title:
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object
  Detection

abstract:
  We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising
anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in
this paper. DINO improves over previous DETR-like models in performance and
efficiency by using a contrastive way for denoising training, a mixed query
selection method for anchor initialization, and a look forward twice scheme for
box prediction. DINO achieves $48.3$AP in $12$ epochs and $51.0$AP in $36$
epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a
significant improvement of $\textbf{+4.9}$\textbf{AP} and
$\textbf{+2.4}$\textbf{AP}, respectively, compared to DN-DETR, the previous
best DETR-like model. DINO scales well in both model size and data size.
Without bells and whistles, after pre-training on the Objects365 dataset with a
SwinL backbone, DINO obtains the best results on both COCO \texttt{val2017}
($\textbf{63.2}$\textbf{AP}) and \texttt{test-dev}
(\textbf{$\textbf{63.3}$AP}). Compared to other models on the leaderboard, DINO
significantly reduces its model size and pre-training data size while achieving
better results. Our code will be available at
\url{https://github.com/IDEACVR/DINO}.


title:
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality
  Enhancement through Islandization

abstract:
  Graph Convolutional Networks (GCNs) have drawn tremendous attention in the
past three years. Compared with other deep learning modalities,
high-performance hardware acceleration of GCNs is as critical but even more
challenging. The hurdles arise from the poor data locality and redundant
computation due to the large size, high sparsity, and irregular non-zero
distribution of real-world graphs.
  In this paper we propose a novel hardware accelerator for GCN inference,
called I-GCN, that significantly improves data locality and reduces unnecessary
computation. The mechanism is a new online graph restructuring algorithm we
refer to as islandization. The proposed algorithm finds clusters of nodes with
strong internal but weak external connections. The islandization process yields
two major benefits. First, by processing islands rather than individual nodes,
there is better on-chip data reuse and fewer off-chip memory accesses. Second,
there is less redundant computation as aggregation for common/shared neighbors
in an island can be reused. The parallel search, identification, and leverage
of graph islands are all handled purely in hardware at runtime working in an
incremental pipeline. This is done without any preprocessing of the graph data
or adjustment of the GCN model structure.
  Experimental results show that I-GCN can significantly reduce off-chip
accesses and prune 38% of aggregation operations, leading to performance
speedups over CPUs, GPUs, the prior art GCN accelerators of 5549x, 403x, and
5.7x on average, respectively.


title: Localization of the continuum directed random polymer

abstract:
  We consider the continuum directed random polymer (CDRP) model that arises as
a scaling limit from $1+1$ dimensional directed polymers in the intermediate
disorder regime. We show that for a point-to-point polymer of length $t$ and
any $p\in (0,1)$, the quenched density of the point on the path which is $pt$
distance away from the origin when centered around its random mode
$\mathcal{M}_{p,t}$ converges in law to an explicit random density function as
$t\to\infty$ without any scaling. Similarly, in the case of point-to-line
polymers of length $t$, the quenched density of the endpoint of the path when
centered around its random mode $\mathcal{M}_{*,t}$ converges in law to an
explicit random density. The limiting random densities are proportional to
$e^{-\mathcal{R}_\sigma(x)}$ where $\mathcal{R}_\sigma(x)$ is a two-sided 3D
Bessel process with appropriate diffusion coefficient $\sigma$. In addition,
the laws of the random modes $\mathcal{M}_{*,t}$, $\mathcal{M}_{p,t}$
themselves converge in distribution upon $t^{2/3}$ scaling to the maximizer of
$\operatorname{Airy}_2$ process minus a parabola and points on the geodesics of
the directed landscape respectively. Our localization results stated above
provide an affirmative case of the folklore "favorite region" conjecture. Our
proof techniques also allow us to prove properties of the KPZ equation such as
ergodicity and limiting Bessel behaviors around the maximum.


title:
Emotion Regulation and Dynamics of Moral Concerns During the Early
  COVID-19 Pandemic

abstract:
  The COVID-19 pandemic has upended daily life around the globe, posing a
threat to public health. Intuitively, we expect that surging cases and deaths
would lead to fear, distress and other negative emotions. However, using
state-of-the-art methods to measure sentiment, emotions, and moral concerns in
social media messages posted in the early stage of the pandemic, we see a
counter-intuitive rise in positive affect. We hypothesize that the increase of
positivity is associated with a decrease of uncertainty and emotion regulation.
Finally, we identify a partisan divide in moral and emotional reactions that
emerged after the first US death. Overall, these results show how collective
emotional states have changed since the pandemic began, and how social media
can provide a useful tool to understand, and even regulate, diverse patterns
underlying human affect.


title: Human-Aware Object Placement for Visual Environment Reconstruction

abstract:
  Humans are in constant contact with the world as they move through it and
interact with it. This contact is a vital source of information for
understanding 3D humans, 3D scenes, and the interactions between them. In fact,
we demonstrate that these human-scene interactions (HSIs) can be leveraged to
improve the 3D reconstruction of a scene from a monocular RGB video. Our key
idea is that, as a person moves through a scene and interacts with it, we
accumulate HSIs across multiple input images, and optimize the 3D scene to
reconstruct a consistent, physically plausible and functional 3D scene layout.
Our optimization-based approach exploits three types of HSI constraints: (1)
humans that move in a scene are occluded or occlude objects, thus, defining the
depth ordering of the objects, (2) humans move through free space and do not
interpenetrate objects, (3) when humans and objects are in contact, the contact
surfaces occupy the same place in space. Using these constraints in an
optimization formulation across all observations, we significantly improve the
3D scene layout reconstruction. Furthermore, we show that our scene
reconstruction can be used to refine the initial 3D human pose and shape (HPS)
estimation. We evaluate the 3D scene layout reconstruction and HPS estimation
qualitatively and quantitatively using the PROX and PiGraphs datasets. The code
and data are available for research purposes at https://mover.is.tue.mpg.de/.


title:
ZippyPoint: Fast Interest Point Detection, Description, and Matching
  through Mixed Precision Discretization

abstract:
  The design of more complex and powerful neural network models has
significantly advanced the state-of-the-art in local feature detection and
description. These advances can be attributed to deeper networks, improved
training methodologies through self-supervision, or the introduction of new
building blocks, such as graph neural networks for feature matching. However,
in the pursuit of increased performance, efficient architectures that generate
lightweight descriptors have received surprisingly little attention. In this
paper, we investigate the adaptations neural networks for detection and
description require in order to enable their use in embedded platforms. To that
end, we investigate and adapt network quantization techniques for use in
real-time applications. In addition, we revisit common practices in descriptor
quantization and propose the use of a binary descriptor normalization layer,
enabling the generation of distinctive length-invariant binary descriptors.
ZippyPoint, our efficient network, runs at 47.2 fps on the Apple M1 CPU. This
is up to 5x faster than other learned detection and description models, making
it the only real-time learned network. ZippyPoint consistently outperforms all
other binary detection and descriptor methods in visual localization and
homography estimation tasks. Code and trained models will be released upon
publication.


title: Gap carving by a migrating planet embedded in a massive debris disc

abstract:
  When considering gaps in debris discs, a typical approach is to invoke
clearing by an unseen planet within the gap, and derive the planet mass using
Wisdom overlap or Hill radius arguments. However, this approach can be invalid
if the disc is massive, because this clearing would also cause planet
migration. This could result in a calculated planet mass that is incompatible
with the inferred disc mass, because the predicted planet would in reality be
too small to carve the gap without significant migration. We investigate the
gap that a single embedded planet would carve in a massive debris disc. We show
that a degeneracy is introduced, whereby an observed gap could be carved by two
different planets: either a high-mass, barely-migrating planet, or a smaller
planet that clears debris as it migrates. We find that, depending on disc mass,
there is a minimum possible gap width that an embedded planet could carve
(because smaller planets, rather than carving a smaller gap, would actually
migrate through the disc and clear a wider region). We provide simple formulae
for the planet-to-debris disc mass ratio at which planet migration becomes
important, the gap width that an embedded planet would carve in a massive
debris disc, and the interaction timescale. We also apply our results to
various systems, and in particular show that the disc of HD 107146 can be
reasonably well-reproduced with a migrating, embedded planet. Finally, we
discuss the importance of planet-debris disc interactions as a tool for
constraining debris disc masses.


title: Induced subgraphs of induced subgraphs of large chromatic number

abstract:
  We prove that, for every graph $F$ with at least one edge, there is a
constant $c_F$ such that there are graphs of arbitrarily large chromatic number
and the same clique number as $F$ in which every $F$-free induced subgraph has
chromatic number at most $c_F$. This generalises recent theorems of Bria\'nski,
Davies and Walczak, and Carbonero, Hompe, Moore and Spirkl. Moreover, we show
an analogous statement where clique number is replaced by odd girth.


title:
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
  in Limit-Order Book Markets

abstract:
  The prediction of financial markets is a challenging yet important task. In
modern electronically-driven markets traditional time-series econometric
methods often appear incapable of capturing the true complexity of the
multi-level interactions driving the price dynamics. While recent research has
established the effectiveness of traditional machine learning (ML) models in
financial applications, their intrinsic inability in dealing with
uncertainties, which is a great concern in econometrics research and real
business applications, constitutes a major drawback. Bayesian methods naturally
appear as a suitable remedy conveying the predictive ability of ML methods with
the probabilistically-oriented practice of econometric research. By adopting a
state-of-the-art second-order optimization algorithm, we train a Bayesian
bilinear neural network with temporal attention, suitable for the challenging
time-series task of predicting mid-price movements in ultra-high-frequency
limit-order book markets. By addressing the use of predictive distributions to
analyze errors and uncertainties associated with the estimated parameters and
model forecasts, we thoroughly compare our Bayesian model with traditional ML
alternatives. Our results underline the feasibility of the Bayesian deep
learning approach and its predictive and decisional advantages in complex
econometric tasks, prompting future research in this direction.


title:
Detecting the periodicity of highly irregularly sampled light-curves
  with Gaussian processes: the case of SDSSJ025214.67-002813.7

abstract:
  Based on a 20-year-long multiband observation of its light-curve, it was
conjectured that the quasar SDSSJ025214.67-002813.7 has a periodicity of ~4.4
years. These observations were acquired at a highly irregular sampling rate and
feature long intervals of missing data. In this setting, the inference over the
light-curve's spectral content requires, in addition to classic Fourier
methods, a proper model of the probability distribution of the missing
observations. In this article, we address the detection of the periodicity of a
light-curve from partial and irregularly-sampled observations using Gaussian
processes, a Bayesian nonparametric model for time series. This methodology
allows us to evaluate the veracity of the claimed periodicity of the
abovementioned quasar and also to estimate its power spectral density. Our main
contribution is the confirmation that considering periodic component definitely
improves the modeling of the data, although being the source originally
selected by a large sample of objects, the possibility that this is a chance
result cannot be ruled out.


