Abstract:
  We propose a novel blocked version of the continuous-time bouncy particle
sampler of [Bouchard-C\^ot\'e et al., 2018] which is applicable to any
differentiable probability density. This alternative implementation is
motivated by blocked Gibbs sampling for state space models [Singh et al., 2017]
and leads to significant improvement in terms of effective sample size per
second, and furthermore, allows for significant parallelization of the
resulting algorithm. The new algorithms are particularly efficient for latent
state inference in high-dimensional state space models, where blocking in both
space and time is necessary to avoid degeneracy of MCMC. The efficiency of our
blocked bouncy particle sampler, in comparison with both the standard
implementation of the bouncy particle sampler and the particle Gibbs algorithm
of Andrieu et al. [2010], is illustrated numerically for both simulated data
and a challenging real-world financial dataset.


Abstract:
  Squeezed light is a key quantum resource that enables quantum advantages for
sensing, networking, and computing applications. The scalable generation and
manipulation of squeezed light with integrated platforms are highly desired for
the development of quantum technology with continuous variables. In this
letter, we demonstrate squeezed light generation with thin-film lithium niobate
integrated photonics. Para-metric down-conversion is realized with quasi-phase
matching using ferroelectric domain engineering. With sub-wavelength mode
confinement, efficient nonlinear processes can be observed with single-pass
configuration. We measure0.56+-0.09dB quadrature squeezing(~3 dB inferred
on-chip). The single-pass configuration further enables the generation of
squeezed light with large spectral bandwidth up to 7 THz. This work represents
a significant step towards the on-chip implementation of continuous-variable
quantum information processing


Abstract:
  This paper considers the data association problem for multi-target tracking.
Multiple hypothesis tracking is a popular algorithm for solving this problem
but it is NP-hard and is is quite complicated for a large number of targets or
for tracking maneuvering targets. To improve tracking performance and enhance
robustness, we propose a randomized multiple model multiple hypothesis tracking
method, which has three distinctive advantages. First, it yields a randomized
data association solution which maximizes the expectation of the logarithm of
the posterior probability and can be solved efficiently by linear programming.
Next, the state estimation performance is improved by the random coefficient
matrices Kalman filter, which mitigates the difficulty introduced by randomized
data association, i.e., where the coefficient matrices of the dynamic system
are random. Third, the probability that the target follows a specific dynamic
model is derived by jointly optimizing the multiple possible models and data
association hypotheses, and it does not require prior mode transition
probabilities. Thus, it is more robust for tracking multiple maneuvering
targets. Simulations demonstrate the efficiency and superior results of the
proposed algorithm over interacting multiple model multiple hypothesis
tracking.


Abstract:
  A new similarity measure for two sets of S-parameters is proposed. It is
constructed with the modified Hausdorff distance applied to S-parameter points
in 3D space with real, imaginary and normalized frequency axes. New
S-parameters similarity measure facilitates automation of the analysis to
measurement validation, comparison of models and measurements obtained with
different tools, as well as finding similar S-parameter models or similar
elements within S-matrices.


Abstract:
  Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor
images recognized from the electroencephalogram (EEG) to control commands. EEG
patterns of different imagination tasks, e.g. hand and foot movements, are
effectively classified with machine learning techniques using band power
features. Recently, also Convolutional Neural Networks (CNNs) that learn both
effective features and classifiers simultaneously from raw EEG data have been
applied. However, CNNs have two major drawbacks: (i) they have a very large
number of parameters, which thus requires a very large number of training
examples; and (ii) they are not designed to explicitly learn features in the
frequency domain. To overcome these limitations, in this work we introduce
Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass
and depthwise convolutional filters. Experimental results obtained on the
publicly available BCI Competition IV Dataset 2a show that our approach
outperforms reference methods in terms of classification accuracy.


Abstract:
  We study map lattices coupled by collision and show how perturbations of
transfer operators associated with the spatially periodic approximation of the
model can be used to extract information about collisions per lattice unit.
More precisely, we study a map on a finite box of $L$ sites with periodic
boundary conditions, coupled by collision. We derive, via a non-trivial first
order approximation for the leading eigenvalue of the rare event transfer
operator, a formula for the first collision rate and a corresponding first
hitting time law. For the former we show that the formula scales at the order
of $L\cdot\varepsilon^2$, where $\varepsilon$ is the coupling strength, and for
the latter, by tracking the $L$ dependency in our arguments, we show that the
error in the law is of order $O\left(C(L)L\varepsilon^2\cdot|\ln
L\varepsilon^2|\right)$ for a specific function $C(L)$. Finally, we derive an
explicit formula for the first collision rate per lattice unit.


Abstract:
  Pretrained language models have achieved state-of-the-art performance when
adapted to a downstream NLP task. However, theoretical analysis of these models
is scarce and challenging since the pretraining and downstream tasks can be
very different. We propose an analysis framework that links the pretraining and
downstream tasks with an underlying latent variable generative model of text --
the downstream classifier must recover a function of the posterior distribution
over the latent variables. We analyze head tuning (learning a classifier on top
of the frozen pretrained model) and prompt tuning in this setting. The
generative model in our analysis is either a Hidden Markov Model (HMM) or an
HMM augmented with a latent memory component, motivated by long-term
dependencies in natural language. We show that 1) under certain non-degeneracy
conditions on the HMM, simple classification heads can solve the downstream
task, 2) prompt tuning obtains downstream guarantees with weaker non-degeneracy
conditions, and 3) our recovery guarantees for the memory-augmented HMM are
stronger than for the vanilla HMM because task-relevant information is easier
to recover from the long-term memory. Experiments on synthetically generated
data from HMMs back our theoretical findings.


Abstract:
  Densest subgraph detection is a fundamental graph mining problem, with a
large number of applications. There has been a lot of work on efficient
algorithms for finding the densest subgraph in massive networks. However, in
many domains, the network is private, and returning a densest subgraph can
reveal information about the network. Differential privacy is a powerful
framework to handle such settings. We study the densest subgraph problem in the
edge privacy model, in which the edges of the graph are private. We present the
first sequential and parallel differentially private algorithms for this
problem. We show that our algorithms have an additive approximation guarantee.
We evaluate our algorithms on a large number of real-world networks, and
observe a good privacy-accuracy tradeoff when the network has high density.


Abstract:
  Protein-protein interactions are the basis of many important physiological
processes and are currently promising, yet difficult, targets for drug
discovery. In this context, inhibitor of apoptosis proteins (IAPs)-mediated
interactions are pivotal for cancer cell survival; the interaction of the BIR1
domain of cIAP2 with TRAF2 was shown to lead the recruitment of cIAPs to the
TNF receptor, promoting the activation of the NF-\kappa B survival pathway. In
this work, using a combined in silico-in vitro approach, we identified a
drug-like molecule, NF023, able to disrupt cIAP2 interaction with TRAF2. We
demonstrated in vitro its ability to interfere with the assembly of the
cIAP2-BIR1/TRAF2 complex and performed a thorough characterization of the
compound's mode of action through 248 parallel unbiased molecular dynamics
simulations of 300 ns (totaling almost 75 {\mu}s of all-atom sampling), which
identified multiple binding modes to the BIR1 domain of cIAP2 via clustering
and ensemble docking. NF023 is, thus, a promising protein-protein interaction
disruptor, representing a starting point to develop modulators of NF-\kappa
B-mediated cell survival in cancer. This study represents a model procedure
that shows the use of large-scale molecular dynamics methods to typify
promiscuous interactors.


Abstract:
  We analysed the shadow cast by charged rotating black hole (BH) in presence
of perfect fluid dark matter (PFDM). We studied the null geodesic equations and
obtained the shadow of the charged rotating BH to see the effects of PFDM
parameter $\gamma$, charge $Q$ and rotation parameter $a$, and it is noticed
that the size as well as the shape of BH shadow is affected due to PFDM
parameter, charge and rotation parameter. Thus, it is seen that the presence of
dark matter around a BH affects its spacetime. We also investigated the
influence of all the parameters (PFDM parameter $\gamma$, BHs charge $Q$ and
rotational parameter $a$) on effective potential, energy emission by graphical
representation, and compare all the results with the non rotating case in usual
general relativity. To this end, we have also explored the effect of PFDM on
the deflection angle and the size of Einstein rings.


Abstract:
  The year 2020 will be remembered for two events of global significance: the
COVID-19 pandemic and 2020 U.S. Presidential Election. In this chapter, we
summarize recent studies using large public Twitter data sets on these issues.
We have three primary objectives. First, we delineate epistemological and
practical considerations when combining the traditions of computational
research and social science research. A sensible balance should be struck when
the stakes are high between advancing social theory and concrete, timely
reporting of ongoing events. We additionally comment on the computational
challenges of gleaning insight from large amounts of social media data. Second,
we characterize the role of social bots in social media manipulation around the
discourse on the COVID-19 pandemic and 2020 U.S. Presidential Election. Third,
we compare results from 2020 to prior years to note that, although bot accounts
still contribute to the emergence of echo-chambers, there is a transition from
state-sponsored campaigns to domestically emergent sources of distortion.
Furthermore, issues of public health can be confounded by political
orientation, especially from localized communities of actors who spread
misinformation. We conclude that automation and social media manipulation pose
issues to a healthy and democratic discourse, precisely because they distort
representation of pluralism within the public sphere.


Abstract:
  We present a new numerical approach for wave induced dynamic fracture. The
method is based on a discontinuous Galerkin approximation of the first-order
hyperbolic system for elastic waves and a phase-field approximation of brittle
fracture driven by the maximum tension. The algorithm is staggered in time and
combines an implicit midpoint rule for the wave propagation followed by an
implicit Euler step for the phase-field evolution. At fracture, the material is
degraded, and the waves are reflected at the diffusive interfaces. Two and
three-dimensional examples demonstrate the advantages of the proposed method
for the computation of crack growth and spalling initiated by reflected and
superposed waves.


