title: An Adaptive Human Driver Model for Realistic Race Car Simulations

abstract:
  Engineering a high-performance race car requires a direct consideration of
the human driver using real-world tests or Human-Driver-in-the-Loop
simulations. Apart from that, offline simulations with human-like race driver
models could make this vehicle development process more effective and efficient
but are hard to obtain due to various challenges. With this work, we intend to
provide a better understanding of race driver behavior and introduce an
adaptive human race driver model based on imitation learning. Using existing
findings and an interview with a professional race engineer, we identify
fundamental adaptation mechanisms and how drivers learn to optimize lap time on
a new track. Subsequently, we use these insights to develop generalization and
adaptation techniques for a recently presented probabilistic driver modeling
approach and evaluate it using data from professional race drivers and a
state-of-the-art race car simulator. We show that our framework can create
realistic driving line distributions on unseen race tracks with almost
human-like performance. Moreover, our driver model optimizes its driving lap by
lap, correcting driving errors from previous laps while achieving faster lap
times. This work contributes to a better understanding and modeling of the
human driver, aiming to expedite simulation methods in the modern vehicle
development process and potentially supporting automated driving and racing
technologies.


title:
Efficient Data Structures for Exploiting Sparsity and Structure in
  Representation of Polynomial Optimization Problems: Implementation in
  SOSTOOLS

abstract:
  We present a new data structure for representation of polynomial variables in
the parsing of sum-of-squares (SOS) programs. In SOS programs, the variables
$s(x;P)$ are polynomial in the independent variables $x$, but linear in the
decision variables $P$. Current SOS parsers, however, fail to exploit the
semi-linear structure of the polynomial variables, treating the decision
variables as independent variables in their representation. This results in
unnecessary overhead in storage and manipulation of the polynomial variables,
prohibiting the parser from addressing larger-scale optimization problems. To
eliminate this computational overhead, we introduce a new representation of
polynomial variables, the "dpvar" structure, that is affine in the decision
variables. We show that the complexity of operations on variables in the dpvar
representation scales favorably with the number of decision variables. We
further show that the required memory for storing polynomial variables is
relatively small using the dpvar structure, particularly when exploiting the
MATLAB sparse storage structure. Finally, we incorporate the dpvar data
structure into SOSTOOLS 4.00, and test the performance of the parser for
several polynomial optimization problems.


title: The Cartier core map for Cartier algebras

abstract:
  Let $R$ be a commutative Noetherian $F$-finite ring of prime characteristic
and let $\mathcal{D}$ be a Cartier algebra. We define a self-map on the
Frobenius split locus of the pair $(R,\mathcal{D})$ by sending a point $P$ to
the splitting prime of $(R_P, \mathcal{D}_P)$. We prove this map is continuous,
containment preserving, and fixes the $\mathcal{D}$-compatible ideals. We show
this map can be extended to arbitrary ideals $J$, where in the Frobenius split
case it gives the largest $\mathcal{D}$-compatible ideal contained in $J$.
Finally, we apply Glassbrenner's criterion to prove that the prime uniformly
$F$-compatible ideals of a Stanley-Reisner rings are the sums of its minimal
primes.


title: Bayesian Spillover Graphs for Dynamic Networks

abstract:
  We present Bayesian Spillover Graphs (BSG), a novel method for learning
temporal relationships, identifying critical nodes, and quantifying uncertainty
for multi-horizon spillover effects in a dynamic system. BSG leverages both an
interpretable framework via forecast error variance decompositions (FEVD) and
comprehensive uncertainty quantification via Bayesian time series models to
contextualize temporal relationships in terms of systemic risk and prediction
variability. Forecast horizon hyperparameter $h$ allows for learning both
short-term and equilibrium state network behaviors. Experiments for identifying
source and sink nodes under various graph and error specifications show
significant performance gains against state-of-the-art Bayesian Networks and
deep-learning baselines. Applications to real-world systems also showcase BSG
as an exploratory analysis tool for uncovering indirect spillovers and
quantifying risk.


title:
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance
  Fields

abstract:
  Thin, reflective objects such as forks and whisks are common in our daily
lives, but they are particularly challenging for robot perception because it is
hard to reconstruct them using commodity RGB-D cameras or multi-view stereo
techniques. While traditional pipelines struggle with objects like these,
Neural Radiance Fields (NeRFs) have recently been shown to be remarkably
effective for performing view synthesis on objects with thin structures or
reflective materials. In this paper we explore the use of NeRF as a new source
of supervision for robust robot vision systems. In particular, we demonstrate
that a NeRF representation of a scene can be used to train dense object
descriptors. We use an optimized NeRF to extract dense correspondences between
multiple views of an object, and then use these correspondences as training
data for learning a view-invariant representation of the object. NeRF's usage
of a density field allows us to reformulate the correspondence problem with a
novel distribution-of-depths formulation, as opposed to the conventional
approach of using a depth map. Dense correspondence models supervised with our
method significantly outperform off-the-shelf learned descriptors by 106%
(PCK@3px metric, more than doubling performance) and outperform our baseline
supervised with multi-view stereo by 29%. Furthermore, we demonstrate the
learned dense descriptors enable robots to perform accurate 6-degree of freedom
(6-DoF) pick and place of thin and reflective objects.


title: Playable Environments: Video Manipulation in Space and Time

abstract:
  We present Playable Environments - a new representation for interactive video
generation and manipulation in space and time. With a single image at inference
time, our novel framework allows the user to move objects in 3D while
generating a video by providing a sequence of desired actions. The actions are
learnt in an unsupervised manner. The camera can be controlled to get the
desired viewpoint. Our method builds an environment state for each frame, which
can be manipulated by our proposed action module and decoded back to the image
space with volumetric rendering. To support diverse appearances of objects, we
extend neural radiance fields with style-based modulation. Our method trains on
a collection of various monocular videos requiring only the estimated camera
parameters and 2D object locations. To set a challenging benchmark, we
introduce two large scale video datasets with significant camera movements. As
evidenced by our experiments, playable environments enable several creative
applications not attainable by prior video synthesis works, including playable
3D video generation, stylization and manipulation. Further details, code and
examples are available at
https://willi-menapace.github.io/playable-environments-website


title:
The dispersion and propagation of topological Langmuir-cyclotron waves
  in cold magnetized plasmas

abstract:
  Topological Langmuir-Cyclotron Wave (TLCW) is a recently identified
topological surface excitation in magnetized plasmas. We show that TLCW
originates from the topological phase transition at the Langmuir wave-cyclotron
wave resonance. By isofrequency surface analysis and 2D and 3D time-dependent
simulations, we demonstrate that the TLCW can propagate robustly along complex
phase transition interfaces in a unidirectional manner and without scattering.
Because of these desirable features, the TLCW could be explored as an effective
mechanism to drive current and flow in magnetized plasmas. The analysis also
establishes a close connection between the newly instituted topological phase
classification of plasmas and the classical CMA diagram of plasma waves.


title: Emergence of local irreversibility in complex interacting systems

abstract:
  Living systems are fundamentally irreversible, breaking detailed balance and
establishing an arrow of time. But how does the evident arrow of time for a
whole system arise from the interactions among its multiple elements? We show
that the local evidence for the arrow of time, which is the entropy production
for thermodynamic systems, can be decomposed. First, it can be split into two
components: an independent term reflecting the dynamics of individual elements
and an interaction term driven by the dependencies among elements. Adapting
tools from non--equilibrium physics, we further decompose the interaction term
into contributions from pairs of elements, triplets, and higher--order terms.
We illustrate our methods on models of cellular sensing and logical
computations, as well as on patterns of neural activity in the retina as it
responds to visual inputs. We find that neural activity can define the arrow of
time even when the visual inputs do not, and that the dominant contribution to
this breaking of detailed balance comes from interactions among pairs of
neurons.


title: Subcritical monotone cellular automata

abstract:
  We study monotone cellular automata (also known as $\mathcal{U}$-bootstrap
percolation) in $\mathbb{Z}^d$ with random initial configurations. Confirming a
conjecture of Balister, Bollob\'as, Przykucki and Smith, who proved the
corresponding result in two dimensions, we show that the critical probability
is non-zero for all subcritical models.


title:
Investigating the limited performance of a deep-learning-based SPECT
  denoising approach: An observer-study-based characterization

abstract:
  Multiple objective assessment of image-quality-based studies have reported
that several deep-learning-based denoising methods show limited performance on
signal-detection tasks. Our goal was to investigate the reasons for this
limited performance. To achieve this goal, we conducted a task-based
characterization of a DL-based denoising approach for individual signal
properties. We conducted this study in the context of evaluating a DL-based
approach for denoising SPECT images. The training data consisted of signals of
different sizes and shapes within a clustered-lumpy background, imaged with a
2D parallel-hole-collimator SPECT system. The projections were generated at
normal and 20% low count level, both of which were reconstructed using an OSEM
algorithm. A CNN-based denoiser was trained to process the low-count images.
The performance of this CNN was characterized for five different signal sizes
and four different SBR by designing each evaluation as an SKE/BKS
signal-detection task. Performance on this task was evaluated using an
anthropomorphic CHO. As in previous studies, we observed that the DL-based
denoising method did not improve performance on signal-detection tasks.
Evaluation using the idea of observer-study-based characterization demonstrated
that the DL-based denoising approach did not improve performance on the
signal-detection task for any of the signal types. Overall, these results
provide new insights on the performance of the DL-based denoising approach as a
function of signal size and contrast. More generally, the observer study-based
characterization provides a mechanism to evaluate the sensitivity of the method
to specific object properties and may be explored as analogous to
characterizations such as modulation transfer function for linear systems.
Finally, this work underscores the need for objective task-based evaluation of
DL-based denoising approaches.


