Abstract:
  The unscented Kalman inversion (UKI) method presented in [1] is a general
derivative-free approach for the inverse problem. UKI is particularly suitable
for inverse problems where the forward model is given as a black box and may
not be differentiable. The regularization strategies, convergence property, and
speed-up strategies [1,2] of the UKI are thoroughly studied, and the method is
capable of handling noisy observation data and solving chaotic inverse
problems. In this paper, we study the uncertainty quantification capability of
the UKI. We propose a modified UKI, which allows to well approximate the mean
and covariance of the posterior distribution for well-posed inverse problems
with large observation data. Theoretical guarantees for both linear and
nonlinear inverse problems are presented. Numerical results, including learning
of permeability parameters in subsurface flow and of the Navier-Stokes initial
condition from solution data at positive times are presented. The results
obtained by the UKI require only $O(10)$ iterations, and match well with the
expected results obtained by the Markov Chain Monte Carlo method.


Abstract:
  Following G.~Gr\"atzer and E.~Knapp, 2009, a planar semimodular lattice $L$
is \emph{rectangular}, if~the left boundary chain has exactly one
doubly-irreducible element, $c_l$, and the right boundary chain has exactly one
doubly-irreducible element, $c_r$, and these elements are complementary.
  The Cz\'edli-Schmidt Sequences, introduced in 2012, construct rectangular
lattices. We use them to prove some structure theorems. In particular, we prove
that for a slim (no $\mathsf{M}_3$ sublattice) rectangular lattice~$L$, the
congruence lattice $\Con L$ has exactly $\length[c_l,1] + \length[c_r,1]$ dual
atoms and a dual atom in $\Con L$ is a congruence with exactly two classes. We
also describe the prime ideals in a slim rectangular lattice.


Abstract:
  We explore the tail of various waiting time datasets of processes that follow
a nonstationary Poisson distribution with a sinusoidal driver. Analytically, we
find that the distribution of large waiting times of such processes can be
described using a power law slope of -2.5. We show that this result applies
more broadly to any nonstationary Poisson process driven periodically. Examples
of such processes include solar flares, coronal mass ejections, geomagnetic
storms, and substorms. We also discuss how the power law specifically relates
to the behavior of driver near its minima.


Abstract:
  The present work looks at semiautomatic rings with automatic addition and
comparisons which are dense subrings of the real numbers and asks how these can
be used to represent geometric objects such that certain operations and
transformations are automatic. The underlying ring has always to be a countable
dense subring of the real numbers and additions and comparisons and
multiplications with constants need to be automatic. It is shown that the ring
can be selected such that equilateral triangles can be represented and
rotations by 30 degrees are possible, while the standard representation of the
b-adic rationals does not allow this.


Abstract:
  To further improve the learning efficiency and performance of reinforcement
learning (RL), in this paper we propose a novel uncertainty-aware model-based
RL (UA-MBRL) framework, and then implement and validate it in autonomous
driving under various task scenarios. First, an action-conditioned ensemble
model with the ability of uncertainty assessment is established as the virtual
environment model. Then, a novel uncertainty-aware model-based RL framework is
developed based on the adaptive truncation approach, providing virtual
interactions between the agent and environment model, and improving RL's
training efficiency and performance. The developed algorithms are then
implemented in end-to-end autonomous vehicle control tasks, validated and
compared with state-of-the-art methods under various driving scenarios. The
validation results suggest that the proposed UA-MBRL method surpasses the
existing model-based and model-free RL approaches, in terms of learning
efficiency and achieved performance. The results also demonstrate the good
ability of the proposed method with respect to the adaptiveness and robustness,
under various autonomous driving scenarios.


Abstract:
  Electronic health records represent a holistic overview of patients'
trajectories. Their increasing availability has fueled new hopes to leverage
them and develop accurate risk prediction models for a wide range of diseases.
Given the complex interrelationships of medical records and patient outcomes,
deep learning models have shown clear merits in achieving this goal. However, a
key limitation of these models remains their capacity in processing long
sequences. Capturing the whole history of medical encounters is expected to
lead to more accurate predictions, but the inclusion of records collected for
decades and from multiple resources can inevitably exceed the receptive field
of the existing deep learning architectures. This can result in missing
crucial, long-term dependencies. To address this gap, we present Hi-BEHRT, a
hierarchical Transformer-based model that can significantly expand the
receptive field of Transformers and extract associations from much longer
sequences. Using a multimodal large-scale linked longitudinal electronic health
records, the Hi-BEHRT exceeds the state-of-the-art BEHRT 1% to 5% for area
under the receiver operating characteristic (AUROC) curve and 3% to 6% for area
under the precision recall (AUPRC) curve on average, and 3% to 6% (AUROC) and
3% to 11% (AUPRC) for patients with long medical history for 5-year heart
failure, diabetes, chronic kidney disease, and stroke risk prediction.
Additionally, because pretraining for hierarchical Transformer is not
well-established, we provide an effective end-to-end contrastive pre-training
strategy for Hi-BEHRT using EHR, improving its transferability on predicting
clinical events with relatively small training dataset.


Abstract:
  Microscopic organisms, such as bacteria, have the ability of colonizing
surfaces and developing biofilms that can determine diseases and infections.
Most bacteria secrete a significant amount of extracellular polymer substances
that are relevant for biofilm stabilization and growth. In this work, we apply
computer simulation and perform experiments to investigate the impact of
polymer size and concentration on early biofilm formation and growth. We
observe as bacterial cells formed loose, disorganized clusters whenever the
effect of diffusion exceeded that of cell growth and division. Addition of
model polymeric molecules induced particle self-assembly and aggregation to
form compact clusters in a polymer size- and concentration-dependent fashion.
We also find that large polymer size or concentration lead to the development
of intriguing stripe-like and dendritic colonies. The results obtained by
Brownian dynamic simulation closely resemble the morphologies that we
experimentally observe in biofilms of a Pseudomonas Putida strain with added
polymers. The analysis of the Brownian dynamic simulation results suggests the
existence of a threshold polymer concentration that distinguishes between two
growth regimes. Below this threshold, the main force driving polymer-induced
compaction is hindrance of bacterial cell diffusion, while collective effects
play a minor role. Above this threshold, especially for large polymers,
polymer-induced compaction is a collective phenomenon driven by depletion
forces. Well above this concentration threshold, severely limited diffusion
drives the formation of filaments and dendritic colonies.


Abstract:
  In this paper, the interfacial motion between two immiscible viscous fluids
in the confined geometry of a Hele-Shaw cell is studied. We consider the
influence of a thin wetting film trailing behind the displaced fluid, which
dynamically affects the pressure drop at the fluid-fluid interface by
introducing a nonlinear dependence on the interfacial velocity. In this
framework, two cases of interest are analyzed: The injection-driven flow
(expanding evolution), and the lifting plate flow (shrinking evolution). In
particular, we investigate the possibility of controlling the development of
fingering instabilities in these two different Hele-Shaw setups when wetting
effects are taken into account. By employing linear stability theory, we find
the proper time-dependent injection rate $Q(t)$ and the time-dependent lifting
speed ${\dot b}(t)$ required to control the number of emerging fingers during
the expanding and shrinking evolution, respectively. Our results indicate that
the consideration of wetting leads to an increase in the magnitude of $Q(t)$
[and ${\dot b}(t)$] in comparison to the non-wetting strategy. Moreover, a
spectrally accurate boundary integral approach is utilized to examine the
validity and effectiveness of the controlling protocols at the fully nonlinear
regime of the dynamics and confirms that the proposed injection and lifting
schemes are feasible strategies to prescribe the morphologies of the resulting
patterns in the presence of the wetting film.


Abstract:
  We revisit the theoretical properties of Hamiltonian stochastic differential
equations (SDES) for Bayesian posterior sampling, and we study the two types of
errors that arise from numerical SDE simulation: the discretization error and
the error due to noisy gradient estimates in the context of data subsampling.
Our main result is a novel analysis for the effect of mini-batches through the
lens of differential operator splitting, revising previous literature results.
The stochastic component of a Hamiltonian SDE is decoupled from the gradient
noise, for which we make no normality assumptions. This leads to the
identification of a convergence bottleneck: when considering mini-batches, the
best achievable error rate is $\mathcal{O}(\eta^2)$, with $\eta$ being the
integrator step size. Our theoretical results are supported by an empirical
study on a variety of regression and classification tasks for Bayesian neural
networks.


Abstract:
  We present the results that are necessary in the ongoing lattice calculations
of the gluon parton distribution functions (PDFs) within the pseudo-PDF
approach. We identify the two-gluon correlator functions that contain the
invariant amplitude determining the gluon PDF in the light-cone $z^2 \to 0$
limit, and perform one-loop calculations in the coordinate representation in an
explicitly gauge-invariant form. Ultraviolet (UV) terms, which contain $\ln
(-z^2)$-dependence cancel in the reduced Ioffe-time distribution (ITD), and we
obtain the matching relation between the reduced ITD and the light-cone ITD.
Using a kernel form, we get a direct connection between lattice data for the
reduced ITD and the normalized gluon PDF.


Abstract:
  Considerable research effort has been guided towards algorithmic fairness but
real-world adoption of bias reduction techniques is still scarce. Existing
methods are either metric- or model-specific, require access to sensitive
attributes at inference time, or carry high development or deployment costs.
This work explores the unfairness that emerges when optimizing ML models solely
for predictive performance, and how to mitigate it with a simple and easily
deployed intervention: fairness-aware hyperparameter optimization (HO). We
propose and evaluate fairness-aware variants of three popular HO algorithms:
Fair Random Search, Fair TPE, and Fairband. We validate our approach on a
real-world bank account opening fraud case-study, as well as on three datasets
from the fairness literature. Results show that, without extra training cost,
it is feasible to find models with 111% mean fairness increase and just 6%
decrease in performance when compared with fairness-blind HO.


