title:
Dynamic Control of Service Systems with Returns: Application to Design
  of Post-Discharge Hospital Readmission Prevention Programs

abstract:
  We study a control problem for queueing systems where customers may return
for additional episodes of service after their initial service completion. At
each service completion epoch, the decision maker can choose to reduce the
probability of return for the departing customer but at a cost that is convex
increasing in the amount of reduction in the return probability. Other costs
are incurred as customers wait in the queue and every time they return for
service. Our primary motivation comes from post-discharge Quality Improvement
(QI) interventions (e.g., follow up phone-calls, appointments) frequently used
in a variety of healthcare settings to reduce unplanned hospital readmissions.
Our objective is to understand how the cost of interventions should be balanced
with the reductions in congestion and service costs. To this end, we consider a
fluid approximation of the queueing system and characterize the structure of
optimal long-run average and bias-optimal transient control policies for the
fluid model. Our structural results motivate the design of intuitive surge
protocols whereby different intensities of interventions (corresponding to
different levels of reduction in the return probability) are provided based on
the congestion in the system. Through extensive simulation experiments, we
study the performance of the fluid policy for the stochastic system and
identify parameter regimes where it leads to significant cost savings compared
to a fixed long-run average optimal policy that ignores holding costs and a
simple policy that uses the highest level of intervention whenever the queue is
non-empty. In particular, we find that in a parameter regime relevant to our
motivating application, dynamically adjusting the intensity of interventions
could result in up to 25.4% reduction in long-run average cost and 33.7% in
finite-horizon costs compared to the simple aggressive policy.


title:
On the decategorification of some higher actions in Heegaard Floer
  homology

abstract:
  We decategorify the higher actions on bordered Heegaard Floer strands
algebras from recent work of Rouquier and the author and identify the
decategorifications with certain actions on exterior powers of homology groups
of surfaces. We also suggest an interpretation for these actions in the
language of open-closed TQFT, and we prove a corresponding gluing formula.


title: Distributed randomized Kaczmarz for the adversarial workers

abstract:
  Developing large-scale distributed methods that are robust to the presence of
adversarial or corrupted workers is an important part of making such methods
practical for real-world problems. Here, we propose an iterative approach that
is adversary-tolerant for least-squares problems. The algorithm utilizes simple
statistics to guarantee convergence and is capable of learning the adversarial
distributions. Additionally, the efficiency of the proposed method is shown in
simulations in the presence of adversaries. The results demonstrate the great
capability of such methods to tolerate different levels of adversary rates and
to identify the erroneous workers with high accuracy.


title: On quantitative hypocoercivity estimates based on Harris-type theorems

abstract:
  This review concerns recent results on the quantitative study of convergence
towards equilibrium for spatially in-homogeneous linear kinetic equations. We
focus on analytical results obtained by means of certain probabilistic
techniques from the ergodic theory of Markov processes. These techniques are
sometimes referred to as Harris-type theorems. They provide constructive proofs
for convergence results in L1 (or total variation) setting for a wide class of
initial data. The convergence rates can be made explicit (both for geometric
and sub-geometric rates) by tracking the constants appearing in the hypotheses.
Harris-type theorems are particularly well-adapted for equations exhibiting
non-explicit and non-equilibrium steady states since they do not require prior
information on the existence of stationary states. This allows for significant
improvements of some already-existing results by relaxing assumptions and
providing explicit convergence rates. We aim at presenting Harris-type
theorems, providing a guideline on how to apply these techniques on kinetic
equations at hand, presenting an exposition of recent quantitative results
obtained for kinetic equations in gas theory and mathematical biology, as well
as giving some perspectives on potential extensions to non-linear equations.


title:
Estimating causal effects with optimization-based methods: A review and
  empirical comparison

abstract:
  In the absence of randomized controlled and natural experiments, it is
necessary to balance the distributions of (observable) covariates of the
treated and control groups in order to obtain an unbiased estimate of a causal
effect of interest; otherwise, a different effect size may be estimated, and
incorrect recommendations may be given. To achieve this balance, there exist a
wide variety of methods. In particular, several methods based on optimization
models have been recently proposed in the causal inference literature. While
these optimization-based methods empirically showed an improvement over a
limited number of other causal inference methods in their relative ability to
balance the distributions of covariates and to estimate causal effects, they
have not been thoroughly compared to each other and to other noteworthy causal
inference methods. In addition, we believe that there exist several unaddressed
opportunities that operational researchers could contribute with their advanced
knowledge of optimization, for the benefits of the applied researchers that use
causal inference tools. In this review paper, we present an overview of the
causal inference literature and describe in more detail the optimization-based
causal inference methods, provide a comparative analysis of the prevailing
optimization-based methods, and discuss opportunities for new methods.


title:
Non-linear Smoothing for the Periodic Generalized Non-linear
  Schr\"odinger Equation

abstract:
  We consider the periodic non-linear Schr\"odinger equation with non-linearity
given by $|u|^{p-1}u$ for odd $p > 1$ in dimension $1$. We first establish that
the difference between the non-linear evolution and a phase rotation of the the
linear evolution is in a smoother space. We then study forced and damped
defocusing non-linear Schr\"odinger equations of the above type and establish
an analogous smoothing statement that extends globally in time. As a corollary
we establish both existence and smootheness for global attractors in the energy
space.


title:
A computational model of self-organized shape dynamics of active
  surfaces in fluids

abstract:
  Mechanochemical processes on surfaces such as the cellular cortex or
epithelial sheets, play a key role in determining patterns and shape changes of
biological systems. To understand the complex interplay of hydrodynamics and
material flows on such active surfaces requires novel numerical tools. Here, we
present a finite-element method for an active deformable surface interacting
with the surrounding fluids. The underlying model couples surface and bulk
hydrodynamics to surface flow of a diffusible species which generates active
contractile forces. The method is validated with previous results based on
linear stability analysis and shows almost perfect agreement regarding
predicted patterning. Away from the linear regime we find rich non-linear
behavior, such as the presence of multiple stationary states. We study the
formation of a contractile ring on the surface and the corresponding shape
changes. Finally, we explore mechanochemical pattern formation on various
surface geometries and find that patterning strongly adapts to local surface
curvature. The developed method provides a basis to analyze a variety of
systems that involve mechanochemical pattern formation on active surfaces
interacting with surrounding fluids.


title:
Making sense of complex systems through resolution, relevance, and
  mapping entropy

abstract:
  Complex systems are characterised by a tight, nontrivial interplay of their
constituents, which gives rise to a multi-scale spectrum of emergent
properties. In this scenario, it is practically and conceptually difficult to
identify those degrees of freedom that mostly determine the behaviour of the
system and separate them from less prominent players. Here, we propose an
analysis pipeline that integrates three measures of statistical information:
resolution, relevance, and mapping entropy. This approach allows one first to
identify, in a quantitative manner, the number of degrees of freedom of the
system that preserve the largest information content about the generative
process that governs it, then to select the specific subset of constituents
that optimally provide a synthetic, yet informative reduced representation of
the whole. The method, which is implemented in a freely available software, is
fully general, as it is shown through the application to three very diverse
systems, namely a toy model of independent binary spins, a coarse-grained
representation of the financial stock market, and a fully atomistic simulation
of a protein.


title: ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction

abstract:
  In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect
prediction. ApacheJIT consists of clean and bug-inducing software changes in
popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239
bug-inducing and 78,435 clean commits). Having a large number of commits makes
ApacheJIT a suitable dataset for machine learning models, especially deep
learning models that require large training sets to effectively generalize the
patterns present in the historical data to future data.


title: Thermal transport in d = 1,2,3 -- a conspiracy enthroning Fourier law

abstract:
  We numerically study the thermal transport in the classical inertial
nearest-neighbor XY ferromagnet in $d=1,2,3$, the total number of sites being
given by $N=L^d$, where $L$ is the linear size of the system. For the thermal
conductivity $\sigma$, we obtain $\sigma(T,L)\, L^{\delta(d)} = A(d)\,
e_{q(d)}^{- B(d)\,[L^{\gamma(d)}T]^{\eta(d)}}$ (with $e_q^z \equiv
[1+(1-q)z]^{1/(1-q)};\,e_1^z=e^z;\,A(d)>0;\,B(d)>0;\,q(d)>1;\,\eta(d)>2;\,\delta
\ge 0; \,\gamma(d)>0)$, for all values of $L^{\gamma(d)}T$ for $d=1,2,3$. In
the $L\to\infty$ limit, we have $\sigma \propto 1/L^{\rho_\sigma(d)}$ with
$\rho_\sigma(d)= \delta(d)+ \gamma(d) \eta(d)/[q(d)-1]$. The material
conductivity is given by $\kappa=\sigma L^d \propto 1/L^{\rho_\kappa(d)}$
($L\to\infty$) with $\rho_\kappa(d)=\rho_\sigma(d)-d$. Our numerical results
are consistent with 'conspiratory' $d$-dependences of $(q,\eta,\delta,\gamma)$,
which comply with normal thermal conductivity (Fourier law) for all dimensions.


title:
The XP Stabiliser Formalism: a Generalisation of the Pauli Stabiliser
  Formalism with Arbitrary Phases

abstract:
  We propose an extension to the Pauli stabiliser formalism that includes
fractional $2\pi/N$ rotations around the $Z$ axis for some integer $N$. The
resulting generalised stabiliser formalism - denoted the XP stabiliser
formalism - allows for a wider range of states and codespaces to be
represented. We describe the states which arise in the formalism, and
demonstrate an equivalence between XP stabiliser states and 'weighted
hypergraph states' - a generalisation of both hypergraph and weighted graph
states. Given an arbitrary set of XP operators, we present algorithms for
determining the codespace and logical operators for an XP code. Finally, we
consider whether measurements of XP operators on XP codes can be classically
simulated.


