Abstract:
  Upper limb and hand functionality is critical to many activities of daily
living and the amputation of one can lead to significant functionality loss for
individuals. From this perspective, advanced prosthetic hands of the future are
anticipated to benefit from improved shared control between a robotic hand and
its human user, but more importantly from the improved capability to infer
human intent from multimodal sensor data to provide the robotic hand perception
abilities regarding the operational context. Such multimodal sensor data may
include various environment sensors including vision, as well as human
physiology and behavior sensors including electromyography and inertial
measurement units. A fusion methodology for environmental state and human
intent estimation can combine these sources of evidence in order to help
prosthetic hand motion planning and control.
  In this paper, we present a dataset of this type that was gathered with the
anticipation of cameras being built into prosthetic hands, and computer vision
methods will need to assess this hand-view visual evidence in order to estimate
human intent. Specifically, paired images from human eye-view and hand-view of
various objects placed at different orientations have been captured at the
initial state of grasping trials, followed by paired video, EMG and IMU from
the arm of the human during a grasp, lift, put-down, and retract style trial
structure. For each trial, based on eye-view images of the scene showing the
hand and object on a table, multiple humans were asked to sort in decreasing
order of preference, five grasp types appropriate for the object in its given
configuration relative to the hand. The potential utility of paired eye-view
and hand-view images was illustrated by training a convolutional neural network
to process hand-view images in order to predict eye-view labels assigned by
humans.


Abstract:
  In both Newtonian gravity and Einstein gravity there is no force on a test
particle located inside a spherical cavity cut out of a static, spherically
symmetric mass distribution. Inside the cavity exterior matter is decoupled and
there is no external field effect that could act on the test particle. However,
for potentials other than the Newtonian potential or for geometries other than
Ricci flat ones this is no longer the case, and there then is an external field
effect. We explore this possibility in various alternate gravity scenarios, and
suggest that such (Machian) external field effects can serve as a diagnostic
for gravitational theory.


Abstract:
  Deep-learning-based video processing has yielded transformative results in
recent years. However, the video analytics pipeline is energy-intensive due to
high data rates and reliance on complex inference algorithms, which limits its
adoption in energy-constrained applications. Motivated by the observation of
high and variable spatial redundancy and temporal dynamics in video data
streams, we design and evaluate an adaptive-resolution optimization framework
to minimize the energy use of multi-task video analytics pipelines. Instead of
heuristically tuning the input data resolution of individual tasks, our
framework utilizes deep reinforcement learning to dynamically govern the input
resolution and computation of the entire video analytics pipeline. By
monitoring the impact of varying resolution on the quality of high-dimensional
video analytics features, hence the accuracy of video analytics results, the
proposed end-to-end optimization framework learns the best non-myopic policy
for dynamically controlling the resolution of input video streams to globally
optimize energy efficiency. Governed by reinforcement learning, optical flow is
incorporated into the framework to minimize unnecessary spatio-temporal
redundancy that leads to re-computation, while preserving accuracy. The
proposed framework is applied to video instance segmentation which is one of
the most challenging computer vision tasks, and achieves better energy
efficiency than all baseline methods of similar accuracy on the YouTube-VIS
dataset.


Abstract:
  Within the scientific goals of the THESEUS ESA/M5 candidate mission, a
critical item is a fast (within a few s) and accurate (<15 arcmin) Gamma-Ray
Burst and high-energy transient location from a few keV up to hard X-ray energy
band. For that purpose, the signal multiplexing based on coded masks is the
selected option to achieve this goal. This contribution is implemented by the
XGIS Imaging System, based on that technique. The XGIS Imaging System has the
heritage of previous payload developments: LEGRI/Minisat-01, INTEGRAL,
UFFO/Lomonosov and ASIM/ISS. In particular the XGIS Imaging System is an
upgrade of the ASIM system in operation since 2018 on the International Space
Station. The scientific goal is similar: to detect a gamma-ray transient. But
while ASIM focuses on Terrestrial Gamma-ray Flashes, THESEUS aims for the GRBs.
For each of the two XGIS Cameras, the coded mask is located at 630 mm from the
detector layer. The coding pattern is implemented in a Tungsten plate (1 mm
thickness) providing a good multiplexing capability up to 150 keV. In that way
both XGIS detector layers (based on Si and CsI detectors) have imaging
capabilities at the medium - hard X-ray domain. This is an improvement achieved
during the current THESEUS Phase-A. The mask is mounted on top of a collimator
that provides the mechanical assembly support, as well as good cosmic X-ray
background shielding. The XGIS Imaging System preliminary structural and
thermal design, and the corresponding analyses, are included in this
contribution, as it is a preliminary performance evaluation.


Abstract:
  Computing the convolution $A\star B$ of two length-$n$ vectors $A,B$ is an
ubiquitous computational primitive. Applications range from string problems to
Knapsack-type problems, and from 3SUM to All-Pairs Shortest Paths. These
applications often come in the form of nonnegative convolution, where the
entries of $A,B$ are nonnegative integers. The classical algorithm to compute
$A\star B$ uses the Fast Fourier Transform and runs in time $O(n\log n)$.
  However, often $A$ and $B$ satisfy sparsity conditions, and hence one could
hope for significant improvements. The ideal goal is an $O(k\log k)$-time
algorithm, where $k$ is the number of non-zero elements in the output, i.e.,
the size of the support of $A\star B$. This problem is referred to as sparse
nonnegative convolution, and has received considerable attention in the
literature; the fastest algorithms to date run in time $O(k\log^2 n)$.
  The main result of this paper is the first $O(k\log k)$-time algorithm for
sparse nonnegative convolution. Our algorithm is randomized and assumes that
the length $n$ and the largest entry of $A$ and $B$ are subexponential in $k$.
Surprisingly, we can phrase our algorithm as a reduction from the sparse case
to the dense case of nonnegative convolution, showing that, under some mild
assumptions, sparse nonnegative convolution is equivalent to dense nonnegative
convolution for constant-error randomized algorithms. Specifically, if $D(n)$
is the time to convolve two nonnegative length-$n$ vectors with success
probability $2/3$, and $S(k)$ is the time to convolve two nonnegative vectors
with output size $k$ with success probability $2/3$, then
$S(k)=O(D(k)+k(\log\log k)^2)$.
  Our approach uses a variety of new techniques in combination with some old
machinery from linear sketching and structured linear algebra, as well as new
insights on linear hashing, the most classical hash function.


Abstract:
  Complex decision making problems such as the privacy policy selection when
sharing content in online social networks can significantly benefit from
artificial intelligence systems. With the use of Computational Argumentation,
it is possible to persuade human users to modify their initial decisions to
avoid potential privacy threats and violations. In this paper, we present a
study performed over 186 teenage users aimed at analysing their behaviour when
we try to persuade them to modify their initial decisions in online social
networks with different arguments. The results of the study revealed that the
personality traits and the social interaction data (e.g., number of comments,
friends, and likes) of our participants were significantly correlated with the
persuasive power of the arguments. The findings from this analysis will help to
model OSN users and define persuasive strategies for argumentation dialogues in
this domain.


Abstract:
  It follows from a theorem of Rosenthal that a compact space is $ccc$ if and
only if every Eberlein continuous image is metrizable. Motivated by this
result, for a class of compact spaces $\mathcal{C}$ we define its orthogonal
$\mathcal{C}^\perp$ as the class of all compact spaces for which every
continuous image in $\mathcal{C}$ is metrizable. We study how this operation
relates classes where centeredness is scarce with classes where it is abundant
(like Eberlein and $ccc$ compacta), and also classes where independence is
scarce (most notably weakly Radon-Nikod\'ym compacta) with classes where it is
abundant. We study these problems for zero-dimensional compact spaces with the
aid of Boolean algebras and show the main difficulties arising when passing to
the general setting. Our main results are the constructions of several relevant
examples.


Abstract:
  The interplay between electronic interactions and strong spin-orbit coupling
is expected to create a plethora of fascinating correlated topological states
of quantum matter. Of particular interest are magnetic Weyl semimetals
originally proposed in the pyrochlore iridates, which are only expected to
reveal their topological nature in thin film form. To date, however, direct
experimental demonstrations of these exotic phases remain elusive, due to the
lack of usable single crystals and the insufficient quality of available films.
Here, we report on the discovery of the long-sought magnetic Weyl semi-metallic
phase in (111)-oriented Eu$_2$Ir$_2$O$_7$ high-quality epitaxial thin films.
The topological magnetic state shows an intrinsic anomalous Hall effect with
colossal coercivity but vanishing net magnetization, which emerges below the
onset of a peculiar magnetic phase with all-in-all-out antiferromagnetic
ordering. The observed anomalous Hall conductivity arises from the non-zero
Berry curvature emanated by Weyl node pairs near the Fermi level that act as
sources and sinks of Berry flux, activated by broken cubic crystal symmetry at
the top and bottom terminations of the thin film.


Abstract:
  Signal transduction is an important and basic mechanism to cell life
activities. The stochastic state transition of receptor induces the release of
signaling molecular, which triggers the state transition of other receptors. It
constructs a nonlinear sigaling network, and leads to robust switchlike
properties which are critical to biological function. Network architectures and
state transitions of receptor affect the performance of this biological
network. In this work, we perform a study of nonlinear signaling on biological
polymorphic network by analyzing network dynamics of the Ca$^{2+}$ induced
Ca$^{2+}$ release mechanism, where fast and slow processes are involved and the
receptor has four conformational states. Three types of networks,
Erd\"os-R\'enyi network, Watts-Strogatz network and BaraB\'asi-Albert network,
are considered with different parameters. The dynamics of the biological
networks exhibit different patterns at different time scales. At short time
scale, the second open state is essential to reproduce the quasi-bistable
regime, which emerges at a critical strength of connection for all three states
involved in the fast processes and disappears at another critical point. The
pattern at short time scale is not sensitive to the network architecture. At
long time scale, only monostable regime is observed, and difference of network
architectures affects the results more seriously. Our finding identifies
features of nonlinear signaling networks with multistate that may underlie
their biological function.