Abstract:
  LIDAR sensors are usually used to provide autonomous vehicles with 3D
representations of their environment. In ideal conditions, geometrical models
could detect the road in LIDAR scans, at the cost of a manual tuning of
numerical constraints, and a lack of flexibility. We instead propose an
evidential pipeline, to accumulate road detection results obtained from neural
networks. First, we introduce RoadSeg, a new convolutional architecture that is
optimized for road detection in LIDAR scans. RoadSeg is used to classify
individual LIDAR points as either belonging to the road, or not. Yet, such
point-level classification results need to be converted into a dense
representation, that can be used by an autonomous vehicle. We thus secondly
present an evidential road mapping algorithm, that fuses consecutive road
detection results. We benefitted from a reinterpretation of logistic
classifiers, which can be seen as generating a collection of simple evidential
mass functions. An evidential grid map that depicts the road can then be
obtained, by projecting the classification results from RoadSeg into grid
cells, and by handling moving objects via conflict analysis. The system was
trained and evaluated on real-life data. A python implementation maintains a 10
Hz framerate. Since road labels were needed for training, a soft labelling
procedure, relying lane-level HD maps, was used to generate coarse training and
validation sets. An additional test set was manually labelled for evaluation
purposes. So as to reach satisfactory results, the system fuses road detection
results obtained from three variants of RoadSeg, processing different LIDAR
features.


Abstract:
  Spiral structure is ubiquitous in the Universe, and the pitch angle of arms
in spiral galaxies provide an important observable in efforts to discriminate
between different mechanisms of spiral arm formation and evolution. In this
paper, we present a hierarchical Bayesian approach to galaxy pitch angle
determination, using spiral arm data obtained through the Galaxy Builder
citizen science project. We present a new approach to deal with the large
variations in pitch angle between different arms in a single galaxy, which
obtains full posterior distributions on parameters. We make use of our pitch
angles to examine previously reported links between bulge and bar strength and
pitch angle, finding no correlation in our data (with a caveat that we use
observational proxies for both bulge size and bar strength which differ from
other work). We test a recent model for spiral arm winding, which predicts
uniformity of the cotangent of pitch angle between some unknown upper and lower
limits, finding our observations are consistent with this model of transient
and recurrent spiral pitch angle as long as the pitch angle at which most
winding spirals dissipate or disappear is larger than 10 degrees.


Abstract:
  Source code spends most of its time in a broken or incomplete state during
software development. This presents a challenge to machine learning for code,
since high-performing models typically rely on graph structured representations
of programs derived from traditional program analyses. Such analyses may be
undefined for broken or incomplete code. We extend the notion of program graphs
to work-in-progress code by learning to predict edge relations between tokens,
training on well-formed code before transferring to work-in-progress code. We
consider the tasks of code completion and localizing and repairing variable
misuse in a work-in-process scenario. We demonstrate that training
relation-aware models with fine-tuned edges consistently leads to improved
performance on both tasks.


Abstract:
  The objective of Federated Learning (FL) is to perform statistical inference
for data which are decentralised and stored locally on networked clients. FL
raises many constraints which include privacy and data ownership, communication
overhead, statistical heterogeneity, and partial client participation. In this
paper, we address these problems in the framework of the Bayesian paradigm. To
this end, we propose a novel federated Markov Chain Monte Carlo algorithm,
referred to as Quantised Langevin Stochastic Dynamics which may be seen as an
extension to the FL setting of Stochastic Gradient Langevin Dynamics, which
handles the communication bottleneck using gradient compression. To improve
performance, we then introduce variance reduction techniques, which lead to two
improved versions coined \texttt{QLSD}$^{\star}$ and \texttt{QLSD}$^{++}$. We
give both non-asymptotic and asymptotic convergence guarantees for the proposed
algorithms. We illustrate their performances using various Bayesian Federated
Learning benchmarks.


Abstract:
  The complex-step derivative approximation is a numerical differentiation
technique that can achieve analytical accuracy, to machine precision, with a
single function evaluation. In this letter, the complex-step derivative
approximation is extended to be compatible with elements of matrix Lie groups.
As with the standard complex-step derivative, the method is still able to
achieve analytical accuracy, up to machine precision, with a single function
evaluation. Compared to a central-difference scheme, the proposed complex-step
approach is shown to have superior accuracy. The approach is applied to two
different pose estimation problems, and is able to recover the same results as
an analytical method when available.


Abstract:
  The problem of simultaneous rigid alignment of multiple unordered point sets
which is unbiased towards any of the inputs has recently attracted increasing
interest, and several reliable methods have been newly proposed. While being
remarkably robust towards noise and clustered outliers, current approaches
require sophisticated initialisation schemes and do not scale well to large
point sets. This paper proposes a new resilient technique for simultaneous
registration of multiple point sets by interpreting the latter as particle
swarms rigidly moving in the mutually induced force fields. Thanks to the
improved simulation with altered physical laws and acceleration of globally
multiply-linked point interactions with a 2^D-tree (D is the space
dimensionality), our Multi-Body Gravitational Approach (MBGA) is robust to
noise and missing data while supporting more massive point sets than previous
methods (with 10^5 points and more). In various experimental settings, MBGA is
shown to outperform several baseline point set alignment approaches in terms of
accuracy and runtime. We make our source code available for the community to
facilitate the reproducibility of the results.


Abstract:
  It is nontrivial to store rapidly growing big data nowadays, which demands
high-performance lossless compression techniques. Likelihood-based generative
models have witnessed their success on lossless compression, where flow based
models are desirable in allowing exact data likelihood optimisation with
bijective mappings. However, common continuous flows are in contradiction with
the discreteness of coding schemes, which requires either 1) imposing strict
constraints on flow models that degrades the performance or 2) coding numerous
bijective mapping errors which reduces the efficiency. In this paper, we
investigate volume preserving flows for lossless compression and show that a
bijective mapping without error is possible. We propose Numerical Invertible
Volume Preserving Flow (iVPF) which is derived from the general volume
preserving flows. By introducing novel computation algorithms on flow models,
an exact bijective mapping is achieved without any numerical error. We also
propose a lossless compression algorithm based on iVPF. Experiments on various
datasets show that the algorithm based on iVPF achieves state-of-the-art
compression ratio over lightweight compression algorithms.


Abstract:
  This paper presents a model addressing welfare optimal policies of demand
responsive transportation service, where passengers cause external travel time
costs for other passengers due to the route changes. Optimal pricing and trip
production policies are modelled both on the aggregate level and on the network
level. The aggregate model is an extension from Jokinen (2016) with flat
pricing model, but occupancy rate is now modelled as an endogenous variable
depending on demand and capacity levels. The network model enables to describe
differences between routes from the viewpoint of occupancy rate and efficient
trip combining. Moreover, the model defines the optimal differentiated pricing
for routes.


Abstract:
  In this article, we introduce a notion of relative mean metric dimension with
potential for a factor map $\pi: (X,d, T)\to (Y, S)$ between two topological
dynamical systems. To link it with ergodic theory, we establish four
variational principles in terms of metric entropy of partitions, Shapira's
entropy, Katok's entropy and Brin-Katok local entropy respectively. Some
results on local entropy with respect to a fixed open cover are obtained in the
relative case. We also answer an open question raised by Shi \cite{Shi}
partially for a very well-partitionable compact metric space, and in general we
obtain a variational inequality involving box dimension of the space.
Corresponding inner variational principles given an invariant measure of
$(Y,S)$ are also investigated.


Abstract:
  We introduce a quantum interferometric scheme that uses states that are sharp
in frequency and delocalized in position. The states are frequency modes of a
quantum field that is trapped at all times in a finite volume potential, such
as a small box potential. This allows for significant miniaturization of
interferometric devices. Since the modes are in contact at all times, it is
possible to estimate physical parameters of global multi-mode channels. As an
example, we introduce a three-mode scheme and calculate precision bounds in the
estimation of parameters of two-mode Gaussian channels. This scheme can be
implemented in several systems, including superconducting circuits, cavity-QED
and cold atoms. We consider a concrete implementation using the ground state
and two phononic modes of a trapped Bose-Einstein condensate. We apply this to
show that frequency interferometry can improve the sensitivity of phononic
gravitational waves detectors by several orders of magnitude, even in the case
that squeezing is much smaller than assumed previously and that the system
suffers from short phononic lifetimes. Other applications range from
magnetometry, gravimetry and gradiometry to dark matter/energy searches.


Abstract:
  Analytic solution is presented of the nonlinear semiclassical dynamics of
superradiant photonic condensate that arises in the Dicke model of two-level
atoms dipolar coupled to the electromagnetic field in the microwave cavity. In
adiabatic limit with respect to photon degree of freedom the system is
approximately integrable and its evolution is expressed via Jacobi elliptic
functions of real time. Periodic trajectories of the semiclassical coordinate
of photonic condensate either localise around two degenerate minima of the
condensate ground state energy or traverse between them over the saddle point.
An exact mapping of the semiclassical dynamics of photonic condensate on the
motion of unstable Lagrange 'sleeping top' is found. Analytic expression is
presented for the frequency dependence of transmission coefficient along a
transmission line inductively coupled to the resonant cavity with superradiant
condensate. Sharp transmission drops reflect Fourier spectrum of the
semiclassical motion of photonic condensate and of 'sleeping top' nodding.


Abstract:
  Molecular ferroelectrics have captured immense attention due to their
superiority over inorganic oxide ferroelectrics, such as environmentally
friendly, low-cost, flexible, foldable. However, the mechanisms of
ferroelectric switching and phase transition for the molecular ferroelectrics
are still missing, leaving the development of novel molecular ferroelectrics
less efficient. In this work, we have provided a methodology combining
molecular dynamics (MD) simulation on a polarized force field named polarized
crystal charge (PCC) and enhanced sampling technique, replica-exchange
molecular dynamics (REMD) to simulate such mechanisms. With this procedure, we
have investigated a promising molecular ferroelectric material,
(R)/(S)-3-quinuclidinol crystal. We have simulated the ferroelectric hysteresis
loops of both enantiomers and obtained spontaneous polarization of 7/8 \mu C
cm-2 and a corresponding coercive electric field of 15 kV cm-1. We also find
the Curie temperature as 380/385 K for ferro-/para-electric phase transition of
both enantiomers. All of the simulated results are highly compatible with
experimental values. Besides of that, we predict a novel Curie temperature of
about 600 K. This finding is further validated by principal component analysis
(PCA). Our work would significantly promote the future exploration of
multifunctional molecular ferroelectrics for the next generation of intelligent
devices.


