title: FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment

abstract:
  We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Unlike previous work, we offer a subject agnostic swapping scheme that can be
applied to pairs of faces without requiring training on those faces. We derive
a novel iterative deep learning--based approach for face reenactment which
adjusts significant pose and expression variations that can be applied to a
single image or a video sequence. For video sequences, we introduce a
continuous interpolation of the face views based on reenactment, Delaunay
Triangulation, and barycentric coordinates. Occluded face regions are handled
by a face completion network. Finally, we use a face blending network for
seamless blending of the two faces while preserving the target skin color and
lighting conditions. This network uses a novel Poisson blending loss combining
Poisson optimization with a perceptual loss. We compare our approach to
existing state-of-the-art systems and show our results to be both qualitatively
and quantitatively superior. This work describes extensions of the FSGAN
method, proposed in an earlier conference version of our work, as well as
additional experiments and results.


title:
Hypercube Quantum Search: Exact Computation of the Probability of
  Success in Polynomial Time

abstract:
  In the emerging domain of quantum algorithms, the Grover's quantum search is
certainly one of the most significant. It is relatively simple, performs a
useful task and more importantly, does it in an optimal way. However, due to
the success of quantum walks in the field, it is logical to study quantum
search variants over several kind of walks. In this paper, we propose an
in-depth study of the quantum search over a hypercube layout. First, through
the analysis of elementary walk operators restricted to suitable eigenspaces,
we show that the acting component of the search algorithm takes place in a
small subspace of the Hilbert workspace that grows linearly with the problem
size. Subsequently, we exploit this property to predict the exact evolution of
the probability of success of the quantum search in polynomial time.


title:
Correlated Disorder Induced Anomalous Transport in Magnetically Doped
  Topological Insulators

abstract:
  We examine the transport properties of magnetically doped topological
insulator (TI) thin films subject to correlated non-magnetic disorder. For the
disorder we choose a quasi-periodic potential with a random phase. We restrict
the disorder to a central region, which is coupled to two leads in a clean
quantum spin Hall insulator (QSHI) state and concentrate on different
orientations of the quasi-periodicity in the two-dimensional central region. In
the case of a diagonally oriented or purely longitudinal quasi-periodicity we
find different topological Anderson insulator (TAI) phases, with a quantum
anomalous Hall insulator (QAHI), a quantum spin Chern insulator (QSCI), or a
QSHI phase being realized before the Anderson insulation takes over at large
disorder strength. Quantized transport from extended bulk states is found for
diagonal quasi-periodicity in addition to the above TAI phases that are also
observed for the case of uncorrelated disorder. For a purely transverse
orientation of the quasi-periodicity the emerging QSHI and QSCI phases persist
to arbitrarily strong disorder potential. These topological phase transitions
(except to the Anderson insulator phase), can be understood from a self
consistent Born approximation.


title: Degenerations of Pascal Lines

abstract:
  Let $\mathcal{K}$ denote a nonsingular conic in the complex projective plane.
Pascal's theorem says that, given six distinct points $A,B,C,D,E,F$ on
$\mathcal{K}$, the three intersection points $AE \cap BF, AD \cap CF, BD \cap
CE$ are collinear. The line containing them is called the Pascal line of the
sextuple. However, this construction may fail when some of the six points come
together. In this paper, we find the indeterminacy locus where the Pascal line
is not well-defined and then use blow-ups along polydiagonals to define it. We
analyse the geometry of Pascals in these degenerate cases. Finally we offer
some remarks about the indeterminacy of other geometric elements in Pascal's
hexagrammum mysticum.


title:
The toy-model of the Boltzmann type equation for the contact force
  distribution in disordered packings of particles

abstract:
  The packing of hard-core particles in contact with their neighbors is
considered as the simplest model of disordered particulate media. We formulate
the statically determinate problem which allows analytical investigation of the
statistical distribution of contact force magnitude. The toy-model of the
Boltzmann type equation for the probability of contact force distribution is
formulated and studied. An experimentally observed exponential distribution is
derived.

categories: cond-mat.stat-mech math-ph math.MP nlin.SI


title:
Learning physics-informed simulation models for soft robotic
  manipulation: A case study with dielectric elastomer actuators

abstract:
  Soft actuators offer a safe and adaptable approach to robotic tasks like
gentle grasping and dexterous movement. Creating accurate models to control
such systems, however, is challenging due to the complex physics of deformable
materials. Accurate Finite Element Method (FEM) models incur prohibitive
computational complexity for closed-loop use. Using a differentiable simulator
is an attractive alternative, but their applicability to soft actuators and
deformable materials remains under-explored. This paper presents a framework
that combines the advantages of both. We learn a differentiable model
consisting of a material properties neural network and an analytical dynamics
model of the remainder of the manipulation task. This physics-informed model is
trained using data generated from FEM and can be used for closed-loop control
and inference. We evaluate our framework on a dielectric elastomer actuator
(DEA) coin-pulling task. We simulate DEA coin pulling in FEM, and design
experiments to evaluate the physics-informed model for simulation, control, and
inference. Our model attains < 5% simulation error compared to FEM, and we use
it as the basis for an MPC controller that outperforms (i.e., requires fewer
iterations to converge) a model-free actor-critic policy, a heuristic policy,
and a PD controller.


title: Virtual permutations and polymorhisms

abstract:
  There is a natural map from a symmetric group $S_n$ to a smaller symmetric
group $S_{n-1}$, we write a decomposition of a permutation into a product of
disjoint cycles and remove the element $n$ from this expression. For this
reason there exists the inverse limit $\mathfrak{S}$ of sets $S_n$. We equip
$S_n$ with the uniform distribution (or more generally with an Ewens
distribution) and get a structure of a measure space on $\mathfrak{S}$ (it is
called 'virtual permutations' or 'Chinese restaurant process'), a double
$S_\infty\times S_\infty $ of an infinite symmetric group acts on
$\mathfrak{S}$ by left and right 'multiplications'. We discuss the closure of
$S_\infty\times S_\infty $ in the semigroup of polymorphisms (spreading maps
with spreaded Radon--Nikodym derivatives) of $\mathfrak{S}$. We get formulas
for some polymorphisms, in particular for the center of the closure.
Expressions are sums of multiple convolutions of Dirichlet distributions,
summation sets are certain collections of dessins d'enfant.


title:
Generalised Gaussian Process Latent Variable Models (GPLVM) with
  Stochastic Variational Inference

abstract:
  Gaussian process latent variable models (GPLVM) are a flexible and non-linear
approach to dimensionality reduction, extending classical Gaussian processes to
an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias
and Lawrence, 2010] uses a variational framework, where the posterior over
latent variables is approximated by a well-behaved variational family, a
factorized Gaussian yielding a tractable lower bound. However, the
non-factories ability of the lower bound prevents truly scalable inference. In
this work, we study the doubly stochastic formulation of the Bayesian GPLVM
model amenable with minibatch training. We show how this framework is
compatible with different latent variable formulations and perform experiments
to compare a suite of models. Further, we demonstrate how we can train in the
presence of massively missing data and obtain high-fidelity reconstructions. We
demonstrate the model's performance by benchmarking against the canonical
sparse GPLVM for high-dimensional data examples.


title: Simulability of high-dimensional quantum measurements

abstract:
  We investigate the compression of quantum information with respect to a given
set $\mathcal{M}$ of high-dimensional measurements. This leads to a notion of
simulability, where we demand that the statistics obtained from $\mathcal{M}$
and an arbitrary quantum state $\rho$ are recovered exactly by first
compressing $\rho$ into a lower dimensional space, followed by some quantum
measurements. A full quantum compression is possible, i.e., leaving only
classical information, if and only if the set $\mathcal{M}$ is jointly
measurable. Our notion of simulability can thus be seen as a quantification of
measurement incompatibility in terms of dimension. After defining these
concepts, we provide an illustrative examples involving mutually unbiased
basis, and develop a method based on semi-definite programming for constructing
simulation models. In turn we analytically construct optimal simulation models
for all projective measurements subjected to white noise or losses. Finally, we
discuss how our approach connects with other concepts introduced in the context
of quantum channels and quantum correlations.


title:
Scalable Gaussian-process regression and variable selection using
  Vecchia approximations

abstract:
  Gaussian process (GP) regression is a flexible, nonparametric approach to
regression that naturally quantifies uncertainty. In many applications, the
number of responses and covariates are both large, and a goal is to select
covariates that are related to the response. For this setting, we propose a
novel, scalable algorithm, coined VGPR, which optimizes a penalized GP
log-likelihood based on the Vecchia GP approximation, an ordered conditional
approximation from spatial statistics that implies a sparse Cholesky factor of
the precision matrix. We traverse the regularization path from strong to weak
penalization, sequentially adding candidate covariates based on the gradient of
the log-likelihood and deselecting irrelevant covariates via a new quadratic
constrained coordinate descent algorithm. We propose Vecchia-based mini-batch
subsampling, which provides unbiased gradient estimators. The resulting
procedure is scalable to millions of responses and thousands of covariates.
Theoretical analysis and numerical studies demonstrate the improved scalability
and accuracy relative to existing methods.


title: Graded Lie-Rinehart algebras

abstract:
  We introduce the class of graded Lie-Rinehart algebras as a natural
generalization of the one of graded Lie algebras. For $G$ an abelian group, we
show that if $L$ is a tight $G$-graded Lie-Rinehart algebra over an associative
and commutative $G$-graded algebra $A$ then $L$ and $A$ decompose as the
orthogonal direct sums $L = \bigoplus_{i \in I}I_i$ and $A = \bigoplus_{j \in
J}A_j$, where any $I_i$ is a non-zero ideal of $L$, any $A_j$ is a non-zero
ideal of $A$, and both decompositions satisfy that for any $i \in I$ there
exists a unique $j \in J$ such that $A_jI_i \neq 0$. Furthermore, any $I_i$ is
a graded Lie-Rinehart algebra over $A_j$. Also, under mild conditions, it is
shown that the above decompositions of $L$ and $A$ are by means of the family
of their, respective, gr-simple ideals.


