Abstract:
  The widespread deployment of surveillance cameras for facial recognition
gives rise to many privacy concerns. This study proposes a privacy-friendly
alternative to large scale facial recognition. While there are multiple
techniques to preserve privacy, our work is based on the minimization principle
which implies minimizing the amount of collected personal data. Instead of
running facial recognition software on all video data, we propose to
automatically extract a high quality snapshot of each detected person without
revealing his or her identity. This snapshot is then encrypted and access is
only granted after legal authorization. We introduce a novel unsupervised face
image quality assessment method which is used to select the high quality
snapshots. For this, we train a variational autoencoder on high quality face
images from a publicly available dataset and use the reconstruction probability
as a metric to estimate the quality of each face crop. We experimentally
confirm that the reconstruction probability can be used as biometric quality
predictor. Unlike most previous studies, we do not rely on a manually defined
face quality metric as everything is learned from data. Our face quality
assessment method outperforms supervised, unsupervised and general image
quality assessment methods on the task of improving face verification
performance by rejecting low quality images. The effectiveness of the whole
system is validated qualitatively on still images and videos.


Abstract:
  Self-triggered control (STC) is a sample-and-hold control method aimed at
reducing communications within networked-control systems; however, existing STC
mechanisms often maximize how late the next sample is, and as such they do not
provide any sampling optimality in the long-term. In this work, we devise a
method to construct self-triggered policies that provide near-maximal average
inter-sample time (AIST) while respecting given control performance
constraints. To achieve this, we rely on finite-state abstractions of a
reference event-triggered control, in which early triggers are also allowed.
These early triggers constitute controllable actions of the abstraction, for
which an AIST-maximizing strategy can be computed by solving a mean-payoff
game. We provide optimality bounds, and how to further improve them through
abstraction refinement techniques.


Abstract:
  Adaptive gradient methods, such as Adam, have achieved tremendous success in
machine learning. Scaling gradients by square roots of the running averages of
squared past gradients, such methods are able to attain rapid training of
modern deep neural networks. Nevertheless, they are observed to generalize
worse than stochastic gradient descent (SGD) and tend to be trapped in local
minima at an early stage during training. Intriguingly, we discover that
substituting the gradient in the second moment estimation term with the
momentumized version in Adam can well solve the issues. The intuition is that
gradient with momentum contains more accurate directional information and
therefore its second moment estimation is a better choice for scaling than that
of the raw gradient. Thereby we propose AdaMomentum as a new optimizer reaching
the goal of training fast while generalizing better. We further develop a
theory to back up the improvement in optimization and generalization and
provide convergence guarantees under both convex and nonconvex settings.
Extensive experiments on a wide range of tasks and models demonstrate that
AdaMomentum exhibits state-of-the-art performance consistently.


Abstract:
  Modern models for event causality identification (ECI) are mainly based on
supervised learning, which are prone to the data lacking problem.
Unfortunately, the existing NLP-related augmentation methods cannot directly
produce the available data required for this task. To solve the data lacking
problem, we introduce a new approach to augment training data for event
causality identification, by iteratively generating new examples and
classifying event causality in a dual learning framework. On the one hand, our
approach is knowledge-guided, which can leverage existing knowledge bases to
generate well-formed new sentences. On the other hand, our approach employs a
dual mechanism, which is a learnable augmentation framework and can
interactively adjust the generation process to generate task-related sentences.
Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show
that 1) our method can augment suitable task-related training data for ECI; 2)
our method outperforms previous methods on EventStoryLine and Causal-TimeBank
(+2.5 and +2.1 points on F1 value respectively).


Abstract:
  We study the black hole information problem within a semiclassically
gravitating AdS$_d$ black hole coupled to and in equilibrium with a
$d$-dimensional thermal conformal bath. We deform the bath state by a relevant
scalar deformation, triggering a holographic RG flow whose "trans-IR" region
deforms from a Schwarzschild geometry to a Kasner universe. The setup manifests
two independent scales which control both the extent of coarse-graining and the
entanglement dynamics when counting Hawking degrees of freedom in the bath. In
tuning either, we find nontrivial changes to the Page time and Page curve. We
consequently view the Page curve as a probe of the holographic RG flow, with a
higher Page time manifesting as a result of increased coarse-graining of the
bath degrees of freedom.


Abstract:
  A longstanding question in QCD is the origin of the mass gap in the
Yang-Mills sector of QCD, i.e., QCD without quarks. In Landau gauge QCD this
mass gap, and hence confinement, is encoded in a mass gap of the gluon
propagator, which is found both in lattice simulations and with functional
approaches. While functional methods are well suited to unravel the mechanism
behind the generation of the mass gap, a fully satisfactory answer has not yet
been found. In this work we solve the coupled Dyson-Schwinger equations for the
ghost propagator, gluon propagator and three-gluon vertex. We corroborate the
findings of earlier works, namely that the mass gap generation is tied to the
longitudinal projection of the gluon self-energy, which acts as an effective
mass term in the equations. Because an explicit mass term is in conflict with
gauge invariance, this leaves two possible scenarios: If it is viewed as an
artifact, only the scaling solution survives; if it is dynamical, gauge
invariance can only be preserved if there are longitudinal massless poles in
either of the vertices. We find that there is indeed a massless pole in the
ghost-gluon vertex, however in our approximation with the assumption of
complete infrared dominance of the ghost this pole is only present for the
scaling solution. We also put forward a possible mechanism that may reconcile
the scaling solution, with an infrared dominance of the ghost, with the
decoupling solutions based on longitudinal poles in the three-gluon vertex as
seen in the PT-BFM scheme.


Abstract:
  This article proves that Tate classes on Siegel modular 3-folds are spanned
by the images of Hilbert modular surfaces at degree 2 and by the images of
Shimura curves at degree 4. The proof involves a careful study of the period
pairing between degree-4 rapidly decreasing cohomological forms and Hilbert
modular surfaces.


Abstract:
  This letter investigates a Branching Dueling Q-Network (BDQ) based online
operation strategy for a microgrid with distributed battery energy storage
systems (BESSs) operating under uncertainties. The developed deep reinforcement
learning (DRL) based microgrid online optimization strategy can achieve a
linear increase in the number of neural network outputs with the number of
distributed BESSs, which overcomes the curse of dimensionality caused by the
charge and discharge decisions of multiple BESSs. Numerical simulations
validate the effectiveness of the proposed method.


Abstract:
  Understanding cell fate selection remains a central challenge in
developmental biology. We present a class of simple yet biologically-motivated
mathematical models for cell differentiation that generically generate
oscillations and hence suggest alternatives to the standard framework based on
Waddington's epigenetic landscape. The models allow us to suggest two generic
dynamical scenarios that describe the differentiation process. In the first
scenario gradual variation of a single control parameter is responsible for
both entering and exiting the oscillatory regime. In the second scenario two
control parameters vary: one responsible for entering, and the other for
exiting the oscillatory regime. We analyse the standard repressilator and four
variants of it and show the dynamical behaviours associated with each scenario.
We present a thorough analysis of the associated bifurcations and argue that
gene regulatory networks with these repressilator-like characteristics are
promising candidates to describe cell fate selection through an oscillatory
process.


Abstract:
  The Inverse Problem for the estimation of a point-wise approximation error
occurring at the discretization and solving of the system of partial
differential equations is addressed. The set of the differences between the
numerical solutions is used as the input data. The analyzed solutions are
obtained by the numerical algorithms of the distinct inner structure on the
same grid. The approximation error is estimated by the Inverse Problem, which
is posed in the variational statement with the zero order Tikhonov
regularization. The numerical tests, performed for the two dimensional inviscid
compressible flows corresponding to Edney-I and Edney-VI shock wave
interference modes, are provided. The analyzed flowfields are computed using
ten different numerical algorithms. The comparison of the estimated
approximation error and the true error, obtained by subtraction of numerical
and analytic solutions, is presented.


Abstract:
  We orchestrate a randomized algorithm for #$k$-SAT which counts the exact
number of satisfying assignments in $2^{o(n)}$ time. The existence of such
algorithm signifies that the #ETH is hereby refuted, and so are $\oplus$ETH,
ETH, #SETH, $\oplus$SETH and SETH.


Abstract:
  Manipulating the strain induced poly-domains and phase transition in
correlated oxide material are important for high performance devices
fabrication. Though the electronic transport in the strained oxide film at
macroscopic scales can be directly measured, the anisotropic electronic state
and the controllable phase separation cross the insulator-to-metal transition
within nanoscale size are still elusive. Here, we selected VO2 crystal film as
a prototypical oxide and achieved the manipulation of anisotropy electronic
phase separation via injecting He+ nanobeam into VO2 film at room temperature.
In addition, this nanoscale phase separation was directly visualized by
infrared near-field imaging measurements, showing the pronounced and unique
cR-axis dependent anisotropy on VO2 surface. Our results offered new insights
towards understanding the anisotropic nanoscale phase separation in strained
metal oxide films.


Abstract:
  In this work, we propose \texttt{TimeGrad}, an autoregressive model for
multivariate probabilistic time series forecasting which samples from the data
distribution at each time step by estimating its gradient. To this end, we use
diffusion probabilistic models, a class of latent variable models closely
connected to score matching and energy-based methods. Our model learns
gradients by optimizing a variational bound on the data likelihood and at
inference time converts white noise into a sample of the distribution of
interest through a Markov chain using Langevin sampling. We demonstrate
experimentally that the proposed autoregressive denoising diffusion model is
the new state-of-the-art multivariate probabilistic forecasting method on
real-world data sets with thousands of correlated dimensions. We hope that this
method is a useful tool for practitioners and lays the foundation for future
research in this area.


Abstract:
  In this paper, we quantify the rate of convergence between the distribution
of number of zeros of random trigonometric polynomials (RTP) with i.i.d.
centered random coefficients and the number of zeros of a stationary centered
Gaussian process G, whose covariance function is given by the sinc function.
First, we find the convergence of the RTP towards G in the Wasserstein-1
distance, which in turn is a consequence of Donsker Theorem. Then, we use this
result to derive the rate of convergence between their respective number of
zeros. Since the number of real zeros of the RTP is not a continuous function,
we use the Kac-Rice formula to express it as the limit of an integral and, in
this way, we approximate it by locally Lipschitz continuous functions.


