Abstract:
  A Lagrangian experimental study of an axisymmetric turbulent water jet is
performed to investigate the highly anisotropic and inhomogeneous flow field.
The measurements were conducted within a Lagrangian exploration module, an
icosahedron apparatus, to facilitate optical access of three cameras. The
stereoscopic particle tracking velocimetry results in three component tracks of
position, velocity and acceleration of the tracer particles within the
vertically-oriented jet with a Taylor-based Reynolds number $\mathcal R_\lambda
\simeq 230$. Analysis is performed at seven locations from 15 diameters up to
45 diameters downstream. Eulerian analysis is first carried out to obtain
critical parameters of the jet and relevant scales, namely the Kolmogorov and
large turnover (integral) scales as well as the energy dissipation rate.
Lagrangian statistical analysis is then performed on velocity components
stationarised following methods inspired by Batchelor (\textit{J. Fluid Mech.},
vol. 3, 1957, pp. 67-80) which aim to extend stationary Lagrangian theory of
turbulent diffusion by Taylor to the case of self-similar flows. The evolution
of typical Lagrangian scaling parameters as a function of the developing jet is
explored and results show validation of the proposed stationarisation. The
universal scaling constant $C_0$ (for the Lagrangian second-order structure
function), as well as Eulerian and Lagrangian integral time scales are
discussed in this context. $C_0$ is found to converge to a constant value (of
the order of $C_0 = 3$) within 30 diameters downstream of the nozzle. Finally,
the existence of finite particle size effects are investigated through
consideration of acceleration dependent quantities.


Abstract:
  The article presents and evaluates a scalable algorithm for validating
solutions of linear programming problems on cluster computing systems. The main
idea of the method is to generate a regular set of points (validation set) on a
small-radius hypersphere centered at the point of the solution under
validation. The objective function is calculated for each point of the
validation set that belongs to the feasible region. If all these values are
less than or equal to the value of the objective function at the point under
validation, then this point is the correct solution. The parallel
implementation of the VaLiPro algorithm is performed in C++ through the
parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based
parallelization of the program. We provide the results of large-scale
computational experiments on a cluster computing system to study the
scalability of the VaLiPro algorithm.


Abstract:
  We present measurements of partial branching fractions of inclusive
semileptonic $B \to X_u \, \ell^+\, \nu_{\ell}$ decays using the full Belle
data set of 711 fb$^{-1}$ of integrated luminosity at the $\Upsilon(4S)$
resonance and for $\ell = e, \mu$. Inclusive semileptonic $B \to X_u \,
\ell^+\, \nu_{\ell}$ decays are CKM suppressed and measurements are complicated
by the large background from CKM-favored $B \to X_c \, \ell^+\, \nu_{\ell}$
transitions, which have a similar signature. Using machine learning techniques,
we reduce this and other backgrounds effectively, whilst retaining access to a
large fraction of the $B \to X_u \, \ell^+\, \nu_{\ell}$ phase space and high
signal efficiency. We measure partial branching fractions in three phase-space
regions covering about $31\%$ to $86\%$ of the accessible $B \to X_u \,
\ell^+\, \nu_{\ell}$ phase space. The most inclusive measurement corresponds to
the phase space with lepton energies of $E_\ell^B > 1 $ GeV, and we obtain
$\Delta \mathcal{B}(B \to X_u \ell^+ \, \nu_\ell) = \left( 1.59 \pm 0.07 \pm
0.16 \right) \times 10^{-3}$ from a two-dimensional fit of the hadronic mass
spectrum and the four-momentum-transfer squared distribution, with the
uncertainties denoting the statistical and systematic error. We find $\left|
V_{ub} \right| = \left( 4.10 \pm 0.09 \pm 0.22 \pm 0.15 \right) \times 10^{-3}$
from an average of four calculations for the partial decay rate with the third
uncertainty denoting the average theory error. This value is higher but
compatible with the determination from exclusive semileptonic decays within 1.3
standard deviations. In addition, we report charmless inclusive partial
branching fractions separately for $B^+$ and $B^0$ mesons as well as for
electron and muon final states. No isospin breaking or lepton flavor
universality violating effects are observed.


Abstract:
  In this work, we are interested in the detailed analysis of complexity
aspects of both time and space that arises from the implementation of a quantum
algorithm on a quantum based hardware. In particular, some steps of the
implementation, as state preparation and readout processes, in most of the
cases can surpass the complexity aspects of the algorithm itself. We present
the complexity involved in the full implementation of quantum algorithms for
solving linear systems of equations and linear system of differential
equations, from state preparation to the number of measurements needed to
obtain good statistics from the final states of the quantum system, in order to
assess the overall complexity of the processes.


Abstract:
  Reinforcement learning (RL) is empirically successful in complex nonlinear
Markov decision processes (MDPs) with continuous state spaces. By contrast, the
majority of theoretical RL literature requires the MDP to satisfy some form of
linear structure, in order to guarantee sample efficient RL. Such efforts
typically assume the transition dynamics or value function of the MDP are
described by linear functions of the state features. To resolve this
discrepancy between theory and practice, we introduce the Effective Planning
Window (EPW) condition, a structural condition on MDPs that makes no linearity
assumptions. We demonstrate that the EPW condition permits sample efficient RL,
by providing an algorithm which provably solves MDPs satisfying this condition.
Our algorithm requires minimal assumptions on the policy class, which can
include multi-layer neural networks with nonlinear activation functions.
Notably, the EPW condition is directly motivated by popular gaming benchmarks,
and we show that many classic Atari games satisfy this condition. We
additionally show the necessity of conditions like EPW, by demonstrating that
simple MDPs with slight nonlinearities cannot be solved sample efficiently.


Abstract:
  Speaker counting is the task of estimating the number of people that are
simultaneously speaking in an audio recording. For several audio processing
tasks such as speaker diarization, separation, localization and tracking,
knowing the number of speakers at each timestep is a prerequisite, or at least
it can be a strong advantage, in addition to enabling a low latency processing.
In a previous work, we addressed the speaker counting problem with a
multichannel convolutional recurrent neural network which produces an
estimation at a short-term frame resolution. In this work, we show that, for a
given frame, there is an optimal position in the input sequence for best
prediction accuracy. We empirically demonstrate the link between that optimal
position, the length of the input sequence and the size of the convolutional
filters.


Abstract:
  We consider the p-ordered characteristic function and its Fourier transform,
the quasidistribution function, of squeezed coherent photons in a thermal state
of photons and calculate the mean number and number variance of squeezed
coherent photons. All the properties calculated from the previous
characteristic function can be used to calculate said properties for photons in
a thermal state of squeezed coherent photons via a parametric transformation.
In particular, one can obtain the mean number and number variance via the
parametric transformation.


Abstract:
  The spread of the coronavirus disease 2019 (COVID-19) caused by severe acute
respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a global health
crisis. The binding affinity of SARS-CoV-2 (in particular the receptor binding
domain, RBD) to its receptor angiotensin converting enzyme 2 (ACE2) and the
antibodies is of great importance in understanding the infectivity of COVID-19
and evaluating the candidate therapeutic for COVID-19. In this work, we propose
a new method based on molecular mechanics/Poisson-Boltzmann surface area
(MM/PBSA) to accurately calculate the free energy of SARS-CoV-2 RBD binding to
ACE2 and antibodies. The calculated binding free energy of SARS-CoV-2 RBD to
ACE2 is -13.3 kcal/mol, and that of SARS-CoV RBD to ACE2 is -11.4 kcal/mol,
which agrees well with experimental result (-11.3 kcal/mol and -10.1 kcal/mol,
respectively). Moreover, we take two recently reported antibodies as the
example, and calculate the free energy of antibodies binding to SARS-CoV-2 RBD,
which is also consistent with the experimental findings. Further, within the
framework of the modified MM/PBSA, we determine the key residues and the main
driving forces for the SARS-CoV-2 RBD/CB6 interaction by the computational
alanine scanning method. The present study offers a computationally efficient
and numerically reliable method to evaluate the free energy of SARS-CoV-2
binding to other proteins, which may stimulate the development of the
therapeutics against the COVID-19 disease in real applications.


Abstract:
  When the users in a MIMO broadcast channel experience different spatial
transmit correlation matrices, a class of gains is produced that is denoted
transmit correlation diversity. This idea was conceived for channels in which
transmit correlation matrices have mutually exclusive eigenspaces, allowing
non-interfering training and transmission. This paper broadens the scope of
transmit correlation diversity to the case of partially and fully overlapping
eigenspaces and introduces techniques to harvest these generalized gains. For
the two-user MIMO broadcast channel, we derive achievable degrees of freedom
(DoF) and achievable rate regions with/without channel state information at the
receiver (CSIR). When CSIR is available, the proposed achievable DoF region is
tight in some configurations of the number of receive antennas and the channel
correlation ranks. We then extend the DoF results to the $K$-user case by
analyzing the interference graph that characterizes the overlapping structure
of the eigenspaces. Our achievability results employ a combination of product
superposition in the common part of the eigenspaces, and pre-beamforming (rate
splitting) to create multiple data streams in non-overlapping parts of the
eigenspaces. Massive MIMO is a natural example in which spatially correlated
link gains are likely to occur. We study the achievable downlink sum rate for a
frequency-division duplex massive MIMO system under transmit correlation
diversity.


Abstract:
  Constructing good representations is critical for learning complex tasks in a
sample efficient manner. In the context of meta-learning, representations can
be constructed from common patterns of previously seen tasks so that a future
task can be learned quickly. While recent works show the benefit of
subspace-based representations, such results are limited to linear-regression
tasks. This work explores a more general class of nonlinear tasks with
applications ranging from binary classification, generalized linear models and
neural nets. We prove that subspace-based representations can be learned in a
sample-efficient manner and provably benefit future tasks in terms of sample
complexity. Numerical results verify the theoretical predictions in
classification and neural-network regression tasks.


