title:
Enhanced physics-constrained deep neural networks for modeling vanadium
  redox flow battery

abstract:
  Numerical modeling and simulation have become indispensable tools for
advancing a comprehensive understanding of the underlying mechanisms and
cost-effective process optimization and control of flow batteries. In this
study, we propose an enhanced version of the physics-constrained deep neural
network (PCDNN) approach [1] to provide high-accuracy voltage predictions in
the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is
to enforce the physics-based zero-dimensional (0D) VRFB model in a neural
network to assure model generalization for various battery operation
conditions. Limited by the simplifications of the 0D model, the PCDNN cannot
capture sharp voltage changes in the extreme SOC regions. To improve the
accuracy of voltage prediction at extreme ranges, we introduce a second
(enhanced) DNN to mitigate the prediction errors carried from the 0D model
itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing
the model prediction with experimental data, we demonstrate that the ePCDNN
approach can accurately capture the voltage response throughout the
charge--discharge cycle, including the tail region of the voltage discharge
curve. Compared to the standard PCDNN, the prediction accuracy of the ePCDNN is
significantly improved. The loss function for training the ePCDNN is designed
to be flexible by adjusting the weights of the physics-constrained DNN and the
enhanced DNN. This allows the ePCDNN framework to be transferable to battery
systems with variable physical model fidelity.


title:
Stellar multiplicity affects the correlation between proto-planetary
  disc masses and accretion rates: binaries explain high-accretors in Upper Sco

abstract:
  In recent years a correlation between mass accretion rates onto new-born
stars and their proto-planetary disc masses was detected in nearby young
star-forming regions. Although such a correlation can be interpreted as due to
viscous-diffusion processes in the disc, highly-accreting sources with low disc
masses in more evolved regions remain puzzling. In this paper, we hypothesise
that the presence of a stellar companion truncating the disc can explain these
outliers. Firstly, we searched the literature for information on stellar
multiplicity in Lupus, Chamaeleon~I and Upper Sco, finding that roughly 20 per
cent of the discs involved in the correlation are in binaries or higher-order
multiple stellar systems. We prove with high statistical significance that at
any disc mass these sources have systematically higher accretion rates than
those in single-stars, with the bulk of the binary population being clustered
around $M_\mathrm{disc}/\dot{M}_\mathrm{acc}\approx0.1\,\mathrm{Myr}$. We then
run coupled gas and dust one-dimensional evolutionary models of tidally
truncated discs to be compared with the data. We find that these models are
able to reproduce well most of the population of observed discs in Lupus and
Upper Sco, even though the unknown eccentricity of each binary prevents an
object by object comparison. In the latter region, the agreement improves if
the grain coagulation efficiency is reduced, as may be expected in discs around
close binaries. Finally, we mention that thermal winds and sub-structures can
be important in explaining few outlying sources.


title: OptiTrap: Optimal Trap Trajectories for Acoustic Levitation Displays

abstract:
  Acoustic levitation has recently demonstrated the ability to create
volumetric content by trapping and quickly moving particles along reference
paths to reveal shapes in mid-air. However, the problem of specifying
physically feasible trap trajectories to display desired shapes remains
unsolved. Even if only the final shape is of interest to the content creator,
the trap trajectories need to determine where and when the traps need to be,
for the particle to reveal the intended shape. We propose OptiTrap, the first
structured numerical approach to compute trap trajectories for acoustic
levitation displays. Our approach generates trap trajectories that are
physically feasible and nearly time-optimal, and reveal generic mid-air shapes,
given only a reference path (i.e., a shape with no time information). We
provide a multi-dimensional model of the acoustic forces around a trap to model
the trap-particle system dynamics and compute optimal trap trajectories by
formulating and solving a non-linear path following problem. We formulate our
approach and evaluate it, demonstrating how OptiTrap consistently produces
feasible and nearly optimal paths, with increases in size, frequency, and
accuracy of the shapes rendered, allowing us to demonstrate larger and more
complex shapes than ever shown to date.


title: Chaotic dynamics of an autophoretic particle

abstract:
  Chemically active, or autophoretic, particles that isotropically emit or
absorb solute molecules are known to undergo spontaneous self-propulsion when
their activity is increased beyond a critical P\'{e}clet number ($Pe$). Here,
we conduct numerical simulations of a spherical rigid autophoretic particle in
unsteady rectilinear translation, which reveal that its motion progresses
through four regimes, as $Pe$ is increased: quiescent, steady, stirring, and
chaos. The particle is stationary in the quiescent regime, and the solute
profile is isotropic about the particle. At $Pe=4$ the fore-aft symmetry in the
solute profile is broken, resulting in its steady self-propulsion, as has been
shown in previous studies. A further increase in $Pe$ gives rise to the
stirring regime at $Pe\approx27$, where the fluid undergoes recirculation, as
the particle remains essentially stationary in a state of dynamic arrest. As
the P\'{e}clet number is increased further, the dynamics of the particle is
marked by chaotic oscillations at $Pe\approx55$ and higher, where its velocity
undergoes rapid reorientations. The mean square displacement of particles in
the chaotic regime exhibits a subdiffusive behavior with an apparently
universal scaling exponent at long times for all values of $Pe$ studied.
However, the time-scale for the decorrelation of the swimming velocity
decreases with an increase in $Pe$, and this time-scale also governs the
transition in the mean square displacement from early-time ballistic to
long-time subdiffusive motion.


title:
Approximation by double second type delayed arithmetic mean of periodic
  functions in $H_{p}^{(\omega, \omega)}$ space

abstract:
  In this paper, we give a degree of approximation of a function in the space
$H_{p}^{(\omega, \omega)}$ by using the second type double delayed arithmetic
means of its Fourier series. Such degree of approximation is expressed via two
functions of moduli of continuity type. To obtain one more general result, we
used the even-type double delayed arithmetic means of Fourier series as well.


title:
From local to global gene co-expression estimation using single-cell
  RNA-seq data

abstract:
  In genomics studies, the investigation of the gene relationship often brings
important biological insights. Currently, the large heterogeneous datasets
impose new challenges for statisticians because gene relationships are often
local. They change from one sample point to another, may only exist in a subset
of the sample, and can be non-linear or even non-monotone. Most previous
dependence measures do not specifically target local dependence relationships,
and the ones that do are computationally costly. In this paper, we explore a
state-of-the-art network estimation technique that characterizes gene
relationships at the single-cell level, under the name of cell-specific gene
networks. We first show that averaging the cell-specific gene relationship over
a population gives a novel univariate dependence measure that can detect any
non-linear, non-monotone relationship. Together with a consistent nonparametric
estimator, we establish its robustness on both the population and empirical
levels. Simulations and real data analysis show that this measure outperforms
existing independence measures like Pearson, Kendall's $\tau$, $\tau^\star$,
distance correlation, HSIC, Hoeffding's D, HHG, and MIC, on various tasks.


title: A Rigidity Theorem for Ext

abstract:
  The goal of this paper is to show that if $R$ is an unramified hypersurface,
if $M$ and $N$ are finitely generated $R$ modules, and if
$\operatorname{Ext}_{R}^{n}(M,N)=0$ for some $n\leq\operatorname{grade}{M}$,
then $\operatorname{Ext}_{R}^{i}(M,N)=0$ for $i\leq n$. A corollary of this
says that $\operatorname{Ext}_{R}^{i}(M,M)\neq 0$ for
$i\leq\operatorname{grade}{M}$ and $M\neq 0$. These results are related to a
question of Jorgensen and results of Dao.


title: On the relevance of language in speaker recognition

abstract:
  This paper presents a new database collected from a bilingual speakers set
(49), in two different languages: Spanish and Catalan. Phonetically there are
significative differences between both languages. These differences have let us
to establish several conclusions on the relevance of language in speaker
recognition, using two methods: vector quantization and covariance matrices


title:
Polarity Sampling: Quality and Diversity Control of Pre-Trained
  Generative Networks via Singular Values

abstract:
  We present Polarity Sampling, a theoretically justified plug-and-play method
for controlling the generation quality and diversity of pre-trained deep
generative networks DGNs). Leveraging the fact that DGNs are, or can be
approximated by, continuous piecewise affine splines, we derive the analytical
DGN output space distribution as a function of the product of the DGN's
Jacobian singular values raised to a power $\rho$. We dub $\rho$ the
$\textbf{polarity}$ parameter and prove that $\rho$ focuses the DGN sampling on
the modes ($\rho < 0$) or anti-modes ($\rho > 0$) of the DGN output-space
distribution. We demonstrate that nonzero polarity values achieve a better
precision-recall (quality-diversity) Pareto frontier than standard methods,
such as truncation, for a number of state-of-the-art DGNs. We also present
quantitative and qualitative results on the improvement of overall generation
quality (e.g., in terms of the Frechet Inception Distance) for a number of
state-of-the-art DGNs, including StyleGAN3, BigGAN-deep, NVAE, for different
conditional and unconditional image generation tasks. In particular, Polarity
Sampling redefines the state-of-the-art for StyleGAN2 on the FFHQ Dataset to
FID 2.57, StyleGAN2 on the LSUN Car Dataset to FID 2.27 and StyleGAN3 on the
AFHQv2 Dataset to FID 3.95. Demo: bit.ly/polarity-demo-colab


title:
Fast Neural Architecture Search for Lightweight Dense Prediction
  Networks

abstract:
  We present LDP, a lightweight dense prediction neural architecture search
(NAS) framework. Starting from a pre-defined generic backbone, LDP applies the
novel Assisted Tabu Search for efficient architecture exploration. LDP is fast
and suitable for various dense estimation problems, unlike previous NAS methods
that are either computational demanding or deployed only for a single subtask.
The performance of LPD is evaluated on monocular depth estimation, semantic
segmentation, and image super-resolution tasks on diverse datasets, including
NYU-Depth-v2, KITTI, Cityscapes, COCO-stuff, DIV2K, Set5, Set14, BSD100,
Urban100. Experiments show that the proposed framework yields consistent
improvements on all tested dense prediction tasks, while being $5\%-315\%$ more
compact in terms of the number of model parameters than prior arts.


