Abstract:
  We develop a stable finite difference method for the elastic wave equations
in bounded media, where the material properties can be discontinuous at curved
interfaces. The governing equations are discretized in second order form by a
fourth or sixth order accurate summation-by-parts operator. The mesh size is
determined by the velocity structure of the material, resulting in
nonconforming grid interfaces with hanging nodes. We use order-preserving
interpolation and the ghost point technique to couple adjacent mesh blocks in
an energy-conserving manner, which is supported by a fully discrete stability
analysis. In numerical experiments, we demonstrate that the convergence rate is
optimal, and is the same as when a globally uniform mesh is used in a single
domain. In addition, with a predictor-corrector time integration method, we
obtain time stepping stability with stepsize almost the same as given by the
usual Courant Friedrichs Lewy condition.


Abstract:
  Flexible manufacturing in the process industry requires control systems to
achieve time-varying setpoints (e.g., product specifications) based on market
demand. Contraction theory provides a useful framework for
reference-independent system analysis and tracking control for nonlinear
systems. However, determination of the control contraction metrics and control
laws can be very difficult for general nonlinear systems. This work develops an
approach to discrete-time contraction analysis and control using neural
networks. The methodology involves training a neural network to learn a
contraction metric and feedback gain. The resulting contraction-based
controller embeds the trained neural network and is capable of achieving
efficient tracking of time-varying references, with a full range of model
uncertainty, without the need for controller structure redesign. This is a
robust approach that can deal with bounded parametric uncertainties in the
process model, which are commonly encountered in industrial (chemical)
processes. Simulation examples are provided to illustrate the above approach.


Abstract:
  We study a novel type of non-Fermi liquid where there exist an infinite
number of critical bosonic modes instead of finite bosonic modes for the
conventional ones. We consider itinerant magnets with both conduction electrons
and fluctuating magnetic moments in three dimensions. With
Dzyaloshinskii-Moriya interaction, the moments fluctuate near a boson surface
in the reciprocal space at low energies when the system approaches an ordering
transition. The infinite number of critical modes on the boson surface strongly
scatter the infinite gapless electrons on the Fermi surface and convert the
metallic sector into a non-Fermi liquid. We explain the novel physical
properties of this non-Fermi liquid. On the ordered side, a conventional
non-Fermi liquid emerges due to the scattering by the gapless Goldstone mode
from the spontaneous breaking of the global rotational symmetry. We discuss the
general structure of the phase diagram in the vicinity of the quantum phase
transition and clarify various crossover behaviors.


Abstract:
  The Clebsh-Gordan coefficients for the Lie algebra
  $\mathfrak{gl}_3$ in the Gelfand-Tsetlin base are calculated. In contrast to
previous papers the result is given as an explicit formula. To obtain the
result a realization of a representation in the space of functions on the group
$GL_3$ is used. The keystone fact that allows to carry the calculation of
Clebsh-Gordan coefficients is the theorem that says that functions
corresponding to Gelfand-Tsetlin base vectors can be expressed through
generalized hypergeometric functions.


Abstract:
  Understanding the generation mechanism of the heating flux is essential for
the design of hypersonic vehicles. We proposed a novel formula to decompose the
heat flux coefficient into the contributions of different terms by integrating
the conservative equation of the total energy. The reliability of the formula
is well demonstrated by the direct numerical simulation results of a hypersonic
transitional boundary layer. Through this formula, the exact process of the
energy transport in the boundary layer can be explained and the dominant
contributors to the heat flux can be explored, which are beneficial for the
prediction of the heat and design of the thermal protection devices


Abstract:
  We define Gaussian assignment process, determine the asymptotic behavior of
its maximum's expectation and suggest an explicit strategy that attains the
corresponding asymptotics.


Abstract:
  We give asymptotic estimates for the mean number of divisors of integers
without small prime factors, integers with bounded ratios of consecutive
divisors, and for practical numbers. In the last case, this confirms a
conjecture of Margenstern.


Abstract:
  The wide adoption of smart meters makes residential load data available and
thus improves the understanding of the energy consumption behavior. Many
existing studies have focused on smart-meter data analysis, but the drivers of
energy consumption behaviors are not well understood. This paper aims to
characterize and estimate users' load patterns based on their demographic and
socioeconomic information. We adopt the symbolic aggregate approximation (SAX)
method to process the load data and use the K-Means method to extract key load
patterns. We develop a deep neural network (DNN) to analyze the relationship
between users' load patterns and their demographic and socioeconomic features.
Using real-world load data, we validate our framework and demonstrate the
connections between load patterns and household demographic and socioeconomic
features. We also take two regression models as benchmarks for comparisons.


Abstract:
  Clockwork models can explain the flavor hierarchies in the Standard Model
quark and lepton spectrum. We construct supersymmetric versions of such flavor
clockwork models. The zero modes of the clockwork are identified with the
fermions and sfermions of the Minimal Supersymmetric Standard Model. In
addition to generating a hierarchical fermion spectrum, the clockwork also
predicts a specific flavor structure for the soft SUSY breaking sfermion
masses. We find sizeable flavor mixing among first and second generation
squarks. Constraints from Kaon oscillations require the masses of either
squarks or gluinos to be above a scale of $\sim 3$ PeV.


Abstract:
  We study a discrete-time dynamical system of wild mosquito population with
parameters: $\beta$ - the birth rate of adults; $\alpha$ - maximum emergence
rete; $\mu>0$ - the death rate of adults; $\gamma$ - Allee effects. We prove
that if $\gamma\geq\frac{\alpha(\beta-\mu)}{\mu^2}$ then the mosquito
population dies and if $\gamma<\frac{\alpha(\beta-\mu)}{\mu^2}$ holds then
extinction or survival of the mosquito population depends on their initial
state.


Abstract:
  We aim to gain insight into the effect of network and faculae on solar
irradiance from their apparent intensity. Taking full-disc observations from
the Solar Dynamics Observatory, we examined the intensity contrast of network
and faculae in the continuum and core of the Fe I 6173 {\AA} line and 1700
{\AA}, including the variation with magnetic flux density, distance from disc
centre, nearby magnetic fields, and time. The brightness of network and faculae
is believed to be suppressed by nearby magnetic fields from its effect on
convection. The difference in intensity contrast between the quiet-Sun network
and active region faculae, noted by various studies, arises because active
regions are more magnetically crowded and is not due to any fundamental
physical differences between network and faculae. These results highlight that
solar irradiance models need to include the effect of nearby magnetic fields on
network and faculae brightness. We found evidence that suggests that departures
from local thermal equilibrium (LTE) might have limited effect on intensity
contrast. This could explain why solar irradiance models that are based on the
intensity contrast of solar surface magnetic features calculated assuming LTE
reproduce the observed spectral variability even where the LTE assumption
breaks down. Certain models of solar irradiance employ chromospheric indices as
direct indications of the effect of network and faculae on solar irradiance.
Based on past studies of the Ca II K line and on the intensity contrast
measurements derived here, we show that the fluctuations in chromospheric
emission from network and faculae are a reasonable estimate of the emission
fluctuations in the middle photosphere, but not of those in the lower
photosphere. The data set, which extends from 2010 to 2018, indicates that
intensity contrast was stable to about 3% in this period.


Abstract:
  Similar to Earth, Saturn's largest moon, Titan, possesses a system of
high-altitude zonal winds (or jets) that encircle the globe. Using the Atacama
Large Millimeter/submillimeter Array (ALMA) in August 2016, Lellouch et al.
(2019) discovered an equatorial jet at much higher altitudes than previously
known, with a surprisingly fast speed of up to ~340 m/s, but the origin of such
high velocities is not yet understood. We obtained spectrally and spatially
resolved ALMA observations in May 2017 to map Titan's 3D global wind field and
compare our results with a reanalysis of the August 2016 data. Doppler wind
velocity maps were derived in the altitude range ~300-1000 km (from the upper
stratosphere to the thermosphere). At the highest, thermospheric altitudes, a
47% reduction in the equatorial zonal wind speed was measured over the 9-month
period (corresponding to L_s = 82-90 degrees on Titan). This is interpreted as
due to a dramatic slowing and loss of confinement (broadening) of the
recently-discovered thermospheric equatorial jet, as a result of dynamical
instability. These unexpectedly-rapid changes in the upper-atmospheric dynamics
are consistent with strong variability of the jet's primary driving mechanism.


Abstract:
  Image registration aims to establish spatial correspondence across pairs, or
groups of images, and is a cornerstone of medical image computing and
computer-assisted-interventions. Currently, most deep learning-based
registration methods assume that the desired deformation fields are globally
smooth and continuous, which is not always valid for real-world scenarios,
especially in medical image registration (e.g. cardiac imaging and abdominal
imaging). Such a global constraint can lead to artefacts and increased errors
at discontinuous tissue interfaces. To tackle this issue, we propose a
weakly-supervised Deep Discontinuity-preserving Image Registration network
(DDIR), to obtain better registration performance and realistic deformation
fields. We demonstrate that our method achieves significant improvements in
registration accuracy and predicts more realistic deformations, in registration
experiments on cardiac magnetic resonance (MR) images from UK Biobank Imaging
Study (UKBB), than state-of-the-art approaches.


Abstract:
  Our goal is to find combinations of facts that optimally summarize data sets.
We consider this problem in the context of voice query interfaces for simple,
exploratory data analysis. Here, the system answers voice queries with a short
summary of relevant data. Finding optimal voice data summaries is
computationally expensive. Prior work in this domain has exploited sampling and
incremental processing. Instead, we rely on a pre-processing stage generating
summaries of data subsets in a batch operation. This step reduces run time
overheads by orders of magnitude.
  We present multiple algorithms for the pre-processing stage, realizing
different tradeoffs between optimality and data processing overheads. We
analyze our algorithms formally and compare them experimentally with prior
methods for generating voice data summaries. We report on multiple user studies
with a prototype system implementing our approach. Furthermore, we report on
insights gained from a public deployment of our system on the Google Assistant
Platform.


