title:
Identifying the Adoption or Rejection of Misinformation Targeting
  COVID-19 Vaccines in Twitter Discourse

abstract:
  Although billions of COVID-19 vaccines have been administered, too many
people remain hesitant. Misinformation about the COVID-19 vaccines, propagating
on social media, is believed to drive hesitancy towards vaccination. However,
exposure to misinformation does not necessarily indicate misinformation
adoption. In this paper we describe a novel framework for identifying the
stance towards misinformation, relying on attitude consistency and its
properties. The interactions between attitude consistency, adoption or
rejection of misinformation and the content of microblogs are exploited in a
novel neural architecture, where the stance towards misinformation is organized
in a knowledge graph. This new neural framework is enabling the identification
of stance towards misinformation about COVID-19 vaccines with state-of-the-art
results. The experiments are performed on a new dataset of misinformation
towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter
discourse. Because CoVaxLies provides a taxonomy of the misinformation about
COVID-19 vaccines, we are able to show which type of misinformation is mostly
adopted and which is mostly rejected.


title: Learning Representations Robust to Group Shifts and Adversarial Examples

abstract:
  Despite the high performance achieved by deep neural networks on various
tasks, extensive studies have demonstrated that small tweaks in the input could
fail the model predictions. This issue of deep neural networks has led to a
number of methods to improve model robustness, including adversarial training
and distributionally robust optimization. Though both of these two methods are
geared towards learning robust models, they have essentially different
motivations: adversarial training attempts to train deep neural networks
against perturbations, while distributional robust optimization aims at
improving model performance on the most difficult "uncertain distributions". In
this work, we propose an algorithm that combines adversarial training and group
distribution robust optimization to improve robust representation learning.
Experiments on three image benchmark datasets illustrate that the proposed
method achieves superior results on robust metrics without sacrificing much of
the standard measures.


title: A Mental-Model Centric Landscape of Human-AI Symbiosis

abstract:
  There has been significant recent interest in developing AI agents capable of
effectively interacting and teaming with humans. While each of these works try
to tackle a problem quite central to the problem of human-AI interaction, they
tend to rely on myopic formulations that obscure the possible inter-relatedness
and complementarity of many of these works. The human-aware AI framework was a
recent effort to provide a unified account for human-AI interaction by casting
them in terms of their relationship to various mental models. Unfortunately,
the current accounts of human-aware AI are insufficient to explain the
landscape of the work doing in the space of human-AI interaction due to their
focus on limited settings. In this paper, we aim to correct this shortcoming by
introducing a significantly general version of human-aware AI interaction
scheme, called generalized human-aware interaction (GHAI), that talks about
(mental) models of six types. Through this paper, we will see how this new
framework allows us to capture the various works done in the space of human-AI
interaction and identify the fundamental behavioral patterns supported by these
works. We will also use this framework to identify potential gaps in the
current literature and suggest future research directions to address these
shortcomings.


title:
Monte Carlo Sensitivity Analysis for Unmeasured Confounding in Dynamic
  Treatment Regimes

abstract:
  Data-driven methods for personalizing treatment assignment have garnered much
attention from clinicians and researchers. Dynamic treatment regimes formalize
this through a sequence of decision rules that map individual patient
characteristics to a recommended treatment. Observational studies are commonly
used for estimating dynamic treatment regimes due to the potentially
prohibitive costs of conducting sequential multiple assignment randomized
trials. However, estimating a dynamic treatment regime from observational data
can lead to bias in the estimated regime due to unmeasured confounding.
Sensitivity analyses are useful for assessing how robust the conclusions of the
study are to a potential unmeasured confounder. A Monte Carlo sensitivity
analysis is a probabilistic approach that involves positing and sampling from
distributions for the parameters governing the bias. We propose a method for
performing a Monte Carlo sensitivity analysis of the bias due to unmeasured
confounding in the estimation of dynamic treatment regimes. We demonstrate the
performance of the proposed procedure with a simulation study and apply it to
an observational study examining tailoring the use of antidepressants for
reducing symptoms of depression using data from Kaiser Permanente Washington
(KPWA).


title:
VaccineLies: A Natural Language Resource for Learning to Recognize
  Misinformation about the COVID-19 and HPV Vaccines

abstract:
  Billions of COVID-19 vaccines have been administered, but many remain
hesitant. Misinformation about the COVID-19 vaccines and other vaccines,
propagating on social media, is believed to drive hesitancy towards
vaccination. The ability to automatically recognize misinformation targeting
vaccines on Twitter depends on the availability of data resources. In this
paper we present VaccineLies, a large collection of tweets propagating
misinformation about two vaccines: the COVID-19 vaccines and the Human
Papillomavirus (HPV) vaccines. Misinformation targets are organized in
vaccine-specific taxonomies, which reveal the misinformation themes and
concerns. The ontological commitments of the Misinformation taxonomies provide
an understanding of which misinformation themes and concerns dominate the
discourse about the two vaccines covered in VaccineLies. The organization into
training, testing and development sets of VaccineLies invites the development
of novel supervised methods for detecting misinformation on Twitter and
identifying the stance towards it. Furthermore, VaccineLies can be a stepping
stone for the development of datasets focusing on misinformation targeting
additional vaccines.


title: Modern Augmented Reality: Applications, Trends, and Future Directions

abstract:
  Augmented reality (AR) is one of the relatively old, yet trending areas in
the intersection of computer vision and computer graphics with numerous
applications in several areas, from gaming and entertainment, to education and
healthcare. Although it has been around for nearly fifty years, it has seen a
lot of interest by the research community in the recent years, mainly because
of the huge success of deep learning models for various computer vision and AR
applications, which made creating new generations of AR technologies possible.
This work tries to provide an overview of modern augmented reality, from both
application-level and technical perspective. We first give an overview of main
AR applications, grouped into more than ten categories. We then give an
overview of around 100 recent promising machine learning based works developed
for AR systems, such as deep learning works for AR shopping (clothing, makeup),
AR based image filters (such as Snapchat's lenses), AR animations, and more. In
the end we discuss about some of the current challenges in AR domain, and the
future directions in this area.


title:
Using Pilot Data to Size Observational Studies for the Estimation of
  Dynamic Treatment Regimes

abstract:
  There has been significant attention given to developing data-driven methods
for tailoring patient care based on individual patient characteristics. Dynamic
treatment regimes formalize this through a sequence of decision rules that map
patient information to a suggested treatment. The data for estimating and
evaluating treatment regimes are ideally gathered through the use of Sequential
Multiple Assignment Randomized Trials (SMARTs) though longitudinal
observational studies are commonly used due to the potentially prohibitive
costs of conducting a SMART. These studies are typically sized for simple
comparisons of fixed treatment sequences or, in the case of observational
studies, a priori sample size calculations are often not performed. We develop
sample size procedures for the estimation of dynamic treatment regimes from
observational studies. Our approach uses pilot data to ensure a study will have
sufficient power for comparing the value of the optimal regime, i.e. the
expected outcome if all patients in the population were treated by following
the optimal regime, with a known comparison mean. Our approach also ensures the
value of the estimated optimal treatment regime is within an a priori set range
of the value of the true optimal regime with a high probability. We examine the
performance of the proposed procedure with a simulation study and use it to
size a study for reducing depressive symptoms using data from electronic health
records.


title:
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early
  Modern French

abstract:
  Language models for historical states of language are becoming increasingly
important to allow the optimal digitisation and analysis of old textual
sources. Because these historical states are at the same time more complex to
process and more scarce in the corpora available, specific efforts are
necessary to train natural language processing (NLP) tools adapted to the data.
In this paper, we present our efforts to develop NLP tools for Early Modern
French (historical French from the 16$^\text{th}$ to the 18$^\text{th}$
centuries). We present the $\text{FreEM}_{\text{max}}$ corpus of Early Modern
French and D'AlemBERT, a RoBERTa-based language model trained on
$\text{FreEM}_{\text{max}}$. We evaluate the usefulness of D'AlemBERT by
fine-tuning it on a part-of-speech tagging task, outperforming previous work on
the test set. Importantly, we find evidence for the transfer learning capacity
of the language model, since its performance on lesser-resourced time periods
appears to have been boosted by the more resourced ones. We release D'AlemBERT
and the open-sourced subpart of the $\text{FreEM}_{\text{max}}$ corpus.


title: Cosmic acceleration in Regge-Teitelboim gravity

abstract:
  The Regge-Teitelboim formulation of gravity, which utilizes dynamical
embeddings in a background space, effectively introduces source terms in the
standard Einstein equations that are not attributable to the energy-momentum
tensor. We show that for a simple class of embeddings of the Robertson-Walker
metric, these source terms naturally generate cosmic acceleration.


title:
Flow-level Coordination of Connected and Autonomous Vehicles in
  Multilane Freeway Ramp Merging Areas

abstract:
  On-ramp merging areas are deemed to be typical bottlenecks for freeway
networks due to the intensive disturbances induced by the frequent merging,
weaving, and lane-changing behaviors. The Connected and Autonomous Vehicles
(CAVs), benefited from their capabilities of real-time communication and
precise motion control, hold an opportunity to promote ramp merging operation
through enhanced cooperation. The existing CAV cooperation strategies are
mainly designed for single-lane freeways, although multilane configurations are
more prevailing in the real-world. In this paper, we present a flow-level CAV
coordination strategy to facilitate merging operation in multilane freeways.
The coordination integrates lane-change rules between mainstream lanes,
proactive creation of large merging gaps, and platooning of ramp vehicles for
enhanced benefits in traffic flow stability and efficiency. The strategy is
formulated under an optimization framework, where the optimal control plan is
determined based on real-time traffic conditions. The impacts of tunable model
parameters on the produced control plan are discussed in detail. The efficiency
of the proposed multilane strategy is demonstrated in a micro-simulation
environment. The results show that the coordination can substantially improve
the overall ramp merging efficiency and prevent recurrent traffic congestions,
especially under high traffic volume conditions.