title: Two Bogoliubov quasiparticles entangled by a spin

abstract:
  Condensed matter is composed of a small set of identical units which
nevertheless show an immense range of behaviour. Recently, an array of atoms
was used to generate long-range spin entanglement, a property of topological
matter. A different approach is to employ the macroscopic coherence of
superconductors. However, the presence of a single localized spin is naively
expected to disrupt any form of long-range spin entanglement by binding to a
quasiparticle from the superconductor to form a localized singlet bound state.
Here we demonstrate that it is possible to attach a second quasiparticle to the
spin, overscreening it into a doublet state carrying triplet correlations
between the quasiparticles over a micrometer distance. To demonstrate this
effect, which is strongest for symmetric bindings, we couple with equal
strength the spin of a quantum dot to two superconducting islands in which the
Coulomb repulsion overcomes Cooper pairing. We show that the doublet state is
stabilized by the Coulomb repulsion and destabilized by the singlet binding. We
predict that this state will carry triplet correlations for an alternating
chain of any odd number of quantum dots and superconducting islands, opening an
alternative route to controllable long-range entanglement in the solid state.


title: Parameterizations of black-hole spacetimes beyond circularity

abstract:
  We discuss parameterizations of black-hole spacetimes in and beyond General
Relativity in view of their symmetry constraints: within the class of
axisymmetric, stationary spacetimes, we propose a parameterization that
includes non-circular spacetimes, both in Boyer-Lindquist as well as in
horizon-penetrating coordinates. We show how existing parameterizations, which
make additional symmetry assumptions (first, circularity; second, a hidden
constant of motion), are included in the new parameterization. Further, we
explain why horizon-penetrating coordinates may be more suitable to
parameterize non-circular deviations from the Kerr geometry. Our investigation
is motivated by our result that the regular, spinning black-hole spacetimes
proposed in [1,2] are non-circular. This particular deviation from circularity
can result in cusps, a dent and an asymmetry in the photon rings surrounding
the black-hole shadow. Finally, we explore a new class of non-circular
deviations from Kerr black holes, which promote the spin parameter to a
function, and find indications that regularity cannot be achieved in this
setting. This result strengthens the case for regular black holes based on a
promotion of the mass parameter to a function.


title: Touching multifunctions on a Hilbert space

abstract:
  We introduce the concept of the touching of two multifunctions on a real
Hilbert space, and deduce that certain multifunctions on the space have a
unique fixed point. These result are applied to the theory of genaralized
cycles and generalized gap vectors for the composition of the projections onto
a finite number of closed convex space in a real Hilbert space.


title: Tunable Cooper pair splitting via Andreev bound states

abstract:
  Two spatially separated quantum dots connected by a common superconductor has
been studied extensively for Cooper pair splitting, and is predicted to be the
building block of an emulated Kitaev chain. The effective couplings between
quantum dots mediated by the middle superconductor is crucial for observing
these intriguing physical phenomena, but unfortunately, a method of tuning the
effective couplings is still lacking, obstructing further experimental
progresses in relevant studies. Here we propose to mediate tunable effective
couplings via Andreev bound states in a semiconductor-superconductor nanowire.
We show explicitly how the coupling strengths depend on the physical parameters
of the Andreev bound states, e.g., chemical potential, induced Zeeman spin
splitting and spin-orbit interaction. Furthermore, we propose to extract the
effective coupling strengths from resonant current in a three-terminal
junction. Our findings will facilitate future experiments on Cooper pair
splitting as well as the experimental realization of the Kitaev chain in
quantum dots.


title:
MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual
  Image Assessment

abstract:
  DeepFakes are synthetic videos generated by swapping a face of an original
image with the face of somebody else. In this paper, we describe our work to
develop general, deep learning-based models to classify DeepFake content. We
propose a novel framework for using Generative Adversarial Network (GAN)-based
models, we call MRI-GAN, that utilizes perceptual differences in images to
detect synthesized videos. We test our MRI-GAN approach and a
plain-frames-based model using the DeepFake Detection Challenge Dataset. Our
plain frames-based-model achieves 91% test accuracy and a model which uses our
MRI-GAN framework with Structural Similarity Index Measurement (SSIM) for the
perceptual differences achieves 74% test accuracy. The results of MRI-GAN are
preliminary and may be improved further by modifying the choice of loss
function, tuning hyper-parameters, or by using a more advanced perceptual
similarity metric.


title:
The first large-scale shell-model calculation of the two-neutrino double
  beta decay of $^{76}$Ge to the ground and excited states in $^{76}$Se

abstract:
  Large-scale shell-model calculations were carried out for the half-lives and
branching ratios of the $2\nu\beta\beta$ decay of $^{76}$Ge to the ground state
and the lowest three excited states $2_1^+$, $0_2^+$ and $2_2^+$ in $^{76}$Se.
In total, the wave functions of more than 10,000 intermediate $1^+$ states in
$^{76}$As were calculated in a three-step procedure allowing an efficient use
of the available computer resources. In the first step, 250 lowest states,
below some 5 MeV of excitation energy, were calculated without truncations
within a full major shell $0f_{5/2}-1p-0g_{9/2}$ for both protons and neutrons.
The wave functions of the rest of the states, up to some 30 MeV, were computed
in two more steps by introducing two consecutive stages of truncation. The
computed magnitudes of the $2\nu\beta\beta$ nuclear matrix elements, $\vert
M_{2\nu}\vert$, converged to the values 0.097, $7.0\times10^{-4}$, 0.070 and
$1.8\times10^{-3}$ for the $0^+_{\rm g.s.}$, $2^+_1$, $0^+_2$ and $2^+_2$
states, respectively. Using up-to-date phase-space integrals, the corresponding
branching ratios were derived to be 99.79%, 9.1$\times10^{-5}$%, 0.21% and
5.0$\times10^{-7}$%. The experimental half-life $(1.926\pm0.094)\times10^{21}$
yr of the ground-state transition was used to derive the value $g_{\rm
A}=1.248\pm0.015$ for the axial-vector coupling, which is consistent with its
free-nucleon value 1.27. The branching ratios for the decays to the excited
states were done using the derived value for $g_{\rm A}$.


title:
An Achievable Rate Region for $3-$User Classical-Quantum Broadcast
  Channels

abstract:
  We consider the scenario of communicating on a $3\mhyphen$user
classical-quantum broadcast channel. We undertake an information theoretic
study and focus on the problem of characterizing an inner bound to its capacity
region. We design a new coding scheme based \textit{partitioned coset codes} -
an ensemble of codes possessing algebraic properties. Analyzing its
information-theoretic performance, we characterize a new inner bound. We
identify examples for which the derived inner bound is strictly larger than
that achievable using IID random codes.


title:
Pedagogical Demonstrations and Pragmatic Learning in Artificial
  Tutor-Learner Interactions

abstract:
  When demonstrating a task, human tutors pedagogically modify their behavior
by either "showing" the task rather than just "doing" it (exaggerating on
relevant parts of the demonstration) or by giving demonstrations that best
disambiguate the communicated goal. Analogously, human learners pragmatically
infer the communicative intent of the tutor: they interpret what the tutor is
trying to teach them and deduce relevant information for learning. Without such
mechanisms, traditional Learning from Demonstration (LfD) algorithms will
consider such demonstrations as sub-optimal. In this paper, we investigate the
implementation of such mechanisms in a tutor-learner setup where both
participants are artificial agents in an environment with multiple goals. Using
pedagogy from the tutor and pragmatism from the learner, we show substantial
improvements over standard learning from demonstrations.


title: GraphWorld: Fake Graphs Bring Real Insights for GNNs

abstract:
  Despite advances in the field of Graph Neural Networks (GNNs), only a small
number (~5) of datasets are currently used to evaluate new models. This
continued reliance on a handful of datasets provides minimal insight into the
performance differences between models, and is especially challenging for
industrial practitioners who are likely to have datasets which look very
different from those used as academic benchmarks. In the course of our work on
GNN infrastructure and open-source software at Google, we have sought to
develop improved benchmarks that are robust, tunable, scalable,and
generalizable. In this work we introduce GraphWorld, a novel methodology and
system for benchmarking GNN models on an arbitrarily-large population of
synthetic graphs for any conceivable GNN task. GraphWorld allows a user to
efficiently generate a world with millions of statistically diverse datasets.
It is accessible, scalable, and easy to use. GraphWorld can be run on a single
machine without specialized hardware, or it can be easily scaled up to run on
arbitrary clusters or cloud frameworks. Using GraphWorld, a user has
fine-grained control over graph generator parameters, and can benchmark
arbitrary GNN models with built-in hyperparameter tuning. We present insights
from GraphWorld experiments regarding the performance characteristics of tens
of thousands of GNN models over millions of benchmark datasets. We further show
that GraphWorld efficiently explores regions of benchmark dataset space
uncovered by standard benchmarks, revealing comparisons between models that
have not been historically obtainable. Using GraphWorld, we also are able to
study in-detail the relationship between graph properties and task performance
metrics, which is nearly impossible with the classic collection of real-world
benchmarks.


title: Understanding Digits in Identifier Names: An Exploratory Study

abstract:
  Before any software maintenance can occur, developers must read the
identifier names found in the code to be maintained. Thus, high-quality
identifier names are essential for productive program comprehension and
maintenance activities. With developers free to construct identifier names to
their liking, it can be difficult to automatically reason about the quality and
semantics behind an identifier name. Studying the structure of identifier names
can help alleviate this problem. Existing research focuses on studying words
within identifiers, but there are other symbols that appear in identifier names
-- such as digits. This paper explores the presence and purpose of digits in
identifier names through an empirical study of 800 open-source Java systems. We
study how digits contribute to the semantics of identifier names and how
identifier names that contain digits evolve over time through renaming. We
envision our findings improving the efficiency of name appraisal and
recommendation tools and techniques.


title:
Tuning the Topological Band Gap of Bismuthene with Silicon-based
  Substrate

abstract:
  Some meta-stable polymorphs of bismuth monolayer (bismuthene) can host
topologically nontrivial phases. However, it remains unclear if these
polymorphs can become stable through interaction with a substrate, whether
their topological properties are preserved, and how to design an optimal
substrate to make the topological phase more robust. Using first-principles
techniques we demonstrate that bismuthene polymorphs can become stable over
silicon carbide (SiC), silicon (Si), silicon dioxide (SiO2) and that the
proximity interaction in the heterostructures has a significant effect on the
electronic structure of the monolayer, even when bonding is weak. We show that
the van der Waals interactions and the breaking of the sublattice symmetry are
the main factors driving changes in the electronic structure. Our work
demonstrates that substrate interaction can strengthen the topological
properties of bismuthene polymorphs and make them accessible for experimental
investigation and technological applications.


