title: Early Stage Diabetes Prediction via Extreme Learning Machine

abstract:
  Diabetes is one of the chronic diseases that has been discovered for decades.
However, several cases are diagnosed in their late stages. Every one in eleven
of the world's adult population has diabetes. Forty-six percent of people with
diabetes have not been diagnosed. Diabetes can develop several other severe
diseases that can lead to patient death. Developing and rural areas suffer the
most due to the limited medical providers and financial situations. This paper
proposed a novel approach based on an extreme learning machine for diabetes
prediction based on a data questionnaire that can early alert the users to seek
medical assistance and prevent late diagnoses and severe illness development.


title: Differentiable and Learnable Robot Models

abstract:
  Building differentiable simulations of physical processes has recently
received an increasing amount of attention. Specifically, some efforts develop
differentiable robotic physics engines motivated by the computational benefits
of merging rigid body simulations with modern differentiable machine learning
libraries. Here, we present a library that focuses on the ability to combine
data driven methods with analytical rigid body computations. More concretely,
our library \emph{Differentiable Robot Models} implements both
\emph{differentiable} and \emph{learnable} models of the kinematics and
dynamics of robots in Pytorch. The source-code is available at
\url{https://github.com/facebookresearch/differentiable-robot-model}


title: Homeostatic Mechanisms in Biological Systems

abstract:
  In this paper we investigate the homeostatic mechanism in two biologically
motivated models: intracellular copper regulation and self immune recognition.
The analysis is based on the notions of infinitesimal homeostasis and
near-perfect homeostasis. We introduce a theoretical background that makes it
possible to consider points of infinitesimal homeostasis that lie at the
boundary of the domain of definition of the input-output function. We show that
the two models display near-perfect homeostasis. Moreover, we show that, unlike
the examples of [M. Reed, J. Best, M. Golubitsky, I. Stewart, and H. F.
Nijhout. Analysis of homeostatic mechanisms in biochemical networks. Bull.
Math. Biol., 79(11):2534-2557, 2017], the obstruction of occurrence of
infinitesimal homeostasis in both of them is caused by the modeling assumptions
that force the point of infinitesimal homeostasis to lie at the boundary of
domain of definition of the respective input-output functions.


title:
No-Regret Learning with Unbounded Losses: The Case of Logarithmic
  Pooling

abstract:
  For each of $T$ time steps, $m$ experts report probability distributions over
$n$ outcomes; we wish to learn to aggregate these forecasts in a way that
attains a no-regret guarantee. We focus on the fundamental and practical
aggregation method known as logarithmic pooling -- a weighted average of log
odds -- which is in a certain sense the optimal choice of pooling method if one
is interested in minimizing log loss (as we take to be our loss function). We
consider the problem of learning the best set of parameters (i.e. expert
weights) in an online adversarial setting. We assume (by necessity) that the
adversarial choices of outcomes and forecasts are consistent, in the sense that
experts report calibrated forecasts. Our main result is an algorithm based on
online mirror descent that learns expert weights in a way that attains
$O(\sqrt{T} \log T)$ expected regret as compared with the best weights in
hindsight.


title:
Type II supernovae from the Carnegie Supernova Project-I. III.
  Understanding SN II diversity through correlations between physical and
  observed properties

abstract:
  SNe II show great photometric and spectroscopic diversity which is attributed
to the varied physical characteristics of their progenitor and explosion
properties. In this study, the third of a series of papers where we analyse a
sample of SNe II observed by the Carnegie Supernova Project-I, we present
correlations between their observed and physical properties. Our analysis shows
that explosion energy is the physical property that correlates with the highest
number of parameters. We recover previously suggested relationships between the
hydrogen-rich envelope mass and the plateau duration, and find that more
luminous SNe II with higher expansion velocities, faster declining light
curves, and higher Ni masses are consistent with higher energy explosions. In
addition, faster declining SNe II are also compatible with more concentrated Ni
in the inner regions of the ejecta. Positive trends are found between the
initial mass, explosion energy, and Ni mass. While the explosion energy spans
the full range explored with our models, the initial mass generally arises from
a relatively narrow range. Observable properties were measured from our grid of
models to determine the effect of each physical parameter on the observed SN II
diversity. We argue that explosion energy is the physical parameter causing the
greatest impact on SN II diversity, when assuming standard single-star
evolution as in the models used in this study. The inclusion of pre-SN models
assuming higher mass loss produces a significant increase in the strength of
some correlations, particularly those between the progenitor hydrogen-rich
envelope mass and the plateau and optically thick phase durations. These
differences clearly show the impact of having different treatments of stellar
evolution, implying that changes in the assumption of standard single-star
evolution are necessary for a complete understanding of SN II diversity.


title: Arbitrary Shape Text Detection using Transformers

abstract:
  Recent text detection frameworks require several handcrafted components such
as anchor generation, non-maximum suppression (NMS), or multiple processing
stages (e.g. label generation) to detect arbitrarily shaped text images. In
contrast, we propose an end-to-end trainable architecture based on Detection
using Transformers (DETR), that outperforms previous state-of-the-art methods
in arbitrary-shaped text detection. At its core, our proposed method leverages
a bounding box loss function that accurately measures the arbitrary detected
text regions' changes in scale and aspect ratio. This is possible due to a
hybrid shape representation made from Bezier curves, that are further split
into piece-wise polygons. The proposed loss function is then a combination of a
generalized-split-intersection-over-union loss defined over the piece-wise
polygons and regularized by a Smooth-$\ln$ regression over the Bezier curve's
control points. We evaluate our proposed model using Total-Text and CTW-1500
datasets for curved text, and MSRA-TD500 and ICDAR15 datasets for
multi-oriented text, and show that the proposed method outperforms the previous
state-of-the-art methods in arbitrary-shape text detection tasks.


title: A modeling approach for estimating dynamic measles case detection rates

abstract:
  The main idea in this paper is that the age associated with reported measles
cases can be used to estimate the number of undetected measles infections.
Somewhat surprisingly, even with age only to the nearest year, estimates of
underreporting can be generated at the much faster, 2 week time-scale
associated with measles transmission. I describe this idea by focusing on the
well-studied, 60 city United Kingdom data set, which covers the transition to
universal healthcare in 1948, and is, as a result, an interesting case study in
infectious disease surveillance. Finally, at the end of the paper, I comment
briefly on how the approach can be modified for application to modern contexts.


title:
Correlation function of a random scalar field evolving with a rapidly
  fluctuating Gaussian process

abstract:
  We consider a scalar field governed by an advection-diffusion equation (or a
more general evolution equation) with rapidly fluctuating, Gaussian distributed
random coefficients. In the white noise limit, we derive the closed evolution
equation for the ensemble average of the random scalar field by three different
strategies, i.e., Feynman-Kac formula, the limit of Ornstein-Uhlenbeck process,
and evaluating the cluster expansion of the propagator on an $n$-simplex. With
the evolution equation of ensemble average, we study the passive scalar
transport problem with two different types of flows, a random periodic flow,
and a random strain flow. For periodic flows, by utilizing the homogenization
method, we show that the $N$-point correlation function of the random scalar
field satisfies an effective diffusion equation at long times. For the strain
flow, we explicit compute the mean of the random scalar field and show that the
statistics of the random scalar field have a connection to the time integral of
geometric Brownian motion. Interestingly, all normalized moment (e.g.,
skewness, kurtosis) of this random scalar field diverges at long times, meaning
that the scalar becomes more and more intermittent during its decay.


title:
Ballistic deposition with memory: a new universality class of surface
  growth with a new scaling law

abstract:
  Motivated by recent experimental studies in microbiology, we suggest a
modification of the classic ballistic deposition model of surface growth, where
the memory of a deposition at a site induces more depositions at that site or
its neighbors. By studying the statistics of surfaces in this model, we obtain
three independent critical exponents: the growth exponent $\beta =5/4$, the
roughening exponent $\alpha = 2$, and the new (size) exponent $\gamma = 1/2$.
The model requires a modification to the Family-Vicsek scaling, resulting in
the dynamical exponent $z = \frac{\alpha+\gamma}{\beta} = 2$. This modified
scaling collapses the surface width vs time curves for various lattice sizes.
This is a previously unobserved universality class of surface growth that could
describe surface properties of a wide range of natural systems.


title: Fixed-time Stability of Discrete-time Autonomous Systems

abstract:
  The fixed-time stability of autonomous nonlinear discrete-time (DT) systems
is studied in this paper. Novel Lyapunov conditions are presented under which
the fixed-time stability of autonomous DT system is certified. Extension to
systems under perturbations is also considered. The sensitivity to
perturbations for fixed-time stable DT systems is analyzed and it is shown that
fixed-time attractiveness can be resulted from the presented Lyapunov
conditions. The fixed upper bound of the settling-time functions is derived for
fixed-time stable and fixed-time attractive systems. An illustrative example
with simulations are also given to verify the introduced results.


title:
Model2Detector: Widening the Information Bottleneck for
  Out-of-Distribution Detection using a Handful of Gradient Steps

abstract:
  Out-of-distribution detection is an important capability that has long eluded
vanilla neural networks. Deep Neural networks (DNNs) tend to generate
over-confident predictions when presented with inputs that are significantly
out-of-distribution (OOD). This can be dangerous when employing machine
learning systems in the wild as detecting attacks can thus be difficult. Recent
advances inference-time out-of-distribution detection help mitigate some of
these problems. However, existing methods can be restrictive as they are often
computationally expensive. Additionally, these methods require training of a
downstream detector model which learns to detect OOD inputs from
in-distribution ones. This, therefore, adds latency during inference. Here, we
offer an information theoretic perspective on why neural networks are
inherently incapable of OOD detection. We attempt to mitigate these flaws by
converting a trained model into a an OOD detector using a handful of steps of
gradient descent. Our work can be employed as a post-processing method whereby
an inference-time ML system can convert a trained model into an OOD detector.
Experimentally, we show how our method consistently outperforms the
state-of-the-art in detection accuracy on popular image datasets while also
reducing computational complexity.