title:
Investigation of long lived activity produced due to neutron emitting
  reactions

abstract:
  In this article, a detailed investigation has been done for the long lived
gamma activity due to neutron emitting experiments. These calculations mainly
focused on the experiments used for energy calibration purposes. Calibrated
energy is one of the most essential features of any accelerator facility.
Several experiments have been used for this purpose. Generally, experiments
having sharp curvature change in cross section of yield are used. Neutron
emitting experiments are one of such. Around the globe reactions like
$^7Li(p,n)$, $^{13}C(p,n)$, $^{19}F(p,n)$, $^{27}Al(p,n)$ etc. are used to
calibrate energy of the beam with accelerator terminal voltage. Neutrons coming
from these experiments can interact with surrounding elements. These
interactions with neutrons can create long lived gamma activity which may
interfere with future measurements. The present study has been done keeping in
mind the new Facility for Research in Experimental Nuclear Astrophysics (FRENA)
at Saha Institute of Nuclear Physics. It is a 3MV tandetron low energy high
current machine.


title:
Multi-objective robust optimization using adaptive surrogate models for
  problems with mixed continuous-categorical parameters

abstract:
  Explicitly accounting for uncertainties is paramount to the safety of
engineering structures. Optimization which is often carried out at the early
stage of the structural design offers an ideal framework for this task. When
the uncertainties are mainly affecting the objective function, robust design
optimization is traditionally considered. This work further assumes the
existence of multiple and competing objective functions that need to be dealt
with simultaneously. The optimization problem is formulated by considering
quantiles of the objective functions which allows for the combination of both
optimality and robustness in a single metric. By introducing the concept of
common random numbers, the resulting nested optimization problem may be solved
using a general-purpose solver, herein the non-dominated sorting genetic
algorithm (NSGA-II). The computational cost of such an approach is however a
serious hurdle to its application in real-world problems. We therefore propose
a surrogate-assisted approach using Kriging as an inexpensive approximation of
the associated computational model. The proposed approach consists of
sequentially carrying out NSGA-II while using an adaptively built Kriging model
to estimate of the quantiles. Finally, the methodology is adapted to account
for mixed categorical-continuous parameters as the applications involve the
selection of qualitative design parameters as well. The methodology is first
applied to two analytical examples showing its efficiency. The third
application relates to the selection of optimal renovation scenarios of a
building considering both its life cycle cost and environmental impact. It
shows that when it comes to renovation, the heating system replacement should
be the priority.


title:
Infinitely many arithmetic hyperbolic rational homology 3-spheres that
  bound geometrically

abstract:
  In this paper we provide the first examples of arithmetic hyperbolic
3-manifolds that are rational homology spheres and bound geometrically either
compact or cusped hyperbolic 4-manifolds.


title:
Counting Molecules: Python based scheme for automated enumeration and
  categorization of molecules in scanning tunneling microscopy images

abstract:
  Scanning tunneling and atomic force microscopies (STM/nc-AFM) are rapidly
progressing to offer unprecedented spatial resolution of a diverse array of
chemical species. In particular, they are employed to characterize on-surface
chemical reactions by directly examining precursors and products. Chiral
effects and self-assembled structures can also be investigated. This open
source, modular, python based scheme automates the categorization of a variety
of molecules present in medium sized (10$\times$10 to 100$\times$100 nm)
scanned probe images.


title:
High Order Robust Adaptive Control Barrier Functions and Exponentially
  Stabilizing Adaptive Control Lyapunov Functions

abstract:
  This paper studies the problem of utilizing data-driven adaptive control
techniques to guarantee stability and safety of uncertain nonlinear systems
with high relative degree. We first introduce the notion of a High Order Robust
Adaptive Control Barrier Function (HO-RaCBF) as a means to compute control
policies guaranteeing satisfaction of high relative degree safety constraints
in the face of parametric model uncertainty. The developed approach guarantees
safety by initially accounting for all possible parameter realizations but
adaptively reduces uncertainty in the parameter estimates leveraging data
recorded online. We then introduce the notion of an Exponentially Stabilizing
Adaptive Control Lyapunov Function (ES-aCLF) that leverages the same data as
the HO-RaCBF controller to guarantee exponential convergence of the system
trajectory. The developed HO-RaCBF and ES-aCLF are unified in a quadratic
programming framework, whose efficacy is showcased via two numerical examples
that, to our knowledge, cannot be addressed by existing adaptive control
barrier function techniques.


title: Certified Newton schemes for the evaluation of low-genus theta functions

abstract:
  Theta functions and theta constants in low genus, especially genus 1 and 2,
can be evaluated at any given point in quasi-linear time in the required
precision using Newton schemes based on Borchardt sequences. Our goal in this
paper is to provide the necessary tools to implement these algorithms in a
provably correct way. In particular, we obtain uniform and explicit convergence
results in the case of theta constants in genus 1 and 2, and theta functions in
genus 1: the associated Newton schemes will converge starting from
approximations to N bits of precision for N=60, 300, and 1600 respectively, for
all suitably reduced arguments. We also describe a uniform quasi-linear time
algorithm to evaluate genus 2 theta constants on the Siegel fundamental domain.
Our main tool is a detailed study of Borchardt means as multivariate analytic
functions.


title: LegalVis: Exploring and Inferring Precedent Citations in Legal Documents

abstract:
  To reduce the number of pending cases and conflicting rulings in the
Brazilian Judiciary, the National Congress amended the Constitution, allowing
the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a
set of understandings that both Executive and lower Judiciary branches must
follow. The STF's justices frequently cite the 58 existing BPs in their
decisions, and it is of primary relevance that judicial experts could identify
and analyze such citations. To assist in this problem, we propose LegalVis, a
web-based visual analytics system designed to support the analysis of legal
documents that cite or could potentially cite a BP. We model the problem of
identifying potential citations (i.e., non-explicit) as a classification
problem. However, a simple score is not enough to explain the results; that is
why we use an interpretability machine learning method to explain the reason
behind each identified citation. For a compelling visual exploration of
documents and BPs, LegalVis comprises three interactive visual components: the
first presents an overview of the data showing temporal patterns, the second
allows filtering and grouping relevant documents by topic, and the last one
shows a document's text aiming to interpret the model's output by pointing out
which paragraphs are likely to mention the BP, even if not explicitly
specified. We evaluated our identification model and obtained an accuracy of
96%; we also made a quantitative and qualitative analysis of the results. The
usefulness and effectiveness of LegalVis were evaluated through two usage
scenarios and feedback from six domain experts.


title:
Depolarising Social Networks: Optimisation of Exposure to Adverse
  Opinions in the Presence of a Backfire Effect

abstract:
  As social networks are ubiquitous in everyday life, problems such as
misinformation, bots and polarisation of opinion gain more and more importance.
This paper focuses on the last one as we propose novel methods to reduce the
amount of polarisation in a social group. We leverage the voter model in which
users hold binary opinions and repeatedly update their beliefs based on others
they connect with. Stubborn agents who never change their minds ("zealots") are
also disseminated through the network. We are interested in two equilibrium
metrics, the diversity of opinions $\sigma$ and the density of active links
$\rho$. The latter is a measure of the exposure to adverse opinions. We derive
formulas to compute them that are valid in any directed, weighted network. Then
we study the problem of finding optimal numbers of zealots in order to maximise
these quantities. We account for the presence of a backfire effect, which may
lead the group to react negatively and reinforce its level of polarisation in
response. We provide exact solutions in the specific context of a complete
unweighted graph, and propose a method for $\sigma$ in the general case.
Finally we apply these problems to the network of the US House of
Representatives. The network exhibits high levels of opinion diversity but
lower levels of active links density. We find optimal numbers of zealots that
maximise these quantities and show that both can be efficiently increased in
some cases.


title:
Nodal Precession of WASP-33b for Eleven Years by Doppler Tomographic and
  Transit Photometric Observations

abstract:
  WASP-33b, a hot Jupiter around a hot star, is a rare system in which nodal
precession has been discovered. We updated the model for the nodal precession
of WASP-33b by adding new observational points. Consequently, we found a motion
of the nodal precession spanning 11 years. We present homogenous Doppler
tomographic analyses of eight datasets, including two new datasets from TS23
and HIDES, obtained between 2008 and 2019, to illustrate the variations in the
projected spin-orbit obliquity of WASP-33b and its impact parameter. We also
present its impact parameters based on photometric transit observations
captured by MuSCAT in 2017 and MuSCAT2 in 2018. We derived its real spin-orbit
obliquity $\psi$, stellar spin inclination $i_{s}$, and stellar gravitational
quadrupole moment $J_2$ from the time variation models of the two orbital
parameters. We obtained $\psi = 108.19^{+0.95}_{-0.97}$ deg, $i_s =
58.3^{+4.6}_{-4.2}$ deg, and $J_2=(1.36^{+0.15}_{-0.12}) \times 10^{-4}$. Our
$J_2$ value was slightly smaller than the theoretically predicted value, which
may indicate that its actual stellar internal structure is different from the
theoretical one. We derived the nodal precession speed
$\dot{\theta}=0.507^{+0.025}_{-0.022}$ deg year$^{-1}$, and its period
$P_{\mathrm{pre}}=709^{+33}_{-34}$ years, and found that WASP-33b transits in
front of WASP-33 for only $\sim$ 20 \% of the entire nodal precession period.


title: Gravitational Portals with Non-Minimal Couplings

abstract:
  We consider the effects of non-minimal couplings to curvature of the form
$\xi_S S^2 R$, for three types of scalars: the Higgs boson, the inflaton, and a
scalar dark matter candidate. We compute the abundance of dark matter produced
by these non-minimal couplings to gravity and compare to similar results with
minimal couplings. We also compute the contribution to the radiation bath
during reheating. The main effect is a potential augmentation of the maximum
temperature during reheating. A model independent limit of
$\mathcal{O}(10^{12})$ GeV is obtained. For couplings $\xi_S \gtrsim
\mathcal{O}(1)$, these dominate over minimal gravitational interactions.


title:
On consistency of constrained spectral clustering under
  representation-aware stochastic block model

abstract:
  Spectral clustering is widely used in practice due to its flexibility,
computational efficiency, and well-understood theoretical performance
guarantees. Recently, spectral clustering has been studied to find balanced
clusters under population-level constraints. These constraints are specified by
additional information available in the form of auxiliary categorical node
attributes. In this paper, we consider a scenario where these attributes may
not be observable, but manifest as latent features of an auxiliary graph.
Motivated by this, we study constrained spectral clustering with the aim of
finding balanced clusters in a given \textit{similarity graph} $\mathcal{G}$,
such that each individual is adequately represented with respect to an
auxiliary graph $\mathcal{R}$ (we refer to this as representation graph). We
propose an individual-level balancing constraint that formalizes this idea. Our
work leads to an interesting stochastic block model that not only plants the
given partitions in $\mathcal{G}$ but also plants the auxiliary information
encoded in the representation graph $\mathcal{R}$. We develop unnormalized and
normalized variants of spectral clustering in this setting. These algorithms
use $\mathcal{R}$ to find clusters in $\mathcal{G}$ that approximately satisfy
the proposed constraint. We also establish the first statistical consistency
result for constrained spectral clustering under individual-level constraints
for graphs sampled from the above-mentioned variant of the stochastic block
model. Our experimental results corroborate our theoretical findings.