title: Slowly rotating black holes in nonlinear electrodynamics

abstract:
  We show how (at least in principle) one can construct electrically and
magnetically charged slowly rotating black hole solutions coupled to non-linear
electrodynamics (NLE). Our generalized Lense-Thirring ansatz is, apart from the
static metric function $f$ and the electrostatic potential $\phi$ inherited
from the corresponding spherical solution, characterized by two new functions
$h$ (in the metric) and $\omega$ (in the vector potential) encoding the effect
of rotation. In the linear Maxwell case, the rotating solutions are completely
characterized by static solution, featuring $h=(f-1)/r^2$ and $\omega=1$. We
show that when the first is imposed, the ansatz is inconsistent with any
restricted (see below) NLE but the Maxwell electrodynamics. In particular, this
implies that the Newman-Janis algorithm cannot be used to generate rotating
solutions for any restricted non-trivial NLE. We present a few explicit
examples of slowly rotating solutions in particular models of NLE, as well as
briefly discuss the NLE charged Taub-NUT spacetimes.


title:
High fidelity state preparation and measurement of ion hyperfine qubits
  with I > 1/2

abstract:
  We present a method for achieving high fidelity state preparation and
measurement (SPAM) using trapped ion hyperfine qubits with nuclear spins higher
than $I = 1/2$. The ground states of these higher nuclear spin isotopes do not
afford a simple frequency-selective state preparation scheme. We circumvent
this limitation by stroboscopically driving strong and weak transitions,
blending fast optical pumping using dipole transitions and narrow microwave or
optical quadrupole transitions. We demonstrate this method with the $I=3/2$
isotope $^{137}\mbox{Ba}^+$ to achieve a SPAM infidelity of $\left(9.0 \pm
1.3\right) \times 10^{-5}$ ($-40.5 \pm 0.6$ dB), facilitating the use of a
wider range of ion isotopes with favorable wavelengths and masses for quantum
computation.


title:
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy
  Quantification

abstract:
  Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue
micro-architecture, which can, in turn, be used to reconstruct tissue
microstructure and white matter pathways. The accuracy of such tasks is
hampered by the low signal-to-noise ratio in dMRI. Today, the noise is
characterized mainly by visual inspection of residual maps and estimated
standard deviation. However, it is hard to estimate the impact of noise on
downstream tasks based only on such qualitative assessments. To address this
issue, we introduce a novel metric, Noise Uncertainty Quantification (NUQ), for
quantitative image quality analysis in the absence of a ground truth reference
image. NUQ uses a recent Bayesian formulation of dMRI models to estimate the
uncertainty of microstructural measures. Specifically, NUQ uses the maximum
mean discrepancy metric to compute a pooled quality score by comparing samples
drawn from the posterior distribution of the microstructure measures. We show
that NUQ allows a fine-grained analysis of noise, capturing details that are
visually imperceptible. We perform qualitative and quantitative comparisons on
real datasets, showing that NUQ generates consistent scores across different
denoisers and acquisitions. Lastly, by using NUQ on a cohort of schizophrenics
and controls, we quantify the substantial impact of denoising on group
differences.


title:
Vision-Language Intelligence: Tasks, Representation Learning, and Large
  Models

abstract:
  This paper presents a comprehensive survey of vision-language (VL)
intelligence from the perspective of time. This survey is inspired by the
remarkable progress in both computer vision and natural language processing,
and recent trends shifting from single modality processing to multiple modality
comprehension. We summarize the development in this field into three time
periods, namely task-specific methods, vision-language pre-training (VLP)
methods, and larger models empowered by large-scale weakly-labeled data. We
first take some common VL tasks as examples to introduce the development of
task-specific methods. Then we focus on VLP methods and comprehensively review
key components of the model structures and training methods. After that, we
show how recent work utilizes large-scale raw image-text data to learn
language-aligned visual representations that generalize better on zero or few
shot learning tasks. Finally, we discuss some potential future trends towards
modality cooperation, unified representation, and knowledge incorporation. We
believe that this review will be of help for researchers and practitioners of
AI and ML, especially those interested in computer vision and natural language
processing.


title: Recovering 3D Human Mesh from Monocular Images: A Survey

abstract:
  Estimating human pose and shape from monocular images is a long-standing
problem in computer vision. Since the release of statistical body models, 3D
human mesh recovery has been drawing broader attention. With the same goal of
obtaining well-aligned and physically plausible mesh results, two paradigms
have been developed to overcome challenges in the 2D-to-3D lifting process: i)
an optimization-based paradigm, where different data terms and regularization
terms are exploited as optimization objectives; and ii) a regression-based
paradigm, where deep learning techniques are embraced to solve the problem in
an end-to-end fashion. Meanwhile, continuous efforts are devoted to improving
the quality of 3D mesh labels for a wide range of datasets. Though remarkable
progress has been achieved in the past decade, the task is still challenging
due to flexible body motions, diverse appearances, complex environments, and
insufficient in-the-wild annotations. To the best of our knowledge, this is the
first survey to focus on the task of monocular 3D human mesh recovery. We start
with the introduction of body models and then elaborate recovery frameworks and
training objectives by providing in-depth analyses of their strengths and
weaknesses. We also summarize datasets, evaluation metrics, and benchmark
results. Open issues and future directions are discussed in the end, hoping to
motivate researchers and facilitate their research in this area. A regularly
updated project page can be found at https://github.com/tinatiansjz/hmr-survey.


title:
Min-Max Bilevel Multi-objective Optimization with Applications in
  Machine Learning

abstract:
  This paper is the first to propose a generic min-max bilevel multi-objective
optimization framework, highlighting applications in representation learning
and hyperparameter optimization. In many machine learning applications such as
meta-learning, multi-task learning, and representation learning, a subset of
the parameters are shared by all the tasks, while each specific task has its
own set of additional parameters. By leveraging the recent advances of
nonconvex min-max optimization, we propose a gradient descent-ascent bilevel
optimization (MORBiT) algorithm which is able to extract a set of shared
parameters that is robust over all tasks and further overcomes the
distributional shift between training and testing tasks. Theoretical analyses
show that MORBiT converges to the first-order stationary point at a rate of
$\mathcal{O}(\sqrt{n}K^{-2/5})$ for a class of nonconvex problems, where $K$
denotes the total number of iterations and $n$ denotes the number of tasks.
Overall, we formulate a min-max bilevel multi-objective optimization problem,
provide a single loop two-timescale algorithm with convergence rate guarantees,
and show theoretical bounds on the generalization abilities of the optimizer.
Experimental results on sinusoid regression and representation learning
showcase the superiority of MORBiT over state-of-the-art methods, validating
our convergence and generalization results.


title: Label-Only Model Inversion Attacks via Boundary Repulsion

abstract:
  Recent studies show that the state-of-the-art deep neural networks are
vulnerable to model inversion attacks, in which access to a model is abused to
reconstruct private training data of any given target class. Existing attacks
rely on having access to either the complete target model (whitebox) or the
model's soft-labels (blackbox). However, no prior work has been done in the
harder but more practical scenario, in which the attacker only has access to
the model's predicted label, without a confidence measure. In this paper, we
introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert
private training data using only the target model's predicted labels. The key
idea of our algorithm is to evaluate the model's predicted labels over a sphere
and then estimate the direction to reach the target class's centroid. Using the
example of face recognition, we show that the images reconstructed by BREP-MI
successfully reproduce the semantics of the private training data for various
datasets and target model architectures. We compare BREP-MI with the
state-of-the-art whitebox and blackbox model inversion attacks and the results
show that despite assuming less knowledge about the target model, BREP-MI
outperforms the blackbox attack and achieves comparable results to the whitebox
attack.


title:
Gaussian processes and effective field theory of $f(T)$ gravity under
  the $H_0$ tension

abstract:
  We consider the effective field theory formulation of torsional gravity in a
cosmological framework to alter the background evolution. Then we use the
latest $H_0$ measurement from the SH0ES Team as well as observational Hubble
data from cosmic chronometer (CC) and radial baryon acoustic oscillations (BAO)
and we reconstruct the $f(T)$ form in a model-independent way by applying
Gaussian processes. Since the special square-root term does not affect the
evolution at the background level, we finally result to a family of functions
that can produce the background evolution required by the data. Finally,
performing a fitting using polynomial functions we find an analytic expression
that may describe the cosmological evolution in reat agreement with
observations.


title:
As Little as Possible, as Much as Necessary: Detecting Over- and
  Undertranslations with Contrastive Conditioning

abstract:
  Omission and addition of content is a typical issue in neural machine
translation. We propose a method for detecting such phenomena with
off-the-shelf translation models. Using contrastive conditioning, we compare
the likelihood of a full sequence under a translation model to the likelihood
of its parts, given the corresponding source or target sequence. This allows to
pinpoint superfluous words in the translation and untranslated words in the
source even in the absence of a reference translation. The accuracy of our
method is comparable to a supervised method that requires a custom quality
estimation model.


title: Label-Free Explainability for Unsupervised Models

abstract:
  Unsupervised black-box models are challenging to interpret. Indeed, most
existing explainability methods require labels to select which component(s) of
the black-box's output to interpret. In the absence of labels, black-box
outputs often are representation vectors whose components do not correspond to
any meaningful quantity. Hence, choosing which component(s) to interpret in a
label-free unsupervised/self-supervised setting is an important, yet unsolved
problem. To bridge this gap in the literature, we introduce two crucial
extensions of post-hoc explanation techniques: (1) label-free feature
importance and (2) label-free example importance that respectively highlight
influential features and training examples for a black-box to construct
representations at inference time. We demonstrate that our extensions can be
successfully implemented as simple wrappers around many existing feature and
example importance methods. We illustrate the utility of our label-free
explainability paradigm through a qualitative and quantitative comparison of
representation spaces learned by various autoencoders trained on distinct
unsupervised tasks.


title:
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and
  Categorical 6D Pose and Size Estimation

abstract:
  This paper studies the complex task of simultaneous multi-object 3D
reconstruction, 6D pose and size estimation from a single-view RGB-D
observation. In contrast to instance-level pose estimation, we focus on a more
challenging problem where CAD models are not available at inference time.
Existing approaches mainly follow a complex multi-stage pipeline which first
localizes and detects each object instance in the image and then regresses to
either their 3D meshes or 6D poses. These approaches suffer from
high-computational cost and low performance in complex multi-object scenarios,
where occlusions can be present. Hence, we present a simple one-stage approach
to predict both the 3D shape and estimate the 6D pose and size jointly in a
bounding-box free manner. In particular, our method treats object instances as
spatial centers where each center denotes the complete shape of an object along
with its 6D pose and size. Through this per-pixel representation, our approach
can reconstruct in real-time (40 FPS) multiple novel object instances and
predict their 6D pose and sizes in a single-forward pass. Through extensive
experiments, we demonstrate that our approach significantly outperforms all
shape completion and categorical 6D pose and size estimation baselines on
multi-object ShapeNet and NOCS datasets respectively with a 12.6% absolute
improvement in mAP for 6D pose for novel real-world object instances.


