title:
The James Webb Space Telescope Absolute Flux Calibration. I. Program
  Design and Calibrator Stars

abstract:
  It is critical for James Webb Space Telescope (JWST) science that
instrumental units are converted to physical units. We detail the design of the
JWST absolute flux calibration program that has the core goal of ensuring a
robust flux calibration internal to and between all the science instruments for
both point and extended source science. This program will observe a sample of
calibration stars that have been extensively vetted based mainly on Hubble
Space Telescope, Spitzer Space Telescope, and Transiting Exoplanet Survey
Satellite observations. The program uses multiple stars of three different,
well understood types (hot stars, A dwarfs, and solar analogs) to allow for the
statistical (within a type) and systematic (between types) uncertainties to be
quantified. The program explicitly includes observations to calibrate every
instrument mode, further vet the set of calibration stars, measure the
instrumental repeatability, measure the relative calibration between subarrays
and full frame, and check the relative calibration between faint and bright
stars. For photometry, we have set up our calibration to directly support both
the convention based on the band average flux density and the convention based
on the flux density at a fixed wavelength.


title:
Clinical trial site matching with improved diversity using fair policy
  learning

abstract:
  The ongoing pandemic has highlighted the importance of reliable and efficient
clinical trials in healthcare. Trial sites, where the trials are conducted, are
chosen mainly based on feasibility in terms of medical expertise and access to
a large group of patients. More recently, the issue of diversity and inclusion
in clinical trials is gaining importance. Different patient groups may
experience the effects of a medical drug/ treatment differently and hence need
to be included in the clinical trials. These groups could be based on
ethnicity, co-morbidities, age, or economic factors. Thus, designing a method
for trial site selection that accounts for both feasibility and diversity is a
crucial and urgent goal. In this paper, we formulate this problem as a ranking
problem with fairness constraints. Using principles of fairness in machine
learning, we learn a model that maps a clinical trial description to a ranked
list of potential trial sites. Unlike existing fairness frameworks, the group
membership of each trial site is non-binary: each trial site may have access to
patients from multiple groups. We propose fairness criteria based on
demographic parity to address such a multi-group membership scenario. We test
our method on 480 real-world clinical trials and show that our model results in
a list of potential trial sites that provides access to a diverse set of
patients while also ensuing a high number of enrolled patients.


title:
Study of Indian English Pronunciation Variabilities relative to Received
  Pronunciation

abstract:
  In contrast to British or American English, labeled pronunciation data on the
phonetic level is scarce for Indian English (IE). This has made it challenging
to study pronunciations of Indian English. Moreover, IE has many varieties,
resulting from various native language influences on L2 English. Indian English
has been studied in the past, by a few linguistic works. They report phonetic
rules for such characterisation, however, the extent to which they can be
applied to a diverse large-scale Indian pronunciation data remains
under-examined. We consider a corpus, IndicTIMIT, which is rich in the
diversity of IE varieties and is curated in a nativity balanced manner. It
contains data from 80 speakers corresponding to various regions of India. We
present an approach to validate the phonetic rules of IE along with reporting
unexplored rules derived using a data-driven manner, on this corpus. We also
provide quantitative information regarding which rules are more prominently
observed than the others, attributing to their relevance in IE accordingly.


title:
Temperature Structures Associated with Different Components of the
  Atmospheric Circulation on Tidally Locked Exoplanets

abstract:
  Observations of time-resolved thermal emission from tidally locked exoplanets
can tell us about their atmospheric temperature structure. Telescopes such as
JWST and ARIEL will improve the quality and availability of these measurements.
This motivates an improved understanding of the processes that determine
atmospheric temperature structure, particularly atmospheric circulation. The
circulation is important in determining atmospheric temperatures, not only
through its ability to transport heat, but also because any circulation pattern
needs to be balanced by horizontal pressure contrasts, therefore implying a
particular temperature structure. In this work, we show how the global
temperature field on a tidally locked planet can be decomposed into
contributions that are balanced by different components of the atmospheric
circulation. These are the superrotating jet, stationary Rossby waves, and the
divergent circulation. To achieve this, we partition the geopotential field
into components balanced by the divergent circulation and the rotational
circulation, with the latter comprising the jet and Rossby waves. The
partitioned geopotential then implies a corresponding partitioning of the
temperature via the hydrostatic relation. We apply these diagnostics to
idealised general circulation model simulations, to show how the separate
rotational and divergent circulations together make up the total
three-dimensional atmospheric temperature structure. We also show how each
component contributes distinct signatures to the thermal phase curve of a
tidally locked planet. We conclude that this decomposition is a physically
meaningful separation of the temperature field that explains its global
structure, and can be used to fit observations of thermal emission.


title:
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific
  Visualization

abstract:
  Since 2016, we have witnessed the tremendous growth of artificial
intelligence+visualization (AI+VIS) research. However, existing survey papers
on AI+VIS focus on visual analytics and information visualization, not
scientific visualization (SciVis). In this paper, we survey related deep
learning (DL) works in SciVis, specifically in the direction of DL4SciVis:
designing DL solutions for solving SciVis problems. To stay focused, we
primarily consider works that handle scalar and vector field data but exclude
mesh data. We classify and discuss these works along six dimensions: domain
setting, research task, learning type, network architecture, loss function, and
evaluation metric. The paper concludes with a discussion of the remaining gaps
to fill along the discussed dimensions and the grand challenges we need to
tackle as a community. This state-of-the-art survey guides SciVis researchers
in gaining an overview of this emerging topic and points out future directions
to grow this research.


title: A joint explanation of W-mass and muon g-2 in 2HDM

abstract:
  Since both $W$-mass and muon $g-2$ can be affected by the mass splittings
among extra Higgs bosons $(H,~A,~H^\pm)$ in a 2HDM, we take a model with
$\mu$-$\tau$ LFV interactions to examine the two anomalies reported
respectively by CDF II and FNAL. We obtain the following observations: (i)
Combined with theoretical constraints, the CDF $W$-mass measurement disfavors
$H$ or $A$ to degenerate in mass with $H^\pm$, but allows $H$ and $A$ to
degenerate. The mass splitting between $H^\pm$ and $H/A$ is required to be
larger than 10 GeV. The $m_{H^\pm}$ and $m_{A}$ are favored to be smaller than
650 GeV for $m_H<120$ GeV, and allowed to have more large values with
increasing of $m_H$. (ii) After imposing other relevant experimental
constraints, there are parameter spaces that simultaneously satisfy (at
$2\sigma$ level) the CDF $W$-mass, the FNAL muon $g-2$ and the data of lepton
universality in $\tau$ decays, but the mass splittings among extra Higgs bosons
are strictly constrained.


title:
Time reversal and $\boldsymbol{CP}$ invariance in Calabi-Yau
  compactifications

abstract:
  We revisit the question of time reversal and $CP$ invariance in Calabi-Yau
compactifications. We show that time reversal invariance is respected by
quantum corrections to the prepotential. In particular, field independent
$\theta$ angles whose presence is dictated by requiring integrality of relevant
monodromy transformations can take precisely the quantized values compatible
with time reversal invariance. Furthermore, monodromy symmetry enlarges the
region on moduli space on which time reversal is not spontaneously broken. We
define the action of the $CP$ transformation for multi-parameter models and
argue that on the slice of moduli space where it is defined, $CP$ is trivially
a symmetry of the theory. For supersymmetric vacua that lie in this slice, we
derive a condition on the third cohomology of the compactification manifold
which determines whether $CP$ preserving fluxes exist that stabilize the moduli
to such points. In the case of one-parameter models, the condition is always
satisfied.


title: Out-of-distribution Detection with Deep Nearest Neighbors

abstract:
  Out-of-distribution (OOD) detection is a critical task for deploying machine
learning models in the open world. Distance-based methods have demonstrated
promise, where testing samples are detected as OOD if they are relatively far
away from in-distribution (ID) data. However, prior methods impose a strong
distributional assumption of the underlying feature space, which may not always
hold. In this paper, we explore the efficacy of non-parametric nearest-neighbor
distance for OOD detection, which has been largely overlooked in the
literature. Unlike prior works, our method does not impose any distributional
assumption, hence providing stronger flexibility and generality. We demonstrate
the effectiveness of nearest-neighbor-based OOD detection on several benchmarks
and establish superior performance. Under the same model trained on
ImageNet-1k, our method substantially reduces the false positive rate
(FPR@TPR95) by 24.77% compared to a strong baseline SSD+, which uses a
parametric approach Mahalanobis distance in detection.


title:
FactGraph: Evaluating Factuality in Summarization with Semantic Graph
  Representations

abstract:
  Despite recent improvements in abstractive summarization, most current
approaches generate summaries that are not factually consistent with the source
document, severely restricting their trust and usage in real-world
applications. Recent works have shown promising improvements in factuality
error identification using text or dependency arc entailments; however, they do
not consider the entire semantic graph simultaneously. To this end, we propose
FactGraph, a method that decomposes the document and the summary into
structured meaning representations (MR), which are more suitable for factuality
evaluation. MRs describe core semantic concepts and their relations,
aggregating the main content in both document and summary in a canonical form,
and reducing data sparsity. FactGraph encodes such graphs using a graph encoder
augmented with structure-aware adapters to capture interactions among the
concepts based on the graph connectivity, along with text representations using
an adapter-based text encoder. Experiments on different benchmarks for
evaluating factuality show that FactGraph outperforms previous approaches by up
to 15%. Furthermore, FactGraph improves performance on identifying content
verifiability errors and better captures subsentence-level factual
inconsistencies.


