title:
Sample, Translate, Recombine: Leveraging Audio Alignments for Data
  Augmentation in End-to-end Speech Translation

abstract:
  End-to-end speech translation relies on data that pair source-language speech
inputs with corresponding translations into a target language. Such data are
notoriously scarce, making synthetic data augmentation by back-translation or
knowledge distillation a necessary ingredient of end-to-end training. In this
paper, we present a novel approach to data augmentation that leverages audio
alignments, linguistic properties, and translation. First, we augment a
transcription by sampling from a suffix memory that stores text and audio data.
Second, we translate the augmented transcript. Finally, we recombine
concatenated audio segments and the generated translation. Besides training an
MT-system, we only use basic off-the-shelf components without fine-tuning.
While having similar resource demands as knowledge distillation, adding our
method delivers consistent improvements of up to 0.9 and 1.1 BLEU points on
five language pairs on CoVoST 2 and on two language pairs on Europarl-ST,
respectively.


title: Quantum Amplitude Interpolation

abstract:
  In this paper we present a method for representing continuous signals with
high precision by interpolating quantum state amplitudes. The method is
inspired by the Nyquist-Shannon sampling theorem, which links continuous and
discrete time signals.
  This method extends our previous method of computing generalized inner
products from integer-valued functions to real-valued functions.


title: UnseenNet: Fast Training Detector for Any Unseen Concept

abstract:
  Training of object detection models using less data is currently the focus of
existing N-shot learning models in computer vision. Such methods use
object-level labels and takes hours to train on unseen classes. There are many
cases where we have large amount of image-level labels available for training
but cannot be utilized by few shot object detection models for training. There
is a need for a machine learning framework that can be used for training any
unseen class and can become useful in real-time situations. In this paper, we
proposed an "Unseen Class Detector" that can be trained within a very short
time for any possible unseen class without bounding boxes with competitive
accuracy. We build our approach on "Strong" and "Weak" baseline detectors,
which we trained on existing object detection and image classification
datasets, respectively. Unseen concepts are fine-tuned on the strong baseline
detector using only image-level labels and further adapted by transferring the
classifier-detector knowledge between baselines. We use semantic as well as
visual similarities to identify the source class (i.e. Sheep) for the
fine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet)
is trained on the ImageNet classification dataset for unseen classes and tested
on an object detection dataset (OpenImages). UnseenNet improves the mean
average precision (mAP) by 10% to 30% over existing baselines (semi-supervised
and few-shot) of object detection on different unseen class splits. Moreover,
training time of our model is <10 min for each unseen class. Qualitative
results demonstrate that UnseenNet is suitable not only for few classes of
Pascal VOC but for unseen classes of any dataset or web. Code is available at
https://github.com/Asra-Aslam/UnseenNet.


title:
Incorporating Multi-Agent Systems Technology in Power and Energy Systems
  of Bangladesh: A Feasibility Study

abstract:
  The power sector of Bangladesh is presently experiencing essential changes as
demand for power services is increasing with rising population and economic
development. With a gradual shift from a rigidly centralized structure to a
more decentralized and fluid setup, fundamentally because of the enormous
advancement of distributed renewable energy sources, the future power system of
the nation requires new control strategies to work efficiently and sustainably
in the face of evolving conditions and constraints. Multi-Agent Systems (MAS)
technology has attributes that meet these prerequisites of modern power systems
and has been shown to be effective in dealing with its distributed and complex
nature. This is a literature-based feasibility study to explore whether MAS
technology is suited to be applied in the context of Bangladesh. For this
preliminary paper, we look at the topic from a holistic perspective and conduct
a meta-review to curate common applications of Multi-Agent System-based
concepts, tools and algorithms on the power and energy sector. We also identify
the top challenges of this domain in Bangladesh and connect the potential
MAS-based solutions to address each challenge. Our qualitative assessment is
motivated to provide a starting point for local researchers eager to experiment
with MAS technology for application in Bangladesh.


title:
Estimator for the anisotropic stress using relativistic effects in
  Large-Scale Structure

abstract:
  The large-scale structure of the Universe is a rich source of information to
test the consistency of General Relativity on cosmological scales. We briefly
describe how the observed distributions of galaxies is affected by
redshift-space distortions, but also by gravitational lensing and other
relativistic effects. Then, we show how one of this relativistic effects, the
gravitational redshift, can be used to build a model independent test that
directly measures the anisotropic stress, i.e. the difference between the two
gravitational potentials that describe spacetime fluctuations of the geometry.


title:
A Cautionary Tale: Dark Energy in Single-Field, Slow-Roll Inflationary
  Models

abstract:
  The current epoch of accelerated cosmic expansion is postulated to be driven
by dark energy, which in the standard model takes the form of a cosmological
constant with equation of state parameter $w=-1$. We propose an innovative
perspective over the nature of dark energy by drawing a parallel with
inflation, which we assume to be driven by a single scalar field, the inflaton.
The inflaton was not a cosmological constant, as indicated by the fact that
inflation ended and by the Planck satellite's constraint of $n_s\neq 1$ at
$8\sigma$ confidence. Therefore, it is interesting to verify whether its
equation of state parameter was measurably different from $-1$. We analyze this
question for a class of single-field slow-roll inflationary models, where the
hierarchy of Hubble slow-roll parameters is truncated at different orders.
Based on the latest Planck and BICEP2/Keck data, we obtain a $68\%$ upper bound
of $1+w < 0.0014$ for the three-parameter model, which gives the best
description to the data. This provides a cautionary tale for drawing
conclusions about the nature of today's dark energy based upon the
non-detection of a deviation from $w=-1$ with current and upcoming cosmological
surveys.


title: Orion A's complete 3D magnetic field morphology

abstract:
  Magnetic fields permeate the interstellar medium and are important in the
star formation process. Determining the 3D magnetic fields of molecular clouds
will allow us to better understand their role in the evolution of these clouds
and formation of stars. We fully reconstruct the approximate three-dimensional
(3D) magnetic field morphology of the Orion A molecular cloud (on scales of a
few to ~100 pc) using Galactic magnetic field models, as well as present
line-of-sight and plane-of-sky magnetic field observations. While previous
studies identified the 3D magnetic field morphology of the Orion A cloud as an
arc shape, in this study we provide the orientation of this arc-shaped field
and its plane-of-sky direction, for the first time. We find that this 3D field
is a tilted, semi-convex (from our point of view) structure and mostly points
in the direction of decreasing latitude and longitude on the plane of the sky,
from our vantage point. The previously identified bubbles and events in this
region were key in shaping this arc-shaped magnetic field morphology.


title:
X-Learner: Learning Cross Sources and Tasks for Universal Visual
  Representation

abstract:
  In computer vision, pre-training models based on largescale supervised
learning have been proven effective over the past few years. However, existing
works mostly focus on learning from individual task with single data source
(e.g., ImageNet for classification or COCO for detection). This restricted form
limits their generalizability and usability due to the lack of vast semantic
information from various tasks and data sources. Here, we demonstrate that
jointly learning from heterogeneous tasks and multiple data sources contributes
to universal visual representation, leading to better transferring results of
various downstream tasks. Thus, learning how to bridge the gaps among different
tasks and data sources is the key, but it still remains an open question. In
this work, we propose a representation learning framework called X-Learner,
which learns the universal feature of multiple vision tasks supervised by
various sources, with expansion and squeeze stage: 1) Expansion Stage:
X-Learner learns the task-specific feature to alleviate task interference and
enrich the representation by reconciliation layer. 2) Squeeze Stage: X-Learner
condenses the model to a reasonable size and learns the universal and
generalizable representation for various tasks transferring. Extensive
experiments demonstrate that X-Learner achieves strong performance on different
tasks without extra annotations, modalities and computational costs compared to
existing representation learning methods. Notably, a single X-Learner model
shows remarkable gains of 3.0%, 3.3% and 1.8% over current pretrained models on
12 downstream datasets for classification, object detection and semantic
segmentation.


title: Efficient conditioned face animation using frontally-viewed embedding

abstract:
  As the quality of few shot facial animation from landmarks increases, new
applications become possible, such as ultra low bandwidth video chat
compression with a high degree of realism. However, there are some important
challenges to tackle in order to improve the experience in real world
conditions. In particular, the current approaches fail to represent profile
views without distortions, while running in a low compute regime. We focus on
this key problem by introducing a multi-frames embedding dubbed Frontalizer to
improve profile views rendering. In addition to this core improvement, we
explore the learning of a latent code conditioning generations along with
landmarks to better convey facial expressions. Our dense models achieves 22% of
improvement in perceptual quality and 73% reduction of landmark error over the
first order model baseline on a subset of DFDC videos containing head
movements. Declined with mobile architectures, our models outperform the
previous state-of-the-art (improving perceptual quality by more than 16% and
reducing landmark error by more than 47% on two datasets) while running on real
time on iPhone 8 with very low bandwidth requirements.


title:
Solving the time-optimal control problem for nonlinear non-autonomous
  linearizable systems

abstract:
  We present the conditions under which the time-optimal control problem for a
nonlinear non-autonomous linearizable system can be solved by the method of
successive approximations, at each step of which a power Markov moment
min-problem is solved. The proposed method can be efficiently implemented by
use of symbolic and numerical calculations.


title: The Degree-Rips Complexes of an Annulus with Outliers

abstract:
  The degree-Rips bifiltration is the most computable of the parameter-free,
density-sensitive bifiltrations in topological data analysis. It is known that
this construction is stable to small perturbations of the input data, but its
robustness to outliers is not well understood. In recent work, Blumberg-Lesnick
prove a result in this direction using the Prokhorov distance and homotopy
interleavings. Based on experimental evaluation, they argue that a more refined
approach is desirable, and suggest the framework of homology inference.
Motivated by these experiments, we consider a probability measure that is
uniform with high density on an annulus, and uniform with low density on the
disc inside the annulus. We compute the degree-Rips complexes of this
probability space up to homotopy type, using the Adamaszek-Adams computation of
the Vietoris-Rips complexes of the circle. These degree-Rips complexes are the
limit objects for the Blumberg-Lesnick experiments. We argue that the homology
inference approach has strong explanatory power in this case, and suggest
studying the limit objects directly as a strategy for further work.


