Abstract:
  COVID19 has impacted Indian engineering institutions (EIs) enormously. It has
tightened its knot around EIs that forced their previous half shut shades
completely down to prevent the risk of spreading COVID19. In such a situation,
fetching new enrollments on EI campuses is a difficult and challenging task, as
students behavior and family preferences have changed drastically due to mental
stress and emotions attached to them. Consequently, it becomes a prerequisite
to examine the choice characteristics influencing the selection of EI during
the COVID-19 pandemic to make it normal for new enrollments.
  The purpose of this study is to critically examine choice characteristics
that affect students choice for EI and consequently to explore relationships
between institutional characteristics and the suitability of EI during the
COVID19 pandemic across students characteristics. The findings of this study
revealed dissimilarities across students characteristics regarding the
suitability of EIs under pandemic conditions. Regression analysis revealed that
EI characteristics such as proximity, image and reputation, quality education
and curriculum delivery have significantly contributed to suitability under
COVID19. At the micro level, multiple relationships were noted between EI
characteristics and the suitability of EI under the pandemic across students
characteristics. The study has successfully demonstrated how choice
characteristics can be executed to regulate the suitability of EI under the
COVID19 pandemic for the inclusion of diversity. It is useful for policy makers
and academicians to reposition EIs that fetch diversity during the pandemic.
This study is the first to provide insights into the performance of choice
characteristics and their relationship with the suitability of EIs under a
pandemic and can be a yardstick in administering new enrollments.


Abstract:
  We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based
algorithm, to sample from a log-concave distribution restricted to a convex
body. We prove that, starting from a warm start, the walk mixes to a
log-concave target distribution $\pi(x) \propto e^{-f(x)}$, where $f$ is
$L$-smooth and $m$-strongly-convex, within accuracy $\varepsilon$ after
$\widetilde O(\kappa d^2 \ell^2 \log (1 / \varepsilon))$ steps for a
well-rounded convex body where $\kappa = L / m$ is the condition number of the
negative log-density, $d$ is the dimension, $\ell$ is an upper bound on the
number of reflections, and $\varepsilon$ is the accuracy parameter. We also
developed an efficient open source implementation of ReHMC and we performed an
experimental study on various high-dimensional data-sets. The experiments
suggest that ReHMC outperfroms Hit-and-Run and Coordinate-Hit-and-Run regarding
the time it needs to produce an independent sample and introduces practical
truncated sampling in thousands of dimensions.


Abstract:
  When flipping a fair coin, let $W = L_1L_2...L_N$ with $L_i\in\{H,T\}$ be a
binary word of length $N=2$ or $N=3$. In this paper, we establish second- and
third-order linear recurrence relations and their generating functions to
discuss the probabilities $p_{W}(n)$ that binary words $W$ appear for the first
time after $n$ coin tosses.


Abstract:
  Climate change and global warming are the significant challenges of the new
century. A viable solution to mitigate greenhouse gas emissions is via a
globally incentivized market mechanism proposed in the Kyoto protocol. In this
view, the carbon dioxide (or other greenhouse gases) emission is considered a
commodity, forming a carbon trading system. There have been attempts in
developing this idea in the past decade with limited success. The main
challenges of current systems are fragmented implementations, lack of
transparency leading to over-crediting and double-spending, and substantial
transaction costs that transfer wealth to brokers and agents. We aim to create
a Carbon Credit Ecosystem using smart contracts that operate in conjunction
with blockchain technology in order to bring more transparency, accessibility,
liquidity, and standardization to carbon markets. This ecosystem includes a
tokenization mechanism to securely digitize carbon credits with clear minting
and burning protocols, a transparent mechanism for distribution of tokens, a
free automated market maker for trading the carbon tokens, and mechanisms to
engage all stakeholders, including the energy industry, project verifiers,
liquidity providers, NGOs, concerned citizens, and governments. This approach
could be used in a variety of other credit/trading systems.


Abstract:
  We demonstrate a successful navigation and docking control system for the
John Deere Tango autonomous mower, using only a single camera as the input.
This vision-only system is of interest because it is inexpensive, simple for
production, and requires no external sensing. This is in contrast to existing
systems that rely on integrated position sensors and global positioning system
(GPS) technologies. To produce our system we combined a state-of-the-art object
detection architecture, You Only Look Once (YOLO), with a reinforcement
learning (RL) architecture, Double Deep QNetworks (Double DQN). The object
detection network identifies features on the mower and passes its output to the
RL network, providing it with a low-dimensional representation that enables
rapid and robust training. Finally, the RL network learns how to navigate the
machine to the desired spot in a custom simulation environment. When tested on
mower hardware, the system is able to dock with centimeter-level accuracy from
arbitrary initial locations and orientations.


Abstract:
  Few researches have been proposed specifically for real-time semantic
segmentation in rainy environments. However, the demand in this area is huge
and it is challenging for lightweight networks. Therefore, this paper proposes
a lightweight network which is specially designed for the foreground
segmentation in rainy environments, named De-raining Semantic Segmentation
Network (DRSNet). By analyzing the characteristics of raindrops, the
MultiScaleSE Block is targetedly designed to encode the input image, it uses
multi-scale dilated convolutions to increase the receptive field, and SE
attention mechanism to learn the weights of each channels. In order to combine
semantic information between different encoder and decoder layers, it is
proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder
employs bilinear interpolation and the output passes through pointwise
convolution, then added element-wise to the lower semantic layer of decoder.
According to the control experiments, the performances of MultiScaleSE Block
and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively
are improved to a certain degree on the Foreground Accuracy index. The
parameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M
and 0.20GFLOPs separately. The state-of-the-art results and real-time
performances are achieved on both the UESTC all-day Scenery add rain
(UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain)
benchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of
DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index
is also the best among the similar magnitude networks on both benchmarks.


Abstract:
  Understanding electrical energy demand at the consumer level plays an
important role in planning the distribution of electrical networks and offering
of off-peak tariffs, but observing individual consumption patterns is still
expensive. On the other hand, aggregated load curves are normally available at
the substation level. The proposed methodology separates substation aggregated
loads into estimated mean consumption curves, called typical curves, including
information given by explanatory variables. In addition, a model-based
clustering approach for substations is proposed based on the similarity of
their consumers typical curves and covariance structures. The methodology is
applied to a real substation load monitoring dataset from the United Kingdom
and tested in eight simulated scenarios.


Abstract:
  The measurements of $V_{us}$ in leptonic $(K_{\mu 2})$ and semileptonic
$(K_{l3})$ kaon decays exhibit a $3\sigma$ disagreement, which could originate
either from physics beyond the Standard Model or some large unidentified
Standard Model systematic effects. Clarifying this issue requires a careful
examination of all existing Standard Model inputs. Making use of a
newly-proposed computational framework and the most recent lattice QCD results,
we perform a comprehensive re-analysis of the electroweak radiative corrections
to the $K_{e3}$ decay rates that achieves an unprecedented level of precision
of $10^{-4}$, which improves the current best results by almost an order of
magnitude. No large systematic effects are found, which suggests that the
electroweak radiative corrections should be removed from the ``list of
culprits'' responsible for the $K_{\mu 2}$--$K_{l3}$ discrepancy.


Abstract:
  We present a new method to capture detailed human motion, sampling more than
1000 unique points on the body. Our method outputs highly accurate 4D
(spatio-temporal) point coordinates and, crucially, automatically assigns a
unique label to each of the points. The locations and unique labels of the
points are inferred from individual 2D input images only, without relying on
temporal tracking or any human body shape or skeletal kinematics models.
Therefore, our captured point trajectories contain all of the details from the
input images, including motion due to breathing, muscle contractions and flesh
deformation, and are well suited to be used as training data to fit advanced
models of the human body and its motion. The key idea behind our system is a
new type of motion capture suit which contains a special pattern with
checkerboard-like corners and two-letter codes. The images from our
multi-camera system are processed by a sequence of neural networks which are
trained to localize the corners and recognize the codes, while being robust to
suit stretching and self-occlusions of the body. Our system relies only on
standard RGB or monochrome sensors and fully passive lighting and the passive
suit, making our method easy to replicate, deploy and use. Our experiments
demonstrate highly accurate captures of a wide variety of human poses,
including challenging motions such as yoga, gymnastics, or rolling on the
ground.


Abstract:
  The application of remaining useful life (RUL) prediction has taken great
importance in terms of energy optimization, cost-effectiveness, and risk
mitigation. The existing RUL prediction algorithms mostly constitute deep
learning frameworks. In this paper, we implement LSTM and GRU models and
compare the obtained results with a proposed genetically trained neural
network. The current models solely depend on Adam and SGD for optimization and
learning. Although the models have worked well with these optimizers, even
little uncertainties in prognostics prediction can result in huge losses. We
hope to improve the consistency of the predictions by adding another layer of
optimization using Genetic Algorithms. The hyper-parameters - learning rate and
batch size are optimized beyond manual capacity. These models and the proposed
architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized
architecture can predict the given hyper-parameters autonomously and provide
superior results.


Abstract:
  In this paper, we study the optimal transmission of a multi-quality tiled 360
virtual reality (VR) video from a multi-antenna server (e.g., access point or
base station) to multiple single-antenna users in a multiple-input
multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA)
system. We minimize the total transmission power with respect to the subcarrier
allocation constraints, rate allocation constraints, and successful
transmission constraints, by optimizing the beamforming vector and subcarrier,
transmission power and rate allocation. The formulated resource allocation
problem is a challenging mixed discrete-continuous optimization problem. We
obtain an asymptotically optimal solution in the case of a large antenna array,
and a suboptimal solution in the general case. As far as we know, this is the
first work providing optimization-based design for 360 VR video transmission in
MIMO-OFDMA systems. Finally, by numerical results, we show that the proposed
solutions achieve significant improvement in performance compared to the
existing solutions.


Abstract:
  We discover that deep ReLU neural network classifiers can see a
low-dimensional Riemannian manifold structure on data. Such structure comes via
the local data matrix, a variation of the Fisher information matrix, where the
role of the model parameters is taken by the data variables. We obtain a
foliation of the data domain and we show that the dataset on which the model is
trained lies on a leaf, the data leaf, whose dimension is bounded by the number
of classification labels. We validate our results with some experiments with
the MNIST dataset: paths on the data leaf connect valid images, while other
leaves cover noisy images.