Abstract:
  We consider a power transmission system monitored with Phasor Measurement
Units (PMUs) placed at significant, but not all, nodes of the system. Assuming
that a sufficient number of distinct single-line faults, specifically pre-fault
state and (not cleared) post-fault state, are recorded by the PMUs and are
available for training, we, first, design a comprehensive sequence of Neural
Networks (NNs) locating the faulty line. Performance of different NNs in the
sequence, including Linear Regression, Feed-Forward NN, AlexNet, Graphical
Convolutional NN, Neural Linear ODE and Neural Graph-based ODE, ordered
according to the type and amount of the power flow physics involved, are
compared for different levels of observability. Second, we build a sequence of
advanced Power-System-Dynamics-Informed and Neural-ODE based Machine Learning
schemes trained, given pre-fault state, to predict the post-fault state and
also, in parallel, to estimate system parameters. Finally, third, and
continuing to work with the first (fault localization) setting we design a
(NN-based) algorithm which discovers optimal PMU placement.


Abstract:
  We investigate the electromagnetic transitions of the singly charmed baryons
with spin 3/2, based on a pion mean-field approach, also known as the chiral
quark-soliton model, taking into account the rotational $1/N_c$ corrections and
the effects of flavor SU(3) symmetry breaking. We examine the valence- and
sea-quark contributions to the electromagnetic transition form factors and find
that the quadrupole form factors of the sea-quark contributions dominate over
those of the valence-quark ones in the smaller $Q^2$ region, whereas the sea
quarks only provide marginal contributions to the magnetic dipole transition
form factors of the baryon sextet with spin 3/2. The effects of the flavor
SU(3) symmetry breaking are in general very small except for the forbidden
transition $\Xi_c^0\gamma\to \Xi_c^{*0}$ by $U$-spin symmetry. We also discuss
the widths of the radiative decays for the baryon sextet with spin 3/2,
comparing the present results with those from other works.


Abstract:
  The recent realisations of hydrogen doped $Ln$FeAsO ($Ln$=Nd and Sm)
superconducting epitaxial thin films call for further investigation of their
structural and electrical transport properties. Here, we report on the
microstructure of a NdFeAs(O,H) epitaxial thin film and its temperature, field,
and orientation dependencies of the resistivity and the critical current
density $J_{\rm c}$. The superconducting transition temperature $T_{\rm c}$ is
comparable to NdFeAs(O,F). Transmission electron microscopy investigation
supported that hydrogen is homogenously substituted for oxygen. A high
self-field $J_{\rm c}$ of over 10 MA/cm$^2$ was recorded at 5 K, which is
likely to be caused by a short London penetration depth. The anisotropic
Ginzburg-Landau scaling for the angle dependence of $J_{\rm c}$ yielded
temperature-dependent scaling parameters $\gamma_{\rm J}$ that decreased from
1.6 at 30 K to 1.3 at 5 K. This is opposite to the behaviour of NdFeAs(O,F).
Additionally, $\gamma_{\rm J}$ of NdFeAs(O,H) is smaller than that of
NdFeAs(O,F). Our results indicate that heavily electron doping by means of
hydrogen substitution for oxygen in $Ln$FeAsO is highly beneficial for
achieving high $J_{\rm c}$ with low anisotropy without compromising $T_{\rm
c}$, which is favourable for high-field magnet applications.


Abstract:
  Triangle centrality is introduced for finding important vertices in a graph
based on the concentration of triangles surrounding each vertex. An important
vertex in triangle centrality is at the center of many triangles, and therefore
it may be in many triangles or none at all.
  We give optimal algorithms that compute triangle centrality in $O(m\sqrt{m})$
time and $O(m+n)$ space. Using fast matrix multiplication it takes
$n^{\omega+o(1)}$ time where $\omega$ is the matrix product exponent.
  On a Concurrent Read Exclusive Write (CREW) Parallel Random Access Memory
(PRAM) machine, we give a near work-optimal algorithm that takes $O(\log n)$
time using $O(m\sqrt{m})$ CREW PRAM processors. In MapReduce, we show it takes
four rounds using $O(m\sqrt{m})$ communication bits, and is therefore optimal.
  We also give a deterministic algorithm to find the triangle neighborhood and
triangle count of each vertex in $O(m\sqrt{m})$ time and $O(m+n)$ space.
  Our empirical results demonstrate that triangle centrality uniquely
identified central vertices thirty-percent of the time in comparison to five
other well-known centrality measures, while being asymptotically faster to
compute on sparse graphs than all but the most trivial of these other measures.


Abstract:
  We present a novel counterfactual framework for both Zero-Shot Learning (ZSL)
and Open-Set Recognition (OSR), whose common challenge is generalizing to the
unseen-classes by only training on the seen-classes. Our idea stems from the
observation that the generated samples for unseen-classes are often out of the
true distribution, which causes severe recognition rate imbalance between the
seen-class (high) and unseen-class (low). We show that the key reason is that
the generation is not Counterfactual Faithful, and thus we propose a faithful
one, whose generation is from the sample-specific counterfactual question: What
would the sample look like, if we set its class attribute to a certain class,
while keeping its sample attribute unchanged? Thanks to the faithfulness, we
can apply the Consistency Rule to perform unseen/seen binary classification, by
asking: Would its counterfactual still look like itself? If ``yes'', the sample
is from a certain class, and ``no'' otherwise. Through extensive experiments on
ZSL and OSR, we demonstrate that our framework effectively mitigates the
seen/unseen imbalance and hence significantly improves the overall performance.
Note that this framework is orthogonal to existing methods, thus, it can serve
as a new baseline to evaluate how ZSL/OSR models generalize. Codes are
available at https://github.com/yue-zhongqi/gcm-cf.


Abstract:
  The excitation ansatz for tensor networks is a powerful tool for simulating
the low-lying quasiparticle excitations above ground states of strongly
correlated quantum many-body systems. Recently, the two-dimensional tensor
network class of infinite entangled pair states gained new ground state
optimization methods based on automatic differentiation, which are at the same
time highly accurate and simple to implement. Naturally, the question arises
whether these new ideas can also be used to optimize the excitation ansatz,
which has recently been implemented in two dimensions as well. In this paper,
we describe a straightforward way to reimplement the framework for excitations
using automatic differentiation, and demonstrate its performance for the
Hubbard model at half filling.


Abstract:
  Accurate prediction of machining cycle times is important in the
manufacturing industry. Usually, Computer Aided Manufacturing (CAM) software
estimates the machining times using the commanded feedrate from the toolpath
file using basic kinematic settings. Typically, the methods do not account for
toolpath geometry or toolpath tolerance and therefore under estimate the
machining cycle times considerably. Removing the need for machine specific
knowledge, this paper presents a data-driven feedrate and machining cycle time
prediction method by building a neural network model for each machine tool
axis. In this study, datasets composed of the commanded feedrate, nominal
acceleration, toolpath geometry and the measured feedrate were used to train a
neural network model. Validation trials using a representative industrial thin
wall structure component on a commercial machining centre showed that this
method estimated the machining time with more than 90% accuracy. This method
showed that neural network models have the capability to learn the behavior of
a complex machine tool system and predict cycle times. Further integration of
the methods will be critical in the implantation of digital twins in Industry
4.0.


Abstract:
  Modern deep neural networks (DNNs) become frail when the datasets contain
noisy (incorrect) class labels. Robust techniques in the presence of noisy
labels can be categorized into two folds: developing noise-robust functions or
using noise-cleansing methods by detecting the noisy data. Recently,
noise-cleansing methods have been considered as the most competitive
noisy-label learning algorithms. Despite their success, their noisy label
detectors are often based on heuristics more than a theory, requiring a robust
classifier to predict the noisy data with loss values. In this paper, we
propose a novel detector for filtering label noise. Unlike most existing
methods, we focus on each data's latent representation dynamics and measure the
alignment between the latent distribution and each representation using the
eigendecomposition of the data gram matrix. Our framework, coined as filtering
noisy instances via their eigenvectors (FINE), provides a robust detector with
derivative-free simple methods having theoretical guarantees. Under our
framework, we propose three applications of the FINE: sample-selection
approach, semi-supervised learning approach, and collaboration with
noise-robust loss functions. Experimental results show that the proposed
methods consistently outperform corresponding baselines for all three
applications on various benchmark datasets.


Abstract:
  Climate change is a burning issue of our time, with the Sustainable
Development Goal (SDG) 13 of the United Nations demanding global climate
action. Realizing the urgency, in 2015 in Paris, world leaders signed an
agreement committing to taking voluntary action to reduce carbon emissions.
However, the scale, magnitude, and climate action processes vary globally,
especially between developed and developing countries. Therefore, from
parliament to social media, the debates and discussions on climate change
gather data from wide-ranging sources essential to the policy design and
implementation. The downside is that we do not currently have the mechanisms to
pool the worldwide dispersed knowledge emerging from the structured and
unstructured data sources.
  The paper thematically discusses how NLP techniques could be employed in
climate policy research and contribute to society's good at large. In
particular, we exemplify symbiosis of NLP and Climate Policy Research via four
methodologies. The first one deals with the major topics related to climate
policy using automated content analysis. We investigate the opinions
(sentiments) of major actors' narratives towards climate policy in the second
methodology. The third technique explores the climate actors' beliefs towards
pro or anti-climate orientation. Finally, we discuss developing a Climate
Knowledge Graph.
  The present theme paper further argues that creating a knowledge platform
would help in the formulation of a holistic climate policy and effective
climate action. Such a knowledge platform would integrate the policy actors'
varied opinions from different social sectors like government, business, civil
society, and the scientific community. The research outcome will add value to
effective climate action because policymakers can make informed decisions by
looking at the diverse public opinion on a comprehensive platform.


Abstract:
  We exploit a recent computational framework to model and detect financial
crises in stock markets, as well as shock events in cryptocurrency markets,
which are characterized by a sudden or severe drop in prices. Our method
manages to detect all past crises in the French industrial stock market
starting with the crash of 1929, including financial crises after 1990 (e.g.
dot-com bubble burst of 2000, stock market downturn of 2002), and all past
crashes in the cryptocurrency market, namely in 2018, and also in 2020 due to
covid-19. We leverage copulae clustering, based on the distance between
probability distributions, in order to validate the reliability of the
framework; we show that clusters contain copulae from similar market states
such as normal states, or crises. Moreover, we propose a novel regression model
that can detect successfully all past events using less than 10% of the
information that the previous framework requires. We train our model by
historical data on the industry assets, and we are able to detect all past
shock events in the cryptocurrency market. Our tools provide the essential
components of our software framework that offers fast and reliable detection,
or even prediction, of shock events in stock and cryptocurrency markets of
hundreds of assets.


