Abstract:
  In this second part of a two-part paper, we discuss numerical simulations of
a head-on merger of two non-spinning black holes. We resolve the fate of the
original two apparent horizons by showing that after intersecting, their world
tubes "turn around" and continue backwards in time. Using the method presented
in the first paper to locate these surfaces, we resolve several such world
tubes evolving and connecting through various bifurcations and annihilations.
This also draws a consistent picture of the full merger in terms of apparent
horizons, or more generally, marginally outer trapped surfaces (MOTSs). The
MOTS stability operator provides a natural mechanism to identify MOTSs which
should be thought of as black hole boundaries. These are the two initial ones
and the final remnant. All other MOTSs lie in the interior and are neither
stable nor inner trapped.


Abstract:
  We complete the task of optimal probabilistic coherence distillation
protocol, whose aim is to transform a general state into a set of n-level
maximally coherent states via strictly incoherent operations (SIO).
Specifically, we present the necessary and sufficient condition for the
transformation from a mixed state into a pure-state ensemble via SIO. With the
aid of this condition and the simplex algorithm, we can accomplish the
probabilistic distillation protocol by presenting an analytical expression of
the maximal average distillable coherence of a general state and constructing
the corresponding operation achieving this bound. Our protocol is a universal
protocol since it can be applied to any coherence measure.


Abstract:
  We study the transverse instability and dynamics of bright soliton stripes in
two-dimensional nonlocal nonlinear media. Using a multiscale perturbation
method, we derive analytically the first-order correction to the soliton shape,
which features an exponential growth in time -- a signature of the transverse
instability. The soliton's characteristic timescale associated with its
exponential growth,is found to depend on the square root of the nonlocality
parameter. This, in turn, highlights the nonlocality-induced suppression of the
transverse instability. Our analytical predictions are corroborated by direct
numerical simulations, with the analytical results being in good agreement with
the numerical ones.


Abstract:
  It is widely believed that range resolution, the ability to distinguish
between two closely situated targets, depends inversely on the bandwidth of the
transmitted radar signal. Here we demonstrate a different type of ranging
system, which possesses superior range resolution that is almost completely
free of bandwidth limitations. By sweeping over the coherence length of the
transmitted signal, the partially coherent radar experimentally demonstrates an
improvement of over an order of magnitude in resolving targets, compared to
standard coherent radars with the same bandwidth.. A theoretical framework is
developed to show that the resolution could be further improved without a
bound, revealing a tradeoff between bandwidth and sweep time. This concept
offers solutions to problems which require high range resolution and accuracy
but available bandwidth is limited, as is the case for the autonomous car
industry, optical imaging, and astronomy to name just few.


Abstract:
  We study families of faces for convex semi-algebraic sets via the normal
cycle which is a semi-algebraic set similar to the conormal variety in
projective duality theory. We propose a convex algebraic notion of a "patch" --
a term recently coined by Ciripoi, Kaihnsa, L\"ohne, and Sturmfels as a tool
for approximating the convex hull of a semi-algebraic set. We discuss geometric
consequences, both for the semi-algebraic and convex geometry of the families
of faces, as well as variations of our definition and their consequences.


Abstract:
  To capture spatial relationships and temporal dynamics in traffic data,
spatio-temporal models for traffic forecasting have drawn significant attention
in recent years. Most of the recent works employed graph neural networks(GNN)
with multiple layers to capture the spatial dependency. However, road junctions
with different hop-distance can carry distinct traffic information which should
be exploited separately but existing multi-layer GNNs are incompetent to
discriminate between their impact. Again, to capture the temporal
interrelationship, recurrent neural networks are common in state-of-the-art
approaches that often fail to capture long-range dependencies. Furthermore,
traffic data shows repeated patterns in a daily or weekly period which should
be addressed explicitly. To address these limitations, we have designed a
Simplified Spatio-temporal Traffic forecasting GNN(SST-GNN) that effectively
encodes the spatial dependency by separately aggregating different neighborhood
representations rather than with multiple layers and capture the temporal
dependency with a simple yet effective weighted spatio-temporal aggregation
mechanism. We capture the periodic traffic patterns by using a novel position
encoding scheme with historical and current data in two different models. With
extensive experimental analysis, we have shown that our model has significantly
outperformed the state-of-the-art models on three real-world traffic datasets
from the Performance Measurement System (PeMS).


Abstract:
  Neutron irradiation tends to promote disorder in ordered alloys through the
action of the thermal spikes that it generates, while simultaneously
introducing point defects and defect clusters. As they migrate, these point
defects will promote reordering of the alloys, acting against
irradiation-induced disordering. In this study, classical molecular dynamics
and a highly parallel accelerated sampling method are used to study the
reordering kinetics of Ni$_3$Al under the diffusion of self-interstitial atoms
(SIA). By monitoring the order parameter and potential energy from atomistic
simulations, we show that the SIA acts as a reordering agent in Ni$_3$Al. A
mean-field rate theory model of the interstitialcy-based reordering kinetics is
introduced, which reproduces simulation data and predicts reordering at
temperatures as low as 500 K.


Abstract:
  We present the concept of a Generalized Feedback Nash Equilibrium (GFNE) in
dynamic games, extending the Feedback Nash Equilibrium concept to games in
which players are subject to state and input constraints. We formalize
necessary and sufficient conditions for (local) GFNE solutions at the
trajectory level, which enable the development of efficient numerical methods
for their computation. Specifically, we propose a Newton-style method for
finding game trajectories which satisfy the necessary conditions, which can
then be checked against the sufficiency conditions. We show that the evaluation
of the necessary conditions in general requires computing a series of nested,
implicitly-defined derivatives, which quickly becomes intractable. To this end,
we introduce an approximation to the necessary conditions which is amenable to
efficient evaluation, and in turn, computation of solutions. We term the
solutions to the approximate necessary conditions Generalized Feedback Quasi
Nash Equilibria (GFQNE), and we introduce numerical methods for their
computation. In particular, we develop a Sequential Linear-Quadratic Game
approach, in which a locally approximate LQ game is solved at each iteration.
The development of this method relies on the ability to compute a GFNE to
inequality- and equality-constrained LQ games, and therefore specific methods
for the solution of these special cases are developed in detail. We demonstrate
the effectiveness of the proposed solution approach on a dynamic game arising
in an autonomous driving application.


Abstract:
  Price discrimination for maximizing expected profit is a well-studied concept
in economics and there are various methods that achieve the maximum given the
user type distribution and the budget constraints. In many applications,
particularly with regards to engineering and computing, it is often the case
than the user type distribution is unknown or not accurately known. In this
paper, we therefore propose and study a mathematical framework for price
discrimination with \emph{target} profits under the contract-theoretic model.
We first consider service providers with a given user type profile and
determine sufficient conditions for achieving a target profit. Our proof is
constructive in that it also provides a method to compute the quality-price tag
menu. Next we consider a dual scenario where the offered service qualities are
predetermined and describe an iterative method to obtain nominal demand values
that best match the qualities offered by the service provider while achieving a
target profit-user satisfaction margin. We also illustrate our methods with
design examples in both cases.


Abstract:
  The (1,2)-TSP is a special case of the TSP where each edge has cost either 1
or 2. In this paper we give a lower bound of $\frac{3}{2}$ for the
approximation ratio of the 2-Opt algorithm for the (1,2)-TSP. Moreover, we show
that the 3-Opt algorithm has an exact approximation ratio of $\frac{11}{8}$ for
the (1,2)-TSP. Furthermore, we introduce the 3-Opt++-algorithm, an improved
version of the 3-Opt algorithm for the (1-2)-TSP with an exact approximation
ratio of $\frac{4}{3}$.


Abstract:
  In this work, we study the extension of two variants of the facility location
problem (FL) to make them robust towards a few distantly located clients.
First, $k$-facility location problem ($k$FL), a common generalization of FL and
$k$ median problems, is a well studied problem in literature. In the second
variant, lower bounded facility location (LBFL), we are given a bound on the
minimum number of clients that an opened facility must serve. Lower bounds are
required in many applications like profitability in commerce and load balancing
in transportation problem. In both the cases, the cost of the solution may be
increased grossly by a few distantly located clients, called the outliers.
Thus, in this work, we extend $k$FL and LBFL to make them robust towards the
outliers. For $k$FL with outliers ($k$FLO) we present the first (constant)
factor approximation violating the cardinality requirement by +1. As a
by-product, we also obtain the first approximation for FLO based on
LP-rounding. For LBFLO, we present a tri-criteria solution with a trade-off
between the violations in lower bounds and the number of outliers. With a
violation of $1/2$ in lower bounds, we get a violation of $2$ in outliers.


Abstract:
  This paper describes the system developed by the NPU team for the 2020
personalized voice trigger challenge. Our submitted system consists of two
independently trained subsystems: a small footprint keyword spotting (KWS)
system and a speaker verification (SV) system. For the KWS system, a
multi-scale dilated temporal convolutional (MDTC) network is proposed to detect
wake-up word (WuW). For SV system, Write something here. The KWS predicts
posterior probabilities of whether an audio utterance contains WuW and
estimates the location of WuW at the same time. When the posterior probability
ofWuW reaches a predefined threshold, the identity information of triggered
segment is determined by the SV system. On evaluation dataset, our submitted
system obtains detection costs of 0.081and 0.091 in close talking and far-field
tasks, respectively.


Abstract:
  The concept of the hafnian first appeared in the works on quantum field
theory by E. R. Caianiello. However, it also has an important combinatorial
property: the hafnian of the adjacency matrix of an undirected weighted graph
is equal to the total sum of the weights of perfect matchings in this graph. In
general, the use of the hafnian is limited by the complexity of its
computation. In this paper, we present an efficient method for the exact
calculation of the hafnian of two-parameter matrices. In terms of graphs, we
count the total sum of the weights of perfect matchings in graphs whose edge
weights take only two values. This method is based on the formula expressing
the hafnian of a sum of two matrices through the product of the hafnians of
their submatrices. The necessary condition for the application of this method
is the possibility to count the number of k-edge matchings in some graphs. We
consider two special cases in detail using a Toeplitz matrix as the
two-parameter matrix. As an example, we propose a new interpretation of some of
the sequences from the On-Line Encyclopedia of Integer Sequences and then
provide new analytical formulas to count the number of certain linear chord
diagrams.


