title: Surface criticality of antiferromagnetic Potts model

abstract:
  We study the three-state antiferromagnetic Potts model on the simple-cubic
lattice, paying attention to the surface critical behaviors. When the nearest
neighboring interactions of the surface is tuned, we obtain a phase diagram
similar to the XY model, owing to the emergent O(2) symmetry of the bulk
critical point. For the ordinary transition, we get $y_{h1}=0.780(3)$,
$\eta_\parallel=1.44(1)$, and $\eta_\perp=0.736(6)$; for the special
transition, we get $y_s=0.59(1)$, $y_{h1}=1.693(2)$,
$\eta_\parallel=-0.391(4)$, and $\eta_\perp=-0.179(5)$; in the
extraordinary-log phase, the surface correlation function $C_\parallel(r)$
decays logarithmically, with decaying exponent $q=0.60(2)$, however, the
correlation $C_\perp(r)$ still decays algebraically, with critical exponent
$\eta_\perp=-0.442(5)$. If the ferromagnetic next nearest neighboring surface
interactions are added, we find two transition points, the first one is a
special point between the ordinary phase and the extraordinary-log phase, the
second one is a transition between the extraordinary-log phase and the $Z_6$
symmetry-breaking phase, with critical exponent $y_{\rm s}=0.41(2)$. The
scaling behaviors of the second transition is very interesting, the surface
spin correlation function $C_\parallel(r)$ and the surface squared staggered
magnetization at this point decays logarithmically, with exponent $q=0.37(1)$;
however, the surface structure factor with the smallest wave vector and the
correlation function $C_\perp(r)$ satisfy power-law decaying, with critical
exponents $\eta_\parallel=-0.69(1)$ and $\eta_\perp=-0.37(1)$, respectively.


title:
Assessment of trends in an intergrated climate metric-Analysis of 200
  mbar zonal wind for the period 1958-2021

abstract:
  Using the ERA5 and JRA-55 reanalysis datasets on latitude bands of nearly
equal area, we analyze the trend of 200 mbar zonal wind anomalies for the
period 1958-2021 as well as annual-mean latitude of the polar jet stream. We
find basically no change of 200 mbar zonal wind in either the JRA-55 or ERA5
dataset. In addition, the polar jet core moves poleward in the Northern
Hemisphere, but the movement is not statistically significant, while no
poleward shift at all has occurred in the Southern Hemisphere.


title: There is a P-measure in the random model

abstract:
  We say that a finitely additive probability measure $\mu$ on $\omega$ is
\emph{a P-measure} if it vanishes on points and for each decreasing sequence
$(E_n)$ of infinite subsets of $\omega$ there is $E\subseteq\omega$ such that
$E\subseteq^* E_n$ for each $n\in\omega$ and $\mu(E) =
\lim_{n\to\infty}\mu(E_n)$. Thus, P-measures generalize in a natural way
P-points and it is known that, similarly as in the case of P-points, their
existence is independent of $\mathsf{ZFC}$. In this paper we show that there is
a P-measure in the model obtained by adding any number of random reals to a
model of $\mathsf{CH}$. As a corollary, we obtain that in the classical random
model $\omega^*$ contains a nowhere dense ccc closed P-set.


title: Estimation of Reliable Proposal Quality for Temporal Action Detection

abstract:
  Temporal action detection (TAD) aims to locate and recognize the actions in
an untrimmed video. Anchor-free methods have made remarkable progress which
mainly formulate TAD into two tasks: classification and localization using two
separate branches. This paper reveals the temporal misalignment between the two
tasks hindering further progress. To address this, we propose a new method that
gives insights into moment and region perspectives simultaneously to align the
two tasks by acquiring reliable proposal quality. For the moment perspective,
Boundary Evaluate Module (BEM) is designed which focuses on local appearance
and motion evolvement to estimate boundary quality and adopts a multi-scale
manner to deal with varied action durations. For the region perspective, we
introduce Region Evaluate Module (REM) which uses a new and efficient sampling
method for proposal feature representation containing more contextual
information compared with point feature to refine category score and proposal
boundary. The proposed Boundary Evaluate Module and Region Evaluate Module
(BREM) are generic, and they can be easily integrated with other anchor-free
TAD methods to achieve superior performance. In our experiments, BREM is
combined with two different frameworks and improves the performance on THUMOS14
by 3.6$\%$ and 1.0$\%$ respectively, reaching a new state-of-the-art (63.6$\%$
average $m$AP). Meanwhile, a competitive result of 36.2\% average $m$AP is
achieved on ActivityNet-1.3 with the consistent improvement of BREM.


title: The family signature theorem

abstract:
  We discuss several versions of the Family Signature Theorem: in rational
cohomology using ideas of Meyer, in $KO[\tfrac{1}{2}]$-theory using ideas of
Sullivan, and finally in symmetric $L$-theory using ideas of Ranicki. Employing
recent developments in Grothendieck--Witt theory, we give a quite complete
analysis of the resulting invariants. As an application we prove that the
signature is multiplicative modulo 4 for fibrations of oriented Poincar\'e
complexes, and discuss the multiplicativity of the de Rham invariant.


title: Basal hydrofractures near sticky patches

abstract:
  Basal crevasses are macroscopic structural discontinuities at the base of ice
sheets and glaciers. Motivated by observations and the mechanics of elastic
fracture, we hypothesise that in the presence of basal water pressure, spatial
variations in basal stress can promote and localise basal crevassing. We
quantify this process in the theoretical context of linear elastic fracture
mechanics. We develop a model evaluating the effect of shear stress variation
on the growth of basal crevasses. Our results indicate that sticky patches
promote the initiation of basal crevasses, increase their length of propagation
into the ice and, under some conditions, give them curved trajectories that
incline upstream. A detailed exploration of the parameter space is conducted to
gain a better understanding of the conditions under which sticky-patch-induced
basal crevassing likely occurs beneath ice sheets and glaciers.


title:
Connecting Commutativity and Classicality for Multi-Time Quantum
  Processes

abstract:
  Understanding the demarcation line between classical and quantum is an
important issue in modern physics. The development of such an understanding
requires a clear picture of the various concurrent notions of `classicality' in
quantum theory presently in use. Here, we focus on the relationship between
Kolmogorov consistency of measurement statistics -- the foundational footing of
classical stochastic processes in standard probability theory -- and the
commutativity (or absence thereof) of measurement operators -- a concept at the
core of quantum theory. Kolmogorov consistency implies that the statistics of
sequential measurements on a (possibly quantum) system could be explained
entirely by means of a classical stochastic process, thereby providing an
operational notion of classicality. On the other hand, commutativity of
measurement operators is a structural property that holds in classical physics
and its breakdown is the origin of the uncertainty principle, a fundamentally
quantum phenomenon. Here, we formalise the connection between these two a
priori independent notions of classicality, demonstrate that they are distinct
in general and detail their implications for memoryless multi-time quantum
processes.


title:
Ab-initio study of magneto-ionic mechanisms in ferromagnet/oxide
  multilayers

abstract:
  The application of gate voltages in heavy metal/ferromagnet/Oxide multilayer
stacks has been identified as one possible candidate to manipulate their
anisotropy at will. However, this method has proven to show a wide variety of
behaviours in terms of reversibility, depending on the nature of the
metal/oxide interface and its degree of oxidation. In order to shed light on
the microscopic mechanism governing the complex magneto-ionic behaviour in
$\text{Ta/CoFeB/}\text{HfO}_2$, we perform ab-initio simulations on various
setups comprising $\text{Fe/O, Fe/HfO}_2$ interfaces with different oxygen atom
interfacial geometries. After the determination of the more stable interfacial
configurations, we calculate the magnetic anisotropy energy on the different
unit cell configurations and formulate a possible mechanism that well describes
the recent experimental observations in $\text{Ta/CoFeB/}\text{HfO}_2$.


title:
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for
  Efficient Feature Matching

abstract:
  Graph Neural Networks (GNNs) with attention have been successfully applied
for learning visual feature matching. However, current methods learn with
complete graphs, resulting in a quadratic complexity in the number of features.
Motivated by a prior observation that self- and cross- attention matrices
converge to a sparse representation, we propose ClusterGNN, an attentional GNN
architecture which operates on clusters for learning the feature matching task.
Using a progressive clustering module we adaptively divide keypoints into
different subgraphs to reduce redundant connectivity, and employ a
coarse-to-fine paradigm for mitigating miss-classification within images. Our
approach yields a 59.7% reduction in runtime and 58.4% reduction in memory
consumption for dense detection, compared to current state-of-the-art GNN-based
matching, while achieving a competitive performance on various computer vision
tasks.


title: Tac2Pose: Tactile Object Pose Estimation from the First Touch

abstract:
  In this paper, we present Tac2Pose, an object-specific approach to tactile
pose estimation from the first touch for known objects. Given the object
geometry, we learn a tailored perception model in simulation that estimates a
probability distribution over possible object poses given a tactile
observation. To do so, we simulate the contact shapes that a dense set of
object poses would produce on the sensor. Then, given a new contact shape
obtained from the sensor, we match it against the pre-computed set using an
object-specific embedding learned using contrastive learning. We obtain contact
shapes from the sensor with an object-agnostic calibration step that maps RGB
tactile observations to binary contact shapes. This mapping, which can be
reused across object and sensor instances, is the only step trained with real
sensor data. This results in a perception model that localizes objects from the
first real tactile observation. Importantly, it produces pose distributions and
can incorporate additional pose constraints coming from other perception
systems, contacts, or priors.
  We provide quantitative results for 20 objects. Tac2Pose provides high
accuracy pose estimations from distinctive tactile observations while
regressing meaningful pose distributions to account for those contact shapes
that could result from different object poses. We also test Tac2Pose on object
models reconstructed from a 3D scanner, to evaluate the robustness to
uncertainty in the object model. Finally, we demonstrate the advantages of
Tac2Pose compared with three baseline methods for tactile pose estimation:
directly regressing the object pose with a neural network, matching an observed
contact to a set of possible contacts using a standard classification neural
network, and direct pixel comparison of an observed contact with a set of
possible contacts.
  Website: http://mcube.mit.edu/research/tac2pose.html


title:
Large-scale quantum diagrammatic reasoning tools, !-boxes vs. scalable
  notations

abstract:
  The application of diagrammatic reasoning techniques to large-scale quantum
processes needs specific tools to describe families of diagrams of arbitrary
size. For now, large-scale diagrammatic reasoning tools in ZH-calculus come in
two flavours, !-boxes and scalable notations. This paper investigates the
interactions between the two approaches by exhibiting correspondences through
various examples from the literature, focusing on (hyper)graph states and
diagrammatic transforms. In doing so, we set up a path toward a neat and tidy
large-scale diagrammatic reasoning toolbox.


