title:
Quantifying resilience and the risk of regime shifts under realistic
  noise conditions

abstract:
  Commonly proposed statistical early warning measures are far away from
realistic applications in which limited data availability, coarse-grained
sampling and strong correlated noise are typical. Even under favourable
simulation conditions the measures are of limited capacity due to their
qualitative nature and sometimes ambiguous trend-to-noise ratio. In order to
solve these shortcomings, we analyse the stability of the system via the slope
of the deterministic term of a Langevin equation, which is hypothesized to
underlie the system dynamics close to the fixed point. The open-source
available method is applied to a previously studied seasonal ecological model
under realistic noise level and correlation scenarios. We compare the results
to autocorrelation, standard deviation, skewness and kurtosis as leading
indicator candidates by a Bayesian model comparison with a linear and a
constant model. We can show that the slope of the deterministic term is a
promising alternative due to its quantitative nature and high robustness
against noise levels and types. The commonly computed indicators apart from the
autocorrelation with deseasonalization fail to provide reliable insights into
the stability of the system in contrast to a previously performed study in
which the standard deviation was found to perform best. In addition, we discuss
the significant influence of the seasonal nature of the data to the robust
computation of the various indicators, before we determine approximately the
minimal amount of data per time window that leads to significant trends for the
drift slope estimations.


title: Direct Sequential Simulation for spherical linear inverse problems

abstract:
  We present a method for obtaining efficient probabilistic solutions to
geostatistical and linear inverse problems in spherical geometry. Our Spherical
Direct Sequential Simulation (SDSSIM) framework combines information from
possibly noisy observations, that provide either point information on the model
or are related to the model by a linear averaging kernel, and statistics
derived from a-priori training models. It generates realizations from marginal
posterior probability distributions of model parameters that are not limited to
be Gaussian. We avoid the restriction to Cartesian geometry built into many
existing geostatistical simulation codes, and work instead with grids in
spherical geometry relevant to problems in Earth and Space sciences.
  We demonstrate our scheme using a synthetic example, showing that it produces
realistic posterior realizations consistent with the known solution while
fitting observations within their uncertainty and reproducing the model
parameter distribution and covariance statistics of a-priori training models.
Secondly, we present an application to real satellite observations, estimating
the posterior probability distribution for the geomagnetic field at the
core-mantle boundary. Our results reproduce well-known features of the
core-mantle boundary magnetic field, and also allow probabilistic
investigations of the magnetic field morphology. Small-length scale features in
the posterior realizations are not determined by the observations but match the
covariance statistics extracted from geodynamo simulation training models. The
framework presented here represents a step towards more general approaches to
probabilistic inversion in spherical geometry.


title:
Recommended Guidelines for Effective MOOCs based on a Multiple-Case
  Study

abstract:
  Massive Open Online Courseware (MOOCs) appeared in 2008 and grew considerably
in the past decade, now reaching millions of students and professionals all
over the world. MOOCs do not replace other educational forms. Instead, they
complement them by offering a powerful educational tool that can reach students
that, otherwise, would not have access to that information. Nevertheless,
designing and implementing a successful MOOC is not straightforward. Simply
recording traditional classes is an approach that does not work, since the
conditions in which a MOOC student learns are very different from the
conventional classroom. In particular, dropout rates in MOOCs are, normally, at
least an order of magnitude higher than in conventional courses. In this paper,
we analyze data from 7 successful MOOCs that have attracted over 150,000
students in the past years. The analysis led to the proposal of a set of
guidelines to help instructors in designing more effective MOOCs. These results
contribute to the existing body of knowledge in the field, bring new insights,
and pose new questions for future research.


title: Categorical Distributions of Maximum Entropy under Marginal Constraints

abstract:
  The estimation of categorical distributions under marginal constraints
summarizing some sample from a population in the most-generalizable way is key
for many machine-learning and data-driven approaches. We provide a
parameter-agnostic theoretical framework that enables this task ensuring (i)
that a categorical distribution of Maximum Entropy under marginal constraints
always exists and (ii) that it is unique. The procedure of iterative
proportional fitting (IPF) naturally estimates that distribution from any
consistent set of marginal constraints directly in the space of probabilities,
thus deductively identifying a least-biased characterization of the population.
The theoretical framework together with IPF leads to a holistic workflow that
enables modeling any class of categorical distributions solely using the
phenomenological information provided.


title:
Detecting Tropical Cyclone from the basic overview of life of Extremely
  Severe Cyclonic Storm, TAUKTAE

abstract:
  The natural energy in the form of sunlight hotness, create low air pressure
in the atmospheric regions. In order to refill the vacated air mass the wind
flows from the higher air pressure regions to lower air pressure regions. If
the lower air pressure occurs in certain hot spots in the land or the coastal
region the moisturized wind from the ocean or sea regions flows to the hot
spots in the track of cyclone trajectories. This causes moisturized cloudy
storms, severe rainfalls and wind whirls in the circulation paths. The
extremely severe cyclonic storms intensified movements are tracked through the
elliptic or circular whirl paths which are commonly known as cyclone's eye. In
this study, we attempt to identify the various technical characteristics of the
extremely severe cyclonic storms caused by tropical cyclones. Due to the
tropical cyclone eye pattern's the storm's intensity and path trajectory
varies. We have compared the eye features of the tropical cyclone
intensification with the life span of the extremely severe cyclonic storm
TAUKTAE. The paper is organized by mentioning the data computation methods,
Overview of the life of ESCS TAUKTAE, Intensity variation for regulate TAUKTAE
according to Dvorak's technique, intensity estimation using ADT9.0 and
comparison with SATCON and IMD best track, Description of the formation of
TAUKTAE Eye, timeframes for eye scenes, sea surface temperature data
comparison, and final thoughts.


title:
Surface Vision Transformers: Flexible Attention-Based Modelling of
  Biomedical Surfaces

abstract:
  Recent state-of-the-art performances of Vision Transformers (ViT) in computer
vision tasks demonstrate that a general-purpose architecture, which implements
long-range self-attention, could replace the local feature learning operations
of convolutional neural networks. In this paper, we extend ViTs to surfaces by
reformulating the task of surface learning as a sequence-to-sequence learning
problem, by proposing patching mechanisms for general surface meshes. Sequences
of patches are then processed by a transformer encoder and used for
classification or regression. We validate our method on a range of different
biomedical surface domains and tasks: brain age prediction in the developing
Human Connectome Project (dHCP), fluid intelligence prediction in the Human
Connectome Project (HCP), and coronary artery calcium score classification
using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART)
dataset, and investigate the impact of pretraining and data augmentation on
model performance. Results suggest that Surface Vision Transformers (SiT)
demonstrate consistent improvement over geometric deep learning methods for
brain age and fluid intelligence prediction and achieve comparable performance
on calcium score classification to standard metrics used in clinical practice.
Furthermore, analysis of transformer attention maps offers clear and
individualised predictions of the features driving each task. Code is available
on Github: https://github.com/metrics-lab/surface-vision-transformers


title: MAESTRO: Matched Speech Text Representations through Modality Matching

abstract:
  We present Maestro, a self-supervised training method to unify
representations learnt from speech and text modalities. Self-supervised
learning from speech signals aims to learn the latent structure inherent in the
signal, while self-supervised learning from text attempts to capture lexical
information. Learning aligned representations from unpaired speech and text
sequences is a challenging task. Previous work either implicitly enforced the
representations learnt from these two modalities to be aligned in the latent
space through multitasking and parameter sharing or explicitly through
conversion of modalities via speech synthesis. While the former suffers from
interference between the two modalities, the latter introduces additional
complexity. In this paper, we propose Maestro, a novel algorithm to learn
unified representations from both these modalities simultaneously that can
transfer to diverse downstream tasks such as Automated Speech Recognition (ASR)
and Speech Translation (ST). Maestro learns unified representations through
sequence alignment, duration prediction and matching embeddings in the learned
space through an aligned masked-language model loss. We establish a new
state-of-the-art (SOTA) on VoxPopuli multilingual ASR with a 11% relative
reduction in Word Error Rate (WER), multidomain SpeechStew ASR (3.7% relative)
and 21 languages to English multilingual ST on CoVoST 2 with an improvement of
2.8 BLEU averaged over 21 languages.


title:
Incremental Prototype Prompt-tuning with Pre-trained Representation for
  Class Incremental Learning

abstract:
  Class incremental learning has attracted much attention, but most existing
works still continually fine-tune the entire representation model, inevitably
resulting in much catastrophic forgetting. Instead of struggling to fight
against such forgetting by replaying or distillation like most of the existing
methods, we take a novel pre-train-and-prompt-tuning paradigm to sequentially
learn new visual concepts based on a fixed semantic-rich pre-trained
representation model. In detail, we incrementally prompt-tune category
prototypes for classification and example prototypes to compensate for semantic
drift, the problem caused by learning bias at different phases. Extensive
experiments conducted on the mainstream incremental learning benchmarks
demonstrate that our method outperforms other state-of-the-art methods.


title: On the u^{\infty}-torsion submodule of prismatic cohomology

abstract:
  We investigate the maximal finite length submodule of the Breuil-Kisin
prismatic cohomology of a smooth proper formal scheme over a p-adic ring of
integers. This submodule governs pathology phenomena in integral p-adic
cohomology theories. Geometric applications include a control, in low degrees
and mild ramifications, of (1) the discrepancy between two naturally associated
Albanese varieties in characteristic p, and (2) kernel of the specialization
map in p-adic \'etale cohomology. As an arithmetic application, we study the
boundary case of the theory due to Fontaine-Laffaille, Fontaine-Messing, and
Kato. Also included is an interesting example, generalized from a construction
in Bhatt-Morrow-Scholze's work, which (1) illustrates some of our theoretical
results being sharp, and (2) negates a question of Breuil.


