title: Teaching Philosophy and Science of Space Exploration (PoSE)

abstract:
  Capitalizing on the enthusiasm about space science in the general public, our
goal as an interdisciplinary group of scholars is to design and teach a new
team-taught interdisciplinary course, "Philosophy and Science of Space
Exploration (PoSE)" at the University of Texas at San Antonio (UTSA) where we
currently teach. We believe that this course will not only help overcome
disciplinary silos to advance our understanding of space and critically examine
its ethical ramifications, but also will better educate the public on how
science works and help overcome the science skepticism that has unfortunately
become more prominent in recent years. In what follows, we first juxtapose two
seemingly contradictory trends: increased interest in space science on the one
hand and increased skepticism about and distrust in science on the other. We
then turn to how our anticipated Philosophy and Science of Space Exploration
(PoSE) course will develop tools that could dismantle distrust in science while
also enhancing the scientific and philosophical understandings of space
science. We explain the content and the questions we will examine in POSE and
conclude with how we will measure our success and progress.


title: Rational twisted series

abstract:
  Rational twisted power series over a (commutative) field are studied. We give
several characterizations of such series, which are similar to the classical
results concerning rational power series over a commutative field. In
particular, we prove a version of Kronecker's lemma for the rationality of
twisted power series.


title:
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural
  Networks

abstract:
  Multiple Instance Learning (MIL) is a weakly supervised learning problem
where the aim is to assign labels to sets or bags of instances, as opposed to
traditional supervised learning where each instance is assumed to be
independent and identically distributed (IID) and is to be labeled
individually. Recent work has shown promising results for neural network models
in the MIL setting. Instead of focusing on each instance, these models are
trained in an end-to-end fashion to learn effective bag-level representations
by suitably combining permutation invariant pooling techniques with neural
architectures. In this paper, we consider modelling the interactions between
bags using a graph and employ Graph Neural Networks (GNNs) to facilitate
end-to-end learning. Since a meaningful graph representing dependencies between
bags is rarely available, we propose to use a Bayesian GNN framework that can
generate a likely graph structure for scenarios where there is uncertainty in
the graph or when no graph is available. Empirical results demonstrate the
efficacy of the proposed technique for several MIL benchmark tasks and a
distribution regression task.


title: Continual Auxiliary Task Learning

abstract:
  Learning auxiliary tasks, such as multiple predictions about the world, can
provide many benefits to reinforcement learning systems. A variety of
off-policy learning algorithms have been developed to learn such predictions,
but as yet there is little work on how to adapt the behavior to gather useful
data for those off-policy predictions. In this work, we investigate a
reinforcement learning system designed to learn a collection of auxiliary
tasks, with a behavior policy learning to take actions to improve those
auxiliary predictions. We highlight the inherent non-stationarity in this
continual auxiliary task learning problem, for both prediction learners and the
behavior learner. We develop an algorithm based on successor features that
facilitates tracking under non-stationary rewards, and prove the separation
into learning successor features and rewards provides convergence rate
improvements. We conduct an in-depth study into the resulting multi-prediction
learning system.


title:
ProtoSound: A Personalized and Scalable Sound Recognition System for
  Deaf and Hard-of-Hearing Users

abstract:
  Recent advances have enabled automatic sound recognition systems for deaf and
hard of hearing (DHH) users on mobile devices. However, these tools use
pre-trained, generic sound recognition models, which do not meet the diverse
needs of DHH users. We introduce ProtoSound, an interactive system for
customizing sound recognition models by recording a few examples, thereby
enabling personalized and fine-grained categories. ProtoSound is motivated by
prior work examining sound awareness needs of DHH people and by a survey we
conducted with 472 DHH participants. To evaluate ProtoSound, we characterized
performance on two real-world sound datasets, showing significant improvement
over state-of-the-art (e.g., +9.7% accuracy on the first dataset). We then
deployed ProtoSound's end-user training and real-time recognition through a
mobile application and recruited 19 hearing participants who listened to the
real-world sounds and rated the accuracy across 56 locations (e.g., homes,
restaurants, parks). Results show that ProtoSound personalized the model
on-device in real-time and accurately learned sounds across diverse acoustic
contexts. We close by discussing open challenges in personalizable sound
recognition, including the need for better recording interfaces and algorithmic
improvements.


title:
Superconducting Instabilities in Strongly-Correlated Infinite-Layer
  Nickelates

abstract:
  The discovery of superconductivity in infinite-layer nickelates has added a
new family of materials to the fascinating growing class of unconventional
superconductors. By incorporating the strongly correlated multi-orbital nature
of the low-energy electronic degrees of freedom, we compute the leading
superconducting instability from magnetic fluctuations relevant for
infinite-layer nickelates. Specifically, by properly including the doping
dependence of the Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals as well as the
self-doping band, we uncover a transition from $d$-wave pairing symmetry to
nodal $s_\pm$ superconductivity, driven by strong fluctuations in the
$d_{z^2}$-dominated orbital states. We discuss the properties of the resulting
superconducting condensates in light of recent tunneling and penetration depth
experiments probing the detailed superconducting gap structure of these
materials.


title:
FlowSense: Monitoring Airflow in Building Ventilation Systems Using
  Audio Sensing

abstract:
  Proper indoor ventilation through buildings' heating, ventilation, and air
conditioning (HVAC) systems has become an increasing public health concern that
significantly impacts individuals' health and safety at home, work, and school.
While much work has progressed in providing energy-efficient and user comfort
for HVAC systems through IoT devices and mobile-sensing approaches, ventilation
is an aspect that has received lesser attention despite its importance. With a
motivation to monitor airflow from building ventilation systems through
commodity sensing devices, we present FlowSense, a machine learning-based
algorithm to predict airflow rate from sensed audio data in indoor spaces. Our
ML technique can predict the state of an air vent-whether it is on or off-as
well as the rate of air flowing through active vents. By exploiting a low-pass
filter to obtain low-frequency audio signals, we put together a
privacy-preserving pipeline that leverages a silence detection algorithm to
only sense for sounds of air from HVAC air vent when no human speech is
detected. We also propose the Minimum Persistent Sensing (MPS) as a
post-processing algorithm to reduce interference from ambient noise, including
ongoing human conversation, office machines, and traffic noises. Together,
these techniques ensure user privacy and improve the robustness of FlowSense.
We validate our approach yielding over 90% accuracy in predicting vent status
and 0.96 MSE in predicting airflow rate when the device is placed within 2.25
meters away from an air vent. Additionally, we demonstrate how our approach as
a mobile audio-sensing platform is robust to smartphone models, distance, and
orientation. Finally, we evaluate FlowSense privacy-preserving pipeline through
a user study and a Google Speech Recognition service, confirming that the audio
signals we used as input data are inaudible and inconstructible.


title:
Harmonic analysis operators associated with Laguerre polynomial
  expansions on variable Lebesgue spaces

abstract:
  In this paper we give sufficient conditions on a measurable function
$p:(0,\infty)^n\rightarrow [1,\infty)$ in order that harmonic analysis
operators (maximal operators, Riesz transforms, Littlewood--Paley functions and
multipliers) associated with $\alpha$-Laguerre polynomial expansions are
bounded on the variable Lebesgue space $L^{p(\cdot)} ((0,\infty)^n,
\mu_\alpha)$, where $d\mu_\alpha (x)=2^n\prod_{j=1}^n \frac{x_j^{2\alpha_j+1}
e^{-x_j^2}}{\Gamma(\alpha_j+1)} dx$, being $\alpha=(\alpha_1, \dots,
\alpha_n)\in [0,\infty)^n$ and $x=(x_1,\dots,x_n)\in (0,\infty)^n$.


title:
Parametric Mie resonances and Kerker effects in time-modulated
  scatterers

abstract:
  We provide a theoretical description of light scattering by a spherical
particle whose permittivity is modulated in time at twice the frequency of the
incident light. Such a particle acts as a finite-sized photonic time crystal
and, despite its sub-wavelength spatial extent, can host optical parametric
amplification. Conditions of parametric Mie resonances in the sphere are
derived. We show that control of the temporal modulation strength provides a
qualitatively new route to tailor the far-field pattern of a scatterer. Two
characteristic spheres are designed, providing amplification of scattered light
and satisfying nearly ideally the first and second Kerker scattering
conditions.


title: Excitons in mesoscopically reconstructed moir\'e heterostructures

abstract:
  Moir\'e effects in twisted or lattice-incommensurate vertical assemblies of
two-dimensional crystals give rise to a new class of quantum materials with
rich transport and optical phenomena, including correlated electron physics in
flat bands of bilayer graphene and moir\'e excitons in semiconductor
heterostructures. These phenomena arise from modulations of interlayer
hybridization on the nanoscale of spatially varying atomic registries of
moir\'e supercells. Due to finite elasticity, however, lattices of
marginally-twisted homobilayers and heterostructures can transform from moir\'e
to periodically reconstructed patterns with triangular or hexagonal tiling.
Here, we expand the notion of nanoscale lattice reconstruction to the
mesoscopic scale of extended samples and demonstrate rich consequences in
optical studies of excitons in MoSe$_2$-WSe$_2$ heterostructures with parallel
and antiparallel alignment. Our results provide a unified perspective on
diverse and partly controversial signatures of moir\'e excitons in
semiconductor heterostructures by identifying domains with exciton properties
of distinct effective dimensionality and establish mesoscopic reconstruction as
a compelling feature of real samples and devices with inherent finite-size
effects and disorder. Generalized to stacks of other two-dimensional materials,
this notion of mesoscale domain formation with emergent topological defects and
percolation networks will instructively expand our understanding of fundamental
electronic, optical, and magnetic properties of van der Waals heterostructures.