title:
Analysis of photoinjector transverse phase space in action and phase
  coordinates

abstract:
  Photoinjectors are the main high brightness electron sources for X-ray free
electron lasers (XFEL). Photoinjector emittance reduction is one of the key
knobs for improving XFEL lasing, so precise emittance measurement is critical.
It's well known that rms emittance is very sensitive to low intensity tails of
particle distributions in the phase space, whose measurement depend on the
signal to noise ratio (SNR) and image processing procedures. Such sensitivities
make the interpretations of beam transverse brightness challenging, leading to
different emittance definitions to reduce the impact of tail particles. In this
paper, transverse phase space is analyzed in action and phase coordinates for
both analytical models and experiments, which give a more intuitive way to
calculate the beam core brightness.


title:
From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of
  Stance, Ontological Commitments and Moral Foundations

abstract:
  While billions of COVID-19 vaccines have been administered, too many people
remain hesitant. Twitter, with its substantial reach and daily exposure, is an
excellent resource for examining how people frame their vaccine hesitancy and
to uncover vaccine hesitancy profiles. In this paper we expose our processing
journey from identifying Vaccine Hesitancy Framings in a collection of
9,133,471 original tweets discussing the COVID-19 vaccines, establishing their
ontological commitments, annotating the Moral Foundations they imply to the
automatic recognition of the stance of the tweet authors toward any of the
CoVaxFrames that we have identified. When we found that 805,336 Twitter users
had a stance towards some CoVaxFrames in either the 9,133,471 original tweets
or their 17,346,664 retweets, we were able to derive nine different Vaccine
Hesitancy Profiles of these users and to interpret these profiles based on the
ontological commitments of the frames they evoked in their tweets and on value
of their stance towards the evoked frames.


title:
Merging Control Strategies of Connected and Autonomous Vehicles at
  Freeway On-Ramps: A Comprehensive Review

abstract:
  On-ramp merging areas are typical bottlenecks in the freeway network, since
merging on-ramp vehicles may cause intensive disturbances on the mainline
traffic flow and lead to various negative impacts on traffic efficiency and
safety. The connected and autonomous vehicles (CAVs), with their capabilities
of real-time communication and precise motion control, hold a great potential
to facilitate ramp merging operation through enhanced coordination strategies.
This paper presents a comprehensive review of the existing ramp merging
strategies leveraging CAVs, focusing on the latest trends and developments in
the research field. The review comprehensively covers 44 papers recently
published in leading transportation journals. Based on the application context,
control strategies are categorized into three categories: merging into
sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed
traffic flows, and merging into multilane freeways. Relevant literature is
reviewed regarding the required technologies, control decision level, applied
methods, and impacts on traffic performance. More importantly, we identify the
existing research gaps and provide insightful discussions on the potential and
promising directions for future research based on the review, which facilitates
further advancement in this research topic.


title: Some results for semi-stable radial solutions of $k$-Hessian equations

abstract:
  We devote this paper to study semi-stable nonconstant radial solutions of
$S_k(D^2u) = w(|x|)g(u)$ on the Euclidean space $R^n$. We establish pointwise
estimates and necessary conditions for the existence of such solutions (not
necessarily bounded) for this equation. For bounded solutions we estimate their
asymptotic behavior at infinity. All the estimates are given in terms of the
spatial dimension $n$, the values of $k$ and the behavior at infinity of the
growth rate function of $w$.


title:
Interactive Visual Pattern Search on Graph Data via Graph Representation
  Learning

abstract:
  Graphs are a ubiquitous data structure to model processes and relations in a
wide range of domains. Examples include control-flow graphs in programs and
semantic scene graphs in images. Identifying subgraph patterns in graphs is an
important approach to understanding their structural properties. We propose a
visual analytics system GraphQ to support human-in-the-loop, example-based,
subgraph pattern search in a database containing many individual graphs. To
support fast, interactive queries, we use graph neural networks (GNNs) to
encode a graph as fixed-length latent vector representation, and perform
subgraph matching in the latent space. Due to the complexity of the problem, it
is still difficult to obtain accurate one-to-one node correspondences in the
matching results that are crucial for visualization and interpretation. We,
therefore, propose a novel GNN for node-alignment called NeuroAlign, to
facilitate easy validation and interpretation of the query results. GraphQ
provides a visual query interface with a query editor and a multi-scale
visualization of the results, as well as a user feedback mechanism for refining
the results with additional constraints. We demonstrate GraphQ through two
example usage scenarios: analyzing reusable subroutines in program workflows
and semantic scene graph search in images. Quantitative experiments show that
NeuroAlign achieves 19-29% improvement in node-alignment accuracy compared to
baseline GNN and provides up to 100x speedup compared to combinatorial
algorithms. Our qualitative study with domain experts confirms the
effectiveness for both usage scenarios.


title: Atomic-scale identification of the active sites of nanocatalysts

abstract:
  Alloy nanocatalysts have found broad applications ranging from fuel cells to
catalytic converters and hydrogenation reactions. Despite extensive studies,
identifying the active sites of nanocatalysts remains a major challenge due to
the heterogeneity of the local atomic environment. Here, we advance atomic
electron tomography to determine the 3D local atomic structure, surface
morphology and chemical composition of PtNi and Mo-doped PtNi nanocatalysts.
Using machine learning trained by density functional theory calculations, we
identify the catalytic active sites for the oxygen reduction reaction from
experimental 3D atomic coordinates, which are corroborated by electrochemical
measurements. By quantifying the structure-activity relationship, we discover a
local environment descriptor to explain and predict the catalytic active sites
at the atomic level. The ability to determine the 3D atomic structure and
chemical species coupled with machine learning is expected to expand our
fundamental understanding of a wide range of nanocatalysts.


title:
Towards Enabling Dynamic Convolution Neural Network Inference for Edge
  Intelligence

abstract:
  Deep learning applications have achieved great success in numerous real-world
applications. Deep learning models, especially Convolution Neural Networks
(CNN) are often prototyped using FPGA because it offers high power efficiency
and reconfigurability. The deployment of CNNs on FPGAs follows a design cycle
that requires saving of model parameters in the on-chip memory during
High-level synthesis (HLS). Recent advances in edge intelligence require CNN
inference on edge network to increase throughput and reduce latency. To provide
flexibility, dynamic parameter allocation to different mobile devices is
required to implement either a predefined or defined on-the-fly CNN
architecture. In this study, we present novel methodologies for dynamically
streaming the model parameters at run-time to implement a traditional CNN
architecture. We further propose a library-based approach to design scalable
and dynamic distributed CNN inference on the fly leveraging
partial-reconfiguration techniques, which is particularly suitable for
resource-constrained edge devices. The proposed techniques are implemented on
the Xilinx PYNQ-Z2 board to prove the concept by utilizing the LeNet-5 CNN
model. The results show that the proposed methodologies are effective, with
classification accuracy rates of 92%, 86%, and 94% respectively


title: A Soft-Switching Single-Stage AC-DC Converter

abstract:
  Partial resonance high frequency AC link converters are recognized to provide
soft-switching, single-stage power conversion. They are highly reliable due to
elimination of DC capacitors. Previous work has focused on proof of concept
topologies and design requirements. In this work, a current regulated AC-DC
converter from this family of converters is presented. The control design and
grid integration requirements are discussed. An active damping method using
grid current feedback is implemented to mitigate the filter resonance
oscillations. The current control is implemented in the synchronous frame using
voltage oriented control which enable power factor and power flow control. The
result of converter operation and its control loops performances are presented


title:
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the
  Dynamics of Panel Data

abstract:
  Panel data involving longitudinal measurements of the same set of
participants taken over multiple time points is common in studies to understand
childhood development and disease modeling. Deep hybrid models that marry the
predictive power of neural networks with physical simulators such as
differential equations, are starting to drive advances in such applications.
The task of modeling not just the observations but the hidden dynamics that are
captured by the measurements poses interesting statistical/computational
questions. We propose a probabilistic model called ME-NODE to incorporate
(fixed + random) mixed effects for analyzing such panel data. We show that our
model can be derived using smooth approximations of SDEs provided by the
Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and
develop (efficient) training algorithms using MC based sampling methods and
numerical ODE solvers. We demonstrate ME-NODE's utility on tasks spanning the
spectrum from simulations and toy data to real longitudinal 3D imaging data
from an Alzheimer's disease (AD) study, and study its performance in terms of
accuracy of reconstruction for interpolation, uncertainty estimates and
personalized prediction.


title:
Geometric Algebra based Embeddings for Static and Temporal Knowledge
  Graph Completion

abstract:
  Recent years, Knowledge Graph Embeddings (KGEs) have shown promising
performance on link prediction tasks by mapping the entities and relations from
a Knowledge Graph (KG) into a geometric space and thus have gained increasing
attentions. In addition, many recent Knowledge Graphs involve evolving data,
e.g., the fact (\textit{Obama}, \textit{PresidentOf}, \textit{USA}) is valid
only from 2009 to 2017. This introduces important challenges for knowledge
representation learning since such temporal KGs change over time. In this work,
we strive to move beyond the complex or hypercomplex space for KGE and propose
a novel geometric algebra based embedding approach, GeomE, which uses
multivector representations and the geometric product to model entities and
relations. GeomE subsumes several state-of-the-art KGE models and is able to
model diverse relations patterns. On top of this, we extend GeomE to TGeomE for
temporal KGE, which performs 4th-order tensor factorization of a temporal KG
and devises a new linear temporal regularization for time representation
learning. Moreover, we study the effect of time granularity on the performance
of TGeomE models. Experimental results show that our proposed models achieve
the state-of-the-art performances on link prediction over four commonly-used
static KG datasets and four well-established temporal KG datasets across
various metrics.


