title: Pursuit-Evasion in Graphs: Zombies, Lazy Zombies and a Survivor

abstract:
  We study zombies and survivor, a variant of the game of cops and robber on
graphs. In this variant, the single survivor plays the role of the robber and
attempts to escape from the zombies that play the role of the cops. The zombies
are restricted, on their turn, to always follow an edge of a shortest path
towards the survivor. Let $z(G)$ be the smallest number of zombies required to
catch the survivor on a graph $G$ with $n$ vertices. We show that there exist
outerplanar graphs and visibility graphs of simple polygons such that $z(G) =
\Theta(n)$. We also show that there exist maximum-degree-$3$ outerplanar graphs
such that $z(G) = \Omega\left(n/\log(n)\right)$.
  Let $z_L(G)$ be the smallest number of lazy zombies (zombies that can stay
still on their turn) required to catch the survivor on a graph $G$. We
establish that lazy zombies are more powerful than normal zombies but less
powerful than cops. We prove that $z_L(G) = 2$ for connected outerplanar
graphs. We show that $z_L(G)\leq k$ for connected graphs with treedepth $k$.
This result implies that $z_L(G)$ is at most $(k+1)\log n$ for connected graphs
with treewidth $k$, $O(\sqrt{n})$ for connected planar graphs, $O(\sqrt{gn})$
for connected graphs with genus $g$ and $O(h\sqrt{hn})$ for connected graphs
with any excluded $h$-vertex minor. Our results on lazy zombies still hold when
an adversary chooses the initial positions of the zombies.


title:
Fractional Graph Coloring for Functional Compression with Side
  Information

abstract:
  We describe a rational approach to reduce the computational and communication
complexities of lossless point-to-point compression for computation with side
information. The traditional method relies on building a characteristic graph
with vertices representing the source symbols and with edges that assign a
source symbol to a collection of independent sets to be distinguished for the
exact recovery of the function. Our approach uses fractional coloring for a
b-fold coloring of characteristic graphs to provide a linear programming
relaxation to the traditional coloring method and achieves coding at a
fine-grained granularity. We derive the fundamental lower bound for
compression, given by the fractional characteristic graph entropy, through
generalizing the notion of K\"orner's graph entropy. We demonstrate the coding
gains of fractional coloring over traditional coloring via a computation
example. We conjecture that the integrality gap between fractional coloring and
traditional coloring approaches the smallest b that attains the fractional
chromatic number to losslessly represent the independent sets for a given
characteristic graph, up to a linear scaling which is a function of the
fractional chromatic number.


title:
A New Era of Quantum Materials Mastery and Quantum Simulators In and Out
  of Equilibrium

abstract:
  We provide a perspective on the burgeoning field of controlling quantum
materials at will and its potential for quantum simulations in and out
equilibrium. After briefly outlining a selection of key recent advances in
controlling materials using novel high fluence lasers as well as in innovative
approaches for novel quantum materials synthesis (especially in the field of
twisted two-dimensional solids), we provide a vision for the future of the
field. By merging state of the art developments we believe it is possible to
enter a new era of quantum materials mastery, in which exotic and for the most
part evasive collective as well as topological phenomena can be controlled in a
versatile manner. This could unlock functionalities of unprecedented
capabilities, which in turn can enable many novel quantum technologies in the
future.

categories: cond-mat.str-el cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.supr-con quant-ph


title: Temporal Relevance Analysis for Video Action Models

abstract:
  In this paper, we provide a deep analysis of temporal modeling for action
recognition, an important but underexplored problem in the literature. We first
propose a new approach to quantify the temporal relationships between frames
captured by CNN-based action models based on layer-wise relevance propagation.
We then conduct comprehensive experiments and in-depth analysis to provide a
better understanding of how temporal modeling is affected by various factors
such as dataset, network architecture, and input frames. With this, we further
study some important questions for action recognition that lead to interesting
findings. Our analysis shows that there is no strong correlation between
temporal relevance and model performance; and action models tend to capture
local temporal information, but less long-range dependencies. Our codes and
models will be publicly available.


title: Superconductivity near a nematoelastic quantum critical point

abstract:
  We study the pairing instability of a two-dimensional metallic system induced
by Ising-nematic quantum fluctuations in the presence of an unavoidable
relevant coupling of the nematic order parameter to the elastic modes (acoustic
phonons) of the lattice. We find that this nematoelastic coupling
$\lambda_\mathrm{latt}$ leads to a decrease of both the superconducting (SC)
critical temperature $T_c$ and the gap function $\Delta$, regardless of the gap
symmetry. Interestingly, we show that $\lambda_\mathrm{latt}$ provides a knob
that allows us to investigate the emergence of the SC phase at low
temperatures, as an instability from either a non-Fermi liquid or a Fermi
liquid normal state. The phase transitions between the SC and these normal
states are characterized by different critical exponents, which may also vary
for each gap symmetry. Finally, we argue that these results might explain the
dependence of $T_c$ in the vicinity of the nematic quantum critical point
exhibited by the compound FeSe$_{1 - x}$S$_x$.


title: Pareto Optimization in Categories

abstract:
  We propose a model of Pareto optimization (multi-objective programming) in
the context of a categorical theory of resources. We describe how to adapt
multi-objective swarm intelligence algorithms to this categorical formulation.


title: Complexes of nearly maximum diameter

abstract:
  The diameter of a strongly connected $d$-dimensional simplicial complex is
the diameter of its dual graph. We provide a probabilistic proof of the
existence of $d$-dimensional simplicial complexes with diameter $ (\frac{1}{d
\cdot d!} - (\log n)^{-\epsilon}) n^d$. Up to the first order term, this is the
best possible lower bound for the maximum diameter of a $d$-complex on $n$
vertices as a simple volume argument shows that the diameter of a
$d$-dimensional simplicial complex is at most $ \frac{1}{d} \binom{n}{d}$. We
also find the right first-order asymptotics for the maximum diameter of a
$d$-pseudomanifold on $n$ vertices.


title:
Cleanformer: A microphone array configuration-invariant, streaming,
  multichannel neural enhancement frontend for ASR

abstract:
  This work introduces the Cleanformer, a streaming multichannel neural based
enhancement frontend for automatic speech recognition (ASR). This model has a
conformer-based architecture which takes as inputs a single channel each of raw
and enhanced signals, and uses self-attention to derive a time-frequency mask.
The enhanced input is generated by a multichannel adaptive noise cancellation
algorithm known as Speech Cleaner, which makes use of noise context to derive
its filter taps. The time-frequency mask is applied to the noisy input to
produce enhanced output features for ASR. Detailed evaluations are presented
with simulated and re-recorded datasets in speech-based and non-speech-based
noise that show significant reduction in word error rate (WER) when using a
large-scale state-of-the-art ASR model. It also will be shown to significantly
outperform enhancement using a beamformer with ideal steering. The enhancement
model is agnostic of the number of microphones and array configuration and,
therefore, can be used with different microphone arrays without the need for
retraining. It is demonstrated that performance improves with more microphones,
up to 4, with each additional microphone providing a smaller marginal benefit.
Specifically, for an SNR of -6dB, relative WER improvements of about 80\% are
shown in both noise conditions.


title: On-demand compute reduction with stochastic wav2vec 2.0

abstract:
  Squeeze and Efficient Wav2vec (SEW) is a recently proposed architecture that
squeezes the input to the transformer encoder for compute efficient
pre-training and inference with wav2vec 2.0 (W2V2) models. In this work, we
propose stochastic compression for on-demand compute reduction for W2V2 models.
As opposed to using a fixed squeeze factor, we sample it uniformly during
training. We further introduce query and key-value pooling mechanisms that can
be applied to each transformer layer for further compression. Our results for
models pre-trained on 960h Librispeech dataset and fine-tuned on 10h of
transcribed data show that using the same stochastic model, we get a smooth
trade-off between word error rate (WER) and inference time with only marginal
WER degradation compared to the W2V2 and SEW models trained for a specific
setting. We further show that we can fine-tune the same stochastically
pre-trained model to a specific configuration to recover the WER difference
resulting in significant computational savings on pre-training models from
scratch.


title: Lifetimes of singly charmed hadrons

abstract:
  We provide an extensive study of the lifetimes of singly charmed baryons and
mesons, within the heavy quark expansion with all known corrections included. A
special attention is devoted to the choice of the charm mass and wavefunctions
of heavy baryons. We give our predictions for lifetimes, lifetime ratios, and
semileptonic branching ratios of singly charmed baryons. Our results
accommodate the experimentally-favoured hierarchy of singly charmed baryon
lifetimes \begin{eqnarray*} \tau\left(\Xi_c^{0}\right) <
\tau\left(\Lambda_c^{+}\right)< \tau\left(\Omega_c^{0}\right) <
\tau\left(\Xi_c^{+}\right)\, \end{eqnarray*} in contrast to earlier theoretical
findings. Predictions for charmed meson lifetimes and semileptonic decay rates
are in agreement with a recent comprehensive study and experimental results
within uncertainties.


title: Discrete-Continuous Smoothing and Mapping

abstract:
  We describe a general approach to smoothing and mapping with a class of
discrete-continuous factor graphs commonly encountered in robotics
applications. While there are openly available tools providing flexible and
easy-to-use interfaces for specifying and solving optimization problems
formulated in terms of either discrete or continuous graphical models, at
present, no similarly general tools exist enabling the same functionality for
hybrid discrete-continuous problems. We aim to address this problem. In
particular, we provide a library, DC-SAM, extending existing tools for
optimization problems defined in terms of factor graphs to the setting of
discrete-continuous models. A key contribution of our work is a novel solver
for efficiently recovering approximate solutions to discrete-continuous
optimization problems. The key insight to our approach is that while joint
inference over continuous and discrete state spaces is often hard, many
commonly encountered discrete-continuous problems can naturally be split into a
"discrete part" and a "continuous part" that can individually be solved easily.
Leveraging this structure, we optimize discrete and continuous variables in an
alternating fashion. In consequence, our proposed work enables straightforward
representation of and approximate inference in discrete-continuous graphical
models. We also provide a method to recover the uncertainty in estimates of
both discrete and continuous variables. We demonstrate the versatility of our
approach through its application to three distinct robot perception
applications: point-cloud registration, robust pose graph optimization, and
object-based mapping and localization.


