title: Diffraction of non-uniformly polarized beams enables beam manipulation

abstract:
  The wave nature of light leads to interference and diffraction. A well known
example of the wave nature is Thomas Young's double slit experiment, where
light propagates through slits in an opaque screen to form a diffraction
pattern at the detector. Here we extend this concept from opaque screens to
spatially polarized wave fronts. The spatial variation of polarization gives
rise to diffraction, opening the possibility to manipulate the beam shape and
propagation direction via polarization control. We describe and simulate the
propagation of a spatially inhomogeneous polarized wavefront and then
experimentally demonstrate polarization control with a Bessel beam. Lensing in
a beam without the need to change the phase-front of a beam is shown. We
further explore how non-uniformly polarized beams may be used in combination
with phase modulation for a high speed point scanning technique. This is in
contrast to traditional methods of beam manipulation that make use of phase or
amplitude modulation alone. Our work shows that diffraction due to polarization
opens new possibilities for flexible and high speed beam control.


title: The Bruce-Roberts Numbers of a Function on an ICIS

abstract:
  We give formulas for the Bruce-Roberts number $\mu_{BR}(f,X)$ and its
relative version $\mu_{BR}^{-}(f,X)$ of a function $f$ with respect to an ICIS
$(X,0)$. We show that $\mu_{BR}^{-}(f,X)=\mu(f^{-1}(0)\cap
X,0)+\mu(X,0)-\tau(X,0)$, where $\mu$ and $\tau$ are the Milnor and Tjurina
numbers, respectively, of the ICIS. The formula for $\mu_{BR}(f,X)$ is more
complicated and also involves $\mu(f)$ and some lengths in terms of the ideals
$I_X$ and $Jf$. We also consider the logarithmic characteristic variety,
$LC(X)$, and its relative version, $LC(X)^{-}$. We show that $LC(X)^{-}$ is
Cohen-Macaulay and that $LC(X)$ is Cohen-Macaulay at any point not in
$X\times\{0\}$. We generalize previous results presented by the authors when
$(X,0)$ has codimension one and by Bruce and Roberts when it is weighted
homogeneous of any codimension.


title: Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning

abstract:
  We study the challenge of learning causal reasoning over procedural text to
answer "What if..." questions when external commonsense knowledge is required.
We propose a novel multi-hop graph reasoning model to 1) efficiently extract a
commonsense subgraph with the most relevant information from a large knowledge
graph; 2) predict the causal answer by reasoning over the representations
obtained from the commonsense subgraph and the contextual interactions between
the questions and context. We evaluate our model on WIQA benchmark and achieve
state-of-the-art performance compared to the recent models.


title:
Day-side Fe I Emission, Day-Night Brightness Contrast and Phase Offset
  of the Exoplanet WASP-33b

abstract:
  We report on Fe I in the day-side atmosphere of the ultra-hot Jupiter
WASP-33b, providing evidence for a thermal inversion in the presence of an
atomic species. We also introduce a new way to constrain the planet's
brightness variation throughout its orbit, including its day-night contrast and
peak phase offset, using high-resolution Doppler spectroscopy alone. We do so
by analyzing high-resolution optical spectra of six arcs of the planet's phase
curve, using ESPaDOnS on the Canada-France-Hawaii telescope and HDS on the
Subaru telescope. By employing a likelihood mapping technique, we explore the
marginalized distributions of parameterized atmospheric models, and detect Fe I
emission at high significance ($>10.4\sigma$) in our combined data sets,
located at $K_{\rm p}=222.1\pm0.4$ km/s and $v_{\rm sys}=-6.5\pm0.3$ km/s. Our
values agree with previous reports. By accounting for WASP-33b's brightness
variation, we find evidence that its night-side flux is $<10\%$ of the day-side
flux and the emission peak is shifted westward of the substellar point,
assuming the spectrum is dominated by Fe I. Our ESPaDOnS data, which cover
phases before and after the secondary eclipse more evenly, weakly constrain the
phase offset to $+22\pm12$ degrees. We caution that the derived
volume-mixing-ratio depends on our choice of temperature-pressure profile, but
note it does not significantly influence our constraints on day-night contrast
or phase offset. Finally, we use simulations to illustrate how observations
with increased phase coverage and higher signal-to-noise ratios can improve
these constraints, showcasing the expanding capabilities of high-resolution
Doppler spectroscopy.


title: Continuous-Stage Runge-Kutta approximation to Differential Problems

abstract:
  In recent years, the efficient numerical solution of Hamiltonian problems has
led to the definition of a class of energy-conserving Runge-Kutta methods named
Hamiltonian Boundary Value Methods (HBVMs). Such methods admit an interesting
interpretation in terms of continuous-stage Runge-Kutta methods, which is here
recalled and revisited for general differential problems.


title:
Improved Sampling-to-Counting Reductions in High-Dimensional Expanders
  and Faster Parallel Determinantal Sampling

abstract:
  We study parallel sampling algorithms for classes of distributions defined
via determinants: symmetric, nonsymmetric, and partition-constrained
determinantal point processes. For these distributions, counting, a.k.a.
computing the partition function, can be reduced to a simple determinant
computation which is highly parallelizable; Csanky proved it is in NC. However,
parallel counting does not automatically translate to parallel sampling, as the
classic reductions between sampling and counting are inherently sequential.
Despite this, we show that for all the aforementioned determinant-based
distributions, a roughly quadratic parallel speedup over sequential sampling
can be achieved. If the distribution is supported on subsets of size $k$ of a
ground set, we show how to approximately produce a sample in
$\widetilde{O}(k^{\frac{1}{2} + c})$ time with polynomially many processors for
any $c>0$. In the special case of symmetric determinantal point processes, our
bound improves to $\widetilde{O}(\sqrt k)$ and we show how to sample exactly in
this case.
  We obtain our results via a generic sampling-to-counting reduction that uses
approximate rejection sampling. As our main technical contribution, we show
that whenever a distribution satisfies a certain form of high-dimensional
expansion called entropic independence, approximate rejection sampling can
achieve a roughly quadratic speedup in sampling via counting. Various forms of
high-dimensional expansion, including the notion of entropic independence we
use in this work, have been the source of major breakthroughs in sampling
algorithms in recent years; thus we expect our framework to prove useful in the
future for distributions beyond those defined by determinants.


title: Robust Visual Tracking by Segmentation

abstract:
  Estimating the target extent poses a fundamental challenge in visual object
tracking. Typically, trackers are box-centric and fully rely on a bounding box
to define the target in the scene. In practice, objects often have complex
shapes and are not aligned with the image axis. In these cases, bounding boxes
do not provide an accurate description of the target and often contain a
majority of background pixels. We propose a segmentation-centric tracking
pipeline that not only produces a highly accurate segmentation mask, but also
works internally with segmentation masks instead of bounding boxes. Thus, our
tracker is able to better learn a target representation that clearly
differentiates the target in the scene from background content. In order to
achieve the necessary robustness for the challenging tracking scenario, we
propose a separate instance localization component that is used to condition
the segmentation decoder when producing the output mask. We infer a bounding
box from the segmentation mask and validate our tracker on challenging tracking
datasets and achieve the new state of the art on LaSOT with a success AUC score
of 69.7%. Since fully evaluating the predicted masks on tracking datasets is
not possible due to the missing mask annotations, we further validate our
segmentation quality on two popular video object segmentation datasets.


title: Transforming Model Prediction for Tracking

abstract:
  Optimization based tracking methods have been widely successful by
integrating a target model prediction module, providing effective global
reasoning by minimizing an objective function. While this inductive bias
integrates valuable domain knowledge, it limits the expressivity of the
tracking network. In this work, we therefore propose a tracker architecture
employing a Transformer-based model prediction module. Transformers capture
global relations with little inductive bias, allowing it to learn the
prediction of more powerful target models. We further extend the model
predictor to estimate a second set of weights that are applied for accurate
bounding box regression. The resulting tracker relies on training and on test
frame information in order to predict all weights transductively. We train the
proposed tracker end-to-end and validate its performance by conducting
comprehensive experiments on multiple tracking datasets. Our tracker sets a new
state of the art on three benchmarks, achieving an AUC of 68.5% on the
challenging LaSOT dataset.


title:
Neuron-like spiking dynamics in the asymmetrically-driven dissipative
  photonic Bose-Hubbard dimer

abstract:
  We demonstrate neuron-like spiking dynamics in the asymmetrically driven
dissipative photonic Bose-Hubbard dimmer model which describes two coupled
nonlinear passive Kerr cavities. Spiking dynamics appear due to the excitable
nature of the system. In this context, excitable excursions in the phase space
correspond to spikes in the temporal evolution of the field variables. In our
case, excitability is mediated by the destruction of an oscillatory state in a
global homoclinic bifurcation. In this type of excitability (known as type-I)
the period of the oscillatory state diverges when approaching the bifurcation.
Beyond this point, the system exhibits excitable dynamics under the application
of suitable perturbations. We have also characterized the effect that additive
Gaussian noise has on the spiking dynamics, showing that the system undergoes a
coherence resonance for a given value of the noise strength.