title: Theoretical Mass-Luminosity Relations in Gaia G-Band

abstract:
  We have analyzed the retrieval of the relation between mass and absolute Gaia
G magnitude as obtained from theoretical models by \cite{2012MNRAS.427..127B}
for the stars of different luminosity classes. For subgiant and main-sequence
stars, we provide approximate analytical direct and inverse relations based on
the most probable value of G magnitude in the course of evolution within the
given stage. A comparison with (albeit few) observational data confirms that
our results can be used for the estimation of the stellar mass from Gaia
photometry in the range of 1 to 10 solar mass. We argue that similar relations
for other luminosity classes are not informative.


title: SHINeS: Space and High-Irradiance Near-Sun Simulator

abstract:
  We present SHINeS, a space simulator which can be used to replicate the
thermal environment in the immediate neighborhood of the Sun down to a
heliocentric distance r~0.06 au. The system consists of three main parts: the
solar simulator which was designed and constructed in-house, a vacuum chamber,
and the probing and recording equipment needed to monitor the experimental
procedures. Our motivation for building this experimental system was to study
the effect of intense solar radiation on the surfaces of asteroids when their
perihelion distances become smaller than the semi-major axis of the orbit of
Mercury. Comparisons between observational data and recent orbit and
size-frequency models of the population of near-Earth asteroids suggest that
asteroids are super-catastrophically destroyed when they approach the Sun.
Whereas the current models are agnostic about the disruption mechanism, SHINeS
was developed to study the mechanism or mechanisms responsible. The system can,
however, be used for other applications that need to study the effects of high
solar radiation on other natural or artificial objects.

categories: astro-ph.EP astro-ph.IM astro-ph.SR physics.space-ph


title: Stable Auslander-Reiten components of monomorphism categories

abstract:
  Let $\Lambda$ be an Artin algebra and $\rm{Gprj}\mbox{-}\Lambda$ the
subcategory of Gorenstein projective $\Lambda$-modules. In this paper, we study
components of the stable Auslander-Reiten quiver of the monomorphism category
$\mathcal{S}(\rm{Gprj}\mbox{-}\Lambda)$ containing certain vertices, which are
called boundary vertices. This kind of the components is linked to the orbits
of an auto-equivalence on the stable category
$\underline{\text{Gprj}}\mbox{-}\Lambda$. As a result, for when $\Lambda$ is a
CM-finite algebra, we apply our results to study the Auslander-Reiten
translation of simple modules of the associated stable Cohen-Macaulay Auslander
algebra $A$ of $\Lambda$, and those components of the stable Auslander-Reiten
quiver $A$ containing a simple module. In particular, we give some applications
for the components of the stable Auslander-Reiten quiver over a preprojective
algebra.


title: The X-ray transform on asymptotically conic spaces

abstract:
  In this paper, partly based on Zachos' PhD thesis, we show that the geodesic
X-ray transform is stably invertible near infinity on a class of asymptotically
conic manifolds which includes perturbations of Euclidean space. In particular
certain kinds of conjugate points are allowed. Further, under a global convex
foliation condition, the transform is globally invertible.
  The key analytic tool, beyond the approach introduced by Uhlmann and Vasy, is
the introduction of a new pseudodifferential operator algebra, which we name
the 1-cusp algebra, and its semiclassical version.


title:
Optimal security hardening over a probabilistic attack graph: a case
  study of an industrial control system using the CySecTool tool

abstract:
  CySecTool is a tool that finds a cost-optimal security controls portfolio in
a given budget for a probabilistic attack graph. A portfolio is a set of
counter-measures, or controls, against vulnerabilities adopted for a computer
system, while an attack graph is a type of a threat scenario model. In an
attack graph, nodes are privilege states of the attacker, edges are
vulnerabilities escalating privileges, and controls reduce the probabilities of
some vulnerabilities being exploited. The tool builds on an optimisation
algorithm published by Khouzani et al. (2019), enabling a user to quickly
create, edit, and incrementally improve models, analyse results for given
portfolios and display the best solutions for all possible budgets in the form
of a Pareto frontier. A case study was performed utilising a system graph and
suspected attack paths prepared by industrial security engineers based on an
industrial source with which they work. The goal of the case study is to model
a supervisory control and data acquisition (SCADA) industrial system which, due
to having the potential to harm people, necessitates strong protection while
not allowing the use of typical penetration tools like vulnerability scanners.
Results are analysed to show how a cyber-security analyst would use CySecTool
to store cyber-security intelligence and draw further conclusions.


title:
Understanding the Relationship Between Core Constraints and
  Core-Selecting Payment Rules in Combinatorial Auctions

abstract:
  Combinatorial auctions (CAs) allow bidders to express complex preferences for
bundles of goods being auctioned. However, the behavior of bidders under
different payment rules is often unclear. In this paper, we aim to understand
how core constraints interact with different core-selecting payment rules. In
particular, we examine the natural and desirable non-decreasing property of
payment rules, which states that bidders cannot decrease their payments by
increasing their bids. Previous work showed that, in general, the widely used
VCG-nearest payment rule violates the non-decreasing property in single-minded
CAs. We prove that under a single effective core constraint, the VCG-nearest
payment rule is non-decreasing. In order to determine in which auctions single
effective core constraints occur, we introduce a conflict graph representation
of single-minded CAs and find sufficient conditions for the single effective
core constraint in CAs. Finally, we study the consequences on the behavior of
the bidders and show that no over-bidding exists in any Nash equilibrium for
non-decreasing core-selecting payment rules.


title: Effective quantum Hamiltonian in thin domains with non-homogeneity

abstract:
  We consider the Laplacian with a non-homogeneous metric in a tubular
neighbourhood of a compact hypersurface in the Euclidean space of arbitrary
dimension, subject to Neumann boundary conditions. It is shown that, in the
limit of the width of the neighbourhood shrinking to zero, the operator
converges in a generalised norm-resolvent sense to an effective
Laplace-Beltrami-type operator on the hypersurface. In this way, we generalise
and give an insight into the convergence of eigenvalues obtained by Yachimura
(arXiv:1706.05027).


title:
Unsteady shallow meandering flows in rectangular reservoirs: A modal
  analysis of URANS modelling

abstract:
  Shallow flows are common in natural and human-made environments. Even for
simple rectangular shallow reservoirs, recent laboratory experiments show that
the developing flow fields are particularly complex, involving large-scale
turbulent structures. For specific combinations of reservoir size and hydraulic
conditions, a meandering jet can be observed. While some aspects of this
pseudo-2D flow pattern can be reproduced using a 2D numerical model, new 3D
simulations, based on the unsteady Reynolds-Averaged Navier-Stokes equations,
show consistent advantages as presented herein. A Proper Orthogonal
Decomposition was used to characterize the four most energetic modes of the
meandering jet at the free surface level, allowing comparison against
experimental data and 2D (depth-averaged) numerical results. Three different
isotropic eddy viscosity models (RNG k-${\epsilon}$, k-${\epsilon}$,
k-${\omega}$) were tested. The 3D models accurately predicted the frequency of
the modes, whereas the amplitudes of the modes and associated energy were
damped for the friction-dominant cases and augmented for non-frictional ones.
The performance of the three turbulence models remained essentially similar,
with slightly better predictions by RNG k-${\epsilon}$ model in the case with
the highest Reynolds number. Finally, the Q-criterion was used to identify
vortices and study their dynamics, assisting on the identification of the
differences between: i) the three-dimensional phenomenon (here reproduced), ii)
its two-dimensional footprint in the free surface (experimental observations)
and iii) the depth-averaged case (represented by 2D models).


title:
Uniqueness of Single Peak Solutions for Coupled Nonlinear
  Gross-Pitaevskii Equations with Potentials

abstract:
  For a couple of singularly perturbed Gross-Pitaevskii equations, we first
prove that the single peak solutions, if they concentrate on the same point,
are unique provided that the Taylor's expansion of potentials around the
concentration point is in the same order along all directions. Among other
assumptions, our results indicate that the peak solutions obtained in
[21,31,38] are unique. Moreover, for the radially symmetric ring-shaped
potential, which attains its minimum at the
spheres$\Gamma_i:=\{x\in\mathbb{R}^N:|x|=A_i>0\},i=1,2,\cdots,l,$ and is
totally degenerate in the tangential space of $\Gamma_i$, we prove that the
positive ground state is cylindrically symmetric and is unique up to rotations
around the origin. Aa far as we know, this is the first uniqueness result for
ground states under radially symmetric but non-monotonic potentials.


title:
Online multiscale model reduction for nonlinear stochastic PDEs with
  multiplicative noise

abstract:
  In this paper, an online multiscale model reduction method is presented for
stochastic partial differential equations (SPDEs) with multiplicative noise,
where the diffusion coefficient is spatially multiscale and the noise
perturbation nonlinearly depends on the diffusion dynamics. It is necessary to
efficiently compute all possible trajectories of the stochastic dynamics for
quantifying model's uncertainty and statistic moments. The multiscale diffusion
and nonlinearity may cause the computation intractable. To overcome the
multiscale difficulty, a constraint energy minimizing generalized multiscale
finite element method (CEM-GMsFEM) is used to localize the computation and
obtain an effective coarse model. However, the nonlinear terms are still
defined on a fine scale space after the Galerkin projection of CEM-GMsFEM is
applied to the nonlinear SPDEs. This significantly impacts on the simulation
efficiency by CEM-GMsFEM. To this end, a stochastic online discrete empirical
interpolation method (DEIM) is proposed to treat the stochastic nonlinearity.
The stochastic online DEIM incorporates offline snapshots and online snapshots.
The offline snapshots consist of the nonlinear terms at the approximate mean of
the stochastic dynamics and are used to construct an offline reduced model. The
online snapshots contain some information of the current new trajectory and are
used to correct the offline reduced model in an increment manner. The
stochastic online DEIM substantially reduces the dimension of the nonlinear
dynamics and enhances the prediction accuracy for the reduced model. Thus, the
online multiscale model reduction is constructed by using CEM-GMsFEM and the
stochastic online DEIM. A priori error analysis is carried out for the
nonlinear SPDEs. We present a few numerical examples with diffusion in
heterogeneous porous media and show the effectiveness of the proposed model
reduction.