Abstract:
  Experimental measurements in deep-inelastic scattering and lepton-pair
production on deuterium targets play an important role in the flavor separation
of $u$ and $d$ (anti)quarks in global QCD analyses of the parton distribution
functions (PDFs) of the nucleon. We investigate the impact of theoretical
corrections accounting for the light-nuclear structure of the deuteron upon the
fitted $u, d$-quark, gluon, and other PDFs in the CJ15 and CT18 families of
next-to-leading order CTEQ global analyses. The investigation is done using the
$L_2$ sensitivity statistical method, which provides a common metric to
quantify the strength of experimental constraints on various PDFs and ratios of
PDFs in the two distinct fitting frameworks. Using the $L_2$ sensitivity and
other approaches, we examine the compatibility of deuteron data sets with other
fitted experiments under varied implementations of the deuteron corrections. We
find that freely-fitted deuteron corrections modify the PDF uncertainty at
large momentum fractions and will be relevant for future PDFs affecting
electroweak precision measurements.


Abstract:
  The Carina Nebula harbors a large population of high-mass stars, including at
least 75 O-type and Wolf-Rayet stars, but the current census is not complete
since further high-mass stars may be hidden in or behind the dense dark clouds
that pervade the association. With the aim of identifying optically obscured O-
and early B-type stars in the Carina Nebula, we performed the first infrared
spectroscopic study of stars in the optically obscured stellar cluster Tr
16-SE, located behind a dark dust lane south of eta Car. We used the
integral-field spectrograph KMOS at the ESO VLT to obtain H- and K-band spectra
with a resolution of R sim 4000 (Delta lambda sim 5 A) for 45 out of the 47
possible OB candidate stars in Tr 16-SE, and we derived spectral types for
these stars. We find 15 stars in Tr 16-SE with spectral types between O5 and B2
(i.e., high-mass stars with M >= 8 Msun, only two of which were known before.
An additional nine stars are classified as (Ae)Be stars (i.e.,
intermediate-mass pre-main-sequence stars), and most of the remaining targets
show clear signatures of being late-type stars and are thus most likely
foreground stars or background giants unrelated to the Carina Nebula. Our
estimates of the stellar luminosities suggest that nine of the 15 O- and early
B-type stars are members of Tr 16-SE, whereas the other six seem to be
background objects. Our study increases the number of spectroscopically
identified high-mass stars (M >= 8 Msun) in Tr 16-SE from two to nine and shows
that Tr 16-SE is one of the larger clusters in the Carina Nebula. Our
identification of three new stars with spectral types between O5 and O7 and
four new stars with spectral types O9 to B1 significantly increases the number
of spectroscopically identified O-type stars in the Carina Nebula.


Abstract:
  Probabilistic deep learning is deep learning that accounts for uncertainty,
both model uncertainty and data uncertainty. It is based on the use of
probabilistic models and deep neural networks. We distinguish two approaches to
probabilistic deep learning: probabilistic neural networks and deep
probabilistic models. The former employs deep neural networks that utilize
probabilistic layers which can represent and process uncertainty; the latter
uses probabilistic models that incorporate deep neural network components which
capture complex non-linear stochastic relationships between the random
variables. We discuss some major examples of each approach including Bayesian
neural networks and mixture density networks (for probabilistic neural
networks), and variational autoencoders, deep Gaussian processes and deep mixed
effects models (for deep probabilistic models). TensorFlow Probability is a
library for probabilistic modeling and inference which can be used for both
approaches of probabilistic deep learning. We include its code examples for
illustration.


Abstract:
  The scale of small-field inflation cannot be constrained via primordial
gravitational waves through measurement of tensor-to-scalar ratio $r$. In this
study, I show that if cosmic strings are produced after symmetry breaking at
the end of hilltop supernatural inflation, this small-field inflation model can
be tested through the production of gravitational waves from cosmic strings.
Future experiments of gravitational wave detectors will determine or further
constrain the parameter space in the model.


Abstract:
  In this work, the structural, electrical, and optical properties of bilayer
SiX (X= N, P, As, and Sb) are studied using density functional theory (DFT).
Five different stacking orders are considered for every compound and their
structural properties are presented. The band structure of these materials
demonstrates that they are indirect semiconductors. The out-of-plane strain has
been applied to tune the bandgap and its electrical properties. The bandgap
increases with tensile strain, whereas, compressive strain leads to
semiconductor-to-metal transition. The sensitivity of the bandgap to the
pressure is investigated and bilayer SiSb demonstrates the highest bandgap
sensitivity to the pressure. These structures exhibit Mexican hat-like valence
band dispersion that can be approved by a singularity in the density of states.
The Mexican-hat coefficient can be tuned by out-of-plane strain. Optical
absorption of these compounds shows that the second and lower valence bands due
to the high density of states display a higher contribution to optical
transitions.


Abstract:
  Signal propagation in an optical fiber can be described by the nonlinear
Schr\"odinger equation (NLSE). The NLSE has no known closed-form solution,
mostly due to the interaction of dispersion and nonlinearities. In this paper,
we present a novel closed-form approximate model for the nonlinear optical
channel, with applications to passive optical networks. The proposed model is
derived using logarithmic perturbation in the frequency domain on the
group-velocity dispersion (GVD) parameter of the NLSE. The model can be seen as
an improvement of the recently proposed regular perturbation (RP) on the GVD
parameter. RP and logarithmic perturbation (LP) on the nonlinear coefficient
have already been studied in the literature, and are hereby compared with RP on
the GVD parameter and the proposed LP model. As an application of the model, we
focus on passive optical networks. For a 20 km PON at 10 Gbaud, the proposed
model improves upon LP on the nonlinear coefficient by 1.5 dB. For the same
system, a detector based on the proposed LP model reduces the uncoded
bit-error-rate by up to 5.4 times at the same input power or reduces the input
power by 0.4 dB at the same information rate.


Abstract:
  Rhombohedral dense forms of carbon, rh-C2 (or hexagonal h-C6), and boron
nitride, rh-BN (or hexagonal h-B3N3), are derived from rhombohedral 3R graphite
based on original crystal chemistry scheme backed with full cell geometry
optimization to minimal energy ground state computations within the quantum
density functional theory. Considering throughout hexagonal settings featuring
extended lattices, the calculation of the hexagonal set of elastic constants,
provide results of large bulk moduli i.e. B0(rh-C2) = 438 GPa close to that of
diamond, and B0(rh-BN) = 369 GPa close to that of cubic BN. The hardness
assessment in the framework of three contemporary models enables both phases to
be considered as ultra-hard. From the electronic band structures calculated in
the hexagonal Brillouin zones, 3R graphite is a small-gap semiconductor,
oppositely to rh-C2 that is characterized by a large band gap close to 5 eV, as
well as the two BN phases.


Abstract:
  Currently, there are more than a dozen Russian-language corpora for sentiment
analysis, differing in the source of the texts, domain, size, number and ratio
of sentiment classes, and annotation method. This work examines publicly
available Russian-language corpora, presents their qualitative and quantitative
characteristics, which make it possible to get an idea of the current landscape
of the corpora for sentiment analysis. The ranking of corpora by annotation
quality is proposed, which can be useful when choosing corpora for training and
testing. The influence of the training dataset on the performance of sentiment
analysis is investigated based on the use of the deep neural network model
BERT. The experiments with review corpora allow us to conclude that on average
the quality of models increases with an increase in the number of training
corpora. For the first time, quality scores were obtained for the corpus of
reviews of ROMIP seminars based on the BERT model. Also, the study proposes the
task of the building a universal model for sentiment analysis.


Abstract:
  An LBYL (`Look Before You Leap') Network is proposed for end-to-end trainable
one-stage visual grounding. The idea behind LBYL-Net is intuitive and
straightforward: we follow a language's description to localize the target
object based on its relative spatial relation to `Landmarks', which is
characterized by some spatial positional words and some descriptive words about
the object. The core of our LBYL-Net is a landmark feature convolution module
that transmits the visual features with the guidance of linguistic description
along with different directions. Consequently, such a module encodes the
relative spatial positional relations between the current object and its
context. Then we combine the contextual information from the landmark feature
convolution module with the target's visual features for grounding. To make
this landmark feature convolution light-weight, we introduce a dynamic
programming algorithm (termed dynamic max pooling) with low complexity to
extract the landmark feature. Thanks to the landmark feature convolution
module, we mimic the human behavior of `Look Before You Leap' to design an
LBYL-Net, which takes full consideration of contextual information. Extensive
experiments show our method's effectiveness in four grounding datasets.
Specifically, our LBYL-Net outperforms all state-of-the-art two-stage and
one-stage methods on ReferitGame. On RefCOCO and RefCOCO+, Our LBYL-Net also
achieves comparable results or even better results than existing one-stage
methods.


Abstract:
  When faced with learning challenging new tasks, humans often follow sequences
of steps that allow them to incrementally build up the necessary skills for
performing these new tasks. However, in machine learning, models are most often
trained to solve the target tasks directly.Inspired by human learning, we
propose a novel curriculum learning approach which decomposes challenging tasks
into sequences of easier intermediate goals that are used to pre-train a model
before tackling the target task. We focus on classification tasks, and design
the intermediate tasks using an automatically constructed label hierarchy. We
train the model at each level of the hierarchy, from coarse labels to fine
labels, transferring acquired knowledge across these levels. For instance, the
model will first learn to distinguish animals from objects, and then use this
acquired knowledge when learning to classify among more fine-grained classes
such as cat, dog, car, and truck. Most existing curriculum learning algorithms
for supervised learning consist of scheduling the order in which the training
examples are presented to the model. In contrast, our approach focuses on the
output space of the model. We evaluate our method on several established
datasets and show significant performance gains especially on classification
problems with many labels. We also evaluate on a new synthetic dataset which
allows us to study multiple aspects of our method.


