title:
A Comparison of Approaches for Imbalanced Classification Problems in the
  Context of Retrieving Relevant Documents for an Analysis

abstract:
  One of the first steps in many text-based social science studies is to
retrieve documents that are relevant for the analysis from large corpora of
otherwise irrelevant documents. The conventional approach in social science to
address this retrieval task is to apply a set of keywords and to consider those
documents to be relevant that contain at least one of the keywords. But the
application of incomplete keyword lists risks drawing biased inferences. More
complex and costly methods such as query expansion techniques, topic
model-based classification rules, and active as well as passive supervised
learning could have the potential to more accurately separate relevant from
irrelevant documents and thereby reduce the potential size of bias. Yet,
whether applying these more expensive approaches increases retrieval
performance compared to keyword lists at all, and if so, by how much, is
unclear as a comparison of these approaches is lacking. This study closes this
gap by comparing these methods across three retrieval tasks associated with a
data set of German tweets (Linder, 2017), the Social Bias Inference Corpus
(SBIC) (Sap et al., 2020), and the Reuters-21578 corpus (Lewis, 1997). Results
show that query expansion techniques and topic model-based classification rules
in most studied settings tend to decrease rather than increase retrieval
performance. Active supervised learning, however, if applied on a not too small
set of labeled training instances (e.g. 1,000 documents), reaches a
substantially higher retrieval performance than keyword lists.


title:
Complete complementarity relations for quantum correlations in neutrino
  oscillations

abstract:
  We analyze quantum correlations and quantum coherence in neutrino
oscillations. To this end, we exploit complete complementarity relations (CCR)
that fully characterize the interplay between different correlations encoded in
a quantum system both for pure and mixed states. We consider the CCR for
neutrino oscillations both in the case of plane-waves (pure state) and of wave
packets (mixed state). In this last case we find a complex structure of
correlations depending on the mixing angle, and we show the connection with the
non local advantage of quantum coherence, a relevant quantifier of coherence.


title:
State-selective EIT for quantum error correction in neutral atom quantum
  computers

abstract:
  We propose a way to measure the qubit state of an arbitrary sub-ensemble of
atoms in an array without significantly disturbing the quantum information in
the unmeasured atoms. The idea is to first site-selectively transfer atoms out
of the qubit basis so that one of the two states at a time is put into an
auxiliary state. Electromagnetically induced transparency (EIT) light will then
protect most states while detection light is scattered from atoms in the
auxiliary state, which is made immune to the EIT protection by angular momentum
selection rules and carefully chosen light polarization. The two states will be
measured in turn, after which it is possible to recool and return the atoms to
a qubit state. These measurements can be the basis of quantum error correction.


title: CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification

abstract:
  Automatically associating social media posts with topics is an important
prerequisite for effective search and recommendation on many social media
platforms. However, topic classification of such posts is quite challenging
because of (a) a large topic space (b) short text with weak topical cues, and
(c) multiple topic associations per post. In contrast to most prior work which
only focuses on post classification into a small number of topics ($10$-$20$),
we consider the task of large-scale topic classification in the context of
Twitter where the topic space is $10$ times larger with potentially multiple
topic associations per Tweet. We address the challenges above by proposing a
novel neural model, CTM that (a) supports a large topic space of $300$ topics
and (b) takes a holistic approach to tweet content modeling -- leveraging
multi-modal content, author context, and deeper semantic cues in the Tweet. Our
method offers an effective way to classify Tweets into topics at scale by
yielding superior performance to other approaches (a relative lift of
$\mathbf{20}\%$ in median average precision score) and has been successfully
deployed in production at Twitter.


title:
An untrained deep learning method for reconstructing dynamic magnetic
  resonance images from accelerated model-based data

abstract:
  The purpose of this work is to implement physics-based regularization as a
stopping condition in tuning an untrained deep neural network for
reconstructing MR images from accelerated data. The ConvDecoder neural network
was trained with a physics-based regularization term incorporating the spoiled
gradient echo equation that describes variable-flip angle (VFA) data.
Fully-sampled VFA k-space data were retrospectively accelerated by factors of
R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the
proposed regularization (CD+r), locally low-rank (LR) reconstruction, and
compressed sensing with L1-wavelet regularization (L1). Final images from CD+r
training were evaluated at the \emph{argmin} of the regularization loss;
whereas the CD, LR, and L1 reconstructions were chosen optimally based on
ground truth data. The performance measures used were the normalized root-mean
square error, the concordance correlation coefficient (CCC), and the structural
similarity index (SSIM). The CD+r reconstructions, chosen using the stopping
condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs
(p=0.95) across R and that were significantly higher than the L1 SSIMs
(p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were
greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps,
respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a
loss of spatially refined details compared to CD+r. We conclude that the use of
an untrained neural network together with a physics-based regularization loss
shows promise as a measure for determining the optimal stopping point in
training without relying on fully-sampled ground truth data.


title: Toward Modeling Creative Processes for Algorithmic Painting

abstract:
  This paper proposes a framework for computational modeling of artistic
painting algorithms, inspired by human creative practices. Based on examples
from expert artists and from the author's own experience, the paper argues that
creative processes often involve two important components: vague, high-level
goals (e.g., "make a good painting"), and exploratory processes for discovering
new ideas. This paper then sketches out possible computational mechanisms for
imitating those elements of the painting process, including underspecified loss
functions and iterative painting procedures with explicit task decompositions.


title: GITz: Graphene-assisted IRS Design for THz Communication

abstract:
  Graphene-based intelligent reflecting surface (GIRS) has been proved to
provide a promising propagation environment to enhance the quality of high
frequency terahertz (THz) wireless communication. In this paper, we
characterize GIRS for THz communication (GITz) using material specific
parameters of graphene to tune the reflection of the incident wave at IRS. In
particular, we propose a GITz design model considering the incident signal
frequency material level parameters like conductivity, Fermi-level, patch width
to control the reflection amplitude (RA) at the communication receiver. We have
obtained the closed-form expression of RA for an accurate design and
characterization of GIRS, which is incomplete in the existing research due to
the inclusion of only phase-shift. The numerical simulation results demonstrate
the effectiveness of the proposed characterization by providing key insights.


title: Modeling and Correcting Bias in Sequential Evaluation

abstract:
  We consider the problem of sequential evaluation, in which an evaluator
observes candidates in a sequence and assigns scores to these candidates in an
online, irrevocable fashion. Motivated by the psychology literature that has
studied sequential bias in such settings -- namely, dependencies between the
evaluation outcome and the order in which the candidates appear -- we propose a
natural model for the evaluator's rating process that captures the lack of
calibration inherent to such a task. We conduct crowdsourcing experiments to
demonstrate various facets of our model. We then proceed to study how to
correct sequential bias under our model by posing this as a statistical
inference problem. We propose a near-linear time, online algorithm for this
task and prove guarantees in terms of two canonical ranking metrics, matched
with lower bounds demonstrating optimality in a certain sense. Our algorithm
outperforms the de facto method of using the rankings induced by the reported
scores.


title:
Local Stochastic Bilevel Optimization with Momentum-Based Variance
  Reduction

abstract:
  Bilevel Optimization has witnessed notable progress recently with new
emerging efficient algorithms and has been applied to many machine learning
tasks such as data cleaning, few-shot learning, and neural architecture search.
However, little attention has been paid to solve the bilevel problems under
distributed setting. Federated learning (FL) is an emerging paradigm which
solves machine learning tasks over distributed-located data. FL problems are
challenging to solve due to the heterogeneity and communication bottleneck.
However, it is unclear how these challenges will affect the convergence of
Bilevel Optimization algorithms. In this paper, we study Federated Bilevel
Optimization problems. Specifically, we first propose the FedBiO, a
deterministic gradient-based algorithm and we show it requires
$O(\epsilon^{-2})$ number of iterations to reach an $\epsilon$-stationary
point. Then we propose FedBiOAcc to accelerate FedBiO with the momentum-based
variance-reduction technique under the stochastic scenario. We show FedBiOAcc
has complexity of $O(\epsilon^{-1.5})$. Finally, we validate our proposed
algorithms via the important Fair Federated Learning task. More specifically,
we define a bilevel-based group fair FL objective. Our algorithms show superior
performances compared to other baselines in numerical experiments.


title: Phase transitions for charged planar solitons in AdS

abstract:
  In this work we study the phase transitions between the planar charged AdS
black hole and the planar charged soliton. The planar soliton is obtained as a
double analytic continuation of the charged black hole metric, which also
involves analytically continuing the electric charge. We show that there are
phase transitions between both solutions depending on the electric potential,
magnetic flux and temperature. The analysis is carried out in the
Grand-Canonical ensemble.


title:
Novel critical phenomena in compressible polar active fluids: A
  functional renormalization group approach

abstract:
  Active matter is not only relevant to living matter and diverse
nonequilibrium systems, but also constitutes a fertile ground for novel
physics. Indeed, dynamic renormalization group (DRG) analyses have uncovered
many new universality classes (UCs) in polar active fluids - an archetype of
active matter systems. However, due to the inherent technical difficulties in
the DRG methodology, almost all previous studies have been restricted to polar
active fluids in the incompressible or infinitely compressible (i.e.,
Malthusian) limits, and, when the $\epsilon$-expansion was used in conjunction,
to the one-loop level. Here, we use functional renormalization group methods to
bypass some of these difficulties and unveil for the first time novel critical
behavior in compressible polar active fluids, and calculate the corresponding
critical exponents beyond the one-loop level. Specifically, focusing on a
multicritical region of the system, we find three novel UCs and quantify their
associate scaling behavior near the upper critical dimension $d_c = 6$.


title: Improved Weakly Private Information Retrieval Codes

abstract:
  We study the problem of weakly private information retrieval (W-PIR), where a
user wishes to retrieve a desired message from $N$ non-colluding servers in a
way that the privacy leakage regarding the desired message's identity is less
than or equal to a threshold. We propose a new code construction which
significantly improves upon the best known result in the literature, based on
the following critical observation. In previous constructions, for the extreme
case of minimum download, the retrieval pattern is to download the message
directly from $N-1$ servers; however this causes leakage to all these $N-1$
servers, and a better retrieval pattern for this extreme case is to download
the message directly from a single server. The proposed code construction
allows a natural transition to such a pattern, and for both the maximal leakage
metric and the mutual information leakage metric, significant improvements can
be obtained. We provide explicit solutions, in contrast to a previous work by
Lin et al., where only numerical solutions were obtained.