title: Impact of Geographic Diversity on Citation of Collaborative Research

abstract:
  Diversity in human capital is widely seen as critical to creating holistic
and high quality research, especially in areas that engage with diverse
cultures, environments, and challenges. Quantifying diverse academic
collaborations and its effect on research quality is lacking, especially at
international scale and across different domains. Here, we present the first
effort to measure the impact of geographic diversity in coauthorships on the
citation of their papers across different academic domains. Our results
unequivocally show that geographic coauthor diversity improves paper citation,
but very long distance collaborations has variable impact. We also discover
"well-trodden" collaboration circles that yield much less impact than similar
travel distances. These relationships are observed to exist across different
subject areas, but with varying strengths. These findings can help academics
identify new opportunities from a diversity perspective, as well as inform
funders on areas that require additional mobility support.


title:
The Causal News Corpus: Annotating Causal Relations in Event Sentences
  from News

abstract:
  Despite the importance of understanding causality, corpora addressing causal
relations are limited. There is a discrepancy between existing annotation
guidelines of event causality and conventional causality corpora that focus
more on linguistics. Many guidelines restrict themselves to include only
explicit relations or clause-based arguments. Therefore, we propose an
annotation schema for event causality that addresses these concerns. We
annotated 3,559 event sentences from protest event news with labels on whether
it contains causal relations or not. Our corpus is known as the Causal News
Corpus (CNC). A neural network built upon a state-of-the-art pre-trained
language model performed well with 81.20% F1 score on test set, and 83.46% in
5-folds cross-validation. CNC is transferable across two external corpora:
CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each of
these external datasets for training, we achieved up to approximately 64% F1 on
the CNC test set without additional fine-tuning. CNC also served as an
effective training and pre-training dataset for the two external corpora.
Lastly, we demonstrate the difficulty of our task to the layman in a
crowd-sourced annotation exercise. Our annotated corpus is publicly available,
providing a valuable resource for causal text mining researchers.


title:
The Galactic 3D large-scale dust distribution via Gaussian process
  regression on spherical coordinates

abstract:
  Knowing the Galactic 3D dust distribution is relevant for understanding many
processes in the interstellar medium and for correcting many astronomical
observations for dust absorption and emission. Here, we aim for a 3D
reconstruction of the Galactic dust distribution with an increase in the number
of meaningful resolution elements by orders of magnitude with respect to
previous reconstructions, while taking advantage of the dust's spatial
correlations to inform the dust map. We use iterative grid refinement to define
a log-normal process in spherical coordinates. This log-normal process assumes
a fixed correlation structure, which was inferred in an earlier reconstruction
of Galactic dust. Our map is informed through 111 Million data points,
combining data of PANSTARRS, 2MASS, Gaia DR2 and ALLWISE. The log-normal
process is discretized to 122 Billion degrees of freedom, a factor of 400 more
than our previous map. We derive the most probable posterior map and an
uncertainty estimate using natural gradient descent and the Fisher-Laplace
approximation. The dust reconstruction covers a quarter of the volume of our
Galaxy, with a maximum coordinate distance of $16\,\text{kpc}$, and meaningful
information can be found up to at distances of $4\,$kpc, still improving upon
our earlier map by a factor of 5 in maximal distance, of $900$ in volume, and
of about eighteen in angular grid resolution. Unfortunately, the maximum
posterior approach chosen to make the reconstruction computational affordable
introduces artifacts and reduces the accuracy of our uncertainty estimate.
Despite of the apparent limitations of the presented 3D dust map, a good part
of the reconstructed structures are confirmed by independent maser
observations. Thus, the map is a step towards reliable 3D Galactic cartography
and already can serve for a number of tasks, if used with care.


title: Masked Image Modeling Advances 3D Medical Image Analysis

abstract:
  Recently, masked image modeling (MIM) has gained considerable attention due
to its capacity to learn from vast amounts of unlabeled data and has been
demonstrated to be effective on a wide variety of vision tasks involving
natural images. Meanwhile, the potential of self-supervised learning in
modeling 3D medical images is anticipated to be immense due to the high
quantities of unlabeled images, and the expense and difficulty of quality
labels. However, MIM's applicability to medical images remains uncertain. In
this paper, we demonstrate that masked image modeling approaches can also
advance 3D medical images analysis in addition to natural images. We study how
masked image modeling strategies leverage performance from the viewpoints of 3D
medical image segmentation as a representative downstream task: i) when
compared to naive contrastive learning, masked image modeling approaches
accelerate the convergence of supervised training even faster (1.40$\times$)
and ultimately produce a higher dice score; ii) predicting raw voxel values
with a high masking ratio and a relatively smaller patch size is non-trivial
self-supervised pretext-task for medical images modeling; iii) a lightweight
decoder or projection head design for reconstruction is powerful for masked
image modeling on 3D medical images which speeds up training and reduce cost;
iv) finally, we also investigate the effectiveness of MIM methods under
different practical scenarios where different image resolutions and labeled
data ratios are applied.


title:
Workhorse minimally empirical dispersion-corrected density functional,
  with tests for dimers and layered materials: r$^{2}$SCAN+rVV10

abstract:
  SCAN+rVV10 has been demonstrated to be a versatile van der Waals (vdW)
density functional that delivers good predictions of both energetic and
structural properties for many types of bonding. Recently, the r$^{2}$SCAN
functional has been devised as a revised form of SCAN with improved numerical
stability. In this work, we refit the rVV10 functional to optimize the
r$^{2}$SCAN+rVV10 vdW density functional, and test its performance for
molecular interactions and layered materials. Our molecular tests demonstrate
that r$^{2}$SCAN+rVV10 outperforms its predecessor SCAN+rVV10 in both
efficiency (numerical stability) and accuracy. This good performance is also
found in lattice constant predictions. In comparison with benchmark results
from higher-level theories or experiments, r$^{2}$SCAN+rVV10 yields excellent
interlayer binding energies and phonon dispersions for layered materials.

categories: cond-mat.mtrl-sci cond-mat.soft physics.atm-clus physics.comp-ph


title:
Predicting Real-time Scientific Experiments Using Transformer models and
  Reinforcement Learning

abstract:
  Life and physical sciences have always been quick to adopt the latest
advances in machine learning to accelerate scientific discovery. Examples of
this are cell segmentation or cancer detection. Nevertheless, these exceptional
results are based on mining previously created datasets to discover patterns or
trends. Recent advances in AI have been demonstrated in real-time scenarios
like self-driving cars or playing video games. However, these new techniques
have not seen widespread adoption in life or physical sciences because
experimentation can be slow. To tackle this limitation, this work aims to adapt
generative learning algorithms to model scientific experiments and accelerate
their discovery using in-silico simulations. We particularly focused on
real-time experiments, aiming to model how they react to user inputs. To
achieve this, here we present an encoder-decoder architecture based on the
Transformer model to simulate real-time scientific experimentation, predict its
future behaviour and manipulate it on a step-by-step basis. As a proof of
concept, this architecture was trained to map a set of mechanical inputs to the
oscillations generated by a chemical reaction. The model was paired with a
Reinforcement Learning controller to show how the simulated chemistry can be
manipulated in real-time towards user-defined behaviours. Our results
demonstrate how generative learning can model real-time scientific
experimentation to track how it changes through time as the user manipulates
it, and how the trained models can be paired with optimisation algorithms to
discover new phenomena beyond the physical limitations of lab experimentation.
This work paves the way towards building surrogate systems where physical
experimentation interacts with machine learning on a step-by-step basis.


title:
On the Performance of Machine Learning Methods for Breakthrough Curve
  Prediction

abstract:
  Reactive flows are important part of numerous technical and environmental
processes. Often monitoring the flow and species concentrations within the
domain is not possible or is expensive, in contrast, outlet concentration is
straightforward to measure. In connection with reactive flows in porous media,
the term breakthrough curve is used to denote the time dependency of the outlet
concentration with prescribed conditions at the inlet. In this work we apply
several machine learning methods to predict breakthrough curves from the given
set of parameters. In our case the parameters are the Damk\"ohler and Peclet
numbers. We perform a thorough analysis for the one-dimensional case and also
provide the results for the three-dimensional case.


title:
Total absorption $\gamma$-ray spectroscopy of the $\beta$ decays of
  $^{96\text{gs,m}}$Y

abstract:
  The $\beta$ decays of the ground state (gs) and isomeric state (m) of
$^{96}$Y have been studied with the total absorption $\gamma$-ray spectroscopy
technique at the Ion Guide Isotope Separator On-Line facility. The separation
of the 8$^{+}$ isomeric state from the 0$^{-}$ ground state was achieved thanks
to the purification capabilities of the JYFLTRAP double Penning trap system.
The $\beta$-intensity distributions of both decays have been independently
determined. In the analyses the de-excitation of the 1581.6 keV level in
$^{96}$Zr, in which conversion electron emission competes with pair production,
has been carefully considered and found to have significant impact on the
$\beta$-detector efficiency, influencing the $\beta$-intensity distribution
obtained. Our results for $^{96\text{gs}}$Y (0$^+$) confirm the large ground
state to ground state $\beta$-intensity probability, although a slightly larger
value than reported in previous studies was obtained, amounting to
$96.6_{-2.1}^{+0.3}\%$ of the total $\beta$ intensity. Given that the decay of
$^{96\text{gs}}$Y is the second most important contributor to the reactor
antineutrino spectrum between 5 and 7 MeV, the impact of the present results on
reactor antineutrino summation calculations has been evaluated. In the decay of
$^{96\text{m}}$Y (8$^{+}$), previously undetected $\beta$ intensity in
transitions to states above 6 MeV has been observed. This shows the importance
of total absorption $\gamma$-ray spectroscopy measurements of $\beta$ decays
with highly fragmented de-excitation patterns. $^{96\text{m}}$Y (8$^{+}$) is a
major contributor to reactor decay heat in uranium-plutonium and
thorium-uranium fuels around 10 s after fission pulses, and the newly measured
average $\beta$ and $\gamma$ energies differ significantly from the previous
values in evaluated databases (...)


title:
Comment on the "Significance of Electromagnetic Potentials in the
  Quantum Theory"

abstract:
  A missing angle-dependent prefactor in the wave functions for half-integer
magnetic flux obtained in [Y. Aharonov and D. Bohm, Phys. Rev. 115, 485 (1959)]
is supplied, without which the result is not single-valued. No physical
conclusions of the paper are affected.