Abstract:
  Offensive language is pervasive in social media. Individuals frequently take
advantage of the perceived anonymity of computer-mediated communication, using
this to engage in behavior that many of them would not consider in real life.
The automatic identification of offensive content online is an important task
that has gained more attention in recent years. This task can be modeled as a
supervised classification problem in which systems are trained using a dataset
containing posts that are annotated with respect to the presence of some
form(s) of abusive or offensive content. The objective of this study is to
provide a description of a classification system built for SemEval-2019 Task 6:
OffensEval. This system classifies a tweet as either offensive or not offensive
(Sub-task A) and further classifies offensive tweets into categories (Sub-tasks
B \& C). We trained machine learning and deep learning models along with data
preprocessing and sampling techniques to come up with the best results. Models
discussed include Naive Bayes, SVM, Logistic Regression, Random Forest and
LSTM.


Abstract:
  Caustics are a striking phenomena in natural optics and hydrodynamics:
high-amplitude characteristic patterns that are singular in the short
wavelength limit. We use exact numerical and approximate semiclassical analytic
methods to study quantum versions of caustics that form in Fock space during
far-from-equilibrium dynamics following a quench in the Bose Hubbard dimer and
trimer models. Due to interparticle interactions, both models are nonlinear and
the trimer is also nonintegrable. The caustics exhibit a hierarchy of
morphologies described by catastrophe theory that are stable against
perturbations and hence occur generically. The dimer case gives rise to
discretized versions of folds and cusps which are the simplest two of Thom's
catastrophes. The extra dimension available in the trimer case allows for
higher catastrophes including the codimension-3 hyperbolic umbilic and elliptic
umbilic catastrophes which we argue are organized by, and projections of, an
8-dimensional corank-2 catastrophe known as $X_9$. These results indicate a
hitherto unrecognized form of universality in quantum many-body dynamics
organized by singularities.


Abstract:
  Quantifying the risk of global warming exceeding critical targets such as 2.0
K requires reliable projections of uncertainty as well as best estimates of
Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST
projections are often calculated heuristically and have several potential
shortcomings. In particular, the uncertainty bands shown in IPCC plume
projections of GMST are based on the distribution of GMST anomalies from
climate model runs and so are strongly determined by model characteristics with
little influence from observations of the real-world. Physically motivated
time-series approaches are proposed based on fitting energy balance models
(EBMs) to climate model outputs and observations in order to constrain future
projections. It is shown that EBMs fitted to one forcing scenario will not
produce reliable projections when different forcing scenarios are applied. The
errors in the EBM projections can be interpreted as arising due to a
discrepancy in the effective forcing felt by the model. A simple time-series
approach to correcting the projections is proposed based on learning the
evolution of the forcing discrepancy so that it can be projected into the
future. These approaches give reliable projections of GMST when tested in a
perfect model setting, and when applied to observations lead to well
constrained projections with lower mean warming and narrower projection bands
than previous estimates. Despite the reduced uncertainty, the lower warming
leads to a greatly reduced probability of exceeding the 2.0 K warming target.


Abstract:
  Magnetic field may distort neutron stars (NSs), but the effect has not been
robustly tested through gravitational-wave observation yet due to the absence
of a fast rotating Galactic magnetar. The central objects of Gamma-ray bursts
(GRBs) could be millisecond magnetars. Under the magnetar scenario on the X-ray
plateaus of GRB afterglows,the spindown evolution modulated by the
gravitational-wave radiation may be inferred from some special samples, so that
the magnetically-induced distorting can be further estimated. According to two
samples, GRB 060807 and GRB 070521, we found that the correlation between the
effective ellipticity, $\varepsilon_{\rm B,eff}$, and effective dipole magnetic
field strength on a neutron star (NS) surface, $B_{\rm eff}$, is
$\varepsilon_{\rm B,eff}\sim 10^{-4} (\frac{B_{\rm eff}}{10^{14}\;\rm G})^{2}$.
This result demands that $B_{\rm eff}\sim 0.01 B_{\rm t}$ with $B_{\rm t}$
being the internal toroidal magnetic field strength of NSs. We suggested that
the torque generated during few unsymmetrical massive-star collapses may induce
differential rotations in proto-NSs to amplify the internal toroidal fields.


Abstract:
  Neutron star (NS) is a unique astronomical compact object where the four
fundamental interactions have been revealed from the observation and studied in
different ways. While the macroscopic properties of NS like mass and radius can
be determined within the General Relativity using a realistic equation of state
(EOS) of NS matter, such an EOS is usually generated by a nuclear structure
model like, e.g., the nuclear mean-field approach to asymmetric nuclear matter.
Given the radius of NS extended to above 10 km and its mass up to twice the
solar mass, NS is expected to be tidally deformed when it is embedded in a
strong tidal field. Such a tidal effect was confirmed unambiguously in the
gravitation wave signals detected recently by the LIGO and Virgo laser
interferometers from GW170817, the first ever direct observation of a binary NS
merger. A nonrelativistic mean-field study is carried out in the present work
within the Hartree-Fock formalism to construct the EOS of NS matter, which is
then used to determine the tidal deformability, gravitational mass, and radius
of NS. The mean-field results are compared with the constraints imposed for
these quantities by the global analysis of the observed GW170817 data, and a
strong impact by the incompressibility of nuclear matter on the hydrostatic
configuration of NS is shown.


Abstract:
  We implement stacked denoising autoencoders, a class of neural networks that
are capable of learning powerful representations of high dimensional data. We
describe stochastic gradient descent for unsupervised training of autoencoders,
as well as a novel genetic algorithm based approach that makes use of gradient
information. We analyze the performance of both optimization algorithms and
also the representation learning ability of the autoencoder when it is trained
on standard image classification datasets.


Abstract:
  The application of the energy-efficient thermal and energy management
mechanism in Geothermal Heat Pumps (GHPs) is indispensable to reduce the
overall energy consumption and carbon emission across the building sector.
Besides, in the Virtual Power Plant (VPP) system, the demand response of
clustered GHP systems can improve the operating flexibility of the power grid.
This paper presents an integration and operation framework of GHPs in the
distribution network, applying a layered communication and optimization method
to coordinate multiple clustered GHPs in a community. In the proposed
hierarchical operation scheme, the operator of regional GHPs collects the
thermal zone information and the disturbance prediction of buildings in a short
time granularity, predicts the energy demand, and transmits the information to
an aggregator. Using a novel linearized optimal power flow model, the
aggregator coordinates and aggregates load resources of GHP systems in the
distribution network. In this way, GHP systems with thermal and energy
management mechanisms can be applied to achieve the demand response in the VPP
and offer more energy flexibility to the community.


Abstract:
  We prove that a closed $n$-manifold $M$ with positive scalar curvature and
abelian fundamental group admits a finite covering $M'$ which is strongly
inessential. The latter means that a classifying map $u:M'\to K(\pi_1(M'),1)$
can be deformed to the $(n-2)$-skeleton. This is proven for all $n$-manifolds
with the exception of 4-manifolds with spin universal coverings.


Abstract:
  The article provides a brief overview of existing models describing guided
flux motion. The first model was proposed by Niessen et al. This model works in
the single-vortex approximation and provides qualitative explanation of the
effect, but in most cases it gives an overestimated guiding angle. The
stochastic model explains the experimentally observed effect of decreasing the
guiding angle, the so-called slipping effect, by the influence of thermal
fluctuations. However, the performed estimates show that thermal fluctuations
should not be so significant. An alternative guiding model is the anisotropic
pinning model. This model works in the critical state approximation, and the
slipping effect is the result of the combined action of anisotropic pinning and
vortex interaction. The predictions of all three models are verified by the
example of a superconducting Nb-Ti tape containing about 6 vol. % {/alpha}-Ti,
which acts as strong pinning centers. Cutting samples at different angles to
rolling allows one to control the direction of the driving force. It was found
that when the standard criterion of the electric field is reached, the guiding
angle varies significantly at different places along the sample, which is
explained by the plastic mode of the vortex matter motion. In this case, the
anisotropic pinning model semi-quantitatively predicts the direction angle
averaged over the sample length. With an increase in the driving force, the
vortex matter motion passes into an elastic mode, and the guiding angle becomes
uniform along the sample.


Abstract:
  Classical model-based imaging methods for ultrasound elasticity inverse
problem require prior constraints about the underlying elasticity patterns,
while finding the appropriate hand-crafted prior for each tissue type is a
challenge. In contrast, standard data-driven methods count solely on supervised
learning on the training data pairs leading to massive network parameters for
unnecessary physical model relearning which might not be consistent with the
governing physical models of the imaging system. Fusing the physical forward
model and noise statistics with data-adaptive priors leads to a united
reconstruction framework that guarantees the learned reconstruction agrees with
the physical models while coping with the limited training data. In this paper,
we propose a new methodology for estimating the elasticity image by solving a
regularized optimization problem which benefits from the physics-based modeling
via a data-fidelity term and adversarially learned priors via a regularization
term. In this method, the regularizer is trained based on the Wasserstein
Generative Adversarial Network (WGAN) objective function which tries to
distinguish the distribution of clean and noisy images. Leveraging such an
adversarial regularizer for parameterizing the distribution of latent images
and using gradient descent (GD) for solving the corresponding regularized
optimization task leads to stability and convergence of the reconstruction
compared to pixel-wise supervised learning schemes. Our simulation results
verify the effectiveness and robustness of the proposed methodology with
limited training datasets.