Abstract:
  Despite significant progress, we show that state of the art 3D human pose and
shape estimation methods remain sensitive to partial occlusion and can produce
dramatically wrong predictions although much of the body is observable. To
address this, we introduce a soft attention mechanism, called the Part
Attention REgressor (PARE), that learns to predict body-part-guided attention
masks. We observe that state-of-the-art methods rely on global feature
representations, making them sensitive to even small occlusions. In contrast,
PARE's part-guided attention mechanism overcomes these issues by exploiting
information about the visibility of individual body parts while leveraging
information from neighboring body-parts to predict occluded parts. We show
qualitatively that PARE learns sensible attention masks, and quantitative
evaluation confirms that PARE achieves more accurate and robust reconstruction
results than existing approaches on both occlusion-specific and standard
benchmarks. The code and data are available for research purposes at {\small
\url{https://pare.is.tue.mpg.de/}}


Abstract:
  This paper presents a summary of the Masked Face Recognition Competitions
(MFR) held within the 2021 International Joint Conference on Biometrics (IJCB
2021). The competition attracted a total of 10 participating teams with valid
submissions. The affiliations of these teams are diverse and associated with
academia and industry in nine different countries. These teams successfully
submitted 18 valid solutions. The competition is designed to motivate solutions
aiming at enhancing the face recognition accuracy of masked faces. Moreover,
the competition considered the deployability of the proposed solutions by
taking the compactness of the face recognition models into account. A private
dataset representing a collaborative, multi-session, real masked, capture
scenario is used to evaluate the submitted solutions. In comparison to one of
the top-performing academic face recognition solutions, 10 out of the 18
submitted solutions did score higher masked face verification accuracy.


Abstract:
  We prove the cosmetic crossing conjecture for genus one knots with
non-trivial Alexander polynomial. We also prove the conjecture for genus one
knots with trivial Alexander polynomial, under some additional assumptions.


Abstract:
  Continuum robotic manipulators are increasingly adopted in minimal invasive
surgery. However, their nonlinear behavior is challenging to model accurately,
especially when subject to external interaction, potentially leading to poor
control performance. In this letter, we investigate the feasibility of adopting
a model-free multiagent reinforcement learning (RL), namely multiagent deep Q
network (MADQN), to control a 2-degree of freedom (DoF) cable-driven continuum
surgical manipulator. The control of the robot is formulated as a one-DoF, one
agent problem in the MADQN framework to improve the learning efficiency.
Combined with a shielding scheme that enables dynamic variation of the action
set boundary, MADQN leads to efficient and importantly safer control of the
robot. Shielded MADQN enabled the robot to perform point and trajectory
tracking with submillimeter root mean square errors under external loads, soft
obstacles, and rigid collision, which are common interaction scenarios
encountered by surgical manipulators. The controller was further proven to be
effective in a miniature continuum robot with high structural nonlinearitiy,
achieving trajectory tracking with submillimeter accuracy under external
payload.


Abstract:
  The generalized Benjamin-Bona-Mahony-Burgers equation (gBBMB) describes the
flow of blood through a long, viscoelastic artery. In this article we introduce
a formulation of gBBMB valid on networks with semi-infinite edges joined at a
single junction, with the network's edges corresponding to a segment of the
arterial tree. To reflect sudden changes in the material properties of blood
vessels, the coefficients of gBBMB are allowed to take different values on each
edge of the network. Critically, our formulation ensures that the total mass of
the solution to gBBMB is constant in time, even in the presence of dissipation.
We also establish local-in-time well-posedness of this new formulation for
sufficiently regular initial data. Then, we show how energy methods can be used
to extend the local solution to a solution valid for all positive times,
provided certain constraints are imposed on the parameters of the model PDE and
the network. To build intuition for how waves scatter off the central junction
of a network with two edges, we demonstrate the results of some numerical
simulations.


Abstract:
  In the past three decades, a large number of metaheuristics have been
proposed and shown high performance in solving complex optimization problems.
While most variation operators in existing metaheuristics are empirically
designed, this paper aims to design new operators automatically, which are
expected to be search space independent and thus exhibit robust performance on
different problems. For this purpose, this work first investigates the
influence of translation invariance, scale invariance, and rotation invariance
on the search behavior and performance of some representative operators. Then,
we deduce the generic form of translation, scale, and rotation invariant
operators. Afterwards, a principled approach is proposed for the automated
design of operators, which searches for high-performance operators based on the
deduced generic form. The experimental results demonstrate that the operators
generated by the proposed approach outperform state-of-the-art ones on a
variety of problems with complex landscapes and up to 1000 decision variables.


Abstract:
  Head-mounted device-based human-computer interaction often requires
egocentric recognition of hand gestures and fingertips detection. In this
paper, a unified approach of egocentric hand gesture recognition and fingertip
detection is introduced. The proposed algorithm uses a single convolutional
neural network to predict the probabilities of finger class and positions of
fingertips in one forward propagation. Instead of directly regressing the
positions of fingertips from the fully connected layer, the ensemble of the
position of fingertips is regressed from the fully convolutional network.
Subsequently, the ensemble average is taken to regress the final position of
fingertips. Since the whole pipeline uses a single network, it is significantly
fast in computation. Experimental results show that the proposed method
outperforms the existing fingertip detection approaches including the Direct
Regression and the Heatmap-based framework. The effectiveness of the proposed
method is also shown in-the-wild scenario as well as in a use-case of virtual
reality.


Abstract:
  $\textit{Thailand-UK Python+Astronomy Summer School}$ (ThaiPASS), a
collaborative project comprising UK and Thai institutions and assess its impact
and possible application to schools in the United Kingdom. Since its inception
in 2018, the annual ThaiPASS have trained around 60 Thai high-school students
in basic data handling skills using Python in the context of various astronomy
topics, using current research from the teaching team. Our impact assessment of
the 5 day summer schools show an overwhelmingly positive response from students
in both years, with over $80\%$ of students scoring the activities above
average in all activities but one. We use this data to suggest possible future
improvements. We also discuss how ThaiPASS may inspire further outreach and
engagement activities within the UK and beyond.


Abstract:
  Digital Twin is an emerging technology at the forefront of Industry 4.0, with
the ultimate goal of combining the physical space and the virtual space. To
date, the Digital Twin concept has been applied in many engineering fields,
providing useful insights in the areas of engineering design, manufacturing,
automation, and construction industry. While the nexus of various technologies
opens up new opportunities with Digital Twin, the technology requires a
framework to integrate the different technologies, such as the Building
Information Model used in the Building and Construction industry. In this work,
an Information Fusion framework is proposed to seamlessly fuse heterogeneous
components in a Digital Twin framework from the variety of technologies
involved. This study aims to augment Digital Twin in buildings with the use of
AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a
drone-based Digital Twin augmentation framework with reusable and customisable
components. A proof of concept is also developed, and extensive evaluation is
conducted for 3D reconstruction and applications of AI for defect detection.


Abstract:
  Estimating the spatially varying microstructures of heterogeneous and locally
anisotropic media non-destructively is necessary for the accurate detection of
flaws and reliable monitoring of manufacturing processes. Conventional
algorithms used for solving this inverse problem come with significant
computational cost, particularly in the case of high dimensional non-linear
tomographic problems. In this paper, we propose a framework which uses deep
neural networks (DNNs) with full aperture, pitch-catch and pulse-echo
transducer configurations to reconstruct material maps of crystallographic
orientation. We also present the first ever application of generative
adversarial networks (GANs) to achieve super resolution of ultrasonic
tomographic images, providing a factor-four increase in image resolution and up
to a 50% increase in structural similarity. The importance of including
appropriate prior knowledge in the GAN training dataset to increase inversion
accuracy is highlighted; known information about the material's structure
should be present in the training data. We show that after a computationally
expensive training process, the DNNs and GANs can be used in less that one
second (0.9 seconds on a standard desktop computer) to provide a high
resolution map of the material's grain orientations.


Abstract:
  Richard P. Feynman's work on gravitation, as can be inferred from several
published and unpublished sources, is reviewed. Feynman was involved with this
subject at least from late 1954 to the late 1960s, giving several pivotal
contributions to it. Even though he published only three papers, much more
material is available, beginning with the records of his many interventions at
the Chapel Hill conference in 1957, which are here analyzed in detail, and show
that he had already considerably developed his ideas on gravity. In addition he
expressed deep thoughts about fundamental issues in quantum mechanics which
were suggested by the problem of quantum gravity, such as superpositions of the
wave functions of macroscopic objects and the role of the observer. Feynman
also lectured on gravity several times. Besides the famous lectures given at
Caltech in 1962-63, he extensively discussed this subject in a series of
lectures delivered at the Hughes Aircraft Company in 1966-67, whose focus was
on astronomy and astrophysics. All this material allows to reconstruct a
detailed picture of Feynman's ideas on gravity and of their evolution until the
late sixties. According to him, gravity, like electromagnetism, has quantum
foundations, therefore general relativity has to be regarded as the classical
limit of an underlying quantum theory; this quantum theory should be
investigated by computing physical processes, as if they were experimentally
accessible. The same attitude is shown with respect to gravitational waves, as
is evident also from an unpublished letter addressed to Victor F. Weisskopf. In
addition, an original approach to gravity, which closely mimics (and probably
was inspired by) the derivation of the Maxwell equations given by Feynman in
that period, is sketched in the unpublished Hughes lectures.


Abstract:
  In addition to the existing strong indications for lepton flavour university
violation (LFUV) in low energy precision experiments, CMS recently released an
analysis of non-resonant di-lepton pairs which could constitute the first sign
of LFUV in high-energy LHC searches. In this article we show that the Cabibbo
angle anomaly, an (apparent) violation of first row and column CKM unitarity
with $\approx3\,\sigma$ significance, and the CMS result can be correlated and
commonly explained in a model independent way by the operator $[Q_{\ell
q}^{(3)}]_{1111} =
(\bar{\ell}_1\gamma^{\mu}\sigma^I\ell_1)(\bar{q}_1\gamma_{\mu}\sigma^Iq_1)$.
This is possible without violating the bounds from the non-resonant di-lepton
search of ATLAS (which interestingly also observed slightly more events than
expected in the electron channel) nor from $R(\pi)=\pi \to\mu\nu/\pi \to e
\nu$. We find a combined preference for the new physics hypothesis of
$4.5\,\sigma$ and predict $1.0004<R(\pi)<1.0009$ (95\%~CL) which can be tested
in the near future with the forthcoming results of the PEN experiment.