Abstract:
  Ceramic $BiFeO_{3}$ samples were prepared by rapid sintering at $880^OC$. Two
compositions were examined. A $56/44\ Bi_{2}O_{3}/Fe_{2}O_{3}\ mole\%$
composition and a $56Bi_{2}O_{3}\cdot44Fe_{2}O_{3}+6.5wt\%\,NaCl$ composition.
The samples were heat treated at different times up to $8$ minutes and the
phase content was examined as a function of the time using XRD measurements and
analysis. It was demonstrated that using both compositions, maximum $BiFeO_{3}$
phase content is obtained after $3.5$ minutes. In the former approximately
$50\%$ of the material transformed to $BiFeO_{3}$ while in the latter $98.5\%$.


Abstract:
  The residual finite-dimensionality of a $\mathrm{C}^*$-algebra is known to be
encoded in a topological property of its space of representations, stating that
finite-dimensional representations should be dense therein. We extend this
paradigm to general (possibly non-self-adjoint) operator algebras. While
numerous subtleties emerge in this greater generality, we exhibit novel tools
for constructing finite-dimensional approximations. One such tool is a notion
of a residually finite-dimensional coaction of a semigroup on an operator
algebra, which allows us to construct finite-dimensional approximations for
operator algebras of functions and operator algebras of semigroups. Our
investigation is intimately related to the question of whether residual
finite-dimensionality of an operator algebra is inherited by its maximal
$\mathrm{C}^*$-cover, which we resolve in many cases of interest.


Abstract:
  We propose a measure, which we call the dissipative spectral form factor
(DSFF), to characterize the spectral statistics of non-Hermitian (and
non-Unitary) matrices. We show that DSFF successfully diagnoses dissipative
quantum chaos, and reveals correlations between real and imaginary parts of the
complex eigenvalues up to arbitrary energy (and time) scale. Specifically, we
provide the exact solution of DSFF for the GinUE and for a Poissonian random
spectrum (Poisson) as minimal models of dissipative quantum chaotic and
integrable systems respectively. For dissipative quantum chaotic systems, we
show that DSFF exhibits an exact rotational symmetry in its complex time
argument $\tau$. Analogous to the spectral form factor (SFF) behaviour for GUE,
DSFF for GinUE shows a ``dip-ramp-plateau'' behavior in $|\tau|$: DSFF
initially decreases, increases at intermediate time scales, and saturates after
a generalized Heisenberg time which scales as the inverse mean level spacing.
Remarkably, for large matrix size, the ``ramp'' of DSFF for GinUE increases
quadratically in $|\tau|$, in contrast to the linear ramp in SFF for Hermitian
ensembles. For dissipative quantum integrable systems, we show that DSFF takes
a constant value except for a region in complex time whose size and behavior
depends on the eigenvalue density. Numerically, we verify the above claims and
show that DSFF for real and quaternion real Ginibre ensembles coincides with
the GinUE behaviour except for a region in complex time plane of measure zero
in the limit of large matrix size. As a physical example, we consider the
quantum kicked top model with dissipation, and show that it falls under the
Ginibre universality class and Poisson as the `kick' is switched on or off.
Lastly, we study spectral statistics of ensembles of random classical
stochastic matrices, and show that these models fall under the Ginibre
universality class.


Abstract:
  Analysis of online reviews has attracted great attention with broad
applications. Often times, the textual reviews are coupled with the numerical
ratings in the data. In this work, we propose a probabilistic model to
accommodate both textual reviews and overall ratings with consideration of
their intrinsic connection for a joint sentiment-topic prediction. The key of
the proposed method is to develop a unified generative model where the topic
modeling is constructed based on review texts and the sentiment prediction is
obtained by combining review texts and overall ratings. The inference of model
parameters are obtained by an efficient Gibbs sampling procedure. The proposed
method can enhance the prediction accuracy of review data and achieve an
effective detection of interpretable topics and sentiments. The merits of the
proposed method are elaborated by the case study from Amazon datasets and
simulation studies.


Abstract:
  We use the energy method to study the well-posedness of initial-boundary
value problems approximated by overset mesh methods in one and two space
dimensions for linear constant-coefficient hyperbolic systems. We show that in
one space dimension, for both scalar equations and systems of equations, the
problem where one domain partially oversets another is well-posed when
characteristic coupling conditions are used. If a system cannot be
diagonalized, as is ususally the case in multiple space dimensions, then the
energy method does not give proper bounds in terms of initial and boundary
data. For those problems, we propose a novel penalty approach. We show, by
using a global energy that accounts for the energy in the overlap region of the
domains, that under well-defined conditions on the coupling matrices the
penalized overset domain problems are energy bounded, conservative, well-posed
and have solutions equivalent to the original single domain problem.


Abstract:
  Planning future operational scenarios of bulk power systems that meet
security and economic constraints typically requires intensive labor efforts in
performing massive simulations. To automate this process and relieve engineers'
burden, a novel multi-stage control approach is presented in this paper to
train centralized and decentralized reinforcement learning agents that can
automatically adjust grid controllers for regulating transmission line flows at
normal condition and under contingencies. The power grid flow control problem
is formulated as Markov Decision Process (MDP). At stage one, centralized soft
actor-critic (SAC) agent is trained to control generator active power outputs
in a wide area to control transmission line flows against specified security
limits. If line overloading issues remain unresolved, stage two is used to
train decentralized SAC agent via load throw-over at local substations. The
effectiveness of the proposed approach is verified on a series of actual
planning cases used for operating the power grid of SGCC Zhejiang Electric
Power Company.


Abstract:
  In the article, hyperelastic material models which state consistent
polynomial expansions of the stored energy function are discussed. The approach
follows from the muliplicative decomposition of the deformation gradient. Some
advantages of the third order expansion model over the five-parameter Rivlin
model using Treloar's experimental data are shown. The models are qualitatively
and quantitatively compared to highlight these advantages of the discussed MV
model.


Abstract:
  We investigate superconducting phase transition in
superconductor(S)/ferromagnet(F) bilayer with Rasba spin-orbit interaction at
S/F interface. This spin-orbit coupling produces spontaneous supercurrents
flowing inside the atomic-thickness region near the interface, which are
compensated by the screening Meissner currents [S. Mironov and A. Buzdin, Phys.
Rev. Lett \textbf{118}, 077001 (2017)]. In the case of thin superconducting
film the emergence of the spontaneous surface currents causes the increase of
the superconducting critical temperature and we calculate the actual value of
the critical temperature shift. We also show that in the case of type-I
superconducting film this phase transition can be of the first order. In the
external magnetic field the critical temperature depends on the relative
orientation of the external magnetic field and the exchange field in the
ferromagnet. Also we predict the in-plane anisotropy of the critical current
which may open an alternative way for the experimental observation of the
spontaneous supercurrents generated by the SOC.


Abstract:
  Warm magnets are magnets that function in normal ambient temperature
conditions. These types are mostly using a soft steel yoke for field
amplification and either Copper or Aluminium coils or permanent magnets to
generate the field. Magnets powered with such normal-conducting coils are often
also called classical, iron dominated or resistive magnets. Since decades these
types of magnets are the workhorse for most linear and circular accelerators
and beam transfer lines.


Abstract:
  One of the main goals of online competitive games is increasing player
engagement by ensuring fair matches. These games use rating systems for
creating balanced match-ups. Rating systems leverage statistical estimation to
rate players' skills and use skill ratings to predict rank before matching
players. Skill ratings of individual players can be aggregated to compute the
skill level of a team. While research often aims to improve the accuracy of
skill estimation and fairness of match-ups, less attention has been given to
how the skill level of a team is calculated from the skill level of its
members. In this paper, we propose two new aggregation methods and compare them
with a standard approach extensively used in the research literature. We
present an exhaustive analysis of the impact of these methods on the predictive
performance of rating systems. We perform our experiments using three popular
rating systems, Elo, Glicko, and TrueSkill, on three real-world datasets
including over 100,000 battle royale and head-to-head matches. Our evaluations
show the superiority of the MAX method over the other two methods in the
majority of the tested cases, implying that the overall performance of a team
is best determined by the performance of its most skilled member. The results
of this study highlight the necessity of devising more elaborated methods for
calculating a team's performance -- methods covering different aspects of
players' behavior such as skills, strategy, or goals.


Abstract:
  A novel non-parametric quantile estimation method for continuous random
variables is introduced, based on a minimal neural network architecture
consisting of a single unit. Its advantage over estimations from ranking the
order statistics is shown, specifically for small sample size. In a regression
context, the method can be used to quantify predictive uncertainty under the
split conformal prediction setting, where prediction intervals are estimated
from the residuals of a pre-trained model on a held-out validation set to
quantify the uncertainty in future predictions. Benchmarking experiments
demonstrate that the method is competitive in quality and coverage with
state-of-the-art solutions, with the added benefit of being more
computationally efficient.


Abstract:
  Cross-modal person re-identification (Re-ID) is critical for modern video
surveillance systems. The key challenge is to align inter-modality
representations according to semantic information present for a person and
ignore background information. In this work, we present AXM-Net, a novel CNN
based architecture designed for learning semantically aligned visual and
textual representations. The underlying building block consists of multiple
streams of feature maps coming from visual and textual modalities and a novel
learnable context sharing semantic alignment network. We also propose
complementary intra modal attention learning mechanisms to focus on more
fine-grained local details in the features along with a cross-modal affinity
loss for robust feature matching. Our design is unique in its ability to
implicitly learn feature alignments from data. The entire AXM-Net can be
trained in an end-to-end manner. We report results on both person search and
cross-modal Re-ID tasks. Extensive experimentation validates the proposed
framework and demonstrates its superiority by outperforming the current
state-of-the-art methods by a significant margin.


Abstract:
  Pre-compiled binary packages provide a convenient way of efficiently
distributing software that has been adopted by most Linux package management
systems. However, the heterogeneity of the Linux ecosystem, combined with the
growing number of R extensions available, poses a scalability problem. As a
result, efforts to bring binary R packages to Linux have been scattered, and
lack a proper mechanism to fully integrate them with R's package manager. This
work reviews past and present of binary distribution for Linux, and presents a
path forward by showcasing the `cran2copr' project, an RPM-based
proof-of-concept implementation of an automated scalable binary distribution
system with the capability of building, maintaining and distributing thousands
of packages, while providing a portable and extensible bridge to the system
package manager.


