Abstract:
  We study integrability properties of the non-chiral intermediate long wave
(ncILW) equation with periodic boundary conditions. The ncILW equation was
recently introduced by the authors as a parity-invariant relative of the
intermediate long wave equation. For this new equation we: (a) derive a Lax
pair, (b) derive a Hirota bilinear form, (c) use the Hirota method to construct
the periodic multi-soliton solutions, (d) derive a B\"{a}cklund transformation,
(e) use the B\"{a}cklund transformation to obtain an infinite number of
conservation laws.


Abstract:
  The advent of digital pathology presents opportunities for computer vision
for fast, accurate, and objective solutions for histopathological images and
aid in knowledge discovery. This work uses deep learning to predict genomic
biomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status,
and intrinsic subtypes, from breast cancer histopathology images. Furthermore,
we attempt to understand the underlying morphology as to how these genomic
biomarkers manifest in images. Since gene sequencing is expensive, not always
available, or even feasible, predicting these biomarkers from images would help
in diagnosis, prognosis, and effective treatment planning. We outperform the
existing works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC
scores across all tasks. We also gain insights that can serve as hypotheses for
further experimentations, including the presence of lymphocytes and
karyorrhexis. Moreover, our fully automated workflow can be extended to other
tasks across other cancer subtypes.


Abstract:
  Images acquired with a telescope are blurred and corrupted by noise. The
blurring is usually modeled by a convolution with the Point Spread Function and
the noise by Additive Gaussian Noise. Recovering the observed image is an
ill-posed inverse problem. Sparse deconvolution is well known to be an
efficient deconvolution technique, leading to optimized pixel Mean Square
Errors, but without any guarantee that the shapes of objects (e.g. galaxy
images) contained in the data will be preserved. In this paper, we introduce a
new shape constraint and exhibit its properties. By combining it with a
standard sparse regularization in the wavelet domain, we introduce the Shape
COnstraint REstoration algorithm (SCORE), which performs a standard sparse
deconvolution, while preserving galaxy shapes. We show through numerical
experiments that this new approach leads to a reduction of galaxy ellipticity
measurement errors by at least 44%.


Abstract:
  This paper aims to gain a better understanding of the perspectives of
contributors to Spanish academic journals regarding open access, open peer
review, and altmetrics. It also explores how age, gender, professional
experience, career history, and perception and use of social media influence
authors opinions toward these developments in scholarly publishing. A sample of
contributors (n-1254) to Spanish academic journals was invited to participate
in a survey about the aforementioned topics. The response rate was 24 per cent
(n-295). Contributors to Spanish scholarly journals hold a favourable opinion
of open access but were more cautious about open peer review and altmetrics.
Younger and female scholars were more reluctant to accept open peer review
practices. A positive attitude toward social networks did not necessarily
translate into enthusiasm for emerging trends in scholarly publishing. Despite
this, ResearchGate users were more aware of altmetrics.


Abstract:
  We investigate the application of a holographic entanglement negativity
construction to bipartite states of single subsystems in $CFT_d$s with a
conserved charge dual to bulk $AdS_{d+1}$ geometries. In this context, we
obtain the holographic entanglement negativity for single subsystems with long
rectangular strip geometry in $CFT_d$s dual to bulk extremal and nonextremal
Reissner-Nordstr{\"o}m (RN)-$AdS_{d+1}$ black holes. Our results demonstrate
that for this configuration the holographic entanglement negativity involves
subtraction of the thermal entropy from the entanglement entropy confirming
earlier results. This conforms to the characterization of entanglement
negativity as the upper bound on the distillable entanglement in quantum
information theory and constitutes an important consistency check for our
higher dimensional construction.


Abstract:
  Compact groups (CGs) of galaxies appear to be the densest galaxy systems
containing a few luminous galaxies in close proximity to each other, which have
a typical size of a few tens kilopacsec in observation. On the other hand, in
the modern hierarchical structure formation paradigm, galaxies are assembled
and grouped in dark matter haloes, which have a typical size of a few hundreds
of kiloparsec. Few studies have explored the physical connection between the
observation based CGs and halo model based galaxy groups to date. In this
study, by matching the largest local CG catalog of Zheng & Shen (2020) to the
halo based group catalog of Yang et al. (2007), we find that the CGs are
physically heterogenous systems and can be mainly separated into two
categories, the isolated systems and those embedded in rich groups or clusters.
By examining the dynamical features of CGs, we find that the isolated CGs have
systematically lower dynamical masses than that of non-compact ones at the same
group luminosity, indicating a more evolved stage of isolated CGs. On the other
hand, the embedded CGs are mixtures of chance alignments in poor clusters and
recent infalling groups (sub-structures) of rich clusters.


Abstract:
  Hybrid Morphology Radio Sources (HyMoRS) are a very rare and newly discovered
subclass of radio galaxies that have mixed FR morphology i.e., these galaxies
have FR-I structure on one side of the core and FR-II structure on the other
side of the core. We systematically searched for HyMoRS using VLA Faint Images
of the Radio Sky at Twenty-cm (FIRST) survey at 1400 MHz and identified
forty-five confirmed HyMoRS and five candidates HyMoRS. Our finding
significantly increased the known sample size of HyMoRS. HyMoRS may play an
essential role in understanding the interaction of jets with the interstellar
medium and a very debated topic of the FR dichotomy. We identified optical/IR
counterparts for thirty-nine sources in our catalogue. In our sample of
sources, five sources had Quasar-like behavior. We had estimated the spectral
index and radio luminosity of HyMoR sources in our catalogue, when possible. We
found that the source J1336+2329 ($\log L=26.93$ W Hz$^{-1}$sr$^{-1}$) was the
most luminous and the source J1204+3801, a Quasar, was the farthest HyMoRS
(with redshift $z$=1.28) in our sample. With the help of a large sample size of
the newly discovered sources, various statistical properties were studied.


Abstract:
  The local Langlands correspondence matches irreducible representations of a
reductive p-adic group G(F) with enhanced L-parameters. It is conjectured by
Hellmann and Zhu that it can be categorified. Then it should become a fully
faithful functor from a derived category of representations to a derived
category of equivariant sheaves on some variety of L-parameters. We approach
this conjecture in the case of finite length G(F)-representations. Then it runs
via graded Hecke algebras associated to Bernstein components or to enhanced
L-parameters. Here we work with graded Hecke algebras H on the Galois side,
those can be constructed entirely in terms of a complex reductive group,
endowed with data from L-parameters. We fix an arbitrary central character
(\sigma,r) of H (which encodes the image of Frobenius by an L-parameter). That
leads to a variety g_N^{\sigma,r} of nilpotent elements in the Lie algebra of G
(possibilities for the monodromy operator from an L-parameter) and to a complex
of equivariant constructible sheaves K_{N,\sigma,r} on g_N^{\sigma,r}. We
relate the (derived) endomorphism algebra of (g_N^{\sigma,r}, K_{N,\sigma,r})
to a localization of H, which yields an equivalence between the appropriate
categories of finite length modules of these algebras. From there we construct
a fully faithful functor between:
  - the bounded derived category of finite length H-modules specified by the
central character (\sigma,r),
  - the equivariant bounded derived category of constructible sheaves on
g_N^{\sigma,r}.
  Also, we explicitly determine the images of standard modules under this
functor. We expect that these results pave the way for more general instances
of the aforementioned conjectural extension of the local Langlands
correspondence.


Abstract:
  Recent work in equivariant deep learning bears strong similarities to
physics. Fields over a base space are fundamental entities in both subjects, as
are equivariant maps between these fields. In deep learning, however, these
maps are usually defined by convolutions with a kernel, whereas they are
partial differential operators (PDOs) in physics. Developing the theory of
equivariant PDOs in the context of deep learning could bring these subjects
even closer together and lead to a stronger flow of ideas. In this work, we
derive a $G$-steerability constraint that completely characterizes when a PDO
between feature vector fields is equivariant, for arbitrary symmetry groups
$G$. We then fully solve this constraint for several important groups. We use
our solutions as equivariant drop-in replacements for convolutional layers and
benchmark them in that role. Finally, we develop a framework for equivariant
maps based on Schwartz distributions that unifies classical convolutions and
differential operators and gives insight about the relation between the two.


Abstract:
  The pyramids of ancient Egypt are said to have shone brilliantly in the sun.
Surfaces of polished limestone would not only have reflected diffusely in all
directions, but would also likely have produced specular reflections in
particular directions. Reflections toward points on the horizon would have been
visible from large distances. On a particular day and time when the sun was
properly situated, an observer stationed at a distant site would have seen a
momentary flash as the sun's reflection moved across the face of the pyramid.
The positions of the sun that are reflected to the horizon are confined to
narrow arcs in the sky, one arc for each side of the pyramid. We model specular
reflections from the pyramid of Khufu and derive the annual dates and times
when they would have been visible at important ancient sites. Certain of these
events might have coincided with significant dates on the Egyptian calendar, as
well as with solar equinoxes, solstices and cross-quarter days. The celebration
of Wepet-Renpet, which at the time of the pyramid's construction occurred near
the spring cross-quarter day, would have been marked by a specular sweep of
sites on the southern horizon. On the autumn and winter cross-quarter days
reflections would have been directed to Heliopolis. We suggest that on those
days the pyramidion of Khafre might have been visible in specular reflection
over the truncated top of Khufu's pyramid.


Abstract:
  The tabla is a unique percussion instrument due to the combined harmonic and
percussive nature of its timbre, and the contrasting harmonic frequency ranges
of its two drums. This allows a tabla player to uniquely emphasize parts of the
rhythmic cycle (theka) in order to mark the salient positions. An analysis of
the loudness dynamics and timing deviations at various cycle positions is an
important part of musicological studies on the expressivity in tabla
accompaniment. To achieve this at a corpus-level, and not restrict it to the
few recordings that manual annotation can afford, it is helpful to have access
to an automatic tabla transcription system. Although a few systems have been
built by training models on labeled tabla strokes, the achieved accuracy does
not necessarily carry over to unseen instruments. In this article, we report
our work towards building an instrument-independent stroke classification
system for accompaniment tabla based on the more easily available tabla solo
audio tracks. We present acoustic features that capture the distinctive
characteristics of tabla strokes and build an automatic system to predict the
label as one of a reduced, but musicologically motivated, target set of four
stroke categories. To address the lack of sufficient labeled training data, we
turn to common data augmentation methods and find the use of pitch-shifting
based augmentation to be most promising. We then analyse the important features
and highlight the problem of their instrument-dependence while motivating the
use of more task-specific data augmentation strategies to improve the diversity
of training data.