Abstract:
  Backhauling services through satellite systems have doubled between 2012 and
2018. There is an increasing demand for this service for which satellite
systems typically allocate a fixed resource. This solution may not help in
optimizing the usage of the scarce satellite resource.
  This study measures the relevance of using dynamic resource allocation
mechanisms for backhaul services through satellite systems. The satellite
system is emulated with OpenSAND, the LTE system with Amarisoft and the
experiments are orchestrated by OpenBACH. We compare the relevance of applying
TCP PEP mechanisms and dynamic resource allocations for different traffic
services by measuring the QoE for web browsing, data transfer and VoIP
applications.
  The main conclusions are the following. When the system is congested, PEP and
layer-2 access mechanisms do not provide significant improvements. When the
system is not congested, data transfer can be greatly improved through
protocols and channel access mechanism optimization. Tuning the Constant Rate
Assignment can help in reducing the cost of the resource and provide QoE
improvements when the network is not loaded.


Abstract:
  Grain boundaries (GBs) are planar lattice defects that govern the properties
of many types of polycrystalline materials. Hence, their structures have been
investigated in great detail. However, much less is known about their chemical
features, owing to the experimental difficulties to probe these features at the
atomic length scale inside bulk material specimens. Atom probe tomography (APT)
is a tool capable of accomplishing this task, with an ability to quantify
chemical characteristics at near-atomic scale. Using APT data sets, we present
here a machine-learning-based approach for the automated quantification of
chemical features of GBs. We trained a convolutional neural network (CNN) using
twenty thousand synthesized images of grain interiors, GBs, or triple
junctions. Such a trained CNN automatically detects the locations of GBs from
APT data. Those GBs are then subjected to compositional mapping and analysis,
including revealing their in-plane chemical decoration patterns. We applied
this approach to experimentally obtained APT data sets pertaining to three case
studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we
extracted GB-specific segregation features as a function of misorientation and
coincidence site lattice character. Secondly, we revealed interfacial excesses
and in-plane chemical features that could not have been found by standard
compositional analyses. Lastly, we tracked the temporal evolution of chemical
decoration from early-stage solute GB segregation in the dilute limit to
interfacial phase separation, characterized by the evolution of complex
composition patterns. This machine-learning-based approach provides
quantitative, unbiased, and automated access to GB chemical analyses, serving
as an enabling tool for new discoveries related to interface thermodynamics,
kinetics, and the associated chemistry-structure-property relations.


Abstract:
  The Fock space $\mathcal{F}(\mathbb{C}^n)$ is the space of holomorphic
functions on $\mathbb{C}^n$ that are square-integrable with respect to the
Gaussian measure on $\mathbb{C}^n$. This space plays an important role in
several subfields of analysis and representation theory. In particular, it has
for a long time been a model to study Toeplitz operators. Esmeral and Maximenko
showed in 2016 that radial Toeplitz operators on $\mathcal{F}(\mathbb{C})$
generate a commutative $C^*$-algebra which is isometrically isomorphic to the
$C^*$-algebra $C_{b,u}(\mathbb{N}_0,\rho_1)$. In this article, we extend the
result to $k$-quasi-radial symbols acting on the Fock space
$\mathcal{F}(\mathbb{C}^n)$. We calculate the spectra of the said Toeplitz
operators and show that the set of all eigenvalue functions is dense in the
$C^*$-algebra $C_{b,u}(\mathbb{N}_0^k,\rho_k)$ of bounded functions on
$\mathbb{N}_0^k$ which are uniformly continuous with respect to the square-root
metric. In fact, the $C^*$-algebra generated by Toeplitz operators with
quasi-radial symbols is $C_{b,u}(\mathbb{N}_0^k,\rho_k)$.


Abstract:
  We present optical follow-up imaging obtained with the Katzman Automatic
Imaging Telescope, Las Cumbres Observatory Global Telescope Network, Nickel
Telescope, Swope Telescope, and Thacher Telescope of the LIGO/Virgo
gravitational wave (GW) signal from the neutron star-black hole (NSBH) merger
GW190814. We searched the GW190814 localization region (19 deg$^{2}$ for the
90th percentile best localization), covering a total of 51 deg$^{2}$ and 94.6%
of the two-dimensional localization region. Analyzing the properties of 189
transients that we consider as candidate counterparts to the NSBH merger,
including their localizations, discovery times from merger, optical spectra,
likely host-galaxy redshifts, and photometric evolution, we conclude that none
of these objects are likely to be associated with GW190814. Based on this
finding, we consider the likely optical properties of an electromagnetic
counterpart to GW190814, including possible kilonovae and short gamma-ray burst
afterglows. Using the joint limits from our follow-up imaging, we conclude that
a counterpart with an $r$-band decline rate of 0.68 mag day$^{-1}$, similar to
the kilonova AT 2017gfo, could peak at an absolute magnitude of at most $-17.8$
mag (50% confidence). Our data are not constraining for ''red'' kilonovae and
rule out ''blue'' kilonovae with $M>0.5 M_{\odot}$ (30% confidence). We
strongly rule out all known types of short gamma-ray burst afterglows with
viewing angles $<$17$^{\circ}$ assuming an initial jet opening angle of
$\sim$$5.2^{\circ}$ and explosion energies and circumburst densities similar to
afterglows explored in the literature. Finally, we explore the possibility that
GW190814 merged in the disk of an active galactic nucleus, of which we find
four in the localization region, but we do not find any candidate counterparts
among these sources.


Abstract:
  Ultrafast lasers are ideal tools to process transparent materials because
they spatially confine the deposition of laser energy within the material's
bulk via nonlinear photoionization processes. Nonlinear propagation and
filamentation were initially regarded as deleterious effects. But in the last
decade, they turned out to be benefits to control energy deposition over long
distances. These effects create very high aspect ratio structures which have
found a number of important applications, particularly for glass separation
with non-ablative techniques. This chapter reviews the developments of
in-volume ultrafast laser processing of transparent materials. We discuss the
basic physics of the processes, characterization means, filamentation of
Gaussian and Bessel beams and provide an overview of present applications.


Abstract:
  Hyperspectral imaging at cryogenic temperatures is used to investigate
exciton and trion propagation in MoSe$_2$ monolayers encapsulated with
hexagonal boron nitride (hBN). Under a tightly focused, continuous-wave laser
excitation, the spatial distribution of neutral excitons and charged trions
strongly differ at high excitation densities. Remarkably, in this regime the
trion distribution develops a halo shape, similar to that previously observed
in WS2 monolayers at room temperature and under pulsed excitation. In contrast,
the exciton distribution only presents a moderate broadening without the
appereance of a halo. Spatially and spectrally resolved luminescence spectra
reveal the buildup of a significant temperature gradient at high excitation
power, that is attributed to the energy relaxation of photoinduced hot
carriers. We show, via a numerical resolution of the transport equations for
excitons and trions, that the halo can be interpreted as thermal drift of
trions due to a Seebeck term in the particle current. The model shows that the
difference between trion and exciton profiles is simply understood in terms of
the very different lifetimes of these two quasiparticles.


Abstract:
  The conformational states of a semiflexible polymer enclosed in a volume
$V:=\ell^{3}$ are studied as stochastic realizations of paths using the
stochastic curvature approach developed in [Rev. E 100, 012503 (2019)], in the
regime whenever $3\ell/\ell_ {p}> 1$, where $\ell_{p}$ is the persistence
length. The cases of a semiflexible polymer enclosed in a cube and sphere are
considered. In these cases, we explore the Spakowitz-Wang type polymer shape
transition, where the critical persistence length distinguishes between an
oscillating and a monotonic phase at the level of the mean-square end-to-end
distance. This shape transition provides evidence of a universal signature of
the behavior of a semiflexible polymer confined in a compact domain.


Abstract:
  We construct a cosmological model from the inception of the
Friedmann-Lem\^aitre-Robertson-Walker metric into the field equations of the
$f(R,L_m)$ gravity theory, with $R$ being the Ricci scalar and $L_m$ being the
matter lagrangian density. The formalism is developed for a particular
$f(R,L_m)$ function, namely $R/16\pi +(1+\sigma R)L_{m}$, with $\sigma$ being a
constant that carries the geometry-matter coupling. Our solutions are
remarkably capable of evading the Big-Bang singularity as well as predict the
cosmic acceleration with no need for the cosmological constant, but simply as a
consequence of the geometry-matter coupling terms in the Friedmann-like
equations.


Abstract:
  Molecular science is governed by the dynamics of electrons, atomic nuclei,
and their interaction with electromagnetic fields. A reliable physicochemical
understanding of these processes is crucial for the design and synthesis of
chemicals and materials of economic value. Although some problems in this field
are adequately addressed by classical mechanics, many require an explicit
quantum mechanical description. Such quantum problems represented by
exponentially large wave function should naturally benefit from quantum
computation on a number of logical qubits that scales only linearly with system
size. In this perspective, we focus on the potential of quantum computing for
solving relevant problems in the molecular sciences -- molecular physics,
chemistry, biochemistry, and materials science.


Abstract:
  This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP
methods of tourism. The fuzzy-AHP process will rank tourism trends based on
data from social media. Social media is one of the channels with the largest
source of data input in determining tourism development. The development uses
social media interactions based on the facilities visited, including reviews,
stories, likes, forums, blogs, and feedback. This experiment aims to prioritize
facilities that are the trend of tourism. The priority ranking uses weight
criteria and the ranking process. The highest rank is in the attractions of the
Park/Picnic Area, with the final weight calculation value of 0.6361. Fuzzy-AHP
can rank optimally with an MSE value of \approx 0.0002.


Abstract:
  Despite their recent success on image denoising, the need for deep and
complex architectures still hinders the practical usage of CNNs. Older but
computationally more efficient methods such as BM3D remain a popular choice,
especially in resource-constrained scenarios. In this study, we aim to find out
whether compact neural networks can learn to produce competitive results as
compared to BM3D for AWGN image denoising. To this end, we configure networks
with only two hidden layers and employ different neuron models and layer widths
for comparing the performance with BM3D across different AWGN noise levels. Our
results conclusively show that the recently proposed self-organized variant of
operational neural networks based on a generative neuron model (Self-ONNs) is
not only a better choice as compared to CNNs, but also provide competitive
results as compared to BM3D and even significantly surpass it for high noise
levels.


