Abstract:
  Personalized image aesthetic assessment (PIAA) has recently become a hot
topic due to its usefulness in a wide variety of applications such as
photography, film and television, e-commerce, fashion design and so on. This
task is more seriously affected by subjective factors and samples provided by
users. In order to acquire precise personalized aesthetic distribution by small
amount of samples, we propose a novel user-guided personalized image aesthetic
assessment framework. This framework leverages user interactions to retouch and
rank images for aesthetic assessment based on deep reinforcement learning
(DRL), and generates personalized aesthetic distribution that is more in line
with the aesthetic preferences of different users. It mainly consists of two
stages. In the first stage, personalized aesthetic ranking is generated by
interactive image enhancement and manual ranking, meanwhile two policy networks
will be trained. The images will be pushed to the user for manual retouching
and simultaneously to the enhancement policy network. The enhancement network
utilizes the manual retouching results as the optimization goals of DRL. After
that, the ranking process performs the similar operations like the retouching
mentioned before. These two networks will be trained iteratively and
alternatively to help to complete the final personalized aesthetic assessment
automatically. In the second stage, these modified images are labeled with
aesthetic attributes by one style-specific classifier, and then the
personalized aesthetic distribution is generated based on the multiple
aesthetic attributes of these images, which conforms to the aesthetic
preference of users better.


Abstract:
  In this work, we propose a method to simultaneously perform (i) biometric
recognition (i.e., identify the individual), and (ii) device recognition,
(i.e., identify the device) from a single biometric image, say, a face image,
using a one-shot schema. Such a joint recognition scheme can be useful in
devices such as smartphones for enhancing security as well as privacy. We
propose to automatically learn a joint representation that encapsulates both
biometric-specific and sensor-specific features. We evaluate the proposed
approach using iris, face and periocular images acquired using near-infrared
iris sensors and smartphone cameras. Experiments conducted using 14,451 images
from 15 sensors resulted in a rank-1 identification accuracy of upto 99.81% and
a verification accuracy of upto 100% at a false match rate of 1%.


Abstract:
  In this paper, we establish a high-dimensional CLT for the sample mean of
$p$-dimensional spatial data observed over irregularly spaced sampling sites in
$\mathbb{R}^d$, allowing the dimension $p$ to be much larger than the sample
size $n$. We adopt a stochastic sampling scheme that can generate irregularly
spaced sampling sites in a flexible manner and include both pure increasing
domain and mixed increasing domain frameworks. To facilitate statistical
inference, we develop the spatially dependent wild bootstrap (SDWB) and justify
its asymptotic validity in high dimensions by deriving error bounds that hold
almost surely conditionally on the stochastic sampling sites. Our dependence
conditions on the underlying random field cover a wide class of random fields
such as Gaussian random fields and continuous autoregressive moving average
random fields. Through numerical simulations and a real data analysis, we
demonstrate the usefulness of our bootstrap-based inference in several
applications, including joint confidence interval construction for
high-dimensional spatial data and change-point detection for spatio-temporal
data.


Abstract:
  Recently cycloarene has been experimentally obtained in a self-assembled
structure, forming graphene-like monoatomic layered systems. Here, we establish
the bandgap engineering/prediction in cycloarene assemblies within a
combination of density functional theory and tight-binding Hamiltonians. Our
results show a weak dependence of the gap with the assembly geometry,
contrasting a strong dependence with the inter-molecule bond density. We
derived a effective model that allows the interpretation of the arising energy
gap for general particle-hole symmetric molecular arranges based on
inter-molecular bond strength.


Abstract:
  We present a paradigm for integrated photonic devices based upon broadband
slab-confined collimated beams that are launched with half-Maxwell fisheye
lenses. Although it is challenging to match to the low-index focus of the lens
whilst maintaining adequate field confinement for a well-defined point source,
integrated dielectric slot waveguides prove highly suitable, yielding
collimators of 90% efficiency and over one octave bandwidth. Terahertz
technology will benefit from such broadband slab-confined beams to replace
free-space optics, toward compact, mass-producible systems that do not require
manual optical alignment. We present two prototype systems to demonstrate the
versatility of this concept, namely a diagonally-set distributed Bragg
reflector as frequency-division diplexer for terahertz communications, and an
attenuated total reflection-based liquid sensor. Both are enabled by oblique
in-slab reflections that are collected at a location other than the originating
lens, which is not attainable using ordinary single-mode channel waveguides.


Abstract:
  The BaBar collaboration has measured the ratio
$R_{\tau\mu}^{\Upsilon(3S)}=\mathcal{B}(\Upsilon(3S)\rightarrow\tau^+\tau^-)/\mathcal{B}(\Upsilon(3S)\rightarrow\mu^+\mu^-)$
with a high level of precision. This measurement utilized a 28 fb$^{-1}$
dataset collected at a center-of-mass energy of 10.355 GeV. The measured ratio,
$R_{\tau\mu}^{\Upsilon(3S)}$, is measured to be
$R_{\tau\mu}^{\Upsilon(3S)}=0.966\pm0.008_{\textrm{stat}}\pm0.014_{\textrm{syst}}$.
This value is within 2 standard deviations of the standard model prediction
$R_{\tau\mu}^{\Upsilon(3S)}$=0.9948. The new measurement is approximately a
factor of 6$\times$ more precise than the only prior measurement. This
increased precision is in part due to a more complete analysis of the radiative
tail in the $\Upsilon(3S)$ decay, in addition to a significant increase in
statistics.


Abstract:
  The Milky Way's metal-poor stars are nearby ancient objects that are used to
study early chemical evolution and the assembly and structure of the Milky Way.
Here we present reliable metallicities of $\sim280,000$ stars with $-3.75
\lesssim$ [Fe/H] $\lesssim -0.75$ down to $g=17$ derived using
metallicity-sensitive photometry from the second data release (DR2) of the
SkyMapper Southern Survey. We use the dependency of the flux through the
SkyMapper $v$ filter on the strength of the Ca II K absorption features, in
tandem with SkyMapper $u,g,i$ photometry, to derive photometric metallicities
for these stars. We find that metallicities derived in this way compare well to
metallicities derived in large-scale spectroscopic surveys, and use such
comparisons to calibrate and quantify systematics as a function of location,
reddening, and color. We find good agreement with metallicities from the
APOGEE, LAMOST, and GALAH surveys, based on a standard deviation of
$\sigma\sim0.25$dex of the residuals of our photometric metallicities with
respect to metallicities from those surveys. We also compare our derived
photometric metallicities to metallicities presented in a number of
high-resolution spectroscopic studies to validate the low metallicity end
([Fe/H] $< -2.5$) of our photometric metallicity determinations. In such
comparisons, we find the metallicities of stars with photometric [Fe/H] $<
-2.5$ in our catalog show no significant offset and a scatter of
$\sigma\sim$0.31dex level relative to those in high-resolution work when
considering the cooler stars ($g-i > 0.65$) in our sample. We also present an
expanded catalog containing photometric metallicities of $\sim720,000$ stars as
a data table for further exploration of the metal-poor Milky Way.


Abstract:
  This paper considers the problem of differentially private semi-supervised
transfer and multi-task learning. The notion of \emph{membership-mapping} has
been developed using measure theory basis to learn data representation via a
fuzzy membership function. An alternative conception of deep autoencoder,
referred to as \emph{Conditionally Deep Membership-Mapping Autoencoder
(CDMMA)}, is considered for transferrable deep learning. Under
practice-oriented settings, an analytical solution for the learning of CDMMA
can be derived by means of variational optimization. The paper proposes a
transfer and multi-task learning approach that combines CDMMA with a tailored
noise adding mechanism to achieve a given level of privacy-loss bound with the
minimum perturbation of the data. Numerous experiments were carried out using
MNIST, USPS, Office, and Caltech256 datasets to verify the competitive robust
performance of the proposed methodology.


Abstract:
  Positivity bounds - constraints on any low-energy effective field theory
imposed by the fundamental axioms of unitarity, causality and locality in the
UV - have recently been used to constrain scalar-tensor theories of dark
energy. However, the coupling to matter fields has so far played a limited
role. We show that demanding positivity when including interactions with
standard matter fields leads to further constraints on the dark energy
parameter space. We demonstrate how implementing these bounds as theoretical
priors affects cosmological parameter constraints and explicitly illustrate the
impact on a specific Effective Field Theory for dark energy. We also show in
this model that the existence of a standard UV completion requires that
gravitational waves must travel superluminally on cosmological backgrounds.


Abstract:
  This paper theoretically characterizes the degrees-of-freedom (DoF) region of
order-$(K-1)$ messages for the $K$-user multiple-input multiple-output (MIMO)
broadcast channel with delayed channel state information at the transmitter
(CSIT) and arbitrary antenna configurations, where the transmitter has $M$
antennas and the receiver $i=1,2,\cdots,K$ has $N_i$ antennas. For the
converse, we first derive the DoF region of order-$(K-1)$ messages for the
$K$-user MIMO broadcast channel with no CSIT and arbitrary antenna
configurations with the aid of the proposed Genie-bound, and then establish the
DoF outer region. For the achievability, we first design a 2-phase transmission
scheme, and then propose a backward/forward cancellation algorithm for
decoding. Specifically, we efficiently derive the achievable DoF region from
the designed transmission scheme by transformation approach. The main
implication of this paper is that for the order-$(K-1)$ messages of $K$-user
MIMO broadcast channel, the DoF region with delayed CSIT is larger than the DoF
region with no CSIT when $N_2<M$, where $N_1 \le N_2 \le \cdots \le N_K$.


Abstract:
  Ordinal pattern dependence is a multivariate dependence measure based on the
co-movement of two time series. In strong connection to ordinal time series
analysis, the ordinal information is taken into account to derive robust
results on the dependence between the two processes. This article deals with
ordinal pattern dependence for long-range dependent time series including mixed
cases of short- and long-range dependence. We investigate the limit
distributions for estimators of ordinal pattern dependence. In doing so we
point out the differences that arise for the underlying time series having
different dependence structures. Depending on these assumptions, central and
non-central limit theorems are proven. The limit distributions for the latter
ones can be included in the class of multivariate Rosenblatt processes.
Finally, a simulation study is provided to illustrate our theoretical findings.