title: Computation by Convective Logic Gates and Thermal Communication

abstract:
  We demonstrate a novel computational architecture based on fluid convection
logic gates and heat flux-mediated information flows. Our previous work
demonstrated that Boolean logic operations can be performed by thermally-driven
convection flows. In this work, we use numerical simulations to demonstrate a
different, but universal Boolean logic operation (NOR), performed by simpler
convective gates. The gates in the present work do not rely on obstacle flows
or periodic boundary conditions, a significant improvement in terms of
experimental realizability. Conductive heat transfer links can be used to
connect the convective gates, and we demonstrate this with the example of
binary half addition. These simulated circuits could be constructed in an
experimental setting with modern, 2-dimensional fluidics equipment, such as a
thin layer of fluid between acrylic plates. The presented approach thus
introduces a new realm of unconventional, thermal fluid-based computation.


title: Unsteady dynamics in a subsonic duct flow with a bluff body

abstract:
  A series of reduced-order numerical simulations on a specific bluff body type
(v-gutters) in a subsonic duct flow is done to assess the unsteady wake
dynamics. Two of the v-gutter's geometrical parameters are varied: the
v-gutter's base angle ($\theta$) and the size of a slit ($\xi$) at the
leading-edge of the v-gutter. Turbulent flow kinematics and pressure field are
analyzed to evaluate the unsteadiness at a freestream Mach number of $M_\infty
= 0.25$ and a freestream Reynolds number based on bluff body's transverse
length (L) of $Re_L=0.1 \times 10^6$. Five v-gutter angles are considered
($\theta$, $^\circ = \pi/6, \pi/4, \pi/3, 5\pi/12, \pi/2$) and three slit sizes
($\xi$, mm =0,0.25,0.5) are considered only for a particular $\theta =
[\pi/6]$. In general, high fluctuations in velocity and pressure are seen for
the bluffest body in consideration ($\theta = \pi/2$) with higher drag ($c_d$)
and total pressure loss ($\Delta p_0$). On the other hand, the presence of a
slit on a streamlined body ($\theta = \pi/6$) tends to efficiently stabilize
the wake and thus, producing almost a periodic shedding structure with lower
$c_d$ and $\Delta p_0$. For $\theta = [\pi/6]$, broadened spectra in vortex
shedding is seen with a peak at $[fL/u_\infty] \sim 0.08$. For $\theta \geq
[\pi/4]$, a dominant discrete shedding frequency is seen with a gradual
spectral decay. Similarly, the effects of $\xi$ on the $\theta = [\pi/6]$ case
produce a discrete shedding frequency instead of a broadened one, as told
before. The shedding frequency increases to a maximum of $[fL/u_\infty] \sim
0.26$ for the maximum slit size of $\xi = 0.5$. More insights on the shedding
vortices, momentum deficit in the wake, varying energy contents in the flow
field, and the dominant spatiotemporal structures are also provided.


title: Robust Dual-Graph Regularized Moving Object Detection

abstract:
  Moving object detection and its associated background-foreground separation
have been widely used in a lot of applications, including computer vision,
transportation and surveillance. Due to the presence of the static background,
a video can be naturally decomposed into a low-rank background and a sparse
foreground. Many regularization techniques, such as matrix nuclear norm, have
been imposed on the background. In the meanwhile, sparsity or smoothness based
regularizations, such as total variation and $\ell_1$, can be imposed on the
foreground. Moreover, graph Laplacians are further imposed to capture the
complicated geometry of background images. Recently, weighted regularization
techniques including the weighted nuclear norm regularization have been
proposed in the image processing community to promote adaptive sparsity while
achieving efficient performance. In this paper, we propose a robust dual-graph
regularized moving object detection model based on the weighted nuclear norm
regularization, which is solved by the alternating direction method of
multipliers (ADMM). Numerical experiments on body movement data sets have
demonstrated the effectiveness of this method in separating moving objects from
background, and the great potential in robotic applications.


title:
Cascade Models in Simulation of Extended Heavy Targets Irradiated by
  Accelerated Proton and Deuteron Beams

abstract:
  The paper presents a survey of the main numerical models used for simulation
of interaction of accelerated particle beams with target nuclei. These models
form the core of the software for simulation of various experiments and
experimental facilities both for scientific and applied purposes. The beam and
target parameters considered in detail in this study (protons and deuterons
with energies from 0.66 to 4 AGeV and bulk U targets) cover the range of
interest in development of new concepts of nuclear power production aided by
accelerated particle beams.


title:
Stochastic Models of Stem Cells and Their Descendants under Different
  Criticality Assumptions

abstract:
  We study time continuous branching processes with exponentially distributed
lifetimes, with two types of cells that proliferate according to binary
fission. A range of possible system dynamics are considered, each of which is
characterized by the mutation rate of the original cells and the survival
probability of the altered cells' progeny. For each system, we derive a
closed-form expression for the joint probability generating function of cell
counts, and perform asymptotic analysis on the behaviors of the cell population
with particular focus on probability of extinction. Part of our results
confirms known properties of branching processes using a different approach
while other are original. While the model is best suited for modeling the fate
of differentiating stem cells, we discuss other scenarios in which these system
dynamics may be applicable in real life. We also discuss the history of the
subject.


title: Meta-AF: Meta-Learning for Adaptive Filters

abstract:
  Adaptive filtering algorithms are pervasive throughout modern society and
have had a significant impact on a wide variety of domains including audio
processing, telecommunications, biomedical sensing, astropyhysics and
cosmology, seismology, and many more. Adaptive filters typically operate via
specialized online, iterative optimization methods such as least-mean squares
or recursive least squares and aim to process signals in unknown or
nonstationary environments. Such algorithms, however, can be slow and laborious
to develop, require domain expertise to create, and necessitate mathematical
insight for improvement. In this work, we seek to go beyond the limits of
human-derived adaptive filter algorithms and present a comprehensive framework
for learning online, adaptive signal processing algorithms or update rules
directly from data. To do so, we frame the development of adaptive filters as a
meta-learning problem in the context of deep learning and use a form of
self-supervision to learn online iterative update rules for adaptive filters.
To demonstrate our approach, we focus on audio applications and systematically
develop meta-learned adaptive filters for five canonical audio problems
including system identification, acoustic echo cancellation, blind
equalization, multi-channel dereverberation, and beamforming. For each
application, we compare against common baselines and/or current
state-of-the-art methods and show we can learn high-performing adaptive filters
that operate in real-time and, in most cases, significantly out perform all
past specially developed methods for each task using a single general-purpose
configuration of our method.


title: On the Kodaira dimension of moduli spaces of Abelian differentials

abstract:
  This paper lays the foundation for determining the Kodaira dimension of the
projectivized strata of Abelian differentials with prescribed zero and pole
orders in large genus. We work with the moduli space of multi-scale
differentials constructed in [BCGGM2] which provides an orbifold
compactification of these strata. We establish the projectivity of the moduli
space of multi-scale differentials, describe the locus of canonical
singularities, and compute a series of effective divisor classes. Moreover, we
exhibit a perturbation of the canonical class which allows the corresponding
pluri-canonical forms to extend over the locus of non-canonical singularities.
  As applications, we certify general type for strata with few zeros as well as
for strata with equidistributed zero orders when g is sufficiently large. In
particular, we show general type for the odd spin components of the minimal
strata for g > 12.


title:
Performance of a MQXF Nb3Sn Quadrupole Magnet Under Different Stress
  Level

abstract:
  In a dipole or in a quadrupole accelerator magnet, the displacement of the
coil turns induced by the electromagnetic forces can cause quenches limiting
the magnet performance. For this reason, an azimuthal preload is applied to
avoid azimuthal movements of the coil up to the required operational current.
However, several tests showed that accelerator magnets can operate with a
partial preload, i.e. that coil unloading during the ramp does not prevent
reaching higher currents. This issue is particularly relevant for Nb3Sn
magnets, where the loads applied to the Nb3Sn filaments can reach the
degradation limits of critical current. In order to investigate the impact of
coil preload on the quench performance, the MQXFS6 short model quadrupole for
the High Luminosity Upgrade was tested under an azimuthal preload at 80% of the
short sample current, reaching 93% of short sample current at 1.9 K. The
preload was then released to 60%, still showing ability to operate in the range
of 80-85% of short sample current as required by HL-LHC project. With this
lower preload, the ability of going above 90% of short sample was lost, and a
significant training appeared above 85%. When the preload was restored to the
original 80% value, the magnet reached with few quenches 95% of short sample
(13.4 T peak field). Magnetic measurements confirm the larger movement of the
coil in the case with lower preload, and agree with finite element simulations.


title:
CDF-II $W$ Boson Mass Anomaly in the Canonical Scotogenic Neutrino-Dark
  Matter Model

abstract:
  The CDF-II collaboration's recent high-precision measurement of $W$ boson
mass indicates new physics contribution(s) beyond the Standard Model. We
investigate the possibility of the well-known canonical Scotogenic model, where
dark matter particle running in the loop generates neutrino masses, to explain
the CDF-II measurement. We show that the doublet scalar carrying a dark parity
charge can account for the W boson mass anomaly through oblique S,T,U parameter
corrections. For both scalar and fermionic dark matter possibilities, we
simultaneously examine the constraints coming from a) neutrino mass,
oscillation, neutrinoless double beta decay and lepton flavour violation
experiments, b) from dark matter relic density and direct detection experiments
c) from the oblique S,T,U parameter values consistent with CDF-II $W$ boson
measurement. We show that the canonical Scotogenic model can simultaneously
explain all the aforementioned issues.


title: Invisible flat bands on a topological chiral edge

abstract:
  We prove that invisible bands associated with zeros of the single-particle
Green's function exist ubiquitously at topological interfaces of 2D Chern
insulators, dual to the chiral edge/domain-wall modes. We verify this statement
in a repulsive Hubbard model with a topological flat band, using real-space
dynamical mean-field theory to study the domain walls of its ferromagnetic
ground state. Moreover, our numerical results show that the chiral modes are
split into branches due to the interaction, and that the branches are connected
by invisible flat bands. Our work provides deeper insight into interacting
topological systems.