title:
Academic mobility as an organizational mechanism of intercultural
  interaction

abstract:
  The trends of academic mobility of students in the context of
internationalization are presented. The results of a study de-voted to the
students' attitudes towards international education, cultural and educational
exchange and academic mobility programs are presented. It is concluded that the
organization of academic mobility, as a part of intercultural environment of
the university, is a necessary tool of motivation for the educational process
and organizational mechanism for intercultural communication.


title: Responsible AI in Healthcare

abstract:
  This article discusses open problems, implemented solutions, and future
research in the area of responsible AI in healthcare. In particular, we
illustrate two main research themes related to the work of two laboratories
within the Department of Informatics, Systems, and Communication at the
University of Milano-Bicocca. The problems addressed concern, in particular,
{uncertainty in medical data and machine advice}, and the problem of online
health information disorder.


title:
Probing the low-energy electron-scattering dynamics in liquids with
  high-harmonic spectroscopy

abstract:
  High-harmonic spectroscopy (HHS) is a nonlinear all-optical technique with
inherent attosecond temporal resolution, which has been applied successfully to
a broad variety of systems in the gas phase and solid state. Here, we extend
HHS to the liquid phase, and uncover the mechanism of high-harmonic generation
(HHG) for this phase of matter. Studying HHG over a broad range of wavelengths
and intensities, we show that the cut-off ($E_c$) is independent of the
wavelength beyond a threshold intensity, and find that $E_c$ is a
characteristic property of the studied liquid. We explain these observations
within an intuitive semi-classical model based on electron trajectories that
are limited by scattering to a characteristic length, which is connected to the
electron mean-free path. Our model is validated against rigorous multi-electron
time-dependent density-functional theory calculations in, both, supercells of
liquid water with periodic boundary conditions, and large clusters of a variety
of liquids. These simulations confirm our interpretation and thereby clarify
the mechanism of HHG in liquids. Our results demonstrate a new, all-optical
access to effective mean-free paths of slow electrons ($\leq$10 eV) in liquids,
in a regime that is inaccessible to accurate calculations, but is critical for
the understanding of radiation damage to living tissue. Our work also
establishes the possibility of resolving sub-femtosecond electron dynamics in
liquids, which offers a novel, all-optical approach to attosecond spectroscopy
of chemical processes in their native liquid environment.

categories: physics.chem-ph physics.atm-clus physics.atom-ph physics.optics


title: Mammograms Classification: A Review

abstract:
  An advanced reliable low-cost form of screening method, Digital mammography
has been used as an effective imaging method for breast cancer detection. With
an increased focus on technologies to aid healthcare, Mammogram images have
been utilized in developing computer-aided diagnosis systems that will
potentially help in clinical diagnosis. Researchers have proved that artificial
intelligence with its emerging technologies can be used in the early detection
of the disease and improve radiologists' performance in assessing breast
cancer. In this paper, we review the methods developed for mammogram mass
classification in two categories. The first one is classifying manually
provided cropped region of interests (ROI) as either malignant or benign, and
the second one is the classification of automatically segmented ROIs as either
malignant or benign. We also provide an overview of datasets and evaluation
metrics used in the classification task. Finally, we compare and discuss the
deep learning approach to classical image processing and learning approach in
this domain.


title: Adaptive Cross-Layer Attention for Image Restoration

abstract:
  Non-local attention module has been proven to be crucial for image
restoration. Conventional non-local attention processes features of each layer
separately, so it risks missing correlation between features among different
layers. To address this problem, we propose Cross-Layer Attention (CLA) module
in this paper. Instead of finding correlated key pixels within the same layer,
each query pixel can attend to key pixels at previous layers of the network. In
order to further enhance the learning capability and reduce the inference cost
of CLA, we further propose Adaptive CLA, or ACLA, as an improved CLA. Two
adaptive designs are proposed for ACLA: 1) adaptively selecting the keys for
non-local attention at each layer; 2) automatically searching for the insertion
locations for ACLA modules. By these two adaptive designs, ACLA dynamically
selects the number of keys to be aggregated for non-local attention at layer.
In addition, ACLA searches for the optimal insert positions of ACLA modules by
a neural architecture search method to render a compact neural network with
compelling performance. Extensive experiments on image restoration tasks,
including single image super-resolution, image denoising, image demosaicing,
and image compression artifacts reduction, validate the effectiveness and
efficiency of ACLA. The code of CLA and ACLA is available at
\url{https://github.com/SDL-ASU/ACLA}.


title:
Prediction of terrorism pattern accompanied by cyber-terrorism and the
  development direction of corresponding legal systems

abstract:
  As the information and communication system has become an essential element
for national operation and people's lives, and the dependence on information
and communication systems such as national infrastructure systems and
facilities increases, cyber terrorism is rapidly emerging as a serious threat
to national security in peacetime. As terrorist groups' access to cyber-attack
assets improves, the traditional form of terrorism is also expected to change
to a form combined with cyber-terrorism. Nevertheless, from a national security
point of view, Korea lacks a legal system to prepare for and respond to cyber
terrorism. In this paper, based on the development process of the modern
military operation concept, we predict the changes in the form of terrorism,
analyze the restrictions on the national response to cyber-terrorism based on
the current legal system, and propose the development directions.


title:
Triple Motion Estimation and Frame Interpolation based on Adaptive
  Threshold for Frame Rate Up-Conversion

abstract:
  In this paper, we propose a novel motion-compensated frame rate up-conversion
(MC-FRUC) algorithm. The proposed algorithm creates interpolated frames by
first estimating motion vectors using unilateral (jointing forward and
backward) and bilateral motion estimation. Then motion vectors are combined
based on adaptive threshold, in order to creates high-quality interpolated
frames and reduce block artifacts. Since motion-compensated frame interpolation
along unilateral motion trajectories yields holes, a new algorithm is
introduced to resolve this problem. The experimental results show that the
quality of the interpolated frames using the proposed algorithm is much higher
than the existing algorithms.


title:
Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity
  Measurement

abstract:
  A stroke occurs when an artery in the brain ruptures and bleeds or when the
blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's
tissues due to the rupture or obstruction resulting in tissue death. The Middle
cerebral artery (MCA) is the largest cerebral artery and the most commonly
damaged vessel in stroke. The quick onset of a focused neurological deficit
caused by interruption of blood flow in the territory supplied by the MCA is
known as an MCA stroke. Alberta stroke programme early CT score (ASPECTS) is
used to estimate the extent of early ischemic changes in patients with MCA
stroke. This study proposes a deep learning-based method to score the CT scan
for ASPECTS. Our work has three highlights. First, we propose a novel method
for medical image segmentation for stroke detection. Second, we show the
effectiveness of AI solution for fully-automated ASPECT scoring with reduced
diagnosis time for a given non-contrast CT (NCCT) Scan. Our algorithms show a
dice similarity coefficient of 0.64 for the MCA anatomy segmentation and 0.72
for the infarcts segmentation. Lastly, we show that our model's performance is
inline with inter-reader variability between radiologists.


title:
Measurement-conditioned Denoising Diffusion Probabilistic Model for
  Under-sampled Medical Image Reconstruction

abstract:
  We propose a novel and unified method, measurement-conditioned denoising
diffusion probabilistic model (MC-DDPM), for under-sampled medical image
reconstruction based on DDPM. Different from previous works, MC-DDPM is defined
in measurement domain (e.g. k-space in MRI reconstruction) and conditioned on
under-sampling mask. We apply this method to accelerate MRI reconstruction and
the experimental results show excellent performance, outperforming full
supervision baseline and the state-of-the-art score-based reconstruction
method. Due to its generative nature, MC-DDPM can also quantify the uncertainty
of reconstruction. Our code is available on github.


title:
Fusion-Correction Network for Single-Exposure Correction and
  Multi-Exposure Fusion

abstract:
  The photographs captured by digital cameras usually suffer from over-exposure
or under-exposure problems. The Single-Exposure Correction (SEC) and
Multi-Exposure Fusion (MEF) are two widely studied image processing tasks for
image exposure enhancement. However, current SEC and MEF methods ignore the
internal correlation between SEC and MEF, and are proposed under distinct
frameworks. What's more, most MEF methods usually fail at processing a sequence
containing only under-exposed or over-exposed images. To alleviate these
problems, in this paper, we develop an integrated framework to simultaneously
tackle the SEC and MEF tasks. Built upon the Laplacian Pyramid (LP)
decomposition, we propose a novel Fusion-Correction Network (FCNet) to fuse and
correct an image sequence sequentially in a multi-level scheme. In each LP
level, the image sequence is feed into a Fusion block and a Correction block
for consecutive image fusion and exposure correction. The corrected image is
upsampled and re-composed with the high-frequency detail components in
next-level, producing the base sequence for the next-level blocks. Experiments
on the benchmark dataset demonstrate that our FCNet is effective on both the
SEC and MEF tasks.


title:
Self-consistent equation for torsion arising as a consequence of the
  Dirac sea quantum fluctuations in external classical electromagnetic and
  gravitational fields

abstract:
  The quantum fluctuations of the Dirac field in external classical
gravitational and electromagnetic fields are studied. A self-consistent
equation for torsion is calculated, which is obtained using one-loop fermion
diagrams.


title: Coordinate Translator for Learning Deformable Medical Image Registration

abstract:
  The majority of deep learning (DL) based deformable image registration
methods use convolutional neural networks (CNNs) to estimate displacement
fields from pairs of moving and fixed images. This, however, requires the
convolutional kernels in the CNN to not only extract intensity features from
the inputs but also understand image coordinate systems. We argue that the
latter task is challenging for traditional CNNs, limiting their performance in
registration tasks. To tackle this problem, we first introduce Coordinate
Translator (CoTr), a differentiable module that identifies matched features
between the fixed and moving image and outputs their coordinate correspondences
without the need for training. It unloads the burden of understanding image
coordinate systems for CNNs, allowing them to focus on feature extraction. We
then propose a novel deformable registration network, im2grid, that uses
multiple CoTr's with the hierarchical features extracted from a CNN encoder and
outputs a deformation field in a coarse-to-fine fashion. We compared im2grid
with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic
resonance image registration. Our experiments show that im2grid outperforms
these methods both qualitatively and quantitatively.