Abstract:
  Given a monoid $S$ with $E$ any non-empty subset of its idempotents, we
present a novel one-sided version of idempotent completion we call left
$E$-completion. In general, the construction yields a one-sided variant of a
small category called a constellation by Gould and Hollings. Under certain
conditions, this constellation is inductive, meaning that its partial
multiplication may be extended to give a left restriction semigroup, a type of
unary semigroup whose unary operation models domain. We study the properties of
those pairs $S,E$ for which this happens, and characterise those left
restriction semigroups that arise as such left $E$-completions of their monoid
of elements having domain $1$. As first applications, we decompose the left
restriction semigroup of partial functions on the set $X$ and the right
restriction semigroup of left total partitions on $X$ as left and right
$E$-completions respectively of the transformation semigroup $T_X$ on $X$, and
decompose the left restriction semigroup of binary relations on $X$ under
demonic composition as a left $E$-completion of the left-total binary
relations. In many cases, including these three examples, the construction
embeds in a semigroup Zappa-Sz\'{e}p product.


Abstract:
  Synthesizing data for semantic parsing has gained increasing attention
recently. However, most methods require handcrafted (high-precision) rules in
their generative process, hindering the exploration of diverse unseen data. In
this work, we propose a generative model which features a (non-neural) PCFG
that models the composition of programs (e.g., SQL), and a BART-based
translation model that maps a program to an utterance. Due to the simplicity of
PCFG and pre-trained BART, our generative model can be efficiently learned from
existing data at hand. Moreover, explicitly modeling compositions using PCFG
leads to a better exploration of unseen programs, thus generate more diverse
data. We evaluate our method in both in-domain and out-of-domain settings of
text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider,
respectively. Our empirical results show that the synthesized data generated
from our model can substantially help a semantic parser achieve better
compositional and domain generalization.


Abstract:
  Detecting pedestrians is a crucial task in autonomous driving systems to
ensure the safety of drivers and pedestrians. The technologies involved in
these algorithms must be precise and reliable, regardless of environment
conditions. Relying solely on RGB cameras may not be enough to recognize road
environments in situations where cameras cannot capture scenes properly. Some
approaches aim to compensate for these limitations by combining RGB cameras
with TOF sensors, such as LIDARs. However, there are few works that address
this problem using exclusively the 3D geometric information provided by LIDARs.
In this paper, we propose a PointNet++ based architecture to detect pedestrians
in dense 3D point clouds. The aim is to explore the potential contribution of
geometric information alone in pedestrian detection systems. We also present a
semi-automatic labeling system that transfers pedestrian and non-pedestrian
labels from RGB images onto the 3D domain. The fact that our datasets have RGB
registered with point clouds enables label transferring by back projection from
2D bounding boxes to point clouds, with only a light manual supervision to
validate results. We train PointNet++ with the geometry of the resulting 3D
labelled clusters. The evaluation confirms the effectiveness of the proposed
method, yielding precision and recall values around 98%.


Abstract:
  Being generated, the relic neutrino background contained equal fractions of
electron $\nu_e$, muon $\nu_\mu$, and taon $\nu_\tau$ neutrinos. We show that
the gravitational field of our Galaxy and other nearby cosmic objects changes
this composition near the Solar System, enriching it with the heaviest neutrino
$nu_3$. This mass state is almost free of the electron component (only $\sim
2\%$ of $\nu_e$) and contains more muon component than the tau one. As a
result, the relic background becomes enriched with taon and particularly muon
neutrinos. The electron relic neutrinos are the rarest for a terrestrial
observer: instead of $1/3$, the relic background may contain only $\gtrsim
20\%$ of them.


Abstract:
  If $(X, \le_X)$ is a partially ordered set satisfying certain necessary
conditions for $X$ to be order-isomorphic to the spectrum of a Noetherian
domain of dimension two, we describe a new poset $(\text{str } X,
\le_{\text{str } X})$ that completely determines $X$ up to isomorphism. The
order relation $\le_{\text{str } X}$ imposed on $\text{str } X$ is modeled
after R. Wiegand's well-known "P5" condition that can be used to determine when
a given partially ordered set $(U, \le_U)$ of a certain type is
order-isomorphic to $(\text{Spec } \mathbb Z[x], \subseteq).$


Abstract:
  We demonstrate a new approach to supercontinuum generation and
carrier-envelope-offset detection in dispersion-engineered nanophotonic
waveguides based on saturated second-harmonic generation of femtosecond pulses.
In contrast with traditional approaches based on self-phase modulation, this
technique simultaneously broadens both harmonics by generating rapid amplitude
modulations of the field envelopes. The generated supercontinuum produces
coherent carrier-envelope-offset beatnotes in the overlap region that remain in
phase across 100's of nanometers of bandwidth while requiring $<$10 picojoules
of pulse energy.


Abstract:
  Egocentric segmentation has attracted recent interest in the computer vision
community due to their potential in Mixed Reality (MR) applications. While most
previous works have been focused on segmenting egocentric human body parts
(mainly hands), little attention has been given to egocentric objects. Due to
the lack of datasets of pixel-wise annotations of egocentric objects, in this
paper we contribute with a semantic-wise labeling of a subset of 2124 images
from the RGB-D THU-READ Dataset. We also report benchmarking results using
Thundernet, a real-time semantic segmentation network, that could allow future
integration with end-to-end MR applications.


Abstract:
  The connection of single-phase microgrids (MG) and loads to three-phase MGs
creates power quality problems such as unbalanced voltage and voltage rise at
the point of common coupling (PCC) of the MGs. In this paper, a modified
reverse droop control (MRDC) scheme in the Energy Storage System (ESS) is
proposed to improve the three-phase PCC voltage quality in multi-microgrids
(MMG). The MRDC consists of a reactive power compensator (RPC) and a voltage
compensator. The controller regulates the reactive power and voltage unbalance
of the MMG by using the reactive power produced by the ESS. The effectiveness
of this proposed scheme is verified in real-time simulation using the Opal-RT
OP5600 real-time simulator. The voltage unbalance factor (VUF) at the PCC is
decreased from 3.6 percent to 0.25 percent, while the reactive power is reduced
significantly at the single-phase load.


Abstract:
  Generative Adversarial Networks (GANs) are machine learning networks based
around creating synthetic data. Voice Conversion (VC) is a subset of voice
translation that involves translating the paralinguistic features of a source
speaker to a target speaker while preserving the linguistic information. The
aim of non-parallel conditional GANs for VC is to translate an acoustic speech
feature sequence from one domain to another without the use of paired data. In
the study reported here, we investigated the interpretability of
state-of-the-art implementations of non-parallel GANs in the domain of VC. We
show that the learned representations in the repeating layers of a particular
GAN architecture remain close to their original random initialised parameters,
demonstrating that it is the number of repeating layers that is more
responsible for the quality of the output. We also analysed the learned
representations of a model trained on one particular dataset when used during
transfer learning on another dataset. This showed extremely high levels of
similarity across the entire network. Together, these results provide new
insight into how the learned representations of deep generative networks change
during learning and the importance in the number of layers.


Abstract:
  Time-resolved mapping of lattice dynamics in real- and momentum-space is
essential to understand better several ubiquitous phenomena such as heat
transport, displacive phase transition, thermal conductivity, and many more. In
this regard, time-resolved diffraction and microscopy methods are employed to
image the induced lattice dynamics within a pump-probe configuration. In this
work, we demonstrate that inelastic scattering methods, with the aid of
theoretical simulation, are competent to provide similar information as one
could obtain from the time-resolved diffraction and imaging measurements. To
illustrate the robustness of the proposed method, our simulated result of
lattice dynamics in germanium is in excellent agreement with the time-resolved
x-ray diffuse scattering measurement performed using x-ray free-electron laser.
For a given inelastic scattering data in energy and momentum space, the
proposed method is useful to image in-situ lattice dynamics under different
environmental conditions of temperature, pressure, and magnetic field.
Moreover, the technique will profoundly impact where time-resolved diffraction
within the pump-probe setup is not feasible, for instance, in inelastic neutron
scattering.


Abstract:
  In clinical trials, there often exist multiple historical studies for the
same or related treatment investigated in the current trial. Incorporating
historical data in the analysis of the current study is of great importance, as
it can help to gain more information, improve efficiency, and provide a more
comprehensive evaluation of treatment. Enlightened by the unit information
prior (UIP) concept in the reference Bayesian test, we propose a new
informative prior called UIP from an information perspective that can
adaptively borrow information from multiple historical datasets. We consider
both binary and continuous data and also extend the new UIP methods to linear
regression settings. Extensive simulation studies demonstrate that our method
is comparable to other commonly used informative priors, while the
interpretation of UIP is intuitive and its implementation is relatively easy.
One distinctive feature of UIP is that its construction only requires summary
statistics commonly reported in the literature rather than the patient-level
data. By applying our UIP methods to phase III clinical trials for
investigating the efficacy of memantine in Alzheimer's disease, we illustrate
its ability of adaptively borrowing information from multiple historical
datasets in the real application.


Abstract:
  The initial period of vaccination shows strong heterogeneity between
countries' vaccinations rollout, both in the terms of the start of the
vaccination process and in the dynamics of the number of people that are
vaccinated. A predominant thesis in the ongoing debate on the drivers of this
observed heterogeneity is that a key determinant of the swift and extensive
vaccine rollout is state capacity. Here, we utilize two measures that quantify
different aspects of the state capacity: i) the external capacity (measured
through the soft power and the economic power of the country) and ii) the
internal capacity (measured via the country's government effectiveness) and
investigate their relationship with the coronavirus vaccination outcome in the
initial period (up to 30th January 2021). By using data on 189 countries and a
two-step Heckman approach, we find that the economic power of the country and
its soft power are robust determinants of whether a country has started with
the vaccination process. In addition, the government effectiveness is a key
factor that determines vaccine roll-out. Altogether, our findings are in line
with the hypothesis that state capacity determines the observed heterogeneity
between countries in the initial period of COVID-19 vaccines rollout.