title:
Solving Large-Scale Dynamic Vehicle Routing Problems with Stochastic
  Requests

abstract:
  Dynamic vehicle routing problems (DVRPs) arise in several applications such
as technician routing, meal delivery, and parcel shipping. We consider the DVRP
with stochastic customer requests (DVRPSR), in which vehicles must be routed
dynamically with the goal of maximizing the number of served requests. We model
the DVRPSR as a multi-stage optimization problem, where the first-stage
decision defines route plans for serving scheduled requests. Our main
contributions are knapsack-based linear models to approximate accurately the
expected reward-to-go, measured as the number of accepted requests, at any
state of the stochastic system. These approximations are based on representing
each vehicle as a knapsack with a capacity given by the remaining service time
available along the vehicle's route. We combine these approximations with
optimal acceptance and assignment decision rules and derive efficient and
high-performing online scheduling policies. We further leverage good
predictions of the expected reward-to-go to design initial route plans that
facilitate serving dynamic requests. Computational experiments on very large
instances based on a real street network demonstrate the effectiveness of the
proposed methods in prescribing high-quality offline route plans and online
scheduling decisions.


title: Wearable uBrain: Fabric Based-Spiking Neural Network

abstract:
  On garment intelligence influenced by artificial neural networks and
neuromorphic computing is emerging as a research direction in the e-textile
sector. In particular, bio inspired Spiking Neural Networks mimicking the
workings of the brain show promise in recent ICT research applications. Taking
such technological advancements and new research directions driving forward the
next generation of e-textiles and smart materials, we present a wearable micro
Brain capable of event driven artificial spiking neural network computation in
a fabric based environment. We demonstrate a wearable Brain SNN prototype with
multi-layer computation, enabling scalability and flexibility in terms of
modifications for hidden layers to be augmented to the network. The wearable
micro Brain provides a low size, weight and power artificial on-garment
intelligent wearable solution with embedded functionality enabling offline
adaptive learning through the provision of interchangeable resistor synaptic
weightings. The prototype has been evaluated for fault tolerance, where we have
determine the robustness of the circuit when certain parts are damaged.
Validations were also conducted for movements to determine if the circuit can
still perform accurate computation.


title: OCR-IDL: OCR Annotations for Industry Document Library Dataset

abstract:
  Pretraining has proven successful in Document Intelligence tasks where deluge
of documents are used to pretrain the models only later to be finetuned on
downstream tasks. One of the problems of the pretraining approaches is the
inconsistent usage of pretraining data with different OCR engines leading to
incomparable results between models. In other words, it is not obvious whether
the performance gain is coming from diverse usage of amount of data and
distinct OCR engines or from the proposed models. To remedy the problem, we
make public the OCR annotations for IDL documents using commercial OCR engine
given their superior performance over open source OCR models. The contributed
dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope
that OCR-IDL can be a starting point for future works on Document Intelligence.
All of our data and its collection process with the annotations can be found in
https://github.com/furkanbiten/idl_data.


title: Extracting Effective Subnetworks with Gumebel-Softmax

abstract:
  Large and performant neural networks are often overparameterized and can be
drastically reduced in size and complexity thanks to pruning. Pruning is a
group of methods, which seeks to remove redundant or unnecessary weights or
groups of weights in a network. These techniques allow the creation of
lightweight networks, which are particularly critical in embedded or mobile
applications. In this paper, we devise an alternative pruning method that
allows extracting effective subnetworks from larger untrained ones. Our method
is stochastic and extracts subnetworks by exploring different topologies which
are sampled using Gumbel Softmax. The latter is also used to train probability
distributions which measure the relevance of weights in the sampled topologies.
The resulting subnetworks are further enhanced using a highly efficient
rescaling mechanism that reduces training time and improves performance.
Extensive experiments conducted on CIFAR10 show the outperformance of our
subnetwork extraction method against the related work.


title: A Brief Survey on Adaptive Video Streaming Quality Assessment

abstract:
  Quality of experience (QoE) assessment for adaptive video streaming plays a
significant role in advanced network management systems. It is especially
challenging in case of dynamic adaptive streaming schemes over HTTP (DASH)
which has increasingly complex characteristics including additional playback
issues. In this paper, we provide a brief overview of adaptive video streaming
quality assessment. Upon our review of related works, we analyze and compare
different variations of objective QoE assessment models with or without using
machine learning techniques for adaptive video streaming. Through the
performance analysis, we observe that hybrid models perform better than both
quality-of-service (QoS) driven QoE approaches and signal fidelity measurement.
Moreover, the machine learning-based model slightly outperforms the model
without using machine learning for the same setting. In addition, we find that
existing video streaming QoE assessment models still have limited performance,
which makes it difficult to be applied in practical communication systems.
Therefore, based on the success of deep learned feature representations for
traditional video quality prediction, we also apply the off-the-shelf deep
convolutional neural network (DCNN) to evaluate the perceptual quality of
streaming videos, where the spatio-temporal properties of streaming videos are
taken into consideration. Experiments demonstrate its superiority, which sheds
light on the future development of specifically designed deep learning
frameworks for adaptive video streaming quality assessment. We believe this
survey can serve as a guideline for QoE assessment of adaptive video streaming.


title:
Search for a W' boson decaying to a vector-like quark and a top or
  bottom quark in the all-jets final state at $\sqrt{s}$ = 13 TeV

abstract:
  A search is presented for a heavy W' boson resonance decaying to a B or T
vector-like quark and a t or a b quark, respectively. The analysis is performed
using proton-proton collisions collected with the CMS detector at the LHC. The
data correspond to an integrated luminosity of 138 fb$^{-1}$ at a
center-of-mass energy of 13 TeV. Both decay channels result in a signature with
a t quark, a Higgs or Z boson, and a b quark, each produced with a significant
Lorentz boost. The all-hadronic decays of the Higgs or Z boson and of the t
quark are selected using jet substructure techniques to reduce standard model
backgrounds, resulting in a distinct three-jet W' boson decay signature. No
significant deviation in data with respect to the standard model background
prediction is observed. Upper limits are set at 95% confidence level on the
product of the W' boson cross section and the final state branching fraction. A
W' boson with a mass below 3.1 TeV is excluded, given the benchmark model
assumption of democratic branching fractions. In addition, limits are set based
on generalizations of these assumptions. These are the most sensitive limits to
date for this final state.


title: Flexible variable selection in the presence of missing data

abstract:
  In many applications, it is of interest to identify a parsimonious set of
features, or panel, from multiple candidates that achieves a desired level of
performance in predicting a response. This task is often complicated in
practice by missing data arising from the sampling design or other random
mechanisms. Most recent work on variable selection in missing data contexts
relies in some part on a finite-dimensional statistical model (e.g., a
generalized or penalized linear model). In cases where this model is
misspecified, the selected variables may not all be truly scientifically
relevant and can result in panels with suboptimal classification performance.
To address this limitation, we propose several nonparametric variable selection
algorithms combined with multiple imputation to develop flexible panels in the
presence of missing-at-random data. We outline strategies based on the proposed
algorithms that achieve control of commonly used error rates. Through
simulations, we show that our proposals have good operating characteristics and
result in panels with higher classification performance compared to several
existing penalized regression approaches. Finally, we use the proposed methods
to develop biomarker panels for separating pancreatic cysts with differing
malignancy potential in a setting where complicated missingness in the
biomarkers arose due to limited specimen volumes.


title:
Role of Zirconium Conversion Coating in Corrosion Performance of
  Aluminum Alloys: An Integrated First-Principles and Multiphysics Modeling
  Approach

abstract:
  A variety of chromate-free conversion coatings are being actively
investigated to improve the corrosion performance of light-weight alloys for
aerospace and defense applications. Advancing conversion coating, however,
requires an in-depth understanding of the underlying corrosion mechanisms in
order to rationally design sustainable coatings. Here, we present a multiscale
modeling approach to predict corrosion performance of metallic materials, with
a focus on localized corrosion of Cu-containing aluminum alloys coated with
ZrO2 layer. First-principles and transition-state theory are used to implement
the kinetics model, which includes electrolyte-metal interfacial reactions. The
modeling framework systematically characterizes and couples multiple
electrochemical and physical (e.g., transport) phenomena to explore
interrelationships between pit morphology, surface chemistry, and local
environment. This multiscale model can quantitatively link the corrosion rate
of ZrO2-coated aluminum alloys with the evolution of interfacial reactions
during immersion, which is very difficult to establish using in situ
experiments. We have evaluated the presented multiscale model using available
experimental data. The rate of corrosion and pit stability were quantitatively
assessed for various environmental parameters and applied potentials. Results
show that Zr-based conversion coating strongly enhances the corrosion
performance of aluminum alloys due to zirconium involvement in interfacial
kinetics.


title:
Attacks and Faults Injection in Self-Driving Agents on the Carla
  Simulator -- Experience Report

abstract:
  Machine Learning applications are acknowledged at the foundation of
autonomous driving, because they are the enabling technology for most driving
tasks. However, the inclusion of trained agents in automotive systems exposes
the vehicle to novel attacks and faults, that can result in safety threats to
the driv-ing tasks. In this paper we report our experimental campaign on the
injection of adversarial attacks and software faults in a self-driving agent
running in a driving simulator. We show that adversarial attacks and faults
injected in the trained agent can lead to erroneous decisions and severely
jeopardize safety. The paper shows a feasible and easily-reproducible approach
based on open source simula-tor and tools, and the results clearly motivate the
need of both protective measures and extensive testing campaigns.


title:
Decomposing past and future: Integrated information decomposition based
  on shared probability mass exclusions

abstract:
  A core feature of complex systems is that the interactions between elements
in the present causally constrain each-other as the system evolves through
time. To fully model all of these interactions (between elements, as well as
ensembles of elements), we can decompose the total information flowing from
past to future into a set of non-overlapping temporal interactions that
describe all the different modes by which information can flow. To achieve
this, we propose a novel information-theoretic measure of temporal dependency
($I_{\tau sx}$) based on informative and misinformative local probability mass
exclusions. To demonstrate the utility of this framework, we apply the
decomposition to spontaneous spiking activity recorded from dissociated neural
cultures of rat cerebral cortex to show how different modes of information
processing are distributed over the system. Furthermore, being a localizable
analysis, we show that $I_{\tau sx}$ can provide insight into the computational
structure of single moments. We explore the time-resolved computational
structure of neuronal avalanches and find that different types of information
atoms have distinct profiles over the course of an avalanche, with the majority
of non-trivial information dynamics happening before the first half of the
cascade is completed. These analyses allow us to move beyond the historical
focus on single measures of dependency such as information transfer or
information integration, and explore a panoply of different relationships
between elements (and groups of elements) in complex systems.