title:
The Right Spin: Learning Object Motion from Rotation-Compensated Flow
  Fields

abstract:
  Both a good understanding of geometrical concepts and a broad familiarity
with objects lead to our excellent perception of moving objects. The human
ability to detect and segment moving objects works in the presence of multiple
objects, complex background geometry, motion of the observer and even
camouflage. How humans perceive moving objects so reliably is a longstanding
research question in computer vision and borrows findings from related areas
such as psychology, cognitive science and physics. One approach to the problem
is to teach a deep network to model all of these effects. This contrasts with
the strategy used by human vision, where cognitive processes and body design
are tightly coupled and each is responsible for certain aspects of correctly
identifying moving objects. Similarly from the computer vision perspective,
there is evidence that classical, geometry-based techniques are better suited
to the "motion-based" parts of the problem, while deep networks are more
suitable for modeling appearance. In this work, we argue that the coupling of
camera rotation and camera translation can create complex motion fields that
are difficult for a deep network to untangle directly. We present a novel
probabilistic model to estimate the camera's rotation given the motion field.
We then rectify the flow field to obtain a rotation-compensated motion field
for subsequent segmentation. This strategy of first estimating camera motion,
and then allowing a network to learn the remaining parts of the problem, yields
improved results on the widely used DAVIS benchmark as well as the recently
published motion segmentation data set MoCA (Moving Camouflaged Animals).


title:
Enhancing Satellite Imagery using Deep Learning for the Sensor To
  Shooter Timeline

abstract:
  The sensor to shooter timeline is affected by two main variables: satellite
positioning and asset positioning. Speeding up satellite positioning by adding
more sensors or by decreasing processing time is important only if there is a
prepared shooter, otherwise the main source of time is getting the shooter into
position. However, the intelligence community should work towards the
exploitation of sensors to the highest speed and effectiveness possible.
Achieving a high effectiveness while keeping speed high is a tradeoff that must
be considered in the sensor to shooter timeline. In this paper we investigate
two main ideas, increasing the effectiveness of satellite imagery through image
manipulation and how on-board image manipulation would affect the sensor to
shooter timeline. We cover these ideas in four scenarios: Discrete Event
Simulation of onboard processing versus ground station processing, quality of
information with cloud cover removal, information improvement with super
resolution, and data reduction with image to caption. This paper will show how
image manipulation techniques such as Super Resolution, Cloud Removal, and
Image to Caption will improve the quality of delivered information in addition
to showing how those processes effect the sensor to shooter timeline.


title: The Visible Spectro-Polarimeter of the Daniel K. Inouye Solar Telescope

abstract:
  The Daniel K. Inouye Solar Telescope (DKIST) Visible Spectro-Polarimeter
(ViSP) is a traditional slit-scanning spectrograph, with the ability to observe
solar regions up to a $120\times78~\mathrm{arcsec}^2$ area. The design
implements dual-beam polarimetry, a polychromatic polarization modulator, a
high-dispersion echelle grating, and three spectral channels that can be
automatically positioned. A defining feature of the instrument is its
capability to tune anywhere within the 380-900~nm range of the solar spectrum,
allowing for a virtually infinite number of combinations of three wavelengths
to be observed simultaneously. This enables the ViSP user to pursue
well-established spectro-polarimetric studies of the magnetic structure and
plasma dynamics of the solar atmosphere, as well as completely novel
investigations of the solar spectrum. Within the suite of first-generation
instruments at the DKIST, ViSP is the only wavelength-versatile
spectro-polarimeter available to the scientific community. It was specifically
designed to be a discovery instrument, for the exploration of new spectroscopic
and polarimetric diagnostics, and to test improved models of polarized line
formation, through high spatial-, spectral-, and temporal-resolution
observations of the Sun's polarized spectrum. In this instrument article, we
describe the science requirements and design drivers of ViSP, and we present
preliminary science data collected during the commissioning of the instrument.


title: A Gelfand Transform for Spinor Fields on Embedded Riemannian Manifolds

abstract:
  A classical result of Gelfand shows that the topologized spectrum of
characters on commutative Banach algebra is homeomorphic to the underlying
space. This fact is used in solving the Calder\'on problem in dimension 2 via
the boundary control (BC) method. To apply the BC method in dimension 3, the
algebra of complex holomorphic functions can be replaced by the space of
harmonic quaternion fields, but this space is no longer an algebra and is not
commutative. Nonetheless, it has been shown that a suitable notion of a
spectrum exists and in the case when the underlying space is convex, the
spectrum is homeomorphic to the ball. Our goal is to generalize this result to
more general manifolds in arbitrary dimension. To do so, we use Clifford
algebras of multivector fields and the set of functions we care about is the
space monogenic spinor fields. The spectrum consists of spinor valued
functionals that respect the module and subalgebra structure of the space of
monogenic spinor fields. We prove that for compact regions in $\mathbb{R}^n$,
the spectrum is homeomorphic to the manifold itself. Furthermore, some
essential lemmas and propositions are proven for arbitrary compact Riemannian
$n$-manifolds. Finally, we end with a Stone--Weierstrass theorem which shows
the algebra generated by the monogenic spinor fields on arbitrary compact $M$
with boundary is dense in the algebra of continuous spinor fields.


title:
Efficient Task Allocation in Smart Warehouses with Multi-delivery
  Stations and Heterogeneous Robots

abstract:
  The task allocation problem in multi-robot systems (MRTA) is an NP-hard
problem whose viable solutions are usually found by heuristic algorithms.
Considering the increasing need of improvement on logistics, the use of robots
for increasing the efficiency of logistics warehouses is becoming a
requirement. In a smart warehouse the main tasks consist of employing a fleet
of automated picking and mobile robots that coordinate by picking up items from
a set of orders from the shelves and dropping them at the delivery stations.
Two aspects generally justify multi-robot task allocation complexity: (i)
environmental aspects, such as multi-delivery stations and dispersed robots
(since they remain in constant motion) and (ii) fleet heterogeneity, where
robots' traffic speed and capacity loads are different from each other. Despite
these properties have been widely researched in the literature, they usually
are investigated separately. This work proposes a scalable and efficient task
allocation algorithm for smart warehouses with multi-delivery stations and
heterogeneous fleets. Our strategy employs a novel cost estimator, which
computes costs as a function of the robots' variable characteristics and
capacity while they receive new tasks. For validating the strategy a series of
experiments is performed simulating the operation of smart warehouses with
multiple delivery stations and heteregenous fleets. The results show that our
strategy generates routes costing up to $33$\% less than the routes generated
by a state-of-the-art task allocation algorithm and $96$\% faster in test
instances representing our target scenario. Considering single-delivery
stations and non-dispersed robots, we reduced the number of robots by up to
$18$\%, allocating tasks $92$\% faster, and generating routes whose costs are
statistically similar to the routes generated by the state-of-the-art
algorithm.


title:
Neural Ordinary Differential Equations for Nonlinear System
  Identification

abstract:
  Neural ordinary differential equations (NODE) have been recently proposed as
a promising approach for nonlinear system identification tasks. In this work,
we systematically compare their predictive performance with current
state-of-the-art nonlinear and classical linear methods. In particular, we
present a quantitative study comparing NODE's performance against neural
state-space models and classical linear system identification methods. We
evaluate the inference speed and prediction performance of each method on
open-loop errors across eight different dynamical systems. The experiments show
that NODEs can consistently improve the prediction accuracy by an order of
magnitude compared to benchmark methods. Besides improved accuracy, we also
observed that NODEs are less sensitive to hyperparameters compared to neural
state-space models. On the other hand, these performance gains come with a
slight increase of computation at the inference time.


title: Dense Gas History of the Universe: from ASPECS to the ngVLA

abstract:
  We review the evolution of the cosmic average molecular gas density to large
look-back times, using observations of rotational transitions of CO. Molecular
gas is the fuel for star formation in galaxies. Deep searches for CO emission
from distant galaxies have delineated the density of molecular gas back to $z
\sim 5$, or within 1~Gyr of the Big Bang. The results show a rise and fall in
the gas density that parallels, and likely drives, the rise and fall of the
cosmic star formation rate density. We present the potential for the next
generation Very Large Array to image the distribution and dynamics of the
molecular gas in early galaxies, and to make a precise measurement of the dense
gas history of the Universe.


title:
Uniqueness for nonlinear Fokker-Planck equations and for McKean-Vlasov
  SDEs: The degenerate case

abstract:
  This work is concerned with the existence and uniqueness of generalized (mild
or distributional) solutions to (possibly degenerate) Fokker-Planck equations
(possibly degenerate) $\rho_t-\Delta\beta(\rho)+{\rm div}(Db(\rho)\rho)=0$ in
$(0,\infty)\times\mathbb{R}^d,$ $\rho(0,x) \equiv \rho_0(x)$. Under suitable
assumptions on $\beta:\mathbb{R}\to\mathbb{R},\,b:\mathbb{R}\to\mathbb{R}$ and
$D:\mathbb{R}^d\to\mathbb{R}^d$, $d\ge1$, this equation generates a unique flow
$\rho(t)=S(t)\rho_0:[0,\infty)\to L^1(\mathbb{R}^d)$ as a mild solution in the
sense of nonlinear semigroup theory. This flow is also unique in the class of
$L^\infty((0,T)\times\mathbb{R}^d)\cap L^1((0,T)\times\mathbb{R}^d),$ $\forall
T>0$, Schwartz distributional solutions on $(0,\infty)\times\mathbb{R}^d$.
Moreover, for $\rho_0\in L^1(\mathbb{R}^d)\cap H^{-1}(\mathbb{R}^d)$, $t\to
S(t)\rho_0$ is differentiable from the right on $[0,\infty)$ in
$H^{-1}(\mathbb{R}^d)$-norm. As a main application, the weak uniqueness of the
corresponding McKean-Vlasov SDEs is proven.


title:
Rectifying homographies for stereo vision: analytical solution for
  minimal distortion

abstract:
  Stereo rectification is the determination of two image transformations (or
homographies) that map corresponding points on the two images, projections of
the same point in the 3D space, onto the same horizontal line in the
transformed images. Rectification is used to simplify the subsequent stereo
correspondence problem and speeding up the matching process. Rectifying
transformations, in general, introduce perspective distortion on the obtained
images, which shall be minimised to improve the accuracy of the following
algorithm dealing with the stereo correspondence problem. The search for the
optimal transformations is usually carried out relying on numerical
optimisation. This work proposes a closed-form solution for the rectifying
homographies that minimise perspective distortion. The experimental comparison
confirms its capability to solve the convergence issues of the previous
formulation. Its Python implementation is provided.