Abstract:
  One of the top remaining science challenges in astronomical optics is the
direct imaging and characterization of extrasolar planets and planetary
systems. Directly imaging exoplanets from ground-based observatories requires
combining high-order adaptive optics with a stellar coronagraph observing at
wavelengths ranging from the visible to the mid-IR. A limiting factor in
achieving the required contrast (planet-to-star intensity ratio) is
quasi-static speckles, caused largely by non-common path aberrations (NCPA) in
the coronagraph. Starting with a realistic simulator of a telescope with an AO
system and a coronagraph, this article provides simulations of several closely
related millisecond regression models requiring inputs of the measured
wavefronts and science camera images. The simplest regression model, called the
naive estimator, does not treat the noise and other sources of information loss
in the WFS. The naive estimator provided a useful estimate of the NCPA of
$\sim$ 0.5 radian RMS, with an accuracy of $\sim$ 0.06 radian RMS in one minute
of simulated sky time on a magnitude 8 star. The bias-corrected estimator
generalizes the regression model to account for the noise and information loss
in the WFS. A simulation of the bias-corrected estimator with four minutes of
sky time included an NCPA of $\sim 0.05 \,$ radian RMS and an extended
exoplanet scene. The joint regression of the bias-corrected estimator
simultaneously achieved an NCPA estimate with an accuracy of $\sim
5\times10^{-3} \,$radian and contrast of $\sim 10^{-5}$ on the exoplanet scene.
In addition, the estimate of the exoplanet image was completely free of the
subtraction artifacts that always plague differential imaging. The estimate of
the exoplanet image obtained by the joint regression was nearly identical to
the image obtained by subtraction of a perfectly known point-spread function.


Abstract:
  Learning from streaming tasks leads a model to catastrophically erase unique
experiences it absorbs from previous episodes. While regularization techniques
such as LWF, SI, EWC have proven themselves as an effective avenue to overcome
this issue by constraining important parameters of old tasks from changing when
accepting new concepts, these approaches do not exploit common information of
each task which can be shared to existing neurons. As a result, they do not
scale well to large-scale problems since the parameter importance variables
quickly explode. An Inter-Task Synaptic Mapping (ISYANA) is proposed here to
underpin knowledge retention for continual learning. ISYANA combines
task-to-neuron relationship as well as concept-to-concept relationship such
that it prevents a neuron to embrace distinct concepts while merely accepting
relevant concept. Numerical study in the benchmark continual learning problems
has been carried out followed by comparison against prominent continual
learning algorithms. ISYANA exhibits competitive performance compared to state
of the arts. Codes of ISYANA is made available in
\url{https://github.com/ContinualAL/ISYANAKBS}.


Abstract:
  Let $k$ be an infinite field of characteristic 0, and $X$ a del Pezzo surface
of degree $d$ with at least one $k$-rational point. Various methods from
algebraic geometry and arithmetic statistics have shown the Zariski density of
the set $X(k)$ of $k$-rational points in $X$ for $d\geq2$ (under an extra
condition for $d=2$), but fail to work in generality when the degree of $X$ is
1, leaving a large class of del Pezzo surfaces for which the question of
density of rational points is still open. In this paper, we prove the Zariski
density of $X(k)$ when $X$ has degree 1 and is represented in the weighted
projective space $\mathbb{P}(2,3,1,1)$ with coordinates $x,y,z,w$ by an
equation of the form $y^2=x^3+az^6+bz^3w^3+cw^6$ for $a,b,c\in k$ with $a,c$
non-zero, under the condition that the elliptic surface obtained by blowing up
the base point of the anticanonical linear system $|-K_X|$ contains a smooth
fiber above a point in $\mathbb{P}^1\setminus\{(1:0),(0:1)\}$ with positive
rank over $k$. When $k$ is of finite type over $\mathbb{Q}$, this condition is
sufficient and necessary.


Abstract:
  We consider decentralized stochastic optimization problems where a network of
agents each owns a local cost function cooperate to find a minimizer of the
global-averaged cost. A widely studied decentralized algorithm for this problem
is D-SGD in which each node applies a stochastic gradient descent step, then
averages its estimate with its neighbors. D-SGD is attractive due to its
efficient single-iteration communication and can achieve linear speedup in
convergence (in terms of the network size). However, D-SGD is very sensitive to
the network topology. For smooth objective functions, the transient stage
(which measures how fast the algorithm can reach the linear speedup stage) of
D-SGD is on the order of $O(n/(1-\beta)^2)$ and $O(n^3/(1-\beta)^4)$ for
strongly convex and generally convex cost functions, respectively, where
$1-\beta \in (0,1)$ is a topology-dependent quantity that approaches $0$ for a
large and sparse network. Hence, D-SGD suffers from slow convergence for large
and sparse networks.
  In this work, we study the non-asymptotic convergence property of the
D$^2$/Exact-diffusion algorithm. By eliminating the influence of data
heterogeneity between nodes, D$^2$/Exact-diffusion is shown to have an enhanced
transient stage that are on the order of $O(n/(1-\beta))$ and
$O(n^3/(1-\beta)^2)$ for strongly convex and generally convex cost functions,
respectively. Moreover, we provide a lower bound of the transient stage of
D-SGD under homogeneous data distributions, which coincides with the transient
stage of D$^2$/Exact-diffusion in the strongly-convex setting. These results
show that removing the influence of data heterogeneity can ameliorate the
network topology dependence of D-SGD. Compared with existing decentralized
algorithms bounds, D$^2$/Exact-diffusion is least sensitive to network
topology.


Abstract:
  We give an example of a semisimple symmetric space $G/H$ and an irreducible
representation of $G$ which has multiplicity 1 in $L^2(G/H)$ and multiplicity 2
in $C^\infty(G/H)$.


Abstract:
  This paper studies video inpainting detection, which localizes an inpainted
region in a video both spatially and temporally. In particular, we introduce
VIDNet, Video Inpainting Detection Network, which contains a two-stream
encoder-decoder architecture with attention module. To reveal artifacts encoded
in compression, VIDNet additionally takes in Error Level Analysis frames to
augment RGB frames, producing multimodal features at different levels with an
encoder. Exploring spatial and temporal relationships, these features are
further decoded by a Convolutional LSTM to predict masks of inpainted regions.
In addition, when detecting whether a pixel is inpainted or not, we present a
quad-directional local attention module that borrows information from its
surrounding pixels from four directions. Extensive experiments are conducted to
validate our approach. We demonstrate, among other things, that VIDNet not only
outperforms by clear margins alternative inpainting detection methods but also
generalizes well on novel videos that are unseen during training.


Abstract:
  Recent DFT calculations for Ba2CoO4 (BCO) and neutron scattering experiments
for SrRuO3 (SRO) have shown that oxygen develops a magnetic polarization.
Moreover, DFT calculations for these compounds also unveiled unexpected nodes
in the spin density, both along Co-O and Ru-O. For BCO, the overall
antiferromagnetic state in its triangular lattice contains unusual zigzag spin
patterns. Here, using simple model calculations supplemented by DFT we explain
and extend these results. We predict that ligands that in principle should be
spinless, such as O$^{2-}$, will develop a net polarization when they act as
electronic bridges between transition metal (TM) spins ferromagnetically
ordered, regardless of the number of intermediate ligand atoms. The reason is
the hybridization between atoms and mobility of the electrons with spins
opposite to those of the closest TM atoms. Moreover, for bonds with TMs
antiferromagnetically ordered, counterintuitively our calculations show that
oxygens should also have a net magnetization for the super-super-exchange cases
TM-O-O-TM while for only one oxygen, as in Cu-O-Cu, the O-polarization should
cancel. Our simple model also allows us to explain the presence of nodes based
on the antibonding character of the dominant singly occupied molecular orbitals
along the TM-O bonds. Finally, the zigzag pattern order becomes the ground
state mainly due to the influence of the Hubbard $U$, that creates the moments,
in combination with a robust easy-axis anisotropy that suppresses the competing
120$^{\circ}$ degree antiferromagnetic order of a triangular lattice. Our
predictions are generic and should be applicable to any other compound with
characteristics similar to those of BCO and SRO.


Abstract:
  Assuming that the free energy of a gas depends non-locally on the logarithm
of its mass density, the body force in the resulting equation of motion
consists of the sum of density gradient terms. Truncating this series after the
second term, Bohm's quantum potential and the Madelung equation are identically
obtained, showing explicitly that some of the hypotheses that led to the
formulation of quantum mechanics admit a classical interpretation based on
non-locality.


Abstract:
  For research results to be comparable, it is important to have common
datasets for experimentation and evaluation. The size of such datasets,
however, can be an obstacle to their use. The Vimeo Creative Commons Collection
(V3C) is a video dataset designed to be representative of video content found
on the web, containing roughly 3800 hours of video in total, split into three
shards. In this paper, we present insights on the second of these shards (V3C2)
and discuss their implications for research areas, such as video retrieval, for
which the dataset might be particularly useful. We also provide all the
extracted data in order to simplify the use of the dataset.


Abstract:
  In comparative effectiveness research, treated and control patients might
have a different start of follow up, e.g. when treatment is started later in
the disease trajectory. When the follow up period of control patients starts
earlier in the disease trajectory than follow up of the treated, estimation of
the treatment effect suffers from different types of survival/survivorship
bias. We study unobserved heterogeneity and illustrate how failing to account
for the time difference in recruitment between treated and controls leads to
bias in the estimated treatment effect. We explore five methods to adjust for
this survivorship bias by including the time between diagnosis and treatment
initiation (wait time) in the analysis in different ways. We first conducted a
simulation study on whether these methods reduce survivorship bias, then
applied our methods to fertility data on insemination. The five methods were:
first, to regress on wait time as an additional covariate in the analysis
model. Second, to match on wait time. Third, to select treated who started
treatment immediately. Fourth, to select controls who survived up to the median
time of treatment initiation. Fifth, to consider the wait time as the time of
left truncation. All methods reduced survivorship bias in the simulation. In
the application to fertility data, failing to adjust for survivorship bias led
to a hazard ratio (HR) of 1.63 (95%CI: 1.13-1.65) whereas a HR of 2.15
(1.71-2.69) was expected when using a time-varying covariate for treatment,
which in this prospective cohort coincided with the left truncation approach.
In agreement with our simulations, the method in which adjustment corrected the
HR upwards the most was left truncation. We conclude that the wait time between
diagnosis and treatment initiation should be taken into account in the analysis
to respect the chronology of the disease and treatment trajectory.