Abstract:
  We use a Monte Carlo radiative transfer model (MCRTM) to simulate the UBVRI
light curves, images and linear polarization of a light echo from supernova
SN$~$1987A in the Large Magellanic Cloud (LMC) using various dust cloud shapes,
sizes, and optical properties. We compare the theoretical simulations to the
observations of AT2019xis, a light echo detected at a large angular distance
(4.05$^{'}$) from SN$~$1987A. We estimate the size and optical thickness of the
dust cloud based on the simulation results and the observations of Optical
Gravitational Lensing Experiment (OGLE-IV) Transient Detection System (OTDS)
I-band light curve. The mass of the dust cloud is calculated using the
estimated size, optical thickness and extinction coefficient. If the dust cloud
is assumed to correspond to a gas-to-dust ratio of 300, the total mass of the
dust cloud is approximately 7.8-9.3 $M_{\odot}$. Based on these theoretical
models, we show that the morphological shapes of the light echoes in the
wavelength range in or shorter than the U-band to be very different from those
in the longer wavelength bands, and the difference carries important
information on the early UV radiation of SN$~$1987A.


Abstract:
  For real-world deployments, it is critical to allow robots to navigate in
complex environments autonomously. Traditional methods usually maintain an
internal map of the environment, and then design several simple rules, in
conjunction with a localization and planning approach, to navigate through the
internal map. These approaches often involve a variety of assumptions and prior
knowledge. In contrast, recent reinforcement learning (RL) methods can provide
a model-free, self-learning mechanism as the robot interacts with an initially
unknown environment, but are expensive to deploy in real-world scenarios due to
inefficient exploration. In this paper, we focus on efficient navigation with
the RL technique and combine the advantages of these two kinds of methods into
a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of
time. First, we use the rule of wall-following to generate a closed-loop
trajectory. Second, we employ a reduction rule to shrink the trajectory, which
in turn effectively reduces the redundant exploration space. Besides, we give
the detailed theoretical guarantee that the optimal navigation path is still in
the reduced space. Third, in the reduced space, we utilize the Pledge rule to
guide the exploration strategy for accelerating the RL process at the early
stage. Experiments conducted on real robot navigation problems in hex-grid
environments demonstrate that RuRL can achieve improved navigation performance.


Abstract:
  Quest for new states of matter near an ordered phase is a promising route for
making modern physics forward. By probing thermal properties of a ferroelectric
(FE) crystal Ba1-xSrxAl2O4, we have clarified that low-energy excitation of
acoustic phonons is remarkably enhanced with critical behavior at the border of
the FE phase. The phonon spectrum is significantly damped toward the FE phase
boundary and transforms into glasslike phonon excitation which is reminiscent
of a boson peak. This system thus links long-standing issues of amorphous
solids and structural instability in crystals to pave the way to controlling
lattice fluctuation as a new tuning parameter.


Abstract:
  This study is about public-private research collaboration. In particular, we
want to measure how the propensity of academics to collaborate with their
colleagues from private firms varies over time and whether the typical profile
of such academics change. Furthermore, we investigate the change of the weights
of main drivers underlying the academics' propensity to collaborate with
industry. In order to achieve such goals, we apply an inferential model on a
dataset of professors working in Italian universities in two subsequent
periods, 2010-2013 and 2014-2017. Results can be useful for supporting the
definition of policies aimed at fostering public-private research
collaborations, and should be taken into account when assessing their
effectiveness afterwards.


Abstract:
  We propose a framework for Bayesian Likelihood-Free Inference (LFI) based on
Generalized Bayesian Inference. To define the generalized posterior, we use
Scoring Rules (SRs), which evaluate probabilistic models given an observation.
As in LFI we can sample from the model (but not evaluate the likelihood), we
employ SRs with easy empirical estimators. Our framework includes novel
approaches and popular LFI techniques (such as Bayesian Synthetic Likelihood),
which benefit from the generalized Bayesian interpretation. Our method enjoys
posterior consistency in a well-specified setting when a strictly-proper SR is
used (i.e., one whose expectation is uniquely minimized when the model
corresponds to the data generating process). Further, we prove a finite sample
generalization bound and outlier robustness for the Kernel and Energy Score
posteriors, and propose a strategy suitable for the LFI setup for tuning the
learning rate in the generalized posterior. We run simulations studies with
pseudo-marginal Markov Chain Monte Carlo (MCMC) and compare with related
approaches, which we show do not enjoy robustness and consistency.


Abstract:
  The emergence of order in collective dynamics is a fascinating phenomenon
that characterizes many natural systems consisting of coupled entities.
Synchronization is such an example where individuals, usually represented by
either linear or nonlinear oscillators, can spontaneously act coherently with
each other when the interactions' configuration fulfills certain conditions.
However, synchronization is not always perfect, and the coexistence of coherent
and incoherent oscillators, broadly known in the literature as chimera states,
is also possible. Although several attempts have been made to explain how
chimera states are created, their emergence, stability, and robustness remain a
long-debated question. We propose an approach that aims to establish a robust
mechanism through which chimeras originate. We first introduce a
stability-breaking method where clusters of synchronized oscillators can
emerge. Similarly, one or more clusters of oscillators may remain incoherent
within yielding a particular class of patterns that we here name cluster
chimera states.


Abstract:
  The Cosserat continuum is used in this paper to regularize the ill-posed
governing equations of the Cauchy/Maxwell continuum. Most available
constitutive models adopt yield and plastic potential surfaces with a circular
deviatoric section. This is a too crude an approximation which hinders the
application of the Cosserat continuum into practice, particularly in the
geotechnical domain. An elasto-plastic constitutive model for the linear
formulation of the Cosserat continuum is here presented, which features
non-associated flow and hardening/softening behaviour, whilst linear
hyper-elasticity is adopted to reproduce the recoverable response. For the
formulation of the yield and plastic potential functions, a definition of the
\textit{equivalent von Mises stress} is used which is based on Hencky's
interpretation of the von Mises criterion and also on the theory of
representations. The dependency on the Lode's angle of both the yield and
plastic potential functions is introduced through the adoption of a recently
proposed \textit{Generalized classical} criterion, which rigorously defines
most of the classical yield and failure criteria.


Abstract:
  Despite thousands of spectroscopic detections, only four isolated white
dwarfs exhibit Balmer emission lines. The temperature inversion mechanism is a
puzzle over 30 years old that has defied conventional explanations. One
hypothesis is a unipolar inductor that achieves surface heating via ohmic
dissipation of a current loop between a conducting planet and a magnetic white
dwarf. To investigate this model, new time-resolved spectroscopy,
spectropolarimetry, and photometry of the prototype GD 356 are studied. The
emission features vary in strength on the rotational period, but in anti-phase
with the light curve, consistent with a cool surface spot beneath an optically
thin chromosphere. Possible changes in the line profiles are observed at the
same photometric phase, potentially suggesting modest evolution of the emission
region, while the magnetic field varies by 10 per cent over a full rotation.
These comprehensive data reveal neither changes to the photometric period, nor
additional signals such as might be expected from an orbiting body. A closer
examination of the unipolar inductor model finds points of potential failure:
the observed rapid stellar rotation will inhibit current carriers due to the
centrifugal force, there may be no supply of magnetospheric ions, and no
anti-phase flux changes are expected from ohmic surface heating. Together with
the highly similar properties of the four cool, emission-line white dwarfs,
these facts indicate that the chromospheric emission is intrinsic. A
tantalizing possibility is that intrinsic chromospheres may manifest in
(magnetic) white dwarfs, and in distinct parts of the HR diagram based on
structure and composition.


Abstract:
  We discuss reduced-scaling strategies employing recently introduced
sub-system embedding sub-algebras coupled-cluster formalism (SES-CC) to
describe many-body systems. These strategies utilize properties of the SES-CC
formulations where the equations describing certain classes of sub-systems can
be integrated into a computational flows composed coupled eigenvalue problems
of reduced dimensionality. Additionally, these flows can be determined at the
level of the CC Ansatz by the inclusion of selected classes of cluster
amplitudes, which define the wave function "memory" of possible partitionings
of the many-body system into constituent sub-systems. One of the possible ways
of solving these coupled problems is through implementing procedures, where the
information is passed between the sub-systems in a self-consistent manner. As a
special case, we consider local flow formulations where the so-called local
character of correlation effects can be closely related to properties of
sub-system embedding sub-algebras employing localized molecular basis. We also
generalize flow equations to the time domain and to downfolding methods
utilizing double exponential unitary CC Ansatz (DUCC), where reduced
dimensionality of constituent sub-problems offer a possibility of efficient
utilization of limited quantum resources in modeling realistic systems.


Abstract:
  Thermodynamics of quantum systems and quantum thermal machines are rapidly
developing fields, which have already delivered several promising results, as
well as raised many intriguing questions. Many-body quantum machines present
new opportunities stemming from many-body effects. At the same time, they pose
new challenges related to many-body physics. In this short review we discuss
some of the recent developments on technologies based on many-body quantum
systems. We mainly focus on many-body effects in quantum thermal machines. We
also briefly address the role played by many-body systems in the development of
quantum batteries and quantum probes.


Abstract:
  We state and prove that a certain class of smooth functions said to be
BH-separable is a meagre subset for the Fr\'echet topology. Because these
functions are the only admissible Hamiltonians for Arnold-Liouville systems
admitting a bi-Hamiltonian structure, we get that, generically,
Arnold-Liouville systems cannot be bi-Hamiltonian. At the end of the paper, we
determine, both as a concrete representation of our general result and as an
illustrative list, which polynomial Hamiltonians $H$ of the form
$H(x,y)=xy+ax^3+bx^2y+cxy^2+dy^3$ are BH-separable.


Abstract:
  We investigate the incompressible and compressible heat conducting boundary
layer with applying the two-dimensional self-similar Ansatz. Analytic solutions
can be found for the incompressible case which can be expressed with special
functions. The parameter dependencies are studied and discussed in details. In
the last part of our study we present the ordinary differential equation (ODE)
system which is obtained for compressible boundary layers.


Abstract:
  The fluctuation-dissipation theorem is a fundamental result in statistical
physics that establishes a connection between the response of a system subject
to a perturbation and the fluctuations associated with observables in
equilibrium. Here we derive its version within a resource-theoretic framework,
where one investigates optimal quantum state transitions under thermodynamic
constraints. More precisely, we first characterise optimal thermodynamic
distillation processes, and then prove a relation between the amount of free
energy dissipated in such processes and the free energy fluctuations of the
initial state of the system. Our results apply to initial states given by
either asymptotically many identical pure systems or arbitrary number of
independent energy-incoherent systems, and allow not only for a state
transformation, but also for the change of Hamiltonian. The
fluctuation-dissipation relations we derive enable us to find the optimal
performance of thermodynamic protocols such as work extraction, information
erasure and thermodynamically-free communication, up to second-order
asymptotics in the number $N$ of processed systems. We thus provide a first
rigorous analysis of these thermodynamic protocols for quantum states with
coherence between different energy eigenstates in the intermediate regime of
large but finite $N$.


