Abstract:
  Reflecting on the last few years, the biggest breakthroughs in deep
reinforcement learning (RL) have been in the discrete action domain. Robotic
manipulation, however, is inherently a continuous control environment, but
these continuous control reinforcement learning algorithms often depend on
actor-critic methods that are sample-inefficient and inherently difficult to
train, due to the joint optimisation of the actor and critic. To that end, we
explore how we can bring the stability of discrete action RL algorithms to the
robot manipulation domain. We extend the recently released ARM algorithm, by
replacing the continuous next-best pose agent with a discrete next-best pose
agent. Discretisation of rotation is trivial given its bounded nature, while
translation is inherently unbounded, making discretisation difficult. We
formulate the translation prediction as the voxel prediction problem by
discretising the 3D space; however, voxelisation of a large workspace is memory
intensive and would not work with a high density of voxels, crucial to
obtaining the resolution needed for robotic manipulation. We therefore propose
to apply this voxel prediction in a coarse-to-fine manner by gradually
increasing the resolution. In each step, we extract the highest valued voxel as
the predicted location, which is then used as the centre of the
higher-resolution voxelisation in the next step. This coarse-to-fine prediction
is applied over several steps, giving a near-lossless prediction of the
translation. We show that our new coarse-to-fine algorithm is able to
accomplish RLBench tasks much more efficiently than the continuous control
equivalent, and even train some real-world tasks, tabular rasa, in less than 7
minutes, with only 3 demonstrations. Moreover, we show that by moving to a
voxel representation, we are able to easily incorporate observations from
multiple cameras.


Abstract:
  Stack Overflow (SO) platform has a huge dataset of questions and answers
driven by interactions between users. But the count of unanswered questions is
continuously rising. This issue is common across various community Question &
Answering platforms (Q&A) such as Yahoo, Quora and so on. Clustering is one of
the approaches used by these communities to address this challenge.
Specifically, Intent-based clustering could be leveraged to answer unanswered
questions using other answered questions in the same cluster and can also
improve the response time for new questions. It is here, we propose SOCluster,
an approach and a tool to cluster SO questions based on intent using a
graph-based clustering approach. We selected four datasets of 10k, 20k, 30k &
40k SO questions without code-snippets or images involved, and performed
intent-based clustering on them. We have done a preliminary evaluation of our
tool by analyzing the resultant clusters using the commonly used metrics of
Silhouette coefficient, Calinkski-Harabasz Index, & Davies-Bouldin Index. We
performed clustering for 8 different threshold similarity values and analyzed
the intriguing trends reflected by the output clusters through the three
evaluation metrics. At 90% threshold similarity, it shows the best value for
the three evaluation metrics on all four datasets. The source code and tool are
available for download on Github at: https://github.com/Liveitabhi/SOCluster,
and the demo can be found here: https://youtu.be/uyn8ie4h3NY.


Abstract:
  We consider the Euler equations for the incompressible flow of an ideal fluid
with an additional rough-in-time, divergence-free, Lie-advecting vector field.
In recent work, we have demonstrated that this system arises from Clebsch and
Hamilton-Pontryagin variational principles with a perturbative geometric rough
path Lie-advection constraint. In this paper, we prove local well-posedness of
the system in $L^2$-Sobolev spaces $H^m$ with integer regularity $m\ge \lfloor
d/2\rfloor+2$ and establish a Beale-Kato-Majda (BKM) blow-up criterion in terms
of the $L^1_tL^\infty_x$-norm of the vorticity. In dimension two, we show that
the $L^p$-norms of the vorticity are conserved, which yields global
well-posedness and a Wong-Zakai approximation theorem for the stochastic
version of the equation.


Abstract:
  The topologically non-trivial nano-whirls, called magnetic skyrmions, are
often considered attractive for spintronic applications like the racetrack data
storage device. However, skyrmions do not move parallel to applied currents and
typically do not coexist with other nano-objects, making the realization of
such storage concepts difficult. Herein we consider materials with an
anisotropic DMI, like tetragonal Heusler materials, and show that four distinct
types of topologically trivial bubbles can coexist. We show that each of them
can be written by spin torques and that they can be distinguished using a
single magnetic tunneling junction. Due to their trivial topology, the four
types of bubbles move parallel to applied electrical currents and remain
equidistant under motion. Still, the bubbles have a small size and high
stability comparable to topologically non-trivial spin textures. This allows to
construct a quaternary-digit-based racetrack storage device that overcomes the
two mentioned drawbacks of skyrmion-based racetracks.


Abstract:
  One possible progenitor of short gamma-ray bursts (GRBs) is thought to be
from a double neutron star (NS) merger, and the remnant of such a merger may be
a supramassive NS, which is supported by rigid rotation and through its
survival of hundreds of seconds before collapsing into a black hole (BH). If
this is the case, an optical/infrared transient (namely merger-nova) is
generated from the ejected materials and it is powered by radioactive decay
from $r$-process, spin-down energy from a supramassive NS, as well as the
magnetic wind from a newborn BH. In this paper, we systematically search for
the signature of a supramassive NS central engine by analyzing the X-ray
emission of short GRBs with internal plateau observed by {\em Swift}, and we
find that five candidates of short GRBs have such feature with redshift
measurement. Then, we calculate the possible merger-nova emission from those
candidates for given the typical model parameters by considering the above
three energy sources, and compare its brightness with the sensitivity of some
optical telescopes. We find that the merger-nova emission of GRB 060801 in K-,
r-, and U-bands with variations of $M_{\rm ej}$ ($10^{-4}-10^{-2} M_{\odot}$),
$\kappa$ ($0.1-10 ~\rm cm^{2}~g^{-1}$), and $\beta$ ($0.1-0.3$) is very
difficult to detect using the Vera C. Rubin, Panoramic Survey Telescope and
Rapid Response System (Pan-STARRS), the Zwicky Transient Facility, and the
Roman Space Telescope (Roman), except for the case of large ejecta mass $M_{\rm
ej}=10^{-2} M_{\odot}$. However, we are very hopeful to detect the merger-nova
emission of GRBs 090515, 100625A, and 101219A using more sensitive instruments,
such as Vera C. Rubin, Pan-STARRS, and Roman. Moreover, the merger-nova
emission of GRB 160821B is bright enough to detect in our calculations, and it
is also consistent with current real observations of merger-nova emission.


Abstract:
  COVID-19 has hugely changed our lives, work, and interactions with people.
With more and more online activities, people are easily exposed to privacy
threats. In this paper, we explore how users self-disclose on social media and
privacy concerns raised from these behaviors. Based on recent news, techniques,
and research, we indicate three increasing privacy threats caused by the
COVID-19 pandemic. After that, we provide a systematic analysis of potential
privacy issues related to the COVID pandemic. Furthermore, we propose a series
of research directions about online user self-disclosure and privacy issues for
future work as well as possible solutions.


Abstract:
  Metric of axially symmetric asymptotically flat black holes in an arbitrary
metric theory of gravity can be represented in the general form which depends
on infinite number of parameters. We constrain this general class of metrics by
requiring the existence of additional symmetries, which lead to the separation
of variables in the Hamilton-Jacobi and Klein-Gordon equations, and show that
once the metric functions change sufficiently moderately in some region near
the black hole, the black-hole shadow depends on a few deformation parameters
only. We analyze the influence of these parameters on the black-hole shadow. We
also show that the shadow of the rotating black hole in the
Einstein-dilaton-Gauss-Bonnet theory is well approximated if the terms
violating the separation of variables are neglected in the metric.


Abstract:
  Internetwork (IN) magnetic fields are highly dynamic, short-lived magnetic
structures that populate the interior of supergranular cells. Since they emerge
all over the Sun, these small-scale fields bring a substantial amount of flux,
and therefore energy, to the solar surface. Because of this, IN fields are
crucial for understanding the quiet Sun (QS) magnetism. However, they are weak
and produce very small polarization signals, which is the reason why their
properties and impact on the energetics and dynamics of the solar atmosphere
are poorly known. Here we use coordinated, high-resolution, multiwavelength
observations obtained with the Swedish 1-m Solar Telescope (SST) and the
\textit{Interface Region Imaging Spectrograph} (IRIS) to follow the evolution
of IN magnetic loops as they emerge into the photosphere and reach the
chromosphere and transition region. We studied in this paper three flux
emergence events having total unsigned magnetic fluxes of $1.9\times10^{18}$,
$2.5\times10^{18}$, and $5.3\times10^{18}$~Mx. The footpoints of the emerging
IN bipoles are clearly seen to appear in the photosphere and to rise up through
the solar atmosphere, as observed in \ion{Fe}{1} 6173 \AA\/ and \ion{Mg}{1}
b$_2$ 5173 \AA\/ magnetograms, respectively. For the first time, our
polarimetric measurements taken in the chromospheric \ion{Ca}{2} 8542 \AA\/
line provide direct observational evidence that IN fields are capable of
reaching the chromosphere. Moreover, using IRIS data, we study the effects of
these weak fields on the heating of the chromosphere and transition region.


Abstract:
  We address the global-in-time existence, stability and long time behaviour of
weak solutions of the three-dimensional compressible Navier-Stokes equations
with potential force. We show the details of the $\alpha$-dependence of
different smoothing rates for weak solutions near $t=0$ under the assumption on
the initial velocity $u_0$ that $u_0\in H^\alpha$ for
$\alpha\in(\frac{1}{2},1]$ and obtain long time convergence of weak solutions
in various norms. We then make use of the Lagrangean framework in comparing the
instantaneous states of corresponding fluid particles in two different
solutions. The present work provides qualitative results on the long time
behaviour of weak solutions and how the weak solutions depend continuously on
initial data and steady states.


Abstract:
  CNN (Convolutional Neural Network) models have been successfully used for
segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance
Imaging), providing clinical measurements. In practice, two questions arise
with deployment of CNNs: 1) when is it better to use a shallow model instead of
a deeper one? 2) how the size of a dataset might change the network
performance? We propose a framework to answer them, by experimenting with deep
and shallow versions of three U-Net families, trained from scratch in six
subsets varying from 100 to 10,000 images, different network sizes, learning
rates and regularization values. 1620 models were evaluated using 5-fold
cross-validation by loss and DICE. The results indicate that: sample size
affects performance more than architecture or hyper-parameters; in small
samples the performance is more sensitive to hyper-parameters than
architecture; the performance difference between shallow and deeper networks is
not the same across families.