Abstract:
  An open challenge in physics is to expand the frontiers of the validity of
quantum mechanics by evidencing nonclassicality of the centre of mass state of
a macroscopic object. Yet another equally important task is to evidence the
essential nonclassicality of the interactions which act between macroscopic
objects. Here we introduce a new tool to meet these challenges: massive spatial
qubits. In particular, we show that if two distinct localized states of a mass
are used as the $|0\rangle$ and $|1\rangle$ states of a qubit, then we can
measure this encoded spatial qubit with a high fidelity in the $\sigma_x,
\sigma_y$ and $\sigma_z$ bases simply by measuring its position after different
durations of free evolution. We show how this technique can be used to reveal
an irreducible nonclassicality through a Bell-inequality violation arising from
the entanglement of the centre of mass of a nano-crystal with its spin in a
Stern-Gerlach setup. Secondly, we show how our methodology, in conjuction with
the Casimir interaction, offers a powerful method to create and certify
non-Gaussian entanglement between two neutral nano-objects. Fundamentally, the
generation of such an entanglement provides an empirical means for
demonstrating an inherent quantumness of the Casimir interaction.


Abstract:
  In this paper, we survey some recent results on statistical inference
(parametric and nonparametric statistical estimation, hypotheses testing) about
the spectrum of stationary models with tapered data, as well as, a question
concerning robustness of inferences, carried out on a linear stationary process
contaminated by a small trend. We also discuss some question concerning tapered
Toeplitz matrices and operators, central limit theorems for tapered Toeplitz
type quadratic functionals, and tapered Fej\'er-type kernels and singular
integrals. These are the main tools for obtaining the corresponding results,
and also are of interest in themselves. The processes considered will be
discrete-time and continuous-time Gaussian, linear or L\'evy-driven linear
processes with memory.


Abstract:
  Machine learning-based malware detection systems are often vulnerable to
evasion attacks, in which a malware developer manipulates their malicious
software such that it is misclassified as benign. Such software hides some
properties of the real class or adopts some properties of a different class by
applying small perturbations. A special case of evasive malware hides by
repackaging a bonafide benign mobile app to contain malware in addition to the
original functionality of the app, thus retaining most of the benign properties
of the original app. We present a novel malware detection system based on
metamorphic testing principles that can detect such benign-seeming malware
apps. We apply metamorphic testing to the feature representation of the mobile
app rather than to the app itself. That is, the source input is the original
feature vector for the app and the derived input is that vector with selected
features removed. If the app was originally classified benign and is indeed
benign, the output for the source and derived inputs should be the same class,
i.e., benign, but if they differ, then the app is exposed as likely malware.
Malware apps originally classified as malware should retain that classification
since only features prevalent in benign apps are removed. This approach enables
the machine learning model to classify repackaged malware with reasonably few
false negatives and false positives. Our training pipeline is simpler than many
existing ML-based malware detection methods, as the network is trained
end-to-end to learn appropriate features and perform classification. We
pre-trained our classifier model on 3 million apps collected from the
widely-used AndroZoo dataset. We perform an extensive study on other publicly
available datasets to show our approach's effectiveness in detecting repackaged
malware with more than94% accuracy, 0.98 precision, 0.95 recall, and 0.96 F1
score.


Abstract:
  Consider the compressed sensing setup where the support $s^*$ of an
$m$-sparse $d$-dimensional signal $x$ is to be recovered from $n$ linear
measurements with a given algorithm. Suppose that the measurements are such
that the algorithm does not guarantee perfect support recovery and that true
features may be missed. Can they efficiently be retrieved? This paper addresses
this question through a simple error-correction module referred to as LiRE.
LiRE takes as input an estimate $s_{in}$ of the true support $s^*$, and outputs
a refined support estimate $s_{out}$. In the noiseless measurement setup,
sufficient conditions are established under which LiRE is guaranteed to recover
the entire support, that is $s_{out}$ contains $s^*$. These conditions imply,
for instance, that in the high-dimensional regime LiRE can correct a sublinear
in $m$ number of errors made by Orthogonal Matching Pursuit (OMP). The
computational complexity of LiRE is $O(mnd)$. Experimental results with random
Gaussian design matrices show that LiRE substantially reduces the number of
measurements needed for perfect support recovery via Compressive Sampling
Matching Pursuit, Basis Pursuit (BP), and OMP. Interestingly, adding LiRE to
OMP yields a support recovery procedure that is more accurate and significantly
faster than BP. This observation carries over in the noisy measurement setup.
Finally, as a standalone support recovery algorithm with a random
initialization, experiments show that LiRE's reconstruction performance lies
between OMP and BP. These results suggest that LiRE may be used generically, on
top of any suboptimal baseline support recovery algorithm, to improve support
recovery or to operate with a smaller number of measurements, at the cost of a
relatively small computational overhead. Alternatively, LiRE may be used as a
standalone support recovery algorithm that is competitive with respect to OMP.


Abstract:
  Clinical images are vital for diagnosing and monitoring skin diseases, and
their importance has increased with the growing popularity of machine learning.
Lack of standards has stifled innovation in dermatological imaging, unlike
other image-intensive specialties such as radiology. We investigate the
meta-requirements for utilizing the popular DICOM standard for metadata
management of images in dermatology. We propose practical design solutions and
provide open-source tools to integrate dermatologists' workflow with enterprise
imaging systems. Using the tool, dermatologists can tag, search, organize and
convert clinical images to the DICOM format. We believe that our less
disruptive approach will improve the adoption of standards in the specialty.


Abstract:
  Drug-target interaction (DTI) prediction plays a crucial role in drug
discovery, and deep learning approaches have achieved state-of-the-art
performance in this field. We introduce an ensemble of deep learning models
(EnsembleDLM) for DTI prediction. EnsembleDLM only uses the sequence
information of chemical compounds and proteins, and it aggregates the
predictions from multiple deep neural networks. This approach not only achieves
state-of-the-art performance in Davis and KIBA datasets but also reaches
cutting-edge performance in the cross-domain applications across different
bio-activity types and different protein classes. We also demonstrate that
EnsembleDLM achieves a good performance (Pearson correlation coefficient and
concordance index > 0.8) in the new domain with approximately 50% transfer
learning data, i.e., the training set has twice as much data as the test set.


Abstract:
  This article presents a new way to classify geodesics on a cone in the
Euclidean 3-space. This proof is obtained considering our main result, which
establishes the necessary and sufficient conditions that a curve in space must
satisfy: to be rectifying curve or that its trace is contained in the sphere
centered at the origin.


Abstract:
  Generative adversarial networks (GANs) are often billed as "universal
distribution learners", but precisely what distributions they can represent and
learn is still an open question. Heavy-tailed distributions are prevalent in
many different domains such as financial risk-assessment, physics, and
epidemiology. We observe that existing GAN architectures do a poor job of
matching the asymptotic behavior of heavy-tailed distributions, a problem that
we show stems from their construction. Additionally, when faced with the
infinite moments and large distances between outlier points that are
characteristic of heavy-tailed distributions, common loss functions produce
unstable or near-zero gradients. We address these problems with the Pareto GAN.
A Pareto GAN leverages extreme value theory and the functional properties of
neural networks to learn a distribution that matches the asymptotic behavior of
the marginal distributions of the features. We identify issues with standard
loss functions and propose the use of alternative metric spaces that enable
stable and efficient learning. Finally, we evaluate our proposed approach on a
variety of heavy-tailed datasets.


Abstract:
  Photonic bound states in the continuum (BICs) are special localized and
non-decaying states of a photonic system with a frequency embedded into the
spectrum of scattered states. The simplest photonic structure displaying a
single BIC is provided by two waveguides side-coupled to a common waveguide
lattice, where the BIC is protected by symmetry. Here we consider such a simple
photonic structure and show that, breaking mirror symmetry and allowing for
non-nearest neighbor couplings, a doublet of quasi-BIC states can be sustained,
enabling weakly-damped embedded Rabi oscillations of photons between the
waveguides.


Abstract:
  This paper proposes a dynamic process of portfolio risk measurement to
address potential information loss. The proposed model takes advantage of
financial big data to incorporate out-of-target-portfolio information that may
be missed when one considers the Value at Risk (VaR) measures only from certain
assets of the portfolio. We investigate how the curse of dimensionality can be
overcome in the use of financial big data and discuss where and when benefits
occur from a large number of assets. In this regard, the proposed approach is
the first to suggest the use of financial big data to improve the accuracy of
risk analysis. We compare the proposed model with benchmark approaches and
empirically show that the use of financial big data improves small portfolio
risk analysis. Our findings are useful for portfolio managers and financial
regulators, who may seek for an innovation to improve the accuracy of portfolio
risk estimation.


Abstract:
  Axion-like particles (ALPs) appear in various new physics models with
spontaneous global symmetry breaking. When the ALP mass is in the range of MeV
to GeV, the cosmology and astrophysics bounds are so far quite weak. In this
work, we investigate such light ALPs through the ALP-strahlung production
processes $pp \to W^\pm a, Z a$ with the sequential decay $a \to \gamma\gamma$
at the 14 TeV LHC with an integrated luminosity of 3000 fb$^{-1}$ (HL-LHC).
Building on the concept of jet image which uses calorimeter towers as the
pixels of the image and measures a jet as an image, we investigate the
potential of machine learning techniques based on convolutional neural network
(CNN) to identify the highly boosted ALPs which decay to a pair of highly
collimated photons. With the CNN tagging algorithm, we demonstrate that our
approach can extend current LHC sensitivity and probe the ALP mass range from
0.3~GeV to 5~GeV. The obtained bounds are stronger than the existing limits on
the ALP-photon coupling.


Abstract:
  We consider an exponentially growing population of cells undergoing mutations
and ask about the effect of reproductive fluctuations (genetic drift) on its
long-term evolution. We combine first step analysis with the stochastic
dynamics of a birth-death process to analytically calculate the probability
that the parent of a given genotype will go extinct. We compare the results
with numerical simulations and show how this turnover of genetic clones can be
used to infer the rates underlying the population dynamics. Our work is
motivated by growing populations of tumour cells, the epidemic spread of
viruses, and bacterial growth.


