Abstract:
  Multi-access edge computing (MEC) emerges as an essential part of the
upcoming Fifth Generation (5G) and future beyond-5G mobile communication
systems. It brings computation power to the edge of cellular networks, which is
close to the energy-constrained user devices, and therewith allows the users to
offload tasks to the edge computing nodes for a low-latency computation with
low battery consumption. However, due to the high dynamics of user demand and
server load, task congestion may occur at the edge nodes, leading to long
queuing delay. Such delays can significantly degrade the quality of experience
(QoE) of some latency-sensitive applications, raise the risk of service outage,
and cannot be efficiently resolved by conventional queue management solutions.
  In this article, we study an latency-outage critical scenario, where the
users intend to reduce the risk of latency outage. We propose an
impatience-based queuing strategy for such users to intelligently choose
between MEC offloading and local computation, allowing them to rationally
renege from the task queue. The proposed approach is demonstrated by numerical
simulations as efficient for generic service model, when a perfect queue
information is available. For the practical case where the users obtain no
perfect queue information, we design a optimal online learning strategy to
enable its application in Poisson service scenarios.


Abstract:
  Reinforcement learning competitions have formed the basis for standard
research benchmarks, galvanized advances in the state-of-the-art, and shaped
the direction of the field. Despite this, a majority of challenges suffer from
the same fundamental problems: participant solutions to the posed challenge are
usually domain-specific, biased to maximally exploit compute resources, and not
guaranteed to be reproducible. In this paper, we present a new framework of
competition design that promotes the development of algorithms that overcome
these barriers. We propose four central mechanisms for achieving this end:
submission retraining, domain randomization, desemantization through domain
obfuscation, and the limitation of competition compute and environment-sample
budget. To demonstrate the efficacy of this design, we proposed, organized, and
ran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning. In
this work, we describe the organizational outcomes of the competition and show
that the resulting participant submissions are reproducible, non-specific to
the competition environment, and sample/resource efficient, despite the
difficult competition task.


Abstract:
  We use a cosmological counterpart of the cylindrical Halilsoy solution to
illustrate properties of cross-polarized standing gravitational waves.


Abstract:
  Dynamic Algorithm Configuration (DAC) aims to dynamically control a target
algorithm's hyperparameters in order to improve its performance. Several
theoretical and empirical results have demonstrated the benefits of dynamically
controlling hyperparameters in domains like evolutionary computation, AI
Planning or deep learning. Replicating these results, as well as studying new
methods for DAC, however, is difficult since existing benchmarks are often
specialized and incompatible with the same interfaces. To facilitate
benchmarking and thus research on DAC, we propose DACBench, a benchmark library
that seeks to collect and standardize existing DAC benchmarks from different AI
domains, as well as provide a template for new ones. For the design of
DACBench, we focused on important desiderata, such as (i) flexibility, (ii)
reproducibility, (iii) extensibility and (iv) automatic documentation and
visualization. To show the potential, broad applicability and challenges of
DAC, we explore how a set of six initial benchmarks compare in several
dimensions of difficulty.


Abstract:
  The observational structure function of the scintillations of the radio
pulsar PSR B0950+08, was fitted, a decade ago, with a power law with index $1
\pm 0.01$. This was interpreted as an {\em appreciable deviation} from the,
commonly observed index of $5/3$, expected for Kolmogorov turbulence. In this
paper it is suggested that the observations are consistent with a Kolmogorov
turbulence and that the {\em apparent} deviation is due to a turbulent region
with an effective depth which is {\em comparable} to the observed lateral
scales on the plane of the sky, spanned by the pulsar beam. Alternatively, the
fitted index of $1$ is consistent with an underlying compressive turbulence and
an even {\it smaller} depth. In the first interpretation the depth is $(5.5 \pm
1.8) \times 10^8 cm$. In the second one, the depth is $\lesssim 4\times 10^7 cm
$. These estimates lend support for the existence of extremely thin, ionized
scattering screens in the local interstellar cloud, that have been proposed a
decade ago.


Abstract:
  Deep networks are now ubiquitous in large-scale multi-center imaging studies.
However, the direct aggregation of images across sites is contraindicated for
downstream statistical and deep learning-based image analysis due to
inconsistent contrast, resolution, and noise. To this end, in the absence of
paired data, variations of Cycle-consistent Generative Adversarial Networks
have been used to harmonize image sets between a source and target domain.
Importantly, these methods are prone to instability, contrast inversion,
intractable manipulation of pathology, and steganographic mappings which limit
their reliable adoption in real-world medical imaging. In this work, based on
an underlying assumption that morphological shape is consistent across imaging
sites, we propose a segmentation-renormalized image translation framework to
reduce inter-scanner heterogeneity while preserving anatomical layout. We
replace the affine transformations used in the normalization layers within
generative networks with trainable scale and shift parameters conditioned on
jointly learned anatomical segmentation embeddings to modulate features at
every level of translation. We evaluate our methodologies against recent
baselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on
datasets with and without lesions. Segmentation-renormalization for translation
GANs yields superior image harmonization as quantified by Inception distances,
demonstrates improved downstream utility via post-hoc segmentation accuracy,
and improved robustness to translation perturbation and self-adversarial
attacks.


Abstract:
  We propose a new type of locally interacting quantum circuits which are
generated by unitary interactions round-a-face (IRF). Specifically, we discuss
a set (or manifold) of dual-unitary IRFs with local Hilbert space dimension $d$
(DUIRF$(d)$) which generate unitary evolutions both in space and time
directions of an extended 1+1 dimensional lattice. We show how arbitrary
dynamical correlation functions of local observables can be evaluated in terms
of finite dimensional completely positive trace preserving unital maps, in
complete analogy to recently studied circuits made of dual unitary brick gates
(DUBG). In fact, we show that the simplest non-trivial (non-vanishing) local
correlation functions in dual-unitary IRF circuits involve observables
non-trivially supported on at least two sites. We completely characterise the
10-dimensional manifold of DUIRF$(2)$ for qubits ($d=2$) and provide, for
$d=3,4,5,6,7$, empirical estimates of its dimensionality based on numerically
determined dimensions of tangent spaces at an ensemble of random instances of
dual-unitary IRF gates. In parallel, we apply the same algorithm to determine
${\rm dim}\,{\rm DUBG}(d)$ and show that they are of similar order though
systematically larger than ${\rm dim}\,{\rm DUIRF}(d)$ for $d=2,3,4,5,6,7$. It
is remarkable that both sets have rather complex topology for $d\ge 3$ in the
sense that the dimension of the tangent space varies among different randomly
generated points of the set. Finally, we provide additional data on
dimensionality of the chiral extension of DUBG circuits with distinct local
Hilbert spaces of dimensions $d\neq d'$ residing at even/odd lattice sites.


Abstract:
  The dynamic state index (DSI) is a scalar field that combines variational
information on the total energy and enstrophy of a flow field with the second
law of thermodynamics. Its magnitude is a combined local measure for
non-stationarity, diabaticity, and dissipation in the flow, and it has been
shown to provide good qualitative indications for the onset and presence of
precipitation and the organization of storms. The index has been derived thus
far for ideal fluid models only, however, so that one may expect more detailed
insights from a revised definition of the quantity that includes more complex
aerothermodynamics. The present paper suggests definitions of DSI-like
indicators for flows of moist air with phase changes and precipitation. In this
way, the DSI is generalized to signal deviations from a variety of different
types of balanced states. A comparison of these indices evaluated with respect
to one and the same flow field enables the user to test whether the flow
internally balances any combination of the physical processes encoded in the
generalized DSI-indices.


Abstract:
  In this paper we introduce a technique to determine the sumset $A+A$, where
$A$ is the indicator function of the 0's occurring in a fixed point $x$ of a
substitution on the alphabet $\{0,1\}$.


Abstract:
  We investigate the phenomenology of an extension of the Standard Model (SM)
by a non-abelian gauge group $SU(2)_{HS}$ where all SM particles are singlets
under this gauge group, and a new scalar representation $\phi$ that is singlet
under SM gauge group and doublet under $SU(2)_{HS}$. In this model, the dark
matter (DM) candidates are the three mass degenerate dark photons $A_{i}$
$(i=1,2,3)$ of $SU(2)_{HS}$; and the hidden sector interacts with the (SM)
particles through the Higgs portal interactions. Consequently, there will be a
new CP-even scalar $\eta$ that could be either heavier or lighter than the
SM-like Higgs. By taking into account all theoretical and experimental
constraints such as perturbativity, unitarity, vacuum stability, non-SM Higgs
decays, DM direct detection, DM relic density, we found viable DM is possible
in the range from GeV to TeV. Within the viable parameters space, the both of
the triple Higgs coupling and the di-Higgs production at LHC14 could be
enhanced or reduced depending on the scalar mixing and the mass of the scalar
particle $\eta$.


Abstract:
  In this paper we study dispersive wave equation using the method of multiple
scales (MMS) and perform several numerical tests to investigate its accuracy.
The key feature of our MMS solution is the linearity of the amplitude equation
and the complex nature of the time-frequency. The MMS is tested as an initial
value problem using three choices of the dispersion model, one toy and two
Lorentz models. Depending on the parameters of the problem, the amplitude
equation can be both well- or ill-posed. Despite the ill-posedness, the MMS
solution remains a valid approximation of the solution to the original
nonlinear model.


Abstract:
  The Schr\"odinger bridge problem (SBP) finds the most likely stochastic
evolution between two probability distributions given a prior stochastic
evolution. As well as applications in the natural sciences, problems of this
kind have important applications in machine learning such as dataset alignment
and hypothesis testing. Whilst the theory behind this problem is relatively
mature, scalable numerical recipes to estimate the Schr\"odinger bridge remain
an active area of research. We prove an equivalence between the SBP and maximum
likelihood estimation enabling direct application of successful machine
learning techniques. We propose a numerical procedure to estimate SBPs using
Gaussian process and demonstrate the practical usage of our approach in
numerical simulations and experiments.