Abstract:
  As a master's student, knowing how to manage your personal research is not
only useful for keeping track of your work, but it is also a process that
should be learned as a part of your training. As a doctoral student, however,
research management is a fundamental part of your overall methodology and it
should be a well-planned process. Long-term research requires a good approach
to documentation, otherwise you risk getting lost among your many surveyed
papers, carried experiments, and results. This approach should be systematic,
accessible (mainly to you), low-effort, and a natural part of your daily
workflow - it should be there to help you, and not the other way around. In
this article, we describe how we relied on a wiki to organize literature,
datasets, experiments and results, and we also show how such a systematic
approach can lead to better insights through automated meta-analysis. In
addition to this content, we provide a docker-based installation of a
preconfigured wiki, with the required templates and extensions, along with some
examples pages, as well as a Jupyter notebook to analyze your documented work.
So read on.


Abstract:
  The direct and inverse scattering problems are analyzed for a first-order
discrete system associated with the semi-discrete version of the derivative NLS
system. The Jost solutions, the scattering coefficients, the bound-state
dependency and norming constants are investigated and related to the
corresponding quantities for two particular discrete linear systems associated
with the semi-discrete version of the NLS system. The bound-state data set with
any multiplicities is described in an elegant manner in terms of a pair of
constant matrix triplets. Several methods are presented to the solve the
inverse problem. One of these methods involves a discrete Marchenko system
using as input the scattering data set consisting of the scattering
coefficients and the bound-state information, and this method is presented in a
way generalizable to other first-order systems both in the discrete and
continuous cases for which a Marchenko system is not yet available. Finally,
using the time-evolved scattering data set, the inverse scattering transform is
applied on the corresponding semi-discrete derivative NLS system, and in the
reflectionless case certain explicit solution formulas are presented in closed
form expressed in terms of the two matrix triplets.


Abstract:
  We develop two new classes of tests for the Weibull distribution based on
Stein's method. The proposed tests are applied in the full sample case as well
as in the framework of random right censoring. We investigate the finite sample
performance of the new tests using a comprehensive Monte Carlo study. In both
the absence and presence of censoring, it is found that the newly proposed
classes of tests outperform competing tests against the majority of the
distributions considered. In the cases where censoring is present we consider
various censoring distributions. Some remarks on the asymptotic properties of
the proposed tests are included. The paper presents another result of
independent interest; the test initially proposed in Krit (2014) for use with
full samples is amended to allow for testing for the Weibull distribution in
the presence of censoring. The techniques developed in the paper are
illustrated using two practical examples. In the first, we consider the
survival times of patients with a certain type of leukemia. The second example
is concerned with the initial remission times of leukemia patients, where the
observed remission times are subject to random right censoring. We further
include some concluding remarks along with avenues for future research.


Abstract:
  In lattice calculations, the approach to the continuum limit is hindered by
the severe freezing of the topological charge, which prevents ergodic sampling
in configuration space. In order to significantly reduce the autocorrelation
time of the topological charge, we develop a density of states approach with a
smooth constraint and use it to study SU(3) pure Yang Mills gauge theory near
the continuum limit. Our algorithm relies on simulated tempering across a range
of couplings, which guarantees the decorrelation of the topological charge and
ergodic sampling of topological sectors. Particular emphasis is placed on
testing the accuracy, efficiency and scaling properties of the method. In their
most conservative interpretation, our results provide firm evidence of a
sizeable reduction of the exponent z related to the growth of the
autocorrelation time as a function of the inverse lattice spacing.


Abstract:
  In It\^{o}-diffusion environments, we introduce and analyze $N$-player and
common-noise mean-field games in the context of optimal portfolio choice in a
common market. The players invest in a finite horizon and also interact, driven
either by competition or homophily. We study an incomplete market model in
which the players have constant individual risk tolerance coefficients (CARA
utilities). We also consider the general case of random individual risk
tolerances and analyze the related games in a complete market setting. This
randomness makes the problem substantially more complex as it leads to ($N$ or
a continuum of) auxiliary ''individual'' It\^{o}-diffusion markets. For all
cases, we derive explicit or closed-form solutions for the equilibrium
stochastic processes, the optimal state processes, and the values of the games.


Abstract:
  In this paper, we propose methods for functional predictor selection and the
estimation of smooth functional coefficients simultaneously in a
scalar-on-function regression problem under high-dimensional multivariate
functional data setting. In particular, we develop two methods for functional
group-sparse regression under a generic Hilbert space of infinite dimension. We
show the convergence of algorithms and the consistency of the estimation and
the selection (oracle property) under infinite-dimensional Hilbert spaces.
Simulation studies show the effectiveness of the methods in both the selection
and the estimation of functional coefficients. The applications to the
functional magnetic resonance imaging (fMRI) reveal the regions of the human
brain related to ADHD and IQ.


Abstract:
  There are considerable advancements in medical health care in recent years,
resulting in rising older population. As the workforce for such a population is
not keeping pace, there is an urgent need to address this problem. Having
robots to stimulating recreational activities for older adults can reduce the
workload for caretakers and give them time to address the emotional needs of
the elderly. In this paper, we investigate the effects of the humanoid social
robot Nadine as an activity host for the elderly. This study aims to analyse if
the elderly feels comfortable and enjoy playing game/activity with the humanoid
robot Nadine. We propose to evaluate this by placing Nadine humanoid social
robot in a nursing home as a caretaker where she hosts bingo game. We record
sessions with and without Nadine to understand the difference and acceptance of
these two scenarios. We use computer vision methods to analyse the activities
of the elderly to detect emotions and their involvement in the game. We
envision that such humanoid robots will make recreational activities more
readily available for the elderly. Our results present positive enforcement
during recreational activity, Bingo, in the presence of Nadine.


Abstract:
  Recently, attributed community search, a related but different problem to
community detection and graph clustering, has been widely studied in the
literature. Compared with the community detection that finds all existing
static communities from a graph, the attributed community search (ACS) is more
challenging since it aims to find dynamic communities with both cohesive
structures and homogeneous node attributes given arbitrary queries. To solve
the ACS problem, the most popular paradigm is to simplify the problem as two
sub-problems, that is, structural matching and attribute filtering and deal
with them separately. However, in real-world graphs, the community structure
and the node attributes are actually correlated to each other. In this vein,
current studies cannot capture these correlations which are vital for the ACS
problem.
  In this paper, we propose Query-Driven Graph Convolutional Networks (QD-GCN),
an end-to-end framework that unifies the community structure as well as node
attribute to solve the ACS problem. In particular, QD-GCN leverages the Graph
Convolutional Networks, which is a powerful tool to encode the graph topology
and node attributes concurrently, as the backbones to extract the
query-dependent community information from the original graph. By utilizing
this query-dependent community information, QD-GCN is able to predict the
target community given any queries. Experiments on real-world graphs with
ground-truth communities demonstrate that QD-GCN outperforms existing
attributed community search algorithms in terms of both efficiency and
effectiveness.


Abstract:
  We present benchmarks of the parity transformation for the Quantum
Approximate Optimization Algorithm (QAOA). We analyse the gate resources
required to implement a single QAOA cycle for real-world scenarios. In
particular, we consider random spin models with higher order terms, as well as
the problems of predicting financial crashes and finding the ground states of
electronic structure Hamiltonians. For the spin models studied our findings
imply a significant advantage of the parity mapping compared to the standard
gate model. In combination with full parallelizability of gates this has the
potential to boost the race for demonstrating quantum advantage.


Abstract:
  This paper addresses a new variant of the multi-trip single vehicle routing
problem where the nodes are related to each other through AND-type precedence
constraints. The problem aims at determining a sequence of trips to visit all
the nodes respecting every precedence constraint within and among the routes so
to minimize the total traveling cost. Our motivation comes from routing
problems where a node may have a set of predecessors (not just single one
proposed in the dial-a-ride or pickup and delivery problems) resulting in a set
of pairwise relations that specify which customers need to be visited before
which other ones. We develop three Mixed Integer Programming (MIP) models to
formulate the problem. The models are experimentally compared to determine the
best one. Moreover, a solution approach based on the Logic-Based Benders
Decomposition (LBBD) algorithm is developed which allows to decompose the
original problem into an assignment master problem and independent sequencing
subproblems. A new valid optimality cut is devised to achieve faster
convergence. The cut performance is experimentally investigated by comparing
with a recently proposed one in the literature. We further relax the algorithm
to find the sub-optimal solution and demonstrate its efficiency. Extensive
computational experiments are conducted to assess the proposed algorithms in
terms of solution quality and CPU time.


Abstract:
  Weakly-Supervised Temporal Action Localization (WS-TAL) task aims to
recognize and localize temporal starts and ends of action instances in an
untrimmed video with only video-level label supervision. Due to lack of
negative samples of background category, it is difficult for the network to
separate foreground and background, resulting in poor detection performance. In
this report, we present our 2021 HACS Challenge - Weakly-supervised Learning
Track solution that based on BaSNet to address above problem. Specifically, we
first adopt pre-trained CSN, Slowfast, TDN, and ViViT as feature extractors to
get feature sequences. Then our proposed Local-Global Background Modeling
Network (LGBM-Net) is trained to localize instances by using only video-level
labels based on Multi-Instance Learning (MIL). Finally, we ensemble multiple
models to get the final detection results and reach 22.45% mAP on the test set


Abstract:
  Polycrystal microstructures, with their distinct physical, chemical,
structural and topological entities, play an important role in determining the
effective properties of materials. Particularly for computational studies, the
well-known Voronoi tessellation technique is regularly used for obtaining
microstructures. Standard Voronoi tessellations, however, exhibit statistics
that are generally far removed from those in real microstructures.
Nevertheless, such tessellations can be optimized to obtain certain key
features and statistics seen in real microstructures. In this work, we develop
the open-source software package OptiMic that enables the generation of
optimized microstructures for both finite element as well as atomistic
simulations. OptiMic allows for both monodispersive grains as well as irregular
grains obtained currently via Voronoi tessellations. These initial
microstructures can then be optimized to reflect desired statistical features.
A key feature of the tool is that it gives the user extensive control on the
optimization process via customizable cost functions. The software currently
performs tessellations with the Voronoi method and can be easily extended to
include other methods like grain-growth, phase-field etc.