title: On classification of strategic agents who can both game and improve

abstract:
  In this work, we consider classification of agents who can both game and
improve. For example, people wishing to get a loan may be able to take some
actions that increase their perceived credit-worthiness and others that also
increase their true credit-worthiness. A decision-maker would like to define a
classification rule with few false-positives (does not give out many bad loans)
while yielding many true positives (giving out many good loans), which includes
encouraging agents to improve to become true positives if possible. We consider
two models for this problem, a general discrete model and a linear model, and
prove algorithmic, learning, and hardness results for each. For the general
discrete model, we give an efficient algorithm for the problem of maximizing
the number of true positives subject to no false positives, and show how to
extend this to a partial-information learning setting. We also show hardness
for the problem of maximizing the number of true positives subject to a nonzero
bound on the number of false positives, and that this hardness holds even for a
finite-point version of our linear model. We also show that maximizing the
number of true positives subject to no false positive is NP-hard in our full
linear model. We additionally provide an algorithm that determines whether
there exists a linear classifier that classifies all agents accurately and
causes all improvable agents to become qualified, and give additional results
for low-dimensional data.


title:
Pervasive beyond room-temperature ferromagnetism in a doped van der
  Waals magnet: Ni doped Fe$_5$GeTe$_2$ with $T_{\text{C}}$ up to 478 K

abstract:
  The existence of long range magnetic order in low dimensional magnetic
systems, such as the quasi-two-dimensional (2D) van der Waals (vdW) magnets,
has attracted intensive studies of new physical phenomena. The vdW
Fe$_N$GeTe$_2$ ($N$ = 3, 4, 5; FGT) family is exceptional owing to its vast
tunability of magnetic properties. Particularly, a ferromagnetic ordering
temperature ($T_{\text{C}}$) above room temperature at $N$ = 5 (F5GT) is
observed. Here, our study shows that, by nickel (Ni) substitution of iron (Fe)
in F5GT, a record high $T_{\text{C}}$ = 478(6) K is achieved. Importantly,
pervasive, beyond-room-temperature ferromagnetism exists in almost the entire
doping range of the phase diagram of Ni-F5GT. We argue that this striking
observation in Ni-F5GT can be possibly due to several contributing factors, in
which the structural alteration enhanced 3D magnetic couplings might be
critical for enhancing the ferromagnetic order.


title:
Learning Low-Dimensional Nonlinear Structures from High-Dimensional
  Noisy Data: An Integral Operator Approach

abstract:
  We propose a kernel-spectral embedding algorithm for learning low-dimensional
nonlinear structures from high-dimensional and noisy observations, where the
datasets are assumed to be sampled from an intrinsically low-dimensional
manifold and corrupted by high-dimensional noise. The algorithm employs an
adaptive bandwidth selection procedure which does not rely on prior knowledge
of the underlying manifold. The obtained low-dimensional embeddings can be
further utilized for downstream purposes such as data visualization, clustering
and prediction. Our method is theoretically justified and practically
interpretable. Specifically, we establish the convergence of the final
embeddings to their noiseless counterparts when the dimension and size of the
samples are comparably large, and characterize the effect of the
signal-to-noise ratio on the rate of convergence and phase transition. We also
prove convergence of the embeddings to the eigenfunctions of an integral
operator defined by the kernel map of some reproducing kernel Hilbert space
capturing the underlying nonlinear structures. Numerical simulations and
analysis of three real datasets show the superior empirical performance of the
proposed method, compared to many existing methods, on learning various
manifolds in diverse applications.


title:
Refining perovskite structures to pair distribution function data using
  collective Glazer modes as a basis

abstract:
  Structural modelling of octahedral tilts in perovskites is typically done
using the symmetry constraints of the resulting space group. In most cases,
this introduces more degrees of freedom than those strictly necessary to
describe only the octahedral tilts. It can therefore be a challenge to
disentangle the octahedral tilts from other structural distortions such as
cation displacements and octahedral distortions. This paper reports on the
development of constraints for modelling pure octahedral tilts and implemented
the constraints in diffpy-CMI, a powerful package to analyse pair distribution
function (PDF) data. The program allows features in the PDF that come from
rigid tilts to be separated from non-rigid relaxations, provides an intuitive
picture of the tilting, and as it has many fewer refinable variables than the
unconstrained space-group fits, provides robust and stable refinements of the
tilt components. It further demonstrates the use of the model on the canonical
tilted perovskite CaTiO$_3$ which has a known Glazer tilt system $\alpha^+
\beta^- \beta^-$. The Glazer model fits comparably to the corresponding space
group model $Pnma$ below $r$ = 14 A and becomes progressively worse than the
space group model at higher $r$ due to non-rigid distortions in the real
material.


title: Learning Neural Hamiltonian Dynamics: A Methodological Overview

abstract:
  The past few years have witnessed an increased interest in learning
Hamiltonian dynamics in deep learning frameworks. As an inductive bias based on
physical laws, Hamiltonian dynamics endow neural networks with accurate
long-term prediction, interpretability, and data-efficient learning. However,
Hamiltonian dynamics also bring energy conservation or dissipation assumptions
on the input data and additional computational overhead. In this paper, we
systematically survey recently proposed Hamiltonian neural network models, with
a special emphasis on methodologies. In general, we discuss the major
contributions of these models, and compare them in four overlapping directions:
1) generalized Hamiltonian system; 2) symplectic integration, 3) generalized
input form, and 4) extended problem settings. We also provide an outlook of the
fundamental challenges and emerging opportunities in this area.


title: BlazeNeo: Blazing fast polyp segmentation and neoplasm detection

abstract:
  In recent years, computer-aided automatic polyp segmentation and neoplasm
detection have been an emerging topic in medical image analysis, providing
valuable support to colonoscopy procedures. Attentions have been paid to
improving the accuracy of polyp detection and segmentation. However, not much
focus has been given to latency and throughput for performing these tasks on
dedicated devices, which can be crucial for practical applications. This paper
introduces a novel deep neural network architecture called BlazeNeo, for the
task of polyp segmentation and neoplasm detection with an emphasis on
compactness and speed while maintaining high accuracy. The model leverages the
highly efficient HarDNet backbone alongside lightweight Receptive Field Blocks
for computational efficiency, and an auxiliary training mechanism to take full
advantage of the training data for the segmentation quality. Our experiments on
a challenging dataset show that BlazeNeo achieves improvements in latency and
model size while maintaining comparable accuracy against state-of-the-art
methods. When deploying on the Jetson AGX Xavier edge device in INT8 precision,
our BlazeNeo achieves over 155 fps while yielding the best accuracy among all
compared methods.


title:
Paper Plain: Making Medical Research Papers Approachable to Healthcare
  Consumers with Natural Language Processing

abstract:
  When seeking information not covered in patient-friendly documents, like
medical pamphlets, healthcare consumers may turn to the research literature.
Reading medical papers, however, can be a challenging experience. To improve
access to medical papers, we introduce a novel interactive interface-Paper
Plain-with four features powered by natural language processing: definitions of
unfamiliar terms, in-situ plain language section summaries, a collection of key
questions that guide readers to answering passages, and plain language
summaries of the answering passages. We evaluate Paper Plain, finding that
participants who use Paper Plain have an easier time reading and understanding
research papers without a loss in paper comprehension compared to those who use
a typical PDF reader. Altogether, the study results suggest that guiding
readers to relevant passages and providing plain language summaries, or
"gists," alongside the original paper content can make reading medical papers
easier and give readers more confidence to approach these papers.


title:
A Multi-scale Transformer for Medical Image Segmentation: Architectures,
  Model Efficiency, and Benchmarks

abstract:
  Transformers have emerged to be successful in a number of natural language
processing and vision tasks, but their potential applications to medical
imaging remain largely unexplored due to the unique difficulties of this field.
In this study, we present UTNetV2, a simple yet powerful backbone model that
combines the strengths of the convolutional neural network and Transformer for
enhancing performance and efficiency in medical image segmentation. The
critical design of UTNetV2 includes three innovations: (1) We used a hybrid
hierarchical architecture by introducing depthwise separable convolution to
projection and feed-forward network in the Transformer block, which brings
local relationship modeling and desirable properties of CNNs (translation
invariance) to Transformer, thus eliminate the requirement of large-scale
pre-training. (2) We proposed efficient bidirectional attention (B-MHA) that
reduces the quadratic computation complexity of self-attention to linear by
introducing an adaptively updated semantic map. The efficient attention makes
it possible to capture long-range relationship and correct the fine-grained
errors in high-resolution token maps. (3) The semantic maps in the B-MHA allow
us to perform semantically and spatially global multi-scale feature fusion
without introducing much computational overhead. Furthermore, we provide a fair
comparison codebase of CNN-based and Transformer-based on various medical image
segmentation tasks to evaluate the merits and defects of both architectures.
UTNetV2 demonstrated state-of-the-art performance across various settings,
including large-scale datasets, small-scale datasets, 2D and 3D settings.


title: On Testability and Goodness of Fit Tests in Missing Data Models

abstract:
  Significant progress has been made in developing identification and
estimation techniques for missing data problems where modeling assumptions can
be described via a directed acyclic graph. The validity of results using such
techniques rely on the assumptions encoded by the graph holding true; however,
verification of these assumptions has not received sufficient attention in
prior work. In this paper, we provide new insights on the testable implications
of three broad classes of missing data graphical models, and design
goodness-of-fit tests around them. The classes of models explored are:
sequential missing-at-random and missing-not-at-random models which can be used
for modeling longitudinal studies with dropout/censoring, and a kind of no
self-censoring model which can be applied to cross-sectional studies and
surveys.


