Abstract:
  The relative entropy of entanglement $E_R$ is defined as the distance of a
multi-partite quantum state from the set of separable states as measured by the
quantum relative entropy. We show that this optimisation is always achieved,
i.e. any state admits a (unique) closest separable state, even in infinite
dimension; also, $E_R$ is everywhere lower semi-continuous. These results,
which seem to have gone unnoticed so far, hold not only for the relative
entropy of entanglement and its multi-partite generalisations, but also for
many other similar resource quantifiers, such as the relative entropy of
non-Gaussianity, of non-classicality, of Wigner negativity -- more generally,
all relative entropy distances from the sets of states with non-negative
$\lambda$-quasi-probability distribution. The crucial hypothesis underpinning
all these applications is the weak*-closedness of the cone generated by free
states. We complement our findings by giving explicit and asymptotically tight
continuity estimates for $E_R$ and closely related quantities in the presence
of an energy constraint.


Abstract:
  We consider the incompressible 2D Navier-Stokes equations with periodic
boundary conditions driven by a deterministic time periodic forcing and a
degenerate stochastic forcing. We show that the system possesses a unique
ergodic periodic invariant measure which is exponentially mixing under a
Wasserstein metric. We also prove the weak law of large numbers for the
continuous time inhomogeneous solution process. In addition, we obtain the weak
law of large numbers and central limit theorem by restricting the inhomogeneous
solution process to periodic times. The results are independent of the strength
of the noise and hold true for any value of viscosity with a lower bound
$\nu_1$ characterized by the Grashof number $G_1$ associated with the
deterministic forcing. In the laminar case, there is a larger lower bound
$\nu_2$ of the viscosity characterized by the Grashof number $G_2$ associated
with both the deterministic and random forcing. We prove that in this laminar
case, the system has trivial dynamics for any viscosity larger than $\nu_2$ by
demonstrating the existence of a unique globally exponentially stable random
periodic solution that supports the unique periodic invariant measure.


Abstract:
  Audio-visual event localization aims to localize an event that is both
audible and visible in the wild, which is a widespread audio-visual scene
analysis task for unconstrained videos. To address this task, we propose a
Multimodal Parallel Network (MPN), which can perceive global semantics and
unmixed local information parallelly. Specifically, our MPN framework consists
of a classification subnetwork to predict event categories and a localization
subnetwork to predict event boundaries. The classification subnetwork is
constructed by the Multimodal Co-attention Module (MCM) and obtains global
contexts. The localization subnetwork consists of Multimodal Bottleneck
Attention Module (MBAM), which is designed to extract fine-grained
segment-level contents. Extensive experiments demonstrate that our framework
achieves the state-of-the-art performance both in fully supervised and weakly
supervised settings on the Audio-Visual Event (AVE) dataset.


Abstract:
  We show that monopoles can be pair produced by cosmological magnetic fields
in the early universe. The pair production gives rise to relic monopoles, and
at the same time induces a self-screening of the magnetic fields. By studying
these effects we derive limits on the monopole mass, and also on the initial
amplitude of primordial magnetic fields. Monopoles of GUT scale mass can even
be produced if primordial magnetic fields exist at sufficiently high redshifts.


Abstract:
  Gaussian process optimization is a successful class of algorithms(e.g.
GP-UCB) to optimize a black-box function through sequential evaluations.
However, for functions with continuous domains, Gaussian process optimization
has to rely on either a fixed discretization of the space, or the solution of a
non-convex optimization subproblem at each evaluation. The first approach can
negatively affect performance, while the second approach puts requires a heavy
computational burden. A third option, only recently theoretically studied, is
to adaptively discretize the function domain. Even though this approach avoids
the extra non-convex optimization costs, the overall computational complexity
is still prohibitive. An algorithm such as GP-UCB has a runtime of $O(T^4)$,
where $T$ is the number of iterations. In this paper, we introduce Ada-BKB
(Adaptive Budgeted Kernelized Bandit), a no-regret Gaussian process
optimization algorithm for functions on continuous domains, that provably runs
in $O(T^2 d_\text{eff}^2)$, where $d_\text{eff}$ is the effective dimension of
the explored space, and which is typically much smaller than $T$. We
corroborate our theoretical findings with experiments on synthetic non-convex
functions and on the real-world problem of hyper-parameter optimization,
confirming the good practical performances of the proposed approach.


Abstract:
  We estimate galaxy clustering under a modified gravitational potential. In
particular, the modifications in gravitational potential energy occur due to a
power-law and cosmological constant terms. We derive a canonical partition
function for the system of galaxies interacting under such a modified
gravitational potential. Moreover, we compute various thermodynamical equation
of states for the system. We do comparative analysis in order to emphasize the
effect of corrections on thermodynamics of the system. Interestingly, the
modifications in thermodynamical quantities are embedded in clustering
parameter only.


Abstract:
  Organisations are currently lacking in developing and implementing business
systems in meaningful ways to motivate and engage their staff. This is
particularly salient as the average employee spends eleven cumulative years of
their life at work, however less than one third of the workforce are actually
engaged in their duties throughout their career. Such low levels of engagement
are particularly prominent with younger employees, referred to as Generation Y
(GenY), who are the least engaged of all groups at work. However, they will
dedicate around five cumulative years of their life immersed playing video
games such as Clash of Clans, whether for social, competitive, extrinsic, or
intrinsic motivational factors. Using behavioural concepts derived from video
games, and applying game design elements in business systems to motivate
employees in the digital economy, is a concept which has come to be recognised
as Business Gamification. Thus, the purpose of this research paper is to
further our understanding of game design elements for business, and investigate
their properties from design to implementation in gamified systems. Following a
two-year ethnographic style study with both a system development, and a
communication agency largely staffed with GenY employees, findings suggest
properties in game design elements are emergent and temporal in their
instantiations.


Abstract:
  Lepton number is promoted to an $U(1)_L$ gauge symmetry in a simple extension
of the standard model. The spontaneous breaking of $U(1)_L$ by three units
allows a conserved $Z_3^L$ lepton symmetry to remain, guaranteeing that
neutrinos are Dirac fermions, which acquire naturally small masses from a
previously proposed mechanism. Dark matter appears as a singlet scalar, with
dark symmetry $Z_3^D$ derivable from $Z_3^L$. Muon $g-2$ may be explained.


Abstract:
  We propose a space mapping-based optimization algorithm for microscopic
interacting particle dynamics which are inappropriate for direct optimization.
This is of relevance for example in applications with bounded domains such that
the microscopic optimization is difficult. The space mapping algorithm exploits
the relationship of the microscopic description of the interacting particle
system and the corresponding macroscopic description as partial differential
equation in the "many particle limit". We validate the approach with the help
of a toy problem that allows for direct optimization. Then we study the
performance of the algorithm in two applications. An evacuation dynamic is
considered and the transportation of goods on a conveyor belt is optimized. The
numerical results underline the feasibility of the proposed approach.


Abstract:
  Game theory has been increasingly applied in settings where the game is not
known outright, but has to be estimated by sampling. For example, meta-games
that arise in multi-agent evaluation can only be accessed by running a
succession of expensive experiments that may involve simultaneous deployment of
several agents. In this paper, we focus on $\alpha$-rank, a popular
game-theoretic solution concept designed to perform well in such scenarios. We
aim to estimate the $\alpha$-rank of the game using as few samples as possible.
Our algorithm maximizes information gain between an epistemic belief over the
$\alpha$-ranks and the observed payoff. This approach has two main benefits.
First, it allows us to focus our sampling on the entries that matter the most
for identifying the $\alpha$-rank. Second, the Bayesian formulation provides a
facility to build in modeling assumptions by using a prior over game payoffs.
We show the benefits of using information gain as compared to the confidence
interval criterion of ResponseGraphUCB (Rowland et al. 2019), and provide
theoretical results justifying our method.


Abstract:
  We study the existence and uniqueness of mild and strong solutions of
nonlocal nonlinear diffusion problems of $p$-Laplacian type with nonlinear
boundary conditions posed in metric random walk spaces. These spaces include,
among others, weighted discrete graphs and $\mathbb{R}^N$ with a random walk
induced by a nonsingular kernel. We also study the case of nonlinear dynamical
boundary conditions. The generality of the nonlinearities considered allow us
to cover the nonlocal counterparts of a large scope of local diffusion
problems: Stefan problems, Hele-Shaw problems, diffusion in porous media
problems, obstacle problems, and more. Nonlinear semigroup theory is the basis
for this study.


Abstract:
  An agent that is capable of predicting what happens next can perform a
variety of tasks through planning with no additional training. Furthermore,
such an agent can internally represent the complex dynamics of the real-world
and therefore can acquire a representation useful for a variety of visual
perception tasks. This makes predicting the future frames of a video,
conditioned on the observed past and potentially future actions, an interesting
task which remains exceptionally challenging despite many recent advances.
Existing video prediction models have shown promising results on simple narrow
benchmarks but they generate low quality predictions on real-life datasets with
more complicated dynamics or broader domain. There is a growing body of
evidence that underfitting on the training data is one of the primary causes
for the low quality predictions. In this paper, we argue that the inefficient
use of parameters in the current video models is the main reason for
underfitting. Therefore, we introduce a new architecture, named FitVid, which
is capable of severe overfitting on the common benchmarks while having similar
parameter count as the current state-of-the-art models. We analyze the
consequences of overfitting, illustrating how it can produce unexpected
outcomes such as generating high quality output by repeating the training data,
and how it can be mitigated using existing image augmentation techniques. As a
result, FitVid outperforms the current state-of-the-art models across four
different video prediction benchmarks on four different metrics.


Abstract:
  We derive a simple proof, based on information theoretic inequalities, of an
upper bound on the largest rates of $q$-ary $\overline{2}$-separable codes that
improves recent results of Wang for any $q\geq 13$. For the case $q=2$, we
recover a result of Lindstr\"om, but with a much simpler derivation. The method
easily extends to give bounds on $B_2$ codes which, although not improving on
Wang's results, use much simpler tools and might be useful for future
applications.