title:
Electron correlations and charge density wave in the topological kagome
  metal FeGe

abstract:
  Charge order in kagome metals is of extensive current interest. Recently, a
charge density wave was discovered in the magnetic binary kagome metal FeGe. In
analogy to its predecessor, the non-magnetic $A$V$_3$Sb$_5$ ($A$=K, Cs, Rb),
the in-plane ordering occurs at the $M$ point. In contrast, however, the system
manifestly shows effects of substantial correlations. Here we identify the
topological bands crossing the Fermi energy (E$_F$) in FeGe and characterize
the correlation-induced renormalization of these bands. We then derive a charge
order from an effective model comprising topological kagome `flat' bands in the
presence of a magnetic order. We demonstrate edge states as well as excess
out-of-plane magnetic moment associated with the charge order; both are
fingerprints of non-trivial band topology and consistent with recent
experimental observations. Our results point to FeGe as an ideal platform to
realize and elucidate correlated topological physics.


title:
Spitzer and Herschel studies of dust in supernova remnants in the Small
  Magellanic Cloud

abstract:
  With the entire Small Magellanic Cloud (SMC) mapped by the Spitzer Space
Telescope and Herschel Space Observatory, we were able to search 8-250 micron
images in order to identify infrared (IR) emission associated with SMC
supernova remnants (SNRs). A valid detection had to correspond with known
X-ray, Halpha and radio emission from the SNRs. From the 24 known SNRs, we made
5 positive detections with another 5 possible detections. Two detections are
associated with pulsars or pulsar wind nebula, and another three detections are
part of the extended nebulous emission from the SNRs. We modelled dust emission
where fast moving electrons are predicted to collide and heat dust grains which
then radiate in IR. With known distance (62.44+-0.47kpc), measured SNR sizes,
electron densities, temperatures from X-ray emission as well as hydrogen
densities, the modelling of SMC SNRs is straightforward.
  If the higher range of hydrogen and electron densities were to be accepted,
we would expect almost all SMC SNRs to be detected in the IR, at least at 24
micron, but the actual detection rate is only 25%. One possible and common
explanation for this discrepancy is that small grains have been destroyed by
the SNRs shockwave. However, within the uncertainties of hydrogen and electron
densities, we find that infrared dust emission can be explained reasonably
well, without invoking dust destruction. There is no conclusive evidence that
SNRs destroy swept-up ISM dust.


title: Contextual Attention Network: Transformer Meets U-Net

abstract:
  Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the
de facto standard and attained immense success in medical image segmentation.
However, as a downside, CNN based methods are a double-edged sword as they fail
to build long-range dependencies and global context connections due to the
limited receptive field that stems from the intrinsic characteristics of the
convolution operation. Hence, recent articles have exploited Transformer
variants for medical image segmentation tasks which open up great opportunities
due to their innate capability of capturing long-range correlations through the
attention mechanism. Although being feasibly designed, most of the cohort
studies incur prohibitive performance in capturing local information, thereby
resulting in less lucidness of boundary areas. In this paper, we propose a
contextual attention network to tackle the aforementioned limitations. The
proposed method uses the strength of the Transformer module to model the
long-range contextual dependency. Simultaneously, it utilizes the CNN encoder
to capture local semantic information. In addition, an object-level
representation is included to model the regional interaction map. The extracted
hierarchical features are then fed to the contextual attention module to
adaptively recalibrate the representation space using the local information.
Then, they emphasize the informative regions while taking into account the
long-range contextual dependency derived by the Transformer module. We validate
our method on several large-scale public medical image segmentation datasets
and achieve state-of-the-art performance. We have provided the implementation
code in https://github.com/rezazad68/TMUnet.


title:
Temporal Context Matters: Enhancing Single Image Prediction with Disease
  Progression Representations

abstract:
  Clinical outcome or severity prediction from medical images has largely
focused on learning representations from single-timepoint or snapshot scans. It
has been shown that disease progression can be better characterized by temporal
imaging. We therefore hypothesized that outcome predictions can be improved by
utilizing the disease progression information from sequential images. We
present a deep learning approach that leverages temporal progression
information to improve clinical outcome predictions from single-timepoint
images. In our method, a self-attention based Temporal Convolutional Network
(TCN) is used to learn a representation that is most reflective of the disease
trajectory. Meanwhile, a Vision Transformer is pretrained in a self-supervised
fashion to extract features from single-timepoint images. The key contribution
is to design a recalibration module that employs maximum mean discrepancy loss
(MMD) to align distributions of the above two contextual representations. We
train our system to predict clinical outcomes and severity grades from
single-timepoint images. Experiments on chest and osteoarthritis radiography
datasets demonstrate that our approach outperforms other state-of-the-art
techniques.


title:
Quality or Quantity: Toward a Unified Approach for Multi-organ
  Segmentation in Body CT

abstract:
  Organ segmentation of medical images is a key step in virtual imaging trials.
However, organ segmentation datasets are limited in terms of quality (because
labels cover only a few organs) and quantity (since case numbers are limited).
In this study, we explored the tradeoffs between quality and quantity. Our goal
is to create a unified approach for multi-organ segmentation of body CT, which
will facilitate the creation of large numbers of accurate virtual phantoms.
Initially, we compared two segmentation architectures, 3D-Unet and DenseVNet,
which were trained using XCAT data that is fully labeled with 22 organs, and
chose the 3D-Unet as the better performing model. We used the XCAT-trained
model to generate pseudo-labels for the CT-ORG dataset that has only 7 organs
segmented. We performed two experiments: First, we trained 3D-UNet model on the
XCAT dataset, representing quality data, and tested it on both XCAT and CT-ORG
datasets. Second, we trained 3D-UNet after including the CT-ORG dataset into
the training set to have more quantity. Performance improved for segmentation
in the organs where we have true labels in both datasets and degraded when
relying on pseudo-labels. When organs were labeled in both datasets, Exp-2
improved Average DSC in XCAT and CT-ORG by 1. This demonstrates that quality
data is the key to improving the model's performance.


title: E-CIR: Event-Enhanced Continuous Intensity Recovery

abstract:
  A camera begins to sense light the moment we press the shutter button. During
the exposure interval, relative motion between the scene and the camera causes
motion blur, a common undesirable visual artifact. This paper presents E-CIR,
which converts a blurry image into a sharp video represented as a parametric
function from time to intensity. E-CIR leverages events as an auxiliary input.
We discuss how to exploit the temporal event structure to construct the
parametric bases. We demonstrate how to train a deep learning model to predict
the function coefficients. To improve the appearance consistency, we further
introduce a refinement module to propagate visual features among consecutive
frames. Compared to state-of-the-art event-enhanced deblurring approaches,
E-CIR generates smoother and more realistic results. The implementation of
E-CIR is available at https://github.com/chensong1995/E-CIR.


title:
Comparison of LSTM autoencoder based deep learning enabled Bayesian
  inference using two time series reconstruction approaches

abstract:
  In this work, we use a combination of Bayesian inference, Markov chain Monte
Carlo and deep learning in the form of LSTM autoencoders to build and test a
framework to provide robust estimates of injection rate from ground surface
data in coupled flow and geomechanics problems. We use LSTM autoencoders to
reconstruct the displacement time series for grid points on the top surface of
a faulting due to water injection problem. We then deploy this LSTM autoencoder
based model instead of the high fidelity model in the Bayesian inference
framework to estimate injection rate from displacement input.


title:
Semantic-guided Image Virtual Attribute Learning for Noisy Multi-label
  Chest X-ray Classification

abstract:
  Deep learning methods have shown outstanding classification accuracy in
medical image analysis problems, which is largely attributed to the
availability of large datasets manually annotated with clean labels. However,
such manual annotation can be expensive to obtain for large datasets, so we may
rely on machine-generated noisy labels. Many Chest X-ray (CXR) classifiers are
modelled from datasets with machine-generated labels, but their training
procedure is in general not robust to the presence of noisy-label samples and
can overfit those samples to produce sub-optimal solutions. Furthermore, CXR
datasets are mostly multi-label, so current noisy-label learning methods
designed for multi-class problems cannot be easily adapted. To address such
noisy multi-label CXR learning problem, we propose a new learning method based
on estimating image virtual attributes using semantic information from the
label to assist in the identification and correction of noisy multi-labels from
training samples. Our experiments on diverse noisy multi-label training sets
and clean testing sets show that our model has state-of-the-art accuracy and
robustness across all datasets.


title:
Anisotropic compact stars in $D\rightarrow 4$ limit of Gauss-Bonnet
  gravity

abstract:
  In the frame of Gauss-Bonnet gravity and in the limit of $D\to 4$, based on
the fact that spherically symmetric solution derived using any of
regularization schemes will be the same form as the original theory
\cite{Banerjee:2020yhu,Hennigar:2020lsl,Casalino:2020kbt,Aoki:2020lig}, we
derive a new interior spherically symmetric solution assuming specific forms of
the metric potentials that have two constants . Using the junction condition we
determine these two constants. By using the data of the star EXO $1785-248 $,
whose mass is $ { M=1.3\pm0.2 \,{\textrm M}_\odot}$ and radius $ {
l=8.849\pm0.4\,{\textrm km}}$, we calculate the numerical values of these
constants, in terms of the dimensionful coupling parameter of the Gauss-Bonnet
term, and eventually, we get real values for these constants. In this regard,
we show that the components of the energy-momentum tensor has a finite value at
the center of the star as well as a smaller value to the surface of the star.
Moreover, we show that the equations of the state behave in a non-linear way
due to the impact of the Gauss-Bonnet term. Using the
Tolman-Oppenheimer-Volkoff equation, the adiabatic index, and stability in the
static state we show that the model under consideration is always stable.
Finally, the solution of this study is matched with observational data of other
pulsars showing satisfactory results


title: New Features of the Pulsar B0950+08 Radiation at the Frequency of 111MHz

abstract:
  Results of long time observations of the pulsar B0950+08 are given. These
observations were carried out at the LPA radio telescope at the frequency of
111 MHz from January of 2016 to May of 2019 (450 days). A strong variability in
emission of this pulsar has been detected with changes of signal to noise
ratios hundreds of times. Part of the long-time flux density variability can be
explained by refractive scintillations in the interstellar medium. The
existence of radiation between the interpulse (IP) and main pulse (MP) was
confirmed. It was more powerful than at high frequencies. We detected the
unusual interpulse and precursor (Pr) radiation on August 1, 2017. On the base
of strong 65 interpulses we found the correlations between energies of IP and
Pr and between the phase of IP and the distance Pr-IP. It is shown that the
observed peculiarities of this pulsar can be explained in the frame of the
aligned rotator model. We estimated distances of radiation levels from the
center of the neutron star. The calculated value of the initial period of 0.2
sec means that not all pulsars are born with millisecond periods. The large age
of the pulsar (6.8 millions of years) and the small angle between its magnetic
moment and the rotation axis (less than 20$^{\circ}$) confirm the suggestion on
the pulsar evolution to an alignment.


