title:
Wetting dynamics under periodic switching on different scales:
  Characterization and mechanisms

abstract:
  The development of substrates with a switchable wettability is on a fast
pace. The limit of switching frequencies and contact angle differences between
substrate states are steadily pushed further. We investigate the behavior of a
droplet on a homogeneous substrate, which is switched between two wettabilities
for a large range of switching frequencies. Here, we are particularly
interested in the dependence of the wetting behavior on the switching
frequency. We show, that results obtained on the particle level via molecular
dynamics simulations and on the continuum level via the thin-film model are
consistent. Predictions of simple models as the molecular theory of wetting
(MKT) and analytical calculations based on the MKT also show good agreement and
offer deeper insights into the underlying mechanisms.


title:
Direct Detection of Sub-GeV Dark Matter with $s>1$ through the
  DM-Electron Scattering (I): case study for $s=3/2$ and $s=2$ with
  (pseudo)scalar mediator

abstract:
  The observable signal of higher-spin particles is rare in current particle
collider experiments. In the meantime, there have no evident constraints on the
spin of the dark matter particles. And thereby it is natural to consider the
possibility that the higher-spin particles behaves as dark matter. In this
work, we investigate the direct detection of (spin-$3/2$) gravitino dark matter
and (spin-$2$) massive KK-graviton dark matter respectively through the
DM-electron scattering. Both the spin-independent (SI) and spin-independent
(SD) interactions of DM are considered. Using the experiment datas of XENON10,
XENON100 and XENON1T, we could give the restriction relation between the DM
mass and cross section. For simplification, we only compute the corss section
at leading order, while merely focus on the (pseudo)scalar mediators. Moreover,
by evaluating and analyzing the form factor of DM, we uncover that if the
mediator is scalar there have no significant signals to distinguish the DM with
spin $3/2,2$ from the one with spin $0,1/2,1$.


title: Salt Detection Using Segmentation of Seismic Image

abstract:
  In this project, a state-of-the-art deep convolution neural network (DCNN) is
presented to segment seismic images for salt detection below the earth's
surface. Detection of salt location is very important for starting mining.
Hence, a seismic image is used to detect the exact salt location under the
earth's surface. However, precisely detecting the exact location of salt
deposits is difficult. Therefore, professional seismic imaging still requires
expert human interpretation of salt bodies. This leads to very subjective,
highly variable renderings. Hence, to create the most accurate seismic images
and 3D renderings, we need a robust algorithm that automatically and accurately
identifies if a surface target is a salt or not. Since the performance of DCNN
is well-known and well-established for object recognition in images, DCNN is a
very good choice for this particular problem and being successfully applied to
a dataset of seismic images in which each pixel is labeled as salt or not. The
result of this algorithm is promising.


title:
Probing Pre-Trained Language Models for Cross-Cultural Differences in
  Values

abstract:
  Language embeds information about social, cultural, and political values
people hold. Prior work has explored social and potentially harmful biases
encoded in Pre-Trained Language models (PTLMs). However, there has been no
systematic study investigating how values embedded in these models vary across
cultures. In this paper, we introduce probes to study which values across
cultures are embedded in these models, and whether they align with existing
theories and cross-cultural value surveys. We find that PTLMs capture
differences in values across cultures, but those only weakly align with
established value surveys. We discuss implications of using mis-aligned models
in cross-cultural settings, as well as ways of aligning PTLMs with value
surveys.


title: Enhanced thermo-optical response by means of anapole excitation

abstract:
  High refractive index dielectric nanostructures offer a versatile platform to
control light-matter interaction at the nanoscale as they can easily support
electric and magnetic modes with low losses. An additional property that makes
them extraordinary is that they can support low radiative modes, so-called
anapole modes. In this work, we propose a spectrally tunable anapole nanoheater
based on the use of a dielectric anapole resonator able to amplify ten-fold the
thermal response of a plasmonic nanoheater. This would allow the use of lower
light intensities to achieve striking heating effects. As proof of concept, we
perform a detailed study of the thermo-plasmonic response of a gold nanoring
used as heating source and a silicon disk, designed to support anapole modes,
located in its center acting as anapolar resonator. Furthermore, we utilize the
anapole excitation to easily shift the thermal response of these structures
from SWIR to the NIR range.


title: PDE-based Dynamic Control and Estimation of Soft Robotic Arms

abstract:
  Compared with traditional rigid-body robots, soft robots not only exhibit
unprecedented adaptation and flexibility, but also present novel challenges in
their modelling and control because of their infinite degrees of freedom. Most
of the existing approaches have mainly relied on approximated models so that
the well-developed finite-dimensional control theory can be exploited. However,
this Approximate-then-Design approach may bring in modelling uncertainty and
performance degradation. Hence, we adopt the opposite approach and exploit
infinite-dimensional analysis for soft robotic systems. Our control design is
based on the increasingly adopted Cosserat rod model, which describes the
kinematics and dynamics of soft robotic arms with a set of nonlinear partial
differential equations (PDE). We design infinite-dimensional state feedback
control laws for the Cosserat PDE model to achieve trajectory tracking in the
task space (consisting of position, rotation, linear and angular velocities),
and prove their uniform tracking convergence. To estimate the feedback states,
we design an infinite-dimensional extended Kalman filter on Lie groups for the
PDE system to estimate all the state variables (including position, rotation,
strains, curvature, linear and angular velocities) using only position
measurements. The proposed algorithms are evaluated using simulations.


title:
Data-driven kinematics-consistent model order reduction of
  fluid-structure interaction problems: application to deformable microcapsules
  in a Stokes flow

abstract:
  In this paper, we present a generic approach of a dynamical data-driven model
order reduction technique for three-dimensional fluid-structure interaction
problems. A low-order continuous linear differential system is identified from
snapshot solutions of a high-fidelity solver. The reduced order model (ROM)
uses different ingredients like proper orthogonal decomposition (POD), dynamic
mode decomposition (DMD) and Tikhonov-based robust identification techniques.
An interpolation method is used to predict the capsule dynamics for any value
of the governing non-dimensional parameters that are not in the training
database. Then a dynamical system is built from the predicted solution.
Numerical evidence shows the ability of the reduced model to predict the
time-evolution of the capsule deformation from its initial state, whatever the
parameter values. Accuracy and stability properties of the resulting low-order
dynamical system are analyzed numerically. The numerical experiments show a
very good agreement, measured in terms of modified Hausdorff distance between
capsule solutions of the full-order and low-order models both in the case of
confined and unconfined flows. This work is a first milestone to move towards
real time simulation of fluid-structure problems, which can be extended to
non-linear low-order systems to account for strong material and flow
non-linearities. It is a valuable innovation tool for rapid design and for the
development of innovative devices.


title:
Resurgent Stokes Data for Painleve Equations and Two-Dimensional Quantum
  (Super) Gravity

abstract:
  Resurgent-transseries solutions to Painleve equations may be recursively
constructed out of these nonlinear differential-equations -- but require Stokes
data to be globally defined over the complex plane. Stokes data explicitly
construct connection-formulae which describe the nonlinear Stokes phenomena
associated to these solutions, via implementation of Stokes transitions acting
on the transseries. Nonlinear resurgent Stokes data lack, however, a
first-principle computational approach, hence are hard to determine
generically. In the Painleve I and Painleve II contexts, nonlinear Stokes data
get further hindered as these equations are resonant, with non-trivial
consequences for the interconnections between transseries sectors, bridge
equations, and associated Stokes coefficients. In parallel to this, the
Painleve I and Painleve II equations are string-equations for two-dimensional
quantum (super) gravity and minimal string theories, where Stokes data have
natural ZZ-brane interpretations. This work computes for the first time the
complete, analytical, resurgent Stokes data for the first two Painleve
equations, alongside their quantum gravity or minimal string incarnations. The
method developed herein, dubbed "closed-form asymptotics", makes sole use of
resurgent large-order asymptotics of transseries solutions -- alongside a
careful analysis of the role resonance plays. Given its generality, it may be
applicable to other distinct (nonlinear, resonant) problems. Results for
analytical Stokes coefficients have natural structures, which are described,
and extensive high-precision numerical tests corroborate all analytical
predictions. Connection-formulae are explicitly constructed, with rather simple
and compact final results encoding the full Stokes data, and further allowing
for exact monodromy checks -- hence for an analytical proof of our results.


title: Topological state transfers in cavity-magnon system

abstract:
  We propose an experimentally feasible scheme for realizing quantum state
transfer via the topological edge states in a one-dimensional cavity-magnon
lattice. We find that the cavity-magnon system can be mapped analytically into
the generalized Su-Schrieffer-Heeger model with tunable cavity-magnon coupling.
It can be shown that the edge state can be served as a quantum channel to
realize the photonic and magnonic state transfers by adjusting the
cavity-cavity coupling strength. Further, our scheme can realize the quantum
state transfer between photonic state and magnonic state by changing the
amplitude of the intracell hopping. With a numerical simulation, we
quantitatively show that the photonic, magnonic and magnon-to-photon state
transfers can be achieved with high fidelity in the cavity-magnon lattice.
Spectacularly, the three different types of quantum state transfer schemes can
be even transformed to each other in a controllable fashion. This system
provides a novel way of realizing quantum state transfer and can be implemented
in quantum computing platforms.


title:
Effect of zirconium doping on mechanical properties of $W_{1-x}Zr_xB_2$
  on the base of ab initio calculations and magnetron sputtered films

abstract:
  Potentially superhard $W_{1-x}Zr_xB_2$ polymorph hP6-P6$_3$/mmc-$WB_2$ with
zirconium doping in the range of x=0.0-0.25 was thoroughly analyzed within the
framework of first-principles density functional theory from the structural and
mechanical point of view. The obtained results were subsequently compared with
properties of material deposited by magnetron sputtering method. All predicted
structures are mechanically and thermodynamically stable. Due to theoretical
calculations zirconium doping reduces hardness and fracture toughness $K_{IC}$
of $WB_2$. Deposited films are characterized by greater hardness $H_v$ but
lower fracture toughness $K_{IC}$. The results of experiments show that not
only solid solution hardening is responsible for strengthening of predicted new
material but also change of microstructure, Hall-Petch effect and boron
vacancies.


