Abstract:
  We investigate asymptotic behavior of polynomials $p^{\omega}_n(z)$
satisfying varying non-Hermitian orthogonality relations $$ \int_{-1}^{1}
x^kp^{\omega}_n(x)h(x) e^{\mathrm{i} \omega x}\mathrm{d} x =0, \quad
k\in\{0,\ldots,n-1\}, $$ where $h(x) = h^*(x) (1 - x)^{\alpha} (1 + x)^{\beta},
\ \omega = \lambda n, \ \lambda \geq 0 $ and $h(x)$ is holomorphic and
non-vanishing in a certain neighborhood in the plane. These polynomials are an
extension of so-called kissing polynomials ($\alpha = \beta = 0$) introduced in
connection with complex Gaussian quadrature rules with uniform good properties
in $\omega$.


Abstract:
  COVID-19 has resulted in over 100 million infections and caused worldwide
lock downs due to its high transmission rate and limited testing options.
Current diagnostic tests can be expensive, limited in availability,
time-intensive and require risky in-person appointments. It has been
established that symptomatic COVID-19 seriously impairs normal functioning of
the respiratory system, thus affecting the coughing acoustics. The 2021 DiCOVA
Challenge @ INTERSPEECH was designed to find scientific and engineering
insights to the question by enabling participants to analyze an acoustic
dataset gathered from COVID-19 positive and non-COVID-19 individuals. In this
report we describe our participation in the Challenge (Track 1). We achieved
82.37% AUC ROC on the blind test outperforming the Challenge's baseline of
69.85%.


Abstract:
  We establish unconditional sharp upper bounds of the $k$-th moments of the
family of quadratic Dirichlet $L$-functions at the central point for $0 \leq k
\leq 2$.


Abstract:
  Vehicle trajectory optimization is essential to ensure vehicles travel
efficiently and safely. This paper presents an infrastructure assisted
constrained connected automated vehicles (CAVs) trajectory optimization method
on curved roads. This paper systematically formulates the problem based on a
curvilinear coordinate which is flexible to model complex road geometries.
Further, to deal with the spatial varying road obstacles, traffic regulations,
and geometric characteristics, two-dimensional vehicle kinematics is given in a
spatial formulation with exact road information provided by the infrastructure.
Consequently, we applied a multi-objective model predictive control (MPC)
approach to optimize the trajectories in a rolling horizon while satisfying the
collision avoidances and vehicle kinematics constraints. To verify the
efficiency of our method, a numerical simulation is conducted. As the results
suggest, the proposed method can provide smooth vehicular trajectories, avoid
road obstacles, and simultaneously follow traffic regulations, which is robust
to road geometries and disturbances.


Abstract:
  WarpX is a general purpose electromagnetic particle-in-cell code that was
originally designed to run on many-core CPU architectures. We describe the
strategy followed to allow WarpX to use the GPU-accelerated nodes on OLCF's
Summit supercomputer, a strategy we believe will extend to the upcoming
machines Frontier and Aurora. We summarize the challenges encountered, lessons
learned, and give current performance results on a series of relevant benchmark
problems.


Abstract:
  Quiver representations arise naturally in many areas across mathematics. Here
we describe an algorithm for calculating the vector space of sections, or
compatible assignments of vectors to vertices, of any finite-dimensional
representation of a finite quiver. Consequently, we are able to define and
compute principal components with respect to quiver representations. These
principal components are solutions to constrained optimisation problems defined
over the space of sections, and are eigenvectors of an associated matrix
pencil.


Abstract:
  We demonstrate that global observations of high-energy cosmic rays contribute
to understanding unique characteristics of a large-scale magnetic flux rope
causing a magnetic storm in August 2018. Following a weak interplanetary shock
on 25 August 2018, a magnetic flux rope caused an unexpectedly large
geomagnetic storm. It is likely that this event became geoeffective because the
flux rope was accompanied by a corotating interaction region and compressed by
high-speed solar wind following the flux rope. In fact, a Forbush decrease was
observed in cosmic-ray data inside the flux rope as expected, and a significant
cosmic-ray density increase exceeding the unmodulated level before the shock
was also observed near the trailing edge of the flux rope. The cosmic-ray
density increase can be interpreted in terms of the adiabatic heating of cosmic
rays near the trailing edge of the flux rope, as the corotating interaction
region prevents free expansion of the flux rope and results in the compression
near the trailing edge. A northeast-directed spatial gradient in the cosmic-ray
density was also derived during the cosmic-ray density increase, suggesting
that the center of the heating near the trailing edge is located northeast of
Earth. This is one of the best examples demonstrating that the observation of
high-energy cosmic rays provides us with information that can only be derived
from the cosmic ray measurements to observationally constrain the
three-dimensional macroscopic picture of the interaction between coronal mass
ejections and the ambient solar wind, which is essential for prediction of
large magnetic storms.


Abstract:
  The generalization of representations learned via contrastive learning
depends crucially on what features of the data are extracted. However, we
observe that the contrastive loss does not always sufficiently guide which
features are extracted, a behavior that can negatively impact the performance
on downstream tasks via "shortcuts", i.e., by inadvertently suppressing
important predictive features. We find that feature extraction is influenced by
the difficulty of the so-called instance discrimination task (i.e., the task of
discriminating pairs of similar points from pairs of dissimilar ones). Although
harder pairs improve the representation of some features, the improvement comes
at the cost of suppressing previously well represented features. In response,
we propose implicit feature modification (IFM), a method for altering positive
and negative samples in order to guide contrastive models towards capturing a
wider variety of predictive features. Empirically, we observe that IFM reduces
feature suppression, and as a result improves performance on vision and medical
imaging tasks. The code is available at: \url{https://github.com/joshr17/IFM}.


Abstract:
  The generalised extreme value (GEV) distribution is a three parameter family
that describes the asymptotic behaviour of properly renormalised maxima of a
sequence of independent and identically distributed random variables. If the
shape parameter $\xi$ is zero, the GEV distribution has unbounded support,
whereas if $\xi$ is positive, the limiting distribution is heavy-tailed with
infinite upper endpoint but finite lower endpoint. In practical applications,
we assume that the GEV family is a reasonable approximation for the
distribution of maxima over blocks, and we fit it accordingly. This implies
that GEV properties, such as finite lower endpoint in the case $\xi>0$, are
inherited by the finite-sample maxima, which might not have bounded support.
This is particularly problematic when predicting extreme observations based on
multiple and interacting covariates. To tackle this usually overlooked issue,
we propose a blended GEV distribution, which smoothly combines the left tail of
a Gumbel distribution (GEV with $\xi=0$) with the right tail of a Fr\'echet
distribution (GEV with $\xi>0$) and, therefore, has unbounded support. Using a
Bayesian framework, we reparametrise the GEV distribution to offer a more
natural interpretation of the (possibly covariate-dependent) model parameters.
Independent priors over the new location and spread parameters induce a joint
prior distribution for the original location and scale parameters. We introduce
the concept of property-preserving penalised complexity (P$^3$C) priors and
apply it to the shape parameter to preserve first and second moments. We
illustrate our methods with an application to NO$_2$ pollution levels in
California, which reveals the robustness of the bGEV distribution, as well as
the suitability of the new parametrisation and the P$^3$C prior framework.


Abstract:
  Long linear carbon-chains have been attracting intense interest arising from
the remarkable properties predicted and their potential applications in future
nanotechnology. Here we comprehensively interrogate the excitonic transitions
and the associated relaxation dynamics of nanotube confined long linear
carbon-chains by using steady state and time-resolved Raman spectroscopies. The
exciton relaxation dynamics on the confined carbon-chains occurs on a hundreds
of picoseconds timescale, in strong contrast to the host dynamics that occurs
on a few picosecond timescale. A prominent time-resolved Raman response is
observed over a broad energy range extending from 1.2 to 2.8 eV, which includes
the strong Raman resonance region around 2.2 eV. Evidence for a strong coupling
between the chain and the nanotube host is found from the dynamics at high
excitation energies which provides a clear evidence for an efficient energy
transfer from the host carbon nanotube to the chain. Our experimental study
presents the first unique characterization of the long linear carbon-chain
exciton dynamics, providing indispensable knowledge for the understanding of
the interactions between different carbon allotropes.


Abstract:
  Recent work has shown that every 3D root system allows the construction of a
correponding 4D root system via an `induction theorem'. In this paper, we look
at the icosahedral case of $H_3\rightarrow H_4$ in detail and perform the
calculations explicitly. Clifford algebra is used to perform group theoretic
calculations based on the versor theorem and the Cartan-Dieudonn\'e theorem,
giving a simple construction of the Pin and Spin covers. Using this connection
with $H_3$ via the induction theorem sheds light on geometric aspects of the
$H_4$ root system (the $600$-cell) as well as other related polytopes and their
symmetries, such as the famous Grand Antiprism and the snub 24-cell. The
uniform construction of root systems from 3D and the uniform procedure of
splitting root systems with respect to subrootsystems into separate invariant
sets allows further systematic insight into the underlying geometry. All
calculations are performed in the even subalgebra of Cl(3), including the
construction of the Coxeter plane, which is used for visualising the
complementary pairs of invariant polytopes, and are shared as supplementary
computational work sheets. This approach therefore constitutes a more
systematic and general way of performing calculations concerning groups, in
particular reflection groups and root systems, in a Clifford algebraic
framework.


