Abstract:
  We consider a generalization of the recently proposed gradient coding
framework where a large dataset is divided across $n$ workers and each worker
transmits to a master node one or more linear combinations of the gradients
over the data subsets assigned to it. Unlike the conventional framework which
requires the master node to recover the sum of the gradients over all the data
subsets in the presence of $s$ straggler workers, we relax the goal of the
master node to computing the sum of at least some $\alpha$ fraction of the
gradients. The broad goal of our work is to study the optimal computation and
communication load per worker for this approximate gradient coding framework.
We begin by deriving a lower bound on the computation load of any feasible
scheme and also propose a strategy which achieves this lower bound, albeit at
the cost of high communication load and a number of data partitions which can
be polynomial in the number of workers $n$. We then restrict attention to
schemes which utilize a number of data partitions equal to $n$ and propose
schemes based on cyclic assignment which have a lower communication load. When
each worker transmits a single linear combination, we also prove lower bounds
on the computation load of any scheme using $n$ data partitions.


Abstract:
  This paper compares BERT-SQuAD and Ab3P on the Abbreviation Definition
Identification (ADI) task. ADI inputs a text and outputs short forms
(abbreviations/acronyms) and long forms (expansions). BERT with reranking
improves over BERT without reranking but fails to reach the Ab3P rule-based
baseline. What is BERT missing? Reranking introduces two new features:
charmatch and freq. The first feature identifies opportunities to take
advantage of character constraints in acronyms and the second feature
identifies opportunities to take advantage of frequency constraints across
documents.


Abstract:
  A series of new flow observables mixed harmonic multi-particle cumulants
(MHC), which allow for the first time to quantify the correlations strength
between different order of flow coefficients with various moments, was
investigated using hydrodynamic model. These new observables are constructed
based on multi-particle cumulants, and thus by design will be less sensitive to
the non-flow contaminations. In addition to the previous study of correlation
involving two flow coefficients with their second moments, both correlations of
three flow coefficients and the correlations of higher order moments of $v_2$
and $v_3$ are systematically investigated using iEBE-VISHNU hybrid model with
two different initial conditions, AMPT and TRENTo, respectively. These
systematic studies using hydrodynamic models will significantly improve the
understanding on the correlations between different orders (and moments) of
flow coefficients. The hydrodynamic predictions shown in this paper and the
future comparisons to experimental measurements will provide more constraints
on theoretical models and extract more information about the transport
properties of the quark-gluon plasma created in heavy-ion collisions.


Abstract:
  An implicit but pervasive hypothesis of modern computer vision research is
that convolutional neural network (CNN) architectures that perform better on
ImageNet will also perform better on other vision datasets. We challenge this
hypothesis through an extensive empirical study for which we train 500 sampled
CNN architectures on ImageNet as well as 8 other image classification datasets
from a wide array of application domains. The relationship between architecture
and performance varies wildly, depending on the datasets. For some of them, the
performance correlation with ImageNet is even negative. Clearly, it is not
enough to optimize architectures solely for ImageNet when aiming for progress
that is relevant for all applications. Therefore, we identify two
dataset-specific performance indicators: the cumulative width across layers as
well as the total depth of the network. Lastly, we show that the range of
dataset variability covered by ImageNet can be significantly extended by adding
ImageNet subsets restricted to few classes.


Abstract:
  Maintaining legacy enterprise information systems is a known necessity in
companies. To date, it remains an expensive and time-consuming process,
requiring high effort and cost to get small changes implemented. MITRAS seeks
to reduce the maintenance cost by providing an automatic maintenance system
model based on graph transformations. This article presents Parthenos, a
different approach to MITRAS, whose goal is to guarantee the correctness of
introduced modifications at a syntax and type semantics level of the source
code. Along with that, it proposes an extensible architecture, which allows the
most varied types of systems to carry out software maintenance. Parthenos was
evaluated through functional tests to evaluate its effectiveness, using
measures of precision, recall, and f-measure.


Abstract:
  We propose a simple extension of residual networks that works simultaneously
in multiple resolutions. Our network design is inspired by the iterative
back-projection algorithm but seeks the more difficult task of learning how to
enhance images. Compared to similar approaches, we propose a novel solution to
make back-projections run in multiple resolutions by using a data pipeline
workflow. Features are updated at multiple scales in each layer of the network.
The update dynamic through these layers includes interactions between different
resolutions in a way that is causal in scale, and it is represented by a system
of ODEs, as opposed to a single ODE in the case of ResNets. The system can be
used as a generic multi-resolution approach to enhance images. We test it on
several challenging tasks with special focus on super-resolution and raindrop
removal. Our results are competitive with state-of-the-arts and show a strong
ability of our system to learn both global and local image features.


Abstract:
  Neural network-based embeddings have been the mainstream approach for
creating a vector representation of the text to capture lexical and semantic
similarities and dissimilarities. In general, existing encoding methods dismiss
the punctuation as insignificant information; consequently, they are routinely
eliminated in the pre-processing phase as they are shown to improve task
performance. In this paper, we hypothesize that punctuation could play a
significant role in sentiment analysis and propose a novel representation model
to improve syntactic and contextual performance. We corroborate our findings by
conducting experiments on publicly available datasets and verify that our model
can identify the sentiments more accurately over other state-of-the-art
baseline methods.


Abstract:
  In Part 1 of the current series of papers, we demonstrate the equivalence
between the Zhang-Zhang polynomial $\text{ZZ}(\boldsymbol{S},x)$ of a
Kekul\'ean regular $m$-tier strip $\boldsymbol{S}$ of length $n$ and the
extended strict order polynomial $\text{E}_{\mathcal{S}}^{\circ}(n,x+1)$ of a
certain partially ordered set (poset) $\mathcal{S}$ associated with
$\boldsymbol{S}$. The discovered equivalence is a consequence of the one-to-one
correspondence between the set $\left\{ K\right\}$ of Kekul\'e structures of
$\boldsymbol{S}$ and the set $\left\{
\mu:\mathcal{S}\supset\mathcal{A}\rightarrow\left[\,n\,\right]\right\}$ of
strictly order-preserving maps from the induced subposets of $\mathcal{S}$ to
the interval $\left[\thinspace n\thinspace\right]$. As a result, the problems
of determining the Zhang-Zhang polynomial of $\boldsymbol{S}$ and of generating
the complete set of Clar covers of $\boldsymbol{S}$ reduce to the problem of
constructing the set $\mathcal{L}(\mathcal{S})$ of linear extensions of the
corresponding poset $\mathcal{S}$ and studying their basic properties. In
particular, the Zhang-Zhang polynomial of $\boldsymbol{S}$ can be written in a
compact form as
$\text{ZZ}(\boldsymbol{S},x)=\sum_{k=0}^{\left|\mathcal{S}\right|}\sum_{w\in\mathcal{L}(\mathcal{S})}\binom{\left|\mathcal{S}\right|-\text{fix}_{\mathcal{S}}(w)}{\,\,k\,\,\hspace{1pt}-\text{fix}_{\mathcal{S}}(w)}\binom{n+\text{des}(w)}{k}\left(1+x\right)^{k}$,
where $\text{des}(w)$ and $\text{fix}_{\mathcal{S}}(w)$ denote the number of
descents and the number of fixed labels, respectively, in the linear extension
$w\in\mathcal{L}(\mathcal{S})$.


Abstract:
  A large number of particle detectors employ liquid argon as their target
material owing to its high scintillation yield and its ability to drift
ionization charge over large distances. Scintillation light from argon is
peaked at 128 nm and a wavelength shifter is required for its efficient
detection. In this work, we directly compare the light yield achieved in two
identical liquid argon chambers, one of which is equipped with PolyEthylene
Naphthalate (PEN) and the other with TetraPhenyl Butadiene (TPB) wavelength
shifter. Both chambers are lined with enhanced specular reflectors and
instrumented with SiPMs with a coverage fraction of approximately 1%, which
represents a geometry comparable to the future large scale detectors. We
measured the light yield of the PEN chamber to be
39.4$\pm$0.4(stat)$\pm$1.9(syst)% of the yield of the TPB chamber. Using a
Monte Carlo simulation this result is used to extract the wavelength shifting
efficiency of PEN relative to TPB equal to 47.2$\pm$5.7%. This result paves the
way for the use of easily available PEN foils as a wavelength shifter, which
can substantially simplify the construction of future liquid argon detectors.


Abstract:
  In this paper, we study the linear transformation model in the most general
setup. This model includes many important and popular models in statistics and
econometrics as special cases. Although it has been studied for many years, the
methods in the literature are based on kernel-smoothing techniques or make use
of only the ranks of the responses in the estimation of the parametric
components. The former approach needs a tuning parameter, which is not easily
optimally specified in practice; and the latter is computationally expensive
and may not make full use of the information in the data. In this paper, we
propose two methods: a pairwise rank likelihood method and a
score-function-based method based on this pairwise rank likelihood. We also
explore the theoretical properties of the proposed estimators. Via extensive
numerical studies, we demonstrate that our methods are appealing in that the
estimators are not only robust to the distribution of the random errors but
also lead to mean square errors that are in many cases comparable to or smaller
than those of existing methods.


Abstract:
  Correlated with the trend of increasing degrees of freedom in robotic systems
is a similar trend of rising interest in Spatio-Temporal systems described by
Partial Differential Equations (PDEs) among the robotics and control
communities. These systems often exhibit dramatic under-actuation, high
dimensionality, bifurcations, and multimodal instabilities. Their control
represents many of the current-day challenges facing the robotics and
automation communities. Not only are these systems challenging to control, but
the design of their actuation is an NP-hard problem on its own. Recent methods
either discretize the space before optimization, or apply tools from linear
systems theory under restrictive linearity assumptions in order to arrive at a
control solution. This manuscript provides a novel sampling-based stochastic
optimization framework based entirely in Hilbert spaces suitable for the
general class of \textit{semi-linear} SPDEs which describes many systems in
robotics and applied physics. This framework is utilized for simultaneous
policy optimization and actuator co-design optimization. The resulting
algorithm is based on variational optimization, and performs joint episodic
optimization of the feedback control law and the actuation design over
episodes. We study first and second order systems, and in doing so, extend
several results to the case of second order SPDEs. Finally, we demonstrate the
efficacy of the proposed approach with several simulated experiments on a
variety of SPDEs in robotics and applied physics including an infinite
degree-of-freedom soft robotic manipulator.