title:
Cosmo-Paleontology: Statistics of Fossil Groups in a Gravity-Only
  Simulation

abstract:
  We present a detailed study of fossil group candidates identified in "Last
Journey", a gravity-only cosmological simulation covering a $(3.4\,
h^{-1}\mathrm{Gpc})^3$ volume with a particle mass resolution of $m_p \approx
2.7 \times 10^9\, h^{-1}\mathrm{M}_\odot$. The simulation allows us to
simultaneously capture a large number of group-scale halos and to resolve their
internal structure. Historically, fossil groups have been characterized by high
X-ray brightness and a large luminosity gap between the brightest and second
brightest galaxy in the group. In order to identify candidate halos that host
fossil groups, we use halo merger tree information to introduce two parameters:
a luminous merger mass threshold ($M_\mathrm{LM}$) and a last luminous merger
redshift cut-off ($z_\mathrm{LLM}$). The final parameter choices are informed
by observational data and allow us to identify a plausible fossil group sample
from the simulation. The candidate halos are characterized by reduced
substructure and are therefore less likely to host bright galaxies beyond the
brightest central galaxy. We carry out detailed studies of this sample,
including analysis of halo properties and clustering. We find that our simple
assumptions lead to fossil group candidates that form early, have higher
concentrations, and are more relaxed compared to other halos in the same mass
range.


title:
Room temperature donor incorporation for quantum devices: arsine on
  germanium

abstract:
  Germanium has emerged as an exceptionally promising material for spintronics
and quantum information applications, with significant fundamental advantages
over silicon. However, efforts to create atomic-scale devices using donor atoms
as qubits have largely focussed on phosphorus in silicon. Positioning
phosphorus in silicon with atomic-scale precision requires a thermal
incorporation anneal, but the low success rate for this step has been shown to
be a fundamental limitation prohibiting the scale-up to large-scale devices.
Here, we present a comprehensive study of arsine (AsH$_3$) on the germanium
(001) surface. We show that, unlike any previously studied dopant precursor on
silicon or germanium, arsenic atoms fully incorporate into substitutional
surface lattice sites at room temperature. Our results pave the way for the
next generation of atomic-scale donor devices combining the superior electronic
properties of germanium with the enhanced properties of arsine/germanium
chemistry that promises scale-up to large numbers of deterministically-placed
qubits.


title:
Manipulation of Dirac band curvature and momentum-dependent g-factor in
  a kagome magnet YMn6Sn6

abstract:
  The Zeeman effect describes the energy change of an atomic quantum state in
magnetic field. The magnitude and the direction of this change depend on the
dimensionless Lande g-factor. In quantum solids, the response of the Bloch
electron states to the magnetic field also exhibits the Zeeman effect with an
effective g-factor that was theoretically predicted to be dependent on the
momentum. While typically negligible in many ordinary solids, the
momentum-dependent variation of the g-factor is theorized to be substantially
enhanced in many topological and magnetic systems. However, the
momentum-dependence of the g-factor is notoriously difficult to extract and it
is yet to be directly experimentally measured. In this work, we report the
experimental discovery of a strongly momentum-dependent g-factor in a kagome
magnet YMn6Sn6. Using spectroscopic-imaging scanning tunneling microscopy, we
map the evolution of a massive Dirac band in the vicinity of the Fermi level as
a function of magnetic field. We find that electronic states at different
lattice momenta exhibit markedly different Zeeman energy shifts, giving rise to
an anomalous g-factor that peaks around the Dirac point. Our work provides the
first momentum-resolved visualization of Dirac band curvature manipulation by
magnetic field, which should in principle be highly relevant to other
topological kagome magnets.


title:
Searches for Baryon Number Violation in Neutrino Experiments: A White
  Paper

abstract:
  Baryon number conservation is not guaranteed by any fundamental symmetry
within the Standard Model, and therefore has been a subject of experimental and
theoretical scrutiny for decades. So far, no evidence for baryon number
violation has been observed. Large underground detectors have long been used
for both neutrino detection and searches for baryon number violating processes.
The next generation of large neutrino detectors will seek to improve upon the
limits set by past and current experiments and will cover a range of lifetimes
predicted by several Grand Unified Theories. In this White Paper, we summarize
theoretical motivations and experimental aspects of searches for baryon number
violation in neutrino experiments.


title: Periodic traveling waves in a taut cable on a bilinear elastic substrate

abstract:
  The wave propagation problem on a taut cable resting on a bilinear substrate
is investigated, without and with a distribute transversal load. The piecewise
nature of the problem offers a sufficiently simple kind of nonlinearity as to
permit a closed form solution both for the wave phase velocity and the wave
form. We show that the solution depends only on the ratio between the two soil
stiffnesses, and that no waves propagate if one side of the substrate is rigid.
Some numerical simulations, based on a finite difference method, are performed
to confirm the analytical findings. The stability of the proposed waves is
discussed analytically and numerically.


title:
Training Data is More Valuable than You Think: A Simple and Effective
  Method by Retrieving from Training Data

abstract:
  Retrieval-based methods have been shown to be effective in NLP tasks via
introducing external knowledge. However, the indexing and retrieving of
large-scale corpora bring considerable computational cost. Surprisingly, we
found that REtrieving from the traINing datA (REINA) only can lead to
significant gains on multiple NLG and NLU tasks. We retrieve the labeled
training instances most similar to the input text and then concatenate them
with the input to feed into the model to generate the output. Experimental
results show that this simple method can achieve significantly better
performance on a variety of NLU and NLG tasks, including summarization, machine
translation, language modeling, and question answering tasks. For instance, our
proposed method achieved state-of-the-art results on XSum, BigPatent, and
CommonsenseQA. Our code is released, https://github.com/microsoft/REINA .


title:
CUE Vectors: Modular Training of Language Models Conditioned on Diverse
  Contextual Signals

abstract:
  We propose a framework to modularize the training of neural language models
that use diverse forms of sentence-external context (including metadata) by
eliminating the need to jointly train sentence-external and within-sentence
encoders. Our approach, contextual universal embeddings (CUE), trains LMs on
one set of context, such as date and author, and adapts to novel metadata
types, such as article title, or previous sentence. The model consists of a
pretrained neural sentence LM, a BERT-based context encoder, and a masked
transformer decoder that estimates LM probabilities using sentence-internal and
sentence-external information. When context or metadata are unavailable, our
model learns to combine contextual and sentence-internal information using
noisy oracle unigram embeddings as a proxy. Real contextual information can be
introduced later and used to adapt a small number of parameters that map
contextual data into the decoder's embedding space. We validate the CUE
framework on a NYTimes text corpus with multiple metadata types, for which the
LM perplexity can be lowered from 36.6 to 27.4 by conditioning on context.
Bootstrapping a contextual LM with only a subset of the context/metadata during
training retains 85\% of the achievable gain. Training the model initially with
proxy context retains 67% of the perplexity gain after adapting to real
context. Furthermore, we can swap one type of pretrained sentence LM for
another without retraining the context encoders, by only adapting the decoder
model. Overall, we obtain a modular framework that allows incremental, scalable
training of context-enhanced LMs.


title:
Practical Conditional Neural Processes Via Tractable Dependent
  Predictions

abstract:
  Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning
models which leverage the flexibility of deep learning to produce
well-calibrated predictions and naturally handle off-the-grid and missing data.
CNPs scale to large datasets and train with ease. Due to these features, CNPs
appear well-suited to tasks from environmental sciences or healthcare.
Unfortunately, CNPs do not produce correlated predictions, making them
fundamentally inappropriate for many estimation and decision making tasks.
Predicting heat waves or floods, for example, requires modelling dependencies
in temperature or precipitation over time and space. Existing approaches which
model output dependencies, such as Neural Processes (NPs; Garnelo et al.,
2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to
train or prohibitively expensive. What is needed is an approach which provides
dependent predictions, but is simple to train and computationally tractable. In
this work, we present a new class of Neural Process models that make correlated
predictions and support exact maximum likelihood training that is simple and
scalable. We extend the proposed models by using invertible output
transformations, to capture non-Gaussian output distributions. Our models can
be used in downstream estimation tasks which require dependent function
samples. By accounting for output dependencies, our models show improved
predictive performance on a range of experiments with synthetic and real data.


title:
Dynamics simulation based on density operator and numerical analysis of
  arbitrary time-dependent $\mathcal{PT}$-symmetric system and the influence of
  quantum noises

abstract:
  $\mathcal{PT}$-symmetric system has attracted extensive attention in recent
years because of its unique properties and applications. How to simulate
$\mathcal{PT}$-symmetric system in traditional quantum mechanical system not
only has basic theoretical significance, but also has practical application
value. We proposal a dynamics simulation scheme of arbitrary time-dependent
$\mathcal{PT}$-symmetric system based on density operator. Based on that, we
further study the influence of quantum noises on the simulation results with
the technique of vectorization of density operator and matrixization of
superoperators (VDMS), and we show the depolarizing (Dep) noise is the most
fatal and should be avoided as much as possible. Meanwhile, we also give a
numerical analysis of the above works. By theoretical analysis and numerical
calculation, we find the problem of chronological product may have to be solved
not only in numerical calculation, but also even in experiment, because the
delated higher-dimensional Hamiltonian is usually time-dependent. And the
solution of chronological product problem is actually one of the key factors to
the accuracy of dynamics simulation of the time-dependent
$\mathcal{PT}$-symmetric system, and even the most key factor. We prove that
the trusted duration of numerical calculation is actually bounded by the
critical time $T_c$ of convergence of Magnus series, while the implemented
duration of experimental running is actually bounded by the critical time $T_l$
of legitimacy of dilation method.


title: Object discovery and representation networks

abstract:
  The promise of self-supervised learning (SSL) is to leverage large amounts of
unlabeled data to solve complex tasks. While there has been excellent progress
with simple, image-level learning, recent methods have shown the advantage of
including knowledge of image structure. However, by introducing hand-crafted
image segmentations to define regions of interest, or specialized augmentation
strategies, these methods sacrifice the simplicity and generality that makes
SSL so powerful. Instead, we propose a self-supervised learning paradigm that
discovers this image structure by itself. Our method, Odin, couples object
discovery and representation networks to discover meaningful image
segmentations without any supervision. The resulting learning paradigm is
simpler, less brittle, and more general, and achieves state-of-the-art transfer
learning results for object detection and instance segmentation on COCO, and
semantic segmentation on PASCAL and Cityscapes, while strongly surpassing
supervised pre-training for video segmentation on DAVIS.


