Abstract:
  Citation recommendation is an important task to assist scholars in finding
candidate literature to cite. Traditional studies focus on static models of
recommending citations, which do not explicitly distinguish differences between
papers that are caused by temporal variations. Although, some researchers have
investigated chronological citation recommendation by adding time related
function or modeling textual topics dynamically. These solutions can hardly
cope with function generalization or cold-start problems when there is no
information for user profiling or there are isolated papers never being cited.
With the rise and fall of science paradigms, scientific topics tend to change
and evolve over time. People would have the time preference when citing papers,
since most of the theoretical basis exist in classical readings that published
in old time, while new techniques are proposed in more recent papers. To
explore chronological citation recommendation, this paper wants to predict the
time preference based on user queries, which is a probability distribution of
citing papers published in different time slices. Then, we use this time
preference to re-rank the initial citation list obtained by content-based
filtering. Experimental results demonstrate that task performance can be
further enhanced by time preference and it's flexible to be added in other
citation recommendation frameworks.


Abstract:
  Forward-looking correlations are of interest in different financial
applications, including factor-based asset pricing, forecasting stock-price
movements or pricing index options. With a focus on non-FX markets, this paper
defines necessary conditions for option implied correlation matrices to be
mathematically and economically feasible and argues, that existing models are
typically not capable of guaranteeing so. To overcome this difficulty, the
problem is addressed from the underlying factor structure and introduces two
approaches to solve it. Under the quantitative approach, the puzzle is
reformulated into a nearest correlation matrix problem which can be used either
as a stand-alone estimate or to re-establish positive-semi-definiteness of any
other model's estimate. From an economic approach, it is discussed how expected
correlations between stocks and risk factors (like CAPM, Fama-French) can be
translated into a feasible implied correlation matrix. Empirical experiments
are carried out on monthly option data of the S\&P 100 and S\&P 500 index
(1996-2020).


Abstract:
  We test the consistency of the data on the nucleon structure functions with
analyticity and the Regge asymptotics of the virtual Compton amplitude. By
solving a functional extremal problem, we derive an optimal lower bound on the
maximum difference between the exact amplitude and the dominant Reggeon
contribution for energies $\nu$ above a certain high value $\nu_h(Q^2)$.
Considering in particular the difference of the amplitudes $T_1^\inel(\nu,
Q^2)$ for the proton and neutron, we find that the lower bound decreases in an
impressive way when $\nu_h(Q^2)$ is increased, and represents a very small
fraction of the magnitude of the dominant Reggeon. While the method cannot rule
out the hypothesis of a fixed Regge pole, the results indicate that the data on
the structure function are consistent with an asymptotic behaviour given by
leading Reggeon contributions. We also show that the minimum of the lower bound
as a function of the subtraction constant $S_1^\inel(Q^2)$ provides a
reasonable estimate of this quantity, in a frame similar, but not identical to
the Reggeon dominance hypothesis.


Abstract:
  The network structure offers in principle the possibility for novel forms of
quantum nonlocal correlations, that are proper to networks and cannot be traced
back to standard quantum Bell nonlocality. Here we define a notion of genuine
network quantum nonlocality. Our approach is operational and views standard
quantum nonlocality as a resource for producing correlations in networks. We
show several examples of correlations that are genuine network nonlocal,
considering the so-called bilocality network of entanglement swapping. In
particular, we present an example of quantum self-testing which relies on the
network structure; the considered correlations are non-bilocal, but are local
according to the usual definition of Bell locality.


Abstract:
  We analyse the canonical quantum dynamics of the isotropic universe, as
emerging from the Hamiltonian formulation of a metric f(R) gravity, viewed in
the Jordan frame. The canonical method of quantization is performed by solving
the Hamiltonian constraint before quantizing and adopting like a relational
time the non-minimally coupled scalar field emerging in the Jordan frame. The
resulting Schr\"oedinger evolution is then investigated both in the vacuum and
in the presence of a massless scalar field, though as the kinetic component of
an inflaton. We show that, in vacuum, the morphology of localized wave packets
is that of a non-spreading profile up to the cosmological singularity. When the
external scalar field is included into the dynamics, we see that the wave
packets acquire the surprising feature of increasing localization of the
universe volume, as it expands. This result suggests that in the metric f(R)
formulation of gravity, a spontaneous mechanism arises for the universe
classicization. Actually, when the phase space of the scalar field is fully
explored, such an increasing localization in the Universe volume is valid up to
a given value of the time, i.e. of the non-minimally coupled mode after which
the wave packets spread again. We conclude our analysis by inferring that
before this critical transition age is reached, the inflationary phase could
take place, here modelled via a cosmological constant. This point of view
provides an interesting scenario for the transition from a Planckian Universe
to a classical de-Sitter phase, which in the f(R) gravity appears more natural
than in the Einsteinian picture.


Abstract:
  In this paper we derive non-classical Tauberian asymptotic at infinity for
the tail, the density and the derivatives thereof of a large class of
exponential functionals of subordinators. More precisely, we consider the case
when the L\'evy measure of the subordinator satisfies the well-known and mild
condition of positive increase. This is achieved via a convoluted application
of the saddle point method to the Mellin transform of these exponential
functionals which is given in terms of Bernstein-gamma functions. To apply the
saddle point method we improved the Stirling type of asymptotic for
Bernstein-gamma functions and the latter is of interest beyond this paper as
the Bernstein-gamma functions are applicable in different settings especially
through their asymptotic behaviour in the complex plane. As an application we
have derived the asymptotic of the density and its derivatives for all
exponential functionals of non-decreasing, potentially compound Poisson
processes which turns out to be precisely as that of an exponentially
distributed random variable. We show further that a large class of densities
are even analytic in a cone of the complex plane.


Abstract:
  Let $\mathcal{D}$ be the class of radial weights on the unit disk which
satisfy both forward and reverse doubling conditions. Let $g$ be an analytic
function on the unit disk $\mathbb{D}$. We characterize bounded and compact
Volterra type integration operators \[
J_{g}(f)(z)=\int_{0}^{z}f(\lambda)g'(\lambda)d\lambda \] between weighted
Bergman spaces $L_{a}^{p}(\omega )$ induced by $\mathcal{D}$ weights and Hardy
spaces $H^{q}$ for $0<p,q<\infty$.


Abstract:
  The conditions in the protoplanetary disc are determinant for the various
planet formation mechanisms. We present a framework which combines
self-consistent disc structures with the calculations of the growth rates of
planetary embryos via pebble accretion, in order to study the formation of
Super-Earths. We first perform 2D hydrodynamical simulations of the inner
discs, considering a grain size distribution with multiple chemical species and
their corresponding size and composition dependent opacities. The resulting
aspect ratios are almost constant with orbital distance, resulting in radially
constant pebble isolation masses, the mass where pebble accretion stops. This
supports the "peas-in-a-pod" constraint from the Kepler observations. The
derived pebble sizes are used to calculate the growth rates of planetary
embryos via pebble accretion. Discs with low levels of turbulence (expressed
through the $\alpha$-viscosity) and/or high dust fragmentation velocities allow
larger particles, hence lead to smaller pebble isolation masses, and the
contrary. At the same time, small pebble sizes lead to low accretion rates. We
find that there is a trade-off between the pebble isolation mass and the growth
timescale with the best set of parameters being an $\alpha$-viscosity of
$10^{-3}$ and a dust fragmentation velocity of 10 m/s, mainly for an initial
gas surface density (at 1 AU) greater than 1000 $\rm{g/cm^2}$. A
self-consistent treatment between the disc structures and the pebble sizes is
thus of crucial importance for planet formation simulations.


Abstract:
  Stars are ubiquitous X-ray emitters and will be a substantial fraction of the
X-ray sources detected in the on-going all-sky survey performed by the eROSITA
instrument aboard the Spectrum Roentgen Gamma (SRG) observatory. We use the
X-ray sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) field
observed during the SRG performance verification phase to investigate different
strategies to identify the stars among other source categories. We focus here
on Support Vector Machine (SVM) and Bayesian approaches, and our approaches are
based on a cross-match with the Gaia catalog, which will eventually contain
counterparts to virtually all stellar eROSITA sources. We estimate that 2060
stars are among the eFEDS sources based on the geometric match distance
distribution, and we identify the 2060 most likely stellar sources with the SVM
and Bayesian methods, the latter being named HamStars in the eROSITA context.
Both methods reach completeness and reliability percentages of almost 90%, and
the agreement between both methods is, incidentally, also about 90%. Knowing
the true number of stellar sources allowed us to derive association
probabilities $p_{ij}$ for the SVM method similar to the Bayesian method so
that one can construct samples with defined completeness and reliability
properties using appropriate cuts in $p_{ij}$. The thus identified stellar
sources show the typical characteristics known for magnetically active stars,
specifically, they are generally compatible with the saturation level, show a
large spread in activity for stars of spectral F to G, and have comparatively
high fractional X-ray luminosities for later spectral types.


Abstract:
  We find excellent agreement between tunneling and optical conductivity gap
values in superconducting granular aluminum films, up to the metal-to-insulator
transition. This behavior, in strong contrast with that recently reported in
atomically disordered samples for which the optical gap becomes smaller than
the tunneling gap, suggests that disorder is not at the origin of the
transition. The large increase seen in the strong coupling ratio and a finite
value of the gap at T_c near the metal-to-insulator transition are consistent
with a BCS to BEC crossover.


Abstract:
  In this paper we study couples of finite separable extensions of the function
field $\mathbb{F}_q(T)$ which are arithmetically equivalent, i.e. such that
prime ideals of $\mathbb{F}_q[T]$ decompose with the same inertia degrees in
the two fields, up to finitely many exceptions. In the first part of this work,
we extend previous results by Cornelissen, Kontogeorgis and Van der Zalm to the
case of non-geometric extensions of $\mathbb{F}_q(T)$, which are fields such
that their field of constants may be bigger than $\mathbb{F}_q$. In the second
part, we explicitly produce examples of non-geometric extensions of
$\mathbb{F}_2(T)$ which are equivalent and non-isomorphic over
$\mathbb{F}_2(T)$ and non-equivalent over $\mathbb{F}_4(T)$, solving a
particular Inverse Galois Problem.