Abstract:
  Background: Due to the finite size of the development sample, predicted
probabilities from a risk prediction model are inevitably uncertain. We apply
Value of Information methodology to evaluate the decision-theoretic
implications of prediction uncertainty.
  Methods: Adopting a Bayesian perspective, we extend the definition of the
Expected Value of Perfect Information (EVPI) from decision analysis to net
benefit calculations in risk prediction. In the context of model development,
EVPI is the expected gain in net benefit by using the correct predictions as
opposed to predictions from a proposed model. We suggest bootstrap methods for
sampling from the posterior distribution of predictions for EVPI calculation
using Monte Carlo simulations. In a case study, we used subsets of data of
various sizes from a clinical trial for predicting mortality after myocardial
infarction to show how EVPI changes with sample size.
  Results: With a sample size of 1,000 and at the pre-specified threshold of 2%
on predicted risks, the gain in net benefit by using the proposed and the
correct models were 0.0006 and 0.0011, respectively, resulting in an EVPI of
0.0005 and a relative EVPI of 87%. EVPI was zero only at unrealistically high
thresholds (>85%). As expected, EVPI declined with larger samples. We summarize
an algorithm for incorporating EVPI calculations into the commonly used
bootstrap method for optimism correction.
  Conclusion: Value of Information methods can be applied to explore
decision-theoretic consequences of uncertainty in risk prediction and can
complement inferential methods when developing risk prediction models. R code
for implementing this method is provided.


Abstract:
  Specific topological excitations of energetically stable "core-and-mantle"
configurations of trapped two-component immiscible Bose-Einstein condensates
are studied numerically within the coupled Gross-Pitaevskii equations.
Non-stationary long-lived coherent structures, that consist of several quantum
vortex filaments penetrating the "mantle" from outside to inside and vice-versa
and demonstrate quite nontrivial dynamics, are observed in simulations for the
first time. The ends of filaments can remain attached to the interface between
the "mantle" and the "core" if the latter is large enough while the surface
tension is not small. The shapes of such "bubbles" are strongly affected by the
vortices and sometimes are far from being spherical.


Abstract:
  We extend previous work concerning rest-frame partial-wave mixing in
Hamiltonian effective field theory to both elongated and moving systems, where
two particles are in a periodic elongated cube or have nonzero total momentum,
respectively. We also consider the combination of the two systems when
directions of the elongation and the moving momentum are aligned. This
extension should also be applicable in any Hamiltonian formalism. As a
demonstration, we analyze lattice QCD results for the spectrum of an isospin-2
$\pi\pi$ scattering system and determine the $s$, $d$, and $g$ partial-wave
scattering information. The inclusion of lattice simulation results from moving
frames significantly improves the uncertainty in the scattering information.


Abstract:
  We study Smith-Purcell radiation from a conducting grating generated by a
vortex electron with an orbital angular momentum $\ell \hbar$, described as a
generalized Laguerre-Gaussian packet, which has an intrinsic magnetic dipole
moment and an electric quadrupole moment. By using a multipole expansion of the
electromagnetic field of such an electron, we employ a generalized
surface-current method, applicable for a wide range of parameters. The radiated
energy contains contributions from the charge, from the magnetic moment, and
from the electric quadrupole moment, as well as from their interference. The
quadrupole contribution grows as the packet spreads while propagating, and it
is enhanced for large $\ell$. In contrast to the linear growth of the radiation
intensity from the charge with a number of strips $N$, the quadrupole
contribution reveals an $N^3$ dependence, which puts a limit on the maximal
grating length for which the radiation losses stay small. We study
spectral-angular distributions of the Smith-Purcell radiation both analytically
and numerically and demonstrate that the electron's vorticity can give rise to
detectable effects for non-relativistic and moderately relativistic electrons.
On a practical side, preparing the incoming electron's state in a form of a
non-Gaussian packet with a quadrupole moment -- such as the vortex electron, an
Airy beam, a Schr\"odinger cat state, and so on -- one can achieve quantum
enhancement of the radiation power compared to the classical linear regime.
Such an enhancement would be a hallmark of a previously unexplored quantum
regime of radiation, in which non-Gaussianity of the packet influences the
radiation properties much stronger than the quantum recoil.


Abstract:
  The QED initial state corrections are calculated to the forward-backward
asymmetry for $e^+e^- \rightarrow \gamma^*/{Z^{0}}^*$ in the leading
logarithmic approximation to $O(\alpha^6 L^6)$ extending the known corrections
up to $O(\alpha^2 L^2)$ in analytic form. We use the method of massive on-shell
operator matrix elements and present the radiators both in Mellin-$N$ and
momentum fraction $z$-space. Numerical results are presented for various
energies around the $Z$-peak by also including energy cuts. These corrections
are of relevance for the precision measurements at the FCC$\_$ee.


Abstract:
  Based on a recent work on traveling waves in spatially nonlocal
reaction-diffusion equations, we investigate the existence of traveling fronts
in reaction-diffusion equations with a memory term. We will explain how such
memory terms can arise from reduction of reaction-diffusion systems if the
diffusion constants of the other species can be neglected. In particular, we
show that two-scale homogenization of spatially periodic systems can induce
spatially homogeneous systems with temporal memory.
  The existence of fronts is proved using comparison principles as well as a
reformulation trick involving an auxiliary speed that allows us to transform
memory terms into spatially nonlocal terms. Deriving explicit bounds and
monotonicity properties of the wave speed of the arising travelingfront, we are
able to establish the existence of true traveling fronts for the original
problem with memory. Our results are supplemented by numerical simulations.


Abstract:
  In recent years, constant applied potential molecular dynamics has allowed to
study the structure and dynamics of the electrochemical double-layer of a large
variety of nanoscale capacitors. Nevertheless it remained impossible to
simulate polarized electrodes at fixed total charge. Here we show that
combining a constant potential electrode with a finite electric displacement
fills this gap by allowing to simulate open circuit conditions. The method can
be extended by applying an electric displacement ramp to perform computational
amperometry experiments at different current intensities. As in experiments,
the full capacitance of the system is obtained at low intensity, but this
quantity decreases when the applied ramp becomes too fast with respect to the
microscopic dynamics of the liquid.


Abstract:
  The main difficulty that arises in the analysis of most machine learning
algorithms is to handle, analytically and numerically, a large number of
interacting random variables. In this Ph.D manuscript, we revisit an approach
based on the tools of statistical physics of disordered systems. Developed
through a rich literature, they have been precisely designed to infer the
macroscopic behavior of a large number of particles from their microscopic
interactions. At the heart of this work, we strongly capitalize on the deep
connection between the replica method and message passing algorithms in order
to shed light on the phase diagrams of various theoretical models, with an
emphasis on the potential differences between statistical and algorithmic
thresholds. We essentially focus on synthetic tasks and data generated in the
teacher-student paradigm. In particular, we apply these mean-field methods to
the Bayes-optimal analysis of committee machines, to the worst-case analysis of
Rademacher generalization bounds for perceptrons, and to empirical risk
minimization in the context of generalized linear models. Finally, we develop a
framework to analyze estimation models with structured prior informations,
produced for instance by deep neural networks based generative models with
random weights.


Abstract:
  Influence competition finds its significance in many applications, such as
marketing, politics and public events like COVID-19. Existing work tends to
believe that the stronger influence will always win and dominate nearly the
whole network, i.e., "winner takes all". However, this finding somewhat
contradicts with our common sense that many competing products are actually
coexistent, e.g., Android vs. iOS. This contradiction naturally raises the
question: will the winner take all?
  To answer this question, we make a comprehensive study into influence
competition by identifying two factors frequently overlooked by prior art: (1)
the incomplete observation of real diffusion networks; (2) the existence of
information overload and its impact on user behaviors. To this end, we attempt
to recover possible diffusion links based on user similarities, which are
extracted by embedding users into a latent space. Following this, we further
derive the condition under which users will be overloaded, and formulate the
competing processes where users' behaviors differ before and after information
overload. By establishing the explicit expressions of competing dynamics, we
disclose that information overload acts as the critical "boundary line", before
which the "winner takes all" phenomenon will definitively occur, whereas after
information overload the share of influences gradually stabilizes and is
jointly affected by their initial spreading conditions, influence powers and
the advent of overload. Numerous experiments are conducted to validate our
theoretical results where favorable agreement is found. Our work sheds light on
the intrinsic driving forces behind real-world dynamics, thus providing useful
insights into effective information engineering.


Abstract:
  With the increasing adoption of private blockchain platforms, consortia
operating in various sectors such as trade, finance, logistics, etc., are
becoming common. Despite having the benefits of a completely decentralized
architecture which supports transparency and distributed control, existing
private blockchains limit the data, assets, and processes within its closed
boundary, which restricts secure and verifiable service provisioning to the
end-consumers. Thus, platforms such as e-commerce with multiple sellers or
cloud federation with a collection of cloud service providers cannot be
decentralized with the existing blockchain platforms. This paper proposes a
decentralized gateway architecture interfacing private blockchain with
end-users by leveraging the unique combination of public and private blockchain
platforms through interoperation. Through the use case of decentralized cloud
federations, we have demonstrated the viability of the solution. Our testbed
implementation with Ethereum and Hyperledger Fabric, with three service
providers, shows that such consortium can operate within an acceptable response
latency while scaling up to 64 parallel requests per second for cloud
infrastructure provisioning. Further analysis over the Mininet emulation
platform indicates that the platform can scale well with minimal impact over
the latency as the number of participating service providers increases.


Abstract:
  While Machine Comprehension (MC) has attracted extensive research interests
in recent years, existing approaches mainly belong to the category of Machine
Reading Comprehension task which mines textual inputs (paragraphs and
questions) to predict the answers (choices or text spans). However, there are a
lot of MC tasks that accept audio input in addition to the textual input, e.g.
English listening comprehension test. In this paper, we target the problem of
Audio-Oriented Multimodal Machine Comprehension, and its goal is to answer
questions based on the given audio and textual information. To solve this
problem, we propose a Dynamic Inter- and Intra-modality Attention (DIIA) model
to effectively fuse the two modalities (audio and textual). DIIA can work as an
independent component and thus be easily integrated into existing MC models.
Moreover, we further develop a Multimodal Knowledge Distillation (MKD) module
to enable our multimodal MC model to accurately predict the answers based only
on either the text or the audio. As a result, the proposed approach can handle
various tasks including: Audio-Oriented Multimodal Machine Comprehension,
Machine Reading Comprehension and Machine Listening Comprehension, in a single
model, making fair comparisons possible between our model and the existing
unimodal MC models. Experimental results and analysis prove the effectiveness
of the proposed approaches. First, the proposed DIIA boosts the baseline models
by up to 21.08% in terms of accuracy; Second, under the unimodal scenarios, the
MKD module allows our multimodal MC model to significantly outperform the
unimodal models by up to 18.87%, which are trained and tested with only audio
or textual data.