title: Communicating Robot Conventions through Shared Autonomy

abstract:
  When humans control robot arms these robots often need to infer the human's
desired task. Prior research on assistive teleoperation and shared autonomy
explores how robots can determine the desired task based on the human's
joystick inputs. In order to perform this inference the robot relies on an
internal mapping between joystick inputs and discrete tasks: e.g., pressing the
joystick left indicates that the human wants a plate, while pressing the
joystick right indicates a cup. This approach works well after the human
understands how the robot interprets their inputs -- but inexperienced users
still have to learn these mappings through trial and error! Here we recognize
that the robot's mapping between tasks and inputs is a convention. There are
multiple, equally efficient conventions that the robot could use: rather than
passively waiting for the human, we introduce a shared autonomy approach where
the robot actively reveals its chosen convention. Across repeated interactions
the robot intervenes and exaggerates the arm's motion to demonstrate more
efficient inputs while also assisting for the current task. We compare this
approach to a state-of-the-art baseline -- where users must identify the
convention by themselves -- as well as written instructions. Our user study
results indicate that modifying the robot's behavior to reveal its convention
outperforms the baselines and reduces the amount of time that humans spend
controlling the robot. See videos of our user study here:
https://youtu.be/jROTVOp469I


title:
Nonconvex Extension of Generalized Huber Loss for Robust Learning and
  Pseudo-Mode Statistics

abstract:
  We propose an extended generalization of the pseudo Huber loss formulation.
We show that using the log-exp transform together with the logistic function,
we can create a loss which combines the desirable properties of the strictly
convex losses with robust loss functions. With this formulation, we show that a
linear convergence algorithm can be utilized to find a minimizer. We further
discuss the creation of a quasi-convex composite loss and provide a
derivative-free exponential convergence rate algorithm.


title:
An LLVM-based C++ Compiler Toolchain for Variational Hybrid
  Quantum-Classical Algorithms and Quantum Accelerators

abstract:
  Variational algorithms are a representative class of quantum computing
workloads that combine quantum and classical computing. This paper presents an
LLVM-based C++ compiler toolchain to efficiently execute variational hybrid
quantum-classical algorithms on a computational system in which the quantum
device acts as an accelerator. We introduce a set of extensions to the C++
language for programming these algorithms. We define a novel Executable and
Linking Format (ELF) for Quantum and create a quantum device compiler component
in the LLVM framework to compile the quantum part of the C++ source and reuse
the host compiler in the LLVM framework to compile the classical computing part
of the C++ source. A variational algorithm runs a quantum circuit repeatedly,
each time with different gate parameters. We add to the quantum runtime the
capability to execute dynamically a quantum circuit with different parameters.
Thus, programmers can call quantum routines the same way as classical routines.
With these capabilities, a variational hybrid quantum-classical algorithm can
be specified in a single-source code and only needs to be compiled once for all
iterations. The single compilation significantly reduces the execution latency
of variational algorithms. We evaluate the framework's performance by running
quantum circuits that prepare Thermofield Double (TFD) states, a
quantum-classical variational algorithm.


title:
On the Hilbert scheme of the moduli space of torsion free sheaves on
  surfaces

abstract:
  The aim of this paper is to determine a bound of the dimension of an
irreducible component of the Hilbert scheme of the moduli space of torsion-free
sheaves on surfaces. Let $X$ be a non-singular irreducible complex surface and
let $E$ be a vector bundle of rank $n$ on $X$. We use the $m$-elementary
transformation of $E$ at a point $x \in X$ to show that there exists an
embedding from the Grassmannian variety $\mathbb{G}(E_x,m)$ into the moduli
space of torsion-free sheaves $\mathfrak{M}_{X,H}(n;c_1,c_2+m)$ which induces
an injective morphism from $X \times M_{X,H}(n;c_1,c_2)$ to $Hilb_{\,
\mathfrak{M}_{X,H}(n;c_1,c_2+m)}$.


title:
Robust Hierarchical Patterns for identifying MDD patients: A Multisite
  Study

abstract:
  Many supervised machine learning frameworks have been proposed for disease
classification using functional magnetic resonance imaging (fMRI) data,
producing important biomarkers. More recently, data pooling has flourished,
making the result generalizable across a large population. But, this success
depends on the population diversity and variability introduced due to the
pooling of the data that is not a primary research interest. Here, we look at
hierarchical Sparse Connectivity Patterns (hSCPs) as biomarkers for major
depressive disorder (MDD). We propose a novel model based on hSCPs to predict
MDD patients from functional connectivity matrices extracted from resting-state
fMRI data. Our model consists of three coupled terms. The first term decomposes
connectivity matrices into hierarchical low-rank sparse components
corresponding to synchronous patterns across the human brain. These components
are then combined via patient-specific weights capturing heterogeneity in the
data. The second term is a classification loss that uses the patient-specific
weights to classify MDD patients from healthy ones. Both of these terms are
combined with the third term, a robustness loss function to improve the
reproducibility of hSCPs. This reduces the variability introduced due to site
and population diversity (age and sex) on the predictive accuracy and pattern
stability in a large dataset pooled from five different sites. Our results show
the impact of diversity on prediction performance. Our model can reduce
diversity and improve the predictive and generalizing capability of the
components. Finally, our results show that our proposed model can robustly
identify clinically relevant patterns characteristic of MDD with high
reproducibility.


title:
Geometrical interpretation of the argument of weak values of general
  observables in N-level quantum systems

abstract:
  Observations in quantum weak measurements are determined by complex numbers
called weak values. We present a geometrical interpretation of the argument of
weak values of general Hermitian observables in $N$-dimensional quantum systems
in terms of geometric phases. We formulate an arbitrary weak value in function
of three real vectors on the unit sphere in $N^2-1$ dimensions, $S^{N^2-2}$.
These vectors are linked to the initial and final states, and to the weakly
measured observable, respectively. We express pure states in the complex
projective space of $N-1$ dimensions, $\mathbb{C}\textrm{P}^{N-1}$, which has a
non-trivial representation as a $2N-2$ dimensional submanifold of $S^{N^2-2}$
(a generalization of the Bloch sphere for qudits). The argument of the weak
value of a projector on a pure state of an $N$-level quantum system describes a
geometric phase associated to the symplectic area of the geodesic triangle
spanned by the vectors representing the pre-selected state, the projector and
the post-selected state in $\mathbb{C}\textrm{P}^{N-1}$. We then proceed to
show that the argument of the weak value of a general observable is equivalent
to the argument of an effective Bargmann invariant. Hence, we extend the
geometrical interpretation of projector weak values to weak values of general
observables. In particular, we consider the generators of SU($N$) given by the
generalized Gell-Mann matrices. Finally, we study in detail the case of the
argument of weak values of general observables in two-level systems and we
illustrate weak measurements in larger dimensional systems by considering
projectors on degenerate subspaces, as well as Hermitian quantum gates.


title: Bordered invariants of pairs of sutured manifolds with torus boundary

abstract:
  We establish a framework for extending invariants of sutured manifolds to
invariants of pairs of sutured manifolds who differ by attaching a basic slice
along a torus boundary component. In the particular case of (bordered-)sutured
Floer homology we show that this recovers the bordered-sutured Floer homology
of a corresponding bordered-sutured three-manifold - though the framework is
broad enough that it should be applicable in the setting of other invariants of
sutured manifolds.


title:
On the Rate of Convergence of Payoff-based Algorithms to Nash
  Equilibrium in Strongly Monotone Games

abstract:
  We derive the rate of convergence to Nash equilibria for the payoff-based
algorithm proposed in \cite{tat_kam_TAC}. These rates are achieved under the
standard assumption of convexity of the game, strong monotonicity and
differentiability of the pseudo-gradient. In particular, we show the algorithm
achieves $O(\frac{1}{T})$ in the two-point function evaluating setting and
$O(\frac{1}{\sqrt{T}})$ in the one-point function evaluation under additional
requirement of Lipschitz continuity of the pseudo-gradient. These rates are to
our knowledge the best known rates for the corresponding problem classes.


title:
Criterion of Bari basis property for $2 \times 2$ Dirac-type operators
  with strictly regular boundary conditions

abstract:
  The paper is concerned with the Bari basis property of a boundary value
problem associated in $L^2([0,1]; \mathbb{C}^2)$ with the following $2 \times
2$ Dirac-type equation for $y = {\rm col}(y_1, y_2)$: $$L_U(Q) y =-i B^{-1} y'
+ Q(x) y = \lambda y , \quad B = \begin{pmatrix} b_1 & 0 \\ 0 & b_2
\end{pmatrix}, \quad b_1 < 0 < b_2, $$ with a potential matrix $Q \in
L^2([0,1]; \mathbb{C}^{2 \times 2})$ and subject to the strictly regular
boundary conditions $Uy :=\{U_1, U_2\}y=0$. If $b_2 = -b_1 =1$ this equation is
equivalent to one dimensional Dirac equation. We show that the system of root
vectors $\{f_n\}_{n \in \mathbb{Z}}$ of the operator $L_U(Q)$ forms a Bari
basis in $L^2([0,1]; \mathbb{C}^2)$ if and only if the unperturbed operator
$L_U(0)$ is self-adjoint. We also give explicit conditions for this in terms of
coefficients in the boundary conditions. The Bari basis criterion is a
consequence of our more general result: Let $Q \in L^p([0,1]; \mathbb{C}^{2
\times 2})$, $p \in [1,2]$, boundary conditions be strictly regular, and let
$\{g_n\}_{n \in \mathbb{Z}}$ be the sequence biorthogonal to the system of root
vectors $\{f_n\}_{n \in \mathbb{Z}}$ of the operator $L_U(Q)$. Then $$ \{\|f_n
- g_n\|_2\}_{n \in \mathbb{Z}} \in (\ell^p(\mathbb{Z}))^*
\quad\Leftrightarrow\quad L_U(0) = L_U(0)^*. $$ These abstract results are
applied to non-canonical initial-boundary value problem for a damped string
equation.


title:
Incorporating social norms into a configurable agent-based model of the
  decision to perform commuting behaviour

abstract:
  Introduction: Active commuting has been recommended as a method to increase
population physical activity, but evidence is mixed. Social norms related to
travel behaviour may influence the uptake of active commuting interventions but
are rarely considered in the design and evaluation of interventions.
  Methods: We developed an agent-based model that incorporates social norms
related to travel behaviour and demonstrate the utility of this through
implementing car-free Wednesdays. A synthetic population of Waltham Forest,
London, UK was generated using a microsimulation approach with data from the UK
Census 2011 and UK HLS datasets. An agent-based model was created using this
synthetic population which modelled how the actions of peers and neighbours,
subculture, habit, weather, bicycle ownership, car ownership, environmental
supportiveness, and congestion affect the decision to travel between four
modes: walking, cycling, driving, and taking public transport.
  Results: In the control scenario, the odds of active travel were plausible at
0.091 (89% HPDI: [0.091, 0.091]). Compared to the control scenario, the odds of
active travel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the
intervention scenario, on non-car-free days; the effect of the intervention is
sustained to non-car-free days.
  Discussion: While these results demonstrate the utility of our agent-based
model, rather than aim to make accurate predictions, they do suggest that by
there being a 'nudge' of car-free days, there may be a sustained change in
active commuting behaviour. The model is a useful tool for investigating the
effect of how social networks and social norms influence the effectiveness of
various interventions. If configured using real-world built environment data,
it may be useful for investigating how social norms interact with the built
environment to cause the emergence of commuting conventions.