title:
Unsteady cavitation dynamics and frequency lock-in of a freely vibrating
  hydrofoil at high Reynolds number

abstract:
  We investigate the influence of unsteady partial cavitation on the
fluid-structure interaction of a freely vibrating hydrofoil section at high
Reynolds numbers. We consider an elastically-mounted NACA66 hydrofoil section
that is free to vibrate in the transverse flow direction. Cavitating flow
dynamics coupled with the transverse vibration are studied at low angles of
attack. For the numerical study, we employ a recently developed unified
variational fluid-structure interaction framework based on homogeneous
mixture-based cavitation with hybrid URANS-LES turbulence modeling. We first
validate the numerical implementation against experimental data for turbulent
cavitating flow. For freely oscillating hydrofoil, we observe large-amplitude
vibrations during unsteady partial cavitating conditions that are absent in the
non-cavitating flow. We identify a frequency lock-in phenomenon as the main
source of sustained large-amplitude vibration whereby the unsteady lift forces
lock into a sub-harmonic of the hydrofoil natural frequency. During cavity
collapse and shedding, we find a periodic generation of clockwise vorticity,
leading to unsteady lift generation. We determine the origin of this flow
unsteadiness near the trailing edge of the hydrofoil via the interplay between
the growing cavity and adverse pressure gradient. The flow-induced structural
vibration is observed to have a consequent impact on the cavity dynamics. In
the frequency lock-in regime, large coherent cavitating structures are seen
over the hydrofoil suction surface undergoing a full cavity
growth-detachment-collapse cycle. For the post-lock-in regime, cavity length is
shorter and the attached cavity is observed to undergo high frequency spatially
localized oscillations. In this regime, cavity shedding is primarily limited to
the cavity trailing end and frequency of a complete cavity detachment and
shedding event is reduced.


title: Hierarchical MPC for coupled subsystems using adjustable tubes

abstract:
  A hierarchical Model Predictive Control (MPC) formulation is presented for
coupled discrete-time linear systems with state and input constraints. Compared
to a centralized approach, a two-level hierarchical controller, with one
controller in the upper-level and one controller per subsystem in the
lower-level, can significantly reduce the computational cost associated with
MPC. Hierarchical coordination is achieved using adjustable tubes, which are
optimized by the upper-level controller and bound permissible lower-level
controller deviations from the system trajectories determined by the
upper-level controller. The size of these adjustable tubes determines the
degree of uncertainty between subsystems and directly affects the required
constraint tightening under a tube-based robust MPC framework. Sets are
represented as zonotopes to enable the ability to optimize the size of these
adjustable tubes and perform the necessary constraint tightening online as part
of the MPC optimization problems. State and input constraint satisfaction is
proven for the two-level hierarchical controller with an arbitrary number of
controllers at the lower-level and a numerical example demonstrates the key
features and performance of the approach.


title: Direct Serendipity and Mixed Finite Elements on Convex Polygons

abstract:
  We construct new families of direct serendipity and direct mixed finite
elements on general planer convex polygons that are $H^1$ and $H(div)$
conforming, respectively, and possess optimal order of accuracy for any order.
They have a minimal number of degrees of freedom subject to the conformity and
accuracy constraints. The name arises because the shape functions are defined
directly on the physical elements, i.e., without using a mapping from a
reference element. The finite element shape functions are defined to be the
full spaces of scalar or vector polynomials plus a space of supplemental
functions. The direct serendipity elements are the precursors of the direct
mixed elements in a de Rham complex. The convergence properties of the finite
elements are shown under a regularity assumption on the shapes of the polygons
in the mesh, as well as some mild restrictions on the choices one can make in
the construction of the supplemental functions. Numerical experiments on
various meshes exhibit the performance of these new families of finite
elements.


title:
Free carrier absorption for high-resolution mode mapping in photonic
  crystal nanocavities

abstract:
  We demonstrate a nonlinear photomodulation spectroscopy method to image the
mode profile of a high-Q photonic crystal resonator (PhCR). This is done by
scanning the PhCR surface with a 405~nm pump beam that modulates the refractive
index by local thermal tuning, while probing the response of the resonance. We
increase resolution by probing at high power, using the thermo-optical
nonlinear response of the PhCR. Spatial resolution of the thermo-optical effect
is typically constrained by the broad thermal profile of the optical pump. Here
we increase the resolution and show that we can approach the diffraction limit
of the pump light. This is due to free carrier absorption that heats up the
PhCR only when there is overlap between the optical pump spot and the optical
mode profile. This is supported with a thermo-optical model that reproduces the
high-resolution mode mapping. Results reveal that the observed enhanced
resolution is reached for surprisingly low carrier density.


title:
Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion
  for Autonomous Driving

abstract:
  Autonomous driving demands accurate perception and safe decision-making. To
achieve this, automated vehicles are now equipped with multiple sensors (e.g.,
camera, Lidar, etc.), enabling them to exploit complementary environmental
context by fusing data from different sensing modalities. With the success of
Deep Convolutional Neural Network(DCNN), the fusion between DCNNs has been
proved as a promising strategy to achieve satisfactory perception accuracy.
However, mainstream existing DCNN fusion schemes conduct fusion by directly
element-wisely adding feature maps extracted from different modalities together
at various stages, failing to consider whether the features being fused are
matched or not. Therefore, we first propose a feature disparity metric to
quantitatively measure the degree of feature disparity between the feature maps
being fused. We then propose Fusion-filter as a feature-matching techniques to
tackle the feature-mismatching issue. We also propose a Layer-sharing technique
in the deep layer that can achieve better accuracy with less computational
overhead. Together with the help of the feature disparity to be an additional
loss, our proposed technologies enable DCNN to learn corresponding feature maps
with similar characteristics and complementary visual context from different
modalities to achieve better accuracy. Experimental results demonstrate that
our proposed fusion technique can achieve better accuracy on KITTI dataset with
less computational resources demand.


title:
Chemical potential influence on the condensation energy from a
  Boson-Fermion model of superconductivity

abstract:
  Influence of the temperature dependent chemical potential on the condensation
energy from a ternary Boson-Fermion model of superconductivity is reported, it
consist of unbound electrons/holes which are fermions plus two-electron and
two-hole Cooper pairs which are bosons. When solving simultaneously the set of
equations of the mixture (two gap-like equations, one for electron pairs and
another one for hole pairs, plus the particle number conservation equation)
within the weak-coupling (BCS regime), the resulting superconducting chemical
potential shows a shift from its normal state counterpart, which is related to
both the magnitude of the temperature-dependent superconducting gap and to the
Fermi energy of the superconductor. As predicted by van der Marel in the early
1990s we also find that the superconducting chemical potential has a prominent
kink at critical temperature $T_c$, which in turn coincides with the normal
state chemical potential. Also there is discontinuity in its first derivative
which directly affects the magnitude in the specific heat jump. We show that
the difference between the superconducting and normal state chemical potentials
is of the same order of magnitude as the corresponding difference between the
thermodynamic potentials for the mixture, and must therefore be accounted for
in the condensation energy calculations instead of ignoring it as is done
often. The condensation energy obtained here shows very good agreement with
experimental data for elemental superconductors.


title: Retrieval Augmented Classification for Long-Tail Visual Recognition

abstract:
  We introduce Retrieval Augmented Classification (RAC), a generic approach to
augmenting standard image classification pipelines with an explicit retrieval
module. RAC consists of a standard base image encoder fused with a parallel
retrieval branch that queries a non-parametric external memory of pre-encoded
images and associated text snippets. We apply RAC to the problem of long-tail
classification and demonstrate a significant improvement over previous
state-of-the-art on Places365-LT and iNaturalist-2018 (14.5% and 6.7%
respectively), despite using only the training datasets themselves as the
external information source. We demonstrate that RAC's retrieval module,
without prompting, learns a high level of accuracy on tail classes. This, in
turn, frees the base encoder to focus on common classes, and improve its
performance thereon. RAC represents an alternative approach to utilizing large,
pretrained models without requiring fine-tuning, as well as a first step
towards more effectively making use of external memory within common computer
vision architectures.


title: A QUBO formulation for the Tree Containment problem

abstract:
  Phylogenetic (evolutionary) trees and networks are leaf-labeled graphs that
are widely used to represent the evolutionary relationships between entities
such as species, languages, cancer cells, and viruses. To reconstruct and
analyze phylogenetic networks, the problem of deciding whether or not a given
rooted phylogenetic network embeds a given rooted phylogenetic tree is of
recurring interest. This problem, formally know as Tree Containment, is
NP-complete in general and polynomial-time solvable for certain classes of
phylogenetic networks. In this paper, we connect ideas from quantum computing
and phylogenetics to present an efficient Quadratic Unconstrained Binary
Optimization formulation for Tree Containment in the general setting. For an
instance (N,T) of Tree Containment, where N is a phylogenetic network with n_N
vertices and T is a phylogenetic tree with n_T vertices, the number of logical
qubits that are required for our formulation is O(n_N n_T).


title: Soliton Motion in Skyrmion Chains

abstract:
  Using a particle-based model we examine the depinning motion of solitons in
skyrmion chains in quasi-one dimensional (1D) and two-dimensional (2D) systems
containing embedded 1D interfaces. The solitons take the form of a particle or
hole in a commensurate chain of skyrmions. Under an applied drive, just above a
critical depinning threshold the soliton moves with a skyrmion Hall angle of
zero. For higher drives, the entire chain depins, and in a 2D system we observe
that both the solitons and chain move at zero skyrmion Hall angle and then
transition to a finite skyrmion Hall angle as the drive increases. In a 2D
system with a 1D interface that is at an angle to the driving direction, there
can be a reversal of the sign of the skyrmion Hall angle from positive to
negative. Our results suggest that solitons in skyrmion systems could be used
as information carriers in racetrack geometries that would avoid the drawbacks
of finite skyrmion Hall angles. The soliton states become mobile at
significantly lower drives than the depinning transition of the skyrmion chains
themselves.