Abstract:
  Geometry problem solving has attracted much attention in the NLP community
recently. The task is challenging as it requires abstract problem understanding
and symbolic reasoning with axiomatic knowledge. However, current datasets are
either small in scale or not publicly available. Thus, we construct a new
large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with
dense annotation in formal language. We further propose a novel geometry
solving approach with formal language and symbolic reasoning, called
Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the
problem text and diagram into formal language automatically via rule-based text
parsing and neural object detecting, respectively. Unlike implicit learning in
existing methods, Inter-GPS incorporates theorem knowledge as conditional rules
and performs symbolic reasoning step by step. Also, a theorem predictor is
designed to infer the theorem application sequence fed to the symbolic solver
for the more efficient and reasonable searching path. Extensive experiments on
the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves
significant improvements over existing methods. The project with code and data
is available at https://lupantech.github.io/inter-gps.


Abstract:
  Heterogeneous multi-task learning (HMTL) is an important topic in multi-task
learning (MTL). Most existing HMTL methods usually solve either scenario where
all tasks reside in the same input (feature) space yet unnecessarily the
consistent output (label) space or scenario where their input (feature) spaces
are heterogeneous while the output (label) space is consistent. However, to the
best of our knowledge, there is limited study on twofold heterogeneous MTL
(THMTL) scenario where the input and the output spaces are both inconsistent or
heterogeneous. In order to handle this complicated scenario, in this paper, we
design a simple and effective multi-task adaptive learning (MTAL) network to
learn multiple tasks in such THMTL setting. Specifically, we explore and
utilize the inherent relationship between tasks for knowledge sharing from
similar convolution kernels in individual layers of the MTAL network. Then in
order to realize the sharing, we weightedly aggregate any pair of convolutional
kernels with their similarity greater than some threshold $\rho$, consequently,
our model effectively performs cross-task learning while suppresses the
intra-redundancy of the entire network. Finally, we conduct end-to-end
training. Our experimental results demonstrate the effectiveness of our method
in comparison with the state-of-the-art counterparts.


Abstract:
  eBPF is a new technology which allows dynamically loading pieces of code into
the Linux kernel. It can greatly speed up networking since it enables the
kernel to process certain packets without the involvement of a userspace
program. So far eBPF has been used for simple packet filtering applications
such as firewalls or Denial of Service protection. We show that it is possible
to develop a flow based network intrusion detection system based on machine
learning entirely in eBPF. Our solution uses a decision tree and decides for
each packet whether it is malicious or not, considering the entire previous
context of the network flow. We achieve a performance increase of over 20\%
compared to the same solution implemented as a userspace program.


Abstract:
  We explore the interplay of electron-electron correlations and surface
effects in the prototypical correlated insulating material, NiO. In particular,
we compute the electronic structure, magnetic properties, and surface energies
of the $(001)$ and $(110)$ surfaces of paramagnetic NiO using a fully charge
self-consistent DFT+DMFT method. Our results reveal a complex interplay between
electronic correlations and surface effects in NiO, with the electronic
structure of the $(001)$ and $(110)$ NiO surfaces being significantly different
from that in bulk NiO. We obtain a sizeable reduction of the band gap at the
surface of NiO, which is most significant for the $(110)$ NiO surface. This
suggests a higher catalytic activity of the $(110)$ NiO surface than that of
the $(001)$ NiO one. Our results reveal a charge-transfer character of the
$(001)$ and $(110)$ surfaces of NiO. Most notably, for the $(110)$ NiO surface
we observe a remarkable electronic state characterized by an alternating
charge-transfer and Mott-Hubbard character of the band gap in the surface and
subsurface NiO layers, respectively. This novel form of electronic order
stabilized by strong correlations is not driven by lattice reconstructions but
of purely electronic origin. We notice the importance of
orbital-differentiation of the Ni $e_g$ states to characterize the Mott-Hubbard
insulating state of the $(001)$ and $(110)$ NiO surfaces. The unoccupied Ni
$e_g$ surface states are seen to split from the lower edge of the conduction
band to form strongly localized states in the fundamental gap of bulk NiO. Our
results for the surface energies of the $(001)$ and $(110)$ NiO surfaces show
that the $(001)$ facet of NiO has significantly lower energy. This implies that
the relative stability of different surfaces, at least from a purely energetic
point of view, does not depend on the presence or absence of magnetic order in
NiO.


Abstract:
  The foundation for the research of summarization in the Czech language was
laid by the work of Straka et al. (2018). They published the SumeCzech, a large
Czech news-based summarization dataset, and proposed several baseline
approaches. However, it is clear from the achieved results that there is a
large space for improvement. In our work, we focus on the impact of named
entities on the summarization of Czech news articles. First, we annotate
SumeCzech with named entities. We propose a new metric ROUGE_NE that measures
the overlap of named entities between the true and generated summaries, and we
show that it is still challenging for summarization systems to reach a high
score in it. We propose an extractive summarization approach Named Entity
Density that selects a sentence with the highest ratio between a number of
entities and the length of the sentence as the summary of the article. The
experiments show that the proposed approach reached results close to the solid
baseline in the domain of news articles selecting the first sentence. Moreover,
we demonstrate that the selected sentence reflects the style of reports
concisely identifying to whom, when, where, and what happened. We propose that
such a summary is beneficial in combination with the first sentence of an
article in voice applications presenting news articles. We propose two
abstractive summarization approaches based on Seq2Seq architecture. The first
approach uses the tokens of the article. The second approach has access to the
named entity annotations. The experiments show that both approaches exceed
state-of-the-art results previously reported by Straka et al. (2018), with the
latter achieving slightly better results on SumeCzech's out-of-domain testing
set.


Abstract:
  Lattice reconstruction in twisted transition-metal dichalcogenide (TMD)
bilayers gives rise to piezo- and ferroelectric moir\'e potentials for
electrons and holes, as well as a modulation of the hybridisation across the
bilayer. Here, we develop hybrid $\mathbf{k}\cdot \mathbf{p}$ tight-binding
models to describe electrons and holes in the relevant valleys of twisted TMD
homobilayers with parallel (P) and anti-parallel (AP) orientations of the
monolayer unit cells. We apply these models to describe moir\'e superlattice
effects in twisted WSe${}_2$ bilayers, in conjunction with microscopic \emph{ab
initio} calculations, and considering the influence of encapsulation, pressure
and an electric displacement field. Our analysis takes into account mesoscale
lattice relaxation, interlayer hybridisation, piezopotentials, and a weak
ferroelectric charge transfer between the layers, and describes a multitude of
possibilities offered by this system, depending on the choices of P or AP
orientation, twist angle magnitude, and electron/hole valley.


Abstract:
  We consider the following time-independent nonlinear $L^2$-critical
Schr\"{o}dinger equation \[ -\Delta
u(x)+V(x)u(x)-a|x|^{-b}|u|^{1+\frac{4-2b}{N}}=\mu u(x)\,\ \hbox{in}\,\
\mathbb{R}^N, \] where $\mu\in\mathbb{R}$, $a>0$, $N\geq 1$, $0<b<\min\{2,N\}$,
and $V(x)$ is an external potential. It is shown that ground states of the
above equation can be equivalently described by minimizers of the corresponding
minimization problem. In this paper, we prove that there is a threshold $a^*>0$
such that minimizer exists for $0<a<a^*$ and minimizer does not exist for any
$a>a^*$. However if $a=a^*$, it is proved that whether minimizer exists depends
sensitively on the value of $V(0)$. Moreover, when there is no minimizer at
threshold $a^*$, we give a detailed concentration behavior of minimizers as
$a\nearrow a^*$, based on which we finally prove that there is a unique
minimizer as $a\nearrow a^*$.


Abstract:
  We study the compactness of composition operators on the Bergman spaces of
certain bounded convex domains in $\mathbb{C}^n$ with non-trivial analytic
discs contained in the boundary. As a consequence we characterize that
compactness of the composition operator with a continuous symbol (up to the
closure) on the Bergman space of the polydisc.


Abstract:
  We examine the possibility of "soft cosmology", namely small deviations from
the usual framework due to the effective appearance of soft-matter properties
in the Universe sectors. One effect of such a case would be the dark energy to
exhibit a different equation-of-state parameter at large scales (which
determine the universe expansion) and at intermediate scales (which determine
the sub-horizon clustering and the large scale structure formation). Concerning
soft dark matter, we show that it can effectively arise due to the dark-energy
clustering, even if dark energy is not soft. We propose a novel parametrization
introducing the "softness parameters" of the dark sectors. As we see, although
the background evolution remains unaffected, due to the extreme sensitivity and
significant effects on the global properties even a slightly non-trivial
softness parameter can improve the clustering behavior and alleviate e.g. the
$f\sigma_8$ tension. Lastly, an extension of the cosmological perturbation
theory and a detailed statistical mechanical analysis, in order to incorporate
complexity and estimate the scale-dependent behavior from first principles, is
necessary and would provide a robust argumentation in favour of soft cosmology.


Abstract:
  Recently, several universal methods have been proposed for online convex
optimization, and attain minimax rates for multiple types of convex functions
simultaneously. However, they need to design and optimize one surrogate loss
for each type of functions, which makes it difficult to exploit the structure
of the problem and utilize the vast amount of existing algorithms. In this
paper, we propose a simple strategy for universal online convex optimization,
which avoids these limitations. The key idea is to construct a set of experts
to process the original online functions, and deploy a meta-algorithm over the
\emph{linearized} losses to aggregate predictions from experts. Specifically,
we choose Adapt-ML-Prod to track the best expert, because it has a second-order
bound and can be used to leverage strong convexity and exponential concavity.
In this way, we can plug in off-the-shelf online solvers as black-box experts
to deliver problem-dependent regret bounds. Furthermore, our strategy inherits
the theoretical guarantee of any expert designed for strongly convex functions
and exponentially concave functions, up to a double logarithmic factor. For
general convex functions, it maintains the minimax optimality and also achieves
a small-loss bound.