title:
Density of the boundary regular set of 2d area minimizing currents with
  arbitrary codimension and multiplicity

abstract:
  In the present work, we consider area minimizing currents in the general
setting of arbitrary codimension and arbitrary boundary multiplicity. We study
the boundary regularity of 2d area minimizing currents, beyond that, several
results are stated in the more general context of $(C_0, \alpha_0, r_0)$-almost
area minimizing currents of arbitrary dimension m and arbitrary codimension
taking the boundary with arbitrary multiplicity. Furthermore, we do not
consider any type of convex barrier assumption on the boundary, in our main
regularity result which states that the regular set, which includes one-sided
and two-sided points, of any 2d area minimizing current $T$ is an open dense
set in $\Gamma$.


title:
Inferences on relations between distant supermassive black holes and
  their hosts complemented by the galaxy fundamental plane

abstract:
  The realization of fundamental relations between supermassive black holes and
their host galaxies would have profound implications in astrophysics. To add
further context to studies of their co-evolution, an investigation is carried
out to gain insight as to whether quasars and their hosts at earlier epochs
follow the local relation between black hole (BH) mass and stellar velocity
dispersion. We use 584 SDSS quasars at 0.2 < z < 0.8 with black hole
measurements, and properties of their hosts from the Hyper Suprime-Cam Subaru
Strategic Program. An inference of the stellar velocity dispersion is achieved
for each based on the stellar mass and size of the host galaxy by using the
galaxy mass fundamental plane for inactive galaxies at similar redshifts. In
agreement with past studies, quasars occupy an elevated position from the local
M_BH-sigma relation, considered as a flattening, while maintaining ratios of
M_BH/M* consistent with local values. Based on a forward-modeling of the
sample, we demonstrate that an evolving intrinsic M_BH-sigma relation can match
the observations. However, we hypothesize that these changes may be a
reflection of a non-evolving intrinsic relationship between M_BH and M*.
Reassuringly, there are signs of migration onto the local M_BH-sigma for
galaxies that are either massive, quiescent or compact. Thus, the majority of
the bulges of quasar hosts at high redshift are in a development stage and
likely to align with their black holes onto the mass scaling relation at later
times.


title:
Multi-photon Monte Carlo event generator KKMCee for lepton and quark
  pair production in lepton colliders

abstract:
  We present the {\tt KKMCee 5.0} Monte Carlo event generator for lepton and
quark pair production for the high energy electron-positron annihilation
process. It is still the most sophisticated event generator for such processes.
Its entire source code is re-written in the modern C++ language. It reproduces
all features of the older \kkmc\ code in Fortran 77. However, a number of
improvements in the Monte Carlo algorithm are also implemented. Most
importantly, it is intended to be a starting point for the future improvements,
which will be mandatory for the future high precision lepton collider projects.
As in the older version, in addition to higher order QED corrections, it
includes so-called \order{\alpha^{1.5}} genuine weak corrections using a
version of the classic {\tt DIZET} library and polarized $\tau$ decays using
{\tt TAUOLA} program. Both {\tt DIZET} and {\tt TAUOLA} external libraries are
still in Fortran 77. In addition, a {\tt HEPMC3} interface to other MC
programs, like parton showers and detector simulation, replaces the older {\tt
HepEvt} interface. The {\tt HEPMC3} interface is also exploited in the
implementation of the additional photon final state emissions in $\tau$ decays
using an external {\tt PHOTOS} library rewritten in C.


title: Strategic Signaling for Utility Control in Audit Games

abstract:
  As an effective method to protect the daily access to sensitive data against
malicious attacks, the audit mechanism has been widely deployed in various
practical fields. In order to examine security vulnerabilities and prevent the
leakage of sensitive data in a timely manner, the database logging system
usually employs an online signaling scheme to issue an alert when suspicious
access is detected. Defenders can audit alerts to reduce potential damage. This
interaction process between a defender and an attacker can be modeled as an
audit game. In previous studies, it was found that sending real-time signals in
the audit game to warn visitors can improve the benefits of the defender.
However, the previous approaches usually assume perfect information of the
attacker, or simply concentrate on the utility of the defender. In this paper,
we introduce a brand-new zero-determinant (ZD) strategy to study the sequential
audit game with online signaling, which empowers the defender to unilaterally
control the utility of visitors when accessing sensitive data. In addition, an
optimization scheme based on the ZD strategy is designed to effectively
maximize the utility difference between the defender and the attacker.
Extensive simulation results show that our proposed scheme enhances the
security management and control capabilities of the defender to better handle
different access requests and safeguard the system security in a cost-efficient
manner.


title: Byzantine-Resilient Counting in Networks

abstract:
  We present two distributed algorithms for the {\em Byzantine counting
problem}, which is concerned with estimating the size of a network in the
presence of a large number of Byzantine nodes.
  In an $n$-node network ($n$ is unknown), our first algorithm, which is {\em
deterministic}, finishes in $O(\log{n})$ rounds and is time-optimal. This
algorithm can tolerate up to $O(n^{1 - \gamma})$ arbitrarily (adversarially)
placed Byzantine nodes for any arbitrarily small (but fixed) positive constant
$\gamma$. It outputs a (fixed) constant factor estimate of $\log{n}$ that would
be known to all but $o(1)$ fraction of the good nodes. This algorithm works for
\emph{any} bounded degree expander network. However, this algorithms assumes
that good nodes can send arbitrarily large-sized messages in a round.
  Our second algorithm is {\em randomized} and most good nodes send only
small-sized messages (Throughout this paper, a small-sized message is defined
to be one that contains $O(\log{n})$ bits in addition to at most a constant
number of node IDs.). This algorithm works in \emph{almost all} $d$-regular
graphs. It tolerates up to $B(n) = n^{\frac{1}{2} - \xi}$ (note that $n$ and
$B(n)$ are unknown to the algorithm) arbitrarily (adversarially) placed
Byzantine nodes, where $\xi$ is any arbitrarily small (but fixed) positive
constant. This algorithm takes $O(B(n)\log^2{n})$ rounds and outputs a (fixed)
constant factor estimate of $\log{n}$ with probability at least $1 - o(1)$. The
said estimate is known to most nodes, i.e., $\geq (1 - \beta)n$ nodes for any
arbitrarily small (but fixed) positive constant $\beta$.
  To complement our algorithms, we also present an impossibility result that
shows that it is impossible to estimate the network size with any reasonable
approximation with any non-trivial probability of success if the network does
not have sufficient vertex expansion.


title:
Comparing correlation components and approximations in Hartree-Fock and
  Kohn-Sham theories via an analytical test case study

abstract:
  The asymmetric Hubbard dimer is a model that allows for explicit expressions
of the Hartree-Fock (HF) and Kohn-Sham (KS) states as analytical functions of
the external potential, $\Delta v$, and of the interaction strength, $U$. We
use this unique circumstance to establish a rigorous comparison between the
individual contributions to the correlation energies stemming from the two
theories in the $\{U,\Delta v\}$ parameter space. Within this analysis of the
Hubbard dimer, we observe a change in the sign of the HF kinetic correlation
energy, compare the indirect repulsion energies, and derive an expression for
the 'traditional' correlation energy, i.e. the one that corrects the HF
estimate, in a pure site-occupation function theory spirit [Eq. (43)]. Next, we
test the performances of the Liu-Burke and the Seidl-Perdew-Levy functionals,
which model the correlation energy based on its weak- and strong-interaction
limit expansions and can be used for both the traditional and the KS
correlation energies. Our results show that, in the Hubbard dimer setting, they
typically work better for the HF reference, despite having been originally
devised for KS. These conclusions are somewhat in line with prior assessments
of these functionals on various chemical data sets. However, the Hubbard dimer
model allows us to show the extent of the error that may occur in using the
strong-interaction ingredient for the KS reference in place of the one for the
HF reference, as has been carried out in most of the prior assessments.


title:
Crystal Transformer: Self-learning neural language model for Generative
  and Tinkering Design of Materials

abstract:
  Self-supervised neural language models have recently achieved unprecedented
success, from natural language processing to learning the languages of
biological sequences and organic molecules. These models have demonstrated
superior performance in the generation, structure classification, and
functional predictions for proteins and molecules with learned representations.
However, most of the masking-based pre-trained language models are not designed
for generative design, and their black-box nature makes it difficult to
interpret their design logic. Here we propose BLMM Crystal Transformer, a
neural network based probabilistic generative model for generative and
tinkering design of inorganic materials. Our model is built on the blank
filling language model for text generation and has demonstrated unique
advantages in learning the "materials grammars" together with high-quality
generation, interpretability, and data efficiency. It can generate chemically
valid materials compositions with as high as 89.7\% charge neutrality and
84.8\% balanced electronegativity, which are more than 4 and 8 times higher
compared to a pseudo random sampling baseline. The probabilistic generation
process of BLMM allows it to recommend tinkering operations based on learned
materials chemistry and makes it useful for materials doping. Combined with the
TCSP crysal structure prediction algorithm, We have applied our model to
discover a set of new materials as validated using DFT calculations. Our work
thus brings the unsupervised transformer language models based generative
artificial intelligence to inorganic materials. A user-friendly web app has
been developed for computational materials doping and can be accessed freely at
\url{www.materialsatlas.org/blmtinker}.


title:
Rapid detection of SARS-CoV-2 nucleocapsid protein using dual-comb
  biosensing

abstract:
  Testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is
essential along with vaccination and inactivation to fight against the
coronavirus disease 2019 (COVID-19) pandemic. Reverse-transcription polymerase
chain reaction (RT-PCR), based on reverse transcription of RNA into DNA and
amplification of specific DNA targets, is the current standard for COVID-19
testing; however, it hampers from laborious and time-consuming multiple steps.
If the testing is largely simplified and shortened, it will be a powerful
deterrent to the spread of COVID-19. Here we demonstrate the optical biosensing
based on optical frequency comb (OFC), enabling the rapid detection of
SARS-CoV-2 nucleocapsid protein. The virus-concentration-dependent optical
spectral shift caused by antigen-antibody interaction and
multimode-interference fiber sensor is transformed into a photonic
radio-frequency (RF) shift by coherent frequency link between optical and RF
regions in OFC, benefiting from high precision, rapid, simple, and low cost in
electric frequency measurements. Furthermore, the active-dummy compensation of
temperature drift with dual-comb configuration extracts the imperceptible
change of virus-concentration-dependent signal from the large background signal
that changes moment by moment. Such the dual-comb biosensing has a potential to
reduce the testing time down of COVID-19 to a few tens minute, which is one
order of magnitude shorter than that of RT-PCR (typically, 5 hours).
Furthermore, it will be applied for sensitive sensing of not only virus of
emerging and re-emerging infectious diseases but also RNA, bio-marker, and
endocrine disruptor by selecting the surface modification of biomolecule
interaction.