Abstract:
  The notion of hypothetical bias (HB) constitutes, arguably, the most
fundamental issue in relation to the use of hypothetical survey methods.
Whether or to what extent choices of survey participants and subsequent
inferred estimates translate to real-world settings continues to be debated.
While HB has been extensively studied in the broader context of contingent
valuation, it is much less understood in relation to choice experiments (CE).
This paper reviews the empirical evidence for HB in CE in various fields of
applied economics and presents an integrative framework for how HB relates to
external validity. Results suggest mixed evidence on the prevalence, extent and
direction of HB as well as considerable context and measurement dependency.
While HB is found to be an undeniable issue when conducting CEs, the empirical
evidence on HB does not render CEs unable to represent real-world preferences.
While health-related choice experiments often find negligible degrees of HB,
experiments in consumer behaviour and transport domains suggest that
significant degrees of HB are ubiquitous. Assessments of bias in environmental
valuation studies provide mixed evidence. Also, across these disciplines many
studies display HB in their total willingness to pay estimates and opt-in rates
but not in their hypothetical marginal rates of substitution (subject to scale
correction). Further, recent findings in psychology and brain imaging studies
suggest neurocognitive mechanisms underlying HB that may explain some of the
discrepancies and unexpected findings in the mainstream CE literature. The
review also observes how the variety of operational definitions of HB prohibits
consistent measurement of HB in CE. The paper further identifies major sources
of HB and possible moderating factors. Finally, it explains how HB represents
one component of the wider concept of external validity.


Abstract:
  Federated learning (FL) has become a prevalent distributed machine learning
paradigm with improved privacy. After learning, the resulting federated model
should be further personalized to each different client. While several methods
have been proposed to achieve personalization, they are typically limited to a
single local device, which may incur bias or overfitting since data in a single
device is extremely limited. In this paper, we attempt to realize
personalization beyond a single client. The motivation is that during FL, there
may exist many clients with similar data distribution, and thus the
personalization performance could be significantly boosted if these similar
clients can cooperate with each other. Inspired by this, this paper introduces
a new concept called federated adaptation, targeting at adapting the trained
model in a federated manner to achieve better personalization results. However,
the key challenge for federated adaptation is that we could not outsource any
raw data from the client during adaptation, due to privacy concerns. In this
paper, we propose PFA, a framework to accomplish Privacy-preserving Federated
Adaptation. PFA leverages the sparsity property of neural networks to generate
privacy-preserving representations and uses them to efficiently identify
clients with similar data distributions. Based on the grouping results, PFA
conducts an FL process in a group-wise way on the federated model to accomplish
the adaptation. For evaluation, we manually construct several practical FL
datasets based on public datasets in order to simulate both the class-imbalance
and background-difference conditions. Extensive experiments on these datasets
and popular model architectures demonstrate the effectiveness of PFA,
outperforming other state-of-the-art methods by a large margin while ensuring
user privacy. We will release our code at: https://github.com/lebyni/PFA.


Abstract:
  Micro-expression can reflect people's real emotions. Recognizing
micro-expressions is difficult because they are small motions and have a short
duration. As the research is deepening into micro-expression recognition, many
effective features and methods have been proposed. To determine which direction
of movement feature is easier for distinguishing micro-expressions, this paper
selects 18 directions (including three types of horizontal, vertical and
oblique movements) and proposes a new low-dimensional feature called the
Histogram of Single Direction Gradient (HSDG) to study this topic. In this
paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP
with Single Direction Gradient (LBP-SDG) and analyze which direction of
movement feature is more discriminative for micro-expression recognition. As
with some existing work, Euler Video Magnification (EVM) is employed as a
preprocessing step. The experiments on the CASME II and SMIC-HS databases
summarize the effective and optimal directions and demonstrate that HSDG in an
optimal direction is discriminative, and the corresponding LBP-SDG achieves
state-of-the-art performance using EVM.


Abstract:
  This paper presents the advantages of alternative data from Super-Apps to
enhance user' s income estimation models. It compares the performance of these
alternative data sources with the performance of industry-accepted bureau
income estimators that takes into account only financial system information;
successfully showing that the alternative data manage to capture information
that bureau income estimators do not. By implementing the TreeSHAP method for
Stochastic Gradient Boosting Interpretation, this paper highlights which of the
customer' s behavioral and transactional patterns within a Super-App have a
stronger predictive power when estimating user' s income. Ultimately, this
paper shows the incentive for financial institutions to seek to incorporate
alternative data into constructing their risk profiles.


Abstract:
  State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promising alternative which leverages a much broader source of
supervision. We demonstrate that the simple pre-training task of predicting
which caption goes with which image is an efficient and scalable way to learn
SOTA image representations from scratch on a dataset of 400 million (image,
text) pairs collected from the internet. After pre-training, natural language
is used to reference learned visual concepts (or describe new ones) enabling
zero-shot transfer of the model to downstream tasks. We study the performance
of this approach by benchmarking on over 30 different existing computer vision
datasets, spanning tasks such as OCR, action recognition in videos,
geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a
fully supervised baseline without the need for any dataset specific training.
For instance, we match the accuracy of the original ResNet-50 on ImageNet
zero-shot without needing to use any of the 1.28 million training examples it
was trained on. We release our code and pre-trained model weights at
https://github.com/OpenAI/CLIP.


Abstract:
  We recast elliptic surfaces over the projective line in terms of the
non-commutative tori with real multiplication. The correspondence is used to
study the Picard numbers, the ranks and the minimal models of such surfaces. As
an example, we calculate the Picard numbers of elliptic surfaces with complex
multiplication.


Abstract:
  In this paper, we perform a detailed analysis of the phase shift phenomenon
of the classical soliton cellular automaton known as the box-ball system,
ultimately resulting in a statement and proof of a formula describing this
phase shift. This phenomenon has been observed since the nineties, when the
system was first introduced by Takahashi and Satsuma, but no explicit global
description was made beyond its observation. By using the
Gessel-Viennot-Lindstr\"{o}m lemma and path-counting arguments, we present here
a novel proof of the classical phase shift formula for the continuous-time Toda
lattice, as discovered by Moser, and use this proof to derive a discrete-time
Toda lattice analogue of the phase shift phenomenon. By carefully analysing the
connection between the box-ball system and the discrete-time Toda lattice,
through the mechanism of tropicalisation/dequantisation, we translate this
discrete-time Toda lattice phase shift formula into our new formula for the
box-ball system phase shift.


Abstract:
  Existing computer vision research in categorization struggles with
fine-grained attributes recognition due to the inherently high intra-class
variances and low inter-class variances. SOTA methods tackle this challenge by
locating the most informative image regions and rely on them to classify the
complete image. The most recent work, Vision Transformer (ViT), shows its
strong performance in both traditional and fine-grained classification tasks.
In this work, we propose a multi-stage ViT framework for fine-grained image
classification tasks, which localizes the informative image regions without
requiring architectural changes using the inherent multi-head self-attention
mechanism. We also introduce attention-guided augmentations for improving the
model's capabilities. We demonstrate the value of our approach by experimenting
with four popular fine-grained benchmarks: CUB-200-2011, Stanford Cars,
Stanford Dogs, and FGVC7 Plant Pathology. We also prove our model's
interpretability via qualitative results.


Abstract:
  Monitoring electronic properties of 2D materials is an essential step to open
a way for applications such as electronic devices and sensors. From this
perspective, Bernal bilayer graphene (BLG) is a fairly simple system that
offers great possibilities for tuning electronic gap and charge carriers'
mobility by selective functionalization (adsorptions of atoms or molecules).
Here, we present a detailed numerical study of BLG electronic properties when
two types of adsorption site are present simultaneously. We focus on realistic
cases that could be realized experimentally with adsorbate concentration c
varying from 0.25% to 5%. For a given value of c, when the electronic doping is
lower than c we show that quantum effects, which are ignored in usual
semi-classical calculations, strongly affect the electronic structure and the
transport properties. A wide range of behaviors is indeed found, such as gap
opening, metallic behavior or abnormal conductivity, which depend on the
adsorbate positions, the c value, the doping, and eventually the coupling
between midgap states which can create a midgap band. These behaviors are
understood by simple arguments based on the fact that BLG lattice is bipartite.
We also analyze the conductivity at low temperature, where multiple scattering
effects cannot be ignored. Moreover, when the Fermi energy lies in the band of
midgap states, the average velocity of charge carriers cancels but conduction
is still possible thanks to quantum fluctuations of the velocity.


Abstract:
  Spear Phishing is a harmful cyber-attack facing business and individuals
worldwide. Considerable research has been conducted recently into the use of
Machine Learning (ML) techniques to detect spear-phishing emails. ML-based
solutions may suffer from zero-day attacks; unseen attacks unaccounted for in
the training data. As new attacks emerge, classifiers trained on older data are
unable to detect these new varieties of attacks resulting in increasingly
inaccurate predictions. Spear Phishing detection also faces scalability
challenges due to the growth of the required features which is proportional to
the number of the senders within a receiver mailbox. This differs from
traditional phishing attacks which typically perform only a binary
classification between phishing and benign emails. Therefore, we devise a
possible solution to these problems, named RAIDER: Reinforcement AIded Spear
Phishing DEtectoR. A reinforcement-learning based feature evaluation system
that can automatically find the optimum features for detecting different types
of attacks. By leveraging a reward and penalty system, RAIDER allows for
autonomous features selection. RAIDER also keeps the number of features to a
minimum by selecting only the significant features to represent phishing emails
and detect spear-phishing attacks. After extensive evaluation of RAIDER over
11,000 emails and across 3 attack scenarios, our results suggest that using
reinforcement learning to automatically identify the significant features could
reduce the dimensions of the required features by 55% in comparison to existing
ML-based systems. It also improves the accuracy of detecting spoofing attacks
by 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection
accuracy even against a sophisticated attack named Known Sender in which
spear-phishing emails greatly resemble those of the impersonated sender.


Abstract:
  Radiative seesaw models have the attractive property of providing dark matter
candidates in addition to generation of neutrino masses. Here we present a
study of neutrino signals from the annihilation of dark matter particles which
have been gravitationally captured in the Sun, in the framework of the
scotogenic model. We compute expected event rates in the IceCube detector in
its 86-string configuration. As fermionic dark matter does not accumulate in
the Sun, we study the case of scalar dark matter, with a scan over the
parameter space. Due to a naturally small mass splitting between the two
neutral scalar components, inelastic scattering processes with nucleons can
occur. We find that for small mass splittings, the model yields very high event
rates. If a detailed analysis at IceCube can exclude these parameter points,
our findings can be translated into a lower limit on one of the scalar
couplings in the model. For larger mass splittings only the elastic case needs
to be considered. We find that in this scenario the XENON1T limits exclude all
points with sufficiently large event rates.