title:
On Strengthening of the Solar f-mode Prior to Active Region Emergence
  Using the Fourier-Hankel Analysis

abstract:
  Recent results of Singh et al. (2016) show that the emergence of an active
region (AR) can be seen in a strengthening of the f-mode power up to two days
prior of the region's formation. In the original work, ring diagram analysis
was used to estimate the power evolution. In this study, we make use of the
Fourier-Hankel method, essentially testing the aforementioned results with an
independent method. The data is acquired from SDO/HMI, studying the ARs 11158,
11072, 11105, 11130, 11242 and 11768. Investigating the total power as a
function of time, we find a similar behavior to the original work, which is an
enhancement of f-mode power about one to three days prior to AR emergence.
Analysis of the absorption coefficient $\alpha$, yielded by a Fourier-Hankel
analysis, shows neither absorption ($\alpha > 0$) nor emission ($\alpha < 0$)
of power during the enhancement. Finding no changes of the absorption
coefficient (i.e. $\alpha = 0$) is an important result, as it narrows down the
possible physical interpretation of the original f-mode power enhancement,
showing that no directional dependence (in the sense of inward and outward
moving waves) is present.


title: Circuit and System Technologies for Energy-Efficient Edge Robotics

abstract:
  As we march towards the age of ubiquitous intelligence, we note that AI and
intelligence are progressively moving from the cloud to the edge. The success
of Edge-AI is pivoted on innovative circuits and hardware that can enable
inference and limited learning in resource-constrained edge autonomous systems.
This paper introduces a series of ultra-low-power accelerator and system
designs on enabling the intelligence in edge robotic platforms, including
reinforcement learning neuromorphic control, swarm intelligence, and
simultaneous mapping and localization. We put an emphasis on the impact of the
mixed-signal circuit, neuro-inspired computing system, benchmarking and
software infrastructure, as well as algorithm-hardware co-design to realize the
most energy-efficient Edge-AI ASICs for the next-generation intelligent and
autonomous systems.


title: Lattices from Linear Codes: Source and Channel Networks

abstract:
  In this paper, we consider the information-theoretic characterization of the
set of achievable rates and distortions in a broad class of multiterminal
communication scenarios with general continuous-valued sources and channels. A
framework is presented which involves fine discretization of the source and
channel variables followed by communication over the resulting discretized
network. In order to evaluate fundamental performance limits, convergence
results for information measures are provided under the proposed discretization
process. Using this framework, we consider point-to-point source coding and
channel coding with side-information, distributed source coding with distortion
constraints, the function reconstruction problems (two-help-one), computation
over multiple access channel, the interference channel, and the
multiple-descriptions source coding problem. We construct lattice-like codes
for general sources and channels, and derive inner-bounds to set of achievable
rates and distortions in these communication scenarios.


title:
A Modified MWPM Decoding Algorithm for Quantum Surface Codes Over
  Depolarizing Channels

abstract:
  Quantum Surface codes are a kind of quantum topological stabilizer codes
whose stabilizers and qubits are geometrically related. Due to their special
structures, surface codes have great potential to lead people to large-scale
quantum computation. In the minimum weight perfect matching (MWPM) decoding of
surface codes, the bit-flip errors and phase-flip errors are assumed to be
independent for simplicity. However, these two kinds of errors are likely to be
correlated in the real world. In this paper, we propose a modification to MWPM
decoding for surface codes to deal with the noises in depolarizing channels
where bit-flip errors and phase-flip errors are correlated. With this
modification, we obtain thresholds of 17% and 15.3% for the surface codes with
mixed boundaries and the surface codes with a hole, respectively


title:
Homeostatic behavioural response to COVID-19 infections returns R to a
  set-point of 1

abstract:
  One clear aspect of behaviour in the COVID-19 pandemic has been people's
focus on, and response to, reported or observed infection numbers in their
community. We describe a simple model of infectious disease spread in a
pandemic situation where people's behaviour is influenced by the current risk
of infection and where this behavioural response acts homeostatically to return
infection risk to a certain preferred level. This model predicts that the
reproduction rate $R$ will be centered around a median value of 1, and that a
related measure of relative change in the number of new infections will follow
the standard Cauchy distribution. Analysis of worldwide COVID-19 data shows
that the estimated reproduction rate has a median of 1, and that this measure
of relative change calculated from reported numbers of new infections closely
follows the standard Cauchy distribution at both an overall and an individual
country level.


title: FUNQUE: Fusion of Unified Quality Evaluators

abstract:
  Fusion-based quality assessment has emerged as a powerful method for
developing high-performance quality models from quality models that
individually achieve lower performances. A prominent example of such an
algorithm is VMAF, which has been widely adopted as an industry standard for
video quality prediction along with SSIM. In addition to advancing the
state-of-the-art, it is imperative to alleviate the computational burden
presented by the use of a heterogeneous set of quality models. In this paper,
we unify "atom" quality models by computing them on a common transform domain
that accounts for the Human Visual System, and we propose FUNQUE, a quality
model that fuses unified quality evaluators. We demonstrate that in comparison
to the state-of-the-art, FUNQUE offers significant improvements in both
correlation against subjective scores and efficiency, due to computation
sharing.


title: Iterative weak approximation and hard bounds for switching diffusion

abstract:
  We establish a novel convergent iteration framework for a weak approximation
of general switching diffusion. The key theoretical basis of the proposed
approach is a restriction of the maximum number of switching so as to untangle
and compensate a challenging system of weakly coupled partial differential
equations to a collection of independent partial differential equations, for
which a variety of accurate and efficient numerical methods are available.
Upper and lower bounding functions for the solutions are constructed using the
iterative approximate solutions. We provide a rigorous convergence analysis for
the iterative approximate solutions, as well as for the upper and lower
bounding functions. Numerical results are provided to examine our theoretical
findings and the effectiveness of the proposed framework.


title:
MLProxy: SLA-Aware Reverse Proxy for Machine Learning Inference Serving
  on Serverless Computing Platforms

abstract:
  Serving machine learning inference workloads on the cloud is still a
challenging task on the production level. Optimal configuration of the
inference workload to meet SLA requirements while optimizing the infrastructure
costs is highly complicated due to the complex interaction between batch
configuration, resource configurations, and variable arrival process.
Serverless computing has emerged in recent years to automate most
infrastructure management tasks. Workload batching has revealed the potential
to improve the response time and cost-effectiveness of machine learning serving
workloads. However, it has not yet been supported out of the box by serverless
computing platforms. Our experiments have shown that for various machine
learning workloads, batching can hugely improve the system's efficiency by
reducing the processing overhead per request.
  In this work, we present MLProxy, an adaptive reverse proxy to support
efficient machine learning serving workloads on serverless computing systems.
MLProxy supports adaptive batching to ensure SLA compliance while optimizing
serverless costs. We performed rigorous experiments on Knative to demonstrate
the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of
serverless deployment by up to 92% while reducing SLA violations by up to 99%
that can be generalized across state-of-the-art model serving frameworks.


title: A Bayesian Deep Learning Approach to Near-Term Climate Prediction

abstract:
  Since model bias and associated initialization shock are serious shortcomings
that reduce prediction skills in state-of-the-art decadal climate prediction
efforts, we pursue a complementary machine-learning-based approach to climate
prediction. The example problem setting we consider consists of predicting
natural variability of the North Atlantic sea surface temperature on the
interannual timescale in the pre-industrial control simulation of the Community
Earth System Model (CESM2). While previous works have considered the use of
recurrent networks such as convolutional LSTMs and reservoir computing networks
in this and other similar problem settings, we currently focus on the use of
feedforward convolutional networks. In particular, we find that a feedforward
convolutional network with a Densenet architecture is able to outperform a
convolutional LSTM in terms of predictive skill. Next, we go on to consider a
probabilistic formulation of the same network based on Stein variational
gradient descent and find that in addition to providing useful measures of
predictive uncertainty, the probabilistic (Bayesian) version improves on its
deterministic counterpart in terms of predictive skill. Finally, we
characterize the reliability of the ensemble of ML models obtained in the
probabilistic setting by using analysis tools developed in the context of
ensemble numerical weather prediction.


title: Exploring Edge Disentanglement for Node Classification

abstract:
  Edges in real-world graphs are typically formed by a variety of factors and
carry diverse relation semantics. For example, connections in a social network
could indicate friendship, being colleagues, or living in the same
neighborhood. However, these latent factors are usually concealed behind mere
edge existence due to the data collection and graph formation processes.
Despite rapid developments in graph learning over these years, most models take
a holistic approach and treat all edges as equal. One major difficulty in
disentangling edges is the lack of explicit supervisions. In this work, with
close examination of edge patterns, we propose three heuristics and design
three corresponding pretext tasks to guide the automatic edge disentanglement.
Concretely, these self-supervision tasks are enforced on a designed edge
disentanglement module to be trained jointly with the downstream node
classification task to encourage automatic edge disentanglement. Channels of
the disentanglement module are expected to capture distinguishable relations
and neighborhood interactions, and outputs from them are aggregated as node
representations. The proposed DisGNN is easy to be incorporated with various
neural architectures, and we conduct experiments on $6$ real-world datasets.
Empirical results show that it can achieve significant performance gains.


title: Learning Neural Networks under Input-Output Specifications

abstract:
  In this paper, we examine an important problem of learning neural networks
that certifiably meet certain specifications on input-output behaviors. Our
strategy is to find an inner approximation of the set of admissible policy
parameters, which is convex in a transformed space. To this end, we address the
key technical challenge of convexifying the verification condition for neural
networks, which is derived by abstracting the nonlinear specifications and
activation functions with quadratic constraints. In particular, we propose a
reparametrization scheme of the original neural network based on loop
transformation, which leads to a convex condition that can be enforced during
learning. This theoretical construction is validated in an experiment that
specifies reachable sets for different regions of inputs.