title:
Safer Autonomous Driving in a Stochastic, Partially-Observable
  Environment by Hierarchical Contingency Planning

abstract:
  When learning to act in a stochastic, partially observable environment, an
intelligent agent should be prepared to anticipate a change in its belief of
the environment state, and be capable of adapting its actions on-the-fly to
changing conditions. As humans, we are able to form contingency plans when
learning a task with the explicit aim of being able to correct errors in the
initial control, and hence prove useful if ever there is a sudden change in our
perception of the environment which requires immediate corrective action. This
is especially the case for autonomous vehicles (AVs) navigating real-world
situations where safety is paramount, and a strong ability to react to a
changing belief about the environment is truly needed.
  In this paper we explore an end-to-end approach, from training to execution,
for learning robust contingency plans and combining them with a hierarchical
planner to obtain a robust agent policy in an autonomous navigation task where
other vehicles' behaviours are unknown, and the agent's belief about these
behaviours is subject to sudden, last-second change. We show that our approach
results in robust, safe behaviour in a partially observable, stochastic
environment, generalizing well over environment dynamics not seen during
training.


title: Phase lines in mean-field models with nonuniform external forces

abstract:
  We look at the influence of external fields on systems described by generic
free energy functional of the order parameter. The external force may have
arbitrary spatial dependence and the coupling to the order parameter may be
nonlinear. The treatment generalizes seemingly disparate works, such as pure
fluids, liquid and polymer mixtures, lipid monolayers, and colloidal
suspensions in electric fields, fluids and nematics in gravity, solutions in an
ultracentrifuge, and liquid mixtures in laser radiation. The phase lines and
thermodynamic behavior are calculated on the mean-field level. We find a
"surface" critical point that can be shifted to higher or lower temperatures
compared to the bulk critical point. Below this point, the transition from a
"gas" phase to a "liquid" phase is first-order, while above it the transition
is second-order. Moreover, the susceptibility may diverge at a finite location
${\bf r}$. Several analytical expressions are given in the limit where a Landau
expansion of the free energy is valid.


title: On Extremal Rates of Secure Storage over Graphs

abstract:
  A secure storage code maps $K$ source symbols, each of $L_w$ bits, to $N$
coded symbols, each of $L_v$ bits, such that each coded symbol is stored in a
node of a graph. Each edge of the graph is either associated with $D$ of the
$K$ source symbols such that from the pair of nodes connected by the edge, we
can decode the $D$ source symbols and learn no information about the remaining
$K-D$ source symbols; or the edge is associated with no source symbols such
that from the pair of nodes connected by the edge, nothing about the $K$ source
symbols is revealed. The ratio $L_w/L_v$ is called the symbol rate of a secure
storage code and the highest possible symbol rate is called the capacity.
  We characterize all graphs over which the capacity of a secure storage code
is equal to $1$, when $D = 1$. This result is generalized to $D> 1$, i.e., we
characterize all graphs over which the capacity of a secure storage code is
equal to $1/D$ under a mild condition that for any node, the source symbols
associated with each of its connected edges do not include a common element.
Further, we characterize all graphs over which the capacity of a secure storage
code is equal to $2/D$.


title: Does depth estimation help object detection?

abstract:
  Ground-truth depth, when combined with color data, helps improve object
detection accuracy over baseline models that only use color. However, estimated
depth does not always yield improvements. Many factors affect the performance
of object detection when estimated depth is used. In this paper, we
comprehensively investigate these factors with detailed experiments, such as
using ground-truth vs. estimated depth, effects of different state-of-the-art
depth estimation networks, effects of using different indoor and outdoor RGB-D
datasets as training data for depth estimation, and different architectural
choices for integrating depth to the base object detector network. We propose
an early concatenation strategy of depth, which yields higher mAP than previous
works' while using significantly fewer parameters.


title: Fluorescence calorimetry of an ion crystal

abstract:
  Motivated by the challenge of identifying intruder ions in a cold ion
crystal, we investigate calorimetry from emitted fluorescence light. Under
continuous Doppler cooling, the ion crystal reaches a temperature equilibrium
with a fixed level of fluorescence intensity and any change in the motional
energy of the crystal results in a modification of this intensity. We
theoretically determine the fluorescence rate of an ion crystal as a function
of the temperature, assuming that laser light is scattered along a two-level
electronic transition, which couples to the crystal's vibrations via the
mechanical effects of light. We analyse how the heat dissipated by collisions
of an incoming intruder ion alters the scattering rate. We argue that an energy
change by an incoming $^{229}$Th$^{10+}$ can be unambiguously detected within
50\,$\mu$s via illuminating 50 ions out of a 10$^{3}$ ion crystal. This method
enables applications including capture and spectroscopy of charged states of
thorium isotopes and investigation of highly charged ions.


title: Scalable Training of Language Models using JAX pjit and TPUv4

abstract:
  Modern large language models require distributed training strategies due to
their size. The challenges of efficiently and robustly training them are met
with rapid developments on both software and hardware frontiers. In this
technical report, we explore challenges and design decisions associated with
developing a scalable training framework, and present a quantitative analysis
of efficiency improvements coming from adopting new software and hardware
solutions.


title: Sentiment Analysis of Political Tweets for Israel using Machine Learning

abstract:
  Sentiment Analysis is a vital research topic in the field of Computer
Science. With the accelerated development of Information Technology and social
networks, a massive amount of data related to comment texts has been generated
on web applications or social media platforms like Twitter. Due to this, people
have actively started proliferating general information and the information
related to political opinions, which becomes an important reason for analyzing
public reactions. Most researchers have used social media specifics or contents
to analyze and predict public opinion concerning political events. This
research proposes an analytical study using Israeli political Twitter data to
interpret public opinion towards the Palestinian-Israeli conflict. The
attitudes of ethnic groups and opinion leaders in the form of tweets are
analyzed using Machine Learning algorithms like Support Vector Classifier
(SVC), Decision Tree (DT), and Naive Bayes (NB). Finally, a comparative
analysis is done based on experimental results from different models.


title:
Decentralized Collaborative Learning Framework for Next POI
  Recommendation

abstract:
  Next Point-of-Interest (POI) recommendation has become an indispensable
functionality in Location-based Social Networks (LBSNs) due to its
effectiveness in helping people decide the next POI to visit. However, accurate
recommendation requires a vast amount of historical check-in data, thus
threatening user privacy as the location-sensitive data needs to be handled by
cloud servers. Although there have been several on-device frameworks for
privacy-preserving POI recommendations, they are still resource-intensive when
it comes to storage and computation, and show limited robustness to the high
sparsity of user-POI interactions. On this basis, we propose a novel
decentralized collaborative learning framework for POI recommendation (DCLR),
which allows users to train their personalized models locally in a
collaborative manner. DCLR significantly reduces the local models' dependence
on the cloud for training, and can be used to expand arbitrary centralized
recommendation models. To counteract the sparsity of on-device user data when
learning each local model, we design two self-supervision signals to pretrain
the POI representations on the server with geographical and categorical
correlations of POIs. To facilitate collaborative learning, we innovatively
propose to incorporate knowledge from either geographically or semantically
similar users into each local model with attentive aggregation and mutual
information maximization. The collaborative learning process makes use of
communications between devices while requiring only minor engagement from the
central server for identifying user groups, and is compatible with common
privacy preservation mechanisms like differential privacy. We evaluate DCLR
with two real-world datasets, where the results show that DCLR outperforms
state-of-the-art on-device frameworks and yields competitive results compared
with centralized counterparts.


title:
Learning Self-Modulating Attention in Continuous Time Space with
  Applications to Sequential Recommendation

abstract:
  User interests are usually dynamic in the real world, which poses both
theoretical and practical challenges for learning accurate preferences from
rich behavior data. Among existing user behavior modeling solutions, attention
networks are widely adopted for its effectiveness and relative simplicity.
Despite being extensively studied, existing attentions still suffer from two
limitations: i) conventional attentions mainly take into account the spatial
correlation between user behaviors, regardless the distance between those
behaviors in the continuous time space; and ii) these attentions mostly provide
a dense and undistinguished distribution over all past behaviors then
attentively encode them into the output latent representations. This is however
not suitable in practical scenarios where a user's future actions are relevant
to a small subset of her/his historical behaviors. In this paper, we propose a
novel attention network, named self-modulating attention, that models the
complex and non-linearly evolving dynamic user preferences. We empirically
demonstrate the effectiveness of our method on top-N sequential recommendation
tasks, and the results on three large-scale real-world datasets show that our
model can achieve state-of-the-art performance.


title:
A pipeline and comparative study of 12 machine learning models for text
  classification

abstract:
  Text-based communication is highly favoured as a communication method,
especially in business environments. As a result, it is often abused by sending
malicious messages, e.g., spam emails, to deceive users into relaying personal
information, including online accounts credentials or banking details. For this
reason, many machine learning methods for text classification have been
proposed and incorporated into the services of most email providers. However,
optimising text classification algorithms and finding the right tradeoff on
their aggressiveness is still a major research problem.
  We present an updated survey of 12 machine learning text classifiers applied
to a public spam corpus. A new pipeline is proposed to optimise hyperparameter
selection and improve the models' performance by applying specific methods
(based on natural language processing) in the preprocessing stage.
  Our study aims to provide a new methodology to investigate and optimise the
effect of different feature sizes and hyperparameters in machine learning
classifiers that are widely used in text classification problems. The
classifiers are tested and evaluated on different metrics including F-score
(accuracy), precision, recall, and run time. By analysing all these aspects, we
show how the proposed pipeline can be used to achieve a good accuracy towards
spam filtering on the Enron dataset, a widely used public email corpus.
Statistical tests and explainability techniques are applied to provide a robust
analysis of the proposed pipeline and interpret the classification outcomes of
the 12 machine learning models, also identifying words that drive the
classification results. Our analysis shows that it is possible to identify an
effective machine learning model to classify the Enron dataset with an F-score
of 94%.