Abstract:
  Two-sided marketplaces are standard business models of many online platforms
(e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers,
buyers or content viewers on one side and producers, sellers or
content-creators on the other. Consumer side measurement of the impact of a
treatment variant can be done via simple online A/B testing. Producer side
measurement is more challenging because the producer experience depends on the
treatment assignment of the consumers. Existing approaches for producer side
measurement are either based on graph cluster-based randomization or on certain
treatment propagation assumptions. The former approach results in low-powered
experiments as the producer-consumer network density increases and the latter
approach lacks a strict notion of error control. In this paper, we propose (i)
a quantification of the quality of a producer side experiment design, and (ii)
a new experiment design mechanism that generates high-quality experiments based
on this quantification. Our approach, called UniCoRn (Unifying Counterfactual
Rankings), provides explicit control over the quality of the experiment and its
computation cost. Further, we prove that our experiment design is optimal to
the proposed design quality measure. Our approach is agnostic to the density of
the producer-consumer network and does not rely on any treatment propagation
assumption. Moreover, unlike the existing approaches, we do not need to know
the underlying network in advance, making this widely applicable to the
industrial setting where the underlying network is unknown and challenging to
predict a priori due to its dynamic nature. We use simulations to validate our
approach and compare it against existing methods. We also deployed UniCoRn in
an edge recommendation application that serves tens of millions of members and
billions of edge recommendations daily.


Abstract:
  The recent low-rank prior based models solve the tensor completion problem
efficiently. However, these models fail to exploit the local patterns of
tensors, which compromises the performance of tensor completion. In this paper,
we propose a novel hierarchical prior regularized matrix factorization model
for tensor completion. This model hierarchically incorporates the low-rank
prior, total variation prior, and sparse coding prior into a matrix
factorization, simultaneously characterizing both the global low-rank property
and the local smoothness of tensors. For solving the proposed model, we use the
alternating direction method of multipliers to establish our algorithm.
Besides, the complexity and convergence are investigated to further validate
the algorithm effectiveness. The proposed scheme is then evaluated through
various data sets. Experiment results verify that, the proposed method
outperforms several state-of-the-art approaches.


Abstract:
  Protecting against multi-step attacks of uncertain duration and timing forces
defenders into an indefinite, always ongoing, resource-intensive response. To
effectively allocate resources, a defender must be able to analyze multi-step
attacks under assumption of constantly allocating resources against an
uncertain stream of potentially undetected attacks. To achieve this goal, we
present a novel methodology that applies a game-theoretic approach to the
attack, attacker, and defender data derived from MITRE's ATT&CK Framework. Time
to complete attack steps is drawn from a probability distribution determined by
attacker and defender strategies and capabilities. This constraints attack
success parameters and enables comparing different defender resource allocation
strategies. By approximating attacker-defender games as Markov processes, we
represent the attacker-defender interaction, estimate the attack success
parameters, determine the effects of attacker and defender strategies, and
maximize opportunities for defender strategy improvements against an uncertain
stream of attacks. This novel representation and analysis of multi-step attacks
enables defender policy optimization and resource allocation, which we
illustrate using the data from MITRE's APT3 ATT&CK Evaluations.


Abstract:
  In this work we analyze strategies for convolutional neural network scaling;
that is, the process of scaling a base convolutional network to endow it with
greater computational complexity and consequently representational power.
Example scaling strategies may include increasing model width, depth,
resolution, etc. While various scaling strategies exist, their tradeoffs are
not fully understood. Existing analysis typically focuses on the interplay of
accuracy and flops (floating point operations). Yet, as we demonstrate, various
scaling strategies affect model parameters, activations, and consequently
actual runtime quite differently. In our experiments we show the surprising
result that numerous scaling strategies yield networks with similar accuracy
but with widely varying properties. This leads us to propose a simple fast
compound scaling strategy that encourages primarily scaling model width, while
scaling depth and resolution to a lesser extent. Unlike currently popular
scaling strategies, which result in about $O(s)$ increase in model activation
w.r.t. scaling flops by a factor of $s$, the proposed fast compound scaling
results in close to $O(\sqrt{s})$ increase in activations, while achieving
excellent accuracy. This leads to comparable speedups on modern memory-limited
hardware (e.g., GPU, TPU). More generally, we hope this work provides a
framework for analyzing and selecting scaling strategies under various
computational constraints.


Abstract:
  This paper formulates a framework for the analysis and distributed control of
interconnected systems from the behavioural perspective. The discussions are
carried out from the viewpoint of set theory and the results are completely
representation-free. The core of a dynamical system can be represented as the
set of all trajectories admissible through the system and interconnections are
interpreted as constraints on the choice of trajectories. We develop a
structure in which the interconnected behaviour can be directly built from the
behaviours of the subsystems in an explicit way without any presumed forms of
representations. We show that the interconnected behaviour can also be fully
obtained from local observations of the subsystem. Furthermore, we develop the
necessary and sufficient conditions for the existence of distributed controller
behaviours and their explicit construction. Due to the entirely
representation-free nature of this framework, it unites various representations
and descriptions of features of dynamical systems (e.g. models, dissipativity,
data, etc.) as behaviours, allowing for the formation of a unified platform for
the analysis and distributed control for interconnected systems.


Abstract:
  Dark photons and mirror matter are well-motivated dark matter candidates. It
is possible that both of them arose during the compactification and symmetry
breaking scenario of the heterotic $E_8\times E_8$ string theory and are
related to each other. In this case, dark photons can become a natural portal
into the mirror world. Unfortunately, the expected magnitude of the induced
interactions of ordinary matter with mirror matter is too small to be of
phenomenological interest.


Abstract:
  In any conformally invariant gravitational theory, the space of exact
solutions is greatly enlarged. The Weyl's conformal invariance can then be
spontaneously broken to spherically symmetric vacuum solutions that exclude the
spacetime region inside the black hole's event horizon from our Universe. We
baptize these solutions ''conformalons''. It turns out that for all such
spacetimes nothing can reach the Schwarzschild event horizon in a finite amount
of proper time for conformally coupled ``massive'' particles, or finite values
of the affine parameter for massless particles. Therefore, for such vacuum
solutions the event horizon is an asymptotic region of the Universe. As a
general feature, all conformalons show a gravitational blueshift instead of a
gravitational redshift at the unattainable event horizon, hence avoiding the
Trans-Planckian problem in the Hawking evaporation process. The latter happens
as usual near the event horizon, but now the annihilation process between the
matter and Hawking's negative energy particles takes place outside of the event
horizon. Indeed, in these solutions the gravitational collapse consists of
matter that falls down forever towards the horizon without ever reaching it.
According to a previous work that has been faithfully adapted to the
conformalons' scenario, the information is not lost in the whole process of
singularity-free collapse and evaporation, as evident from the Penrose diagram.


Abstract:
  Dimensionality reduction (DR) of data is a crucial issue for many machine
learning tasks, such as pattern recognition and data classification. In this
paper, we present a quantum algorithm and a quantum circuit to efficiently
perform linear discriminant analysis (LDA) for dimensionality reduction.
Firstly, the presented algorithm improves the existing quantum LDA algorithm to
avoid the error caused by the irreversibility of the between-class scatter
matrix $S_B$ in the original algorithm. Secondly, a quantum algorithm and
quantum circuits are proposed to obtain the target state corresponding to the
low-dimensional data. Compared with the best-known classical algorithm, the
quantum linear discriminant analysis dimensionality reduction (QLDADR)
algorithm has exponential acceleration on the number $M$ of vectors and a
quadratic speedup on the dimensionality $D$ of the original data space, when
the original dataset is projected onto a polylogarithmic low-dimensional space.
Moreover, the target state obtained by our algorithm can be used as a submodule
of other quantum machine learning tasks. It has practical application value of
make that free from the disaster of dimensionality.


Abstract:
  We study the reciprocal variety to the linear space of symmetric matrices
(LSSM) of catalecticant matrices associated with ternary quartics. With
numerical tools, we obtain 85 to be its degree and 36 to be the ML-degree of
the LSSM. We provide a geometric explanation to why equality between these two
invariants is not reached, as opposed to the case of binary forms, by
describing the intersection of the reciprocal variety and the orthogonal of the
LSSM in the rank loci. Moreover, we prove that only the rank-$1$ locus, namely
the Veronese surface $\nu_4(\mathbb{P}^2)$, contributes to the degree of the
reciprocal variety.


Abstract:
  Gravitational lensing of gravitational waves (GWs) is a powerful probe of the
matter distribution in the universe. Here we study the lensing effect induced
by dark matter (DM) halos on the GW signals from merging massive black holes,
and we revisit the possibility of detection using the Laser Interferometer
Space Antenna (LISA). In particular, we include the halos in the low-mass range
of $10^5-10^9\, M_\odot$ since they are the most numerous according to the cold
DM model. In addition, we employ the matched-filtering technique to search for
weak diffraction signatures when the MBHBs have large impact parameters
($y\sim10^2$). We find that about $(20-40)\%$ of the MBHB in the mass range of
$10^5-10^6M_\odot$ and the redshift range of $4-10$ should show detectable
wave-optics effects. The uncertainty comes mainly from the mass function of DM
halos. Not detecting any signal during the LISA mission would imply that DM
halos are significantly more massive than $10^8\,M_\odot$.


Abstract:
  Sliding is one of the fundamental mechanical movements in machinery. In
macroscopic systems, double-rack pinion machines employ gears to slide two
linear tracks along opposite directions. In microscopic systems, kinesin-5
proteins crosslink and slide apart antiparallel microtubules, promoting spindle
bipolarity and elongation during mitosis. Here we demonstrate an artificial
nanoscopic analog, in which gold nanocrystals can mediate coordinated sliding
of two antiparallel DNA origami filaments powered by DNA fuels. Stepwise and
reversible sliding along opposite directions is in situ monitored and confirmed
using fluorescence spectroscopy. A theoretical model including different energy
transfer mechanisms is developed to understand the observed fluorescence
dynamics. We further show that such sliding can also take place in the presence
of multiple DNA sidelocks that are introduced to inhibit the relative
movements. Our work enriches the toolbox of DNA-based nanomachinery, taking one
step further toward the vision of molecular nanofactories.