title:
Sun-as-a-star spectroscopic observations of the line-of-sight velocity
  of a solar eruption on October 28, 2021

abstract:
  The propagation direction and true velocity of a solar coronal mass ejection,
which are among the most decisive factors for its geo-effectiveness, are
difficult to determine through single-perspective imaging observations. Here we
show that Sun-as-a-star spectroscopic observations, together with imaging
observations, could allow us to solve this problem. Using observations of the
Extreme-ultraviolet Variability Experiment onboard the Solar Dynamics
Observatory, we found clear blue-shifted secondary emission components in
extreme ultraviolet spectral lines during a solar eruption on October 28, 2021.
From simultaneous imaging observations, we found that the secondary components
are caused by a mass ejection from the flare site. We estimated the
line-of-sight (LOS) velocity of the ejecta from both the double Gaussian
fitting method and the red-blue asymmetry analysis. The results of both methods
agree well with each other, giving an average LOS velocity of the plasma of
$\sim 423~\rm{km~s^{-1}}$. From the $304$ \AA~image series taken by the Extreme
Ultraviolet Imager onboard the Solar Terrestrial Relation Observatory-A
(STEREO-A) spacecraft, we estimated the plane-of-sky (POS) velocity from the
STEREO-A viewpoint {to be around $587~\rm{km~s^{-1}}$}. The full velocity of
the bulk motion of the ejecta was then computed by combining the imaging and
spectroscopic observations, which turns out to be around $596~\rm{km~s^{-1}}$
with an angle of $42.4^\circ$ to the west of the Sun-Earth line and
$16.0^\circ$ south to the ecliptic plane.


title: 4DAC: Learning Attribute Compression for Dynamic Point Clouds

abstract:
  With the development of the 3D data acquisition facilities, the increasing
scale of acquired 3D point clouds poses a challenge to the existing data
compression techniques. Although promising performance has been achieved in
static point cloud compression, it remains under-explored and challenging to
leverage temporal correlations within a point cloud sequence for effective
dynamic point cloud compression. In this paper, we study the attribute (e.g.,
color) compression of dynamic point clouds and present a learning-based
framework, termed 4DAC. To reduce temporal redundancy within data, we first
build the 3D motion estimation and motion compensation modules with deep neural
networks. Then, the attribute residuals produced by the motion compensation
component are encoded by the region adaptive hierarchical transform into
residual coefficients. In addition, we also propose a deep conditional entropy
model to estimate the probability distribution of the transformed coefficients,
by incorporating temporal context from consecutive point clouds and the motion
estimation/compensation modules. Finally, the data stream is losslessly entropy
coded with the predicted distribution. Extensive experiments on several public
datasets demonstrate the superior compression performance of the proposed
approach.


title:
Gamma-ray emission in proton-induced nuclear reactions on natC and Mylar
  targets over the incident energy range of $E_p$ = 30-200 MeV. Astrophysical
  implications

abstract:
  We have measured the gamma-ray line production cross sections in
proton-induced nuclear reactions on various target nuclei abundant in
astrophysical sites over the incident energy range of $E_p$ = 30 - 200 MeV. We
carried out experimental campaigns in joint collaboration at the K = 200
cyclotron of iThemba LABS using a high-energy resolution, high-efficiency
detection array composed of 8 Compton-suppressed clover detectors comprising 32
HP-Ge crystals for recording the gamma-ray spectra. In the current paper, we
focus on de-excitation lines produced in proton irradiations of natC and Mylar
targets. In particular, on the prominent 4.439 and 6.129 MeV lines of $^{12}$C
and $^{16}$O which are among the strongest lines emitted in solar flares and in
interactions of low-energy cosmic rays with the gas and dust of the inner
galaxy. We report new gamma-ray production experimental cross section data for
ten nuclear lines that we compare to previous low-energy data sets from the
literature, to the predictions of the TALYS code of modern nuclear reactions
and to a semi-empirical compilation. In first approach, performing calculations
with default input parameters of TALYS we observed substantial deviations
between the predicted cross sections and experimental data. Then, using
modified optical model potential and nuclear level deformation parameters as
input data we generated theoretical excitation functions for the above two main
lines fully consistent with experimental data. In contrast, the experimental
data sets for the other eight analyzed lines from the two proton-irradiated
targets exhibit significant deviations with the predicted cross section values.
We also report line-shape experimental data for the line complex observed at
$E_g$ = 4.44 MeV in irradiations of the two targets. Finally, we emphasize the
astrophysical implications of our results.


title: Terahertz waveform synthesis from integrated lithium niobate circuits

abstract:
  Bridging the "terahertz (THz) gap" relies upon synthesizing arbitrary
waveforms in the THz domain enabling applications that require both narrow band
sources for sensing and few-cycle drives for classical and quantum objects.
However, realization of custom-tailored waveforms needed for these applications
is currently hindered due to limited flexibility for optical rectification of
femtosecond pulses in bulk crystals. Here, we experimentally demonstrate that
thin-film lithium niobate (TFLN) circuits provide a versatile solution for such
waveform synthesis through combining the merits of complex integrated
architectures, low-loss distribution of pump pulses on-chip, and an efficient
optical rectification. Our distributed pulse phase-matching scheme grants
shaping the temporal, spectral, phase, amplitude, and farfield characteristics
of the emitted THz field through designer on-chip components. This strictly
circumvents prior limitations caused by the phase-delay mismatch in
conventional systems and relaxes the requirement for cumbersome spectral
pre-engineering of the pumping light. We provide a toolbox of basic blocks that
produce broadband emission up to 680 GHz with adaptable phase and coherence
properties by using near-infrared pump pulse energies below 100 pJ.


title:
Exponentially decreasing critical detection efficiency for any Bell
  inequality

abstract:
  We address the problem of closing the detection efficiency loophole in Bell
experiments, which is crucial for real-world applications. Every Bell
inequality has the critical detection efficiency $\eta$ that must be surpassed
to avoid the detection loophole. In this paper, we propose a general method for
reducing the critical detection efficiency of any Bell inequality to arbitrary
low values. This is accomplished by entangling two particles in $N$ orthogonal
subspaces (e.g., $N$ degrees of freedom) and conducting $N$ Bell tests in
parallel. Furthermore, the proposed method is based on the introduction of
penalized $N$-product (PNP) Bell inequalities, for which the so-called
simultaneous measurement loophole is closed and the maximum value obtained by
local hidden variable theories is simply the $N$-th power of the initially
considered Bell inequality. We show that, for PNP Bell inequalities, the
critical detection efficiency decays {\em exponentially} with $N$. The strength
of our method is illustrated with a detailed study of the PNP Bell inequalities
resulting from the Clauser-Horne-Shimony-Holt inequality.


title: Block-Circulant Complex Hadamard Matrices

abstract:
  A new method of obtaining a sequence of isolated complex Hadamard matrices
(CHM) of arbitrary order $N\geqslant 7$, based on block-circulant structures,
is presented. Several analytic and numerical examples resulted from the
modification of the Sinkhorn algorithm are discussed. In particular we present
new isolated matrices of order $9$ and $11$ which elements are not roots of
unity. We note novel connections between certain eight-dimensional matrices and
provide new insights towards classification of CHM for $N\geqslant 7$. These
contributions can find applications in Quantum Information Theory and
constructions of new families of Mutually Unbiased Bases or Unitary Error
Bases.


title:
A simple technique for evaluating dipole moments of Bloch states in
  tetrahedral semiconductors

abstract:
  Permanent dipole moments of electronics states in non-centro-symmetric
materials play pivotal role in many phenomena. Correctly evaluating them
presents an arduous task and usually requires full knowledge of the band
structure as well as understanding the intricate concepts of Berry curvature.
Here we show that in a few cases (e.g. zinc blende and wurtzite) a rather
facile first-principle analytical derivation of the permanent dipole moments
using L Hopital rule can be performed, and the values and dispersion of these
dipoles near high symmetry points can be found using just a couple of widely
available material parameters. The results will hopefully contribute to better
understanding of shift currents, optical rectification and other
electro-optical phenomena.


title:
Five key exoplanet questions answered via the analysis of 25 hot Jupiter
  atmospheres in eclipse

abstract:
  Population studies of exoplanets are key to unlocking their statistical
properties. So far the inferred properties have been mostly limited to
planetary, orbital and stellar parameters extracted from, e.g., Kepler, radial
velocity, and GAIA data. More recently an increasing number of exoplanet
atmospheres have been observed in detail from space and the ground. Generally,
however, these atmospheric studies have focused on individual planets, with the
exception of a couple of works which have detected the presence of water vapor
and clouds in populations of gaseous planets via transmission spectroscopy.
Here, using a suite of retrieval tools, we analyse spectroscopic and
photometric data of 25 hot Jupiters, obtained with the Hubble and Spitzer Space
Telescopes via the eclipse technique. By applying the tools uniformly across
the entire set of 25 planets, we extract robust trends in the thermal structure
and chemical properties of hot Jupiters not obtained in past studies. With the
recent launch of JWST and the upcoming missions Twinkle, and Ariel, population
based studies of exoplanet atmospheres, such as the one presented here, will be
a key approach to understanding planet characteristics, formation, and
evolution in our galaxy.


title:
Black hole - black hole total merger mass and the origin of LIGO/Virgo
  sources

abstract:
  The LIGO-Virgo-KAGRA (LVK) Collaboration has reported ~100 BH-BH mergers to
date. The LVK provides estimates of rates, masses, effective spins, and
redshifts for these mergers. Yet, the formation channel(s) of the mergers
remains uncertain. One way to search for a formation site is to contrast
properties of detected BH-BH mergers with different models of BH-BH merger
formation. Here, we assess the usefulness of the average total mass of BH-BH
mergers and its change with redshift to help identify the formation site of
BH-BH mergers. We find that the average intrinsic BH-BH total merger mass shows
exceptionally different behavior for the models that we adopt for our analysis.
In the local universe (z=0) the average merger mass changes from Mtot=25Msun
for CE binary evolution and open clusters channels, to Mtot=30Msun for the RLOF
binary channel, to Mtot=45Msun for the globular cluster channel. These
differences are even more pronounced at larger redshifts. However, these
differences are diminished when considering LVK O3 detector sensitivity.
Comparison with LVK O3 data shows that none of our adopted models can match the
data despite large errors on BH-BH masses and redshifts. We emphasize that our
conclusions are derived from a small set of 5 specific models that are subject
to numerous known uncertainties. We also note that BH-BH mergers may originate
from a mix of several channels and that other BH-BH formation channels may
exist. Our study was designed to investigate the usefulness of the total BH-BH
merger mass in establishing the origin of gravitational-wave sources.


