title:
Factored World Models for Zero-Shot Generalization in Robotic
  Manipulation

abstract:
  World models for environments with many objects face a combinatorial
explosion of states: as the number of objects increases, the number of possible
arrangements grows exponentially. In this paper, we learn to generalize over
robotic pick-and-place tasks using object-factored world models, which combat
the combinatorial explosion by ensuring that predictions are equivariant to
permutations of objects. Previous object-factored models were limited either by
their inability to model actions, or by their inability to plan for complex
manipulation tasks. We build on recent contrastive methods for training
object-factored world models, which we extend to model continuous robot actions
and to accurately predict the physics of robotic pick-and-place. To do so, we
use a residual stack of graph neural networks that receive action information
at multiple levels in both their node and edge neural networks. Crucially, our
learned model can make predictions about tasks not represented in the training
data. That is, we demonstrate successful zero-shot generalization to novel
tasks, with only a minor decrease in model performance. Moreover, we show that
an ensemble of our models can be used to plan for tasks involving up to 12 pick
and place actions using heuristic search. We also demonstrate transfer to a
physical robot.


title:
Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory
  Prediction

abstract:
  In this paper, we study the interaction between pedestrians and vehicles and
propose a novel neural network structure called the Pedestrian-Vehicle
Interaction (PVI) extractor for learning the pedestrian-vehicle interaction. We
implement the proposed PVI extractor on both sequential approaches (long
short-term memory (LSTM) models) and non-sequential approaches (convolutional
models). We use the Waymo Open Dataset that contains real-world urban traffic
scenes with both pedestrian and vehicle annotations. For the LSTM-based models,
our proposed model is compared with Social-LSTM and Social-GAN, and using our
proposed PVI extractor reduces the average displacement error (ADE) and the
final displacement error (FDE) by 7.46% and 5.24%, respectively. For the
convolutional-based models, our proposed model is compared with Social-STGCNN
and Social-IWSTCNN, and using our proposed PVI extractor reduces the ADE and
FDE by 2.10% and 1.27%, respectively. The results show that the
pedestrian-vehicle interaction influences pedestrian behavior, and the models
using the proposed PVI extractor can capture the interaction between
pedestrians and vehicles, and thereby outperform the compared methods.


title: Conditional Uniformity and Hawkes Processes

abstract:
  Classic results show that the Hawkes self-exciting point process can be
viewed as a collection of temporal clusters, where exogenously generated
initial events give rise to endogenously driven descendant events. This
perspective provides the distribution of a cluster's size through a natural
connection to branching processes, but this is irrespective of time. Insight
into the chronology of a Hawkes process cluster has been much more elusive.
Here, we employ this cluster perspective and a novel adaptation of the random
time change theorem to establish an analog of the conditional uniformity
property enjoyed by Poisson processes. Conditional on the number of epochs in a
cluster, we show that the transformed times are jointly uniform within a
particular convex polytope. Furthermore, we find that this polytope leads to a
surprising connection between these continuous state clusters and parking
functions, discrete objects central in enumerative combinatorics and closely
related to Dyck paths on the lattice. In particular, we show that uniformly
random parking functions constitute hidden spines within Hawkes process
clusters. This yields a decomposition that is valuable both methodologically
and practically, which we demonstrate through application to the popular
Markovian Hawkes model and through proposal of a flexible and efficient
simulation algorithm.


title: Dynamic Background Subtraction by Generative Neural Networks

abstract:
  Background subtraction is a significant task in computer vision and an
essential step for many real world applications. One of the challenges for
background subtraction methods is dynamic background, which constitute
stochastic movements in some parts of the background. In this paper, we have
proposed a new background subtraction method, called DBSGen, which uses two
generative neural networks, one for dynamic motion removal and another for
background generation. At the end, the foreground moving objects are obtained
by a pixel-wise distance threshold based on a dynamic entropy map. The proposed
method has a unified framework that can be optimized in an end-to-end and
unsupervised fashion. The performance of the method is evaluated over dynamic
background sequences and it outperforms most of state-of-the-art methods. Our
code is publicly available at https://github.com/FatemeBahri/DBSGen.


title:
Neural Network Training Using Closed-Loop Data: Hazards and an
  Instrumental Variable (IVNN) Solution

abstract:
  An increasing trend in the use of neural networks in control systems is being
observed. The aim of this paper is to reveal that the straightforward
application of learning neural network feedforward controllers with closed-loop
data may introduce parameter inconsistency that degrades control performance,
and to provide a solution. The proposed method employs instrumental variables
to ensure consistent parameter estimates. A nonlinear system example reveals
that the developed instrumental variable neural network (IVNN) approach
asymptotically recovers the optimal solution, while pre-existing approaches are
shown to lead to inconsistent estimates.


title:
Accountability in an Algorithmic Society: Relationality, Responsibility,
  and Robustness in Machine Learning

abstract:
  In 1996, philosopher Helen Nissenbaum issued a clarion call concerning the
erosion of accountability in society due to the ubiquitous delegation of
consequential functions to computerized systems. Using the conceptual framing
of moral blame, Nissenbaum described four types of barriers to accountability
that computerization presented: 1) "many hands," the problem of attributing
moral responsibility for outcomes caused by many moral actors; 2) "bugs," a way
software developers might shrug off responsibility by suggesting software
errors are unavoidable; 3) "computer as scapegoat," shifting blame to computer
systems as if they were moral actors; and 4) "ownership without liability," a
free pass to the tech industry to deny responsibility for the software they
produce. We revisit these four barriers in relation to the recent ascendance of
data-driven algorithmic systems--technology often folded under the heading of
machine learning (ML) or artificial intelligence (AI)--to uncover the new
challenges for accountability that these systems present. We then look ahead to
how one might construct and justify a moral, relational framework for holding
responsible parties accountable, and argue that the FAccT community is uniquely
well-positioned to develop such a framework to weaken the four barriers.


title:
Closure operators: Complexity and applications to classification and
  decision-making

abstract:
  We study the complexity of closure operators, with applications to machine
learning and decision theory. In machine learning, closure operators emerge
naturally in data classification and clustering. In decision theory, they can
model equivalence of choice menus, and therefore situations with a preference
for flexibility. Our contribution is to formulate a notion of complexity of
closure operators, which translate into the complexity of a classifier in ML,
or of a utility function in decision theory.


title: p-adic iterated integration on semistable curves

abstract:
  We reformulate the theory of p-adic iterated integrals on semistable curves
using the unipotent log rigid fundamental group. This fundamental group carries
Frobenius and monodromy operators whose basic properties are established. By
identifying the Frobenius-invariant subgroup of the fundamental group with the
fundamental group of the dual graph, we characterize Berkovich--Coleman
integration, which is path-dependent, as integration along the
Frobenius-invariant lift of a path in the dual graph. We demonstrate the basic
properties for this integration theory. Vologodsky's path-independent
integration theory which was previously described using a monodromy condition
can now be identified as Berkovich--Coleman integration along a combinatorial
canonical path arising from the theory of combinatorial iterated integration as
developed by the first-named author and Cheng. We use this definition to lay
the groundwork for computing iterated integrals.


title:
Galois actions for semifield extensions and Galois coverings on tropical
  curves

abstract:
  For a semifield extension $T /S$, an action of a finite group $G$ on $T$ is
Galois if $(1)$ the $G$-invariant subsemifield of $T$ is $S$ and $(2)$
subgroups of $G$ whose invariant semifields coincide are equal. We show that
for a finite harmonic morphism between tropical curves $\varphi : \varGamma \to
\varGamma^{\prime}$ and an isometric action of a finite group $G$ on
$\varGamma$, $\varphi$ is $G$-Galois if and only if the natural action of $G$
on the rational function semifield $\operatorname{Rat}(\varGamma)$ of
$\varGamma$ induced by the action of $G$ on $\varGamma$ is Galois for the
semifield extension $\operatorname{Rat}(\varGamma) /
\varphi^{\ast}(\operatorname{Rat}(\varGamma^{\prime}))$, where
$\varphi^{\ast}(\operatorname{Rat}(\varGamma^{\prime}))$ stands for the
pull-back of $\operatorname{Rat}(\varGamma^{\prime})$ by $\varphi$.


title: Migration traps as the root cause of the Kepler dichotomy

abstract:
  It is often assumed that the "Kepler dichotomy" -- the apparent excess of
planetary systems with a single detected transiting planet in the Kepler
catalog -- reflects an intrinsic bimodality in the mutual inclinations of
planetary orbits. After conducting 600 simulations of planet formation followed
by simulated Kepler observations, we instead propose that the apparent
dichotomy reflects a divergence in the amount of migration and the separation
of planetary semimajor axes into distinct "clusters". We find that our
simulated high-mass systems migrate rapidly, bringing more planets into orbital
periods of less than 200 days. The outer planets are often caught in a
migration trap -- a range of planet masses and locations in which a dominant
co-rotation torque prevents inward migration -- which splits the system into
two clusters. If clusters are sufficiently separated, the inner cluster remains
dynamically cold, leading to low mutual inclinations and a higher probability
of detecting multiple transiting planets. Conversely, our simulated low-mass
systems typically bring fewer planets inside 200 days, forming a single cluster
that quickly becomes dynamically unstable, leading to collisions and high
mutual inclinations. We propose an alternative explanation for the apparent
Kepler dichotomy in which migration traps during formation lead to fewer
planets inside the Kepler detection window, and where mutual inclinations play
only a secondary role. If our scenario is correct, then Kepler's STIPs (Systems
with Tightly-packed Inner Planets) are a sample of planets that escaped capture
by co-rotation traps, and their sizes may be a valuable probe into the
structure of protoplanetary discs.


title: Coded ResNeXt: a network for designing disentangled information paths

abstract:
  To avoid treating neural networks as highly complex black boxes, the deep
learning research community has tried to build interpretable models allowing
humans to understand the decisions taken by the model. Unfortunately, the focus
is mostly on manipulating only the very high-level features associated with the
last layers. In this work, we look at neural network architectures for
classification in a more general way and introduce an algorithm which defines
before the training the paths of the network through which the per-class
information flows. We show that using our algorithm we can extract a lighter
single-purpose binary classifier for a particular class by removing the
parameters that do not participate in the predefined information path of that
class, which is approximately 60% of the total parameters. Notably, leveraging
coding theory to design the information paths enables us to use intermediate
network layers for making early predictions without having to evaluate the full
network. We demonstrate that a slightly modified ResNeXt model, trained with
our algorithm, can achieve higher classification accuracy on CIFAR-10/100 and
ImageNet than the original ResNeXt, while having all the aforementioned
properties.