Abstract:
  The MOS devices are the basic building block of any digital and analog
circuits, where silicon (Si) is the most commonly used material. The
International Technology Roadmap Semiconductor (ITRS) report predicts the gate
length of the MOS device will shrink to 4.5 nm up to 2023, this may create
severe short channel effects (SCEs). Therefore, new channel materials have been
realized, which have shown their potential to maintain the proper balance
between device performance and SECs. Among them, graphene has shown its strong
presence as an alternative channel material in terms of its fascinating
electrical and mechanical properties. It has ultra-high carrier mobility
(77,000 cm2V-1s-1) and saturation velocity, which makes it compatible with
high-speed circuit applications. This review paper has a detailed report on the
several analytical modeling approaches for graphene-based FET device, which
includes the drift-diffusion, gradual channel approximation, virtual source,
and ballistic approaches. The device modeling plays an important role to
predict the device performance and used to reveal the physics behind it. In
addition, the compact model of the device will use in the development of
electronic design automation (EDA) tools, which are used for the circuits
simulation.


Abstract:
  Quantum error correction is essential for robust quantum information
processing with noisy devices. As bosonic quantum systems play a crucial role
in quantum sensing, communication, and computation, it is important to design
error correction codes suitable for these systems against various different
types of noises. While most efforts aim at protecting qubits encoded into the
infinite dimensional Hilbert space of a bosonic mode, [Phys. Rev. Lett. 125,
080503 (2020)] proposed an error correction code to maintain the
infinite-dimensional-Hilbert-space nature of bosonic systems by encoding a
single bosonic mode into multiple bosonic modes. Enabled by
Gottesman-Kitaev-Preskill states as ancilla, the code overcomes the no-go
theorem of Gaussian error correction. In this work, we generalize the error
correction code to the scenario with general correlated and heterogeneous
Gaussian noises, including memory effects. We introduce Gaussian pre-processing
and post-processing to convert the general noise model to an independent but
heterogeneous collection of additive white Gaussian noise channels and then
apply concatenated codes in an optimized manner. To evaluate the performance,
we develop a theory framework to enable the efficient calculation of the noise
standard deviation after the error correction, despite the non-Gaussian nature
of the codes. Our code provides the optimal scaling of the residue noise
standard deviation with the number of modes and can be widely applied to
distributed sensor-networks, network communication and composite quantum memory
systems.


Abstract:
  An iteration sequence based on the BLUES (beyond linear use of equation
superposition) function method is presented for calculating analytic
approximants to solutions of nonlinear partial differential equations. This
extends previous work using this method for nonlinear ordinary differential
equations with an external source term. Now, the initial condition plays the
role of the source. The method is tested on three examples: a
reaction-diffusion-convection equation, the porous medium equation with growth
or decay, and the nonlinear Black-Scholes equation. A comparison is made with
three other methods: the Adomian decomposition method (ADM), the variational
iteration method (VIM), and the variational iteration method with Green
function (GVIM). As a physical application, a deterministic differential
equation is proposed for interface growth under shear, combining Burgers and
Kardar- Parisi-Zhang nonlinearities. Thermal noise is neglected. This model is
studied with Gaussian and space-periodic initial conditions. A detailed Fourier
analysis is performed and the analytic coefficients are compared with those of
ADM, VIM, GVIM, and standard perturbation theory. The BLUES method turns out to
be a worthwhile alternative to the other methods. The advantages that it offers
ensue from the freedom of choosing judiciously the linear part, with associated
Green function, and the residual containing the nonlinear part of the
differential operator at hand.


Abstract:
  Recently, intensive studies have revealed fascinating physics, such as charge
density wave and superconducting states, in the newly synthesized
kagome-lattice materials $A$V$_3$Sb$_5$ ($A$=K, Rb, Cs). Despite the rapid
progress, fundamental aspects like the magnetic properties and electronic
correlations in these materials have not been clearly understood yet. Here,
based on the density functional theory plus the single-site dynamical
mean-field theory calculations, we investigate the correlated electronic
structure and the magnetic properties of the KV$_3$Sb$_5$ family materials in
the normal state. We show that these materials are good metals with weak local
correlations. The obtained Pauli-like paramagnetism and the absence of local
moments are consistent with recent experiment. We reveal that the band
crossings around the Fermi level form three groups of nodal lines protected by
the spacetime inversion symmetry, each carrying a quantized $\pi$ Berry phase.
Our result suggests that the local correlation strength in these materials
appears to be too weak to generate unconventional superconductivity, and
non-local electronic correlation might be crucial in this kagome system.


Abstract:
  On the dual space of \textit{extended structure}, equations governing the
collective motion of two mutually interacting Lie-Poisson systems are derived.
By including a twisted 2-cocycle term, this novel construction is providing the
most general realization of (de)coupling of Lie-Poisson systems. A double cross
sum (matched pair) of 2-cocycle extensions are constructed. The conditions are
determined for this double cross sum to be a 2-cocycle extension by itself. On
the dual spaces, Lie-Poisson equations are computed. We complement the
discussion by presenting a double cross sum of some symmetric brackets, such as
double bracket, Cartan-Killing bracket, Casimir dissipation bracket, and
Hamilton dissipation bracket. Accordingly, the collective motion of two
mutually interacting irreversible dynamics, as well as mutually interacting
metriplectic flows, are obtained. The theoretical results are illustrated in
three examples. As an infinite-dimensional physical model, decompositions of
the BBGKY hierarchy are presented. As finite-dimensional examples, the coupling
of two Heisenberg algebras and coupling of two copies of $3D$ dynamics are
studied.


Abstract:
  By associating frequencies to larger scales, we provide a simpler way to
derive local uniformity of multiplicative functions on average from the results
of Matom\"aki-Radziwill.


Abstract:
  We propose PeriodNet, a non-autoregressive (non-AR) waveform generation model
with a new model structure for modeling periodic and aperiodic components in
speech waveforms. The non-AR waveform generation models can generate speech
waveforms parallelly and can be used as a speech vocoder by conditioning an
acoustic feature. Since a speech waveform contains periodic and aperiodic
components, both components should be appropriately modeled to generate a
high-quality speech waveform. However, it is difficult to decompose the
components from a natural speech waveform in advance. To address this issue, we
propose a parallel model and a series model structure separating periodic and
aperiodic components. The features of our proposed models are that explicit
periodic and aperiodic signals are taken as input, and external
periodic/aperiodic decomposition is not needed in training. Experiments using a
singing voice corpus show that our proposed structure improves the naturalness
of the generated waveform. We also show that the speech waveforms with a pitch
outside of the training data range can be generated with more naturalness.


Abstract:
  Rapid proliferation of hyperspectral imaging in scanning probe microscopies
creates unique opportunities to systematically capture and categorize higher
dimensional datasets, toward new insights into electronic, mechanical and
chemical properties of materials with nano- and atomic-scale resolution. Here
we demonstrate similarity learning for tunneling spectroscopy acquired on
superconducting material (FeSe) with sparse density of imperfections (Fe
vacancies). Popular methods for unsupervised learning and discrete
representation of the data in terms of clusters of characteristic behaviors
were found to produce inconsistencies with respect to capturing the location
and tunneling characteristics of the vacancy sites. To this end, we applied a
more general, non-linear similarity learning. This approach was found to
outperform several widely used methods for dimensionality reduction and produce
a clear differentiation of the type of tunneling spectra. In particular,
significant spectral weight transfer likely associated with the electronic
reconstruction by the vacancy sites, is systematically captured, as is the
spatial extent of the vacancy region. Given that a great variety of electronic
materials will exhibit similarly smooth variation of the spectral responses due
to random or engineered inhomogeneities in their structure, we believe our
approach will be useful for systematic analysis of hyperspectral imaging with
minimal prior knowledge, as well as prospective comparison of experimental
measurements to theoretical calculations with explicit consideration of
disorder.


Abstract:
  Background: Advanced biological techniques have helped produce more
insightful findings on the genetic etiology of infertility that may lead to
better management of the condition. This review provides an update on genes
predisposing to syndromic and nonsyndromic infertility.
  Main body: The review identified 65 genes linked with infertility and
infertility-related disorders. These genes regulate fertility. However,
mutational loss of the functions of the genes predisposes to infertility.
Twenty-three (23) genes representing 35% were linked with syndromic
infertility, while 42 genes (65%) cause nonsyndromic infertility. Of the 42
nonsyndromic genes, 26 predispose to spermatogenic failure and sperm
morphological abnormalities, 11 cause ovarian failures, and 5 cause sex
reversal and puberty delay. Overall, 31 genes (48%) predispose to male
infertility, 15 genes (23%) cause female infertility, and 19 genes (29%)
predispose to both. The common feature of male infertility was spermatogenic
failure and sperm morphology abnormalities, while ovarian failure has been the
most frequently reported among infertile females. The mechanisms leading to
these pathologies are gene-specific, which, if targeted in the affected, may
lead to improved treatment.
  Conclusions: Mutational loss of the functions of some genes involved in the
development and maintenance of fertility may predispose to syndromic or
nonsyndromic infertility via gene-specific mechanisms. A treatment procedure
that targets the affected gene(s) in individuals expressing infertility may
lead to improved treatment.


Abstract:
  Semantic segmentation of breast cancer metastases in histopathological slides
is a challenging task. In fact, significant variation in data characteristics
of histopathology images (domain shift) make generalization of deep learning to
unseen data difficult. Our goal is to address this challenge by using a
conditional Fully Convolutional Network (co-FCN) whose output can be
conditioned at run time, and which can improve its performance when a properly
selected set of reference slides are used to condition the output. We adapted
to our task a co-FCN originally applied to organs segmentation in volumetric
medical images and we trained it on the Whole Slide Images (WSIs) from three
out of five medical centers present in the CAMELYON17 dataset. We tested the
performance of the network on the WSIs of the remaining centers. We also
developed an automated selection strategy for selecting the conditioning
subset, based on an unsupervised clustering process applied to a
target-specific set of reference patches, followed by a selection policy that
relies on the cluster similarities with the input patch. We benchmarked our
proposed method against a U-Net trained on the same dataset with no
conditioning. The conditioned network shows better performance that the U-Net
on the WSIs with Isolated Tumor Cells and micro-metastases from the medical
centers used as test. Our contributions are an architecture which can be
applied to the histopathology domain and an automated procedure for the
selection of conditioning data.