title:
Combination of survival movement strategies in cyclic game systems
  during an epidemic

abstract:
  Disease outbreaks affect many ecosystems threatening species that also fight
against other natural enemies. We investigate a cyclic game system with $5$
species, whose organisms outcompete according to the rules of a generalised
spatial rock-paper-scissors game, during an epidemic. We study the effects of
behavioural movement strategies that allow individuals of one out of the
species to move towards areas with a low density of disease vectors and a high
concentration of enemies of their enemies. We perform a series of stochastic
simulations to discover the impact of self-preservation strategies in pattern
formation, calculating the species' spatial autocorrelation functions.
Considering organisms with different physical and cognitive abilities, we
compute the benefits of each movement tactic to reduce selection and infection
risks. Our findings show that the maximum profit in terms of territorial
dominance in the cyclic game is achieved if both survival movement strategies
are combined, with individuals prioritising social distancing. In the case of
an epidemic causing symptomatic illness, the drop in infection risk when
organisms identify and avoid disease vectors does not render a rise in the
species population because many refuges are disregarded, limiting the benefits
of safeguarding against natural enemies. Our results may be helpful to the
understanding of the behavioural strategies in ecosystems where organisms adapt
to face living conditions changes.


title: Setting Fair Incentives to Maximize Improvement

abstract:
  We consider the problem of helping agents improve by setting short-term
goals. Given a set of target skill levels, we assume each agent will try to
improve from their initial skill level to the closest target level within reach
or do nothing if no target level is within reach. We consider two models: the
common improvement capacity model, where agents have the same limit on how much
they can improve, and the individualized improvement capacity model, where
agents have individualized limits. Our goal is to optimize the target levels
for social welfare and fairness objectives, where social welfare is defined as
the total amount of improvement, and fairness objectives are considered where
the agents belong to different underlying populations. A key technical
challenge of this problem is the non-monotonicity of social welfare in the set
of target levels, i.e., adding a new target level may decrease the total amount
of improvement as it may get easier for some agents to improve. This is
especially challenging when considering multiple groups because optimizing
target levels in isolation for each group and outputting the union may result
in arbitrarily low improvement for a group, failing the fairness objective.
Considering these properties, we provide algorithms for optimal and
near-optimal improvement for both social welfare and fairness objectives. These
algorithmic results work for both the common and individualized improvement
capacity models. Furthermore, we show a placement of target levels exists that
is approximately optimal for the social welfare of each group. Unlike the
algorithmic results, this structural statement only holds in the common
improvement capacity model, and we show counterexamples in the individualized
improvement capacity model. Finally, we extend our algorithms to learning
settings where we have only sample access to the initial skill levels of
agents.


title:
Investigating the Spatiotemporal Charging Demand and Travel Behavior of
  Electric Vehicles Using GPS Data: A Machine Learning Approach

abstract:
  The increasing market penetration of electric vehicles (EVs) may change the
travel behavior of drivers and pose a significant electricity demand on the
power system. Since the electricity demand depends on the travel behavior of
EVs, which are inherently uncertain, the forecasting of daily charging demand
(CD) will be a challenging task. In this paper, we use the recorded GPS data of
EVs and conventional gasoline-powered vehicles from the same city to
investigate the potential shift in the travel behavior of drivers from
conventional vehicles to EVs and forecast the spatiotemporal patterns of daily
CD. Our analysis reveals that the travel behavior of EVs and conventional
vehicles are similar. Also, the forecasting results indicate that the developed
models can generate accurate spatiotemporal patterns of the daily CD.


title:
Estimating Importation Risk of Covid-19 in Hurricane Evacuations: A
  Prediction Framework Applied to Hurricane Laura in Texas

abstract:
  In August 2020, as Texas was coming down from a large summer COVID-19 surge,
forecasts suggested that Hurricane Laura was tracking towards 6M residents
along the East Texas coastline, threatening to spread COVID-19 across the state
and cause pandemic resurgences. To assist local authorities facing the
dual-threat, we integrated survey expectations of coastal residents and
observed hurricane evacuation rates in a statistical framework that combined
with local pandemic conditions predicts how COVID-19 would spread in response
to a hurricane. For Hurricane Laura, we estimate that 499,500 [90% Credible
Interval (CI): 347,500, 624,000] people evacuated the Texan counties, that no
single county accumulated more than 2.5% of hurricane evacuees, and that there
were 2,900 [90% CI: 1,700, 5,800] exportations of Covid-19 across the state. In
general, reception estimates were concentrated in regions with higher
population densities. Nonetheless, higher importation risk is expected in small
Districts, with a maximum number of importations of 10 per 10,000 residents in
our case study. Overall, we present a flexible and transferable framework that
captures spatial heterogeneity and incorporates geographic components for
predicting population movement in the wake of a natural disaster. As hurricanes
continue to increase in both frequency and strength, our framework can be
deployed in response to anticipated hurricane paths to guide disaster
preparedness and planning.


title:
Learning Cross-Video Neural Representations for High-Quality Frame
  Interpolation

abstract:
  This paper considers the problem of temporal video interpolation, where the
goal is to synthesize a new video frame given its two neighbors. We propose
Cross-Video Neural Representation (CURE) as the first video interpolation
method based on neural fields (NF). NF refers to the recent class of methods
for the neural representation of complex 3D scenes that has seen widespread
success and application across computer vision. CURE represents the video as a
continuous function parameterized by a coordinate-based neural network, whose
inputs are the spatiotemporal coordinates and outputs are the corresponding RGB
values. CURE introduces a new architecture that conditions the neural network
on the input frames for imposing space-time consistency in the synthesized
video. This not only improves the final interpolation quality, but also enables
CURE to learn a prior across multiple videos. Experimental evaluations show
that CURE achieves the state-of-the-art performance on video interpolation on
several benchmark datasets.


title:
Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint
  Segmentation and Motion Prediction in Point Cloud

abstract:
  Environment perception including detection, classification, tracking, and
motion prediction are key enablers for automated driving systems and
intelligent transportation applications. Fueled by the advances in sensing
technologies and machine learning techniques, LiDAR-based sensing systems have
become a promising solution. The current challenges of this solution are how to
effectively combine different perception tasks into a single backbone and how
to efficiently learn the spatiotemporal features directly from point cloud
sequences. In this research, we propose a novel spatiotemporal attention
network based on a transformer self-attention mechanism for joint semantic
segmentation and motion prediction within a point cloud at the voxel level. The
network is trained to simultaneously outputs the voxel level class and
predicted motion by learning directly from a sequence of point cloud datasets.
The proposed backbone includes both a temporal attention module (TAM) and a
spatial attention module (SAM) to learn and extract the complex spatiotemporal
features. This approach has been evaluated with the nuScenes dataset, and
promising performance has been achieved.


title:
Transport coefficients of the quark-gluon plasma at the critical point
  and across the first-order line

abstract:
  A bottom-up Einstein-Maxwell-Dilaton holographic model is used to compute,
for the first time, the behavior of several transport coefficients of the hot
and baryon-rich strongly coupled quark-gluon plasma at the critical point and
also across the first-order phase transition line in the phase diagram. The
observables under study are of the shear and bulk viscosities, baryon
diffusion, thermal conductivity, the jet quenching parameter $\hat{q}$, as well
as the heavy-quark drag force and Langevin diffusion coefficients. These
calculations provide a phenomenologically promising estimate for these
coefficients, given that our model quantitatively reproduces lattice QCD
thermodynamics results, both at zero and finite baryon density, besides
naturally incorporating the nearly-perfect fluidity of the quark-gluon plasma.
We find that the diffusion of baryon charge, and also the shear and bulk
viscosities, are suppressed with increasing baryon density, indicating that the
medium becomes even closer to perfect fluidity at large densities. On the other
hand, the jet quenching parameter and the heavy-quark momentum diffusion are
enhanced with increasing density. The observables display a discontinuity gap
when crossing the first-order phase transition line, while developing an
infinite slope at the critical point. The transition temperatures associated
with different transport coefficients differ in the crossover region but are
found to converge at the critical point.


title: Carrier-phase and IMU based GNSS Spoofing Detection for Ground Vehicles

abstract:
  This paper develops, implements, and validates a powerful single-antenna
carrier-phase-based test to detect Global Navigation Satellite Systems (GNSS)
spoofing attacks on ground vehicles equipped with a low-cost inertial
measurement unit (IMU). Increasingly-automated ground vehicles require precise
positioning that is resilient to unusual natural or accidental events and
secure against deliberate attack. This paper's spoofing detection technique
capitalizes on the carrier-phase fixed-ambiguity residual cost produced by a
well-calibrated carrier-phase-differential GNSS (CDGNSS) estimator that is
tightly coupled with a low-cost IMU. The carrier-phase fixed-ambiguity residual
cost is sensitive at the sub-centimeter-level to discrepancies between measured
carrier phase values and the values predicted by prior measurements and by the
dynamics model, which is based on IMU measurements and on vehicle constraints.
Such discrepancies will arise in a spoofing attack due to the attacker's
practical inability to predict the centimeter-amplitude vehicle movement caused
by roadway irregularities. The effectiveness of the developed spoofing
detection method is evaluated with data captured by a vehicle-mounted sensor
suite in Austin, Texas. The dataset includes both consumer- and
industrial-grade IMU data and a diverse set of multipath environments (open
sky, shallow urban, and deep urban). Artificial worst-case spoofing attacks
injected into the dataset are detected within two seconds.


title:
GA-DRL: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement
  Learning for Robotic Manipulation Tasks

abstract:
  Reinforcement learning (RL) enables agents to make a decision based on a
reward function. However, in the process of learning, the choice of values for
learning algorithm parameters can significantly impact the overall learning
process. In this paper, we proposed a Genetic Algorithm-based Deep
Deterministic Policy Gradient and Hindsight Experience Replay method (called
GA-DRL) to find near-optimal values of learning parameters. We used the
proposed GA-DRL method on fetch-reach, slide, push, pick and place, and door
opening in robotic manipulation tasks. With some modifications, our proposed
GA-DRL method was also applied to the auboreach environment. Our experimental
evaluation shows that our method leads to significantly better performance,
faster than the original algorithm. Also, we provide evidence that GA-DRL
performs better than the existing methods.


title: Mean field Kuramoto models on graphs

abstract:
  One of a classical synchronization model is the Kuramoto model. We propose
both first and second order Kuramoto dynamical models on graphs using discrete
optimal transport dynamics. We analyze the synchronization behaviors for some
examples of Kuramoto models on graphs. We also provide a generalized Hopf-Cole
transformation for discrete optimal transport systems. Focus on the two points
graph, we derive analytical formulas of the Kuramoto dynamics with various
potential induced from entropy functionals. Several numerical examples for the
Kuramoto model on general graphs are presented.