Abstract:
  In addition to high accuracy, robustness is becoming increasingly important
for machine learning models in various applications. Recently, much research
has been devoted to improving the model robustness by training with noise
perturbations. Most existing studies assume a fixed perturbation level for all
training examples, which however hardly holds in real tasks. In fact, excessive
perturbations may destroy the discriminative content of an example, while
deficient perturbations may fail to provide helpful information for improving
the robustness. Motivated by this observation, we propose to adaptively adjust
the perturbation levels for each example in the training process. Specifically,
a novel active learning framework is proposed to allow the model to
interactively query the correct perturbation level from human experts. By
designing a cost-effective sampling strategy along with a new query type, the
robustness can be significantly improved with a few queries. Both theoretical
analysis and experimental studies validate the effectiveness of the proposed
approach.


Abstract:
  Recently, Kostochka and Yancey proved that a conjecture of Ore is
asymptotically true by showing that every $k$-critical graph satisfies
$|E(G)|\geq\left\lceil\left(\frac{k}{2}-\frac{1}{k-1}\right)|V(G)|-\frac{k(k-3)}{2(k-1)}\right\rceil.$
They also characterized the class of graphs that attain this bound and showed
that it is equivalent to the set of $k$-Ore graphs. We show that for any
$k\geq33$ there exists an $\varepsilon>0$ so that if $G$ is a $k$-critical
graph, then
$|E(G)|\geq\left(\frac{k}{2}-\frac{1}{k-1}+\varepsilon_k\right)|V(G)|-\frac{k(k-3)}{2(k-1)}-(k-1)\varepsilon
T(G)$, where $T(G)$ is a measure of the number of disjoint $K_{k-1}$ and
$K_{k-2}$ subgraphs in $G$. This also proves for $k\geq33$ the following
conjecture of Postle regarding the asymptotic density: For every $k\geq4$ there
exists an $\varepsilon_k>0$ such that if $G$ is a $k$-critical $K_{k-2}$-free
graph, then $|E(G)|\geq
\left(\frac{k}{2}-\frac{1}{k-1}+\varepsilon_k\right)|V(G)|-\frac{k(k-3)}{2(k-1)}$.
As a corollary, our result shows that the number of disjoint $K_{k-2}$
subgraphs in a $k$-Ore graph scales linearly with the number of vertices and,
further, that the same is true for graphs whose number of edges is close to
Kostochka and Yancey's bound.


Abstract:
  SrTiO$_3$-based two-dimensional electron gases (2DEGs) can be formed through
the deposition of epitaxial oxides like LaAlO$_3$ or of reactive metals such as
Al. Such 2DEGs possess a finite Rashba spin-orbit coupling that has recently
been harnessed to interconvert charge and spin currents through the direct and
inverse Edelstein and spin Hall effects. Here we compare the formation and
properties of 2DEGs generated in SrTiO$_3$ by the growth of Al, Ta and Y
ultrathin films by magnetron sputtering. By combining in situ and ex situ X-ray
photoelectron spectroscopy (XPS) we gain insight into the reduction of the
SrTiO$_3$ and the appearance of Ti$^{3+}$ states associated with 2DEG
formation, its reoxidation by exposure to the air, and the transformation of
the metal into its binary oxides. We extract the carrier densities through
magnetotransport and compare them with the XPS data. Finally, working with
samples covered by an extra layer of NiFe, we perform spin-pumping
ferromagnetic resonance experiments and investigate spin-charge conversion as a
function of gate voltage. We identify trends in the data across the different
sample systems and discuss them as a function of the carrier density and the
transparency of the metal oxide tunnel barrier.


Abstract:
  The impact of user satisfaction in policy learning task-oriented dialogue
systems has long been a subject of research interest. Most current models for
estimating the user satisfaction either (i) treat out-of-context short-texts,
such as product reviews, or (ii) rely on turn features instead of on
distributed semantic representations. In this work we adopt deep neural
networks that use distributed semantic representation learning for estimating
the user satisfaction in conversations. We evaluate the impact of modelling
context length in these networks. Moreover, we show that the proposed
hierarchical network outperforms state-of-the-art quality estimators.
Furthermore, we show that applying these networks to infer the reward function
in a Partial Observable Markov Decision Process (POMDP) yields to a great
improvement in the task success rate.


Abstract:
  In this paper, we introduce an R software package for simulating plays and
drives using play-by-play data from the National Football League. The
simulations are generated by sampling play-by-play data from previous football
seasons.The sampling procedure adds statistical rigor to any decisions or
inferences arising from examining the simulations. We highlight that the
package is particularly useful as a data-driven tool for evaluating potential
in-game strategies or rule changes within the league. We demonstrate its
utility by evaluating the oft-debated strategy of $\textit{going for it}$ on
fourth down and investigating whether or not teams should pass more than the
current standard.


Abstract:
  Having the ability to acquire inherent skills from environments without any
external rewards or supervision like humans is an important problem. We propose
a novel unsupervised skill discovery method named Information Bottleneck Option
Learning (IBOL). On top of the linearization of environments that promotes more
various and distant state transitions, IBOL enables the discovery of diverse
skills. It provides the abstraction of the skills learned with the information
bottleneck framework for the options with improved stability and encouraged
disentanglement. We empirically demonstrate that IBOL outperforms multiple
state-of-the-art unsupervised skill discovery methods on the
information-theoretic evaluations and downstream tasks in MuJoCo environments,
including Ant, HalfCheetah, Hopper and D'Kitty.


Abstract:
  We present a deep reinforcement learning-based artificial intelligence agent
that could provide optimized development plans given a basic description of the
reservoir and rock/fluid properties with minimal computational cost. This
artificial intelligence agent, comprising of a convolutional neural network,
provides a mapping from a given state of the reservoir model, constraints, and
economic condition to the optimal decision (drill/do not drill and well
location) to be taken in the next stage of the defined sequential field
development planning process. The state of the reservoir model is defined using
parameters that appear in the governing equations of the two-phase flow. A
feedback loop training process referred to as deep reinforcement learning is
used to train an artificial intelligence agent with such a capability. The
training entails millions of flow simulations with varying reservoir model
descriptions (structural, rock and fluid properties), operational constraints,
and economic conditions. The parameters that define the reservoir model,
operational constraints, and economic conditions are randomly sampled from a
defined range of applicability. Several algorithmic treatments are introduced
to enhance the training of the artificial intelligence agent. After appropriate
training, the artificial intelligence agent provides an optimized field
development plan instantly for new scenarios within the defined range of
applicability. This approach has advantages over traditional optimization
algorithms (e.g., particle swarm optimization, genetic algorithm) that are
generally used to find a solution for a specific field development scenario and
typically not generalizable to different scenarios.


Abstract:
  While the prevalence of large pre-trained language models has led to
significant improvements in the performance of NLP systems, recent research has
demonstrated that these models inherit societal biases extant in natural
language. In this paper, we explore a simple method to probe pre-trained
language models for gender bias, which we use to effect a multi-lingual study
of gender bias towards politicians. We construct a dataset of 250k politicians
from most countries in the world and quantify adjective and verb usage around
those politicians' names as a function of their gender. We conduct our study in
7 languages across 6 different language modeling architectures. Our results
demonstrate that stance towards politicians in pre-trained language models is
highly dependent on the language used. Finally, contrary to previous findings,
our study suggests that larger language models do not tend to be significantly
more gender-biased than smaller ones.


Abstract:
  In the early 1960s, P. Malliavin and L. A. Rubel gave a complete description
of pairs of distributions of positive points $Z$ and $W$ such that for each
entire function of exponential type $g\neq 0$ that vanishes on $W$, there is an
entire function of exponential type $f\neq 0$ such that $f$ vanishes on $Z$ and
satisfies the inequality $|f|\leq |g|$ everywhere on the imaginary axis. We
extend this result to much more general distributions of complex points $Z$ and
$W$ lying outside of some pair of vertical angles containing the imaginary axis
as the points approach $\infty$.


Abstract:
  Bigraphs are a universal computational modelling formalism for the spatial
and temporal evolution of a system in which entities can be added and removed.
We extend bigraphs to probablistic bigraphs, and then again to action bigraphs,
which include non-determinism and rewards. The extensions are implemented in
the BigraphER toolkit and illustrated through examples of virus spread in
computer networks and data harvesting in wireless sensor systems. BigraphER
also supports the existing stochastic bigraphs extension of Krivine et al., and
using BigraphER we give, for the first time, a direct implementation of the
membrane budding model used to motivate stochastic bigraphs.


Abstract:
  We make a systematic study of the isospin symmetry of fragmentation functions
by taking decay contributions into account. We assume the isospin symmetry in
strong interactions and show that in the unpolarized case the isospin symmetry
is held for fragmentation functions of $\Lambda$ and only tiny violations are
allowed for other hadrons such as nucleon and pions due to the contributions
from weak decays. We present a rough estimate of the magnitudes of such
violations. In the polarized case, we show that the isospin symmetry violation
for $\Lambda$ production should be tiny and the recent Belle data on the
transverse polarization of $\Lambda$ can be reproduced if the isospin symmetry
is kept in the corresponding polarized fragmentation functions.


Abstract:
  There is growing interest in data-driven weather prediction (DDWP), for
example using convolutional neural networks such as U-NETs that are trained on
data from models or reanalysis. Here, we propose 3 components to integrate with
commonly used DDWP models in order to improve their physical consistency and
forecast accuracy. These components are 1) a deep spatial transformer added to
the latent space of the U-NETs to preserve a property called equivariance,
which is related to correctly capturing rotations and scalings of features in
spatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy
observations and improve the initial conditions for next forecasts, and 3) a
multi-time-step algorithm, which combines forecasts from DDWP models with
different time steps through DA, improving the accuracy of forecasts at short
intervals. To show the benefit/feasibility of each component, we use
geopotential height at 500~hPa (Z500) from ERA5 reanalysis and examine the
short-term forecast accuracy of specific setups of the DDWP framework. Results
show that the equivariance-preserving networks (U-STNs) clearly outperform the
U-NETs, for example improving the forecast skill by $45\%$. Using a sigma-point
ensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we
show that stable, accurate DA cycles are achieved even with high observation
noise. The DDWP+DA framework substantially benefits from large ($O(1000)$)
ensembles that are inexpensively generated with the data-driven forward model
in each DA cycle. The multi-time-step DDWP+DA framework also shows promises,
e.g., it reduces the average error by factors of 2-3.