title:
RG-Induced Modulus Stabilization: Perturbative de Sitter Vacua and
  Improved $\hbox{D3}$-$\overline{\hbox{D3}}$ Inflation

abstract:
  We propose a new mechanism that adapts to string theory a perturbative method
for stabilizing moduli without leaving the domain of perturbative control,
thereby evading the `Dine-Seiberg' problem. The only required nonperturbative
information comes from the standard renormalization-group resummation of
leading logarithms that allow us simultaneously to work to a fixed order in the
perturbative parameter $\alpha$ and to all orders in $\alpha \ln\tau$ where
$\tau$ is a large extra-dimensional modulus. The resulting potential is
naturally minimized for moduli of order $\tau\sim e^{1/\alpha}$ and so can be
exponentially large given ${\cal O}(10)$ input parameters. The mechanism relies
on accidental low-energy scaling symmetries known to be generic and so is
robust against UV details. The resulting compactifications generically break
supersymmetry and 4D de Sitter solutions are relatively easy to achieve without
additional uplifting. Variations on the theme lead to inflationary scenarios
for which the size of the stabilized moduli differ significantly before and
after inflation and so provide a dynamical mechanism whereby inflationary
scales are much larger than late-time physical ($e.g.$~supersymmetry breaking)
scales, with this hierarchy contingent on past cosmic evolution with the
inflaton playing a secondary late-time role as a relaxation field. We apply
this formalism to warped $\hbox{D3}$-$\overline{\hbox{D3}}$ inflation using
non-linearly realized supersymmetry to describe the antibrane tension and the
Coulomb interaction, and show how doing so our perturbative modulus
stabilization mechanism evades the $\eta$-problem that usually plagues this
scenario. We speculate about the relevance of our formalism to tachyon
condensation at later stages of brane-antibrane annihilation.


title: Two-dimensional contact of two different power-law graded elastic bodies

abstract:
  Previous study of contact of power-law graded materials concerned the contact
of a rigid body (punch) with an elastic inhomogeneous foundation whose
inhomogeneity is characterized by the Young modulus varying with depth as a
power function. This paper models Hertzian and adhesive contact of two elastic
inhomogeneous power-law graded bodies with different exponents. The problem is
governed by an integral equation with two different power kernels. A
nonstandard method of Gegenbauer orthogonal polynomials for its solution is
proposed. It leads to infinite system of linear algebraic equations of a
special structure. The integral representations of the system coefficients are
evaluated, and the properties of the system are studied. It is shown that if
the exponents coincide, the infinite system admits a simple exact solution that
corresponds to the case when the Young moduli are different but the exponents
are the same. Formulas for the length of the contact zone, the pressure
distribution, and the surface normal displacements of the contacting bodies are
obtain in the form convenient for computations. Effects of the mismatch in the
Young moduli exponents are studied. A comparative analysis of the Hertzian and
adhesive contact models clarifies the effects of the surface energy density on
the contact pressure, the contact zone size, and the profile of the contacting
bodies outside the contact area.


title:
Experimental semi-device-independent certification of indefinite causal
  order

abstract:
  We report the first experimental certification of indefinite causal order
that relies only on the characterization of the operations of a single party.
We do so in the semi-device-independent scenario with the fewest possible
assumptions of characterization of the parties' local operations in which
indefinite causal order can be demonstrated with the quantum switch. To achieve
this result, we introduce the concept of semi-device-independent causal
inequalities and show that the correlations generated in a tabletop optical
implementation of the quantum switch, in which all parties are able to collect
local outcome statistics, achieve a violation of this inequality of $224$
standard deviations. This result consists of the experimental demonstration of
indefinite causal order with the fewest device-characterization assumptions to
date.


title:
Development and Validation of an AI-Driven Model for the La Rance Tidal
  Barrage: A Generalisable Case Study

abstract:
  In this work, an AI-Driven (autonomous) model representation of the La Rance
tidal barrage was developed using novel parametrisation and Deep Reinforcement
Learning (DRL) techniques. Our model results were validated with experimental
measurements, yielding the first Tidal Range Structure (TRS) model validated
against a constructed tidal barrage and made available to academics. In order
to proper model La Rance, parametrisation methodologies were developed for
simulating (i) turbines (in pumping and power generation modes), (ii)
transition ramp functions (for opening and closing hydraulic structures) and
(iii) equivalent lagoon wetted area. Furthermore, an updated DRL method was
implemented for optimising the operation of the hydraulic structures that
compose La Rance. The achieved objective of this work was to verify the
capabilities of an AI-Driven TRS model to appropriately predict (i) turbine
power and (ii) lagoon water level variations. In addition, the observed
operational strategy and yearly energy output of our AI-Driven model appeared
to be comparable with those reported for the La Rance tidal barrage. The
outcomes of this work (developed methodologies and DRL implementations) are
generalisable and can be applied to other TRS projects. Furthermore, this work
provided insights which allow for more realistic simulation of TRS operation,
enabled through our AI-Driven model.


title: A Stochastic Capital-Labour Model with Logistic Growth Function

abstract:
  We propose and study a stochastic capital-labour model with logistic growth
function. First, we show that the model has a unique positive global solution.
Then, using the Lyapunov analysis method, we obtain conditions for the
extinction of the total labour force. Furthermore, we also prove sufficient
conditions for their persistence in mean. Finally, we illustrate our
theoretical results through numerical simulations.


title:
Robust Parameter Estimation for the Lee-Carter Model: A Probabilistic
  Principal Component Approach

abstract:
  As a traditional and widely-adopted mortality rate projection technique, by
representing the log mortality rate as a simple bilinear form
$\log(m_{x,t})=a_x+b_xk_t$. The Lee-Carter model has been extensively studied
throughout the past 30 years, however, the performance of the model in the
presence of outliers has been paid little attention, particularly for the
parameter estimation of $b_x$. In this paper, we propose a robust estimation
method for Lee-Carter model by formulating it as a probabilistic principal
component analysis (PPCA) with multivariate $t$-distributions, and an efficient
expectation-maximization (EM) algorithm for implementation. The advantages of
the method are threefold. It yields significantly more robust estimates of both
$b_x$ and $k_t$, preserves the fundamental interpretation for $b_x$ as the
first principal component as in the traditional approach and is flexible to be
integrated into other existing time series models for $k_t$. The parameter
uncertainties are examined by adopting a standard residual bootstrap. A
simulation study based on Human Mortality Database shows superior performance
of the proposed model compared to other conventional approaches.


title:
Tensor Network Path Integral Study of Dynamics in B850 LH2 Ring with
  Atomistically Derived Vibrations

abstract:
  The recently introduced multisite tensor network path integral (MS-TNPI)
allows simulation of extended quantum systems coupled to dissipative media. We
use MS-TNPI to simulate the exciton transport and the absorption spectrum of a
B850 bacteriochlorophyll (BChl) ring. The MS-TNPI network is extended to
account for the ring topology of the B850 system. Accurate molecular
dynamics-based descriptions of the molecular vibrations and the protein
scaffold is incorporated through the framework of Feynman-Vernon influence
functional. To relate the present work with the excitonic picture, an
exploration of the absorption spectrum is done by simulating it using
approximate and topologically consistent transition dipole moment vectors.
Comparison of these numerically exact MS-TNPI absorption spectra are shown with
second-order cumulant expansion-based approximations.


title: Bootstrapping PT symmetric Hamiltonians

abstract:
  Bootstrapping in Quantum Mechanics uses positivity condition to derive the
Eigenspectum. For non-hermitian systems usual positivity condition does not
work. In this paper we define positivity condition for special class of
non-hermitian hamiltonian, the PT symmetric Hamiltonian. We illustrate this
modified positivity condition with several examples and obtain eigenspectrum.


title: Domain Adversarial Training: A Game Perspective

abstract:
  The dominant line of work in domain adaptation has focused on learning
invariant representations using domain-adversarial training. In this paper, we
interpret this approach from a game theoretical perspective. Defining optimal
solutions in domain-adversarial training as a local Nash equilibrium, we show
that gradient descent in domain-adversarial training can violate the asymptotic
convergence guarantees of the optimizer, oftentimes hindering the transfer
performance. Our analysis leads us to replace gradient descent with high-order
ODE solvers (i.e., Runge-Kutta), for which we derive asymptotic convergence
guarantees. This family of optimizers is significantly more stable and allows
more aggressive learning rates, leading to high performance gains when used as
a drop-in replacement over standard optimizers. Our experiments show that in
conjunction with state-of-the-art domain-adversarial methods, we achieve up to
3.5% improvement with less than of half training iterations. Our optimizers are
easy to implement, free of additional parameters, and can be plugged into any
domain-adversarial framework.


title: A Gradient-Based Implementation of the Polyhedral Active Set Algorithm

abstract:
  The Polyhedral Active Set Algorithm (PASA) is designed to optimize a general
nonlinear function over a polyhedron. Phase one of the algorithm is a
nonmonotone gradient projection algorithm, while phase two is an active set
algorithm that explores faces of the constraint polyhedron. A gradient-based
implementation is presented, where a projected version of the conjugate
gradient algorithm is employed in phase two. Asymptotically, only phase two is
performed. Comparisons are given with IPOPT using polyhedral constrained
problems from CUTEst and the Maros/Meszaros quadratic programming test set.


title: Optimal Transport for Super Resolution Applied to Astronomy Imaging

abstract:
  Super resolution is an essential tool in optics, especially on interstellar
scales, due to physical laws restricting possible imaging resolution. We
propose using optimal transport and entropy for super resolution applications.
We prove that the reconstruction is accurate when sparsity is known and noise
or distortion is small enough. We prove that the optimizer is stable and robust
to noise and perturbations. We compare this method to a state of the art
convolutional neural network and get similar results for much less
computational cost and greater methodological flexibility.