title:
CARCA: Context and Attribute-Aware Next-Item Recommendation via
  Cross-Attention

abstract:
  In sparse recommender settings, users' context and item attributes play a
crucial role in deciding which items to recommend next. Despite that, recent
works in sequential and time-aware recommendations usually either ignore both
aspects or only consider one of them, limiting their predictive performance. In
this paper, we address these limitations by proposing a context and
attribute-aware recommender model (CARCA) that can capture the dynamic nature
of the user profiles in terms of contextual features and item attributes via
dedicated multi-head self-attention blocks that extract profile-level features
and predicting item scores. Also, unlike many of the current state-of-the-art
sequential item recommendation approaches that use a simple dot-product between
the most recent item's latent features and the target items embeddings for
scoring, CARCA uses cross-attention between all profile items and the target
items to predict their final scores. This cross-attention allows CARCA to
harness the correlation between old and recent items in the user profile and
their influence on deciding which item to recommend next. Experiments on four
real-world recommender system datasets show that the proposed model
significantly outperforms all state-of-the-art models in the task of item
recommendation and achieving improvements of up to 53% in Normalized Discounted
Cumulative Gain (NDCG) and Hit-Ratio. Results also show that CARCA outperformed
several state-of-the-art dedicated image-based recommender systems by merely
utilizing image attributes extracted from a pre-trained ResNet50 in a black-box
fashion.


title: Negative Sampling for Recommendation

abstract:
  How to effectively sample high-quality negative instances is important for
well training a recommendation model. We argue that a high-quality negative
should be both \textit{informativeness} and \textit{unbiasedness}. Although
previous studies have proposed some approaches to address the informativeness
in negative sampling, few has been done to discriminating false negative from
true negative for unbiased negative sampling, not to mention taking both into
consideration. This paper first adopts a parameter learning perspective to
analyze negative informativeness and unbiasedness in loss gradient-based model
training. We argue that both negative sampling and collaborative filtering
include an implicit task of negative classification, from which we report an
insightful yet beneficial finding about the order relation in predicted
negatives' scores. Based on our finding and by regarding negatives as random
variables, we next derive the class condition density of true negatives and
that of false negatives. We also design a Bayesian classifier for negative
classification, from which we define a quantitative unbiasedness measure for
negatives. Finally, we propose to use a harmonic mean of informativeness and
unbiasedness to sample high-quality negatives. Experimental studies validate
the superiority of our negative sampling algorithm over the peers in terms of
better sampling quality and better recommendation performance.


title: Optimizing generalized Gini indices for fairness in rankings

abstract:
  There is growing interest in designing recommender systems that aim at being
fair towards item producers or their least satisfied users. Inspired by the
domain of inequality measurement in economics, this paper explores the use of
generalized Gini welfare functions (GGFs) as a means to specify the normative
criterion that recommender systems should optimize for. GGFs weight individuals
depending on their ranks in the population, giving more weight to worse-off
individuals to promote equality. Depending on these weights, GGFs minimize the
Gini index of item exposure to promote equality between items, or focus on the
performance on specific quantiles of least satisfied users. GGFs for ranking
are challenging to optimize because they are non-differentiable. We resolve
this challenge by leveraging tools from non-smooth optimization and projection
operators used in differentiable sorting. We present experiments using real
datasets with up to 15k users and items, which show that our approach obtains
better trade-offs than the baselines on a variety of recommendation tasks and
fairness criteria.


title: Graph Enhanced BERT for Query Understanding

abstract:
  Query understanding plays a key role in exploring users' search intents and
facilitating users to locate their most desired information. However, it is
inherently challenging since it needs to capture semantic information from
short and ambiguous queries and often requires massive task-specific labeled
data. In recent years, pre-trained language models (PLMs) have advanced various
natural language processing tasks because they can extract general semantic
information from large-scale corpora. Therefore, there are unprecedented
opportunities to adopt PLMs for query understanding. However, there is a gap
between the goal of query understanding and existing pre-training strategies --
the goal of query understanding is to boost search performance while existing
strategies rarely consider this goal. Thus, directly applying them to query
understanding is sub-optimal. On the other hand, search logs contain user
clicks between queries and urls that provide rich users' search behavioral
information on queries beyond their content. Therefore, in this paper, we aim
to fill this gap by exploring search logs. In particular, to incorporate search
logs into pre-training, we first construct a query graph where nodes are
queries and two queries are connected if they lead to clicks on the same urls.
Then we propose a novel graph-enhanced pre-training framework, GE-BERT, which
can leverage both query content and the query graph. In other words, GE-BERT
can capture both the semantic information and the users' search behavioral
information of queries. Extensive experiments on various query understanding
tasks have demonstrated the effectiveness of the proposed framework.


title: Possible Ribose Synthesis in Carbonaceous Planetesimals

abstract:
  The origin of life might be sparked by the polymerization of the first RNA
molecules in Darwinian ponds during wet-dry cycles. The key life-building block
ribose was found in carbonaceous chondrites. Its exogenous delivery onto the
Hadean Earth could be a crucial step toward the emergence of the RNA world.
Here, we investigate the formation of ribose through a simplified version of
the formose reaction inside carbonaceous chondrite parent bodies. Following up
on our previous studies regarding nucleobases with the same coupled
physico-chemical model, we calculate the abundance of ribose within
planetesimals of different sizes and heating histories. We perform laboratory
experiments using catalysts present in carbonaceous chondrites to infer the
yield of ribose among all pentoses (5Cs) forming during the formose reaction.
These laboratory yields are used to tune our theoretical model that can only
predict the total abundance of 5Cs. We found that the calculated abundances of
ribose were similar to the ones measured in carbonaceous chondrites. We discuss
the possibilities of chemical decomposition and preservation of ribose and
derived constraints on time and location in planetesimals. In conclusion, the
aqueous formose reaction might produce most of the ribose in carbonaceous
chondrites. Together with our previous studies on nucleobases, we found that
life-building blocks of the RNA world could be synthesized inside parent bodies
and later delivered onto the early Earth.


title:
Understanding the Spin Crossover Dynamical Effects of the Dioxygen
  Binding and Activation on HOD enzyme

abstract:
  For the cofactor-free 1-H-3-hydroxy-4-oxoquinaldine-2,4-dioxygenase (HOD),
the dioxygen (O2) dependent steps are rate-limiting along with a spin state
crossover to the singlet spin state. Here, the primary triplet O2 molecule
activation on the 2-methyl-3-hydroxy-4(1H)-quinolone (MHQ) is investigated, and
the catalytic role of the intersystem crossing effects is highlighted by
directly comparing results from the Born-Oppenheimer dynamics and non-adiabatic
surface hopping dynamics. This work confirms non-adiabatic dynamical effects
are essential to modulate the O2 activation on the substrate MHQ. The time
scale of the equilibration and conversion from triplet to singlet state should
be in the range of a few hundreds of femtoseconds. We hope this work provides
us a fresh look at the underlying physics of dioxygen activation reactions
involving more than one spin state.


title:
Theoretical proposal of a revolutionary water-splitting photocatalyst:
  The monolayer of boron phosphide

abstract:
  Recently, hydrogen generation by water-splitting photocatalysts is attracting
attention as a sustainable and clean energy resource. Photocatalytic
hydrogen-generation systems are much simpler, cheaper, and easier to scale up
than the coupled systems of electrolysis and solar cells, wind-power
generation, etc. However, photocatalytic hydrogen generation is currently
inefficient. This paper proposes the monolayer of boron phosphide as a stable
highly-efficient water-splitting photocatalyst by high-precision
density-functional theory calculations using a HSE06 functional with a solvent
effect. The monolayer of boron phosphide has a direct allowed energy gap of
about 1.4 eV, and functions as a one-step excitation photocatalyst. It absorbs
sunlight with wavelengths below about 890 nm (ultraviolet, visible, and
near-infrared light) and produces both hydrogen gas and oxygen gas from water
at a suitable pH condition. By calculating the overpotentials of hydrogen and
oxygen evolution reactions, its photocatalytic effectiveness was confirmed. The
monolayers of boron phosphide will realize green hydrogen revolution.


title:
Secondary kinetic peak in the Kohn-Sham potential and its connection to
  the response step

abstract:
  We consider a prototypical 1D model Hamiltonian for a stretched heteronuclear
molecule and construct individual components of the corresponding KS potential,
namely: the kinetic, the N - 1, and the conditional potentials. These
components show very special features, such as peaks and steps, in regions
where the density is drastically low. Some of these features are quite well
known, whereas others, such as a secondary peak in the kinetic potential or a
second bump in the conditional potential, are less or not known at all. We
discuss these features building on the analytical model treated in J. Chem.
Theory Comput. 14, 4151 (2018). In particular, we provide an explanation for
the underlying mechanism which determines the appearance of both peaks in the
kinetic potential and elucidate why these peaks delineate the region over which
the plateau structure, due to the N - 1 potential, stretches. We assess the
validity of the Heitler-London Ansatz at large but finite internuclear
distance, showing that, if optimal orbitals are used, this model is an
excellent approximation to the exact wavefunction. Notably, we find that the
second natural orbital presents an extra node very far out on the side of the
more electronegative atom.


title:
A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility
  Research

abstract:
  Data-intensive machine learning based techniques increasingly play a
prominent role in the development of future mobility solutions - from driver
assistance and automation functions in vehicles, to real-time traffic
management systems realized through dedicated infrastructure. The availability
of high quality real-world data is often an important prerequisite for the
development and reliable deployment of such systems in large scale. Towards
this endeavour, we present the A9-Dataset based on roadside sensor
infrastructure from the 3 km long Providentia++ test field near Munich in
Germany. The dataset includes anonymized and precision-timestamped multi-modal
sensor and object data in high resolution, covering a variety of traffic
situations. As part of the first set of data, which we describe in this paper,
we provide camera and LiDAR frames from two overhead gantry bridges on the A9
autobahn with the corresponding objects labeled with 3D bounding boxes. The
first set includes in total more than 1000 sensor frames and 14000 traffic
objects. The dataset is available for download at https://a9-dataset.com.


abstract:
  Four algorithms for propositional forgetting are compared. The first performs
all possible resolutions and deletes the clauses containing a variable to
forget. The second forgets a variable at time by resolving and then deleting
all clauses that resolve on that variable. The third outputs the result of all
possible linear resolutions on the variables to forget. The fourth generates a
clause from the points of contradiction during a backtracking search. The
latter emerges as the winner, with the second and first having some role in
specific cases. The linear resolution algorithm performs poorly in this
implementation.