title:
Maximizing Sums of Non-monotone Submodular and Linear Functions:
  Understanding the Unconstrained Case

abstract:
  Motivated by practical applications, recent works have considered
maximization of sums of a submodular function $g$ and a linear function $\ell$.
Almost all such works, to date, studied only the special case of this problem
in which $g$ is also guaranteed to be monotone. Therefore, in this paper we
systematically study the simplest version of this problem in which $g$ is
allowed to be non-monotone, namely the unconstrained variant, which we term
Regularized Unconstrained Submodular Maximization (RegularizedUSM).
  Our main algorithmic result is the first non-trivial guarantee for general
RegularizedUSM. For the special case of RegularizedUSM in which the linear
function $\ell$ is non-positive, we prove two inapproximability results,
showing that the algorithmic result implied for this case by previous works is
not far from optimal. Finally, we reanalyze the known Double Greedy algorithm
to obtain improved guarantees for the special case of RegularizedUSM in which
the linear function $\ell$ is non-negative; and we complement these guarantees
by showing that it is not possible to obtain (1/2, 1)-approximation for this
case (despite intuitive arguments suggesting that this approximation guarantee
is natural).


title: On the Price of Locality in Static Fast Rerouting

abstract:
  Modern communication networks feature fully decentralized flow rerouting
mechanisms which allow them to quickly react to link failures. This paper
revisits the fundamental algorithmic problem underlying such local fast
rerouting mechanisms. Is it possible to achieve perfect resilience, i.e., to
define local routing tables which preserve connectivity as long as the
underlying network is still connected? Feigenbaum et al. (ACM PODC'12) and
Foerster et al. (SIAM APOCS'21) showed that, unfortunately, it is impossible in
general.
  This paper charts a more complete landscape of the feasibility of perfect
resilience. We first show a perhaps surprisingly large price of locality in
static fast rerouting mechanisms: even when source and destination remain
connected by a linear number of link-disjoint paths after link failures, local
rerouting algorithms cannot find any of them which leads to a disconnection on
the routing level. This motivates us to study resilience in graphs which
exclude certain dense minors, such as cliques or a complete bipartite graphs,
and in particular, provide characterizations of the possibility of perfect
resilience in different routing models. We provide further insights into the
price of locality by showing impossibility results for few failures and
investigate perfect resilience on Topology Zoo networks.


title:
The role of diamagnetism in the separation of particles and sharp edges
  of the Saturn's ring

abstract:
  The diamagnetism of ice particles of the rings can explain their separation
and the sharp edges of the rings. The existing gravitational theories of the
origin of rings can't explain these observed facts. Taking into account the
magnetic field of Saturn, all the particles of the rings acquire stability in
the horizontal and vertical directions. The force of diamagnetic expulsion of
inhomogeneous magnetic field inside the rings structure forms sharp edges and
separates the particles.


title: Proceedings 16th Logical and Semantic Frameworks with Applications

abstract:
  This volume contains the post-proceedings of the Sixteenth Logical and
Semantic Frameworks with Applications (LSFA 2021). The meeting was held online
on July 23-24, 2021, organised by the Universidad de Buenos Aires, Argentina.
LSFA aims to bring researchers and students interested in theoretical and
practical aspects of logical and semantic frameworks and their applications.
The covered topics include proof theory, type theory and rewriting theory,
specification and deduction languages, and formal semantics of languages and
systems.


title:
A CCBM-based generalized GKB iterative regularizing algorithm for
  inverse Cauchy problems

abstract:
  This paper examines inverse Cauchy problems that are governed by a kind of
elliptic partial differential equation. The inverse problems involve recovering
the missing data on an inaccessible boundary from the measured data on an
accessible boundary, which is severely ill-posed. By using the coupled complex
boundary method (CCBM), which integrates both Dirichlet and Neumann data into a
single Robin boundary condition, we reformulate the underlying problem into an
operator equation. Based on this new formulation, we prove the existence of a
unique solution even in cases with noisy data. A Golub-Kahan bidiagonalization
(GKB) process together with Givens rotation is employed for iteratively solving
the proposed operator equation. The regularizing property of the developed
method, called CCBM-GKB, and its convergence rate results are proved under a
posteriori stopping rule. Finally, a linear finite element method is used for
the numerical realization of CCBM-GKB. Various numerical experiments
demonstrate that CCBM-GKB is a kind of accelerated iterative regularization
method, as it is much faster than the classic Landweber method.


title: Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

abstract:
  Stuttering is a varied speech disorder that harms an individual's
communication ability. Persons who stutter (PWS) often use speech therapy to
cope with their condition. Improving speech recognition systems for people with
such non-typical speech or tracking the effectiveness of speech therapy would
require systems that can detect dysfluencies while at the same time being able
to detect speech techniques acquired in therapy.
  This paper shows that fine-tuning wav2vec 2.0 for the classification of
stuttering on a sizeable English corpus containing stuttered speech, in
conjunction with multi-task learning, boosts the effectiveness of the
general-purpose wav2vec 2.0 features for detecting stuttering in speech; both
within and across languages. We evaluate our method on Fluencybank and the
German therapy-centric Kassel State of Fluency (KSoF) dataset by training
Support Vector Machine classifiers using features extracted from the fine-tuned
models for six different stuttering-related events types: blocks,
prolongations, sound repetitions, word repetitions, interjections, and -
specific to therapy - speech modifications. Using embeddings from the
fine-tuned models leads to relative classification performance gains up to 27\%
w.r.t. F1-score.


title:
Continual Inference: A Library for Efficient Online Inference with Deep
  Neural Networks in PyTorch

abstract:
  We present Continual Inference, a Python library for implementing Continual
Inference Networks (CINs) in PyTorch, a class of Neural Networks designed
specifically for efficient inference in both online and batch processing
scenarios. We offer a comprehensive introduction and guide to CINs and their
implementation in practice, and provide best-practices and code examples for
composing complex modules for modern Deep Learning. Continual Inference is
readily downloadable via the Python Package Index and at
\url{www.github.com/lukashedegaard/continual-inference}.


title: Almost-optimal bulk regularity conditions in the CLT for Wigner matrices

abstract:
  We consider linear spectral statistics of the form $\mathrm{tr} ( \varphi
(H))$ for test functions $\varphi$ of low regularity and Wigner matrices $H$
with smooth entry distribution. We show that for functions $\varphi$ in the
Sobolev space $H^{1/2+\varepsilon}$ or the space $C^{1/2+\varepsilon}$, that
are supported within the spectral bulk of the semicircle distribution, these
linear spectral statistics have asymptotic Gaussian fluctuations with the same
variance as in the CLT for functions of higher regularity, for any $\varepsilon
>0$.


title: On the K-theory of $\mathbb{Z}/p^n$ -- announcement

abstract:
  We announce new methods for using prismatic cohomology to compute the
K-groups of $\mathbb{Z}/p^n$ and related rings. We use computer algebra methods
to compute these K-groups through a large range in specific cases and also
obtain explicit formulas for their orders in large degrees.


title: Self supervised learning for robust voice cloning

abstract:
  Voice cloning is a difficult task which requires robust and informative
features incorporated in a high quality TTS system in order to effectively copy
an unseen speaker's voice. In our work, we utilize features learned in a
self-supervised framework via the Bootstrap Your Own Latent (BYOL) method,
which is shown to produce high quality speech representations when specific
audio augmentations are applied to the vanilla algorithm. We further extend the
augmentations in the training procedure to aid the resulting features to
capture the speaker identity and to make them robust to noise and acoustic
conditions. The learned features are used as pre-trained utterance-level
embeddings and as inputs to a Non-Attentive Tacotron based architecture, aiming
to achieve multispeaker speech synthesis without utilizing additional speaker
features. This method enables us to train our model in an unlabeled
multispeaker dataset as well as use unseen speaker embeddings to copy a
speaker's voice. Subjective and objective evaluations are used to validate the
proposed model, as well as the robustness to the acoustic conditions of the
target utterance.


title:
Asymptotic behavior of positive solutions to the Lane-Emden system in
  dimension two

abstract:
  Consider the Lane-Emden system
  \begin{equation*}\begin{aligned}
  &-\Delta u=v^p,\quad u>0,\quad\text{in}~\Omega,
  &-\Delta v=u^q,\quad v>0,\quad\text{in}~\Omega,
  &u=v=0,\quad\text{on}~\partial\Omega,
  \end{aligned}\end{equation*} where $\Omega$ is a smooth bounded domain in
$\mathbb{R}^N$ with $N\geq 2$ and $q\ge p>0$. The asymptotic behavior of {\it
least energy solutions} of this system was studied for $N\geq 3$. However, the
case $N=2$ is different and remains completely open. In this paper, we study
the case $N=2$ with
  $q=p+\theta_p$ and $\sup_p\theta_p<+\infty$. Under the following natural
condition that holds for least energy solutions
  $$\limsup_{p\to+\infty} p\int_\Omega\nabla u_p\cdot\nabla v_p \mathrm{d}
x<+\infty,$$ we give a complete description of the asymptotic behavior of {\it
positive solutions} $(u_p,v_p)$ (i.e., not only for least energy solutions) as
$p\to+\iy$. This seems the first result for asymptotic behaviors of the
Lane-Emden system in the two dimension case.


title:
The last three years: multiband gravitational-wave observations of
  stellar-mass binary black holes

abstract:
  Understanding the formation and evolution of the stellar-mass binary black
holes discovered by LIGO and Virgo is a challenge that spans many areas of
astrophysics, from stellar evolution, dynamics and accretion disks, to possible
exotic early universe processes. Over the final years of their lives,
stellar-mass binaries radiate gravitational waves that are first observable by
space-based detectors (such as LISA) and then ground-based instruments (such as
LIGO, Virgo and the next generation observatories Cosmic Explorer and the
Einstein Telescope). Using state-of-the-art waveform models and
parameter-estimation pipelines for both ground- and space-based observations,
we show that (the expected handful of) these multiband observations will allow
at least percent-level measurements of all 17 parameters that describe the
binary, the possible identification of a likely host galaxy, and the
forewarning of the merger days in advance allowing telescopes at multiple
wavelengths to search for any electromagnetic signature associated to it.
Multiband sources will therefore be a gold mine for astrophysics, but we also
show that they could be less useful as laboratories for fundamental tests of
general relativity than has been previously suggested.