title: Projective Ranking-based GNN Evasion Attacks

abstract:
  Graph neural networks (GNNs) offer promising learning methods for
graph-related tasks. However, GNNs are at risk of adversarial attacks. Two
primary limitations of the current evasion attack methods are highlighted: (1)
The current GradArgmax ignores the "long-term" benefit of the perturbation. It
is faced with zero-gradient and invalid benefit estimates in certain
situations. (2) In the reinforcement learning-based attack methods, the learned
attack strategies might not be transferable when the attack budget changes. To
this end, we first formulate the perturbation space and propose an evaluation
framework and the projective ranking method. We aim to learn a powerful attack
strategy then adapt it as little as possible to generate adversarial samples
under dynamic budget settings. In our method, based on mutual information, we
rank and assess the attack benefits of each perturbation for an effective
attack strategy. By projecting the strategy, our method dramatically minimizes
the cost of learning a new attack strategy when the attack budget changes. In
the comparative assessment with GradArgmax and RL-S2V, the results show our
method owns high attack performance and effective transferability. The
visualization of our method also reveals various attack patterns in the
generation of adversarial samples.


title:
Generation of Synchronous Unpredictable Oscillations by Coupled Hopfield
  Neural Networks

abstract:
  A new criterion based on generalized synchronization is provided for the
extension of unpredictable oscillations among coupled Hopfield neural networks
(HNNs). It is shown that if a drive HNN possesses an unpredictable oscillation,
then a response HNN also possesses such an oscillation provided that they are
synchronized in the generalized sense. Extension of unpredictability in coupled
4D HNNs are exemplified with simulations. The auxiliary system approach and
conditional Lyapunov exponents are utilized to demonstrate the presence of
synchronization.


title: Near Optimal Reconstruction of Spherical Harmonic Expansions

abstract:
  We propose an algorithm for robust recovery of the spherical harmonic
expansion of functions defined on the d-dimensional unit sphere
$\mathbb{S}^{d-1}$ using a near-optimal number of function evaluations. We show
that for any $f \in L^2(\mathbb{S}^{d-1})$, the number of evaluations of $f$
needed to recover its degree-$q$ spherical harmonic expansion equals the
dimension of the space of spherical harmonics of degree at most $q$ up to a
logarithmic factor. Moreover, we develop a simple yet efficient algorithm to
recover degree-$q$ expansion of $f$ by only evaluating the function on
uniformly sampled points on $\mathbb{S}^{d-1}$. Our algorithm is based on the
connections between spherical harmonics and Gegenbauer polynomials and leverage
score sampling methods. Unlike the prior results on fast spherical harmonic
transform, our proposed algorithm works efficiently using a nearly optimal
number of samples in any dimension d. We further illustrate the empirical
performance of our algorithm on numerical examples.


title:
Seagrass deformation affects fluid instability and tracer exchange in
  canopy flow

abstract:
  Monami is the synchronous waving of a submerged seagrass bed in response to
unidirectional fluid flow. Here we develop a multiphase model for the dynamical
instabilities and flow-driven collective motions of buoyant, deformable
seagrass. We show that the impedance to flow due to the seagrass results in an
unstable velocity shear layer at the canopy interface, leading to a periodic
array of vortices that propagate downstream. Each passing vortex locally
weakens the along-stream velocity at the canopy top, reducing the drag and
allowing the deformed grass to straighten up just beneath it. This causes the
grass to oscillate periodically. Crucially, the maximal grass deflection is out
of phase with the vortices. A phase diagram for the onset of instability shows
its dependence on the fluid Reynolds number and an effective buoyancy
parameter. Less buoyant grass is more easily deformed by the flow and forms a
weaker shear layer, with smaller vortices and less material exchange across the
canopy top. While higher Reynolds number leads to stronger vortices and larger
waving amplitudes of the seagrass, waving is maximized at intermediate grass
buoyancy. All together, our theory and computations correct some misconceptions
in interpretation of the mechanism and provide a robust explanation consistent
with a number of experimental observations.


title:
Self-Supervised and Interpretable Anomaly Detection using Network
  Transformers

abstract:
  Monitoring traffic in computer networks is one of the core approaches for
defending critical infrastructure against cyber attacks. Machine Learning (ML)
and Deep Neural Networks (DNNs) have been proposed in the past as a tool to
identify anomalies in computer networks. Although detecting these anomalies
provides an indication of an attack, just detecting an anomaly is not enough
information for a user to understand the anomaly. The black-box nature of
off-the-shelf ML models prevents extracting important information that is
fundamental to isolate the source of the fault/attack and take corrective
measures. In this paper, we introduce the Network Transformer (NeT), a DNN
model for anomaly detection that incorporates the graph structure of the
communication network in order to improve interpretability. The presented
approach has the following advantages: 1) enhanced interpretability by
incorporating the graph structure of computer networks; 2) provides a
hierarchical set of features that enables analysis at different levels of
granularity; 3) self-supervised training that does not require labeled data.
The presented approach was tested by evaluating the successful detection of
anomalies in an Industrial Control System (ICS). The presented approach
successfully identified anomalies, the devices affected, and the specific
connections causing the anomalies, providing a data-driven hierarchical
approach to analyze the behavior of a cyber network.


title:
Integrated multimodal artificial intelligence framework for healthcare
  applications

abstract:
  Artificial intelligence (AI) systems hold great promise to improve healthcare
over the next decades. Specifically, AI systems leveraging multiple data
sources and input modalities are poised to become a viable method to deliver
more accurate results and deployable pipelines across a wide range of
applications. In this work, we propose and evaluate a unified Holistic AI in
Medicine (HAIM) framework to facilitate the generation and testing of AI
systems that leverage multimodal inputs. Our approach uses generalizable data
pre-processing and machine learning modeling stages that can be readily adapted
for research and deployment in healthcare environments. We evaluate our HAIM
framework by training and characterizing 14,324 independent models based on
MIMIC-IV-MM, a multimodal clinical database (N=34,537 samples) containing 7,279
unique hospitalizations and 6,485 patients, spanning all possible input
combinations of 4 data modalities (i.e., tabular, time-series, text and
images), 11 unique data sources and 12 predictive tasks. We show that this
framework can consistently and robustly produce models that outperform similar
single-source approaches across various healthcare demonstrations (by 6-33%),
including 10 distinct chest pathology diagnoses, along with length-of-stay and
48-hour mortality predictions. We also quantify the contribution of each
modality and data source using Shapley values, which demonstrates the
heterogeneity in data type importance and the necessity of multimodal inputs
across different healthcare-relevant tasks. The generalizable properties and
flexibility of our Holistic AI in Medicine (HAIM) framework could offer a
promising pathway for future multimodal predictive systems in clinical and
operational healthcare settings.


title: Lipschitz bounds for integral functionals with $(p,q)$-growth conditions

abstract:
  We study local regularity properties of local minimizer of scalar integral
functionals of the form $$\mathcal F[u]:=\int_\Omega F(\nabla u)-f u\,dx$$
where the convex integrand $F$ satisfies controlled $(p,q)$-growth conditions.
We establish Lipschitz continuity under sharp assumptions on the forcing term
$f$ and improved assumptions on the growth conditions on $F$ with respect to
the existing literature. Along the way, we establish an
$L^\infty$-$L^2$-estimate for solutions of linear uniformly elliptic equations
in divergence form which is optimal with respect to the ellipticity contrast of
the coefficients.


title:
Robust Estimation of Loss Models for Truncated and Censored Severity
  Data

abstract:
  In this paper, we consider robust estimation of claim severity models in
insurance, when data are affected by truncation (due to deductibles), censoring
(due to policy limits), and scaling (due to coinsurance). In particular, robust
estimators based on the methods of trimmed moments (T-estimators) and
winsorized moments (W-estimators) are pursued and fully developed. The general
definitions of such estimators are formulated and their asymptotic properties
are investigated. For illustrative purposes, specific formulas for T- and
W-estimators of the tail parameter of a single-parameter Pareto distribution
are derived. The practical performance of these estimators is then explored
using the well-known Norwegian fire claims data. Our results demonstrate that
T- and W-estimators offer a robust and computationally efficient alternative to
the likelihood-based inference for models that are affected by deductibles,
policy limits, and coinsurance.


title:
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal
  Arms

abstract:
  We study a sequential decision problem where the learner faces a sequence of
$K$-armed stochastic bandit tasks. The tasks may be designed by an adversary,
but the adversary is constrained to choose the optimal arm of each task in a
smaller (but unknown) subset of $M$ arms. The task boundaries might be known
(the bandit meta-learning setting), or unknown (the non-stationary bandit
setting), and the number of tasks $N$ as well as the total number of rounds $T$
are known ($N$ could be unknown in the meta-learning setting). We design an
algorithm based on a reduction to bandit submodular maximization, and show that
its regret in both settings is smaller than the simple baseline of
$\tilde{O}(\sqrt{KNT})$ that can be obtained by using standard algorithms
designed for non-stationary bandit problems. For the bandit meta-learning
problem with fixed task length $\tau$, we show that the regret of the algorithm
is bounded as $\tilde{O}(N\sqrt{M \tau}+N^{2/3})$. Under additional assumptions
on the identifiability of the optimal arms in each task, we show a bandit
meta-learning algorithm with an improved $\tilde{O}(N\sqrt{M \tau}+N^{1/2})$
regret.