title:
FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac
  Output monitoring using deep artifact suppression and segmentation

abstract:
  Purpose: Real-time monitoring of cardiac output (CO) requires low latency
reconstruction and segmentation of real-time phase contrast MR (PCMR), which
has previously been difficult to perform. Here we propose a deep learning
framework for 'Flow Reconstruction and Segmentation for low latency Cardiac
Output monitoring' (FReSCO).
  Methods: Deep artifact suppression and segmentation U-Nets were independently
trained. Breath hold spiral PCMR data (n=516) was synthetically undersampled
using a variable density spiral sampling pattern and gridded to create aliased
data for training of the artifact suppression U-net. A subset of the data
(n=96) was segmented and used to train the segmentation U-net. Real-time spiral
PCMR was prospectively acquired and then reconstructed and segmented using the
trained models (FReSCO) at low latency at the scanner in 10 healthy subjects
during rest, exercise and recovery periods. CO obtained via FReSCO was compared
to a reference rest CO and rest and exercise Compressed Sensing (CS) CO.
  Results: FReSCO was demonstrated prospectively at the scanner. Beat-to-beat
heartrate, stroke volume and CO could be visualized with a mean latency of
622ms. No significant differences were noted when compared to reference at rest
(Bias = -0.21+-0.50 L/min, p=0.246) or CS at peak exercise (Bias=0.12+-0.48
L/min, p=0.458).
  Conclusion: FReSCO was successfully demonstrated for real-time monitoring of
CO during exercise and could provide a convenient tool for assessment of the
hemodynamic response to a range of stressors.


title: Orbifold resolution via hyperkahler quotients: the $D_2$ ALF manifold

abstract:
  We propose an infinite-dimensional generalization of Kronheimer's
construction of families of hyperkahler manifolds resolving flat orbifold
quotients of $\mathbb{R}^4$. As in [Kro89], these manifolds are constructed as
hyperkahler quotients of affine spaces. This leads to a study of \emph{singular
equivariant instantons} in various dimensions. In this paper, we study singular
equivariant Nahm data to produce the family of $D_2$ asymptotically locally
flat (ALF) manifolds as a deformation of the flat orbifold $(\mathbb{R}^3
\times S^1)/Z_2$. We furthermore introduce a notion of stability for Nahm data
and prove a Donaldson-Uhlenbeck-Yau type theorem to relate real and complex
formulations. We use these results to construct a canonical Ehresmann
connection on the family of non-singular $D_2$ ALF manifolds. In the complex
formulation, we exhibit explicit relationships between these $D_2$ ALF
manifolds and corresponding $A_1$ ALE manifolds. We conjecture analogous
constructions and results for general orbifold quotients of $\mathbb{R}^{4-r}
\times T^r$ with $2 \le r \le 4$. The case $r = 4$ produces K3 manifolds as
hyperkahler quotients.


title: On $(g-2)_\mu$ From Gauged $\mathrm{U}(1)_X$

abstract:
  We investigate an economical explanation for the $(g-2)_\mu$ anomaly with a
neutral vector boson from a spontaneously broken $\mathrm{U}(1)_X$ gauge
symmetry. The Standard Model fermion content is minimally extended by 3
right-handed neutrinos. Using a battery of complementary constraints, we
perform a thorough investigation of the renormalizable, quark flavor-universal,
vector-like $\mathrm{U}(1)_X$ models, allowing for arbitrary kinetic mixing.
Out of 419 models with integer charges not greater than ten, only 7 models are
viable solutions, describing a narrow region in model space. These are either
$L_\mu-L_\tau$ or models with a ratio of electron to baryon number close to
$-2$. The key complementary constraints are from the searches for nonstandard
neutrino interactions. Furthermore, we comment on the severe challenges to
chiral $\mathrm{U}(1)_X$ solutions and show the severe constraints on a
particularly promising such candidate.


title:
Collective and dissipative effects in a common microscopic dynamical
  description

abstract:
  Depending on the energy regime, the dynamics of heavy-ion collisions reveals
a variety of different mechanisms which are attributed to the combination of
collective and dissipative effects. Semi-classical approaches have been
successful in describing chaotic regimes at Fermi-energies but they gradually
lose precision when extending to collective behaviour and in general when
low-energy features become more determinant in the dynamics. To improve on this
description, we propose a theoretical approach starting from the TDHF scheme. A
quantum representation with a moving basis function has been worked out with a
double aim. Firstly, achieving a simplified solution to handle the evolution in
time. Secondly, introducing beyond-mean-field extensions and stochastic
contributions. Applications to nuclear collisions at incident energies around
low to Fermi energy are presented.


title:
Blocks Assemble! Learning to Assemble with Large-Scale Structured
  Reinforcement Learning

abstract:
  Assembly of multi-part physical structures is both a valuable end product for
autonomous robotics, as well as a valuable diagnostic task for open-ended
training of embodied intelligent agents. We introduce a naturalistic
physics-based environment with a set of connectable magnet blocks inspired by
children's toy kits. The objective is to assemble blocks into a succession of
target blueprints. Despite the simplicity of this objective, the compositional
nature of building diverse blueprints from a set of blocks leads to an
explosion of complexity in structures that agents encounter. Furthermore,
assembly stresses agents' multi-step planning, physical reasoning, and bimanual
coordination. We find that the combination of large-scale reinforcement
learning and graph-based policies -- surprisingly without any additional
complexity -- is an effective recipe for training agents that not only
generalize to complex unseen blueprints in a zero-shot manner, but even operate
in a reset-free setting without being trained to do so. Through extensive
experiments, we highlight the importance of large-scale training, structured
representations, contributions of multi-task vs. single-task learning, as well
as the effects of curriculums, and discuss qualitative behaviors of trained
agents.


title: Fractional Integrable Nonlinear Soliton Equations

abstract:
  Nonlinear integrable equations serve as a foundation for nonlinear dynamics,
and fractional equations are well known in anomalous diffusion. We connect
these two fields by presenting the discovery of a new class of integrable
fractional nonlinear evolution equations describing dispersive transport in
fractional media. These equations can be constructed from nonlinear integrable
equations using a widely generalizable mathematical process utilizing
completeness relations, dispersion relations, and inverse scattering transform
techniques. As examples, this general method is used to characterize fractional
extensions to two physically relevant, pervasive integrable nonlinear
equations: the Korteweg-de Vries and nonlinear Schr\"odinger equations. These
equations are shown to predict super-dispersive transport of non-dissipative
solitons in fractional media.


title: Upper limits on the dark matter content in globular clusters

abstract:
  We present a systematic analysis on the possible presence of dark mass
components inside globular clusters (GCs). A spherical Jeans analysis is
applied to the stellar kinematics of 9 nearby GCs. On top of the mass
distribution provided by the luminous stellar component, we add either dark
matter (DM), described by an NFW mass profile, or an intermediate mass
black-hole (IMBH), described by a point-like mass. Their existence would have
important implications in the context of indirect DM searches. After profiling
over the stellar parameters, we find no evidence neither for DM nor for IMBH.
Upper limits on the two components are reported.


title:
Interplay between surface and volume instabilities in heavy-ion
  collisions examined within mean-field extensions

abstract:
  In the transition from nuclear matter to finite nuclei, complex finite-size
effects which characterise open systems arise, in relation with either the
nuclear surface or the bulk. In addition, the non-equilibrium character of the
process, typical of violent heavy-ion collisions (from Fermi energy to the
intermediate-energy domain) adds up as well. The resulting dynamics is the
combination of surface and volume unstable modes which trigger large-amplitude
fluctuations. A rich variety of fragmentation patterns may emerge, ranging from
collimated streams of nuclear clusters to the split of a stretched nuclear
complex into few large fragments. They imply different conditions of density
and surface tension, and result in different chronologies. Such phenomenology
has been observed in experiments, but it is often difficult to recognise and
disentangle the underlying types of instabilities. To draw some example, two
extremely deformed nuclear systems, produced below and above Fermi energy, are
chosen and followed microscopically all along their evolution within the
Boltzmann-Langevin One-Body approach.


title: Using Solver-Aided Languages to Build Package Managers

abstract:
  Open-source software is critical for modern development, but most open-source
packages require large networks of prerequisite packages, or dependencies, in
order to function correctly. Modern software development workflows use package
managers to ease this burden. Given a set of constraints, these systems use
dependency solving to select compatible versions of dependencies before
installing. However, many dependency solvers make ad hoc implementation choices
and use heuristics that affect the set of chosen dependencies, and thus affect
correctness, code size, and other factors of the final bundled software in ways
that are opaque and confusing to programmers.
  We present PacSolve, a unifying formal semantics of dependency solving.
PacSolve can compactly represent the key features and differences between NPM,
PIP and Cargo, and express a wide variety of alternative semantics for
dependency solving. PacSolve lends itself to a solver-aided implementation in
Rosette, which we use to build a drop-in replacement for NPM called MinNPM.
MinNPM allows the user to customize the dependency solving semantics to choose
between different objectives and consistency criteria. We show empirically that
PacSolve is performant and effective on real-world code. For example, on the
top 1000 most downloaded NPM packages, we show that MinNPM can shrink the
footprint of 20% of packages and produce a newer set of dependencies for 14% of
packages. Moreover, MinNPM only takes 1.7s longer than NPM on average. We also
use MinNPM to show that NPM's tree-solving semantics is only necessary for 3%
of these packages.


title:
Nonlinear Field-split Preconditioners for Solving Monolithic Phase-field
  Models of Brittle Fracture

abstract:
  One of the state-of-the-art strategies for predicting crack propagation,
nucleation, and interaction is the phase-field approach. Despite its
reliability and robustness, the phase-field approach suffers from burdensome
computational cost, caused by the non-convexity of the underlying energy
functional and a large number of unknowns required to resolve the damage
gradients. In this work, we propose to solve such nonlinear systems in a
monolithic manner using the Schwarz preconditioned inexact Newton's (SPIN)
method. The proposed SPIN method leverages the field split approach and
minimizes the energy functional separately with respect to displacement and the
phase-field, in an additive and multiplicative manner. In contrast to the
standard alternate minimization, the result of this decoupled minimization
process is used to construct a preconditioner for a coupled linear system,
arising at each Newton's iteration. The overall performance and the convergence
properties of the proposed additive and multiplicative SPIN methods are
investigated by means of several numerical examples. Comparison with
widely-used alternate minimization is also performed and we show a reduction in
the execution time up to a factor of 50. Moreover, we also demonstrate that
this reduction grows even further with increasing problem size and larger
loading increments.


