Abstract:
  In this paper, we prove the existence of full dimensional tori for
$d$-dimensional nonlinear Schr$\ddot{\mbox{o}}$dinger equation with periodic
boundary conditions \begin{equation*}\label{L1} \sqrt{-1}u_{t}+\Delta
u+V*u\pm\epsilon |u|^2u=0,\hspace{12pt}x\in\mathbb{T}^d,\quad d\geq 1,
\end{equation*} where $V*$ is the convolution potential. Here the radius of the
invariant torus satisfies a slower decay, i.e. \begin{equation*}\label{031601}
I_{\textbf n}\sim e^{-r\ln^{\sigma}\left\|\textbf n\right\|},\qquad \mbox{as}\
\left\|\textbf n\right\|\rightarrow\infty, \end{equation*}for any $\sigma>2$
and $r\geq 1$. This result confirms a conjecture by Bourgain [J. Funct. Anal.
229 (2005), no. 1, 62-94].


Abstract:
  Estimating camera wearer's body pose from an egocentric view (egopose) is a
vital task in augmented and virtual reality. Existing approaches either use a
narrow field of view front facing camera that barely captures the wearer, or an
extruded head-mounted top-down camera for maximal wearer visibility. In this
paper, we tackle the egopose estimation from a more natural human vision span,
where camera wearer can be seen in the peripheral view and depending on the
head pose the wearer may become invisible or has a limited partial view. This
is a realistic visual field for user-centric wearable devices like glasses
which have front facing wide angle cameras. Existing solutions are not
appropriate for this setting, and so, we propose a novel deep learning system
taking advantage of both the dynamic features from camera SLAM and the body
shape imagery. We compute 3D head pose, 3D body pose, the figure/ground
separation, all at the same time while explicitly enforcing a certain geometric
consistency across pose attributes. We further show that this system can be
trained robustly with lots of existing mocap data so we do not have to collect
and annotate large new datasets. Lastly, our system estimates egopose in real
time and on the fly while maintaining high accuracy.


Abstract:
  Transition-metal chalcogenides (TMCs) materials have attracted increasing
interest both for fundamental research and industrial applications. Among all
these materials, two-dimensional (2D) compounds with honeycomb-like structure
possess exotic electronic structures. Here, we report a systematic study of TMC
monolayer AgTe fabricated by direct depositing Te on the surface of Ag(111) and
annealing. Few intrinsic defects are observed and studied by scanning tunneling
microscopy, indicating that there are two kinds of AgTe domains and they can
form gliding twin-boundary. Then, the monolayer AgTe can serve as the template
for the following growth of Te film. Meanwhile, some Te atoms are observed in
the form of chains on the top of the bottom Te film. Our findings in this work
might provide insightful guide for the epitaxial growth of 2D materials for
study of novel physical properties and for future quantum devices.


Abstract:
  In quantum electrodynamics with charged chiral fermions, a background
electric field is the source of the chiral anomaly which creates a chirally
imbalanced state of fermions. This chiral state is realized through the
production of entangled pairs of right-moving fermions and left-moving
antifermions (or vice versa, depending on the orientation of the electric
field). Here we show that the statistical Gibbs entropy associated with these
pairs is equal to the entropy of entanglement between the right-moving
particles and left-moving antiparticles. We then derive an asymptotic expansion
for the entanglement entropy in terms of the cumulants of the multiplicity
distribution of produced particles and explain how to re-sum this asymptotic
expansion. Finally, we study the time dependence of the entanglement entropy in
a specific time-dependent pulsed background electric field, the so-called
"Sauter pulse", and illustrate how our re-summation method works in this
specific case. We also find that short pulses (such as the ones created by high
energy collisions) result in an approximately thermal distribution for the
produced particles.


Abstract:
  This article outlines a novel interpretation of quantum theory: the Q-based
interpretation. The core idea underlying this interpretation, recently
suggested for quantum field theories by Drummond and Reid [2020], is to
interpret the phase space function Q -- a transform of the better known Wigner
function -- as a proper probability distribution, roughly analogous to the
probability distribution \rho in classical statistical mechanics.
  Here I motivate the Q-based interpretation, investigate whether it is
empirically adequate, and outline some of its key conceptual features. I argue
that the Q-based interpretation is attractive in that it promises having no
measurement problem, is conceptually parsimonious and has the potential to
apply elegantly to relativistic and field-theoretic contexts.


Abstract:
  In this paper, we establish a structure theorem for projective klt pairs
$(X,\Delta)$ with nef anti-log canonical divisor; specifically, we prove that,
up to replacing $X$ with a finite quasi-\'etale cover, $X$ admits a locally
trivial rationally connected fibration onto a projective klt variety with
numerically trivial canonical divisor. This structure theorem generalizes
previous works for smooth projective varieties and reduces several structure
problems to the singular Beauville-Bogomolov decomposition for Calabi-Yau
varieties. As an application, projective varieties of klt Calabi-Yau type,
which naturally appear as an outcome of the Log Minimal Model Program, are
decomposed into building block varieties: rationally connected varieties and
Calabi-Yau varieties.


Abstract:
  Physical systems that dissipate, mix and develop turbulence also irreversibly
transport statistical density. In statistical physics, laws for these processes
have a mathematical form and tractability that depends on whether the
description is classical or quantum mechanical. Here, we establish a theory for
density transport in any classical dynamical system that is analogous to the
density matrix formulation of quantum mechanics. Defining states in terms of a
classical density matrix leads to generalizations of Liouville's theorem and
Liouville's equation, establishing an alternative computationally-tractable
basis for nonequilibrium statistical mechanics. The formalism is complete with
classical commutators and anti-commutators that embed measures of local
instability and chaos and are directly related to Poisson brackets when the
dynamics are Hamiltonian. It also recovers the traditional Liouville equation
and the Liouville theorem by imposing trace preservation or Hamiltonian
dynamics. Applying to systems that are driven, transient, dissipative, regular,
and chaotic, this formalism has the potential for broad applications.


Abstract:
  Hyper-parameters of time series models play an important role in time series
analysis. Slight differences in hyper-parameters might lead to very different
forecast results for a given model, and therefore, selecting good
hyper-parameter values is indispensable. Most of the existing generic
hyper-parameter tuning methods, such as Grid Search, Random Search, Bayesian
Optimal Search, are based on one key component - search, and thus they are
computationally expensive and cannot be applied to fast and scalable
time-series hyper-parameter tuning (HPT). We propose a self-supervised learning
framework for HPT (SSL-HPT), which uses time series features as inputs and
produces optimal hyper-parameters. SSL-HPT algorithm is 6-20x faster at getting
hyper-parameters compared to other search based algorithms while producing
comparable accurate forecasting results in various applications.


Abstract:
  Microquasars with high-mass companion stars are promising very-high-energy
(VHE; 0.1-100 TeV) gamma-ray emitters, but their behaviors above 10 TeV are
poorly known. Using the High Altitude Water Cherenkov (HAWC) observatory, we
search for excess gamma-ray emission coincident with the positions of known
high-mass microquasars (HMMQs). No significant emission is observed for LS
5039, Cygnus X-1, Cygnus X-3, and SS 433 with 1,523 days of HAWC data. We set
the most stringent limit above 10 TeV obtained to date on each individual
source. Under the assumption that HMMQs produce gamma rays via a common
mechanism, we have performed source-stacking searches, considering two
different scenarios: I) gamma-ray luminosity is a fraction $\epsilon_\gamma$ of
the microquasar jet luminosity, and II) very-high-energy gamma rays are
produced by relativistic electrons up-scattering the radiation field of the
companion star in a magnetic field $B$. We obtain $\epsilon_\gamma < 5.4\times
10^{-6}$ for scenario I, which tightly constrains models that suggest
observable high-energy neutrino emission by HMMQs. In the case of scenario II,
the non-detection of VHE gamma rays yields a strong magnetic field, which
challenges synchrotron radiation as the dominant mechanism of the microquasar
emission between 10 keV and 10 MeV.


Abstract:
  In this reply, we address the comment [arXiv:2105.14908] to our recent paper
[arXiv:2105.09328], where we argued that the Thakurta metric does not describe
cosmological black holes. We clarify that the mass growth of Thakurta black
holes is due to an influx of energy (i.e. accretion), which, by definition, is
not a feature of geometry. The conclusions of [arXiv:2105.09328] are
independent of the interpretation of this energy flux. We show that the average
energy density of primordial Thakurta black holes scales as $a^{-2}$ and
requires an unrealistic and fine-tuned energy transfer from a smooth dark
matter component to the primordial black hole sector.


Abstract:
  Quantum Optical Coherence Tomography (Q-OCT) uses quantum properties of light
to provide several advantages over its classical counterpart, OCT: it achieves
a twice better axial resolution with the same spectral bandwidth and it is
immune to even orders of dispersion. Since these features are very sought-after
in OCT imaging, many hardware and software techniques have been created to
mimic the quantum behaviour of light and achieve these features using
traditional OCT systems. The most recent, purely algorithmic scheme - an
improved version of Intensity Correlation Spectral Domain OCT named ICA-SD-OCT
showed even-order dispersion cancellation and reduction of artefacts. The true
capabilities of this method were unfortunately severely undermined, both in
terms of its relation to Q-OCT and in terms of its main performance parameters.
In this work, we provide experimental demonstrations as well as numerical and
analytical arguments to show that ICA-SD-OCT is a true classical equivalent of
Q-OCT, more specifically its Fourier domain version, and therefore it enables a
true two-fold axial resolution improvement. We believe that clarification of
all the misconceptions about this very promising algorithm will highlight the
great value of this method for OCT and consequently lead to its practical
applications for resolution- and quality-enhanced OCT imaging.


Abstract:
  Short-read DNA sequencing instruments can yield over 1e+12 bases per run,
typically composed of reads 150 bases long. Despite this high throughput, de
novo assembly algorithms have difficulty reconstructing contiguous genome
sequences using short reads due to both repetitive and difficult-to-sequence
regions in these genomes. Some of the short read assembly challenges are
mitigated by scaffolding assembled sequences using paired-end reads. However,
unresolved sequences in these scaffolds appear as "gaps". Here, we introduce
GapPredict, a tool that uses a character-level language model to predict
unresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the
state-of-the-art gap-filling tool Sealer, and observed that the former can fill
65.6% of the sampled gaps that were left unfilled by the latter, demonstrating
the practical utility of deep learning approaches to the gap-filling problem in
genome sequence assembly.