Abstract:
  An important limitation of standard multiple testing procedures is that the
null distribution should be known. Here, we consider a
  null distribution-free approach for multiple testing in the following
semi-supervised setting: the user does not know the null distribution, but has
at hand a sample drawn from this null distribution. In practical situations,
this null training sample (NTS) can come from previous experiments, from a part
of the data under test, from specific simulations, or from a sampling process.
In this work, we present theoretical results that handle such a framework, with
a focus on the false discovery rate (FDR) control and the Benjamini-Hochberg
(BH) procedure. First, we provide upper and lower bounds for the FDR of the BH
procedure based on empirical $p$-values. These bounds match when $\alpha
(n+1)/m$ is an integer, where $n$ is the NTS sample size and $m$ is the number
of tests. Second, we give a power analysis for that procedure suggesting that
the price to pay for ignoring the null distribution is low when $n$ is
sufficiently large in front of $m$; namely $n\gtrsim m/(\max(1,k))$, where $k$
denotes the number of ``detectable'' alternatives. Third, to complete the
picture, we also present a negative result that evidences an intrinsic
transition phase to the general semi-supervised multiple testing problem {and
shows that the empirical BH method is optimal in the sense that its performance
boundary follows this transition phase}. Our theoretical properties are
supported by numerical experiments, which also show that the delineated
boundary is of correct order without further tuning any constant. Finally, we
demonstrate that our work provides a theoretical ground for standard practice
in astronomical data analysis, and in particular for the procedure proposed in
\cite{Origin2020} for galaxy detection.


Abstract:
  Classically, data interpolation with a parametrized model class is possible
as long as the number of parameters is larger than the number of equations to
be satisfied. A puzzling phenomenon in deep learning is that models are trained
with many more parameters than what this classical theory would suggest. We
propose a theoretical explanation for this phenomenon. We prove that for a
broad class of data distributions and model classes, overparametrization is
necessary if one wants to interpolate the data smoothly. Namely we show that
smooth interpolation requires $d$ times more parameters than mere
interpolation, where $d$ is the ambient data dimension. We prove this universal
law of robustness for any smoothly parametrized function class with polynomial
size weights, and any covariate distribution verifying isoperimetry. In the
case of two-layers neural networks and Gaussian covariates, this law was
conjectured in prior work by Bubeck, Li and Nagaraj. We also give an
interpretation of our result as an improved generalization bound for model
classes consisting of smooth functions.


Abstract:
  Motivated by the recent interest in cyber-physical and autonomous robotic
systems, we study the problem of dynamically coupled multi-agent systems under
a set of signal temporal logic tasks. In particular, the satisfaction of each
of these signal temporal logic tasks depends on the behavior of a distinct set
of agents. Instead of abstracting the agent dynamics and the temporal logic
tasks into a discrete domain and solving the problem therein or using
optimization-based methods, we derive collaborative feedback control laws.
These control laws are based on a decentralized control barrier function
condition that results in discontinuous control laws, as opposed to a
centralized condition resembling the single-agent case. The benefits of our
approach are inherent robustness properties typically present in feedback
control as well as satisfaction guarantees for continuous-time multi-agent
systems. More specifically, time-varying control barrier functions are used
that account for the semantics of the signal temporal logic tasks at hand. For
a certain fragment of signal temporal logic tasks, we further propose a
systematic way to construct such control barrier functions. Finally, we show
the efficacy and robustness of our framework in an experiment including a group
of three omnidirectional robots.


Abstract:
  We present the first searches for gravitational waves from r-modes of the
Crab pulsar, coherently and separately integrating data from three stretches of
the first two observing runs of Advanced LIGO using the F-statistic. The second
run was divided in two by a glitch of the pulsar roughly halfway through. The
frequencies and derivatives searched were based on radio measurements of the
pulsar's spin-down parameters as described in Caride et al., Phys. Rev. D 100,
064013 (2019). We did not find any evidence of gravitational waves. Our best
90% confidence upper limits on gravitational wave intrinsic strain were 1.5e-25
for the first run, 1.3e-25 for the first stretch of the second run, and 1.1e-25
for the second stretch of the second run. These are the first upper limits on
gravitational waves from r-modes of a known pulsar to beat its spin-down limit,
and they do so by more than an order of magnitude in amplitude or two orders of
magnitude in luminosity.


Abstract:
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to
learn unknown dynamical systems from data for subsequent use in control design.
While learning-based control has the potential to yield superior performance in
demanding applications, robustness to uncertainty remains an important
challenge. Since Bayesian methods quantify uncertainty of the learning results,
it is natural to incorporate these uncertainties into a robust design. In
contrast to most state-of-the-art approaches that consider worst-case
estimates, we leverage the learning method's posterior distribution in the
controller synthesis. The result is a more informed and, thus, more efficient
trade-off between performance and robustness. We present a novel controller
synthesis for linearized GP dynamics that yields robust controllers with
respect to a probabilistic stability margin. The formulation is based on a
recently proposed algorithm for linear quadratic control synthesis, which we
extend by giving probabilistic robustness guarantees in the form of credibility
bounds for the system's stability.Comparisons to existing methods based on
worst-case and certainty-equivalence designs reveal superior performance and
robustness properties of the proposed method.


Abstract:
  Competitive board games have provided a rich and diverse testbed for
artificial intelligence. This paper contends that collaborative board games
pose a different challenge to artificial intelligence as it must balance
short-term risk mitigation with long-term winning strategies. Collaborative
board games task all players to coordinate their different powers or pool their
resources to overcome an escalating challenge posed by the board and a
stochastic ruleset. This paper focuses on the exemplary collaborative board
game Pandemic and presents a rolling horizon evolutionary algorithm designed
specifically for this game. The complex way in which the Pandemic game state
changes in a stochastic but predictable way required a number of specially
designed forward models, macro-action representations for decision-making, and
repair functions for the genetic operations of the evolutionary algorithm.
Variants of the algorithm which explore optimistic versus pessimistic game
state evaluations, different mutation rates and event horizons are compared
against a baseline hierarchical policy agent. Results show that an evolutionary
approach via short-horizon rollouts can better account for the future dangers
that the board may introduce, and guard against them. Results highlight the
types of challenges that collaborative board games pose to artificial
intelligence, especially for handling multi-player collaboration interactions.


Abstract:
  Hazy images are often subject to color distortion, blurring, and other
visible quality degradation. Some existing CNN-based methods have great
performance on removing homogeneous haze, but they are not robust in
non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the
complicated haze distribution, texture details are easy to be lost during the
dehazing process. Secondly, since the training pairs are hard to be collected,
training on limited data can easily lead to over-fitting problem. To tackle
these two issues, we introduce a novel dehazing network using 2D discrete
wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network
to deal with the aforementioned problems. By utilizing wavelet transform in DWT
branch, our proposed method can retain more high-frequency knowledge in feature
maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted
in the knowledge adaptation branch. Owing to the robust feature representations
of ImageNet pre-training, the generalization ability of our network is improved
dramatically. Finally, a patch-based discriminator is used to reduce artifacts
of the restored images. Extensive experimental results demonstrate that the
proposed method outperforms the state-of-the-arts quantitatively and
qualitatively.


Abstract:
  This expository survey is based on my online talk at the ICCM 2020. It aims
to sketch key steps of the recent proof of the uniform Mordell-Lang conjecture
for curves embedded into Jacobians (a question of Mazur). The full version of
this conjecture is proved by combining Dimitrov-Gao-Habegger
(https://annals.math.princeton.edu/articles/17715) and K\"{u}hne
(arXiv:2101.10272). We include in this survey a detailed proof on how to
combine these two results, which was implicitly done in another short paper of
Dimitrov-Gao-Habegger (arXiv:2009.08505) but not explicitly written in existing
literature. At the end of the survey we state some future aspects.


Abstract:
  In this paper, we study the long-time behavior of global solutions to the
Schr\"odinger-Choquard equation $$i\partial_tu+\Delta
u=-(I_\alpha\ast|\cdot|^b|u|^{p})|\cdot|^b|u|^{p-2}u.$$
  Inspired by Murphy, who gave a simple proof of scattering for the non-radial
inhomogeneous NLS, we prove scattering theory below the ground state for the
intercritical case in energy space without radial assumption.


Abstract:
  We prove that the free energy of directed polymer in Bernoulli environment
converges to the growth rate for the number of open paths in super-critical
oriented percolation as the temperature tends to zero. Our proof is based on
rate of convergence results which hold uniformly in the temperature. We also
prove that the convergence rate is locally uniform in the percolation parameter
inside the super-critical phase, which implies that the growth rate depends
continuously on the percolation parameter.


Abstract:
  We propose a new method for the visual quality assessment of 360-degree
(omnidirectional) videos. The proposed method is based on computing multiple
spatio-temporal objective quality features on viewports extracted from
360-degree videos. A new model is learnt to properly combine these features
into a metric that closely matches subjective quality scores. The main
motivations for the proposed approach are that: 1) quality metrics computed on
viewports better captures the user experience than metrics computed on the
projection domain; 2) the use of viewports easily supports different projection
methods being used in current 360-degree video systems; and 3) no individual
objective image quality metric always performs the best for all types of visual
distortions, while a learned combination of them is able to adapt to different
conditions. Experimental results, based on both the largest available
360-degree videos quality dataset and a cross-dataset validation, demonstrate
that the proposed metric outperforms state-of-the-art 360-degree and 2D video
quality metrics.