title:
Revisiting the relation between the number of globular clusters and
  galaxy mass for low mass galaxies

abstract:
  Using a new method to estimate total galaxy mass (M$_{\rm T}$) and two
samples of low luminosity galaxies containing measurements of the number of
globular clusters (GCs) per galaxy (N$_{\rm GC}$), we revisit the N$_{\rm
GC}-$M$_{\rm T}$ relation using a total of 203 galaxies, 157 of which have
M$_{\rm T}$ $\ \le 10^{10}$ M$_\odot$. We find that the relation is nearly
linear, N$_{\rm GC} \propto$ M$_{\rm T}^{0.92\pm0.08}$ down to at least M$_{\rm
T} \sim 10^{8.75}$ M$_\odot$. Because the relationship extends to galaxies that
average less than one GC per galaxy and to a mass range in which mergers are
relatively rare, the relationship cannot be solely an emergent property of
hierarchical galaxy formation. The character of the radial GC distribution in
low mass galaxies, and the lack of mergers at these galaxy masses, also appears
to challenge models in which the GCs form in central, dissipatively
concentrated high-density, high-pressure regions and are then scattered to
large radius. The slight difference between the fitted power-law exponent and a
value of one, leaves room for a shallow M$_{\rm T}$-dependent variation in the
mean mass per GC that would allow the relation between total mass in GCs and
M$_{\rm T}$ to be linear.


title: Systoles of Arithmetic Hyperbolic 2- and 3-Manifolds

abstract:
  In this paper we study the systoles of arithmetic hyperbolic 2- and
3-manifolds. Our first result is the construction of infinitely many arithmetic
hyperbolic 2- and 3-manifolds which are pairwise noncommensurable, all have the
same systole, and whose volumes are explicitly bounded. Our second result fixes
a positive number x and gives an upper bound for the least volume of an
arithmetic hyperbolic 2- or 3-manifold whose systole is greater than x. We
conclude by determining, for certain small values of x, the least volume of a
principal arithmetic hyperbolic 2-manifold over Q or 3-manifold over Q(i) whose
systole is greater than x.


title: Software Supply Chain Map: How Reuse Networks Expand

abstract:
  Clone-and-own is a typical code reuse approach because of its simplicity and
efficiency. Cloned software components are maintained independently by a new
owner. These clone-and-own operations can be occurred sequentially, that is,
cloned components can be cloned again and owned by other new owners on the
supply chain. In general, code reuse is not documented well, consequently,
appropriate changes like security patches cannot be propagated to descendant
software projects. However, the OpenChain Project defined identifying and
tracking source code reuses as responsibilities of FLOSS software staffs. Hence
supporting source code reuse awareness is in a real need. This paper studies
software reuse relations in FLOSS ecosystem. Technically, clone-and-own reuses
of source code can be identified by file-level clone set detection. Since
change histories are associated with files, we can determine origins and
destinations in reusing across multiple software by considering times. By
building software supply chain maps, we find that clone-and-own is prevalent in
FLOSS development, and set of files are reused widely and repeatedly. These
observations open up future challenges of maintaining and tracking global
software genealogies.


title:
The Heliospheric Ambipolar Potential Inferred from Sunward-Propagating
  Halo Electrons

abstract:
  We provide evidence that the sunward-propagating half of the solar wind
electron halo distribution evolves without scattering in the inner heliosphere.
We assume the particles conserve their total energy and magnetic moment, and
perform a "Liouville mapping" on electron pitch angle distributions measured by
the Parker Solar Probe SPAN-E instrument. Namely, we show that the
distributions are consistent with Liouville's theorem if an appropriate
interplanetary potential is chosen. This potential, an outcome of our fitting
method, is compared against the radial profiles of proton bulk flow energy. We
find that the inferred potential is responsible for nearly 100% of the proton
acceleration in the solar wind at heliocentric distances 0.18-0.79 AU. These
observations combine to form a coherent physical picture: the same
interplanetary potential accounts for the acceleration of the solar wind
protons as well as the evolution of the electron halo. In this picture the halo
is formed from a sunward-propagating population that originates somewhere in
the outer heliosphere by a yet-unknown mechanism.


title: Modified theories of Gravity: Why, How and What?

abstract:
  General Relativity (GR) was proven via the direct detection of gravitational
waves from the mergers of the binary black holes and binary neutron stars by
the Advanced LIGO and Advanced Virgo detectors. These detections confirmed the
prediction of GR and provided the first direct evidence of the existence of
stellar-mass black holes (BHs). However, the occurrence of singularities at the
centers of BHs suggests that GR is inapplicable because of the breakdown of the
equivalence principle at the singularities. The fact that these singularities
exist indicates that GR cannot be a universal theory of space-time. In the
low-energy limit, the theoretical and observational challenges faced by the
$\Lambda$CDM model also indicate that we might have to look beyond GR as the
underlying theory of gravity. Unlike GR, whose field equations contain only up
to second-order derivatives, the modified theories with higher derivative
Ricci/Riemann tensor gravity models include higher derivatives. Therefore, one
expects significant differences between GR and modified theories. Since there
are many ways of modifying GR in the strong-gravity and cosmological distances,
each model has unique features. This leads to the following crucial question:
Are there a set of unique signatures that distinguish GR from modified gravity
(MG) theories? This review discusses three aspects of MG theories: (1) Why do
we need to consider MG theories? (2) How to modify GR? and (3) What are the
observational consequences? The review is written in a pedagogical style with
the expectation that it will serve as a useful reference for theorists and
observers and those interested in bridging the divide between theory and
observations.


title:
A High-Quality Entropy Source Using van der Waals Heterojunction for
  True Random Number Generation

abstract:
  Generators of random sequences used in high-end applications such as
cryptography rely on entropy sources for their indeterminism. Physical
processes governed by the laws of quantum mechanics are excellent sources of
entropy available in nature. However, extracting enough entropy from such
systems for generating truly random sequences is challenging while maintaining
the feasibility of the extraction procedure for real-world applications. Here,
we present a compact and an all-electronic van der Waals (vdW)
heterostructure-based device capable of detecting discrete charge fluctuations
for extracting entropy from physical processes and use it for the generation of
independent and identically distributed (IID) true random sequences. We extract
a record high value ($>0.98~bits/bit$) of min-entropy using the proposed
scheme. We demonstrate an entropy generation rate tunable over multiple orders
of magnitude and show the persistence of the underlying physical process for
temperatures ranging from cryogenic to ambient conditions. We verify the random
nature of the generated sequences using tests such as NIST SP 800-90B standard
and other statistical measures and verify the suitability our random sequence
for cryptographic applications using the NIST SP 800-22 standard. The generated
random sequences are then used in implementing various randomized algorithms
without any preconditioning steps.

categories: quant-ph cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph


title: Multilingual Event Linking to Wikidata

abstract:
  We present a task of multilingual linking of events to a knowledge base. We
automatically compile a large-scale dataset for this task, comprising of 1.8M
mentions across 44 languages referring to over 10.9K events from Wikidata. We
propose two variants of the event linking task: 1) multilingual, where event
descriptions are from the same language as the mention, and 2) crosslingual,
where all event descriptions are in English. On the two proposed tasks, we
compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and
multilingual adaptations of the biencoder and crossencoder architectures from
BLINK (Wu et al., 2020). In our experiments on the two task variants, we find
both biencoder and crossencoder models significantly outperform the BM25+
baseline. Our results also indicate that the crosslingual task is in general
more challenging than the multilingual task. To test the out-of-domain
generalization of the proposed linking systems, we additionally create a
Wikinews-based evaluation set. We present qualitative analysis highlighting
various aspects captured by the proposed dataset, including the need for
temporal reasoning over context and tackling diverse event descriptions across
languages.


title: 4D Tracking: Present Status and Perspective

abstract:
  The past ten years have seen the advent of silicon-based precise timing
detectors for charged particle tracking. The underlying reason for this
evolution is a design innovation: the Low-Gain Avalanche Diode (LGAD). In its
simplicity, the LGAD design is an obvious step with momentous consequences: low
gain leads to large signals maintaining sensors stability and low noise,
allowing sensor segmentation. Albeit introduced for a different reason, to
compensate for charge trapping in irradiated silicon sensors, LGAD found
fertile ground in the design of silicon-based timing detectors. Spurred by this
design innovation, solid-state-based timing detectors for charged particles are
going through an intense phase of R&D, and hybrid and monolithic sensors, with
or without internal gain, are being explored. This contribution offers a review
of this booming field.


title:
From nonlocality quantifiers for behaviors to nonlocality quantifiers
  for states

abstract:
  We define an alternative way of quantifying nonlocality of states based on
Bell nonlocality of behaviors, called the trace-weighted nonlocal volume. The
construction is based on the nonlocal volume, a quantifier of nonlocality for
states that counts the volume of the set of measurements that give rise to
nonlocal behaviors when applied to this state, plus the trace distance, a
quantifier of nonlocality for behaviors based on the distance between the
behavior and the local set. The key difference from preceding candidates was
the introduction of a quantifier of nonlocality to weight each contribution
from behaviors in the nonlocal volume. We list some interesting properties of
this quantifier and investigate the (2, 2, 2) and (2, 3, 2) scenarios. We show
that the weak anomaly of nonlocality for the (2, 2, 3) scenario persists, but
the local minimum for nonlocality with the trace-weighted nonlocal volume
occurs in a different state as compared to the minimum for the non-weighted
version, showing that the weak anomaly is not an intrinsic characteristic of
the scenario, but is strongly dependent of the choice of quantifier.


title: The dynamics of scalar-field Quintom cosmological models

abstract:
  We shall present a complete (compactified) dynamical systems analysis of the
Quintom model comprised of an interacting quintessence scalar field and a
phantom scalar field. We find that there is a range for the model parameters
$\kappa, \lambda$ such that there are expanding Quintom cosmologies that
undergo two inflationary periods, and that this behaviour is not destabilized
by spatial curvature. We also discuss a class of bouncing cosmologies.