Abstract:
  A substantial fraction of Cataclysmic Variables (CVs) reveals non-solar
abundances. A comprehensive list of CVs which includes those that have been
examined for these abundances is given. Three possible sources of these
non-solar abundances on the secondary are accretion during the red giant common
envelope phase, an Evolved Main Sequence secondary and nova-processed material.
Use of the secondary's cross-section just on the escaping nova material to
change the abundances of its convective region has been the killing objection
for considering nova-processed material. The key element, ignored in other
studies, is that a thermonuclear runaway on a white dwarf causes a strong
propagating shock wave which not only ejects material, but also produces a
large amount of non-ejected material which forms a common envelope. This
nova-produced common envelope contains a large amount of non-solar material. We
demonstrate that the secondary has the capacity and time to re-accrete enough
of this material to acquire a significant non-solar convective region. This
same envelope interacting with the binary will produce a Frictional Angular
Momentum Loss which can be the Consequential Angular Momentum Loss needed for
the average CV white dwarf mass, WD mass accretion rates, the period minimum,
the orbital period distribution, and the space density of CVs problems. This
interaction will decrease the orbital period which can cause the recently
observed sudden period decreases across nova eruptions. A simple, rapid
evolutionary model of the secondary that includes the swept-up nova-produced
material and the increasing convective region is developed and applied to
individual CVs.


Abstract:
  As known, the degree of entanglement of biphoton states with respect to the
transverse components of photon wave vectors (momenta) or coordinates can be
characterized either by the parameter K associated with the Schmidt
decompositions, or by the parameter R, defined as the ratio of the widths of
the unconditional and conditional single-particle distributions of biphoton
states. As entanglement is a fundamental characteristics of a state, it must be
independent of a choice of its different representations, i.e. its entanglement
parameters must be identical in the coordinate and momentum representations.
Likewise, entanglement itself and its characterization parameters must remain
constant along the photon propagation length. It's known, however, that only
the parameter K obeys these requirements, whereas the parameter R in the
coordinate representation deviates from K beyond the near zone and, thus,
becomes inapplicable as a measure of the degree of entablement. But, as shown
below, there is a way of saving the parameter R by means of modifying its
definition in such a way that the modified parameter R turns out to be as good
as the parameter K both in the momentum and coordinate representations, as well
as in the near and far zones and everywhere between them. We will discuss also
possible ways of measuring this newly defined parameter R experimentally.


Abstract:
  When an expert operates a perilous dynamic system, ideal constraint
information is tacitly contained in their demonstrated trajectories and
controls. The likelihood of these demonstrations can be computed, given the
system dynamics and task objective, and the maximum likelihood constraints can
be identified. Prior constraint inference work has focused mainly on
deterministic models. Stochastic models, however, can capture the uncertainty
and risk tolerance that are often present in real systems of interest.
  This paper extends maximum likelihood constraint inference to stochastic
applications by using maximum causal entropy likelihoods. Furthermore, we
propose an efficient algorithm that computes constraint likelihood and risk
tolerance in a unified Bellman backup, allowing us to generalize to stochastic
systems without increasing computational complexity.


Abstract:
  Background: Measurement errors in terms of quantification or classification
frequently occur in epidemiologic data and can strongly impact inference.
Measurement errors may occur when ascertaining, recording or extracting data.
Although the effects of measurement errors can be severe and are well
described, simple straight forward general analytic solutions are not readily
available for statistical analysis and measurement error is frequently not
acknowledged or accounted for. Generally, to account for measurement error
requires some data where we can observe the variables once with and once
without error, to establish the relationship between the two. Methods: Here we
describe a general method accounting for measurement error in outcome and/or
predictor variables for the parametric regression setting when there is a
validation subsample where variables are measured once with and once without
error. The method does not describe and thus does not depend on the particular
relation between the variables measured with and without error, and is
generally robust to the type of measurement error, for example nondifferential,
differential or Berkson errors. Results: Simulation studies show how the method
reduces bias compared to models based upon variables measured with error alone
and reduces variances compared to models based upon the variables measured
without error in the validation subsample alone. Conclusion: The proposed
estimator has favorable properties in terms of bias and variance, is easily
derived empirically, and is robust to different types of measurement error.
This method should be a valuable tool in the analysis of data with measurement
error.


Abstract:
  The estimation of functions with varying degrees of smoothness is a
challenging problem in the nonparametric function estimation. In this paper, we
propose the LABS (L\'{e}vy Adaptive B-Spline regression) model, an extension of
the LARK models, for the estimation of functions with varying degrees of
smoothness. LABS model is a LARK with B-spline bases as generating kernels. The
B-spline basis consists of piecewise k degree polynomials with k-1 continuous
derivatives and can express systematically functions with varying degrees of
smoothness. By changing the orders of the B-spline basis, LABS can
systematically adapt the smoothness of functions, i.e., jump discontinuities,
sharp peaks, etc. Results of simulation studies and real data examples support
that this model catches not only smooth areas but also jumps and sharp peaks of
functions. The proposed model also has the best performance in almost all
examples. Finally, we provide theoretical results that the mean function for
the LABS model belongs to the certain Besov spaces based on the orders of the
B-spline basis and that the prior of the model has the full support on the
Besov spaces.


Abstract:
  We present an estimation of lifetimes of massive galaxies with distinct UV
colors at $0.5 \le z \le 2.5$ in the COSMOS/UltraVISTA field. After dividing
the galaxy sample into subsamples of red sequence (RS), blue cloud (BC), and
green valley (GV) galaxies in different redshift bins, according to their
rest-frame extinction-corrected UV colors, we derive their lifetimes using
clustering analyses. Several essentials that may influence the lifetime
estimation have been explored, including the dark matter (DM) halo mass
function (HMF), the width of redshift bin, the growth of DM halos within each
redshift bin, and the stellar mass. We find that the HMF difference results in
scatters of $\sim0.2$ dex on lifetime estimation; adopting a redshift bin width
of $\Delta z = 0.5$ is good enough to estimate the lifetime; and no significant
effect on lifetime estimation is found due to the growth of DM halos within
each redshift bin. The galaxy subsamples with higher stellar masses generally
have shorter lifetimes; however, the lifetimes among different subsamples at z
> 1:5 tend to be independent of stellar mass. Consistently, the
clustering-based lifetime for each galaxy subsample agrees well with that
inferred using the spectral energy distribution modeling. Moreover, the
lifetimes of the RS and BC galaxies also coincide well with their typical gas
depletion timescales attributed to the consumption of star formation.
Interestingly, the distinct lifetime behaviors of the GV galaxies at $z \le
1.5$ and $z>1.5$ can not be fully accounted for by their gas depletion
timescales. Instead, this discrepancy between the lifetimes and gas depletion
timescales of the GV galaxies suggests that there are additional physical
processes, such as feedback of active galactic nuclei, accelerating the
quenching of GV galaxies at high redshifts.


Abstract:
  The iterative and incremental nature of software development using models
typically makes a model of a system incomplete (i.e., partial) until a more
advanced and complete stage of development is reached. Existing model execution
approaches (interpretation of models or code generation) do not support the
execution of partial models. Supporting the execution of partial models at the
early stages of software development allows early detection of defects, which
can be fixed more easily and at a lower cost. This paper proposes a conceptual
framework for the execution of partial models, which consists of three steps:
static analysis, automatic refinement, and input-driven execution. First, a
static analysis that respects the execution semantics of models is applied to
detect problematic elements of models that cause problems for the execution.
Second, using model transformation techniques, the models are refined
automatically, mainly by adding decision points where missing information can
be supplied. Third, refined models are executed, and when the execution reaches
the decision points, it uses inputs obtained either interactively or by a
script that captures how to deal with partial elements. We created an execution
engine called PMExec for the execution of partial models of UML-RT (i.e., a
modeling language for the development of soft real-time systems) that embodies
our proposed framework. We evaluated PMExec based on several use-cases that
show that the static analysis, refinement, and application of user input can be
carried out with reasonable performance and that the overhead of approach,
which is mostly due to the refinement and the increase in model complexity it
causes, is manageable. We also discuss the properties of the refinement
formally and show how the refinement preserves the original behaviors of the
model.


Abstract:
  The author makes use of infinite compositions and a limiting function to
construct a $\mathcal{C}^\infty$ tetration function $\mathcal{F}(t) = e \tet
t$. As a tetration function, $\mathcal{F}$ satisfies $e^{\mathcal{F}(t)} =
\mathcal{F}(t+1)$. Of it, $\mathcal{F}$ takes $(-2,\infty) \to \mathbb{R}$
bijectively with strictly monotone growth, and is continuously differentiable
here. We then iterate this construction to derive arbitrary hyper-operations
$e\up^k t$. These hyper-operations are $\mathcal{C}^\infty$ strictly monotone
bijections of $(\alpha_k,\infty) \to \mathbb{R}$ for $k$ even ($1-k > \alpha_k
\ge -k$), and $\mathcal{C}^\infty$ strictly monotone bijections of $\mathbb{R}
\to (\alpha_k, \infty)$ for $k$ odd. These hyper-operations satisfy the
functional equation $e \up^{k-1} (e \up^k t) = e \up^k (t+1)$ with the initial
conditions $e \up^1 t = e^t$ and $e \up^k 0 = 1$.


Abstract:
  We use a sample of 809 photometrically classified type Ia supernovae (SNe Ia)
discovered by the Dark Energy Survey (DES) along with 40415 field galaxies to
calculate the rate of SNe Ia per galaxy in the redshift range $0.2 < z <0.6$.
We recover the known correlation between SN Ia rate and galaxy stellar mass
across a broad range of scales $8.5 \leq \log(M_*/\mathrm{M}_{\odot}) \leq
11.25$. We find that the SN Ia rate increases with stellar mass as a power-law
with index $0.63 \pm 0.02$, which is consistent with previous work. We use an
empirical model of stellar mass assembly to estimate the average star-formation
histories (SFHs) of galaxies across the stellar mass range of our measurement.
Combining the modelled SFHs with the SN Ia rates to estimate constraints on the
SN Ia delay time distribution (DTD), we find the data are fit well by a
power-law DTD with slope index $\beta = -1.13 \pm 0.05$ and normalisation $A =
2.11 \pm0.05 \times
10^{-13}~\mathrm{SNe}~{\mathrm{M}_{\odot}}^{-1}~\mathrm{yr}^{-1}$, which
corresponds to an overall SN Ia production efficiency $N_{\mathrm{Ia}}/M_* =
0.9~_{-0.7}^{+4.0} \times 10^{-3}~\mathrm{SNe}~\mathrm{M}_{\odot}^{-1}$. Upon
splitting the SN sample by properties of the light curves, we find a strong
dependence on DTD slope with the SN decline rate, with slower-declining SNe
exhibiting a steeper DTD slope. We interpret this as a result of a relationship
between intrinsic luminosity and progenitor age, and explore the implications
of the result in the context of SN Ia progenitors.