Abstract:
  In the context of scalar-tensor theories, the inclusion of new degrees of
freedom coupled non-minimally to the gravitational sector might produce
additional imprints on cosmological observables. We investigate this premise by
including a canonical SU(2) Yang-Mills field to the total energy budget of the
universe coupled to the standard quintessential field by a disformal
transformation. The background dynamics study is addressed by a dynamical
system analysis from which novel anisotropic scaling solutions with a
non-vanishing gauge field, supporting a preferred spatial direction, are
obtained. After establishing the dynamical character of the fixed points, the
phenomenological consequences of the model on the background evolution of the
universe are assessed by means of numerical analysis. As an interesting result,
the disformal coupling changes the equation of state of the gauge field from
radiation to matter at some stages of the evolution of the universe, thereby
the gauge field can contribute to some fraction of the total dark matter. We
have also quantified the redshift-dependent contribution of the gauge field in
the form of dark radiation during the radiation era to the effective number of
relativistic species. This depends essentially on the initial conditions and,
more importantly, on the disformal coupling function. Phenomenological
couplings and the Abelian version of the model are discussed in order to check
the generality of our results. Finally, the phenomenological advantages of this
model are discussed in the light of the current tensions in the $\Lambda$CDM
model.


Abstract:
  Let $\mathbb{Z}^{ab}$ be the ring of integers of $\mathbb{Q}^{ab}$, the
maximal abelian extension of $\mathbb{Q}$. We show that there exists an
algorithm to decide whether a system of equations and inequations, with integer
coefficients, has a solution in $\mathbb{Z}^{ab}$ modulo every rational prime.


Abstract:
  High temperature superconductivity in cuprates arises from doping a parent
Mott insulator by electrons or holes. A central issue is how the Mott gap
evolves and the low-energy states emerge with doping. Here we report
angle-resolved photoemission spectroscopy measurements on a cuprate parent
compound by sequential in situ electron doping. The chemical potential jumps to
the bottom of the upper Hubbard band upon a slight electron doping, making it
possible to directly visualize the charge transfer band and the full Mott gap
region. With increasing doping, the Mott gap rapidly collapses due to the
spectral weight transfer from the charge transfer band to the gapped region and
the induced low-energy states emerge in a wide energy range inside the Mott
gap. These results provide key information on the electronic evolution in
doping a Mott insulator and establish a basis for developing microscopic
theories for cuprate superconductivity.


Abstract:
  A $(d,k)$-set is a subset of $\mathbb{R}^d$ containing a $k$-dimensional unit
ball of all possible orientations. Using an approach of D.~Oberlin we prove
various Fourier dimension estimates for compact $(d,k)$-sets. Our main interest
is in restricted $(d,k)$-sets, where the set only contains unit balls with a
restricted set of possible orientations $\Gamma$. In this setting our estimates
depend on the Hausdorff dimension of $\Gamma$ and can sometimes be improved if
additional geometric properties of $\Gamma$ are assumed. We are led to consider
cones and prove that the cone in $\mathbb{R}^{d+1}$ has Fourier dimension
$d-1$, which may be of interest in its own right.


Abstract:
  We give an explicit formula for the Waring rank of every binary binomial form
with complex coefficients. We give several examples to illustrate this, and
compare the Waring rank and the real Waring rank for binary binomial forms.


Abstract:
  Open Information Extraction (OIE) systems seek to compress the factual
propositions of a sentence into a series of n-ary tuples. These tuples are
useful for downstream tasks in natural language processing like knowledge base
creation, textual entailment, and natural language understanding. However,
current OIE datasets are limited in both size and diversity. We introduce a new
dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset
(LSOIE). Our LSOIE dataset is 20 times larger than the next largest
human-annotated OIE dataset. We construct and evaluate several benchmark OIE
models on LSOIE, providing baselines for future improvements on the task. Our
LSOIE data, models, and code are made publicly available


Abstract:
  Software products have become an integral part of human lives, and therefore
need to account for human values such as privacy, fairness, and equality.
Ignoring human values in software development leads to biases and violations of
human values: racial biases in recidivism assessment and facial recognition
software are well-known examples of such issues. One of the most critical steps
in software development is Software Release Planning (SRP), where decisions are
made about the presence or absence of the requirements (features) in the
software. Such decisions are primarily guided by the economic value of the
requirements, ignoring their impacts on a broader range of human values. That
may result in ignoring (selecting) requirements that positively (negatively)
impact human values, increasing the risk of value breaches in the software. To
address this, we have proposed an Integer Programming approach to considering
human values in software release planning. In this regard, an Integer Linear
Programming (ILP) model has been proposed, that explicitly accounts for human
values in finding an "optimal" subset of the requirements. The ILP model
exploits the algebraic structure of fuzzy graphs to capture dependencies and
conflicts among the values of the requirements.


Abstract:
  Without contamination from the final state interactions, the calculation of
the branching ratios of semileptonic decays $\Xi^{(')}_{c}\to\Xi+e^+\nu_e$ may
provide us more information about the inner structure of charmed baryons.
Moreover, by studying those processes, one can better determine the form
factors of $\Xi_c\to\Xi$ which can be further applied to relevant estimates. In
this work, we use the light-front quark model to carry out the computations
where the three-body vertex functions for $\Xi_c$ and $\Xi$ are employed. To
fit the new data of the Belle II, we re-adjust the model parameters and obtain
$\beta_{s[sq]}=1.07$ GeV which is 2.9 times larger than $\beta_{s\bar s}=0.366$
GeV. This value may imply that the $ss$ pair in $\Xi$ constitutes a more
compact subsystem. Furthermore, we also investigate the non-leptonic decays of
$\Xi^{(')}_c\to \Xi$ which will be experimentally measured soon, so our model
would be tested by consistency with the new data.


Abstract:
  Node influence metrics have been applied to many applications, including
ranking web pages on internet, or locations on spatial networks. PageRank is a
popular and effective algorithm for estimating node influence. However,
conventional PageRank method considers neither the heterogeneity of network
structures nor additional network information, causing a major impedance to
performance improvement and an underestimation of non-hub nodes' importance. As
these problems are only partially studied, existing solutions are still not
satisfying. This paper addresses the problems by presenting a general
PageRank-based model framework, dubbed Hetero-NodeRank, that accounts for
heterogeneous network topology and incorporates node attribute information to
capture both link- and node-based effects in measuring node influence.
Moreover, the framework enables the calibration of the proposed model against
empirical data, which transforms the original deductive approach into an
inductive one that could be useful for hypothesis testing and causal-effect
analysis. As the original unsupervised task becomes a supervised one,
optimization methods can be leveraged for model calibrations. Experiments on
real data from the national city network of China demonstrated that the
proposed model outperforms several widely used algorithms.


Abstract:
  This review summarizes more than 100 years of research on spinel compounds,
mainly focusing on the progress in understanding their magnetic, electronic,
and polar properties during the last two decades. Many spinel compounds are
magnetic insulators or semiconductors; however, a number of spinel-type metals
exists including superconductors and some rare examples of d-derived
heavy-fermion compounds. In the early days, they gained importance as
ferrimagnetic or even ferromagnetic insulators with relatively high saturation
magnetization and high ordering temperatures, with magnetite being the first
magnetic mineral known to mankind. However, spinels played an outstanding role
in the development of concepts of magnetism, in testing and verifying the
fundamentals of magnetic exchange, in understanding orbital-ordering and
charge-ordering phenomena. In addition, the A- site as well as the B-site
cations in the spinel structure form lattices prone to strong frustration
effects resulting in exotic ground-state properties. In case the A-site cation
is Jahn-Teller active, additional entanglements of spin and orbital degrees of
freedom appear, which can give rise to a spin-orbital liquid or an orbital
glass state. The B-site cations form a pyrochlore lattice, one of the strongest
contenders of frustration in three dimensions. In addition, in spinels with
both cation lattices carrying magnetic moments, competing magnetic exchange
interactions become important, yielding ground states like the time-honoured
triangular Yafet-Kittel structure. Finally, yet importantly, there exists a
long-standing dispute about the possibility of a polar ground state in spinels,
despite their reported overall cubic symmetry. Indeed, over the years number of
multiferroic spinels were identified.


Abstract:
  We study a class of gauge groups that can automatically yield a
perturbatively exact Peccei-Quinn symmetry, and we outline a model in which the
axion quality problem is solved at all operator dimensions. Gauge groups
belonging to this class can also enforce and protect accidental symmetries of
the clockwork type, and we present a toy model where an `invisible' axion
arises from a single breaking of the gauge and global symmetries.


Abstract:
  I briefly review several important formal theory developments in quantum
field theory and string theory that were reported at ICHEP conferences in past
decades, and explain how they underlie a new research area referred to as
physical or quantum mathematics. To illustrate these ideas in some specific
context, I discuss certain aspects of topological string theory and a recently
discovered knots-quivers correspondence.


Abstract:
  Convolution is one of the basic building blocks of CNN architectures. Despite
its common use, standard convolution has two main shortcomings:
Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive,
while further increasing the computational overhead. Depth-wise convolution is
a lightweight variant, but it usually leads to a drop in CNN performance or
requires a larger number of channels. In this work, we propose the Decoupled
Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings.
Inspired by recent advances in attention, DDF decouples a depth-wise dynamic
filter into spatial and channel dynamic filters. This decomposition
considerably reduces the number of parameters and limits computational costs to
the same level as depth-wise convolution. Meanwhile, we observe a significant
boost in performance when replacing standard convolution with DDF in
classification networks. ResNet50 / 101 get improved by 1.9% and 1.3% on the
top-1 accuracy, while their computational costs are reduced by nearly half.
Experiments on the detection and joint upsampling networks also demonstrate the
superior performance of the DDF upsampling variant (DDF-Up) in comparison with
standard convolution and specialized content-adaptive layers.


Abstract:
  School performance measures are published annually in England to hold schools
to account and to support parental school choice. This article reviews and
evaluates the Progress 8 secondary school accountability system for
state-funded schools. We assess the statistical strengths and weaknesses of
Progress 8 relating to: choice of pupil outcome attainment measures; potential
adjustments for pupil input attainment and background characteristics;
decisions around which schools and pupils are excluded from the measure;
presentation of Progress 8 to users, choice of statistical model, and
calculation of statistical uncertainty; and issues related to the volatility of
school performance over time, including scope for reporting multi-year
averages. We then discuss challenges for Progress 8 raised by the COVID-19
pandemic. Six simple recommendations follow to improve Progress 8 and school
accountability in England.