Abstract:
  We consider a unitary circuit where the underlying gates are chosen to be
R-matrices satisfying the Yang-Baxter equation and correlation functions can be
expressed through a transfer matrix formalism. These transfer matrices are no
longer Hermitian and differ from the ones guaranteeing local conservation laws,
but remain mutually commuting at different values of the spectral parameter
defining the circuit. Exact eigenstates can still be constructed as a Bethe
ansatz, but while these transfer matrices are diagonalizable in the
inhomogeneous case, the homogeneous limit corresponds to an exceptional point
where multiple eigenstates coalesce and Jordan blocks appear. Remarkably, the
complete set of (generalized) eigenstates is only obtained when taking into
account a combinatorial number of nontrivial vacuum states. In all cases, the
Bethe equations reduce to those of the integrable spin-1 chain and exhibit a
global SU(2) symmetry, significantly reducing the total number of eigenstates
required in the calculation of correlation functions. A similar construction is
shown to hold for the calculation of out-of-time-order correlations.


Abstract:
  This paper focuses on understanding how the generalization error scales with
the amount of the training data for deep neural networks (DNNs). Existing
techniques in statistical learning require computation of capacity measures,
such as VC dimension, to provably bound this error. It is however unclear how
to extend these measures to DNNs and therefore the existing analyses are
applicable to simple neural networks, which are not used in practice, e.g.,
linear or shallow ones or otherwise multi-layer perceptrons. Moreover, many
theoretical error bounds are not empirically verifiable. We derive estimates of
the generalization error that hold for deep networks and do not rely on
unattainable capacity measures. The enabling technique in our approach hinges
on two major assumptions: i) the network achieves zero training error, ii) the
probability of making an error on a test point is proportional to the distance
between this point and its nearest training point in the feature space and at a
certain maximal distance (that we call radius) it saturates. Based on these
assumptions we estimate the generalization error of DNNs. The obtained estimate
scales as O(1/(\delta N^{1/d})), where N is the size of the training data and
is parameterized by two quantities, the effective dimensionality of the data as
perceived by the network (d) and the aforementioned radius (\delta), both of
which we find empirically. We show that our estimates match with the
experimentally obtained behavior of the error on multiple learning tasks using
benchmark data-sets and realistic models. Estimating training data requirements
is essential for deployment of safety critical applications such as autonomous
driving etc. Furthermore, collecting and annotating training data requires a
huge amount of financial, computational and human resources. Our empirical
estimates will help to efficiently allocate resources.


Abstract:
  We study a class of elliptic and parabolic equations in non-divergence form
with singular coefficients in an upper half space with the homogeneous
Dirichlet boundary condition. Intrinsic weighted Sobolev spaces are found in
which the existence and uniqueness of strong solutions are proved when the
partial oscillations of coefficients in small parabolic cylinders are small.
Our results are new even when the coefficients are constants


Abstract:
  The influence of forward speed on stochastic free-surface crossing, in a
Gaussian wave field, is investigated. The case of a material point moving with
a constant forward speed is considered; the wave field is assumed stationary in
time, and homogeneous in space. The focus is on up-crossing events, which are
defined as the material point crossing the free surface, into the water domain.
The effect of the Doppler shift (induced by the forward speed) on the
up-crossing frequency, and the related conditional joint distribution of wave
kinematic variables is analytically investigated. Some general trends are
illustrated through different examples, where three kinds of wave direction
distribution are considered: unidirectional, short-crested anisotropic, and
isotropic. The way the developed approach may be used in the context of
slamming on marine structures is briefly discussed.


Abstract:
  In this paper, we consider the use of cross-layer network coding (CLNC),
caching, and device-to-device (D2D) communications to jointly optimize the
delivery of a set of popular contents to a set of user devices (UDs). In the
considered D2D network, a group of near-by UDs cooperate with each other and
use NC to combine their cached files, so as the completion time required for
delivering all requested contents to all UDs is minimized. Unlike the previous
work that considers only one transmitting UD at a time, our work allows
multiple UDs to transmit simultaneously given the interference among the active
links is small. Such configuration brings a new trade-off among scheduling UDs
to transmitting UDs, selecting the coding decisions and the transmission
rate/power. Therefore, we consider the completion time minimization problem
that involves scheduling multiple transmitting UDs, determining their
transmission rates/powers and file combinations. The problem is shown to be
intractable because it involves all future coding decisions. To tackle the
problem at each transmission slot, we first design a graph called herein the
D2D Rate-Aware IDNC graph where its vertices have weights that judiciously
balance between the rates/powers of the transmitting UDs and the number of
their scheduled UDs. Then, we propose an innovative and efficient CLNC solution
that iteratively selects a set of transmitting UDs only if the interference
caused by the transmissions of the newly selected UDs does not significantly
impact the overall completion time. Simulation results show that the proposed
solution offers significant completion time reduction compared with the
existing algorithms.


Abstract:
  Massive machine type communications (mMTC) is one of the cornerstone services
that have to be supported by 5G systems. 3GPP has already introduced LTE-M and
NB-IoT, often referred to as cellular IoT, in 3GPP Releases 13, 14, and 15 and
submitted these technologies as part of 3GPP IMT-2020 (i.e., 5G) technology
submission to ITU-R. Even though NB-IoT and LTE-M have shown to satisfy 5G mMTC
requirements defined by ITU-R, it is expected that these cellular IoT solutions
will not address all aspects of IoT and ongoing digitalization, including the
support for direct communication between "things" with flexible deployments,
different business models, as well as support for even higher node densities
and enhanced coverage. In this paper, we introduce the DECT-2020 standard
recently published by ETSI for mMTC communications. We evaluate its performance
and compare it to the existing LPWAN solutions showing that it outperforms
those in terms of supported density of nodes while still keeping delay and loss
guarantees at the required level.


Abstract:
  The paths leading to future networks are pointing towards a data-driven
paradigm to better cater to the explosive growth of mobile services as well as
the increasing heterogeneity of mobile devices, many of which generate and
consume large volumes and variety of data. These paths are also hampered by
significant challenges in terms of security, privacy, services provisioning,
and network management. Blockchain, which is a technology for building
distributed ledgers that provide an immutable log of transactions recorded in a
distributed network, has become prominent recently as the underlying technology
of cryptocurrencies and is revolutionizing data storage and processing in
computer network systems. For future data-driven networks (DDNs), blockchain is
considered as a promising solution to enable the secure storage, sharing, and
analytics of data, privacy protection for users, robust, trustworthy network
control, and decentralized routing and resource managements. However, many
important challenges and open issues remain to be addressed before blockchain
can be deployed widely to enable future DDNs. In this article, we present a
survey on the existing research works on the application of blockchain
technologies in computer networks, and identify challenges and potential
solutions in the applications of blockchains in future DDNs. We identify
application scenarios in which future blockchain-empowered DDNs could improve
the efficiency and security, and generally the effectiveness of network
services.


Abstract:
  Cross-language authorship attribution problems rely on either translation to
enable the use of single-language features, or language-independent feature
extraction methods. Until recently, the lack of datasets for this problem
hindered the development of the latter, and single-language solutions were
performed on machine-translated corpora. In this paper, we present a novel
language-independent feature for authorship analysis based on dependency graphs
and universal part of speech tags, called DT-grams (dependency tree grams),
which are constructed by selecting specific sub-parts of the dependency graph
of sentences. We evaluate DT-grams by performing cross-language authorship
attribution on untranslated datasets of bilingual authors, showing that, on
average, they achieve a macro-averaged F1 score of 0.081 higher than previous
methods across five different language pairs. Additionally, by providing
results for a diverse set of features for comparison, we provide a baseline on
the previously undocumented task of untranslated cross-language authorship
attribution.


Abstract:
  As the 5G standards mature and awareness of the capabilities of the
technology increases, industry verticals are becoming more eager to test new
services and develop them to the level of maturity required for market
adoption. Network slicing, i.e. multiple virtual networks running on a common
infrastructure, is considered a key mechanism to serve the multitude of tenants
(e.g. vertical industries) targeted by forthcoming fifth generation (5G)
systems, in a flexible and cost-efficient manner. It is predicted that one of
the most popular models for customers will be the Network Slice as a Service
(NSaaS) model. This model allows a Network Service Customer to order and
configure a Network Slice and offered it as a service. This work presents
Openlice a Service based, opens-ource OSS for delivering NSaaS following
emerging standards from SDOs. We strongly believe that such open source
solutions make it easier for organizations to enable complex scenarios
especially in the are of Non-Public Networks.


Abstract:
  We have measured trigonometric parallaxes for four water masers associated
with distant massive young stars in the inner regions of the Galaxy using the
VLBA as part of the BeSSeL Survey. G026.50$+$0.28. is located at the near end
of the Galactic bar, perhaps at the origin of the Norma spiral arm.
G020.77$-$0.05 is in the Galactic Center region and is likely associated with a
far-side extension of the Scutum arm. G019.60$-$0.23 and G020.08$-$0.13 are
likely associated and lie well past the Galactic Center. These sources appear
to be in the Sagittarius spiral arm, but an association with the Perseus arm
cannot be ruled out.


Abstract:
  The goal of this paper is to adapt speaker embeddings for solving the problem
of speaker diarisation. The quality of speaker embeddings is paramount to the
performance of speaker diarisation systems. Despite this, prior works in the
field have directly used embeddings designed only to be effective on the
speaker verification task. In this paper, we propose three techniques that can
be used to better adapt the speaker embeddings for diarisation: dimensionality
reduction, attention-based embedding aggregation, and non-speech clustering. A
wide range of experiments is performed on various challenging datasets. The
results demonstrate that all three techniques contribute positively to the
performance of the diarisation system achieving an average relative improvement
of 25.07% in terms of diarisation error rate over the baseline.


