Abstract:
  We study quantitative aspects and concentration phenomena for ground states
of the following nonlocal Schr\"odinger equation $
  (-\Delta)^s u+V(x)u= u^{2_s^*-1-\varepsilon} \ \
  \text{in}\ \ \mathbb{R}^N, $ where $\varepsilon>0$, $s\in (0,1)$,
$2^*_s:=\frac{2N}{N-2s}$, $N>4s$. We show that the ground state
$u_{\varepsilon}$ blows up and precisely with the following rate
$\|u_{\varepsilon}\|_{L^\infty(\mathbb{R}^N)}\sim
\varepsilon^{-\frac{N-2s}{4s}}$, as $\epsilon\rightarrow 0^+$. We also localize
the concentration points and, in the case of radial potentials $V$, we prove
local uniqueness of sequences of ground states which exhibit a concentrating
behavior.


Abstract:
  We present the first data release (DR1) of the VST Early-type GAlaxy Survey
(VEGAS). This is a deep multi-band (u'g'r'i') imaging survey, carried out with
the ESO VLT Survey Telescope (VST). To date, using about 90% of the total
observing time, VEGAS has already collected 43 targets (groups and clusters of
galaxies) covering a total area on the sky of about 95 square degrees. Taking
advantage of the wide (1 deg^2) field-of-view of OmegaCAM@VST, the long
integration time and the wide variety of targets, VEGAS has proven to be a gold
mine to explore the structure of galaxies down to the faintest surface
brightness levels of about 27-30 mag/arcsec^2 in the SDSS g' band, for the
dense clusters of galaxies and for the unexplored poor groups of galaxies.
Based on the analysed data, VEGAS allowed us to i) study the galaxy outskirts,
detect the intra-cluster light and low-surface brightness features in the
intra-cluster/group space, ii) trace the mass assembly in galaxies, by
estimating the accreted mass fraction in the stellar halos and provide results
that can be directly compared with the predictions of galaxy formation models,
iii) trace the spatial distribution of candidate globular clusters, and iv)
detect the ultra-diffuse galaxies. With the DR1, we provide the reduced VST
mosaics of 10 targets, which have been presented in the VEGAS publications. The
data products are available via the ESO Science Portal (see
http://www.eso.org/sci/observing/phase3/news.html#VEGAS-DR1).


Abstract:
  We study the statistical behavior of multimoded optical systems under
equilibrium conditions. We investigate the role of variations of the system
parameters in the thermodynamic description and derive, an optical analogue of
the first law of thermodynamics, a generic expression for the work done to the
system, and an optical Gibbs-Duhem equation. To demonstrate these effects, we
focus in the case of two-dimensional photonic lattices. We study the conditions
under which the entropy in such waveguide arrays can be considered as
extensive. In this respect, small deviations from the extensive character of
the entropy give rise to stress and strain terms. We examine how the
conservation laws in such array configurations are affected by variations in
the system parameters, and furthermore, we analyze the respective thermodynamic
processes (isentropic and Joule-type expansions).


Abstract:
  In this article, the mechanism of the unexpected high fluidity in SiOx
nanowire under modest irradiation was proposed, the high fluidity is attributed
to the long lifetime of irradiation-induced holes, which arise from formation
of small polarons. The holes created in irradiation could have a long lifetime,
and localized in space, such missing of bonding electron could suppress the
energy barrier(athermal activation effect) for a Pachner move of the network.
The atomic level dynamics of the system is proposed by interaction of phonon
and local configuration, the activation effect was then studied with passing
rate of corresponding stochastic dynamic equation, calculation shows an
exponential dependent of the time-lapse of Pachner move to lifetime of the
activation, furthermore, connection between the local configuration time and
viscosity of the fluid indicates a strong sensitivity of viscosity to lifetime
of the athermal activation, such mechanism would give an effective
interpretation to the unexpected high fluidity together with the passivation
effect of the conductor on the material.


Abstract:
  We present the analytical singular value decomposition of the stoichiometry
matrix for a spatially discrete reaction-diffusion system on a one dimensional
domain. The domain has two subregions which share a single common boundary.
Each of the subregions is further partitioned into a finite number of
compartments. Chemical reactions can occur within a compartment, whereas
diffusion is represented as movement between adjacent compartments. Inspired by
biology, we study both 1) the case where the reactions on each side of the
boundary are different and only certain species diffuse across the boundary as
well as 2) the case with spatially homogenous reactions and diffusion. We write
the stoichiometry matrix for these two classes of systems using a Kronecker
product formulation. For the first scenario, we apply linear perturbation
theory to derive an approximate singular value decomposition in the limit as
diffusion becomes much faster than reactions. For the second scenario, we
derive an exact analytical singular value decomposition for all relative
diffusion and reaction time scales. By writing the stoichiometry matrix using
Kronecker products, we show that the singular vectors and values can also be
written concisely using Kronecker products.
  Ultimately, we find that the singular value decomposition of the
reaction-diffusion stoichiometry matrix depends on the singular value
decompositions of smaller matrices. These smaller matrices represent modified
versions of the reaction-only stoichiometry matrices and the analytically known
diffusion-only stoichiometry matrix. Our results provide a mathematical
framework that can be used to study complex biochemical systems with metabolic
compartments. MATLAB code for calculating the SVD equations is available at
\url{www.github.com/MathBioCU/ReacDiffStoicSVD}.


Abstract:
  This paper sketches a new approach using Fuzzy Cognitive Maps (FCMs) to
operably map and simulate digital transformation in architecture and urban
planning. Today these processes are poorly understood. Many current studies on
digital transformation are only treating questions of economic efficiency.
Sustainability and social impact only play a minor role. Decisive definitions,
concepts and terms stay unclear. Therefore this paper develops an open
experimental testbed for sustainable and innovative environments (ETSIE) for
three different digital transformation scenarios using FCMs. A traditional
growth-oriented scenario, a COVID-19 scenario and an innovative and sustainable
COVID-19 scenario are modeled and tested. All three scenarios have the same
number of components, connections and the same driver components. Only the
initial state vectors are different and the internal correlations are weighted
differently. This allows for comparing all three scenarios on an equal basis.
The mental modeler software is used (Gray et al. 2013). This paper presents one
of the first applications of FCMs in the context of digital transformation. It
is shown, that the traditional growth-oriented scenario is structurally very
similar to the current COVID-19 scenario. The current pandemic is able to
accelerate digital transformation to a certain extent. But the pandemic does
not guarantee for a distinct sustainable and innovative future development.
Only by changing the initial state vectors and the weights of the connections
an innovative and sustainable turnaround in a third scenario becomes possible.


Abstract:
  When constructing a Bayesian Machine Learning model, we might be faced with
multiple different prior distributions and thus are required to properly
consider them in a sensible manner in our model. While this situation is
reasonably well explored for classical Bayesian Statistics, it appears useful
to develop a corresponding method for complex Machine Learning problems. Given
their underlying Bayesian framework and their widespread popularity, Gaussian
Processes are a good candidate to tackle this task. We therefore extend the
idea of Mixture models for Gaussian Process regression in order to work with
multiple prior beliefs at once - both a analytical regression formula and a
Sparse Variational approach are considered. In addition, we consider the usage
of our approach to additionally account for the problem of prior
misspecification in functional regression problems.


Abstract:
  We present closed-form expressions of unrefined instanton partition functions
for gauge groups of type $BCD$ as sums over Young diagrams. For
$\mathrm{SO}(n)$ gauge groups, we provide a fivebrane web picture of our
formula based on the vertex-operator formalism of the topological vertex with a
new type called O-vertex for an O5-plane.


Abstract:
  An adversarial patch can arbitrarily manipulate image pixels within a
restricted region to induce model misclassification. The threat of this
localized attack has gained significant attention because the adversary can
mount a physically-realizable attack by attaching patches to the victim object.
Recent provably robust defenses generally follow the PatchGuard framework by
using CNNs with small receptive fields and secure feature aggregation for
robust model predictions. In this paper, we extend PatchGuard to PatchGuard++
for provably detecting the adversarial patch attack to boost both provable
robust accuracy and clean accuracy. In PatchGuard++, we first use a CNN with
small receptive fields for feature extraction so that the number of features
corrupted by the adversarial patch is bounded. Next, we apply masks in the
feature space and evaluate predictions on all possible masked feature maps.
Finally, we extract a pattern from all masked predictions to catch the
adversarial patch attack. We evaluate PatchGuard++ on ImageNette (a 10-class
subset of ImageNet), ImageNet, and CIFAR-10 and demonstrate that PatchGuard++
significantly improves the provable robustness and clean performance.


Abstract:
  An emerging optimisation problem from the real-world applications, named the
multi-point dynamic aggregation (MPDA) problem, has become one of the active
research topics of the multi-robot system. This paper focuses on a
multi-objective MPDA problem which is to design an execution plan of the robots
to minimise the number of robots and the maximal completion time of all the
tasks. The strongly-coupled relationships among robots and tasks, the
redundancy of the MPDA encoding, and the variable-size decision space of the
MO-MPDA problem posed extra challenges for addressing the problem effectively.
To address the above issues, we develop a hybrid decomposition-based
multi-objective evolutionary algorithm (HDMOEA) using $ \varepsilon
$-constraint method. It selects the maximal completion time of all tasks as the
main objective, and converted the other objective into constraints. HDMOEA
decomposes a MO-MPDA problem into a series of scalar constrained optimization
subproblems by assigning each subproblem with an upper bound robot number. All
the subproblems are optimized simultaneously with the transferring knowledge
from other subproblems. Besides, we develop a hybrid population initialisation
mechanism to enhance the quality of initial solutions, and a reproduction
mechanism to transmit effective information and tackle the encoding redundancy.
Experimental results show that the proposed HDMOEA method significantly
outperforms the state-of-the-art methods in terms of several most-used metrics.


Abstract:
  This paper studies random fields on the unit sphere. Traditionally, isotropic
Gaussian random fields are considered as the underlying statistical model of
the cosmic microwave background (CMB) data. This paper discusses the
generalized multifractional Brownian motion and its pointwise H\"older exponent
on the sphere. The multifractional approach is used to investigate the CMB data
from the Planck mission. These data consist of CMB radiation measurements at
narrow angles of the sky sphere. The obtained results suggest that the
estimated H\"older exponents for different CMB regions do change from location
to location. Therefore, CMB data are multifractional. Then the developed
methodology is used to suggest two approaches for detecting regions with
anomalies in cleaned CMB maps.