Abstract:
  Thermodynamic quantities, like heat, entropy, or work, are random variables,
in stochastic systems. Here, we investigate the statistics of the heat
exchanged by a Brownian particle subjected to a logarithm-harmonic potential.
We derive analytically the characteristic function and its moments for the
heat. Through numerical integration, and numerical simulation, we calculate the
probability distribution as well, characterizing fully the statistical behavior
of the heat. The results are also investigated in the asymptotic limit, where
we encounter the characteristic function in terms of hypergeometric functions.


Abstract:
  Efficient integration of solar energy into the electricity mix depends on a
reliable anticipation of its intermittency. A promising approach to forecast
the temporal variability of solar irradiance resulting from the cloud cover
dynamics is based on the analysis of sequences of ground-taken sky images or
satellite images. Despite encouraging results, a recurrent limitation of
existing deep learning approaches lies in the ubiquitous tendency of reacting
to past observations rather than actively anticipating future events. This
leads to a frequent temporal lag and limited ability to predict sudden events.
To address this challenge, we introduce ECLIPSE, a spatio-temporal neural
network architecture that models cloud motion from sky images to not only
predict future irradiance levels but also segmented images, which provide
richer information on the local irradiance map. We show that ECLIPSE
anticipates critical events and reduces temporal delay while generating
visually realistic futures.


Abstract:
  The spin effect of electrons/positrons ($e^-$/$e^+$) and polarization effect
of $\gamma$ photons are investigated in the interaction of two
counter-propagating linearly polarized 10-PW-class laser pulses with a thin
foil target. The processes of nonlinear Compton scattering and nonlinear
Breit-Wheeler pair production based on spin- and polarization-resolved
probabilities are implemented into the particle-in-cell (PIC) algorithm by
Monte Carlo methods. It is found from PIC simulations that the average degree
of linear polarization of emitted $\gamma$ photons can exceed $50\%$. This
polarization effect leads to reduced positron yield by about $10\%$. At some
medium positron energies, the reduction can reach $20\%$. Furthermore, we also
observe that the local spin polarization of $e^-$/$e^+$ leads to a slight
decrease of the positron yield about $2\%$ and some anomalous phenomena about
the positron spectrum and photon polarization at the high-energy range, due to
spin-dependent photon emissions. Our results indicate that spin and
polarization effects should be considered in calculating the pair production
and laser-plasma interaction with the laser power of 10-PW class.


Abstract:
  We prove that if $P(D)$ is some constant coefficient partial differential
operator and $f$ is a scalar field such that $P(D)f$ vanishes in a given open
set, then the integrals of $f$ over all lines intersecting that open set
determine the scalar field uniquely everywhere. This is done by proving a
unique continuation property of fractional Laplacians which implies uniqueness
for the partial data problem. We also apply our results to partial data
problems of vector fields.


Abstract:
  Wavelet transformation stands as a cornerstone in modern data analysis and
signal processing. Its mathematical essence is an invertible transformation
that discerns slow patterns from fast patterns in the frequency domain, which
repeats at each level. Such an invertible transformation can be learned by a
designed normalizing flow model. With a factor-out scheme resembling the
wavelet downsampling mechanism, a mutually independent prior, and parameter
sharing along the depth of the network, one can train normalizing flow models
to factor-out variables corresponding to fast patterns at different levels,
thus extending linear wavelet transformations to non-linear learnable models.
In this paper, a concrete way of building such flows is given. Then, a
demonstration of the model's ability in lossless compression task, progressive
loading, and super-resolution (upsampling) task. Lastly, an analysis of the
learned model in terms of low-pass/high-pass filters is given.


Abstract:
  In recent years, several magnetic Mott insulators with strong spin-orbit
couplings were suggested to be proximate to the Kitaev quantum spin liquid
(QSL) whose one of the most exciting features is the fractionalization of spin
excitations into itinerant Majorana fermions and static $Z_2$ fluxes. Motivated
by the emergence of this plethora of 4d and 5d transition metal Kitaev
materials and by the fact that some level of disorder is inevitable in real
materials, here we study how the Kitaev QSL responds to various forms of
disorder, such as vacancies, impurities, and bond randomness. First, we argue
that the presence of the quenched disorder in the Kitaev QSL can lead to the
Anderson localization of Majorana fermions and the appearance of Lifshitz
tails. We point out that the Anderson localization of low-energy states is
particularly strong in the extended Kitaev model with the time reversal
symmetry breaking term. Second, we show that the disorder effects on the
low-energy Majorana fermion modes can be detected in thermal transport. Third,
we show that at finite temperatures the $Z_2$ fluxes become thermally excited
and give rise to an additional disorder for the Majorana fermions. This source
of the disorder dominates at high temperatures. Fourth, we demonstrate that
both the structure of the energy spectrum and the thermal transport properties
of the disordered Kitaev QSL depend strongly on the character of disorder.
While we find that both the site disorder and the bond randomness suppress the
longitudinal thermal conductivity, the low-energy localization is stronger in
the case of the site disorder.


Abstract:
  We introduce diversified risk parity embedded with various reward-risk
measures and more generic allocation rules for portfolio construction. We
empirically test advanced reward-risk parity strategies and compare their
performance with an equally-weighted risk portfolio in various asset universes.
The reward-risk parity strategies we tested exhibit consistent outperformance
evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The
alternative allocations also reflect less downside risks in Value-at-Risk,
conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced
performance and reward-risk profile, transaction costs can be reduced by
lowering turnover rates. The Carhart four-factor analysis also indicates that
the diversified reward-risk parity allocations gain superior performance.


Abstract:
  Transfer learning is a machine learning paradigm where the knowledge from one
task is utilized to resolve the problem in a related task. On the one hand, it
is conceivable that knowledge from one task could be useful for solving a
related problem. On the other hand, it is also recognized that if not executed
properly, transfer learning algorithms could in fact impair the learning
performance instead of improving it - commonly known as "negative transfer". In
this paper, we study the online transfer learning problems where the source
samples are given in an offline way while the target samples arrive
sequentially. We define the expected regret of the online transfer learning
problem and provide upper bounds on the regret using information-theoretic
quantities. We also obtain exact expressions for the bounds when the sample
size becomes large. Examples show that the derived bounds are accurate even for
small sample sizes. Furthermore, the obtained bounds give valuable insight on
the effect of prior knowledge for transfer learning in our formulation. In
particular, we formally characterize the conditions under which negative
transfer occurs.


Abstract:
  We develop an analytical framework for self-stabilizing optical manipulation
of freestanding metasurfaces in three dimensions. Our framework reveals that
the challenging problem of stabilization against translational and rotational
perturbations in three dimensions is reduced to a simpler scattering analysis
of the metasurface unit cell in two dimensions. We derive universal analytical
stiffness coefficients applicable to arbitrary three-dimensional radial
metasurfaces and radial beam intensity profiles. The analytical nature of our
framework facilitates highly efficient discovery of optimal optomechanical
metasurfaces. Such use of metasurfaces for mechanical stabilization enables
macroscale and long-range control in collimated, but otherwise unfocused light
beams, and could open up new avenues for manipulation beyond traditional
optical tweezing and transport.


Abstract:
  This paper reports on the derivation and implementation of a shape
optimization procedure for the minimization of hemolysis induction in
biomedical devices. Hemolysis is a blood damaging phenomenon that may occur in
mechanical blood-processing applications where large velocity gradients are
found. An increased level of damaged blood can lead to deterioration of the
immune system and quality of life. It is, thus, important to minimize
flow-induced hemolysis by improving the design of next-generation biomedical
machinery. Emphasis is given to the formulation of a continuous adjoint
complement to a power-law hemolysis prediction model dedicated to efficiently
identifying the shape sensitivity to hemolysis. The computational approach is
verified against the analytical solutions of a benchmark problem and computed
sensitivity derivatives are validated by a finite differences study on a
generic 2D stenosed geometry. The application included addresses a 3D ducted
geometry which features typical characteristics of biomedical devices. An
optimized shape, leading to a potential improvement in hemolysis induction up
to 22%, is identified. It is shown, that the improvement persists for
different, literature-reported hemolysis-evaluation parameters.


Abstract:
  In this paper, we prove the existence of infinitely many solutions for a
class of quasilinear elliptic $m(x)$-polyharmonic Kirchhoff equations where the
nonlinear function has a quasicritical growth at infinity and without assuming
the Ambrosetti and Rabinowitz type condition. The new aspect consists in
employing the notion of a Schauder basis to verify the geometry of the
symmetric mountain pass theorem. Furthermore, for the case $m(x)\equiv Const$,
we introduce a positive quantity $\lambda_M$ similar to the first eigenvalue of
the $m$-polyharmonic operator to find a mountain pass solution, and also to
discuss the sublinear case under large growth conditions at infinity and at
zero. Our results are an improvement and generalization of the corresponding
results obtained by Colasuonno-Pucci (Nonlinear Analysis: Theory, Methods and
Applications, $2011$) and Bae-Kim (Mathematical Methods in the Applied
Sciences, $2020$).


Abstract:
  We give a modified definition of a reproducing kernel Hilbert $C^*$-module
(shortly, $RKHC^*M$) without using the condition of self-duality and discuss
some related aspects; in particular, an interpolation theorem is presented. We
investigate the exterior tensor product of $RKHC^*M$s and find their
reproducing kernel. In addition, we deal with left multipliers of $RKHC^*M$s.
Under some mild conditions, it is shown that one can make a new $RKHC^*M$ via a
left multiplier. Moreover, we introduce the Berezin transform of an operator in
the context of $RKHC^*M$s and construct a unital subalgebra of the unital
$C^*$-algebra consisting of adjointable maps on an $RKHC^*M$ and show that it
is closed with respect to a certain topology. Finally, the Papadakis theorem is
extended to the setting of $RKHC^*M$, and in order for the multiplication of
two specific functions to be in the Papadakis $RKHC^*M$, some conditions are
explored.