title: Generating Fast and Slow: Scene Decomposition via Reconstruction

abstract:
  We consider the problem of segmenting scenes into constituent entities, i.e.
underlying objects and their parts. Current supervised visual detectors though
impressive within their training distribution, often fail to segment
out-of-distribution scenes into their constituent entities. Recent slot-centric
generative models break such dependence on supervision, by attempting to
segment scenes into entities unsupervised, by reconstructing pixels. However,
they have been restricted thus far to toy scenes as they suffer from a
reconstruction-segmentation trade-off: as the entity bottleneck gets wider,
reconstruction improves but then the segmentation collapses. We propose
GFS-Nets (Generating Fast and Slow Networks) that alleviate this issue with two
ingredients: i) curriculum training in the form of primitives, often missing
from current generative models and, ii) test-time adaptation per scene through
gradient descent on the reconstruction objective, what we call slow inference,
missing from current feed-forward detectors. We show the proposed curriculum
suffices to break the reconstruction-segmentation trade-off, and slow inference
greatly improves segmentation in out-of-distribution scenes. We evaluate
GFS-Nets in 3D and 2D scene segmentation benchmarks of PartNet, CLEVR, Room
Diverse++, and show large ( 50%) performance improvements against SOTA
supervised feed-forward detectors and unsupervised object discovery methods


title: Manipulating Generalized Dirac Cones In Quantum Metasurfaces

abstract:
  We discuss the emergence and manipulation of generalised Dirac cones in the
subradiant collective modes of quantum metasurfaces. We consider a collection
of single quantum emitters arranged in a honeycomb lattice with subwavelength
periodicity. While conventional honeycomb quantum metasurfaces host bound modes
that display Dirac cones at the K and K' points, we show that introducing
uniaxial anisotropy in the lattice results in modified dispersion relations.
These include the tilting of Dirac cones, which changes the local density of
states at the Dirac point from vanishing (type I) to diverging (types II and
III), the emergence of semi-Dirac points, with linear and quadratic dispersions
in orthogonal directions, and the anisotropic movement of Dirac cones away from
the K and K' points. Such energy dispersions can modify substantially the
dynamics of local probes, such as quantum emitters, to which they have been
shown to induce anisotropic, power-law interactions


title:
Performance of Deep Learning models with transfer learning for
  multiple-step-ahead forecasts in monthly time series

abstract:
  Deep Learning and transfer learning models are being used to generate time
series forecasts; however, there is scarce evidence about their performance
prediction that it is more evident for monthly time series. The purpose of this
paper is to compare Deep Learning models with transfer learning and without
transfer learning and other traditional methods used for monthly forecasts to
answer three questions about the suitability of Deep Learning and Transfer
Learning to generate predictions of time series. Time series of M4 and M3
competitions were used for the experiments. The results suggest that deep
learning models based on TCN, LSTM, and CNN with transfer learning tend to
surpass the performance prediction of other traditional methods. On the other
hand, TCN and LSTM, trained directly on the target time series, got similar or
better performance than traditional methods for some forecast horizons.


title: Teachable Reinforcement Learning via Advice Distillation

abstract:
  Training automated agents to complete complex tasks in interactive
environments is challenging: reinforcement learning requires careful
hand-engineering of reward functions, imitation learning requires specialized
infrastructure and access to a human expert, and learning from intermediate
forms of supervision (like binary preferences) is time-consuming and extracts
little information from each human intervention. Can we overcome these
challenges by building agents that learn from rich, interactive feedback
instead? We propose a new supervision paradigm for interactive learning based
on "teachable" decision-making systems that learn from structured advice
provided by an external teacher. We begin by formalizing a class of
human-in-the-loop decision making problems in which multiple forms of
teacher-provided advice are available to a learner. We then describe a simple
learning algorithm for these problems that first learns to interpret advice,
then learns from advice to complete tasks even in the absence of human
supervision. In puzzle-solving, navigation, and locomotion domains, we show
that agents that learn from advice can acquire new skills with significantly
less human supervision than standard reinforcement learning algorithms and
often less than imitation learning.


title:
Comment on "Tests and calibrations of nuclear track detectors (CR39) for
  operation in high neutron flux" by E.E. Kading et al., Phys. Rev. Res. 2,
  023279 (2020)

abstract:
  The experimental conditions described in the article E.E Kading et al. [Phys.
Rev. Res. 2, 023279 (2020)] show striking flaws. The interpretation and
extraction of quantitative results from this experiment are considered
unreliable. All results of cross sections based on this article must be
discarded.


title:
Distinguishing Non-natural from Natural Adversarial Samples for More
  Robust Pre-trained Language Model

abstract:
  Recently, the problem of robustness of pre-trained language models (PrLMs)
has received increasing research interest. Latest studies on adversarial
attacks achieve high attack success rates against PrLMs, claiming that PrLMs
are not robust. However, we find that the adversarial samples that PrLMs fail
are mostly non-natural and do not appear in reality. We question the validity
of current evaluation of robustness of PrLMs based on these non-natural
adversarial samples and propose an anomaly detector to evaluate the robustness
of PrLMs with more natural adversarial samples. We also investigate two
applications of the anomaly detector: (1) In data augmentation, we employ the
anomaly detector to force generating augmented data that are distinguished as
non-natural, which brings larger gains to the accuracy of PrLMs. (2) We apply
the anomaly detector to a defense framework to enhance the robustness of PrLMs.
It can be used to defend all types of attacks and achieves higher accuracy on
both adversarial samples and compliant samples than other defense frameworks.


title:
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with
  Heterophily

abstract:
  Due to the homophily assumption in graph convolution networks, a common
consensus is that graph neural networks (GNNs) perform well on homophilic
graphs but may fail on heterophilic graphs with many inter-class edges. In this
work, we re-examine the heterophily problem of GNNs and investigate the feature
aggregation of inter-class neighbors. Instead of treating the inter-class edges
as harmful, under some conditions, we argue that they may provide helpful
information for node classification from an entire neighbor's perspective. To
better evaluate whether the neighbor is helpful for the downstream tasks, we
present the concept of the neighbor effect of each node and use the von Neumann
entropy to measure the randomness/identifiability of the neighbor distribution
for each class. Moreover, we propose a Conv-Agnostic GNNs framework (CAGNNs) to
enhance the performance of GNNs on heterophily datasets by learning the
neighbor effect for each node. Specifically, we first decouple the feature of
each node into the discriminative feature for downstream tasks and the
aggregation feature for graph convolution. Then, we propose a shared mixer
module for all layers to adaptively evaluate the neighbor effect of each node
to incorporate the neighbor information. Experiments are performed on nine
well-known benchmark datasets for the node classification task. The results
indicate that our framework is able to improve the average prediction
performance by 9.81%, 25.81%, and 20.61% for GIN, GAT, and GCN, respectively.
Extensive ablation studies and robustness analysis further verify the
effectiveness, robustness, and interpretability of our framework.


title: Efficient Neural Network Analysis with Sum-of-Infeasibilities

abstract:
  Inspired by sum-of-infeasibilities methods in convex optimization, we propose
a novel procedure for analyzing verification queries on neural networks with
piecewise-linear activation functions. Given a convex relaxation which
over-approximates the non-convex activation functions, we encode the violations
of activation functions as a cost function and optimize it with respect to the
convex relaxation. The cost function, referred to as the Sum-of-Infeasibilities
(SoI), is designed so that its minimum is zero and achieved only if all the
activation functions are satisfied. We propose a stochastic procedure, DeepSoI,
to efficiently minimize the SoI. An extension to a canonical
case-analysis-based complete search procedure can be achieved by replacing the
convex procedure executed at each search state with DeepSoI. Extending the
complete search with DeepSoI achieves multiple simultaneous goals: 1) it guides
the search towards a counter-example; 2) it enables more informed branching
decisions; and 3) it creates additional opportunities for bound derivation. An
extensive evaluation across different benchmarks and solvers demonstrates the
benefit of the proposed techniques. In particular, we demonstrate that SoI
significantly improves the performance of an existing complete search
procedure. Moreover, the SoI-based implementation outperforms other
state-of-the-art complete verifiers. We also show that our technique can
efficiently improve upon the perturbation bound derived by a recent adversarial
attack algorithm.


title:
The eigenvalues and eigenfunctions of the toroidal dipole operator in a
  mesoscopic system

abstract:
  We give analytical expressions for the eigenvalues and generalized
eigenfunctions of $\hat{T}_3$, the $z$-axis projection of the toroidal dipole
operator, in a system consisting of a particle confined in a thin film bent
into a torus shape. We find the quantization rules for the eigenvalues, which
are essential for describing measurements of $\hat{T}_3$. The eigenfunctions
are not square-integrable, so they do not belong to the Hilbert space of wave
functions, but they can be interpreted in the formalism of rigged Hilbert
spaces as kernels of distributions. While these kernels appear to be
problematic at first glance due to singularities, they can actually be used in
practical computations. In order to illustrate this, we prescribe their action
explicitly and we also provide a normalization procedure.


title:
Reinforcement learning for automatic quadrilateral mesh generation: a
  soft actor-critic approach

abstract:
  This paper proposes, implements, and evaluates a Reinforcement Learning (RL)
based computational framework for automatic mesh generation. Mesh generation,
as one of six basic research directions identified in NASA Vision 2030, is an
important area in computational geometry and plays a fundamental role in
numerical simulations in the area of finite element analysis (FEA) and
computational fluid dynamics (CFD). Existing mesh generation methods suffer
from high computational complexity, low mesh quality in complex geometries, and
speed limitations. By formulating the mesh generation as a Markov decision
process (MDP) problem, we are able to use soft actor-critic, a state-of-the-art
RL algorithm, to learn the meshing agent's policy from trials automatically,
and achieve a fully automatic mesh generation system without human intervention
and any extra clean-up operations, which are typically needed in current
commercial software. In our experiments and comparison with a number of
representative commercial software, our system demonstrates promising
performance with respect to generalizability, robustness, and effectiveness.


title: Gravitational Waves from Quasi-stable Strings

abstract:
  We estimate the stochastic gravitational wave spectrum emitted from a network
of cosmic strings in which the latter are effectively stable against breaking
by monopole pair creation. The monopoles are produced at a higher scale from an
earlier symmetry breaking and experience significant inflation before
reentering the horizon. This gives rise to monopole-antimonopole pairs
connected by string segments and the string loop formation essentially ceases.
As a consequence, the lower frequency portion of the gravitational wave
spectrum is suppressed relative to the no-inflation case with stable strings,
which evades the stringent PPTA bound on the dimensionless string tension
$G\mu$. We display the modified spectrum, accessible in the ongoing and future
experiments, for $G\mu$ values in the range $10^{-10} - 10^{-15}$. We show how
this `quasi-stable' string network is realized in realistic grand unified
theories.


title:
VinDr-Mammo: A large-scale benchmark dataset for computer-aided
  diagnosis in full-field digital mammography

abstract:
  Mammography, or breast X-ray, is the most widely used imaging modality to
detect cancer and other breast diseases. Recent studies have shown that deep
learning-based computer-assisted detection and diagnosis (CADe or CADx) tools
have been developed to support physicians and improve the accuracy of
interpreting mammography. However, most published datasets of mammography are
either limited on sample size or digitalized from screen-film mammography
(SFM), hindering the development of CADe and CADx tools which are developed
based on full-field digital mammography (FFDM). To overcome this challenge, we
introduce VinDr-Mammo - a new benchmark dataset of FFDM for detecting and
diagnosing breast cancer and other diseases in mammography. The dataset
consists of 5,000 mammography exams, each of which has four standard views and
is double read with disagreement (if any) being resolved by arbitration. It is
created for the assessment of Breast Imaging Reporting and Data System
(BI-RADS) and density at the breast level. In addition, the dataset also
provides the category, location, and BI-RADS assessment of non-benign findings.
We make VinDr-Mammo publicly available on PhysioNet as a new imaging resource
to promote advances in developing CADe and CADx tools for breast cancer
screening.


