title: On possible life-dispersal patterns beyond the Earth

abstract:
  We model hypothetical bio-dispersal within a single Galactic region using the
stochastic infection dynamics process, which is inspired by these local
properties of life dispersal on Earth. We split the population of stellar
systems into different categories regarding habitability and evolved them
through time using probabilistic cellular automata rules analogous to the
model. As a dynamic effect, we include the existence of natural dispersal
vectors (e.g., dust, asteroids) in a way that avoids assumptions about their
agency. By assuming that dispersal vectors have a finite velocity and range,
the model includes the parameter of 'optical depth of life spreading'. The
effect of the oscillatory infection rate on the long-term behavior of the
dispersal flux, which adds a diffusive component to its progression, is also
taken into account. We found that phase space is separated into subregions of
long-lasting transmission, rapidly terminated transmission, and a transition
region between the two. We observed that depending on the amplitude of the
oscillatory life spreading rate, life-transmission in the Galactic patch might
take on different geometrical shapes. Even if some host systems are
uninhabited, life transmission has a certain threshold, allowing a patch to be
saturated with viable material over a long period. Although stochastic
fluctuations in the local density of habitable systems allow for clusters that
can continuously infect one another, the spatial pattern disappears when life
transmission is below the observed threshold, so that transmission process is
not permanent in time. Both findings suggest that a habitable planet in a
densely populated region may remain uninfected.


title: Noise correction of large deviations with anomalous scaling

abstract:
  We present a path integral calculation of the probability distribution
associated with the time-integrated moments of the Ornstein-Uhlenbeck process
that includes the Gaussian prefactor in addition to the dominant path or
instanton term obtained in the low-noise limit. The instanton term was obtained
recently [D. Nickelsen, H. Touchette, Phys. Rev. Lett. 121, 090602 (2018)] and
shows that the large deviations of the time-integrated moments are anomalous in
the sense that the logarithm of their distribution scales nonlinearly with the
integration time. The Gaussian prefactor gives a correction to the low-noise
approximation and leads us to define an instanton variance giving some insights
as to how anomalous large deviations are created in time. The results are
compared with simulations based on importance sampling, extending our previous
results based on direct Monte Carlo simulations. We conclude by explaining why
many of the standard analytical and numerical methods of large deviation theory
fail in the case of anomalous large deviations.


title:
IF-City: Intelligible Fair City Planning to Measure, Explain and
  Mitigate Inequality

abstract:
  With the increasing pervasiveness of Artificial Intelligence (AI), many
visual analytics tools have been proposed to examine fairness, but they mostly
focus on data scientist users. Instead, tackling fairness must be inclusive and
involve domain experts with specialized tools and workflows. Thus,
domain-specific visualizations are needed for algorithmic fairness.
Furthermore, while much work on AI fairness has focused on predictive
decisions, less has been done for fair allocation and planning, which require
human expertise and iterative design to integrate myriad constraints. We
propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages
explanations of causal attribution (Why), contrastive (Why Not) and
counterfactual reasoning (What If, How To) to aid domain experts to assess and
alleviate unfairness in allocation problems. We apply the framework to fair
urban planning for designing cities that provide equal access to amenities and
benefits for diverse resident types. Specifically, we propose an interactive
visual tool, Intelligible Fair City Planner (IF-City), to help urban planners
to perceive inequality across groups, identify and attribute sources of
inequality, and mitigate inequality with automatic allocation simulations and
constraint-satisfying recommendations. We demonstrate and evaluate the usage
and usefulness of IF-City on a real neighborhood in New York City, US, with
practicing urban planners from multiple countries, and discuss generalizing our
findings, application, and framework to other use cases and applications of
fair allocation.


title: Generalisation and the Risk--Entropy Curve

abstract:
  In this paper we show that the expected generalisation performance of a
learning machine is determined by the distribution of risks or equivalently its
logarithm -- a quantity we term the risk entropy -- and the fluctuations in a
quantity we call the training ratio. We show that the risk entropy can be
empirically inferred for deep neural network models using Markov Chain Monte
Carlo techniques. Results are presented for different deep neural networks on a
variety of problems. The asymptotic behaviour of the risk entropy acts in an
analogous way to the capacity of the learning machine, but the generalisation
performance experienced in practical situations is determined by the behaviour
of the risk entropy before the asymptotic regime is reached. This performance
is strongly dependent on the distribution of the data (features and targets)
and not just on the capacity of the learning machine.


title:
An $\mathfrak{sl}_2$-type tensor category for the Virasoro algebra at
  central charge $25$

abstract:
  Let $\mathcal{O}_{25}$ be the vertex algebraic braided tensor category of
finite-length modules for the Virasoro Lie algebra at central charge $25$ whose
composition factors are the irreducible quotients of reducible Verma modules.
We show that $\mathcal{O}_{25}$ is rigid and that its simple objects generate a
semisimple tensor subcategory that is braided tensor equivalent to an abelian
$3$-cocycle twist of the category of finite-dimensional
$\mathfrak{sl}_2$-modules. We also show that this $\mathfrak{sl}_2$-type
subcategory is braid-reversed tensor equivalent to a similar category for the
Virasoro algebra at central charge $1$. As an application, we obtain a new
conformal vertex algebra of central charge $26$ as an extension of the tensor
product of Virasoro vertex operator algebras at central charges $1$ and $25$,
analogous to the modified regular representations of the Virasoro algebra
constructed previously from Virasoro vertex operator algebras at generic
central charges by I. Frenkel--Styrkas and I. Frenkel--M. Zhu.


title: Calomplification -- The Power of Generative Calorimeter Models

abstract:
  Motivated by the high computational costs of classical simulations,
machine-learned generative models can be extremely useful in particle physics
and elsewhere. They become especially attractive when surrogate models can
efficiently learn the underlying distribution, such that a generated sample
outperforms a training sample of limited size. This kind of GANplification has
been observed for simple Gaussian models. We show the same effect for a physics
simulation, specifically photon showers in an electromagnetic calorimeter.


title:
Intra-night optical variability monitoring of $\gamma$-ray emitting
  blazars

abstract:
  We present the results obtained from our campaign to characterize the
intra-night-optical variability properties of blazars detected by the {\it
Fermi} Large Area Telescope. This involves R-band monitoring observations of a
sample of 18 blazars, that includes five flat-spectrum radio quasars (FSRQs)
and thirteen BL Lac objects (BL Lacs) covering the redshift range z =
0.085$-$1.184. Our observations, carried out using the 1.3 m J.C. Bhattacharya
Telescope cover a total of 40 nights ($\sim$200 hrs) between the period 2016
December and 2020 March. We characterized variability using the power enhanced
$F-$test. We found a duty cycle (DC) of the variability of about 11\% for FSRQs
and 12\% for BL Lacs. Dividing the sample into different sub-classes based on
the position of the synchrotron peak in their broadband spectral energy
distribution (SED), we found DC of $\sim$16\%, $\sim$10\% and $\sim$7\% for
low-synchrotron peaked (LSP), intermediate synchrotron peaked (ISP) and high
synchrotron peaked (HSP) blazars. Such high DC of variability in LSP blazars
could be understood in the context of the R-band tracing the falling part
(contributed by high energy electrons) of the synchrotron component of the
broadband SED. Also, the R-band tracing the rising synchrotron part (produced
by low energy electrons) in the case of ISP and HSP blazars, could cause lesser
variability in them. Thus, the observed high DC of variability in LSP blazars
relative to ISP and HSP blazars is in accordance with the leptonic model of
emission from blazar jets.


title:
Diffusion-controlled reactions triggered by multiple particles with
  reversible target-binding kinetics

abstract:
  We investigate a class of diffusion-controlled reactions that are triggered
when a prescribed number $K$ among $N$ independently diffusing particles are
simultaneously bound to a target region. If particles that bind to the target
stay there forever, the reaction time in this irreversible binding limit is the
$K$-th fastest first-passage time to the target, whose distribution is
well-known. In turn, reversible binding, which is common for most applications,
renders theoretical analysis much more challenging and drastically changes the
distribution of reaction times. We develop a renewal-based approach to derive
an approximate solution for the probability density of the reaction time. This
approximation turns out to be remarkably accurate for a broad range of
parameters. We also analyze the dependence of the mean reaction time or,
equivalently, the inverse reaction rate, on the main parameters such as $K$,
$N$, and binding/unbinding constants. Some biophysical applications and further
perspectives are discussed.


title:
Velocity correlations in the high-density phase of active Brownian
  particles

abstract:
  Active Brownian particle is an important model of active matter. It exhibits
many intriguing behaviors since its non-equilibrium nature. Recently, the
velocity correlation in this model has been observed in the absence of thermal
fluctuations. However, it will disappear if the fluctuations play a key role.
In our simulation, we find in the presence of fluctuations this correlation
still exists while using mean velocities instead of instantaneous ones. The
cluster formed by particles whose local structures are symmetrical behaves like
a rigid body. Particles in this rigid body move coherently leading to the
velocity correlation domain.


title: Realistic Counterfactual Explanations by Learned Relations

abstract:
  Many existing methods of counterfactual explanations ignore the intrinsic
relationships between data attributes and thus fail to generate realistic
counterfactuals. Moreover, the existing methods that account for relationships
between data attributes require domain knowledge, which limits their
applicability in complex real-world applications. In this paper, we propose a
novel approach to realistic counterfactual explanations that preserve
relationships between data attributes. The model directly learns the
relationships by a variational auto-encoder without domain knowledge and then
learns to disturb the latent space accordingly. We conduct extensive
experiments on both synthetic and real-world datasets. The results demonstrate
that the proposed method learns relationships from the data and preserves these
relationships in generated counterfactuals.