Abstract:
  We report on new stability conditions for evolutionary dynamics in the
context of population games. We adhere to the prevailing framework consisting
of many agents, grouped into populations, that interact noncooperatively by
selecting strategies with a favorable payoff. Each agent is repeatedly allowed
to revise its strategy at a rate referred to as revision rate. Previous
stability results considered either that the payoff mechanism was a memoryless
potential game, or allowed for dynamics (in the payoff mechanism) at the
expense of precluding any explicit dependence of the agents' revision rates on
their current strategies. Allowing the dependence of revision rates on
strategies is relevant because the agents' strategies at any point in time are
generally unequal. To allow for strategy-dependent revision rates and payoff
mechanisms that are dynamic (or memoryless games that are not potential), we
focus on an evolutionary dynamics class obtained from a straightforward
modification of one that stems from the so-called impartial pairwise comparison
strategy revision protocol. Revision protocols consistent with the modified
class retain from those in the original one the advantage that the agents
operate in a fully decentralized manner and with minimal information
requirements - they need to access only the payoff values (not the mechanism)
of the available strategies. Our main results determine conditions under which
system-theoretic passivity properties are assured, which we leverage for
stability analysis.


Abstract:
  Collective action demands that individuals efficiently coordinate how much,
where, and when to cooperate. Laboratory experiments have extensively explored
the first part of this process, demonstrating that a variety of
social-cognitive mechanisms influence how much individuals choose to invest in
group efforts. However, experimental research has been unable to shed light on
how social cognitive mechanisms contribute to the where and when of collective
action. We leverage multi-agent deep reinforcement learning to model how a
social-cognitive mechanism--specifically, the intrinsic motivation to achieve a
good reputation--steers group behavior toward specific spatial and temporal
strategies for collective action in a social dilemma. We also collect
behavioral data from groups of human participants challenged with the same
dilemma. The model accurately predicts spatial and temporal patterns of group
behavior: in this public goods dilemma, the intrinsic motivation for reputation
catalyzes the development of a non-territorial, turn-taking strategy to
coordinate collective action.


Abstract:
  In this paper we study zero-noise limits of $\alpha -$stable noise perturbed
ODE's which are driven by an irregular vector field $A$ with asymptotics $%
A(x)\sim \overline{a}(\frac{x}{\left\vert x\right\vert })\left\vert
x\right\vert ^{\beta -1}x$ at zero, where $\overline{a}>0$ is a continuous
function and $\beta \in (0,1)$. The results established in this article can be
considered a generalization of those in the seminal works of Bafico \cite% {Ba}
and Bafico, Baldi \cite{BB} to the multi-dimensional case.\ Our approach for
proving these results is inspired by techniques in \cite% {PP_self_similar} and
based on the analysis of an SDE for $t\longrightarrow \infty $, which is
obtained through a transformation of the perturbed ODE.


Abstract:
  A Private Set Operation (PSO) protocol involves at least two parties with
their private input sets. The goal of the protocol is for the parties to learn
the output of a set operation, i.e. set intersection, on their input sets,
without revealing any information about the items that are not in the output
set. Commonly, the outcome of the set operation is revealed to parties and
no-one else. However, in many application areas of PSO the result of the set
operation should be learned by an external participant whom does not have an
input set. We call this participant the decider. In this paper, we present new
variants of multi-party PSO, where there is a decider who gets the result. All
parties expect the decider have a private set. Other parties neither learn this
result, nor anything else about this protocol. Moreover, we present a generic
solution to the problem of PSO.


Abstract:
  One of the biggest challenges in multi-agent reinforcement learning is
coordination, a typical application scenario of this is traffic signal control.
Recently, it has attracted a rising number of researchers and has become a hot
research field with great practical significance. In this paper, we propose a
novel method called MetaVRS~(Meta Variational RewardShaping) for traffic signal
coordination control. By heuristically applying the intrinsic reward to the
environmental reward, MetaVRS can wisely capture the agent-to-agent interplay.
Besides, latent variables generated by VAE are brought into policy for
automatically tradeoff between exploration and exploitation to optimize the
policy. In addition, meta learning was used in decoder for faster adaptation
and better approximation. Empirically, we demonstate that MetaVRS substantially
outperforms existing methods and shows superior adaptability, which predictably
has a far-reaching significance to the multi-agent traffic signal coordination
control.


Abstract:
  We present the first measurement of the homogeneity index, $\mathcal{H}$, a
fractal or Hausdorff dimension of the early Universe from the Planck CMB
temperature variations $\delta T$ in the sky. This characterization of the
isotropy scale is model-free and purely geometrical, independent of the
amplitude of $\delta T$. We find evidence of homogeneity ($\mathcal{H}=0$) for
scales larger than $\theta_{\mathcal{H}} = 65.9 \pm 9.2 \deg $ on the CMB sky.
This finding is at odds with the $\Lambda$CDM prediction, which assumes a scale
invariant infinite universe. Such anomaly is consistent with the well known low
quadrupule amplitude in the angular $\delta T$ spectrum, but quantified in a
direct and model independent way. We estimate the significance of our finding
for $\mathcal{H}=0$ using a principal component analysis from the sampling
variations of the observed sky. This analysis is validated with an independent
theoretical prediction of the covariance matrix based purely on data. Assuming
translation invariance (and flat geometry $k=0$) we can convert the isotropy
scale $\theta_\mathcal{H}$ into a (comoving) homogeneity scale of
$\chi_\mathcal{H} \simeq 3.3 c/H_0$, which is very close to the trapped surface
generated by the observed cosmological constant $\Lambda$.


Abstract:
  XRISM is an X-ray astronomical mission by the JAXA, NASA, ESA and other
international participants, that is planned for launch in 2022 (Japanese fiscal
year), to quickly restore high-resolution X-ray spectroscopy of astrophysical
objects. To enhance the scientific outputs of the mission, the Science
Operations Team (SOT) is structured independently from the instrument teams and
the Mission Operations Team. The responsibilities of the SOT are divided into
four categories: 1) guest observer program and data distributions, 2)
distribution of analysis software and the calibration database, 3) guest
observer support activities, and 4) performance verification and optimization
activities. As the first step, lessons on the science operations learned from
past Japanese X-ray missions are reviewed, and 15 kinds of lessons are
identified. Among them, a) the importance of early preparation of the
operations from the ground stage, b) construction of an independent team for
science operations separate from the instrument development, and c) operations
with well-defined duties by appointed members are recognized as key lessons.
Then, the team structure and the task division between the mission and science
operations are defined; the tasks are shared among Japan, US, and Europe and
are performed by three centers, the SOC, SDC, and ESAC, respectively. The SOC
is designed to perform tasks close to the spacecraft operations, such as
spacecraft planning, quick-look health checks, pre-pipeline processing, etc.,
and the SDC covers tasks regarding data calibration processing, maintenance of
analysis tools, etc. The data-archive and user-support activities are covered
both by the SOC and SDC. Finally, the science-operations tasks and tools are
defined and prepared before launch.


Abstract:
  We study an expanding two-fluid model of non-relativistic dark matter and
radiation which are allowed to interact during a certain time span and to
establish an approximate thermal equilibrium. Such interaction which generates
an effective bulk viscous pressure at background level is expected to be
relevant for times around the transition from radiation to matter dominance. We
quantify the magnitude of this pressure for dark matter particles masses within
the range $1 {\rm eV} \lesssim m_{\chi} \lesssim 10 {\rm eV}$ around the
matter-radiation equality epoch (i.e., redshift $z_{\rm eq}\sim 3400$) and
demonstrate that the existence of a transient bulk viscosity has consequences
which may be relevant for addressing current tensions of the standard
cosmological model: i) the additional (negative) pressure contribution modifies
the expansion rate around $z_{\rm eq}$, yielding a larger $H_0$ value and ii)
large-scale structure formation is impacted by suppressing the amplitude of
matter overdensity growth via a new viscous friction-term contribution to the
M\'esz\'aros effect. As a result, both the $H_0$ and the $S_8$ tensions of the
current standard cosmological model are significantly alleviated.


Abstract:
  For an autonomous robotic system, monitoring surgeon actions and assisting
the main surgeon during a procedure can be very challenging. The challenges
come from the peculiar structure of the surgical scene, the greater similarity
in appearance of actions performed via tools in a cavity compared to, say,
human actions in unconstrained environments, as well as from the motion of the
endoscopic camera. This paper presents ESAD, the first large-scale dataset
designed to tackle the problem of surgeon action detection in endoscopic
minimally invasive surgery. ESAD aims at contributing to increase the
effectiveness and reliability of surgical assistant robots by realistically
testing their awareness of the actions performed by a surgeon. The dataset
provides bounding box annotation for 21 action classes on real endoscopic video
frames captured during prostatectomy, and was used as the basis of a recent
MIDL 2020 challenge. We also present an analysis of the dataset conducted using
the baseline model which was released as part of the challenge, and a
description of the top performing models submitted to the challenge together
with the results they obtained. This study provides significant insight into
what approaches can be effective and can be extended further. We believe that
ESAD will serve in the future as a useful benchmark for all researchers active
in surgeon action detection and assistive robotics at large.


Abstract:
  Traditionally Genetic Algorithm has been used for optimization of unimodal
and multimodal functions. Earlier researchers worked with constant
probabilities of GA control operators like crossover, mutation etc. for tuning
the optimization in specific domains. Recent advancements in this field
witnessed adaptive approach in probability determination. In Adaptive mutation
primarily poor individuals are utilized to explore state space, so mutation
probability is usually generated proportionally to the difference between
fitness of best chromosome and itself (fMAX - f). However, this approach is
susceptible to nature of fitness distribution during optimization. This paper
presents an alternate approach of mutation probability generation using
chromosome rank to avoid any susceptibility to fitness distribution.
Experiments are done to compare results of simple genetic algorithm (SGA) with
constant mutation probability and adaptive approaches within a limited resource
constraint for unimodal, multimodal functions and Travelling Salesman Problem
(TSP). Measurements are done for average best fitness, number of generations
evolved and percentage of global optimum achievements out of several trials.
The results demonstrate that the rank-based adaptive mutation approach is
superior to fitness-based adaptive approach as well as SGA in a multimodal
problem space.


Abstract:
  A new class of test functions for black box optimization is introduced.
Affine OneMax (AOM) functions are defined as compositions of OneMax and
invertible affine maps on bit vectors. The black box complexity of the class is
upper bounded by a polynomial of large degree in the dimension. The proof
relies on discrete Fourier analysis and the Kushilevitz-Mansour algorithm.
Tunable complexity is achieved by expressing invertible linear maps as finite
products of transvections. The black box complexity of sub-classes of AOM
functions is studied. Finally, experimental results are given to illustrate the
performance of search algorithms on AOM functions.