title:
Low complexity joint position and channel estimation at millimeter wave
  based on multidimensional orthogonal matching pursuit

abstract:
  Compressive approaches provide a means of effective channel high resolution
channel estimates in millimeter wave MIMO systems, despite the use of analog
and hybrid architectures. Such estimates can also be used as part of a joint
channel estimation and localization solution. Achieving good localization
performance, though, requires high resolution channel estimates and better
methods to exploit those channels. In this paper, we propose a low complexity
multidimensional orthogonal matching pursuit strategy for compressive channel
estimation based by operating with a product of independent dictionaries for
the angular and delay domains, instead of a global large dictionary. This leads
to higher quality channel estimates but with lower complexity than
generalizations of conventional solutions. We couple this new algorithm with a
novel localization formulation that does not rely on the absolute time of
arrival of the LoS path and exploits the structure of reflections in indoor
channels. We show how the new approach is able to operate in realistic 3D
scenarios to estimate the communication channel and locate devices in an indoor
simulation setting.


title:
Complete flux scheme for variable velocity fields: coupling between the
  advection-diffusion equation and the Poisson equation for the velocity field

abstract:
  In this work, we consider an advection-diffusion equation, coupled to a
Poisson equation for the velocity field. This type of coupling is typically
encountered in models arising from plasma physics or porous media flow. The aim
of this work is to build upon the complete flux scheme (an improvement over the
Scharfetter-Gummel scheme by considering the contribution of the source term),
so that its second-order convergence, which is uniform in P\'eclet numbers,
carries over to these models. This is done by considering a piecewise linear
approximation of the velocity field, which is then used for defining
upwind-adjusted P\'eclet numbers.


title:
The Influence of a Parameter that Controls the Asymmetry of a Potential
  Energy Surface with an Entrance Channel and Two Potential Wells

abstract:
  In this paper we study an asymmetric valley-ridge inflection point (VRI)
potential, whose energy surface (PES) features two sequential index-1 saddles
(the upper and the lower), with one saddle having higher energy than the other
and two potential wells separated by the lower index-1 saddle. We show how the
depth and the flatness of our potential changes as we modify the parameter that
controls the asymmetry as well as how the branching ratio (ratio of the
trajectories that enter each well) is changing as we modify the same parameter
and its correlation with the area of the lobes as they have formulated by the
stable and unstable manifolds that have been extracted from the gradient of the
LD scalar fields.


title:
Stabilisation of a viscous conservation law by a one-dimensional
  external force

abstract:
  We study a damped scalar conservation law driven by the sum of a fixed
external force and a localised one-dimensional control. The problem is
considered in a bounded domain and is supplemented with the Dirichlet boundary
condition. It is proved that any solution of the uncontrolled equation can be
exponentially stabilised. As a consequence, we obtain the global approximate
controllability to trajectories by a one-dimensional localised control.


title: Detecting Vocal Fatigue with Neural Embeddings

abstract:
  Vocal fatigue refers to the feeling of tiredness and weakness of voice due to
extended utilization. This paper investigates the effectiveness of neural
embeddings for the detection of vocal fatigue. We compare x-vectors,
ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English.
Low-dimensional mappings of the data reveal that neural embeddings capture
information about the change in vocal characteristics of a speaker during
prolonged voice usage. We show that vocal fatigue can be reliably predicted
using all three kinds of neural embeddings after only 50 minutes of continuous
speaking when temporal smoothing and normalization are applied to the extracted
embeddings. We employ support vector machines for classification and achieve
accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and
82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score
of 76%, when the trained system is applied to a different speaker and recording
environment without any adaptation.


title: Finding Counterfactual Explanations through Constraint Relaxations

abstract:
  Interactive constraint systems often suffer from infeasibility (no solution)
due to conflicting user constraints. A common approach to recover infeasibility
is to eliminate the constraints that cause the conflicts in the system. This
approach allows the system to provide an explanation as: "if the user is
willing to drop out some of their constraints, there exists a solution".
However, one can criticise this form of explanation as not being very
informative. A counterfactual explanation is a type of explanation that can
provide a basis for the user to recover feasibility by helping them understand
which changes can be applied to their existing constraints rather than removing
them. This approach has been extensively studied in the machine learning field,
but requires a more thorough investigation in the context of constraint
satisfaction. We propose an iterative method based on conflict detection and
maximal relaxations in over-constrained constraint satisfaction problems to
help compute a counterfactual explanation.


title:
Thermodynamic and cosmological parameters of early stages of the
  Universe

abstract:
  The early Universe was characterized by the presence of heavy particles that
decoupled at different temperatures leading to different phases of the
Universe. This had a consequences on the time evolution of the thermodynamic
and the cosmological parameters characterizing each phase of the early
Universe. In this study, we derive the analytic expressions of the equations
governing the time evolution of these parameters in the early eras of the
Universe namely, the radiation era, the quark-gluon plasma era, the hadron era
and the mixed era. The parameters under concern include the energy density, the
entropy density, the temperature, the pressure in addition to Hubble parameter
and the scale factor. Having these expressions allows us to give estimations of
the times corresponding to the beginning and ending of each era of the Universe
as will be presented in this work.


title:
Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With
  Class-Dependent Confidence

abstract:
  Energy-efficient machine learning models that can run directly on edge
devices are of great interest in IoT applications, as they can reduce network
pressure and response latency, and improve privacy. An effective way to obtain
energy-efficiency with small accuracy drops is to sequentially execute a set of
increasingly complex models, early-stopping the procedure for "easy" inputs
that can be confidently classified by the smallest models. As a stopping
criterion, current methods employ a single threshold on the output
probabilities produced by each model. In this work, we show that such a
criterion is sub-optimal for datasets that include classes of different
complexity, and we demonstrate a more general approach based on per-classes
thresholds. With experiments on a low-power end-node, we show that our method
can significantly reduce the energy consumption compared to the
single-threshold approach.


title:
Collective patterns and stable misunderstandings in networks striving
  for consensus without a common value system

abstract:
  Collective phenomena in systems of interacting agents have helped us
understand diverse social, ecological and biological observations. The
corresponding explanations are challenged by incorrect information processing.
In particular, the models typically assume a shared understanding of signals or
a common truth or value system, i.e., an agreement of whether the measurement
or perception of information is `right' or `wrong'. It is an open question
whether a collective consensus can emerge without these conditions. Here we
introduce a model of interacting agents that strive for consensus, however,
each with only a subjective perception of the world. Our communication model
does not presuppose a definition of right or wrong and the actors can hence not
distinguish between correct and incorrect observations. Depending on a single
parameter that governs how responsive the agents are to changing their
world-view we observe a transition between an unordered phase of individuals
that are not able to communicate with each other and a phase of an emerging
shared signalling framework. We find that there are two types of
convention-aligned clusters: one, where all social actors in the cluster have
the same set of conventions, and one, where neighbouring actors have different
but compatible conventions (`stable misunderstandings').


title:
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under
  Advanced Persistent Threats

abstract:
  Despite its technological benefits, Internet of Things (IoT) has cyber
weaknesses due to the vulnerabilities in the wireless medium. Machine learning
(ML)-based methods are widely used against cyber threats in IoT networks with
promising performance. Advanced persistent threat (APT) is prominent for
cybercriminals to compromise networks, and it is crucial to long-term and
harmful characteristics. However, it is difficult to apply ML-based approaches
to identify APT attacks to obtain a promising detection performance due to an
extremely small percentage among normal traffic. There are limited surveys to
fully investigate APT attacks in IoT networks due to the lack of public
datasets with all types of APT attacks. It is worth to bridge the
state-of-the-art in network attack detection with APT attack detection in a
comprehensive review article. This survey article reviews the security
challenges in IoT networks and presents the well-known attacks, APT attacks,
and threat models in IoT systems. Meanwhile, signature-based, anomaly-based,
and hybrid intrusion detection systems are summarized for IoT networks. The
article highlights statistical insights regarding frequently applied ML-based
methods against network intrusion alongside the number of attacks types
detected. Finally, open issues and challenges for common network intrusion and
APT attacks are presented for future research.


title: Motivic spectra and universality of $K$-theory

abstract:
  We develop a theory of motivic spectra in a broad generality; in particular
$\mathbb{A}^1$-homotopy invariance is not assumed. As an application, we prove
that $K$-theory of schemes is a universal Zariski sheaf of spectra which is
equipped with an action of the Picard stack and satisfies projective bundle
formula.


title:
The depleted Higgs boson: searches for universal coupling suppression,
  invisible decays, and mixed-in scalars

abstract:
  There are two simple ways that the standard signals of the Standard Model
Higgs boson can be depleted. Its couplings to fermions and gauge bosons can be
suppressed by a universal factor, and part of its branching fraction can be
drained into invisible final states. A large class of theories can impose one
or both of these depletion factors, even if mild, by way of additional scalar
bosons that are singlets under the Standard Model but mix with the Higgs boson.
We perform a comprehensive survey of the present status of the depleted Higgs
boson, and discuss future prospects for detecting the presence of either
depletion factor. We also survey the constraints status and future detection
prospects for the generic case of extra mixed-in scalars which generically lead
to these depletion factors for the Higgs boson. We find, for example, that
precision study of the Higgs boson in many cases is more powerful than searches
for the extra scalar states, given the slate of next-generation experiments
that are on the horizon.


title:
A unified theory of non-overlapping Robin-Schwarz methods -- continuous
  and discrete, including cross points

abstract:
  Non-overlapping Schwarz methods with generalized Robin transmission
conditions were originally introduced by B. Despr\'es for time-harmonic wave
propagation problems and have largely developed over the past thirty years. The
aim of the paper is to provide both a review of the available formulations and
methods as well as a consistent theory applicable to more general cases than
studied until to date. An abstract variational framework is provided
reformulating the original problem by the well-known form involving a
scattering operator and an interface exchange operator, and the equivalence
between the formulations is discussed thoroughly. The framework applies to a
series of wave propagation problems throughout the de Rham complex, such as the
scalar Helmholtz equation, Maxwell's equations, a dual formulation of the
Helmholtz equation in H(div), as well as any conforming finite element
discretization thereof, and it applies also to coercive problems. Three
convergence results are shown. The first one (using compactness) and the second
one (based on absorbtion) generalize Despr\'es' early findings and apply as
well to the FETI-2LM formulation. The third result, oriented on the work by
Collino, Ghanemi, and Joly, establishes a convergence rate and covers cases
with cross points, while not requiring any regularity of the solution. The key
ingredient is a global interface exchange operator, proposed originally by X.
Claeys and further developed by Claeys and Parolin, here worked out in full
generality. The third type of convergence theory is applicable at the discrete
level as well, where the exchange operator is allowed to be even local. The
resulting scheme can be viewed as a generalization of the 2-Lagrange-multiplier
method introduced by S. Loisel, and connections are drawn to another technique
proposed by Gander and Santugini.