Abstract:
  The peculiarities of the inverse square law of Newtonian gravity in standard
Big Bang Cosmology are discussed. It is shown that the incorporation of an
additive term to Newtonian gravitation, as the inverse Yukawa-like field,
allows remove the incompatibility between the flatness of the Universe and the
density of matter in the Friedmann equation, provides a new approach for dark
energy, and enable theoretical deduce the Hubble-Lemaitre's law. The source of
this inverse Yukawa-like field is the ordinary baryonic matter and represents
the large-scale contribution of gravity in accordance with the Mach's
principle. It's heuristically build from a specular reflection of the Yukawa
potential, in agreement with astronomical and laboratory observables, result
null in the inner solar system, weakly attractive in ranges of interstellar
distances, very attractive in distance ranges comparable to the clusters of
galaxies and repulsive in cosmic scales. Its implications in the missing mass
of Zwicky, Virial Theorem, Kepler's Third Law in Globular Clusters, rotations
curves of galaxies, gravitational redshift and the Jean's mass are discussed.
The inclusion of the inverse Yukawa-like field in Newtonian gravitation
predicts a graviton mass of at least 10-64 kg and could be an alternative to
the paradigm of non-baryonic dark matter concomitant with the observables of
the Big Bang.


Abstract:
  It is proved that a generic simple, closed, piecewise regular curve in space
can be the boundary of only finitely many developable surfaces with
nonvanishing mean curvature. The relevance of this result in the context of the
dynamics of developable surfaces is discussed.


Abstract:
  The tower number $\mathfrak t$ and the ultrafilter number $\mathfrak u$ are
cardinal characteristics from set theory. They are based on combinatorial
properties of classes of subsets of~$\omega$ and the almost inclusion relation
$\subseteq^*$ between such subsets. We consider analogs of these cardinal
characteristics in computability theory.
  We show that the mass problem of ultrafilter bases is equivalent to the mass
problem of computing a function that dominates all computable functions, and
hence, by Martin's characterization, it captures highness. On the other hand,
the mass problem for maximal towers is below the mass problem of computing a
non-low set. We also show that some, but not all, noncomputable low sets
compute maximal towers: Every noncomputable (low) c.e.\ set computes a maximal
tower but no 1-generic $\Delta^0_2$-set does so.
  We finally consider the mass problems of maximal almost disjoint, and of
maximal independent families. We show that they are Medvedev equivalent to
maximal towers, and to ultrafilter bases, respectively.


Abstract:
  ML-enabled software systems have been incorporated in many public
demonstrations for automated driving (AD) systems. Such solutions have also
been considered as a crucial approach to aim at SAE Level 5 systems, where the
passengers in such vehicles do not have to interact with the system at all
anymore. Already in 2016, Nvidia demonstrated a complete end-to-end approach
for training the complete software stack covering perception, planning and
decision making, and the actual vehicle control. While such approaches show the
great potential of such ML-enabled systems, there have also been demonstrations
where already changes to single pixels in a video frame can potentially lead to
completely different decisions with dangerous consequences. In this paper, a
structured analysis has been conducted to explore video degradation effects on
the performance of an ML-enabled pedestrian detector. Firstly, a baseline of
applying YOLO to 1,026 frames with pedestrian annotations in the KITTI Vision
Benchmark Suite has been established. Next, video degradation candidates for
each of these frames were generated using the leading video codecs libx264,
libx265, Nvidia HEVC, and AV1: 52 frames for the various compression presets
for color and gray-scale frames resulting in 104 degradation candidates per
original KITTI frame and 426,816 images in total. YOLO was applied to each
image to compute the intersection-over-union (IoU) metric to compare the
performance with the original baseline. While aggressively lossy compression
settings result in significant performance drops as expected, it was also
observed that some configurations actually result in slightly better IoU
results compared to the baseline. The findings show that carefully chosen lossy
video configurations preserve a decent performance of particular ML-enabled
systems while allowing for substantial savings when storing or transmitting
data.


Abstract:
  We study the behaviour of the solutions to the quasilinear heat equation with
a reaction restricted to a half-line $$ u_t=(u^m)_{xx}+a(x) u^p, $$ $m, p>0$
and $a(x)=1$ for $x>0$, $a(x)=0$ for $x<0$. We first characterize the global
existence exponent $p_0=1$ and the Fujita exponent $p_c=m+2$. Then we pass to
study the grow-up rate in the case $p\le1$ and the blow-up rate for $p>1$. In
particular we show that the grow-up rate is different as for global reaction if
$p>m$ or $p=1\neq m$.


Abstract:
  Understanding the electrodes' surface morphology influence on the ions'
distribution is essential for designing the supercapacitors with enhanced
energy density characteristics. We develop a model for the structure of
electrolytes near the rough surface of electrodes. The model describes an
effective electrostatic field's increase and associated intensification of
ions' spatial separation at the electrode-electrolyte interface. These
adsorption-induced local electric and structure properties result in notably
increased values and sharpened form of the DC dependence on the applied
potential. Such capacitance behavior is observed in many published simulations,
and its description is beyond the capabilities of the established
flat-electrodes theories. The proposed approach could extend the quantitatively
verified models providing a new instrument of the electrodes surface-parameters
optimization for specific electrolytes.


Abstract:
  This paper introduces a plug-and-play descriptor that can be effectively
adopted for image retrieval tasks without prior initialization or preparation.
The description method utilizes the recently proposed Vision Transformer
network while it does not require any training data to adjust parameters. In
image retrieval tasks, the use of Handcrafted global and local descriptors has
been very successfully replaced, over the last years, by the Convolutional
Neural Networks (CNN)-based methods. However, the experimental evaluation
conducted in this paper on several benchmarking datasets against 36
state-of-the-art descriptors from the literature demonstrates that a neural
network that contains no convolutional layer, such as Vision Transformer, can
shape a global descriptor and achieve competitive results. As fine-tuning is
not required, the presented methodology's low complexity encourages adoption of
the architecture as an image retrieval baseline model, replacing the
traditional and well adopted CNN-based approaches and inaugurating a new era in
image retrieval approaches.


Abstract:
  Automated predictions require explanations to be interpretable by humans. One
type of explanation is a rationale, i.e., a selection of input features such as
relevant text snippets from which the model computes the outcome. However, a
single overall selection does not provide a complete explanation, e.g.,
weighing several aspects for decisions. To this end, we present a novel
self-interpretable model called ConRAT. Inspired by how human explanations for
high-level decisions are often based on key concepts, ConRAT extracts a set of
text snippets as concepts and infers which ones are described in the document.
Then, it explains the outcome with a linear aggregation of concepts. Two
regularizers drive ConRAT to build interpretable concepts. In addition, we
propose two techniques to boost the rationale and predictive performance
further. Experiments on both single- and multi-aspect sentiment classification
tasks show that ConRAT is the first to generate concepts that align with human
rationalization while using only the overall label. Further, it outperforms
state-of-the-art methods trained on each aspect label independently.


Abstract:
  As one of the most popular methods in the field of reinforcement learning,
Q-learning has received increasing attention. Recently, there have been more
theoretical works on the regret bound of algorithms that belong to the
Q-learning class in different settings. In this paper, we analyze the
cumulative regret when conducting Nash Q-learning algorithm on 2-player
turn-based stochastic Markov games (2-TBSG), and propose the very first gap
dependent logarithmic upper bounds in the episodic tabular setting. This bound
matches the theoretical lower bound only up to a logarithmic term. Furthermore,
we extend the conclusion to the discounted game setting with infinite horizon
and propose a similar gap dependent logarithmic regret bound. Also, under the
linear MDP assumption, we obtain another logarithmic regret for 2-TBSG, in both
centralized and independent settings.


Abstract:
  We say that a subgroup $H$ is isolated in a group $G$ if for every $x\in G$
we have either $x\in H$ or $\langle x\rangle\cap H=1$. In this short note, we
describe the set of isolated subgroups of a finite abelian group. The technique
used is based on an interesting connection between isolated subgroups and the
function sum of element orders of a finite group.


Abstract:
  While radar and video data can be readily fused at the detection level,
fusing them at the pixel level is potentially more beneficial. This is also
more challenging in part due to the sparsity of radar, but also because
automotive radar beams are much wider than a typical pixel combined with a
large baseline between camera and radar, which results in poor association
between radar pixels and color pixel. A consequence is that depth completion
methods designed for LiDAR and video fare poorly for radar and video. Here we
propose a radar-to-pixel association stage which learns a mapping from radar
returns to pixels. This mapping also serves to densify radar returns. Using
this as a first stage, followed by a more traditional depth completion method,
we are able to achieve image-guided depth completion with radar and video. We
demonstrate performance superior to camera and radar alone on the nuScenes
dataset. Our source code is available at https://github.com/longyunf/rc-pda.


Abstract:
  This paper analytically and numerically investigates misalignment and
mode-mismatch induced power coupling coefficients and losses as a function of
Hermite Gauss (HG) mode order. We show that higher-order HG modes are more
susceptible to beam perturbations when, for example, coupling into optical
cavities: the misalignment and mode-mismatch-induced power coupling losses
scale linearly and quadratically with respect to the mode indices respectively.
As a result, the mode-mismatch tolerance for the $\mathrm{HG}_{3,3}$ mode is
reduced to a factor of 0.28 relative to the currently-used $\mathrm{HG}_{0,0}$
mode. This is a potential hurdle to using higher-order modes to reduce thermal
noise in future gravitational-wave detectors.


Abstract:
  We study a nonlinear evolutionary partial differential equation that can be
viewed as a generalization of the heat equation where the temperature gradient
is a~priori bounded but the heat flux provides merely \mbox{$L^1$-coercivity}.
Applying higher differentiability techniques in space and time, choosing a
special weighted norm (equivalent to the Euclidean norm in $\mathbb{R}^d$),
incorporating finer properties of integrable functions and using the concept of
renormalized solution, we prove long-time and large-data existence and
uniqueness of weak solution, with an $L^1$-integrable flux, to an initial
spatially-periodic problem for all values of a positive model parameter. If
this parameter is smaller than $2/(d+1)$, where $d$ denotes the spatial
dimension, we obtain higher integrability of the flux. As the developed
approach is not restricted to a scalar equation, we also present an analogous
result for nonlinear parabolic systems in which the nonlinearity, being the
gradient of a strictly convex function, gives an a-priori $L^\infty$-bound on
the gradient of the unknown solution.