title: Thermodynamics of the spin-half square-kagome lattice antiferromagnet

abstract:
  Over the last decade, the interest in the spin-$1/2$ Heisenberg
antiferromagnet (HAF) on the square-kagome (also called shuriken) lattice has
been growing as a model system of quantum magnetism with a quantum paramagnetic
ground state, flat-band physics near the saturation field, and quantum scars.
Here, we present large-scale numerical investigations of the specific heat
$C(T)$, the entropy $S(T)$ as well as the susceptibility $\chi(T)$ by means of
the finite-temperature Lanczos method for system sizes of
$N=18,24,30,36,42,48$, and $N=54$. We find that the specific heat exhibits a
low-temperature shoulder below the major maximum which can be attributed to
low-lying singlet excitations filling the singlet-triplet gap, which is
significantly larger than the singlet-singlet gap. This observation is further
supported by the behavior of the entropy $S(T)$, where a change in the
curvature is present just at about $T/J=0.2$, the same temperature where the
shoulder in $C$ sets in. For the susceptibility the low-lying singlet
excitations are irrelevant, and the singlet-triplet gap leads to an
exponentially activated low-temperature behavior. The maximum in $\chi(T)$ is
found at a pretty low temperature $T_{\rm max}/J=0.146$ (for $N=42$) compared
to $T_{\rm max}/J=0.935$ for the unfrustrated square-lattice HAF signaling the
crucial role of frustration also for the susceptibility. We find a striking
similarity of our square-kagome data with the corresponding ones for the kagome
HAF down to very low $T$. The magnetization process featuring plateaus and
jumps and the field dependence of the specific heat that exhibits
characteristic peculiarities attributed to the existence of a flat one-magnon
band are as well discussed.


title:
A Unified Framework for Masked and Mask-Free Face Recognition via
  Feature Rectification

abstract:
  Face recognition under ideal conditions is now considered a well-solved
problem with advances in deep learning. Recognizing faces under occlusion,
however, still remains a challenge. Existing techniques often fail to recognize
faces with both the mouth and nose covered by a mask, which is now very common
under the COVID-19 pandemic. Common approaches to tackle this problem include
1) discarding information from the masked regions during recognition and 2)
restoring the masked regions before recognition. Very few works considered the
consistency between features extracted from masked faces and from their
mask-free counterparts. This resulted in models trained for recognizing masked
faces often showing degraded performance on mask-free faces. In this paper, we
propose a unified framework, named Face Feature Rectification Network
(FFR-Net), for recognizing both masked and mask-free faces alike. We introduce
rectification blocks to rectify features extracted by a state-of-the-art
recognition model, in both spatial and channel dimensions, to minimize the
distance between a masked face and its mask-free counterpart in the rectified
feature space. Experiments show that our unified framework can learn a
rectified feature space for recognizing both masked and mask-free faces
effectively, achieving state-of-the-art results. Project code:
https://github.com/haoosz/FFR-Net


title: textless-lib: a Library for Textless Spoken Language Processing

abstract:
  Textless spoken language processing research aims to extend the applicability
of standard NLP toolset onto spoken language and languages with few or no
textual resources. In this paper, we introduce textless-lib, a PyTorch-based
library aimed to facilitate research in this research area. We describe the
building blocks that the library provides and demonstrate its usability by
discuss three different use-case examples: (i) speaker probing, (ii) speech
resynthesis and compression, and (iii) speech continuation. We believe that
textless-lib substantially simplifies research the textless setting and will be
handful not only for speech researchers but also for the NLP community at
large. The code, documentation, and pre-trained models are available at
https://github.com/facebookresearch/textlesslib/ .


title:
Multimodal Driver Referencing: A Comparison of Pointing to Objects
  Inside and Outside the Vehicle

abstract:
  Advanced in-cabin sensing technologies, especially vision based approaches,
have tremendously progressed user interaction inside the vehicle, paving the
way for new applications of natural user interaction. Just as humans use
multiple modes to communicate with each other, we follow an approach which is
characterized by simultaneously using multiple modalities to achieve natural
human-machine interaction for a specific task: pointing to or glancing towards
objects inside as well as outside the vehicle for deictic references. By
tracking the movements of eye-gaze, head and finger, we design a multimodal
fusion architecture using a deep neural network to precisely identify the
driver's referencing intent. Additionally, we use a speech command as a trigger
to separate each referencing event. We observe differences in driver behavior
in the two pointing use cases (i.e. for inside and outside objects), especially
when analyzing the preciseness of the three modalities eye, head, and finger.
We conclude that there is no single modality that is solely optimal for all
cases as each modality reveals certain limitations. Fusion of multiple
modalities exploits the relevant characteristics of each modality, hence
overcoming the case dependent limitations of each individual modality.
Ultimately, we propose a method to identity whether the driver's referenced
object lies inside or outside the vehicle, based on the predicted pointing
direction.


title:
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell
  Electrodes

abstract:
  Anomaly detection in X-ray images has been an active and lasting research
area in the last decades, especially in the domain of medical X-ray images. For
this work, we created a real-world labeled anomaly dataset, consisting of
16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst
solution and perform anomaly detection on the dataset using a deep learning
approach. The dataset contains a diverse set of anomalies with 11 identified
common anomalies where the electrodes contain e.g. scratches, bubbles, smudges
etc. We experiment with 16-bit image to 8-bit image conversion methods to
utilize pre-trained Convolutional Neural Networks as feature extractors
(transfer learning) and find that we achieve the best performance by maximizing
the contrasts globally across the dataset during the 16-bit to 8-bit
conversion, through histogram equalization. We group the fuel cell electrodes
with anomalies into a single class called abnormal and the normal fuel cell
electrodes into a class called normal, thereby abstracting the anomaly
detection problem into a binary classification problem. We achieve a balanced
accuracy of 85.18\%. The anomaly detection is used by the company, Serenergy,
for optimizing the time spend on the quality control of the fuel cell
electrodes


title: MuLD: The Multitask Long Document Benchmark

abstract:
  The impressive progress in NLP techniques has been driven by the development
of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks
focus on tasks for one or two input sentences, there has been exciting work in
designing efficient techniques for processing much longer inputs. In this
paper, we present MuLD: a new long document benchmark consisting of only
documents over 10,000 tokens. By modifying existing NLP tasks, we create a
diverse benchmark which requires models to successfully model long-term
dependencies in the text. We evaluate how existing models perform, and find
that our benchmark is much more challenging than their `short document'
equivalents. Furthermore, by evaluating both regular and efficient
transformers, we show that models with increased context length are better able
to solve the tasks presented, suggesting that future improvements in these
models are vital for solving similar long document problems. We release the
data and code for baselines to encourage further research on efficient NLP
models.


title: Periodic H\"older waves in nonsmooth fractional KdV equations

abstract:
  We prove the existence of highest, cusped, periodic travelling-wave solutions
with exact and optimal $ \alpha $-H\"older continuity in a family of
negative-order fractional Korteweg--de Vries equations of the form
\begin{equation*} u_t + (| \mathrm{D} |^{- \alpha} u + n(u) )_x = 0
\end{equation*} for every $ \alpha \in (0, 1) $ with homogeneous Fourier
multiplier $ | \mathrm{D} |^{ - \alpha} $. We tackle nonlinearities $ n(u) $ of
the type $ | u |^p $ or $ u | u |^{p - 1} $ for all real $ p > 1 $, and show
that when $ n $ is odd, the waves also feature antisymmetry and thus contain
inverted cusps. The analysis for nonsmooth $ n $ is applicable to other
negative-order nonlocal equations. Both the construction of highest
antisymmetric waves and the regularisation of nonsmooth terms to an analytic
bifurcation setting, we believe are new in this context.


title: Zero-Shot Assistance in Novel Decision Problems

abstract:
  We consider the problem of creating assistants that can help agents - often
humans - solve novel sequential decision problems, assuming the agent is not
able to specify the reward function explicitly to the assistant. Instead of
aiming to automate, and act in place of the agent as in current approaches, we
give the assistant an advisory role and keep the agent in the loop as the main
decision maker. The difficulty is that we must account for potential biases
induced by limitations or constraints of the agent which may cause it to
seemingly irrationally reject advice. To do this we introduce a novel
formalization of assistance that models these biases, allowing the assistant to
infer and adapt to them. We then introduce a new method for planning the
assistant's advice which can scale to large decision making problems. Finally,
we show experimentally that our approach adapts to these agent biases, and
results in higher cumulative reward for the agent than automation-based
alternatives.


title: A Statistical Learning View of Simple Kriging

abstract:
  In the Big Data era, with the ubiquity of geolocation sensors in particular,
massive datasets exhibiting a possibly complex spatial dependence structure are
becoming increasingly available. In this context, the standard probabilistic
theory of statistical learning does not apply directly and guarantees of the
generalization capacity of predictive rules learned from such data are left to
establish. We analyze here the simple Kriging task, the flagship problem in
Geostatistics: the values of a square integrable random field $X=\{X_s\}_{s\in
S}$, $S\subset \mathbb{R}^2$, with unknown covariance structure are to be
predicted with minimum quadratic risk, based upon observing a single
realization of the spatial process at a finite number of locations $s_1,\;
\ldots,\; s_n$ in $S$. Despite the connection of this minimization problem with
kernel ridge regression, establishing the generalization capacity of empirical
risk minimizers is far from straightforward, due to the non i.i.d. nature of
the spatial data $X_{s_1},\; \ldots,\; X_{s_n}$ involved. In this article,
nonasymptotic bounds of order $O_{\mathbb{P}}(1/n)$ are proved for the excess
risk of a plug-in predictive rule mimicking the true minimizer in the case of
isotropic stationary Gaussian processes observed at locations forming a regular
grid. These theoretical results, as well as the role played by the technical
conditions required to establish them, are illustrated by various numerical
experiments and hopefully pave the way for further developments in statistical
learning based on spatial data.


title: An Amp\'{e}re-Nernst-Planck Framework for Modeling Charge Dynamics

abstract:
  Understanding the properties of charge dynamics is crucial to many practical
applications, such as electrochemical energy devices and transmembrane ion
channels. This work proposes an Amp\'{e}re-Nernst-Planck (ANP) framework for
the description of charge dynamics. The proposed ANP model is shown to be a
gradient flow of a convex free-energy functional of ionic concentrations and
dielectric displacement, and demonstrated to be equivalent to the
Poisson-Nernst-Planck model. By the energy dissipation law, the steady state of
the ANP model reproduces the charge conserving Poisson--Boltzmann (PB) theory,
providing an alternative energy stable approach to study the PB theory from the
perspective of a gradient flow. In order to achieve the curl-free condition,
the ANP model is equipped with a local curl-free relaxation algorithm, which is
shown to preserve the Gauss's law naturally and have robust convergence and
linear computational complexity. Many-body effects such as ionic steric effects
and Coulomb correlations can be incorporated within the ANP framework to derive
modified ANP models for problems in which the mean-field approximation fails.
Numerical results on the charge dynamics with such beyond mean-field effects in
inhomogeneous dielectric environments are presented to demonstrate the
performance of the ANP models in the description of charge dynamics,
illustrating that the proposed ANP model provides a general framework for
modeling charge dynamics.