title: Performance Modeling of Metric-Based Serverless Computing Platforms

abstract:
  Analytical performance models are very effective in ensuring the quality of
service and cost of service deployment remain desirable under different
conditions and workloads. While various analytical performance models have been
proposed for previous paradigms in cloud computing, serverless computing lacks
such models that can provide developers with performance guarantees. Besides,
most serverless computing platforms still require developers' input to specify
the configuration for their deployment that could affect both the performance
and cost of their deployment, without providing them with any direct and
immediate feedback. In previous studies, we built such performance models for
steady-state and transient analysis of scale-per-request serverless computing
platforms (e.g., AWS Lambda, Azure Functions, Google Cloud Functions) that
could give developers immediate feedback about the quality of service and cost
of their deployments. In this work, we aim to develop analytical performance
models for the latest trend in serverless computing platforms that use
concurrency value and the rate of requests per second for autoscaling
decisions. Examples of such serverless computing platforms are Knative and
Google Cloud Run (a managed Knative service by Google). The proposed
performance model can help developers and providers predict the performance and
cost of deployments with different configurations which could help them tune
the configuration toward the best outcome. We validate the applicability and
accuracy of the proposed performance model by extensive real-world
experimentation on Knative and show that our performance model is able to
accurately predict the steady-state characteristics of a given workload with
minimal amount of data collection.


title: Controlling conservation laws II: compressible Navier-Stokes equations

abstract:
  We propose, study, and compute solutions to a class of optimal control
problems for hyperbolic systems of conservation laws and their viscous
regularization. We take barotropic compressible Navier--Stokes equations (BNS)
as a canonical example. We first apply the entropy--entropy flux--metric
condition for BNS. We select an entropy function and rewrite BNS to a summation
of flux and metric gradient of entropy. We then develop a metric variational
problem for BNS, whose critical points form a primal-dual BNS system. We design
a finite difference scheme for the variational system. The numerical
approximations of conservation laws are implicit in time. We solve the
variational problem with an algorithm inspired by the primal-dual hybrid
gradient method. This includes a new method for solving implicit time
approximations for conservation laws, which seems to be unconditionally stable.
Several numerical examples are presented to demonstrate the effectiveness of
the proposed algorithm.


title:
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A
  Survey and Virtual Design Element Taxonomy

abstract:
  Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI)
has been gaining considerable attention in research in recent years. However,
the HRI community lacks a set of shared terminology and framework for
characterizing aspects of mixed reality interfaces, presenting serious problems
for future research. Therefore, it is important to have a common set of terms
and concepts that can be used to precisely describe and organize the diverse
array of work being done within the field. In this paper, we present a novel
taxonomic framework for different types of VAM-HRI interfaces, composed of four
main categories of virtual design elements (VDEs). We present and justify our
taxonomy and explain how its elements have been developed over the last 30
years as well as the current directions VAM-HRI is headed in the coming decade.


title: Tight Dynamic Problem Lower Bounds from Generalized BMM and OMv

abstract:
  The main theme of this paper is using $k$-dimensional generalizations of the
combinatorial Boolean Matrix Multiplication (BMM) hypothesis and the
closely-related Online Matrix Vector Multiplication (OMv) hypothesis to prove
new tight conditional lower bounds for dynamic problems.
  The combinatorial $k$-Clique hypothesis, which is a standard hypothesis in
the literature, naturally generalizes the combinatorial BMM hypothesis. In this
paper, we prove tight lower bounds for several dynamic problems under the
combinatorial $k$-Clique hypothesis. For instance, we show that:
  * The Dynamic Range Mode problem has no combinatorial algorithms with
$\mathrm{poly}(n)$ pre-processing time, $O(n^{2/3-\epsilon})$ update time and
$O(n^{2/3-\epsilon})$ query time for any $\epsilon > 0$, matching the known
upper bounds for this problem. Previous lower bounds only ruled out algorithms
with $O(n^{1/2-\epsilon})$ update and query time under the OMv hypothesis.
  Other examples include tight combinatorial lower bounds for Dynamic Subgraph
Connectivity, Dynamic 2D Orthogonal Range Color Counting, Dynamic 2-Pattern
Document Retrieval, and Dynamic Range Mode in higher dimensions.
  Furthermore, we propose the OuMv$_k$ hypothesis as a natural generalization
of the OMv hypothesis. Under this hypothesis, we prove tight lower bounds for
various dynamic problems. For instance, we show that:
  * The Dynamic Skyline Points Counting problem in $(2k-1)$-dimensional space
has no algorithm with $\mathrm{poly}(n)$ pre-processing time and
$O(n^{1-1/k-\epsilon})$ update and query time for $\epsilon > 0$, even if the
updates are semi-online.
  Other examples include tight conditional lower bounds for (semi-online)
Dynamic Klee's measure for unit cubes, and high-dimensional generalizations of
Erickson's problem and Langerman's problem.


title:
Low-memory Krylov subspace methods for optimal rational matrix function
  approximation

abstract:
  We describe a Lanczos-based algorithm for approximating the product of a
rational matrix function with a vector. This algorithm, which we call the
Lanczos method for optimal rational matrix function approximation (Lanczos-OR),
returns the optimal approximation from a Krylov subspace in a norm induced by
the rational function's denominator, and can be computed using the information
from a slightly larger Krylov subspace. We also provide a low-memory
implementation which only requires storing a number of vectors proportional to
the denominator degree of the rational function. Finally, we show that
Lanczos-OR can also be used to derive algorithms for computing other matrix
functions, including the matrix sign function and quadrature based rational
function approximations. In many cases, it improves on the approximation
quality of prior approaches, including the standard Lanczos method, with little
additional computational overhead.


title:
Thermodynamics of non-linearly charged Anti-de Sitter black holes in
  four-dimensional Critical Gravity

abstract:
  In this work, we provide new examples of Anti-de Sitter black holes with a
planar base manifold in four-dimensional Critical Gravity by considering
nonlinear electrodynamics as a matter source. We find a general solution
characterized by the presence of only one integration constant where, for a
suitable election of coupling constants, we can show the existence of one or
more horizons. Additionally, we compute its non-null thermodynamical quantities
through a variety of techniques, testing the validity of the first law of
thermodynamics as well as a Smarr formula. Finally, we analyze the local
thermodynamical stability of the solutions. To our knowledge, these charged
configurations are the first example with Critical Gravity where their
thermodynamical quantities are not zero.


title:
Branching Brownian motion in a periodic environment and existence of
  pulsating travelling waves

abstract:
  We study the limits of the additive and derivative martingales of
one-dimensional branching Brownian motion in a periodic environment. Then we
prove the existence of pulsating travelling wave solutions of the corresponding
F-KPP equation in the supercritical and critical cases by representing the
solutions probabilistically in terms of the limits of the additive and
derivative martingales. We also prove that there is no pulsating travelling
wave solution in the subcritical case. Our main tools are the spine
decomposition and martingale change of measures.


title:
Graph Partitioning in Connected Components with Minimum Size Constraints
  via Mixed Integer Programming

abstract:
  In this work, a graph partitioning problem in a fixed number of connected
components is considered. Given an undirected graph with costs on the edges,
the problem consists on partitioning the set of nodes into a fixed number of
subsets with minimum size, where each subset induces a connected subgraph with
minimal edge cost. Mixed Integer Programming formulations together with a
variety of valid inequalities are demonstrated and implemented in a Branch &
Cut framework. A column generation approach is also proposed for this problem
with additional cuts. Finally, the methods are tested for several simulated
instances and computational results are discussed.


title:
Branching Brownian motion in a periodic environment and uniqueness of
  pulsating travelling waves

abstract:
  Using one-dimensional branching Brownian motion in a periodic environment, we
give probabilistic proofs of the asymptotics and uniqueness of pulsating
travelling waves of the F-KPP equation in a periodic environment. This paper is
a sequel to [Ren et al. Branching Brownian motion in a periodic environment and
existence of pulsating travelling waves], in which we proved the existence of
the pulsating travelling waves in the supercritical and critical cases using
the limits of the additive and derivative martingales of branching Brownian
motion in a periodic environment.


title: On generalized lc pairs with $\mathrm{\textbf b}$-log abundant nef part

abstract:
  We study the behavior of generalized lc pairs with $\mathrm{\textbf b}$-log
abundant nef part, a meticulously designed structure on algebraic varieties. We
show that this structure is preserved under the canonical bundle formula and
sub-adjunction formulas, and is also compatible with the non-vanishing
conjecture and the abundance conjecture in the classical minimal model program.


title: ML-Aided Collision Recovery for UHF-RFID Systems

abstract:
  We propose a collision recovery algorithm with the aid of machine learning
(ML-aided) for passive Ultra High Frequency (UHF) Radio Frequency
Identification (RFID) systems. The proposed method aims at recovering the tags
under collision to improve the system performance. We first estimate the number
of tags from the collided signal by utilizing machine learning tools and show
that the number of colliding tags can be estimated with high accuracy. Second,
we employ a simple yet effective deep learning model to find the experienced
channel coefficients. The proposed method allows the reader to separate each
tag's signal from the received one by applying maximum likelihood decoding. We
perform simulations to illustrate that the use of deep learning is highly
beneficial and demonstrate that the proposed approach boosts the throughput
performance of the standard framed slotted ALOHA (FSA) protocol from 0.368 to
1.837, where the receiver is equipped with a single antenna and capable of
decoding up to 4 tags.


title:
Many processors, little time: MCMC for partitions via optimal transport
  couplings

abstract:
  Markov chain Monte Carlo (MCMC) methods are often used in clustering since
they guarantee asymptotically exact expectations in the infinite-time limit. In
finite time, though, slow mixing often leads to poor performance. Modern
computing environments offer massive parallelism, but naive implementations of
parallel MCMC can exhibit substantial bias. In MCMC samplers of continuous
random variables, Markov chain couplings can overcome bias. But these
approaches depend crucially on paired chains meetings after a small number of
transitions. We show that straightforward applications of existing coupling
ideas to discrete clustering variables fail to meet quickly. This failure
arises from the "label-switching problem": semantically equivalent cluster
relabelings impede fast meeting of coupled chains. We instead consider chains
as exploring the space of partitions rather than partitions' (arbitrary)
labelings. Using a metric on the partition space, we formulate a practical
algorithm using optimal transport couplings. Our theory confirms our method is
accurate and efficient. In experiments ranging from clustering of genes or
seeds to graph colorings, we show the benefits of our coupling in the highly
parallel, time-limited regime.