Abstract:
  The advent of cost effective cloud computing over the past decade and
ever-growing accumulation of high-fidelity clinical data in a modern hospital
setting is leading to new opportunities for translational medicine. Machine
learning is driving the appetite of the research community for various types of
signal data such as patient vitals. Health care systems, however, are ill
suited for massive processing of large volumes of data. In addition, due to the
sheer magnitude of the data being collected, it is not feasible to retain all
of the data in health care systems in perpetuity. This gold mine of information
gets purged periodically thereby losing invaluable future research
opportunities. We have developed a highly scalable solution that: a) siphons
off patient vital data on a nightly basis from on-premises bio-medical systems
to a cloud storage location as a permanent archive, b) reconstructs the
database in the cloud, c) generates waveforms, alarms and numeric data in a
research-ready format, and d) uploads the processed data to a storage location
in the cloud ready for research.
  The data is de-identified and catalogued such that it can be joined with
Electronic Medical Records (EMR) and other ancillary data types such as
electroencephalogram (EEG), radiology, video monitoring etc. This technique
eliminates the research burden from health care systems. This highly scalable
solution is used to process high density patient monitoring data aggregated by
the Philips Patient Information Center iX (PIC iX) hospital surveillance system
for archival storage in the Philips Data Warehouse Connect enterprise-level
database. The solution is part of a broader platform that supports a secure
high performance clinical data science platform.


Abstract:
  Recently a new class of quantum algorithms that are based on the quantum
computation of the connected moment expansion has been reported to find the
ground and excited state energies. In particular, the Peeters-Devreese-Soldatov
(PDS) formulation is found variational and bearing the potential for further
combining with the existing variational quantum infrastructure. Here we find
that the PDS formulation can be considered as a new energy functional of which
the PDS energy gradient can be employed in a conventional variational quantum
solver. In comparison with the usual variational quantum eigensolver (VQE) and
the original static PDS approach, this new variational quantum solver offers an
effective approach to navigate the dynamics to be free from getting trapped in
the local minima that refer to different states, and achieve high accuracy at
finding the ground state and its energy through the rotation of the trial wave
function of modest quality, thus improves the accuracy and efficiency of the
quantum simulation. We demonstrate the performance of the proposed variational
quantum solver for toy models, H$_2$ molecule, and strongly correlated planar
H$_4$ system in some challenging situations. In all the case studies, the
proposed variational quantum approach outperforms the usual VQE and static PDS
calculations even at the lowest order. We also discuss the limitations of the
proposed approach and its preliminary execution for model Hamiltonian on the
NISQ device.


Abstract:
  We report on some findings concerning Connes' noncommutative distance $d$ on
a weighted undirected graph $G$. Our main result is the lower bound
$\ell/\Delta(G)\le d$ where $\ell$ is the geodesic distance and $\Delta(G)$ the
degree of $G$. It is obtained thanks to an auxiliary spectral triple on the
collection of the edges of $G$.


Abstract:
  The directions of an infinite graph $G$ are a tangle-like description of its
ends: they are choice functions that choose compatibly for all finite vertex
sets $X\subseteq V(G)$ a component of $G-X$. Although every direction is
induced by a ray, there exist directions of graphs that are not uniquely
determined by any countable subset of their choices. We characterise these
directions and their countably determined counterparts in terms of star-like
substructures or rays of the graph. Curiously, there exist graphs whose
directions are all countably determined but which cannot be distinguished all
at once by countably many choices. We structurally characterise the graphs
whose directions can be distinguished all at once by countably many choices,
and we structurally characterise the graphs which admit no such countably many
choices. Our characterisations are phrased in terms of normal trees and
tree-decompositions. Our four (sub)structural characterisations imply
combinatorial characterisations of the four classes of infinite graphs that are
defined by the first and second axiom of countability applied to their end
spaces: the two classes of graphs whose end spaces are first countable or
second countable, respectively, and the complements of these two classes.


Abstract:
  A Bayesian functorial characterization of the classical relative entropy (KL
divergence) of finite probabilities was recently obtained by Baez and Fritz.
This was then generalized to standard Borel spaces by Gagn\'e and Panangaden.
Here, we provide preliminary calculations suggesting that the
finite-dimensional quantum (Umegaki) relative entropy might be characterized in
a similar way. Namely, we explicitly prove that it defines an affine functor in
the special case where the relative entropy is finite. A recent non-commutative
disintegration theorem provides a key ingredient in this proof.


Abstract:
  In this paper, we present a Neural Network (NN) model based on Neural
Architecture Search (NAS) and self-learning for received signal strength (RSS)
map reconstruction out of sparse single-snapshot input measurements, in the
case where data-augmentation by side deterministic simulations cannot be
performed. The approach first finds an optimal NN architecture and
simultaneously train the deduced model over some ground-truth measurements of a
given (RSS) map. These ground-truth measurements along with the predictions of
the model over a set of randomly chosen points are then used to train a second
NN model having the same architecture. Experimental results show that signal
predictions of this second model outperforms non-learning based interpolation
state-of-the-art techniques and NN models with no architecture search on five
large-scale maps of RSS measurements.


Abstract:
  Recent developments in neural networks have shown the potential of estimating
drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a
large high-fidelity dataset to train neural networks is deterring their use in
practical applications. In this study, we propose a transfer learning framework
to model the drag on irregular rough surfaces even with a limited amount of
direct numerical simulations. We show that transfer learning of empirical
correlations, reported in the literature, can significantly improve the
performance of neural networks for drag prediction. This is because empirical
correlations include `approximate knowledge' of the drag dependency in
high-fidelity physics. The `approximate knowledge' allows neural networks to
learn the surface statistics known to affect drag more efficiently. The
developed framework can be applied to applications where acquiring a large
dataset is difficult, but empirical correlations have been reported.


Abstract:
  Importance of monsoon rainfall cannot be ignored as it affects round the year
activities ranging from agriculture to industrial. Accurate rainfall estimation
and prediction is very helpful in decision making in the sectors of water
resource management and agriculture. Due to dynamic nature of monsoon rainfall,
it's accurate prediction becomes very challenging task. In this paper, we
analyze and evaluate various deep learning approaches such as one dimensional
Convolutional Neutral Network, Multi-layer Perceptron and Wide Deep Neural
Networks for the prediction of summer monsoon rainfall in Indian state of
Rajasthan.For our analysis purpose we have used two different types of datasets
for our experiments. From IMD grided dataset, rainfall data of 484 coordinates
are selected which lies within the geographical boundaries of Rajasthan. We
have also collected rainfall data of 158 rain gauge station from water
resources department. The comparison of various algorithms on both these data
sets is presented in this paper and it is found that Deep Wide Neural Network
based model outperforms the other two approaches.


Abstract:
  We study the influence of strong spin-orbit interaction on the formation of
flat bands in relaxed twisted bilayer WSe$_2$. Flat bands, well separated in
energy, emerge at the band edges for twist angles ($\theta$) close to 0$^o$ and
60$^o$. For $\theta$ close to 0$^o$, the interlayer hybridization together with
a moir\'{e} potential determines the electronic structure. The bands near the
valence band edge have non-trivial topology, with Chern numbers equal to +1 or
$-$1. We propose that this can be probed experimentally for twist angles less
than a critical angle of 3.5$^o$. For $\theta$ near 60$^o$, the flattening of
the bands arising from the K point of the unit cell Brillouin zone is a result
of atomic rearrangements in the individual layers. Our findings on the flat
bands and the localization of their wavefunctions for both ranges of $\theta$
match well with recent experimental observations [1,2].


Abstract:
  We construct a nonseparable Banach space $\mathcal X$ (actually, of density
continuum) such that any uncountable subset $\mathcal Y$ of the unit sphere of
$\mathcal X$ contains uncountably many points distant by less than $1$ (in
fact, by less then $1-\varepsilon$ for some $\varepsilon>0$). This solves in
the negative the central problem of the search for a nonseparable version of
Kottman's theorem which so far has produced many deep positive results for
special classes of Banach spaces and has related the global properties of the
spaces to the distances between points of uncountable subsets of the unit
sphere.
  The property of our space is strong enough to imply that it contains neither
an uncountable Auerbach system nor an uncountable equilateral set. The space is
a strictly convex renorming of the Johnson-Lindenstrauss space induced by an
$\mathbb R$-embeddable almost disjoint family of subsets of $\mathbb N$. We
also show that this special feature of the almost disjoint family is essential
to obtain the above properties.


Abstract:
  The existence of stationary distributions to distribution dependent
stochastic differential equations are investigated by using the ergodicity of
the associated decoupled equation and the Schauder fixed point theorem. By
using Zvonkin's transformation, we also establish the existence result for
equations with singular coefficients. Instead of the uniqueness, the
non-uniqueness of stationary distributions are considered for equations with
regular coefficients. Concrete examples including McKean-Vlasov stochastic
equations with the quadratic interaction and the non-quadratic interaction, and
equations with a bounded and discontinuous drift are presented to illustrate
our non-uniqueness results.


Abstract:
  We present methods and preliminary observations of two pulse Direct Laser
Acceleration in a Laser-Driven Plasma Accelerator. This acceleration mechanism
uses a second co-propagating laser pulse to overlap and further accelerate
electrons in a wakefield bubble, increasing energy at the cost of emittance
when compared to traditional laser wakefield acceleration (LWFA). To this end,
we introduce a method of femtosecond scale control of time delay between two
co-propagating pulses. We show energy enhancement when the separation between
the two pulses approaches the bubble radius.


Abstract:
  The ability to extend the field of view of laparoscopy images can help the
surgeons to obtain a better understanding of the anatomical context. However,
due to tissue deformation, complex camera motion and significant
three-dimensional (3D) anatomical surface, image pixels may have non-rigid
deformation and traditional mosaicking methods cannot work robustly for
laparoscopy images in real-time. To solve this problem, a novel two-dimensional
(2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed
in this paper, which is able to compensate for the deformation of pixels and
perform image mosaicking in real-time. The key algorithm of this 2D non-rigid
SLAM system is the expectation maximization and dual quaternion (EMDQ)
algorithm, which can generate smooth and dense deformation field from sparse
and noisy image feature matches in real-time. An uncertainty-based loop closing
method has been proposed to reduce the accumulative errors. To achieve
real-time performance, both CPU and GPU parallel computation technologies are
used for dense mosaicking of all pixels. Experimental results on \textit{in
vivo} and synthetic data demonstrate the feasibility and accuracy of our
non-rigid mosaicking method.