title:
$^1$S$_0$ pairing gaps, chemical potential and entrainment matrix in
  superfluid neutron-star cores for the Brussels-Montreal functionals

abstract:
  Temperature and velocity-dependent $^1$S$_0$ pairing gaps, chemical
potentials and entrainment matrix in dense homogeneous neutron-proton
superfluid mixtures constituting the outer core of neutron stars, are
determined fully self-consistently by solving numerically the time-dependent
Hartree-Fock-Bogoliubov equations over the whole range of temperatures and flow
velocities for which superfluidity can exist. Calculations have been made for
$npe\mu$ in beta-equilibrium using the Brussels-Montreal functional BSk24. The
accuracy of various approximations is assessed and the physical meaning of the
different velocities and momentum densities appearing in the theory is
clarified. Together with the unified equation of state published earlier, the
present results provide consistent microscopic inputs for modeling superfluid
neutron-star cores.


title: Snowmass 2021 White Paper: The Selena Neutrino Experiment

abstract:
  Imaging devices made from an ionization target layer of amorphous selenium
(aSe) coupled to a silicon complementary metal-oxide-semiconductor (CMOS)
active pixel array for charge readout are a promising technology for neutrino
physics. The high spatial resolution in a solid-state target provides
unparalleled rejection of backgrounds from natural radioactivity in the search
for neutrinoless $\beta\beta$ decay and for electron neutrino ($\nu_e$)
spectroscopy with $^{82}$Se. In this white paper, we summarize the broad
scientific program of a large imaging detector with a 10-ton target of
$^{82}$Se. We review the detector technology, and outline the ongoing research
program to realize this experiment.


title: Frustration on a centred pyrochlore lattice in metal-organic frameworks

abstract:
  Geometric frustration inhibits magnetic systems from ordering, opening a
window beyond Landau's paradigm into topological matter and emergent gauge
fields, with potential applications to quantum computing. In most systems
frustration stems from one of only two elementary units of spins: either
triangles or tetrahedra. Here we explore an alternative route for frustration
via molecular design in the metal-azolate framework [Mn(II)(ta)2], whose units
are centred tetrahedra made of five spins. Despite a Curie-Weiss temperature of
-21 K indicating the energy scale of magnetic interactions, [Mn(II)(ta)2]
orders only at 0.43 K, putting it firmly in the category of highly frustrated
magnets. Using a combination of simulations and field theory, we show that this
material is proximate to a spin liquid described as a three-dimensional "slab"
of a four-dimensional emergent electric field. The large degeneracy and
robustness of this spin liquid, and additional chemical possibilities in the
metal-organic frameworks beyond the pyrochlore structure, unlock a new door in
frustrated magnetism.


title:
GLASS: A Citizen-Centric Distributed Data-Sharing Model within an
  e-Governance Architecture

abstract:
  E-governance is a process that aims to enhance a government's ability to
simplify all the processes that may involve government, citizens, businesses,
and so on. The rapid evolution of digital technologies has often created the
necessity for the establishment of an e-Governance model. There is often a need
for an inclusive e-governance model with integrated multiactor governance
services and where a single market approach can be adopted. e-Governance often
aims to minimise bureaucratic processes, while at the same time including a
digital-by-default approach to public services. This aims at administrative
efficiency and the reduction of bureaucratic processes. It can also improve
government capabilities, and enhances trust and security, which brings
confidence in governmental transactions. However, solid implementations of a
distributed data sharing model within an e-governance architecture is far from
a reality; hence, citizens of European countries often go through the tedious
process of having their confidential information verified. This paper focuses
on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed
file-exchange network for security, transparency, cost-effectiveness and trust
(GLASS) model, which aims to ensure that a citizen can control their
relationship with governmental agencies. The paper thus proposes an approach
that integrates a permissioned blockchain with the InterPlanetary File System
(IPFS). This method demonstrates how we may encrypt and store verifiable
credentials of the GLASS ecosystem, such as academic awards, ID documents and
so on, within IPFS in a secure manner and thus only allow trusted users to read
a blockchain record, and obtain the encryption key. This allows for the
decryption of a given verifiable credential that stored on IPFS. This paper
outlines the creation of a demonstrator that proves the principles of the GLASS
approach.


title: The Mercedes water Cherenkov detector

abstract:
  The concept of a small, single-layer water Cherenkov detector, with three
photomultiplier tubes (PMT), placed at its bottom in a $120^{\circ}$ star
configuration (\emph{Mercedes} WCD) is presented. The PMTs are placed near the
lateral walls of the stations with an adjustable inclination and may be
installed inside or outside the water volume. To illustrate the technical
viability of this concept and obtain a first-order estimation of its cost, an
engineering design was elaborated. The sensitivity of these stations to low
energy EAS electrons, photons and muons is discussed, both in compact and
sparse array configurations. It is shown that the analysis of the intensity and
time patterns of the PMT signals, using Machine Learning techniques, enables
the tagging of muons, achieving an excellent gamma/hadron discrimination for
TeV showers. This concept minimises the station production and maintenance
costs, allowing for a highly flexible and fast installation. Mercedes WCDs are
thus well-suited for use in high-altitude large gamma-ray observatories
covering an extended energy range from the low energies, closing the gap
between satellite and ground-based measurements, to very high energy regions,
beyond the PeV scale.

categories: physics.ins-det astro-ph.IM hep-ex hep-ph


title: $2$-Categories from a Gray Perspective

abstract:
  In this paper we present $2$-category theory from the perspective of
Gray-categories using the graphical calculus of separated surface diagrams. As
an extended example we consider cones and limits of $2$-functors. Then we use
the canonical adjunction between $2$-computads and $2$-categories to interpret
the comparison structure of lax functors and extend the surface diagram
calculus with compositor sheets in order to represent and reason about them.


title:
Liquid Heterostructures: Generation of Liquid-Liquid Interfaces in
  Free-Flowing Liquid Sheets

abstract:
  Chemical reactions and biological processes are often governed by the
structure and transport dynamics of the interface between two liquid phases.
Despite their importance, our microscopic understanding of liquid-liquid
interfaces has been severely hindered by difficulty in accessing the interface
through the bulk liquid. Here we demonstrate a method for generating large-area
liquid-liquid interfaces within free-flowing liquid sheets, which we call
liquid heterostructures. These sheets can be made thin enough to transmit
photons from across the spectrum, which also minimizes the amount of bulk
liquid relative to the interface and makes them ideal targets for a wide range
of spectroscopies and scattering experiments. The sheets are produced with a
microfluidic nozzle that impinges two converging jets of one liquid onto two
sides of a third jet of another liquid. The hydrodynamic forces provided by the
colliding jets both produce a multilayered laminar liquid sheet with the
central jet is flattened in the middle. Infrared microscopy, white light
reflectivity, and imaging ellipsometry measurements demonstrate that the buried
layer has a tunable thickness and displays well-defined liquid-liquid
interfaces, and that the inner layer can be thinner than 100 nm.


title:
FEM modeling and simulation of broadband ultrasonic transducers with
  randomized inhomogeneous backing material

abstract:
  A FEM application for the accurate design of composite backing of ultrasonic
transducers is presented. The idea is to obtain the dependence between the
volume ratio of the tungsten powder in an epoxy matrix used as a backing and
the final pulsed RF signal emitted by the backed transducer. An inhomogeneous
material model for the backing was adopted instead of the classic complex
geometrical FEM. The model was developed in Comsol 5.5 using two of its physics
interfaces. The first based on classic FEM or continuous Galerkin for
ultrasonic propagation in the backing and the second based on a discontinuous
Galerkin option for fluid pulsed propagation. In this way it is possible to
avoid the use of an impedance-based model. The calculations for the resulting
bandwidth are good enough as predicted for Desilets for the 1D case by using
the KLM circuital model. We worked in a 2D space, where the transducer face
vibration is far away from the assumed piston-like considered by the
unidimensional KLM model.


title: Finite Sample t-Tests for High-Dimensional Means

abstract:
  Size distortion can occur if an asymptotic testing procedure requiring
diverging sample sizes, is implemented to data with very small sample sizes. In
this paper, we consider one-sample and two-sample tests for mean vectors when
data are high-dimensional but sample sizes are very small. We establish
asymptotic t-distributions of one-sample and two-sample U-statistics, which
only require data dimensionality to diverge but sample sizes to be fixed and no
less than 3. Simulation studies confirm the theoretical results that the
proposed tests maintain accurate empirical sizes for a wide range of sample
sizes and data dimensionalities. We apply the proposed tests to an fMRI dataset
to demonstrate the practical implementation of the methods.


title:
Exploring Variational Graph Auto-Encoders for Extract Class Refactoring
  Recommendation

abstract:
  The code smell is a sign of design and development flaws in a software system
that reduces the reusability and maintainability of the system. Refactoring is
done as an ongoing practice to remove the code smell from the program code.
Among different code smells, the God class or Blob is one of the most common
code smells. A god class contains too many responsibilities, violating
object-oriented programming design's low coupling and high cohesiveness
principles. This paper proposes an automatic approach to extracting a God class
into multiple smaller classes with more specific responsibilities. To do this,
we first construct a graph of methods (as nodes) for the concerning god class.
The edge between any two methods is determined by their structural similarity,
and the feature for each method is initialized using different semantic
representation methods. Then, the variational graph auto-encoder is used to
learn a vector representation for each method. Finally, the learned vectors are
used to cluster methods into different groups to be recommended as refactored
classes. We assessed the proposed framework using three different class
cohesion metrics on sixteen actual God Classes collected from two well-known
open-source systems. We also conducted a comparative study of our approach with
a similar existing approach and found that the proposed approach generated
better results for almost all the God Classes used in the experiment.


title: Are Shortest Rationales the Best Explanations for Human Understanding?