title:
Attacks, Defenses, And Tools: A Framework To Facilitate Robust AI/ML
  Systems

abstract:
  Software systems are increasingly relying on Artificial Intelligence (AI) and
Machine Learning (ML) components. The emerging popularity of AI techniques in
various application domains attracts malicious actors and adversaries.
Therefore, the developers of AI-enabled software systems need to take into
account various novel cyber-attacks and vulnerabilities that these systems may
be susceptible to. This paper presents a framework to characterize attacks and
weaknesses associated with AI-enabled systems and provide mitigation techniques
and defense strategies. This framework aims to support software designers in
taking proactive measures in developing AI-enabled software, understanding the
attack surface of such systems, and developing products that are resilient to
various emerging attacks associated with ML. The developed framework covers a
broad spectrum of attacks, mitigation techniques, and defensive and offensive
tools. In this paper, we demonstrate the framework architecture and its major
components, describe their attributes, and discuss the long-term goals of this
research.


title:
Does ridesourcing respond to unplanned rail disruptions? A natural
  experiment analysis of mobility resilience and disparity

abstract:
  Urban rail transit networks provide critical access to opportunities and
livelihood in many urban systems. Ensuring that these services are resilient
(that is, exhibiting efficient response to and recovery from disruptions) is a
key economic and social priority. Increasingly, the ability of urban rail
systems to cope with disruptions is a function of a complex patchwork of
mobility options, wherein alternative modes can complement and fill service
gaps. This study analyzes the role of ridesourcing in providing adaptive
mobility capacity that could be leveraged to fill no-notice gaps in rail
transit services, addressing the question of distributional impacts of
resilience. Using a natural experiment, we systematically identify 28 major
transit disruptions over the period of one year in Chicago and match them, both
temporally and spatially, with ridesourcing trip data. Using multilevel mixed
modeling, we quantify variation in the adaptive use of on-demand mobility
across the racially and economically diverse city of Chicago. Our findings show
that the gap-filling potential of adaptive ridesourcing during rail transit
disruptions is significantly influenced by station-, community-, and
district-level factors. Specifically, greater shifts to ridesourcing occur
during weekdays, nonholidays, and more severe disruptions, in community areas
that have higher percentages of White residents and transit commuters, and in
the more affluent North district of the city. These findings suggest that while
ridesourcing appears to provide adaptive capacity during rail disruptions, its
benefits do not appear to be equitable for lower-income communities of color
that already experience limited mobility options.


title:
Predictive Coding: Towards a Future of Deep Learning beyond
  Backpropagation?

abstract:
  The backpropagation of error algorithm used to train deep neural networks has
been fundamental to the successes of deep learning. However, it requires
sequential backward updates and non-local computations, which make it
challenging to parallelize at scale and is unlike how learning works in the
brain. Neuroscience-inspired learning algorithms, however, such as
\emph{predictive coding}, which utilize local learning, have the potential to
overcome these limitations and advance beyond current deep learning
technologies. While predictive coding originated in theoretical neuroscience as
a model of information processing in the cortex, recent work has developed the
idea into a general-purpose algorithm able to train neural networks using only
local computations. In this survey, we review works that have contributed to
this perspective and demonstrate the close theoretical connections between
predictive coding and backpropagation, as well as works that highlight the
multiple advantages of using predictive coding models over
backpropagation-trained neural networks. Specifically, we show the
substantially greater flexibility of predictive coding networks against
equivalent deep neural networks, which can function as classifiers, generators,
and associative memories simultaneously, and can be defined on arbitrary graph
topologies. Finally, we review direct benchmarks of predictive coding networks
on machine learning classification tasks, as well as its close connections to
control theory and applications in robotics.


title: Sample Efficient Grasp Learning Using Equivariant Models

abstract:
  In planar grasp detection, the goal is to learn a function from an image of a
scene onto a set of feasible grasp poses in $\mathrm{SE}(2)$. In this paper, we
recognize that the optimal grasp function is $\mathrm{SE}(2)$-equivariant and
can be modeled using an equivariant convolutional neural network. As a result,
we are able to significantly improve the sample efficiency of grasp learning,
obtaining a good approximation of the grasp function after only 600 grasp
attempts. This is few enough that we can learn to grasp completely on a
physical robot in about 1.5 hours.


title:
Discrete subgroups generated by lattices in opposite horospherical
  subgroups of $SL(3,\mathbb{C})$ following Hee Oh

abstract:
  We prove that a discrete subgroup generated by two lattices in opposite
minimal horospherical subgroups of $SL(3,\mathbb{C})$ is arithmetic and thus by
a Borel and Harish-Chandra also a lattice. We follow the method and ideas used
by Oh in \cite{Ref13}. There, Oh proves the same result for lattices in minimal
horospherical subgroups of $SL(n,\mathbb{R})$. The method consists of studying
the orbits of the generating lattices in the horospherical subgroups under the
conjugation action of the commutator of the corresponding Levi subgroup in
order to obtain a rational structure for $SL(3,\mathbb{C})$. A theorem of
Ratner reduces the possibilities for the closure of the orbits. Then, by the
discreteness of the generated subgroup, it can be shown the closeness of the
orbits. Finally, using this information, it's possible to find a rational form
such that the subgroup generated by the lattices is arithmetic.


title:
Automated Attack Synthesis by Extracting Finite State Machines from
  Protocol Specification Documents

abstract:
  Automated attack discovery techniques, such as attacker synthesis or
model-based fuzzing, provide powerful ways to ensure network protocols operate
correctly and securely. Such techniques, in general, require a formal
representation of the protocol, often in the form of a finite state machine
(FSM). Unfortunately, many protocols are only described in English prose, and
implementing even a simple network protocol as an FSM is time-consuming and
prone to subtle logical errors. Automatically extracting protocol FSMs from
documentation can significantly contribute to increased use of these techniques
and result in more robust and secure protocol implementations.
  In this work we focus on attacker synthesis as a representative technique for
protocol security, and on RFCs as a representative format for protocol prose
description. Unlike other works that rely on rule-based approaches or use
off-the-shelf NLP tools directly, we suggest a data-driven approach for
extracting FSMs from RFC documents. Specifically, we use a hybrid approach
consisting of three key steps: (1) large-scale word-representation learning for
technical language, (2) focused zero-shot learning for mapping protocol text to
a protocol-independent information language, and (3) rule-based mapping from
protocol-independent information to a specific protocol FSM. We show the
generalizability of our FSM extraction by using the RFCs for six different
protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP. We demonstrate how automated
extraction of an FSM from an RFC can be applied to the synthesis of attacks,
with TCP and DCCP as case-studies. Our approach shows that it is possible to
automate attacker synthesis against protocols by using textual specifications
such as RFCs.


title: Non-abelian Cohen--Lenstra Heuristics in the presence of roots of unity

abstract:
  For a Galois extension $K/\mathbb{F}_q(t)$ of Galois group $\Gamma$ with
$\gcd(q,|\Gamma|)=1$, we define an invariant $\omega_K$, and show that it
determines the Weil pairing of the curve corresponding to $K$ and it descends
to the prime-to-$|\Gamma|$-torsion part of the lifting invariants of Hurwitz
schemes introduced by Ellenberg--Venkatesh--Westerland and Wood. By keeping
track of the image of $\omega_K$, we compute, as $K$ varies and $q\to \infty$,
the average number of surjections from the Galois group of maximal unramified
extension of $K$ to $H$, for any $\Gamma$-group $H$ whose order is prime to
$q|\Gamma|$. Motivated by this result, we modify the conjecture of Wood,
Zureick-Brown and the author about non-abelian Cohen--Lenstra, for both
function fields and number fields, to cover the cases when the base field
contains extra roots of unity. We also discuss how to use the invariant
$\omega_K$ to construct a random group model, and prove in a special case that
the model produces the same moments as our function field result.


title: FedEmbed: Personalized Private Federated Learning

abstract:
  Federated learning enables the deployment of machine learning to problems for
which centralized data collection is impractical. Adding differential privacy
guarantees bounds on privacy while data are contributed to a global model.
Adding personalization to federated learning introduces new challenges as we
must account for preferences of individual users, where a data sample could
have conflicting labels because one sub-population of users might view an input
positively, but other sub-populations view the same input negatively. We
present FedEmbed, a new approach to private federated learning for
personalizing a global model that uses (1) sub-populations of similar users,
and (2) personal embeddings. We demonstrate that current approaches to
federated learning are inadequate for handling data with conflicting labels,
and we show that FedEmbed achieves up to 45% improvement over baseline
approaches to personalized private federated learning.


title: Long Run Risk in Stationary Structural Vector Autoregressive Models

abstract:
  This paper introduces a local-to-unity/small sigma process for a stationary
time series with strong persistence and non-negligible long run risk. This
process represents the stationary long run component in an unobserved short-
and long-run components model involving different time scales. More
specifically, the short run component evolves in the calendar time and the long
run component evolves in an ultra long time scale. We develop the methods of
estimation and long run prediction for the univariate and multivariate
Structural VAR (SVAR) models with unobserved components and reveal the
impossibility to consistently estimate some of the long run parameters. The
approach is illustrated by a Monte-Carlo study and an application to
macroeconomic data.


title:
Activity Mediated Globule to Coil Transition of a Flexible Polymer in
  Poor Solvent

abstract:
  Understanding the role of self-propulsion on the conformational properties of
active filamentous objects has relevance in biology. In this context, we
consider a flexible bead-spring model polymer for which along with both
attractive and repulsive interactions among the non-bonded monomers, activity
for each bead works along its intrinsic direction of self-propulsion. We study
its kinetics in the overdamped limit, following a quench from good to poor
solvent condition. We observe that with low activities, though the kinetic
pathways remain similar, the scaling exponent for the relaxation time of
globule formation becomes smaller than that for the passive case.
Interestingly, for higher activities when self-propulsion dominates over
interaction energy, the polymer becomes more extended. In its steady state, the
variation of the spatial extension of the polymer, measured via its gyration
radius, shows two completely different scaling regimes: The corresponding Flory
exponent changes from $1/3$ to $3/5$ similar to a transition of the polymer
from a globular state to a self-avoiding walk. This can be explained by an
interplay among three energy scales present in the system, viz., the
"ballistic", thermal, and interaction energy.