Abstract:
  We introduce an approach based on the Chapman-Kolmogorov equation to model
heterogeneous stochastic circuits, namely, the circuits combining binary or
multi-state stochastic memristive devices and continuum reactive components
(capacitors and/or inductors). Such circuits are described in terms of
occupation probabilities of memristive states that are functions of reactive
variables. As an illustrative example, the series circuit of a binary memristor
and capacitor is considered in detail. Some analytical solutions are found. Our
work offers a novel analytical/numerical tool for modeling complex stochastic
networks, which may find a broad range of applications.


Abstract:
  Ground-based Whole Sky Imagers (WSIs) are increasingly being used for various
remote sensing applications. While the fundamental requirements of a WSI are to
make it climate-proof with an ability to capture high resolution images, cost
also plays a significant role for wider scale adoption. This paper proposes an
extremely low-cost alternative to the existing WSIs. In the designed model,
high resolution images are captured with auto adjusting shutter speeds based on
the surrounding light intensity. Furthermore, a manual data backup option using
a portable memory drive is implemented for remote locations with no internet
access.


Abstract:
  Graph classification is a critical research problem in many applications from
different domains. In order to learn a graph classification model, the most
widely used supervision component is an output layer together with
classification loss (e.g.,cross-entropy loss together with softmax or margin
loss). In fact, the discriminative information among instances are more
fine-grained, which can benefit graph classification tasks. In this paper, we
propose the novel Label Contrastive Coding based Graph Neural Network (LCGNN)
to utilize label information more effectively and comprehensively. LCGNN still
uses the classification loss to ensure the discriminability of classes.
Meanwhile, LCGNN leverages the proposed Label Contrastive Loss derived from
self-supervised learning to encourage instance-level intra-class compactness
and inter-class separability. To power the contrastive learning, LCGNN
introduces a dynamic label memory bank and a momentum updated encoder. Our
extensive evaluations with eight benchmark graph datasets demonstrate that
LCGNN can outperform state-of-the-art graph classification models. Experimental
results also verify that LCGNN can achieve competitive performance with less
training data because LCGNN exploits label information comprehensively.


Abstract:
  Precise segmentation of a lesion area is important for optimizing its
treatment. Deep learning makes it possible to detect and segment a lesion field
using annotated data. However, obtaining precisely annotated data is very
challenging in the medical domain. Moreover, labeling uncertainty and
imprecision make segmentation results unreliable. In this paper, we address the
uncertain boundary problem by a new evidential neural network with an
information fusion strategy, and the scarcity of annotated data by
semi-supervised learning. Experimental results show that our proposal has
better performance than state-of-the-art methods.


Abstract:
  In this paper, it is proved that the weak convergence of the $L_p$ Guassian
surface area measures implies the convergence of the corresponding convex
bodies in the Hausdorff metric for $p\geq 1$. Moreover, this paper obtains the
solution to the Guassian Minkowski problem is continuous with respect to $p$.


Abstract:
  In this paper we provide a way of taking $L^p$, $p > \frac{m}{2}$ bounds on a
$m-$ dimensional Riemannian metric and transforming that into H\"{o}lder bounds
for the corresponding distance function. One can think of this new estimate as
a type of Morrey inequality for Riemannian manifolds where one thinks of a
Riemannian metric as the gradient of the corresponding distance function so
that the $L^p$, $p > \frac{m}{2}$ bound analogously implies H\"{o}lder control
on the distance function. This new estimate is then used to state a compactness
theorem, another theorem which guarantees convergence to a particular
Riemmanian manifold, and a new scalar torus stability result. We expect these
results to be useful for proving geometric stability results in the presence of
scalar curvature bounds when Gromov-Hausdorff convergence is expected.


Abstract:
  We investigate here the nonlinear elliptic H\'enon type equation:
  $$\D^{2} u= |x|^a|u|^{p-1}u \; \,\,\mbox{in}\,\,\,\, \R^{n}_{+}, \quad \quad
u =\frac{\partial u}{\partial x_n} = 0 \quad \mbox{in}\,\,\,\, \partial
\R^{n}_{+},$$ with $p>1$ and $n\geq 2$. In particular, we prove some Liouville
type theorems for stable at infinity solutions. The main methods used are the
integral estimates, the Pohozaev-type identity and the monotonicity formula.


Abstract:
  Anisotropic, submicrometer sized particles are versatile systems providing
interesting features in creating ordering in 2-dimensional systems. Combining
hard ellipsoids with a soft shell further enhances the opportunities to trigger
and control order and alignment. In this work, we report a rich 2D phase
behavior and show how softness affects the ordering of anisotropic particles at
fluid oil-water interfaces. Three different core-shell systems were synthesized
such that they have the same elliptical hematite-silica core but differ with
respect to thickness and stiffness of the soft microgel shell. Compression
isotherms, the shape of individual core-shell microgel as well as their 2D
order at a decane-water interface are investigated by means of the
Langmuir-Blodgett technique combined with ex-situ Atomic Force Microscopy (AFM)
imaging, as well as by Disspiative Particle Dynamics (DPD) simulations. We show
how softness, size and anisotropy of the microgel shell affect the side-to-side
vs. tip-to-tip ordering of anisotropic hybrid microgels as well as the
alignment with respect to the direction of compression in the Langmuir trough.
A large and soft microgel shell leads to an ordered structure with a tip-to-tip
alignment directed perpendicular to the direction of compression. In contrast,
a thin and harder microgel shell leads to side-to-side ordering orientated
parallel to the compression direction. In addition, the thin and harder
microgel shell induces clustering of the microgels in the dilute state
indicating the presence of strong capillary interactions. Our findings
highlight the relevance of softness for the complex ordering of anisotropic
hybrid microgels at interfaces.


Abstract:
  Crystallization in a dense suspension of anisotropic spherical colloidal
particles with a Yukawa potential is numerically investigated in a
two-dimensional plane. It is found that a strong anisotropy can hinder the
particles from crystallizing, while a weak anisotropy but super-strong coupling
facilitates colloids to freeze into a hexagonal crystal. Different criterions
are employed to describe the phase transition, one can find that a competition
between anisotropic degree and coupling strength shall widened the transition
region in the phase diagram, where the heterogeneous structures coexist, which
render as a quasi-platform stretched across the probability distribution curve
of the local order parameter. Our study maybe helpful for the experiments
relating to the crystallizing behavior in statistical physics, materials
science and biophysical systems.


Abstract:
  As the scene information, including objectness and scene type, are important
for people with visual impairment, in this work we present a multi-task
efficient perception system for the scene parsing and recognition tasks.
Building on the compact ResNet backbone, our designed network architecture has
two paths with shared parameters. In the structure, the semantic segmentation
path integrates fast attention, with the aim of harvesting long-range
contextual information in an efficient manner. Simultaneously, the scene
recognition path attains the scene type inference by passing the semantic
features into semantic-driven attention networks and combining the semantic
extracted representations with the RGB extracted representations through a
gated attention module. In the experiments, we have verified the systems'
accuracy and efficiency on both public datasets and real-world scenes. This
system runs on a wearable belt with an Intel RealSense LiDAR camera and an
Nvidia Jetson AGX Xavier processor, which can accompany visually impaired
people and provide assistive scene information in their navigation tasks.


Abstract:
  Neutrinos from a Galactic core-collapse supernova will be measured by
neutrino detectors minutes to days before an optical signal reaches Earth. We
present a novel calculation showing the ability of current and near-future
neutrino detectors to make fast predictions of the progenitor distance and
place constraints on the zero-age main sequence mass in order to inform the
observing strategy for electromagnetic follow-up. We show that for typical
Galactic supernovae, the distance can be constrained with an uncertainty of
$\sim$5\% using IceCube or Hyper-K and, furthermore, the zero-age main sequence
mass can be constrained for extremal values of compactness.


Abstract:
  In \cite{JianQuan Liao 1} and \cite{ZhongKai Li 3}, $H_{\lambda}^p(\RR)$ is
introduced to extend the results about the Hankel transform of Muckenhoupt and
Stein in \cite{MS}. In this paper, we use the same way as
Burkholder-Gundy-Silverstein theorem in\,\cite{BGS}, and obtain the Real
Characterization of the class $H_{\lambda}^p(\RR)$ for
$\frac{2\lambda}{2\lambda+1}<p\leq1$.


Abstract:
  The increasing availability of textual corpora and data fetched from social
networks is fuelling a huge production of works based on the model proposed by
psychologist Robert Plutchik, often referred simply as the ``Plutchik Wheel''.
Related researches range from annotation tasks description to emotions
detection tools. Visualisation of such emotions is traditionally carried out
using the most popular layouts, as bar plots or tables, which are however
sub-optimal. The classic representation of the Plutchik's wheel follows the
principles of proximity and opposition between pairs of emotions: spatial
proximity in this model is also a semantic proximity, as adjacent emotions
elicit a complex emotion (a primary dyad) when triggered together; spatial
opposition is a semantic opposition as well, as positive emotions are opposite
to negative emotions. The most common layouts fail to preserve both features,
not to mention the need of visually allowing comparisons between different
corpora in a blink of an eye, that is hard with basic design solutions. We
introduce PyPlutchik, a Python library specifically designed for the
visualisation of Plutchik's emotions in texts or in corpora. PyPlutchik draws
the Plutchik's flower with each emotion petal sized after how much that emotion
is detected or annotated in the corpus, also representing three degrees of
intensity for each of them. Notably, PyPlutchik allows users to display also
primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We
substantiate our claim that PyPlutchik outperforms other classic visualisations
when displaying Plutchik emotions and we showcase a few examples that display
our library's most compelling features.