title:
Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning
  and Random Sampling

abstract:
  A fully automated system for interpreting abdominal computed tomography (CT)
scans with multiple phases of contrast enhancement requires an accurate
classification of the phases. This work aims at developing and validating a
precise, fast multi-phase classifier to recognize three main types of contrast
phases in abdominal CT scans. We propose in this study a novel method that uses
a random sampling mechanism on top of deep CNNs for the phase recognition of
abdominal CT scans of four different phases: non-contrast, arterial, venous,
and others. The CNNs work as a slice-wise phase prediction, while the random
sampling selects input slices for the CNN models. Afterward, majority voting
synthesizes the slice-wise results of the CNNs, to provide the final prediction
at scan level. Our classifier was trained on 271,426 slices from 830
phase-annotated CT scans, and when combined with majority voting on 30% of
slices randomly chosen from each scan, achieved a mean F1-score of 92.09% on
our internal test set of 358 scans. The proposed method was also evaluated on 2
external test sets: CTPAC-CCRCC (N = 242) and LiTS (N = 131), which were
annotated by our experts. Although a drop in performance has been observed, the
model performance remained at a high level of accuracy with a mean F1-score of
76.79% and 86.94% on CTPAC-CCRCC and LiTS datasets, respectively. Our
experimental results also showed that the proposed method significantly
outperformed the state-of-the-art 3D approaches while requiring less
computation time for inference.


title: Hybrid training of optical neural networks

abstract:
  Optical neural networks are emerging as a promising type of machine learning
hardware capable of energy-efficient, parallel computation. Today's optical
neural networks are mainly developed to perform optical inference after in
silico training on digital simulators. However, various physical imperfections
that cannot be accurately modelled may lead to the notorious reality gap
between the digital simulator and the physical system. To address this
challenge, we demonstrate hybrid training of optical neural networks where the
weight matrix is trained with neuron activation functions computed optically
via forward propagation through the network. We examine the efficacy of hybrid
training with three different networks: an optical linear classifier, a hybrid
opto-electronic network, and a complex-valued optical network. We perform a
comparative study to in silico training, and our results show that hybrid
training is robust against different kinds of static noise. Our
platform-agnostic hybrid training scheme can be applied to a wide variety of
optical neural networks, and this work paves the way towards advanced
all-optical training in machine intelligence.


title:
An efficient heuristic approach combining maximal itemsets and area
  measure for compressing voluminous table constraints

abstract:
  Constraint Programming is a powerful paradigm to model and solve
combinatorial problems. While there are many kinds of constraints, the table
constraint is perhaps the most significant-being the most well-studied and has
the ability to encode any other constraints defined on finite variables.
However, constraints can be very voluminous and their size can grow
exponentially with their arity. To reduce space and the time complexity,
researchers have focused on various forms of compression. In this paper we
propose a new approach based on maximal frequent itemsets technique and area
measure for enumerating the maximal frequent itemsets relevant for compressing
table constraints. Our experimental results show the effectiveness and
efficiency of this approach on compression and on solving compressed table
constraints.


title:
On the Effect of Pre-Processing and Model Complexity for Plastic
  Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging

abstract:
  The importance of plastic waste recycling is undeniable. In this respect,
computer vision and deep learning enable solutions through the automated
analysis of short-wave-infrared hyper-spectral images of plastics. In this
paper, we offer an exhaustive empirical study to show the importance of
efficient model selection for resolving the task of hyper-spectral image
segmentation of various plastic flakes using deep learning. We assess the
complexity level of generic and specialized models and infer their performance
capacity: generic models are often unnecessarily complex. We introduce two
variants of a specialized hyper-spectral architecture, PlasticNet, that
outperforms several well-known segmentation architectures in both performance
as well as computational complexity. In addition, we shed lights on the
significance of signal pre-processing within the realm of hyper-spectral
imaging. To complete our contribution, we introduce the largest, most versatile
hyper-spectral dataset of plastic flakes of four primary polymer types.


title:
Disentangling Patterns and Transformations from One Sequence of Images
  with Shape-invariant Lie Group Transformer

abstract:
  An effective way to model the complex real world is to view the world as a
composition of basic components of objects and transformations. Although humans
through development understand the compositionality of the real world, it is
extremely difficult to equip robots with such a learning mechanism. In recent
years, there has been significant research on autonomously learning
representations of the world using the deep learning; however, most studies
have taken a statistical approach, which requires a large number of training
data. Contrary to such existing methods, we take a novel algebraic approach for
representation learning based on a simpler and more intuitive formulation that
the observed world is the combination of multiple independent patterns and
transformations that are invariant to the shape of patterns. Since the shape of
patterns can be viewed as the invariant features against symmetric
transformations such as translation or rotation, we can expect that the
patterns can naturally be extracted by expressing transformations with
symmetric Lie group transformers and attempting to reconstruct the scene with
them. Based on this idea, we propose a model that disentangles the scenes into
the minimum number of basic components of patterns and Lie transformations from
only one sequence of images, by introducing the learnable shape-invariant Lie
group transformers as transformation components. Experiments show that given
one sequence of images in which two objects are moving independently, the
proposed model can discover the hidden distinct objects and multiple
shape-invariant transformations that constitute the scenes.


title:
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement
  Learning

abstract:
  Despite notable results in various fields over the recent years, deep
reinforcement learning (DRL) algorithms lack transparency, affecting user trust
and hindering their deployment to high-risk tasks. Causal confusion refers to a
phenomenon where an agent learns spurious correlations between features which
might not hold across the entire state space, preventing safe deployment to
real tasks where such correlations might be broken. In this work, we examine
whether an agent relies on spurious correlations in critical states, and
propose an alternative subset of features on which it should base its decisions
instead, to make it less susceptible to causal confusion. Our goal is to
increase transparency of DRL agents by exposing the influence of learned
spurious correlations on its decisions, and offering advice to developers about
feature selection in different parts of state space, to avoid causal confusion.
We propose ReCCoVER, an algorithm which detects causal confusion in agent's
reasoning before deployment, by executing its policy in alternative
environments where certain correlations between features do not hold. We
demonstrate our approach in taxi and grid world environments, where ReCCoVER
detects states in which an agent relies on spurious correlations and offers a
set of features that should be considered instead.


title: Comment on "Universal Bound on Energy Cost of Bit Reset in Finite Time"

abstract:
  We present a quantum-mechanical generalization of the work penalty equation
(Eq. (6) in Phys. Rev. Lett., 127, 190602 (2021)) with an alternative
derivation.


title: ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation

abstract:
  Glioma is the most common and aggressive brain tumor. Magnetic resonance
imaging (MRI) plays a vital role to evaluate tumors for the arrangement of
tumor surgery and the treatment of subsequent procedures. However, the manual
segmentation of the MRI image is strenuous, which limits its clinical
application. With the development of deep learning, a large number of automatic
segmentation methods have been developed, but most of them stay in 2D images,
which leads to subpar performance. Moreover, the serious voxel imbalance
between the brain tumor and the background as well as the different sizes and
locations of the brain tumor makes the segmentation of 3D images a challenging
problem. Aiming at segmenting 3D MRI, we propose a model for brain tumor
segmentation with multiple encoders. The structure contains four encoders and
one decoder. The four encoders correspond to the four modalities of the MRI
image, perform one-to-one feature extraction, and then merge the feature maps
of the four modalities into the decoder. This method reduces the difficulty of
feature extraction and greatly improves model performance. We also introduced a
new loss function named "Categorical Dice", and set different weights for
different segmented regions at the same time, which solved the problem of voxel
imbalance. We evaluated our approach using the online BraTS 2020 Challenge
verification. Our proposed method can achieve promising results in the
validation set compared to the state-of-the-art approaches with Dice scores of
0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhanced
tumor, respectively.


title: Status on the DESIR High Resolution Separator Commissioning

abstract:
  Many nuclear reactions used to create radioactive isotopes for nuclear
research produce, in addition to the isotope of interest, many contaminants,
which are often produced in much larger amounts than the isotope of interest.
Many installations using the ISOL approach are therefore equipped with
high-resolution mass separators to remove at least isotopes with a different
mass number. In the present paper, we present the results of the commissioning
of the DESIR HRS presently under development at LP2I Bordeaux (formerly CENBG).
Optical aberrations are corrected up to 3rd order and a mass resolution of
M/$\Delta$M of 25000 is reached with a transmission of about 70% for a 133Cs+
beam at 25 keV.


title:
On the shortcomings of the Shapiro delay tests of the equivalence
  principle

abstract:
  There are several shortcomings in the "standard" Shapiro delay tests of the
equivalence principle on cosmological scales \cite{minazzoli:2019pr}. Although
many people in the community already acknowledged this in the literature, and
proposed alternative ways to compare potential Shapiro delays over cosmological
scales -- e.g. \cite{bartlett:2021pr,hashimoto:2021pr} and references therein
-- papers are still submitted to journals with the usual issues.


title: The Conceptual VAE

abstract:
  In this report we present a new model of concepts, based on the framework of
variational autoencoders, which is designed to have attractive properties such
as factored conceptual domains, and at the same time be learnable from data.
The model is inspired by, and closely related to, the Beta-VAE model of
concepts, but is designed to be more closely connected with language, so that
the names of concepts form part of the graphical model. We provide evidence
that our model -- which we call the Conceptual VAE -- is able to learn
interpretable conceptual representations from simple images of coloured shapes
together with the corresponding concept labels. We also show how the model can
be used as a concept classifier, and how it can be adapted to learn from fewer
labels per instance. Finally, we formally relate our model to Gardenfors'
theory of conceptual spaces, showing how the Gaussians we use to represent
concepts can be formalised in terms of "fuzzy concepts" in such a space.