Abstract:
  We study the Cauchy problem for a class of third order linear anisotropic
evolution equations with complex valued lower order terms depending both on
time and space variables. Under suitable decay assumptions for $|x| \to \infty$
on these coefficients, we prove a well posedness result in Gevrey-type spaces.


Abstract:
  In this work, we have considered the recently proposed new Tsallis Agegraphic
Dark Energy model (NTADE) (Mod. Phys. Lett. A 34, 1950086, 2019) within the
framework of a flat Friedmann-Robertson-Walker(FRW)
  Universe by taking various values of the parameter $\delta$. The NTADE model
shows the current phase transition of the Universe from
  decelerated to accelerated phase. The NTADE EoS parameter shows a rich
behaviour as it can be quintessence-like or phantom-like depending on the value
of $\delta$. For discriminating the NTADE model from $\Lambda$CDM, we have
plotted the statefinder parameters $r(z)$, $s(z)$ and $(r, s)$, $(r, q)$ pair.
The NTADE model shows distinct evolutionary trajectories of their evolution in
($ r, s$) and ($ r, q$) plane. An analysis using the snap parameter and
  the $\omega_{D}-\omega_{D}^{'}$ pair dynamical analysis have also been
performed.


Abstract:
  Functional magnetic resonance imaging (fMRI) is a non-invasive and in-vivo
imaging technique essential for measuring brain activity. Functional
connectivity is used to study associations between brain regions either at rest
or while study subjects perform tasks. In this paper, we propose a rigorous
definition of task-evoked functional connectivity at the population level
(ptFC). Importantly, our proposed ptFC is interpretable in the context of
task-fMRI studies. An algorithm for estimating ptFC is provided. We present the
performance of the proposed algorithm compared to existing functional
connectivity estimation approaches using simulations. Lastly, we apply the
proposed framework to estimate task-evoked functional connectivity in a
motor-task study from the Human Connectome Project. We show that the proposed
algorithm identifies associations regions of the brain related to the
performance of motor tasks as expected.


Abstract:
  Sazdanovic and Yip defined a categorification of Stanley's chromatic function
called the chromatic symmetric homology. In this paper we prove that (as
conjectured by Chandler, Sazdanovic, Stella and Yip), if a graph $G$ is
non-planar, then its chromatic symmetric homology in bidegree (1,0) contains
$\mathbb{Z}_2$-torsion. Our proof follows a recursive argument based on
Kuratowsky's theorem.


Abstract:
  Detecting similar code fragments, usually referred to as code clones, is an
important task. In particular, code clone detection can have significant uses
in the context of vulnerability discovery, refactoring and plagiarism
detection. However, false positives are inevitable and always require manual
reviews. In this paper, we propose Twin-Finder+, a novel closed-loop approach
for pointer-related code clone detection that integrates machine learning and
symbolic execution techniques to achieve precision. Twin-Finder+ introduces a
formal verification mechanism to automate such manual reviews process. Our
experimental results show Twin-Finder+ that can remove 91.69% false positives
in average. We further conduct security analysis for memory safety using
real-world applications, Links version 2.14 and libreOffice-6.0.0.1.
Twin-Finder+ is able to find 6 unreported bugs in Links version 2.14 and one
public patched bug in libreOffice-6.0.0.1.


Abstract:
  Lithium niobate on insulator (LNOI), as an emerging and promising optical
integration platform, faces shortages of on-chip active devices including
lasers and amplifiers. Here, we report the fabrication on-chip erbium-doped
LNOI waveguide amplifiers based on electron beam lithography and inductively
coupled plasma reactive ion etching. A net internal gain of ~30 dB/cm in
communication band was achieved in the fabricated waveguide amplifiers under
the pump of a 974-nm continuous laser. This work develops new active devices on
LNOI and will promote the development of LNOI integrated photonics.


Abstract:
  The scientific image integrity area presents a challenging research
bottleneck, the lack of available datasets to design and evaluate forensic
techniques. Its data sensitivity creates a legal hurdle that prevents one to
rely on real tampered cases to build any sort of accessible forensic benchmark.
To mitigate this bottleneck, we present an extendable open-source library that
reproduces the most common image forgery operations reported by the research
integrity community: duplication, retouching, and cleaning. Using this library
and realistic scientific images, we create a large scientific forgery image
benchmark (39,423 images) with an enriched ground-truth. In addition, concerned
about the high number of retracted papers due to image duplication, this work
evaluates the state-of-the-art copy-move detection methods in the proposed
dataset, using a new metric that asserts consistent match detection between the
source and the copied region. The dataset and source-code will be freely
available upon acceptance of the paper.


Abstract:
  Visual interpretability of Convolutional Neural Networks (CNNs) has gained
significant popularity because of the great challenges that CNN complexity
imposes to understanding their inner workings. Although many techniques have
been proposed to visualize class features of CNNs, most of them do not provide
a correspondence between inputs and the extracted features in specific layers.
This prevents the discovery of stimuli that each layer responds better to. We
propose an approach to visually interpret CNN features given a set of images by
creating corresponding images that depict the most informative features of a
specific layer. Exploring features in this class-agnostic manner allows for a
greater focus on the feature extractor of CNNs. Our method uses a
dual-objective activation maximization and distance minimization loss, without
requiring a generator network nor modifications to the original model. This
limits the number of FLOPs to that of the original network. We demonstrate the
visualization quality on widely-used architectures.


Abstract:
  Exploding granules have drawn renewed interest because of their interaction
with the magnetic field. Especially the newly forming downflow lanes developing
in their centre seem to be eligible candidates for the intensification of
magnetic fields. We analyse spectroscopic data from two different instruments
in order to study the intricate velocity pattern within the newly forming
downflow lanes in detail. We aim to examine general properties of a number of
exploding granules. To gain a better understanding of the formation process of
the developing intergranular lane in exploding granules, we study the temporal
evolution and height dependence of the line-of-sight velocities at their
formation location. Additionally, we search for evidence that exploding
granules act as acoustic sources. We investigated the evolution of several
exploding granules using data taken with the Interferometric Bidimensional
Spectrometer and the Imaging Magnetograph eXperiment. Velocities for different
heights of the solar atmosphere were determined by computing bisectors of the
Fe I 6173.0{\AA} and the Fe I 5250.2{\AA} lines. We performed a wavelet
analysis to study the intensity and velocity oscillations within and around
exploding granules. We also compared our findings with predictions of numerical
simulations. We found that exploding granules have significantly longer
lifetimes than regular granules. Exploding granules larger than 3.8 arcsec form
an independent intergranular lane during their decay phase, while smaller
granules usually fade away or disappear into the intergranular area. For all
exploding granules that form a new intergranular downflow lane, we find a
temporal height-dependent shift with respect to the maximum of the downflow
velocity. Our suggestion that this results from a complex atmospheric structure
within the newly forming downflow lane is supported by the simulations.


Abstract:
  Designing agents that acquire knowledge autonomously and use it to solve new
tasks efficiently is an important challenge in reinforcement learning.
Knowledge acquired during an unsupervised pre-training phase is often
transferred by fine-tuning neural network weights once rewards are exposed, as
is common practice in supervised domains. Given the nature of the reinforcement
learning problem, we argue that standard fine-tuning strategies alone are not
enough for efficient transfer in challenging domains. We introduce Behavior
Transfer (BT), a technique that leverages pre-trained policies for exploration
and that is complementary to transferring neural network weights. Our
experiments show that, when combined with large-scale pre-training in the
absence of rewards, existing intrinsic motivation objectives can lead to the
emergence of complex behaviors. These pre-trained policies can then be
leveraged by BT to discover better solutions than without pre-training, and
combining BT with standard fine-tuning strategies results in additional
benefits. The largest gains are generally observed in domains requiring
structured exploration, including settings where the behavior of the
pre-trained policies is misaligned with the downstream task.


Abstract:
  The American Physical Society calls on its members to improve the diversity
of physics by supporting an inclusive culture that encourages women and Black,
Indigenous, and people of color to become physicists. In the current
educational system, it is unlikely for a student to become a physicist if they
do not share the same attitudes about what it means to learn and do physics as
those held by most professional physicists. Evidence shows college physics
courses and degree programs do not support students in developing these
attitudes. Rather physics education filters out students who do not enter
college physics courses with these attitudes. To better understand the role of
attitudes in the lack of diversity in physics, we investigated the intersecting
relationships between racism and sexism in inequities in student attitudes
about learning and doing physics using a critical quantitative framework. The
analyses used hierarchical linear models to examine students attitudes as
measured by the Colorado learning attitudes about science survey. The data came
from the LASSO database and included 2170 students in 46 calculus-based
mechanics courses and 2503 students in 49 algebra-based mechanics courses
taught at 18 institutions. Like prior studies, we found that attitudes either
did not change or slightly decreased for most groups. Results identified large
differences across intersecting race and gender groups representing educational
debts society owes these students. White students, particularly White men in
calculus-based courses, tended to have more expert-like attitudes than any
other group of students. Instruction that addresses society's educational debts
can help move physics toward an inclusive culture supportive of diverse
students and professionals.


Abstract:
  Efficient control of a magnetization without an application of the external
magnetic fields is the ultimate goal of spintronics. We demonstrate, that in
monolayers of $\text{CrI}_3$, magnetization can be switched all optically, by
application of the resonant pulses of circularly polarized light. This happens
because of the efficient coupling of the lattice magnetization with bright
excitonic transition. $\text{CrI}_3$ is thus perspective functional material
with high potential for applications in the domains of spintronics and
ultra-fast magnetic memory.