title:
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel
  UNet for enhancing super-resolution of dynamic MRI

abstract:
  Magnetic resonance imaging (MRI) provides high spatial resolution and
excellent soft-tissue contrast without using harmful ionising radiation.
Dynamic MRI is an essential tool for interventions to visualise movements or
changes of the target organ. However, such MRI acquisition with high temporal
resolution suffers from limited spatial resolution - also known as the
spatio-temporal trade-off of dynamic MRI. Several approaches, including deep
learning based super-resolution approaches, have been proposed to mitigate this
trade-off. Nevertheless, such an approach typically aims to super-resolve each
time-point separately, treating them as individual volumes. This research
addresses the problem by creating a deep learning model which attempts to learn
both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet,
is proposed - which takes the low-resolution volume of the current time-point
along with a prior image volume. Initially, the network is supplied with a
static high-resolution planning scan as the prior image along with the
low-resolution input to super-resolve the first time-point. Then it continues
step-wise by using the super-resolved time-points as the prior image while
super-resolving the subsequent time-points. The model performance was tested
with 3D dynamic data that was undersampled to different in-plane levels. The
proposed network achieved an average SSIM value of 0.951$\pm$0.017 while
reconstructing the lowest resolution data (i.e. only 4\% of the k-space
acquired) - which could result in a theoretical acceleration factor of 25. The
proposed approach can be used to reduce the required scan-time while achieving
high spatial resolution.


title: Network Interference in Micro-Randomized Trials

abstract:
  The micro-randomized trial (MRT) is an experimental design that can be used
to develop optimal mobile health interventions. In MRTs, interventions in the
form of notifications or messages are sent through smart phones to individuals,
targeting a health-related outcome such as physical activity or weight
management. Often, mobile health interventions have a social media component;
an individual's outcome could thus depend on other individuals' treatments and
outcomes. In this paper, we study the micro-randomized trial in the presence of
such cross-unit interference. We model the cross-unit interference with a
network interference model; the outcome of one individual may affect the
outcome of another individual if and only if they are connected by an edge in
the network. Assuming the dynamics can be represented as a Markov decision
process, we analyze the behavior of the outcomes in large sample asymptotics
and show that they converge to a mean-field limit when the sample size goes to
infinity. Based on the mean-field result, we give characterization results and
estimation strategies for various causal estimands including the short-term
direct effect of a binary intervention, its long-term direct effect and its
long-term total effect.


title: Structured light dark-field microscope

abstract:
  A resolution-enhanced dark-field microscope by structured light illumination
is proposed to improve resolution and contrast. A set of phase-shifted fringes
are projected to the sample plane at large angle to capture modulated
dark-field images, from which resolution- and contrast-enhanced dark-field
image, as well as sectioned dark-field image, can be obtained. Human tissue
samples are tested to demonstrate the resolution and contrast enhancement. The
system can be implemented in transmission-mode and reflectance-mode, with
potential applications ranging from defect detection to biomedical imaging.


title:
Tuning magnetostructural phase transition in CoMn$_{0.88}$Cu$_{0.12}$Ge
  by application of hydrostatic pressure

abstract:
  Structural and magnetic properties of the CoMn$_{0.88}$Cu$_{0.12}$Ge compound
have been investigated using X-ray diffraction (XRD) and differential scanning
calorimetry (DSC) accompanied with investigation of magnetic properties under
applied hydrostatic pressure up to 12 kbar. It has been found that the
orthorhombic crystal structure of the TiNiSi-type, which is dominant at low
temperatures, turns into the hexagonal Ni$_2$In-type one, slightly above 200 K.
The structural transition is of the first-order type as confirmed by presence
of distinct thermal hysteresis exceeding 20 K. The ac magnetic measurements
indicate a ferromagnetic order below the Curie temperature $T_C$ of 238 K,
followed by additional anomaly at lower temperatures - the latter one being
related to the structural transition. Application of hydrostatic pressure leads
to temperature separation of the purely magnetic transition, whose Curie
temperature $T_C$ slowly increases with applied pressure, and the
magnetostructural transition characterized by critical temperature showing much
rapid decrease with increasing pressure. Comparison of the DSC data for the
investigated compound and the isostructural CoMn$_{0.95}$Cu$_{0.05}$Ge one has
made it possible to determine both the structural and magnetic components of
entropy change.


title: Uniform distribution and geometric incidence theory

abstract:
  A celebrated unit distance conjecture due to Erd\H os says that that the unit
distances cannot arise more than $C_{\epsilon}n^{1+\epsilon}$ times (for any
$\epsilon>0$) among $n$ points in the Euclidean plane (see e.g. \cite{SST84}
and the references contained therein). In three dimensions, the conjectured
bound is $Cn^{\frac{4}{3}}$ (see e.g. \cite{KMSS12} and \cite{Z19}). In
dimensions four and higher, this problem, in its general formulation, loses
meaning because the Lens example shows that one can construct a set of $n$
points in dimension $4$ and higher where the unit distance arises $\approx n^2$
times (see e.g. \cite{B97}). However, the Lens example is one-dimension in
nature, which raises the possibility that the unit distance conjecture is still
quite interesting in higher dimensions under additional structural assumptions
on the point set. This point of view was explored in \cite{I19}, \cite{IS16},
\cite{IMT12}, \cite{IRU14}, \cite{OO15} and has led to some interesting
connections between the unit distance problem and its continuous counterparts,
especially the Falconer distance conjecture (\cite{Falc85}).
  In this paper, we study the unit distance problem and its variants under the
assumption that the underlying family of point sets is uniformly distributed.
We prove several incidence bounds in this setting and clarify some key
properties of uniformly distributed sequences in the context of incidence
problems in combinatorial geometry.


title: Formalized functional analysis with semilinear maps

abstract:
  Semilinear maps are a generalization of linear maps between vector spaces
where we allow the scalar action to be twisted by a ring homomorphism such as
complex conjugation. In particular, this generalization unifies the concepts of
linear and conjugate-linear maps. We implement this generalization in Lean's
\textsf{mathlib} library, along with a number of important results in
functional analysis which previously were impossible to formalize properly.
Specifically, we prove the Fr\'echet--Riesz representation theorem and the
spectral theorem for compact self-adjoint operators generically over real and
complex Hilbert spaces. We also show that semilinear maps have applications
beyond functional analysis by formalizing the one-dimensional case of a theorem
of Dieudonn\'e and Manin that classifies the isocrystals over an algebraically
closed field with positive characteristic.


title: Lieb's most useful contribution to density functional theory?

abstract:
  The importance of the Lieb-Simon proof of the relative exactness of
Thomas-Fermi theory in the large-Z limit to modern density functional theory
(DFT) is explored. The principle, that there is a specific semiclassical limit
in which functionals become local, implies that there exist well-defined
leading functional corrections to local approximations that become relatively
exact for the error in local approximations in this limit. It is argued that
this principle might be used to greatly improve the accuracy of the thousand or
so DFT calculations that are now published each week. A key question is how to
find the leading corrections to any local density approximation as this limit
is approached. These corrections have been explicitly derived in ridiculously
simple model systems to ridiculously high order, yielding ridiculously accurate
energies. Much analytic work is needed to use this principle to improve
realistic calculations of molecules and solids.


title:
Giant magnetoresistance, Fermi surface topology, Shoenberg effect and
  vanishing quantum oscillations in type-II Dirac semimetal candidates MoSi$_2$
  and WSi$_2$

abstract:
  We performed comprehensive theoretical and experimental studies of the
electronic structure and the Fermi surface topology of two novel quantum
materials, MoSi$_2$ and WSi$_2$. The theoretical predictions of the electronic
structure in the vicinity of the Fermi level was verified experimentally by
thorough analysis of the observed quantum oscillations in both electrical
resistivity and magnetostriction. We established that the Fermi surface sheets
in MoSi$_2$ and WSi$_2$ consist of 3D dumbbell-shaped hole-like pockets and
rosette-shaped electron-like pockets, with nearly equal volumes. Based on this
finding, both materials were characterized as almost perfectly compensated
semimetals. In conjunction, the magnetoresistance attains giant values of
$10^4$ and $10^5\,\%$ for WSi$_2$ and MoSi$_2$, respectively. In turn, the
anisotropic magnetoresistance achieves $-95$ and $-98\,\%$ at $T=2\,$K and in
$B=14\,$T for WSi$_2$ and MoSi$_2$, respectively. Furthermore, for both
compounds we observed the Shoenberg effect in their Shubnikov-de Haas
oscillations that persisted at as high temperature as $T=25\,$K in MoSi$_2$ and
$T=12\,$K in WSi$_2$. In addition, we found for MoSi$_2$ a rarely observed
spin-zero phenomenon. Remarkably, the electronic structure calculations
revealed type-II Dirac cones located near 480 meV and 710 meV above the Fermi
level in MoSi$_2$ and WSi$_2$, respectively.


title:
Heavy polarons in ultracold atomic Fermi superfluids at the BEC-BCS
  crossover: formalism and applications

abstract:
  We investigate the system of a heavy impurity embedded in a paired
two-component Fermi gas at the crossover from a Bose-Einstein condensate (BEC)
to a Bardeen--Cooper--Schrieffer (BCS) superfluid via an extension of the
functional determinant approach (FDA). FDA is an exact numerical approach
applied to study manifestations of Anderson\textquoteright s orthogonality
catastrophe (OC) in the system of a static impurity immersed in an ideal Fermi
gas. Here, we extend the FDA to a strongly correlated superfluid background
described by a BCS mean-field wavefunction. In contrast to the ideal Fermi gas
case, the pairing gap in the BCS superfluid prevents the OC and leads to
genuine polaron signals in the spectrum. Thus, our exactly solvable model can
provide a deeper understanding of polaron physics. In addition, we find that
the polaron spectrum can be used to measure the superfluid pairing gap, and in
the case of a magnetic impurity, the energy of the sub-gap Yu-Shiba-Rusinov
(YSR) bound state. Our theoretical predictions can be examined with
state-of-art cold-atom experiments.