title: Sharp universal rate for stable blow-up of corotational wave maps

abstract:
  We consider the energy-critical (corotational) 1-equivariant wave maps into
the two-sphere. By the seminal work [53] of Rapha\"el and Rodnianski, there is
an open set of initial data whose forward-in-time development blows up in
finite time with the blow-up rate
$\lambda(t)=(T-t)e^{-\sqrt{|\log(T-t)|}+O(1)}$. In this paper, we show that
this $e^{O(1)}$-factor in fact converges to the universal constant $2e^{-1}$,
and hence these solutions contract at the universal rate
$\lambda(t)=2e^{-1}(T-t)e^{-\sqrt{|\log(T-t)|}}(1+o_{t\to T}(1))$. Our proof is
inspired by recent works on type-II blow-up dynamics for parabolic equations.
The key improvement is in the construction of an explicit invariant subspace
decomposition for the linearized operator perturbed by the scaling generator in
the dispersive case, from which we obtain a more precise ODE system determining
$\lambda(t)$.


title: Generalized effective completeness for continuous logic

abstract:
  In this paper, we present a generalized effective completeness theorem for
continuous logic. The primary result is that any continuous theory is satisfied
in a structure which admits a presentation of the same Turing degree. It then
follows that any decidable theory is satisfied by a computably presentable
structure. This modifies and extends previous partial effective completeness
theorems for continuous logic given by Calvert and Didehvar, Ghasemloo, and
Pourmahdian.


title: Axion Electrodynamics and the Axionic Casimir Effect

abstract:
  A general scheme for axion electrodynamics is given, in which a surrounding
medium of constant permittivity and permeability is assumed. Then, as an
application we give simple numerical estimates for the electromagnetic current
density produced by the electrically neutral time-dependent axions $a=a(t)$ in
a strong magnetic field. As is known, the assumption $a=a(t)$ is common under
astrophysical conditions. In the third part of the paper, we consider the
implications by assuming instead an axion amplitude $a(z)$ depending on one
coordinate $z$ only. If such an axion field is contained within two large metal
plates one obtains an axion-generated splitting of the eigenmodes for the
dispersion relation. These modes yield equal, though opposite, contributions to
the pressure on the plates. We calculate the magnitude of the splitting effect,
in a simple one-dimensional model.


title: The Weighted Ambient Metric

abstract:
  We prove the existence and uniqueness of weighted ambient metrics and
weighted Poincar\'e metrics for smooth metric measure spaces.


title: Parallel MCMC Without Embarrassing Failures

abstract:
  Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel
computing to scale Bayesian inference to large datasets by using a two-step
approach. First, MCMC is run in parallel on (sub)posteriors defined on data
partitions. Then, a server combines local results. While efficient, this
framework is very sensitive to the quality of subposterior sampling. Common
sampling problems such as missing modes or misrepresentation of low-density
regions are amplified -- instead of being corrected -- in the combination
phase, leading to catastrophic failures. In this work, we propose a novel
combination strategy to mitigate this issue. Our strategy, Parallel Active
Inference (PAI), leverages Gaussian Process (GP) surrogate modeling and active
learning. After fitting GPs to subposteriors, PAI (i) shares information
between GP surrogates to cover missing modes; and (ii) uses active sampling to
individually refine subposterior approximations. We validate PAI in challenging
benchmarks, including heavy-tailed and multi-modal posteriors and a real-world
application to computational neuroscience. Empirical results show that PAI
succeeds where previous methods catastrophically fail, with a small
communication overhead.


title: Gluing Formulas for Volume Forms on Representation Varieties of Surfaces

abstract:
  Let $\Sigma_{g,n}$ be a compact oriented surface with genus $g\geq 2$
bordered by $n$ circles. Due to Witten, the twisted Reidemeister torsion
coincides with a power of the Atiyah-Bott-Goldman-Narasimhan symplectic form on
the space of representations of $\pi_1(\Sigma_{g,0})$ in any semi-simple Lie
group. In the present paper, we first obtain a multiplicative gluing formula
for the twisted Reidemeister torsion of $\Sigma_{g,0}$ in terms of torsions of
$\Sigma_{2,2},$ $\Sigma_{2,1},$ and boundary circles $\mathbb{S}^1.$ Then, by
using Heusener and Porti's results on $\Sigma_{g,n},$ we show that the
symplectic volume form on the representation variety of $\Sigma_{g,0}$ can be
expressed as a product of the holomorphic symplectic volume forms on the
relative representation varieties of surfaces $\Sigma_{2,1}$ and
$\Sigma_{2,2}.$


title:
Emergence of non-linear electrodynamic theories from $T\bar{T}$-like
  deformations

abstract:
  In this letter, we investigate the deformation of the ModMax theory, as a
unique Lagrangian of non-linear electrodynamics preserving both conformal and
electromagnetic-duality invariance, under $T\bar{T}$-like flows. We will show
that the deformed theory is the generalized non-linear Born-Infeld
electrodynamics. Being inspired by the invariance under the flow equation for
Born-Infeld theories, we propose another $T\bar{T}$-like operator generating
the ModMax and generalized Born-Infeld non-linear electrodynamic theories from
the usual Maxwell and Born-Infeld theories, respectively.


title: Tilted Poincar\'e Sphere Geodesics

abstract:
  We provide the first experimental demonstration of geometric phase generated
in association with closed Poincar\'e Sphere trajectories comprised of geodesic
arcs that do not start, end, or necessarily even include, the north and south
poles that represent pure Laguerre- Gaussian modes. Arbitrarily tilted
(elliptical) single vortex states are prepared with a spatial light modulator,
and Poincar\'e Sphere circuits are driven by beam transit through a series of
{\pi}-converters and Dove prisms.


title:
Investigating the nature of the ultraluminous X-ray sources in the
  galaxy NGC 925

abstract:
  Variability is a powerful tool to investigate properties of X-ray binaries
(XRB), in particular for Ultraluminous X-ray sources (ULXs) that are mainly
detected in the X-ray band. For most ULXs the nature of the accretor is
unknown, although a few ULXs have been confirmed to be accreting at
super-Eddington rates onto a neutron star (NS). Monitoring these sources is
particularly useful both to detect transients and to derive periodicities,
linked to orbital and super-orbital modulations. Here we present the results of
our monitoring campaign of the galaxy NGC 925, performed with the Neil Gehrels
Swift Observatory. We also include archival and literature data obtained with
Chandra, XMM-Newton and NuSTAR. We have studied spectra, light-curves and
variability properties on days to months time-scales. All the three ULXs
detected in this galaxy show flux variability. ULX-1 is one of the most
luminous ULXs known, since only 10% of the ULXs exceed a luminosity of
$\sim$5$\times$10$^{40}$ erg s$^{-1}$, but despite its high flux variability we
found only weak spectral variability. We classify it as in a hard ultraluminous
regime of super-Eddington accretion. ULX-2 and ULX-3 are less luminous but also
variable in flux and possibly also in spectral shape. We classify them as in
between the hard and the soft ultraluminous regimes. ULX-3 is a transient
source: by applying a Lomb-Scargle algorithm we derive a periodicity of $\sim$
126 d, which could be associated with an orbital or super-orbital origin.


title:
RIS-Enabled Self-Localization: Leveraging Controllable Reflections With
  Zero Access Points

abstract:
  Reconfigurable intelligent surfaces (RISs) are one of the most promising
technological enablers of the next (6th) generation of wireless systems. In
this paper, we introduce a novel use-case of the RIS technology in radio
localization, which is enabling the user to estimate its own position via
transmitting orthogonal frequency-division multiplexing (OFDM) pilots and
processing the signal reflected from the RIS. We demonstrate that user
localization in this scenario is possible by deriving Cramer-Rao lower bounds
on the positioning error and devising a low-complexity position estimation
algorithm. We consider random and directional RIS phase profiles and apply a
specific temporal coding to them, such that the reflected signal from the RIS
can be separated from the uncontrolled multipath. Finally, we assess the
performance of our position estimator for an example system and show that the
proposed algorithm can attain the derived bound at high signal-to-noise ratio
values.


title: Understanding Queries by Conditional Instances

abstract:
  A powerful way to understand a complex query is by observing how it operates
on data instances. However, specific database instances are not ideal for such
observations: they often include large amounts of superfluous details that are
not only irrelevant to understanding the query but also cause cognitive
overload; and one specific database may not be enough. Given a relational
query, is it possible to provide a simple and generic "representative" instance
that (1) illustrates how the query can be satisfied, (2) summarizes all
specific instances that would satisfy the query in the same way by abstracting
away unnecessary details? Furthermore, is it possible to find a collection of
such representative instances that together completely characterize all
possible ways in which the query can be satisfied? This paper takes initial
steps towards answering these questions. We design what these representative
instances look like, define what they stand for, and formalize what it means
for them to satisfy a query in "all possible ways." We argue that this problem
is undecidable for general domain relational calculus queries, and develop
practical algorithms for computing a minimum collection of such instances
subject to other constraints. We evaluate the efficiency of our approach
experimentally, and show its effectiveness in helping users debug relational
queries through a user study.


title:
Impact of Channel Correlation on Subspace-Based Activity Detection in
  Grant-Free NOMA

abstract:
  In this paper, we consider the problem of activity detection in grant-free
code-domain non-orthogonal multiple access (NOMA). We focus on performing
activity detection via subspace methods under a setup where the data and pilot
spreading signatures are of different lengths, and consider a realistic
frame-structure similar to existing mobile networks. We investigate the impact
of channel correlation on the activity detection performance; first, we
consider the case where the channel exhibits high correlation in time and
frequency and show how it can heavily deteriorate the performance. To tackle
that, we propose to apply user-specific masking sequences overlaid on top of
the pilot signatures. Second, we consider the other extreme with the channel
being highly selective, and show that it can also negatively impact the
performance. We investigate possible pilots' reallocation strategies that can
help reduce its impact.


