title: The pseudofinite monadic second order theory of words

abstract:
  We analyse the pseudofinite monadic second order theory of words over a fixed
finite alphabet. In particular we present an axiomatisation of this theory,
working in a one-sorted first order framework. The analysis hinges on the fact
that concatenation of words interacts nicely with monadic second order logic.
More precisely, give a signature under which for each natural number k,
equivalence of (monadic second order versions of) words with respect to
formulas of quantifier depth at most k is a congruence for concatenation. We
use our analysis to present an alternative proof of a theorem connecting
recognisable languages and finitely generated free profinite monoids via
extended Stone duality, due to Gehrke, Grigorieff, and Pin.


title:
The Promising Marriage of Mobile Edge Computing and Cell-Free Massive
  MIMO

abstract:
  This paper considers a mobile edge computing-enabled cell-free massive MIMO
wireless network. An optimization problem for the joint allocation of uplink
powers and remote computational resources is formulated, aimed at minimizing
the total uplink power consumption under latency constraints, while
simultaneously also maximizing the minimum SE throughout the network. Since the
considered problem is non-convex, an iterative algorithm based on sequential
convex programming is devised. A detailed performance comparison between the
proposed distributed architecture and its co-located counterpart, based on a
multi-cell massive MIMO deployment, is provided. Numerical results reveal the
natural suitability of cell-free massive MIMO in supporting
computation-offloading applications, with benefits over users' transmit power
and energy consumption, the offloading latency experienced, and the total
amount of allocated remote computational resources.


title:
Characterization of Multianode Photomultiplier Tubes for use in the
  CLAS12 RICH Detector

abstract:
  We present results of the detailed study of several hundred Hamamatsu H12700
Multianode Photomultiplier Tubes (MAPMTs), characterizing their response to the
Cherenkov light photons in the second Ring Imaging Cherenkov detector, a part
of the CLAS12 upgrade at Jefferson Lab. The total number of pixels studied was
25,536. The single photoelectron spectra were measured for each pixel at
different high voltages and light intensities of the laser test setup. Using
the same dedicated front-end electronics as in the first RICH detector, the
setup allowed us to characterize each pixel's properties such as gain, quantum
efficiency, signal crosstalk between neighboring pixels, and determine the
signal threshold values to optimize their efficiency to detect Cherenkov
photons. A recently published state-of-the-art mathematical model, describing
photon detector response functions measured in low light conditions, was
extended to include the description of the crosstalk contributions to the
spectra. The database of extracted parameters will be used for the final
selection of the MAPMTs, their arrangement in the new RICH detector, and the
optimization of the operational settings of the front-end electronics. The
results show that the characteristics of the H12700 MAPMTs satisfy our
requirements for the position-sensitive single photoelectron detectors.


title: Generalizing continuous flexible Kokotsakis belts of the isogonal type

abstract:
  Kokotsakis studied the following problem in 1932: Given is a rigid closed
polygonal line (planar or non-planar), which is surrounded by a polyhedral
strip, where at each polygon vertex three faces meet. Determine the geometries
of these closed strips with a continuous mobility. On the one side, we
generalize this problem by allowing the faces, which are adjacent to polygon
line-segments, to be skew; i.e to be non-planar. But on the other side, we
restrict to the case where the four angles associated with each polygon vertex
fulfill the so-called isogonality condition that both pairs of opposite angles
are equal or supplementary. In more detail, we study the case where the
polygonal line is a skew quad, as this corresponds to a (3x3) building block of
a so-called V-hedra composed of skew quads. The latter also gives a positive
answer to a question posed by Robert Sauer in his book of 1970 whether
continuous flexible skew quad surfaces exist.


title: Beyond Deterministic Translation for Unsupervised Domain Adaptation

abstract:
  In this work we challenge the common approach of using a one-to-one mapping
('translation') between the source and target domains in unsupervised domain
adaptation (UDA). Instead, we rely on stochastic translation to capture
inherent translation ambiguities. This allows us to (i) train more accurate
target networks by generating multiple outputs conditioned on the same source
image, leveraging both accurate translation and data augmentation for
appearance variability, (ii) impute robust pseudo-labels for the target data by
averaging the predictions of a source network on multiple translated versions
of a single target image and (iii) train and ensemble diverse networks in the
target domain by modulating the degree of stochasticity in the translations. We
report improvements over strong recent baselines, leading to state-of-the-art
UDA results on two challenging semantic segmentation benchmarks.


title:
Binary Classification for High Dimensional Data using Supervised
  Non-Parametric Ensemble Method

abstract:
  Medical Research data used for prognostication deals with binary
classification problems in most of the cases. The endocrinological disorders
have data available and it can be leveraged using Machine Learning. The dataset
for Polycystic Ovary Syndrome is available, which is termed as an
endocrinological disorder in women. Non-Parametric Supervised Ensemble machine
learning methods can be used for prediction of the disorder in early stages. In
this paper we present the Bootstrap Aggregation Supervised Ensemble
Non-parametric method for prognostication that competes state-of-the-art
performance with accuracy of over 92% along with in depth analysis of the data.


title:
Optimal intervention strategies for minimizing total incidence during an
  epidemic

abstract:
  This article considers the minimization of the total number of infected
individuals over the course of an epidemic in which the rate of infectious
contacts can be reduced by time-dependent nonpharmaceutical interventions. The
societal and economic costs of interventions are taken into account using a
linear budget constraint which imposes a trade-off between short-term heavy
interventions and long-term light interventions. We search for an optimal
intervention strategy in an infinite-dimensional space of controls containing
multiple consecutive lockdowns, gradually imposed and lifted restrictions, and
various heuristic controls based for example on tracking the effective
reproduction number. Mathematical analysis shows that among all such
strategies, the global optimum is achieved by a single constant-level lockdown
of maximum possible magnitude. Numerical simulations highlight the need of
careful timing of such interventions, and illustrate their benefits and
disadvantages compared to strategies designed for minimizing peak prevalence.
Rather counterintuitively, adding restrictions prior to the start of a
well-planned intervention strategy may even increase the total incidence.


title:
Direction of Arrival Estimation and Phase-Correction for Non-Coherent
  Sub-Arrays: A Convex Optimization Approach

abstract:
  Estimating the direction of arrival (DOA) of sources is an important problem
in aerospace and vehicular communication, localization and radar. In this
paper, we consider a challenging multi-source DOA estimation task, where the
receiving antenna array is composed of non-coherent sub-arrays, i.e.,
sub-arrays that observe different unknown phase shifts at every snapshot (e.g.,
due to waiving the demanding synchronization of local oscillators across the
entire array). We formulate this problem as the reconstruction of joint sparse
and low-rank matrices, and solve the problem's convex relaxation. To scale the
optimization complexity with the number of snapshots better than
general-purpose solvers, we design an optimization scheme, based on integrating
the alternating direction method of multipliers and the accelerated proximal
gradient techniques, that exploits the structure of the problem. While the DOAs
can be estimated from the solution of the aforementioned convex problem, we
further show how an improvement is obtained if, instead, one estimates from
this solution only the sub-arrays' phase shifts. This is done using another,
computationally-light, convex relaxation that is practically tight. Using the
estimated phase shifts, "phase-corrected" observations are created and a final
plain ("coherent") DOA estimation method can be applied. Numerical experiments
show the performance advantages of the proposed strategies over existing
methods.


title:
Recommendations for extending the GFF3 specification for improved
  interoperability of genomic data

abstract:
  The GFF3 format is a common, flexible tab-delimited format representing the
structure and function of genes or other mapped features
(https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md).
However, with increasing re-use of annotation data, this flexibility has become
an obstacle for standardized downstream processing. Common software packages
that export annotations in GFF3 format model the same data and metadata in
different notations, which puts the burden on end-users to interpret the data
model. The AgBioData consortium is a group of genomics, genetics and breeding
databases and partners working towards shared practices and standards.
Providing concrete guidelines for generating GFF3, and creating a standard
representation of the most common biological data types would provide a major
increase in efficiency for AgBioData databases and the genomics research
community that use the GFF3 format in their daily operations. The AgBioData
GFF3 working group has developed recommendations to solve common problems in
the GFF3 format. We suggest improvements for each of the GFF3 fields, as well
as the special cases of modeling functional annotations, and standard
protein-coding genes. We welcome further discussion of these recommendations.
We request the genomics and bioinformatics community to utilize the github
repository (https://github.com/NAL-i5K/AgBioData_GFF3_recommendation) to
provide feedback via issues or pull requests.


title:
Subadditive Theorems and First Passage Percolation in Time-Dependent
  Environments

abstract:
  We prove time-dependent versions of Kingman's subadditive ergodic theorem,
which can be used to study processes such as first passage percolation or
propagation of solutions to PDE in time-dependent environments.


title: Dark Sector as Origin of Light Lepton Mass and Its Phenomenology

abstract:
  We discuss a model with a dark sector, in which smallness of mass for charged
leptons and neutrinos can naturally be explained by one-loop effects mediated
by particles in the dark sector. These new particles, including dark matter
candidates, also contribute to the anomalous magnetic dipole moment, denoted by
$(g-2)$, for charged leptons. We show that our model can explain the muon
$(g-2)$ anomaly and observed neutrino oscillations under the constraints from
lepton flavor violating decays of charged leptons. We illustrate that the
scenario with scalar dark matter is highly constrained by direct searches at
the LHC, while that with fermionic dark matter allows for considering dark
scalars with masses of order 100 GeV. Our scenario can be tested by a precise
measurement of the muon Yukawa coupling as well as the direct production of
dark scalar bosons at future electron-positron colliders.


title: Predictability and Surprise in Large Generative Models

abstract:
  Large-scale pre-training has recently emerged as a technique for creating
capable, general purpose, generative models such as GPT-3, Megatron-Turing NLG,
Gopher, and many others. In this paper, we highlight a counterintuitive
property of such models and discuss the policy implications of this property.
Namely, these generative models have an unusual combination of predictable loss
on a broad training distribution (as embodied in their "scaling laws"), and
unpredictable specific capabilities, inputs, and outputs. We believe that the
high-level predictability and appearance of useful capabilities drives rapid
development of such models, while the unpredictable qualities make it difficult
to anticipate the consequences of model deployment. We go through examples of
how this combination can lead to socially harmful behavior with examples from
the literature and real world observations, and we also perform two novel
experiments to illustrate our point about harms from unpredictability.
Furthermore, we analyze how these conflicting properties combine to give model
developers various motivations for deploying these models, and challenges that
can hinder deployment. We conclude with a list of possible interventions the AI
community may take to increase the chance of these models having a beneficial
impact. We intend this paper to be useful to policymakers who want to
understand and regulate AI systems, technologists who care about the potential
policy impact of their work, and academics who want to analyze, critique, and
potentially develop large generative models.