title:
Multi-channel deep convolutional neural networks for multi-classifying
  thyroid disease

abstract:
  Thyroid disease instances have been continuously increasing since the 1990s,
and thyroid cancer has become the most rapidly rising disease among all the
malignancies in recent years. Most existing studies focused on applying deep
convolutional neural networks for detecting thyroid cancer. Despite their
satisfactory performance on binary classification tasks, limited studies have
explored multi-class classification of thyroid disease types; much less is
known of the diagnosis of co-existence situation for different types of thyroid
diseases. Therefore, this study proposed a novel multi-channel convolutional
neural network (CNN) architecture to address the multi-class classification
task of thyroid disease. The multi-channel CNN merits from computed tomography
to drive a comprehensive diagnostic decision for the overall thyroid gland,
emphasizing the disease co-existence circumstance. Moreover, this study also
examined alternative strategies to enhance the diagnostic accuracy of CNN
models through concatenation of different scales of feature maps. Benchmarking
experiments demonstrate the improved performance of the proposed multi-channel
CNN architecture compared with the standard single-channel CNN architecture.
More specifically, the multi-channel CNN achieved an accuracy of 0.909,
precision of 0.944, recall of 0.896, specificity of 0.994, and F1 of 0.917, in
contrast to the single-channel CNN, which obtained 0.902, 0.892, 0.909, 0.993,
0.898, respectively. In addition, the proposed model was evaluated in different
gender groups; it reached a diagnostic accuracy of 0.908 for the female group
and 0.901 for the male group. Collectively, the results highlight that the
proposed multi-channel CNN has excellent generalization and has the potential
to be deployed to provide computational decision support in clinical settings.


title: Remark on Right Continuous Exponential Martingales

abstract:
  Using <M^c>, jump measure \mu and its compensator \nu we characterize the
event where the stochastic exponential E(M) equals to zero.


title: Black-Bounce in $f(T)$ Gravity

abstract:
  We study new solutions of black bounce spacetimes formulated in $f(T)$
gravity in four dimensions. First, we present the case of a diagonal tetrad,
where a constraint arises in the equations of motion, which is divided into the
cases of null torsion, constant torsion, and Teleparallel. The Null Energy
Condition (NEC) is still always violated, which implies that the other energy
conditions are violated. The solutions are regular in all spacetime and the
solution with null torsion exhibits discontinuity between the energy conditions
outside and inside the event horizon. Second, we present the case of
non-diagonal tetrads. This case is divided into a Simpson-Visser type model and
a quadratic model. The NEC continues to be violated, implying a violation of
the other energy conditions. The solutions are regular in all spacetimes. An
interesting result is that due to the possibility that the area associated with
the metric is different from $4\pi r^2$, the no-go theorem established in the
usual $f(T)$ is violated, appearing the new possibility $g_{00}=-g^{11}$, for
the components of the metric.


title: Quantum Algorithm to Estimate the Mean Value of a Function

abstract:
  This paper proposes a quantum circuit for computing the mean value from a
given set of numbers or function evaluations. Suppose a Quantum Random Access
Memory is given as a black-box function, which allows us to store and read the
values of a set as quantum states. The proposed quantum algorithm estimate the
mean value of the function by using superposition, interference, and
entanglement phenomena, in $\mathcal{O}(\log{N})$ complexity or in
$\mathcal{O}(1)$ query of the black-box.


title:
Student Become Decathlon Master in Retinal Vessel Segmentation via
  Dual-teacher Multi-target Domain Adaptation

abstract:
  Unsupervised domain adaptation has been proposed recently to tackle the
so-called domain shift between training data and test data with different
distributions. However, most of them only focus on single-target domain
adaptation and cannot be applied to the scenario with multiple target domains.
In this paper, we propose RVms, a novel unsupervised multi-target domain
adaptation approach to segment retinal vessels (RVs) from multimodal and
multicenter retinal images. RVms mainly consists of a style augmentation and
transfer (SAT) module and a dual-teacher knowledge distillation (DTKD) module.
SAT augments and clusters images into source-similar domains and
source-dissimilar domains via B\'ezier and Fourier transformations. DTKD
utilizes the augmented and transformed data to train two teachers, one for
source-similar domains and the other for source-dissimilar domains. Afterwards,
knowledge distillation is performed to iteratively distill different domain
knowledge from teachers to a generic student. The local relative intensity
transformation is employed to characterize RVs in a domain invariant manner and
promote the generalizability of teachers and student models. Moreover, we
construct a new multimodal and multicenter vascular segmentation dataset from
existing publicly-available datasets, which can be used to benchmark various
domain adaptation and domain generalization methods. Through extensive
experiments, RVms is found to be very close to the target-trained Oracle in
terms of segmenting the RVs, largely outperforming other state-of-the-art
methods.


title:
On the stability of orthogonally Jensen additive and quadratic
  functional equation

abstract:
  We consider the stability of the orthogonal Jensen additive and quadratic
equations in $F$-spaces, through applying and extending the approach to the
proof of a 2010 result of W.Frchner and J.Sikorska, we presenting a new method
to get the stability. Moreover, we work in a more general and natural condition
than considered before by other antuors.


title:
Tensors and Algebras: An algebraic spacetime interpretation for tensor
  models

abstract:
  The quest for a consistent theory for quantum gravity is one of the most
challenging problems in theoretical high-energy physics. An often-used approach
is to describe the gravitational degrees of freedom by the metric tensor or
related variables, and finding a way to quantise this. In the canonical tensor
model, the gravitational degrees of freedom are encoded in a tensorial quantity
$P_{abc}$, and this quantity is subsequently quantised. This makes the
quantisation much more straightforward mathematically, but the interpretation
of this tensor as a spacetime is less evident. In this work we take a first
step towards fully understanding the relationship to spacetime. By considering
$P_{abc}$ as the generator of an algebra of functions, we first describe how we
can recover the topology and the measure of a compact Riemannian manifold.
Using the tensor rank decomposition, we then generalise this principle in order
to have a well-defined notion of the topology and geometry for a large class of
tensors $P_{abc}$. We provide some examples of the emergence of a topology and
measure of both exact and perturbed Riemannian manifolds, and of a purely
algebraically-defined space called the semi-local circle.


title:
BPM-Net: non-contact blood pressure measuring network based on face
  videos

abstract:
  Blood pressure indicates cardiac function and peripheral vascular resistance
and is critical for disease diagnosis. Traditionally, blood pressure data are
mainly acquired through contact sensors, which require high maintenance and may
be inconvenient and unfriendly to some people (e.g., burn patients). In this
paper, we proposed an efficient non-contact blood pressure measurement network
based on face videos. First, an innovative oversampling training strategy is
proposed to handle the unbalanced data distribution. The input video sequences
are first normalized and converted to our proposed YUVT color space. Then the
spatio-temporal slicer encodes it into a multi-domain spatio-temporal mapping.
Finally, the feature extractor composed of a series backbone network and LSTM
fits the high-dimensional feature, which is fed into blood pressure classifier
to locates the blood pressure interval. The blood pressure calculator combines
the results of the feature extractor and the blood pressure classifier to
output the final blood pressure value. We tested BPM-Net on MMSE-HR dataset,
the MAE of systolic blood pressure reached 12.35 mmHg and that of diastolic
blood pressure reached 9.5 mmHg. Experimental results on MMSE-HR show that the
network outperforms existing state-of-the-art methods.


title: Stepwise Feature Fusion: Local Guides Global

abstract:
  Colonoscopy, currently the most efficient and recognized colon polyp
detection technology, is necessary for early screening and prevention of
colorectal cancer. However, due to the varying size and complex morphological
features of colonic polyps as well as the indistinct boundary between polyps
and mucosa, accurate segmentation of polyps is still challenging. Deep learning
has become popular for accurate polyp segmentation tasks with excellent
results. However, due to the structure of polyps image and the varying shapes
of polyps, it easy for existing deep learning models to overfitting the current
dataset. As a result, the model may not process unseen colonoscopy data. To
address this, we propose a new State-Of-The-Art model for medical image
segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve
the generalization ability of models. Specifically, our proposed Progressive
Locality Decoder can be adapted to the pyramid Transformer backbone to
emphasize local features and restrict attention dispersion. The SSFormer
achieves statet-of-the-art performance in both learning and generalization
assessment.


title:
Clustering and classification of low-dimensional data in explicit
  feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of
  a colon in a liver

abstract:
  Application of artificial intelligence in medicine brings in highly accurate
predictions achieved by complex models, the reasoning of which is hard to
interpret. Their generalization ability can be reduced because of the lack of
pixel wise annotated images that occurs in frozen section tissue analysis. To
partially overcome this gap, this paper explores the approximate explicit
feature map (aEFM) transform of low-dimensional data into a low-dimensional
subspace in Hilbert space. There, with a modest increase in computational
complexity, linear algorithms yield improved performance and keep
interpretability. They remain amenable to incremental learning that is not a
trivial issue for some nonlinear algorithms. We demonstrate proposed
methodology on a very large-scale problem related to intraoperative pixel-wise
semantic segmentation and clustering of adenocarcinoma of a colon in a liver.
Compared to the results in the input space, logistic classifier achieved
statistically significant performance improvements in micro balanced accuracy
and F1 score in the amounts of 12.04% and 12.58%, respectively. Support vector
machine classifier yielded the increase of 8.04% and 9.41%. For clustering,
increases of 0.79% and 0.85% are obtained with ultra large-scale spectral
clustering algorithm. Results are supported by a discussion of interpretability
using Shapely additive explanation values for predictions of linear classifier
in input space and aEFM induced space.


title: Terrestrial volcanic eruptions and their association with solar activity

abstract:
  Frequencies of volcanic eruptions in the past 270 years are compared with
variations of solar activity and summary curve of principal components of the
solar background magnetic field (SBMF).Frequency analysis with Morlet wavelet
reveals the most pronounced period of volcanic eruptions of 22 years. There is
a strong correlation (-0.84) between volcanic frequencies and the summary curve
of SBMF for 11 cycles after 1868. The maxima of volcanic eruptions are shown to
occur during solar activity cycles with the southern magnetic polarity. The
next anticipated maximum of volcanic eruptions is expected to occur during
cycle 26, when SBMF have a southern magnetic polarity.


title:
Unsupervised Image Registration Towards Enhancing Performance and
  Explainability in Cardiac And Brain Image Analysis

abstract:
  Magnetic Resonance Imaging (MRI) typically recruits multiple sequences
(defined here as "modalities"). As each modality is designed to offer different
anatomical and functional clinical information, there are evident disparities
in the imaging content across modalities. Inter- and intra-modality affine and
non-rigid image registration is an essential medical image analysis process in
clinical imaging, as for example before imaging biomarkers need to be derived
and clinically evaluated across different MRI modalities, time phases and
slices. Although commonly needed in real clinical scenarios, affine and
non-rigid image registration is not extensively investigated using a single
unsupervised model architecture. In our work, we present an un-supervised deep
learning registration methodology which can accurately model affine and
non-rigid trans-formations, simultaneously. Moreover, inverse-consistency is a
fundamental inter-modality registration property that is not considered in deep
learning registration algorithms. To address inverse-consistency, our
methodology performs bi-directional cross-modality image synthesis to learn
modality-invariant latent rep-resentations, while involves two factorised
transformation networks and an inverse-consistency loss to learn
topology-preserving anatomical transformations. Overall, our model (named
"FIRE") shows improved performances against the reference standard baseline
method on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI
data experiments.