title:
Color Space-based HoVer-Net for Nuclei Instance Segmentation and
  Classification

abstract:
  Nuclei segmentation and classification is the first and most crucial step
that is utilized for many different microscopy medical analysis applications.
However, it suffers from many issues such as the segmentation of small objects,
imbalance, and fine-grained differences between types of nuclei. In this paper,
multiple different contributions were done tackling these problems present.
Firstly, the recently released "ConvNeXt" was used as the encoder for HoVer-Net
model since it leverages the key components of transformers that make them
perform well. Secondly, to enhance the visual differences between nuclei, a
multi-channel color space-based approach is used to aid the model in extracting
distinguishing features. Thirdly, Unified Focal loss (UFL) was used to tackle
the background-foreground imbalance. Finally, Sharpness-Aware Minimization
(SAM) was used to ensure generalizability of the model. Overall, we were able
to outperform the current state-of-the-art (SOTA), HoVer-Net, on the
preliminary test set of the CoNiC Challenge 2022 by 12.489% mPQ+.


title: Autoregressive Image Generation using Residual Quantization

abstract:
  For autoregressive (AR) modeling of high-resolution images, vector
quantization (VQ) represents an image as a sequence of discrete codes. A short
sequence length is important for an AR model to reduce its computational costs
to consider long-range interactions of codes. However, we postulate that
previous VQ cannot shorten the code sequence and generate high-fidelity images
together in terms of the rate-distortion trade-off. In this study, we propose
the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and
RQ-Transformer, to effectively generate high-resolution images. Given a fixed
codebook size, RQ-VAE can precisely approximate a feature map of an image and
represent the image as a stacked map of discrete codes. Then, RQ-Transformer
learns to predict the quantized feature vector at the next position by
predicting the next stack of codes. Thanks to the precise approximation of
RQ-VAE, we can represent a 256$\times$256 image as 8$\times$8 resolution of the
feature map, and RQ-Transformer can efficiently reduce the computational costs.
Consequently, our framework outperforms the existing AR models on various
benchmarks of unconditional and conditional image generation. Our approach also
has a significantly faster sampling speed than previous AR models to generate
high-quality images.


title: The Maximum Isotropic Equivalent Energy Of Gamma Ray Bursts

abstract:
  The canonball model, which unifies gamma ray bursts (GRBs) and cosmic ray
bursts (CRBs), is used to predict the maximum isotropic equivalent gamma ray
energy release in a single GRB. The predicted maximum is consistent with that
concluded from uptodate GRB observations. It is based on the recently
discovered knee in the energy spectrum of Galactic cosmic ray electrons, and on
the Amati corelation in GRBs, both of which were predicted by the cannonball
model of CRBs and GRBs.


title: Non-helical Large-Scale Dynamo in Stellar Radiative Zones

abstract:
  Our understanding of large-scale magnetic fields in stellar radiative zones
remains fragmented and incomplete. Such fields, which must be produced by some
form of dynamo mechanism, are thought to dominate angular-momentum transport,
making them crucial to stellar evolution more generally. A major difficulty is
the effect of stable stratification, which generally suppresses dynamo action.
We propose a non-helical large-scale dynamo (LSD) mechanism that we find can
operate in a stably stratified plasma alongside two ingredients: mean shear and
non-helical magnetic fluctuations. Both ingredients are easily sourced in the
presence of differential rotation. Our idealized direct numerical simulations,
supported by mean-field theory, demonstrate the generation of near
equipartition large-scale toroidal fields. The mechanism is robust to
increasing stable stratification as long as a source of non-helical magnetic
fluctuations is present, e.g. from a small-scale dynamo. Additionally, a scan
over magnetic Reynolds number shows no change in the growth or saturation of
the LSD, providing good numerical evidence of a quenching-free dynamo
mechanism, which has been an issue for helical dynamos. These properties -- the
lack of quenching and robustness to stable stratification -- make the mechanism
a plausible candidate for generating in-situ large-scale magnetic fields in
stellar radiative zones.


title:
A multi-stream convolutional neural network for classification of
  progressive MCI in Alzheimer's disease using structural MRI images

abstract:
  Early diagnosis of Alzheimer's disease and its prodromal stage, also known as
mild cognitive impairment (MCI), is critical since some patients with
progressive MCI will develop the disease. We propose a multi-stream deep
convolutional neural network fed with patch-based imaging data to classify
stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's
disease with cognitively normal subjects to identify distinct anatomical
landmarks using a multivariate statistical test. These landmarks are then used
to extract patches that are fed into the proposed multi-stream convolutional
neural network to classify MRI images. Next, we train the architecture in a
separate scenario using samples from Alzheimer's disease images, which are
anatomically similar to the progressive MCI ones and cognitively normal images
to compensate for the lack of progressive MCI training data. Finally, we
transfer the trained model weights to the proposed architecture in order to
fine-tune the model using progressive MCI and stable MCI data. Experimental
results on the ADNI-1 dataset indicate that our method outperforms existing
methods for MCI classification, with an F1-score of 85.96%.


title:
Phenomenological Aspects of Axion-Like Particles in Cosmology and
  Astrophysics

abstract:
  Cosmology and particle physics are closer today than ever before, with
several searches underway at the interface between cosmology, particle physics,
and field theory. The mystery of dark matter (DM) is one of the greatest common
unsolved problems between these fields. It is established now based on many
astrophysical and cosmological observations that only a small fraction of the
total matter content of the universe is made of baryonic matter, while the vast
majority is constituted by dark matter. However, the nature of such a component
is still unknown. One theoretically well-motivated approach to understanding
the nature of dark matter would be through looking for light pseudo-scalar
candidates for dark matter such as axions and axion-like particles (ALPs).
Axions are hypothetical elementary particles resulting from the Peccei-Quinn
(PQ) solution to the strong CP (charge-parity) problem in quantum
chromodynamics (QCD). Furthermore, many theoretically well-motivated extensions
to the standard model of particle physics (SMPP) predicted the existence of
more pseudo-scalar particles similar to the QCD axion and called ALPs. Axions
and ALPs are characterized by their coupling with two photons. While the
coupling parameter for axions is related to the axion mass, there is no direct
relation between the coupling parameter and the mass of ALPs. Nevertheless, it
is expected that ALPs share the same phenomenology of axions. In the past
years, axions and ALPs regained popularity and slowly became one of the most
appealing candidates that possibly contribute to the dark matter density of the
universe. In this thesis, we focus on studying the phenomenology of axions and
ALPs interactions with photons to constrain some of their properties.


title:
Sterile neutrino portals to Majorana dark matter: effective operators
  and UV completions

abstract:
  Stringent constraints on the interactions of dark matter with the Standard
Model suggest that dark matter does not take part in gauge interactions. In
this regard, the possibility of communicating between the visible and dark
sectors via gauge singlets seems rather natural. We consider a framework where
the dark matter talks to the Standard Model through its coupling to sterile
neutrinos, which generate active neutrino masses. We focus on the case of
Majorana dark matter, with its relic abundance set by thermal freeze-out
through annihilations into sterile neutrinos. We use an effective field theory
approach to study the possible sterile neutrino portals to dark matter. We find
that both lepton-number-conserving and lepton-number-violating operators are
possible, yielding an interesting connection with the Dirac/Majorana character
of active neutrinos. In a second step, we open the different operators and
outline the possible renormalisable models. We analyse the phenomenology of the
most promising ones, including a particular case in which the Majorana mass of
the sterile neutrinos is generated radiatively.


title:
Tidal Wave Breaking in the Eccentric Lead-In to Mass Transfer and Common
  Envelope Phases

abstract:
  The evolution of many close binary and multiple star systems is defined by
phases of mass exchange and interaction. As these systems evolve into contact,
tidal dissipation is not always sufficient to bring them into circular,
synchronous orbits. In these cases, encounters of increasing strength occur
while the orbit remains eccentric. This paper focuses on the outcomes of close
tidal passages in eccentric orbits. Close eccentric passages excite dynamical
oscillations about the stars' equilibrium configurations. These tidal
oscillations arise from the transfer of orbital energy into oscillation mode
energy. When these oscillations reach sufficient amplitude, they break near the
stellar surface. The surface wave-breaking layer forms a shock-heated
atmosphere that surrounds the object. The continuing oscillations in the star's
interior launch shocks that dissipate into the this atmosphere, damping the
tidal oscillations. We show that the rapid, nonlinear dissipation associated
with the wave breaking of fundamental oscillation modes therefore comes with
coupled mass loss to the wave breaking atmosphere. The mass ratio is an
important characteristic that defines the relative importance of mass loss and
energy dissipation and therefore determines the fate of systems evolving under
the influence of nonlinear dissipation. The outcome can be rapid tidal
circularization ($q\ll1$) or runaway mass exchange ($q\gg1$).