Abstract:
  In this paper we consider the classical capacity problem for Gaussian
measurement channels without imposing any kind of threshold condition. We prove
Gaussianity of the average state of the optimal ensemble in general and discuss
the Hypothesis of Gaussian Maximizers concerning the structure of the ensemble.
The proof uses an approach of Wolf, Giedke and Cirac adapted to the convex
closure of the output differential entropy. Then we discuss the case of one
mode in detail, including the dual problem of accessible information of a
Gaussian ensemble.
  In quantum communications there are several studies of the classical capacity
in the transmission scheme where not only the Gaussian channel but also the
receiver is fixed, and the optimization is performed over certain set of the
input ensembles. These studies are practically important in view of the
complexity of the optimal receiver in the Quantum Channel Coding (HSW) theorem.
Our findings are relevant to such a situation where the receiver is Gaussian
and concatenation of the channel and the receiver can be considered as one
Gaussian measurement channel. Our efforts in this and preceding papers are then
aimed at establishing full Gaussianity of the optimal ensemble (usually taken
as an assumption) in such schemes.


Abstract:
  The mechanism behind the nematicity of FeSe is not known. Through
elastoresitivity measurements it has been shown to be an electronic
instability. However, so far measurements have extended only to small strains,
where the response is linear. Here, we apply large elastic strains to FeSe, and
perform two types of measurements. (1) Using applied strain to control
twinning, the nematic resistive anisotropy at temperatures below the nematic
transition temperature Ts is determined. (2) Resistive anisotropy is measured
as nematicity is induced through applied strain at fixed temperature above Ts.
In both cases, as nematicity strengthens the resistive anisotropy peaks about
about 7%, then decreases. Below ~40 K, the nematic resistive anisotropy changes
sign. We discuss possible implications of this behaviour for theories of
nematicity. We report in addition: (1) Under experimentally accessible
conditions with bulk crystals, stress, rather than strain, is the conjugate
field to the nematicity of FeSe. (2) At low temperatures the twin boundary
resistance is ~10% of the sample resistance, and must be properly subtracted to
extract intrinsic resistivities. (3) Biaxial inplane compression increases both
in-plane resistivity and the superconducting critical temperature Tc,
consistent with a strong role of the yz orbital in the electronic correlations.


Abstract:
  Let $(M,\nabla,\langle\;,\;\rangle)$ be a manifold endowed with a flat
torsionless connection $\nabla$ and a Riemannian metric $\langle\;,\;\rangle$
and $(T^kM)_{k\geq1}$ the sequence of tangent bundles given by
$T^kM=T(T^{k-1}M)$ and $T^1M=TM$. We show that, for any $k\geq1$, $T^kM$
carries a Hermitian structure $(J_k,g_k)$ and a flat torsionless connection
$\nabla^k$ and when $M$ is a Lie group and $(\nabla,\langle\;,\;\rangle)$ are
left invariant there is a Lie group structure on each $T^kM$ such that
$(J_k,g_k,\nabla^k)$ are left invariant.
  It is well-known that $(TM,J_1,g_1)$ is K\"ahler if and only if
$\langle\;,\;\rangle$ is Hessian, i.e, in each system of affine coordinates
$(x_1,\ldots,x_n)$,
$\langle\partial_{x_i},\partial_{x_j}\rangle=\frac{\partial^2\phi}{\partial_{x_i}\partial_{x_j}}$.
  Having in mind many generalizations of the K\"ahler condition introduced
recently, we give the conditions on $(\nabla,\langle\;,\;\rangle)$ so that
$(TM,J_1,g_1)$ is balanced, locally conformally balanced, locally conformally
K\"ahler, pluriclosed, Gauduchon, Vaismann or Calabi-Yau with torsion.
Moreover, we can control at the level of $(\nabla,\langle\;,\;\rangle)$ the
conditions insuring that some $(T^kM,J_k,g_k)$ or all of them satisfy a
generalized K\"ahler condition.
  For instance, we show that there are some classes of
$(M,\nabla,\langle\;,\;\rangle)$ such that, for any $k\geq1$, $(T^kM,J_k,g_k)$
is balanced non-K\"ahler and Calabi-Yau with torsion. By carefully studying the
geometry of $(M,\nabla,\langle\;,\;\rangle)$, we develop a powerful machinery
to build a large classes of generalized K\"ahler manifolds.


Abstract:
  Deep learning demonstrated major abilities in solving many kinds of different
real-world problems in computer vision literature. However, they are still
strained by simple reasoning tasks that humans consider easy to solve. In this
work, we probe current state-of-the-art convolutional neural networks on a
difficult set of tasks known as the same-different problems. All the problems
require the same prerequisite to be solved correctly: understanding if two
random shapes inside the same image are the same or not. With the experiments
carried out in this work, we demonstrate that residual connections, and more
generally the skip connections, seem to have only a marginal impact on the
learning of the proposed problems. In particular, we experiment with DenseNets,
and we examine the contribution of residual and recurrent connections in
already tested architectures, ResNet-18, and CorNet-S respectively. Our
experiments show that older feed-forward networks, AlexNet and VGG, are almost
unable to learn the proposed problems, except in some specific scenarios. We
show that recently introduced architectures can converge even in the cases
where the important parts of their architecture are removed. We finally carry
out some zero-shot generalization tests, and we discover that in these
scenarios residual and recurrent connections can have a stronger impact on the
overall test accuracy. On four difficult problems from the SVRT dataset, we can
reach state-of-the-art results with respect to the previous approaches,
obtaining super-human performances on three of the four problems.


Abstract:
  A point process on a space is a random bag of elements of that space. In this
paper we explore programming with point processes in a monadic style. To this
end we identify point processes on a space X with probability measures of bags
of elements in X. We describe this view of point processes using the
composition of the Giry and bag monads on the category of measurable spaces and
functions and prove that this composition also forms a monad using a
distributive law for monads. Finally, we define a morphism from a point process
to its intensity measure, and show that this is a monad morphism. A special
case of this monad morphism gives us Wald's Lemma, an identity used to
calculate the expected value of the sum of a random number of random variables.
Using our monad we define a range of point processes and point process
operations and compositionally compute their corresponding intensity measures
using the monad morphism.


Abstract:
  Generative Adversarial Networks (GANs) have recently achieved unprecedented
success in photo-realistic image synthesis from low-dimensional random noise.
The ability to synthesize high-quality content at a large scale brings
potential risks as the generated samples may lead to misinformation that can
create severe social, political, health, and business hazards. We propose
SubsetGAN to identify generated content by detecting a subset of anomalous
node-activations in the inner layers of pre-trained neural networks. These
nodes, as a group, maximize a non-parametric measure of divergence away from
the expected distribution of activations created from real data. This enable us
to identify synthesised images without prior knowledge of their distribution.
SubsetGAN efficiently scores subsets of nodes and returns the group of nodes
within the pre-trained classifier that contributed to the maximum score. The
classifier can be a general fake classifier trained over samples from multiple
sources or the discriminator network from different GANs. Our approach shows
consistently higher detection power than existing detection methods across
several state-of-the-art GANs (PGGAN, StarGAN, and CycleGAN) and over different
proportions of generated content.


Abstract:
  Assume $k$ is a field and let $F:C\rightarrow Vect_{k}$ be a small $k$-linear
functor from a $k$-linear abelian category $C$ to the category of vector spaces
over the field $k$, the purpose of this note is to use a little knowledge of
linear algebra and category to give the description of $end(F)$ and $coend(F)$,
and then we give the reconstruction theorem of bialgebras by using this
description. We use a constructive approach to understand $end(F),coend(F)$ and
we describe the bialgebra structure of $coend(F)$ concretely when $F$ is a
tensor functor.


Abstract:
  This paper investigates the dynamic voltage support (DVS) control of
inverter-based resources (IBRs) under voltage sags to enhance the low-voltage
ride-through performance. We first revisit the prevalent droop control from an
optimization perspective to elaborate on why it usually suffers from
suboptimality. Then, we formulate the DVS problem as an optimization program
that maximizes the positive-sequence voltage magnitude at the point of common
coupling (PCC) subject to the current, active power, and stability constraints.
The program is inherently nonconvex due to the active power limits, of which
the global optimality is not guaranteed by off-the-shelf solvers. In this
context, we perform the optimality analysis to explore the global optimum
analytically. It is found that the unique global optimum has three
scenarios/stages (S1--S3), which depends on the specific relationship among
grid voltage, grid strength, as well as physical limits of IBRs. The
closed-form solutions in S1 and S3 are derived and the optimality conditions
for S2 are provided, which guarantees the optimality and compatibility with the
fast real-time control. We implement the optimum with a grid-connected
photovoltaic (PV) power plant by integrating a DVS controller. Dynamic
simulations are carried out under different scenarios to test our proposal and
compare it with other existing methods. Additionally, the robustness of
optimality against model errors is discussed and numerically demonstrated.


Abstract:
  In 2019, the Event Horizon Telescope Collaboration (EHTC) has published the
first image of a supermassive black hole (SMBH) obtained via the Very Large
Baseline Interferometry (VLBI) technique. In the future, it is expected that
additional and more sensitive VLBI observations will be pursued for other
nearby Active Galactic Nuclei (AGN), and it is therefore important to
understand which possible features can be expected in such images. In this
paper, we post-process General Relativistic Magneto-Hydrodynamical (GR-MHD)
simulations which include resistivity, thus providing a self-consistent jet
formation model, including resistive mass loading of a wind launched from a
disc in Keplerian rotation. The ray-tracing is done using the General
Relativistic Ray-Tracing code GRTRANS assuming synchrotron emission. We study
the appearance of the black hole environment including the accretion disc,
winds and jets under a large range of condition, varying black hole mass,
accretion rate, spin, inclination angle, disc parameters and observed
frequency. When we adopt M87-like parameters, we show that we can reproduce a
ring-like feature (similar as observed by the EHT) for some of our simulations.
The latter suggests that such Keplerian disc models thus could be consistent
with the observed results. Depending on their masses, accretion rates, spin and
the sensitivity of the observation, we note that other SMBHs may show
additional features like winds and jets in the observations.