title:
Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching
  Points

abstract:
  Dynamic algorithm selection can be beneficial for solving numerical black-box
problems, in which we implement an online switching mechanism between
optimization algorithms. In this approach, we need to decide when a switch
should take place and which algorithm to pick for the switching. Intuitively,
this approach chains the algorithms for combining the well-performing segments
from the performance profile of the algorithms. To realize efficient chaining,
we investigate two important aspects - how the switching point influences the
overall performance and how to warm-start an algorithm with information stored
in its predecessor. To delve into those aspects, we manually construct a
portfolio comprising five state-of-the-art optimization algorithms and only
consider a single switch between each algorithm pair. After benchmarking those
algorithms with the BBOB problem set, we choose the switching point for each
pair by maximizing the theoretical performance gain. The theoretical gain is
compared to the actual gain obtained by executing the switching procedure with
the corresponding switching point. Moreover, we devise algorithm-specific
warm-starting methods for initializing the algorithm after the switching with
the information learned from its predecessor. Our empirical results show that
on some BBOB problems, the theoretical gain is realized or even surpassed by
the actual gain. More importantly, this approach discovers a chain that
outperforms the single best algorithm on many problem instances. Also, we show
that a proper warm-starting procedure is crucial to achieving high actual
performance gain for some algorithm pairs. Lastly, with a sensitivity analysis,
we find the actual performance gain is hugely affected by the switching point,
and in some cases, the switching point yielding the best actual performance
differs from the one computed from the theoretical gain.


title:
Time series features for supporting hydrometeorological explorations and
  predictions in ungauged locations using large datasets

abstract:
  Regression-based frameworks for streamflow regionalization are built around
catchment attributes that traditionally originate from catchment hydrology,
flood frequency analysis and their interplay. In this work, we deviated from
this traditional path by formulating and extensively investigating the first
regression-based streamflow regionalization frameworks that largely emerge from
general-purpose time series features for data science and, more precisely, from
a large variety of such features. We focused on 28 features that included
(partial) autocorrelation, entropy, temporal variation, seasonality, trend,
lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others.
We estimated these features for daily temperature, precipitation and streamflow
time series from 511 catchments, and then merged them within regionalization
contexts with traditional topographic, land cover, soil and geologic
attributes. Precipitation and temperature features (e.g., the spectral entropy,
seasonality strength and lag-1 autocorrelation of the precipitation time
series, and the stability and trend strength of the temperature time series)
were found to be useful predictors of many streamflow features. The same
applies to traditional attributes, such as the catchment mean elevation.
Relationships between predictor and dependent variables were also revealed,
while the spectral entropy, the seasonality strength and several
autocorrelation features of the streamflow time series were found to be more
regionalizable than others.


title:
The $W\ell\nu$-vertex corrections to W-boson mass in the R-parity
  violating MSSM

abstract:
  Inspired by the astonishing $7\sigma$ discrepancy between the recent CDF-II
measurement and the Standard Model prediction on the mass of $W$-boson, we
investigate the unique $\lambda'$- corrections to the vertex of $\mu\to\nu_\mu
e\bar{\nu_e}$ decay in the context of the $R$-parity violating minimal
supersymmetric standard model. These corrections can raise the $W$-boson mass
independently. Combined with recent $Z$-pole measurements, $m_W\lesssim 80.38$
GeV can be reached. We find that these vertex corrections cannot explain the
CDF result entirely. However, these corrections, together with the oblique
ones, can increase $m_W$ to around $80.422$ GeV, which is accordant with the
CDF result at the edge of $1\sigma$ level.


title: Imprinting persistent currents in tunable fermionic rings

abstract:
  Persistent currents in annular geometries have played an important role in
disclosing the quantum phase coherence of superconductors and mesoscopic
electronic systems. Ultracold atomic gases in multiply connected traps also
exhibit long-lived supercurrents, and have attracted much interest both for
fundamental studies of superfluid dynamics and as prototypes for atomtronic
circuits. Here, we report on the realization of supercurrents in homogeneous,
tunable fermionic rings. We gain exquisite, rapid control over quantized
persistent currents in all regimes of the BCS-BEC crossover through a universal
phase-imprinting technique, attaining on-demand circulations $w$ as high as 8.
High-fidelity read-out of the superfluid circulation state is achieved by
exploiting an interferometric protocol, which also yields local information
about the superfluid phase around the ring. In the absence of externally
introduced perturbations, we find the induced metastable supercurrents to be as
long-lived as the atomic sample. Conversely, we trigger and inspect the
supercurrent decay by inserting a single small obstacle within the ring. For
circulations higher than a critical value, the quantized current is observed to
dissipate via the emission of vortices, i.e., quantized phase slips, which we
directly image, in good agreement with numerical simulations. The critical
circulation at which the superflow becomes unstable is found to depend starkly
on the interaction strength, taking its maximum value for the unitary Fermi
gas. Our results demonstrate fast and accurate control of quantized collective
excitations in a macroscopic quantum system, and establish strongly interacting
fermionic superfluids as excellent candidates for atomtronic applications.


title:
Sharing the Load: Considering Fairness in De-energization Scheduling to
  Mitigate Wildfire Ignition Risk using Rolling Optimization

abstract:
  Wildfires are a threat to public safety and have increased in both frequency
and severity due to climate change. To mitigate wildfire ignition risks,
electric power system operators proactively de-energize high-risk power lines
during "public safety power shut-off" (PSPS) events. Line de-energizations can
cause communities to lose power, which may result in negative economic, health,
and safety impacts. Furthermore, the same communities may repeatedly experience
power shutoffs over the course of a wildfire season, which compounds these
negative impacts. However, there are often many combinations of power lines
whose de-energization will result in about the same reduction of wildfire risk,
but the associated power loss affects different communities. Therefore, one may
raise concerns regarding the fairness of de-energization decisions.
Accordingly, this paper proposes a model-predictive-control-inspired framework
to select lines to de-energize in order to balance wildfire risk reduction,
total load shedding, and fairness considerations. The goal of the framework is
to prevent a small fraction of communities from disproportionally being
impacted by PSPS events, and to instead more equally share the burden of power
outages. For a geolocated test case in the southwestern United States, we use
actual California demand data as well as real wildfire risk forecasts to
simulate PSPS events during the 2021 wildfire season and compare the
performance of various methods for promoting fairness. Our results demonstrate
that the proposed formulation can provide significantly more fair outcomes with
limited impacts on system-wide performance.


title:
Features of the Earth's seasonal hydroclimate: Characterizations and
  comparisons across the Koppen-Geiger climates and across continents

abstract:
  Detailed feature investigations and comparisons across climates, continents
and time series types can progress our understanding and modelling ability of
the Earth's hydroclimate and its dynamics. As a step towards these important
directions, we here propose and extensively apply a multifaceted and
engineering-friendly methodological framework for the thorough characterization
of seasonal hydroclimatic dependence, variability and change at the global
scale. We apply this framework using over 13 000 quarterly temperature,
precipitation and river flow time series. In these time series, the seasonal
hydroclimatic behaviour is represented by 3-month means of earth-observed
variables. In our analyses, we also adopt the well-established Koppen-Geiger
climate classification system and define continental-scale regions with large
or medium density of observational stations. In this context, we provide in
parallel seasonal hydroclimatic feature summaries and comparisons in terms of
autocorrelation, seasonality, temporal variation, entropy, long-range
dependence and trends. We find notable differences to characterize the
magnitudes of most of these features across the various Koppen-Geiger climate
classes, as well as between several continental-scale geographical regions. We,
therefore, deem that the consideration of the comparative summaries could be
more beneficial in water resources engineering contexts than the also provided
global summaries. Lastly, we apply explainable machine learning to compare the
investigated features with respect to how informative they are in explaining
and predicting either the main Koppen-Geiger climate or the continental-scale
region, with the entropy, long-range dependence and trend features being
(roughly) found to be less informative than the remaining ones at the seasonal
time scale.


title:
A stakeholder-oriented multi-criteria optimization model for decentral
  multi-energy systems

abstract:
  The decarbonization of municipal and district energy systems requires
economic and ecologic efficient transformation strategies in a wide spectrum of
technical options. Especially under the consideration of multi-energy systems,
which connect energy domains such as heat and electricity supply, expansion and
operational planning of so-called decentral multi-energy systems (DMES) holds a
multiplicity of complexities. This motivates the use of optimization problems,
which reach their limitations with regard to computational feasibility in
combination with the required level of detail. With an increased focus on DMES
implementation, this problem is aggravated since, moving away from the
traditional system perspective, a user-centered, market-integrated perspective
is assumed. Besides technical concepts it requires the consideration of market
regimes, e.g. self-consumption and the broader energy sharing. This highlights
the need for DMES optimization models which cover a microeconomic perspective
under consideration of detailed technical options and energy regulation, in
order to understand mutual technical and socio-economic and -ecologic
interactions of energy policies. In this context we present a
stakeholder-oriented multi-criteria optimization model for DMES, which
addresses technical aspects, as well as market and services coverage towards a
real-world implementation. The current work bridges a gap between the required
modelling level of detail and computational feasibility of DMES expansion and
operation optimization. Model detail is achieved by the application of a hybrid
combination of mathematical methods in a nested multi-level decomposition
approach, including a Genetic Algorithm, Benders Decomposition and Lagrange
Relaxation. This also allows for distributed computation on multi-node high
performance computer clusters.


title: Better Uncertainty Quantification for Machine Translation Evaluation

abstract:
  Neural-based machine translation (MT) evaluation metrics are progressing
fast. However, these systems are often hard to interpret and might produce
unreliable scores when human references or assessments are noisy or when data
is out-of-domain. Recent work leveraged uncertainty quantification techniques
such as Monte Carlo dropout and deep ensembles to provide confidence intervals,
but these techniques (as we show) are limited in several ways. In this paper we
investigate more powerful and efficient uncertainty predictors for MT
evaluation metrics and their potential to capture aleatoric and epistemic
uncertainty. To this end we train the COMET metric with new heteroscedastic
regression, divergence minimization, and direct uncertainty prediction
objectives. Our experiments show improved results on WMT20 and WMT21 metrics
task datasets and a substantial reduction in computational costs. Moreover,
they demonstrate the ability of our predictors to identify low quality
references and to reveal model uncertainty due to out-of-domain data.