Abstract:
  Normal-mode oscillation frequencies computed from stellar models differ from
those which would be measured from stars with identical interior structures,
because of modelling errors in the near-surface layers. These frequency
differences are referred to as the asteroseismic "surface term". The vast
majority of solar-like oscillators which have been observed, and which are
expected to be observed in the near future, are evolved stars which exhibit
mixed modes. For these evolved stars, the inference of stellar properties from
these mode frequencies has been shown to depend on how this surface term is
corrected for. We show that existing parametrisations of the surface term
account for mode mixing only to first order in perturbation theory, if at all,
and therefore may not be adequate for evolved stars. Moreover, existing
nonparametric treatments of the surface term do not account for mode mixing. We
derive both a first-order construction, and a more general approach, for one
particular class of nonparametric methods. We illustrate the limits of
first-order approximations from both analytic considerations and using
numerical injection-recovery tests on stellar models. First-order corrections
for the surface term are strictly only applicable where the size of the surface
term is much smaller than both the coupling strength between the mixed p- and
g-modes, as well as the local g-mode spacing. Our more general matrix
construction may be applied to evolved stars, where perturbation theory cannot
be relied upon.


Abstract:
  Detecting anomalies has become increasingly critical to the financial service
industry. Anomalous events are often indicative of illegal activities such as
fraud, identity theft, network intrusion, account takeover, and money
laundering. Financial anomaly detection use cases face serious challenges due
to the dynamic nature of the underlying patterns especially in adversarial
environments such as constantly changing fraud tactics. While retraining the
models with the new patterns is absolutely essential; keeping up with the rapid
changes introduces other challenges as it moves the model away from older
patterns or continuously grows the size of the training data. The resulting
data growth is hard to manage and it reduces the agility of the models'
response to the latest attacks. Due to the data size limitations and the need
to track the latest patterns, older time periods are often dropped in practice,
which in turn, causes vulnerabilities. In this study, we propose a label
augmentation approach to utilize the learning from older models to boost the
latest. Experimental results show that the proposed approach provides a
significant reduction in training time, while providing potential performance
improvement.


Abstract:
  In this paper, we present a ship detection pipeline for low-cost medium
resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet
Labs Dove constellations. This optical satellite imagery is readily available
for any place on Earth and underutilized in the maritime domain, compared to
existing solutions based on synthetic-aperture radar (SAR) imagery. We
developed a ship detection method based on a state-of-the-art
deep-learning-based object detection method which was developed and evaluated
on a large-scale dataset that was collected and automatically annotated with
the help of Automatic Identification System (AIS) data.


Abstract:
  We introduce Explearn, an online algorithm that learns to jointly output
predictions and explanations for those predictions. Explearn leverages Gaussian
Processes (GP)-based contextual bandits. This brings two key benefits. First,
GPs naturally capture different kinds of explanations and enable the system
designer to control how explanations generalize across the space by virtue of
choosing a suitable kernel. Second, Explearn builds on recent results in
contextual bandits which guarantee convergence with high probability. Our
initial experiments hint at the promise of the approach.


Abstract:
  In this paper we study the relationship between Lyapunov exponents and the
induced map on cohomology for $C^{1}-$diffeomorphisms on compact manifolds. We
show that if the induced map on cohomology has spectral radius strictly larger
than 1, then the diffeomorphism has an invariant ergodic measure with at least
one positive Lyapunov exponent. Furthermore, if the diffeomorphism also
preserves a continuous volume form then it has an invariant ergodic measure
with at least one positive and one negative Lyapunov exponent, in agreement
with Shub's entropy conjecture. We also consider diffeomorphisms preserving a
measure equivalent to volume. In this case we show that if the Lyapunov metric
satisfies an integrability condition and the induced map on cohomology has
spectral radius strictly larger than 1, then the diffeomorphism has non-zero
Lyapunov exponents on a set of positive volume.


Abstract:
  Precise control of charged particles in radio-frequency (Paul) traps requires
minimising excess micromotion induced by stray electric fields. We present a
method to detect and compensate such fields through amplitude modulation of the
radio-frequency trapping field. Modulation at frequencies close to the motional
modes of the trapped particle excites coherent motion whose amplitude linearly
depends on the stray field. In trapped-ion experiments, this motion can be
detected by recording the arrival times of photons scattered during laser
cooling. Only a single laser beam is required to resolve fields in multiple
directions. In a demonstration using a $^{88}\mathrm{Sr}^{+}$ ion in a surface
electrode trap, we achieve a sensitivity of $0.1\, \mathrm{V}\,
\mathrm{m}^{-1}\, /\, \sqrt{\mathrm{Hz}}$ and a minimal uncertainty of $0.015\,
\mathrm{V}\, \mathrm{m}^{-1}$.


Abstract:
  Cerebrovascular accident, or commonly known as stroke, is an acute disease
with extreme impact on patients and healthcare systems and is the second
largest cause of death worldwide. Fast and precise stroke lesion detection and
location is an extreme important process with regards to stroke diagnosis,
treatment, and prognosis. Except from the manual segmentation approach, machine
learning based segmentation methods are the most promising ones when
considering efficiency and accuracy, and convolutional neural network based
models are the first of its kind. However, most of these neural network models
do not really align with the brain anatomical structures. Intuitively, this
work presents a more brain alike model which mimics the anatomical structure of
the human visual cortex. Through the preliminary experiments on the stroke
lesion segmentation task, the proposed model is found to be able to perform
equally well or better to the de-facto standard U-Net. Part of the
implementation will be made available at https://github.com/DarkoBomer/VCA-Net.


Abstract:
  During the expansion of a heavy ion collision, the system passes close to the
$O(4)$ critical point of QCD, and thus the fluctuations of the order parameter
$(\sigma, \vec{\pi})$ are expected to be enhanced. Our goal is to compute how
these enhanced fluctuations modify the transport coefficients of QCD near the
pseudo-critical point. We also make a phenomenological estimate for how chiral
fluctuations could effect the momentum spectrum of soft pions.
  We first formulate the appropriate stochastic hydrodynamic equations close to
the $O(4)$ critical point. Then, working in mean field, we determine the
correlation functions of the stress tensor and the currents which result from
this stochastic real time theory, and use these correlation functions to
determine the scaling behavior of the transport coefficients. The hydrodynamic
theory also describes the propagation of pion waves, fixing the scaling
behavior of the dispersion curve of soft pions. We present scaling functions
for the shear viscosity and the charge conductivities near the pseudo-critical
point, and estimate the absolute magnitude of the critical fluctuations to
these parameters and the bulk viscosity.
  Using the calculated pion dispersion curve, we estimate the expected critical
enhancement of soft pion yields, and this estimate provides a plausible
explanation for the excess seen in experiment relative to ordinary hydrodynamic
computations. Our results motivate further phenomenological and numerical work
on the implications of chiral symmetry on real time properties of thermal QCD
near the pseudo-critical point.


Abstract:
  In this article, we present an elementary proof of a special case of
Ramanujan's formula for $ \zeta(2m +1)$ in terms of hyperbolic cotangent sums
using Mittag-Leffler expansion of hyperbolic cotangent and Euler's formula for
$ \zeta(2m) $. We also show that our approach produces an elementary proof of
the more involved Ramanujan's formula for $ \zeta(2m+1)$.


Abstract:
  Multimode fibers (MMFs) show great promise as miniature probes for sensing,
imaging and spectroscopy applications. Different parameters of the fibers, such
as numerical aperture, refractive index profile and length, have been already
optimized for better performance. Here we investigate the role of the core
shape, in particular for wavefront shaping applications where a focus is formed
at the output of the MMF. We demonstrate that in contrast to a conventional
round-core MMF, a square-core design doesn't suffer from focus aberrations.
Moreover, we find that how the interference pattern behind a square-core fiber
decorrelates with the input frequency is largely independent of the input light
coupling. Finally, we demonstrate that a square core shape provides an
on-average uniform distribution of the output intensity, free from the
input-output correlations seen in round fibers, showing great promise for
imaging and spectroscopy applications.


Abstract:
  We apply the automated AnomalyFinder algorithm of Paper I (Zang et al. 2021b)
to 2018-2019 light curves from the $\simeq 13\,{\rm deg}^2$ covered by the six
KMTNet prime fields, with cadences $\Gamma \geq 2\,{\rm hr}^{-1}$. We find a
total of 11 planets with mass ratios $q<2\times 10^{-4}$, including six newly
discovered planets, one planet that was reported in Paper I, and recovery of
four previously discovered planets. One of the new planets,
OGLE-2018-BLG-0977Lb, is in a planetary-caustic event, while the other five
(OGLE-2018-BLG-0506Lb, OGLE-2018-BLG-0516Lb, OGLE-2019-BLG-1492Lb,
KMT-2019-BLG-0253, and KMT-2019-BLG-0953) are revealed by a "dip" in the light
curve as the source crosses the host-planet axis on the opposite side of the
planet. These subtle signals were missed in previous by-eye searches. The
planet-host separations (scaled to the Einstein radius), $s$, and planet-host
mass ratios, $q$, are, respectively, $(s,q\times 10^5) = (0.88, 4.1)$,
$(0.96\pm 0.10, 8.3)$, $(0.94\pm 0.07, 13)$, $(0.97\pm 0.07, 18)$,
$(0.97\pm0.04,4.1)$, and $(0.74,18)$, where the "$\pm$" indicates a discrete
degeneracy. The 11 planets are spread out over the range $-5<\log q < -3.7$.
Together with the two planets previously reported with $q\sim 10^{-5}$ from the
2018-2019 non-prime KMT fields, this result suggests that planets toward the
bottom of this mass-ratio range may be more common than previously believed.


