Abstract:
  Let $V$ be an elementary abelian $2$-group and $X$ be a finite
$V$-CW-complex. In this memoir we study two cochain complexes of modules over
the mod2 Steenrod algebra $\mathrm{A}$, equipped with an action of
$\mathrm{H}^{*}V$, the mod2 cohomology of $V$, both associated with $X$. The
first, which we call the "topological complex", is defined using the orbit
filtration of $X$. The second, which we call the "algebraic complex", is
defined just in terms of the unstable $\mathrm{A}$-module $\mathrm{H}^*_V X$,
the mod2 equivariant cohomology of $X$. Our study makes intensive use of the
theory of unstable $\mathrm{H}^{*}V$-$\mathrm{A}$-modules which is a by-product
of the researches on Sullivan conjecture. There is a noteworthy overlap between
the topological part of our memoir and the paper "Syzygies in equivariant
cohomology in positive characteristic", by Allday, Franz and Puppe, which has
just appeared; however our techniques are quite different from theirs (the name
"Steenrod" does not show up in their article).


Abstract:
  Sun-like stars form from the contraction of cold and dense interstellar
clouds. How the collapse proceeds and what are the main physical processes
driving it, however, is still under debate and a final consensus on the
timescale of the process has not been reached. Does this contraction proceed
slowly, sustained by strong magnetic fields and ambipolar diffusion, or is it
driven by fast collapse with gravity dominating the entire process? One way to
answer this question is to measure the age of prestellar cores through
statistical methods based on observations or via reliable chemical
chronometers, which should better reflect the physical conditions of the cores.
Here we report APEX observations of ortho-H$_2$D$^+$ and para-D$_2$H$^+$ for
six cores in the Ophiuchus complex and combine them with detailed
three-dimensional magneto-hydrodynamical simulations including chemistry,
providing a range of ages for the observed cores up to 200 kyr. The outcome of
our simulations and subsequent analysis provides a good match with the
observational results in terms of physical (core masses and volume densities)
and dynamical parameters such as the Mach number and the virial parameter. We
show that models of fast collapse successfully reproduce the observed range of
chemical abundance ratios as the timescales to reach the observed stages is
comparable to the dynamical time of the cores (i.e. the free-fall time) and
much shorter than the ambipolar diffusion time, measured from the electron
fraction in the simulations. To confirm that this ratio can be used to
distinguish between different star-formation scenarios a larger (statistically
relevant) sample of star-forming cores should be explored.


Abstract:
  The experiments involving a slider moving on top of granular media consisting
of photoelastic particles in two dimensions have uncovered elaborate dynamics
that may vary from continuous motion to crackling, periodic motion, and
stick-slip type of behavior. We establish that there is a clear correlation
between the slider dynamics and the response of the force network that
spontaneously develop in the granular system. This correlation is established
by application of the persistence homology that allows for formulation of
objective measures for quantification of time-dependent force networks. We find
that correlation between the slider dynamics and the force network properties
is particularly strong in the dynamical regime characterized by well-defined
stick-slip type of dynamics.


Abstract:
  We investigate resource allocation scheme to reduce the energy consumption of
federated learning (FL) in the integrated fog-cloud computing enabled
Internet-of-things (IoT) networks. In the envisioned system, IoT devices are
connected with the centralized cloud server (CS) via multiple fog access points
(F-APs). We consider two different scenarios for training the local models. In
the first scenario, local models are trained at the IoT devices and the F-APs
upload the local model parameters to the CS. In the second scenario, local
models are trained at the F-APs based on the collected data from the IoT
devices and the F-APs collaborate with the CS for updating the model
parameters. Our objective is to minimize the overall energy-consumption of both
scenarios subject to FL time constraint. Towards this goal, we devise a joint
optimization of scheduling of IoT devices with the F-APs, transmit power
allocation, computation frequency allocation at the devices and F-APs and
decouple it into two subproblems. In the first subproblem, we optimize the IoT
device scheduling and power allocation, while in the second subproblem, we
optimize the computation frequency allocation. For each scenario, we develop a
conflict graph based solution to iteratively solve the two subproblems.
Simulation results show that the proposed two schemes achieve a considerable
performance gain in terms of the energy consumption minimization. The presented
simulation results interestingly reveal that for a large number of IoT devices
and large data sizes, it is more energy efficient to train the local models at
the IoT devices instead of the F-APs.