title: The MeLa BitChute Dataset

abstract:
  In this paper we present a near-complete dataset of over 3M videos from 61K
channels over 2.5 years (June 2019 to December 2021) from the social video
hosting platform BitChute, a commonly used alternative to YouTube.
Additionally, we include a variety of video-level metadata, including comments,
channel descriptions, and views for each video. The MeLa-BitChute dataset can
be found at:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KRD1VS.


title:
Many-body Green's function approaches to the doped Fr\"ohlich solid:
  Exact solutions and anomalous mass enhancement

abstract:
  In polar semiconductors and insulators, the Fr\"ohlich interaction between
electrons and long-wavelength longitudinal optical phonons induces a many-body
renormalization of the carrier effective masses and the appearence of
characteristic phonon sidebands in the spectral function, commonly dubbed
'polaron satellites'. The simplest model that captures these effects is the
Fr\"ohlich model, whereby electrons in a parabolic band interact with a
dispersionless longitudinal optical phonon. The Fr\"ohlich model has been
employed in a number of seminal papers, from early perturbation-theory
approaches to modern diagrammatic Monte Carlo calculations. One limitation of
this model is that it focuses on undoped systems, thus ignoring carrier
screening and Pauli blocking effects that are present in real experiments on
doped samples. To overcome this limitation, we here extend the Fr\"ohlich model
to the case of doped systems, and we provide exact solutions for the electron
spectral function, mass enhancement, and polaron satellites. We perform the
analysis using two approaches, namely Dyson's equation with the Fan-Migdal
self-energy, and the second-order cumulant expansion. We find that these two
approaches provide qualitatively different results. In particular, the Dyson's
approach yields better quasiparticle masses and worse satellites, while the
cumulant approach provides better satellite structures, at the price of worse
quasiparticle masses. Both approaches yield an anomalous enhancement of the
electron effective mass at finite doping levels, which in turn leads to a
breakdown of the quasiparticle picture in a significant portion of the phase
diagram.


title:
Exact quasiparticle properties of a heavy polaron in BCS Fermi
  superfluids

abstract:
  We present the Ramsey response and radio-frequency spectroscopy of a heavy
impurity immersed in an interacting Fermi superfluid, using exact functional
determinant approach. We describe the Fermi superfluid through the conventional
Bardeen-Cooper-Schrieffer theory and investigate the role of the pairing gap on
quasiparticle properties revealed by the two spectroscopies. The energy cost
for pair breaking prevents Anderson\textquoteright s orthogonality catastrophe
that occurs in a non-interacting Fermi gas and allows the existence of polaron
quasiparticles in the exactly solvable heavy impurity limit. Hence, we
rigorously confirm the remarkable features such as dark continuum,
molecule-hole continuum and repulsive polaron. For a magnetic impurity
scattering at finite temperature, we predict additional resonances related to
the sub-gap Yu-Shiba-Rusinov bound state, whose positions can be used to
measure the superfluid pairing gap. For a non-magnetic scattering at zero
temperature, we surprisingly find undamped repulsive polarons. These exact
results might be readily observed in quantum gas experiments with Bose-Fermi
mixtures that have a large-mass ratio.


title: Projecting local and global symmetries to the Planck scale

abstract:
  The Standard Model of the elementary particles is controlled by more than 20
parameters, of which it is not known today how they can be linked to deeper
principles. Any attempt to clean up this theory, in general results in
producing more such parameters rather than less. Yet it is clear that, at
distance scales of the order of the Planck length, the gravitational force
presents itself in such a way that the need for new physical principles is
evident. A stand-in-the-way is then quantum mechanics, a theory that demands
the occurrence of superpositions of physical states in such a way that, when
combined with general relativity, space and time themselves may require new
formalisms for being used as primary frames for the descriptions of events. In
previous papers the author proposed that quantum mechanics as a theory for the
elementary particles should be rephrased as originating from a combination of
deterministic evolution laws and discreteness at the Planck scale. This may
well have a drastic effect on the symmetry structures and algebras. Local,
discrete and continuous symmetries do not emerge without a cause, and we
suggest that the symmetries can tell us more about fundamental constants, among
which the Higgs mass is the most peculiar and the most challenging one.


title:
Uniaxial stress effect on the quasi-one-dimensional Kondo lattice
  CeCo$_2$Ga$_8$

abstract:
  Quantum critical phenomena in the quasi-one-dimensional limit remains an open
issue. We report the uniaxial stress effect on the quasi-one-dimensional Kondo
lattice CeCo$_2$Ga$_8$ by electric transport and AC heat capacity measurements.
CeCo$_2$Ga$_8$ is speculated to sit in close vicinity but on the
quantum-disordered side of a quantum critical point. Upon compressing the ${c}$
axis, parallel to the Ce-Ce chain, the onset of coherent Kondo effect is
enhanced. In contrast, the electronic specific heat diverges more rapidly at
low temperature when the intra-chain distance is elongated by compressions
along {a} or {b} axes. These results suggest that a tensile intra-chain strain
($\varepsilon_c >0$) pushes CeCo$_2$Ga$_8$ closer to a quantum critical point,
while a compressive intra-chain strain ($\varepsilon_c <0$) likely causes
departure. Our work provides a rare paradigm of manipulation near a quantum
critical point in a quasi-1D Kondo lattice by uniaxial stress, and paves the
way for further investigations on the unique feature of quantum criticality in
the quasi-1D limit.


title: Raman Imaging of Atoms Inside a High-bandwidth Cavity

abstract:
  High-bandwidth, fiber-based optical cavities are a promising building block
for future quantum networks. They are used to resonantly couple stationary
qubits such as single or multiple atoms with photons routing quantum
information into a fiber network at high rates. In high-bandwidth cavities,
standard fluorescence imaging on the atom-cavity resonance line for controlling
atom positions is impaired since the Purcell effect strongly suppresses
all-directional fluorescence. Here, we restore imaging of $^{87}$Rb atoms
strongly coupled to such a fiber Fabry-P\'erot cavity by detecting the repumper
fluorescence which is generated by continuous and three-dimensional Raman
sideband cooling. We have carried out a detailed spectroscopic investigation of
the repumper-induced differential light shifts affecting the Raman resonance,
dependent on intensity and detuning. Our analysis identifies a compromise
regime between imaging signal-to-noise ratio and survival rate, where physical
insight into the role of dipole-force fluctuations in the heating dynamics of
trapped atoms is gained.


title:
Bayesian learning of COVID-19 Vaccine safety while incorporating Adverse
  Events ontology

abstract:
  While vaccines are crucial to end the COVID-19 pandemic, public confidence in
vaccine safety has always been vulnerable. Many statistical methods have been
applied to VAERS (Vaccine Adverse Event Reporting System) database to study the
safety of COVID-19 vaccines. However, all these methods ignored the adverse
event (AE) ontology. AEs are naturally related; for example, events of
retching, dysphagia, and reflux are all related to an abnormal digestive
system. Explicitly bringing AE relationships into the model can aid in the
detection of true AE signals amid the noise while reducing false positives. We
propose a Bayesian graphical model to estimate all AEs while incorporating the
AE ontology simultaneously. We proposed strategies to construct conjugate forms
leading to an efficient Gibbs sampler. Built upon the posterior distributions,
we proposed a negative control approach to mitigate reporting bias and an
enrichment approach to detect AE groups of concern. The proposed methods were
evaluated using simulation studies and were further illustrated on studying the
safety of COVID-19 vaccines. The proposed methods were implemented in R package
\textit{BGrass} and source code are available at
https://github.com/BangyaoZhao/BGrass.


title:
Matrix concentration inequalities and efficiency of random universal
  sets of quantum gates

abstract:
  For a random set $\mathcal{S}$ of quantum gates we provide bounds on the
probability that $\mathcal{S}$ forms a $\delta$-approximate $t$-design. In
particular we show how many random gates are needed to have a
$\delta$-approximate $t$-design with high probability. We also analyze how
$\delta$ concentrates around its expected value $\mathbb{E}\delta$ for random
$\mathcal{S}$. Our results are valid for both symmetric and non-symmetric sets
of gates.


title:
Hydrodynamics of Clustered Clouds: Drafting, Survival, Condensation, and
  Ablation

abstract:
  For et al., who catalogued Magellanic Stream (MS) clouds, suggested that
there is substantial large-scale turbulence in the MS. Here we follow up with a
series of FLASH simulations that model the hydrodynamic effects that clouds
have on each other. The suite of simulations includes a range of cloud
separation distances and densities. The ambient conditions are similar to those
surrounding the MS but also relevant to the circumgalactic medium and
intergalactic medium. Ten simulations are presented, eight of which model
clustered clouds and two of which model isolated clouds. The isolated clouds
are used as controls for comparison with the multicloud simulations. We find
that if the clouds are initially near each other, then hydrodynamical drafting
helps the trailing cloud to catch the leading cloud and mix together. We
present the measured acceleration due to drafting and find that lower-density
clouds in lower-density environments experience more acceleration due to
drafting than their denser cohorts. We find that the clustering of clouds also
increases the condensation of ambient material and affects longevity. We
analyze the velocity dispersion of the clouds using a single component method
and a multicomponent decomposition method. We find that the presence of a
second cloud increases the velocity dispersion behind the trailing cloud at
some times. We find that the velocity dispersion due to gas motion in our
simulations is significantly less than the actual dispersion observed by For et
al., indicating that the thermal component must dominate in the MS.


title:
Adaptive Control of Distributed Energy Resources for Distribution Grid
  Voltage Stability

abstract:
  Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters
provide mechanisms to ensure system voltage magnitudes and power factors remain
within acceptable limits. However, these control functions can become unstable,
introducing oscillations in system voltages when not appropriately configured
or maliciously altered during a cyberattack. In the event that Volt-VAR and
Volt-Watt control functions in a portion of PV smart inverters in a
distribution grid are unstable, the proposed adaptation scheme utilizes the
remaining and stably-behaving PV smart inverters and other Distributed Energy
Resources to mitigate the effect of the instability. The adaptation mechanism
is entirely decentralized, model-free, communication-free, and requires
virtually no external configuration. We provide a derivation of the adaptive
control approach and validate the algorithm in experiments on the IEEE 37 and
8500 node test feeders.