Abstract:
  We address the task of multi-view image-to-image translation for person image
generation. The goal is to synthesize photo-realistic multi-view images with
pose-consistency across all views. Our proposed end-to-end framework is based
on a joint learning of multiple unpaired image-to-image translation models, one
per camera viewpoint. The joint learning is imposed by constraints on the
shared 3D human pose in order to encourage the 2D pose projections in all views
to be consistent. Experimental results on the CMU-Panoptic dataset demonstrate
the effectiveness of the suggested framework in generating photo-realistic
images of persons with new poses that are more consistent across all views in
comparison to a standard Image-to-Image baseline. The code is available at:
https://github.com/sony-si/MultiView-Img2Img


Abstract:
  The first repeating fast radio burst source, FRB 121102, is observed to emit
bursts periodically. We show that FRB 121102 can be interpreted as an
interacting neutron star binary system with an orbital period of 159 days. We
develop a binary comb model by introducing an eccentricity in the orbit.
Besides the original funnel mode of the binary comb model, which was applied to
FRB 180916.J0158+65 by Ioka and Zhang 2020, we also identify two new modes of
the binary comb model, i.e. the tau-crossing mode and the inverse funnel mode,
and apply them to interpret FRB 121102. These new developments expand the
applicable parameter space, allowing the companion star to be a massive star, a
massive black hole, or a supermassive black hole, with the latter two having
larger parameter spaces. These models are also consistent with other
observations, such as the persistent bright radio counterpart associated with
the source. We also argue that the observed frequency dependence of the active
window does not disfavor the binary comb model, in contrast to recent claims,
and propose two possible scenarios to interpret the data.


Abstract:
  In order to interact with the world, agents must be able to predict the
results of the world's dynamics. A natural approach to learn about these
dynamics is through video prediction, as cameras are ubiquitous and powerful
sensors. Direct pixel-to-pixel video prediction is difficult, does not take
advantage of known priors, and does not provide an easy interface to utilize
the learned dynamics. Object-centric video prediction offers a solution to
these problems by taking advantage of the simple prior that the world is made
of objects and by providing a more natural interface for control. However,
existing object-centric video prediction pipelines require dense object
annotations in training video sequences. In this work, we present
Object-centric Prediction without Annotation (OPA), an object-centric video
prediction method that takes advantage of priors from powerful computer vision
models. We validate our method on a dataset comprised of video sequences of
stacked objects falling, and demonstrate how to adapt a perception model in an
environment through end-to-end video prediction training.


Abstract:
  Context.Coherence in the characteristics of neighboring sources in 2D and 3D
space may suggest the existence of large-scale cosmic structures, which are
useful for cosmological studies. Numerous works have been conducted to detect
such features in global scalesas well as in confined areas of the sky. However,
results are often contradictory and their interpretation remains controversial.
Aims.We investigate the potential alignment of parsec-scale radio jets in
localized regions of the coordinates-redshift space. Methods.We use data from
the Astrogeo VLBI FITS image database to deduce jet directions of radio
sources. We perform the search for statistical alignments between nearby
sources and explore the impact of instrumental biases. Results.We unveil four
regions for which the alignment between jet directions deviates from randomness
at a significance level of more than 5 sigma and is unlikely due to
instrumental systematics. Intriguingly, their locations coincide with other
known large-scale cosmic structures and/or regions of alignments.
Conclusions.If the alignments found are the result of physical processes, the
discovered regions may designate some of the largest structures known to date.


Abstract:
  {\alpha}-Sn with a diamond-type crystal structure provides an ideal avenue to
investigate novel topological properties owing to its rich diagram of
topological phases and simple elemental material structure. Thus far, however,
realisation of high-quality {\alpha}-Sn remains a challenge, which limits our
understanding of its quantum transport properties and device applications.
Here, we present epitaxial growth of {\alpha}-Sn on InSb (001) with the highest
quality thus far and reveal that it is a topological Dirac semimetal (TDS) by
quantum transport investigations together with first-principles calculations.
We realise unprecedentedly high quantum mobilities of both the surface state
(30000 cm^2/Vs), which is ten times higher than the previously reported values,
and the bulk heavy-hole (HH) state (1700 cm^2/Vs), which has never been
obtained experimentally. These excellent features allow us, for the first time,
to quantitatively characterise the nontrivial interfacial and bulk band
structure of {\alpha}-Sn via Shubnikov-de Haas oscillations at various
temperatures and under various magnetic field directions. These results reveal
the existence of a topological surface state (TSS) and a bulk HH band, both
with nontrivial phase shifts, indicating that the TDS phase of {\alpha}-Sn is
established. Furthermore, we demonstrate a crossover from the TDS to a
two-dimensional topological insulator (2D-TI) and a subsequent phase transition
to a trivial insulator when varying the thickness of {\alpha}-Sn. Our work
indicates that {\alpha}-Sn is an excellent model system to study novel
topological phases and a prominent material candidate for topological devices.


Abstract:
  Video-based person re-identification (Re-ID) aims to retrieve video sequences
of the same person under non-overlapping cameras. Previous methods usually
focus on limited views, such as spatial, temporal or spatial-temporal view,
which lack of the observations in different feature domains. To capture richer
perceptions and extract more comprehensive video representations, in this paper
we propose a novel framework named Trigeminal Transformers (TMT) for
video-based person Re-ID. More specifically, we design a trigeminal feature
extractor to jointly transform raw video data into spatial, temporal and
spatial-temporal domain. Besides, inspired by the great success of vision
transformer, we introduce the transformer structure for video-based person
Re-ID. In our work, three self-view transformers are proposed to exploit the
relationships between local features for information enhancement in spatial,
temporal and spatial-temporal domains. Moreover, a cross-view transformer is
proposed to aggregate the multi-view features for comprehensive video
representations. The experimental results indicate that our approach can
achieve better performance than other state-of-the-art approaches on public
Re-ID benchmarks. We will release the code for model reproduction.


Abstract:
  Recent years have witnessed the rapid advance in neural machine translation
(NMT), the core of which lies in the encoder-decoder architecture. Inspired by
the recent progress of large-scale pre-trained language models on machine
translation in a limited scenario, we firstly demonstrate that a single
language model (LM4MT) can achieve comparable performance with strong
encoder-decoder NMT models on standard machine translation benchmarks, using
the same training data and similar amount of model parameters. LM4MT can also
easily utilize source-side texts as additional supervision. Though modeling the
source- and target-language texts with the same mechanism, LM4MT can provide
unified representations for both source and target sentences, which can better
transfer knowledge across languages. Extensive experiments on pivot-based and
zero-shot translation tasks show that LM4MT can outperform the encoder-decoder
NMT model by a large margin.


Abstract:
  A local artificial neural network (LANN) framework is developed for
turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms
are reconstructed by artificial neural network (ANN) based on the local
coordinate system which is orthogonal to the curved walls. We verify the
proposed model for the flows over periodic hills. The correlation coefficients
of the RANS unclosed terms predicted by the LANN model can be made larger than
0.96 in an a priori analysis, and the relative error of the unclosed terms can
be made smaller than 18%. In an a posteriori analysis, detailed comparisons are
made on the results of RANS simulations using the LANN and Spalart-Allmaras
(SA) models. It is shown that the LANN model performs better than the SA model
in the prediction of the average velocity, wall-shear stress and average
pressure, which gives the results that are essentially indistinguishable from
the direct numerical simulation (DNS) data. The LANN model trained in low
Reynolds number Re = 2800 can be directly applied in the cases of high Reynolds
numbers Re = 5600, 10595, 19000, 37000 with accurate predictions. Furthermore,
the LANN model is verified for flows over periodic hills with varying slopes.
These results suggest that the LANN framework has a great potential to be
applied to complex turbulent flows with curved walls.


Abstract:
  Predicting the future motion of actors in a traffic scene is a crucial part
of any autonomous driving system. Recent research in this area has focused on
trajectory prediction approaches that optimize standard trajectory error
metrics. In this work, we describe three important properties -- physical
realism guarantees, system maintainability, and sample efficiency -- which we
believe are equally important for developing a self-driving system that can
operate safely and practically in the real world. Furthermore, we introduce
PTNet (PathTrackingNet), a novel approach for vehicle trajectory prediction
that is a hybrid of the classical pure pursuit path tracking algorithm and
modern graph-based neural networks. By combining a structured robotics
technique with a flexible learning approach, we are able to produce a system
that not only achieves the same level of performance as other state-of-the-art
methods on traditional trajectory error metrics, but also provides strong
guarantees about the physical realism of the predicted trajectories while
requiring half the amount of data. We believe focusing on this new class of
hybrid approaches is an useful direction for developing and maintaining a
safety-critical autonomous driving system.


Abstract:
  Surgical skill directly affects surgical procedure outcomes; thus, effective
training is needed to ensure satisfactory results. Many objective assessment
metrics have been developed and some are widely used in surgical training
simulators. These objective metrics provide the trainee with descriptive
feedback about their performance however, often lack feedback on how to proceed
to improve performance. The most effective training method is one that is
intuitive, easy to understand, personalized to the user and provided in a
timely manner. We propose a framework to enable user-adaptive training using
near-real-time detection of performance, based on intuitive styles of surgical
movements (e.g., fluidity, smoothness, crispness, etc.), and propose a haptic
feedback framework to assist with correcting styles of movement. We evaluate
the ability of three types of force feedback (spring, damping, and spring plus
damping feedback), computed based on prior user positions, to improve different
stylistic behaviors of the user during kinematically constrained reaching
movement tasks. The results indicate that four out of the six styles studied
here were statistically significantly improved (p<0.05) using spring guidance
force feedback and a significant reduction in task time was also found using
spring feedback. The path straightness and targeting error in the task were
other task performance metrics studied which were improved significantly using
the spring-damping feedback. This study presents a groundwork for adaptive
training in robotic surgery based on near-real-time human-centric models of
surgical behavior.