title:
A New Approach to Compute Information Theoretic Outer Bounds and Its
  Application to Regenerating Codes

abstract:
  The study of the fundamental limits of information systems is a central theme
in information theory. Both the traditional analytical approach and the
recently proposed computational approach have significant limitations, where
the former is mainly due to its reliance on human ingenuity, and the latter due
to its exponential memory and computational complexity. In this work, we
propose a new computational approach to tackle the problem with much lower
memory and computational requirements, which can naturally utilize certain
intuitions, but also can maintain the strong computational advantage of the
existing computational approach. A reformulation of the underlying optimization
problem is first proposed, which converts the large linear program to a maximin
problem. This leads to an iterative solving procedure, which uses the LP dual
to carry over learned evidence between iterations. The key in the reformulated
problem is the selection of good information inequalities, with which a relaxed
LP can be formed. A particularly powerful intuition is a potentially optimal
code construction, and we provide a method that directly utilizes it in the new
algorithm. As an application, we derive a tighter outer bound for the
storage-repair tradeoff for the $(6,5,5)$ regenerating code problem, which
involves at least 30 random variables and is impossible to compute with the
previously known computational approach.


title:
Conditioned backward and forward times of diffusion with stochastic
  resetting: a renewal theory approach

abstract:
  Stochastic resetting can be naturally understood as a renewal process
governing the evolution of an underlying stochastic process. In this work, we
formally derive well-known results of diffusion with resets from a renewal
theory perspective. Parallel to the concepts from renewal theory, we introduce
the conditioned backward and forward times for stochastic processes with
resetting to be the times since the last and until the next reset, given that
the current state of the system is known. We focus on studying diffusion under
Markovian and non-Markovian resetting. For these cases, we find the conditioned
backward and forward time PDFs, comparing them with numerical simulations of
the process. In particular, we find that for power-law reset time PDFs with
asymptotic form $\varphi(t)\sim t^{-1-\alpha}$, significant changes in the
properties of the conditioned backward and forward times happen at half-integer
values of $\alpha$. This is due to the composition between the long-time
scaling of diffusion $P(x,t)\sim 1/\sqrt{t}$ and the reset time PDF.


title: Automatic Segmentation of Aircraft Dents in Point Clouds

abstract:
  Dents on the aircraft skin are frequent and may easily go undetected during
airworthiness checks, as their inspection process is tedious and extremely
subject to human factors and environmental conditions. Nowadays, 3D scanning
technologies are being proposed for more reliable, human-independent
measurements, yet the process of inspection and reporting remains laborious and
time consuming because data acquisition and validation are still carried out by
the engineer. For full automation of dent inspection, the acquired point cloud
data must be analysed via a reliable segmentation algorithm, releasing humans
from the search and evaluation of damage. This paper reports on two
developments towards automated dent inspection. The first is a method to
generate a synthetic dataset of dented surfaces to train a fully convolutional
neural network. The training of machine learning algorithms needs a substantial
volume of dent data, which is not readily available. Dents are thus simulated
in random positions and shapes, within criteria and definitions of a Boeing 737
structural repair manual. The noise distribution from the scanning apparatus is
then added to reflect the complete process of 3D point acquisition on the
training. The second proposition is a surface fitting strategy to convert 3D
point clouds to 2.5D. This allows higher resolution point clouds to be
processed with a small amount of memory compared with state-of-the-art methods
involving 3D sampling approaches. Simulations with available ground truth data
show that the proposed technique reaches an intersection-over-union of over
80%. Experiments over dent samples prove an effective detection of dents with a
speed of over 500 000 points per second.


title:
Global semiconcavity of solutions to first-order Hamilton-Jacobi
  equations with state constraints

abstract:
  We focus on the global semiconcavity of solutions to first-order
Hamilton--Jacobi equations with state constraints, especially for the
Hamiltonian $H(x, \beta):=|\beta|^p-f(x)$ with $p \in (1, 2]$. We first show
that the solution is locally semiconcave, and the semiconcavity constant at
each point depends on the first time a corresponding minimizing curve emanating
from this point hits the boundary. Then, with appropriate conditions on $Df$,
we prove that for any such minimizing curve, the time it takes to hit the
boundary of the domain is $+\infty$, and as a consequence, the solution is
globally semiconcave. Moreover, the condition on $Df$ is essentially optimal
with examples in one-dimensional space. The proofs employ the Euler-Lagrange
equations and techniques in weak KAM theory.


title: Data assimilation with agent-based models using Markov chain sampling

abstract:
  Every day, weather forecasting centres around the world make use of noisy,
incomplete observations of the atmosphere to update their weather forecasts.
This process is known as data assimilation, data fusion or state estimation and
is best expressed as Bayesian inference: given a set of observations, some
prior beliefs and a model of the target system, what is the probability
distribution of some set of unobserved quantities or latent variables at some
time, possibly in the future?
  While data assimilation has developed rapidly in some areas, relatively
little progress has been made in performing data assimilation with agent-based
models. This has hampered the use of agent-based models to make quantitative
claims about real-world systems.
  Here we present an algorithm that uses Markov-Chain-Monte-Carlo methods to
generate samples of the parameters and trajectories of an agent-based model
over a window of time given a set of possibly noisy, aggregated and incomplete
observations of the system. This can be used as-is, or as part of a data
assimilation cycle or sequential-MCMC algorithm.
  Our algorithm is applicable to time-stepping, agent-based models whose agents
have a finite set of states and a finite number of ways of acting on the world.
As presented the algorithm is only practical for agents with a few bytes of
internal state although we discuss ways of removing this restriction. We
demonstrate the algorithm by performing data assimilation with an agent-based,
spatial predator-prey model.


title: Functors in Lorentzian geometry -- three variations on a theme

abstract:
  We review three examples of functors from Lorentzian categories and their
applications in finiteness results, singularity theorems and boundary
constructions. The third example is a novel functor from the category of
ordered measure spaces to the category of Lorentzian pre-length spaces in the
sense of Kunzinger-S\"amann.


title:
GUE via Frobenius Manifolds. I. From Matrix Gravity to Topological
  Gravity and Back

abstract:
  Dubrovin establishes the relationship between the GUE partition function and
the partition function of Gromov--Witten invariants of the complex projective
line. In this paper, we give a direct proof of Dubrovin's result. We also
present in a diagram the recent progress on topological gravity and matrix
gravity.


title: Cosmological evolution with negative energy densities

abstract:
  For general number of spatial dimensions we investigate the cosmological
dynamics driven by a cosmological constant and by a source with barotropic
equation of state. It is assumed that for both those sources the energy density
can be either positive or negative. Exact solutions of the cosmological
equations are provided for flat models. For models with curved space and with
zero cosmological constant the general solutions are expressed in terms of the
hypergeometric function. The qualitative evolution is described for all values
of the equation of state parameter. We specify the values of that parameter and
the combinations of the signs for the cosmological constant and matter energy
density for which the cosmological dynamics is nonsingular. An example is
considered with positive cosmological constant and negative matter energy
density induced by the polarization of the hyperbolic vacuum.


title:
OmniKnight: Multilingual Neural Machine Translation with
  Language-Specific Self-Distillation

abstract:
  Although all-in-one-model multilingual neural machine translation (MNMT) has
achieved remarkable progress in recent years, its selected best overall
checkpoint fails to achieve the best performance simultaneously in all language
pairs. It is because that the best checkpoints for each individual language
pair (i.e., language-specific best checkpoints) scatter in different epochs. In
this paper, we present a novel training strategy dubbed Language-Specific
Self-Distillation (LSSD) for bridging the gap between language-specific best
checkpoints and the overall best checkpoint. In detail, we regard each
language-specific best checkpoint as a teacher to distill the overall best
checkpoint. Moreover, we systematically explore three variants of our LSSD,
which perform distillation statically, selectively, and adaptively.
Experimental results on two widely-used benchmarks show that LSSD obtains
consistent improvements towards all language pairs and achieves the
state-of-the-art


title:
Machine Learning-Assisted Multi-Objective Optimization of Battery
  Manufacturing from Synthetic Data Generated by Physics-Based Simulations

abstract:
  The optimization of the electrodes manufacturing process constitutes one of
the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells,
in particular for automotive applications. Because LIB electrode manufacturing
is a complex process involving multiple steps and interdependent parameters, we
have shown in our previous works that 3D-resolved physics-based models
constitute very useful tools to provide insights about the impact of the
manufacturing process parameters on the textural and performance properties of
the electrodes. However, their high-throughput application for electrode
properties optimization and inverse design of manufacturing parameters is
limited due to the high computational cost associated with this kind of model.
In this work, we tackle this issue by proposing an innovative approach,
supported by a deterministic machine learning (ML)-assisted pipeline for
multi-objective optimization of LIB electrode properties and inverse design of
its manufacturing process. Firstly, the pipeline generates a synthetic dataset
from physics-based simulations with low discrepancy sequences, that allow to
sufficiently represent the manufacturing parameters space. Secondly, the
generated dataset is used to train deterministic ML models for the
implementation of a fast multi-objective optimization, to identify an optimal
electrode and the manufacturing parameters to adopt in order to fabricate it.
Lastly, this electrode was successfully fabricated experimentally, proving that
our modeling pipeline prediction is physical-relevant. Here, we demonstrate our
pipeline for the simultaneous minimization of the electrode tortuosity factor
and maximization of the effective electronic conductivity, the active surface
area, and the density, all being parameters that affect the Li$^+$
(de-)intercalation kinetics, ionic, and electronic transport properties of the
electrode.