title:
A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower
  Luminosities

abstract:
  We present a spectroscopic and photometric analysis of a sample of 416,288
galaxies from the Sloan Digital Sky Survey (SDSS) matched to mid-infrared
(mid-IR) data from the Wide-Field Infrared Survey Explorer (WISE). By using a
new spectroscopic fitting package, GELATO (Galaxy/AGN Emission Line Analysis
TOol), we are able to retrieve emission line fluxes and uncertainties for SDSS
spectra and robustly determine the presence of broad lines and outflowing
components, enabling us to investigate WISE color space as a function of
optical spectroscopic properties. In addition, we pursue SED template fitting
to assess the relative AGN contribution and nuclear obscuration to compare to
existing mid-IR selection criteria with WISE. We present a selection criterion
in mid-IR color space to select Active Galactic Nuclei (AGNs) with a $\sim$80%
accuracy and a completeness of $\sim$16%. This is the first mid-IR color
selection defined by solely using the distribution of Type I and Type II
optical spectroscopic AGNs in WISE mid-IR color space. Our selection is an
improvement of $\sim$50% in the completeness of targeting spectroscopic AGNs
with WISE down to an SDSS $r<17.77$ mag. In addition, our new criterion targets
a less luminous population of AGNs, with on average lower [O III] luminosities
by $\sim$30% ($>0.1$ dex) compared to typical WISE color-color selections. With
upcoming large photometric surveys without corresponding spectroscopy, our
method presents a way to select larger populations of AGNs at lower AGN
luminosities and higher nuclear obscuration levels than traditional mid-IR
color selections.


title: Creation of inclusive spaces with astromimicry

abstract:
  The Universe can inspire us to design communities that foster equity and
inclusion.


title: Casting the $H_0$ tension as a fitting problem of cosmologies

abstract:
  The discrepancy between the value of the Hubble constant $H_0$ in the late,
local universe and the one obtained from the Planck collaboration representing
an all-sky value for the early universe reached the 5-$\sigma$ level.
Approaches to alleviate the tension contain a wide range of ansatzes:
increasing uncertainties in data acquisition, reducing biases in the
astrophysical models that underly the probes, or taking into account
observer-dependent variances in the parameters of the cosmological background
model. Another way of interpreting the tension is to consider the density
parameters in the Friedmann equations effective background fields which are
probed at two different cosmic times, i.e. using the data of the early universe
and the late, more complex universe for two different fits of a flat
Friedmann-Lema\^itre-Robertson-Walker cosmology. The two sets of parameter
values can differ and tensions arise if these background fits and perturbing
biases are not consistently calibrated with respect to each other when they are
linked to observables because these observables are all based on common
standards. This consistent model-fitting calibration is lacking between the two
$H_0$ values mentioned above, thus causing a tension. As shown here, this
interpretation resolves the $H_0$ tension, if 15% of the matter-density
parameter obtained from the fit to the cosmic microwave background,
$\Omega_\mathrm{m}=0.315$, are assigned to decoupled perturbations yielding
$\Omega_\mathrm{m}=0.267$ for the fit at redshifts of the supernova
observations. Existing theoretical analyses and data evaluations which support
this solution are given.


title: Cosmology from the vacuum

abstract:
  We argue that standard tools of holography can be used to describe fully
non-perturbative microscopic models of cosmology in which a period of
accelerated expansion may result from the positive potential energy of
time-dependent scalar fields evolving towards a region with negative potential.
In these models, the fundamental cosmological constant is negative, and the
universe eventually recollapses in a time-reversal symmetric way. The
microscopic description naturally selects a special state for the cosmology. In
this framework, physics in the cosmological spacetime is dual to the vacuum
physics in a static planar asymptotically AdS Lorentzian wormhole spacetime, in
the sense that the background spacetimes and observables are related by
analytic continuation. The dual spacetime is weakly curved everywhere, so any
cosmological observables can be computed in the dual picture via effective
field theory without detailed knowledge of the UV completion or the physics
near the big bang. In particular, while inflation may explain the origin of
perturbations in the cosmology picture, the perturbations can be deduced from
the dual picture without any knowledge of the inflationary potential.


title: Nematic phases and elastoresistivity from a multiorbital strange metal

abstract:
  We propose and study a two-orbital lattice extension of the Sachdev-Ye-Kitaev
model in the large-$N$ limit. The phase diagram of this model features a high
temperature isotropic strange metal which undergoes a first-order thermal
transition into a nematic insulator or a continuous thermal transition into
nematic metal phase, separated by a tunable tricritical point. These phases
arise from spontaneous partial orbital polarization of the multiorbital
non-Fermi liquid. We explore the spectral and transport properties of this
model, including the d.c. elastoresistivity, which exhibits a peak near the
nematic transition, as well as the nonzero frequency elastoconductivity. Our
work offers a useful perspective on nematic phases and transport in correlated
multiorbital systems.


title: Lepton-flavor violating axions at MEG II

abstract:
  We study the sensitivity of the existing MEG data to lepton flavor violating
axion-like particles produced through $\mu^+ \to e^+ a \gamma$ and estimate the
discovery potential for the upcoming MEG II experiment in this channel. The MEG
II signal efficiency can be improved significantly if a new trigger can be
implemented in a dedicated run with a reduced beam intensity. This search would
establish the world leading measurement in this channel with only 1 month of
data taking.


title: Star Formation in the Central Molecular Zone of the Milky Way

abstract:
  The Central Molecular Zone (CMZ) is a ring-like accumulation of molecular gas
in the innermost few hundred parsecs of the Milky Way, generated by the inward
transport of matter driven by the Galactic bar. The CMZ is the most extreme
star-forming environment in the Galaxy. The unique combination of large-scale
dynamics and extreme interstellar medium conditions, characterised by high
densities, temperatures, pressures, turbulent motions, and strong magnetic
fields, make the CMZ an ideal region for testing current star and planet
formation theories. We review the recent observational and theoretical advances
in the field, and combine these to draw a comprehensive, multi-scale and
multi-physics picture of the cycle of matter and energy in the context of star
formation in the closest galactic nucleus.


title:
NLO QCD Renormalization Group Evolution for Non-Leptonic $\Delta F=2$
  Transitions in the SMEFT

abstract:
  We present for the first time NLO QCD Renormalization Group (RG) evolution
matrices for non-leptonic $\Delta F=2$ transitions in the Standard Model
Effective Field Theory (SMEFT). To this end we transform first the known
two-loop QCD anomalous dimension matrices (ADMs) of the BSM operators in the
so-called BMU basis into the ones in the common Weak Effective Theory (WET)
basis (the so-called JMS basis) for which tree-level and one-loop matching to
the SMEFT are already known. This allows us subsequently to find the two-loop
QCD ADMs for the SMEFT non-leptonic $\Delta F=2$ operators in the Warsaw basis.
Having all these ingredients we investigate the impact of these NLO QCD effects
on the QCD RG evolution of SMEFT Wilson coefficients for non-leptonic $\Delta
F=2$ transitions from the new physics scale $\Lambda$ down to the electroweak
scale $\mu_\text{ew}$. The main benefit of these new contributions is that they
allow to remove renormalization scheme dependences present both in the one-loop
matchings between the WET and SMEFT and also between SMEFT and a chosen UV
completion. But the NLO QCD effects, calculated here in the NDR scheme, turn
out to be small, in the ballpark of a few percent but larger than one-loop
Yukawa top effects when only the $\Delta F=2$ operators are considered. The
technology developed in our paper allows to obtain the ADMs in the SMEFT from
the ones of the BMU basis also for non-leptonic $\Delta F=1$ decays and the
results of this more involved analysis will be presented soon in another
publication.


title:
A topological principle for photovoltaics: Shift current in
  intrinsically polar insulators

abstract:
  To realize an efficient solar cell without inhomogeneous doping, one would
like to maximize the shift component of the bulk photovoltaic current, in
noncentric semiconductors with wide band gaps. I achieve this maximization for
a new class of topological insulators whose band topology is only compatible
with a polar crystal class. For such insulators, it is impossible to
continuously tune the $\boldsymbol{k}$-dependent electron-hole dipole moment
(or `shift vector') to zero throughout the Brillouin zone. Averaging the shift
vector over all high-symmetry cross-sections of the Brillouin zone gives
exactly a rational multiple of a Bravais lattice vector, which points parallel
to the polar axis. Even with wide band gaps, the frequency-integrated shift
conductivity of intrinsically polar insulators greatly exceeds $e^3/h^2$, and
is at least three orders of magnitude larger than the conductivity of the
prototypical ferroelectric BaTiO$_3$, challenging a widely-held expectation
that small band gaps are necessary for large shift currents in topological
materials. Close to a topological phase transition, the integrated conductivity
diverges as $|E_g|^{-1/2}$ with $E_g$ the band gap, suggesting an application
to ultrafast infrared detection.


title:
Snowmass2021 Cosmic Frontier CF6 White Paper: Multi-Experiment Probes
  for Dark Energy -- Transients

abstract:
  This invited Snowmass 2021 White Paper highlights the power of joint-analysis
of astronomical transients in advancing HEP Science and presents research
activities that can realize the opportunities that come with current and
upcoming projects. Transients of interest include gravitational wave events,
neutrino events, strongly-lensed quasars and supernovae, and Type~Ia supernovae
specifically. These transients can serve as probes of cosmological distances in
the Universe and as cosmic laboratories of extreme strong-gravity, high-energy
physics. Joint analysis refers to work that requires significant coordination
from multiple experiments or facilities so encompasses Multi-Messenger
Astronomy and optical transient discovery and distributed follow-up programs.


title:
Hydrodynamics and survivability during post-main-sequence planetary
  engulfment

abstract:
  The engulfment of substellar bodies (SBs) such as brown dwarfs and planets
has been invoked as a possible explanation for the presence of SBs orbiting
subdwarfs and white dwarfs, rapidly rotating giants, and lithium-rich giants.
We perform three-dimensional hydrodynamical simulations of the flow in the
vicinity of an SB engulfed in a stellar envelope. We model the SB as a rigid
body with a reflective boundary because it cannot accrete. This reflective
boundary changes the flow morphology to resemble that of engulfed compact
objects with outflows. We measure the drag coefficients for the ram pressure
and gravitational drag forces acting on the SB, and use them to integrate its
trajectory during engulfment. We find that SB engulfment can increase the
stellar luminosity of a $1M_\odot$ star by up to a few orders of magnitude for
timescales of up to a few thousand years when the star is $\approx10R_\odot$
and up to a few decades at the tip of the red giant branch. We find that no SBs
can eject the envelope of a $1M_\odot$ star before it evolves to
$\approx10R_\odot$. In contrast, SBs as small as $\approx10M_\text{Jup}$ can
eject the envelope at the tip of the red giant branch, shrinking their orbits
by several orders of magnitude in the process. The numerical framework we
introduce here can be used to study the dynamics of planetary engulfment in a
simplified setting that captures the physics of the flow at the scale of the
SB.