title:
Euclid-Roman joint microlensing survey: early mass measurement, free
  floating planets and exomoons

abstract:
  As the Kepler mission has done for hot exoplanets, the ESA Euclid and NASA
Roman missions have the potential to create a breakthrough in our understanding
of the demographics of cool exoplanets, including unbound, or "free-floating",
planets (FFPs). In this study, we demonstrate the complementarity of the two
missions and propose two joint-surveys to better constrain the mass and
distance of microlensing events. We first demonstrate that an early brief
Euclid survey (7 h) of the Roman microlensing fields will allow the measurement
of a large fraction of events relative proper motions and lens magnitudes.
Then, we study the potential of simultaneous observations by Roman and Euclid
to enable the measurement of the microlensing parallax for the shortest
microlensing events. Using detailed simulations of the joint detection yield we
show that within one year Roman-Euclid observations will be at least an order
of magnitude more sensitive than current ground-based measurements. Depending
on the exact distribution of FFP, a joint Roman-Euclid campaign should detect
around 130 FFP events within a year, including 110 with measured parallax that
strongly constrain the FFP mass, and around 30 FFP events with direct mass and
distance measurements. The ability of the joint survey to completely break the
microlens mass-distance-velocity degeneracy for a significant subset of events
provides a unique opportunity to verify unambiguously the FFP hypothesis or
else place abundance limits for FFPs between Earth and Jupiter masses that are
up to two orders of magnitude stronger than provided by ground-based surveys.
Finally, we study the capabilities of the joint survey to enhance the detection
and charcterization of exomoons, and found that it could lead to the detection
of the first exomoon.


title:
Mixing of Condensable Constituents with H-He During the Formation &
  Evolution of Jupiter

abstract:
  Simulations of Jupiter's formation are presented that incorporate mixing of
H-He with denser material entering the planet as solids. Heavy compounds and
gas mix substantially when the planet becomes roughly as massive as Earth,
because incoming planetesimals can fully vaporize. Supersaturation of vaporized
silicates causes the excess to sink as droplets, but water remains at higher
altitudes. Because the mean molecular weight decreases rapidly outward, some of
the compositional inhomogeneities produced during formation can survive for
billions of years. After 4.57 Gyr, our Jupiter model retains compositional
gradients; proceeding outwards one finds: i) an inner heavy-element core, the
outer part derived from hot supersaturated rain-out; ii) a composition-gradient
region, containing most of the heavy elements, where H-He abundance increases
outward, reaching about 0.9 mass fraction at 0.3 of the radius, with silicates
enhanced relative to water in the lower parts and depleted in the upper parts;
iii) a uniform composition region (neglecting He immiscibility) that is
enriched over protosolar and contains most of the planet's mass; and iv) an
outer region where cloud formation (condensation) of heavy constituents occurs.
This radial compositional profile has heavy elements more broadly distributed
than predicted by classical formation models, but less diluted than suggested
by Juno-constrained gravity models. The compositional gradients in the region
containing the bulk of the heavy elements prevent convection, in both our
models and those fitting current gravity, resulting in a hot interior where
much of the accretion energy remains trapped.


title: The Inter-magic Spectra of Trees

abstract:
  For any positive integer $h$, a graph $G=(V,E)$ is said to be $h$-magic if
there exists a labeling $l:E(G)\to \mathbb{Z}_h -\{0\} $ such that the induced
vertex set labeling $\ l^+ : V(G) \to \mathbb{Z}_h \ $ defined by $$ l^+
(v)=\sum_{uv \in E(G)} \ l(uv) $$ is a constant map. The integer-magic spectrum
of a graph $G$, denoted by $IM(G)$, is the set of all $h \in \mathbb{N} $ for
which $G$ is $h$-magic. So far, only the integer-magic spectra of trees of
diameter at most five have been determined. In this paper, we determine the
integer-magic spectra of trees of diameter six and higher.


title:
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in
  Bayesian Deep Neural Networks

abstract:
  Uncertainty estimation in deep models is essential in many real-world
applications and has benefited from developments over the last several years.
Recent evidence suggests that existing solutions dependent on simple Gaussian
formulations may not be sufficient. However, moving to other distributions
necessitates Monte Carlo (MC) sampling to estimate quantities such as the KL
divergence: it could be expensive and scales poorly as the dimensions of both
the input data and the model grow. This is directly related to the structure of
the computation graph, which can grow linearly as a function of the number of
MC samples needed. Here, we construct a framework to describe these computation
graphs, and identify probability families where the graph size can be
independent or only weakly dependent on the number of MC samples. These
families correspond directly to large classes of distributions. Empirically, we
can run a much larger number of iterations for MC approximations for larger
architectures used in computer vision with gains in performance measured in
confident accuracy, stability of training, memory and training time.


title:
Quantum circuits for the preparation of spin eigenfunctions on quantum
  computers

abstract:
  The application of quantum algorithms to the study of many-particle quantum
systems requires the ability to prepare wavefunctions that are relevant in the
behavior of the system under study. Hamiltonian symmetries are an important
instrument, to classify relevant many-particle wavefunctions, and to improve
the efficiency of numerical simulations. In this work, quantum circuits for the
exact and approximate preparation of total spin eigenfunctions on quantum
computers are presented. Two different strategies are discussed and compared:
exact recursive construction of total spin eigenfunctions based on the addition
theorem of angular momentum, and heuristic approximation of total spin
eigenfunctions based on the variational optimization of a suitable cost
function. The construction of these quantum circuits is illustrated in detail,
and the preparation of total spin eigenfunctions is demonstrated on IBM quantum
devices, focusing on 3- and 5-spin systems on graphs with triangle
connectivity.


title: Reciprocity in Machine Learning

abstract:
  Machine learning is pervasive. It powers recommender systems such as Spotify,
Instagram and YouTube, and health-care systems via models that predict sleep
patterns, or the risk of disease. Individuals contribute data to these models
and benefit from them. Are these contributions (outflows of influence) and
benefits (inflows of influence) reciprocal? We propose measures of outflows,
inflows and reciprocity building on previously proposed measures of training
data influence. Our initial theoretical and empirical results indicate that
under certain distributional assumptions, some classes of models are
approximately reciprocal. We conclude with several open directions.


title: TransDreamer: Reinforcement Learning with Transformer World Models

abstract:
  The Dreamer agent provides various benefits of Model-Based Reinforcement
Learning (MBRL) such as sample efficiency, reusable knowledge, and safe
planning. However, its world model and policy networks inherit the limitations
of recurrent neural networks and thus an important question is how an MBRL
framework can benefit from the recent advances of transformers and what the
challenges are in doing so. In this paper, we propose a transformer-based MBRL
agent, called TransDreamer. We first introduce the Transformer State-Space
Model, a world model that leverages a transformer for dynamics predictions. We
then share this world model with a transformer-based policy network and obtain
stability in training a transformer-based RL agent. In experiments, we apply
the proposed model to 2D visual RL and 3D first-person visual RL tasks both
requiring long-range memory access for memory-based reasoning. We show that the
proposed model outperforms Dreamer in these complex tasks.


title:
Effects of noise on leaky integrate-and-fire neuron models for
  neuromorphic computing applications

abstract:
  Artificial neural networks (ANNs) have been extensively used for the
description of problems arising from biological systems and for constructing
neuromorphic computing models. The third generation of ANNs, namely, spiking
neural networks (SNNs), inspired by biological neurons enable a more realistic
mimicry of the human brain. A large class of the problems from these domains is
characterized by the necessity to deal with the combination of neurons, spikes
and synapses via integrate-and-fire neuron models. Motivated by important
applications of the integrate-and-fire of neurons in neuromorphic computing for
bio-medical studies, the main focus of the present work is on the analysis of
the effects of random inputs on leaky integrate-and-fire (LIF) neuron models.
One of the models examined here is based on a classical LIF model, while the
other model is a LIF synaptic conductance neuron model. Our analysis is carried
out via Langevin stochastic dynamics in a numerical setting describing cell
membrane potentials. We provide the details of the models, as well as
representative numerical examples, and discuss the effects of noise on the time
evolution of the membrane potential as well as the spiking activities of
neurons in both LIF models scrutinized here. Furthermore, our numerical results
demonstrate that the presence of random inputs in LIF systems may substantially
influence the spiking activities of neurons.


title: Data-Driven Mitigation of Adversarial Text Perturbation

abstract:
  Social networks have become an indispensable part of our lives, with billions
of people producing ever-increasing amounts of text. At such scales, content
policies and their enforcement become paramount. To automate moderation,
questionable content is detected by Natural Language Processing (NLP)
classifiers. However, high-performance classifiers are hampered by misspellings
and adversarial text perturbations. In this paper, we classify intentional and
unintentional adversarial text perturbation into ten types and propose a
deobfuscation pipeline to make NLP models robust to such perturbations. We
propose Continuous Word2Vec (CW2V), our data-driven method to learn word
embeddings that ensures that perturbations of words have embeddings similar to
those of the original words. We show that CW2V embeddings are generally more
robust to text perturbations than embeddings based on character ngrams. Our
robust classification pipeline combines deobfuscation and classification, using
proposed defense methods and word embeddings to classify whether Facebook posts
are requesting engagement such as likes. Our pipeline results in engagement
bait classification that goes from 0.70 to 0.67 AUC with adversarial text
perturbation, while character ngram-based word embedding methods result in
downstream classification that goes from 0.76 to 0.64.