title:
N-electron valence perturbation theory with reference wavefunctions from
  quantum computing: application to the relative stability of hydroxide anion
  and hydroxyl radical

abstract:
  Quantum simulations of the hydroxide anion and hydroxyl radical are reported,
employing variational quantum algorithms for near-term quantum devices. The
energy of each species is calculated along the dissociation curve, to obtain
information about the stability of the molecular species being investigated. It
is shown that simulations restricted to valence spaces incorrectly predict the
hydroxyl radical to be more stable than the hydroxide anion. Inclusion of
dynamical electron correlation from non-valence orbitals is demonstrated,
through the integration of the variational quantum eigensolver and quantum
subspace expansion methods in the workflow of N-electron valence perturbation
theory, and shown to correctly predict the hydroxide anion to be more stable
than the hydroxyl radical, provided that basis sets with diffuse orbitals are
also employed.


title:
Decision Making in Non-Stationary Environments with Policy-Augmented
  Monte Carlo Tree Search

abstract:
  Decision-making under uncertainty (DMU) is present in many important
problems. An open challenge is DMU in non-stationary environments, where the
dynamics of the environment can change over time. Reinforcement Learning (RL),
a popular approach for DMU problems, learns a policy by interacting with a
model of the environment offline. Unfortunately, if the environment changes the
policy can become stale and take sub-optimal actions, and relearning the policy
for the updated environment takes time and computational effort. An alternative
is online planning approaches such as Monte Carlo Tree Search (MCTS), which
perform their computation at decision time. Given the current environment, MCTS
plans using high-fidelity models to determine promising action trajectories.
These models can be updated as soon as environmental changes are detected to
immediately incorporate them into decision making. However, MCTS's convergence
can be slow for domains with large state-action spaces. In this paper, we
present a novel hybrid decision-making approach that combines the strengths of
RL and planning while mitigating their weaknesses. Our approach, called Policy
Augmented MCTS (PA-MCTS), integrates a policy's actin-value estimates into
MCTS, using the estimates to seed the action trajectories favored by the
search. We hypothesize that PA-MCTS will converge more quickly than standard
MCTS while making better decisions than the policy can make on its own when
faced with nonstationary environments. We test our hypothesis by comparing
PA-MCTS with pure MCTS and an RL agent applied to the classical CartPole
environment. We find that PC-MCTS can achieve higher cumulative rewards than
the policy in isolation under several environmental shifts while converging in
significantly fewer iterations than pure MCTS.


title:
SBbadger: Biochemical Reaction Networks with Definable Degree
  Distributions

abstract:
  An essential step in the development of computational tools for biochemical
model inference and simulation is to gauge the performance of those tools
against earlier efforts and on an appropriate set of benchmarks. In this work
we present SBbadger, a software tool for the generation of synthetic
biochemical reaction or metabolic networks with user defined indegree and
outdegree distributions with multiple types of kinetics. SBbadger provides a
means to create models with these and other user-specified properties, thus
enabling flexibility to define sets of benchmarks that reflect model properties
targeted by the computational tools being developed.


title: Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs

abstract:
  The paper discusses an intelligent vision-based control solution for
autonomous tracking and landing of Vertical Take-Off and Landing (VTOL) capable
Unmanned Aerial Vehicles (UAVs) on ships without utilizing GPS signal. The
central idea involves automating the Navy helicopter ship landing procedure
where the pilot utilizes the ship as the visual reference for long-range
tracking; however, refers to a standardized visual cue installed on most Navy
ships called the "horizon bar" for the final approach and landing phases. This
idea is implemented using a uniquely designed nonlinear controller integrated
with machine vision. The vision system utilizes machine learning-based object
detection for long-range ship tracking and classical computer vision for the
estimation of aircraft relative position and orientation utilizing the horizon
bar during the final approach and landing phases. The nonlinear controller
operates based on the information estimated by the vision system and has
demonstrated robust tracking performance even in the presence of uncertainties.
The developed autonomous ship landing system was implemented on a quad-rotor
UAV equipped with an onboard camera, and approach and landing were successfully
demonstrated on a moving deck, which imitates realistic ship deck motions.
Extensive simulations and flight tests were conducted to demonstrate vertical
landing safety, tracking capability, and landing accuracy.


title:
Weakly Supervised Instance Segmentation using Motion Information via
  Optical Flow

abstract:
  Weakly supervised instance segmentation has gained popularity because it
reduces high annotation cost of pixel-level masks required for model training.
Recent approaches for weakly supervised instance segmentation detect and
segment objects using appearance information obtained from a static image.
However, it poses the challenge of identifying objects with a
non-discriminatory appearance. In this study, we address this problem by using
motion information from image sequences. We propose a two-stream encoder that
leverages appearance and motion features extracted from images and optical
flows. Additionally, we propose a novel pairwise loss that considers both
appearance and motion information to supervise segmentation. We conducted
extensive evaluations on the YouTube-VIS 2019 benchmark dataset. Our results
demonstrate that the proposed method improves the Average Precision of the
state-of-the-art method by 3.1.


title: Compressed Matrix Computations

abstract:
  Frugal computing is becoming an important topic for environmental reasons. In
this context, several techniques have been proposed to reduce the storage of
scientific data by dedicated compression methods specially tailored for arrays
of floating-point numbers. While these techniques are quite efficient to save
memory, they introduce additional computations to compress and decompress the
data before processing them. In this article, we introduce a new lossy,
fixed-rate compression technique for 2D-arrays of floating-point numbers which
allows one to compute directly on the compressed data, without decompressing
them. We obtain important speedups since less operations are needed to compute
among the compressed data and since no decompression and re-compression is
needed. More precisely, our technique makes it possible to perform basic linear
algebra operations such as addition, multiplication by a constant among
compressed matrices and dot product and matrix multiplication among partly
uncompressed matrices. This work has been implemented into a tool named blaz
and a comparison with the well-known compressor zfp in terms of execution-time
and accuracy is presented.


title: Characterization of a Meso-Scale Wearable Robot for Bathing Assistance

abstract:
  Robotic bathing assistance has long been considered an important and
practical task in healthcare. Yet, achieving flexible and efficient cleaning
tasks on the human body is challenging, since washing the body involves direct
human-robot physical contact and simple, safe, and effective devices are needed
for bathing and hygiene. In this paper, we present a meso-scale wearable robot
that can locomote along the human body to provide bathing and skin care
assistance. We evaluated the cleaning performance of the robot system under
different scenarios. The experiments on the pipe show that the robot can
achieve cleaning percentage over 92% with two types of stretchable fabrics. The
robot removed most of the debris with average values of 94% on a human arm and
93% on a manikin torso. The results demonstrate that the robot exhibits high
performance in cleaning tasks.


title:
Long-range dispersion-inclusive machine learning potentials for
  structure search and optimization of hybrid organic-inorganic interfaces

abstract:
  The computational prediction of the structure and stability of hybrid
organic-inorganic interfaces provides important insights into the measurable
properties of electronic thin film devices, coatings, and catalyst surfaces and
plays an important role in their rational design. However, the rich diversity
of molecular configurations and the important role of long-range interactions
in such systems make it difficult to use machine learning (ML) potentials to
facilitate structure exploration that otherwise require computationally
expensive electronic structure calculations. We present an ML approach that
enables fast, yet accurate, structure optimizations by combining two different
types of deep neural networks trained on high-level electronic structure data.
The first model is a short-ranged interatomic ML potential trained on local
energies and forces, while the second is an ML model of effective atomic
volumes derived from atoms-in-molecules partitioning. The latter can be used to
connect short-range potentials to well-established density-dependent long-range
dispersion correction methods. For two systems, specifically gold nanoclusters
on diamond (110) surfaces and organic $\pi$-conjugated molecules on silver
(111) surfaces, we train models on sparse structure relaxation data from
density functional theory and show the ability of the models to deliver highly
efficient structure optimizations and semi-quantitative energy predictions of
adsorption structures.


title: Isometric Actions are Quasidiagonal

abstract:
  We show every isometric action is quasidiagonal in a strong sense. This shows
that reduced crossed products by such actions are quasidiagonal or MF whenever
the reduced algebra of the acting group is quasidiagonal or MF.


title: Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks

abstract:
  Fueled by advances in hardware and algorithm design, large-scale automated
explorations of chemical reaction space have become possible. Here, we present
our approach to an open-source, extensible framework for explorations of
chemical reaction mechanisms based on the first principles of quantum
mechanics. It is intended to facilitate reaction network explorations for
diverse chemical problems with a wide range of goals such as mechanism
elucidation, reaction path optimization, retrosynthetic path validation,
reagent design, and microkinetic modeling. The stringent first-principles basis
of all algorithms in our framework is key for the general applicability that
avoids any restrictions to specific chemical systems. Such an agile framework
requires multiple specialized software components of which we present three
modules in this work. The key module, Chemoton,drives the exploration of
reaction networks. For the exploration itself, we introduce two new algorithms
for elementary-step searches that are based on Newton trajectories. The
performance of these algorithms is assessed for a variety of reactions
characterized by a broad chemical diversity in terms of bonding patterns and
chemical elements. We reproduce and significantly extend what is known about
these reactions and provide the resulting data to be used as a starting point
for further explorations and for future reference.


title:
Modeling of effective interactions between ligand coated nanoparticles
  through symmetry functions

abstract:
  Ligand coated nanoparticles are complex objects consisting of a metallic or
semiconductor core with organic ligands grafted on their surface. These organic
ligands provide stability to a nanoparticle suspension. In solutions, the
effective interactions between such nanoparticles are mediated through a
complex interplay of interactions between the nanoparticle cores, the
surrounding ligands and the solvent molecules. While it is possible to compute
these interactions using fully atomistic molecular simulations, such
computations are too expensive for studying self-assembly of a large number of
nanoparticles. The problem can be made tractable by removing the degrees of
freedom associated with the ligand chains and solvent molecules and using the
potentials of mean force (PMF) between nanoparticles. In general, the
functional dependence of the PMFs on the inter-particle distance is unknown and
can be quite complex. In this article, we present a method to model the
two-body and three-body potentials of mean force between ligand coated
nanoparticles through a linear combination of symmetry functions. The method is
quite general and can be extended to model interactions between different types
of macromolecules.