title: Functional reduction of one-loop Feynman integrals with arbitrary masses

abstract:
  A method of functional reduction for the dimensionally regularized one-loop
Feynman integrals with massive propagators is described in detail. The method
is based on a repeated application of the functional relations proposed by the
author. Explicit formulae are given for reducing one-loop scalar integrals to a
simpler ones, the arguments of which are the ratios of polynomials in the
masses and kinematic invariants. We show that a general scalar $n$-point
integral, depending on $n(n+1)/2$ generic masses and kinematic variables, can
be expressed as a linear combination of integrals depending only on $n$
variables. The latter integrals are given explicitly in terms of hypergeometric
functions of $(n-1)$ dimensionless variables. Analytic expressions for the 2-,
3- and 4-point integrals, that depend on the minimal number of variables, were
also obtained by solving the dimensional recurrence relations. The resulting
expressions for these integrals are given in terms of Gauss' hypergeometric
function $_2F_1$, the Appell function $F_1$ and the hypergeometric Lauricella -
Saran function $F_S$. A modification of the functional reduction procedure for
some special values of kinematical variables is considered.


title:
The Concordance Index decomposition: a measure for a deeper
  understanding of survival prediction models

abstract:
  The Concordance Index (C-index) is a commonly used metric in Survival
Analysis to evaluate how good a prediction model is. This paper proposes a
decomposition of the C-Index into a weighted harmonic mean of two quantities:
one for ranking observed events versus other observed events, and the other for
ranking observed events versus censored cases. This decomposition allows a more
fine-grained analysis of the pros and cons of survival prediction methods. The
utility of the decomposition is demonstrated using three benchmark survival
analysis models (Cox Proportional Hazard, Random Survival Forest, and Deep
Adversarial Time-to-Event Network) together with a new variational generative
neural-network-based method (SurVED), which is also proposed in this paper. The
demonstration is done on four publicly available datasets with varying
censoring levels. The analysis with the C-index decomposition shows that all
methods essentially perform equally well when the censoring level is high
because of the dominance of the term measuring the ranking of events versus
censored cases. In contrast, some methods deteriorate when the censoring level
decreases because they do not rank the events versus other events well.


title: Getting There and Back Again

abstract:
  "There and Back Again" (TABA) is a programming pattern where the recursive
calls traverse one data structure and the subsequent returns traverse another.
This article presents new TABA examples, refines existing ones, and formalizes
both their control flow and their data flow using the Coq Proof Assistant. Each
formalization mechanizes a pen-and-paper proof, thus making it easier to "get"
TABA. In addition, this article identifies and illustrates a tail-recursive
variant of TABA, There and Forth Again (TAFA) that does not come back but goes
forth instead with more tail calls.


title:
VaultDB: A Real-World Pilot of Secure Multi-Party Computation within a
  Clinical Research Network

abstract:
  Electronic health records represent a rich and growing source of clinical
data for research. Privacy, regulatory, and institutional concerns limit the
speed and ease of data sharing in this context. We introduce VaultDB, a
framework for securely computing SQL queries over the union of private data
from two or more sources. VaultDB evaluates queries using secure multiparty
computation: cryptographic protocols that securely evaluate a function such
that the only information revealed from running them is the query answer. We
describe the development of a HIPAA-compliant version of this framework using
the Chicago Area Patient Centered Outcomes Research Network (CAPriCORN), a
multi-institutional clinical research network that spans the electronic health
records of nearly 13M patients over hundreds of clinics and hospitals in the
Chicago metropolitan area. Our results from deploying this technology at three
health systems within this network demonstrate its efficiency and scalability
to conduct multiparty clinical research analyses without moving patient records
from their site of origin.


title:
Arbitrarily high order implicit ODE integration by correcting a neural
  network approximation with Newton's method

abstract:
  As a method of universal approximation deep neural networks (DNNs) are
capable of finding approximate solutions to problems posed with little more
constraints than a suitably-posed mathematical system and an objective
function. Consequently, DNNs have considerably more flexibility in applications
than classical numerical methods. On the other hand they offer an uncontrolled
approximation to the sought-after mathematical solution. This suggests that
hybridization of classical numerical methods with DNN-based approximations may
be a desirable approach. In this work a DNN-based approximator inspired by the
physics-informed neural networks (PINNs) methodology is used to provide an
initial guess to a Newton's method iteration of a very-high order implicit
Runge-Kutta (IRK) integration of a nonlinear system of ODEs, namely the Lorenz
system. In the usual approach many explicit timesteps are needed to provide a
guess to the implicit system's nonlinear solver, requiring enough work to make
the IRK method infeasible. The DNN-based approach described in this work
enables large implicit time-steps to be taken to any desired degree of accuracy
for as much effort as it takes to converge the DNN solution to within a few
percent accuracy. This work also develops a general formula for the matrix
elements of the IRK method for an arbitrary quadrature order.


title: Improved iterative methods for solving risk parity portfolio

abstract:
  Risk parity, also known as equal risk contribution, has recently gained
increasing attention as a portfolio allocation method. However, solving
portfolio weights must resort to numerical methods as the analytic solution is
not available. This study improves two existing iterative methods: the cyclical
coordinate descent (CCD) and Newton methods. We enhance the CCD method by
simplifying the formulation using a correlation matrix and imposing an
additional rescaling step. We also suggest an improved initial guess inspired
by the CCD method for the Newton method. Numerical experiments show that the
improved CCD method performs the best and is approximately three times faster
than the original CCD method, saving more than 40% of the iterations.


title:
Understanding Brownian yet non-Gaussian diffusion via long-range
  molecular interactions

abstract:
  In the last years, a few experiments in the fields of biological and soft
matter physics in colloidal suspensions have reported normal diffusion with a
Laplacian probability distribution in the particles displacements (i.e.,
Brownian yet non Gaussian diffusion). To model this behavior different
stochastic models had been proposed, with all of them introducing new random
elements that incorporate our lack of information about the media. Although
these models work in practice, due to their own nature a thorough understanding
of how the media interacts with itself and with the Brownian particle in
Brownian yet non Gaussian diffusion is outside of their aim and scope. For this
reason, a comprehensive mathematical model to explain Brownian yet non Gaussian
diffusion that includes molecular interactions is proposed in this paper. Based
on the theory of interfaces by Gennes and Langevin dynamics, it is shown that
long-range interactions in a weakly interacting fluid and in a microscopic
regime of zero viscosity leads to a Laplacian probability distribution in the
particles displacements. Further, it is shown that a phase transition can
explain a high diffusivity and causes this Laplacian distribution to evolve
towards a Gaussian via a transition probability in the interval of time as it
was observed in experiments. To validate these model predictions, the
experimental data of the Brownian motion of colloidal beads on phospholipid
bilayer by Wang et al. is used and compared with the results of the theory.
This comparison suggests that the proposed model not only is able to explain
qualitatively the Brownian yet non-Gaussian diffusion, but also quantitatively.


title:
Explaining RADAR features for detecting spoofing attacks in Connected
  Autonomous Vehicles

abstract:
  Connected autonomous vehicles (CAVs) are anticipated to have built-in AI
systems for defending against cyberattacks. Machine learning (ML) models form
the basis of many such AI systems. These models are notorious for acting like
black boxes, transforming inputs into solutions with great accuracy, but no
explanations support their decisions. Explanations are needed to communicate
model performance, make decisions transparent, and establish trust in the
models with stakeholders. Explanations can also indicate when humans must take
control, for instance, when the ML model makes low confidence decisions or
offers multiple or ambiguous alternatives. Explanations also provide evidence
for post-incident forensic analysis. Research on explainable ML to security
problems is limited, and more so concerning CAVs. This paper surfaces a
critical yet under-researched sensor data \textit{uncertainty} problem for
training ML attack detection models, especially in highly mobile and
risk-averse platforms such as autonomous vehicles. We present a model that
explains \textit{certainty} and \textit{uncertainty} in sensor input -- a
missing characteristic in data collection. We hypothesize that model
explanation is inaccurate for a given system without explainable input data
quality. We estimate \textit{uncertainty} and mass functions for features in
radar sensor data and incorporate them into the training model through
experimental evaluation. The mass function allows the classifier to categorize
all spoofed inputs accurately with an incorrect class label.


title:
Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine
  Learning

abstract:
  Machine learning techniques including neural networks are popular tools for
materials and chemical scientists with applications that may provide viable
alternative methods in the analysis of structure and energetics of systems
ranging from crystals to biomolecules. However, efforts are less abundant for
prediction of dynamics. Here we explore the ability of three well established
recurrent neural network architectures for forecasting the energetics of a
macromolecular polymer-lipid aggregate solvated in ethyl acetate at ambient
conditions. Data models generated from recurrent neural networks are trained
and tested on nanoseconds-long time series of the intra-macromolecules
potential energy and their interaction energy with the solvent generated from
Molecular Dynamics and containing half million points. Our exhaustive analyses
convey that the three recurrent neural network investigated generate data
models with limited capability of reproducing the energetic fluctuations and
yielding short or long term energetics forecasts with underlying distribution
of points inconsistent with the input series distributions. We propose an in
silico experimental protocol consisting on forming an ensemble of artificial
network models trained on an ensemble of series with additional features from
time series containing pre-clustered time patterns of the original series. The
forecast process improves by predicting a band of forecasted time series with a
spread of values consistent with the molecular dynamics energy fluctuations
span. However, the distribution of points from the band of forecasts is not
optimal. Although the three inspected recurrent neural networks were unable of
generating single models that reproduce the actual fluctuations of the
inspected molecular system energies in thermal equilibrium at the nanosecond
scale, the proposed protocol provides useful estimates of the molecular fate


title:
The Harbourne-Hirschowitz condition and the Anticanonical Orthogonal
  Property for surfaces

abstract:
  In this paper we study the Harbourne-Hirschowitz condition and the
Anticanonical Orthogonal Property for regular surfaces. To do so, we consider
the Kodaira dimension of the surfaces and study the cases based on the
Enriques-Kodaira classification.