title:
Sakurai's Object revisited: new laboratory data for carbonates and
  melilites suggest the carrier of 6.9 $\mu$m excess absorption is a carbonate

abstract:
  We present new room-temperature 1100 - 1800 cm^{-1} spectra of melilite
silicates and 600 - 2000 cm^{-1} spectra of three randomly orientated
fine-grained carbonates to determine the possible carrier(s) of a 6.9~micron
absorption feature observed in a variety of dense astronomical environments
including young stellar objects and molecular clouds. We focus on the low-mass
post-AGB star Sakurai's Object which has been forming substantial quantities of
carbonaceous dust since an eruptive event in the 1990s. Large melilite grains
cannot be responsible for the 6.9-micron absorption feature because the
similarly-shaped feature in the laboratory spectrum was produced by very low
(0.1 per cent by mass) carbonate contamination which was not detected at other
wavelengths. Due to the high band-strength of the 6.9-micron feature in
carbonates, we conclude that carbonates carry the astronomical 6.9~micron
feature. Replacement of melilite with carbonates in models of Sakurai's object
improves fits to the 6 - 7-micron Spitzer spectra without significantly
altering other conclusions of Bowey's previous models except that there is no
link between the feature and the abundance of melilite in meteorites. With
magnesite (MgCO3), the abundance of 25-micron-sized SiC grains is increased by
10 - 50 per cent and better constrained. The mass of carbonate dust is similar
to the mass of PAH dust. Existing experiments suggest carbonates are stable
below 700~K, however it is difficult to ascertain the applicability of these
experiments to astronomical environments and more studies are required.


title: Influence of the magnetic field on the formation of protostellar disks

abstract:
  We numerically model the collapse of magnetic rotating protostellar clouds
with mass of 10 $M_{sun}$. The simulations are carried out with the help of 2D
MHD code Enlil. The structure of the cloud at the isothermal stage of the
collapse is investigated for the cases of weak, moderate, and strong initial
magnetic field. Simulations reveal the universal hierarchical structure of
collapsing protostellar clouds, consisting of flattened envelope with the
qausi-magnetostatc disk inside and the first core in its center. The size of
the primary disk increases with the initial magnetic energy of the cloud. The
magnetic braking efficiently transports the angular momentum from the primary
disk into the envelope in the case, when initial magnetic energy of the cloud
is more than 20 % of its gravitational energy. Intensity of the outflows
launched from the region near the boundary of the first core increases with
initial magnetic energy. The `dead' zone with small ionization fraction,
$x<10^{-11}$, forms inside the first hydrostatic core and at the base of the
outflow. Ohmic dissipation and ambipolar diffusion determine conditions for
further formation of the protostellar disk in this region.


title:
Half-sibling regression meets exoplanet imaging: PSF modeling and
  subtraction using a flexible, domain knowledge-driven, causal framework

abstract:
  High-contrast imaging of exoplanets hinges on powerful post-processing
methods to denoise the data and separate the signal of a companion from its
host star, which is typically orders of magnitude brighter. Existing
post-processing algorithms do not use all prior domain knowledge that is
available about the problem. We propose a new method that builds on our
understanding of the systematic noise and the causal structure of the
data-generating process. Our algorithm is based on a modified version of
half-sibling regression (HSR), a flexible denoising framework that combines
ideas from the fields of machine learning and causality. We adapt the method to
address the specific requirements of high-contrast exoplanet imaging data
obtained in pupil tracking mode. The key idea is to estimate the systematic
noise in a pixel by regressing the time series of this pixel onto a set of
causally independent, signal-free predictor pixels. We use regularized linear
models in this work; however, other (non-linear) models are also possible. In a
second step, we demonstrate how the HSR framework allows us to incorporate
observing conditions such as wind speed or air temperature as additional
predictors. When we apply our method to four data sets from the VLT/NACO
instrument, our algorithm provides a better false-positive fraction than
PCA-based PSF subtraction, a popular baseline method in the field.
Additionally, we find that the HSR-based method provides direct and accurate
estimates for the contrast of the exoplanets without the need to insert
artificial companions for calibration in the data sets. Finally, we present
first evidence that using the observing conditions as additional predictors can
improve the results. Our HSR-based method provides an alternative, flexible and
promising approach to the challenge of modeling and subtracting the stellar PSF
and systematic noise in exoplanet imaging data.


title: Task-Aware Active Learning for Endoscopic Image Analysis

abstract:
  Semantic segmentation of polyps and depth estimation are two important
research problems in endoscopic image analysis. One of the main obstacles to
conduct research on these research problems is lack of annotated data.
Endoscopic annotations necessitate the specialist knowledge of expert
endoscopists and due to this, it can be difficult to organise, expensive and
time consuming. To address this problem, we investigate an active learning
paradigm to reduce the number of training examples by selecting the most
discriminative and diverse unlabelled examples for the task taken into
consideration. Most of the existing active learning pipelines are task-agnostic
in nature and are often sub-optimal to the end task. In this paper, we propose
a novel task-aware active learning pipeline and applied for two important tasks
in endoscopic image analysis: semantic segmentation and depth estimation. We
compared our method with the competitive baselines. From the experimental
results, we observe a substantial improvement over the compared baselines.
Codes are available at https://github.com/thetna/endo-active-learn.


title:
Assessing the accuracy of compound formation energies with quantum Monte
  Carlo

abstract:
  Accurately predicting the formation energy of a compound, which describes its
thermodynamic stability, is a key challenge in materials physics. Here, we
employ many-body quantum Monte Carlo (QMC) with single-reference trial
functions to compute the formation energy of two electronically disparate
compounds, the intermetallic VPt$_2$ and the semiconductor CuI, for which
standard density functional theory (DFT) predictions using both the
Perdew-Burke Ernzerhof (PBE) and the strongly constrained and appropriately
normed (SCAN) approximations deviate markedly from available experimental
values. For VPt$_2$, we find an agreement between QMC, SCAN, and PBE0
estimates, which therefore remain in disagreement with the much less exothermic
experimental value. For CuI, the QMC result agrees with neither SCAN nor PBE
pointing towards DFT exchange-correlation biases, likely related to the
localized Cu $3d$ electrons. Compared to DFT, spin-averaged QMC exhibits a
smaller but still appreciable deviation when compared to experiment. The QMC
result is slightly improved by incorporating spin-orbit corrections for CuI and
solid I$_2$, so that experiment and theory are brought into imperfect but
reasonable agreement within about 120 meV/atom.


title:
Spontaneous time-reversal symmetry breaking in twisted double bilayer
  graphene

abstract:
  Twisted double bilayer graphene (tDBG) comprises two Bernal-stacked bilayer
graphene sheets with a twist between them. Gate voltages applied to top and
back gates of a tDBG device tune both the flatness and topology of the
electronic bands, enabling an unusual level of experimental control. Broken
spin/valley symmetry metallic states have been observed in tDBG devices with
twist angles $\sim $ 1.2-1.3$^\circ$, but the topologies and order parameters
of these states have remained unclear. We report the observation of an
anomalous Hall effect in the correlated metal state of tDBG, with hysteresis
loops spanning 100s of mT in out-of-plane magnetic field ($B_{\perp}$) that
demonstrate spontaneously broken time-reversal symmetry. The $B_{\perp}$
hysteresis persists for in-plane fields up to several Tesla, suggesting valley
(orbital) ferromagnetism. At the same time, the resistivity is strongly
affected by even mT-scale values of in-plane magnetic field, pointing to
spin-valley coupling or to a direct orbital coupling between in-plane field and
the valley degree of freedom.


title: On Dunkl Schr\"odinger semigroups with Green bounded potentials

abstract:
  On $\mathbb R^N$ equipped with a normalized root system $R$, a multiplicity
function $k(\alpha) > 0$, and the associated measure
  $$ dw(\mathbf x)=\prod_{\alpha\in R}|\langle \mathbf
x,\alpha\rangle|^{k(\alpha)}\, d\mathbf x, $$ we consider a Dunkl Schr\"odinger
operator $L=-\Delta_k+V$, where $\Delta_k$ is the Dunkl Laplace operator and
$V\in L^1_{\rm loc} (dw)$ is a non-negative potential. Let $h_t(\mathbf
x,\mathbf y)$ and $k^{\{V\}}_t(\mathbf x,\mathbf y)$ denote the Dunkl heat
kernel and the integral kernel of the semigroup generated by $-L$ respectively.
We prove that $k^{\{V\}}_t(\mathbf x,\mathbf y)$ satisfies the following heat
kernel lower bounds: there are constants $C, c>0$ such that $$ h_{ct}(\mathbf
x,\mathbf y)\leq C k^{\{V\}}_t(\mathbf x,\mathbf y)$$ if and only if $$
\sup_{\mathbf x\in\mathbb R^N} \int_0^\infty \int_{\mathbb R^N} V(\mathbf
y)w(B(\mathbf x,\sqrt{t}))^{-1}e^{-\|\mathbf x-\mathbf y\|^2/t}\, dw(\mathbf
y)\, dt<\infty, $$ where $B(\mathbf x,\sqrt{t})$ stands for the Euclidean ball
centered at $\mathbf x \in \mathbb{R}^N$ and radius $\sqrt{t}$.


title: Deep Visual Geo-localization Benchmark

abstract:
  In this paper, we propose a new open-source benchmarking framework for Visual
Geo-localization (VG) that allows to build, train, and test a wide range of
commonly used architectures, with the flexibility to change individual
components of a geo-localization pipeline. The purpose of this framework is
twofold: i) gaining insights into how different components and design choices
in a VG pipeline impact the final results, both in terms of performance
(recall@N metric) and system requirements (such as execution time and memory
consumption); ii) establish a systematic evaluation protocol for comparing
different methods. Using the proposed framework, we perform a large suite of
experiments which provide criteria for choosing backbone, aggregation and
negative mining depending on the use-case and requirements. We also assess the
impact of engineering techniques like pre/post-processing, data augmentation
and image resizing, showing that better performance can be obtained through
somewhat simple procedures: for example, downscaling the images' resolution to
80% can lead to similar results with a 36% savings in extraction time and
dataset storage requirement. Code and trained models are available at
https://deep-vg-bench.herokuapp.com/.