Abstract:
  The ring of q-character polynomials is a q-analog of the classical ring of
character polynomials for the symmetric groups. This ring consists of certain
class functions defined simultaneously on the groups $Gl_n(F_q)$ for all n,
which we also interpret as statistics on matrices. Here we evaluate these
statistics on all matrices and work towards computing the structure constants
of the product in this ring. We show that the statistics are periodically
polynomial in q, and governed by universal polynomials $P_{\lambda,\mu}(q)$
which we compute explicitly, indexed by pairs of integer partitions. The
product structure is similarly polynomial in q in many cases, governed by
polynomials $R_{\lambda,\mu}^{\nu}(q)$ indexed by triples of partitions, which
we compute in some cases.
  Our calculations seem to exhibit several unexpected patterns. Mainly, we
conjecture that certain indecomposable statistics generate the whole ring, and
indeed prove this for statistics associated with matrices consisting of up to 2
Jordan blocks. Furthermore, the coefficients we compute exhibit surprising
stability phenomena, which in turn reflect stabilizations of joint moments as
well as multiplicities in the irreducible decomposition of tensor products of
representations of $Gl_n(F_q)$ for $n\gg 1$. We use this stabilization to
compute the correlation of the number of unipotent Jordan blocks of two sizes.


Abstract:
  When modeling superconducting devices based on REBCO tapes andworking near or
above the critical current value (i.e. I> Ic), the power-law model is not
always accurate. In our previous works, we proposed the overcritical current
model, based on a combination of fast pulsed current measurements and finite
elementan alysis. The overcritical current model was provided in the form of
look-up tables and was validated experimentally. We showed that the
overcritical current model could better reproduce experimental measurements
than the power-law model, and that the power-law model predicts a faster quench
than the overcritical current model. In this contribution, we propose the
eta-beta model, a mathematical expression to model analytically the
overcritical current model, based on measurements performed between 77 K and 90
K in self-field conditions. The proposed model is verified by comparing DC
fault measurements with the results of numerical simulations using the eta-beta
model to represent the electrical resistivity of the superconducting layer of
REBCO tapes.


Abstract:
  We introduce an exact numerical technique to solve the nuclear pairing
Hamiltonian and to determine properties such as the even-odd mass differences
or spectral functions for any element within the periodic table for any number
of nuclear shells. In particular, we show that the nucleus is a system with
small entanglement and can thus be described efficiently using a
one-dimensional tensor network (matrix-product state) despite the presence of
long-range interactions. Our approach is numerically cheap and accurate to
essentially machine precision, even for large nuclei.
  We apply this framework to compute the even-odd mass differences of all known
lead isotopes from $^{178}$Pb to $^{220}$Pb in the very large configuration
space of 13 shells between the neutron magic numbers 82 and 184 (i.e., two
major shells) and find good agreement with the experiment. To go beyond the
ground state, we calculate the two-neutron removal spectral function of
$^{210}$Pb which relates to a two-neutron pickup experiment that probes
neutron-pair excitations across the gap of $^{208}$Pb. Finally, we discuss the
capabilities of our method to treat pairing with non-zero angular momentum.
This is numerically more demanding, but one can still determine the lowest
excited states in the full configuration space of one major shell with modest
effort, which we demonstrate for the $N=126$, $Z\geq 82$ isotones.


Abstract:
  A treatment of many-electron polar and $s-d(f)$ exchange models is carried
out in connection with the development of the theory of magnetism of transition
and rare-earth metals, as well as their compounds. Particular emphasis is
placed on the derivation of the many-electron Heisenberg, Hubbard, Anderson and
$t-J$ models, as well as the internal relationship between them. Among the
topics discussed are many-electron approaches in describing systems of $d$- and
$f$-electrons, atomic representation of Hubbard's $X$-operators, slave-particle
representations, the problem of strong itinerant magnetism and the formation of
local moments, the role of non-quasiparticle (incoherent states). The
application of these concepts to strongly correlated systems, in particular, to
half-metallic ferromagnets and Kondo lattices, is discussed. A comparison is
performed with modern field-theoretical and topological approaches.


Abstract:
  In this paper, we derive the symmetry operators ($J$'s) in the asymmetric
two-photon quantum Rabi models in terms of Bogoliubov operator approaches. $
J^2$ can be expressed as a polynomial in terms of the Hamiltonian, which
uncovers the $\mathbb{Z}_{2}$ nature of the hidden symmetry in this two-photon
model rigorously. The previous symmetry operators in the asymmetric one-photon
quantum Rabi models are reproduced readily in terms of Bogoliubov operator
approaches, and the obtained operators are expressed much more concisely. It is
found that the polynomial degree of $J^2$ in the two-photon model is twice of
that in the one-photon model.


Abstract:
  Artificially created two-dimensional (2D) interfaces or structures are ideal
for seeking exotic phase transitions due to their highly tunable carrier
density and interfacially enhanced many-body interactions. Here, we report the
discovery of a metal-insulator transition (MIT) and an emergent gapped phase in
the metal-semiconductor interface that is created in 2H-MoTe$_2$ via
alkali-metal deposition. Using angle-resolved photoemission spectroscopy, we
found that the electron-phonon coupling is strong at the interface as
characterized by a clear observation of replica shake-off bands. Such strong
electron-phonon coupling interplays with disorder scattering, leading to an
Anderson localization of polarons which could explain the MIT. The domelike
emergent gapped phase could then be attributed to a polaron extended state or
phonon-mediated superconductivity. Our results demonstrate the capability of
alkali-metal deposition as an effective method to enhance the many-body
interactions in 2D semiconductors. The surface-doped 2H-MoTe$_2$ is a promising
candidate for realizing polaronic insulator and high-$T_c$ superconductivity.


Abstract:
  How to leverage dynamic contextual information in end-to-end speech
recognition has remained an active research area. Previous solutions to this
problem were either designed for specialized use cases that did not generalize
well to open-domain scenarios, did not scale to large biasing lists, or
underperformed on rare long-tail words. We address these limitations by
proposing a novel solution that combines shallow fusion, trie-based deep
biasing, and neural network language model contextualization. These techniques
result in significant 19.5% relative Word Error Rate improvement over existing
contextual biasing approaches and 5.4%-9.3% improvement compared to a strong
hybrid baseline on both open-domain and constrained contextualization tasks,
where the targets consist of mostly rare long-tail words. Our final system
remains lightweight and modular, allowing for quick modification without model
re-training.


Abstract:
  Motivated by applications to congested traffic problems, we establish higher
integrability results for the gradient of local weak solutions to the strongly
degenerate or singular elliptic PDE $-\mathrm{div}\left((\vert\nabla
u\vert-1)_{+}^{q-1}\frac{\nabla u}{\vert\nabla u\vert}\right)=f$,
$\mathrm{in}\,\,\Omega$, where $\Omega$ is a bounded domain in $\mathbb{R}^{n}$
for $n\geq2$, $1<q<\infty$ and $\left(\,\cdot\,\right)_{+}$ stands for the
positive part. We assume that the datum $f$ belongs to a suitable Sobolev or
Besov space. The main novelty here is that we deal with the case of
subquadratic growth, i.e. $1<q<2$, which has so far been neglected. In the
latter case, we also prove the higher fractional differentiability of the
solution to a variational problem, which is characterized by the above
equation. For the sake of completeness, we finally give a Besov regularity
result also in the case $q\geq2$.


Abstract:
  Lumen formation plays an essential role in the morphogenesis of tissues
during development. Here we review the physical principles that play a role in
the growth and coarsening of lumens. Solute pumping by the cell, hydraulic
flows driven by differences of osmotic and hydrostatic pressures, balance of
forces between extracellular fluids and cell-generated cytoskeletal forces, and
electro-osmotic effects have been implicated in determining the dynamics and
steady-state of lumens. We use the framework of linear irreversible
thermodynamics to discuss the relevant force, time and length scales involved
in these processes. We focus on order of magnitude estimates of physical
parameters controlling lumen formation and coarsening.


Abstract:
  Human-in-the-loop machine learning is widely used in artificial intelligence
(AI) to elicit labels for data points from experts or to provide feedback on
how close the predicted results are to the target. This simplifies away all the
details of the decision-making process of the expert. In this work, we allow
the experts to additionally produce decision rules describing their
decision-making; the rules are expected to be imperfect but to give additional
information. In particular, the rules can extend to new distributions, and
hence enable significantly improving performance for cases where the training
and testing distributions differ, such as in domain adaptation. We apply the
proposed method to lifelong learning and domain adaptation problems and discuss
applications in other branches of AI, such as knowledge acquisition problems in
expert systems. In simulated and real-user studies, we show that decision rule
elicitation improves domain adaptation of the algorithm and helps to propagate
expert's knowledge to the AI model.


Abstract:
  Modern drug discovery is often time-consuming, complex and cost-ineffective
due to the large volume of molecular data and complicated molecular properties.
Recently, machine learning algorithms have shown promising results in virtual
screening of automated drug discovery by predicting molecular properties. While
emerging learning methods such as graph neural networks and recurrent neural
networks exhibit high accuracy, they are also notoriously computation-intensive
and memory-intensive with operations such as feature embeddings or deep
convolutions. In this paper, we propose a viable alternative to existing
learning methods by presenting \model, a method based on brain-inspired
hyperdimensional computing (HDC) for molecular property prediction. We develop
HDC encoders to project SMILES representation of a molecule into
high-dimensional vectors that are used for HDC training and inference. We
perform an extensive evaluation using 29 classification tasks from 3
widely-used molecule datasets (Clintox, BBBP, SIDER) under three splits methods
(random, scaffold, and stratified). By an comprehensive comparison with 8
existing learning models including SOTA graph/recurrent neural networks, we
show that MoleHD is able to achieve highest ROC-AUC score on random and
scaffold splits on average across 3 datasets and achieve second-highest on
stratified split. Importantly, MoleHD achieves such performance with
significantly reduced computing cost and training efforts. To the best of our
knowledge, this is the first HDC-based method for drug discovery. The promising
results presented in this paper can potentially lead to a novel path in drug
discovery research.