title:
New frontiers in characterising ZrB$_2$-MoSi$_2$ ultra-high temperature
  ceramics

abstract:
  The structure-property(S-P) correlation,especially in the context of
high-temperature oxidation, in ZrB$_2$-MoSi$_2$ based Ultra High-Temperature
Ceramic Matrix Composites (UHTCMCs) have been extensively investigated for
quite some time since the last 25 years and a countless amount of published
data is presently available in this field.On the other hand,emergence of
correlative microscopy [1] has completely revolutionised the world of materials
research, in a number of ways.However,owing to the challenges of sample
preparation, there has hardly been a literature aimed at understanding the
aforesaid phenomenon of understanding S-P correlation,based on oxidation in
ZrB$_2$-MoSi$_2$ UHTCMCs,which,in future, may open up new frontiers of research
in UHTCMCs.The present review intends to discuss some of the most interesting
data published existing in this field and intends to provide a brief overview
of the challenges associated along with some of the currently unexplored
avenues in this field,especially in terms of fundamental research.However,in
view of the enormous amount of research already done in understanding
oxidation-based S-P correlation in these materials,the author does not claim to
address all the issues which may be associated with understanding the same.


title: Magnetic Field Analysis for Fermilab Muon $g-2$

abstract:
  The Muon g-2 experiment E989 at Fermilab measures the anomalous magnetic
moment of the muon $a_\mu$ with improved precision compared to the Brookhaven
(E821) experiments. The Brookhaven results are in tension with the Standard
Model by more than $3\sigma$. The determination of $a_\mu$ requires the
measurement of both the muon anomaly frequency, $\omega_a$, and the magnetic
field, $\mathbf{B}$, that confines muons in a storage ring. The field is
monitored by a set of coordinated nuclear magnetic resonance (NMR)
measurements. NMR probes at fixed locations above and below the storage region
constantly monitor the field. An in-vacuum trolley equipped with 17 NMR probes
maps the muon storage region, and a special water-based NMR probe provides the
calibration for the trolley probes. This presentation focuses on the
determination of the time-dependent field maps from combining the fixed probe
measurements and the trolley maps. The field maps are combined with the muon
distribution to derive the average field observed by the muons during the
measurement. These proceedings will cover the analysis from the first data run.


title:
A weak Galerkin method and its two-grid algorithm for the quasi-linear
  elliptic problems of non-monotone type

abstract:
  In this article, a weak Galerkin method is firstly presented and analyzed for
the quasi-linear elliptic problem of non-monotone type. By using Brouwer's
fixed point technique, the existence of WG solution and error estimates in both
the energy-like norm and the $L^2$ norm are derived. Then an efficient two-grid
WG method is introduced to improve the computational efficiency. The
convergence error of the two-grid WG method is analyzed in the energy-like
norm. Numerical experiments are presented to verify our theoretical findings.


title: Ramified cover of varieties with nef cotangent bundle

abstract:
  We construct examples to show that having nef cotangent bundle is not
preserved under finite ramified covers. Our examples also show that a
projective manifold with Stein universal cover may not have nef cotangent
bundle, disproving a conjecture of Liu-Maxim-Wang.


title: Generalized Fubini transform with two variable

abstract:
  This paper aims to investigate a new class of generalized Fubini polynomials.
Firstly, we derive a general identity which generalizes the Fubini transform of
a sequence. Secondly, we give in particular case and we establish a
relationship between generalized Fubini polynomials and more polynomials as
Fubini, Bell, Eulerian and Frobenius-Euler polynomials. Finally, we give the
probabilistic representation and the degenerate of this class.


title: Fly ash composites: A review

abstract:
  The present decade has witnessed numerous investigations focussed on the
determination of the mechanical properties of Fly ash composites.These
composites have attracted attention,especially in the form of reinforcements
owing to their excellent tensile and compressive properties coupled with impact
and hardness response.A number of techniques viz.in-situ deposition, hand-up
lay and compo-casting techniques have been reported to fabricate these
composites.However,in the context of these composites,a systematic
structure-property correlation has not been established till date.The present
review is aimed at highlighting the current state of research in the avenue of
Fly ash composites from two different viewpoints,viz.(i)fabrication technique
and (ii)mechanical properties of the fabricated composites.Moreover,the
necessity of establishing systematic structure-property correlation in these
materials has also been briefly discussed from the author viewpoint.


title:
"You have to prove the threat is real": Understanding the needs of
  Female Journalists and Activists to Document and Report Online Harassment

abstract:
  Online harassment is a major societal challenge that impacts multiple
communities. Some members of community, like female journalists and activists,
bear significantly higher impacts since their profession requires easy
accessibility, transparency about their identity, and involves highlighting
stories of injustice. Through a multi-phased qualitative research study
involving a focus group and interviews with 27 female journalists and
activists, we mapped the journey of a target who goes through harassment. We
introduce PMCR framework, as a way to focus on needs for Prevention,
Monitoring, Crisis and Recovery. We focused on Crisis and Recovery, and
designed a tool to satisfy a target's needs related to documenting evidence of
harassment during the crisis and creating reports that could be shared with
support networks for recovery. Finally, we discuss users' feedback to this
tool, highlighting needs for targets as they face the burden and offer
recommendations to future designers and scholars on how to develop tools that
can help targets manage their harassment.


title:
Neural Speech Synthesis on a Shoestring: Improving the Efficiency of
  LPCNet

abstract:
  Neural speech synthesis models can synthesize high quality speech but
typically require a high computational complexity to do so. In previous work,
we introduced LPCNet, which uses linear prediction to significantly reduce the
complexity of neural synthesis. In this work, we further improve the efficiency
of LPCNet -- targeting both algorithmic and computational improvements -- to
make it usable on a wide variety of devices. We demonstrate an improvement in
synthesis quality while operating 2.5x faster. The resulting open-source LPCNet
algorithm can perform real-time neural synthesis on most existing phones and is
even usable in some embedded devices.


title: Multi-fidelity reinforcement learning framework for shape optimization

abstract:
  Deep reinforcement learning (DRL) is a promising outer-loop intelligence
paradigm which can deploy problem solving strategies for complex tasks.
Consequently, DRL has been utilized for several scientific applications,
specifically in cases where classical optimization or control methods are
limited. One key limitation of conventional DRL methods is their episode-hungry
nature which proves to be a bottleneck for tasks which involve costly
evaluations of a numerical forward model. In this article, we address this
limitation of DRL by introducing a controlled transfer learning framework that
leverages a multi-fidelity simulation setting. Our strategy is deployed for an
airfoil shape optimization problem at high Reynolds numbers, where our
framework can learn an optimal policy for generating efficient airfoil shapes
by gathering knowledge from multi-fidelity environments and reduces
computational costs by over 30\%. Furthermore, our formulation promotes policy
exploration and generalization to new environments, thereby preventing
over-fitting to data from solely one fidelity. Our results demonstrate this
framework's applicability to other scientific DRL scenarios where
multi-fidelity environments can be used for policy learning.


title: Network community detection and clustering with random walks

abstract:
  We present a novel approach to partitioning network nodes into
non-overlapping communities - a key step in revealing network modularity and
hierarchical organization. Our methodology, applicable to networks with both
weighted and unweighted symmetric edges, uses random walks to explore
neighboring nodes in the same community. The walk-likelihood algorithm (WLA)
produces an optimal partition of network nodes into a given number of
communities. The walk-likelihood community finder (WLCF) employs WLA to predict
both the optimal number of communities and the corresponding network partition.
We have extensively benchmarked both algorithms, finding that they outperform
or match other methods in terms of the modularity of predicted partitions and
the number of links between communities. Making use of the computational
efficiency of our approach, we investigated a large-scale map of roads and
intersections in the state of Colorado. Our clustering yielded geographically
sensible boundaries between neighboring communities.


title:
The First Measurement of the Muon Anomalous Magnetic Moment from the
  Fermilab Muon $g-2$ Collaboration

abstract:
  This paper will cover the physics and methods behind Fermilab's Muon g-2
Experiment, along with the long-awaited results from Run-1. The experiment was
undertaken to resolve the tension between the Standard Model and the previous
measurement taken at Brookhaven National Laboratory. The measured value of the
muon magnetic anomaly is $a_\mu(FNAL)=116592040(54)\times10^{-11}$. This result
is in good agreement with Brookhaven's previous measurement. The new world
average, $a_\mu(Exp)=116592061(41)\times10^{-11}$, shows a difference from the
theoretical value of the Standard Model (SM),
$a_\mu(SM)=116591810(43)\times10^{-11}$, of 4.2 standard deviations, strongly
hinting at physics beyond the Standard Model. The experiment requires the
simultaneous measurement of the muon precession frequency, the magnetic field,
and the muons' distribution in the field. All three of these measurements will
be discussed in context, along with the main systematic corrections and
uncertainties.


title: On a generalized Cahn-Hilliard model with p-Laplacian

abstract:
  A generalized Cahn-Hilliard model in a bounded interval of the real line with
no-flux boundary conditions is considered. The label "generalized" refers to
the fact that we consider a concentration dependent mobility, the $p$-Laplace
operator with $p>1$ and a double well potential of the form
$F(u)=\frac{1}{2\theta}|1-u^2|^\theta$, with $\theta>1$; these terms replace,
respectively, the constant mobility, the linear Laplace operator and the $C^2$
potential satisfying $F"(\pm1)>0$, which are typical of the standard
Cahn-Hilliard model. After investigating the associated stationary problem and
highlighting the differences with the standard results, we focus the attention
on the long time dynamics of solutions when $\theta\geq p>1$. In the $critical$
$\theta=p>1$, we prove $exponentially$ $slow$ $motion$ of profiles with a
transition layer structure, thus extending the well know results of the
standard model, where $\theta=p=2$; conversely, in the $supercritical$ case
$\theta>p>1$, we prove $algebraic$ $slow$ $motion$ of layered profiles.


title:
The Composition and Power of the Jet of the Broad-Line Radio Galaxy 3C
  120

abstract:
  We calculated the electron-positron pair production rate at the base of the
jet of 3C 120 by collisions of photons from the hot accretion flow using the
measurement of its average soft gamma-ray spectrum by Compton Gamma Ray
Observatory. We found that this rate approximately equals the flow rate of the
leptons emitting the observed synchrotron radio-to-IR spectrum of the jet core,
calculated using the extended jet model following Blandford \& Konigl. This
coincidence shows the jet composition is likely to be pair-dominated. We then
calculated the jet power in the bulk motion of ions, and found it greatly
exceeds that achievable by the magnetically arrested disk scenario for the
maximum black hole spin unless the jet contains mostly pairs. Next, we found
that the magnetic flux through the synchrotron-emitting jet equals the maximum
poloidal flux that can thread the black hole. Finally, we compared two
estimates of the magnetization parameter at the onset of the synchrotron
emission and found they are in agreement only if pairs dominate the jet
content.


title: A note on Cauchy's formula

abstract:
  We use the correlation functions of vertex operators to give a proof of
Cauchy's formula.