title:
Using transcription-based detectors to emulate the behaviour of
  sequential probability ratio-based concentration detectors

abstract:
  The sequential probability ratio test (SPRT) from statistics is known to have
the least mean decision time compared to other sequential or fixed-time tests
for given error rates. In some circumstances, cells need to make decisions
accurately and quickly, therefore it has been suggested the SPRT may be used to
understand the speed-accuracy tradeoff in cellular decision making. It is
generally thought that in order for cells to make use of the SPRT, it is
necessary to find biochemical circuits that can compute the log-likelihood
ratio needed for the SPRT. However, this paper takes a different approach. We
recognise that the high-level behaviour of the SPRT is defined by its positive
detection or hit rate, and the computation of the log-likelihood ratio is just
one way to realise this behaviour. In this paper, we will present a method
which uses a transcription-based detector to emulate the hit rate of the SPRT
without computing the exact log-likelihood ratio. We consider the problem of
using a promoter with multiple binding sites to detect whether the
concentration of a transcription factor is above a target level. We show that
it is possible to find binding and unbinding rates of the transcription factor
to the promoter's binding sites so that the probability that the amount of mRNA
produced will be higher than a threshold is approximately equal to the hit rate
of the SPRT detector. Moreover, we show that the average time that this
transcription-based detector needs to make a positive detection is less than or
equal to that of the SPRT for a wide range of concentrations. We remark that
the last statement does not contradict Wald's optimality result because our
transcription-based detector uses an open-ended test.


title: VLA detects CO(1-0) emission in the z=3.65 quasar SDSS J160705+533558

abstract:
  We present CO(1--0) observations of the high-redshift quasar SDSS
J160705+533558 ($z=3.653$) using the Karl G. Jansky Very Large Array (VLA). We
detect CO emission associated with the quasar and at $\sim16.8\,\rm kpc$
projected distance from it, separated by $\sim800\,\rm km\,s^{-1}$ in velocity.
The total molecular gas mass of this system is $\sim5\times10^{10}\,\rm
M_{\odot}$. By comparing our CO detections with previous submillimetre (submm)
observations of the source, an offset between the different emission components
is revealed: the peak of the submm emission is offset from the quasar and from
the CO companion detected in our VLA data. To explain our findings, we propose
a scenario similar to that for the Antennae galaxies: SDSS J160705+533558 might
be a merger system in which the quasar and the CO companion are the merging
galaxies, whose interaction resulted in the formation of a dusty, star-forming
overlap region between the galaxies that is dominant at the submm wavelengths.


title: A Low-Parametric Model for Bit-Rate Estimation of VVC Residual Coding

abstract:
  There are many tasks within video compression which require fast bit rate
estimation. As an example, rate-control algorithms are only feasible because it
is possible to estimate the required bit rate without needing to encode the
entire block. With residual coding technology becoming more and more
sophisticated, the corresponding bit rate models require more advanced
features. In this work, we propose a set of four features together with a
linear model, which is able to estimate the rate of arbitrary residual blocks
which were compressed using the VVC standard. Our method outperforms other
methods which were used for the same task both in terms of mean absolute error
and mean relative error. Our model deviates by less than 4 bit on average over
a large dataset of natural images.


title: A high angular resolution interferometric backscatter meter

abstract:
  Backscatter limits many interferometric measurements, including gravitational
wave detectors, by creating spurious interference. We describe an experimental
method to directly and quantitatively measure the backscatter interference. We
derive and verify experimentally a relation between backscatter interference,
beam radius and the scattering sample bidirectional reflectance distribution
function. We also demonstrate that our method is able to measure backscatter
from high quality optics for angles as low as 500 urad with a 160 urad angular
resolution.


title: Personalized Prompt Learning for Explainable Recommendation

abstract:
  Providing user-understandable explanations to justify recommendations could
help users better understand the recommended items, increase the system's ease
of use, and gain users' trust. A typical approach to realize it is natural
language generation. However, previous works mostly adopt recurrent neural
networks to meet the ends, leaving the potentially more effective pre-trained
Transformer models under-explored. In fact, user and item IDs, as important
identifiers in recommender systems, are inherently in different semantic space
as words that pre-trained models were already trained on. Thus, how to
effectively fuse IDs into such models becomes a critical issue. Inspired by
recent advancement in prompt learning, we come up with two solutions: find
alternative words to represent IDs (called discrete prompt learning), and
directly input ID vectors to a pre-trained model (termed continuous prompt
learning). In the latter case, ID vectors are randomly initialized but the
model is trained in advance on large corpora, so they are actually in different
learning stages. To bridge the gap, we further propose two training strategies:
sequential tuning and recommendation as regularization. Extensive experiments
show that our continuous prompt learning approach equipped with the training
strategies consistently outperforms strong baselines on three datasets of
explainable recommendation.


title:
Towards machine learning for microscopic mechanisms: a formula search
  for crystal structure stability based on atomic properties

abstract:
  Machine Learning (ML) techniques are revolutionizing the way to perform
efficient materials modeling. Nevertheless, not all the ML approaches allow for
the understanding of microscopic mechanisms at play in different phenomena. To
address the latter aspect, we propose a combinatorial machine-learning approach
to obtain physical formulas based on simple and easily-accessible ingredients,
such as atomic properties. The latter are used to build materials features that
are finally employed, through Linear Regression, to predict the energetic
stability of semiconducting binary compounds with respect to zincblende and
rocksalt crystal structures. The adopted models are trained using dataset built
from first-principles calculations. Our results show that already
one-dimensional (1D) formulas well describe the energetics; a simple
grid-search optimization of the automatically-obtained 1D-formulas enhances the
prediction performances at a very small computational cost. In addition, our
approach allows to highlight the role of the different atomic properties
involved in the formulas. The computed formulas clearly indicate that "spatial"
atomic properties (i.e. radii indicating maximum probability densities for
$s,p,d$ electronic shells) drive the stabilization of one crystal structure
with respect to the other, suggesting the major relevance of the radius
associated to the $p$-shell of the cation species.


title: The linear mode analysis from spin transport equation

abstract:
  In this paper, a detailed analysis on normal modes of the linearized hermite
collision operator is presented, which comes from linearizing the nonlocal
collision term for massive fermions proposed in \cite{Weickgenannt:2021cuo} and
neglecting the non-diagonal or polarized part of the transition rate. With the
assumption of the conservation of total angular momentum, the kinetic theory we
adopt is proved to well describe the equilibrium state and give the right
collisional invariants, thus is relevant for the research on local spin
polarization. Following the familiar fashion as used in quantum mechanics, we
treat the problem of solving the normal modes as a degenerate perturbation
problem and calculate the dispersion relations for our concerned eleven zero
modes, which corresponds to all collisional invariants. We find that the
results of spinless mode appearing in ordinary hydrodynamics are consistent
with available conclusions in some textbooks. As for the new spin-related modes
without reference, we obtain the frequencies up to second-order in wave vector
of which the second corrections are only formal solutions to be further
determined. In addition, the relaxation of spin density is related to our
linear mode analysis, which shall play a big role in investigating the issues
of the local spin polarization in the relativistic heavy-ion collisions.


title: The paradox of classical reasoning

abstract:
  As shown in the paper, the applicability of the quantum mechanical
description to macroscopic systems requires the formal language of Hilbert
space theory to be a metalanguage with respect to the formal language of set
theory that expresses the mathematical formalism of classical mechanics.
However, such a requirement cannot be met - the language of the theory of
Hilbert spaces is less expressive than that of the theory of sets. This fact
puts in doubt the quantum-classical correspondence and undermines the
reductionist approach to the physical world.


title:
Optomechanical Modulation Spectroscopy of Bound-States-In-The-Continuum
  in a dielectric metasurface

abstract:
  Elusive features in photonic and electronic devices can be detected by means
of advanced, time-domain spectroscopic techniques. In this letter we introduce
a novel kind of modulation spectroscopy, based on the optomechanical
interaction of photonic and mechanical modes. Applying the technique to a Si
metasurface and its drum-like mechanical modes, we detect narrow-band
quasi-Bound-State-in-the-Continuum (q-BIC) modes close to normal incidence,
where their measurement can be hindered by a high symmetry protection and
undesired background modes. Showing a visibility enhancement of more than one
order of magnitude, the optomechanical modulation spectroscopy can be an
innovative tool for precise spectroscopy of a wide set of photonic devices,
including the goal of measuring purely symmetry protected BIC resonances.


title:
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for
  Supervised Query Performance Prediction

abstract:
  Motivated by the recent success of end-to-end deep neural models for ranking
tasks, we present here a supervised end-to-end neural approach for query
performance prediction (QPP). In contrast to unsupervised approaches that rely
on various statistics of document score distributions, our approach is entirely
data-driven. Further, in contrast to weakly supervised approaches, our method
also does not rely on the outputs from different QPP estimators. In particular,
our model leverages information from the semantic interactions between the
terms of a query and those in the top-documents retrieved with it. The
architecture of the model comprises multiple layers of 2D convolution filters
followed by a feed-forward layer of parameters. Experiments on standard test
collections demonstrate that our proposed supervised approach outperforms other
state-of-the-art supervised and unsupervised approaches.