title:
Compression-Complexity with Ordinal Patterns for Robust Causal Inference
  in Irregularly-Sampled Time Series

abstract:
  Distinguishing cause from effect is a scientific challenge resisting
solutions from mathematics, statistics, information theory and computer
science. Compression-Complexity Causality (CCC) is a recently proposed
interventional measure of causality, inspired by Wiener-Granger's idea. It
estimates causality based on change in dynamical compression-complexity (or
compressibility) of the effect variable, given the cause variable. CCC works
with minimal assumptions on given data and is robust to irregular-sampling,
missing-data and finite-length effects. However, it only works for
one-dimensional time series. We propose an ordinal pattern symbolization scheme
to encode multidimensional patterns into one-dimensional symbolic sequences,
and thus introduce the Permutation CCC (PCCC), which retains all advantages of
the original CCC and can be applied to data from multidimensional systems with
potentially hidden variables. PCCC is tested on numerical simulations and
applied to paleoclimate data characterized by irregular and uncertain sampling
and limited numbers of samples.


title:
Dielectron production at midrapidity at low transverse momentum in
  peripheral and semi-peripheral Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} =
  5.02$ TeV

abstract:
  The first measurement of the ${\rm e}^{+}{\rm e}^{-}$ pair production at low
lepton pair transverse momentum ($p_{\rm T,ee}$) and low invariant mass
($m_{\rm ee}$) in non-central Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$
TeV at the LHC is presented. The dielectron production is studied with the
ALICE detector at midrapidity ($|\eta_{\rm e}| < 0.8$) as a function of
invariant mass ($0.4 \leq m_{\rm ee} < 2.7$ GeV/$c^2$) in the 50$-$70% and
70$-$90% centrality classes for $p_{\rm T,ee} < 0.1$ GeV/$c$, and as a function
of $p_{\rm T,ee}$ in three $m_{\rm ee}$ intervals in the most peripheral
Pb$-$Pb collisions. Below a $p_{\rm T,ee}$ of 0.1 GeV/$c$, a clear excess of
${\rm e}^{+}{\rm e}^{-}$ pairs is found compared to the expectations from known
hadronic sources and predictions of thermal radiation from the medium. The
$m_{\rm ee}$ excess spectra are reproduced, within uncertainties, by different
predictions of the photon$-$photon production of dielectrons, where the photons
originate from the extremely strong electromagnetic fields generated by the
highly Lorentz-contracted Pb nuclei. Lowest-order quantum electrodynamic (QED)
calculations, as well as a model that takes into account the impact-parameter
dependence of the average transverse momentum of the photons, also provide a
good description of the $p_{\rm T,ee}$ spectra. The measured $\sqrt{\langle
p_{\rm T,ee}^{2} \rangle}$ of the excess $p_{\rm T,ee}$ spectrum in peripheral
Pb$-$Pb collisions is found to be comparable to the values observed previously
at RHIC in a similar phase-space region.


title:
Linear topological invariants for kernels of convolution and
  differential operators

abstract:
  We establish the condition $(\Omega)$ for smooth kernels of various types of
convolution and differential operators. By the $(DN)$-$(\Omega)$ splitting
theorem of Vogt and Wagner, this implies that these operators are surjective on
the corresponding spaces of vector-valued smooth functions with values in a
product of Montel $(DF)$-spaces whose strong duals satisfy the condition
$(DN)$, e.g., the space $\mathscr{D}'(X)$ of distributions over an open set $X
\subseteq \mathbb{R}^d$ or the space $\mathscr{S}'(\mathbb{R}^d)$ of tempered
distributions. Most notably, we show that:
  $(i)$ $\mathscr{E}_P(X) = \{ f \in \mathscr{E}(X) \, | \, P(D)f = 0 \}$
satisfies $(\Omega)$ for any differential operator $P(D)$ and any open convex
set $X \subseteq \mathbb{R}^d$.
  $(ii)$ Let $P\in\mathbb{C}[X_1,X_2]$ and $X \subseteq \mathbb{R}^2$ open be
such that $P(D):\mathscr{E}(X)\rightarrow\mathscr{E}(X)$ is surjective. Then,
$\mathscr{E}_P(X)$ satisfies $(\Omega)$.
  $(iii)$ Let $\mu \in \mathscr{E}'(\mathbb{R}^d)$ be such that $
\mathscr{E}(\mathbb{R}^d) \rightarrow \mathscr{E}(\mathbb{R}^d), \, f \mapsto
\mu \ast f$ is surjective. Then, $ \{ f \in \mathscr{E}(\mathbb{R}^d) \, | \,
\mu \ast f = 0 \}$ satisfies $(\Omega)$.
  The central result in this paper states that the space of smooth zero
solutions of a general convolution equation satisfies the condition $(\Omega)$
if and only if the space of distributional zero solutions of the equation
satisfies the condition $(P\Omega)$. The above and related results then follow
from known results concerning $(P\Omega)$ for distributional kernels of
convolution and differential operators.


title: Enhancing quantum cryptography with quantum dot single-photon sources

abstract:
  Quantum cryptography harnesses quantum light, in particular single photons,
to provide security guarantees that cannot be reached by classical means. For
each cryptographic task, the security feature of interest is directly related
to the photons' non-classical properties. Quantum dot-based single-photon
sources are remarkable candidates, as they can in principle emit
deterministically, with high brightness and low multiphoton contribution. Here,
we show that these sources provide additional security benefits, thanks to the
tunability of coherence in the emitted photon-number states. Generating either
mixed or coherent states of light allows for enhanced performance of many
quantum cryptography applications. We identify the optimal optical pumping
scheme for the main quantum-cryptographic primitives, and benchmark their
performance with respect to Poisson-distributed sources such as attenuated
laser states and down-conversion sources. The presented results will guide
future developments in solid-state and quantum information science for photon
sources that are tailored to quantum communication tasks.


title: Forecasting Electricity Prices

abstract:
  Forecasting electricity prices is a challenging task and an active area of
research since the 1990s and the deregulation of the traditionally monopolistic
and government-controlled power sectors. Although it aims at predicting both
spot and forward prices, the vast majority of research is focused on short-term
horizons which exhibit dynamics unlike in any other market. The reason is that
power system stability calls for a constant balance between production and
consumption, while being weather (both demand and supply) and business activity
(demand only) dependent. The recent market innovations do not help in this
respect. The rapid expansion of intermittent renewable energy sources is not
offset by the costly increase of electricity storage capacities and
modernization of the grid infrastructure. On the methodological side, this
leads to three visible trends in electricity price forecasting research as of
2022. Firstly, there is a slow, but more noticeable with every year, tendency
to consider not only point but also probabilistic (interval, density) or even
path (also called ensemble) forecasts. Secondly, there is a clear shift from
the relatively parsimonious econometric (or statistical) models towards more
complex and harder to comprehend, but more versatile and eventually more
accurate statistical/machine learning approaches. Thirdly, statistical error
measures are nowadays regarded as only the first evaluation step. Since they
may not necessarily reflect the economic value of reducing prediction errors,
more and more often, they are complemented by case studies comparing profits
from scheduling or trading strategies based on price forecasts obtained from
different models.


title:
KnowAugNet: Multi-Source Medical Knowledge Augmented Medication
  Prediction Network with Multi-Level Graph Contrastive Learning

abstract:
  Predicting medications is a crucial task in many intelligent healthcare
systems. It can assist doctors in making informed medication decisions for
patients according to electronic medical records (EMRs). However, medication
prediction is a challenging data mining task due to the complex relations
between medical codes. Most existing studies focus on utilizing inherent
relations between homogeneous codes of medical ontology graph to enhance their
representations using supervised methods, and few studies pay attention to the
valuable relations between heterogeneous or homogeneous medical codes from
history EMRs, which further limits the prediction performance and application
scenarios. Therefore, to address these limitations, this paper proposes
KnowAugNet, a multi-sourced medical knowledge augmented medication prediction
network which can fully capture the diverse relations between medical codes via
multi-level graph contrastive learning framework. Specifically, KnowAugNet
first leverages the graph contrastive learning using graph attention network as
the encoder to capture the implicit relations between homogeneous medical codes
from the medical ontology graph and obtains the knowledge augmented medical
codes embedding vectors. Then, it utilizes the graph contrastive learning using
a weighted graph convolutional network as the encoder to capture the
correlative relations between homogeneous or heterogeneous medical codes from
the constructed medical prior relation graph and obtains the relation augmented
medical codes embedding vectors. Finally, the augmented medical codes embedding
vectors and the supervised medical codes embedding vectors are retrieved and
input to the sequential learning network to capture the temporal relations of
medical codes and predict medications for patients.


title: Phase diagram of superconductivity in the integer quantum Hall regime

abstract:
  An interplay between pairing and topological orders has been predicted to
give rise to superconducting states supporting exotic emergent particles, such
as Majorana particles obeying non-Abelian braid statistics. We consider a
system of spinless electrons on a Hofstadter lattice with nearest neighbor
attractive interaction, and solve the mean-field Bogoliubov-de Gennes equations
in a self-consistent fashion, leading to gauge invariant solutions that display
a rich phase diagram as a function of the chemical potential, magnetic field
and the interaction. As the strength of the attractive interaction is
increased, the system first makes a transition from a quantum Hall phase to a
skyrmion lattice phase that is fully gapped in the bulk but has topological
chiral edge current, characterizing a topologically non-trivial state. This is
followed by a vortex phase in which the vortices carrying Majorana modes form a
lattice; the spectrum contains a low-energy Majorana band arising from the
coupling between neighboring vortex-core Majorana modes but does not have
chiral edge currents. For some parameters, a dimer vortex lattice occurs with
no low energy Majorana band. The experimental feasibility and the observable
consequences of skyrmions as well as Majorana fermions are indicated.


title:
Extreme Mass-Ratio Binary Black Hole Merger: Universal Characteristics
  of the Test-Particle Limit

abstract:
  We study binary black hole mergers in the extreme mass-ratio limit. We
determine the energy, angular momentum, and linear momentum of the post-merger,
remnant black hole. Unlike previous works, we perform our analysis directly in
the test-particle limit, by solving the Regge-Wheeler-Zerilli wave equation
with a source that moves along a geodesic. We build on the fact that towards
the merger, small mass-ratio binary systems follow a Geodesic Universal Infall
(GUI) trajectory. This formalism captures the final pre-merger stages of small
mass-ratio binaries and thus provides a straightforward universal description
in a region inaccessible to numerical relativity simulations. We present a
general waveform template that may be used in the search for gravitational wave
bursts from small and intermediate mass-ratio binary systems. Finally, this
formalism gives a formal proof that the recoil velocity is quadratic in the
symmetric mass-ratio $\nu$. Specifically, the velocity is given by $V/c\approx
0.0467 \nu^2$. This result is about $4\%$ larger than previously estimated.
Most of this difference stems from the inclusion of higher multipoles in our
calculation.