title: Counterdiabatic Optimised Local Driving

abstract:
  Adiabatic protocols are employed across a variety of quantum technologies,
from implementing state preparation and individual operations that are building
blocks of larger devices, to higher-level protocols in quantum annealing and
adiabatic quantum computation. The problem of speeding up these processes has
garnered a large amount of interest, resulting in a menagerie of approaches,
most notably quantum optimal control and shortcuts to adiabaticity. The two
approaches are complementary: optimal control manipulates control fields to
steer the dynamics in the minimum allowed time while shortcuts to adiabaticity
aim to retain the adiabatic condition upon speed-up. We outline a new method
which combines the two methodologies and takes advantage of the strengths of
each. The new technique improves upon approximate local counterdiabatic driving
- an approach that aims to suppress nonadiabatic transitions - with the
addition of time-dependent control fields that increase its effectiveness. We
refer to this new method as counterdiabatic optimised local driving (COLD) and
we show that it can result in a substantial improvement when applied to
annealing protocols, state preparation schemes and population transfer on a
lattice. COLD can be enhanced with existing advanced optimal control methods
and we explore this using the chopped randomised basis method. Furthermore, we
outline extensions of COLD, which include its potential implementation when the
system dynamics cannot be efficiently classically computed.


title:
All-purpose, all-sky photometric redshifts for the Legacy Imaging
  Surveys Data Release 8

abstract:
  In this paper we present photometric redshift (photo-$z$) estimates for the
Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently
the most sensitive optical survey covering the majority of the extra-galactic
sky. Our photo-$z$ methodology is based on a machine-learning approach, using
sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that
allow regions of parameter space to be identified and trained separately in a
purely data-driven way. The same GMMs are also used to calculate cost-sensitive
learning weights that mitigate biases in the spectroscopic training sample. By
design, this approach aims to produce reliable and unbiased predictions for all
parts of the parameter space present in wide area surveys. Compared to previous
literature estimates using the same underlying photometry, our photo-$z$s are
significantly less biased and more accurate at $z > 1$, with negligible loss in
precision or reliability for resolved galaxies at $z < 1$. Our photo-$z$
estimates offer accurate predictions for rare high-value populations within the
parent sample, including optically selected quasars at the highest redshifts
($z > 6$), as well as X-ray or radio continuum selected populations across a
broad range of flux (densities) and redshift. Deriving photo-$z$ estimates for
the full Legacy Imaging Surveys Data Release 8, the catalogues provided in this
work offer photo-$z$ estimates predicted to be high quality for
$\gtrsim9\times10^{8}$ galaxies over $\sim 19\,400\,\text{deg}^{2}$ and
spanning $0 < z \lesssim 7$, offering one of the most extensive samples of
redshift estimates ever produced.


title:
Exploiting a future galactic supernova to probe neutrino magnetic
  moments

abstract:
  A core-collapse supernova (SN) offers an excellent astrophysical laboratory
to test non-zero neutrino magnetic moments. In particular, the neutronization
burst phase, which lasts for few tens of milliseconds post-bounce, is dominated
by electron neutrinos and can offer exceptional discovery potential for
transition magnetic moments. We simulate the neutrino spectra from the burst
phase in forthcoming neutrino experiments like the Deep Underground Neutrino
Experiment (DUNE), and the Hyper-Kamiokande (HK), by taking into account
spin-flavour conversions of SN neutrinos, caused by interactions with ambient
magnetic fields. We find that the neutrino transition magnetic moments which
can be explored by these experiments for a galactic SN are an order to several
orders of magnitude better than the current terrestrial and astrophysical
limits. Additionally, we also discuss how this realization might shed light on
three important neutrino properties: (a) the Dirac/Majorana nature, (b) the
neutrino mass ordering, and (c) the neutrino mass-generation mechanism.


title: Exploring the orthosymplectic zoo

abstract:
  We study the Higgs branch of the SCFT limit of 5d SO(6) and SO(8) gauge
theory with hypermultiplets in the spinor and vector representations. In the
case of SO(6) gauge theories, we contrast the magnetic quivers obtained with
those of SU(4) gauge theory with hypermultiplets in the fundamental and second
rank antisymmetric representations. Since SU(4) gauge theories admit several
different values of the Chern-Simons level, we make some observations about how
to distinguish those theories from the brane webs of the SO(6) theories. In the
case of SO(8) gauge theories, we use SO(8) triality to propose (naively)
inequivalent magnetic quivers, which will turn out to have the same moduli
spaces of vacua, at least locally around their most singular loci. We encounter
several interesting new phenomena occurring in the magnetic quivers, such as
hypermultiplets between neighbouring symplectic gauge nodes and matter in
two-index representations of unitary nodes. We also give a prescription for
computing the local Coulomb branch Hilbert series for quivers involving bad
USp(2) gauge nodes.


title:
Flavor hierarchies, flavor anomalies, and Higgs mass from a warped extra
  dimension

abstract:
  The recent B-meson anomalies are coherently explained at the TeV scale by
4321 gauge models with hierarchical couplings reminiscent of the Standard Model
Yukawas. We show that such models arise as the low-energy limit of a complete
theory of flavor, based on a warped fifth dimension where each Standard Model
family is quasi-localized in a different brane. The Higgs is identified as a
pseudo-Nambu-Goldstone boson emerging from the same dynamics responsible for
4321 symmetry breaking. This novel construction unifies quarks and leptons in a
flavor non-universal manner, provides a natural description of flavor
hierarchies, and addresses the electroweak hierarchy problem.


title:
An extended stellar halo discovered in the Fornax dwarf spheroidal using
  Gaia EDR3

abstract:
  We have studied the extent of the Red Giant Branch stellar population in the
Fornax dwarf spheroidal galaxy using the spatially extended and homogeneous
data set from Gaia EDR3. Our preselection of stars belonging to Fornax is based
on their proper motions, parallaxes and color-magnitude diagram. The latter
criteria provide a Fornax star sample, which we further restrict by color and
magnitude to eliminate contaminations due to either Milky Way stars or QSOs.
The precision of the data has been sufficient to reach extremely small
contaminations (0.02 to 0.3%), allowing us to reach to a background level 12
magnitudes deeper than the central surface brightness of Fornax. We discover a
break in the density profile, which reveals the presence of an additional
component that extents 2.1 degree in radius, i.e. 5.4 kpc, and almost seven
times the half-light radius of Fornax. The extended new component represents
10% of the stellar mass of Fornax, and behaves like an extended halo. The
absence of tidally elongated features at such an unprecedented depth
(equivalent to $37.94\pm0.16$ mag ${\rm arcsec}^{-2}$ in V-band) rules out a
possible role of tidal stripping. We suggest instead that Fornax is likely at
first infall, and has lost its gas very recently, which consequently leads to a
lack of gravity implying that residual stars have spherically expanded to form
the newly discovered stellar halo of Fornax.


title:
The Flaring TESS Objects of Interest: Flare Rates for all Two Minute
  Cadence TESS Planet Candidates

abstract:
  Although more than 5000 TESS Objects of Interest have been cataloged, no
comprehensive survey of the flare rates of their host stars exists. We perform
the first flare survey of all 2250 non-retired TOIs with 2 min cadence light
curves to measure or place upper limits on their flare rates. We find 93
candidates orbit flare stars and measure their flare frequency distributions.
Across the sample, TOIs of <1.5R_Earth orbit flare stars more frequently than
do TOIs of 1.5<R<2.75R_Earth, 2.75<R<4R_Earth, or R<4R_Earth. We sort all TOI
host stars by their flare rate/upper limit, stellar mass, and distance to
create a flare ranking metric (FRM) to determine suitability for follow-up. The
FRM of each TOI is then checked against the expected signal-to-noise of
atmospheric features in transmission spectroscopy to locate the most promising
targets. We find 1/4 of terrestrial M-dwarf planets amenable to transmission
spectroscopy orbit flare stars. However, none of the M-dwarf hosts to
terrestrial planets are currently flaring at sufficient levels for >99.9%
atmospheric ozone depletion. We give the first upper limits on the flare rate
of the host star to TOI 700 d and explore the flare rates incident on young
planets such as DS Tuc Ab.


title: Neutrino Self-Interactions: A White Paper

abstract:
  Neutrinos are the Standard Model (SM) particles which we understand the
least, often due to how weakly they interact with the other SM particles.
Beyond this, very little is known about interactions among the neutrinos, i.e.,
their self-interactions. The SM predicts neutrino self-interactions at a level
beyond any current experimental capabilities, leaving open the possibility for
beyond-the-SM interactions across many energy scales. In this white paper, we
review the current knowledge of neutrino self-interactions from a vast array of
probes, from cosmology, to astrophysics, to the laboratory. We also discuss
theoretical motivations for such self-interactions, including neutrino masses
and possible connections to dark matter. Looking forward, we discuss the
capabilities of searches in the next generation and beyond, highlighting the
possibility of future discovery of this beyond-the-SM physics.


title: Complex Saddles and Euclidean Wormholes in the Lorentzian Path Integral

abstract:
  We study complex saddles of the Lorentzian path integral for 4D axion gravity
and its dual description in terms of a 3-form flux, which include the
Giddings-Strominger Euclidean wormhole. Transition amplitudes are computed
using the Lorentzian path integral and with the help of Picard-Lefschetz
theory. The number and nature of saddles is shown to qualitatively change in
the presence of a bilocal operator that could arise, for example, as a result
of considering higher-topology transitions. We also analyze the stability of
the Giddings-Strominger wormhole in the 3-form picture, where we find that it
represents a perturbatively stable Euclidean saddle of the gravitational path
integral. This calls into question the ultimate fate of such solutions in an
ultraviolet-complete theory of quantum gravity.


title:
Anomalous gapped boundaries between surface topological orders in
  higher-order topological insulators and superconductors with inversion
  symmetry

abstract:
  We show that the gapless boundary signatures - namely, chiral/helical hinge
modes or localized zero modes - of three-dimensional higher-order topological
insulators and superconductors with inversion symmetry can be gapped without
symmetry breaking upon the introduction of non-Abelian surface topological
order. In each case, the fractionalization pattern that appears on surface is
`anomalous' in the sense that it can be made consistent with symmetry only on
the surface of a three dimensional higher-order insulator/superconductor. Our
results show that the interacting manifestation of higher-order topology is the
appearance of `anomalous gapped boundaries' between distinct topological orders
whose quasiparticles are related by inversion, possibly in conjunction with
other protecting symmetries such as TRS and charge conservation.