Abstract:
  We study the generalizations of the known equivalent reformulations of
condition moderate growth from the single weight sequence to the weight matrix
setting. This condition, also known in the literature under the name stability
under ultradifferentiable operators, plays a significant role in the theory of
ultradifferentiable (and ultraholomorophic) function classes defined in terms
of weight sequences and its generalization becomes relevant when dealing with
classes defined by weight matrices. In the matrix setting, we prove that the
different mixed conditions are in general not equivalently satisfied anymore
and we focus on weight matrices associated with (associated) weight functions.


Abstract:
  The I-table contains luminous intensity values over the range of angles for
the luminaires used in the road lighting in accordance with technical report
CIE 121:1996. A limited number of angles causes smoothing of the luminous
intensity diagram omitting possible local extremal values which affect the
calculations of the photometric parameters such as average illuminance, average
luminance, uniformity or treshold increment. The interpolating methods used to
calculate the luminous intensity can significantly improve the accuracy of the
calculations and redound to more effective and reliable road lighting design.
In the paper standard interpolation methods used up to now are compared with
newly proposed hybrid adaptive splines. Calculated values of luminous intensity
are compared and verified by goniophotometric measurements.


Abstract:
  We propose an Exclusive Topic Modeling (ETM) for unsupervised text
classification, which is able to 1) identify the field-specific keywords though
less frequently appeared and 2) deliver well-structured topics with exclusive
words. In particular, a weighted Lasso penalty is imposed to reduce the
dominance of the frequently appearing yet less relevant words automatically,
and a pairwise Kullback-Leibler divergence penalty is used to implement topics
separation. Simulation studies demonstrate that the ETM detects the
field-specific keywords, while LDA fails. When applying to the benchmark NIPS
dataset, the topic coherence score on average improves by 22% and 10% for the
model with weighted Lasso penalty and pairwise Kullback-Leibler divergence
penalty, respectively.


Abstract:
  We present TweeNLP, a one-stop portal that organizes Twitter's natural
language processing (NLP) data and builds a visualization and exploration
platform. It curates 19,395 tweets (as of April 2021) from various NLP
conferences and general NLP discussions. It supports multiple features such as
TweetExplorer to explore tweets by topics, visualize insights from Twitter
activity throughout the organization cycle of conferences, discover popular
research papers and researchers. It also builds a timeline of conference and
workshop submission deadlines. We envision TweeNLP to function as a collective
memory unit for the NLP community by integrating the tweets pertaining to
research papers with the NLPExplorer scientific literature search engine. The
current system is hosted at http://nlpexplorer.org/twitter/CFP .


Abstract:
  The rapid solidification of a binary mixture in the region of the interface
velocities $V$ close to the diffusion speed in the bulk of the liquid phase
$V_D$ is considered within the framework of the local nonequilibrium approach.
In this high-speed region the derivation of the analytical expression for the
response function "temperature-velocity" representing kinetic phase diagram is
given without using the concept of the equilibrium phase diagram.
  The modes of movement of the interface both without and with the drag effect
are analyzed. It is shown that the drag effect can be accompanied by a local
interface temperature maximum at $V = V_D$.


Abstract:
  In this work we introduce a new class of gradient-free global optimization
methods based on a binary interaction dynamics governed by a Boltzmann type
equation. In each interaction the particles act taking into account both the
best microscopic binary position and the best macroscopic collective position.
In the mean-field limit we show that the resulting Fokker-Planck partial
differential equations generalize the current class of consensus based
optimization (CBO) methods. For the latter methods, convergence to the global
minimizer can be shown for a large class of functions. Algorithmic
implementations inspired by the well-known direct simulation Monte Carlo
methods in kinetic theory are derived and discussed. Several examples on
prototype test functions for global optimization are reported including
applications to machine learning.


Abstract:
  Deep neural network is an effective choice to automatically recognize human
actions utilizing data from various wearable sensors. These networks automate
the process of feature extraction relying completely on data. However, various
noises in time series data with complex inter-modal relationships among sensors
make this process more complicated. In this paper, we have proposed a novel
multi-stage training approach that increases diversity in this feature
extraction process to make accurate recognition of actions by combining
varieties of features extracted from diverse perspectives. Initially, instead
of using single type of transformation, numerous transformations are employed
on time series data to obtain variegated representations of the features
encoded in raw data. An efficient deep CNN architecture is proposed that can be
individually trained to extract features from different transformed spaces.
Later, these CNN feature extractors are merged into an optimal architecture
finely tuned for optimizing diversified extracted features through a combined
training stage or multiple sequential training stages. This approach offers the
opportunity to explore the encoded features in raw sensor data utilizing
multifarious observation windows with immense scope for efficient selection of
features for final convergence. Extensive experimentations have been carried
out in three publicly available datasets that provide outstanding performance
consistently with average five-fold cross-validation accuracy of 99.29% on UCI
HAR database, 99.02% on USC HAR database, and 97.21% on SKODA database
outperforming other state-of-the-art approaches.


Abstract:
  Let $G$ be a compact connected Lie group. We show that the category
$\mathbf{Loc}_{\infty}(BG)$ of $\infty$-local systems on the classifying space
of $G$, can be described infinitesimally as the category
$\mathbf{InfLoc}_{\infty}(\mathfrak{g})$ of basic $\mathfrak{g}$-$L_\infty$
spaces. Moreover, we show that, given a principal bundle $\pi \colon P
\rightarrow X$ with structure group $G$ and any connection $\theta$ on $P$,
there is a DG functor $$\mathcal{CW}_{\theta} \colon
\mathbf{InfLoc}_{\infty}(\mathfrak{g}) \longrightarrow
\mathbf{Loc}_{\infty}(X), $$ which corresponds to the pullback functor by the
classifying map of $P$. The DG functors associated to different connections are
related by an $A_\infty$-natural isomorphism. This construction provides a
categorification of the Chern-Weil homomorphism, which is recovered by applying
the functor $\mathcal{CW}_{\theta}$ to the endomorphisms of the constant local
system.


Abstract:
  The Fowler-Nordheim tunneling current formula has been widely used in the
design of devices based on metal/insulator (metal/semiconductor)
heterojunctions with triangle potential barriers, such as the flash memory.
Here we adopt the model that was used to derive the Landauer formula at finite
temperature, the nearly-free electron approximation to describe the electronic
states in semi-infinite metal electrode and the Wentzel-Kramers-Brillouin (WKB)
approximation to describe the transmission coefficient, and derive a tunneling
current formula for metal/insulator heterojunctions under large bias and
electric field. In contrast to the Fowler-Nordheim formula which is the limit
at zero temperature, our formula is generalized to the finite temperature (with
the thermal excitation of electrons in metal electrode considered) and the
potential barriers beyond triangle ones, which may be used for the design of
more complicated heterojunction devices based on the carrier tunneling.


Abstract:
  We study the problem of matrix completion in this paper. A spectral scaled
Student prior is exploited to favour the underlying low-rank structure of the
data matrix. Importantly, we provide a thorough theoretical investigation for
our approach, while such an analysis is hard to obtain and limited in
theoretical understanding of Bayesian matrix completion. More precisely, we
show that our Bayesian approach enjoys a minimax-optimal oracle inequality
which guarantees that our method works well under model misspecification and
under general sampling distribution. Interestingly, we also provide efficient
gradient-based sampling implementations for our approach by using Langevin
Monte Carlo which is novel in Bayesian matrix completion. More specifically, we
show that our algorithms are significantly faster than Gibbs sampler in this
problem. To illustrate the attractive features of our inference strategy, some
numerical simulations are conducted and an application to image inpainting is
demonstrated.


Abstract:
  We present a time dependent quantum perturbation result, uniform in the
Planck constant, for perturbations of potentials whose gradients are Lipschitz
continuous by potentials whose gradients are only bounded a.e.. Though this low
regularity of the full potential is not enough to provide the existence of the
classical underlying dynamics, at variance with the quantum one, our result
shows that the classical limit of the perturbed quantum dynamics remains in a
tubular neighbourhood of the classical unperturbed one of size of order of the
square root of the size of the perturbation. We treat both Schr\"odinger and
von Neumann-Heisenberg equations.


Abstract:
  We review progress in active hydrodynamic descriptions of flowing media on
curved and deformable manifolds: the state-of-the-art in continuum descriptions
of single-layers of epithelial and/or other tissues during development. First,
after a brief overview of activity, flows and hydrodynamic descriptions, we
highlight the generic challenge of identifying the dependence on dynamical
variables of so-called active kinetic coefficients -- active counterparts to
dissipative Onsager coefficients. We go on to describe some of the subtleties
concerning how curvature and active flows interact, and the issues that arise
when surfaces are deformable. We finish with a broad discussion around the
utility of such theories in developmental biology. This includes limitations to
analytical techniques, challenges associated with numerical integration,
fitting-to-data and inference, and potential tools for the future, such as
discrete differential geometry.


Abstract:
  High-finesse, open-geometry microcavities have recently emerged as a
versatile tool for enhancing interactions between photons and material systems,
with a range of applications in quantum optics and quantum information science.
However, mechanical vibrations pose a considerable challenge to their operation
within a closed-cycle cryostat, particularly when spatial tunability and
free-space optical access are required. Here, we present the design and
characterization of a system that can achieve $\sim$16 pm-rms passive
mechanical stability between two high-finesse mirrors while permitting both
three-dimensional positioning of the cavity mode and free-space confocal
imaging. The design relies on two cascaded vibration isolation stages connected
by leaf springs that decouple axial and lateral motion, and incorporates
tuned-mass and magnetic damping. Furthermore, we present a technique for
quantifying cavity length displacements similar to or larger than the cavity
linewidth, allowing in-situ measurement of vibrations with and without active
feedback. Our results facilitate operation of a tunable, high-finesse cavity
within a closed-cycle cryostat, representing an enabling technology for cavity
coupling to a variety of solid-state systems.