Abstract:
  In performative prediction, predictions guide decision-making and hence can
influence the distribution of future data. To date, work on performative
prediction has focused on finding performatively stable models, which are the
fixed points of repeated retraining. However, stable solutions can be far from
optimal when evaluated in terms of the performative risk, the loss experienced
by the decision maker when deploying a model. In this paper, we shift attention
beyond performative stability and focus on optimizing the performative risk
directly. We identify a natural set of properties of the loss function and
model-induced distribution shift under which the performative risk is convex, a
property which does not follow from convexity of the loss alone. Furthermore,
we develop algorithms that leverage our structural assumptions to optimize the
performative risk with better sample efficiency than generic methods for
derivative-free convex optimization.


Abstract:
  Current deep quantization methods that produce binary code representations
for efficient image retrieval mostly learn codewords from data. They rarely
investigate the effect of their inherent distribution on the quantization and
the learning metrics presently used are insufficient for face image retrieval
task. To address this, this paper integrates product quantization into an
end-to-end deep learning framework to retrieve face images. We propose a novel
scheme that uses predefined orthonormal vectors as codewords, to enhance the
quantization informativeness and reduce redundancy in the codewords. A tailored
loss function maximizes discriminability among identities in each quantization
subspace for both the quantized and original features. An entropy-based
regularization term is imposed to reduce the quantization error. Experiments
were conducted on three commonly-used datasets for both single- and
cross-domain retrieval. The proposed method outperformed all the deep
hashing/quantization methods it was compared with under both settings. We
observe that the proposed orthonormal codewords consistently improved both
models' standard retrieval performance and generalization ability. Therefore,
the proposed method is more suitable for scalable face image retrieval than
deep hashing methods.


Abstract:
  The effect of ionisation quenching for ions is critical for experiments
relying on the measurement of low energy recoils, such as direct Dark Matter
searches. We present ionisation quenching factor estimates over a range of
energies for protons, $\alpha$-particles, and heavier ions in H$_{2}$,
CH$_{4}$, N$_{2}$, Ar, CO$_{2}$, and C$_{3}$H$_{8}$ gases, estimated from the
respective reference W-value measurements. The resulting ionisation quenching
factors are compared with predictions from SRIM.


Abstract:
  Face Presentation Attack Detection (PAD) has drawn increasing attentions to
secure the face recognition systems that are widely used in many applications.
Conventional face anti-spoofing methods have been proposed, assuming that
testing is from the same domain used for training, and so cannot generalize
well on unseen attack scenarios. The trained models tend to overfit to the
acquisition sensors and attack types available in the training data. In light
of this, we propose an end-to-end learning framework based on Domain Adaptation
(DA) to improve PAD generalization capability. Labeled source-domain samples
are used to train the feature extractor and classifier via cross-entropy loss,
while unsupervised data from the target domain are utilized in adversarial DA
approach causing the model to learn domain-invariant features. Using DA alone
in face PAD fails to adapt well to target domain that is acquired in different
conditions with different devices and attack types than the source domain. And
so, in order to keep the intrinsic properties of the target domain, deep
clustering of target samples is performed. Training and deep clustering are
performed end-to-end, and experiments performed on several public benchmark
datasets validate that our proposed Deep Clustering guided Unsupervised Domain
Adaptation (DCDA) can learn more generalized information compared with the
state-of-the-art classification error on the target domain.


Abstract:
  We propose a new discretization method for PDEs on moving domains in the
setting of unfitted finite element methods, which is provably higher-order
accurate in space and time. In the considered setting, the physical domain that
evolves essentially arbitrarily through a time-independent computational
background domain, is represented by a level set function. For the time
discretization, the application of standard time stepping schemes that are
based on finite difference approximations of the time derivative is not
directly possible, as the degrees of freedom may get active or inactive across
such a finite difference stencil in time. In [Lehrenfeld, Olshanskii. An
Eulerian finite element method for PDEs in time-dependent domains. ESAIM: M2AN,
53:585--614, 2019] this problem is overcome by extending the discrete solution
at every timestep to a sufficiently large neighborhood so that all the degrees
of freedom that are relevant at the next time step stay active. But that paper
focuses on low-order methods. We advance these results with introducing and
analyzing realizable techniques for the extension to higher order. To obtain
higher-order convergence in space and time, we combine the BDF time stepping
with the isoparametric unfitted FEM. The latter has been used and analyzed for
several stationary problems before. However, for moving domains the key
ingredient in the method, the transformation of the underlying mesh, becomes
time-dependent which gives rise to some technical issues. We treat these with
special care, carry out an a priori error analysis and two numerical
experiments.


Abstract:
  Cross-ratio degrees count configurations of points $z_1,\ldots, z_n \in
\mathbb{P}^1$ satisfying $n - 3$ cross-ratio constraints, up to isomorphism.
These numbers arise in multiple contexts in algebraic and tropical geometry,
and may be viewed as combinatorial invariants of certain hypergraphs. We prove
an upper bound on cross-ratio degrees in terms of the theory of perfect
matchings on bipartite graphs. We also discuss several of the many perspectives
on cross-ratio degrees -- including a connection to Gromov-Witten theory -- and
give many example computations.


Abstract:
  Let $R$ be a subring of $\mathbb{C}[[z]]$, and let $X \in \mathbb{C}[[z]]$.
The Newton-Puiseux Theorem implies that if the coefficients of $X$ grow
sufficiently rapidly relative to the coefficients of the series in $R$, then
$X$ is transcendental over $R$. We prove an alternative proof of this result by
establishing a relationship between the coefficients of $A(X)$ and
$A^\prime(X)$, where $A(T)$ is a polynomial over $\mathbb{C}[[z]]$.


Abstract:
  Deterministic dynamics is an essential part of many MCMC algorithms, e.g.
Hybrid Monte Carlo or samplers utilizing normalizing flows. This paper presents
a general construction of deterministic measure-preserving dynamics using
autonomous ODEs and tools from differential geometry. We show how Hybrid Monte
Carlo and other deterministic samplers follow as special cases of our theory.
We then demonstrate the utility of our approach by constructing a continuous
non-sequential version of Gibbs sampling in terms of an ODE flow and extending
it to discrete state spaces. We find that our deterministic samplers are more
sample efficient than stochastic counterparts, even if the latter generate
independent samples.


Abstract:
  Texture matrices are a way of mitigating the redundancy inherent in the
description of flavor physics via Yukawa couplings by eliminating some entries
in order to identify relevant parameters. A four-zero texture scheme has been
used in the literature to successfully describe fermion masses and mixing.
However, as we show in this work, improving experimental constraints require an
update to this analysis. In this paper the implications of a 2-zero texture
mass matrix is studied for quarks and leptons. We show that the introduction of
a new parameter in each mass matrix allow us to reach good results with
relative low cost in predictability. We report a numerical study using a
hybridized nature-inspired/cellular automata search algorithm. We find that
leptons and quarks can be described by the same 1-zero structure. We describe
some scenarios where a simplified description can be achieved, including a
narrow region in parameter space where the same values describe charged leptons
and neutrinos, which is a stronger version of a previously proposed Universal
Texture Constraint.


Abstract:
  The paper addresses two unusual scaling regimes (types of critical behaviour)
predicted by the field-theoretic renormalization group analysis for a
self-organized critical system with turbulent motion of the environment. The
system is modelled by the anisotropic stochastic equation for a "running
sandpile" introduced by Hwa and Kardar in [{\it Phys. Rev. Lett.} {\bf 62}:
1813 (1989)]. The turbulent motion is described by the isotropic
Kazantsev-Kraichnan "rapid-change" velocity ensemble for an incompressible
fluid. The original Hwa-Kardar equation allows for independent scaling of the
spatial coordinates $x_{\parallel}$ (the coordinate along the preferred
dimension) and ${\bf x_{\bot}}$ (the coordinates in the orthogonal subspace to
the preferred direction) that becomes impossible once the isotropic velocity
ensemble is coupled to the equation. However, it is found that one of the
regimes of the system's critical behaviour (the one where the isotropic
turbulent motion is irrelevant) recovers the anisotropic scaling through
"dimensional transmutation." The latter manifests as a dimensionless ratio
acquiring nontrivial canonical dimension. The critical regime where both the
velocity ensemble and the nonlinearity of the Hwa-Kardar equation are relevant
simultaneously is also characterized by "atypical" scaling. While the ordinary
scaling with fixed infra-red irrelevant parameters is impossible in this
regime, the "restricted" scaling where the times, the coordinates, and the
dimensionless ratio are scaled becomes possible. This result brings to mind
scaling hypotheses modifications (Stell's weak scaling or Fisher's generalized
scaling) for systems with significantly different characteristic scales.


Abstract:
  This technical report presents our solution for temporal action detection
task in AcitivityNet Challenge 2021. The purpose of this task is to locate and
identify actions of interest in long untrimmed videos. The crucial challenge of
the task comes from that the temporal duration of action varies dramatically,
and the target actions are typically embedded in a background of irrelevant
activities. Our solution builds on BMN, and mainly contains three steps: 1)
action classification and feature encoding by Slowfast, CSN and ViViT; 2)
proposal generation. We improve BMN by embedding the proposed Proposal Relation
Network (PRN), by which we can generate proposals of high quality; 3) action
detection. We calculate the detection results by assigning the proposals with
corresponding classification results. Finally, we ensemble the results under
different settings and achieve 44.7% on the test set, which improves the
champion result in ActivityNet 2020 by 1.9% in terms of average mAP.