title:
Thermodynamical aspects of Bianchi type-I Universe in quadratic form of
  $f\left( Q\right) $ gravity

abstract:
  In this paper, we study a LRS Bianchi type-I cosmological model in the
framework of symmetric teleparallel $f(Q)$ gravity in which the non-metricity
term $(Q)$ is responsible for the gravitational interaction. We have considered
a special form of the $f\left(Q\right)$ gravity function which can be cast as
$f\left( Q\right) =\gamma{\;} Q^{n}$ (where $\gamma$ and $n$ are both the
dynamical model constant parameters). Such a choice can be viewed as a hybrid
scale factor and the cosmic time and the redshift are related via
$t=\left(\frac{\alpha}{\beta}\right)W\left[\frac{\beta}{\alpha}\left(
\frac{1}{a_{1}(1+z)}\right)^{1/\alpha}\right]$ which describes a $\Lambda$CDM
model of the Universe with the expansion evolving from decelerating to an
acceleration phase. We further study the validity of our model with an
investigation of the thermodynamical quantities and energy conditions along
with some physical variables such as the EoS, jerk, and statefinder parameters.
Our results are discussed in the light of current observational data and
trends.


title:
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch
  Decoder Network

abstract:
  Fully convolutional neural networks have made promising progress in joint
liver and liver tumor segmentation. Instead of following the debates over 2D
versus 3D networks (for example, pursuing the balance between large-scale 2D
pretraining and 3D context), in this paper, we novelly identify the wide
variation in the ratio between intra- and inter-slice resolutions as a crucial
obstacle to the performance. To tackle the mismatch between the intra- and
inter-slice information, we propose a slice-aware 2.5D network that emphasizes
extracting discriminative features utilizing not only in-plane semantics but
also out-of-plane coherence for each separate slice. Specifically, we present a
slice-wise multi-input multi-output architecture to instantiate such a design
paradigm, which contains a Multi-Branch Decoder (MD) with a Slice-centric
Attention Block (SAB) for learning slice-specific features and a Densely
Connected Dice (DCD) loss to regularize the inter-slice predictions to be
coherent and continuous. Based on the aforementioned innovations, we achieve
state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS)
dataset. Besides, we also test our model on the ISBI 2019 Segmentation of
THoracic Organs at Risk (SegTHOR) dataset, and the result proves the robustness
and generalizability of the proposed method in other segmentation tasks.


title: Algebras of slowly growing length

abstract:
  We investigate the class of finite dimensional not necessary associative
algebras that have slowly growing length, that is, for any algebra in this
class its length is less than or equal to its dimension. We show that this
class is considerably big, in particular, finite dimensional Lie algebras as
well as many other important classical finite dimensional algebras belong to
this class, for example, Leibniz algebras, Novikov algebras, and Zinbiel
algebras. An exact upper bounds for the length of these algebras is proved. To
do this we transfer the method of characteristic sequences to non-unital
algebras and find certain polynomial conditions on the algebra elements that
guarantee the slow growth of the length function.


title:
Observable Signatures of Cosmic Rays Transport in Starburst Galaxies on
  Gamma-ray and Neutrino Observations

abstract:
  The gamma-ray emission from Starburst and Starforming Galaxies (SBGs and
SFGs) strongly suggest a correlation between star-forming activity and
gamma-ray luminosity. However, the very nature of cosmic-ray (CR) transport and
the degree of their confinement within SBG cores are still open questions. We
aim at probing the imprints left by CR transport on gamma-ray and neutrino
observations of point-like SFGs and SBGs, looking into quantitative ways to
discriminate among different transport models. Moreover, following the reported
scenarios, we quantitatively assess the SBGs and SFGs contribution to the
Extra-galactic Gamma-Ray Background (EGB data) and the IceCube diffuse
observations (HESE data). We analyse the 10-year Fermi-LAT spectral energy
distributions of 13 nearby galaxies with two different CR transport models,
taking into account the corresponding IR and UV observations. We generate mock
gamma-ray data to simulate the CTA performance in detecting these sources. In
the way, we propose a test to discriminate between the two CR models,
quantifying the statistical confidence at which one model can be preferred over
the other. We point out that current data already give a slight preference to
CR models which are dominated by advection in their nucleus. Moreover, we show
that CTA will allow us to firmly disfavour models dominated by diffusion over
self-induced turbulence, compared to advection-dominated models, with Bayes
factors which can be as large as $10^7$ for some of the SBGs. Finally, we
estimate the diffuse gamma-ray and neutrino fluxes of SFGs and SBGs, showing
that they can explain $25\%$ of the diffuse HESE data, while remaining
consistent with gamma-ray limits on non-blazar sources.


title:
Cross-correlating dark sirens and galaxies: measurement of $H_0$ from
  GWTC-3 of LIGO-Virgo-KAGRA

abstract:
  We measure the Hubble constant of the Universe using spatial
cross-correlation between gravitational wave (GW) sources without
electromagnetic counterparts from the third GW Transient Catalog (GWTC-3), and
the photometric galaxy surveys 2MPZ and WISE-SuperCOSMOS. Using the eight
well-localised GW events, we obtain Hubble constant $H_0= 68.2_{-6.2}^{+26.0}$
km/s/Mpc (median and 68.3$\%$ equal-tailed interval (ETI)) after marginalizing
over the matter density and the GW bias parameters. Though the constraints are
weak due to a limited number of GW sources and poor sky localization, they are
not subject to assumptions regarding the GW mass distribution. By combining
this measurement with the Hubble constant measurement from binary neutron star
GW170817, we find a value of Hubble constant $H_0= 67.0_{-3.8}^{+6.3}$ km/s/Mpc
(median and 68.3$\%$ ETI).


title:
One Loop to Rule Them All: Eight and Nine Dimensional String Vacua from
  Junctions

abstract:
  String and 5-brane junctions are shown to succinctly classify all known 8d
${\cal N}$=1 string vacua. This requires an extension of the description for
ordinary $[p,q]$-7-branes to consistently include O7$^+$-planes, which then
naturally encodes the dynamics of $\mathfrak{sp}_n$ gauge algebras, including
their higher-form center symmetries. Central to this analysis are loop
junctions, i.e., strings/5-branes which encircle stacks of 7-branes and
O7$^+$'s. Loop junctions further signal the appearance of affine symmetries of
emergent 9d descriptions at the 8d moduli space's boundaries. Such limits
reproduce all 9d string vacua, including the two disconnected rank (1,1) moduli
components.


title:
The in situ origin of the globular cluster NGC 6388 from abundances of
  Sc, V, and Zn of a large sample of stars

abstract:
  Chemical tagging of globular clusters (GCs) is often done using abundances of
alpha-elements. The iron-peak elements Sc, V, and in particular Zn were
proposed as an alternative to alpha-elements to tag accreted GCs in the
metal-rich regime, where the dwarf galaxy Sagittarius and its GCs show
peculiarly marked under-abundances of these heavier species with respect to
Milky Way stars. A handful of stars in NGC 6388 was used to suggest an accreted
origin for this GC, contradicting the results from dynamics. We tested the
efficiency of the iron-peak method by using large samples of stars in NGC 6388,
compared to thousands of field stars in the disc and the bulge of the Milky
Way. Our abundance ratios of Sc (185 stars) and V (35 stars) for NGC 6388 are
within about 1.5 sigma from the average for the field stars with a similar
metallicity, and they are in perfect agreement for Zn (31 stars), claimed to be
the most sensitive element concerning the accretion pattern. Moreover, the
chemo-dynamical plots, coupled to the bifurcated age-metallicity relation of
GCs in the Galaxy, clearly rule out any association of NGC 6388 to the groups
of accreted GCs. Using a large set of GC abundances from the literature, we
also show that the new method with Sc, V, and Zn seems to be efficient in
picking up GCs related to the Sagittarius dwarf galaxy. Whether this is also
generally true for accreted GCs seems to be less evident, and it should be
verified with larger and homogeneous samples of stars both in the field and in
GCs.


title: Hardware-Tailored Diagonalization Circuits

abstract:
  A central building block of many quantum algorithms is the diagonalization of
Pauli operators. Although it is always possible to construct a quantum circuit
that simultaneously diagonalizes a given set of commuting Pauli operators, only
resource-efficient circuits are reliably executable on near-term quantum
computers. Generic diagonalization circuits can lead to an unaffordable
Swap-gate overhead on quantum devices with limited hardware connectivity. A
common alternative is excluding two-qubit gates, however, this comes at the
cost of restricting the class of diagonalizable sets of Pauli operators to
tensor product bases (TPBs). In this letter, we introduce a theoretical
framework for constructing hardware-tailored (HT) diagonalization circuits. We
apply our framework to group the Pauli operators occurring in the decomposition
of a given Hamiltonian into jointly-HT-diagonalizable sets. We investigate
several classes of popular Hamiltonians and observe that our approach requires
a smaller number of measurements than conventional TPB approaches. Finally, we
experimentally demonstrate the practical applicability of our technique, which
showcases the great potential of our circuits for near-term quantum computing.