title: Missing Data Infill with Automunge

abstract:
  Missing data is a fundamental obstacle in the practice of data science. This
paper surveys a few conventions for imputation as available in the Automunge
open source python library platform for tabular data preprocessing, including
"ML infill" in which auto ML models are trained for target features from
partitioned extracts of a training set. A series of validation experiments were
performed to benchmark imputation scenarios towards downstream model
performance, in which it was found for the given benchmark sets that in many
cases ML infill outperformed for both numeric and categoric target features,
and was otherwise at minimum within noise distributions of the other imputation
scenarios. Evidence also suggested supplementing ML infill with the addition of
support columns with boolean integer markers signaling presence of infill was
usually beneficial to downstream model performance. We consider these results
sufficient to recommend defaulting to ML infill for tabular learning, and
further recommend supplementing imputations with support columns signaling
presence of infill, each as can be prepared with push-button operation in the
Automunge library. Our contributions include an auto ML derived missing data
imputation library for tabular learning in the python ecosystem, fully
integrated into a preprocessing platform with an extensive library of feature
transformations, with a novel production friendly implementation that bases
imputation models on a designated train set for consistent basis towards
additional data.


title: Bayesian inference for link travel time correlation of a bus route

abstract:
  Estimation of link travel time correlation of a bus route is essential to
many bus operation applications, such as timetable scheduling, travel time
forecasting and transit service assessment/improvement. Most previous studies
rely on either independent assumptions or simplified local spatial correlation
structures. In the real world, however, link travel time on a bus route could
exhibit complex correlation structures, such as long-range correlations,
negative correlations, and time-varying correlations. Therefore, before
introducing strong assumptions, it is essential to empirically quantify and
examine the correlation structure of link travel time from real-world bus
operation data. To this end, this paper develops a Bayesian Gaussian model to
estimate the link travel time correlation matrix of a bus route using
smart-card-like data. Our method overcomes the small-sample-size problem in
correlation matrix estimation by borrowing/integrating those incomplete
observations (i.e., with missing/ragged values and overlapped link segments)
from other bus routes. Next, we propose an efficient Gibbs sampling framework
to marginalize over the missing and ragged values and obtain the posterior
distribution of the correlation matrix. Three numerical experiments are
conducted to evaluate model performance. We first conduct a synthetic
experiment and our results show that the proposed method produces an accurate
estimation for travel time correlations with credible intervals. Next, we
perform experiments on a real-world bus route with smart card data; our results
show that both local and long-range correlations exist on this bus route.
Finally, we demonstrate an application of using the estimated covariance matrix
to make probabilistic forecasting of link and trip travel time.


title: Suitability of Different Metric Choices for Concept Drift Detection

abstract:
  The notion of concept drift refers to the phenomenon that the distribution,
which is underlying the observed data, changes over time; as a consequence
machine learning models may become inaccurate and need adjustment. Many
unsupervised approaches for drift detection rely on measuring the discrepancy
between the sample distributions of two time windows. This may be done
directly, after some preprocessing (feature extraction, embedding into a latent
space, etc.), or with respect to inferred features (mean, variance, conditional
probabilities etc.). Most drift detection methods can be distinguished in what
metric they use, how this metric is estimated, and how the decision threshold
is found. In this paper, we analyze structural properties of the drift induced
signals in the context of different metrics. We compare different types of
estimators and metrics theoretically and empirically and investigate the
relevance of the single metric components. In addition, we propose new choices
and demonstrate their suitability in several experiments.


title: SAGE: SLAM with Appearance and Geometry Prior for Endoscopy

abstract:
  In endoscopy, many applications (e.g., surgical navigation) would benefit
from a real-time method that can simultaneously track the endoscope and
reconstruct the dense 3D geometry of the observed anatomy from a monocular
endoscopic video. To this end, we develop a Simultaneous Localization and
Mapping system by combining the learning-based appearance and optimizable
geometry priors and factor graph optimization. The appearance and geometry
priors are explicitly learned in an end-to-end differentiable training pipeline
to master the task of pair-wise image alignment, one of the core components of
the SLAM system. In our experiments, the proposed SLAM system is shown to
robustly handle the challenges of texture scarceness and illumination variation
that are commonly seen in endoscopy. The system generalizes well to unseen
endoscopes and subjects and performs favorably compared with a state-of-the-art
feature-based SLAM system. The code repository is available at
https://github.com/lppllppl920/SAGE-SLAM.git.


title:
An Operator Learning Approach via Function-valued Reproducing Kernel
  Hilbert Space for Differential Equations

abstract:
  Much recent work has addressed the solution of a family of partial
differential equations by computing the inverse operator map between the input
and solution space. Toward this end, we incorporate function-valued reproducing
kernel Hilbert spaces in our operator learning model. We use neural networks to
parameterize Hilbert-Schmidt integral operator and propose an architecture.
Experiments including several typical datasets show that the proposed
architecture has desirable accuracy on linear and nonlinear partial
differential equations even with a small amount of data. By learning the
mappings between function spaces, the proposed method can find the solution of
a high-resolution input after learning from lower-resolution data.


title: Shaping Advice in Deep Reinforcement Learning

abstract:
  Reinforcement learning involves agents interacting with an environment to
complete tasks. When rewards provided by the environment are sparse, agents may
not receive immediate feedback on the quality of actions that they take,
thereby affecting learning of policies. In this paper, we propose to methods to
augment the reward signal from the environment with an additional reward termed
shaping advice in both single and multi-agent reinforcement learning. The
shaping advice is specified as a difference of potential functions at
consecutive time-steps. Each potential function is a function of observations
and actions of the agents. The use of potential functions is underpinned by an
insight that the total potential when starting from any state and returning to
the same state is always equal to zero. We show through theoretical analyses
and experimental validation that the shaping advice does not distract agents
from completing tasks specified by the environment reward. Theoretically, we
prove that the convergence of policy gradients and value functions when using
shaping advice implies the convergence of these quantities in the absence of
shaping advice. We design two algorithms- Shaping Advice in Single-agent
reinforcement learning (SAS) and Shaping Advice in Multi-agent reinforcement
learning (SAM). Shaping advice in SAS and SAM needs to be specified only once
at the start of training, and can easily be provided by non-experts.
Experimentally, we evaluate SAS and SAM on two tasks in single-agent
environments and three tasks in multi-agent environments that have sparse
rewards. We observe that using shaping advice results in agents learning
policies to complete tasks faster, and obtain higher rewards than algorithms
that do not use shaping advice.


title: Gradient-Based Optimization of Optical Vortex Beam Emitters

abstract:
  Vortex beams are stable solutions of Maxwell's equations that carry phase
singularities and orbital angular momentum, unique properties that give rise to
many applications in the basic sciences, optical communications, and quantum
technologies. Scalable integration and fabrication of vortex beam emitters will
allow these applications to flourish and enable new applications not possible
with traditional optics. Here we present a general framework to generate
integrated vortex beam emitters using photonic inverse design. We
experimentally demonstrate generation of vortex beams with angular momentum
spanning -3$\hbar$ to 3$\hbar$. We show the generality of this design procedure
by designing a vortex beam multiplexer capable of exciting a custom vortex beam
fiber. Finally, we produce foundry-fabricated beam emitters with
wide-bandwidths and high-efficiencies that take advantage of a multi-layer
heterogeneous integration.


title: Arbitrarily slow decay in the M\"{o}bius disjointness conjecture

abstract:
  Sarnak's M\"{o}bius disjointness conjecture asserts that for any zero entropy
dynamical system $(X,T)$, $\frac{1}{N} \sum_{n=1} ^N f(T^n x) \mu (n)= o(1)$
for every $f\in \mathcal{C}(X)$ and every $x\in X$. We construct examples
showing that this $o(1)$ can go to zero arbitrarily slowly. In fact, our
methods yield a more general result, where in lieu of $\mu(n)$ one can put any
bounded sequence such that the Ces\`aro mean of the corresponding sequence of
absolute values does not tend to zero.


title:
Highlighting Object Category Immunity for the Generalization of
  Human-Object Interaction Detection

abstract:
  Human-Object Interaction (HOI) detection plays a core role in activity
understanding. As a compositional learning problem (human-verb-object),
studying its generalization matters. However, widely-used metric mean average
precision (mAP) fails to model the compositional generalization well. Thus, we
propose a novel metric, mPD (mean Performance Degradation), as a complementary
of mAP to evaluate the performance gap among compositions of different objects
and the same verb. Surprisingly, mPD reveals that previous methods usually
generalize poorly. With mPD as a cue, we propose Object Category (OC) Immunity
to boost HOI generalization. The idea is to prevent model from learning
spurious object-verb correlations as a short-cut to over-fit the train set. To
achieve OC-immunity, we propose an OC-immune network that decouples the inputs
from OC, extracts OC-immune representations, and leverages uncertainty
quantification to generalize to unseen objects. In both conventional and
zero-shot experiments, our method achieves decent improvements. To fully
evaluate the generalization, we design a new and more difficult benchmark, on
which we present significant advantage. The code is available at
https://github.com/Foruck/OC-Immunity.


title: A New Type of Gr\"obner Basis and Its Complexity

abstract:
  The new type of ideal basis introduced herein constitutes a compromise
between the Gr\"obner bases based on the Buchberger's algorithm and the
characteristic sets based on the Wu's method. It reduces the complexity of the
traditional Gr\"obner bases and subdues the notorious intermediate expression
swell problem and intermediate coefficient swell problem to a substantial
extent. The computation of an $S$-polynomial for the new bases requires at most
$O(m\ln^2m\ln\ln m)$ word operations whereas $O(m^6\ln^2m)$ word operations are
requisite in the Buchberger's algorithm. Here $m$ denotes the upper bound for
the numbers of terms both in the leading coefficients and for the rest of the
polynomials. The new bases are for zero-dimensional polynomial ideals and based
on univariate pseudo-divisions. However in contrast to the pseudo-divisions in
the Wu's method for the characteristic sets, the new bases retain the algebraic
information of the original ideal and in particular, solve the ideal membership
problem. In order to determine the authentic factors of the eliminant, we
analyze the multipliers of the pseudo-divisions and develop an algorithm over
principal quotient rings with zero divisors.