Abstract:
  Robustness is a key concern for Rust library development because Rust
promises no risks of undefined behaviors if developers use safe APIs only.
Fuzzing is a practical approach for examining the robustness of programs.
However, existing fuzzing tools are not directly applicable to library APIs due
to the absence of fuzz targets. It mainly relies on human efforts to design
fuzz targets case by case which is labor-intensive. To address this problem,
this paper proposes a novel automated fuzz target generation approach for
fuzzing Rust libraries via API dependency graph traversal. We identify several
essential requirements for library fuzzing, including validity and
effectiveness of fuzz targets, high API coverage, and efficiency. To meet these
requirements, we first employ breadth-first search with pruning to find API
sequences under a length threshold, then we backward search longer sequences
for uncovered APIs, and finally we optimize the sequence set as a set covering
problem. We implement our fuzz target generator and conduct fuzzing experiments
with AFL++ on several real-world popular Rust projects. Our tool finally
generates 7 to 118 fuzz targets for each library with API coverage up to 0.92.
We exercise each target with a threshold of 24 hours and find 30
previously-unknown bugs from seven libraries.


Abstract:
  Eigenmode coalescence imparts remarkable properties to non-hermitian time
evolution, culminating in a purely non-hermitian spectral degeneracy known as
an exceptional point (EP). Here, we revisit time evolution at the EP and
classify two-level non-hermitian Hamiltonians in terms of the M\"obius group.
We then leverage that classification to study dynamical EP encircling, by
applying it to periodically-modulated (Floquet) Hamiltonians. This reveals that
Floquet non-hermitian systems exhibit rich physics whose complexity is not
captured by an EP-encircling rule. For example, Floquet EPs can occur without
encircling and vice-versa. Instead, we show that the elaborate interplay
between non-hermitian and modulation instabilities is better understood through
the lens of parametric resonance.


Abstract:
  Black hole superradiance is a powerful tool in the search for ultra-light
bosons. Constraints on the existence of such particles have been derived from
the observation of highly spinning black holes, absence of continuous
gravitational-wave signals, and of the associated stochastic background.
However, these constraints are only strictly speaking valid in the limit where
the boson's interactions can be neglected. In this work we investigate the
extent to which the superradiant growth of an ultra-light dark photon can be
quenched via scattering processes with ambient electrons. For dark photon
masses $m_{\gamma^\prime} \gtrsim 10^{-17}\,{\rm eV}$, and for reasonable
values of the ambient electron number density, we find superradiance can be
quenched prior to extracting a significant fraction of the black-hole spin. For
sufficiently large $m_{\gamma^\prime}$ and small electron number densities, the
in-medium suppression of the kinetic mixing can be efficiently removed, and
quenching occurs for mixings $\chi_0 \gtrsim \mathcal{O}(10^{-8})$; at low
masses, however, in-medium effects strongly inhibit otherwise efficient
scattering processes from dissipating energy. Intriguingly, this quenching
leads to a time- and energy-oscillating electromagnetic signature, with
luminosities potentially extending up to $\sim 10^{57}\,{\rm erg / s}$,
suggesting that such events should be detectable with existing telescopes. As a
byproduct we also show that superradiance cannot be used to constrain a small
mass for the Standard Model photon.


Abstract:
  The modular structure of brain networks supports specialized information
processing, complex dynamics, and cost-efficient spatial embedding.
Inter-individual variation in modular structure has been linked to differences
in performance, disease, and development. There exist many data-driven methods
for detecting and comparing modular structure, the most popular of which is
modularity maximization. Although modularity maximization is a general
framework that can be modified and reparamaterized to address domain-specific
research questions, its application to neuroscientific datasets has, thus far,
been narrow. Here, we highlight several strategies in which the
``out-of-the-box'' version of modularity maximization can be extended to
address questions specific to neuroscience. First, we present approaches for
detecting ``space-independent'' modules and for applying modularity
maximization to signed matrices. Next, we show that the modularity maximization
frame is well-suited for detecting task- and condition-specific modules.
Finally, we highlight the role of multi-layer models in detecting and tracking
modules across time, tasks, subjects, and modalities. In summary, modularity
maximization is a flexible and general framework that can be adapted to detect
modular structure resulting from a wide range of hypotheses. This article
highlights opens multiple frontiers for future research and applications.


Abstract:
  We select 37 most common and realistic dense matter equation of states to
integrate the general relativistic stellar structure equations for static
spherically symmetric matter configurations. For all these models, we check the
compliance of the acceptability conditions that every stellar model should
satisfy. It was found that some of the non-relativistic equation of states
violate the causality and/or the dominant energy condition and that adiabatic
instabilities appear in the inner crust for all equation of state considered.


Abstract:
  Let $G$ be a graph on $n$ vertices, its adjacency matrix and degree diagonal
matrix are denoted by $A(G)$ and $D(G)$, respectively. In 2017, Nikiforov
\cite{0007} introduced the matrix $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G)$
for $\alpha\in [0, 1].$ The $A_\alpha$-spectrum of a graph $G$ consists of all
the eigenvalues (including the multiplicities) of $A_\alpha(G).$ A graph $G$ is
said to be determined by the generalized $A_{\alpha}$-spectrum (or,
DGA$_\alpha$S for short) if whenever $H$ is a graph such that $H$ and $G$ share
the same $A_{\alpha}$-spectrum and so do their complements, then $H$ is
isomorphic to $G$. In this paper, when $\alpha$ is rational, we present a
simple arithmetic condition for a graph being DGA$_\alpha$S. More precisely,
put $A_{c_\alpha}:={c_\alpha}A_\alpha(G),$ here ${c_\alpha}$ is the smallest
positive integer such that $A_{c_\alpha}$ is an integral matrix. Let
$\tilde{W}_{{\alpha}}(G)=\left[{\bf 1},\frac{A_{c_\alpha}{\bf
1}}{c_\alpha},\ldots, \frac{A_{c_\alpha}^{n-1}{\bf 1}}{c_\alpha}\right]$, where
${\bf 1}$ denotes the all-ones vector. We prove that if $\frac{\det
\tilde{W}_{{\alpha}}(G)}{2^{\lfloor\frac{n}{2}\rfloor}}$ is an odd and
square-free integer and the rank of $\tilde{W}_{{\alpha}}(G)$ is full over
$\mathbb{F}_p$ for each odd prime divisor $p$ of $c_\alpha$, then $G$ is
DGA$_\alpha$S except for even $n$ and odd $c_\alpha\,(\geqslant 3)$. By our
obtained results in this paper we may deduce the main results in \cite{0005}
and \cite{0002}.


Abstract:
  Advanced manufacturing (AM) technologies, such as nanoscale additive
manufacturing process, enable the fabrication of nanoscale architected
materials which has received great attention due to their prominent properties.
However, few studies delve into the functional gradient cellular architecture
on nanoscale. This work studied the gradient nano-Gyroid architected material
made of copper (Cu) by molecular dynamic (MD) simulations. The result reveals
that, unlike homogeneous architecture, gradient Gyroid not only shows novel
layer-by-layer deformation behavior, but also processes significantly better
energy absorption ability. Moreover, this deformation behavior and energy
absorption are predictable and designable, which demonstrates its highly
programmable potential.


Abstract:
  Quantum computing offers a powerful new paradigm of information processing
that has the potential to transform a wide range of industries. In the pursuit
of the tantalizing promises of a universal quantum computer, a multitude of new
knowledge and expertise has been developed, enabling the construction of novel
quantum algorithms as well as increasingly robust quantum hardware. In
particular, we have witnessed rapid progress in the circuit quantum
electrodynamics (cQED) technology, which has emerged as one of the most
promising physical systems that is capable of addressing the key challenges in
realizing full-stack quantum computing on a large scale. In this article, we
present some of the most crucial building blocks developed by the cQED
community in recent years and a pr\'{e}cis of the latest achievements towards
robust universal quantum computation. More importantly, we aim to provide a
synoptic outline of the core techniques that underlie most cQED experiments and
offer a practical guide for a novice experimentalist to design, construct, and
characterize their first quantum device


Abstract:
  The advantages of operating selected transmission lines at frequencies other
than the standard 50 or 60 Hz are numerous, encompassing increased power
transfer capacity and better utilization of existing infrastructure. While high
voltage DC (HVDC) is by far the most well-established example, there has been
an emerging interest low frequency AC (LFAC) transmission in applications
ranging from offshore wind to railway systems and mining. In this paper, we
investigate the use of LFAC as a transmission upgrade and propose models and
analysis methods to determine the optimal choice of frequency. The paper first
presents an optimal power flow model with frequency as a variable, assuming
modular multilevel converters for frequency conversion. Using this model, we
analyze LFAC as an embedded upgrade in a transmission system using existing
lines. We quantify the system-wide advantages from improved power flow control
and frequency reduction and find that an LFAC upgrade achieves similar and
sometimes better results compared with HVDC upgrades. Finally, we analyze the
factors which determine the optimal frequency for these upgraded transmission
lines, and we demonstrate the benefits of changing the frequency in response to
different system topologies and operating conditions.


Abstract:
  Understanding and explaining the mistakes made by trained models is critical
to many machine learning objectives, such as improving robustness, addressing
concept drift, and mitigating biases. However, this is often an ad hoc process
that involves manually looking at the model's mistakes on many test samples and
guessing at the underlying reasons for those incorrect predictions. In this
paper, we propose a systematic approach, conceptual counterfactual
explanations(CCE), that explains why a classifier makes a mistake on a
particular test sample(s) in terms of human-understandable concepts (e.g. this
zebra is misclassified as a dog because of faint stripes). We base CCE on two
prior ideas: counterfactual explanations and concept activation vectors, and
validate our approach on well-known pretrained models, showing that it explains
the models' mistakes meaningfully. In addition, for new models trained on data
with spurious correlations, CCE accurately identifies the spurious correlation
as the cause of model mistakes from a single misclassified test sample. On two
challenging medical applications, CCE generated useful insights, confirmed by
clinicians, into biases and mistakes the model makes in real-world settings.
The code for CCE is publicly available and can easily be applied to explain
mistakes in new models.