Abstract:
  Phenotype transition takes place in many biological processes such as
differentiation, and understanding how a cell reprograms its global gene
expression profile is a problem of rate theories. A cell phenotype transition
accompanies with switching of expression rates of clusters of genes, analogous
to domain flipping in an Ising system. Here through analyzing single cell RNA
sequencing data in the framework of transition path theory, we set to study how
such a genome-wide expression program switching proceeds in three different
cell transition processes. For each process after reconstructing a Markov
transition model in the cell state space, we formed an ensemble of shortest
paths connecting the initial and final cell states, reconstructed a reaction
coordinate describing the transition progression, and inferred the gene
regulation network (GRN) along the reaction coordinate. In all three processes
we observed common pattern that the frustration of gene regulatory network
(GRN), defined as overall confliction between the regulation received by genes
and their expression states, first increases then decreases when approaching a
new phenotype. The results support a mechanism of concerted silencing of genes
that are active in the initial phenotype and activation of genes that are
active in the final phenotype.


Abstract:
  With the advent of continuous health monitoring with wearable devices, users
now generate their unique streams of continuous data such as minute-level step
counts or heartbeats. Summarizing these streams via scalar summaries often
ignores the distributional nature of wearable data and almost unavoidably leads
to the loss of critical information. We propose to capture the distributional
nature of wearable data via user-specific quantile functions (QF) and use these
QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an
alternative approach, we also propose to represent QFs via user-specific
L-moments, robust rank-based analogs of traditional moments, and use L-moments
as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually
consistent interpretations: in terms of quantile levels by SOQFR and in terms
of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal
distributional data via Joint and Individual Variation Explained (JIVE) using
L-moments. The proposed methods are illustrated in a study of association of
digital gait biomarkers with cognitive function in Alzheimer's disease (AD).
Our analysis shows that the proposed methods demonstrate higher predictive
performance and attain much stronger associations with clinical cognitive
scales compared to simple distributional summaries.


Abstract:
  We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a
model-free, adaptive decision-making algorithm to coordinate a large fleet of
automated taxis in a dynamic urban environment to minimize expected customer
waiting times. Our scalable algorithm exploits the natural transportation
network company topology by switching between two behaviors: distributed
temporal-difference learning computed locally at each taxi and infrequent
centralized Bellman updates computed at the dispatch center. We derive a regret
bound and design the trigger condition between the two behaviors to explicitly
control the trade-off between computational complexity and the individual taxi
policy's bounded sub-optimality; this advances the state of the art by enabling
distributed operation with bounded-suboptimality. Additionally, unlike recent
reinforcement learning dispatch methods, this policy estimation is adaptive and
robust to out-of-training domain events. This result is enabled by a two-step
modelling approach: the policy is learned on an agent-agnostic, cell-based
Markov Decision Process and individual taxis are coordinated using the learned
policy in a distributed game-theoretic task assignment. We validate our
algorithm against a receding horizon control baseline in a Gridworld
environment with a simulated customer dataset, where the proposed solution
decreases average customer waiting time by 50% over a wide range of parameters.
We also validate in a Chicago city environment with real customer requests from
the Chicago taxi public dataset where the proposed solution decreases average
customer waiting time by 26% over irregular customer distributions during a
2016 Major League Baseball World Series game.


Abstract:
  Image virtual try-on replaces the clothes on a person image with a desired
in-shop clothes image. It is challenging because the person and the in-shop
clothes are unpaired. Existing methods formulate virtual try-on as either
in-painting or cycle consistency. Both of these two formulations encourage the
generation networks to reconstruct the input image in a self-supervised manner.
However, existing methods do not differentiate clothing and non-clothing
regions. A straight-forward generation impedes virtual try-on quality because
of the heavily coupled image contents. In this paper, we propose a Disentangled
Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce
highly-realistic try-on images by disentangling important components of virtual
try-on including clothes warping, skin synthesis, and image composition. To
this end, DCTON can be naturally trained in a self-supervised manner following
cycle consistency learning. Extensive experiments on challenging benchmarks
show that DCTON outperforms state-of-the-art approaches favorably.


Abstract:
  Model-free off-policy actor-critic methods are an efficient solution to
complex continuous control tasks. However, these algorithms rely on a number of
design tricks and hyperparameters, making their application to new domains
difficult and computationally expensive. This paper creates an evolutionary
approach that automatically tunes these design decisions and eliminates the
RL-specific hyperparameters from the Soft Actor-Critic algorithm. Our design is
sample efficient and provides practical advantages over baseline approaches,
including improved exploration, generalization over multiple control
frequencies, and a robust ensemble of high-performance policies. Empirically,
we show that our agent outperforms well-tuned hyperparameter settings in
popular benchmarks from the DeepMind Control Suite. We then apply it to less
common control tasks outside of simulated robotics to find high-performance
solutions with minimal compute and research effort.


Abstract:
  In this paper, we estimate the high dimensional precision matrix under the
weak sparsity condition where many entries are nearly zero. We study a
Lasso-type method for high dimensional precision matrix estimation and derive
general error bounds under the weak sparsity condition. The common
irrepresentable condition is relaxed and the results are applicable to the weak
sparse matrix. As applications, we study the precision matrix estimation for
the heavy-tailed data, the non-paranormal data, and the matrix data with the
Lasso-type method.


Abstract:
  We construct a model of type theory enjoying parametricity from an arbitrary
one. A type in the new model is a semi-cubical type in the old one,
illustrating the correspondence between parametricity and cubes.
  Our construction works not only for parametricity, but also for similar
interpretations of type theory and in fact similar interpretations of any
generalized algebraic theory. To be precise we consider a functor forgetting
unary operations and equations defining them recursively in a generalized
algebraic theory. We show that it has a right adjoint.
  We use techniques from locally presentable category theory, as well as from
quotient inductive-inductive types.


Abstract:
  The broad range of requirements of Internet of Things applications has lead
to the development of several dedicated communication technologies, each
tailored to meet a specific feature set. A solution combining different
wireless technologies in one device, can overcome the disadvantages of any
individual technology. The design of such Multiple Radio Access Technology
solutions based on the diverse characteristics of the technologies offers
interesting opportunities. In this work we analyze the potential of combining
LoRaWAN and NB-IoT in a Multi-RAT solution for IoT. To that end we evaluate key
IoT node requirements in function of payload size and link quality: (1) energy
efficiency, (2) coverage, (3) payload size, (4) latency performance, (5)
Quality of Service, and (6) cost efficiency. Our theoretical assessment and
experimental validation of these IoT features show the merits of a Multi-RAT
solution. Notably, energy consumption in use cases with only sporadic large
payload requirements, can be improved by a factor of at least 4 with respect to
either single-mode technologies. Moreover, latency-critical messages can get
delivered on time and coverage can be extended elegantly where needed.


Abstract:
  In this article we establish an asymptotic formula for the number of rational
points, with bounded denominators, within a given distance to a compact
submanifold $\mathcal{M}$ of $\mathbb{R}^M$ with a certain curvature condition.
Our result generalises earlier work of Huang for hypersurfaces [J.-J. Huang,
The density of rational points near hypersurfaces, Duke Math. J. 169 (2020),
2045--2077.], as our curvature condition reduces to Gaussian curvature being
bounded away from $0$ when $M - dim \mathcal{M} = 1$. An interesting feature of
our result is that the asymptotic formula holds beyond the conjectured range of
the distance to $\mathcal{M}$. Furthermore, we obtain an upper bound for the
number of rational points on $\mathcal{M}$ with additional power saving to the
bound in the analogue of Serre's dimension growth conjecture for compact
submanifolds of $\mathbb{R}^M$ when $M - dim \mathcal{M} > 1$.


Abstract:
  In recent years, locomotion mechanisms exhibited by vertebrate animals have
been the inspiration for the improvement in the performance of robotic systems.
These mechanisms include the adaptability of their locomotion to any change
registered in the environment through their biological sensors. In this regard,
we aim to replicate such kind of adaptability in legged robots through a
Spiking Central Pattern Generator. This Spiking Central Pattern Generator
generates different locomotion (rhythmic) patterns which are driven by an
external stimulus, that is, the output of a Force Sensitive Resistor connected
to the robot to provide feedback. The Spiking Central Pattern Generator
consists of a network of five populations of Leaky Integrate-and-Fire neurons
designed with a specific topology in such a way that the rhythmic patterns can
be generated and driven by the aforementioned external stimulus. Therefore, the
locomotion of the end robotic platform (any-legged robot) can be adapted to the
terrain by using any sensor as input. The Spiking Central Pattern Generator
with adaptive learning has been numerically validated at software and hardware
level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for
the latest. In particular, our experiments clearly show an adaptation in the
oscillation frequencies between the spikes produced in the populations of the
Spiking Central Pattern Generator while the input stimulus varies. To validate
the robustness and adaptability of the Spiking Central Pattern Generator, we
have performed several tests by variating the output of the sensor. These
experiments were carried out in Brian 2 and SpiNNaker; both implementations
showed a similar behavior with a Pearson correlation coefficient of 0.905.


Abstract:
  The content of two additional Ward identities exhibited by the $U(1)$ Higgs
model is exploited. These novel Ward identities can be derived only when a pair
of local composite operators providing a gauge invariant setup for the Higgs
particle and the massive vector boson is introduced in the theory from the
beginning. Among the results obtained from the above mentioned Ward identities,
we underline a new exact relationship between the stationary condition for the
vacuum energy, the vanishing of the tadpoles and the vacuum expectation value
of the gauge invariant scalar operator. We also present a characterization of
the two-point correlation function of the composite operator corresponding to
the vector boson in terms of the two-point function of the elementary gauge
fields. Finally, a discussion on the connection between the cartesian and the
polar parametrization of the complex scalar field is presented in the light of
the Equivalence Theorem. The latter can in the current case be understood in
the language of a constrained cohomology, which also allows to rewrite the
action in terms of the aforementioned gauge invariant operators. We also
comment on the diminished role of the global $U(1)$ symmetry and its breaking.