Abstract:
  The aberrations in an optical microscope are commonly measured and corrected
at one location in the field of view, within the so-called isoplanatic patch.
Full-field correction is desirable for high-resolution imaging of large
specimens. Here we present a novel wavefront detector, based on pupil sampling
with sub-apertures, which measures the aberrated wavefront phase at each
position of the specimen. Based on this measurement, we propose a region-wise
deconvolution that provides an anisoplanatic reconstruction of the sample
image. Our results indicate that the measurement and correction of the
aberrations can be performed in a wide-field fluorescence microscope over its
entire field of view.


Abstract:
  A rapid decline in mortality and fertility has become major issues in many
developed countries over the past few decades. A precise model for forecasting
demographic movements is important for decision making in social welfare
policies and resource budgeting among the government and many industry sectors.
This article introduces a novel non-parametric approach using Gaussian process
regression with a natural cubic spline mean function and a spectral mixture
covariance function for mortality and fertility modelling and forecasting.
Unlike most of the existing approaches in demographic modelling literature,
which rely on time parameters to decide the movements of the whole mortality or
fertility curve shifting from one year to another over time, we consider the
mortality and fertility curves from their components of all age-specific
mortality and fertility rates and assume each of them following a Gaussian
process over time to fit the whole curves in a discrete but intensive style.
The proposed Gaussian process regression approach shows significant
improvements in terms of preciseness and robustness compared to other
mainstream demographic modelling approaches in the short-, mid- and long-term
forecasting using the mortality and fertility data of several developed
countries in our numerical experiments.


Abstract:
  Driving is a routine activity for many, but it is far from simple. Drivers
deal with multiple concurrent tasks, such as keeping the vehicle in the lane,
observing and anticipating the actions of other road users, reacting to
hazards, and dealing with distractions inside and outside the vehicle. Failure
to notice and respond to the surrounding objects and events can cause
accidents. The ongoing improvements of the road infrastructure and vehicle
mechanical design have made driving safer overall. Nevertheless, the problem of
driver inattention has remained one of the primary causes of accidents.
Therefore, understanding where the drivers look and why they do so can help
eliminate sources of distractions and identify unsafe attention patterns.
Research on driver attention has implications for many practical applications
such as policy-making, improving driver education, enhancing road
infrastructure and in-vehicle infotainment systems, as well as designing
systems for driver monitoring, driver assistance, and automated driving. This
report covers the literature on changes in drivers' visual attention
distribution due to factors, internal and external to the driver. Aspects of
attention during driving have been explored across multiple disciplines,
including psychology, human factors, human-computer interaction, intelligent
transportation, and computer vision, each offering different perspectives,
goals, and explanations for the observed phenomena. We link cross-disciplinary
theoretical and behavioral research on driver's attention to practical
solutions. Furthermore, limitations and directions for future research are
discussed. This report is based on over 175 behavioral studies, nearly 100
practical papers, 20 datasets, and over 70 surveys published since 2010. A
curated list of papers used for this report is available at
\url{https://github.com/ykotseruba/attention_and_driving}.


Abstract:
  Magnetic anisotropies have key role to taylor magnetic behavior in
ferromagnetic systems. Further, they are also essential elements to manipulate
the thermoelectric response in Anomalous Nernst (ANE) and Longitudinal Spin
Seebeck systems (LSSE). We propose here a theoretical approach and explore the
role of magnetic anisotropies on the magnetization and thermoelectric response
of noninteracting multidomain ferromagnetic systems. The magnetic behavior and
the thermoelectric curves are calculated from a modified Stoner Wohlfarth model
for an isotropic system, a uniaxial magnetic one, as well as for a system
having a mixture of uniaxial and cubic magnetocrystalline magnetic
anisotropies. It is verified remarkable modifications of the magnetic behavior
with the anisotropy and it is shown that the thermoelectric response is
strongly affected by these changes. Further, the fingerprints of the energy
contributions to the thermoelectric response are disclosed. To test the
robustness of our theoretical approach, we engineer films having the specific
magnetic properties and compare directly experimental data with theoretical
results. Thus, experimental evidence is provided to confirm the validity of our
theoretical approach. The results go beyond the traditional reports focusing on
magnetically saturated films and show how the thermoelectric effect behaves
during the whole magnetization curve. Our findings reveal a promising way to
explore the ANE and LSSE effects as a powerful tool to study magnetic
anisotropies, as well as to employ systems with magnetic anisotropy as sensing
or elements in technological applications.


Abstract:
  IW And stars are a recently recognized subgroup of dwarf novae which are
characterized by (often repetitive) slowly rising standstills terminated by
brightening, but the exact mechanism for this variation is not yet identified.
We have identified BO Cet, which had been considered as a novalike cataclysmic
variable, as a new member of IW And stars based on the behavior in 2019-2020.
In addition to this, the object showed dwarf nova-type outbursts in 2020-2021,
and superhumps having a period 7.8% longer than the orbital one developed at
least during one long outburst. This object has been confirmed as an SU
UMa-type dwarf nova with an exceptionally long orbital period (0.1398 d). BO
Cet is thus the first cataclysmic variable showing both SU UMa-type and IW
And-type features. We obtained a mass ratio (q) of 0.31-0.34 from the
superhumps in the growing phase (stage A superhumps). At this q, the radius of
the 3:1 resonance, responsible for tidal instability and superhumps, and the
tidal truncation radius are very similar. We interpret that in some occasions
this object showed IW And-type variation when the disk size was not large
enough, but that the radius of the 3:1 resonance could be reached as the result
of thermal instability. We also discuss that there are SU UMa-type dwarf novae
above q=0.30, which is above the previously considered limit (q~0.25) derived
from numerical simulations and that this is possible since the radius of the
3:1 resonance is inside the tidal truncation radius. We constrained the mass of
the white dwarf larger than 1.0Msol, which may be responsible for the IW
And-type behavior and the observed strength of the He II emission. The exact
reason, however, why this object is unique in that it shows both SU UMa-type
and IW And-type features is still unsolved.


Abstract:
  We develop time-splitting finite difference methods, using implicit
Backward-Euler and semi-implicit Crank-Nicolson discretization schemes, to
study the spin-orbit coupled spinor Bose Einstein condensates with coherent
coupling in quasi-one and quasi-two-dimensional traps. The split equations
involving kinetic energy and spin-orbit coupling operators are solved using
either time-implicit Backward-Euler or semi-implicit Crank-Nicolson methods. We
explicitly develop the method for pseudospin-1/2, spin-1, and spin-2
condensates. The results for ground states obtained with time-splitting
Backward-Euler and Crank-Nicolson methods are in excellent agreement with
time-splitting Fourier spectral method which is one of the popular methods to
solve the mean-field models for spin-orbit coupled spinor condensates. We
confirm the emergence of different phases in spin-orbit coupled pseudospin-1/2,
spin-1, and spin-2 condensates with coherent coupling.


Abstract:
  We consider two models of random cones together with their duals. Let
$Y_1,\dots,Y_n$ be independent and identically distributed random vectors in
$\mathbb R^d$ whose distribution satisfies some mild condition. The random
cones $G_{n,d}^A$ and $G_{n,d}^B$ are defined as the positive hulls
$\text{pos}\{Y_1-Y_2,\dots,Y_{n-1}-Y_n\}$, respectively
$\text{pos}\{Y_1-Y_2,\dots,Y_{n-1}-Y_n,Y_n\}$, conditioned on the event that
the respective positive hull is not equal to $\mathbb R^d$. We prove limit
theorems for various expected geometric functionals of these random cones, as
$n$ and $d$ tend to infinity in a coordinated way. This includes limit theorems
for the expected number of $k$-faces and the $k$-th conic quermassintegrals, as
$n$, $d$ and sometimes also $k$ tend to infinity simultaneously. Moreover, we
uncover a phase transition in high dimensions for the expected statistical
dimension for both models of random cones.


Abstract:
  A proper orthogonal decomposition-based B-splines B\'ezier elements method
(POD-BSBEM) is proposed as a non-intrusive reduced-order model for uncertainty
propagation analysis for stochastic time-dependent problems. The method uses a
two-step proper orthogonal decomposition (POD) technique to extract the reduced
basis from a collection of high-fidelity solutions called snapshots. A third
POD level is then applied on the data of the projection coefficients associated
with the reduced basis to separate the time-dependent modes from the stochastic
parametrized coefficients. These are approximated in the stochastic parameter
space using B-splines basis functions defined in the corresponding B\'ezier
element. The accuracy and the efficiency of the proposed method are assessed
using benchmark steady-state and time-dependent problems and compared to the
reduced order model-based artificial neural network (POD-ANN) and to the
full-order model-based polynomial chaos expansion (Full-PCE). The POD-BSBEM is
then applied to analyze the uncertainty propagation through a flood wave flow
stemming from a hypothetical dam-break in a river with a complex bathymetry.
The results confirm the ability of the POD-BSBEM to accurately predict the
statistical moments of the output quantities of interest with a substantial
speed-up for both offline and online stages compared to other techniques.


Abstract:
  Nowadays, datacenters lean on a computer-centric approach based on monolithic
servers which include all necessary hardware resources (mainly CPU, RAM,
network and disks) to run applications. Such an architecture comes with two
main limitations: (1) difficulty to achieve full resource utilization and (2)
coarse granularity for hardware maintenance. Recently, many works investigated
a resource-centric approach called disaggregated architecture where the
datacenter is composed of self-content resource boards interconnected using
fast interconnection technologies, each resource board including instances of
one resource type. The resource-centric architecture allows each resource to be
managed (maintenance, allocation) independently. LegoOS is the first work which
studied the implications of disaggregation on the operating system, proposing
to disaggregate the operating system itself. They demonstrated the suitability
of this approach, considering mainly CPU and RAM resources. However, they
didn't study the implication of disaggregation on network resources. We
reproduced a LegoOS infrastructure and extended it to support disaggregated
networking. We show that networking can be disaggregated following the same
principles, and that classical networking optimizations such as DMA, DDIO or
loopback can be reproduced in such an environment. Our evaluations show the
viability of the approach and the potential of future disaggregated
infrastructures.