title: Why adversarial training can hurt robust accuracy

abstract:
  Machine learning classifiers with high test accuracy often perform poorly
under adversarial attacks. It is commonly believed that adversarial training
alleviates this issue. In this paper, we demonstrate that, surprisingly, the
opposite may be true -- Even though adversarial training helps when enough data
is available, it may hurt robust generalization in the small sample size
regime. We first prove this phenomenon for a high-dimensional linear
classification setting with noiseless observations. Our proof provides
explanatory insights that may also transfer to feature learning models.
Further, we observe in experiments on standard image datasets that the same
behavior occurs for perceptible attacks that effectively reduce class
information such as mask attacks and object corruptions.


title:
NuSTAR spectral analysis of three Seyfert galaxies: NGC 3227, NGC 5548
  and MR 2251$-$178

abstract:
  The observed nuclear X-ray emission in the radio-quiet category of active
galactic nuclei (AGN) is believed to be from a compact region, the corona
situated in the vicinity of the central supermassive black holes (SMBH). The
shape of the X-ray continuum, among other factors, depends on the temperature
of the corona ($\rm{kT_{e}}$). The launch of the Nuclear Spectroscopic
Telescope Array (NuSTAR) has led to the determination of the high energy
cut-off ($\rm{E_{cut}}$; and thereby $\rm{kT_{e}}$) in many AGN. In a handful
of sources, multiple observations with NuSTAR have also revealed changes in
$\rm{E_{cut}}$. In this work we aimed to investigate the variation in
$\rm{kT_{e}}$ in three AGN, namely NGC 3227, NGC 5548 and MR 2251$-$178 using
more than one epoch of data on a source from NuSTAR. We carried out spectral
analysis of multiple epochs of data acquired using NuSTAR on the three sources
including a few new observations not published so far. By fitting
Comptonization model to the data we determined the temperature of the corona
and also investigated changes in $\rm{kT_{e}}$ if any in these sources. In NGC
3227, we found evidence for variation in $\rm{kT_{e}}$. We found no correlation
of $\rm{kT_{e}}$, photon index ($\Gamma$), reflection fraction ($R$) and
optical depth ($\tau$) with flux, while, $\tau$ is found to anti-correlate with
$\rm{kT_{e}}$. These could be due to more than one physical process at work in
the source that causes the change in $\rm{kT_{e}}$. Conclusive evidence for the
variation in $\rm{kT_{e}}$ is not found in MR 2251$-$178 and NGC 5548.


title: A Non-Hydrostatic Multi-Phase Mass Flow Model

abstract:
  Modeling mass flows is classically based on hydrostatic balance equations.
However, if momentum transfers scale similarly in slope parallel and flow depth
directions, then the gravity and acceleration can have the same order of
magnitude effects, urging for a non-hydrostatic model formulation. Here, we
extend existing single-phase Boussinesq-type models by developing a new
non-hydrostatic model for multi-phase mass flows consisting of solid and
fine-solid particles and viscous fluid (Pudasaini & Mergili, 2019). The new
model includes enhanced gravity and dispersion considering various interfacial
momentum transfers. We outline new contributions in the non-hydrostatic
Boussinesq-type multi-phase gravity waves emerging from phase-interactions. We
present a general, well-structured framework of multi-phase mass flows with
enhanced gravity and dispersion, setting a foundation for comprehensive
simulations. We discuss situations where non-hydrostatic, dispersive effects
are more pronounced for such flows. Reduced models demonstrate the importance
of non-hydrostatic contributions. Simplified analytical solutions reveal how
the new dispersive model can be reduced to non-dispersive ones, yet largely
generalizing existing models. We postulate a novel, spatially varying
dissipative force, called the prime-force, which physically controls the
dynamics and run-out of the mass flow in a precise way. Practitioners and
engineers may find this force very useful in technical applications. This
illuminates the need of formally including the prime-force in momentum
equations. A simple dispersion equation is derived highlighting the essence of
dispersion on mass flow dynamics. Dispersion produces a wavy velocity field
about a reference state without dispersion, the first of this kind for
avalanching debris mass. Dispersion intensity increases energetically as the
solid volume fraction or friction decreases.


title:
Counting points on abelian surfaces over finite fields with Elkies's
  method

abstract:
  We generalize Elkies's method, an essential ingredient in the SEA algorithm
to count points on elliptic curves over finite fields of large characteristic,
to the setting of p.p. abelian surfaces. Under reasonable assumptions related
to the distribution of Elkies primes, we obtain improvements over Schoof's
method in two cases. If the abelian surface A over Fq has RM by a fixed
quadratic field F, we reach the same asymptotic complexity Otilde(log4 q) as
the SEA algorithm up to constant factors depending on F. If A is defined over a
number field, we count points on A modulo sufficiently many primes in
Otilde(log6 q) binary operations on average. Numerical experiments demonstrate
the practical usability of our methods.


title:
No-gold-standard evaluation of quantitative imaging methods in the
  presence of correlated noise

abstract:
  Objective evaluation of quantitative imaging (QI) methods with patient data
is highly desirable, but is hindered by the lack or unreliability of an
available gold standard. To address this issue, techniques that can evaluate QI
methods without access to a gold standard are being actively developed. These
techniques assume that the true and measured values are linearly related by a
slope, bias, and Gaussian-distributed noise term, where the noise between
measurements made by different methods is independent of each other. However,
this noise arises in the process of measuring the same quantitative value, and
thus can be correlated. To address this limitation, we propose a
no-gold-standard evaluation (NGSE) technique that models this correlated noise
by a multi-variate Gaussian distribution parameterized by a covariance matrix.
We derive a maximum-likelihood-based approach to estimate the parameters that
describe the relationship between the true and measured values, without any
knowledge of the true values. We then use the estimated slopes and diagonal
elements of the covariance matrix to compute the noise-to-slope ratio (NSR) to
rank the QI methods on the basis of precision. The proposed NGSE technique was
evaluated with multiple numerical experiments. Our results showed that the
technique reliably estimated the NSR values and yielded accurate rankings of
the considered methods for ~ 83% of 160 trials. In particular, the technique
correctly identified the most precise method for ~ 97% of the trials. Overall,
this study demonstrates the efficacy of the NGSE technique to accurately rank
different QI methods when the correlated noise is present, and without access
to any knowledge of the ground truth. The results motivate further validation
of this technique with realistic simulation studies and patient data.


title: The starting dates of COVID-19 multiple waves

abstract:
  The severe acute respiratory syndrome of coronavirus 2 spread globally very
quickly, causing great concern at the international level due to the severity
of the associated respiratory disease, the so-called COVID-19. Considering Rio
de Janeiro city (Brazil) as an example, the first diagnosis of this disease
occurred in March 2020, but the exact moment when the local spread of the virus
started is uncertain as the Brazilian epidemiological surveillance system was
not widely prepared to detect suspected cases of COVID-19 at that time.
Improvements in this surveillance system occurred over the pandemic, but due to
the complex nature of the disease transmission process, specifying the exact
moment of emergence of new community contagion outbreaks is a complicated task.
This work aims to propose a general methodology to determine possible start
dates for the multiple community outbreaks of COVID-19, using for this purpose
a parametric statistical approach that combines surveillance data, nonlinear
regression, and information criteria to obtain a statistical model capable of
describing the multiple waves of contagion observed. The dynamics of COVID-19
in the city of Rio de Janeiro is taken as a case study, and the results suggest
that the original strain of the virus was already circulating in Rio de Janeiro
city as early as late February 2020, probably being massively disseminated in
the population during the carnival festivities.


title: Localized Quantum Chemistry on Quantum Computers

abstract:
  Quantum chemistry calculations of large, strongly correlated systems are
typically limited by the computation cost that scales exponentially with the
size of the system. Quantum algorithms, designed specifically for quantum
computers, can alleviate this, but the resources required are still too large
for today's quantum devices. Here we present a quantum algorithm that combines
a localization of multireference wave functions of chemical systems with
quantum phase estimation (QPE) and variational unitary coupled cluster singles
and doubles (UCCSD) to compute their ground state energy. Our algorithm, termed
"local active space unitary coupled cluster" (LAS-UCC), scales linearly with
system size for certain geometries, providing a polynomial reduction in the
total number of gates compared with QPE, while providing accuracy above that of
the variational quantum eigensolver using the UCCSD ansatz and also above that
of the classical local active space self-consistent field. The accuracy of
LAS-UCC is demonstrated by dissociating (H$_2$)$_2$ into two H$_2$ molecules
and by breaking the two double bonds in trans-butadiene and resources estimates
are provided for linear chains of up to 20 H$_2$ molecules.


title:
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled
  Local Explanations

abstract:
  The ability for a human to understand an Artificial Intelligence (AI) model's
decision-making process is critical in enabling stakeholders to visualize model
behavior, perform model debugging, promote trust in AI models, and assist in
collaborative human-AI decision-making. As a result, the research fields of
interpretable and explainable AI have gained traction within AI communities as
well as interdisciplinary scientists seeking to apply AI in their subject
areas. In this paper, we focus on advancing the state-of-the-art in
interpreting multimodal models - a class of machine learning methods that
tackle core challenges in representing and capturing interactions between
heterogeneous data sources such as images, text, audio, and time-series data.
Multimodal models have proliferated numerous real-world applications across
healthcare, robotics, multimedia, affective computing, and human-computer
interaction. By performing model disentanglement into unimodal contributions
(UC) and multimodal interactions (MI), our proposed approach, DIME, enables
accurate and fine-grained analysis of multimodal models while maintaining
generality across arbitrary modalities, model architectures, and tasks. Through
a comprehensive suite of experiments on both synthetic and real-world
multimodal tasks, we show that DIME generates accurate disentangled
explanations, helps users of multimodal models gain a deeper understanding of
model behavior, and presents a step towards debugging and improving these
models for real-world deployment. Code for our experiments can be found at
https://github.com/lvyiwei1/DIME.