title: Saturated Kripke Structures as Vietoris Coalgebras

abstract:
  We show that the category of coalgebras for the compact Vietoris endofunctor
$\mathbb{V}$ on the category Top of topological spaces and continuous mappings
is isomorphic to the category of all modally saturated Kripke structures.
Extending a result of Bezhanishvili, Fontaine and Venema, we also show that
Vietoris subcoalgebras as well as bisimulations admit topological closure and
that the category of Vietoris coalgebras has a terminal object.


title: Trustworthy Anomaly Detection: A Survey

abstract:
  Anomaly detection has a wide range of real-world applications, such as bank
fraud detection and cyber intrusion detection. In the past decade, a variety of
anomaly detection models have been developed, which lead to big progress
towards accurately detecting various anomalies. Despite the successes, anomaly
detection models still face many limitations. The most significant one is
whether we can trust the detection results from the models. In recent years,
the research community has spent a great effort to design trustworthy machine
learning models, such as developing trustworthy classification models. However,
the attention to anomaly detection tasks is far from sufficient. Considering
that many anomaly detection tasks are life-changing tasks involving human
beings, labeling someone as anomalies or fraudsters should be extremely
cautious. Hence, ensuring the anomaly detection models conducted in a
trustworthy fashion is an essential requirement to deploy the models to conduct
automatic decisions in the real world. In this brief survey, we summarize the
existing efforts and discuss open problems towards trustworthy anomaly
detection from the perspectives of interpretability, fairness, robustness, and
privacy-preservation.


title:
Stiefel-Whitney Classes of Representations of Some Finite Groups of Lie
  Type

abstract:
  In this note we present the Stiefel-Whitney classes (SWCs) for orthogonal
representations of several finite groups of Lie type, namely for
$G=\text{SL}(2,q),$ $\text{SL}(3,q),$ $\text{Sp}(4,q)$, and $\text{Sp}(6,q)$,
with $q$ odd. We also describe the SWCs for $G=\text{SL}(2,q)$ when $q$ is
even.


title: Safe Reinforcement Learning by Imagining the Near Future

abstract:
  Safe reinforcement learning is a promising path toward applying reinforcement
learning algorithms to real-world problems, where suboptimal behaviors may lead
to actual negative consequences. In this work, we focus on the setting where
unsafe states can be avoided by planning ahead a short time into the future. In
this setting, a model-based agent with a sufficiently accurate model can avoid
unsafe states. We devise a model-based algorithm that heavily penalizes unsafe
trajectories, and derive guarantees that our algorithm can avoid unsafe states
under certain assumptions. Experiments demonstrate that our algorithm can
achieve competitive rewards with fewer safety violations in several continuous
control tasks.


title: Speech Denoising in the Waveform Domain with Self-Attention

abstract:
  In this work, we present CleanUNet, a causal speech denoising model on the
raw waveform. The proposed model is based on an encoder-decoder architecture
combined with several self-attention blocks to refine its bottleneck
representations, which is crucial to obtain good results. The model is
optimized through a set of losses defined over both waveform and
multi-resolution spectrograms. The proposed method outperforms the
state-of-the-art models in terms of denoised speech quality from various
objective and subjective evaluation metrics.


title:
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP
  models

abstract:
  In the last year, new neural architectures and multilingual pre-trained
models have been released for Russian, which led to performance evaluation
problems across a range of language understanding tasks.
  This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after
GLUE for Russian NLP models. The new version includes a number of technical,
user experience and methodological improvements, including fixes of the
benchmark vulnerabilities unresolved in the previous version: novel and
improved tests for understanding the meaning of a word in context (RUSSE) along
with reading comprehension and common sense reasoning (DaNetQA, RuCoS, MuSeRC).
Together with the release of the updated datasets, we improve the benchmark
toolkit based on \texttt{jiant} framework for consistent training and
evaluation of NLP-models of various architectures which now supports the most
recent models for Russian. Finally, we provide the integration of Russian
SuperGLUE with a framework for industrial evaluation of the open-source models,
MOROCCO (MOdel ResOurCe COmparison), in which the models are evaluated
according to the weighted average metric over all tasks, the inference speed,
and the occupied amount of RAM. Russian SuperGLUE is publicly available at
https://russiansuperglue.com/.


title:
Efficient Content Delivery in Cache-Enabled VEN with
  Deadline-Constrained Heterogeneous Demands: A User-Centric Approach

abstract:
  Modern connected vehicles (CVs) frequently require diverse types of content
for mission-critical decision-making and onboard users' entertainment. These
contents are required to be fully delivered to the requester CVs within
stringent deadlines that the existing radio access technology (RAT) solutions
may fail to ensure. Motivated by the above consideration, this paper exploits
content caching with a software-defined user-centric virtual cell (VC) based
RAT solution for delivering the requested contents from a proximity edge
server. Moreover, to capture the heterogeneous demands of the CVs, we introduce
a preference-popularity tradeoff in their content request model. To that end,
we formulate a joint optimization problem for content placement, CV scheduling,
VC configuration, VC-CV association and radio resource allocation to minimize
long-term content delivery delay. However, the joint problem is highly complex
and cannot be solved efficiently in polynomial time. As such, we decompose the
original problem into a cache placement problem and a content delivery delay
minimization problem given the cache placement policy. We use deep
reinforcement learning (DRL) as a learning solution for the first sub-problem.
Furthermore, we transform the delay minimization problem into a priority-based
weighted sum rate (WSR) maximization problem, which is solved leveraging
maximum bipartite matching (MWBM) and a simple linear search algorithm. Our
extensive simulation results demonstrate the effectiveness of the proposed
method.


title: Heuristic computation of exact treewidth

abstract:
  We are interested in computing the treewidth $\tw(G)$ of a given graph $G$.
Our approach is to design heuristic algorithms for computing a sequence of
improving upper bounds and a sequence of improving lower bounds, which would
hopefully converge to $\tw(G)$ from both sides. The upper bound algorithm
extends and simplifies Tamaki's unpublished work on a heuristic use of the
dynamic programming algorithm for deciding treewidth due to Bouchitt\'{e} and
Todinca. The lower bound algorithm is based on the well-known fact that, for
every minor $H$ of $G$, we have $\tw(H) \leq \tw(G)$. Starting from a greedily
computed minor $H_0$ of $G$, the algorithm tries to construct a sequence of
minors $H_0$, $H_1$, \ldots $H_k$ with $\tw(H_i) < \tw(H_{i + 1})$ for $0 \leq
i < k$ and hopefully $\tw(H_k) = \tw(G)$.
  We have implemented a treewidth solver based on this approach and have
evaluated it on the bonus instances from the exact treewidth track of PACE 2017
algorithm implementation challenge. The results show that our approach is
extremely effective in tackling instances that are hard for conventional
solvers. Our solver has an additional advantage over conventional ones in that
it attaches a compact certificate to the lower bound it computes.


title:
Observability of superconductivity in Sr-doped Bi2Se3 at the surface
  using scanning tunneling microscope

abstract:
  The superconducting materials family of doped Bi2Se3 remains intensively
studied in the field of condensed matter physics due to strong experimental
evidence for topologically non-trivial superconductivity in the bulk. However,
at the surface of these materials, even the observation of superconductivity
itself is still controversial. We use scanning tunneling microscopy (STM) down
to 0.4 K to show that on the surface of bulk superconducting SrxBi2Se3, no gap
in the density of states is observed around the Fermi energy as long as clean
metallic probe tips are used. Nevertheless, using scanning electron microscopy
and energy-dispersive X-ray analysis, we find that micron-sized flakes of
SrxBi2Se3 are easily transferred from the sample onto the STM probe tip and
that such flakes consistently show a superconducting gap in the density of
states. We argue that the superconductivity in SrxBi2Se3 crystals does not
extend to the surface when the topological surface state (TSS) is intact, but
in micro-flakes the TSS has been destroyed due to strain and allows the
superconductivity to extend to the surface. To understand this phenomenon, we
propose that the local electric field, always found in electron doped Bi2Se3 in
the presence of the TSS due to an intrinsic upward band bending, works against
superconductivity at the surface.

categories: cond-mat.supr-con cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el


title:
The Biosensor based on electrochemical dynamics of fermentation in yeast
  Saccharomyces Cerevisiae

abstract:
  The zymase activity of the yeast Saccharomyces Cerevisiae is sensitive to
environmental parameters and is therefore used as a microbiological sensor for
water quality assessment, ecotoxicological characterization or environmental
monitoring. Comparing to bacterial bioluminescence approach, this method has no
toxicity, excludes usage of genetically modified microorganisms, and enables
low-cost express analysis. This work focuses on measuring the yeast
fermentation dynamics based on multichannel pressure sensing and
electrochemical impedance spectroscopy (EIS). Measurement results are compared
with each other in terms of accuracy, reproducibility and ease of use in the
field conditions. It has been shown that EIS provides more information about
ionic dynamics of metabolic processes and requires less complex measurements.
The conducted experiments demonstrated the sensitivity of this approach for
assessing biophotonic phenomena, non-chemical water treatments and impact of
environmental stressors.

categories: q-bio.QM cs.RO physics.bio-ph physics.ins-det


title: Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

abstract:
  Scalp electroencephalogram (EEG) signals inherently have a low
signal-to-noise ratio due to the way the signal is electrically transduced.
Temporal and spatial information must be exploited to achieve accurate
detection of seizure events. Most popular approaches to seizure detection using
deep learning do not jointly model this information or require multiple passes
over the signal, which makes the systems inherently non-causal. In this paper,
we exploit both simultaneously by converting the multichannel signal to a
grayscale image and using transfer learning to achieve high performance. The
proposed system is trained end-to-end with only very simple pre- and
postprocessing operations which are computationally lightweight and have low
latency, making them conducive to clinical applications that require real-time
processing. We have achieved a performance of 42.05% sensitivity with 5.78
false alarms per 24 hours on the development dataset of v1.5.2 of the Temple
University Hospital Seizure Detection Corpus. On a single-core CPU operating at
1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of
memory, and has a latency of 300 msec.