title:
A new DG method for a pure--stress formulation of the Brinkman problem
  with strong symmetry

abstract:
  A strongly symmetric stress approximation is proposed for the Brinkman
equations with mixed boundary conditions. The resulting formulation solves for
the Cauchy stress using a symmetric interior penalty discontinuous Galerkin
method. Pressure and velocity are readily post-processed from stress, and a
second post-process is shown to produce exactly divergence-free discrete
velocities. We demonstrate the stability of the method with respect to a
DG-energy norm and obtain error estimates that are explicit with respect to the
coefficients of the problem. We derive optimal rates of convergence for the
stress and for the post-processed variables. Moreover, under appropriate
assumptions on the mesh, we prove optimal $L^2$-error estimates for the stress.
Finally, we provide numerical examples in 2D and 3D.


title: Harmonic forms and the Rumin complex on Sasakian manifolds

abstract:
  We show that the kernel of the Rumin Laplacian agrees with that of the
Hodge-de Rham Laplacian on compact Sasakian manifolds. As a corollary, we
obtain another proof of primitiveness of harmonic forms. Moreover, the space of
harmonic forms coincides with the sub-Riemann limit of Hodge-de Rham Laplacian
when its limit converges. Finally, we express the analytic torsion function
associated with the Rumin complex in terms of the Reeb vector field.


title:
Debiasing the estimate of treatment effect on the treated with
  time-varying counfounders

abstract:
  With the increased availability of large health databases comes the
opportunity of evaluating treatment effect on new data sources.Through these
databases time-dependent outcomes can be analysed as events that can be
measured using counting processes. Estimating average treatment effect on the
treated (ATT) requires modelling of time-varying covariate and time-dependent
treatment and outcome. Gran et al. proposed an easy-to-implement method based
on additive intensity regression to estimate ATT. We introduce a debiased
estimate of the ATT based on a generalization of the Gran's model for a
potentially repeated outcome and in the presence of multiple time-dependent
covariates and baseline covariates. Simulation analyses show that our corrected
estimator outperforms Gran's uncorrected estimator. Our method is applied to
intensive care real-life data from MIMIC-III databases to estimate
vasoppressors effect on patients with sepsis.


title: Light Cone Sum Rules and Form Factors for $p\rightarrow e^+ \gamma$

abstract:
  Proton decay is a baryon number violating process, and hence is forbidden in
the Standard Model (SM) of particle physics. Baryon number violation is
expected to be an important criteria to explain the matter-anti-matter
asymmetry of the universe. Any detection of the proton decay will be a direct
evidence of physics beyond the SM. In SMEFT, proton decay is possible via
baryon number violating dimension-6 operators. In this work, we pay attention
to the decay channel $p\rightarrow e^+ \gamma$ which is expected to be an
experimentally cleaner channel due to less nuclear absorption. The gauge
invariant amplitude of this process involves two form factors. We calculate
these form factors in the framework of light cone sum rules (LCSR) using photon
DAs upto two particle twist-3 accuracy as well as proton DAs of twist-3
accuracy. We find that the form factors calculated using photon DAs are more
reliable.


title: Column competent tensors and tensor complementarity problem

abstract:
  In multilinear algebra, some special classes of matrices are extended to
higher order structured tensors. The local $w$-uniqueness solution to the
linear complementarity problem can be identified by the column competent
matrix. Motivated by this $w$-uniqueness property, we introduce column
competent tensor in the context of tensor complementarity problem. We consider
some important properties. In the theory of linear complementarity problem,
column competent matrices are introduced to study local $w$-uniqueness property
of LCP solution. We present the inheritance property and invariance property of
column competent tensors. We study the tensor complementarity problem using
column competent tensors and several results are established. Some examples are
illustrated to support the results. Keywords: Tensor complementarity problem,
column competent tensor, nondegenerate tensor, $\omega$-solution. AMS subject
classifications: 90C33, 90C30, 15A69, 46G25.


title:
Reputation as insurance: how reputation moderates public backlash
  following a company's decision to profiteer

abstract:
  We examine whether a company's corporate reputation gained from their CSR
activities and a company leader's reputation, one that is unrelated to his or
her business acumen, can impact economic action fairness appraisals. We provide
experimental evidence that good corporate reputation causally buffers
individuals' negative fairness judgment following the firm's decision to
profiteer from an increase in the demand. Bad corporate reputation does not
make the decision to profiteer as any less acceptable. However, there is
evidence that individuals judge as more unfair an ill-reputed firm's decision
to raise their product's price to protect against losses. Thus, our results
highlight the importance of a good reputation in protecting a firm against
severe negative judgments from making an economic decision that the public
deems unfair.


title: A sub-Riemannian Gauss-Bonnet theorem for surfaces in contact manifolds

abstract:
  We obtain a sub-Riemannian version of the classical Gauss-Bonnet theorem. We
consider 3 dimensional contact sub-Riemannian manifolds with a corresponding
metric extension and obtain a pure sub-Riemannian metric in the limit for
subsurfaces. In particular, we are able to recover topological information
concentrated around the characteristic set of points where the tangent space to
the surface and contact structure coincide.


title:
Physics is organized around transformations connecting contextures in a
  polycontextural world

abstract:
  The rich body of physical theories defines the foundation of our
understanding of the world. Its mathematical formulation is based on classical
Aristotelian (binary) logic. In the philosophy of science the ambiguities,
paradoxes, and the possibility of subjective interpretations of facts have
challenged binary logic, leading, among other developments, to Gotthard
G\"unther's theory of polycontexturality (often also termed 'transclassical
logic'). G\"unther's theory explains how observers with subjective perception
can become aware of their own subjectivity and provides means to describe
contradicting or even paradox observations in a logically sound formalism. Here
we summarize the formalism behind G\"unther's theory and apply it to two
well-known examples from physics where different observers operate in distinct
and only locally valid logical systems. Using polycontextural logic we show how
the emerging awareness of these limitations of logical systems entails the
design of mathematical transformations, which then become an integral part of
the theory. In our view, this approach offers a novel perspective on the
structure of physical theories and, at the same time, emphasizes the relevance
of the theory of polycontexturality in modern sciences.


title:
Open Wilson chain numerical renormalization group approach to Green's
  functions

abstract:
  By combining Wilson's numerical renormalization group with a modified
Bloch-Redfield approach we are able to eliminate the artificial broadening of
the Lehmann representation of quantum impurity spectral functions required by
the standard numerical renormalization group algorithm. Our approach is based
on the exact reproduction of the continuous coupling function in the original
quantum impurity model. It augments each chain site of the Wilson chain by a
coupling to an additional reservoir. This open Wilson chain is constructed by a
continuous fraction expansion and the coupling function is treated in second
order in the context of the Bloch-Redfield approach. The eigenvalues of the
resulting Bloch-Redfield tensor generate a finite life time of the numerical
renormalization group excitations that leads to a natural broadening of the
spectral functions. We combine this approach with z-averaging and an analytical
exact expression for the correlation part of the self-energy to obtain an
accurate representation of the spectral function of the original continuum
model in the absence and presence of an external magnetic field.


title: Variational dynamics as a ground-state problem on a quantum computer

abstract:
  We propose a variational quantum algorithm to study the real time dynamics of
quantum systems as a ground-state problem. The method is based on the original
proposal of Feynman and Kitaev to encode time into a register of auxiliary
qubits. We prepare the Feynman-Kitaev Hamiltonian acting on the composed system
as a qubit operator and find an approximate ground state using the Variational
Quantum Eigensolver. We apply the algorithm to the study of the dynamics of a
transverse field Ising chain with an increasing number of spins and time steps,
proving a favorable scaling in terms of the number of two qubit gates. Through
numerical experiments, we investigate its robustness against noise, showing
that the method can be use to evaluate dynamical properties of quantum systems
and detect the presence of dynamical quantum phase transitions by measuring
Loschmidt echoes.


title:
Limitations of variational quantum algorithms: a quantum optimal
  transport approach

abstract:
  The impressive progress in quantum hardware of the last years has raised the
interest of the quantum computing community in harvesting the computational
power of such devices. However, in the absence of error correction, these
devices can only reliably implement very shallow circuits or comparatively
deeper circuits at the expense of a nontrivial density of errors. In this work,
we obtain extremely tight limitation bounds for standard NISQ proposals in both
the noisy and noiseless regimes, with or without error-mitigation tools. The
bounds limit the performance of both circuit model algorithms, such as QAOA,
and also continuous-time algorithms, such as quantum annealing. In the noisy
regime with local depolarizing noise $p$, we prove that at depths
$L=\cO(p^{-1})$ it is exponentially unlikely that the outcome of a noisy
quantum circuit outperforms efficient classical algorithms for combinatorial
optimization problems like Max-Cut. Although previous results already showed
that classical algorithms outperform noisy quantum circuits at constant depth,
these results only held for the expectation value of the output. Our results
are based on newly developed quantum entropic and concentration inequalities,
which constitute a homogeneous toolkit of theoretical methods from the quantum
theory of optimal mass transport whose potential usefulness goes beyond the
study of variational quantum algorithms.


title: Few-Shot Forecasting of Time-Series with Heterogeneous Channels

abstract:
  Learning complex time series forecasting models usually requires a large
amount of data, as each model is trained from scratch for each task/data set.
Leveraging learning experience with similar datasets is a well-established
technique for classification problems called few-shot classification. However,
existing approaches cannot be applied to time-series forecasting because i)
multivariate time-series datasets have different channels and ii) forecasting
is principally different from classification. In this paper we formalize the
problem of few-shot forecasting of time-series with heterogeneous channels for
the first time. Extending recent work on heterogeneous attributes in vector
data, we develop a model composed of permutation-invariant deep set-blocks
which incorporate a temporal embedding. We assemble the first meta-dataset of
40 multivariate time-series datasets and show through experiments that our
model provides a good generalization, outperforming baselines carried over from
simpler scenarios that either fail to learn across tasks or miss temporal
information.