title:
Sign and Basis Invariant Networks for Spectral Graph Representation
  Learning

abstract:
  Many machine learning tasks involve processing eigenvectors derived from
data. Especially valuable are Laplacian eigenvectors, which capture useful
structural information about graphs and other geometric objects. However,
ambiguities arise when computing eigenvectors: for each eigenvector $v$, the
sign flipped $-v$ is also an eigenvector. More generally, higher dimensional
eigenspaces contain infinitely many choices of basis eigenvectors. These
ambiguities make it a challenge to process eigenvectors and eigenspaces in a
consistent way. In this work we introduce SignNet and BasisNet -- new neural
architectures that are invariant to all requisite symmetries and hence process
collections of eigenspaces in a principled manner. Our networks are universal,
i.e., they can approximate any continuous function of eigenvectors with the
proper invariances. They are also theoretically strong for graph representation
learning -- they can approximate any spectral graph convolution, can compute
spectral invariants that go beyond message passing neural networks, and can
provably simulate previously proposed graph positional encodings. Experiments
show the strength of our networks for molecular graph regression, learning
expressive graph representations, and learning implicit neural representations
on triangle meshes. Our code is available at
https://github.com/cptq/SignNet-BasisNet .


title:
Model Checking on Interpretations of Classes of Bounded Local
  Cliquewidth

abstract:
  We present a fixed-parameter tractable algorithm for first-order model
checking on interpretations of graph classes with bounded local cliquewidth.
Notably, this includes interpretations of planar graphs, and more generally, of
classes of bounded genus. To obtain this result we develop a new tool which
works in a very general setting of dependent classes and which we believe can
be an important ingredient in achieving similar results in the future.


title: Outside-Obstacle Representations with All Vertices on the Outer Face

abstract:
  An \emph{obstacle representation} of a graph $G$ consists of a set of
polygonal obstacles and a drawing of $G$ as a \emph{visibility graph} with
respect to the obstacles: vertices are mapped to points and edges to
straight-line segments such that each edge avoids all obstacles whereas each
non-edge intersects at least one obstacle. Obstacle representations have been
investigated quite intensely over the last few years.
  Here we focus on \emph{outside-obstacle representations} that use only one
obstacle in the outer face of the drawing. It is known that every outerplanar
graph admits such a representation [Alpert, Koch, Laison; DCG 2010]. We
strengthen this result by showing that every partial 2-tree has an
outside-obstacle representation. We also consider a restricted version of
outside-obstacle representations where the vertices lie on a regular polygon.
We construct such regular representations for partial outerpaths, partial
cactus graphs, and partial grids.


title:
Bounds on Determinantal Complexity of Two Types of Generalized
  Permanents

abstract:
  We define two new families of polynomials that generalize permanents and
prove upper and lower bounds on their determinantal complexities comparable to
the known bounds for permanents. One of these families is obtained by replacing
permutations by signed permutations, and the other by replacing permutations by
surjective functions with preimages of prescribed sizes.


title:
Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes
  under Natural Illumination

abstract:
  This paper presents a process for estimating the spatially varying surface
reflectance of complex scenes observed under natural illumination. In contrast
to previous methods, our process is not limited to scenes viewed under
controlled lighting conditions but can handle complex indoor and outdoor scenes
viewed under arbitrary illumination conditions. An end-to-end process uses a
model of the scene's geometry and several images capturing the scene's surfaces
from arbitrary viewpoints and under various natural illumination conditions. We
develop a differentiable path tracer that leverages least-square conformal
mapping for handling multiple disjoint objects appearing in the scene. We
follow a two-step optimization process and introduce a multi-view gradient
consistency loss which results in up to 30-50% improvement in the image
reconstruction loss and can further achieve better disentanglement of the
diffuse and specular BRDFs compared to other state-of-the-art. We demonstrate
the process in real-world indoor and outdoor scenes from images in the wild and
show that we can produce realistic renders consistent with actual images using
the estimated reflectance properties. Experiments show that our technique
produces realistic results for arbitrary outdoor scenes with complex geometry.
The source code is publicly available at:
https://gitlab.com/alen.joy/multi-view-gradient-consistency-for-svbrdf-estimation-of-complex-scenes-under-natural-illumination


title:
HCIL: Hierarchical Class Incremental Learning for Longline Fishing
  Visual Monitoring

abstract:
  The goal of electronic monitoring of longline fishing is to visually monitor
the fish catching activities on fishing vessels based on cameras, either for
regulatory compliance or catch counting. The previous hierarchical
classification method demonstrates efficient fish species identification of
catches from longline fishing, where fishes are under severe deformation and
self-occlusion during the catching process. Although the hierarchical
classification mitigates the laborious efforts of human reviews by providing
confidence scores in different hierarchical levels, its performance drops
dramatically under the class incremental learning (CIL) scenario. A CIL system
should be able to learn about more and more classes over time from a stream of
data, i.e., only the training data for a small number of classes have to be
present at the beginning and new classes can be added progressively. In this
work, we introduce a Hierarchical Class Incremental Learning (HCIL) model,
which significantly improves the state-of-the-art hierarchical classification
methods under the CIL scenario.


title:
Hierarchical symbolic regression for identifying key physical parameters
  correlated with bulk properties of perovskites

abstract:
  Symbolic regression identifies key physical parameters describing materials
properties by uncovering correlations as nonlinear analytical expressions.
However, the pool of expressions grows rapidly with complexity, compromising
its efficiency. We tackle this challenge by a hierarchical approach: identified
expressions are used as input parameters for obtaining more complex
expressions. Crucially, this framework can transfer knowledge among properties,
highlighting physical relationships. We demonstrate this strategy by using the
Sure-Independence-Screening-and-Sparsifying-Operator (SISSO) approach to
identify expressions correlated with the lattice constant and cohesive energy,
which are then used to model the bulk modulus of ABO3 perovskites.


title:
Energy balance and Alfv\'en Mach numbers in compressible
  magnetohydrodynamic turbulence with a large-scale magnetic field

abstract:
  Energy equipartition is a powerful theoretical tool for understanding
astrophysical plasmas. It is invoked, for example, to measure magnetic fields
in the interstellar medium (ISM), as evidence for small-scale turbulent dynamo
action, and, in general, to estimate the energy budget of star-forming
molecular clouds. In this study we motivate and explore the role of the
volume-averaged root-mean-squared (rms) magnetic coupling term between the
turbulent, $\delta\mathbf{B}$ and large-scale, $\mathbf{B}_0$ fields, $\left<
(\delta\mathbf{B}\cdot\mathbf{B}_0)^{2} \right>^{1/2}_{\mathcal{V}}$. By
considering the second moments of the energy balance equations we show that the
rms coupling term is in energy equipartition with the volume-averaged turbulent
kinetic energy for turbulence with a sub-Alfv\'enic large-scale field. Under
the assumption of exact energy equipartition between these terms, we derive
relations for the magnetic and coupling term fluctuations, which provide
excellent, parameter-free agreement with time-averaged data from 280 numerical
simulations of compressible MHD turbulence. Furthermore, we explore the
relation between the turbulent, mean-field and total Alfv\'en Mach numbers, and
demonstrate that sub-Alfv\'enic turbulence can only be developed through a
strong, large-scale magnetic field, which supports an extremely
super-Alfv\'enic turbulent magnetic field. This means that the magnetic field
fluctuations are significantly subdominant to the velocity fluctuations in the
sub-Alfv\'enic large-scale field regime. Throughout our study, we broadly
discuss the implications for observations of magnetic fields and understanding
the dynamics in the magnetised ISM.


title: Building a visual semantics aware object hierarchy

abstract:
  The semantic gap is defined as the difference between the linguistic
representations of the same concept, which usually leads to misunderstanding
between individuals with different knowledge backgrounds. Since linguistically
annotated images are extensively used for training machine learning models,
semantic gap problem (SGP) also results in inevitable bias on image annotations
and further leads to poor performance on current computer vision tasks. To
address this problem, we propose a novel unsupervised method to build visual
semantics aware object hierarchy, aiming to get a classification model by
learning from pure-visual information and to dissipate the bias of linguistic
representations caused by SGP. Our intuition in this paper comes from
real-world knowledge representation where concepts are hierarchically
organized, and each concept can be described by a set of features rather than a
linguistic annotation, namely visual semantic. The evaluation consists of two
parts, firstly we apply the constructed hierarchy on the object recognition
task and then we compare our visual hierarchy and existing lexical hierarchies
to show the validity of our method. The preliminary results reveal the
efficiency and potential of our proposed method.


title:
Arbitrary Order Energy and Enstrophy Conserving Finite Element Methods
  for 2D Incompressible Fluid Dynamics and Drift-Reduced Magnetohydrodynamics

abstract:
  Maintaining conservation laws in the fully discrete setting is critical for
accurate long-time behavior of numerical simulations and requires accounting
for discrete conservation properties in both space and time. This paper derives
arbitrary order finite element exterior calculus spatial discretizations for
the two-dimensional (2D) Navier-Stokes and drift-reduced magnetohydrodynamic
equations that conserve both energy and enstrophy to machine precision when
coupled with generally symplectic time-integration methods. Both continuous and
discontinuous-Galerkin (DG) weak formulations can ensure conservation, but only
generally symplectic time integration methods, such as the implicit midpoint
method, permit exact conservation in time. Moreover, the symplectic implicit
midpoint method yields an order of magnitude speedup over explicit schemes. The
methods are implemented using the MFEM library and the solutions are verified
for an extensive suite of 2D neutral fluid turbulence test problems. Numerical
solutions are verified via comparison to a semi-analytic linear eigensolver as
well as to the finite difference Global Drift Ballooning (GDB) code. However,
it is found that turbulent simulations that conserve both energy and enstrophy
tend to have too much power at high wavenumber and that this part of the
spectrum should be controlled by reintroducing artificial dissipation. The DG
formulation allows upwinding of the advection operator which dissipates
enstrophy while still maintaining conservation of energy. Coupling upwinded DG
with implicit symplectic integration appears to offer the best compromise of
allowing mid-range wavenumbers to reach the appropriate amplitude while still
controlling the high-wavenumber part of the spectrum.