Abstract:
  In dense star clusters, such as globular and open clusters, dynamical
interactions between stars and black holes (BHs) can be extremely frequent,
leading to various astrophysical transients. Close encounters between a star
and a stellar mass BH make it possible for the star to be tidally disrupted by
the BH. Due to the relative low mass of the BH and the small cross section of
the tidal disruption event (TDE) for cases with high penetration, disruptions
caused by close encounters are usually partial disruptions. The existence of
the remnant stellar core and its non-negligible mass compared to the stellar
mass BH alters the accretion process significantly. We study this problem with
SPH simulations using the code {\tt Phantom}, with the inclusion of radiation
pressure, which is important for small mass BHs. Additionally, we develop a
new, more general method of computing the fallback rate which does not rely on
any approximation. Our study shows that the powerlaw slope of the fallback rate
has a strong dependence on the mass of the BH in the stellar mass BH regime.
Furthermore, in this regime, self-gravity of the fallback stream and local
instabilities become more significant, and cause the disrupted material to
collapse into small clumps before returning to the BH. This results in an
abrupt increase of the fallback rate, which can significantly deviate from a
powerlaw. Our results will help in the identification of TDEs by stellar mass
BHs in dense clusters.


Abstract:
  We consider Gaussian measures $\mu, \tilde{\mu}$ on a separable Hilbert
space, with fractional-order covariance operators $A^{-2\beta}$ resp.
$\tilde{A}^{-2\tilde{\beta}}$, and derive necessary and sufficient conditions
on $A, \tilde{A}$ and $\beta, \tilde{\beta} > 0$ for I. equivalence of the
measures $\mu$ and $\tilde{\mu}$, and II. uniform asymptotic optimality of
linear predictions for $\mu$ based on the misspecified measure $\tilde{\mu}$.
These results hold, e.g., for Gaussian processes on compact metric spaces. As
an important special case, we consider the class of generalized
Whittle-Mat\'ern Gaussian random fields, where $A$ and $\tilde{A}$ are elliptic
second-order differential operators, formulated on a bounded Euclidean domain
$\mathcal{D}\subset\mathbb{R}^d$ and augmented with homogeneous Dirichlet
boundary conditions. Our outcomes explain why the predictive performances of
stationary and non-stationary models in spatial statistics often are
comparable, and provide a crucial first step in deriving consistency results
for parameter estimation of generalized Whittle-Mat\'ern fields.


Abstract:
  The primordial heavy or superheavy dark matter that could be created during
the reheating or preheating stage of the Universe can undergo QCD cascade decay
process to produce leptons or $\gamma$ as end products. Although these could be
rare decays, the energy involved in such decay process can influence 21-cm
signal of hyperfine transition of neutral hydrogen during the reionization era.
We explore in this work, possible multimessenger signals of such heavy dark
matter decays. One of which could be the source of ultra high energy neutrino
(of $\sim$ PeV energy regime) signals at IceCube detector whereas the other
signal attributes to the cooling/heating of the baryons by the exchange of
energy involved in this decay process and its consequent influence on 21-cm
signal. The effect of evaporation of primordial black holes and baryon
scattering with light cold dark matter are also included in relation to the
evolution of the 21-cm signal temperature and their influence are also
discussed.


Abstract:
  Street network analysis holds appeal as a tool for the assessment of
pedestrian connectivity and its relation to the intensity and mix of land-uses;
however, application within urban-design triggers a range of questions on
implementary specifics due to a variety of theories, methods, and
considerations and it is not immediately clear which of these might be the most
applicable at the pedestrian scale in relation to land-uses. It is,
furthermore, difficult to directly evaluate differing approaches on a
like-for-like basis without recourse to the underlying algorithms and
computational workflows. To this end, the cityseer-api Python package is here
used to develop, compute, and compare a range of centrality methods which are
then applied to the Ordnance Survey Open Roads dataset for Greater London. The
centralities are correlated to high-resolution land-use and mixed-used measures
computed from the Ordnance Survey Points of Interest dataset for the same
points of analysis using a spatially precise methodology based on network
distances to premise locations. The comparisons show that mixed-uses correlate
more strongly against closeness than betweenness centralities; segmented
measures tend to offer slightly stronger correlations than node-based
equivalents; weighted variants offer correlations similar to unweighted
versions, but with a greater degree of spatial specificity; simplest-path
methods confer an advantage in the context of local high-street mixed-uses but
not necessarily for district-wide mixed-uses or land-use accessibilities; and
the application of centrality measures to the dual network does not offer
tangible benefits over the primal network.


Abstract:
  Synthesis of face images from visual attributes is an important problem in
computer vision and biometrics due to its applications in law enforcement and
entertainment. Recent advances in deep generative networks have made it
possible to synthesize high-quality face images from visual attributes.
However, existing methods are specifically designed for generating unimodal
images (i.e visible faces) from attributes. In this paper, we propose a novel
generative adversarial network that simultaneously synthesizes identity
preserving multimodal face images (i.e. visible, sketch, thermal, etc.) from
visual attributes without requiring paired data in different domains for
training the network. We introduce a novel generator with multimodal
stretch-out modules to simultaneously synthesize multimodal face images.
Additionally, multimodal stretch-in modules are introduced in the discriminator
which discriminates between real and fake images. Extensive experiments and
comparisons with several state-of-the-art methods are performed to verify the
effectiveness of the proposed attribute-based multimodal synthesis method.


Abstract:
  We present a case study of using a novel method to identify red supergiant
(RSG) candidates in NGC 6822, based on their 1.6 $\mu$m "H-bump". We collected
32 bands of photometric data for NGC 6822 ranging from optical to MIR. By using
the theoretical spectra from MARCS, we demonstrate that there is a prominent
difference around 1.6 $\mu$m ("H-bump") between low-surface-gravity (LSG) and
high-surface-gravity (HSG) targets. Taking advantage of this feature, we
identify efficient color-color diagrams (CCDs) of rzH and rzK to separate HSG
and LSG targets from crossmatching of optical and NIR data. Moreover, synthetic
photometry from ATLAS9 also give similar results. Further separating RSG
candidates from the rest of the LSG candidates is done by using semi-empirical
criteria on NIR CMDs and resulted in 323 RSG candidates. Meanwhile, the
simulation of foreground stars from Besan\c{c}on models also indicates that our
selection criteria is largely free from the contamination of Galactic giants.
In addition to the "H-bump" method, we also use the traditional BVR method as a
comparison and/or supplement, by applying a slightly aggressive cut to select
as much as possible RSG candidates (358 targets). Furthermore, the Gaia
astrometric solution is used to constrain the sample, where 181 and 193 targets
were selected from the "H-bump" and BVR method, respectively. The percentages
of selected targets in both methods are similar as $\sim$60\%, indicating the
comparable accuracy of the two methods. In total, there are 234 RSG candidates
after combining targets from both methods with 140 ($\sim$60\%) of them in
common. The final RSG candidates are in the expected locations on the MIR CMDs,
while the spatial distribution is also coincident with the FUV-selected star
formation regions, suggesting the selection is reasonable and reliable.


Abstract:
  We compared high-contrast near-infrared images of the core of R136 taken by
VLT/SPHERE, in two epochs separated by 3.06 years. For the first time we
monitored the dynamics of the detected sources in the core of R136 from a
ground-based telescope with adaptive optics. The aim of these observations was
to search for High prOper Motion cAndidates (HOMAs) in the central region of
R136 (r<6") where it has been challenging for other instruments. Two bright
sources (K<15mag and V<16mag) are located near R136a1 and R136c (massive WR
stars) and have been identified as potential HOMAs. These sources have
significantly shifted in the images with respect to the mean shift of all
reliable detected sources and their neighbours, and six times their own
astrometric errors. We calculate their proper motions to be 1.36\pm0.22 mas/yr
(321\pm52 km/s) and 1.15\pm0.11 mas/yr (273\pm26 km/s). We discuss different
possible scenarios to explain the magnitude of such extreme proper motions, and
argue for the necessity to conduct future observations to conclude on the
nature of HOMAs in the core of R136.


Abstract:
  Most methods for dimensionality reduction are based on either tensor
representation or local geometry learning. However, the tensor-based methods
severely rely on the assumption of global and multilinear structures in
high-dimensional data; and the manifold learning methods suffer from the
out-of-sample problem. In this paper, bridging the tensor decomposition and
manifold learning, we propose a novel method, called Hypergraph Regularized
Nonnegative Tensor Factorization (HyperNTF). HyperNTF can preserve
nonnegativity in tensor factorization, and uncover the higher-order
relationship among the nearest neighborhoods. Clustering analysis with HyperNTF
has low computation and storage costs. The experiments on four synthetic data
show a desirable property of hypergraph in uncovering the high-order
correlation to unfold the curved manifolds. Moreover, the numerical experiments
on six real datasets suggest that HyperNTF robustly outperforms
state-of-the-art algorithms in clustering analysis.


Abstract:
  A chiral photonic interface is a quantum system that has different
probabilities for emitting photons to the left and right. An on-chip compatible
chiral interface is attractive for both fundamental studies of light-matter
interactions and applications to quantum information processing. We propose
such a chiral interface based on superconducting circuits, which has wide
bandwidth, rich tunability, and high tolerance to fabrication variations. The
proposed interface consists of a core that uses Cooper-pair boxes (CPBs) to
break time-reversal symmetry, and two superconducting transmons that connect
the core to a waveguide in the manner reminiscent of a ``giant atom.'' The
transmons form a state decoupled from the core, akin to dark states of atomic
physics, rendering the whole interface insensitive to the CPB charge noise. The
proposed interface can be extended to realize a broadband fully passive on-chip
circulator for microwave photons.


Abstract:
  Learning-from-demonstrations is an emerging paradigm to obtain effective
robot control policies for complex tasks via reinforcement learning without the
need to explicitly design reward functions. However, it is susceptible to
imperfections in demonstrations and also raises concerns of safety and
interpretability in the learned control policies. To address these issues, we
use Signal Temporal Logic to evaluate and rank the quality of demonstrations.
Temporal logic-based specifications allow us to create non-Markovian rewards,
and also define interesting causal dependencies between tasks such as
sequential task specifications. We validate our approach through experiments on
discrete-world and OpenAI Gym environments, and show that our approach
outperforms the state-of-the-art Maximum Causal Entropy Inverse Reinforcement
Learning.