Abstract:
  The controlled functionalization of single-walled carbon nanotubes with
luminescent sp3-defects has created the potential to employ them as
quantum-light sources in the near-infrared. For that, it is crucial to control
their spectral diversity. The emission wavelength is determined by the binding
configuration of the defects rather than the molecular structure of the
attached groups. However, current functionalization methods produce a variety
of binding configurations and thus emission wavelengths. We introduce a simple
reaction protocol for the creation of only one type of luminescent defect in
polymer-sorted (6,5) nanotubes, which is more red-shifted and exhibits longer
photoluminescence lifetimes than the commonly obtained binding configurations.
We demonstrate single-photon emission at room temperature and expand this
functionalization to other polymer-wrapped nanotubes with emission further in
the near-infrared. As the selectivity of the reaction with various aniline
derivatives depends on the presence of an organic base we propose nucleophilic
addition as the reaction mechanism.


Abstract:
  We determine theoretically the interaction between two magnetic impurities
embedded in a spin-split $s$-wave superconductor. The spin-splitting in the
superconductor gives rise to two different interaction types between the
impurity spins, depending on whether their spins lie in the plane perpendicular
to the spin-splitting field (Heisenberg) or not (Ising). For impurity
separation distances exceeding $\xi_S$, we find that the magnitude of the
spin-splitting can determine whether an antiferromagnetic or ferromagnetic
alignment of the impurity spins is preferred by the RKKY interaction. Moreover,
the Ising and Heisenberg terms of the RKKY interaction alternate on being the
dominant term and their magnitudes oscillate as a function of distance between
the impurities.


Abstract:
  Learning to program an audio production VST synthesizer is a time consuming
process, usually obtained through inefficient trial and error and only mastered
after years of experience. As an educational and creative tool for sound
designers, we propose SerumRNN: a system that provides step-by-step
instructions for applying audio effects to change a user's input audio towards
a desired sound. We apply our system to Xfer Records Serum: currently one of
the most popular and complex VST synthesizers used by the audio production
community. Our results indicate that SerumRNN is consistently able to provide
useful feedback for a variety of different audio effects and synthesizer
presets. We demonstrate the benefits of using an iterative system and show that
SerumRNN learns to prioritize effects and can discover more efficient effect
order sequences than a variety of baselines.


Abstract:
  We study modules over the commutative ring spectrum $H\mathbb F_2\wedge
H\mathbb F_2$, whose coefficient groups are quotients of the dual Steenrod
algebra by collections of the Milnor generators. We show that very few of these
quotients admit algebra structures, but those that do can be constructed
simply: killing a generator $\xi_k$ in the category of associative algebras
freely kills the higher generators $\xi_{k+n}$. Using new information about the
conjugation operation in the dual Steenrod algebra, we also consider quotients
by families of Milnor generators and their conjugates. This allows us to
produce a family of associative $H\mathbb F_2\wedge H\mathbb F_2$-algebras
whose coefficient rings are finite-dimensional and exhibit unexpected duality
features. We then use these algebras to give detailed computations of the
homotopy groups of several modules over this ring spectrum.


Abstract:
  In this paper the Feynman Green function for Maxwell's theory in curved
space-time is studied by using the Fock-Schwinger-DeWitt asymptotic expansion;
the point-splitting method is then applied, since it is a valuable tool for
regularizing divergent observables. Among these, the stress-energy tensor is
expressed in terms of second covariant derivatives of the Hadamard Green
function, which is also closely linked to the effective action; therefore one
obtains a series expansion for the stress-energy tensor. Its divergent part can
be isolated, and a concise formula is here obtained: by dimensional analysis
and combinatorics, there are two kinds of terms: quadratic in curvature tensors
(Riemann, Ricci tensors and scalar curvature) and linear in their second
covariant derivatives. This formula holds for every space-time metric; it is
made even more explicit in the physically relevant particular cases of
Ricci-flat and maximally symmetric spaces, and fully evaluated for some
examples of physical interest: Kerr and Schwarzschild metrics and de Sitter
space-time.


Abstract:
  The purpose of the paper is to show that, in low dimensions, the WDVV
equations are bi-Hamiltonian. The invariance of the bi-Hamiltonian formalism is
proved for $N=3$. More examples in higher dimensions show that the result might
hold in general. The invariance group of the bi-Hamiltonian pairs that we find
for WDVV equations is the group of projective transformations. The significance
of projective invariance of WDVV equations is discussed in detail. The computer
algebra programs that were used for calculations throughout the paper are
provided in a GitHub repository.


Abstract:
  The Influenza type A virus can be considered as one of the most severe
viruses that can infect multiple species with often fatal consequences to the
hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a
target for antiviral drug development realised through accurate identification
of its sub-types and possible the targeted hosts. In this paper, to accurately
predict if an Influenza type A virus has the capability to infect human hosts,
by using only the HA gene, is therefore developed and tested. The predictive
model follows three main steps; (i) decoding the protein sequences into
numerical signals using EIIP amino acid scale, (ii) analysing these sequences
by using Discrete Fourier Transform (DFT) and extracting DFT-based features,
(iii) using a predictive model, based on Artificial Neural Networks and using
the features generated by DFT. In this analysis, from the Influenza Research
Database, 30724, 18236 and 8157 HA protein sequences were collected for Human,
Avian and Swine, respectively. Given this set of the proteins, the proposed
method yielded 97.36% (+- 0.04%), 97.26% (+- 0.26%), 0.978 (+- 0.004), 0.963
(+- 0.005) and 0.945 (+- 0.005) for the training accuracy validation accuracy,
precision, recall and Mathews Correlation Coefficient (MCC) respectively, based
on a 10-fold cross-validation. The classification model generated by using one
of the largest dataset, if not the largest, yields promising results that could
lead to early detection of such species and help develop precautionary
measurements for possible human infections.


Abstract:
  The pre-training on the graph neural network model can learn the general
features of large-scale networks or networks of the same type by
self-supervised methods, which allows the model to work even when node labels
are missing. However, the existing pre-training methods do not take network
evolution into consideration. This paper proposes a pre-training method on
dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph
generation tasks to simultaneously learn the structure, semantics, and
evolution features of the graph. The method includes two steps: 1) dynamic
sub-graph sampling, and 2) pre-training with dynamic attributed graph
generation task. Comparative experiments on three realistic dynamic network
datasets show that the proposed method achieves the best results on the link
prediction fine-tuning task.


Abstract:
  This work tackles the issue of fairness in the context of generative
procedures, such as image super-resolution, which entail different definitions
from the standard classification setting. Moreover, while traditional group
fairness definitions are typically defined with respect to specified protected
groups -- camouflaging the fact that these groupings are artificial and carry
historical and political motivations -- we emphasize that there are no ground
truth identities. For instance, should South and East Asians be viewed as a
single group or separate groups? Should we consider one race as a whole or
further split by gender? Choosing which groups are valid and who belongs in
them is an impossible dilemma and being "fair" with respect to Asians may
require being "unfair" with respect to South Asians. This motivates the
introduction of definitions that allow algorithms to be \emph{oblivious} to the
relevant groupings.
  We define several intuitive notions of group fairness and study their
incompatibilities and trade-offs. We show that the natural extension of
demographic parity is strongly dependent on the grouping, and \emph{impossible}
to achieve obliviously. On the other hand, the conceptually new definition we
introduce, Conditional Proportional Representation, can be achieved obliviously
through Posterior Sampling. Our experiments validate our theoretical results
and achieve fair image reconstruction using state-of-the-art generative models.


Abstract:
  The complex-valued quantum mechanics considers quantum motion on the complex
plane instead of on the real axis, and studies the variations of a particle
complex position, momentum and energy along a complex trajectory. On the basis
of quantum Hamilton-Jacobi formalism in the complex space, we point out that
having complex-valued motion is a universal property of quantum systems,
because every quantum system is actually accompanied with an intrinsic complex
Hamiltonian originating from the equation. It is revealed that the conventional
real-valued quantum mechanics is a special case of the complex-valued quantum
mechanics in that the eigen-structures of real and complex quantum systems,
such as their eigenvalues, eigenfunctions and eigen-trajectories, are invariant
under linear complex mapping. In other words, there is indeed no distinction
between Hermitian systems, PT-symmetric systems, and non PT-symmetric systems
when viewed from a complex domain. Their eigen-structures can be made
coincident through linear transformation of complex coordinates.


Abstract:
  Learned optimizers are increasingly effective, with performance exceeding
that of hand designed optimizers such as Adam~\citep{kingma2014adam} on
specific tasks \citep{metz2019understanding}. Despite the potential gains
available, in current work the meta-training (or `outer-training') of the
learned optimizer is performed by a hand-designed optimizer, or by an optimizer
trained by a hand-designed optimizer \citep{metz2020tasks}. We show that a
population of randomly initialized learned optimizers can be used to train
themselves from scratch in an online fashion, without resorting to a hand
designed optimizer in any part of the process. A form of population based
training is used to orchestrate this self-training. Although the randomly
initialized optimizers initially make slow progress, as they improve they
experience a positive feedback loop, and become rapidly more effective at
training themselves. We believe feedback loops of this type, where an optimizer
improves itself, will be important and powerful in the future of machine
learning. These methods not only provide a path towards increased performance,
but more importantly relieve research and engineering effort.


Abstract:
  We conduct a randomized experiment that varies one-time health insurance
subsidy amounts (partial and full) in Ghana to study the impacts of subsidies
on insurance enrollment and health care utilization. We find that both partial
and full subsidies promote insurance enrollment in the long run, even after the
subsidies expired. Although the long run enrollment rate and selective
enrollment do not differ by subsidy level, long run health care utilization
increased only for the partial subsidy group. We show that this can plausibly
be explained by stronger learning-through-experience behavior in the partial
than in the full subsidy group.


Abstract:
  In task allocation for real-time domains, such as disaster response, a
limited number of agents is deployed across a large area to carry out numerous
tasks, each with its prerequisites, profit, time window and workload. To
maximize profits while minimizing time penalties, agents need to cooperate by
forming, disbanding and reforming coalitions. In this paper, we name this
problem Multi-Agent Routing and Scheduling through Coalition formation (MARSC)
and show that it generalizes the important Team Orienteering Problem with Time
Windows. We propose a binary integer program and an anytime and scalable
heuristic to solve it. Using public London Fire Brigade records, we create a
dataset with 347588 tasks and a test framework that simulates the mobilization
of firefighters. In problems with up to 150 agents and 3000 tasks, our
heuristic finds solutions up to 3.25 times better than the Earliest Deadline
First approach commonly used in real-time systems. Our results constitute the
first large-scale benchmark for the MARSC problem.