title: The aperiodic Domino problem in higher dimension

abstract:
  The classical Domino problem asks whether there exists a tiling in which none
of the forbidden patterns given as input appear. In this paper, we consider the
aperiodic version of the Domino problem: given as input a family of forbidden
patterns, does it allow an aperiodic tiling? The input may correspond to a
subshift of finite type, a sofic subshift or an effective subshift.
arXiv:1805.08829 proved that this problem is co-recursively enumerable
($\Pi_0^1$-complete) in dimension 2 for geometrical reasons. We show that it is
much harder, namely analytic ($\Sigma_1^1$-complete), in higher dimension: $d
\geq 4$ in the finite type case, $d \geq 3$ for sofic and effective subshifts.
The reduction uses a subshift embedding universal computation and two
additional dimensions to control periodicity. This complexity jump is
surprising for two reasons: first, it separates 2- and 3-dimensional subshifts,
whereas most subshift properties are the same in dimension 2 and higher;
second, it is unexpectedly large.


title:
Computing Black Scholes with Uncertain Volatility-A Machine Learning
  Approach

abstract:
  In financial mathematics, it is a typical approach to approximate financial
markets operating in discrete time by continuous-time models such as the Black
Scholes model. Fitting this model gives rise to difficulties due to the
discrete nature of market data. We thus model the pricing process of financial
derivatives by the Black Scholes equation, where the volatility is a function
of a finite number of random variables. This reflects an influence of uncertain
factors when determining volatility. The aim is to quantify the effect of this
uncertainty when computing the price of derivatives. Our underlying method is
the generalized Polynomial Chaos (gPC) method in order to numerically compute
the uncertainty of the solution by the stochastic Galerkin approach and a
finite difference method. We present an efficient numerical variation of this
method, which is based on a machine learning technique, the so-called
Bi-Fidelity approach. This is illustrated with numerical examples.


title:
NuSTAR and AstroSat observations of thermonuclear X-ray bursts with
  short-recurrence times in 4U 1636$-$536

abstract:
  We report results from the spectro-timing analysis of the atoll source 4U
1636$-$536 observed with NuSTAR and AstroSat during its hard spectral state. In
three observations of 207 ks total exposure, we identify 31 thermonuclear X-ray
bursts including five doublets and a triplet. Recurrence time as short as 3.8
minutes is seen in one of the doublets. To the best of our knowledge, this is
the shortest recurrence time known for this source. Our time-averaged
spectroscopy during the bursts indicate the presence of an additional powerlaw
or a blackbody component in a few cases, perhaps due to varying temperatures
during bursts or plausible deviation from ideal blackbody behavior, however, it
is difficult to probe this using the time-resolved spectroscopy owing to
limited statistics. Time-resolved bursts are well fit using an absorbed
blackbody model with temperatures varying between 1.7 and 2.2 keV. Burst
oscillations around 581 Hz are detected with 3$\sigma$ confidence during the
decay phase in two of the X-ray bursts. One of the burst oscillations is seen
at 582 Hz, a frequency observed during the 2001 superburst in this source.


title:
GuaranTEE: Introducing Control-Flow Attestation for Trusted Execution
  Environments

abstract:
  The majority of cloud providers offers users the possibility to deploy
Trusted Execution Environments (TEEs) in order to protect their data and
processes from high privileged adversaries. This offer is intended to address
concerns of users when moving critical tasks into the cloud. However, TEEs only
allow to attest the integrity of the environment at launch-time. To also enable
the attestation of a TEE's integrity at run-time, we present GuaranTEE.
GuaranTEE uses control-flow attestation to ensure the integrity of a service
running within a TEE. By additionally placing all components of GuaranTEE in
TEEs, we are able to not only detect a compromised target, but are also able to
protect ourselves from malicious administrators. We show the practicability of
GuaranTEE by providing a detailed performance and security evaluation of our
prototype based on Intel SGX in Microsoft Azure. Our evaluation shows that the
need to transfer information between TEEs and the additional verification
process add considerable overhead. Yet, we are able to reduce this overhead by
securely caching collected information and by performing the analysis in
parallel to executing the application. In summary, our results show that
GuaranTEE is able to provide a practical solution for cloud users focused on
protecting the integrity of their data and processes at run-time.


title:
Monolithic multigrid for implicit Runge-Kutta discretizations of
  incompressible fluid flow

abstract:
  Most research on preconditioners for time-dependent PDEs has focused on
implicit multi-step or diagonally-implicit multi-stage temporal
discretizations. In this paper, we consider monolithic multigrid
preconditioners for fully-implicit multi-stage Runge-Kutta (RK) time
integration methods. These temporal discretizations have very attractive
accuracy and stability properties, but they couple the spatial degrees of
freedom across multiple time levels, requiring the solution of very large
linear systems. We extend the classical Vanka relaxation scheme to implicit RK
discretizations of saddle point problems. We present numerical results for the
incompressible Stokes, Navier-Stokes, and resistive magnetohydrodynamics
equations, in two and three dimensions, confirming that these relaxation
schemes lead to robust and scalable monolithic multigrid methods for a
challenging range of incompressible fluid-flow models.


title: Phase Vocoder Done Right

abstract:
  The phase vocoder (PV) is a widely spread technique for processing audio
signals. It employs a short-time Fourier transform (STFT)
analysis-modify-synthesis loop and is typically used for time-scaling of
signals by means of using different time steps for STFT analysis and synthesis.
The main challenge of PV used for that purpose is the correction of the STFT
phase. In this paper, we introduce a novel method for phase correction based on
phase gradient estimation and its integration. The method does not require
explicit peak picking and tracking nor does it require detection of transients
and their separate treatment. Yet, the method does not suffer from the typical
phase vocoder artifacts even for extreme time stretching factors.


title: Regular non-semisimple Dubrovin-Frobenius manifolds

abstract:
  We study regular non-semisimple Dubrovin-Frobenius manifolds in dimensions
2,3,4. We focus on the case where the Jordan canonical form of the operator of
multiplication by the Euler vector field has a single Jordan block. Our results
rely on the existence of special local coordinates introduced in [4] for
regular flat F-manifolds with Euler vector field. In such coordinates the
invariant metric of the Dubrovin-Frobenius manifold takes a special form which
is the starting point of our construction.


title:
The Phase-I Trigger Readout Electronics Upgrade of the ATLAS Liquid
  Argon Calorimeters

abstract:
  The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon
calorimeters enhances the physics reach of the experiment during the upcoming
operation at increasing Large Hadron Collider luminosities. The new system,
installed during the second Large Hadron Collider Long Shutdown, increases the
trigger readout granularity by up to a factor of ten as well as its precision
and range. Consequently, the background rejection at trigger level is improved
through enhanced filtering algorithms utilizing the additional information for
topological discrimination of electromagnetic and hadronic shower shapes. This
paper presents the final designs of the new electronic elements, their custom
electronic devices, the procedures used to validate their proper functioning,
and the performance achieved during the commissioning of this system.


title:
Interplay of Beam Polarisation and Systematic Uncertainties in
  Electroweak Precision Measurements at Future $e^+ e^-$ Colliders

abstract:
  Future high-energy $\textrm{e}^+\textrm{e}^-$ colliders will provide some of
the most precise tests of the Standard Model. Statistical uncertainties on
electroweak precision observables and triple gauge couplings are expected to
improve by orders of magnitude over current measurements. This provides a new
challenge in accurately assessing and minimizing the impact of experimental
systematic uncertainties. Beam polarization may hold a unique potential to
isolate and determine the size of systematic effects. So far, studies have
mainly focused on the statistical improvements from beam polarisation. This
study aims to assess, for the first time, its impact on systematic
uncertainties. A combined fit of precision observables, such as chiral fermion
couplings and anomalous triple gauge couplings, together with experimental
systematic effects is performed on generator-level differential distribution of
2-fermion and 4-fermion final-states. Different configurations of available
beam polarisations and luminosities are tested with and without systematic
effects, and will be discussed in the context of the existing projections on
fermion and gauge boson couplings from detailed experimental simulations.


title:
$\mu$-hybrid Inflation, Gravitino Dark Matter and Stochastic
  Gravitational Wave Background from Cosmic Strings

abstract:
  We present a successful realization of supersymmetric $\mu$-hybrid inflation
model based on a gauged $U(1)_{B-L}$ extension of the minimal supersymmetric
standard model, with the soft supersymmetry breaking terms are playing an
important role. Successful non-thermal leptogenesis with gravitino dark matter
yields a reheat temperature in the range $2 \times 10^{7} \lesssim T_R \lesssim
5 \times 10^{9}$ GeV. This corresponds to the predictions $2 \times 10^{-18}
\lesssim r\lesssim 4 \times 10^{-13}$ for the tensor to scalar ratio, and $-2
\times 10^{-6} \lesssim dn_s/d\ln k \lesssim -5 \times 10^{-11}$ for the
running of the scalar spectral index. The $B-L$ breaking scale is estimated as
$ 6 \times 10^{14}\lesssim M/ \text{GeV}\lesssim 10^{16}$, calculated at the
central value of the scalar spectral index, $n_s =0.9655$, reported by Planck
2018. Finally, in a grand unified theory setup the dimensionless string tension
parameter associated with the metastable strings is in the range $ 10^{-9}
\lesssim G\mu_\text{cs} \lesssim 10^{-6}$ corresponding to a stochastic
gravitational wave background lying within the 2$\sigma$ bound of the recent
NANOGrav 12.5-yr data.


title: Waves in weakly ionised solar plasmas

abstract:
  Here we study the nature and characteristics of waves propagating in
partially ionised plasmas in the weakly ionised limit, typical for the lower
part of the solar atmosphere. The framework in which the properties of waves
are discussed depends on the relative magnitude of collisions between
particles, but also on the relative magnitude of the collisional frequencies
compared to the gyro-frequency of charged particles. Our investigation shows
that the weakly ionised solar atmospheric plasma can divided into two regions
and this division occurs, roughly, at the base of the chromosphere. In the
solar photosphere the plasma is non-magnetised and the dynamics can described
within the three-fluid framework where acoustic waves associated to each
species can propagate. Due to the very high concentration of neutrals, the
neutral sound waves propagates with no damping, while for the other two modes
the damping rate is determined by collisions with neutrals. The ion and
electron-related acoustic modes propagate with a cut-off determined by the
collisional frequency of these species with neutrals. In the weakly ionised
chromosphere only electrons are magnetised, however, the strong coupling of
charged particles reduces the working framework to a two-fluid model. The
disassociation of charged particles creates electric currents that can
influence the characteristic of waves. The propagation properties of waves with
respect to the angle of propagation are studied with the help of polar
diagrams.