Abstract:
  This work presents ReBeatICG, a real-time, low-complexity beat-to-beat
impedance cardiography (ICG) delineation algorithm that allows hemodynamic
parameters monitoring. The proposed procedure relies only on the ICG signal
compared to most algorithms found in the literature that rely on synchronous
electrocardiogram signal (ECG) recordings. ReBeatICG was designed with
implementation on an ultra-low-power microcontroller (MCU) in mind. The
detection accuracy of the developed algorithm is tested against points manually
labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%,
98.6%, 90.3%, and 84.3% for the B, C, X, and O points, respectively.
Furthermore, several hemodynamic parameters were calculated based on annotated
characteristic points and compared with values generated from the
cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11ms,
9.72ms, 8.32ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude,
respectively.


Abstract:
  We present a detailed convergence analysis for an operator splitting scheme
proposed in [C. Liu et al.,J. Comput. Phys., 436, 110253, 2021] for a
reaction-diffusion system with detailed balance. The numerical scheme has been
constructed based on a recently developed energetic variational formulation, in
which the reaction part is reformulated in terms of the reaction trajectory,
and both the reaction and diffusion parts dissipate the same free energy. The
scheme is energy stable and positivity-preserving. In this paper, the detailed
convergence analysis and error estimate are performed for the operator
splitting scheme. The nonlinearity in the reaction trajectory equation, as well
as the implicit treatment of nonlinear and singular logarithmic terms, impose
challenges in numerical analysis. To overcome these difficulties, we make use
of the convex nature of the logarithmic nonlinear terms, which are treated
implicitly in the chemical reaction stage. In addition, a combination of a
rough error estimate and a refined error estimate leads to a desired bound of
the numerical error in the reaction stage, in the discrete maximum norm.
Furthermore, a discrete maximum principle yields the evolution bound of the
numerical error function at the diffusion stage. As a direct consequence, a
combination of the numerical error analysis at different stages and the
consistency estimate for the operator splitting results in the convergence
estimate of the numerical scheme for the full reaction-diffusion system.


Abstract:
  Rapid and large scanning of a dissipative Kerr-microresonator soliton comb
with the characterization of all comb modes along with the separation of the
comb modes is imperative for the emerging applications of the frequency-scanned
soliton combs. However, the scan speed is limited by the gain of feedback
systems and the measurement of the frequency shift of all comb modes has not
been demonstrated. To overcome the limitation of the feedback, we incorporate
the feedback with the feedforward. With the additional gain of > 40 dB by a
feedforward signal, a dissipative Kerr-microresonator soliton comb is scanned
by 70 GHz in 500 $\mu$s, 50 GHz in 125 $\mu$s, and 25 GHz in 50 $\mu$s (= 500
THz/s). Furthermore, we propose and demonstrate a method to measure the
frequency shift of all comb modes, in which an imbalanced Mach-Zehnder
interferometer with two outputs with different wavelengths is used. Because of
the two degrees of freedom of optical frequency combs, the measurement at the
two different wavelengths enables the estimation of the frequency shift of all
comb modes.


Abstract:
  In this work we investigate the existence and uniqueness of Struwe-like
solutions for a system of partial differential equations modeling the dynamics
of magnetoviscoelastic fluids. The considered system couples a Navier-Stokes
type equation with a dissipative equation for the deformation tensor and a
Landau-Lifshitz-Gilbert type equation for the magnetization field. The main
purpose is to establish a well-posedness theory in a two-dimensional periodic
domain under standard assumption of critical regularity for the (possibly
large) initial data. We prove that the considered weak solutions are everywhere
smooth, except for a discrete set of time values. The proof of the uniqueness
is based on suitable energy estimates for the solutions within a functional
framework which is less regular than the one of the Struwe energy level. These
estimates rely on several techniques of harmonic analysis and paradifferential
calculus.


Abstract:
  We study reinforcement learning for the optimal control of Branching Markov
Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov
Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities
of various types that, while spawning other entities, generate a payoff. In
comparison with BMCs, where the evolution of a each entity of the same type
follows the same probabilistic pattern, BMDPs allow an external controller to
pick from a range of options. This permits us to study the best/worst behaviour
of the system. We generalise model-free reinforcement learning techniques to
compute an optimal control strategy of an unknown BMDP in the limit. We present
results of an implementation that demonstrate the practicality of the approach.


Abstract:
  Multi-intent SLU can handle multiple intents in an utterance, which has
attracted increasing attention. However, the state-of-the-art joint models
heavily rely on autoregressive approaches, resulting in two issues: slow
inference speed and information leakage. In this paper, we explore a
non-autoregressive model for joint multiple intent detection and slot filling,
achieving more fast and accurate. Specifically, we propose a Global-Locally
Graph Interaction Network (GL-GIN) where a local slot-aware graph interaction
layer is proposed to model slot dependency for alleviating uncoordinated slots
problem while a global intent-slot graph interaction layer is introduced to
model the interaction between multiple intents and all slots in the utterance.
Experimental results on two public datasets show that our framework achieves
state-of-the-art performance while being 11.5 times faster.


Abstract:
  The Bell's inequality is a strong criterion to distinguish classical and
quantum mechanical aspects of reality. Its violation is the net effect of the
existence of non-locality in systems, an advantage for quantum mechanics (QM)
over classical physics. The quantum mechanical world is under the control of
the Heisenberg uncertainty principle (HUP) that is generalized by quantum
gravity (QG) scenarios, called generalized uncertainty principle (GUP). Here,
the effects of GUP on the square of Bell operators of qubits and qutrits are
studied. The achievements claim that the violation quality of the square of
Bell inequalities may be a tool to get a better understanding of the quantum
features of gravity. In this regard, it is obtained that the current accuracy
of the Stern-Gerlach experiments implies upper bounds on the values of the GUP
parameters.


Abstract:
  Clinical notes are unstructured text generated by clinicians during patient
encounters. Clinical notes are usually accompanied by a set of metadata codes
from the International Classification of Diseases(ICD). ICD code is an
important code used in various operations, including insurance, reimbursement,
medical diagnosis, etc. Therefore, it is important to classify ICD codes
quickly and accurately. However, annotating these codes is costly and
time-consuming. So we propose a model based on bidirectional encoder
representations from transformers (BERT) using the sequence attention method
for automatic ICD code assignment. We evaluate our approach on the medical
information mart for intensive care III (MIMIC-III) benchmark dataset. Our
model achieved performance of macro-averaged F1: 0.62898 and micro-averaged F1:
0.68555 and is performing better than a performance of the state-of-the-art
model using the MIMIC-III dataset. The contribution of this study proposes a
method of using BERT that can be applied to documents and a sequence attention
method that can capture important sequence in-formation appearing in documents.


Abstract:
  We investigate the role of gentle algebras in higher homological algebra. In
the first part of the paper, we show that if the module category of a gentle
algebra $\Lambda$ contains a $d$-cluster tilting subcategory for some $d \geq
2$, then $\Lambda$ is a radical square zero Nakayama algebra. This gives a
complete classification of weakly $d$-representation finite gentle algebras. In
the second part, we use a geometric model of the derived category to prove a
similar result in the triangulated setup. More precisely, we show that if
$\mathcal{D}^b(\Lambda)$ contains a $d$-cluster tilting subcategory that is
closed under $[d]$, then $\Lambda$ is derived equivalent to an algebra of
Dynkin type $A$. Furthermore, our approach gives a geometric characterization
of all $d$-cluster tilting subcategories of $\mathcal{D}^b(\Lambda)$ that are
closed under $[d]$.


Abstract:
  Model-based paradigms for decision-making and control are becoming ubiquitous
in robotics. They rely on the ability to efficiently learn a model of the
system from data. Structured Mechanical Models (SMMs) are a data-efficient
black-box parameterization of mechanical systems, typically fit to data by
minimizing the error between predicted and observed accelerations or next
states. In this work, we propose a methodology for fitting SMMs to data by
minimizing the discrete Euler-Lagrange residual. To study our methodology, we
fit models to joint-angle time-series from undamped and damped
double-pendulums, studying the quality of learned models fit to data with and
without observation noise. Experiments show that our methodology learns models
that are better in accuracy to those of the conventional schemes for fitting
SMMs. We identify use cases in which our method is a more appropriate
methodology. Source code for reproducing the experiments is available at
https://github.com/sisl/delsmm.


Abstract:
  Consider the focusing inhomogeneous nonlinear Schr\"odinger equation in
$H^1(\mathbb{R}^N)$, $$iu_t + \Delta u + |x|^{-b}|u|^{p-1}u=0,$$ when $b > 0$
and $N \geq 3$ in the intercritical case $0 < s_c <1$. In previous works, the
second author, as well as Farah, Guzm\'an and Murphy, applied the
concentration-compactness approach to prove scattering below the mass-energy
threshold for radial and non-radial data. Recently, the first author adapted
the Dodson-Murphy approach for radial data, followed by Murphy, who proved
scattering for non-radial solutions in the 3d cubic case, for $b<1/2$. This
work generalizes the recent result of Murphy, allowing a broader range of
values for the parameters $p$ and $b$, as well as allowing any dimension $N
\geq 3$. It also gives a simpler proof for scattering nonradial, avoiding the
Kenig-Merle road map. We exploit the decay of the nonlinearity, which, together
with Virial-Morawetz-type estimates, allows us to drop the radial assumption.