Abstract:
  This paper investigates the energy efficiency (EE) optimization in downlink
multi-cell massive multiple-input multiple-output (MIMO). In our research, the
statistical channel state information (CSI) is exploited to reduce the
signaling overhead. To maximize the minimum EE among the neighbouring cells, we
design the transmit covariance matrices for each base station (BS).
Specifically, optimization schemes for this max-min EE problem are developed,
in the centralized and distributed ways, respectively. To obtain the transmit
covariance matrices, we first find out the closed-form optimal transmit
eigenmatrices for the BS in each cell, and convert the original transmit
covariance matrices designing problem into a power allocation one. Then, to
lower the computational complexity, we utilize an asymptotic approximation
expression for the problem objective. Moreover, for the power allocation
design, we adopt the minorization maximization method to address the
non-convexity of the ergodic rate, and use Dinkelbach's transform to convert
the max-min fractional problem into a series of convex optimization
subproblems. To tackle the transformed subproblems, we propose a centralized
iterative water-filling scheme. For reducing the backhaul burden, we further
develop a distributed algorithm for the power allocation problem, which
requires limited inter-cell information sharing. Finally, the performance of
the proposed algorithms are demonstrated by extensive numerical results.


Abstract:
  Optimal Mass Transport (OMT) is a well studied problem with a variety of
applications in a diverse set of fields ranging from Physics to Computer Vision
and in particular Statistics and Data Science. Since the original formulation
of Monge in 1781 significant theoretical progress been made on the existence,
uniqueness and properties of the optimal transport maps. The actual numerical
computation of the transport maps, particularly in high dimensions, remains a
challenging problem. By Brenier's theorem, the continuous OMT problem can be
reduced to that of solving a non-linear PDE of Monge-Ampere type whose solution
is a convex function. In this paper, building on recent developments of input
convex neural networks and physics informed neural networks for solving PDE's,
we propose a Deep Learning approach to solve the continuous OMT problem.
  To demonstrate the versatility of our framework we focus on the ubiquitous
density estimation and generative modeling tasks in statistics and machine
learning. Finally as an example we show how our framework can be incorporated
with an autoencoder to estimate an effective probabilistic generative model.


Abstract:
  In this paper, we develop a numerical scheme to handle interfaces across
computational domains in multi-block schemes for the approximation of systems
of conservation laws. We are interested in transmitting shock discontinuities
without lowering the overall precision of the method. We want to accomplish
this without using information from interior points of adjacent grids, that is,
sharing only information from boundary points of those grids. To achieve this,
we choose to work with the second-order Kurganov-Tadmor (KT) method at interior
points, relaxing it to first order at interfaces. This allows us to keep
second-order overall accuracy (in the relevant norm) and at the same time
preserve the TVD property of the original scheme. After developing the method
we performed several standard one and two-dimensional tests. Among them, we
used the one-dimensional advection and Burgers equations to verify the
second-order convergence of the method. We also tested the two-dimensional
Euler equations with an implosion and a Gresho vortex\cite{liska2003}. In
particular, in the two-dimensional implosion test we can see that regardless of
the orientation of shocks with respect to the interface, they travel across
them without appreciable deformation both in amplitude and front direction.


Abstract:
  In this paper, we want to show the potential benefit of a dynamic auto-tuning
approach for the inference process in the Deep Neural Network (DNN) context,
tackling the object detection challenge. We benchmarked different neural
networks to find the optimal detector for the well-known COCO 17 database, and
we demonstrate that even if we only consider the quality of the prediction
there is not a single optimal network. This is even more evident if we also
consider the time to solution as a metric to evaluate, and then select, the
most suitable network. This opens to the possibility for an adaptive
methodology to switch among different object detection networks according to
run-time requirements (e.g. maximum quality subject to a time-to-solution
constraint).
  Moreover, we demonstrated by developing an ad hoc oracle, that an additional
proactive methodology could provide even greater benefits, allowing us to
select the best network among the available ones given some characteristics of
the processed image. To exploit this method, we need to identify some image
features that can be used to steer the decision on the most promising network.
Despite the optimization opportunity that has been identified, we were not able
to identify a predictor function that validates this attempt neither adopting
classical image features nor by using a DNN classifier.


Abstract:
  Due to the lack of natural scene and haze prior information, it is greatly
challenging to completely remove the haze from single image without distorting
its visual content. Fortunately, the real-world haze usually presents
non-homogeneous distribution, which provides us with many valuable clues in
partial well-preserved regions. In this paper, we propose a Non-Homogeneous
Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph
reasoning. Firstly, we employ the gamma correction iteratively to simulate
artificial multiple shots under different exposure conditions, whose haze
degrees are different and enrich the underlying scene prior. Secondly, beyond
utilizing the local neighboring relationship, we build a bidimensional graph
reasoning module to conduct non-local filtering in the spatial and channel
dimensions of feature maps, which models their long-range dependency and
propagates the natural scene prior between the well-preserved nodes and the
nodes contaminated by haze. We evaluate our method on different benchmark
datasets. The results demonstrate that our method achieves superior performance
over many state-of-the-art algorithms for both the single image dehazing and
hazy image understanding tasks.


Abstract:
  Let $H$ be a complex Hilbert space. Consider the ortho-Grassmann graph
$\Gamma^{\perp}_{k}(H)$ whose vertices are $k$-dimensional subspaces of $H$
(projections of rank $k$) and two subspaces are connected by an edge in this
graph if they are compatible and adjacent (the corresponding rank-$k$
projections commute and their difference is an operator of rank $2$). Our main
result is the following: if $\dim H\ne 2k$, then every automorphism of
$\Gamma^{\perp}_{k}(H)$ is induced by a unitary or anti-unitary operator; if
$\dim H=2k\ge 6$, then every automorphism of $\Gamma^{\perp}_{k}(H)$ is induced
by a unitary or anti-unitary operator or it is the composition of such an
automorphism and the orthocomplementary map. For the case when $\dim H=2k=4$
the statement fails. To prove this statement we compare geodesics of length two
in ortho-Grassmann graphs and characterise compatibility (commutativity) in
terms of geodesics in Grassmann and ortho-Grassmann graphs. At the end, we
extend this result on generalised ortho-Grassmann graphs associated to
conjugacy classes of finite-rank self-adjoint operators.


Abstract:
  We introduce an algebraic method to study local stability in the Newtonian
$n$-body problem when certain symmetries are present. We use representation
theory of groups to simplify the calculations of certain eigenvalue problems.
The method should be applicable in many cases, we give two main examples here:
the square central configurations with four equal masses, and the equilateral
triangular configurations with three equal masses plus an additional mass of
arbitrary size at the center. We explicitly found the eigenvalues of certain
8x8 Hessians in these examples, with only some simple calculations of traces.
We also studied the local stability properties of corresponding relative
equilibria in the four-body problems.


Abstract:
  Spatial gaseous structures in the Beta Lyrae system have been studied with
the fact of change in the longitudinal component of the donor's magnetic field
during the orbital period in mind. The investigation was based primarily on the
study of the dynamics of the circumstellar structures surrounding the binary
system as a whole. The special emphasis was placed on the study of complex
helium lines, in particular those arising from metastable levels. A number of
different observable facts from the ultraviolet to the red spectral region were
analyzed. The configuration of the donor magnetic field is a factor that not
only enhances mass transfer and influences the formation of spatial gas
structures between stellar components but, to some extent, also affects the
outflow of matter and the formation of external gas structures around this
interacting binary system. Together with previous articles
\citep{Skulskyy2020a, Skulskyy2020b}, the pieces of evidence of this work,
confirming the reflection of magnetically controlled matter in circumbinary
structures, define the basis for a coherent picture of the mass exchange
between components and outflows of matter outwards


Abstract:
  In this article we consider a version of the challenging problem of learning
from datasets whose size is too limited to allow generalisation beyond the
training set. To address the challenge we propose to use a transfer learning
approach whereby the model is first trained on a synthetic dataset replicating
features of the original objects. In this study the objects were smartphone
photographs of near-complete Roman terra sigillata pottery vessels from the
collection of the Museum of London. Taking the replicated features from
published profile drawings of pottery forms allowed the integration of expert
knowledge into the process through our synthetic data generator. After this
first initial training the model was fine-tuned with data from photographs of
real vessels. We show, through exhaustive experiments across several popular
deep learning architectures, different test priors, and considering the impact
of the photograph viewpoint and excessive damage to the vessels, that the
proposed hybrid approach enables the creation of classifiers with appropriate
generalisation performance. This performance is significantly better than that
of classifiers trained exclusively on the original data which shows the promise
of the approach to alleviate the fundamental issue of learning from small
datasets.


Abstract:
  We calculate, for the first time, the NNLO QCD corrections to identified
heavy hadron production at hadron colliders. The calculation is based on a
flexible numeric framework which allows the calculation of any distribution of
a single identified heavy hadron plus jets and non-QCD particles. As a first
application we provide NNLO QCD predictions for several differential
distributions of $B$ hadrons in $t\bar t$ events at the LHC. Among others,
these predictions are needed for the precise determination of the top quark
mass. The extension of our results to other processes, like open or associated
$B$ and charm production is straightforward. We also explore the prospects for
extracting heavy flavor fragmentation functions from LHC data.


Abstract:
  Reward shaping (RS) is a powerful method in reinforcement learning (RL) for
overcoming the problem of sparse or uninformative rewards. However, RS
typically relies on manually engineered shaping-reward functions whose
construction is time-consuming and error-prone. It also requires domain
knowledge which runs contrary to the goal of autonomous learning. We introduce
Reinforcement Learning Optimising Shaping Algorithm (ROSA), an automated RS
framework in which the shaping-reward function is constructed in a novel Markov
game between two agents. A reward-shaping agent (Shaper) uses switching
controls to determine which states to add shaping rewards and their optimal
values while the other agent (Controller) learns the optimal policy for the
task using these shaped rewards. We prove that ROSA, which easily adopts
existing RL algorithms, learns to construct a shaping-reward function that is
tailored to the task thus ensuring efficient convergence to high performance
policies. We demonstrate ROSA's congenial properties in three carefully
designed experiments and show its superior performance against state-of-the-art
RS algorithms in challenging sparse reward environments.