Abstract:
  In Si quantum dots, valley degree of freedom, in particular the generally
small valley splitting and the dot-dependent valley-orbit phase, adds
complexities to the low-energy electron dynamics and the associated spin qubit
manipulation. Here we propose a four-level model to extract tunnel coupling
information for a Si double quantum dot (DQD). This scheme is based on a charge
sensing measurement on the ground state as proposed in the widely used protocol
for a GaAs double dot [DiCarlo et. al., PRL 92. 226801]. Our theory can help
determine both intra- and inter-valley tunnel coupling with high accuracy, and
is robust against system parameters such as valley splittings in the individual
quantum dots.


Abstract:
  Diffusion is the movement of molecules from a region of higher concentration
to a region of lower concentration. It can be used to describe the interactions
among data points. In many machine learning problems including transductive
semi-supervised learning and few-shot learning, the relationship between
labeled and unlabeled data points is a critical component for high
classification accuracy. In this paper, inspired by the convection-diffusion
ODE, we propose a novel diffusion residual network (Diff-ResNet) to introduce
diffusion mechanism into neural networks internally. Under the structured data
assumption, it is proved that the diffusion mechanism can increase the
distance-diameter ratio that improves the separability of inter-class points
and reduces the distance among local intra-class points. This property can be
easily adopted by the residual networks for constructing the separable
hyperplanes. Extensive experiments of synthetic binary classification,
semi-supervised graph node classification and few-shot image classification in
various datasets validate the effectiveness of the proposed diffusion
mechanism.


Abstract:
  Stochastic dynamic models have been extensively used for the description of
processes with uncertainties arising in the operations research, behavioral
sciences, and many other application areas. A large class of the problems from
these domains is characterized by the necessity to deal with several distinct
groups of populations, which are usually labeled as "active" and "passive".
Motivated by important applications of queueing networks and neuroscience, the
main focus of the present work is on the analysis of reflecting stochastic
dynamics of such mixed populations. We develop a general mathematical modeling
framework to describe the reflecting stochastic dynamics for active-passive
populations. The analysis of this model is carried out via a combination of
low- and high-delity results obtained from the solution of the underlying
coupled system of SDEs and from the simulations with a
statistical-mechanics-based lattice gas model, where we employ a kinetic Monte
Carlo procedure. We provide details of the queueing theory and neuronal models
and discuss a relationship between reflecting SDEs and a model of queueing
theory via a limit theorem. Furthermore, we present several representative
numerical examples, and discuss an intrinsic interconnection between active and
passive particles in the underlying stochastic process. Finally, possible
extensions of the proposed methodology have been highlighted.


Abstract:
  A 1D mechanical model for nanogranular films, based on a structural
interface, is here presented. The analytical dispersion relation for the
frequency and lifetimes of the acoustics breathing modes is obtained in terms
of the interface layer thickness and porosity. The model is successfully
benchmarked both against 3D Finite Element Method simulations and experimental
photoacoustic data on a paradigmatic system available from the literature. A
simpler 1D model, based on an homogenized interface, is also presented and its
limitations and pitfalls discussed at the light of the more sophisticated
pillar model. The pillar model captures the relevant physics responsible for
acoustic dissipation at a disordered interface. Furthermore, the present
findings furnish to the experimentalist an easy-to-adopt, benchmarked
analytical tool to extract the interface layer physical parameters upon fitting
of the acoustic data. The model is scale invariant and may be deployed, other
than the case of granular materials, where a patched interface is involved.


Abstract:
  The theory of unified product and extending structures for alternative and
pre-alternative algebras are developed. It is proved that the extending
structures of these algebras can be classified by using some non-abelian
cohomology and deformation map theory.


Abstract:
  We study a problem concerning parabolic induction in certain $p$-adic unitary
groups. More precisely, for $E/F$ a quadratic extension of $p$-adic fields the
associated unitary group $G=\mathrm{U}(n,n+1)$ contains a parabolic subgroup
$P$ with Levi component $L$ isomorphic to $\mathrm{GL}_n(E) \times
\mathrm{U}_1(E)$. Let $\pi$ be an irreducible supercuspidal representation of
$L$ of depth zero. We use Hecke algebra methods to determine when the
parabolically induced representation $\iota_P^G \pi$ is reducible.


Abstract:
  One aim shared by multiple settings, such as continual learning or transfer
learning, is to leverage previously acquired knowledge to converge faster on
the current task. Usually this is done through fine-tuning, where an implicit
assumption is that the network maintains its plasticity, meaning that the
performance it can reach on any given task is not affected negatively by
previously seen tasks. It has been observed recently that a pretrained model on
data from the same distribution as the one it is fine-tuned on might not reach
the same generalisation as a freshly initialised one. We build and extend this
observation, providing a hypothesis for the mechanics behind it. We discuss the
implication of losing plasticity for continual learning which heavily relies on
optimising pretrained models.