title:
Line-of-sight Elongation and Hydrostatic Mass Bias of the Frontier
  Fields Galaxy Cluster Abell 370

abstract:
  We present a detailed weak-lensing and X-ray study of the Frontier Fields
galaxy cluster Abell 370, one of the most massive known lenses on the sky,
using wide-field BRz Subaru/Sprime-Cam and Chandra X-ray observations. By
combining 2D shear and azimuthally averaged magnification constraints derived
from Subaru data, we perform a lensing mass reconstruction in a free-form
manner, which allows us to determine both radial structure and 2D morphology of
the cluster mass distribution. In a triaxial framework assuming an NFW density
profile, we constrain the intrinsic structure and geometry of the cluster halo
by forward modeling the reconstructed mass map. We obtain a halo mass
$M_{200}=(1.54 \pm 0.29)\times 10^{15}h^{-1}M_\odot$, a halo concentration
$c_{200}=5.27 \pm 1.28$, and a minor-major axis ratio $q_a=0.62 \pm 0.23$ with
uninformative priors. Using a prior on the line-of-sight alignment of the halo
major axis derived from binary merger simulations constrained by multi-probe
observations, we find that the data favor a more prolate geometry with lower
mass and lower concentration. From triaxial lens modeling with the
line-of-sight prior, we find a spherically enclosed gas mass fraction of
$f_\mathrm{gas}=(8.4 \pm 1.0)\%$ at $0.7h^{-1}$ Mpc. When compared to the
hydrostatic mass estimate from Chandra observations, our triaxial weak-lensing
analysis yields spherically enclosed mass ratios of $1-b=0.56 \pm 0.09$ and
$0.51 \pm 0.09$ at $0.7h^{-1}$ Mpc with and without using the line-of-sight
prior, respectively. Since the cluster is in a highly disturbed dynamical
state, this represents the likely maximum level of hydrostatic bias in galaxy
clusters. We also obtain a model-independent constraint of
$M_\mathrm{2D}(<2.3h^{-1}\mathrm{Mpc})=(3.11 \pm 0.47)\times
10^{15}h^{-1}M_\odot$ for the projected mass of the whole system, including any
currently unbound material around the cluster.


title:
The SAMI Galaxy Survey: The internal orbital structure and mass
  distribution of passive galaxies from triaxial orbit-superposition
  Schwarzschild models

abstract:
  Dynamical models are crucial for uncovering the internal dynamics of
galaxies, however, most of the results to date assume axisymmetry, which is not
representative for a significant fraction of massive galaxies. Here, we build
triaxial Schwarschild orbit-superposition models of galaxies taken from the
SAMI Galaxy Survey, in order to reconstruct their inner orbital structure and
mass distribution. The sample consists of 161 passive galaxies with total
stellar masses in the range $10^{9.5}$ to $10^{12} M_{\odot}$. We find that the
changes in internal structures within 1$R_{\rm e}$ are correlated with the
total stellar mass of the individual galaxies. The majority of the galaxies in
the sample ($73\% \pm 3\%$) are oblate, while $19\% \pm 3\%$ are mildly
triaxial and $8\% \pm 2\%$ have triaxial/prolate shape. Galaxies with $\log
M_{\star}/M_{\odot} > 10.50$ are more likely to be non-oblate. We find a mean
dark matter fraction of $f_{\rm{DM}} = 0.28 \pm 0.20$, within 1$R_{\rm e}$.
Galaxies with higher intrinsic ellipticity (flatter) are found to have more
negative velocity anisotropy $\beta_r$ (tangential anisotropy). $\beta_r$ also
shows an anti-correlation with the edge-on spin parameter \lam, so that
$\beta_r$ decreases with increasing \lam. We see evidence of an increasing
fraction of hot orbits with increasing stellar mass, while warm and cold orbits
show a decreasing trend. We also find that galaxies with different ($V/\sigma$
- $h_3$) kinematic signatures have distinct combinations of orbits. These
results are in agreement with a formation scenario in which slow- and
fast-rotating galaxies form through two main channels.


title: A depolarizing HI tidal tail in the western lobe of Fornax A

abstract:
  Recent MeerKAT neutral hydrogen (HI) observations of Fornax A reveal tidal
material intersecting in projection the western lobe of this radio galaxy. We
found a spatial coincidence between the northern HI tail and a depolarized
structure observed for the first time with the Australian Square Kilometre
Array Pathfinder (ASKAP) at 1.2 GHz. We analyzed the properties of the rotation
measure (RM) image obtained with ASKAP data at the location of the HI tail and
in its neighborhood. We modeled the observed RM structure function to
investigate the magnetic field power spectrum at the location of the HI tail
and in a nearby control region. We found that the observed RM, in the control
region and in a region enclosing the HI tail, cannot be due to the intracluster
Faraday screen caused by the Fornax cluster. An intragroup magnetized medium
with a central magnetic field strength of 18.5 $\rm\mu$G can explain the
control region RM, but it is clear that there is an excess in correspondence
with the HI tail region. We evaluated several scenarios in which the HI tail is
either in the lobe foreground or embedded in the lobe. We determined a magnetic
field strength on the order of $\sim$9.5$-$11 $\mu$G in the HI tail, a value
consistent with constraints derived from narrowband H$\alpha$ imaging of the
ionized gas. The spatial coincidence between HI tail and depolarization
analyzed in this paper could be the first observed evidence of a magnetic field
that either has passed through a radio galaxy lobe or has survived the lobe
expansion.


title: Kitaev spin-orbital bilayers and their moir\'e superlattices

abstract:
  We determine the phase diagram of a bilayer, Kitaev spin-orbital model with
inter-layer interactions (J), for several stackings and moir\'e superlattices.
For AA stacking, a gapped Z2 quantum spin liquid phase emerges at a finite Jc.
We show that this phase survives in the well-controlled large-J limit, where an
isotropic honeycomb toric code emerges. For moir\'e superlattices, a finite-q
inter-layer hybridization is stabilized. This connects inequivalent Dirac
points, effectively `untwisting' the system. Our study thus provides insight
into the spin-liquid phases of bilayer spin-orbital Kitaev materials.


title:
Deep learning and Bayesian inference of gravitational-wave populations:
  hierarchical black-hole mergers

abstract:
  The catalog of gravitational-wave events is growing, and so are our hopes of
constraining the underlying astrophysics of stellar-mass black-hole mergers by
inferring the distributions of, e.g., masses and spins. While conventional
analyses parametrize this population with simple phenomenological models, we
propose an innovative physics-first approach that compares gravitational-wave
data against astrophysical simulations. We combine state-of-the-art
deep-learning techniques with hierarchical Bayesian inference and exploit our
approach to constrain the properties of repeated black-hole mergers from the
gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural
networks allow us to (i) construct a flexible population model that accurately
emulates simulations of hierarchical mergers, (ii) estimate selection effects,
and (iii) recover the branching ratios of repeated-merger generations. Among
our results we find that: the distribution of host-environment escape speeds
favors values <100 km s$^{-1}$ but is relatively flat; first-generation black
holes are born with a maximum mass that is compatible with current estimates
from pair-instability supernovae; there is multimodal substructure in both the
mass and spin distributions due to repeated mergers; and binaries with a
higher-generation component make up at least 15% of the underlying population.
The deep-learning pipeline we present is ready to be used in conjunction with
realistic astrophysical population-synthesis predictions.


title: Manipulating quantum impurity spins via dynamical modes of nanomagnets

abstract:
  Quantum impurity (QI) spins offer promising information processing and
sensing applications by harnessing up to room-temperature quantum coherence.
Challenged by the requirement of designing local coherent drives and improving
sensitivity to various signals for such applications, the search of hybrid
systems coupling QI spins with matter excitations have garnered significant
recent interest. We propose and theoretically study a hybrid system that
couples spin-1 QI with the dynamical excitations of nanomagnets, which are
controlled by mechanisms uncovered in classical spintronics. We show that in
such systems the QI-spin decoherence, due to coupling to thermally excited
modes of the nanomagnet, can be designed across a wide range by exploiting the
control over nanomagnet's mode ellipticity and the chiral nature of the
coupling between the QI spin and nanomagnet. On the other hand, when activated
electrically via voltage-induced torques, we demonstrate that QI spins can be
driven coherently with large quality factors at room temperature by leveraging
inherent non-linear precessional modes of the nanomagnet. Our results provide
theoretical guidance for enabling unique quantum spintronic functionalities,
such as local coherent driving of QI spins up to ambient conditions, and the
design of nanomagnet-enhanced QI-based hybrid sensors.


title:
3D elemental abundances of stars at formation across the histories of
  Milky Way-mass galaxies in the FIRE simulations

abstract:
  We characterize the 3-D spatial variations of [Fe/H], [Mg/H], and [Mg/Fe] in
stars at the time of their formation, across 11 simulated Milky Way (MW)- and
M31-mass galaxies in the FIRE-2 simulations, to inform initial conditions for
chemical tagging. The overall scatter in [Fe/H] within a galaxy decreased with
time until $\approx 7$ Gyr ago, after which it increased to today: this arises
from a competition between a reduction of azimuthal scatter and a steepening of
the radial gradient in abundance over time. The radial gradient is generally
negative, and it steepened over time from an initially flat gradient $\gtrsim
12$ Gyr ago. The strength of the present-day abundance gradient does not
correlate with when the disk `settled'; instead, it best correlates with the
radial velocity dispersion within the galaxy. The strength of azimuthal
variation is nearly independent of radius, and the 360 degree scatter decreased
over time, from $\lesssim 0.17$ dex at $t_{\rm lb} = 11.6$ Gyr to $\sim 0.04$
dex at present day. Consequently, stars at $t_{\rm lb} \gtrsim 8$ Gyr formed in
a disk with primarily azimuthal scatter in abundances. All stars formed in a
vertically homogeneous disk, $\Delta$[Fe/H] $\leq 0.02$ dex within $1$ kpc of
the galactic midplane, with the exception of the young stars in the inner
$\approx 4$ kpc at $z \sim 0$. These results generally agree with our previous
analysis of gas-phase elemental abundances, which reinforces the importance of
cosmological disk evolution and azimuthal scatter in the context of stellar
chemical tagging. We provide analytic fits to our results for use in
chemical-tagging analyses.


title: Multiparticle Amplitudes in a Scalar EFT

abstract:
  At sufficiently high energies the production of a very large number of
particles is kinematically allowed. However, it is well-known that already in
the simplest case of a weakly-coupled massive $\lambda \varphi^4$ theory,
$n$-particle amplitudes become non-perturbative in the limit where $n$ scales
with energy. In this case, the effective expansion parameter, $\lambda n$, is
no longer small and the perturbative approach breaks down. In general, the
associated $n$-particle production rates were argued to be described by an
exponential that, depending on the specifics of the underlying Quantum Field
Theory model, could be either growing or decaying in the large-$n$ regime. We
investigate such processes in general settings of Effective Field Theory (EFT),
involving arbitrary higher-dimensional operators of $\varphi$. We perform the
resummation of all leading loop corrections arising from EFT vertices for
amplitudes at the multiparticle threshold. We find that the net effect of
higher-dimensional operators amounts to an exponentially growing factor. We
show that if an exponential growth was already generated by the renormalizable
interactions, it would then be further enhanced by the EFT contributions. On
the other hand, if the multiparticle rates computed in the renormalizable part
of the theory were suppressed, this suppression would not be lifted in the EFT.