title: Partial Regularity for $\mathbb{A}$-quasiconvex Functionals

abstract:
  We establish partial H\"older regularity for (local) generalised minimisers
of variational problems involving strongly quasi-convex integrands of linear
growth, where the full gradient is replaced by a first order homogeneous
differential operator $\mathbb{A}$ with constant coefficients. Working under
the assumption of $\mathbb{A}$ being $\mathbb{C}$-elliptic, this is achieved by
adapting a method recently introduced by Gmeineder (Partial Regularity for
Symmetric Quasiconvex Functionals on BD, J. Math. Pures Appl. 145 (2021), Issue
9, pp. 83--129) and Gmeineder & Kristensen (Partial regularity for BV
Minimizers, Arch. Ration. Mech. Anal. 232 (2019), Issue 3, pp. 1429--1473).


title:
When to Crossover from Earth to Space for Lower Latency Data
  Communications?

abstract:
  For data communications over long distances, optical wireless satellite
networks (OWSNs) can offer lower latency than optical fiber terrestrial
networks (OFTNs). However, when is it beneficial to switch or crossover from an
OFTN to an OWSN for lower latency data communications? In this work, we
introduce a crossover function that enables to find the crossover distance,
i.e., a distance between two points on the surface of the Earth beyond which
switching or crossing over from an OFTN to an OWSN for data communications
between these points is useful in terms of latency. Numerical results reveal
that a higher refractive index of optical fiber (or $i$) in an OFTN and a lower
altitude of satellites (or $h$) in an OWSN result in a shorter crossover
distance. To account for the variation in the end-to-end propagation distance
that occurs over the OWSN, we examine the crossover function in four different
scenarios. Numerical results indicate that the crossover distance varies with
the end-to-end propagation distance over an OWSN and is different for different
scenarios. We calculate the average crossover distance over all scenarios for
different $h$ and $i$ and use it to evaluate the simulation results.
Furthermore, for a comparative analysis of OFTNs and OWSNs in terms of latency,
we study three different OFTNs having different refractive indices and three
different OWSNs having different satellite altitudes in three different
scenarios for long-distance inter-continental data communications, including
connections between New York and Dublin, Sao Paulo and London, and Toronto and
Sydney.


title:
Performance of Distribution Regression with Doubling Measure under the
  seek of Closest Point

abstract:
  We study the distribution regression problem assuming the distribution of
distributions has a doubling measure larger than one. First, we explore the
geometry of any distributions that has doubling measure larger than one and
build a small theory around it. Then, we show how to utilize this theory to
find one of the nearest distributions adaptively and compute the regression
value based on these distributions. Finally, we provide the accuracy of the
suggested method here and provide the theoretical analysis for it.


title:
Preemptive Motion Planning for Human-to-Robot Indirect Placement
  Handovers

abstract:
  As technology advances, the need for safe, efficient, and collaborative
human-robot-teams has become increasingly important. One of the most
fundamental collaborative tasks in any setting is the object handover.
Human-to-robot handovers can take either of two approaches: (1) direct
hand-to-hand or (2) indirect hand-to-placement-to-pick-up. The latter approach
ensures minimal contact between the human and robot but can also result in
increased idle time due to having to wait for the object to first be placed
down on a surface. To minimize such idle time, the robot must preemptively
predict the human intent of where the object will be placed. Furthermore, for
the robot to preemptively act in any sort of productive manner, predictions and
motion planning must occur in real-time. We introduce a novel
prediction-planning pipeline that allows the robot to preemptively move towards
the human agent's intended placement location using gaze and gestures as model
inputs. In this paper, we investigate the performance and drawbacks of our
early intent predictor-planner as well as the practical benefits of using such
a pipeline through a human-robot case study.


title:
Simultaneous Semantic and Instance Segmentation for Colon Nuclei
  Identification and Counting

abstract:
  We address the problem of automated nuclear segmentation, classification, and
quantification from Haematoxylin and Eosin stained histology images, which is
of great relevance for several downstream computational pathology applications.
In this work, we present a solution framed as a simultaneous semantic and
instance segmentation framework. Our solution is part of the Colon Nuclei
Identification and Counting (CoNIC) Challenge. We first train a semantic and
instance segmentation model separately. Our framework uses as backbone HoverNet
and Cascade Mask-RCNN models. We then ensemble the results with a custom
Non-Maximum Suppression embedding (NMS). In our framework, the semantic model
computes a class prediction for the cells whilst the instance model provides a
refined segmentation. We demonstrate, through our experimental results, that
our model outperforms the provided baselines by a large margin.


title:
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for
  Memory-Efficient Graph Convolutional Neural Networks

abstract:
  Graph convolutional neural networks (GCNs) have emerged as a key technology
in various application domains where the input data is relational. A unique
property of GCNs is that its two primary execution stages, aggregation and
combination, exhibit drastically different dataflows. Consequently, prior GCN
accelerators tackle this research space by casting the aggregation and
combination stages as a series of sparse-dense matrix multiplication. However,
prior work frequently suffers from inefficient data movements, leaving
significant performance left on the table. We present GROW, a GCN accelerator
based on Gustavson's algorithm to architect a row-wise product based
sparse-dense GEMM accelerator. GROW co-designs the software/hardware that
strikes a balance in locality and parallelism for GCNs, achieving significant
energy-efficiency improvements vs. state-of-the-art GCN accelerators.


title: Limit distribution theory for smooth $p$-Wasserstein distances

abstract:
  The Wasserstein distance is a metric on a space of probability measures that
has seen a surge of applications in statistics, machine learning, and applied
mathematics. However, statistical aspects of Wasserstein distances are
bottlenecked by the curse of dimensionality, whereby the number of data points
needed to accurately estimate them grows exponentially with dimension. Gaussian
smoothing was recently introduced as a means to alleviate the curse of
dimensionality, giving rise to a parametric convergence rate in any dimension,
while preserving the Wasserstein metric and topological structure. To
facilitate valid statistical inference, in this work, we develop a
comprehensive limit distribution theory for the empirical smooth Wasserstein
distance. The limit distribution results leverage the functional delta method
after embedding the domain of the Wasserstein distance into a certain dual
Sobolev space, characterizing its Hadamard directional derivative for the dual
Sobolev norm, and establishing weak convergence of the smooth empirical process
in the dual space. To estimate the distributional limits, we also establish
consistency of the nonparametric bootstrap. Finally, we use the limit
distribution theory to study applications to generative modeling via minimum
distance estimation with the smooth Wasserstein distance, showing asymptotic
normality of optimal solutions for the quadratic cost.


title: Semantic Sentence Composition Reasoning for Multi-Hop Question Answering

abstract:
  Due to the lack of insufficient data, existing multi-hop open domain question
answering systems require to effectively find out relevant supporting facts
according to each question. To alleviate the challenges of semantic factual
sentences retrieval and multi-hop context expansion, we present a semantic
sentence composition reasoning approach for a multi-hop question answering
task, which consists of two key modules: a multi-stage semantic matching module
(MSSM) and a factual sentence composition module (FSC). With the combination of
factual sentences and multi-stage semantic retrieval, our approach can provide
more comprehensive contextual information for model training and reasoning.
Experimental results demonstrate our model is able to incorporate existing
pre-trained language models and outperform the existing SOTA method on the QASC
task with an improvement of about 9%.


title: On Testability of the Front-Door Model via Verma Constraints

abstract:
  The front-door criterion can be used to identify and compute causal effects
despite the existence of unmeasured confounders between a treatment and
outcome. However, the key assumptions -- (i) the existence of a variable (or
set of variables) that fully mediates the effect of the treatment on the
outcome, and (ii) which simultaneously does not suffer from similar issues of
confounding as the treatment-outcome pair -- are often deemed implausible. This
paper explores the testability of these assumptions. We show that under mild
conditions involving an auxiliary variable, the assumptions encoded in the
front-door model (and simple extensions of it) may be tested via generalized
equality constraints a.k.a Verma constraints. We propose two goodness-of-fit
tests based on this observation, and evaluate the efficacy of our proposal on
real and synthetic data. We also provide theoretical and empirical comparisons
to instrumental variable approaches to handling unmeasured confounding.


title: Do Transformers use variable binding?

abstract:
  Increasing the explainability of deep neural networks (DNNs) requires
evaluating whether they implement symbolic computation. One central symbolic
capacity is variable binding: linking an input value to an abstract variable
held in system-internal memory. Prior work on the computational abilities of
DNNs has not resolved the question of whether their internal processes involve
variable binding. We argue that the reason for this is fundamental, inherent in
the way experiments in prior work were designed. We provide the first
systematic evaluation of the variable binding capacities of the
state-of-the-art Transformer networks BERT and RoBERTa. Our experiments are
designed such that the model must generalize a rule across disjoint subsets of
the input vocabulary, and cannot rely on associative pattern matching alone.
The results show a clear discrepancy between classification and
sequence-to-sequence tasks: BERT and RoBERTa can easily learn to copy or
reverse strings even when trained on task-specific vocabularies that are
switched in the test set; but both models completely fail to generalize across
vocabularies in similar sequence classification tasks. These findings indicate
that the effectiveness of Transformers in sequence modelling may lie in their
extensive use of the input itself as an external "memory" rather than
network-internal symbolic operations involving variable binding. Therefore, we
propose a novel direction for future work: augmenting the inputs available to
circumvent the lack of network-internal variable binding.


title: Symmetry and Asymmetry in the 1+N Coorbital Problem

abstract:
  The relative equilibria of planar Newtonian $N$-body problem become coorbital
around a central mass in the limit when all but one of the masses becomes zero.
We prove a variety of results about the coorbital relative equilibria, with an
emphasis on the relation between symmetries of the configurations and
symmetries in the masses, or lack thereof. We prove that in the $N=4$, $N=6$,
and $N=8$ Newtonian coorbital problems there exist symmetric relative
equilibria with asymmetric positive masses. This result can be generalized to
other homogeneous potentials, and we conjecture similar results hold for larger
even numbers of infinitesimal masses. We prove that some equalities of the
masses in the $1+4$ and $1+5$ coorbital problems imply symmetry of a class of
convex relative equilibria. We also prove there is at most one convex central
configuration of the symmetric $1+5$ problem.