title:
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using
  Few-Shot Learning

abstract:
  Wi-Fi sensing can classify human activities because each activity causes
unique changes to the channel state information (CSI). Existing WiFi sensing
suffers from limited scalability as the system needs to be retrained whenever
new activities are added, which cause overheads of data collection and
retraining. Cross-domain sensing may fail because the mapping between
activities and CSI variations is destroyed when a different environment or user
(domain) is involved. This paper proposed a few-shot learning-based WiFi
sensing system, named FewSense, which can recognise novel classes in unseen
domains with only few samples. Specifically, a feature extractor was
pre-trained offline using the source domain data. When the system was applied
in the target domain, few samples were used to fine-tune the feature extractor
for domain adaptation. Inference was made by computing the cosine similarity.
FewSense can further boost the classification accuracy by collaboratively
fusing inference from multiple receivers. We evaluated the performance using
three public datasets, i.e., SignFi, Widar, and Wiar. The results show that
FewSense with five-shot learning recognised novel classes in unseen domains
with an accuracy of 90.3\%, 96.5\% ,82.7\% on SignFi, Widar, and Wiar datasets,
respectively. Our collaborative sensing model improved system performance by an
average of 30\%.


title: Common valuations of division polynomials

abstract:
  In this note we prove a formula for the cancellation exponent $k_{v,n}$
between division polynomials $\psi_n$ and $\phi_n$ associated with a sequence
$\{nP\}_{n\in\mathbb{N}}$ of points on an elliptic curve $E$ defined over a
discrete valuation field $K$. The formula is identical with the result of
Yabuta-Voutier for the case of finite extension of $\mathbb{Q}_{p}$ and
generalizes to the case of non-standard Kodaira types for non-perfect residue
fields.


title:
Interventions, Where and How? Experimental Design for Causal Models at
  Scale

abstract:
  Causal discovery from observational and interventional data is challenging
due to limited data and non-identifiability which introduces uncertainties in
estimating the underlying structural causal model (SCM). Incorporating these
uncertainties and selecting optimal experiments (interventions) to perform can
help to identify the true SCM faster. Existing methods in experimental design
for causal discovery from limited data either rely on linear assumptions for
the SCM or select only the intervention target. In this paper, we incorporate
recent advances in Bayesian causal discovery into the Bayesian optimal
experimental design framework, which allows for active causal discovery of
nonlinear, large SCMs, while selecting both the target and the value to
intervene with. We demonstrate the performance of the proposed method on
synthetic graphs (Erdos-R\`enyi, Scale Free) for both linear and nonlinear SCMs
as well as on the in-silico single-cell gene regulatory network dataset, DREAM.


title:
Sieve of Eratosthenes for Bose-Einstein Condensates in Optical Moir\'e
  Lattices

abstract:
  We catalog known optical moir\'e lattices and uncover exotic lattice
configurations following a geometric analog of the ancient sieve of
Eratosthenes algorithm for finding prime numbers. Rich dynamics of
Bose-Einstein condensates loaded into these optical lattices is revealed from
numerical simulations of time-of-flight interference patterns. What sets this
method apart is the ability to tune the periodicity of the optical lattices
without changing the wavelength of the laser, yet maintaining the local
potential at the individual lattices sites. In addition, we discuss the ability
to spatially translate the optical lattice through applying a structured phase
only.


title:
Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge
  Transfer Networks

abstract:
  How can we make predictions for nodes in a heterogeneous graph when an entire
type of node (e.g. user) has no labels (perhaps due to privacy issues) at all?
Although heterogeneous graph neural networks (HGNNs) have shown superior
performance as powerful representation learning techniques, there is no direct
way to learn using labels rooted at different node types. Domain adaptation
(DA) targets this setting, however, existing DA can not be applied directly to
HGNNs. In heterogeneous graphs, the source and target domains have different
modalities, thus HGNNs provide different feature extractors to them, while most
of DA assumes source and target domains share a common feature extractor. In
this work, we address the issue of zero-shot domain adaptation in HGNNs. We
first theoretically induce a relationship between source and target domain
features extracted from HGNNs, then propose a novel domain adaptation method,
Knowledge Transfer Networks for HGNNs (HGNN-KTN). HGNN-KTN learns the
relationship between source and target features, then maps the target
distributions into the source domain. HGNN-KTN outperforms state-of-the-art
baselines, showing up to 73.3% higher in MRR on 18 different domain adaptation
tasks running on real-world benchmark graphs.


title: Color Transparency and Light Front Holographic QCD

abstract:
  Color transparency, the reduction of initial- or final-state interactions in
coherent nuclear processes, is a natural prediction of QCD provided that
small-sized or point-like configurations (PLCs) are responsible for
high-momentum transfer, high-energy, semi-exclusive processes. I use the FMS
criteria for the existence of PLCs to show that the wave functions of light
front holographic QCD, as currently formulated, do not contain a PLC.


title: Nonlinear predictive models computation in ADPCM schemes

abstract:
  Recently several papers have been published on nonlinear prediction applied
to speech coding. At ICASSP98 we presented a system based on an ADPCM scheme
with a nonlinear predictor based on a neural net. The most critical parameter
was the training procedure in order to achieve good generalization capability
and robustness against mismatch between training and testing conditions. In
this paper, we propose several new approaches that improve the performance of
the original system in up to 1.2dB of SEGSNR (using bayesian regularization).
The variance of the SEGSNR between frames is also minimized, so the new scheme
produces a more stable quality of the output.


title:
Beam dynamics corrections to the Run-1 measurement of the muon anomaly
  $a_{\mu}$ at Fermilab

abstract:
  The Fermi National Accelerator Laboratory (FNAL) Muon $g-2$ Experiment has
measured the positive muon magnetic anomaly $a_{\mu} \equiv (g_{\mu}-2)/2$ with
a precision of 0.46 parts per million, with data collected during its first
physics run in 2018. The experimental result combined with the measurement from
the former experiment at Brookhaven National Laboratory increases the tension
with the Standard Model expectation to $4.2\sigma$, thus strengthening evidence
for new physics. The magnetic anomaly is determined from the precision
measurements of the muon spin precession frequency, relative to the muon
momentum, and the average magnetic field seen by the beam. In an ideal case
with muons orbiting in a horizontal plane with a uniform vertical magnetic
field, the anomalous precession frequency $\omega_a$ is given by the difference
between the spin~($s$) and cyclotron~($c$) frequencies, $\omega_a = \omega_s -
\omega_c$. The observable $\omega_a$ is proportional to $a_{\mu}$. This
proceeding presents the beam dynamics systematic corrections that are required
to adjust the measured muon precession frequency $\omega^{m}_{a}$ to its true
physical value $\omega_{a}$.


title:
Astrometric and photometric verification of faint blue white dwarfs in
  the Gaia catalogue of nearby stars

abstract:
  The Gaia catalogue of nearby stars (GCNS) divided all objects with parallaxes
$>$10mas into GCNS-selected and GCNS-rejected 100pc samples. Below the white
dwarf (WD) sequence in the complete GCNS color-magnitude diagram (CMD), at
$Gabs>14.7+4.7(G-RP)$, there appear 60 GCNS-selected faint blue white dwarfs
(FBWDs). However this CMD region is also populated by 411 GCNS-rejected
objects, mainly from crowded regions towards the Galactic centre and the
Magellanic Clouds. The WD catalog of Gentile Fusillo et al. (2021) lists only
47 GCNS-selected but also 8 GCNS-rejected objects. I confirm 59 of the
GCNS-selected but none of the GCNS-rejected objects as FBWDs from visual
inspection and a proper motion check using additional optical sky surveys.
Hence FBWDs form an additional branch in the CMD. Compared to the full
GCNS-selected 100pc sample, FBWDs have relatively high proper motions and
tangential velocities. They represent interesting targets for studies of
ultracool or infrared-faint, and possibly also ultramassive WDs.


title: High Welfare Matching Markets via Descending Price

abstract:
  We consider design of monetary mechanisms for two-sided matching. Mechanisms
in the tradition of the deferred acceptance algorithm, even in variants
incorporating money, tend to focus on the criterion of stability. Instead, in
this work we seek a simple auction-inspired mechanism with social welfare
guarantees. We consider a descending-price mechanism called the Marshallian
Match, proposed (but not analyzed) by Waggoner and Weyl (2019). When all values
for potential matches are positive, we show the Marshallian Match with a
"rebate" payment rule achieves constant price of anarchy. This result extends
to models with costs for acquiring information about one's values, and also to
matching on hypergraphs. With possibly-negative valuations, which capture e.g.
job markets, the problem becomes harder. We introduce notions of approximate
stability and show that they have beneficial welfare implications. However, the
main problem of proving constant factor welfare guarantees in "ex ante stable
equilibrium" remains open.


title:
Imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron
  Microscopy

abstract:
  Efforts to map atomic-scale chemistry at low doses with minimal noise using
electron microscopes are fundamentally limited by inelastic interactions. Here,
fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR)
recovery of material chemistry at nano- and atomic- resolution by coupling
correlated information encoded within both elastic scattering (high-angle
annular dark field (HAADF)) and inelastic spectroscopic signals (electron
energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these
simultaneously acquired signals, or modalities, the chemical distribution
within nanomaterials can be imaged at significantly lower doses with existing
detector hardware. In many cases, the dose requirements can be reduced by over
one order of magnitude. This high SNR recovery of chemistry is tested against
simulated and experimental atomic resolution data of heterogeneous
nanomaterials.


title: Online Balanced Experimental Design

abstract:
  e consider the experimental design problem in an online environment, an
important practical task for reducing the variance of estimates in randomized
experiments which allows for greater precision, and in turn, improved decision
making. In this work, we present algorithms that build on recent advances in
online discrepancy minimization which accommodate both arbitrary treatment
probabilities and multiple treatments. The proposed algorithms are
computational efficient, minimize covariate imbalance, and include
randomization which enables robustness to misspecification. We provide worst
case bounds on the expected mean squared error of the causal estimate and show
that the proposed estimator is no worse than an implicit ridge regression,
which are within a logarithmic factor of the best known results for offline
experimental design. We conclude with a detailed simulation study showing
favorable results relative to complete randomization as well as to offline
methods for experimental design with time complexities exceeding our algorithm.