Abstract:
  We use a combined generalized spectroscopic ellipsometry and density
functional theory approach to determine and analyze the anisotropic dielectric
functions of an $\alpha$-Ga$_2$O$_3$ thin film. The sample is grown epitaxially
by plasma-assisted molecular beam epitaxy on $m$-plane sapphire. Generalized
spectroscopic ellipsometry data from multiple sample azimuths in the spectral
range from 0.73 eV to 8.75 eV are simultaneously analyzed. Density functional
theory is used to calculate the valence and conduction band structure. We
identify, for the indirect-bandgap material, two direct band-to-band
transitions with $M_0$-type van Hove singularities for polarization
perpendicular to the $c$ axis, $E_{0,\perp}=5.46(6)$ eV and
$E_{0,\perp}=6.04(1)$ eV, and one direct band-to-band transition with
$M_1$-type van Hove singularity for polarization parallel with
$E_{0,||}=5.44(2)$ eV. We further identify excitonic contributions with small
binding energy of 7 meV associated with the lowest ordinary transition, and a
hyperbolic exciton at the $M_1$-type critical point with large binding energy
of 178 meV.


Abstract:
  The stability conditions of a relativistic hydrodynamic theory can be derived
directly from the requirement that the entropy should be maximised in
equilibrium. Here we use a simple geometrical argument to prove that, if the
hydrodynamic theory is stable according to this entropic criterion, then
localised perturbations to the equilibrium state cannot propagate outside their
future light-cone. In other words, within relativistic hydrodynamics, acausal
theories must be thermodynamically unstable, at least close to equilibrium. We
show that the physical origin of this deep connection between stability and
causality lies in the relationship between entropy and information. Our result
may be interpreted as an ``equilibrium conservation theorem'', which
generalizes the Hawking-Ellis vacuum conservation theorem to finite temperature
and chemical potential.


Abstract:
  Most of the existing isolated sound event datasets comprise a small number of
sound event classes, usually 10 to 15, restricted to a small domain, such as
domestic and urban sound events. In this work, we introduce GISE-51, a dataset
spanning 51 isolated sound events belonging to a broad domain of event types.
We also release GISE-51-Mixtures, a dataset of 5-second soundscapes with
hard-labelled event boundaries synthesized from GISE-51 isolated sound events.
We conduct baseline sound event recognition (SER) experiments on the
GISE-51-Mixtures dataset, benchmarking prominent convolutional neural networks,
and models trained with the dataset demonstrate strong transfer learning
performance on existing audio recognition benchmarks. Together, GISE-51 and
GISE-51-Mixtures attempt to address some of the shortcomings of recent sound
event datasets, providing an open, reproducible benchmark for future research
along with the freedom to adapt the included isolated sound events for
domain-specific applications.


Abstract:
  In the present letter we show the existence of a fully nonlinear dynamical
mechanism for the formation of scalarized black holes which is different from
the spontaneous scalarization. We consider a class of scalar-Gauss-Bonnet
gravity theories within which no tachyonic instability can occur. Although the
Schwarzschild black holes are linearly stable against scalar perturbations, we
show dynamically that for certain choices of the coupling function they are
unstable against nonlinear scalar perturbations. This nonlinear instability
leads to the formation of new black holes with scalar hair. The fully nonlinear
and self-consistent study of the equilibrium black holes reveals that the
spectrum of solutions is more complicated and more than one scalarized branch
can exist. We have also considered classes of scalar-Gauss-Bonnet theories
where both the standard and the nonlinear scalarization can be present, and
they are smoothly connected that completes in an interesting way the picture of
black hole scalarization. The fully nonlinear (de)scalarization of a
Schwarzschild black hole will always happen with a jump because the stable
"scalarized branch" is not continuously connected to the Schwarzschild one that
can leave clear observational signatures.


Abstract:
  We have investigated the directional dichroism of magnetic resonance spectra
in the polar ferromagnet GaV$_4$S$_8$. While four types of structural domains
are energetically degenerated under zero field, the magnetic resonance for each
domain is well separated by applying magnetic fields due to uniaxial magnetic
anisotropy. Consequently, the directional dichroism as large as 20 % is clearly
observed without domain cancellation. The present observation therefore
demonstrates that not only magnetoelectric mono-domain crystals but also
magnetoelectric multi-domain specimens can be used to realize microwave
(optical) diodes owing to the lack of inversion domains.


Abstract:
  An important transition from a homogeneous steady state to an inhomogeneous
steady state via the Turing bifurcation in coupled oscillators was reported in
[Phys. Rev. Lett. {\bf 111}, 024103 (2013)]. However, the same in the quantum
domain is yet to be observed. In this paper, we discover the quantum analogue
of the Turing bifurcation in coupled quantum oscillators. We show that a
homogeneous steady state is transformed into an inhomogeneous steady state
through this bifurcation in coupled quantum van der Pol oscillators. We
demonstrate our results by a direct simulation of the quantum master equation
in the Lindblad form. We further support our observations through an analytical
treatment of the noisy classical model. Our study explores the paradigmatic
Turing bifurcation at the quantum-classical interface and opens up the door
towards its broader understanding.


Abstract:
  Motivational digital systems offer capabilities to engage and motivate
end-users to foster behavioral changes towards a common goal. In general these
systems use gamification principles in non-games contexts. Over the years,
gamification has gained consensus among researchers and practitioners as a tool
to motivate people to perform activities with the ultimate goal of promoting
behavioural change, or engaging the users to perform activities that can offer
relevant benefits but which can be seen as unrewarding and even tedious.
  There exists a plethora of heterogeneous application scenarios towards
reaching the common good that can benefit from gamification. However, an open
problem is how to effectively combine multiple motivational campaigns to
maximise the degree of participation without exposing the system to
counterproductive behaviours.
  We conceive motivational digital systems as multi-agent systems:
self-adaptation is a feature of the overall system, while individual agents may
self-adapt in order to leverage other agents' resources, functionalities and
capabilities to perform tasks more efficiently and effectively. Consequently,
multiple campaigns can be run and adapted to reach common good. At the same
time, agents are grouped into micro-communities in which agents contribute with
their own social capital and leverage others' capabilities to balance their
weaknesses.
  In this paper we propose our vision on how the principles at the base of the
autonomous and multi-agent systems can be exploited to design multi-challenge
motivational systems to engage smart communities towards common goals. We
present an initial version of a general framework based on the MAPE-K loop and
a set of research challenges that characterise our research roadmap for the
implementation of our vision.


Abstract:
  We are interested in the general Choquard equation \begin{multline*}
  \sqrt{\strut -\Delta + m^2} \ u - mu + V(x)u - \frac{\mu}{|x|} u
  =
  \left( \int_{\mathbb{R}^N} \frac{F(y,u(y))}{|x-y|^{N-\alpha}} \, dy \right)
  f(x,u) - K (x) |u|^{q-2}u \end{multline*} under suitable assumptions on the
bounded potential \(V\) and on the nonlinearity \(f\). Our analysis extends
recent results by the second and third author on the problem with $\mu = 0$ and
pure-power nonlinearity $f(x,u)=|u|^{p-2}u$. We show that, under appropriate
assumptions on the potential, whether the ground state does exist or not.
Finally, we study the asymptotic behaviour of ground states as $\mu \to 0^+$.


Abstract:
  Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for
shielding user-level applications from the OS. While enclaves are a useful
starting point, code running in the enclave requires additional checks whenever
control or data is transferred to/from the untrusted OS. The enclave-OS
interface on SGX, however, can be extremely large if we wish to run existing
unmodified binaries inside enclaves. This paper presents Ratel, a dynamic
binary translation engine running inside SGX enclaves on Linux. Ratel offers
complete interposition, the ability to interpose on all executed instructions
in the enclave and monitor all interactions with the OS. Instruction-level
interposition offers a general foundation for implementing a large variety of
inline security monitors in the future.
  We take a principled approach in explaining why complete interposition on SGX
is challenging. We draw attention to 5 design decisions in SGX that create
fundamental trade-offs between performance and ensuring complete interposition,
and we explain how to resolve them in the favor of complete interposition. To
illustrate the utility of the Ratel framework, we present the first attempt to
offer binary compatibility with existing software on SGX. We report that Ratel
offers binary compatibility with over 200 programs we tested, including
micro-benchmarks and real applications such as Linux shell utilities. Runtimes
for two programming languages, namely Python and R, tested with standard
benchmarks work out-of-the-box on Ratel without any specialized handling.


Abstract:
  In this paper we study the large N limit of the $O(N)$-invariant linear sigma
model, which is a vector-valued generalization of the $\Phi^4$ quantum field
theory, on the three dimensional torus. We study the problem via its stochastic
quantization, which yields a coupled system of N interacting SPDEs. We prove
tightness of the invariant measures in the large N limit. For large enough mass
or small enough coupling constant, they converge to the (massive) Gaussian free
field at a rate of order $1/\sqrt N$ with respect to the Wasserstein distance.
We also obtain tightness results for certain $O(N)$ invariant observables.
These generalize some of the results in \cite{SSZZ20} from two dimensions to
three dimensions. The proof leverages the method recently developed by
\cite{GH18} and combines many new techniques such as uniform in $N$ estimates
on perturbative objects as well as the solutions.


Abstract:
  Magnesium (Mg) has the lowest density of all structural metals and has
excellent potential for wide use in structural applications. While pure Mg has
inferior mechanical properties; the addition of further elements at various
concentrations has produced alloys with enhanced mechanical performance and
corrosion resistance. An important consequence of adding such elements is that
the saturated Mg matrix can locally decompose to form solute clusters and
intermetallic particles, often referred to as precipitates. Controlling the
shape, number density, volume fraction, and spatial distribution of solute
clusters and precipitates significantly impacts the alloy's plastic response.
Conversely, plastic deformation during thermomechanical processing can
dramatically impact solute clustering and precipitation. In this paper, we
first discuss how solute atoms, solute clusters, and precipitates can improve
the mechanical properties of Mg alloys. We do so by primarily comparing three
alloy systems: Mg-Al, Mg-Zn, and Mg-Y-based alloys. In the second part, we
provide strategies for optimizing such microstructures by controlling
nucleation and growth of solute clusters and precipitates during
thermomechanical processing. In the third part, we briefly highlight how one
can enable inverse design of Mg alloys by a more robust Integrated
Computational Materials Design (ICMD) approach.