title:
Extended Kalman filter based observer design for semilinear
  infinite-dimensional systems

abstract:
  In many physical applications, the system's state varies with spatial
variables as well as time. The state of such systems is modelled by partial
differential equations and evolves on an infinite-dimensional space. Systems
modelled by delay-differential equations are also infinite-dimensional systems.
The full state of these systems cannot be measured. Observer design is an
important tool for estimating the state from available measurements. For linear
systems, both finite- and infinite-dimensional, the Kalman filter provides an
estimate with minimum-variance on the error, if certain assumptions on the
noise are satisfied. The extended Kalman filter (EKF) is one type of extension
to nonlinear finite-dimensional systems. In this paper we provide an extension
of the EKF to semilinear infinite-dimensional systems. Under mild assumptions
we prove the well-posedness of equations defining the EKF. Local exponential
stability of the error dynamics is shown. Only detectability is assumed, not
observability, so this result is new even for finite-dimensional systems. The
results are illustrated with implementation of finite-dimensional
approximations of the infinite-dimensional EKF on an example.


title:
BB-ML: Basic Block Performance Prediction using Machine Learning
  Techniques

abstract:
  Recent years have seen the adoption of Machine Learning (ML) techniques to
predict the performance of large-scale applications, mostly at a coarse level.
In contrast, we propose to use ML techniques for performance prediction at much
finer granularity, namely at the levels of Basic Block (BB), which are the
single entry-single exit code blocks that are used as analysis tools by all
compilers to break down a large code into manageable pieces. Utilizing ML and
BB analysis together can enable scalable hardware-software co-design beyond the
current state of the art. In this work, we extrapolate the basic block
execution counts of GPU applications for large inputs sizes from the counts of
smaller input sizes of the same application.
  We employ two ML models, a Poisson Neural Network (PNN) and a Bayesian
Regularization Backpropagation Neural Network (BR-BPNN). We train both models
using the lowest input values of the application and random input values to
predict basic block counts. Results show that our models accurately predict the
basic block execution counts of 16 benchmark applications. For PNN and BR-BPNN
models, we achieve an average accuracy of 93.5% and 95.6%, respectively, while
extrapolating the basic block counts for large input sets when the model is
trained using smaller input sets. Additionally, the models show an average
accuracy of 97.7% and 98.1%, respectively, while predicting basic block counts
on random instances.


title:
A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges
  and Future Directions

abstract:
  After the advent of the Internet of Things and 5G networks, edge computing
became the center of attraction. The tasks demanding high computation are
generally offloaded to the cloud since the edge is resource-limited. The Edge
Cloud is a promising platform where the devices can offload delay-sensitive
workloads. In this regard, scheduling holds great importance in offloading
decisions in the Edge Cloud collaboration. The ultimate objectives of
scheduling are the quality of experience, minimizing latency, and increasing
performance. An abundance of efforts on scheduling has been done in the past.
In this paper, we have surveyed proposed scheduling strategies in the context
of edge cloud computing in various aspects such as advantages and demerits, QoS
parameters, and fault tolerance. We have also surveyed such scheduling
approaches to evaluate which one is feasible under what circumstances. We first
classify all the algorithms into heuristic algorithms and meta-heuristics, and
we subcategorize algorithms in each class further based on extracted attributes
of algorithms. We hope that this survey will be very thoughtful in the
development of new scheduling techniques. Issues, challenges, and future
directions have also been examined.


title:
Not All Patches are What You Need: Expediting Vision Transformers via
  Token Reorganizations

abstract:
  Vision Transformers (ViTs) take all the image patches as tokens and construct
multi-head self-attention (MHSA) among them. Complete leverage of these image
tokens brings redundant computations since not all the tokens are attentive in
MHSA. Examples include that tokens containing semantically meaningless or
distractive image backgrounds do not positively contribute to the ViT
predictions. In this work, we propose to reorganize image tokens during the
feed-forward process of ViT models, which is integrated into ViT during
training. For each forward inference, we identify the attentive image tokens
between MHSA and FFN (i.e., feed-forward network) modules, which is guided by
the corresponding class token attention. Then, we reorganize image tokens by
preserving attentive image tokens and fusing inattentive ones to expedite
subsequent MHSA and FFN computations. To this end, our method EViT improves
ViTs from two perspectives. First, under the same amount of input image tokens,
our method reduces MHSA and FFN computation for efficient inference. For
instance, the inference speed of DeiT-S is increased by 50% while its
recognition accuracy is decreased by only 0.3% for ImageNet classification.
Second, by maintaining the same computational cost, our method empowers ViTs to
take more image tokens as input for recognition accuracy improvement, where the
image tokens are from higher resolution images. An example is that we improve
the recognition accuracy of DeiT-S by 1% for ImageNet classification at the
same computational cost of a vanilla DeiT-S. Meanwhile, our method does not
introduce more parameters to ViTs. Experiments on the standard benchmarks show
the effectiveness of our method. The code is available at
https://github.com/youweiliang/evit


title: Measuring Laser Beams with a Neural Network

abstract:
  A deep neural network (NN) is used to simultaneously detect laser beams in
images and measure their center coordinates, radii and angular orientations. A
dataset of images containing simulated laser beams and a dataset of images with
experimental laser beams, generated using a spatial light modulator, are used
to train and evaluate the NN. After training on the simulated dataset the NN
achieves beam parameter rootmean-square-errors (RMSEs) of less than 3.4% on the
experimental dataset. Subsequent training on the experimental dataset causes
the RMSEs to fall below 1.1%. The NN method can be used as a stand-alone
measurement of the beam parameters or can compliment other beam profiling
methods by providing an accurate region-of-interest.


title:
Generative Adversarial Network-Driven Detection of Adversarial Tasks in
  Mobile Crowdsensing

abstract:
  Mobile Crowdsensing systems are vulnerable to various attacks as they build
on non-dedicated and ubiquitous properties. Machine learning (ML)-based
approaches are widely investigated to build attack detection systems and ensure
MCS systems security. However, adversaries that aim to clog the sensing
front-end and MCS back-end leverage intelligent techniques, which are
challenging for MCS platform and service providers to develop appropriate
detection frameworks against these attacks. Generative Adversarial Networks
(GANs) have been applied to generate synthetic samples, that are extremely
similar to the real ones, deceiving classifiers such that the synthetic samples
are indistinguishable from the originals. Previous works suggest that GAN-based
attacks exhibit more crucial devastation than empirically designed attack
samples, and result in low detection rate at the MCS platform. With this in
mind, this paper aims to detect intelligently designed illegitimate sensing
service requests by integrating a GAN-based model. To this end, we propose a
two-level cascading classifier that combines the GAN discriminator with a
binary classifier to prevent adversarial fake tasks. Through simulations, we
compare our results to a single-level binary classifier, and the numeric
results show that proposed approach raises Adversarial Attack Detection Rate
(AADR), from $0\%$ to $97.5\%$ by KNN/NB, from $45.9\%$ to $100\%$ by Decision
Tree. Meanwhile, with two-levels classifiers, Original Attack Detection Rate
(OADR) improves for the three binary classifiers, with comparison, such as NB
from $26.1\%$ to $61.5\%$.


title: An Exploration of Higher Order Flavor Sum Rules

abstract:
  We explore the idea of higher-order flavor sum rules, i.e. sum rules that
hold to higher orders in the flavor symmetry-breaking parameters. In
particular, we consider isospin sum rules based on $B \to n\pi$ decays, and
U-spin sum rules based on $B_d$ and $B_s$ decays to $n$ charged pions and/or
kaons. We also demonstrate that sum rules exist to arbitrary orders by
considering decay modes with increasing number of final state mesons $n$.
Finally, we identify various theoretical and practical issues that need to be
addressed in utilizing these sum rules.


title: Decays of Roper-like singly heavy baryons in a chiral model

abstract:
  We present a chiral model describing decays of the Roper-like
$\Lambda_c(2765)$ and $\Xi_c(2970)$ as well as those of the heavy quark
spin-doublet $\{\Sigma_c(2455),\Sigma_c(2520)\}$ and $\{\Xi_c',\Xi_c(2645)\}$,
by focusing on chiral representations of diquarks inside the heavy baryons.
Based on our chiral model together with the heavy quark spin symmetry, we
derive a relation satisfied by axial charges of the heavy baryons that controls
magnitude of their one pion (kaon) decays. Besides, it is shown that the large
decay widths of the Roper-like $\Lambda_c(2765)$ and $\Xi_c(2970)$, which
cannot be implemented by conventional non-relativistic quark models, are
explained by reasonable values of the axial charges. In addition, we apply the
chiral model to bottom baryons and predict decay widths of the undiscovered
Roper-like $\Xi_b$. We expect that our present investigation leads to
elucidation of dynamical properties of the excited heavy baryons from the
viewpoint of chiral symmetry.


title:
Momentum flux fluctuations in wall turbulence: a formula beyond the law
  of the wall

abstract:
  Within wall turbulence, there is a sublayer where the mean wall-normal flux
of the streamwise momentum is constant and related to the logarithmic
wall-normal profile of the mean streamwise velocity. This relation, i.e., the
law of the wall, has been used to estimate the mean stress at the wall surface.
However, the momentum flux exhibits large temporal fluctuations. To relate them
theoretically to those of the streamwise velocity at the same position from the
wall, we consider an orthogonal decomposition of the fluctuations on a plane of
the streamwise and wall-normal velocities. Since a large timescale is expected
for the component that would dominate the momentum flux, it is singled out by
temporal smoothing. The resultant formula is consistent with time-series data
of a boundary layer in a wind tunnel. We also extend the formula to thermally
stratified cases.