Abstract:
  A quantum stabilizer code over GF$(q)$ corresponds to a classical additive
code over GF$(q^2)$ that is self-orthogonal with respect to a symplectic inner
product. We study the decoding of quantum low-density parity-check (LDPC) codes
over binary finite fields GF$(q=2^l)$ by the sum-product algorithm, also known
as belief propagation (BP). Conventionally, a message in a nonbinary BP for
quantum codes over GF$(2^l)$ represents a probability vector over GF$(2^{2l})$,
inducing high decoding complexity. In this paper, we explore the property of
the symplectic inner product and show that scalar messages suffice for BP
decoding of nonbinary quantum codes, rather than vector messages necessary for
the conventional BP. Consequently, we propose a BP decoding algorithm for
quantum codes over GF$(2^l)$ by passing scalar messages so that it has low
computation complexity. The algorithm is specified in log domain by using
log-likelihood ratios (LLRs) of the channel statistics to have a low
implementation cost. Moreover, techniques such as message normalization or
offset can be naturally applied in this algorithm to mitigate the effects of
short cycles to improve BP performance. This is important for nonbinary quantum
codes since they may have more short cycles compared to binary quantum codes.
Several computer simulations are provided to demonstrate these advantages. The
scalar-based strategy can also be used to improve the BP decoding of classical
linear codes over GF$(2^l)$ with many short cycles.


Abstract:
  Collective excitations in topologically non-trivial systems have attracted
considerable attention in recent years. Here we study plasmons in the
Su-Schrieffer-Heeger model whose low-energy electronic band is only partially
filled, such that the system is metallic. Using the random phase approximation,
we calculate the intra- and inter-band polarization functions and determine the
bulk plasmonic dispersion from the dielectric function within the random phase
approximation. We find that the sub-lattice basis states strongly affect the
polarization functions and therefore control the system's plasmonic
excitations. By varying the real-space separation of these local orbitals, one
can thus selectively enhance or suppress the plasmonic energies via a tunable
trade-off between intra-band and inter-band screening processes. Specifically,
this mechanism can be used to stabilize undamped high energy plasmons that have
already been reported in related models. We propose scenarios on how to control
and observe these effects in experiments.


Abstract:
  Totally symmetric sets are a recently introduced tool for studying
homomorphisms between groups. In this paper, we give full classifications of
totally symmetric sets in certain families of groups and bound their sizes in
others. As a consequence, we derive restrictions on possible homomorphisms
between these groups. One sample application of our results is that any
homomorphism of a braid group to a direct product of solvable groups must have
cyclic image.


Abstract:
  We study in this work the 2D dynamics of an experimental system of
disk-shaped rotors, fluidized by a turbulent upflow. Our experiments show a
complex chirality behavior. In particular, as average kinetic energy increases,
the system evolves from positive chirality (one vortex rotating in the same
direction as particles spin), to complex chirality (several vortexes of both
signs) and negative chirality (one vortex in opposite sense to particle spin).
We find that these transitions are determined by the combined action of heat
dissipation at the boundaries and statistical correlations between particles
spin and translational velocities. Moreover, we show that the decay to negative
chirality is produced as a consequence of particles spin syncronization.
Therefore, we elucidate a control mechanism of chirality, via the adjustment of
spin in a system of active rotors.


Abstract:
  A polynomial threshold function (PTF) $f:\mathbb{R}^n \rightarrow \mathbb{R}$
is a function of the form $f(x) = \mathsf{sign}(p(x))$ where $p$ is a
polynomial of degree at most $d$. PTFs are a classical and well-studied
complexity class with applications across complexity theory, learning theory,
approximation theory, quantum complexity and more. We address the question of
designing pseudorandom generators (PRG) for polynomial threshold functions
(PTFs) in the gaussian space: design a PRG that takes a seed of few bits of
randomness and outputs a $n$-dimensional vector whose distribution is
indistinguishable from a standard multivariate gaussian by a degree $d$ PTF.
  Our main result is a PRG that takes a seed of $d^{O(1)}\log ( n /
\varepsilon)\log(1/\varepsilon)/\varepsilon^2$ random bits with output that
cannot be distinguished from $n$-dimensional gaussian distribution with
advantage better than $\varepsilon$ by degree $d$ PTFs. The best previous
generator due to O'Donnell, Servedio, and Tan (STOC'20) had a quasi-polynomial
dependence (i.e., seedlength of $d^{O(\log d)}$) in the degree $d$. Along the
way we prove a few nearly-tight structural properties of restrictions of PTFs
that may be of independent interest.


Abstract:
  Bound-states-in-the-continuum (BIC)is a wave-mechanical concept that
generates resonances with vanishing spectral linewidths. It has many practical
applications in Optics, such as narrow-band filters, mirror-less lasing, and
nonlinear harmonic generation. As true BIC optical modes non-radiative and
confined to the near field of nanostructures, they cannot be excited using
propagating light. As a result, their direct experimental observation has been
elusive. Rather than using light, we demonstrate probing BIC modes on arrays of
silicon nanoantennas using a focused beam of electrons in a tranmission
electron microscope. By combining cathodoluminescence (CL) and monochromated
electron energy-loss spectroscopy (EELS) with controlled nanofabrication, we
provide direct experimental evidence of "true" BIC modes, and demonstrate a BIC
mode in the visible spectrum at 720 nm. The ability to observe and quantify
these guided resonances with a spatial precision more than two orders of
magnitude higher than previous far-field measurements allows the probing of
individual elements in the nano-antenna arrays. The high-resolution
experimental results are supported by numerical simulations as well as
multipolar decomposition analysis, allowing us to demonstrate that the coherent
interaction length of the quasi-BIC resonance requires at least 6 neighboring
antenna elements, achieving over 60 times higher emissivity than for
unpatterned silicon.


Abstract:
  Let $G$ be a finite abelian group viewed a $\mathbb{Z}$-module and let
$\mathcal{G} = (V, E)$ be a simple graph. In this paper, we consider a graph
$\Gamma(G)$ called as a \textit{group-annihilator} graph. The vertices of
$\Gamma(G)$ are all elements of $G$ and two distinct vertices $x$ and $y$ are
adjacent in $\Gamma(G)$ if and only if $[x : G][y : G]G = \{0\}$, where $x,
y\in G$ and $[x : G] = \{r\in\mathbb{Z} : rG \subseteq \mathbb{Z}x\}$ is an
ideal of a ring $\mathbb{Z}$. We discuss in detail the graph structure realised
by the group $G$. Moreover, we study the creation sequence, hyperenergeticity
and hypoenergeticity of group-annihilator graphs. Finally, we conclude the
paper with a discussion on Laplacian eigen values of the group-annhilator
graph. We show that the Laplacian eigen values are representatives of orbits of
the group action: $Aut(\Gamma(G)) \times G \rightarrow G$.


Abstract:
  In this paper, we outline an approach for automatic generation of challenging
road networks for virtual testing of an automated lane keep system. Based on a
set of control points, we construct a parametric curve that represents a road
network, which defines the dynamic driving task an automated lane keep system
equipped vehicle has to perform. Changing control points has global influence
on the resulting road geometry. Our approach uses search to find a set of
control points that results in a challenging road geometry, eventually forcing
the vehicle to leave the intended path. We evaluated our approach in three
different search-configurations regarding test efficiency and practical
applicability for automatic virtual testing of an automated lane keep system.


Abstract:
  LHAASO detected 12 gamma-ray sources above 100 TeV which are the possible
origins of Galactic cosmic-rays. We summarize the neutrino measurements by
IceCube and ANTARES in the vicinity of LHAASO sources to constrain the
contribution of hadronic gamma-rays in these sources. We find that the current
observations constrain that the hadronic gamma-rays contribute no more than
~60% of the gamma-rays from Crab Nebula. Gamma-rays from two LHAASO sources,
LHAASO J1825-1326 and LHAASO J1907+0626, are dominated by leptonic components
up to ~200 TeV, under the hypotheses in the analysis by IceCube. The
uncertainties of the constraint on the hadronic gamma-ray emission are
discussed. We also constrain the total 100 TeV gamma-ray emission from TeV PWNe
relying on the remarkable sensitivity of LHAASO at that energies.


Abstract:
  CAV platooning technology has received considerable attention in the past few
years, driven by the next generation smart transportation systems. Unlike most
of the existing platooning methods that focus on linear vehicle dynamics of
CAVs, this paper considers nonlinear vehicle dynamics and develops fully
distributed optimization based CAV platooning control schemes via the model
predictive control (MPC) approach for a possibly heterogeneous CAV platoon. The
nonlinear vehicle dynamics leads to several major difficulties in distributed
algorithm development and control analysis and design. Specifically, the
underlying MPC optimization problem is nonconvex and densely coupled. Further,
the closed loop dynamics becomes a time-varying nonlinear system subject to
external perturbations, making closed loop stability analysis rather
complicated. To overcome these difficulties, we formulate the underlying MPC
optimization problem as a locally coupled, albeit nonconvex, optimization
problem and develop a sequential convex programming based fully distributed
scheme for a general MPC horizon. Such a scheme can be effectively implemented
for real-time computing using operator splitting methods. To analyze the closed
loop stability, we apply various tools from global implicit function theorems,
stability of linear time-varying systems, and Lyapunov theory for
input-to-state stability to show that the closed loop system is locally
input-to-state stable uniformly in all small coefficients pertaining to the
nonlinear dynamics. Numerical tests on homogeneous and heterogeneous CAV
platoons demonstrate the effectiveness of the proposed fully distributed
schemes and CAV platooning control.


Abstract:
  The Continual Learning (CL) problem involves performing well on a sequence of
tasks under limited compute. Current algorithms in the domain are either slow,
offline or sensitive to hyper-parameters. La-MAML, an optimization-based
meta-learning algorithm claims to be better than other replay-based,
prior-based and meta-learning based approaches. According to the MER paper [1],
metrics to measure performance in the continual learning arena are Retained
Accuracy (RA) and Backward Transfer-Interference (BTI). La-MAML claims to
perform better in these values when compared to the SOTA in the domain. This is
the main claim of the paper, which we shall be verifying in this report.


Abstract:
  There is a big difference in the tone of color of skin between dark and light
skinned people. Despite this fact, most face recognition tasks almost all
classical state-of-the-art models are trained on datasets containing an
overwhelming majority of light skinned face images. It is tedious to collect a
huge amount of data for dark skinned faces and train a model from scratch. In
this paper, we apply transfer learning on VGGFace to check how it works on
recognising dark skinned mainly Ethiopian faces. The dataset is of low quality
and low resource. Our experimental results show above 95\% accuracy which
indicates that transfer learning in such settings works.