Abstract:
  Analog meters equipped with one or multiple pointers are wildly utilized to
monitor vital devices' status in industrial sites for safety concerns. Reading
these legacy meters {\bi autonomously} remains an open problem since estimating
pointer origin and direction under imaging damping factors imposed in the wild
could be challenging. Nevertheless, high accuracy, flexibility, and real-time
performance are demanded. In this work, we propose the Vector Detection Network
(VDN) to detect analog meters' pointers given their images, eliminating the
barriers for autonomously reading such meters using intelligent agents like
robots. We tackled the pointer as a two-dimensional vector, whose initial point
coincides with the tip, and the direction is along tail-to-tip. The network
estimates a confidence map, wherein the peak pixels are treated as vectors'
initial points, along with a two-layer scalar map, whose pixel values at each
peak form the scalar components in the directions of the coordinate axes. We
established the Pointer-10K dataset composing of real-world analog meter images
to evaluate our approach due to no similar dataset is available for now.
Experiments on the dataset demonstrated that our methods generalize well to
various meters, robust to harsh imaging factors, and run in real-time.


Abstract:
  We establish a method for calculating the Poincar\'e series of moduli spaces
constructed as quotients of smooth varieties by suitable non-reductive group
actions; examples of such moduli spaces include moduli spaces of unstable
vector or Higgs bundles on a smooth projective curve, with a Harder-Narasimhan
type of length two. To do so, we first prove a result concerning the smoothness
of fixed point sets for suitable non-reductive group actions on smooth
varieties. This enables us to prove that quotients of smooth varieties by such
non-reductive group actions, which can be constructed using Non-Reductive GIT
via a sequence of blow-ups, have at worst finite quotient singularities. We
conclude the paper by providing explicit formulae for the Poincar\'e series of
these non-reductive GIT quotients.


Abstract:
  The dipole blockade phenomenon is a direct consequence of strong
dipole-dipole interaction, where only single atom can be excited because the
doubly excited state is shifted out of resonance. The corresponding two-body
entanglement with non-zero concurrence induced by the dipole blockade effect is
an important resource for quantum information processing. Here, we propose a
novel physical mechanism for realizing dipole blockade without the
dipole-dipole interaction, where two qubits coupled to a cavity, are driven by
a coherent field. By suitably chosen placements of the qubits in the cavity and
by adjusting the relative decay strengths of the qubits and cavity field, we
kill many unwanted excitation pathways. This leads to dipole blockade. In
addition, we show that these two qubits are strongly entangled over a broad
regime of the system parameters. We show that a strong signature of this dipole
blockade is the bunching property of the cavity photons which thus provides a
possible measurement of the dipole blockade. We present dynamical features of
the dipole blockade without dipole-dipole interaction. The proposal presented
in this work can be realized not only in traditional cavity QED, but also in
non-cavity topological photonics involving edge modes.


Abstract:
  The Euler tour technique is a classical tool for designing parallel graph
algorithms, originally proposed for the PRAM model. We ask whether it can be
adapted to run efficiently on GPU. We focus on two established applications of
the technique: (1) the problem of finding lowest common ancestors (LCA) of
pairs of nodes in trees, and (2) the problem of finding bridges in undirected
graphs. In our experiments, we compare theoretically optimal algorithms using
the Euler tour technique against simpler heuristics supposed to perform
particularly well on typical instances. We show that the Euler tour-based
algorithms not only fulfill their theoretical promises and outperform practical
heuristics on hard instances, but also perform on par with them on easy
instances.


Abstract:
  Fast radio bursts (FRBs) are mysterious radio transients with millisecond
durations. Recently, a periodic activity of $\sim$16 day and a possible
periodicity of $\sim$159 day were detected to arise from FRB 180916.J0158+65
and FRB 121102, respectively, and the spin period of a slow-rotation magnetar
was further considered to be one of possibilities to explain the periodic
activities of repeating FRBs. For isolated neutron stars, the spin evolution
suggests that it's difficult to reach several hours. In this work, we mainly
focus on the possible maximum spin period of isolated NSs / magnetars dominated
by an interaction between star's magnetic field and the disk. We find that the
disk wind plays an important role in spin evolution, whose influence varies the
power law index in the evolution equation of mass flow rate. For a magnetar
without disk wind, the longest spin period is tens of hours. When the disk wind
with a classical parameter is involved, the maximum spin period can reach
hundreds of hours. But for a much extremely large index of mass flow rate due
to disk wind or other angular momentum extraction processes, a spin period of
$\sim$(16-160) days is still possible.


Abstract:
  We study the extent to which wide neural networks may be approximated by
Gaussian processes when initialized with random weights. It is a
well-established fact that as the width of a network goes to infinity, its law
converges to that of a Gaussian process. We make this quantitative by
establishing explicit convergence rates for the central limit theorem in an
infinite-dimensional functional space, metrized with a natural transportation
distance. We identify two regimes of interest; when the activation function is
polynomial, its degree determines the rate of convergence, while for
non-polynomial activations, the rate is governed by the smoothness of the
function.


Abstract:
  Increasing deployment of photovoltaics (PV) plants demands for cheap and fast
inspection. A viable tool for this task is thermographic imaging by unmanned
aerial vehicles (UAV). In this work, we develop a computer vision tool for the
semi-automatic extraction of PV modules from thermographic UAV videos. We use
it to curate a dataset containing 4.3 million IR images of 107842 PV modules
from thermographic videos of seven different PV plants. To demonstrate its use
for automated PV plant inspection, we train a ResNet-50 to classify ten common
module anomalies with more than 90 % test accuracy. Experiments show that our
tool generalizes well to different PV plants. It successfully extracts PV
modules from 512 out of 561 plant rows. Failures are mostly due to an
inappropriate UAV trajectory and erroneous module segmentation. Including all
manual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV
installations per day, potentially scaling to multi-gigawatt plants due to its
parallel nature. While we present an effective method for automated PV plant
inspection, we are also confident that our approach helps to meet the growing
demand for large thermographic datasets for machine learning tasks, such as
power prediction or unsupervised defect identification.


Abstract:
  In this paper we present grammatical interpretations of the alternating
Eulerian polynomials of types A and B. As applications, we derive several
properties of the type B alternating Eulerian polynomials, including
combinatorial expansions, recurrence relations and generating functions. We
establish an interesting connection between alternating Eulerian polynomials of
type B and left peak polynomials of permutations in the symmetric group, which
implies that the type B alternating Eulerian polynomials have gamma-vectors
alternate in sign.


Abstract:
  Classical approaches in cluster analysis are typically based on a feature
space analysis. However, many applications lead to datasets with additional
spatial information and a ground truth with spatially coherent classes, which
will not necessarily be reconstructed well by standard clustering methods.
Motivated by applications in hyperspectral imaging, we introduce in this work
clustering models based on orthogonal nonnegative matrix factorization, which
include an additional total variation (TV) regularization procedure on the
cluster membership matrix to enforce the needed spatial coherence in the
clusters. We propose several approaches with different optimization techniques,
where the TV regularization is either performed as a subsequent postprocessing
step or included into the clustering algorithm. Finally, we provide a numerical
evaluation of all proposed methods on a hyperspectral dataset obtained from a
matrix-assisted laser desorption/ionisation imaging measurement, which leads to
significantly better clustering results compared to classical clustering
models.


Abstract:
  The aim of this paper is to introduce the notion of intuitionistic fuzzy Lie
subalgebras and intutionistic fuzzy Lie ideals of n-Lie algebras. It is a
generalization of intuitionistic fuzzy Lie algebras. Then, we investigate some
of characteristics of intutionistic fuzzy Lie ideals (resp. subalgebras) of
n-Lie algeras. Finally, we define the image and the inverse image of
intuitionistic fuzzy Lie subalgebra under n-Lie algebra homomorphism. The
properties of intuitionistic fuzzy n-Lie subalgebras and intuitionistic fuzzy
Lie ideals under homomorphisms of n-Lie algebras are studied. Finally, we
define the intuitionistic fuzzy quotient n-Lie algebra by an intuitionistic
fuzzy ideal of n-Lie algebra and prove that it is a n-Lie algebra.


Abstract:
  We give an algorithm that generates a uniformly random contingency table with
specified marginals, i.e. a matrix with non-negative integer values and
specified row and column sums. Such algorithms are useful in statistics and
combinatorics. When $\Delta^4< M/5$, where $\Delta$ is the maximum of the row
and column sums and $M$ is the sum of all entries of the matrix, our algorithm
runs in time linear in $M$ in expectation. Most previously published algorithms
for this problem are approximate samplers based on Markov chain Monte Carlo,
whose provable bounds on the mixing time are typically polynomials with rather
large degrees.


Abstract:
  One of the most serious public health problems in Peru and worldwide is
Tuberculosis (TB), which is produced by a bacterium known as Mycobacterium
tuberculosis. The purpose of this work is to facilitate and automate the
diagnosis of tuberculosis using the MODS method and using lens-free microscopy,
as it is easier to calibrate and easier to use by untrained personnel compared
to lens microscopy. Therefore, we employed a U-Net network on our collected
data set to perform automatic segmentation of cord shape bacterial accumulation
and then predict tuberculosis. Our results show promising evidence for
automatic segmentation of TB cords, and thus good accuracy for TB prediction.


Abstract:
  We consider the problem of simultaneous approximation of real numbers
$\theta_1, \ldots,\theta_n$ with rationals and the dual problem of
approximating zero with the values of the linear form
$x_0+\theta_1x_1+\ldots+\theta_nx_n$ at integer points. In this setting we
analyse two transference inequalities obtained by Schmidt and Summerer. We
present a rather simple geometric observation, which proves their result. We
also derive several corollaries previously unknown. Particularly, we show that,
together with the transference inequalities for uniform exponents, Schmidt and
Summerer's inequalities imply the inequalities by Bugeaud and Laurent and "one
half" of the inequalities by Marnat and Moshchevitin. Besides that, we show
that our main construction provides a rather simple proof of Nesterenko's
linear independence criterion.


Abstract:
  Quantum sensing is one of the key areas which exemplifies the superiority of
quantum technologies. Nonetheless, most quantum sensing protocols operate
efficiently only when the unknown parameters vary within a very narrow region,
i.e., local sensing. Here, we provide a systematic formulation for quantifying
the precision of a probe for multi-parameter global sensing when there is no
prior information about the parameters. In many-body probes, in which extra
tunable parameters exist, our protocol can tune the performance for harnessing
the quantum criticality over arbitrarily large sensing intervals. For the
single-parameter sensing, our protocol optimizes a control field such that an
Ising probe is tuned to always operate around its criticality. This
significantly enhances the performance of the probe even when the interval of
interest is so large that the precision is bounded by the standard limit. For
the multi-parameter case, our protocol optimizes the control fields such that
the probe operates at the most efficient point along its critical line.
Interestingly, for an Ising probe, it is predominantly determined by the
longitudinal field. Finally, we show that even a simple magnetization
measurement significantly benefits from our optimization and moderately
delivers the theoretical precision.