title: Code Generation for Unknown Libraries via Reading API Documentations

abstract:
  Open-domain code generation is a challenging problem because the set of
functions and classes that we use are frequently changed and extended in
programming communities. We consider the challenge of code generation for
unknown libraries without additional training. In this paper, we explore a
framework of code generation that can refer to relevant API documentations like
human programmers to handle unknown libraries. As a first step of this
direction, we implement a model that can extract relevant code signatures from
API documentations based on a natural language intent and copy primitives from
the extracted signatures. Moreover, to evaluate code generation for unknown
libraries and our framework, we extend an existing dataset of open-domain code
generation and resplit it so that the evaluation data consist of only examples
using the libraries that do not appear in the training data. Experiments on our
new split show that baseline encoder-decoder models cannot generate code using
primitives of unknown libraries as expected. In contrast, our model outperforms
the baseline on the new split and can properly generate unknown primitives when
extracted code signatures are noiseless.


title: Creating stars orbiting in AdS

abstract:
  We propose a method to create a star orbiting in an asymptotically AdS
spacetime using the AdS/CFT correspondence. We demonstrate that by applying an
appropriate source in the quantum field theory defined on a 2-sphere, the
localized star gradually appears in the dual asymptotically AdS geometry. Once
the star is created, the angular position can be observed from the response
function. The relationship between the parameters of the created star and those
of the source is studied. We show that information regarding the bulk geometry
can be extracted from the observation of stellar motion in the bulk geometry.


title: Policy Learning and Evaluation with Randomized Quasi-Monte Carlo

abstract:
  Reinforcement learning constantly deals with hard integrals, for example when
computing expectations in policy evaluation and policy iteration. These
integrals are rarely analytically solvable and typically estimated with the
Monte Carlo method, which induces high variance in policy values and gradients.
In this work, we propose to replace Monte Carlo samples with low-discrepancy
point sets. We combine policy gradient methods with Randomized Quasi-Monte
Carlo, yielding variance-reduced formulations of policy gradient and
actor-critic algorithms. These formulations are effective for policy evaluation
and policy improvement, as they outperform state-of-the-art algorithms on
standardized continuous control benchmarks. Our empirical analyses validate the
intuition that replacing Monte Carlo with Quasi-Monte Carlo yields
significantly more accurate gradient estimates.


title: Computing binary curves of genus five

abstract:
  Genus 5 curves can be hyperelliptic, trigonal, or non-hyperelliptic
non-trigonal, whose model is a complete intersection of three quadrics in
$\mathbb{P}^4$. We present and explain algorithms we used to determine, up to
isomorphism over $\mathbb{F}_2$, all genus 5 curves defined over
$\mathbb{F}_2$, and we do that separately for each of the three mentioned
types. We consider these curves in terms of isogeny classes over $\mathbb{F}_2$
of their Jacobians or their Newton polygons, and for each of the three types,
we compute the number of curves over $\mathbb{F}_2$ weighted by the size of
their $\mathbb{F}_2$-automorphism groups.


title:
Another Type of Anomalous Velocity Appearing at the Boundary of the
  Magnetic First Brillouin Zone

abstract:
  The quantum Hall effect is known to be caused by the anomalous velocity that
is expressed as the Berry curvature of the magnetic Bloch band. We have
investigated the dynamics of a wave packet composed of magnetic Bloch
functions. It is shown that another type of the anomalous velocity appears if
the wave packet approaches to the boundary of the magnetic first Brillouin zone
(MBZ), in addition to the group velocity and the anomalous velocity of the
magnetic Bloch state. This another type of the anomalous velocity is
perpendicular to the electric field and may contribute to the Hall current if
the wave packet approaches to the boundary of the MBZ. Furthermore, we present
the acceleration theorem that describes the motion of the wave packet in the
MBZ. It is found that the acceleration theorem contains characteristic terms
appearing at the boundary of the MBZ.


title:
Wind properties of Milky Way and SMC massive stars: empirical Z
  dependence from CMFGEN models

abstract:
  Detailed knowledge about stellar winds and evolution at different
metallicities is crucial for understanding stellar populations and feedback in
the Local Group of galaxies and beyond. Despite efforts in the literature, we
still lack a comprehensive, empirical view of the dependence of wind properties
on metallicity ($Z$). Here, we investigate the winds of O and B stars in the
Milky Way (MW) and Small Magellanic Cloud (SMC). We gathered a sample of 96
stars analyzed by means of the NLTE code CMFGEN. We explored their wind
strengths and terminal velocities to address the $Z$ dependence, over a large
luminosity range. The empirical wind-luminosity relation (WLR) obtained updates
and extends previous results in the literature. It reveals a luminosity and $Z$
dependence, in agreement with the radiatively driven wind theory. For bright
objects ($\log L/L_\odot \gtrsim 5.4$), we infer that $\dot{M} \sim
Z^{0.5-0.8}$. However, this dependence seems to get weaker or vanish at lower
luminosities. The analysis of the terminal velocities suggests a shallow $Z^n$
dependence, with $n \sim 0.1-0.2$, but it should be confirmed with a larger
sample and more accurate $V_{\infty}$ determinations. Recent results on SMC
stars based on the PoWR code support our inferred WLR. On the other hand,
recent bow-shocks measurements stand mostly above our derived WLR. Theoretical
calculations of the WLR are not precise, specially at low $L$, where the
results scatter. Deviations between our results and recent predictions are
identified to be due to the weak wind problem and the extreme terminal
velocities predicted by the models. The Z dependence suggested by our analysis
deserves further investigations, given its astrophysical implications.


title: Code-routing: a new attack on position-verification

abstract:
  The cryptographic task of position-verification attempts to verify one
party's location in spacetime by exploiting constraints on quantum information
and relativistic causality. A popular verification scheme known as $f$-routing
involves requiring the prover to redirect a quantum system based on the value
of a Boolean function $f$. Cheating strategies for the $f$-routing scheme
require the prover use pre-shared entanglement, and security of the scheme
rests on assumptions about how much entanglement a prover can manipulate. Here,
we give a new cheating strategy in which the quantum system is encoded into a
secret-sharing scheme, and the authorization structure of the secret-sharing
scheme is exploited to direct the system appropriately. This strategy completes
the $f$-routing task using $O(SP_p(f))$ EPR pairs, where $SP_p(f)$ is the
minimal size of a span program over the field $\mathbb{Z}_p$ computing $f$.
This shows we can efficiently attack $f$-routing schemes whenever $f$ is in the
complexity class $\text{Mod}_p\text{L}$, after allowing for local
pre-processing. The best earlier construction achieved the class L, which is
believed to be strictly inside of $\text{Mod}_p\text{L}$. We also show that the
size of a quantum secret sharing scheme with indicator function $f_I$ upper
bounds entanglement cost of $f$-routing on the function $f_I$.


title:
On the wave-like energy estimates of Klein-Gordon type equations with
  time dependent potential

abstract:
  We consider the conditions for the time dependent potential in which the
energy of the Cauchy problem of Klein-Gordon type equation asymptotically
behaves like the energy of the wave equation. The conclusion of this paper is
that the condition is not always given by the order of the potential itself,
but should be given by "generalized zero mean condition", which is represented
by the integral of the potential. We also introduce "generalized modified
energy conservation" in order to describe the appropriate energy for our
problem.


title: Moments of quadratic Dirichlet $L$-functions over Function Fields

abstract:
  In this paper, we establish the expected order of magnitude of the
$k$th-moment of quadratic Dirichlet $L$-functions associated to hyperelliptic
curves of genus $g$ as well as of prime moduli over a fixed finite field
$\mathbb{F}_{q}$ for all real $k \geq 0$.


title:
Applying adversarial networks to increase the data efficiency and
  reliability of Self-Driving Cars

abstract:
  Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images
when small perturbations are present. With the increasing prevalence of CNNs in
self-driving cars, it is vital to ensure these algorithms are robust to prevent
collisions from occurring due to failure in recognizing a situation. In the
Adversarial Self-Driving framework, a Generative Adversarial Network (GAN) is
implemented to generate realistic perturbations in an image that cause a
classifier CNN to misclassify data. This perturbed data is then used to train
the classifier CNN further. The Adversarial Self-driving framework is applied
to an image classification algorithm to improve the classification accuracy on
perturbed images and is later applied to train a self-driving car to drive in a
simulation. A small-scale self-driving car is also built to drive around a
track and classify signs. The Adversarial Self-driving framework produces
perturbed images through learning a dataset, as a result removing the need to
train on significant amounts of data. Experiments demonstrate that the
Adversarial Self-driving framework identifies situations where CNNs are
vulnerable to perturbations and generates new examples of these situations for
the CNN to train on. The additional data generated by the Adversarial
Self-driving framework provides sufficient data for the CNN to generalize to
the environment. Therefore, it is a viable tool to increase the resilience of
CNNs to perturbations. Particularly, in the real-world self-driving car, the
application of the Adversarial Self-Driving framework resulted in an 18 %
increase in accuracy, and the simulated self-driving model had no collisions in
30 minutes of driving.


title:
ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in
  Text-to-Speech

abstract:
  Expressive text-to-speech (TTS) has become a hot research topic recently,
mainly focusing on modeling prosody in speech. Prosody modeling has several
challenges: 1) the extracted pitch used in previous prosody modeling works have
inevitable errors, which hurts the prosody modeling; 2) different attributes of
prosody (e.g., pitch, duration and energy) are dependent on each other and
produce the natural prosody together; and 3) due to high variability of prosody
and the limited amount of high-quality data for TTS training, the distribution
of prosody cannot be fully shaped. To tackle these issues, we propose
ProsoSpeech, which enhances the prosody using quantized latent vectors
pre-trained on large-scale unpaired and low-quality text and speech data.
Specifically, we first introduce a word-level prosody encoder, which quantizes
the low-frequency band of the speech and compresses prosody attributes in the
latent prosody vector (LPV). Then we introduce an LPV predictor, which predicts
LPV given word sequence. We pre-train the LPV predictor on large-scale text and
low-quality speech data and fine-tune it on the high-quality TTS dataset.
Finally, our model can generate expressive speech conditioned on the predicted
LPV. Experimental results show that ProsoSpeech can generate speech with richer
prosody compared with baseline methods.