Abstract:
  Prevention and early diagnosis of breast cancer (BC) is an essential
prerequisite for the selection of proper treatment. The substantial pressure
due to the increase of demand for faster and more precise diagnostic results
drives for automatic solutions. In the past decade, deep learning techniques
have demonstrated their power over several domains, and Computer-Aided (CAD)
diagnostic became one of them. However, when it comes to the analysis of Whole
Slide Images (WSI), most of the existing works compute predictions from levels
independently. This is, however, in contrast to the histopathologist expert
approach who requires to see a global architecture of tissue structures
important in BC classification.
  We present a deep learning-based solution and framework for processing WSI
based on a novel approach utilizing the advantages of image levels. We apply
the weighing of information extracted from several levels into the final
classification of the malignancy. Our results demonstrate the profitability of
global information with an increase of accuracy from 72.2% to 84.8%.


Abstract:
  In the present paper we address double parton scattering (DPS) in quasi-real
photon-proton interactions. By using electromagnetic and hadronic models of the
photon light cone wave functions, we compute the so-called effective
cross-section, $\sigma_{eff}^{\gamma p}$ which allows us to calculate the DPS
contribution to these processes under dedicated assumptions. In particular, for
the four-jet photoproduction in HERA kinematics we found a sizeable DPS
contribution. We show that if the photon virtuality $Q^2$ could be measured and
thus the dependence of $\sigma_{eff}^{\gamma p}$ on such a parameter exposed,
information on the transverse distance between partons active in proton could
be extracted. To this aim, we set lower limits on the integrated luminosity
needed to observe such an effect which would allow the extraction of novel
information on the proton structure.


Abstract:
  This paper studies the dissipative generalized surface quasi-geostrophic
equations in a supercritical regime where the order of the dissipation is small
relative to order of the velocity, and the velocities are less regular than the
advected scalar by up to one order of derivative. We also consider a
non-degenerate modification of the endpoint case in which the velocity is less
smooth than the advected scalar by slightly more than one order. The existence
and uniqueness theory of these equations in the borderline Sobolev spaces is
addressed, as well as the instantaneous smoothing effect of their corresponding
solutions. In particular, it is shown that solutions emanating from initial
data belonging to these Sobolev classes immediately enter a Gevrey class. Such
results appear to be the first of its kind for a quasilinear parabolic equation
whose coefficients are of higher order than its linear term; they rely on an
approximation scheme which modifies the flux in such a way that preserves the
underlying commutator structure lost by having to work in the critical space
setting, as well as delicate adaptations of well-known commutator estimates to
Gevrey classes.


Abstract:
  A nonnoetherian spacetime is a Lorentzian manifold that contains a set of
causal curves with no distinct interior points. We show that on such a
spacetime, hidden within the free Dirac Lagrangian is the entire standard model
(with four massive neutral scalar bosons), with the correct spin, electric
charge, color charge, and relative mass orderings of each particle.
Furthermore, using two 'fusion rules', we are able to reproduce almost all of
the standard model trivalent vertices, as well as electroweak parity violation.
Finally, we find that on a nonnoetherian spacetime, C, P, and T each sit in a
different connected component of the full Lorentz group, and their product is
the identity.


Abstract:
  We use theory and numerical computation to determine the shape of an
axisymmetric fluid membrane with a resistance to bending and constant area. The
membrane connects two rings in the classic geometry that produces a catenoidal
shape in a soap film. In our problem, we find infinitely many branches of
solutions for the shape and external force as functions of the separation of
the rings, analogous to the infinite family of eigenmodes for the Euler
buckling of a slender rod. Special attention is paid to the catenoid, which
emerges as the shape of maximal allowable separation when the area is less than
a critical area equal to the planar area enclosed by the two rings. A
perturbation theory argument directly relates the tension of catenoidal
membranes to the stability of catenoidal soap films in this regime. When the
membrane area is larger than the critical area, we find additional cylindrical
tether solutions to the shape equations at large ring separation, and that
arbitrarily large ring separations are possible. These results apply for the
case of vanishing Gaussian curvature modulus; when the Gaussian curvature
modulus is nonzero and the area is below the critical area, the force and the
membrane tension diverge as the ring separation approaches its maximum value.
We also examine the stability of our shapes and analytically show that
catenoidal membranes have markedly different stability properties than their
soap film counterparts.


Abstract:
  Since the mapping relationship between definitized intra-interventional X-ray
and undefined pre-interventional Computed Tomography(CT) is uncertain,
auxiliary positioning devices or body markers, such as medical implants, are
commonly used to determine this relationship. However, such approaches can not
be widely used in clinical due to the complex realities. To determine the
mapping relationship, and achieve a initializtion post estimation of human body
without auxiliary equipment or markers, proposed method applies image
segmentation and deep feature matching to directly match the X-ray and CT
images. As a result, the well-trained network can directly predict the spatial
correspondence between arbitrary X-ray and CT. The experimental results show
that when combining our approach with the conventional approach, the achieved
accuracy and speed can meet the basic clinical intervention needs, and it
provides a new direction for intra-interventional registration.


Abstract:
  Billions of photos are uploaded to the web daily through various types of
social networks. Some of these images receive millions of views and become
popular, whereas others remain completely unnoticed. This raises the problem of
predicting image popularity on social media. The popularity of an image can be
affected by several factors, such as visual content, aesthetic quality, user,
post metadata, and time. Thus, considering all these factors is essential for
accurately predicting image popularity. In addition, the efficiency of the
predictive model also plays a crucial role. In this study, motivated by
multimodal learning, which uses information from various modalities, and the
current success of convolutional neural networks (CNNs) in various fields, we
propose a deep learning model, called visual-social convolutional neural
network (VSCNN), which predicts the popularity of a posted image by
incorporating various types of visual and social features into a unified
network model. VSCNN first learns to extract high-level representations from
the input visual and social features by utilizing two individual CNNs. The
outputs of these two networks are then fused into a joint network to estimate
the popularity score in the output layer. We assess the performance of the
proposed method by conducting extensive experiments on a dataset of
approximately 432K images posted on Flickr. The simulation results demonstrate
that the proposed VSCNN model significantly outperforms state-of-the-art
models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in
terms of Spearman's Rho, mean absolute error, and mean squared error,
respectively.


Abstract:
  We consider some energy integrals under slow growth and we prove that the
local minimizers are locally Lipschitz continuous. Many examples are given,
either with subquadratic $p,q-$growth and/or anisotropic growth.


Abstract:
  Making use of the gauge/string duality, it is possible to study some aspects
of the string breaking phenomenon in the three quark system. Our results point
out that the string breaking distance is not universal and depends on quark
geometry. The estimates of the ratio of the string breaking distance in the
three quark system to that in the quark-antiquark system would range
approximately from $\frac{2}{3}$ to $1$. In addition, it is shown that there
are special geometries which allow more than one breaking distance.


Abstract:
  We present a measurement of the Hubble constant $H_0$ from surface brightness
fluctuation (SBF) distances for 63 bright, mainly early-type galaxies out to
100 Mpc observed with the Wide Field Camera 3 Infrared Channel (WFC3/IR) on the
Hubble Space Telescope (HST). The sample is drawn from several independent HST
imaging programs using the F110W bandpass of WFC3/IR. The majority of galaxies
are in the 50 to 80 Mpc range and come from the MASSIVE galaxy survey. The
median statistical uncertainty on individual distance measurements is 4%. We
construct the Hubble diagram with these IR SBF distances and constrain $H_0$
using {four} different treatments of the galaxy velocities. For the SBF zero
point calibration, we use both the existing tie to Cepheid variables, updated
for consistency with the latest determination of the distance to the Large
Magellanic Cloud from detached eclipsing binaries, and a new tie to the tip of
the red giant branch (TRGB) calibrated from the maser distance to NGC4258.
These two SBF calibrations are consistent with each other and with theoretical
predictions from stellar population models. From a weighted average of the
Cepheid and TRGB calibrations, we derive $H_0=73.3{\,\pm\,}0.7{\,\pm\,}2.4$
km/s/Mpc, where the error bars reflect the statistical and systematic
uncertainties. This result accords well with recent measurements of $H_0$ from
Type~Ia supernovae, time delays in multiply lensed quasars, and water masers.
The systematic uncertainty could be reduced to below 2% by calibrating the SBF
method with precision TRGB distances for a statistical sample of massive
early-type galaxies out to the Virgo cluster measured with the James Webb Space
Telescope.


Abstract:
  Despite significant advancements in the field of multi-agent navigation,
agents still lack the sophistication and intelligence that humans exhibit in
multi-agent settings. In this paper, we propose a framework for learning a
human-like general collision avoidance policy for agent-agent interactions in
fully decentralized, multi-agent environments. Our approach uses knowledge
distillation with reinforcement learning to shape the reward function based on
expert policies extracted from human trajectory demonstrations through behavior
cloning. We show that agents trained with our approach can take human-like
trajectories in collision avoidance and goal-directed steering tasks not
provided by the demonstrations, outperforming the experts as well as
learning-based agents trained without knowledge distillation.


Abstract:
  In this note we show that the Linet-Tian family of solutions of the vacuum
Einstein equations with a cosmological constant are a restricted set of the
solutions of the Einstein field equations for a rotating perfect fluid
previously found by A. Krasi\'nski.


Abstract:
  This paper provides an overview of the current state of the stripping model
for short gamma-ray bursts. After the historical joint detection of the
gravitational wave event GW170817 and the accompanying gamma-ray burst
GRB170817A, the relation between short gamma-ray bursts and neutron star
mergers has been reliably confirmed. We show that many properties of
GRB170817A, which turned out to be peculiar in comparison with other short
gamma-ray bursts, are naturally explained in the context of the stripping
model, specifically, the time (1.7 s) between the peak of the gravitational
wave signal and the detection of the gamma-ray burst, its total isotropic
energy, and the parameters of the red and blue components of the accompanying
kilonova.


Abstract:
  Increasingly important photomechanical materials produce stress and
mechanical work when illuminated. We propose experimentally accessible
performance metrics for photostress and photowork, enabling comparison of
materials performance. We relate these metrics to material properties,
providing a framework for the design and optimization of photomechanical
materials.