title: High Dimensional Quantum Machine Learning With Small Quantum Computers

abstract:
  Quantum computers hold great promise to enhance machine learning, but their
current qubit counts restrict the realisation of this promise. In an attempt to
placate this limitation techniques can be applied for evaluating a quantum
circuit using a machine with fewer qubits than the circuit naively requires.
These techniques work by evaluating many smaller circuits on the smaller
machine, that are then combined in a polynomial to replicate the output of the
larger machine. This scheme requires more circuit evaluations than are
practical for general circuits. However, we investigate the possibility that
for certain applications many of these subcircuits are superfluous, and that a
much smaller sum is sufficient to estimate the full circuit. We construct a
machine learning model that may be capable of approximating the outputs of the
larger circuit with much fewer circuit evaluations. We successfully apply our
model to the task of digit recognition, using simulated quantum computers much
smaller than the data dimension. The model is also applied to the task of
approximating a random 10 qubit PQC with simulated access to a 5 qubit
computer, even with only relatively modest number of circuits our model
provides an accurate approximation of the 10 qubit PQCs output, superior to a
neural network attempt. The developed method might be useful for implementing
quantum models on larger data throughout the NISQ era.


title:
A generalized precision matrix for t-Student distributions in portfolio
  optimization

abstract:
  The Markowitz model is still the cornerstone of modern portfolio theory. In
particular, when focusing on the minimum-variance portfolio, the covariance
matrix or better its inverse, the so-called precision matrix, is the only input
required. So far, most scholars worked on improving the estimation of the
input, however little attention has been given to the limitations of the
inverse covariance matrix when capturing the dependence structure in a
non-Gaussian setting. While the precision matrix allows to correctly understand
the conditional dependence structure of random vectors in a Gaussian setting,
the inverse of the covariance matrix might not necessarily result in a reliable
source of information when Gaussianity fails. In this paper, exploiting the
local dependence function, different definitions of the generalized precision
matrix (GPM), which holds for a general class of distributions, are provided.
In particular, we focus on the multivariate t-Student distribution and point
out that the interaction in random vectors does not depend only on the inverse
of the covariance matrix, but also on additional elements. We test the
performance of the proposed GPM using a minimum-variance portfolio set-up by
considering S\&P 100 and Fama and French industry data. We show that portfolios
relying on the GPM often generate statistically significant lower out-of-sample
variances than state-of-art methods.


title:
Estimation of high dimensional Gamma convolutions through random
  projections

abstract:
  Multivariate generalized Gamma convolutions are distributions defined by a
convolutional semi-parametric structure. Their flexible dependence structures,
the marginal possibilities and their useful convolutional expression make them
appealing to the practitioner. However, fitting such distributions when the
dimension gets high is a challenge. We propose stochastic estimation procedures
based on the approximation of a Laguerre integrated square error via (shifted)
cumulants approximation, evaluated on random projections of the dataset.
Through the analysis of our loss via tools from Grassmannian cubatures, sparse
optimization on measures and Wasserstein gradient flows, we show the
convergence of the stochastic gradient descent to a proper estimator of the
high dimensional distribution. We propose several examples on both low and
high-dimensional settings.


title: An inverse boundary value problem for isotropic nonautonomous heat flows

abstract:
  We study an inverse boundary value problem on the determination of principal
order coefficients in isotropic nonautonomous heat flows stated as follows;
given a medium, and in the absence of heat sources and sinks, can the
time-dependent thermal conductivity and volumetric heat capacity of the medium
be uniquely determined from the Cauchy data of temperature and heat flux
measurements on its boundary? We prove uniqueness in all dimensions under an
assumption on the thermal diffusivity of the medium, which is defined as the
ratio of the thermal conductivity and volumetric heat capacity. As a corollary
of our result for isotropic media, we also obtain a uniqueness result, up to a
natural gauge, in two-dimensional anisotropic media. Our assumption on the
thermal diffusivity is related to construction of certain families of
exponential solutions to the heat equation.


title: Obstruction theory and the level $n$ elliptic genus

abstract:
  Given a height $\leq 2$ Landweber exact $\mathbb{E}_\infty$-ring $E$ whose
homotopy is concentrated in even degrees, we show that any complex orientation
of $E$ which satisfies the Ando criterion admits a unique lift to an
$\mathbb{E}_\infty$-complex orientation $\mathrm{MU} \to E$. As a consequence,
we give a short proof that the level $n$ elliptic genus lifts uniquely to an
$\mathbb{E}_\infty$-complex orientation $\mathrm{MU} \to \mathrm{tmf}_1 (n)$
for all $n \geq 2$.


title:
A discrete-module-finite-element hydroelasticity method in analyzing
  dynamic response of floating flexible structures

abstract:
  A discrete-module-finite element (DMFE) based hydroelasticity method has been
proposed and well developed. Firstly, a freely floating flexible structure is
discretized into several macro-submodules in two horizontal directions to
perform a multi-rigid-body hydrodynamic analysis. Each macro-submodule is then
abstracted to a lumped mass at the center of gravity that bears the external
forces including inertia force, hydrodynamic force and hydrostatic force. Apart
from external forces, all lumped masses are also subjected to structural forces
that reflect the structural deformation features of the original flexible
structure. The key to calculating the structural forces is derivation of the
equivalent overall structural stiffness matrix with respect to the
displacements of all lumped masses, which is tackled following the finite
element procedure. More specifically, each macro-submodule is discretized into
a number of microelements to derive the corresponding structural stiffness
matrix, which is manipulated to a new one including only the nodes at the
position of the lumped masses and surrounding boundaries by using the
substructure approach, and subsequently the target overall stiffness matrix is
obtained by combining together all macro-submodules. Finally, based on
equivalence between external and structural forces, the DMFE method establishes
the hydroelastic equation on all lumped masses with their displacements as
unknown variables. Solving the equation gives the displacement response of all
lumped masses. Displacement and structural force responses are first calculated
on the interfaces of every two adjacent macro-submodules, after which at any
given position of the flexible structure, the recovery of displacement is based
on the structural stiffness matrix of the corresponding macro-submodule and the
recovery of structural force uses the spline interpolation scheme.


title:
Weak solutions for singular multiplicative SDEs via regularization by
  noise

abstract:
  We study multiplicative SDEs perturbed by an additive fractional Brownian
motion on another probability space. Provided the Hurst parameter is chosen in
a specified regime, we establish existence of probabilistically weak solutions
to the SDE if the measurable diffusion coefficient merely satisfies an
integrability condition. In particular, this allows to consider certain
singular diffusion coefficients.


title: Code Smells for Machine Learning Applications

abstract:
  The popularity of machine learning has wildly expanded in recent years.
Machine learning techniques have been heatedly studied in academia and applied
in the industry to create business value. However, there is a lack of
guidelines for code quality in machine learning applications. In particular,
code smells have rarely been studied in this domain. Although machine learning
code is usually integrated as a small part of an overarching system, it usually
plays an important role in its core functionality. Hence ensuring code quality
is quintessential to avoid issues in the long run. This paper proposes and
identifies a list of 22 machine learning-specific code smells collected from
various sources, including papers, grey literature, GitHub commits, and Stack
Overflow posts. We pinpoint each smell with a description of its context,
potential issues in the long run, and proposed solutions. In addition, we link
them to their respective pipeline stage and the evidence from both academic and
grey literature. The code smell catalog helps data scientists and developers
produce and maintain high-quality machine learning application code.


title:
Non-Eisenstein cohomology of locally symmetric spaces for $GL_2$ over a
  CM field

abstract:
  Let $F$ be a CM field, let $p$ be a prime number. The goal of this paper is
to show, under mild conditions, that the modulo $p$ cohomology of the locally
symmetric spaces $X$ for $GL_2(F)$ with level prime to $p$ is concentrated in
degrees belonging to the Borel-Wallach range $[q_0,q_0+\ell_0]$ after
localizing at a "strongly non-Eisenstein" maximal ideal of the Hecke algebra.
From this result, we deduce expected consequences on the structure of the first
and last cohomology groups as modules over the Hecke algebra.


title:
On the derivation of the homogeneous kinetic wave equation for a
  nonlinear random matrix model

abstract:
  We consider a nonlinear system of ODEs, where the underlying linear dynamics
are determined by a Hermitian random matrix ensemble. We prove that the leading
order dynamics in the weakly nonlinear, infinite volume limit are determined by
a solution to the corresponding kinetic wave equation on a non-trivial
timescale. Our proof relies on estimates for Haar-distributed unitary matrices
obtained from Weingarten calculus, which may be of independent interest.


title: Recovery of black hole mass from a single quasinormal mode

abstract:
  We study the determination of the mass of a de Sitter-Schwarzschild black
hole from one quasinormal mode. We prove a local uniqueness result with a
H\"older type stability estimate.


title: Direction of Parsec-Scales Jets for 9220 Active Galactic Nuclei

abstract:
  The direction of parsec-scale jets in active galactic nuclei (AGNs) is
essential information for many astrophysical and astrometric studies, including
linear polarization and magnetic field structure, frequency-dependent
synchrotron opacity, proper motion, and reference frame alignment. We developed
a rigorous, simple, and completely automated method to measure the directions
from calibrated interferometric visibility data at frequencies ranging from 1.4
GHz to 86 GHz. We publish the results for 9220 AGNs with the typical accuracy
below 10 degrees. An internal check of the method comparing the directions
between different observing frequencies as well as with previous publications
verifies the robustness of the measured values.


title: Efficient-VDVAE: Less is more

abstract:
  Hierarchical VAEs have emerged in recent years as a reliable option for
maximum likelihood estimation. However, instability issues and demanding
computational requirements have hindered research progress in the area. We
present simple modifications to the Very Deep VAE to make it converge up to
$2.6\times$ faster, save up to $20\times$ in memory load and improve stability
during training. Despite these changes, our models achieve comparable or better
negative log-likelihood performance than current state-of-the-art models on all
$7$ commonly used image datasets we evaluated on. We also make an argument
against using 5-bit benchmarks as a way to measure hierarchical VAE's
performance due to undesirable biases caused by the 5-bit quantization.
Additionally, we empirically demonstrate that roughly $3\%$ of the hierarchical
VAE's latent space dimensions is sufficient to encode most of the image
information, without loss of performance, opening up the doors to efficiently
leverage the hierarchical VAEs' latent space in downstream tasks. We release
our source code and models at https://github.com/Rayhane-mamah/Efficient-VDVAE .