abstract:
  Existing self-explaining models typically favor extracting the shortest
possible rationales - snippets of an input text "responsible for" corresponding
output - to explain the model prediction, with the assumption that shorter
rationales are more intuitive to humans. However, this assumption has yet to be
validated. Is the shortest rationale indeed the most human-understandable? To
answer this question, we design a self-explaining model, LimitedInk, which
allows users to extract rationales at any target length. Compared to existing
baselines, LimitedInk achieves compatible end-task performance and
human-annotated rationale agreement, making it a suitable representation of the
recent class of self-explaining models. We use LimitedInk to conduct a user
study on the impact of rationale length, where we ask human judges to predict
the sentiment label of documents based only on LimitedInk-generated rationales
with different lengths. We show rationales that are too short do not help
humans predict labels better than randomly masked text, suggesting the need for
more careful design of the best human rationales.


title: Koszul Monoids in Quasi-abelian Categories

abstract:
  Suppose that we have a bicomplete closed symmetric monoidal quasi-abelian
category $\mathcal{E}$ with enough projectives, such as the category of
complete bornological spaces $\textbf{CBorn}_k$ or the category of inductive
limits of Banach spaces $\textbf{IndBan}_k$. Working with monoids in
$\mathcal{E}$, we can generalise and extend the Koszul duality theory of
Beilinson, Ginzburg, Soergel. We use an element-free approach to define the
notions of Koszul monoids, and quadratic monoids and their duals. Schneiders'
embedding of a quasi-abelian category into an abelian category, its left heart,
allows us to prove an equivalence of certain subcategories of the derived
categories of graded modules over Koszul monoids and their duals.


title: Analysis of a Gray-Box Operator for Vertex Cover

abstract:
  Combinatorial optimization problems are a prominent application area of
evolutionary algorithms, where the (1+1) EA is one of the most investigated. We
extend this algorithm by introducing some problem knowledge with a specialized
mutation operator which works under the assumption that the number of 1s of a
solution is critical, as frequently happens in combinatorial optimization. This
slight modification increases the chance to correct wrongly placed bits while
preserving the simplicity and problem independence of the (1+1) EA. As an
application of our algorithm we examine the vertex cover problem on certain
instances, where we show that it leads to asymptotically better runtimes and
even finds with higher probability optimal solutions in comparison with the
usual (1+1) EA. Precisely, we compare the performance of both algorithms on
paths and on complete bipartite graphs of size $n$. Regarding the path we prove
that, for a particular initial configuration, the \alg1+1 takes in expectation
$\Theta(n^4)$ iterations while the modification reduces this to $\Theta(n^3)$,
and present experimental evidence that such a configuration is reached.
Concerning the complete bipartite graph our modification finds the optimum in
polynomial time with probability $1-1/2^{\Omega(n^\xi)}$ for every positive
constant $\xi < 1$, which improves the known probability of
$1-1/\text{poly}(n)$ for the (1+1) EA..


title:
Pontryagin algebras and the LS-category of moment-angle complexes in the
  flag case

abstract:
  For any flag simplicial complex $K$, we describe the multigraded Poincare
series, the minimal number of relations and the degrees of these relations in
the Pontryagin algebra of the corresponding moment-angle complex $Z_K$. We
compute the LS-category of $Z_K$ for flag complexes and give a lower bound in
the general case. The key observation is that the Milnor-Moore spectral
sequence collapses at the second sheet for flag $K$. We also show that the
results of Panov and Ray about the Pontryagin algebras of Davis- Januszkiewicz
spaces are valid for arbitrary ring coefficients, and introduce the
$\mathbb{Z}\times\mathbb{Z}^m$-grading on the Pontryagin algebras similar to
the multigrading on the cohomology of $Z_K$.


title:
PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical
  Research

abstract:
  There has been significant progress in machine learning algorithms for human
pose estimation that may provide immense value in rehabilitation and movement
sciences. However, there remain several challenges to routine use of these
tools for clinical practice and translational research, including: 1) high
technical barrier to entry, 2) rapidly evolving space of algorithms, 3)
challenging algorithmic interdependencies, and 4) complex data management
requirements between these components. To mitigate these barriers, we developed
a human pose estimation pipeline that facilitates running state-of-the-art
algorithms on data acquired in clinical context. Our system allows for running
different implementations of several classes of algorithms and handles their
interdependencies easily. These algorithm classes include subject
identification and tracking, 2D keypoint detection, 3D joint location
estimation, and estimating the pose of body models. The system uses a database
to manage videos, intermediate analyses, and data for computations at each
stage. It also provides tools for data visualization, including generating
video overlays that also obscure faces to enhance privacy. Our goal in this
work is not to train new algorithms, but to advance the use of cutting-edge
human pose estimation algorithms for clinical and translation research. We show
that this tool facilitates analyzing large numbers of videos of human movement
ranging from gait laboratories analyses, to clinic and therapy visits, to
people in the community. We also highlight limitations of these algorithms when
applied to clinical populations in a rehabilitation setting.


title:
On integral mixed Cayley graphs over non-abelian finite groups admitting
  an abelian subgroup of index 2

abstract:
  Recently, several works by a number of authors have provided with
characterizations of integral simple Cayley graphs over generalized dihedral
groups and generalized dicyclic groups. We generalize and unify these results
in two different ways. Firstly, we work over arbitrary non-abelian finite
groups $G$ admitting an abelian subgroup of index 2. Secondly, our main result
actually characterizes integral mixed Cayley graphs over such finite groups
$G$, in the spirit of a very recent result of Kadyan--Bhattarcharjya in the
abelian case.


title: Double sweep LU decomposition for American options under negative rates

abstract:
  The classic Brennan-Schwartz algorithm to solve the linear complementary
problem, which arises from the finite difference discretization of the partial
differential equation related to American option pricing does not lead to the
exact solution under negative interest rates. This is due to the two exercise
boundaries which may appear under negative interest rate, while the algorithm
was proven to lead to the exact solution in the case of a single exercise
boundary only. This paper explains that two sweeps of the Brennan-Schwartz
algorithm in two directions is enough to recover the exact solution.


title: Zero Pixel Directional Boundary by Vector Transform

abstract:
  Boundaries are among the primary visual cues used by human and computer
vision systems. One of the key problems in boundary detection is the label
representation, which typically leads to class imbalance and, as a consequence,
to thick boundaries that require non-differential post-processing steps to be
thinned. In this paper, we re-interpret boundaries as 1-D surfaces and
formulate a one-to-one vector transform function that allows for training of
boundary prediction completely avoiding the class imbalance issue.
Specifically, we define the boundary representation at any point as the unit
vector pointing to the closest boundary surface. Our problem formulation leads
to the estimation of direction as well as richer contextual information of the
boundary, and, if desired, the availability of zero-pixel thin boundaries also
at training time. Our method uses no hyper-parameter in the training loss and a
fixed stable hyper-parameter at inference. We provide theoretical
justification/discussions of the vector transform representation. We evaluate
the proposed loss method using a standard architecture and show the excellent
performance over other losses and representations on several datasets.


title:
A Continual Learning Framework for Adaptive Defect Classification and
  Inspection

abstract:
  Machine-vision-based defect classification techniques have been widely
adopted for automatic quality inspection in manufacturing processes. This
article describes a general framework for classifying defects from high volume
data batches with efficient inspection of unlabelled samples. The concept is to
construct a detector to identify new defect types, send them to the inspection
station for labelling, and dynamically update the classifier in an efficient
manner that reduces both storage and computational needs imposed by data
samples of previously observed batches. Both a simulation study on image
classification and a case study on surface defect detection via 3D point clouds
are performed to demonstrate the effectiveness of the proposed method.


title:
An asynchronous variational integrator for the phase field approach to
  dynamic fracture

abstract:
  The phase field approach is widely used to model fracture behaviors due to
the absence of the need to track the crack topology and the ability to predict
crack nucleation and branching. In this work, the asynchronous variational
integrators (AVI) is adapted for the phase field approach of dynamic brittle
fractures. The AVI is derived from Hamilton's principle and allows each element
in the mesh to have its own local time step that may be different from others'.
While the displacement field is explicitly updated, the phase field is
implicitly solved, with upper and lower bounds strictly and conveniently
enforced. In particular, the AT1 and AT2 variants are equally easily
implemented. Three benchmark problems are used to study the performances of
both AT1 and AT2 models and the results show that AVI for phase field approach
significantly speeds up the computational efficiency and successfully captures
the complicated dynamic fracture behavior.


title: Quantized scaling dimension of Cooper pair from the black hole interior

abstract:
  We have shown that in holographic superconductivity theory for 3+1
dimensional system, the scaliing dimension of the Cooper pair operator, $
\Delta$, is quantized if we request that the scalar condensation is well
defined inside the black hole as well as outside. We suggest that our method
can be regarded as a calculational tool for $ \Delta$ which in other method
would be highly non-trivial.


title:
The minimal entanglement wedge cross section in the GMMG/GCFT flat
  holography

abstract:
  We focus on a proper candidate for the entanglement wedge in asymptotically
flat bulk geometries that are described by the generalized minimal massive
gravity (GMMG) in the context of the flat holography. To this end, we describe
the boundary by two dimensional Galilean conformal field theory (GCFT) at the
bipartite mixed state of the two disjoint intervals. We make a conjecture on
the minimal entanglement wedge cross section (EWCS) and we find that the
results are consistent with the previous computations on the holographic
entanglement negativity.


title: Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline

abstract:
  Particle tracking is a challenging pattern recognition task at the Large
Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms,
such as those based on the Kalman Filter, achieve excellent performance in
reconstructing the prompt tracks from the collision points. However, they
require dedicated configuration and additional computing time to efficiently
reconstruct the large radius tracks created away from the collision points. We
developed an end-to-end machine learning-based track finding algorithm for the
HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic
about global track positions. In this work, we study the performance of the
Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in
the event, the pipeline simultaneously reconstructs prompt tracks and large
radius tracks with high efficiencies. This new capability offered by the
Exa.TrkX pipeline may enable us to search for new physics in real time.