categories: physics.flu-dyn cs.NA math.NA physics.comp-ph


title: Quickest Change Detection in Anonymous Heterogeneous Sensor Networks

abstract:
  The problem of quickest change detection (QCD) in anonymous heterogeneous
sensor networks is studied. There are $n$ heterogeneous sensors and a fusion
center. The sensors are clustered into $K$ groups, and different groups follow
different data-generating distributions. At some unknown time, an event occurs
in the network and changes the data-generating distribution of the sensors. The
goal is to detect the change as quickly as possible, subject to false alarm
constraints. The anonymous setting is studied, where at each time step, the
fusion center receives $n$ unordered samples, and the fusion center does not
know which sensor each sample comes from, and thus does not know its exact
distribution. A simple optimality proof is first derived for the mixture
likelihood ratio test, which was constructed and proved to be optimal for the
non-sequential anonymous setting in (Chen and Wang, 2019). For the QCD problem,
a mixture CuSum algorithm is further constructed, and is further shown to be
optimal under Lorden's criterion. For large networks, a computationally
efficient test is proposed and a novel theoretical characterization of its
false alarm rate is developed. Numerical results are provided to validate the
theoretical results.


title: ASSIST: Towards Label Noise-Robust Dialogue State Tracking

abstract:
  The MultiWOZ 2.0 dataset has greatly boosted the research on dialogue state
tracking (DST). However, substantial noise has been discovered in its state
annotations. Such noise brings about huge challenges for training DST models
robustly. Although several refined versions, including MultiWOZ 2.1-2.4, have
been published recently, there are still lots of noisy labels, especially in
the training set. Besides, it is costly to rectify all the problematic
annotations. In this paper, instead of improving the annotation quality
further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt
dIalogue State Tracking), to train DST models robustly from noisy labels.
ASSIST first generates pseudo labels for each sample in the training set by
using an auxiliary model trained on a small clean dataset, then puts the
generated pseudo labels and vanilla noisy labels together to train the primary
model. We show the validity of ASSIST theoretically. Experimental results also
demonstrate that ASSIST improves the joint goal accuracy of DST by up to
$28.16\%$ on MultiWOZ 2.0 and $8.41\%$ on MultiWOZ 2.4, compared to using only
the vanilla noisy labels.


title: Sofic Lie Algebras

abstract:
  We introduce and study soficity for Lie algebras, modelled after linear
soficity in associative algebras. We introduce equivalent definitions of
soficity, one involving metric ultraproducts and the other involving almost
representations. We prove that any Lie algebra of subexponential growth is
sofic. We also prove that a Lie algebra over a field of characteristic 0 is
sofic if and only if its universal enveloping algebra is linearly sofic.
Finally, we give explicit families of almost representations for the Witt and
Virasoro algebras.


title: Static Analysis Deployment Pitfalls

abstract:
  Organizational, political, and configuration mistakes in the deployment of a
static source code analysis tool within a software development organization can
result in most of the value of the tool being lost, even while apparently
meeting management goals. A list of pitfalls encountered as a static analysis
consultant is presented, with discussion of techniques for avoiding or
mitigating them. This is part of a work in progress, tentatively entitled
"Handbook of Static Analysis Deployment."


title:
Modeling the propagation of tumor fronts with shortest path and
  diffusion models -- implications for the definition of the clinical target
  volume

abstract:
  Objective: The overarching objective is to make the definition of the
clinical target volume (CTV) in radiation oncology less subjective and more
scientifically based. The specific objective of this study is to investigate
similarities and differences between two methods that model tumor spread beyond
the visible gross tumor volume (GTV): 1. The shortest path model, which is the
standard method of adding a geometric GTV-CTV margin, and 2. The
reaction-diffusion model.
  Approach: These two models to capture the invisible tumor "fire front" are
defined and compared in mathematical terms. The models are applied to geometric
example cases that represent tumor spread in non-uniform and anisotropic media
with anatomical barriers.
  Main Results: The two seemingly disparate models bring forth traveling waves
that can be associated with the front of tumor growth outward from the GTV. The
shape of the fronts is similar for both models. Differences are seen in cases
where the diffusive flow is reduced due to anatomical barriers, and in complex
spatially non-uniform cases. The diffusion model generally leads to smoother
fronts. The smoothness can be controlled with a parameter defined by the ratio
of the diffusion coefficient and the proliferation rate.
  Significance: Defining the CTV has been described as the weakest link of the
radiotherapy chain. There are many similarities in the mathematical description
and the behavior of the common geometric GTV-CTV expansion method, and the
definition of the CTV tumor front via the reaction-diffusion model. Its
mechanistic basis and the controllable smoothness make the diffusion model an
attractive alternative to the standard GTV-CTV margin model.


title: Healthsheet: Development of a Transparency Artifact for Health Datasets

abstract:
  Machine learning (ML) approaches have demonstrated promising results in a
wide range of healthcare applications. Data plays a crucial role in developing
ML-based healthcare systems that directly affect people's lives. Many of the
ethical issues surrounding the use of ML in healthcare stem from structural
inequalities underlying the way we collect, use, and handle data. Developing
guidelines to improve documentation practices regarding the creation, use, and
maintenance of ML healthcare datasets is therefore of critical importance. In
this work, we introduce Healthsheet, a contextualized adaptation of the
original datasheet questionnaire ~\cite{gebru2018datasheets} for
health-specific applications. Through a series of semi-structured interviews,
we adapt the datasheets for healthcare data documentation. As part of the
Healthsheet development process and to understand the obstacles researchers
face in creating datasheets, we worked with three publicly-available healthcare
datasets as our case studies, each with different types of structured data:
Electronic health Records (EHR), clinical trial study data, and
smartphone-based performance outcome measures. Our findings from the
interviewee study and case studies show 1) that datasheets should be
contextualized for healthcare, 2) that despite incentives to adopt
accountability practices such as datasheets, there is a lack of consistency in
the broader use of these practices 3) how the ML for health community views
datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface
the limitations and strength of datasets and 4) the relative importance of
different fields in the datasheet to healthcare concerns.


title: Dynamics of the threshold model on hypergraphs

abstract:
  The threshold model has been widely adopted as a prototype for studying
contagion processes on social networks. In this paper, we consider individual
interactions in groups of three or more vertices and study the threshold model
on hypergraphs. To understand how high-order interactions affect the breakdown
of the system, we develop a theoretical framework based on generating function
technology to derive the cascade condition and the giant component of
vulnerable vertices, which depend on both hyperedges and hyperdegrees. First,
we find a dual role of the hyperedge in propagation: when the average
hyperdegree is small, increasing the size of the hyperedges may make the system
fragile, while the average hyperdegree is relatively large, the increase of the
hyperedges causes the system to be robust. Then, we identify the effects of
threshold, hyperdegree, and hyperedge heterogeneities. The heterogeneity of
individual thresholds causes the system to be more fragile, while the
heterogeneity of individual hyperdegrees or hyperedges increases the robustness
of the system. Finally, we show that the higher hyperdegree a vertex has, the
larger possibility and faster speed it will get activated. We verify these
results by simulating meme spreading on both random hypergraph models and
hypergraphs constructed from empirical data.


title: Lattice dynamics in an emergent Zeeman lattice

abstract:
  Periodic band structures are a hallmark phenomenon of condensed matter
physics. While often imposed by external potentials, periodicity can also arise
through the interplay of couplings that are not necessarily spatially periodic
on their own. Here, we investigate dynamics in a lattice structure that emerges
from the simultaneous application of Raman and radio frequency coupling to a
dilute-gas Bose-Einstein condensate. We demonstrate a variety of techniques
including Kapitza-Dirac scattering, Bloch oscillations, and lattice shaking
with spin and momentum resolved measurements. This combined coupling scheme
allows for exceptional tunability and control, enabling future investigations
into unconventional band structures such as quasi-flat ground bands and those
with semimetal-like band gaps.


title: Scotogenic $A_5 \to A_4$ Dirac Neutrinos with Freeze-In Dark Matter

abstract:
  Radiative Dirac neutrino masses and their mixing are linked to dark matter
through the non-Abelian discrete symmetry $A_5$ of the 4-dimensional pentatope,
softly broken to $A_4$ of the 3-dimensional tetrahedron. This unifying
understanding of neutrino family structure from dark matter is made possible
through the interplay of gauge symmetry, renormalizable Lagrangian field
theory, and softly broken discrete symmetries. Dark neutral fermions are
produced through Higgs decay.


title: Topology optimization of 3D flow fields for flow batteries

abstract:
  As power generated from renewables becomes more readily available, the need
for power-efficient energy storage devices, such as redox flow batteries,
becomes critical for successful integration of renewables into the electrical
grid. An important component in a redox flow battery is the planar flow field,
which is usually composed of two-dimensional channels etched into a backing
plate. As reactant-laden electrolyte flows into the flow battery, the channels
in the flow field distribute the fluid throughout the reactive porous
electrode. We utilize topology optimization to design flow fields with full
three-dimensional geometry variation, i.e., 3D flow fields. Specifically, we
focus on vanadium redox flow batteries and use the optimization algorithm to
generate 3D flow fields evolved from standard interdigitated flow fields by
minimizing the electrical and flow pressure power losses. To understand how
these designs improve performance, we analyze the polarization of the reactant
concentration and exchange current within the electrode to highlight how the
designed flow fields mitigate the presence of electrode dead zones. While
interdigitated flow fields can be heuristically engineered to yield high
performance by tuning channel and land dimensions, such a process can be
tedious; this work provides a framework for automating that design process.