Abstract:
  In this paper we begin mapping out the space of rank-2 $\mathcal{N}=2$
superconformal field theories (SCFTs) in four dimensions. This represents an
ideal set of theories which can be potentially classified using purely quantum
field-theoretic tools, thus providing a precious case study to probe the
completeness of the current understanding of SCFTs, primarily derived from
string theory constructions. Here, we collect and systematize a large amount of
field theoretic data characterizing each theory. We also provide a detailed
description of each case and determine the theories' Coulomb, Higgs and Mixed
branch stratification. The theories naturally organize themselves into series
connected by RG flows but which have gaps suggesting that our current
understanding is not complete.


Abstract:
  Let the group $G$ act transitively on the finite set $\Omega$. We show that
random Schreier graphs on $O(\log|\Omega|)$ elements are expanders with high
probability, magnifying a famous theorem of Alon and Roichman. On the other
side, depending on the particular action of $G$ on $\Omega$, we give a lower
bound on the number of elements which are necessary to provide expansion. We
apply this method to estimate the spectral gap in the case where $G$ is
nilpotent.


Abstract:
  We present lambda layers -- an alternative framework to self-attention -- for
capturing long-range interactions between an input and structured contextual
information (e.g. a pixel surrounded by other pixels). Lambda layers capture
such interactions by transforming available contexts into linear functions,
termed lambdas, and applying these linear functions to each input separately.
Similar to linear attention, lambda layers bypass expensive attention maps, but
in contrast, they model both content and position-based interactions which
enables their application to large structured inputs such as images. The
resulting neural network architectures, LambdaNetworks, significantly
outperform their convolutional and attentional counterparts on ImageNet
classification, COCO object detection and COCO instance segmentation, while
being more computationally efficient. Additionally, we design LambdaResNets, a
family of hybrid architectures across different scales, that considerably
improves the speed-accuracy tradeoff of image classification models.
LambdaResNets reach excellent accuracies on ImageNet while being 3.2 - 4.4x
faster than the popular EfficientNets on modern machine learning accelerators.
When training with an additional 130M pseudo-labeled images, LambdaResNets
achieve up to a 9.5x speed-up over the corresponding EfficientNet checkpoints.


Abstract:
  Beamforming technology is widely used in millimeter wave systems to combat
path losses, and beamformers are usually selected from a predefined codebook.
Unfortunately, the traditional codebook design neglects the beam squint effect,
and this will cause severe performance degradation when the bandwidth is large.
In this letter, we consider that a codebook with fixed size is adopted in the
wideband beamforming system. First, we analyze how beam squint affects system
performance when all beams have the same width. The expression of average
spectrum efficiency is derived based on the ideal beam pattern. Next, we
formulate the optimization problem to design the optimal codebook. Simulation
results demonstrate that the proposed codebook deals with beam squint by
spreading the beam coverage and significantly mitigates the performance
degradation.


Abstract:
  While the predictive performance of modern statistical dependency parsers
relies heavily on the availability of expensive expert-annotated treebank data,
not all annotations contribute equally to the training of the parsers. In this
paper, we attempt to reduce the number of labeled examples needed to train a
strong dependency parser using batch active learning (AL). In particular, we
investigate whether enforcing diversity in the sampled batches, using
determinantal point processes (DPPs), can improve over their diversity-agnostic
counterparts. Simulation experiments on an English newswire corpus show that
selecting diverse batches with DPPs is superior to strong selection strategies
that do not enforce batch diversity, especially during the initial stages of
the learning process. Additionally, our diversityaware strategy is robust under
a corpus duplication setting, where diversity-agnostic sampling strategies
exhibit significant degradation.


Abstract:
  We present BIEBER (Byte-IdEntical Binary parsER), the first system to model
and regenerate a full working parser from instrumented program executions. To
achieve this, BIEBER exploits the regularity (e.g., header fields and
array-like data structures) that is commonly found in file formats. Key
generalization steps derive strided loops that parse input file data and
rewrite concrete loop bounds with expressions over input file header bytes.
These steps enable BIEBER to generalize parses of specific input files to
obtain parsers that operate over input files of arbitrary size. BIEBER also
incrementally and efficiently infers a decision tree that reads file header
bytes to route input files of different types to inferred parsers of the
appropriate type. The inferred parsers and decision tree are expressed in an
IR; separate backends (C and Perl in our prototype) can translate the IR into
the same language as the original program (for a safer drop-in replacement), or
automatically port to a different language. An empirical evaluation shows that
BIEBER can successfully regenerate parsers for six file formats (waveform audio
[1654 files], MT76x0 .BIN firmware containers [5 files], OS/2 1.x bitmap images
[9 files], Windows 3.x bitmaps [9971 files], Windows 95/NT4 bitmaps [133
files], and Windows 98/2000 bitmaps [859 files]), correctly parsing 100% (>=
99.98% when using standard held-out cross-validation) of the corresponding
corpora. The regenerated parsers contain automatically inserted safety checks
that eliminate common classes of errors such as memory errors. We find that
BIEBER can help reverse-engineer file formats, because it automatically
identifies predicates for the decision tree that relate to key semantics of the
file format. We also discuss how BIEBER helped us detect and fix two new bugs
in stb_image as well as independently rediscover and fix a known bug.


Abstract:
  Irreversibility is usually captured by a comparison between the process that
happens and a corresponding "reverse process". In the last decades, this
comparison has been extensively studied through fluctuation relations. Here we
revisit fluctuation relations from the standpoint, suggested decades ago by
Watanabe, that the comparison should involve the prediction and the
retrodiction on the unique process, rather than two processes. We identify a
necessary and sufficient condition for a retrodictive reading of a fluctuation
relation. The retrodictive narrative also brings to the fore the possibility of
deriving fluctuation relations based on various statistical divergences, and
clarifies some of the traditional assumptions as arising from the choice of a
reference prior.


Abstract:
  Let $GP(q,d)$ be the $d$-Paley graph defined on the finite field
$\mathbb{F}_q$. It is notoriously difficult to improve the trivial upper bound
$\sqrt{q}$ on the clique number of $GP(q,d)$. In this paper, we investigate the
connection between Gauss sums over a finite field and the maximum cliques of
their corresponding generalized Paley graphs. We show that the trivial upper
bound on the clique number of $GP(q,d)$ is tight if and only if $d \mid
(\sqrt{q}+1)$, which strengthens the previous related results by
Broere-D\"oman-Ridley and Schneider-Silva. We also obtain a new simple proof of
Stickelberger's theorem on evaluating semi-primitive Gauss sums.


Abstract:
  The nearest prototype classification is a less computationally intensive
replacement for the $k$-NN method, especially when large datasets are
considered. In metric spaces, centroids are often used as prototypes to
represent whole clusters. The selection of cluster prototypes in non-metric
spaces is more challenging as the idea of computing centroids is not directly
applicable.
  In this paper, we present CRS, a novel method for selecting a small yet
representative subset of objects as a cluster prototype. Memory and
computationally efficient selection of representatives is enabled by leveraging
the similarity graph representation of each cluster created by the NN-Descent
algorithm. CRS can be used in an arbitrary metric or non-metric space because
of the graph-based approach, which requires only a pairwise similarity measure.
As we demonstrate in the experimental evaluation, our method outperforms the
state of the art techniques on multiple datasets from different domains.


Abstract:
  We report on the results of multi-wavelength follow-up observations with
Gemini, VLA, and ATCA, to search for a host galaxy and any persistent radio
emission associated with FRB 180309. This FRB is among the most luminous FRB
detections to date, with a luminosity of $> 8.7\times 10^{32}$ erg Hz$^{-1}$ at
the dispersion-based redshift upper limit of 0.32. We used the
high-significance detection of FRB 180309 with the Parkes Telescope and a beam
model of the Parkes Multibeam Receiver to improve the localization of the FRB
to a region spanning approximately $\sim2'\times2'$. We aimed to seek bright
galaxies within this region to determine the strongest candidates as the
originator of this highly luminous FRB. We identified optical sources within
the localization region above our r-band magnitude limit of 24.27, fourteen of
which have photometric redshifts whose fitted mean is consistent with the
redshift upper limit ($z < 0.32$) of our FRB. Two of these galaxies are
coincident with marginally detected "persistent" radio sources of flux density
24.3$\mu$Jy beam$^{-1}$ and 22.1$\mu$Jy beam$^{-1}$ respectively. Our
redshift-dependent limit on the luminosity of any associated persistent radio
source is comparable to the luminosity limits for other localized FRBs. We
analyze several properties of the candidate hosts we identified, including
chance association probability, redshift, and presence of radio emission,
however it remains possible that any of these galaxies could be the host of
this FRB. Follow-up spectroscopy on these objects to explore their H$\alpha$
emission and ionization contents, as well as to obtain more precisely measured
redshifts, may be able to isolate a single host for this luminous FRB.


Abstract:
  Spin waves are promising chargeless information carriers for the future,
energetically efficient beyond-CMOS systems. Among many advantages there are
the ease of achieving nonlinearity, the variety of possible interactions, and
excitation types. Although the rapidly developing magnonic research has already
yielded impressive realizations, multi-mode nonlinear effects, particularly
propagating waves and their nanoscale realizations, are still an open research
problem. We study theoretically the dynamic interactions of the spin waves
confined to the edge of a thin ferromagnetic film with the spin-wave beam
incident at this edge. We found the inelastically scattered spin-wave beams at
frequencies increased and decreased by the frequency of the edge spin-wave
relative to the specularly reflected beam. We observed a strong dependence of
the angular shift of the inelastic scattered spin-wave beam on the edge-mode
frequency, which allowed us to propose a magnonic demultiplexing of the signal
encoded in spin waves propagating along the edge. Since dynamic magnetostatic
interactions, which are ubiquitous in the spin-wave dynamics, are decisive in
this process, this indicates the possibility of implementing the presented
effects, also in other configurations and their use in magnonic systems.


Abstract:
  Spectral clustering is a popular algorithm that clusters points using the
eigenvalues and eigenvectors of Laplacian matrices derived from the data. For
years, spectral clustering has been working mysteriously. This paper explains
spectral clustering by dividing it into two categories based on whether the
graph Laplacian is fully connected or not. For a fully connected graph, this
paper demonstrates the dimension reduction part by offering an objective
function: the covariance between the original data points' similarities and the
mapped data points' similarities. For a multi-connected graph, this paper
proves that with a proper $k$, the first $k$ eigenvectors are the indicators of
the connected components. This paper also proves there is an equivalence
between spectral embedding and PCA.