Abstract:
  Transformer is a powerful tool for many natural language tasks which is based
on self-attention, a mechanism that encodes the dependence of other tokens on
each specific token, but the computation of self-attention is a bottleneck due
to its quadratic time complexity. There are various approaches to reduce the
time complexity and approximation of matrix is one such. In Nystr\"omformer,
the authors used Nystr\"om based method for approximation of softmax. The
Nystr\"om method generates a fast approximation to any large-scale symmetric
positive semidefinite (SPSD) matrix using only a few columns of the SPSD
matrix. However, since the Nystr\"om approximation is low-rank when the
spectrum of the SPSD matrix decays slowly, the Nystr\"om approximation is of
low accuracy. Here an alternative method is proposed for approximation which
has a much stronger error bound than the Nystr\"om method. The time complexity
of this same as Nystr\"omformer which is $O\left({n}\right)$.


Abstract:
  A thermodynamically consistent model for soft deformable viscoelastic magnets
is formulated in actual space (Eulerian) coordinates. The possibility of a
ferro-paramagnetic-type (or ferri-antiferromagnetic) transition exploiting
Landau-Lifshitz phase transition theory as well as mechanical melting or
solidification is considered, being motivated and applicable to paleomagnetism
(involving both thermo- and isothermal and viscous remanent magnetization) in
rocks in Earth's crust and to rock-magma transition. The temperature-dependent
Jeffreys rheology in the deviatoric part combined with the Kelvin-Voigt
rheology in the spherical (volumetric) part is used. The energy balance and the
entropy imbalance behind the model are demonstrated, and its analysis is
performed by time discretization, proving existence of weak solutions.


Abstract:
  This paper presents an automatic merging algorithm for autonomous driving
vehicles, which decouples the specific motion planning problem into a
Dual-Layer Automatic Merge Planning (DL_AMP) and a Descent-Based Trajectory
Optimization (DBTO). This work leads to great improvements in finding the best
merge opportunity, lateral and longitudinal merge planning and control,
trajectory postprocessing and driving comfort.


Abstract:
  In imperfect-information games, subgame solving is significantly more
challenging than in perfect-information games, but in the last few years, such
techniques have been developed. They were the key ingredient to the milestone
of superhuman play in no-limit Texas hold'em poker. Current subgame-solving
techniques analyze the entire common-knowledge closure of the player's current
information set, that is, the smallest set of nodes within which it is common
knowledge that the current node lies. While this is acceptable in games like
poker where the common-knowledge closure is relatively small, many practical
games have more complex information structure, which renders the
common-knowledge closure impractically large to enumerate or even reasonably
approximate. We introduce an approach that overcomes this obstacle, by instead
working with only low-order knowledge. Our approach allows an agent, upon
arriving at an infoset, to basically prune any node that is no longer
reachable, thereby massively reducing the game tree size relative to the
common-knowledge subgame. We prove that, as is, our approach can increase
exploitability compared to the blueprint strategy. However, we develop three
avenues by which safety can be guaranteed. Even without the safety-guaranteeing
additions, experiments on medium-sized games show that our approach always
reduced exploitability in practical games even when applied at every infoset,
and a depth-limited version of it led to -- to our knowledge -- the first
strong AI for the challenge problem dark chess.


Abstract:
  The spin-orbit coupling field, an atomic magnetic field inside a Kramer's
system, or discrete symmetries can create a topological torus in the Brillouin
Zone and provide protected edge or surface states, which can contain
relativistic fermions, namely, Dirac and Weyl Fermions. The topology-protected
helical edge or surface states and the bulk electronic energy band define
different quantum or topological phases of matters, offering an excellent
prospect for some unique device applications. Device applications of the
quantum materials rely primarily on understanding the topological properties,
their mutual conversion processes under different external stimuli, and the
physical system for achieving the phase conversion. There have been tremendous
efforts in finding new topological materials with exotic topological phases.
However, the application of the topological properties in devices is still
limited due to the slow progress in developing the physical structures for
controlling the topological phase conversions. Such control systems often
require extreme tuning conditions or the fabrication of complex multi-layered
topological structures. This review article highlights the details of the
topological phases, their conversion processes, along with their potential
physical systems, and the prospective application fields. A general overview of
the critical factors for topological phases and the materials properties are
further discussed to provide the necessary background for the following
sections.


Abstract:
  We consider thermodynamically consistent autonomous Markov jump processes
displaying a macroscopic limit in which the logarithm of the probability
distribution is proportional to a scale-independent rate function (i.e., a
large deviations principle is satisfied). In order to provide an explicit
expression for the probability distribution valid away from equilibrium, we
propose a linear response theory performed at the level of the rate function.
We show that the first order non-equilibrium contribution to the steady state
rate function, $g(x)$, satisfies $u(x)\cdot \nabla g(x) = -\beta \dot W(x)$
where the vector field $u(x)$ defines the macroscopic deterministic dynamics,
and the scalar field $\dot W(x)$ equals the rate at which work is performed on
the system in a given state $x$. This equation provides a practical way to
determine $g(x)$, significantly outperforms standard linear response theory
applied at the level of the probability distribution, and approximates the rate
function surprisingly well in some far-from-equilibrium conditions. The method
applies to a wealth of physical and chemical systems, that we exemplify by two
analytically tractable models - an electrical circuit and an autocatalytic
chemical reaction network - both undergoing a non-equilibrium transition from a
monostable phase to a bistable phase. Our approach can be easily generalized to
transient probabilities and non-autonomous dynamics. Moreover, its recursive
application generates a virtual flow in the probability space which allows to
determine the steady state rate function arbitrarily far from equilibrium.


Abstract:
  Globular clusters (GCs) are bright objects that span a wide range of
galactocentric distances, and are thus probes of the structure of dark matter
(DM) haloes. In this work, we explore whether the projected radial profiles of
GCs can be used to infer the structural properties of their host DM haloes. We
use the simulated GC populations in a sample of 166 central galaxies from the
$(34.4~\rm cMpc)^3$ periodic volume of the E-MOSAICS project. We find that more
massive galaxies host stellar and GC populations with shallower density
profiles that are more radially extended. In addition, the metal-poor GC
subpopulations tend to have shallower and more extended profiles than the
metal-rich subsamples, which we relate to the preferentially accreted origin of
the metal-poor GCs. We find strong correlations between the slopes and
effective radii of the radial profiles of the GC populations and the structural
properties of the DM haloes, such as their power-law slopes, scale radii, and
concentration parameters. Accounting for a dependence on the galaxy stellar
mass decreases the scatter of the two-dimensional relations. This suggests that
the projected number counts of GCs, combined with their galaxy mass, trace the
density profile of the DM halo of their host galaxy. When applied to
extragalactic GC systems, we recover the scale radii and the extent of the DM
haloes of a sample of ETGs with uncertainties smaller than $0.2~\rm dex$. Thus,
extragalactic GC systems provide a novel avenue to explore the structure of DM
haloes beyond the Local Group.


Abstract:
  Architecture information is vital for Open Source Software (OSS) development,
and mailing list is one of the widely used channels for developers to share and
communicate architecture information. This work investigates the nature of
architecture information communication (i.e., why, who, when, and what) by OSS
developers via developer mailing lists. We employed a multiple case study
approach to extract and analyze the architecture information communication from
the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during
their development life-cycle of over 18 years. Our main findings are: (a)
architecture negotiation and interpretation are the two main reasons (i.e.,
why) of architecture communication; (b) the amount of architecture information
communicated in developer mailing lists decreases after the first stable
release (i.e., when); (c) architecture communications centered around a few
core developers (i.e., who); (d) and the most frequently communicated
architecture elements (i.e., what) are Architecture Rationale and Architecture
Model. There are a few similarities of architecture communication between the
two OSS projects. Such similarities point to how OSS developers naturally
gravitate towards the four aspects of architecture communication in OSS
development.


Abstract:
  Magnetic Resonance Imaging (MRI) is a principal diagnostic approach used in
the field of radiology to create images of the anatomical and physiological
structure of patients. MRI is the prevalent medical imaging practice to find
abnormalities in soft tissues. Traditionally they are analyzed by a radiologist
to detect abnormalities in soft tissues, especially the brain. The process of
interpreting a massive volume of patient's MRI is laborious. Hence, the use of
Machine Learning methodologies can aid in detecting abnormalities in soft
tissues with considerable accuracy. In this research, we have curated a novel
dataset and developed a framework that uses Deep Transfer Learning to perform a
multi-classification of tumors in the brain MRI images. In this paper, we
adopted the Deep Residual Convolutional Neural Network (ResNet50) architecture
for the experiments along with discriminative learning techniques to train the
model. Using the novel dataset and two publicly available MRI brain datasets,
this proposed approach attained a classification accuracy of 86.40% on the
curated dataset, 93.80% on the Harvard Whole Brain Atlas dataset, and 97.05%
accuracy on the School of Biomedical Engineering dataset. Results of our
experiments significantly demonstrate our proposed framework for transfer
learning is a potential and effective method for brain tumor
multi-classification tasks.


Abstract:
  We present a three-periodic, chiral, tensegrity structure and demonstrate
that it is auxetic. Our tensegrity structure is constructed using the chiral
symmetry {\Pi}+ cylinder packing, transforming the cylinders themselves to the
elastic elements and cylinder contacts to incompressible rods. The resulting
structure displays local re-entrant geometry at its vertices, and is shown to
be auxetic when modelled as an equilibrium configuration of spatial constraints
subject to a quasi-static deformation. When the tensegrity structure is
subsequently modelled as a lattice material with elastic elements, the auxetic
behaviour is again confirmed through finite element modelling as well as
demonstrated with a 3d printed example. The cubic symmetry of the structure
means that the behaviour is independent of the chosen stretching direction and
the auxetic behaviour is observed in both perpendicular directions. This
structure could be the simplest three-dimensional analogue to the
two-dimensional re-entrant honeycomb. This, alongside the chirality of the
structure, make it an interesting design target for multifunctional materials.