title: Low Mass Stars as Tracers of Star and Cluster Formation

abstract:
  We review the use of young low mass stars and protostars, or young stellar
objects (YSOs), as tracers of star formation. Observations of molecular clouds
at visible, infrared, radio and X-ray wavelengths can identify and characterize
the YSOs populating these clouds, with the ability to detect deeply embedded
objects and all evolutionary stages. Surveys with the Spitzer, Herschel,
XMM-Newton and Chandra space telescopes have measured the spatial distribution
of YSOs within a number of nearby (< 2.5 kpc) molecular clouds, showing surface
densities varying by more than three orders of magnitude. These surveys have
been used to measure the spatially varying star formation rates and
efficiencies within clouds, and when combined with maps of the molecular gas,
have led to the discovery of star-forming relations within clouds. YSO surveys
can also characterize the structures, ages, and star formation histories of
embedded clusters, and they illuminate the relationship of the clusters to the
networks of filaments, hubs and ridges in the molecular clouds from which they
form. Measurements of the proper motions and radial velocities of YSOs trace
the evolving kinematics of clusters from the deeply embedded phases through gas
dispersal, providing insights into the factors that shape the formation of
bound clusters. On 100 pc scales that encompass entire star-forming complexes,
Gaia is mapping the young associations of stars that have dispersed their natal
gas and exist alongside molecular clouds. These surveys reveal the complex
structures and motions in associations, and show evidence for supernova driven
expansions. Remnants of these associations have now been identified by Gaia,
showing that traces of star-forming structures can persist for a few hundred
million years.


title:
Terahertz displacive excitation of a coherent Raman-active phonon in
  V$_2$O$_3$

abstract:
  Nonlinear processes involving frequency-mixing of light fields set the basis
for ultrafast coherent spectroscopy of collective modes in solids. In certain
semimetals and semiconductors, generation of coherent phonon modes can occur by
a displacive force on the lattice at the difference-frequency mixing of a laser
pulse excitation on the electronic system. Here, as a low-frequency counterpart
of this process, we demonstrate that coherent phonon excitations can be induced
by the sum-frequency components of an intense terahertz light field, coupled to
intraband electronic transitions. This nonlinear process leads to
charge-coupled coherent dynamics of Raman-active phonon modes in the strongly
correlated metal V$_2$O$_3$. Our results show a new up-conversion pathway for
the optical control of Raman-active modes in solids mediated by
terahertz-driven electronic excitation.


title:
The archival discovery of a strong Lyman-$\alpha$ and [CII] emitter at z
  = 7.677

abstract:
  We report the archival discovery of Lyman-$\alpha$ emission from the bright
ultraviolet galaxy Y002 at $z=7.677$, spectroscopically confirmed by its
ionized carbon [CII] 158$\mu$m emission line. The Ly$\alpha$ line is spatially
associated with the rest-frame UV stellar emission ($M_{\rm UV}$~-22, 2x
brighter than $M^\star_{\rm UV}$) and it appears offset from the peak of the
extended [CII] emission at the current ~1" spatial resolution. We derive an
estimate of the unobscured SFR(UV)=$(22\pm1)\,M_\odot$ yr$^{-1}$ and set an
upper limit of SFR(IR)$<15\,M_\odot$ yr$^{-1}$ from the far-infrared wavelength
range, which globally place Y002 on the SFR(UV+IR)-L([CII]) correlation
observed at lower redshifts. In terms of velocity, the peak of the Ly$\alpha$
emission is redshifted by $\Delta v$(Ly$\alpha$)~500 km s$^{-1}$ from the
systemic redshift set by [CII] and a high-velocity tail extends to up to ~1000
km s$^{-1}$. The velocity offset is up to ~3.5x higher than the average
estimate for similarly UV-bright emitters at z~6-7, which might suggest that we
are witnessing the merging of two clumps. A combination of strong outflows and
the possible presence of an extended ionized bubble surrounding Y002 would
likely facilitate the escape of copious Ly$\alpha$ light, as indicated by the
large equivalent width EW(Ly$\alpha$)=$24^{+5}_{-6}$ \r{A}. Assuming that [CII]
traces the neutral hydrogen, we estimate a HI gas fraction of $M({\rm
HI})/M_\star\gtrsim8$ for Y002 as a system and speculate that patches of high
HI column densities could contribute to explain the observed spatial offsets
between Ly$\alpha$ and [CII] emitting regions. The low dust content, implied by
the non-detection of the far-infrared continuum emission at rest-frame ~160
$\mu$m, would be sufficient to absorb any potential Ly$\alpha$ photons produced
within the [CII] clump as a result of large HI column densities.


title: Molecular gas in z~6 quasar host galaxies

abstract:
  We investigate the molecular gas content of z~6 quasar host galaxies using
the IRAM / Northern Extended Millimeter Array. We target the 3mm dust
continuum, and the line emission from CO(6-5), CO(7-6), [CI]2-1 in 10
infra-red-luminous quasars that have been previously studied in their 1mm dust
continuum and [CII] line emission. We detect CO(7-6) at various degrees of
significance in all the targeted sources, thus doubling the number of such
detections in z~6 quasars. The 3mm to 1mm flux density ratios are consistent
with a modified black body spectrum with a dust temperature $T_{dust}$~47 K and
an optical depth $\tau_{\nu}$=0.2 at the [CII] frequency. Our study provides us
with four independent ways to estimate the molecular gas mass, $M_{H2}$, in the
targeted quasars. This allows us to set constraints on various parameters used
in the derivation of molecular gas mass estimates, such as the mass per
luminosity ratios $\alpha_{CO}$ and $\alpha_{[CII]}$, the gas-to-dust ratio
$\delta_{g/d}$, and the carbon abundance [C]/H2. Leveraging either on the dust,
CO, [CI], or [CII] emission yields mass estimates of the entire sample in the
range $M_{H2}$~$10^{10}$ to $10^{11}$ M$_{\odot}$. We compare the observed
luminosities of dust, [CII], [CI], and CO(7-6) with predictions from
photo-dissociation and X-ray dominated regions. We find that the former provide
better model fits to our data, assuming that the bulk of the emission arises
from dense ($n_H>10^4$ cm$^{-3}$) clouds with a column density
$N_{H}$~$10^{23}$ cm$^{-2}$, exposed to a radiation field with intensity
$G_0$~$10^3$ (in Habing units). Our analysis reiterates the presence of massive
reservoirs of molecular gas fueling star formation and nuclear accretion in
$z$~6 quasar host galaxies. It also highlights the power of combined 3mm and
1mm observations for quantitative studies of the dense gas content in massive
galaxies at cosmic dawn.


title:
A gap in the double white dwarf separation distribution caused by the
  common-envelope evolution: astrometric evidence from Gaia

abstract:
  The trajectory of the center of light of an unresolved binary is different
from that of its center of mass. Binary-induced stellar centroid wobbling can
therefore be detected as an excess in the goodness-of-fit of the single-star
astrometric model. We use reduced $\chi^2$ of the astrometric fit in the {\it
Gaia} Early Data Release 3 to detect the likely unresolved double white dwarfs
(DWDs). Using parallax-based distances we convert the excess of reduced
$\chi^2$ into the amplitude of the centroid wobble $\delta a$, which is
proportional to the binary separation $a$. The measured $\delta a$ distribution
drops towards larger wobble amplitudes and shows a break around $\delta a
\approx 0.2$ where it steepens. The integral of the distribution yields DWD
fraction of $6.5 \pm 3.7$ per cent in the range $0.01 < a (\text{au}) < 2$.
Using synthetic models of the Galactic DWDs we demonstrate that the break in
the $\delta a$ distribution corresponds to one side of a deep gap in the DWD
separation distribution at around $a\approx 1$ au. Model DWDs with separations
less than several au shrink dramatically due to (al least one) common envelope
phase, reshaping the original separation distribution, clearing a gap and
creating a pile-up of systems with $a\approx 0.01$ au and $\delta a < 0.01$.
Our models reproduce the overall shape of the observed $\delta a$ distribution
and its normalisation, however the predicted drop in the numbers of DWDs beyond
the break is steeper than in the data.


title:
Minimal Consistent Dark Matter models for systematic experimental
  characterisation: Fermion Dark Matter

abstract:
  The search for a Dark Matter particle is the new grail and hard-sought
nirvana of the particle physics community. From the theoretical side, it is the
main challenge to provide a consistent and model-independent tool for comparing
the bounds and reach of the diverse experiments. We propose a first complete
classification of minimal consistent Dark Matter models, which provides the
missing link between experiments and top-down models. Consistency is achieved
by imposing renormalisability and invariance under the full Standard Model
symmetries. We apply this paradigm to fermionic Dark multiplets with up to one
mediator. We also reconsider the one-loop contributions to direct detection,
including the relevant effect of (small) mass splits in the Dark multiplet. Our
work highlights the presence of unexplored viable models, and paves the way for
the ultimate systematic hunt for the Dark Matter particle.


title: Comment on "Quantum principle of relativity"

abstract:
  A. Dragan and A. Ekert [New Journal of Physics, 22(3), p.033038.] have
recently claimed that fundamental properties of quantum physics (e.g.
fundamental indeterminism and the principle of superposition) can be derived
solely from relativistic considerations, if one takes as physically meaningful
superluminal reference frames. In this comment we show that their arguments are
flawed and their claims therefore unwarranted.


title:
Reconstruction of Missing Resonances Combining Nearest Neighbors
  Regressors and Neural Network Classifiers

abstract:
  Neutrinos, dark matter, and long-lived neutral particles traverse the
particle detectors unnoticed, carrying away information about their parent
particles and interaction sources needed to reconstruct key variables like
resonance peaks in invariant mass distributions. In this work, we show that a
$k$-nearest neighbors regressor algorithm combined with deep neural network
classifiers, a $k$NN, is able to accurately recover binned distributions of the
fully leptonic $WW$ mass of a new heavy Higgs boson and its Standard Model
backgrounds from the observable detector level information at disposal. The
output of the regressor can be used to train even stronger classifiers to
separate signals and backgrounds in the fully leptonic case and guarantee the
selection of on-mass-shell Higgs bosons with enhanced statistical significance.
The method assumes previous knowledge of the event classes and model
parameters, thus suitable for post-discovery studies.