title:
Controlled Growth of the Self-Modulation of a Relativistic Proton Bunch
  in Plasma

abstract:
  A long, narrow, relativistic charged particle bunch propagating in plasma is
subject to the self-modulation (SM) instability. We show that SM of a proton
bunch can be seeded by the wakefields driven by a preceding electron bunch. SM
timing reproducibility and control are at the level of a small fraction of the
modulation period. With this seeding method, we independently control the
amplitude of the seed wakefields with the charge of the electron bunch and the
growth rate of SM with the charge of the proton bunch. Seeding leads to larger
growth of the wakefields than in the instability case.


title:
A roadmap to explore the vector-like quarks decaying to a new
  (pseudo)scalar

abstract:
  The current experimental data allow for a sub-TeV colourless weak-singlet
scalar or pseudoscalar. If such a singlet field is present together with
TeV-range vector-like top and bottom partners, there is a possibility that the
heavy quarks decay dominantly to the singlet state and a third-generation
quark, and the singlet state can decay to quark and boson pairs. Such a
possibility may arise in various models but has not been explored
experimentally, especially in the context of the vectorlike-quark searches. We
consider some minimal models, covering the possible weak representations of the
top and bottom partners, that can be mapped to many well-motivated
ultra-violate complete theories. We chart out the possible interesting and
unexplored signatures of the exotic decay of vector-like quarks and identify
benchmark points representing different signal topologies for high luminosity
LHC. We perform a general scan of the parameter space with the relevant direct
search bounds and find that large regions, which do not require any
fine-tuning, remain open for the unexplored channels. We also perform a simple
projection study in the cleanest channel and indicate how other new but
experimentally challenging channels can be used to probe more regions of the
parameter space.


title: Fast fluorescence lifetime imaging analysis via extreme learning machine

abstract:
  We present a fast and accurate analytical method for fluorescence lifetime
imaging microscopy (FLIM) using the extreme learning machine (ELM). We used
extensive metrics to evaluate ELM and existing algorithms. First, we compared
these algorithms using synthetic datasets. Results indicate that ELM can obtain
higher fidelity, even in low-photon conditions. Afterwards, we used ELM to
retrieve lifetime components from human prostate cancer cells loaded with gold
nanosensors, showing that ELM also outperforms the iterative fitting and
non-fitting algorithms. By comparing ELM with a computational efficient neural
network, ELM achieves comparable accuracy with less training and inference
time. As there is no back-propagation process for ELM during the training
phase, the training speed is much higher than existing neural network
approaches. The proposed strategy is promising for edge computing with online
training.


title:
Integrable Fractional Modified Korteweg-de Vries, Sine-Gordon, and
  Sinh-Gordon Equations

abstract:
  The inverse scattering transform allows explicit construction of solutions to
many physically significant nonlinear wave equations. Notably, this method can
be extended to fractional nonlinear evolution equations characterized by
anomalous dispersion using completeness of suitable eigenfunctions of the
associated linear scattering problem. In anomalous diffusion, the mean squared
displacement is proportional to $t^{\alpha}$, $\alpha>0$, while in anomalous
dispersion, the speed of localized waves is proportional to $A^{\alpha}$, where
$A$ is the amplitude of the wave. Fractional extensions of the modified
Korteweg-deVries (mKdV), sine-Gordon (sineG) and sinh-Gordon (sinhG) and
associated hierarchies are obtained. Using symmetries present in the linear
scattering problem, these equations can be connected with a scalar family of
nonlinear evolution equations of which fractional mKdV (fmKdV), fractional
sineG (fsineG), and fractional sinhG (fsinhG) are special cases. Completeness
of solutions to the scalar problem is obtained and, from this, the nonlinear
evolution equation is characterized in terms of a spectral expansion. In
particular, fmKdV, fsineG, and fsinhG are explicitly written. One-soliton
solutions are derived for fmKdV and fsineG using the inverse scattering
transform and these solitons are shown to exhibit anomalous dispersion.


title:
Min-Max Polarization for Certain Classes of Sharp Configurations on the
  Sphere

abstract:
  We consider the problem of finding an $N$-point configuration on the sphere
$S^d\subset \RR^{d+1}$ with the smallest absolute maximum value over $S^d$ of
its total potential. The potential induced by each point ${\bf y}$ in a given
configuration at a point ${\bf x}\in S^d$ is $f\(\left|{\bf x}-{\bf
y}\right|^2\)$, where $f$ is continuous on $[0,4]$ and completely monotone on
$(0,4]$, and $\left|{\bf x}-{\bf y}\right|$ is the Euclidean distance between
points~${\bf x}$ and ${\bf y}$. We show that any sharp point configuration
$\OL\omega_N$ on $S^d$, which is antipodal or is a spherical design of an even
strength is a solution to this problem. We also prove that the absolute maximum
over $S^d$ of the potential of any such configuration $\OL\omega_N$ is attained
at points of $\OL\omega_N$.


title:
The Cosmological Constant Problem and Running Vacuum in the Expanding
  Universe

abstract:
  It is well-known that quantum field theory (QFT) induces a huge value of the
cosmological constant, $\Lambda$, which is outrageously inconsistent with
cosmological observations. We review here some aspects of this fundamental
theoretical conundrum (`the cosmological constant problem') and strongly argue
in favor of the possibility that the cosmic vacuum density $\rho_{\rm vac}$ may
be mildly evolving with the expansion rate $H$. Such a `running vacuum model'
(RVM) proposal predicts an effective dynamical dark energy without postulating
new ad hoc fields (quintessence and the like). Using the method of adiabatic
renormalization within QFT in curved spacetime we find that $\rho_{\rm vac}(H)$
acquires a dynamical component ${\cal O}(H^2)$ caused by the quantum matter
effects. There are also ${\cal O}(H^n)$ ($n=4,6,..$) contributions, some of
which may trigger inflation in the early universe. Remarkably, the evolution of
the adiabatically renormalized $\rho_{\rm vac}(H)$ is not affected by dangerous
terms proportional to the quartic power of the masses ($\sim m^4$) of the
fields. Traditionally, these terms have been the main source of trouble as they
are responsible for the extreme fine tuning feature of the cosmological
constant problem. In the context under study, however, the late time $\rho_{\rm
vac}(H)$ around $H_0$ is given by a dominant term ($\rho_{\rm vac}^0$) plus the
aforementioned mild dynamical component $\propto \nu (H^2-H_0^2)$ (with
$|\nu|\ll1$), which makes the RVM to mimic quintessence. Finally, on the
phenomenological side we show that the RVM may be instrumental in alleviating
some of the most challenging problems (so-called `tensions') afflicting
nowadays the observational consistency of the `concordance' $\Lambda$CDM model,
such as the $H_0$ and $\sigma_8$ tensions.


title: Puzzles in the $B^0_s \rightarrow D_s^{\pm} K^{\mp} $ System

abstract:
  Non-leptonic $B^0_s \rightarrow D_s^{\pm} K^{\mp}$ transitions are
particularly interesting processes to test the Standard Model. As these decays
occur via pure tree diagrams, they allow a theoretically clean determination of
the angle $\gamma$ of the unitarity triangle. Considering recent LHCb results,
an intriguing picture arises, showing tension with the Standard Model.
Extracting the branching ratios of the underlying $\bar{B}^0_s \rightarrow
D_s^{\pm} K^{\mp}$ modes and combining them with information from semileptonic
$B_{(s)}$ decays, we arrive at another puzzling situation, in accordance with
similar decays. These patterns could be footprints of New Physics. We present a
model-independent strategy to include such New-Physics effects and apply it to
the data. Interestingly, new contributions of moderate size could accommodate
the data. This formalism offers an exciting probe for new sources of CP
violation at the future high-precision B physics era.


title:
Constraining the multi-scale dark-matter distribution in CASSOWARY 31
  with strong gravitational lensing and stellar dynamics

abstract:
  We study the inner structure of the group-scale lens CASSOWARY 31 (CSWA 31)
by adopting both strong lensing and dynamical modeling. CSWA 31 is a peculiar
lens system. The brightest group galaxy (BGG) is an ultra-massive elliptical
galaxy at z = 0.683 with a weighted mean velocity dispersion of $\sigma = 432
\pm 31$ km s$^{-1}$. It is surrounded by group members and several lensed arcs
probing up to ~150 kpc in projection. Our results significantly improve
previous analyses of CSWA 31 thanks to the new HST imaging and MUSE
integral-field spectroscopy. From the secure identification of five sets of
multiple images and measurements of the spatially-resolved stellar kinematics
of the BGG, we conduct a detailed analysis of the multi-scale mass distribution
using various modeling approaches, both in the single and multiple lens-plane
scenarios. Our best-fit mass models reproduce the positions of multiple images
and provide robust reconstructions for two background galaxies at z = 1.4869
and z = 2.763. The relative contributions from the BGG and group-scale halo are
remarkably consistent in our three reference models, demonstrating the
self-consistency between strong lensing analyses based on image position and
extended image modeling. We find that the ultra-massive BGG dominates the
projected total mass profiles within 20 kpc, while the group-scale halo
dominates at larger radii. The total projected mass enclosed within $R_{eff}$ =
27.2 kpc is $1.10_{-0.04}^{+0.02} \times 10^{13}$ M$_\odot$. We find that CSWA
31 is a peculiar fossil group, strongly dark-matter dominated towards the
central region, and with a projected total mass profile similar to higher-mass
cluster-scale halos. The total mass-density slope within the effective radius
is shallower than isothermal, consistent with previous analyses of early-type
galaxies in overdense environments.