title: Performance optimizations for porting the openQ$^\star$D package to GPUs

abstract:
  OpenQ$^\star$D code has been used by the RC$^\star$ collaboration for the
generation of fully dynamical QCD+QED gauge configurations with C$^\star$
boundary conditions. In this talk, optimization of solvers provided with the
openQ$^\star$D package relevant for porting the code on GPU-accelerated
supercomputing platforms is discussed. We present the analysis of the current
implementations of the GCR solver preconditioned with Schwarz alternating
procedure for ill-conditioned Dirac-operators. With the goal of enabling
support for GPUs from various vendors, a novel method of adaptive
CPU/GPU-hybrid implementation is proposed.


title: Spam four ways: Making sense of text data

abstract:
  The world is full of text data, yet text analytics has not traditionally
played a large part in statistics education. We consider four different ways to
provide students with opportunities to explore whether email messages are
unwanted correspondence (spam). Text from subject lines are used to identify
features that can be used in classification. The approaches include use of a
Model Eliciting Activity, exploration with CODAP, modeling with a specially
designed Shiny app, and coding more sophisticated analyses using R. The
approaches vary in their use of technology and code but all share the common
goal of using data to make better decisions and assessment of the accuracy of
those decisions.


title: Practical Testing of a C99 Compiler Using Output Comparison

abstract:
  A simple technique is presented for testing a C99 compiler, by comparison of
its output with output from preexisting tools. The advantage to this approach
is that new test cases can be added in bulk from existing sources, reducing the
need for in-depth investigation of correctness issues, and for creating new
test code by hand. This technique was used in testing the PalmSource Palm OS
Cobalt ARM C/C++ cross-compiler for Palm-Powered personal digital assistants,
primarily for standards-compliance and correct execution of generated code. The
technique described here found several hundred bugs, mostly in our in-house
code, but also in longstanding high-quality front- and back-end code from
Edison Design Group and Apogee Software. It also found eighteen bugs in the GNU
C compiler, as well as a bug specific to the Apple version of GCC, a bug
specific to the Suse version of GCC, and a dozen bugs in versions of GCC for
the ARM processor, several of them critical.


title:
Exploring Deep Reinforcement Learning-Assisted Federated Learning for
  Online Resource Allocation in EdgeIoT

abstract:
  Federated learning (FL) has been increasingly considered to preserve data
training privacy from eavesdropping attacks in mobile edge computing-based
Internet of Thing (EdgeIoT). On the one hand, the learning accuracy of FL can
be improved by selecting the IoT devices with large datasets for training,
which gives rise to a higher energy consumption. On the other hand, the energy
consumption can be reduced by selecting the IoT devices with small datasets for
FL, resulting in a falling learning accuracy. In this paper, we formulate a new
resource allocation problem for EdgeIoT to balance the learning accuracy of FL
and the energy consumption of the IoT device. We propose a new federated
learning-enabled twin-delayed deep deterministic policy gradient (FLDLT3)
framework to achieve the optimal accuracy and energy balance in a continuous
domain. Furthermore, long short term memory (LSTM) is leveraged in FL-DLT3 to
predict the time-varying network state while FL-DLT3 is trained to select the
IoT devices and allocate the transmit power. Numerical results demonstrate that
the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while
the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to
existing state-of-the-art benchmark.


title:
Topology induced first-order phase transitions in lattice quantum
  gravity

abstract:
  Causal Dynamical Triangulations (CDT) is a lattice formulation of quantum
gravity, suitable for Monte-Carlo simulations which have been used to study the
phase diagram of the model. It has four phases characterized by different
dominant geometries, denoted phase $A$, $B$, $C$ and $C_b$. In this article we
analyse the $A-B$ and the $B-C$ {phase} transitions in the case where the
topology of space is that of the three-torus. This completes the phase diagram
of CDT for such a spatial topology. We observe that the order of a phase
transition of spacetime geometries can depend on the topology of spacetime.


title: First-order thermodynamics of scalar-tensor cosmology

abstract:
  A new thermodynamics of scalar-tensor gravity is applied to spatially
homogeneous and isotropic cosmologies in this class of theories and tested on
analytical solutions. A forever-expanding universe approaches the Einstein
"state of equilibrium" with zero effective temperature at late times and
departs from it near spacetime singularities. "Cooling" by expansion and
"heating" by singularities compete near the Big Rip, where it is found that the
effective temperature diverges in the case of a conformally coupled scalar
field.


title:
IEC61850 Sample-Value Service Based on Reduced Application Service Data
  Unit for Energy IOT

abstract:
  With the development of 5G technology and low-power wireless communication
technology, a large number of IOT devices are introduced into energy systems.
Existing IOT communication protocols such as MQQT and COAP cannot meet the
requirements of high reliability and real-time performance. However, the
61850-9-2 Sample value protocol is relatively complex and the message length is
large, difficult to ensure real-time transmission for IOT devices with limited
transmission rate. This paper proposes a 9-2 Sample Value protocol for IOT
controller based on Application Service Data Unit. The communication protocol
is strictly in accordance with IEC61850-9-2 and can be recognized by existing
intelligent electronic devices such as merging units. The protocol simplifies
and trims some parameters, and changes the floating point value to integer
data. Considering the instability of wireless communication, uni-cast or
multicast UDP/IP is utilized to send SV Payload based on the 2.4GHz WIFI. The
maximum transmission rate can be up to 30 Mbps. The hardware to implement
reduced SV adopts ESP32-S, which is a dual-core MCU supporting WIFI with
frequency of 240MHz. Software is based on FreeRTOS, LWIP and Libiec61850. A PC
or raspberry PI is used as the Host to receive and analyze packets, verifying
feasibility of reduced SV protocols.


title: Integrability of Three Dimensional Gravity Field Equations

abstract:
  We show that the tree dimensional Einstein vacuum field equations with
cosmological constant are integrable. Using the $sl(2,R)$ valued soliton
connections we obtain the metric of the spacetime in terms of the dynamical
variables of the integrable nonlinear partial differential equations.


title: Noncommutative Bispectral Algebras and their Presentations

abstract:
  We prove a general result on presentations of finitely-generated algebras and
apply it to obtain nice presentations for some noncommutative algebras arising
in the matrix bispectral problem. By "nice presentation" we mean a presentation
that has as few as possible defining relations. This in turn, has potential
applications in computer algebra implementations and examples.
  Our results can be divided into three parts. In the first two, we consider
bispectral algebras with the eigenvalue in the physical equation to be
scalar-valued for $2\times 2$ and $3\times 3$ matrix-valued eigenfunctions. In
the third part, we assume the eigenvalue in the physical equation to be
matrix-valued and draw an important connection with spin Calogero-Moser
systems. In all cases, we show that these algebras are finitely presented. As a
byproduct, we answer positively a conjecture of F.~A.~Gr\"{u}nbaum about these
algebras.


title:
General Solution and Canonical Quantization of the Conic Path
  Constrained Second-Class System

abstract:
  We consider the problem of constrained motion along a conic path under a
given external potential function. The model is described as a second-class
system capturing the behavior of a certain class of specific quantum field
theories. By exhibiting a suitable integration factor, we obtain the general
solution for the associated non-linear differential equations. We perform the
canonical quantization in a consistent way in terms of the corresponding Dirac
brackets. We apply the Dirac-Bergmann algorithm to unravel and classify the
whole internal constraints structure inherent to its dynamical Hamiltonian
description, obtain the proper extended Hamiltonian function, determine the
Lagrange multiplier and compute all relevant Poisson brackets among the
constraints, Hamiltonian and Lagrange multiplier. The complete Dirac brackets
algebra in phase space as well as its physical realization in terms of
differential operators is explicitly obtained.


title:
A numerical energy minimisation approach for semilinear
  diffusion-reaction boundary value problems based on steady state iterations

abstract:
  We present a novel energy-based numerical analysis of semilinear
diffusion-reaction boundary value problems. Based on a suitable variational
setting, the proposed computational scheme can be seen as an energy
minimisation approach. More specifically, this procedure aims to generate a
sequence of numerical approximations, which results from the iterative solution
of related (stabilised) linearised discrete problems, and tends to a local
minimum of the underlying energy functional. Simultaneously, the
finite-dimensional approximation spaces are adaptively refined; this is
implemented in terms of a new mesh refinement strategy in the context of finite
element discretisations, which again relies on the energy structure of the
problem under consideration, and does not involve any a posteriori error
indicators. In combination, the resulting adaptive algorithm consists of an
iterative linearisation procedure on a sequence of hierarchically refined
discrete spaces, which we prove to converge towards a solution of the
continuous problem in an appropriate sense. Numerical experiments demonstrate
the robustness and reliability of our approach for a series of examples.


title: Interpreting a Machine Learning Model for Detecting Gravitational Waves

abstract:
  We describe a case study of translational research, applying interpretability
techniques developed for computer vision to machine learning models used to
search for and find gravitational waves. The models we study are trained to
detect black hole merger events in non-Gaussian and non-stationary advanced
Laser Interferometer Gravitational-wave Observatory (LIGO) data. We produced
visualizations of the response of machine learning models when they process
advanced LIGO data that contains real gravitational wave signals, noise
anomalies, and pure advanced LIGO noise. Our findings shed light on the
responses of individual neurons in these machine learning models. Further
analysis suggests that different parts of the network appear to specialize in
local versus global features, and that this difference appears to be rooted in
the branched architecture of the network as well as noise characteristics of
the LIGO detectors. We believe efforts to whiten these "black box" models can
suggest future avenues for research and help inform the design of interpretable
machine learning models for gravitational wave astrophysics.


title:
Mixed boundary conditions as limits of dissipative boundary conditions
  in dynamic perfect plasticity

abstract:
  This paper addresses the well posedness of a dynamical model of perfect
plasticity with mixed boundary conditions for general closed and convex
elasticity sets. The proof relies on an asymptotic analysis of the solution of
a perfect plasticity model with relaxed dissipative boundary conditions
obtained in [7]. One of the main issues consists in extending the measure
theoretic duality pairing between stresses and plastic strains, as well as a
convexity inequality to a more general context where deviatoric stresses are
not necessarily bounded. Complete answers are given in the pure Dirichlet and
pure Neumann cases. For general mixed boundary conditions, partial answers are
given in dimension $2$ and $3$ under additional geometric hypothesis on the
elasticity set and the reference configuration.