title:
Validation and interpretation of three-dimensional configuration of a
  magnetic cloud flux rope

abstract:
  One "strong" magnetic cloud (MC) with the magnetic field magnitude reaching
$\sim$ 40 nT at 1 au during 2012 June 16-17 is examined in association with a
pre-existing magnetic flux rope (MFR) identified on the Sun. The MC is
characterized by a quasi-three dimensional (3D) flux rope model based on in
situ measurements from the Wind spacecraft. The magnetic flux contents and
other parameters are quantified. In addition, a correlative study with the
corresponding measurements of the same structure crossed by the Venus Express
(VEX) spacecraft at a heliocentric distance 0.7 au and with an angular
separation $\sim 6^\circ$ in longitude is performed to validate the MC modeling
results. The spatial variation between the Wind and VEX magnetic field
measurements is attributed to the 3D configuration of the structure as featured
by a knotted bundle of flux. The comparison of the magnetic flux contents
between the MC and the source region on the Sun indicates that the 3D
reconnection process accompanying an M1.9 flare may correspond to the magnetic
reconnection between the field lines of the pre-existing MFR rooted in the
opposite polarity footpoints. Such a process reduces the amount of the axial
magnetic flux in the erupted flux rope, by approximately 50\%, in this case.


title: Video Diffusion Models

abstract:
  Generating temporally coherent high fidelity video is an important milestone
in generative modeling research. We make progress towards this milestone by
proposing a diffusion model for video generation that shows very promising
initial results. Our model is a natural extension of the standard image
diffusion architecture, and it enables jointly training from image and video
data, which we find to reduce the variance of minibatch gradients and speed up
optimization. To generate long and higher resolution videos we introduce a new
conditional sampling technique for spatial and temporal video extension that
performs better than previously proposed methods. We present the first results
on a large text-conditioned video generation task, as well as state-of-the-art
results on an established unconditional video generation benchmark.
Supplementary material is available at https://video-diffusion.github.io/


title: Compatible topologies on mixed lattice vector spaces

abstract:
  A mixed lattice vector space is a partially ordered vector space with two
partial orderings, generalizing the notion of a Riesz space. Whereas the
algebraic theory of mixed lattice structures dates back to the 1970s, the
topological theory of mixed lattice spaces remains largely unexplored. The
purpose of this paper is to develop the basic topological theory of mixed
lattice spaces. A vector topology is said to be compatible with the mixed
lattice structure if the mixed lattice operations are continuous. We obtain a
characterization of compatible mixed lattice topologies, which is similar to
the well known Roberts-Namioka characterization of locally solid Riesz spaces.
Moreover, we study locally convex topologies and the associated seminorms, as
well as connections between mixed lattice topologies and locally solid
topologies on Riesz spaces. We also briefly discuss asymmetric norms and cone
norms on mixed lattice spaces.


title:
Classical and quantum harmonic oscillators subject to a time dependent
  force

abstract:
  In this work we address the problem of the quantization of a simple harmonic
oscillator that is perturbed by a time dependent force. The approach consists
of removing the perturbation by a canonical change of coordinates. Since the
quantization procedure uses the classical Hamiltonian formalism as staring
point, the change of variables is carried out using canonical transformations,
and to transform between the quantized systems the canonical transformation is
implemented as a unitary transformation mapping the states of the perturbed and
unperturbed system onto each other.


title: The CR Almost Schur Lemma and the positivity conditions

abstract:
  We establish a new version of the CR almost Schur Lemma which gives an
estimation of the pseudohermitian scalar curvature on a compact strictly
pseudoconvex pseudohermitian manifold to be a constant in terms of the norm of
the traceless Webster Ricci tensor and the pseudohermitian torsion under a
certain positivity condition. In the torsion-free case, i.e. for a compact
Sasakian manifold, our positivity condition coincides with the known one and we
obtain a better estimate


title: Ramsey non-goodness involving books

abstract:
  In 1983, Burr and Erd\H{o}s initiated the study of Ramsey goodness
problems.Nikiforov and Rousseau (2009) resolved almost all goodness questions
raised by Burr and Erd\H{o}s, in which the bounds on the parameters are of
tower type since their proofs rely on the regularity lemma. Let $B_{k,n}$ be
the book graph on $n$ vertices which consists of $n-k$ copies of $K_{k+1}$ all
sharing a common $K_k$, and let $H=K_p(a_1,\dots,a_{p})$ be the complete
$p$-partite graph with parts of sizes $a_1,\dots,a_{p}$.
  Recently, avoiding use of the regularity lemma, Fox, He and Wigderson (2021)
revisit several Ramsey goodness results involving books. They comment that it
would be very interesting to see how far one can push these ideas. In
particular, they conjecture that for all integers $k, p, t\ge 2$, there exists
some $\delta>0$ such that for all $n\ge 1$, $1\leq a_1\le\cdots\le a_{p-1}\le
t$ and $a_p \le \delta n$, we have $r(H, B_{k,n})=
(p-1)(n-1)+d_k(n,K_{a_1,a_2})+1,$ where $d_k(n,K_{a_1,a_2})$ is the maximum $d$
for which there is an $(n+d-1)$-vertex $K_{a_1,a_2}$-free graph in which at
most $k-1$ vertices have degree less than $d$.They verify the conjecture when
$a_1=a_2=1$.
  Building upon the work of Fox et al. (2021), we make a substantial step by
showing that the conjecture "roughly" holds if $a_1=1$ and $a_2|(n-1-k)$, i.e.
$a_2$ divides $n-1-k$. Moreover, avoiding use of the regularity lemma, we prove
that for every $k, a\geq 1$ and $p\ge2$, there exists $\delta>0$ such that for
all large $n$ and $b\le \delta\ln n$, $r(K_p(1,a,b,\dots,b), B_{k,n})=
(p-1)(n-1)+k(p-1)(a-1)+1$ if $a|(n-1-k)$, where the case when $a=1$ has been
proved by Nikiforov and Rousseau (2009) using the regularity lemma. The bounds
on $1/\delta$ we obtain are not of tower-type since our proofs do not rely on
the regularity lemma.


title: Maps preserving triple transition pseudo-probabilities

abstract:
  Let $e$ and $v$ be minimal tripotents in a JBW$^*$-triple $M$. We introduce
the notion of triple transition pseudo-probability from $e$ to $v$ as the
complex number $TTP(e,v)= \varphi_v(e),$ where $\varphi_v$ is the unique
extreme point of the closed unit ball of $M_*$ at which $v$ attains its norm.
In the case of two minimal projections in a von Neumann algebra, this
correspond to the usual transition probability. We prove that every bijective
transformation $\Phi$ preserving triple transition pseudo-probabilities between
the lattices of tripotents of two atomic JBW$^*$-triples $M$ and $N$ admits an
extension to a bijective {\rm(}complex{\rm)} linear mapping between the socles
of these JBW$^*$-triples. If we additionally assume that $\Phi$ preserves
orthogonality, then $\Phi$ can be extended to a surjective (complex-)linear
{\rm(}isometric{\rm)} triple isomorphism from $M$ onto $N$. In case that $M$
and $N$ are two spin factors or two type 1 Cartan factors we show, via
techniques and results on preservers, that every bijection preserving triple
transition pseudo-probabilities between the lattices of tripotents of $M$ and
$N$ automatically preserves orthogonality, and hence admits an extension to a
triple isomorphism from $M$ onto $N$.


title: High-T$_c$ cuprates: a story of two electron subsystems

abstract:
  A review of the phenomenology and microscopy of cuprate superconductors is
presented, with particular attention to universal conductance features, which
reveal the existence of two electronic subsystems. The overall electronic
system consists of $1+p$ charges, where $p$ is the doping. At low dopings
exactly one hole is localized per planar copper-oxygen unit, while upon
increasing doping and temperature, the hole is gradually delocalized and
becomes itinerant. Remarkably, the itinerant holes exhibit identical
Fermi-liquid character across the cuprate phase diagram. This universality
enables a simple count of carrier density and yields comprehensive
understanding of the key features in the normal and superconducting state. A
possible superconducting mechanism is presented, compatible with the key
experimental facts. The base of this mechanism is the interaction of fast
Fermi-liquid carriers with localized holes. A change in the microscopic nature
of chemical bonding in the copper-oxide planes, from ionic to covalent, is
invoked to explain the phase diagram of these fascinating compounds.


title:
BERTuit: Understanding Spanish language in Twitter through a native
  transformer

abstract:
  The appearance of complex attention-based language models such as BERT,
Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of
scenarios. However, when applied to specific domains, these models encounter
considerable difficulties. This is the case of Social Networks such as Twitter,
an ever-changing stream of information written with informal and complex
language, where each message requires careful evaluation to be understood even
by humans given the important role that context plays. Addressing tasks in this
domain through Natural Language Processing involves severe challenges. When
powerful state-of-the-art multilingual language models are applied to this
scenario, language specific nuances use to get lost in translation. To face
these challenges we present \textbf{BERTuit}, the larger transformer proposed
so far for Spanish language, pre-trained on a massive dataset of 230M Spanish
tweets using RoBERTa optimization. Our motivation is to provide a powerful
resource to better understand Spanish Twitter and to be used on applications
focused on this social network, with special emphasis on solutions devoted to
tackle the spreading of misinformation in this platform. BERTuit is evaluated
on several tasks and compared against M-BERT, XLM-RoBERTa and XLM-T, very
competitive multilingual transformers. The utility of our approach is shown
with applications, in this case: a zero-shot methodology to visualize groups of
hoaxes and profiling authors spreading disinformation.
  Misinformation spreads wildly on platforms such as Twitter in languages other
than English, meaning performance of transformers may suffer when transferred
outside English speaking communities.