Abstract:
  The spin-1/2 antiferromagnetic Heisenberg systems are studied on three
typical diamond-type hierarchical lattices (systems A, B and C) with fractal
dimensions 1.63, 2 and 2.58, respectively, and the phase diagrams, critical
phenomena and quantum correlations are calculated by a combination of the
equivalent transformation and real-space renormalization group methods. We find
that there exist a reentrant behavior for system A and a finite temperature
transition in the isotropic Heisenberg limit for system C (not for system B).
Unlike the ferromagnetic case, the Neel temperatures of antiferromagnetic
systems A and B are inversely proportional to ln(Delta_c-Delta) (when
Delta->Delta_c) and ln Delta (when Delta->0), respectively. And we also find
that there is a turning point of quantum correlation in the isotropic
Heisenberg limit Delta=0 where there is a peak of the contour and no matter how
large the size of system is, quantum correlation will change to zero in the
Ising limit for the three systems. The quantum correlation decreases with the
increase of lattice size L and it is almost zero when L>=30 for system A, and
for systems B and C, they still exist when L is larger than that of system A.
Moreover, as an example, we discuss the error of result in system A, which is
induced by the noncommutativity.


Abstract:
  The effectiveness of resource allocation under emergencies especially
hurricane disasters is crucial. However, most researchers focus on emergency
resource allocation in a ground transportation system. In this paper, we
propose Learning-to-Dispatch (L2D), a reinforcement learning (RL) based air
route dispatching system, that aims to add additional flights for hurricane
evacuation while minimizing the airspace's complexity and air traffic
controller's workload. Given a bipartite graph with weights that are learned
from the historical flight data using RL in consideration of short- and
long-term gains, we formulate the flight dispatch as an online maximum weight
matching problem. Different from the conventional order dispatch problem, there
is no actual or estimated index that can evaluate how the additional evacuation
flights influence the air traffic complexity. Then we propose a multivariate
reward function in the learning phase and compare it with other univariate
reward designs to show its superior performance. The experiments using the
real-world dataset for Hurricane Irma demonstrate the efficacy and efficiency
of our proposed schema.


Abstract:
  We construct the general permutation invariant Gaussian 2-matrix model for
matrices of arbitrary size $D$. The parameters of the model are given in terms
of variables defined using the representation theory of the symmetric group
$S_D$. A correspondence is established between the permutation invariant
polynomial functions of the matrix variables (the observables of the model) and
directed colored graphs, which sheds light on stability properties in the large
$D$ counting of these invariants. The refined counting of the graphs is given
in terms of double cosets involving permutation groups defined by the local
structure of the graphs. Linear and quadratic observables are transformed to an
$S_D$ representation theoretic basis and are used to define the convergent
Gaussian measure. The perturbative rules for the computation of expectation
values of graph-basis observables of any degree are given in terms of the
representation theoretic parameters. Explicit results for a number of
observables of degree up to four are given along with a Sage programme that
computes general expectation values.


Abstract:
  Continued follow-up of WISEA J153429.75-104303.3, announced in Meisner et al
(2020), has proven it to have an unusual set of properties. New imaging data
from Keck/MOSFIRE and HST/WFC3 show that this object is one of the few faint
proper motion sources known with J-ch2 > 8 mag, indicating a very cold
temperature consistent with the latest known Y dwarfs. Despite this, it has
W1-W2 and ch1-ch2 colors ~1.6 mag bluer than a typical Y dwarf. A new
trigonometric parallax measurement from a combination of WISE, Spitzer, and HST
astrometry confirms a nearby distance of $16.3^{+1.4}_{-1.2}$ pc and a large
transverse velocity of $207.4{\pm}15.9$ km/s. The absolute J, W2, and ch2
magnitudes are in line with the coldest known Y dwarfs, despite the highly
discrepant W1-W2 and ch1-ch2 colors. We explore possible reasons for the unique
traits of this object and conclude that it is most likely an old, metal-poor
brown dwarf and possibly the first Y subdwarf. Given that the object has an HST
F110W magnitude of 24.7 mag, broad-band spectroscopy and photometry from JWST
are the best options for testing this hypothesis.


Abstract:
  Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a
central component in online learning and AI in Education. In this paper, we
present a simple, yet effective strategy to improve the generalization ability
of KT models: we propose three types of novel data augmentation, coined
replacement, insertion, and deletion, along with corresponding regularization
losses that impose certain consistency or monotonicity biases on the model's
predictions for the original and augmented sequence. Extensive experiments on
various KT benchmarks show that our regularization scheme consistently improves
the model performances, under 3 widely-used neural networks and 4 public
benchmarks, e.g., it yields 6.3% improvement in AUC under the DKT model and the
ASSISTmentsChall dataset.


Abstract:
  We devise a condition strictly between the existence of an $n$-ary and an
$n{+}1$-ary near-unanimity term. We evaluate exactly the distributivity and
modularity levels implied by such a condition.


Abstract:
  Administrative data, or non-probability sample data, are increasingly being
used to obtain official statistics due to their many benefits over survey
methods. In particular, they are less costly, provide a larger sample size, and
are not reliant on the response rate. However, it is difficult to obtain an
unbiased estimate of the population mean from such data due to the absence of
design weights. Several estimation approaches have been proposed recently using
an auxiliary probability sample which provides representative covariate
information of the target population. However, when this covariate information
is high-dimensional, variable selection is not a straight-forward task even for
a subject matter expert. In the context of efficient and doubly robust
estimation approaches for estimating a population mean, we develop two data
adaptive methods for variable selection using the outcome adaptive LASSO and a
collaborative propensity score, respectively. Simulation studies are performed
in order to verify the performance of the proposed methods versus competing
methods. Finally, we presented an anayisis of the impact of Covid-19 on
Canadians.


Abstract:
  Breaking of time-reversal and point-group spatial symmetries can have a
profound impact on superconductivity. One of the most extraordinary effects,
due to the application of a magnetic field, is represented by the Abrikosov
vortices with charged supercurrents circulating around their cores. Whether a
similar phenomenon can be obtained by exploiting spatial symmetry breaking,
e.g. through electric fields or mechanical strain, is a fundamentally relevant
but not yet fully settled problem. Here, we show that in two-dimensional
spin-singlet superconductors with unusually low degree of spatial symmetry
content, vortices with supercurrents carrying angular momentum around the core
can form and be energetically stable. The vortex has zero net magnetic flux
since it is made up of counter-propagating Cooper pairs with opposite orbital
moments. By solving self-consistently the Bogoliubov - de Gennes equations in
real space, we demonstrate that the orbital vortex is stable and we unveil the
spatial distribution of the superconducting order parameter around its core.
The resulting amplitude has a characteristic pattern with a pronounced angular
anisotropy that deviates from the profile of conventional magnetic vortices.
These hallmarks guide predictions and proposals for the experimental detection.


Abstract:
  We study the performance of the gradient play algorithm for stochastic games
(SGs), where each agent tries to maximize its own total discounted reward by
making decisions independently based on current state information which is
shared between agents. Policies are directly parameterized by the probability
of choosing a certain action at a given state. We show that Nash equilibria
(NEs) and first-order stationary policies are equivalent in this setting, and
give a local convergence rate around strict NEs. Further, for a subclass of SGs
called Markov potential games (which includes the cooperative setting with
identical rewards among agents as an important special case), we design a
sample-based reinforcement learning algorithm and give a non-asymptotic global
convergence rate analysis for both exact gradient play and our sample-based
learning algorithm. Our result shows that the number of iterations to reach an
$\epsilon$-NE scales linearly, instead of exponentially, with the number of
agents. Local geometry and local stability are also considered, where we prove
that strict NEs are local maxima of the total potential function and
fully-mixed NEs are saddle points.


Abstract:
  The quest for practical waveguides operating in the terahertz range faces two
major hurdles: large losses and high rigidity. While recent years have been
marked by remarkable progress in lowering the impact of material losses using
hollow-core guidance, such waveguides are typically not flexible. Here we
experimentally and numerically investigate antiresonant dielectric waveguides
made of polyurethane, a commonly used dielectric with a low Young's modulus.
The hollow-core nature of antiresonant fibers leads to low transmission losses
using simple structures, whereas the low Young's modulus of polyurethane makes
them extremely flexible. The structures presented enable millimeter-wave
manipulation in centimeter-thick waveguides in the same spirit as conventional
(visible- and near-IR-) optical fibers, i.e. conveniently and reconfigurably.
We investigate two canonical antiresonant geometries formed by one- and
six-tubes, experimentally comparing their transmission, bend losses and mode
profiles. The waveguides under investigation have loss below 1 dB/cm in their
sub-THz transmission bands, increasing by 1 dB/cm for a bend radius of about 10
cm, which is analogous to bending standard $125 \mu{\rm m}$ diameter fiber to a
1.2 mm radius.


Abstract:
  A better understanding of dispersion in natural streams requires knowledge of
longitudinal dispersion coefficient(LDC). Various methods have been proposed
for predictions of LDC. Those studies can be grouped into three types:
analytical, statistical and ML-driven researches(Implicit and explicit).
However, a comprehensive evaluation of them is still lacking. In this paper, we
first present an in-depth analysis of those methods and find out their defects.
This is carried out on an extensive database composed of 660 samples of
hydraulic and channel properties worldwide. The reliability and
representativeness of utilized data are enhanced through the deployment of the
Subset Selection of Maximum Dissimilarity(SSMD) for testing set selection and
the Inter Quartile Range(IQR) for removal of the outlier. The evaluation
reveals the rank of those methods as: ML-driven method > the statistical method
> the analytical method. Whereas implicit ML-driven methods are black-boxes in
nature, explicit ML-driven methods have more potential in prediction of LDC.
Besides, overfitting is a universal problem in existing models. Those models
also suffer from a fixed parameter combination. To establish an interpretable
model for LDC prediction with higher performance, we then design a novel
symbolic regression method called evolutionary symbolic regression
network(ESRN). It is a combination of genetic algorithms and neural networks.
Strategies are introduced to avoid overfitting and explore more parameter
combinations. Results show that the ESRN model has superiorities over other
existing symbolic models in performance. The proposed model is suitable for
practical engineering problems due to its advantage in low requirement of
parameters (only w and U* are required). It can provide convincing solutions
for situations where the field test cannot be carried out or limited field
information can be obtained.