Abstract:
  We study the problem of training a model that must obey demographic fairness
conditions when the sensitive features are not available at training time -- in
other words, how can we train a model to be fair by race when we don't have
data about race? We adopt a fairness pipeline perspective, in which an
"upstream" learner that does have access to the sensitive features will learn a
proxy model for these features from the other attributes. The goal of the proxy
is to allow a general "downstream" learner -- with minimal assumptions on their
prediction task -- to be able to use the proxy to train a model that is fair
with respect to the true sensitive features. We show that obeying multiaccuracy
constraints with respect to the downstream model class suffices for this
purpose, and provide sample- and oracle efficient-algorithms and generalization
bounds for learning such proxies. In general, multiaccuracy can be much easier
to satisfy than classification accuracy, and can be satisfied even when the
sensitive features are hard to predict.


Abstract:
  We consider learning Ising tree models when the observations from the nodes
are corrupted by independent but non-identically distributed noise with unknown
statistics. Katiyar et al. (2020) showed that although the exact tree structure
cannot be recovered, one can recover a partial tree structure; that is, a
structure belonging to the equivalence class containing the true tree. This
paper presents a systematic improvement of Katiyar et al. (2020). First, we
present a novel impossibility result by deriving a bound on the necessary
number of samples for partial recovery. Second, we derive a significantly
improved sample complexity result in which the dependence on the minimum
correlation $\rho_{\min}$ is $\rho_{\min}^{-8}$ instead of $\rho_{\min}^{-24}$.
Finally, we propose Symmetrized Geometric Averaging (SGA), a more statistically
robust algorithm for partial tree recovery. We provide error exponent analyses
and extensive numerical results on a variety of trees to show that the sample
complexity of SGA is significantly better than the algorithm of Katiyar et al.
(2020). SGA can be readily extended to Gaussian models and is shown via
numerical experiments to be similarly superior.


Abstract:
  Carbon nanoscrolls (CNSs) are nanomaterials with geometry resembling graphene
layers rolled up into a spiral (papyrus-like) form. Effects of hydrogenation
and temperature on the self-scrolling process of two nanoribbons interacting
with a carbon nanotube (CNT) have been studied by molecular dynamics
simulations for three configurations: (1) graphene/graphene/CNT; (2)
graphene/graphane/CNT, and (3) graphane/graphane/CNT. Graphane refers to a
fully hydrogenated graphene nanoribbon. Nanoscroll formation is observed for
configuration (1) and (2) for temperatures 300-1000 and 300-600 K,
respectively, while nanoribbons wrap CNT without nanoscroll formation for
configuration (3).


Abstract:
  Convolutional video models have an order of magnitude larger computational
complexity than their counterpart image-level models. Constrained by
computational resources, there is no model or training method that can train
long video sequences end-to-end. Currently, the main-stream method is to split
a raw video into clips, leading to incomplete fragmentary temporal information
flow. Inspired by natural language processing techniques dealing with long
sentences, we propose to treat videos as serial fragments satisfying Markov
property, and train it as a whole by progressively propagating information
through the temporal dimension in multiple steps. This progressive training
(PGT) method is able to train long videos end-to-end with limited resources and
ensures the effective transmission of information. As a general and robust
training method, we empirically demonstrate that it yields significant
performance improvements on different models and datasets. As an illustrative
example, the proposed method improves SlowOnly network by 3.7 mAP on Charades
and 1.9 top-1 accuracy on Kinetics with negligible parameter and computation
overhead. Code is available at https://github.com/BoPang1996/PGT.


Abstract:
  We consider a sequence of finite quantum graphs with few loops, so that they
converge, in the sense of Benjamini-Schramm, to a random infinite quantum tree.
We assume these quantum trees are spectrally delocalized in some interval $I$,
in the sense that their spectrum in $I$ is purely absolutely continuous and
their Green's functions are well controlled near the real axis. We furthermore
suppose that the underlying sequence of discrete graphs is expanding. We deduce
a quantum ergodicity result, showing that the eigenfunctions with eigenvalues
lying in $I$ are spatially delocalized.


Abstract:
  We propose a method to reconstruct the 3-D molecular structure from
micrographs collected at just one sample tilt angle in the random conical tilt
scheme in cryo-electron microscopy. Our method uses autocorrelation analysis on
the micrographs to estimate features of the molecule which are invariant under
certain nuisance parameters such as the positions of molecular projections in
the micrographs. This enables us to reconstruct the molecular structure
directly from micrographs, completely circumventing the need for particle
picking. We demonstrate reconstructions with simulated data and investigate the
effect of the missing-cone region. These results show promise to reduce the
size limit for single particle reconstruction in cryo-electron microscopy.


Abstract:
  Vehicular Ad Hoc Networks (VANETs) improves traffic management and reduce the
amount of road accidents by providing safety applications. However, VANETs are
vulnerable to variety of security attacks from malicious entities. An
authentication is an integral a neighborhood of trust establishment and secure
communications between vehicles. The Road-side Unit (RSU) evaluates trust-value
and the Agent Trusted Authority (ATA) helps in computing the trust-value of
auto supported its reward-points. The communication between nodes is enhanced,
this can reduce 50% of road accidents. The security of the VANET is improved.
We propose the utilization of Elliptic Curve Cryptography in the design of an
efficient data encryption/decryption system for sensor nodes in a wireless
network. Elliptic Curve Cryptography can provide impressive levels of security
standards while keeping down the cost of certain issues, primarily storage
space. Sensors will benefit from having to store relatively smaller keys
coupled with increased computational capability and this will be a stronger
design as the bit-level security is improved. Thus, reducing the time delay
between the nodes and to provide better results between them we have made use
of this method. The implementation of this work is done with NS2 software.


Abstract:
  With the remarkable success of representation learning for prediction
problems, we have witnessed a rapid expansion of the use of machine learning
and deep learning for the analysis of digital pathology and biopsy image
patches. However, learning over patch-wise features using convolutional neural
networks limits the ability of the model to capture global contextual
information and comprehensively model tissue composition. The phenotypical and
topological distribution of constituent histological entities play a critical
role in tissue diagnosis. As such, graph data representations and deep learning
have attracted significant attention for encoding tissue representations, and
capturing intra- and inter- entity level interactions. In this review, we
provide a conceptual grounding for graph analytics in digital pathology,
including entity-graph construction and graph architectures, and present their
current success for tumor localization and classification, tumor invasion and
staging, image retrieval, and survival prediction. We provide an overview of
these methods in a systematic manner organized by the graph representation of
the input image, scale, and organ on which they operate. We also outline the
limitations of existing techniques, and suggest potential future research
directions in this domain.


Abstract:
  In this paper we consider the discounted 0-1 knapsack problem (DKP), which is
an extension of the classical knapsack problem where a set of items is
decomposed into groups of three items. At most one item can be chosen from each
group and the aim is to maximize the total profit of the selected items while
respecting the knapsack capacity constraint. The DKP is a relatively recent
problem in the literature. In this paper we propose a two-phase approach in
which the problem is reduced by applying exact and / or heuristic fixation
rules in a first phase that can be viewed as a preprocessing phase. The
remaining problem can then be solved by dynamic programming. Experiments
performed on available instances in the literature show that the fixation
techniques are very useful to solve these instances. Indeed, the preprocessing
phase greatly reduces the size of these instances, leading to a significant
reduction in the time required for dynamic programming to provide an optimal
solution.


Abstract:
  In the paper, we study the moments $\langle \xi_{2;a_1}^{\|;n}\rangle$ of the
axial-vector $a_1(1260)$-meson distribution amplitude by using the QCD sum
rules approach under the background field theory. By considering the vacuum
condensates up to dimension-six and the perturbative part up to next-to-leading
order QCD corrections, its first five moments at an initial scale $\mu_0=1~{\rm
GeV}$ are $\langle \xi_{2;a_1}^{\|;2}\rangle|_{\mu_0} = 0.210 \pm 0.018$,
$\langle\xi_{2;a_1}^{\|;4}\rangle|_{\mu_0} = 0.091 \pm 0.007$,
$\langle\xi_{2;a_1}^{\|;6}\rangle |_{\mu_0} = 0.052 \pm 0.004$,
$\langle\xi_{2;a_1}^{\|;8}\rangle |_{\mu_0} = 0.036 \pm 0.003$ and
$\langle\xi_{2;a_1}^{\|;10}\rangle |_{\mu_0} = 0.025 \pm 0.002$, respectively.
We then construct a light-cone harmonic oscillator model for $a_1(1260)$-meson
longitudinal twist-2 distribution amplitude $\phi_{2;a_1}^{\|}(x,\mu)$, whose
input parameters are fitted by using the least square method. As an application
of $\phi_{2;a_1}^\|(x,\mu)$, we calculate the transition form factors (TFFs) of
$D\to a_1(1260)$ in large and intermediate momentum transfers by using the QCD
light-cone sum rules approach. And at the largest recoil point ($q^2=0$), we
obtain $ A(0) = 0.130_{ - 0.015}^{ + 0.013}$, $V_1(0) =
1.900_{-0.125}^{+0.121}$, $V_2(0) = 0.210_{-0.020}^{ + 0.018}$, and $V_0(0) =
0.236_{ - 0.024}^{ + 0.026}$. By applying the extrapolated TFFs to the
semi-leptonic decay $D^{0(+)} \to a_1^{-(0)}(1260)\ell^+\nu_\ell$, we obtain
${\cal B}(D^0\to a_1^-(1260) e^+\nu_e) = (5.458_{-0.681}^{+0.720}) \times
10^{-5}$, ${\cal B}(D^+\to a_1^0(1260) e^+\nu_e) = (6.922_{-0.864}^{+0.913})
\times 10^{-5}$, ${\cal B}(D^0\to a_1^-(1260) \mu^+
\nu_\mu)=(4.897_{-0.627}^{+0.663}) \times 10^{-5}$, ${\cal B}(D^+ \to
a_1^0(1260) \mu^+ \nu_\mu)=(6.210_{-0.796}^{+0.767}) \times 10^{-5}$.