Abstract:
  Recent work has focused on the potential and pitfalls of causal
identification in observational studies with multiple simultaneous treatments.
On the one hand, a latent variable model fit to the observed treatments can
identify essential aspects of the distribution of unobserved confounders. On
the other hand, it has been shown that even when the latent confounder
distribution is known exactly, causal effects are still not point identifiable.
Thus, the practical benefits of latent variable modeling in multi-treatment
settings remain unclear. We clarify these issues with a sensitivity analysis
method that can be used to characterize the range of causal effects that are
compatible with the observed data. Our method is based on a copula
factorization of the joint distribution of outcomes, treatments, and
confounders, and can be layered on top of arbitrary observed data models. We
propose a practical implementation of this approach making use of the Gaussian
copula, and establish conditions under which causal effects can be bounded. We
also describe approaches for reasoning about effects, including calibrating
sensitivity parameters, quantifying robustness of effect estimates, and
selecting models which are most consistent with prior hypotheses.


Abstract:
  In this work, we introduce an unconditional video generative model,
InMoDeGAN, targeted to (a) generate high quality videos, as well as to (b)
allow for interpretation of the latent space. For the latter, we place emphasis
on interpreting and manipulating motion. Towards this, we decompose motion into
semantic sub-spaces, which allow for control of generated samples. We design
the architecture of InMoDeGAN-generator in accordance to proposed Linear Motion
Decomposition, which carries the assumption that motion can be represented by a
dictionary, with related vectors forming an orthogonal basis in the latent
space. Each vector in the basis represents a semantic sub-space. In addition, a
Temporal Pyramid Discriminator analyzes videos at different temporal
resolutions. Extensive quantitative and qualitative analysis shows that our
model systematically and significantly outperforms state-of-the-art methods on
the VoxCeleb2-mini and BAIR-robot datasets w.r.t. video quality related to (a).
Towards (b) we present experimental results, confirming that decomposed
sub-spaces are interpretable and moreover, generated motion is controllable.


Abstract:
  To broaden the scope of van der Waals 2D magnets, we report the synthesis and
magnetism of covalent 2D magnetic Cr$_2$Te$_3$ with a thickness down to
one-unit-cell. The 2D Cr$_2$Te$_3$ crystals exhibit robust ferromagnetism with
a Curie temperature of 180 K, a large perpendicular anisotropy of 7*105 J m-3,
and a high coercivity of ~ 4.6 kG at 20 K. First-principles calculations
further show a transition from canted to collinear ferromagnetism, a transition
from perpendicular to in-plane anisotropy, and emergent half-metallic behavior
in atomically-thin Cr$_2$Te$_3$, suggesting its potential application for
injecting carriers with high spin polarization into spintronic devices.


Abstract:
  We give a lower bound of the $\delta$-invariants of ample line bundles in
terms of Seshadri constants. As applications, we prove the uniform K-stability
of infinitely many families of Fano hypersurfaces of arbitrarily large index,
as well as the uniform K-stability of most families of smooth Fano threefolds
of Picard number one.


Abstract:
  One of the main characteristics of the penumbra of sunspots is the radially
outward-directed Evershed flow. Only recently have penumbral regions been
reported with similar characteristics to normal penumbral filaments, but with
an opposite direction of the flow. Such flows directed towards the umbra are
known as counter Evershed flows (CEFs). We aim to determine the frequency of
occurrence of CEFs in active regions (ARs) and to characterize their lifetime
and the prevailing conditions in the ARs. We analysed the continuum images,
Dopplergrams, and magnetograms recorded by SDO/HMI of 97 ARs that appeared from
2011 to 2017. We followed the ARs for $9.6\pm1.4$ days on average. We found 384
CEFs in total, with a median value of 6 CEFs per AR. CEFs are a rather common
feature, they occur in 83.5% of all ARs regardless of the magnetic complexity
of the AR. However, CEFs were observed on average only during 5.9% of the mean
total duration of all the observations analyzed here. The lifetime of CEFs
follows a log-normal distribution with a median value of 10.6$_{-6.0}^{+12.4}$
hr. In addition, we report two populations of CEFs depending on whether they
are associated with light bridges, or not. We explain that the rarity of
reports of CEFs in the literature is a result of highly incomplete coverage of
ARs with spectropolarimetric data. By using the continuous observations now
routinely available from space, we are able to overcome this limitation.


Abstract:
  SHINE, as the first hard x-ray free-electron-laser (FEL) facility in China,
is designed to provide high-brightness FEL lasing under high-repetition-rate
operation mode. In order to drive x-ray FEL pulses with high qualities, the
photoinjector section is deployed to provide the specified electron beam with
low transverse emittance and high brightness. Normally the multi-objective
optimization algorithm is employed in the injector beam dynamics design. In
this paper, the many-objective optimization algorithm NSGA-III is introduced to
the injector physical design for optimizing the 4 beam quality properties for
the first time. The results of the optimization are presented and analyzed. The
application of NSGA-III can provide guidance for further physical research as
well as improve the beam dynamics optimization efficiency.


Abstract:
  Singular equations with rank-deficient Jacobians arise frequently in
algebraic computing applications. As shown in case studies in this paper,
direct and intuitive modeling of algebraic problems often results in
nonisolated singular solutions. The challenges become formidable when the
problems need to be solved from empirical data of limited accuracy. A newly
discovered low-rank Newton's iteration emerges as an effective regularization
mechanism that enables solving singular equations accurately with an error
bound in the same order as the data error. This paper elaborates applications
of new methods on solving singular algebraic equations such as singular linear
systems, polynomial GCD and factorizations as well as matrix defective
eigenvalue problems.


Abstract:
  We consider random filtered complexes built over marked point processes on
Euclidean spaces. Examples of our filtered complexes include a filtration of
$\check{\textrm{C}}$ech complexes of a family of sets with various sizes,
growths, and shapes. We establish the law of large numbers for persistence
diagrams as the size of the convex window observing a marked point process
tends to infinity.


Abstract:
  This paper introduces and analyzes an improved Q-learning algorithm for
discrete-time linear time-invariant systems. The proposed method does not
require any knowledge of the system dynamics, and it enjoys significant
efficiency advantages over other data-based optimal control methods in the
literature. This algorithm can be fully executed off-line, as it does not
require to apply the current estimate of the optimal input to the system as in
on-policy algorithms. It is shown that a persistently exciting input, defined
from an easily tested matrix rank condition, guarantees the convergence of the
algorithm. Moreover, the method avoids the use of linear matrix inequalities
(LMIs) for control design, decreasing the corresponding computational
complexity. A data-based method is proposed to design the initial stabilizing
feedback gain that the algorithm requires. Robustness of the algorithm in the
presence of noisy measurements is analyzed. Both theoretical and simulation
comparisons are performed to show the advantages of this algorithm against
other model-free control design methods.


Abstract:
  Close to a magical angle, twisted bilayer graphene (TBLG) systems exhibit
isolated flat electronic bands and, accordingly, strong electron localization.
TBLGs have hence been ideal platforms to explore superconductivity, correlated
insulating states, magnetism, and quantized anomalous Hall states in reduced
dimension. Below a threshold twist angle ($\sim$ $1.1^\circ$), the TBLG
superlattice undergoes lattice reconstruction, leading to a periodic moir\'e
structure which presents a marked atomic corrugation. Using a tight-binding
framework, this research demonstrates that superlattice reconstruction affects
significantly the electronic structure of small-angle TBLGs. The first magic
angle at $\sim$ $1.1^\circ$ is found to be a critical case presenting globally
maximized electron localization, thus separating reconstructed TBLGs into two
classes with clearly distinct electronic properties. While low-energy Dirac
fermions are still preserved at large twist angles $> 1.1 ^\circ$, small-angle
($\lesssim 1.1^\circ$) TBLG systems present common features such as large
spatial variation and strong electron localization observed in unfavorable AA
stacking regions. However, for small twist angles below $1.1 ^\circ$, the
relative contribution of the local AA regions is progressively reduced, thus
precluding the emergence of further magic angles, in very good agreement with
existing experimental evidence.


Abstract:
  The paper is concerned with an inverse point source problem for the Helmholtz
equation. It consists of recovering the locations and amplitudes of a finite
number of radiative point sources inside a given inhomogeneous medium from the
knowledge of a single boundary measurement. The main result of the paper is a
new H\"{o}lder type stability estimate for the inversion under the assumption
that the point sources are well separated. The proof of the stability is based
on a combination of Carleman estimates and a technique for proving uniqueness
of the Cauchy problem.


Abstract:
  Using a combination of general-relativistic magnetohydrodynamics simulations
and ray tracing of synchrotron emission, we study the effect of modest (24
degrees) misalignment between the black hole spin and plasma angular momentum,
focusing on the variability of total flux, image centroids, and image sizes. We
consider both millimeter and infrared (IR) observables motivated by Sagittarius
A* (Sgr A*), though our results apply more generally to optically thin flows.
For most quantities, tilted accretion is more variable, primarily due to a
significantly hotter and denser "coronal" region well off the disk midplane. We
find (1) a 150% increase in millimeter light curve variability when adding tilt
to the flow; (2) the tilted image centroid in the millimeter shifts on a scale
of 3.7 microarcseconds over 28 hours (5000 gravitational times) for some
electron temperature models; (3) tilted disk image diameters in the millimeter
can be 10% larger (52 versus 47 microarcseconds) than those of aligned disks at
certain viewing angles; (4) the tilted models produce significant IR flux,
similar to that seen in Sgr A*, with comparable or even greater variability
than observed; (5) for some electron models, the tilted IR centroid moves by
more than 50 microarcseconds over several hours, in a similar fashion to the
centroid motion detected by the GRAVITY interferometer.


Abstract:
  In many health domains such as substance-use, outcomes are often counts with
an excessive number of zeros (EZ) - count data having zero counts at a rate
significantly higher than that expected of a standard count distribution (e.g.,
Poisson). However, an important gap exists in sample size estimation
methodology for planning sequential multiple assignment randomized trials
(SMARTs) for comparing dynamic treatment regimens (DTRs) using longitudinal
count data. DTRs, also known as treatment algorithms or adaptive interventions,
mimic the individualized and evolving nature of patient care through the
specification of decision rules guiding the type, timing and modality of
delivery, and dosage of treatments to address the unique and changing needs of
individuals. To close this gap, we develop a Monte Carlo-based approach to
sample size estimation. A SMART for engaging alcohol and cocaine-dependent
patients in treatment is used as motivation.