Abstract:
  Real-world data is usually segmented by attributes and distributed across
different parties. Federated learning empowers collaborative training without
exposing local data or models. As we demonstrate through designed attacks, even
with a small proportion of corrupted data, an adversary can accurately infer
the input attributes. We introduce an adversarial learning based procedure
which tunes a local model to release privacy-preserving intermediate
representations. To alleviate the accuracy decline, we propose a defense method
based on the forward-backward splitting algorithm, which respectively deals
with the accuracy loss and privacy loss in the forward and backward gradient
descent steps, achieving the two objectives simultaneously. Extensive
experiments on a variety of datasets have shown that our defense significantly
mitigates privacy leakage with negligible impact on the federated learning
task.


Abstract:
  Humans tend to build environments with structure, which consists of mainly
planar surfaces. From the intersection of planar surfaces arise straight lines.
Lines have more degrees-of-freedom than points. Thus, line-based
Structure-from-Motion (SfM) provides more information about the environment. In
this paper, we present solutions for SfM using lines, namely, incremental SfM.
These approaches consist of designing state observers for a camera's dynamical
visual system looking at a 3D line. We start by presenting a model that uses
spherical coordinates for representing the line's moment vector. We show that
this parameterization has singularities, and therefore we introduce a more
suitable model that considers the line's moment and shortest viewing ray.
Concerning the observers, we present two different methodologies. The first
uses a memory-less state-of-the-art framework for dynamic visual systems. Since
the previous states of the robotic agent are accessible -- while performing the
3D mapping of the environment -- the second approach aims at exploiting the use
of memory to improve the estimation accuracy and convergence speed. The two
models and the two observers are evaluated in simulation and real data, where
mobile and manipulator robots are used.


Abstract:
  We show that, by utilising temporal quantum correlations as expressed by
pseudo-density operators (PDOs), it is possible to recover formally the
standard quantum dynamical evolution as a sequence of teleportations in time.
We demonstrate that any completely positive evolution can be formally
reconstructed by teleportation with different temporally correlated states.
This provides a different interpretation of maximally correlated PDOs, as
resources to induce quantum time-evolution. Furthermore, we note that the
possibility of this protocol stems from the strict formal correspondence
between spatial and temporal entanglement in quantum theory. We proceed to
demonstrate experimentally this correspondence, by showing a multipartite
violation of generalised temporal and spatial Bell inequalities and verifying
agreement with theoretical predictions to a high degree of accuracy, in
high-quality photon qubits.


Abstract:
  Shear bands originating from in situ tensile tests of
Al$_{88}$Y$_{7}$Fe$_{5}$ melt-spun ribbons conducted in a transmission electron
microscope are compared with ones which had formed ex situ during cold rolling.
During in situ straining, the observations of a spearhead-like shear front, a
meniscus-like foil thickness reduction and no apparent shear steps to
accommodate strain suggest shear band initiation by a rejuvenating shear front
followed by shearing along the already softened paths. This leads to necking
and subsequent failure under the reduced constraint of a 2D geometry in the
thin foil and thus explains the observed lack of ductility under tension. In
contrast, shear bands formed during cold rolling display distinct alternating
density changes and shear off-sets. An explanation for this difference may be
that in situ shear bands rip before such features could develop. Moreover, both
in and ex situ experiments suggest that initiation, propagation and arrest of
shear bands occur during different stages.


Abstract:
  We consider the problem of sensor selection for designing observer and filter
for continuous linear time invariant systems such that the sensor precisions
are minimized, and the estimation errors are bounded by the prescribed
$\mathcal{H}_2/\mathcal{H}_{\infty}$ performance criteria. The proposed
integrated framework formulates the precision minimization as a convex
optimization problem subject to linear matrix inequalities, and it is solved
using an algorithm based on the alternating direction method of multipliers
(ADMM). We also present a greedy approach for sensor selection and demonstrate
the performance of the proposed algorithms using numerical simulations.


Abstract:
  We study the quantization of the corner symmetry algebra of 3d gravity, that
is the algebra of observables associated with 1d spatial boundaries. In the
continuum field theory, at the classical level, this symmetry algebra is given
by the central extension of the Poincar\'e loop algebra. At the quantum level,
we construct a discrete current algebra based on a quantum symmetry group given
by the Drinfeld double $\mathcal{D}\mathrm{SU}(2)$. Those discrete currents
depend on an integer $N$, a discreteness parameter, understood as the number of
quanta of geometry on the 1d boundary: low $N$ is the deep quantum regime,
while large $N$ should lead back to a continuum picture. We show that this
algebra satisfies two fundamental properties. First, it is compatible with the
quantum space-time picture given by the Ponzano-Regge state-sum model, which
provides discrete path integral amplitudes for 3d quantum gravity. The integer
$N$ then counts the flux lines attached to the boundary. Second, we analyse the
refinement, coarse-graining and fusion processes as $N$ changes, and we show
that the $N\rightarrow\infty$ limit is a classical limit where we recover the
Poincar\'e current algebra. Identifying such a discrete current algebra on
quantum boundaries is an important step towards understanding how conformal
field theories arise on spatial boundaries in quantized space-times such as in
loop quantum gravity.


Abstract:
  Gate-all-around nanowire transistor, due to its extremely tight electrostatic
control and vertical integration capability, is a highly promising candidate
for sub-5 nm technology node. In particular, the junctionless nanowire
transistors are highly scalable with reduced variability due to avoidance of
steep source/drain junction formation by ion implantation. Here we demonstrate
a dual-gated junctionless nanowire \emph{p}-type field effect transistor using
tellurium nanowire as the channel. The dangling-bond-free surface due to the
unique helical crystal structure of the nanowire, coupled with an integration
of dangling-bond-free, high quality hBN gate dielectric, allows us to achieve a
phonon-limited field effect hole mobility of $570\,\mathrm{cm^{2}/V\cdot s}$ at
270 K, which is well above state-of-the-art strained Si hole mobility. By
lowering the temperature, the mobility increases to
$1390\,\mathrm{cm^{2}/V\cdot s}$ and becomes primarily limited by Coulomb
scattering. \txc{The combination of an electron affinity of $\sim$4 eV and a
small bandgap of tellurium provides zero Schottky barrier height for hole
injection at the metal-contact interface}, which is remarkable for reduction of
contact resistance in a highly scaled transistor. Exploiting these properties,
coupled with the dual-gated operation, we achieve a high drive current of
$216\,\mathrm{\mu A/\mu m}$ while maintaining an on-off ratio in excess of
$2\times10^4$. The findings have intriguing prospects for alternate channel
material based next-generation electronics.


Abstract:
  In [I.R. Khairulin et al., submitted to Phys. Rev. Lett.] we propose a method
for amplifying a train of sub-femtosecond pulses of circularly or elliptically
polarized extreme ultraviolet (XUV) radiation, constituted by high-order
harmonics of an infrared (IR) laser field, in a neon-like active medium of a
plasma-based X-ray laser, additionally irradiated with a replica of a
fundamental frequency laser field used to generate harmonics, and show the
possibility of maintaining or enhancing the ellipticity of high-harmonic
radiation during its amplification. In the present paper we describe this
process in detail both for a single harmonic component and a sub-femtosecond
pulse train formed by a set of harmonics. We derive the analytical theory and
describe both analytically and numerically the evolution of the high-harmonic
field during its propagation through the medium. We discuss also the
possibility of an experimental implementation of the suggested technique in an
active medium of an X-ray laser based on neon-like Ti^{12+} ions irradiated by
an IR laser field with a wavelength of 3.9 microns.


Abstract:
  Bottom-up approaches for image-based multi-person pose estimation consist of
two stages: (1) keypoint detection and (2) grouping of the detected keypoints
to form person instances. Current grouping approaches rely on learned embedding
from only visual features that completely ignore the spatial configuration of
human poses. In this work, we formulate the grouping task as a graph
partitioning problem, where we learn the affinity matrix with a Graph Neural
Network (GNN). More specifically, we design a Geometry-aware Association GNN
that utilizes spatial information of the keypoints and learns local affinity
from the global context. The learned geometry-based affinity is further fused
with appearance-based affinity to achieve robust keypoint association. Spectral
clustering is used to partition the graph for the formation of the pose
instances. Experimental results on two benchmark datasets show that our
proposed method outperforms existing appearance-only grouping frameworks, which
shows the effectiveness of utilizing spatial context for robust grouping.
Source code is available at: https://github.com/jiahaoLjh/PoseGrouping.


Abstract:
  Existing works for aspect-based sentiment analysis (ABSA) have adopted a
unified approach, which allows the interactive relations among subtasks.
However, we observe that these methods tend to predict polarities based on the
literal meaning of aspect and opinion terms and mainly consider relations
implicitly among subtasks at the word level. In addition, identifying multiple
aspect-opinion pairs with their polarities is much more challenging. Therefore,
a comprehensive understanding of contextual information w.r.t. the aspect and
opinion are further required in ABSA. In this paper, we propose Deep
Contextualized Relation-Aware Network (DCRAN), which allows interactive
relations among subtasks with deep contextual information based on two modules
(i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies).
Especially, we design novel self-supervised strategies for ABSA, which have
strengths in dealing with multiple aspects. Experimental results show that
DCRAN significantly outperforms previous state-of-the-art methods by large
margins on three widely used benchmarks.


Abstract:
  A common feature of electromagnetic emission from solar flares is the
presence of intensity pulsations that vary as a function of time. Known as
quasi-periodic pulsations (QPPs), these variations in flux appear to include
periodic components and characteristic time-scales. Here, we analyse a GOES
M3.7 class flare exhibiting pronounced QPPs across a broad band of wavelengths
using imaging and time-series analysis. We identify QPPs in the timeseries of
X-ray, low frequency radio and EUV wavelengths using wavelet analysis, and
localise the region of the flare site from which the QPPs originate via X-ray
and EUV imaging. It was found that the pulsations within the 171 \.A, 1600 \.A,
soft X-ray (SXR), and hard X-ray (HXR) light curves yielded similar periods of
$\sim$122 s, $\sim$131s, $\sim$123 s, and $\sim$137 s, respectively, indicating
a common progenitor. The low frequency radio emission at 2.5 MHz contained a
longer period of $\sim$231 s. Imaging analysis indicates that the location of
the X-ray and EUV pulsations originates from a HXR footpoint linked to a system
of nearby open magnetic field lines. Our results suggest that intermittent
particle acceleration, likely due to 'bursty' magnetic reconnection, is
responsible for the QPPs. The precipitating electrons accelerated towards the
chromosphere produce the X-ray and EUV pulsations, while the escaping electrons
result in low frequency radio pulses in the form of type III radio bursts. The
modulation of the reconnection process, resulting in episodic particle
acceleration, explains the presence of these QPPs across the entire spatial
range of flaring emission.