Abstract:
  The building of a stellar structure requires knowing the Rosseland mean
opacity at each layer of the model. This mean opacity is very often
interpolated in pre-computed tables due to the overwhelming time to compute it
from monochromatic cross sections. The main drawback to using tables is that
the opacities can be inconsistent with the actual local chemical composition,
for instance in the regions of the star where nucleosynthesis occurs. We
developed a strategy that allows very fast calculations of Rosseland opacities
from monochromatic cross sections. This method has been implemented in the
Toulouse-Geneva evolution code, which we used to compute evolutionary tracks
with models whose Rosseland opacities are fully consistent with the chemical
mix everywhere in the star. Our self-consistent models show very small
structural differences compared to models where the Rosseland opacity is
computed with a fixed metal mixture. The main-sequence evolutionary tracks are
almost the same for models of mass ranging from 2 to 8 solar masses. At a given
surface gravity the relative difference in age is lower than 2% and generally
below 1% between the two kinds of calculations, the self-consistent model being
younger most of the time. Unless such a precision in age is sought out, the use
of tabulated Rosseland opacities with a metal content defined globally is still
acceptable, at least in main-sequence stars where the chemical mix changes only
through nucleosynthesis.


Abstract:
  We investigate how the production of gravitational waves (GWs) is affected by
the GW velocity $(c_T)$ during preheating after inflation. For instance, we
simulate the production of GWs after $\lambda\phi^4$ chaotic inflation, and
find that GW spectrum is enhanced for $c_T<1$, but distorted (suppressed at low
frequency, but enhanced at high frequency) for $c_T>1$.


Abstract:
  Electroactive polymer thin films undergo repeated reversible structural
change during operation in electrochemical applications. While synchrotron
X-ray scattering is powerful for the characterization of stand-alone and
ex-situ organic thin films, in situ structural characterization has been
underutilized--in large part due to complications arising from supporting
electrolyte scattering. This has greatly hampered the development of
application relevant structure property relationships. Therefore, we have
developed a new methodology for in situ and operando X-ray characterization
that separates the incident and scattered X-ray beam path from the electrolyte.
As a proof of concept, we demonstrate the in situ structural changes of
weakly-scattering, organic mixed ionic-electronic conductor thin films in an
aqueous electrolyte environment, enabling access to previously unexplored
changes in the pi-pi peak and diffuse scatter in situ, while capturing the
solvent swollen thin film structure which was inaccessible in previous ex situ
studies. These in situ measurements improve the sensitivity to structural
changes, capturing minute changes not possible ex situ, and have multimodal
potential such as combined Raman measurements that also serve to validate the
true in situ/operando conditions of the cell. Finally, we examine new
directions enabled by this operando cell design and compare state of the art
measurements.


Abstract:
  Small-Signal Stability (SSS) is crucial for the control of power grids.
However, False Data Injection (FDI) attacks against SSS can impact the grid
stability, hence, the security of SSS needs to be studied. This paper proposes
a formal method of synthesizing FDI attack vectors (i.e., a set of measurements
to be altered) that can destabilize power systems. We formulate an FDI attack
as an optimization problem using AC power flow, SSS model, and stability
constraints. The attacker capability is modeled as the accessibility to a
limited set of measurements. The solution of the proposed FDI attack model
provides a destabilizing attack vector if exists. We implement the proposed
mechanism and evaluate its performance by conducting several case studies using
the WSCC 3-machine 9-bus system. The case study results showed that the
possibility of random FDIs (i.e., with no knowledge of the power system) in
launching a destabilizing attack is too low to be successful. However, an
intelligent attacker can leverage the grid knowledge to make the system
unstable, even with limited access to the measurements.


Abstract:
  A fundamental problem in numerical analysis and approximation theory is
approximating smooth functions by polynomials. A much harder version under
recent consideration is to enforce bounds constraints on the approximating
polynomial. In this paper, we consider the problem of approximating functions
by polynomials whose Bernstein coefficients with respect to a given degree
satisfy such bounds, which implies such bounds on the approximant. We frame the
problem as an inequality-constrained optimization problem and give an algorithm
for finding the Bernstein coefficients of the exact solution. Additionally, our
method can be modified slightly to include equality constraints such as mass
preservation. It also extends naturally to multivariate polynomials over a
simplex.


Abstract:
  An unconfined strongly swirled flow is investigated for different Reynolds
numbers using particle image velocimetry (PIV) and Large Eddy Simulation (LES)
with a Thickened Flame (TF) model. Both reacting and non-reacting flow results
are presented. In the LES-TF approach, the flame front is resolved on the
computational grid through artificial thickening and the individual species
transport equations are directly solved with the reaction rates specified using
Arrhenius chemistry. Good agreement is found when comparing predictions with
the experimental data. Also the predicted RMS fluctuations exhibit a
double-peak profile with one peak in the burnt and the other in the un-burnt
region. The measured and predicted heat release distributions are in
qualitative agreement with each other and exhibit the highest values along the
inner edge of the shear layer. The precessing vortex core (PVC) is clearly
observed in both the non-reacting and reacting cases. However, it appears more
axially-elongated for the reacting cases and the oscillations in the PVC are
damped with reactions.


Abstract:
  Tukey order are used to compare the cofinal complexity of partially order
sets (posets). We prove that there is a $2^\mathfrak{c}$-sized collection of
sub-posets in $2^\omega$ which forms an antichain in the sense of Tukey
ordering. Using the fact that any boundedly-complete sub-poset of
$\omega^\omega$ is a Tukey quotient of $\omega^\omega$, we answer two open
questions published in \cite{FKL16}.
  The relation between $P$-base and strong Pytkeev$^\ast$ property is
investigated. Let $P$ be a poset equipped with a second-countable topology in
which every convergent sequence is bounded. Then we prove that any topological
space with a $P$-base has the strong Pytkeev$^\ast$ property. Furthermore, we
prove that every uncountably-dimensional locally convex space (lcs) with a
$P$-base contains an infinite-dimensional metrizable compact subspace. Examples
in function spaces are given.


Abstract:
  We study the phase transitions of a fluid confined in a capillary slit made
from two adjacent walls each of which are a periodic composite of stripes of
two different materials. For wide slits the capillary condensation occurs at a
pressure which is described accurately by a combination of the Kelvin equation
and the Cassie law for an averaged contact angle. However, for narrow slits the
condensation occurs in two steps involving an intermediate bridging phase, with
the corresponding pressures described by two new Kelvin equations. These are
characterised by different contact angles due to interfacial pinning, with one
larger and one smaller than the Cassie angle. We determine the triple point and
predict two types of dispersion force induced Derjaguin-like corrections due to
mesoscopic volume reduction and the singular free-energy contribution from
nano-droplets and bubbles. We test these predictions using a fully microscopic
density functional model which confirms their validity even for molecularly
narrow slits. Analogous mesoscopic corrections are also predicted for two
dimensional systems arising from thermally induced interfacial wandering.


Abstract:
  In this work, we propose a novel convolutional autoencoder based architecture
to generate subspace specific feature representations that are best suited for
classification task. The class-specific data is assumed to lie in low
dimensional linear subspaces, which could be noisy and not well separated,
i.e., subspace distance (principal angle) between two classes is very low. The
proposed network uses a novel class-specific self expressiveness (CSSE) layer
sandwiched between encoder and decoder networks to generate class-wise subspace
representations which are well separated. The CSSE layer along with encoder/
decoder are trained in such a way that data still lies in subspaces in the
feature space with minimum principal angle much higher than that of the input
space. To demonstrate the effectiveness of the proposed approach, several
experiments have been carried out on state-of-the-art machine learning datasets
and a significant improvement in classification performance is observed over
existing subspace based transformation learning methods.


Abstract:
  Recent advances in multi-agent reinforcement learning have been largely
limited in training one model from scratch for every new task. The limitation
is due to the restricted model architecture related to fixed input and output
dimensions. This hinders the experience accumulation and transfer of the
learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs
6 multi-agent games). In this paper, we make the first attempt to explore a
universal multi-agent reinforcement learning pipeline, designing one single
architecture to fit tasks with the requirement of different observation and
action configurations. Unlike previous RNN-based models, we utilize a
transformer-based model to generate a flexible policy by decoupling the policy
distribution from the intertwined input observation with an importance weight
measured by the merits of the self-attention mechanism. Compared to a standard
transformer block, the proposed model, named as Universal Policy Decoupling
Transformer (UPDeT), further relaxes the action restriction and makes the
multi-agent task's decision process more explainable. UPDeT is general enough
to be plugged into any multi-agent reinforcement learning pipeline and equip
them with strong generalization abilities that enables the handling of multiple
tasks at a time. Extensive experiments on large-scale SMAC multi-agent
competitive games demonstrate that the proposed UPDeT-based multi-agent
reinforcement learning achieves significant results relative to
state-of-the-art approaches, demonstrating advantageous transfer capability in
terms of both performance and training speed (10 times faster).


Abstract:
  Physical representations of data offer physical and spatial ways of looking
at, navigating, and interacting with data. While digital fabrication has
facilitated the creation of objects with data-driven geometry, rendering data
as a physically fabricated object is still a daunting leap for many
physicalization designers. Rendering in the scope of this research refers to
the back-and-forth process from digital design to digital fabrication and its
specific challenges. We developed a corpus of example data physicalizations
from research literature and physicalization practice. This survey then unpacks
the "rendering" phase of the extended InfoVis pipeline in greater detail
through these examples, with the aim of identifying ways that researchers,
artists, and industry practitioners "render" physicalizations using digital
design and fabrication tools.