title: Provable and Efficient Continual Representation Learning

abstract:
  In continual learning (CL), the goal is to design models that can learn a
sequence of tasks without catastrophic forgetting. While there is a rich set of
techniques for CL, relatively little understanding exists on how
representations built by previous tasks benefit new tasks that are added to the
network. To address this, we study the problem of continual representation
learning (CRL) where we learn an evolving representation as new tasks arrive.
Focusing on zero-forgetting methods where tasks are embedded in subnetworks
(e.g., PackNet), we first provide experiments demonstrating CRL can
significantly boost sample efficiency when learning new tasks. To explain this,
we establish theoretical guarantees for CRL by providing sample complexity and
generalization error bounds for new tasks by formalizing the statistical
benefits of previously-learned representations. Our analysis and experiments
also highlight the importance of the order in which we learn the tasks.
Specifically, we show that CL benefits if the initial tasks have large sample
size and high "representation diversity". Diversity ensures that adding new
tasks incurs small representation mismatch and can be learned with few samples
while training only few additional nonzero weights. Finally, we ask whether one
can ensure each task subnetwork to be efficient during inference time while
retaining the benefits of representation learning. To this end, we propose an
inference-efficient variation of PackNet called Efficient Sparse PackNet (ESPN)
which employs joint channel & weight pruning. ESPN embeds tasks in
channel-sparse subnets requiring up to 80% less FLOPs to compute while
approximately retaining accuracy and is very competitive with a variety of
baselines. In summary, this work takes a step towards data and
compute-efficient CL with a representation learning perspective. GitHub page:
https://github.com/ucr-optml/CtRL


title: Towards Mining OSS Skills from GitHub Activity

abstract:
  Open source software (OSS) development relies on diverse skill sets. However,
to our knowledge, there are no tools which detect OSS-related skills. In this
paper, we present a novel method to detect OSS skills and prototype it in a
tool called Disko. Our approach relies on identifying relevant signals, which
are measurable activities or cues associated with a skill. Our tool detects how
contributors 1) teach others to be involved in OSS projects, 2) show commitment
towards an OSS project, 3) have knowledge in specific programming languages,
and 4) are familiar with OSS practices. We then evaluate the tool by
administering a survey to 455 OSS contributors. We demonstrate that Disko
yields promising results: it detects the presence of these skills with
precision scores between 77% to 97%. We also find that over 54% of participants
would display their high-proficiency skills. Our approach can be used to
transform existing OSS experiences, such as identifying collaborators, matching
mentors to mentees, and assigning project roles. Given the positive results and
potential impact of our approach, we outline future research opportunities in
interpreting and sharing OSS skills.


title: Solar cycle 25 prediction using length-to-amplitude relations

abstract:
  We propose a simple method for prediction of the 11-year solar cycle maximum
that is based on two relations. One of them is well known Waldmeier's rule that
binds the amplitude of a cycle and the length of its ascending phase. The
second rule relates the length of a given cycle from minimum to minimum and the
amplitude of the next one. Using corresponding linear regressions we obtain for
the amplitide of cycle 25 in the scale of 13-month smoothed monthly total
revised sunspot number ${\rm SN}_{\rm max}(25) = 181\pm46$ and for the moment
of the maximum $T_{\rm max}(25) = 2024.2\pm1.0$. Therefore, according to the
prediction, cycle 25 will be higher than the previous one (${\rm SN}_{\rm
max}(24) = 116$) with probability 0.92.


title: Hyper-Kamiokande Experiment: A Snowmass White Paper

abstract:
  Hyper-Kamiokande (HK) is the next generation underground water Cherenkov
detector that builds on the highly successful Super-Kamiokande (SK) experiment.
The 260,000-ton detector has an 8.4 times larger fiducial volume than its
predecessor. HK's low energy threshold combined with the very large fiducial
volume make the detector unique; HK is expected to acquire an unprecedented
exposure of 3.8 Mton-year over a period of 20 years starting in 2027. It has an
extremely diverse science program including long-baseline neutrino oscillation
measurements, nucleon decay searches, atmospheric neutrinos, neutrinos from the
sun and supernova explosions, and neutrinos from other astrophysical origins.
Like DUNE, the flagship project of the U.S. high-energy physics program, HK
measures fundamental properties of neutrinos such as the search for leptonic CP
violation and neutrino physics beyond the Standard Model.


title:
Post-Error Correction for Quantum Annealing Processor using
  Reinforcement Learning

abstract:
  Finding the ground state of the Ising spin-glass is an important and
challenging problem (NP-hard, in fact) in condensed matter physics. However,
its applications spread far beyond physic due to its deep relation to various
combinatorial optimization problems, such as travelling salesman or protein
folding. Sophisticated and promising new methods for solving Ising instances
rely on quantum resources. In particular, quantum annealing is a quantum
computation paradigm, that is especially well suited for Quadratic
Unconstrained Binary Optimization (QUBO). Nevertheless, commercially available
quantum annealers (i.e., D-Wave) are prone to various errors, and their ability
to find low energetic states (corresponding to solutions of superior quality)
is limited. This naturally calls for a post-processing procedure to correct
errors (capable of lowering the energy found by the annealer). As a
proof-of-concept, this work combines the recent ideas revolving around the
DIRAC architecture with the Chimera topology and applies them in a real-world
setting as an error-correcting scheme for quantum annealers. Our preliminary
results show how to correct states output by quantum annealers using
reinforcement learning. Such an approach exhibits excellent scalability, as it
can be trained on small instances and deployed for large ones. However, its
performance on the chimera graph is still inferior to a typical algorithm one
could incorporate in this context, e.g., simulated annealing.


title: Scaling Relations for Auxin Waves

abstract:
  We analyze an 'up-the-gradient' model for the formation of transport channels
of the phytohormone auxin, through auxin-mediated polarization of the PIN1
auxin transporter. We show that this model admits a family of travelling wave
solutions that is parameterized by the height of the auxin-pulse. We uncover
scaling relations for the speed and width of these waves and verify these
rigorous results with numerical computations. In addition, we provide explicit
expressions for the leading-order wave profiles, which allows the influence of
the biological parameters in the problem to be readily identified. Our proofs
are based on a generalization of the scaling principle developed by Friesecke
and Pego to construct pulse solutions to the classic Fermi-Pasta-Ulam-Tsingou
model, which describes a one-dimensional chain of coupled nonlinear springs.


title: On linear chaos in the space of convergent sequences

abstract:
  We show that linear chaos in the space $c(\mathbb{N})$ of convergent
sequences cannot be arrived at by merely extending the weighted backward shifts
in the space $c_0(\mathbb{N})$ of vanishing sequences. Applying a newly found
sufficient condition for linear chaos, we furnish concise proofs of the
chaoticity of the foregoing operators along with their powers and also itemize
their spectral structure. We further construct bounded and unbounded linear
chaotic operators in $c(\mathbb{N})$ as conjugate to the chaotic backward
shifts in $c_0(\mathbb{Z}_+)$ via a homeomorphic isomorphism between the two
spaces.


title:
Search for Pauli Exclusion Principle Violation by Pair Production in the
  Majorana Demonstrator Calibration Data

abstract:
  The energy calibration data collected for the \textsc{Majorana Demonstrator}
is used to search for a Pauli Exclusion Principle (PEP) violating process in an
atomic system where a new fermion is introduced through the electron-positron
pair production. The signature of this PEP violating process would be found
through the cascade of a pair-produced electron to a forbidden atomic level of
Ge. An improved limit on the parameter (1/2)$\beta^{2}$, which quantifies the
probability of a PEP violating process, is measured to be $<6.3 \times 10^{-4}$
at 95% CL for this process.


title: Data-Efficient and Interpretable Tabular Anomaly Detection

abstract:
  Anomaly detection (AD) plays an important role in numerous applications. We
focus on two understudied aspects of AD that are critical for integration into
real-world applications. First, most AD methods cannot incorporate labeled data
that are often available in practice in small quantities and can be crucial to
achieve high AD accuracy. Second, most AD methods are not interpretable, a
bottleneck that prevents stakeholders from understanding the reason behind the
anomalies. In this paper, we propose a novel AD framework that adapts a
white-box model class, Generalized Additive Models, to detect anomalies using a
partial identification objective which naturally handles noisy or heterogeneous
features. In addition, the proposed framework, DIAD, can incorporate a small
amount of labeled data to further boost anomaly detection performances in
semi-supervised settings. We demonstrate the superiority of our framework
compared to previous work in both unsupervised and semi-supervised settings
using diverse tabular datasets. For example, under 5 labeled anomalies DIAD
improves from 86.2\% to 89.4\% AUC by learning AD from unlabeled data. We also
present insightful interpretations that explain why DIAD deems certain samples
as anomalies.


title:
Baba is Y'all 2.0: Design and Investigation of a Collaborative
  Mixed-Initiative System

abstract:
  This paper describes a new version of the mixed-initiative collaborative
level designing system: Baba is Y'all, as well as the results of a user study
on the system. Baba is Y'all is a prototype for AI-assisted game design in
collaboration with others. The updated version includes a more user-friendly
interface, a better level-evolver and recommendation system, and extended site
features. The system was evaluated via a user study where participants were
required to play a previously submitted level from the site and then create
their own levels using the editor. They reported on their individual process
creating the level and their overall experience interacting with the site. The
results have shown both the benefits and limitations of a mixed-initiative
system and how it can help with creating a diversity of `Baba is You' levels
that are both human and AI designed while maintaining their quality.


title: Universal supercritical behavior for some skew-product maps

abstract:
  We consider skew-product maps over circle rotations $x\mapsto x+\alpha$ (mod
1) with factors that take values in SL(2,R) In numerical experiments with
$\alpha$ the inverse golden mean, Fibonacci iterates of almost Mathieu maps
with rotation number $1/4$ and positive Lyapunov exponent exhibit asymptotic
scaling behavior. We prove the existence of such asymptotic scaling for
"periodic" rotation numbers and for large Lyapunov exponent. The phenomenon is
universal, in the sense that it holds for open sets of maps, with the scaling
limit being independent of the maps. The set of maps with a given periodic
rotation number is a real analytic codimension $1$ manifold in a suitable space
of maps.