title: Magnetic freeze-out and anomalous Hall effect in ZrTe$_5$

abstract:
  The ultra-quantum limit is achieved when a magnetic field confines an
electron gas in its lowest spin-polarised Landau level. Here we show that in
this limit, electron doped ZrTe$_5$ shows a metal-insulator transition followed
by a sign change of the Hall and Seebeck effects at low temperature. We
attribute this transition to a magnetic freeze-out of charge carriers on the
ionised impurities. The reduction of the charge carrier density gives way to an
anomalous Hall response of the spin-polarised electrons. This behaviour, at
odds with the usual magnetic freeze-out scenario, occurs in this Dirac metal
because of its tiny Fermi energy, extremely narrow band gap and a large
$g$-factor. We discuss the different possible sources (intrinsic or extrinsic)
for this anomalous Hall contribution.


title:
A survey of sodium absorption in ten giant exoplanets with
  high-resolution transmission spectroscopy

abstract:
  The alkali metal sodium (Na) is one of the most commonly detected chemical
species in the upper atmospheres of giant exoplanets. In this work we conducted
a homogeneous survey of Na in a diverse sample of ten highly irradiated giant
exoplanets using high-resolution transmission spectroscopy. Our sample includes
nine planets with previous Na detections and one new detection. We confirm
previous detections and assess multiple approaches for deriving Na line
properties from high-resolution transmission spectra. The homogeneously
measured sodium line depths were used to constrain the atmospheric heights
($H_{\text{Na}}$) with respect to the planetary radii ($R_{\text{p}}$). We
assess an empirical trend describing the relative atmospheric height
($H_{\text{Na}}/R_{\text{p}}$) as a function of planetary equilibrium
temperature ($T_{\text{eq}}$) and surface gravity ($g$), in which
$H_{\text{Na}}/R_{\text{p}}$ decreases exponentially with $\xi \propto
gT_{\text{eq}}$, approaching a constant at large $\xi$. We also report the
sodium D2/D1 line ratios across our sample and find that seven targets have
line ratios which are consistent with unity. Finally, we measured net
blueshifted offsets of the sodium absorption lines from their rest frame
wavelengths for all ten planets, corresponding to day-night wind velocities of
a few km s$^{-1}$. This suggests that the broad sample of exoplanets share
common underlying processes which govern atmospheric dynamics. Our study
highlights a promising avenue for using high-resolution transmission
spectroscopy to further our understanding of how atmospheric characteristics
vary over a diverse sample of exoplanets.


title:
Practical Saccade Prediction for Head-Mounted Displays: Towards a
  Comprehensive Model

abstract:
  Eye-tracking technology is an integral component of new display devices such
as virtual and augmented reality headsets. Applications of gaze information
range from new interaction techniques exploiting eye patterns to
gaze-contingent digital content creation. However, system latency is still a
significant issue in many of these applications because it breaks the
synchronization between the current and measured gaze positions. Consequently,
it may lead to unwanted visual artifacts and degradation of user experience. In
this work, we focus on foveated rendering applications where the quality of an
image is reduced towards the periphery for computational savings. In foveated
rendering, the presence of latency leads to delayed updates to the rendered
frame, making the quality degradation visible to the user. To address this
issue and to combat system latency, recent work proposes to use saccade landing
position prediction to extrapolate the gaze information from delayed
eye-tracking samples. While the benefits of such a strategy have already been
demonstrated, the solutions range from simple and efficient ones, which make
several assumptions about the saccadic eye movements, to more complex and
costly ones, which use machine learning techniques. Yet, it is unclear to what
extent the prediction can benefit from accounting for additional factors. This
paper presents a series of experiments investigating the importance of
different factors for saccades prediction in common virtual and augmented
reality applications. In particular, we investigate the effects of saccade
orientation in 3D space and smooth pursuit eye-motion (SPEM) and how their
influence compares to the variability across users. We also present a simple
yet efficient correction method that adapts the existing saccade prediction
methods to handle these factors without performing extensive data collection.


title: Toward Robust Spiking Neural Network Against Adversarial Perturbation

abstract:
  As spiking neural networks (SNNs) are deployed increasingly in real-world
efficiency critical applications, the security concerns in SNNs attract more
attention. Currently, researchers have already demonstrated an SNN can be
attacked with adversarial examples. How to build a robust SNN becomes an urgent
issue. Recently, many studies apply certified training in artificial neural
networks (ANNs), which can improve the robustness of an NN model promisely.
However, existing certifications cannot transfer to SNNs directly because of
the distinct neuron behavior and input formats for SNNs. In this work, we first
design S-IBP and S-CROWN that tackle the non-linear functions in SNNs' neuron
modeling. Then, we formalize the boundaries for both digital and spike inputs.
Finally, we demonstrate the efficiency of our proposed robust training method
in different datasets and model architectures. Based on our experiment, we can
achieve a maximum $37.7\%$ attack error reduction with $3.7\%$ original
accuracy loss. To the best of our knowledge, this is the first analysis on
robust training of SNNs.


title: Automated Learning of Interpretable Models with Quantified Uncertainty

abstract:
  Interpretability and uncertainty quantification in machine learning can
provide justification for decisions, promote scientific discovery and lead to a
better understanding of model behavior. Symbolic regression provides inherently
interpretable machine learning, but relatively little work has focused on the
use of symbolic regression on noisy data and the accompanying necessity to
quantify uncertainty. A new Bayesian framework for genetic-programming-based
symbolic regression (GPSR) is introduced that uses model evidence (i.e.,
marginal likelihood) to formulate replacement probability during the selection
phase of evolution. Model parameter uncertainty is automatically quantified,
enabling probabilistic predictions with each equation produced by the GPSR
algorithm. Model evidence is also quantified in this process, and its use is
shown to increase interpretability, improve robustness to noise, and reduce
overfitting when compared to a conventional GPSR implementation on both
numerical and physical experiments.


title:
Emergent universal statistics in nonequilibrium systems with dynamical
  scale selection

abstract:
  Pattern-forming nonequilibrium systems are ubiquitous in nature, from driven
quantum matter and biological life forms to atmospheric and interstellar gases.
Identifying universal aspects of their far-from-equilibrium dynamics and
statistics poses major conceptual and practical challenges due to the absence
of energy and momentum conservation laws. Here, we experimentally and
theoretically investigate the statistics of prototypical nonequilibrium systems
in which inherent length-scale selection confines the dynamics near a mean
energy hypersurface. Guided by spectral analysis of the field modes and scaling
arguments, we derive a universal nonequilibrium distribution for kinetic field
observables. We confirm the predicted energy distributions in experimental
observations of Faraday surface waves, and in quantum chaos and active
turbulence simulations. Our results indicate that pattern dynamics and
transport in driven physical and biological matter can often be described
through monochromatic random fields, suggesting a path towards a unified
statistical field theory of nonequilibrium systems with length-scale selection.


title:
SynopSet: Multiscale Visual Abstraction Set for Explanatory Analysis of
  DNA Nanotechnology Simulations

abstract:
  We propose a new abstraction set (SynopSet) that has a continuum of visual
representations for the explanatory analysis of molecular dynamics simulations
(MDS) in the DNA nanotechnology domain. By re-purposing the commonly used
progress bar and designing novel visuals, as well as transforming the data from
the domain format to a format that better fits the newly designed visuals, we
compose this new set of representations. This set is also designed to be
capable of showing all spatial and temporal details, and all structural
complexity, or abstracting these to various degrees, enabling both the slow
playback of the simulation for detailed examinations or very fast playback for
an overview that helps to efficiently identify events of interest, as well as
several intermediate levels between these two extremes. For any pair of
successive representations, we demonstrate smooth, continuous transitions,
enabling users to keep track of relevant information from one representation to
the next. By providing multiple representations suited to different temporal
resolutions and connected by smooth transitions, we enable time-efficient
simulation analysis, giving users the opportunity to examine and present
important phases in great detail, or leverage abstract representations to go
over uneventful phases much faster. Domain experts can thus gain actionable
insight about their simulations and communicate it in a much shorter time.
Further, the novel representations are more intuitive and also enable
researchers unfamiliar with MDS analysis graphs to better understand the
simulation results. We assessed the effectiveness of SynopSet on 12 DNA
nanostructure simulations together with a domain expert. We have also shown
that our set of representations can be systematically located in a
visualization space, dubbed SynopSpace.

categories: q-bio.QM cs.CV cs.GR physics.bio-ph physics.comp-ph


title:
AutoFi: Towards Automatic WiFi Human Sensing via Geometric
  Self-Supervised Learning

abstract:
  WiFi sensing technology has shown superiority in smart homes among various
sensors for its cost-effective and privacy-preserving merits. It is empowered
by Channel State Information (CSI) extracted from WiFi signals and advanced
machine learning models to analyze motion patterns in CSI. Many learning-based
models have been proposed for kinds of applications, but they severely suffer
from environmental dependency. Though domain adaptation methods have been
proposed to tackle this issue, it is not practical to collect high-quality,
well-segmented and balanced CSI samples in a new environment for adaptation
algorithms, but randomly captured CSI samples can be easily collected. In this
paper, we firstly explore how to learn a robust model from these low-quality
CSI samples, and propose AutoFi, an automatic WiFi sensing model based on a
novel geometric self-supervised learning algorithm. The AutoFi fully utilizes
unlabeled low-quality CSI samples that are captured randomly, and then
transfers the knowledge to specific tasks defined by users, which is the first
work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented
on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge
from randomly collected CSI samples into human gait recognition and achieves
state-of-the-art performance. Furthermore, we simulate cross-task transfer
using public datasets to further demonstrate its capacity for cross-task
learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory
results on activity recognition and gesture recognition without any prior
training. We believe that the AutoFi takes a huge step toward automatic WiFi
sensing without any developer engagement while overcoming the cross-site issue.


title:
Liquid interface shaping and transport phenomena induced by spatially
  inhomogeneous vibrations

abstract:
  Vibrations can dynamically stabilize otherwise unstable liquid interfaces and
produce new dynamic equilibria, called vibro-equilibria. Typically, the
vibrations are homogeneous in the liquid and the liquid interface remains
approximately flat. Here, we produce controlled vertical vibration gradients by
taking advantage of the resonant oscillations of sinking submerged bubbles. The
locally increased amplitude of the vibrations induces a local elevation of the
liquid interface that can be controlled and engineered. The mean elevation of
the interface can be linked theoretically with the local vibration amplitude by
a simple formula that is tested experimentally. In addition, the transport of a
floating body at the interface can be induced by secondary flows triggered by
the amplitude gradients of the liquid vibrations.