Abstract:
  Time series forecasting is an extensively studied subject in statistics,
economics, and computer science. Exploration of the correlation and causation
among the variables in a multivariate time series shows promise in enhancing
the performance of a time series model. When using deep neural networks as
forecasting models, we hypothesize that exploiting the pairwise information
among multiple (multivariate) time series also improves their forecast. If an
explicit graph structure is known, graph neural networks (GNNs) have been
demonstrated as powerful tools to exploit the structure. In this work, we
propose learning the structure simultaneously with the GNN if the graph is
unknown. We cast the problem as learning a probabilistic graph model through
optimizing the mean performance over the graph distribution. The distribution
is parameterized by a neural network so that discrete graphs can be sampled
differentiably through reparameterization. Empirical evaluations show that our
method is simpler, more efficient, and better performing than a recently
proposed bilevel learning approach for graph structure learning, as well as a
broad array of forecasting models, either deep or non-deep learning based, and
graph or non-graph based.


Abstract:
  Recent work has shown that entanglement and the structure of spacetime are
intimately related. One way to investigate this is to begin with an
entanglement entropy in a conformal field theory (CFT) and use the AdS/CFT
correspondence to calculate the bulk metric. We perform this calculation for
ABJM, a particular 3-dimensional supersymmetric CFT (SCFT), in its ground
state. In particular we are able to reconstruct the pure AdS4 metric from the
holographic entanglement entropy of the boundary ABJM theory in its ground
state. Moreover, we are able to predict the correct AdS radius purely from
entanglement. We also address the general philosophy of relating entanglement
and spacetime through the Holographic Principle, as well as some of the
philosophy behind our calculations.


Abstract:
  Fine-tuning from a collection of models pre-trained on different domains (a
"model zoo") is emerging as a technique to improve test accuracy in the
low-data regime. However, model selection, i.e. how to pre-select the right
model to fine-tune from a model zoo without performing any training, remains an
open topic. We use a linearized framework to approximate fine-tuning, and
introduce two new baselines for model selection -- Label-Gradient and
Label-Feature Correlation. Since all model selection algorithms in the
literature have been tested on different use-cases and never compared directly,
we introduce a new comprehensive benchmark for model selection comprising of:
i) A model zoo of single and multi-domain models, and ii) Many target tasks.
Our benchmark highlights accuracy gain with model zoo compared to fine-tuning
Imagenet models. We show our model selection baseline can select optimal models
to fine-tune in few selections and has the highest ranking correlation to
fine-tuning accuracy compared to existing algorithms.


Abstract:
  While current state-of-the-art Automatic Speech Recognition (ASR) systems
achieve high accuracy on typical speech, they suffer from significant
performance degradation on disordered speech and other atypical speech
patterns. Personalization of ASR models, a commonly applied solution to this
problem, is usually performed in a server-based training environment posing
problems around data privacy, delayed model-update times, and communication
cost for copying data and models between mobile device and server
infrastructure. In this paper, we present an approach to on-device based ASR
personalization with very small amounts of speaker-specific data. We test our
approach on a diverse set of 100 speakers with disordered speech and find
median relative word error rate improvement of 71% with only 50 short
utterances required per speaker. When tested on a voice-controlled home
automation platform, on-device personalized models show a median task success
rate of 81%, compared to only 40% of the unadapted models.


Abstract:
  The World Trade Web (WTW) is the network of international trade relationships
among world countries. Characterizing both the local link weights (observed
trade volumes) and the global network structure (large-scale topology) of the
WTW via a single model is still an open issue. While the traditional Gravity
Model (GM) successfully replicates the observed trade volumes by employing
macroeconomic properties such as GDP and geographic distance, it,
unfortunately, predicts a fully connected network, thus returning a completely
unrealistic topology of the WTW. To overcome this problem, two different
classes of models have been introduced in econometrics and statistical physics.
Econometric approaches interpret the traditional GM as the expected value of a
probability distribution that can be chosen arbitrarily and tested against
alternative distributions. Statistical physics approaches construct
maximum-entropy probability distributions of (weighted) graphs from a chosen
set of measurable structural constraints and test distributions resulting from
different constraints. Here we compare and integrate the two approaches by
considering a class of maximum-entropy models that can incorporate
macroeconomic properties used in standard econometric models. We find that the
integrated approach achieves a better performance than the purely econometric
one. These results suggest that the maximum-entropy construction can serve as a
viable econometric framework wherein extensive and intensive margins can be
separately controlled for, by combining topological constraints and dyadic
macroeconomic variables.


Abstract:
  Morse foliated open books were introduced by the autors (along with abstract
and embedded versions) as a tool for studying contact manifolds with boundary,
and this article illustrates the advantages of the Morse perspective. We use
this to extend the definition of \textit{right-veering} to foliated open books
and we show that it plays a similar role in detecting overtwistedness as in
other versions of open books.


Abstract:
  The Cooper pairs in superconducting condensates are shown to acquire a
temperature-dependent dc magnetic moment under the effect of the circularly
polarized electromagnetic radiation. The mechanisms of this inverse Faraday
effect are investigated within the simplest version of the phenomenological
dynamic theory for superfluids, namely, the time-dependent Ginzburg-Landau (GL)
model. The light-induced magnetic moment is shown to be strongly affected by
the nondissipative oscillatory contribution to the superconducting order
parameter dynamics which appears due to the nonzero imaginary part of the GL
relaxation time. The relevance of the latter quantity to the Hall effect in
superconducting state allows to establish the connection between the direct and
inverse Faraday phenomena.


Abstract:
  Using high-resolution Hubble Space Telescope imaging data, we perform a
visual morphological classification of $\sim 36,000$ galaxies at $z < 1$ in the
DEVILS/COSMOS region. As the main goal of this study, we derive the stellar
mass function (SMF) and stellar mass density (SMD) sub-divided by morphological
types. We find that visual morphological classification using optical imaging
is increasingly difficult at $z > 1$ as the fraction of irregular galaxies and
merger systems (when observed at rest-frame UV/blue wavelengths) dramatically
increases. We determine that roughly two-thirds of the total stellar mass of
the Universe today was in place by $z \sim 1$. Double-component galaxies
dominate the SMD at all epochs and increase in their contribution to the
stellar mass budget to the present day. Elliptical galaxies are the second most
dominant morphological type and increase their SMD by $\sim 2.5$ times, while
by contrast, the pure-disk population significantly decreases by $\sim 85\%$.
According to the evolution of both high- and low-mass ends of the SMF, we find
that mergers and in-situ evolution in disks are both present at $z < 1$, and
conclude that double-component galaxies are predominantly being built by the
in-situ evolution in disks (apparent as the growth of the low-mass end with
time), while mergers are likely responsible for the growth of ellipticals
(apparent as the increase of intermediate/high-mass end).


Abstract:
  In recent years, a vast increase in spectroscopic observations of transiting
exoplanets has for the first time allowed us to search for broad trends in
their atmospheric properties. Analysis of these observations has revealed that,
even for the highly irradiated hot Jupiters, aerosol is a common presence and
must be accounted for in modelling efforts. An additional challenge for hot
Jupiters is the large variation in temperature across the planet, which is
likely to result in partial or patchy cloud cover. As our observational
capability is due to increase further with the launch of the James Webb Space
Telescope, anticipated in autumn 2021, community efforts are underway to
prepare modelling and analysis tools capable of recovering information about
variable and patchy cloud coverage on hot exoplanets


Abstract:
  We consider intersections of n diagonal forms of degrees k 1 < $\bullet$
$\bullet$ $\bullet$ < kn, and we prove an asymptotic formula for the number of
rational points of bounded height on these varieties. The proof uses the
Hardy-Littlewood method and recent breakthroughs on the Vinogradov system. We
also give a sharper result for one specific value of (k 1 ,. .. , kn), using a
technique due to Wooley and an estimate on exponential sums derived from a
recent approach in the van der Corput's method.


Abstract:
  Machine Learning (ML) techniques have been rapidly adopted by smart
Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful
decision-making capabilities. However, they are vulnerable to various security
and reliability threats, at both hardware and software levels, that compromise
their accuracy. These threats get aggravated in emerging edge ML devices that
have stringent constraints in terms of resources (e.g., compute, memory,
power/energy), and that therefore cannot employ costly security and reliability
measures. Security, reliability, and vulnerability mitigation techniques span
from network security measures to hardware protection, with an increased
interest towards formal verification of trained ML models.
  This paper summarizes the prominent vulnerabilities of modern ML systems,
highlights successful defenses and mitigation techniques against these
vulnerabilities, both at the cloud (i.e., during the ML training phase) and
edge (i.e., during the ML inference stage), discusses the implications of a
resource-constrained design on the reliability and security of the system,
identifies verification methodologies to ensure correct system behavior, and
describes open research challenges for building secure and reliable ML systems
at both the edge and the cloud.


Abstract:
  We show that the non-perturbative dynamics of $\mathcal{N}=2$ super
Yang-Mills theories in a self-dual $\Omega$-background and with an arbitrary
simple gauge group is fully determined by studying renormalization group
equations of vevs of surface operators generating one-form symmetries. The
corresponding system of equations is a {\it non-autonomous} Toda chain, the
time being the RG scale. We obtain new recurrence relations which provide a
systematic algorithm computing multi-instanton corrections from the tree-level
one-loop prepotential as the asymptotic boundary condition of the RGE. We
exemplify by computing the $E_6$ and $G_2$ cases up to two-instantons.