title:
JAX-FLUIDS: A fully-differentiable high-order computational fluid
  dynamics solver for compressible two-phase flows

abstract:
  Physical systems are governed by partial differential equations (PDEs). The
Navier-Stokes equations describe fluid flows and are representative of
nonlinear physical systems with complex spatio-temporal interactions. Fluid
flows are omnipresent in nature and engineering applications, and their
accurate simulation is essential for providing insights into these processes.
While PDEs are typically solved with numerical methods, the recent success of
machine learning (ML) has shown that ML methods can provide novel avenues of
finding solutions to PDEs. ML is becoming more and more present in
computational fluid dynamics (CFD). However, up to this date, there does not
exist a general-purpose ML-CFD package which provides 1) powerful
state-of-the-art numerical methods, 2) seamless hybridization of ML with CFD,
and 3) automatic differentiation (AD) capabilities. AD in particular is
essential to ML-CFD research as it provides gradient information and enables
optimization of preexisting and novel CFD models. In this work, we propose
JAX-FLUIDS: a comprehensive fully-differentiable CFD Python solver for
compressible two-phase flows. JAX-FLUIDS allows the simulation of complex fluid
dynamics with phenomena like three-dimensional turbulence, compressibility
effects, and two-phase flows. Written entirely in JAX, it is straightforward to
include existing ML models into the proposed framework. Furthermore, JAX-FLUIDS
enables end-to-end optimization. I.e., ML models can be optimized with
gradients that are backpropagated through the entire CFD algorithm, and
therefore contain not only information of the underlying PDE but also of the
applied numerical methods. We believe that a Python package like JAX-FLUIDS is
crucial to facilitate research at the intersection of ML and CFD and may pave
the way for an era of differentiable fluid dynamics.


title: Electrical control of excitons in GaN/(Al,Ga)N quantum wells

abstract:
  A giant built-in electric field in the growth direction makes excitons in
wide GaN/(Al, Ga)N quantum wells spatially indirect even in the absence of any
external bias. Significant densities of indirect excitons can accumulate in
electrostatic traps imprinted in the quantum well plane by a thin metal layer
deposited on top of the heterostructure. By jointly measuring
spatially-resolved photoluminescence and photo-induced current, we demonstrate
that exciton density in the trap can be controlled via an external electric
bias, which is capable of altering the trap depth. Application of a negative
bias deepens the trapping potential, but does not lead to any additional
accumulation of excitons in the trap. This is due to exciton dissociation
instigated by the lateral electric field at the electrode edges. The resulting
carrier losses are detected as an increased photo-current and reduced
photoluminescence intensity. By contrast, application of a positive bias washes
out the electrode-induced trapping potential. Thus, excitons get released from
the trap and recover free propagation in the plane that we reveal by
spatially-resolved photoluminescence.


title: A World-Self Model Towards Understanding Intelligence

abstract:
  Artificial intelligence has achieved tremendous successes in various tasks,
while it is still out of question that there are big gaps between artificial
and human intelligence, and the nature of intelligence is still in darkness. In
this work we will first stress the importance of defining the scope of
discussion and choosing the right physical and informational granularity of
investigation. We will carefully compare human and artificial intelligence, and
propose that the information abstraction mechanism of human intelligence is the
key to connect perception and cognition, and the lack of a new model is
preventing the understanding and next-level implementation of intelligence. We
will present the broader idea of "concept", the principles and mathematical
frameworks of the new model World-Self Model (WSM) of intelligence, and finally
an unified general framework of intelligence based on WSM. Rather than focusing
on solving a specific problem or discussing a certain kind of intelligence, our
work is instead towards a better understanding of the nature of the general
phenomenon of intelligence, independent of the task or system of investigation.


title: Spectroscopic signatures of nonpolarons : the case of diamond

abstract:
  Polarons are quasi-particles made from electrons interacting with vibrations
in crystal lattices. They derive their name from the strong electron-vibration
polar interaction in ionic systems, that induces associated spectroscopic and
optical signatures of such quasi-particles in these materials. In this paper,
we focus on diamond, a non-polar crystal with inversion symmetry which
nevertheless shows characteristic signatures of polarons, better denoted
"nonpolarons" in this case. The polaronic effects are produced by short-range
crystal fields with only a small influence of long-range quadrupoles. The
many-body spectral function has a characteristic energy dependence, showing a
plateau structure that is similar to but distinct from the satellites observed
in the polar Fr\"{o}hlich case. The temperature-dependent spectral function of
diamond is determined by two methods: the standard Dyson-Migdal approach, which
calculates electron-phonon interactions within the lowest-order expansion of
the self-energy, and the cumulant expansion, which includes higher orders of
electron-phonon interactions. The latter corrects the nonpolaron energies and
broadening, providing a more realistic spectral function, which we examine in
detail for both conduction and valence band edges.


title:
An Assessment of the In-Situ Growth of the Intracluster Light in the
  High Redshift Galaxy Cluster SpARCS1049+56

abstract:
  The formation of the stellar mass within galaxy cluster cores is a poorly
understood process. It features the complicated physics of cooling flows, AGN
feedback, star formation and more. Here, we study the growth of the stellar
mass in the vicinity of the Brightest Cluster Galaxy (BCG) in a z = 1.7
cluster, SpARCS1049+56. We synthesize a reanalysis of existing HST imaging, a
previously published measurement of the star formation rate, and the results of
new radio molecular gas spectroscopy. These analyses represent the past,
present and future star formation respectively within this system. We show that
a large amount of stellar mass -- between $(2.2 \pm 0.5) \times 10^{10} \:
M_\odot$ and $(6.6 \pm 1.2) \times 10^{10}\: M_\odot$ depending on the data
processing -- exists in a long and clumpy tail-like structure that lies roughly
12 kpc off the BCG. Spatially coincident with this stellar mass is a similarly
massive reservoir ($(1.0 \pm 0.7) \times 10^{11} \: M_\odot$) of molecular gas
that we suggest is the fuel for the immense star formation rate of $860 \pm 130
\: M_\odot$/yr, as measured by infrared observations. Hlavacek-Larrondo et al.
2021 surmised that massive, runaway cooling of the hot intracluster X-ray gas
was feeding this star formation, a process that had not been observed before at
high-redshift. We conclude, based on the amount of fuel and current stars, that
this event may be rare in the lifetime of a cluster, producing roughly 15 to
21% of the Intracluster Light (ICL) mass in one go, though perhaps a common
event for all galaxy clusters.


title: Rainbow Tur\'an Methods for Trees

abstract:
  The rainbow Tur\'an number, a natural extension of the well studied
traditional Tur\'an number, was introduced in 2007 by Keevash, Mubayi, Sudakov
and Verstra\"ete. The rainbow Tur\'an number of a graph $H$, $ex^{*}(n,H)$, is
the largest number of edges for an $n$ vertex graph $G$ which can be properly
edge colored with no rainbow $H$ subgraph.
  We explore the reduction method for finding upper bounds on rainbow Tur\'an
numbers, and use this to inform results for the rainbow Tur\'an numbers of
double stars, caterpillars, and perfect binary trees. In addition, we define
$k$-unique colorings and the related $k$-unique Tur\'an numbers. We provide
preliminary results on this new variant on the classic problem.


title:
Straightening skewed markets with an index tracking optimizationless
  portfolio

abstract:
  Among professionals and academics alike, it is well known that active
portfolio management is unable to provide additional risk-adjusted returns
relative to their benchmarks. For this reason, passive wealth management has
emerged in recent decades to offer returns close to benchmarks at a lower cost.
In this article, we first refine the existing results on the theoretical
properties of oblique Brownian motion. Then, assuming that the returns follow
skew geometric Brownian motions and that they are correlated, we describe some
statistical properties for the \emph{ex-post}, the \emph{ex-ante} tracking
errors, and the forecasted tracking portfolio. To this end, we develop an
innovative statistical methodology, based on a benchmark-asset principal
component factorization, to determine a tracking portfolio that replicates the
performance of a benchmark by investing in a subset of the investable universe.
This strategy, named hybrid Principal Component Analysis (hPCA), is applied
both on normal and skew distributions. In the case of skew-normal returns, we
propose a framework for calibrating the model parameters, based on the maximum
likelihood estimation method. For testing and validation, we compare four
alternative models for index tracking. The first two are based on the hPCA when
returns are assumed to be normal or skew-normal. The third model adopts a
standard optimization-based approach and the last one is used in the financial
sector by some practitioners. For validation and testing, we present a thorough
comparison of these strategies on real-world data, both in terms of performance
and computational efficiency. A noticeable result is that, not only, the
suggested lean PCA-based portfolio selection approach compares well versus
cumbersome algorithms for optimization-based portfolios, but, also, it could
provide a better service to the asset management industry.


title: Exponential Sums Equations and Tropical Geometry

abstract:
  We show a case of Zilber's Exponential-Algebraic Closedness Conjecture,
establishing that the conjecture holds for varieties which split as the product
of a linear subspace of the additive group $\mathbb{C}^n$ and an algebraic
subvariety of the multiplicative group $(\mathbb{C}^\times)^n$. This amounts to
solving certain systems of exponential sums equations, and it generalizes old
results of Zilber, which required stronger assumptions on the variety such as
the linear space being defined over the real numbers. The proofs use the theory
of amoebas and tropical geometry.


title: The CAFAna framework for neutrino analysis

abstract:
  The CAFAna framework, initially developed for oscillation analysis with NOvA,
is now a key tool in the NOvA, SBN, and DUNE experiments for day-to-day
analysis and the production of final oscillation results.


title:
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting
  and Recent Behavior Changes

abstract:
  The negative effects of misinformation filter bubbles in adaptive systems
have been known to researchers for some time. Several studies investigated,
most prominently on YouTube, how fast a user can get into a misinformation
filter bubble simply by selecting wrong choices from the items offered. Yet, no
studies so far have investigated what it takes to burst the bubble, i.e.,
revert the bubble enclosure. We present a study in which pre-programmed agents
(acting as YouTube users) delve into misinformation filter bubbles by watching
misinformation promoting content (for various topics). Then, by watching
misinformation debunking content, the agents try to burst the bubbles and reach
more balanced recommendation mixes. We recorded the search results and
recommendations, which the agents encountered, and analyzed them for the
presence of misinformation. Our key finding is that bursting of a filter bubble
is possible, albeit it manifests differently from topic to topic. Moreover, we
observe that filter bubbles do not truly appear in some situations. We also
draw a direct comparison with a previous study. Sadly, we did not find much
improvements in misinformation occurrences, despite recent pledges by YouTube.