Abstract:
  Attempts to find black hole microstates using the Hamiltonian phase space
approach have been made on the Schwarzschild spacetime. Since the Schwarzschild
spacetime is also in the larger family of the Kerr spacetimes, and both are
asymptotically flat, the Kerr black hole is a good option for the method
development. The Kerr black hole is a spinning one. We perform this analysis on
the Kerr spacetime and we obtain promising results using the covariant phase
space analysis. Although we have forced ourselves to use the Bondi fall-off
conditions, we find the gauge degrees of freedom that could be good candidates
for the black hole microstates. The charge algebra on the boundary could be a
Virasoro algebra that has a different central term than the Schwarzschild black
hole. The two dimensional theory on the black hole boundary is conjectured to
be conformally invariant.


Abstract:
  How do you incentivize self-interested agents to $\textit{explore}$ when they
prefer to $\textit{exploit}$? We consider complex exploration problems, where
each agent faces the same (but unknown) MDP. In contrast with traditional
formulations of reinforcement learning, agents control the choice of policies,
whereas an algorithm can only issue recommendations. However, the algorithm
controls the flow of information, and can incentivize the agents to explore via
information asymmetry. We design an algorithm which explores all reachable
states in the MDP. We achieve provable guarantees similar to those for
incentivizing exploration in static, stateless exploration problems studied
previously. To the best of our knowledge, this is the first work to consider
mechanism design in a stateful, reinforcement learning setting.


Abstract:
  Multiport interferometers based on integrated beamsplitter meshes are widely
used in photonic technologies. While the rectangular mesh is favored for its
compactness and uniformity, its geometry resists conventional
self-configuration approaches, which are essential to programming large meshes
in the presence of fabrication error. Here, we present a new configuration
algorithm, related to the $2\times 2$ block decomposition of a unitary matrix,
that overcomes this limitation. Our proposed algorithm is robust to errors,
requires no prior knowledge of the process variations, and relies only on
external sources and detectors. We show that self-configuration using this
technique reduces the effect of fabrication errors by the same quadratic factor
observed in triangular meshes. This relaxes a significant limit to the size of
multiport interferometers, removing a major roadblock to the scaling of optical
quantum and machine-learning hardware.


Abstract:
  Ion channels are important proteins for physiological information transfer
and functional control. To predict the microscopic origins of their
voltage-conductance characteristics, we here applied dissipation-corrected
targeted Molecular Dynamics in combination with Langevin equation simulations
to potassium diffusion through the Gramicidin A channel as a test system.
Performing a non-equilibrium principal component analysis on backbone dihedral
angles, we find coupled protein-ion dynamics to occur during ion transfer. The
dissipation-corrected free energy profiles correspond well to predictions from
other biased simulation methods. The incorporation of an external electric
field in Langevin simulations enables the prediction of macroscopic observables
in the form of I-V characteristics.


Abstract:
  This paper is the second part of a two part paper which introduces the study
of the Whitney Equisingularity of families of Symmetric determinantal
singularities. This study reveals how to use the multiplicity of polar curves
associated to a generic deformation of a singularity to control the Whitney
equisingularity type of these curves.


Abstract:
  We construct a class of lattice Hamiltonians that exhibit fractional Hall
conductivity. These Hamiltonians, while not being exactly solvable, can be
controllably solved in their low energy sectors, through a combination of
perturbative and exact techniques. Our construction demonstrates a systematic
way to circumvent the Kapustin-Fidkowski no-go theorem and is generalizable.


Abstract:
  The age-metallicity relation is a fundamental tool for constraining the
chemical evolution of the Galactic disc. In this work we analyse the
observational properties of this relation using binary stars that have not
interacted consisting of a white dwarf - from which we can derive the total age
of the system - and a main sequence star - from which we can derive the
metallicity as traced by the [Fe/H] abundances. Our sample consists of 46
widely separated, but unresolved spectroscopic binaries identified within the
Sloan Digital Sky Survey, and 189 white dwarf plus main sequence common proper
motion pairs identified within the second data release of Gaia. This is
currently the largest white dwarf sample for which the metallicity of their
progenitors have been determined. We find a flat age-metallicity relation
displaying a scatter of [Fe/H] abundances of approximately 0.5 dex around the
solar metallicity at all ages. This independently confirms the lack of
correlation between age and metallicity in the solar neighbourhood that is
found in previous studies focused on analysing single main sequence stars and
open clusters.


Abstract:
  Handwritten Text Recognition (HTR) remains a challenging problem to date,
largely due to the varying writing styles that exist amongst us. Prior works
however generally operate with the assumption that there is a limited number of
styles, most of which have already been captured by existing datasets. In this
paper, we take a completely different perspective -- we work on the assumption
that there is always a new style that is drastically different, and that we
will only have very limited data during testing to perform adaptation. This
results in a commercially viable solution -- the model has the best shot at
adaptation being exposed to the new style, and the few samples nature makes it
practical to implement. We achieve this via a novel meta-learning framework
which exploits additional new-writer data through a support set, and outputs a
writer-adapted model via single gradient step update, all during inference. We
discover and leverage on the important insight that there exists few key
characters per writer that exhibit relatively larger style discrepancies. For
that, we additionally propose to meta-learn instance specific weights for a
character-wise cross-entropy loss, which is specifically designed to work with
the sequential nature of text data. Our writer-adaptive MetaHTR framework can
be easily implemented on the top of most state-of-the-art HTR models.
Experiments show an average performance gain of 5-7% can be obtained by
observing very few new style data. We further demonstrate via a set of ablative
studies the advantage of our meta design when compared with alternative
adaption mechanisms.


Abstract:
  Throughout cosmological simulations, the properties of the matter density
field in the initial conditions have a decisive impact on the features of the
structures formed today. In this paper we use a random-forest classification
algorithm to infer whether or not dark matter particles, traced back to the
initial conditions, would end up in dark matter halos whose masses are above
some threshold. This problem might be posed as a binary classification task,
where the initial conditions of the matter density field are mapped into
classification labels provided by a halo finder program. Our results show that
random forests are effective tools to predict the output of cosmological
simulations without running the full process. These techniques might be used in
the future to decrease the computational time and to explore more efficiently
the effect of different dark matter/dark energy candidates on the formation of
cosmological structures.


Abstract:
  Continuous-time quantum walks (CTQW) have shown the capability to perform
efficiently the spatial search of a marked site on many kinds of graphs.
However, most of such graphs are hard to realize in an experimental setting.
Here we study CTQW spatial search on a planar triangular lattice by means of
both numerical simulations and experiments. The experiments are performed using
three-dimensional waveguide arrays fabricated by femtosecond laser pulses,
illuminated by coherent light. We show that the retrieval of the marked site by
the quantum walker is accomplished with higher probability than the classical
counterpart, in a convenient time window placed early in the evolution.


Abstract:
  Many synthetic quantum systems allow particles to have dispersion relations
that are neither linear nor quadratic functions. Here, we explore
single-particle scattering in general spatial dimension $D\geq 1$ when the
density of states diverges at a specific energy. To illustrate the underlying
principles in an experimentally relevant setting, we focus on waveguide quantum
electrodynamics (QED) problems (i.e. $D=1$) with dispersion relation
$\epsilon(k)=\pm |d|k^m$, where $m\geq 2$ is an integer. For a large class of
these problems for any positive integer $m$, we rigorously prove that when
there are no bright zero-energy eigenstates, the $S$-matrix evaluated at an
energy $E\to 0$ converges to a universal limit that is only dependent on $m$.
We also give a generalization of a key index theorem in quantum scattering
theory known as Levinson's theorem -- which relates the scattering phases to
the number of bound states -- to waveguide QED scattering for these more
general dispersion relations. We then extend these results to general integer
dimensions $D \geq 1$, dispersion relations $\epsilon(\boldsymbol{k}) =
|\boldsymbol{k}|^a$ for a $D$-dimensional momentum vector $\boldsymbol{k}$ with
any real positive $a$, and separable potential scattering.


Abstract:
  APR techniques can be extremely time consuming since (1) a large number of
patches can be generated for a given bug, and (2) each patch needs to be
executed on the original tests to ensure its correctness. In the literature,
various techniques (e.g., based on learning, mining, and constraint solving)
have been proposed/studied to reduce the number of patches. However, there is
limited study on the impact of test selection for each patch (e.g., only the
tests affected by the patch need to be executed as the other tests would keep
the same outcomes and can be skipped), and few APR systems actually apply test
selection. Therefore, this paper conducts the first extensive study to
investigate the impact of Regression Test Selection (RTS) on APR. More
specifically, we implemented widely-used RTS techniques at different levels for
12 state-of-the-art APR systems with over 2M patches. Our study reveals various
practical guidelines for future APR, including: (1) the number of patches
widely used for measuring APR efficiency can incur skewed conclusions, and the
use of inconsistent RTS configurations can further skew the conclusion; (2) all
studied RTS techniques can substantially improve APR efficiency and should be
considered in future APR work; (3) method- and statement-level RTS outperform
class-level RTS substantially, and should be preferred; (4) RTS techniques can
substantially outperform state-of-the-art test prioritization techniques for
APR, and combining them can further improve APR efficiency; and (5) traditional
regression test prioritization widely studied in regression testing performs
even better than APR-specific test prioritization when combined with most RTS
techniques. Furthermore, we also present the detailed impact of different patch
categories and patch validation strategies on our findings.