Abstract:
  We study the phase synchronization problem with noisy measurements
$Y=z^*z^{*H}+\sigma W\in\mathbb{C}^{n\times n}$, where $z^*$ is an
$n$-dimensional complex unit-modulus vector and $W$ is a complex-valued
Gaussian random matrix. It is assumed that each entry $Y_{jk}$ is observed with
probability $p$. We prove that an SDP relaxation of the MLE achieves the error
bound $(1+o(1))\frac{\sigma^2}{2np}$ under a normalized squared $\ell_2$ loss.
This result matches the minimax lower bound of the problem, and even the
leading constant is sharp. The analysis of the SDP is based on an equivalent
non-convex programming whose solution can be characterized as a fixed point of
the generalized power iteration lifted to a higher dimensional space. This
viewpoint unifies the proofs of the statistical optimality of three different
methods: MLE, SDP, and generalized power method. The technique is also applied
to the analysis of the SDP for $\mathbb{Z}_2$ synchronization, and we achieve
the minimax optimal error $\exp\left(-(1-o(1))\frac{np}{2\sigma^2}\right)$ with
a sharp constant in the exponent.


Abstract:
  Noise injection-based regularization, such as Dropout, has been widely used
in image domain to improve the performance of deep neural networks (DNNs).
However, efficient regularization in the point cloud domain is rarely
exploited, and most of the state-of-the-art works focus on data
augmentation-based regularization. In this paper, we, for the first time,
perform systematic investigation on noise injection-based regularization for
point cloud-domain DNNs. To be specific, we propose a series of regularization
techniques, namely DropFeat, DropPoint and DropCluster, to perform noise
injection on the point feature maps at the feature level, point level and
cluster level, respectively. We also empirically analyze the impacts of
different factors, including dropping rate, cluster size and dropping position,
to obtain useful insights and general deployment guidelines, which can
facilitate the adoption of our approaches across different datasets and DNN
architectures.
  We evaluate our proposed approaches on various DNN models for different point
cloud processing tasks. Experimental results show our approaches enable
significant performance improvement. Notably, our DropCluster brings 1.5%, 1.3%
and 0.8% higher overall accuracy for PointNet, PointNet++ and DGCNN,
respectively, on ModelNet40 shape classification dataset. On ShapeNet part
segmentation dataset, DropCluster brings 0.5%, 0.5% and 0.2% mean
Intersection-over-union (IoU) increase for PointNet, PointNet++ and DGCNN,
respectively. On S3DIS semantic segmentation dataset, DropCluster improves the
mean IoU of PointNet, PointNet++ and DGCNN by 3.2%, 2.9% and 3.7%,
respectively. Meanwhile, DropCluster also enables the overall accuracy increase
for these three popular backbone DNNs by 2.4%, 2.2% and 1.8%, respectively.


Abstract:
  Self-supervised clustering methods have achieved increasing accuracy in
recent years but do not yet perform as well as supervised classification
methods. This contrasts with the situation for feature learning, where
self-supervised features have recently surpassed the performance of supervised
features on several important tasks. We hypothesize that the performance gap is
due to the difficulty of specifying, without supervision, which features
correspond to class differences that are semantic to humans. To reduce the
performance gap, we introduce the "single-noun" prior - which states that
semantic clusters tend to correspond to concepts that humans label by a
single-noun. By utilizing a pre-trained network that maps images and sentences
into a common space, we impose this prior obtaining a constrained optimization
task. We show that our formulation is a special case of the facility location
problem, and introduce a simple-yet-effective approach for solving this
optimization task at scale. We test our approach on several commonly reported
image clustering datasets and obtain significant accuracy gains over the best
existing approaches.


Abstract:
  We consider high order approximations of the solution of the stochastic
filtering problem, derive their pathwise representation in the spirit of the
earlier work of Clark and Davis and prove their robustness property. In
particular, we show that the high order discretised filtering functionals can
be represented by Lipschitz continuous functions defined on the observation
path space. This property is important from the practical point of view as it
is in fact the pathwise version of the filtering functional that is sought in
numerical applications. Moreover, the pathwise viewpoint will be a stepping
stone into the rigorous development of machine learning methods for the
filtering problem. This work is a continuation of a recent work by two of the
authors where a discretisation of the solution of the filtering problem of
arbitrary order has been established. We expand the previous work by showing
that robust approximations can be derived from the discretisations therein.


Abstract:
  We report multi-wavelength study of a complex M-class solar eruptive flare
that consists of three different sets of flare ribbons, viz. circular,
parallel, and remote ribbons. Magnetic field modeling of source active region
NOAA 12242 exhibits the presence of 3D null-point magnetic topology which
encompasses an inner bipolar region. The event initiates with the faint
signatures of the circular ribbon along with remote brightening right from the
pre-flare phase which points toward the ongoing slow yet persistent null-point
reconnection. We first detected flux cancellation and an associated
brightening, which are likely signatures of tether-cutting reconnection that
builds the flux rope near the polarity inversion line (PIL) of the inner
bipolar region. In the next stage, with the onset of M8.7 flare, there is a
substantial enhancement in the brightening of circular ribbon which essentially
suggests an increase in the rate of ongoing null-point reconnection. Finally,
the eruption of underlying flux rope triggers ``standard flare reconnection"
beneath it producing an abrupt rise in the intensity of the parallel ribbons as
well as enhancing the rate of null-point reconnection by external forcing. We
show that within the the fan dome, the region with magnetic decay index n>1.5
borders the null-point QSL. Our analysis suggests that both the torus
instability and the breakout model have played role toward the triggering
mechanism for the eruptive flare. This event is a nice example of the dynamical
evolution of a flux rope initially confined in a null-point topology, that
subsequently activates and erupts with the progression of the circular -- cum
-- parallel ribbon flare.


Abstract:
  This paper considers a multiagent, connected, robotic fleet where the primary
functionality of the agents is sensing. A distributed multi-sensor control
strategy maximizes the value of the collective sensing capability of the fleet,
using an information-driven approach. Each agent individually performs sensor
processing (Kalman Filtering and Joint Probabilistic Data Association) to
identify trajectories (and associated distributions). Using communications with
its neighbors the agents enhance the prediction of the trajectories using a
Consensus of Information approach that iteratively calculates the
Kullback-Leibler average of trajectory distributions, enabling the calculation
of the value of the collective information for the fleet. The dynamics of the
agents, the evolution of the identified trajectories for each agent, and the
dynamics of individual observed objects are captured as a Partially Observable
Markov Decision Process (POMDP). Using this POMDP and applying rollout with
receding horizon control, an optimized non-myopic control policy that maximizes
the collective fleet information value is synthesized. Simulations are
performed for a scenario with three heterogeneous UAVs performing coordinated
target tracking that illustrate the proposed methodology and compare the
centralized approach with a contemporary sequential multiagent distributed
decision technique.


Abstract:
  Most existing approaches to train a unified multi-organ segmentation model
from several single-organ datasets require simultaneously access multiple
datasets during training. In the real scenarios, due to privacy and ethics
concerns, the training data of the organs of interest may not be publicly
available. To this end, we investigate a data-free incremental organ
segmentation scenario and propose a novel incremental training framework to
solve it. We use the pretrained model instead of its own training data for
privacy protection. Specifically, given a pretrained $K$ organ segmentation
model and a new single-organ dataset, we train a unified $K+1$ organ
segmentation model without accessing any data belonging to the previous
training stages. Our approach consists of two parts: the background label
alignment strategy and the uncertainty-aware guidance strategy. The first part
is used for knowledge transfer from the pretained model to the training model.
The second part is used to extract the uncertainty information from the
pretrained model to guide the whole knowledge transfer process. By combing
these two strategies, more reliable information is extracted from the
pretrained model without original training data. Experiments on multiple
publicly available pretrained models and a multi-organ dataset MOBA have
demonstrated the effectiveness of our framework.


Abstract:
  Automated and accurate segmentations of left atrium (LA) and atrial scars
from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are
in high demand for quantifying atrial scars. The previous quantification of
atrial scars relies on a two-phase segmentation for LA and atrial scars due to
their large volume difference (unbalanced atrial targets). In this paper, we
propose an inter-cascade generative adversarial network, namely JAS-GAN, to
segment the unbalanced atrial targets from LGE CMR images automatically and
accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive
attention cascade to automatically correlate the segmentation tasks of the
unbalanced atrial targets. The adaptive attention cascade mainly models the
inclusion relationship of the two unbalanced atrial targets, where the
estimated LA acts as the attention map to adaptively focus on the small atrial
scars roughly. Then, an adversarial regularization is applied to the
segmentation tasks of the unbalanced atrial targets for making a consistent
optimization. It mainly forces the estimated joint distribution of LA and
atrial scars to match the real ones. We evaluated the performance of our
JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the
state-of-the-art methods, our proposed approach yielded better segmentation
performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and
0.821 for LA and atrial scars, respectively), which indicated the effectiveness
of our proposed approach for segmenting unbalanced atrial targets.


Abstract:
  This study proposes an efficient exact k-flexible aggregate nearest neighbor
(k-FANN) search algorithm in road networks using the M-tree. The
state-of-the-art IER-kNN algorithm used the R-tree and pruned off unnecessary
nodes based on the Euclidean coordinates of objects in road networks. However,
IER-kNN made many unnecessary accesses to index nodes since the Euclidean
distances between objects are significantly different from the actual
shortest-path distances between them. In contrast, our algorithm proposed in
this study can greatly reduce unnecessary accesses to index nodes compared with
IER-kNN since the M-tree is constructed based on the actual shortest-path
distances between objects. To the best of our knowledge, our algorithm is the
first exact FANN algorithm that uses the M-tree. We prove that our algorithm
does not cause any false drop. In conducting a series of experiments using
various real road network datasets, our algorithm consistently outperformed
IER-kNN by up to 6.92 times.