Abstract:
  This work investigates the feasibility of using input-output data-driven
control techniques for building control and their susceptibility to
data-poisoning techniques. The analysis is performed on a digital replica of
the KTH Livein Lab, a non-linear validated model representing one of the KTH
Live-in Lab building testbeds. This work is motivated by recent trends showing
a surge of interest in using data-based techniques to control cyber-physical
systems. We also analyze the susceptibility of these controllers to
data-poisoning methods, a particular type of machine learning threat geared
towards finding imperceptible attacks that can undermine the performance of the
system under consideration. We consider the Virtual Reference Feedback Tuning
(VRFT), a popular data-driven control technique, and show its performance on
the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks
can be crafted and illustrate the impact of such attacks. Numerical experiments
reveal the feasibility of using data-driven control methods for finding
efficient control laws. However, a subtle change in the datasets can
significantly deteriorate the performance of VRFT.


Abstract:
  Scanning real-life scenes with modern registration devices typically give
incomplete point cloud representations, mostly due to the limitations of the
scanning process and 3D occlusions. Therefore, completing such partial
representations remains a fundamental challenge of many computer vision
applications. Most of the existing approaches aim to solve this problem by
learning to reconstruct individual 3D objects in a synthetic setup of an
uncluttered environment, which is far from a real-life scenario. In this work,
we reformulate the problem of point cloud completion into an object
hallucination task. Thus, we introduce a novel autoencoder-based architecture
called HyperPocket that disentangles latent representations and, as a result,
enables the generation of multiple variants of the completed 3D point clouds.
We split point cloud processing into two disjoint data streams and leverage a
hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the
missing object parts. As a result, the generated point clouds are not only
smooth but also plausible and geometrically consistent with the scene. Our
method offers competitive performances to the other state-of-the-art models,
and it enables a~plethora of novel applications.


Abstract:
  Using five year monitoring observations, we did a blind search for pulses for
rotating radio transient (RRAT) J0139+33 and PSR B0320+39. At the interval \pm
1.5m of the time corresponding to the source passing through the meridian, we
detected 39377 individual pulses for the pulsar B0320+39 and 1013 pulses for
RRAT J0139+33. The share of registered pulses from the total number of observed
periods for the pulsar B0320+39 is 74%, and for the transient J0139+33 it is
0.42%. Signal-to-noise ratio (S/N) for the strongest registered pulses is
approximately equal to: S/N = 262 (for B0320+39) and S/N = 154 (for J0139+33).
  Distributions of the number of detected pulses in S/N units for the pulsar
and for the rotating transient are obtained. The distributions could be
approximated with a lognormal and power dependencies. For B0320+39 pulsar, the
dependence is lognormal, it turns into a power dependence at high values of
S/N, and for RRAT J0139+33, the distribution of pulses by energy is described
by a broken (bimodal) power dependence with an exponent of about 0.4 and 1.8
(S/N < 19 and S/N > 19).
  We have not detected regular (pulsar) emission of J0139+33. Analysis of the
obtained data suggests that RRAT J0139+33 is a pulsar with giant pulses.


Abstract:
  A Banach space X has the SHAI (surjective homomorphisms are injective)
property provided that for every Banach space Y, every continuous surjective
algebra homomorphism from the bounded linear operators on X onto the bounded
linear operators on Y is injective. The main result gives a sufficient
condition for X to have the SHAI property. The condition is satisfied for L^p
(0, 1) for 1 < p < \infty, spaces with symmetric bases that have finite cotype,
and the Schatten p-spaces for 1 < p < \infty.


Abstract:
  This paper introduces our systems for all three subtasks of SemEval-2021 Task
4: Reading Comprehension of Abstract Meaning. To help our model better
represent and understand abstract concepts in natural language, we well-design
many simple and effective approaches adapted to the backbone model (RoBERTa).
Specifically, we formalize the subtasks into the multiple-choice question
answering format and add special tokens to abstract concepts, then, the final
prediction of question answering is considered as the result of subtasks.
Additionally, we employ many finetuning tricks to improve the performance.
Experimental results show that our approaches achieve significant performance
compared with the baseline systems. Our approaches achieve eighth rank on
subtask-1 and tenth rank on subtask-2.


Abstract:
  How should we understand the social and political effects of the datafication
of human life? This paper argues that the effects of data should be understood
as a constitutive shift in social and political relations. We explore how
datafication, or quantification of human and non-human factors into binary
code, affects the identity of individuals and groups. This fundamental shift
goes beyond economic and ethical concerns, which has been the focus of other
efforts to explore the effects of datafication and AI. We highlight that
technologies such as datafication and AI (and previously, the printing press)
both disrupted extant power arrangements, leading to decentralization, and
triggered a recentralization of power by new actors better adapted to
leveraging the new technology. We use the analogy of the printing press to
provide a framework for understanding constitutive change. The printing press
example gives us more clarity on 1) what can happen when the medium of
communication drastically alters how information is communicated and stored; 2)
the shift in power from state to private actors; and 3) the tension of
simultaneously connecting individuals while driving them towards narrower
communities through algorithmic analyses of data.


Abstract:
  The concept of an angle is one that often causes difficulties in metrology.
These are partly caused by a confusing mixture of several mathematical terms,
partly by real mathematical difficulties and finally by imprecise terminology.
The purpose of this publication is to clarify misunderstandings and to explain
why strict terminology is important. It will also be shown that most
misunderstandings regarding the `radian' can be avoided if some simple rules
are obeyed.


Abstract:
  We explore the parameter space of a U(1) extension of the standard model --
also called the super-weak model -- from the point of view of explaining the
observed dark matter energy density in the Universe. The new particle spectrum
contains a complex scalar singlet and three right-handed neutrinos, among which
the lightest one is the dark matter candidate. We explore both freeze-in and
freeze-out mechanisms of dark matter production. In both cases, we find regions
in the plane of the super-weak coupling vs. the mass of the new gauge boson
that are not excluded by current experimental constraints. These regions are
distinct and the one for freeze-out will be explored in searches for neutral
gauge boson in the near future.


Abstract:
  This paper focuses on a core task in computational sustainability and
statistical ecology: species distribution modeling (SDM). In SDM, the
occurrence pattern of a species on a landscape is predicted by environmental
features based on observations at a set of locations. At first, SDM may appear
to be a binary classification problem, and one might be inclined to employ
classic tools (e.g., logistic regression, support vector machines, neural
networks) to tackle it. However, wildlife surveys introduce structured noise
(especially under-counting) in the species observations. If unaccounted for,
these observation errors systematically bias SDMs. To address the unique
challenges of SDM, this paper proposes a framework called StatEcoNet.
Specifically, this work employs a graphical generative model in statistical
ecology to serve as the skeleton of the proposed computational framework and
carefully integrates neural networks under the framework. The advantages of
StatEcoNet over related approaches are demonstrated on simulated datasets as
well as bird species data. Since SDMs are critical tools for ecological science
and natural resource management, StatEcoNet may offer boosted computational and
analytical powers to a wide range of applications that have significant social
impacts, e.g., the study and conservation of threatened species.


Abstract:
  We study phase contributions of wave functions that occur in the evolution of
Gaussian surface gravity water wave packets with nonzero initial momenta
propagating in the presence and absence of an effective external linear
potential. Our approach takes advantage of the fact that in contrast to matter
waves, water waves allow us to measure both their amplitudes and phases.


Abstract:
  Non-Hermitian systems show a non-Hermitian skin effect, where the bulk states
are localized at a boundary of the systems with open boundary conditions. In
this paper, we study dependence of the localization length of the eigenstates
on a system size in a specific non-Hermitian model with a critical
non-Hermitian skin effect, where the energy spectrum undergoes discontinuous
transition in the thermodynamic limit. We analytically show that the
eigenstates exhibit remarkable localization, known as scale-free localization,
where the localization length is proportional to a system size. Our result
gives a theoretical support for the scale-free localization, which has been
proposed only numerically in previous works.


Abstract:
  Recently, [8] has proposed that heterogeneity of infectiousness (and
susceptibility) across individuals in infectious diseases, plays a major role
in affecting the Herd Immunity Threshold (HIT). Such heterogeneity has been
observed in COVID-19 and is recognized as overdispersion (or
"super-spreading"). The model of [8] suggests that super-spreaders contribute
significantly to the effective reproduction factor, R, and that they are likely
to get infected and immune early in the process. Consequently, under R_0 = 3
(attributed to COVID-19), the Herd Immunity Threshold (HIT) is as low as 5%, in
contrast to 67% according to the traditional models [1, 2, 4, 10]. This work
follows up on [8] and proposes that heterogeneity of infectiousness
(susceptibility) has two "faces" whose mix affects dramatically the HIT: (1)
Personal-Trait-, and (2) Event-Based- Infectiousness (Susceptibility). The
former is a personal trait of specific individuals (super-spreaders) and is
nullified once those individuals are immune (as in [8]). The latter is
event-based (e.g cultural super-spreading events) and remains effective
throughout the process, even after the super-spreaders immune. We extend [8]'s
model to account for these two factors, analyze it and conclude that the HIT is
very sensitive to the mix between (1) and (2), and under R_0 = 3 it can vary
between 5% and 67%. Preliminary data from COVID-19 suggests that herd immunity
is not reached at 5%. We address operational aspects and analyze the effects of
lockdown strategies on the spread of a disease. We find that herd immunity (and
HIT) is very sensitive to the lockdown type. While some lockdowns affect
positively the disease blocking and increase herd immunity, others have adverse
effects and reduce the herd immunity.