Abstract:
  Hyperspectral image fusion (HIF) is critical to a wide range of applications
in remote sensing and many computer vision applications. Most traditional HIF
methods assume that the observation model is predefined or known. However, in
real applications, the observation model involved are often complicated and
unknown, which leads to the serious performance drop of many advanced HIF
methods. Also, deep learning methods can achieve outstanding performance, but
they generally require a large number of image pairs for model training, which
are difficult to obtain in realistic scenarios. Towards these issues, we
proposed a self-regression learning method that alternatively reconstructs
hyperspectral image (HSI) and estimate the observation model. In particular, we
adopt an invertible neural network (INN) for restoring the HSI, and two
fully-connected network (FCN) for estimating the observation model. Moreover,
\emph{SoftMax} nonlinearity is applied to the FCN for satisfying the
non-negative, sparsity and equality constraints. Besides, we proposed a local
consistency loss function to constrain the observation model by exploring
domain specific knowledge. Finally, we proposed an angular loss function to
improve spectral reconstruction accuracy. Extensive experiments on both
synthetic and real-world dataset show that our model can outperform the
state-of-the-art methods


Abstract:
  We propose a dense indirect visual odometry method taking as input externally
estimated optical flow fields instead of hand-crafted feature correspondences.
We define our problem as a probabilistic model and develop a generalized-EM
formulation for the joint inference of camera motion, pixel depth, and
motion-track confidence. Contrary to traditional methods assuming
Gaussian-distributed observation errors, we supervise our inference framework
under an (empirically validated) adaptive log-logistic distribution model.
Moreover, the log-logistic residual model generalizes well to different
state-of-the-art optical flow methods, making our approach modular and agnostic
to the choice of optical flow estimators. Our method achieved top-ranking
results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced
implementation is inherently GPU-friendly with only linear computational and
storage growth.


Abstract:
  Session-based recommendation aims at predicting the next item given a
sequence of previous items consumed in the session, e.g., on e-commerce or
multimedia streaming services. Specifically, session data exhibits some unique
characteristics, i.e., session consistency and sequential dependency over items
within the session, repeated item consumption, and session timeliness. In this
paper, we propose simple-yet-effective linear models for considering the
holistic aspects of the sessions. The comprehensive nature of our models helps
improve the quality of session-based recommendation. More importantly, it
provides a generalized framework for reflecting different perspectives of
session data. Furthermore, since our models can be solved by closed-form
solutions, they are highly scalable. Experimental results demonstrate that the
proposed linear models show competitive or state-of-the-art performance in
various metrics on several real-world datasets.


Abstract:
  We probe type IIA geometries with \emph{folded} semiclassical strings those
encode the strong coupling dynamics associated with long operators in a class
of SCFTs pertaining to spacetimes whose dimension vary from $6d$ down to $2d$.
We particularly focus on folded string configurations those are extended
through stack of flavor $ Dp $ ($ p=6,8 $) branes localized along the internal
manifold. Considering some specific examples within the realm of $
\mathcal{N}=2 $ linear quivers, we show that the associated string spectrum
exhibits a pole as the string approaches flavor D6 branes. We identify this as
a \emph{fragmentation} of long operators in the dual $ \mathcal{N}=2 $ SCFTs.
On the other hand, a similar analysis for $ \mathcal{N}=(1,0) $ SCFTs in $6d$
as well as $ \mathcal{N}=(0,4) $ SCFTs in $ 4d $ reveals a set of new
dispersion relations at strong coupling. Based on semiclassical calculations,
we set an argument that explains either of these cases.


Abstract:
  We investigate different methods for regularizing quantile regression when
predicting either a subset of quantiles or the full inverse CDF. We show that
minimizing an expected pinball loss over a continuous distribution of quantiles
is a good regularizer even when only predicting a specific quantile. For
predicting multiple quantiles, we propose achieving the classic goal of
non-crossing quantiles by using deep lattice networks that treat the quantile
as a monotonic input feature, and we discuss why monotonicity on other features
is an apt regularizer for quantile regression. We show that lattice models
enable regularizing the predicted distribution to a location-scale family.
Lastly, we propose applying rate constraints to improve the calibration of the
quantile predictions on specific subsets of interest and improve fairness
metrics. We demonstrate our contributions on simulations, benchmark datasets,
and real quantile regression problems.


Abstract:
  To a simple graph we associate a so-called graph series, which can be viewed
as the Hilbert--Poincar\'e series of a certain infinite jet scheme. We study
new $q$-representations and examine modular properties of several examples
including Dynkin diagrams of finite and affine type. Notably, we obtain new
formulas for graph series of type $A_7$ and $A_8$ in terms of "sum of tails"
series, and of type $D_4$ and $D_5$ in the form of indefinite theta functions
of signature $(1,1)$. We also study examples related to sums of powers of
divisors corresponding to $5$-cycles. For several examples of graphs, we prove
that graph series are so-called mixed quantum modular forms.


Abstract:
  We consider a game in which the action set of each player is uncountable, and
show that, from weak assumptions on the common prior, any mixed strategy has an
approximately equivalent pure strategy. The assumption of this result can be
further weakened if we consider the purification of a Nash equilibrium.
Combined with the existence theorem for a Nash equilibrium, we derive an
existence theorem for a pure strategy approximated Nash equilibrium under
sufficiently weak assumptions. All of the pure strategies we derive in this
paper can take a finite number of possible actions.


Abstract:
  We study the conormal sheaves and singular schemes of 1-dimensional
foliations on smooth projective varieties $X$ of dimension 3 and Picard rank 1.
We prove that if the singular scheme has dimension 0, then the conormal sheaf
is $\mu$-stable whenever the tangent bundle $TX$ is stable, and apply this fact
to the characterization of certain irreducible components of the moduli space
of rank 2 reflexive sheaves on $\mathbb{P}^3$ and on a smooth quadric
hypersurface $Q_3\subset\mathbb{P}^4$. Finally, we give a classification of
local complete intersection foliations, that is, foliations with locally free
conormal sheaves, of degree 0 and 1 on $Q_3$.


Abstract:
  Fabrication process variations can significantly influence the performance
and yield of nano-scale electronic and photonic circuits. Stochastic spectral
methods have achieved great success in quantifying the impact of process
variations, but they suffer from the curse of dimensionality. Recently,
low-rank tensor methods have been developed to mitigate this issue, but two
fundamental challenges remain open: how to automatically determine the tensor
rank and how to adaptively pick the informative simulation samples. This paper
proposes a novel tensor regression method to address these two challenges. We
use a $\ell_{q}/ \ell_{2}$ group-sparsity regularization to determine the
tensor rank. The resulting optimization problem can be efficiently solved via
an alternating minimization solver. We also propose a two-stage adaptive
sampling method to reduce the simulation cost. Our method considers both
exploration and exploitation via the estimated Voronoi cell volume and
nonlinearity measurement respectively. The proposed model is verified with
synthetic and some realistic circuit benchmarks, on which our method can well
capture the uncertainty caused by 19 to 100 random variables with only 100 to
600 simulation samples.


Abstract:
  Let $a\in \mathbb{Z}$ and $\rho$ be a root of $f_a(x)=x^3-ax^2-(a+3)x-1$,
then the number field $K_a=\mathbb{Q}(\rho)$ is called a simplest cubic field.
In this paper we consider the family of unit equations $u_1+u_2=n$ where
$u_1,u_2\in \mathbb{Z}[\rho]^*$ and $n\in \mathbb{Z}$. We completely solve the
unit equations under the restriction $|n|\leq \max\{1,|a|^{1/3}\}$.


Abstract:
  This paper reports the use of a qualitative methodology for conducting
longitudinal case study research on software development. We provide a detailed
description and explanation of appropriate methods of qualitative data
collection and analysis that can be utilized by other researchers in the
software engineering field. Our aim is to illustrate the utility of
longitudinal case study research, as a complement to existing methodologies for
studying software development, so as to enable the community to develop a
fuller and richer understanding of this complex, multi-dimensional phenomenon.
We discuss the insights gained and lessons learned from applying a longitudinal
qualitative approach to an empirical case study of a software development
project in a large multi-national organization. We evaluate the methodology
used to emphasize its strengths and to address the criticisms traditionally
made of qualitative research.


Abstract:
  We determine the 1-form symmetry group for any 4d N = 2 class S theory
constructed by compactifying a 6d N=(2,0) SCFT on a Riemann surface with
arbitrary regular untwisted and twisted punctures. The 6d theory has a group of
mutually non-local dimension-2 surface operators, modulo screening.
Compactifying these surface operators leads to a group of mutually non-local
line operators in 4d, modulo screening and flavor charges. Complete
specification of a 4d theory arising from such a compactification requires a
choice of a maximal subgroup of mutually local line operators, and the 1-form
symmetry group of the chosen 4d theory is identified as the Pontryagin dual of
this maximal subgroup. We also comment on how to generalize our results to
compactifications involving irregular punctures. Finally, to complement the
analysis from 6d, we derive the 1-form symmetry from a Type IIB realization of
class S theories.


Abstract:
  We develop a novel algorithm for large-scale holographic reconstruction of 3D
particle fields. Our method is based on a multiple-scattering beam propagation
method (BPM) combined with sparse regularization that enables recovering dense
3D particles of high refractive index contrast from a single hologram. We show
that the BPM-computed hologram generates intensity statistics closely matching
with the experimental measurements and provides up to 9$\times$ higher accuracy
than the single-scattering model. To solve the inverse problem, we devise a
computationally efficient algorithm, which reduces the computation time by two
orders of magnitude as compared to the state-of-the-art
multiple-scattering-based technique. We demonstrate superior reconstruction
accuracy in both simulations and experiments under different scattering
strengths. We show that the BPM reconstruction significantly outperforms the
single-scattering method in particular for deep imaging depths and high
particle densities.


Abstract:
  We study Landau-Ginzburg orbifolds $(f,G)$ with $f=x_1^n+\ldots+x_N^n$ and
$G=S\ltimes G^d$, where $S\subseteq S_N$ and $G^d$ is either the maximal group
of scalar symmetries of $f$ or the intersection of the maximal diagonal
symmetries of $f$ with $\mathrm{SL}_N(\mathbb{C})$. We construct a mirror map
between the corresponding phase spaces and prove that it is an isomorphism
restricted to a certain subspace of the phase space when $n=N$ is a prime
number. When $S$ satisfies the condition PC of Ebeling and Gusein-Zade this
subspace coincides with the full space. We also show that two phase spaces are
isomorphic for $n=N=5$.


Abstract:
  Balancing is a fundamental need for legged robots due to their unstable
floating-base nature. Balance control has been thoroughly studied for simple
models such as the linear inverted pendulum thanks to the concept of the
instantaneous capture point (ICP), yet the constant center of mass height
assumption limits the application. This paper explores balancing of the
variable-height inverted pendulum (VHIP) model by introducing the
\emph{instantaneous capture input} (ICI), an extension of the ICP based on its
key properties. Namely, the ICI can be computed as a function of the state, and
when this function is used as the control policy, the ICI is rendered
stationary and the system will eventually come to a stop. This characterization
induces an analytical region of capturable states for the VHIP, which can be
used to conceptually guide where to step. To further address state and control
constraints during recovery, we present and theoretically analyze an explicit
ICI-based controller with online optimal feedback gains. Simulations
demonstrate the validity of our controller for capturability maintenance
compared to an approach based on the divergent component of motion.