Abstract:
  We demonstrate the DC-Kerr effect in PECVD Silicon-rich Nitride (SRN) and use
it to demonstrate a third order nonlinear susceptibility, \c{hi}^((3)), as high
as (6 +/- 0.58)x10-19 m2/v2. We employ spectral shift versus applied voltage
measurements in a racetrack ring resonator as a tool by which to characterize
the nonlinear susceptibilities of these films. In doing so we demonstrate a
\c{hi}^((3)) larger than that of silicon and argue that PECVD SRN can provide a
versatile platform for employing optical phase-shifters while maintain a low
thermal budget using a deposition technique readily available in CMOS process
flows.


Abstract:
  Entity Linking is one of the essential tasks of information extraction and
natural language understanding. Entity linking mainly consists of two tasks:
recognition and disambiguation of named entities. Most studies address these
two tasks separately or focus only on one of them. Moreover, most of the
state-of-the -art entity linking algorithms are either supervised, which have
poor performance in the absence of annotated corpora or language-dependent,
which are not appropriate for multi-lingual applications. In this paper, we
introduce an Unsupervised Language-Independent Entity Disambiguation (ULIED),
which utilizes a novel approach to disambiguate and link named entities.
Evaluation of ULIED on different English entity linking datasets as well as the
only available Persian dataset illustrates that ULIED in most of the cases
outperforms the state-of-the-art unsupervised multi-lingual approaches.


Abstract:
  Let $K$ be a local field with residue characteristic $p$ and let $L/K$ be a
totally ramified extension of degree $p^k$. In this paper we show that if $L/K$
has only two distinct indices of inseparability then there exists a uniformizer
$\pi_L$ for $L$ whose minimum polynomial over $K$ has at most three terms. This
leads to an explicit classification of extensions with two indices of
inseparability. Our classification extends work of Amano, who considered the
case $k=1$.


Abstract:
  One of the most popular approaches being pursued to achieve a quantum
advantage with practical hardware are superconducting circuit devices. Although
significant progress has been made over the previous two decades, substantial
engineering efforts are required to scale these devices so they can be used to
solve many problems of interest. Unfortunately, much of this exciting field is
described using technical jargon and concepts from physics that are unfamiliar
to a classically trained electromagnetic engineer. As a result, this work is
often difficult for engineers to become engaged in. We hope to lower the
barrier to this field by providing an accessible review of one of the most
prevalently used quantum bits (qubits) in superconducting circuit systems, the
transmon qubit. Most of the physics of these systems can be understood
intuitively with only some background in quantum mechanics. As a result, we
avoid invoking quantum mechanical concepts except where it is necessary to ease
the transition between details in this work and those that would be encountered
in the literature. We believe this leads to a gentler introduction to this
fascinating field, and hope that more researchers from the classical
electromagnetic community become engaged in this area in the future.


Abstract:
  Determining the border between ergodic and localized behavior is of central
interest for interacting many-body systems. We consider here the recently very
popular spin-chain model that is periodically excited. A convenient description
of such a many-body system is achieved by the dual operator that evolves the
system in contrast to the time-evolution operator not in time but in particle
direction. We identify in this paper the largest eigenvalue of a function based
on the dual operator as a convenient tool to determine if the system shows
ergodic or many-body localized features. By perturbation theory in the vicinity
of the noninteracting system we explain analytically the eigenvalue structure
and compare it with numerics in [P. Braun, D. Waltner, M. Akila, B. Gutkin, T.
Guhr, Phys. Rev. E $\bf{101}$, 052201 (2020)] for small times. Furthermore we
identify a quantity that allows based on extensive large-time numerical
computations of the spectral form factor to distinguish between localized and
ergodic system features and to determine the Thouless time, i.e. the transition
time between these regimes in the thermodynamic limit.


Abstract:
  Rerandomization discards assignments with covariates unbalanced in the
treatment and control groups to improve the estimation and inference
efficiency. However, the acceptance-rejection sampling method used by
rerandomization is computationally inefficient. As a result, it is
time-consuming for classical rerandomization to draw numerous independent
assignments, which are necessary for constructing Fisher randomization tests.
To address this problem, we propose a pair-switching rerandomization method to
draw balanced assignments much efficiently. We show that the
difference-in-means estimator is unbiased for the average treatment effect and
the Fisher randomization tests are valid under pair-switching rerandomization.
In addition, our method is applicable in both non-sequentially and sequentially
randomized experiments. We conduct comprehensive simulation studies to compare
the finite-sample performances of the proposed method and classical
rerandomization. Simulation results indicate that pair-switching
rerandomization leads to comparable power of Fisher randomization tests and is
4-18 times faster than classical rerandomization. Finally, we apply the
pair-switching rerandomization method to analyze two clinical trial data sets,
both demonstrating the advantages of our method.


Abstract:
  The Leggett-Garg inequality (LGI) distinguishes nonmacrorealistic channels
from macrorealistic ones by constraining the experimental outcomes of the
underlying system. In this work, we propose a class of channels which,
initially, cannot violate the LGI (in the form of the temporal Bell inequality)
but can violate it after the application of stochastic pre- and post-
operations (SPPOs). As a proof-of-principle experiment, we demonstrate the
stochastic pre- and post- operations in an amplitude-damping channel with
photonic qubits. We denote the above phenomenon as hidden nonmacrorealistic
channels. We also discuss the relationship between this hidden
nonmacrorealistic channels (in terms of the temporal Clauser-Horne-Shimony-Holt
(CHSH) inequality) and the strongly nonlocality-breaking channel, which breaks
the hidden spatial CHSH nonlocality for arbitrary states. In general, if the
channel satisfies hidden nonmacrorealism, it is not a strongly CHSH
nonlocality-breaking channel.


Abstract:
  A practical application of universal wall scalings is near-wall turbulence
modeling. In this paper, we exploit temperature's semi-local scaling [Patel,
Boersma, and Pecnik, {Scalar statistics in variable property turbulent channel
flows}, Phys. Rev. Fluids, 2017, 2(8), 084604] and derive an eddy conductivity
closure for wall-modeled large-eddy simulation of high-speed flows. We show
that while the semi-local scaling does not collapse high-speed direct numerical
simulation (DNS) data, the resulting eddy conductivity and the wall model work
fairly well. The paper attempts to answer the following outstanding question:
why the semi-local scaling fails but the resulting eddy conductivity works
well. We conduct DNSs of Couette flows at Mach numbers from $M=1.4$ to 6. We
add a source term in the energy equation to get a cold, a close-to-adiabatic
wall, and a hot wall. Detailed analysis of the flows' energy budgets shows that
aerodynamic heating is the answer to our question: aerodynamic heating is not
accounted for in Patel et al.'s semi-local scaling but is modeled in the
equilibrium wall model. We incorporate aerodynamic heating in semi-local
scaling and show that the new scaling successfully collapses the high-speed DNS
data. We also show that incorporating aerodynamic heating or not, the
semi-local scaling gives rise to the exact same eddy conductivity, thereby
answering the outstanding question.


Abstract:
  State-of-the-art deep neural networks require large-scale labeled training
data that is often expensive to obtain or not available for many tasks. Weak
supervision in the form of domain-specific rules has been shown to be useful in
such settings to automatically generate weakly labeled training data. However,
learning with weak rules is challenging due to their inherent heuristic and
noisy nature. An additional challenge is rule coverage and overlap, where prior
work on weak supervision only considers instances that are covered by weak
rules, thus leaving valuable unlabeled data behind.
  In this work, we develop a weak supervision framework (ASTRA) that leverages
all the available data for a given task. To this end, we leverage task-specific
unlabeled data through self-training with a model (student) that considers
contextualized representations and predicts pseudo-labels for instances that
may not be covered by weak rules. We further develop a rule attention network
(teacher) that learns how to aggregate student pseudo-labels with weak rule
labels, conditioned on their fidelity and the underlying context of an
instance. Finally, we construct a semi-supervised learning objective for
end-to-end training with unlabeled data, domain-specific rules, and a small
amount of labeled data. Extensive experiments on six benchmark datasets for
text classification demonstrate the effectiveness of our approach with
significant improvements over state-of-the-art baselines.


Abstract:
  Real-time data analytics systems such as SAP HANA, MemSQL, and IBM Wildfire
employ hybrid data layouts, in which data are stored in different formats
throughout their lifecycle. Recent data are stored in a row-oriented format to
serve OLTP workloads and support high data rates, while older data are
transformed to a column-oriented format for OLAP access patterns. We observe
that a Log-Structured Merge (LSM) Tree is a natural fit for a lifecycle-aware
storage engine due to its high write throughput and level-oriented structure,
in which records propagate from one level to the next over time. To build a
lifecycle-aware storage engine using an LSM-Tree, we make a crucial
modification to allow different data layouts in different levels, ranging from
purely row-oriented to purely column-oriented, leading to a Real-Time LSM-Tree.
We give a cost model and an algorithm to design a Real-Time LSM-Tree that is
suitable for a given workload, followed by an experimental evaluation of LASER
- a prototype implementation of our idea built on top of the RocksDB key-value
store. In our evaluation, LASER is almost 5x faster than Postgres (a pure
row-store) and two orders of magnitude faster than MonetDB (a pure
column-store) for real-time data analytics workloads.


Abstract:
  Over the past several years, in order to solve the problem of malicious abuse
of facial manipulation technology, face manipulation detection technology has
obtained considerable attention and achieved remarkable progress. However, most
existing methods have very impoverished generalization ability and robustness.
In this paper, we propose a novel method for face manipulation detection, which
can improve the generalization ability and robustness by bag-of-local-feature.
Specifically, we extend Transformers using bag-of-feature approach to encode
inter-patch relationships, allowing it to learn local forgery features without
any explicit supervision. Extensive experiments demonstrate that our method can
outperform competing state-of-the-art methods on FaceForensics++, Celeb-DF and
DeeperForensics-1.0 datasets.