title:
Reformulating Scalar-Tensor Field Theories as Scalar-Scalar Field
  Theories Using a Novel Geometry

abstract:
  In this paper I shall show how the notions of Finsler geometry can be used to
construct a similar geometry using a scalar field, f, on the cotangent bundle
of a differentiable manifold M. This will enable me to use the second vertical
derivatives of f, along with the differential of a scalar field phi on M, to
construct a Lorentzian metric on M that depends upon phi. I refer to a field
theory based upon a manifold with such a Lorentzian structure as a
scalar-scalar field theory. We shall study such a field theory when f is chosen
so that the resultant metric on M has the form of a
Friedmann-Lemaitre-Robertson-Walker metric, and the Lagrangian has a
particularly simple form. It will be shown that the scalar-scalar theory
determined by this Lagrangian can generate self-inflating universes, which can
be pieced together to form multiverses with non-Hausdorff topologies, in which
the global time function multifurcates at t=0. Some of the universes in these
multiverses begin explosively, and then settle down to a period of much quieter
accelerated expansion, which can be followed by a collapse to its original
pre-expansion state. I conclude the paper with a discussion of how
probabilities can be assigned to the various universes of a multiverse. This is
accomplished by using the action of the universes, with universes having action
closer to zero being more likely than universes with large positive values for
their action. In order to assure that universe models similar to our own
universe are likely to exist I found it necessary to introduce a second scalar
field on M, and to modify the original Lagrangian. In the end my theory has
three scalar fields, two on the manifold M and one on the cotangent bundle of
M.


title: On the fixed part of pluricanonical systems for surfaces

abstract:
  We show that $|mK_X|$ defines a birational map and has no fixed part for some
bounded positive integer $m$ for any $\frac{1}{2}$-lc surface $X$ such that
$K_X$ is big and nef. For every positive integer $n\geq 3$, we construct a
sequence of projective surfaces $X_{n,i}$, such that $K_{X_{n,i}}$ is ample,
${\rm{mld}}(X_{n,i})>\frac{1}{n}$ for every $i$,
$\lim_{i\rightarrow+\infty}{\rm{mld}}(X_{n,i})=\frac{1}{n}$, and for any
positive integer $m$, there exists $i$ such that $|mK_{X_{n,i}}|$ has non-zero
fixed part. These results answer the surface case of a question of Xu.


title:
Extending the baseline for SMC X-1's spin and orbital behavior with
  NuSTAR stray light

abstract:
  StrayCats, the catalog of NuSTAR stray light observations, contains data from
bright X-ray sources that fall within crowded source regions. These
observations offer unique additional data with which to monitor sources like
X-ray binaries that show variable timing behavior. In this work, we present a
timing analysis of stray light data of the high mass X-ray binary SMC X-1, the
first scientific analysis of a single source from the StrayCats project. We
describe the process of screening stray light data for scientific analysis,
verify the orbital ephemeris, and create both time and energy resolved pulse
profiles. We find that the orbital ephemeris of SMC X-1 is unchanged and
confirm a long-term spin up rate of $\dot{\nu}=(2.52\pm0.03)\times10^{-11}$ Hz
s$^{-1}$. We also note that the shape of SMC X-1's pulse profile, while
remaining double-peaked, varies significantly with time and only slightly with
energy.


title: Exposure theory for learning complex networks with random walks

abstract:
  Random walks are a common model for exploration and discovery of complex
networks. While numerous algorithms have been proposed to map out an unknown
network, a complementary question arises: in a known network, which nodes and
edges are most likely to be discovered by a random walker in finite time? Here
we introduce exposure theory, a statistical mechanics framework that predicts
the learning of nodes and edges across several types of networks, including
weighted and temporal, and show that edge learning follows a universal
trajectory. While the learning of individual nodes and edges is noisy, exposure
theory produces a highly accurate prediction of aggregate exploration
statistics.


title:
Recurrence relations for the generalized Laguerre and Charlier
  orthogonal polynomials and discrete Painlev\'e equations on the $D_{6}^{(1)}$
  Sakai surface

abstract:
  In this paper we consider two examples of certain recurrence relations, or
nonlinear discrete dynamical systems, that appear in the theory of orthogonal
polynomials, from the point of view of Sakai's geometric theory of Painlev\'e
equations. On one hand, this gives us new examples of the appearance of
discrete Painlev\'e equations in the theory of orthogonal polynomials. On the
other hand, it serves as a good illustration of the effectiveness of a recently
proposed procedure on how to reduce such recurrences to some canonical discrete
Painlev\'e equations. Of particular interest is the fact that both recurrences
are regularized on the same family of rational algebraic surfaces, but at the
same time their dynamics are non-equivalent.


title: Blockchain Framework for Artificial Intelligence Computation

abstract:
  Blockchain is an essentially distributed database recording all transactions
or digital events among participating parties. Each transaction in the records
is approved and verified by consensus of the participants in the system that
requires solving a hard mathematical puzzle, which is known as proof-of-work.
To make the approved records immutable, the mathematical puzzle is not trivial
to solve and therefore consumes substantial computing resources. However, it is
energy-wasteful to have many computational nodes installed in the blockchain
competing to approve the records by just solving a meaningless puzzle. Here, we
pose proof-of-work as a reinforcement-learning problem by modeling the
blockchain growing as a Markov decision process, in which a learning agent
makes an optimal decision over the environment's state, whereas a new block is
added and verified. Specifically, we design the block verification and
consensus mechanism as a deep reinforcement-learning iteration process. As a
result, our method utilizes the determination of state transition and the
randomness of action selection of a Markov decision process, as well as the
computational complexity of a deep neural network, collectively to make the
blocks not easy to recompute and to preserve the order of transactions, while
the blockchain nodes are exploited to train the same deep neural network with
different data samples (state-action pairs) in parallel, allowing the model to
experience multiple episodes across computing nodes but at one time. Our method
is used to design the next generation of public blockchain networks, which has
the potential not only to spare computational resources for industrial
applications but also to encourage data sharing and AI model design for common
problems.


title:
Central black hole mass in the distant tidal disruption event candidate
  of {\it Swift} J2058.4+0516

abstract:
  In the manuscript, central black hole (BH) mass is estimated in the distant
TDE (tidal disruption event) of {\it Swift} J2058.4+0516 as the second
candidate of relativistic jet birth related to TDE. {\it Swift} J2058.4+0516
have quite different BH masses estimated through different indirect methods in
the literature. Therefore, it is necessary and interesting to determine the
central BH mass in {\it Swift} J2058.4+0516 by one another independent method.
Here, based on the theoretical TDE model applied to describe the long-term
time-dependent X-ray variabilities of {\it Swift} J2058.4+0516, the central BH
mass can be well determined to be around $1.05_{-0.29}^{+0.39}\times10^5{\rm
M_\odot}$, after kind considerations of suggested intrinsic beaming effects
from such relativistic jet tightly related to TDE. Moreover, the {\it Swift}
J2058.4+0516 is an unique object in the space of BH masses versus energy
transfer efficiencies of the reported TDE candidates, providing interesting
clues to detect and/or anticipate candidates of relativistic TDE to make the
birth of relativistic jets.


title: Margin-distancing for safe model explanation

abstract:
  The growing use of machine learning models in consequential settings has
highlighted an important and seemingly irreconcilable tension between
transparency and vulnerability to gaming. While this has sparked sizable debate
in legal literature, there has been comparatively less technical study of this
contention. In this work, we propose a clean-cut formulation of this tension
and a way to make the tradeoff between transparency and gaming. We identify the
source of gaming as being points close to the \emph{decision boundary} of the
model. And we initiate an investigation on how to provide example-based
explanations that are expansive and yet consistent with a version space that is
sufficiently uncertain with respect to the boundary points' labels. Finally, we
furnish our theoretical results with empirical investigations of this tradeoff
on real-world datasets.


title: Odd coloring of sparse graphs and planar graphs

abstract:
  An {\it odd $c$-coloring} of a graph is a proper $c$-coloring such that each
non-isolated vertex has a color appearing an odd number of times on its
neighborhood. Recently, Cranston investigated odd colorings of graphs with
bounded maximum average degree, and conjectured that every graph $G$ with
$mad(G)\leq \frac{4c-4}{c+1}$ has an odd $c$-coloring for $c\geq 4$, and proved
the conjecture for $c\in\{5, 6\}$. In particular, planar graphs with girth at
least $7$ and $6$ have an odd $5$-coloring and an odd $6$-coloring,
respectively.
  We completely resolve Cranston's conjecture. For $c\geq 7$, we show that the
conjecture is true, in a stronger form that was implicitly suggested by
Cranston, but for $c=4$, we construct counterexamples, which all contain
$5$-cycles. On the other hand, we show that a graph $G$ with
$mad(G)<\frac{22}{9}$ and no induced $5$-cycles has an odd $4$-coloring. This
implies that a planar graph with girth at least 11 has an odd $4$-coloring. We
also prove that a planar graph with girth at least 5 has an odd $6$-coloring.


title:
Designing Decision Support Systems for Emergency Response: Challenges
  and Opportunities

abstract:
  Designing effective emergency response management (ERM) systems to respond to
incidents such as road accidents is a major problem faced by communities. In
addition to responding to frequent incidents each day (about 240 million
emergency medical services calls and over 5 million road accidents in the US
each year), these systems also support response during natural hazards.
Recently, there has been a consistent interest in building decision support and
optimization tools that can help emergency responders provide more efficient
and effective response. This includes a number of principled subsystems that
implement early incident detection, incident likelihood forecasting and
strategic resource allocation and dispatch policies. In this paper, we
highlight the key challenges and provide an overview of the approach developed
by our team in collaboration with our community partners.