title:
Analysis of the use of color and its emotional relationship in visual
  creations based on experiences during the context of the COVID-19 pandemic

abstract:
  Color is a complex communicative element that helps us understand and
evaluate our environment. At the level of artistic creation, this component
influences both the formal aspects of the composition and the symbolic weight,
directly affecting the construction and transmission of the message that you
want to communicate, creating a specific emotional reaction. During the
COVID-19 pandemic, people generated countless images transmitting this event's
subjective experiences. Using the repository of images created in the Instagram
account CAM (The COVID Art Museum), we propose a methodology to understand the
use of color and its emotional relationship in this context. The process
considers two stages in parallel that are then combined. First, emotions are
extracted and classified from the CAM dataset images through a convolutional
neural network. Second, we extract the colors and their harmonies through a
clustering process. Once both processes are completed, we combine the results
generating an expanded discussion on the usage of color, harmonies, and
emotion. The results indicate that warm colors are prevalent in the sample,
with a preference for analog compositions over complementary ones. The
relationship between emotions and these compositions shows a trend in positive
emotions, reinforced by the results of the algorithm a priori and the emotional
relationship analysis of the attributes of color (hue, chroma, and lighting).


title:
Investigating the effect of noise channels on the quality of unitary
  t-designs

abstract:
  Unitary t-designs have a wide variety of applications in quantum information
theory, such as quantum data encryption and randomised benchmarking. However,
experimental realisations of t-designs are subject to noise. Here we
investigate the effect of noise channels on the quality of single-qubit
t-designs. The noise channels we study are bit flips, phase flips, bit and
phase flips, phase damping, amplitude damping and depolarising noise. We
consider two noise models: the first has noise applied before the t-design
unitary operations, while the second has noise applied after the unitary
operations. We show that for single qubits the 1-design is completely
unaffected by an arbitrary noise channel, for both noise models, while numeric
results obtained for the 2-, 3-, 4- and 5-design suggest that a 2t-design is
significantly more sensitive to noise than a (2t-1)-design and that, with the
exception of amplitude damping, a (2t+1)-design is as sensitive to noise as a
2t-design. Numeric results also reveal substantial variations in sensitivity to
noise throughout the Bloch sphere. In particular, t-designs appear to be most
sensitive to noise when acting on pure states and least sensitive to noise for
the maximally mixed state. For depolarising noise, we show that our two noise
models are equivalent, and for the other noise channels, numeric results
suggest that applying a noise channel after the unitary operations effectively
transforms the channel into a depolarising channel, an effect exploited in
randomised benchmarking with 2-designs.


title: On homogeneous Hermite-Lorentz spaces of low dimension

abstract:
  We classify irreducible homogeneous almost Hermite-Lorentz spaces of complex
dimension 3.


title: Experimental Implementation of a Two-Stroke Quantum Heat Engine

abstract:
  We put forth an experimental simulation of a stroboscopic two-stroke thermal
engine in the IBMQ processor. The system consists of a quantum spin chain
connected to two baths at their boundaries, prepared at different temperatures
using the variational quantum thermalizer algorithm. The dynamics alternates
between heat and work strokes, which can be separately designed using
independent quantum circuits. The results show good agreement with theoretical
predictions, showcasing IBMQ as a powerful tool to study thermodynamics in the
quantum regime, as well as the implementation of variational quantum algorithms
in real-world quantum computers. It also opens the possibility of simulating
quantum heat transport across a broad range of chains geometries and
interactions.


title: Picosecond timing of charged particles using the TORCH detector

abstract:
  TORCH is a large-area, high-precision time-of-flight (ToF) detector designed
to provide charged-particle identification in the 2-20 GeV$/c$ momentum range.
Prompt Cherenkov photons emitted by charged hadrons as they traverse a 10mm
quartz radiator are propagated to the periphery of the detector, where they are
focused onto an array of micro-channel plate photomultiplier tubes (MCP-PMTs).
The position and arrival times of the photons are used to infer the particles'
time of entry in the radiator, to identify hadrons based on their ToF. The
MCP-PMTs were developed with an industrial partner to satisfy the stringent
requirements of the TORCH detector. The requirements include a finely segmented
anode, excellent time resolution, and a long lifetime. Over an approximately
10m flight distance, the difference in ToF between a kaon and a pion with
10GeV$/c$ momentum is 35ps, leading to a 10-15ps per track timing resolution
requirement. On average 30 photons per hadron are detected, which translates to
a single-photon time resolution of 70ps. The TORCH research and development
program aims to demonstrate the validity of the detector concept through
laboratory and beam tests, results from which are presented. A timing
resolution of 70-100ps was reached in beam tests, approaching the TORCH design
goal. Laboratory timing tests consist of operating the MCP-PMTs coupled to the
TORCH readout electronics. A time resolution of about 50ps was measured,
meeting the TORCH target timing resolution.


title:
Interlopers speak out: Studying the dark universe using small-scale
  lensing anisotropies

abstract:
  Strongly lensed systems are powerful probes of the distribution of dark
matter on small scales. In this paper, we show that line-of-sight halos between
the source and the observers give rise to a distinct anisotropic signature in
the two-point function of the effective lensing deflection field. We show in
particular that the nonlinear coupling between line-of-sight halos and the main
lens plane imprints a characteristic quadrupole moment on this two-point
function whose amplitude reflects the abundance of such halos within the
strongly lensed field. We discuss how, by taking ratios of different multipole
moments, such observables could be made robust under the mass-sheet transform.
We also demonstrate that future extremely large telescopes have the ability to
detect the quadrupole moment due to this unique anisotropic signature. Our
approach opens the door to statistically distinguish the effect of
line-of-sight halos from that of the main-lens substructure on lensed images,
hence allowing one to probe dark matter physics in a new way.


title:
Sharp adaptive similarity testing with pathwise stability for ergodic
  diffusions

abstract:
  Within the nonparametric diffusion model, we develop a multiple test to infer
about similarity of an unknown drift $b$ to some reference drift $b_0$: At
prescribed significance, we simultaneously identify those regions where
violation from similiarity occurs, without a priori knowledge of their number,
size and location. This test is shown to be minimax-optimal and adaptive. At
the same time, the procedure is robust under small deviation from Brownian
motion as the driving noise process. A detailed investigation for fractional
driving noise, which is neither a semimartingale nor a Markov process, is
provided for Hurst indices close to the Brownian motion case.


title: Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion

abstract:
  Human behavior has the nature of indeterminacy, which requires the pedestrian
trajectory prediction system to model the multi-modality of future motion
states. Unlike existing stochastic trajectory prediction methods which usually
use a latent variable to represent multi-modality, we explicitly simulate the
process of human motion variation from indeterminate to determinate. In this
paper, we present a new framework to formulate the trajectory prediction task
as a reverse process of motion indeterminacy diffusion (MID), in which we
progressively discard indeterminacy from all the walkable areas until reaching
the desired trajectory. This process is learned with a parameterized Markov
chain conditioned by the observed trajectories. We can adjust the length of the
chain to control the degree of indeterminacy and balance the diversity and
determinacy of the predictions. Specifically, we encode the history behavior
information and the social interactions as a state embedding and devise a
Transformer-based diffusion model to capture the temporal dependencies of
trajectories. Extensive experiments on the human trajectory prediction
benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the
superiority of our method. Code is available at
https://github.com/gutianpei/MID.


title:
L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and
  BERT models

abstract:
  Social media platforms are used by a large number of people prominently to
express their thoughts and opinions. However, these platforms have contributed
to a substantial amount of hateful and abusive content as well. Therefore, it
is important to curb the spread of hate speech on these platforms. In India,
Marathi is one of the most popular languages used by a wide audience. In this
work, we present L3Cube-MahaHate, the first major Hate Speech Dataset in
Marathi. The dataset is curated from Twitter, annotated manually. Our dataset
consists of over 25000 distinct tweets labeled into four major classes i.e
hate, offensive, profane, and not. We present the approaches used for
collecting and annotating the data and the challenges faced during the process.
Finally, we present baseline classification results using deep learning models
based on CNN, LSTM, and Transformers. We explore mono-lingual and multi-lingual
variants of BERT like MahaBERT, IndicBERT, mBERT, and xlm-RoBERTa and show that
mono-lingual models perform better than their multi-lingual counterparts. The
MahaBERT model provides the best results on L3Cube-MahaHate Corpus. The data
and models are available at https://github.com/l3cube-pune/MarathiNLP .


title: Origins of Low-dimensional Adversarial Perturbations

abstract:
  In this note, we initiate a rigorous study of the phenomenon of
low-dimensional adversarial perturbations in classification. These are
adversarial perturbations wherein, unlike the classical setting, the attacker's
search is limited to a low-dimensional subspace of the feature space. The goal
is to fool the classifier into flipping its decision on a nonzero fraction of
inputs from a designated class, upon the addition of perturbations from a
subspace chosen by the attacker and fixed once and for all. It is desirable
that the dimension $k$ of the subspace be much smaller than the dimension $d$
of the feature space, while the norm of the perturbations should be negligible
compared to the norm of a typical data point. In this work, we consider binary
classification models under very general regularity conditions, which are
verified by certain feedforward neural networks (e.g., with sufficiently
smooth, or else ReLU activation function), and compute analytical lower-bounds
for the fooling rate of any subspace. These bounds explicitly highlight the
dependence that the fooling rate has on the margin of the model (i.e., the
ratio of the output to its $L_2$-norm of its gradient at a test point), and on
the alignment of the given subspace with the gradients of the model w.r.t.
inputs. Our results provide a theoretical explanation for the recent success of
heuristic methods for efficiently generating low-dimensional adversarial
perturbations. Moreover, our theoretical results are confirmed by experiments.


title: Detraction of decoherence that arises from acceleration process

abstract:
  The possibility of detracting the decoherence due to the acceleration process
of the two-qutrit system is investigated, where we examined the behaviour of
the relative entropy and the non-local information. For this purpose, the
accelerated subsystems are allowed to pass through local or global noisy
channels. It is shown that, the detraction potential depends on the type of the
used noisy channel, local or global, and the initial settings of the
accelerated qutrit systems, whether it is prepared in free or bound entangled
intervals. The improving rate that depicted for systems prepared in the free
entangled intervals is much better than those prepared in the bound entangled
interval. The maximum bounds of the non-local information in the presence of
the amplitude damping channels are larger than those passes in the dephasing
channel.