title: Snowmass White Paper: the Double Copy and its Applications

abstract:
  The double copy is, in essence, a map between scattering amplitudes in a
broad variety of familiar field and string theories. In addition to the
mathematically rich intrinsic structure, it underlies a multitude of active
research directions and has a range of interesting applications in quantum,
classical and effective field theories, including broad topics such as string
theory, particle physics, astrophysics, and cosmology. This Snowmass white
paper provides a brief introduction to the double copy, its applications,
current research and future challenges.


title: Dynamic Programming of Stochastic Burgers Equation Driven by Levy Noise

abstract:
  In this work, we study the optimal control of stochastic Burgers equation
perturbed by Gaussian and Levy type noises with distributed control process
acting on the state equation. We use the dynamic programming approach for the
second order Hamilton-Jacobi- Bellman (HJB) equation consisting of an
integro-differential operator with Levy measure associated with the stochastic
control problem. Using the regularizing properties of the transition semigroup
corresponding to the stochastic Burgers equation and compactness arguments, we
solve the HJB equation and the resultant feedback control problem.


title:
Optimally Designing Cybersecurity Insurance Contracts to Encourage the
  Sharing of Medical Data

abstract:
  Though the sharing of medical data has the potential to lead to breakthroughs
in health care, the sharing process itself exposes patients and health care
providers to various risks. Patients face risks due to the possible loss in
privacy or livelihood that can occur when medical data is stolen or used in
non-permitted ways, whereas health care providers face risks due to the
associated liability. For medical data, these risks persist even after
anonymizing/deidentifying, according to the standards defined in existing
legislation, the data sets prior to sharing, because shared medical data can
often be deanonymized/reidentified using advanced artificial intelligence and
machine learning methodologies. As a result, health care providers are hesitant
to share medical data. One possible solution to encourage health care providers
to responsibly share data is through the use of cybersecurity insurance
contracts. This paper studies the problem of designing optimal cybersecurity
insurance contracts, with the goal of encouraging the sharing of the medical
data. We use a principal-agent model with moral hazard to model various
scenarios, derive the optimal contract, discuss its implications, and perform
numerical case studies. In particular, we consider two scenarios: the first
scenario is where a health care provider is selling medical data to a
technology firm who is developing an artificial intelligence algorithm using
the shared data. The second scenario is where a group of health care providers
share health data amongst themselves for the purpose of furthering medical
research using the aggregated medical data.


title:
Improving adaptability to new environments and removing catastrophic
  forgetting in Reinforcement Learning by using an eco-system of agents

abstract:
  Adapting a Reinforcement Learning (RL) agent to an unseen environment is a
difficult task due to typical over-fitting on the training environment. RL
agents are often capable of solving environments very close to the trained
environment, but when environments become substantially different, their
performance quickly drops. When agents are retrained on new environments, a
second issue arises: there is a risk of catastrophic forgetting, where the
performance on previously seen environments is seriously hampered. This paper
proposes a novel approach that exploits an ecosystem of agents to address both
concerns. Hereby, the (limited) adaptive power of individual agents is
harvested to build a highly adaptive ecosystem. This allows to transfer part of
the workload from learning to inference. An evaluation of the approach on two
distinct distributions of environments shows that our approach outperforms
state-of-the-art techniques in terms of adaptability/generalization as well as
avoids catastrophic forgetting.


title: Simulations of nonradiative processes in semiconductor nanocrystals

abstract:
  The description of carrier dynamics in spatially confined semiconductor
nanocrystals (NCs), which have enhanced electron-hole and exciton-phonon
interactions, is a great challenge for modern computational science. These NCs
typically contain thousands of atoms and tens of thousands of valence electrons
with a discrete spectrum at low excitation energies, similar to atoms and
molecules, that converges to the continuum bulk limit at higher energies.
Computational methods developed for molecules are limited to very small
nanoclusters, and methods for bulk systems with periodic boundary conditions
are not suitable due to the lack of translational symmetry in NCs. This
perspective focuses on our recent efforts in developing a unified atomistic
model based on the semiempirical pseudopotential approach, which is
parametrized by first-principles calculations and validated against
experimental measurements, to describe two of the main nonradiative relaxation
processes of quantum confined excitons: exciton cooling and Auger
recombination. We focus on the description of both electron-hole and
exciton-phonon interactions in our approach and discuss the role of size,
shape, and interfacing on the electronic properties and dynamics for II-VI and
III-V semiconductor NCs.


title: Neural Vector Fields for Surface Representation and Inference

abstract:
  Neural implicit fields have recently been shown to represent 3D shapes
accurately, opening up various applications in 3D shape analysis. Up to now,
such implicit fields for 3D representation are scalar, encoding the signed
distance or binary volume occupancy and more recently the unsigned distance.
However, the first two can only represent closed shapes, while the unsigned
distance has difficulties in accurate and fast shape inference. In this paper,
we propose a Neural Vector Field for shape representation in order to overcome
the two aforementioned problems. Mapping each point in space to the direction
towards the closest surface, we can represent any type of shape. Similarly the
shape mesh can be reconstructed by applying the marching cubes algorithm, with
proposed small changes, on top of the inferred vector field. We compare the
method on ShapeNet where the proposed new neural implicit field shows superior
accuracy in representing both closed and open shapes outperforming previous
methods.


title:
Coarse Graining Empirical Densities and Currents in Continuous-Space
  Steady States

abstract:
  We present the conceptual and technical background required to describe and
understand the correlations and fluctuations of the empirical density and
current of steady-state diffusion processes on all time scales -- observables
central to statistical mechanics and thermodynamics on the level of individual
trajectories. We focus on the important and non-trivial effect of a spatial
coarse graining. Making use of a generalized time-reversal symmetry we provide
deeper insight about the physical meaning of fluctuations of the coarse-grained
empirical density and current, and explain why a systematic variation of the
coarse-graining scale offers an efficient method to infer bounds on a system's
dissipation. Moreover, we discuss emerging symmetries in the statistics of the
empirical density and current, and the statistics in the large deviations
regime. More broadly our work promotes the application of stochastic calculus
as a powerful direct alternative to Feynman-Kac theory and path-integral
methods.


title:
Integrable sigma models at RG fixed points: quantisation as affine
  Gaudin models

abstract:
  The goal of this paper is to make first steps towards the quantisation of
integrable non-linear sigma models using the formalism of affine Gaudin models,
by approaching these theories through their conformal limits. We focus mostly
on the example of the Klim\v{c}\'{i}k model, which is a two-parameter
deformation of the Principal Chiral Model on a Lie group $G$. We show that the
UV fixed point of this theory is described classically by two decoupled chiral
affine Gaudin models, encoding its left- and right-moving degrees of freedom,
and give a detailed analysis of the chiral and integrable structures of these
models. Their quantisation is then explored within the framework of Feigin and
Frenkel. We study the quantum local integrals of motion using the formalism of
quantised affine Gaudin models and show agreement of the first two integrals
with known results in the literature for $G={\rm SU}(2)$. Evidence is given for
the existence of a monodromy matrix satisfying the Yang-Baxter algebra for this
model, thus paving the way for the quantisation of the non-local integrals of
motion. We conclude with various perspectives, including on generalisations of
this program to a larger class of integrable sigma models and applications of
the ODE/IQFT correspondence to the description of their quantum spectrum.


title: Fast Few-shot Debugging for NLU Test Suites

abstract:
  We study few-shot debugging of transformer based natural language
understanding models, using recently popularized test suites to not just
diagnose but correct a problem. Given a few debugging examples of a certain
phenomenon, and a held-out test set of the same phenomenon, we aim to maximize
accuracy on the phenomenon at a minimal cost of accuracy on the original test
set. We examine several methods that are faster than full epoch retraining. We
introduce a new fast method, which samples a few in-danger examples from the
original training set. Compared to fast methods using parameter distance
constraints or Kullback-Leibler divergence, we achieve superior original
accuracy for comparable debugging accuracy.


title:
Gravitational Bremsstrahlung with tidal effects in the post-Minkowskian
  expansion

abstract:
  We compute the mass and current quadrupole tidal corrections to the
four-momentum radiated during the scattering of two spinless bodies, at leading
order in $G$ and at all orders in the velocities, using the effective field
theory worldline approach. Extending our previous work, we compute the
conserved stress-energy tensor linearly coupled to gravity generated by the two
bodies, including tidal fields. The total emitted momentum is obtained by
phase-space integration of the graviton momentum weighted by the modulo squared
of the radiation amplitude, obtained from the stress-energy tensor. The
integral is solved using scattering amplitude techniques. We show that our
expressions are consistent with existing results up to the
next-to-next-to-leading order in the post-Newtonian expansion. Finally, we
derive the asymptotic gravitational wave waveform in direct space.


title: Exciton nature of plasma phase transition in warm dense fluid hydrogen

abstract:
  The transition of a warm dense fluid hydrogen from insulator to a conducting
state at pressures of the order of 20-300 GPa and temperatures of 1000-5000 K
has been the subject of active scientific research over the past few decades.
The use of various experimental methods has not yet led to obtaining reliable
consistent results, and despite numerous attempts, theoretical methods have not
yet been able to explain the existing discrepancies, as well as the microscopic
nature of the mechanism for the transition of hydrogen fluid to a conducting
state. In this work, we have discovered a key mechanism of the transition
associated with formation and dissociation of exciton pairs, which allows to
explain several stages of the transition of hydrogen from molecular state to
plasma. This mechanism is able to give quantitative description of several
experimental results as well as to resolve the discrepancies between different
experimental studies.


title: Controllable Video Generation through Global and Local Motion Dynamics

abstract:
  We present GLASS, a method for Global and Local Action-driven Sequence
Synthesis. GLASS is a generative model that is trained on video sequences in an
unsupervised manner and that can animate an input image at test time. The
method learns to segment frames into foreground-background layers and to
generate transitions of the foregrounds over time through a global and local
action representation. Global actions are explicitly related to 2D shifts,
while local actions are instead related to (both geometric and photometric)
local deformations. GLASS uses a recurrent neural network to transition between
frames and is trained through a reconstruction loss. We also introduce
W-Sprites (Walking Sprites), a novel synthetic dataset with a predefined action
space. We evaluate our method on both W-Sprites and real datasets, and find
that GLASS is able to generate realistic video sequences from a single input
image and to successfully learn a more advanced action space than in prior
work.