Abstract:
  The infinite-dimensional Iwahori--Hecke algebras $\mathcal{H}_\infty(q)$ are
direct limits of the usual finite-dimensional Iwahori--Hecke algebras. They
arise in a natural way as convolution algebras of bi-invariant functions on
groups $\mathrm{GLB}(\mathbb{F}_q)$ of infinite-dimensional matrices over
finite-fields having only finite number of non-zero matrix elements under the
diagonal. In 1988 Vershik and Kerov classified all indecomposable positive
traces on $\mathcal{H}_\infty(q)$. Any such trace generates a representation of
the double $\mathcal{H}_\infty(q)\otimes \mathcal{H}_\infty(q)$ and of the
double $\mathrm{GLB}(\mathbb{F}_q)\times \mathrm{GLB}(\mathbb{F}_q)$. We
present constructions of such representations; the traces are some
distinguished matrix elements. We also obtain some (simple) general statements
on relations between unitary representations of groups and representations of
convolution algebras of measures bi-invariant with respect to compact
subgroups.


Abstract:
  In one-dimensional, heterogeneous systems, the whole traffic dynamics depend
strongly on the behavior of the leading vehicle. This result holds for a class
of vehicular traffic models satisfying the following properties. The
interactions are unidirectional. The dynamics of the particles maximize the
velocity or reduces the gap between particles. The particles are hard. We use
this result to show a link between traffic models and graphs theory with the
consequence that as driving styles spread through social contagion and appear
randomly, the inhomogeneities of the system becomes dynamical, or
\textit{annealed}, toward specific regions in the space of parameters.
Interpreting parameters as entries of vectors defined in the parameters space
appear analogies between the evolutionary dynamics of these systems and
asymptotic behaviors of the \textit {n-vector model}. When the time-scale ratio
of imitation to the mutation processes, $ \tau_i / \tau_m $, is small an
organized state where "orientation" corresponds to the set of parameters of the
slowest strategies is favored, and if this ratio is big an unorganized state
without a preferential orientation is favored.


Abstract:
  Industrial processes produce a considerable volume of data and thus
information. Whether it is structured sensory data or semi- to unstructured
textual data, the knowledge that can be derived from it is critical to the
sustainable development of the industrial process. A key challenge of this
sustainability is the intelligent management of the generated data, as well as
the knowledge extracted from it, in order to utilize this knowledge for
improving future procedures. This challenge is a result of the tailored
documentation methods and domain-specific requirements, which include the need
for quick visibility of the documented knowledge. In this paper, we utilize the
expert knowledge documented in chip-design failure reports in supporting user
access to information that is relevant to a current chip design. Unstructured,
free, textual data in previous failure documentations provides a valuable
source of lessons-learned, which expert design-engineers have experienced,
solved and documented. To achieve a sustainable utilization of knowledge within
the company, not only the inherent knowledge has to be mined from unstructured
textual data, but also the relations between the lessons-learned, uncovering
potentially unknown links. In this research, a knowledge graph is constructed,
in order to represent and use the interconnections between reported design
failures. A search engine is developed and applied onto the graph to answer
queries. In contrast to mere keyword-based searching, the searchability of the
knowledge graph offers enhanced search results beyond direct matches and acts
as a mean for generating explainable results and result recommendations.
Results are provided to the design engineer through an interactive search
interface, in which, the feedback from the user is used to further optimize
relations for future iterations of the knowledge graph.


Abstract:
  The creation of unstable heavy particles at the Large Hadron Collider is the
most direct way to address some of the deepest open questions in physics.
Collisions typically produce variable-size sets of observed particles which
have inherent ambiguities complicating the assignment of observed particles to
the decay products of the heavy particles. Current strategies for tackling
these challenges in the physics community ignore the physical symmetries of the
decay products and consider all possible assignment permutations and do not
scale to complex configurations. Attention based deep learning methods for
sequence modelling have achieved state-of-the-art performance in natural
language processing, but they lack built-in mechanisms to deal with the unique
symmetries found in physical set-assignment problems. We introduce a novel
method for constructing symmetry-preserving attention networks which reflect
the problem's natural invariances to efficiently find assignments without
evaluating all permutations. This general approach is applicable to arbitrarily
complex configurations and significantly outperforms current methods, improving
reconstruction efficiency between 19\% - 35\% on typical benchmark problems
while decreasing inference time by two to five orders of magnitude on the most
complex events, making many important and previously intractable cases
tractable.
  A full code repository containing a general library, the specific
configuration used, and a complete dataset release, are avaiable at
https://github.com/Alexanders101/SPANet


Abstract:
  Spatial and channel attentions, modelling the semantic interdependencies in
spatial and channel dimensions respectively, have recently been widely used for
semantic segmentation. However, computing spatial and channel attentions
separately sometimes causes errors, especially for those difficult cases. In
this paper, we propose Channelized Axial Attention (CAA) to seamlessly
integrate channel attention and spatial attention into a single operation with
negligible computation overhead. Specifically, we break down the dot-product
operation of the spatial attention into two parts and insert channel relation
in between, allowing for independently optimized channel attention on each
spatial location. We further develop grouped vectorization, which allows our
model to run with very little memory consumption without slowing down the
running speed. Comparative experiments conducted on multiple benchmark
datasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate
that our CAA outperforms many state-of-the-art segmentation models (including
dual attention) on all tested datasets.


Abstract:
  The existence of Ulrich modules for local domains has been a difficult and
elusive open question. For over thirty years, it was unknown whether local
domains always have Ulrich modules. In this paper, we answer the question of
existence for both Ulrich modules and weakly lim Ulrich sequences -- a weaker
notion recently introduced by Ma -- in the negative. We construct many local
domains in all dimensions $d \geq 2$ that do not have any Ulrich modules.
Moreover, we show that when $d = 2$, these local domains do not have weakly lim
Ulrich sequences. A key insight in our proofs is the classification of MCM
$R$-modules via the $S_2$-ification of $R$. For local domains of dimension $2$,
we show that the existence of weakly lim Ulrich sequences implies the existence
of lim Ulrich sequences.


Abstract:
  Line-intensity mapping observations will find fluctuations of integrated line
emission are attenuated by varying degrees at small scales due to the width of
the line emission profiles. This attenuation may significantly impact estimates
of astrophysical or cosmological quantities derived from measurements. We
consider a theoretical treatment of the effect of line broadening on both the
clustering and shot-noise components of the power spectrum of a generic
line-intensity power spectrum using a halo model. We then consider possible
simplifications to allow easier application in analysis, particularly in the
context of inferences that require numerous, repeated, fast computations of
model line-intensity signals across a large parameter space. For the CO Mapping
Array Project (COMAP) and the CO(1-0) line-intensity field at $z\sim3$ serving
as our primary case study, we expect a $\sim10\%$ attenuation of the
spherically averaged power spectrum on average at relevant scales of
$k\approx0.2$-$0.3$ Mpc$^{-1}$, compared to $\sim25\%$ for the interferometric
Millimetre-wave Intensity Mapping Experiment (mmIME) targeting shot noise from
CO lines at $z\sim1$-$5$ at scales of $k\gtrsim1$ Mpc$^{-1}$. We also consider
the nature and amplitude of errors introduced by simplified treatments of line
broadening, and find that while an approximation using a single effective
velocity scale is sufficient for spherically-averaged power spectra, a more
careful treatment is necessary when considering other statistics such as higher
multipoles of the anisotropic power spectrum or the voxel intensity
distribution.


Abstract:
  In this article, we show and discuss our experience in applying the flipped
classroom method for teaching Conditional Random Fields in a Natural Language
Processing course. We present the activities that we developed together with
their relationship to a cognitive complexity model (Bloom's taxonomy). After
this, we provide our own reflections and expectations of the model itself.
Based on the evaluation got from students, it seems that students learn about
the topic and also that the method is rewarding for some students.
Additionally, we discuss some shortcomings and we propose possible solutions to
them. We conclude the paper with some possible future work.


Abstract:
  Software bots have been facilitating several development activities in Open
Source Software (OSS) projects, including code review. However, these bots may
bring unexpected impacts to group dynamics, as frequently occurs with new
technology adoption. Understanding and anticipating such effects is important
for planning and management. To analyze these effects, we investigate how
several activity indicators change after the adoption of a code review bot. We
employed a regression discontinuity design on 1,194 software projects from
GitHub. We also interviewed 12 practitioners, including open-source maintainers
and contributors. Our results indicate that the adoption of code review bots
increases the number of monthly merged pull requests, decreases monthly
non-merged pull requests, and decreases communication among developers. From
the developers' perspective, these effects are explained by the transparency
and confidence the bot comments introduce, in addition to the changes in the
discussion focused on pull requests. Practitioners and maintainers may leverage
our results to understand, or even predict, bot effects on their projects.


Abstract:
  We consider a simple extension of the standard model, which could give a
solution for its $CP$ issues such as the origin of both CKM and PMNS phases and
the strong $CP$ problem. The model is extended with singlet scalars which could
cause spontaneous $CP$ violation to result in these phases at low energy
regions. The singlet scalars could give a good inflaton candidate if they have
a suitable nonminimal coupling with the Ricci scalar. $CP$ issues and inflation
could be closely related through these singlet scalars in a natural way. In a
case where inflaton is a mixture of the singlet scalars, we study reheating and
leptogenesis as notable phenomena affected by the fields introduced in this
extension.


Abstract:
  Some cosmological solutions of massive strings are obtained in Bianchi I
space-time following the techniques used by Letelier and Stachel. A class of
solutions corresponds to string cosmology associated with/without a magnetic
field and the other class consists of pure massive strings, obeying the
Takabayashi equation of state.