Abstract:
  Thermal reverberation in accretion discs of active galactic nuclei is thought
to be the reason of the continuum UV/optical time lags seen in these sources.
Recently, we studied thermal reverberation of a standard Novikov-Thorne
accretion disc illuminated by an X-ray point-like source, and we derived an
analytic prescription for the time lags as function of wavelength. In this
work, we use this analytic function to fit the time-lags spectra of seven
Seyferts, that have been intensively monitored, in many wave-bands, in the last
few years. We find that thermal reverberation can explain the observed
UV/optical time lags in all these sources. Contrary to previous claims, the
magnitude of the observed UV/optical time-lags is exactly as expected in the
case of a standard accretion disc in the lamp-post geometry, given the black
hole mass and the accretion rate estimates for the objects we study. We derive
estimates of the disc accretion rates and corona height for a non-spinning and
a maximally spinning black hole scenarios. We also find that the modelling of
the continuum optical/UV time-lags can be used to estimate the black hole spin,
when combined with additional information. We also find that the model
under-predicts the observed X-ray to UV time-lags, but this difference is
probably due to the broad X-ray auto-correlation function of these sources.


Abstract:
  Knowledge distillation (KD) is commonly used to construct synthetic data for
training non-autoregressive translation (NAT) models. However, there exists a
discrepancy on low-frequency words between the distilled and the original data,
leading to more errors on predicting low-frequency words. To alleviate the
problem, we directly expose the raw data into NAT by leveraging pretraining. By
analyzing directed alignments, we found that KD makes low-frequency source
words aligned with targets more deterministically but fails to align sufficient
low-frequency words from target to source. Accordingly, we propose reverse KD
to rejuvenate more alignments for low-frequency target words. To make the most
of authentic and synthetic data, we combine these complementary approaches as a
new training strategy for further boosting NAT performance. We conduct
experiments on five translation benchmarks over two advanced architectures.
Results demonstrate that the proposed approach can significantly and
universally improve translation quality by reducing translation errors on
low-frequency words. Encouragingly, our approach achieves 28.2 and 33.9 BLEU
points on the WMT14 English-German and WMT16 Romanian-English datasets,
respectively. Our code, data, and trained models are available at
\url{https://github.com/longyuewangdcu/RLFW-NAT}.


Abstract:
  We prove the existence of infinitely many solutions to an elliptic problem by
borrowing the techniques from algebraic topology. The solution(s) thus obtained
will also be proved to be bounded.


Abstract:
  We analyzed Medical Subject Headings (MeSH) from 21.6 million research
articles indexed by PubMed to map this vast space of entities and their
relations, providing insights into the origins and future of biomedical
convergence. Detailed analysis of MeSH co-occurrence networks identifies three
robust knowledge clusters: the vast universe of microscopic biological entities
and structures; systems, disease and diagnostics; and emergent biological and
social phenomena underlying the complex problems driving the health, behavioral
and brain science frontiers. These domains integrated from the 1990s onward by
way of technological and informatic capabilities that introduced highly
controllable, scalable and permutable research processes and invaluable imaging
techniques for illuminating fundamental structure-function-behavior questions.
Article-level analysis confirms a positive relationship between team size and
topical diversity, and shows convergence to be increasing in prominence but
with recent saturation. Together, our results invite additional policy support
for cross-disciplinary team assembly to harness transdisciplinary convergence.


Abstract:
  We initiate the study of fairness for ordinal regression, or ordinal
classification. We adapt two fairness notions previously considered in fair
ranking and propose a strategy for training a predictor that is approximately
fair according to either notion. Our predictor consists of a threshold model,
composed of a scoring function and a set of thresholds, and our strategy is
based on a reduction to fair binary classification for learning the scoring
function and local search for choosing the thresholds. We can control the
extent to which we care about the accuracy vs the fairness of the predictor via
a parameter. In extensive experiments we show that our strategy allows us to
effectively explore the accuracy-vs-fairness trade-off and that it often
compares favorably to "unfair" state-of-the-art methods for ordinal regression
in that it yields predictors that are only slightly less accurate, but
significantly more fair.


Abstract:
  We consider a sequential assortment selection problem where the user choice
is given by a multinomial logit (MNL) choice model whose parameters are
unknown. In each period, the learning agent observes a $d$-dimensional
contextual information about the user and the $N$ available items, and offers
an assortment of size $K$ to the user, and observes the bandit feedback of the
item chosen from the assortment. We propose upper confidence bound based
algorithms for this MNL contextual bandit. The first algorithm is a simple and
practical method which achieves an $\tilde{\mathcal{O}}(d\sqrt{T})$ regret over
$T$ rounds. Next, we propose a second algorithm which achieves a
$\tilde{\mathcal{O}}(\sqrt{dT})$ regret. This matches the lower bound for the
MNL bandit problem, up to logarithmic terms, and improves on the best known
result by a $\sqrt{d}$ factor. To establish this sharper regret bound, we
present a non-asymptotic confidence bound for the maximum likelihood estimator
of the MNL model that may be of independent interest as its own theoretical
contribution. We then revisit the simpler, significantly more practical, first
algorithm and show that a simple variant of the algorithm achieves the optimal
regret for a broad class of important applications.


Abstract:
  Manganese oxide-coated TiO2 nanotube array (NTA) was synthesized and applied
for the (electro)catalytic oxidation of sulfide. By setting the
electrodeposition parameters and calcination temperature, the coating obtained
was MnO2 or Mn2O3, with rod or needle like morphology. Excellent catalytic
activity of the Ti/TiO2 NTA/MnxOy anodes resulted in robust and selective
oxidation of sulfide to elemental sulfur via an inner sphere complex between
the sulfide ion and Mn-oxide. The penetration of the MnxOy inside the NTA
improved adhesion and enhanced the electron transfer, thus enabling complete
and rapid electrocatalytic re-oxidation of the Mn-oxide coating. At pH 8, the
final product of sulfide oxidation was colloidal sulfur, S8, which prevented
anode passivation and enabled a stable (electro)catalytic activity in the
subsequent applications. This was observed in both synthetic electrolyte and
real sewage, thus demonstrating the efficiency of the Ti/TiO2 NTA/MnxOy anode
for sulfide oxidation in complex waste streams.


Abstract:
  In this paper, we present a field-trial C-band 72Gbit/s optical on-off keying
(OOK) system over 18.8km dispersion-uncompensated submarine optical cable in
the South China Sea. Chromatic dispersion (CD) of 18.8km submarine optical
cable causes four spectral nulls on the 36GHz bandwidth of 72Gbit/s OOK signal,
which is the main obstacle for achieving an acceptable bit-error-rate (BER)
performance. Decision feedback equalizer (DFE) is effective to compensate for
the spectral nulls. However, DFE has a serious defect of burst-error
propagation when the burst errors emerge due to the unstable submarine
environment. Weighted DFE (WDFE) can be used to mitigate the burst-error
propagation, but it cannot fully compensate for the spectral nulls because only
a part of feedback symbols is directly decided. Fortunately, maximum likelihood
sequence estimation (MLSE) can be added after the WDFE to simultaneously
eliminate the resisting spectral distortions and implement optimal detection.
Compared to the joint DFE and MLSE algorithm, the joint WDFE and MLSE algorithm
can effectively suppress the burst-error propagation to obtain a maximum 2.9dB
improvement of $\boldsymbol{Q}$ factor and eliminate the phenomenon of BER
floor. In conclusion, the joint WDFE and MLSE algorithm can solve the
burst-error propagation for the field-trial fiber-optic communications.


Abstract:
  More and more, optimization methods are used to find diverse solution sets.
We compare solution diversity in multi-objective optimization, multimodal
optimization, and quality diversity in a simple domain. We show that
multiobjective optimization does not always produce much diversity, multimodal
optimization produces higher fitness solutions, and quality diversity is not
sensitive to genetic neutrality and creates the most diverse set of solutions.
An autoencoder is used to discover phenotypic features automatically, producing
an even more diverse solution set with quality diversity. Finally, we make
recommendations about when to use which approach.


Abstract:
  The amount of CO$_2$ emitted per kilowatt-hour on an electricity grid varies
by time of day and substantially varies by location due to the types of
generation. Networked collections of warehouse scale computers, sometimes
called Hyperscale Computing, emit more carbon than needed if operated without
regard to these variations in carbon intensity. This paper introduces Google's
system for Carbon-Intelligent Compute Management, which actively minimizes
electricity-based carbon footprint and power infrastructure costs by delaying
temporally flexible workloads. The core component of the system is a suite of
analytical pipelines used to gather the next day's carbon intensity forecasts,
train day-ahead demand prediction models, and use risk-aware optimization to
generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all
datacenter clusters across Google's fleet. VCCs impose hourly limits on
resources available to temporally flexible workloads while preserving overall
daily capacity, enabling all such workloads to complete within a day. Data from
operation shows that VCCs effectively limit hourly capacity when the grid's
energy supply mix is carbon intensive and delay the execution of temporally
flexible workloads to "greener" times.


Abstract:
  We present an $(e^{O(p)} \frac{\log \ell}{\log\log\ell})$-approximation
algorithm for socially fair clustering with the $\ell_p$-objective. In this
problem, we are given a set of points in a metric space. Each point belongs to
one (or several) of $\ell$ groups. The goal is to find a $k$-medians,
$k$-means, or, more generally, $\ell_p$-clustering that is simultaneously good
for all of the groups. More precisely, we need to find a set of $k$ centers $C$
so as to minimize the maximum over all groups $j$ of $\sum_{u \text{ in group
}j} d(u,C)^p$. The socially fair clustering problem was independently proposed
by Ghadiri, Samadi, and Vempala [2021] and Abbasi, Bhaskara, and
Venkatasubramanian [2021]. Our algorithm improves and generalizes their
$O(\ell)$-approximation algorithms for the problem. The natural LP relaxation
for the problem has an integrality gap of $\Omega(\ell)$. In order to obtain
our result, we introduce a strengthened LP relaxation and show that it has an
integrality gap of $\Theta(\frac{\log \ell}{\log\log\ell})$ for a fixed $p$.
Additionally, we present a bicriteria approximation algorithm, which
generalizes the bicriteria approximation of Abbasi et al. [2021].