Abstract:
  We present a scenario for the formation and the morphology of the
arrow-shaped dwarf irregular galaxy NGC 1427A in the Fornax Cluster. This
galaxy shows intriguing stellar and gaseous tails pointing in different
directions for which alternative but not conclusive formation scenarios have
been proposed in the literature. We performed N-body/SPH simulations of dwarf
galaxies falling into a model of the Fornax cluster, exhibiting a
jellyfish-like appearance while undergoing ram-pressure stripping. We noted
that some of our models show interesting tail morphologies similar to that of
NGC 1427A. In this way, the peculiar NGC 1427A structure can be studied using
models whose stellar and neutral gas photometry and kinematics are in good
agreement with the observed ones, without the need of invoking an interaction
with a nearby galaxy. Thanks to the tails, we can identify the requirements for
a galaxy to expose such a structure and assess the possible position and
velocity of the galaxy in the cluster. This puts constraints on the orbit of
the galaxy, its position in the cluster and the time since its pericentre
passage. From the statistics of identified snapshots following our modelling,
we found that the most likely position of the galaxy is around 200 kpc in front
of the cluster centre, travelling towards the cluster with a velocity angle
with respect to the line-of-sight direction of around 50 deg. This analysis can
be useful in future observations of similar galaxies in clusters to
characterise their position and velocity in the cluster and their formation.


Abstract:
  Many mobile robotic platforms rely on an accurate knowledge of the extrinsic
calibration parameters, especially systems performing visual stereo matching.
Although a number of accurate stereo camera calibration methods have been
developed, which provide good initial "factory" calibrations, the determined
parameters can lose their validity over time as the sensors are exposed to
environmental conditions and external effects. Thus, on autonomous platforms
on-board diagnostic methods for an early detection of the need to repeat
calibration procedures have the potential to prevent critical failures of
crucial systems, such as state estimation or obstacle detection. In this work,
we present a novel data-driven method to estimate the calibration quality and
detect discrepancies between the original calibration and the current system
state for stereo camera systems. The framework consists of a novel dataset
generation pipeline to train CalQNet, a deep convolutional neural network.
CalQNet can estimate the calibration quality using a new metric that
approximates the degree of miscalibration in stereo setups. We show the
framework's ability to predict from a single stereo frame if a state-of-the-art
stereo-visual odometry system will diverge due to a degraded calibration in two
real-world experiments.


Abstract:
  Weakly-supervised temporal action localization aims to localize action
instances temporal boundary and identify the corresponding action category with
only video-level labels. Traditional methods mainly focus on foreground and
background frames separation with only a single attention branch and class
activation sequence. However, we argue that apart from the distinctive
foreground and background frames there are plenty of semantically ambiguous
action context frames. It does not make sense to group those context frames to
the same background class since they are semantically related to a specific
action category. Consequently, it is challenging to suppress action context
frames with only a single class activation sequence. To address this issue, in
this paper, we propose an action-context modeling network termed ACM-Net, which
integrates a three-branch attention module to measure the likelihood of each
temporal point being action instance, context, or non-action background,
simultaneously. Then based on the obtained three-branch attention values, we
construct three-branch class activation sequences to represent the action
instances, contexts, and non-action backgrounds, individually. To evaluate the
effectiveness of our ACM-Net, we conduct extensive experiments on two benchmark
datasets, THUMOS-14 and ActivityNet-1.3. The experiments show that our method
can outperform current state-of-the-art methods, and even achieve comparable
performance with fully-supervised methods. Code can be found at
https://github.com/ispc-lab/ACM-Net


Abstract:
  The log-aesthetic curve has a significant factor in the field of aesthetic
design to meet the high industrial requirements. It has much exceptional
property and a large number of research papers are published, since its
introduction. It can be parametrized by tangential angle, and it is
autoevolute, meaning that its evolute is also a log-aesthetic curve. In this
paper, we will examine the isoptics of the log-aesthetic curve and determine if
it is autoisoptic as well. We will show that the log-aesthetic curve only
coincides its isoptic in some specific cases. We will also provide a new
formula to calculate the isoptic curve of any tangential angle parametrized
curves.


Abstract:
  With the advent of the Internet of Things (IoT), establishing a secure
channel between smart devices becomes crucial. Recent research proposes
zero-interaction pairing (ZIP), which enables pairing without user assistance
by utilizing devices' physical context (e.g., ambient audio) to obtain a shared
secret key. The state-of-the-art ZIP schemes suffer from three limitations: (1)
prolonged pairing time (i.e., minutes or hours), (2) vulnerability to
brute-force offline attacks on a shared key, and (3) susceptibility to attacks
caused by predictable context (e.g., replay attack) because they rely on
limited entropy of physical context to protect a shared key. We address these
limitations, proposing FastZIP, a novel ZIP scheme that significantly reduces
pairing time while preventing offline and predictable context attacks. In
particular, we adapt a recently introduced Fuzzy Password-Authenticated Key
Exchange (fPAKE) protocol and utilize sensor fusion, maximizing their
advantages. We instantiate FastZIP for intra-car device pairing to demonstrate
its feasibility and show how the design of FastZIP can be adapted to other ZIP
use cases. We implement FastZIP and evaluate it by driving four cars for a
total of 800 km. We achieve up to three times shorter pairing time compared to
the state-of-the-art ZIP schemes while assuring robust security with
adversarial error rates below 0.5%.


Abstract:
  We review recent progress in understanding nearly integrable models within
the framework of generalized hydrodynamics (GHD). Integrable systems have
infinitely many conserved quantities and stable quasiparticle excitations: when
integrability is broken, only a few residual conserved quantities survive,
eventually leading to thermalization, chaotic dynamics and conventional
hydrodynamics. In this review, we summarize recent efforts to take into account
small integrability breaking terms, and describe the transition from GHD to
standard hydrodynamics. We discuss the current state of the art, with emphasis
on weakly inhomogeneous potentials, generalized Boltzmann equations and
collision integrals, as well as bound-state recombination effects. We also
identify important open questions for future works.


Abstract:
  This paper describes the ExKaldi-RT online automatic speech recognition (ASR)
toolkit that is implemented based on the Kaldi ASR toolkit and Python language.
ExKaldi-RT provides tools for building online recognition pipelines. While
similar tools are available built on Kaldi, a key feature of ExKaldi-RT that it
works on Python, which has an easy-to-use interface that allows online ASR
system developers to develop original research, such as by applying neural
network-based signal processing and by decoding model trained with deep
learning frameworks. We performed benchmark experiments on the minimum
LibriSpeech corpus, and it showed that ExKaldi-RT could achieve competitive ASR
performance in real-time recognition.


Abstract:
  Answering questions about why characters perform certain actions is central
to understanding and reasoning about narratives. Despite recent progress in QA,
it is not clear if existing models have the ability to answer "why" questions
that may require commonsense knowledge external to the input narrative. In this
work, we introduce TellMeWhy, a new crowd-sourced dataset that consists of more
than 30k questions and free-form answers concerning why characters in short
narratives perform the actions described. For a third of this dataset, the
answers are not present within the narrative. Given the limitations of
automated evaluation for this task, we also present a systematized human
evaluation interface for this dataset. Our evaluation of state-of-the-art
models show that they are far below human performance on answering such
questions. They are especially worse on questions whose answers are external to
the narrative, thus providing a challenge for future QA and narrative
understanding research.


Abstract:
  We have revisited the chemistry of chlorine-bearing species in the diffuse
interstellar medium with new observations of the HCl$^+$ molecular ion and new
astrochemical models. Using the GREAT instrument on board SOFIA, we observed
the $^2\Pi_{3/2}\, J = 5/2 - 3/2$ transition of HCl$^+$ near 1444 GHz toward
the bright THz continuum source W49N. We detected absorption by diffuse
foreground gas unassociated with the background source, and were able to
thereby measure the distribution of HCl$^+$ along the sight-line. We
interpreted the observational data using an updated version of an astrochemical
model used previously in a theoretical study of Cl-bearing interstellar
molecules. The abundance of HCl$^+$ was found to be almost constant relative to
the related H$_2$Cl$^+$ ion, but the observed $n({\rm H_2Cl^+})/n({\rm HCl^+})$
abundance ratio exceeds the predictions of our astrochemical model by an
order-of-magnitude. This discrepancy suggests that the rate of the primary
destruction process for ${\rm H_2Cl^+}$, dissociative recombination, has been
significantly overestimated. For HCl$^+$, the model predictions can provide a
satisfactory fit to the observed column densities along the W49N sight-line
while simultaneously accounting for the ${\rm OH^+}$ and ${\rm H_2O^+}$ column
densities.


Abstract:
  We consider functional differential equations(FDEs) which are perturbations
of smooth ordinary differential equations(ODEs). The FDE can involve multiple
state-dependent delays or distributed delays (forward or backward). We show
that, under some mild assumptions, if the ODE has a nondegenerate periodic
orbit, then the FDE has a smooth periodic orbit. Moreover, we get smooth
dependence of the periodic orbit and its frequency on parameters with high
regularity.
  The result also applies to FDEs which are perturbations of some evolutionary
partial differential equations(PDEs).
  The proof consists in solving functional equations satisfied by the
parameterization of the periodic orbit and the frequency using a fixed point
approach. We do not need to consider the smoothness of the evolution or even
the phase space of the FDEs.


Abstract:
  Microbially induced calcite precipitation (MICP) is a technology for sealing
leakage paths to ensure the safe storage of CO$_2$ in geological formations. In
this work we introduce a numerical simulator of MICP for field-scale studies.
This simulator is implemented in the open porous media (OPM) framework. We
compare the numerical results to simulations using an upgraded implementation
of the mathematical model in the MATLAB reservoir simulation toolbox (MRST).
Finally, we consider a 3D system consisting of two aquifers separated by
caprock with a leakage path across the width of the reservoir. We study a
strategy where microbial solution is injected only at the beginning of the
treatment and subsequently either growth solution or cementation solution is
injected for biofilm development or calcite precipitation. By applying this
strategy, the numerical results show that the MICP technology could be used to
seal these leakage paths.