title:
Cross-view and Cross-domain Underwater Localization based on Optical
  Aerial and Acoustic Underwater Images

abstract:
  Cross-view image matches have been widely explored on terrestrial image
localization using aerial images from drones or satellites. This study expands
the cross-view image match idea and proposes a cross-domain and cross-view
localization framework. The method identifies the correlation between color
aerial images and underwater acoustic images to improve the localization of
underwater vehicles that travel in partially structured environments such as
harbors and marinas. The approach is validated on a real dataset acquired by an
underwater vehicle in a marina. The results show an improvement in the
localization when compared to the dead reckoning of the vehicle.


title: Introduction to Radio Astronomical Polarimetry

abstract:
  These workshop notes present an introduction to the concepts and mathematical
foundations of polarimetry. One of the main goals of this workshop is to
develop an understanding of the relationships between a physical description of
the signal path (e.g. gain, delay, rotation, coupling, etc.), the corresponding
transformations of the electric field vector, and the equivalent
transformations of the Stokes parameters. The adopted algebraic/geometric
approach is either directly copied from or heavily inspired by the work of
Britton (2000) and Hamaker (2000), and some justification is provided for
preferring these over other approaches and parameterizations.


title:
Exact solutions for charged spheres and their stability. I. Perfect
  Fluids

abstract:
  We study exact solutions of the Einstein-Maxwell equations for the interior
gravitational field of static spherically symmetric charged compact spheres.
The spheres are composed of a perfect fluid with a charge distribution that
creates a static radial electric field. The inertial mass density of the fluid
has the form $\rho (r) = \rho_o + \alpha r^2$ ($\rho_o$ and $\alpha$ are
constants) and the total charge $q(r)$ within a sphere of radius $r$ has the
form $q = \beta r^3$ ($\beta$ is a constant). We evaluate the critical values
of $M/R$ for these spheres as a function of $Q/R$ and compare these values with
those given by the Andr\'{e}asson formula.


title:
A Survey of Semen Quality Evaluation in Microscopic Videos Using
  Computer Assisted Sperm Analysis

abstract:
  The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male
reproductive health diagnosis and Infertility treatment. With the development
of the computer industry in recent years, a great of accurate algorithms are
proposed. With the assistance of those novel algorithms, it is possible for
CASA to achieve a faster and higher quality result. Since image processing is
the technical basis of CASA, including pre-processing,feature extraction,
target detection and tracking, these methods are important technical steps in
dealing with CASA. The various works related to Computer Assisted Sperm
Analysis methods in the last 30 years (since 1988) are comprehensively
introduced and analysed in this survey. To facilitate understanding, the
methods involved are analysed in the sequence of general steps in sperm
analysis. In other words, the methods related to sperm detection (localization)
are first analysed, and then the methods of sperm tracking are analysed. Beside
this, we analyse and prospect the present situation and future of CASA.
According to our work, the feasible for applying in sperm microscopic video of
methods mentioned in this review is explained. Moreover, existing challenges of
object detection and tracking in microscope video are potential to be solved
inspired by this survey.


title:
A Projected Subgradient Method for the Computation of Adapted Metrics
  for Dynamical Systems

abstract:
  In this paper, we extend a recently established subgradient method for the
computation of Riemannian metrics that optimizes certain singular value
functions associated with dynamical systems. This extension is threefold.
First, we introduce a projected subgradient method which results in Riemannian
metrics whose parameters are confined to a compact convex set and we can thus
prove that a minimizer exists; second, we allow inexact subgradients and study
the effect of the errors on the computed metrics; and third, we analyze the
subgradient algorithm for three different choices of step sizes: constant,
exogenous and Polyak. The new methods are illustrated by application to
dimension and entropy estimation of the H\'enon map.


title: Experimental realization of two-dimensional weak topological insulators

abstract:
  We report the experimental realization of two-dimensional (2D) weak
topological insulator (WTI) in spinless Su-Schrieffer-Heeger circuits with
parity-time and chiral symmetries. Strong and weak $\mathbb{Z}_2$ topological
indexes are adopted to explain the experimental findings that a Dirac semimetal
(DSM) phase and four WTI phases emerge in turn when we modulate the
centrosymmetric circuit deformations. In DSM phase, it is found that the Dirac
cone is highly anisotropic and not pinned to any high-symmetry points but can
widely move within the Brillouin zone, which eventually leads to the phase
transition between WTIs. In addition, we observe a pair of flat-band domain
wall states by designing spatially inhomogeneous node connections. Our work
provides the first experimental evidence for 2D WTIs, which significantly
advances our understanding on the strong and weak nature of topological
insulators, the robustness of flat bands, and the itinerant and anisotropic
feature of Dirac cones.


title:
Segmentation and Risk Score Prediction of Head and Neck Cancers in
  PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks

abstract:
  We utilized a 3D nnU-Net model with residual layers supplemented by squeeze
and excitation (SE) normalization for tumor segmentation from PET/CT images
provided by the Head and Neck Tumor segmentation chal-lenge (HECKTOR). Our
proposed loss function incorporates the Unified Fo-cal and Mumford-Shah losses
to take the advantage of distribution, region, and boundary-based loss
functions. The results of leave-one-out-center-cross-validation performed on
different centers showed a segmentation performance of 0.82 average Dice score
(DSC) and 3.16 median Hausdorff Distance (HD), and our results on the test set
achieved 0.77 DSC and 3.01 HD. Following lesion segmentation, we proposed
training a case-control proportional hazard Cox model with an MLP neural net
backbone to predict the hazard risk score for each discrete lesion. This hazard
risk prediction model (CoxCC) was to be trained on a number of PET/CT radiomic
features extracted from the segmented lesions, patient and lesion demographics,
and encoder features provided from the penultimate layer of a multi-input 2D
PET/CT convolutional neural network tasked with predicting time-to-event for
each lesion. A 10-fold cross-validated CoxCC model resulted in a c-index
validation score of 0.89, and a c-index score of 0.61 on the HECKTOR challenge
test dataset.


title: RNGDet: Road Network Graph Detection by Transformer in Aerial Images

abstract:
  Road network graphs provide critical information for autonomous vehicle
applications, such as motion planning on drivable areas. However, manually
annotating road network graphs is inefficient and labor-intensive.
Automatically detecting road network graphs could alleviate this issue, but
existing works are either segmentation-based approaches that could not ensure
satisfactory topology correctness, or graph-based approaches that could not
present precise enough detection results. To provide a solution to these
problems, we propose a novel approach based on transformer and imitation
learning named RNGDet (\underline{R}oad \underline{N}etwork \underline{G}raph
\underline{Det}ection by Transformer) in this paper. In view of that
high-resolution aerial images could be easily accessed all over the world
nowadays, we make use of aerial images in our approach. Taken as input an
aerial image, our approach iteratively generates road network graphs
vertex-by-vertex. Our approach can handle complicated intersection points of
various numbers of road segments. We evaluate our approach on a publicly
available dataset. The superiority of our approach is demonstrated through the
comparative experiments.


title: Reducing Overconfidence Predictions for Autonomous Driving Perception

abstract:
  In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid
functions are most commonly employed as the predictor outputs. Such layers
often produce overconfident predictions rather than proper probabilistic
scores, which can thus harm the decision-making of `critical' perception
systems applied in autonomous driving and robotics. Given this, the experiments
in this work propose a probabilistic approach based on distributions calculated
out of the Logit layer scores of pre-trained networks. We demonstrate that
Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) functions are more
suitable for probabilistic interpretations than SoftMax and Sigmoid-based
predictions for object recognition. We explore distinct sensor modalities via
RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5
datasets, where our approach shows promising performance compared to the usual
SoftMax and Sigmoid layers, with the benefit of enabling interpretable
probabilistic predictions. Another advantage of the approach introduced in this
paper is that the ML and MAP functions can be implemented in existing trained
networks, that is, the approach benefits from the output of the Logit layer of
pre-trained networks. Thus, there is no need to carry out a new training phase
since the ML and MAP functions are used in the test/prediction phase.


title:
CenGCN: Centralized Convolutional Networks with Vertex Imbalance for
  Scale-Free Graphs

abstract:
  Graph Convolutional Networks (GCNs) have achieved impressive performance in a
wide variety of areas, attracting considerable attention. The core step of GCNs
is the information-passing framework that considers all information from
neighbors to the central vertex to be equally important. Such equal importance,
however, is inadequate for scale-free networks, where hub vertices propagate
more dominant information due to vertex imbalance. In this paper, we propose a
novel centrality-based framework named CenGCN to address the inequality of
information. This framework first quantifies the similarity between hub
vertices and their neighbors by label propagation with hub vertices. Based on
this similarity and centrality indices, the framework transforms the graph by
increasing or decreasing the weights of edges connecting hub vertices and
adding self-connections to vertices. In each non-output layer of the GCN, this
framework uses a hub attention mechanism to assign new weights to connected
non-hub vertices based on their common information with hub vertices. We
present two variants CenGCN\_D and CenGCN\_E, based on degree centrality and
eigenvector centrality, respectively. We also conduct comprehensive
experiments, including vertex classification, link prediction, vertex
clustering, and network visualization. The results demonstrate that the two
variants significantly outperform state-of-the-art baselines.


title:
Semilocal approximations to the Kohn-Sham exchange potential as applied
  to a metal surface

abstract:
  Several semilocal exchange potentials usually employed in the framework of
density-functional theory (DFT) are tested and compared with their exact
counterpart, the exchange Optimized Effective Potential (OEP), as applied to
the jellium-slab model of a metal-vacuum interface. Driven by their explicit
dependence on the ground-state density, its gradient, and its kinetic-energy
density, the three analyzed semilocal exchange potentials approach their
respective asymptotic limits faster than in the case of the OEP, all of them
having an asymptotic scaling of the form $-\alpha\,e^2/z + V_{\infty}$, with
$\alpha < 1$. Here we provide the leading analytic asymptotics of the three
model potentials under study, and we find that none of them exhibits the exact
OEP slab asymptotics $-\;e^2/z$. While the so-called Becke-Roussel potential's
leading asymptote is close to its exact OEP counterpart, the other two model
potentials under study approach a material-dependent positive constant value
far into the vacuum, resulting in considerably overestimated ionization
potentials.