Abstract:
  Objective: The spinous process angle (SPA) is one of the essential parameters
to denote three-dimensional (3-D) deformity of spine. We propose an automatic
segmentation method based on Stacked Hourglass Network (SHN) to detect the
spinous processes (SP) on ultrasound (US) spine images and to measure the SPAs
of clinical scoliotic subjects. Methods: The network was trained to detect
vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images
and validated on 100 images. All the processed transverse images with
highlighted SP and laminae were reconstructed into a 3D image volume, and the
SPAs were measured on the projected coronal images. The trained network was
tested on 400 images by calculating the percentage of correct keypoints (PCK);
and the SPA measurements were evaluated on 50 scoliotic subjects by comparing
the results from US images and radiographs. Results: The trained network
achieved a high average PCK (86.8%) on the test datasets, particularly the PCK
of SP detection was 90.3%. The SPAs measured from US and radiographic methods
showed good correlation (r>0.85), and the mean absolute differences (MAD)
between two modalities were 3.3{\deg}, which was less than the clinical
acceptance error (5{\deg}). Conclusion: The vertebral features can be
accurately segmented on US spine images using SHN, and the measurement results
of SPA from US data was comparable to the gold standard from radiography.


Abstract:
  Screening for the diagnosis of glaucoma through a fundus image can be
determined by the optic cup to disc diameter ratio (CDR), which requires the
segmentation of the cup and disc regions. In this paper, we propose two novel
approaches, namely Parameter-Shared Branched Network (PSBN) andWeak Region of
Interest Model-based segmentation (WRoIM) to identify disc and cup boundaries.
Unlike the previous approaches, the proposed methods are trained end-to-end
through a single neural network architecture and use dynamic cropping instead
of manual or traditional computer vision-based cropping. We are able to achieve
similar performance as that of state-of-the-art approaches with less number of
network parameters. Our experiments include comparison with different best
known methods on publicly available Drishti-GS1 and RIM-ONE v3 datasets. With
$7.8 \times 10^6$ parameters our approach achieves a Dice score of 0.96/0.89
for disc/cup segmentation on Drishti-GS1 data whereas the existing
state-of-the-art approach uses $19.8\times 10^6$ parameters to achieve a dice
score of 0.97/0.89.


Abstract:
  We study disorder operator, defined as a symmetry transformation applied to a
finite region, across a continuous quantum phase transition in $(2+1)d$. We
show analytically that at a conformally-invariant critical point with U(1)
symmetry, the disorder operator with a small U(1) rotation angle defined on a
rectangle region exhibits power-law scaling with the perimeter of the
rectangle. The exponent is proportional to the current central charge of the
critical theory. Such a universal scaling behavior is due to the sharp corners
of the region and we further obtain a general formula for the exponent when the
corner is nearly smooth. To probe the full parameter regime, we carry out
systematic computation of the U(1) disorder parameter in the square lattice
Bose-Hubbard model across the superfluid-insulator transition with large-scale
quantum Monte Carlo simulations, and confirm the presence of the universal
corner correction. The exponent of the corner term determined from numerical
simulations agrees well with the analytical predictions.


Abstract:
  The Multi-slit Solar Explorer (MUSE) is a proposed NASA MIDEX mission,
currently in Phase A, composed of a multi-slit EUV spectrograph (in three
narrow spectral bands centered around 171A, 284A, and 108A) and an EUV context
imager (in two narrow passbands around 195A and 304A). MUSE will provide
unprecedented spectral and imaging diagnostics of the solar corona at high
spatial (<0.5 arcsec), and temporal resolution (down to ~0.5s) thanks to its
innovative multi-slit design. By obtaining spectra in 4 bright EUV lines (Fe IX
171A , Fe XV 284A, Fe XIX-Fe XXI 108A) covering a wide range of transition
region and coronal temperatures along 37 slits simultaneously, MUSE will for
the first time be able to "freeze" (at a cadence as short as 10 seconds) with a
spectroscopic raster the evolution of the dynamic coronal plasma over a wide
range of scales: from the spatial scales on which energy is released (~0.5
arcsec) to the large-scale often active-region size (170 arcsec x 170 arcsec)
atmospheric response. We use advanced numerical modeling to showcase how MUSE
will constrain the properties of the solar atmosphere on the spatio-temporal
scales (~0.5 arcsec, ~20 seconds) and large field-of-view on which various
state-of-the-art models of the physical processes that drive coronal heating,
solar flares and coronal mass ejections (CMEs) make distinguishing and testable
predictions. We describe how the synergy between MUSE, the single-slit,
high-resolution Solar-C EUVST spectrograph, and ground-based observatories
(DKIST and others) can address how the solar atmosphere is energized, and the
critical role MUSE plays because of the multi-scale nature of the physical
processes involved. In this first paper, we focus on how comparisons between
MUSE observations and theoretical models will significantly further our
understanding of coronal heating mechanisms.


Abstract:
  In developed countries like the USA, Germany, and the UK, the security forces
used highly sophisticated equipment, fast vehicles, drones, and helicopters to
catch offenders' vehicles. Whereas, in developing countries with limited
resources such schemes cannot be utilized due to management cost and other
constraints. In this paper, we proposed a framework called CVEH that enables
developing countries to profile the offender vehicle movements through
crowdsourcing technique and act as an early warning system to the law forcing
agencies. It also engages citizens to play their role in improving security
conditions. The proposed CVEH framework allows Vehicle-to-Infrastructure (V2I)
communication to monitor the movement of the offender's vehicle and shared its
information with the Command and Control (CC) centre. The CC centre projects
the path and engages nearly located law enforcement agencies. CVEH is developed
and evaluated on android smartphones. Simulations conducted for this study
exhibit the effectiveness of our framework.


Abstract:
  In a simple extension of the standard model (SM), a pair of vector like
lepton doublets ($L_1$ and $L_2$) and a $SU(2)_L$ scalar doublet ($\eta$) have
been introduced to help in accommodating the discrepancy in determination of
the anomalous magnetic moments of the light leptons, namely, $e$ and $\mu$.
Moreover, to make our scenario friendly to a Dirac like neutrino and also for a
consistent dark matter phenomenology, we specifically add a singlet scalar
($S$) and a singlet fermion ($\psi$) in the set-up. A discrete symmetry
$\mathcal{Z}_2\times\mathcal{Z}_2^\prime$ has been imposed under which all the
SM particles are even while the new particles may be assumed to have odd
charges. In a bottom-up approach, with a minimal particle content, we
systematically explore the available parameter space in terms of couplings and
masses of the new particles. Here a number of observables associated with the
SM leptons have been considered, e.g., masses and mixings of neutrinos, $(g-2)$
anomalies of $e$, $\mu$, charged lepton flavor violating (cLFV) observables and
the dark matter (DM) phenomenology of a singlet-doublet dark matter. Neutrinos,
promoted as the Dirac type states, acquire mass at one loop level after the
discrete $\mathcal{Z}_2^\prime$ symmetry gets softly broken, while the unbroken
$\mathcal{Z}_2$ keeps the dark matter stable. The mixing between the singlet
$\psi$ and the doublet vector lepton can be constrained to satisfy the
electroweak precision observables and the spin independent (SI) direct
detection (DD) cross section of the dark matter. In this analysis, potentially
important LHC bounds have also been discussed.


Abstract:
  Inspired by the formulation of quantum-electrodynamical time-dependent
density functional theory (QED-TDDFT) by Rubio and coworkers, we propose an
implementation that uses dimensionless amplitudes for describing the photonic
contributions to QED-TDDFT electron-photon eigenstates. The leads to a
symmetric QED-TDDFT coupling matrix, which is expected to facilitate the future
development of analytic derivatives. Through a Gaussian atomic basis
implementation of the QED-TDDFT method, we examined the effect of dipole
self-energy, rotating wave approximation, and the Tamm-Dancoff approximation on
the QED-TDDFT eigenstates of model compounds (ethene, formaldehyde, and
benzaldehyde) in an optical cavity. We highlight, in the strong coupling
regime, the role of higher-energy and off-resonance excited states with large
transition dipole moments in the direction of the photonic field, which are
automatically accounted for in our QED-TDDFT calculations and might
substantially affect the energy and composition of polaritons associated with
lower-energy electronic states.


Abstract:
  Many ML models are opaque to humans, producing decisions too complex for
humans to easily understand. In response, explainable artificial intelligence
(XAI) tools that analyze the inner workings of a model have been created.
Despite these tools' strength in translating model behavior, critiques have
raised concerns about the impact of XAI tools as a tool for `fairwashing` by
misleading users into trusting biased or incorrect models. In this paper, we
created a framework for evaluating explainable AI tools with respect to their
capabilities for detecting and addressing issues of bias and fairness as well
as their capacity to communicate these results to their users clearly. We found
that despite their capabilities in simplifying and explaining model behavior,
many prominent XAI tools lack features that could be critical in detecting
bias. Developers can use our framework to suggest modifications needed in their
toolkits to reduce issues likes fairwashing.


Abstract:
  In the past few years, we have witnessed an increased interest in the use of
2D materials for the realization of hybrid photonic nonlinear waveguides.
Although graphene has attracted most of the attention, other families of 2D
materials such as transition metal dichalcogenides have also shown promising
nonlinear performances. In this work, we propose a strategy for designing
silicon nitride waveguide structures embedded with molybdenum disulfide for
nonlinear applications. The transverse geometry of the hybrid waveguides
structure is optimized for high third order nonlinear effects using
optogeometrical engineering and multiple layers of molybdenum disulfide.
Stacking multiple monolayers, results in an improvement of 2 orders of
magnitude in comparison with standard silicon nitride waveguides. The
performance of the hybrid waveguides is then investigated in terms of four wave
mixing enhancement in micro ring resonator configurations. A 6,3 dB signal
idler conversion efficiency is reached around 1550 nm wavelength for a 5 mW
pumping level.


Abstract:
  This note discusses the Wigner function representation from the standpoint of
establishing a holography-like correspondence between the descriptions of a
generic quantum system in the phase space ('bulk') picture versus its spacetime
('boundary') counterpart. Under certain circumstances the former might reduce
to the classical dynamics of a local metric-like variable while the latter
takes on the form of some bosonized collective field hydrodynamics. This
generic pseudo-holographic duality neither relies on any particular symmetry of
the system in question, nor does it require any connection to an underlying
'string theory', as in the various 'ad hoc' scenarios of applied holography.