title: Lie algebras of curves and loop-bundles on surfaces

abstract:
  W. Goldman and V. Turaev defined a Lie bialgebra structure on the $\mathbb
Z$-module generated by free homotopy classes of loops of an oriented surface
(i.e. the conjugacy classes of its fundamental group). We develop a
generalization of this construction replacing homotopies by thin homotopies,
based on the combinatorial approach given by M. Chas. We use it to give a
geometric proof of a characterization of simple curves in terms of the
Goldman-Turaev bracket, which was conjectured by Chas.


title:
Robust Counterexample-guided Optimization for Planning from
  Differentiable Temporal Logic

abstract:
  Signal temporal logic (STL) provides a powerful, flexible framework for
specifying complex autonomy tasks; however, existing methods for planning based
on STL specifications have difficulty scaling to long-horizon tasks and are not
robust to external disturbances. In this paper, we present an algorithm for
finding robust plans that satisfy STL specifications. Our method alternates
between local optimization and local falsification, using automatically
differentiable temporal logic to iteratively optimize its plan in response to
counterexamples found during the falsification process. We benchmark our
counterexample-guided planning method against state-of-the-art planning methods
on two long-horizon satellite rendezvous missions, showing that our method
finds high-quality plans that satisfy STL specifications despite adversarial
disturbances. We find that our method consistently finds plans that are robust
to adversarial disturbances and requires less than half the time of competing
methods. We provide an implementation of our planner at
https://github.com/MIT-REALM/architect.


title: Self-similar solution for Hardy operator

abstract:
  We describe the large-time asymptotics of solutions to the heat equation for
the fractional Laplacian with added subcritical or even critical Hardy-type
potential. The asymptotics is governed by a self-similar solution of the
equation, obtained as a normalized limit at the origin of the kernel of the
corresponding Feynman-Kac semigroup.


title: A note on the conflict-free chromatic index

abstract:
  Let $G$ be a graph with maximum degree $\Delta$ and without isolated
vertices. An edge colouring $c$ of $G$ is conflict-free if the closed
neighbourhood of every edge includes a uniquely coloured element. The least
number of colours admitting such $c$ is the conflict-free chromatic index of
$G$, denoted by $\chi'_{CF}(G)$. In "Conflict-free chromatic number versus
conflict-free chromatic index" [J. Graph Theory, 2022; 99: 349--358] it was
recently proved by means of the probabilistic method that $\chi'_{CF}(G)\leq
C_1\log_2\Delta+C_2$, where $C_1>337$ and $C_2$ are constants, whereas there
are families of graphs with $\chi'_{CF}(G)\geq (1-o(1))\log_2\Delta$. In this
note we provide an explicit simple proof of the fact that $\chi'_{CF}(G)\leq
3\log_2\Delta+1$, which is a corollary of a stronger result: $\chi'_{CF}(G)\leq
3\log_2\chi(G)+1$. For this aim we prove a few auxiliary observations, implying
in particular that $\chi'_{CF}(G)\leq 4$ for bipartite graphs.


title: Evolution of Stars and Gas in Galaxies

abstract:
  Essentially everything of astronomical interest is either part of a galaxy,
or from a galaxy, or otherwise relevant to the origin or evolution of galaxies.
Diverse examples are that the isotropic composition of meteorites provides
clues to the history of star formation billions of years ago, and cosmological
tests for the deceleration of the Universe are strongly affected by changes in
the luminosities of galaxies during the lookback time sampled. The aim of this
article is to review some of the vital connections that galaxy evolution makes
among many astronomical phenomena.


title:
Automatic Detection and Segmentation of Postoperative Cerebellar Damage
  Based on Normalization

abstract:
  Surgical resection is a common procedure in the treatment of pediatric
posterior fossa tumors. However, surgical damage is often unavoidable and its
association with postoperative complications is not well understood. A reliable
localization and measure of cerebellar damage is fundamental to study the
relationship between the damaged cerebellar regions and postoperative
neurological outcomes. Existing cerebellum normalization methods are not
reliable on postoperative scans, therefore current approaches to measure
surgical damage rely on manual labelling. In this work, we develop a robust
algorithm to automatically detect and measure cerebellum damage due to surgery
using postoperative 3D T1 magnetic resonance imaging. In our proposed approach,
normal brain tissues are first segmented using a Bayesian algorithm customized
for postoperative scans. Next, the cerebellum is isolated by nonlinear
registration of a whole brain template to the native space. The isolated
cerebellum is then normalized into the spatially unbiased atlas (SUIT) space
using anatomical information derived from the previous step. Finally, the
damage is detected in the atlas space by comparing the normalized cerebellum
and the SUIT template. We evaluated our damage detection tool on postoperative
scans of 153 patients diagnosed with medulloblastoma based on inspection by
human expects. We also designed a simulation to test the proposed approach
without human intervention. Our results show that the proposed approach has
superior performance on various scenarios.


title: Viterbo's conjecture as a worm problem

abstract:
  In this paper, we relate Viterbo's conjecture from symplectic geometry to
Minkowski versions of worm problems which are inspired by the well-known Moser
worm problem from geometry. For the special case of Lagrangian products this
relation provides a connection to systolic Minkowski billiard inequalities and
Mahler's conjecture from convex geometry. Moreover, we use the above relation
in order to transfer Viterbo's conjecture to a conjecture for the longstanding
open Wetzel problem which also can be expressed as a systolic Euclidean
billiard inequality and for which we discuss an algorithmic approach in order
to find a new lower bound. Finally, we point out that the above mentioned
relation between Viterbo's conjecture and Minkowski worm problems has a
structural similarity to the known relationship between Bellmann's
lost-in-a-forest problem and the original Moser worm problem.


title: Design requirements to improve laparoscopy via XR

abstract:
  Laparoscopic surgery has the advantage of avoiding large open incisions and
thereby decreasing blood loss, pain, and discomfort to patients. However, on
the other side, it is hampered by restricted workspace, ambiguous
communication, and surgeon fatigue caused by non-ergonomic head positioning. We
aimed to identify critical problems and suggest design requirements and
solutions. We used user and task analysis methods to learn about practices
performed in an operating room by observing surgeons in their working
environment to understand how they performed tasks and achieved their intended
goals. Drawing on observations and analysis from recorded laparoscopic
surgeries, we have identified several constraints and design requirements to
propose potential solutions to address the issues. Surgeons operate in a dimly
lit environment, surrounded by monitors, and communicate through verbal
commands and pointing gestures. Therefore, performing user and task analysis
allowed us to better understand the existing problems in laparoscopy while
identifying several communication constraints and design requirements, which a
solution has to follow to address those problems. Our contributions include
identifying design requirements for laparoscopy surgery through a user and task
analysis. These requirements propose design solutions towards improved
surgeons' comfort and make the surgical procedure less laborious.


title: Serre weights, Galois deformation rings, and local models

abstract:
  We survey some recent progress on generalizations of conjectures of Serre
concerning the cohomology of arithmetic groups, focusing primarily on the
"weight" aspect. This is intimately related to (generalizations of) a
conjecture of Breuil and M\'ezard relating the geometry of potentially
semistable deformation rings to modular representation theory. Recently, B.
Levin, S. Morra, and the authors established these conjectures in tame generic
contexts by constructing projective varieties (local models) in mixed
characteristic whose singularities model, in generic cases, those of tamely
potentially crystalline Galois deformation rings for unramified extensions of
$\mathbb{Q}_p$ with small regular Hodge-Tate weights.


title:
On the diversity of mixing and helium core masses of B-type dwarfs from
  gravity-mode asteroseismology

abstract:
  The chemical evolution of the Galaxy is largely guided by the yields from
massive stars. Their evolution is heavily influenced by their internal mixing,
allowing the stars to live longer and yield a more massive helium core at the
end of their main-sequence evolution. Asteroseismology is a powerful tool for
studying stellar interiors by providing direct probes of the interior physics
of the oscillating stars. This work revisits the recently derived internal
mixing profiles of 26 slowly pulsating B stars observed by the Kepler space
telescope, in order to investigate how well the mixing profiles can in fact be
distinguished from one another as well as provide predictions for the expected
helium core masses obtained at the end of the main-sequence evolution. We find
that for five of these stars the mixing profile is derived unambiguously, while
the remaining stars have at least one other mixing profile which explains the
oscillations equally well. Convective penetration is preferred over exponential
diffusive overshoot for ~55% of the stars, while stratified mixing is preferred
in the envelope (~39%). We estimate the expected helium core masses obtained at
the end of the main-sequence evolution and find them to be highly influenced by
the estimated amount of mixing occurring in the envelopes of the stars.


title: Evolution of X-ray Activity in <25 Myr Old Pre-Main Sequence Stars

abstract:
  Measuring the evolution of X-ray emission from pre-main sequence (PMS) stars
gives insight into two issues: the response of magnetic dynamo processes to
changes in interior structure and the effects of high-energy radiation on
protoplanetary disks and primordial planetary atmospheres. We present a sample
of 6,003 stars with ages [7-25] Myr in ten nearby open clusters from Chandra
X-ray and Gaia-EDR3 surveys. Combined with previous results in large samples of
younger (<5 Myr) stars in MYStIX and SFiNCs star forming regions,
mass-stratified activity-age relations are derived for early phases of stellar
evolution. X-ray luminosity (Lx) is constant during the first few Myr, possibly
due to the presence of extended X-ray coronas insensitive to temporal changes
in stellar size. Lx then decays during the [7-25] Myr period, more rapidly as
stellar mass increases. This decay is interpreted as decreasing efficiency of
the alpha^2 dynamo as radiative cores grow and a solar-type alpha-Omega dynamo
emerges. For more massive [2-7] Msun fully radiative stars, the X-ray emission
plummets indicating lack of an effective magnetic dynamos. The findings provide
improved measurements of high energy radiation effects on circumstellar
material, first the protoplanetary disk and then the atmospheres of young
planets. The observed X-ray luminosities can be so high that an inner
Earth-mass rocky, unmagnetized planet around a solar-mass PMS star might lose
its primary and secondary atmospheres within a few-several million years. PMS
X-ray emission may thus have a significant impact on evolution of early
planetary atmospheres and the conditions promoting the rise of habitability.