title: Quantum jumbled pattern matching

abstract:
  Let $S_1, S_2 \in \Sigma^*$ strings, we say that $S_1$ {\em jumble match}
$S_2$ if they are permutations of each other. Given a text $T$ of size $N$ and
a string $S \in \Sigma^*$, the problem of \emph{Jumbled Pattern Matching} (JPM)
is to determine all substrings in $T$ jumbled matching $S$. In classical
computing, a widespread conjecture is that JPM requires
$\Omega(N^{2-\epsilon})$ preprocessing time and space for $O(1)$ query time, or
$\Omega(N^{1-\delta})$ query time in the online version, with $\epsilon, \delta
>0$. In this paper, we present a quantum algorithm for the online JPM in
$O(\sqrt{N})$ time.


title: A descriptive approach to higher derived limits

abstract:
  We present a new aspect of the study of higher derived limits. More
precisely, we introduce a complexity measure for the elements of higher derived
limits over the directed set $\Omega$ of functions from $\mathbb{N}$ to
$\mathbb{N}$ and prove that cocycles of this complexity are images of cochains
of the roughly the same complexity. In the course of this work, we isolate a
partition principle for powers $D^{n+1}$ of products of directed sets $D$ and
show that whenever this principle holds, the corresponding derived limit
$\mathrm{lim}^n$ is additive; vanishing results for this limit are the typical
corollary. The formulation of this partition hypothesis synthesizes and
clarifies some recent advances in this area.


title: Dvoretzky-type theorem for locally finite subsets of a Hilbert space

abstract:
  The main result of the paper: Given any $\varepsilon>0$, every locally finite
subset of $\ell_2$ admits a $(1+\varepsilon)$-bilipschitz embedding into an
arbitrary infinite-dimensional Banach space.
  The result is based on two results which are of independent interest:
  (1) A direct sum of two finite-dimensional Euclidean spaces contains a
sub-sum of a controlled dimension which is $\varepsilon$-close to a direct sum
with respect to a $1$-unconditional basis in a two-dimensional space.
  (2) For any finite-dimensional Banach space $Y$ and its direct sum $X$ with
itself with respect to a $1$-unconditional basis in a two-dimensional space,
there exists a $(1+\varepsilon)$-bilipschitz embedding of $Y$ into $X$ which on
a small ball coincides with the identity map onto the first summand and on a
complement of a large ball coincides with the identity map onto the second
summand.


title: Nonlocal diffusion models with consistent local and fractional limits

abstract:
  For some spatially nonlocal diffusion models with a finite range of nonlocal
interactions measured by a positive parameter $\delta$, we review their
formulation defined on a bounded domain subject to various conditions that
correspond to some inhomogeneous data. We consider their consistency to similar
inhomogeneous boundary value problems of classical partial differential
equation (PDE) models as the nonlocal interaction kernel gets localized in the
local $\delta\to 0$ limit, and at the same time, for rescaled fractional type
kernels, to corresponding inhomogeneous nonlocal boundary value problems of
fractional equations in the global $\delta\to \infty$ limit. Such discussions
help to delineate issues related to nonlocal problems defined on a bounded
domain with inhomogeneous data.


title: Aspects of $Z_{cs}(3985)$ and $Z_{cs}(4000)$

abstract:
  In the present work we investigate the $\eta_c K$, $J/\psi K$, $\eta_c K^*$
and $J/\psi K^*$ hidden-charm decay modes for the $c\bar{c}s\bar{u}$ four-quark
system in the molecular and compact tetraquark scenarios using the
quark-exchange model. Our theoretical results indicate that if the newly
observed states $Z_{cs}(3985)$ and $Z_{cs}(4000)$ are two different states,
$Z_{cs}(4000)$ may be interpreted as the mixture
$\frac{1}{\sqrt{2}}(D^0D_s^{*-}+D^{*0}D_s^{-})$ of which the $J/\psi K$ partial
decay width is about $\Gamma\sim2.89$ MeV, while $Z_{cs}(3985)$ may be
explained as the mixture $\frac{1}{\sqrt{2}}(-D^0D_s^{*-}+D^{*0}D_s^{-})$ of
which the $J/\psi K$ partial decay width is small to zero. Moreover, if the
state $Z_{cs}(4000)$ can be explained as the mixed state
$\frac{1}{\sqrt{2}}(D^0D_s^{*-}+D^{*0}D_s^{-})$ indeed, the partial decay width
ratio between $J/\psi K$ and $\eta_cK^*$ is close to unit, which indicates the
decay channel $\eta_cK^*$ may be a ideal channel as well to decode the inner
structure of $Z_{cs}(4000)$. In addition, the partial decay width for the
tensor molecular state $|D^{*0}D_s^{*-}\rangle_{2^+}$ decaying into $J/\psi
K^*$ can reach up to a few MeV, which shows this tensor molecular state has a
good potential to be observed in this decay channel.


title: Baire spaces of homogeneous structures in which Borel sets are Ramsey

abstract:
  We prove that for any homogeneous structure $\mathbf{K}$ in a language with
finitely many relation symbols of arity at most two satisfying SDAP$^+$, there
are natural topological spaces of subcopies of $\mathbf{K}$, forming subspaces
of the Baire space, in which all Borel sets are Ramsey. As a corollary, we
obtain an analogue of the Nash-Williams Theorem which recovers exact big Ramsey
degrees for these structures, answering a question raised by Todorcevic at the
2019 Luminy Workshop on Set Theory. Moreover, we show that the rationals and
similar homogeneous structures satisfy an analogue of the Ellentuck theorem.


title: On the functional central limit theorem with mean-uncertainty

abstract:
  We introduce a new basic model for independent and identical distributed
sequence on the canonical space
$(\mathbb{R}^\mathbb{N},\mathcal{B}(\mathbb{R}^\mathbb{N}))$ via probability
kernels with model uncertainty. Thanks to the well-defined upper and lower
variances, we obtain a new functional central limit theorem with
mean-uncertainty by the means of martingale central limit theorem and stability
of stochastic integral in the classical probability theory. Then we extend it
from the canonical space to the general sublinear expectation space. The
corresponding proofs are purely probabilistic and do not rely on the nonlinear
partial differential equation.


title:
A Standardized Pipeline for Colon Nuclei Identification and Counting
  Challenge

abstract:
  Nuclear segmentation and classification is an essential step for
computational pathology. TIA lab from Warwick University organized a nuclear
segmentation and classification challenge (CoNIC) for H&E stained
histopathology images in colorectal cancer with two highly correlated tasks,
nuclei segmentation and classification task and cellular composition task.
There are a few obstacles we have to address in this challenge, 1) limited
training samples, 2) color variation, 3) imbalanced annotations, 4) similar
morphological appearance among classes. To deal with these challenges, we
proposed a standardized pipeline for nuclear segmentation and classification by
integrating several pluggable components. First, we built a GAN-based model to
automatically generate pseudo images for data augmentation. Then we trained a
self-supervised stain normalization model to solve the color variation problem.
Next we constructed a baseline model HoVer-Net with cost-sensitive loss to
encourage the model pay more attention on the minority classes. According to
the results of the leaderboard, our proposed pipeline achieves 0.40665 mPQ+
(Rank 49th) and 0.62199 r2 (Rank 10th) in the preliminary test phase.


title:
Enhancing Local Feature Learning for 3D Point Cloud Processing using
  Unary-Pairwise Attention

abstract:
  We present a simple but effective attention named the unary-pairwise
attention (UPA) for modeling the relationship between 3D point clouds. Our idea
is motivated by the analysis that the standard self-attention (SA) that
operates globally tends to produce almost the same attention maps for different
query positions, revealing difficulties for learning query-independent and
query-dependent information jointly. Therefore, we reformulate the SA and
propose query-independent (Unary) and query-dependent (Pairwise) components to
facilitate the learning of both terms. In contrast to the SA, the UPA ensures
query dependence via operating locally. Extensive experiments show that the UPA
outperforms the SA consistently on various point cloud understanding tasks
including shape classification, part segmentation, and scene segmentation.
Moreover, simply equipping the popular PointNet++ method with the UPA even
outperforms or is on par with the state-of-the-art attention-based approaches.
In addition, the UPA systematically boosts the performance of both standard and
modern networks when it is integrated into them as a compositional module.


title: Oncology Dose Finding Using Approximate Bayesian Computation Design

abstract:
  In the development of new cancer treatment, an essential step is to determine
the maximum tolerated dose (MTD) via phase I clinical trials. Generally
speaking, phase I trial designs can be classified as either model-based or
algorithm-based approaches. Model-based phase I designs are typically more
efficient by using all observed data, while there is a potential risk of model
misspecification that may lead to unreliable dose assignment and incorrect MTD
identification. In contrast, most of the algorithm-based designs are less
efficient in using cumulative information, because they tend to focus on the
observed data in the neighborhood of the current dose level for dose movement.
To use the data more efficiently yet without any model assumption, we propose a
novel approximate Bayesian computation (ABC) approach for phase I trial design.
Not only is the ABC design free of any dose--toxicity curve assumption, but it
can also aggregate all the available information accrued in the trial for dose
assignment. Extensive simulation studies demonstrate its robustness and
efficiency compared with other phase I designs. We apply the ABC design to the
MEK inhibitor selumetinib trial to demonstrate its satisfactory performance.
The proposed design can be a useful addition to the family of phase I clinical
trial designs due to its simplicity, efficiency and robustness.


title:
Chemical variations across the TMC-1 boundary: molecular tracers from
  translucent phase to dense phase

abstract:
  We investigated the chemical evolutions of gas phase and grain surface
species across the Taurus molecular cloud-1 (TMC-1) filament from translucent
phase to dense phase. By comparing observations with modeling results from an
up-to-date chemical network, we examined the conversion processes for the
carbon-, oxygen-, nitrogen- and sulfur-bearing species, i.e.from their initial
atomic form to their main molecular reservoir form both in the gas phase and on
the grain surface. The conversion processes were found to depend on the species
and A$_V$. The effect of initial carbon to oxygen elemental abundances ratio
(C/O) by varying O on the chemistry was explored, and an initial carbon
elemental abundance of 2.5 $\times$ 10$^{-4}$ and a C/O ratio of 0.5 could best
reproduce the abundances of most observed molecules at TMC-1 CP, where more
than 90 molecules have been identified. Based on the TMC-1 condition, we
predicted a varied grain ice composition during the evolutions of molecular
clouds, with H$_2$O ice as the dominant ice composition at A$_V$ $>$ 4 mag,
CO$_2$ ice as the dominant ice composition at A$_V$ $<$ 4 mag, while CO ice
severely decreased at A$_V$ around 4--5 mag.