Abstract:
  This paper proposes a novel voice conversion (VC) method based on
non-autoregressive sequence-to-sequence (NAR-S2S) models. Inspired by the great
success of NAR-S2S models such as FastSpeech in text-to-speech (TTS), we extend
the FastSpeech2 model for the VC problem. We introduce the
convolution-augmented Transformer (Conformer) instead of the Transformer,
making it possible to capture both local and global context information from
the input sequence. Furthermore, we extend variance predictors to variance
converters to explicitly convert the source speaker's prosody components such
as pitch and energy into the target speaker. The experimental evaluation with
the Japanese speaker dataset, which consists of male and female speakers of
1,000 utterances, demonstrates that the proposed model enables us to perform
more stable, faster, and better conversion than autoregressive S2S (AR-S2S)
models such as Tacotron2 and Transformer.


Abstract:
  The specific heat $C$ of the cuprate superconductors La$_{2-x}$Sr$_x$CuO$_4$
and Bi$_{2+y}$Sr$_{2-x-y}$La$_x$CuO$_{6+\delta}$ was measured at low
temperature (down to $0.5~{\rm K}$), for dopings $p$ close to $p^\star$, the
critical doping for the onset of the pseudogap phase. A magnetic field up to
$35~{\rm T}$ was applied to suppress superconductivity, giving direct access to
the normal state at low temperature, and enabling a determination of $C_e$, the
electronic contribution to the normal-state specific heat, at $T \to 0$. In
La$_{2-x}$Sr$_x$CuO$_4$ at $x=p = 0.22$, $0.24$ and $0.25$, $C_e / T =
15-16~{\rm mJmol}^{-1}{\rm K}^{-2}$ at $T = 2~{\rm K}$, values that are twice
as large as those measured at higher doping ($p > 0.3$) and lower doping ($p <
0.15$). This confirms the presence of a broad peak in the doping dependence of
$C_e$ at $p^\star\simeq 0.19$, as previously reported for samples in which
superconductivity was destroyed by Zn impurities. Moreover, at those three
dopings, we find a logarithmic growth as $T \to 0$, such that $C_e / T \sim
{\rm B}\ln(T_0/T)$. The peak vs $p$ and the logarithmic dependence vs $T$ are
the two typical thermodynamic signatures of quantum criticality. In the very
different cuprate Bi$_{2+y}$Sr$_{2-x-y}$La$_x$CuO$_{6+\delta}$, we again find
that $C_e / T \sim {\rm B}\ln(T_0/T$) at $p \simeq p^\star$, strong evidence
that this $\ln(1/T)$ dependence - first discovered in the cuprates
La$_{1.8-x}$Eu$_{0.2}$Sr$_x$CuO$_4$ and La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4$ -
is a universal property of the pseudogap critical point. All four materials
display similar values of the $\rm B$ coefficient, indicating that they all
belong to the same universality class.


Abstract:
  In facing with the explosive Internet traffic growth, optical transport
networks based on WDM technologies forming the core part of Internet
infrastructure carrying multi-Tb/s has to be re-considered from both designing,
planning, operation and management perspectives to attain greater efficiency.
Thanks to the convergence of significant advances in optical transmission
technologies, and photonic switching, transparent (all-optical) architecture
has come into practice, paving the way for eliminating the over-utilization of
costly optical-electrical-optical (O-E-O) interfaces and hence, yielding
remarkable savings of cost and energy consumption compared to opaque
architecture. Traditional designs for transparent optical networks based on
single-objective optimization model aiming at optimizing solely a single
performance metric appears to be insufficient to capture the nuances of
practical designs while conventional multi-objective approach tends to reach
(non-) optimal solutions. Different from existing works, we present a new
framework for multi-objective WDM network designs capturing several goals on
one hand and on the other hand, achieving optimal solutions. Moreover, our
proposal exploits the characteristics of each constituent objectives to lay the
foundation for setting up weight coefficient so that the order of optimization
is guaranteed. Equally important, our proposal is pragmatic in the sense that
the complexity of the optimization model remains the same as the
single-objective model while the quality of solution has been greatly improved.
We have extensively tested realistic optical core networks topologies, that is,
COST239 and NSFNET, with various network traffic conditions and it turns out
that our design brings about a saving of wavelength link usage up to roughly
$28\%$ in the most favorable cases while $14\%$ is expected for the least
favorable cases.


Abstract:
  In this paper, we outline a way to deploy a privacy-preserving protocol for
multiparty Randomized Controlled Trials on the scale of 500 million rows of
data and more than a billion gates. Randomized Controlled Trials (RCTs) are
widely used to improve business and policy decisions in various sectors such as
healthcare, education, criminology, and marketing. A Randomized Controlled
Trial is a scientifically rigorous method to measure the effectiveness of a
treatment. This is accomplished by randomly allocating subjects to two or more
groups, treating them differently, and then comparing the outcomes across
groups. In many scenarios, multiple parties hold different parts of the data
for conducting and analyzing RCTs. Given privacy requirements and expectations
of each of these parties, it is often challenging to have a centralized store
of data to conduct and analyze RCTs.
  We accomplish this by a three-stage solution. The first stage uses the
Private Secret Share Set Intersection (PS$^3$I) solution to create a joined set
and establish secret shares without revealing membership, while discarding
individuals who were placed into more than one group. The second stage runs
multiple instances of a general purpose MPC over a sharded database to
aggregate statistics about each experimental group while discarding individuals
who took an action before they received treatment. The third stage adds
distributed and calibrated Differential Privacy (DP) noise to the aggregate
statistics and uncertainty measures, providing formal two-sided privacy
guarantees.
  We also evaluate the performance of multiple open source general purpose MPC
libraries for this task. We additionally demonstrate how we have used this to
create a working ads effectiveness measurement product capable of measuring
hundreds of millions of individuals per experiment.


Abstract:
  Deep learning models in large-scale machine learning systems are often
continuously trained with enormous data from production environments. The sheer
volume of streaming training data poses a significant challenge to real-time
training subsystems and ad-hoc sampling is the standard practice. Our key
insight is that these deployed ML systems continuously perform forward passes
on data instances during inference, but ad-hoc sampling does not take advantage
of this substantial computational effort. Therefore, we propose to record a
constant amount of information per instance from these forward passes. The
extra information measurably improves the selection of which data instances
should participate in forward and backward passes. A novel optimization
framework is proposed to analyze this problem and we provide an efficient
approximation algorithm under the framework of Mini-batch gradient descent as a
practical solution. We also demonstrate the effectiveness of our framework and
algorithm on several large-scale classification and regression tasks, when
compared with competitive baselines widely used in industry.


Abstract:
  Bound states in the continuum (BICs) enable unique features in tailoring
light-matter interaction on nanoscale. These radiationless localized states
drive theoretically infinite quality factors and lifetimes for modern
nanophotonics, making room for a variety of emerging applications. Here we use
the peculiar properties possessed by the so-called $\mathcal{PT}$-symmetric
optical structures to propose the novel mechanism for the quasi-BIC
manifestation governed by the $\mathcal{PT}$-symmetry breaking. In particular,
we study regularities of the spontaneous $\mathcal{PT}$-symmetry breaking in
trilayer structures with the outer loss and gain layers consisting of materials
with permittivity close to zero. We reveal singular points on the curves
separating $\mathcal{PT}$-symmetric and broken-$\mathcal{PT}$-symmetry states
in the parametric space of the light frequency and the angle of incidence.
These singularities remarkably coincide with the BIC positions at the frequency
of volume plasmon excitation, where the dielectric permittivity vanishes. The
loss and gain value acts as an asymmetry parameter that disturbs conditions of
the ideal BIC inducing the quasi-BIC. Fascinating properties of these
quasi-BICs having ultrahigh quality factors and almost perfect transmission can
be utilized in sensing, nonlinear optics, and other applications.


Abstract:
  The massive shock of the COVID-19 pandemic is already showing its negative
effects on economies around the world, unprecedented in recent history.
COVID-19 infections and containment measures have caused a general slowdown in
research and new knowledge production. Because of the link between R&D spending
and economic growth, it is to be expected then that a slowdown in research
activities will slow in turn the global recovery from the pandemic. Many recent
studies also claim an uneven impact on scientific production across gender. In
this paper, we investigate the phenomenon across countries, analysing preprint
depositions. Differently from other works, that compare the number of preprint
depositions before and after the pandemic outbreak, we analyse the depositions
trends across geographical areas, and contrast after-pandemic depositions with
expected ones. Differently from common belief and initial evidence, in few
countries female scientists increased their scientific output while males
plunged.


Abstract:
  In this paper, we resort to the TensorFlow framework to investigate the
benefits of applying data vectorization and fitness caching methods to domain
evaluation in Genetic Programming. For this purpose, an independent engine was
developed, TensorGP, along with a testing suite to extract comparative timing
results across different architectures and amongst both iterative and
vectorized approaches. Our performance benchmarks demonstrate that by
exploiting the TensorFlow eager execution model, performance gains of up to two
orders of magnitude can be achieved on a parallel approach running on dedicated
hardware when compared to a standard iterative approach.


Abstract:
  Imaging the vibrations of nanomechanical resonators means measuring their
flexural mode shapes from the dependence of their frequency response on
in-plane position. Applied to two-dimensional resonators, this technique
provides a wealth of information on the mechanical properties of
atomically-thin membranes. We present a simple and robust system to image the
vibrations of few layer graphene (FLG) resonators at room temperature and in
vacuum with an in-plane displacement precision of $\approx0.20$ $\mu$m. It
consists of a sturdy vacuum enclosure mounted on a three-axis micropositioning
stage and designed for free space optical measurements of vibrations. The
system is equipped with ultra-flexible radio frequency waveguides to
electrically actuate resonators. With it we characterize the lowest frequency
mode of a FLG resonator by measuring its frequency response as a function of
position on the membrane. The resonator is suspended over a nanofabricated
local gate electrode acting both as a mirror and as a capacitor plate to
actuate vibrations at radio frequencies. From these measurements, we estimate
the ratio of thermal expansion coefficient to thermal conductivity of the
membrane, and we measure the effective mass of the lowest frequency mode. We
complement our study with a globally gated resonator and image its first three
vibration modes. There, we find that folds in the membrane locally suppress
vibrations.