title:
The non-GRS properties for the twisted generalized Reed-Solomon code and
  its extended code

abstract:
  In 2017, Beelen et al. firstly introduced twisted generalized Reed-Solomon
(in short, TGRS) codes, and constructed a large subclass of MDS TGRS codes.
Later, they proved that TGRS code is non-GRS when the code rate is less than
one half. In this letter, basing on the dual code of the TGRS code or the
extended TGRS code, by using the Schur product, we prove that almost all of
TGRS codes and extended TGRS codes are non-GRS when the code rate more than one
half.


title: Spontaneous Emergence of Computation in Network Cascades

abstract:
  Neuronal network computation and computation by avalanche supporting networks
are of interest to the fields of physics, computer science (computation theory
as well as statistical or machine learning) and neuroscience. Here we show that
computation of complex Boolean functions arises spontaneously in threshold
networks as a function of connectivity and antagonism (inhibition), computed by
logic automata (motifs) in the form of computational cascades. We explain the
emergent inverse relationship between the computational complexity of the
motifs and their rank-ordering by function probabilities due to motifs, and its
relationship to symmetry in function space. We also show that the optimal
fraction of inhibition observed here supports results in computational
neuroscience, relating to optimal information processing.

categories: physics.soc-ph cond-mat.dis-nn cs.AI cs.FL


title: Defect line coarsening and refinement in active nematics

abstract:
  Active matter naturally performs out-of-equilibrium which results in the
emergence of diverse dynamic steady-states, including the omnipresent chaotic
dynamic state known as active turbulence. However, much less is known how such
active systems dynamically depart out of these configurations, such as get
excited or damped to a different dynamic steady state. In this Letter, we
demonstrate the transitional dynamics towards and between different dynamic
steady states of three-dimensional active nematic turbulence, which we show is
determined by the coarsening or refinement of the topological defect lines.
Specifically, using theory and numerical modelling, we are able to predict the
evolution of the active defect density away from the steady state as due to
time-dependent activity or viscoelastic material properties, establishing a
single length scale phenomenological description of defect line
coarsening/refinement in a three-dimensional active nematic. The approach is
first applied to growth dynamics of a single active defect loop, and then to a
full three-dimensional active defect line network. The analysis also points out
an interesting analogy with the coarsening dynamics of cosmic strings.


title: Multiaxial fields improve SABRE efficiency by preserving hydride order

abstract:
  Signal Amplification By Reversible Exchange (SABRE) and the heteronuclear
variant, X-SABRE, increase the sensitivity of magnetic resonance techniques
using order derived from reversible binding of para-hydrogen. One current
limitations of SABRE is suboptimal polarization transfer over the lifetime of
the complex. Here, we demonstrate a multiaxial low-field pulse sequence which
allows optimal polarization build-up during a low-field evolution pulse,
followed by a high-field mixing pulse which permits proton decoupling along an
orthogonal axis. This preserves the singlet character of the parahydrides while
allowing exchange to replenish the ligands on the iridium catalyst. This
strategy leads to a 2.5-fold improvement over continuous field SABRE SHEATH
experimentally which was confirmed with numerical simulation.


title: The parabolic coset structure of Bruhat intervals in Coxeter groups

abstract:
  In this paper, we study the decomposition of Bruhat intervals in a Coxeter
group with respect to cosets of a parabolic subgroup. Our main result is that
the intersection of a lower Bruhat interval with a parabolic coset contains a
unique maximal element. As an application, we give a decomposition formula for
the Poincar\'{e} polynomial of a Coxeter group element. We also show that the
fibers of standard parabolic projection maps on Schubert varieties are
themselves Schubert varieties.


title: The equivalence of GRS codes and EGRS codes

abstract:
  Generalized Reed-Solomon and extended generalized Reed-Solomon (abbreviation
to GRS and EGRS) codes are the most well-known family of MDS codes with wide
applications in coding theory and practice. Let $\mathbb{F}_q$ be the $q$
elements finite field, where $q$ is the power of a prime. For a linear code
$\mathcal{C}$ over $\mathbb{F}_q$ with length $2\le n\le q$, we prove that
$\mathcal{C}$ is a GRS code if and only if $\mathcal{C}$ is a EGRS code.


title:
Questionnaires to PDEs: From Disorganized Data to Emergent Generative
  Dynamic Models

abstract:
  Starting with sets of disorganized observations of spatially varying and
temporally evolving systems, obtained at different (also disorganized) sets of
parameters, we demonstrate the data-driven derivation of parameter dependent,
evolutionary partial differential equation (PDE) models capable of generating
the data. This tensor type of data is reminiscent of shuffled
(multi-dimensional) puzzle tiles. The independent variables for the evolution
equations (their "space" and "time") as well as their effective parameters are
all "emergent", i.e., determined in a data-driven way from our disorganized
observations of behavior in them. We use a diffusion map based "questionnaire"
approach to build a parametrization of our emergent space/time/parameter space
for the data. This approach iteratively processes the data by successively
observing them on the "space", the "time", and the "parameter" axes of a
tensor. Once the data are organized, we use machine learning (here, neural
networks) to approximate the operators governing the evolution equations in
this emergent space. Our illustrative example is based on a previously
developed vertex-plus-signaling model of Drosophila embryonic development. This
allows us to discuss features of the process like symmetry breaking,
translational invariance, and autonomousness of the emergent PDE model, as well
as its interpretability.


title: On bounded ratios of minors of totally positive matrices

abstract:
  We provide several examples of bounded Laurent monomials of minors of a
totally positive matrix, which can not be factored into a product of so called
primitive ratios, thus showing that the conjecture about factorization of
bounded ratios stated in [3] by Fallat, Gekhtman, and Johnson does not hold.
However, all found examples satisfy subtraction-free conjecture stated also in
[3]. In addition, we show that the set of all bounded ratios form a polyhedral
cone of dimension $\binom{2n}{n}-2n$.


title:
Positive and negative exact boundary controllability results for the
  linear Biharmonic Schr\"odinger equation

abstract:
  In this paper, we study the exact boundary controllability of the linear
Biharmonic Schr\"odinger equation $i\partial_ty=-\partial_x^4y+
\gamma\partial_x^2y$ on a bounded domain with hinged boundary conditions and
boundary control acts on the second spatial derivative at the {left} endpoint,
where the parameter $\gamma<0$. We prove that this system is exactly
controllable in time $T>0$, if and only if, the parameter $\gamma$ does not
belong to a critical countable set of negative real numbers. The analysis in
this work is based on spectral analysis together with the nonharmonic Fourier
series method.


title: SceneTrilogy: On Scene Sketches and its Relationship with Text and Photo

abstract:
  We for the first time extend multi-modal scene understanding to include that
of free-hand scene sketches. This uniquely results in a trilogy of scene data
modalities (sketch, text, and photo), where each offers unique perspectives for
scene understanding, and together enable a series of novel scene-specific
applications across discriminative (retrieval) and generative (captioning)
tasks. Our key objective is to learn a common three-way embedding space that
enables many-to-many modality interactions (e.g, sketch+text $\rightarrow$
photo retrieval). We importantly leverage the information bottleneck theory to
achieve this goal, where we (i) decouple intra-modality information by
minimising the mutual information between modality-specific and
modality-agnostic components via a conditional invertible neural network, and
(ii) align \textit{cross-modalities information} by maximising the mutual
information between their modality-agnostic components using InfoNCE, with a
specific multihead attention mechanism to allow many-to-many modality
interactions. We spell out a few insights on the complementarity of each
modality for scene understanding, and study for the first time a series of
scene-specific applications like joint sketch- and text-based image retrieval,
sketch captioning.


title: Bug Characteristics in Quantum Software Ecosystem

abstract:
  With the advance in quantum computing in recent years, quantum software
becomes vital for exploring the full potential of quantum computing systems.
Quantum programming is different from classical programming, for example, the
state of a quantum program is probabilistic in nature, and a quantum computer
is error-prone due to the instability of quantum mechanisms. Therefore, the
characteristics of bugs in quantum software projects may be very different from
that of classical software projects. This work aims to understand the
characteristics of bugs in quantum software projects, in order to provide
insights to help devise effective testing and debugging mechanisms. To achieve
this goal, we conduct an empirical study on the bug reports of 125 quantum
software projects. We observe that quantum software projects are more buggy
than classical software projects and that quantum project bugs are more costly
to fix than classical project bugs. We also identify the types of the bugs and
the quantum programming components where they occurred. Our study shows that
the bugs are spread across different components, but quantum-specific bugs
particularly appear in the compiler, gate operation, and state preparation
components. The three most occurring types of bugs are Program anomaly bugs,
Configuration bugs, and Data type and structure bugs. Our study highlights some
particularly challenging areas in quantum software development, such as the
lack of scientific quantum computation libraries that implement comprehensive
mathematical functions for quantum computing. Quantum developers also seek
specialized data manipulation libraries for quantum software engineering like
Numpy for quantum computing. Our findings also provide insights for future work
to advance the quantum program development, testing, and debugging of quantum
software, such as providing tooling support for debugging low-level circuits.


title:
Estimating and Penalizing Induced Preference Shifts in Recommender
  Systems

abstract:
  The content that a recommender system (RS) shows to users influences them.
Therefore, when choosing which recommender to deploy, one is implicitly also
choosing to induce specific internal states in users. Even more, systems
trained via long-horizon optimization will have direct incentives to manipulate
users, e.g. shift their preferences so they are easier to satisfy. In this work
we focus on induced preference shifts in users. We argue that - before
deployment - system designers should: estimate the shifts a recommender would
induce; evaluate whether such shifts would be undesirable; and even actively
optimize to avoid problematic shifts. These steps involve two challenging
ingredients: estimation requires anticipating how hypothetical policies would
influence user preferences if deployed - we do this by using historical user
interaction data to train predictive user model which implicitly contains their
preference dynamics; evaluation and optimization additionally require metrics
to assess whether such influences are manipulative or otherwise unwanted - we
use the notion of "safe shifts", that define a trust region within which
behavior is safe. In simulated experiments, we show that our learned preference
dynamics model is effective in estimating user preferences and how they would
respond to new recommenders. Additionally, we show that recommenders that
optimize for staying in the trust region can avoid manipulative behaviors while
still generating engagement.