Abstract:
  One-shot learning is proposed to make a pretrained classifier workable on a
new dataset based on one labeled samples from each pattern. However, few of
researchers consider whether the dataset itself supports one-shot learning. In
this paper, we propose a set of definitions to explain what kind of datasets
can support one-shot learning and propose the concept "absolute
generalization". Based on these definitions, we proposed a method to build an
absolutely generalizable classifier. The proposed method concatenates two
samples as a new single sample, and converts a classification problem to an
identity identification problem or a similarity metric problem. Experiments
demonstrate that the proposed method is superior to baseline on one-shot
learning datasets and artificial datasets.


Abstract:
  In this article, the authors introduce the spaces of Lipschitz type on spaces
of homogeneous type in the sense of Coifman and Weiss, and discuss their
relations with Besov and Triebel-Lizorkin spaces. As an application, the
authors establish the difference characterization of Besov and Triebel-Lizorkin
spaces on spaces of homogeneous type. A major novelty of this article is that
all results presented in this article get rid of the dependence on the reverse
doubling assumption of the considered measure of the underlying space
${\mathcal X}$ via using the geometrical property of ${\mathcal X}$ expressed
by its dyadic reference points, dyadic cubes, and the (local) lower bound.
Moreover, some results when $p\le 1$ but near to $1$ are new even when
${\mathcal X}$ is an RD-space.


Abstract:
  We investigate the onset and evolution of zonal flows in a growing convective
layer when a stably-stratified fluid with a composition gradient is cooled from
above. This configuration allows the study of zonal flows for a wide range of
values of the Rayleigh number, $Ra$, and aspect ratio of the convection zone
within a given simulation. We perform a series of 2D simulations using the
Boussinesq approximation, with aspect ratio of the computational domain between
$1$ and $5$, and Prandtl number $Pr = 0.1$, 0.5, 1, and $7$. We find that for
square domains zonal flows appear when the aspect ratio of the convective layer
is smaller than two, and the evolution of the system depends on the Prandtl
number. For $Pr\leq 1$, the fluid experiences bursts of convective transport
with negligible convective transport between bursts. The magnitude and
frequency of the bursts are smaller at low $Pr$, which suggests that the
bursting regime is stronger in a narrow range around $Pr=1$, as observed in
previous studies of thermal convection. For $Pr=7$, the structure of the flow
consists of tilted convective plumes, and the convective transport is sustained
at all times. In wider domains, the aspect ratio of the convective zone is
always much larger than two and zonal flows do not appear. These results
confirm and extend to fluids with stable composition gradients previous
findings on thermal convection. The fact that zonal flows can be avoided by
using computational domains with large aspect ratios opens up the possibility
of 2D studies of convective overshoot, layer formation and transport properties
across diffusive interfaces.


Abstract:
  The Cartesian-tree pattern matching is a recently introduced scheme of
pattern matching that detects fragments in a sequential data stream which have
a similar structure as a query pattern. Formally, Cartesian-tree pattern
matching seeks all substrings $S'$ of the text string $S$ such that the
Cartesian tree of $S'$ and that of a query pattern $P$ coincide. In this paper,
we present a new indexing structure for this problem called the Cartesian-tree
Position Heap (CPH). Let $n$ be the length of the input text string $S$, $m$
the length of a query pattern $P$, and $\sigma$ the alphabet size. We show that
the CPH of $S$, denoted $\mathsf{CPH}(S)$, supports pattern matching queries in
$O(m (\sigma + \log (\min\{h, m\})) + occ)$ time with $O(n)$ space, where $h$
is the height of the CPH and $occ$ is the number of pattern occurrences. We
show how to build $\mathsf{CPH}(S)$ in $O(n \log \sigma)$ time with $O(n)$
working space. Further, we extend the problem to the case where the text is a
labeled tree (i.e. a trie). Given a trie $T$ with $N$ nodes, we show that the
CPH of $T$, denoted $\mathsf{CPH}(T)$, supports pattern matching queries on the
trie in $O(m (\sigma^2 + \log (\min\{h, m\})) + occ)$ time with $O(N \sigma)$
space. We also show a construction algorithm for $\mathsf{CPH}(T)$ running in
$O(N \sigma)$ time and $O(N \sigma)$ working space.


Abstract:
  We present a splitting method for the one-dimensional Saint-Venant-Exner
equations used for describing the bed evolution in shallow water systems. We
adapt the flux vector splitting approach of Toro and Vazquez-Cend\`on and
identify one subsystem of conservative equations (advection system) and one of
non-conservative equations (pressure system), both having a very simple
eigenstructure compared to the full system. The final numerical scheme is
constructed using a Godunov-type path-conservative scheme for the pressure
system and a simple conservative Godunov method for the advection system and
solved following a coupled solution strategy. The resulting first-order
accurate method is extended to second order of accuracy in space and time via
the ADER approach together with an AENO reconstruction technique. Accuracy,
robustness and well-balanced properties of the resulting scheme are assessed
through a carefully selected suite of testcases. The scheme is exceedingly
simple, accurate and robust as the sophisticated Godunov methods. A distinctive
feature of the novel scheme is its flexibility in the choice of the sediment
transport closure formula, which makes it particularly attractive for
scientific and engineering applications.


Abstract:
  We define an integer-valued virtual count of embedded pseudo-holomorphic
curves of two times a primitive homology class and arbitrary genus in
symplectic Calabi--Yau $3$-folds, which can be viewed as an extension of
Taubes' Gromov invariant. The construction depends on a detailed study of
bifurcations of moduli spaces of embedded pseudo-holomorphic curves which is
partially motivated by Wendl's recent solution of Bryan--Pandharipande's
super-rigidity conjecture.


Abstract:
  By treating black hole as the macroscopic stable state on the free energy
landscape, we propose that the stochastic dynamics of the black hole phase
transition can be effectively described by the Langevin equation or
equivalently by the Fokker-Planck equation in phase space. We demonstrate the
turnover of the kinetics for the charged anti-de Sitter black hole phase
transition, which shows that the mean first passage time is linear with the
friction in the high damping regime and inversely proportional to the friction
in the low damping regime. The fluctuations in the kinetics are shown to be
large/small in the high/low damping regime and the switching behavior from the
small fluctuations to the large fluctuations takes place at the kinetic
turnover point. Because the friction is a reflection of the microscopic degrees
of freedom acting on the order parameter of the black hole, the turnover and
the corresponding fluctuations of the phase transition kinetics can be used to
probe the black hole microstructure.


Abstract:
  Lattice results on sigma terms and global analysis of parton momentum
fractions are used to give the quark and glue fractions of the proton mass and
rest energy. The mass decomposition in terms of the trace of the
energy-momentum tensor is renormalization group invariant. The decomposition of
the rest energy from the Hamiltonian and the gravitational form factors are
scheme and scale dependent. The separation of the energy-momentum tensor into
the traceless part which is composed of the quark and glue parton momentum
fractions and the trace part has the minimum scheme dependence.
  We identify the glue part of the trace anomaly $\langle H_{\beta}\rangle $ as
the vacuum energy from the glue condensate in the vacuum. From the metric term
of the gravitational form factors, which is the stress part of the
stress-energy-momentum tensor, we find that the trace part of the rest energy,
dominated by $\langle H_{\beta}\rangle$, gives a {\it constant} restoring
pressure which balances that from the traceless part of the Hamiltonian to
confine the hadron, much like the cosmological constant Einstein introduced for
a static universe. From a lattice calculation of $\langle H_{\beta}\rangle$ in
the charmonium, we deduce the associated string tension which turns out to be
in good agreement with that from a Cornell potential which fits the charmonium
spectrum.


Abstract:
  As an alternative to traditional fault injection-based methodologies and to
explore the applicability of modern machine learning algorithms in the field of
reliability engineering, this paper proposes a systemic framework that explores
gate-level netlist circuit abstractions to extract and exploit relevant feature
representations in a low-dimensional vector space. A scalable feature learning
method on a graphical domain called node2vec algorithm had been utilized for
efficiently extracting structural features of the netlist, providing a valuable
database to exercise a selection of machine learning (ML) or deep learning (DL)
algorithms aiming at predicting fault propagation metrics. The current work
proposes to model the gate-level netlist as a Probabilistic Bayesian Graph
(PGB) in the form of a Graph Modeling Language (GML) format. To accomplish this
goal, a Verilog Procedural Interface (VPI) library linked to standard
simulation tools has been built to map gate-level netlist into the graph model.
The extracted features have been used for predicting the Functional Derating
(FDR) factors of individual flip-flops of a given circuit through Support
Vector Machine (SVM) and Deep Neural Network (DNN) algorithms. The results of
the approach have been compared against data obtained through first-principles
approaches. The whole experiment was implemented on the features extracted from
the 10-Gigabit Ethernet MAC IEEE 802.3 standard circuit.


Abstract:
  Annotating a large-scale in-the-wild person re-identification dataset
especially of marathon runners is a challenging task. The variations in the
scenarios such as camera viewpoints, resolution, occlusion, and illumination
make the problem non-trivial. Manually annotating bounding boxes in such
large-scale datasets is cost-inefficient. Additionally, due to crowdedness and
occlusion in the videos, aligning the identity of runners across multiple
disjoint cameras is a challenge. We collected a novel large-scale in-the-wild
video dataset of marathon runners. The dataset consists of hours of recording
of thousands of runners captured using 42 hand-held smartphone cameras and
covering real-world scenarios. Due to the presence of crowdedness and occlusion
in the videos, the annotation of runners becomes a challenging task. We propose
a new scheme for tackling the challenges in the annotation of such large
dataset. Our technique reduces the overall cost of annotation in terms of time
as well as budget. We demonstrate performing fps analysis to reduce the effort
and time of annotation. We investigate several annotation methods for
efficiently generating tight bounding boxes. Our results prove that
interpolating bounding boxes between keyframes is the most efficient method of
bounding box generation amongst several other methods and is 3x times faster
than the naive baseline method. We introduce a novel way of aligning the
identity of runners in disjoint cameras. Our inter-camera alignment tool
integrated with the state-of-the-art person re-id system proves to be
sufficient and effective in the alignment of the runners across multiple
cameras with non-overlapping views. Our proposed framework of annotation
reduces the annotation cost of the dataset by a factor of 16x, also effectively
aligning 93.64% of the runners in the cross-camera setting.