title:
A New Generation of Perspective API: Efficient Multilingual
  Character-level Transformers

abstract:
  On the world wide web, toxic content detectors are a crucial line of defense
against potentially hateful and offensive messages. As such, building highly
effective classifiers that enable a safer internet is an important research
area. Moreover, the web is a highly multilingual, cross-cultural community that
develops its own lingo over time. As such, it is crucial to develop models that
are effective across a diverse range of languages, usages, and styles. In this
paper, we present the fundamentals behind the next version of the Perspective
API from Google Jigsaw. At the heart of the approach is a single multilingual
token-free Charformer model that is applicable across a range of languages,
domains, and tasks. We demonstrate that by forgoing static vocabularies, we
gain flexibility across a variety of settings. We additionally outline the
techniques employed to make such a byte-level model efficient and feasible for
productionization. Through extensive experiments on multilingual toxic comment
classification benchmarks derived from real API traffic and evaluation on an
array of code-switching, covert toxicity, emoji-based hate, human-readable
obfuscation, distribution shift, and bias evaluation settings, we show that our
proposed approach outperforms strong baselines. Finally, we present our
findings from deploying this system in production.


title: A Barrier-Based Scenario Approach to Verify Safety-Critical Systems

abstract:
  In this letter, we detail our randomized approach to safety-critical system
verification. Our method requires limited system data to make a strong
verification statement. Specifically, our method first randomly samples initial
conditions and parameters for a controlled, continuous-time system and records
the ensuing state trajectory at discrete intervals. Then, we evaluate these
states under a candidate barrier function $h$ to determine the constraints for
a randomized linear program. The solution to this program then provides either
a probabilistic verification statement or a counterexample. To show the
validity of our results, we verify the robotarium simulator and identify
counterexamples for its hardware counterpart. We also provide numerical
evidence to validate our verification statements in the same setting.
Furthermore, we show that our method is system-independent by performing the
same verification method on a quadrupedal system in a multi-agent setting as
well.


title:
Event-based Imaging Velocimetry -- An Assessment of Event-based Cameras
  for the Measurement of Fluid Flows

abstract:
  Contrary to conventional frame-based imaging, event-based vision (EBV) or
dynamic vision sensing (DVS) asynchronously records binary signals of intensity
changes for given pixels with microsecond resolution. The present work explores
the possibilities of harnessing the potentials of event-based vision for fluid
flow measurement. The described implementations of event-based imaging
velocimetry (EBIV) rely on the imaging small particles that are illuminated by
a laser light sheet which is similar to classical two-dimensional,
two-component (2d-2c) PIV with the difference that a continuously operating
laser-light sheet is used without modulation of the laser or camera. The moving
particles generate continuous time-stamped events on the detector that are
later used to infer their velocity using patch-wise processing schemes. Two
flow estimation algorithms are proposed; one uses a "motion compensation" that
maximizes the local contrast, the other is based on a sum-of-correlations
approach. The underlying motion detection schemes along with the complete
absence of background signal allows straightforward retrieval of the events
associated with individual particles thereby allowing the reconstruction of
individual particle tracks. Alternatively, the event data can be processed with
conventional PIV algorithms using images reconstructed from the event data
stream. The concepts are demonstrated on simple flows in water and air.


title:
Transient Characteristics of $\beta$-Ga$_2$O$_3$ Nanomembrane Schottky
  Barrier Diodes on Various Substrates

abstract:
  In this paper, a transient delayed rising and fall time of
$\beta$-Ga$_2$O$_3$ NMs Schottky barrier diodes (SBDs) formed on four different
substrates (diamond, Si, sapphire, and polyimide) were measured using a
sub-micron second resolution time-resolved electrical measurement system under
a different temperature condition. The devices exhibited noticeably
less-delayed turn-on-/off- the transient time when $\beta$-Ga$_2$O$_3$ NMs SBDs
were transfer-printed on a high-k substrate. Furthermore, a relationship
between the $\beta$-Ga$_2$O$_3$ NM thicknesses and their transient
characteristics were systematically investigated and found that phonon
scattering plays an important role in heat dissipation as the thickness of
$\beta$-Ga$_2$O$_3$ NMs get thinner which is also verified by the Multiphysics
simulator. Overall, our result reveals the impact of various substrates with
different thermal properties and different \b{eta}- Ga2O3 NMs thickness with
the performance of $\beta$-Ga$_2$O$_3$ NMs based devices. Hence, these results
can guide further efforts us to optimize the performance of future
$\beta$-Ga$_2$O$_3$ devices by maximizing heat dissipation from the
$\beta$-Ga$_2$O$_3$ layer.


title:
Selecting cells in a raster database for maximal impact intervention in
  the presence of spatial interaction: Computational complexity of a Multiple
  vs. a Single Flow Direction Method

abstract:
  To minimize the sediment flowing to the outlet of a river catchment with
minimal effort or cost, it is important to select the best areas to perform a
certain intervention, e.g., afforestation. CAMF (Cellular Automata based
heuristic for Minimizing Flow) is a method that performs this selection process
iteratively in a raster geodatabase environment. To simulate the flow paths,
the original CAMF uses a Single Flow Direction (SFD) algorithm. However, SFD
fails to reflect the real nature of flow transport, especially in areas with
low relief. This paper describes and analyzes the integration of a Multiple
Flow Direction (MFD) algorithm in CAMF, in order to provide a more realistic
flow simulation. We compare the computational complexity of CAMF-SFD and
CAMF-MFD and we discuss the scalability w.r.t. the number of cells involved. We
evaluate the behavior of both variants for sediment yield minimization by
afforestation in two catchments with different properties.


title: Minimal quantum walk simulation of Dirac fermions in curved space-times

abstract:
  The problem of simulating through quantum walks Dirac fermions in arbitrary
curved space-times and coordinates is revisited, taking (1 + 1)D space-times as
an example. A new shift or translation operator on the grid is introduced, to
take into account arbitrary geometries. The new, generalised quantum walks
built with this operator can simulate Dirac fermions in arbitrary curved
space-times and coordinates, and their wave functions have exactly the same
number of components as standard Dirac spinors, and not twice that number, as
previously believed. In particular, in $(1 + 1)$D space-times, only one qubit
is needed at each lattice point, which makes it easier to perform quantum
simulations of the Dirac dynamics on current NISQs quantum devices. Numerical
simulations of the Dirac dynamics in the post Newtonian, so-called
Gravitoelectromagnetism regime are presented as an illustration.


title:
Boolean models of the transport, synthesis, and metabolism of tryptophan
  in Escherichia Coli

abstract:
  The tryptophan (trp) operon in E. coli codes for the proteins responsible for
the synthesis of the amino acid tryptophan from chorismic acid, and has been
one of the most well-studied gene networks since its discovery in the 1960s.
The tryptophanase (tna) operon codes for proteins needed to transport and
metabolize it. Both of these have been modeled individually with delay
differential equations under the assumption of mass-action kinetics. Recent
work has provided strong evidence for bistable behavior of the tna operon. The
authors of (Orozco, 2019) identified a medium range of tryptophan in which the
system has two stable steady-states, and they reproduced these experimentally.
In this paper, we will show how a Boolean model can capture this bistability.
We will also develop and analyze a Boolean model of the trp operon. Finally, we
will combine these two to create a single Boolean model of the transport,
synthesis, and metabolism of tryptophan. In this amalgamated model, the
bistability disappears, presumably reflecting the ability of the trp operon to
produce tryptophan and drive the system toward homeostasis. All of these models
have longer attractors that we call "artifacts of synchrony", which disappear
in the asynchronous automata. This curiously matches the behavior of a recent
Boolean model of the arabinose operon in E. coli, and we discuss some
open-ended questions that arise along these lines.


title: Systemic Risk in Financial Systems: Properties of Equilibria

abstract:
  Eisenberg and Noe (2001) analyze systemic risk for financial institutions
linked by a network of liabilities. They show that the solution to their model
is unique when the financial system is satisfies a regularity condition
involving risk orbits. We show that this condition is not needed: a unique
solution always exists.


title: Level compressibility of certain random unitary matrices

abstract:
  The value of spectral form factor at the origin, called level
compressibility, is an important characteristic of random spectra. The paper is
devoted to analytical calculations of this quantity for different random
unitary matrices describing models with intermediate spectral statistics. The
computations are based on the approach developed by G. Tanner in [J. Phys. A:
Math. Gen. 34, 8485 (2001)] for chaotic systems. The main ingredient of the
method is the determination of eigenvalues of a transition matrix whose matrix
elements equal squared moduli of matrix elements of the initial unitary matrix.
The principal result of the paper is the proof that the level compressibility
of random unitary matrices derived from the exact quantisation of barrier
billiards and consequently of barrier billiards themselves is equal to $1/2$
irrespectively of the height and the position of the barrier.


title: Schubert structure coefficients via derivatives

abstract:
  Schubert structure coefficients $c_{u,v}^w$ describe the multiplicative
structure of the cohomology rings of flag varieties. Much work has been done on
the problem of giving combinatorial formulas for these coefficients in special
cases. In particular, the Littlewood-Richardson rule computes $c_{u,v}^w$ in
the case that $u$, $v$, and $w$ are all $p$-Grassmannian permutations for some
common $p$.
  Building on work on Wyser (2013), we introduce backstable clans to prove a
"dual" positive combinatorial rule that computes $c_{u,v}^w$ when $u^{-1}$ is
$p$-Grassmannian and $v^{-1}$ is $q$-Grassmannian. We derive new families of
linear relations among Schubert structure coefficients, which we then use to
give a further positive combinatorial rule for $c_{u,v}^w$ in the case that
$u^{-1}$ is $p$-Grassmannian and $v^{-1}$ is covered in weak Bruhat order by a
$q$-Grassmannian permutation.