Abstract:
  This paper proposes a composable "Just in Time Architecture" for Data Science
(DS) Pipelines named JITA-4DS and associated resource management techniques for
configuring disaggregated data centers (DCs). DCs under our approach are
composable based on vertical integration of the application,
middleware/operating system, and hardware layers customized dynamically to meet
application Service Level Objectives (SLO - application-aware management).
Thereby, pipelines utilize a set of flexible building blocks that can be
dynamically and automatically assembled and re-assembled to meet the dynamic
changes in the workload's SLOs. To assess disaggregated DC's, we study how to
model and validate their performance in large-scale settings.


Abstract:
  This paper proposes a 1D residual convolutional neural network (CNN)
architecture for music genre classification and compares it with other recent
1D CNN architectures. The 1D CNNs learn a representation and a discriminant
directly from the raw audio signal. Several convolutional layers capture the
time-frequency characteristics of the audio signal and learn various filters
relevant to the music genre recognition task. The proposed approach splits the
audio signal into overlapped segments using a sliding window to comply with the
fixed-length input constraint of the 1D CNNs. As a result, music genre
classification can be carried out on a single audio segment or on the
aggregation of the predictions on several audio segments, which improves the
final accuracy. The performance of the proposed 1D residual CNN is assessed on
a public dataset of 1,000 audio clips. The experimental results have shown that
it achieves 80.93% of mean accuracy in classifying music genres and outperforms
other 1D CNN architectures.


Abstract:
  Finding an expert plays a crucial role in driving successful collaborations
and speeding up high-quality research development and innovations. However, the
rapid growth of scientific publications and digital expertise data makes
identifying the right experts a challenging problem. Existing approaches for
finding experts given a topic can be categorised into information retrieval
techniques based on vector space models, document language models, and
graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new
ensemble model for expert finding, that integrates a novel $N$-gram vector
space model, denoted as $n$VSM, and a graph-based model, denoted as
$\textit{$\mu$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm.
The key of $n$VSM is to exploit recent inverse document frequency weighting
method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into
$\textit{$\mu$CO-HITS}$ to achieve expert finding. We comprehensively evaluate
$\textit{ExpFinder}$ on four different datasets from the academic domains in
comparison with six different expert finding models. The evaluation results
show that $\textit{ExpFinder}$ is a highly effective model for expert finding,
substantially outperforming all the compared models in 19% to 160.2%.


Abstract:
  In this paper, we introduce a self-supervised learning method to enhance the
graph-level representations with the help of a set of subgraphs. For this
purpose, we propose a universal framework to generate subgraphs in an
auto-regressive way and then using these subgraphs to guide the learning of
graph representation by Graph Neural Networks. Under this framework, we can get
a comprehensive understanding of the graph structure in a learnable way. And to
fully capture enough information of original graphs, we design three
information aggregators: \textbf{attribute-conv}, \textbf{layer-conv} and
\textbf{subgraph-conv} to gather information from different aspects. And to
achieve efficient and effective contrastive learning, a Head-Tail contrastive
construction is proposed to provide abundant negative samples. Under all
proposed components which can be generalized to any Graph Neural Networks, in
the unsupervised case, we achieve new state-of-the-art results in several
benchmarks. We also evaluate our model on semi-supervised learning tasks and
make a fair comparison to state-of-the-art semi-supervised methods.


Abstract:
  Effective computation of resultants is a central problem in elimination
theory and polynomial system solving. Commonly, we compute the resultant as a
quotient of determinants of matrices and we say that there exists a
determinantal formula when we can express it as a determinant of a matrix whose
elements are the coefficients of the input polynomials. We study the resultant
in the context of mixed multilinear polynomial systems, that is multilinear
systems with polynomials having different supports, on which determinantal
formulas were not known. We construct determinantal formulas for two kind of
multilinear systems related to the Multiparameter Eigenvalue Problem (MEP):
first, when the polynomials agree in all but one block of variables; second,
when the polynomials are bilinear with different supports, related to a
bipartite graph. We use the Weyman complex to construct Koszul-type
determinantal formulas that generalize Sylvester-type formulas. We can use the
matrices associated to these formulas to solve square systems without computing
the resultant. The combination of the resultant matrices with the eigenvalue
and eigenvector criterion for polynomial systems leads to a new approach for
solving MEP.


Abstract:
  In this work we propose a deep neural network based surrogate model for a
plasma shadowgraph - a technique for visualization of perturbations in a
transparent medium. We are substituting the numerical code by a computationally
cheaper projection based surrogate model that is able to approximate the
electric fields at a given time without computing all preceding electric fields
as required by numerical methods. This means that the projection based
surrogate model allows to recover the solution of the governing 3D partial
differential equation, 3D wave equation, at any point of a given compute domain
and configuration without the need to run a full simulation. This model has
shown a good quality of reconstruction in a problem of interpolation of data
within a narrow range of simulation parameters and can be used for input data
of large size.


Abstract:
  We present two classical algorithms for the simulation of universal quantum
circuits on $n$ qubits constructed from $c$ instances of Clifford gates and $t$
arbitrary-angle $Z$-rotation gates such as $T$ gates. Our algorithms complement
each other by performing best in different parameter regimes. The
$\tt{Estimate}$ algorithm produces an additive precision estimate of the Born
rule probability of a chosen measurement outcome with the only source of
run-time inefficiency being a linear dependence on the stabilizer extent (which
scales like $\approx 1.17^t$ for $T$ gates). Our algorithm is state-of-the-art
for this task: as an example, in approximately $13$ hours (on a standard
desktop computer), we estimated the Born rule probability to within an additive
error of $0.03$, for a $50$ qubit, $60$ non-Clifford gate quantum circuit with
more than $2000$ Clifford gates. Our second algorithm, $\tt{Compute}$,
calculates the probability of a chosen measurement outcome to machine precision
with run-time $O(2^{t-r} t)$ where $r$ is an efficiently computable,
circuit-specific quantity. With high probability, $r$ is very close to $\min
\{t,n-w\}$ for random circuits with many Clifford gates, where $w$ is the
number of measured qubits. $\tt{Compute}$ can be effective in surprisingly
challenging parameter regimes, e.g., we can randomly sample Clifford+$T$
circuits with $n=55$, $w=5$, $c=10^5$ and $t=80$ $T$ gates, and then compute
the Born rule probability with a run-time consistently less than $10$ minutes
using a single core of a standard desktop computer. We provide a C+Python
implementation of our algorithms.


Abstract:
  In this work we solve the problem of robustly learning a high-dimensional
Gaussian mixture model with $k$ components from $\epsilon$-corrupted samples up
to accuracy $\widetilde{O}(\epsilon)$ in total variation distance for any
constant $k$ and with mild assumptions on the mixture. This robustness
guarantee is optimal up to polylogarithmic factors. The main challenge is that
most earlier works rely on learning individual components in the mixture, but
this is impossible in our setting, at least for the types of strong robustness
guarantees we are aiming for. Instead we introduce a new framework which we
call {\em strong observability} that gives us a route to circumvent this
obstacle.


Abstract:
  Several researches and evidence show the increasing likelihood of pandemics
(large-scale outbreaks of infectious disease) which has far reaching sequels in
all aspects of human lives ranging from rapid mortality rates to economic and
social disruption across the world. In the recent time, COVID-19 (Coronavirus
Disease 2019) pandemic disrupted normal human lives, and motivated by the
urgent need of combating COVID-19, researchers have put significant efforts in
modelling and analysing the disease spread patterns for effective preventive
measures (in addition to developing pharmaceutical solutions, like vaccine). In
this regards, it is absolutely necessary to develop an analytics framework by
extracting and incorporating the knowledge of heterogeneous datasources to
deliver insights in improving administrative policy and enhance the
preparedness to combat the pandemic. Specifically, human mobility, travel
history and other transport statistics have significant impacts on the spread
of any infectious disease. In this direction, this paper proposes a
spatio-temporal knowledge mining framework, named STOPPAGE to model the impact
of human mobility and other contextual information over large geographic area
in different temporal scales. The framework has two major modules: (i)
Spatio-temporal data and computing infrastructure using fog/edge based
architecture; and (ii) Spatio-temporal data analytics module to efficiently
extract knowledge from heterogeneous data sources. Typically, we develop a
Pandemic-knowledge graph to discover correlations among mobility information
and disease spread, a deep learning architecture to predict the next hot-spot
zones; and provide necessary support in home-health monitoring utilizing
Femtolet and fog/edge based solutions. The experimental evaluations on
real-life datasets related to COVID-19 in India illustrate the efficacy of the
proposed methods.


Abstract:
  Many healthcare decisions involve navigating through a multitude of treatment
options in a sequential and iterative manner to find an optimal treatment
pathway with the goal of an optimal patient outcome. Such optimization problems
may be amenable to reinforcement learning. A reinforcement learning agent could
be trained to provide treatment recommendations for physicians, acting as a
decision support tool. However, a number of difficulties arise when using RL
beyond benchmark environments, such as specifying the reward function, choosing
an appropriate state representation and evaluating the learned policy.


Abstract:
  Regret minimization has proved to be a versatile tool for tree-form
sequential decision making and extensive-form games. In large two-player
zero-sum imperfect-information games, modern extensions of counterfactual
regret minimization (CFR) are currently the practical state of the art for
computing a Nash equilibrium. Most regret-minimization algorithms for tree-form
sequential decision making, including CFR, require (i) an exact model of the
player's decision nodes, observation nodes, and how they are linked, and (ii)
full knowledge, at all times t, about the payoffs -- even in parts of the
decision space that are not encountered at time t. Recently, there has been
growing interest towards relaxing some of those restrictions and making regret
minimization applicable to settings for which reinforcement learning methods
have traditionally been used -- for example, those in which only black-box
access to the environment is available. We give the first, to our knowledge,
regret-minimization algorithm that guarantees sublinear regret with high
probability even when requirement (i) -- and thus also (ii) -- is dropped. We
formalize an online learning setting in which the strategy space is not known
to the agent and gets revealed incrementally whenever the agent encounters new
decision points. We give an efficient algorithm that achieves $O(T^{3/4})$
regret with high probability for that setting, even when the agent faces an
adversarial environment. Our experiments show it significantly outperforms the
prior algorithms for the problem, which do not have such guarantees. It can be
used in any application for which regret minimization is useful: approximating
Nash equilibrium or quantal response equilibrium, approximating coarse
correlated equilibrium in multi-player games, learning a best response,
learning safe opponent exploitation, and online play against an unknown
opponent/environment.