title:
Pairing enhancement in a two-neutron transfer process through the
  continuum

abstract:
  Enhancement, due to constructive interference through the many possible
reaction channels, occurs in two-neutron transfer reactions from a bound system
$A$ to a weakly-bound system ($A$+2), whenever the intermediate system is
unbound and consists of a continuum with a resonant state. We elucidate this
phenomenon in the case of $^6$He, modeled as two neutrons in the orbitals of an
intermediate $^5$He nucleus, comparing the realistic case of an unbound ($A$+1)
system with an artificially bound case. A properly modeled continuum in these
calculations is found to be crucial and leads to significant enhancement due to
pairing correlations.


title: Heisenberg-picture evolution without evolution

abstract:
  In an elegant model, Page and Wootters (Page and Wootters, 1983) showed how
time can emerge in a timeless quantum universe through the entanglement between
its subsystems. The Page-Wootters model for an "evolution without evolution" is
usually formulated in the Schr\"odinger picture or in other ways that hide the
quantum nature of time. In this work, I formulate the model in the Heisenberg
picture starting from its Schr\"odinger picture version. This is achieved by
transferring all the entanglement from the state vector of the universe to the
operators. In this formulation, the observables become functions of a clock
operator, thus making explicit the quantum nature of time. The usual Heisenberg
evolution of the observables in terms of a classical time parameter can be
recovered in a relational way. I also extend the model to include mixed states
of the universe and apply these results to some simple scenarios.


title:
Antiferromagnetic Spin Orientation and Magnetic Domain Structure in
  Epitaxially Grown MnN Studied using Optical Second Harmonic Generation

abstract:
  MnN is a centrosymmetric collinear antiferromagnet belonging to the
transition metal nitride family with a high Neel temperature, a low anisotropy
field, and a large magnetic moment per Mn atom. Despite several recent
experimental and theoretical studies, the spin symmetry (magnetic point group)
and magnetic domain structure of the material remain unknown. In this work, we
use optical second harmonic generation (SHG) to study the magnetic structure of
thin epitaxially-grown single-crystal (001) MnN films. Our work shows that spin
moments in MnN are tilted away from the [001] direction and the components of
the spin moments in the (001) plane are aligned along one of the two possible
in-plane symmetry axes ([100] or [110]) resulting in a magnetic point group
symmetry of 2/m1'. Our work rules out magnetic point group symmetries 4/mmm1'
and mmm1' that have been previously discussed in the literature. Four different
spin domains consistent with the 2/m1' magnetic point group symmetry are
possible in MnN. A statistical model based on the observed variations in the
polarization-dependent intensity of the second harmonic signal collected over
large sample areas puts an upper bound of 0.65 microns on the mean domain size.
Our results show that SHG can be used to probe the magnetic order in metallic
antiferromagnets. This work is expected to contribute to the recent efforts in
using antiferromagnets for spintronic applications.


title: Discovering changes in birthing narratives during COVID-19

abstract:
  We investigate whether, and if so how, birthing narratives written by new
parents on Reddit changed during COVID-19. Our results indicate that the
presence of family members significantly decreased and themes related to
induced labor significantly increased in the narratives during COVID-19. Our
work builds upon recent research that analyze how new parents use Reddit to
describe their birthing experiences.


title: Structure-preserving numerical method for Ampere-Nernst-Planck model

abstract:
  Charge dynamics play essential role in many practical applications such as
semiconductors, electrochemical devices and transmembrane ion channels. An
Amp\'{e}re-Nernst-Planck (ANP) model that describes charge dynamics via
concentrations and the electric displacement is able to take effects beyond
mean-field approximations into account. To obtain physically faithful numerical
solutions, we develop a structure-preserving numerical method for the ANP model
whose solution has several physical properties of importance. By the Slotboom
transform with entropy-mean approximations, a positivity preserving scheme with
Scharfetter-Gummel fluxes is derived for the generalized Nernst-Planck
equations. To deal with the curl-free constraint, the dielectric displacement
from the Amp\'{e}re equation is further updated with a local relaxation
algorithm of linear computational complexity. We prove that the proposed
numerical method unconditionally preserves the mass conservation and the
solution positivity at the discrete level, and satisfies the discrete energy
dissipation law with a mild time-step restriction. Numerical experiments verify
that our numerical method has expected accuracy and structure-preserving
properties. Applications to ion transport with large convection, arising from
boundary-layer electric field and Born solvation interactions, further
demonstrate that the ANP formulation with the proposed numerical scheme has
attractive performance and can effectively describe charge dynamics with large
convection of high numerical cell P\'{e}clet numbers.


title: Stability Preserving Data-driven Models With Latent Dynamics

abstract:
  In this paper, we introduce a data-driven modeling approach for dynamics
problems with latent variables. The state-space of the proposed model includes
artificial latent variables, in addition to observed variables that can be
fitted to a given data set. We present a model framework where the stability of
the coupled dynamics can be easily enforced. The model is implemented by
recurrent cells and trained using backpropagation through time. Numerical
examples using benchmark tests from order reduction problems demonstrate the
stability of the model and the efficiency of the recurrent cell implementation.
As applications, two fluid-structure interaction problems are considered to
illustrate the accuracy and predictive capability of the model.

categories: math.OC cs.LG cs.NA math.NA physics.comp-ph


title: Quasi-exact ground-state algorithm for the random-field Potts model

abstract:
  The use of combinatorial optimization algorithms has contributed
substantially to the major progress that has occurred in recent years in the
understanding of the physics of disordered systems, such as the random-field
Ising model. While for this system exact ground states can be computed
efficiently in polynomial time, the related random-field Potts model is {\em
NP\} hard computationally. While thus exact ground states cannot be computed
for large systems in this case, approximation schemes based on graph cuts and
related techniques can be used. Here we show how a combination of such methods
with repeated runs allows for a systematic extrapolation of relevant system
properties to the ground state. The method is benchmarked on a special class of
disorder samples for which exact ground states are available.


title: SPHERE adaptive optics performance for faint targets

abstract:
  Context: High contrast imaging is a powerful technique to detect and
characterize planetary companions at large orbital separations from their
parent stars. Aims: We aim at studying the limiting magnitude of the VLT/SPHERE
Adaptive Optics system and the corresponding instrument performance for faint
targets (G $\ge$ 11.0 mag). Methods: We computed coronagraphic H-band raw
contrast at 300 [mas] and FWHM of the non-coronagraphic PSF, for a total of 111
different stars observed between 2016 and 2022 with IRDIS. For this, we
processed a large number of individual frames that were obtained under
different atmospheric conditions. We then compared the resulting raw contrast
and the PSF shape as a function of the visible wave front sensor instant flux
which scales with the G-band stellar magnitude. We repeated this analysis for
the top 10\% and 30\% best turbulence conditions in Cerro Paranal. Results: We
found a strong decrease in the coronagraphic achievable contrast for star
fainter than G $\sim$ 12.5 mag, even under the best atmospheric conditions. In
this regime, the AO correction is dominated by the read-out noise of the WFS
detector. In particular we found roughly a factor ten decrease in the raw
contrast ratio between stars with G $\sim$ 12.5 and G $\sim$ 14.0 mag.
Similarly, we observe a sharp increase in the FWHM of the non-coronagraphic PSF
beyond G $\sim$ 12.5 mag, and a corresponding decrease in the strehl ratio from
$\sim$ 50\% to $\sim$ 20\% for the faintest stars. Although these trend are
observed for the two turbulence categories, the decrease in the contrast ratio
and PSF sharpness is more pronounced for poorer conditions.


title:
A feasibility study proposal of the predictive model to enable the
  prediction of population susceptibility to COVID-19 by analysis of vaccine
  utilization for advising deployment of a booster dose

abstract:
  With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or
Omicron spreading around the globe, there is concern that the COVID-19 pandemic
will not end soon and that it will be a race against time until a more
contagious and virulent variant emerges. One of the most promising approaches
for preventing virus propagation is to maintain continuous high vaccination
efficacy among the population, thereby strengthening the population protective
effect and preventing the majority of infection in the vaccinated population,
as is known to occur with the Omicron variant frequently. Countries must
structure vaccination programs in accordance with their populations'
susceptibility to infection, optimizing vaccination efforts by delivering
vaccines progressively enough to protect the majority of the population. We
present a feasibility study proposal for maintaining optimal continuous
vaccination by assessing the susceptible population, the decline of vaccine
efficacy in the population, and advising booster dosage deployment to maintain
the population's protective efficacy through the use of a predictive model.
Numerous studies have been conducted in the direction of analyzing vaccine
utilization; however, very little study has been conducted to substantiate the
optimal deployment of booster dosage vaccination with the help of a predictive
model based on machine learning algorithms.


title: Optimal Discrete Decisions when Payoffs are Partially Identified

abstract:
  We derive optimal statistical decision rules for discrete choice problems
when the decision maker is unable to discriminate among a set of payoff
distributions. In this problem, the decision maker must confront both model
uncertainty (about the identity of the true payoff distribution) and
statistical uncertainty (the set of payoff distributions must be estimated). We
derive "efficient-robust decision rules" which minimize maximum risk or regret
over the set of payoff distributions and which use the data to learn
efficiently about features of the set of payoff distributions germane to the
choice problem. We discuss implementation of these decision rules via the
bootstrap and Bayesian methods, for both parametric and semiparametric models.
Using a limits of experiments framework, we show that efficient-robust decision
rules are optimal and can dominate seemingly natural alternatives. We present
applications to treatment assignment using observational data and optimal
pricing in environments with rich unobserved heterogeneity.


title:
Y-Systems for Generalised Gibbs Ensembles in Integrable Quantum Field
  Theory

abstract:
  The thermodynamic Bethe ansatz approach to the study of integrable quantum
field theories was introduced in the early 90s. Since then it has been known
that the thermodynamic Bethe ansatz equations can be recast in the form of
$Y$-systems. These $Y$-systems have a number of interesting properties, notably
in the high-temperature limit their solutions are constants from which the
central charge of the ultraviolet fixed point can be obtained and they are
typically periodic functions, with period proportional to the dimension of the
perturbing field. In this letter we discuss the derivation of $Y$-systems when
the standard thermodynamic Bethe ansatz equations are replaced by generalised
versions, describing generalised Gibbs ensembles. We shown that for many
theories, there is a large class of distinct generalised Gibbs ensembles which
share the same $Y$-system.