Abstract:
  Limited-valency colloidal particles can self-assemble into polymeric
structures analogous to molecules. While their structural equilibrium
properties have attracted wide attention, insight into their dynamics has
proven challenging. Here, we investigate the polymerization dynamics of
semiflexible polymers in two dimensions (2D) by direct observation of
assembling divalent particles, bonded by critical Casimir forces. The
reversible critical Casimir force creates living polymerization conditions with
tunable chain dissociation, association and bending rigidity. We find that
unlike dilute polymers that show exponential size distributions in excellent
agreement with Flory theory, concentrated samples exhibit arrest of rotational
and translational diffusion due to a continuous isotropic-to-nematic transition
in 2D, slowing down the growth kinetics. These effects are circumvented by
addition of higher-valency particles, cross-linking the polymers into networks.
Our results connecting polymer flexibility, polymer interactions and the
peculiar isotropic-nematic transition in 2D offer insight into polymerization
processes of synthetic two-dimensional polymers, and biopolymers at membranes
and interfaces.


Abstract:
  Understanding differences in hospital case-fatality rates (HCFRs) of
coronavirus disease (COVID-19) may help evaluate its severity and the capacity
of the healthcare system to reduce mortality. We examined the variability in
HCFRs of COVID-19 in relation to spatial inequalities in socioeconomic factors
across the city of Sao Paulo, Brazil. We found that HCFRs were higher for men
and for individuals aged 60 years and older. Our models identified per capita
income as a significant factor that is negatively associated with the HCFRs of
COVID-19, even after adjusting by age, sex and presence of risk factors.


Abstract:
  With the significant rise in demand for same-day instant deliveries, several
courier services are exploring alternatives to transport packages in a cost-
and time-effective, as well as, sustainable manner. Motivated by a real-life
case study, this paper focuses on developing a simulation algorithm that
assists same-day package delivery companies to serve customers instantly. The
proposed recommender system provides the best solution with respect to three
criteria: cost, time, and sustainability, considering the variation in travel
time and cost parameters. The decision support tool provides recommendations on
the best alternative for transporting products based on factors, such as source
and destination locations, time of the day, package weight, and volume. Besides
considering existing new technologies like electric-assisted cargo bikes, we
also analyze the impact of emerging methods of deliveries, such as robots and
air taxis. Finally, this paper also considers the best delivery alternative
during the presence of a pandemic, such as COVID-19. For the purpose of
illustrating our approach, we consider the delivery options in New York City.
We believe that the proposed tool is the first to provide solutions to courier
companies considering evolving modes of transportation and under logistics
disruptions due to pandemic.
  Keywords: Instant package delivery; Courier services; Simulation algorithm;
Recommender system; Emerging technologies; COVID-19 pandemic.


Abstract:
  Iron selenide (FeSe) is an iron-based superconductor which shows unique
properties, including strongly anisotropic superconducting gap, paramagnetism
in undoped compound and extremely small Fermi pocket size. In this work, we
demonstrate that the sizes of electron and hole pockets in FeSe monolayer
become much smaller than those in bulk. The Fermi energy is in the order of a
few meV and can be fine-tuned by the thickness of graphene layers underneath.
Despite the low carrier density, the FeSe monolayers grown on trilayer or
multi-layer graphene are superconducting. The superconducting gap size is
sensitive to the Fermi energy of the hole band. Remarkably, the FeSe monolayer
provides the opportunity to study the physics in the crossover regime where the
Fermi energy and superconducting gap are comparable to each other.


Abstract:
  We define the notion of a specialization morphism from a locally noetherian
analytic adic space to a scheme. This captures the (classical) specialization
morphism associated to a formal scheme. There is a well behaved theory of
compactifications and it turns out that the classical specialization morphism
is \emph{proper} in this setup. As an application, we show that the nearby
cycles functor commutes with lower shriek in great generality.


Abstract:
  We discuss two problems concerning the class Eberlein compacta, i.e., weakly
compact subspaces of Banach spaces. The first one deals with preservation of
some classes of scattered Eberlein compacta under continuous images. The second
one concerns the known problem of the existence of nonmetrizable compact spaces
without nonmetrizable zero-dimensional closed subspaces. We show that the
existence of such Eberlein compacta is consistent with ZFC. We also show that
it is consistent with ZFC that each Eberlein compact space of weight $>
\omega_1$ contains a nonmetrizable closed zero-dimensional subspace.


Abstract:
  We study the cluster structure of 21Ne and 21Na within the framework of the
cluster shell model (CSM) and show that they have a complex cluster structure
with the coexistence of a 20Ne+n, 20}Ne+p structure and a 19Ne+2n, 19}F+2p
structure. Seven rotational bands are identified in 21Ne and four in 21Na and
assigned to single-particle cluster states, single-hole cluster states and
vibrational states. The single-particle states are associated with the 20Ne+n
and 20Ne+p cluster structure, while the single-hole states are associated with
the 19Ne+2n and 19F+2p structure.


Abstract:
  Clinical trials of a vaccine during an epidemic face particular challenges,
such as the pressure to identify an effective vaccine quickly to control the
epidemic, and the effect that time-space-varying infection incidence has on the
power of a trial. We illustrate how the operating characteristics of different
trial design elements may be evaluated using a network epidemic and trial
simulation model, based on COVID-19 and individually randomised two-arm trials
with a binary outcome. We show that "ring" recruitment strategies, prioritising
participants at high risk of infection, can result in substantial improvement
in terms of power, if sufficiently many contacts of observed cases are at high
risk. In addition, we introduce a novel method to make more efficient use of
the data from the earliest cases of infection observed in the trial, whose
infection may have been too early to be vaccine-preventable. Finally, we
compare several methods of response-adaptive randomisation, discussing their
advantages and disadvantages in this two-arm context and identifying particular
adaptation strategies that preserve power and estimation properties, while
slightly reducing the number of infections, given an effective vaccine.


Abstract:
  Medical imaging systems are commonly assessed and optimized by use of
objective-measures of image quality (IQ) that quantify the performance of an
observer at specific tasks. Variation in the objects to-be-imaged is an
important source of variability that can significantly limit observer
performance. This object variability can be described by stochastic object
models (SOMs). In order to establish SOMs that can accurately model realistic
object variability, it is desirable to use experimental data. To achieve this,
an augmented generative adversarial network (GAN) architecture called
AmbientGAN has been developed and investigated. However, AmbientGANs cannot be
immediately trained by use of advanced GAN training methods such as the
progressive growing of GANs (ProGANs). Therefore, the ability of AmbientGANs to
establish realistic object models is limited. To circumvent this, a
progressively-growing AmbientGAN (ProAmGAN) has been proposed. However,
ProAmGANs are designed for generating two-dimensional (2D) images while medical
imaging modalities are commonly employed for imaging three-dimensional (3D)
objects. Moreover, ProAmGANs that employ traditional generator architectures
lack the ability to control specific image features such as fine-scale textures
that are frequently considered when optimizing imaging systems. In this study,
we address these limitations by proposing two advanced AmbientGAN
architectures: 3D ProAmGANs and Style-AmbientGANs (StyAmGANs). Stylized
numerical studies involving magnetic resonance (MR) imaging systems are
conducted. The ability of 3D ProAmGANs to learn 3D SOMs from imaging
measurements and the ability of StyAmGANs to control fine-scale textures of
synthesized objects are demonstrated.


Abstract:
  Semantic segmentation, which refers to pixel-wise classification of an image,
is a fundamental topic in computer vision owing to its growing importance in
robot vision and autonomous driving industries. It provides rich information
about objects in the scene such as object boundary, category, and location.
Recent methods for semantic segmentation often employ an encoder-decoder
structure using deep convolutional neural networks. The encoder part extracts
feature of the image using several filters and pooling operations, whereas the
decoder part gradually recovers the low-resolution feature maps of the encoder
into a full input resolution feature map for pixel-wise prediction. However,
the encoder-decoder variants for semantic segmentation suffer from severe
spatial information loss, caused by pooling operations or convolutions with
stride, and does not consider the context in the scene. In this paper, we
propose a dense upsampling convolution method based on guided filtering to
effectively preserve the spatial information of the image in the network. We
further propose a novel local context convolution method that not only covers
larger-scale objects in the scene but covers them densely for precise object
boundary delineation. Theoretical analyses and experimental results on several
benchmark datasets verify the effectiveness of our method. Qualitatively, our
approach delineates object boundaries at a level of accuracy that is beyond the
current excellent methods. Quantitatively, we report a new record of 82.86% and
81.62% of pixel accuracy on ADE20K and Pascal-Context benchmark datasets,
respectively. In comparison with the state-of-the-art methods, the proposed
method offers promising improvements.


Abstract:
  Recent works have shown exciting results in unsupervised image de-rendering
-- learning to decompose 3D shape, appearance, and lighting from single-image
collections without explicit supervision. However, many of these assume
simplistic material and lighting models. We propose a method, termed RADAR,
that can recover environment illumination and surface materials from real
single-image collections, relying neither on explicit 3D supervision, nor on
multi-view or multi-light images. Specifically, we focus on rotationally
symmetric artefacts that exhibit challenging surface properties including
specular reflections, such as vases. We introduce a novel self-supervised
albedo discriminator, which allows the model to recover plausible albedo
without requiring any ground-truth during training. In conjunction with a shape
reconstruction module exploiting rotational symmetry, we present an end-to-end
learning framework that is able to de-render the world's revolutionary
artefacts. We conduct experiments on a real vase dataset and demonstrate
compelling decomposition results, allowing for applications including
free-viewpoint rendering and relighting.


Abstract:
  A novel algorithm that produces a quad layout based on imposed set of
singularities is proposed. In this paper, we either use singularities that
appear naturally, e.g., by minimizing Ginzburg-Landau energy, or use as an
input user-defined singularity pattern, possibly with high valence
singularities that do not appear naturally in cross-field computations. The
first contribution of the paper is the development of a formulation that allows
computing a cross-field from a given set of singularities through the
resolution of two linear PDEs. A specific mesh refinement is applied at the
vicinity of singularities to accommodate the large gradients of cross
directions that appear in the vicinity of singularities of high valence. The
second contribution of the paper is a correction scheme that repairs limit
cycles and/or non-quadrilateral patches. Finally, a high quality
block-structured quad mesh is generated from the quad layout and per-partition
parameterization.