Abstract:
  Previous methods decompose the blind super-resolution (SR) problem into two
sequential steps: \textit{i}) estimating the blur kernel from given
low-resolution (LR) image and \textit{ii}) restoring the SR image based on the
estimated kernel. This two-step solution involves two independently trained
models, which may not be well compatible with each other. A small estimation
error of the first step could cause a severe performance drop of the second
one. While on the other hand, the first step can only utilize limited
information from the LR image, which makes it difficult to predict a highly
accurate blur kernel. Towards these issues, instead of considering these two
steps separately, we adopt an alternating optimization algorithm, which can
estimate the blur kernel and restore the SR image in a single model.
Specifically, we design two convolutional neural modules, namely
\textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR
image based on the predicted kernel, and \textit{Estimator} estimates the blur
kernel with the help of the restored SR image. We alternate these two modules
repeatedly and unfold this process to form an end-to-end trainable network. In
this way, \textit{Estimator} utilizes information from both LR and SR images,
which makes the estimation of the blur kernel easier. More importantly,
\textit{Restorer} is trained with the kernel estimated by \textit{Estimator},
instead of the ground-truth kernel, thus \textit{Restorer} could be more
tolerant to the estimation error of \textit{Estimator}. Extensive experiments
on synthetic datasets and real-world images show that our model can largely
outperform state-of-the-art methods and produce more visually favorable results
at a much higher speed. The source code is available at
\url{https://github.com/greatlog/DAN.git}.


Abstract:
  We consider a certain lattice branching random walk with on-site competition
and in an environment which is heterogeneous at a macroscopic scale
$1/\varepsilon$ in space and time. This can be seen as a model for the spatial
dynamics of a biological population in a habitat which is heterogeneous at a
large scale (mountains, temperature or precipitation gradient...). The model
incorporates another parameter, $K$, which is a measure of the local population
density. We study the model in the limit when first $\varepsilon\to 0$ and then
$K\to\infty$. In this asymptotic regime, we show that the rescaled position of
the front as a function of time converges to the solution of an explicit ODE.
We further discuss the relation with another popular model of population
dynamics, the Fisher-KPP equation, which arises in the limit $K\to\infty$.
Combined with known results on the Fisher-KPP equation, our results show in
particular that the limits $\varepsilon\to0$ and $K\to\infty$ do not commute in
general. We conjecture that an interpolating regime appears when $\log K$ and
$1/\varepsilon$ are of the same order.


Abstract:
  A long-held belief is that shock energy induces initiation of an energetic
material through an indirect energy up-pumping mechanism involving phonon
scattering through doorway modes. In this paper, a 3-phonon theoretical
analysis of energy up-pumping in RDX is presented that involves both direct and
indirect pathways where the direct energy transfer dominates. The calculation
considers individual phonon modes which are then analyzed in bands. Scattering
is handled up to the third order term in the Hamiltonian based on Fermi's
Golden Rule. On average, modes with frequencies up to 90 cm-1 scatter quickly
and redistribute the energy to all the modes. This direct stimulation occurs
rapidly, within 0.16 ps, and involves distortions to NN bonds. Modes from 90 to
1839 cm-1 further up-pump the energy to NN bond distortion modes through an
indirect route within 5.6 ps. The highest frequency modes have the lowest
contribution to energy transfer due to their lower participation in
phonon-phonon scattering. The modes stimulated directly by the shock with
frequencies up to 90 cm-1 are estimated to account for 52 to 89\% of the total
energy transfer to various NN bond distorting modes.


Abstract:
  A new paradigm called physical reservoir computing has recently emerged,
where the nonlinear dynamics of high-dimensional and fixed physical systems are
harnessed as a computational resource to achieve complex tasks. Via extensive
simulations based on a dynamic truss-frame model, this study shows that an
origami structure can perform as a dynamic reservoir with sufficient computing
power to emulate high-order nonlinear systems, generate stable limit cycles,
and modulate outputs according to dynamic inputs. This study also uncovers the
linkages between the origami reservoir's physical designs and its computing
power, offering a guideline to optimize the computing performance.
Comprehensive parametric studies show that selecting optimal feedback crease
distribution and fine-tuning the underlying origami folding designs are the
most effective approach to improve computing performance. Furthermore, this
study shows how origami's physical reservoir computing power can apply to soft
robotic control problems by a case study of earthworm-like peristaltic crawling
without traditional controllers. These results can pave the way for
origami-based robots with embodied mechanical intelligence.


Abstract:
  We study the algebraic conditions leading to the chain property of complexes
for vertex operator algebra $n$-point functions with differential being defined
through reduction formulas. The notion of the reduction cohomology of Riemann
surfaces is introduced. Algebraic, geometrical, and cohomological meanings of
reduction formulas is clarified. A counterpart of the Bott-Segal theorem for
Riemann surfaces in terms of the reductions cohomology is proven. It is shown
that the reduction cohomology is given by the cohomology of $n$-point
connections over the vertex operator algebra bundle defined on a genus $g$
Riemann surface $\Sigma^{(g)}$. The reduction cohomology for a vertex operator
algebra with formal parameters identified with local coordinates around marked
points on $\Sigma^{(g)}$ is found in terms of the space of analytical
continuations of solutions to Knizhnik-Zamolodchikov equations. For the
reduction cohomology, the Euler-Poincare formula is derived. Examples for
various genera and vertex operator cluster algebras are provided.


Abstract:
  For a commutative ring $R$, we define the notions of deformed Picard
algebroids and deformed twisted differential operators on a smooth, separated,
locally of finite type $R$-scheme and prove these are in a natural bijection.
We then define the pullback of a sheaf of twisted differential operators that
reduces to the classical definition when $R=\mathbb{C}$. Finally, for modules
over twisted differential operators, we prove a theorem for the descent under a
locally trivial torsor.


Abstract:
  Motivated by questions in number theory, Myerson asked how small the sum of 5
complex nth roots of unity can be. We obtain a uniform bound of O(n^{-4/3}) by
perturbing the vertices of a regular pentagon, improving to O(n^{-7/3})
infinitely often.
  The corresponding configurations were suggested by examining exact minimum
values computed for n <= 221000. These minima can be explained at least in part
by selection of the best example from multiple families of competing
configurations related to close rational approximations.


Abstract:
  The orientation completion problem for a class of oriented graphs asks
whether a given partially oriented graph can be completed to an oriented graph
in the class by orienting the unoriented edges of the partially oriented graph.
Orientation completion problems have been studied recently for several classes
of oriented graphs, yielding both polynomial time solutions as well as
NP-completeness results. Local tournaments are a well-structured class of
oriented graphs that generalize tournaments and their underlying graphs are
intimately related to proper circular-arc graphs. According to Skrien, a
connected graph can be oriented as a local tournament if and only if it is a
proper circular-arc graph. Proper interval graphs are precisely the graphs
which can be oriented as acyclic local tournaments. It has been proved that the
orientation completion problems for the classes of local tournaments and
acyclic local tournaments are both polynomial time solvable. In this paper we
characterize the partially oriented graphs that can be completed to local
tournaments by determining the complete list of obstructions. These are in a
sense minimal partially oriented graphs that cannot be completed to local
tournaments. The result may be viewed as an extension of the well-known
forbidden subgraph characterization of proper circular-arc graphs obtained by
Tucker. The complete list of obstructions for acyclic local tournament
orientation completions has been given in a companion paper.


Abstract:
  We derive a thermodynamic uncertainty relation (TUR) for first-passage times
(FPTs) on continuous time Markov chains. The TUR utilizes the entropy
production coming from bidirectional transitions, and the net flux coming from
unidirectional transitions, to provide a lower bound on FPT fluctuations. As
every bidirectional transition can also be seen as a pair of separate
unidirectional ones, our approach typically yields an ensemble of TURs. The
tightest bound on FPT fluctuations can then be obtained from this ensemble by a
simple and physically motivated optimization procedure. The results presented
herein are valid for arbitrary initial conditions, out-of-equilibrium dynamics,
and are therefore well suited to describe the inherently irreversible
first-passage event. They can thus be readily applied to a myriad of
first-passage problems that arise across a wide range of disciplines.


Abstract:
  A hyperlink is a finite set of non-intersecting simple closed curves in
$\mathbb{R}^4 \equiv \mathbb{R} \times \mathbb{R}^3$, each curve is either a
matter or geometric loop. We consider an equivalence class of such hyperlinks,
up to time-like isotopy, preserving time-ordering. Using an equivalence class
and after coloring each matter component loop with an irreducible
representation of $\mathfrak{su}(2) \times \mathfrak{su}(2)$, we can define its
Wilson Loop observable using an Einstein-Hilbert action, which is now thought
of as a functional acting on the set containing equivalence classes of
hyperlink. Construct a vector space using these functionals, which we now term
as quantum states. To make it into a Hilbert space, we need to define a
counting probability measure on the space containing equivalence classes of
hyperlinks. In our previous work, we defined area, volume and curvature
operators, corresponding to given geometric objects like surface and a compact
solid spatial region. These operators act on the quantum states and by
deliberate construction of the Hilbert space, are self-adjoint and possibly
unbounded operators. Using these operators and Einstein's field equations, we
can proceed to construct a quantized stress operator and also a Hamiltonian
constraint operator for the quantum system. We will also use the area operator
to derive the Bekenstein entropy of a black hole. In the concluding section, we
will explain how Loop Quantum Gravity predicts the existence of gravitons,
implies causality and locality in quantum gravity, and formulate the principle
of equivalence mathematically in its framework.


Abstract:
  We use a replica trick construction to propose a definition of branch-point
twist operators in two dimensional momentum space and compute their two-point
function. The result is then tentatively interpreted as a pseudo R\'enyi
entropy for momentum modes.


Abstract:
  Deep learning semantic segmentation algorithms can localise abnormalities or
opacities from chest radiographs. However, the task of collecting and
annotating training data is expensive and requires expertise which remains a
bottleneck for algorithm performance. We investigate the effect of image
augmentations on reducing the requirement of labelled data in the semantic
segmentation of chest X-rays for pneumonia detection. We train fully
convolutional network models on subsets of different sizes from the total
training data. We apply a different image augmentation while training each
model and compare it to the baseline trained on the entire dataset without
augmentations. We find that rotate and mixup are the best augmentations amongst
rotate, mixup, translate, gamma and horizontal flip, wherein they reduce the
labelled data requirement by 70% while performing comparably to the baseline in
terms of AUC and mean IoU in our experiments.