Abstract:
  pyGDM is a python toolkit for electro-dynamical simulations of individual
nano-structures, based on the Green Dyadic Method (GDM). pyGDM uses the concept
of a generalized propagator, which allows to solve cost-efficiently
monochromatic problems with a large number of varying illumination conditions
such as incident angle scans or focused beam raster-scan simulations. We
provide an overview of new features added since the initial publication
[Wiecha, Computer Physics Communications 233, pp.167-192 (2018)]. The updated
version of pyGDM is implemented in pure python, removing the former dependency
on fortran-based binaries. In the course of this re-write, the toolkit's
internal architecture has been completely redesigned to offer a much wider
range of possibilities to the user such as the choice of the dyadic Green's
functions describing the environment. A new class of dyads allows to perform 2D
simulations of infinitely long nanostructures. While the Green's dyads in pyGDM
are based on a quasistatic description for interfaces, we also provide as new
external python package pyGDM2_retard a module with retarded Green's tensors
for an environment with two interfaces. We have furthermore added
functionalities for simulations using fast-electron excitation, namely electron
energy loss spectroscopy and cathodoluminescence. Along with several further
new tools and improvements, the update includes also the possibility to
calculate the magnetic field and the magnetic LDOS inside nanostructures,
field-gradients in- and outside a nanoparticle, optical forces or the chirality
of nearfields. All new functionalities remain compatible with the evolutionary
optimization module of pyGDM for nano-photonics inverse design.


Abstract:
  Bubble-sort star graphs are a combination of star graphs and bubble sort
graphs. They are bipartite graphs and also form a family of Cayley graphs. The
decycling number of a graph is the minimum number of vertices whose removal
from the graph results in an acyclic subgraph. In this paper, we prove the
decycling number D(n) of an n-dimensional bubble-sort star graph for n <= 5. We
also show D(n) satisfies the inequalities for n >= 6.


Abstract:
  In insulators, Born effective charges describe the electrical polarization
induced by the displacement of individual atomic sublattices. Such a physical
property is at first sight irrelevant for metals and doped semiconductors,
where the macroscopic polarization is ill-defined. Here we show that, in clean
conductors, going beyond the adiabatic approximation results in nonadiabatic
Born effective charges that are well defined in the low-frequency limit. In
addition, we find that the sublattice sum of the nonadiabatic Born effective
charges does not vanish as it does in the insulating case, but instead is
proportional to the Drude weight. We demonstrate these formal results with
density functional perturbation theory calculations of Al, and electron-doped
SnS$_2$ and SrTiO$_3$.


Abstract:
  The compact set of homogeneous quadratic polynomials in $n$ real variables
with modulus bounded by 1 on the unit sphere $S^{n-1}$ is trivially
semi-definite representable. The compact set of homogeneous ternary quartics
with modulus bounded by 1 on the unit sphere $S^2$ is also semi-definite
representable. This suggests that the compact set of homogeneous ternary cubics
with modulus bounded by 1 on $S^2$ is semi-definite representable. We deduce an
explicit semi-definite representation of this norm ball. More generally, we
provide a semi-definite description of the cone of inhomogeneous ternary cubics
which are nonnegative on $S^2$.


Abstract:
  We analyze dynamics of chiral gauge theories based on the $SU(N)$ gauge group
with one anti-symmetric tensor $A$ and $(N-4)$ anti-fundamentals $F_{i}$ when
$N$ is odd. Based on the continuity to the supersymmetric gauge theories with
anomaly-mediated supersymmetry breaking, we claim that the global $SU(N-4)$
symmetry is spontaneously broken to $Sp(N-5)$. There are $N-5$ massless
fermions as a fundamental representation of $Sp(N-5)$, and another massless
fermion, together saturating the anomaly matching conditions. When $N$ is even,
the unbroken flavor symmetry is $Sp(N-4)$ while there are no massless fermions.
Our result is different from the dynamics suggested by tumbling where the full
$SU(N-4)$ symmetry is unbroken, but the tumbling picture can be modified via
the addition of a second condensate to produce the symmetry breaking pattern
predicted from our method.


Abstract:
  In this paper, we expand on results from our previous paper "The Case Against
Smooth Null Infinity I: Heuristics and Counter-Examples" [1] by showing that
the failure of "peeling" (and, thus, of smooth null infinity) in a
neighbourhood of $i^0$ derived therein translates into logarithmic corrections
at leading order to the well-known Price's law asymptotics near $i^+$. This
suggests that the non-smoothness of $\mathcal{I}^+$ is physically measurable.
More precisely, we consider the linear wave equation $\Box_g \phi=0$ on a fixed
Schwarzschild background ($M>0$), and we show the following: If one imposes
conformally smooth initial data on an ingoing null hypersurface (extending to
$\mathcal{H}^+$ and terminating at $\mathcal{I}^-$) and vanishing data on
$\mathcal{I}^-$ (this is the no incoming radiation condition), then the precise
leading-order asymptotics of the solution $\phi$ are given by
$r\phi|_{\mathcal{I}^+}=C u^{-2}\log u+\mathcal{O}(u^{-2})$ along future null
infinity, $\phi|_{r=R>2M}=2C\tau^{-3}\log\tau+\mathcal{O}(\tau^{-3})$ along
hypersurfaces of constant $r$, and $\phi|_{\mathcal{H}^+}=2Cv^{-3}\log
v+\mathcal{O}(v^{-3})$ along the event horizon. Moreover, the constant $C$ is
given by $C=4M I_0^{(\mathrm{past})}[\phi]$, where
$I_0^{(\mathrm{past})}[\phi]:=\lim_{u\to -\infty}
r^2\partial_u(r\phi_{\ell=0})$ is the past Newman--Penrose constant of $\phi$
on $\mathcal{I}^-$. Thus, the precise late-time asymptotics of $\phi$ are
completely determined by the early-time behaviour of the spherically symmetric
part of $\phi$ near $\mathcal{I}^-$. Similar results are obtained for
polynomially decaying timelike boundary data. The paper uses methods developed
by Angelopoulos--Aretakis--Gajic and is essentially self-contained.


Abstract:
  Compton scattering of gamma rays propagating in a pair plasma can drive the
formation of a relativistic electron positron beam. This process is scrutinised
theoretically and numerically via particle-in-cell simulations. In addition, we
determine in which conditions the beam can prompt a beam-plasma instability and
convert its kinetic energy into magnetic energy. We argue that such conditions
can be met at the photosphere radius of bright Gamma Ray Bursts.


Abstract:
  With the emergence of Internet-of-Things (IoT) and ever-increasing demand for
the newly connected devices, there is a need for more effective storage and
processing paradigms to cope with the data generated from these devices. In
this study, we have discussed different paradigms for data processing and
storage including Cloud, Fog, and Edge computing models and their suitability
in integrating with the IoT. Moreover, a detailed discussion on low latency and
massive connectivity requirements of future cellular networks in accordance
with machine-type communication (MTC) is also presented. Furthermore, the need
to bring IoT devices to Internet connectivity and a standardized protocol stack
to regulate the data transmission between these devices is also addressed while
keeping in view the resource constraint nature of IoT devices.


Abstract:
  Machine learning models that incorporate concept learning as an intermediate
step in their decision making process can match the performance of black-box
predictive models while retaining the ability to explain outcomes in human
understandable terms. However, we demonstrate that the concept representations
learned by these models encode information beyond the pre-defined concepts, and
that natural mitigation strategies do not fully work, rendering the
interpretation of the downstream prediction misleading. We describe the
mechanism underlying the information leakage and suggest recourse for
mitigating its effects.


Abstract:
  We study the application of iterative first-order methods to the problem of
computing equilibria of large-scale two-player extensive-form games.
First-order methods must typically be instantiated with a regularizer that
serves as a distance-generating function for the decision sets of the players.
For the case of two-player zero-sum games, the state-of-the-art theoretical
convergence rate for Nash equilibrium is achieved by using the dilated entropy
function. In this paper, we introduce a new entropy-based distance-generating
function for two-player zero-sum games, and show that this function achieves
significantly better strong convexity properties than the dilated entropy,
while maintaining the same easily-implemented closed-form proximal mapping.
Extensive numerical simulations show that these superior theoretical properties
translate into better numerical performance as well. We then generalize our new
entropy distance function, as well as general dilated distance functions, to
the scaled extension operator. The scaled extension operator is a way to
recursively construct convex sets, which generalizes the decision polytope of
extensive-form games, as well as the convex polytopes corresponding to
correlated and team equilibria. By instantiating first-order methods with our
regularizers, we develop the first accelerated first-order methods for
computing correlated equilibra and ex-ante coordinated team equilibria. Our
methods have a guaranteed $1/T$ rate of convergence, along with linear-time
proximal updates.


Abstract:
  Retrosynthesis, of which the goal is to find a set of reactants for
synthesizing a target product, is an emerging research area of deep learning.
While the existing approaches have shown promising results, they currently lack
the ability to consider availability (e.g., stability or purchasability) of the
reactants or generalize to unseen reaction templates (i.e., chemical reaction
rules). In this paper, we propose a new approach that mitigates the issues by
reformulating retrosynthesis into a selection problem of reactants from a
candidate set of commercially available molecules. To this end, we design an
efficient reactant selection framework, named RetCL (retrosynthesis via
contrastive learning), for enumerating all of the candidate molecules based on
selection scores computed by graph neural networks. For learning the score
functions, we also propose a novel contrastive training scheme with hard
negative mining. Extensive experiments demonstrate the benefits of the proposed
selection-based approach. For example, when all 671k reactants in the USPTO
{database} are given as candidates, our RetCL achieves top-1 exact match
accuracy of $71.3\%$ for the USPTO-50k benchmark, while a recent
transformer-based approach achieves $59.6\%$. We also demonstrate that RetCL
generalizes well to unseen templates in various settings in contrast to
template-based approaches.


Abstract:
  In inverse optimal control, an optimal controller is synthesized with respect
to a meaningful, a posteriori, defined cost functional. Our work illustrates
the usefulness of this approach in the control of converter-based power systems
and networked systems in general, and thereby in designing controllers with
topological structure and known optimality properties. In particular, we design
an inverse optimal feedback controller that stabilizes the phase angles of
voltage source-controlled DC/AC converters at an induced steady state with {\em
zero} frequency error. The distributed angular droop controller yields active
power to angle droop behavior at steady state. Moreover, we suggest a practical
implementation of the controller and corroborate our results through
simulations on a three-converter system and a numerical comparison with
standard frequency droop control.