Abstract:
  Recent advances have lead towards first prototypes of a quantum internet in
which entanglement is distributed by sources producing bipartite entangled
states with high fidelities. This raises the question which states can be
generated in quantum networks based on bipartite sources using local operations
and classical communication. In this work we study state transformations under
finite rounds of local operations and classical communication in networks based
on maximally entangled two-qubit states. We first derive the symmetries for
arbitrary network structures as these determine which transformations are
possible. Then we show that contrary to tree graphs for which it has already
been shown that any state within the same entanglement class can be reached
there exist states which can be reached probabilistically but not
deterministically if the network contains a cycle. Furthermore, we provide a
systematic way to determine states which are not reachable in networks
consisting of a cycle. Moreover, we provide a complete characterization of the
states which can be reached in a cycle network with a protocol where each party
measures only once and each step of the protocol results in a deterministic
transformation. Finally, we present an example which cannot be reached with
such a simple protocol.


Abstract:
  By telescoping method, Sun gave some hypergeometric series whose sums are
related to $\pi$ recently. We investigate these series from the point of view
of Gosper's algorithm. Given a hypergeometric term $t_k$, we consider the
Gosper summability of $r(k)t_k$ for $r(k)$ being a rational function of $k$. We
give an upper bound and a lower bound on the degree of the numerator of $r(k)$
such that $r(k)t_k$ is Gosper summable. We also show that the denominator of
the $r(k)$ can read from the Gosper representation of $t_{k+1}/t_k$. Based on
these results, we give a systematic method to construct series whose sums can
be derived from the known ones. We also illustrated the corresponding
super-congruences and the $q$-analogue of the approach.


Abstract:
  Conditional Variational Auto Encoders (VAE) are gathering significant
attention as an Explainable Artificial Intelligence (XAI) tool. The codes in
the latent space provide a theoretically sound way to produce counterfactuals,
i.e. alterations resulting from an intervention on a targeted semantic feature.
To be applied on real images more complex models are needed, such as
Hierarchical CVAE. This comes with a challenge as the naive conditioning is no
longer effective. In this paper we show how relaxing the effect of the
posterior leads to successful counterfactuals and we introduce VAEX an
Hierarchical VAE designed for this approach that can visually audit a
classifier in applications.


Abstract:
  We present a self-improving, Neural Tree Expansion (NTE) method for
multi-robot online planning in non-cooperative environments, where each robot
attempts to maximize its cumulative reward while interacting with other
self-interested robots. Our algorithm adapts the centralized, perfect
information, discrete-action space method from AlphaZero to a decentralized,
partial information, continuous action space setting for multi-robot
applications. Our method has three interacting components: (i) a centralized,
perfect-information "expert" Monte Carlo Tree Search (MCTS) with large
computation resources that provides expert demonstrations, (ii) a
decentralized, partial-information "learner" MCTS with small computation
resources that runs in real-time and provides self-play examples, and (iii)
policy & value neural networks that are trained with the expert demonstrations
and bias both the expert and the learner tree growth. Our numerical experiments
demonstrate Neural Tree Expansion's computational advantage by finding better
solutions than a MCTS with 20 times more resources. The resulting policies are
dynamically sophisticated, demonstrate coordination between robots, and play
the Reach-Target-Avoid differential game significantly better than the
state-of-the-art control-theoretic baseline for multi-robot, double-integrator
systems. Our hardware experiments on an aerial swarm demonstrate the
computational advantage of Neural Tree Expansion, enabling online planning at
20Hz with effective policies in complex scenarios.


Abstract:
  We provide statistical analysis methods for samples of curves when the image
but not the parametrisation of the curves is of interest. A parametrisation
invariant analysis can be based on the elastic distance of the curves modulo
warping, but existing methods have limitations in common realistic settings
where curves are irregularly and potentially sparsely observed. We provide
methods and algorithms to approximate the elastic distance for such curves via
interpreting them as polygons. Moreover, we propose to use spline curves for
modelling smooth or polygonal Fr\'echet means of open or closed curves with
respect to the elastic distance and show identifiability of the spline model
modulo warping. We illustrate the use of our methods for elastic mean and
distance computation by application to two datasets. The first application
clusters sparsely sampled GPS tracks based on the elastic distance and computes
smooth means for each cluster to find new paths on Tempelhof field in Berlin.
The second classifies irregularly sampled handwritten spirals of Parkinson's
patients and controls based on the elastic distance to a mean spiral curve
computed using our approach. All developed methods are implemented in the
\texttt{R}-package \texttt{elasdics} and evaluated in simulations.


Abstract:
  In this work we prove a strong maximum principle for fractional elliptic
problems with mixed Dirichlet-Neumann boundary data which extends the one
proved by J. D\'avila to the fractional setting. In particular, we present a
comparison result for two solutions of the fractional Laplace equation
involving the spectral fractional Laplacian endowed with homogeneous mixed
boundary condition. This result represents a non-local counterpart to a Hopf's
Lemma for fractional elliptic problems with mixed boundary data.


Abstract:
  Examining cables using many conductor transmission line theory has shed light
on the modes supported by various cable types. However, so far the theory
disregards the fundamental surface wave mode whose lateral confinement
increases with frequency and hence is expected to play an important role in
high frequency applications. To address this issue, we propose an extension to
the theory which incorporates surface waves on uncoated, cylindrical wires.
Crucially, this requires new definitions of the per unit length transmission
line parameters which are derived using the single wire surface wave solution.
By closely examining a two wire and three wire system, we show that these new
parameters can predict surface waves as well as modes found using conventional
many conductor transmission line theory. Furthermore, all calculated modes are
validated experimentally by diagonalization of a measured channel transfer
matrix. Additionally, the theoretically predicted propagation constants for the
modes are validated against full numerical simulation for the two wire case and
good agreement is observed when proximity effects can be neglected.


Abstract:
  Cervical cancer is a very common and fatal type of cancer in women.
Cytopathology images are often used to screen for this cancer. Given that there
is a possibility that many errors can occur during manual screening, a
computer-aided diagnosis system based on deep learning has been developed. Deep
learning methods require a fixed dimension of input images, but the dimensions
of clinical medical images are inconsistent. The aspect ratios of the images
suffer while resizing them directly. Clinically, the aspect ratios of cells
inside cytopathological images provide important information for doctors to
diagnose cancer. Therefore, it is difficult to resize directly. However, many
existing studies have resized the images directly and have obtained highly
robust classification results. To determine a reasonable interpretation, we
have conducted a series of comparative experiments. First, the raw data of the
SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets.
Then, the datasets are resized to 224 x 224 pixels. Finally, 22 deep learning
models are used to classify the standard and scaled datasets. The results of
the study indicate that deep learning models are robust to changes in the
aspect ratio of cells in cervical cytopathological images. This conclusion is
also validated via the Herlev dataset.


Abstract:
  Single Image Super-resolution (SISR) produces high-resolution images with
fine spatial resolutions from aremotely sensed image with low spatial
resolution. Recently, deep learning and generative adversarial networks(GANs)
have made breakthroughs for the challenging task of single image
super-resolution (SISR). However, thegenerated image still suffers from
undesirable artifacts such as, the absence of texture-feature representationand
high-frequency information. We propose a frequency domain-based spatio-temporal
remote sensingsingle image super-resolution technique to reconstruct the HR
image combined with generative adversarialnetworks (GANs) on various frequency
bands (TWIST-GAN). We have introduced a new method incorporatingWavelet
Transform (WT) characteristics and transferred generative adversarial network.
The LR image hasbeen split into various frequency bands by using the WT,
whereas, the transfer generative adversarial networkpredicts high-frequency
components via a proposed architecture. Finally, the inverse transfer of
waveletsproduces a reconstructed image with super-resolution. The model is
first trained on an external DIV2 Kdataset and validated with the UC Merceed
Landsat remote sensing dataset and Set14 with each image sizeof 256x256.
Following that, transferred GANs are used to process spatio-temporal remote
sensing images inorder to minimize computation cost differences and improve
texture information. The findings are comparedqualitatively and qualitatively
with the current state-of-art approaches. In addition, we saved about 43% of
theGPU memory during training and accelerated the execution of our simplified
version by eliminating batchnormalization layers.


Abstract:
  Medical imaging has been employed to support medical diagnosis and treatment.
It may also provide crucial information to surgeons to facilitate optimal
surgical preplanning and perioperative management. Essentially, semi-automatic
organ and tumor segmentation has been studied by many researchers. Recently,
with the development of Deep Learning (DL) algorithms, automatic organ
segmentation has been gathered lots of attention from the researchers. This
paper addresses to propose the most efficient DL architectures for Liver
segmentation by adapting and comparing state-of-the-art DL frameworks, studied
in different disciplines. These frameworks are implemented and adapted into a
Commercial software, 'LiverVision'. It is aimed to reveal the most effective
and accurate DL architecture for fully automatic liver segmentation. Equal
conditions were provided to all architectures in the experiments so as to
measure the effectiveness of algorithms accuracy, and Dice coefficient metrics
were also employed to support comparative analysis. Experimental results prove
that 'U-Net' and 'SegNet' have been superior in line with the experiments
conducted considering the concepts of time, cost, and effectiveness.
Considering both architectures, 'SegNet' was observed to be more successful in
eliminating false-positive values. Besides, it was seen that the accuracy
metric used to measure effectiveness in image segmentation alone was not
enough. Results reveal that DL algorithms are able to automate organ
segmentation from DICOM images with high accuracy. This contribution is
critical for surgical preplanning and motivates author to apply this approach
to the different organs and field of medicine.


Abstract:
  Mechanical metamaterials owe their extraordinary properties and
functionalities to their micro-/nanoscale design of which shape, including both
geometry and topology, is perhaps the most important aspect. 4D printing
enables programmed, predictable, and precise change in the shape of mechanical
metamaterials to achieve multi-functionality, adaptive properties, and the
other types of desired behaviors that cannot be achieved using simple 3D
printing. This paper presents an overview of 4D printing as applied to
mechanical metamaterials. It starts by presenting a systematic definition of
what 4D printing is and what shape aspects (e.g., geometry, topology) are
relevant for the 4D printing of mechanical metamaterials. Instead of focusing
on different printing processes and materials, the paper addresses the most
fundamental aspects of the shapeshifting behaviors required for transforming a
flat construct to a target 3D shape (i.e., 2D to 3D shapeshifting) or
transforming a 3D shape to another 3D shape (i.e., 3D to 3D shapeshifting). In
either case, we will discuss the rigid-body shape morphing (e.g., rigid
origami) as well as deformable-body shapeshifting. The paper concludes with a
discussion of the major challenges ahead of us for applying 4D printing to
mechanical metamaterials and suggests several areas for future research.