Abstract:
  Graph Neural Networks (GNNs) are deep learning methods which provide the
current state of the art performance in node classification tasks. GNNs often
assume homophily -- neighboring nodes having similar features and labels--, and
therefore may not be at their full potential when dealing with non-homophilic
graphs. In this work, we focus on addressing this limitation and enable Graph
Attention Networks (GAT), a commonly used variant of GNNs, to explore the
structural information within each graph locality. Inspired by the positional
encoding in the Transformers, we propose a framework, termed Graph Attentional
Networks with Positional Embeddings (GAT-POS), to enhance GATs with positional
embeddings which capture structural and positional information of the nodes in
the graph. In this framework, the positional embeddings are learned by a model
predictive of the graph context, plugged into an enhanced GAT architecture,
which is able to leverage both the positional and content information of each
node. The model is trained jointly to optimize for the task of node
classification as well as the task of predicting graph context. Experimental
results show that GAT-POS reaches remarkable improvement compared to strong GNN
baselines and recent structural embedding enhanced GNNs on non-homophilic
graphs.


Abstract:
  Image-based age estimation aims to predict a person's age from facial images.
It is used in a variety of real-world applications. Although end-to-end deep
models have achieved impressive results for age estimation on benchmark
datasets, their performance in-the-wild still leaves much room for improvement
due to the challenges caused by large variations in head pose, facial
expressions, and occlusions. To address this issue, we propose a simple yet
effective method to explicitly incorporate facial semantics into age
estimation, so that the model would learn to correctly focus on the most
informative facial components from unaligned facial images regardless of head
pose and non-rigid deformation. To this end, we design a face parsing-based
network to learn semantic information at different scales and a novel face
parsing attention module to leverage these semantic features for age
estimation. To evaluate our method on in-the-wild data, we also introduce a new
challenging large-scale benchmark called IMDB-Clean. This dataset is created by
semi-automatically cleaning the noisy IMDB-WIKI dataset using a constrained
clustering method. Through comprehensive experiment on IMDB-Clean and other
benchmark datasets, under both intra-dataset and cross-dataset evaluation
protocols, we show that our method consistently outperforms all existing age
estimation methods and achieves a new state-of-the-art performance. To the best
of our knowledge, our work presents the first attempt of leveraging face
parsing attention to achieve semantic-aware age estimation, which may be
inspiring to other high level facial analysis tasks.


Abstract:
  We introduce several geometric notions, including thick-thin decompositions
and the width of a homology class, to the theory of persistent homology. These
ideas provide geometric interpretations of persistence diagrams. Indeed, we
give quantitative and geometric descriptions of the ``size'' or ``persistence''
of a homology class. As a case study, we analyze the power filtration on
unweighted graphs, and provide explicit bounds for the life spans of homology
classes in persistence diagrams in all dimensions.


Abstract:
  Training of Artificial Neural Networks is a complex task of great importance
in supervised learning problems. Evolutionary Algorithms are widely used as
global optimization techniques and these approaches have been used for
Artificial Neural Networks to perform various tasks. An optimization algorithm,
called Group Search Optimizer (GSO), was proposed and inspired by the search
behaviour of animals. In this article we present two new hybrid approaches:
CGSO-Hk-WD and CGSO-Sk-WD. Cooperative GSOs are based on the divide-and-conquer
paradigm, employing cooperative behaviour between GSO groups to improve the
performance of the standard GSO. We also apply the weight decay strategy (WD,
acronym for Weight Decay) to increase the generalizability of the networks. The
results show that cooperative GSOs are able to achieve better performance than
traditional GSO for classification problems in benchmark datasets such as
Cancer, Diabetes, Ecoli and Glass datasets.


Abstract:
  The transport of virus-laden aerosols from a host to a susceptible person is
governed by complex turbulent airflow, and physics related to breathing,
coughing and sneezing, mechanical and passive ventilation, thermal buoyancy
effects, surface deposition, masks, and air filtration. In this paper, we study
the infection risk via airborne transmission on an urban bus using unsteady
Reynolds-Averaged Navier--Stokes equations and a passive-scalar model of the
virus-laden aerosol concentration. Results from these simulations are directly
compared to the widely-used well mixed model, and show significant differences
in the concentration field and number of inhaled particles. Specifically, in
the limit of low mechanical ventilation rate, the well-mixed model will
over-predict concentration far from the infected passenger, and substantially
underpredict concentration near the infected passenger. The results reported
herein also apply to other enclosed spaces.


Abstract:
  Recently, a special kind of graph, i.e., supernet, which allows two nodes
connected by multi-choice edges, has exhibited its power in neural architecture
search (NAS) by searching for better architectures for computer vision (CV) and
natural language processing (NLP) tasks. In this paper, we discover that the
design of such discrete architectures also appears in many other important
learning tasks, e.g., logical chain inference in knowledge graphs (KGs) and
meta-path discovery in heterogeneous information networks (HINs). Thus, we are
motivated to generalize the supernet search problem on a broader horizon.
However, none of the existing works are effective since the supernet topology
is highly task-dependent and diverse. To address this issue, we propose to
tensorize the supernet, i.e., unify the subgraph search problems by a tensor
formulation and encode the topology inside the supernet by a tensor network. We
further propose an efficient algorithm that admits both stochastic and
deterministic objectives to solve the search problem. Finally, we perform
extensive experiments on diverse learning tasks, i.e., architecture design for
CV, logic inference for KG, and meta-path discovery for HIN. Empirical results
demonstrate that our method leads to better performance and architectures.


Abstract:
  In this letter, we have carried out an independent validation of the Gaia
EDR3 photometry using about 10,000 Landolt standard stars from Clem & Landolt
(2013). Using a machine learning technique, the UBVRI magnitudes are converted
into the Gaia magnitudes and colors and then compared to those in the EDR3,
with the effect of metallicity incorporated. Our result confirms the
significant improvements in the calibration process of the Gaia EDR3. Yet
modest trends up to 10 mmag with G magnitude are found for all the magnitudes
and colors for the 10 < G < 19 mag range, particularly for the bright and faint
ends. With the aid of synthetic magnitudes computed on the CALSPEC spectra with
the Gaia EDR3 passbands, absolute corrections are further obtained, paving the
way for optimal usage of the Gaia EDR3 photometry in high accuracy
investigations.


Abstract:
  Clustering algorithms partition a dataset into groups of similar points. The
clustering problem is very general, and different partitions of the same
dataset could be considered correct and useful. To fully understand such data,
it must be considered at a variety of scales, ranging from coarse to fine. We
introduce the Multiscale Environment for Learning by Diffusion (MELD) data
model, which is a family of clusterings parameterized by nonlinear diffusion on
the dataset. We show that the MELD data model precisely captures latent
multiscale structure in data and facilitates its analysis. To efficiently learn
the multiscale structure observed in many real datasets, we introduce the
Multiscale Learning by Unsupervised Nonlinear Diffusion (M-LUND) clustering
algorithm, which is derived from a diffusion process at a range of temporal
scales. We provide theoretical guarantees for the algorithm's performance and
establish its computational efficiency. Finally, we show that the M-LUND
clustering algorithm detects the latent structure in a range of synthetic and
real datasets.


Abstract:
  The topological evolution of classic eigenmodes including
Hermite-Laguerre-Gaussian and (helical) InceGaussian modes is exploited to
construct coherent state modes, which unifies the representations of
travelingwave (TW) and standing-wave (SW) ray-wave structured light for the
first time and realizes the TW-SW unified ray-wave geometric beam with topology
of raytrajectories splitting effect, breaking the boundary of TW and SW
structured light. We experimentally generate these new modes with high purity
and dynamic control by digital holography method, revealing potential
applications in optical manipulation and communication.


Abstract:
  In this paper we introduce the characteristic gluing problem for the Einstein
vacuum equations. We present a codimension-$10$ gluing construction for
characteristic initial data which are close to the Minkowski data and we show
that the $10$-dimensional obstruction space consists of gauge-invariant charges
which are conserved by the linearized null constraint equations. By relating
these $10$ charges to the ADM energy, linear momentum, angular momentum and the
center-of-mass we prove that asymptotically flat data can be characteristically
glued (including the $10$ charges) to the data of a suitably chosen Kerr
spacetime, obtaining as a corollary an alternative proof of the Corvino--Schoen
spacelike gluing construction. Moreover, we derive a localized version of our
construction where the given data restricted on an angular sector is
characteristically glued to the Minkowski data restricted on another angular
sector. As a corollary we obtain an alternative proof of the Carlotto-Schoen
localized spacelike gluing construction. Our method yields no loss of decay in
the transition region, resolving an open problem. We also discuss a number of
other applications.


Abstract:
  We study communication systems over band-limited Additive White Gaussian
Noise (AWGN) channels in which the transmitter output is constrained to be
symmetric binary (bi-polar). In this work we improve the original
Ozarov-Wyner-Ziv (OWZ) lower bound on capacity by introducing new achievability
schemes with two advantages over the studied OWZ scheme which is based on
peak-power constrained pulse-amplitude modulation. Our schemes achieve a
moderately improved information rate and do so with much less sign transitions
of the binary signal. The gap between the known upper-bound based on spectral
constrains of bi-polar signals and our achievable lower bound is reduced to
0.93 bits per Nyquist interval at high SNR.


Abstract:
  Scenarios requiring humans to choose from multiple seemingly optimal actions
are commonplace, however standard imitation learning often fails to capture
this behavior. Instead, an over-reliance on replicating expert actions induces
inflexible and unstable policies, leading to poor generalizability in an
application. To address the problem, this paper presents the first imitation
learning framework that incorporates Bayesian variational inference for
learning flexible non-parametric multi-action policies, while simultaneously
robustifying the policies against sources of error, by introducing and
optimizing disturbances to create a richer demonstration dataset. This
combinatorial approach forces the policy to adapt to challenging situations,
enabling stable multi-action policies to be learned efficiently. The
effectiveness of our proposed method is evaluated through simulations and
real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm.
Results show that, through improved flexibility and robustness, the learning
performance and control safety are better than comparison methods.