title:
The Coma cluster at LOFAR frequencies II: the halo, relic, and a new
  accretion relic

abstract:
  We present LOw Frequency ARray observations of the Coma cluster field at
144\,MHz. The cluster hosts one of the most famous radio halos, a relic, and a
low surface-brightness bridge. We detect new features that allow us to make a
step forward in the understanding of particle acceleration in clusters. The
radio halo extends for more than 2 Mpc, which is the largest extent ever
reported. To the North-East of the cluster, beyond the Coma virial radius, we
discover an arc-like radio source that could trace particles accelerated by an
accretion shock. To the West of the halo, coincident with a shock detected in
the X-rays, we confirm the presence of a radio front, with different spectral
properties with respect to the rest of the halo. We detect a radial steepening
of the radio halo spectral index between 144 MHz and 342 MHz, at $\sim
30^{\prime}$ from the cluster centre, that may indicate a non constant
re-acceleration time throughout the volume. We also detect a mild steepening of
the spectral index towards the cluster centre. For the first time, a radial
change in the slope of the radio-X-ray correlation is found, and we show that
such a change could indicate an increasing fraction of cosmic ray versus
thermal energy density in the cluster outskirts. Finally, we investigate the
origin of the emission between the relic and the source NGC 4789, and we argue
that NGC4789 could have crossed the shock originating the radio emission
visible between its tail and the relic.


title: Detection of Gravitational Redshift in Open Cluster non-degenerate stars

abstract:
  A key observational prediction of Einstein's Equivalence Principle is that
light undergoes redshift when it escapes from a gravitational field. Although
astrophysics provides a wide variety of physical conditions in which this
redshift should be significant, till very recently the observational evidence
for this gravitational effect was limited to the light emitted by the Sun and
white dwarfs. \textit{Gaia}-DR2 astrometric and kinematic data, in combination
with other spectroscopic observations, provides a test bench to validate such
predictions in statistical terms. The aim of this paper is to analyze several
thousand main-sequence and giant stars in open clusters (OCs) to measure the
gravitational redshift effect. Observationally, a spectral shift will depend on
the stellar mass-to-radius ratio as expected from the theoretical estimation of
relativity. After the analysis, the obtained correlation coefficient between
theoretical predictions and observations for 28 (51) OCs is $a= 0.977 \pm
0.218$ ($0.899 \pm 0.137$). The result has proven to be statistically robust
and with little dependence on the details of the methodology or sample
selection criteria. This study represents one of the more extensive validations
of a fundamental prediction of gravity theories.


title:
A hot and luminous source at the site of the fast transient AT2018cow at
  2-3 years after its explosion

abstract:
  We report the discovery of a luminous late-time source at the position of the
fast blue optical transient (FBOT) AT2018cow on images taken by the Hubble
Space Telescope (HST) at 714 d and 1136 d after its explosion. This source is
detected at both UV and optical wavelengths and has prominent H$\alpha$
emission. It has a very stable brightness between the two epochs and a very
blue spectral energy distribution (SED) consistent with $f_\lambda$ $\propto$
$\lambda^{-4.1 \pm 0.1}$, i.e. the Rayleigh-Jeans tail of a hot blackbody with
a very high temperature of log($T$/K) $>$ 4.6 and luminosity of
log($L$/$L_\odot$) $>$ 7.0. This late-time source is unlikely to be an
unrelated object in chance alignment, or due to a light echo of AT2018cow.
Other possible scenarios also have some difficulties in explaining this
late-time source, including companion star(s), star cluster, the survived
progenitor star, interaction with circumstellar medium (CSM), magnetar, or
delayed accretion in a tidal disruption event (TDE). Long-term and
multi-wavelength monitoring will help to resolve its nature and finally reveal
the origin of the "Cow".


title: Quantum Error Correction in the Black Hole Interior

abstract:
  We study the quantum error correction properties of the black hole interior
in a toy model for an evaporating black hole: Jackiw-Teitelboim gravity
entangled with a non-gravitational bath. After the Page time, the black hole
interior degrees of freedom in this system are encoded in the bath Hilbert
space. We use the gravitational path integral to show that the interior density
matrix is correctable against the action of quantum operations on the bath
which (i) do not have prior access to details of the black hole microstates,
and (ii) do not have a large, negative coherent information with respect to the
maximally mixed state on the bath, with the lower bound controlled by the black
hole entropy and code subspace dimension. Thus, the encoding of the black hole
interior in the radiation is robust against generic, low-rank quantum
operations. For erasure errors, gravity comes within an $O(1)$ distance of
saturating the Singleton bound on the tolerance of error correcting codes. For
typical errors in the bath to corrupt the interior, they must have a rank that
is a large multiple of the bath Hilbert space dimension, with the precise
coefficient set by the black hole entropy and code subspace dimension.


title: Anisotropic deconfined criticality in Dirac spin liquids

abstract:
  We analyze a Higgs transition from a U(1) Dirac spin liquid to a gapless
$\mathbb{Z}_2$ spin liquid. This $\mathbb{Z}_2$ spin liquid is of relevance to
the spin $S=1/2$ square lattice antiferromagnet, where recent numerical studies
have given evidence for such a phase existing in the regime of high frustration
between nearest neighbor and next-nearest neighbor antiferromagnetic
interactions (the $J_1$-$J_2$ model), appearing in a parameter regime between
the vanishing of N\'eel order and the onset of valence bond solid ordering. The
proximate Dirac spin liquid is unstable to monopole proliferation on the square
lattice, ultimately leading to N\'eel or valence bond solid ordering. As such,
we conjecture that this Higgs transition describes the critical theory
separating the gapless $\mathbb{Z}_2$ spin liquid of the $J_1$-$J_2$ model from
one of the two proximate ordered phases. The transition into the other ordered
phase can be described in a unified manner via a transition into an unstable
SU(2) spin liquid, which we have analyzed in prior work. By studying the
deconfined critical theory separating the U(1) Dirac spin liquid from the
gapless $\mathbb{Z}_2$ spin liquid in a $1/N_f$ expansion, with $N_f$
proportional to the number of fermions, we find a stable fixed point with an
anisotropic spinon dispersion and a dynamical critical exponent $z \neq 1$. We
analyze the consequences of this anisotropic dispersion by calculating the
angular profiles of the equal-time N\'eel and valence bond solid correlation
functions, and we find them to be distinct. We also note the influence of the
anisotropy on the scaling dimension of monopoles.


title:
Breaching the capillary time-step constraint using a coupled VOF method
  with implicit surface tension

abstract:
  The capillary time-step constraint is the dominant limitation on the
applicable time-step in many simulations of interfacial flows with surface
tension and, consequently, governs the execution time of these simulations. We
propose a fully-coupled pressure-based algorithm based on an algebraic
Volume-of-Fluid (VOF) method in conjunction with an implicit linearised surface
tension treatment that can breach the capillary time-step constraint. The
advection of the interface is solved together with the momentum and continuity
equations of the interfacial flow in a single system of linearised equations,
providing an implicit coupling between pressure, velocity and the VOF colour
function used to distinguish the interacting fluids. Surface tension is treated
with an implicit formulation of the Continuum Surface Force (CSF) model,
whereby both the interface curvature and the gradient of the colour function
are treated implicitly with respect to the colour function. The presented
results demonstrate that a time-step larger than the capillary time-step can be
applied with this new numerical framework, as long as other relevant time-step
restrictions are satisfied, including a time-step restriction associated with
surface tension, density as well as viscosity.


title:
Quantum $f$-divergences via Nussbaum-Szko{\l}a Distributions with
  applications to Petz-R\'enyi and von Neumann Relative Entropy

abstract:
  We prove that the quantum $f$-divergence of two states (density operators on
a Hilbert space of finite or infinite dimensions) is same as the classical
$f$-divergence of the corresponding Nussbaum-Szko{\l}a distributions. This
provides a general framework to study most of the known quantum entropic
quantities using the corresponding classical entities. In this spirit, we study
the Petz-R\'enyi and von Neumann relative entropy and prove that these quantum
entropies are equal to the corresponding classical counterparts of the
Nussbaum-Szko{\l}a distributions. This is a generalization of a finite
dimensional result that was proved by Nussbaum and Szko{\l}a [Ann. Statist. 37,
2009, 2] to the infinite dimensions. We apply classical results about R\'enyi
and Kullback-Leibler divergences to obtain new results and new proofs for some
known results about the quantum relative entropies. Most important among these
are (i) a quantum Pinsker type inequality in the infinite dimensions, and (ii)
necessary and sufficient conditions for the finiteness of the Petz-R\'enyi
$\alpha$-relative entropy for any order $\alpha \in [0, \infty]$. Furthermore,
we construct an example to show that the information theoretic definition of
the von Neumann relative entropy is different from Araki's definition of
relative entropy when the dimension of the Hilbert space is infinite. This
discrepancy can be bridged using the notion of the distribution of an unbounded
positive selfadjoint operator with respect to a positive compact operator and
Haagerup's extension of the trace. Our results are valid in both finite and
infinite dimensions and hence can be applied to continuous variable systems as
well.


title: Null test BSM searches with rare charm baryon decays

abstract:
  Rare $\vert \Delta c \vert=\vert \Delta u \vert=1$ processes uniquely probe
flavor in the Standard Model and beyond from the up-type quark sector.
Opportunities to search for BSM physics in charm arise from the severe GIM
suppression which kills SM contributions to leptonic axial-vector contributions
and suppresses CP violation. Semileptonic decays of charmed hadrons offer a
variety of clean null test observables, featuring new physics effects which are
even enhanced by resonance contributions. In particular, angular observables in
three- and self-analyzing four-body baryon decays, such as $\Lambda_c\to p
\ell^+\ell^-$ and $\Xi_c^+\to\Sigma^+(\to p\pi^0)\ell^+\ell^-$,
$\Xi_c^0\to\Lambda^0\,(\to p \pi^-)\ell^+\ell^-$ and $\Omega_c^0\to\Xi^0\,(\to
\Lambda^{0} \pi^0)\ell^+\ell^-$ disentangle possible new physics effects in
electromagnetic dipole couplings $C_7^{(\prime)}$ and (axial-)vector 4-fermion
ones $C^{(\prime)}_{9\,(10)}$. There is sensitivity to BSM couplings as small
as $\sim 0.01$.