Abstract:
  The temporal Credit Assignment Problem (CAP) is a well-known and challenging
task in AI. While Reinforcement Learning (RL), especially Deep RL, works well
when immediate rewards are available, it can fail when only delayed rewards are
available or when the reward function is noisy. In this work, we propose
delegating the CAP to a Neural Network-based algorithm named InferNet that
explicitly learns to infer the immediate rewards from the delayed rewards. The
effectiveness of InferNet was evaluated on two online RL tasks: a simple
GridWorld and 40 Atari games; and two offline RL tasks: GridWorld and a
real-life Sepsis treatment task. For all tasks, the effectiveness of using the
InferNet inferred rewards is compared against the immediate and the delayed
rewards with two settings: with noisy rewards and without noise. Overall, our
results show that the effectiveness of InferNet is robust against noisy reward
functions and is an effective add-on mechanism for solving temporal CAP in a
wide range of RL tasks, from classic RL simulation environments to a real-world
RL problem and for both online and offline learning.


Abstract:
  We study the stratified gas in a rapidly rotating centrifuge as a model for
the Earth's atmosphere. Based on methods of perturbation theory, it is shown
that in certain regimes, internal waves in the gas centrifuge have the same
dispersion relation to leading order as their atmospheric siblings. Assuming an
air filled centrifuge with a radius of circa 50 cm, the optimal rotational
frequency for realistic atmosphere-like waves is around 10 000 rounds per
minute. Using gases of lower heat capacities at constant pressure, like xenon,
the rotational frequencies can be even halved to obtain the same results.
Similar to the atmosphere, it is feasible in the gas centrifuge to generate a
clear scale separation of wave frequencies and therefore phase speeds between
acoustic waves and internal waves. In addition to the centrifugal force, the
Coriolis force acts in the same plane. However, its influence on axially
homogeneous internal waves appears only as a higher-order correction. We
conclude that the gas centrifuge provides an unprecedented opportunity to
investigate atmospheric internal waves experimentally with a compressible
working fluid.


Abstract:
  Neural networks represent a field of research that can quickly achieve very
good results in the field of medical image segmentation using a GPU. A possible
way to achieve good results without GPUs are random forests. For this purpose,
two random forest approaches were compared with a state-of-the-art deep
convolutional neural network. To make the comparison the PhC-C2DH-U373 and the
retinal imaging datasets were used. The evaluation showed that the deep
convolutional neutral network achieved the best results. However, one of the
random forest approaches also achieved a similar high performance. Our results
indicate that random forest approaches are a good alternative to deep
convolutional neural networks and, thus, allow the usage of medical image
segmentation without a GPU.


Abstract:
  We have obtained Hubble Space Telescope (HST) coronagraphic observations of
the circumstellar disk around M star TWA 7 using the STIS instrument in visible
light. Together with archival observations including HST/NICMOS using the F160W
filter and Very Large Telescope/SPHERE at $H$-band in polarized light, we
investigate the system in scattered light. By studying this nearly face-on
system using geometric disk models and Henyey--Greenstein phase functions, we
report new discovery of a tertiary ring and a clump. We identify a layered
architecture: three rings, a spiral, and an ${\approx}150$ au$^2$ elliptical
clump. The most extended ring peaks at $28$ au, and the other components are on
its outskirts. Our point source detection limit calculations demonstrate the
necessity of disk modeling in imaging fainter planets. Morphologically, we
witness a clockwise spiral motion, and the motion pattern is consistent with
both solid body and local Keplerian; we also observe underdensity regions for
the secondary ring that might result from mean motion resonance or moving
shadows: both call for re-observations to determine their nature. Comparing
multi-instrument observations, we obtain blue STIS-NICMOS color, STIS-SPHERE
radial distribution peak difference for the tertiary ring, and high
SPHERE-NICMOS polarization fraction; these aspects indicate that TWA 7 could
retain small dust particles. By viewing the debris disk around M star TWA 7 at
a nearly face-on vantage point, our study allows for the understanding of such
disks in scattered light in both system architecture and dust property.


Abstract:
  We propose a learning-based compression scheme that envelopes a standard
codec between pre and post-processing deep CNNs. Specifically, we demonstrate
improvements over prior approaches utilizing a compression-decompression
network by introducing: (a) an edge-aware loss function to prevent blurring
that is commonly occurred in prior works & (b) a super-resolution convolutional
neural network (CNN) for post-processing along with a corresponding
pre-processing network for improved rate-distortion performance in the low rate
regime. The algorithm is assessed on a variety of datasets varying from low to
high resolution namely Set 5, Set 7, Classic 5, Set 14, Live 1, Kodak, General
100, CLIC 2019. When compared to JPEG, JPEG2000, BPG, and recent CNN approach,
the proposed algorithm contributes significant improvement in PSNR with an
approximate gain of 20.75%, 8.47%, 3.22%, 3.23% and 24.59%, 14.46%, 10.14%,
8.57% at low and high bit-rates respectively. Similarly, this improvement in
MS-SSIM is approximately 71.43%, 50%, 36.36%, 23.08%, 64.70% and 64.47%,
61.29%, 47.06%, 51.52%, 16.28% at low and high bit-rates respectively. With
CLIC 2019 dataset, PSNR is found to be superior with approximately 16.67%,
10.53%, 6.78%, and 24.62%, 17.39%, 14.08% at low and high bit-rates
respectively, over JPEG2000, BPG, and recent CNN approach. Similarly, the
MS-SSIM is found to be superior with approximately 72%, 45.45%, 39.13%, 18.52%,
and 71.43%, 50%, 41.18%, 17.07% at low and high bit-rates respectively,
compared to the same approaches. A similar type of improvement is achieved with
other datasets also.


Abstract:
  Although deep-learning has been successfully applied in a variety of science
and engineering problems owing to its strong high-dimensional nonlinear mapping
capability, it is of limited use in scientific knowledge discovery. In this
work, we propose a deep-learning based framework to discover the macroscopic
governing equation of viscous gravity current based on high-resolution
microscopic simulation data without the need for prior knowledge of underlying
terms. For two typical scenarios with different viscosity ratios, the
deep-learning based equations exactly capture the same dominated terms as the
theoretically derived equations for describing long-term asymptotic behaviors,
which validates the proposed framework. Unknown macroscopic equations are then
obtained for describing short-term behaviors, and additional deep-learned
compensation terms are eventually discovered. Comparison of posterior tests
shows that the deep-learning based PDEs actually perform better than the
theoretically derived PDEs in predicting evolving viscous gravity currents for
both long-term and short-term regimes. Moreover, the proposed framework is
proven to be very robust against non-biased data noise for training, which is
up to 20%. Consequently, the presented deep-learning framework shows
considerable potential for discovering unrevealed intrinsic laws in scientific
semantic space from raw experimental or simulation results in data space.


Abstract:
  One of the main challenges since the advancement of convolutional neural
networks is how to connect the extracted feature map to the final
classification layer. VGG models used two sets of fully connected layers for
the classification part of their architectures, which significantly increases
the number of models' weights. ResNet and next deep convolutional models used
the Global Average Pooling (GAP) layer to compress the feature map and feed it
to the classification layer. Although using the GAP layer reduces the
computational cost, but also causes losing spatial resolution of the feature
map, which results in decreasing learning efficiency. In this paper, we aim to
tackle this problem by replacing the GAP layer with a new architecture called
Wise-SrNet. It is inspired by the depthwise convolutional idea and is designed
for processing spatial resolution and also not increasing computational cost.
We have evaluated our method using three different datasets: Intel Image
Classification Challenge, MIT Indoors Scenes, and a part of the ImageNet
dataset. We investigated the implementation of our architecture on several
models of Inception, ResNet and DensNet families. Applying our architecture has
revealed a significant effect on increasing convergence speed and accuracy. Our
Experiments on images with 224x224 resolution increased the Top-1 accuracy
between 2% to 8% on different datasets and models. Running our models on
512x512 resolution images of the MIT Indoors Scenes dataset showed a notable
result of improving the Top-1 accuracy within 3% to 26%. We will also
demonstrate the GAP layer's disadvantage when the input images are large and
the number of classes is not few. In this circumstance, our proposed
architecture can do a great help in enhancing classification results. The code
is shared at https://github.com/mr7495/image-classification-spatial.


Abstract:
  Link prediction is a very fundamental task on graphs. Inspired by traditional
path-based methods, in this paper we propose a general and flexible
representation learning framework based on paths for link prediction.
Specifically, we define the representation of a pair of nodes as the
generalized sum of all path representations, with each path representation as
the generalized product of the edge representations in the path. Motivated by
the Bellman-Ford algorithm for solving the shortest path problem, we show that
the proposed path formulation can be efficiently solved by the generalized
Bellman-Ford algorithm. To further improve the capacity of the path
formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general
graph neural network framework that solves the path formulation with learned
operators in the generalized Bellman-Ford algorithm. The NBFNet parameterizes
the generalized Bellman-Ford algorithm with 3 neural components, namely
INDICATOR, MESSAGE and AGGREGATE functions, which corresponds to the boundary
condition, multiplication operator, and summation operator respectively. The
NBFNet is very general, covers many traditional path-based methods, and can be
applied to both homogeneous graphs and multi-relational graphs (e.g., knowledge
graphs) in both transductive and inductive settings. Experiments on both
homogeneous graphs and knowledge graphs show that the proposed NBFNet
outperforms existing methods by a large margin in both transductive and
inductive settings, achieving new state-of-the-art results.


Abstract:
  The standard way to estimate the parameters $\Theta_\text{SEIR}$ (e.g., the
transmission rate $\beta$) of an SEIR model is to use grid search, where
simulations are performed on each set of parameters, and the parameter set
leading to the least $L_2$ distance between predicted number of infections and
observed infections is selected. This brute-force strategy is not only time
consuming, as simulations are slow when the population is large, but also
inaccurate, since it is impossible to enumerate all parameter combinations. To
address these issues, in this paper, we propose to transform the
non-differentiable problem of finding optimal $\Theta_\text{SEIR}$ to a
differentiable one, where we first train a recurrent net to fit a small number
of simulation data. Next, based on this recurrent net that is able to
generalize SEIR simulations, we are able to transform the objective to a
differentiable one with respect to $\Theta_\text{SEIR}$, and straightforwardly
obtain its optimal value. The proposed strategy is both time efficient as it
only relies on a small number of SEIR simulations, and accurate as we are able
to find the optimal $\Theta_\text{SEIR}$ based on the differentiable objective.
On two COVID-19 datasets, we observe that the proposed strategy leads to
significantly better parameter estimations with a smaller number of
simulations.