Abstract:
  The new two-dimensional (2D) kagome superconductor CsV$_3$Sb$_5$ has
attracted much recent attention due to the coexistence of superconductivity,
charge order, topology and kagome physics. A key issue in this field is to
unveil the unique reconstructed electronic structure, which successfully
accommodates different orders and interactions to form a fertile ground for
emergent phenomena. Here, we report angle-resolved photoemission spectroscopy
(ARPES) evidence for two distinct band reconstructions in CsV$_3$Sb$_5$. The
first one is characterized by the appearance of new electron energy band at low
temperature. The new band is theoretically reproduced when the three
dimensionality of the charge order is considered for a band-folding along the
out-of-plane direction. The second reconstruction is identified as a surface
induced orbital-selective shift of the electron energy band. Our results
provide the first evidence for the three dimensionality of the charge order in
single-particle spectral function, highlighting the importance of long-range
out-of-plane electronic correlations in this layered kagome superconductor.
They also point to the feasibility of orbital-selective control of the band
structure via surface modification, which would open a new avenue for
manipulating exotic phenomena in this system, including superconductivity.


Abstract:
  Transformer networks are effective at modeling long-range contextual
information and have recently demonstrated exemplary performance in the natural
language processing domain. Conventionally, the temporal action proposal
generation (TAPG) task is divided into two main sub-tasks: boundary prediction
and proposal confidence prediction, which rely on the frame-level dependencies
and proposal-level relationships separately. To capture the dependencies at
different levels of granularity, this paper intuitively presents a unified
temporal action proposal generation framework with original Transformers,
called TAPG Transformer, which consists of a Boundary Transformer and a
Proposal Transformer. Specifically, the Boundary Transformer captures long-term
temporal dependencies to predict precise boundary information and the Proposal
Transformer learns the rich inter-proposal relationships for reliable
confidence evaluation. Extensive experiments are conducted on two popular
benchmarks: ActivityNet-1.3 and THUMOS14, and the results demonstrate that TAPG
Transformer outperforms state-of-the-art methods. Equipped with the existing
action classifier, our method achieves remarkable performance on the temporal
action localization task. Codes and models will be available.


Abstract:
  Infrastructure systems, such as power, transportation, telecommunication, and
water systems, are composed of multiple components which are interconnected and
interdependent to produce and distribute essential goods and services. So, the
robustness of infrastructure systems to resist disturbances is crucial for the
durable performance of modern societies. Multilayer networks have been used to
model the multiplicity and interrelation of infrastructure systems and
percolation theory is the most common approach to quantify the robustness of
such networks. This survey systematically reviews literature published between
2010 and 2021, on applying percolation theory to assess the robustness of
infrastructure systems modeled as multilayer networks. We discussed all network
properties applied to build infrastructure models. Among all properties,
interdependency strength and communities were the most common network property
whilst very few studies considered realistic attributes of infrastructure
systems such as directed links and feedback conditions. The review highlights
that the properties produced approximately similar model outcomes, in terms of
detecting improvement or deterioration in the robustness of multilayer
infrastructure networks, with few exceptions. Most of the studies focused on
highly simpliffied synthetic models rather than models built by real datasets.
Thus, this review suggests analyzing multiple properties in a single model to
assess whether they boost or weaken the impact of each other. In addition, the
effect size of different properties on the robustness of infrastructure systems
should be quantiffied. It can support the design and planning of robust
infrastructure systems by arranging and prioritizing the most effective
properties.


Abstract:
  We study the energy-density dynamics at finite momentum of the
two-dimensional Kitaev spin-model on the honeycomb lattice. Due to
fractionalization of magnetic moments, the energy relaxation occurs through
mobile Majorana matter, coupled to a static $\mathbb{Z}_2$ gauge field. At
finite temperatures, the $\mathbb{Z}_2$ flux excitations act as an emergent
disorder, which strongly affects the energy dynamics. We show that sufficiently
far above the flux proliferation temperature, but not yet in the classical
regime, gauge disorder modifies the coherent low-temperature energy-density
dynamics into a form which is almost diffusive, with hydrodynamic momentum
scaling of a diffusion-kernel, which however remains retarded, primarily due to
the presence of two distinct relaxation channels of particle-hole and
particle-particle nature. Relations to thermal conductivity are clarified. Our
analysis is based on complementary calculations in the low-temperature
homogeneous gauge and a mean-field treatment of thermal gauge fluctuations,
valid at intermediate and high temperatures.


Abstract:
  The electronic behaviour in graphene under arbitrary uniaxial deformations,
such as foldings or flexural fields, is studied by including in the Dirac
equation pseudo-electromagnetic fields. General foldings are thus studied by
showing that uniaxial deformations can be considered as pseudo-magnetic fields
in the Coulomb gauge norm. This allows to give an expression for the Fermi
(zero) energy modes wavefunctions. For random deformations, contact is made
with previous works on the quantum Hall effect under random magnetic fields,
showing that the density of states has a power law behaviour and that the zero
energy modes wavefunctions are multifractal. This hints of an unusual electron
velocity distribution. Also, it is shown that a strong Aharonov-Bohm
pseudo-effect is produced. For more general non-uniaxial general flexural
strain, it is not possible to use the Coulomb gauge. The results presented here
allow to tailor-made graphene uniaxial deformations to achieve specific
wave-functions and electronic properties.


Abstract:
  The direct linearisation framework is presented for the two-dimensional Toda
equations associated with the infinite-dimensional Lie algebras $A_\infty$,
$B_\infty$ and $C_\infty$, as well as the Kac--Moody algebras $A_{r}^{(1)}$,
$A_{2r}^{(2)}$, $C_{r}^{(1)}$ and $D_{r+1}^{(2)}$ for arbitrary integers
$r\in\mathbb{Z}^+$, from the aspect of a set of linear integral equations in a
certain form. Such a scheme not only provides a unified perspective to
understand the underlying integrability structure, but also induces the direct
linearising type solution potentially leading to the universal solution space,
for each class of the two-dimensional Toda system. As particular applications
of this framework to the two-dimensional Toda lattices, we rediscover the Lax
pairs and the adjoint Lax pairs and simultaneously construct the generalised
Cauchy matrix solutions.


Abstract:
  Mobile contact tracing apps are -- in principle -- a perfect aid to condemn
the human-to-human spread of an infectious disease such as COVID-19 due to the
wide use of smartphones worldwide. Yet, the unknown accuracy of contact
estimation by wireless technologies hinders the broader use. We address this
challenge by conducting a measurement study with a custom testbed to show the
capabilities and limitations of Bluetooth Low Energy (BLE) in different
scenarios. Distance estimation is based on interpreting the signal pathloss
with a basic linear and a logarithmic model. Further, we compare our results
with accurate ultra-wideband (UWB) distance measurements. While the results
indicate that distance estimation by BLE is not accurate enough, a contact
detector can detect contacts below 2.5 m with a true positive rate of 0.65 for
the logarithmic and of 0.54 for the linear model. Further, the measurements
reveal that multi-path signal propagation reduces the effect of body shielding
and thus increases detection accuracy in indoor scenarios.


Abstract:
  The interstellar turbulence is magnetized and thus anisotropic. The
anisotropy of turbulent magnetic fields and velocities is imprinted in the
related observables, rotation measures (RMs), and velocity centroids (VCs).
This anisotropy provides valuable information on both the direction and
strength of the magnetic field. However, its measurement is difficult
especially in highly supersonic turbulence in cold interstellar phases due to
the distortions by isotropic density fluctuations. By using 3D simulations of
supersonic and sub-Alfv\'enic magnetohydrodynamic(MHD) turbulence, we find that
the problem can be alleviated when we selectively sample the volume-filling
low-density regions in supersonic MHD turbulence. Our results show that in
these low-density regions, the anisotropy of RM and VC fluctuations depends on
the Alfv\'enic Mach number as $\rm M_A^{-4/3}$. This anisotropy-$\rm M_A$
relation is theoretically expected for sub-Alfv 'enic MHD turbulence and
confirmed by our synthetic observations of $^{12}$CO emission. It provides a
new method for measuring the plane-of-the-sky magnetic fields in cold
interstellar phases.


Abstract:
  Robotic nursing aid is one of the heavily researched areas in robotics
nowadays. Several robotic assistants exist that only focus on a specific
function related to nurses assistance or functions related to patient aid.
There is a need for a unified system that not only performs tasks that would
assist nurses and reduce their burden but also perform tasks that help a
patient. In recent times, due to the COVID-19 pandemic, there is also an
increase in the need for robotic assistants that have teleoperation
capabilities to provide better protection against the virus spread. To address
these requirements, we propose a novel Multi-purpose Intelligent Nurse Aid
(MINA) robotic system that is capable of providing walking assistance to the
patients and perform teleoperation tasks with an easy-to-use and intuitive
Graphical User Interface (GUI). This paper also presents preliminary results
from the walking assistant task that improves upon the current state-of-the-art
methods and shows the developed GUI for teleoperation.


Abstract:
  In this paper, we propose a direct Eulerian generalized Riemann problem (GRP)
scheme for a blood flow model in arteries. It is an extension of the Eulerian
GRP scheme, which is developed by Ben-Artzi, et. al. in J. Comput. Phys.,
218(2006). By using the Riemann invariants, we diagonalize the blood flow
system into a weakly coupled system, which is used to resolve rarefaction wave.
We also use Rankine-Hugoniot condition to resolve the local GRP formulation. We
pay special attention to the acoustic case as well as the sonic case. The
extension to the two dimensional case is carefully obtained by using the
dimensional splitting technique. We test that the derived GRP scheme is second
order accuracy.