title: NetRCA: An Effective Network Fault Cause Localization Algorithm

abstract:
  Localizing the root cause of network faults is crucial to network operation
and maintenance. However, due to the complicated network architectures and
wireless environments, as well as limited labeled data, accurately localizing
the true root cause is challenging. In this paper, we propose a novel algorithm
named NetRCA to deal with this problem. Firstly, we extract effective derived
features from the original raw data by considering temporal, directional,
attribution, and interaction characteristics. Secondly, we adopt multivariate
time series similarity and label propagation to generate new training data from
both labeled and unlabeled data to overcome the lack of labeled samples.
Thirdly, we design an ensemble model which combines XGBoost, rule set learning,
attribution model, and graph algorithm, to fully utilize all data information
and enhance performance. Finally, experiments and analysis are conducted on the
real-world dataset from ICASSP 2022 AIOps Challenge to demonstrate the
superiority and effectiveness of our approach.


title: Spontaneous magnetic skyrmions in single-layer CrInX3(X=Te, Se)

abstract:
  The realization of magnetic skyrmions in nanostructures holds great promise
for both fundamental research and device applications. Despite recent progress,
intrinsic magnetic skyrmions in two-dimensional lattice are still rarely
explored. Here, using first-principles calculations and Monte-Carlo
simulations, we report the identification of spontaneous magnetic skyrmions in
single-layer CrInX3 (X = Te, Se). Due to the joint effect of broken inversion
symmetry and strong spin-orbit coupling, inherent large Dzyaloshinskii-Moriya
interaction occurs in both systems, endowing the intriguing Neel-type skyrmions
in the absence of magnetic field. By further imposing moderate magnetic field,
the skyrmion phase can be obtained and is stable within a wide temperature
range. Particularly for single-layer CrInTe3, the size of skyrmions is sub-10
nm and the skyrmion phase can be maintained at an elevated temperature of 180
K. In addition, the phase diagrams of their topological spin textures under the
variation of magnetic parameters of D, J, and K are mapped out. Our results
greatly enrich the research of 2D skyrmionics physics.


title: ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints

abstract:
  Robotic navigation has been approached as a problem of 3D reconstruction and
planning, as well as an end-to-end learning problem. However, long-range
navigation requires both planning and reasoning about local traversability, as
well as being able to utilize general knowledge about global geography, in the
form of a roadmap, GPS, or other side information providing important cues. In
this work, we propose an approach that integrates learning and planning, and
can utilize side information such as schematic roadmaps, satellite maps and GPS
coordinates as a planning heuristic, without relying on them being accurate.
Our method, ViKiNG, incorporates a local traversability model, which looks at
the robot's current camera observation and a potential subgoal to infer how
easily that subgoal can be reached, as well as a heuristic model, which looks
at overhead maps for hints and attempts to evaluate the appropriateness of
these subgoals in order to reach the goal. These models are used by a heuristic
planner to identify the best waypoint in order to reach the final destination.
Our method performs no explicit geometric reconstruction, utilizing only a
topological representation of the environment. Despite having never seen
trajectories longer than 80 meters in its training dataset, ViKiNG can leverage
its image-based learned controller and goal-directed heuristic to navigate to
goals up to 3 kilometers away in previously unseen environments, and exhibit
complex behaviors such as probing potential paths and backtracking when they
are found to be non-viable. ViKiNG is also robust to unreliable maps and GPS,
since the low-level controller ultimately makes decisions based on egocentric
image observations, using maps only as planning heuristics. For videos of our
experiments, please check out our project page
https://sites.google.com/view/viking-release.


title:
Anharmonic effects on lattice dynamics and thermal transport of
  two-dimensional InTe Monolayer

abstract:
  The lattice thermal conductivity plays a key role in the performance of
thermoelectric materials, where the lower values lead to a higher figure of
merit values. Two-dimensional group III-VI monolayers such as InTe are
promising materials for TE energy generation owing to their low that leads to
high TE figure of merit values. In this work, we investigate the influence of
the lattice anharmonicity on the lattice thermal conductivity of the InTe
monolayer. The thermodynamic parameters are calculated by using the
self-consistent phonon theory. The lattice thermal conductivity value of the
InTe monolayer is obtained to be 0.30 by using the standard Boltzmann transport
equation (BTE) approach, while it is 3.58 by using SCP + BTE approach. These
results confirm the importance of the anharmonic effects on the lattice thermal
conductivity value, where it was found to be significantly higher (91%) using
the SCP + BTE approach than that obtained using the standard BTE approach.


title: A solvable extended logarithm of the Johnson homomorphism

abstract:
  We suggest an extension of a certain logarithm of the total Johnson map in
terms of solvable Lie groups. Here, the domain of the map is extended to a
subset consisting of exponential solvable elements in the mapping class group
of a surface.


title:
Forward and inverse modeling of water flow in unsaturated soils with
  discontinuous hydraulic conductivities using physics-informed neural networks
  with domain decomposition

abstract:
  Modeling water flow in unsaturated soils is vital for describing various
hydrological and ecological phenomena. Soil water dynamics is described by
well-established physical laws (Richardson-Richards equation (RRE)). Solving
the RRE is difficult due to the inherent non-linearity of the processes, and
various numerical methods have been proposed to solve the issue. However,
applying the methods to practical situations is challenging because they
require well-defined initial and boundary conditions. Here, we present a
physics-informed neural networks (PINNs) method that approximates the solution
to the RRE using neural networks while concurrently matching available soil
moisture data. Although the ability of PINNs to solve partial differential
equations, including the RRE, has been demonstrated previously, its potential
applications and limitations are not fully known. This study conducted a
comprehensive analysis of PINNs and carefully tested the accuracy of the
solutions by comparing them with analytical solutions and accepted traditional
numerical solutions. We demonstrated that the solutions by PINNs with adaptive
activation functions are comparable with those by traditional methods. We
showed that soil moisture dynamics in layered soils with discontinuous
hydraulic conductivities are correctly simulated by PINNs with domain
decomposition. We demonstrated that the unspecified upper boundary condition
can be estimated from sparse soil moisture measurements. Nevertheless, there
remain challenges that require further development. Chiefly, PINNs are
sensitive to the initialization of NNs and are significantly slower than
traditional numerical methods.


title:
Electromagnetically induced moir\'{e} optical lattices in a coherent
  atomic gas

abstract:
  Electromagnetically induced optical (or photonic) lattices via atomic
coherence in atomic ensembles have recently received great theoretical and
experimental interest. We here conceive a way to generate electromagnetically
induced moir\'{e} optical lattices -- a twisted periodic pattern when two
identical periodic patterns (lattices) are overlapped in a twisted angle
($\theta$) -- in a three-level coherent atomic gas working under
electromagnetically induced transparency. We show that, changing the twisted
angle and relative strength between the two constitutive sublattices, the
moir\'{e} Bloch bands that are extremely flattened can always appear,
resembling the typical flat-band and moir\'{e} physics found in other contexts.
Dynamics of light propagation in the induced periodic structures demonstrating
the unique linear localization and delocalization properties are also revealed.
Our scheme can be implemented in a Rubidium atomic medium, where the predicted
moir\'{e} optical lattices and flattened bands are naturally observable.


title:
Nearest neighbor ratio imputation with incomplete multi-nomial outcome
  in survey sampling

abstract:
  Nonresponse is a common problem in survey sampling. Appropriate treatment can
be challenging, especially when dealing with detailed breakdowns of totals.
Often, the nearest neighbor imputation method is used to handle such incomplete
multinomial data. In this article, we investigate the nearest neighbor ratio
imputation estimator, in which auxiliary variables are used to identify the
closest donor and the vector of proportions from the donor is applied to the
total of the recipient to implement ratio imputation. To estimate the
asymptotic variance, we first treat the nearest neighbor ratio imputation as a
special case of predictive matching imputation and apply the linearization
method of \cite{yang2020asymptotic}. To account for the non-negligible sampling
fractions, parametric and generalized additive models are employed to
incorporate the smoothness of the imputation estimator, which results in a
valid variance estimator. We apply the proposed method to estimate expenditures
detail items based on empirical data from the 2018 collection of the Service
Annual Survey, conducted by the United States Census Bureau. Our simulation
results demonstrate the validity of our proposed estimators and also confirm
that the derived variance estimators have good performance even when the
sampling fraction is non-negligible.


title:
Minimax Optimal Quantization of Linear Models: Information-Theoretic
  Limits and Efficient Algorithms

abstract:
  We consider the problem of quantizing a linear model learned from
measurements $\mathbf{X} = \mathbf{W}\boldsymbol{\theta} + \mathbf{v}$. The
model is constrained to be representable using only $dB$-bits, where $B \in (0,
\infty)$ is a pre-specified budget and $d$ is the dimension of the model. We
derive an information-theoretic lower bound for the minimax risk under this
setting and show that it is tight with a matching upper bound. This upper bound
is achieved using randomized embedding based algorithms. We propose randomized
Hadamard embeddings that are computationally efficient while performing
near-optimally. We also show that our method and upper-bounds can be extended
for two-layer ReLU neural networks. Numerical simulations validate our
theoretical claims.


title:
Joint Channel Estimation, Activity Detection and Decoding using Dynamic
  Message-Scheduling for Machine-Type Communications

abstract:
  In this work, we present a joint channel estimation, activity detection and
data decoding scheme for massive machine-type communications. By including the
channel and the a priori activity factor in the factor graph, we present the
bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that
uses the channel decoder beliefs to refine the activity detection and data
decoding. We include two message-scheduling strategies based on the residual
belief propagation and the activity user detection in which messages are
evaluated and scheduled in every new iteration. An analysis of the convergence
of BiMSGAMP along with a study of its computational complexity is carried out.
Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms,
highlighting the gains achieved by using the dynamic scheduling strategies and
the effects of the channel decoding part in the system.


title:
An End-to-End Cascaded Image Deraining and Object Detection Neural
  Network

abstract:
  While the deep learning-based image deraining methods have made great
progress in recent years, there are two major shortcomings in their application
in real-world situations. Firstly, the gap between the low-level vision task
represented by rain removal and the high-level vision task represented by
object detection is significant, and the low-level vision task can hardly
contribute to the high-level vision task. Secondly, the quality of the
deraining dataset needs to be improved. In fact, the rain lines in many
baselines have a large gap with the real rain lines, and the resolution of the
deraining dataset images is generally not ideally. Meanwhile, there are few
common datasets for both the low-level vision task and the high-level vision
task. In this paper, we explore the combination of the low-level vision task
with the high-level vision task. Specifically, we propose an end-to-end object
detection network for reducing the impact of rainfall, which consists of two
cascaded networks, an improved image deraining network and an object detection
network, respectively. We also design the components of the loss function to
accommodate the characteristics of the different sub-networks. We then propose
a dataset based on the KITTI dataset for rainfall removal and object detection,
on which our network surpasses the state-of-the-art with a significant
improvement in metrics. Besides, our proposed network is measured on driving
videos collected by self-driving vehicles and shows positive results for rain
removal and object detection.