title: Efficient Recognition of Subgraphs of Planar Cubic Bridgeless Graphs

abstract:
  It follows from the work of Tait and the Four-Color-Theorem that a planar
cubic graph is 3-edge-colorable if and only if it contains no bridge. We
consider the question of which planar graphs are subgraphs of planar cubic
bridgeless graphs, and hence 3-edge-colorable. We provide an efficient
recognition algorithm that given an $n$-vertex planar graph, augments this
graph in $\mathcal{O}(n^2)$ steps to a planar cubic bridgeless supergraph, or
decides that no such augmentation is possible. The main tools involve the
GeneralizedFactor-problem for the fixed embedding case, and SPQR-trees for the
variable embedding case.


title: Adversarial Attention for Human Motion Synthesis

abstract:
  Analysing human motions is a core topic of interest for many disciplines,
from Human-Computer Interaction, to entertainment, Virtual Reality and
healthcare. Deep learning has achieved impressive results in capturing human
pose in real-time. On the other hand, due to high inter-subject variability,
human motion analysis models often suffer from not being able to generalise to
data from unseen subjects due to very limited specialised datasets available in
fields such as healthcare. However, acquiring human motion datasets is highly
time-consuming, challenging, and expensive. Hence, human motion synthesis is a
crucial research problem within deep learning and computer vision. We present a
novel method for controllable human motion synthesis by applying
attention-based probabilistic deep adversarial models with end-to-end training.
We show that we can generate synthetic human motion over both short- and
long-time horizons through the use of adversarial attention. Furthermore, we
show that we can improve the classification performance of deep learning models
in cases where there is inadequate real data, by supplementing existing
datasets with synthetic motions.


title:
Trading Hard Negatives and True Negatives: A Debiased Contrastive
  Collaborative Filtering Approach

abstract:
  Collaborative filtering (CF), as a standard method for recommendation with
implicit feedback, tackles a semi-supervised learning problem where most
interaction data are unobserved. Such a nature makes existing approaches highly
rely on mining negatives for providing correct training signals. However,
mining proper negatives is not a free lunch, encountering with a tricky
trade-off between mining informative hard negatives and avoiding false ones. We
devise a new approach named as Hardness-Aware Debiased Contrastive
Collaborative Filtering (HDCCF) to resolve the dilemma. It could sufficiently
explore hard negatives from two-fold aspects: 1) adaptively sharpening the
gradients of harder instances through a set-wise objective, and 2) implicitly
leveraging item/user frequency information with a new sampling strategy. To
circumvent false negatives, we develop a principled approach to improve the
reliability of negative instances and prove that the objective is an unbiased
estimation of sampling from the true negative distribution. Extensive
experiments demonstrate the superiority of the proposed model over existing CF
models and hard negative mining methods.


title: Partitioning Problems with Splitting and Interval Targets

abstract:
  We consider a variant of the $n$-way number partitioning problem, in which
some fixed number $s$ of items can be split between two or more bins. We show a
two-way polynomial-time reduction between this variant and a second variant, in
which the maximum bin sum must be within a pre-specified interval. We prove
that the second variant can be solved in polynomial time if the length of the
allowed interval is at least $(n-2)/n$ times the maximum item size, and it is
NP-hard otherwise. Using the equivalence between the variants, we prove that
number-partitioning with $s$ split items can be solved in polynomial time if
$s\geq n-2$, and it is NP-hard otherwise.


title:
P(VDF-TrFE)/BaTiO3 nanoparticle composite films mediate piezoelectric
  stimulation and promote differentiation of SH-SY5Y neuroblastoma cells

abstract:
  Poly(vinylidene fluoride-trifluoroethylene, P(VDF-TrFE)) and
P(VDF-TrFE)/barium titanate nanoparticle (BTNP) films are prepared and tested
as substrates for neuronal stimulation through direct piezoelectric effect.
Films are characterized in terms of surface, mechanical, and piezoelectric
features before in vitro testing on SH-SY5Y cells. In particular, BTNPs
significantly improve piezoelectric properties of the films (4.5-fold increased
d31). Both kinds of films support good SH-SY5Y viability and differentiation.
Ultrasound (US) stimulation is proven to elicit Ca2+ transients and to enhance
differentiation in cells grown on the piezoelectric substrates. For the first
time in the literature, this study demonstrates the suitability of
polymer/ceramic composite films and US for neuronal stimulation through direct
piezoelectric effect.


title: $W$-mass and 96 GeV excess in type-III 2HDM

abstract:
  Recently the CDF collaboration reported a new measurement of the W boson mass
using their full data set of 8.8 fb$^{-1}$ in $p\bar{p}$ collision at CDF
detector (Fermilab Tevatron), which deviates from the Standard Model (SM)
prediction about 7$\sigma$. By including this new measurement, however, we
explore the implications in the framework of general two Higgs doublet model
type-III in presence of a specific Yukawa texture, wherein Lepton Flavour
Violating (LFV) (neutral) currents are induced at tree level. The paper shows
the preferred values of oblique parameters $S$ and $T$ which is simultaneously
consistent with the 96 GeV excesses observed by LEP and CMS at about
2.8$\sigma$ When combined with current LHC data and electroweak precision
measurements, we show that the parameter space that accommodates the excesses
can also significantly contribute to $M_W$ disfavors degenerates charged Higgs
mass with neutral CP-odd Higgs when $H$ being Standard Model like particle. We
show that 96 GeV excesses may persist only in the case $m_{H^\pm} > m_A$. Both
Branching ratios of $H^\pm$ and $A$ are strictly constrained.


title: Nonparametric Multiple-Output Center-Outward Quantile Regression

abstract:
  Based on the novel concept of multivariate center-outward quantiles
introduced recently in Chernozhukov et al. (2017) and Hallin et al. (2021), we
are considering the problem of nonparametric multiple-output quantile
regression. Our approach defines nested conditional center-outward quantile
regression contours and regions with given conditional probability content
irrespective of the underlying distribution; their graphs constitute nested
center-outward quantile regression tubes. Empirical counterparts of these
concepts are constructed, yielding interpretable empirical regions and contours
which are shown to consistently reconstruct their population versions in the
Pompeiu-Hausdorff topology. Our method is entirely non-parametric and performs
well in simulations including heteroskedasticity and nonlinear trends; its
power as a data-analytic tool is illustrated on some real datasets.


title: Parallel coarsening of graph data with spectral guarantees

abstract:
  Finding coarse representations of large graphs is an important computational
problem in the fields of scientific computing, large scale graph partitioning,
and the reduction of geometric meshes. Of particular interest in all of these
fields is the preservation of spectral properties with regards to the original
graph. While many methods exist to perform this task, they typically require
expensive linear algebraic operations and yield high work complexities. We
adapt a spectral coarsening bound from the literature in order to develop a
coarsening algorithm with a work complexity that is drastically smaller than
previous work. We further show that this algorithm is easily parallelizable and
presents impressive scaling results on meshes.


title: Shape driven confluent rigidity transition in curved biological tissues

abstract:
  Collective cell motions underlie structure formation during embryonic
development. Tissues exhibit emergent multicellular characteristics such as
jamming, rigidity transitions, and glassy dynamics, but there remain questions
about how those tissue scale dynamics derive from local cell level properties.
Specifically, there has been little consideration of the interplay between
local tissue geometry and cellular properties influencing larger scale tissue
behaviours. Here we consider a simple two dimensional computational vertex
model for confluent tissue monolayers, which exhibits a rigidity phase
transition controlled by the shape index (ratio of perimeter to square root
area) of cells, on a spherical surface. We show that the critical point for the
rigidity transition is a function of curvature such that more highly curved
systems are more likely to be in a less rigid, more fluid, phase. A phase
diagram we generate for the curvature and shape index constitutes a testable
prediction from the model. The curvature dependence is interesting because it
suggests a natural explanation for more dynamic tissue remodelling and facile
growth in regions of higher surface curvature, without invoking the need for
biochemical or other physical differences. This has potential ramifications for
our understanding of morphogenesis of budding and branching structures.


title:
A Multi-Wavelength Study of the Highly Asymmetrical Debris Disk Around
  HD 111520

abstract:
  We observed the nearly edge-on debris disk system HD 111520 at $J$, $H$, &
$K1$ near infrared (NIR) bands using both the spectral and polarization modes
of the Gemini Planet Imager (GPI). With these new observations, we have
performed an empirical analysis in order to better understand the disk
morphology and its highly asymmetrical nature. We find that the disk features a
large brightness and radial asymmetry, most prominent at shorter wavelengths.
We also find that the radial location of the peak polarized intensity differs
on either side of the star by 11 AU, suggesting that the disk may be eccentric,
although, such an eccentricity does not fully explain the large brightness and
radial asymmetry observed. Observations of the disk halo with HST also show the
disk to be warped at larger separations, with a bifurcation feature in the
northwest, further suggesting that there may be a planet in this system
creating an asymmetrical disk structure. Measuring the disk color shows that
the brighter extension is bluer compared to the dimmer extension, suggesting
that the two sides have different dust grain properties. This finding, along
with the large brightness asymmetry, are consistent with the hypothesis that a
giant impact occurred between two large bodies in the northern extension of the
disk, although confirming this based on NIR observations alone is not feasible.
Follow-up imaging with ALMA to resolve the asymmetry in the dust mass
distribution is essential in order to confirm this scenario.


title:
A new model for preferential attachment scheme with time-varying
  parameters

abstract:
  We propose an extension of the preferential attachment scheme by allowing the
connecting probability to depend on time t. We estimate the parameters involved
in the model by minimizing the expected squared difference between the number
of vertices of degree one and its conditional expectation. The asymptotic
properties of the estimators are also investigated when the parameters are
time-varying by establishing the central limit theorem (CLT) of the number of
vertices of degree one. We propose a new statistic to test whether the
parameters have change points. We also offer some methods to estimate the
number of change points and detect the locations of change points. Simulations
are conducted to illustrate the performances of the above results.


title: Certification of Maass cusp forms of arbitrary level and character

abstract:
  We present a method for certifying the existence of an arbitrary Maass cusp
form for any level and character. This is accomplished by producing a bound on
the difference between the $\Delta$-eigenvalue of an authentic Maass cusp form
and a purported approximation of a $\Delta$-eigenvalue, arrived at by any
means. We apply this method to a proposed non-CM level 5 form with quadratic
character, to present the first certified $\Delta$-eigenvalue of such a form.
This work generalises the method for certifying level 1 forms presented by
Booker, Str\"ombergsson and Venkatesh, and is motivated by the production of
purported Maass cusp forms of arbitrary level and character via methods
developed by Hejhal and Str\"omberg.