Abstract:
  Apollonius of Perga, showed that for two given points $A,B$ in the Euclidean
plane and a positive real number $k\neq 1$, geometric locus of the points $X$
that satisfies the equation $|XA|=k|XB|$ is a circle. This circle is called
Apollonius circle. In this paper we generalize the definition of the Apollonius
circle for two given circles $\Gamma_1,\Gamma_2$ and we show that geometric
locus of the points $X$ with the ratio of the power with respect to the circles
$\Gamma_1,\Gamma_2$ is constant, is also a circle. Using this we generalize the
definition of Apollonius Circle, and generalize some results about Apollonius
Circle.


Abstract:
  Polymer-based hydrogels have been widely applied for chronic wound
therapeutics, due to their well-acclaimed wound exudate management capability.
At the same time, there is still an unmet clinical need for simple wound
diagnostic tools to assist clinical decision-making at the point of care and
deliver on the vision of patient-personalised wound management. To explore this
challenge, we present a one-step synthetic strategy to realise a
redox-responsive, hyaluronic acid (HA)-based hydrogel that is sensitive to
wound environment-related variations in glutathione (GSH) concentration. By
selecting aminoethyl disulfide (AED) as a GSH-sensitive crosslinker and
considering GSH concentration variations in active and non-self-healing wounds,
we investigated the impact of GSH-induced AED cleavage on hydrogel dimensions,
aiming to build GSH-size relationships for potential point-of-care wound
diagnosis. The hydrogel was also found to be non-cytotoxic and aided L929
fibroblast growth and proliferation over seven days in vitro. Such a material
offers a very low-cost tool for the visual detection of a target analyte that
varies dependent on the status of the cells and tissues (wound detection) and
may be further exploited as an implant for fibroblast growth and tissue
regeneration (wound repair).


Abstract:
  Face parsing aims to predict pixel-wise labels for facial components of a
target face in an image. Existing approaches usually crop the target face from
the input image with respect to a bounding box calculated during
pre-processing, and thus can only parse inner facial Regions of
Interest~(RoIs). Peripheral regions like hair are ignored and nearby faces that
are partially included in the bounding box can cause distractions. Moreover,
these methods are only trained and evaluated on near-frontal portrait images
and thus their performance for in-the-wild cases has been unexplored. To
address these issues, this paper makes three contributions. First, we introduce
iBugMask dataset for face parsing in the wild, which consists of 21,866
training images and 1,000 testing images. The training images are obtained by
augmenting an existing dataset with large face poses. The testing images are
manually annotated with $11$ facial regions and there are large variations in
sizes, poses, expressions and background. Second, we propose RoI Tanh-polar
transform that warps the whole image to a Tanh-polar representation with a
fixed ratio between the face area and the context, guided by the target
bounding box. The new representation contains all information in the original
image, and allows for rotation equivariance in the convolutional neural
networks~(CNNs). Third, we propose a hybrid residual representation learning
block, coined HybridBlock, that contains convolutional layers in both the
Tanh-polar space and the Tanh-Cartesian space, allowing for receptive fields of
different shapes in CNNs. Through extensive experiments, we show that the
proposed method improves the state-of-the-art for face parsing in the wild and
does not require facial landmarks for alignment.


Abstract:
  Spin waves in magnetic insulators are low-damping signal carriers that could
enable a new generation of spintronic devices. The excitation, control, and
detection of spin waves by metal electrodes is crucial for interfacing these
devices to electrical circuits. It is therefore important to understand
metal-induced damping of spin-wave transport, but characterizing this process
requires access to the underlying magnetic films. Here we show that spins in
diamond enable imaging of spin waves that propagate underneath metals in
magnetic insulators, and then use this capability to reveal a 100-fold increase
in spin-wave damping. By analyzing spin-wave-induced currents in the metal, we
derive an effective damping parameter that matches these observations well. We
furthermore detect buried scattering centers, highlighting the technique's
power for assessing spintronic device quality. Our results open new avenues for
studying metal - spin-wave interaction and provide access to interfacial
processes such as spin-wave injection via the spin-Hall effect.


Abstract:
  Photodetector nonlinearity, the main limiting factor in terms of optical
power in the detection chain, is corrected to improve the signal-to-noise ratio
of a short-time measurement in dual-comb spectroscopy. An iterative correction
algorithm minimizing out-of-band spectral artifacts based on nonlinearity
correction methods used in classical Fourier-transform spectrometers is
presented. The exactitude of the nonlinearity correction is validated using a
low power linear measurement. Spectroscopic lines of H$^{12}$CN are provided
and the error caused by the saturation of the detector is corrected yielding
residuals limited by the measurement noise.


Abstract:
  The presence of hierarchy in many real-world networks is not yet fully
explained. Complex interaction networks are often coarse-grain models of vast
modular networks, where tightly connected subgraphs are agglomerated into nodes
for simplicity of representation and feasibility of analysis. The emergence of
hierarchy in growing complex networks may stem from one particular property of
these ignored subgraphs: their graph conductance. Being a quantification of the
main bottleneck of flow on the subgraph, all such subgraphs will then have a
specific structural limitation associated with this scalar value. This supports
the consideration of heterogeneous degree restrictions on a randomly growing
network for which a hidden variable model is proposed based on the basic
\textit{rich-get-richer} scheme. Such node degree restrictions are drawn from
various probability distributions, and it is shown that restriction generally
leads to increased measures of hierarchy, while altering the tail of the degree
distribution. Thus, a general mechanism is provided whereby inherent
limitations lead to hierarchical self-organization.


Abstract:
  In this article, we have described the Rao distance (due to C.R. Rao) and
ideas of conformal mappings on 3D objects with angle preservations. Three
propositions help us to construct distances between the points within the 3D
objects in \mathbb{R}^{3} and line integrals within complex planes. We
highlight the application of these concepts to virtual tourism.


Abstract:
  The question of how a pure fiat currency is enforced and comes to have a
non-zero value has been much debated \cite{10.2307/2077948}. What is less often
addressed is, in the case where the enforcement is taken for granted and we ask
what value (in terms of goods and services) the currency will end up taking.
Establishing a decentralised mechanism for price formation has proven a
challenge for economists: "Since no decentralized out-of-equilibrium adjustment
mechanism has been discovered, we currently have no acceptable dynamical model
of the Walrasian system" (Gintis 2006). In his paper, Gintis put forward a
model for price discovery based on the evolution of the model's agents, i.e.
"poorly performing agents dying and being replaced by copies of the well
performing agents." It seems improbable that this mechanism is the driving
force behind price discovery in the real world. This paper proposes a more
realistic mechanism and presents results from a corresponding agent based
model.


Abstract:
  We consider the Newton stratification on Iwahori double cosets for a
connected reductive group. We prove the existence of Newton strata whose
closures cannot be expressed as a union of strata, and show how this is implied
by the existence of non-equidimensional affine Deligne-Lusztig varieties. We
also give an explicit example for a group of type $A_4$.


Abstract:
  We study Shimura (special) subvarieties in the moduli space $A_{p,D}$ of
complex abelian varieties of dimension $p$ and polarization type $D$. These
subvarieties arise from families of covers compatible with a fixed group action
on the base curve such that the quotient of the base curve by the group is
isomorphic to $\mathbb{P}^1$. We give a criterion for the image of these
families under the Prym map to be a special subvariety and, using computer
algebra, obtain 210 Shimura subvarieties contained in the Prym locus.


Abstract:
  The Meissner effect is one of the defining properties of superconductivity,
with a conventional superconductor completely repelling an external magnetic
field. In contrast to this diamagnetic behavior, odd-frequency superconducting
pairing has often been seen to produce a paramagnetic Meissner effect, which
instead makes the superconductor unstable due to the attraction of magnetic
field. In this work we study how both even- and odd-frequency superconducting
pairing contributes to the Meissner effect in a generic two-orbital
superconductor with a tunable odd-frequency pairing component. By dividing the
contributions to the Meissner effect into intra- and inter-band processes, we
find that the odd-frequency pairing actually generates both dia- and
paramagnetic Meissner responses, determined by the normal-state band structure.
More specifically, for materials with two electron-like (hole-like) low-energy
bands we find that the odd-frequency inter-band contribution is paramagnetic
but nearly canceled by a diamagnetic odd-frequency intra-band contribution.
Combined with a diamagnetic even-frequency contribution, such superconductors
thus always display a large diamagnetic Meissner response to an external
magnetic field, even in the presence of large odd-frequency pairing. For
materials with an inverted, or topological, band structure, we find the
odd-frequency inter-band contribution to instead be diamagnetic and even the
dominating contribution to the Meissner effect in the near-metallic regime.
Taken together, our results show that odd-frequency pairing in multi-orbital
superconductors does not generate a destabilizing paramagnetic Meissner effect
and can even generate a diamagnetic response in topological materials.


Abstract:
  Here I investigate some mathematical aspects of the maximum entropy theory of
ecology (METE). In particular I address the geometrical structure of METE
endowed by information geometry. As novel results, the macrostate entropy is
calculated analytically by the Legendre transformation of the log-normalizer in
METE. This result allows for the calculation of the metric terms in the
information geometry arising from METE and, by consequence, the covariance
matrix between METE variables.


Abstract:
  In this work, we study the convergence of the normalized Yamabe flow with
positive Yamabe constant on a class of pseudo-manifolds that includes
stratified spaces with iterated cone-edge metrics. We establish convergence
under a low energy condition. We also prove a concentration - compactness
dichotomy, and investigate what the alternatives to convergence are. We end by
investigating a non-convergent example due to Viaclovsky in more detail.


Abstract:
  Significant progress has been made in boundary detection with the help of
convolutional neural networks. Recent boundary detection models not only focus
on real object boundary detection but also "crisp" boundaries (precisely
localized along the object's contour). There are two methods to evaluate crisp
boundary performance. One uses more strict tolerance to measure the distance
between the ground truth and the detected contour. The other focuses on
evaluating the contour map without any postprocessing. In this study, we
analyze both methods and conclude that both methods are two aspects of crisp
contour evaluation. Accordingly, we propose a novel network named deep
refinement network (DRNet) that stacks multiple refinement modules to achieve
richer feature representation and a novel loss function, which combines
cross-entropy and dice loss through effective adaptive fusion. Experimental
results demonstrated that we achieve state-of-the-art performance for several
available datasets.