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


title: Base-Rate Fallacy Redux and a Deep Dive Review in Cybersecurity

abstract:
  This paper examines the current state of the science underlying cybersecurity
research with an emphasis on the non-signature-based intrusion detection
domain. First, the paper re-examines the base-rate fallacy originally published
by Axelsson, putting the impact of false positives into context. Given the
relative high numbers of false positives, the paper argues for deeper analysis
of false positives, akin to the analysis that true positives are treated to.
The second section of the paper examines the metrics being used to analyze
non-signature intrusion detection techniques, the current status quo of
employed metrics, and the impact of the status quo on scientific advancement.
Finally, the paper analyzes the use of online attack graphs and their
applicability, especially in scenarios of constrained environments, such as
Internet of Things devices. The use of offline attack graphs in such
constrained environments is also examined. In essence, a deep dive review
identified multiple areas throughout the field in which the effectiveness and
validity of the scientific method can be greatly improved, e.g., through
removal of logical fallacies.


title:
Physics-Informed Neural Networks with Adaptive Localized Artificial
  Viscosity

abstract:
  Physics-informed Neural Network (PINN) is a promising tool that has been
applied in a variety of physical phenomena described by partial differential
equations (PDE). However, it has been observed that PINNs are difficult to
train in certain "stiff" problems, which include various nonlinear hyperbolic
PDEs that display shocks in their solutions. Recent studies added a diffusion
term to the PDE, and an artificial viscosity (AV) value was manually tuned to
allow PINNs to solve these problems. In this paper, we propose three approaches
to address this problem, none of which rely on an a priori definition of the
artificial viscosity value. The first method learns a global AV value, whereas
the other two learn localized AV values around the shocks, by means of a
parametrized AV map or a residual-based AV map. We applied the proposed methods
to the inviscid Burgers equation and the Buckley-Leverett equation, the latter
being a classical problem in Petroleum Engineering. The results show that the
proposed methods are able to learn both a small AV value and the accurate shock
location and improve the approximation error over a nonadaptive global AV
alternative method.


title: The European Spallation Source neutrino Super Beam

abstract:
  In this Snowmass 2021 white paper, we summarise the Conceptual Design of the
European Spallation Source neutrino Super Beam (ESSvSB) experiment and its
synergies with the possible future muon based facilities, e.g. a Low Energy
nuSTORM and the Muon Collider. The ESSvSB will benefit from the high power, 5
MW, of the European Spallation Source (ESS) LINAC in Lund-Sweden to produce the
world most intense neutrino beam, enabling measurements to be made at the
second oscillation maximum. Assuming a ten-year exposure, physics simulations
show that the CP-invariance violation can be established with a significance of
5 sigma over more than 70% of all values of delta CP and with an error in the
measurement of the delta CP angle of less than 8 degree for all values of delta
CP.
  However, several technological and physics challenges must be further studied
before achieving a final Technical Design. Measuring at the 2nd oscillation
maximum necessitates a very intense neutrino beam with the appropriate energy.
For this, the ESS proton beam LINAC, which is designed to produce the world's
most intense neutron beam, will need to be upgraded to 10 MW power, 2.5 GeV
energy and 28 Hz beam pulse repetition rate. An accumulator ring will be
required for the compression of the ESS LINAC beam pulse from 2.86 ms to 1.3
mus. A high power target station facility will be needed to produce a
well-focused intense (super) mu-neutrino beam. The physics performance of that
neutrino Super Beam in conjunction with a megaton underground Water Cherenkov
neutrino far detector installed at a distance of either 360 km or 540 km from
the ESS, the baseline, has been evaluated.


title: Snowmass'21 Whitepaper -- IsoDAR Overview

abstract:
  IsoDAR@Yemilab is a unique facility for underground neutrino physics. The
system comprises an accelerator-driven $\bar{\nu}_e$ source located next to the
Yemilab LSC 2.3 kt detector. Because this facility is first-of-its-kind, it
opens new approaches to Beyond Standard Model (BSM) physics searches. The
program is most well-known for its capability to perform searches for new
oscillation signatures at high statistics in a model-agnostic manner.
IsoDAR@Yemilab can definitively resolve the question of $\bar{\nu}_e$
disappearance at short baselines. Beyond this, IsoDAR offers a broad range of
searches for new neutrino properties and new particles. The facility uses a
state-of-the art cyclotron, that is now fully designed and is undergoing
protoyping. Preliminary approval to run at Yemilab in South Korea has led to
the completed excavation of caverns. While the accelerator is designed to run
underground, IsoDAR accelerators can also be constructed on the surface,
allowing this project to contribute to the opportunity for production of
life-saving medical isotopes. The capabilites, technical elements, and
deployment studies are well-documented in articles on arXiv, and appear in
multiple Snowmass'21 whitepapers. Rather than repeat this text, this whitepaper
provides a "table of contents" to these documents.


title: Quantum Computing for Data Analysis in High-Energy Physics

abstract:
  Some of the biggest achievements of the modern era of particle physics, such
as the discovery of the Higgs boson, have been made possible by the tremendous
effort in building and operating large-scale experiments like the Large Hadron
Collider or the Tevatron. In these facilities, the ultimate theory to describe
matter at the most fundamental level is constantly probed and verified. These
experiments often produce large amounts of data that require storing,
processing, and analysis techniques that often push the limits of traditional
information processing schemes. Thus, the High-Energy Physics (HEP) field has
benefited from advancements in information processing and the development of
algorithms and tools for large datasets. More recently, quantum computing
applications have been investigated in an effort to understand how the
community can benefit from the advantages of quantum information science. In
this manuscript, we provide an overview of the state-of-the-art applications of
quantum computing to data analysis in HEP, discuss the challenges and
opportunities in integrating these novel analysis techniques into a day-to-day
analysis workflow, and whether there is potential for a quantum advantage.


title:
New directions for surrogate models and differentiable programming for
  High Energy Physics detector simulation

abstract:
  The computational cost for high energy physics detector simulation in future
experimental facilities is going to exceed the current available resources. To
overcome this challenge, new ideas on surrogate models using machine learning
methods are being explored to replace computationally expensive components.
Additionally, differentiable programming has been proposed as a complementary
approach, providing controllable and scalable simulation routines. In this
document, new and ongoing efforts for surrogate models and differential
programming applied to detector simulation are discussed in the context of the
2021 Particle Physics Community Planning Exercise (`Snowmass').

categories: hep-ph cs.LG hep-ex physics.comp-ph physics.ins-det


title:
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical
  Image Set

abstract:
  Access to dermatological care is a major issue, with an estimated 3 billion
people lacking access to care globally. Artificial intelligence (AI) may aid in
triaging skin diseases. However, most AI models have not been rigorously
assessed on images of diverse skin tones or uncommon diseases. To ascertain
potential biases in algorithm performance in this context, we curated the
Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly
curated, and pathologically confirmed image dataset with diverse skin tones.
Using this dataset of 656 images, we show that state-of-the-art dermatology AI
models perform substantially worse on DDI, with receiver operator curve area
under the curve (ROC-AUC) dropping by 27-36 percent compared to the models'
original test results. All the models performed worse on dark skin tones and
uncommon diseases, which are represented in the DDI dataset. Additionally, we
find that dermatologists, who typically provide visual labels for AI training
and test datasets, also perform worse on images of dark skin tones and uncommon
diseases compared to ground truth biopsy annotations. Finally, fine-tuning AI
models on the well-characterized and diverse DDI images closed the performance
gap between light and dark skin tones. Moreover, algorithms fine-tuned on
diverse skin tones outperformed dermatologists on identifying malignancy on
images of dark skin tones. Our findings identify important weaknesses and
biases in dermatology AI that need to be addressed to ensure reliable
application to diverse patients and diseases.


title:
Neural-Network-Directed Genetic Programmer for Discovery of Governing
  Equations

abstract:
  We develop a symbolic regression framework for extracting the governing
mathematical expressions from observed data. The evolutionary approach, faiGP,
is designed to leverage the properties of a function algebra that have been
encoded into a grammar, providing a theoretical guarantee of universal
approximation and a way to minimize bloat. In this framework, the choice of
operators of the grammar may be informed by a physical theory or symmetry
considerations. Since there is currently no theory that can derive the
'constants of nature', an empirical investigation on extracting these
coefficients from an evolutionary process is of methodological interest. We
quantify the impact of different types of regularizers, including a diversity
metric adapted from studies of the transcriptome and a complexity measure, on
the performance of the framework. Our implementation, which leverages neural
networks and a genetic programmer, generates non-trivial symbolically
equivalent expressions ("Ramanujan expressions") or approximations with
potentially interesting numerical applications. To illustrate the framework, a
model of ligand-receptor binding kinetics, including an account of gene
regulation by transcription factors, and a model of the regulatory range of the
cistrome from omics data are presented. This study has important implications
on the development of data-driven methodologies for the discovery of governing
equations in experimental data derived from new sensing systems and
high-throughput screening technologies.


title: Broadening the scope of Education, Career and Open Science in HEP

abstract:
  High Energy Particle Physics (HEP) faces challenges over the coming decades
with a need to attract young people to the field and STEM careers, as well as a
need to recognize, promote and sustain those in the field who are making
important contributions to the research effort across the many specialties
needed to deliver the science. Such skills can also serve as attractors for
students who may not want to pursue a PhD in HEP but use them as a springboard
to other STEM careers. This paper reviews the challenges and develops
strategies to correct the disparities to help transform the particle physics
field into a stronger and more diverse ecosystem of talent and expertise, with
the expectation of long-lasting scientific and societal benefits.


title:
Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On
  Federated Learning using Multiview Pseudo-Labeling

abstract:
  Speech Emotion Recognition (SER) application is frequently associated with
privacy concerns as it often acquires and transmits speech data at the
client-side to remote cloud platforms for further processing. These speech data
can reveal not only speech content and affective information but the speaker's
identity, demographic traits, and health status. Federated learning (FL) is a
distributed machine learning algorithm that coordinates clients to train a
model collaboratively without sharing local data. This algorithm shows enormous
potential for SER applications as sharing raw speech or speech features from a
user's device is vulnerable to privacy attacks. However, a major challenge in
FL is limited availability of high-quality labeled data samples. In this work,
we propose a semi-supervised federated learning framework, Semi-FedSER, that
utilizes both labeled and unlabeled data samples to address the challenge of
limited labeled data samples in FL. We show that our Semi-FedSER can generate
desired SER performance even when the local label rate l=20 using two SER
benchmark datasets: IEMOCAP and MSP-Improv.