Abstract:
  For a sequence of extrinsic or intrinsic biharmonic maps $u_j: M_j\rightarrow
N$ from a sequence of non-collapsed degenerating closed Einstein 4-manifolds
$(M_j,g_j)$ with bounded Einstein constants, bounded diameters and bounded
$L^2$ curvature energy into a compact Riemannian manifold $(N,h)$ with
uniformly bounded biharmonic energy, we establish a compactness theory modular
finitely many bubbles, which are finite energy biharmonic maps from
$\mathbb{R}^4$, or from $\mathbb{R}^4 / \Gamma$ for some nontrivial finite
group $\Gamma \subset SO(4)$, or from some complete, noncompact, Ricci flat,
non-flat ALE 4-manifold (orbifold). To achieve this, we develop a sophisticated
asymptotic analysis for solutions over degenerating neck regions.


Abstract:
  Reinforcement Learning (RL) methods have been widely applied for robotic
manipulations via sim-to-real transfer, typically with proprioceptive and
visual information. However, the incorporation of tactile sensing into RL for
contact-rich tasks lacks investigation. In this paper, we model a tactile
sensor in simulation and study the effects of its feedback in RL-based robotic
control via a zero-shot sim-to-real approach with domain randomization. We
demonstrate that learning and controlling with feedback from tactile sensor
arrays at the gripper, both in simulation and reality, can enhance grasping
stability, which leads to a significant improvement in robotic manipulation
performance for a door opening task. In real-world experiments, the door open
angle was increased by 45% on average for transferred policies with tactile
sensing over those without it.


Abstract:
  Data-driven dynamic models of cell biology can be used to predict cell
response to unseen perturbations. Recent work (CellBox) had demonstrated the
derivation of interpretable models with explicit interaction terms, in which
the parameters were optimized using machine learning techniques. While the
previous work was tested only in a single biological setting, this work aims to
extend the range of applicability of this model inference approach to a
diversity of biological systems. Here we adapted CellBox in Julia differential
programming and augmented the method with adjoint algorithms, which has
recently been used in the context of neural ODEs. We trained the models using
simulated data from both abstract and biology-inspired networks, which afford
the ability to evaluate the recovery of the ground truth network structure. The
resulting accuracy of prediction by these models is high both in terms of low
error against data and excellent agreement with the network structure used for
the simulated training data. While there is no analogous ground truth for real
life biological systems, this work demonstrates the ability to construct and
parameterize a considerable diversity of network models with high predictive
ability. The expectation is that this kind of procedure can be used on real
perturbation-response data to derive models applicable to diverse biological
systems.


Abstract:
  A thermodynamic framework has been developed for a class of amorphous
polymers used in fused deposition modeling (FDM), in order to predict the
residual stresses and the accompanying distortion of the geometry of the
printed part (warping). When a polymeric melt is cooled, the inhomogeneous
distribution of temperature causes spatially varying volumetric shrinkage
resulting in the generation of residual stresses. Shrinkage is incorporated
into the framework by introducing an isotropic volumetric expansion/contraction
in the kinematics of the body. We show that the parameter for shrinkage also
appears in the systematically derived rate-type constitutive relation for the
stress. The solidification of the melt around the glass transition temperature
is emulated by drastically increasing the viscosity of the melt.
  In order to illustrate the usefulness and efficacy of the derived
constitutive relation, we consider four ribbons of polymeric melt stacked on
each other such as those extruded using a flat nozzle: each layer laid
instantaneously and allowed to cool for one second before another layer is laid
on it. Each layer cools, shrinks and warps until a new layer is laid, at which
time the heat from the newly laid layer flows and heats up the bottom layers.
The residual stresses of the existing and newly laid layers readjust to satisfy
equilibrium. Such mechanical and thermal interactions amongst layers result in
a complex distribution of residual stresses. The plane strain approximation
predicts nearly equibiaxial tensile stress conditions in the core region of the
solidified part, implying that a pre-existing crack in that region is likely to
propagate and cause failure of the part during service. The free-end of the
interface between the first and the second layer is subjected to the largest
magnitude of combined shear and tension in the plane with a propensity for
delamination.


Abstract:
  Twin-field quantum key distribution (TF-QKD) and its variants can overcome
the fundamental rate-distance limit of QKD which has been demonstrated in the
laboratory and field while their physical implementations with side channels
remains to be further researched. We find the external modulation of different
intensity states through the test, required in those TF-QKD with post-phase
compensation, shows a side channel in frequency domain. Based on this, we
propose a complete and undetected eavesdropping attack, named passive frequency
shift attack, on sending or not-sending (SNS) TF-QKD protocol given any
difference between signal and decoy states in frequency domain which can be
extended to other imperfections with distinguishable decoy states. We analyze
this attack by giving the formula of upper bound of real secure key rate and
comparing it with lower bound of secret key rate under Alice and Bob's
estimation with the consideration of actively odd-parity pairing (AOPP) method
and finite key effects. The simulation results show that Eve can get full
information about the secret key bits without being detected at long distance.
Our results emphasize the importance of practical security at source and might
provide a valuable reference for the practical implementation of TF-QKD.


Abstract:
  An algorithm is proposed to verify whether a finite game is a weighted
potential game (WPG) without pre-knowledge on its weights. Then the algorithm
is also applied to find the closest WPG for a given finite game. The concept
and criterion of near weighted potential games (NWPGs) are given, indicating
the evolutionary equivalence between a finite game and its closest WPG. Based
on this, a method is proposed for replacing PGs by NWPGs in game theoretic
control, which improved the applicability of PG-based optimization.


Abstract:
  Triangular numbers that are multiple of other triangular numbers are
investigated. It is known that for any positive non-square integer multiplier,
there is an infinity of multiples of triangular numbers which are triangular
numbers. If the multiplier is a squared integer, there is either one or no
solution, depending on the multiplier value. Instead of recurrent relations, we
develop in this paper closed form equations to calculate directly the values of
triangular numbers and their indices without the need of knowing the previous
solutions. We develop the theoretical equations for four cases of ranks from 1
to 4 and we give several examples for non-square multipliers 2, 3, 5 and 8.


Abstract:
  This paper developed a fatigue-driven residual strength model considering the
effects of low-velocity impact (LVI) damage and stress ratio. New fatigue
failure criteria based on fatigue-driven residual strength concept and fatigue
progressive damage model were developed to simulate fatigue damage growth and
predict fatigue life for plain-weave composite laminates with LVI damage. To
validate the proposed model, LVI tests of plain-weave glass fibre reinforced
polymer 3238A/EW250F laminates were conducted, followed by post-impact constant
amplitude tension-tension, compression-compression fatigue tests and multi-step
fatigue tests. Experimental results indicate that the LVI damage degrades
fatigue strength of plain-weave glass fibre composite laminate drastically. The
load history also plays an important role on the fatigue accumulation damage of
post-impact laminates. The new fatigue progressive damage model achieves a good
agreement with fatigue life of post-impact laminates and is able to capture the
load sequence effect, opening a new avenue to predict fatigue failure of
composite laminates.


Abstract:
  We theoretically study an optomechanical system, which consists of a
two-sided cavity and a mechanical membrane that is placed outside of it. The
membrane is positioned close to one of its mirrors, and the cavity is coupled
to the external light field through the other mirror. Our study is focused on
the regime where the dispersive optomechanical coupling in the system vanishes.
Such a regime is found to be possible if the membrane is less reflecting than
the adjacent mirror, yielding a potentially very strong dissipative
optomechanical coupling. Specifically, if the absolute values of amplitude
transmission coefficients of the membrane and the mirror, $t$ and $t_m$
respectively, obey the condition $ t_m^2< t\ll t_m\ll 1$, the dissipative
coupling constant of the setup exceeds the dispersive coupling constant for an
optomechanical cavity of the same length. The dissipative coupling constant and
the corresponding optomechanical cooperativity of the proposed system are also
compared with those of the Michelson-Sagnac interferometer and the so-called
"membrane-at-the-edge" system, which are known for a strong optomechanical
dissipative interaction. It is shown that under the above condition, the system
proposed here is advantageous in both aspects. It also enables an efficient
realization of the two-port configuration, which was recently proposed as a
promising optomechanical system, providing, among other benefits, a possibility
of quantum limited optomechanical measurements in a system, which does not
suffer from any optomechanical instability.


Abstract:
  To identify the 3alpha BEC state with the excess neutron, we have
investigated the monopole strength of the excited states of 13C by using the
theoretical framework of the real-time evolution method. The calculations have
revealed several candidates of the Hoyle-analog states in a highly excited
region.


Abstract:
  A.Regev proved that the codimension growth of an associative PI-algebra is at
most exponential. The author established a scale for the codimension growth of
Lie PI-algebras, which includes a series of functions between exponential and
factorial functions. We prove that the same scale stratifies the ordinary
codimension growth of Poisson PI-algebras satisfying Lie identical relations.
As a byproduct, we obtain a new bound on the codimension growth of Lie
PI-algebras in terms of complexity functions. We also study very fast
codimension growth of some Poisson algebras without Lie identities. We
essentially use techniques of exponential generating functions and growth of
respective fast growing entire functions.


Abstract:
  Current anchor-free object detectors are quite simple and effective yet lack
accurate label assignment methods, which limits their potential in competing
with classic anchor-based models that are supported by well-designed assignment
methods based on the Intersection-over-Union~(IoU) metric. In this paper, we
present \textbf{Pseudo-Intersection-over-Union~(Pseudo-IoU)}: a simple metric
that brings more standardized and accurate assignment rule into anchor-free
object detection frameworks without any additional computational cost or extra
parameters for training and testing, making it possible to further improve
anchor-free object detection by utilizing training samples of good quality
under effective assignment rules that have been previously applied in
anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end
single-stage anchor-free object detection framework, we observe consistent
improvements in their performance on general object detection benchmarks such
as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also
achieves comparable performance to other recent state-of-the-art anchor-free
methods without bells and whistles. Our code is based on mmdetection toolbox
and will be made publicly available at
https://github.com/SHI-Labs/Pseudo-IoU-for-Anchor-Free-Object-Detection.