Abstract:
  We study LiFeP, LiFeAs and NaFeAs in their paramagnetic metallic phase
including dynamical electronic correlations within a density functional theory
+ slave-spin mean-field framework. The three compounds are found to lie next to
the crossover between a normal and a Hund's metal, where a region of enhanced
electronic compressibility that may boost superconductivity is systematically
present in this type of systems. We find that LiFeP lies in the normal metallic
regime, LiFeAs at the crossover, and NaFeAs is in the Hund's metal regime,
which possibly explains the different experimental trends for the pressure- and
doping-dependence of superconductivity in these compounds. Our picture captures
the orbitally-resolved mass renormalizations measured in these materials, while
an analysis of the Sommerfeld specific-heat coefficient highlights some
limitations of currently used implementations of density-functional theory for
the correct prediction of the details of band structures in the iron-based
superconductors.


Abstract:
  We study sets of local dimensions for self-similar measures in $\mathbb{R}$
satisfying the finite neighbour condition, which is formally stronger than the
weak separation condition but satisfied in all known examples. Under a mild
technical assumption, we establish that the set of attainable local dimensions
is a finite union of (possibly singleton) compact intervals. The number of
intervals is bounded above by the number of non-trivial maximal strongly
connected components of a finite directed graph construction depending only on
the governing iterated function system. We also explain how our results allow
computations of the sets of local dimensions in many explicit cases. This
contextualizes and generalizes a vast amount of prior work on sets of local
dimensions for self-similar measures satisfying the weak separation condition.


Abstract:
  Hundreds of millions of people learn something new online every day.
Simultaneously, the study of online education has blossomed within the human
computer interaction community, with new systems, experiments, and observations
creating and exploring previously undiscovered online learning environments. In
this study we endeavor to characterize this entire landscape of online learning
experiences using a national survey of 2260 US adults who are balanced to match
the demographics of the U.S. We examine the online learning resources that they
consult, and we analyze the subjects that they pursue using those resources.
Furthermore, we compare both formal and informal online learning experiences on
a larger scale than has ever been done before, to our knowledge, to better
understand which subjects people are seeking for intensive study. We find that
there is a core set of online learning experiences that are central to other
experiences and these are shared among the majority of people who learn online.
We conclude by showing how looking outside of these core online learning
experiences can reveal opportunities for innovation in online education.


Abstract:
  With a growing demand for adopting ML models for a varietyof application
services, it is vital that the frameworks servingthese models are capable of
delivering highly accurate predic-tions with minimal latency along with reduced
deploymentcosts in a public cloud environment. Despite high latency,prior works
in this domain are crucially limited by the accu-racy offered by individual
models. Intuitively, model ensem-bling can address the accuracy gap by
intelligently combiningdifferent models in parallel. However, selecting the
appro-priate models dynamically at runtime to meet the desiredaccuracy with low
latency at minimal deployment cost is anontrivial problem. Towards this, we
proposeCocktail, a costeffective ensembling-based model serving
framework.Cock-tailcomprises of two key components: (i) a dynamic
modelselection framework, which reduces the number of modelsin the ensemble,
while satisfying the accuracy and latencyrequirements; (ii) an adaptive
resource management (RM)framework that employs a distributed proactive
autoscalingpolicy combined with importance sampling, to efficiently allo-cate
resources for the models. The RM framework leveragestransient virtual machine
(VM) instances to reduce the de-ployment cost in a public cloud. A prototype
implementationofCocktailon the AWS EC2 platform and exhaustive evalua-tions
using a variety of workloads demonstrate thatCocktailcan reduce deployment cost
by 1.45x, while providing 2xreduction in latency and satisfying the target
accuracy for upto 96% of the requests, when compared to
state-of-the-artmodel-serving frameworks.


Abstract:
  The combinatorial invariance conjecture (due independently to G. Lusztig and
M. Dyer) predicts that if $[x,y]$ and $[x',y']$ are isomorphic Bruhat posets
(of possibly different Coxeter systems), then the corresponding Kazhdan-Lusztig
polynomials are equal, i.e., $P_{x,y}(q)=P_{x',y'}(q)$. We prove this
conjecture for the affine Weyl group of type $\widetilde{A}_2$. This is the
first non-trivial case where this conjecture is verified for an infinite group.


Abstract:
  Two-dimensional fully kinetic particle-in-cell simulations of an
electrodeless plasma thruster, which uses a magnetic nozzle, were conducted to
investigate the thrust generation induced by the internal plasma current. The
results clearly show that the $\bf{E} \times \bf{B}$ and diamagnetic current
densities are the major components of the internal plasma current. The
simulated pressure structures reproduced the experimentally observed structures
well. The results for various magnetic field strengths reveal that the $\bf{E}
\times \bf{B}$ effect decreases and the diamagnetic effect becomes dominant
with an increase in the magnetic field strength; this demonstrates the
significant contribution of the diamagnetic effect in thrust generation.


Abstract:
  For an infinity of number rings we express stable motivic invariants in terms
of topological data determined by the complex numbers, the real numbers, and
finite fields. We use this to extend Morel's identification of the endomorphism
ring of the motivic sphere with the Grothendieck-Witt ring of quadratic forms
to deeper base schemes.


Abstract:
  Quantum-well (QW) devices have been extensively investigated in semiconductor
structures. More recently, spin-polarized QWs were integrated into magnetic
tunnel junctions (MTJs). In this work, we demonstrate the spin-based control of
the quantized states in iron $3d$-band QWs, as observed in experiments and
theoretical calculations. We find that the magnetization rotation in the Fe QWs
significantly shifts the QW quantization levels, which modulate the
resonant-tunneling current in MTJs, resulting in a tunneling anisotropic
magnetoresistance (TAMR) effect of QWs. This QW-TAMR effect is sizable compared
to other types of TAMR effect, and it is present above the room-temperature. In
a QW MTJ of Cr/Fe/MgAl$_2$O$_4$/top electrode, where the QW is formed by a
mismatch between Cr and Fe in the $d$ band with $\Delta_1$ symmetry, a QW-TAMR
ratio of up to 5.4 % was observed at 5 K, which persisted to 1.2 % even at
380K. The magnetic control of QW transport can open new applications for
spin-coupled optoelectronic devices, ultra-thin sensors, and memories.


Abstract:
  Multi-agent reinforcement learning methods have shown remarkable potential in
solving complex multi-agent problems but mostly lack theoretical guarantees.
Recently, mean field control and mean field games have been established as a
tractable solution for large-scale multi-agent problems with many agents. In
this work, driven by a motivating scheduling problem, we consider a
discrete-time mean field control model with common environment states. We
rigorously establish approximate optimality as the number of agents grows in
the finite agent case and find that a dynamic programming principle holds,
resulting in the existence of an optimal stationary policy. As exact solutions
are difficult in general due to the resulting continuous action space of the
limiting mean field Markov decision process, we apply established deep
reinforcement learning methods to solve the associated mean field control
problem. The performance of the learned mean field control policy is compared
to typical multi-agent reinforcement learning approaches and is found to
converge to the mean field performance for sufficiently many agents, verifying
the obtained theoretical results and reaching competitive solutions.


Abstract:
  E-commerce with major online retailers is changing the way people consume.
The goal of increasing delivery speed while remaining cost-effective poses
significant new challenges for supply chains as they race to satisfy the
growing and fast-changing demand. In this paper, we consider a warehouse with a
Robotic Mobile Fulfillment System (RMFS), in which a fleet of robots stores and
retrieves shelves of items and brings them to human pickers. To adapt to
changing demand, uncertainty, and differentiated service (e.g., prime vs.
regular), one can dynamically modify the storage allocation of a shelf. The
objective is to define a dynamic storage policy to minimise the average cycle
time used by the robots to fulfil requests. We propose formulating this system
as a Partially Observable Markov Decision Process, and using a Deep Q-learning
agent from Reinforcement Learning, to learn an efficient real-time storage
policy that leverages repeated experiences and insightful forecasts using
simulations. Additionally, we develop a rollout strategy to enhance our method
by leveraging more information available at a given time step. Using
simulations to compare our method to traditional storage rules used in the
industry showed preliminary results up to 14\% better in terms of travelling
times.


Abstract:
  The Colonel Blotto Problem proposed by Borel in 1921 has served as a widely
applicable model of budget-constrained simultaneous winner-take-all
competitions in the social sciences. Applications include elections,
advertising, R&D and more. However, the classic Blotto problem and variants
limit the study to competitions over a finite set of discrete battlefields. In
this paper, we extend the classical theory to study multiplayer Blotto games
over arbitrary measurable battlegrounds, provide an algorithm to efficiently
sample equilibria of symmetric "equipartionable" Generalized Blotto games, and
characterize the symmetric fair equilibria of the Blotto game over the unit
interval.


Abstract:
  A novel quadcopter capable of changing shape mid-flight is presented,
allowing for operation in four configurations with the capability of sustained
hover in three. This is accomplished without requiring actuators beyond the
four motors typical of a quadcopter. Morphing is achieved through
freely-rotating hinges that allow the vehicle arms to fold downwards by either
reducing or reversing thrust forces. Constraints placed on the control inputs
of the vehicle prevent the arms from folding or unfolding unexpectedly. This
allows for the use of existing quadcopter controllers and trajectory generation
algorithms with only minimal added complexity. For our experimental vehicle at
hover, we find that these constraints result in a 36% reduction of the maximum
yaw torque the vehicle can produce, but do not result in a reduction of the
maximum thrust or roll and pitch torques. Experimental results show that, for a
typical maneuver, the added limits have a negligible effect on trajectory
tracking performance. Finally, the ability to change configurations is shown to
enable the vehicle to traverse small passages, perch on hanging wires, and
perform limited grasping tasks.


Abstract:
  We reconstruct spectra of secondary X-rays from a tunable 250-350 MeV laser
wakefield electron accelerator from single-shot X-ray depth-energy measurements
in a compact (7.5 $\times$ 7.5 $\times$ 15 cm), modular X-ray calorimeter made
of alternating layers of absorbing materials and imaging plates. X-rays range
from few-keV betatron to few-MeV inverse Compton to >100 MeV bremsstrahlung
emission, and are characterized both individually and in mixtures. Geant4
simulations of energy deposition of single-energy X-rays in the stack generate
an energy-vs-depth response matrix for a given stack configuration. An
iterative reconstruction algorithm based on analytic models of betatron,
inverse Compton and bremsstrahlung photon energy distributions then unfolds
X-ray spectra, typically within a minute. We discuss uncertainties, limitations
and extensions of both measurement and reconstruction methods.