title:
Measurement of infrared magic wavelength for an all-optical trapping of
  $^{40}$Ca$^{+}$ ion clock

abstract:
  For the first time, we experimentally determine the infrared magic wavelength
for the $^{40}$Ca$^{+}$ $4s\, ^{2}\!S_{1/2} \rightarrow 3d\,^{2}\!D_{5/2}$
electric quadrupole transition by observation of the light shift canceling in
$^{40}$Ca$^{+}$ optical clock. A "magic" magnetic field direction is chosen to
make the magic wavelength insensitive to both the linear polarization purity
and the polarization direction of the laser. The determined magic wavelength
for this transition is 1056.37(9)~nm, which is not only in good agreement with
theoretical predictions but also more precise by a factor of about 300. Using
this measured magic wavelength we also derive the differential static
polarizability to be $-44.32(32)$~a.u., which will be an important input for
the evaluation of the blackbody radiation shift at room temperatures. Our work
paves a way for all-optical-trapping of $^{40}$Ca$^{+}$ optical clock.


title: Spatial Transformer K-Means

abstract:
  K-means defines one of the most employed centroid-based clustering algorithms
with performances tied to the data's embedding. Intricate data embeddings have
been designed to push $K$-means performances at the cost of reduced theoretical
guarantees and interpretability of the results. Instead, we propose preserving
the intrinsic data space and augment K-means with a similarity measure
invariant to non-rigid transformations. This enables (i) the reduction of
intrinsic nuisances associated with the data, reducing the complexity of the
clustering task and increasing performances and producing state-of-the-art
results, (ii) clustering in the input space of the data, leading to a fully
interpretable clustering algorithm, and (iii) the benefit of convergence
guarantees.


title: Higgsino Dark Matter in Pure Gravity Mediated Supersymmetry

abstract:
  We consider the prospects for the direct detection of dark matter in pure
gravity meditation (PGM) models of supersymmetry breaking. Minimal PGM models
require only two parameters, the gravitino mass, $m_{3/2}$, which sets the UV
mass for all scalar masses, and $\tan\beta$. Gaugino masses are generated
through anomaly mediation. Typically the lightest supersymmetric state (the
dark matter candidate) is a wino. Here, we consider a one-parameter extension
of the minimal model by allowing the Higgs soft masses to deviate from
universality. For simplicity, we take these to be equal and use the $\mu$-term
as a surrogate. We also consider non-universal stop masses. When $|\mu| \sim 1$
TeV, the Higgsino is a viable dark matter candidate when the gravitino mass is
of order $\sim 1$ PeV and $\tan\beta \simeq 2$. We calculate the spin-dependent
and spin-independent cross sections for dark matter scattering on protons. For
spin-independent scattering, existing experimental limits place constraints on
the PGM parameter space. Much of the currently allowed parameter space lies
above the irreducible neutrino background. Thus, future direct detection
experiments will be able to probe much of the remaining PGM parameter space.


title:
CycleGAN for Undamaged-to-Damaged Domain Translation for Structural
  Health Monitoring and Damage Detection

abstract:
  The recent advances in the data science field in the last few decades have
benefitted many other fields including Structural Health Monitoring (SHM).
Particularly, Artificial Intelligence (AI) such as Machine Learning (ML) and
Deep Learning (DL) methods for vibration-based damage diagnostics of civil
structures has been utilized extensively due to the observed high performances
in learning from data. Along with diagnostics, damage prognostics is also
vitally important for estimating the remaining useful life of civil structures.
Currently, AI-based data-driven methods used for damage diagnostics and
prognostics centered on historical data of the structures and require a
substantial amount of data for prediction models. Although some of these
methods are generative-based models, they are used to perform ML or DL tasks
such as classification, regression, clustering, etc. after learning the
distribution of the data. In this study, a variant of Generative Adversarial
Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with
Gradient Penalty (CycleWDCGAN-GP) model is developed to investigate the
"transition of structural dynamic signature from an undamaged-to-damaged state"
and "if this transition can be employed for predictive damage detection". The
outcomes of this study demonstrate that the proposed model can accurately
generate damaged responses from undamaged responses or vice versa. In other
words, it will be possible to understand the damaged condition while the
structure is still in a healthy (undamaged) condition or vice versa with the
proposed methodology. This will enable a more proactive approach in overseeing
the life-cycle performance as well as in predicting the remaining useful life
of structures.


title:
Heterogeneous Graph Learning for Explainable Recommendation over
  Academic Networks

abstract:
  With the explosive growth of new graduates with research degrees every year,
unprecedented challenges arise for early-career researchers to find a job at a
suitable institution. This study aims to understand the behavior of academic
job transition and hence recommend suitable institutions for PhD graduates.
Specifically, we design a deep learning model to predict the career move of
early-career researchers and provide suggestions. The design is built on top of
scholarly/academic networks, which contains abundant information about
scientific collaboration among scholars and institutions. We construct a
heterogeneous scholarly network to facilitate the exploring of the behavior of
career moves and the recommendation of institutions for scholars. We devise an
unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax)
which aggregates attention mechanism and mutual information for institution
recommendation. Moreover, we propose scholar attention and meta-path attention
to discover the hidden relationships between several meta-paths. With these
mechanisms, HAI provides ordered recommendations with explainability. We
evaluate HAI upon a real-world dataset against baseline methods. Experimental
results verify the effectiveness and efficiency of our approach.


title: Square-root higher-order Weyl semimetals

abstract:
  The mathematical foundation of quantum mechanics is built on linear algebra,
while the application of nonlinear operators can lead to outstanding
discoveries under some circumstances. In this Letter, we propose a model of
square-root higher-order Weyl semimetal (SHOWS) by inheriting features from its
parent Hamiltonians. It is found that the SHOWS hosts both "Fermi-arc" surface
and hinge states that connect the projection of the Weyl points. We
theoretically construct and experimentally observe the exotic SHOWS state in
three-dimensional (3D) stacked electric circuits with honeycomb-kagome
hybridizations and double-helix interlayer couplings. Our results open the door
for realizing the square-root topology in 3D solid-state platforms.


title:
Quantum algorithms for computing observables of nonlinear partial
  differential equations

abstract:
  We construct quantum algorithms to compute physical observables of nonlinear
PDEs with M initial data. Based on an exact mapping between nonlinear and
linear PDEs using the level set method, these new quantum algorithms for
nonlinear Hamilton-Jacobi and scalar hyperbolic PDEs can be performed with a
computational cost that is independent of M, for arbitrary nonlinearity.
Depending on the details of the initial data, it can also display up to
exponential advantage in both the dimension of the PDE and the error in
computing its observables. For general nonlinear PDEs, quantum advantage with
respect to M is possible in the large M limit.


title:
Privacy-Preserving Graph Neural Network Training and Inference as a
  Cloud Service

abstract:
  Graphs are widely used to model the complex relationships among entities. As
a powerful tool for graph analytics, graph neural networks (GNNs) have recently
gained wide attention due to its end-to-end processing capabilities. With the
proliferation of cloud computing, it is increasingly popular to deploy the
services of complex and resource-intensive model training and inference in the
cloud due to its prominent benefits. However, GNN training and inference
services, if deployed in the cloud, will raise critical privacy concerns about
the information-rich and proprietary graph data (and the resulting model).
While there has been some work on secure neural network training and inference,
they all focus on convolutional neural networks handling images and text rather
than complex graph data with rich structural information. In this paper, we
design, implement, and evaluate SecGNN, the first system supporting
privacy-preserving GNN training and inference services in the cloud. SecGNN is
built from a synergy of insights on lightweight cryptography and machine
learning techniques. We deeply examine the procedure of GNN training and
inference, and devise a series of corresponding secure customized protocols to
support the holistic computation. Extensive experiments demonstrate that SecGNN
achieves comparable plaintext training and inference accuracy, with practically
affordable performance.


title: View Composition Algebra for Ad Hoc Comparison

abstract:
  Comparison is a core task in visual analysis. Although there are numerous
guidelines to help users design effective visualizations to aid known
comparison tasks, there are few techniques available when users want to make ad
hoc comparisons between marks, trends, or charts during data exploration and
visual analysis. For instance, to compare voting count maps from different
years, two stock trends in a line chart, or a scatterplot of country GDPs with
a textual summary of the average GDP. Ideally, users can directly select the
comparison targets and compare them, however what elements of a visualization
should be candidate targets, which combinations of targets are safe to compare,
and what comparison operations make sense? This paper proposes a conceptual
model that lets users compose combinations of values, marks, legend elements,
and charts using a set of composition operators that summarize, compute
differences, merge, and model their operands. We further define a View
Composition Algebra (VCA) that is compatible with datacube-based
visualizations, derive an interaction design based on this algebra that
supports ad hoc visual comparisons, and illustrate its utility through several
use cases.


title:
Application of Long Short-Term Memory Recurrent Neural Networks Based on
  the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability
  Problems

abstract:
  Reliability is an important tool for evaluating the performance of modern
networks. Currently, it is NP-hard and #P-hard to calculate the exact
reliability of a binary-state network when the reliability of each component is
assumed to be fixed. However, this assumption is unrealistic because the
reliability of each component always varies with time. To meet this practical
requirement, we propose a new algorithm called the LSTM-BAT-MCS, based on long
short-term memory (LSTM), the Monte Carlo simulation (MCS), and the
binary-adaption-tree algorithm (BAT). The superiority of the proposed
LSTM-BAT-MCS was demonstrated by experimental results of three benchmark
networks with at most 10-4 mean square error.


title: Quantization: History and Problems

abstract:
  In this work, I explore the concept of quantization as a mapping from
classical phase space functions to quantum operators. I discuss the early
history of this notion of quantization with emphasis on the works of
Schr\"odinger and Dirac, and how quantization fit into their overall
understanding of quantum theory in the 1920's. Dirac, in particular, proposed a
quantization map which should satisfy certain properties, including the
property that quantum commutators should be related to classical Poisson
brackets in a particular way. However, in 1946, Groenewold proved that Dirac's
mapping was inconsistent, making the problem of defining a rigorous
quantization map more elusive than originally expected. This result, known as
the Groenewold-Van Hove theorem, is not often discussed in physics texts, but
here I will give an account of the theorem and what it means for potential
"corrections" to Dirac's scheme. Other proposals for quantization have arisen
over the years, the first major one being that of Weyl in 1927, which was later
developed by many, including Groenewold, and which has since become known as
Weyl Quantization in the mathematical literature. Another, known as Geometric
Quantization, formulates quantization in differential-geometric terms by
appealing to the character of classical phase spaces as symplectic manifolds;
this approach began with the work of Souriau, Kostant, and Kirillov in the
1960's. I will describe these proposals for quantization and comment on their
relation to Dirac's original program. Along the way, the problem of operator
ordering and of quantizing in curvilinear coordinates will be described, since
these are natural questions that immediately present themselves when thinking
about quantization.