Abstract:
  There is great need for high intensity proton beams from compact particle
accelerators in particle physics, medical isotope production, and materials-
and energy-research. To address this need, we present, for the first time, a
design for a compact isochronous cyclotron that will be able to deliver 10 mA
of 60 MeV protons - an order of magnitude higher than on-market compact
cyclotrons and a factor four higher than research machines. A key breakthrough
is that vortex motion is incorporated in the design of a cyclotron, leading to
clean extraction. Beam losses on the septa of the electrostatic extraction
channels stay below 50 W (a factor four below the required safety limit), while
maintaining good beam quality. We present a set of highly accurate
particle-in-cell simulations, and an uncertainty quantification of select beam
input parameters using machine learning, showing the robustness of the design.
This design can be utilized for beams for experiments in particle and nuclear
physics, materials science and medical physics as well as for industrial
applications.


Abstract:
  The macroscopic behavior of many materials is complex and the end result of
mechanisms that operate across a broad range of disparate scales. An imperfect
knowledge of material behavior across scales is a source of epistemic
uncertainty of the overall material behavior. However, assessing this
uncertainty is difficult due to the complex nature of material response and the
prohibitive computational cost of integral calculations. In this paper, we
exploit the multiscale and hierarchical nature of material response to develop
an approach to quantify the overall uncertainty of material response without
the need for integral calculations. Specifically, we bound the uncertainty at
each scale and then combine the partial uncertainties in a way that provides a
bound on the overall or integral uncertainty. The bound provides a conservative
estimate on the uncertainty. Importantly, this approach does not require
integral calculations that are prohibitively expensive. We demonstrate the
framework on the problem of ballistic impact of a polycrystalline magnesium
plate. Magnesium and its alloys are of current interest as promising
light-weight structural and protective materials. Finally, we remark that the
approach can also be used to study the sensitivity of the overall response to
particular mechanisms at lower scales in a materials-by-design approach.


Abstract:
  We propose in this paper to consider the stock market as a physical system
assimilate to a fluid evolving in a macroscopic space subject to a Force that
influences its movement over time where this last is arising from the collision
between the supply and the demand of Financial agents. In fluid mechanics, this
Force also results from the collisions of fluid molecules led by its physical
property such as density, viscosity, and surface tension. The purpose of this
article is to show that the dynamism of the stock market behavior can be
explained qualitatively and quantitatively by considering the supply & demand
collision as the result of Financial agents physical properties defined by
Stokes Law. The first objective of this article is to show theoretically that
fluid mechanics equations can be used to describe stock market physical
properties. The second objective based on the knowledge of stock market
physical properties is to propose an Econophysics analog of the stock market
viscosity and Reynolds number to measure stock market conditions, whether
laminar, transitory, or turbulent. The Reynolds Number defined in this way can
be applied in research into the study and classification of stock market
dynamics phases through for instance the creation of Econophysics analog of
Moddy diagram, this last could be seen as a physical way to quantify asset and
stock index idiosyncratic risk. The last objective is to present evidence from
a computer simulation that the stock market behavior can be a priori, and
posteriori explained by physical properties (viscosity & density) quantifiable
by fluid mechanics law (Stokes law) and measurable with the stock market
Reynolds Number.


Abstract:
  We present a simplified model of a vehicle driving on a nonplanar road. A
parametric surface is used to describe the nonplanar road which can describe
any combination of curvature, bank and slope. We show that the proposed
modeling approach generalizes planar vehicle models that reference a
centerline, such as the Frenet model.
  We use the proposed approach for vehicle path planning and following using
model predictive control. We also model and control vehicle contact with the
road surface. We demonstrate that the proposed controller improves speed and
lane following on complex roads compared to planar vehicle controllers, and
mitigates loss of control on complex road surfaces including off-camber turns.


Abstract:
  Countries, companies, and universities are increasingly competing over
top-tier artificial intelligence (AI) researchers. Where are these researchers
likely to immigrate and what affects their immigration decisions? We conducted
a survey $(n = 524)$ of the immigration preferences and motivations of
researchers that had papers accepted at one of two prestigious AI conferences:
the Conference on Neural Information Processing Systems (NeurIPS) and the
International Conference on Machine Learning (ICML). We find that the U.S. is
the most popular destination for AI researchers, followed by the U.K., Canada,
Switzerland, and France. A country's professional opportunities stood out as
the most common factor that influences immigration decisions of AI researchers,
followed by lifestyle and culture, the political climate, and personal
relations. The destination country's immigration policies were important to
just under half of the researchers surveyed, while around a quarter noted
current immigration difficulties to be a deciding factor. Visa and immigration
difficulties were perceived to be a particular impediment to conducting AI
research in the U.S., the U.K., and Canada. Implications of the findings for
the future of AI talent policies and governance are discussed.


Abstract:
  In this paper, the sharp interface limit for the compressible non-isentropic
Navier-Stokes/Allen-Cahn system is derived by the method of matched asymptotic
expansion. We show that the leading order problem satisfies the compressible
Navier-Stokes equations with the interface being a free boundary. We discuss
two cases in terms of different phase field diffusion coefficients. One is
$M_{\epsilon}=O(1)$ and $M_{\epsilon}=O(\frac{1}{\epsilon})$, where $\epsilon$
is the interface thickness. We have observed that the velocity and the
temperature of the compressible immiscible two-phase fluids continuously
through the interface. There is a jump for the tension tensor at the interface,
this jump depends on the surface tension and the mean curvature of the
interface. In particular, for the first case $M_{\epsilon}=O(1)$, no matter how
the density changes through the interface, the velocity of the interface in the
normal direction is the same as the normal velocity of the fluid along the
interface. But for the second case $M_{\epsilon}=O(\frac{1}{\epsilon})$, This
phenomenon cann't occur where the density passes continuously through the
interface.In fact, on this part of the interface, the normal velocity of the
interface is determined by the mean curvature of the interface, the velocity
and the density of the compressible immiscible two-phase fluids. That's where
the phase transition happens.


Abstract:
  We prove the relative Grauert-Riemenschneider vanishing, Kawamata-Viehweg
vanishing, and Koll\'ar injectivity theorems for $\mathbf{Q}$-schemes, solving
conjectures of Boutot and Kawakita. Our proof uses Grothendieck's limit theorem
for sheaf cohomology and Zariski-Riemann spaces. As an application, we extend
Boutot's theorem to the case of locally quasi-excellent $\mathbf{Q}$-algebras
by showing that if $R \to R'$ is a cyclically pure homomorphism of locally
quasi-excellent $\mathbf{Q}$-algebras, and $R'$ has rational singularities,
then $R$ has rational singularities. This solves a conjecture of Boutot and
answers a question of Schoutens in the locally quasi-excellent case.


Abstract:
  We construct Markov processes for modeling the rupture of edges in a
two-dimensional foam. We first describe a network model for tracking
topological information of foam networks with a state space of combinatorial
embeddings. Through a mean-field rule for randomly selecting neighboring cells
of a rupturing edge, we consider a simplified version of the network model in
the sequence space $\ell_1(\mathbb N)$ which counts total numbers of cells with
$n\ge 3$ sides ($n$-gons). Under a large cell limit, we show that number
densities of $n$-gons in the mean field model are solutions of an infinite
system of nonlinear kinetic equations. This system is comparable to the
Smoluchowski coagulation equation for coalescing particles under a
multiplicative collision kernel, suggesting gelation behavior. Numerical
simulations reveal gelation in the mean-field model, and also comparable
statistical behavior between the network and mean-field models.


Abstract:
  Photonic cluster states are a powerful resource for measurement-based quantum
computing and loss-tolerant quantum communication. Proposals to generate
multi-dimensional lattice cluster states have identified coupled spin-photon
interfaces, spin-ancilla systems, and optical feedback mechanisms as potential
schemes. Following these, we propose the generation of multi-dimensional
lattice cluster states using a single, efficient spin-photon interface coupled
strongly to a nuclear register. Our scheme makes use of the contact hyperfine
interaction to enable universal quantum gates between the interface spin and a
local nuclear register and funnels the resulting entanglement to photons via
the spin-photon interface. Among several quantum emitters, we identify the
silicon-29 vacancy centre in diamond, coupled to a nanophotonic structure, as
possessing the right combination of optical quality and spin coherence for this
scheme. We show numerically that using this system a 2x5-sized cluster state
with a lower-bound fidelity of 0.5 and repetition rate of 65 kHz is achievable
under currently realised experimental performances and with feasible technical
overhead. Realistic gate improvements put 100-photon cluster states within
experimental reach.


Abstract:
  This paper presents an approach for constructing distributed storage system
based on micro-service architecture. By building storage functionalities using
micro services, we can provide new capabilities to a distributed storage system
in a flexible way. We take erasure coding and compression as two case studies
to show how to build a micro-service based distributed storage system. We also
show that by building erasure coding and compression as micro-services, the
distributed storage system still achieves reasonable performance compared to
the monolithic one.


Abstract:
  The focus of our paper is the identification and correction of non-word
errors in OCR text. Such errors may be the result of incorrect insertion,
deletion, or substitution of a character, or the transposition of two adjacent
characters within a single word. Or, it can be the result of word boundary
problems that lead to run-on errors and incorrect-split errors. The traditional
N-gram correction methods can handle single-word errors effectively. However,
they show limitations when dealing with split and merge errors. In this paper,
we develop an unsupervised method that can handle both errors. The method we
develop leads to a sizable improvement in the correction rates. This tutorial
paper addresses very difficult word correction problems - namely incorrect
run-on and split errors - and illustrates what needs to be considered when
addressing such problems. We outline a possible approach and assess its success
on a limited study.


Abstract:
  One of the advantages of the phase-field method (PFM) is its ability to
incorporate elastic interactions that dominate solid-state processes including
phase transformations and plastic deformation. As mechanical equilibrium is
attained much faster than chemical equilibrium, the former should be used as a
constraint explicitly in deriving the governing equations of time-evolution of
PFM order parameters. Current models for elastically anisotropic and
inhomogeneous media in the literature do not impose such a constraint in their
governing equations. In particular, they ignore the dependence of the total
strain on the order parameters while evaluating the variational derivative of
the elastic energy (VDEE). There is no mathematical proof to support this
treatment and the fundamental thermodynamic consistency of such a models could
be challenged. In this work, we present a rigorous and physically transparent
formulation of PFM governing equations for elastically anisotropic and
inhomogeneous media in which the mechanical equilibrium is explicitly used as a
constraint. From our formulation, we show that VDEE computed in the
Leo-Lowengrub-Jou (1998) (LLJ) model is thermodynamically consistent. We also
show that the Wang-Jin-Khachaturyan (2003) (WJK) model has significant error in
VDEE calculation. We proposed a first-order correction term to the WJK model
which nullifies the error in VDEE and makes the model thermodynamically
consistent. Also, we computed the VDEE by a numerical method with-out using any
assumptions and compared the numerical VDEE with the LLJ, WJK and modified WJK
models.