Abstract:
  We investigate the behavior of vortex bound states in the quantum limit by
self-consistently solving the Bogoliubov-de Gennes equation. We find that the
energies of the vortex bound states deviates from the analytical result
$E_\mu=\mu\Delta^2/E_F$ with the half-integer angular momentum $\mu$ in the
extreme quantum limit. Specifically, the energy ratio for the first three
orders is more close to $1:2:3$ instead of $1:3:5$ at extremely low
temperature. The local density of states reveals an Friedel-like behavior
associated with that of the pair potential in the extreme quantum limit, which
will be smoothed out by thermal effect above a certain temperature even the
quantum limit condition, namely $T/T_c<\Delta/E_F$ is still satisfied. Our
studies show that the vortex bound states can exhibit very distinct features in
different temperature regimes, which provides a comprehensive understanding and
should stimulate more experimental efforts for verifications.


Abstract:
  Predicting molecular conformations (or 3D structures) from molecular graphs
is a fundamental problem in many applications. Most existing approaches are
usually divided into two steps by first predicting the distances between atoms
and then generating a 3D structure through optimizing a distance geometry
problem. However, the distances predicted with such two-stage approaches may
not be able to consistently preserve the geometry of local atomic
neighborhoods, making the generated structures unsatisfying. In this paper, we
propose an end-to-end solution for molecular conformation prediction called
ConfVAE based on the conditional variational autoencoder framework.
Specifically, the molecular graph is first encoded in a latent space, and then
the 3D structures are generated by solving a principled bilevel optimization
program. Extensive experiments on several benchmark data sets prove the
effectiveness of our proposed approach over existing state-of-the-art
approaches. Code is available at
\url{https://github.com/MinkaiXu/ConfVAE-ICML21}.


Abstract:
  Search strategies for the third-generation leptoquarks (LQs) are distinct
from other LQ searches, especially when they decay to a top quark and a $\tau$
lepton. We investigate the cases of all TeV-scale scalar and vector LQs that
decay to either a top-tau pair (charge-$1/3$ and $5/3$ LQs) or a top-neutrino
pair (charge-$2/3$ LQs). One can then use the boosted top (which can be tagged
efficiently using jet-substructure techniques) and high-$p_{\rm T}$ $\tau$
leptons to search for these LQs. We consider two search channels with either
one or two taus along with at least one hadronically decaying boosted top
quark. We estimate the high luminosity LHC (HL-LHC) search prospects of these
LQs by considering both symmetric and asymmetric pair and single production
processes. Our selection criteria are optimised to retain events from both pair
and single production processes. The combined signal has better prospects than
the traditional searches. We include new three-body single production processes
to enhance the single production contributions to the combined signal. We
identify the interference effect that appears in the dominant single production
channel of charge-$1/3$ scalar LQ ($S^{1/3}$). This interference is
constructive if $S^{1/3}$ is weak-triplet and destructive, for a singlet one.
As a result, their LHC prospects differ appreciably.


Abstract:
  We present a detailed analysis to clarify what determines the growth of the
low-$T/|W|$ instability in the context of rapidly rotating core-collapse of
massive stars. To this end, we perform three-dimensional core-collapse
supernova (CCSN) simulations of a $27 M_{\odot}$ star including several updates
in the general relativistic correction to gravity, the multi-energy treatment
of heavy-lepton neutrinos, and the nuclear equation of state. Non-axisymmetric
deformations are analyzed from the point of view of the time evolution of the
pattern frequency and the corotation radius. The corotation radius is found to
coincide with the convective layer in the proto neutron star (PNS). We propose
a new mechanism to account for the growth of the low-$T/|W|$ instability in the
CCSN environment. Near the convective boundary where a small
Brunt-V\"ais\"al\"a frequency is expected, Rossby waves propagating in the
azimuthal direction at mid latitude induce non-axisymmetric unstable modes, in
both hemispheres. They merge with each other and finally become the spiral arm
in the equatorial plane. We also investigate how the growth of the low-$T/|W|$
instability impacts the neutrino and gravitational-wave signatures.


Abstract:
  An optical neural network is proposed and demonstrated with programmable
matrix transformation and nonlinear activation function of photodetection
(square-law detection). Based on discrete phase-coherent spatial modes, the
dimensionality of programmable optical matrix operations is 30~37, which is
implemented by spatial light modulators. With this architecture, all-optical
classification tasks of handwritten digits, objects and depth images are
performed on the same platform with high accuracy. Due to the parallel nature
of matrix multiplication, the processing speed of our proposed architecture is
potentially as high as7.4T~74T FLOPs per second (with 10~100GHz detector)


Abstract:
  In this note, we give a characterisation in terms of identities of the join
of $\mathbf{V}$ with the variety of finite locally trivial semigroups
$\mathbf{LI}$ for several well-known varieties of finite monoids $\mathbf{V}$
by using classical algebraic-automata-theoretic techniques. To achieve this, we
use the new notion of essentially-$\mathbf{V}$ stamps defined by Grosshans,
McKenzie and Segoufin and show that it actually coincides with the join of
$\mathbf{V}$ and $\mathbf{LI}$ precisely when some natural condition on the
variety of languages corresponding to $\mathbf{V}$ is verified.This work is a
kind of rediscovery of the work of J. C. Costa around 20 years ago from a
rather different angle, since Costa's work relies on the use of advanced
developments in profinite topology, whereas what is presented here essentially
uses an algebraic, language-based approach.


Abstract:
  The Transiting Exoplanet Survey Satellite (\textit{TESS}) mission was
designed to perform an all-sky search of planets around bright and nearby
stars. Here we report the discovery of two sub-Neptunes orbiting around the TOI
1062 (TIC 299799658), a V=10.25 G9V star observed in the TESS Sectors 1, 13, 27
& 28. We use precise radial velocity observations from HARPS to confirm and
characterize these two planets. TOI 1062b has a radius of
2.265^{+0.095}_{-0.091} Re, a mass of 11.8 +\- 1.4 Me, and an orbital period of
4.115050 +/- 0.000007 days. The second planet is not transiting, has a minimum
mass of 7.4 +/- 1.6 Me and is near the 2:1 mean motion resonance with the
innermost planet with an orbital period of 8.13^{+0.02}_{-0.01} days. We
performed a dynamical analysis to explore the proximity of the system to this
resonance, and to attempt at further constraining the orbital parameters. The
transiting planet has a mean density of 5.58^{+1.00}_{-0.89} g cm^-3 and an
analysis of its internal structure reveals that it is expected to have a small
volatile envelope accounting for 0.35% of the mass at maximum. The star's
brightness and the proximity of the inner planet to the "radius gap" make it an
interesting candidate for transmission spectroscopy, which could further
constrain the composition and internal structure of TOI 1062b.


Abstract:
  This paper describes the design, implementation, and verification of a
test-bed for determining the noise temperature of radio antennas operating
between 400-800MHz. The requirements for this test-bed were driven by the HIRAX
experiment, which uses antennas with embedded amplification, making system
noise characterization difficult in the laboratory. The test-bed consists of
two large cylindrical cavities, each containing radio-frequency (RF) absorber
held at different temperatures (300K and 77K), allowing a measurement of system
noise temperature through the well-known 'Y-factor' method. The apparatus has
been constructed at Yale, and over the course of the past year has undergone
detailed verification measurements. To date, three preliminary noise
temperature measurement sets have been conducted using the system, putting us
on track to make the first noise temperature measurements of the HIRAX feed and
perform the first analysis of feed repeatability.


Abstract:
  We establish concentration inequalities in the class of ultra log-concave
distributions. In particular, we show that ultra log-concave distributions
satisfy Poisson concentration bounds. As an application, we derive
concentration bounds for the intrinsic volumes of a convex body, which
generalizes and improves a result of Lotz, McCoy, Nourdin, Peccati, and Tropp
(2019).


Abstract:
  What does bumping into things in a scene tell you about scene geometry? In
this paper, we investigate the idea of learning from collisions. At the heart
of our approach is the idea of collision replay, where we use examples of a
collision to provide supervision for observations at a past frame. We use
collision replay to train convolutional neural networks to predict a
distribution over collision time from new images. This distribution conveys
information about the navigational affordances (e.g., corridors vs open spaces)
and, as we show, can be converted into the distance function for the scene
geometry. We analyze this approach with an agent that has noisy actuation in a
photorealistic simulator.


Abstract:
  We propose a leptoquark model with two scalar leptoquarks $S^{}_1 \left(
\bar{3},1,\frac{1}{3} \right)$ and $\widetilde{R}^{}_2 \left(3,2,\frac{1}{6}
\right)$ to give a combined explanation of neutrino masses, lepton flavor
mixing and the anomaly of muon $g-2$, satisfying the constraints from the
radiative decays of charged leptons. The neutrino masses are generated via
one-loop corrections resulting from a mixing between $S^{}_1$ and
$\widetilde{R}^{}_2$. With a set of specific textures for the leptoquark Yukawa
coupling matrices, the neutrino mass matrix possesses an approximate
$\mu$-$\tau$ reflection symmetry with $\left( M^{}_\nu \right)^{}_{ee} = 0$
only in favor of the normal neutrino mass ordering. We show that this model can
successfully explain the anomaly of muon $g-2$ and current experimental
neutrino oscillation data under the constraints from the radiative decays of
charged leptons.


Abstract:
  Face detection is a crucial first step in many facial recognition and face
analysis systems. Early approaches for face detection were mainly based on
classifiers built on top of hand-crafted features extracted from local image
regions, such as Haar Cascades and Histogram of Oriented Gradients. However,
these approaches were not powerful enough to achieve a high accuracy on images
of from uncontrolled environments. With the breakthrough work in image
classification using deep neural networks in 2012, there has been a huge
paradigm shift in face detection. Inspired by the rapid progress of deep
learning in computer vision, many deep learning based frameworks have been
proposed for face detection over the past few years, achieving significant
improvements in accuracy. In this work, we provide a detailed overview of some
of the most representative deep learning based face detection methods by
grouping them into a few major categories, and present their core architectural
designs and accuracies on popular benchmarks. We also describe some of the most
popular face detection datasets. Finally, we discuss some current challenges in
the field, and suggest potential future research directions.


Abstract:
  A Cayley (di)hypergraph is a hypergraph that its automorphism group contains
a subgroup acting regularly on (hyper)vertices. In this paper, we study Cayley
(di)hypergraph and its automorphism group.