categories: physics.hist-ph math-ph math.MP quant-ph


title:
Relativistic Wind Farm Effect: Possibly Turbulent Flow of a Charged,
  Massless Relativistic Fluid in Graphene

abstract:
  At low Reynolds numbers, the wind flow in the wake of a single wind turbine
is generally not turbulent. However, turbines in wind farms affect each other's
wakes so that a turbulent flow can arise. In the present work, an analogue of
this effect for the massless charge carrier flow around obstacles in graphene
is outlined. We use a relativistic hydrodynamic simulation to analyze the flow
in a sample containing impurities. Depending on the density of impurities in
the sample, we indeed find evidence for potentially turbulent flow and discuss
experimental consequences.


title:
Orbit-transfer torque driven field-free switching of perpendicular
  magnetization

abstract:
  The reversal of perpendicular magnetization (PM) by electric control is
crucial for high-density integration of low-power magnetic random-access memory
(MRAM). Although the spin-transfer torque (STT) and spin-orbit torque (SOT)
technologies have been used to switch the magnetization of a free layer with
perpendicular magnetic anisotropy, the former has limited endurance because of
the high current density directly through the junction, while the latter
requires an external magnetic field or unconventional configuration to break
the symmetry. Here we propose and realize the orbit-transfer torque (OTT), that
is, exerting torque on the magnetization using the orbital magnetic moments,
and thus demonstrate a new strategy for current-driven PM reversal without
external magnetic field. The perpendicular polarization of orbital magnetic
moments is generated by a direct current in a few-layer WTe2 due to the
existence of nonzero Berry curvature dipole, and the polarization direction can
be switched by changing the current polarity. Guided by this principle, we
construct the WTe2/Fe3GeTe2 heterostructures, where the OTT driven field-free
deterministic switching of PM is achieved.


title:
Learning Deep Direct-Path Relative Transfer Function for Binaural Sound
  Source Localization

abstract:
  Direct-path relative transfer function (DP-RTF) refers to the ratio between
the direct-path acoustic transfer functions of two microphone channels. Though
DP-RTF fully encodes the sound spatial cues and serves as a reliable
localization feature, it is often erroneously estimated in the presence of
noise and reverberation. This paper proposes to learn DP-RTF with deep neural
networks for robust binaural sound source localization. A DP-RTF learning
network is designed to regress the binaural sensor signals to a real-valued
representation of DP-RTF. It consists of a branched convolutional neural
network module to separately extract the inter-channel magnitude and phase
patterns, and a convolutional recurrent neural network module for joint feature
learning. To better explore the speech spectra to aid the DP-RTF estimation, a
monaural speech enhancement network is used to recover the direct-path
spectrograms from the noisy ones. The enhanced spectrograms are stacked onto
the noisy spectrograms to act as the input of the DP-RTF learning network. We
train one unique DP-RTF learning network using many different binaural arrays
to enable the generalization of DP-RTF learning across arrays. This way avoids
time-consuming training data collection and network retraining for a new array,
which is very useful in practical application. Experimental results on both
simulated and real-world data show the effectiveness of the proposed method for
direction of arrival (DOA) estimation in the noisy and reverberant environment,
and a good generalization ability to unseen binaural arrays.


title: Weakly nonlinear surface waves on the plasma-vacuum interface

abstract:
  We consider the free boundary problem for a plasma--vacuum interface in ideal
incompressible magnetohydrodynamics. Unlike the classical statement, where the
vacuum magnetic field obeys the div-curl system of pre-Maxwell dynamics, we do
not neglect the displacement current in the vacuum region and consider the
Maxwell equations for electric and magnetic fields. Our aim is to construct
highly oscillating surface wave solutions in weakly nonlinear regime to this
plasma--vacuum interface problem.
  Under a necessary and sufficient stability condition for a piecewise constant
background state, we construct approximate solutions at any arbitrarily large
order of accuracy to the free boundary problem in three space dimensions when
the initial discontinuity displays high frequency oscillations. Moreover, such
approximate surface waves have nontrivial residual non-oscillatory components.


title: PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation

abstract:
  An unsupervised point cloud object retrieval and pose estimation method,
called PCRP, is proposed in this work. It is assumed that there exists a
gallery point cloud set that contains point cloud objects with given pose
orientation information. PCRP attempts to register the unknown point cloud
object with those in the gallery set so as to achieve content-based object
retrieval and pose estimation jointly, where the point cloud registration task
is built upon an enhanced version of the unsupervised R-PointHop method.
Experiments on the ModelNet40 dataset demonstrate the superior performance of
PCRP in comparison with traditional and learning based methods.


title:
Data Capsule: A Self-Contained Data Model as an Access Policy
  Enforcement Strategy

abstract:
  In this paper, we introduce a data capsule model, a self-contained and
self-enforcing data container based on emerging self-sovereign identity
standards, blockchain, and attribute-based encryption. A data capsule allows
for a transparent, privacy-respecting, and secure exchange of personal data,
enabling a progressive trust scheme in a semi-trusted environment. Each data
capsule is bundled with its own access policy structure and verifiable data,
drastically reducing the number of interactions needed among the user, the
service providers, and data custodians. Moreover, by relying on the
decentralized nature of blockchain and attribute-based encryption our proposed
model ensures the access policies published by service providers are public,
transparent, and strictly followed.


title: Near-optimal Top-k Pattern Mining

abstract:
  Nowadays, frequent pattern mining (FPM) on large graphs receives increasing
attention, since it is crucial to a variety of applications, e.g., social
analysis. Informally, the FPM problem is defined as finding all the patterns in
a large graph with frequency above a user-defined threshold. However, this
problem is nontrivial due to the unaffordable computational and space costs in
the mining process. In light of this, we propose a cost-effective approach to
mining near-optimal top-k patterns. Our approach applies a "level-wise"
strategy to incrementally detect frequent patterns, hence is able to terminate
as soon as top-k patterns are discovered. Moreover, we develop a technique to
compute the lower bound of support with smart traverse strategy and compact
data structures. Extensive experimental studies on real-life and synthetic
graphs show that our approach performs well, i.e., it outperforms traditional
counterparts in efficiency, memory footprint, recall and scalability.


abstract:
  Knowledge distillation aims to enhance the performance of a lightweight
student model by exploiting the knowledge from a pre-trained cumbersome teacher
model. However, in the traditional knowledge distillation, teacher predictions
are only used to provide the supervisory signal for the last layer of the
student model, which may result in those shallow student layers lacking
accurate training guidance in the layer-by-layer back propagation and thus
hinders effective knowledge transfer. To address this issue, we propose
Deeply-Supervised Knowledge Distillation (DSKD), which fully utilizes class
predictions and feature maps of the teacher model to supervise the training of
shallow student layers. A loss-based weight allocation strategy is developed in
DSKD to adaptively balance the learning process of each shallow layer, so as to
further improve the student performance. Extensive experiments show that the
performance of DSKD consistently exceeds state-of-the-art methods on various
teacher-student models, confirming the effectiveness of our proposed method.


title:
Giant oscillatory tunnel magnetoresistance in CoFe/MgO/CoFe(001)
  junctions

abstract:
  The tunnel magnetoresistance (TMR) effect observed in magnetic tunnel
junctions (MTJs) is commonly used in many spintronic applications because the
effect can easily convert from local magnetic states to electric signals in a
wide range of device resistances. In this study, we demonstrated TMR ratios of
up to 631% at room temperature (RT), which is two or more times larger than
those used currently for magnetoresistive random access memory (MRAM) devices,
using CoFe/MgO/CoFe(001) epitaxial MTJs. The TMR ratio increased up to 1143% at
10 K, which corresponds to an effective tunneling spin polarization of 0.923.
The observed large TMR ratios resulted from the fine-tuning of atomic-scale
structures of the MTJs, such as crystallographic orientations and MgO interface
oxidation, in which the well-known Delta1 coherent tunneling mechanism for the
giant TMR effect is expected to be pronounced. However, behavior that is not
covered by the standard coherent tunneling theory was unexpectedly manifested;
i.e., (i) TMR saturation at a thick MgO barrier region and (ii) enhanced TMR
oscillation with a 0.32 nm period in MgO thickness. Particularly, the TMR
oscillatory behavior dominates the transport in a wide range of MgO
thicknesses; the peak-to-valley difference of the TMR oscillation exceeded 140%
at RT, attributable to the appearance of large oscillatory components in
resistance area product (RA). Further, we found that the oscillatory behaviors
of the TMR ratio and RA survive, even under a +-1 V bias voltage application,
indicating the robustness of the oscillation. Our demonstration of the giant
RT-TMR ratio will be an essential step for establishing spintronic
architectures, such as large-capacity MRAMs and spintronic artificial neural
networks. More essentially, the present observations can trigger us to revisit
the true TMR mechanism in crystalline MTJs.


title:
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI
  Workloads

abstract:
  Lowering costs by driving high utilization across deep learning workloads is
a crucial lever for cloud providers. We present Singularity, Microsoft's
globally distributed scheduling service for highly-efficient and reliable
execution of deep learning training and inference workloads. At the heart of
Singularity is a novel, workload-aware scheduler that can transparently preempt
and elastically scale deep learning workloads to drive high utilization without
impacting their correctness or performance, across a global fleet of AI
accelerators (e.g., GPUs, FPGAs).
  All jobs in Singularity are preemptable, migratable, and dynamically
resizable (elastic) by default: a live job can be dynamically and transparently
(a) preempted and migrated to a different set of nodes, cluster, data center or
a region and resumed exactly from the point where the execution was preempted,
and (b) resized (i.e., elastically scaled-up/down) on a varying set of
accelerators of a given type. Our mechanisms are transparent in that they do
not require the user to make any changes to their code or require using any
custom libraries that may limit flexibility. Additionally, our approach
significantly improves the reliability of deep learning workloads. We show that
the resulting efficiency and reliability gains with Singularity are achieved
with negligible impact on the steady-state performance. Finally, our design
approach is agnostic of DNN architectures and handles a variety of parallelism
strategies (e.g., data/pipeline/model parallelism).