Abstract:
  We study the Seebeck effect in the three-dimensional Dirac electron system
based on the linear response theory with Luttinger's gravitational potential.
The Seebeck coefficient $S$ is defined by $S = L_{12} / L_{11} T$, where $T$ is
the temperature, and $L_{11}$ and $L_{12}$ are the longitudinal response
coefficients of the charge current to the electric field and to the temperature
gradient, respectively; $L_{11}$ is the electric conductivity and $L_{12}$ is
the thermo-electric conductivity. We consider randomly-distributed impurity
potentials as the source of the momentum relaxation of electrons and
microscopically calculate the relaxation rate and the vertex corrections of
$L_{11}$ and $L_{12}$ due to the impurities. It is confirmed that $L_{11}$ and
$L_{12}$ are related through Mott's formula in low temperatures when the
chemical potential lies above the gap ($|\mu| > \Delta$), irrespective of the
linear dispersion of the Dirac electrons and unconventional energy dependence
of the lifetime of electrons. On the other hand, when the chemical potential
lies in the band gap ($|\mu| < \Delta$), Seebeck coefficient behaves just as in
conventional semiconductors: Its dependences on the chemical potential $\mu$
and the temperature $T$ are partially captured by $S \propto (\Delta - \mu) /
\kB T$ for $\mu > 0$. The Seebeck coefficient takes the relatively large value
$|S| \simeq 1.7 \,\mathrm{m V/K}$ at $T \simeq 8.7\,\mathrm{K}$ for $\Delta =
15 \,\mathrm{m eV}$ by assuming doped bismuth.


Abstract:
  Replacing a fossil fuel-powered car with an electric model can halve
greenhouse gas emissions over the course of the vehicle's lifetime and reduce
the noise pollution in urban areas. In green logistics, a well-scheduled
charging ensures an efficient operation of transportation and power systems
and, at the same time, provides economical and satisfactory charging services
for drivers. This paper presents a taxonomy of current electric vehicle
charging scheduling problems in green logistics by analyzing its unique
features with some typical use cases, such as space assignment, routing and
energy management; discusses the challenges, i.e., the information availability
and stakeholders' strategic behaviors that arise in stochastic and
decentralized environments; and classifies the existing approaches, as
centralized, distributed and decentralized ones, that apply to these
challenges. Moreover, we discuss research opportunities in applying
market-based mechanisms, which shall be coordinated with stochastic
optimization and machine learning, to the decentralized, dynamic and
data-driven charging scheduling problems for the management of the future green
logistics.


Abstract:
  We consider an analog of particle production in a quartic $O(N)$ quantum
oscillator with time-dependent frequency, which is a toy model of particle
production in the dynamical Casimir effect and de Sitter space. We calculate
exact quantum averages, Keldysh propagator, and particle number using two
different methods. First, we employ a kind of rotating wave approximation to
estimate these quantities for small deviations from stationarity. Second, we
extend these results to arbitrarily large deviations using the
Schwinger-Keldysh diagrammatic technique. We show that in strongly
nonstationary situations, including resonant oscillations, loop corrections to
the tree-level expressions effectively result in an additional degree of
freedom, $N \to N + \frac{3}{2}$, which modifies the average number and energy
of created particles.


Abstract:
  In this paper we investigate the commutator relations for prenilpotent roots
which are nested. These commutator relations are trivial in a lot of cases.


Abstract:
  We extend a recent breakthrough result relating expectation thresholds and
actual thresholds to include rainbow versions.


Abstract:
  A symmetry-preserving treatment of mesons, within a Dyson-Schwinger and
Bethe-Salpeter equations approach, demands an interconnection between the
kernels of the quark gap equation and meson Bethe-Salpeter equation. Appealing
to those symmetries expressed by the vector and axial-vector
Ward-Green-Takahashi identitiges (WGTI), we construct a two-body Bethe-Salpeter
kernel and study its implications in the vector channel; particularly, we
analyze the structure of the quark-photon vertex, which explicitly develops a
vector meson pole in the timelike axis and the quark anomlaous magnetic moment
term, as well as a variety of $\rho$ meson properties: mass and decay
constants, electromagnetic form factors, and valence-quark distribution
amplitudes.


Abstract:
  An equation is derived for analyzing the self-action of a wave packets with
few optical cycles in multicore fibers (MCF). A new class of stable
out-of-phase spatio-temporal solitons with few cycle durations in the MCF with
cores located in a ring is found and analyzed. The stability boundary of the
obtained solutions is determined. As an example of using such solitons, we
considered the problem of their self-compression in the process of multisoliton
dynamics in the MCF. The formation of laser pulses with a duration of few
optical cycles at the output of a ten-core MCF is shown.


Abstract:
  The least squares problem with L1-regularized regressors, called Lasso, is a
widely used approach in optimization problems where sparsity of the regressors
is desired. This formulation is fundamental for many applications in signal
processing, machine learning and control. As a motivating problem, we
investigate a sparse data predictive control problem, run at a cloud service to
control a system with unknown model, using L1-regularization to limit the
behavior complexity. The input-output data collected for the system is
privacy-sensitive, hence, we design a privacy-preserving solution using
homomorphically encrypted data. The main challenges are the non-smoothness of
the L1-norm, which is difficult to evaluate on encrypted data, as well as the
iterative nature of the Lasso problem. We use a distributed ADMM formulation
that enables us to exchange substantial local computation for little
communication between multiple servers. We first give an encrypted multi-party
protocol for solving the distributed Lasso problem, by approximating the
non-smooth part with a Chebyshev polynomial, evaluating it on encrypted data,
and using a more cost effective distributed bootstrapping operation. For the
example of data predictive control, we prefer a non-homogeneous splitting of
the data for better convergence. We give an encrypted multi-party protocol for
this non-homogeneous splitting of the Lasso problem to a non-homogeneous set of
servers: one powerful server and a few less powerful devices, added for
security reasons. Finally, we provide numerical results for our proposed
solutions.


Abstract:
  Many platforms for benchmarking optimization algorithms offer users the
possibility of sharing their experimental data with the purpose of promoting
reproducible and reusable research. However, different platforms use different
data models and formats, which drastically inhibits identification of relevant
data sets, their interpretation, and their interoperability. Consequently, a
semantically rich, ontology-based, machine-readable data model is highly
desired.
  We report in this paper on the development of such an ontology, which we name
OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides
the vocabulary needed for semantic annotation of the core entities involved in
the benchmarking process, such as algorithms, problems, and evaluation
measures. It also provides means for automated data integration, improved
interoperability, powerful querying capabilities and reasoning, thereby
enriching the value of the benchmark data. We demonstrate the utility of OPTION
by annotating and querying a corpus of benchmark performance data from the BBOB
workshop data - a use case which can be easily extended to cover other
benchmarking data collections.


Abstract:
  Transmission electron microscopy (TEM) is one of the primary tools to show
microstructural characterization of materials as well as film thickness.
However, manual determination of film thickness from TEM images is
time-consuming as well as subjective, especially when the films in question are
very thin and the need for measurement precision is very high. Such is the case
for head overcoat (HOC) thickness measurements in the magnetic hard disk drive
industry. It is therefore necessary to develop software to automatically
measure HOC thickness. In this paper, for the first time, we propose a HOC
layer segmentation method using NASNet-Large as an encoder and then followed by
a decoder architecture, which is one of the most commonly used architectures in
deep learning for image segmentation. To further improve segmentation results,
we are the first to propose a post-processing layer to remove irrelevant
portions in the segmentation result. To measure the thickness of the segmented
HOC layer, we propose a regressive convolutional neural network (RCNN) model as
well as orthogonal thickness calculation methods. Experimental results
demonstrate a higher dice score for our model which has lower mean squared
error and outperforms current state-of-the-art manual measurement.


Abstract:
  Objectives: Federal open data initiatives that promote increased sharing of
federally collected data are important for transparency, data quality, trust,
and relationships with the public and state, tribal, local, and territorial
(STLT) partners. These initiatives advance understanding of health conditions
and diseases by providing data to more researchers, scientists, and
policymakers for analysis, collaboration, and valuable use outside CDC
responders. This is particularly true for emerging conditions such as COVID-19
where we have much to learn and have evolving data needs. Since the beginning
of the outbreak, CDC has collected person-level, de-identified data from
jurisdictions and currently has over 8 million records, increasing each day.
This paper describes how CDC designed and produces two de-identified public
datasets from these collected data.
  Materials and Methods: Data elements were included based on the usefulness,
public request, and privacy implications; specific field values were suppressed
to reduce risk of reidentification and exposure of confidential information.
Datasets were created and verified for privacy and confidentiality using data
management platform analytic tools as well as R scripts.
  Results: Unrestricted data are available to the public through Data.CDC.gov
and restricted data, with additional fields, are available with a data use
agreement through a private repository on GitHub.com.
  Practice Implications: Enriched understanding of the available public data,
the methods used to create these data, and the algorithms used to protect
privacy of de-identified individuals allow for improved data use. Automating
data generation procedures allows greater and more timely sharing of data.


Abstract:
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive
machine type communications (mMTC) in 5G networks and beyond. In this paper,
NOMA is applied to improve the random access efficiency in high-density
spatially-distributed multi-cell wireless IoT networks, where IoT devices
contend for accessing the shared wireless channel using an adaptive
p-persistent slotted Aloha protocol. To enable a capacity-optimal network, a
novel formulation of random channel access management is proposed, in which the
transmission probability of each IoT device is tuned to maximize the geometric
mean of users' expected capacity. It is shown that the network optimization
objective is high dimensional and mathematically intractable, yet it admits
favourable mathematical properties that enable the design of efficient
data-driven algorithmic solutions which do not require a priori knowledge of
the channel model or network topology. A centralized model-based algorithm and
a scalable distributed model-free algorithm, are proposed to optimally tune the
transmission probabilities of IoT devices and attain the maximum capacity. The
convergence of the proposed algorithms to the optimal solution is further
established based on convex optimization and game-theoretic analysis. Extensive
simulations demonstrate the merits of the novel formulation and the efficacy of
the proposed algorithms.