title:
A New BAT and PageRank algorithm for Propagation Probability in Social
  Networks

abstract:
  Social networks have increasingly become important and popular in modern
times. Moreover, the influence of social networks plays a vital role in various
organizations including government organizations, academic research or
corporate organizations. Therefore, how to strategize the optimal propagation
strategy in social networks has also become more important. By increasing the
precision of evaluating the propagation probability of social network, it can
indirectly influence the investment of cost, manpower and time for information
propagation to achieve the best return. This study proposes a new algorithm,
which includes a scale-free network, Barabasi-Albert model,
Binary-Addition-Tree (BAT) algorithm, PageRank algorithm, personalized PageRank
algorithm and a new BAT algorithm, to calculate the propagation probability in
social networks. The results obtained after implementing the simulation
experiment of social network models show the studied model and the proposed
algorithm provide an effective method to increase the efficiency of information
propagation in social networks. In this way, the maximum propagation efficiency
is achieved with the minimum investment.


title:
Coronal Mass Ejections and Dimmings: A Comparative Study using MHD
  Simulations and SDO Observations

abstract:
  Solar coronal dimmings have been observed extensively in the past two
decades. Due to their close association with coronal mass ejections (CMEs),
there is a critical need to improve our understanding of the physical processes
that cause dimmings as well as their relationship with CMEs. In this study, we
investigate coronal dimmings by combining simulation and observational efforts.
By utilizing a data-constrained global magnetohydrodynamics model (AWSoM:
Alfven-wave Solar Model), we simulate coronal dimmings resulting from different
CME energetics and flux rope configurations. We synthesize the emissions of
different EUV spectral bands/lines and compare with SDO/AIA and EVE
observations. A detailed analysis of the simulation and observation data
suggests that the transient dimming / brightening are related to plasma heating
processes, while the long-lasting core and remote dimmings are caused by mass
loss process induced by the CME. Moreover, the interaction between the erupting
flux rope with different orientations and the global solar corona could
significantly influence the coronal dimming patterns. Using metrics such as
dimming depth and dimming slope, we investigate the relationship between
dimmings and CME properties (e.g., CME mass, CME speed) in the simulation. Our
result suggests that coronal dimmings encode important information about the
associated CMEs, which provides a physical basis for detecting stellar CMEs
from distant solar-like stars.


title: Jacobi equations of geodetic brane gravity

abstract:
  We consider brane gravity as described by the Regge-Teitelboim geometric
model, in any codimension. In brane gravity our spacetime is modeled as the
time-like world volume spanned by a space-like brane in its evolution, seen as
a manifold embedded in an ambient background Minkowski spacetime of higher
dimension. Although the equations of motion of the model are well known,
apparently their linearization has not been considered before. Using a direct
approach, we linearize the equations of motion about a solution, obtaining the
Jacobi equations of the Regge- Teitelboim model. They take a formidable aspect.
Some of their features are commented upon. By identifying the Jacobi equations,
we derive an explicit expression for the Morse index of the model. To be
concrete, we apply the Jacobi equations to the study of the stability of a
four-dimensional Schwarzschild spacetime embedded in a six-dimensional
Minkowski spacetime. We find that it is unstable under small linear
deformations.


title: New error bounds for the extended vertical LCP

abstract:
  In this paper, by making use of this fact that for $a_{j}, b_{j}\in
\mathbb{R}$, $j=1,2,\ldots,n$, there are $\lambda_{j}\in [0,1]$ with
$\sum_{j=1}^{n}\lambda_{j}=1$ such that \[ \min_{1\leq j\leq
n}\{a_{j}\}-\min_{1\leq j\leq
n}\{b_{j}\}=\sum_{j=1}^{n}\lambda_{j}(a_{j}-b_{j}), \] some new error bounds of
the extended vertical LCP under the row $\mathcal{W}$-property are obtained,
which cover the error bounds in [Math. Program., 106 (2006) 513-525] and
[Comput. Optim. Appl., 42 (2009) 335-352]. Not only that, these new error
bounds skillfully avoid the inconvenience caused by the row rearrangement
technique for error bounds to achieve the goal of reducing the computation
workload, which was introduced in the latter paper mentioned above. Besides,
with respect to the row $\mathcal{W}$-property, two new sufficient and
necessary conditions are obtained.


title:
Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge
  Computing Networks: A Contract-Stackelberg Approach

abstract:
  With the popularity of mobile devices and development of computationally
intensive applications, researchers are focusing on offloading computation to
Mobile Edge Computing (MEC) server due to its high computational efficiency and
low communication delay. As the computing resources of an MEC server are
limited, vehicles in the urban area who have abundant idle resources should be
fully utilized. However, offloading computing tasks to vehicles faces many
challenging issues. In this paper, we introduce a vehicular fog-edge computing
paradigm and formulate it as a multi-stage Stackelberg game to deal with these
issues. Specifically, vehicles are not obligated to share resources, let alone
disclose their private information (e.g., stay time and the amount of
resources). Therefore, in the first stage, we design a contract-based incentive
mechanism to motivate vehicles to contribute their idle resources. Next, due to
the complicated interactions among vehicles, road-side unit (RSU), MEC server
and mobile device users, it is challenging to coordinate the resources of all
parties and design a transaction mechanism to make all entities benefit. In the
second and third stages, based on Stackelberg game, we develop pricing
strategies that maximize the utilities of all parties. The analytical forms of
optimal strategies for each stage are given. Simulation results demonstrate the
effectiveness of our proposed incentive mechanism, reveal the trends of energy
consumption and offloading decisions of users with various parameters, and
present the performance comparison between our framework and existing MEC
offloading paradigm in vehicular networks.


title:
Homeostatic and Adaptive Energetics: Nonequilibrium Fluctuations Beyond
  Detailed Balance in Voltage-Gated Ion Channels

abstract:
  Stochastic thermodynamics has largely succeeded in analyzing both equilibrium
and arbitrarily far-from-equilibrium systems. It remains underapplied, however,
in mesoscopic complex systems -- particularly, biological ones -- whose
effective dynamics often violate detailed balance and whose microscopic degrees
of freedom are often unknown or intractable, assumptions that stochastic
thermodynamics commonly invokes. Using its tools, we review how to delineate
excess and housekeeping energetics -- the adaptive and homeostatic components
of a system's thermodynamic behavior. Then, we extend stochastic thermodynamics
with a trajectory class fluctuation theorem for nonequilibrium steady-state,
nondetailed-balanced complex systems. Finally, we consider two neurobiological
examples -- voltage-gated sodium and potassium ion channels -- to illustrate
the results. These highlight the progress possible in understanding the
thermodynamics of complex systems without restrictive assumptions and
exhaustive knowledge of every underlying degree of freedom.

categories: cond-mat.stat-mech nlin.AO physics.bio-ph q-bio.NC


title:
A Rich Satellite Population of the NGC 4437 Group and Implications of a
  Magnitude Gap for Galaxy Group Assembly History

abstract:
  Both observations and cosmological simulations have recently shown that there
is a large scatter in the number of satellites of Milky Way (MW)-like galaxies.
In this study, we investigate the relation between the satellite number and
galaxy group assembly history, using the $r-$band magnitude gap ($\Delta
m_{12}$) between the first and the second brightest galaxy as an indicator.
From 20 deg$^2$ of Hyper Suprime-Cam Subaru Strategic Program Wide layer, we
identify 17 dwarf satellite candidates around NGC 4437, a spiral galaxy with
about one-fourth of the MW stellar mass. We estimate their distances using the
surface brightness fluctuation (SBF) method. Then we confirm five candidates as
members of the NGC 4437 group, resulting in a total of seven group members.
Combining the NGC 4437 group (with $\Delta m_{12} = 2.5$ mag) with other groups
in the literature, we find a stratification of the satellite number by $\Delta
m_{12}$ for a given host stellar mass. The satellite number for given host
stellar mass decreases as $\Delta m_{12}$ increases. The same trend is found in
simulated galaxy groups in IllustrisTNG50 simulations. We also find that the
host galaxies in groups with a smaller $\Delta m_{12}$ (like NGC 4437) have
assembled their halo mass more recently than those in larger gap groups, and
that their stellar-to-halo mass ratios (SHMRs) increase as $\Delta m_{12}$
increases. These results show that the large scatter in the satellite number is
consistent with a large range of $\Delta m_{12}$, indicating diverse group
assembly histories.


title: Iterative Genetic Improvement: Scaling Stochastic Program Synthesis

abstract:
  Program synthesis aims to {\it automatically} find programs from an
underlying programming language that satisfy a given specification. While this
has the potential to revolutionize computing, how to search over the vast space
of programs efficiently is an unsolved challenge in program synthesis. In cases
where large programs are required for a solution, it is generally believed that
{\it stochastic} search has advantages over other classes of search techniques.
Unfortunately, existing stochastic program synthesizers do not meet this
expectation very well, suffering from the scalability issue. Here we propose a
new framework for stochastic program synthesis, called iterative genetic
improvement to overcome this problem, a technique inspired by the practice of
the software development process. The key idea of iterative genetic improvement
is to apply genetic improvement to improve a current reference program, and
then iteratively replace the reference program by the best program found.
Compared to traditional stochastic synthesis approaches, iterative genetic
improvement can build up the complexity of programs incrementally in a more
robust way. We evaluate the approach on two program synthesis domains: list
manipulation and string transformation. Our empirical results indicate that
this method has considerable advantages over several representative stochastic
program synthesizer techniques, both in terms of scalability and of solution
quality.


title: Towards Revenue Maximization with Popular and Profitable Products

abstract:
  Economic-wise, a common goal for companies conducting marketing is to
maximize the return revenue/profit by utilizing the various effective marketing
strategies. Consumer behavior is crucially important in economy and targeted
marketing, in which behavioral economics can provide valuable insights to
identify the biases and profit from customers. Finding credible and reliable
information on products' profitability is, however, quite difficult since most
products tends to peak at certain times w.r.t. seasonal sales cycle in a year.
On-Shelf Availability (OSA) plays a key factor for performance evaluation.
Besides, staying ahead of hot product trends means we can increase marketing
efforts without selling out the inventory. To fulfill this gap, in this paper,
we first propose a general profit-oriented framework to address the problem of
revenue maximization based on economic behavior, and compute the 0n-shelf
Popular and most Profitable Products (OPPPs) for the targeted marketing. To
tackle the revenue maximization problem, we model the k-satisfiable product
concept and propose an algorithmic framework for searching OPPP and its
variants. Extensive experiments are conducted on several real-world datasets to
evaluate the effectiveness and efficiency of the proposed algorithm.