Abstract:
  In Jiang et al. (2020), we reported a possible bright flash (hereafter
GN-z11-flash) from a galaxy GN-z11 at z ~ 11. Recently, Steinhardt et al.
(2021; arXiv:2101.12738) found 27 images with transient signals in Keck MOSFIRE
archival data and claimed that GN-z11-flash was more likely from a moving
object in our Solar system. We show that the Steinhardt et al.'s definition of
the chance probability and their methodology of finding GN-z11-flash-like
transients are problematic in several aspects. In particular, none of their
transients is analogous to GN-z11-flash, and none of them is positionally
coincident with a known object in their imaging data. In Jiang et al., we
performed a comprehensive analysis of the origin of GN-z11-flash and ruled out,
to the best of our knowledge, the possibility of known man-made objects or
moving objects in the Solar system, based on all available information and our
current understanding of these objects. Steinhardt et al. did not use such
information and did not analyse the GN-z11-flash event itself. The majority of
their transients are apparently low-Earth orbit satellites or aircrafts.
Therefore, their analysis can neither prove nor disprove our results. Finally,
we present a method to estimate the chance probability of finding
GN-z11-flash-like transients in archival data. Based on this method and the
archival data used by Steinhardt et al., we obtain a loose upper limit of the
probability that actually support the original results of Jiang et al. (2020).


Abstract:
  Characterization of the molecular properties of surfaces under ambient or
chemically reactive conditions is a fundamental scientific challenge. Moreover,
many traditional analytical techniques used for probing surfaces often lack
dynamic or molecular selectivity, which limits their applicability for
mechanistic and kinetic studies under realistic chemical conditions. Nuclear
magnetic resonance spectroscopy (NMR) is a widely used technique and would be
ideal for probing interfaces due to the molecular information it provides
noninvasively. However, it lacks the sensitivity to probe the small number of
spins at surfaces. Here, we use nitrogen vacancy (NV) centers in diamond as
quantum sensors to optically detect nuclear magnetic resonance signals from
chemically modified aluminum oxide surfaces, prepared with atomic layer
deposition (ALD). With the surface NV-NMR technique, we are able to monitor in
real-time the formation kinetics of a self assembled monolayer (SAM) based on
phosphonate anchoring chemistry to the surface. This demonstrates the
capability of quantum sensors as a new surface-sensitive tool with
sub-monolayer sensitivity for in-situ NMR analysis with the additional
advantage of a strongly reduced technical complexity.


Abstract:
  This monograph is centred at the intersection of three mathematical topics,
that are theoretical in nature, yet with motivations and relevance deep rooted
in applications: the linear inverse problems on abstract, in general
infinite-dimensional Hilbert space; the notion of Krylov subspace associated to
an inverse problem, i.e., the cyclic subspace built upon the datum of the
inverse problem by repeated application of the linear operator; the possibility
to solve the inverse problem by means of Krylov subspace methods, namely
projection methods where the finite-dimensional truncation is made with respect
to the Krylov subspace and the approximants converge to an exact solution to
the inverse problem.


Abstract:
  In 2019, Eliud Chipgoke ran a sub-two hour marathon in an unofficial race
wearing last-generation shoes. The legitimacy of this feat was naturally
questioned due to unusual racing conditions and suspicions of technological
doping. In this work, we assess the likelihood of a sub-two hour marathon in an
official race, and the potential influence of Vaporfly shoes, by studying the
evolution of running top performances from 2001 to 2019 for distances ranging
from 10k to marathon. The analysis is performed using extreme value theory, a
field of statistics dealing with analysis of records. We find a significant
evidence of technological doping with a 12% increase of the probability that a
new world record for marathon-man discipline is set in 2021. However, results
suggest that achieving a sub-two hour marathon in an official event in 2021 is
still very unlikely, and exceeds 10% probability only by 2025.


Abstract:
  We perform a detailed photometric and astrometric analysis of stars in the
Jet stream using data from the first data release of the DECam Local Volume
Exploration Survey (DELVE) DR1 and \emph{Gaia} EDR3. We discover that the
stream extends over $\sim 29 ^{\circ}$ on the sky (increasing the known length
by $18^{\circ}$), which is comparable to the kinematically cold Phoenix, ATLAS,
and GD-1 streams. Using blue horizontal branch stars, we resolve a distance
gradient along the Jet stream of 0.2 kpc/deg, with distances ranging from
$D_\odot \sim 27-34$ kpc. We use natural splines to simultaneously fit the
stream track, width, and intensity to quantitatively characterize density
variations in the Jet stream, including a large gap, and identify substructure
off the main track of the stream. Furthermore, we report the first measurement
of the proper motion of the Jet stream and find that it is well-aligned with
the stream track suggesting the stream has likely not been significantly
perturbed perpendicular to the line of sight. Finally, we fit the stream with a
dynamical model and find that the stream is on a retrograde orbit, and is well
fit by a gravitational potential including the Milky Way and Large Magellanic
Cloud. These results indicate the Jet stream is an excellent candidate for
future studies with deeper photometry, astrometry, and spectroscopy to study
the potential of the Milky Way and probe perturbations from baryonic and dark
matter substructure.


Abstract:
  The atomic cluster expansion is a general polynomial expansion of the atomic
energy in multi-atom basis functions. Here we implement the atomic cluster
expansion in the performant C++ code \verb+PACE+ that is suitable for use in
large scale atomistic simulations. We briefly review the atomic cluster
expansion and give detailed expressions for energies and forces as well as
efficient algorithms for their evaluation. We demonstrate that the atomic
cluster expansion as implemented in \verb+PACE+ shifts a previously established
Pareto front for machine learning interatomic potentials towards faster and
more accurate calculations. Moreover, general purpose parameterizations are
presented for copper and silicon and evaluated in detail. We show that the new
Cu and Si potentials significantly improve on the best available potentials for
highly accurate large-scale atomistic simulations.


Abstract:
  We study quasi-locally complete locally convex spaces and generalize this
concept to quasi-locally Baire locally convex spaces. It is shown that an
inductive limit of strictly webbed spaces is regular if it is quasi-locally
Baire. This extends Qiu's theorem on regularity. Additionally, if each step is
strictly webbed and quasi-locally Baire, then the inductive limit is
quasi-locally Baire if it is regular. Relevant examples are provided.


Abstract:
  For a connected finite poset $P$, let $E(P)$ be the poset induced by the
extremal points of $P$. We show that the fixed point property of $E(P)$ implies
the fixed point property of $P$. On the other hand, we show that a homomorphism
$f : E(P) \rightarrow Q$ can be extended to $P$ if $Q$ is a flat poset not
containing a 4-crown. We conclude that every retract-crown of $E(P)$ with more
than four points is a retract-crown of $P$, too. We see that for $P$ having the
fixed point property but $E(P)$ not, every edge of every crown in $E(P)$ must
belong to a so-called improper 4-crown, with additional specifications if $P$
has height two. The results provide several sufficient and necessary conditions
for $P$ having the fixed point property, and these conditions refer to objects
simpler than $P$.


Abstract:
  Establishing the origin of the water D/H ratio in the Solar System is central
to our understanding of the chemical trail of water during the star and planet
formation process. Recent modeling suggests that comparisons of the D$_2$O/HDO
and HDO/H$_2$O ratios are a powerful way to trace the chemical evolution of
water and, in particular, determine whether the D/H ratio is inherited from the
molecular cloud or established locally. We seek to determine the D$_2$O column
density and derive the D$_2$O/HDO ratios in the warm region toward the low-mass
Class 0 sources B335 and L483. The results are compared with astrochemical
models and previous observations to determine their implications for the
chemical evolution of water. We present ALMA observations of the D$_2$O
transition at 316.8 GHz toward B335 and L483 at $<$0.5" ($<$ 100 au)
resolution, probing the inner warm envelope gas. The column densities of
D$_2$O, HDO, and H$_{2}^{18}$O are determined by synthetic spectrum modeling
and direct Gaussian fitting, under the assumption of a single excitation
temperature and similar spatial extent for the three water isotopologs. D$_2$O
is detected toward both sources in the inner warm envelope. The derived
D$_2$O/HDO ratios is $(1.0\pm0.2)\times10^{-2}$ for L483 and
$(1.4\pm0.1)\times10^{-2}$ for B335. The high D$_2$O/HDO ratios are a strong
indication of chemical inheritance of water from the prestellar phase down to
the inner warm envelope. This implies that the local cloud conditions in the
prestellar phase, such as temperatures and timescales, determine the water
chemistry at later stages and could provide a source of chemical
differentiation in young systems. In addition, the observed D$_2$O/H$_2$O
ratios support an observed dichotomy in the deuterium fractionation of water
toward isolated and clustered protostars, namely, a higher D/H ratio toward
isolated sources


Abstract:
  The goal of this work is to find the simplest UV completion of Accidental
Composite Dark Matter Models (ACDM) that can dynamically generate an asymmetry
for the DM candidate, the lightest \textit{dark baryon} (DCb), and
simultaneously annihilate the symmetric component. In this framework the DCb is
a bound state of a confining $\text{SU}(N)_{\text{DC}}$ gauge group, and can
interact weakly with the visible sector. The constituents of the DCb can
possess non-trivial charges under the Standard Model gauge group. The
generation of asymmetry for such candidate is a two-flavor variation of the
\emph{out-of-equilibrium} decay of a heavy scalar, with mass $M_\phi\gtrsim
10^{10}$ GeV. Below the scale of the scalars, the models recover accidental
stability, or long-livedness, of the DM candidate. The symmetric component is
annihilated by residual confined interactions provided that the mass of the DCb
$m_{\text{DCb}} \lesssim 75$ TeV. We implement the mechanism of asymmetry
generation, or a variation of it, in all the original ACDM models, managing to
generate the correct asymmetry for DCb of masses in this range. For some of the
models found, the stability of the DM candidate is not spoiled even considering
generic GUT completions or asymmetry generation mechanisms in the visible
sector.