title: Numerical conservation laws of time fractional diffusion PDEs

abstract:
  The first part of this paper introduces sufficient conditions to determine
conservation laws of diffusion equations of arbitrary fractional order in time.
Numerical methods that satisfy a discrete analogue of these conditions have
conservation laws that approximate the continuous ones. In the second part of
the paper, we propose a method that combines a finite difference method in
space with a spectral integrator in time. The time integrator has already been
applied in literature to solve time fractional equations with Caputo fractional
derivative of order $\alpha\in(0,1)$. It is here generalised to approximate
Caputo and Riemann-Liouville fractional derivatives of arbitrary order. We
apply the method to subdiffusion and superdiffusion equations with
Riemann-Liouville fractional derivative and derive its conservation laws.
Finally, we present a range of numerical experiments to show the convergence of
the method and its conservation properties.


title: Global solutions of quasi-geostrophic shallow-water fronts

abstract:
  In this paper, we consider a family of piecewise constant solutions of the
quasi-geostrophic shallow-water (QGSW) equation. We derive the contour dynamics
equation of the QGSW front, which is a nonlinear, nonlocal dispersive equation,
and prove the global existence of the solutions when the initial data is
sufficiently close to a flat front.


title: Learning Time-optimized Path Tracking with or without Sensory Feedback

abstract:
  In this paper, we present a learning-based approach that allows a robot to
quickly follow a reference path defined in joint space without exceeding limits
on the position, velocity, acceleration and jerk of each robot joint. Contrary
to offline methods for time-optimal path parameterization, the reference path
can be changed during motion execution. In addition, our approach can utilize
sensory feedback, for instance, to follow a reference path with a bipedal robot
without losing balance. With our method, the robot is controlled by a neural
network that is trained via reinforcement learning using data generated by a
physics simulator. From a mathematical perspective, the problem of tracking a
reference path in a time-optimized manner is formalized as a Markov decision
process. Each state includes a fixed number of waypoints specifying the next
part of the reference path. The action space is designed in such a way that all
resulting motions comply with the specified kinematic joint limits. The reward
function finally reflects the trade-off between the execution time, the
deviation from the desired reference path and optional additional objectives
like balancing. We evaluate our approach with and without additional objectives
and show that time-optimized path tracking can be successfully learned for both
industrial and humanoid robots. In addition, we demonstrate that networks
trained in simulation can be successfully transferred to a real Kuka robot.


title:
Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and
  no Retraining

abstract:
  Retrospective analysis of brain MRI scans acquired in the clinic has the
potential to enable neuroimaging studies with sample sizes much larger than
those found in research datasets. However, analysing such clinical images "in
the wild" is challenging, since subjects are scanned with highly variable
protocols (MR contrast, resolution, orientation, etc.). Nevertheless, recent
advances in convolutional neural networks (CNNs) and domain randomisation for
image segmentation, best represented by the publicly available method SynthSeg,
may enable morphometry of clinical MRI at scale. In this work, we first
evaluate SynthSeg on an uncurated, heterogeneous dataset of more than 10,000
scans acquired at Massachusetts General Hospital. We show that SynthSeg is
generally robust, but frequently falters on scans with low signal-to-noise
ratio or poor tissue contrast. Next, we propose SynthSeg+, a novel method that
greatly mitigates these problems using a hierarchy of conditional segmentation
and denoising CNNs. We show that this method is considerably more robust than
SynthSeg, while also outperforming cascaded networks and state-of-the-art
segmentation denoising methods. Finally, we apply our approach to a
proof-of-concept volumetric study of ageing, where it closely replicates
atrophy patterns observed in research studies conducted on high-quality, 1mm,
T1-weighted scans. The code and trained model are publicly available at
https://github.com/BBillot/SynthSeg.


title:
Boundary fluctuation dynamics of a phase-separated domain in planar
  geometry

abstract:
  Using theories of phase ordering kinetics and of renormalization group, we
derive analytically the relaxation times of the long wave-length fluctuations
of a phase-separated domain boundary in the vicinity of (and below) the
critical temperature, in the planar Ising universality class. For a conserved
order parameter, the relaxation time grows like $\Lambda^3$ at wave-length
$\Lambda$ and can be expressed in terms of parameters relevant at the
microscopic scale: lattice spacing, bulk diffusion coefficient of the minority
phase, and temperature. These results are supported by numerical simulations of
2D Ising models, enabling in addition to calculate the non-universal numerical
prefactor. We discuss the applications of these findings to the determination
of the time-scale associated with elementary Monte Carlo moves from the
measurement of long wave-length relaxation times on experimental systems or
Molecular Dynamics simulations.


title: Spectrum of the Laplacian on a domain perturbed by small resonators

abstract:
  It is widely known that the spectrum of the Dirichlet Laplacian is stable
under small perturbations of a domain, while in the case of the Neumann or
mixed boundary conditions the spectrum may abruptly change. In this work we
discuss an example of such a domain perturbation. Let $\Omega$ be a (not
necessary bounded) domain in $\mathbb{R}^n$. We perturb it to $
\Omega_\varepsilon=\Omega\setminus \cup_{k=1}^m S_{k,\varepsilon},$ where
$S_{k,\varepsilon}$ are closed surfaces with small suitably scaled holes
("windows") through which the bounded domains enclosed by these surfaces
("resonators") are connected to the outer domain. When $\varepsilon$ goes to
zero, the resonators shrink to points. We prove that in the limit
$\varepsilon\to 0$ the spectrum of the Laplacian on $\Omega_\varepsilon$ with
the Neumann boundary conditions on $S_{k,\varepsilon}$ and the Dirichlet
boundary conditions on the outer boundary converges to the union of the
spectrum of the Dirichlet Laplacian on $\Omega$ and the numbers $\gamma_k$,
$k=1,\dots,m$, being equal $1/4$ times the limit of the ratio between the
capacity of the $k$th window and the volume of the $k$th resonator. We obtain
an estimate on the rate of this convergence with respect to the Hausdorff-type
metrics. Also, an application of this result is presented: we construct an
unbounded waveguide-like domain with inserted resonators such that the
eigenvalues of the Laplacian on this domain lying below the essential spectrum
threshold do coincide with prescribed numbers.


title:
Analyzing rotational bands in odd-mass nuclei using Effective Field
  Theory and Bayesian methods

abstract:
  We recently developed an Effective Field Theory (EFT) for rotational bands in
odd-mass nuclei. Here we use EFT expressions to perform a Bayesian analysis of
data on the rotational energy levels of $^{99}$Tc, ${}^{155,157}$Gd,
${}^{159}$Dy, ${}^{167, 169}$Er, ${}^{167, 169}$Tm, ${}^{183}$W, ${}^{235}$U
and ${}^{239}$Pu. The error model in our Bayesian analysis includes both
experimental and EFT truncation uncertainties. It also accounts for the fact
that low-energy constants (LECs) at even and odd orders are expected to have
different sizes. We use Markov Chain Monte Carlo (MCMC) sampling to explore the
joint posterior of the EFT and error-model parameters and show both the LECs
and the expansion parameter, $Q$, can be reliably determined. We extract the
LECs up to fourth order in the EFT and find that, provided we correctly account
for EFT truncation errors in our likelihood, results for lower-order LECs are
stable as we go to higher orders. LEC results are also stable with respect to
the addition of higher-energy data. We extract the expansion parameter for all
the nuclei listed above and find a clear correlation between the extracted and
the expected $Q$ based on the single-particle and vibrational energy scales.
However, the $Q$ that actually determines the convergence of the EFT expansion
is markedly smaller than would be naively expected based on those scales.


title: Kleinian sphere packings, reflection groups, and arithmeticity

abstract:
  In this paper we study crystallographic sphere packings and Kleinian sphere
packings, introduced first by Kontorovich and Nakamura in 2017 and then studied
further by Kapovich and Kontorovich in 2021. In particular, we solve the
problem of existence of crystallographic sphere packings in certain higher
dimensions posed by Kontorovich and Nakamura. In addition, we present a
geometric doubling procedure allowing to obtain sphere packings from some
Coxeter polyhedra without isolated roots, and study "properly integral"
packings (that is, ones which are integral but not superintegral).


title: Towards Rich, Portable, and Large-Scale Pedestrian Data Collection

abstract:
  Recently, pedestrian behavior research has shifted towards machine learning
based methods and converged on the topic of modeling pedestrian interactions.
For this, a large-scale dataset that contains rich information is needed. We
propose a data collection system that is portable, which facilitates accessible
large-scale data collection in diverse environments. We also couple the system
with a semi-autonomous labeling pipeline for fast trajectory label production.
We demonstrate the effectiveness of our system by further introducing a dataset
we have collected -- the TBD pedestrian dataset. Compared with existing
pedestrian datasets, our dataset contains three components: human verified
labels grounded in the metric space, a combination of top-down and perspective
views, and naturalistic human behavior in the presence of a socially
appropriate "robot". In addition, the TBD pedestrian dataset is larger in
quantity compared to similar existing datasets and contains unique pedestrian
behavior.


title: Nudged elastic band approach to nuclear fission pathways

abstract:
  The nuclear fission process is a dramatic example of the large-amplitude
collective motion in which the nucleus undergoes a series of shape changes
before splitting into distinct fragments. This motion can be represented by a
pathway in the many-dimensional space of collective coordinates. The collective
action along the fission pathway determines the spontaneous fission half-lives
as well as mass and charge distributions of fission fragments.
  We study the performance and precision of various methods to determine the
minimum action and minimum-energy fission trajectories in the collective space.
  We apply the nudged elastic band method (NEB), grid-based methods, and Euler
Lagrange approach to the collective action minimization in two and three
dimensional collective spaces.
  The performance of various approaches to the fission pathway problem is
assessed by studying the collective motion along both analytic energy surfaces
and realistic potential energy surfaces obtained with the
Hartree-Fock-Bogoliubov theory. The uniqueness and stability of the solutions
is studied. The NEB method is capable of efficient determination of the exit
points on the outer turning surface that characterize the most probable fission
pathway and constitute the key input for fission studies. This method can also
be used to accurately compute the critical points (i.e., local minima and
saddle points) on the potential energy surface of the fissioning nucleus that
determine the static fission path.
  The NEB method is the tool of choice for finding the least-action and minimum
energy fission trajectories. It will be particularly useful in large-scale
fission calculation of superheavy nuclei and neutron-rich fissioning nuclei
contributing to the astrophysical r-process recycling.