title: Local analysis of fast magnetic reconnection

abstract:
  Fast magnetic reconnection is defined by the topology of the magnetic field
lines changing on a timescale that is approximately an order of magnitude
longer than the topology-conserving ideal-evolution timescale. Fast
reconnection is an intrinsic property of Faraday's law when the evolving
magnetic field depends non-trivially on all three spatial coordinates and is
commonly observed -- even when the effects that allow topology breaking are
arbitrarily small. The associated current density need only be enhanced by a
factor of approximately ten and flows in thin but broad ribbons along the
magnetic field. These results follow from the variation in the separation of
neighboring pairs of magnetic field lines, which in an ideal evolution
typically increases exponentially with time, and the existence of a spatial
scale below which magnetic field lines freely change their identities due to
non-ideal effects such as resistivity. Traditional reconnection theory ignores
exponentially large variations and relies on the current density reaching a
magnitude that is exponentially larger than is actually required. Here, an
analysis of the behavior of magnetic field lines in the neighborhood of an
arbitrarily chosen line is used to obtain more precise and rigorous results on
intrinsic reconnection. The maximum parallel kinetic energy of collisionless
charged particles is shown to have an exponential increase in time during a
generic magnetic evolution.


title: Sorting Balls and Water: Equivalence and Computational Complexity

abstract:
  Various forms of sorting problems have been studied over the years. Recently,
two kinds of sorting puzzle apps are popularized. In these puzzles, we are
given a set of bins filled with colored units, balls or water, and some empty
bins. These puzzles allow us to move colored units from a bin to another when
the colors involved match in some way or the target bin is empty. The goal of
these puzzles is to sort all the color units in order. We investigate
computational complexities of these puzzles. We first show that these two
puzzles are essentially the same from the viewpoint of solvability. That is, an
instance is sortable by ball-moves if and only if it is sortable by
water-moves. We also show that every yes-instance has a solution of polynomial
length, which implies that these puzzles belong to in NP. We then show that
these puzzles are NP-complete. For some special cases, we give polynomial-time
algorithms. We finally consider the number of empty bins sufficient for making
all instances solvable and give non-trivial upper and lower bounds in terms of
the number of filled bins and the capacity of bins.


title: Numeric Encoding Options with Automunge

abstract:
  Mainstream practice in machine learning with tabular data may take for
granted that any feature engineering beyond scaling for numeric sets is
superfluous in context of deep neural networks. This paper will offer arguments
for potential benefits of extended encodings of numeric streams in deep
learning by way of a survey of options for numeric transformations as available
in the Automunge open source python library platform for tabular data
pipelines, where transformations may be applied to distinct columns in "family
tree" sets with generations and branches of derivations. Automunge
transformation options include normalization, binning, noise injection,
derivatives, and more. The aggregation of these methods into family tree sets
of transformations are demonstrated for use to present numeric features to
machine learning in multiple configurations of varying information content, as
may be applied to encode numeric sets of unknown interpretation. Experiments
demonstrate the realization of a novel generalized solution to data
augmentation by noise injection for tabular learning, as may materially benefit
model performance in applications with underserved training data.


title: Gradient Estimation with Discrete Stein Operators

abstract:
  Gradient estimation -- approximating the gradient of an expectation with
respect to the parameters of a distribution -- is central to the solution of
many machine learning problems. However, when the distribution is discrete,
most common gradient estimators suffer from excessive variance. To improve the
quality of gradient estimation, we introduce a variance reduction technique
based on Stein operators for discrete distributions. We then use this technique
to build flexible control variates for the REINFORCE leave-one-out estimator.
Our control variates can be adapted online to minimize the variance and do not
require extra evaluations of the target function. In benchmark generative
modeling tasks such as training binary variational autoencoders, our gradient
estimator achieves substantially lower variance than state-of-the-art
estimators with the same number of function evaluations.


title: Parsed Categoric Encodings with Automunge

abstract:
  The Automunge open source python library platform for tabular data
pre-processing automates feature engineering data transformations of numerical
encoding and missing data infill to received tidy data on bases fit to
properties of columns in a designated train set for consistent and efficient
application to subsequent data pipelines such as for inference, where
transformations may be applied to distinct columns in "family tree" sets with
generations and branches of derivations. Included in the library of
transformations are methods to extract structure from bounded categorical
string sets by way of automated string parsing, in which comparisons between
entries in the set of unique values are parsed to identify character subset
overlaps which may be encoded by appended columns of boolean overlap detection
activations or by replacing string entries with identified overlap partitions.
Further string parsing options, which may also be applied to unbounded
categoric sets, include extraction of numeric substring partitions from entries
or search functions to identify presence of specified substring partitions. The
aggregation of these methods into "family tree" sets of transformations are
demonstrated for use to automatically extract structure from categoric string
compositions in relation to the set of entries in a column, such as may be
applied to prepare categoric string set encodings for machine learning without
human intervention.


title: A higher Hodge extension of the Feigin-Tsygan Theorem

abstract:
  We show that the extended noncommutative de Rham complex of a cofibrant
resolution, when completed at a certain Hodge filtration, is (reduced)
quasi-isomorphic to the periodic cyclic complex, while each of its filtration
piece is quasi-isomorphic to the negative cyclic complex. This extends a
classical result of Feigin and Tsygan, which corresponds to the Hodge degree
$0$ part of our quasi-isomorphism. This result is applied to the study of
Calabi-Yau categories in \cite{Yeu1, Yeu2}.


title:
Exact Instability Margin Analysis and Minimum Norm Strong Stabilization
  -- phase change rate maximization --

abstract:
  This paper is concerned with a new control related optimization problem named
"phase change rate maximization" for single-input-single-output (SISO) linear
time-invariant (LTI) systems. The problem relates to two control problems,
namely robust instability analysis against stable perturbations and minimum
norm strong stabilization. We define an index of the instability margin called
"robust instability radius (RIR)" as the smallest $H_\infty$ norm of a stable
perturbation that stabilizes a given unstable system. This paper has two main
contributions. It is first shown that the problem of finding the exact RIR can
be transformed into a problem of maximizing the phase change rate at the peak
frequency with a phase constraint. Then, we show that the maximum is attained
by a first-order all-pass function and derive conditions, under which the RIR
can be exactly characterized, in terms of the phase change rate. Two extensions
with illustrative examples are also provided.


title: The Adaptive Spectral Koopman Method for Dynamical Systems

abstract:
  Dynamical systems have a wide range of applications in mechanics, electrical
engineering, chemistry, and so on. In this work, we propose the adaptive
spectral Koopman (ASK) method to solve nonlinear autonomous dynamical systems.
This novel numerical method leverages the spectral-collocation (i.e.,
pseudo-spectral) method and properties of the Koopman operator to obtain the
solution of a dynamical system. Specifically, this solution is represented as a
linear combination of the multiplication of Koopman operator's eigenfunctions
and eigenvalues, and these eigenpairs are approximated using the spectral
method. Unlike conventional time evolution algorithms such as Euler's scheme
and the Runge-Kutta scheme, ASK is mesh-free, and hence is more flexible when
evaluating the solution. Numerical experiments demonstrate high accuracy of ASK
for solving one-, two- and three-dimensional dynamical systems.


title:
Graph Spring Network and Informative Anchor Selection for Session-based
  Recommendation

abstract:
  Session-based recommendation (SBR) aims at predicting the next item for an
ongoing anonymous session. The major challenge of SBR is how to capture richer
relations in between items and learn ID-based item embeddings to capture such
relations. Recent studies propose to first construct an item graph from
sessions and employ a Graph Neural Network (GNN) to encode item embedding from
the graph. Although such graph-based approaches have achieved performance
improvements, their GNNs are not suitable for ID-based embedding learning for
the SBR task. In this paper, we argue that the objective of such ID-based
embedding learning is to capture a kind of \textit{neighborhood affinity} in
that the embedding of a node is similar to that of its neighbors' in the
embedding space. We propose a new graph neural network, called Graph Spring
Network (GSN), for learning ID-based item embedding on an item graph to
optimize neighborhood affinity in the embedding space. Furthermore, we argue
that even stacking multiple GNN layers may not be enough to encode potential
relations for two item nodes far-apart in a graph. In this paper, we propose a
strategy that first selects some informative item anchors and then encode
items' potential relations to such anchors. In summary, we propose a GSN-IAS
model (Graph Spring Network and Informative Anchor Selection) for the SBR task.
We first construct an item graph to describe items' co-occurrences in all
sessions. We design the GSN for ID-based item embedding learning and propose an
\textit{item entropy} measure to select informative anchors. We then design an
unsupervised learning mechanism to encode items' relations to anchors. We next
employ a shared gated recurrent unit (GRU) network to learn two session
representations and make two next item predictions. Finally, we design an
adaptive decision fusion strategy to fuse two predictions to make the final
recommendation.


title:
On the recurrence times of neutron star X-ray binary transients and the
  nature of the Galactic Centre quiescent X-ray binaries

abstract:
  The presence of some X-ray sources in the Galactic Centre region which show
variability, but do not show outbursts in over a decade of monitoring has been
used to argue for the presence of a large population of stellar mass black
holes in this region. A core element of the arguments that these objects are
accreting black holes is the claim that neutron stars (NSs) in low mass X-ray
binaries (LMXBs) do not have long transient recurrence times. We demonstrate in
this paper that about half of the known transient LMXBs with clear signatures
for NS primaries have recurrence times in excess of a decade for outbursts at
the sensitivity of MAXI. We furthermore show that, in order to reconcile the
expected total population of NS LMXBs with the observed one and with the
millisecond radio pulsar (MSRP) population of the Galaxy, systems with
recurrence times well in excess of a century for outbursts detectable by
instruments like MAXI must be the dominant population of NS LMXBs, and that few
of these systems have yet been discovered.


title: Tools and Recommendations for Reproducible Teaching

abstract:
  It is recommended that teacher-scholars of data science adopt reproducible
workflows in their research as scholars and teach reproducible workflows to
their students. In this paper, we propose a third dimension to reproducibility
practices and recommend that regardless of whether they teach reproducibility
in their courses or not, data science instructors adopt reproducible workflows
for their own teaching. We consider computational reproducibility,
documentation, and openness as three pillars of reproducible teaching
framework. We share tools, examples, and recommendations for the three pillars.