Abstract:
  Ultrafunctions are a particular class of functions defined on a Non
Archimedean field E. They have been introduced and studied in some previous
works. In this paper we develop the notion of fine ultrafunctions which
improves the older definitions in many crucial points. Some applications are
given to show how ultrafunctions can be applied in studing Partial Differential
Equations. In particular, it is possible to prove the existence of
ultrafunction solutions to ill posed evolution poblems.


Abstract:
  In scientific paper, we will show a proof of Legendre's conjecture based on a
scheme for finding elements of "active" set ${\rm H}_{m^{4}}$ and "critical"
element $C_{m^{4}}$ for number $m^{4}$ at each $m \ge 3$.


Abstract:
  We consider the hadroproduction of a massive charged lepton plus the
corresponding neutrino through the Drell-Yan mechanism. We present a new
computation of the mixed QCD-EW corrections to this process. The cancellation
of soft and collinear singularities is achieved by using a formulation of the
$q_T$ subtraction formalism derived from the next-to-next-to-leading order QCD
calculation for heavy-quark production. For the first time, all the real and
virtual contributions due to initial- and final-state radiation are
consistently included without any approximation, except for the finite part of
the two-loop virtual correction, which is computed in the pole approximation
and suitably improved through a reweighting procedure. We demonstrate that our
calculation is reliable in both on-shell and off-shell regions, thereby
providing the first prediction of the mixed QCD-EW corrections in the entire
region of the lepton transverse momentum. The computed corrections are in
qualitative agreement with what we obtain in a factorised approach of QCD and
EW corrections. At large values of the lepton $p_T$, the mixed QCD-EW
corrections are negative and increase in size, to about $-20\%$ with respect to
the next-to-leading order QCD result at $p_T=500\,$GeV.


Abstract:
  The recursive model index (RMI) has recently been introduced as a
machine-learned replacement for traditional indexes over sorted data, achieving
remarkably fast lookups. Follow-up work focused on explaining RMI's performance
and automatically configuring RMIs through enumeration. Unfortunately,
configuring RMIs involves setting several hyperparameters, the enumeration of
which is often too time-consuming in practice. Therefore, in this work, we
conduct the first inventor-independent broad analysis of RMIs with the goal of
understanding the impact of each hyperparameter on performance. In particular,
we show that in addition to model types and layer size, error bounds and search
algorithms must be considered to achieve the best possible performance. Based
on our findings, we develop a simple-to-follow guideline for configuring RMIs.
We evaluate our guideline by comparing the resulting RMIs with a number of
state-of-the-art indexes, both learned and traditional. We show that our simple
guideline is sufficient to achieve competitive performance with other learned
indexes and RMIs whose configuration was determined using an expensive
enumeration procedure. In addition, while carefully reimplementing RMIs, we are
able to improve the build time by 2.5x to 6.3x.


Abstract:
  We improve on a result by Svetlana Jitomirskaya and Wencai Liu dealing with
inhomogeneous Diophantine approximation in the coprime setting.


Abstract:
  Spark NLP is a Natural Language Processing (NLP) library built on top of
Apache Spark ML. It provides simple, performant and accurate NLP annotations
for machine learning pipelines that can scale easily in a distributed
environment. Spark NLP comes with 1100 pre trained pipelines and models in more
than 192 languages. It supports nearly all the NLP tasks and modules that can
be used seamlessly in a cluster. Downloaded more than 2.7 million times and
experiencing nine times growth since January 2020, Spark NLP is used by 54% of
healthcare organizations as the worlds most widely used NLP library in the
enterprise.


Abstract:
  Recent experimental developments in hydrogen-rich materials in high pressures
have put this class of materials above others in the race toward room
temperature superconductivity. As it is the basis of all the materials in this
class, the efforts to determine the properties of pure solid hydrogen at high
pressures remain intense. Most notably, a recent experimental study of the
metallization of hydrogen identified the crystal phase of the solid as the
C2/c-24 molecular phase up to ~425 GPa. It is possible that the observed
metallization is caused by band structure effects and not a structural phase
transition, and the material remains in this crystal phase up to higher
pressures. Therefore it is of crucial importance to determine the
superconducting properties of the C2/c-24 phase. Here, we employ a Wannier
function-based dense k-point and q-point sampling to compute the
electron-phonon coupling and superconducting properties of molecular hydrogen
in the C2/c-24 phase. We find that the material has a high superconducting
transition temperature of 242 K at 500 GPa. We also find that the transition
temperature rapidly increases with pressure in the 400 - 500 GPa range.


Abstract:
  Quantum key distribution (QKD), the distribution of quantum secured keys
useful for data encryption, is expected to have a crucial impact in the next
decades. However, although the notable achievements accomplished in the last
twenty years, many practical and serious challenges are limiting the full
deployment of this novel quantum technology in the current telecommunication
infrastructures. In particular, the co-propagation of quantum signals and
high-speed data traffic within the same optical fiber, is not completely
resolved, due to the intrinsic noise caused by the high intensity of the
classical signals. As a consequence, current co-propagation schemes limit the
amount of classical optical power in order to reduce the overall link noise.
However, this ad-hoc solution restrains the overall range of possibilities for
a large-scale QKD deployment. Here, we propose and demonstrate a new method,
based on up-conversion assisted receiver, for co-propagating classical light
and QKD signals. In addition, we compare the performances of this scheme with
an off-the-shelf quantum receiver, equipped with a standard InGaAs detector,
over different lengths of an installed fiber link. Our proposal exhibits higher
tolerance for noise in comparison to the standard receiver, thus enabling the
distribution of secret keys in the condition of 4 dB-higher classical power.


Abstract:
  Aims: We aim to determine whether Jupiter's obliquity is bound to remain
exceptionally small in the Solar System, or if it could grow in the future and
reach values comparable to those of the other giant planets.
  Methods: The spin axis of Jupiter is subject to the gravitational torques
from its regular satellites and from the Sun. These torques evolve over time
due to the long-term variations of its orbit and to the migration of its
satellites. With numerical simulations, we explore the future evolution of
Jupiter's spin axis for different values of its moment of inertia and for
different migration rates of its satellites. Analytical formulas show the
location and properties of all relevant resonances.
  Results: Because of the migration of the Galilean satellites, Jupiter's
obliquity is currently increasing, as it adiabatically follows the drift of a
secular spin-orbit resonance with the nodal precession mode of Uranus. Using
the current estimates of the migration rate of the satellites, the obliquity of
Jupiter can reach values ranging from 6{\deg} to 37{\deg} after 5 Gyrs from
now, according to the precise value of its polar moment of inertia. A faster
migration for the satellites would produce a larger increase in obliquity, as
long as the drift remains adiabatic.
  Conclusions: Despite its peculiarly small current value, the obliquity of
Jupiter is no different from other obliquities in the Solar System: It is
equally sensitive to secular spin-orbit resonances and it will probably reach
comparable values in the future.


Abstract:
  We revise the steady vortex surface theory following the recent finding of
asymmetric vortex sheets (AM,2021). These surfaces avoid the Kelvin-Helmholtz
instability by adjusting their discontinuity and shape. The vorticity collapses
to the sheet only in an exceptional case considered long ago by Burgers and
Townsend, where it decays as a Gaussian on both sides of the sheet. In generic
asymmetric vortex sheets (Shariff,2021), vorticity leaks to one side or
another, making such sheets inadequate for vortex sheet statistics and
anomalous dissipation.
  We conjecture that the vorticity in a turbulent flow collapses on a special
kind of surface (confined vortex surface, or CVS), satisfying some equations
involving the tangent components of the local strain tensor.
  The most important qualitative observation is that the inequality needed for
this solution's stability breaks the Euler dynamics' time reversibility. We
interpret this as dynamic irreversibility. We have also represented the
enstrophy as a surface integral, conserved in the Navier-Stokes equation in the
turbulent limit, with vortex stretching and viscous diffusion terms exactly
canceling each other on the CVS surfaces.
  We have studied the CVS equations for the cylindrical vortex surface for an
arbitrary constant background strain with two different eigenvalues. This
equation reduces to a particular version of the stationary Birkhoff-Rott
equation for the 2D flow with an extra nonanalytic term. We study some general
properties of this equation and reduce its solution to a fixed point of a map
on a sphere, guaranteed to exist by the Brouwer theorem.


Abstract:
  Let $p$ be a prime, and $\mathbb{F}_p$ the field with $p$ elements. We prove
that if $G$ is a mild pro-$p$ group with quadratic $\mathbb{F}_p$-cohomology
algebra $H^\bullet(G,\mathbb{F}_p)$, then the algebras
$H^\bullet(G,\mathbb{F}_p)$ and $\mathrm{gr}\mathbb{F}_p[\![G]\!]$ - the latter
being induced by the quotients of consecutive terms of the $p$-Zassenhaus
filtration of $G$ - are both Koszul, and they are quadratically dual to each
other. Consequently, if the maximal pro-$p$ Galois group of a field is mild,
then Positselski's and Weigel's Koszulity conjectures hold true for such a
field.


Abstract:
  Approaches based on deep neural networks have achieved striking performance
when testing data and training data share similar distribution, but can
significantly fail otherwise. Therefore, eliminating the impact of distribution
shifts between training and testing data is crucial for building
performance-promising deep models. Conventional methods assume either the known
heterogeneity of training data (e.g. domain labels) or the approximately equal
capacities of different domains. In this paper, we consider a more challenging
case where neither of the above assumptions holds. We propose to address this
problem by removing the dependencies between features via learning weights for
training samples, which helps deep models get rid of spurious correlations and,
in turn, concentrate more on the true connection between discriminative
features and labels. Extensive experiments clearly demonstrate the
effectiveness of our method on multiple distribution generalization benchmarks
compared with state-of-the-art counterparts. Through extensive experiments on
distribution generalization benchmarks including PACS, VLCS, MNIST-M, and NICO,
we show the effectiveness of our method compared with state-of-the-art
counterparts.