title:
Towards Sub-Quadratic Diameter Computation in Geometric Intersection
  Graphs

abstract:
  We initiate the study of diameter computation in geometric intersection
graphs from the fine-grained complexity perspective. A geometric intersection
graph is a graph whose vertices correspond to some shapes in $d$-dimensional
Euclidean space, such as balls, segments, or hypercubes, and whose edges
correspond to pairs of intersecting shapes. The diameter of a graph is the
largest distance realized by a pair of vertices in the graph.
  Computing the diameter in near-quadratic time is possible in several classes
of intersection graphs [Chan and Skrepetos 2019], but it is not at all clear if
these algorithms are optimal, especially since in the related class of planar
graphs the diameter can be computed in $\widetilde{\mathcal{O}}(n^{5/3})$ time
[Cabello 2019, Gawrychowski et al. 2021].
  In this work we (conditionally) rule out sub-quadratic algorithms in several
classes of intersection graphs, i.e., algorithms of running time
$\mathcal{O}(n^{2-\delta})$ for some $\delta>0$. In particular, there are no
sub-quadratic algorithms already for fat objects in small dimensions: unit
balls in $\mathbb{R}^3$ or congruent equilateral triangles in $\mathbb{R}^2$.
For unit segments and congruent equilateral triangles, we can even rule out
strong sub-quadratic approximations already in $\mathbb{R}^2$. It seems that
the hardness of approximation may also depend on dimensionality: for
axis-parallel unit hypercubes in~$\mathbb{R}^{12}$, distinguishing between
diameter 2 and 3 needs quadratic time (ruling out $(3/2-\varepsilon)$-
approximations), whereas for axis-parallel unit squares, we give an algorithm
that distinguishes between diameter $2$ and $3$ in near-linear time.
  Note that many of our lower bounds match the best known algorithms up to
sub-polynomial factors.


title:
Unsupervised Domain Adaptation with Contrastive Learning for OCT
  Segmentation

abstract:
  Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography
images is key for diagnosis and personalized treatment of eye diseases. While
deep learning has been successful at this task, trained supervised models often
fail for images that do not resemble labeled examples, e.g. for images acquired
using different devices. We hereby propose a novel semi-supervised learning
framework for segmentation of volumetric images from new unlabeled domains. We
jointly use supervised and contrastive learning, also introducing a contrastive
pairing scheme that leverages similarity between nearby slices in 3D. In
addition, we propose channel-wise aggregation as an alternative to conventional
spatial-pooling aggregation for contrastive feature map projection. We evaluate
our methods for domain adaptation from a (labeled) source domain to an
(unlabeled) target domain, each containing images acquired with different
acquisition devices. In the target domain, our method achieves a Dice
coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive
framework), and leads to results comparable to an upper bound with supervised
training in that domain. In the source domain, our model also improves the
results by 5.4% Dice, by successfully leveraging information from many
unlabeled images.


title:
Distributed Consensus of Stochastic Multi-agent Systems with Prescribed
  Performance Constraints

abstract:
  This paper focuses on the problem of distributed consensus control of
multi-agent systems while considering two main practical concerns (i)
stochastic noise in the agent dynamics and (ii) predefined performance
constraints over evolutions of multi-agent systems. In particular, we consider
that each agent is driven by a stochastic differential equation with
state-dependent noise which makes the considered problem more challenging
compared to non-stochastic agents. The work provides sufficient conditions
under which the proposed timevarying distributed control laws ensure consensus
in expectation and almost sure consensus of stochastic multi-agent systems
while satisfying prescribed performance constraints over evolutions of the
systems in the sense of the qth moment. Finally, we demonstrate the
effectiveness of the proposed results with a numerical example.


title:
Exploring $\Lambda$CDM extensions with SPT-3G and Planck data: 4$\sigma$
  evidence for neutrino masses, full resolution of the Hubble crisis by dark
  energy with phantom crossing, and all that

abstract:
  We present new cosmological constraints in a set of motivated extensions of
the $\Lambda$CDM model using the polarization and gravitational lensing
measurements from the South Polar Telescope and the Planck CMB temperature
observations at large angular scales. It was shown in Ref.
\cite{Chudaykin:2020acu} that this CMB setup is free from the Planck anomalies
which could affect the cosmological inference in extended models. Combining the
SPT-3G, SPTPol and Planck large-scale temperature data with the latest
full-shape BOSS and BAO measurements and information from the weak lensing
surveys we find a $4\sigma$ evidence for nonzero neutrino mass, $\sum
m_\nu=0.23\pm0.06$ eV. In $\rm \Lambda$CDM+$N_{\rm eff}$ scenario we
demonstrate that the Planck anomalies do not propagate into cosmological
constraints. Then we examine two promising scenarios with modified late-time
cosmology: one introduces a phantom crossing in dark energy equation of state,
another provides with a sharp transition in the dark energy evolution. We find
that the scenario with a phantom crossing completely alleviate the cosmological
tensions yielding $H_0=73.92\pm1.09{\rm \,\,km\,s^{-1}Mpc^{-1}}$ and
$S_8=0.774\pm0.010$. The Bayesian approach indicates that the model with
phantom crossing is favoured by data as compared to $\Lambda$CDM. For the
transitional dark energy we find no strong evidence for a rapid change in its
equation of state after including the BAO from the BOSS DR12. Our
proof-of-concept analysis shows that the Planck anomalies severely obscure the
cosmological inference in some extensions of the $\Lambda$CDM model. More
precise CMB data is needed to validate our conclusions on neutrino masses and
phantom crossing in the dark energy sector.


title:
Fluctuations in the stress energy tensor of spinor fields evolving in
  general FRW spacetimes

abstract:
  In this work, we study quantum fluctuations in the stress energy tensor of
spinor fields evolving in general FRW spacetimes. We quantify these
fluctuations by the noise kernel of spinor fields. For the particular case of
de Sitter spacetime, we place the spinor field in what is called the fermionic
Bunch Davies vacuum and study the variation of the noise kernel with the mass
of the field. We then make use of the conformal invariance of massless spinor
fields and employ an equivalence that relates a massless spinor field in any
given FRW spacetime with a corresponding massless spinor field in de Sitter
spacetime. Using this equivalence, we study the behaviour of the noise kernel
of massless spinor fields in general FRW spacetimes while placing the fields in
the Bunch Davies vacuum of the corresponding massless spinor field of the de
Sitter spacetime. We extend this analysis to the coordinate invariant quantity
of the energy energy correlator. We compare these results for spinor fields in
considered spacetimes with the analogous results for scalar fields. This study
helps us better understand whether the spin 1/2 fermionic matter remains
strongly correlated in considered spacetimes leading to considerable
backreaction or not.


title: A Typology to Explore and Guide Explanatory Interactive Machine Learning

abstract:
  Recently, more and more eXplanatory Interactive machine Learning (XIL)
methods have been proposed with the goal of extending a model's learning
process by integrating human user supervision on the model's explanations.
These methods were often developed independently, provide different motivations
and stem from different applications. Notably, up to now, there has not been a
comprehensive evaluation of these works. By identifying a common set of basic
modules and providing a thorough discussion of these modules, our work, for the
first time, comes up with a unification of the various methods into a single
typology. This typology can thus be used to categorize existing and future XIL
methods based on the identified modules. Moreover, our work contributes by
surveying six existing XIL methods. In addition to benchmarking these methods
on their overall ability to revise a model, we perform additional benchmarks
regarding wrong reason revision, interaction efficiency, robustness to feedback
quality, and the ability to revise a strongly corrupted model. Apart from
introducing these novel benchmarking tasks, for improved quantitative
evaluations, we further introduce a novel Wrong Reason (\wrnospace) metric
which measures the average wrong reason activation in a model's explanations to
complement a qualitative inspection. In our evaluations, all methods prove to
revise a model successfully. However, we found significant differences between
the methods on individual benchmark tasks, revealing valuable
application-relevant aspects not only for comparing current methods but also to
motivate the necessity of incorporating these benchmarks in the development of
future XIL methods.


title: A simple guide from Machine Learning outputs to statistical criteria

abstract:
  In this paper we propose ways to incorporate Machine Learning training
outputs into a study of statistical significance. We describe these methods in
supervised classification tasks using a CNN and a DNN output, and unsupervised
learning based on a VAE. As use cases, we consider two physical situations
where Machine Learning are often used: high-$p_T$ hadronic activity, and
boosted Higgs in association with a massive vector boson.


title:
eV Hubble Scale Inflation with Radiative Plateau: Very light Inflaton,
  Reheating & Dark Matter in B-L Extensions

abstract:
  We study radiative plateau-like inflation \& Z$_{BL}$-portal freeze-in
fermionic dark matter (DM) in minimal (B-L) extended model. The U(1)$_{B-L}$
Higgs, responsible for heavy neutrino masses, also drives inflation in early
universe, thanks to radiative corrections from the heavy neutrinos \& the
Z$_{BL}$ boson. The benchmark parameters (U(1)$_{B-L}$ gauge coupling
$g_{B-L}\sim10^{-4}$) makes it within the reach of current and future lifetime
frontier searches (FASER and FASER 2 at the LHC, SHiP, Belle II, LHCb, etc.) in
context to light Z$_{BL}$ candidates and freeze-in dark matter. For such
benchmark points, the Hubble scale of inflation ($H_{inf}$) is required to be
very low ($H_{inf} \sim \mathcal{O}(100)$ eV) \& the inflaton turns out to be
of very light mass ($\mathcal{O}(1)$ eV), and consequently the decay width of
inflaton is small. We investigate the \it{2-field-system} still find that
sufficient reheating occurs along the the Standard Model Higgs direction
component of the tangent along the trajectory in the field space, near the
water-fall of the potential. For our parameter choices, along with the chosen
DM mass $m_\chi\sim\mathcal{O}(10)$ GeV, we find the DM ($\chi$) is produced
via freeze-in.


title:
Parity flipping mediated by a quantum dot in Majorana Josephson
  junctions

abstract:
  With the increasing experimentally measurable signatures for Majorana bound
states (MBSs), how to manipulate qubits by MBSs turns into a crucial issue. In
this work, we introduce a quantum dot (QD) to Majorana Josephson junctions
(MJJs). The parity characteristics of Majorana qubits can be manipulated by
modulating the QD energy. We study the voltage induced by parity-flipping using
fast or adiabatic evolution approach, and find an interesting $4\pi$-period
hopping behavior of the electron occupying the QD at the robust $4\pi$-period
voltage state, which can be applied to fabricate hybrid quantum circuits. We
further investigate the Landau-Zener-St\"{u}celberg interference under distinct
driving frequencies. It shows a cosinoidal QD occupation probability without
parity-flipping and a controllable voltage state with parity-flipping,
respectively. Furthermore, we find the Rabi oscillation in our system is
suppressed by a damping. These properties can help to detect MBSs and realize
quantum computation.