Abstract:
  Purpose: Develop a processing scheme for Gradient Echo (GRE) phase to enable
restoration of susceptibility-related (SuR) features in regions affected by
imperfect phase unwrapping, background suppression and low signal-to-noise
ratio (SNR) due to phase dispersion. Theory and Methods: The predictable
components sampled across the echo dimension in a multi-echo GRE sequence are
recovered by rank minimizing a Hankel matrix formed using the complex
exponential of the background suppressed phase. To estimate the single
frequency component that relates to the susceptibility induced field, it is
required to maintain consistency with the measured phase after background
suppression, penalized by a unity rank approximation (URA) prior. This is
formulated as an optimization problem, implemented using the alternating
direction method of multiplier (ADMM). Results: With in vivo multi-echo GRE
data, the magnitude susceptibility weighted image (SWI) reconstructed using URA
prior shows additional venous structures that are obscured due to phase
dispersion and noise in regions subject to remnant non-local field variations.
The performance is compared with the susceptibility map weighted imaging (SMWI)
and the standard SWI. It is also shown using numerical simulation that
quantitative susceptibility map (QSM) computed from the reconstructed phase
exhibits reduced artifacts and quantification error. In vivo experiments reveal
iron depositions in insular, motor cortex and superior frontal gyrus that are
not identified in standard QSM. Conclusion: URA processed GRE phase is less
sensitive to imperfections in the phase pre-processing techniques, and thereby
enable robust estimation of SWI and QSM.


Abstract:
  We provide a comprehensive analysis of the two-parameter Beta distributions
seen from the perspective of second-order stochastic dominance. By changing its
parameters through a bijective mapping, we work with a bounded subset D instead
of an unbounded plane. We show that a mean-preserving spread is equivalent to
an increase of the variance, which means that higher moments are irrelevant to
compare the riskiness of Beta distributions. We then derive the lattice
structure induced by second-order stochastic dominance, which is feasible
thanks to the topological closure of D. Finally, we consider a standard
(expected-utility based) portfolio optimization problem in which its inputs are
the parameters of the Beta distribution. We explicitly characterize the subset
of D for which the optimal solution consists of investing 100% of the wealth in
the risky asset and we provide an exhaustive numerical analysis of this optimal
solution through (color-coded) graphs.


Abstract:
  We report the growth, structural and magnetic properties of the less studied
Eu-oxide phase, Eu$_3$O$_4$, thin films grown on a Si/SiO$_2$ substrate and
Si/SiO$_2$/graphene using molecular beam epitaxy. The X-ray diffraction scans
show that highly-textured crystalline Eu$_3$O$_4$(001) films are grown on both
substrates, whereas the film deposited on graphene has a better crystallinity
than that grown on the Si/SiO$_2$ substrate. The SQUID measurements show that
both films have a Curie temperature of about 5.5 K, with a magnetic moment of
0.0032 emu/g at 2 K. The mixed-valency of the Eu cations has been confirmed by
the qualitative analysis of the depth-profile X-ray photoelectron spectroscopy
measurements with the Eu$^{2+}$ : Eu$^{3+}$ ratio of 28 : 72. However,
surprisingly, our films show no metamagnetic behaviour as reported for the bulk
and powder form. Furthermore, the Raman spectroscopy scans show that the growth
of the Eu$_3$O$_4$ thin films has no damaging effect on the underlayer graphene
sheet. Therefore, the graphene layer is expected to retain its properties.


Abstract:
  We study the Choquard equation with a local perturbation \begin{equation*}
-\Delta u=\lambda u+(I_\alpha\ast|u|^p)|u|^{p-2}u+\mu|u|^{q-2}u,\ x\in
\mathbb{R}^{N} \end{equation*} having prescribed mass \begin{equation*}
\int_{\mathbb{R}^N}|u|^2dx=a^2. \end{equation*} For a $L^2$-critical or
$L^2$-supercritical perturbation $\mu|u|^{q-2}u$, we prove nonexistence,
existence and symmetry of normalized ground states, by using the mountain pass
lemma, the Poho\v{z}aev constraint method, the Schwartz symmetrization
rearrangements and some theories of polarizations. In particular, our results
cover the Hardy-Littlewood-Sobolev upper critical exponent case
$p=(N+\alpha)/(N-2)$. Our results are a nonlocal counterpart of the results in
\cite{{Li 2021-4},{Soave JFA},{Wei-Wu 2021}}.


Abstract:
  We study an invariant of compact metric spaces which combines the notion of
curvature sets introduced by Gromov in the 1980s together with the notion of
Vietoris-Rips persistent homology. For given integers $k\geq 0$ and $n\geq 1$
these invariants arise by considering the degree $k$ Vietoris-Rips persistence
diagrams of all subsets of a given metric space with cardinality at most $n$.
We call these invariants \emph{persistence sets} and denote them as
$D_{n,k}^\mathrm{VR}$. We argue that computing these invariants could be
significantly easier than computing the usual Vietoris-Rips persistence
diagrams. We establish stability results as for these invariants and we also
precisely characterize some of them in the case of spheres with geodesic and
Euclidean distances. We identify a rich family of metric graphs for which
$D_{4,1}^{\mathrm{VR}}$ fully recovers their homotopy type. Along the way we
prove some useful properties of Vietoris-Rips persistence diagrams.


Abstract:
  Let $K$ be the connected sum of knots $K_1,\ldots,K_n$. It is known that the
$\mathrm{SL}_2(\mathbb{C})$-character variety of the knot exterior of $K$ has a
component of dimension $\geq 2$ as the connected sum admits a so-called
bending. We show that there is a natural way to define the adjoint Reidemeister
torsion for such a high-dimensional component and prove that it is locally
constant on a subset of the character variety where the trace of a meridian is
constant. We also prove that the adjoint Reidemeister torsion of $K$ satisfies
the vanishing identity if each $K_i$ does so.


Abstract:
  Grammatical error correction (GEC) suffers from a lack of sufficient parallel
data. Therefore, GEC studies have developed various methods to generate pseudo
data, which comprise pairs of grammatical and artificially produced
ungrammatical sentences. Currently, a mainstream approach to generate pseudo
data is back-translation (BT). Most previous GEC studies using BT have employed
the same architecture for both GEC and BT models. However, GEC models have
different correction tendencies depending on their architectures. Thus, in this
study, we compare the correction tendencies of the GEC models trained on pseudo
data generated by different BT models, namely, Transformer, CNN, and LSTM. The
results confirm that the correction tendencies for each error type are
different for every BT model. Additionally, we examine the correction
tendencies when using a combination of pseudo data generated by different BT
models. As a result, we find that the combination of different BT models
improves or interpolates the F_0.5 scores of each error type compared with that
of single BT models with different seeds.


Abstract:
  Deep learning recommendation models (DLRMs) are used across many
business-critical services at Facebook and are the single largest AI
application in terms of infrastructure demand in its data-centers. In this
paper we discuss the SW/HW co-designed solution for high-performance
distributed training of large-scale DLRMs. We introduce a high-performance
scalable software stack based on PyTorch and pair it with the new evolution of
Zion platform, namely ZionEX. We demonstrate the capability to train very large
DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup
in terms of time to solution over previous systems. We achieve this by (i)
designing the ZionEX platform with dedicated scale-out network, provisioned
with high bandwidth, optimal topology and efficient transport (ii) implementing
an optimized PyTorch-based training stack supporting both model and data
parallelism (iii) developing sharding algorithms capable of hierarchical
partitioning of the embedding tables along row, column dimensions and load
balancing them across multiple workers; (iv) adding high-performance core
operators while retaining flexibility to support optimizers with fully
deterministic updates (v) leveraging reduced precision communications,
multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we
develop and briefly comment on distributed data ingestion and other supporting
services that are required for the robust and efficient end-to-end training in
production environments.


Abstract:
  Bitcoin and Ethereum transactions present one of the largest real-world
complex networks that are publicly available for study, including a detailed
picture of their time evolution. As such, they have received a considerable
amount of attention from the network science community, beside analysis from an
economic or cryptography perspective. Among these studies, in an analysis on
the early instance of the Bitcoin network, we have shown the clear presence of
the preferential attachment, or "rich-get-richer" phenomenon. Now, we revisit
this question, using a recent version of the Bitcoin network that has grown
almost 100-fold since our original analysis. Furthermore, we additionally carry
out a comparison with Ethereum, the second most important cryptocurrency. Our
results show that preferential attachment continues to be a key factor in the
evolution of both the Bitcoin and Ethereum transactoin networks. To facilitate
further analysis, we publish a recent version of both transaction networks, and
an efficient software implementation that is able to evaluate linking
statistics necessary for learn about preferential attachment on networks with
several hundred million edges.


Abstract:
  Distributed hardware of acoustic sensor networks bears inconsistency of local
sampling frequencies, which is detrimental to signal processing. Fundamentally,
sampling rate offset (SRO) nonlinearly relates the discrete-time signals
acquired by different sensor nodes. As such, retrieval of SRO from the
available signals requires nonlinear estimation, like double-cross-correlation
processing (DXCP), and frequently results in biased estimation. SRO
compensation by asynchronous sampling rate conversion (ASRC) on the signals
then leaves an unacceptable residual. As a remedy to this problem, multi-stage
procedures have been devised to diminish the SRO residual with multiple
iterations of SRO estimation and ASRC over the entire signal. This paper
converts the mechanism of offline multi-stage processing into a continuous
feedback-control loop comprising a controlled ASRC unit followed by an online
implementation of DXCP-based SRO estimation. To support the design of an
optimum internal model control unit for this closed-loop system, the paper
deploys an analytical dynamical model of the proposed online DXCP. The
resulting control architecture then merely applies a single treatment of each
signal frame, while efficiently diminishing SRO bias with time. Evaluations
with both speech and Gaussian input demonstrate that the high accuracy of
multi-stage processing is maintained at the low complexity of single-stage
(open-loop) processing.


Abstract:
  We examine a class of random walks in random environments on $\mathbb{Z}$
with bounded jumps, a generalization of the classic one-dimensional model. The
environments we study have i.i.d. transition probability vectors drawn from
Dirichlet distributions. For this model, we characterize recurrence and
transience, and in the transient case we characterize ballisticity. For
ballisticity, we give two parameters, $\kappa_0$ and $\kappa_1$. The parameter
$\kappa_0$ governs finite trapping effects, and $\kappa_1$ governs repeated
traversals of arbitrarily large regions of the graph. We show that the walk is
right-transient if and only if $\kappa_1>0$, and in that case it is ballistic
if and only if $\min(\kappa_0,\kappa_1)>1$.