title:
Two-loop scattering amplitude for heavy-quark pair production through
  light-quark annihilation in QCD

abstract:
  We present the first full analytic evaluation of the scattering amplitude for
the process $q {\bar q} \to Q {\bar Q}$ up-to two loops in Quantum
Chromodynamics, for a massless $(q)$ and a massive $(Q)$ quark flavour. The
interference terms of the one- and two-loop amplitudes with the Born amplitude,
decomposed in terms of gauge invariant form factors depending on the colour and
flavour structure, are analytically calculated by keeping complete dependence
on the squared center-of-mass energy, the squared momentum transfer, and the
heavy-quark mass. The results are expressed as Laurent series around four
space-time dimensions, with coefficients given in terms of generalised
polylogarithms and transcendental constants up-to weight four. Our results
validate the known, purely numerical calculations of the squared amplitude, and
extend the analytic knowledge, previously limited to a subset of form factors,
to their whole set, coming from both planar and non-planar diagrams, up-to the
second order corrections in the strong coupling constant.


title: Jacobian Norm for Unsupervised Source-Free Domain Adaptation

abstract:
  Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer
knowledge from a well-trained source model to a related but unlabeled target
domain. In such a scenario, all conventional adaptation methods that require
source data fail. To combat this challenge, existing USFDAs turn to transfer
knowledge by aligning the target feature to the latent distribution hidden in
the source model. However, such information is naturally limited. Thus, the
alignment in such a scenario is not only difficult but also insufficient, which
degrades the target generalization performance. To relieve this dilemma in
current USFDAs, we are motivated to explore a new perspective to boost their
performance. For this purpose and gaining necessary insight, we look back upon
the origin of the domain adaptation and first theoretically derive a new-brand
target generalization error bound based on the model smoothness. Then,
following the theoretical insight, a general and model-smoothness-guided
Jacobian norm (JN) regularizer is designed and imposed on the target domain to
mitigate this dilemma. Extensive experiments are conducted to validate its
effectiveness. In its implementation, just with a few lines of codes added to
the existing USFDAs, we achieve superior results on various benchmark datasets.


title:
Measurements of $\mu\to 3e$ Decay with Polarised Muons as a Probe of New
  Physics

abstract:
  Working within the Standard Model effective field theory approach, we examine
the possibility to test charged lepton flavour violating (cLFV) new physics
(NP) by angular measurements of the outgoing electron and positrons in the
polarised $\mu^{+}\to e^{+}e^{+}e^{-}$ decay. This decay is planned to be
studied with a sensitivity to the branching ratio of $B_{\mu\to3e}\sim
10^{-15}$ by the Mu3e experiment at PSI. To illustrate the potential of these
measurements, we consider a set of eight effective operators generating the
decay at the tree level, including dimension-five dipole operators and
dimension-six scalar and vector four-fermion operators of different
chiralities. We show that if the polarised $\mu^{+}\to e^{+}e^{+}e^{-}$ decay
is observed and is induced by a single operator, data from three angular
observables - two P-odd asymmetries and one T-odd asymmetry - can allow to
discriminate between all considered operators with the exception of scalar and
vector operators of opposite chirality. For these two types of operators, the
P-odd asymmetry is maximal in magnitude; they can be distinguished, in
principle, by measuring the helicities of the outgoing positrons. The
observation of a non-zero T-odd asymmetry would indicate the presence in the
decay rate of a dipole and vector operator interference term that is T (CP)
violating.


title: Sharp threshold sequence and universality for Ising perceptron models

abstract:
  We study a family of Ising perceptron models with $\{0,1\}$-valued activation
functions. This includes the classical half-space models, as well as some of
the symmetric models considered in recent works. For each of these models we
show that the free energy is self-averaging, there is a sharp threshold
sequence, and the free energy is universal with respect to the disorder. A
prior work of Xu (2019) used very different methods to show a sharp threshold
sequence in the half-space Ising perceptron with Bernoulli disorder. Recent
works of Perkins--Xu (2021) and Abbe--Li--Sly (2021) determined the sharp
threshold and limiting free energy in a symmetric perceptron model. The results
of this paper apply in more general settings, and are based on new "add one
constraint" estimates extending Talagrand's estimates for the half-space model
(1999, 2011).


title: Limits of P\'olya urns with innovations

abstract:
  We consider a version of the classical P\'olya urn scheme which incorporates
innovations. The space $S$ of colors is an arbitrary measurable set. After each
sampling of a ball in the urn, one returns $C$ balls of the same color and
additional balls of different colors given by some finite point process $\xi$
on $S$. When the number of steps goes to infinity, the empirical distribution
of the colors in the urn converges to the normalized intensity measure of
$\xi$, and we analyze the fluctuations. The ratio $\rho= E(C)/E(R)$ of the
average number of copies to the average total number of balls returned plays a
key role.


title:
DynLight: Realize dynamic phase duration with multi-level traffic signal
  control

abstract:
  Adopting reinforcement learning (RL) for traffic signal control (TSC) is
increasingly popular, and RL has become a promising solution for traffic signal
control. However, several challenges still need to be overcome. Firstly, most
RL methods use fixed action duration and select the green phase for the next
state, which makes the phase duration less dynamic and flexible. Secondly, the
phase sequence of RL methods can be arbitrary, affecting the real-world
deployment which may require a cyclical phase structure. Lastly, the average
travel time and throughput are not fair metrics to evaluate TSC performance. To
address these challenges, we propose a multi-level traffic signal control
framework, DynLight, which uses an optimization method Max-QueueLength (M-QL)
to determine the phase and uses a deep Q-network to determine the duration of
the corresponding phase. Based on DynLight, we further propose DynLight-C which
adopts a well-trained deep Q-network of DynLight and replace M-QL with a
cyclical control policy that actuates a set of phases in fixed cyclical order
to realize cyclical phase structure. Comprehensive experiments on multiple
real-world datasets demonstrate that DynLight achieves a new state-of-the-art.
Furthermore, the deep Q-network of DynLight can learn well on determining the
phase duration and DynLight-C demonstrates high performance for deployment.


title:
Distance Learning in Primary School During the COVID 19 Pandemic:
  Results of the "SMART KIDS" Experiment

abstract:
  The paper analyzes the results of the introduction of the distance learning
form (DLF) using electronic educational resources (EER) and the teacher's
virtual classroom in primary school. The experiment took place within the
framework of the "Smart Kids" All-Ukrainian project during the long quarantine
caused by the COVID-19 pandemic. The educational process took place both
synchronously and asynchronously. The present paper substantiates the model of
organization of distance learning of primary school students using EER and
outlines its three main components: the organization of learning, conducting
online classes (explaining new material or practicing skills by students) and
monitoring the quality of students' independent performance of tasks. The
results of the experiment prove that it is necessary to provide teachers and
students with computer equipment, Internet access, digital resources for
teaching and assessment to implement DLF. It has been established that EER in
distance learning can be used both on a regular basis - in each class, and
periodically - to explain new material or train skills, the quality of tasks
performed by students can be monitored in the virtual office of the teacher and
shape an individual trajectory of students' development. The teachers
identified the following main problems of DLF implementation: internet
interruptions, problems with providing new computer equipment to students and
some teachers; lack of state aid in providing EER to all participants in the
educational process; limited access to students' computers during complete
isolation due to online work of parents. Despite the outlined problems, the
quality of distance learning of primary school students during the pandemic
using EER was positively and highly assessed by teachers.


title:
The Number of Roots of a Random Polynomial over The Field of $p$-adic
  Numbers

abstract:
  We study the roots of a random polynomial over the field of p-adic numbers.
For a random monic polynomial with coefficients in $\mathbb{Z}_p$, we obtain an
asymptotic formula for the factorial moments of the number of roots of this
polynomial. In addition, we show the probability that a random polynomial of
degree $n$ has more than $\log n$ roots is $O\big(n^{-K}\big)$ for some $K >
0$.


title:
Area-minimizing horizontal graphs with low-regularity in the sub-Finsler
  Heisenberg group $\mathbb{H}^1$

abstract:
  In the Heisenberg group $\mathbb{H}^1$ equipped with a left invariant and not
necessarily symmetric norm in the horizontal distribution, we provide examples
of entire area-minimizing horizontal graphs which are locally Lipschitz in
Euclidean sense. A large number of them fail to have further regularity
properties. The examples are obtained prescribing as singular set a horizontal
line or a finite union of horizontal half-lines extending from a given point.
We also provide examples of families of area-minimizing cones.


title:
Solving ImageNet: a Unified Scheme for Training any Backbone to Top
  Results

abstract:
  ImageNet serves as the primary dataset for evaluating the quality of
computer-vision models. The common practice today is training each architecture
with a tailor-made scheme, designed and tuned by an expert. In this paper, we
present a unified scheme for training any backbone on ImageNet. The scheme,
named USI (Unified Scheme for ImageNet), is based on knowledge distillation and
modern tricks. It requires no adjustments or hyper-parameters tuning between
different models, and is efficient in terms of training times. We test USI on a
wide variety of architectures, including CNNs, Transformers, Mobile-oriented
and MLP-only. On all models tested, USI outperforms previous state-of-the-art
results. Hence, we are able to transform training on ImageNet from an
expert-oriented task to an automatic seamless routine. Since USI accepts any
backbone and trains it to top results, it also enables to perform methodical
comparisons, and identify the most efficient backbones along the speed-accuracy
Pareto curve. Implementation is available
at:https://github.com/Alibaba-MIIL/Solving_ImageNet


title:
ProbNVS: Fast Novel View Synthesis with Learned Probability-Guided
  Sampling

abstract:
  Existing state-of-the-art novel view synthesis methods rely on either fairly
accurate 3D geometry estimation or sampling of the entire space for neural
volumetric rendering, which limit the overall efficiency. In order to improve
the rendering efficiency by reducing sampling points without sacrificing
rendering quality, we propose to build a novel view synthesis framework based
on learned MVS priors that enables general, fast and photo-realistic view
synthesis simultaneously. Specifically, fewer but important points are sampled
under the guidance of depth probability distributions extracted from the
learned MVS architecture. Based on the learned probability-guided sampling, a
neural volume rendering module is elaborately devised to fully aggregate source
view information as well as the learned scene structures to synthesize
photorealistic target view images. Finally, the rendering results in uncertain,
occluded and unreferenced regions can be further improved by incorporating a
confidence-aware refinement module. Experiments show that our method achieves
15 to 40 times faster rendering compared to state-of-the-art baselines, with
strong generalization capacity and comparable high-quality novel view synthesis
performance.