Abstract:
  In this paper we consider the influence of intercellular communication on the
development and progression of Glioblastoma Multiforme (GBM), a grade IV
malignant glioma which is defined by an interplay Grow i.e. self renewal and Go
i.e. invasiveness potential of multiple malignant glioma stem cells. Firstly,
we performed wet lab experiments with U87 malignant glioma cells to study the
node-stem growth pattern of GBM. Next we develop a model accounting for the
structural influence of multiple transmitter and receiver glioma stem cells
resulting in the node-stem growth structure of GBM tumour. By using information
theory we study different properties associated with this communication model
to show that the growth of GBM in a particular direction (node to stem) is
related to an increase in mutual information. We further show that information
flow between glioblastoma cells for different levels of invasiveness vary at
different points between node and stem. These findings are expected to
contribute significantly in the design of future therapeutic mechanisms for
GBM.


Abstract:
  We consider the boomerang uniformity of an infinite class of (locally-APN)
power maps and show that its boomerang uniformity over the finite field
$\F_{2^n}$ is $2$ and $4$, when $n \equiv 0 \pmod 4$ and $n \equiv 2 \pmod 4$,
respectively. As a consequence, we show that for this class of power maps, the
differential uniformity is strictly greater than its boomerang uniformity.


Abstract:
  Traditional reactive approach of blacklisting botnets fails to adapt to the
rapidly evolving landscape of cyberattacks. An automated and proactive approach
to detect and block botnet hosts will immensely benefit the industry.
Behavioral analysis of botnet is shown to be effective against a wide variety
of attack types. Current works, however, focus solely on analyzing network
traffic from and to the bots. In this work we take a different approach of
analyzing the chain of commands input by attackers in a compromised host. We
have deployed several honeypots to simulate Linux shells and allowed attackers
access to the shells to collect a large dataset of commands. We have further
developed an automated mechanism to analyze these data. For the automation we
have developed a system called CYbersecurity information Exchange with Privacy
(CYBEX-P). Finally, we have done a sequential analysis on the dataset to show
that we can successfully predict attacker behavior from the shell commands
without analyzing network traffic like previous works.


Abstract:
  We re-consider Leadbetter's extremal index for stationary sequences. It has
interpretation as reciprocal of the expected size of an extremal cluster above
high thresholds. We focus on heavy-tailed time series, in particular on
regularly varying stationary sequences, and discuss recent research in extreme
value theory for these models. A regularly varying time series has multivariate
regularly varying finite-dimensional distributions. Thanks to results by Basrak
and Segers we have explicit representations of the limiting cluster structure
of extremes, leading to explicit expressions of the limiting point process of
exceedances and the extremal index as a summary measure of extremal clustering.
The extremal index appears in various situations which do not seem to be
directly related, like the convergence of maxima and point processes. We
consider different representations of the extremal index which arise from the
considered context. We discuss the theory and apply it to a regularly varying
AR(1) process and the solution to an affine stochastic recurrence equation


Abstract:
  Galactic outflows driven by starbursts can modify the galactic magnetic
fields and drive them away from the galactic planes. Here, we quantify how
these fields may magnetize the intergalactic medium. We estimate the strength
and structure of the fields in the starburst galaxy M82 using thermal polarized
emission observations from SOFIA/HAWC+ and a potential field extrapolation
commonly used in solar physics. We modified the Davis-Chandrasekhar-Fermi
method to account for the large-scale flow and the turbulent field. Results
show that the observed magnetic fields arise from the combination of a
large-scale ordered potential field associated with the outflow and a
small-scale turbulent field associated with bow-shock-like features. Within the
central $900$ pc radius, the large-scale field accounts for $53\pm4$% of the
observed turbulent magnetic energy with a median field strength of $305\pm15$
$\mu$G, while small-scale turbulent magnetic fields account for the remaining
$40\pm5$% with a median field strength of $222\pm19$ $\mu$G. We estimate that
the turbulent kinetic and turbulent magnetic energies are in close
equipartition up to $\sim2$ kpc (measured), while the turbulent kinetic energy
dominates at $\sim7$ kpc (extrapolated). We conclude that the fields are frozen
into the ionized outflowing medium and driven away kinetically. The magnetic
field lines in the galactic wind of M82 are `open,' providing a direct channel
between the starburst core and the intergalactic medium. Our novel approach
offers the tools needed to quantify the effects of outflows on galactic
magnetic fields as well as their influence on the intergalactic medium and
evolution of energetic particles.


Abstract:
  Systems composed of interacting self-propelled particles (SPPs) display
different forms of order-disorder phase transitions relevant to collective
motion. In this paper we propose a generalization of the Vicsek model
characterized by an angular noise term following an arbitrary probability
density function, which might depend on the state of the system and thus have a
multiplicative character. We show that the well established vectorial Vicsek
model can be expressed in this general formalism by deriving the corresponding
angular probability density function, as well as we propose two new
multiplicative models consisting on a bivariate Gaussian and a wrapped Gaussian
distributions. With the proposed formalism, the mean-field system can be solved
using the mean resultant length of the angular stochastic term. Accordingly,
when the SPPs interact globally, the character of the phase transition depends
on the choice of the noise distribution, being first-order with an hybrid
scaling for the vectorial and wrapped Gaussian distributions, and second order
for the bivariate Gaussian distribution. Numerical simulations reveal that this
scenario also holds when the interactions among SPPs are given by a static
complex network. On the other hand, using spatial short-range interactions
displays, in all the considered instances, a discontinuous transition with a
coexistence region, consistent with the original formulation of the Vicsek
model.


Abstract:
  Animals ranging from rats to humans can demonstrate cognitive map
capabilities. We evolved weights in a biologically plausible recurrent neural
network (RNN) using an evolutionary algorithm to replicate the behavior and
neural activity observed in rats during a spatial and working memory task in a
triple T-maze. The rat was simulated in the Webots robot simulator and used
vision, distance and accelerometer sensors to navigate a virtual maze. After
evolving weights from sensory inputs to the RNN, within the RNN, and from the
RNN to the robot's motors, the Webots agent successfully navigated the space to
reach all four reward arms with minimal repeats before time-out. Our current
findings suggest that it is the RNN dynamics that are key to performance, and
that performance is not dependent on any one sensory type, which suggests that
neurons in the RNN are performing mixed selectivity and conjunctive coding.
Moreover, the RNN activity resembles spatial information and
trajectory-dependent coding observed in the hippocampus. Collectively, the
evolved RNN exhibits navigation skills, spatial memory, and working memory. Our
method demonstrates how the dynamic activity in evolved RNNs can capture
interesting and complex cognitive behavior and may be used to create RNN
controllers for robotic applications.


Abstract:
  Urban 3D modeling from satellite images requires accurate semantic
segmentation to delineate urban features, multiple view stereo for 3D
reconstruction of surface heights, and 3D model fitting to produce compact
models with accurate surface slopes. In this work, we present a cumulative
assessment metric that succinctly captures error contributions from each of
these components. We demonstrate our approach by providing challenging public
datasets and extending two open source projects to provide an end-to-end 3D
modeling baseline solution to stimulate further research and evaluation with a
public leaderboard.


Abstract:
  This paper is concerned with the problem of state estimation for
discrete-time linear systems in the presence of additional (equality or
inequality) constraints on the state (or estimate). By use of the minimum
variance duality, the estimation problem is converted into an optimal control
problem. Two algorithmic solutions are described: the full information
estimator (FIE) and the moving horizon estimator (MHE). The main result is to
show that the proposed estimator is stable in the sense of an observer. The
proposed algorithm is distinct from the standard algorithm for constrained
state estimation based upon the use of the minimum energy duality. The two are
compared numerically on the benchmark batch reactor process model.


Abstract:
  We take the tensor network describing explicit p-adic CFT partition functions
proposed in [1], and considered boundary conditions of the network describing a
deformed Bruhat-Tits (BT) tree geometry. We demonstrate that this geometry
satisfies an emergent graph Einstein equation in a unique way that is
consistent with the bulk effective matter action encoding the same correlation
function as the tensor network, at least in the perturbative limit order by
order away from the pure BT tree. Moreover, the (perturbative) definition of
the graph curvature in the Mathematics literature naturally emerges from the
consistency requirements of the emergent Einstein equation. This could provide
new insights into the understanding of gravitational dynamics potentially
encoded in more general tensor networks.


Abstract:
  The problem of fermion dynamics is studied using the Q-function for fermions.
This is a probabilistic phase-space representation, which we express using
Majorana operators, so that the phase-space variable is a real antisymmetric
matrix. We consider a general interaction Hamiltonian with four Majorana
operators and arbitrary properties. Our model includes the Majorana Hubbard and
Fermi Hubbard Hamiltonians, as well as general quantum field theories of
interacting fermions. Using the Majorana Q-function we derive a generalized
Fokker-Planck equation, with results for the drift and diffusion terms. The
diffusion term is proved to be traceless, which gives a dynamical
interpretation as a forwards-backwards stochastic process. This approach leads
to a model of quantum measurement in terms of an ontology with real vacuum
fluctuations.


Abstract:
  We show that the Plebanski-Demianski spacetime persists as a solution of
General Relativity when the theory is supplemented with both, a conformally
coupled scalar theory and with quadratic curvature corrections. The quadratic
terms are of two types and are given by quadratic combinations of the Riemann
tensor as well as a higher curvature interaction constructed with a scalar
field which is conformally coupled to quadratic terms in the curvature. The
later is built in terms of a four-rank tensor $S_{\mu\nu}^{\ \ \lambda\rho}$
that depends on the Riemann tensor and the scalar field, and that transforms
covariantly under local Weyl rescallings. Due to the generality of the
Plebanski-Demianski family, several new hairy black hole solutions are obtained
in this higher curvature model. We pay particular attention to the C-metric
spacetime and the stationary Taub-NUT metric, which in the hyperbolic case can
be analytically extended leading to healthy, asymptotically AdS, wormhole
configurations. Finally, we present a new general model for higher derivative,
conformally coupled scalars, depending on an arbitrary function and that we
have dubbed Conformal K-essence. We also construct spherically symmetric hairy
black holes for these general models.