title: The Spectrum of the Quaquaversal Operator is Real

abstract:
  The Hecke operator associated with the Quaquaversal tiling, a highly
anisotropic tiling introduced by Conway and Radin, is shown to have a real
spectrum. Answering a question of Draco, Sadun, and Van Wieren. We also
explicitly compute about three quarters of this operator's eigenvalues.


title: From Quantum Graph Computing to Quantum Graph Learning: A Survey

abstract:
  Quantum computing (QC) is a new computational paradigm whose foundations
relate to quantum physics. Notable progress has been made, driving the birth of
a series of quantum-based algorithms that take advantage of quantum
computational power. In this paper, we provide a targeted survey of the
development of QC for graph-related tasks. We first elaborate the correlations
between quantum mechanics and graph theory to show that quantum computers are
able to generate useful solutions that can not be produced by classical systems
efficiently for some problems related to graphs. For its practicability and
wide-applicability, we give a brief review of typical graph learning techniques
designed for various tasks. Inspired by these powerful methods, we note that
advanced quantum algorithms have been proposed for characterizing the graph
structures. We give a snapshot of quantum graph learning where expectations
serve as a catalyst for subsequent research. We further discuss the challenges
of using quantum algorithms in graph learning, and future directions towards
more flexible and versatile quantum graph learning solvers.


title:
PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step
  Point Moving Paths

abstract:
  Point cloud completion concerns to predict missing part for incomplete 3D
shapes. A common strategy is to generate complete shape according to incomplete
input. However, unordered nature of point clouds will degrade generation of
high-quality 3D shapes, as detailed topology and structure of unordered points
are hard to be captured during the generative process using an extracted latent
code. We address this problem by formulating completion as point cloud
deformation process. Specifically, we design a novel neural network, named
PMP-Net++, to mimic behavior of an earth mover. It moves each point of
incomplete input to obtain a complete point cloud, where total distance of
point moving paths (PMPs) should be the shortest. Therefore, PMP-Net++ predicts
unique PMP for each point according to constraint of point moving distances.
The network learns a strict and unique correspondence on point-level, and thus
improves quality of predicted complete shape. Moreover, since moving points
heavily relies on per-point features learned by network, we further introduce a
transformer-enhanced representation learning network, which significantly
improves completion performance of PMP-Net++. We conduct comprehensive
experiments in shape completion, and further explore application on point cloud
up-sampling, which demonstrate non-trivial improvement of PMP-Net++ over
state-of-the-art point cloud completion/up-sampling methods.


title:
Who Are the Best Adopters? User Selection Model for Free Trial Item
  Promotion

abstract:
  With the increasingly fierce market competition, offering a free trial has
become a potent stimuli strategy to promote products and attract users. By
providing users with opportunities to experience goods without charge, a free
trial makes adopters know more about products and thus encourages their
willingness to buy. However, as the critical point in the promotion process,
finding the proper adopters is rarely explored. Empirically winnowing users by
their static demographic attributes is feasible but less effective, neglecting
their personalized preferences. To dynamically match the products with the best
adopters, in this work, we propose a novel free trial user selection model
named SMILE, which is based on reinforcement learning (RL) where an agent
actively selects specific adopters aiming to maximize the profit after free
trials. Specifically, we design a tree structure to reformulate the action
space, which allows us to select adopters from massive user space efficiently.
The experimental analysis on three datasets demonstrates the proposed model's
superiority and elucidates why reinforcement learning and tree structure can
improve performance. Our study demonstrates technical feasibility for
constructing a more robust and intelligent user selection model and guides for
investigating more marketing promotion strategies.


title: PETCI: A Parallel English Translation Dataset of Chinese Idioms

abstract:
  Idioms are an important language phenomenon in Chinese, but idiom translation
is notoriously hard. Current machine translation models perform poorly on idiom
translation, while idioms are sparse in many translation datasets. We present
PETCI, a parallel English translation dataset of Chinese idioms, aiming to
improve idiom translation by both human and machine. The dataset is built by
leveraging human and machine effort. Baseline generation models show
unsatisfactory abilities to improve translation, but structure-aware
classification models show good performance on distinguishing good
translations. Furthermore, the size of PETCI can be easily increased without
expertise. Overall, PETCI can be helpful to language learners and machine
translation systems.


title: Dynamic Transaction Storage Strategies for a Sustainable Blockchain

abstract:
  As the core technology behind Bitcoin, Blockchain's decentralized,
tamper-proof, and traceable features make it the preferred platform for
organizational innovation. In current Bitcoin, block reward is halved every
four years, and transaction fees are expected to become the majority of miner
revenues around 2140. When transaction fee dominates mining rewards, strategic
deviations such as Selfish Mining, Undercutting, and Mining Gap could threaten
the integrity and security of the Blockchain. This paper proposes a set of
Dynamic Transaction Storage (DTS) strategies for maintaining a sustainable
Blockchain under the transaction-fee regime. We demonstrate that block
incentive volatility can be reduced through systematic simulation by applying
DTS strategies and avoiding strategic deviations. With DTS, public Blockchains
such as Bitcoin become sustainable when the mining reward is solely based on
the transaction fee.


title: NFTCert: NFT-Based Certificates With Online Payment Gateway

abstract:
  Nowadays, academic certificates are still widely issued in paper format.
Traditional certificate verification is a lengthy, manually intensive, and
sometimes expensive process. In this paper, we propose a novel NFT-based
certificate framework called NFTCert, which enables the establishment of links
between a legitimate certificate and its owner through a Blockchain. In this
paper, we describe the implementation of the NFTCert framework, including
schema definition, minting, verification, and revocation of NFT-based
certificates. We also introduce a payment gateway into the minting process,
which enables NFTCert to be used by a wider audience. Therefore, participants
of NFTCerts do not need to rely on cryptocurrency for transactions. All in all,
the proposed framework is designed to achieve usability, authenticity,
confidentiality, transparency, and availability properties when it is compared
to existing Blockchain-based systems.


title:
Distributed non-negative RESCAL with Automatic Model Selection for
  Exascale Data

abstract:
  With the boom in the development of computer hardware and software, social
media, IoT platforms, and communications, there has been an exponential growth
in the volume of data produced around the world. Among these data, relational
datasets are growing in popularity as they provide unique insights regarding
the evolution of communities and their interactions. Relational datasets are
naturally non-negative, sparse, and extra-large. Relational data usually
contain triples, (subject, relation, object), and are represented as
graphs/multigraphs, called knowledge graphs, which need to be embedded into a
low-dimensional dense vector space. Among various embedding models, RESCAL
allows learning of relational data to extract the posterior distributions over
the latent variables and to make predictions of missing relations. However,
RESCAL is computationally demanding and requires a fast and distributed
implementation to analyze extra-large real-world datasets. Here we introduce a
distributed non-negative RESCAL algorithm for heterogeneous CPU/GPU
architectures with automatic selection of the number of latent communities
(model selection), called pyDRESCALk. We demonstrate the correctness of
pyDRESCALk with real-world and large synthetic tensors, and the efficacy
showing near-linear scaling that concurs with the theoretical complexities.
Finally, pyDRESCALk determines the number of latent communities in an
11-terabyte dense and 9-exabyte sparse synthetic tensor.


title: Soft streaming -- flow rectification via elastic boundaries

abstract:
  Viscous streaming is an efficient mechanism to exploit inertia at the
microscale for flow control. While streaming from rigid features has been
thoroughly investigated, when body compliance is involved, as in biological
settings, little is known. Here, we investigate body elasticity effects on
streaming in the minimal case of an immersed soft cylinder. Our study reveals
an additional streaming process, available even in Stokes flows. Paving the way
for advanced forms of flow manipulation, we illustrate how gained insights may
translate to complex geometries beyond circular cylinders.


title:
Robust Reinforcement Learning as a Stackelberg Game via
  Adaptively-Regularized Adversarial Training

abstract:
  Robust Reinforcement Learning (RL) focuses on improving performances under
model errors or adversarial attacks, which facilitates the real-life deployment
of RL agents. Robust Adversarial Reinforcement Learning (RARL) is one of the
most popular frameworks for robust RL. However, most of the existing literature
models RARL as a zero-sum simultaneous game with Nash equilibrium as the
solution concept, which could overlook the sequential nature of RL deployments,
produce overly conservative agents, and induce training instability. In this
paper, we introduce a novel hierarchical formulation of robust RL - a
general-sum Stackelberg game model called RRL-Stack - to formalize the
sequential nature and provide extra flexibility for robust training. We develop
the Stackelberg Policy Gradient algorithm to solve RRL-Stack, leveraging the
Stackelberg learning dynamics by considering the adversary's response. Our
method generates challenging yet solvable adversarial environments which
benefit RL agents' robust learning. Our algorithm demonstrates better training
stability and robustness against different testing conditions in the
single-agent robotics control and multi-agent highway merging tasks.


title:
SPNet: A novel deep neural network for retinal vessel segmentation based
  on shared decoder and pyramid-like loss

abstract:
  Segmentation of retinal vessel images is critical to the diagnosis of
retinopathy. Recently, convolutional neural networks have shown significant
ability to extract the blood vessel structure. However, it remains challenging
to refined segmentation for the capillaries and the edges of retinal vessels
due to thickness inconsistencies and blurry boundaries. In this paper, we
propose a novel deep neural network for retinal vessel segmentation based on
shared decoder and pyramid-like loss (SPNet) to address the above problems.
Specifically, we introduce a decoder-sharing mechanism to capture multi-scale
semantic information, where feature maps at diverse scales are decoded through
a sequence of weight-sharing decoder modules. Also, to strengthen
characterization on the capillaries and the edges of blood vessels, we define a
residual pyramid architecture which decomposes the spatial information in the
decoding phase. A pyramid-like loss function is designed to compensate possible
segmentation errors progressively. Experimental results on public benchmarks
show that the proposed method outperforms the backbone network and the
state-of-the-art methods, especially in the regions of the capillaries and the
vessel contours. In addition, performances on cross-datasets verify that SPNet
shows stronger generalization ability.