title:
Heisenberg's uncertainty principle in the PTOLEMY project: a theory
  update

abstract:
  We discuss the consequences of the quantum uncertainty on the spectrum of the
electron emitted by the $\beta$-processes of a tritium atom bound to a graphene
sheet. We analyze quantitatively the issue recently raised in [Cheipesh et al.,
Phys. Rev. D 104, 116004 (2021)], and discuss the relevant time scales and the
degrees of freedom that can contribute to the intrinsic spread in the electron
energy. We propose an experimental signature to test this theoretical
understanding. We also perform careful calculations of the potential between
tritium and graphene with different coverages and geometries. With this at
hand, we propose possible avenues to mitigate the effect of the quantum
uncertainty.


title: Asteroid Taxonomy from Cluster Analysis of Spectrometry and Albedo

abstract:
  The classification of the minor bodies of the Solar System based on
observables has been continuously developed and iterated over the past 40
years. While prior iterations followed either the availability of large
observational campaigns or new instrumental capabilities opening novel
observational dimensions, we see the opportunity to improve primarily upon the
established methodology. We develop an iteration of the asteroid taxonomy which
allows for classification of partial and complete observations, i.e. visible,
near-infrared, and visible-near-infrared spectrometry, and which re-introduces
the visual albedo into the classification observables. The resulting class
assignments are given probabilistically, enabling to quantify the uncertainty
of a classification. We build the taxonomy based on 2983 observations of 2125
unique asteroids, representing an almost tenfold increase of sample size
compared with the previous taxonomy. The asteroid classes are identified in a
lower-dimensional representation of the observations using a Mixture of Common
Factor Analyzers model. We identify 17 classes split into the three complexes
C, M, and S, including the novel Z-class for extremely-red objects in the Main
Belt. The visual albedo information resolves the spectral degeneracy of the
X-complex and establishes the P-class as part of the C-complex. We present a
classification tool which computes probabilistic class assignments within this
taxonomic scheme from asteroid observations, intrinsically accounting for
degeneracies between classes based on the observed wavelength region. The
ability to classify partial observations and the re-introduction of the visual
albedo into the classification provide a taxonomy which is well suited for the
current and future datasets of asteroid observations, in particular provided by
Gaia, the MITHNEOS-, and the SPHEREx surveys.


title: CP-violating axion interactions in effective field theory

abstract:
  Axions are introduced to explain the observed smallness of the $\bar \theta $
term of QCD. Standard Model extensions typically contain new sources of CP
violation, for instance to account for the baryon asymmetry of the universe. In
the presence of additional CP-violating sources a Peccei-Quinn mechanism does
not remove all CP violation, leading to CP-odd interactions among axions and
Standard Model fields. In this work, we use effective field theory to
parametrize generic sources of beyond-the-Standard-Model CP violation. We
systematically compute the resulting CP-odd couplings of axions to leptons and
hadrons by using chiral perturbation theory. We discuss in detail the
phenomenology of the CP-odd axion couplings and compare limits from axion
searches, such as fifth force and monopole-dipole searches and astrophysics, to
direct limits on the CP-violating operators from electric dipole moment
experiments. While limits from electric dipole moment searches are tight, the
proposed ARIADNE experiment can potentially improve the existing constraints in
a window of axion masses.


title: Three-loop non-singlet matching coefficients for heavy quark currents

abstract:
  We compute the matching coefficients between QCD and non-relativistic QCD for
external vector, axial-vector, scalar and pseudo-scalar currents up to
three-loop order. We concentrate on the non-singlet contributions and present
precise numerical results with an accuracy of about ten digits. For the vector
current the results from arXiv:1401.3004 are confirmed, increasing the accuracy
by several orders of magnitude.


title:
AGN-driven outflows and the formation of Ly$\alpha$ nebulae around
  high-z quasars

abstract:
  The detection of Ly$\alpha$ nebulae around $z\gtrsim 6$ quasars provides
evidence for extended gas reservoirs around the first rapidly growing
supermassive black holes. Observations of $z > 6$ quasars can be explained by
cosmological models provided that the black holes by which they are powered
evolve in rare, massive dark matter haloes. Whether these theoretical models
also explain the observed extended Ly$\alpha$ emission remains an open
question. We post-process a suite of cosmological, radiation-hydrodynamic
simulations targeting a quasar host halo at $z>6$ with the Ly$\alpha$ radiative
transfer code RASCAS. A combination of recombination radiation from
photo-ionised hydrogen and emission from collisionally excited gas powers
Ly$\alpha$ nebulae with a surface brightness profile in close agreement with
observations. We also find that, even on its own, resonant scattering of the
Ly$\alpha$ line associated to the quasar's broad line region can also generate
Ly$\alpha$ emission on $\sim 100 \, \rm kpc$ scales, resulting in comparable
agreement with observed surface brightness profiles. Even if powered by a broad
quasar Ly$\alpha$ line, Ly$\alpha$ nebulae can have narrow line-widths
$\lesssim 1000 \, \rm km \, s^{-1}$, consistent with observational constraints.
Even if there is no quasar, we find that halo gas cooling produces a faint,
extended Ly$\alpha$ glow. However, to light-up extended Ly$\alpha$ nebulae with
properties in line with observations, our simulations unambiguously require
quasar-powered outflows to clear out the galactic nucleus and allow the
Ly$\alpha$ flux to escape and still remain resonant with halo gas. The close
match between observations and simulations with quasar outflows suggests that
AGN feedback already operates before $z \, = \, 6$ and confirms that high-$z$
quasars reside in massive haloes tracing overdensities.


title:
Grid of pseudo-2D chemistry models for tidally locked exoplanets -- II.
  The role of photochemistry

abstract:
  Photochemistry is expected to change the chemical composition of the upper
atmospheres of irradiated exoplanets through the dissociation of species, such
as methane and ammonia, and the association of others, such as hydrogen
cyanide. Although primarily the high altitude day side should be affected by
photochemistry, it is still unclear how dynamical processes transport
photochemical species throughout the atmosphere, and how these chemical
disequilibrium effects scale with different parameters. In this work we
investigate the influence of photochemistry in a two-dimensional context, by
synthesizing a grid of photochemical models across a large range of
temperatures. We find that photochemistry can strongly change the atmospheric
composition, even up to depths of several bar in cool exoplanets. We further
identify a sweet spot for the photochemical production of hydrogen cyanide and
acetylene, two important haze precursors, between effective temperatures of 800
and 1400 K. The night sides of most cool planets (effective temperature < 1800
K) are shown to host photochemistry products, transported from the day side by
horizontal advection. Synthetic transmission spectra are only marginally
affected by photochemistry, but we suggest that observational studies probing
higher altitudes, such as high-resolution spectroscopy, take photochemistry
into account.


title:
Unveiling the population of dual- and lensed- AGNs at sub-arcsec
  separations with Gaia

abstract:
  [Abridged] We present a novel method that takes advantage of the unique
capabilities of the Gaia satellite to obtain large and reliable samples of dual
or lensed AGN candidates with sub-arcsec separations, by selecting targets
showing multiple peaks in the Gaia scans. With this method, we have identified
260 multiple system candidates, 26 of which have archival Hubble Space
Telescope (HST) images. All these images show two or more point-sources at
sub-arcsec separations, thus confirming that the proposed technique can be
extremely efficient at small separations. Thirteen out of these 26 systems are
known gravitational lenses, and two have been previously classified as dual
AGNs. One of these two systems has an HST/STIS spatially resolved spectrum
showing two distinct AGNs at z=2.95 with 3.6 kpc (0.46 arcsec) separation,
confirming the previous identification as dual AGN. Dedicated adaptive-optics
(AO) assisted high-resolution imaging at the Large Binocular Telescope (LBT) of
5 systems also detected multiple components in all the targets, further
confirming the selection method. The measured separations are between 0.33 and
0.66 arcsec (2.7 - 5.6 kpc at the distance of the primary AGN). The nature of
the unclassified sources is uncertain. Several tests demonstrate that
contamination from foreground stars can only account for ~30% of these multiple
systems, and the analysis of the observed colours suggests that most of the
selected systems are expected to be dual/lensed AGNs with physical separations
between 3 and 5 kpc. Our results show that this is a very efficient technique
to select compact dual/lensed AGN systems with sub-arcsec separations,
complementing other methods. This method samples separation down to 2 kpc at
z>1 and thus allows us to probe the physical processes driving the inspiralling
of the pairs of SMBH inside a single galaxy.


title: Dynamics of Transmon Ionization

abstract:
  Qubit measurement and control in circuit QED rely on microwave drives, with
higher drive amplitudes ideally leading to faster processes. However,
degradation in qubit coherence time and readout fidelity has been observed even
under moderate drive amplitudes corresponding to few photons populating the
measurement resonator. Here, we numerically explore the dynamics of a driven
transmon-resonator system under strong and nearly resonant measurement drives,
and find clear signatures of transmon ionization where the qubit escapes out of
its cosine potential. Using a semiclassical model, we interpret this ionization
as resulting from resonances occurring at specific resonator photon
populations. We find that the photon populations at which these spurious
transitions occur are strongly parameter dependent and that they can occur at
low resonator photon population, something which may explain the experimentally
observed degradation in measurement fidelity.


title:
The cosmic optical background excess, dark matter, and line-intensity
  mapping

abstract:
  Recent studies using New Horizons LORRI images have returned the most precise
measurement of the cosmic optical background to date, yielding a flux that
exceeds that expected from deep galaxy counts by roughly a factor of two. We
investigate whether this excess, detected at $\sim 4\sigma$ significance, is
due to dark matter that decays to a monoenergetic photon with a rest-frame
energy in the range $0.5-10$ eV. We derive the spectral energy distribution
from such decays and the contribution to the flux measured by LORRI. The
parameter space that explains the measured excess with decays to photons with
energies $E\gtrsim 4$ eV is unconstrained to date. If the excess arises from
dark-matter decay to a photon line, there will be a significant signal in
forthcoming line-intensity mapping measurements. Moreover, the ultraviolet
instrument aboard New Horizons (which will have better sensitivity and probe a
different range of the spectrum) and future studies of very high-energy
$\gamma$-ray attenuation will also test this hypothesis and expand the search
for dark matter to a wider range of frequencies.