Abstract:
  We present a novel method for local image feature matching. Instead of
performing image feature detection, description, and matching sequentially, we
propose to first establish pixel-wise dense matches at a coarse level and later
refine the good matches at a fine level. In contrast to dense methods that use
a cost volume to search correspondences, we use self and cross attention layers
in Transformer to obtain feature descriptors that are conditioned on both
images. The global receptive field provided by Transformer enables our method
to produce dense matches in low-texture areas, where feature detectors usually
struggle to produce repeatable interest points. The experiments on indoor and
outdoor datasets show that LoFTR outperforms state-of-the-art methods by a
large margin. LoFTR also ranks first on two public benchmarks of visual
localization among the published methods.


Abstract:
  We analyze some relevant features of the primordial Universe as viewed in the
Jordan frame formulation of the f(R)-gravity, especially when the potential
term of the non-minimally coupled scalar field is negligible. We start
formulating the Hamiltonian picture in the Jordan frame, using the 3-metric
determinant as a basic variable and we outline that its conjugated momentum
appears linearly only in the scalar constraint. Then, we construct the basic
formalism to characterize the dynamics of a generic inhomogeneous cosmological
model and specialize it in order to describe behaviors of the Bianchi
Universes, both on a classical and a quantum regime. As a fundamental issue, we
demonstrate that, when the potential term of the additional scalar mode is
negligible near enough to the initial singularity, the Bianchi IX cosmology is
no longer affected by the chaotic behavior, typical in vacuum of the standard
Einsteinian dynamics. In fact, the presence of stable Kasner stability region
and its actractive character are properly characterized. Finally, we
investigate the canonical quantization of the Bianchi I model, using as time
variable the non-minimally coupled scalar field and showing that the existence
of a conserved current is outlined for the corresponding Wheeler-DeWitt
equation. The behavior of a localized wave-packet for the isotropic Universe is
also evolved, demonstrating that the singularity is still present in this
revised quantum dynamics.


Abstract:
  Time inconsistency is prevalent in dynamic choice problems: a plan of actions
to be taken in the future that is optimal for an agent today may not be optimal
for the same agent in the future. If the agent is aware of this intra-personal
conflict but unable to commit herself in the future to following the optimal
plan today, the rational strategy for her today is to reconcile with her future
selves, namely to correctly anticipate her actions in the future and then act
today accordingly. Such a strategy is named intra-personal equilibrium and has
been studied since as early as in the 1950s. A rigorous treatment in
continuous-time settings, however, had not been available until a decade ago.
Since then, the study on intra-personal equilibrium for time-inconsistent
problems in continuous time has grown rapidly. In this chapter, we review the
classical results and some recent development in this literature.


Abstract:
  This paper explores the use of hyperbolic geometry and deep learning
techniques for recommendation. We present Hyperbolic Neural Collaborative
Recommender (HNCR), a deep hyperbolic representation learning method that
exploits mutual semantic relations among users/items for collaborative
filtering (CF) tasks. HNCR contains two major phases: neighbor construction and
recommendation framework. The first phase introduces a neighbor construction
strategy to construct a semantic neighbor set for each user and item according
to the user-item historical interaction. In the second phase, we develop a deep
framework based on hyperbolic geometry to integrate constructed neighbor sets
into recommendation. Via a series of extensive experiments, we show that HNCR
outperforms its Euclidean counterpart and state-of-the-art baselines.


Abstract:
  Granular flow out of a silo is studied experimentally and numerically. The
time evolution of the discharge rate as well as the normal force (apparent
weight) at the bottom of the container is monitored. We show, that particle
stiffness has a strong effect on the qualitative features of silo discharge.
For deformable grains with a Young's modulus of about $Y_m\approx 40$ kPa in a
silo with basal pressure of the order of 4 kPa lowering the friction
coefficient leads to a gradual change in the discharge curve: the flow rate
becomes filling height dependent, it decreases during the discharge process.
For hard grains with a Young's modulus of about $Y_m\approx 500$ MPa the flow
rate is much less sensitive to the value of the friction coefficient. Using DEM
data combined with a coarse-graining methodology allows us to compute all the
relevant macroscopic fields, namely, linear momentum, density and stress
tensors. The observed difference in the discharge in the low friction limit is
connected to a strong difference in the pressure field: while for hard grains
Janssen-screening is effective, leading to high vertical stress near the silo
wall and small pressure above the orifice region, for deformable grains the
pressure above the orifice is larger and gradually decreases during the
discharge process. We have analyzed the momentum balance in the region of the
orifice (near the location of the outlet) for the case of soft particles with
low friction coefficient, and proposed a phenomenological formulation that
predicts the linear decrease of the flow rate with decreasing filling height.


Abstract:
  Recent works have revealed that quantum extremal islands can contribute to
the fine-grained entropy of black hole radiation reproducing the unitary Page
curve. In this paper, we use these results to assess if an observer in de
Sitter space can decode information hidden behind their cosmological horizon.
By computing the fine-grained entropy of the Gibbons-Hawking radiation in a
region where gravity is weak we find that this is possible, but the observer's
curiosity comes at a price. At the same time the island appears, which happens
much earlier than the Page time, a singularity forms which the observer will
eventually hit. We arrive at this conclusion by studying Jackiw-Teitelboim
gravity in de Sitter space. We emphasize the role of the observer collecting
radiation, breaking the thermal equilibrium studied so far in the literature.
By analytically solving for the backreacted geometry we show how an island
appears in this out-of-equilibrium state.


Abstract:
  Whereas existing literature on unsupervised machine translation (MT) focuses
on exploiting unsupervised techniques for low-resource language pairs where
bilingual training data is scare or unavailable, we investigate whether
unsupervised MT can also improve translation quality of high-resource language
pairs where sufficient bitext does exist. We compare the style of correct
translations generated by either supervised or unsupervised MT and find that
the unsupervised output is less monotonic and more natural than supervised
output. We demonstrate a way to combine the benefits of unsupervised and
supervised MT into a single system, resulting in better human evaluation of
quality and fluency. Our results open the door to discussions about the
potential contributions of unsupervised MT in high-resource settings, and how
supervised and unsupervised systems might be mutually-beneficial.


Abstract:
  Filters are fast, small and approximate set membership data structures. They
are often used to filter out expensive accesses to a remote set S for negative
queries (that is, a query x not in S). Filters have one-sided errors: on a
negative query, a filter may say "present" with a tunable false-positve
probability of epsilon. Correctness is traded for space: filters only use log
(1/\epsilon) + O(1) bits per element.
  The false-positive guarantees of most filters, however, hold only for a
single query. In particular, if x is a false positive of a filter, a subsequent
query to x is a false positive with probability 1, not epsilon. With this in
mind, recent work has introduced the notion of an adaptive filter. A filter is
adaptive if each query has false positive epsilon, regardless of what queries
were made in the past. This requires "fixing" false positives as they occur.
  Adaptive filters not only provide strong false positive guarantees in
adversarial environments but also improve performance on query practical
workloads by eliminating repeated false positives.
  Existing work on adaptive filters falls into two categories. First, there are
practical filters based on cuckoo filters that attempt to fix false positives
heuristically, without meeting the adaptivity guarantee. Meanwhile, the broom
filter is a very complex adaptive filter that meets the optimal theoretical
bounds.
  In this paper, we bridge this gap by designing a practical, provably adaptive
filter: the telescoping adaptive filter. We provide theoretical false-positive
and space guarantees of our filter, along with empirical results where we
compare its false positive performance against state-of-the-art filters. We
also test the throughput of our filters, showing that they achieve comparable
performance to similar non-adaptive filters.


Abstract:
  Optimally solving a multi-armed bandit problem suffers the curse of
dimensionality. Indeed, resorting to dynamic programming leads to an
exponential growth of computing time, as the number of arms and the horizon
increase. We introduce a decompositioncoordination heuristic, DeCo, that turns
the initial problem into parallelly coordinated one-armed bandit problems. As a
consequence, we obtain a computing time which is essentially linear in the
number of arms. In addition, the decomposition provides a theoretical lower
bound on the regret. For the two-armed bandit case, dynamic programming
provides the exact solution, which is almost matched by the DeCo heuristic.
Moreover, in numerical simulations with up to 100 rounds and 20 arms, DeCo
outperforms classic algorithms (Thompson sampling and Kullback-Leibler
upper-confidence bound) and almost matches the theoretical lower bound on the
regret for 20 arms.


Abstract:
  Topological notions in physics have become a powerful perspective that leads
to the discoveries of topological interface states (TISs). In this work, we
present a scheme to achieve negative refraction by leveraging the properties of
TISs in a valley photonic crystal (VPC). Due to the chiral characteristics, one
type of the TISs deterministically possesses negative dispersion relation,
which can cause an obliquely incident wave to undergo a negative lateral shift.
By stacking multiple VPC interfaces, the TIS-induced lateral shifts can relay
in the transmitted wave towards the far side of incidence. The resultant
outgoing wave appears to have undergone negative refraction. This finding is
verified in microwave experiments. Our scheme opens new application scenarios
for topological systems in bulk wave manipulations.


Abstract:
  Single photon emitters in atomically-thin semiconductors can be
deterministically positioned using strain induced by underlying
nano-structures. Here, we couple monolayer WSe$_2$ to high-refractive-index
gallium phosphide dielectric nano-antennas providing both optical enhancement
and monolayer deformation. For single photon emitters formed on such
nano-antennas, we find very low (femto-Joule) saturation pulse energies and up
to 10$^4$ times brighter photoluminescence than in WSe$_2$ placed on
low-refractive-index SiO$_2$ pillars. We show that the key to these
observations is the increase on average by a factor of 5 in the quantum
efficiency of the emitters coupled to the nano-antennas. This further allowed
us to gain new insights into their photoluminescence dynamics, revealing the
roles of the dark exciton reservoir and Auger processes. We also find that the
coherence time of such emitters is limited by intrinsic dephasing processes.
Our work establishes dielectric nano-antennas as a platform for high-efficiency
quantum light generation in monolayer semiconductors.