title: On the codimension-two cohomology of $\mathrm{SL}_n(\mathbb{Z})$

abstract:
  Borel--Serre proved that $\mathrm{SL}_n(\mathbb{Z})$ is a virtual duality
group of dimension $n \choose 2$ and the Steinberg module
$\mathrm{St}_n(\mathbb{Q})$ is its dualizing module. This module is the
top-dimensional homology group of the Tits building associated to
$\mathrm{SL}_n(\mathbb{Q})$. We determine the "relations among the relations"
of this Steinberg module. That is, we construct an explicit partial resolution
of length two of the $\mathrm{SL}_n(\mathbb{Z})$-module
$\mathrm{St}_n(\mathbb{Q})$. We use this partial resolution to show the
codimension-2 rational cohomology group $H^{{n \choose 2}
-2}(\mathrm{SL}_n(\mathbb{Z});\mathbb{Q})$ of $\mathrm{SL}_n(\mathbb{Z})$
vanishes for $n \geq 3$. This resolves a case of a conjecture of
Church--Farb--Putman. We also produce lower bounds for the codimension-1
cohomology of certain congruence subgroups of $\mathrm{SL}_n(\mathbb{Z})$.


title: Some remarks on critical sets of Laplace eigenfunctions

abstract:
  We study the set of critical points of a solution to $\Delta u = \lambda
\cdot u$ and in particular components of the critical set that have codimension
1. We show, for example, that if a second Neumann eigenfunction of a simply
connected polygon $P$ has infinitely many critical points, then $P$ is a
rectangle.


title:
Oscillations and confluence in three-magnon scattering of ferromagnetic
  resonance

abstract:
  We have performed a comprehensive study and developed a semi-analytical model
of the transient behavior of three-magnon scattering of ferromagnetic
resonance. For powers above the Suhl instability, we observe a secondary
nonlinear regime in which the resonance amplitude oscillates while relaxing to
its steady-state value. This behavior is in both qualitative and quantitative
agreement with our model despite the onset of higher-order interactions. As the
power increases, the oscillations lead to 180-degree phase shifts in the
resonance, which results in switching between three-magnon splitting and
confluence. These findings deepen the understanding of the transient behavior
and directionality of three-magnon scattering.


title:
Visual Acuity Prediction on Real-Life Patient Data Using a Machine
  Learning Based Multistage System

abstract:
  In ophthalmology, intravitreal operative medication therapy (IVOM) is
widespread treatment for diseases such as the age-related macular degeneration
(AMD), the diabetic macular edema (DME), as well as the retinal vein occlusion
(RVO). However, in real-world settings, patients often suffer from loss of
vision on time scales of years despite therapy, whereas the prediction of the
visual acuity (VA) and the earliest possible detection of deterioration under
real-life conditions is challenging due to heterogeneous and incomplete data.
In this contribution, we present a workflow for the development of a
research-compatible data corpus fusing different IT systems of the department
of ophthalmology of a German maximum care hospital. The extensive data corpus
allows predictive statements of the expected progression of a patient and his
or her VA in each of the three diseases. Within our proposed multistage system,
we classify the VA progression into the three groups of therapy "winners",
"stabilizers", and "losers" (WSL scheme). Our OCT biomarker classification
using an ensemble of deep neural networks results in a classification accuracy
(F1-score) of over 98 %, enabling us to complete incomplete OCT documentations
while allowing us to exploit them for a more precise VA modelling process. Our
VA prediction requires at least four VA examinations and optionally OCT
biomarkers from the same time period to predict the VA progression within a
forecasted time frame. While achieving a prediction accuracy of up to 69 %
(macro average F1-score) when considering all three WSL-based progression
groups, this corresponds to an improvement by 11 % in comparison to our
ophthalmic expertise (58 %).


title: Optical vortex Brillouin laser

abstract:
  Optical vortices, which have been extensively studied over the last decades,
offer an additional degree of freedom useful in many applications, such as
optical tweezers and quantum control. Stimulated Brillouin scattering,
providing a narrow linewidth and a strong nonlinear response, has been used to
realise quasi-continuous wave (CW) lasers. Here, we report stable oscillation
of optical vortices and acoustic modes in a Brillouin laser based on chiral
photonic crystal fibre, which robustly supports helical Bloch modes (HBMs) that
carry circularly-polarized optical vortex and display circular birefringence.
We implement a narrow-linewidth Brillouin fibre laser that stably emits 1st-
and 2nd-order vortex-carrying HBMs. Angular momentum conservation selection
rules dictate that pump and backward Brillouin signals have opposite
topological charge and spin. Additionally, we show that when the chiral PCF is
placed within a laser ring cavity, the linewidth-narrowing associated with
lasing permits the peak of the Brillouin gain that corresponds to acoustic mode
to be measured with resolution of 10 kHz and accuracy of 520 kHz. The results
pave the way to a new generation of vortex-carrying SBS systems with
applications in quantum information processing, vortex-carrying nonreciprocal
systems.


title:
Gate-Level Side-Channel Leakage Assessment with Architecture Correlation
  Analysis

abstract:
  While side-channel leakage is traditionally evaluated from a fabricated chip,
it is more time-efficient and cost-effective to do so during the design phase
of the chip. We present a methodology to rank the gates of a design according
to their contribution to the side-channel leakage of the chip. The methodology
relies on logic synthesis, logic simulation, gate-level power estimation, and
gate leakage assessment to compute a ranking. The ranking metric can be defined
as a specific test by correlating gate-level activity with a leakage model, or
else as a non-specific test by evaluating gate-level activity in response to
distinct test vector groups. Our results show that only a minority of the gates
in a design contribute most of the side-channel leakage. We demonstrate this
property for several designs, including a hardware AES coprocessor and a
cryptographic hardware/software interface in a five-stage pipelined RISC
processor.


title: On Automorphism Criteria for Comparing Amounts of Mathematical Structure

abstract:
  Wilhelm (2021) has recently defended a criterion for comparing structure of
mathematical objects, which he calls Subgroup. He argues that Subgroup is
better than SYM * , another widely adopted criterion. We argue that this is
mistaken; Subgroup is strictly worse than SYM *. We then formulate a new
criterion that improves on both SYM * and Subgroup, answering Wilhelm's
criticisms of SYM * along the way. We conclude by arguing that no criterion
that looks only to the automorphisms of mathematical objects to compare their
structure can be fully satisfactory.


title:
$Z'$s and sterile neutrinos from heterotic string models: exploring $Z'$
  mass exclusion limits

abstract:
  We investigate the impact of sterile neutrinos on the decay rate of extra
$Z'$s with mass in the TeV range in heterotic string derived models. We explore
the impact of sterile neutrinos on the current $Z'$ mass exclusion limits at
the LHC, and how these bounds change when the parameter space of this specific
class of models is modified.


title:
A Mini-Neptune from TESS and CHEOPS Around the 120 Myr Old AB Dor member
  HIP 94235

abstract:
  The TESS mission has enabled discoveries of the brightest transiting planet
systems around young stars. These systems are the benchmarks for testing
theories of planetary evolution. We report the discovery of a mini-Neptune
transiting a bright star in the AB Doradus moving group. HIP 94235 (TOI-4399,
TIC 464646604) is a Vmag=8.31 G-dwarf hosting a 3.00 -0.28/+0.32 Rearth
mini-Neptune in a 7.7 day period orbit. HIP 94235 is part of the AB Doradus
moving group, one of the youngest and closest associations. Due to its youth,
the host star exhibits significant photometric spot modulation, lithium
absorption, and X-ray emission. Three 0.06% transits were observed during
Sector-27 of the TESS Extended Mission, though these transit signals are
dwarfed by the 2% peak-to-peak photometric variability exhibited by the host
star. Follow-up observations with CHEOPS confirmed the transit signal and
prevented the erosion of the transit ephemeris. HIP 94235 is part of a 50 AU
G-M binary system. We make use of diffraction limited observations spanning 11
years, and astrometric accelerations from Hipparchos and Gaia, to constrain the
orbit of HIP 94235 B. HIP 94235 is one of the tightest stellar binaries to host
an inner planet. As part of a growing sample of bright, young planet systems,
HIP 94235 b is ideal for follow-up transit observations, such as those that
investigate the evaporative processes driven by high-energy radiation that may
sculpt the valleys and deserts in the Neptune population.


title: Quantum diagrammatics for $F_4$

abstract:
  We introduce a graphical calculus for the representation theory of the
quantized enveloping algebra of type $F_4$. We do this by giving a diagrammatic
description of the category of invariant tensors on the 26-dimensional
fundamental representation.


title: Surfaces of section for geodesic flows of closed surfaces

abstract:
  We prove several results concerning the existence of surfaces of section for
the geodesic flows of closed orientable Riemannian surfaces. The surfaces of
section $\Sigma$ that we construct are either Birkhoff sections, meaning that
they intersect every sufficiently long orbit segment of the geodesic flow, or
at least they have some hyperbolic components in $\partial\Sigma$ as limit sets
of the orbits of the geodesic flow that do not return to $\Sigma$. In order to
prove these theorems, we provide a study of configurations of simple closed
geodesics of closed orientable Riemannian surfaces, which may have independent
interest. Our arguments are based on Grayson's curve shortening flow.


title:
Remote laser-speckle sensing of heart sounds for health assessment and
  biometric identification

abstract:
  Assessment of heart sounds is the cornerstone of cardiac examination, but it
requires a stethoscope, skills and experience, and a direct contact with the
patient. We developed a contactless, machine-learning assisted method for
heart-sound identification and quantification based on the remote measurement
of the reflected laser speckle from the neck skin surface in healthy
individuals. We compare the performance of this method to standard digital
stethoscope recordings on an example task of heart-beat sound biometric
identification. We show that our method outperforms the stethoscope even
allowing identification on the test data taken on different days. This method
might allow development of devices for remote monitoring of cardiovascular
health in different settings.