title:
A general approach to deriving diagnosability results of interconnection
  networks

abstract:
  We generalize an approach to deriving diagnosability results of various
interconnection networks in terms of the popular $g$-good-neighbor and
$g$-extra fault-tolerant models, as well as mainstream diagnostic models such
as the PMC and the MM* models.
  As demonstrative examples, we show how to follow this constructive, and
effective, process to derive the $g$-extra diagnosabilities of the hypercube,
the $(n, k)$-star, and the arrangement graph. These results agree with those
achieved individually, without duplicating structure independent technical
details. Some of them come with a larger applicable range than those already
known, and the result for the arrangement graph in terms of the MM* model is
new.


title: Group separation strikes back

abstract:
  We consider group languages, which are those recognized by a finite group, or
equivalently by a permutation automaton (i.e., each letter induces a
permutation on the set of states). We investigate the separation problem for
this class: given two regular languages as input, decide whether there exists a
group language containing the first, while being disjoint from the second. We
prove that covering, which generalizes separation, is decidable. So far, this
result could only be obtained as a corollary of an independent algebraic
theorem by Ash, whose proof relies on involved algebraic notions. In contrast,
our algorithm and its proof rely exclusively on standard notions from automata
theory. Additionally, we prove that covering is also decidable for two strict
subclasses: languages recognized by commutative groups, and modulo languages.
Both algorithms rely on the construction made for group languages, but the
proof for commutative groups builds on independent ideas.


title:
A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex
  Stochastic Optimization

abstract:
  In this paper we present and analyze a zeroth-order proximal stochastic
gradient method suitable for the minimization of weakly convex stochastic
optimization problems. We consider non-smooth and nonlinear stochastic
composite problems, for which (sub-)gradient information might be unavailable.
The proposed algorithm utilizes the well-known Gaussian smoothing technique,
which yields unbiased zeroth-order gradient estimators of a related partially
smooth surrogate problem. This allows us to employ a standard proximal
stochastic gradient scheme for the approximate solution of the surrogate
problem, which is determined by a single smoothing parameter, and without the
utilization of first-order information. We provide state-of-the-art convergence
rates for the proposed zeroth-order method, utilizing a much simpler analysis
that requires less restrictive assumptions, as compared with a double Gaussian
smoothing alternative recently analyzed in the literature. The proposed method
is numerically compared against its (sub-)gradient-based counterparts to
demonstrate its viability on a standard phase retrieval problem. Further, we
showcase the usefulness and effectiveness of our method for the unique setting
of automated hyper-parameter tuning. In particular, we focus on automatically
tuning the parameters of optimization algorithms by minimizing a novel
heuristic model. The proposed approach is tested on a proximal alternating
direction method of multipliers for the solution of
$\mathcal{L}_1/\mathcal{L}_2$-regularized PDE-constrained optimal control
problems, with evident empirical success.


title:
Multi-view Geometry: Correspondences Refinement Based on Algebraic
  Properties

abstract:
  Correspondences estimation or feature matching is a key step in the
image-based 3D reconstruction problem. In this paper, we propose two algebraic
properties for correspondences. The first is a rank deficient matrix construct
from the correspondences of at least nine key-points on two images (two-view
correspondences) and the second is also another rank deficient matrix built
from the other correspondences of six key-points on at least five images
(multi-view correspondences). To our knowledge, there are no theoretical
results for multi-view correspondences prior to this paper. To obtain accurate
correspondences, multi-view correspondences seem to be more useful than
two-view correspondences. From these two algebraic properties, we propose an
refinement algorithm for correspondences. This algorithm is a combination of
correspondences refinement, outliers recognition and missing key-points
recovery. Real experiments from the project of reconstructing Buddha statue
show that the proposed refinement algorithm can reduce the average error from
77 pixels to 55 pixels on the correspondences estimation. This drop is
substantial and it validates our results.


title: Coarse sheaf cohomology

abstract:
  A certain Grothendieck topology assigned to a metric space gives rise to a
sheaf cohomology theory which sees the coarse structure of the space. Already
constant coefficients produce interesting cohomology groups. In degree 0 they
see the number of ends of the space. In this paper a resolution of the constant
sheaf via cochains is developed. It serves to be a valuable tool for computing
cohomology. In addition coarse homotopy invariance of coarse cohomology with
constant coefficients is established. This property can be used to compute
cohomology of Riemannian manifolds. The Higson corona of a proper metric space
is shown to reflect sheaves and sheaf cohomology. Thus we can use topological
tools on compact Hausdorff spaces in our computations. In particular if the
asymptotic dimension of a proper metric space is finite then higher cohomology
groups vanish. We compute a few examples. As it turns out finite abelian groups
are best suited as coefficients on finitely generated groups.


title: Atomtronic multi-terminal Aharonov-Bohm interferometer

abstract:
  Manipulating the flow of cold atoms promises practical devices for quantum
technologies. Here, we study a three-terminal atomic ring circuit pierced by a
synthetic magnetic flux as a powerful multi-functional device. The flux
controls the transport through the ring via the Aharonov-Bohm effect. The
device can measure the flux in-situ via a flux-induced transition of the
reflections from negative Andreev-like to positive. By exciting the system we
break an emergent symmetry of the flux. This allows us to direct the atomic
current into specific output ports via the flux, realizing a flexible
non-reciprocal switch to connect multiple atomic systems together. Our system
allows us to study the time-dependent Aharonov-Bohm effect in a controllable
manner. While the Aharonov-Bohm effect for time-dependent magnetic fields and
electrons is an open problem, for cold atoms we observe a clear periodic
modulation of the current with a time-dependent synthetic flux. This allows us
to realize an atomic frequency generator as well as a converter between direct
and alternating current. Our results can be directly implemented in experiments
to create novel quantum technological devices.


title: Novel cosmological bounds on thermally-produced axion-like particles

abstract:
  We constrain the coupling of thermally-produced axion-like particles (here
axions) with photons and gluons, using data from the cosmic microwave
background (CMB) spectra and baryon acoustic oscillations. The axion possesses
an explicit soft breaking mass term and it is produced thermally in the early
Universe from either axion-photon or axion-gluon processes, accounting for the
recent progresses in the field. We derive the most stringent bounds on the
axion-gluon coupling to date on the mass range considered $10^{-4} \lesssim
m_a/{\rm eV} \lesssim 100$, superseding the current bounds from SN1987A. The
bounds on the axion-photon coupling are competitive with the results from the
CAST collaboration for the axion mass $m_a \gtrsim 3\,$eV. We comment on the
forecast reaches that will be available given the sensitivity of future CMB-S4
missions.


title:
Asymptotic Independence of the Sum and Maximum of Dependent Random
  Variables with Applications to High-Dimensional Tests

abstract:
  For a set of dependent random variables, without stationary or the strong
mixing assumptions, we derive the asymptotic independence between their sums
and maxima. Then we apply this result to high-dimensional testing problems,
where we combine the sum-type and max-type tests and propose a novel test
procedure for the one-sample mean test, the two-sample mean test and the
regression coefficient test in high-dimensional setting. Based on the
asymptotic independence between sums and maxima, the asymptotic distributions
of test statistics are established. Simulation studies show that our proposed
tests have good performance regardless of data being sparse or not. Examples on
real data are also presented to demonstrate the advantages of our proposed
methods.


title:
Dynamic and Context-Dependent Stock Price Prediction Using Attention
  Modules and News Sentiment

abstract:
  The growth of machine-readable data in finance, such as alternative data,
requires new modeling techniques that can handle non-stationary and
non-parametric data. Due to the underlying causal dependence and the size and
complexity of the data, we propose a new modeling approach for financial time
series data, the $\alpha_{t}$-RIM (recurrent independent mechanism). This
architecture makes use of key-value attention to integrate top-down and
bottom-up information in a context-dependent and dynamic way. To model the data
in such a dynamic manner, the $\alpha_{t}$-RIM utilizes an exponentially
smoothed recurrent neural network, which can model non-stationary times series
data, combined with a modular and independent recurrent structure. We apply our
approach to the closing prices of three selected stocks of the S\&P 500
universe as well as their news sentiment score. The results suggest that the
$\alpha_{t}$-RIM is capable of reflecting the causal structure between stock
prices and news sentiment, as well as the seasonality and trends. Consequently,
this modeling approach markedly improves the generalization performance, that
is, the prediction of unseen data, and outperforms state-of-the-art networks
such as long short-term memory models.


title:
Adaptive Traffic Signal Control for Developing Countries Using Fused
  Parameters Derived from Crowd-Source Data

abstract:
  Advancement of mobile technologies has enabled economical collection,
storage, processing, and sharing of traffic data. These data are made
accessible to intended users through various application program interfaces
(API) and can be used to recognize and mitigate congestion in real time. In
this paper, quantitative (time of arrival) and qualitative (color-coded
congestion levels) data were acquired from the Google traffic APIs. New
parameters that reflect heterogeneous traffic conditions were defined and
utilized for real-time control of traffic signals while maintaining the
green-to-red time ratio. The proposed method utilizes a congestion-avoiding
principle commonly used in computer networking. Adaptive congestion levels were
observed on three different intersections of Delhi (India), in peak hours. It
showed good variation, hence sensitive for the control algorithm to act
efficiently. Also, simulation study establishes that proposed control algorithm
decreases waiting time and congestion. The proposed method provides an
inexpensive alternative for traffic sensing and tracking technologies.