Abstract:
  In certain systems which are subject to significant constant external forcing
such as an airplane in wind or an underwater glider in ocean currents, the
ability to detect the forcing depends on both the measurements available and
whether appropriate control is being applied. Using analytical nonlinear
observability techniques, we define the necessary characteristics required of a
measurement function for systems with unicycle dynamics subject to constant
forcing to be observable. We further consider the necessary associated motion
characteristics required of this class of systems to enable the desired sensing
capabilities. We then apply these results in combination with the empirical
Gramian to quantify relative observability, optimal sensor selection, and
sensing quality for different motion primitive modes for a Dubins path.


Abstract:
  We argue that the global causal structure of the singularity is not a purely
geometric property but also depends on the collapsing matter-field leading to
its formation. To show this, we investigate the global visibility of the end
state of a spherically symmetric marginally bound Lemaitre-Tolman-Bondi
collapsing cloud (which is well studied in general relativity) in the framework
of modified gravity having the generalized Lagrangian $R+\alpha R^2$ in the
Einstein-Hilbert action. Here $R$ is the Ricci scalar, and $\alpha>0$ is a
constant. As an example, we depict that for the same LTB metric governing the
spacetime formed due to two different matter-fields, i.e., dust in general
relativity and imperfect viscous fluid in $f(R)$ gravity, the singularity is
locally visible in the former case, and globally visible in the latter case.


Abstract:
  A resilient multi-vehicle system cooperatively performs tasks by exchanging
information, detecting, and removing cyber attacks that have the intent of
hijacking or diminishing performance of the entire system. In this paper, we
propose a framework to: i) detect and isolate misbehaving vehicles in the
network, and ii) securely encrypt information among the network to alert and
attract nearby vehicles toward points of interest in the environment without
explicitly broadcasting safety-critical information. To accomplish these goals,
we leverage a decentralized virtual spring-damper mesh physics model for
formation control on each vehicle. To discover inconsistent behavior of any
vehicle in the network, we consider an approach that monitors for changes in
sign behavior of an inter-vehicle residual that does not match with an
expectation. Similarly, to disguise important information and trigger vehicles
to switch to different behaviors, we leverage side-channel information on the
state of the vehicles and characterize a hidden spring-damper signature model
detectable by neighbor vehicles. Our framework is demonstrated in simulation
and experiments on formations of unmanned ground vehicles (UGVs) in the
presence of malicious man-in-the-middle communication attacks.


Abstract:
  The coronal mass ejections (CMEs) from the Sun are known for their space
weather and geomagnetic consequences. Among all CMEs, so-called radio-loud (RL)
and halo CMEs are considered the most energetic in the sense that they are
usually faster and wider than the general population of CMEs. Hence the study
of RL and halo CMEs has become important and the prediction of their occurrence
rate in a future cycle will give a warning in advance. In the present paper,
the occurrence rates of RL and halo CMEs in solar cycle (SC) 25 are predicted.
For this, we obtained good correlations between the numbers of RL and halo CMEs
in each year and the yearly mean sunspot numbers in the previous two cycles.
The predicted values of sunspot numbers in SC 25 by NOAA/NASA were considered
as representative indices and the corresponding numbers of RL and halo CMEs
have been determined using linear relations. Our results show that the maximum
number of RL and halo CMEs will be around 39 $/pm$ 3 and 45 $/pm$ 4,
respectively. Removing backside events, a set of front-side events was also
considered separately and the front-side events alone in SC 25 are predicted
again. The peak values of front-side RL and halo events have been estimated to
be around 31 $/pm$ 3 and 29 $/pm$ 3 respectively. These results are discussed
in comparison with the predicted values of sunspots by different authors.


Abstract: We prove Burkholder inequality using Bregman divergence.


Abstract:
  In this paper, we propose a recent and under-researched paradigm for the task
of event detection (ED) by casting it as a question-answering (QA) problem with
the possibility of multiple answers and the support of entities. The extraction
of event triggers is, thus, transformed into the task of identifying answer
spans from a context, while also focusing on the surrounding entities. The
architecture is based on a pre-trained and fine-tuned language model, where the
input context is augmented with entities marked at different levels, their
positions, their types, and, finally, the argument roles. Experiments on the
ACE~2005 corpus demonstrate that the proposed paradigm is a viable solution for
the ED task and it significantly outperforms the state-of-the-art models.
Moreover, we prove that our methods are also able to extract unseen event
types.


Abstract:
  In a recent work [\textit{J. Mol. Liq}, 2020, \textbf{320}, 114420], using
molecular dynamics simulations and a core-softened potential approach, we have
shown that adding a simple solute as methanol can "kill" the density anomalous
behavior as the LLCP is suppressed by the spontaneous crystallization in a
hexagonal closed packing (HCP) crystal near the LLPT. Now, we extend this work
in order to realize how longer-chain alcohols will affect the complex behavior
of water-alcohol mixtures in the supercooled regime. Besides core-softened (CS)
methanol, ethanol and 1-propanol were added to a system of identical particles
that interact through the continuous shouldered well (CSW) potential. We
observed that the density anomaly gradually decreases its extension in phase
diagrams until disappearing with the growth of the non-polar chain and the
alcohol concentration, differently from the liquid-liquid phase transition (and
the LLCP), which remained present in all analyzed mixtures, in according to
\textit{Nature}, 2001, \textbf{409}, 692. For our model, the longer non-polar
chains and higher concentrations gradually impact the competition between the
scales in the CS potential, leading to a gradual disappearing of the anomalies
until the TMD total disappearance is observed when the first coordination shell
structure is also affected: the short-range ordering is favored, leading to
less competition between short- and long-range ordering and, consequently, to
the extinction of anomalies. Also, the non-polar chain size and concentration
have an effect on the solid phases, favoring the hexagonal closed packed (HCP)
solid and the amorphous solid phase over the body-centered cubic (BCC) crystal.


Abstract:
  Several recent studies have shown that Deep Neural Network (DNN)-based
classifiers are vulnerable against model extraction attacks. In model
extraction attacks, an adversary exploits the target classifier to create a
surrogate classifier imitating the target classifier with respect to some
criteria. In this paper, we investigate the hardness degree of samples and
demonstrate that the hardness degree histogram of model extraction attacks
samples is distinguishable from the hardness degree histogram of normal
samples. Normal samples come from the target classifier's training data
distribution. As the training process of DNN-based classifiers is done in
several epochs, we can consider this process as a sequence of subclassifiers so
that each subclassifier is created at the end of an epoch. We use the sequence
of subclassifiers to calculate the hardness degree of samples. We investigate
the relation between hardness degree of samples and the trust in the classifier
outputs. We propose Hardness-Oriented Detection Approach (HODA) to detect the
sample sequences of model extraction attacks. The results demonstrate that HODA
can detect the sample sequences of model extraction attacks with a high success
rate by only watching 100 attack samples. We also investigate the hardness
degree of adversarial examples and indicate that the hardness degree histogram
of adversarial examples is distinct from the hardness degree histogram of
normal samples.


Abstract:
  In this work, we study the effects that interacting quark matter has on the
stellar structure of strange and charm quark stars. Additionally, their
stability against radial pulsations is analyzed using a first-order formalism
for adiabatic general relativistic oscillations. Besides, the early stage of
stellar evolution of neutron stars after the supernovae explosion, i.e.
protoneutron stars, is investigated by considering the possibility of a
first-order phase transition to quark matter rich in leptons, where the
dynamics of conversion between phases is studied within a thermal nucleation
model. For each kind of compact star mentioned we use for the quark phase the
equation of state calculated within cold perturbative QCD (pQCD), parametrized
only by its renormalization scale. We note that the original pQCD framework is
manipulated appropriately to include neutrinos and extended to add heavy quarks
to the original system composed only by up, down and strange quark flavors.


Abstract:
  In this article, we have investigated the role of bulk viscosity to study the
accelerated expansion of the universe in the framework of modified $f(Q)$
gravity. The gravitational action in this modified gravity theory has the form
$f(Q)$, where $Q$ denote the non-metricity scalar. In the present manuscript,
we have considered a bulk viscous matter-dominated cosmological model with the
bulk viscosity coefficient of the form $\xi =\xi _{0}+\xi _{1}H+\xi _{2}\left(
\frac{\dot{H}}{H}+H\right) $ which is proportional to the velocity and
acceleration of the expanding universe. Two sets of limiting conditions on the
bulk viscous parameters $\xi _{0},$ $\xi _{1},$ $% \xi _{2}$ and model
parameter $\alpha $ arose here out of which one condition favours the present
scenario of cosmic acceleration with a phase transition and corresponds to the
universe with a Big Bang origin. Moreover, we have discussed the cosmological
behaviour of some geometrical parameters. Then, we have obtained the best
fitting values of the model parameters $\xi _{0},$ $\xi _{1},$ $\xi _{2}$ and
$\alpha $ by constraining our model with updated Hubble datasets consisting of
$57$ data points and recently released Pantheon datasets consisting of $1048$
data points which show that our obtained model has good compatibility with
observations. Further, we have also included the Baryon Acoustic Oscillation
(BAO) datasets of six data points with the Hubble \& Pantheon datasets and
obtained slightly different values of the model parameters. Finally, we have
analyzed our model with the statefinder diagnostic analysis and found some
interesting results and are discussed in details.


Abstract:
  Smooth planar vector fields containing two hyperbolic saddles may possess
contours formed by heteroclinic connections between these saddles. We present
an overview of the bifurcations of these contours based on papers by J.W. Reyn
and A.V. Dukov. Additionally, two new explicit polynomial systems containing
such contours are derived, which are studied using the bifurcation software
MatCont and are shown to exhibit the theoretically predicted phenomena,
including series of heteroclinic connections.


Abstract:
  We present a polynomial-time $\frac{3}{2}$-approximation algorithm for the
problem of finding a maximum-cardinality stable matching in a many-to-many
matching model with ties and laminar constraints on both sides. We formulate
our problem using a bipartite multigraph whose vertices are called workers and
firms, and edges are called contracts. Our algorithm is described as the
computation of a stable matching in an auxiliary instance, in which each
contract is replaced with three of its copies and all agents have strict
preferences on the copied contracts. The construction of this auxiliary
instance is symmetric for the two sides, which facilitates a simple symmetric
analysis. We use the notion of matroid-kernel for computation in the auxiliary
instance and exploit the base-orderability of laminar matroids to show the
approximation ratio.
  In a special case in which each worker is assigned at most one contract and
each firm has a strict preference, our algorithm defines a
$\frac{3}{2}$-approximation mechanism that is strategy-proof for workers.