title:
Learning a Shield from Catastrophic Action Effects: Never Repeat the
  Same Mistake

abstract:
  Agents that operate in an unknown environment are bound to make mistakes
while learning, including, at least occasionally, some that lead to
catastrophic consequences. When humans make catastrophic mistakes, they are
expected to learn never to repeat them, such as a toddler who touches a hot
stove and immediately learns never to do so again. In this work we consider a
novel class of POMDPs, called POMDP with Catastrophic Actions (POMDP-CA) in
which pairs of states and actions are labeled as catastrophic. Agents that act
in a POMDP-CA do not have a priori knowledge about which (state, action) pairs
are catastrophic, thus they are sure to make mistakes when trying to learn any
meaningful policy. Rather, their aim is to maximize reward while never
repeating mistakes.
  As a first step of avoiding mistake repetition, we leverage the concept of a
shield which prevents agents from executing specific actions from specific
states.
  In particular, we store catastrophic mistakes (unsafe pairs of states and
actions) that agents make in a database. Agents are then forbidden to pick
actions that appear in the database. This approach is especially useful in a
continual learning setting, where groups of agents perform a variety of tasks
over time in the same underlying environment. In this setting, a task-agnostic
shield can be constructed in a way that stores mistakes made by any agent, such
that once one agent in a group makes a mistake the entire group learns to never
repeat that mistake. This paper introduces a variant of the PPO algorithm that
utilizes this shield, called ShieldPPO, and empirically evaluates it in a
controlled environment.
  Results indicate that ShieldPPO outperforms PPO, as well as baseline methods
from the safe reinforcement learning literature, in a range of settings.


title: Deep Learning for Hate Speech Detection: A Comparative Study

abstract:
  Automated hate speech detection is an important tool in combating the spread
of hate speech, particularly in social media. Numerous methods have been
developed for the task, including a recent proliferation of deep-learning based
approaches. A variety of datasets have also been developed, exemplifying
various manifestations of the hate-speech detection problem. We present here a
large-scale empirical comparison of deep and shallow hate-speech detection
methods, mediated through the three most commonly used datasets. Our goal is to
illuminate progress in the area, and identify strengths and weaknesses in the
current state-of-the-art. We particularly focus our analysis on measures of
practical performance, including detection accuracy, computational efficiency,
capability in using pre-trained models, and domain generalization. In doing so
we aim to provide guidance as to the use of hate-speech detection in practice,
quantify the state-of-the-art, and identify future research directions. Code
and dataset are available at
https://github.com/jmjmalik22/Hate-Speech-Detection.


title:
Distributed Out-of-Memory NMF of Dense and Sparse Data on CPU/GPU
  Architectures with Automatic Model Selection for Exascale Data

abstract:
  The need for efficient and scalable big-data analytics methods is more
essential than ever due to the exploding size and complexity of globally
emerging datasets. Nonnegative Matrix Factorization (NMF) is a well-known
explainable unsupervised learning method for dimensionality reduction, latent
feature extraction, blind source separation, data mining, and machine learning.
In this paper, we introduce a new distributed out-of-memory NMF method, named
pyDNMF-GPU, designed for modern heterogeneous CPU/GPU architectures that is
capable of factoring exascale-sized dense and sparse matrices. Our method
reduces the latency associated with local data transfer between the GPU and
host using CUDA streams, and reduces the latency associated with collective
communications (both intra-node and inter-node) via NCCL primitives. In
addition, sparse and dense matrix multiplications are significantly accelerated
with GPU cores, resulting in good scalability. We set new benchmarks for the
size of the data being analyzed: in experiments, we measure up to 76x
improvement on a single GPU over running on a single 18 core CPU and we show
good weak scaling on up to 4096 multi-GPU cluster nodes with approximately
25,000 GPUs, when decomposing a dense 340 Terabyte-size matrix and a 11
Exabyte-size sparse matrix of density 10e-6. Finally, we integrate our method
with an automatic model selection method. With this integration, we introduce a
new tool that is capable of analyzing, compressing, and discovering explainable
latent structures in extremely large sparse and dense data.


title:
The four-fifths rule is not disparate impact: a woeful tale of epistemic
  trespassing in algorithmic fairness

abstract:
  Computer scientists are trained to create abstractions that simplify and
generalize. However, a premature abstraction that omits crucial contextual
details creates the risk of epistemic trespassing, by falsely asserting its
relevance into other contexts. We study how the field of responsible AI has
created an imperfect synecdoche by abstracting the four-fifths rule (a.k.a. the
4/5 rule or 80% rule), a single part of disparate impact discrimination law,
into the disparate impact metric. This metric incorrectly introduces a new
deontic nuance and new potentials for ethical harms that were absent in the
original 4/5 rule. We also survey how the field has amplified the potential for
harm in codifying the 4/5 rule into popular AI fairness software toolkits. The
harmful erasure of legal nuances is a wake-up call for computer scientists to
self-critically re-evaluate the abstractions they create and use, particularly
in the interdisciplinary field of AI ethics.


title:
Elasto-plastic and adhesive contact: an improved linear model and its
  application

abstract:
  An improved linear model is developed for elasto-plastic and adhesive
contact. New correlations are proposed and validated to estimate the key input
parameters of the model, including contact stiffness, yield point, maximum
pull-off force and time step. The newly proposed contact model is applied to
the analysis of single particle contact behaviour upon impact and bulk particle
flow behaviour by DEM simulations. The results show that both single particle
and bulk powder behave more cohesively if contact plastic deformation is taken
into consideration. A cohesion yield number is proposed to describe and govern
the extent of yielding when cohesive particles are in contact with each other.
There is critical particle size, below which the effect of plastic deformation
must be considered. This provides a new framework and criteria for
elasto-plastic and adhesive contact model, and a step towards understanding the
effect of plastic deformation on the behaviour of cohesive particles.


title:
On the tunability of energetic highly-elliptical higher harmonics
  generated by intense two-color counter rotating laser fields

abstract:
  Tunability of energetic highly-elliptical high-harmonics is demonstrated.
Harmonics are generated in Ar gas, driven by intense two-color counter rotating
laser electric fields, namely of the fundamental and second harmonic frequency.
For the experiment a MAZEL-TOV-like device is used, combined with loose
focusing geometry. By properly tuning the central frequency of the second
harmonic, the central frequency of the extreme ultraviolet (XUV) high harmonic
radiation is continuously tuned.


title:
Assisting Living by Wireless Sensing: The Role of Integrated Sensing and
  Communications in 6G Era

abstract:
  Advance in wireless communication and signal processing facilitates
integrated sensing and communication (ISAC) -- a technology that combines
sensing and communication functionalities to efficiently utilize congested
wireless/hardware resources, and to pursue mutual benefits. Consequently, the
future communications network will be perceptive. In this article, we provide a
review of human-related sensing in the context of ISAC. We first present a
general ISAC receiver signal processing framework, with a focus on human
activity recognition (HAR). Based on geographical deployments, we then
categorize current ISAC HAR into three classical configurations, namely
monostatic, bi-static and distributed deployments, and discuss their
properties, critical research problems and solutions. In order to facilitate
system realization and improve the recognition performance, we further explore
inherent connections between physical-layer system parameters and HAR
performance metrics. Finally, we review major technical challenges and identify
key open research problems.


title:
Finiteness of meromorphic functions on annuli sharing finite sets with
  truncated multiplicity

abstract:
  The purpose of this paper has two fold. The first is to establish a second
main theorem for meromorphic functions on annuli and meromorphic function
targets (may not be small functions) with truncated counting function
(truncation level 1) and with more detailed estimate for error term. The second
is to show that if the polynomial $$P_S(w)=(w-a_1)\cdots (w-a_q)$$ is a
uniqueness polynomial for meromorphic functions on an annulus $\mathbb A(R_0)$
such that $P'_S(w)$ has exactly $k$ distinct zero and
$q>\frac{(5k+3)l}{2l-175}$ then the set $S=\{a_1,\ldots,a_q\}$ is a finiteness
range set with truncation level $l$ for admissible meromorphic functions on
$\mathbb A(R_0)$. This result extends the previous result on the finiteness
range set (with truncation level $l=\infty$) for holomorphic functions on
$\mathbb C$ of H. Fujimoto.


title:
DRL-based Joint Beamforming and BS-RIS-UE Association Design for
  RIS-Assisted mmWave Networks

abstract:
  Reconfigurable intelligent surface (RIS) is considered as an extraordinarily
promising technology to solve the blockage problem of millimeter wave (mmWave)
communications owing to its capable of establishing a reconfigurable wireless
propagation. In this paper, we focus on a RIS-assisted mmWave communication
network consisting of multiple base stations (BSs) serving a set of user
equipments (UEs). Considering the BS-RIS-UE association problem which
determines that the RIS should assist which BS and UEs, we joint optimize
BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate
of the system. To solve this intractable non-convex problem, we propose a soft
actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming
and BS-RIS-UE association design algorithm, which can learn the best policy by
interacting with the environment using less prior information and avoid falling
into the local optimal solution by incorporating with the maximization of
policy information entropy. The simulation results demonstrate that the
proposed SAC-DRL algorithm can achieve significant performance gains compared
with benchmark schemes.


title: Powers of posinormal operators

abstract:
  Square of a posinormal operator is not necessarily posinormal$.$ But (i)
powers of quasiposinormal operators are quasiposinormal and, under closed
ranges assumption, powers of (ii) posinormal operators are posinormal, (iii) of
operators that are both posinormal and coposinormal are posinormal and
coposinormal, and (iv) of semi-Fredholm posinormal operators are posinormal.


title: Trees of hyperbolic spaces

abstract:
  We give an alternative proof of the Bestvina--Feighn combination theorem for
trees hyperbolic spaces and describe uniform quasigeodesics in such spaces. As
one of the applications, we prove the existence of Cannon-Thurston maps for
inclusion maps of total spaces of subtrees of hyperbolic spaces.