Abstract:
  Designing broadband enhanced chirality is of strong interest to the emerging
fields of chiral chemistry and sensing, or to control the spin orbital momentum
of photons in recently introduced nanophotonic chiral quantum and classical
optical applications. However, chiral light-matter interactions have an
extremely weak nature, are difficult to be controlled and enhanced, and cannot
be made tunable or broadband. In addition, planar ultrathin nanophotonic
structures to achieve strong, broadband, and tunable chirality at the
technologically important visible to ultraviolet spectrum still remain elusive.
Here, we tackle these important problems by experimentally demonstrating and
theoretically verifying spectrally tunable, extremely large, and broadband
chiroptical response by nanohelical metamaterials. The reported new designs of
all-dielectric and dielectric-metallic (hybrid) plasmonic metamaterials permit
the largest and broadest ever measured chiral Kuhn dissymmetry factor achieved
by a large-scale nanophotonic structure. In addition, the strong circular
dichroism of the presented bottom-up fabricated optical metamaterials can be
tuned by varying their dimensions and proportions between their dielectric and
plasmonic helical subsections. The currently demonstrated ultrathin optical
metamaterials are expected to provide a substantial boost to the developing
field of chiroptics leading to significantly enhanced and broadband chiral
light-matter interactions at the nanoscale.


Abstract:
  Recent works find that AI algorithms learn biases from data. Therefore, it is
urgent and vital to identify biases in AI algorithms. However, the previous
bias identification pipeline overly relies on human experts to conjecture
potential biases (e.g., gender), which may neglect other underlying biases not
realized by humans. To help human experts better find the AI algorithms'
biases, we study a new problem in this work -- for a classifier that predicts a
target attribute of the input image, discover its unknown biased attribute.
  To solve this challenging problem, we use a hyperplane in the generative
model's latent space to represent an image attribute; thus, the original
problem is transformed to optimizing the hyperplane's normal vector and offset.
We propose a novel total-variation loss within this framework as the objective
function and a new orthogonalization penalty as a constraint. The latter
prevents trivial solutions in which the discovered biased attribute is
identical with the target or one of the known-biased attributes. Extensive
experiments on both disentanglement datasets and real-world datasets show that
our method can discover biased attributes and achieve better disentanglement
w.r.t. target attributes. Furthermore, the qualitative results show that our
method can discover unnoticeable biased attributes for various object and scene
classifiers, proving our method's generalizability for detecting biased
attributes in diverse domains of images. The code is available at
https://git.io/J3kMh.


Abstract:
  We study a fundamental problem in computational chemistry known as molecular
conformation generation, trying to predict stable 3D structures from 2D
molecular graphs. Existing machine learning approaches usually first predict
distances between atoms and then generate a 3D structure satisfying the
distances, where noise in predicted distances may induce extra errors during 3D
coordinate generation. Inspired by the traditional force field methods for
molecular dynamics simulation, in this paper, we propose a novel approach
called ConfGF by directly estimating the gradient fields of the log density of
atomic coordinates. The estimated gradient fields allow directly generating
stable conformations via Langevin dynamics. However, the problem is very
challenging as the gradient fields are roto-translation equivariant. We notice
that estimating the gradient fields of atomic coordinates can be translated to
estimating the gradient fields of interatomic distances, and hence develop a
novel algorithm based on recent score-based generative models to effectively
estimate these gradients. Experimental results across multiple tasks show that
ConfGF outperforms previous state-of-the-art baselines by a significant margin.


Abstract:
  This paper will suggest a new finite element method to find a $P^4$-velocity
and a $P^3$-pressure solving Stokes equations. The method solves first the
decoupled equation for the $P^4$-velocity. Then, four kinds of local
$P^3$-pressures and one $P^0$-pressure will be calculated in a successive way.
If we superpose them, the resulting $P^3$-pressure shows the optimal order of
convergence same as a $P^3$-projection. The chief time cost of the new method
is on solving two linear systems for the $P^4$-velocity and $P^0$-pressure,
respectively.


Abstract:
  Based on the fact that the constituent quark model reproduces the recent
lattice result on baryon-baryon repulsion at short distance and that it
includes the quark dynamics with confinement, we analyze to what extent the
quarkyonic modes appear in the phase space of baryons as one increases the
density before only quark dynamics and hence deconfinement occurs. We find that
as one increases the baryon density, the initial quark mode that appears will
involve the $d(u)$-quark from a neutron (proton), which will leave the most
attractive ($ud$) diquark intact.


Abstract:
  Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the
impacted patients have been diagnosed with high fever, dry cough, and soar
throat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19
from lung imaging is proved to be a major evidence for early diagnosis of the
disease. Although nucleic acid detection using real-time reverse-transcriptase
polymerase chain reaction (rRT-PCR) remains a gold standard for the detection
of COVID-19, the proposed approach focuses on the automated diagnosis and
prognosis of the disease from a non-contrast chest computed tomography (CT)scan
for timely diagnosis and triage of the patient. The prognosis covers the
quantification and assessment of the disease to help hospitals with the
management and planning of crucial resources, such as medical staff,
ventilators and intensive care units (ICUs) capacity. The approach utilises
deep learning techniques for automated quantification of the severity of
COVID-19 disease via measuring the area of multiple rounded ground-glass
opacities (GGO) and consolidations in the periphery (CP) of the lungs and
accumulating them to form a severity score. The severity of the disease can be
correlated with the medicines prescribed during the triage to assess the
effectiveness of the treatment. The proposed approach shows promising results
where the classification model achieved 93% accuracy on hold-out data.


Abstract:
  We investigate errors in tangents and adjoints of implicit functions
resulting from errors in the primal solution due to approximations computed by
a numerical solver.
  Adjoints of systems of linear equations turn out to be unconditionally
numerically stable. Tangents of systems of linear equations can become instable
as well as both tangents and adjoints of systems of nonlinear equations, which
extends to optima of convex unconstrained objectives. Sufficient conditions for
numerical stability are derived.


Abstract:
  Recently, it was argued in [Phys. Rev. Lett. {\bf126}, 031102 (2021)] that
the WCCC can serve as a constraint to high-order effective field theories.
However, we find there exists a key error in their approximate black hole
solution. After correcting it, their calculation cannot show the ability of
WCCC to constrain the gravitational theories.


Abstract:
  We present a dynamical description of (anti)proton number fluctuations
cumulants and correlation functions in central Au-Au collisions at
$\sqrt{s_{\rm NN}} = 7.7-200$ GeV by utilizing viscous hydrodynamics
simulations. The cumulants of proton and baryon number are calculated in a
given momentum acceptance analytically, via an appropriately extended
Cooper-Frye procedure describing particlization of an interacting hadron
resonance gas. The effects of global baryon number conservation are taken into
account using a generalized subensemble acceptance method. The experimental
data of the STAR collaboration are consistent at $\sqrt{s_{\rm NN}} \gtrsim 20$
GeV with simultaneous effects of global baryon number conservation and
repulsive interactions in baryon sector, the latter being in line with the
behavior of baryon number susceptibilities observed in lattice QCD. The data at
lower collision energies show possible indications for sizable attractive
interactions among baryons. The data also indicate sizable negative
two-particle correlations between antiprotons that are not satisfactorily
described by baryon conservation and excluded volume effects. We also discuss
differences between cumulants and correlation functions (factorial cumulants)
of (anti)proton number distribution, proton versus baryon number fluctuations,
and effects of hadronic afterburner.


Abstract:
  We consider the problem where $n$ clients transmit $d$-dimensional
real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the
receiver to approximately reconstruct their mean. Such compression problems
naturally arise in distributed and federated learning. We provide novel
mathematical results and derive computationally efficient algorithms that are
more accurate than previous compression techniques. We evaluate our methods on
a collection of distributed and federated learning tasks, using a variety of
datasets, and show a consistent improvement over the state of the art.


Abstract:
  Recently, the SARS-CoV-2 variants from the United Kingdom (UK), South Africa,
and Brazil have received much attention for their increased infectivity,
potentially high virulence, and possible threats to existing vaccines and
antibody therapies. The question remains if there are other more infectious
variants transmitted around the world. We carry out a large-scale study of
252,874 SARS-CoV-2 genome isolates from patients to identify many other rapidly
growing mutations on the spike (S) protein receptor-binding domain (RDB). We
reveal that 88 out of 95 significant mutations that were observed more than 10
times strengthen the binding between the RBD and the host
angiotensin-converting enzyme 2 (ACE2), indicating the virus evolves toward
more infectious variants. In particular, we discover new fast-growing RBD
mutations N439K, L452R, S477N, S477R, and N501T that also enhance the RBD and
ACE2 binding. We further unveil that mutation N501Y involved in United Kingdom
(UK), South Africa, and Brazil variants may moderately weaken the binding
between the RBD and many known antibodies, while mutations E484K and K417N
found in South Africa and Brazilian variants can potentially disrupt the
binding between the RDB and many known antibodies. Among three newly identified
fast-growing RBD mutations, L452R, which is now known as part of the California
variant B.1.427, and N501T are able to effectively weaken the binding of many
known antibodies with the RBD. Finally, we hypothesize that RBD mutations that
can simultaneously make SARS-CoV-2 more infectious and disrupt the existing
antibodies, called vaccine escape mutations, will pose an imminent threat to
the current crop of vaccines. A list of most likely vaccine escape mutations is
given, including N501Y, L452R, E484K, N501T, S494P, and K417N.


Abstract:
  We relate the geometry of Schubert varieties in twisted affine Grassmannian
and the nilpotent varieties in symmetric spaces. This extends some results of
Achar-Henderson in the twisted setting. We also get some applications to the
geometry of the order 2 nilpotent varieties in certain classical symmetric
spaces.