categories: astro-ph.SR astro-ph.EP astro-ph.HE hep-ph


title:
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through
  Self-Supervision With Supervoxels

abstract:
  Recent work has shown that label-efficient few-shot learning through
self-supervision can achieve promising medical image segmentation results.
However, few-shot segmentation models typically rely on prototype
representations of the semantic classes, resulting in a loss of local
information that can degrade performance. This is particularly problematic for
the typically large and highly heterogeneous background class in medical image
segmentation problems. Previous works have attempted to address this issue by
learning additional prototypes for each class, but since the prototypes are
based on a limited number of slices, we argue that this ad-hoc solution is
insufficient to capture the background properties. Motivated by this, and the
observation that the foreground class (e.g., one organ) is relatively
homogeneous, we propose a novel anomaly detection-inspired approach to few-shot
medical image segmentation in which we refrain from modeling the background
explicitly. Instead, we rely solely on a single foreground prototype to compute
anomaly scores for all query pixels. The segmentation is then performed by
thresholding these anomaly scores using a learned threshold. Assisted by a
novel self-supervision task that exploits the 3D structure of medical images
through supervoxels, our proposed anomaly detection-inspired few-shot medical
image segmentation model outperforms previous state-of-the-art approaches on
two representative MRI datasets for the tasks of abdominal organ segmentation
and cardiac segmentation.


title:
A trust-region augmented Hessian implementation for state-specific and
  state-averaged CASSCF wave functions

abstract:
  In this work, we present a one-step second-order converger for state-specific
(SS) and state-averaged (SA) complete active space self-consistent field
(CASSCF) wave functions. Robust convergence is achieved through step
restrictions using a trust-region augmented Hessian (TRAH) algorithm. To avoid
numerical instabilities, an exponential parametrization of variational
configuration parameters is employed, which works with a nonredundant
orthogonal complement basis. This is a common approach for SS-CASSCF and is
extended to SA-CASSCF wave functions, in this work. Our implementation is
integral direct and based on intermediates that are formulated either in the
sparse atomic-orbital or small active molecular-orbital basis. Thus, it
benefits from a combination with efficient integral decomposition techniques,
such as the resolution-of-the-identity or the chain-of-spheres for exchange
approximations. This facilitates calculations on large molecules such as a
Ni(II) complex with 231 atoms and 5154 basis functions. The runtime performance
of TRAH-CASSCF is competitive with other state-of-the-art implementations of
approximate and full second-order algorithms. In comparison with a
sophisticated first-order converger, TRAH-CASSCF calculations usually take more
iterations to reach convergence and, thus, have longer runtimes. However,
TRAH-CASSCF calculations still converge reliably to a true minimum even if the
first-order algorithm fails.


title:
Autonomous and Resilient Control for Optimal LEO Satellite Constellation
  Coverage Against Space Threats

abstract:
  LEO satellite constellation coverage has served as the base platform for
various space applications. However, the rapidly evolving security environment
such as orbit debris and adversarial space threats are greatly endangering the
security of satellite constellation and integrity of the satellite
constellation coverage. As on-orbit repairs are challenging, a distributed and
autonomous protection mechanism is necessary to ensure the adaptation and
self-healing of the satellite constellation coverage from different attacks. To
this end, we establish an integrative and distributed framework to enable
resilient satellite constellation coverage planning and control in a single
orbit. Each satellite can make decisions individually to recover from
adversarial and non-adversarial attacks and keep providing coverage service. We
first provide models and methodologies to measure the coverage performance.
Then, we formulate the joint resilient coverage planning-control problem as a
two-stage problem. A coverage game is proposed to find the equilibrium
constellation deployment for resilient coverage planning and an agent-based
algorithm is developed to compute the equilibrium. The multi-waypoint Model
Predictive Control (MPC) methodology is adopted to achieve autonomous
self-healing control. Finally, we use a typical LEO satellite constellation as
a case study to corroborate the results.


abstract:
  Unsupervised learning plays an important role in many fields, such as
artificial intelligence, machine learning, and neuroscience. Compared to static
data, methods for extracting low-dimensional structure for dynamic data are
lagging. We developed a novel information-theoretic framework, Compressed
Predictive Information Coding (CPIC), to extract useful representations from
dynamic data. CPIC selectively projects the past (input) into a linear subspace
that is predictive about the compressed data projected from the future
(output). The key insight of our framework is to learn representations by
minimizing the compression complexity and maximizing the predictive information
in latent space. We derive variational bounds of the CPIC loss which induces
the latent space to capture information that is maximally predictive. Our
variational bounds are tractable by leveraging bounds of mutual information. We
find that introducing stochasticity in the encoder robustly contributes to
better representation. Furthermore, variational approaches perform better in
mutual information estimation compared with estimates under a Gaussian
assumption. We demonstrate that CPIC is able to recover the latent space of
noisy dynamical systems with low signal-to-noise ratios, and extracts features
predictive of exogenous variables in neuroscience data.


title: A Unified Spatially Coupled Code Design: Threshold, Cycles, and Locality

abstract:
  Spatially-Coupled (SC)-LDPC codes are known to have outstanding
error-correction performance and low decoding latency. Whereas previous works
on LDPC and SC-LDPC codes mostly take either an asymptotic or a finite-length
design approach, in this paper we present a unified framework for jointly
optimizing the codes' thresholds and cycle counts to address both regimes. The
framework is based on efficient traversal and pruning of the code search space,
building on the fact that the performance of a protograph-based SC-LDPC code
depends on some characteristics of the code's partitioning matrix, which by
itself is much smaller than the code's full parity-check matrix. We then
propose an algorithm that traverses all nonequivalent partitioning matrices,
and outputs a list of codes, each offering an attractive point on the trade-off
between asymptotic and finite-length performance. We further extend the
framework to designing SC-LDPC codes with sub-block locality, which is a
recently introduced feature offering fast access to sub-blocks within the code
block. Our simulations show that our framework results in SC-LDPC codes that
outperform the state-of-the-art constructions, and that it offers the
flexibility to choose low-SNR, high-SNR, or in-between SNR region as the
primary design target.


title:
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive
  Representation Learning

abstract:
  We present modality gap, an intriguing geometric phenomenon of the
representation space of multi-modal models. Specifically, we show that
different data modalities (e.g. images and text) are embedded at arm's length
in their shared representation in multi-modal models such as CLIP. Our
systematic analysis demonstrates that this gap is caused by a combination of
model initialization and contrastive learning optimization. In model
initialization, we show empirically and theoretically that the representation
of a common deep neural network is restricted to a narrow cone. As a
consequence, in a multi-modal model with two encoders, the representations of
the two modalities are clearly apart when the model is initialized. During
optimization, contrastive learning keeps the different modalities separate by a
certain distance, which is influenced by the temperature parameter in the loss
function. Our experiments further demonstrate that varying the modality gap
distance has a significant impact in improving the model's downstream zero-shot
classification performance and fairness. Our code and data are available at
https://modalitygap.readthedocs.io/


title:
Collision-Aware Fast Simulation for Soft Robots by Optimization-Based
  Geometric Computing

abstract:
  Soft robots are able to safely interact with environments because of their
mechanical compliance. Self-collision is also employed in the modern design of
soft robots to enhance their performance in different tasks. However,
developing an efficient and reliable simulator which can handle the collision
response well, is still a challenging task in the research of soft robotics.
This paper presents a collision-aware simulator based on geometric
optimization, in which we develop a highly efficient and realistic collision
checking / response model incorporating a hyperelastic material property. Both
actuated deformation and collision response for soft robots are formulated as
geometry-based objectives. The collision-free body of a soft robot can be
obtained by minimizing the geometry-based objective function. Different from
the FEA-based physical simulation, the proposed pipeline performs a much lower
computational cost. Moreover, adaptive remeshing is applied to achieve the
improvement of the convergence when dealing with soft robots having large
volume variations. Experimental tests are conducted on different soft robots to
verify the performance of our approach.


title: Deep Latent-Variable Models for Text Generation

abstract:
  Text generation aims to produce human-like natural language output for
down-stream tasks. It covers a wide range of applications like machine
translation, document summarization, dialogue generation and so on. Recently
deep neural network-based end-to-end architectures have been widely adopted.
The end-to-end approach conflates all sub-modules, which used to be designed by
complex handcrafted rules, into a holistic encode-decode architecture. Given
enough training data, it is able to achieve state-of-the-art performance yet
avoiding the need of language/domain-dependent knowledge. Nonetheless, deep
learning models are known to be extremely data-hungry, and text generated from
them usually suffer from low diversity, interpretability and controllability.
As a result, it is difficult to trust the output from them in real-life
applications. Deep latent-variable models, by specifying the probabilistic
distribution over an intermediate latent process, provide a potential way of
addressing these problems while maintaining the expressive power of deep neural
networks. This dissertation presents how deep latent-variable models can
improve over the standard encoder-decoder model for text generation.


title: Symmetry Structured Convolutional Neural Networks

abstract:
  We consider Convolutional Neural Networks (CNNs) with 2D structured features
that are symmetric in the spatial dimensions. Such networks arise in modeling
pairwise relationships for a sequential recommendation problem, as well as
secondary structure inference problems of RNA and protein sequences. We develop
a CNN architecture that generates and preserves the symmetry structure in the
network's convolutional layers. We present parameterizations for the
convolutional kernels that produce update rules to maintain symmetry throughout
the training. We apply this architecture to the sequential recommendation
problem, the RNA secondary structure inference problem, and the protein contact
map prediction problem, showing that the symmetric structured networks produce
improved results using fewer numbers of machine parameters.


title: Interpretable Latent Variables in Deep State Space Models

abstract:
  We introduce a new version of deep state-space models (DSSMs) that combines a
recurrent neural network with a state-space framework to forecast time series
data. The model estimates the observed series as functions of latent variables
that evolve non-linearly through time. Due to the complexity and non-linearity
inherent in DSSMs, previous works on DSSMs typically produced latent variables
that are very difficult to interpret. Our paper focus on producing
interpretable latent parameters with two key modifications. First, we simplify
the predictive decoder by restricting the response variables to be a linear
transformation of the latent variables plus some noise. Second, we utilize
shrinkage priors on the latent variables to reduce redundancy and improve
robustness. These changes make the latent variables much easier to understand
and allow us to interpret the resulting latent variables as random effects in a
linear mixed model. We show through two public benchmark datasets the resulting
model improves forecasting performances.