title: Defect engineering of silicon with ion pulses from laser acceleration

abstract:
  Defect engineering is foundational to classical electronic device development
and for emerging quantum devices. Here, we report on defect engineering of
silicon single crystals with ion pulses from a laser accelerator with ion flux
levels up to 10^22 ions/cm^2/s. Low energy ions from plasma expansion of the
laser-foil target are implanted near the surface and then diffuse into silicon
samples that were locally pre-heated by high energy ions. We observe low energy
ion fluences of ~10^16 cm^-2, about four orders of magnitude higher than the
fluence of high energy (MeV) ions. In the areas of highest energy deposition,
silicon crystals exfoliate from single ion pulses. Color centers, predominantly
W and G-centers, form directly in response to ion pulses without a subsequent
annealing step. We find that the linewidth of G-centers increase in areas with
high ion flux much more than the linewidth of W-centers, consistent with
density functional theory calculations of their electronic structure. Laser ion
acceleration generates aligned pulses of high and low energy ions that expand
the parameter range for defect engineering and doping of semiconductors with
tunable balances of ion flux, damage rates and local heating.


title:
First observation of the direct production of the $\chi_{c1}$ in
  $e^+e^-$ annihilation

abstract:
  We study the direct production of the $J^{PC}=1^{++}$ charmonium state
$\chi_{c1}(1P)$ in electron-positron annihilation by carrying out an energy
scan around the mass of the $\chi_{c1}(1P)$. The data was collected with the
BESIII detector at the BEPCII collider. An interference pattern between the
signal process $e^+e^-\to \chi_{c1}(1P) \to \gamma J/\psi\to\gamma\mu^+\mu^-$
and the background processes $e^+e^-\to \gamma_{\mathrm{ISR}}
J/\psi\to\gamma_{\rm{ISR}}\mu^+\mu^-$ and
$e^+e^-\to\gamma_{\rm{ISR}}\mu^+\mu^-$ is observed by combining all the data
samples. The $\chi_{c1}(1P)$ signal is observed with a significance of
5.1$\sigma$. This is the first observation of a $C$-even state directly
produced in $e^+e^-$ annihilation. The electronic width of the $\chi_{c1}(1P)$
resonance is determined to be $\Gamma_{ee}=(0.12^{+0.13}_{-0.08})~\mathrm{eV}$,
which is of the same order of magnitude as theoretical calculations.


title: Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation

abstract:
  A key challenge in metabolomics is annotating measured spectra from a
biological sample with chemical identities. Currently, only a small fraction of
measurements can be assigned identities. Two complementary computational
approaches have emerged to address the annotation problem: mapping candidate
molecules to spectra, and mapping query spectra to molecular candidates. In
essence, the candidate molecule with the spectrum that best explains the query
spectrum is recommended as the target molecule. Despite candidate ranking being
fundamental in both approaches, no prior works utilized rank learning tasks in
determining the target molecule. We propose a novel machine learning model,
Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes
advantage of prior neural network-based annotation models that utilize
multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on
the ranking results of the MLP and GNN-based models, ESP learns a weighting for
the outputs of MLP and GNN spectral predictors to generate a spectral
prediction for a query molecule. Importantly, training data is stratified by
molecular formula to provide candidate sets during model training. Further,
baseline MLP and GNN models are enhanced by considering peak dependencies
through multi-head attention mechanism and multi-tasking on spectral topic
distributions. ESP improves average rank by 41% and 30% over the MLP and GNN
baselines, respectively, demonstrating remarkable performance gain over
state-of-the-art neural network approaches. We show that annotation
performance, for ESP and other models, is a strong function of the number of
molecules in the candidate set and their similarity to the target molecule.


title: Disadvantaged Communities Have Lower Access to Urban Infrastructure

abstract:
  Disparity in spatial accessibility is strongly associated with growing
inequalities among urban communities. Since improving levels of accessibility
for certain communities can provide them with upward social mobility and
address social exclusion and inequalities in cities, it is important to
understand the nature and distribution of spatial accessibility among urban
communities. To support decision-makers in achieving inclusion and fairness in
policy interventions in cities, we present an open-source and data-driven
framework to understand the spatial nature of accessibility to infrastructure
among the different demographics. We find that accessibility to a wide range of
infrastructure in any city (54 cities) converges to a Zipf's law, suggesting
that inequalities also appear proportional to growth processes in these cities.
Then, assessing spatial inequalities among the socioeconomically clustered
urban profiles for 10 of those cities, we find urban communities are distinctly
segregated along social and spatial lines. We find low accessibility scores for
populations who have a larger share of minorities, earn less, and have a
relatively lower number of individuals with a university degree. These findings
suggest that the reproducible framework we propose may be instrumental in
understanding processes leading to spatial inequalities and in supporting
cities to devise targeted measures for addressing inequalities for certain
underprivileged communities.


title: Classical physics from amplitudes on curved backgrounds

abstract:
  We generalise the Kosower-Maybee-O'Connell (KMOC) formalism relating
classical observables and scattering amplitudes to curved backgrounds. We show
how to compute the final semiclassical state for a particle moving in a curved
background in terms of scattering amplitudes on that background. Two-point
amplitudes in this framework correspond to conservative physics with
background-dependent memory effects. As an application, we consider plane wave
and shockwave backgrounds both in electromagnetism and general relativity. We
determine the final semiclassical state, showing it satisfies a notion of
double copy on curved backgrounds. We then conclude by computing the impulse of
a particle on such backgrounds, deriving exact results and velocity memory
effects.


title:
The $\bar B_s^0 \to J/\psi \pi^0 \eta$ decay and the $a_0(980)-f_0(980)$
  mixing

abstract:
  We study the $\bar B_s^0 \to J/\psi f_0(980)$ and $\bar B_s^0 \to J/\psi
a_0(980)$ reactions, and pay attention to the different sources of isospin
violation and mixing of $f_0(980)$ and $a_0(980)$ resonances where these
resonances are dynamically generated from meson-meson interaction. We find that
the main reason of isospin violation is the isospin breaking in the meson-meson
transition $T$ matrices, and the other source is that the loops involving kaons
in the production mechanism provide a source of isospin violation because they
do not cancel by the different mass of the charged and neutral kaons. We obtain
a branching ratio for $a_0(980)$ production of the order of $5 \times 10^{-6}$.
Future experiments can address this problem and the production rate and shape
of the $\pi^0 \eta$ mass distribution will definitely help to better understand
the nature of scalar resonances.


title:
A Hybrid Framework for Sequential Data Prediction with End-to-End
  Optimization

abstract:
  We investigate nonlinear prediction in an online setting and introduce a
hybrid model that effectively mitigates, via an end-to-end architecture, the
need for hand-designed features and manual model selection issues of
conventional nonlinear prediction/regression methods. In particular, we use
recursive structures to extract features from sequential signals, while
preserving the state information, i.e., the history, and boosted decision trees
to produce the final output. The connection is in an end-to-end fashion and we
jointly optimize the whole architecture using stochastic gradient descent, for
which we also provide the backward pass update equations. In particular, we
employ a recurrent neural network (LSTM) for adaptive feature extraction from
sequential data and a gradient boosting machinery (soft GBDT) for effective
supervised regression. Our framework is generic so that one can use other deep
learning architectures for feature extraction (such as RNNs and GRUs) and
machine learning algorithms for decision making as long as they are
differentiable. We demonstrate the learning behavior of our algorithm on
synthetic data and the significant performance improvements over the
conventional methods over various real life datasets. Furthermore, we openly
share the source code of the proposed method to facilitate further research.


title:
Forecasting F(Q) cosmology with $\Lambda$CDM background using standard
  sirens

abstract:
  Forecast constraints for a Symmetric Teleparallel Gravity model with a
$\Lambda$CDM background are made using forthcoming ground and space based
gravitational waves observatories. A Bayesian analysis resorting to generated
mock catalogs shows that LIGO is not expected to be able to distinguish this
model from $\Lambda$CDM, while both LISA and the ET will, with the ET
outperforming LISA. We also show that low redshift events are favored in order
to improve the quality of the constrains.


title:
FLUTE: A Scalable, Extensible Framework for High-Performance Federated
  Learning Simulations

abstract:
  In this paper we introduce "Federated Learning Utilities and Tools for
Experimentation" (FLUTE), a high-performance open source platform for federated
learning research and offline simulations. The goal of FLUTE is to enable rapid
prototyping and simulation of new federated learning algorithms at scale,
including novel optimization, privacy, and communications strategies. We
describe the architecture of FLUTE, enabling arbitrary federated modeling
schemes to be realized, we compare the platform with other state-of-the-art
platforms, and we describe available features of FLUTE for experimentation in
core areas of active research, such as optimization, privacy and scalability.
We demonstrate the effectiveness of the platform with a series of experiments
for text prediction and speech recognition, including the addition of
differential privacy, quantization, scaling and a variety of optimization and
federation approaches.


title:
The echo chamber effect resounds on financial markets: a social media
  alert system for meme stocks

abstract:
  The short squeeze of Gamestop (GME) has revealed to the world how retail
investors pooling through social media can severely impact financial markets.
In this paper, we devise an early warning signal to detect suspicious users'
social network activity, which might affect the financial market stability. We
apply our approach to the subreddit r/WallStreetBets, selecting two meme stocks
(GME and AMC) and two non-meme stocks (AAPL and MSFT) as case studies. The
alert system is structured in two stpng; the first one is based on
extraordinary activity on the social network, while the second aims at
identifying whether the movement seeks to coordinate the users to a bulk
action. We run an event study analysis to see the reaction of the financial
markets when the alert system catches social network turmoil. A regression
analysis witnesses the discrepancy between the meme and non-meme stocks in how
the social networks might affect the trend on the financial market.


title: The Companion Model -- a Canonical Model in Graph Signal Processing

abstract:
  This paper introduces a $\textit{canonical}$ graph signal model defined by a
$\textit{canonical}$ graph and a $\textit{canonical}$ shift, the
$\textit{companion}$ graph and the $\textit{companion}$ shift. These are
canonical because, under standard conditions, we show that any graph signal
processing (GSP) model can be transformed into the canonical model. The
transform that obtains this is the graph $z$-transform ($\textrm{G$z$T}$) that
we introduce. The GSP canonical model comes closest to the discrete signal
processing (DSP) time signal models: the structure of the companion shift
decomposes into a line shift and a signal continuation just like the DSP shift
and the GSP canonical graph is a directed line graph with a terminal condition
reflecting the signal continuation condition. We further show that,
surprisingly, in the canonical model, convolution of graph signals is fast
convolution by the DSP FFT.