title:
$t\bar{t}b\bar{b}$ at the LHC: On the size of off-shell effects and
  prompt $b$-jet identification

abstract:
  We investigate full off-shell effects in $t\bar{t}b\bar{b}$ production in the
dilepton channel at the LHC with the center-of-mass energy $\sqrt{s} = 13$ TeV.
Specifically, we compute NLO QCD corrections to the $pp \to e^+ \nu_e \mu^-
\bar{\nu}_\mu b \bar{b} b \bar{b} + X$ process and provide a prescription for
$b$-jet identification to distinguish prompt $b$ jets from $b$ jets originating
from the decay of the top quarks. As an important irreducible background to $pp
\to t\bar{t}H (H\to b\bar{b})$, $t\bar{t}$ production in association with two
prompt $b$ jets is a primary source of uncertainty in the measurement of
$t\bar{t}H (H\to b\bar{b})$. In quantifying full off-shell effects, we perform
comparisons between the state-of-the-art full off-shell computation and the
calculation in the narrow width approximation. The former includes all double-,
single- and non-resonant Feynman diagrams, interferences as well as
finite-width effects of the top quarks and $W$ gauge bosons. The latter
restricts the unstable top quarks and $W$ gauge bosons to on-shell states and
includes for the first time NLO QCD corrections to both production and decays.
We observe that full off-shell effects are subdominant compared to the scale
uncertainties for the integrated fiducial cross section and for the majority of
differential observables in the phase-space regions that we investigated.
However, for a number of observables related to beyond the Standard Model
searches, full off-shell effects are significant. Furthermore, with our $b$-jet
labelling prescription, the prompt $b$ jets and the $b$ jets from top-quark
decays can be successfully disentangled.


title: A new X-ray tidal disruption event candidate with fast variability

abstract:
  During a close encounter between a star and a supermassive black hole, the
star can get disrupted by the black hole's tidal forces, resulting in a tidal
disruption event (TDE). The accretion of the star's material onto the black
hole produces strong emission in different wavelength regimes. Here we report
the discovery with ROSAT of an X-ray-selected transient source in an optically
non-active galaxy. At the location RA: 13h31m57.66s and Dec:
-32deg3arcmin19.7arsec a sudden rise in X-ray luminosity by a factor of 8
within 8 days has been observed. Additionally, a very soft X-ray spectrum with
a black-body temperature kT=0.1 keV and a peak luminosity of at least 10^43
erg/s suggest a TDE interpretation, and the observed properties are very
similar to previously identified soft X-ray (ROSAT) TDEs. An optical spectrum
taken of the galaxy at the position of RXJ133157.6-324319.7 six years after the
X-ray outburst does not show any emission lines as would be expected from a
persistent active galactic nucleus (AGN). The redshift of the galaxy is
determined to be 0.051 based on absorption lines. It is therefore likely a
member of the galaxy cluster Abell 3560. The rise in X-ray luminosity happens
within 8 days and thus appears to be fast for such an event. No X-ray emission
was detected 170 days before and 165 days after the event, and none was
detected 25 years later with the Neil Gehrels Swift Observatory. The change in
X-ray luminosity is at least a factor of 40.


title:
SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in
  POSGs with Sparse Interactions

abstract:
  This work introduces sIPOMDPLite-net, a deep neural network (DNN)
architecture for decentralized, self-interested agent control in partially
observable stochastic games (POSGs) with sparse interactions between agents.
The network learns to plan in contexts modeled by the interactive partially
observable Markov decision process (I-POMDP) Lite framework and uses
hierarchical value iteration networks to simulate the solution of nested MDPs,
which I-POMDP Lite attributes to the other agent to model its behavior and
predict its intention. We train sIPOMDPLite-net with expert demonstrations on
small two-agent Tiger-grid tasks, for which it accurately learns the underlying
I-POMDP Lite model and near-optimal policy, and the policy continues to perform
well on larger grids and real-world maps. As such, sIPOMDPLite-net shows good
transfer capabilities and offers a lighter learning and planning approach for
individual, self-interested agents in multiagent settings.


title:
Mathematical foundation of sparsity-based multi-illumination
  super-resolution

abstract:
  It is well-known that the resolution of a traditional optical imaging system
is limited by the so-called Rayleigh limit, which is of several hundreds of
nanometers. By employing fluorescence techniques, modern microscopic methods
can resolve point scatterers separated much lower than the Rayleigh limit.
Localization-based fluorescence subwavelength imaging techniques such as PALM
and STORM can achieve a spatial resolution of several tens of nanometers.
However, these techniques have limited temporal resolution as they require tens
of thousands of exposures. Employing sparsity-based models and recovery
algorithms is a natural way to reduce the number of exposures and hence obtain
high temporal resolution. Recently, a new multi-illumination imaging technique
called Brownian Excitation Amplitude Modulation microscopy (BEAM) is
introduced. BEAM achieves a threefold resolution improvement by applying a
compressive sensing recovery algorithm over only few frames. Motivated by BEAM,
our aim in this paper is to pioneer the mathematical foundation for
sparsity-based multi-illumination super-resolution. We consider several
diffraction-limited images from samples exposed to different illumination
patterns and recover the source by considering the sparsest solution. We
estimate the minimum separation distance between point scatterers so that they
could be stably recovered. By this estimation, we reveal the dependence of the
resolution on the cut-off frequency of the imaging system, the SNR, the
sparsity of point scatterers, and the incoherence of illumination patterns. Our
theory particularly highlights the importance of the high incoherence of
illumination patterns in enhancing the resolution. It also demonstrates that
super-resolution can be achieved using sparsity-based multi-illumination
imaging with very few frames, whereby the spatio-temporal super-resolution
becomes possible.


title: Neural Network based Successor Representations of Space and Language

abstract:
  How does the mind organize thoughts? The hippocampal-entorhinal complex is
thought to support domain-general representation and processing of structural
knowledge of arbitrary state, feature and concept spaces. In particular, it
enables the formation of cognitive maps, and navigation on these maps, thereby
broadly contributing to cognition. It has been proposed that the concept of
multi-scale successor representations provides an explanation of the underlying
computations performed by place and grid cells. Here, we present a neural
network based approach to learn such representations, and its application to
different scenarios: a spatial exploration task based on supervised learning, a
spatial navigation task based on reinforcement learning, and a non-spatial task
where linguistic constructions have to be inferred by observing sample
sentences. In all scenarios, the neural network correctly learns and
approximates the underlying structure by building successor representations.
Furthermore, the resulting neural firing patterns are strikingly similar to
experimentally observed place and grid cell firing patterns. We conclude that
cognitive maps and neural network-based successor representations of structured
knowledge provide a promising way to overcome some of the short comings of deep
learning towards artificial general intelligence.


title: Real Time Adaptive Estimation of Li-ion Battery Bank Parameters

abstract:
  This paper proposes an accurate and efficient Universal Adaptive Stabilizer
(UAS) based online parameters estimation technique for a 400 V Li-ion battery
bank. The battery open circuit voltage, parameters modeling the transient
response, and series resistance are all estimated in a single real-time test.
In contrast to earlier UAS based work on individual battery packs, this work
does not require prior offline experimentation or any post-processing. Real
time fast convergence of parameters' estimates with minimal experimental effort
enables self-update of battery parameters in run-time. The proposed strategy is
mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah
Li-ion battery bank powering the induction motor driven prototype electric
vehicle (EV) traction system.


title: Modal Estimation on a Warped Frequency Axis for Linear System Modeling

abstract:
  Linear systems such as room acoustics and string oscillations may be modeled
as the sum of mode responses, each characterized by a frequency, damping and
amplitude. Here, we consider finding the mode parameters from impulse response
measurements, and estimate the mode frequencies and decay rates as the
generalized eigenvalues of Hankel matrices of system response samples, similar
to ESPRIT. For greater resolution at low frequencies, such as desired in room
acoustics and musical instrument modeling, the estimation is done on a warped
frequency axis. The approach has the benefit of selecting the number of modes
to achieve a desired fidelity to the measured impulse response. An optimization
to further refine the frequency and damping parameters is presented. The method
is used to model coupled piano strings and room impulse responses, with its
performance comparing favorably to FZ-ARMA.


title:
On the connection between the Hardy and $L^2-$Cowling--Price Gaussian
  spaces in the subcritical case

abstract:
  We prove that the classes of Schwartz functions present in Cowling-Price's
and Hardy's uncertainty principle are almost-equivalent. This has the precise
meaning that, if a function $f$ decays in the $L^2$ sense in space and
frequency as a Gaussian $e^{-ax^2}$, then it also decays in the pointwise sense
as $e^{-bx^2}$, for any $b < a$.
  We also discuss an endpoint conjecture version of our main result, exhibiting
particular classes where it holds, and present applications of our main
theorem. In particular, by making use of that main result and the recent
machinery of Escauriaza, Kenig, Ponce and Vega, we improve the current bounds
on pointwise Gaussian decay of solutions to harmonic oscillators, settling a
conjecture of Vemuri in all but a discrete set of times.


title:
r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme
  Conversion by Controlled noise introducing and Contextual information
  incorporation

abstract:
  Grapheme-to-phoneme (G2P) conversion is the process of converting the written
form of words to their pronunciations. It has an important role for
text-to-speech (TTS) synthesis and automatic speech recognition (ASR) systems.
In this paper, we aim to evaluate and enhance the robustness of G2P models. We
show that neural G2P models are extremely sensitive to orthographical
variations in graphemes like spelling mistakes. To solve this problem, we
propose three controlled noise introducing methods to synthesize noisy training
data. Moreover, we incorporate the contextual information with the baseline and
propose a robust training strategy to stabilize the training process. The
experimental results demonstrate that our proposed robust G2P model (r-G2P)
outperforms the baseline significantly (-2.73\% WER on Dict-based benchmarks
and -9.09\% WER on Real-world sources).


title: Recommendations to Develop, Distribute and Market Sonification Apps

abstract:
  After decades of research, sonification is still rarely adopted in consumer
electronics, software and user interfaces. Outside the science and arts scenes
the term sonification seems not well known to the public. As a means of science
communication, and in order to make software developers, producers of consumer
electronics and end users aware of sonification, we developed, distributed, and
promoted Tiltification. This smartphone app utilizes sonification to inform
users about the tilt angle of their phone, so that they can use it as a torpedo
level. In this paper we report on our app development, distribution and
promotion strategies and reflect on their success in making the app in
particular, and sonification in general, better known to the public. Finally,
we give recommendations on how to develop, distribute and market sonification
apps. This article is dedicated to research institutions without commercial
interests.


title:
Backdoor Defense in Federated Learning Using Differential Testing and
  Outlier Detection

abstract:
  The goal of federated learning (FL) is to train one global model by
aggregating model parameters updated independently on edge devices without
accessing users' private data. However, FL is susceptible to backdoor attacks
where a small fraction of malicious agents inject a targeted misclassification
behavior in the global model by uploading polluted model updates to the server.
In this work, we propose DifFense, an automated defense framework to protect an
FL system from backdoor attacks by leveraging differential testing and two-step
MAD outlier detection, without requiring any previous knowledge of attack
scenarios or direct access to local model parameters. We empirically show that
our detection method prevents a various number of potential attackers while
consistently achieving the convergence of the global model comparable to that
trained under federated averaging (FedAvg). We further corroborate the
effectiveness and generalizability of our method against prior defense
techniques, such as Multi-Krum and coordinate-wise median aggregation. Our
detection method reduces the average backdoor accuracy of the global model to
below 4% and achieves a false negative rate of zero.