title:
The Optimal Beam-loading in Two-bunch Nonlinear Plasma Wakefield
  Accelerators

abstract:
  Plasma wakefield acceleration shows a great potential for applications in
future light sources and linear electron-positron colliders, in which trailing
beams are required to be optimally loaded to maintain a low energy spread while
extracting energy efficiently from the wake during the acceleration. Due to the
highly nonlinear nature of this effect, it is at present not possible to
analytically determine the beam parameters needed in a two-bunch plasma
wakefield accelerator with the optimal beam-loading. Therefore in this paper,
by using the Broyden-Fletcher-Goldfarb-Shanno algorithm for the parameter
scanning with the code QuickPIC and the polynomial regression together with
k-fold cross-validation method, we obtain two fitting formulas for calculating
the tri-Gaussian electron beams' parameters when minimizing the energy spread
based on the beam loading effect in a nonlinear plasma wakefield accelerator.
One formula allows the optimization of the normalized charge per unit length of
a trailing beam to achieve the minimal energy spread, i.e. the optimal
beam-loading. The other one directly gives the transformer ratio when the
trailing beam achieves the optimal beam-loading. A simple scaling law for
beams' charges is obtained from the fitting formula, which indicates that for a
certain plasma and the beams' length and separation, optimal beam loading is
always obtained for a given charge ratio of the two beams. When the plasma
density changes, the fitting formula for optimal beam-loading shows that when
the drive beam has a fixed charge and beams' length and separation are fixed,
there are two optimal plasma densities for the maximum trailing beam's charge
and the maximum acceleration efficiency respectively. These two fitting
formulas will significantly enhance the efficiency for designing and optimizing
a two-bunch plasma wakefield acceleration stage.


title:
SODAR: Segmenting Objects by DynamicallyAggregating Neighboring Mask
  Representations

abstract:
  Recent state-of-the-art one-stage instance segmentation model SOLO divides
the input image into a grid and directly predicts per grid cell object masks
with fully-convolutional networks, yielding comparably good performance as
traditional two-stage Mask R-CNN yet enjoying much simpler architecture and
higher efficiency. We observe SOLO generates similar masks for an object at
nearby grid cells, and these neighboring predictions can complement each other
as some may better segment certain object part, most of which are however
directly discarded by non-maximum-suppression. Motivated by the observed gap,
we develop a novel learning-based aggregation method that improves upon SOLO by
leveraging the rich neighboring information while maintaining the architectural
efficiency. The resulting model is named SODAR. Unlike the original per grid
cell object masks, SODAR is implicitly supervised to learn mask representations
that encode geometric structure of nearby objects and complement adjacent
representations with context. The aggregation method further includes two novel
designs: 1) a mask interpolation mechanism that enables the model to generate
much fewer mask representations by sharing neighboring representations among
nearby grid cells, and thus saves computation and memory; 2) a deformable
neighbour sampling mechanism that allows the model to adaptively adjust
neighbor sampling locations thus gathering mask representations with more
relevant context and achieving higher performance. SODAR significantly improves
the instance segmentation performance, e.g., it outperforms a SOLO model with
ResNet-101 backbone by 2.2 AP on COCO \texttt{test} set, with only about 3\%
additional computation. We further show consistent performance gain with the
SOLOv2 model.


title:
Deep learning and differential equations for modeling changes in
  individual-level latent dynamics between observation periods

abstract:
  When modeling longitudinal biomedical data, often dimensionality reduction as
well as dynamic modeling in the resulting latent representation is needed. This
can be achieved by artificial neural networks for dimension reduction, and
differential equations for dynamic modeling of individual-level trajectories.
However, such approaches so far assume that parameters of individual-level
dynamics are constant throughout the observation period. Motivated by an
application from psychological resilience research, we propose an extension
where different sets of differential equation parameters are allowed for
observation sub-periods. Still, estimation for intra-individual sub-periods is
coupled for being able to fit the model also with a relatively small dataset.
We subsequently derive prediction targets from individual dynamic models of
resilience in the application. These serve as interpretable resilience-related
outcomes, to be predicted from characteristics of individuals, measured at
baseline and a follow-up time point, and selecting a small set of important
predictors. Our approach is seen to successfully identify individual-level
parameters of dynamic models that allows us to stably select predictors, i.e.,
resilience factors. Furthermore, we can identify those characteristics of
individuals that are the most promising for updates at follow-up, which might
inform future study design. This underlines the usefulness of our proposed deep
dynamic modeling approach with changes in parameters between observation
sub-periods.


title: Dehnen halo effect on a black hole in an ultra-faint dwarf galaxy

abstract:
  There had been recent advancement toward the detection of ultra-faint dwarf
galaxies, which may serve as a useful laboratory for dark matter exploration
since some of them contains almost 99$\%$ of pure dark matter. The majority of
these galaxies contain no black hole that inhabits them. Recently, there had
been reports that some dwarf galaxies may have a black hole within. In this
study, we construct a black hole solution combined with the Dehnen dark matter
halo profile, which is commonly used for dwarf galaxies. We aim to find out
whether there would be deviations relative to the standard black hole
properties which might allow determining whether the dark matter profile in
ultra-faint dwarf galaxies is cored or cuspy. To make the model more realistic,
we applied the modified Newman-Janis prescription to obtain the rotating
metric. We analyzed the black hole properties such as the event horizon,
ergoregion, geodesics of time-like and null particle, and the black hole
shadow. To broaden the scope of this study, we also calculated the weak
deflection angle to examine the effect of the Dehnen profile. Results revealed
that the Dehnen profile causes very tiny but interesting deviations to the
known black hole properties and it shows that the cuspy profile shows more
deviation than the cored profile. In determining whether the dark matter
profile in a dwarf galaxy is cored or cuspy, the implication of the study
revealed the impossibility with the use of present space technology.


title:
Mean first-passage time to a small absorbing target in three-dimensional
  elongated domains

abstract:
  We derive an approximate formula for the mean first-passage time (MFPT) to a
small absorbing target of arbitrary shape inside an elongated domain of a
slowly varying axisymmetric profile. For this purpose, the original Poisson
equation in three dimensions is reduced an effective one-dimensional problem on
an interval with a semi-permeable semi-absorbing membrane. The approximate
formula captures correctly the dependence of the MFPT on the distance to the
target, the radial profile of the domain, and the size and the shape of the
target. This approximation is validated by Monte Carlo simulations.


title:
Impact of the X ray edge singularity on detection of relic neutrinos in
  the PTOLEMY project

abstract:
  Direct detection of relic neutrinos in a beta-decay experiment is an
ambitious goal, which has for a long time been beyond the reach of available
technology. One of the toughest practical difficulties that such an experiment
has to overcome is that it needs to deal with a large amount of radioactive
material in such a way as to not compromise the energy resolution required for
the separation of useful events from the massive beta-decay background. The
PTOLEMY project offers an innovative approach to this problem based on
deposition of radioactive material on graphene. While such an approach is
expected to resolve the main difficulty, new challenges arise from the
proximity of the beta decayers to a solid state system. In this work, we focus
on the effect of the shakeup of the graphene electron system due to a
beta-decay event. We calculate the distortion of the relic neutrino peaks as
resulting from such a shakeup, analyse the impact of the distortion on the
visibility of neutrino capture events and discuss what technological solutions
could be used to improve the visibility of neutrino capture events.


title: The $\infty$-elastica problem on a Riemannian manifold

abstract:
  We consider the following problem: on any given complete Riemannian manifold
$(M,g)$, among all curves which have fixed length as well as fixed end-points
and tangents at the end-points, minimise the $L^\infty$ norm of the curvature.
We show that the solutions of this problem, as well as a wider class of curves,
must satisfy a second order ODE system. From this system we obtain some
geometric information about the behaviour of the curves.


title: Coupled surface and bulk diffusion in crystals

abstract:
  We analyze point defect bulk and surface diffusion near the crystal-vacuum
interface and show that bulk diffusion is altered by atomic reactions at the
surface-vacuum boundary. A new atomic mechanism for point defect generation and
recombination is presented. In this mechanism, atomic steps are considered as
sources and sinks not only for adsorbed atoms and surface vacancies but also
for bulk native point defects -- vacancies and self-interstitials. We
demonstrate that bulk diffusion is coupled with the diffusion of adatoms and
surface vacancies at the surface via the atomic processes in the intermediate
subsurface layer, containing adsorbed self-interstitials and bulk vacancies.
The results show the existence of the fundamental relation between equilibrium
concentrations of the point defects in bulk and at the surface of the crystal.


title:
A Lower Bounding Framework for Motion Planning amid Dynamic Obstacles in
  2D

abstract:
  This work considers a Motion Planning Problem with Dynamic Obstacles (MPDO)
in 2D that requires finding a minimum-arrival-time collision-free trajectory
for a point robot between its start and goal locations amid dynamic obstacles
moving along known trajectories. Existing methods, such as continuous Dijkstra
paradigm, can find an optimal solution by restricting the shape of the
obstacles or the motion of the robot, while this work makes no such
assumptions. Other methods, such as search-based planners and sampling-based
approaches can compute a feasible solution to this problem but do not provide
approximation bounds. Since finding the optimum is challenging for MPDO, this
paper develops a framework that can provide tight lower bounds to the optimum.
These bounds acts as proxies for the optimum which can then be use to bound the
deviation of a feasible solution from the optimum. To accomplish this, we
develop a framework that consists of (i) a bi-level discretization approach
that converts the MPDO to a relaxed path planning problem, and (ii) an
algorithm that can solve the relaxed problem to obtain lower bounds. We also
present preliminary numerical results to corroborate the performance of the
proposed framework. These results show that the bounds obtained by our approach
for some instances are three times larger than a naive baseline approach
showcasing potential advantages of the proposed approach.