title:
A Deep Learning-Based Approach for Cell Outage Compensation in NOMA
  Networks

abstract:
  Cell outage compensation enables a network to react to a catastrophic cell
failure quickly and serve users in the outage zone uninterruptedly. Utilizing
the promising benefits of non-orthogonal multiple access (NOMA) for improving
the throughput of cell edge users, we propose a newly NOMA-based cell outage
compensation scheme. In this scheme, the compensation is formulated as a mixed
integer non-linear program (MINLP) where outage zone users are associated to
neighboring cells and their power are allocated with the objective of
maximizing spectral efficiency, subject to maintaining the quality of service
for the rest of the users. Owing to the importance of immediate management of
cell outage and handling the computational complexity, we develop a
low-complexity suboptimal solution for this problem in which the user
association scheme is determined by a newly heuristic algorithm, and power
allocation is set by applying an innovative deep neural network (DNN). The
complexity of our proposed method is in the order of polynomial basis, which is
much less than the exponential complexity of finding an optimal solution.
Simulation results demonstrate that the proposed method approaches the optimal
solution. Moreover, the developed scheme greatly improves fairness and
increases the number of served users.


title: Star-dust geometry main determinant of dust attenuation in galaxies

abstract:
  Analysing a large representative sample of local galaxies (8707), we find
that the variation in the shape of their dust attenuation curves is driven
primarily by their structure, i.e., distribution of stars (and dust) within
them. The attenuation curve for spheroid dominated galaxies, as compared to the
disc dominated ones, is nearly twice as steep. Both structural types cover
distinct ranges of attenuation slope values. Similar findings are reflected in
the case of star-forming and passive galaxies. Spheroids and passive galaxies
witness minimal attenuation in the optical compared to UV wavelengths
underlining the lack of dusty birth-clouds that define complex star-dust
geometry. The distinction in the attenuation properties of spheroids and discs
is maintained in each stellar mass range emphasising that structure is the
primal cause of variation. However, within a structural group, the attenuation
curve becomes shallower with both the increase in total stellar mass and
optical depth of the galaxy. Overall, with the extinction curve fixed to be the
same for all galaxies, the star-dust geometry emerges to be the prime
determinant of the variation in their attenuation properties.


title:
Delta Keyword Transformer: Bringing Transformers to the Edge through
  Dynamically Pruned Multi-Head Self-Attention

abstract:
  Multi-head self-attention forms the core of Transformer networks. However,
their quadratically growing complexity with respect to the input sequence
length impedes their deployment on resource-constrained edge devices. We
address this challenge by proposing a dynamic pruning method, which exploits
the temporal stability of data across tokens to reduce inference cost. The
threshold-based method only retains significant differences between the
subsequent tokens, effectively reducing the number of multiply-accumulates, as
well as the internal tensor data sizes. The approach is evaluated on the Google
Speech Commands Dataset for keyword spotting, and the performance is compared
against the baseline Keyword Transformer. Our experiments show that we can
reduce ~80% of operations while maintaining the original 98.4% accuracy.
Moreover, a reduction of ~87-94% operations can be achieved when only degrading
the accuracy by 1-4%, speeding up the multi-head self-attention inference by a
factor of ~7.5-16.


title: Phase Transitions in de Sitter: Quantum Corrections

abstract:
  We investigate the decay rate of a false vacuum state in de Sitter space at
high Hubble rates, using two methods: the Hawking-Moss instanton method which
is fully quantum mechanical but relies on the saddle-point approximation, and
the Starobinsky-Yokoyama stochastic approach which is non-perturbative but does
not include quantum effects. We use the flux-over-population method to compute
the Hawking-Moss decay rate at one-loop order, and demonstrate that in its
domain of validity, it is reproduced by the stochastic calculation using the
one-loop constraint effective potential. This suggests that the stochastic
approach together with the constraint effective potential can be used to
accurately describe vacuum decay beyond the saddle-point approximation.


title:
Serendipitous discovery of radio flaring behaviour from a nearby M dwarf
  with MeerKAT

abstract:
  We report on the detection of MKT J174641.0$-$321404, a new radio transient
found in untargeted searches of wide-field MeerKAT radio images centred on the
black hole X-ray binary H1743$-$322. MKT J174641.0$-$321404 is highly variable
at 1.3 GHz and was detected three times during 11 observations of the field in
late 2018, reaching a maximum flux density of 590 $\pm$ 60 $\mu$Jy. We
associate this radio transient with a high proper motion, M dwarf star
SCR~1746$-$3214 12 pc away from the Sun. Multiwavelength observations of this M
dwarf indicate flaring activity across the electromagnetic spectrum, consistent
with emission expected from dMe stars, and providing upper limits on quiescent
brightness in both the radio and X-ray regimes. \textit{TESS} photometry
reveals a rotational period for SCR~1746$-$3214 of $0.2292 \pm 0.0025$ days,
which at its estimated radius makes the star a rapid rotator, comparable to
other low mass systems. Dedicated spectroscopic follow up confirms the star as
a mid-late spectral M dwarf with clear magnetic activity indicated by strong
H$\alpha$ emission. This transient's serendipitous discovery by MeerKAT, along
with multiwavelength characterisation, make it a prime demonstration of both
the capabilities of the current generation of radio interferometers and the
value of simultaneous observations by optical facilities such as MeerLICHT. Our
results build upon the literature of of M dwarfs' flaring behaviour,
particularly relevant to the habitability of their planetary systems.


title:
Constraints on the ultralight scalar boson from Advanced LIGO and
  Advanced Virgo's first three observing runs using the stochastic
  gravitational-wave background

abstract:
  Ultralight bosons are promising dark matter candidates and can trigger
superradiant instabilities of spinning black holes (BHs), resulting in
long-lived rotating "bosonic clouds" around the BHs and dissipating their
energy through the emission of monochromatic gravitational waves (GWs). We
focus on the scalar bosons minimally coupled with both isolated stellar-origin
BHs (SBH) and their binary merger remnants, and perform Bayesian data analysis
to search for the stochastic GW background from all the unstable modes that can
trigger the superradiant instabilities using the data of Advanced LIGO and
Advanced Virgo's first three observing runs. We find no evidence for such
signal, and hence rule out the scalar bosons within the mass range $[1.5,
16]\times10^{-13}$ eV, $[1.9, 8.3]\times10^{-13}$ eV and $[1.3,
17]\times10^{-13}$ eV at $95\%$ confidence level for isolated SBHs having a
uniform dimensionless spin distribution in $[0,1]$, $[0,0.5]$ and $[0.5,1]$,
respectively.


title: Fusion for the Yang-Baxter equation and the braid group

abstract:
  These are the extended notes of a mini-course given at the school
WinterBraids X. We discuss algebras simultaneously related to: the braid group,
the Yang-Baxter equation and the representation theory of quantum groups. The
main goal is to explain the idea of the fusion procedure for the Yang-Baxter
equation and to show how it leads to new examples of such algebras: the fused
Hecke algebras.


title: Commitment games with conditional information revelation

abstract:
  The conditional commitment abilities of mutually transparent computer agents
have been studied in previous work on commitment games and program equilibrium.
This literature has shown how these abilities can help resolve Prisoner's
Dilemmas and other failures of cooperation in complete information settings.
But inefficiencies due to private information have been neglected thus far in
this literature, despite the fact that these problems are pervasive and might
also be addressed by greater mutual transparency. In this work, we introduce a
framework for commitment games with a new kind of conditional commitment
device, which agents can use to conditionally reveal private information. We
prove a folk theorem for this setting that provides sufficient conditions for
ex post efficiency, and thus represents a model of ideal cooperation between
agents without a third-party mediator. Connecting our framework with the
literature on strategic information revelation, we explore cases where
conditional revelation can be used to achieve full cooperation while
unconditional revelation cannot. Finally, extending previous work on program
equilibrium, we develop an implementation of conditional information
revelation. We show that this implementation forms program $\epsilon$-Bayesian
Nash equilibria corresponding to the Bayesian Nash equilibria of these
commitment games.


title: Nonlinear Kalman Filter Using Cramer Rao Bound

abstract:
  This paper studies the optimal state estimation for a dynamic system, whose
transfer function can be nonlinear and the input noise can be of arbitrary
distribution. Our algorithm differs from the conventional extended Kalman
filter (EKF) and the particle filter (PF) in that it estimates not only the
state vector but also the Cramer-Rao bound (CRB), which serves as an accuracy
indicator. Combining the state estimation, the CRB, and the incoming new
measurement, the algorithm updates the state estimation according to the
maximum likelihood (ML) criterion. To illustrate the effectiveness of the
proposed method for autonomous driving, we apply it to estimate the position
and velocity of a vehicle based on the noisy measurements of distance and
Doppler offset. Simulation results show that the proposed algorithm can achieve
estimation significantly more accurate than the standard EKF and the PF.


title:
Application of a dispersive wave hydro-sediment-morphodynamic model in
  the Ria Formosa lagoon

abstract:
  Results of an application of a dispersive wave hydro-sediment-morphodynamic
model in the western circulation cell of the Ria Formosa lagoon located in the
Algarve region of the southern Portugal are presented. This area of interest
has a couple of features that complicate the application of the dispersive wave
model: (1) the area has a complex irregular geometry with a number of barrier
islands that separate the lagoon from the Atlantic Ocean, artificial and
naturally occurring tidal inlets, and a number of curling channels inside the
lagoon that interconnect the inlets and serve as waterways between the lagoon
settlements; (2) the tidal range in the area can reach up to 3.5 m; therefore,
the terrain inside the lagoon is characterized by vast salt marshes and tidal
flats, and the wetting-drying process is a key component of any hydrodynamic
simulation in this area. A model representation of the area has been developed
by generating an unstructured finite element mesh of the circulation cell, and
collecting data on parameters that characterize the tidal waves in the area,
and bottom friction and sediment transport models used in the simulations. The
results of the simulations indicate that the dispersive wave model can be
applied in coastal areas with nontrivial underlying physical processes, and
complex irregular geometries. Moreover, the dispersive term of the model is
capable of capturing additional flow characteristics that are otherwise not
present in hydrodynamic simulations that involve the nonlinear shallow water
equations; and these additional flow features can, in their turn, affect the
resulting sediment transport and bed morphodynamic process simulations.