Abstract:
  The quantum operator $\hat{T}_3$, corresponding to the projection of the
toroidal moment on the $z$ axis, admits several self-adjoint extensions, when
defined on the whole $\mathbb{R}^3$ space. $\hat{T}_3$ commutes with
$\hat{L}_3$ (the projection of the angular momentum operator on the $z$ axis)
and they have a \textit{natural set of coordinates} $(k,u,\phi)$ where $\phi$
is the azimuthal angle. The second set of \textit{natural coordinates} is
$(k_1,k_2,u)$, where $k_1 = k\cos\phi$, $k_2 = k\sin\phi$. In both sets,
$\hat{T}_3 = -i\hbar\partial/\partial u$, so any operator that is a function of
$k$ and the partial derivatives with respect to the \textit{natural variables}
$(k, u, \phi)$ commute with $\hat{T}_3$ and $\hat{L}_3$. Similarly, operators
that are functions of $k_1$, $k_2$, and the partial derivatives with respect to
$k_1$, $k_2$, and $u$ commute with $\hat{T}_3$. Therefore, we introduce here
the operators $\hat{p}_{k} \equiv -i \hbar \partial/\partial k$,
$\hat{p}^{(k1)} \equiv -i \hbar \partial/\partial k_1$, and $\hat{p}^{(k2)}
\equiv -i \hbar \partial/\partial k_2$ and express them in the $(x,y,z)$
coordinates. One may also invert the relations and write the typical operators,
like the momentum $\hat{\bf p} \equiv -i\hbar {\bf \nabla}$ or the kinetic
energy $\hat{H}_0 \equiv -\hbar^2\Delta/(2m)$ in terms of the "toroidal"
operators $\hat{T}_3$, $\hat{p}^{(k)}$, $\hat{p}^{(k1)}$, $\hat{p}^{(k2)}$,
and, eventually, $\hat{L}_3$. The formalism may be applied to specific physical
systems, like nuclei, condensed matter systems, or metamaterials. We exemplify
it by calculating the momentum operator and the free particle Hamiltonian in
terms of \textit{natural coordinates} in a thin torus, where the general
relations get considerably simplified.


Abstract:
  Electronic states in the gap of a superconductor inherit intriguing many-body
properties from the superconductor. Here, we create these in-gap states by
manipulating Cr atomic chains on the $\beta$-Bi$_2$Pd superconductor. We find
that the topological properties of the in-gap states can greatly vary depending
on the crafted spin chain. These systems make an ideal platform for non-trivial
topological phases because of the large atom-superconductor interactions and
the existence of a large Rashba coupling at the Bi-terminated surface. We study
two spin chains, one with atoms two-lattice-parameter apart and one with
square-root-of-two lattice parameters. Of these, only the second one is in a
topologically non-trivial phase, in correspondence with the spin interactions
for this geometry.


Abstract:
  Using density functional theory combined with nonequilibrium Green's function
method, the transport properties of borophene-based nano gas sensors with gold
electrodes are calculated, and comprehensive understandings regarding the
effects of gas molecules, MoS$_2$ substrate and gold electrodes to the
transport properties of borophene are made. Results show that borophene-based
sensors can be used to detect and distinguish CO, NO, NO$_2$ and NH$_3$ gas
molecules, MoS$_2$ substrate leads to a non-linear behavior on the
current-voltage characteristic, and gold electrodes provide charges to
borophene and form a potential barrier, which reduced the current values
compared to the current of the systems without gold electrodes. Our studies not
only provide useful information on the computationally design of
borophene-based gas sensors, but also help understand the transport behaviors
and underlying physics of 2D metallic materials with metal electrodes.


Abstract:
  RX J0123.4-7321 is a well-established Be star X-ray binary system (BeXRB) in
the Small Magellanic Cloud (SMC). Like many such systems the variable X-ray
emission is driven by the underlying behaviour of the mass donor Be star.
Previous work has shown that the optical and X-ray were characterised by
regular outbursts at the proposed binary period of 119 d. However around
February 2008 the optical behaviour changed substantially, with the previously
regular optical outbursts ending. Reported here are new optical (OGLE) and
X-ray (Swift) observations covering the period after 2008 which suggest an
almost total circumstellar disc loss followed by a gradual recovery. This
indicates the probable transition of a Be star to a B star, and back again.
However, at the time of the most recent OGLE data (March 2020) the
characteristic periodic outbursts had yet to return to their early state,
indicating that the disk still had some re-building yet to complete.


Abstract:
  Convection has been discussed in the field of accretion discs for several
decades, both as a means of angular momentum transport and also because of its
role in controlling discs' vertical structure via heat transport. If the gas is
sufficiently ionized and threaded by a weak magnetic field, convection might
interact in non-trivial ways with the magnetorotational instability (MRI).
Recently, vertically stratified local simulations of the MRI have reported
considerable variation in the angular momentum transport, as measured by the
stress to thermal pressure ratio $\alpha$, when convection is thought to be
present. Although MRI turbulence can act as a heat source for convection, it is
not clear how the instabilities will interact dynamically. Here we aim to
investigate the interplay between the two instabilities in controlled numerical
experiments, and thus isolate the generic features of their interaction. We
perform vertically stratified, 3D MHD shearing box simulations with a perfect
gas equation of state with the conservative, finite-volume code PLUTO. We find
two characteristic outcomes of the interaction between the two instabilities:
straight MRI and MRI/convective cycles, with the latter exhibiting alternating
phases of convection-dominated flow (during which the turbulent transport is
weak) and MRI-dominated flow. During the latter phase we find that $\alpha$ is
enhanced by nearly an order of magnitude, reaching peak values of $\sim 0.08$.
In addition, we find that convection in the non-linear phase takes the form of
large-scale and oscillatory convective cells. Convection can also help the MRI
persist to lower Rm than it would otherwise do. Finally we discuss how our
results help interpret simulations of Dwarf Novae.


Abstract:
  In this paper we study Chow motives whose identity map is killed by a natural
number. Examples of such objects were constructed by Gorchinskiy-Orlov. We
introduce various invariants of torsion motives, in particular, the $p$-level.
We show that this invariant bounds from below the dimension of the variety a
torsion motive $M$ is a direct summand of and imposes restrictions on motivic
and singular cohomology of $M$. We study in more details the $p$-torsion
motives of surfaces, in particular, the Godeaux torsion motive. We show that
such motives are in 1-to-1 correspondence with certain Rost cycle submodules of
free modules over $H^*_{et}$. This description is parallel to that of mod-$p$
reduced motives of curves.


Abstract:
  A very useful identity for Parseval frames for Hilbert spaces was obtained by
Balan, Casazza, Edidin, and Kutyniok. In this paper, we obtain a similar
identity for Parseval p-approximate Schauder frames for Banach spaces which
admits a homogeneous semi-inner product in the sense of Lumer-Giles.


Abstract:
  We construct the hydrodynamic theory of coherent collective motion
("flocking") at a solid-liquid interface. The polar order parameter and
concentration of a collection of "active" (self-propelled) particles at a
planar interface between a passive, isotropic bulk fluid and a solid surface
are dynamically coupled to the bulk fluid. We find that such systems are
stable, and have long-range orientational order, over a wide range of
parameters. When stable, these systems exhibit "giant number fluctuations",
i.e., large fluctuations of the number of active particles in a fixed large
area. Specifically, these number fluctuations grow as the $3/4$th power of the
mean number within the area. Stable systems also exhibit anomalously rapid
diffusion of tagged particles suspended in the passive fluid along any
directions in a plane parallel to the solid-liquid interface, whereas the
diffusivity along the direction perpendicular to the plane is non-anomalous. In
other parameter regimes, the system becomes unstable.


Abstract:
  As the fundamental physical process with many astrophysical implications, the
diffusion of cosmic rays (CRs) is determined by their interaction with
magnetohydrodynamic (MHD) turbulence. We consider the magnetic mirroring effect
arising from MHD turbulence on the diffusion of CRs. Due to the intrinsic
superdiffusion of turbulent magnetic fields, CRs with large pitch angles that
undergo mirror reflection, i.e., bouncing CRs, are not trapped between magnetic
mirrors, but move diffusively along the turbulent magnetic field, leading to a
new type of parallel diffusion, i.e., mirror diffusion. This mirror diffusion
is in general slower than the diffusion of non-bouncing CRs with small pitch
angles that undergo gyroresonant scattering. The critical pitch angle at the
balance between magnetic mirroring and pitch-angle scattering is important for
determining the diffusion coefficients of both bouncing and non-bouncing CRs
and their scalings with the CR energy. We find non-universal energy scalings of
diffusion coefficients, depending on the properties of MHD turbulence.


Abstract:
  Floquet engineering is the concept of tailoring a system by a periodic drive.
It has been very successful in opening new classes of Hamiltonians to the study
with ultracold atoms in optical lattices, such as artificial gauge fields,
topological band structures and density-dependent tunneling. Furthermore,
driven systems provide new physics without static counterpart such as anomalous
Floquet topological insulators. In this review article, we provide an overview
of the exciting developments in the field and discuss the current challenges
and perspectives.


Abstract:
  Convex clustering is an attractive clustering algorithm with favorable
properties such as efficiency and optimality owing to its convex formulation.
It is thought to generalize both k-means clustering and agglomerative
clustering. However, it is not known whether convex clustering preserves
desirable properties of these algorithms. A common expectation is that convex
clustering may learn difficult cluster types such as non-convex ones. Current
understanding of convex clustering is limited to only consistency results on
well-separated clusters. We show new understanding of its solutions. We prove
that convex clustering can only learn convex clusters. We then show that the
clusters have disjoint bounding balls with significant gaps. We further
characterize the solutions, regularization hyperparameters, inclusterable cases
and consistency.


Abstract:
  In this paper we derive sharp lower and upper bounds for the covariance of
two bounded random variables when knowledge about their expected values,
variances or both is available. When only the expected values are known, our
result can be viewed as an extension of the Bhatia-Davis Inequality for
variances. We also provide a number of different ways to standardize
covariance. For a binary pair random variables, one of these standardized
measures of covariation agrees with a frequently used measure of dependence
between genetic variants.


Abstract:
  The production, application, and/or measurement of polarised X-/gamma rays
are key to the fields of synchrotron science and X-/gamma-ray astronomy. The
design, development and optimisation of experimental equipment utilised in
these fields typically relies on the use of Monte Carlo radiation transport
modelling toolkits such as Geant4. In this work the Geant4 "G4LowEPPhysics"
electromagnetic physics constructor has been reconfigured to offer a "best set"
of electromagnetic physics models for studies exploring the transport of low
energy polarised X-/gamma rays. An overview of the physics models implemented
in "G4LowEPPhysics", and it's experimental validation against Compton X-ray
polarimetry measurements of the BL38B1 beamline at the SPring-8 synchrotron
(Sayo, Japan) is reported. "G4LowEPPhysics" is shown to be able to reproduce
the experimental results obtained at the BL38B1 beamline (SPring-8) to within a
level of accuracy on the same order as Geant4's X-/gamma ray interaction
cross-sectional data uncertainty (approximately $\pm$ 5 \%).