Abstract:
  A quantum version of the Monge--Kantorovich optimal transport problem is
analyzed. The transport cost is minimized over the set of all bipartite
coupling states $\rho^{AB}$, such that both of its reduced density matrices
$\rho^A$ and $\rho^B$ of dimension $N$ are fixed. We show that, selecting the
quantum cost matrix to be proportional to the projector on the antisymmetric
subspace, the minimal transport cost leads to a semidistance between $\rho^A$
and $\rho^B$, which is bounded from below by the rescaled Bures distance and
from above by the root infidelity. In the single qubit case we provide a
semi-analytic expression for the optimal transport cost between any two states
and prove that its square root satisfies the triangle inequality and yields an
analogue of the Wasserstein distance of order two on the set of density
matrices. We introduce an associated measure of proximity of quantum states,
called SWAP-fidelity, and discuss its properties and applications in quantum
machine learning.


Abstract:
  Cells and tissues exhibit oscillatory deformations during remodelling,
migration or embryogenesis. Although it has been shown that these oscillations
correlate with cell biochemical signalling, it is yet unclear the role of these
oscillations in triggering drastic cell reorganisation events or instabilities,
and the coupling of this oscillatory response with tested visco-elastic
properties.
  We here present a rheological model that incorporates elastic, viscous and
frictional components, and that is able to generate oscillatory response
through a delay adaptive process of the rest-length. We analyse its stability
properties as a function of the model parameters and deduce analytical bounds
of the stable domain. While increasing values of the delay and remodelling rate
render the model unstable, we also show that increasing friction with the
substrate destabilise the oscillatory response. Furthermore, we numerically
verify that the extension of the model to non-linear strain measures is able to
generate sustained oscillations that alternate between stable and unstable
regions.


Abstract:
  We show that the number of partial triangulations of a set of $n$ points on
the plane is at least the $(n-2)$-nd Catalan number. This is tight for convex
$n$-gons. We also describe all the equality cases.


Abstract:
  Ultra-cold atoms provide ideal platforms for interferometry. The macroscopic
matter-wave property of ultra-cold atoms leads to large coherent length and
long coherent time, which enable high accuracy and sensitivity to measurement.
Here, we review our efforts to improve the performance of the interferometer.
We demonstrate a shortcut method for manipulating ultra-cold atoms in an
optical lattice. Compared with traditional ones, this shortcut method can
reduce manipulation time by up to three orders of magnitude. We construct a
matter-wave Ramsey interferometer for trapped motional quantum states and
significantly increase its coherence time by one order of magnitude with an
echo technique based on this method. Efforts have also been made to enhance the
resolution by multimode scheme. Application of a noise-resilient
multi-component interferometer shows that increasing the number of paths could
sharpen the peaks in the time-domain interference fringes, which leads to a
resolution nearly twice compared with that of a conventional double-path
two-mode interferometer. With the shortcut method mentioned above, improvement
of the momentum resolution could also be fulfilled, which leads to atomic
momentum patterns less than 0.6 $\hbar k_L$. To identify and remove systematic
noises, we introduce the methods based on the principal component analysis
(PCA) that reduce the noise in detection close to the $1/\sqrt{2}$ of the
photon-shot noise and separate and identify or even eliminate noises.
Furthermore, we give a proposal to measure precisely the local gravity
acceleration within a few centimeters based on our study of ultracold atoms in
precision measurements.


Abstract:
  Within the last decade much progress has been made in the experimental
realisation of quantum computing hardware based on a variety of physical
systems. Rapid progress has been fuelled by the conviction that sufficiently
powerful quantum machines will herald enormous computational advantages in many
fields, including chemical research. A quantum computer capable of simulating
the electronic structures of complex molecules would be a game changer for the
design of new drugs and materials. Given the potential implications of this
technology, there is a need within the chemistry community to keep abreast with
the latest developments as well as becoming involved in experimentation with
quantum prototypes. To facilitate this, here we review the types of quantum
computing hardware that have been made available to the public through cloud
services. We focus on three architectures, namely superconductors, trapped ions
and semiconductors. For each one we summarise the basic physical operations,
requirements and performance. We discuss to what extent each system has been
used for molecular chemistry problems and highlight the most pressing hardware
issues to be solved for a chemistry-relevant quantum advantage to eventually
emerge.


Abstract:
  In the present work, a general formulation is proposed to implement the
contact angle boundary conditions for the second-order Phase-Field models,
which is applicable to $N$-phase $(N \geqslant 2)$ moving contact line
problems. To remedy the issue of mass change due to the contact angle boundary
condition, a Lagrange multiplier is added to the original second-order
Phase-Field models, which is determined by the consistent and conservative
volume distribution algorithm so that the summation of the order parameters and
the \textit{consistency of reduction} are not influenced. To physically couple
the proposed formulation to the hydrodynamics, especially for
large-density-ratio problems, the consistent formulation is employed. The
reduction-consistent conservative Allen-Cahn models are chosen as examples to
illustrate the application of the proposed formulation. The numerical scheme
that preserves the consistency and conservation of the proposed formulation is
employed to demonstrate its effectiveness. Results produced by the proposed
formulation are in good agreement with the exact and/or asymptotic solutions.
The proposed method captures complex dynamics of moving contact line problems
having large density ratios.


Abstract:
  The numerical simulation of the physical systems has become in recent years a
fundamental tool to perform analyses and predictions in several application
fields, spanning from industry to the academy. As far as large scale
simulations are concerned, one of the most computationally expensive task is
the solution of linear systems arising from the discretization of the partial
differential equations governing the physical processes.This work presents
Chronos, a collection of linear algebra functions specifically designed for the
solution of large, sparse linear systems on massively parallel computers
(https://www.m3eweb.it/chronos/). Its emphasis is on modern, effective and
scalable AMG preconditioners for High Performance Computing (HPC). This work
describes the numerical algorithms and the main structures of this software
suite, especially from the implementation standpoint. Several numerical results
arising from practical mechanics and fluid dynamics applications with hundreds
of millions of unknowns are addressed and compared with other state-of-the-art
linear solvers, proving Chronos efficiency and robustness.


Abstract:
  We shortly describe the algorithms behind some of the functions provided by
the Macaulay2 package MultiprojectiveVarieties, a package for multi-projective
varieties and rational maps between them.


Abstract:
  This study presents torsional and transverse vibration analysis of a solar
panel including a rectangular thin plate locally supported by an elastic beam.
The plate is totally free in all boundaries, except for the local part attached
to the beam. The response of the system, which is subjected to a combination of
torsional and transverse vibration, identifies with a couple of PDEs developed
by the Euler-Bernoulli assumption and classical plate theory. To calculate the
system's natural frequencies, the domain of the solution is discretized by
zeroes of the Chebyshev polynomials to apply the Modified Generalized
Differential Quadrature method (MGDQ). Furthermore, governing equations along
with continuity and boundary conditions are discretized. After obtaining
solutions to the eigenvalue problem, several studies are investigated to
validate the accuracy of the proposed method. As can be concluded from the
tables, MGDQ improves the accuracy of results obtained by GDQ. Results for
various case studies reveal that MGDQ is properly devised for the vibration
analysis of systems with local boundary and continuity conditions.


Abstract:
  We introduce a modification of standard Martin-Lof type theory in which we
eliminate definitional equality and replace all computation rules by
propositional equalities. We show that type checking for such a system can be
done in quadratic time and that it has a natural homotopy-theoretic semantics.


Abstract:
  Fifth generation cellular systems support a broad range of services,
including mobile broadband, critical and massive Internet of Things and are
used in a variety of scenarios. In many of these scenarios, the main challenge
is maintaining high throughput and ensuring proper quality of service (QoS) in
irregular topologies. In multiple input multiple output systems, this challenge
translates to designing linear transmit and receive beamformers that maximize
the system throughput and manage QoS constraints. In this paper, we argue that
this basic design task in 5G and beyond systems must be extended such that
beamforming design and user scheduling are managed jointly. Specifically, we
propose a fully decentralized joint beamforming design and user scheduling
algorithm that manages QoS. A novel feature of this scheme is its ability to
reduce the initial rate requirements in case of infeasibility. By means of
simulations that model contemporary 5G scenarios, we show that the proposed
decentralized scheme outperforms benchmarking algorithms that do not support
minimum rate requirements and previously proposed algorithms that support QoS
requirements.


Abstract:
  Pommerman is a hybrid cooperative/adversarial multi-agent environment, with
challenging characteristics in terms of partial observability, limited or no
communication, sparse and delayed rewards, and restrictive computational time
limits. This makes it a challenging environment for reinforcement learning (RL)
approaches. In this paper, we focus on developing a curriculum for learning a
robust and promising policy in a constrained computational budget of 100,000
games, starting from a fixed base policy (which is itself trained to imitate a
noisy expert policy). All RL algorithms starting from the base policy use
vanilla proximal-policy optimization (PPO) with the same reward function, and
the only difference between their training is the mix and sequence of opponent
policies. One expects that beginning training with simpler opponents and then
gradually increasing the opponent difficulty will facilitate faster learning,
leading to more robust policies compared against a baseline where all available
opponent policies are introduced from the start. We test this hypothesis and
show that within constrained computational budgets, it is in fact better to
"learn in the school of hard knocks", i.e., against all available opponent
policies nearly from the start. We also include ablation studies where we study
the effect of modifying the base environment properties of ammo and bomb blast
strength on the agent performance.