Abstract:
  The large and diffuse galaxies NGC1052-DF2 and NGC1052-DF4 have been found to
have very low dark matter content and a population of luminous globular
clusters. Accurate distance measurements are key to interpreting these
observations. Recently, the distance to NGC1052-DF4 was found to be $20.0\pm
1.6$ Mpc by identifying the tip of the red giant branch (TRGB) in 12 orbits of
Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) imaging. Here we
present 40 orbits of HST ACS data for NGC1052-DF2 and use these data to measure
its TRGB. The TRGB is readily apparent in the color-magnitude diagram. Using a
forward model that incorporates photometric uncertainties, we find a TRGB
magnitude of $m_{\rm F814W, TRGB} = 27.67 \pm 0.10$ mag. The inferred distance
is $D_{\rm TRGB} = 22.1 \pm 1.2$ Mpc, consistent with the previous surface
brightness fluctuation distances to the bright elliptical galaxy NGC1052. The
new HST distance rules out the idea that some of NGC1052-DF2's unusual
properties can be explained if it were at $\sim 13$ Mpc; instead, it implies
that the galaxy's globular clusters are even more luminous than had been
derived using the previous distance of 20 Mpc. The distance from NGC1052-DF2 to
NGC1052-DF4 is well-determined at $2.1\pm 0.5$ Mpc, significantly larger than
the virial diameter of NGC1052. We discuss the implications for formation
scenarios of the galaxies and for the external field effect, which has been
invoked to explain the intrinsic dynamics of these objects in the context of
modified Newtonian dynamics.


Abstract:
  In epidemiology, the effective reproduction number $R_e$ is used to
characterize the growth rate of an epidemic outbreak. In this paper, we
investigate properties of $R_e$ for a modified SEIR model of COVID-19 in the
city of Houston, TX USA, in which the population is divided into low-risk and
high-risk subpopulations. The response of $R_e$ to two types of control
measures (testing and distancing) applied to the two different subpopulations
is characterized. A nonlinear cost model is used for control measures, to
include the effects of diminishing returns. We propose three types of heuristic
strategies for mitigating COVID-19 that are targeted at reducing $R_e$, and we
exhibit the tradeoffs between strategy implementation costs and number of
deaths. We also consider two variants of each type of strategy: basic
strategies, which consider only the effects of controls on $R_e$, without
regard to subpopulation; and high-risk prioritizing strategies, which maximize
control of the high-risk subpopulation. Results showed that of the three
heuristic strategy types, the most cost-effective involved setting a target
value for $R_e$ and applying sufficient controls to attain that target value.
This heuristic led to strategies that begin with strict distancing of the
entire population, later followed by increased testing. Strategies that
maximize control on high-risk individuals were less cost-effective than basic
strategies that emphasize reduction of the rate of spreading of the disease.
The model shows that delaying the start of control measures past a certain
point greatly worsens strategy outcomes. We conclude that the effective
reproduction can be a valuable real-time indicator in determining
cost-effective control strategies.


Abstract:
  We introduce the notion of squeezing function corresponding to $d$-balanced
domains motivated by the concept of generalized squeezing function given by
Rong and Yang. In this work we study some of its properties and its relation
with Fridman invariant.


Abstract:
  The past decade has witnessed a groundbreaking rise of machine learning for
human language analysis, with current methods capable of automatically
accurately recovering various aspects of syntax and semantics - including
sentence structure and grounded word meaning - from large data collections.
Recent research showed the promise of such tools for analyzing acoustic
communication in nonhuman species. We posit that machine learning will be the
cornerstone of future collection, processing, and analysis of multimodal
streams of data in animal communication studies, including bioacoustic,
behavioral, biological, and environmental data. Cetaceans are unique non-human
model species as they possess sophisticated acoustic communications, but
utilize a very different encoding system that evolved in an aquatic rather than
terrestrial medium. Sperm whales, in particular, with their highly-developed
neuroanatomical features, cognitive abilities, social structures, and discrete
click-based encoding make for an excellent starting point for advanced machine
learning tools that can be applied to other animals in the future. This paper
details a roadmap toward this goal based on currently existing technology and
multidisciplinary scientific community effort. We outline the key elements
required for the collection and processing of massive bioacoustic data of sperm
whales, detecting their basic communication units and language-like
higher-level structures, and validating these models through interactive
playback experiments. The technological capabilities developed by such an
undertaking are likely to yield cross-applications and advancements in broader
communities investigating non-human communication and animal behavioral
research.


Abstract:
  We consider a single run-and-tumble particle (RTP) moving in one dimension.
We assume that the velocity of the particle is drawn independently at each
tumbling from a zero-mean Gaussian distribution and that the run times are
exponentially distributed. We investigate the probability distribution $P(X,N)$
of the position $X$ of the particle after $N$ runs, with $N\gg 1$. We show that
in the regime $ X \sim N^{3/4}$ the distribution $P(X,N)$ has a large deviation
form with a rate function characterized by a discontinuous derivative at the
critical value $X=X_c>0$. The same is true for $X=-X_c$ due to the symmetry of
$P(X,N)$. We show that this singularity corresponds to a first-order
condensation transition: for $X>X_c$ a single large jump dominates the RTP
trajectory. We consider the participation ratio of the single-run displacements
as the order parameter of the system, showing that this quantity is
discontinuous at $X=X_c$. Our results are supported by numerical simulations
performed with a constrained Markov chain Monte Carlo algorithm.


Abstract:
  The smartphone and laptop can be unlocked by face or fingerprint recognition,
while neural networks which confront numerous requests every day have little
capability to distinguish between untrustworthy and credible users. It makes
model risky to be traded as a commodity. Existed research either focuses on the
intellectual property rights ownership of the commercialized model, or traces
the source of the leak after pirated models appear. Nevertheless, active
identifying users legitimacy before predicting output has not been considered
yet. In this paper, we propose Model-Lock (M-LOCK) to realize an end-to-end
neural network with local dynamic access control, which is similar to the
automatic locking function of the smartphone to prevent malicious attackers
from obtaining available performance actively when you are away. Three kinds of
model training strategy are essential to achieve the tremendous performance
divergence between certified and suspect input in one neural network. Extensive
experiments based on MNIST, FashionMNIST, CIFAR10, CIFAR100, SVHN and GTSRB
datasets demonstrated the feasibility and effectiveness of the proposed scheme.


Abstract:
  In many machine learning applications where massive and privacy-sensitive
data are generated on numerous mobile or IoT devices, collecting data in a
centralized location may be prohibitive. Thus, it is increasingly attractive to
estimate parameters over mobile or IoT devices while keeping data localized.
Such learning setting is known as cross-device federated learning. In this
paper, we propose the first theoretically guaranteed algorithms for general
minimax problems in the cross-device federated learning setting. Our algorithms
require only a fraction of devices in each round of training, which overcomes
the difficulty introduced by the low availability of devices. The communication
overhead is further reduced by performing multiple local update steps on
clients before communication with the server, and global gradient estimates are
leveraged to correct the bias in local update directions introduced by data
heterogeneity. By developing analyses based on novel potential functions, we
establish theoretical convergence guarantees for our algorithms. Experimental
results on AUC maximization, robust adversarial network training, and GAN
training tasks demonstrate the efficiency of our algorithms.


Abstract:
  In this article we review the results of our recent work on the difference
between the Borel representations of tau hadronic spectral function moments
obtained with the CIPT and FOPT methods. For the presentation of the
theoretical results we focus on the large-$\beta_0$ approximation, where all
expressions can be written down in closed form, and we comment on the
generalization to full QCD. The results may explain the discrepancy in the
behavior of the FOPT and CIPT series that has been the topic of intense
discussions in previous literature and which represents a major part of the
theoretical uncertainties in current strong coupling determinations from
hadronic tau decays. The findings also imply that the OPE corrections for FOPT
and CIPT differ and that the OPE corrections for CIPT do not have standard
form.


Abstract:
  Collapsing supermassive stars ($M \gtrsim 3 \times 10^4 M_{\odot}$) at high
redshifts can naturally provide seeds and explain the origin of the
supermassive black holes observed in the centers of nearly all galaxies. During
the collapse of supermassive stars, a burst of non-thermal neutrinos is
generated with a luminosity that could greatly exceed that of a conventional
core collapse supernova explosion. In this work, we investigate the extent to
which the neutrinos produced in these explosions can be observed via coherent
elastic neutrino-nucleus scattering (CE$\nu$NS). Large scale direct dark matter
detection experiments provide particularly favorable targets. We find that
upcoming $\mathcal{O}(100)$ tonne-scale experiments will be sensitive to the
collapse of individual supermassive stars at distances as large as
$\mathcal{O}(10)$ Mpc.


Abstract:
  In this paper, we introduce a new perspective on training deep neural
networks capable of state-of-the-art performance without the need for the
expensive over-parameterization by proposing the concept of In-Time
Over-Parameterization (ITOP) in sparse training. By starting from a random
sparse network and continuously exploring sparse connectivities during
training, we can perform an Over-Parameterization in the space-time manifold,
closing the gap in the expressibility between sparse training and dense
training. We further use ITOP to understand the underlying mechanism of Dynamic
Sparse Training (DST) and indicate that the benefits of DST come from its
ability to consider across time all possible parameters when searching for the
optimal sparse connectivity. As long as there are sufficient parameters that
have been reliably explored during training, DST can outperform the dense
neural network by a large margin. We present a series of experiments to support
our conjecture and achieve the state-of-the-art sparse training performance
with ResNet-50 on ImageNet. More impressively, our method achieves dominant
performance over the overparameterization-based sparse methods at extreme
sparsity levels. When trained on CIFAR-100, our method can match the
performance of the dense model even at an extreme sparsity (98%). Code can be
found https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization.


Abstract:
  We consider the isomonodromic formulation of the Calogero-Painlev\'e
multi-particle systems and proceed to their canonical quantization. We then
proceed to the quantum Hamiltonian reduction on a special representation to
radial variables, in analogy with the classical case and also with the theory
of quantum Calogero equations. This quantized version is compared to the
generalization of a result of Nagoya on integral representations of certain
solutions of the quantum Painlev\'e equations. We also provide multi-particle
generalizations of these integral representations.


Abstract:
  We prove that, for every one-dimendional exponentially ergodic probabilistic
cellular automaton with positive rates, there exists a locally defined
coupling-from-the-past flow whose coalescence time has a finite exponential
moment.


Abstract:
  Semantic code search is about finding semantically relevant code snippets for
a given natural language query. In the state-of-the-art approaches, the
semantic similarity between code and query is quantified as the distance of
their representation in the shared vector space. In this paper, to improve the
vector space, we introduce tree-serialization methods on a simplified form of
AST and build the multimodal representation for the code data. We conduct
extensive experiments using a single corpus that is large-scale and
multi-language: CodeSearchNet. Our results show that both our tree-serialized
representations and multimodal learning model improve the performance of code
search. Last, we define intuitive quantification metrics oriented to the
completeness of semantic and syntactic information of the code data, to help
understand the experimental findings.