Abstract:
  The thermal transport properties of biphenylene network (BPN), a novel sp2
-hybridized two-dimensional allotrope of carbon atoms recently realized in
experiments [Fan et al., Science, 372 852-856 (2021)], are studied using the
density functional theory-driven solution of the Boltzmann transport equation.
The thermal transport in BPN is anisotropic and the obtained thermal
conductivities are more than an order of magnitude lower than that in graphene,
despite similar sp2-hybridized planar-structure of both allotropes. The lower
thermal conductivity in BPN is found to originate from enhanced anharmonicity
which in turn is a result of reduced crystal symmetry of BPN.


Abstract:
  Modeling complex physical dynamics is a fundamental task in science and
engineering. Traditional physics-based models are sample efficient,
interpretable but often rely on rigid assumptions. Furthermore, direct
numerical approximation is usually computationally intensive, requiring
significant computational resources and expertise. While deep learning (DL)
provides novel alternatives for efficiently recognizing complex patterns and
emulating nonlinear dynamics, its predictions do not necessarily obey the
governing laws of physical systems, nor do they generalize well across
different systems. Thus, the study of physics-guided DL emerged and has gained
great progress. Physics-guided DL aims to take the best from both physics-based
modeling and state-of-the-art DL models to better solve scientific problems. In
this paper, we provide a structured overview of existing methodologies of
integrating prior physical knowledge or physics-based modeling into DL, with a
special emphasis on learning dynamical systems. We also discuss the fundamental
challenges and emerging opportunities in the area.


Abstract:
  The Beyond-Linear-Use-of-Equation-Superposition (BLUES) function method is
extended to coupled nonlinear ordinary differential equations and applied to
the epidemiological SIRS model with vaccination. Accurate analytic
approximations are obtained for the time evolution of the susceptible and
infected population fractions. The results are compared with those obtained
with alternative methods, notably Adomian decomposition, variational iteration
and homotopy perturbation. In contrast with these methods, the BLUES iteration
converges rapidly, globally, and captures the exact asymptotic behavior for
long times. The time of the infection peak is calculated using the BLUES
approximants and the results are compared with numerical solutions, which
indicate that the method is able to generate useful analytic expressions that
coincide with the (numerically) exact ones already for a small number of
iterations.


Abstract:
  Wide field small aperture telescopes (WFSATs) are mainly used to obtain
scientific information of point--like and streak--like celestial objects.
However, qualities of images obtained by WFSATs are seriously affected by the
background noise and variable point spread functions. Developing high speed and
high efficiency data processing method is of great importance for further
scientific research. In recent years, deep neural networks have been proposed
for detection and classification of celestial objects and have shown better
performance than classical methods. In this paper, we further extend abilities
of the deep neural network based astronomical target detection framework to
make it suitable for photometry and astrometry. We add new branches into the
deep neural network to obtain types, magnitudes and positions of different
celestial objects at the same time. Tested with simulated data, we find that
our neural network has better performance in photometry than classical methods.
Because photometry and astrometry are regression algorithms, which would obtain
high accuracy measurements instead of rough classification results, the
accuracy of photometry and astrometry results would be affected by different
observation conditions. To solve this problem, we further propose to use
reference stars to train our deep neural network with transfer learning
strategy when observation conditions change. The photometry framework proposed
in this paper could be used as an end--to--end quick data processing framework
for WFSATs, which can further increase response speed and scientific outputs of
WFSATs.


Abstract:
  A major strategy to prevent the spread of COVID-19 is the limiting of
in-person contacts. However, this is impractical or impossible for the many
disabled people who do not live in care facilities, but still require
caregivers. We seek to determine which interventions can prevent infections
among disabled people and their caregivers. We simulate transmission with a
model that includes susceptible, exposed, asymptomatic, symptomatically ill,
hospitalized, and removed individuals. The networks on which we simulate
disease spread incorporate heterogeneity in the risks of different types of
interactions, time-dependent lockdown and reopening measures, and contact
distributions for four different groups (caregivers, disabled people, essential
workers, and the general population). We find the probability of becoming
infected is largest for caregivers and second largest for disabled people. Our
analysis of network structure illustrates that caregivers have the largest
modal eigenvector centrality. We find that two interventions --
contact-limiting by all groups and mask-wearing by disabled people and
caregivers -- most reduce the cases among disabled people and caregivers. We
also test which group spreads COVID-19 most readily by seeding infections in a
subset of each group. We find caregivers are the most potent spreaders of
COVID-19, particularly to other caregivers and to disabled people. We test
where to use limited vaccine doses most effectively and find (1) vaccinating
caregivers better protects disabled people than vaccinating the general
population or essential workers and (2) vaccinating caregivers protects
disabled people about as much as vaccinating disabled people themselves. Our
results highlight the potential effectiveness of mask-wearing, contact-limiting
throughout society, and strategic vaccination for limiting the exposure of
disabled people and their caregivers to COVID-19.


Abstract:
  This paper is concerned with analyzing a class of fractional calculus of
variations problems and their associated Euler-Lagrange (fractional
differential) equations. Unlike the existing fractional calculus of variations
which is based on the classical notion of fractional derivatives, the
fractional calculus of variations considered in this paper is based on a newly
developed notion of weak fractional derivatives and their associated fractional
order Sobolev spaces. Since fractional derivatives are direction-dependent,
using one-sided fractional derivatives and their combinations leads to new
types of calculus of variations and fractional differential equations as well
as nonstandard Neumann boundary operators. The primary objective of this paper
is to establish the well-posedness and regularities for a class of fractional
calculus of variations problems and their Euler-Lagrange (fractional
differential) equations. This is achieved first for one-sided Dirichlet energy
functionals which lead to one-sided fractional Laplace equations, then for more
general energy functionals which give rise to more general fractional
differential equations.


Abstract:
  We employ MUSE/VLT data to study the ionised and highly ionised gas phases of
the feedback in Circinus, the closest Seyfert 2 galaxy to us. The analysis of
the nebular emission allowed us to detect a remarkable high-ionisation gas
outflow beyond the galaxy plane traced by the coronal lines [Fe VII]
$\lambda$6089 and [Fe X] $\lambda$6374, extending up to 700 pc and 350 pc NW
from the nucleus, respectively. This is the first time that the [Fe X] emission
is observed at such distances from the central engine in an AGN. The gas
kinematics reveals expanding gas shells with velocities of a few hundred km
s$^{-1}$, spatially coincident with prominent hard X-ray emission detected by
Chandra. Density and temperature sensitive line ratios show that the extended
high-ionisation gas is characterized by a temperature reaching 25000 K and an
electron density > 10$^2$ cm$^{-3}$. We found that local gas excitation by
shocks produced by the passage of a radio jet leads to the spectacular
high-ionisation emission in this object. This hypothesis is fully supported by
photoionisation models that accounts for the combined effects of the central
engine and shocks. They reproduce the observed emission line spectrum at
different locations inside and outside of the NW ionisation cone. The energetic
outflow produced by the radio jet is spatially located close to an extended
molecular outflow recently reported using ALMA which suggests that they both
represent different phases of the same feedback process acting on the AGN.


Abstract:
  In this paper, we propose an invertible neural network for image denoising
(DnINN) inspired by the transform-based denoising framework. The proposed DnINN
consists of an invertible neural network called LINN whose architecture is
inspired by the lifting scheme in wavelet theory and a sparsity-driven
denoising network which is used to remove noise from the transform
coefficients. The denoising operation is performed with a single
soft-thresholding operation or with a learned iterative shrinkage thresholding
network. The forward pass of LINN produces an over-complete representation
which is more suitable for denoising. The denoised image is reconstructed using
the backward pass of LINN using the output of the denoising network. The
simulation results show that the proposed DnINN method achieves results
comparable to the DnCNN method while only requiring 1/4 of learnable
parameters.


Abstract:
  According to Mostow's celebrated rigidity theorem, the geometry of closed
hyperbolic 3-manifolds is already determined by their topology. In particular,
the volume of such manifolds is a topological invariant and, as such, has been
investigated for half a century.
  Motivated by the algorithmic study of 3-manifolds, Maria and Purcell have
recently shown that every closed hyperbolic 3-manifold M with volume vol(M)
admits a triangulation with dual graph of treewidth at most C vol(M), for some
universal constant C.
  Here we improve on this result by showing that the volume provides a linear
upper bound even on the pathwidth of the dual graph of some triangulation,
which can potentially be much larger than the treewidth. Our proof relies on a
synthesis of tools from 3-manifold theory: generalized Heegaard splittings,
amalgamations, and the thick-thin decomposition of hyperbolic 3-manifolds. We
provide an illustrated exposition of this toolbox and also discuss the
algorithmic consequences of the result.


Abstract:
  We perform a fit of the finite-volume QCD spectrum of three pions at maximal
isospin to constrain the three-body force. We use the unitarity-based
relativistic three-particle quantization condition, with the GWUQCD spectrum
obtained at 315 MeV and 220 MeV pion mass in two-flavor QCD. For the heavier
pion mass we find that the data is consistent with a constant contact term
close to zero, whereas for the lighter mass we see a statistically significant
energy dependence in tension with the prediction of leading order ChPT. Our
results also suggest that with enough three-body energy levels, the two-body
amplitude could be constrained.


Abstract:
  In recent years, the implementation of the demand response (DR) programs in
the power systems scheduling and operation is increased. DR is used to improve
the consumers' and power providers economic condition. That said, optimal power
flow is a fundamental concept in the power system operation and control. The
impact of exploiting DR programs in the power management of the systems is of
significant importance. In this paper, the effect of time-based DR programs on
the cost of 24-hour operation of a power system is presented. The effect of the
time of use and real-time pricing programs with different participation factors
are investigated. In addition, the systems operation cost is studied to analyze
the DR programs' role in the current power grids. For this aim, the 14-bus IEEE
test system is used to properly implement and simulate the proposed approach.