Abstract:
  The overwhelming amount of biomedical scientific texts calls for the
development of effective language models able to tackle a wide range of
biomedical natural language processing (NLP) tasks. The most recent dominant
approaches are domain-specific models, initialized with general-domain textual
data and then trained on a variety of scientific corpora. However, it has been
observed that for specialized domains in which large corpora exist, training a
model from scratch with just in-domain knowledge may yield better results.
Moreover, the increasing focus on the compute costs for pre-training recently
led to the design of more efficient architectures, such as ELECTRA. In this
paper, we propose a pre-trained domain-specific language model, called
ELECTRAMed, suited for the biomedical field. The novel approach inherits the
learning framework of the general-domain ELECTRA architecture, as well as its
computational advantages. Experiments performed on benchmark datasets for
several biomedical NLP tasks support the usefulness of ELECTRAMed, which sets
the novel state-of-the-art result on the BC5CDR corpus for named entity
recognition, and provides the best outcome in 2 over the 5 runs of the 7th
BioASQ-factoid Challange for the question answering task.


Abstract:
  Payment channel networks are a promising approach to improve the scalability
of cryptocurrencies: they allow to perform transactions in a peer-to-peer
fashion, along multi-hop routes in the network, without requiring consensus on
the blockchain. However, during the discovery of cost-efficient routes for the
transaction, critical information may be revealed about the transacting
entities.
  This paper initiates the study of privacy-preserving route discovery
mechanisms for payment channel networks. In particular, we present LightPIR, an
approach which allows a source to efficiently discover a shortest path to its
destination without revealing any information about the endpoints of the
transaction. The two main observations which allow for an efficient solution in
LightPIR are that: (1) surprisingly, hub labelling algorithms - which were
developed to preprocess "street network like" graphs so one can later
efficiently compute shortest paths - also work well for the graphs underlying
payment channel networks, and that (2) hub labelling algorithms can be directly
combined with private information retrieval.
  LightPIR relies on a simple hub labeling heuristic on top of existing hub
labeling algorithms which leverages the specific topological features of
cryptocurrency networks to further minimize storage and bandwidth overheads. In
a case study considering the Lightning network, we show that our approach is an
order of magnitude more efficient compared to a privacy-preserving baseline
based on using private information retrieval on a database that stores all
pairs shortest paths.


Abstract:
  As an essential characteristics of fractional calculus, the memory effect is
served as one of key factors to deal with diverse practical issues, thus has
been received extensive attention since it was born. By combining the
fractional derivative with memory effects and grey modeling theory, this paper
aims to construct an unified framework for the commonly-used fractional grey
models already in place. In particular, by taking different kernel and
normalization functions, this framework can deduce some other new fractional
grey models. To further improve the prediction performance, the four popular
intelligent algorithms are employed to determine the emerging coefficients for
the UFGM(1,1) model. Two published cases are then utilized to verify the
validity of the UFGM(1,1) model and explore the effects of fractional
accumulation order and initial value on the prediction accuracy, respectively.
Finally, this model is also applied to dealing with two real examples so as to
further explain its efficacy and equally show how to use the unified framework
in practical applications.


Abstract:
  B-splines are widely used in the fields of reverse engineering and
computer-aided design, due to their superior properties. Traditional B-spline
surface interpolation algorithms usually assume regularity of the data
distribution. In this paper, we introduce a novel B-spline surface
interpolation algorithm: KPI, which can interpolate sparsely and non-uniformly
distributed data points. As a two-stage algorithm, our method generates the
dataset out of the sparse data using Kriging, and uses the proposed KPI
(Key-Point Interpolation) method to generate the control points. Our algorithm
can be extended to higher dimensional data interpolation, such as
reconstructing dynamic surfaces. We apply the method to interpolating the
temperature of Shanxi Province. The generated dynamic surface accurately
interpolates the temperature data provided by the weather stations, and the
preserved dynamic characteristics can be useful for meteorology studies.


Abstract:
  Software projects are regularly updated with new functionality and bug fixes
through so-called releases. In recent years, many software projects have been
shifting to shorter release cycles and this can affect the bug handling
activity. Past research has focused on the impact of switching from traditional
to rapid release cycles with respect to bug handling activity, but the effect
of the rapid release cycle duration has not yet been studied. We empirically
investigate releases of 420 open source projects with rapid release cycles to
understand the effect of variable and rapid release cycle durations on bug
handling activity. We group the releases of these projects into five categories
of release cycle durations. For each project, we investigate how the sequence
of releases is related to bug handling activity metrics and we study the effect
of the variability of cycle durations on bug fixing. Our results did not reveal
any statistically significant difference for the studied bug handling activity
metrics in the presence of variable rapid release cycle durations. This
suggests that the duration of fast release cycles does not seem to impact bug
handling activity.


Abstract:
  We derive major parts of the eigenvalue spectrum of the operators on the
squashed seven-sphere that appear in the compactification of eleven-dimensional
supergravity. These spectra determine the mass spectrum of the fields in
$AdS_4$ and are important for the corresponding ${\mathcal N} =1$
supermultiplet structure. This work is a continuation of the work in [1] where
the complete spectrum of irreducible isometry representations of the fields in
$AdS_4$ was derived for this compactification. Some comments are also made
concerning the $G_2$ holonomy and its implications on the structure of the
operator equations on the squashed seven-sphere.


Abstract:
  We present a quantum error correcting code with dynamically generated logical
qubits. When viewed as a subsystem code, the code has no logical qubits.
Nevertheless, our measurement patterns generate logical qubits, allowing the
code to act as a fault-tolerant quantum memory. Our particular code gives a
model very similar to the two-dimensional toric code, but each measurement is a
two-qubit Pauli measurement.


Abstract:
  Dilatancy associated with fault slip produces a transient pore pressure drop
which increases frictional strength. This effect is analysed in a steadily
propagating rupture model that includes frictional weakening, slip-dependent
fault dilation and fluid flow. Dilatancy is shown to increase the stress
intensity factor required to propagate the rupture tip. With increasing rupture
speed, an undrained (strengthened) region develops near the tip and extends
beyond the frictionally weakened zone. Away from the undrained region, pore
fluid diffusion gradually recharges the fault and strength returns to the
drained, weakened value. For sufficiently large rupture dimensions, the
dilation-induced strength increase near the tip is equivalent to an increase in
toughness that is proportional to the square root of the rupture speed. In
general, dilation has the effect of increasing the stress required for rupture
growth by decreasing the stress drop along the crack. Thermal pressurisation
has the potential to compensate for the dilatant strengthening effect, at the
expense of an increased heating rate, which might lead to premature frictional
melting. Using reasonable laboratory parameters, the dilatancy-toughening
effect leads to rupture dynamics that is quantitatively consistent with the
dynamics of observed slow slip events in subduction zones.


Abstract:
  We develop a geometrical micro-local analysis of contact Anosov flow, such as
geodesic flow on negatively curved manifold. We use the method of wave-packet
transform discussed in arXiv:1706.09307 and observe that the transfer operator
is well approximated (in the high frequency limit) by the quantization of an
induced transfer operator acting on sections of some vector bundle on the
trapped set. This gives a few important consequences: The discrete eigenvalues
of the generator of transfer operators, called Ruelle spectrum, are structured
into vertical bands. If the right-most band is isolated from the others, most
of the Ruelle spectrum in it concentrate along a line parallel to the imaginary
axis and, further, the density satisfies a Weyl law as the imaginary part tend
to infinity. Some of these results were announced in arXiv:1301.5525.


Abstract:
  While sophisticated Visual Question Answering models have achieved remarkable
success, they tend to answer questions only according to superficial
correlations between question and answer. Several recent approaches have been
developed to address this language priors problem. However, most of them
predict the correct answer according to one best output without checking the
authenticity of answers. Besides, they only explore the interaction between
image and question, ignoring the semantics of candidate answers. In this paper,
we propose a select-and-rerank (SAR) progressive framework based on Visual
Entailment. Specifically, we first select the candidate answers relevant to the
question or the image, then we rerank the candidate answers by a visual
entailment task, which verifies whether the image semantically entails the
synthetic statement of the question and each candidate answer. Experimental
results show the effectiveness of our proposed framework, which establishes a
new state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.


Abstract:
  The architecture of circuital quantum computers requires computing layers
devoted to compiling high-level quantum algorithms into lower-level circuits of
quantum gates. The general problem of quantum compiling is to approximate any
unitary transformation that describes the quantum computation, as a sequence of
elements selected from a finite base of universal quantum gates. The existence
of an approximating sequence of one qubit quantum gates is guaranteed by the
Solovay-Kitaev theorem, which implies sub-optimal algorithms to establish it
explicitly. Since a unitary transformation may require significantly different
gate sequences, depending on the base considered, such a problem is of great
complexity and does not admit an efficient approximating algorithm. Therefore,
traditional approaches are time-consuming tasks, unsuitable to be employed
during quantum computation. We exploit the deep reinforcement learning method
as an alternative strategy, which has a significantly different trade-off
between search time and exploitation time. Deep reinforcement learning allows
creating single-qubit operations in real time, after an arbitrary long training
period during which a strategy for creating sequences to approximate unitary
operators is built. The deep reinforcement learning based compiling method
allows for fast computation times, which could in principle be exploited for
real-time quantum compiling.


Abstract:
  We solve the large deviations of the Kardar-Parisi-Zhang (KPZ) equation in
one dimension at short time by introducing an approach which combines field
theoretical, probabilistic and integrable techniques. We expand the program of
the weak noise theory, which maps the large deviations onto a non-linear
hydrodynamic problem, and unveil its complete solvability through a connection
to the integrability of the Zakharov-Shabat system. Exact solutions, depending
on the initial condition of the KPZ equation, are obtained using the inverse
scattering method and a Fredholm determinant framework recently developed.
These results, explicit in the case of the droplet geometry, open the path to
obtain the complete large deviations for general initial conditions.


Abstract:
  Grounding natural language instructions on the web to perform previously
unseen tasks enables accessibility and automation. We introduce a task and
dataset to train AI agents from open-domain, step-by-step instructions
originally written for people. We build RUSS (Rapid Universal Support Service)
to tackle this problem. RUSS consists of two models: First, a BERT-LSTM with
pointers parses instructions to ThingTalk, a domain-specific language we design
for grounding natural language on the web. Then, a grounding model retrieves
the unique IDs of any webpage elements requested in ThingTalk. RUSS may
interact with the user through a dialogue (e.g. ask for an address) or execute
a web operation (e.g. click a button) inside the web runtime. To augment
training, we synthesize natural language instructions mapped to ThingTalk. Our
dataset consists of 80 different customer service problems from help websites,
with a total of 741 step-by-step instructions and their corresponding actions.
RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single
instructions. It outperforms state-of-the-art models that directly map
instructions to actions without ThingTalk. Our user study shows that RUSS is
preferred by actual users over web navigation.