Abstract:
  To model is to represent. The threshold of decidability defines two
epistemological choices: one model (or a finite number of models) suffices for
representing the dynamics below the undecidable; above this threshold (defined
as G-complexity), every model is partial, no complete modeling is possible.


Abstract:
  It is known that the estimating equations for quantile regression (QR) can be
solved using an EM algorithm in which the M-step is computed via weighted least
squares, with weights computed at the E-step as the expectation of independent
generalized inverse-Gaussian variables. This fact is exploited here to extend
QR to allow for random effects in the linear predictor. Convergence of the
algorithm in this setting is established by showing that it is a generalized
alternating minimization (GAM) procedure. Another modification of the EM
algorithm also allows us to adapt a recently proposed method for variable
selection in mean regression models to the QR setting. Simulations show the
resulting method significantly outperforms variable selection in QR models
using the lasso penalty. Applications to real data include a frailty QR
analysis of hospital stays, and variable selection for age at onset of lung
cancer and for riboflavin production rate using high-dimensional gene
expression arrays for prediction.


Abstract:
  The Zwicky Transient Facility recently announced the detection of an optical
transient AT2020blt at redshift $z=2.9$, consistent with the afterglow of a
gamma-ray burst. No prompt emission was observed. We analyse AT2020blt with
detailed models, showing the data are best explained as the afterglow of an
on-axis long gamma-ray burst, ruling out other hypotheses such as a cocoon and
a low-Lorentz factor jet. We search \textit{Fermi} data for prompt emission,
setting deeper upper limits on the prompt emission than in the original
detection paper. Together with \konus{} observations, we show that the
gamma-ray efficiency of AT2020blt is $\lesssim 2.8\%$, lower than $98.4\%$ of
observed gamma-ray bursts. We speculate that AT2020blt and AT2021any belong to
the low-efficiency tail of long gamma-ray burst distributions that are
beginning to be readily observed due to the capabilities of new observatories
like the Zwicky Transient Facility.


Abstract:
  Conditioning is crucial in applied science when inference involving time
series is involved. Belief calculus is an effective way of handling such
inference in the presence of epistemic uncertainty -- unfortunately, different
approaches to conditioning in the belief function framework have been proposed
in the past, leaving the matter somewhat unsettled. Inspired by the geometric
approach to uncertainty, in this paper we propose an approach to the
conditioning of belief functions based on geometrically projecting them onto
the simplex associated with the conditioning event in the space of all belief
functions. We show here that such a geometric approach to conditioning often
produces simple results with straightforward interpretations in terms of
degrees of belief. This raises the question of whether classical approaches,
such as for instance Dempster's conditioning, can also be reduced to some form
of distance minimisation in a suitable space. The study of families of
combination rules generated by (geometric) conditioning rules appears to be the
natural prosecution of the presented research.


Abstract:
  This paper studies linear convergence of the subspace constrained mean shift
(SCMS) algorithm, a well-known algorithm for identifying a density ridge
defined by a kernel density estimator. By arguing that the SCMS algorithm is a
special variant of a subspace constrained gradient ascent (SCGA) algorithm with
an adaptive step size, we derive linear convergence of such SCGA algorithm.
While the existing research focuses mainly on density ridges in the Euclidean
space, we generalize density ridges and the SCMS algorithm to directional data.
In particular, we establish the stability theorem of density ridges with
directional data and prove the linear convergence of our proposed directional
SCMS algorithm.


Abstract:
  We describe a general approach to modeling rational decision-making agents
who adopt either quantum or classical mechanics based on the Quantum Bayesian
(QBist) approach to quantum theory. With the additional ingredient of a scheme
by which the properties of one agent may influence another, we arrive at a
flexible framework for treating multiple interacting quantum and classical
Bayesian agents. We present simulations in several settings to illustrate our
construction: quantum and classical agents receiving signals from an exogenous
source, two interacting classical agents, two interacting quantum agents, and
interactions between classical and quantum agents. A consistent treatment of
multiple interacting users of quantum theory may allow us to properly interpret
existing multi-agent protocols and could suggest new approaches in other areas
such as quantum algorithm design.


Abstract:
  An important issue when using Machine Learning algorithms in recent research
is the lack of interpretability. Although these algorithms provide accurate
point predictions for various learning problems, uncertainty estimates
connected with point predictions are rather sparse. A contribution to this gap
for the Random Forest Regression Learner is presented here. Based on its
Out-of-Bag procedure, several parametric and non-parametric prediction
intervals are provided for Random Forest point predictions and theoretical
guarantees for its correct coverage probability is delivered. In a second part,
a thorough investigation through Monte-Carlo simulation is conducted evaluating
the performance of the proposed methods from three aspects: (i) Analyzing the
correct coverage rate of the proposed prediction intervals, (ii) Inspecting
interval width and (iii) Verifying the competitiveness of the proposed
intervals with existing methods. The simulation yields that the proposed
prediction intervals are robust towards non-normal residual distributions and
are competitive by providing correct coverage rates and comparably narrow
interval lengths, even for comparably small samples.


Abstract:
  Speech evaluation is an essential component in computer-assisted language
learning (CALL). While speech evaluation on English has been popular, automatic
speech scoring on low resource languages remains challenging. Work in this area
has focused on monolingual specific designs and handcrafted features stemming
from resource-rich languages like English. Such approaches are often difficult
to generalize to other languages, especially if we also want to consider
suprasegmental qualities such as rhythm. In this work, we examine three
different languages that possess distinct rhythm patterns: English
(stress-timed), Malay (syllable-timed), and Tamil (mora-timed). We exploit
robust feature representations inspired by music processing and vector
representation learning. Empirical validations show consistent gains for all
three languages when predicting pronunciation, rhythm and intonation
performance.


Abstract:
  The $\textit{planar slope number}$ $psn(G)$ of a planar graph $G$ is the
minimum number of edge slopes in a planar straight-line drawing of $G$. It is
known that $psn(G) \in O(c^\Delta)$ for every planar graph $G$ of degree
$\Delta$. This upper bound has been improved to $O(\Delta^5)$ if $G$ has
treewidth three, and to $O(\Delta)$ if $G$ has treewidth two. In this paper we
prove $psn(G) \in \Theta(\Delta)$ when $G$ is a Halin graph, and thus has
treewidth three. Furthermore, we present the first polynomial upper bound on
the planar slope number for a family of graphs having treewidth four. Namely we
show that $O(\Delta^2)$ slopes suffice for nested pseudotrees.


Abstract:
  We survey Natural Language Processing (NLP) approaches to summarizing,
simplifying, and generating patents' text. While solving these tasks has
important practical applications - given patents' centrality in the R&D process
- patents' idiosyncrasies open peculiar challenges to the current NLP state of
the art. This survey aims at a) describing patents' characteristics and the
questions they raise to the current NLP systems, b) critically presenting
previous work and its evolution, and c) drawing attention to directions of
research in which further work is needed. To the best of our knowledge, this is
the first survey of generative approaches in the patent domain.


Abstract:
  A splitting of the fundamental optical modes in micro/nano-cavities
comprising semiconductor heterostructures is commonly observed. Given that this
splitting plays an important role for the light-matter interaction and hence
quantum technology applications, a method for controlling the mode-splitting is
important. In this work we use an open microcavity composed of a "bottom"
semiconductor distributed Bragg reflector (DBR) incorporating an n-i-p
heterostructure, paired with a "top" curved dielectric DBR. We measure the
mode-splitting as a function of wavelength across the stopband. We demonstrate
a reversible in-situ technique to tune the mode-splitting by applying uniaxial
stress to the semiconductor DBR. The method exploits the photoelastic effect of
the semiconductor materials. We achieve a maximum tuning of $\sim$11 GHz. The
stress applied to the heterostructure is determined by observing the
photoluminescence of quantum dots embedded in the sample, converting a spectral
shift to a stress via deformation potentials. A thorough study of the
mode-splitting and its tuning across the stop-band leads to a quantitative
understanding of the mechanism behind the results.


Abstract:
  {We present a study of neutron star models that contain dark matter (DM) in
the core. The DM is assumed to have a particle nature and to be
self-interacting. Using constraints on the mass and radius of neutron stars, we
investigate the allowed properties of either bosonic or fermionic DM particles.
We consider cases where it constitutes up to 15\% of the mass of the star, even
though conventional mechanisms cannot generate such large fractions. For this
purpose three different models of neutron stars are considered, the first
involving nucleons only, the second including hyperons, and the last involving
strange matter in the core. Different EoSs are constructed for the various
cases of fermionic and bosonic DM. These EoSs are solved for selected
properties of the DM particles and the results are tested against mass, radius
and tidal deformability constraints for neutron stars. The distribution of
energy density of DM and ordinary matter inside the neutron stars is also
presented. It is found that if the DM is fermionic in nature it does not just
sit in the core but it is present everywhere in the star, from the centre to
outside the surface and may even envelop it.


Abstract:
  Evaluating robustness of machine-learning models to adversarial examples is a
challenging problem. Many defenses have been shown to provide a false sense of
security by causing gradient-based attacks to fail, and they have been broken
under more rigorous evaluations. Although guidelines and best practices have
been suggested to improve current adversarial robustness evaluations, the lack
of automatic testing and debugging tools makes it difficult to apply these
recommendations in a systematic manner. In this work, we overcome these
limitations by (i) defining a set of quantitative indicators which unveil
common failures in the optimization of gradient-based attacks, and (ii)
proposing specific mitigation strategies within a systematic evaluation
protocol. Our extensive experimental analysis shows that the proposed
indicators of failure can be used to visualize, debug and improve current
adversarial robustness evaluations, providing a first concrete step towards
automatizing and systematizing current adversarial robustness evaluations. Our
open-source code is available at:
https://github.com/pralab/IndicatorsOfAttackFailure.