Abstract:
  Modern kernel-based two-sample tests have shown great success in
distinguishing complex, high-dimensional distributions with appropriate learned
kernels. Previous work has demonstrated that this kernel learning procedure
succeeds, assuming a considerable number of observed samples from each
distribution. In realistic scenarios with very limited numbers of data samples,
however, it can be challenging to identify a kernel powerful enough to
distinguish complex distributions. We address this issue by introducing the
problem of meta two-sample testing (M2ST), which aims to exploit (abundant)
auxiliary data on related tasks to find an algorithm that can quickly identify
a powerful test on new target tasks. We propose two specific algorithms for
this task: a generic scheme which improves over baselines and a more tailored
approach which performs even better. We provide both theoretical justification
and empirical evidence that our proposed meta-testing schemes out-perform
learning kernel-based tests directly from scarce observations, and identify
when such schemes will be successful.


Abstract:
  Virtual screening is a computational technique widely used for identifying
small molecules which are most likely to bind to a protein target. Here, we
performed a molecular docking study to propose potential candidates to prevent
the RBD/ACE2 attachment. These candidates are sixteen different flavonoids
present in the peppermint leaf. Results showed that Luteolin
7-O-neohesperidoside is the peppermint flavonoid with a higher binding affinity
regarding the RBD/ACE2 complex (about -9.18 Kcal/mol). On the other hand,
Sakuranetin presented the lowest affinity (about -6.38 Kcal/mol). Binding
affinities of the other peppermint flavonoids ranged from -6.44 Kcal/mol up to
-9.05 Kcal/mol. The binding site surface analysis showed pocket-like regions on
the RBD/ACE2 complex that yield several interactions (mostly hydrogen bonds)
between the flavonoid and the amino acid residues of the proteins. This study
can open channels for the understanding of the roles of flavonoids against
COVID-19 infection.


Abstract:
  In this paper, we compute and demonstrate the equivalence of the joint
distribution of the first letter and descent statistics on six avoidance
classes of permutations corresponding to two patterns of length four. This
distribution is in turn shown to be equivalent to the distribution on a
restricted class of inversion sequences for the statistics that record the last
letter and number of distinct positive letters, affirming a recent conjecture
of Lin and Kim. Members of each avoidance class of permutations and also of the
class of inversion sequences are enumerated by the $n$-th large Schr\"oder
number and thus one obtains a new bivariate refinement of these numbers as a
consequence. We make use of auxiliary combinatorial statistics, special
generating functions (specific to each class) and the kernel method to
establish our results. In some cases, we utilize the conjecture itself in a
creative way to aid in solving the system of functional equations satisfied by
the associated generating functions.


Abstract:
  Recently the drift-reduced Landau fluid six-field turbulence model within the
BOUT++ framework has been upgraded. In particular, this new model employs a new
normalization, adds a volumetric flux-driven source option, the Landau fluid
closure for parallel heat flux and a Laplacian inversion solver which is able
to capture n=0 axisymmetric mode evolution in realistic tokamak configurations.
These improvements substantially extended model's capability to study a wider
range of tokamak edge phenomena, and are essential to build a fully
self-consistent edge turbulence model capable of both transient (e.g., ELM,
disruption) and transport time-scale simulations.


Abstract:
  We enumerate Hurwitz orbits of shortest reflection factorizations of an
arbitrary element in the infinite family $G(m, p, n)$ of complex reflection
groups. As a consequence, we characterize the elements for which the action is
transitive and give a simple criterion to tell when two shortest reflection
factorizations belong to the same Hurwitz orbit. We also characterize the
quasi-Coxeter elements (those with a shortest reflection factorization that
generates the whole group) in $G(m, p, n)$.


Abstract:
  We consider the problem of constructing nonparametric undirected graphical
models for high-dimensional functional data. Most existing statistical methods
in this context assume either a Gaussian distribution on the vertices or linear
conditional means. In this article we provide a more flexible model which
relaxes the linearity assumption by replacing it by an arbitrary additive form.
The use of functional principal components offers an estimation strategy that
uses a group lasso penalty to estimate the relevant edges of the graph. We
establish statistical guarantees for the resulting estimators, which can be
used to prove consistency if the dimension and the number of functional
principal components diverge to infinity with the sample size. We also
investigate the empirical performance of our method through simulation studies
and a real data application.


Abstract:
  We show fully polynomial time randomized approximation schemes (FPRAS) for
counting matchings of a given size, or more generally sampling/counting
monomer-dimer systems in planar, not-necessarily-bipartite, graphs. While
perfect matchings on planar graphs can be counted exactly in polynomial time,
counting non-perfect matchings was shown by [Jer87] to be #P-hard, who also
raised the question of whether efficient approximate counting is possible. We
answer this affirmatively by showing that the multi-site Glauber dynamics on
the set of monomers in a monomer-dimer system always mixes rapidly, and that
this dynamics can be implemented efficiently on downward-closed families of
graphs where counting perfect matchings is tractable. As further applications
of our results, we show how to sample efficiently using multi-site Glauber
dynamics from partition-constrained strongly Rayleigh distributions, and
nonsymmetric determinantal point processes.
  In order to analyze mixing properties of the multi-site Glauber dynamics, we
establish two notions for generating polynomials of discrete set-valued
distributions: sector-stability and fractional log-concavity. These notions
generalize well-studied properties like real-stability and log-concavity, but
unlike them robustly degrade under useful transformations applied to the
distribution. We relate these notions to pairwise correlations in the
underlying distribution and the notion of spectral independence introduced by
[ALO20], providing a new tool for establishing spectral independence based on
geometry of polynomials. As a byproduct of our techniques, we show that
polynomials avoiding roots in a sector of the complex plane must satisfy what
we call fractional log-concavity; this extends a classic result established by
[Gar59] who showed homogeneous polynomials that have no roots in a half-plane
must be log-concave over the positive orthant.


Abstract:
  Nuclear magnetic resonance imaging (MRI) at the atomic scale offers exciting
prospects for determining the structure and function of individual molecules
and proteins. Quantum defects in diamond have recently emerged as a promising
platform towards reaching this goal, and allowed for the detection and
localization of single nuclear spins under ambient conditions. We present an
efficient strategy for extending imaging to large nuclear spin clusters,
fulfilling an important requirement towards a single-molecule MRI technique.
Our method combines the concepts of weak quantum measurements, phase encoding
and simulated annealing to detect three-dimensional positions from many nuclei
in parallel. Detection is spatially selective, allowing us to probe nuclei at a
chosen target radius while avoiding interference from strongly-coupled proximal
nuclei. We demonstrate our strategy by imaging clusters containing more than 20
carbon-13 nuclear spins within a radius of 2.4 nm from single, near-surface
nitrogen--vacancy centers at room temperature. The radius extrapolates to 7-8
nm for protons. Beside taking an important step in nanoscale MRI, our
experiment also provides an efficient tool for the characterization of large
nuclear spin registers in the context of quantum simulators and quantum network
nodes.


Abstract:
  Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen
Oxide (NOx) emissions. In order to achieve a cost-effective reduction of
greenhouse gas emissions and to combat climate change, the European Union (EU)
introduced an Emissions Trading System (ETS) where organizations can buy or
receive emission allowances as needed. The current ETS is a centralized one,
consisting of a set of complex rules. It is currently administered at the
organizational level and is used for fixed-point sources of pollution such as
factories, power plants, and refineries. However, the current ETS cannot
efficiently cope with vehicle mobility, even though vehicles are one of the
primary sources of CO2 and NOx emissions. In this study, we propose a new
distributed Blockchain-based emissions allowance trading system called B-ETS.
This system enables transparent and trustworthy data exchange as well as
trading of allowances among vehicles, relying on vehicle-to-vehicle
communication. In addition, we introduce an economic incentive-based mechanism
that appeals to individual drivers and leads them to modify their driving
behavior in order to reduce emissions. The efficiency of the proposed system is
studied through extensive simulations, showing how increased vehicle
connectivity can lead to a reduction of the emissions generated from those
vehicles. We demonstrate that our method can be used for full life-cycle
monitoring and fuel economy reporting. This leads us to conjecture that the
proposed system could lead to important behavioral changes among the drivers


Abstract:
  Recently, the chiral superconductivity of the cosmic string in the axion
model has gathered attention. The superconductive nature can alter the standard
understanding of the cosmology of the axion model. For example, a string loop
with a sizable superconducting current can become a stable configuration, which
is called a Vorton. The superconductive nature can also affect the cosmological
evolution of the string network. In this paper, we study the stability of the
superconducting current in the string. We find the superconductivity is indeed
stable for a straight string or infinitely small string core size, even if the
carrier particles are unstable in the vacuum. However we also find that the
carrier particle decays in a curved string in typical axion models, if the
carrier particles are unstable in the vacuum. Accordingly, the lifetime of the
Vorton is not far from that of the carrier particle in the vacuum.


Abstract:
  Diabetic retinopathy (DR) remains the most prevalent cause of vision
impairment and irreversible blindness in the working-age adults. Due to the
renaissance of deep learning (DL), DL-based DR diagnosis has become a promising
tool for the early screening and severity grading of DR. However, training deep
neural networks (DNNs) requires an enormous amount of carefully labeled data.
Noisy label data may be introduced when labeling plenty of data, degrading the
performance of models. In this work, we propose a novel label management
mechanism (LMM) for the DNN to overcome overfitting on the noisy data. LMM
utilizes maximum posteriori probability (MAP) in the Bayesian statistic and
time-weighted technique to selectively correct the labels of unclean data,
which gradually purify the training data and improve classification
performance. Comprehensive experiments on both synthetic noise data (Messidor
\& our collected DR dataset) and real-world noise data (ANIMAL-10N)
demonstrated that LMM could boost performance of models and is superior to
three state-of-the-art methods.