title: Mesoscopic M\"obius ladder lattices as non-Hermitian model systems

abstract:
  While classic quantum chaos originated from the idea to set into context
nonlinear physics and Hermitian quantum mechanics, non-Hermitian models have
enhanced the field in recent years. At the same time, low-dimensional effective
matrix models have proven to be a powerful tool in accessing the physical
properties of a system in a semiquantitative manner. Here, we focus on two
realizations of non-Hermitian physics in mesoscopic systems. First, we consider
spiral optical microcavities in which the asymmetric scattering between
whispering gallery modes induces the non-Hermitian behaviour. Second, for
parity-time (PT) symmetric ladder lattices we compare circular and M\"obius
geometries. We find the effective coupling between even and odd parity modes to
be symmetric but complex in a microscopically derived 2 x 2 matrix model,
resulting in non-Hermitian behaviour as well. Most importantly, the M\"obius
topology acts like a scatterer that induces a qualitatively new form of
(avoided) level crossing - a PT-broken phase terminated by exceptional points -
resulting from the symmetric but non-Hermitian coupling.


title: Exact renormalization groups and transportation of measures

abstract:
  This note provides a new perspective on Polchinski's equation for the exact
renormalization group by explaining how it gives rise, via the multiscale
Bakry-\'Emery criterion, to Lipschitz transport maps between Gaussian free
fields and Euclidean quantum field theories. Consequently, many functional
inequalities can be verified for the latter field theories, going beyond the
current known results.


title: Cross-Domain Object Detection with Mean-Teacher Transformer

abstract:
  Recently, DEtection TRansformer (DETR), an end-to-end object detection
pipeline, has achieved promising performance. However, it requires large-scale
labeled data and suffers from domain shift, especially when no labeled data is
available in the target domain. To solve this problem, we propose an end-to-end
cross-domain detection transformer based on the mean teacher knowledge transfer
(MTKT), which transfers knowledge between domains via pseudo labels. To improve
the quality of pseudo labels in the target domain, which is a crucial factor
for better domain adaptation, we design three levels of source-target feature
alignment strategies based on the architecture of the Transformer, including
domain query-based feature alignment (DQFA), bi-level-graph-based prototype
alignment (BGPA), and token-wise image feature alignment (TIFA). These three
levels of feature alignment match the global, local, and instance features
between source and target, respectively. With these strategies, more accurate
pseudo labels can be obtained, and knowledge can be better transferred from
source to target, thus improving the cross-domain capability of the detection
transformer. Extensive experiments demonstrate that our proposed method
achieves state-of-the-art performance on three domain adaptation scenarios,
especially the result of Sim10k to Cityscapes scenario is remarkably improved
from 52.6 mAP to 57.9 mAP. Code will be released.


title: Towards URLLC with Proactive HARQ Adaptation

abstract:
  In this work, we propose a dynamic decision maker algorithm to improve the
proactive HARQ protocol for beyond 5G networks. Based on Lyapunov stochastic
optimization, our adaptation control framework dynamically selects the number
of proactive retransmissions for intermittent URLLC traffic scenarios under
time-varying channel conditions without requiring any prior knowledge
associated with this stochastic process. It then better exploits the trade-off
between Radio Access Network (RAN) latency, reliability and resource
efficiency, which is still limited in its realization on current HARQ designs.
We then evaluate the performance of several HARQ strategies and show that our
proposal further improves latency over the reactive regime without affecting
the resource efficiency such as fixed proactive retransmission while
maintaining target reliability.


title:
On a conjecture that strengthens the $k$-factor case of Kundu's
  $k$-factor Theorem

abstract:
  In 1974, Kundu showed that for even $n$ if $\pi=(d_{1},\ldots,d_{n})$ is a
non-increasing degree sequence such that
$\mathcal{D}_{k}(\pi)=(d_{1}-k,\ldots,d_{n}-k)$ is graphic, then some
realization of $\pi$ has a $k$-factor. In 1978, Brualdi and then Busch et al.\
in 2012, conjectured that not only is there a $k$-factor, but there is
$k$-factor that can be partitioned into $k$ edge-disjoint $1$-factors. Busch et
al.\ showed that if $k\leq 3$, $d_{1}\leq \frac{n}{2}+1$, or $d_{n}\geq
\frac{n}{2}+k-2$, then the conjecture holds. Later, Seacrest extended this to
$k\leq 5$. We explore this conjecture by first developing new tools that
generalize edge-exchanges. With these new tools, we can drop the assumption
$\mathcal{D}_{k}(\pi)$ is graphic and show that if $d_{d_{1}-d_{n}+k}\geq
d_{1}-d_{n}+k-1$, then $\pi$ has a realization with $k$ edge-disjoint
$1$-factors. From this we show that if $d_{n}\geq \frac{d_{1}+k-1}{2}$ or
$\mathcal{D}_{k}(\pi)$ is graphic and $d_{1}\leq \max
\{n/2+d_{n}-k,(n+d_{n})/2\}$, then the conjecture holds. With a different
approach we show the conjecture holds when $\mathcal{D}_{k}(\pi)$ is graphic
and $d_{\min\{\frac{n}{2},m(\pi)-1\}}> \big\lceil\frac{n+3k-8}{2}\big\rceil$
where $m(\pi)=\max\{i : d_{i}\geq i-1\}$. For $r\leq 2$, Busch et al.\ and
later Seacrest for $r\leq 4$ showed that if $\mathcal{D}_{k}(\pi)$ is graphic,
then there is a realization with a $k$-factor whose edges can be partitioned
into a $(k-r)$-factor and $r$ edge-disjoint $1$-factors. We improve this for
any $r\leq \max\big\{\min\{k,4\},\frac{k+3}{3}\big\}$. As a result, we can show
that if $\mathcal{D}_{k}(\pi)$ is graphic, then there is a realization with at
least $2\big\lfloor \frac{k}{3}\big\rfloor$ edge-disjoint $1$-factors.


title:
Implementation of an efficient, portable and platform-agnostic
  cryptocurrency mining algorithm for Internet of Things devices

abstract:
  Recently, there has been a remarkable amount of research being done in both,
the fields of Blockchain and Internet of Things (IoT). Blockchain technology
synergises well with IoT, solving key problems such as privacy, concerns with
interoperability and security. However, the consensus mechanisms that allows
trustless parties to maintain an agreement, the same algorithms that underpins
cryptocurrency mining, are usually extremely computationally expensive, making
implementation on low-power IoT devices difficult. More importantly, mining
requires downloading and synchronizing hundred of gigabytes worth of blocks
which is far beyond the capabilities of most IoT devices. In this paper, we
present an efficient, portable and platform-agnostic cryptocurrency mining
algorithm using the Stratum protocol to avoid downloading the entire
blockchain. We implement the algorithm in four different platforms- PC, ESP32,
an emulator and an old PlayStation Portable (PSP) to demonstrate that it is
indeed possible for any device to mine cryptocurrencies with no assumptions
except the ability to connect to the internet. To make sure of ease of
portability on any platform and for reproducibility of the reported results we
make the implementation publicly available with detailed instructions at:
https://anonymous.4open.science/r/cryptominer.


title:
Intelligent Trajectory Design for RIS-NOMA aided Multi-robot
  Communications

abstract:
  A novel reconfigurable intelligent surface-aided multi-robot network is
proposed, where multiple mobile robots are served by an access point (AP)
through non-orthogonal multiple access (NOMA). The goal is to maximize the
sum-rate of whole trajectories for multi-robot system by jointly optimizing
trajectories and NOMA decoding orders of robots, phase-shift coefficients of
the RIS, and the power allocation of the AP, subject to predicted initial and
final positions of robots and the quality of service (QoS) of each robot. To
tackle this problem, an integrated machine learning (ML) scheme is proposed,
which combines long short-term memory (LSTM)-autoregressive integrated moving
average (ARIMA) model and dueling double deep Q-network (D$^{3}$QN) algorithm.
For initial and final position prediction for robots, the LSTM-ARIMA is able to
overcome the problem of gradient vanishment of non-stationary and non-linear
sequences of data. For jointly determining the phase shift matrix and robots'
trajectories, D$^{3}$QN is invoked for solving the problem of action value
overestimation. Based on the proposed scheme, each robot holds a global optimal
trajectory based on the maximum sum-rate of a whole trajectory, which reveals
that robots pursue long-term benefits for whole trajectory design. Numerical
results demonstrated that: 1) LSTM-ARIMA model provides high accuracy
predicting model; 2) The proposed D$^{3}$QN algorithm can achieve fast average
convergence; 3) The RIS with higher resolution bits offers a bigger sum-rate of
trajectories than lower resolution bits; and 4) RIS-NOMA networks have superior
network performance compared to RIS-aided orthogonal counterparts.


title:
The zeroth law of black hole thermodynamics in arbitrary higher
  derivative theories of gravity

abstract:
  We consider diffeomorphism invariant theories of gravity with arbitrary
higher derivative terms in the Lagrangian as corrections to the leading two
derivative theory of Einstein's general relativity. We construct a proof of the
zeroth law of black hole thermodynamics in such theories. We assume that a
stationary black hole solution in an arbitrary higher derivative theory can be
obtained by starting with the corresponding stationary solution in general
relativity and correcting it order by order in a perturbative expansion in the
coupling constants of the higher derivative Lagrangian. We prove that surface
gravity remains constant on its horizon when computed for such stationary black
holes, which is the zeroth law. We argue that the constancy of surface gravity
on the horizon is related to specific components of the equations of motion in
such theories. We further use a specific boost symmetry of the near horizon
space-time of the stationary black hole to constrain the off-shell structure of
the equations of motion. Our proof for the zeroth law is valid up to arbitrary
order in the expansion in the higher derivative couplings.