title: Analysis Cyberinfrastructure: Challenges and Opportunities

abstract:
  Analysis cyberinfrastructure refers to the combination of software and
computer hardware used to support late-stage data analysis in High Energy
Physics (HEP). For the purposes of this white paper, late-stage data analysis
refers specifically to the step of transforming the most reduced common data
format produced by a given experimental collaboration (for example, nanoAOD for
the CMS experiment) into histograms. In this white paper, we reflect on
observations gathered from a recent experience with data analysis using a
recent, python-based analysis framework, and extrapolate these experiences
though the High-Luminosity LHC era as way of highlighting potential R\&D topics
in analysis cyberinfrastructure.


title:
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on
  Screening Mammography

abstract:
  A major limitation in applying deep learning to artificial intelligence (AI)
systems is the scarcity of high-quality curated datasets. We investigate strong
augmentation based self-supervised learning (SSL) techniques to address this
problem. Using breast cancer detection as an example, we first identify a
mammogram-specific transformation paradigm and then systematically compare four
recent SSL methods representing a diversity of approaches. We develop a method
to convert a pretrained model from making predictions on uniformly tiled
patches to whole images, and an attention-based pooling method that improves
the classification performance. We found that the best SSL model substantially
outperformed the baseline supervised model. The best SSL model also improved
the data efficiency of sample labeling by nearly 4-fold and was highly
transferrable from one dataset to another. SSL represents a major breakthrough
in computer vision and may help the AI for medical imaging field to shift away
from supervised learning and dependency on scarce labels.


title: Example Perplexity

abstract:
  Some examples are easier for humans to classify than others. The same should
be true for deep neural networks (DNNs). We use the term example perplexity to
refer to the level of difficulty of classifying an example. In this paper, we
propose a method to measure the perplexity of an example and investigate what
factors contribute to high example perplexity. The related codes and resources
are available at https://github.com/vaynexie/Example-Perplexity.


title: Compact relativistic geometries in $f(R,G)$ gravity

abstract:
  One of the possible potential candidates for describing the universe's rapid
expansion is modified gravity. In the framework of the modified theory of
gravity $f(R,G)$, the present work features the materialization of anisotropic
matter, such as compact stars. Specifically, to learn more about the physical
behavior of compact stars, the radial, and tangential pressures as well as the
energy density of six stars namely $Her X-1$, $SAXJ1808.4-3658$, $4U1820-30$,
$PSR J 1614 2230$, $VELA X-1$, and $Cen X-3$ are calculated. Herein, the
modified theory of gravity $f(R,G)$ is disintegrated into two parts i.e. the
$\tanh$ hyperbolic $f(R)$ model and the three different $f(G)$ model. The study
focuses on graphical analysis of compact stars wherein the stability aspects,
energy conditions, and anisotropic measurements are mainly addressed. Our
calculation revealed that, for the positive value of parameter n of the model
$f(G)$, all the six stars behave normally.


title: QUBOs for Sorting Lists and Building Trees

abstract:
  We show that the fundamental tasks of sorting lists and building search trees
or heaps can be modeled as quadratic unconstrained binary optimization problems
(QUBOs). The idea is to understand these tasks as permutation problems and to
devise QUBOs whose solutions represent appropriate permutation matrices. We
discuss how to construct such QUBOs and how to solve them using Hopfield nets
or adiabatic) quantum computing. In short, we show that neurocomputing methods
or quantum computers can solve problems usually associated with abstract data
structures.


title: Comparison of the Parkes and FAST FRB DM Distribution

abstract:
  We model the Fast Radio Burst (FRB) dispersion measure (DM) distribution for
the Five-hundred-meter Aperture Spherical Telescope (FAST) and compare this
with the four FRBs published in the literature to date. We compare the DM
distribution of Parkes and FAST, taking advantage of the similarity between
their multibeam receivers. Notwithstanding the limited sample size, we observe
a paucity of events at low DM for all evolutionary models considered, resulting
in a sharp rise in the observed cumulative distribution function (CDF) in the
region of $1000\lesssim\mathrm{DM}\lesssim2000$ pc cm$^{-3}$. These traits
could be due to statistical fluctuations ($0.12 \le p \le 0.22$), a complicated
energy distribution or break in an energy distribution power law, spatial
clustering, observational bias or outliers in the sample (e.g., an excessive
DM_${HOST}$ as recently found for FRB 20190520B). The energy distribution in
this regime is unlikely to be adequately constrained until further events are
detected. Modelling suggests that FAST may be well placed to discriminate
between redshift evolutionary models and to probe the helium ionisation signal
of the IGM.


title:
Experiments on the Electrostatic Transport of Charged Anorthite
  Particles under Electron Beam Irradiation

abstract:
  To reveal the effect of secondary electron emission on the charging
properties of a surface covered by micron-sized insulating dust particles and
the migration characteristics of these particles, for the first time, we used a
laser Doppler method to measure the diameters and velocities of micron-sized
anorthite particles under electron beam irradiation with an incident energy of
350 eV. Here, anorthite particles are being treated as a proxy for lunar
regolith. We experimentally confirm that the vertical transport of anorthite
particles is always dominant, although the horizontal transport occurs. In our
experiments, some anorthite particles were observed to have large vertical
velocities up to 9.74 m~s$^{-1}$ at the measurement point. The upper boundary
of the vertical velocities $V_{\rm{z}}$ of these high-speed anorthite particles
are well constrained by its diameter $D$, that is, $V_{\rm{z}}^2$ linearly
depends on $D^{-2}$. These velocity-diameter data provide strong constraints on
the dust charging and transportation mechanisms. The shared charge model could
not explain the observed velocity-diameter data. Both the isolated charge model
and patched charge model appear to require a large dust charging potential of
$-$350 to $-$78 V to reproduce the observed data. The micro-structures of the
dusty surface may play an important role in producing this charging potential
and in understanding the pulse migration phenomenon observed in our experiment.
The presented results and analysis in this paper are helpful for understanding
the dust charging and electrostatic transport mechanisms in airless celestial
bodies such as the Moon and asteroids in various plasma conditions.

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


title: A fully Lagrangian DEM-MPS mesh-free model for ice-wave dynamics

abstract:
  This paper develops and evaluates a novel 3D fully-Lagrangian
(particle-based) numerical model, based on the hybrid discrete element method
(DEM) and moving particle semi-implicit (MPS) mesh-free techniques, for
modeling the highly-dynamic ice-wave interactions. Both MPS and DEM belong to
mesh-free Lagrangian (particle) techniques. The model considers ice-wave
dynamics as a multiphase continuum-discrete system. While the MPS solves the
continuum equations of free-surface flow in a Lagrangian particle-based domain,
the DEM uses a multi-sphere Hertzian contact dynamic model to simulate the ice
floes motion and interaction. The hybrid model predicts the motion and
collision of ice floes as well as their interaction with water, boundaries, and
any obstacle in their way. Considering the mesh-free Lagrangian nature of both
DEM and MPS, the developed model has an inherent ability to predict the
free-drift (absence of internal stress) movements of the ice floes, e.g.,
sliding, rolling, colliding, and piling-up, in violent free-surface flow. A
small-scale and challenging experiment based on dam-break flow over dry and wet
beds with floating block floes, which mimics the characteristics of an
idealized jam release, has been conducted to provide useful and comprehensive
quality data for the validation of the proposed model, as well as other
numerical models. Experimental and numerical results of the free-surface
profile and the position of the blocks are compared. The results show the
ability of the model to numerically reproduce and predict the complex
three-dimensional dynamic behavior of wave-ice floes interaction. Overall, this
study is a first effort toward developing an ice-wave dynamics within a fully
Lagrangian framework, and its results can be extended to bring an in-depth
understanding of the physics of the real-scale ice-wave or river ice dynamic
problems in the future.


title:
Hierarchical Clustering and Matrix Completion for the Reconstruction of
  World Input-Output Tables

abstract:
  World Input-Output (I/O) matrices provide the networks of within- and
cross-country economic relations. In the context of I/O analysis, the
methodology adopted by national statistical offices in data collection raises
the issue of obtaining reliable data in a timely fashion and it makes the
reconstruction of (part of) the I/O matrices of particular interest. In this
work, we propose a method combining hierarchical clustering and Matrix
Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing
entries of a partially unknown I/O matrix. Through simulations based on
synthetic matrices we study the effectiveness of the proposed method to predict
missing values from both previous years data and current data related to
countries similar to the one for which current data are obscured. To show the
usefulness of our method, an application based on World Input-Output Database
(WIOD) tables - which are an example of industry-by-industry I/O tables - is
provided. Strong similarities in structure between WIOD and other I/O tables
are also found, which make the proposed approach easily generalizable to them.


title: DePS: An improved deep learning model for de novo peptide sequencing

abstract:
  De novo peptide sequencing from mass spectrometry data is an important method
for protein identification. Recently, various deep learning approaches were
applied for de novo peptide sequencing and DeepNovoV2 is one of the
represetative models. In this study, we proposed an enhanced model, DePS, which
can improve the accuracy of de novo peptide sequencing even with missing signal
peaks or large number of noisy peaks in tandem mass spectrometry data. It is
showed that, for the same test set of DeepNovoV2, the DePS model achieved
excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino
acid precision and peptide recall respectively. Furthermore, the results
suggested that DePS outperforms DeepNovoV2 on the cross species dataset.


title: Low Background kTon-Scale Liquid Argon Time Projection Chambers

abstract:
  We find that it is possible to increase sensitivity to low energy physics in
a third or fourth DUNE-like module with careful controls over radiopurity and
some modifications to a detector similar to the DUNE Far Detector design. In
particular, sensitivity to supernova and solar neutrinos can be enhanced with
improved MeV-scale reach. A neutrinoless double beta decay search with
$^{136}$Xe loading appears feasible. Furthermore, sensitivity to
Weakly-Interacting Massive Particle (WIMP) Dark Matter (DM) becomes competitive
with the planned world program in such a detector, offering a unique seasonal
variation detection that is characteristic for the nature of WIMPs.