Abstract:
  Let $F_n(\Sigma_{g,1})$ denote the configuration space of $n$ ordered points
on the surface $\Sigma_{g,1}$ and let $\Gamma_{g,1}$ denote the mapping class
group of $\Sigma_{g,1}$. We prove that the action of $\Gamma_{g,1}$ on
$H_i(F_n(\Sigma_{g,1});\mathbb{Z})$ is trivial when restricted to the $i^{th}$
stage of the Johnson filtration $\mathcal{J}(i)\subset \Gamma_{g,1}$. We give
examples showing that $\mathcal{J}(2)$ acts nontrivially on
$H_3(F_3(\Sigma_{g,1}))$ for $g\ge 2$, and provide two new conceptual
reinterpretations of a certain group introduced by Moriyama.


Abstract:
  The Pauli Exclusion Operator (PEO) which ensures proper symmetry of the
eigenstates of multi-electron systems with respect to exchange of each pair of
electrons is introduced. Once PEO is added to the Hamiltonian, no additional
constraints on multi-electron wave function due to the Pauli exclusion
principle are needed. For two-electron states in two dimensions ($2D$) the PEO
can be expressed in a closed form in terms of momentum operators, while in the
position representation PEO is a non-local operator. Generalizations of PEO for
multi-electron systems is introduced. Several approximations to PEO are
discussed. Examples of analytical and numerical calculations of PEO are given
for isotropic and anisotropic Hooke's atom in~$2D$. Application of approximate
and kernel forms of PEO for calculations of energies and states in~$2D$ Hooke's
atom are analyzed. Relation of PEO to standard variational calculations with
the use of Slater determinant is discussed.


Abstract:
  Generative adversarial networks (GANs) are able to generate high resolution
photo-realistic images of objects that "do not exist." These synthetic images
are rather difficult to detect as fake. However, the manner in which these
generative models are trained hints at a potential for information leakage from
the supplied training data, especially in the context of synthetic faces. This
paper presents experiments suggesting that identity information in face images
can flow from the training corpus into synthetic samples without any
adversarial actions when building or using the existing model. This raises
privacy-related questions, but also stimulates discussions of (a) the face
manifold's characteristics in the feature space and (b) how to create
generative models that do not inadvertently reveal identity information of real
subjects whose images were used for training. We used five different face
matchers (face_recognition, FaceNet, ArcFace, SphereFace and Neurotechnology
MegaMatcher) and the StyleGAN2 synthesis model, and show that this identity
leakage does exist for some, but not all methods. So, can we say that these
synthetically generated faces truly do not exist? Databases of real and
synthetically generated faces are made available with this paper to allow full
replicability of the results discussed in this work.


Abstract:
  We propose a novel reinforcement learning based framework PoBRL for solving
multi-document summarization. PoBRL jointly optimizes over the following three
objectives necessary for a high-quality summary: importance, relevance, and
length. Our strategy decouples this multi-objective optimization into different
subproblems that can be solved individually by reinforcement learning.
Utilizing PoBRL, we then blend each learned policies together to produce a
summary that is a concise and complete representation of the original input.
Our empirical analysis shows state-of-the-art performance on several
multi-document datasets. Human evaluation also shows that our method produces
high-quality output.


Abstract:
  A rigorous and comprehensive testing plays a key role in training
self-driving cars to handle variety of situations that they are expected to see
on public roads.
  The physical testing on public roads is unsafe, costly, and not always
reproducible. This is where testing in simulation helps fill the gap, however,
the problem with simulation testing is that it is only as good as the simulator
used for testing and how representative the simulated scenarios are of the real
environment. In this paper, we identify key requirements that a good simulator
must have. Further, we provide a comparison of commonly used simulators. Our
analysis shows that CARLA and LGSVL simulators are the current state-of-the-art
simulators for end to end testing of self-driving cars for the reasons
mentioned in this paper. Finally, we also present current challenges that
simulation testing continues to face as we march towards building fully
autonomous cars.


Abstract:
  We study the propagation in time of $1/2$-Gelfand-Shilov singularities, i.e.
global analytic singularities, of tempered distributional solutions of the
initial value problem \begin{align*} \begin{cases} u_t + q^w(x,D) u = 0 \\
u|_{t=0} = u_0, \end{cases} \end{align*} on $\mathbb{R}^n$, where $u_0$ is a
tempered distribution on $\mathbb{R}^n$, $q=q(x,\xi)$ is a complex-valued
quadratic form on $\mathbb{R}^{2n} = \mathbb{R}^n_x \times \mathbb{R}^n_\xi$
with nonnegative real part $\textrm{Re} \ q \ge 0$, and $q^w(x,D)$ is the Weyl
quantization of $q$. We prove that the $1/2$-Gelfand-Shilov singularities of
the initial data that are contained within a distinguished linear subspace of
the phase space $\mathbb{R}^{2n}$, called the singular space of $q$, are
transported by the Hamilton flow of $\textrm{Im} \ q$, while all other
$1/2$-Gelfand-Shilov singularities are instantaneously regularized. Our result
extends the observation of Hitrik, Pravda-Starov, and Viola '18 that this
evolution is instantaneously globally analytically regularizing when the
singular space of $q$ is trivial.


Abstract:
  A novel explainable AI method called CLEAR Image is introduced in this paper.
CLEAR Image is based on the view that a satisfactory explanation should be
contrastive, counterfactual and measurable. CLEAR Image explains an image's
classification probability by contrasting the image with a corresponding image
generated automatically via adversarial learning. This enables both salient
segmentation and perturbations that faithfully determine each segment's
importance. CLEAR Image was successfully applied to a medical imaging case
study where it outperformed methods such as Grad-CAM and LIME by an average of
27% using a novel pointing game metric. CLEAR Image excels in identifying cases
of "causal overdetermination" where there are multiple patches in an image, any
one of which is sufficient by itself to cause the classification probability to
be close to one.


Abstract:
  A single spin in a Josephson junction can reverse the flow of the
supercurrent. At mesoscopic length scales, such $\pi$-junctions are employed in
various instances from finding the pairing symmetry to quantum computing. In
Yu-Shiba-Rusinov (YSR) states, the atomic scale counterpart of a single spin in
a superconducting tunnel junction, the supercurrent reversal so far has
remained elusive. Using scanning tunneling microscopy (STM), we demonstrate
such a 0 to $\pi$ transition of a Josephson junction through a YSR state as we
continuously change the impurity-superconductor coupling. We detect the sign
change in the critical current by exploiting a second transport channel as
reference in analogy to a superconducting quantum interference device (SQUID),
which provides the STM with the required phase sensitivity. The measured change
in the Josephson current is a signature of the quantum phase transition and
allows its characterization with unprecedented resolution.


Abstract:
  Temporal receptive fields of models play an important role in action
segmentation. Large receptive fields facilitate the long-term relations among
video clips while small receptive fields help capture the local details.
Existing methods construct models with hand-designed receptive fields in
layers. Can we effectively search for receptive field combinations to replace
hand-designed patterns? To answer this question, we propose to find better
receptive field combinations through a global-to-local search scheme. Our
search scheme exploits both global search to find the coarse combinations and
local search to get the refined receptive field combination patterns further.
The global search finds possible coarse combinations other than human-designed
patterns. On top of the global search, we propose an expectation guided
iterative local search scheme to refine combinations effectively. Our
global-to-local search can be plugged into existing action segmentation methods
to achieve state-of-the-art performance.


Abstract:
  A new exactly solvable case in strong-field quantum electrodynamics with a
time-dependent external electric field is presented. The corresponding field is
given by an analytic function, which is asymmetric (in contrast to Sauter-like
electric field) with respect to the time instant, where it reaches its maximum
value, that is why we call it the analytic asymmetric electric field. We
managed to exactly solve the Dirac equation with such a field, which made it
possible to calculate characteristics of the corresponding vacuum instability
nonperturbatively. We construct the so-called in- and out-solutions and with
their help calculate mean differential and total numbers of created charged
particles, probability of the vacuum to remain a vacuum, vacuum mean values of
current density and energy-momentum tensor of the particles. We study the
vacuum instability in regimes of rapidly and slowly changing analytic
asymmetric electric field, and compare the obtained results with corresponding
ones obtained earlier for the case of the symmetric Sauter-like electric field.
We also compare exact results in the regime of slowly changing field with
corresponding results obtained within the slowly varying field approximation
recently proposed by two of the authors, thus demonstrating the effectiveness
of such an approximation.


Abstract:
  How to attribute responsibility for autonomous artificial intelligence (AI)
systems' actions has been widely debated across the humanities and social
science disciplines. This work presents two experiments ($N$=200 each) that
measure people's perceptions of eight different notions of moral responsibility
concerning AI and human agents in the context of bail decision-making. Using
real-life adapted vignettes, our experiments show that AI agents are held
causally responsible and blamed similarly to human agents for an identical
task. However, there was a meaningful difference in how people perceived these
agents' moral responsibility; human agents were ascribed to a higher degree of
present-looking and forward-looking notions of responsibility than AI agents.
We also found that people expect both AI and human decision-makers and advisors
to justify their decisions regardless of their nature. We discuss policy and
HCI implications of these findings, such as the need for explainable AI in
high-stakes scenarios.


Abstract:
  In this note, we establish a Vorono\"i--Oppenheim summation formula for
divisor functions over an arbitrary number field.


Abstract:
  The discovery of structure from time series data is a key problem in fields
of study working with complex systems. Most identifiability results and
learning algorithms assume the underlying dynamics to be discrete in time.
Comparatively few, in contrast, explicitly define dependencies in infinitesimal
intervals of time, independently of the scale of observation and of the
regularity of sampling. In this paper, we consider score-based graph learning
for the study of dynamical systems. We prove that for vector fields
parameterized in a large class of neural networks, least squares optimization
with adaptive regularization schemes consistently recovers directed graphs of
local independencies in systems of stochastic differential equations. Using
this insight, we propose a score-based learning algorithm based on penalized
Neural Ordinary Differential Equations (modelling the mean process) that we
show to be applicable to the general setting of irregularly-sampled
multivariate time series and to outperform the state of the art across a range
of dynamical systems.