Abstract:
  A few years ago Avetissian {\it et al.} \cite{Avetissian2014,Avetissian2015}
discovered that the exponential growth rate of the stimulated annihilation
photons from a singlet positronium Bose-Einstein condensate should be
proportional to the square root of the positronium number density, not to the
number density itself. In order to elucidate this surprising result obtained
via a field-theoretical analysis, we point out that the basic physics involved
is the same as that of resonant subharmonic transitions between two quantum
oscillators. Using this model, we show that nonlinearities of the type
discovered by Avetissian {\it et al.} are not unique to positronium and in fact
will be encountered in a wide range of systems that can be modeled as
nonlinearly coupled quantum oscillators.


Abstract:
  This paper proposes an approach to addresses the control challenges posed by
a fault-induced uncertainty in both the dynamics and control input
effectiveness of a class of hierarchical nonlinear systems in which the
high-level dynamics is nonlinearly coupled with a multi-agent low-level
dynamics. The high-level dynamics has a multiplicative uncertainty in the
control input effectiveness and is subjected to an exogenous disturbance input.
On the other hand, the low-level system is subjected to actuator faults causing
a time-varying multiplicative uncertainty in the dynamical model and associated
control effectiveness. Moreover, the nonlinear coupling between the high-level
and the low-level dynamics makes the problem even more challenging. To address
this problem, an online parameter estimation algorithm is designed, coupled
with an adaptive splitting mechanism which automatically distributes the
control action among low level multi-agent systems. A nonlinear
$\mathcal{L}_2$-gain-based controller, and then a state-feedback controller are
designed in the high-level, and the low-level, respectively, to recover the
system from faults with high performance in the transient response, and reject
the exogenous disturbance. The resulting analysis guarantees a robust tracking
of the high-level reference command signal.


Abstract:
  Modern policy gradient algorithms such as Proximal Policy Optimization (PPO)
rely on an arsenal of heuristics, including loss clipping and gradient
clipping, to ensure successful learning. These heuristics are reminiscent of
techniques from robust statistics, commonly used for estimation in outlier-rich
(``heavy-tailed'') regimes. In this paper, we present a detailed empirical
study to characterize the heavy-tailed nature of the gradients of the PPO
surrogate reward function. We demonstrate that the gradients, especially for
the actor network, exhibit pronounced heavy-tailedness and that it increases as
the agent's policy diverges from the behavioral policy (i.e., as the agent goes
further off policy). Further examination implicates the likelihood ratios and
advantages in the surrogate reward as the main sources of the observed
heavy-tailedness. We then highlight issues arising due to the heavy-tailed
nature of the gradients. In this light, we study the effects of the standard
PPO clipping heuristics, demonstrating that these tricks primarily serve to
offset heavy-tailedness in gradients. Thus motivated, we propose incorporating
GMOM, a high-dimensional robust estimator, into PPO as a substitute for three
clipping tricks. Despite requiring less hyperparameter tuning, our method
matches the performance of PPO (with all heuristics enabled) on a battery of
MuJoCo continuous control tasks.


Abstract:
  With increasing availability of high dimensional, multi-source data, the
identification of joint and data specific patterns of variability has become a
subject of interest in many research areas. Several matrix decomposition
methods have been formulated for this purpose, for example JIVE (Joint and
Individual Variation Explained), and its angle based variation, aJIVE. Although
the effect of data contamination on the estimated joint and individual
components has not been considered in the literature, gross errors and outliers
in the data can cause instability in such methods, and lead to incorrect
estimation of joint and individual variance components. We focus on the aJIVE
factorization method and provide a thorough analysis of the effect outliers on
the resulting variation decomposition. After showing that such effect is not
negligible when all data-sources are contaminated, we propose a robust
extension of aJIVE (RaJIVE) that integrates a robust formulation of the
singular value decomposition into the aJIVE approach. The proposed RaJIVE is
shown to provide correct decompositions even in the presence of outliers and
improves the performance of aJIVE. We use extensive simulation studies with
different levels of data contamination to compare the two methods. Finally, we
describe an application of RaJIVE to a multi-omics breast cancer dataset from
The Cancer Genome Atlas. We provide the R package RaJIVE with a ready-to-use
implementation of the methods and documentation of code and examples.


Abstract:
  Firth-type logistic regression has become a standard approach for the
analysis of binary outcomes with small samples. Whereas it reduces the bias in
maximum likelihood estimates of coefficients, bias towards 1/2 is introduced in
the predicted probabilities. The stronger the imbalance of the outcome, the
more severe is the bias in the predicted probabilities. We propose two simple
modifications of Firth-type logistic regression resulting in unbiased predicted
probabilities. The first corrects the predicted probabilities by a post-hoc
adjustment of the intercept. The other is based on an alternative formulation
of Firth-types estimation as an iterative data augmentation procedure. Our
suggested modification consists in introducing an indicator variable which
distinguishes between original and pseudo observations in the augmented data.
In a comprehensive simulation study these approaches are compared to other
attempts to improve predictions based on Firth-type penalization and to other
published penalization strategies intended for routine use. For instance, we
consider a recently suggested compromise between maximum likelihood and
Firth-type logistic regression. Simulation results are scrutinized both with
regard to prediction and regression coefficients. Finally, the methods
considered are illustrated and compared for a study on arterial closure devices
in minimally invasive cardiac surgery.


Abstract:
  Current deep learning techniques for style transfer would not be optimal for
design support since their "one-shot" transfer does not fit exploratory design
processes. To overcome this gap, we propose parametric transcription, which
transcribes an end-to-end style transfer effect into parameter values of
specific transformations available in an existing content editing tool. With
this approach, users can imitate the style of a reference sample in the tool
that they are familiar with and thus can easily continue further exploration by
manipulating the parameters. To enable this, we introduce a framework that
utilizes an existing pretrained model for style transfer to calculate a
perceptual style distance to the reference sample and uses black-box
optimization to find the parameters that minimize this distance. Our
experiments with various third-party tools, such as Instagram and Blender, show
that our framework can effectively leverage deep learning techniques for
computational design support.


Abstract:
  We give an overview of the practice and science of call center workforce
planning, where we evaluate the commonly used methods by their quality and the
theory by its applicability. As such this paper is useful for developers and
consultants interested in the background and advanced methodology of workforce
management, and for researchers interested in practically relevant science.


Abstract:
  Computing Education Research (CER) is critical for supporting the increasing
number of students who need to learn computing skills. To systematically
advance knowledge, publications must be clear enough to support replications,
meta-analyses, and theory-building. The goal of this study is to characterize
the reporting of empiricism in CER literature by identifying whether
publications include information to support replications, meta-analyses, and
theory building. The research questions are: RQ1) What percentage of papers in
CER venues have empirical evaluation? RQ2) What are the characteristics of the
empirical evaluation? RQ3) Do the papers with empirical evaluation follow
reporting norms (both for inclusion and for labeling of key information)? We
conducted an SLR of 427 papers published during 2014 and 2015 in five CER
venues: SIGCSE TS, ICER, ITiCSE, TOCE, and CSE. We developed and applied the
CER Empiricism Assessment Rubric. Over 80% of papers had some form of empirical
evaluation. Quantitative evaluation methods were the most frequent. Papers most
frequently reported results on interventions around pedagogical techniques,
curriculum, community, or tools. There was a split in papers that had some type
of comparison between an intervention and some other data set or baseline. Many
papers lacked properly reported research objectives, goals, research questions,
or hypotheses, description of participants, study design, data collection, and
threats to validity. CER authors are contributing empirical results to the
literature; however, not all norms for reporting are met. We encourage authors
to provide clear, labeled details about their work so readers can use the
methodologies and results for replications and meta-analyses. As our community
grows, our reporting of CER should mature to help establish computing education
theory to support the next generation of computing learners.


Abstract:
  The spatial discretization of the single-cone Dirac Hamiltonian on the
surface of a topological insulator or superconductor needs a special
"staggered" grid, to avoid the appearance of a spurious second cone in the
Brillouin zone. We adapt the Stacey discretization from lattice gauge theory to
produce a generalized eigenvalue problem, of the form ${\mathcal H}\psi=E
{\mathcal P}\psi$, with Hermitian tight-binding operators ${\mathcal H}$,
${\mathcal P}$, a locally conserved particle current, and preserved chiral and
symplectic symmetries. This permits the study of the spectral statistics of
Dirac fermions in each of the four symmetry classes A, AII, AIII, and D.


Abstract:
  We propose a construction with which to resolve the black hole singularity
and enable an anisotropic cosmology to emerge from the inside of the hole. The
model relies on the addition of an S-brane to the effective action which
describes the geometry of space-time. This space-like defect is located inside
of the horizon on a surface where the Weyl curvature reaches a limiting value.
We study how metric fluctuations evolve from the outside of the black hole to
the beginning of the cosmological phase to the future of the S-brane. Our setup
addresses i) the black hole singularity problem, ii) the cosmological
singularity problem and iii) the information loss paradox since the outgoing
Hawking radiation is entangled with the state inside the black hole which
becomes the new universe.


Abstract:
  We prove large (and moderate) deviations for a class of linear combinations
of spacings generated by i.i.d. exponentially distributed random variables. We
allow a wide class of coefficients which can be expressed in terms of
continuous functions defined on [0, 1] which satisfy some suitable conditions.
In this way we generalize some recent results by Giuliano et al. (2015) which
concern the empirical cumulative entropies defined in Di Crescenzo and
Longobardi (2009a).


Abstract:
  Let $F$ be a finite unramified extension of $\mathbb Q\_p$ and $\bar\rho$ be
an absolutely irreducible mod~$p$ $2$-dimensional representation of the
absolute Galois group of $F$. Let $t$ be a tame inertial type of $F$. We
conjecture that the deformation space parametrizing the potentially
Barsotti--Tate liftings of $\bar\rho$ having type $t$ depends only on the Kisin
variety attached to the situation, enriched with its canonical embedding into
$(\mathbb P^1)^f$ and its shape stratification. We give evidences towards this
conjecture by proving that the Kisin variety determines the cardinality of the
set of common Serre weights $D(t,\bar\rho) = D(t) \cap D(\bar\rho)$. Besides,
we prove that this dependance is nondecreasing (the smaller is the Kisin
variety, the smaller is the number of common Serre weights) and compatible with
products (if the Kisin variety splits as a product, so does the number of
weights).