title: Learning Enriched Features for Fast Image Restoration and Enhancement

abstract:
  Given a degraded input image, image restoration aims to recover the missing
high-quality image content. Numerous applications demand effective image
restoration, e.g., computational photography, surveillance, autonomous
vehicles, and remote sensing. Significant advances in image restoration have
been made in recent years, dominated by convolutional neural networks (CNNs).
The widely-used CNN-based methods typically operate either on full-resolution
or on progressively low-resolution representations. In the former case, spatial
details are preserved but the contextual information cannot be precisely
encoded. In the latter case, generated outputs are semantically reliable but
spatially less accurate. This paper presents a new architecture with a holistic
goal of maintaining spatially-precise high-resolution representations through
the entire network, and receiving complementary contextual information from the
low-resolution representations. The core of our approach is a multi-scale
residual block containing the following key elements: (a) parallel
multi-resolution convolution streams for extracting multi-scale features, (b)
information exchange across the multi-resolution streams, (c) non-local
attention mechanism for capturing contextual information, and (d) attention
based multi-scale feature aggregation. Our approach learns an enriched set of
features that combines contextual information from multiple scales, while
simultaneously preserving the high-resolution spatial details. Extensive
experiments on six real image benchmark datasets demonstrate that our method,
named as MIRNet-v2 , achieves state-of-the-art results for a variety of image
processing tasks, including defocus deblurring, image denoising,
super-resolution, and image enhancement. The source code and pre-trained models
are available at https://github.com/swz30/MIRNetv2


title: Non-Markovian dynamical heat engines

abstract:
  We discuss whether, and under which conditions, it is possible to realize a
heat engine simply by dynamically modulating the couplings between the quantum
working medium and thermal reservoirs. For that purpose, we consider the
paradigmatic model of a quantum harmonic oscillator, exposed to a minimal
modulation, that is, a monochromatic driving of the coupling to only one of the
thermal baths. We show that in this setup non-Markovianity of the bath is a
necessary condition to obtain a heat engine. In addition, we identify suitable
structured environments for the engine to approach the ideal Carnot efficiency.
Our results open up new possibilities for the use of non-Markovian open quantum
systems for the construction and optimization of quantum thermal machines.


title: Unique Characterisability and Learnability of Temporal Instance Queries

abstract:
  We aim to determine which temporal instance queries can be uniquely
characterised by a (polynomial-size) set of positive and negative temporal data
examples. We start by considering queries formulated in fragments of
propositional linear temporal logic LTL that correspond to conjunctive queries
(CQs) or extensions thereof induced by the until operator. Not all of these
queries admit polynomial characterisations but by restricting them further to
path-shaped queries we identify natural classes that do. We then investigate
how far the obtained characterisations can be lifted to temporal knowledge
graphs queried by 2D languages combining LTL with concepts in description
logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope
of description logic constructors can destroy polynomial characterisability, we
obtain general transfer results for the case when description logic
constructors are within the scope of temporal operators. Finally, we apply our
characterisations to establish (polynomial) learnability of temporal instance
queries using membership queries in the active learning framework.


title: Episodic Memory Question Answering

abstract:
  Egocentric augmented reality devices such as wearable glasses passively
capture visual data as a human wearer tours a home environment. We envision a
scenario wherein the human communicates with an AI agent powering such a device
by asking questions (e.g., where did you last see my keys?). In order to
succeed at this task, the egocentric AI assistant must (1) construct
semantically rich and efficient scene memories that encode spatio-temporal
information about objects seen during the tour and (2) possess the ability to
understand the question and ground its answer into the semantic memory
representation. Towards that end, we introduce (1) a new task - Episodic Memory
Question Answering (EMQA) wherein an egocentric AI assistant is provided with a
video sequence (the tour) and a question as an input and is asked to localize
its answer to the question within the tour, (2) a dataset of grounded questions
designed to probe the agent's spatio-temporal understanding of the tour, and
(3) a model for the task that encodes the scene as an allocentric, top-down
semantic feature map and grounds the question into the map to localize the
answer. We show that our choice of episodic scene memory outperforms naive,
off-the-shelf solutions for the task as well as a host of very competitive
baselines and is robust to noise in depth, pose as well as camera jitter. The
project page can be found at: https://samyak-268.github.io/emqa .


title: A Counterexample to the Generalisation of Witten's Conjecture

abstract:
  This note provides a counterexample to a conjecture by March\'e about the
structure of the Kauffman bracket skein module for closed compact oriented
3-manifolds over the ring of Laurent polynomials.


title:
Entanglement Signature of Hinge Arcs, Fermi Arcs, and Crystalline
  Symmetry Protection in Higher-Order Weyl Semimetals

abstract:
  The existence of $1/2$ modes in the entanglement spectrum (ES) has been shown
to be a powerful quantum-informative signature of boundary states of gapped
topological phases of matter, e.g., topological insulators and topological
superconductors, where the finite bulk gap allows us to establish a
crystal-clear correspondence between $1/2$ modes and boundary states. Here we
investigate the recently proposed higher-order Weyl semimetals (HOWSM), where
bulk supports gapless higher-order Weyl nodes and boundary supports hinge arcs
and Fermi arcs. We find that the aim of unambiguously identifying higher-order
boundary states ultimately drives us to make full use of eigen quantities of
the entanglement Hamiltonian: ES as well as Schmidt vectors (entanglement
wavefunctions, abbr. EWF). We demonstrate that, while both hinge arcs and Fermi
arcs contribute to $1/2$ modes, the EWFs corresponding to hinge arcs and Fermi
arcs are respectively localized on the virtual hinges and surfaces of the
partition. Besides, by means of various symmetry-breaking partitions, we can
identify the minimal crystalline symmetries that protect boundary states.
Therefore, for gapless topological phases such as HOWSMs, we can combine ES and
EWF to universally identify boundary states and potential symmetry requirement.
While HOWSMs are prototypical examples of gapless phases, our work sheds light
on general theory of entanglement signature in gapless topological phases of
matter.


title:
Effective Field Theory Islands from Perturbative and Nonperturbative
  Four-Graviton Amplitudes

abstract:
  Theoretical data obtained from physically sensible field and string theory
models suggest that gravitational Effective Field Theories (EFTs) live on
islands that are tiny compared to current general bounds determined from
unitarity, causality, crossing symmetry, and a good high-energy behavior. In
this work, we present explicit perturbative and nonperturbative $2 \to 2$
graviton scattering amplitudes and their associated low-energy expansion in
spacetime dimensions $D\geq 4$ to support this notion. Our new results include
a first nonperturbative example consisting of a $D=4$, $\mathcal{N}=1$
supersymmetric field theory that is coupled weakly to gravity. We show that
this nonperturbative model lies on the same islands identified using
four-dimensional perturbative models based on string theory and
minimally-coupled matter circulating a loop. Furthermore, we generalize the
previous four-dimensional perturbative models based on string theory and
minimally-coupled massive spin-0 and spin-1 states circulating in the loop to
$D$ dimensions. Remarkably, we again find that the low-energy EFT coefficients
lie on small islands. These results offer a useful guide towards constraining
possible extensions of Einstein gravity.


title:
GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense
  Mapping

abstract:
  We present a robust and accurate depth refinement system, named GeoRefine,
for geometrically-consistent dense mapping from monocular sequences. GeoRefine
consists of three modules: a hybrid SLAM module using learning-based priors, an
online depth refinement module leveraging self-supervision, and a global
mapping module via TSDF fusion. The proposed system is online by design and
achieves great robustness and accuracy via: (i) a robustified hybrid SLAM that
incorporates learning-based optical flow and/or depth; (ii) self-supervised
losses that leverage SLAM outputs and enforce long-term geometric consistency;
(iii) careful system design that avoids degenerate cases in online depth
refinement. We extensively evaluate GeoRefine on multiple public datasets and
reach as low as $5\%$ absolute relative depth errors.


title: Cross-modal Representation Learning for Zero-shot Action Recognition

abstract:
  We present a cross-modal Transformer-based framework, which jointly encodes
video data and text labels for zero-shot action recognition (ZSAR). Our model
employs a conceptually new pipeline by which visual representations are learned
in conjunction with visual-semantic associations in an end-to-end manner. The
model design provides a natural mechanism for visual and semantic
representations to be learned in a shared knowledge space, whereby it
encourages the learned visual embedding to be discriminative and more
semantically consistent. In zero-shot inference, we devise a simple semantic
transfer scheme that embeds semantic relatedness information between seen and
unseen classes to composite unseen visual prototypes. Accordingly, the
discriminative features in the visual structure could be preserved and
exploited to alleviate the typical zero-shot issues of information loss,
semantic gap, and the hubness problem. Under a rigorous zero-shot setting of
not pre-training on additional datasets, the experiment results show our model
considerably improves upon the state of the arts in ZSAR, reaching encouraging
top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets. Code will
be made available.


title:
Some new invariants of Noetherian local rings related to square of the
  maximal ideal

abstract:
  We introduce two new invariants of a Noetherian (standard graded) local ring
$(R, \mathfrak m)$ that measure the number of generators of certain kinds of
reductions of $\mathfrak m,$ and we study their properties. Explicitly, we
consider the minimum among the number of generators of ideals $I$ such that
either $I^2 = \mathfrak m^2$ or $I \supseteq \mathfrak m^2$ holds. We
investigate subsequently the case that $R$ is the quotient of a polynomial ring
$k[x_1, \dots, x_n]$ by an ideal $I$ generated by homogeneous quadratic forms,
and we compute these invariants. We devote specific attention to the case that
$R$ is the quotient of a polynomial ring $k[x_1, \dots, x_n]$ by the edge ideal
of a finite simple graph $G.$


title: On the origin of molecular oxygen on the surface of Ganymede

abstract:
  Since its first identification on the surface of Ganymede in 1995, molecular
oxygen (O2) ice has been at the center of a scientific debate as the surface
temperature of the Jovian moon is on average well above the freezing point of
O2. Laboratory evidence suggested that solid O2 may either exist in a cold (<50
K) subsurface layer of the icy surface of Ganymede, or it is in an atmospheric
haze of the moon. Alternatively, O2 is constantly replenished at the surface
through ion irradiation of water-containing ices. A conclusive answer on the
existence of solid O2 on the surface of Ganymede is hampered by the lack of
detailed, extensive observational datasets. We present new ground-based,
high-resolution spectroscopic observations of Ganymede's surface obtained at
the Telescopio Nazionale Galileo. These are combined with dedicated laboratory
measurements of ultraviolet-visible (UV-vis) photoabsorption spectra of O2 ice,
both pure and mixed with other species of potential interest for the Galilean
satellites. Our study confirms that the two bands identified in the visible
spectra of Ganymede's surface are due to the (1,0) and (0,0) transition bands
of O2 ice. Oxygen-rich ice mixtures including water (H2O) and carbon dioxide
(CO2) can reproduce observational reflectance data of the Ganymede's surface
better than pure O2 ice in the temperature range 20-35 K. Solid H2O and CO2
also provide an environment where O2 ice can be trapped at higher temperatures
than its pure ice desorption under vacuum space conditions. Our experiments at
different temperatures show also that the (1,0)/(0,0) ratio in case of the
CO2:O2=1:2 ice mixture at 35 K has the closest value to observations, while at
30 K the (1,0)/(0,0) ratio seems to be mixture independent with the exception
of the N2:O2=1:2 ice mixture. The present work will support the ESA/JUICE
mission to the Jovian system.