Abstract:
  The focus of the project will be an examination of obtaining the highest
score in the Axelrod Tournament. The initial design of the highest score in the
Axelrod Tournament consisted of looking at the Cooperation rates of the top
strategies currently in the Axelrod Library. After creating an initial Finite
State Machine strategy that utilized the Cooperation Rates of the top players;
our ten-state FSM finished within the top 35 players out of 220 in the short
run time strategies in the Axelrod Library. After a quick evolutionary
algorithm, 50 generations, our original ten-state FSM was changed into an
eight-state FSM, which finished within the top 5 of the short run time
strategies in the Axelrod Library. This eight-state FSM was then evolved again
using a full evolutionary algortihm process, which lasted 500 generations,
where the eight-state FSM evolved into a another eight-state FSM which finishes
first in the Axelrod Library among the short run time strategies and the full
Axelrod Tournament. From that final FSM, two of the eight states are
inaccessible, so the final FSM strategy is a six-state FSM that finishes first
in the Full Axelrod Tournament, against the short run time strategies as well
as the long run time strategies.


Abstract:
  An important task when working with terrain models is computing viewsheds:
the parts of the terrain visible from a given viewpoint. When the terrain is
modeled as a polyhedral terrain, the viewshed is composed of the union of all
the triangle parts that are visible from the viewpoint. The complexity of a
viewshed can vary significantly, from constant to quadratic in the number of
terrain vertices, depending on the terrain topography and the viewpoint
position.
  In this work we study a new topographic attribute, the \emph{prickliness},
that measures the number of local maxima in a terrain from all possible
perspectives. We show that the prickliness effectively captures the potential
of 2.5D terrains to have high complexity viewsheds, and we present near-optimal
algorithms to compute the prickliness of 1.5D and 2.5D terrains. We also report
on some experiments relating the prickliness of real word 2.5D terrains to the
size of the terrains and to their viewshed complexity.


Abstract:
  In this paper we provide a detailed description of the eigenvalue $
E_{D}(x_0)\leq 0$ (respectively $ E_{N}(x_0)\leq 0$) of the self-adjoint
Hamiltonian operator representing the negative Laplacian on the positive
half-line with a Dirichlet (resp. Neuman) boundary condition at the origin
perturbed by an attractive Dirac distribution $-\lambda \delta(x-x_0)$ for any
fixed value of the magnitude of the coupling constant. We also investigate the
$\lambda$-dependence of both eigenvalues for any fixed value of $x_0$.
Furthermore, we show that both systems exhibit resonances as poles of the
analytic continuation of the resolvent. These results will be connected with
the study of the ground state energy of two remarkable three-dimensional
self-adjoint operators, studied in depth in Albeverio's monograph, perturbed by
an attractive $\delta$-distribution supported on the spherical shell of radius
$r_0$.


Abstract:
  The Simons Observatory (SO) is a Cosmic Microwave Background (CMB) experiment
to observe the microwave sky in six frequency bands from 30GHz to 290GHz. The
Observatory -- at $\sim$5200m altitude -- comprises three Small Aperture
Telescopes (SATs) and one Large Aperture Telescope (LAT) at the Atacama Desert,
Chile. This research note describes the design and current status of the LAT
along with its future timeline.


Abstract:
  In this paper we study the elementary theory of graph products of groups and
show that under natural conditions on the vertex groups we can recover (the
core of) the underlying graph and the associated vertex groups. More precisely,
we require the vertex groups to satisfy a non-generic almost positive sentence,
a condition which generalizes a range of natural ``non-freeness conditions"
such as the satisfaction of a group law, having nontrivial center or being
boundedly simple.
  As a corollary, we determine an invariant of the elementary theory of a
right-angled Artin group, the core of the defining graph, which we conjecture
to determine the elementary class of the RAAG. We further combine our results
with the results of Sela on free products of groups to describe all finitely
generated groups elementarily equivalent to certain RAAGs. We also deduce
rigidity results on the elementary classification of graph products of groups
for specific types of vertex groups, such as finite, nilpotent or classical
linear groups.


Abstract:
  Hand Gesture Recognition (HGR) based on inertial data has grown considerably
in recent years, with the state-of-the-art approaches utilizing a single
handheld sensor and a vocabulary comprised of simple gestures.
  In this work we explore the benefits of using multiple inertial sensors.
Using WaveGlove, a custom hardware prototype in the form of a glove with five
inertial sensors, we acquire two datasets consisting of over $11000$ samples.
  To make them comparable with prior work, they are normalized along with $9$
other publicly available datasets, and subsequently used to evaluate a range of
Machine Learning approaches for gesture recognition, including a newly proposed
Transformer-based architecture. Our results show that even complex gestures
involving different fingers can be recognized with high accuracy.
  An ablation study performed on the acquired datasets demonstrates the
importance of multiple sensors, with an increase in performance when using up
to three sensors and no significant improvements beyond that.


Abstract:
  An important result by Agmon implies that an eigenfunction of a
Schr\"{o}dinger operator in $\mathbb{R}^n$ with eigenvalue $E$ below the bottom
of the essential spectrum decays exponentially if the associated classically
allowed region $\{x \in \mathbb{R}^n~:~ V(x) \leq E \}$ is compact. We extend
this result to a class of Schr\"{o}dinger operators with eigenvalues, for which
the classically allowed region is not necessarily compactly supported: We show
that integrability of the characteristic function of the classically allowed
region with respect to an increasing weight function of bounded logarithmic
derivative leads to $L^2$-decay of the eigenfunction with respect to the same
weight. Here, the decay is measured in the Agmon metric, which takes into
account anisotropies of the potential. In particular, for a power law (or,
respectively, exponential) weight, our main result implies that power law (or,
respectively, exponential) decay of "the size of the classically allowed
region" allows to conclude power law (or, respectively, exponential) decay, in
the Agmon metric, of the eigenfunction.


Abstract:
  Detailed parcel-level crop type mapping for the whole European Union (EU) is
necessary for the evaluation of agricultural policies. The Copernicus program,
and Sentinel-1 (S1) in particular, offers the opportunity to monitor
agricultural land at a continental scale and in a timely manner. However, so
far the potential of S1 has not been explored at such a scale. Capitalizing on
the unique LUCAS 2018 Copernicus in-situ survey, we present the first
continental crop type map at 10-m spatial resolution for the EU based on S1A
and S1B Synthetic Aperture Radar observations for the year 2018. Random forest
classification algorithms are tuned to detect 19 different crop types. We
assess the accuracy of this EU crop map with three approaches. First, the
accuracy is assessed with independent LUCAS core in-situ observations over the
continent. Second, an accuracy assessment is done specifically for main crop
types from farmers declarations from 6 EU member countries or regions totaling
>3M parcels and 8.21 Mha. Finally, the crop areas derived by classification are
compared to the subnational (NUTS 2) area statistics reported by Eurostat. The
overall accuracy for the map is reported as 80.3% when grouping main crop
classes and 76% when considering all 19 crop type classes separately. Highest
accuracies are obtained for rape and turnip rape with user and produced
accuracies higher than 96%. The correlation between the remotely sensed
estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99
(rape and turnip rape). Finally, we discuss how the framework presented here
can underpin the operational delivery of in-season high-resolution based crop
mapping.


Abstract:
  An investigation to optimize the application of the third-generation charge
optimized many-body (COMB3) interatomic potential and associated input
parameters was carried out through the study of solid-liquid interactions in
classical molecular dynamics (MD) simulations. The rates of these molecular
interactions are understood though the wetting rates of water nano droplets on
a bare copper (111) surface. Implementing the Langevin thermostat, the
influence of simulation time step, the number of atoms in the system, the
frequency at which charge equilibration is performed, and the temperature
relaxation rate are all examined. The results indicate that time steps of 0.4
fs are possible when using longer relaxation times for the system temperature,
which is almost double the typical time step used for reactive potentials. The
use of the QEq charge equilibration allows for a fewer atomic layers to be used
in the Cu slab. In addition, charge equilibrium schemes do not need to be
performed every time step to ensure accurate charge transfer. Interestingly,
the rate of wetting for the nanodroplets is dominantly dependent on the
temperature relaxation time which are predicted to significantly change the
viscosity of the water droplets. This work provides a pathway for optimizing
simulations using the COMB3 reactive interatomic potential.


Abstract:
  In this paper we present a direct formula for the solution of the general
second order linear ordinary differential equation as our main result such that
the parameters required for the formula are determined using another
differential equation, which happens to be a Riccati Equation. We also derive
other results based on the main result which include special cases for the
concerned differential equation with variable coefficients, formula for
solution of concerned differential equation with constant coefficients and
formula for the solution of the concerned differential equation with one
complimentary solution known.


Abstract:
  Diabetes is currently one of the most common, dangerous, and costly diseases
in the world that is caused by an increase in blood sugar or a decrease in
insulin in the body. Diabetes can have detrimental effects on people's health
if diagnosed late. Today, diabetes has become one of the challenges for health
and government officials. Prevention is a priority, and taking care of people's
health without compromising their comfort is an essential need. In this study,
the Ensemble training methodology based on genetic algorithms are used to
accurately diagnose and predict the outcomes of diabetes mellitus. In this
study, we use the experimental data, real data on Indian diabetics on the
University of California website. Current developments in ICT, such as the
Internet of Things, machine learning, and data mining, allow us to provide
health strategies with more intelligent capabilities to accurately predict the
outcomes of the disease in daily life and the hospital and prevent the
progression of this disease and its many complications. The results show the
high performance of the proposed method in diagnosing the disease, which has
reached 98.8%, and 99% accuracy in this study.