title:
Mixed QCD-electroweak corrections to dilepton production at the LHC in
  the high invariant mass region

abstract:
  We compute mixed QCD-electroweak corrections to the neutral-current Drell-Yan
production of a pair of massless leptons in the high invariant mass region. Our
computation is fully differential with respect to the final state particles. At
relatively low values of the dilepton invariant mass, $m_{\ell \ell} \sim 200$
GeV, we find unexpectedly large mixed QCD-electroweak corrections at the level
of -1%. At higher invariant masses, $m_{\ell \ell} \sim 1$ TeV, we observe that
these corrections can be well approximated by the product of QCD and
electroweak corrections. Hence, thanks to the well-known Sudakov enhancement of
the latter, they increase at large invariant mass and reach e.g. -3% at
$m_{\ell \ell} = 3$ TeV. Finally, we note that the inclusion of mixed
corrections reduces the theoretical uncertainty related to the choice of
electroweak input parameters to below the percent level.


title:
Accurate Determination of the Electron Spin Polarization In Magnetized
  Iron and Nickel Foils for M{\o}ller Polarimetry

abstract:
  The M{\o}ller polarimeter in Hall A at Jefferson Lab in Newport News, VA, has
provided reliable measurements of electron beam polarization for the past two
decades reaching the typically required $\pm$1\% level of absolute uncertainty.
However, the upcoming proposed experimental program including MOLLER and SoLID
have stringent requirements on beam polarimetry precision at the level of 0.4\%
\cite{MOLLER2014, SoLID2019}, requiring a systematic re-examination of all the
contributing uncertainties.
  M{\o}ller polarimetry uses the double polarized scattering asymmetry of a
polarized electron beam on a target with polarized atomic electrons. The target
is a ferromagnetic material magnetized to align the spins in a given direction.
In Hall A, the target is a pure iron foil aligned perpendicular to the beam and
magnetized out of plane parallel or antiparallel to the beam direction. The
acceptance of the detector is engineered to collect scattered electrons close
to 90$^{\circ}$ in the center of mass frame where the analyzing power is a
maximum (-7/9).
  One of the leading systematic errors comes from determination of the target
foil polarization. Polarization of a magnetically saturated target foil
requires knowledge of both the saturation magnetization and $g^\prime$, the
electron $g$-factor which includes components from both spin and orbital
angular momentum from which the spin fraction of magnetization is determined.
This paper utilizes the existing world data to provide a best estimate for
target polarization for both nickel and iron foils including uncertainties in
magnetization, high-field and temperature dependence, and fractional
contribution to magnetization from orbital effects. We determine the foil
electron spin polarization at 294~K to be 0.08020$\pm$0.00018 (@4~T applied
field) for iron and 0.018845$\pm0.000053$ (@2~T applied field) for nickel.


title:
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and
  Quantization

abstract:
  Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve
state-of-the-art performance on many generative NLP tasks. However, such models
pose a great challenge in resource-constrained scenarios owing to their large
memory requirements and high latency. To alleviate this issue, we propose to
jointly distill and quantize the model, where knowledge is transferred from the
full-precision teacher model to the quantized and distilled low-precision
student model. Empirical analyses show that, despite the challenging nature of
generative tasks, we were able to achieve a 16.5x model footprint compression
ratio with little performance drop relative to the full-precision counterparts
on multiple summarization and QA datasets. We further pushed the limit of
compression ratio to 27.7x and presented the performance-efficiency trade-off
for generative tasks using pre-trained models. To the best of our knowledge,
this is the first work aiming to effectively distill and quantize
sequence-to-sequence pre-trained models for language generation tasks.


title: At the End of the World: Local Dynamical Cobordism

abstract:
  The Cobordism Conjecture states that any Quantum Gravity configuration
admits, at topological level, a boundary ending spacetime. We study the
dynamical realization of cobordism, as spacetime dependent solutions of
Einstein gravity coupled to scalars containing such end-of-the-world "branes".
The latter appear in effective theory as a singularity at finite spacetime
distance at which scalars go off to infinite field space distance. We provide a
local description near the end-of-the-world branes, in which the solutions
simplify dramatically and are characterized in terms of a critical exponent,
which controls the asymptotic profiles of fields and the universal scaling
relations among the spacetime distance to the singularity, the field space
distance, and the spacetime curvature. The analysis does not rely on
supersymmetry. We study many explicit examples of such Local Dynamical
Cobordisms in string theory, including 10d massive IIA, the 10d
non-supersymmetric $USp(32)$ theory, Bubbles of Nothing, 4d $ \mathcal{N}=1 $
cosmic string solutions, the Klebanov-Strassler throat, D$p$-brane solutions,
brane configurations related to the D1/D5 systems, and small black holes. Our
framework encompasses diverse recent setups in which scalars diverge at the
core of defects, by regarding them as suitable end-of-the-world branes. We
explore the interplay of Local Dynamical Cobordisms with the Distance
Conjecture and other swampland constraints.


title:
The medium-modified $g\to c\bar{c}$ splitting function in the BDMPS-Z
  formalism

abstract:
  The formalism of Baier-Dokshitzer-Mueller-Peign\'e-Schiff and Zakharov
determines modifications of parton splittings in the QCD plasma that arise from
medium-induced gluon radiation. Here, we extend this BDMPS-Z formalism to
medium-modifications of the gluon splitting into a quark--anti-quark pair. We
derive a compact path-integral formulation that resums effects from an
arbitrary number of interactions with the medium. Analyses in the $N=1$ opacity
and the saddle point approximations reveal two phenomena: a medium-induced
momentum broadening that increases the invariant mass of quark--anti-quark
pairs, and a medium-enhanced production of such pairs. We note that both
effects are numerically sizeable if the average momentum transfer from the
medium is comparable to the quark mass. In ultra-relativistic heavy-ion
collisions, this condition is satisfied for charm quarks. We therefore focus
our numerical analysis on the medium-modification of $g\to c\bar{c}$, although
our derivation applies equally well to $g\to b\bar{b}$ and to gluons splitting
into light-flavoured quark--anti-quark pairs.


title:
A survey on GANs for computer vision: Recent research, analysis and
  taxonomy

abstract:
  In the last few years, there have been several revolutions in the field of
deep learning, mainly headlined by the large impact of Generative Adversarial
Networks (GANs). GANs not only provide an unique architecture when defining
their models, but also generate incredible results which have had a direct
impact on society. Due to the significant improvements and new areas of
research that GANs have brought, the community is constantly coming up with new
researches that make it almost impossible to keep up with the times. Our survey
aims to provide a general overview of GANs, showing the latest architectures,
optimizations of the loss functions, validation metrics and application areas
of the most widely recognized variants. The efficiency of the different
variants of the model architecture will be evaluated, as well as showing the
best application area; as a vital part of the process, the different metrics
for evaluating the performance of GANs and the frequently used loss functions
will be analyzed. The final objective of this survey is to provide a summary of
the evolution and performance of the GANs which are having better results to
guide future researchers in the field.


title:
Probing Planets with Exomoons: The Cases of Kepler-1708 b and
  Kepler-1625 b

abstract:
  The tidal interactions between a planet and moon can provide insight into the
properties of the host planet. The recent exomoon candidates Kepler-1708 b-i
and Kepler-1625 b-i are Neptune-sized satellites orbiting Jupiter-like planets
and provide an opportunity to apply such methods. We show that if the tidal
migration time is roughly equal to the age of these systems, then the tidal
dissipation factor Q for the planets Kepler-1708 b and Kepler-1625 b have
values of ~$3\times10^5-3\times10^6$ and ~$1.5\times10^5-4\times10^5$,
respectively. In each case, these are consistent with estimates for gas giant
planets. Even though some work suggests an especially large semimajor axis for
Kepler-1625 b-i, we find that this would imply a surprisingly low Q~2000 for a
gas giant unless the moon formed at essentially its current position. More
detailed predictions for the moons' initial semimajor axis could provide even
better constraints on Q, and we discuss the formation scenarios for a moon in
this context. Similar arguments can be used as more exomoons are discovered in
the future to constrain exoplanet interior properties. This could be especially
useful for exoplanets near the sub-Neptune/super-Earth radius gap where the
planet structure is uncertain.


title: Semistability of cubulated groups

abstract:
  We prove that all cubulated groups are semistable at infinity. In doing so we
prove two further results about cubulations of groups. The first of these
states that any one-ended cubulated group has a cubulation for which all
halfspaces are one-ended. The second states that any cubulated group has a
cubulation for which all quarterspaces are deep -- analogous to the fact that
passing to the essential core of a given cubulation ensures that all halfspaces
are deep.


title:
Embedding cyclic causal structures in acyclic spacetimes: no-go results
  for process matrices

abstract:
  Causality can be defined in terms of a space-time structure or based on
information-theoretic structures, which correspond to very different notions of
causation. When analysing physical experiments, these notions must be put
together in a compatible manner. The process matrix framework describes quantum
indefinite causal structures in the information-theoretic sense, but the
physicality of such processes remains an open question. At the same time, there
are several experiments in Minkowski spacetime (which implies a definite
spacetime notion of causality) that claim to have implemented indefinite
information-theoretic causal structures, suggesting an apparent tension between
these notions. To address this, we develop a general framework that
disentangles these two notions and characterises their compatibility in
scenarios where quantum systems may be delocalised over a spacetime. The
framework first describes a composition of quantum maps through feedback loops,
and then the embedding of the resulting (possibly cyclic) signalling structure
in an acyclic spacetime. Relativistic causality then corresponds to the
compatibility of the two notions of causation. We reformulate the process
matrix framework here and derive a number of no-go results for physical
implementations of process matrices in a spacetime. These reveal that it is
impossible to physically implement indefinite causal order processes with
spacetime localised systems, and also characterise the degree to which they
must be delocalised. Further, we show that any physical implementation of an
indefinite order process can ultimately be fine-grained to one that admits a
fixed acyclic information-theoretic causal order that is compatible with the
spacetime causal order, thus resolving the apparent paradox. Our work sheds
light on the operational meaning of indefinite causal structures which we
discuss in detail.


title: Introducing DMRadio-GUT, a search for GUT-scale QCD axions

abstract:
  The QCD axion is a leading dark matter candidate that emerges as part of the
solution to the strong CP problem in the Standard Model. The coupling of the
axion to photons is the most common experimental probe, but much parameter
space remains unexplored. The coupling of the QCD axion to the Standard Model
scales linearly with the axion mass; therefore, the highly-motivated region
0.4-120 neV, corresponding to a GUT-scale axion, is particularly difficult to
reach. This paper presents the design requirements for a definitive search for
GUT-scale axions and reviews the technological advances needed to enable this
program.