title: Quantum phase transition in ferroelectric-paraelectric heterostructures

abstract:
  Phase transition between ferroelectricity and quantum paraelectricity via
non-thermal tuning parameters can lead to quantum critical behavior and
associated emergent phenomena. Ferroelectric quantum critical systems are,
however, rare despite the abundance of ferroelectric materials. Here, we show
theoretically that in ferroelectric-paraelectric heterostructures, it is
plausible to induce quantum paraelectricity where the quantum temperature
(i.e., the temperature below with the onset of ferroelectricity is suppressed
by quantum fluctuations) can be tuned by the thickness ratio. This, in turn,
can effect a quantum phase transition between effective ferroelectric and
quantum paraelectric states, using the thickness ratio as the tuning parameter.
The associated quantum critical region offers unexpected prospects in the field
of ferroelectric quantum criticality.


title:
IR characteristic emission and dust properties of star-forming galaxies
  at 4.5 $<$ z $<$ 6.2

abstract:
  The luminosity functions at z < 4 - 5 suggest that most galaxies have a
relatively low stellar mass (logM_star = 10) and a low dust attenuation (A_FUV
= 1.0). The physical properties of these objects are quite homogeneous. We used
an approach where we combined their rest-frame far-infrared and submillimeter
emissions and utilized the universe and the redshift as a spectrograph to
increase the amount of information in a collective way. From a subsample of 27
ALMA-detected galaxies at z > 4.5, we built an infrared spectral energy
distribution composite template. It was used to fit, with CIGALE, the 105
galaxies (detections and upper limits) in the sample from the FUV to the FIR.
The derived physical parameters provide information to decipher the nature of
the dust cycle and of the stellar populations in these galaxies. The derived IR
composite template is consistent with the galaxies in the studied sample. A
delayed star formation history with tau_main = 500 Myrs is slightly favored by
the statistical analysis as compared to a delayed with a final burst or a
continuous star formation history. The position of the sample in the star
formation rate (SFR)- M_star diagram is consistent with previous papers. The
redshift evolution of the log M_star versus A_FUV relation is in agreement with
evolution in the redshift of this relation. This evolution is necessary to
explain the cosmic evolution of the average dust attenuation of galaxies.
Evolution is also observed in the L_dust/ L_FUV (IRX) versus UV slope beta_FUV
diagram: younger galaxies have bluer beta_FUV. We modeled the shift of galaxies
in the IRX versus the beta_FUV diagram with the mass-weighted age as a free
parameter, and we provide an equation to make predictions.


title:
Thermographic detection of internal defects using 2D photothermal super
  resolution reconstruction with sequential laser heating

abstract:
  Thermographic photothermal super resolution reconstruction enables the
resolution of internal defects/inhomogeneities below the classical limit which
is governed by the diffusion properties of thermal wave propagation. Based on a
combination of the application of special sampling strategies and a subsequent
numerical optimization step in post-processing, thermographic super resolution
has already proven to be superior to standard thermographic methods in the
detection of one-dimensional defect/inhomogeneity structures. In our work, we
report an extension of the capabilities of the method for efficient detection
and resolution of defect cross sections with fully two-dimensional structured
laser-based heating. The reconstruction is carried out using one of two
different algorithms which are proposed within this work. Both algorithms
utilize the combination of several coherent measurements using convex
optimization and exploit the sparse nature of defects/inhomogeneities as is
typical for most nondestructive testing scenarios. Finally, the performance of
each algorithm is rated on reconstruction quality and algorithmic complexity.
The presented experimental approach is based on repeated spatially structured
heating by a high power laser. As a result, a two-dimensional sparse
defect/inhomogeneity map can be obtained. In addition, the obtained results are
compared with those of conventional thermographic inspection methods which make
use of homogeneous illumination. Due to the sparse nature of the reconstructed
defect/inhomogeneity map, this comparison is performed qualitatively.


title: Dyson's crank and unimodal compositions

abstract:
  The crank is a partition statistic requested by Dyson in 1944 in order to
combinatorially prove a Ramanujan congruence of Euler's partition function
$p(n)$. In this paper, we provide connections between Dyson's crank and
unimodal compositions. Somewhat unrelated, we give a combinatorial proof of a
new truncated Euler pentagonal number theorem due to Xia and Zhao.


title:
Radiative energy bandgap of nanostructures coupled with quantum emitters
  around the epsilon-near-zero (ENZ) frequency

abstract:
  Epsilon-near-zero (ENZ) materials have been demonstrated to exhibit unique
electromagnetic properties. Here we propose the concept of radiative energy
bandgap for an ENZ nanoparticle coupled with a quantum emitter (QE). The
radiative emission of the coupled QE-nanoparticle can be significantly
suppressed around the ENZ frequency and substantially enhanced otherwise,
yielding an effective energy bandgap for radiation. This suppression is
effectively invariant with respect to the particle size and is therefore an
intrinsic property of the ENZ material. Our concept also heralds an alternative
pathway to quench the emission from a QE, which may find potential application
in quantum information storage.


title:
Design and Fabrication of a Differential MOEMS Accelerometer Based on
  Fabry Perot micro-cavities

abstract:
  In this paper, a differential MOEMS accelerometer based on the Fabry-Perot
(FP) micro-cavities is presented. The optical system of the device consists of
two FP cavities and the mechanical system is composed of a proof mass that is
suspended by four springs. The applied acceleration tends to move the PM from
its resting position. This mechanical displacement can be measured by the FP
interferometer formed between the proof mass cross-section and the optical
fiber end face. The proposed sensor is fabricated on a silicon on insulator
(SOI) wafer using the bulk micromachining method. The results of the sensor
characterization show that the accelerometer has a linear response in the range
of 1g. Also, the optical sensitivity and resolution of the sensor in the static
characterization are 6.52 nm/g and 153ug. The sensor sensitivity in the power
measurement is 49.6 mV/g and its resonant is at 1372 Hz. Using the differential
measurement method increases the sensitivity of the accelerometer. Based on
experimental data, the sensor sensitivity is two times as high as that of a
similar MOEMS accelerometer with one FP cavity.


title:
Programmable Optical Data Transmission Through Multimode Fibres Enabling
  Confidentiality by Physical Layer Security

abstract:
  Complex light transportation phenomena of multimode fibres can be exploited
for information-theoretically secure data transmission. The approach called
physical layer security is not crackable by quantum computers as it does not
rely on mathematical complexity, but on targeted leveraging of properties that
are obscured in the physical channel. For its implementation only knowledge of
the channel conditions, i.e. the transmission matrix, is required. This allows
wiretap code generation, which determine the appropriate mode combination
providing secure data transmission. Once the proper combination is launched at
the transmitter-side, the message is delivered to the legitimate receiver and,
simultaneously, the full decipherment for an eavesdropper is destroyed. This is
demonstrated experimentally at presence of an almighty eavesdropper and the
fundamental theory is introduced. We harness effects that have long been
considered limiting and have restricted the widespread use of multimode fibres
opening new perspectives on information-theoretic security in spatial
multiplexing communication systems.

categories: physics.app-ph cs.CR cs.IT math.IT physics.optics quant-ph


title: Spinorial discrete symmetries and adjoint structures

abstract:
  Extending the investigations about the theory of duals, we analyse duals
built up with the aid of discrete symmetry operators. We scrutinize algebraic
and physical constraints (encompassing them in a theoretical scope) in order to
verify which combination of discrete symmetries may compose a physical and
mathematical well-posed spinorial dual. In this scenario, we relate the
Lounesto classification with several other spinor classification possibilities,
attempting to connect classes and physical constraints. Several possibilities
are investigated.


title:
NuSTAR discovery of the hard X-ray emission and a wide-band X-ray
  spectrum from the Pictor A western hot spot

abstract:
  Utilizing \textit{Chandra}, \textit{XMM-Newton} and \textit{NuSTAR}, a
wide-band X-ray spectrum through 0.2 to 20 keV is reported from the western hot
spot of Pictor A. In particular, the X-ray emission is significantly detected
in the 3 to 20 keV band at 30 sigma by \textit{NuSTAR}. This is the first
detection of hard X-rays with energies above 10 keV from a jet termination hot
spot of active galactic nuclei. The hard X-ray spectrum is well described with
a power-law model with a photon index of $\mathit{\Gamma}=1.8\pm0.2$, and the
flux is obtained to be $(4.5\pm0.4)\times10^{-13}$ erg s$^{-1}$ cm$^{-2}$ in
the 3 to 20 keV band. The obtained spectrum is smoothly connected with those
soft X-ray spectra observed by \textit{Chandra} and \textit{XMM-Newton}. The
wide-band spectrum shows a single power-law spectrum with a photon index of
$\mathit{\Gamma}=2.07\pm0.03$, excluding any cut-off/break features. Assuming
the X-rays as synchrotron radiation of the electrons, the energy index of the
electrons is estimated as $p=2\mathit{\Gamma}-1=3.14\pm0.06$ from the wide-band
spectrum. Given that the X-ray synchrotron emitting electrons quickly lose
their initial energies via synchrotron radiation, the energy index of electrons
at acceleration sites is estimated as $p_\mathrm{acc}=p-1=2.14\pm0.06$. This is
consistent with the prediction of the diffusive shock acceleration. Since the
spectrum has no cut-off feature up to 20 keV, the maximum electron energy is
estimated to be no less than 40 TeV.


title: Heat transport in a hierarchy of reduced-order convection models

abstract:
  Reduced-order models (ROMs) are systems of ordinary differential equations
(ODEs) designed to approximate the dynamics of partial differential equations
(PDEs). In this work, a distinguished hierarchy of ROMs is constructed for
Rayleigh's 1916 model of natural thermal convection. These models are
distinguished in the sense that they preserve energy and vorticity balances
derived from the governing equations, and each is capable of modeling zonal
flow. Various models from the hierarchy are analyzed to determine the maximal
heat transport in a given model, measured by the dimensionless Nusselt number,
for a given Rayleigh number. Lower bounds on the maximal heat transport are
ascertained by computing the Nusselt number among equilibria of the chosen
model using numerical continuation. A method known as sum-of-squares
optimization is applied to construct upper bounds on the time-averaged Nusselt
number. In this case, the sum-of-squares approach involves constructing a
polynomial quantity whose global nonnegativity implies the upper bound along
all solutions to a chosen ROM. The minimum such bound is determined through a
type of convex optimization called semidefinite programming. For the ROMs
studied in this work, the Nusselt number is maximized by equilibria whenever
the Rayleigh number is sufficiently small. In this range of Rayleigh number,
the equilibria maximizing heat transport are those that bifurcate first from
the zero state. Analyzing this primary equilibrium branch provides a possible
mechanism for the increase in heat transport near the onset of convection.