Abstract:
  The goal of this paper is to open up a new research direction aimed at
understanding the power of preprocessing in speeding up algorithms that solve
NP-hard problems exactly. We explore this direction for the classic Feedback
Vertex Set problem on undirected graphs, leading to a new type of graph
structure called antler decomposition, which identifies vertices that belong to
an optimal solution. It is an analogue of the celebrated crown decomposition
which has been used for Vertex Cover. We develop the graph structure theory
around such decompositions and develop fixed-parameter tractable algorithms to
find them, parameterized by the number of vertices for which they witness
presence in an optimal solution. This reduces the search space of
fixed-parameter tractable algorithms parameterized by the solution size that
solve Feedback Vertex Set.


Abstract:
  Experiments have shown that hepatitis C virus (HCV) infections in vitro
disseminate both distally via the release and diffusion of cell-free virus
through the medium, and locally via direct, cell-to-cell transmission. To
determine the relative contribution of each mode of infection to HCV
dissemination, we developed an agent-based model (ABM) that explicitly
incorporates both distal and local modes of infection. The ABM tracks the
concentration of extracellular infectious virus in the supernatant and the
number of intracellular HCV RNA segments within each infected cell over the
course of simulated in vitro HCV infections. Experimental data for in vitro HCV
infections conducted in the presence and absence of free-virus neutralizing
antibodies was used to validate the ABM and constrain the value of its
parameters. We found that direct, cell-to-cell infection accounts for 99%
(84%$-$100%, 95% credible interval) of infection events, making it the dominant
mode of HCV dissemination in vitro. Yet, when infection via the free-virus
route is blocked, a 57% reduction in the number of infection events at 72 hpi
is observed experimentally; a result consistent with that found by our ABM.
Taken together, these findings suggest that while HCV spread via cell-free
virus contributes little to the total number of infection events in vitro, it
plays a critical role in enhancing cell-to-cell HCV dissemination by providing
access to distant, uninfected areas, away from the already established large
infection foci.


Abstract:
  We investigate the second harmonic generation of light field carrying orbital
angular momentum in bulk $\chi^{(2)}$ material. We show that due to
conservation of energy and momentum, the frequency doubled beam light has a
modified spatial distribution and mode characteristics. Through rigorous phase
matching conditions, we demonstrate efficient mode and frequency conversion
based on three wave nonlinear optical mixing.


Abstract:
  We develop two parallel machine-learning pipelines to estimate the
contribution of cosmic strings (CSs), conveniently encoded in their tension
($G\mu$), to the anisotropies of the cosmic microwave background radiation
observed by {\it Planck}. The first approach is tree-based and feeds on certain
map features derived by image processing and statistical tools. The second uses
convolutional neural network with the goal to explore possible non-trivial
features of the CS imprints. The two pipelines are trained on {\it Planck}
simulations and when applied to {\it Planck} \texttt{SMICA} map yield the
$3\sigma$ upper bound of $G\mu\lesssim 8.6\times 10^{-7}$. We also train and
apply the pipelines to make forecasts for futuristic CMB-S4-like surveys and
conservatively find their minimum detectable tension to be $G\mu_{\rm min}\sim
1.9\times 10^{-7}$.


Abstract:
  We explore quantitative descriptors that herald when a many-particle system
in $d$-dimensional Euclidean space $\mathbb{R}^d$ approaches a hyperuniform
state as a function of the relevant control parameter. We establish
quantitative criteria to ascertain the extent of hyperuniform and
nonhyperuniform distance-scaling regimes n terms of the ratio $B/A$, where $A$
is "volume" coefficient and $B$ is "surface-area" coefficient associated with
the local number variance $\sigma^2(R)$ for a spherical window of radius $R$.
To complement the known direct-space representation of the coefficient $B$ in
terms of the total correlation function $h({\bf r})$, we derive its
corresponding Fourier representation in terms of the structure factor $S({\bf
k})$, which is especially useful when scattering information is available
experimentally or theoretically. We show that the free-volume theory of the
pressure of equilibrium packings of identical hard spheres that approach a
strictly jammed state either along the stable crystal or metastable disordered
branch dictates that such end states be exactly hyperuniform. Using the ratio
$B/A$, the hyperuniformity index $H$ and the direct-correlation function length
scale $\xi_c$, we study three different exactly solvable models as a function
of the relevant control parameter, either density or temperature, with end
states that are perfectly hyperuniform. We analyze equilibrium hard rods and
"sticky" hard-sphere systems in arbitrary space dimension $d$ as a function of
density. We also examine low-temperature excited states of many-particle
systems interacting with "stealthy" long-ranged pair interactions as the
temperature tends to zero. The capacity to identify hyperuniform scaling
regimes should be particularly useful in analyzing experimentally- or
computationally-generated samples that are necessarily of finite size.


Abstract:
  We develop an approach to choice principles and their contrapositive
bar-induction principles as extensionality schemes connecting an "intensional"
or "effective" view of respectively ill-and well-foundedness properties to an
"extensional" or "ideal" view of these properties. After classifying and
analysing the relations between different intensional definitions of
ill-foundedness and well-foundedness, we introduce, for a domain $A$, a
codomain $B$ and a "filter" $T$ on finite approximations of functions from $A$
to $B$, a generalised form GDC$_{A,B,T}$ of the axiom of dependent choice and
dually a generalised bar induction principle GBI$_{A,B,T}$ such that:
  GDC$_{A,B,T}$ intuitionistically captures the strength of
  $\bullet$ the general axiom of choice expressed as $\forall a\exists b R(a,
b) \Rightarrow\exists\alpha\forall \alpha R(\alpha,\alpha(a))$ when $T$ is a
filter that derives point-wise from a relation $R$ on $A \times B$ without
introducing further constraints,
  $\bullet$ the Boolean Prime Filter Theorem / Ultrafilter Theorem if $B$ is
the two-element set $\mathbb{B}$ (for a constructive definition of prime
filter),
  $\bullet$ the axiom of dependent choice if $A = \mathbb{N}$,
  $\bullet$ Weak K{\"o}nig's Lemma if $A = \mathbb{N}$ and $B = \mathbb{B}$ (up
to weak classical reasoning)
  GBI$_{A,B,T}$ intuitionistically captures the strength of
  $\bullet$ G{\"o}del's completeness theorem in the form validity implies
provability for entailment relations if $B = \mathbb{B}$,
  $\bullet$ bar induction when $A = \mathbb{N}$,
  $\bullet$ the Weak Fan Theorem when $A = \mathbb{N}$ and $B = \mathbb{B}$.
  Contrastingly, even though GDC$_{A,B,T}$ and GBI$_{A,B,T}$ smoothly capture
several variants of choice and bar induction, some instances are inconsistent,
e.g. when $A$ is $\mathbb{B}^\mathbb{N}$ and $B$ is $\mathbb{N}$.


Abstract:
  The Cyber Science Lab (CSL) and Smart Cyber-Physical System (SCPS) Lab at the
University of Guelph conduct a market study of cybersecurity technology
adoption and requirements for smart and precision farming in Canada. We
conducted 17 stakeholder/key opinion leader interviews in Canada and the USA,
as well as conducting extensive secondary research, to complete this study.
Each interview generally required 15-20 minutes to complete. Interviews were
conducted using a client-approved interview guide. Secondary and primary
research focussed on the following areas of investigation: Market size and
segmentation Market forecast and growth rate Competitive landscape Market
challenges/barriers to entry Market trends/growth drivers
Adoption/commercialization of the technology


Abstract:
  AGBs and YSOs often share the same domains in IR color-magnitude or
color-color diagrams leading to potential mis-classification. We extracted a
list of AGB interlopers from the published YSO catalogues using the periodogram
analysis on NEOWISE time series data. YSO IR variability is typically
stochastic and linked to episodic mass accretion. Furthermore, most variable
YSOs are at an early evolutionary stage, with significant surrounding envelope
and/or disk material. In contrast, AGBs are often identified by a well defined
sinusoidal variability with periods of a few hundreds days. From our
periodogram analysis of all known low mass YSOs in the Gould Belt, we find 85
AGB candidates, out of which 62 were previously classified as late-stage Class
III YSOs. Most of these new AGB candidates have similar IR colors to O-rich
AGBs. We observed 73 of these AGB candidates in the H2O, CH3OH and SiO maser
lines to further reveal their nature. The SiO maser emission was detected in 10
sources, confirming them as AGBs since low mass YSOs, especially Class III
YSOs, do not show such maser emission. The H2O and CH3OH maser lines were
detected in none of our targets.


Abstract:
  In multi-component scalar dark matter scenarios, a single $Z_N$ ($N\geq 4$)
symmetry may account for the stability of different dark matter particles. Here
we study the case where $N$ is even ($N=2n$) and two species, a complex scalar
and a real scalar, contribute to the observed dark matter density. We perform a
phenomenological analysis of three scenarios based on the $Z_4$ and $Z_6$
symmetries, characterizing their viable parameter spaces and analyzing their
detection prospects. Our results show that, thanks to the new interactions
allowed by the $Z_{2n}$ symmetry, current experimental constraints can be
satisfied over a wider range of dark matter masses, and that these scenarios
may lead to observable signals in direct detection experiments. Finally, we
argue that these three scenarios serve as prototypes for other two-component
$Z_{2n}$ models with one complex and one real dark matter particle.


Abstract:
  Astrophysical black holes are thought to be the Kerr black holes predicted by
general relativity, but macroscopic deviations from the Kerr solution can be
expected from a number of scenarios involving new physics. In Paper I, we
studied the reflection features in NuSTAR and XMM-Newton spectra of the
supermassive black hole at the center of the galaxy MCG-06-30-15 and we
constrained a set of deformation parameters proposed by Konoplya, Rezzolla &
Zhidenko (Phys. Rev. D93, 064015, 2016). In the present work, we analyze the
X-ray data of a stellar-mass black hole within the same theoretical framework
in order to probe a different curvature regime. We consider a NuSTAR
observation of the X-ray binary EXO 1846-031 during its outburst in 2019. As in
the case of Paper I, all our fits are consistent with the Kerr black hole
hypothesis, but some deformation parameters cannot be constrained well.