Abstract:
  G0.253+0.016, aka 'the Brick', is one of the most massive (> 10^5 Msun) and
dense (> 10^4 cm-3) molecular clouds in the Milky Way's Central Molecular Zone.
Previous observations have detected tentative signs of active star formation,
most notably a water maser that is associated with a dust continuum source. We
present ALMA Band 6 observations with an angular resolution of 0.13" (1000 AU)
towards this 'maser core', and report unambiguous evidence of active star
formation within G0.253+0.016. We detect a population of eighteen continuum
sources (median mass ~ 2 Msun), nine of which are driving bi-polar molecular
outflows as seen via SiO (5-4) emission. At the location of the water maser, we
find evidence for a protostellar binary/multiple with multi-directional outflow
emission. Despite the high density of G0.253+0.016, we find no evidence for
high-mass protostars in our ALMA field. The observed sources are instead
consistent with a cluster of low-to-intermediate-mass protostars. However, the
measured outflow properties are consistent with those expected for
intermediate-to-high-mass star formation. We conclude that the sources are
young and rapidly accreting, and may potentially form intermediate and
high-mass stars in the future. The masses and projected spatial distribution of
the cores are generally consistent with thermal fragmentation, suggesting that
the large-scale turbulence and strong magnetic field in the cloud do not
dominate on these scales, and that star formation on the scale of individual
protostars is similar to that in Galactic disc environments.


Abstract:
  Authenticated Append-Only Skiplists (AAOSLs) enable maintenance and querying
of an authenticated log (such as a blockchain) without requiring any single
party to store or verify the entire log, or to trust another party regarding
its contents. AAOSLs can help to enable efficient dynamic participation (e.g.,
in consensus) and reduce storage overhead.
  In this paper, we formalize an AAOSL originally described by Maniatis and
Baker, and prove its key correctness properties. Our model and proofs are
machine checked in Agda. Our proofs apply to a generalization of the original
construction and provide confidence that instances of this generalization can
be used in practice. Our formalization effort has also yielded some
simplifications and optimizations.


Abstract:
  We define a spectral flow for paths of selfadjoint Fredholm operators that
are equivariant under the orthogonal action of a compact Lie group as an
element of the representation ring of the latter. This $G$-equivariant spectral
flow shares all common properties of the integer valued classical spectral
flow, and it can be non-trivial even if the classical spectral flow vanishes.
Our main theorem uses the $G$-equivariant spectral flow to study bifurcation of
periodic solutions for autonomous Hamiltonian systems with symmetries.


Abstract:
  Deterioration of the operation parameters of Al/SiO2/p-type Si surface
barrier detector upon irradiation with alpha-particles at room temperature was
investigated. As a result of 40-days irradiation with a total fluence of 8*10^9
{\alpha}-particles, an increase of {\alpha}-peak FWHM from 70 keV to 100 keV
was observed and explained by increase of the detector reverse current due to
formation of a high concentration of near mid-gap defect levels. Performed CV
measurements revealed the appearance of at least 6*10^12 cm-3 radiation-induced
acceptors at the depths where according to the TRIM simulations the highest
concentration of vacancy-interstitial pairs was created by the incoming
{\alpha}-particles. The studies carried out by current-DLTS technique allowed
to associate the observed increase of the acceptor concentration with the near
mid-gap acceptor level at EV+0.56 eV. This level can be apparently associated
with V2O defects recognized previously to be responsible for the space charge
sign inversion in the irradiated n-type Si detectors.


Abstract:
  For a function $f\colon [0,1]\to\mathbb R$, we consider the set $E(f)$ of
points at which $f$ cuts the real axis. Given $f\colon [0,1]\to\mathbb R$ and a
Cantor set $D\subset [0,1]$ with $\{0,1\}\subset D$, we obtain conditions
equivalent to the conjunction $f\in C[0,1]$ (or $f\in C^\infty [0,1]$) and
$D\subset E(f)$. This generalizes some ideas of Zabeti. We observe that, if $f$
is continuous, then $E(f)$ is a closed nowhere dense subset of $f^{-1}[\{ 0\}]$
where each $x\in \{0,1\}\cap E(f)$ is an accumulation point of $E(f)$. Our main
result states that, for a closed nowhere dense set $F\subset [0,1]$ with each
$x\in \{0,1\}\cap E(f)$ being an accumulation point of $F$, there exists $f\in
C^\infty [0,1]$ such that $F=E(f)$.


Abstract:
  Graph-based causal discovery methods aim to capture conditional
independencies consistent with the observed data and differentiate causal
relationships from indirect or induced ones. Successful construction of
graphical models of data depends on the assumption of causal sufficiency: that
is, that all confounding variables are measured. When this assumption is not
met, learned graphical structures may become arbitrarily incorrect and effects
implied by such models may be wrongly attributed, carry the wrong magnitude, or
mis-represent direction of correlation. Wide application of graphical models to
increasingly less curated "big data" draws renewed attention to the unobserved
confounder problem.
  We present a novel method that aims to control for the latent space when
estimating a DAG by iteratively deriving proxies for the latent space from the
residuals of the inferred model. Under mild assumptions, our method improves
structural inference of Gaussian graphical models and enhances identifiability
of the causal effect. In addition, when the model is being used to predict
outcomes, it un-confounds the coefficients on the parents of the outcomes and
leads to improved predictive performance when out-of-sample regime is very
different from the training data. We show that any improvement of prediction of
an outcome is intrinsically capped and cannot rise beyond a certain limit as
compared to the confounded model. We extend our methodology beyond GGMs to
ordinal variables and nonlinear cases. Our R package provides both PCA and
autoencoder implementations of the methodology, suitable for GGMs with some
guarantees and for better performance in general cases but without such
guarantees.


Abstract:
  The Multichannel Subtractive Double Pass (MSDP) is an imaging spectroscopy
technique, which allows observations of spectral line profiles over a 2D field
of view with high spatial and temporal resolution. It has been intensively used
since 1977 on various spectrographs (Meudon, Pic du Midi, the German Vacuum
Tower Telescope, THEMIS, Wroc{\l}aw). We summarize previous developments and
describe the capabilities of a new design that has been developed at Meudon and
that has higher spectral resolution and increased channel number: Spectral
Sampling with Slicer for Solar Instrumentation (S4I), which can be combined
with a new and fast polarimetry analysis. This new generation MSDP technique is
well adapted to large telescopes. Also presented are the goals of a derived
compact version of the instrument, the Solar Line Emission Dopplerometer
(SLED), dedicated to dynamic studies of coronal loops observed in the forbidden
iron lines, and prominences. It is designed for observing total solar eclipses,
and for deployment on the Wroc{\l}aw and Lomnicky peak coronagraphs
respectively for prominence and coronal observations.


Abstract:
  We exhibit a non-hyperelliptic curve C of genus 3 such that the class of the
Ceresa cycle [C]-[(-1)*C] in JC modulo algebraic equivalence is torsion.


Abstract:
  This work presents the results of project CONECT4, which addresses the
research and development of new non-intrusive communication methods for the
generation of a human-machine learning ecosystem oriented to predictive
maintenance in the automotive industry. Through the use of innovative
technologies such as Augmented Reality, Virtual Reality, Digital Twin and
expert knowledge, CONECT4 implements methodologies that allow improving the
efficiency of training techniques and knowledge management in industrial
companies. The research has been supported by the development of content and
systems with a low level of technological maturity that address solutions for
the industrial sector applied in training and assistance to the operator. The
results have been analyzed in companies in the automotive sector, however, they
are exportable to any other type of industrial sector. -- --
  En esta publicaci\'on se presentan los resultados del proyecto CONECT4, que
aborda la investigaci\'on y desarrollo de nuevos m\'etodos de comunicaci\'on no
intrusivos para la generaci\'on de un ecosistema de aprendizaje
hombre-m\'aquina orientado al mantenimiento predictivo en la industria de
automoci\'on. A trav\'es del uso de tecnolog\'ias innovadoras como la Realidad
Aumentada, la Realidad Virtual, el Gemelo Digital y conocimiento experto,
CONECT4 implementa metodolog\'ias que permiten mejorar la eficiencia de las
t\'ecnicas de formaci\'on y gesti\'on de conocimiento en las empresas
industriales. La investigaci\'on se ha apoyado en el desarrollo de contenidos y
sistemas con un nivel de madurez tecnol\'ogico bajo que abordan soluciones para
el sector industrial aplicadas en la formaci\'on y asistencia al operario. Los
resultados han sido analizados en empresas del sector de automoci\'on, no
obstante, son exportables a cualquier otro tipo de sector industrial.


Abstract:
  This paper reports 209 O-type stars found with LAMOST. All 135 new O-type
stars discovered so far with LAMOST so far are given. Among them, 94 stars are
firstly presented in this sample. There are 1 Iafpe star, 5 Onfp stars, 12 Oe
stars, 1 Ofc stars, 3 ON stars, 16 double-lined spectroscopic binaries, and 33
single-lined spectroscopic binaries. All O-type stars are determined based on
LAMOST low-resolution spectra (R ~ 1800), with their LAMOST median-resolution
spectra (R~7500) as supplements.


Abstract:
  A world-wide COVID-19 pandemic intensified strongly the studies of molecular
mechanisms related to the coronaviruses. The origin of coronaviruses and the
risks of human-to-human, animal-to-human, and human-to-animal transmission of
coronaviral infections can be understood only on a broader evolutionary level
by detailed comparative studies. In this paper, we studied ribonucleocapsid
assembly-packaging signals (RNAPS) in the genomes of all seven known pathogenic
human coronaviruses, SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-HKU1,
HCoV-229E, and HCoV-NL63 and compared them with RNAPS in the genomes of the
related animal coronaviruses including SARS-Bat-CoV, MERS-Camel-CoV, MHV,
Bat-CoV MOP1, TGEV, and one of camel alphacoronaviruses. RNAPS in the genomes
of coronaviruses were evolved due to weakly specific interactions between
genomic RNA and N proteins in helical nucleocapsids. Combining transitional
genome mapping and Jaccard correlation coefficients allows us to perform the
analysis directly in terms of underlying motifs distributed over the genome. In
all coronaviruses RNAPS were distributed quasi-periodically over the genome
with the period about 54 nt biased to 57 nt and to 51 nt for the genomes longer
and shorter than that of SARS-CoV, respectively. The comparison with the
experimentally verified packaging signals for MERS-CoV, MHV, and TGEV proved
that the distribution of particular motifs is strongly correlated with the
packaging signals. We also found that many motifs were highly conserved in both
characters and positioning on the genomes throughout the lineages that make
them promising therapeutic targets. The mechanisms of encapsidation can affect
the recombination and co-infection as well.