title: Topological Multipartite Entanglement in a Fermi Liquid

abstract:
  We show that the topology of the Fermi sea of a $D$-dimensional Fermi gas is
reflected in the multipartite entanglement characterizing $D+1$ regions that
meet at a point. For odd $D$ we introduce the multipartite mutual information,
and show that it exhibits a $\log^D L$ divergence as a function of system size
$L$ with a universal coefficient that is proportional to the Euler
characteristic $\chi_F$ of the Fermi sea. This generalizes the well known
result for $D=1$ that expresses the $\log L$ divergence of the bipartite
entanglement entropy in terms of the central charge $c$ characterizing a
conformal field theory. For even $D$ we introduce a charge-weighted
entanglement entropy that is manifestly odd under a particle-hole
transformation. We show that the corresponding charge-weighted mutual
information exhibits a similar $\log^D L$ divergence proportional to $\chi_F$.
Our analysis relates the universal behavior of the multipartite mutual
information in the absence of interactions to the $D+1$'th order equal-time
density correlation function, which we show exhibits a universal behavior in
the long wavelength limit proportional to $\chi_F$. Our analytic results are
based on the replica method. In addition we perform a numerical study of the
charge-weighted mutual information for $D=2$ that confirms several aspects of
the analytic theory. Finally, we consider the effect of interactions
perturbatively within the replica theory. We show that for $D=3$ the $\log^3 L$
divergence of the topological mutual information is not perturbed by weak
short-ranged interactions, though for $D=2$ the charge-weighted mutual
information is perturbed. Thus, for $D=3$ the multipartite mutual information
provides a robust classification that distinguishes distinct topological Fermi
liquid phases.


title: The dynamic stall dilemma for vertical-axis wind turbines

abstract:
  Vertical-axis wind turbines (VAWT) are excellent candidates to complement
traditional wind turbines and increase the total wind energy capacity.
Development of VAWT has been hampered by their low efficiency and structural
unreliability, which are related to the occurrence of dynamic stall. Dynamic
stall consists of the formation, growth, and shedding of large-scale dynamic
stall vortices, followed by massive flow separation. The vortex shedding is
detrimental to the turbine's efficiency and causes significant load
fluctuations that jeopardise the turbine's structural integrity. We present a
comprehensive experimental characterisation of dynamic stall on a VAWT blade
including time-resolved load and velocity field measurements. Particular
attention is dedicated to the dilemma faced by VAWT to either operate at lower
tip-speed ratios to maximise aerodynamic performance but experience dynamic
stall, or to avoid dynamic stall at the cost of loosing performance. Based on
the results, we map turbine operating conditions to one of three regimes: deep
stall, light stall, and no stall. The light stall regime offers VAWT the best
compromise in the dynamic stall dilemma as it yields positive tangential forces
during the upwind and downwind rotation and reduces load transients by 75%
compared to the deep stall regime.


title: Breakdown of quantisation in a Hubbard-Thouless pump

abstract:
  The fate of topological transport in the strongly correlated regime raises
fundamental questions on the role of geometry in quantum many-body physics.
Recently, different platforms have emerged as possible probes for many-body
transport beyond the traditional Hall response; these include ultracold quantum
gases, Rydberg atoms, and photonics. A paradigm of quantised transport is the
topological Thouless pump, which represents the one-dimensional, dynamic
analogue of the quantum Hall effect. A few experiments have explored the
effects of interactions on Thouless pumping in two-body and optical mean-field
systems, but the strongly correlated regime has so far remained out of reach.
Here, we experimentally detect the breakdown of topological transport due to
strong Hubbard interactions in a fermionic Thouless pump, facilitated by a
dynamical optical superlattice. We observe the deviation from quantised pumping
for repulsive Hubbard $U$, which we attribute to pinned atoms in a Mott
insulator. On the attractive side another mechanism is reducing the pumping
efficiency: a smaller energy gap for pair pumping makes the evolution less
adiabatic. The dynamical superlattice operated at a single frequency
establishes a novel platform for studying topology in the presence of strong
correlations in one, two, and possibly three dimensions, including avenues to
fractional transport.


title: Estimating Structural Disparities for Face Models

abstract:
  In machine learning, disparity metrics are often defined by measuring the
difference in the performance or outcome of a model, across different
sub-populations (groups) of datapoints. Thus, the inputs to disparity
quantification consist of a model's predictions $\hat{y}$, the ground-truth
labels for the predictions $y$, and group labels $g$ for the data points.
Performance of the model for each group is calculated by comparing $\hat{y}$
and $y$ for the datapoints within a specific group, and as a result, disparity
of performance across the different groups can be calculated. In many real
world scenarios however, group labels ($g$) may not be available at scale
during training and validation time, or collecting them might not be feasible
or desirable as they could often be sensitive information. As a result,
evaluating disparity metrics across categorical groups would not be feasible.
On the other hand, in many scenarios noisy groupings may be obtainable using
some form of a proxy, which would allow measuring disparity metrics across
sub-populations. Here we explore performing such analysis on computer vision
models trained on human faces, and on tasks such as face attribute prediction
and affect estimation. Our experiments indicate that embeddings resulting from
an off-the-shelf face recognition model, could meaningfully serve as a proxy
for such estimation.


title: Character-focused Video Thumbnail Retrieval

abstract:
  We explore retrieving character-focused video frames as candidates for being
video thumbnails. To evaluate each frame of the video based on the character(s)
present in it, characters (faces) are evaluated in two aspects:
Facial-expression: We train a CNN model to measure whether a face has an
acceptable facial expression for being in a video thumbnail. This model is
trained to distinguish faces extracted from artworks/thumbnails, from faces
extracted from random frames of videos. Prominence and interactions:
Character(s) in the thumbnail should be important character(s) in the video, to
prevent the algorithm from suggesting non-representative frames as candidates.
We use face clustering to identify the characters in the video, and form a
graph in which the prominence (frequency of appearance) of the character(s),
and their interactions (co-occurrence) are captured. We use this graph to infer
the relevance of the characters present in each candidate frame. Once every
face is scored based on the two criteria above, we infer frame level scores by
combining the scores for all the faces within a frame.


title: Non-Invertible Higher-Categorical Symmetries

abstract:
  We sketch a procedure to capture general non-invertible symmetries of a
d-dimensional quantum field theory in the data of a higher-category, which
captures the local properties of topological defects associated to the
symmetries. We also discuss global fusions of topological defects, where some
of the directions of the defects are wrapped along compact sub-manifolds in
spacetime while some others are left non-compact, and argue that such a global
fusion is described as a local fusion plus a gauging of a higher-categorical
symmetry localized along the compact sub-manifold. Recently some fusions of
topological defects were discussed in the literature where the dimension of
topological defects seems to jump under fusion. We explain how such fusions can
be understood in terms of fusion operations of a higher-category, where the
dimension does not jump. We also discuss how a 0-form sub-symmetry of a
higher-categorical symmetry can be gauged and describe the higher-categorical
symmetry of the theory obtained after gauging. This provides a procedure for
constructing non-invertible higher-categorical symmetries starting from
invertible higher-form or higher-group symmetries and gauging a 0-form
symmetry. We illustrate this procedure by constructing non-invertible
2-categorical symmetries in 4d gauge theories and non-invertible 3-categorical
symmetries in 5d and 6d theories. We check some of the results obtained using
our approach against the results obtained using a recently proposed approach
based on 't Hooft anomalies.


title: Impurity bands in magnetic superconductors with spin density wave

abstract:
  Magnetic superconductors define a broad class of strongly correlated
materials in which superconductivity may coexist with either localized or
itinerant long-range magnetic order. In this work we consider a multiband model
of a disordered magnetic superconductor which realizes coexistence of
unconventional superconductivity and a spin-density-wave. We derive an exact
$T$-matrix and compute a single particle density of states in this system. In a
purely superconducting state the interband scattering potential leads to an
appearance of the localized Yu-Shiba-Rusinov bound states. Our main finding is
that in the fairly broad swath of the coexistence region superconductivity
remains fully gapped despite the presence of the impurity bands. We also
discuss the effects of spatial inhomogeneities on the density of states in
strongly contaminated superconductors.


title: Superpotentials from Singular Divisors

abstract:
  We study Euclidean D3-branes wrapping divisors $D$ in Calabi-Yau orientifold
compactifications of type IIB string theory. Witten's counting of fermion zero
modes in terms of the cohomology of the structure sheaf $\mathcal{O}_D$ applies
when $D$ is smooth, but we argue that effective divisors of Calabi-Yau
threefolds typically have singularities along rational curves. We generalize
the counting of fermion zero modes to such singular divisors, in terms of the
cohomology of the structure sheaf $\mathcal{O}_{\overline{D}}$ of the
normalization $\overline{D}$ of $D$. We establish this by detailing
compactifications in which the singularities can be unwound by passing through
flop transitions, giving a physical incarnation of the normalization process.
Analytically continuing the superpotential through the flops, we find that
singular divisors whose normalizations are rigid can contribute to the
superpotential: specifically,
$h^{\bullet}_{+}(\mathcal{O}_{\overline{D}})=(1,0,0)$ and
$h^{\bullet}_{-}(\mathcal{O}_{\overline{D}})=(0,0,0)$ give a sufficient
condition for a contribution. The examples that we present feature infinitely
many isomorphic geometric phases, with corresponding infinite-order monodromy
groups $\Gamma$. We use the action of $\Gamma$ on effective divisors to
determine the exact effective cones, which have infinitely many generators. The
resulting nonperturbative superpotentials are Jacobi theta functions, whose
modular symmetries suggest the existence of strong-weak coupling dualities
involving inversion of divisor volumes.