title: MFA-DVR: Direct Volume Rendering of MFA Models

abstract:
  3D volume rendering is widely used to reveal insightful intrinsic patterns of
volumetric datasets across many domains. However, the complex structures and
varying scales of datasets make generating a high-quality volume rendering
results efficiently a challenging task. Multivariate functional approximation
(MFA) is a new data model that addresses some of the key challenges of volume
visualization. MFA provides high-order evaluation of values and derivatives
anywhere in the spatial domain, mitigating the artifacts caused by the zero- or
first-order interpolation commonly implemented in existing volume visualization
algorithms. MFA's compact representation improves the space complexity for
large-scale volumetric data visualization, while its uniform representation of
both structured and unstructured data allows the same ray casting algorithm to
be used for a variety of input data types. In this paper, we present MFA-DVR,
the first direct volume rendering pipeline utilizing the MFA model, for both
structured and unstructured volumetric datasets. We demonstrate improved
rendering quality using MFA-DVR on both synthetic and real datasets through a
comparative study with raw and compressed data. We show that MFA-DVR not only
generates more faithful volume rendering results with less memory footprint,
but also performs faster than traditional algorithms when rendering
unstructured datasets. MFA-DVR is implemented in the existing volume rendering
pipeline of the Visualization Toolkit (VTK) in order to be accessible by the
scientific visualization community.


title:
Three-dimensional Newtonian gravity with cosmological constant and
  torsion

abstract:
  In this paper we present an alternative cosmological extension of the
three-dimensional extended Newtonian Chern-Simons gravity by switching on the
torsion. The theory is obtained as a non-relativistic limit of an enhancement
and $U(1)$-enlargement of the so-called teleparallel algebra and can be seen as
the teleparallel analogue of the Newtonian gravity theory. The
infinite-dimensional extension of our result is also explored through the Lie
algebra expansion method. An infinite-dimensional torsional Galilean gravity
model is presented which in the vanishing cosmological constant limit
reproduces the infinite-dimensional extension of the Galilean gravity theory.


title:
Current Status of Hard X-Ray Nano-Tomography on the Transmission
  Microscope at the ANATOMIX Beamline

abstract:
  The transmission X-ray microscope (TXM) on the Anatomix beamline welcomed its
first nano-tomography users in 2019. The instrument is based on diffractive
optics and works in the range of energies from 7 keV to 21 keV. A spatial
resolution in 3D volumes of better than 100 nm can be achieved. The design
allows imaging samples in air, and local tomography as well as off-axis
tomography scans are possible. Scans below and above K-edges can be made to
access elemental distribution. The TXM serves materials science and the
bio-medical field.


title:
LightDefectNet: A Highly Compact Deep Anti-Aliased Attention Condenser
  Neural Network Architecture for Light Guide Plate Surface Defect Detection

abstract:
  Light guide plates are essential optical components widely used in a diverse
range of applications ranging from medical lighting fixtures to back-lit TV
displays. An essential step in the manufacturing of light guide plates is the
quality inspection of defects such as scratches, bright/dark spots, and
impurities. This is mainly done in industry through manual visual inspection
for plate pattern irregularities, which is time-consuming and prone to human
error and thus act as a significant barrier to high-throughput production.
Advances in deep learning-driven computer vision has led to the exploration of
automated visual quality inspection of light guide plates to improve inspection
consistency, accuracy, and efficiency. However, given the cost constraints in
visual inspection scenarios, the widespread adoption of deep learning-driven
computer vision methods for inspecting light guide plates has been greatly
limited due to high computational requirements. In this study, we explore the
utilization of machine-driven design exploration with computational and
"best-practices" constraints as well as L$_1$ paired classification discrepancy
loss to create LightDefectNet, a highly compact deep anti-aliased attention
condenser neural network architecture tailored specifically for light guide
plate surface defect detection in resource-constrained scenarios. Experiments
show that LightDetectNet achieves a detection accuracy of $\sim$98.2% on the
LGPSDD benchmark while having just 770K parameters ($\sim$33$\times$ and
$\sim$6.9$\times$ lower than ResNet-50 and EfficientNet-B0, respectively) and
$\sim$93M FLOPs ($\sim$88$\times$ and $\sim$8.4$\times$ lower than ResNet-50
and EfficientNet-B0, respectively) and $\sim$8.8$\times$ faster inference speed
than EfficientNet-B0 on an embedded ARM processor.


title:
CellDefectNet: A Machine-designed Attention Condenser Network for
  Electroluminescence-based Photovoltaic Cell Defect Inspection

abstract:
  Photovoltaic cells are electronic devices that convert light energy to
electricity, forming the backbone of solar energy harvesting systems. An
essential step in the manufacturing process for photovoltaic cells is visual
quality inspection using electroluminescence imaging to identify defects such
as cracks, finger interruptions, and broken cells. A big challenge faced by
industry in photovoltaic cell visual inspection is the fact that it is
currently done manually by human inspectors, which is extremely time consuming,
laborious, and prone to human error. While deep learning approaches holds great
potential to automating this inspection, the hardware resource-constrained
manufacturing scenario makes it challenging for deploying complex deep neural
network architectures. In this work, we introduce CellDefectNet, a highly
efficient attention condenser network designed via machine-driven design
exploration specifically for electroluminesence-based photovoltaic cell defect
detection on the edge. We demonstrate the efficacy of CellDefectNet on a
benchmark dataset comprising of a diversity of photovoltaic cells captured
using electroluminescence imagery, achieving an accuracy of ~86.3% while
possessing just 410K parameters (~13$\times$ lower than EfficientNet-B0,
respectively) and ~115M FLOPs (~12$\times$ lower than EfficientNet-B0) and
~13$\times$ faster on an ARM Cortex A-72 embedded processor when compared to
EfficientNet-B0.


title:
A Model of Homologous Confined and Ejective Eruptions Involving Kink
  Instability and Flux Cancellation

abstract:
  In this study, we model a sequence of a confined and a full eruption,
employing the relaxed end state of the confined eruption of a kink-unstable
flux rope as the initial condition for the ejective one. The full eruption, a
model of a coronal mass ejection, develops as a result of converging motions
imposed at the photospheric boundary, which drive flux cancellation. In this
process, parts of the positive and negative external flux converge toward the
polarity inversion line, reconnect, and cancel each other. Flux of the same
amount as the canceled flux transfers to a flux rope, increasing the free
magnetic energy of the coronal field. With sustained flux cancellation and the
associated progressive weakening of the magnetic tension of the overlying flux,
we find that a flux reduction of $\approx\!11\%$ initiates the torus
instability of the flux rope, which leads to a full eruption. These results
demonstrate that a homologous full eruption, following a confined one, can be
driven by flux cancellation.


title:
Explicitly correlated double hybrid DFT: a comprehensive analysis of the
  basis set convergence on the GMTKN55 database

abstract:
  Double-hybrid density functional theory (DHDFT) offers a pathway to
accuracies approaching composite wavefunction approaches like G4 theory.
However, the GLPT2 (G{\"o}rling 2nd order perturbation theory) term causes them
to partially inherit the slow $\propto L^{-3}$ (with $L$ the maximum angular
momentum) basis set convergence of correlated wavefunction methods. This could
potentially be remedied by introducing F12 explicit correlation: we investigate
the basis set convergence of both DHDFT and DHDFT-F12 for the large and
chemically diverse GMTKN55 (general main-group thermochemistry, kinetics, and
noncovalent interactions) benchmark suite. The B2GP-PLYP-D3(BJ) and
revDSD-PBEP86-D4 double hybrid density functionals (DHDFs) are investigated as
test cases, together with orbital basis sets as large as aug-cc-pV5Z and F12
basis sets as large as cc-pV(Q+d)Z-F12. We show that F12 greatly accelerates
basis set convergence of DHDFs, to the point that even the modest cc-pVDZ-F12
basis set is closer to the basis set limit than cc-pV(Q+d)Z or def2-QZVPP in
orbital-based approaches, and in fact comparable in quality to cc-pV(5+d)Z.
Somewhat surprisingly, aug-cc-pVDZ-F12 is not required even for the anionic
subsets. In conclusion, DHDF-F12/VDZ-F12 eliminates concerns about basis set
convergence in both the development and application of double-hybrid
functionals.


title:
Multi-scale reconstruction of undersampled spectral-spatial OCT data for
  coronary imaging using deep learning

abstract:
  Coronary artery disease (CAD) is a cardiovascular condition with high
morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has
been considered as an optimal imagining system for the diagnosis and treatment
of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high
resolving power to delineate cellular structures/ features. There is a
trade-off between high spatial resolution and fast scanning rate for coronary
imaging. In this paper, we propose a viable spectral-spatial acquisition method
that down-scales the sampling process in both spectral and spatial domain while
maintaining high quality in image reconstruction. The down-scaling schedule
boosts data acquisition speed without any hardware modifications. Additionally,
we propose a unified multi-scale reconstruction framework, namely Multiscale-
Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled
(compressed) OCT images with flexible magnification factors. We incorporate the
proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary
samples with clinical features such as stent and calcified lesions. Our
experimental results demonstrate that spectral-spatial downscaled data can be
better reconstructed than data that is downscaled solely in either spectral or
spatial domain. Moreover, we observe better reconstruction performance using
MSSMN than using existing reconstruction methods. Our acquisition method and
multi-scale reconstruction framework, in combination, may allow faster SD-OCT
inspection with high resolution during coronary intervention.


title: Higher rank thin monodromy in O(5)

abstract:
  In this article, we study 19 examples of higher rank orthogonal
hypergeometric groups of degree five. These examples appeared in [3, Table 6].
We establish the thinness of 12 of these hypergeometric groups. Some of these
examples are associated with Calabi-Yau 4-folds. This produces the first
examples in the cyclotomic family of Zariski dense non-arithmetic orthogonal
hypergeometric monodromy groups of real rank two.


title: General Interpolation and Strong Amalgamation for Contiguous Arrays

abstract:
  Interpolation is an essential tool in software verification, where
first-order theories are used to constrain datatypes manipulated by programs.
In this paper, we introduce the datatype theory of contiguous arrays with
maxdiff, where arrays are completely defined in their allocation memory and for
which maxdiff returns the max index where they differ. This theory is strictly
more expressive than the array theories previously studied. By showing via an
algebraic analysis that its models strongly amalgamate, we prove that this
theory admits quantifier-free interpolants and, notably, that interpolation
transfers to theory combinations. Finally, we provide an algorithm that
significantly improves the ones for related array theories: it relies on a
polysize reduction to general interpolation in linear arithmetics, thus
avoiding impractical full terms instantiations and unbounded loops.