Abstract:
  Intuitively, one would expect the accuracy of a trained neural network's
prediction on a test sample to correlate with how densely that sample is
surrounded by seen training samples in representation space. In this work we
provide theory and experiments that support this hypothesis. We propose an
error function for piecewise linear neural networks that takes a local region
in the network's input space and outputs smooth empirical training error, which
is an average of empirical training errors from other regions weighted by
network representation distance. A bound on the expected smooth error for each
region scales inversely with training sample density in representation space.
Empirically, we verify this bound is a strong predictor of the inaccuracy of
the network's prediction on test samples. For unseen test sets, including those
with out-of-distribution samples, ranking test samples by their local region's
error bound and discarding samples with the highest bounds raises prediction
accuracy by up to 20% in absolute terms, on image classification datasets.


Abstract:
  Current deep learning models for classification tasks in computer vision are
trained using mini-batches. In the present article, we take advantage of the
relationships between samples in a mini-batch, using graph neural networks to
aggregate information from similar images. This helps mitigate the adverse
effects of alterations to the input images on classification performance.
Diverse experiments on image-based object and scene classification show that
this approach not only improves a classifier's performance but also increases
its robustness to image perturbations and adversarial attacks. Further, we also
show that mini-batch graph neural networks can help to alleviate the problem of
mode collapse in Generative Adversarial Networks.


Abstract:
  Two main methods have been proposed to derive the acoustical radiation force
and torque applied by an arbitrary acoustic field on a particle: The first one
relies on the plane wave angular spectrum decomposition of the incident field
(see [Sapozhnikov and Bailey, J. Acoust. Soc. Am. 133, 661 (2013)] for the
force and [Gong and Baudoin, J. Acoust. Soc. Am. 148, 3131 (2020)] for the
torque), while the second one relies on the decomposition of the incident field
into a sum of spherical waves, the so-called multipole expansion (see [Silva,
J. Acoust. Soc. Am. 130, 3541 (2011)] and [Baresh et al., J. Acoust. Soc. Am.
133, 25 (2013)] for the force, and [Silva et al., EPL 97, 54003 (2012)] and
[Gong et al., Phys. Rev. Applied 11, 064022 (2019)] for the torque). In this
paper, we formally establish the equivalence between the expressions obtained
with these two methods for both the force and torque.


Abstract:
  B-doped $\delta$-layers were fabricated in Si(100) using BCl$_{3}$ as a
dopant precursor in ultrahigh vacuum. BCl$_{3}$ adsorbed readily at room
temperature, as revealed by scanning tunneling microscopy (STM) imaging.
Annealing at elevated temperatures facilitated B incorporation into the Si
substrate. Secondary ion mass spectrometry (SIMS) depth profiling demonstrated
a peak B concentration $>$ 1.2(1) $\times$ 10$^{21}$ cm$^{-3}$ with a total
areal dose of 1.85(1) $\times$ 10$^{14}$ cm$^{-2}$ resulting from a 30 L
BCl$_{3}$ dose at 150 $^{\circ}$C. Hall bar measurements of a similar sample
were performed at 3.0 K revealing a sheet resistance of $R_{\mathrm{s}}$ = 1.91
k$\Omega\square^{-1}$, a hole concentration of $n$ = 1.90 $\times$ 10$^{14}$
cm$^{-2}$ and a hole mobility of $\mu$ = 38.0 cm$^{2}$V$^{-1}$s$^{-1}$ without
performing an incorporation anneal. Further, the conductivity of several
B-doped $\delta$-layers showed a log dependence on temperature suggestive of a
two-dimensional system. Selective-area deposition of BCl$_{3}$ was also
demonstrated using both H- and Cl-based monatomic resists. In comparison to a
dosed area on bare Si, adsorption selectivity ratios for H and Cl resists were
determined by SIMS to be 310(10):1 and 1529(5):1, respectively, further
validating the use of BCl$_{3}$ as a dopant precursor for atomic precision
fabrication of acceptor-doped devices in Si.


Abstract:
  This paper proposes a novel way to solve transient linear, and non-linear
solid dynamics for compressible, nearly incompressible, and incompressible
material in the updated Lagrangian framework for tetrahedral unstructured
finite elements. It consists of a mixed formulation in both displacement and
pressure, where the momentum equation of the continuum is complemented with a
pressure equation that handles incompresibility inherently. It is obtained
through the deviatoric and volumetric split of the stress, that enables us to
solve the problem in the incompressible limit. The Varitaional Multi-Scale
method (VMS) is developed based on the orthogonal decomposition of the
variables, which damps out spurious pressure fields for piece wise linear
tetrahedral elements. Various numerical examples are presented to assess the
robustness, accuracy and capabilities of our scheme in bending dominated
problems, and for complex geometries.


Abstract:
  Coronary artery disease (CAD) has posed a leading threat to the lives of
cardiovascular disease patients worldwide for a long time. Therefore, automated
diagnosis of CAD has indispensable significance in clinical medicine. However,
the complexity of coronary artery plaques that cause CAD makes the automatic
detection of coronary artery stenosis in Coronary CT angiography (CCTA) a
difficult task. In this paper, we propose a Transformer network (TR-Net) for
the automatic detection of significant stenosis (i.e. luminal narrowing > 50%)
while practically completing the computer-assisted diagnosis of CAD. The
proposed TR-Net introduces a novel Transformer, and tightly combines
convolutional layers and Transformer encoders, allowing their advantages to be
demonstrated in the task. By analyzing semantic information sequences, TR-Net
can fully understand the relationship between image information in each
position of a multiplanar reformatted (MPR) image, and accurately detect
significant stenosis based on both local and global information. We evaluate
our TR-Net on a dataset of 76 patients from different patients annotated by
experienced radiologists. Experimental results illustrate that our TR-Net has
achieved better results in ACC (0.92), Spec (0.96), PPV (0.84), F1 (0.79) and
MCC (0.74) indicators compared with the state-of-the-art methods. The source
code is publicly available from the link (https://github.com/XinghuaMa/TR-Net).


Abstract:
  We introduce large-scale Augmented Granger Causality (lsAGC) as a method for
connectivity analysis in complex systems. The lsAGC algorithm combines
dimension reduction with source time-series augmentation and uses predictive
time-series modeling for estimating directed causal relationships among
time-series. This method is a multivariate approach, since it is capable of
identifying the influence of each time-series on any other time-series in the
presence of all other time-series of the underlying dynamic system. We
quantitatively evaluate the performance of lsAGC on synthetic directional
time-series networks with known ground truth. As a reference method, we compare
our results with cross-correlation, which is typically used as a standard
measure of connectivity in the functional MRI (fMRI) literature. Using
extensive simulations for a wide range of time-series lengths and two different
signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms
cross-correlation at accurately detecting network connections, using Receiver
Operator Characteristic Curve (ROC) analysis, across all tested time-series
lengths and noise levels. In addition, as an outlook to possible clinical
application, we perform a preliminary qualitative analysis of connectivity
matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical
controls, using a subset of 59 subjects of the Autism Brain Imaging Data
Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by
extracting sparse connectivity matrices, may be useful for network analysis in
complex systems, and may be applicable to clinical fMRI analysis in future
research, such as targeting disease-related classification or regression tasks
on clinical data.


Abstract:
  Frustrated Mott insulators such as, transition metal dichalcogenide
1T-TaS$_{2}$ present an ideal platform for the experimental realization of
disorder induced insulator-metal transition. In this letter we present the
first non perturbative theoretical investigation of the disorder induced
insulator-metal transition in copper (Cu) intercalated 1T-TaS$_{2}$, in the
framework of Anderson-Hubbard model on a triangular lattice. Based on the
magnetic, spectroscopic and transport signatures we map out the thermal phase
diagram of this system. Our results show that over a regime of moderate
disorder strength this material hosts an antiferromagnetic metal. The emergent
metal is a non Fermi liquid, governed by resilient quasiparticles, that survive
as the relevant low energy excitations even after the break down of Fermi
liquid description. The system undergoes a crossover from a non Fermi liquid
metal to a bad metallic phase, as a function of temperature. Our results on
spectral line shape are found to be in excellent agreement with the
experimental observations on Cu intercalated 1T-TaS$_{2}$. The optical and
spectroscopic signatures discussed in this letter are expected to serve as
important benchmark for future experiments on this and related class of
materials. The numerical technique discussed herein serves as a computational
breakthrough to address systems for which most of the existing methods fall
short.


Abstract:
  In this work, we propose a Model Predictive Control (MPC)-based Reinforcement
Learning (RL) method for Autonomous Surface Vehicles (ASVs). The objective is
to find an optimal policy that minimizes the closed-loop performance of a
simplified freight mission, including collision-free path following, autonomous
docking, and a skillful transition between them. We use a parametrized
MPC-scheme to approximate the optimal policy, which considers
path-following/docking costs and states (position, velocity)/inputs (thruster
force, angle) constraints. The Least Squares Temporal Difference (LSTD)-based
Deterministic Policy Gradient (DPG) method is then applied to update the policy
parameters. Our simulation results demonstrate that the proposed MPC-LSTD-based
DPG method could improve the closed-loop performance during learning for the
freight mission problem of ASV.


Abstract:
  Pore structures and gas transport properties in porous separators for polymer
electrolyte fuel cells are evaluated both experimentally and through
simulations. In the experiments, the gas permeabilities of two porous samples,
a conventional sample and one with low electrical resistivity, are measured by
a capillary flow porometer, and the pore size distributions are evaluated with
mercury porosimetry. Local pore structures are directly observed with micro
X-ray computed tomography (CT). In the simulations, the effective diffusion
coefficients of oxygen and the air permeability in porous samples are
calculated using random walk Monte Carlo simulations and computational fluid
dynamics (CFD) simulations, respectively, based on the X-ray CT images. The
calculated porosities and air permeabilities of the porous samples are in good
agreement with the experimental values. The simulation results also show that
the in-plane permeability is twice the through-plane permeability in the
conventional sample, whereas it is slightly higher in the low-resistivity
sample. The results of this study show that CFD simulation based on micro X-ray
CT images makes it possible to evaluate anisotropic gas permeabilities in
anisotropic porous media.


Abstract:
  We describe how some problems (interpretability,lack of object-orientedness)
of modern deep networks potentiallycould be solved by adapting a biologically
plausible saccadicmechanism of perception. A sketch of such a saccadic
visionmodel is proposed. Proof of concept experimental results areprovided to
support the proposed approach.