Abstract:
  Boosting is a popular machine learning algorithm in regression and
classification problems. Boosting can combine a sequence of regression trees to
obtain accurate prediction. In the presence of outliers, traditional boosting,
based on optimizing convex loss functions, may show inferior results. In this
article, a unified robust boosting is proposed for more resistant estimation.
The method utilizes a recently developed concave-convex family for robust
estimation, composite optimization by conjugation operator, and functional
decent boosting. As a result, an iteratively reweighted boosting algorithm can
be conveniently constructed with existing software. Applications in robust
regression, classification and Poisson regression are demonstrated in the R
package ccboost.


Abstract:
  The number of social media sites have increased exponentially with new ones
cashing in on the weaknesses of older ones and others going beyond community
guidelines by offering uncensored content. The vendors of these platforms in
order to have a wider reach do not place restrictions on viewing age, promises
young people with fame, and other such attractive offers that make the youths
addicted to the site. The possibility of hacking into accounts of users and
using same for fraud is another rave among Nigerian youths with desire for
quick riches. The crash in prices of data, smart phones, and related digital
devices have increased availability and access thereby closing digital divide
and widening its adverse effects on the youths morals and academic pursuits. It
is important that the Nigerian government understand factors that contribute to
the dwindling performance level of students in government owned institutions to
put in place policies and infrastructure that would help combat the challenges.
This study investigated the effects of social media on students academic
activities, mood and time management abilities. The result indicated that
association between social media and academic activities is statistically
significant. However, a negative association exists between them which implies
that the high the level of social media activity, the lower academic activities
participation. Similar association was observed on the effects of social media
on students time management ability.


Abstract:
  A photonic-assisted multiple radio frequency (RF) measurement approach based
on stimulated Brillouin scattering (SBS) and frequency-to-time mapping with
high accuracy and high-frequency resolution is reported. A two-tone signal is
single-sideband (SSB) modulated on an optical carrier via a dual-parallel
Mach-Zehnder modulator to construct one SBS gain and two SBS losses for SBS
gain bandwidth reduction. The unknown RF signal is also SSB modulated on a
carrier that has been modulated by a sweep signal, thus the unknown RF signal
is converted to a sweep optical signal along with the sweep optical carrier.
The bandwidth-reduced SBS gain spectrum is detected by the sweep optical
signals at different specific time, mapping the RF frequencies to the time
domain. An experiment is performed. RF frequencies from 0.3 to 7.6 GHz are
simultaneously measured with a root mean square error of less than 1 MHz. In
addition, the frequency resolution of the measurement can be much lower than 10
MHz, which is now the best result in the RF frequency measurement methods
employing the SBS effect.


Abstract:
  We investigate a single field model in the context of the constant-roll
inflation in which inflaton moves down to the minimum point of the potential
with a constant rate of rolling. We use a quintessential inflationary model
obtained by a Lorentzian function which is dependent on the number of e-folds.
We present the inflationary analysis for the model and find the observational
constraints on the parameters space using the observations of CMB anisotropies
\textit{i.e.} the Planck and Keck/array datasets. We find the observationally
acceptable values of the Width of the Lorentzian function as
$0.3<\Gamma\leq0.5$ at the $68\%$ CL and $\Gamma\leq0.3$ at the $95\%$ CL when
$\xi=120$, $|\beta|=0.02$ and $N=60$. Also, we acquire the observationally
favoured values of the amplitude of the Lorentzian function as $400<\xi\leq600$
at the $68\%$ CL and $\xi\leq400$ at the $95\%$ CL when $\Gamma=0.1$,
$|\beta|=0.02$ and $N=60$. Moreover, we study the model from the Weak Gravity
Conjecture approach using the swampland criteria.


Abstract:
  Engineering sketches form the 2D basis of parametric Computer-Aided Design
(CAD), the foremost modeling paradigm for manufactured objects. In this paper
we tackle the problem of learning based engineering sketch generation as a
first step towards synthesis and composition of parametric CAD models. We
propose two generative models, CurveGen and TurtleGen, for engineering sketch
generation. Both models generate curve primitives without the need for a sketch
constraint solver and explicitly consider topology for downstream use with
constraints and 3D CAD modeling operations. We find in our perceptual
evaluation using human subjects that both CurveGen and TurtleGen produce more
realistic engineering sketches when compared with the current state-of-the-art
for engineering sketch generation.


Abstract:
  The Poisson-Boltzmann equation provides a mean-field theory of electrolyte
solutions at interfaces and in confinement, with numerous applications ranging
from colloid science to nanofluidics. This formulary gathers important formulas
for the Poisson-Boltzmann description of a Z:Z electrolyte solution inside slit
and cylindrical channels. Different approximated solutions and their range of
validity are discussed, together with the full solution for the slit channel.
This formulary is meant to continuously evolve following feedback from the
readers.


Abstract:
  In recent years, significant effort has been invested verifying the
reproducibility and robustness of research claims in social and behavioral
sciences (SBS), much of which has involved resource-intensive replication
projects. In this paper, we investigate prediction of the reproducibility of
SBS papers using machine learning methods based on a set of features. We
propose a framework that extracts five types of features from scholarly work
that can be used to support assessments of reproducibility of published
research claims. Bibliometric features, venue features, and author features are
collected from public APIs or extracted using open source machine learning
libraries with customized parsers. Statistical features, such as p-values, are
extracted by recognizing patterns in the body text. Semantic features, such as
funding information, are obtained from public APIs or are extracted using
natural language processing models. We analyze pairwise correlations between
individual features and their importance for predicting a set of human-assessed
ground truth labels. In doing so, we identify a subset of 9 top features that
play relatively more important roles in predicting the reproducibility of SBS
papers in our corpus. Results are verified by comparing performances of 10
supervised predictive classifiers trained on different sets of features.


Abstract:
  Pseudo-normality synthesis, which computationally generates a pseudo-normal
image from an abnormal one (e.g., with lesions), is critical in many
perspectives, from lesion detection, data augmentation to clinical surgery
suggestion. However, it is challenging to generate high-quality pseudo-normal
images in the absence of the lesion information. Thus, expensive lesion
segmentation data have been introduced to provide lesion information for the
generative models and improve the quality of the synthetic images. In this
paper, we aim to alleviate the need of a large amount of lesion segmentation
data when generating pseudo-normal images. We propose a Semi-supervised Medical
Image generative LEarning network (SMILE) which not only utilizes limited
medical images with segmentation masks, but also leverages massive medical
images without segmentation masks to generate realistic pseudo-normal images.
Extensive experiments show that our model outperforms the best state-of-the-art
model by up to 6% for data augmentation task and 3% in generating high-quality
images. Moreover, the proposed semi-supervised learning achieves comparable
medical image synthesis quality with supervised learning model, using only 50
of segmentation data.


Abstract:
  We extend the Zee model by introducing a vector-like lepton doublet and a
flavor dependent global $U(1)$ symmetry. Flavor changing neutral currents in
the quark sector can be naturally forbidden at tree level due to the $U(1)$
symmetry, while sufficient amount of lepton flavor violation is provided to
explain current neutrino oscillation data. In our model, additional sources of
CP-violation appear in the lepton sector, but their contribution to electric
dipole moments is much smaller than current experimental bounds due to the
Yukawa structure constrained by the $U(1)$ symmetry. We find that there is a
parameter region where the strongly first order electroweak phase transition
can be realized, which is necessary for the successful scenario of the
electroweak baryogenesis in addition to new CP-violating phases. In the
benchmark points satisfying neutrino data, lepton flavor violation data and the
strongly first order phase transition, we show that an additional CP-even Higgs
boson $H$ mainly decays into a lighter CP-odd Higgs boson $A$, i.e., $H \to AZ$
or $H \to AA$ with a characteristic pattern of lepton flavor violating decays
of $A$.


Abstract:
  A major challenge in flow through porous media is to better understand the
link between microstructure and macroscale flow and transport. For idealised
microstructures, the mathematical framework of homogenisation theory can be
used for this purpose. Here, we consider a two-dimensional microstructure
comprising an array of obstacles of smooth but arbitrary shape, the size and
spacing of which can vary along the length of the porous medium. We use
homogenisation via the method of multiple scales to systematically upscale a
novel problem involving cells of varying area to obtain effective continuum
equations for macroscale flow and transport. The equations are characterised by
the local porosity, a local anisotropic flow permeability, an effective local
anisotropic solute diffusivity, and an effective local adsorption rate. These
macroscale properties depend nontrivially on the two degrees of microstructural
geometric freedom in our problem: obstacle size, and obstacle spacing. We
exploit this dependence to construct and compare scenarios where the same
porosity profile results from different combinations of obstacle size and
spacing. We focus on a simple example geometry comprising circular obstacles on
a rectangular lattice, for which we numerically determine the macroscale
permeability and effective diffusivity. We investigate scenarios where the
porosity is spatially uniform but the permeability and diffusivity are not. Our
results may be useful in the design of filters, or for studying the impact of
deformation on transport in soft porous media.


Abstract:
  Recovering the 3D geometric structure of a face from a single input image is
a challenging active research area in computer vision. In this paper, we
present a novel method for reconstructing 3D heads from a single or multiple
image(s) using a hybrid approach based on deep learning and geometric
techniques. We propose an encoder-decoder network based on the U-net
architecture and trained on synthetic data only. It predicts both pixel-wise
normal vectors and landmarks maps from a single input photo. Landmarks are used
for the pose computation and the initialization of the optimization problem,
which, in turn, reconstructs the 3D head geometry by using a parametric
morphable model and normal vector fields. State-of-the-art results are achieved
through qualitative and quantitative evaluation tests on both single and
multi-view settings. Despite the fact that the model was trained only on
synthetic data, it successfully recovers 3D geometry and precise poses for
real-world images.


Abstract:
  We have investigated the Kondo physics of a single magnetic impurity embedded
in multi-Dirac (Weyl) node fermionic systems. By using a generic effective
model for the host material and employing a numerical renormalization group
approach we access the low temperature behavior of the system, identifying the
existence of Kondo screening in single-, double-, and triple-Dirac (Weyl) node
models. We find that in any multi-Dirac node systems the low-energy regime lies
within one of the known classes of pseudogap Kondo problem, extensively studied
in the literature. Kondo screening is also observed for time reversal symmetry
broken Weyl systems. This is, however, possible only in the particle-hole
symmetry broken regime obtained for finite chemical potential $\mu$. Although
weakly, breaking time-reversal symmetry suppresses the Kondo resonance,
especially in the single-node Weyl semimetals. More interesting Kondo screening
regimes are obtained for inversion symmetry broken multi-Weyl fermions. In
these systems the Kondo regimes of double- and triple-Weyl node models are much
richer than in the single-Weyl node model. While in the single-Weyl node model
the Kondo temperature increases monotonically with $|\mu|$ regardless the value
of the inversion symmetry breaking parameter $Q_0$, in double- and triple-Weyl
node models there are two distinct regimes: (i) For $Q_0< |\mu|$ the Kondo
temperature depends strongly on $\mu$, while (ii) for $Q_0 > |\mu|$ the Kondo
temperature depends very weakly on $\mu$, resembling the flat-band single
impurity Anderson model.