Abstract:
  Let $(X,p)$ be a Ricci limit space. We show that for any $\epsilon > 0$ and
$x \in X$, there exists $r< \epsilon$, depending on $\epsilon$ and $x$, so that
any loop in $B_{r}(x)$ is contractible in $B_{\epsilon}(x)$. In particular, $X$
is semi-locally simply connected. Then we show that the generalized Margulis
lemma holds for Ricci limit spaces of $n$-manifolds.


Abstract:
  Intelligent resident surveillance is one of the most essential smart
community services. The increasing demand for security needs surveillance
systems to be able to detect anomalies in surveillance scenes. Employing
high-capacity computational devices for intelligent surveillance in residential
societies is costly and not feasible. Therefore, we propose anomaly detection
for intelligent surveillance using CPU-only edge devices. A modular framework
to capture object-level inferences and tracking is developed. To cope with
partial occlusions, posture deformations, and complex scenes, we employed
feature encoding and trajectory association governed by two metrices
complementing to each other. The elements of an anomaly detection framework are
optimized to run on CPU-only edge devices with sufficient frames per second
(FPS). The experimental results indicate the proposed method is feasible and
achieves satisfactory results in real-life scenarios.


Abstract:
  Model information can be used to predict future trajectories, so it has huge
potential to avoid dangerous region when implementing reinforcement learning
(RL) on real-world tasks, like autonomous driving. However, existing studies
mostly use model-free constrained RL, which causes inevitable constraint
violations. This paper proposes a model-based feasibility enhancement technique
of constrained RL, which enhances the feasibility of policy using generalized
control barrier function (GCBF) defined on the distance to constraint boundary.
By using the model information, the policy can be optimized safely without
violating actual safety constraints, and the sample efficiency is increased.
The major difficulty of infeasibility in solving the constrained policy
gradient is handled by an adaptive coefficient mechanism. We evaluate the
proposed method in both simulations and real vehicle experiments in a complex
autonomous driving collision avoidance task. The proposed method achieves up to
four times fewer constraint violations and converges 3.36 times faster than
baseline constrained RL approaches.


Abstract:
  Although quasi-Keplerian discs are among the most common astrophysical
structures, computation of secular angular momentum transport within them
routinely presents a considerable practical challenge. In this work, we
investigate the secular small-inclination dynamics of a razor-thin particle
disc as the continuum limit of a discrete Lagrange-Laplace secular perturbative
theory and explore the analogy between the ensuing secular evolution --
including non-local couplings of self-gravitating discs -- and quantum
mechanics. We find the 'quantum' Hamiltonian that describes the time evolution
of the system and demonstrate the existence of a conserved inner product. The
lowest-frequency normal modes are numerically approximated by performing a Wick
rotation on the equations of motion. These modes are used to quantify the
accuracy of a much simpler local-coupling model, revealing that it predicts the
shape of the normal modes to a high degree of accuracy, especially in narrow
annuli, even though it fails to predict their eigenfrequencies.


Abstract:
  In this work, we study global existence, eventual smoothness and asymptotical
behavior of positive solutions for a two-species chemotaxis consumption model
in a bounded smooth but not necessarily convex domain $\Omega\subset
\mathbb{R}^n (n=2,3,4,5)$ with nonnegative initial data and homogeneous Neumann
boundary data Under a smallness condition, boundedness of classical solutions
and stabilization to constant equilibrium is known. Here, without any smallness
condition, we show global existence and uniform-in-time boundedness of
classical solutions in 2D and global existence, eventual smoothness and
asymptotical behavior (in convex domains) of weak solutions in nD (n=3,4,5).
Our findings also extend and improve the one-species chemotaxis-consumption
model studied in relevant literature.


Abstract:
  Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming
highly resolvable, enabling accurate vehicle localization and mapping. We
propose a novel method of radio simultaneous localization and mapping (SLAM)
with the Dirichlet process (DP). The DP, which can estimate the number of
clusters as well as clustering, is capable of identifying the locations of
reflectors by classifying signals when such 5G signals are reflected and
received from various objects. We generate birth points using the measurements
from 5G mmWave signals received by the vehicle and classify objects by
clustering birth points generated over time. Each time we use the DP clustering
method, we can map landmarks in the environment in challenging situations where
false alarms exist in the measurements and change the cardinality of received
signals. Simulation results demonstrate the performance of the proposed scheme.
By comparing the results with the SLAM based on the Rao-Blackwellized
probability hypothesis density filter, we confirm a slight drop in SLAM
performance, but as a result, we validate that it has a significant gain in
computational complexity.


Abstract:
  We analyze the possible effect of rings on orbital velocities in galaxies.
The superposition of the central force with the gravitational forces induced by
the rings opens up various possibilities of the course of orbital velocities.
The orbital velocity depends on the position of the star in the ring. We
illustrate this dependence on several models, where we show the course of
potential curves and the curves of field strength.


Abstract:
  Blur and noise corrupting Computed Tomography (CT) images can hide or distort
small but important details, negatively affecting the diagnosis. In this paper,
we present a novel gradient-based Plug-and-Play algorithm, constructed on the
Half-Quadratic Splitting scheme, and we apply it to restore CT images. In
particular, we consider different schemes encompassing external and internal
denoisers as priors, defined on the image gradient domain. The internal prior
is based on the Total Variation functional. The external denoiser is
implemented by a deep Convolutional Neural Network (CNN) trained on the
gradient domain (and not on the image one, as in state-of-the-art works). We
also prove a general fixed-point convergence theorem under weak assumptions on
both internal and external denoisers. The experiments confirm the effectiveness
of the proposed framework in restoring blurred noisy CT images, both in
simulated and real medical settings. The achieved enhancements in the restored
images are really remarkable, if compared to the results of many
state-of-the-art methods.


Abstract:
  The Pacific Northwest Smart Grid Demonstration was an electricity grid
modernization project conducted in the Northwest U.S. This paper presents the
analysis of renewable generation at the Renewable Energy Park located in the
City of Ellensburg, WA. The community energy park concept is an intriguing
model for community investment in renewable resources,but the lessons in this
paper should be considered.


Abstract:
  The Institutional Analysis and Development (IAD) framework is a conceptual
toolbox put forward by Elinor Ostrom and colleagues in an effort to identify
and delineate the universal common variables that structure the immense variety
of human interactions. The framework identifies rules as one of the core
concepts to determine the structure of interactions, and acknowledges their
potential to steer a community towards more beneficial and socially desirable
outcomes. This work presents the first attempt to turn the IAD framework into a
computational model to allow communities of agents to formally perform what-if
analysis on a given rule configuration. To do so, we define the Action
Situation Language -- or ASL -- whose syntax is hgighly tailored to the
components of the IAD framework and that we use to write descriptions of social
interactions. ASL is complemented by a game engine that generates its semantics
as an extensive-form game. These models, then, can be analyzed with the
standard tools of game theory to predict which outcomes are being most
incentivized, and evaluated according to their socially relevant properties.


Abstract:
  Two dimensional crystalline membranes in isotropic embedding space exhibit a
flat phase with anomalous elasticity, relevant e.g., for graphene. Here we
study their thermal fluctuations in the absence of exact rotational invariance
in the embedding space. An example is provided by a membrane in an
orientational field, tuned to a critical buckling point by application of
in-plane stresses. Through a detailed analysis, we show that the transition is
in a new universality class. The self-consistent screening method predicts a
second order transition, with modified anomalous elasticity exponents at
criticality, while the RG suggests a weakly first order transition.


Abstract:
  We address the problem of determining optimal sensor precisions for
estimating the states of linear time-varying discrete-time stochastic dynamical
systems, with guaranteed bounds on the estimation errors. This is performed in
the Kalman filtering framework, where the sensor precisions are treated as
variables. They are determined by solving a constrained convex optimization
problem, which guarantees the specified upper bound on the posterior error
variance. Optimal sensor precisions are determined by minimizing the l1 norm,
which promotes sparseness in the solution and indirectly addresses the sensor
selection problem. The theory is applied to realistic flight mechanics and
astrodynamics problems to highlight its engineering value. These examples
demonstrate the application of the presented theory to a) determine redundant
sensing architectures for linear time invariant systems, b) accurately estimate
states with low-cost sensors, and c) optimally schedule sensors for linear
time-varying systems.


Abstract:
  Due to the significant delay and cost associated with experimental tests, a
model based evaluation of concrete compressive strength is of high value, both
for the purpose of strength prediction as well as the mixture optimization. In
this regard, several recent studies have employed state-of-the-art regression
models in order to achieve a good prediction model, employing available
experimental data sets. Nevertheless, while each of the employed models can
better adapt to a specific nature of the input data, the accuracy of each
individual model is limited due to the sensitivity to the choice of
hyperparameters and the learning strategy. In the present work, we take a
further step towards improving the accuracy of the prediction model via the
weighted combination of multiple regression methods. Moreover, a (GA)-based
multi-objective mixture optimization is proposed, building on the obtained
multi-regression model. In particular, we present a data aided framework where
the regression methods based on artificial neural network, random forest
regression, and polynomial regression are jointly implemented to predict the
compressive strength of concrete. The outcome of the individual regression
models are then combined via a linear weighting strategy and optimized over the
training data set as a quadratic convex optimization problem. It is worth
mentioning that due to the convexity of the formulated problem, the globally
optimum weighting strategy is obtained via standard numerical solvers.
Employing the proposed GA-based optimization, a Pareto front of the cost-CS
trade-of has been obtained employing the available data set. Moreover, the
resulting accuracy of the proposed multi-model prediction method is shown to
outperform the available single-model regression methods in the literature by a
valuable margin, via numerical simulations.