title: The Assouad dimension of self-affine measures on sponges

abstract:
  We derive upper and lower bounds for the Assouad and lower dimensions of
self-affine measures in $\mathbb{R}^d$ generated by diagonal matrices and
satisfying suitable separation conditions. The upper and lower bounds always
coincide for $d=2,3$ yielding precise explicit formulae for the dimensions.
Moreover, there are easy to check conditions guaranteeing that the bounds
coincide for $d \geq 4$.
  An interesting consequence of our results is that there can be a `dimension
gap' for such self-affine constructions, even in the plane. That is, we show
that for some self-affine carpets of `Bara\'nski type' the Assouad dimension of
all associated self-affine measures strictly exceeds the Assouad dimension of
the carpet by some fixed $\delta>0$ depending only on the carpet. We also
provide examples of self-affine carpets of `Bara\'nski type' where there is no
dimension gap and in fact the Assouad dimension of the carpet is equal to the
Assouad dimension of a carefully chosen self-affine measure.


title:
Design Considerations for 2D Dirac-Source FETs: Device Parameters,
  Non-Idealities and Benchmarking

abstract:
  Dirac-source field-effect transistors (DS-FETs) have been proposed as
promising candidates for low-power switching devices by leveraging the Dirac
cone of graphene as a low-pass energy filter. In particular, using
two-dimensional (2D) materials as the channel in a DS-FET is of interest for
ultimate scaling purposes. In this paper, we investigate the design
considerations for 2D DS-FETs using ballistic simulations based on Landauer
formalism. We study the impact of several key device parameters on the device
performance, such as graphene doping, Schottky barrier heights, and effective
mass of the 2D channel. In addition, we study the impact of non-idealities on
the performance of DS-FETs, such as graphene disorder and rethermalization, as
well as ways to mitigate them. Finally, we benchmark the performance of DS-FETs
for different channel materials, providing a guide for the proper choice of
material for 2D DS-FETs.


title:
Photonuclear reactions $^{nat}$Ni($\gamma$,xn)$^{57}$Ni and
  $^{nat}$Ni($\gamma$,xn)$^{56}$Ni in the energy range E$_{{\gamma}max}$ =
  35...94 MeV

abstract:
  The total bremsstrahlung flux-averaged cross-sections
<$\sigma$(E$_{{\gamma}max}$)> for the photonuclear reactions
$^{nat}$Ni($\gamma$,xn)$^{57}$Ni and $^{nat}$Ni($\gamma$,xn)$^{56}$Ni have been
measured in the range of end-point energies E$_{{\gamma}max}$ = 35...94 MeV.
The experiments were performed with the beam from the NSC KIPT electron linear
accelerator LUE-40 with the use of the activation and off-line $\gamma$-ray
spectrometric technique. The calculation of the flux-average cross-sections
<$\sigma$(E$_{{\gamma}max}$)>$_{th}$ was carried out using the partial
cross-section values $\sigma$(E) computed with the TALYS1.95 code and
bremsstrahlung $\gamma$-flux calculated by GEANT4.9.2. The experimental results
are compared with theoretical calculations and data from the literature. The
obtained <$\sigma$(E$_{{\gamma}max}$)> values expand the energy range of
previously known experimental data.


title:
An Adaptive Optics Census of Companions to Northern Stars Within 25 pc
  with Robo-AO

abstract:
  In order to assess the multiplicity statistics of stars across spectral types
and populations in a volume-limited sample, we censused nearby stars for
companions with Robo-AO. We report on observations of 1157 stars of all
spectral types within 25 pc with decl. $>-13^{\circ}$ searching for tight
companions. We detected 154 companion candidates with separations ranging from
$\sim$0.15$''$ to 4.0$''$ and magnitude differences up to
$\Delta$m$_{\textit{i'}}\le$7 using the robotic adaptive optics instrument
Robo-AO. We confirmed physical association from Gaia EDR3 astrometry for 53 of
the companion candidates, 99 remain to be confirmed, and 2 were ruled out as
background objects. We complemented the high-resolution imaging companion
search with a search for co-moving objects with separations out to 10,000 AU in
Gaia EDR3, which resulted in an additional 147 companions registered. Of the
301 total companions reported in this study, 49 of them are new discoveries.
Out of the 191 stars with significant acceleration measurements in the
Hipparcos-Gaia catalog of accelerations, we detect companions around 115 of
them, with the significance of the acceleration increasing as the companion
separation decreases. From this survey, we report the following multiplicity
fractions (compared to literature values): 40.9%$\pm$3.0% (44%) for FGK stars
and 28.2%$\pm$2.3% (27%) for M stars, as well as higher-order fractions of
5.5%$\pm$1.1% (11%) and 3.9%$\pm$0.9% (5%) for FGK stars and M type stars,
respectively.


title:
Word-of-Mouth on Action: Analysis of Operational Decisions When
  Customers are Resentful

abstract:
  Word-of-Mouth (WoM) communication, via online reviews, plays an important
role in customers' purchasing decisions. As such, retailers must consider the
impact of WoM to manage customer perceptions and future demand. In this paper,
we consider an online shopping system with premium and regular customers.
Building on the behavioral and operations management literature, we model
customer preferences based on the perceived service quality indicated by WoM
and integrate this into the retailer's operational problem to determine a
shipment policy. First, we study the e-tailer's problem when she has no
knowledge of WoM, and only reacts to the changes in demand. We analyze the
long-term behavior of customer demand and show that potential market size and
customer sensitivity are the key parameters determining this behavior. Then, we
build a model to integrate knowledge of WoM into operational decision making
and partially characterize the optimal solution. We show that (i) beating the
competition in the market may not always benefit the company under strict
operational constraints as it can result in undesired customer switching
behavior, (ii) relaxations in operational constraints may hurt profitability
due to the associated difficulties of managing perceptions, and (iii) seeking a
stationary policy can lead to suboptimal solutions, therefore cyclic policies
should also be considered when appropriate.


title:
Energy analysis of bursting Hindmarsh-Rose neurons with time-delayed
  coupling

abstract:
  Mathematical modeling is an important tool to study the role of delay in
neural systems and to evaluate its effects on the signaling activity of coupled
neurons. Models for delayed neurons are often used to represent the dynamics of
real neurons, but rarely to assess the energy required to maintain these
dynamics. In this work, we address these questions from an energy perspective
by considering a pair of Hindmarsh-Rose burst neurons coupled by reciprocal
time-delayed coupling with electrical and chemical synapses. We examine the
average energy consumption required to maintain cooperative behavior and
quantify the contribution of synapses to total energy consumption. We show that
unlike electrical coupling, where the time delay appears to reduce the
instantaneous average relative weight of the synaptic contribution, in chemical
coupling this average synaptic contribution appears to be much higher in
delayed coupling than in instantaneous coupling, except at certain values of
coupling strength where the instantaneous synaptic contribution is more
important.


title:
A framework for scale-sensitive, spatially explicit accuracy assessment
  of binary built-up surface layers

abstract:
  To better understand the dynamics of human settlements, thorough knowledge of
the uncertainty in geospatial built-up surface datasets is critical. While
frameworks for localized accuracy assessments of categorical gridded data have
been proposed to account for the spatial non-stationarity of classification
accuracy, such approaches have not been applied to (binary) built-up land data.
Such data differs from other data such as land cover data, due to considerable
variations of built-up surface density across the rural-urban continuum
resulting in switches of class imbalance, causing sparsely populated confusion
matrices based on small underlying sample sizes. In this paper, we aim to fill
this gap by testing common agreement measures for their suitability and
plausibility to measure the localized accuracy of built-up surface data. We
examine the sensitivity of localized accuracy to the assessment support, as
well as to the unit of analysis, and analyze the relationships between local
accuracy and density / structure-related properties of built-up areas, across
rural-urban trajectories and over time. Our experiments are based on the
multi-temporal Global Human Settlement Layer (GHSL) and a reference database
for the state of Massachusetts (USA). We find strong variation of suitability
among commonly used agreement measures, and varying levels of sensitivity to
the assessment support. We then apply our framework to assess localized GHSL
data accuracy over time from 1975 to 2014. Besides increasing accuracy along
the rural-urban gradient, we find that accuracy generally increases over time,
mainly driven by peri-urban densification processes in our study area.
Moreover, we find that localized densification measures derived from the GHSL
tend to overestimate peri-urban densification processes that occurred between
1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.


title:
A note on hyperelliptic curves with ordinary reduction over 2-adic
  fields

abstract:
  We study a class of semistable ordinary hyperelliptic curves over 2-adic
fields and the special fibre of their minimal regular model. We show that these
curves can be controlled using `cluster pictures', similarly to the case of odd
residue characteristic.


title:
Effective Dynamics of Interacting Fermions from Semiclassical Theory to
  the Random Phase Approximation

abstract:
  I review results concerning the derivation of effective equations for the
dynamics of interacting Fermi gases in a high-density regime of mean-field
type. Three levels of effective theories, increasing in precision, can be
distinguished: the semiclassical theory given by the Vlasov equation, the
mean-field theory given by the Hartree-Fock equation, and the description of
the dominant effects of non-trivial entanglement by the random phase
approximation. Particular attention is given to the discussion of admissible
initial data, and I present an example of a realistic quantum quench that can
be approximated by Hartree-Fock dynamics.