Abstract:
  CO2+H2 greenhouse warming has recently emerged as a promising scenario to
sufficiently warm the early martian surface to allow the formation of valley
networks and lakes. Here we present numerical 3-D global climate simulations of
the early martian climate that we have performed assuming dense CO2+H2
atmospheres. Our climate model, derived from earlier works by Forget et al.
(2013) and Wordsworth et al. (2013), is coupled to an asynchronous model of the
long-term evolution of martian glaciers and lakes. Simulations were carried out
at 40{\deg} obliquity to investigate how (i) water content and (ii) H2 content
(added to 1 or 2 bars of CO2) can shape the climate and hydrologic cycle of
early Mars. We show that the adiabatic cooling mechanism (Wordsworth et al.
2013) that leads to the accumulation of ice deposits in the southern highlands
in cold climate (the so called 'icy highland scenario') also works in warm
climates, with impact crater lakes acting as the main water reservoirs. This
produces rainfall mainly localized in the southern highlands of Mars. If one
adjust (i) the amount of CO2 and H2, (ii) the size and location of the water
reservoirs, and (iii) the ancient topography (i.e. by removing Tharsis), the
spatial patterns of surface runoff (from rainfall or snowmelt) in the
simulations can match -- with a few exceptions -- the observed distribution of
valley networks and impact crater lakes. Although our results are obtained for
CO2-dominated atmospheres enriched with H2, they should also apply to assess
the impact of any combination of powerful long-lived greenhouse gases on early
Mars.


Abstract:
  The one-dimensional ($1D$) Galerkin-truncated Burgers equation, with both
dissipation and noise terms included, is studied using spectral methods. When
the truncation-scale Reynolds number $R_{\rm min}$ is varied, from very small
values to order $1$ values, the scale-dependent correlation time $\tau(k)$ is
shown to follow the expected crossover from the short-distance $\tau(k) \sim
k^{-2}$ Edwards-Wilkinson scaling to the universal long-distance
Kardar-Parisi-Zhang scaling $\tau(k) \sim k^{-3/2}$. In the inviscid limit:
$R_{\rm min}\to \infty$, we show that the system displays {\it another}
crossover to the Galerkin-truncated inviscid-Burgers regime that admits
thermalised solutions with $\tau(k) \sim k^{-1}$. The scaling form of the
time-correlation functions are shown to follow the known analytical laws and
the skewness and excess kurtosis of the interface increments distributions are
characterised.


Abstract:
  In the break of COVID-19 pandemic, mass testing has become essential to
reduce the spread of the virus. Several recent studies suggest that a
significant number of COVID-19 patients display no physical symptoms
whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19
test, which increases their chances of unintentionally spreading the virus.
Currently, the primary diagnostic tool to detect COVID-19 is RT-PCR test on
collected respiratory specimens from the suspected case. This requires patients
to travel to a laboratory facility to be tested, thereby potentially infecting
others along the way.It is evident from recent researches that asymptomatic
COVID-19 patients cough and breath in a different way than the healthy people.
Several research groups have created mobile and web-platform for crowdsourcing
the symptoms, cough and breathing sounds from healthy, COVID-19 and Non-COVID
patients. Some of these data repositories were made public. We have received
such a repository from Cambridge University team under data-sharing agreement,
where we have cough and breathing sound samples for 582 and 141 healthy and
COVID-19 patients, respectively. 87 COVID-19 patients were asymptomatic, while
rest of them have cough. We have developed an Android application to
automatically screen COVID-19 from the comfort of people homes. Test subjects
can simply download a mobile application, enter their symptoms, record an audio
clip of their cough and breath, and upload the data anonymously to our servers.
Our backend server converts the audio clip to spectrogram and then apply our
state-of-the-art machine learning model to classify between cough sounds
produced by COVID-19 patients, as opposed to healthy subjects or those with
other respiratory conditions. The system can detect asymptomatic COVID-19
patients with a sensitivity more than 91%.


Abstract:
  Recently, a new cosmological framework, dubbed Ricci Cosmology, has been
proposed. Such a framework has emerged from the study of relativistic dynamics
of fluids out of equilibrium in a curved background and is characterised by the
presence of deviations from the equilibrium pressure in the energy-momentum
tensor which are due to linear terms in the Ricci scalar and the Ricci tensor.
The coefficients in front of such terms are called the second order transport
coefficients and they parametrise the fluid response to the pressure terms
arising from the spacetime curvature.
  Under the preliminary assumption that the second order transport coefficients
are constant, we find the simplest solution of Ricci cosmology in which the
presence of pressure terms causes a departure from the perfect fluid redshift
scaling for matter components filling the Universe. In order to test the
viability of this solution, we make four different ans\"{a}tze on the transport
coefficients, giving rise to four different cases of our model. On the physical
ground of the second law of thermodynamics for fluids with non-equilibrium
pressure, we find some theoretical bounds (priors) on the parameters of the
models. Our main concern is then the check of each of the case against the
standard set of cosmological data in order to obtain the observational bounds
on the second order transport coefficients. We find those bounds also realising
that Ricci cosmology model is compatible with $\Lambda$CDM cosmology for all
the ans\"{a}tze.


Abstract:
  Due to a high heterogeneity in pose and size and to a limited number of
available data, segmentation of pediatric images is challenging for deep
learning methods. In this work, we propose a new CNN architecture that is pose
and scale invariant thanks to the use of Spatial Transformer Network (STN). Our
architecture is composed of three sequential modules that are estimated
together during training: (i) a regression module to estimate a similarity
matrix to normalize the input image to a reference one; (ii) a differentiable
module to find the region of interest to segment; (iii) a segmentation module,
based on the popular UNet architecture, to delineate the object. Unlike the
original UNet, which strives to learn a complex mapping, including pose and
scale variations, from a finite training dataset, our segmentation module
learns a simpler mapping focusing on images with normalized pose and size.
Furthermore, the use of an automatic bounding box detection through STN allows
saving time and especially memory, while keeping similar performance. We test
the proposed method in kidney and renal tumor segmentation on abdominal
pediatric CT scanners. Results indicate that the estimated STN homogenization
of size and pose accelerates the segmentation (25h), compared to standard
data-augmentation (33h), while obtaining a similar quality for the kidney
(88.01\% of Dice score) and improving the renal tumor delineation (from 85.52\%
to 87.12\%).


Abstract:
  The ultrasound (US) screening of the infant hip is vital for the early
diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH
refers to measuring alpha and beta angles that quantify hip joint development.
These two angles are calculated from key anatomical landmarks and structures of
the hip. However, this measurement process is not trivial for sonographers and
usually requires a thorough understanding of complex anatomical structures. In
this study, we propose a multi-task framework to learn the relationships among
landmarks and structures jointly and automatically evaluate DDH. Our multi-task
networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as
the basic framework to detect and segment key anatomical structures and add one
landmark detection branch to form a new multi-task framework. Secondly, we
propose a novel shape similarity loss to refine the incomplete anatomical
structure prediction robustly and accurately. Thirdly, we further incorporate
the landmark-structure consistent prior to ensure the consistency of the bony
rim estimated from the segmented structure and the detected landmark. In our
experiments, 1,231 US images of the infant hip from 632 patients are collected,
of which 247 images from 126 patients are tested. The average errors in alpha
and beta angles are 2.221 degrees and 2.899 degrees. About 93% and 85%
estimates of alpha and beta angles have errors less than 5 degrees,
respectively. Experimental results demonstrate that the proposed method can
accurately and robustly realize the automatic evaluation of DDH, showing great
potential for clinical application.


Abstract:
  We investigate the interaction of weak light fields with two-dimensional
lattices of atoms, in which two-photon coupling establishes conditions of
electromagnetically induced transparency and excites high lying atomic Rydberg
states. This system features different interactions that act on disparate
length scales, from zero-range defect scattering of atomic excitations and
finite-range dipole exchange interactions to long-range Rydberg-state
interactions that span the entire array. Analyzing their interplay, we identify
conditions that yield a nonlinear quantum mirror which coherently splits
incident fields into correlated photon-pairs in a single transverse mode, while
transmitting single photons unaffected. Such strong photon-photon interactions
in the absence of otherwise detrimental photon losses in Rydberg-EIT arrays
opens up a promising approach for the generation and manipulation of quantum
light, and the exploration of many-body phenomena with interacting photons.


Abstract:
  Dark matter may self-interact through a continuum of low-mass states. This
happens if dark matter couples to a strongly-coupled nearly-conformal hidden
sector. This type of theory is holographically described by brane-localized
dark matter interacting with bulk fields in a slice of 5D anti-de Sitter space.
The long-range potential in this scenario depends on a non-integer power of the
spatial separation, in contrast to the Yukawa potential generated by the
exchange of a single 4D mediator. The resulting self-interaction cross section
scales like a non-integer power of velocity. We identify the Born, classical
and resonant regimes and investigate them using state-of-the-art numerical
methods. We demonstrate the viability of our continuum-mediated framework to
address the astrophysical small-scale structure anomalies. Investigating the
continuum-mediated Sommerfeld enhancement, we demonstrate that a pattern of
resonances can occur depending on the non-integer power. We conclude that
continuum mediators introduce novel power-law scalings which open new
possibilities for dark matter self-interaction phenomenology.


Abstract:
  To traverse complex three-dimensional terrainwith large obstacles, animals
and robots must transition across different modes. However, the most
mechanistic understanding of terrestrial locomotion concerns how to generate
and stabilize near-steady-state, single-mode locomotion (e.g. walk, run). We
know little about how to use physical interaction to make robust locomotor
transitions. Here, we review our progress towards filling this gap by
discovering terradynamic principles of multi-legged locomotor transitions,
using simplified model systems representing distinct challenges in complex
three-dimensional terrain. Remarkably, general physical principles emerge
across diverse model systems, by modelling locomotor-terrain interaction using
a potential energy landscape approach. The animal and robots' stereotyped
locomotor modes are constrained by physical interaction. Locomotor transitions
are stochastic, destabilizing, barrier-crossing transitions on the landscape.
They can be induced by feed-forward self-propulsion and are facilitated by
feedbackcontrolled active adjustment. General physical principles and
strategies from our systematic studies already advanced robot performance in
simple model systems. Efforts remain to better understand the intelligence
aspect of locomotor transitions and how to compose larger-scale potential
energy landscapes of complex three-dimensional terrains from simple landscapes
of abstracted challenges. This will elucidate how the neuromechanical control
system mediates physical interaction to generate multi-pathway locomotor
transitions and lead to advancements in biology, physics, robotics and
dynamical systems theory.