title:
Fluctuation-driven dynamics in nanoscale thin-film flows: physical
  insights from numerical investigations

abstract:
  The effects of thermal fluctuations on nanoscale flows are captured by a
numerical scheme that is underpinned by fluctuating hydrodynamics. A stochastic
lubrication equation (SLE) is solved on non-uniform adaptive grids to study a
series of nanoscale thin-film flows. The Fornberg scheme is used for
high-resolution spatial discretisation and a fully-implicit time-marching
scheme is designed for numerical stability. The accuracy of the numerical
method is verified against theoretical results for thermal capillary waves
during the linear stage of their development. The framework is then used to
study the nonlinear behaviour of three bounded thin-film flows: (i) droplet
spreading, where new power laws are derived; (ii) droplet coalescence, where
molecular dynamics results are reproduced by the SLE at a fraction of the
computational cost and it is discovered that thermal fluctuations decelerate
the process, in contrast to previously investigated phenomena; and (iii)
thin-film rupture, where, in the regime considered, disjoining pressure
dominates the final stages of rupture.


title: Quantum fluctuation theorem for initial near-equilibrium system

abstract:
  Quantum work fluctuation theorem (FT) commonly requires the system initially
prepared in an equilibrium state. Whether there exists universal exact quantum
work FT for initial state beyond equilibrium is still unknown. Here I
initialize the system in a near-equilibrium state, and derive the corresponding
modified Jarzynski equality. The correction is nontrivial because it directly
leads to the principle of maximum work or the second law of thermodynamics for
near-equilibrium system and also gives a more tighter bound beyond the
principle of maximum work or the second law of thermodynamics for a given
process. I also verify my theoretical results by considering a concrete
many-body system, and reveal a fundamental connection between quantum critical
phenomenon and near-equilibrium state at really high temperature.


title:
Knowledge Graph Reasoning with Logics and Embeddings: Survey and
  Perspective

abstract:
  Knowledge graph (KG) reasoning is becoming increasingly popular in both
academia and industry. Conventional KG reasoning based on symbolic logic is
deterministic, with reasoning results being explainable, while modern
embedding-based reasoning can deal with uncertainty and predict plausible
knowledge, often with high efficiency via vector computation. A promising
direction is to integrate both logic-based and embedding-based methods, with
the vision to have advantages of both. It has attracted wide research attention
with more and more works published in recent years. In this paper, we
comprehensively survey these works, focusing on how logics and embeddings are
integrated. We first briefly introduce preliminaries, then systematically
categorize and discuss works of logic and embedding-aware KG reasoning from
different perspectives, and finally conclude and discuss the challenges and
further directions.


title: Post Newtonian Feasibility of the Closed String Massless Sector

abstract:
  We perform post Newtonian analysis of Double Field Theory taken as the
gravitational theory of closed string massless sector. We identify the
Eddington-Robertson-Schiff parameters $\beta_{{{\rm{PPN}}}}$,
$\gamma_{{{\rm{PPN}}}}$ with the charges of electric $H$-flux and dilaton
respectively, and further relate them to stress-energy tensor. We show
${\beta_{{{\rm{PPN}}}}=1}$ from weak energy condition and argue that the
observation of $\gamma_{{{\rm{PPN}}}}\simeq 1$ signifies the ultrarelativistic
equation of state in baryons, or the suppression of gluon condensate.


title: Interpretable Reinforcement Learning with Multilevel Subgoal Discovery

abstract:
  We propose a novel Reinforcement Learning model for discrete environments,
which is inherently interpretable and supports the discovery of deep subgoal
hierarchies. In the model, an agent learns information about environment in the
form of probabilistic rules, while policies for (sub)goals are learned as
combinations thereof. No reward function is required for learning; an agent
only needs to be given a primary goal to achieve. Subgoals of a goal G from the
hierarchy are computed as descriptions of states, which if previously achieved
increase the total efficiency of the available policies for G. These state
descriptions are introduced as new sensor predicates into the rule language of
the agent, which allows for sensing important intermediate states and for
updating environment rules and policies accordingly.


title: NeuPL: Neural Population Learning

abstract:
  Learning in strategy games (e.g. StarCraft, poker) requires the discovery of
diverse policies. This is often achieved by iteratively training new policies
against existing ones, growing a policy population that is robust to exploit.
This iterative approach suffers from two issues in real-world games: a) under
finite budget, approximate best-response operators at each iteration needs
truncating, resulting in under-trained good-responses populating the
population; b) repeated learning of basic skills at each iteration is wasteful
and becomes intractable in the presence of increasingly strong opponents. In
this work, we propose Neural Population Learning (NeuPL) as a solution to both
issues. NeuPL offers convergence guarantees to a population of best-responses
under mild assumptions. By representing a population of policies within a
single conditional model, NeuPL enables transfer learning across policies.
Empirically, we show the generality, improved performance and efficiency of
NeuPL across several test domains. Most interestingly, we show that novel
strategies become more accessible, not less, as the neural population expands.


title:
The Membership Problem for Hypergeometric Sequences with Rational
  Parameters

abstract:
  We investigate the Membership Problem for hypergeometric sequences: given a
hypergeometric sequence $\langle u_n \rangle_{n=0}^\infty$ of rational numbers
and a target $t \in \mathbb{Q}$, decide whether $t$ occurs in the sequence. We
show decidability of this problem under the assumption that in the defining
recurrence $p(n)u_{n+1}=q(n)u_n$, the roots of the polynomials $p(x)$ and
$q(x)$ are all rational numbers. Our proof relies on bounds on the density of
primes in arithmetic progressions. We also observe a relationship between the
decidability of the Membership problem (and variants) and the Rohrlich-Lang
conjecture in transcendence theory.


title:
Cooperative effect of electrons spin polarization in a hybrid
  nanostructure of a magnetic thin film with adsorbed chiral molecules studied
  with non-spin-polarized scanning tunneling microscopy

abstract:
  Polyalanine molecules (PA) with an {\alpha}-helix conformation gathered
recently a lot of interest as the propagation of electrons through the chiral
backbone structure comes along with spin polarization of the transmitted
electrons. By means of scanning tunneling microscopy and spectroscopy at
ambient conditions, PA molecules adsorbed on surfaces of epitaxial magnetic
Al2O3/Pt/Au/Co/Au nanostructures with perpendicular anisotropy were studied.
Thereby, a correlation between the PA molecules ordering at the surface with
the electron tunneling across this hybrid system as a function of the substrate
magnetization orientation as well as the coverage density and helicity of the
was observed. The highest spin polarization values, P, were found for
well-ordered self-assembled monolayers and with a defined chemical coupling of
the molecules to the magnetic substrate surface, showing that the current
induced spin selectivity is a cooperative effect. Thereby, P deduced from the
electron transmission along unoccupied molecular orbitals of the helical
molecules is larger as compared to values derived from the occupied molecular
orbitals. Apparently, the larger orbital overlap is resulting in a higher
electron mobility yielding a higher P value. By switching the magnetization
direction of the Co-layer, it was demonstrated that the non-spin-polarized STM
can be used to study chiral molecules with a sub-molecular resolution, to
detect properties of buried magnetic layers and to detect the spin polarization
of the molecules from the change of the magnetoresistance of such hybrid
structures.


title: Bending Waves in Velocity Space: a First Look at the THINGS sample

abstract:
  Detection of bending waves is a highly challenging task even in nearby disc
galaxies due to their sub-kpc bending amplitudes. However, simulations show
that the harmonic bending of a Milky Way like disc galaxy is associated with a
harmonic fluctuation in the measured line of sight (los) velocities as well,
and can be regarded as a kinematic signature of a manifested bending wave.
Here, we look for similar kinematic signatures of bending waves in \HI discs,
as they extend to much beyond the optical radii.
  We present a multipole analysis of the \HI los residual velocity fields of
six nearby spiral galaxies from the THINGS sample, which uncovers the bending
wave-induced velocity peaks. This allows us to identify the radial positions
and amplitudes of the different bending modes present in the galaxies. We find
that all of our sample discs show a combined kinematic signature of
superposition of a few lower-order bending modes, suggesting that bending waves
are a common phenomenon. The identified velocity peaks are found to be of modes
$m=2,3$ and $4$, not more than 15 km s$^{-1}$ in amplitude and spread across
the entire \HI disc. Interestingly, they appear to be concentrated near the
optical edge of their host galaxies. Also, $m=2$ appears to be more common than
the other two modes.


title: Characterising Cybercriminals: A Review

abstract:
  This review provides an overview of current research on the known
characteristics and motivations of offenders engaging in cyber-dependent
crimes. Due to the shifting dynamics of cybercriminal behaviour, and the
availability of prior reviews in 2013, this review focuses on original research
conducted from 2012 onwards, although some older studies that were not included
in prior reviews are also considered. As a basis for interpretation of results,
a limited quality assessment was also carried out on included studies through
examination of key indicators.


title: A tensor-based entanglement measure for mixed states

abstract:
  Entanglement is a key resource for quantum technologies and is at the root of
exciting many-body phenomena. Nevertheless, quantifying the amount of
entanglement in a real-world quantum system is challenging when it interacts
with its environment. Here, we devise an entanglement measure for such
realistic open systems. Our measure relies on a tensor-network representation
of the system's density matrix and is readily computed using our presented
algorithm. We showcase the virtues of our scheme using a case study of spinless
particles moving on a chain in presence of dephasing. Crucially, our approach
distinguishes classical from quantum correlations in a broad plethora of
systems and motivates efficient experimental entanglement quantification.


title: Adversarial Attacks and Defense Methods for Power Quality Recognition

abstract:
  Vulnerability of various machine learning methods to adversarial examples has
been recently explored in the literature. Power systems which use these
vulnerable methods face a huge threat against adversarial examples. To this
end, we first propose a signal-specific method and a universal signal-agnostic
method to attack power systems using generated adversarial examples. Black-box
attacks based on transferable characteristics and the above two methods are
also proposed and evaluated. We then adopt adversarial training to defend
systems against adversarial attacks. Experimental analyses demonstrate that our
signal-specific attack method provides less perturbation compared to the FGSM
(Fast Gradient Sign Method), and our signal-agnostic attack method can generate
perturbations fooling most natural signals with high probability. What's more,
the attack method based on the universal signal-agnostic algorithm has a higher
transfer rate of black-box attacks than the attack method based on the
signal-specific algorithm. In addition, the results show that the proposed
adversarial training improves robustness of power systems to adversarial
examples.