Abstract:
  Data is the engine of modern computer vision, which necessitates collecting
large-scale datasets. This is expensive, and guaranteeing the quality of the
labels is a major challenge. In this paper, we investigate efficient annotation
strategies for collecting multi-class classification labels for a large
collection of images. While methods that exploit learnt models for labeling
exist, a surprisingly prevalent approach is to query humans for a fixed number
of labels per datum and aggregate them, which is expensive. Building on prior
work on online joint probabilistic modeling of human annotations and
machine-generated beliefs, we propose modifications and best practices aimed at
minimizing human labeling effort. Specifically, we make use of advances in
self-supervised learning, view annotation as a semi-supervised learning
problem, identify and mitigate pitfalls and ablate several key design choices
to propose effective guidelines for labeling. Our analysis is done in a more
realistic simulation that involves querying human labelers, which uncovers
issues with evaluation using existing worker simulation methods. Simulated
experiments on a 125k image subset of the ImageNet100 show that it can be
annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a
2.7x and 6.7x improvement over prior work and manual annotation, respectively.
Project page: https://fidler-lab.github.io/efficient-annotation-cookbook


Abstract:
  There is currently a gap between the natural language expression of scholarly
publications and their structured semantic content modeling to enable
intelligent content search. With the volume of research growing exponentially
every year, a search feature operating over semantically structured content is
compelling. The SemEval-2021 Shared Task NLPContributionGraph (a.k.a. 'the NCG
task') tasks participants to develop automated systems that structure
contributions from NLP scholarly articles in the English language. Being the
first-of-its-kind in the SemEval series, the task released structured data from
NLP scholarly articles at three levels of information granularity, i.e. at
sentence-level, phrase-level, and phrases organized as triples toward Knowledge
Graph (KG) building. The sentence-level annotations comprised the few sentences
about the article's contribution. The phrase-level annotations were scientific
term and predicate phrases from the contribution sentences. Finally, the
triples constituted the research overview KG. For the Shared Task,
participating systems were then expected to automatically classify contribution
sentences, extract scientific terms and relations from the sentences, and
organize them as KG triples.
  Overall, the task drew a strong participation demographic of seven teams and
27 participants. The best end-to-end task system classified contribution
sentences at 57.27% F1, phrases at 46.41% F1, and triples at 22.28% F1. While
the absolute performance to generate triples remains low, in the conclusion of
this article, the difficulty of producing such data and as a consequence of
modeling it is highlighted.


Abstract:
  The paper utilizes H\"older graphical derivatives for characterizing H\"older
strong subregularity, isolated calmness and sharp minimum. As applications, we
characterize H\"older isolated calmness in linear semi-infinite optimization
and H\"older sharp minimizers of some penalty functions for constrained
optimization.


Abstract:
  Given the prominence of current 3D sensors, a fine-grained analysis on the
basic point cloud data is worthy of further investigation. Particularly, real
point cloud scenes can intuitively capture complex surroundings in the real
world, but due to 3D data's raw nature, it is very challenging for machine
perception. In this work, we concentrate on the essential visual task, semantic
segmentation, for large-scale point cloud data collected in reality. On the one
hand, to reduce the ambiguity in nearby points, we augment their local context
by fully utilizing both geometric and semantic features in a bilateral
structure. On the other hand, we comprehensively interpret the distinctness of
the points from multiple resolutions and represent the feature map following an
adaptive fusion method at point-level for accurate semantic segmentation.
Further, we provide specific ablation studies and intuitive visualizations to
validate our key modules. By comparing with state-of-the-art networks on three
different benchmarks, we demonstrate the effectiveness of our network.


Abstract:
  Vehicle safety systems have substantially decreased motor vehicle
crash-related injuries and fatalities, but injuries to the lumbar spine still
have been reported. Experimental and computational analyses of upright and,
particularly, reclined occupants in frontal crashes have shown that the lumbar
spine can be subjected to axial compression followed by combined
compression-flexion loading. Lumbar spine failure tolerance in combined
compression-flexion has not been widely explored in the literature. Therefore,
the goal of this study was to measure the failure tolerance of the lumbar spine
in combined compression and flexion. Forty 3-vertebra lumbar spine segments
were pre-loaded with axial compression and then subjected to dynamic flexion
bending until failure. Clinically relevant middle vertebra fractures were
observed in twenty-one of the specimens, including compression and burst
fractures. The remaining nineteen specimens experienced failure at the potting
grip interface. Since specimen characteristics and pre-test axial load varied
widely within the sample, failure forces (mean 3.4 kN, range 1.6-5.1 kN) and
moments (mean 73 Nm, range 0-181 Nm) also varied widely. Tobit univariate
regressions were performed to determine the relationship between censored
failure tolerance and specimen sex, segment type (upper/lower), age, and
cross-sectional area. Age, sex, and cross-sectional area significantly affected
failure force and moment individually (p<0.0024). These data can be used to
develop injury prediction tools for lumbar spine fractures and further research
in future safety systems.


Abstract:
  As more and more robots are envisioned to cooperate with humans sharing the
same space, it is desired for robots to be able to predict others' trajectories
to navigate in a safe and self-explanatory way. We propose a Convolutional
Neural Network-based approach to learn, detect, and extract patterns in
sequential trajectory data, known here as Social Pattern Extraction Convolution
(Social-PEC). A set of experiments carried out on the human trajectory
prediction problem shows that our model performs comparably to the state of the
art and outperforms in some cases. More importantly, the proposed approach
unveils the obscurity in the previous use of a pooling layer, presenting a way
to intuitively explain the decision-making process.


Abstract:
  It is widely believed that natural image data exhibits low-dimensional
structure despite the high dimensionality of conventional pixel
representations. This idea underlies a common intuition for the remarkable
success of deep learning in computer vision. In this work, we apply dimension
estimation tools to popular datasets and investigate the role of
low-dimensional structure in deep learning. We find that common natural image
datasets indeed have very low intrinsic dimension relative to the high number
of pixels in the images. Additionally, we find that low dimensional datasets
are easier for neural networks to learn, and models solving these tasks
generalize better from training to test data. Along the way, we develop a
technique for validating our dimension estimation tools on synthetic data
generated by GANs allowing us to actively manipulate the intrinsic dimension by
controlling the image generation process. Code for our experiments may be found
here https://github.com/ppope/dimensions.


Abstract:
  We derive explicitly the structural properties of the $p$-adic special
orthogonal groups in dimension three, for all primes $p$, and, along the way,
the two-dimensional case. In particular, starting from the unique definite
quadratic form in three dimensions (up to linear equivalence and rescaling), we
show that every element of $SO(3)_p$ is a rotation around an axis. An important
part of the analyis is the classification of all definite forms in two
dimensions, yielding a description of the rotation subgroups around any fixed
axis, which all turn out to be abelian and parametrised naturally by the
projective line.
  Furthermore, we find that for odd primes $p$, the entire group $SO(3)_p$
admits a representation in terms of Cardano angles of rotations around the
reference axes, in close analogy to the real orthogonal case. However, this
works only for certain orderings of the product of rotations around the
coordinate axes, depending on the prime; furthermore, there is no general Euler
angle decomposition. For $p=2$, no Euler or Cardano decomposition exists.


Abstract:
  Direct-to-satellite (DtS) communication has gained importance recently to
support globally connected Internet of things (IoT) networks. However,
relatively long distances of densely deployed satellite networks around the
Earth cause a high path loss. In addition, since high complexity operations
such as beamforming, tracking and equalization have to be performed in IoT
devices partially, both the hardware complexity and the need for high-capacity
batteries of IoT devices increase. The reconfigurable intelligent surfaces
(RISs) have the potential to increase the energy-efficiency and to perform
complex signal processing over the transmission environment instead of IoT
devices. But, RISs need the information of the cascaded channel in order to
change the phase of the incident signal. This study proposes graph attention
networks (GATs) for the challenging channel estimation problem and examines the
performance of DtS IoT networks for different RIS configurations under GAT
channel estimation.


Abstract:
  Mechatronic systems are commonly used in the industry, where fast and
accurate motion performance is always required to guarantee manufacturing
precision and efficiency. Nevertheless, the system model and parameters are
difficult to be obtained accurately. Moreover, the high-order modes, strong
coupling in the multi-axis systems, or unmodeled frictions will bring uncertain
dynamics to the system. To overcome the above-mentioned issues and enhance the
motion performance, this paper introduces a novel intelligent and totally
model-free control method for mechatronic systems with unknown dynamics. In
detail, a 2-degree-of-freedom (DOF) architecture is designed, which organically
merges a generalized super-twisting algorithm with a unique iterative learning
law. The controller solely utilizes the input-output data collected in
iterations such that it works without any knowledge of the system parameters.
The rigorous proof of convergence ability is given and a case study on
flexture-joint dual-drive H-gantry stage is shown to validate the effectiveness
of the proposed method.


Abstract:
  We construct a new class of efficient Monte Carlo methods based on
continuous-time piecewise deterministic Markov processes (PDMP) suitable for
inference in high dimensional sparse models, i.e. models for which there is
prior knowledge that many coordinates are likely to be exactly $0$. This is
achieved with the fairly simple idea of endowing existing PDMP samplers with
sticky coordinate axes, coordinate planes etc. Upon hitting those subspaces, an
event is triggered, during which the process sticks to the subspace, this way
spending some time in a sub-model. That introduces non-reversible jumps between
different (sub-)models. The approach can also be combined with local
implementations of PDMP samplers to target measures that additionally exhibit a
sparse dependency structure. We illustrate the new method for a number of
statistical models where both the sample size $N$ and the dimensionality $d$ of
the parameter space are large.