Abstract:
  In this paper, we present a theoretical analysis of separating images
consisting of pointlike and $C^{ \beta}$-curvelike structures, where $\beta \in
(1,2] $. Our approach is based on $l_1$-minimization, in which the sparsity of
the desired solution is exploited by two sparse representation systems. It is
well known that for such components wavelets provide an optimally sparse
representation for point singularities, whereas $\alpha$-shearlet type with
$\alpha$=$\frac{2}{\beta}$ might be best adapted to the $C^{\beta}$-curvilinear
singularities. In our analysis, we first propose a reconstruction framework
with a theoretical guarantee on convergence, which is extended to use general
frames instead of Parseval frames. We then construct a dual pair of bandlimited
$\alpha$-shearlets which possesses a good time and frequency localization.
Finally, we apply the result to derive an asymptotic accuracy of the
reconstructions. In addition, we show that it is possible to separate these two
components as long as $\alpha <2$, i.e., bandlimited $\alpha$-shearlets which
range from wavelet to shearlet type do not coincide with wavelets in the sense
of isotropic fashion.


Abstract:
  For the study of polarization reversal features in the structure of "mixed
ionic-electronic conductor - on - ferroelectric" type, the hydrogen bonded
molecular ferroelectric crystal triglycine sulphate (TGS) is used. The dynamic
currant-voltage (I-V-) loops of thin TGS plates of polar (010) cut with
dissimilar electrodes on the opposite surfaces (vacuum deposited Ag and
Ag-paste diluted with a water-ethanol mixture) are investigated. After
electroforming in the low-voltage vicinity of coercive voltage, during further
cycling, a reversible transformation of I-V-loop shape occurs from typical of
non-linear dielectrics to typical of memristive systems and than to typical of
ferroelectrics (and vice versa) with a sequential increase (decrease) of
drawing voltage amplitude. Such "awakening" and "falling asleep" of I-V-loops
of the memristor type is apparently associated with reversible electromigration
and accumulation of charged species (e.g., protons), in the hydrated surface
layer of TGS as a component of electrochemical capacitor-on-ferroelectric
structure. The observed transformation can be explained by considering the
coupled ion transfer and polarization reversal taking into account the swinging
of the boundary of the built-in chemical inhomogeneity under conditions of
linear and/or nonlinear dynamics of ion drift and ionic concentration
polarization caused by charged species transfer under the applied voltage.


Abstract:
  We study heating dynamics in isolated quantum many-body systems driven
periodically at high frequency and large amplitude. Combining the
high-frequency expansion for the Floquet Hamiltonian with Fermi's golden rule
(FGR), we develop a master equation termed the Floquet FGR. Unlike the
conventional one, the Floquet FGR correctly describes heating dynamics,
including the prethermalization regime, even for strong drives, under which the
Floquet Hamiltonian is significantly dressed, and nontrivial Floquet
engineering is present. The Floquet FGR depends on system size only weakly,
enabling us to analyze the thermodynamic limit with small-system calculations.
Our results also indicate that, during heating, the system approximately stays
in the thermal state for the Floquet Hamiltonian with a gradually rising
temperature.


Abstract:
  Osteoporosis is a common chronic metabolic bone disease that is often
under-diagnosed and under-treated due to the limited access to bone mineral
density (BMD) examinations, Dual-energy X-ray Absorptiometry (DXA). In this
paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the
most common, accessible, and low-cost medical image examinations. Our method
first automatically detects Regions of Interest (ROIs) of local and global bone
structures from the CXR. Then a multi-ROI model is developed to exploit both
local and global information in the chest X-ray image for accurate BMD
estimation. Our method is evaluated on 329 CXR cases with ground truth BMD
measured by DXA. The model predicted BMD has a strong correlation with the gold
standard DXA BMD (Pearson correlation coefficient 0.840). When applied for
osteoporosis screening, it achieves a high classification performance (AUC
0.936). As the first effort in the field to use CXR scans to predict the spine
BMD, the proposed algorithm holds strong potential in enabling early
osteoporosis screening through routine chest X-rays and contributing to the
enhancement of public health.


Abstract:
  Purpose: To develop a method for banding-free bSSFP cardiac cine with
substantially reduced flow artifacts.
  Methods: A projection-reconstruction (PR) trajectory is proposed for a
frequency-modulated cine sequence, facilitating reconstruction of three phase
cycles and a field-map time series from a short, breath-held scan. Data is also
acquired during the gradient rewinders to enable generation of field maps using
BMART, B$_0$ mapping using rewinding trajectories, where the rewind data forms
the second TE image for calculating the field map. A field-map-based
combination method is developed which weights the phase-cycle component images
to include only passband signal in the final cine images, and exclude stopband
and near-band flow artifacts.
  Results: The weights derived from the BMART-generated field maps mask out
banding and near-band flow artifacts in and around the heart. Therefore, the
field-map-based phase-cycle combination, which is facilitated by the PR
acquisition with BMART, results in more homogeneous blood pools and reduced
hyperintense regions than root-sum-of-squares.
  Conclusion: With the proposed techniques, using a non-Cartesian trajectory
for a frequency-modulated cine sequence enables flow-artifact-reduced
banding-free cardiac imaging within a short breath-hold.


Abstract:
  We propose a means of omni-directional contact detection using accelerometers
instead of tactile sensors for object shape estimation using touch. Unlike
tactile sensors, our contact-based detection method tends to induce a degree of
uncertainty with false-positive contact data because the sensors may react not
only to actual contact but also to the unstable behavior of the robot.
Therefore, it is crucial to consider a robust shape estimation method capable
of handling such false-positive contact data. To realize this, we introduce the
concept of heteroscedasticity into the contact data and propose a robust shape
estimation algorithm based on Gaussian process implicit surfaces (GPIS). We
confirmed that our algorithm not only reduces shape estimation errors caused by
false-positive contact data but also distinguishes false-positive contact data
more clearly than the GPIS through simulations and actual experiments using a
quadcopter.


Abstract:
  We address the estimation of conditional average treatment effects (CATEs)
for structured treatments (e.g., graphs, images, texts). Given a weak condition
on the effect, we propose the generalized Robinson decomposition, which (i)
isolates the causal estimand (reducing regularization bias), (ii) allows one to
plug in arbitrary models for learning, and (iii) possesses a quasi-oracle
convergence guarantee under mild assumptions. In experiments with small-world
and molecular graphs we demonstrate that our approach outperforms prior work in
CATE estimation.


Abstract:
  A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition
of multiple MR pulse sequences, which are required for a reliable diagnosis.
Each sequence can be parameterized through multiple acquisition parameters
affecting MR image contrast, signal-to-noise ratio, resolution, or scan time.
With the rise of generative deep learning models, approaches for the synthesis
of MR images are developed to either synthesize additional MR contrasts,
generate synthetic data, or augment existing data for AI training. However,
current generative approaches for the synthesis of MR images are only trained
on images with a specific set of acquisition parameter values, limiting the
clinical value of these methods as various sets of acquisition parameter
settings are used in clinical practice. Therefore, we trained a generative
adversarial network (GAN) to generate synthetic MR knee images conditioned on
various acquisition parameters (repetition time, echo time, image orientation).
This approach enables us to synthesize MR images with adjustable image
contrast. In a visual Turing test, two experts mislabeled 40.5% of real and
synthetic MR images, demonstrating that the image quality of the generated
synthetic and real MR images is comparable. This work can support radiologists
and technologists during the parameterization of MR sequences by previewing the
yielded MR contrast, can serve as a valuable tool for radiology training, and
can be used for customized data generation to support AI training.


Abstract:
  A nanoelectronic device made of twisted bilayer graphene (TBLG) is proposed
to steer the direction of the current flow. The ballistic electron current,
injected at one edge of the bottom layer, can be guided predominantly to one of
the lateral edges of the top layer. The current is steered to the opposite
lateral edge, if either the twist angle is reversed or the electrons are
injected in the valence band instead of the conduction band, making it possible
to control the current flow by electric gates. When both graphene layers are
aligned, the current passes straight through the system without changing its
initial direction. The observed steering angle exceeds well the twist angle and
emerges for a broad range of experimentally accessible parameters. It is
explained by the trigonal shape of the energy bands beyond the van Hove
singularity due to the Moir\'e interference pattern. As the shape of the energy
bands depends on the valley degree of freedom, the steered current is partially
valley polarized. Our findings show how to control and manipulate the current
flow in TBLG. Technologically, they are of relevance for applications in
twistronics and valleytronics.


Abstract:
  Let $O$ be chosen uniformly at random from the group of $(N+L) \times (N+L)$
orthogonal matrices. Denote by $\tilde{O}$ the upper-left $N \times N$ corner
of $O$, which we refer to as a truncation of $O$. In this paper we prove two
conjectures of Forrester, Ipsen and Kumar (2020) on the number of real
eigenvalues $N^{(m)}_{\mathbb{R}}$ of the product matrix $\tilde{O}_{1}\ldots
\tilde{O}_{m}$, where the matrices $\{\tilde{O}_{j}\}_{j=1}^{m}$ are
independent copies of $\tilde{O}$. When $L$ grows in proportion to $N$, we
prove that $$ \mathbb{E}(N^{(m)}_{\mathbb{R}}) = \sqrt{\frac{2m
L}{\pi}}\,\mathrm{arctanh}\left(\sqrt{\frac{N}{N+L}}\right) + O(1), \qquad N
\to \infty. $$ We also prove the conjectured form of the limiting real
eigenvalue distribution of the product matrix. Finally, we consider the
opposite regime where $L$ is fixed with respect to $N$, known as the regime of
weak non-orthogonality. In this case each matrix in the product is very close
to an orthogonal matrix. We show that $\mathbb{E}(N^{(m)}_{\mathbb{R}}) \sim
c_{L,m}\,\log(N)$ as $N \to \infty$ and compute the constant $c_{L,m}$
explicitly. These results generalise the known results in the one matrix case
due to Khoruzhenko, Sommers and \.{Z}yczkowski (2010).