Abstract:
  Direct optimization of IR metrics has often been adopted as an approach to
devise and develop ranking-based recommender systems. Most methods following
this approach aim at optimizing the same metric being used for evaluation,
under the assumption that this will lead to the best performance. A number of
studies of this practice bring this assumption, however, into question. In this
paper, we dig deeper into this issue in order to learn more about the effects
of the choice of the metric to optimize on the performance of a ranking-based
recommender system. We present an extensive experimental study conducted on
different datasets in both pairwise and listwise learning-to-rank scenarios, to
compare the relative merit of four popular IR metrics, namely RR, AP, nDCG and
RBP, when used for optimization and assessment of recommender systems in
various combinations. For the first three, we follow the practice of loss
function formulation available in literature. For the fourth one, we propose
novel loss functions inspired by RBP for both the pairwise and listwise
scenario. Our results confirm that the best performance is indeed not
necessarily achieved when optimizing the same metric being used for evaluation.
In fact, we find that RBP-inspired losses perform at least as well as other
metrics in a consistent way, and offer clear benefits in several cases.
Interesting to see is that RBP-inspired losses, while improving the
recommendation performance for all uses, may lead to an individual performance
gain that is correlated with the activity level of a user in interacting with
items. The more active the users, the more they benefit. Overall, our results
challenge the assumption behind the current research practice of optimizing and
evaluating the same metric, and point to RBP-based optimization instead as a
promising alternative when learning to rank in the recommendation context.


Abstract:
  Advances in unmanned aerial vehicle (UAV) design have opened up applications
as varied as surveillance, firefighting, cellular networks, and delivery
applications. Additionally, due to decreases in cost, systems employing fleets
of UAVs have become popular. The uniqueness of UAVs in systems creates a novel
set of trajectory or path planning and coordination problems. Environments
include many more points of interest (POIs) than UAVs, with obstacles and
no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement
learning algorithm inspired by economic transactions to distribute tasks
between UAVs. This system revolves around an economic theory, in particular an
auction mechanism where UAVs trade assigned POIs. We formulate the path
planning problem as a multi-agent economic game, where agents can cooperate and
compete for resources. We then translate the problem into a Partially
Observable Markov decision process (POMDP), which is solved using a
reinforcement learning (RL) model deployed on each agent. As the system
computes task distributions via UAV cooperation, it is highly resilient to any
change in the swarm size. Our proposed network and economic game architecture
can effectively coordinate the swarm as an emergent phenomenon while
maintaining the swarm's operation. Evaluation results prove that REPlanner
efficiently outperforms conventional RL-based trajectory search.


Abstract:
  Strain is ubiquitous in solid-state materials, but despite its fundamental
importance and technological relevance, leveraging externally applied strain to
gain control over material properties is still in its infancy. In particular,
strain control over the diverse phase transitions and topological states in
two-dimensional (2D) transition metal dichalcogenides (TMDs) remains an open
challenge. Here, we exploit uniaxial strain to stabilize the long-debated
structural ground state of the 2D topological semimetal IrTe$_2$, which is
hidden in unstrained samples. Combined angle-resolved photoemission
spectroscopy (ARPES) and scanning tunneling microscopy (STM) data reveal the
strain-stabilized phase has a 6x1 periodicity and undergoes a Lifshitz
transition, granting unprecedented spectroscopic access to previously
inaccessible type-II topological Dirac states that dominate the modified
inter-layer hopping. Supported by density functional theory (DFT) calculations,
we show that strain induces a charge transfer strongly weakening the
inter-layer Te bonds and thus reshaping the energetic landscape of the system
in favor of the 6x1 phase. Our results highlight the potential to exploit
strain-engineered properties in layered materials, particularly in the context
of tuning inter-layer behavior.


Abstract:
  We present a novel control-theoretic understanding of online optimization and
learning in games, via the notion of passivity. Passivity is a fundamental
concept in control theory, which abstracts energy conservation and dissipation
in physical systems. It has become a standard tool in analysis of general
feedback systems, to which game dynamics belong. Our starting point is to show
that all continuous-time Follow-the-Regularized-Leader (FTRL) dynamics, which
include the well-known Replicator Dynamic, are lossless, i.e. it is passive
with no energy dissipation. Interestingly, we prove that passivity implies
bounded regret, connecting two fundamental primitives of control theory and
online optimization.
  The observation of energy conservation in FTRL inspires us to present a
family of lossless learning dynamics, each of which has an underlying energy
function with a simple gradient structure. This family is closed under convex
combination; as an immediate corollary, any convex combination of FTRL dynamics
is lossless and thus has bounded regret. This allows us to extend the framework
of Fox and Shamma [Games, 2013] to prove not just global asymptotic stability
results for game dynamics, but Poincar\'e recurrence results as well.
Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled
with lossless learning dynamics, their feedback interconnection is also
lossless, which results in a pendulum-like energy-preserving recurrent
behavior, generalizing the results of Piliouras and Shamma [SODA, 2014] and
Mertikopoulos, Papadimitriou and Piliouras [SODA, 2018].


Abstract:
  Neural text generation models are typically trained by maximizing
log-likelihood with the sequence cross entropy loss, which encourages an exact
token-by-token match between a target sequence with a generated sequence. Such
training objective is sub-optimal when the target sequence is not perfect,
e.g., when the target sequence is corrupted with noises, or when only weak
sequence supervision is available. To address this challenge, we propose a
novel Edit-Invariant Sequence Loss (EISL), which computes the matching loss of
a target n-gram with all n-grams in the generated sequence. Drawing
inspirations from the classical convolutional networks (ConvNets) which capture
shift-invariance in image modeling, EISL is designed to be robust to the shift
of n-grams to tolerate various noises and edits in the target sequences.
Moreover, the EISL computation is essentially a convolution operation with
target n-grams as kernels, which is easy to implement and efficient to compute
with existing libraries. To demonstrate the effectiveness of EISL, we conduct
experiments on a wide range of tasks, including machine translation with noisy
target sequences, unsupervised text style transfer with only weak training
signals, and non-autoregressive generation with non-predefined generation
order. Experimental results show our method significantly outperforms the
common cross-entropy loss and other strong baselines on all the tasks.


Abstract:
  Aims: We aim to investigate the consequences of a fast massive stellar
remnant - a black hole (BH) or a neutron star (NS) - encountering a planetary
system. Methods: We modelled a close encounter between the actual Solar System
(SS) and a $2\,M_\odot$ NS and a $10\,M_\odot$ BH, using a few-body symplectic
integrator. We used a range of impact parameters, orbital phases at the start
of the simulation derived from the current SS orbital parameters, encounter
velocities, and incidence angles relative to the plane of the SS. Results: We
give the distribution of possible outcomes, such as when the SS remains bound,
when it suffers a partial or complete disruption, and in which cases the
intruder is able to capture one or more planets, yielding planetary systems
around a BH or a NS. We also show examples of the long-term stability of the
captured planetary systems.


Abstract:
  The purpose of this project was to derive a reliable estimate of the
frequency of occurrence of the 30 phonemes - plus consonant geminated
counterparts - of the Italian language, based on four selected written texts.
Since no comparable dataset was found in previous literature, the present
analysis may serve as a reference in future studies. Four textual sources were
considered: Come si fa una tesi di laurea: le materie umanistiche by Umberto
Eco, I promessi sposi by Alessandro Manzoni, a recent article in Corriere della
Sera (a popular daily Italian newspaper), and In altre parole by Jhumpa Lahiri.
The sources were chosen to represent varied genres, subject matter, time
periods, and writing styles. Results of the analysis, which also included an
analysis of variance, showed that, for all four sources, the frequencies of
occurrence reached relatively stable values after about 6,000 phonemes (approx.
1,250 words), varying by <0.025%. Estimated frequencies are provided for each
single source and as an average across sources.


Abstract:
  In this work we consider the problem of repeated hyperparameter and neural
architecture search (HNAS). We propose an extension of Successive Halving that
is able to leverage information gained in previous HNAS problems with the goal
of saving computational resources. We empirically demonstrate that our solution
is able to drastically decrease costs while maintaining accuracy and being
robust to negative transfer. Our method is significantly simpler than competing
transfer learning approaches, setting a new baseline for transfer learning in
HNAS.


Abstract:
  The cascaded decay in a four-level quantum emitter is a well established
mechanism to generate polarization entangled photon pairs, the building blocks
of many applications in quantum technologies. The four most prominent maximally
entangled photon pair states are the Bell states. In a typical experiment based
on an undriven emitter only one type of Bell state entanglement can be observed
in a given polarization basis. Other types of Bell state entanglement in the
same basis can be created by continuously driving the system by an external
laser. In this work we propose a protocol for time-dependent entanglement
switching in a four-level quantum emitter--cavity system that can be operated
by changing the external driving strength. By selecting different two-photon
resonances between the laser-dressed states, we can actively switch back and
forth between the different types of Bell state entanglement in the same basis
as well as between entangled and nonentangled photon pairs. This remarkable
feature demonstrates the possibility to achieve a controlled, time-dependent
manipulation of the entanglement type that could be used in many innovative
applications.


Abstract:
  An overlapped continuous model framework, for the Helmholtz wave propagation
problem in unbounded regions comprising bounded heterogeneous media, was
recently introduced and analyzed by the authors ({\tt J. Comput. Phys., {\bf
403}, 109052, 2020}). The continuous Helmholtz system incorporates a radiation
condition (RC) and our equivalent hybrid framework facilitates application of
widely used finite element methods (FEM) and boundary element methods (BEM),
and the resulting discrete systems retain the RC exactly. The FEM and BEM
discretizations, respectively, applied to the designed interior heterogeneous
and exterior homogeneous media Helmholtz systems include the FEM and BEM
solutions matching in artificial interface domains, and allow for computations
of the exact ansatz based far-fields. In this article we present rigorous
numerical analysis of a discrete two-dimensional FEM-BEM overlapped coupling
implementation of the algorithm. We also demonstrate the efficiency of our
discrete FEM-BEM framework and analysis using numerical experiments, including
applications to non-convex heterogeneous multiple particle Janus
configurations. Simulations of the far-field induced differential scattering
cross sections (DSCS) of heterogeneous configurations and orientation-averaged
(OA) counterparts are important for several applications, including inverse
wave problems. Our robust FEM-BEM framework facilities computations of such
quantities of interest, without boundedness or homogeneity or shape
restrictions on the wave propagation model.