Abstract:
  Automatic defect detection is a challenging task because of the variability
in texture and type of fabric defects. An effective defect detection system
enables manufacturers to improve the quality of processes and products.
Automation across the textile manufacturing systems would reduce fabric wastage
and increase profitability by saving cost and resources. There are different
contemporary research on automatic defect detection systems using image
processing and machine learning techniques. These techniques differ from each
other based on the manufacturing processes and defect types. Researchers have
also been able to establish real-time defect detection system during weaving.
Although, there has been research on patterned fabric defect detection, these
defects are related to weaving faults such as holes, and warp and weft defects.
But, there has not been any research that is designed to detect defects that
arise during such as spot and print mismatch. This research has fulfilled this
gap by developing a print fabric database and implementing deep convolutional
neural network (CNN).


Abstract:
  Let $A$ be a square complex matrix and $z$ a complex number. The distance,
with respect to the spectral norm, from $A$ to the set of matrices which have
$z$ as an eigenvalue is less than or equal to the distance from $z$ to the
spectrum of $A$. If these two distances are equal for a sufficiently large
finite set of numbers $z$ which are not in the spectrum of $A$, then the matrix
$A$ is normal.


Abstract:
  Simulating inhomogeneous flows with different characteristic scales in
different coordinate directions using the collide-and-stream based lattice
Boltzmann methods (LBM) can be accomplished efficiently using rectangular
lattice grids. We develop and investigate a new rectangular central moment LBM
based on non-orthogonal moment basis (referred to as RC-LBM). The equilibria to
which the central moments relax under collision in this approach are obtained
from matching with those corresponding to the continuous Maxwell distribution.
A Chapman-Enskog analysis is performed to derive the correction terms to the
second order moment equilibria involving the grid aspect ratio and velocity
gradients that restores the isotropy of the viscous stress tensor and
eliminates the non-Galilean invariant cubic velocity terms of the resulting
hydrodynamical equations. A special case of this rectangular formulation
involving the raw moments (referred to as the RNR-LBM) is also constructed. The
resulting schemes represent a considerable simplification, especially for the
transformation matrices and isotropy corrections, and improvement over the
existing MRT-LB schemes on rectangular lattice grids that use orthogonal moment
basis. Numerical validation study of both the RC-LBM and RNR-LBM for a variety
of benchmark flow problems are performed that show good accuracy at various
grid aspect ratios. The ability of our proposed schemes to simulate flows using
relatively lower grid aspect ratios than considered in prior rectangular LB
approaches is demonstrated. Furthermore, simulations reveal the superior
stability characteristics of the RC-LBM over RNR-LBM in handling shear flows at
lower viscosities and/or higher characteristic velocities. In addition,
computational advantages of using our rectangular LB formulation in lieu of
that based on the square lattice is shown.


Abstract:
  We prove that among the finite dimensional algebras of finite representation
type those that are string algebras are precisely the ones that have the
property that the middle term of an arbitrary extension of indecomposable
modules has at most two direct factors. On the other hand, we show that
non-domestic string algebras are very far from having that property.


Abstract:
  We obtain Gabor frame characterisations of modulation spaces defined via a
class of translation-modulation invariant Banach spaces of distributions that
was recently introduced in $[10]$. We show that these spaces admit an atomic
decomposition through Gabor expansions and that they are characterised by
summability properties of their Gabor coefficients. Furthermore, we construct a
large space of admissible windows. This generalises several fundamental results
for the classical modulation spaces $ M^{p,q}_{w}$. Due to the absence of
solidity assumptions on the Banach spaces defining these modulation spaces, the
methods used for the spaces $M^{p,q}_{w}$ (or, more generally, in coorbit space
theory) fail in our setting and we develop here a new approach based on the
twisted convolution.


Abstract:
  We study the convergence of the discontinuous Galerkin (DG) method applied to
the advection-reaction equation on meshes with reentrant faces. On such meshes,
the upwind numerical flux is not smooth, and so the numerical integration of
the resulting face terms can only be expected to be first-order accurate.
Despite this inexact integration, we prove that the DG method converges with
order $\mathcal{O}(h^{p+1/2})$, which is the same rate as in the case of exact
integration. Consequently, specialized quadrature rules that accurately
integrate the non-smooth numerical fluxes are not required for high-order
accuracy. These results are numerically corroborated on examples of linear
advection and discrete ordinates transport equations.


Abstract:
  Coulomb interaction, following an inverse-square force-law, quantifies the
amount of force between two stationary and electrically charged particles. The
long-range nature of Coulomb interactions poses a major challenge to molecular
dynamics simulations which are major tools for problems at the nano-/micro-
scale. Various algorithms are developed to calculate the pairwise Coulomb
interactions to a linear scaling but the poor scalability limits the size of
simulated systems. Here, we conduct an efficient molecular dynamics algorithm
with the random batch Ewald method on all-atom systems where the complete
Fourier components in the Coulomb interaction are replaced by randomly selected
mini-batches. By simulating the $N$-body systems up to 100 million particles
using $10$ thousand CPU cores, we show that this algorithm furnishes $O(N)$
complexity, almost perfect scalability and an order of magnitude faster
computational speed when compared to the existing state-of-the-art algorithms.
Further examinations of our algorithm on distinct systems, including pure
water, micro-phase-separated electrolyte and protein solution demonstrate that
the spatiotemporal information on all time and length scales investigated and
thermodynamic quantities derived from our algorithm are in perfect agreement
with those obtained from the existing algorithms. Therefore, our algorithm
provides a breakthrough solution on scalability of computing the Coulomb
interaction. It is particularly useful and cost-effective to simulate
ultra-large systems, which was either impossible or very costing to conduct
using existing algorithms, thus would benefit the broad community of sciences.


Abstract:
  In this letter, we present the photonic nanojet as a phenomenon in a
structured light generator system that is implemented to modify the source
focal spot size and emission angle. The optical system comprises a microlens
array that is illuminated by a focused Gaussian beam to generate a structured
pattern in the far-field. By introducing a spheroid with different aspect
ratios in the focus of the Gaussian beam, the source optical characteristics
change and a photonic nanojet is generated which will engineer the far-field
distribution. To probe the light fields we implement a high-resolution
interferometry setup to extract both the phase and intensity at different
planes. We both numerically and experimentally demonstrate that the pattern
distribution in the far-field can be engineered by photonic nanojet. As an
example, we examine prolate, sphere, and ablate geometries. An interesting
finding is that depending on the spheroid geometry, a smaller transverse FWHM
of a photonic nanojet with a higher diverges angle produces an increased
pattern field of view at the same physical size of the optical system.


Abstract:
  We investigate the groups of units of one-relator and special inverse
monoids. These are inverse monoids which are defined by presentations where all
the defining relations are of the form $r=1$. We develop new approaches for
finding presentations for the group of units of a special inverse monoid, and
apply these methods to give conditions under which the group admits a
presentation with the same number of defining relations as the monoid. In
particular our results give sufficient conditions for the group of units of a
one-relator inverse monoid to be a one-relator group. When these conditions are
satisfied these results give inverse semigroup theoretic analogues of classical
results of Adjan for one-relator monoids, and Makanin for special monoids. In
contrast, we show that in general these classical results to not hold for
one-relator and special inverse monoids. In particular, we show that there
exists a one-relator special inverse monoid whose group of units is not a
one-relator group (with respect to any generating set), and we show that there
exists a finitely presented special inverse monoid whose group of units is not
finitely presented.


Abstract:
  Nearly complete intersection ideals were introduced by A. Boocher and J.
Seiner (2018) and defines a special class of monomial ideals in a polynomial
ring. These ideals were used to give a lower bound of the total sum of betti
numbers that appear a minimal free resolution of a monomial ideal. In this note
we give a graph theoretic classification of nearly complete intersection ideals
generated in degree two. In doing so, we define a novel graph operation (the
inversion) that is motivated by the definition of this new class of ideals.


Abstract:
  Purpose: This study evaluates the effectiveness and impact of automated
order-based protocol assignment for magnetic resonance imaging (MRI) exams
using natural language processing (NLP) and deep learning (DL).
  Methods: NLP tools were applied to retrospectively process orders from over
116,000 MRI exams with 200 unique sub-specialized protocols ("Local" protocol
class). Separate DL models were trained on 70\% of the processed data for
"Local" protocols as well as 93 American College of Radiology ("ACR") protocols
and 48 "General" protocols. The DL Models were assessed in an "auto-protocoling
(AP)" inference mode which returns the top recommendation and in a "clinical
decision support (CDS)" inference mode which returns up to 10 protocols for
radiologist review. The accuracy of each protocol recommendation was computed
and analyzed based on the difference between the normalized output score of the
corresponding neural net for the top two recommendations.
  Results: The top predicted protocol in AP mode was correct for 82.8%, 73.8%,
and 69.3% of the test cases for "General", "ACR", and "Local" protocol classes,
respectively. Higher levels of accuracy over 96% were obtained for all protocol
classes in CDS mode. However, at current validation performance levels, the
proposed models offer modest, positive, financial impact on large-scale imaging
networks.
  Conclusions: DL-based protocol automation is feasible and can be tuned to
route substantial fractions of exams for auto-protocoling, with higher accuracy
with more general protocols. Economic analyses of the tested algorithms
indicate that improved algorithm performance is required to yield a practical
exam auto-protocoling tool for sub-specialized imaging exams.


Abstract:
  INTRODUCTION: Lateral ventricles are reliable and sensitive indicators of
brain atrophy and disease progression in behavioral variant frontotemporal
dementia (bvFTD). We aimed to investigate whether an automated tool using
ventricular features could improve diagnostic accuracy in bvFTD across
neurodegenerative diseases. METHODS: Using 678 subjects (2750 timepoints),
differences in ventricular features were assessed between bvFTD, normal
controls and other dementia cohorts. RESULTS: Ventricular antero-posterior
ratio (APR) was the only feature that was significantly different and increased
faster in bvFTD compared to all other cohorts. We achieved a 10-fold
cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in
differentiating bvFTD from all other cohorts with ventricular features, and 76%
accuracy using only the single APR feature. DISCUSSION: APR could be a useful
and easy-to-implement feature to aid bvFTD diagnosis. We have made our
ventricle feature estimation and bvFTD diagnostic tool publicly available,
allowing application of our model in other studies.