title: Low SNR Multiframe Registration for Cubesats

abstract:
  We present a registration algorithm which jointly estimates motion and the
ground truth image from a set of noisy frames under rigid, constant
translation. The algorithm is non-iterative and needs no hyperparameter tuning.
It requires a fixed number of FFT, multiplication, and downsampling operations
for a given input size, enabling fast implementation on embedded platforms like
cubesats where on-board image fusion can greatly save on limited downlink
bandwidth. The algorithm is optimal in the maximum likelihood sense for
additive white Gaussian noise and non-stationary Gaussian approximations of
Poisson noise. Accurate registration is achieved for very low SNR, even when
visible features are below the noise floor.


title:
Generalized Label Shift Correction via Minimum Uncertainty Principle:
  Theory and Algorithm

abstract:
  As a fundamental problem in machine learning, dataset shift induces a
paradigm to learn and transfer knowledge under changing environment. Previous
methods assume the changes are induced by covariate, which is less practical
for complex real-world data. We consider the Generalized Label Shift (GLS),
which provides an interpretable insight into the learning and transfer of
desirable knowledge. Current GLS methods: 1) are not well-connected with the
statistical learning theory; 2) usually assume the shifting conditional
distributions will be matched with an implicit transformation, but its explicit
modeling is unexplored. In this paper, we propose a conditional adaptation
framework to deal with these challenges. From the perspective of learning
theory, we prove that the generalization error of conditional adaptation is
lower than previous covariate adaptation. Following the theoretical results, we
propose the minimum uncertainty principle to learn conditional invariant
transformation via discrepancy optimization. Specifically, we propose the
\textit{conditional metric operator} on Hilbert space to characterize the
distinctness of conditional distributions. For finite observations, we prove
that the empirical estimation is always well-defined and will converge to
underlying truth as sample size increases. The results of extensive experiments
demonstrate that the proposed model achieves competitive performance under
different GLS scenarios.


title:
A combined multiscale finite element method based on the LOD technique
  for the multiscale elliptic problems with singularities

abstract:
  In this paper, we construct a combined multiscale finite element method
(MsFEM) using the Local Orthogonal Decomposition (LOD) technique to solve the
multiscale problems which may have singularities in some special portions of
the computational domain. For example, in the simulation of steady flow
transporting through highly heterogeneous porous media driven by extraction
wells, the singularities lie in the near-well regions. The basic idea of the
combined method is to utilize the traditional finite element method (FEM)
directly on a fine mesh of the problematic part of the domain and using the
LOD-based MsFEM on a coarse mesh of the other part. The key point is how to
define local correctors for the basis functions of the elements near the coarse
and fine mesh interface, which require meticulous treatment. The proposed
method takes advantages of the traditional FEM and the LOD-based MsFEM, which
uses much less DOFs than the standard FEM and may be more accurate than the
LOD-based MsFEM for problems with singularities. The error analysis is carried
out for highly varying coefficients, without any assumptions on scale
separation or periodicity. {Numerical examples with periodic and random highly
oscillating coefficients}, as well as the multiscale problems on the L-shaped
domain, and multiscale problems with high-contrast channels or
well-singularities are presented to demonstrate the efficiency and accuracy of
the proposed method.


title:
Effects of Polydispersity on the Plastic Behaviors of Dense 2D Granular
  Systems Under Shear

abstract:
  We study particle-scale motion in sheared highly polydisperse amorphous
materials, in which the largest particles are as much as ten times the size of
the smallest. We find strikingly different behavior from the more commonly
studied amorphous systems with low polydispersity. In particular, analysis of
the nonaffine motion of particles reveals qualitative differences between large
and small particles: the smaller particles have dramatically more nonaffine
motion, which is induced by the presence of the large particles. We
characterize the crossover in nonaffine motion from the low- to
high-polydispersity regime, and demonstrate a quantitative way to distinguish
between "large" and "small" particles in systems with broad distributions of
particle sizes.


title:
A Scalable Graph-Theoretic Distributed Framework for Cooperative
  Multi-Agent Reinforcement Learning

abstract:
  The main challenge of large-scale cooperative multi-agent reinforcement
learning (MARL) is two-fold: (i) the RL algorithm is desired to be distributed
due to limited resource for each individual agent; (ii) issues on convergence
or computational complexity emerge due to the curse of dimensionality.
Unfortunately, most of existing distributed RL references only focus on
ensuring that the individual policy-seeking process of each agent is based on
local information, but fail to solve the scalability issue induced by high
dimensions of the state and action spaces when facing large-scale networks. In
this paper, we propose a general distributed framework for cooperative MARL by
utilizing the structures of graphs involved in this problem. We introduce three
graphs in MARL, namely, the coordination graph, the observation graph and the
reward graph. Based on these three graphs, and a given communication graph, we
propose two distributed RL approaches. The first approach utilizes the inherent
decomposability property of the problem itself, whose efficiency depends on the
structures of the aforementioned four graphs, and is able to produce a high
performance under specific graphical conditions. The second approach provides
an approximate solution and is applicable for any graphs. Here the
approximation error depends on an artificially designed index. The choice of
this index is a trade-off between minimizing the approximation error and
reducing the computational complexity. Simulations show that our RL algorithms
have a significantly improved scalability to large-scale MASs compared with
centralized and consensus-based distributed RL algorithms.


title:
AugESC: Large-scale Data Augmentation for Emotional Support Conversation
  with Pre-trained Language Models

abstract:
  Crowd-sourcing is commonly adopted for dialog data collection. However, it is
highly costly and time-consuming, and the collected data is limited in scale
and topic coverage. In this paper, aiming to generate emotional support
conversations, we propose exploiting large-scale pre-trained language models
for data augmentation, and provide key findings in our pilot exploration. Our
adopted approach leverages the 6B-parameter GPT-J model and utilizes publicly
available dialog posts to trigger conversations on various topics. Then we
construct AugESC, a machine-augmented dataset for emotional support
conversation. It is two orders of magnitude larger than the original ESConv
dataset in scale, covers more diverse topics, and is shown to be of high
quality by human evaluation. Lastly, we demonstrate with interactive evaluation
that AugESC can further enhance dialog models tuned on ESConv to handle various
conversation topics and to provide significantly more effective emotional
support.


title:
Bayesian Ridge Regression Based Model to Predict Fault Location in HVdc
  Network

abstract:
  This paper discusses a method for accurately estimating the fault location in
multi-terminal High Voltage direct current (HVdc) transmission network using
single ended current and voltage measurements. The post-fault voltage and
current signatures are a function of multiple factors and thus accurately
locating faults on a multi-terminal network is challenging. We discuss a novel
data-driven Bayes Regression based method for accurately predicting fault
locations. The sensitivity of the proposed algorithm to measurement noise,
fault location, resistance and current limiting inductance are performed on a
radial three-terminal MTdc network. The test system is designed in Power System
Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).


title: 5-point Toponogov theorem

abstract:
  We give an if-and-only-if condition on a five-point metric spaces that admit
embeddings into nonnegatively curved Riemannian manifolds.


title: Optical flow-based branch segmentation for complex orchard environments

abstract:
  Machine vision is a critical subsystem for enabling robots to be able to
perform a variety of tasks in orchard environments. However, orchards are
highly visually complex environments, and computer vision algorithms operating
in them must be able to contend with variable lighting conditions and
background noise. Past work on enabling deep learning algorithms to operate in
these environments has typically required large amounts of hand-labeled data to
train a deep neural network or physically controlling the conditions under
which the environment is perceived. In this paper, we train a neural network
system in simulation only using simulated RGB data and optical flow. This
resulting neural network is able to perform foreground segmentation of branches
in a busy orchard environment without additional real-world training or using
any special setup or equipment beyond a standard camera. Our results show that
our system is highly accurate and, when compared to a network using manually
labeled RGBD data, achieves significantly more consistent and robust
performance across environments that differ from the training set.


title:
Three-dimensional Propagation of the Global EUV Wave associated with a
  solar eruption on 2021 October 28

abstract:
  We present a case study for the global extreme ultraviolet (EUV) wave and its
chromospheric counterpart `Moreton-Ramsey wave' associated with the second
X-class flare in Solar Cycle 25 and a halo coronal mass ejection (CME). The EUV
wave was observed in the H$\alpha$ and EUV passbands with different
characteristic temperatures. In the 171 {\AA} and 193/195 {\AA} images, the
wave propagates circularly with an initial velocity of 600-720 km s$^{-1}$ and
a deceleration of 110-320 m s$^{-2}$. The local coronal plasma is heated from
log(T/K)=5.9 to log(T/K)=6.2 during the passage of the wavefront. The H$\alpha$
and 304 {\AA} images also reveal signatures of wave propagation with a velocity
of 310-540 km s$^{-1}$. With multi-wavelength and dual-perspective
observations, we found that the wavefront likely propagates forwardly inclined
to the solar surface with a tilt angle of ~53.2$^{\circ}$. Our results suggest
that this EUV wave is a fast-mode magnetohydrodynamic wave or shock driven by
the expansion of the associated CME, whose wavefront is likely a dome-shaped
structure that could impact the upper chromosphere, transition region and
corona.


title:
Arbitrary high-order structure-preserving methods for the quantum
  Zakharov system

abstract:
  In this paper, we present a new methodology to develop arbitrary high-order
structure-preserving methods for solving the quantum Zakharov system. The key
ingredients of our method are: (i) the original Hamiltonian energy is
reformulated into a quadratic form by introducing a new quadratic auxiliary
variable; (ii) the original system is then rewritten into a new equivalent
system which inherits the quadratic energy based on the energy variational
principle; (iii) the resulting system is discretized by symplectic Runge-Kutta
method in time combining with the Fourier pseudo-spectral method in space. The
proposed method achieves arbitrary high-order accurate in time and can preserve
the discrete mass and Hamiltonian energy exactly. Moreover, an efficient
iterative solver is presented to solve the resulting discrete nonlinear
equations. Finally, extensive numerical examples are provided to demonstrate
the theoretical claims and illustrate the efficiency of our method.