title:
Trials with Irregular and Informative Assessment Times: A Sensitivity
  Analysis Approach

abstract:
  Many trials are designed to collect outcomes at pre-specified times after
randomization. However, in practice, there is often substantial variability in
the times at which participants are actually assessed, which poses a challenge
to learning about the treatment effects at the targeted assessment times. Any
method for analyzing a trial with such irregular assessment times relies on
untestable assumptions. Therefore, conducting a sensitivity analysis is an
important step that can strengthen conclusions from such trials. However, no
sensitivity analysis methodology has been developed. We develop a methodology
that accounts for possibly informative assessment times, where assessment at
time t may be related to the outcome at that time, even after accounting for
observed past history. We implement our methodology using a new augmented
inverse-intensity-weighted estimator, and we apply it to a trial of low-income
participants with uncontrolled asthma. We also evaluate the performance of our
estimation procedure in a realistic simulation study.


title: Decentralisation Conscious Players And System Reliability

abstract:
  We propose a game-theoretic model of the reliability of decentralised systems
based on Varian's model of system reliability, to which we add a new normalised
total effort case that models \textit{decentralisation conscious players} who
prioritise decentralisation. We derive the Nash equilibria in the normalised
total effort game. In these equilibria, either one or two values are played by
players that do not free ride. The speed at which players can adjust their
contributions can determine how an equilibrium is reached and equilibrium
values. The behaviour of decentralisation conscious players is robust to
deviations by other players. Our results highlight the role that
decentralisation conscious players can play in maintaining decentralisation.
They also highlight, however, that by supporting an equilibrium that requires
an important contribution they cannot be expected to increase decentralisation
as contributing the equilibrium value may still imply a loss for many players.
We also discuss practical constraints on decentralisation in the context of our
model.


title:
End-to-end Mapping in Heterogeneous Systems Using Graph Representation
  Learning

abstract:
  To enable heterogeneous computing systems with autonomous programming and
optimization capabilities, we propose a unified, end-to-end, programmable graph
representation learning (PGL) framework that is capable of mining the
complexity of high-level programs down to the universal intermediate
representation, extracting the specific computational patterns and predicting
which code segments would run best on a specific core in heterogeneous hardware
platforms. The proposed framework extracts multi-fractal topological features
from code graphs, utilizes graph autoencoders to learn how to partition the
graph into computational kernels, and exploits graph neural networks (GNN) to
predict the correct assignment to a processor type. In the evaluation, we
validate the PGL framework and demonstrate a maximum speedup of 6.42x compared
to the thread-based execution, and 2.02x compared to the state-of-the-art
technique.


title:
BronchoPose: an analysis of data and model configuration for
  vision-based bronchoscopy pose estimation

abstract:
  Vision-based bronchoscopy (VB) models require the registration of the virtual
lung model with the frames from the video bronchoscopy to provide effective
guidance during the biopsy. The registration can be achieved by either tracking
the position and orientation of the bronchoscopy camera or by calibrating its
deviation from the pose (position and orientation) simulated in the virtual
lung model. Recent advances in neural networks and temporal image processing
have provided new opportunities for guided bronchoscopy. However, such progress
has been hindered by the lack of comparative experimental conditions.
  In the present paper, we share a novel synthetic dataset allowing for a fair
comparison of methods. Moreover, this paper investigates several neural network
architectures for the learning of temporal information at different levels of
subject personalization. In order to improve orientation measurement, we also
present a standardized comparison framework and a novel metric for camera
orientation learning. Results on the dataset show that the proposed metric and
architectures, as well as the standardized conditions, provide notable
improvements to current state-of-the-art camera pose estimation in video
bronchoscopy.


title: Remarks on a combinatorial solution of a stochastic aggregation model

abstract:
  In a recent series of papers, an exact combinatorial solution was claimed for
a variant of the so-called Marcus--Lushnikov model of aggregation. In this
model, a finite number of aggregates, are initially assumed to be present in
the form of monomers. At each time step, two aggregates are chosen according to
certain size-dependent proabbilities and irreversibly joined to form an
aggregate of higher mass. The claimed result given an expression for the full
probability distribution over all possible size distributions in terms of the
so-called Bell polynomials. In this paper, we develop the asymptotics of this
solution in order to check whether the exact solution yields correct
expressions for the average cluster size distribution as obtained from the
Smoluchowski equations. The answer is surprisingly involved: for the generic
case of an arbitrary reaction rate, it is negative, but for the so-called {\em
classical\/} rate kernels, constant, additive and multiplicative, the solutions
obtained are indeed exact. On the other hand, for the multiplicative kernel, a
discrepancy is found in the full solution between the combinatorial solution
and the exact solution. The reasons for this puzzling pattern of agreement and
disagreement are unclear. A better understanding of the combinatorial
solution's derivation is needed, the better to understand its range of
validity.


title: Counting geodesics on compact symmetric spaces

abstract:
  We describe the inverse image of the Riemannian exponential map at a
basepoint of a compact symmetric space as the disjoint union of so called focal
orbits through a maximal torus. These are orbits of a subgroup of the isotropy
group acting in the tangent space at the basepoint. We show how their
dimensions (infinitesimal data) and connected components (topological data) are
encoded in the diagram, multiplicities, Weyl group and lattice of the symmetric
space. Obtaining this data is precisely what we mean by counting geodesics.
This extends previous results on compact Lie groups. We apply our results to
give short independent proofs of known results on compact symmetric spaces.


title: When adversarial examples are excusable

abstract:
  Neural networks work remarkably well in practice and theoretically they can
be universal approximators. However, they still make mistakes and a specific
type of them called adversarial errors seem inexcusable to humans. In this
work, we analyze both test errors and adversarial errors on a well controlled
but highly non-linear visual classification problem. We find that, when
approximating training on infinite data, test errors tend to be close to the
ground truth decision boundary. Qualitatively speaking these are also more
difficult for a human. By contrast, adversarial examples can be found almost
everywhere and are often obvious mistakes. However, when we constrain
adversarial examples to the manifold, we observe a 90\% reduction in
adversarial errors. If we inflate the manifold by training with Gaussian noise
we observe a similar effect. In both cases, the remaining adversarial errors
tend to be close to the ground truth decision boundary. Qualitatively, the
remaining adversarial errors are similar to test errors on difficult examples.
They do not have the customary quality of being inexcusable mistakes.


title:
New bounds on the existence of $(n_{5})$ and $(n_{6})$ configurations:
  the Gr\"{u}nbaum Calculus revisited

abstract:
  The "Gr\"unbaum Incidence Calculus" is the common name of a collection of
operations introduced by Branko Gr\"unbaum to produce new $(n_{4})$
configurations from various input configurations. In a previous paper, we
generalized two of these operations to produce operations on arbitrary $(n_k)$
configurations, and we showed that for each $k$, there exists an integer
$N_{k}$ such that for all $n \geq N_{k}$, there exists at least one $(n_{k})$
configuration, with current records $N_{5}\leq 576$ and $N_{6}\leq 7350$. In
this paper, we further extend the Gr\"unbaum calculus; using these operations,
as well as a collection of previously known and novel ad hoc constructions, we
refine the bounds for $k = 5$ and $k = 6$. Namely, we show that $N_5 \leq 166$
and $N_{6}\leq 585$.


title: Inherent Symmetries of graphs, paths, and Traveling Salesperson Problems

abstract:
  Without imposing restrictions on a weighted graph's arc lengths, symmetry
structures cannot be expected. But, they exist. To find them, the graphs are
decomposed into a component that dictates all closed path properties (e.g.,
shortest and longest paths), and a superfluous component that can be removed.
The simpler remaining graph exposes inherent symmetry structures that form the
basis for all closed path properties. For certain asymmetric problems, the
symmetry is that of three-cycles; for the general undirected setting it is a
type of four-cycles; for general directed problems with asymmetric costs, it is
a product of three and four cycles. Everything extends immediately to
incomplete graphs.


title:
Search for heavy neutral leptons in decays of $W$ bosons using a
  dilepton displaced vertex in $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS
  detector

abstract:
  A search for a long-lived, heavy neutral lepton ($\mathcal{N}$) in 139
fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collision data collected by the ATLAS
detector at the Large Hadron Collider is reported. The $\mathcal{N}$ is
produced via $W \rightarrow \mathcal{N} \mu$ or $W \rightarrow \mathcal{N} e$
and decays into two charged leptons and a neutrino, forming a displaced vertex.
The $\mathcal{N}$ mass is used to discriminate between signal and background.
No signal is observed, and limits are set on the squared mixing parameters of
the $\mathcal{N}$ with the left-handed neutrino states for the $\mathcal{N}$
mass range $3$ GeV $< m_{\mathcal{N}} < 15$ GeV. For the first time, limits are
given for both single-flavor and multiflavor mixing scenarios motivated by
neutrino flavor oscillation results for both the normal and inverted
neutrino-mass hierarchies.


title:
C3: Continued Pretraining with Contrastive Weak Supervision for Cross
  Language Ad-Hoc Retrieval

abstract:
  Pretrained language models have improved effectiveness on numerous tasks,
including ad-hoc retrieval. Recent work has shown that continuing to pretrain a
language model with auxiliary objectives before fine-tuning on the retrieval
task can further improve retrieval effectiveness. Unlike monolingual retrieval,
designing an appropriate auxiliary task for cross-language mappings is
challenging. To address this challenge, we use comparable Wikipedia articles in
different languages to further pretrain off-the-shelf multilingual pretrained
models before fine-tuning on the retrieval task. We show that our approach
yields improvements in retrieval effectiveness.


title: Compactness of the Bloom sparse operators and applications

abstract:
  We establish the characterization of compactness for the sparse operator
(associated with symbol in weighted VMO space) in the two weight setting on the
spaces of homogeneous type in the sense of Coifman and Weiss. As a direct
application we obtain the compactness characterization for the maximal
commutators with respect to the weighted VMO functions and the commutator of
Calder\'on-Zygmund operators on the homogeneous spaces. Furthermore, our
approach can be applied to compactness characterization for operators in the
multilinear Bloom setting.


title: Implications of W-boson mass anomaly for atomic parity violation

abstract:
  We consider the implication of the recent measurement of the W-boson mass
$M_W$ [Science 376, 170 (2022)] for atomic parity violation experiments. We
show that the change in $M_W$ shifts the Standard Model prediction for the
${}^{133}$Cs nuclear weak charge to $Q_W({}^{133}{\rm Cs})=-72.85(6)$, i.e. by
$5.5\sigma$ from its current value. This brings existing experimental result
for $Q_W({}^{133}{\rm Cs})$ into an essential agreement with the Standard
Model. Using our revised value for $Q_W({}^{133}{\rm Cs})$, we readjust
constraints on physics beyond the Standard Model.