Abstract:
  In this paper we propose the adaptive lasso for predictive quantile
regression (ALQR). Reflecting empirical findings, we allow predictors to have
various degrees of persistence and exhibit different signal strengths. The
number of predictors is allowed to grow with the sample size. We study
regularity conditions under which stationary, local unit root, and cointegrated
predictors are present simultaneously. We next show the convergence rates and
model selection consistency of ALQR. We apply the proposed method to the
out-of-sample quantile prediction problem of stock returns and find that it
outperforms the existing alternatives. We also provide numerical evidence from
additional Monte Carlo experiments, supporting the theoretical results.


Abstract:
  Deep neural networks' remarkable ability to correctly fit training data when
optimized by gradient-based algorithms is yet to be fully understood. Recent
theoretical results explain the convergence for ReLU networks that are wider
than those used in practice by orders of magnitude. In this work, we take a
step towards closing the gap between theory and practice by significantly
improving the known theoretical bounds on both the network width and the
convergence time. We show that convergence to a global minimum is guaranteed
for networks with widths quadratic in the sample size and linear in their depth
at a time logarithmic in both. Our analysis and convergence bounds are derived
via the construction of a surrogate network with fixed activation patterns that
can be transformed at any time to an equivalent ReLU network of a reasonable
size. This construction can be viewed as a novel technique to accelerate
training, while its tight finite-width equivalence to Neural Tangent Kernel
(NTK) suggests it can be utilized to study generalization as well.


Abstract:
  A Rb deficiency by a factor two with respect to the Sun has been found in M
dwarfs of solar metallicity. This deficiency is difficult to understand from
both the observational and nucleosynthesis point of views. To test the
reliability of this Rb deficiency, we study the Rb and Zr abundances in a
sample of KM-type giant stars in a similar metallicity range extracted from the
AMBRE Project. We derive Rb and Zr abundances in 54 giant stars with
metallicity close to solar by spectral synthesis in LTE and NLTE. The impact of
the Zeeman broadening in the RbI line is also studied. The LTE analysis results
in a Rb deficiency in giant stars smaller than that obtained in M dwarfs, but
the NLTE [Rb/Fe] ratios are very close to solar in the full metallicity range.
This contrasts with the figure found in M dwarfs. We investigate the effect of
gravitational settling and magnetic activity as possible causes of the Rb
deficiency found in M dwarfs. While, the former phenomenon has a negligible
impact on the surface Rb abundance, the existence of an average magnetic field
with intensity typical of that observed in M dwarfs may result in systematic Rb
abundance underestimations if the Zeeman broadening is not considered in the
spectral synthesis. The new [Rb,Zr/Fe] vs. [Fe/H] relationships can be
explained when the Rb production by rotating massive stars and
low-and-intermediate mass stars are considered, without the need of any
deviation from the standard s-process nucleosynthesis in AGB stars as
previously suggested.


Abstract:
  The complexity and size increase of software has extended the delay for
developers as they wait for code analysis and code merge. With the larger and
more complex software, more developers nowadays are developing software with
large source code repositories. The tendency for software platforms to
immediately update software packages with feature updates and bug-fixes is a
significant obstacle. Continuous integration systems may help prevent software
flaws during the active development of software packages, even when they are
deployed and updated frequently. Herein, we present a portable and modular code
review automation system that inspects incoming code changes such as code
format and style, performance regression, static analysis, build and deployment
tests, and dynamic analysis before merging and changing code. The proposed
mechanisms are sufficiently lightweight to be hosted on a regular desktop
computer even for numerous developers. The resulting reduced costs allow
developers to apply the proposed mechanism to many source code repositories.
Experimental results demonstrate that the proposed mechanism drastically
reduces overheads and improves usability compared with conventional mechanisms:
execution time (6x faster), CPU usage (40% lower), memory consumption (1/180),
and no out-of-memory occurrence.


Abstract:
  We review theoretical findings, astrophysical modeling, and current
gravitational-wave evidence of hierarchical stellar-mass black-hole mergers.
While most of the compact binary mergers detected by LIGO and Virgo are
expected to consist of first-generation black holes formed from the collapse of
stars, others might instead be of second (or higher) generation, containing the
remnants of previous black-hole mergers. Such a subpopulation of hierarchically
assembled black holes presents distinctive gravitational-wave signatures,
namely higher masses, possibly within the pair-instability mass gap, and
dimensionless spins clustered at the characteristic value of $\sim$0.7. In
order to produce hierarchical mergers, astrophysical environments need to
overcome the relativistic recoils imparted to black-hole merger remnants, a
condition which prefers hosts with escape speeds $\gtrsim$ 100 km/s. Promising
locations for efficient production of hierarchical mergers include nuclear star
clusters and accretion disks surrounding active galactic nuclei, though
environments that are less efficient at retaining merger products such as
globular clusters may still contribute significantly to the detectable
population of repeated mergers. While GW190521 is the single most promising
hierarchical-merger candidate to date, constraints coming from large population
analyses are becoming increasingly more powerful.


Abstract:
  Biological agents have adopted the principle of attention to limit the rate
of incoming information from the environment. One question that arises is if an
artificial agent has access to only a limited view of its surroundings, how can
it control its attention to effectively solve tasks? We propose an approach for
learning how to control a hard attention window by maximizing the mutual
information between the environment state and the attention location at each
step. The agent employs an internal world model to make predictions about its
state and focuses attention towards where the predictions may be wrong.
Attention is trained jointly with a dynamic memory architecture that stores
partial observations and keeps track of the unobserved state. We demonstrate
that our approach is effective in predicting the full state from a sequence of
partial observations. We also show that the agent's internal representation of
the surroundings, a live mental map, can be used for control in two partially
observable reinforcement learning tasks. Videos of the trained agent can be
found at https://sites.google.com/view/hard-attention-control.


Abstract:
  Young stellar associations hold a star formation record that can persist for
millions of years, revealing the progression of star formation long after the
dispersal of the natal cloud. To identify nearby young stellar populations that
trace this progression, we have designed a comprehensive framework for the
identification of young stars, and use it to identify $\sim$3$\times 10^4$
candidate young stars within a distance of 333 pc using Gaia DR2. Applying the
HDBSCAN clustering algorithm to this sample, we identify 27 top-level groups,
nearly half of which have little to no presence in previous literature. Ten of
these groups have visible substructure, including notable young associations
such as Orion, Perseus, Taurus, and Sco-Cen. We provide a complete
subclustering analysis on all groups with substructure, using age estimates to
reveal each region's star formation history. The patterns we reveal include an
apparent star formation origin for Sco-Cen along a semicircular arc, as well as
clear evidence for sequential star formation moving away from that arc with a
propagation speed of $\sim$4 km s$^{-1}$ ($\sim$4 pc Myr$^{-1}$). We also
identify earlier bursts of star formation in Perseus and Taurus that predate
current, kinematically identical active star-forming events, suggesting that
the mechanisms that collect gas can spark multiple generations of star
formation, punctuated by gas dispersal and cloud regrowth. The large spatial
scales and long temporal scales on which we observe star formation offer a
bridge between the processes within individual molecular clouds and the broad
forces guiding star formation at galactic scales.


Abstract:
  Flying Gyroscopes are fascinating flight objects, which, due to gyroscopic
stabilization, can achieve surprisingly long flight distances when thrown with
rapid spin. The most common example hereby is a traditional Frisbee disc. This
paper focuses on a similar object called X-Zylo, that shows a remarkable
straight flight despite its simple geometry. The main aim of the present study
is to investigate the flight behavior of the X-Zylo and to build a reliable
groundwork for further quantitative parameter studies on ring wing
configurations. To achieve this goal, a six degree of freedom model to predict
the flight trajectory was developed. The trajectory computation uses
interpolated high-fidelity CFD simulation data to calculate the acting moments
and forces on the object during flight. A launch contraption was built to be
able to validate the theory systematically and reproducible in experiments
without human factors involved in the launch. Despite the complexity of the
flight, the theoretical simulations match the real world data qualitatively,
however quantitative differences still prevail. The investigation shows that
the deviation between theory and experiment mostly stems from uncertainties in
the CFD data as well as the optical recording of the experimental data. Despite
the methods outperforming those of prior studies, advancements still have to be
made in those areas in order to obtain better quantitative accordance between
theory and experiment.


Abstract:
  With computers getting more and more powerful and integrated in our daily
lives, the focus is increasingly shifting towards more human-friendly
interfaces, making Automatic Speech Recognition (ASR) a central player as the
ideal means of interaction with machines. Consequently, interest in speech
technology has grown in the last few years, with more systems being proposed
and higher accuracy levels being achieved, even surpassing \textit{Human
Accuracy}. While ASR systems become increasingly powerful, the computational
complexity also increases, and the hardware support have to keep pace. In this
paper, we propose a technique to improve the energy-efficiency and performance
of ASR systems, focusing on low-power hardware for edge devices. We focus on
optimizing the DNN-based Acoustic Model evaluation, as we have observed it to
be the main bottleneck in state-of-the-art ASR systems, by leveraging run-time
information from the Beam Search. By doing so, we reduce energy and execution
time of the acoustic model evaluation by 25.6% and 25.9%, respectively, with
negligible accuracy loss.


Abstract:
  The symmetry energy and its density dependence are pivotal for many nuclear
physics and astrophysics applications, as they determine properties ranging
from the neutron-skin thickness of nuclei to the crust thickness and the radius
of neutron stars. Recently, PREX-II reported a value of $0.283\pm0.071$ fm for
the neutron-skin thickness of $^{208}$Pb, $R_{\rm skin}^{^{208}\text{Pb}}$,
implying a symmetry-energy slope parameter $L$ of $106\pm37$ MeV, larger than
most ranges obtained from microscopic calculations and other nuclear
experiments. We use a nonparametric equation of state representation based on
Gaussian processes to constrain the symmetry energy $S_0$, $L$, and $R_{\rm
skin}^{^{208}\text{Pb}}$ directly from observations of neutron stars with
minimal modeling assumptions. The resulting astrophysical constraints from
heavy pulsar masses, LIGO/Virgo, and NICER favor smaller values of the neutron
skin and $L$, as well as negative symmetry incompressibilities. Combining
astrophysical data with chiral effective field theory ($\chi$EFT) and PREX-II
constraints yields $S_0 = 33.0^{+2.0}_{-1.8}$ MeV, $L=53^{+14}_{-15}$ MeV, and
$R_{\rm skin}^{^{208}\text{Pb}} = 0.17^{+0.04}_{-0.04}$ fm. We also examine the
consistency of several individual $\chi$EFT calculations with astrophysical
observations and terrestrial experiments. We find that there is only mild
tension between $\chi$EFT, astrophysical data, and PREX-II's
$R_\mathrm{skin}^{^{208}\mathrm{Pb}}$ measurement ($p$-value $= 12.3\%$) and
that there is excellent agreement between $\chi$EFT, astrophysical data, and
other nuclear experiments.