Abstract:
  Silent speech interfaces (SSI) aim to reconstruct the speech signal from a
recording of the articulatory movement, such as an ultrasound video of the
tongue. Currently, deep neural networks are the most successful technology for
this task. The efficient solution requires methods that do not simply process
single images, but are able to extract the tongue movement information from a
sequence of video frames. One option for this is to apply recurrent neural
structures such as the long short-term memory network (LSTM) in combination
with 2D convolutional neural networks (CNNs). Here, we experiment with another
approach that extends the CNN to perform 3D convolution, where the extra
dimension corresponds to time. In particular, we apply the spatial and temporal
convolutions in a decomposed form, which proved very successful recently in
video action recognition. We find experimentally that our 3D network
outperforms the CNN+LSTM model, indicating that 3D CNNs may be a feasible
alternative to CNN+LSTM networks in SSI systems.


Abstract:
  Model of a partial current-carrying torus loop anchored to the photosphere is
analyzed. Conditions of the catastrophic loss of equilibrium are considered and
corresponding value of the critical decay index of external magnetic field is
found. Taking into account line-tying conditions leads to non-monotonous
dependence of the critical decay index on the height of the apex and length of
the flux rope (its endpoints separation). For relatively short flux ropes, the
critical decay index is significantly lower than unity, which is in contrast to
widespread models with the typical critical decay index above unity. The steep
decrease of the critical index with height at low heights is due to the sharp
increase of the curvature of the flux-rope axis that transforms from a nearly
straight line to a crescent.


Abstract:
  We consider in this paper the nonlinear elliptic equation with Neumann
boundary condition \begin{align*} \begin{cases} \Delta u=a|u|^{m-1}u\,\,\mbox{
in }\,\,\rnp\\ \dfrac{\partial u}{\partial t}=b|u|^{\eta-1}u+f\,\,\mbox{ on
}\,\,\partial\rnp. \end{cases} \end{align*} For $a,b\neq 0$,
$m>\frac{n+1}{n-1}$, $(n>1)$, $\eta=\frac{m+1}{2}$ and small data $f\in
L^{\frac{nq}{n+1},\infty}(\partial\rnp)$, $q=\frac{(n+1)(m-1)}{m+1}$ we prove
that the problem is solvable. More precisely, we establish existence,
uniqueness and continuous dependence of solutions on the boundary data $f$ in
the function space $\X^{q}_{\infty}$ where
\[\|u\|_{\X^{q}_{\infty}}=\sup_{t>0}t^{\frac{n+1}{q}-1}\|u(\cdot,t)\|_{L^{\infty}(\partial\rnp)}+\|u\|_{L^{\frac{q(m+1)}{2},\infty}(\rnp)}+\|\nabla
u\|_{L^{q,\infty}(\rnp)}. \] As a direct consequence, we obtain the local
regularity property $C^{1,\nu}_{loc}$, $\nu\in (0,1)$ of these solutions as
well as energy estimates for certain values of $m$. Boundary values decaying
faster than $|x|^{-(m+1)/(m-1)}$, $x\in \rn\setminus\{0\}$ yield solvability
and this decay property is shown to be sharp for positive nonlinearities.
  Moreover, we are able to show that solutions inherit qualitative features of
the boundary data such as positivity, rotational symmetry with respect to the
$(n+1)$-axis, radial monotonicity in the tangential variable and homogeneity.
When $a,b>0$, the critical exponent $m_c$ for the existence of positive
solutions is identified, $m_c=(n+1)/(n-1)$.


Abstract:
  In this paper, an innovative threshold measurement equation is proposed to be
employed in a Realized-GARCH framework. The proposed framework employs a
nonlinear threshold regression specification to consider the leverage effect
and model the contemporaneous dependence between the observed realized measures
and hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted
and employed for the model estimation and forecasting, with its validity
assessed via a simulation study. The usefulness of the proposed measurement
equation in a Realized-GARCH model has been evaluated via a comprehensive
empirical study, by forecasting the 1% and 2.5% Value-at-Risk and Expected
Shortfall on six market indices. The proposed framework is shown to be capable
of producing competitive tail risk forecasting results, compared to the
original Realized-GARCH. Especially, the proposed model is favoured during the
high volatility 2008 Global Financial Crisis period.


Abstract:
  Intelligent reflecting surface (IRS) is envisioned to play a significant role
in future wireless communication systems as an effective means of reconfiguring
the radio signal propagation environment. In this paper, we study a new
multi-IRS aided massive multiple-input multiple-output (MIMO) system, where a
multi-antenna BS transmits independent messages to a set of remote
single-antenna users using orthogonal beams that are subsequently reflected by
different groups of IRSs via their respective multi-hop passive beamforming
over pairwise line-of-sight (LoS) links. We aim to select optimal IRSs and
their beam routing path for each of the users, along with the active/passive
beamforming at the BS/IRSs, such that the minimum received signal power among
all users is maximized. This problem is particularly difficult to solve due to
a new type of path separation constraints for avoiding the IRS-reflected signal
induced interference among different users. To tackle this difficulty, we first
derive the optimal BS/IRS active/passive beamforming solutions based on their
practical codebooks given the reflection paths. Then we show that the resultant
multi-beam multi-hop routing problem can be recast as an equivalent
graph-optimization problem, which is however NP-complete. To solve this
challenging problem, we propose an efficient recursive algorithm to partially
enumerate the feasible routing solutions, which is able to effectively balance
the performance-complexity trade-off. Numerical results demonstrate that the
proposed algorithm achieves near-optimal performance with low complexity and
outperforms other benchmark schemes. Useful insights into the optimal
multi-beam multi-hop routing design are also drawn under different setups of
the multi-IRS aided massive MIMO network.


Abstract:
  The wisdom of the crowd has long become the de facto approach for eliciting
information from individuals or experts in order to predict the ground truth.
However, classical democratic approaches for aggregating individual
\emph{votes} only work when the opinion of the majority of the crowd is
relatively accurate. A clever recent approach, \emph{surprisingly popular
voting}, elicits additional information from the individuals, namely their
\emph{prediction} of other individuals' votes, and provably recovers the ground
truth even when experts are in minority. This approach works well when the goal
is to pick the correct option from a small list, but when the goal is to
recover a true ranking of the alternatives, a direct application of the
approach requires eliciting too much information. We explore practical
techniques for extending the surprisingly popular algorithm to ranked voting by
partial votes and predictions and designing robust aggregation rules. We
experimentally demonstrate that even a little prediction information helps
surprisingly popular voting outperform classical approaches.


Abstract:
  We report the orbital X-ray variability of high mass X-ray binary (HMXB)
GX301--2. GX301--2 undergone a spin up process in 2018--2020 with the period
evolving from $\sim$ 685 s to 670 s. The energy resolved pulse-profiles of the
pulsar in 1--60 keV varied from single peaked and sinusoidal shapes to
multi-peaked across different orbital phases. Pulse fractions evolving over
orbit had negative correlations with the X-ray flux. The broad-band X-ray
energy spectrum of the pulsar can be described with a partial covering negative
positive cutoff power-law continuum model. Near the periastron passage of the
pulsar we found a strong variation in the additional column density ($NH_{2}$),
which correlated with variation of the flux. Curves of growth for both Fe
K$\alpha$ and Fe K$\beta$ lines were plotted to investigate the distribution of
matter around neutron star. We have also found the evidence of two cyclotron
absorption lines in the phase-averaged spectra in GX301--2, with one line of
30--42 keV and the other line varying in 48--56 keV. Both two line's centroid
energies show the similar relationship with X-ray luminosity: positive
correlation in lower luminosity range, and negative relation above a critical
luminosity of $10^{37}$ erg s$^{-1}$. We estimated the surface magnetic field
of the neutron star in GX301--2 at ~$(0.5-2)\times 10^{13}$ G. Two cyclotron
line energies have a nearly fixed ratio of ~1.63 while having a low strength
ratio (~0.05), suggesting that these two features may actually be one line.


Abstract:
  In this paper, we propose an $H(\text{curl}^2)$-conforming quadrilateral
spectral element method to solve quad-curl problems. Starting with generalized
Jacobi polynomials, we first introduce quasi-orthogonal polynomial systems for
vector fields over rectangles. $H(\text{curl}^2)$-conforming elements over
arbitrary convex quadrilaterals are then constructed explicitly in a
hierarchical pattern using the contravariant transform together with the
bilinear mapping from the reference square onto each quadrilateral. It is
astonishing that both the simplest rectangular and quadrilateral spectral
elements possess only 8 degrees of freedom on each physical element. In the
sequel, we propose our $H(\text{curl}^2)$-conforming quadrilateral spectral
element approximation based on the mixed weak formulation to solve the
quad-curl equation and its eigenvalue problem. Numerical results show the
effectiveness and efficiency of our method.


Abstract:
  We establish a congruence on sums of central $q$-binomial coefficients. From
this $q$-congruence, we derive the divisibility of the $q$-trinomial
coefficients introduced by Andrews and Baxter.


Abstract:
  Deep learning-based image reconstruction approaches have demonstrated
impressive empirical performance in many imaging modalities. These approaches
generally require a large amount of high-quality training data, which is often
not available. To circumvent this issue, we develop a novel unsupervised
knowledge-transfer paradigm for learned iterative reconstruction within a
Bayesian framework. The proposed approach learns an iterative reconstruction
network in two phases. The first phase trains a reconstruction network with a
set of ordered pairs comprising of ground truth images and measurement data.
The second phase fine-tunes the pretrained network to the measurement data
without supervision. Furthermore, the framework delivers uncertainty
information over the reconstructed image. We present extensive experimental
results on low-dose and sparse-view computed tomography, showing that the
proposed framework significantly improves reconstruction quality not only
visually, but also quantitatively in terms of PSNR and SSIM, and is competitive
with several state-of-the-art supervised and unsupervised reconstruction
techniques.