title:
Offline Vehicle Routing Problem with Online Bookings: A Novel Problem
  Formulation with Applications to Paratransit

abstract:
  Vehicle routing problems (VRPs) can be divided into two major categories:
offline VRPs, which consider a given set of trip requests to be served, and
online VRPs, which consider requests as they arrive in real-time. Based on
discussions with public transit agencies, we identify a real-world problem that
is not addressed by existing formulations: booking trips with flexible pickup
windows (e.g., 3 hours) in advance (e.g., the day before) and confirming tight
pickup windows (e.g., 30 minutes) at the time of booking. Such a service model
is often required in paratransit service settings, where passengers typically
book trips for the next day over the phone. To address this gap between offline
and online problems, we introduce a novel formulation, the offline vehicle
routing problem with online bookings. This problem is very challenging
computationally since it faces the complexity of considering large sets of
requests -- similar to offline VRPs -- but must abide by strict constraints on
running time -- similar to online VRPs. To solve this problem, we propose a
novel computational approach, which combines an anytime algorithm with a
learning-based policy for real-time decisions. Based on a paratransit dataset
obtained from the public transit agency of Chattanooga, TN, we demonstrate that
our novel formulation and computational approach lead to significantly better
outcomes in this setting than existing algorithms.


title:
Signatures of a magnetic-field-induced Lifshitz transition in the
  ultra-quantum limit of the topological semimetal ZrTe$_5$

abstract:
  The quantum limit (QL) of an electron liquid, realised at strong magnetic
fields, has long been proposed to host a wealth of strongly correlated states
of matter. Electronic states in the QL are, for example, quasi-one dimensional
(1D), which implies perfectly nested Fermi surfaces prone to instabilities.
Whereas the QL typically requires unreachably strong magnetic fields, the
topological semimetal ZrTe$_5$ has been shown to reach the QL at fields of only
a few Tesla. Here, we characterize the QL of ZrTe$_5$ at fields up to 64 T by a
combination of electrical-transport and ultrasound measurements. We find that
the Zeeman effect in ZrTe$_5$ enables an efficient tuning of the 1D Landau band
structure with magnetic field. This results in a Lifshitz transition to a 1D
Weyl regime in which perfect charge neutrality can be achieved. Since no
instability-driven phase transitions destabilise the 1D electron liquid for the
investigated field strengths and temperatures, our analysis establishes
ZrTe$_5$ as a thoroughly understood platform for potentially inducing more
exotic interaction-driven phases at lower temperatures.


title:
Contrastive learning-based computational histopathology predict
  differential expression of cancer driver genes

abstract:
  Digital pathological analysis is run as the main examination used for cancer
diagnosis. Recently, deep learning-driven feature extraction from pathology
images is able to detect genetic variations and tumor environment, but few
studies focus on differential gene expression in tumor cells. In this paper, we
propose a self-supervised contrastive learning framework, HistCode, to infer
differential gene expressions from whole slide images (WSIs). We leveraged
contrastive learning on large-scale unannotated WSIs to derive slide-level
histopathological feature in latent space, and then transfer it to tumor
diagnosis and prediction of differentially expressed cancer driver genes. Our
extensive experiments showed that our method outperformed other
state-of-the-art models in tumor diagnosis tasks, and also effectively
predicted differential gene expressions. Interestingly, we found the higher
fold-changed genes can be more precisely predicted. To intuitively illustrate
the ability to extract informative features from pathological images, we
spatially visualized the WSIs colored by the attentive scores of image tiles.
We found that the tumor and necrosis areas were highly consistent with the
annotations of experienced pathologists. Moreover, the spatial heatmap
generated by lymphocyte-specific gene expression patterns was also consistent
with the manually labeled WSI.


title:
Ergomagnetosphere, Ejection Disc, Magnetopause in M87. I Global Flow of
  Mass, Angular Momentum, Energy and Current

abstract:
  We interpret the 1.3mm VLBI observations made by the Event Horizon Telescope
of the black hole in M87. It is proposed that, instead of being a torus of
accreting gas, the ring is a rotating, magnetically-dominated ergomagnetosphere
that can transmit electromagnetic angular momentum and energy outward to the
disc through a combination of large scale magnetic torque and small scale
instabilities. It is further proposed that energy can be extracted by magnetic
flux threading the ergosphere through the efficient emission of long wavelength
electromagnetic disturbances onto negative energy orbits, when the invariant
$B^2-E^2$ becomes negative. In this way, the spinning black hole and its
ergosphere not only power the jets but also the ejection disc so as to drive
away most of the gas supplied near the Bondi radius. This outflow takes the
form of a MHD wind, extending over many decades of radius, with a
unidirectional magnetic field, that is collimated by the infalling gas across a
magnetopause. This wind, in turn, collimates the relativistic jets and the
emission observed from the jet sheath may be associated with a return current.
A model for the global flow of mass, angular momentum, energy and current, on
scales from the horizon to the Bondi radius, is presented and discussed.


title:
Understanding Creep Suppression Mechanism in Polymer Nanocomposites
  through Machine Learning

abstract:
  While recent efforts have shown how local structure plays an essential role
in the dynamic heterogeneity of homogeneous glass-forming materials, systems
containing interfaces such as thin films or composite materials remain poorly
understood. It is known that interfaces perturb the molecular packing nearby,
however, numerous studies show the dynamics are modified over a much larger
range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a
combination of simulations and experiments and quantitatively separate the role
of polymer packing from other effects on the dynamics, as a function of
distance from the nanoparticle surfaces. After showing good qualitative
agreement between the simulations and experiments in glassy structure and creep
compliance, we use a recently developed machine learning technique to decompose
polymer dynamics in our simulated PNCs into structure-dependent and
structure-independent processes. With this decomposition, the free energy
barrier for polymer rearrangement can be described as a combination of
packing-dependent and packing-independent barriers. We find both barriers are
higher near nanoparticles and decrease with applied stress, quantitatively
demonstrating that the slow interfacial dynamics is not solely due to polymer
packing differences, but also the change of structure-dynamics relationships.
Finally, we present how this decomposition can be used to accurately predict
strain-time creep curves for PNCs from their static configuration, providing
additional insights into the effects of polymer-nanoparticle interfaces on
creep suppression in PNCs.


title: Regularized zero-range interactions in the three-body problem

abstract:
  To regularize the three-body problem, Minlos and Faddeev suggested a
modification of zero-range model, which diminishes interaction at the
triple-collision point. The analysis reveals that this regularization results
in four alternatives depending on the regularization parameter $ \sigma $.
Explicitly, Efimov or Thomas effects remain for $ \sigma < \sigma_c $, the
additional boundary conditions of two types should be imposed at the
triple-collision point for $ \sigma_c \le \sigma < \sigma_e $ and $ \sigma_e <
\sigma < \sigma_r $, and the problem is regularized for $ \sigma \ge \sigma_r
$. Critical values $ \sigma_c < \sigma_e < \sigma_r $ separating different
alternatives are determined both for a two-component three-body system and for
three identical bosons.


title:
BeAGLE: Benchmark $e$A Generator for LEptoproduction in high energy
  lepton-nucleus collisions

abstract:
  The upcoming Electron-Ion Collider (EIC) will address several outstanding
puzzles in modern nuclear physics. Topics such as the partonic structure of
nucleons and nuclei, the origin of their mass and spin, among others, can be
understood via the study of high energy electron-proton ($ep$) and
electron-nucleus ($e$A) collisions. Achieving the scientific goals of the EIC
will require a novel electron-hadron collider and detectors capable to perform
high-precision measurements, but also dedicated tools to model and interpret
the data. To aid in the latter, we present a general-purpose $e$A Monte Carlo
(MC) generator - BeAGLE. In this paper, we provide a general description of the
models integrated into BeAGLE, applications of BeAGLE in $e$A physics,
implications for detector requirements at the EIC, and the tuning of the
parameters in BeAGLE based on available experimental data. Specifically, we
focus on a selection of model and data comparisons in particle production in
both $ep$ and $e$A collisions, where baseline particle distributions provide
essential information to characterize the event. In addition, we investigate
the collision geometry determination in $e$A collisions, which could be used as
an experimental tool for varying the nuclear density.

categories: physics.comp-ph hep-ph nucl-ex nucl-th physics.data-an physics.ins-det


title:
On the current failure -- but bright future -- of topology-driven
  biological network alignment

abstract:
  The function of a protein is defined by its interaction partners. Thus,
topology-driven network alignment of the protein-protein interaction (PPI)
networks of two species should uncover similar interaction patterns and allow
identification of functionally similar proteins. Howver, few of the fifty or
more algorithms for PPI network alignment have demonstrated a significant link
between network topology and functional similarity, and none have recovered
orthologs using network topology alone. We find that the major contributing
factors to this failure are: (i) edge densities in current PPI networks are too
low to expect topological network alignment to succeed; (ii) when edge
densities are high enough, some measures of topological similarity easily
uncover functionally similar proteins while others do not; and (iii) most
network alignment algorithms fail to optimize their own topological objective
functions, hampering their ability to use topology effectively. We demonstrate
that SANA-the Simulated Annealing Network Aligner-significantly outperforms
existing aligners at optimizing their own objective functions, even achieving
near-optimal solutions when optimal solution is known. We offer the first
demonstration of global network alignments based on topology alone that align
functionally similar proteins with p-values in some cases below 1e-300. We
predict that topological network alignment has a bright future as edge
densities increase towards the value where good alignments become possible. We
demonstrate that when enough common topology is present at high enough edge
densities-for example in the recent, partly synthetic networks of the
Integrated Interaction Database-topological network alignment easily recovers
most orthologs, paving the way towards high-throughput functional prediction
based on topology-driven network alignment.


title: AI Personification: Estimating the Personality of Language Models

abstract:
  Technology for open-ended language generation, a key application of
artificial intelligence, has advanced to a great extent in recent years.
Large-scale language models, which are trained on large corpora of text, are
being used in a wide range of applications everywhere, from virtual assistants
to conversational bots. While these language models output fluent text,
existing research shows that these models can and do capture human biases. Many
of these biases, especially those that could potentially cause harm, are being
well investigated. On the other hand, studies that infer and change personality
traits inherited by these models have been scarce or non-existent. In this
work, we explore the personality traits of several large-scale language models
designed for open-ended text generation and the datasets used for training
them. Our work builds on the popular Big Five factors and develops robust
methods that quantify the personality traits of these models and their
underlying datasets. In particular, we trigger the models with a questionnaire
designed for personality assessment and subsequently classify the text
responses into quantifiable traits using a Zero-shot classifier. Our
classification sheds light on an important anthropomorphic element found in
such AI models and can help stakeholders decide how they should be applied and
how society could perceive them. We augment our analysis by studying approaches
that can alter these personalities.