title:
Gravitational waves from accretion-induced descalarization in massive
  scalar-tensor theory

abstract:
  Many classes of extended scalar-tensor theories predict that dynamical
instabilities can take place at high energies, leading to the formation of
scalarized neutron stars. Depending on the theory parameters, stars in a
scalarized state can form a solution-space branch that shares a lot of
similarities with the so-called mass twins in general relativity appearing for
equations of state containing first-order phase transitions. Members of this
scalarized branch have a lower maximum mass and central energy density compared
to Einstein ones. In such cases, a scalarized star could potentially
over-accrete beyond the critical mass limit, thus triggering a gravitational
phase transition where the star sheds its scalar hair and migrates over to its
non-scalarized counterpart. Such an event resembles, though is distinct from, a
nuclear or thermodynamic phase transition. We dynamically track a gravitational
transition by first constructing hydrostatic, scalarized equilibria for
realistic equations of state, and then allowing additional material to fall
onto the stellar surface. The resulting bursts of monopolar radiation are
dispersively-stretched to form a quasi-continuous signal that persists for
decades, carrying strains of order $\gtrsim10^{-22} (\text{kpc}/L)^{3/2} \text{
Hz}^{-1/2}$ at frequencies of $\lesssim300 \text{ Hz}$, detectable with the
existing interferometer network out to distances of $L\lesssim10 \text{ kpc}$,
and out to a few hundred kpc with the inclusion of the Einstein Telescope.
Electromagnetic signatures of such events, involving gamma-ray and neutrino
bursts, are also considered.


title:
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment
  of Implicit Graph Generators

abstract:
  We propose and analyse a novel statistical procedure, coined AgraSSt, to
assess the quality of graph generators that may not be available in explicit
form. In particular, AgraSSt can be used to determine whether a learnt graph
generating process is capable of generating graphs that resemble a given input
graph. Inspired by Stein operators for random graphs, the key idea of AgraSSt
is the construction of a kernel discrepancy based on an operator obtained from
the graph generator. AgraSSt can provide interpretable criticisms for a graph
generator training procedure and help identify reliable sample batches for
downstream tasks. Using Stein`s method we give theoretical guarantees for a
broad class of random graph models. We provide empirical results on both
synthetic input graphs with known graph generation procedures, and real-world
input graphs that the state-of-the-art (deep) generative models for graphs are
trained on.


title: M5-brane prongs, string soliton bound states and wall-crossing

abstract:
  We study abelian M5-brane field configurations representing BPS bound states
of self-dual string solitons whose locations correspond to the endlines of
M2-branes ending on the M5-branes. The BPS equations are obtained from
appropriate Bogomolny completion of the effective abelian low energy functional
with two transverse scalars, using two vectors representing the directions
along which these endline strings extend. Then we impose boundary conditions on
the scalars near the string soliton cores. This leads to a molecule-like
equilibrium structure of two non-parallel string solitons at fixed transverse
separations, with the M5-brane "prong" deformations comprising two "spikes",
each shaped like a ridge. The resulting picture becomes increasingly accurate
as one approaches the wall of marginal stability, on which these states decay.
There are various parallels with wall-crossing phenomena for string web
configurations obtained from D3-brane deformations.


title: The Sources of Apparently Non-Repeating FRB

abstract:
  There are insufficient catastrophic events (collapse, explosion or merger of
stars or compact objects) to explain the cosmologically local rate of
apparently non-repeating FRB if each such catastrophic event produces a single
FRB. Unless produced by some novel and unsuspected but comparatively frequent
event, apparently non-repeating FRB must actually repeat many times in the
lifetimes of their sources. Yet no such infrequent repetitions (in contrast to
the frequent activity of FRB known to repeat) have been observed, constraining
their repetition rates and active lifetimes. The absence of more frequent
weaker but detectable repetitive outbursts in apparent non-repeaters resembles
the distribution of SGR outbursts, with a large gap between giant outbursts and
lesser outbursts. This suggests mini-SGR as sources, more energetic than SGR
1935$+$2154 associated with FRB 200428 but less energetic than SGR 1806$-$20
that had no associated FRB. Their largest radio outbursts would, at
cosmological distances, be apparently non-repeating FRB and their X-ray and
gamma-ray outbursts would be undetectable. The large gap between the strongest
outburst of FRB 200428 and its lesser outbursts resembles the gamma-ray
properties of individual well-observed SGR; at twenty times its actual
distance, FRB 200428 would have been an apparent non-repeater.


title:
Microscopic insights of magnetism in Sm$ _{2} $NiMnO$ _{6} $ double
  perovskite

abstract:
  The functional characteristics of double perovskites with unique
ferromagnetic-insulator ground state have been controversial due to the
unavoidable presence of anti-site disorders (ASDs). Here, we aim to investigate
the origin of magnetic ordering on local and global scales in
Sm$_{2}$NiMnO$_{6}$ (SNMO) double perovskite system. Different calcination
routes are exploited to generate different cation arrangements in SNMO and the
corresponding magnetic configurations are examined using the high energy (E $
\sim $0.3 eV) `hot neutrons', which has helped to overcome Sm absorption as
well as to record total (Bragg's+diffuse) scattering profiles with high
momentum transfer (Q$ _{max} \sim $24 angstrom$ ^{-1} $). We have observed that
the Ni-Mn sublattice adopts long range collinear ferromagnetic $ F_{x}F_{z} $
structure with commensurate $k$=(0, 0, 0) propagation vector, below ordering
temperature T $ \lesssim $ 160 K, irrespective of variable ASD concentrations.
In addition, the signatures indicating the antiparallel polarization of Sm
paramagnetic moments with respect to Ni-Mn network, are noticed in the vicinity
of anomalous magnetic transitions at T $ \lesssim $ 35 K. The real space pair
distribution function calculations have provided a direct visualization of ASDs
by means of broadening in Ni/Mn-Mn/Ni linkage. Employing the Reverse Monte
Carlo approach on diffuse magnetic scattering profiles, we have observed the
negative spin-spin correlation function which suggests the Ni-Ni
antiferromagnetic exchange interactions ranging up to first nearest neighbor
distance. These results confirm that the existence of ASDs in cation ordered
host matrix leads to competing ferromagnetic-antiferromagnetic phases in a
broad temperature range, which quantitatively governs the temperature dependent
bulk magnetic observables of SNMO system.


title:
Object-centric and memory-guided normality reconstruction for video
  anomaly detection

abstract:
  This paper addresses video anomaly detection problem for videosurveillance.
Due to the inherent rarity and heterogeneity of abnormal events, the problem is
viewed as a normality modeling strategy, in which our model learns
object-centric normal patterns without seeing anomalous samples during
training. The main contributions consist in coupling pretrained object-level
action features prototypes with a cosine distance-based anomaly estimation
function, therefore extending previous methods by introducing additional
constraints to the mainstream reconstruction-based strategy. Our framework
leverages both appearance and motion information to learn object-level behavior
and captures prototypical patterns within a memory module. Experiments on
several well-known datasets demonstrate the effectiveness of our method as it
outperforms current state-of-the-art on most relevant spatio-temporal
evaluation metrics.


title: An Infrared view of the obscured AGN environment in NGC 4945

abstract:
  NGC 4945 harbors one of the nearest active galactic nuclei (AGN), which
allows reaching high spatial resolution with the current observational
facilities. The Seyfert 2 nucleus is deeply obscured by an edge-on disk with
$A_V\sim14$, requiring infrared observations to study circumnuclear structures
and the interstellar medium. In this work, we present an imaging and longslit
spectroscopic study of the nuclear region with a spatial resolution of 6.5 pc,
based on Flamingos-2 (F2) and T-ReCS data taken at the Gemini South
observatory. We report sub-arcsecond photometric measurements of the nucleus in
$J$, $H$ and $K_{\rm s}$ filters, and at larger apertures. We do not detect
nuclear variability. The nuclear spectra confirm that even in $K$-band the AGN
emission-line features are completely obscured by dust. We detect a
circumnuclear disk in $K$-band as well as in the mid-infrared (MIR) $N$- and
$Q_{\rm a}$-bands, with a radial scale length of $\sim$120 pc. The disk shows
knots mostly in a ring-like arrangement that has been previously detected with
HST Pa $\alpha$ observations, indicating that these are deeply embedded,
massive young star clusters. We present here the spectrum of one of the
brightest and unresolved object (R$<5$pc), which we identify as a super star
cluster candidate with M$_{K_{\rm s}} -16.6\pm0.4$. For the circumnuclear
region, a detailed rotation curve allows to measure a nuclear mass of $M=(4.4
\pm 3)\times 10^{6} M_\odot$ within a radius of $\sim6.5$ pc. We also report
the detection of hot dust ($\sim 1000$ K) out to a nuclear distance of 80 pc
measured along the semi-major axis.


title: Quasar photometric redshifts from incomplete data using Deep Learning

abstract:
  Forthcoming astronomical surveys are expected to detect new sources in such
large numbers that measuring their spectroscopic redshift measurements will be
not be practical. Thus, there is much interest in using machine learning to
yield the redshift from the photometry of each object. We are particularly
interested in radio sources (quasars) detected with the Square Kilometre Array
and have found Deep Learning, trained upon a large optically-selected sample of
quasi-stellar objects, to be effective in the prediction of the redshifts in
three external samples of radio-selected sources. However, the requirement of
nine different magnitudes, from the near-infrared, optical and ultra-violet
bands, has the effect of significantly reducing the number of sources for which
redshifts can be predicted. Here we explore the possibility of using machine
learning to impute the missing features. We find that for the training sample,
simple imputation is sufficient, particularly replacing the missing magnitude
with the maximum for that band, thus presuming that the non-detection is at the
sensitivity limit. For the test samples, however, this does not perform as well
as multivariate imputation, which suggests that many of the missing magnitudes
are not limits, but have indeed not been observed. From extensive testing of
the models, we suggest that the imputation is best restricted to two missing
values per source. Where the sources overlap on the sky, in the worst case,
this increases the fraction of sources for which redshifts can be estimated
from 46% to 80%, with >90% being reached for the other samples.


title:
Nonreciprocal vortex isolator by stimulated Brillouin scattering in
  chiral photonic crystal fibre

abstract:
  Optical non-reciprocity, which breaks the symmetry between forward and
backward propagating optical waves, has become vital in photonic systems and
enables many key devices, such as optical isolators, circulators and optical
routers. Most conventional optical isolators involve magneto-optic materials,
but devices based on optical nonlinearities, optomechanically induced
transparency and stimulated Brillouin scattering (SBS) have also been
demonstrated. So far, however, they have only been implemented for linearly or
randomly polarized LP01-like fundamental modes. Here we report a light-driven
nonreciprocal isolator for optical vortex modes, based on topology-selective
SBS in chiral photonic crystal fibre. The device can be reconfigured as an
amplifier or an isolator by adjusting the frequency of the control signal. The
experimental results show vortex isolation of 22 dB, which is at the
state-of-the-art in fundamental mode isolators using SBS. This unique device
may find applications in optical communications, fibre lasers, quantum
information processing and optical tweezers.