title: Analogue viscous current flow near the onset of superconductivity

abstract:
  Spatially resolved transport in two-dimensional quantum materials can reveal
dynamics which is invisible in conventional bulk transport measurements. We
predict striking patterns in spatially inhomogeneous transport just above the
critical temperature in two-dimensional superconducting thin films, where
electrical current will appear to flow as if it were a viscous fluid obeying
the Navier-Stokes equations. Compared to viscous electron fluids in ultrapure
metals such as graphene, this analogue viscous vortex fluid can exhibit a far
more tunable crossover, as a function of temperature, from Ohmic to non-Ohmic
transport, with the latter arising on increasingly large length scales close to
the critical temperature. Experiments using nitrogen vacancy center
magnetometry, or transport through patterned thin films, could reveal this
analogue viscous flow in a wide variety of materials.


title:
Universal gravothermal evolution of isolated self-interacting dark
  matter halos for velocity-dependent cross sections

abstract:
  We study the evolution of isolated self-interacting dark matter (SIDM) halos
using spherically-symmetric gravothermal equations allowing for the scattering
cross section to be velocity dependent. We focus our attention on the large
class of models where the core is in the long mean free path regime for a
substantial time. We find that the temporal evolution exhibits an approximate
universality that allows velocity-dependent models to be mapped onto
velocity-independent models in a well-defined way using the scattering
timescale computed when the halo achieves its minimum central density. We show
how this timescale depends on the halo parameters and an average cross section
computed at the central velocity dispersion when the central density is
minimum. The predicted collapse time is fully defined by the scattering
timescale, with negligible variation due to the velocity dependence of the
cross section. We derive new self-similar solutions that provide an analytic
understanding of the numerical results.


title:
Overestimated inclinations of Milgromian disc galaxies: the case of the
  ultradiffuse galaxy AGC 114905

abstract:
  We present hydrodynamical star-forming simulations in the Milgromian dynamics
(MOND) framework of a gas-rich disc galaxy with properties similar to AGC
114905, which has recently been argued to have a rotation curve (RC) that is
inconsistent with the MOND prediction. Our first model considers the galaxy in
isolation, while our second model includes an external field of $0.05 \,
a_{_0}$, the estimated gravitational field from large-scale structure. We show
that isophotes in the face-on view can differ from circular at the 50% level.
This could mislead observers into overestimating the inclination $i$ between
disc and sky planes. Because RCs require a correction factor of $1/\sin i$, the
actual RC could be much higher than reported by observers. This plausibly
reconciles AGC 114905 with MOND expectations.


title:
Tuning the Topological $\theta$-Angle in Cold-Atom Quantum Simulators of
  Gauge Theories

abstract:
  The topological $\theta$-angle in gauge theories engenders a series of
fundamental phenomena, including violations of charge-parity (CP) symmetry,
dynamical topological transitions, and confinement--deconfinement transitions.
At the same time, it poses major challenges for theoretical studies, as it
implies a sign problem in numerical simulations. Analog quantum simulators open
the promising prospect of treating quantum many-body systems with such
topological terms, but, contrary to their digital counterparts, they have not
yet demonstrated the capacity to control the $\theta$-angle. Here, we
demonstrate how a tunable topological $\theta$-term can be added to a prototype
theory with $\mathrm{U}(1)$ gauge symmetry, a discretized version of quantum
electrodynamics in one spatial dimension. As we show, the model can be realized
experimentally in a single-species Bose--Hubbard model in an optical
superlattice with three different spatial periods, thus requiring only standard
experimental resources. Through numerical calculations obtained from the
time-dependent density matrix renormalization group method and exact
diagonalization, we benchmark the model system, and illustrate how salient
effects due to the $\theta$-term can be observed. These include charge
confinement, the weakening of quantum many-body scarring, as well as the
disappearance of Coleman's phase transition due to explicit breaking of CP
symmetry. This work opens the door towards studying the rich physics of
topological gauge-theory terms in large-scale cold-atom quantum simulators.

categories: cond-mat.quant-gas cond-mat.str-el hep-lat hep-ph quant-ph


title: Charge and Antipodal Matching across Spatial Infinity

abstract:
  We derive the antipodal matching relations used to demonstrate the
equivalence between soft graviton theorems and BMS charge conservation across
spatial infinity. To this end we provide a precise map between Bondi data at
null infinity $\mathscr{I}$ and Beig-Schmidt data at spatial infinity $i^0$ in
a context appropriate to the gravitational scattering problem and celestial
holography. In addition, we explicitly match the various proposals of BMS
charges at $\mathscr{I}$ found in the literature with the conserved charges at
$i^0$.


title:
Spatially Resolved Gas-phase Metallicity in FIRE-2 Dwarfs: Late-Time
  Evolution of Metallicity Relations in Simulations with Feedback and Mergers

abstract:
  We present an analysis of spatially resolved gas-phase metallicity relations
in five dwarf galaxies ($M_{halo} \approx 10^{11} M_\odot$, $M_\star \approx
10^{8.8}-10^{9.6} M_\odot$) from the FIRE-2 (Feedback in Realistic
Environments) cosmological zoom-in simulation suite, which include an explicit
model for sub-grid turbulent mixing of metals in gas, near $z\approx 0$, over a
period of 1.4 Gyrs, and compare our findings with observations. While these
dwarf galaxies represent a diverse sample, we find that all simulated galaxies
match the observed mass-metallicity (MZR) and mass-metallicity gradient (MZGR)
relations. We note that in all five galaxies, the metallicities are effectively
identical between phases of the interstellar medium (ISM), with 95$\%$ being
within $\pm$0.1 dex between various ISM phases, including the cold and dense
gas ($T < 500$ K and $n_{\rm H} > 1$ cm$^{-3}$), ionized gas (near the
H$\alpha$ $T \approx 10^4$ K ridge-line), and nebular regions (ionized gas
where the 10 Myr-averaged star formation rate is non-zero). We find that most
of the scatter in relative metallicity between cold and dense gas and ionized
gas/nebular regions can be attributed to either local starburst events or
metal-poor inflows. We also note the presence of a major merger in one of our
galaxies, m11e, with a substantial impact on the metallicity distribution in
the spatially resolved map, showing two strong metallicity peaks and triggering
a starburst in the main galaxy.


title:
Ground States and Dynamical Properties of $S>1/2$ Quantum Heisenberg
  Model on the 1/5-Depleted Square Lattice

abstract:
  We study the $S>1/2$ antiferromagnetic Heisenberg model on the 1/5-depleted
square lattice as a function of the ratio of the intra-plaquette coupling to
the inter-plaquette coupling. Using stochastic series expansion quantum Monte
Carlo simulations, we numerically identify three quantum phases, including the
dimer phase, N\'eel phase and plaquette valence bond solid phase. We also
obtain the accurate quantum critical points that belong to the O(3)
universality class using the large-scale finite-size scaling. Most importantly,
we study the dynamic spin structure factors of different phases, which can be
measured by inelastic neutron scattering experiments. The low-energy
excitations can be explained as triplons in the dimer phase and plaquette
valence bond solid phase. While in the N\'eel phase, the more prominent magnon
mode can be found as the spin magnitude increases. Furthermore, we find a
broader continuum at smaller $S$, which may be the dynamical signature of
nearly deconfined spinon excitations.


title: Moir\'e Gravity and Cosmology

abstract:
  The vacuum catastrophe is a fundamental puzzle, where the observed scales of
the cosmological constant are many orders of magnitude smaller than the natural
scales expected in the theory. This work proposes a new "bi-world" construction
that may offer an insight into the cosmological constant problem. The model
includes a (3+1)-dimensional manifold with two different geometries and matter
fields residing on them. The diffeomorphism invariance and causality highly
constrain the two metrics to be conformally related, $\eta_{\mu \nu} = \phi^2
g_{\mu \nu}$. This reduces the theory to a standard single-world description,
but introduces a new inherently geometrical "moir\'e field," $\phi$.
Interestingly, the moir\'e field has the character of both a dilaton and Higgs
field familiar in the conventional theory. Integrating out the moir\'e field
naturally gives rise to the Starobinsky action and inflationary dynamics. In
the framework of the Friedmann-Lemaitre-Robertson-Walker solution, we reduce an
effective action for the moir\'e field to that of a particle moving in a
Mexican hat potential. The equations of motion are then solved numerically and
the moir\'e field is shown to approach a Mexican-hat minimum in an oscillatory
fashion, which is accompanied by the decay of the Hubble parameter. Under
additional reasonable assumptions, the vacuum energy asymptotically approaches
zero in the end of inflationary evolution. The physics presented here shares
similarities with the moir\'e phenomena in condensed matter and elsewhere,
where two similar structures superimposed upon give rise to a superstructure
with low emergent energy scales compared to the native theories.


title:
Discovery and analysis of three magnetic hot subdwarf stars: evidence
  for merger-induced magnetic fields

abstract:
  Magnetic fields can play an important role in stellar evolution. Among white
dwarfs, the most common stellar remnant, the fraction of magnetic systems is
more than 20 per cent. The origin of magnetic fields in white dwarfs, which
show strengths ranging from 40 kG to hundreds of MG, is still a topic of
debate. In contrast, only one magnetic hot subdwarf star has been identified
out of thousands of known systems. Hot subdwarfs are formed from binary
interaction, a process often associated with the generation of magnetic fields,
and will evolve to become white dwarfs, which makes the lack of detected
magnetic hot subdwarfs a puzzling phenomenon. Here we report the discovery of
three new magnetic hot subdwarfs with field strengths in the range 300-500 kG.
Like the only previously known system, they are all helium-rich O-type stars
(He-sdOs). We analysed multiple archival spectra of the three systems and
derived their stellar properties. We find that they all lack radial velocity
variability, suggesting formation via a merger channel. However, we derive
higher than typical hydrogen abundances for their spectral type, which are in
disagreement with current model predictions. Our findings suggest a lower limit
to the magnetic fraction of hot subdwarfs of 0.147 (+0.143/-0.047) per cent,
and provide evidence for merger-induced magnetic fields which could explain
white dwarfs with field strengths of 50-150 MG, assuming magnetic flux
conservation.