title:
An Epidemic Compartment Model for Economic Policy Directions for
  Managing Future Pandemic

abstract:
  In this research, we develop a framework to analyze the interaction between
the economy and the Covid-19 pandemic using an extension of SIR epidemic model.
At the outset, we assume there are two health related investments including
general medical expenditures and the other for a direct investment for
controlling the pandemic. We incorporate the learning dynamics associated with
the management of the virus into our model. Given that the labor force in a
society depends on the state of the epidemic, we allow birth, death, and
vaccination to occur in our model and assume labor force consists of the
susceptible, vaccinated, and recovered individuals. We also assume parameters
in our epidemic compartmental model depend on investment amount for directly
controlling the epidemic, the health stock of individual representative agents
in the society, and the knowledge or learning about the epidemic in the
community. By controlling consumption, the general medical expenditure, and the
direct investment of funds for controlling the epidemic, we optimize the
utility realized by the representative individuals because of consumption. This
problem is nontrivial since the disease dynamics results in a non-convex
optimization problem.


title:
Bilinear forms in the Jacobian module and binding of $N$-spectral chains
  of a hypersurface with an isolated singularity

abstract:
  Using his deep and beautiful idea of cutting with a Hyperplane, Lefschetz
explained how the homology groups of a projective smooth variety could be
constructed from basic pieces, that he called primitive homology. This idea can
be applied every time we have a vector space with a biliner form (i.e. homology
with cup product) and a $\pm$-symmetric nilpotent operator (i.e. cutting with a
hyperplane). We will illustrate this in the context of Singularity Theory: A
germ of an isolated singular point of a hypersurface defined by $f=0$. We begin
with the algebraic setting in the Jacobian (or Milnor) Algebra of the
singularity, with Grothendieck pairing as bilinear form and multiplication by
$f$ as a symmetric nilpotent operator. We continue in the topological setting
of vanishing cohomology with bilinear form induced from cup product and as
nilpotent map the logarithm of the unipotent map of the monodromy as an
anti-symmetric operator. We then show how these 2 very different settings are
tied up using the Brieskorn lattice as a D-module, on using results of
Brieskorn (1970), A. Varchenko (1980s), M. Saito (1989), and C. Hertling (1999,
2004, 2005), inducing a Polarized Mixed Hodge structure at the singularity
(Steenbrink, 1976) and bringing the spectrum of the singularity as a deeper
invariant than the eigenvalues of the Algebraic Monodromy. In particular, we
show how an $f$-Jordan chain is obtained from several $N$-Jordan chains by
gluing them in the Brieskorn lattice.


title:
Development of a Scalable Quantum Memory Platform -- Materials Science
  of Erbium-Doped TiO$_2$ Thin Films on Silicon

abstract:
  Rare-earth ions (REI) have emerged as an attractive candidate for solid-state
qubits, particularly as a quantum memory. Their 4f-4f transitions are shielded
by filled 5s and 5p orbitals, offering a degree of protection from external
electric fields. Embedded within a thin film oxide host, REIs could enable a
qubit platform with significant memory capabilities. Furthermore, a
silicon-compatible thin film form factor would enable the use of standard
semiconductor fabrication processes to achieve chip-based integrability and
scalability for functional quantum networks. Towards this goal, we have carried
out optical and microstructural studies of erbium-doped polycrystalline and
epitaxial TiO$_2$ thin films on Si (100), r-sapphire, and SrTiO$_3$ (100). We
observe that the inhomogeneous optical linewidth of the Er photoluminescence is
comparable or better for polycrystalline Er:TiO$_2$(grown on Si) in comparison
to single crystal epitaxial films on sapphire or SrTiO$_3$, implying a relative
insensitivity to extended defects. We investigated the effect of the
film/substrate and film/air interface and found that the inhomogeneous
linewidth and spectral diffusion can be significantly improved via bottom
buffer and top capping layers of undoped TiO$_2$. Using such approaches, we
obtain inhomogeneous linewidths of 5.2 GHz and spectral diffusion of 180 MHz in
Er:TiO$_2$ /Si(100) films and have demonstrated the engineerability of
quantum-relevant properties in these thin films.


title:
Multisummability of formal solutions for a family of generalized
  singularly perturbed moment differential equations

abstract:
  The notion of moment differentiation is extended to the set of generalized
multisums of formal power series via an appropriate integral representation and
accurate estimates of the moment derivatives. The main result is applied to
characterize generalized multisummability of the formal solution to a family of
singularly perturbed moment differential equations in the complex domain,
broadening widely the range of singularly perturbed functional equations to be
considered in practice, such as singularly perturbed differential equations and
singularly perturbed fractional differential equations.


title:
Theory of perturbatively nonlinear quantum transport II: Hilbert space
  truncation, gauge invariance, and second order transport in a spatially
  uniform, time-varying electric field

abstract:
  This article is the second of a trilogy that addresses the perturbative
response of general quantum systems, with possibly nontrivial ground state
geometry, beyond linear order. Here, we establish concise, general formulae for
second order response to a spatially uniform, time-varying electric field in
the velocity gauge that are $\textit{manifestly}$ free of static limit spurious
divergences. We first discuss general quantum evolution in a curved space, then
detail how such a situation is a natural byproduct of Hilbert space truncation,
and point out crucial subtleties associated with the resulting finite
curvatures. We then present a geometric perspective of the two popular gauges
often used in quantum transport theories, the velocity gauge and the length
gauge, and discuss how they, taking truncation-induced curvature effects into
account, naturally lead to the same results in spite of the truncation. We
highlight subtle formal discrepancies in the literature. Finally, we provide a
general scheme for removing static limit spurious divergences in the velocity
gauge $\textit{without}$ frequency expansions and present concise and
comprehensive Green's function formulae for responses up to second order. As an
application of specific aspects of our theory, second order charge current
responses in selected cases are analyzed in parts I and III.


title:
Theory of perturbatively nonlinear quantum transport I: general
  formulation and structure of the retarded correlator

abstract:
  This article is the first of a trilogy that addresses various aspects of the
perturbative response of general quantum systems, with possibly nontrivial
ground state geometry, beyond linear order. Here, we use group theoretical
considerations to investigate the structure of retarded correlators, and
demonstrate how they decompose according to irreducible representations of a
`time-reversal group' and relevant permutation groups, with the former probing
dissipative and time-reversal properties, and the latter discerning
configurational properties -- longitudinal, transverse and their
generalizations. We establish second order fluctuation-dissipation and
fluctuation-reaction theorems, and connect them to well-known second order
transport effects such as the shift and injection currents. Exploiting the
Schur-Weyl duality between irreducible representations of general linear groups
and irreducible representations of permutation groups, we show how to decide
which terms in the decomposition based on the latter can be supported by
different crystals described via the 32 point groups, and perform the full
point group classification for rank 3 and 4 polar and axial tensors. Our
results provide a formal basis for the extraction of uniquely differing
physical effects from retarded correlators. Applications to second order charge
current responses in selected cases are given in part III.


title: Voltage operations on maniplexes

abstract:
  Classical geometric and topological operations on polyhedra, maps and
polytopes often give rise to structures with the same symmetry group as the
original one, but with more flags. In this paper we introduce the notion of
voltage operations on maniplexes, as a way to generalize such operations. Our
technique provides a way to study classical operations in a graph theory
setting. In fact, voltage operations can be applied to symmetry type graphs,
and more generally to n-valent properly n-edge colored graphs. We focus on
studying the interactions between voltage operations and the symmetries of the
operated object, and show that they can be potentially used to build maniplexes
with prescribed symmetry type graphs. Moreover, a complete characterization of
when an operation can be seen as a voltage operation is given.


title: Quantum effects in rotating reference frames

abstract:
  We consider the time delay of interfering single photons oppositely traveling
in the Kerr metric of a rotating massive object. Classically, the time delay
shows up as a phase difference between coherent sources of light. In quantum
mechanics, the loss in visibility due to the indistinguishability of
interfering photons is directly related to the time delay. We can thus observe
the Kerr frame-dragging effect using the Hong-Ou-Mandel (HOM) dip, a purely
quantum mechanical effect. By Einstein's equivalence principle, we can
analogously consider a rotating turntable to simulate the Kerr metric. We look
at the feasibility of such an experiment using optical fibre, and note a
cancellation in the second-order dispersion but a direction-dependent
difference in group velocity. However, for the chosen experimental parameters,
we can effectively assume light propagating through a vacuum.


title:
Give me a knee radiograph, I will tell you where the knee joint area is:
  a deep convolutional neural network adventure

abstract:
  Knee pain is undoubtedly the most common musculoskeletal symptom that impairs
quality of life, confines mobility and functionality across all ages. Knee pain
is clinically evaluated by routine radiographs, where the widespread adoption
of radiographic images and their availability at low cost, make them the
principle component in the assessment of knee pain and knee pathologies, such
as arthritis, trauma, and sport injuries. However, interpretation of the knee
radiographs is still highly subjective, and overlapping structures within the
radiographs and the large volume of images needing to be analyzed on a daily
basis, make interpretation challenging for both naive and experienced
practitioners. There is thus a need to implement an artificial intelligence
strategy to objectively and automatically interpret knee radiographs,
facilitating triage of abnormal radiographs in a timely fashion. The current
work proposes an accurate and effective pipeline for autonomous detection,
localization, and classification of knee joint area in plain radiographs
combining the You Only Look Once (YOLO v3) deep convolutional neural network
with a large and fully-annotated knee radiographs dataset. The present work is
expected to stimulate more interest from the deep learning computer vision
community to this pragmatic and clinical application.


title:
Including Facial Expressions in Contextual Embeddings for Sign Language
  Generation

abstract:
  State-of-the-art sign language generation frameworks lack expressivity and
naturalness which is the result of only focusing manual signs, neglecting the
affective, grammatical and semantic functions of facial expressions. The
purpose of this work is to augment semantic representation of sign language
through grounding facial expressions. We study the effect of modeling the
relationship between text, gloss, and facial expressions on the performance of
the sign generation systems. In particular, we propose a Dual Encoder
Transformer able to generate manual signs as well as facial expressions by
capturing the similarities and differences found in text and sign gloss
annotation. We take into consideration the role of facial muscle activity to
express intensities of manual signs by being the first to employ facial action
units in sign language generation. We perform a series of experiments showing
that our proposed model improves the quality of automatically generated sign
language.