title: Primordial helium-3 redux: The helium isotope ratio of the Orion nebula

abstract:
  We report the first direct measurement of the helium isotope ratio, 3He/4He,
outside of the Local Interstellar Cloud, as part of science verification
observations with the upgraded CRyogenic InfraRed Echelle Spectrograph
(CRIRES). Our determination of 3He/4He is based on metastable HeI* absorption
along the line-of-sight towards Tet02 Ori A in the Orion Nebula. We measure a
value 3He/4He=(1.77+/-0.13)x10^{-4}, which is just ~40 per cent above the
primordial relative abundance of these isotopes, assuming the Standard Model of
particle physics and cosmology, (3He/4He)_p = (1.257+/-0.017)x10^-4. We
calculate a suite of galactic chemical evolution simulations to study the
Galactic build up of these isotopes, using the yields from Limongi & Chieffi
(2018) for stars in the mass range M=8-100 M_sun and Lagarde (2011,2012) for
M=0.8-8 M_sun. We find that these simulations simultaneously reproduce the
Orion and protosolar 3He/4He values if the calculations are initialized with a
primordial ratio (3He/4He)_p=(1.043+/-0.089)x10^-4. Even though the quoted
error does not include the model uncertainty, this determination agrees with
the Standard Model value to within ~2sigma. We also use the present-day
Galactic abundance of deuterium (D/H), helium (He/H), and 3He/4He to infer an
empirical limit on the primordial 3He abundance, (3He/H)_p <
(1.09+/-0.18)x10^-5, which also agrees with the Standard Model value. We point
out that it is becoming increasingly difficult to explain the discrepant
primordial 7Li/H abundance with non-standard physics, without breaking the
remarkable simultaneous agreement of three primordial element ratios (D/H,
4He/H, and 3He/4He) with the Standard Model values.


title: Accretion Variability as a Guide to Stellar Mass Assembly

abstract:
  Variable accretion in young stellar objects reveals itself photometrically
and spectroscopically over a continuum of timescales and amplitudes. Most
dramatic are the large outbursts (e.g., FU Ori, V1647 Ori, and EX Lup type
events), but more frequent are the less coherent, smaller burst-like variations
in accretion rate. Improving our understanding of time-variable accretion
directly addresses the fundamental question of how stars gain their masses. We
review variability phenomena, as characterized from observations across the
wavelength spectrum, and how those observations probe underlying physical
conditions. The diversity of observed lightcurves and spectra at optical and
infrared wavelengths defies a simple classification of outbursts and bursts
into well-defined categories. Mid-infrared and sub-millimeter wavelengths are
sensitive to lower-temperature phenomena, and it is currently unclear if
observed flux variations probe similar or distinct physics relative to the
shorter wavelengths. We highlight unresolved issues and emphasize the value of
spectroscopy, multiwavelength studies, and ultimately patience in using
variable accretion to understand stellar mass assembly.


title: Efficient Classification of Long Documents Using Transformers

abstract:
  Several methods have been proposed for classifying long textual documents
using Transformers. However, there is a lack of consensus on a benchmark to
enable a fair comparison among different approaches. In this paper, we provide
a comprehensive evaluation of the relative efficacy measured against various
baselines and diverse datasets -- both in terms of accuracy as well as time and
space overheads. Our datasets cover binary, multi-class, and multi-label
classification tasks and represent various ways information is organized in a
long text (e.g. information that is critical to making the classification
decision is at the beginning or towards the end of the document). Our results
show that more complex models often fail to outperform simple baselines and
yield inconsistent performance across datasets. These findings emphasize the
need for future studies to consider comprehensive baselines and datasets that
better represent the task of long document classification to develop robust
models.


title: Covariant singularities: a brief review

abstract:
  The Hawking-Penrose theorem is not covariant under field redefinitions.
Should the invariance under such transformations be a true principle in Nature,
spacetime singularities become dubious objects. We here review the concept of
covariant singularities, that is, singularities that are invariant under both
spacetime diffeomorphisms and field redefinitions.


title: The symmetric plank problem, revisited

abstract:
  We present a streamlined proof of K. Ball's symmetric plank theorem in
$\mathbb{R}^d$, which solves the affine plank problem raised by Th. Bang for
symmetric convex bodies.


title: Healthy Twitter discussions? Time will tell

abstract:
  Studying misinformation and how to deal with unhealthy behaviours within
online discussions has recently become an important field of research within
social studies. With the rapid development of social media, and the increasing
amount of available information and sources, rigorous manual analysis of such
discourses has become unfeasible. Many approaches tackle the issue by studying
the semantic and syntactic properties of discussions following a supervised
approach, for example using natural language processing on a dataset labeled
for abusive, fake or bot-generated content. Solutions based on the existence of
a ground truth are limited to those domains which may have ground truth.
However, within the context of misinformation, it may be difficult or even
impossible to assign labels to instances. In this context, we consider the use
of temporal dynamic patterns as an indicator of discussion health. Working in a
domain for which ground truth was unavailable at the time (early COVID-19
pandemic discussions) we explore the characterization of discussions based on
the the volume and time of contributions. First we explore the types of
discussions in an unsupervised manner, and then characterize these types using
the concept of ephemerality, which we formalize. In the end, we discuss the
potential use of our ephemerality definition for labeling online discourses
based on how desirable, healthy and constructive they are.


title: 2D Ising Field Theory in a Magnetic Field: The Yang-Lee Singularity

abstract:
  We study Ising Field Theory (the scaling limit of Ising model near the Curie
critical point) in pure imaginary external magnetic field. We put particular
emphasis on the detailed structure of the Yang-Lee edge singularity. While the
leading singular behavior is controlled by the Yang-Lee fixed point ($=$
minimal CFT ${\cal M}_{2/5}$), the fine structure of the subleading singular
terms is determined by the effective action which involves a tower of
irrelevant operators. We use numerical data obtained through the "Truncated
Free Fermion Space Approach" to estimate the couplings associated with two
least irrelevant operators. One is the operator $T{\bar T}$, and we use the
universal properties of the $T{\bar T}$ deformation to fix the contributions of
higher orders in the corresponding coupling parameter $\alpha$. Another
irrelevant operator we deal with is the descendant $L_{-4}{\bar L}_{-4}\phi$ of
the relevant primary $\phi$ in ${\cal M}_{2/5}$. The significance of this
operator is that it is the lowest dimension operator which breaks integrability
of the effective theory. We also establish analytic properties of the particle
mass $M$ ($=$ inverse correlation length) as the function of complex magnetic
field.


title:
Assessing trade-offs among electrification and grid decarbonization in a
  clean energy transition: Application to New York State

abstract:
  A modeling framework is presented to investigate trade-offs among
decarbonization from increased low-carbon electricity generation and
electrification of heating and vehicles. The model is broadly applicable but
relies on high-fidelity parameterization of existing infrastructure and
anticipated electrified loads; this study applies it to New York State where
detailed data is available. Trade-offs are investigated between end use
electrification and renewable energy deployment in terms of supply costs,
generation and storage capacities, renewable resource mix, and system
operation. Results indicate that equivalent emissions reductions can be
achieved at lower costs to the grid by prioritizing electrification with 40-70%
low-carbon electricity supply instead of aiming for complete grid
decarbonization. With 60% electrification and 50% low-carbon electricity,
approximately 1/3 emissions reductions can be achieved at current supply costs;
with only 20% electrification, 90% low-carbon electricity is required to
achieve the same emissions reductions, resulting in 43% higher grid costs. In
addition, three primary cost drivers are identified for a system undergoing
decarbonization: (1) decreasing per-unit costs of existing infrastructure with
increasing electrified demand, (2) higher in-state generation costs from
low-carbon sources relative to gas-based and hydropower generation, and (3)
increasing integration costs at high percentages of low-carbon electricity.


title: When Do Stars Go BOOM?

abstract:
  The maximum mass of a star that can produce a white dwarf (WD) is an
important astrophysical quantity. One of the best approaches to establishing
this limit is to search for WDs in young star clusters in which only massive
stars have had time to evolve and where the mass of the progenitor can be
established from the cooling time of the WD together with the age of the
cluster. Searches in young Milky Way clusters have not thus far yielded WD
members more massive than about 1.1$~M_{\odot}$, well below the Chandrasekhar
mass of $1.38~M_{\odot}$, nor progenitors with masses in excess of about
$6~M_{\odot}$. However, the hunt for potentially massive WDs that escaped their
cluster environs is yielding interesting candidates. To expand the cluster
sample further, we used HST to survey four young and massive star clusters in
the Magellanic Clouds for bright WDs that could have evolved from stars as
massive as 10$~M_{\odot}$. We located five potential WD candidates in the
oldest of the four clusters examined, the first extragalactic single WDs thus
far discovered. As these hot WDs are very faint at optical wavelengths, final
confirmation will likely have to await spectroscopy with 30-metre class
telescopes.


title: Curry and Howard Meet Borel

abstract:
  We show that an intuitionistic version of counting propositional logic
corresponds, in the sense of Curry and Howard, to an expressive type system for
the probabilistic event lambda-calculus, a vehicle calculus in which both
call-by-name and call-by-value evaluation of discrete randomized functional
programs can be simulated. Remarkably, proofs (respectively, types) do not only
guarantee that validity (respectively, termination) holds, but also reveal the
underlying probability. We finally show that by endowing the type system with
an intersection operator, one obtains a system precisely capturing the
probabilistic behavior of lambda-terms.


title:
Enriching and Characterizing T-Cell Repertoires from 3' Barcoded
  Single-Cell Whole Transcriptome Amplification Products

abstract:
  Antigen-specific T cells play an essential role in immunoregulation and
diseases such as cancer. Characterizing the T cell receptor (TCR) sequences
that encode T cell specificity is critical for elucidating the antigenic
determinants of immunological diseases and designing therapeutic remedies.
However, methods of obtaining single-cell TCR sequencing data are labor and
cost intensive, requiring cell sorting and full length single-cell
RNA-sequencing (scRNA-seq). New high-throughput 3' cell-barcoding scRNA-seq
methods can simplify and scale this process; however, they do not routinely
capture TCR sequences during library preparation and sequencing. While 5'
cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at
single-cell resolution, it requires specialized reagents which cannot be
applied to samples previously processed using 3' cell-barcoding methods. Here,
we outline a method for sequencing TCR$\alpha$ and TCR$\beta$ transcripts from
samples already processed using 3' cell-barcoding scRNA-seq platforms, ensuring
TCR recovery at a single-cell resolution. In short, a fraction of the 3'
barcoded whole transcriptome amplification (WTA) product typically used to
generate a massively parallel 3' scRNA-seq library is enriched for TCR
transcripts using biotinylated probes, and further amplified using the same
universal primer sequence from WTA. Primer extension using TCR V-region primers
and targeted PCR amplification results in a 3' barcoded single-cell
CDR3-enriched library that can be sequenced with custom sequencing primers.
Coupled with 3' scRNA-seq of the same WTA, this method enables simultaneous
analysis of single-cell transcriptomes and TCR sequences which can help
interpret inherent heterogeneity among antigen-specific T cells and salient
disease biology. This method can be adapted to enrich other transcripts of
interest from 3' and 5' barcoded WTA libraries.