Abstract:
  Thermoelectric (TE) materials are among very few sustainable yet feasible
energy solutions of present time. This huge promise of energy harvesting is
contingent on identifying/designing materials having higher efficiency than
presently available ones. However, due to the vastness of the chemical space of
materials, only its small fraction was scanned experimentally and/or
computationally so far. Employing a compressed-sensing based symbolic
regression in an active-learning framework, we have not only identified a trend
in materials' compositions for superior TE performance, but have also predicted
and experimentally synthesized several extremely high performing novel TE
materials. Among these, we found Ag$_{0.55}$Cu$_{0.45}$GaTe$_2$ to possess an
experimental figure of merit as high as ~2.8 at 827 K, which is a breakthrough
in the field. The presented methodology demonstrates the importance and
tremendous potential of physically informed descriptors in material science, in
particular for relatively small data sets typically available from experiments
at well-controlled conditions.


Abstract:
  Stellar evolution and numerical hydrodynamics simulations depend critically
on access to fast, accurate, thermodynamically consistent equations of state.
We present Skye, a new equation of state for fully-ionized matter. Skye
includes the effects of positrons, relativity, electron degeneracy, Coulomb
interactions, non-linear mixing effects, and quantum corrections. Skye
determines the point of Coulomb crystallization in a self-consistent manner,
accounting for mixing and composition effects automatically. A defining feature
of this equation of state is that it uses analytic free energy terms and
provides thermodynamic quantities using automatic differentiation machinery.
Because of this, Skye is easily extended to include new effects by simply
writing new terms in the free energy. We also introduce a novel thermodynamic
extrapolation scheme for extending analytic fits to the free energy beyond the
range of the fitting data while preserving desirable properties like positive
entropy and sound speed. We demonstrate Skye in action in the MESA stellar
evolution software instrument by computing white dwarf cooling curves.


Abstract:
  Applying the triplicate form of the extended Gould--Hsu inverse series
relations to Dougall's summation theorem for the well--poised $_7F_6$-series,
we establish, from the dual series, several interesting Ramanujan--like
infinite series expressions for $\pi^2$ and $\pi^{\pm1}$ with convergence rate
"$-\frac{1}{27}$".


Abstract:
  Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of
documents bound together by internal hyperlinks. These links form the building
blocks of a large network whose structure contains important information on the
concepts covered in this encyclopedia. The presence of a link between two
articles, materialised by an anchor text in the source page pointing to the
target page, can increase readers' understanding of a topic. However, the
process of linking follows specific editorial rules to avoid both under-linking
and over-linking. In this paper, we study the transductive and the inductive
tasks of link prediction on several subsets of the English Wikipedia and
identify some key challenges behind automatic linking based on anchor text
information. We propose an appropriate evaluation sampling methodology and
compare several algorithms. Moreover, we propose baseline models that provide a
good estimation of the overall difficulty of the tasks.


Abstract:
  In this work, we propose a multigrid preconditioner for Jacobian-free
Newton-Krylov (JFNK) methods. Our multigrid method does not require to store
any representation of the Jacobian at any level of the multigrid hierarchy. As
it is common in standard multigrid methods, the proposed method also relies on
three building blocks: transfer operators, smoothers, and a coarse level
solver. In addition to the restriction and prolongation operator, we also use a
projection operator to transfer the current Newton iterate to a coarser level.
The three-level Chebyshev semi-iterative method is employed as a smoother, as
it has good smoothing properties and does not require the representation of the
Jacobian matrix. We replace the direct solver on the coarsest-level with a
matrix-free Krylov subspace method, thus giving rise to a truly Jacobian-free
multigrid preconditioner. We will discuss all building blocks of our multigrid
preconditioner in detail and demonstrate the robustness and the efficiency of
the proposed method using several numerical examples.


Abstract:
  Digital contact tracing has been proposed to support the health authorities
in fighting the current Covid-19 pandemic. In this paper we propose two
centralised protocols for digital contact tracing that, contrary to the common
hypothesis that this is an inherent risk, do not allow (retroactive) tracking
of the location of a device over time. The first protocol does not rely on
synchronised clocks. The second protocol does not require a handshake between
two devices, at the expense of relying on real-time communication with a
central server. We stress that digital contact tracing is a form of
technological solutionism that should be used with care, especially given the
inherent mass surveillance nature of such systems.


Abstract:
  The Vehicle Routing Problem (VRP) is the combinatorial optimization problem
of designing routes for vehicles to visit customers in such a fashion that a
cost function, typically the number of vehicles, or the total travelled
distance is minimized. The problem finds applications in industrial scenarios,
for example where Automated Guided Vehicles run through the plant to deliver
components from the warehouse. This specific problem, henceforth called the
Electric Conflict-Free Vehicle Routing Problem (CF-EVRP), involves constraints
such as limited operating range of the vehicles, time windows on the delivery
to the customers, and limited capacity on the number of vehicles the road
segments can accommodate at the same time. Such a complex system results in a
large model that cannot easily be solved to optimality in reasonable time. We
therefore developed a compositional model that breaks down the problem into
smaller and simpler sub-problems and provides sub-optimal, feasible solutions
to the original problem. The algorithm exploits the strengths of SMT solvers,
which proved in our previous work to be an efficient approach to deal with
scheduling problems. Compared to a monolithic model for the CF-EVRP, written in
the SMT standard language and solved using a state-of-the-art SMT solver the
compositional model was found to be significantly faster.


Abstract:
  In the present study, the structural and hitherto uninvestigated mechanical
(elastic stiffness constants, machinability index, Cauchy pressure, anisotropy
indices, brittleness/ductility, Poissons ratio), electronic, optical, and
thermodynamic properties of novel boron-rich compounds B6X (X = S, Se) have
been explored using density functional theory. The estimated structural lattice
parameters were consistent with the prior report. The mechanical and dynamical
stability of these compounds have been established theoretically. The materials
are brittle in nature and elastically anisotropic. The value of fracture
toughness, KIC for the B6S and B6Se are ~ 2.07 MPam0.5, evaluating the
resistance to limit the crack propagation inside the materials. Both B6S and
B6Se compounds possess high hardness values in the range 31-35 GPa, and have
the potential to be prominent members of the class of hard compounds. Strong
covalent bonding and sharp peak at low energy below the Fermi level confirmed
by partial density of states (PDOS) resulted in the high hardness. The profile
of band structure, as well as DOS, assesses the indirect semiconducting nature
of the titled compounds. The comparatively high value of Debye temperature
({\Theta}D), minimum thermal conductivity (Kmin), lattice thermal conductivity
(kph), low thermal expansion coefficient, and low density suggest that both
boron-rich chalcogenides might be used as thermal management materials. Large
absorption capacities in the mid ultraviolet region (3.2-15 eV) of the studied
materials and low reflectivity (~16 %) are significantly noted. Such favorable
features give promise to the compounds under investigation to be used in UV
surface-disinfection devices as well as medical sterilizer equipment
applications. Excellent correlations are found among all the studied physical
properties of these compounds.


Abstract:
  We demonstrate that it is feasible to diacritize Hebrew script without any
human-curated resources other than plain diacritized text. We present NAKDIMON,
a two-layer character level LSTM, that performs on par with much more
complicated curation-dependent systems, across a diverse array of modern Hebrew
sources.


Abstract:
  We consider 1D integrable systems supporting ballistic propagation of
excitations, perturbed by a localised defect that breaks most conservation laws
and induces chaotic dynamics. Focusing on classical systems, we study an
out-of-equilibrium protocol engineered activating the defect in an initially
homogeneous and far from the equilibrium state. We find that large enough
defects induce full thermalisation at their center, but nonetheless the
outgoing flow of carriers emerging from the defect is non-thermal due to a
generalization of the celebrated Boundary Thermal Resistance effect, occurring
at the edges of the chaotic region. Our results are obtained combining
ab-initio numerical simulations for relatively small-sized defects, with the
solution of the Boltzmann equation, which becomes exact in the scaling limit of
large, but weak defects.


Abstract:
  Streaming codes represent a packet-level FEC scheme for achieving reliable,
low-latency communication. In the literature on streaming codes, the
commonly-assumed Gilbert-Elliott channel model, is replaced by a more
tractable, delay-constrained, sliding-window (DCSW) channel model that can
introduce either random or burst erasures. The known streaming codes that are
rate optimal over the DCSW channel model are constructed by diagonally
embedding a scalar block code across successive packets. These code
constructions have field size that is quadratic in the delay parameter $\tau$
and have a somewhat complex structure with an involved decoding procedure. This
led to the introduction of simple streaming (SS) codes in which diagonal
embedding is replaced by staggered-diagonal embedding (SDE). The SDE approach
reduces the impact of a burst of erasures and makes it possible to construct
near-rate-optimal streaming codes using Maximum Distance Separable (MDS) code
having linear field size. The present paper takes this development one step
further, by retaining the staggered-diagonal feature, but permitting the
placement of more than one code symbol from a given scalar codeword within each
packet. These generalized, simple streaming codes allow us to improve upon the
rate of SS codes, while retaining the simplicity of working with MDS codes. We
characterize the maximum code rate of streaming codes under a constraint on the
number of contiguous packets over which symbols of the underlying scalar code
are dispersed. Such a constraint leads to simplified code construction and
reduced-complexity decoding.


Abstract:
  The relativistic charged spinor matter field is quantized in the background
of a straight cosmic string with nonvanishing transverse size. The most general
boundary conditions ensuring the impossibility for matter to penetrate through
the edge of the string core are considered. The role of discrete symmetries is
elucidated, and analytic expressions for the temporal and spatial components of
the induced vacuum current are derived in the case of either $P$ or $CT$
invariant boundary condition with two parameters varying arbitrarily from point
to point of the edge. The requirement of physical plausibility for the global
induced vacuum characteristics is shown to remove completely an arbitrariness
in boundary conditions. We find out that a magnetic field is induced in the
vacuum and that a sheath in the form of a tube of the magnetic flux lines
encloses a cosmic string. The dependence of the induced vacuum magnetic field
strength on the string flux and tension, as well as on the transverse size of
the string and on the distance from the string, is unambiguously determined.