title:
Understanding robustness and generalization of artificial neural
  networks through Fourier masks

abstract:
  Despite the enormous success of artificial neural networks (ANNs) in many
disciplines, the characterization of their computations and the origin of key
properties such as generalization and robustness remain open questions. Recent
literature suggests that robust networks with good generalization properties
tend to be biased towards processing low frequencies in images. To explore the
frequency bias hypothesis further, we develop an algorithm that allows us to
learn modulatory masks highlighting the essential input frequencies needed for
preserving a trained network's performance. We achieve this by imposing
invariance in the loss with respect to such modulations in the input
frequencies. We first use our method to test the low-frequency preference
hypothesis of adversarially trained or data-augmented networks. Our results
suggest that adversarially robust networks indeed exhibit a low-frequency bias
but we find this bias is also dependent on directions in frequency space.
However, this is not necessarily true for other types of data augmentation. Our
results also indicate that the essential frequencies in question are
effectively the ones used to achieve generalization in the first place.
Surprisingly, images seen through these modulatory masks are not recognizable
and resemble texture-like patterns.


title: Rescuing High-Scale Leptogenesis using Primordial Black Holes

abstract:
  We explore the interplay between light primordial black holes (PBH) and
high-scale baryogenesis, with a particular emphasis on leptogenesis. We first
review a generic baryogenesis scenario where a heavy particle, $X$, with mass,
$M_X$, produced solely from PBH evaporation decays to generate a baryon
asymmetry. We show that the viable parameter space is bounded from above by
$M_X \lesssim 10^{17}$ GeV and increases with decreasing $M_X$. We demonstrate
that regions of the leptogenesis parameter space where the lightest
right-handed neutrino (RHN) mass $M_{N_{1}}\gtrsim 10^{15}\,{\rm GeV}$ and
neutrino mass scale $m_\nu\gtrsim 0.1$ eV, excluded in standard cosmology due
to $\Delta L=2$ washout processes, becomes viable with the assistance of light
PBHs. This scenario of PBH-assisted leptogenesis occurs because the PBHs
radiate RHNs via Hawking evaporation late in the Universe's evolution when the
temperature of the thermal plasma is low relative to the RHN mass.
Subsequently, these RHNs can decay and produce a lepton asymmetry while the
washout processes are suppressed.


title: Forecasting dark showers at Belle II

abstract:
  Dark showers from strongly interacting dark sectors that confine at the GeV
scale can give rise to novel signatures at $e^+e^-$ colliders. In this work, we
study the sensitivity of $B$ factory experiments to dark showers produced
through an effective interaction arising from a heavy off-shell mediator. We
show that a prospective search for displaced vertices from GeV-scale long-lived
particles at Belle II can improve the sensitivity to dark showers substantially
compared to an existing search at BaBar. We compare the sensitivity of searches
for displaced signals to searches for promptly produced resonances at BaBar and
KLOE and calculate sensitivity projections for a single-photon search at Belle
II to invisible dark showers produced through an effective interaction. The
underlying structure of the effective interaction can be resolved at
higher-energy experiments, where the mediator can be produced on-shell. To
study the resulting constraints, we update electroweak precision bounds on
kinetically mixed $Z'$ bosons and reinterpret a search for low-mass di-muon
resonances at LHCb in terms of dark showers. We find that LHCb and Belle II are
most sensitive to different particle decay lengths, underscoring the
complementarity of LHC and intensity frontier experiments.


title:
Complementary Signals of Lepton Flavor Violation at a High-Energy Muon
  Collider

abstract:
  A muon collider would be a powerful probe of flavor violation in new physics.
There is a strong complementary case for collider measurements and precision
low-energy probes of lepton flavor violation (as well as CP violation). We
illustrate this by studying the collider reach in a supersymmetric scenario
with flavor-violating slepton mixing. We find that the collider could discover
sleptons and measure the slepton and neutralino masses with high precision,
enabling event reconstruction that could cleanly separate flavor-violating new
physics signals from Standard Model backgrounds. The discovery reach of a
high-energy muon collider would cover a comparably large, and overlapping,
range of parameter space to future $\mu \to e$ conversion and electron EDM
experiments, and unlike precision experiments could immediately shed light on
the nature of new physics responsible for flavor violation. This
complementarity strengthens the case that a muon collider could be an ideal
energy-frontier laboratory in the search for physics beyond the Standard Model.


title: Quantum simulation with just-in-time compilation

abstract:
  We present a first attempt to perform circuit-based quantum simulation using
the just-in-time (JIT) compilation technique on multiple hardware architectures
and configurations based on single-node central processing units (CPUs) and
graphics processing units (GPUs). One of the major challenges in scientific
code development is to balance the level of complexity between algorithms and
programming techniques without losing performance or degrading code
readability. In this context, we have developed qibojit: a new module for the
Qibo quantum computing framework, which uses a just-in-time compilation
approach through Python. We perform systematic performance benchmarks between
our JIT approach and a subset of relevant publicly available libraries for
quantum computing. We show that our novel approach simplifies the complex
aspects of the implementation without deteriorating performance.


title:
Discovering the building blocks of dark matter halo density profiles
  with neural networks

abstract:
  The density profiles of dark matter halos are typically modeled using
empirical formulae fitted to the density profiles of relaxed halo populations.
We present a neural network model that is trained to learn the mapping from the
raw density field containing each halo to the dark matter density profile. We
show that the model recovers the widely-used Navarro-Frenk-White (NFW) profile
out to the virial radius, and can additionally describe the variability in the
outer profile of the halos. The neural network architecture consists of a
supervised encoder-decoder framework, which first compresses the density inputs
into a low-dimensional latent representation, and then outputs $\rho(r)$ for
any desired value of radius $r$. The latent representation contains all the
information used by the model to predict the density profiles. This allows us
to interpret the latent representation by quantifying the mutual information
between the representation and the halos' ground-truth density profiles. A
two-dimensional representation is sufficient to accurately model the density
profiles up to the virial radius; however, a three-dimensional representation
is required to describe the outer profiles beyond the virial radius. The
additional dimension in the representation contains information about the
infalling material in the outer profiles of dark matter halos, thus discovering
the splashback boundary of halos without prior knowledge of the halos'
dynamical history.


title: Robust quantum many-body scars in lattice gauge theories

abstract:
  Quantum many-body scarring is a paradigm of weak ergodicity breaking arising
due to the presence of special nonthermal many-body eigenstates that possess
low entanglement entropy, are equally spaced in energy, and concentrate in
certain parts of the Hilbert space. Though scars have been shown to be
intimately connected to gauge theories, their stability in such experimentally
relevant models is still an open question, and it is generally considered that
they exist only under fine-tuned conditions. In this work, we show through
Krylov-based time-evolution methods how quantum many-body scars can be made
robust in the presence of experimental errors through utilizing terms linear in
the gauge-symmetry generator or a simplified pseudogenerator in $\mathrm{U}(1)$
and $\mathbb{Z}_2$ lattice gauge theories. Our findings are explained by the
concept of quantum Zeno dynamics. Our experimentally feasible methods can be
readily implemented in existing large-scale ultracold-atom quantum simulators
and setups of Rydberg atoms with optical tweezers.

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


title:
Distinguishing 6d (1,0) SCFTs: an extension to the geometric
  construction

abstract:
  We provide a new extension to the geometric construction of 6d $(1,0)$ SCFTs
that encapsulates Higgs branch structures with identical global symmetry but
different spectra. In particular, we find that there exist distinct 6d $(1,0)$
SCFTs that may appear to share their tensor branch description, flavor symmetry
algebras, and central charges. For example, such subtleties arise for the very
even nilpotent Higgsing of $(\mathfrak{so}_{4k}, \mathfrak{so}_{4k})$ conformal
matter; we propose a method to predict at which conformal dimension the Higgs
branch operators of the two theories differ via augmenting the tensor branch
description with the Higgs branch chiral ring generators of the building block
theories. Torus compactifications of these 6d $(1,0)$ SCFTs give rise to 4d
$\mathcal{N}=2$ SCFTs of class $\mathcal{S}$ and the Higgs branch of such 4d
theories are captured via the Hall--Littlewood index. We confirm that the
resulting 4d theories indeed differ in their spectra in the predicted conformal
dimension from their Hall--Littlewood indices. We highlight how this ambiguity
in the tensor branch description arises beyond the very even nilpotent Higgsing
of $(\mathfrak{so}_{4k}, \mathfrak{so}_{4k})$ conformal matter, and hence
should be understood for more general classes of 6d $(1,0)$ SCFTs.


title: Weak Ergodicity Breaking in the Schwinger Model

abstract:
  As a paradigm of weak ergodicity breaking in disorder-free nonintegrable
models, quantum many-body scars (QMBS) can offer deep insights into the
thermalization dynamics of gauge theories. Having been first discovered in a
spin-$1/2$ quantum link formulation of the Schwinger model, it is a fundamental
question as to whether QMBS persist for $S>1/2$ since such theories converge to
the lattice Schwinger model in the large-$S$ limit, which is the appropriate
version of lattice QED in one spatial dimension. In this work, we address this
question by exploring QMBS in spin-$S$ $\mathrm{U}(1)$ quantum link models
(QLMs) with staggered fermions. We find that QMBS persist at $S>1/2$, with the
resonant scarring regime, which occurs for a zero-mass quench, arising from
simple high-energy gauge-invariant initial states. We furthermore find evidence
of detuned scarring regimes, which occur for finite-mass quenches starting in
the physical vacua and the charge-proliferated state. Our results conclusively
show that QMBS exist in a wide class of lattice gauge theories in one spatial
dimension represented by spin-$S$ QLMs coupled to dynamical fermions.