title:
Toward an efficient hybrid method for pricing barrier options on assets
  with stochastic volatility

abstract:
  We combine the one-dimensional Monte Carlo simulation and the semi-analytical
one-dimensional heat potential method to design an efficient technique for
pricing barrier options on assets with correlated stochastic volatility. Our
approach to barrier options valuation utilizes two loops. First we run the
outer loop by generating volatility paths via the Monte Carlo method. Second,
we condition the price dynamics on a given volatility path and apply the method
of heat potentials to solve the conditional problem in closed-form in the inner
loop. We illustrate the accuracy and efficacy of our semi-analytical approach
by comparing it with the two-dimensional Monte Carlo simulation and a hybrid
method, which combines the finite-difference technique for the inner loop and
the Monte Carlo simulation for the outer loop. We apply our method for
computation of state probabilities (Green function), survival probabilities,
and values of call options with barriers. Our approach provides better accuracy
and is orders of magnitude faster than the existing methods. s a by-product of
our analysis, we generalize Willard's (1997) conditioning formula for valuation
of path-independent options to path-dependent options and derive a novel
expression for the joint probability density for the value of drifted Brownian
motion and its running minimum.


title:
Large Intrinsic Valley Polarization and High Curie Temperature in Stable
  Two-dimensional Ferrovalley YX$_2$(X=I,Br and Cl)

abstract:
  Ferrovalley materials with spontaneous valley polarization are crucial to
valleytronic application. Based on first-principles calculations, we
demonstrate that two-dimensional (2D) YX$_2$(X= I, Br,and Cl) in 2H structure
constitute a series of promising ferrovalley semiconductors with large
spontaneous valley polarization and high Curie temperature. Our calculations
reveal that YX$_2$ are dynamically and thermally stable 2D ferromagnetic
semiconductors with a Curie temperature above 200 K. Due to the natural
noncentrosymmetric structure, intrinsic ferromagnetic order and strong spin
orbital coupling, the large spontaneous valley polarizations of 108.98, 57.70
and 22.35 meV are also predicted in single-layer YX$_2$(X = I, Br and
Cl),respectively. The anomalous valley Hall effect is also proposed based on
the valley contrasting Berry curvature. Moreover, the ferromagnetism and valley
polarization are found to be effectively tuning by applying a biaxial strain.
Interestingly, the suppressed valley physics of YBr$_2$ and YCl$_2$ can be
switched on via applying a moderate compression strain. The present findings
promise YX$_2$ as competitive candidates for the further experimental studies
and practical applications in valleytronics.


title: Mean Convex Smoothing of Mean Convex Cones

abstract:
  We show that any minimizing hypercone can be perturbed into one side to a
properly embedded smooth minimizing hypersurface in the Euclidean space, and
every viscosity mean convex cone admits a properly embedded smooth mean convex
self-expander asymptotic to it near infinity. These two together confirm a
conjecture of Lawson \cite[Problem 5.7]{Brothers86_OpenProblems}.


title:
Period Changes of 14,127 Contact Eclipsing Binaries in the Galactic
  Bulge

abstract:
  We present the orbital period variations of 14,127 contact eclipsing binaries
(CEBs) based on the OGLE-III\&IV observations in the Galactic bulge. New times
of minimum lights for the CEBs were derived by binary modeling for the full
seasonal light curves, which were made from survey observations at an interval
of 1 year. The orbital period changes of the systems were classified based on
the statistical inference, multiple-hypothesis testing error measure, and
visual inspection of the eclipse timing diagrams. As the results, we identified
13,716 CEBs with a parabola, 307 CEBs with a sinusoid, and 104 CEBs with the
two variations. The period distributions of the inner close binaries and the
outer companions were in the ranges of $0.235-0.990$ days and $5.0-14.0$ years,
respectively. In our sample of 13,820 CEBs showing a parabolic variation, the
highest decreasing and increasing period rates were determined to be $\dot
P=-1.38\pm0.06\times10^{-5}$ day year$^{-1}$ for OGLE-BLG-ECL-169991 and $\dot
P=+8.99\pm0.44\times10^{-6}$ day year$^{-1}$ for OGLE-BLG-ECL-189805,
respectively. The secular period change rates were distributed almost
symmetrically around zero, and most of them lie within $\dot
P=\pm5.0\times10^{-6}$ day year$^{-1}$.


title: A deep dive into the consistently toxic 1% of Twitter

abstract:
  Misbehavior in online social networks (OSN) is an ever-growing phenomenon.
The research to date tends to focus on the deployment of machine learning to
identify and classify types of misbehavior such as bullying, aggression, and
racism to name a few. The main goal of identification is to curb natural and
mechanical misconduct and make OSNs a safer place for social discourse. Going
beyond past works, we perform a longitudinal study of a large selection of
Twitter profiles, which enables us to characterize profiles in terms of how
consistently they post highly toxic content. Our data spans 14 years of tweets
from 122K Twitter profiles and more than 293M tweets. From this data, we
selected the most extreme profiles in terms of consistency of toxic content and
examined their tweet texts, and the domains, hashtags, and URLs they shared. We
found that these selected profiles keep to a narrow theme with lower diversity
in hashtags, URLs, and domains, they are thematically similar to each other (in
a coordinated manner, if not through intent), and have a high likelihood of
bot-like behavior (likely to have progenitors with intentions to influence).
Our work contributes a substantial and longitudinal online misbehavior dataset
to the research community and establishes the consistency of a profile's toxic
behavior as a useful factor when exploring misbehavior as potential accessories
to influence operations on OSNs.


title:
Broken symmetry of recruitment fluctuations in marine fishes:
  L\'evy-stable laws and beyond

abstract:
  Recruitment is calculated by summing random offspring-numbers entering the
population, where the number of summands (i.e. spawning population size) is
also a random process. A priori, it is not clear that individual reproductive
variability would have a significant impact on aggregate measures for
monitoring populations. Usually these variations are averaged out in a large
population, and the aggregate output is merely influenced by population-wide
environmental disturbances such as climate and fisheries. However, such
arguments break down if the distribution of the individual offspring numbers is
heavy-tailed. In a world with power-law offspring-number distribution with
exponent $1<\alpha<2$, the recruitment distribution has a putative power-law
regime in the tail with the same $\alpha$. The question is to what extent
individual reproductive variability can have a noticeable impact on the
recruitment under environmentally driven population fluctuations. This question
is answered by considering the L\'evy-stable fluctuations as embedded in a
randomly varying environment. I report fluctuation scaling and asymmetric
fluctuations in recruitment of commercially exploited fish stocks throughout
the North Atlantic. The linear scaling of recruitment standard deviation with
recruitment level implies that the individual reproductive variability is
dominated by population fluctuations. The totally asymmetric (skewed to the
right) character is a sign of idiosyncratic variation in reproductive success.


title:
Conversational Speech Recognition By Learning Conversation-level
  Characteristics

abstract:
  Conversational automatic speech recognition (ASR) is a task to recognize
conversational speech including multiple speakers. Unlike sentence-level ASR,
conversational ASR can naturally take advantages from specific characteristics
of conversation, such as role preference and topical coherence. This paper
proposes a conversational ASR model which explicitly learns conversation-level
characteristics under the prevalent end-to-end neural framework. The highlights
of the proposed model are twofold. First, a latent variational module (LVM) is
attached to a conformer-based encoder-decoder ASR backbone to learn role
preference and topical coherence. Second, a topic model is specifically adopted
to bias the outputs of the decoder to words in the predicted topics.
Experiments on two Mandarin conversational ASR tasks show that the proposed
model achieves a maximum 12% relative character error rate (CER) reduction.


title: The NLP Task Effectiveness of Long-Range Transformers

abstract:
  Transformer models cannot easily scale to long sequences due to their O(N^2)
time and space complexity. This has led to Transformer variants seeking to
lessen computational complexity, such as Longformer and Performer. While such
models have theoretically greater efficiency, their effectiveness on real NLP
tasks has not been well studied. We benchmark 7 variants of Transformer models
on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the
effect of pretraining and hyperparameter settings, to focus on their capacity
for long-range attention. Moreover, we present various methods to investigate
attention behaviors, to illuminate model details beyond metric scores. We find
that attention of long-range transformers has advantages on content selection
and query-guided decoding, but they come with previously unrecognized drawbacks
such as insufficient attention to distant tokens.


title:
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time
  Series

abstract:
  Anomaly detection is a widely studied task for a broad variety of data types;
among them, multiple time series appear frequently in applications, including
for example, power grids and traffic networks. Detecting anomalies for multiple
time series, however, is a challenging subject, owing to the intricate
interdependencies among the constituent series. We hypothesize that anomalies
occur in low density regions of a distribution and explore the use of
normalizing flows for unsupervised anomaly detection, because of their superior
quality in density estimation. Moreover, we propose a novel flow model by
imposing a Bayesian network among constituent series. A Bayesian network is a
directed acyclic graph (DAG) that models causal relationships; it factorizes
the joint probability of the series into the product of easy-to-evaluate
conditional probabilities. We call such a graph-augmented normalizing flow
approach GANF and propose joint estimation of the DAG with flow parameters. We
conduct extensive experiments on real-world datasets and demonstrate the
effectiveness of GANF for density estimation, anomaly detection, and
identification of time series distribution drift.


title: ITTC @ TREC 2021 Clinical Trials Track

abstract:
  This paper describes the submissions of the Natural Language Processing (NLP)
team from the Australian Research Council Industrial Transformation Training
Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021
Clinical Trials Track. The task focuses on the problem of matching eligible
clinical trials to topics constituting a summary of a patient's admission
notes. We explore different ways of representing trials and topics using NLP
techniques, and then use a common retrieval model to generate the ranked list
of relevant trials for each topic. The results from all our submitted runs are
well above the median scores for all topics, but there is still plenty of scope
for improvement.