Abstract:
  The Aubry-Andr\'e-Harper model provides a paradigmatic example of aperiodic
order in a one-dimensional lattice displaying a delocalization-localization
phase transition at a finite critical value $V_c$ of the quasiperiodic
potential amplitude $V$. In terms of dynamical behavior of the system, the
phase transition is discontinuous when one measures the quantum diffusion
exponent $\delta$ of wave packet spreading, with $\delta=1$ in the delocalized
phase $V<V_c$ (ballistic transport), $\delta \simeq 1/2$ at the critical point
$V=V_c$ (diffusive transport), and $\delta=0$ in the localized phase $V>V_c$
(dynamical localization). However, the phase transition turns out to be smooth
when one measures, as a dynamical variable, the speed $v(V)$ of excitation
transport in the lattice, which is a continuous function of potential amplitude
$V$ and vanishes as the localized phase is approached. Here we consider a
non-Hermitian extension of the Aubry-Andr\'e-Harper model, in which hopping
along the lattice is asymmetric, and show that the dynamical
localization-delocalization transition is discontinuous not only in the
diffusion exponent $\delta$, but also in the speed $v$ of ballistic transport.
This means that, even very close to the spectral phase transition point, rather
counter-intuitively ballistic transport with a finite speed is allowed in the
lattice. Also, we show that the ballistic velocity can increase as $V$ is
increased above zero, i.e. surprisingly disorder in the lattice can result in
an enhancement of transport.


Abstract:
  In this paper we present a system that exploits different pre-trained
Language Models for assigning domain labels to WordNet synsets without any kind
of supervision. Furthermore, the system is not restricted to use a particular
set of domain labels. We exploit the knowledge encoded within different
off-the-shelf pre-trained Language Models and task formulations to infer the
domain label of a particular WordNet definition. The proposed zero-shot system
achieves a new state-of-the-art on the English dataset used in the evaluation.


Abstract:
  We present nonlinear (NL) and higher twist (HT) corrections to the color
dipole model (CDM) bounds at low values of $x$ and $Q^{2}$ using the
parameterization method. Consistency between the bounds at this region describe
that a transition from the linear to the nonlinear behavior is dependence on
the behavior of the gluon distribution function. The parameters in the color
dipole model are comparable with the color dipole bounds at low values of
$Q^{2}$. Consequently, the obtained reduced cross sections at low and moderate
$Q^{2}$ values due to the NL+HT effects show a good agreement with the H1 data.


Abstract:
  The material and exact shape of a nanostructure determine its optical
response, which is especially strong for plasmonic metals. Unfortunately, only
a very few plasmonic metals are available, which limits the spectral range
where these strong optical effects can be utilized. Alloying different
plasmonic metals can overcome this limitation, at the expense of using a high
temperature alloying process, which adversely destroys the nanostructure shape.
Here, we develop a low temperature alloying process at only 300{\deg}C and
fabricate Au-Ag nanostructures with a broad diversity of shapes, aspect ratios
and stoichiometries. EDX and XPS analyses confirm the homogeneous alloying
through the entire sample. Varying the alloy stoichiometry tunes the optical
response of the nanostructure and controls spectral features such as Fano
resonances. Binary metasurfaces that combine nanostructures with different
stoichiometries are fabricated using multiple-step electron beam lithography,
and their optical function as hologram or Fresnel zone plate is demonstrated at
the visible wavelength of 532 nm. This low temperature annealing technique
provides a versatile and cost-effective way of fabricating complex Au-Ag
nanostructures with arbitrary stoichiometry.


Abstract:
  Traditional computer vision approaches, based on neural networks (NN), are
typically trained on a large amount of image data. By minimizing the
cross-entropy loss between a prediction and a given class label, the NN and its
visual embedding space are learned to fulfill a given task. However, due to the
sole dependence on the image data distribution of the training domain, these
models tend to fail when applied to a target domain that differs from their
source domain. To learn a more robust NN to domain shifts, we propose the
knowledge graph neural network (KG-NN), a neuro-symbolic approach that
supervises the training using image-data-invariant auxiliary knowledge. The
auxiliary knowledge is first encoded in a knowledge graph with respective
concepts and their relationships, which is then transformed into a dense vector
representation via an embedding method. Using a contrastive loss function,
KG-NN learns to adapt its visual embedding space and thus its weights according
to the image-data invariant knowledge graph embedding space. We evaluate KG-NN
on visual transfer learning tasks for classification using the mini-ImageNet
dataset and its derivatives, as well as road sign recognition datasets from
Germany and China. The results show that a visual model trained with a
knowledge graph as a trainer outperforms a model trained with cross-entropy in
all experiments, in particular when the domain gap increases. Besides better
performance and stronger robustness to domain shifts, these KG-NN adapts to
multiple datasets and classes without suffering heavily from catastrophic
forgetting.


Abstract:
  Relationship between agents can be conveniently represented by graphs. When
these relationships have different modalities, they are better modelled by
multilayer graphs where each layer is associated with one modality. Such graphs
arise naturally in many contexts including biological and social networks.
Clustering is a fundamental problem in network analysis where the goal is to
regroup nodes with similar connectivity profiles. In the past decade, various
clustering methods have been extended from the unilayer setting to multilayer
graphs in order to incorporate the information provided by each layer. While
most existing works assume - rather restrictively - that all layers share the
same set of nodes, we propose a new framework that allows for layers to be
defined on different sets of nodes. In particular, the nodes not recorded in a
layer are treated as missing. Within this paradigm, we investigate several
generalizations of well-known clustering methods in the complete setting to the
incomplete one and prove some consistency results under the Multi-Layer
Stochastic Block Model assumption. Our theoretical results are complemented by
thorough numerical comparisons between our proposed algorithms on synthetic
data, and also on real datasets, thus highlighting the promising behaviour of
our methods in various settings.


Abstract:
  Multifunctional semiconductors widen the application scope of existing
semiconductor devices. Here we report on the ferromagnetic/semiconductor
coupling obtained through nickel and porous-GaN composites. We realised
nickel-infiltrated and nickel-coated porous-GaN structures, and examined the
subsequent magnetic and magnetotransport properties. We found that the
magnetization of the porous-GaN/Ni composites strongly depended on the amount
of deposited nickel and evolves from relatively isotropic and remanent (up to
90% remanent-to-saturation magnetization) response to an anisotropic response
characteristic of thin-films. The magnetoresistance of nickel sputter-coated
porous-GaN structures was measured at 300 and 200 K. The temperature dependant
measurements suggested that transport in the GaN layers is dominating the
current. Nonetheless, depending on sample pore size, the magnetoresistance
displayed high (12-fold) out-of-plane/in-plane anisotropy, and significant
hysteresis. These results are uncharacteristic of GaN transport and point
towards magneto-piezo-resistive coupling between the nickel and the porous GaN.
These results encourage deeper investigation of magnetic nanostructure property
tuning and of magnetic property coupling to GaN and similar materials.


Abstract:
  Purpose: Image classification is perhaps the most fundamental task in imaging
AI. However, labeling images is time-consuming and tedious. We have recently
demonstrated that reinforcement learning (RL) can classify 2D slices of MRI
brain images with high accuracy. Here we make two important steps toward
speeding image classification: Firstly, we automatically extract class labels
from the clinical reports. Secondly, we extend our prior 2D classification work
to fully 3D image volumes from our institution. Hence, we proceed as follows:
in Part 1, we extract labels from reports automatically using the SBERT natural
language processing approach. Then, in Part 2, we use these labels with RL to
train a classification Deep-Q Network (DQN) for 3D image volumes.
  Methods: For Part 1, we trained SBERT with 90 radiology report impressions.
We then used the trained SBERT to predict class labels for use in Part 2. In
Part 2, we applied multi-step image classification to allow for combined Deep-Q
learning using 3D convolutions and TD(0) Q learning. We trained on a set of 90
images. We tested on a separate set of 61 images, again using the classes
predicted from patient reports by the trained SBERT in Part 1. For comparison,
we also trained and tested a supervised deep learning classification network on
the same set of training and testing images using the same labels.
  Results: Part 1: Upon training with the corpus of radiology reports, the
SBERT model had 100% accuracy for both normal and metastasis-containing scans.
Part 2: Then, using these labels, whereas the supervised approach quickly
overfit the training data and as expected performed poorly on the testing set
(66% accuracy, just over random guessing), the reinforcement learning approach
achieved an accuracy of 92%. The results were found to be statistically
significant, with a p-value of 3.1 x 10^-5.


Abstract:
  We report piezoelectric response in liquid phase exfoliated MoS2 nanosheets
with desired structure and morphology. The piezoelectric effect in liquid phase
exfoliated few layers of MoS2 flakes is interesting as it may allow the
scalable fabrication of electronic devices such as self-powered electronics,
piezoelectric transformers, antennas and more. The piezo force microscopy (PFM)
measurements were used to quantify the amplitude and phase loop, which shows
strong piezoelectric coefficient. Herein, the piezoelectric response in few
layers of MoS2 is attributed to the defects formed in it during the synthesis
procedure. The presence of defects is confirmed by XPS analysis


Abstract:
  In this paper we propose a method to compute a freeform reflector system for
collimating and shaping a beam from a point source. We construct these
reflectors such that the radiant intensity of the source is converted into a
desired target. An important generalization in our approach compared to
previous research is that the output beam can be in an arbitrary direction. The
design problem is approached by using a generalized Monge-Amp\`ere equation.
This equation is solved using a least-squares algorithm for non-quadratic cost
functions. This algorithm calculates the optical map, from which we can then
compute the surfaces. We test our algorithm on two cases. First we consider a
uniform source and target distribution. Next, we use the model of a laser diode
light source and a ring-shaped target distribution.