Abstract:
  The direct imaging of rocky exoplanets is one of the major science goals for
upcoming large telescopes. The contrast requirement for imaging such planets is
challenging. However, the mid-IR (InfraRed) regime provides the optimum
contrast to directly detect the thermal signatures of exoplanets in our solar
neighbourhood. We aim to exploit novel fast chopping techniques newly developed
for astronomy with the aid of adaptive optics to look for thermal signatures of
exoplanets around bright stars in the solar neighbourhood. We use the upgraded
VISIR (Very Large Telescope Imager and Spectrometer for the mid-InfraRed)
instrument with high contrast imaging (HCI) capability optimized for
observations at 10~$\mu$m to look for exoplanets around five nearby ($d$ < 4
pc) stars. The instrument provides an improved signal-to-noise (S/N) by a
factor of $\sim$4 in the N-band compared to standard VISIR for a given S/N and
time. In this work we achieve a detection sensitivity of sub-mJy, which is
sufficient to detect few Jupiter mass planets in nearby systems. Although no
detections are made we achieve most sensitive limits within $<2''$ for all the
observed targets compared to previous campaigns. For $\epsilon$ Indi A and
$\epsilon$ Eri we achieve detection limits very close to the giant planets
discovered by RV, with the limits on $\epsilon$ Indi A being the most sensitive
to date. Our non-detection therefore supports an older age for $\epsilon$ Indi
A. The results presented here show the promise for high contrast imaging and
exoplanet detections in the mid-IR regime.


Abstract:
  Effectively recognising and applying emotions to interactions is a highly
desirable trait for social robots. Implicitly understanding how subjects
experience different kinds of actions and objects in the world is crucial for
natural HRI interactions, with the possibility to perform positive actions and
avoid negative actions. In this paper, we utilize the NICO robot's appearance
and capabilities to give the NICO the ability to model a coherent affective
association between a perceived auditory stimulus and a temporally asynchronous
emotion expression. This is done by combining evaluations of emotional valence
from vision and language. NICO uses this information to make decisions about
when to extend conversations in order to accrue more affective information if
the representation of the association is not coherent. Our primary contribution
is providing a NICO robot with the ability to learn the affective associations
between a perceived auditory stimulus and an emotional expression. NICO is able
to do this for both individual subjects and specific stimuli, with the aid of
an emotion-driven dialogue system that rectifies emotional expression
incoherences. The robot is then able to use this information to determine a
subject's enjoyment of perceived auditory stimuli in a real HRI scenario.


Abstract:
  Core Damage Frequency (CDF) is a risk metric often employed by nuclear
regulatory bodies worldwide. Numerical values for this metric are required by
U.S. regulators, prior to reactor licensing, and reported values can trigger
regulatory inspections. CDF is reported as a constant, sometimes accompanied by
a confidence interval. It is well understood that CDF characterizes the arrival
rate of a stochastic point process modeling core damage events. However,
consequences of the assumptions imposed on this stochastic process as a
computational necessity are often overlooked. Herein, we revisit CDF in the
context of modern point process theory. We argue that the assumptions required
to yield a constant CDF are typically unrealistic. We further argue that
treating CDF as an informative approximation is suspect, because of the
inherent difficulties in quantifying its quality as an approximation.


Abstract:
  Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the
gap for semantic segmentation performance from pixel-level supervision to
image-level supervision. Most advanced approaches are based on class activation
maps (CAMs) to generate pseudo-labels to train the segmentation network. The
main limitation of WSSS is that the process of generating pseudo-labels from
CAMs that use an image classifier is mainly focused on the most discriminative
parts of the objects. To address this issue, we propose Puzzle-CAM, a process
that minimizes differences between the features from separate patches and the
whole image. Our method consists of a puzzle module and two regularization
terms to discover the most integrated region in an object. Puzzle-CAM can
activate the overall region of an object using image-level supervision without
requiring extra parameters. % In experiments, Puzzle-CAM outperformed previous
state-of-the-art methods using the same labels for supervision on the PASCAL
VOC 2012 test dataset. In experiments, Puzzle-CAM outperformed previous
state-of-the-art methods using the same labels for supervision on the PASCAL
VOC 2012 dataset. Code associated with our experiments is available at
https://github.com/OFRIN/PuzzleCAM.


Abstract:
  Recent work has revealed two classes of Globular Clusters (GCs), dubbed
Type-I and Type-II. Type-II GCs are characterized by a blue- and a red- red
giant branch composed of stars with different metallicities, often coupled with
distinct abundances in the slow-neutron capture elements (s-elements). Here we
continue the chemical tagging of Type-II GCs by adding the two least-massive
clusters of this class, NGC1261 and NGC6934. Based on both spectroscopy and
photometry, we find that red stars in NGC1261 are slightly enhanced in [Fe/H]
by ~0.1 dex and confirm that red stars of NGC 6934 are enhanced in iron by ~0.2
dex. Neither NGC1261 nor NGC6934 show internal variations in the s-elements,
which suggests a GC mass threshold for the occurrence of s-process enrichment.
We found a significant correlation between the additional Fe locked in the red
stars of Type-II GCs and the present-day mass of the cluster. Nevertheless,
most Type II GCs retained a small fraction of Fe produced by SNe II, lower than
the 2%; NGC6273, M54 and omega Centauri are remarkable exceptions. In the
appendix, we infer for the first time chemical abundances of Lanthanum, assumed
as representative of the s-elements, in M54, the GC located in the nucleus of
the Sagittarius dwarf galaxy. Red-sequence stars are marginally enhanced in
[La/Fe] by 0.10\pm0.06 dex, in contrast with the large [La/Fe] spread of most
Type II GCs. We suggest that different processes are responsible for the
enrichment in iron and s-elements in Type-II GCs.


Abstract:
  A set of quantum states is said to be absolutely entangled, when at least one
state in the set remains entangled for any definition of subsystems, i.e. for
any choice of the global reference frame. In this work we investigate the
properties of absolutey entangled sets (AES) of pure quantum states. For the
case of a two-qubit system, we present a sufficient condition to detect an AES,
and use it to construct families of $N$ states such that $N-3$ (the maximal
possible number) remain entangled for any definition of subsystems. For a
general bipartition $d=d_1d_2$, we prove that sets of
$N>\left\lfloor{(d_{1}+1)(d_{2}+1)/2}\right \rfloor$ states are AES with Haar
measure 1. Then, we define AES for multipartitions. We derive a general lower
bound on the number of states in an AES for a given multipartition, and also
construct explicit examples. In particular, we exhibit an AES with respect to
any possible multi-partitioning of the total system.


Abstract:
  We study the numerical approximation of stochastic evolution equations with a
monotone drift driven by an infinite-dimensional Wiener process. To discretize
the equation, we combine a drift-implicit two-step BDF method for the temporal
discretization with an abstract Galerkin method for the spatial discretization.
After proving well-posedness of the BDF2-Maruyama scheme, we establish a
convergence rate of the strong error for equations under suitable Lipschitz
conditions. We illustrate our theoretical results through various numerical
experiments and compare the performance of the BDF2-Maruyama scheme to the
backward Euler--Maruyama scheme.


Abstract:
  Many long-term goals, such as learning a language, require people to
regularly practice every day to achieve mastery. At the same time, people
regularly surf the web and read social news feeds in their spare time. We have
built a browser extension that teaches vocabulary to users in the context of
Facebook feeds and arbitrary websites, by showing users interactive quizzes
they can answer without leaving the website. On Facebook, the quizzes show up
as part of the news feed, while on other sites, the quizzes appear where
advertisements normally would. In our user study, we examined the effectiveness
of inserting microlearning tasks into social news feeds. We compared vocabulary
learning rates when we inserted interactive quizzes into feeds, versus
inserting links that lead them to a website where they could do the quizzes.
Our results suggest that users engage with and learn from our embedded quizzes,
and engagement increases when the quizzes can be done directly within their
feeds.


Abstract:
  The $s,p-d$ exchange coupling between the spins of band carriers and of
transition metal (TM) dopants ranging from Ti to Cu in ZnO is studied within
the density functional theory. The $+U$ corrections are included to reproduce
the experimental ZnO band gap and the dopant levels. The $p-d$ coupling reveals
unexpectedly complex features. In particular, (i) the $p-d$ coupling constants
$N_0\beta$ vary about 10 times when going from V to Cu, (ii) not only the value
but also the sign of $N_0\beta$ depends on the charge state of the dopant,
(iii) the $p-d$ coupling with the heavy holes and the light holes is not the
same; in the case of Fe, Co and Ni, $N_0\beta$s for the two subbands can differ
twice, and for Cu the opposite sign of the coupling is found for light and
heavy holes. The main features of the $p-d$ coupling are determined by the
$p-d$ hybridization between the $d$(TM) and $p$(O) orbitals. In contrast, the
$s-d$ coupling constant $N_0\alpha$ is almost the same for all TM ions, and
does not depend on the charge state of the dopant. The TM-induced spin
polarization of the $p$(O) orbitals contributes to the $s-d$ coupling,
enhancing $N_0\alpha$.


Abstract:
  We present results on electron transport in quasi-one dimensional (1D)
quantum wires in GaAs/AlGaAs heterostructures obtained using an asymmetric
confinement potential. The variation of the energy levels of the spatially
quantized states is followed from strong confinement through weak confinement
to the onset of two-dimensionality. An anticrossing of the initial ground and
first excited states is found as the asymmetry of the potential is varied
giving rise to two anticrossing events which occur on either side of symmetric
confinement. We present results analysing this behaviour and showing how it can
be affected by the inhomogeneity in background potential. The use of an
enhanced source-drain voltage to alter the energy levels is shown to be a
significant validation of the analysis by showing the formation of double rows
of electrons which correlate with the anticrossing.


Abstract:
  For any given positive integer $l$, we prove that every plane deformation of
a circle which preserves the $1/2$ and $1/(2l+1)$-rational caustics is trivial
i.e. the deformation consists only of similarities (rescalings plus
isometries).