Abstract:
  Multifidelity Monte Carlo methods often rely on a preprocessing phase
consisting of standard Monte Carlo sampling to estimate correlation
coefficients between models of different fidelity to determine the weights and
number of samples for each level. For computationally intensive models, as are
often encountered in simulations of chaotic systems, this up-front cost can be
prohibitive. In this work, a correlation estimation procedure is developed for
the case in which the highest and next highest fidelity models are generated
via discretizing the same mathematical model using different resolution. The
procedure uses discretization error estimates to estimate the required
correlation coefficient without the need to sample the highest fidelity model,
which can dramatically decrease the cost of the preprocessing phase. The method
is extended to chaotic problems by using discretization error estimates that
account for the statistical nature of common quantities of interest and the
accompanying finite sampling errors that pollute estimates of such quantities
of interest. The methodology is then demonstrated on a model problem based on
the Kuramoto-Sivashinsky equation.


Abstract:
  The manuscript New Theory of Sunspots was identified and attributed to the
Jesuit astronomer and hydrographer from Marseille Esprit Pezenas (1692-1776)
during the year 2002, during the systematic search for papers concerning him in
the various French funds. The manuscript, in large part unpublished, New Theory
of Sunspots was written by astronomer and professor of Marseilles Jesuit
hydrography, Father Esprit Pezenas (1692-1776). The text was composed and
revised between the years 1766 and 1772. In this manuscript, of which we are
going to study the conditions of its composition, P. Pezenas gives one of the
last geometric methods, related graphical methods, allowing the inclination of
the Sun's equator to be deduced on ecliptic, using three observations of a
sunspot, at given times, and in taking into account the movement of the Earth
with respect to the Sun during the duration of observations. This text contains
all the digital elements allowing to follow and understand the application of
geometric processing methods of observations and observation techniques
mentioned above in the introduction: passages of edges and Sun spots at the
meridian, use of objective micrometers and wire micrometers. At detour of his
calculations, Father Pezenas announces a duration of about 26 days and 9 hours
for the rotation of the Sun around its axis, comments on and corrects some
observation data published in Le Monnier's Heavenly History.


Abstract:
  We propose a low-rank tensor approach to approximate linear transport and
nonlinear Vlasov solutions and their associated flow maps. The approach takes
advantage of the fact that the differential operators in the Vlasov equation is
tensor friendly, based on which we propose a novel way to dynamically and
adaptively build up low-rank solution basis by adding new basis functions from
discretization of the PDE, and removing basis from an SVD-type truncation
procedure. For the discretization, we adopt a high order finite difference
spatial discretization and a second order strong stability preserving
multi-step time discretization. We apply the same procedure to evolve the
dynamics of the flow map in a low-rank fashion, which proves to be advantageous
when the flow map enjoys the low rank structure, while the solution suffers
from high rank or displays filamentation structures. Hierarchical Tucker
decomposition is adopted for high dimensional problems. An extensive set of
linear and nonlinear Vlasov test examples are performed to show the high order
spatial and temporal convergence of the algorithm with mesh refinement up to
SVD-type truncation, the significant computational savings of the proposed
low-rank approach especially for high dimensional problems, the improved
performance of the flow map approach for solutions with filamentations.


Abstract:
  Grasping in dynamic environments presents a unique set of challenges. A
stable and reachable grasp can become unreachable and unstable as the target
object moves, motion planning needs to be adaptive and in real time, the delay
in computation makes prediction necessary. In this paper, we present a dynamic
grasping framework that is reachability-aware and motion-aware. Specifically,
we model the reachability space of the robot using a signed distance field
which enables us to quickly screen unreachable grasps. Also, we train a neural
network to predict the grasp quality conditioned on the current motion of the
target. Using these as ranking functions, we quickly filter a large grasp
database to a few grasps in real time. In addition, we present a seeding
approach for arm motion generation that utilizes solution from previous time
step. This quickly generates a new arm trajectory that is close to the previous
plan and prevents fluctuation. We implement a recurrent neural network (RNN)
for modelling and predicting the object motion. Our extensive experiments
demonstrate the importance of each of these components and we validate our
pipeline on a real robot.


Abstract:
  Semiconductor quantum dots embedded in optical cavities are promising
on-demand sources of single photons. Here, we theoretically study single photon
emission from an optically driven two-photon Raman transition between the
biexciton and the ground state of a quantum dot. The advantage of this process
is that it allows all-optical control of the properties of the emitted single
photon with a laser pulse. However, with the presence of other decay channels
and excitation-induced quantum interference, on-demand emission of the single
Raman photon is generally difficult to achieve. Here we show that laser pulses
with non-trivial shapes can be used to maintain excitation conditions for which
with increasing pulse intensities the on-demand regime is reached. To provide a
realistic picture of the achievable system performance, we include
phonon-mediated processes in the theoretical caluclations. While preserving
both high photon purity and indistinguishability, we find that although based
on a higher-order emission process, for realistic system parameters on-demand
Raman photon emission is indeed achievable with suitably tailored laser pulses.


Abstract:
  Stringent constraints on both reliability and latency must be guaranteed in
ultra-reliable low-latency communication (URLLC). To fulfill these constraints
with computationally constrained receivers, such as low-budget IoT receivers,
optimal transmission parameters need to be studied in detail. In this paper, we
introduce a multi-objective optimization framework for the optimal design of
URLLC in the presence of decoding complexity constraints. We consider
transmission of short-blocklength codewords that are encoded with linear block
encoders, transmitted over a binary-input AWGN channel, and finally decoded
with order-statistics (OS) decoder. We investigate the optimal selection of a
transmission rate and power pair, while satisfying the constraints. For this
purpose, a multi-objective optimization problem (MOOP) is formulated. Based on
the empirical model that accurately quantifies the trade-off between the
performance of an OS decoder and its computational complexity, the MOOP is
solved and the Pareto boundary is derived. In order to assess the overall
performance among several Pareto-optimal transmission pairs, two scalarization
methods are investigated. To exemplify the importance of the MOOP, a case study
on a battery-powered communication system is provided. It is shown that,
compared to the classical fixed rate-power transmissions, the MOOP provides the
optimum usage of the battery and increases the energy efficiency of the
communication system while maintaining the constraints.


Abstract:
  Few-shot object detection has made substantial progressby representing novel
class objects using the feature representation learned upon a set of base class
objects. However,an implicit contradiction between novel class classification
and representation is unfortunately ignored. On the one hand, to achieve
accurate novel class classification, the distributions of either two base
classes must be far away fromeach other (max-margin). On the other hand, to
precisely represent novel classes, the distributions of base classes should be
close to each other to reduce the intra-class distance of novel classes
(min-margin). In this paper, we propose a class margin equilibrium (CME)
approach, with the aim to optimize both feature space partition and novel class
reconstruction in a systematic way. CME first converts the few-shot detection
problem to the few-shot classification problem by using a fully connected layer
to decouple localization features. CME then reserves adequate margin space for
novel classes by introducing simple-yet-effective class margin loss during
feature learning. Finally, CME pursues margin equilibrium by disturbing the
features of novel class instances in an adversarial min-max fashion.
Experiments on Pascal VOC and MS-COCO datasets show that CME significantly
improves upon two baseline detectors (up to $3\sim 5\%$ in average), achieving
state-of-the-art performance. Code is available at
https://github.com/Bohao-Lee/CME .


Abstract:
  The fundamental nature of dark matter is entirely unknown. A compelling
candidate is Twin Higgs mirror matter, invisible hidden-sector cousins of the
Standard Model particles and forces. This generically predicts mirror neutron
stars, degenerate objects made entirely of mirror nuclear matter. We find their
structure using realistic equations of state, robustly modified based on
first-principle quantum chromodynamic calculations. We predict their
detectability with gravitational waves and binary pulsars, suggesting an
impressive discovery potential and ability to probe the dark sector.


Abstract:
  We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to
synthesise speech with an improved coarse and fine-grained prosody. We present
a generic multi-scale spectrogram prediction mechanism where the system first
predicts coarser scale mel-spectrograms that capture the suprasegmental
information in speech, and later uses these coarser scale mel-spectrograms to
predict finer scale mel-spectrograms capturing fine-grained prosody.
  We present details for two specific versions of MSS called Word-level MSS and
Sentence-level MSS where the scales in our system are motivated by the
linguistic units. The Word-level MSS models word, phoneme, and frame-level
spectrograms while Sentence-level MSS models sentence-level spectrogram in
addition.
  Subjective evaluations show that Word-level MSS performs statistically
significantly better compared to the baseline on two voices.


Abstract:
  We review the correspondence between integrable sigma models with complex
homogeneous target spaces and chiral bosonic (and possibly mixed
bosonic/fermionic) Gross-Neveu models. Mathematically, the latter are models
with quiver variety phase spaces, which reduce to the more conventional sigma
models in special cases. We discuss the geometry of the models, as well as
their trigonometric and elliptic deformations, Ricci flow and the inclusion of
fermions.


Abstract:
  Many approaches to 3D image segmentation are based on hierarchical clustering
of supervoxels into image regions. Here we describe a distributed algorithm
capable of handling a tremendous number of supervoxels. The algorithm works
recursively, the regions are divided into chunks that are processed
independently in parallel by multiple workers. At each round of the recursive
procedure, the chunk size in all dimensions are doubled until a single chunk
encompasses the entire image. The final result is provably independent of the
chunking scheme, and the same as if the entire image were processed without
division into chunks. This is nontrivial because a pair of adjacent regions is
scored by some statistical property (e.g. mean or median) of the affinities at
the interface, and the interface may extend over arbitrarily many chunks. The
trick is to delay merge decisions for regions that touch chunk boundaries, and
only complete them in a later round after the regions are fully contained
within a chunk. We demonstrate the algorithm by clustering an affinity graph
with over 1.5 trillion edges between 135 billion supervoxels derived from a 3D
electron microscopic brain image.