title:
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer
  Among Related Languages

abstract:
  Pre-trained multilingual language models such as mBERT and XLM-R have
demonstrated great potential for zero-shot cross-lingual transfer to low
web-resource languages (LRL). However, due to limited model capacity, the large
difference in the sizes of available monolingual corpora between high
web-resource languages (HRL) and LRLs does not provide enough scope of
co-embedding the LRL with the HRL, thereby affecting downstream task
performance of LRLs. In this paper, we argue that relatedness among languages
in a language family along the dimension of lexical overlap may be leveraged to
overcome some of the corpora limitations of LRLs. We propose Overlap BPE
(OBPE), a simple yet effective modification to the BPE vocabulary generation
algorithm which enhances overlap across related languages. Through extensive
experiments on multiple NLP tasks and datasets, we observe that OBPE generates
a vocabulary that increases the representation of LRLs via tokens shared with
HRLs. This results in improved zero-shot transfer from related HRLs to LRLs
without reducing HRL representation and accuracy. Unlike previous studies that
dismissed the importance of token-overlap, we show that in the low-resource
related language setting, token overlap matters. Synthetically reducing the
overlap to zero can cause as much as a four-fold drop in zero-shot transfer
accuracy.


title: Audio-Visual Object Classification for Human-Robot Collaboration

abstract:
  Human-robot collaboration requires the contactless estimation of the physical
properties of containers manipulated by a person, for example while pouring
content in a cup or moving a food box. Acoustic and visual signals can be used
to estimate the physical properties of such objects, which may vary
substantially in shape, material and size, and also be occluded by the hands of
the person. To facilitate comparisons and stimulate progress in solving this
problem, we present the CORSMAL challenge and a dataset to assess the
performance of the algorithms through a set of well-defined performance scores.
The tasks of the challenge are the estimation of the mass, capacity, and
dimensions of the object (container), and the classification of the type and
amount of its content. A novel feature of the challenge is our
real-to-simulation framework for visualising and assessing the impact of
estimation errors in human-to-robot handovers.


title: Region-of-Interest Based Neural Video Compression

abstract:
  Humans do not perceive all parts of a scene with the same resolution, but
rather focus on few regions of interest (ROIs). Traditional Object-Based codecs
take advantage of this biological intuition, and are capable of non-uniform
allocation of bits in favor of salient regions, at the expense of increased
distortion the remaining areas: such a strategy allows a boost in perceptual
quality under low rate constraints. Recently, several neural codecs have been
introduced for video compression, yet they operate uniformly over all spatial
locations, lacking the capability of ROI-based processing. In this paper, we
introduce two models for ROI-based neural video coding. First, we propose an
implicit model that is fed with a binary ROI mask and it is trained by
de-emphasizing the distortion of the background. Secondly, we design an
explicit latent scaling method, that allows control over the quantization
binwidth for different spatial regions of latent variables, conditioned on the
ROI mask. By extensive experiments, we show that our methods outperform all our
baselines in terms of Rate-Distortion (R-D) performance in the ROI. Moreover,
they can generalize to different datasets and to any arbitrary ROI at inference
time. Finally, they do not require expensive pixel-level annotations during
training, as synthetic ROI masks can be used with little to no degradation in
performance. To the best of our knowledge, our proposals are the first
solutions that integrate ROI-based capabilities into neural video compression
models.


title: Theory of polar domains in moir\'e heterostructures

abstract:
  The discovery of unconventional ferroelectric behavior in twisted bilayers
has prompted the consideration of moir\'e heterostructures as polar materials.
However, misconceptions about the nature and origin of the observed
ferroelectricity indicate that a better theoretical understanding of the polar
properties of moir\'e heterostructures is needed. In this paper, it is
proposed, and verified with first-principles calculations, that all moir\'e
heterostructures exhibit an out-of-plane moir\'e polar domain (MPD) structure.
In transition metal dichalcogenide bilayers, an interlayer charge transfer
occurs due to the change in stacking arrangements throughout the moir\'e
superlattice, leading to a local out-of-plane dipole moment, with the magnitude
and shape of the MPDs being dominated by the chalcogen atoms. While the MPDs in
all heterostructures are sensitive to the moir\'e period, it is only in the
homo-bilayers that they can be tuned with an out-of-plane electric field. The
misconceptions about ferroelectricity in moir\'e heterostructures are
addressed, and it is proposed that the only scenario in which the MPDs can be
considered ferroelectric domains is via a global van der Waals sliding by one
third of a unit cell in a homo-bilayer. Finally, a general theoretical
discussion of the polar properties of moir\'e heterostructures is provided.


title: The role of mass loss in chemodynamical evolution of galaxies

abstract:
  Thanks to the long-term collaborations between nuclear and astrophysics, we
have good understanding on the origin of elements in the universe, except for
the elements around Ti and some neutron-capture elements. From the comparison
between observations of nearby stars and Galactic chemical evolution models, a
rapid neutron-capture process associated with core-collapse supernovae is
required. The production of C, N, F and some minor isotopes depends on the
rotation of massive stars, and the observations of distant galaxies with ALMA
indicate rapid cosmic enrichment. It might be hard to find very metal-poor or
Population III (and dust-free) galaxies at very high redshifts even with JWST.


title: PIONEER: Studies of Rare Pion Decays

abstract:
  A next-generation rare pion decay experiment, PIONEER, is strongly motivated
by several inconsistencies between Standard Model (SM) predictions and data
pointing towards the potential violation of lepton flavor universality. It will
probe non-SM explanations of these anomalies through sensitivity to quantum
effects of new particles even if their masses are at very high scales.
Measurement of the charged-pion branching ratio to electrons vs. muons
$R_{e/\mu}$ is extremely sensitive to new physics effects. At present, the SM
prediction for $R_{e/\mu}$ is known to 1 part in $10^4$, which is 15 times more
precise than the current experimental result. An experiment reaching the
theoretical accuracy will test lepton flavor universality at an unprecedented
level, probing mass scales up to the PeV range. Measurement of pion beta decay,
$\pi^+\to \pi^0 e^+ \nu (\gamma)$, with 3 to 10-fold improvement in
sensitivity, will determine $V_{ud}$ in a theoretically pristine manner and
test CKM unitarity, which is very important in light of the recently emerged
tensions. In addition, various exotic rare decays involving sterile neutrinos
and axions will be searched for with unprecedented sensitivity. The experiment
design benefits from experience with the recent PIENU and PEN experiments at
TRIUMF and the Paul Scherrer Institut (PSI). Excellent energy and time
resolutions, greatly increased calorimeter depth, high-speed detector and
electronics response, large solid angle coverage, and complete event
reconstruction are all critical aspects of the approach. The PIONEER experiment
design includes a 3$\pi$ sr 25 radiation length calorimeter, a segmented low
gain avalanche detector stopping target, a positron tracker, and other
detectors. Using intense pion beams, and state-of-the-art instrumentation and
computational resources, the experiments can be performed at the PSI ring
cyclotron.


title:
Gravitationally induced decoherence vs space-time diffusion: testing the
  quantum nature of gravity

abstract:
  We consider two interacting systems when one is treated classically while the
other system remains quantum. Consistent dynamics of this coupling has been
shown to exist, and explored in the context of treating space-time classically.
Here, we prove that such hybrid dynamics necessarily results in decoherence of
the quantum system, and a breakdown in predictability in the classical phase
space. We further prove that a trade-off between the rate of this decoherence
and the degree of diffusion induced in the classical system is a general
feature of all classical quantum dynamics; long coherence times require strong
diffusion in phase-space relative to the strength of the coupling. Applying the
trade-off relation to gravity, we find a relationship between the strength of
gravitationally-induced decoherence versus diffusion of the metric and its
conjugate momenta. This provides an experimental signature of theories in which
gravity is fundamentally classical. Bounds on decoherence rates arising from
current interferometry experiments, combined with precision measurements of
mass, place significant restrictions on theories where Einstein's classical
theory of gravity interacts with quantum matter. We find that part of the
parameter space of such theories are already squeezed out, and provide figures
of merit which can be used in future mass measurements and interference
experiments.


title:
Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces
  in End-to-End Robot Learning

abstract:
  Action representation is an important yet often overlooked aspect in
end-to-end robot learning with deep networks. Choosing one action space over
another (e.g. target joint positions, or Cartesian end-effector poses) can
result in surprisingly stark performance differences between various downstream
tasks -- and as a result, considerable research has been devoted to finding the
right action space for a given application. However, in this work, we instead
investigate how our models can discover and learn for themselves which action
space to use. Leveraging recent work on implicit behavioral cloning, which
takes both observations and actions as input, we demonstrate that it is
possible to present the same action in multiple different spaces to the same
policy -- allowing it to learn inductive patterns from each space.
Specifically, we study the benefits of combining Cartesian and joint action
spaces in the context of learning manipulation skills. To this end, we present
Implicit Kinematic Policies (IKP), which incorporates the kinematic chain as a
differentiable module within the deep network. Quantitative experiments across
several simulated continuous control tasks -- from scooping piles of small
objects, to lifting boxes with elbows, to precise block insertion with
miscalibrated robots -- suggest IKP not only learns complex prehensile and
non-prehensile manipulation from pixels better than baseline alternatives, but
also can learn to compensate for small joint encoder offset errors. Finally, we
also run qualitative experiments on a real UR5e to demonstrate the feasibility
of our algorithm on a physical robotic system with real data. See
https://tinyurl.com/4wz3nf86 for code and supplementary material.


title: The case of equality for the spacetime positive mass theorem

abstract:
  The rigidity of the spacetime positive mass theorem states that an initial
data set satisfying the dominant energy condition with vanishing mass can be
isometrically embedded into Minkowski space. This has been established by
Beig-Chrusciel and Huang-Lee under additional decay assumptions for the energy
and momentum densities $\mu$ and $J$. In this note we give a new and elementary
proof in dimension 3 which removes these additional decay assumptions. Our
argument is based on spacetime harmonic functions and Liouville's theorem.