Abstract:
  The progress in neuromorphic computing is fueled by the development of novel
nonvolatile memories capable of storing analog information and implementing
neural computation efficiently. However, like most other analog circuits, these
devices and circuits are prone to imperfections, such as temperature
dependency, noise, tuning error, etc., often leading to considerable
performance degradation in neural network implementations. Indeed,
imperfections are major obstacles in the path of further progress and ultimate
commercialization of these technologies. Hence, a practically viable approach
should be developed to deal with these nonidealities and unleash the full
potential of nonvolatile memories in neuromorphic systems. Here, for the first
time, we report a comprehensive characterization of critical imperfections in
two analog-grade memories, namely passively-integrated memristors and
redesigned eFlash memories, which both feature long-term retention, high
endurance, analog storage, low-power operation, and compact nano-scale
footprint. Then, we propose a holistic approach that includes modifications in
the training, tuning algorithm, memory state optimization, and circuit design
to mitigate these imperfections. Our proposed methodology is corroborated on a
hybrid software/experimental framework using two benchmarks: a moderate-size
convolutional neural network and ResNet-18 trained on CIFAR-10 and ImageNet
datasets, respectively. Our proposed approaches allow 2.5x to 9x improvements
in the energy consumption of memory arrays during inference and sub-percent
accuracy drop across 25-100 C temperature range. The defect tolerance is
improved by >100x, and a sub-percent accuracy drop is demonstrated in deep
neural networks built with 64x64 passive memristive crossbars featuring 25%
normalized switching threshold variations.


Abstract:
  We study the behavior of R\'enyi entropies for pure states from standard
assumptions about chaos in the high-energy spectrum of the Hamiltonian of a
many-body quantum system. We compute the exact long-time averages of R\'enyi
entropies and show that the quantum noise around these values is exponentially
suppressed in the microcanonical entropy. For delocalized states over the
microcanonical band, the long-time average approximately reproduces the
equilibration proposal of H. Liu and S. Vardhan, with extra structure arising
at the order of non-planar permutations. We analyze the equilibrium
approximation for AdS/CFT systems describing black holes in equilibrium in a
box. We extend our analysis to the situation of an evaporating black hole, and
comment on the possible gravitational description of the new terms in our
approximation.


Abstract:
  Beam breakup instability is a potential issue for all particle accelerators
and is often the limiting factor for the maximum beam current that can be
achieved. This is particularly relevant for Energy Recovery Linacs with
multiple passes where a relatively small amount of charge can result in a large
beam current. Recent studies have shown that the choice of filling pattern and
recirculation scheme for a multi-pass energy recovery linac can drastically
affect the interactions between the beam and RF system. In this paper we
further explore this topic to study how filling patterns affect the beam
breakup instability and how this can allow us to optimise the design in order
to minimise this effect. We present a theoretical model of the beam-RF
interaction as well as numerical modeling and show that the threshold current
can vary by factors of 2-4, and potentially even more depending on the machine
design parameters. Therefore a judicious choice of filling pattern can greatly
increase the onset of BBU, expanding the utility of future ERLs.


Abstract:
  This paper aims to devise a generalized maximum likelihood (ML) estimator to
robustly detect signals with unknown noise statistics in multiple-input
multiple-output (MIMO) systems. In practice, there is little or even no
statistical knowledge on the system noise, which in many cases is non-Gaussian,
impulsive and not analyzable. Existing detection methods have mainly focused on
specific noise models, which are not robust enough with unknown noise
statistics. To tackle this issue, we propose a novel ML detection framework to
effectively recover the desired signal. Our framework is a fully probabilistic
one that can efficiently approximate the unknown noise distribution through a
normalizing flow. Importantly, this framework is driven by an unsupervised
learning approach, where only the noise samples are required. To reduce the
computational complexity, we further present a low-complexity version of the
framework, by utilizing an initial estimation to reduce the search space.
Simulation results show that our framework outperforms other existing
algorithms in terms of bit error rate (BER) in non-analytical noise
environments, while it can reach the ML performance bound in analytical noise
environments. The code of this paper is available at
https://github.com/skypitcher/manfe.


Abstract:
  The applicability of process mining techniques hinges on the availability of
event logs capturing the execution of a business process. In some use cases,
particularly those involving customer-facing processes, these event logs may
contain private information. Data protection regulations restrict the use of
such event logs for analysis purposes. One way of circumventing these
restrictions is to anonymize the event log to the extent that no individual can
be singled out using the anonymized log. This paper addresses the problem of
anonymizing an event log in order to guarantee that, upon disclosure of the
anonymized log, the probability that an attacker may single out any individual
represented in the original log, does not increase by more than a threshold.
The paper proposes a differentially private disclosure mechanism, which
oversamples the cases in the log and adds noise to the timestamps to the extent
required to achieve the above privacy guarantee. The paper reports on an
empirical evaluation of the proposed approach using 14 real-life event logs in
terms of data utility loss and computational efficiency.


Abstract:
  Recently, Rendle has warned that the use of sampling-based top-$k$ metrics
might not suffice. This throws a number of recent studies on deep
learning-based recommendation algorithms, and classic non-deep-learning
algorithms using such a metric, into jeopardy. In this work, we thoroughly
investigate the relationship between the sampling and global top-$K$ Hit-Ratio
(HR, or Recall), originally proposed by Koren[2] and extensively used by
others. By formulating the problem of aligning sampling top-$k$ ($SHR@k$) and
global top-$K$ ($HR@K$) Hit-Ratios through a mapping function $f$, so that
$SHR@k\approx HR@f(k)$, we demonstrate both theoretically and experimentally
that the sampling top-$k$ Hit-Ratio provides an accurate approximation of its
global (exact) counterpart, and can consistently predict the correct winners
(the same as indicate by their corresponding global Hit-Ratios).


Abstract:
  We look for minimal conditions on a two-dimensional metric surface $X$ of
locally finite Hausdorff $2$-measure under which $X$ admits an (almost)
parametrization with good geometric and analytic properties. Only assuming that
$X$ is locally geodesic, we show that Jordan domains in $X$ of finite boundary
length admit a quasiconformal almost parametrization. If $X$ satisfies some
further conditions then such an almost parametrization can be upgraded to a
geometrically quasiconformal homeomorphism or a quasisymmetric homeomorphism.
In particular, we recover Rajala's recent quasiconformal uniformization theorem
in the special case that $X$ is locally geodesic as well as Bonk-Kleiner's
quasisymmetric uniformization theorem. On the way we establish the existence of
Sobolev discs spanning a given Jordan curve in $X$ under nearly minimal
assumptions on $X$ and prove the continuity of energy minimizers.


Abstract:
  In Japan, teacher and student is randomly matched in the first year of
elementary school. Under the quasi-natural experimental setting, we examine how
learning in female teacher homeroom class in the elementary school influence
pupils' smoking behavior after they become adult. We found that pupils are
unlikely to smoke later in life if they belonged to female teacher homeroom
class in pupil's first year of school.


Abstract:
  Colloidal gels formed by strongly attractive particles at low particle volume
fractions are composed of space-spanning networks of uniformly sized clusters.
We study the thermal fluctuations of the clusters using differential dynamic
microscopy by decomposing them into two modes of dynamics, and link them to the
macroscopic viscoelasticity via rheometry. The first mode, dominant at early
times, represents the localized, elastic fluctuations of individual clusters.
The second mode, pronounced at late times, reflects the collective,
viscoelastic dynamics facilitated by the connectivity of the clusters. By
mixing two types of particles of distinct attraction strengths in different
proportions, we control the transition time at which the collective mode starts
to dominate, and hence tune the frequency dependence of the linear viscoelastic
moduli of the binary gels.


Abstract:
  In the paper we continue to study Special Bohr-Sommerfeld geometry of compact
symplectic manifolds. Using natural deformation parameters we avoid the
difficulties appeared in the definition of the moduli space of Special
Bohr-Sommerfeld cycles for compact simply connected algebraic varieties. As a
byproduct we present certain remarks on the Weinstein structures and Eliashberg
conjectures.


Abstract:
  Because all stars contribute to its gravitational potential, stellar clusters
amplify perturbations collectively. In the limit of small fluctuations, this is
described through linear response theory, via the so-called response matrix.
While the evaluation of this matrix is somewhat straightforward for unstable
modes (i.e. with a positive growth rate), it requires a careful analytic
continuation for damped modes (i.e. with a negative growth rate). We present a
generic method to perform such a calculation in spherically symmetric stellar
clusters. When applied to an isotropic isochrone cluster, we recover the
presence of a low-frequency weakly damped $\ell = 1$ mode. We finally use a set
of direct $N$-body simulations to test explicitly this prediction through the
statistics of the correlated random walk undergone by a cluster's density
centre.


Abstract:
  Convergence of order $O(1/\sqrt{n})$ is obtained for the distance in total
variation between the Poisson distribution and the distribution of the number
of fixed size cycles in generalized random graphs with random vertex weights.
The weights are assumed to be independent identically distributed random
variables which have a power-law distribution. The proof is based on the
Chen--Stein approach and on the derived properties of the ratio of the sum of
squares of random variables and the sum of these variables. These properties
can be applied to other asymptotic problems related to generalized random
graphs.


Abstract:
  Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is
the task of predicting the entailment relation between a pair of sentences
(premise and hypothesis). This task has been described as a valuable testing
ground for the development of semantic representations, and is a key component
in natural language understanding evaluation benchmarks. Models that understand
entailment should encode both, the premise and the hypothesis. However,
experiments by Poliak et al. revealed a strong preference of these models
towards patterns observed only in the hypothesis, based on a 10 dataset
comparison. Their results indicated the existence of statistical irregularities
present in the hypothesis that bias the model into performing competitively
with the state of the art. While recast datasets provide large scale generation
of NLI instances due to minimal human intervention, the papers that generate
them do not provide fine-grained analysis of the potential statistical patterns
that can bias NLI models. In this work, we analyze hypothesis-only models
trained on one of the recast datasets provided in Poliak et al. for word-level
patterns. Our results indicate the existence of potential lexical biases that
could contribute to inflating the model performance.