title: On the Lie algebra structure of integrable derivations

abstract:
  Building on work of Gerstenhaber, we show that the space of integrable
derivations on an Artin algebra $A$ forms a Lie algebra, and a restricted Lie
algebra if $A$ contains a field of characteristic $p$. We deduce that the space
of integrable classes in $\HH^1(A)$ forms a (restricted) Lie algebra that is
invariant under derived equivalences, and under stable equivalences of Morita
type between self-injective algebras. We also provide negative answers to
questions about integrable derivations posed by Linckelmann and by Farkas,
Geiss and Marcos.


title:
Radio masers on WX UMa: hints of a Neptune-sized planet, or
  magnetospheric reconnection?

abstract:
  The nearby M dwarf WX UMa has recently been detected at radio wavelengths
with LOFAR. The combination of its observed brightness temperature and circular
polarisation fraction suggests that the emission is generated via the
electron-cyclotron maser instability. Two distinct mechanisms have been
proposed to power such emission from low-mass stars: either a sub-Alfv\'enic
interaction between the stellar magnetic field and an orbiting planet, or
reconnection at the edge of the stellar magnetosphere. In this paper, we
investigate the feasibility of both mechanisms, utilising the information about
the star's surrounding plasma environment obtained from modelling its stellar
wind. Using this information, we show that a Neptune-sized exoplanet with a
magnetic field strength of 10-100 G orbiting at ~0.034 au can accurately
reproduce the observed radio emission from the star, with corresponding orbital
periods of 7.4 days. Due to the stellar inclination, a planet in an equatorial
orbit is unlikely to transit the star. While such a planet could induce radial
velocity semi-amplitudes from 7 to 396 m s$^{-1}$, it is unlikely that this
signal could be detected with current techniques due to the activity of the
host star. The application of our planet-induced radio emission model here
illustrates its exciting potential as a new tool for identifying planet-hosting
candidates from long-term radio monitoring. We also develop a model to
investigate the reconnection-powered emission scenario. While this approach
produces less favourable results than the planet-induced scenario, it
nevertheless serves as a potential alternative emission mechanism which is
worth exploring further.


title:
A Prototype Compact Accelerator Driven Neutron Source for Canada
  Supporting Medical and Scientific Applications

abstract:
  Canada recently lost its major supply of neutron beams with the closure of
the National Research Universal reactor at Chalk River Laboratories in March
2018. This issue is further exacerbated by the closure of other reactors
abroad, which also served as centers for neutron research. Consequently, there
is a demand for new sources, both for Canada and internationally, as the global
supply shrinks. Compact accelerator driven neutron sources provide an avenue to
realize an intense source of pulsed neutron beams, with a capital cost
significantly lower than spallation sources. In an effort to close the neutron
gap in Canada, a Prototype Canadian compact accelerator driven neutron source
(PC-CANS) is proposed for installation at the University of Windsor. The
PC-CANS is envisaged to serve two neutron science instruments, a boron neutron
capture therapy (BNCT) station and a beamline for fluorine-18 radioisotope
production for positive emission tomography. To serve these diverse
applications of neutron beams, a linear accelerator solution is selected to
provide 10 MeV protons with a peak current of 10 mA within a 5\% duty cycle.
The accelerator is based on an RFQ and a DTL with a post-DTL pulsed kicker
system to simultaneously deliver macro-pulses to each end-station. The neutron
production targets for both neutron science and BNCT will be made of beryllium
and engineered to handle the high beam power density. Conceptual studies of the
accelerator and benchmarking studies of neutron production and moderation with
FLUKA and MCNP, in support of the target-moderator-reflector design are
reported


title: Adversarial Training for High-Stakes Reliability

abstract:
  In the future, powerful AI systems may be deployed in high-stakes settings,
where a single failure could be catastrophic. One technique for improving AI
safety in high-stakes settings is adversarial training, which uses an adversary
to generate examples to train on in order to achieve better worst-case
performance.
  In this work, we used a language generation task as a testbed for achieving
high reliability through adversarial training. We created a series of
adversarial training techniques -- including a tool that assists human
adversaries -- to find and eliminate failures in a classifier that filters text
completions suggested by a generator. In our simple "avoid injuries" task, we
determined that we can set very conservative classifier thresholds without
significantly impacting the quality of the filtered outputs. With our chosen
thresholds, filtering with our baseline classifier decreases the rate of unsafe
completions from about 2.4% to 0.003% on in-distribution data, which is near
the limit of our ability to measure. We found that adversarial training
significantly increased robustness to the adversarial attacks that we trained
on, without affecting in-distribution performance. We hope to see further work
in the high-stakes reliability setting, including more powerful tools for
enhancing human adversaries and better ways to measure high levels of
reliability, until we can confidently rule out the possibility of catastrophic
deployment-time failures of powerful models.


title:
An Efficient Method Generating Discrete Uniform Distribution From A
  Biased Coin

abstract:
  In this article, we propose an efficient method for generating discrete
uniform distribution on $p_1$ elements from a biased random source for a prime
number $p_1$. The method improves Dijkstra's method in
\cite{dijkstra1990making} to make it computationally efficient, and it is a
generalization of Von Neumann's method. Besides, according to the first
proposed method and the prime factorization of integer, we also design a method
generating discrete uniform distribution on any finite set from a biased random
source. The time complexity is overall sublinear $O(n/\log n)$ and hence it can
give better results on some composite numbers.


title: Error Metrics for Partially Coherent Wavefields

abstract:
  Lensless imaging methods that account for partial coherence have become very
common in the past decade. However, there are no metrics in use for comparing
partially coherent light fields, despite the widespread use of such metrics to
compare fully coherent objects and wavefields. Here, we show how reformulating
the mean squared error and Fourier ring correlation in terms of quantum state
fidelity naturally generalizes them to partially coherent wavefields. These
results fill an important gap in the lensless imaging literature and will
enable quantitative assessments of the reliability and resolution of
reconstructed partially coherent wavefields.


title:
DANBO: Disentangled Articulated Neural Body Representations via Graph
  Neural Networks

abstract:
  Deep learning greatly improved the realism of animatable human models by
learning geometry and appearance from collections of 3D scans, template meshes,
and multi-view imagery. High-resolution models enable photo-realistic avatars
but at the cost of requiring studio settings not available to end users. Our
goal is to create avatars directly from raw images without relying on expensive
studio setups and surface tracking. While a few such approaches exist, those
have limited generalization capabilities and are prone to learning spurious
(chance) correlations between irrelevant body parts, resulting in implausible
deformations and missing body parts on unseen poses. We introduce a three-stage
method that induces two inductive biases to better disentangled pose-dependent
deformation. First, we model correlations of body parts explicitly with a graph
neural network. Second, to further reduce the effect of chance correlations, we
introduce localized per-bone features that use a factorized volumetric
representation and a new aggregation function. We demonstrate that our model
produces realistic body shapes under challenging unseen poses and shows
high-quality image synthesis. Our proposed representation strikes a better
trade-off between model capacity, expressiveness, and robustness than competing
methods. Project website: https://lemonatsu.github.io/danbo.


title:
Particle motion under the conservative piece of the self-force is
  Hamiltonian

abstract:
  We consider the motion of a point particle in a stationary spacetime under
the influence of a scalar, electromagnetic or gravitational self-force. We show
that the conservative piece of the first-order self-force gives rise to
Hamiltonian dynamics, and we derive an explicit expression for the Hamiltonian
on phase space. Specialized to the Kerr spacetime, our result generalizes the
Hamiltonian function previously obtained by Fujita et. al., which is valid only
for non-resonant orbits. We discuss implications for the first law of binary
black hole mechanics.


title: End-to-End Visual Editing with a Generatively Pre-Trained Artist

abstract:
  We consider the targeted image editing problem: blending a region in a source
image with a driver image that specifies the desired change. Differently from
prior works, we solve this problem by learning a conditional probability
distribution of the edits, end-to-end. Training such a model requires
addressing a fundamental technical challenge: the lack of example edits for
training. To this end, we propose a self-supervised approach that simulates
edits by augmenting off-the-shelf images in a target domain. The benefits are
remarkable: implemented as a state-of-the-art auto-regressive transformer, our
approach is simple, sidesteps difficulties with previous methods based on
GAN-like priors, obtains significantly better edits, and is efficient.
Furthermore, we show that different blending effects can be learned by an
intuitive control of the augmentation process, with no other changes required
to the model architecture. We demonstrate the superiority of this approach
across several datasets in extensive quantitative and qualitative experiments,
including human studies, significantly outperforming prior work.


title: Improved stellar limits on a light CP-even scalar

abstract:
  We derive improved stellar luminosity limits on a generic light CP-even
scalar field $S$ mixing with the Standard Model (SM) Higgs boson from the
supernova SN1987A, the Sun, red giants (RGs) and white dwarfs (WDs). For the
first time, we include the geometric effects for the decay and absorption of
$S$ particles in the stellar interior. For SN1987A and the Sun, we also take
into account the detailed stellar profiles. We find that a broad range of the
scalar mass and mixing angle can be excluded by our updated astrophysical
constraints. For instance, SN1987A excludes $1.0\times10^{-7} \lesssim
\sin\theta \lesssim 4.0\times 10^{-5}$ and scalar mass up to 290 MeV, which
covers the cosmological blind spot with a high reheating temperature. The
updated solar limit excludes the mixing angle in the range of $3.8\times
10^{-11} < \sin\theta < 0.27$, with scalar mass up to 39 keV. The RG and WD
limits are updated to $4.3\times 10^{-13} < \sin \theta < 8.6\times 10^{-5}$
and $2.3\times 10^{-18} < \sin \theta < 3.5\times 10^{-8}$, with scalar mass up
to 398 keV and 292 keV, respectively.