Abstract:
  The knapsack problem for groups was introduced by Miasnikov, Nikolaev, and
Ushakov. It is defined for each finitely generated group $G$ and takes as input
group elements $g_1,\ldots,g_n,g\in G$ and asks whether there are
$x_1,\ldots,x_n\ge 0$ with $g_1^{x_1}\cdots g_n^{x_n}=g$. We study the knapsack
problem for wreath products $G\wr H$ of groups $G$ and $H$. Our main result is
a characterization of those wreath products $G\wr H$ for which the knapsack
problem is decidable. The characterization is in terms of decidability
properties of the indiviual factors $G$ and $H$. To this end, we introduce two
decision problems, the intersection knapsack problem and its restriction, the
positive intersection knapsack problem. Moreover, we apply our main result to
$H_3(\mathbb{Z})$, the discrete Heisenberg group, and to Baumslag-Solitar
groups $\mathsf{BS}(1,q)$ for $q\ge 1$. First, we show that the knapsack
problem is undecidable for $G\wr H_3(\mathbb{Z})$ for any $G\ne 1$. This
implies that for $G\ne 1$ and for infinite and virtually nilpotent groups $H$,
the knapsack problem for $G\wr H$ is decidable if and only if $H$ is virtually
abelian and solvability of systems of exponent equations is decidable for $G$.
Second, we show that the knapsack problem is decidable for
$G\wr\mathsf{BS}(1,q)$ if and only if solvability of systems of exponent
equations is decidable for $G$.


Abstract:
  The first of a two-part series, this paper assumes a weak local energy decay
estimate holds and proves that solutions to the linear wave equation with
variable coefficients in $\mathbb R^{1+3}$, first-order terms, and a potential
decay at a rate depending on how rapidly the vector fields of the metric,
first-order terms, and potential decay at spatial infinity. We prove results
for both stationary and nonstationary metrics. The proof uses local energy
decay to prove an initial decay rate, and then uses the one-dimensional
reduction repeatedly to achieve the full decay rate.


Abstract:
  The effect of coupling between pairing and quadrupole triaxial shape
vibrations on the low-energy collective states of $\gamma$-soft nuclei is
investigated using a model based on the framework of nuclear energy density
functionals (EDFs). Employing a constrained self-consistent mean-field (SCMF)
method that uses universal EDFs and pairing interactions, potential energy
surfaces of characteristic $\gamma$-soft Os and Pt nuclei with $A\approx190$
are calculated as functions of the pairing and triaxial quadrupole
deformations. Collective spectroscopic properties are computed using a
number-nonconserving interacting boson model (IBM) Hamiltonian, with parameters
determined by mapping the SCMF energy surface onto the expectation value of the
Hamiltonian in the boson condensate state. It is shown that, by simultaneously
considering both the shape and pairing collective degrees of freedom, the
EDF-based IBM successfully reproduces data on collective structures based on
low-energy $0^{+}$ states, as well as $\gamma$-vibrational bands.


Abstract:
  We consider the capacity of entanglement in models related with the
gravitational phase transitions. The capacity is labeled by the replica
parameter which plays a similar role to the inverse temperature in
thermodynamics. In the end of the world brane model of a radiating black hole
the capacity has a peak around the Page time indicating the phase transition
between replica wormhole geometries of different types of topology. Similarly,
in a moving mirror model describing Hawking radiation the capacity typically
shows a discontinuity when the dominant saddle switches between two phases,
which can be seen as a formation of island regions. In either case we find the
capacity can be an invaluable diagnostic for a black hole evaporation process.


Abstract:
  The tension between inferences of Hubble constant ($H_0$) is found in a large
array of datasets combinations. Modification to the late expansion history is
the most direct solution to this discrepancy. In this work we examine the
viability of restoring the cosmological concordance within the scenarios of
late dark energy. We explore two representative parameterizations: a novel
version of transitional dark energy (TDE) and modified emergent dark energy
(MEDE). We find that, the main anchors for the cosmic distance scale: cosmic
microwave background (CMB), baryon acoustic oscillation (BAO), and SNe Ia
calibrated by Cepheids form a ``impossible trinity'', i.e., it's plausible to
reconcile with any of the two but unlikely to accommodate them all.
Particularly, the tension between BAO and the calibrated SNe Ia can not be
reconciled within the scenarios of late dark energy. Nevertheless, we still
find a positive evidence for TDE model in analysis of all datasets
combinations, while with the the exclusion of BOSS datasets, the tensions with
SH0ES drops from $3.1\sigma$ to $1.1\sigma$. For MEDE model, the tension with
$H_0$ is much alleviated with the exclusion of SNe dataset. But unfortunately,
in both TDE and MEDE scenarios, the $S_8$ tension is not relieved nor
exacerbated.


Abstract:
  The Raman peak position and linewidth provide insight into phonon
anharmonicity and electron-phonon interactions (EPI) in materials. For
monolayer graphene, prior first-principles calculations have yielded decreasing
linewidth with increasing temperature, which is opposite to measurement
results. Here, we explicitly consider four-phonon anharmonicity, phonon
renormalization, and electron-phonon coupling, and find all to be important to
successfully explain both the $G$ peak frequency shift and linewidths in our
suspended graphene sample at a wide temperature range. Four-phonon scattering
contributes a prominent linewidth that increases with temperature, while
temperature dependence from EPI is found to be reversed above a doping
threshold ($\hbar\omega_G/2$, with $\omega_G$ being the frequency of the $G$
phonon).


Abstract:
  We propose a novel neural network module that transforms an existing
single-frame semantic segmentation model into a video semantic segmentation
pipeline. In contrast to prior works, we strive towards a simple, fast, and
general module that can be integrated into virtually any single-frame
architecture. Our approach aggregates a rich representation of the semantic
information in past frames into a memory module. Information stored in the
memory is then accessed through an attention mechanism. In contrast to previous
memory-based approaches, we propose a fast local attention layer, providing
temporal appearance cues in the local region of prior frames. We further fuse
these cues with an encoding of the current frame through a second
attention-based module. The segmentation decoder processes the fused
representation to predict the final semantic segmentation. We integrate our
approach into two popular semantic segmentation networks: ERFNet and PSPNet. We
observe an improvement in segmentation performance on Cityscapes by 1.7% and
2.1% in mIoU respectively, while increasing inference time of ERFNet by only
1.5ms.


Abstract:
  We study the problem of dynamically trading multiple futures whose underlying
asset price follows a multiscale central tendency Ornstein-Uhlenbeck (MCTOU)
model. Under this model, we derive the closed-form no-arbitrage prices for the
futures contracts. Applying a utility maximization approach, we solve for the
optimal trading strategies under different portfolio configurations by
examining the associated system of Hamilton-Jacobi-Bellman (HJB) equations. The
optimal strategies depend on not only the parameters of the underlying asset
price process but also the risk premia embedded in the futures prices.
Numerical examples are provided to illustrate the investor's optimal positions
and optimal wealth over time.


Abstract:
  Let $ K $ be a number field over $ \mathbb{Q} $ and let $ a_K(m) $ denote the
number of integral ideals of $ K $ of norm equal to $ m\in\mathbb{N} $. In this
paper we obtain asymptotic formulae for sums of the form $ \sum_{m\leq X}
a^l_K(m) $ thereby generalizing the previous works on the problem. Previously
such asymptotics were known only in the case when $ K $ is Galois or when $K$
was a non normal cubic extension and $ l=2,3 $. The present work subsumes both
these cases.


Abstract:
  Automatic transcription of monophonic/polyphonic music is a challenging task
due to the lack of availability of large amounts of transcribed data. In this
paper, we propose a data augmentation method that converts natural speech to
singing voice based on vocoder based speech synthesizer. This approach, called
voice to singing (V2S), performs the voice style conversion by modulating the
F0 contour of the natural speech with that of a singing voice. The V2S model
based style transfer can generate good quality singing voice thereby enabling
the conversion of large corpora of natural speech to singing voice that is
useful in building an E2E lyrics transcription system. In our experiments on
monophonic singing voice data, the V2S style transfer provides a significant
gain (relative improvements of 21%) for the E2E lyrics transcription system. We
also discuss additional components like transfer learning and lyrics based
language modeling to improve the performance of the lyrics transcription
system.


Abstract:
  This paper provides a multivariate extension of Bertoin's pathwise
construction of a L\'evy process conditioned to stay positive/negative. Thus
obtained processes conditioned to stay in half-spaces are closely related to
the original process on a compact time interval seen from its directional
extremal points. In the case of a correlated Brownian motion the law of the
conditioned process is obtained by a linear transformation of a standard
Brownian motion and an independent Bessel-3 process. Further motivation is
provided by a limit theorem corresponding to zooming in on a L\'evy process
with a Brownian part at the point of its directional infimum. Applications to
zooming in at the point furthest from the origin are envisaged.


Abstract:
  The US Census Bureau plans to protect the privacy of 2020 Census respondents
through its Disclosure Avoidance System (DAS), which attempts to achieve
differential privacy guarantees by adding noise to the Census microdata. By
applying redistricting simulation and analysis methods to DAS-protected 2010
Census data, we find that the protected data are not of sufficient quality for
redistricting purposes. We demonstrate that the injected noise makes it
impossible for states to accurately comply with the One Person, One Vote
principle. Our analysis finds that the DAS-protected data are biased against
certain areas, depending on voter turnout and partisan and racial composition,
and that these biases lead to large and unpredictable errors in the analysis of
partisan and racial gerrymanders. Finally, we show that the DAS algorithm does
not universally protect respondent privacy. Based on the names and addresses of
registered voters, we are able to predict their race as accurately using the
DAS-protected data as when using the 2010 Census data. Despite this, the
DAS-protected data can still inaccurately estimate the number of
majority-minority districts. We conclude with recommendations for how the
Census Bureau should proceed with privacy protection for the 2020 Census.


Abstract:
  For analytic functions $g$ on the unit disc with non-negative Maclaurin
coefficients, we describe the boundedness and compactness of the integral
operator $T_g(f)(z)=\int_0^zf(\zeta)g'(\zeta)\,d\zeta$ from a space $X$ of
analytic functions in the unit disc to $H^\infty$, in terms of neat and useful
conditions on the Maclaurin coefficients of $g$. The choices of $X$ that will
be considered contain the Hardy and the Hardy-Littlewood spaces, the
Dirichlet-type spaces $D^p_{p-1}$, as well as the classical Bloch and BMOA
spaces.