Abstract:
  COVID-19 has been devastating the world since the end of 2019 and has
continued to play a significant role in major national and worldwide events,
and consequently, the news. In its wake, it has left no life unaffected. Having
earned the world's attention, social media platforms have served as a vehicle
for the global conversation about COVID-19. In particular, many people have
used these sites in order to express their feelings, experiences, and
observations about the pandemic. We provide a multi-faceted analysis of
critical properties exhibited by these conversations on social media regarding
the novel coronavirus pandemic. We present a framework for analysis, mining,
and tracking the critical content and characteristics of social media
conversations around the pandemic. Focusing on Twitter and Reddit, we have
gathered a large-scale dataset on COVID-19 social media conversations. Our
analyses cover tracking potential reports on virus acquisition, symptoms,
conversation topics, and language complexity measures through time and by
region across the United States. We also present a BERT-based model for
recognizing instances of hateful tweets in COVID-19 conversations, which
achieves a lower error-rate than the state-of-the-art performance. Our results
provide empirical validation for the effectiveness of our proposed framework
and further demonstrate that social media data can be efficiently leveraged to
provide public health experts with inexpensive but thorough insight over the
course of an outbreak.


Abstract:
  As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption
Spectroscopy (TDLAS) tomography has been widely used for imaging of
two-dimensional temperature distributions in reactive flows. Compared with the
computational tomographic algorithms, Convolutional Neural Networks (CNNs) have
been proofed to be more robust and accurate for image reconstruction,
particularly in case of limited access of laser beams in the Region of Interest
(RoI). In practice, flame in the RoI that requires to be reconstructed with
good spatial resolution is commonly surrounded by low-temperature background.
Although the background is not of high interest, spectroscopic absorption still
exists due to heat dissipation and gas convection. Therefore, we propose a
Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses
efficiently the training and learning resources for temperature imaging in the
RoI with good spatial resolution, and (b) reconstructs the less spatially
resolved background temperature by adequately addressing the integrity of the
spectroscopic absorption model. In comparison with the traditional CNN, the
newly introduced pseudo inversion of the RoI sensitivity matrix is more
penetrating for revealing the inherent correlation between the projection data
and the RoI to be reconstructed, thus prioritising the temperature imaging in
the RoI with high accuracy and high computational efficiency. In this paper,
the proposed algorithm was validated by both numerical simulation and lab-scale
experiment, indicating good agreement between the phantoms and the
high-fidelity reconstructions.


Abstract:
  This paper presents a game, controlled by computer vision, in identification
of hand gestures (hand-tracking). The proposed work is based on image
segmentation and construction of a convex hull with Jarvis Algorithm , and
determination of the pattern based on the extraction of area characteristics in
the convex hull.


Abstract:
  Hypotheses: Additives are commonly used to tune macromolecular conformational
transitions. Among additives, trehalose is an excellent bioprotectant and among
responsive polymers, PNIPAM is the most studied material. Nevertheless, their
interaction mechanism so far has only been hinted without direct investigation,
and, crucially, never elucidated in comparison to proteins. Detailed insights
would help understand to what extent PNIPAM microgels can effectively be used
as synthetic biomimetic materials, to reproduce and study, at the colloidal
scale, isolated protein behavior and its sensitivity to interactions with
specific cosolvents or cosolutes. Experiments: The effect of trehalose on the
swelling behavior of PNIPAM microgels was monitored by dynamic light
scattering; Raman spectroscopy and molecular dynamics simulations were used to
explore changes of solvation and dynamics across the swelling-deswelling
transition at the molecular scale. Findings: Strongly hydrated trehalose
molecules develop water-mediated interactions with PNIPAM microgels, thereby
preserving polymer hydration below and above the transition while drastically
inhibiting local motions of the polymer and of its hydration shell. Our study,
for the first time, demonstrates that slowdown of dynamics and preferential
exclusion are the principal mechanisms governing trehalose effect on PNIPAM
microgels, at odds with preferential adsorption of alcohols, but in full
analogy with the behavior observed in trehalose-protein systems.


Abstract:
  In this paper we extensively study the notion of Hamiltonian structure for
nonabelian differential-difference systems, exploring the link between the
different algebraic (in terms of double Poisson algebras and vertex algebras)
and geometric (in terms of nonabelian Poisson bivectors) definitions. We
introduce multiplicative double Poisson vertex algebras (PVAs) as the suitable
noncommutative counterpart to multiplicative PVAs, used to describe Hamiltonian
differential-difference equations in the commutative setting, and prove that
these algebras are in one-to-one correspondence with the Poisson structures
defined by difference operators, providing a sufficient condition for the
fulfilment of the Jacobi identity. Moreover, we define nonabelian polyvector
fields and their Schouten brackets, for both finitely generated noncommutative
algebras and infinitely generated difference ones: this allows us to provide a
unified characterisation of Poisson bivectors and double quasi-Poisson algebra
structures. Finally, as an application we obtain some results towards the
classification of local scalar Hamiltonian difference structures and construct
the Hamiltonian structures for the nonabelian Kaup, Ablowitz-Ladik and
Chen-Lee-Liu integrable lattices.


Abstract:
  We discuss the scalability of superconducting quantum computers, especially
in a wiring problem. The number of wiring inside a cryostat is almost
proportional to the number of qubits in current wiring architectures. We
introduce regularity, modularity, and hierarchy to an architecture design of
superconducting quantum computers. The key to the wiring elimination is found
in the quantum error correction codes having thresholds and spatial
translational symmetry, i.e., the surface code. We show a
superconducting-digital-logic-based architecture and introduce a stacked
heterogeneous structure of the quantum module.


Abstract:
  We prove the (optimal) $W^{1,\infty}$-regularity of weak solutions to the
equation $-\Delta u = Q \; \mathcal{H}^{n-1} \llcorner \Gamma$ in a domain
$\Omega \subset \mathbb{R}^n$ with Dirichlet boundary conditions, where $\Gamma
\subset \subset \Omega$ is a compact (Lipschitz) manifold and $Q \in
L^\infty(\Gamma)$.
  We also discuss optimality and necessity of the assumptions on $Q$ and
$\Gamma$.
  Our findings can be applied to study the regularity of solutions for several
free boundary problems, in particular the biharmonic Alt-Caffarelli Problem.


Abstract:
  Inter-observer variation is a significant problem in clinical target
volume(CTV) segmentation in postoperative settings, where there is no gross
tumor present. In this scenario, the CTV is not an anatomically established
structure, but one determined by the physician based on the clinical guideline
used, the preferred tradeoff between tumor control and toxicity, their
experience and training background, and other factors. This results in high
inter-observer variability between physicians. This variability has been
considered an issue, but the absence of multiple physician CTV contours for
each patient and the significant amount of time required for dose planning have
made it impractical to study its dosimetric consequences. In this study, we
analyze the impact that variations in physician style have on dose to
organs-at-risk(OAR) by simulating the clinical workflow via deep learning. For
a given patient previously treated by one physician, we use deep learning-based
tools to simulate how other physicians would contour the CTV and how the
corresponding dose distributions would look for this patient. To simulate
multiple physician styles, we use a previously developed in-house CTV
segmentation model that can produce physician style-aware segmentations. The
corresponding dose distribution is predicted using another in-house deep
learning tool, which, can predict dose within 3% of the prescription dose, on
average, on the test data. For every test patient, four different physician
style CTVs are considered, and four different dose distributions are analyzed.
OAR dose metrics are compared, showing that even though physician style
variations result in organs getting different doses, all the important dose
metrics except Maximum Dose point are within the clinically acceptable limit.


Abstract:
  Darija Open Dataset (DODa) is an open-source project for the Moroccan
dialect. With more than 10,000 entries DODa is arguably the largest open-source
collaborative project for Darija-English translation built for Natural Language
Processing purposes. In fact, besides semantic categorization, DODa also adopts
a syntactic one, presents words under different spellings, offers verb-to-noun
and masculine-to-feminine correspondences, contains the conjugation of hundreds
of verbs in different tenses, and many other subsets to help researchers better
understand and study Moroccan dialect. This data paper presents a description
of DODa, its features, how it was collected, as well as a first application in
Image Classification using ImageNet labels translated to Darija. This
collaborative project is hosted on GitHub platform under MIT's Open-Source
license and aims to be a standard resource for researchers, students, and
anyone who is interested in Moroccan Dialect


Abstract:
  In this paper, we study an asymptotic expansion of the heat kernel for a
Laplace operator on a smooth Riemannian manifold without a boundary at enough
small values of the proper time. The Seeley-DeWitt coefficients of this
decomposition satisfy a set of recurrence relations, which we use to construct
two function families of a special kind. Using these functions, we find the
expansion of a heat kernel for the inverse Laplace operator for an arbitrary
dimension of space. We show that the new functions have some important
properties. For example, we can consider the Laplace operator on the function
set as a shift one. Also we describe various applications useful in theoretical
physics and, in particular, we find the decomposition of Green functions in
terms of new functions.


Abstract:
  Both performance and efficiency are crucial factors for sequence labeling
tasks in many real-world scenarios. Although the pre-trained models (PTMs) have
significantly improved the performance of various sequence labeling tasks,
their computational cost is expensive. To alleviate this problem, we extend the
recent successful early-exit mechanism to accelerate the inference of PTMs for
sequence labeling tasks. However, existing early-exit mechanisms are
specifically designed for sequence-level tasks, rather than sequence labeling.
In this paper, we first propose a simple extension of sentence-level early-exit
for sequence labeling tasks. To further reduce the computational cost, we also
propose a token-level early-exit mechanism that allows partial tokens to exit
early at different layers. Considering the local dependency inherent in
sequence labeling, we employed a window-based criterion to decide for a token
whether or not to exit. The token-level early-exit brings the gap between
training and inference, so we introduce an extra self-sampling fine-tuning
stage to alleviate it. The extensive experiments on three popular sequence
labeling tasks show that our approach can save up to 66%-75% inference cost
with minimal performance degradation. Compared with competitive compressed
models such as DistilBERT, our approach can achieve better performance under
the same speed-up ratios of 2X, 3X, and 4X.