Abstract:
  Person detection is a crucial task for mobile robots navigating in
human-populated environments and LiDAR sensors are promising for this task,
given their accurate depth measurements and large field of view. This paper
studies existing LiDAR-based person detectors with a particular focus on mobile
robot scenarios (e.g. service robot or social robot), where persons are
observed more frequently and in much closer ranges, compared to the driving
scenarios. We conduct a series of experiments, using the recently released
JackRabbot dataset and the state-of-the-art detectors based on 3D or 2D LiDAR
sensors (CenterPoint and DR-SPAAM respectively). These experiments revolve
around the domain gap between driving and mobile robot scenarios, as well as
the modality gap between 3D and 2D LiDAR sensors. For the domain gap, we aim to
understand if detectors pretrained on driving datasets can achieve good
performance on the mobile robot scenarios, for which there are currently no
trained models readily available. For the modality gap, we compare detectors
that use 3D or 2D LiDAR, from various aspects, including performance, runtime,
localization accuracy, robustness to range and crowdedness. The results from
our experiments provide practical insights into LiDAR-based person detection
and facilitate informed decisions for relevant mobile robot designs and
applications.


Abstract:
  Data augmentation methods have been shown to be a fundamental technique to
improve generalization in tasks such as image, text and audio classification.
Recently, automated augmentation methods have led to further improvements on
image classification and object detection leading to state-of-the-art
performances. Nevertheless, little work has been done on time-series data, an
area that could greatly benefit from automated data augmentation given the
usually limited size of the datasets. We present two sample-adaptive automatic
weighting schemes for data augmentation: the first learns to weight the
contribution of the augmented samples to the loss, and the second method
selects a subset of transformations based on the ranking of the predicted
training loss. We validate our proposed methods on a large, noisy financial
dataset and on time-series datasets from the UCR archive. On the financial
dataset, we show that the methods in combination with a trading strategy lead
to improvements in annualized returns of over 50$\%$, and on the time-series
data we outperform state-of-the-art models on over half of the datasets, and
achieve similar performance in accuracy on the others.


Abstract:
  Wireless power transfer using inductive coupling is commonly used for medical
implantable devices. The design of the secondary coil on the implantable device
is important as it will affect the power transfer efficiency, the size of the
implant, and also the data transmission between the implant and the in-vitro
controller. In this paper, we present a design of the secondary coil on a
polyimide-based flexible substrate to achieve high power transfer efficiency.
Load shift keying modulation is used for the data communication between the
primary and secondary coils. A thorough analysis is done for the ideal and
practical scenario and it shows that a mismatched secondary LC tank will affect
the communication range and communication correctness. A solution to achieve
robust data transmission is proposed and then verified by SPICE simulations.


Abstract:
  We show that, for points along the moment curve, the bar-and-joint rigidity
matroid and the hyperconnectivity matroid coincide, and that both coincide with
the $C^{d-2}_{d-1}$-cofactor rigidity of points along any (non-degenerate)
conic in the plane. We also show that, for hyperconnectivity in dimension two,
having the points in the moment curve is no loss of generality.


Abstract:
  We present numerical methods based on the fast Fourier transform (FFT) to
solve convolution integral equations on a semi-infinite interval (Wiener-Hopf
equation) or on a finite interval (Fredholm equation). We extend and improve a
FFT-based method for the Wiener-Hopf equation due to Henery, expressing it in
terms of the Hilbert transform, and computing the latter in a more
sophisticated way with sinc functions. We then generalise our method to the
Fredholm equation reformulating it as two coupled Wiener-Hopf equations and
solving them iteratively. We provide numerical tests and open-source code.


Abstract:
  Ultracold YbAg molecules have been recently proposed as promising candidates
for electron electric dipole moment searches [Verma, Jayich, and Vutha, Phys.
Rev. Lett. 125, 153201 (2020)]. Here, we calculate potential energy curves,
permanent electric dipole and quadrupole moments, and static electric dipole
polarizabilities for the YbCu, YbAg, and YbAu molecules in their ground
electronic states. We use the coupled cluster method restricted to single,
double, and noniterative triple excitations with large Gaussian basis sets,
while the scalar relativistic effects are included within the small-core
energy-consistent pseudopotentials. We find that the studied molecules are
relatively strongly bound with the well depths of 5708$\,$cm$^{-1}$,
5253$\,$cm$^{-1}$, 13349$\,$cm$^{-1}$ and equilibrium distances of
5.50$\,$bohr, 5.79$\,$bohr, 5.55$\,$bohr for the YbCu, YbAg, and YbAu,
respectively. They have large permanent electric dipole moments of 3.2$\,$D,
3.3$\,$D, and 5.3$\,$D at equilibrium distances, respectively. We also
calculate equilibrium geometries and energies of corresponding trimers. The
studied molecules are chemically reactive unless they are segregated in an
optical lattice or shielded with external fields. The investigated molecules
may find application in ultracold controlled chemistry, dipolar many-body
physics, or precision measurement experiments.


Abstract:
  Hyperspectral Image (HSI) classification using Convolutional Neural Networks
(CNN) is widely found in the current literature. Approaches vary from using
SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other
approaches do not consider both the spectral and spatial features together for
HSI classification task, thereby resulting in poor performances. 3D CNNs are
computationally heavy and are not widely used, while 2D CNNs do not consider
multi-resolution processing of images, and only limits itself to the spatial
features. Even though 3D-2D CNNs try to model the spectral and spatial features
their performance seems limited when applied over multiple dataset. In this
article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN
for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet
transform to bring out spectral features. Computing a wavelet transform is
lighter than computing 3D CNN. The spectral features extracted are then
connected to the 2D CNN which bring out the spatial features, thereby creating
a spatial-spectral feature vector for classification. Overall a better model is
achieved that can classify multi-resolution HSI data with high accuracy.
Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines,
University of Pavia, and Salinas Scenes confirm the superiority of proposed
SpectralNET with respect to the state-of-the-art methods. The code is publicly
available in https://github.com/tanmay-ty/SpectralNET.


Abstract:
  We consider the offline reinforcement learning (RL) setting where the agent
aims to optimize the policy solely from the data without further environment
interactions. In offline RL, the distributional shift becomes the primary
source of difficulty, which arises from the deviation of the target policy
being optimized from the behavior policy used for data collection. This
typically causes overestimation of action values, which poses severe problems
for model-free algorithms that use bootstrapping. To mitigate the problem,
prior offline RL algorithms often used sophisticated techniques that encourage
underestimation of action values, which introduces an additional set of
hyperparameters that need to be tuned properly. In this paper, we present an
offline RL algorithm that prevents overestimation in a more principled way. Our
algorithm, OptiDICE, directly estimates the stationary distribution corrections
of the optimal policy and does not rely on policy-gradients, unlike previous
offline RL algorithms. Using an extensive set of benchmark datasets for offline
RL, we show that OptiDICE performs competitively with the state-of-the-art
methods.


Abstract:
  We consider a market setting of agents with additive valuations over
heterogeneous divisible resources. Agents are assigned a budget of tokens
(possibly unequal budgets) they can use to obtain resources; leftover tokens
are worthless. We show how to support any Pareto efficient allocation in
equilibrium, using anonymous resource prices and agent specific budgets. We
also give computationally efficient algorithms for those tasks. In particular,
this allows us to support the Rawlsian max-min allocation.


Abstract:
  We study two dyadic models for incompressible ideal magnetohydrodynamics, one
with a uni-directional energy cascade and the other one with both forward and
backward energy cascades. Global existence of weak solutions and local
well-posedness are established for both models. In addition, solutions to the
model with uni-directional energy cascade associated with positive initial data
are shown to develop blow-up at a finite time. Moreover, a set of fixed points
is found for each model. Linear instability about some particular fixed points
is proved.


Abstract:
  When fitting statistical models to variables in geoscientific disciplines
such as hydrology, it is a customary practice to regionalize - to divide a
large spatial domain into multiple regions and study each region separately -
instead of fitting a single model on the entire data (also known as
unification). Traditional wisdom in these fields suggests that models built for
each region separately will have higher performance because of homogeneity
within each region. However, by partitioning the training data, each model has
access to fewer data points and cannot learn from commonalities between
regions. Here, through two hydrologic examples (soil moisture and streamflow),
we argue that unification can often significantly outperform regionalization in
the era of big data and deep learning (DL). Common DL architectures, even
without bespoke customization, can automatically build models that benefit from
regional commonality while accurately learning region-specific differences. We
highlight an effect we call data synergy, where the results of the DL models
improved when data were pooled together from characteristically different
regions. In fact, the performance of the DL models benefited from more diverse
rather than more homogeneous training data. We hypothesize that DL models
automatically adjust their internal representations to identify commonalities
while also providing sufficient discriminatory information to the model. The
results here advocate for pooling together larger datasets, and suggest the
academic community should place greater emphasis on data sharing and
compilation.


Abstract:
  Trading frictions are stochastic. They are, moreover, in many instances
fast-mean reverting. Here, we study how to optimally trade in a market with
stochastic price impact and study approximations to the resulting optimal
control problem using singular perturbation methods. We prove, by constructing
sub- and super-solutions, that the approximations are accurate to the specified
order. Finally, we perform some numerical experiments to illustrate the effect
that stochastic trading frictions have on optimal trading.


Abstract:
  Biologically inspired algorithms for simultaneous localization and mapping
(SLAM) such as RatSLAM have been shown to yield effective and robust robot
navigation in both indoor and outdoor environments. One drawback however is the
sensitivity to perceptual aliasing due to the template matching of
low-dimensional sensory templates. In this paper, we propose an unsupervised
representation learning method that yields low-dimensional latent state
descriptors that can be used for RatSLAM. Our method is sensor agnostic and can
be applied to any sensor modality, as we illustrate for camera images, radar
range-doppler maps and lidar scans. We also show how combining multiple sensors
can increase the robustness, by reducing the number of false matches. We
evaluate on a dataset captured with a mobile robot navigating in a
warehouse-like environment, moving through different aisles with similar
appearance, making it hard for the SLAM algorithms to disambiguate locations.