title:
Learning Multi-step Robotic Manipulation Policies from Visual
  Observation of Scene and Q-value Predictions of Previous Action

abstract:
  In this work, we focus on multi-step manipulation tasks that involve
long-horizon planning and considers progress reversal. Such tasks interlace
high-level reasoning that consists of the expected states that can be attained
to achieve an overall task and low-level reasoning that decides what actions
will yield these states. We propose a sample efficient Previous Action
Conditioned Robotic Manipulation Network (PAC-RoManNet) to learn the
action-value functions and predict manipulation action candidates from visual
observation of the scene and action-value predictions of the previous action.
We define a Task Progress based Gaussian (TPG) reward function that computes
the reward based on actions that lead to successful motion primitives and
progress towards the overall task goal. To balance the ratio of
exploration/exploitation, we introduce a Loss Adjusted Exploration (LAE) policy
that determines actions from the action candidates according to the Boltzmann
distribution of loss estimates. We demonstrate the effectiveness of our
approach by training PAC-RoManNet to learn several challenging multi-step
robotic manipulation tasks in both simulation and real-world. Experimental
results show that our method outperforms the existing methods and achieves
state-of-the-art performance in terms of success rate and action efficiency.
The ablation studies show that TPG and LAE are especially beneficial for tasks
like multiple block stacking. Additional experiments on Ravens-10 benchmark
tasks suggest good generalizability of the proposed PAC-RoManNet.


title: Three Dimensional Activity Volcano Plot under External Electric Field

abstract:
  An external electric field (EEF) can impact a broad range of catalytic
processes beyond redox systems. Computational design of catalysts under EEFs
targeting specific operation conditions essentially requires accurate
predictions of the response of a complex physicochemical system to collective
parameters such as EEF strength/direction and temperature. Here, we develop a
first-principles-based multiscale approach that enables efficient EEF-dependent
kinetic modeling of heterogeneous catalysis. Taking steam reforming of methanol
as an example, we find that the methanol conversion rate exhibits strong
nonlinear response to temperature and EEF, and the optimal field line and
constant carbon concentration line defined in the temperature--EEF parameter
space serve as powerful metrics for catalyst design. Assisted with a deep
neural network, we establish a three-dimensional activity volcano plot under
EEFs for thousands of metallic alloys.


title: Simple models for macro-parasite distributions in hosts

abstract:
  Negative binomial distribution is the most used distribution to model
macro-parasite burden in hosts. However reliable maximum likelihood parameter
estimation from data is far from trivial. No closed formula is available and
numerical estimation requires sophisticated methods. Using data from the
literature we show that simple alternatives to negative binomial, like
zero-inflated geometric or hurdle geometric distributions, produce a good and
even better fit to data than negative binomial distribution. We derived closed
simple formulas for the maximum likelihood parameter estimation which
constitutes a significant advantage of these distributions over negative
binomial distribution.


title:
Better Modelling Out-of-Distribution Regression on Distributed Acoustic
  Sensor Data Using Anchored Hidden State Mixup

abstract:
  Generalizing the application of machine learning models to situations where
the statistical distribution of training and test data are different has been a
complex problem. Our contributions in this paper are threefold: (1) we
introduce an anchored-based Out of Distribution (OOD) Regression Mixup
algorithm, leveraging manifold hidden state mixup and observation similarities
to form a novel regularization penalty, (2) we provide a first of its kind,
high resolution Distributed Acoustic Sensor (DAS) dataset that is suitable for
testing OOD regression modelling, allowing other researchers to benchmark
progress in this area, and (3) we demonstrate with an extensive evaluation the
generalization performance of the proposed method against existing approaches,
then show that our method achieves state-of-the-art performance. Lastly, we
also demonstrate a wider applicability of the proposed method by exhibiting
improved generalization performances on other types of regression datasets,
including Udacity and Rotation-MNIST datasets.


title:
A 5.3 GHz Al0.76Sc0.24N Two-Dimensional Resonant Rods Resonator with a
  kt2 of 23.9%

abstract:
  This work reports on the measured performance of an Aluminum Scandium Nitride
(AlScN) Two-Dimensional Resonant Rods resonator (2DRR), fabricated by using a
Sc-doping concentration of 24%, characterized by a low off-resonance impedance
(~25 Ohm) and exhibiting a record electromechanical coupling coefficient (kt2)
of 23.9% for AlScN resonators. In order to achieve such performance, we
identified and relied on optimized deposition and etching processes for
highly-doped AlScN films, aiming at achieving high crystalline quality, low
density of abnormally oriented grains in the 2DRR's active region and sharp
lateral sidewalls. Also, the 2DRR's unit-cell has been acoustically engineered
to maximize the piezo-generated mechanical energy within each rod and to ensure
a low transduction of spurious modes around resonance. Due to its unprecedented
kt2, the reported 2DRR opens exciting scenarios towards the development of next
generation monolithic integrated radio-frequency (RF) filtering components. In
fact, we show that 5th-order 2DRR-based ladder filters with fractional
bandwidths (BW) of ~11%, insertion-loss (I.L) values of ~2.5 dB and with >30 dB
out-of-band rejections can now be envisioned, paving an unprecedented path
towards the development of ultra-wide band (UWB) filters for next-generation
Super-High-Frequency (SHF) radio front-ends.


title: Neural Generalised AutoRegressive Conditional Heteroskedasticity

abstract:
  We propose Neural GARCH, a class of methods to model conditional
heteroskedasticity in financial time series. Neural GARCH is a neural network
adaptation of the GARCH 1,1 model in the univariate case, and the diagonal BEKK
1,1 model in the multivariate case. We allow the coefficients of a GARCH model
to be time varying in order to reflect the constantly changing dynamics of
financial markets. The time varying coefficients are parameterised by a
recurrent neural network that is trained with stochastic gradient variational
Bayes. We propose two variants of our model, one with normal innovations and
the other with Students t innovations. We test our models on a wide range of
univariate and multivariate financial time series, and we find that the Neural
Students t model consistently outperforms the others.


title: Deep Recurrent Modelling of Granger Causality with Latent Confounding

abstract:
  Inferring causal relationships in observational time series data is an
important task when interventions cannot be performed. Granger causality is a
popular framework to infer potential causal mechanisms between different time
series. The original definition of Granger causality is restricted to linear
processes and leads to spurious conclusions in the presence of a latent
confounder. In this work, we harness the expressive power of recurrent neural
networks and propose a deep learning-based approach to model non-linear Granger
causality by directly accounting for latent confounders. Our approach leverages
multiple recurrent neural networks to parameterise predictive distributions and
we propose the novel use of a dual-decoder setup to conduct the Granger tests.
We demonstrate the model performance on non-linear stochastic time series for
which the latent confounder influences the cause and effect with different time
lags; results show the effectiveness of our model compared to existing
benchmarks.


title: LPF-Defense: 3D Adversarial Defense based on Frequency Analysis

abstract:
  Although 3D point cloud classification has recently been widely deployed in
different application scenarios, it is still very vulnerable to adversarial
attacks. This increases the importance of robust training of 3D models in the
face of adversarial attacks. Based on our analysis on the performance of
existing adversarial attacks, more adversarial perturbations are found in the
mid and high-frequency components of input data. Therefore, by suppressing the
high-frequency content in the training phase, the models robustness against
adversarial examples is improved. Experiments showed that the proposed defense
method decreases the success rate of six attacks on PointNet, PointNet++ ,, and
DGCNN models. In particular, improvements are achieved with an average increase
of classification accuracy by 3.8 % on drop100 attack and 4.26 % on drop200
attack compared to the state-of-the-art methods. The method also improves
models accuracy on the original dataset compared to other available methods.


title:
Fast algebraic multigrid for block-structured dense and
  Toeplitz-like-plus-Cross systems arising from nonlocal diffusion problems

abstract:
  Algebraic multigrid (AMG) is one of the most efficient iterative methods for
solving large sparse system of equations. However, how to build/check
restriction and prolongation operators in practical of AMG methods for
nonsymmetric {\em sparse} systems is still an interesting open question
[Brezina, Manteuffel, McCormick, Runge, and Sanders, SIAM J. Sci. Comput.
(2010); Manteuffel and Southworth, SIAM J. Sci. Comput. (2019)]. This paper
deals with the block-structured dense and Toeplitz-like-plus-Cross systems,
including {\em nonsymmetric} indefinite, symmetric positive definite (SPD),
arising from nonlocal diffusion problem and peridynamic problem. The simple
(traditional) restriction operator and prolongation operator are employed in
order to handle such block-structured dense and Toeplitz-like-plus-Cross
systems, which is convenient and efficient when employing a fast AMG. We focus
our efforts on providing the detailed proof of the convergence of the two-grid
method for such SPD situations. The numerical experiments are performed in
order to verify the convergence with a computational cost of only
$\mathcal{O}(N \mbox{log} N)$ arithmetic operations, by using few fast Fourier
transforms, where $N$ is the number of the grid points. To the best of our
knowledge, this is the first contribution regarding Toeplitz-like-plus-Cross
linear systems solved by means of a fast AMG.