Abstract:
  We present thermal observations of Ganymede from the Atacama Large Millimeter
Array (ALMA) in 2016-2019 at a spatial resolution of 300-900 km (0.1-0.2''
angular resolution) and frequencies of 97.5, 233, and 343.5 GHz (wavelengths of
3, 1.3, and 0.87 mm); the observations collectively covered all Ganymede
longitudes. We determine the global thermophysical properties using a thermal
model that considers subsurface emission and depth- and temperature-dependent
thermophysical and dielectric properties, in combination with a retrieval
algorithm. The data are sensitive to emission from the upper $\sim$0.5 meter of
the surface, and we find a millimeter emissivity of 0.75-0.78 and (sub)surface
porosities of 10-40%, corresponding to effective thermal inertias of 400-800 J
m^{-2} K^{-1} s^{-1/2}. Combined with past infrared results, as well as
modeling presented here of a previously-unpublished Galileo PPR nighttime
infrared observation, the multi-wavelength constraints are consistent with a
compaction profile whereby the porosity drops from ~85% at the surface to
10{+30/-10}% at depth over a compaction length scale of tens of cm. We present
maps of temperature residuals from the best-fit global models which indicate
localized variations in thermal surface properties at some (but not all) dark
terrains and at impact craters, which appear 5-8 K colder than the model.
Equatorial regions are warmer than predicted by the model, in particular near
the centers of the leading and trailing hemispheres, while the mid-latitudes
(~30-60 degrees) are generally colder than predicted; these trends are
suggestive of an exogenic origin.


Abstract:
  Let $R$ be a ring and $S$ a multiplicative subset of $R$. An $R$-module $T$
is called uniformly $S$-torsion provided that $sT=0$ for some $s\in S$. The
notion of $S$-exact sequences is also introduced from the viewpoint of
uniformity. An $R$-module $F$ is called $S$-flat provided that the induced
sequence $0\rightarrow A\otimes_RF\rightarrow B\otimes_RF\rightarrow
C\otimes_RF\rightarrow 0$ is $S$-exact for any $S$-exact sequence $0\rightarrow
A\rightarrow B\rightarrow C\rightarrow 0$. A ring $R$ is called $S$-von Neumann
regular provided there exists an element $s\in S$ satisfies that for any $a\in
R$ there exists $r\in R$ such that $sa=ra^2$. We obtain that a ring $R$ an
$S$-von Neumann regular ring if and only if any $R$-module is $S$-flat. Several
properties of $S$-flat modules and $S$-von Neumann regular rings are obtained.


Abstract:
  The majority of stochastic channel models rely on the electromagnetic
far-field assumption. This assumption breaks down in future applications that
push towards the electromagnetic near-field region such as those where the use
of very large antenna arrays is envisioned. Motivated by this consideration, we
show how physical principles can be used to derive a channel model that is also
valid in the electromagnetic near-field. We show that wave propagation through
a three-dimensional scattered medium can be generally modeled as a linear and
space-variant system. We first review the physics principles that lead to a
closed-form deterministic angular representation of the channel response. This
serves as a basis for deriving a stochastic representation of the channel in
terms of statistically independent Gaussian random coefficients for randomly
spatially-stationary propagation environments. The very desirable property of
spatial stationarity can always be retained by excluding reactive propagation
mechanisms confined in the extreme near-field propagation region. Remarkably,
the provided stochastic representation is directly connected to the Fourier
spectral representation of a general spatially-stationary random field.


Abstract:
  We present a novel LSTM cell architecture capable of learning both intra- and
inter-perspective relationships available in visual sequences captured from
multiple perspectives. Our architecture adopts a novel recurrent joint learning
strategy that uses additional gates and memories at the cell level. We
demonstrate that by using the proposed cell to create a network, more effective
and richer visual representations are learned for recognition tasks. We
validate the performance of our proposed architecture in the context of two
multi-perspective visual recognition tasks namely lip reading and face
recognition. Three relevant datasets are considered and the results are
compared against fusion strategies, other existing multi-input LSTM
architectures, and alternative recognition solutions. The experiments show the
superior performance of our solution over the considered benchmarks, both in
terms of recognition accuracy and complexity. We make our code publicly
available at https://github.com/arsm/MPLSTM.


Abstract:
  This paper describes FBK's system submission to the IWSLT 2021 Offline Speech
Translation task. We participated with a direct model, which is a
Transformer-based architecture trained to translate English speech audio data
into German texts. The training pipeline is characterized by knowledge
distillation and a two-step fine-tuning procedure. Both knowledge distillation
and the first fine-tuning step are carried out on manually segmented real and
synthetic data, the latter being generated with an MT system trained on the
available corpora. Differently, the second fine-tuning step is carried out on a
random segmentation of the MuST-C v2 En-De dataset. Its main goal is to reduce
the performance drops occurring when a speech translation model trained on
manually segmented data (i.e. an ideal, sentence-like segmentation) is
evaluated on automatically segmented audio (i.e. actual, more realistic testing
conditions). For the same purpose, a custom hybrid segmentation procedure that
accounts for both audio content (pauses) and for the length of the produced
segments is applied to the test data before passing them to the system. At
inference time, we compared this procedure with a baseline segmentation method
based on Voice Activity Detection (VAD). Our results indicate the effectiveness
of the proposed hybrid approach, shown by a reduction of the gap with manual
segmentation from 8.3 to 1.4 BLEU points.


Abstract:
  Discrete time crystals (DTCs) refer to a novel many-body steady state that
spontaneously breaks the discrete time-translational symmetry in a
periodically-driven quantum system. Here, we study DTCs in a Bose-Einstein
condensate (BEC) bouncing resonantly on an oscillating mirror, using a two-mode
model derived from a standard quantum field theory. We investigate the validity
of this model and apply it to study the long-time behavior of our system. A
wide variety of initial states based on two Wannier modes are considered. We
find that in previous studies the investigated phenomena in the evolution
time-window ($\lessapprox$2000 driving periods) are actually "short-time"
transient behavior though DTC formation signaled by the sub-harmonic responses
is still shown if the inter-boson interaction is strong enough. After a much
longer (about 20 times) evolution time, initial states with no "long-range"
correlations relax to a steady state, where time-symmetry breaking can be
unambiguously defined. Quantum revivals also eventually occur. This long-time
behavior can be understood via the many-body Floquet quasi-eigenenergy spectrum
of the two-mode model. A symmetry-breaking edge for DTC formation appears in
the spectrum for strong enough interaction, where all quasi-eigenstates below
the edge are symmetry-breaking while those above the edge are symmetric. The
late-time steady state's time-translational symmetry depends solely on whether
the initial energy is above or below the symmetry-breaking edge. A phase
diagram showing regions of symmetry-broken and symmetric phases for differing
initial energies and interaction strengths is presented. We find that according
to this two-mode model, the discrete time crystal survives for times out to at
least 250,000 driving periods.


Abstract:
  The test bench time needed for model-based calibration can be reduced with
active learning methods for test design. This paper presents an improved
strategy for active output selection. This is the task of learning multiple
models in the same input dimensions and suits the needs of calibration tasks.
Compared to an existing strategy, we take into account the noise estimate,
which is inherent to Gaussian processes. The method is validated on three
different toy examples. The performance compared to the existing best strategy
is the same or better in each example. In a best case scenario, the new
strategy needs at least 10% less measurements compared to all other active or
passive strategies. Further efforts will evaluate the strategy on a real-world
application. Moreover, the implementation of more sophisticated active-learning
strategies for the query placement will be realized.


Abstract:
  We study a certain wormholing phenomenon that takes place in the
Koll\'ar--Shepherd-Barron--Alexeev (KSBA) compactification of the moduli space
of surfaces of general type. It occurs because of the appearance of particular
extremal P-resolutions in surfaces on the KSBA boundary. We state a general
wormhole conjecture, and we prove it for a wide range of cases. At the end, we
discuss some topological properties and open questions.


Abstract:
  With the aim of evaluating image forensics tools, we propose a methodology to
create forgeries traces, leaving intact the semantics of the image. Thus, the
only forgery cues left are the specific alterations of one or several aspects
of the image formation pipeline. This methodology creates automatically forged
images that are challenging to detect for forensic tools and overcomes the
problem of creating convincing semantic forgeries. Based on this methodology,
we create the Trace database and conduct an evaluation of the main
state-of-the-art image forensics tools.


Abstract:
  The strong-field approximation for high-harmonic generation in near-infrared
and infrared laser pulses is formulated in the accelerated Kramers-Henneberger
frame. The accompanying physical picture is discussed and the nature of the
leading-order term is contrasted with that of the three-step model following
the strong-field-approximation formulation in the length or velocity gauges.
The theory is illustrated by high-harmonic generation spectra for atomic
hydrogen.


Abstract:
  The axion has emerged in recent years as a leading particle candidate to
provide the mysterious dark matter in the cosmos, as we review here for a
general scientific audience. We describe first the historical roots of the
axion in the Standard Model of particle physics and the problem of
charge-parity invariance of the strong nuclear force. We then discuss how the
axion emerges as a dark matter candidate, and how it is produced in the early
Universe. The symmetry properties of the axion dictate the form of its
interactions with ordinary matter. Astrophysical considerations restrict the
particle mass and interaction strengths to a limited range, which facilitates
the planning of experiments to detect the axion. A companion review discusses
the exciting prospect that the axion could indeed be detected in the near term
in the laboratory.


Abstract:
  A continuous-time quantum walk on a dynamic graph evolves by Schr\"odinger's
equation with a sequence of Hamiltonians encoding the edges of the graph. This
process is universal for quantum computing, but in general, the dynamic graph
that implements a quantum circuit can be quite complicated. In this paper, we
give six scenarios under which a dynamic graph can be simplified, and they
exploit commuting graphs, identical graphs, perfect state transfer,
complementary graphs, isolated vertices, and uniform mixing on the hypercube.
As examples, we simplify dynamic graphs, in some instances allowing
single-qubit gates to be implemented in parallel.