Abstract:
  Variational quantum algorithms aim at harnessing the power of noisy
intermediate-scale quantum computers, by using a classical optimizer to train a
parameterized quantum circuit to solve tractable quantum problems. The
variational quantum eigensolver is one of the aforementioned algorithms
designed to determine the ground-state of many-body Hamiltonians. Here, we
apply the variational quantum eigensolver to study the ground-state properties
of $N$-component fermions. With such knowledge, we study the persistent current
of interacting SU($N$) fermions, which is employed to reliably map out the
different quantum phases of the system. Our approach lays out the basis for a
current-based quantum simulator of many-body systems that can be implemented on
noisy intermediate-scale quantum computers.


Abstract:
  Can we complete pre-training of Vision Transformers (ViT) without natural
images and human-annotated labels? Although a pre-trained ViT seems to heavily
rely on a large-scale dataset and human-annotated labels, recent large-scale
datasets contain several problems in terms of privacy violations, inadequate
fairness protection, and labor-intensive annotation. In the present paper, we
pre-train ViT without any image collections and annotation labor. We
experimentally verify that our proposed framework partially outperforms
sophisticated Self-Supervised Learning (SSL) methods like SimCLRv2 and MoCov2
without using any natural images in the pre-training phase. Moreover, although
the ViT pre-trained without natural images produces some different
visualizations from ImageNet pre-trained ViT, it can interpret natural image
datasets to a large extent. For example, the performance rates on the CIFAR-10
dataset are as follows: our proposal 97.6 vs. SimCLRv2 97.4 vs. ImageNet 98.0.


Abstract:
  Animal brains evolved to optimize behavior in dynamically changing
environments, selecting actions that maximize future rewards. A large body of
experimental work indicates that such optimization changes the wiring of neural
circuits, appropriately mapping environmental input onto behavioral outputs. A
major unsolved scientific question is how optimal wiring adjustments, which
must target the connections responsible for rewards, can be accomplished when
the relation between sensory inputs, action taken, environmental context with
rewards is ambiguous. The computational problem of properly targeting cues,
contexts and actions that lead to reward is known as structural, contextual and
temporal credit assignment respectively. In this review, we survey prior
approaches to these three types of problems and advance the notion that the
brain's specialized neural architectures provide efficient solutions. Within
this framework, the thalamus with its cortical and basal ganglia interactions
serve as a systems-level solution to credit assignment. Specifically, we
propose that thalamocortical interaction is the locus of meta-learning where
the thalamus provides cortical control functions that parametrize the cortical
activity association space. By selecting among these control functions, the
basal ganglia hierarchically guide thalamocortical plasticity across two
timescales to enable meta-learning. The faster timescale establishes contextual
associations to enable rapid behavioral flexibility while the slower one
enables generalization to new contexts. Incorporating different thalamic
control functions under this framework clarifies how thalamocortical-basal
ganglia interactions may simultaneously solve the three credit assignment
problems.


Abstract:
  Media bias can lead to increased political polarization, and thus, the need
for automatic mitigation methods is growing. Existing mitigation work displays
articles from multiple news outlets to provide diverse news coverage, but
without neutralizing the bias inherent in each of the displayed articles.
Therefore, we propose a new task, a single neutralized article generation out
of multiple biased articles, to facilitate more efficient access to balanced
and unbiased information. In this paper, we compile a new dataset NeuWS, define
an automatic evaluation metric, and provide baselines and multiple analyses to
serve as a solid starting point for the proposed task. Lastly, we obtain a
human evaluation to demonstrate the alignment between our metric and human
judgment.


Abstract:
  In phylogenetics, it is important for the phylogenetic network model
parameters to be identifiable so that the evolutionary histories of a group of
species can be consistently inferred. However, as the complexity of the
phylogenetic network models grows, the identifiability of network models
becomes increasingly difficult to analyze. As an attempt to analyze the
identifiability of network models, we check whether two networks are
distinguishable. In this paper, we specifically study the distinguishability of
phylogenetic network models associated with level-2 networks. Using an
algebraic approach, namely using discrete Fourier transformation, we present
some results on the distinguishability of some level-2 networks, which
generalize earlier work on the distinguishability of level-1 networks. In
particular, we study simple and semisimple level-2 networks. Simple and
semisimple level-2 networks can be thought as generalizations of level-1 sunlet
and cycle networks, respectively. Moreover, we also compare the varieties
associated with semisimple level-2 and cycle networks.


Abstract:
  We demonstrate the roles of $D_{(s)}^\ast$ and their contributions in the
quasi-two-body decays $B_{(s)} \to D_{(s)} h h^\prime$ ($h, h^\prime = \{\pi,
K\}$) in the perturbative QCD approach, stemming from the quark flavour
changing $\bar{b} \to \bar{c} \, q_2 \, \bar{q}_1$ and $\bar{b} \to c \,
\bar{q_1} \, \bar{q_2}$ with $q_1, q_2 = \{s/d, u\}$. The main motivation of
this study is the measurements of significant derivations from the simple
phase-space model in the channels $B_{(s)} \to D_{(s)} h h^\prime$ at $B$
factories and LHC, which is now clarified as the Breit-Wigner-tail effects from
the corresponding intermediate resonant states $D_{(s)}^\ast$. We confirm that
these effect from $D^\ast$ is small ($\sim 5\%$) in the quasi-two-body $B_{(s)}
\to D \pi \pi (K)$ decaying channels, and predict the tiny ($< 1 \%$)
contributions from $D^\ast$ in the $B_{(s)} \to D_s K \pi (K)$ decaying
channels, our result for the $B_s \to D K \pi (K)$ decaying channels
contributed only from the Breit-Wigner-tail effect of $D_s^\ast$ is in
agreement with the current LHCb measurement. We recommend the Belle-II and the
LHCb collaborations to restudy the processes $B^+\to \bar D^{\ast 0}\pi^+(K^+)
\to D^-\pi^+\pi^+(K^+)$ to reveal the structure of $D^{\ast 0}$ and the strong
decay $D^{\ast 0} \to D^+ \pi^-$.


Abstract:
  The paper proposes a new method to determine a binary control signal for an
analog linear system such that the state, or some output, of the system follows
a given target trajectory. The method can also be used for digital-to-analog
conversion. The heart of the proposed method is a new binary-enforcing NUV
prior (normal with unknown variance). The resulting computations, for each
planning period, amount to iterating forward-backward Gaussian message passing
recursions (similar to Kalman smoothing), with a complexity (per iteration)
that is linear in the planning horizon. In consequence, the proposed method is
not limited to a short planning horizon.


Abstract:
  In this paper we prove a higher differentiability result for the solutions to
a class of obstacle problems in the form
  \begin{equation*} \label{obst-def0} \min\left\{\int_\Omega F(x,Dw) dx : w\in
\mathcal{K}_{\psi}(\Omega)\right\} \end{equation*} where $\psi\in
W^{1,p(x)}(\Omega)$ is a fixed function called obstacle and
$\mathcal{K}_{\psi}=\{w \in W^{1,p(x)}_{0}(\Omega)+u_0: w \ge \psi \,\,
\textnormal{a.e. in $\Omega$}\}$ is the class of the admissible functions, for
a suitable boundary value $ u_0 $. We deal with a convex integrand $F$ which
satisfies the $p(x)$-growth conditions
\begin{equation*}\label{growth}|\xi|^{p(x)}\le F(x,\xi)\le
C(1+|\xi|^{p(x)}),\quad p(x)>1 \end{equation*}


Abstract:
  The {\em override} operation $\sqcup$ is a natural one in computer science,
and has connections with other areas of mathematics such as hyperplane
arrangements. For arbitrary functions $f$ and $g$, $f\sqcup g$ is the function
with domain $dom(f)\cup dom(g)$ that agrees with $f$ on $dom(f)$ and with $g$
on $dom(g) \backslash dom(f)$. Jackson and the author have shown that there is
no finite axiomatisation of algebras of functions of signature $(\sqcup)$. But
adding operations (such as {\em update}) to this minimal signature can lead to
finite axiomatisations. For the functional signature $(\sqcup,\backslash)$
where $\backslash$ is set-theoretic difference, Cirulis has given a finite
equational axiomatisation as subtraction o-semilattices. Define $f\curlyvee
g=(f\sqcup g)\cap (g\sqcup f)$ for all functions $f$ and $g$; this is the
largest domain restriction of the binary relation $f\cup g$ that gives a
partial function. Now $f\cap g=f\backslash(f\backslash g)$ and $f\sqcup
g=f\curlyvee(f\curlyvee g)$ for all functions $f,g$, so the signatures
$(\curlyvee)$ and $(\sqcup,\cap)$ are both intermediate between $(\sqcup)$ and
$(\sqcup,\backslash)$ in expressive power. We show that each is finitely
axiomatised, with the former giving a proper quasivariety and the latter the
variety of associative distributive o-semilattices in the sense of Cirulis.


Abstract:
  ARM TrustZone is the de-facto hardware TEE implementation on mobile devices
like smartphones. As a vendor-centric TEE, TrustZone greatly overlooks the
strong protection demands and requirements from the App developers. Several
security solutions have been proposed to enable the TEE-assisted isolation in
the Normal World of ARM, attempting to balance the security and usability.
However, they are still not full-fledged in serving Apps' needs. In this paper,
we introduce LEAP, which is a lightweight App developer Centric TEE solution in
the Normal World. LEAP offers the auto DevOps tool to help developers to
prepare the codes running on it, enables isolated codes to execute in parallel
and access peripheral (e.g. mobile GPUs) with ease, and dynamically manage
system resources upon Apps' requests. We implement the LEAP prototype on the
off-the-shelf ARM platform without any hardware change. We perform the
comprehensive analyses and experiments to demonstrate that LEAP is efficient in
design, comprehensive in support, and convenient in adoption.