Abstract:
  We present a calculation of the $cos (2 \phi_h)$ asymmetry in $J/\psi$
production in electron-proton collision at the future electron-ion collider
(EIC), a useful channel to probe the gluon TMDs. We calculate the asymmetry at
next-to-leading order (NLO) in $\alpha_s$ in the framework of generalized
factorization. The dominating sub-process is $\gamma^* +g \rightarrow
J/\psi+g$. The production of $J/\psi$ is calculated in the non-relativistic
QCD(NRQCD) framework with the inclusion of both color singlet and color octet
contributions. Numerical estimates of the $cos(2\phi_h)$ asymmetry are given in
the kinematical region to be accessed by the future EIC. The asymmetry depends
on the parameterization of the gluon TMDs, as well as on the long distance
matrix elements (LDMEs). We use both Gaussian-type parameterization and
McLerran-Venugopalan model for the TMDs in the kinematical region of small-$x$,
where the gluons play a dominant role. We obtain sizable asymmetry, which may
be useful to probe the ratio of the linearly polarized and the unpolarized
gluon distribution in the proton.


Abstract:
  This paper provides a scheme for generating maximally entangled qubit states
in the anti-Jaynes-Cummings interaction mechanism, so called entangled
anti-polariton qubit states. We demonstrate that in an initial vacuum-field,
Rabi oscillations in a cavity mode in the anti-Jaynes-Cummings interaction
process, occur in the reverse sense relative to the Jaynes-Cummings interaction
process and that time evolution of entanglement in the anti-Jaynes-Cummings
interaction process takes the same form as in the Jaynes-Cummings interaction
process. With the generated anti-polariton qubit state as one of the initial
qubits, we present quantum teleportation of an atomic quantum state by applying
entanglement swapping protocol achieving an impressive maximal teleportation
fidelity~$F_\rho=1$.


Abstract:
  We characterize the tail behavior of the distribution of the PageRank of a
uniformly chosen vertex in a directed preferential attachment graph and show
that it decays as a power law with an explicit exponent that is described in
terms of the model parameters. Interestingly, this power law is heavier than
the tail of the limiting in-degree distribution, which goes against the
commonly accepted {\em power law hypothesis}. This deviation from the power law
hypothesis points at the structural differences between the inbound
neighborhoods of typical vertices in a preferential attachment graph versus
those in static random graph models where the power law hypothesis has been
proven to hold (e.g., directed configuration models and inhomogeneous random
digraphs). In addition to characterizing the PageRank distribution of a typical
vertex, we also characterize the explicit growth rate of the PageRank of the
oldest vertex as the network size grows.


Abstract:
  In this paper, we propose a novel conditional convolution network, named
location-variable convolution, to model the dependencies of the waveform
sequence. Different from the use of unified convolution kernels in WaveNet to
capture the dependencies of arbitrary waveform, the location-variable
convolution uses convolution kernels with different coefficients to perform
convolution operations on different waveform intervals, where the coefficients
of kernels is predicted according to conditioning acoustic features, such as
Mel-spectrograms. Based on location-variable convolutions, we design LVCNet for
waveform generation, and apply it in Parallel WaveGAN to design more efficient
vocoder. Experiments on the LJSpeech dataset show that our proposed model
achieves a four-fold increase in synthesis speed compared to the original
Parallel WaveGAN without any degradation in sound quality, which verifies the
effectiveness of location-variable convolutions.


Abstract:
  Click-through rate (CTR) estimation plays as a core function module in
various personalized online services, including online advertising, recommender
systems, and web search etc. From 2015, the success of deep learning started to
benefit CTR estimation performance and now deep CTR models have been widely
applied in many industrial platforms. In this survey, we provide a
comprehensive review of deep learning models for CTR estimation tasks. First,
we take a review of the transfer from shallow to deep CTR models and explain
why going deep is a necessary trend of development. Second, we concentrate on
explicit feature interaction learning modules of deep CTR models. Then, as an
important perspective on large platforms with abundant user histories, deep
behavior models are discussed. Moreover, the recently emerged automated methods
for deep CTR architecture design are presented. Finally, we summarize the
survey and discuss the future prospects of this field.


Abstract:
  The ground-state properties of two-component repulsive Fermi gases in two
dimensions are investigated by means of fixed-node diffusion Monte Carlo
simulations. The energy per particle is determined as a function of the
intercomponent interaction strength and of the population imbalance. The regime
of universality in terms of the s-wave scattering length is identified by
comparing results for hard-disk and for soft-disk potentials. In the large
imbalance regime, the equation of state turns out to be well described by a
Landau-Pomeranchuk functional for two-dimensional polarons. To fully
characterize this expansion, we determine the polarons' effective mass and
their coupling parameter, complementing previous studies on their chemical
potential. Furthermore, we extract the magnetic susceptibility from
low-imbalance data, finding only small deviations from the mean-field
prediction. While the mean-field theory predicts a direct transition from a
paramagnetic to a fully ferromagnetic phase, our diffusion Monte Carlo results
suggest that the partially ferromagnetic phase is stable in a narrow interval
of the interaction parameter. This finding calls for further analyses on the
effects due to the fixed-node constraint.


Abstract:
  Exact Hausdorff dimensions are computed for singular continuous components of
the spectral measures of a class of Schr\"odinger operators in bounded
intervals.


Abstract:
  We computed the orbital evolution and the emitted gravitational radiation of
the supermassive black hole binary OJ 287. Here we used the initial data
provided by the outburst structure of the system. We considered the spin-spin,
spin-orbit, and the next-to-next leading order fourth post-Newtonian (4PN)
corrections in our analysis. In this way, we could make an accurate examination
of unstable orbits ($3M < r <6M$) of the secondary black hole. We tested the
4PN terms by analyzing the total and radiated energy, compared the
post-Newtonian parameters and the separation of the two black holes for 3PN and
4PN. In conclusion, the 4PN corrections provided a significantly more accurate
tool for analyzing unstable orbits than earlier 3PN terms. Furthermore, in this
paper, we show the gravitational waves emitted by OJ 287 during its complete
orbital evolution including unstable orbits.


Abstract:
  This paper proposes an attentional network for the task of Continuous Sign
Language Recognition. The proposed approach exploits co-independent streams of
data to model the sign language modalities. These different channels of
information can share a complex temporal structure between each other. For that
reason, we apply attention to synchronize and help capture entangled
dependencies between the different sign language components. Even though Sign
Language is multi-channel, handshapes represent the central entities in sign
interpretation. Seeing handshapes in their correct context defines the meaning
of a sign. Taking that into account, we utilize the attention mechanism to
efficiently aggregate the hand features with their appropriate spatio-temporal
context for better sign recognition. We found that by doing so the model is
able to identify the essential Sign Language components that revolve around the
dominant hand and the face areas. We test our model on the benchmark dataset
RWTH-PHOENIX-Weather 2014, yielding competitive results.


Abstract:
  Pathogens like the SARS-CoV-2 are transmitted not only through violent
expiratory events like coughing, but also through routine activities like
breathing/speaking/singing. We perform direct numerical simulations of the
turbulent transport of potentially infectious aerosols in short conversations.
It is shown that a two-way conversation significantly reduces the aerosol
exposure compared to a relative monologue by one person and relative silence of
the other. This is because the interaction of the jets ejected from the mouth
of each speaker produce a "canceling" effect. Unequal conversation is shown to
significantly increase the risk of infection to the person who talks less.
Interestingly, a small height difference is worse for infection spread, due to
reduced interference between the two speech jets, than two faces at the same
level! For small axial separation, speech jets show large oscillations and
reach the other person intermittently. We suggest a range of lateral
separations between two people to minimize transmission risk. A realistic
estimate of the infection probability is provided by including exposure through
eyes and mouth, in addition to the more common method of using inhaled virions
alone. We expect that our results will provide useful inputs to epidemiological
models and to disease management.


Abstract:
  An important parameter for COVID-19 is the case fatality rate (CFR). It has
been applied to wide applications, including the measure of the severity of the
infection, the estimation of the number of infected cases, risk assessment etc.
However, there remains a lack of understanding on several aspects of CFR,
including population factors that are important to CFR, the apparent
discrepancy of CFRs in different countries, and how the age effect comes into
play. We analyze the CFRs at two different time snapshots, July 6 and Dec 28,
2020, with one during the first wave and the other a second wave of the
COVID-19 pandemic. We consider two important population covariates, age and GDP
as a proxy for the quality and abundance of public health. Extensive
exploratory data analysis leads to some interesting findings. First, there is a
clear exponential age effect among different age groups, and, more importantly,
the exponential index is almost invariant across countries and time in the
pandemic. Second, the roles played by the age and GDP are a little surprising:
during the first wave, age is a more significant factor than GDP, while their
roles have switched during the second wave of the pandemic, which may be
partially explained by the delay in time for the quality and abundance of
public health and medical research to factor in.


Abstract:
  We investigate coincidences of the (one-variable) Jones polynomial amongst
rational knots, what we call `Jones rational coincidences'. We provide moves on
the continued fraction expansion of the associated rational which we prove do
not change the Jones polynomial and conjecture (based on experimental evidence
from all rational knots with determinant $<900$) that these moves are
sufficient to generate all Jones rational coincidences. These coincidences are
generically not mutants, as is verified by checking the HOMFLYPT polynomial. In
the process we give a new formula for the Jones polynomial of a rational knot
based on a continued fraction expansion of the associated rational, which has
significantly fewer terms than other formulae known to us. The paper is based
on the second author's Ph.D. thesis and gives an essentially self-contained
account.


Abstract:
  We study the maximum capture problem in facility location under random
utility models, i.e., the problem of seeking to locate new facilities in a
competitive market such that the captured user demand is maximized, assuming
that each customer chooses among all available facilities according to a random
utility maximization model. We employ the generalized extreme value (GEV)
family of discrete choice models and show that the objective function in this
context is monotonic and submodular. This finding implies that a simple greed
heuristic can always guarantee an (1-1/e) approximation solution. We further
develop a new algorithm combining a greedy heuristic, a gradient-based local
search and an exchanging procedure to efficiently solve the problem. We conduct
experiments using instances of difference sizes and under different discrete
choice models, and we show that our approach significantly outperforms prior
approaches in terms of both returned objective value and CPU time. Our
algorithm and theoretical findings can be applied to the maximum capture
problems under various random utility models in the literature, including the
popular multinomial logit, nested logit, cross nested logit, and the mixed
logit models.