title:
Real-time Over-the-air Adversarial Perturbations for Digital
  Communications using Deep Neural Networks

abstract:
  Deep neural networks (DNNs) are increasingly being used in a variety of
traditional radiofrequency (RF) problems. Previous work has shown that while
DNN classifiers are typically more accurate than traditional signal processing
algorithms, they are vulnerable to intentionally crafted adversarial
perturbations which can deceive the DNN classifiers and significantly reduce
their accuracy. Such intentional adversarial perturbations can be used by RF
communications systems to avoid reactive-jammers and interception systems which
rely on DNN classifiers to identify their target modulation scheme. While
previous research on RF adversarial perturbations has established the
theoretical feasibility of such attacks using simulation studies, critical
questions concerning real-world implementation and viability remain unanswered.
This work attempts to bridge this gap by defining class-specific and
sample-independent adversarial perturbations which are shown to be effective
yet computationally feasible in real-time and time-invariant. We demonstrate
the effectiveness of these attacks over-the-air across a physical channel using
software-defined radios (SDRs). Finally, we demonstrate that these adversarial
perturbations can be emitted from a source other than the communications
device, making these attacks practical for devices that cannot manipulate their
transmitted signals at the physical layer.


title:
The anomalous process in singlet fission kinetic model with
  time-dependent coefficient

abstract:
  In the third generation photovoltaic device, the main physical mechanism that
the photoelectric conversion efficiency is enhanced is the singlet fission
(SF). In order to accurately describe the SF and reveal physical process in
theoretically, we introduce the anomalous process (AP) in SF dynamics based on
previous model, including anomalous fission, anomalous fusion, anomalous
dissociation and combination of triplet pair states, anomalous decay and
diffusion of single triplet exciton. The effects of the AP on SF are
investigated by the kinetic model with time-dependent coefficient. Further,
according to the results we make the optimal simulations for the experimental
data [G. B. Piland et al., J. Phys. Chem. C, 2013, 117, 1224] by adjusting the
rate coefficients and exponents in the mended kinetic equations. The results
show that the model considered AP is more accurate than previous that to
describe SF dynamics, demonstrating that the AP do exist in SF. The model also
provides the theoretical foundation for how varies experimentally physical
factors to make SF occur to tend to required direction.


title: Differentially Private Regression with Unbounded Covariates

abstract:
  We provide computationally efficient, differentially private algorithms for
the classical regression settings of Least Squares Fitting, Binary Regression
and Linear Regression with unbounded covariates. Prior to our work, privacy
constraints in such regression settings were studied under strong a priori
bounds on covariates. We consider the case of Gaussian marginals and extend
recent differentially private techniques on mean and covariance estimation
(Kamath et al., 2019; Karwa and Vadhan, 2018) to the sub-gaussian regime. We
provide a novel technical analysis yielding differentially private algorithms
for the above classical regression settings. Through the case of Binary
Regression, we capture the fundamental and widely-studied models of logistic
regression and linearly-separable SVMs, learning an unbiased estimate of the
true regression vector, up to a scaling factor.


title:
Quantum Distributed Deep Learning Architectures: Models, Discussions,
  and Applications

abstract:
  Although deep learning (DL) has already become a state-of-the-art technology
for various data processing tasks, data security and computational overload
problems often arise due to their high data and computational power dependency.
To solve this problem, quantum deep learning (QDL) and distributed deep
learning (DDL) has emerged to complement existing DL methods. Furthermore, a
quantum distributed deep learning (QDDL) technique that combines and maximizes
these advantages is getting attention. This paper compares several model
structures for QDDL and discusses their possibilities and limitations to
leverage QDDL for some representative application scenarios.


title: Unravelling Token Ecosystem of EOSIO Blockchain

abstract:
  Being the largest Initial Coin Offering project, EOSIO has attracted great
interest in cryptocurrency markets. Despite its popularity and prosperity
(e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5,
2020), there is almost no work investigating the EOSIO token ecosystem. To fill
this gap, we are the first to conduct a systematic investigation on the EOSIO
token ecosystem by conducting a comprehensive graph analysis on the entire
on-chain EOSIO data (nearly 135 million blocks). We construct token creator
graphs, token-contract creator graphs, token holder graphs, and token transfer
graphs to characterize token creators, holders, and transfer activities.
Through graph analysis, we have obtained many insightful findings and observed
some abnormal trading patterns. Moreover, we propose a fake-token detection
algorithm to identify tokens generated by fake users or fake transactions and
analyze their corresponding manipulation behaviors. Evaluation results also
demonstrate the effectiveness of our algorithm.


title: Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning

abstract:
  Indiscriminate data poisoning attacks are quite effective against supervised
learning. However, not much is known about their impact on unsupervised
contrastive learning (CL). This paper is the first to consider indiscriminate
data poisoning attacks on contrastive learning, demonstrating the feasibility
of such attacks, and their differences from indiscriminate poisoning of
supervised learning. We also highlight differences between contrastive learning
algorithms, and show that some algorithms (e.g., SimCLR) are more vulnerable
than others (e.g., MoCo). We differentiate between two types of data poisoning
attacks: sample-wise attacks, which add specific noise to each image, cause the
largest drop in accuracy, but do not transfer well across SimCLR, MoCo, and
BYOL. In contrast, attacks that use class-wise noise, though cause a smaller
drop in accuracy, transfer well across different CL algorithms. Finally, we
show that a new data augmentation based on matrix completion can be highly
effective in countering data poisoning attacks on unsupervised contrastive
learning.


title: Label-Smoothed Backdoor Attack

abstract:
  By injecting a small number of poisoned samples into the training set,
backdoor attacks aim to make the victim model produce designed outputs on any
input injected with pre-designed backdoors. In order to achieve a high attack
success rate using as few poisoned training samples as possible, most existing
attack methods change the labels of the poisoned samples to the target class.
This practice often results in severe over-fitting of the victim model over the
backdoors, making the attack quite effective in output control but easier to be
identified by human inspection or automatic defense algorithms.
  In this work, we proposed a label-smoothing strategy to overcome the
over-fitting problem of these attack methods, obtaining a
\textit{Label-Smoothed Backdoor Attack} (LSBA). In the LSBA, the label of the
poisoned sample $\bm{x}$ will be changed to the target class with a probability
of $p_n(\bm{x})$ instead of 100\%, and the value of $p_n(\bm{x})$ is
specifically designed to make the prediction probability the target class be
only slightly greater than those of the other classes. Empirical studies on
several existing backdoor attacks show that our strategy can considerably
improve the stealthiness of these attacks and, at the same time, achieve a high
attack success rate. In addition, our strategy makes it able to manually
control the prediction probability of the design output through manipulating
the applied and activated number of LSBAs\footnote{Source code will be
published at \url{https://github.com/v-mipeng/LabelSmoothedAttack.git}}.


title: Study of Feature Importance for Quantum Machine Learning Models

abstract:
  Predictor importance is a crucial part of data preprocessing pipelines in
classical and quantum machine learning (QML). This work presents the first
study of its kind in which feature importance for QML models has been explored
and contrasted against their classical machine learning (CML) equivalents. We
developed a hybrid quantum-classical architecture where QML models are trained
and feature importance values are calculated from classical algorithms on a
real-world dataset. This architecture has been implemented on ESPN Fantasy
Football data using Qiskit statevector simulators and IBM quantum hardware such
as the IBMQ Mumbai and IBMQ Montreal systems. Even though we are in the Noisy
Intermediate-Scale Quantum (NISQ) era, the physical quantum computing results
are promising. To facilitate current quantum scale, we created a data tiering,
model aggregation, and novel validation methods. Notably, the feature
importance magnitudes from the quantum models had a much higher variation when
contrasted to classical models. We can show that equivalent QML and CML models
are complementary through diversity measurements. The diversity between QML and
CML demonstrates that both approaches can contribute to a solution in different
ways. Within this paper we focus on Quantum Support Vector Classifiers (QSVC),
Variational Quantum Circuit (VQC), and their classical counterparts. The ESPN
and IBM fantasy football Trade Assistant combines advanced statistical analysis
with the natural language processing of Watson Discovery to serve up
personalized trade recommendations that are fair. Here, player valuation data
of each player has been considered and this work can be extended to calculate
the feature importance of other QML models such as Quantum Boltzmann machines.


title:
Constant matters: Fine-grained Complexity of Differentially Private
  Continual Observation

abstract:
  We study fine-grained error bounds for differentially private algorithms for
averaging and counting under continual observation. Our main insight is that
the factorization mechanism when using lower-triangular matrices, can be used
in the continual observation model. We give explicit factorizations for two
fundamental matrices, namely the counting matrix $M_{\mathsf{count}}$ and the
averaging matrix $M_{\mathsf{average}}$ and show fine-grained bounds for the
additive error of the resulting mechanism using the {\em completely bounded
norm} (cb-norm) or {\em factorization norm}. Our bound on the cb-norm for
$M_{\mathsf{count}}$ is tight up an additive error of 1 and the bound for
$M_{\mathsf{average}}$ is tight up to $\approx 0.64$. This allows us to give
the first algorithm for averaging whose additive error has $o(\log^{3/2} T)$
dependence. Furthermore, we are the first to give concrete error bounds for
various problems under continual observation such as binary counting,
maintaining a histogram, releasing an approximately cut-preserving synthetic
graph, many graph-based statistics, and substring and episode counting.
Finally, we present a fine-grained error bound for non-interactive local
learning.


title: Functional Parcellation of fMRI data using multistage k-means clustering

abstract:
  Purpose: Functional Magnetic Resonance Imaging (fMRI) data acquired through
resting-state studies have been used to obtain information about the
spontaneous activations inside the brain. One of the approaches for analysis
and interpretation of resting-state fMRI data require spatially and
functionally homogenous parcellation of the whole brain based on underlying
temporal fluctuations. Clustering is often used to generate functional
parcellation. However, major clustering algorithms, when used for fMRI data,
have their limitations. Among commonly used parcellation schemes, a tradeoff
exists between intra-cluster functional similarity and alignment with
anatomical regions. Approach: In this work, we present a clustering algorithm
for resting state and task fMRI data which is developed to obtain brain
parcellations that show high structural and functional homogeneity. The
clustering is performed by multistage binary k-means clustering algorithm
designed specifically for the 4D fMRI data. The results from this multistage
k-means algorithm show that by modifying and combining different algorithms, we
can take advantage of the strengths of different techniques while overcoming
their limitations. Results: The clustering output for resting state fMRI data
using the multistage k-means approach is shown to be better than simple k-means
or functional atlas in terms of spatial and functional homogeneity. The
clusters also correspond to commonly identifiable brain networks. For task
fMRI, the clustering output can identify primary and secondary activation
regions and provide information about the varying hemodynamic response across
different brain regions. Conclusion: The multistage k-means approach can
provide functional parcellations of the brain using resting state fMRI data.
The method is model-free and is data driven which can be applied to both
resting state and task fMRI.