title:
Reversible to Irreversible Transitions for Cyclically Driven Disks on
  Periodic Obstacle Arrays

abstract:
  We examine the collective dynamics of disks moving through a square array of
obstacles under cyclic square wave driving. Below a critical density we find
that system organizes into a reversible state in which the disks return to the
same positions at the end of every drive cycle. Above this density, the
dynamics are irreversible and the disks do not return to the same positions
after each cycle. The critical density depends strongly on the angle $\theta$
between the driving direction and a symmetry axis of the obstacle array, with
the highest critical densities appearing at commensurate angles such as
$\theta=0^\circ$ and $\theta=45^\circ$ and the lowest critical densities
falling at $\theta = \arctan(0.618)$, the inverse of the golden ratio, where
the flow is the most frustrated. As the density increases, the number of cycles
required to reach a reversible state grows as a power law with an exponent near
$\nu=1.36$, similar to what is found in periodically driven colloidal and
superconducting vortex systems.


title: Cyclops: Open Platform for Scale Truck Platooning

abstract:
  Cyclops, introduced in this paper, is an open research platform for everyone
that wants to validate novel ideas and approaches in the area of self-driving
heavy-duty vehicle platooning. The platform consists of multiple 1/14 scale
semi-trailer trucks, a scale proving ground, and associated computing,
communication and control modules that enable self-driving on the proving
ground. A perception system for each vehicle is composed of a lidar-based
object tracking system and a lane detection/control system. The former is to
maintain the gap to the leading vehicle and the latter is to maintain the
vehicle within the lane by steering control. The lane detection system is
optimized for truck platooning where the field of view of the front-facing
camera is severely limited due to a small gap to the leading vehicle. This
platform is particularly amenable to validate mitigation strategies for
safety-critical situations. Indeed, a simplex structure is adopted in the
embedded module for testing various fail safe operations. We illustrate a
scenario where camera sensor fails in the perception system but the vehicle
operates at a reduced capacity to a graceful stop. Details of the Cyclops
including 3D CAD designs and algorithm source codes are released for those who
want to build similar testbeds.


title:
Something Can Come of Nothing: Quantum Fluctuations and the Casimir
  Force

abstract:
  The Casimir force provides a striking example of the effects of quantum
fluctuations in a mesoscopic system. Because it arises from the objects'
electromagnetic response, the necessary calculations in quantum field theory
are most naturally expressed in terms of electromagnetic scattering from each
object. In this review we illustrate a variety of such techniques, with a focus
on those that can be expressed in terms of surface effects, including both
idealized boundary conditions and their physical realization in terms of
material properties.


title:
TwHIN: Embedding the Twitter Heterogeneous Information Network for
  Personalized Recommendation

abstract:
  Social networks, such as Twitter, form a heterogeneous information network
(HIN) where nodes represent domain entities (e.g., user, content, advertiser,
etc.) and edges represent one of many entity interactions (e.g, a user
re-sharing content or "following" another). Interactions from multiple relation
types can encode valuable information about social network entities not fully
captured by a single relation; for instance, a user's preference for accounts
to follow may depend on both user-content engagement interactions and the other
users they follow. In this work, we investigate knowledge-graph embeddings for
entities in the Twitter HIN (TwHIN); we show that these pretrained
representations yield significant offline and online improvement for a diverse
range of downstream recommendation and classification tasks: personalized ads
rankings, account follow-recommendation, offensive content detection, and
search ranking. We discuss design choices and practical challenges of deploying
industry-scale HIN embeddings, including compressing them to reduce end-to-end
model latency and handling parameter drift across versions.


title:
Massively parallel pixel-by-pixel nanophotonic optimization using a
  Green's function formalism

abstract:
  We introduce an efficient parallelization scheme to implement pixel-by-pixel
nanophotonic optimization using a Green's function based formalism. The crucial
insight in our proposal is the reframing of the optimization algorithm as a
large-scale data processing pipeline, which allows for the efficient
distribution of computational tasks across thousands of workers. We demonstrate
the utility of our implementation by exercising it to optimize a high numerical
aperture focusing metalens at problem sizes that would otherwise be far out of
reach for the Green's function based method. Finally, we highlight the
connection to powerful ideas from reinforcement learning as a natural corollary
of reinterpreting the nanophotonic inverse design problem as a graph traversal
enabled by the pixel-by-pixel optimization paradigm.


title:
Investigating cognitive ability using action-based models of structural
  brain networks

abstract:
  Recent developments in network neuroscience have highlighted the importance
of developing techniques for analyzing and modeling brain networks. A
particularly powerful approach for studying complex neural systems is to
formulate generative models that use wiring rules to synthesize networks
closely resembling the topology of a given connectome. Successful models can
highlight the principles by which a network is organized (identify structural
features that arise from wiring rules versus those that emerge) and potentially
uncover the mechanisms by which it grows and develops. Previous research has
shown that such models can validate the effectiveness of spatial embedding and
other (non-spatial) wiring rules in shaping the network topology of the human
connectome. In this research, we propose variants of the action-based model
that combine a variety of generative factors capable of explaining the topology
of the human connectome. We test the descriptive validity of our models by
evaluating their ability to explain between-subject variability. Our analysis
provides evidence that geometric constraints are vital for connectivity between
brain regions, and an action-based model relying on both topological and
geometric properties can account for between-subject variability in structural
network properties. Further, we test correlations between parameters of
subject-optimized models and various measures of cognitive ability and find
that higher cognitive ability is associated with an individual's tendency to
form long-range or non-local connections.


title: 1/f Noise Under Drift And Thermal Agitation In Semiconductor Materials

abstract:
  Voss and Clarke observed 1/f noise in the square of Johnson noise across
samples in thermal equilibrium without applying a current. We refer to this
phenomenon as thermal 1/f noise. Voss and Clarke suggested spatially correlated
temperature fluctuations as an origin of thermal 1/f noise; they also showed
that thermal 1/f noise closely matches the 1/f spectrum obtained by passing a
current through the sample. An intermittent generation-recombination (g-r)
process has recently been introduced to interpret 1/f noise in semiconductors.
The square of this intermittent g-r process generates a 1/f noise component
which correlates with Voss and Clarke's empirical findings. Traps which
intermittently rather than continuously generate g-r pulses are suggested as
the origin of 1/f noise under drift and thermal agitation. We see no need to
introduce correlated temperature fluctuations or oxide traps with a large
distribution of time constants to explain 1/f noise.


title:
Comment on "Standard and non-standard Lagrangians for dissipative
  dynamical systems with variable coefficientes"

abstract:
  Z.E. Musielak has reported in 2008 J. Phys. A: Math. Theor. {\bf 41} 055205
methods to obtain standard and non-standard Lagrangians and identify classes of
equations of motion that admit a Lagrangian description. In this comment we
show how to obtain new non-standard Lagrangians using the non-standard
Lagrangians previously found. In particular, it is demonstrated that for every
non-standard Lagrangian one can generate a new non-standard Lagrangian
associated to a new equation of motion.


title: Classical model for survival resonances close to Talbot time

abstract:
  We present a classical approximation for the peaks of survival resonances
occurring when diffracting matter waves from absorption potentials. Generally
our simplified model describes the absorption-diffraction process around the
Talbot time very well. Classical treatments of this process are presently
lacking. For purely imaginary potentials, the classical model duplicates
quantum mechanical calculations. The classical model allows for simple
evolution of phase-space probability densities, which in the limit of the
effective Planck's constant going to zero allows for a compact analytical
expression of the survival probability as a function of remaining parameters.
Our work extends the range of processes that can be described through classical
analogues.


title:
Li\'enard-Wiechert Retarded Potentials Derived from the Electrodynamic
  Doppler Effect

abstract:
  Here electrodynamic retarded potentials are derived considering the Doppler
shifted electromagnetic (e.m.) field generated by a charge moving along a
generic trajectory. This alternative route allows a direct physical
interpretation of the Doppler term $(v/c)$ characterizing these potentials.
Meanwhile, it does not incur mathematically questionable aspects or it does not
hide the physical nature of these potentials when the charge volume is taken to
the infinitesimal limit of a point charge or when mathematical developments
derived from special relativity are used. The process outlined here is
mathematically rigorous and elegant and sheds light on the physics underlying
the origin of the retarded potential, making the understanding of the radiation
processes by a moving charge more accessible to students, teachers, and
scholars.


title:
A Density-Dependent Diffusion Model of an Interacting System of Brownian
  Particles

abstract:
  Density-dependent diffusion is a widespread phenomenon in nature. We have
examined the density-dependent diffusion behavior of some biological processes
such as tumor growth and invasion [23]. Here, we extend our previous work by
developing computational techniques to analyze the density-dependent diffusion
behavior of one-dimensional interacting particle systems, which have been used
to model numerous microscopic processes [17-19], and we apply our techniques to
an interacting system of Brownian particles, with hard-core interactions and
nearest-neighbor adhesion, known as single-file dynamics. Through large-scale
numerical simulations that exploit Monte-Carlo techniques and high-performance
computing resources, we show that the diffusion rate in such systems depends on
the average particle density. Extensions to the techniques we present here
enable researchers to examine the density-dependent diffusion behavior of many
physical systems in nature that undergo one-dimensional diffusion associated
with a change in the particle density; such as ion transport processes,
channeling in zeolites, etc.