title:
Explainable COVID-19 Infections Identification and Delineation Using
  Calibrated Pseudo Labels

abstract:
  The upheaval brought by the arrival of the COVID-19 pandemic has continued to
bring fresh challenges over the past two years. During this COVID-19 pandemic,
there has been a need for rapid identification of infected patients and
specific delineation of infection areas in computed tomography (CT) images.
Although deep supervised learning methods have been established quickly, the
scarcity of both image-level and pixellevel labels as well as the lack of
explainable transparency still hinder the applicability of AI. Can we identify
infected patients and delineate the infections with extreme minimal
supervision? Semi-supervised learning (SSL) has demonstrated promising
performance under limited labelled data and sufficient unlabelled data.
Inspired by SSL, we propose a model-agnostic calibrated pseudo-labelling
strategy and apply it under a consistency regularization framework to generate
explainable identification and delineation results. We demonstrate the
effectiveness of our model with the combination of limited labelled data and
sufficient unlabelled data or weakly-labelled data. Extensive experiments have
shown that our model can efficiently utilize limited labelled data and provide
explainable classification and segmentation results for decision-making in
clinical routine.


title:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex
  Hazard Structures in Survival Analysis

abstract:
  Survival analysis (SA) is an active field of research that is concerned with
time-to-event outcomes and is prevalent in many domains, particularly
biomedical applications. Despite its importance, SA remains challenging due to
small-scale data sets and complex outcome distributions, concealed by
truncation and censoring processes. The piecewise exponential additive mixed
model (PAMM) is a model class addressing many of these challenges, yet PAMMs
are not applicable in high-dimensional feature settings or in the case of
unstructured or multimodal data. We unify existing approaches by proposing
DeepPAMM, a versatile deep learning framework that is well-founded from a
statistical point of view, yet with enough flexibility for modeling complex
hazard structures. We illustrate that DeepPAMM is competitive with other
machine learning approaches with respect to predictive performance while
maintaining interpretability through benchmark experiments and an extended case
study.


title:
The potential of artificial intelligence for achieving healthy and
  sustainable societies

abstract:
  In this chapter we extend earlier work (Vinuesa et al., Nature Communications
11, 2020) on the potential of artificial intelligence (AI) to achieve the 17
Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for
the 2030 Agenda. The present contribution focuses on three SDGs related to
healthy and sustainable societies, i.e. SDG 3 (on good health), SDG 11 (on
sustainable cities) and SDG 13 (on climate action). This chapter extends the
previous study within those three goals, and goes beyond the 2030 targets.
These SDGs are selected because they are closely related to the coronavirus
disease 19 (COVID-19) pandemic, and also to crises like climate change, which
constitute important challenges to our society.


title:
Algebraic function based Banach space valued ordinary and fractional
  neural network approximations

abstract:
  Here we research the univariate quantitative approximation, ordinary and
fractional, of Banach space valued continuous functions on a compact interval
or all the real line by quasi-interpolation Banach space valued neural network
operators. These approximations are derived by establishing Jackson type
inequalities involving the modulus of continuity of the engaged function or its
Banach space valued high order derivative of fractional derivatives. Our
operators are defined by using a density function generated by an algebraic
sigmoid function. The approximations are pointwise and of the uniform norm. The
related Banach space valued feed-forward neural networks are with one hidden
layer.


title: Peripheral elements in reduced Alexander modules

abstract:
  We discuss meridians and longitudes in reduced Alexander modules of classical
and virtual links. When these elements are suitably defined, each link
component will have many meridians, along with a single longitude that is
uniquely determined up to module isomorphisms that preserve the sets of
meridians. Enhancing the reduced Alexander module by singling out these
peripheral elements provides a significantly stronger link invariant. In
particular, the enhanced module determines all linking numbers in a link; in
contrast, the module alone does not even detect how many linking numbers are
$0$.


title:
On the Complementarity of Images and Text for the Expression of Emotions
  in Social Media

abstract:
  Authors of posts in social media communicate their emotions and what causes
them with text and images. While there is work on emotion and stimulus
detection for each modality separately, it is yet unknown if the modalities
contain complementary emotion information in social media. We aim at filling
this research gap and contribute a novel, annotated corpus of English
multimodal Reddit posts. On this resource, we develop models to automatically
detect the relation between image and text, an emotion stimulus category and
the emotion class. We evaluate if these tasks require both modalities and find
for the image-text relations, that text alone is sufficient for most categories
(complementary, illustrative, opposing): the information in the text allows to
predict if an image is required for emotion understanding. The emotions of
anger and sadness are best predicted with a multimodal model, while text alone
is sufficient for disgust, joy, and surprise. Stimuli depicted by objects,
animals, food, or a person are best predicted by image-only models, while
multimodal models are most effective on art, events, memes, places, or
screenshots.


title:
Learning Contextually Fused Audio-visual Representations for
  Audio-visual Speech Recognition

abstract:
  With the advance in self-supervised learning for audio and visual modalities,
it has become possible to learn a robust audio-visual speech representation.
This would be beneficial for improving the audio-visual speech recognition
(AVSR) performance, as the multi-modal inputs contain more fruitful information
in principle. In this paper, based on existing self-supervised representation
learning methods for audio modality, we therefore propose an audio-visual
representation learning approach. The proposed approach explores both the
complementarity of audio-visual modalities and long-term context dependency
using a transformer-based fusion module and a flexible masking strategy. After
pre-training, the model is able to extract fused representations required by
AVSR. Without loss of generality, it can be applied to single-modal tasks, e.g.
audio/visual speech recognition by simply masking out one modality in the
fusion module. The proposed pre-trained model is evaluated on speech
recognition and lipreading tasks using one or two modalities, where the
superiority is revealed.


title: The ${\rm SL}(2,\mathbb{C})$-character variety of the Borromean link

abstract:
  For the Borromean link, we determine its irreducible ${\rm
SL}(2,\mathbb{C})$-character variety, and compute the twisted Alexander
polynomial associated to each irreducible representation.


title:
Europa's Surface Water-ice Crystallinity and Correlations between Lineae
  and Hydrate Composition

abstract:
  Europa's surface composition and evidence for cryovolcanic activity can
provide insight into the properties and composition of the subsurface ocean,
allowing the evaluation of its potential habitability. One promising avenue for
revealing the surface processing and subsurface activity are the relative
fractions of crystalline and amorphous water-ice observed on the surface, which
are influenced by temperature, charged particle bombardment, vapor deposition,
and cryovolcanic activity. The crystallinity observed on Europa's leading
hemisphere cannot be reproduced by thermophysical and particle flux modeling
alone, indicating that there may be additional processes influencing the
surface. We performed a spectral mixture analysis on hyperspectral image cubes
from the Galileo Near-Infrared Mapping Spectrometer (NIMS) to identify how
surface crystallinity is influenced by physical processing at a high spatial
resolution scale. We focus specifically on two image cubes, 15e015 closer to
the equator and 17e009 closer to the south pole, both on the leading
hemisphere. We performed a nonnegative least-squares spectral mixture analysis
to reveal both the non-ice composition and the water-ice crystallinity of the
surface. We found that amorphous water-ice dominates the spectrum at the
equator and the south pole. We estimated a mean crystallinity of ~35% within
the 15e015 NIMS cube and a mean crystallinity of ~15% within the 17e009 NIMS
cube, which is consistent with ground-based spectroscopically derived
crystallinities. We also identified a correlation of magnesium sulfate,
magnesium chloride, and hydrated sulfuric acid with lineae and ridges, which
may provide evidence for surface processing by upwelling subsurface material.


title:
A survey of recent studies concerning the extreme properties of
  Morris-Thorne wormholes

abstract:
  It has been known for a long time that Morris-Thorne wormholes can only be
held open by violating the null energy condition, which can be expressed in the
form $\tau-\rho c^2>0$, where $\tau$ is the radial tension. Matter that
violates this condition is usually referred to as "exotic." For any wormhole
having a moderately-sized throat, the radial tension is equal to that at the
center of a massive neutron star. Attributing this outcome to exotic matter
seems reasonable enough, but it ignores the fact that exotic matter was
introduced for a completely different reason. Moreover, its problematical
nature suggests that the amount of exotic matter be held to a minimum, but this
would make the high radial tension harder to explain. If the amount is
infinitely small, this explanation breaks down entirely. By invoking $f(R)$
modified gravity, the need for exotic matter at the throat could actually be
eliminated, but the negation of the above condition, i.e., $\tau <\rho c^2$,
shows that we have not necessarily eliminated the high radial tension. This
survey discusses various ways to account for the high radial tension and, in
some cases, the possible origin of exotic matter. We conclude with some
comments on a possible multiply-connected Universe.


title: A precortical module for robust CNNs to light variations

abstract:
  We present a simple mathematical model for the mammalian low visual pathway,
taking into account its key elements: retina, lateral geniculate nucleus (LGN),
primary visual cortex (V1). The analogies between the cortical level of the
visual system and the structure of popular CNNs, used in image classification
tasks, suggests the introduction of an additional preliminary convolutional
module inspired to precortical neuronal circuits to improve robustness with
respect to global light intensity and contrast variations in the input images.
We validate our hypothesis on the popular databases MNIST, FashionMNIST and
SVHN, obtaining significantly more robust CNNs with respect to these
variations, once such extra module is added.


title:
Sharp phase-field modeling of isotropic solidification with a super
  efficient spatial resolution

abstract:
  The numerical resolution efficiency of phase-field models is limited by grid
friction, grid anisotropy and pinning. The 1D sharp phase-field model
eliminates grid friction and pinning by a global restoration of Translational
Invariance (TI) in the discretized phase-field equation (Phys. Rev. Lett. 121,
025501, 2018). In 3D global TI restricts the beneficial modeling properties to
a finite number of fixed interface orientations. We propose an accurate scheme
to restore TI locally in the local interface normal direction. At
one-grid-point interface resolutions, the new model captures the formation of
isotropic seaweed structures without spurious dendritic selection by grid
anisotropy.