title:
Reconstruction and Normalization of Anselin's Local Indicators of
  Spatial Association (LISA)

abstract:
  The local indicators of spatial association (LISA) are significant measures
for spatial autocorrelation analysis. However, there is an inadvertent fault in
Anselin's mathematical processes so that the local Moran and Geary indicators
do not satisfy his second basic requirement, i.e., the sum of the local
indicators is proportional to a global indicator. Based on Anselin's original
intention, this paper is devoted to reconstructing the calculation formulae of
the local Moran indexes and Geary coefficients through mathematical derivation
and empirical evidence. Two sets of LISAs were clarified by mathematical
reasoning. One set of LISAs is based on no normalized weights and centralized
variable (MI1 and GC1), and the other set is but the second the set cannot.
Then, the third set of LISA was proposed, treated as canonical forms (MI3 and
GC3). The local Moran indexes are based on global normalized weights and
standardized variable based on population standard deviation, while the local
Geary coefficients are based on global normalized weights and standardized
variable based on sample standard deviation. This set of LISAs satisfies the
second requirement of based on row normalized weights and standardized variable
(MI2 and GC2). The results show that the first set of LISAs satisfy Anselin's
second requirement,Anselin's. The observational data of city population and
traffic mileage in Beijing-Tianjin-Hebei region of China were employed to
verify the theoretical results. This study helps to clarify the
misunderstandings about LISAs in the field of geospatial analysis.


title:
Democratizing Aviation Emissions Estimation: Development of an
  Open-Source, Data-Driven Methodology

abstract:
  Through an aviation emissions estimation tool that is both
publicly-accessible and comprehensive, researchers, planners, and community
advocates can help shape a more sustainable and equitable U.S. air
transportation system. To this end, we develop an open-source, data-driven
methodology to calculate the system-wide emissions of the U.S. domestic civil
aviation industry. This process utilizes and integrates six different public
datasets provided by the Bureau of Transportation Statistics (BTS), the Federal
Aviation Agency (FAA), EUROCONTROL, and the International Civil Aviation
Organization (ICAO). At the individual flight level, our approach examines the
specific aircraft type, equipped engine, and time in stage of flight to produce
a more granular estimate than competing approaches. Enabled by our methodology,
we then calculate system-wide emissions, considering four different greenhouse
gases (CO2, NOx, CO, HC) during the LTO and CCD flight cycles. Our results
elucidate that emissions on a particular route can vary significantly due to
aircraft and engine choice, and that emission rates differ significantly from
airline to airline. We also find that CO2 alone is not a sufficient proxy for
emissions, as NOx, when converted to its CO2-equivalency, exceeds CO2 during
both LTO and CCD.


title:
Inertial focusing of spherical particles in curved microfluidic ducts at
  moderate Dean numbers

abstract:
  We examine the effect of Dean number on the inertial focusing of spherical
particles suspended in flow through curved microfluidic ducts. Previous
modelling of particle migration in curved ducts assumed the flow rate was small
enough that a leading order approximation of the background flow with respect
to the Dean number produces a reasonable model. Herein, we extend our model to
situations having a moderate Dean number (in the microfluidics context) while
the particle Reynolds number remains small. This extension allows us to capture
changes in the background flow that occur with increasing flow rate, namely a
shift in local extrema towards the outside wall. The change in the axial
velocity profile of the background flow has an effect on the inertial lift
force, while the change in the cross-sectional components directly affects the
secondary flow drag. In keeping the particle Reynolds number small we
approximate the inertial lift force in a similar manner to previous studies
while capturing subtle effects do to the modified background flow profile.
Capturing and understanding these effects is an important step towards
accurately modelling inertial migration across a wide range of practical
applications. Our results reveal how the changing background flow profile
modifies the inertial focusing of particles. We illustrate enhanced lateral
separation of particles by size in a number of scenarios and find that focusing
times can be roughly separated into two regimes. These results suggest our
model might aid with parameter choices for separation of particles by size.


title:
Pointwise convergence of the heat and subordinates of the heat
  semigroups associated with the Laplace operator on homogeneous trees and two
  weighted $L^p$ maximal inequalities

abstract:
  In this paper we consider the heat semigroup $\{W_t\}_{t>0}$ defined by the
combinatorial Laplacian and two subordinated families of $\{W_t\}_{t>0}$ on
homogeneous trees $X$. We characterize the weights $u$ on $X$ for which the
pointwise convergence to initial data of the above families holds for every
$f\in L^{p}(X,\mu,u)$ with $1\le p<\infty$, where $\mu$ represents the counting
measure in $X$ . We prove that this convergence property in $X$ is equivalent
to the fact that the maximal operator on $t\in (0,R)$, for some $R>0$, defined
by the semigroup is bounded from $L^{p}(X,\mu,u)$ into $L^{p}(X,\mu,v)$ for
some weight $v$ on $X$.


title: Max/Min Puzzles in Geometry III

abstract:
  The first two installments of this series of papers dealt with the maximum
area polygons: Parallelogram, Rectangle, Square or Equilateral Triangle, in
given triangles. Minimum area polygons were also considered in the second paper
on Equilateral Triangles. In this paper the puzzle will be turned the other way
around. Given the regular unit polygons, a Square, Pentagon, or Hexagon, we
searched for the smallest area triangle(s) which contains it. The Dynamic
Software Sketchpad v5.10 BETA was used for all examples and figures throughout
the work.


title:
Metrical properties for the weighted products of multiple partial
  quotients in continued fractions

abstract:
  The classical Khintchine and Jarn\'ik theorems, generalizations of a
consequence of Dirichlet's theorem, are fundamental results in the theory of
Diophantine approximation. These theorems are concerned with the size of the
set of real numbers for which the partial quotients in their continued fraction
expansions grows with a certain rate. Recently it was observed that the growth
of product of pairs of consecutive partial quotients in the continued fraction
expansion of a real number is associated with improvements to Dirichlet's
theorem. In this paper we consider the products of several consecutive partial
quotients raised to different powers. Namely, we find the Lebesgue measure and
the Hausdorff dimension of the following set: $$ {\D_{\mathbf
t}}(\psi):=\left\{x\in[0, 1): \prod\limits_{i=0}^{m-1}{a^{t_i}_{n+i}(x)} \ge
\Psi(n)\ {\text{for infinitely many}} \ n\in \N \right\}, $$ where
$t_i\in\mathbb R_+$ for all ${0\leq i\leq m-1}$, and $\Psi:\N\to\R_{\ge 1}$ is
a positive function.


title:
From Resource Auction to Service Auction: An Auction Paradigm Shift in
  Wireless Networks

abstract:
  In 5G and beyond, the newly emerging services, such as edge
computing/intelligence services, may demand the provision of heterogeneous
communications, computing, and storage (CCS) resources on and across network
entities multihop apart. In such cases, traditional resource-oriented auction
schemes, where buyers place bids on resources, may not be effective in
providing end-to-end (E2E) quality-of-service (QoS) guarantees. To overcome
these limitations, in this article, we coin the concept of E2E service auction
where the auction commodities are E2E services rather than certain resource.
Under this framework, buyers simply bid for services with E2E QoS requirements
without having to know the inner working (which resources are behind). To
guarantee E2E QoS for winning bids while ensuring essential economic
properties, E2E service auction requires addressing the joint problem of
network optimization and auction design with both economical and QoS
constraints. To substantiate the mechanism design, we illustrate how to devise
E2E service auctions for edge computing systems under various scenarios. We
also identify the research opportunities on E2E service auction mechanism
design for other critical use cases, including edge intelligence.


title:
FourCastNet: A Global Data-driven High-resolution Weather Model using
  Adaptive Fourier Neural Operators

abstract:
  FourCastNet, short for Fourier Forecasting Neural Network, is a global
data-driven weather forecasting model that provides accurate short to
medium-range global predictions at $0.25^{\circ}$ resolution. FourCastNet
accurately forecasts high-resolution, fast-timescale variables such as the
surface wind speed, precipitation, and atmospheric water vapor. It has
important implications for planning wind energy resources, predicting extreme
weather events such as tropical cyclones, extra-tropical cyclones, and
atmospheric rivers. FourCastNet matches the forecasting accuracy of the ECMWF
Integrated Forecasting System (IFS), a state-of-the-art Numerical Weather
Prediction (NWP) model, at short lead times for large-scale variables, while
outperforming IFS for variables with complex fine-scale structure, including
precipitation. FourCastNet generates a week-long forecast in less than 2
seconds, orders of magnitude faster than IFS. The speed of FourCastNet enables
the creation of rapid and inexpensive large-ensemble forecasts with thousands
of ensemble-members for improving probabilistic forecasting. We discuss how
data-driven deep learning models such as FourCastNet are a valuable addition to
the meteorology toolkit to aid and augment NWP models.


title: Nuclear Excitation by Muon Capture

abstract:
  Efficient excitation of nuclei via exchange of a real or virtual photon has a
fundamental importance for nuclear science and technology development. Here, we
present a new mechanism of nuclear excitation based on the capture of a free
muon into the atomic orbits (NE$\mu$C). The cross section of such a new process
is evaluated using the Feshbach projection operator formalism and compared to
other known excitation phenomena, i.e. photo-excitation and nuclear excitation
by electron capture (NEEC), showing up to ten orders of magnitude increase in
cross section. NE$\mu$C is particularly interesting for MeV excitations that
become accessible thanks to the stronger binding of muons to the nucleus. The
binding energies of muonic atoms have been calculated introducing a state of
the art modification to the Flexible Atomic Code (FAC). An analysis of an
experimental scenarios in the context of modern muon production facilities
shows that the effect can be detectable for selected isotopes. The total
probability of NE$\mu$C is predicted to be $ P \approx 10^{-6}$ per incident
muon in a beam-based scenario. Given the high transition energy provided by
muons, NE$\mu$C can have important consequences for isomer feeding and
particle-induced fission.