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


title:
Study of the Intermediate Mass Ratio Black Hole Binary Merger up to
  1000:1 with Numerical Relativity

abstract:
  We explicitly demonstrate that current numerical relativity technology is
able to accurately evolve black hole binaries with mass ratios of the order of
1000:1. This has direct implications for future third generation (3G)
gravitational wave detectors and space mission LISA, as by purely numerical
methods we are able to accurately compute gravitational waves, as directly
predicted by general relativity. We perform a sequence of simulations in the
intermediate to small mass ratio regime, $m_1^p/m_2^p = 1/7, 1/16, 1/32, 1/64,
1/128, 1/256, 1/512, 1/1024$, with the small hole starting from rest at a
proper distance $D\approx13M$. We compare this full numerical evolutions with
the corresponding semianalytic perturbative results finding a striking
agreement for the total gravitational radiated energy and linear momentum as
well as for the gravitational waveforms. We display numerical convergence of
the results and identify the minimal numerical resolutions required to
accurately solve these very low amplitude gravitational waves. We conclude that
we have the numerical technology to build up template banks in time for 3G
detectors and LISA.


title: Near-Zone Symmetries of Kerr Black Holes

abstract:
  We study the near-zone symmetries of a massless scalar field on
four-dimensional black hole backgrounds. We provide a geometric understanding
that unifies various recently discovered symmetries as part of an SO(4,2)
group. Of these, a subset are exact symmetries of the static sector and give
rise to the ladder symmetries responsible for the vanishing of Love numbers. In
the Kerr case, we compare different near-zone approximations in the literature,
and focus on the implementation that retains the symmetries of the static
limit. We also describe the relation to spin-1 and 2 perturbations.


title: Axions in String Theory and the Hydra of Dark Radiation

abstract:
  It is widely believed that string theory easily allows for a QCD axion in the
cosmologically favored mass range. The required small decay constant, $f_a\ll
M_P$, can be implemented by using a large compactification volume. This points
to the Large Volume Scenario (LVS), which in turn makes certain cosmological
predictions: First, the axion behaves similarly to a field-theoretic axion in
the pre-inflationary scenario, i.e. the initial value can be tuned but one is
constrained by isocurvature fluctuations. In addition, the volume naively
represents a long-lived modulus, that may lead to an early matter-dominated
phase. Finally, the volume modulus' decay to its own axion tends to produce
excessive dark radiation. In this paper we aim to carefully analyze the
cosmology by studying models that not only allow for a QCD axion but also
include inflation. Quite generally, limits on isocurvature fluctuations
restrict us to relatively low-scale inflation, which in the present stringy
context points to Kahler or blowup inflation models. Moreover, we find as a
novel and at first sight encouraging feature that the lightest (volume) modulus
is likely to couple strongly to the Higgs. It hence quickly decays to the
Standard Model, thus seemingly resolving the dark radiation problem. This decay
is much faster than that of the inflaton such that the latter comes to dominate
the Universe. Since the inflaton distributes its energy equally between the QCD
axion and the Standard Model, this turns out to be a curse rather than a
blessing: Generically, the dark radiation abundance remains too high. We
briefly discuss possibilities to circumvent this issue. In particular, the
rapid decay of the volume modulus into Higgses demotes dark radiation from a
generic LVS problem to an issue resolvable by inflationary model building.


title: Fragmented spin ice and multi-$k$ ordering in rare-earth antiperovskites

abstract:
  We study near-neighbour and dipolar Ising models on a lattice of
corner-sharing octahedra. In an extended parameter range of both models,
frustration between antiferromagnetism and a spin-ice-like three-in-three-out
rule stabilises a Coulomb phase with correlated dipolar and quadrupolar spin
textures, both yielding distinctive neutron-scattering signatures. Strong
further-neighbour perturbations cause the two components to order
independently, resulting in unusual multi-$k$ orders. We propose experimental
realisations of our model in rare-earth antiperovskites.


title:
The Compact Structures of Massive $z\sim0.7$ Post-Starburst Galaxies in
  the SQuIGG$\vec{L}$E Sample

abstract:
  We present structural measurements of 145 spectroscopically selected
intermediate-redshift (z$\sim$0.7), massive ($M_\star \sim 10^{11} \ M_\odot$)
post-starburst galaxies from the SQuIGG$\vec{L}$E Sample measured using
wide-depth Hyper Suprime-Cam i-band imaging. This deep imaging allows us to
probe the sizes and structures of these galaxies, which we compare to a control
sample of star forming and quiescent galaxies drawn from the LEGA-C Survey. We
find that post-starburst galaxies systematically lie $\sim0.1$ dex below the
quiescent mass-size (half-light radius) relation, with a scatter of $\sim0.2$
dex. This finding is bolstered by non-parametric measures, such as the Gini
coefficient and the concentration, which also reveal these galaxies to have
more compact light profiles than both quiescent and star-forming populations at
similar mass and redshift. The sizes of post-starburst galaxies show either
negative or no correlation with the time since quenching, such that more
recently quenched galaxies are larger or similarly sized. This empirical
finding disfavors the formation of post-starburst galaxies via a purely central
burst of star formation that simultaneously shrinks the galaxy and shuts off
star formation. We show that the central densities of post-starburst and
quiescent galaxies at this epoch are very similar, in contrast with their
effective radii. The structural properties of z$\sim$0.7 post-starburst
galaxies match those of quiescent galaxies that formed in the early universe,
suggesting that rapid quenching in the present epoch is driven by a similar
mechanism to the one at high redshift.


title: Theoretical Analysis of Single-ion Anisotropy in d$^3$ Mott Insulators

abstract:
  An effective spin model for Mott insulators is determined by the symmetries
involved among magnetic sites, electron fillings, and their interactions. Such
a spin Hamiltonian offers insight to mechanisms of magnetic orders and magnetic
anisotropy beyond the Heisenberg model. For a spin moment S bigger than 1/2,
single-ion anisotropy is in principle allowed. However, for $d^3$ Mott
insulators with large cubic crystal field splitting, the single-ion anisotropy
is absent within the LS coupling, despite S=3/2 local moment. On the other
hand, preferred magnetic moment directions in $d^3$ materials have been
reported which calls for a further theoretical investigation. Here we derive
the single-ion anisotropy interaction using the strong-coupling perturbation
theory. The cubic crystal field splitting including $e_g$ orbtals, trigonal
distortions, Hund's coupling, and spin-orbit coupling beyond the LS scheme are
taken into account. For compressed distortion, the spin-orbit coupling at
magnetic sites favors the easy-plane while that of anions leads to easy-axis
anisotropy. We apply the theory on $\rm{CrX}_3$ with X = Cl and I, and show the
dependence of the single-ion anisotropy on the ratio of the spin-orbit
couplings of magnetic and anion sites. Significance of the single-ion
anisotropy in ideal two-dimensional magnets is also discussed.


title: First and second sound in a dilute Bose gas across the BKT transition

abstract:
  We study the propagation of the two sound modes in two-dimensional Bose gases
across the Berezinksii-Kosterlitz-Thouless (BKT) transition using
classical-field dynamics, which is motivated by recent measurements of
Christodoulou et al. Nature 594, 191 (2021). Based on the dynamic structure
factor, we identify the two sound modes as the Bogoliubov (B) and the
non-Bogoliubov (NB) sound mode below the transition, and as the diffusive and
the normal sound mode above the transition. The NB sound mode velocity is
higher than the B sound mode velocity, which we refer to as the weak-coupling
regime of the sound modes. We excite the sound modes by driving the system as
in the experiment and by perturbing the density with a step-pulse perturbation,
as a secondary comparison. The driven response depends on the driving strength
and results in higher velocities for the B sound mode at high temperatures near
the transition, compared to the sound results of the dynamic structure factor
and step-pulse excitation. We show that the higher mode velocity has a weak
temperature dependence across the transition, which is consistent with the
experimental observation.


title:
Bayesian field-level inference of primordial non-Gaussianity using
  next-generation galaxy surveys

abstract:
  Detecting and measuring a non-Gaussian signature of primordial origin in the
density field is a major science goal of next-generation galaxy surveys. The
signal will permit us to determine primordial physics processes and constrain
models of cosmic inflation. While traditional approaches utilise a limited set
of statistical summaries of the galaxy distribution to constrain primordial
non-Gaussianity, we present a field-level approach by Bayesian
forward-modelling the entire three-dimensional galaxy survey. Our method
naturally and fully self-consistently exploits the entirety of the large-scale
structure, e.g., higher-order statistics, peculiar velocity fields, and
scale-dependent galaxy bias, to extract information on the local
non-Gaussianity parameter, $\fnl$. We demonstrate the performance of our
approach through various tests with mock galaxy data emulating relevant
features of the \sdssiii{}-like survey, and additional tests with a
\textit{Stage IV} mock data set. These tests reveal that the method infers
unbiased values of $\fnl$ by accurately handling survey geometries, noise, and
unknown galaxy biases. We demonstrate that our method can achieve constraints
of $\sigma_{\fnl} \approx 8.78$ for \sdssiii{}-like data, an improvement of a
factor $\sim 2.5$ over currently published constraints. Tests with
next-generation mock data show that significant further improvements are
feasible with sufficiently high resolution. Furthermore, the results
demonstrate that our method can consistently marginalise all nuisance
parameters of the data model. The method further provides an inference of the
three-dimensional primordial density field, providing opportunities to explore
additional signatures of primordial physics.


title:
Holographic Relations and Alternative Boundary Conditions for Lovelock
  Gravity

abstract:
  We obtain the holographic relation for the general Lovelock gravity and
decompose the full Lagrangian into the bulk term and the surface term,
expressed as a covariant total derivative $\partial_\mu J^\mu$. By holographic
relation, we mean that $J^\mu$ is determined completely by the bulk term. We
find that the bulk term is not degenerate, or first order in this
foliation-independent approach. We then consider the Arnowitt-Deser-Misner
(ADM) formalism where the foliation coordinate $w$ is treated as special. We
obtain the holographic-degenerate relation with the first-order bulk term that
does not involve higher than one derivative of $w$. For Einstein gravity, the
two approaches lead to the same bulk term, but different ones for higher-order
Lovelock gravities. The holographic-degenerate formulation in the ADM approach
allows us to consider alternative boundary conditions in the variation
principle with different Gibbons-Hawking-York terms. We show in the
semiclassical approximation that the black hole entropy in all cases is the
same as the one obtained under the standard Dirichlet boundary condition. We
also generalize the formalism to general $f(L_{\rm Lovelock}^{(k)})$-gravity.