title:
Observations (from 2016 to 2020) of the Geminids from different regions
  of Russia by an amateur astronomer

abstract:
  I present the results of my observations (visual and photographic) of the
Geminid meteor shower in 2016, 2018, 2019 and 2020. I observed meteors from
different regions (Moscow and Primorsky Krai) of Russia, under different
observation conditions: light pollution, Moon phases and weather. I used a DSLR
camera with a lens to photograph meteor tracks. I compare the results of my
visual observations in different years and determine the coordinates of the
meteors from the photographs to graphically demonstrate the radiant.


title:
The scope for AI-augmented interpretation of building blueprints in
  commercial and industrial property insurance

abstract:
  This report, commissioned by the WTW research network, investigates the use
of AI in property risk assessment. It (i) reviews existing work on risk
assessment in commercial and industrial properties and automated information
extraction from building blueprints; and (ii) presents an exploratory 'proof-of
concept-solution' exploring the feasibility of using machine learning for the
automated extraction of information from building blueprints to support
insurance risk assessment.


title:
Branch & Learn for Recursively and Iteratively Solvable Problems in
  Predict+Optimize

abstract:
  This paper proposes Branch & Learn, a framework for Predict+Optimize to
tackle optimization problems containing parameters that are unknown at the time
of solving. Given an optimization problem solvable by a recursive algorithm
satisfying simple conditions, we show how a corresponding learning algorithm
can be constructed directly and methodically from the recursive algorithm. Our
framework applies also to iterative algorithms by viewing them as a degenerate
form of recursion. Extensive experimentation shows better performance for our
proposal over classical and state-of-the-art approaches.


title:
A Deep Learning-based Integrated Framework for Quality-aware
  Undersampled Cine Cardiac MRI Reconstruction and Analysis

abstract:
  Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard
for cardiac function evaluation. However, cine CMR acquisition is inherently
slow and in recent decades considerable effort has been put into accelerating
scan times without compromising image quality or the accuracy of derived
results. In this paper, we present a fully-automated, quality-controlled
integrated framework for reconstruction, segmentation and downstream analysis
of undersampled cine CMR data. The framework enables active acquisition of
radial k-space data, in which acquisition can be stopped as soon as acquired
data are sufficient to produce high quality reconstructions and segmentations.
This results in reduced scan times and automated analysis, enabling robust and
accurate estimation of functional biomarkers. To demonstrate the feasibility of
the proposed approach, we perform realistic simulations of radial k-space
acquisitions on a dataset of subjects from the UK Biobank and present results
on in-vivo cine CMR k-space data collected from healthy subjects. The results
demonstrate that our method can produce quality-controlled images in a mean
scan time reduced from 12 to 4 seconds per slice, and that image quality is
sufficient to allow clinically relevant parameters to be automatically
estimated to within 5% mean absolute difference.


title:
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust
  Classification of Breast Cancer

abstract:
  Robust self-training (RST) can augment the adversarial robustness of image
classification models without significantly sacrificing models'
generalizability. However, RST and other state-of-the-art defense approaches
failed to preserve the generalizability and reproduce their good adversarial
robustness on small medical image sets. In this work, we propose the
Multi-instance RST with a drop-max layer, namely MIRST-DM, which involves a
sequence of iteratively generated adversarial instances during training to
learn smoother decision boundaries on small datasets. The proposed drop-max
layer eliminates unstable features and helps learn representations that are
robust to image perturbations. The proposed approach was validated using a
small breast ultrasound dataset with 1,190 images. The results demonstrate that
the proposed approach achieves state-of-the-art adversarial robustness against
three prevalent attacks.


title: Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI

abstract:
  Fetal brain segmentation is an important first step for slice-level motion
correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate
segmentation of the fetal brain on fetal MRI is required to achieve real-time
fetal head pose estimation and motion tracking for slice re-acquisition and
steering. To address this critical unmet need, in this work we analyzed the
speed-accuracy performance of a variety of deep neural network models, and
devised a symbolically small convolutional neural network that combines spatial
details at high resolution with context features extracted at lower
resolutions. We used multiple branches with skip connections to maintain high
accuracy while devising a parallel combination of convolution and pooling
operations as an input downsampling module to further reduce inference time. We
trained our model as well as eight alternative, state-of-the-art networks with
manually-labeled fetal brain MRI slices and tested on two sets of normal and
challenging test cases. Experimental results show that our network achieved the
highest accuracy and lowest inference time among all of the compared
state-of-the-art real-time segmentation methods. We achieved average Dice
scores of 97.99\% and 84.04\% on the normal and challenging test sets,
respectively, with an inference time of 3.36 milliseconds per image on an
NVIDIA GeForce RTX 2080 Ti. Code, data, and the trained models are available at
https://github.com/bchimagine/real_time_fetal_brain_segmentation.


title:
FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for
  Fundus Images Quality Grading

abstract:
  Objective: Ophthalmological pathologies such as glaucoma, diabetic
retinopathy and age-related macular degeneration are major causes of blindness
and vision impairment. There is a need for novel decision support tools that
can simplify and speed up the diagnosis of these pathologies. A key step in
this process is to automatically estimate the quality of the fundus images to
make sure these are interpretable by a human operator or a machine learning
model. We present a novel fundus image quality scale and deep learning (DL)
model that can estimate fundus image quality relative to this new scale.
  Methods: A total of 1,245 images were graded for quality by two
ophthalmologists within the range 1-10, with a resolution of 0.5. A DL
regression model was trained for fundus image quality assessment. The
architecture used was Inception-V3. The model was developed using a total of
89,947 images from 6 databases, of which 1,245 were labeled by the specialists
and the remaining 88,702 images were used for pre-training and semi-supervised
learning. The final DL model was evaluated on an internal test set (n=209) as
well as an external test set (n=194).
  Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute
error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary
classification model on the public DRIMDB database as an external test set the
model obtained an accuracy of 99%.
  Significance: the proposed algorithm provides a new robust tool for automated
quality grading of fundus images.


title:
ASTROMER: A transformer-based embedding for the representation of light
  curves

abstract:
  Taking inspiration from natural language embeddings, we present ASTROMER, a
transformer-based model to create representations of light curves. ASTROMER was
trained on millions of MACHO R-band samples, and it can be easily fine-tuned to
match specific domains associated with downstream tasks. As an example, this
paper shows the benefits of using pre-trained representations to classify
variable stars. In addition, we provide a python library including all
functionalities employed in this work. Our library includes the pre-trained
models that can be used to enhance the performance of deep learning models,
decreasing computational resources while achieving state-of-the-art results.


title: Large Payload Quantum Steganography Based on Cavity QED

abstract:
  A large payload quantum steganography protocol based on cavity quantum
electrodynamics (QED) is presented in the paper, which effectively uses the
evolution law of atom in cavity QED. The protocol builds up hidden channel to
transmit secret messages using entanglement swapping between one GHZ state and
one Bell state in cavity QED together with the Hadamard operation. The quantum
steganography protocol is insensitive to cavity decay and thermal field. The
capacity, imperceptibility and security against eavesdropping are analyzed in
detail in the protocol. It turns out that the protocol not only has good
imperceptibility but also possesses good security against eavesdropping. In
addition, its capacity of hidden channel achieves five bits, larger than most
of those previous quantum steganography protocols.


title:
Physics to the Rescue: Deep Non-line-of-sight Reconstruction for
  High-speed Imaging

abstract:
  Computational approach to imaging around the corner, or non-line-of-sight
(NLOS) imaging, is becoming a reality thanks to major advances in imaging
hardware and reconstruction algorithms. A recent development towards practical
NLOS imaging, Nam et al. demonstrated a high-speed non-confocal imaging system
that operates at 5Hz, 100x faster than the prior art. This enormous gain in
acquisition rate, however, necessitates numerous approximations in light
transport, breaking many existing NLOS reconstruction methods that assume an
idealized image formation model. To bridge the gap, we present a novel deep
model that incorporates the complementary physics priors of wave propagation
and volume rendering into a neural network for high-quality and robust NLOS
reconstruction. This orchestrated design regularizes the solution space by
relaxing the image formation model, resulting in a deep model that generalizes
well on real captures despite being exclusively trained on synthetic data.
Further, we devise a unified learning framework that enables our model to be
flexibly trained using diverse supervision signals, including target intensity
images or even raw NLOS transient measurements. Once trained, our model renders
both intensity and depth images at inference time in a single forward pass,
capable of processing more than 5 captures per second on a high-end GPU.
Through extensive qualitative and quantitative experiments, we show that our
method outperforms prior physics and learning based approaches on both
synthetic and real measurements. We anticipate that our method along with the
fast capturing system will accelerate future development of NLOS imaging for
real world applications that require high-speed imaging.


title:
Penrose's 1965 singularity theorem: From geodesic incompleteness to
  cosmic censorship

abstract:
  Supplementing earlier literature by e.g. Tipler, Clarke, & Ellis (1980),
Israel (1987), Thorne, (1994), Earman (1999), Senovilla & Garfinkle (2015),
Curiel (2019ab), and Landsman (2021ab), I provide a historical and conceptual
analysis of Penrose's path-breaking 1965 singularity (or incompleteness)
theorem. The emphasis is on the nature and historical origin of the assumptions
and definitions used in-or otherwise relevant to-the theorem, as well as on the
discrepancy between the (astro)physical goals of the theorem and its actual
content: even if its assumptions are met, the theorem fails to prove the
existence or formation of black holes. Penrose himself was well aware of this
gap, which he subsequently tried to overcome with his visionary and influential
cosmic censorship conjectures. Roughly speaking, to infer from (null) geodesic
incompleteness that there is a "black" object one needs weak cosmic censorship,
whereas in addition a "hole" exists (as opposed to a boundary of an extendible
space-time causing the incompleteness of geodesics) if strong cosmic censorship
holds.