Abstract:
  Nanostructured materials enable high thermal transport tunability, holding
promises for thermal management and heat harvesting applications. Predicting
the effect that nanostructuring has on thermal conductivity requires models,
such as the Boltzmann transport equation (BTE), that capture the non-diffusive
transport of phonons. Although the BTE has been well validated against several
key experiments, notably those on nanoporous materials, its applicability is
computationally expensive. Several effective model theories have been put
forward to estimate the effective thermal conductivity; however, most of them
are either based on simple geometries, e.g., thin films, or simplified material
descriptions such as the gray-model approximation. To fill this gap, we propose
a model that takes into account the whole mean-free-path (MFP) distribution as
well as the complexity of the material's boundaries in infinitely thick films
with extruded porosity using uniparameter logistic regression. We validate our
approach, which is called the "Ballistic Correction Model" (BCM), against full
BTE simulations of a selection of three base materials (GaAs, InAs, and Si)
with nanoscale porosity, obtaining excellent agreement. While the key
parameters of our method, associated with the geometry of the bulk material,
are obtained from the BTE, they can be decoupled and used in arbitrary
combinations and scales. We tabulated these parameters for a few cases,
enabling the exploration of systems that are beyond those considered in this
work. Providing a simple yet accurate estimation of thermal transport in
nanostructures, our work sets out to accelerate the discovery of materials for
thermal-related applications.


Abstract:
  Recent applications employ publish/subscribe (Pub/Sub) systems so that
publishers can easily receive attentions of customers and subscribers can
monitor useful information generated by publishers. Due to the prevalence of
smart devices and social networking services, a large number of objects that
contain both spatial and keyword information have been generated continuously,
and the number of subscribers also continues to increase. This poses a
challenge to Pub/Sub systems: they need to continuously extract useful
information from massive objects for each subscriber in real time. In this
paper, we address the problem of k nearest neighbor monitoring on a
spatial-keyword data stream for a large number of subscriptions. To scale well
to massive objects and subscriptions, we propose a distributed solution, namely
DkM-SKS. Given m workers, DkM-SKS divides a set of subscriptions into m
disjoint subsets based on a cost model so that each worker has almost the same
kNN-update cost, to maintain load balancing. DkM-SKS allows an arbitrary
approach to updating kNN of each subscription, so with a suitable in-memory
index, DkM-SKS can accelerate update efficiency by pruning irrelevant
subscriptions for a given new object. We conduct experiments on real datasets,
and the results demonstrate the efficiency and scalability of DkM-SKS.


Abstract:
  Robotic applications nowadays are widely adopted to enhance operational
automation and performance of real-world Cyber-Physical Systems (CPSs)
including Industry 4.0, agriculture, healthcare, and disaster management. These
applications are composed of latency-sensitive, data-heavy, and
compute-intensive tasks. The robots, however, are constrained in the
computational power and storage capacity. The concept of multi-agent cloud
robotics enables robot-to-robot cooperation and creates a complementary
environment for the robots in executing large-scale applications with the
capability to utilize the edge and cloud resources. However, in such a
collaborative environment, the optimal resource allocation for robotic tasks is
challenging to achieve. Heterogeneous energy consumption rates and application
of execution costs associated with the robots and computing instances make it
even more complex. In addition, the data transmission delay between local
robots, edge nodes, and cloud data centres adversely affects the real-time
interactions and impedes service performance guarantee. Taking all these issues
into account, this paper comprehensively surveys the state-of-the-art on
resource allocation and service provisioning in multi-agent cloud robotics. The
paper presents the application domains of multi-agent cloud robotics through
explicit comparison with the contemporary computing paradigms and identifies
the specific research challenges. A complete taxonomy on resource allocation is
presented for the first time, together with the discussion of resource pooling,
computation offloading, and task scheduling for efficient service provisioning.
Furthermore, we highlight the research gaps from the learned lessons, and
present future directions deemed beneficial to further advance this emerging
field.


Abstract:
  We build a rigorous statistical framework to provide constraints on the
chemical and dust evolution parameters for nearby late-type galaxies with a
wide range of gas fractions ($3\%<f_g<94\%$). A Bayesian Monte Carlo Markov
Chain framework provides statistical constraints on the parameters used in
chemical evolution models. Nearly a million one-zone chemical and dust
evolution models were compared to 340 galaxies. Relative probabilities were
calculated from the $\chi^2$ between data and models, marginalised over the
different time steps, galaxy masses and star formation histories. We applied
this method to find `best fitting' model parameters related to metallicity, and
subsequently fix these metal parameters to study the dust parameters. For the
metal parameters, a degeneracy was found between the choice of initial mass
function, supernova metal yield tables and outflow prescription. For the dust
parameters, the uncertainties on the best fit values are often large except for
the fraction of metals available for grain growth, which is well constrained.
We find a number of degeneracies between the dust parameters, limiting our
ability to discriminate between chemical models using observations only. For
example, we show that the low dust content of low-metallicity galaxies can be
resolved by either reducing the supernova dust yields and/or including
photo-fragmentation. We also show that supernova dust dominates the dust mass
for low metallicity galaxies and grain growth dominates for high metallicity
galaxies. The transition occurs around $12+\log({\rm O/H})=7.75$, which is
lower than found in most studies in the literature.


Abstract:
  This paper presents a new Vision Transformer (ViT) architecture Multi-Scale
Vision Longformer, which significantly enhances the ViT of
\cite{dosovitskiy2020image} for encoding high-resolution images using two
techniques. The first is the multi-scale model structure, which provides image
encodings at multiple scales with manageable computational cost. The second is
the attention mechanism of vision Longformer, which is a variant of Longformer
\cite{beltagy2020longformer}, originally developed for natural language
processing, and achieves a linear complexity w.r.t. the number of input tokens.
A comprehensive empirical study shows that the new ViT significantly
outperforms several strong baselines, including the existing ViT models and
their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent
work \cite{wang2021pyramid}, on a range of vision tasks, including image
classification, object detection, and segmentation. The models and source code
are released at \url{https://github.com/microsoft/vision-longformer}.


Abstract:
  A balanced sampling design should always be the adopted strategies if
auxiliary information is available. Besides, integrating a stratified structure
of the population in the sampling process can considerably reduce the variance
of the estimators. We propose here a new method to handle the selection of a
balanced sample in a highly stratified population. The method improves
substantially the commonly used sampling design and reduces the time-consuming
problem that could arise if inclusion probabilities within strata do not sum to
an integer.


Abstract:
  I briefly discuss recent results of numerical simulations addressing the
generation of a cosmological gravitational wave background produced by
turbulence sources in the early universe. Contribution to the 2021 Gravitation
session of the 55th Rencontres de Moriond.


Abstract:
  Petroni et al. (2019) demonstrated that it is possible to retrieve world
facts from a pre-trained language model by expressing them as cloze-style
prompts and interpret the model's prediction accuracy as a lower bound on the
amount of factual information it encodes. Subsequent work has attempted to
tighten the estimate by searching for better prompts, using a disjoint set of
facts as training data. In this work, we make two complementary contributions
to better understand these factual probing techniques. First, we propose
OptiPrompt, a novel and efficient method which directly optimizes in continuous
embedding space. We find this simple method is able to predict an additional
6.4% of facts in the LAMA benchmark. Second, we raise a more important
question: Can we really interpret these probing results as a lower bound? Is it
possible that these prompt-search methods learn from the training data too? We
find, somewhat surprisingly, that the training data used by these methods
contains certain regularities of the underlying fact distribution, and all the
existing prompt methods, including ours, are able to exploit them for better
fact prediction. We conduct a set of control experiments to disentangle
"learning" from "learning to recall", providing a more detailed picture of what
different prompts can reveal about pre-trained language models.


Abstract:
  Much of what is known about high-temperature cuprate superconductors stems
from studies based on two surface analytical tools, angle-resolved
photoemission spectroscopy (ARPES) and spectroscopic imaging scanning tunneling
microscopy (SI-STM). A question of general interest is whether and when the
surface properties probed by ARPES and SI-STM are representative of the
intrinsic properties of bulk materials. We find this question is prominent in
thin films of a rarely studied cuprate DBCO. We synthesize DBCO films by oxide
molecular beam epitaxy and study them by in situ ARPES and SI-STM. Both ARPES
and SI-STM show that the surface DBCO layer is different from the bulk of the
film. It is heavily underdoped, while the doping level in the bulk is close to
optimal doping evidenced by bulk-sensitive mutual inductance measurements.
ARPES shows the typical electronic structure of a heavily underdoped CuO2 plane
and two sets of one-dimensional bands originating from the CuO chains with one
of them gapped. SI-STM reveals two different energy scales in the local density
of states, with one corresponding to the superconductivity and the other one to
the pseudogap. While the pseudogap shows large variations over the length scale
of a few nanometers, the superconducting gap is very homogeneous. This
indicates that the pseudogap and superconductivity are of different origins.


Abstract:
  Deep Learning (DL) is a machine learning procedure for artificial
intelligence that analyzes the input data in detail by increasing neuron sizes
and number of the hidden layers. DL has a popularity with the common
improvements on the graphical processing unit capabilities. Increasing number
of the neuron sizes at each layer and hidden layers is directly related to the
computation time and training speed of the classifier models. The
classification parameters including neuron weights, output weights, and biases
need to be optimized for obtaining an optimum model. Most of the popular DL
algorithms require long training times for optimization of the parameters with
feature learning progresses and back-propagated training procedures. Reducing
the training time and providing a real-time decision system are the basic focus
points of the novel approaches. Deep Extreme Learning machines (Deep ELM)
classifier model is one of the fastest and effective way to meet fast
classification problems. In this study, Deep ELM model, its superiorities and
weaknesses are discussed, the problems that are more suitable for the
classifiers against Convolutional neural network based DL algorithms.