title:
Classification of Computer Aided Engineering (CAE) Parts Using Graph
  Convolutional Networks

abstract:
  CAE engineers work with hundreds of parts spread across multiple body models.
A Graph Convolutional Network (GCN) was used to develop a CAE parts classifier.
As many as 866 distinct parts from a representative body model were used as
training data. The parts were represented as a three-dimensional (3-D) Finite
Element Analysis (FEA) mesh with values of each node in the x, y, z coordinate
system. The GCN based classifier was compared to fully connected neural network
and PointNet based models. Performance of the trained models was evaluated with
a test set that included parts from the training data, but with additional
holes, rotation, translation, mesh refinement/coarsening, variation of mesh
schema, mirroring along x and y axes, variation of topographical features, and
change in mesh node ordering. The trained GCN model was able to achieve 88.5%
classification accuracy on the test set i.e., it was able to find the correct
matching part from the dataset of 866 parts despite significant variation from
the baseline part. A CAE parts classifier demonstrated in this study could be
very useful for engineers to filter through CAE parts spread across several
body models to find parts that meet their requirements.


title: Equivariant Bordism of 2-Torus Manifolds and Unitary Toric Manifolds

abstract:
  The equivariant bordism classification of manifolds with group actions is an
essential subject in the study of transformation groups. We are interesting in
the action of 2-torus group $\mathbb{Z}_2^n$ and torus group $T^n$, and study
the equivariant bordism of 2-torus manifolds and unitary toric manifolds. In
this paper, we give a new description of the group
$\mathcal{Z}_n(\mathbb{Z}_2^n)$ of 2-torus manifolds, and determine the
dimention of $\mathcal{Z}_n(\mathbb{Z}_2^n)$ as a $\mathbb{Z}_2$-vector space.
With the help of toric topology, L\"u and Tan proved that the bordism groups
$\mathcal{Z}_n(\mathbb{Z}_2^n)$ are generated by small covers. We will give a
new proof to this result. These results can be generalized to the equivariant
bordism of unitary toric manifolds, that is, we will give a new description of
the group $\mathcal{Z}_n^U(T^n)$ of unitary torus manifolds, and prove that
$\mathcal{Z}_n^U(T^n)$ can be generated by quasitoric manifolds with
omniorientations.


title: Spin-Phonon-Photon Strong Coupling in a Piezomechanical Nanocavity

abstract:
  We introduce a hybrid tripartite quantum system for strong coupling between a
semiconductor spin, a mechanical phonon, and a microwave photon. Consisting of
a piezoelectric resonator with an integrated diamond strain concentrator, this
system achieves microwave-acoustic and spin-acoustic coupling rates $\sim$MHz
or greater, allowing for simultaneous ultra-high cooperativities ($\sim 10^3$
and $\sim 10^2$, respectively). From finite-element modeling and master
equation simulations, we estimate photon-to-spin quantum state transfer
fidelities exceeding 0.97 based on separately demonstrated device parameters.
We anticipate that this device will enable hybrid quantum architectures that
leverage the advantages of both superconducting circuits and solid-state spins
for information processing, memory, and networking.


title: Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

abstract:
  Unsupervised point cloud registration algorithm usually suffers from the
unsatisfied registration precision in the partially overlapping problem due to
the lack of effective inlier evaluation. In this paper, we propose a
neighborhood consensus based reliable inlier evaluation method for robust
unsupervised point cloud registration. It is expected to capture the
discriminative geometric difference between the source neighborhood and the
corresponding pseudo target neighborhood for effective inlier distinction.
Specifically, our model consists of a matching map refinement module and an
inlier evaluation module. In our matching map refinement module, we improve the
point-wise matching map estimation by integrating the matching scores of
neighbors into it. The aggregated neighborhood information potentially
facilitates the discriminative map construction so that high-quality
correspondences can be provided for generating the pseudo target point cloud.
Based on the observation that the outlier has the significant structure-wise
difference between its source neighborhood and corresponding pseudo target
neighborhood while this difference for inlier is small, the inlier evaluation
module exploits this difference to score the inlier confidence for each
estimated correspondence. In particular, we construct an effective graph
representation for capturing this geometric difference between the
neighborhoods. Finally, with the learned correspondences and the corresponding
inlier confidence, we use the weighted SVD algorithm for transformation
estimation. Under the unsupervised setting, we exploit the Huber function based
global alignment loss, the local neighborhood consensus loss, and spatial
consistency loss for model optimization. The experimental results on extensive
datasets demonstrate that our unsupervised point cloud registration method can
yield comparable performance.


title: SIN, COS, EXP and LOG of Liouville numbers

abstract:
  For any Liouville number $\alpha$, all of the following are transcendental
numbers: $\textrm{e}^\alpha$, $\log_\textrm{e}\alpha$, $\sin \alpha$,
$\cos\alpha$, $\tan\alpha$, $\sinh\alpha$, $\cosh\alpha$, $\tanh\alpha$,
$\arcsin\alpha$ and the inverse functions evaluated at $\alpha$ of the listed
trigonometric and hyperbolic functions, noting that wherever multiple values
are involved, every such value is transcendental. This remains true if
"Liouville number" is replaced by "$U$-number", where $U$ is one of Mahler's
classes of transcendental numbers.


title: Maximal Independent Sets in Polygonal Cacti

abstract:
  Counting the number of maximal independent sets of graphs was started over
$50$ years ago by Erd\H{o}s and Mooser. The problem has been continuously
studied with a number of variations. In this paper, we applied the concept of
bivariate generating function to establish the recurrence relations and their
asymptotic behavior of the number of maximal independent sets of regualr
$n$-gonal cacti when $3 \leq n \leq 6$.


title:
Continual learning-based probabilistic slow feature analysis for
  multimode dynamic process monitoring

abstract:
  In this paper, a novel multimode dynamic process monitoring approach is
proposed by extending elastic weight consolidation (EWC) to probabilistic slow
feature analysis (PSFA) in order to extract multimode slow features for online
monitoring. EWC was originally introduced in the setting of machine learning of
sequential multi-tasks with the aim of avoiding catastrophic forgetting issue,
which equally poses as a major challenge in multimode dynamic process
monitoring. When a new mode arrives, a set of data should be collected so that
this mode can be identified by PSFA and prior knowledge. Then, a regularization
term is introduced to prevent new data from significantly interfering with the
learned knowledge, where the parameter importance measures are estimated. The
proposed method is denoted as PSFA-EWC, which is updated continually and
capable of achieving excellent performance for successive modes. Different from
traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and
forward transfer ability. The significant features of previous modes are
retained while consolidating new information, which may contribute to learning
new relevant modes. Compared with several known methods, the effectiveness of
the proposed method is demonstrated via a continuous stirred tank heater and a
practical coal pulverizing system.


title: Reinforcement Learning in Practice: Opportunities and Challenges

abstract:
  This article is a gentle discussion about the field of reinforcement learning
in practice, about opportunities and challenges, touching a broad range of
topics, with perspectives and without technical details. The article is based
on both historical and recent research papers, surveys, tutorials, talks,
blogs, books, (panel) discussions, and workshops/conferences. Various groups of
readers, like researchers, engineers, students, managers, investors, officers,
and people wanting to know more about the field, may find the article
interesting.
  In this article, we first give a brief introduction to reinforcement learning
(RL), and its relationship with deep learning, machine learning and AI. Then we
discuss opportunities of RL, in particular, products and services, games,
bandits, recommender systems, robotics, transportation, finance and economics,
healthcare, education, combinatorial optimization, computer systems, and
science and engineering. Then we discuss challenges, in particular, 1)
foundation, 2) representation, 3) reward, 4) exploration, 5) model, simulation,
planning, and benchmarks, 6) off-policy/offline learning, 7) learning to learn
a.k.a. meta-learning, 8) explainability and interpretability, 9) constraints,
10) software development and deployment, 11) business perspectives, and 12)
more challenges. We conclude with a discussion, attempting to answer: "Why has
RL not been widely adopted in practice yet?" and "When is RL helpful?".


title: Minimum time trajectory generation for surveying using UAVs

abstract:
  In this paper, we present a motion planning strategy for UAVs that generates
a time-optimal trajectory to survey a given target area. There are several
situations where completing an aerial survey is time sensitive, such as gaining
situational awareness for first responders, surveying hazardous environments,
etc. One of the challenges in such cases is to plan a time-optimal trajectory
for the drone. To this end, we present an autonomous aerial survey framework
that minimizes the time taken to completely explore a target area or volume
using drones. In this work, (i) we present an approach, where for a known
flight survey pattern, the planner can generate time-optimal flight paths in
3-D (ii) we frame the planning problem as a discrete non-linear program, and
reduce the time taken to compute its solution by using an SOCP relaxation (iii)
The given path is then executed using a simple trajectory tracking controller
on a quadrotor to demonstrate its capability on hardware.


title:
Is Global Asymptotic Stability Necessarily Uniform for Time-Delay
  Systems?

abstract:
  For time-invariant finite-dimensional systems, it is known that global
asymptotic stability (GAS) is equivalent to uniform global asymptotic stability
(UGAS), in which the decay rate and transient overshoot of solutions are
requested to be uniform on bounded sets of initial states. This paper
investigates this relationship for time-invariant delay systems. We show that
UGAS and GAS are equivalent for this class of systems under the assumption of
robust forward completeness, i.e. under the assumption that the reachable set
from any bounded set of initial states on any finite time horizon is bounded.
We also show that, if the state space is a space in a particular family of
Sobolev or Holder spaces, then GAS is equivalent to UGAS and that robust
forward completeness holds. Based on these equivalences, we provide a novel
Lyapunov characterization of GAS (and UGAS) in the aforementioned spaces.


title: Knowledge Augmented BERT Mutual Network in Multi-turn Spoken Dialogues

abstract:
  Modern spoken language understanding (SLU) systems rely on sophisticated
semantic notions revealed in single utterances to detect intents and slots.
However, they lack the capability of modeling multi-turn dynamics within a
dialogue particularly in long-term slot contexts. Without external knowledge,
depending on limited linguistic legitimacy within a word sequence may overlook
deep semantic information across dialogue turns. In this paper, we propose to
equip a BERT-based joint model with a knowledge attention module to mutually
leverage dialogue contexts between two SLU tasks. A gating mechanism is further
utilized to filter out irrelevant knowledge triples and to circumvent
distracting comprehension. Experimental results in two complicated multi-turn
dialogue datasets have demonstrate by mutually modeling two SLU tasks with
filtered knowledge and dialogue contexts, our approach has considerable
improvements compared with several competitive baselines.