Abstract:
  Recent experimental advances in strongly coupled light-matter systems has
sparked the development of general ab-initio methods capable of describing
interacting light-matter systems from first principles. One of these methods,
quantum-electrodynamical density-functional theory (QEDFT), promises
computationally efficient calculations for large correlated light-matter
systems with the quality of the calculation depending on the underlying
approximation for the exchange-correlation functional. So far no true
density-functional approximation has been introduced limiting the efficient
application of the theory. In this paper, we introduce the first gradient-based
density functional for the QEDFT exchange-correlation energy derived from the
adiabatic-connection fluctuation-dissipation theorem. We benchmark this
simple-to-implement approximation on small systems in optical cavities and
demonstrate its relatively low computational costs for fullerene molecules up
to C$_{180}$ coupled to 400,000 photon modes in a dissipative optical cavity.
This work now makes first principle calculations of much larger systems
possible within the QEDFT framework effectively combining quantum optics with
large-scale electronic structure theory.


Abstract:
  We show that the Weyl phase in $\rm Sm_2Ir_2O_7$ is protracted up to at least
2~\% alloying with Bi by an anomalous negative lattice expansion with $\rm
\Delta a \sim- 0.01$\AA. With further doping, the magnetic ordering disappears
and electrical resistivity decreases by orders of magnitude; the resistivity
upturn remains but with $\rm 1/T$ dependence of Weyl phase changed to $\rm
-lnT$ dependence characteristic of the Quadratic Band Touching (QBT). At the
Weyl-QBT phase boundary, a new phase is evidenced whose resistivity scales as
$\rm -T^{1/4}$ possibly due to proximity to a quantum critical point proposed
several years ago [Phys. Rev. X 4, 041027 (2014)], but whose experimental
evidence has remained elusive thus far.


Abstract:
  We generalize soft theorems of the nonlinear sigma model beyond the
$\mathcal{O} (p^2)$ amplitudes and the coset of $\text{SU} (N) \times \text{SU}
(N) / \text{SU} (N) $. We first discuss the universal flavor ordering of the
amplitudes for the Nambu-Goldstone bosons, so that we can reinterpret the known
$\mathcal{O} (p^2)$ single soft theorem for $\text{SU} (N) \times \text{SU} (N)
/ \text{SU} (N) $ in the context of a general symmetry group representation. We
then investigate the special case of the fundamental representation of
$\text{SO} (N)$, where a special flavor ordering of the "pair basis" is
available. We provide novel amplitude relations and a Cachazo-He-Yuan formula
for such a basis, and derive the corresponding single soft theorem. Next, we
extend the single soft theorem for a general group representation to
$\mathcal{O} (p^4)$, where for at least two specific choices of the
$\mathcal{O} (p^4)$ operators, the leading non-vanishing pieces can be
interpreted as new extended theory amplitudes involving bi-adjoint scalars, and
the corresponding soft factors are the same as at $\mathcal{O} (p^2)$. Finally,
we compute the general formula for the double soft theorem, valid to all
derivative orders, where the leading part in the soft momenta is fixed by the
$\mathcal{O}(p^2)$ Lagrangian, while any possible corrections to the subleading
part are determined by the $\mathcal{O}(p^4)$ Lagrangian alone. Higher order
terms in the derivative expansion do not contribute any new corrections to the
double soft theorem.


Abstract:
  Unconventional photon blockade refers to the suppression of multi-photon
states in weakly nonlinear optical resonators via the destructive interference
of different excitation pathways. It has been studied in a pair of coupled
nonlinear resonators and other few-mode systems. Here, we show that
unconventional photon blockade can be greatly enhanced in a chain of coupled
resonators. Specifically, the strength of the nonlinearity in each resonator
needed to achieve unconventional photon blockade is suppressed exponentially
with lattice size. The analytic derivation, based on a weak drive
approximation, is validated by wavefunction Monte Carlo simulations. These
findings show that customized lattices of coupled resonators can be powerful
tools for controlling multi-photon quantum states.


Abstract:
  For the first time a dedicated investigation of the target size of a
nanodosimeter device has been carried out in order to investigate to what
extent measured ionisation cluster size distributions can serve as benchmark
data for modelling approaches, with particular focus on the target size in
terms of liquid H2O. To this end, measurements with alpha particles from a
241Am source were carried out using three different target gases, H2O, C3H8 and
C4H8O. For each of the three target gases, three different drift-time windows
were applied to realise three different target sizes. A method has been
developed to determine the dimensions of the simulated nanometric target volume
in liquid H2O for cylindrical and spherical shape, as often used in approaches
to model radiation effects to DNA. Simulations with nanometric targets of
dimensions determined with this method agree very well with the corresponding
measurements. Scaling of the spatial distribution of the extraction efficiency
for different target gases and drift-time windows, which corresponds to the
nanodosimeter's target volume, in terms of liquid H2O using
($\rho\lambda_{ion}$)-ratios has also been investigated and proved to yield an
estimate of the target volume in liquid H2O. Thus, it can be concluded that
ionisation cluster size distributions measured with a nanodosimeter device are
suited as benchmark data for approaches that model radiation induced damage to
DNA in nanometric volumes of liquid H2O in simple geometries such as cylinders
or spheres, provided that the nanodosimeter's target volume is characterised
accordingly. By proper selection of drift-time window length as well as target
gas and density a wide range of target volume dimensions in terms of liquid H2O
can be realised with the PTB Ion Counter nanodosimeter according to specific
requirements of modelling approaches.


Abstract:
  The aviation industry is of great importance for a globally connected
economy. Customer satisfaction with airlines and airport performance is
considerably influenced by how much flights are delayed. But how should the
delay be quantified with thousands of flights for each airport and airline?
Here, we present a statistical analysis of arrival delays at several UK
airports between 2018 and 2020. We establish a procedure to compare both mean
delay and extreme events among airlines and airports, identifying a power-law
decay of large delays. Furthermore, we note drastic changes in plane delay
statistics during the COVID-19 pandemic. Finally, we find that delays are
described by a superposition of simple distributions, leading to a
superstatistics.


Abstract:
  Human re-rendering from a single image is a starkly under-constrained
problem, and state-of-the-art algorithms often exhibit undesired artefacts,
such as over-smoothing, unrealistic distortions of the body parts and garments,
or implausible changes of the texture. To address these challenges, we propose
a new method for neural re-rendering of a human under a novel user-defined pose
and viewpoint, given one input image. Our algorithm represents body pose and
shape as a parametric mesh which can be reconstructed from a single image and
easily reposed. Instead of a colour-based UV texture map, our approach further
employs a learned high-dimensional UV feature map to encode appearance. This
rich implicit representation captures detailed appearance variation across
poses, viewpoints, person identities and clothing styles better than learned
colour texture maps. The body model with the rendered feature maps is fed
through a neural image-translation network that creates the final rendered
colour image. The above components are combined in an end-to-end-trained neural
network architecture that takes as input a source person image, and images of
the parametric body model in the source pose and desired target pose.
Experimental evaluation demonstrates that our approach produces higher quality
single image re-rendering results than existing methods.


Abstract:
  The proliferation of insecure Internet-connected devices gave rise to the IoT
botnets which can grow very large rapidly and may perform high-impact
cyber-attacks. The related studies for tackling IoT botnets are concerned with
either capturing or analyzing IoT botnet samples, using honeypots and
sandboxes, respectively. The lack of integration between the two implies that
the samples captured by the honeypots must be manually submitted for analysis
in sandboxes, introducing a delay during which a botnet may change its
operation. Furthermore, the effectiveness of the proposed sandboxes is limited
by the potential use of anti-analysis techniques and the inability to identify
features for effective detection and identification of IoT botnets. In this
paper, we propose and evaluate a novel framework, the IoT-BDA framework, for
automated capturing, analysis, identification, and reporting of IoT botnets.
The framework consists of honeypots integrated with a novel sandbox that
supports a wider range of hardware and software configurations, and can
identify indicators of compromise and attack, along with anti-analysis,
persistence, and anti-forensics techniques. These features can make botnet
detection and analysis, and infection remedy more effective. The framework
reports the findings to a blacklist and abuse service to facilitate botnet
suspension. The paper also describes the discovered anti-honeypot techniques
and the measures applied to reduce the risk of honeypot detection. Over the
period of seven months, the framework captured, analyzed, and reported 4077
unique IoT botnet samples. The analysis results show that some IoT botnets used
anti-analysis, persistence, and anti-forensics techniques typically seen in
traditional botnets.


Abstract:
  Laboratory tests play a major role in clinical decision making because they
are essential for the confirmation of diagnostics suspicions and influence
medical decisions. The number of different laboratory tests available to
physicians in our age has been expanding very rapidly due to the rapid advances
in laboratory technology. To find the correct desired tests within this
expanding plethora of elements, the Health Information System must provide a
powerful search engine and the practitioner need to remember the exact name of
the laboratory test to correctly select the bag of tests to order. Recommender
systems are platforms which suggest appropriate items to a user after learning
the users' behaviour. A neighbourhood-based collaborative filtering method was
used to model the recommender system, where similar bags, clustered using
nearest neighbours algorithm, are used to make recommendations of tests for
each other similar bag of laboratory tests. The recommender system developed in
this paper achieved 95.54 % in the mean average precision metric. A fully
documented Python package named LaboRecommender was developed to implement the
algorithm proposed in this paper


Abstract:
  Purpose: Magnetization transfer (MT) and inhomogeneous MT (ihMT) contrasts
are used in MRI to provide information about macromolecular tissue content. In
particular, MT is sensitive to macromolecules and ihMT appears to be specific
to myelinated tissue. This study proposes a technique to characterize MT and
ihMT properties from a single acquisition, producing both semiquantitative
contrast ratios, and quantitative parameter maps.
  Theory and Methods: Building upon previous work that uses multiband
radiofrequency (RF) pulses to efficiently generate ihMT contrast, we propose a
cyclic-steady-state approach that cycles between multiband and single-band
pulses to boost the achieved contrast. Resultant time-variable signals are
reminiscent of a magnetic resonance fingerprinting (MRF) acquisition, except
that the signal fluctuations are entirely mediated by magnetization transfer
effects. A dictionary-based low-rank inversion method is used to reconstruct
the resulting images and to produce both semiquantitative MT ratio (MTR) and
ihMT ratio (ihMTR) maps, as well as quantitative parameter estimates
corresponding to an ihMT tissue model.
  Results: Phantom and in vivo brain data acquired at 1.5T demonstrate the
expected contrast trends, with ihMTR maps showing contrast more specific to
white matter (WM), as has been reported by others. Quantitative estimation of
semisolid fraction and dipolar T1 was also possible and yielded measurements
consistent with literature values in the brain.
  Conclusions: By cycling between multiband and single-band pulses, an entirely
magnetization transfer mediated 'fingerprinting' method was demonstrated. This
proof-of-concept approach can be used to generate semiquantitative maps and
quantitatively estimate some macromolecular specific tissue parameters.