title:
Robust, Deep, and Reinforcement Learning for Management of Communication
  and Power Networks

abstract:
  This thesis develops data-driven machine learning algorithms to managing and
optimizing the next-generation highly complex cyberphysical systems, which
desperately need ground-breaking control, monitoring, and decision making
schemes that can guarantee robustness, scalability, and situational awareness.
The present thesis first develops principled methods to make generic machine
learning models robust against distributional uncertainties and adversarial
data. Particular focus will be on parametric models where some training data
are being used to learn a parametric model. The developed framework is of high
interest especially when training and testing data are drawn from "slightly"
different distribution. We then introduce distributionally robust learning
frameworks to minimize the worst-case expected loss over a prescribed ambiguity
set of training distributions quantified via Wasserstein distance. Later, we
build on this robust framework to design robust semi-supervised learning over
graph methods. The second part of this thesis aspires to fully unleash the
potential of next-generation wired and wireless networks, where we design
"smart" network entities using (deep) reinforcement learning approaches.
Finally, this thesis enhances the power system operation and control. Our
contribution is on sustainable distribution grids with high penetration of
renewable sources and demand response programs. To account for unanticipated
and rapidly changing renewable generation and load consumption scenarios, we
specifically delegate reactive power compensation to both utility-owned control
devices (e.g., capacitor banks), as well as smart inverters of distributed
generation units with cyber-capabilities.


title:
Enhancing ASR for Stuttered Speech with Limited Data Using Detect and
  Pass

abstract:
  It is estimated that around 70 million people worldwide are affected by a
speech disorder called stuttering. With recent advances in Automatic Speech
Recognition (ASR), voice assistants are increasingly useful in our everyday
lives. Many technologies in education, retail, telecommunication and healthcare
can now be operated through voice. Unfortunately, these benefits are not
accessible for People Who Stutter (PWS). We propose a simple but effective
method called 'Detect and Pass' to make modern ASR systems accessible for
People Who Stutter in a limited data setting. The algorithm uses a context
aware classifier trained on a limited amount of data, to detect acoustic frames
that contain stutter. To improve robustness on stuttered speech, this extra
information is passed on to the ASR model to be utilized during inference. Our
experiments show a reduction of 12.18% to 71.24% in Word Error Rate (WER)
across various state of the art ASR systems. Upon varying the threshold of the
associated posterior probability of stutter for each stacked frame used in
determining low frame rate (LFR) acoustic features, we were able to determine
an optimal setting that reduced the WER by 23.93% to 71.67% across different
ASR systems.


title: Neural Architecture Search for Energy Efficient Always-on Audio Models

abstract:
  Mobile and edge computing devices for always-on audio classification require
energy-efficient neural network architectures. We present a neural architecture
search (NAS) that optimizes accuracy, energy efficiency and memory usage. The
search is run on Vizier, a black-box optimization service. We present a search
strategy that uses both Bayesian and regularized evolutionary search with
particle swarms, and employs early-stopping to reduce the computational burden.
The search returns architectures for a sound-event classification dataset based
upon AudioSet with similar accuracy to MobileNetV1/V2 implementations but with
an order of magnitude less energy per inference and a much smaller memory
footprint.


title: Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

abstract:
  MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the
interaction of spin-momentum locked surface electrons and intrinsic magnetism,
and it exhibits novel magnetic and topological phenomena. Recent studies
suggested that the interaction of electrons and magnetism can be affected by
the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects.
Here we report an observation of nonreciprocal transport, i.e.
current-direction-dependent resistance, in a bilayer composed of
antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal
response below the N\'eel temperature confirms a correlation between
nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular
dependence of the nonreciprocal transport indicates that nonreciprocal response
originates from the asymmetry scattering of electrons at the surface of MBT
mediated by magnon. Our work provides an insight into nonreciprocity arising
from the correlation between magnetism and Dirac surface electrons in intrinsic
magnetic topological insulators.


title: XRISM Quick Reference

abstract:
  This document was prepared by the XRISM Science Team to introduce the XRISM
mission, its onboard instruments, figures of merit, and examples of
high-resolution X-ray spectroscopy to general astronomers and students.


title:
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG
  Learning for Emotion Recognition

abstract:
  We propose PARSE, a novel semi-supervised architecture for learning strong
EEG representations for emotion recognition. To reduce the potential
distribution mismatch between the large amounts of unlabeled data and the
limited amount of labeled data, PARSE uses pairwise representation alignment.
First, our model performs data augmentation followed by label guessing for
large amounts of original and augmented unlabeled data. This is then followed
by sharpening of the guessed labels and convex combinations of the unlabeled
and labeled data. Finally, representation alignment and emotion classification
are performed. To rigorously test our model, we compare PARSE to several
state-of-the-art semi-supervised approaches which we implement and adapt for
EEG learning. We perform these experiments on four public EEG-based emotion
recognition datasets, SEED, SEED-IV, SEED-V and AMIGOS (valence and arousal).
The experiments show that our proposed framework achieves the overall best
results with varying amounts of limited labeled samples in SEED, SEED-IV and
AMIGOS (valence), while approaching the overall best result (reaching the
second-best) in SEED-V and AMIGOS (arousal). The analysis shows that our
pairwise representation alignment considerably improves the performance by
reducing the distribution alignment between unlabeled and labeled data,
especially when only 1 sample per class is labeled.


title:
Multivariate distance matrix regression for a manifold-valued response
  variable

abstract:
  In this paper, we propose the use of geodesic distances in conjunction with
multivariate distance matrix regression, called geometric-MDMR, as a powerful
first step analysis method for manifold-valued data. Manifold-valued data is
appearing more frequently in the literature from analyses of earthquake to
analysing brain patterns. Accounting for the structure of this data increases
the complexity of your analysis, but allows for much more interpretable results
in terms of the data. To test geometric-MDMR, we develop a method to simulate
functional connectivity matrices for fMRI data to perform a simulation study,
which shows that our method outperforms the current standards in fMRI analysis.


title:
Do People Engage Cognitively with AI? Impact of AI Assistance on
  Incidental Learning

abstract:
  When people receive advice while making difficult decisions, they often make
better decisions in the moment and also increase their knowledge in the
process. However, such incidental learning can only occur when people
cognitively engage with the information they receive and process this
information thoughtfully. How do people process the information and advice they
receive from AI, and do they engage with it deeply enough to enable learning?
To answer these questions, we conducted three experiments in which individuals
were asked to make nutritional decisions and received simulated AI
recommendations and explanations. In the first experiment, we found that when
people were presented with both a recommendation and an explanation before
making their choice, they made better decisions than they did when they
received no such help, but they did not learn. In the second experiment,
participants first made their own choice, and only then saw a recommendation
and an explanation from AI; this condition also resulted in improved decisions,
but no learning. However, in our third experiment, participants were presented
with just an AI explanation but no recommendation and had to arrive at their
own decision. This condition led to both more accurate decisions and learning
gains. We hypothesize that learning gains in this condition were due to deeper
engagement with explanations needed to arrive at the decisions. This work
provides some of the most direct evidence to date that it may not be sufficient
to include explanations together with AI-generated recommendation to ensure
that people engage carefully with the AI-provided information. This work also
presents one technique that enables incidental learning and, by implication,
can help people process AI recommendations and explanations more carefully.


title: Learning Temporal Rules from Noisy Timeseries Data

abstract:
  Events across a timeline are a common data representation, seen in different
temporal modalities. Individual atomic events can occur in a certain temporal
ordering to compose higher level composite events. Examples of a composite
event are a patient's medical symptom or a baseball player hitting a home run,
caused distinct temporal orderings of patient vitals and player movements
respectively. Such salient composite events are provided as labels in temporal
datasets and most works optimize models to predict these composite event labels
directly. We focus on uncovering the underlying atomic events and their
relations that lead to the composite events within a noisy temporal data
setting. We propose Neural Temporal Logic Programming (Neural TLP) which first
learns implicit temporal relations between atomic events and then lifts logic
rules for composite events, given only the composite events labels for
supervision. This is done through efficiently searching through the
combinatorial space of all temporal logic rules in an end-to-end differentiable
manner. We evaluate our method on video and healthcare datasets where it
outperforms the baseline methods for rule discovery.


title: Regularized Q-learning

abstract:
  Q-learning is widely used algorithm in reinforcement learning community.
Under the lookup table setting, its convergence is well established. However,
its behavior is known to be unstable with the linear function approximation
case. This paper develops a new Q-learning algorithm that converges when linear
function approximation is used. We prove that simply adding an appropriate
regularization term ensures convergence of the algorithm. We prove its
stability using a recent analysis tool based on switching system models.
Moreover, we experimentally show that it converges in environments where
Q-learning with linear function approximation has known to diverge. We also
provide an error bound on the solution where the algorithm converges.


title: Weight polytopes and saturation of Demazure characters

abstract:
  For $G$ a reductive group and $T\subset B$ a maximal torus and Borel
subgroup, Demazure modules are certain $B$-submodules, indexed by elements of
the Weyl group, of the finite irreducible representations of $G$. In order to
describe the $T$-weight spaces that appear in a Demazure module, we study the
convex hull of these weights - the Demazure polytope. We characterize these
polytopes both by vertices and by inequalities, and we use these results to
prove that Demazure characters are saturated, in the case that $G$ is simple of
classical Lie type. Specializing to $G=GL_n$, we recover results of Fink,
M\'esz\'aros, and St. Dizier, and separately Fan and Guo, on key polynomials,
originally conjectured by Monical, Tokcan, and Yong.


title:
Generating 77 T using a portable pulse magnet for single shot quantum
  beam experiments

abstract:
  We devised a portable system that generates pulsed high magnetic fields up to
77 T with 3 $\mu$s duration. The system employs the single turn coil method, a
destructive way of field generation. The system consists of a capacitor of 10.4
$\mu$F, a 30 kV charger, a mono air-gap switch, a triggering system, and a
magnet clamp, which weighs less than 1.0 tons in total and is transportable.
The system offers opportunities for single-shot experiments at ultrahigh
magnetic fields in combinations with novel quantum beams. The single-shot x-ray
diffraction experiment using x-ray free-electron laser at 65 T is presented. We
comment on the possible update of the system for the generation of 100 T.


title: Liquid Time Crystals

abstract:
  We have constructed and characterised an instrument to study gravitationally
bouncing droplets of fluid, subjected to periodic driving force. Our system
incorporates a droplet printer that enables an on-demand computer controlled
deposition of droplets on the fluid surface. We demonstrate the operation of
this instrument by creating and observing long-lived and interacting time
crystals whose evolution we have witnessed for more than one hundred thousand
oscillation periods. Our observations provide points of comparison for
experiments that differentiate between quantum and classical time crystal
behaviours in driven non-equilibrium systems.