Abstract:
  We investigate the pure annihilation type radiative $B$ meson decays $B^0 \to
\phi \gamma$ and $B_s \to \rho^0(\omega)\gamma$ in the soft-collinear effective
theory. We consider three types of contributions to the decay amplitudes,
including the direct annihilation topology, the contribution from the
electro-magnetic penguin operator and the contribution of the neutral vector
meson mixings. The numerical analysis shows that the decay amplitudes are
dominated by the $\omega-\phi$ mixing effect in the $B^0 \to \phi\gamma$ and
$B_s \to \omega\gamma$ modes. The corresponding decay branching ratios are
enhanced about three orders of magnitudes relative to the pure annihilation
type contribution in these two decay channels. The decay rate of $B_s \to
\rho^0\gamma$ is much smaller than that of $B_s \to \omega\gamma$ because of
the smaller $\rho^0-\phi$ mixing. The predicted branching ratios
$B(B^{0}\rightarrow\phi\gamma)=(3.99^{+1.67}_{-1.46}
)\times10^{-9},\,B(B_s\rightarrow\omega\gamma)=(2.01^{+0.81}_{-0.71}
)\times10^{-7}$ are to be tested by the Belle-II and LHC-b experiments.


Abstract:
  MCBTE solves the linearized Boltzmann transport equation for phonons in three
dimensions using a variance-reduced Monte Carlo solution approach. The
algorithm is suited for both transient and steady-state analysis of thermal
transport in structured materials with size features in the nanometer to
hundreds of microns range. The code is portable and integrated with both
first-principles density functional theory calculations and empirical relations
for the input of phonon frequency, group velocity, and mean free path required
for calculating the thermal properties. The program outputs space- and
time-resolved temperature and heat flux for the transient study. For the
steady-state simulations, the frequency-resolved contribution of phonons to
temperature and heat flux is written to the output files, thus allowing the
study of cumulative thermal conductivity as a function of phonon frequency or
mean free path. We provide several illustrative examples, including ballistic
and quasi-ballistic thermal transport, the thermal conductivity of thin films
and periodic nanostructures, to demonstrate the functionality and to benchmark
our code against available theoretical/analytical/computational results from
the literature. Moreover, we parallelize the code using the Matlab Distributed
Computing Server, providing near-linear scaling with the number of processors.


Abstract:
  In the paper the relation of homology groups of the direct limit of cochain
complexes and the inverse limit of homology groups of corresponding chain
complexes is studied. As a corollary, the tautness property of a homology
theory is obtained. Moreover, for the defined exact homology theory the
continuous property is obtained on the category of compact pairs.


Abstract:
  Semiconductor superlattice multipliers have emerged as a nonlinear medium
capable to generate radiation in a wide frequency range. This property
facilitates the potential of sources suitable for sensing and spectroscopy
applications. In this study, we further investigate the consequences on
harmonic generation in a superlattice multiplier after excitation by an input
signal oscillating at different frequencies. Here we provide a rigorous
description of our theoretical model including a semiclassical Boltzmann
approach to nonlinear miniband transport and non-equilibrium Greens functions
calculations treating scattering processes under forward and reverse bias. To
fully exploit the features of this radiation source, we focus on the effects of
elastic scattering and systemat-ic imperfections in the superlattice structure
which lead to asymmetric current flow.


Abstract:
  We study the zeroes of a family of random holomorphic functions on the unit
disc, distinguished by their invariance with respect to the hyperbolic
geometry. Our main finding is a transition in the limiting behaviour of the
number of zeroes in a large hyperbolic disc.
  We find a normal distribution if the covariance decays faster than a certain
critical value. In contrast, in the regime of 'long-range dependence' when the
covariance decays slowly, the limiting distribution is skewed. For a closely
related model we emphasise a link with Gaussian multiplicative chaos.


Abstract:
  The stability of the solution to the equation $(*)\dot{u} = F(t,u)+f(t)$,
$t\ge 0$, $u(0)=u_0$ is studied. Here $F(t,u)$ is a nonlinear operator in a
Banach space $\mathcal{X}$ for any fixed $t\ge 0$ and $F(t,0)=0$, $\forall t\ge
0$. We assume that the Fr\'echet derivative of $F(t,u)$ is H\"{o}lder
continuous of order $q>0$ with respect to $u$ for any fixed $t\ge 0$, i.e.,
$\|F'_u(t,w) - F'_u(t,v)\|\le \alpha(t)\|v - w\|^{q}$, $q>0$. We proved that
the equilibrium solution $v=0$ to the equation $\dot{v} = F(t,v)$ is Lyapunov
stable under persistently acting perturbation $f(t)$ if $\sup_{t\ge 0}\int_0^t
\alpha(\xi)\|U(t,\xi)\|\, d\xi<\infty$ and $\sup_{t\ge 0}\|U(t)\|<\infty$.
Here, $U(t):=U(t,0)$ and $U(t,\xi)$ is the solution to the equation
$\frac{d}{dt}{U}(t,\xi) = F'_u(t,0)U(t,\xi)$, $t\ge \xi$, $U(\xi,\xi)=I$, where
$I$ is the identity operator in $\mathcal{X}$. Sufficient conditions for the
solution $u(t)$ to equation (*) to be bounded and for $\lim_{t\to\infty}u(t) =
0$ are proposed and justified. Stability of solutions to equations with
unbounded operators in Hilbert spaces is also studied.


Abstract:
  We prove $L^p\to L^q$ estimates for local maximal operators associated with
dilates of codimension two spheres in Heisenberg groups; these are sharp up to
two endpoints. The results can be applied to improve currently known bounds on
sparse domination for global maximal operators. We also consider lacunary
variants, and extensions to M\'etivier groups.


Abstract:
  In this paper, the role of secret key with finite rate is studied to enhance
the secrecy performance of the system when users are operating in interference
limited scenarios. To address this problem, a 2-user Gaussian Z-IC with secrecy
constraint at the receiver is considered. One of the fundamental problems here
is how to use the secret key as a part of the encoding process. The paper
proposes novel achievable schemes, where the schemes differ from each other
based on how the key has been used in the encoding process. The first
achievable scheme uses one part of the key for one-time pad and remaining part
of the key for wiretap coding. The encoding is performed such that the receiver
experiencing interference can decode some part of the interference without
violating the secrecy constraint. As a special case of the derived result, one
can obtain the secrecy rate region when the key is completely used for one-time
pad or part of the wiretap coding. The second scheme uses the shared key to
encrypt the message using one-time pad and in contrast to the previous case no
interference is decoded at the receiver. The paper also derives an outer bound
on the sum rate and secrecy rate of the transmitter which causes interference.
The main novelty of deriving outer bound lies in the selection of side
information provided to the receiver and using the secrecy constraint at the
receiver. The derived outer bounds are found to be tight depending on the
channel conditions and rate of the key. The scaling behaviour of key rate is
also explored for different schemes using the notion of secure GDOF. The
optimality of different schemes is characterized for some specific cases. The
developed results show the importance of key rate splitting in enhancing the
secrecy performance of the system when users are operating under interference
limited environment.


Abstract:
  We introduce a general framework for robust data-enabled predictive control
(DeePC) for linear time-invariant (LTI) systems. The proposed framework enables
us to obtain model-free optimal control for LTI systems based on noisy
input/output data. More specifically, robust DeePC solves a min-max
optimization problem to compute the optimal control sequence that is resilient
to all possible realizations of the uncertainties in the input/output data
within a prescribed uncertainty set. We present computationally tractable
reformulations of the min-max problem with various uncertainty sets.
Furthermore, we show that even though an accurate prediction of the future
behavior is unattainable in practice due to inaccessibility of the perfect
input/output data, the obtained robust optimal control sequence provides
performance guarantees for the actually realized input/output cost. We further
show that the robust DeePC generalizes and robustifies the regularized DeePC
(with quadratic regularization or 1-norm regularization) proposed in the
literature. Finally, we demonstrate the performance of the proposed robust
DeePC algorithm on high-fidelity, nonlinear, and noisy simulations of a
grid-connected power converter system.


Abstract:
  We present a new discretization for advection-diffusion problems with Robin
boundary conditions on complex time-dependent domains. The method is based on
second order cut cell finite volume methods introduced by Bochkov et al. to
discretize the Laplace operator and Robin boundary condition. To overcome the
small cell problem, we use a splitting scheme that uses a semi-Lagrangian
method to treat advection. We demonstrate second order accuracy in the $L^1$,
$L^2$, and $L^\infty$ norms for both analytic test problems and numerical
convergence studies. We also demonstrate the ability of the scheme to handle
conversion of one concentration field to another across a moving boundary.


Abstract:
  Optimal Transport (OT) metrics allow for defining discrepancies between two
probability measures. Wasserstein distance is for longer the celebrated
OT-distance frequently-used in the literature, which seeks probability
distributions to be supported on the $\textit{same}$ metric space. Because of
its high computational complexity, several approximate Wasserstein distances
have been proposed based on entropy regularization or on slicing, and
one-dimensional Wassserstein computation. In this paper, we propose a novel
extension of Wasserstein distance to compare two incomparable distributions,
that hinges on the idea of $\textit{distributional slicing}$, embeddings, and
on computing the closed-form Wassertein distance between the sliced
distributions. We provide a theoretical analysis of this new divergence, called
$\textit{heterogeneous Wasserstein discrepancy (HWD)}$, and we show that it
preserves several interesting properties including rotation-invariance. We show
that the embeddings involved in HWD can be efficiently learned. Finally, we
provide a large set of experiments illustrating the behavior of HWD as a
divergence in the context of generative modeling and in query framework.


Abstract:
  Using Levi-Civita's theory of ideal fluids, we derive the complex Korteweg-de
Vries (KdV) equation, describing the complex velocity of a shallow fluid up to
first order. We use perturbation theory, and the long wave, slowly varying
velocity approximations for shallow water. The complex KdV equation describes
the nontrivial dynamics of all water particles from the surface to the bottom
of the water layer. A crucial new step made in our work is the proof that a
natural consequence of the complex KdV theory is that the wave elevation is
described by the real KdV equation. The complex KdV approach in the theory of
shallow fluids is thus more fundamental than the one based on the real KdV
equation. We demonstrate how it allows direct calculation of the particle
trajectories at any point of the fluid, and that these results agree well with
numerical simulations of other authors.