title:
Measuring Discrepancies in Airbnb Guest Acceptance Rates Using
  Anonymized Demographic Data

abstract:
  In order to make technological systems and platforms more equitable,
organizations must be able to measure the scale of potential inequities as well
as the efficacy of proposed solutions. In this paper, we present a system that
measures discrepancies in platform user experience that are attributable to
perceived race (experience gaps) using anonymized data. This allows for
progress to be made in this area while limiting any potential privacy risk.
Specifically, the system enforces the privacy model of p-sensitive k-anonymity
to conduct measurement without ever storing or having access to a 1:1 mapping
between user identifiers and perceived race. We test this system in the context
of the Airbnb guest booking experience. Our simulation-based power analysis
shows that the system can measure the efficacy of proposed platform-wide
interventions with comparable precision to non-anonymized data. Our work
establishes that measurement of experience gaps with anonymized data is
feasible and can be used to guide the development of policies to promote
equitable outcomes for users of Airbnb as well as other technology platforms.


title:
Towards the detection of ultra-low energetic neutrinos with plasma
  metamaterials

abstract:
  Experiments as IceCube or Super-Kamiokande have been successful in detecting
highly energetic neutrinos in the. Neutrinos in the ultra-low energy range
($\mathcal{E}<1.0~\rm{eV}$) have been theoretically predicted but their
observation remain elusive, and no concrete experimental scheme has been
proposed for that job. Here, we propose a novel scheme based on graphene
plasmonic metamaterials to designed to detect ultra-low energetic neutrinos. We
claim that slow neutrino fluxes, interacting with solid-state plasmas, can
generate an instability due to the weak neutrino-plasmon interaction, which is
reminiscent of the beam-plasma instability taking place in astrophysics and
laboratory plasmas. We make use of the semi-classical limit of the weak
interaction to describe the coupling between the neutrinos and electrons in
graphene. To render the scheme practical, we investigate the neutrino-plasma
instability produced in a graphene metamaterial, composed by a periodic
stacking of graphene layers. Our findings reveal that the controlled excitation
of plasma waves in such graphene metamaterial allows for the detection of
neutrinos in the energy range $\sim 1.0~\rm{\mu eV}-100~\rm{meV}$, and fluxes
in the range $10^{4}-10^{10} \rm{cm^{-2} s^{-1}}$.


title: Convex Cylinders and the Symmetric Gaussian Isoperimetric Problem

abstract:
  Let $\Omega$ be a measurable Euclidean set in $\mathbb{R}^{n}$ that is
symmetric, i.e. $\Omega=-\Omega$, such that $\Omega\times\mathbb{R}$ has the
smallest Gaussian surface area among all measurable symmetric sets of fixed
Gaussian volume. We conclude that either $\Omega$ or $\Omega^{c}$ is convex.
Moreover, except for the case $H(x)=\langle x,N(x)\rangle+\lambda$ with
$H\geq0$ and $\lambda<0$, we show there exist a radius $r>0$ and an integer
$0\leq k\leq n-1$ such that after applying a rotation, the boundary of $\Omega$
must satisfy $\partial\Omega= rS^{k}\times\mathbb{R}^{n-k-1}$, with
$\sqrt{n-1}\leq r\leq\sqrt{n+1}$ when $k\geq1$. Here $S^{k}$ denotes the unit
sphere of $\mathbb{R}^{k+1}$ centered at the origin, and $n\geq1$ is an
integer. One might say this result nearly resolves the symmetric Gaussian
conjecture of Barthe from 2001.


title:
Regimes in astrophysical lensing: refractive optics, diffractive optics,
  and the Fresnel scale

abstract:
  Astrophysical lensing has typically been studied in two regimes: diffractive
optics and refractive optics. Diffractive optics is characterized by a
perturbative expansion of the Kirchhoff-Fresnel diffraction integral, while
refractive optics is characterized by the stationary phase approximation.
Previously, it has been assumed that the Fresnel scale, $R_F$ , is the relevant
physical scale that separates these two regimes. With the recent introduction
of Picard-Lefschetz theory to the field of lensing, it has become possible to
generalize the refractive description of discrete images to all wave
parameters, and, in particular, exactly evaluate the diffraction integral at
all frequencies. In this work, we assess the regimes of validity of refractive
and diffractive approximations for a simple one-dimensional lens model through
comparison with this exact evaluation. We find that, contrary to previous
assumptions, the true separation scale between these regimes is given by $R_F /
\sqrt{\kappa}$, where $\kappa$ is the convergence of the lens. Thus, when the
lens is strong, refractive optics can hold for arbitrarily small scales. We
also argue that intensity variations in diffractive optics are generically
small, which has implications for the study of strong diffractive scintillation
(DISS).


title:
gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics
  Identification

abstract:
  A parametric adaptive physics-informed greedy Latent Space Dynamics
Identification (gLaSDI) method is proposed for accurate, efficient, and robust
data-driven reduced-order modeling of high-dimensional nonlinear dynamical
systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic
nonlinear latent representations of high-dimensional data, while dynamics
identification (DI) models capture local latent-space dynamics. An interactive
training algorithm is adopted for the autoencoder and local DI models, which
enables identification of simple latent-space dynamics and enhances accuracy
and efficiency of data-driven reduced-order modeling. To maximize and
accelerate the exploration of the parameter space for the optimal model
performance, an adaptive greedy sampling algorithm integrated with a
physics-informed residual-based error indicator and random-subset evaluation is
introduced to search for the optimal training samples on-the-fly. Further, to
exploit local latent-space dynamics captured by the local DI models for an
improved modeling accuracy with a minimum number of local DI models in the
parameter space, an efficient k-nearest neighbor convex interpolation scheme is
employed. The effectiveness of the proposed framework is demonstrated by
modeling various nonlinear dynamical problems, including Burgers equations,
nonlinear heat conduction, and radial advection. The proposed adaptive greedy
sampling outperforms the conventional predefined uniform sampling in terms of
accuracy. Compared with the high-fidelity models, gLaSDI achieves 66 to 4,417x
speed-up with 1 to 5% relative errors.

categories: eess.SY cs.CE cs.LG cs.NA cs.SY math.NA physics.comp-ph


title: Parametric Dynamic Mode Decomposition for Reduced Order Modeling

abstract:
  Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby
spatial modes of fixed temporal frequencies are extracted from numerical or
experimental data sets. The DMD low-rank or reduced operator is typically
obtained by singular value decomposition of the temporal data sets. For
parameter-dependent models, as found in many multi-query applications such as
uncertainty quantification or design optimization, the only parametric DMD
technique developed was a stacked approach, with data sets at multiples
parameter values were aggregated together, increasing the computational work
needed to devise low-rank dynamical reduced-order models. In this paper, we
present two novel approach to carry out parametric DMD: one based on the
interpolation of the reduced-order DMD eigenpair and the other based on the
interpolation of the reduced DMD (Koopman) operator. Numerical results are
presented for diffusion-dominated nonlinear dynamical problems, including a
multiphysics radiative transfer example. All three parametric DMD approaches
are compared.


title:
Assessing the ability of generative adversarial networks to learn
  canonical medical image statistics

abstract:
  In recent years, generative adversarial networks (GANs) have gained
tremendous popularity for potential applications in medical imaging, such as
medical image synthesis, restoration, reconstruction, translation, as well as
objective image quality assessment. Despite the impressive progress in
generating high-resolution, perceptually realistic images, it is not clear if
modern GANs reliably learn the statistics that are meaningful to a downstream
medical imaging application. In this work, the ability of a state-of-the-art
GAN to learn the statistics of canonical stochastic image models (SIMs) that
are relevant to objective assessment of image quality is investigated. It is
shown that although the employed GAN successfully learned several basic first-
and second-order statistics of the specific medical SIMs under consideration
and generated images with high perceptual quality, it failed to correctly learn
several per-image statistics pertinent to the these SIMs, highlighting the
urgent need to assess medical image GANs in terms of objective measures of
image quality.


title:
Virtual Reality Applications in Software Engineering Education: A
  Systematic Review

abstract:
  Requirement Engineering (RE) is a Software Engineering (SE) process of
defining, documenting, and maintaining the requirements from a problem. It is
one of the most complex processes of SE because it addresses the relation
between customer and developer. RE learning may be abstract and complex for
most students because many of them cannot visualize the subject directly
applied. Through the advancement of technology, Virtual Reality (VR) hardware
is becoming increasingly more accessible, and it is not rare to use it in
education. Little research and systematic studies explain the integration
between SE and VR, and even less between RE and VR. Hence, this systematic
review proposes to select and present studies that relate the use of VR
applications to teach SE and RE concepts. We selected nine studies to include
in this review. Despite the lack of articles addressing the topic, the results
from this study showed that the use of VR technologies for learning SE is still
very seminal. The projects based essentially on visualization. There are lack
of tasks to build modeling artifacts, and also interaction with stakeholders
and other software engineers. Learning tasks and the monitoring of students'
progress by teachers also need to be considered.


title: Nodal Set Openings on Perturbed Rectangular Domains

abstract:
  We study the effects of perturbing the boundary of a rectangle on the nodal
sets of eigenfunctions of the Laplacian. Namely, for a rectangle of a given
aspect ratio $N$, we identify the first Dirichlet mode to feature a crossing in
its nodal set and perturb one of the sides of the rectangle by a close to flat,
smooth curve. Such perturbations will often "open" the crossing in the nodal
set, splitting it into two curves, and we study the separation between these
curves and their regularity. The main technique used is an approximate
separation of variables that allows us to restrict study to the first two
Fourier modes in an eigenfunction expansion. We show how the nature of the
boundary perturbation provides conditions on the orientation of the opening and
estimates on its size. In particular, several features of the perturbed nodal
set are asymptotically independent of the aspect ratio, which contrasts with
prior works. Numerical results supporting our findings are also presented.