title: On Some Hypergeometric Supercongruence Conjectures of Long

abstract:
  In 2003, Rodriguez Villegas conjectured 14 supercongruences between
hypergeometric functions arising as periods of certain families of rigid
Calabi-Yau threefolds and the Fourier coefficients of weight 4 modular forms.
Uniform proofs of these supercongruences were given in 2019 by Long, Tu, Yui,
and Zudilin. Using p-adic techniques of Dwork, they reduce the original
supercongruences to related congruences which involve only the hypergeometric
series. We generalize their techniques to consider six further supercongruences
recently conjectured by Long. In particular we prove an analogous version of
Long, Tu, Yui, and Zudilin's reduced congruences for each of these six cases.
We also conjecture a generalization of Dwork's work which has been observed
computationally and which would, together with a proof of modularity for Galois
representations associated to our hypergeometric data, yield a full proof of
Long's conjectures.


title:
DFT+$U$ within the framework of linear combination of numerical atomic
  orbitals

abstract:
  We present a formulation and implementation of the DFT+\textit{U} method
within the framework of linear combination of numerical atomic orbitals (NAO).
Our implementation not only enables single-point total energy and
electronic-structure calculations but also provides access to atomic forces and
stresses, hence allowing for full structure relaxations of periodic systems.
Furthermore, our implementation allows one to deal with non-collinear spin
texture, with the spin-orbit coupling (SOC) effect treated self-consistently.
The key aspect behind our implementation is a suitable definition of the
correlated subspace when multiple atomic orbitals with the same angular
momentum are used, and this is addressed via the "Mulliken charge projector"
constructed in terms of the first (most localized) atomic orbital within the
$d/f$ angular momentum channel. The important Hubbard $U$ and Hund $J$
parameters can be estimated from a screened Coulomb potential of the Yukawa
type, with the screening parameter either chosen semi-empirically or determined
from the Thomas-Fermi screening model. Benchmark calculations are performed for
four late transition metal monoxide bulk systems, i.e., MnO, FeO, CoO, and NiO,
and for the 5$d$-electron compounds IrO$_2$. For the former type of systems, we
check the performance of our DFT+$U$ implementation for calculating band gaps,
magnetic moments, electronic band structures, as well as forces and stresses;
for the latter, the efficacy of our DFT+$U$+SOC implementation is assessed.
Systematic comparisons with available experimental results, and especially with
the results from other implementation schemes are carried out, which
demonstrate the validity of our NAO-based DFT+$U$ formalism and implementation.


title:
An estimate of the stochastic gravitational wave background from the
  MassiveBlackII simulation

abstract:
  A population of super-massive black hole binaries is expected to generate a
stochastic gravitational wave background (SGWB) in the pulsar timing array
(PTA) frequency range of $10^{-9}$--$10^{-7}$ Hz. Detection of this signal is a
current observational goal and so predictions of its characteristics are of
significant interest. In this work we use super-massive black hole binary
mergers from the MassiveBlackII simulation to estimate the characteristic
strain of the stochastic background. We examine both a gravitational wave
driven model of binary evolution and a model which also includes the effects of
stellar scattering and a circumbinary gas disk. Results are consistent with PTA
upper limits and similar to estimates in the literature. The characteristic
strain at a reference frequency of $1 yr^{-1}$ is found to be $A_{yr^{-1}} =
6.9 \times 10^{-16}$ and $A_{yr^{-1}} = 6.4 \times 10^{-16}$ in the
gravitational-wave driven and stellar scattering/gas disk cases, respectively.
Using the latter approach, our models show that the SGWB is mildly suppressed
compared to the purely gravitational wave driven model as frequency decreases
inside the PTA frequency band.


title: Incremental Learning of Structured Memory via Closed-Loop Transcription

abstract:
  This work proposes a minimal computational model for learning a structured
memory of multiple object classes in an incremental setting. Our approach is
based on establishing a closed-loop transcription between multiple classes and
their corresponding subspaces, known as a linear discriminative representation,
in a low-dimensional feature space. Our method is both simpler and more
efficient than existing approaches to incremental learning, in terms of model
size, storage, and computation: it requires only a single, fixed-capacity
autoencoding network with a feature space that is used for both discriminative
and generative purposes. All network parameters are optimized simultaneously
without architectural manipulations, by solving a constrained minimax game
between the encoding and decoding maps over a single rate reduction-based
objective. Experimental results show that our method can effectively alleviate
catastrophic forgetting, achieving significantly better performance than prior
work for both generative and discriminative purposes.


title:
Checking Continuous Stochastic Logic against Quantum Continuous-Time
  Markov Chains

abstract:
  Verifying quantum systems has attracted a lot of interest in the last
decades. In this paper, we study the quantitative model-checking of quantum
continuous-time Markov chains (quantum CTMCs). The branching-time properties of
quantum CTMCs are specified by continuous stochastic logic (CSL), which is
famous for verifying real-time systems, including classical CTMCs. The core of
checking the CSL formulas lies in tackling multiphase until formulas. We
develop an algebraic method using proper projection, matrix exponentiation, and
definite integration to symbolically calculate the probability measures of path
formulas. Thus the decidability of CSL is established. To be efficient,
numerical methods are incorporated to guarantee that the time complexity is
polynomial in the encoding size of the input model and linear in the size of
the input formula. A running example of Apollonian networks is further provided
to demonstrate our method.


title:
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air
  Pollution Data

abstract:
  Analyzing air pollution data is challenging as there are various analysis
focuses from different aspects: feature (what), space (where), and time (when).
As in most geospatial analysis problems, besides high-dimensional features, the
temporal and spatial dependencies of air pollution induce the complexity of
performing analysis. Machine learning methods, such as dimensionality
reduction, can extract and summarize important information of the data to lift
the burden of understanding such a complicated environment. In this paper, we
present a methodology that utilizes multiple machine learning methods to
uniformly explore these aspects. With this methodology, we develop a visual
analytic system that supports a flexible analysis workflow, allowing domain
experts to freely explore different aspects based on their analysis needs. We
demonstrate the capability of our system and analysis workflow supporting a
variety of analysis tasks with multiple use cases.


title:
Gradient estimates of very weak solutions to general quasilinear
  elliptic equations

abstract:
  We establish a gradient estimate for a very weak solution to a quasilinear
elliptic equation with a nonstandard growth condition, which is a natural
generalization of the $p$-Laplace equation. We investigate the maximum extent
for the gradient estimate to hold without imposing any regularity assumption on
the nonlinearity other than basic structure assumptions. Our results also
include a higher integrability result of the gradient and the existence for the
very weak solutions to such nonlinear problems.


title:
Asymptotic expansion of 2-dimensional gradient graph with vanishing mean
  curvature at infinity

abstract:
  In this paper, we establish the asymptotic expansion at infinity of gradient
graph in dimension 2 with vanishing mean curvature at infinity. This
corresponds to our previous results in higher dimensions and generalizes the
results for minimal gradient graph on exterior domain in dimension 2. Different
from the strategies for higher dimensions, instead of the equivalence of
Green's function on unbounded domains, we apply a version of iteration methods
from Bao--Li--Zhang [Calc.Var PDE, 52(2015), pp. 39-63] that is refined by
spherical harmonic expansions to provide a more explicit asymptotic behavior
than known results.


title:
FAAG: Fast Adversarial Audio Generation through Interactive Attack
  Optimisation

abstract:
  Automatic Speech Recognition services (ASRs) inherit deep neural networks'
vulnerabilities like crafted adversarial examples. Existing methods often
suffer from low efficiency because the target phases are added to the entire
audio sample, resulting in high demand for computational resources. This paper
proposes a novel scheme named FAAG as an iterative optimization-based method to
generate targeted adversarial examples quickly. By injecting the noise over the
beginning part of the audio, FAAG generates adversarial audio in high quality
with a high success rate timely. Specifically, we use audio's logits output to
map each character in the transcription to an approximate position of the
audio's frame. Thus, an adversarial example can be generated by FAAG in
approximately two minutes using CPUs only and around ten seconds with one GPU
while maintaining an average success rate over 85%. Specifically, the FAAG
method can speed up around 60% compared with the baseline method during the
adversarial example generation process. Furthermore, we found that appending
benign audio to any suspicious examples can effectively defend against the
targeted adversarial attack. We hope that this work paves the way for inventing
new adversarial attacks against speech recognition with computational
constraints.


title: Galactic Positrons from Thermonuclear Supernovae

abstract:
  Type Ia Supernovae (SNe Ia) may originate from a wide variety of explosion
scenarios and progenitor channels. They exhibit a factor of about 10 difference
in brightness and, thus, a differentiation in the mass of 56Ni->56Co->56 Fe. We
present a study on the fate of positrons within SNe Ia in order to evaluate
their escape fractions and energy spectra. Our detailed Monte Carlo transport
simulations for positrons and gamma-rays include both beta + decay of 56 Co and
pair production. We simulate a wide variety of explosion scenarios, including
the explosion of white dwarfs (WD) close to the Chandrasekhar mass, M(Ch),
He-triggered explosions of sub-M Ch WDs, and dynamical mergers of two WDs. For
each model, we study the influence of the size and morphology of the progenitor
magnetic field between 1 and 1E13 G. Population synthesis based on the observed
brightness distribution of SNe Ia was used to estimate the overall
contributions to Galactic positrons due to escape from SN Ia. We find that this
is dominated by normal-bright SNe Ia, where variations in the distribution of
emitted positrons are small. We estimate a total SNe Ia contribution to the
Galactic positrons of < 2% and, depending on the magnetic field morphology,
less than 6...20% for M(Ch) and sub-M(Ch), respectively.


title: The salient crossings of a crown diagram

abstract:
  A crown diagram of a smooth, closed oriented 4-manifold can be thought of as
the projection of a link in the product of a closed surface and the circle,
with chords in the circle direction connecting the strands of each crossing.
This paper uses a straightforward assignment of integers to these chords to
show that the smooth structures on the topological 4-manifold underlying a pair
of Fintushel-Stern knot surgery 4-manifolds which are known to have the same
Seiberg-Witten invariant are not isotopic. A natural question is to determine
if the argument may be strengthened to show these manifolds are not
diffeomorphic.