Abstract:
  A recently-derived alternative method for computing the relative gain array
(RGA) for singular and/or non-square systems has been proposed that provably
guarantees unit invariance. This property is not offered by the conventional
method that uses the Moore-Penrose (MP) pseudoinverse. In this paper we note
that the absence of the scale-invariance property by the conventional MP-RGA
does not necessarily imply a practical disadvantage in real-world applications.
In other words, while it is true that performance of a controller should not
depend on the choice of units on its input and output variables, this does not
{\em necessarily} imply that the resulting MP-RGA measures of component
interaction lead to different controller-design input-output pairings. In this
paper we consider the application of the MP-RGA to a realistic system (a Sakai
fractional distillation system) to assess whether or not the choice of unit,
which in this case relates to temperature, affects the choice of input-output
pairings determined by the resulting RGA matrix. Our results show that it does,
thus confirming that unit-sensitivity of the MP-RGA undermines its rigorous use
for MIMO controller design.


Abstract:
  This paper presents a Material Mask Overlay Strategy topology optimization
approach with improved material assignment at the element level for achieving
close to black-and-white designs for pressure-loaded problems. Hexagonal
elements are employed to parametrize the design domain as this tessellation
provides nonsingular local connectivity. Elliptical negative masks are used to
find the optimized material layout. The material dilation and material erosion
variables of each mask are systematically varied in association with a
gray-scale measure constraint to achieve designs close to 0-1. Darcy's law in
association with a drainage term is used to formulate the pressure field. The
obtained pressure field is converted into the consistent nodal forces using
Wachspress shape functions. Sensitivities of the objective and pressure load
are evaluated using the adjoint-variable method. The approach is demonstrated
by solving various pressure-loaded structures and pressure-actuated compliant
mechanisms. Compliance is minimized for loadbearing structures, whereas a
multicriteria objective is minimized for mechanism designs.


Abstract:
  Small-angle neutron scattering is used in combination with transport
measurements to investigate the current-induced effects on the morphology of
the intermediate mixed state domains in the intertype superconductor niobium.
We report the robust self-organisation of the vortex lattice domains to
elongated parallel stripes perpendicular to the applied current in a
steady-state. The experimental results for the formation of the superstructure
are supported by theoretical calculations, which highlight important details of
the vortex matter evolution. The investigation demonstrates a mechanism of a
spontaneous pattern formation that is closely related to the universal physics
governing the intermediate mixed state in low-$\kappa$ superconductors.


Abstract:
  Causal inference is a challenging problem with observational data alone. The
task becomes easier when having access to data from perturbing the underlying
system, even when happening in a non-randomized way: this is the setting we
consider, encompassing also latent confounding variables. To identify causal
relations among a collections of covariates and a response variable, existing
procedures rely on at least one of the following assumptions: i) the response
variable remains unperturbed, ii) the latent variables remain unperturbed, and
iii) the latent effects are dense. In this paper, we examine a perturbation
model for interventional data, which can be viewed as a mixed-effects linear
structural causal model, over a collection of Gaussian variables that does not
satisfy any of these conditions. We propose a maximum-likelihood estimator --
dubbed DirectLikelihood -- that exploits system-wide invariances to uniquely
identify the population causal structure from unspecific perturbation data, and
our results carry over to linear structural causal models without requiring
Gaussianity. We illustrate the utility of our framework on synthetic data as
well as real data involving California reservoirs and protein expressions.


Abstract:
  Through examples, we illustrate how to compute differential operators on a
quotient of an affine semigroup ring by a radical monomial ideal, when working
over an algebraically closed field of characteristic 0.


Abstract:
  Although it is possible to increase confidence in Free and Open Source
Software (FOSS) by reviewing its source code, trusting code is not the same as
trusting its executable counterparts. These are typically built and distributed
by third-party vendors, with severe security consequences if their supply
chains are compromised. In this paper, we present reproducible builds, an
approach that can determine whether generated binaries correspond with their
original source code. We first define the problem, and then provide insight
into the challenges of making real-world software build in a "reproducible"
manner-this is, when every build generates bit-for-bit identical results.
Through the experience of the Reproducible Builds project making the Debian
Linux distribution reproducible, we also describe the affinity between
reproducibility and quality assurance (QA).


Abstract:
  Recently, there has been an increasing interest in neural speech synthesis.
While the deep neural network achieves the state-of-the-art result in
text-to-speech (TTS) tasks, how to generate a more emotional and more
expressive speech is becoming a new challenge to researchers due to the
scarcity of high-quality emotion speech dataset and the lack of advanced
emotional TTS model. In this paper, we first briefly introduce and publicly
release a Mandarin emotion speech dataset including 9,724 samples with audio
files and its emotion human-labeled annotation. After that, we propose a simple
but efficient architecture for emotional speech synthesis called EMSpeech.
Unlike those models which need additional reference audio as input, our model
could predict emotion labels just from the input text and generate more
expressive speech conditioned on the emotion embedding. In the experiment
phase, we first validate the effectiveness of our dataset by an emotion
classification task. Then we train our model on the proposed dataset and
conduct a series of subjective evaluations. Finally, by showing a comparable
performance in the emotional speech synthesis task, we successfully demonstrate
the ability of the proposed model.


Abstract:
  A key limiting factor of present day state-of-the-art laser frequency
stabilization using Fabry-P\'erot cavities are length changes due to
thermo-mechanical noise, originating from, for example, Brownian motion. We
present a laser-frequency stabilization concept using an optical cavity with a
strong slow-light effect to reduce the impact of cavity length changes on the
frequency stability. The resulting noise-reduction factor is proportional to
the ratio between the light phase and group velocities in the highly dispersive
cavity spacer. We experimentally demonstrate a proof-of-principle
implementation of this laser-frequency stabilization technique using a
rare-earth doped crystalline cavity spacer in conjunction with semi-permanent
spectral tailoring to achieve precise control of the dispersive properties of
the cavity. A reduction in frequency sensitivity of four orders of magnitude
was achieved compared to the same setup in the absence of the slow-light
effect.


Abstract:
  In this work, we propose the $4S$-$3D$ mixing scheme to assign the
$\Upsilon(10753)$ into the conventional bottomonium family. Under this
interpretation, we further study its hidden-bottom hadronic decays with a
$\eta^{(\prime)}$ or $\omega$ emission, which include
$\Upsilon(10753)\to\Upsilon(1S)\eta^{(\prime)}$, $\Upsilon(10753)\to
h_{b}(1P)\eta$ and $\Upsilon(10753)\to\chi_{bJ}\omega$ ($J$=0,1,2) processes.
Since the $\Upsilon(10753)$ is above the $B\bar{B}$ threshold, the
coupled-channel effect cannot be ignored, thus, when calculating partial decay
widths of these $\Upsilon(10753)$ hidden-bottom decays, we apply the hadronic
loop mechanism. Our result shows that these discussed decay processes own
considerable branching fractions with the order of magnitude of $10^{-4}\sim
10^{-3}$, which can be accessible at Belle II and other future experiments.


Abstract:
  Robust implementable output regulator design approaches are studied for
general linear continuous-time \mbox{systems} with periodically sampled
measurements, consisting of both the regulation errors and extra measurements
that are generally non-vanishing in steady state. A digital regulator is first
developed via the conventional emulation-based approach, rendering the
regulation errors asymptotically bounded with a small sampling period. We then
develop a hybrid design framework by incorporating a generalized hold device,
which transforms the original problem into the problem of designing an output
feedback controller fulfilling two conditions for a discrete-time system. We
show that such a controller can always be obtained by designing a discrete-time
internal model, a discrete-time washout filter, and a discrete-time output
feedback stabilizer. As a result, the regulation errors are shown to be
globally exponentially convergent to zero, while the sampling period is fixed
but can be arbitrarily large. This design framework is further developed for a
multi-rate digital regulator with a large sampling period of the measurements
and a small control execution period.


Abstract:
  We establish an equivalence-singularity dichotomy for a large class of
one-dimensional Markov measures. Our approach is new in that we deal with
one-sided and two-sided chains simultaneously, and in that we do not appeal to
any 0-1 law. In fact we deduce a new 0-1 law from the dichotomy.


Abstract:
  We study the problem of minimizing the sum of potentially non-differentiable
convex cost functions with partially overlapping dependences in an asynchronous
manner, where communication in the network is not coordinated. We study the
behavior of an asynchronous algorithm based on dual decomposition and block
coordinate subgradient methods under assumptions weaker than those used in the
literature. At the same time, we allow different agents to use local stepsizes
with no global coordination. Sufficient conditions are provided for almost sure
convergence to the solution of the optimization problem. Under additional
assumptions, we establish a sublinear convergence rate that in turn can be
strengthened to linear convergence rate if the problem is strongly convex and
has Lipschitz gradients. We also extend available results in the literature by
allowing multiple and potentially overlapping blocks to be updated at the same
time with non-uniform and potentially time varying probabilities assigned to
different blocks. A numerical example is provided to illustrate the
effectiveness of the algorithm.


Abstract:
  In this article we study and classify optimal martingales in the dual
formulation of optimal stopping problems. In this respect we distinguish
between weakly optimal and surely optimal martingales. It is shown that the
family of weakly optimal and surely optimal martingales may be quite large. On
the other hand it is shown that the Doob-martingale, that is, the martingale
part of the Snell envelope, is in a certain sense the most robust surely
optimal martingale under random perturbations. This new insight leads to a
novel randomized dual martingale minimization algorithm that doesn't require
nested simulation. As a main feature, in a possibly large family of optimal
martingales the algorithm efficiently selects a martingale that is as close as
possible to the Doob martingale. As a result, one obtains the dual upper bound
for the optimal stopping problem with low variance.