Abstract:
  Blindly decoding a signal requires estimating its unknown transmit
parameters, compensating for the wireless channel impairments, and identifying
the modulation type. While deep learning can solve complex problems, digital
signal processing (DSP) is interpretable and can be more computationally
efficient. To combine both, we propose the dual path network (DPN). It consists
of a signal path of DSP operations that recover the signal, and a feature path
of neural networks that estimate the unknown transmit parameters. By
interconnecting the paths over several recovery stages, later stages benefit
from the recovered signals and reuse all the previously extracted features. The
proposed design is demonstrated to provide 5% improvement in modulation
classification compared to alternative designs lacking either feature sharing
or access to recovered signals. The estimation results of DPN along with its
blind decoding performance are shown to outperform a blind signal processing
algorithm for BPSK and QPSK on a simulated dataset. An over-the-air
software-defined-radio capture was used to verify DPN results at high SNRs. DPN
design can process variable length inputs and is shown to outperform relying on
fixed length inputs with prediction averaging on longer signals by up to 15% in
modulation classification.


Abstract:
  There has been a significant recent progress in the field of Embodied AI with
researchers developing models and algorithms enabling embodied agents to
navigate and interact within completely unseen environments. In this paper, we
propose a new dataset and baseline models for the task of Rearrangement. We
particularly focus on the task of Room Rearrangement: an agent begins by
exploring a room and recording objects' initial configurations. We then remove
the agent and change the poses and states (e.g., open/closed) of some objects
in the room. The agent must restore the initial configurations of all objects
in the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement
settings involving 72 different object types in 120 scenes. Our experiments
show that solving this challenging interactive task that involves navigation
and object interaction is beyond the capabilities of the current
state-of-the-art techniques for embodied tasks and we are still very far from
achieving perfect performance on these types of tasks. The code and the dataset
are available at: https://ai2thor.allenai.org/rearrangement


Abstract:
  Self-supervised speech representation learning (speech SSL) has demonstrated
the benefit of scale in learning rich representations for Automatic Speech
Recognition (ASR) with limited paired data, such as wav2vec 2.0. We investigate
the existence of sparse subnetworks in pre-trained speech SSL models that
achieve even better low-resource ASR results. However, directly applying widely
adopted pruning methods such as the Lottery Ticket Hypothesis (LTH) is
suboptimal in the computational cost needed. Moreover, we show that the
discovered subnetworks yield minimal performance gain compared to the original
dense network. We present Prune-Adjust-Re-Prune (PARP), which discovers and
finetunes subnetworks for much better performance, while only requiring a
single downstream ASR finetuning run. PARP is inspired by our surprising
observation that subnetworks pruned for pre-training tasks need merely a slight
adjustment to achieve a sizeable performance boost in downstream ASR tasks.
Extensive experiments on low-resource ASR verify (1) sparse subnetworks exist
in mono-lingual/multi-lingual pre-trained speech SSL, and (2) the computational
advantage and performance gain of PARP over baseline pruning methods. In
particular, on the 10min Librispeech split without LM decoding, PARP discovers
subnetworks from wav2vec 2.0 with an absolute 10.9%/12.6% WER decrease compared
to the full model. We further demonstrate the effectiveness of PARP via:
cross-lingual pruning without any phone recognition degradation, the discovery
of a multi-lingual subnetwork for 10 spoken languages in 1 finetuning run, and
its applicability to pre-trained BERT/XLNet for natural language tasks.


Abstract:
  We report on time-resolved measurements of electron number density by
continuous-wave laser absorption in a low-energy nanosecond-scale
laser-produced spark in atmospheric pressure air. Laser absorption is a result
of free-free and bound-free electron excitation, with the absorption
coefficient modeled and evaluated using estimates of the time-variation in
electron temperature and probe laser absorption path length. Plasma electron
number densities are determined to be as high as $n_\text{e}=7\times10^{19}$
cm$^{-3}$, and decay to $1/e$ of their peak values over a period of about 50 ns
following plasma formation using a 20 mJ, 10 ns pulse width frequency-doubled
Nd:YAG laser. The measured plasma densities at later times are shown to be in
reasonable agreement with Stark broadening measurements of the
3s[$^5S{^o}$]-3p[$^5P$] electronic transition in atomic oxygen at 777 nm. This
study provides support for the use of such continuous wave laser absorption for
time resolved electron density measurements in low energy spark discharges in
air, provided that an estimate of the electron temperature and laser path
length can be made by accompanying diagnostics.


Abstract:
  Mader conjectured that for any tree $T$ of order $m$, every $k$-connected
graph $G$ with minimum degree at least $\lfloor\frac{3k}{2}\rfloor +m-1$
contains a subtree $T'\cong T$ such that $G-V(T')$ is $k$-connected. In this
paper, we give a characterization for a subgraph to contain an embedding of a
specified tree avoiding some vertex. As a corollary, we confirm Mader's
conjecture for $k\leq3$.


Abstract:
  We study the triply heavy baryons $\Omega_{QQQ}$ $(Q=c, b)$ in the QCD sum
rules by performing the first calculation of the next-to-leading order (NLO)
contribution to the perturbative QCD part of the correlation functions.
Compared with the leading order (LO) result, the NLO contribution is found to
be very important to the $\Omega_{QQQ}$. This is because the NLO not only
results in a large correction, but also reduces the parameter dependence,
making the Borel platform more distinct, especially for the $\Omega_{bbb}$ in
the $\overline{\rm{MS}}$ scheme, where the platform appears only at NLO but not
at LO. Particularly, owing to the inclusion of the NLO contribution, the
renormalization schemes ($\bar {MS}$ and On-Shell) dependence and the scale
dependence are significantly reduced. Consequently, after including the NLO
contribution to the perturbative part in the QCD sum rules, the masses are
estimated to be $4.53^{+0.26}_{-0.11}$ GeV for $\Omega_{ccc}$ and
$14.27^{+0.33}_{-0.32}$ GeV for $\Omega_{bbb}$, where the results are obtained
at $\mu=M_B$ with errors including those from the variation of the
renormalization scale $\mu$ in the range $(0.8-1.2) M_B$. A careful study of
the $\mu$ dependence in a wide range is further performed, which shows that the
LO results are very sensitive to the choice of $\mu$ whereas the NLO results
are considerably better. In addition to the $\mu=M_B$ result, a more stable
value, (4.75-4.80) GeV, for the $\Omega_{ccc}$ mass is found in the range of
$\mu=(1.2-2.0) M_B$, which should be viewed as a more relevant prediction in
our NLO approach because of $\mu$ dependence.


Abstract:
  Affordances are the possibilities of actions the environment offers to the
individual. Ordinary objects (hammer, knife) usually have many affordances
(grasping, pounding, cutting), and detecting these allow artificial agents to
understand what are their possibilities in the environment, with obvious
application in Robotics. Proposed benchmarks and state-of-the-art prediction
models for supervised affordance segmentation are usually modifications of
popular object segmentation models such as Mask R-CNN. We observe that
theoretically, these popular object segmentation methods should be sufficient
for detecting affordances masks. So we ask the question: is it necessary to
tailor new architectures to the problem of learning affordances? We show that
applying the out-of-the-box Mask R-CNN to the problem of affordances
segmentation outperforms the current state-of-the-art. We conclude that the
problem of supervised affordance segmentation is included in the problem of
object segmentation and argue that better benchmarks for affordance learning
should include action capacities.


Abstract:
  Supersymmetric circular Wilson loops in $\mathcal{N}=4$ Super-Yang-Mills
theory are discussed starting from their Gaussian matrix model representations.
Previous results on the generating functions of Wilson loops are reviewed and
extended to the more general case of two different loop contours, which is
necessary to discuss coincident loops with opposite orientations. A
combinatorial formula representing the connected correlators of multiply wound
Wilson loops in terms of the matrix model solution is derived. Two new results
are obtained on the expectation value of the circular Wilson loop, the
expansion of which into a series in $1/N$ and to all orders in the 't~Hooft
coupling $\lambda$ was derived by Drukker and Gross about twenty years ago. The
connected correlators of two multiply wound Wilson loops with arbitrary winding
numbers are calculated as a series in $1/N$. The coefficient functions are
derived not only as power series in $\lambda$, but also to all orders in
$\lambda$ by expressing them in terms of the coefficients of the Drukker and
Gross series. This provides an efficient way to calculate the $1/N$ series,
which can probably be generalized to higher-point correlators.


Abstract:
  We construct a Connes spectral triple or `Dirac operator' on the non-reduced
fuzzy sphere $C_\lambda[S^2]$ as realised using quantum Riemannian geometry
with a central quantum metric $g$ of Euclidean signature and its associated
quantum Levi-Civita connection. The Dirac operator is characterised uniquely up
to unitary equivalence within our quantum Riemannian geometric setting and an
assumption that the spinor bundle is trivial and rank 2 with a central basis.
The spectral triple has KO dimension 3 and in the case of the round metric,
essentially recovers a previous proposal motivated by rotational symmetry.


Abstract:
  We investigate modifications of a stochastic polymer picture through a shift
in the boundary between the system and an external environment. A conventional
bead-and-spring model serving as the coarse-graining model is given by the
Langevin equation for all the monomers subject to white noise. However,
stochastic motion for only a tagged monomer is observed to occur in the
presence of colored noise. The qualitative change in the observations arises
from the boundary shift decided by the observer. The Langevin dynamics analyses
interpret the colored noise as the emergence of the polymeric elastic force,
resulting in additional heat in the tagged monomer observation. Being
distinguished from coarse-graining based on scale separation, the projection of
comparable internal degrees of freedom is also discussed in light of the
fluctuation theorem and the stochastic polymer thermodynamics.


Abstract:
  We propose a model of the substructural logic of Bunched Implications (BI)
that is suitable for reasoning about quantum states. In our model, the
separating conjunction of BI describes separable quantum states. We develop a
program logic where pre- and post-conditions are BI formulas describing quantum
states -- the program logic can be seen as a counterpart of separation logic
for imperative quantum programs. We exercise the logic for proving the security
of quantum one-time pad and secret sharing, and we show how the program logic
can be used to discover a flaw in Google Cirq's tutorial on the Variational
Quantum Algorithm (VQA).


Abstract:
  The main purpose of this paper is to study generalized (self-) reciprocal
Appell polynomials, which play a certain role in connection with Faulhaber-type
polynomials. More precisely, we show for any Appell sequence when satisfying a
reflection relation that the Appell polynomials can be described by
Faulhaber-type polynomials, which arise from a quadratic variable substitution.
Furthermore, the coefficients of the latter polynomials are given by values of
derivatives of generalized reciprocal Appell polynomials. Subsequently, we show
some applications to the Bernoulli and Euler polynomials. In the context of
power sums the results transfer to the classical Faulhaber polynomials.