title:
Visual-based Safe Landing for UAVs in Populated Areas: Real-time
  Validation in Virtual Environments

abstract:
  Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated
areas is a crucial aspect for successful urban deployment, particularly in
emergency landing situations. Nonetheless, validating autonomous landing in
real scenarios is a challenging task involving a high risk of injuring people.
In this work, we propose a framework for real-time safe and thorough evaluation
of vision-based autonomous landing in populated scenarios, using
photo-realistic virtual environments. We propose to use the Unreal graphics
engine coupled with the AirSim plugin for drone's simulation, and evaluate
autonomous landing strategies based on visual detection of Safe Landing Zones
(SLZ) in populated scenarios. Then, we study two different criteria for
selecting the "best" SLZ, and evaluate them during autonomous landing of a
virtual drone in different scenarios and conditions, under different
distributions of people in urban scenes, including moving people. We evaluate
different metrics to quantify the performance of the landing strategies,
establishing a baseline for comparison with future works in this challenging
task, and analyze them through an important number of randomized iterations.
The study suggests that the use of the autonomous landing algorithms
considerably helps to prevent accidents involving humans, which may allow to
unleash the full potential of drones in urban environments near to people.


title:
Optical constants of several multilayer transition metal dichalcogenides
  measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index,
  anisotropy, and hyperbolicity

abstract:
  Transition metal dichalcogenides (TMDs) attract significant attention due to
their exceptional optical and excitonic properties. It was understood already
in the 1960s, and recently rediscovered, that many TMDs possess high refractive
index and optical anisotropy, which make them attractive for nanophotonic
applications. However, accurate analysis and predictions of nanooptical
phenomena require knowledge of dielectric constants along both in- and
out-of-plane directions and over a broad spectral range -- information, which
is often inaccessible or incomplete. Here, we present an experimental study of
optical constants from several exfoliated TMD multilayers obtained using
spectroscopic ellipsometry in the broad range of 300--1700 nm. The specific
materials studied include semiconducting WS$_2$, WSe$_2$, MoS$_2$, MoSe$_2$,
MoTe$_2$, as well as, in-plane anisotropic ReS$_2$, WTe$_2$, and metallic
TaS$_2$, TaSe$_2$, and NbSe$_2$. The extracted parameters demonstrate
high-index ($n$ up till $\approx 4.84$ for MoTe$_2$), significant anisotropy
($n_{\parallel}-n_{\perp} \approx 1.54$ for MoTe$_2$), and low absorption in
the near infrared region. Moreover, metallic TMDs show potential for combined
plasmonic-dielectric behavior and hyperbolicity, as their plasma frequency
occurs at around $\sim$1000--1300 nm depending on the material. The knowledge
of optical constants of these materials opens new experimental and
computational possibilities for further development of all-TMD nanophotonics.


title:
Relative energies without electronic perturbations via integral
  transform

abstract:
  We show that the energy of a perturbed system can be fully recovered from the
unperturbed electron density. We derive an alchemical integral transform by
parametrizing space in terms of changes of the potential, the chain rule and
integration by parts. Within the radius of convergence, the zeroth order yields
the energy expansion at all orders, narrowing the textbook statement that the
$p$-th order wave function derivative is necessary to describe the $(2p+1)$-th
energy derivative. Results are shown for the harmonic oscillator, the
hydrogen-like atom, and multi-electron atoms with up to ten electrons.


title: A Unified Approach to Gelfand and de Vries Dualities

abstract:
  We develop a unified approach to Gelfand and de Vries dualities for compact
Hausdorff spaces, which is based on appropriate modifications of the classic
results of Dieudonn\'{e} (analysis), Dilworth (lattice theory), and
Kat{\v{e}}tov-Tong (topology).


title: Homodyne Time-Domain Acousto-Optic Imaging for Low-Gain Photodetectors

abstract:
  Acousto-optics imaging (AOI) is a hybrid imaging modality that is capable of
mapping the light fluence rate in deep tissue by local ultrasound modulation of
the diffused photons. Since the intensity of the modulated photons is
relatively low, AOI systems often rely on high-gain photodetectors, e.g.
photomultiplier tubes (PMTs), which limit scalability due to size and cost and
may significantly increase the relative shot-noise in the detected signal due
to low quantum yields or gain noise. In this Letter, we have developed a
homodyne AOI scheme in which the modulated photons are amplified by
interference with a reference beam, enabling their detection with low-gain
photodetectors. We experimentally demonstrate our approach with a silicon
photodiode, achieving over a 4-fold improvement in SNR in comparison to a
PMT-based setup.


title:
Sequential matched randomization and a case for covariate-adaptive
  randomization

abstract:
  Background: Sequential Matched Randomization (SMR) is one of multiple recent
covariate-adaptive randomization (CAR) procedures that utilize a distance
matrix to improve covariate-balance and estimation efficiency. Randomization
occurs within mates whose distance meet an a-priori, fixed similarity quantile
of random distances. Methods: We extend SMR to allow multiple participants to
be randomized simultaneously, to allow matches to break and rematch if a better
match later enrolls (Sequential Rematched Randomization; SRR), and to use a
dynamic threshold. In simplified settings which vary covariate distribution and
association upon outcome, we compare end-study covariate-balance and estimator
efficiency in SMR before and after extensions. In a real-world application, we
compare covariate-balance, power, and estimator efficiency of SMR before and
after extensions when adjusting for priority covariates and all covariates of
interest. We compare with Complete Randomization (CR) and CR followed by a
flexible, covariate-adjusted regression model. As side-by-side comparisons, we
include stratified randomization, D$_A$ optimality biased coin design
(D$_A$-BCD), and Pairwise Sequential Randomization (PSR). Results: In both the
simplified and real-world application, we observe benefits of each extension
upon covariate balance and estimator efficiency. In the real-world application,
SRR with a dynamic threshold, D$_A$-BCD, and PSR provide greater power than CR
followed by a covariate-adjusted regression model. Matching methods achieved
greater covariate-balance when adjusting for all covariates yet greater power
and efficiency when adjusting for priority covariates. Conclusion: We improve
upon SMR and show the potential for CAR methods -- that adjusting for
covariates in randomization can outperform covariate adjustment in a flexible
regression model.


title: Non-inner amenability of the Higman-Thompson groups

abstract:
  We prove that the Higman-Thompson groups $T_n$ and $V_n$ are non-inner
amenable for all $n\ge 2$. This extends Haagerup and Olesen's result that
Thompson's groups $T=T_2$ and $V=V_2$ are non-inner amenable. Their proof
relied on machinery only available in the $n=2$ case, namely Thurston's
piecewise-projective model for Thompson's group $T$, so our approach
necessarily utilizes different tools. This also provides an alternate proof of
Haagerup-Olesen's result when $n=2$.


title: Gravity-constrained point cloud registration

abstract:
  Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms
benefit from the Inertial Measurement Unit (IMU) modality. The high-rate
inertial data complement the other lower-rate modalities. Moreover, in the
absence of constant acceleration, the gravity vector makes two attitude angles
out of three observable in the global coordinate frame. In visual odometry,
this is already being used to reduce the 6-Degrees Of Freedom (DOF) pose
estimation problem to 4-DOF. In lidar SLAM, the gravity measurements are often
used as a penalty in the back-end global map optimization to prevent map
deformations. In this work, we propose an Iterative Closest Point (ICP)-based
front-end which exploits the observable DOF and provides pose estimates aligned
with the gravity vector. We believe that this front-end has the potential to
support the loop closure identification, thus speeding up convergences of
global map optimizations. The presented approach has been extensively tested in
large-scale outdoor environments as well as in the Subterranean Challenge
organized by Defense Advanced Research Projects Agency (DARPA). We show that it
can reduce the localization drift by 30% when compared to the standard 6-DOF
ICP. Moreover, the code is readily available to the community as a part of the
libpointmatcher library.


title: Continuous Dynamic-NeRF: Spline-NeRF

abstract:
  The problem of reconstructing continuous functions over time is important for
problems such as reconstructing moving scenes, and interpolating between time
steps. Previous approaches that use deep-learning rely on regularization to
ensure that reconstructions are approximately continuous, which works well on
short sequences. As sequence length grows, though, it becomes more difficult to
regularize, and it becomes less feasible to learn only through regularization.
We propose a new architecture for function reconstruction based on classical
Bezier splines, which ensures $C^0$ and $C^1$-continuity, where $C^0$
continuity is that $\forall c:\lim\limits_{x\to c} f(x)
  = f(c)$, or more intuitively that there are no breaks at any point in the
function. In order to demonstrate our architecture, we reconstruct dynamic
scenes using Neural Radiance Fields, but hope it is clear that our approach is
general and can be applied to a variety of problems. We recover a Bezier spline
$B(\beta, t\in[0,1])$, parametrized by the control points $\beta$. Using Bezier
splines ensures reconstructions have $C^0$ and $C^1$ continuity, allowing for
guaranteed interpolation over time. We reconstruct $\beta$ with a multi-layer
perceptron (MLP), blending machine learning with classical animation
techniques. All code is available at https://github.com/JulianKnodt/nerf_atlas,
and datasets are from prior work.


title: Noisy monitored quantum dynamics of ergodic multi-qubit systems

abstract:
  I employ random-matrix methods to set up and solve statistical models of
noisy nonunitary dynamics that appear in the context of monitored quantum
systems. The models cover a range of scenarios combining random dynamics and
measurements of variable strength of one or several qubits. The combined
dynamics drive the system into states whose statistics reflect the competition
of randomizing unitary evolution and the measurement-induced backaction
collapsing the state. These effects are mediated by entanglement, as I describe
in detail by analytical results. For the paradigmatic case of monitoring via a
single designated qubit, this reveals a simple statistical mechanism, in which
the monitoring conditions the state of the monitored qubit, which then imposes
statistical constraints on the remaining quantities of the system. For the case
of monitoring several qubits with prescribed strength, the developed formalism
allows one to set up the statistical description and solve it numerically.
Finally, I also compare the analytical results to the monitored dynamics of a
quantum kicked top, revealing two regimes where the statistical model either
describes the full stationary dynamics, or resolves time scales during
particular parts of the evolution.