Abstract:
  The performance of wireless networks is fundamentally limited by the
aggregate interference, which depends on the spatial distributions of the
interferers, channel conditions, and user traffic patterns (or queueing
dynamics). These factors usually exhibit spatial and temporal correlations and
thus make the performance of large-scale networks environment-dependent (i.e.,
dependent on network topology, locations of the blockages, etc.). The
correlation can be exploited in protocol designs (e.g., spectrum-, load-,
location-, energy-aware resource allocations) to provide efficient wireless
services. For this, accurate system-level performance characterization and
evaluation with spatio-temporal correlation are required. In this context,
stochastic geometry models and random graph techniques have been used to
develop analytical frameworks to capture the spatio-temporal interference
correlation in large-scale wireless networks. The objective of this article is
to provide a tutorial on the stochastic geometry analysis of large-scale
wireless networks that captures the spatio-temporal interference correlation
(and hence the signal-to-interference ratio (SIR) correlation). We first
discuss the importance of spatio-temporal performance analysis, different
parameters affecting the spatio-temporal correlation in the SIR, and the
different performance metrics for spatio-temporal analysis. Then we describe
the methodologies to characterize spatio-temporal SIR correlations for
different network configurations (independent, attractive, repulsive
configurations), shadowing scenarios, user locations, queueing behavior,
relaying, retransmission, and mobility. We conclude by outlining future
research directions in the context of spatio-temporal analysis of emerging
wireless communications scenarios.


Abstract:
  Vortex based spin torque nano oscillators (STVOs) can present more complex
dynamics than the spin torque induced gyrotropic (G) motion of the vortex core.
The respective dynamic modes and the transition between them can be controlled
by experimental parameters such as the applied dc current. An interesting
behavior is the stochastic transition from the G- to a dynamic C-state
occurring for large current densities. Moreover, the C-state oscillations
exhibit a constant active magnetic volume. We present noise measurements in the
different dynamic states that allow accessing specific properties of the
stochastic transition, such as the characteristic state transition frequency.
Furthermore,we confirm, as theoretically predicted, an increase of flicker
noise with $I_{dc}^2$ when the oscillation volume remains constant with the
current. These results bring insight into the potential optimization of noise
properties sought for many potential rf applications with spin torque
oscillators. Furthermore, the investigated stochastic characteristics open up
new potentialities, for instance in the emerging field of neuromorphic
computing schemes.


Abstract:
  Temperature is a deceptively simple concept that still raises deep questions
at the forefront of quantum physics research. The observation of thermalisation
in completely isolated quantum systems, such as cold-atom quantum simulators,
implies that a temperature can be assigned even to individual, pure quantum
states. Here, we propose a scheme to measure the temperature of such pure
states through quantum interference. Our proposal involves interferometry of an
auxiliary qubit probe, which is prepared in a superposition state and
subsequently undergoes decoherence due to weak coupling with a closed,
thermalised many-body system. Using only a few basic assumptions about chaotic
quantum systems -- namely, the eigenstate thermalisation hypothesis and the
emergence of hydrodynamics at long times -- we show that the qubit undergoes
pure exponential decoherence at a rate that depends on the temperature of its
surroundings. We verify our predictions by numerical experiments on a quantum
spin chain that thermalises after absorbing energy from a periodic drive. Our
work provides a general method to measure the temperature of isolated, strongly
interacting systems under minimal assumptions.


Abstract:
  The study of hyper-compact (HC) or ultra-compact (UC) HII regions is
fundamental to understanding the process of massive (> 8 M_sun) star formation.
We employed Atacama Large Millimeter/submillimeter Array (ALMA) 1.4 mm Cycle 6
observations to investigate at high angular resolution (~0.050", corresponding
to 330 au) the HC HII region inside molecular core A1 of the high-mass
star-forming cluster G24.78+0.08. We used the H30alpha emission and different
molecular lines of CH3CN and 13CH3CN to study the kinematics of the ionized and
molecular gas, respectively. At the center of the HC HII region, at radii <~500
au, we observe two mutually perpendicular velocity gradients, which are
directed along the axes at PA = 39 deg and PA = 133 deg, respectively. The
velocity gradient directed along the axis at PA = 39 deg has an amplitude of 22
km/s mpc^(-1), which is much larger than the other's, 3 km/s mpc^(-1). We
interpret these velocity gradients as rotation around, and expansion along, the
axis at PA = 39 deg. We propose a scenario where the H30alpha line traces the
ionized heart of a disk-jet system that drives the formation of the massive
star (~20 M_sun) responsible for the HC HII region. Such a scenario is also
supported by the position-velocity plots of the CH3CN and 13CH3CN lines along
the axis at PA = 133 deg, which are consistent with Keplerian rotation around a
20 M_sun star. Toward the HC HII region in G24.78+0.08, the coexistence of mass
infall (at radii of ~5000 au), an outer molecular disk (from <~4000 au to >~500
au), and an inner ionized disk (<~500 au) indicates that the massive ionizing
star is still actively accreting from its parental molecular core. To our
knowledge, this is the first example of a molecular disk around a high-mass
forming star that, while becoming internally ionized after the onset of the HII
region, continues to accrete mass onto the ionizing star.


Abstract:
  Numerous congruences for partitions with designated summands have been proven
since first being introduced and studied by Andrews, Lewis, and Lovejoy. This
paper explicitly characterizes the number of partitions with designated
summands whose parts are not divisible by $2^\ell$, $2$, and $3^\ell$ working
modulo $2,\ 4,$ and $3$, respectively, greatly extending previous results on
the subject. We provide a few applications of our characterizations throughout
in the form of congruences and a computationally fast recurrence. Moreover, we
illustrate a previously undocumented connection between the number of
partitions with designated summands and the number of partitions with odd
multiplicities.


Abstract:
  The quadratic rough Heston model provides a natural way to encode Zumbach
effect in the rough volatility paradigm. We apply multi-factor approximation
and use deep learning methods to build an efficient calibration procedure for
this model. We show that the model is able to reproduce very well both SPX and
VIX implied volatilities. We typically obtain VIX option prices within the
bid-ask spread and an excellent fit of the SPX at-the-money skew. Moreover, we
also explain how to use the trained neural networks for hedging with
instantaneous computation of hedging quantities.


Abstract:
  Compact objects inspiraling into supermassive black holes, known as
extreme-mass-ratio inspirals, are an important source for future space-borne
gravitational-wave detectors. When constructing waveform templates, usually the
adiabatic approximation is employed to treat the compact object as a test
particle for a short duration, and the radiation reaction is reflected in the
changes of the constants of motion. However, the mass of the compact object
should have contributions to the background. In the present paper, employing
the effective-one-body formalism, we analytically calculate the trajectories of
a compact object around a massive Kerr black hole with generally
three-dimensional orbits and express the fundamental orbital frequencies in
explicit forms. In addition, by constructing an approximate "constant" similar
to the Carter constant, we transfer the dynamical quantities such as energy,
angular momentum, and the "Carter constant" to the semilatus rectum,
eccentricity, and orbital inclination with mass-ratio corrections. The linear
mass-ratio terms in the formalism may not be sufficient for accurate waveforms,
but our analytical method for solving the equations of motion could be useful
in various approaches to building waveform models.


Abstract:
  We study the electronic phase diagram of the excitonic insulator candidates
Ta$_2$Ni(Se$_{1-x}$S$_x$)$_5$ [x=0, ... ,1] using Raman spectroscopy. Critical
excitonic fluctuations are observed, that diminish with $x$ and ultimately
shift to high energies, characteristic of a quantum phase transition.
Nonetheless, a symmetry-breaking transition at finite temperatures is detected
for all $x$, exposing a cooperating lattice instability that takes over for
large $x$. Our study reveals a failed excitonic quantum phase transition,
masked by a preemptive structural order.


Abstract:
  Spectropolarimetric measurements of gamma-ray burst (GRB) optical afterglows
contain polarization information for both continuum and absorption lines. Based
on the Zeeman effect, an absorption line in a strong magnetic field is
polarized and split into a triplet. In this paper, we solve the polarization
radiative transfer equations of the absorption lines, and obtain the degree of
linear polarization of the absorption lines as a function of the optical depth.
In order to effectively measure the degree of linear polarization for the
absorption lines, a magnetic field strength of at least $10^3$ G is required.
The metal elements that produce the polarized absorption lines should be
sufficiently abundant and have large oscillation strengths or Einstein
absorption coefficients. We encourage both polarization measurements and
high-dispersion observations of the absorption lines in order to detect the
triplet structure in early GRB optical afterglows.


Abstract:
  Existing deterministic variational inference approaches for diffusion
processes use simple proposals and target the marginal density of the
posterior. We construct the variational process as a controlled version of the
prior process and approximate the posterior by a set of moment functions. In
combination with moment closure, the smoothing problem is reduced to a
deterministic optimal control problem. Exploiting the path-wise Fisher
information, we propose an optimization procedure that corresponds to a natural
gradient descent in the variational parameters. Our approach allows for richer
variational approximations that extend to state-dependent diffusion terms. The
classical Gaussian process approximation is recovered as a special case.


Abstract:
  In this paper, a distributed formation flight control topology for
Leader-Follower formation structure is presented. Such topology depends in the
first place on online generation of the trajectories that should be followed by
the agents in the formation. The trajectory of each agent is planned during
execution depending on its neighbors and considering that the desired reference
trajectory is only given to the leader. Simulation using MATLAB/SIMULINK is
done on a formation of quadrotor UAVs to illustrate the proposed method. The
topology shows very good results in achieving the formation and following the
reference trajectory.


Abstract:
  A theory of a pseudogap phase of high-temperature superconductors where
current carriers are translation invariant bipolarons is developed. A
temperature T* of a transition from a pseudogap phase to a normal one is
calculated. For the temperature of a transition to the pseudogap phase, the
isotope coefficient is found. It is shown that the results obtained, in
particular, the possibility of negative values of the isotope coefficient are
consistent with the experiment. New experiments on the influence of the
magnetic field on the isotope coefficient are proposed.


Abstract:
  FloodNet is a high-resolution image dataset acquired by a small UAV platform,
DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a
unique challenge of advancing the damage assessment process for post-disaster
scenarios using unlabeled and limited labeled dataset. We propose a solution to
address their classification and semantic segmentation challenge. We approach
this problem by generating pseudo labels for both classification and
segmentation during training and slowly incrementing the amount by which the
pseudo label loss affects the final loss. Using this semi-supervised method of
training helped us improve our baseline supervised loss by a huge margin for
classification, allowing the model to generalize and perform better on the
validation and test splits of the dataset. In this paper, we compare and
contrast the various methods and models for image classification and semantic
segmentation on the FloodNet dataset.