Abstract:
  We present the theoretical framework to efficiently solve the Jeans equations
for multi-component axisymmetric stellar systems, focusing on the scaling of
all quantities entering them. The models may include an arbitrary number of
stellar distributions, a dark matter halo, and a central supermassive black
hole; each stellar distribution is implicitly described by a two- or
three-integral distribution function, and the stellar components can have
different structural (density profile, flattening, mass, scale-length),
dynamical (rotation, velocity dispersion anisotropy), and population (age,
metallicity, initial mass function, mass-to-light ratio) properties. In order
to determine the ordered rotational velocity and the azimuthal velocity
dispersion fields of each component, we introduce a decomposition that can be
used when the commonly adopted Satoh decomposition cannot be applied. The
scheme developed is particularly suitable for a numerical implementation; we
describe its realisation within our code JASMINE2, optimised to maximally
exploit the scalings allowed by the Poisson and the Jeans equations, also in
the post-processing procedures. As applications, we illustrate the building of
three multi-component galaxy models with two distinct stellar populations, a
central black hole, and a dark matter halo; we also study the solution of the
Jeans equations for an exponential thick disc, and for its multi-component
representation as the superposition of three Miyamoto-Nagai discs. A useful
general formula for the numerical evaluation of the gravitational potential of
factorised thick discs is finally given.


Abstract:
  These are the guidelines for the application of SNACS (Semantic Network of
Adposition and Case Supersenses; Schneider et al. 2018) to Modern Standard
Hindi of Delhi. SNACS is an inventory of 50 supersenses (semantic labels) for
labelling the use of adpositions and case markers with respect to both
lexical-semantic function and relation to the underlying context. The English
guidelines (Schneider et al., 2020) were used as a model for this document.
  Besides the case system, Hindi has an extremely rich adpositional system
built on the oblique genitive, with productive incorporation of loanwords even
in present-day Hinglish.
  This document is aligned with version 2.5 of the English guidelines.


Abstract:
  This paper introduces our solution for the Track2 in AI City Challenge 2021
(AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the
real-world data and synthetic data. We mainly focus on four points, i.e.
training data, unsupervised domain-adaptive (UDA) training, post-processing,
model ensembling in this challenge. (1) Both cropping training data and using
synthetic data can help the model learn more discriminative features. (2) Since
there is a new scenario in the test set that dose not appear in the training
set, UDA methods perform well in the challenge. (3) Post-processing techniques
including re-ranking, image-to-track retrieval, inter-camera fusion, etc,
significantly improve final performance. (4) We ensemble CNN-based models and
transformer-based models which provide different representation diversity. With
aforementioned techniques, our method finally achieves 0.7445 mAP score,
yielding the first place in the competition. Codes are available at
https://github.com/michuanhaohao/AICITY2021_Track2_DMT.


Abstract:
  It is well-known that Microsoft Word/Excel compatible documents or PDF files
can contain malicious content. LaTeX files are unfortunately no exception
either. LaTeX users often include third-party codes through sources or packages
(.sty or .cls files). But those packages can execute malicious commands on the
users' system, in order to capture sensitive information or to perform denial
of service attacks. Checkoway et al. [3] were the first to warn LaTeX users of
these threats. Collaborative cloud-based LaTeX editors and services compiling
LaTeX sources are particularly concerned. In this paper, we have created a
LaTeX package that collects system data and hides them inside the PDF file
produced by the target. Then, we have measured what can be recovered by hackers
using malicious LaTeX file on online services, and which measures those
services have enforced to thwart the threats. Services defend themselves using
sandbox or commands restrictions. Commands restrictions are more difficult to
setup and we found one service (PMLatex) which is too permissive.


Abstract:
  Background: COVID-19 is impacting people worldwide and is currently a leading
cause of death in many countries. This study sought to redefine the Healthy
People 2030 SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we
aim to provide a blueprint and implement a prototype for the Urban Population
Health Observatory (UPHO), a web-based platform that integrates classified
group-level SDoH indicators to individual- and aggregate-level population
health data. The process of building the UPHO involves collecting and
integrating data from several sources, classifying the collected data into
drivers and outcomes, incorporating data science techniques for calculating
measurable indicators from the raw variables, and studying the extent to which
interventions are identified or developed to mitigate drivers that lead to the
undesired outcomes. We generated and classified the indicators of social
determinants of health, which are linked to COVID-19. To display the
functionalities of the UPHO platform, we presented a prototype design to
demonstrate its features. We provided a use case scenario for 4 different
users. UPHO serves as an apparatus for implementing effective interventions and
can be adopted as a global platform for chronic and infectious diseases. The
UPHO surveillance platform provides a novel approach and novel insights into
immediate and long-term health policy responses to the COVID-19 pandemic and
other future public health crises. The UPHO assists public health organizations
and policymakers in their efforts in reducing health disparities, achieving
health equity, and improving urban population health.


Abstract:
  We study Ramsey expansions of certain homogeneous 3-hypertournaments. We show
that they exhibit an interesting behaviour and, in one case, they seem not to
submit to current gold-standard methods for obtaining Ramsey expansions. This
makes these examples very interesting from the point of view of structural
Ramsey theory as there is a large demand for novel examples.


Abstract:
  A low-complexity convolutional neural network estimator which learns the
minimum mean squared error channel estimator for single-antenna users was
recently proposed. We generalize the architecture to the estimation of MIMO
channels with multiple-antenna users and incorporate complexity-reducing
assumptions based on the channel model. Learning is used in this context to
combat the mismatch between the assumptions and real scenarios where the
assumptions may not hold. We derive a high-level description of the estimator
for arbitrary choices of the pilot sequence. It turns out that the proposed
estimator has the implicit structure of a two-layered convolutional neural
network, where the derived quantities can be relaxed to learnable parameters.
We show that by using discrete Fourier transform based pilots the number of
learnable network parameters decreases significantly and the online run time of
the estimator is reduced considerably, where we can achieve linearithmic order
of complexity in the number of antennas. Numerical results demonstrate
performance gains compared to state-of-the-art algorithms from the field of
compressive sensing or covariance estimation of the same or even higher
computational complexity. The simulation code is available online.


Abstract:
  We present a fully lock-free variant of the recent Montage system for
persistent data structures. Our variant, nbMontage, adds persistence to almost
any nonblocking concurrent structure without introducing significant overhead
or blocking of any kind. Like its predecessor, nbMontage is buffered durably
linearizable: it guarantees that the state recovered in the wake of a crash
will represent a consistent prefix of pre-crash execution. Unlike its
predecessor, nbMontage ensures wait-free progress of the persistence frontier,
thereby bounding the number of recent updates that may be lost on a crash, and
allowing a thread to force an update of the frontier (i.e., to perform a sync
operation) without the risk of blocking. As an extra benefit, the helping
mechanism employed by our wait-free sync significantly reduces its latency.
  Performance results for nonblocking queues, skip lists, trees, and hash
tables rival custom data structures in the literature -- dramatically faster
than achieved with prior general-purpose systems, and generally within 50% of
equivalent non-persistent structures placed in DRAM.


Abstract:
  We present the analysis of XMM-Newton observations of two X-ray luminous cool
core clusters, RXCJ1504.1-0248 and Abell 1664. The Reflection Grating
Spectrometer reveals a radiative cooling rate of $180\pm 40\, \rm
M_{\odot}\rm\,yr^{-1}$ and $34\pm 6\, \rm M_{\odot}\rm\,yr^{-1}$ in
RXCJ1504.1-0248 and Abell 1664 for gas above 0.7 keV, respectively. These
cooling rates are higher than the star formation rates observed in the
clusters, and support simultaneous star formation and molecular gas mass growth
on a timescale of 3$\times 10^8$ yr or longer. At these rates, the energy of
the X-ray cooling gas is inadequate to power the observed UV/optical
line-emitting nebulae, which suggests additional strong heating. No significant
residual cooling is detected below 0.7 keV in RXCJ1504.1-0248. By
simultaneously fitting the first and second order spectra, we place an upper
limit on turbulent velocity of 300 km$\rm s^{-1}$ at 90 per cent confidence
level for the soft X-ray emitting gas in both clusters. The turbulent energy
density is considered to be less than 8.9 and 27 per cent of the thermal energy
density in RXCJ1504.1-0248 and Abell 1664, respectively. This means it is
insufficient for AGN heating to fully propagate throughout the cool core via
turbulence. We find the cool X-ray component of Abell 1664 ($\sim$0.8 keV) is
blueshifted from the systemic velocity by 750$^{+800}_{-280}$ km$\rm s^{-1}$.
This is consistent with one component of the molecular gas in the core and
suggests a similar dynamical structure for the two phases. We find that an
intrinsic absorption model allows the cooling rate to increase to $520\pm 30\,
\rm M_{\odot}\rm\,yr^{-1}$ in RXCJ1504.1-0248.


Abstract:
  Co-Salient Object Detection (CoSOD) aims at simulating the human visual
system to discover the common and salient objects from a group of relevant
images. Recent methods typically develop sophisticated deep learning based
models have greatly improved the performance of CoSOD task. But there are still
two major drawbacks that need to be further addressed, 1) sub-optimal
inter-image relationship modeling; 2) lacking consideration of inter-image
separability. In this paper, we propose the Co-Salient Object Detection
Transformer (CoSformer) network to capture both salient and common visual
patterns from multiple images. By leveraging Transformer architecture, the
proposed method address the influence of the input orders and greatly improve
the stability of the CoSOD task. We also introduce a novel concept of
inter-image separability. We construct a contrast learning scheme to modeling
the inter-image separability and learn more discriminative embedding space to
distinguish true common objects from noisy objects. Extensive experiments on
three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate
that our CoSformer outperforms cutting-edge models and achieves the new
state-of-the-art. We hope that CoSformer can motivate future research for more
visual co-analysis tasks.