Abstract:
  SAR (VV and VH polarization) and optical data are widely used in image fusion
to use the complimentary information of each other and to obtain the
better-quality image (in terms of spatial and spectral features) for the
improved classification results. This paper uses anisotropic diffusion with PCA
for the fusion of SAR and optical data and patch-based SVM Classification with
LBP (LBP-PSVM). Fusion results with VV polarization performed better than VH
polarization using considered fusion method. For classification, the
performance of LBP-PSVM using S1 (VV) with S2, S1 (VH) with S2 is compared with
SVM classifier (without patch) and PSVM classifier (with patch), respectively.
Classification results suggests that the LBP-PSVM classifier is more effective
in comparison to SVM and PSVM classifiers for considered data.


Abstract:
  The manuscript presents the $LU$ approach to matrix biorthogonal polynomials
when all the even ordered entries in the Gram matrix are zero. This arises in
case of a quadratic transformation which is briefly discussed. Further, the
main diagonal of the Gram matrix is a zero diagonal and we present the theory
that follows from this fact. Precisely, we discuss the Christoffel
transformation and matrix representations of the kernel polynomials, usually
called the ABC Theorem. Finally, we provide an illustration of our results
assuming the Gram matrix has Hankel symmetry.


Abstract:
  The relevance of the Key Information Extraction (KIE) task is increasingly
important in natural language processing problems. But there are still only a
few well-defined problems that serve as benchmarks for solutions in this area.
To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister
Charity). They involve a mix of scanned and born-digital long formal
English-language documents. In these datasets, an NLP system is expected to
find or infer various types of entities by employing both textual and
structural layout features. The Kleister Charity dataset consists of 2,788
annual financial reports of charity organizations, with 61,643 unique pages and
21,612 entities to extract. The Kleister NDA dataset has 540 Non-disclosure
Agreements, with 3,229 unique pages and 2,160 entities to extract. We provide
several state-of-the-art baseline systems from the KIE domain (Flair, BERT,
RoBERTa, LayoutLM, LAMBERT), which show that our datasets pose a strong
challenge to existing models. The best model achieved an 81.77% and an 83.57%
F1-score on respectively the Kleister NDA and the Kleister Charity datasets. We
share the datasets to encourage progress on more in-depth and complex
information extraction tasks.


Abstract:
  The research of the derivative nonlinear Schrodinger equation (DNLS) has
attracted more and more extensive attention in theoretical analysis and
physical application. The improved physicsinformed neural network (IPINN)
approach with neuron-wise locally adaptive activation function is presented to
derive the data-driven localized wave solutions, which contain rational
solution, soliton solution, rogue wave, periodic wave and rogue periodic wave
for the DNLS with initial and boundary conditions in complex space. Especially,
the data-driven periodic wave and rogue periodic wave of the DNLS are
investigated by employing the IPINN method for the first time. Furthermore, the
relevant dynamical behaviors, error analysis and vivid plots have been
exhibited in detail. The numerical results indicate the IPINN method can well
simulate the localized wave solutions of the DNLS under a small data set.


Abstract:
  The Internet of Vehicles (IoV) is an application of the Internet of things
(IoT). It faces two main security problems: (1) the central server of the IoV
may not be powerful enough to support the centralized authentication of the
rapidly increasing connected vehicles, (2) the IoV itself may not be robust
enough to single-node attacks. To solve these problems, this paper proposes
SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of
Vehicles, which is based on a distributed blockchain structure. The distributed
structure can reduce the pressure on the central server and decrease the risk
of single-node attacks. The SG-PBFT consensus algorithm improves the
traditional PBFT consensus algorithm by using a score grouping mechanism to
achieve a higher consensus efficiency. The experimental result shows that our
method can greatly improve the consensus efficiency and prevent single-node
attacks. Specifically, when the number of consensus nodes reaches 1000, the
consensus time of our algorithm is only about 27% of what is required for the
state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile
and can be used in other application scenarios which require high consensus
efficiency.


Abstract:
  The cultivation of orchard meadows provides an ecological benefit for
biodiversity, which is significantly higher than in intensively cultivated
orchards. The goal of this research is to create a tree model to automatically
determine possible pruning points for stand-alone trees within meadows. The
algorithm which is presented here is capable of building a skeleton model based
on a pre-segmented photogrammetric 3D point cloud. Good results were achieved
in assigning the points to their leading branches and building a virtual tree
model, reaching an overall accuracy of 95.19 %. This model provided the
necessary information about the geometry of the tree for automated pruning.


Abstract:
  Currently, memristor devices suffer from variability between devices and from
cycle to cycle. In this work, we study the impact of device variations on
memristive Material Implication (IMPLY). New constraints for different
parameters and variables are analytically derived and compared to extensive
simulation results, covering single gate and 1T1R crossbar structures. We show
that a static analysis based on switching conditions is not sufficient for an
overall assessment of robustness against device variability. Furthermore, we
outline parameter ranges within which the IMPLY gate is predicted to produce
correct output values. Our study shows that threshold voltage is the most
critical parameter. This work helps scientists and engineers to understand the
pitfalls of designing reliable IMPLY-based calculation units better and design
them with more ease. Moreover, these analyses can be used to determine whether
a certain memristor technology is suitable for implementation of IMPLY-based
circuits and systems.


Abstract:
  I point out critical errors in the paper "Bell's Theorem Versus Local Realism
in a Quaternionic Model of Physical Space" by J. Christian, published in IEEE
Access. Christian's paper in fact contains several conflicting models. None of
them form counterexamples to Bell's theorem. Most of Christian's paper is
devoted to a model based on the detection loophole due to Pearle (1970).


Abstract:
  Operations on the cohomology of spaces are important tools enhancing the
descriptive power of this computable invariant. For cohomology with mod 2
coefficients, Steenrod squares are the most significant of these operations.
Their effective computation relies on a cup-$i$ construction, a structure on
(co)chains which is important in its own right, having connections to, among
others, lattice field theory, convex geometry and higher category theory. In
this article we present new formulas defining a cup-$i$ construction, and use
them to introduce a fast algorithm for the computation of Steenrod squares on
the cohomology of simplicial complexes.


Abstract:
  We demonstrate on-chip light amplification with integrated optical waveguide
fabricated on erbium-doped thin film lithium niobate on insulator (TFLNOI)
using the photolithography assisted chemo-mechanical etching (PLACE) technique.
A maximum internal net gain of 18 dB in the small-signal-gain regime is
measured at the peak emission wavelength of 1530 nm for a waveguide length of
3.6 cm, indicating a differential gain per unit length of 5 dB/cm. This work
paves the way to the monolithic integration of diverse active and passive
photonic components on the TFLNOI platform.


Abstract:
  We obtain various new limit theorems for random walks on SL_2(C) under low
moment conditions. For non-elementary measures with a finite second moment, we
prove a Local Limit Theorem for the norm cocycle, yielding the optimal version
of a theorem of E. Le Page. For measures with a finite third moment, we obtain
the Local Limit Theorem for the matrix coefficients, improving a recent result
of Grama-Quint-Xiao and the authors, and Berry-Esseen bounds with optimal rate
$O(1 / \sqrt n)$ for the norm cocycle and the matrix coefficients. The main
tool is a detailed study of the spectral properties of the Markov operator and
its purely imaginary perturbations acting on different function spaces. We
introduce, in particular, a new function space derived from the Sobolev space
$W^{1,2}$ that provides uniform estimates.


Abstract:
  Proactive tile-based virtual reality video streaming computes and delivers
the predicted tiles to be requested before playback. All existing works
overlook the important fact that computing and communication (CC) tasks for a
segment may squeeze the time for the tasks for the next segment, which will
cause less and less available time for the latter segments. In this paper, we
jointly optimize the durations for CC tasks to maximize the completion rate of
CC tasks under the task duration-squeezing-aware constraint. To ensure the
latter segments remain enough time for the tasks, the CC tasks for a segment
are not allowed to squeeze the time for computing and delivering the subsequent
segment. We find the closed-form optimal solution, from which we find a
minimum-resource-limited, an unconditional and a conditional resource-tradeoff
regions, which are determined by the total time for proactive CC tasks and the
playback duration of a segment. Owing to the duration-squeezing-prohibited
constraints, the increase of the configured resources may not be always useful
for improving the completion rate of CC tasks. Numerical results validate the
impact of the duration-squeezing-prohibited constraints and illustrate the
three regions.


Abstract:
  Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free
technique for biomedical and mineralogical imaging which can suffer from low
signal to noise ratios. Here we demonstrate the use of an unsupervised deep
learning neural network for rapid and automatic denoising of SRS images: UHRED
(Unsupervised Hyperspectral Resolution Enhancement and Denoising). UHRED is
capable of one-shot learning; only one hyperspectral image is needed, with no
requirements for training on previously labelled datasets or images.
Furthermore, by applying a k-means clustering algorithm to the processed data,
we demonstrate automatic, unsupervised image segmentation, yielding, without
prior knowledge of the sample, intuitive chemical species maps, as shown here
for a lithium ore sample.


Abstract:
  String theory's holographic QCD duality makes predictions for hadron physics
by building models that live in five-dimensional (5D) curved space. In this
pedagogical note, we explain how finding the hadron mass spectrum in these
models amounts to finding the eigenvalues of a time-independent,
one-dimensional Schroedinger equation. Changing the structure of the 5D curved
space is equivalent to altering the potential in the Schroedinger equation,
which in turn alters the hadron spectrum. We illustrate this concept with three
holographic QCD models possessing exact analogs in basic quantum mechanics: the
free particle, the infinite square well, and the harmonic oscillator. In
addition to making aspects of holographic QCD accessible to undergraduates,
this formulation can provide students with intuition for the meaning of curved
space. This paper is intended primarily as a tool for researchers interested in
involving early-stage undergraduates in research, but is also a suitable
introduction to elements of holographic QCD for advanced undergraduate- and
beginning graduate students with some knowledge of general relativity and
classical field theory.