Abstract:
  We analyze a diffuse interface model that couples a viscous Cahn-Hilliard
equation for the phase variable with a diffusion-reaction equation for the
nutrient concentration. The system under consideration also takes into account
some important mechanisms like chemotaxis, active transport as well as nonlocal
interaction of Oono's type. When the spatial dimension is three, we prove the
existence and uniqueness of global weak solutions to the model with singular
potentials including the physically relevant logarithmic potential. Then we
obtain some regularity properties of the weak solutions when $t>0$. In
particular, with the aid of the viscous term, we prove the so-called
instantaneous separation property of the phase variable such that it stays away
from the pure states $\pm1$ as long as $t>0$. Furthermore, we study long-time
behavior of the system, by proving the existence of a global attractor and
characterizing its $\omega$-limit set.


Abstract:
  The action of qubit channels on projective measurements on a qubit state is
used to establish an equivalence between channels and properties of generalized
measurements characterized by bias and sharpness parameters. This can be
interpreted as shifting the description of measurement dynamics from the
Schrodinger to the Heisenberg picture. In particular, unital quantum channels
are shown to induce unbiased measurements. The Markovian channels are found to
be equivalent to measurements for which sharpness is a monotonically decreasing
function of time. These results are illustrated by considering various noise
channels. Further, the effect of bias and sharpness parameters on the energy
cost of a measurement and its interplay with non-Markovianity of dynamics is
also discussed


Abstract:
  The graduates careers are the most spectacular and visible outcome of
excellent university education. This is also important for the university
performance assessment when its graduates can easily find jobs in the labor
market. The information about graduates matching their qualifications and
fields of studies versus undertaken employment, creates an important set of
data for future students and employers to be analyzed. Additionally, there is
business environment pressure to transform workplaces and whole organizations
towards a more green and sustainable form. Green Jobs (GJ) are the elements of
the whole economic transformation. This change is based on the green
qualifications and green careers which translate theoretical assumptions into
business language. Therefore, the choice of future career path is based on
specified criteria, which were examined by surveys performed among graduates by
the career office at Wroclaw University of Technology (WUT) in Poland. The aim
of this article was to address the question about the most significant criteria
of green career paths among graduates of WUT in 2019. Special attention was
paid to the GJ understood as green careers. In this article, the multi-criteria
Bellinger method was explained, presented, and then used to analyze chosen
factors of choice graduates career paths and then compared with Gale-Shapley
algorithm results in a comparative analysis. Future research can develop a
graduate profile willing to be employed in GJ.


Abstract:
  Soft cellular systems, such as foams or biological tissues, exhibit highly
complex rheological properties, even in the quasistatic regime, that numerical
modeling can help to apprehend. We present a numerical implementation of
quasistatic strain within the widely used cellular Potts model. The accuracy of
the method is tested by simulating the quasistatic strain 2D dry foams, both
ordered and disordered. The implementation of quasistatic strain in CPM allows
the investigation of sophisticated interplays between stress-strain
relationship and structural changes that take place in cellular systems.


Abstract:
  In this paper, we apply the angle-resolved Optically Detected Magnetic
Resonance (ODMR) technique to study series of strained (Cd, Mn)Te/(Cd, Mg)Te
quantum wells (QWs) produced by molecular beam epitaxy. By analyzing
characteristic features of ODMR angular scans, we determine strain-induced
axial-symmetry spin Hamiltonian parameter D with neV precision. Furthermore, we
use low-temperature optical reflectivity measurements and X-ray diffraction
scans to evaluate the local strain present in QW material. In our analysis, we
take into account different thermal expansion coefficients of GaAs substrate
and CdTe buffer. The additional deformation due to the thermal expansion
effects has the same magnitude as deformation origination from the different
compositions of the samples. Based on the evaluated deformations and values of
strain-induced axial-symmetry spin Hamiltonian parameter D, we find strain
spin-lattice coefficient G11 = (72.2 +- 1.9) neV for Mn2+ in CdTe and shear
deformation potential b = (-0.94 +- 0.11) eV for CdTe.


Abstract:
  Neural networks have enabled applications in artificial intelligence through
machine learning, and neuromorphic computing. Software implementations of
neural networks on conventional computers that have separate memory and
processor (and that operate sequentially) are limited in speed and energy
efficiency. Neuromorphic engineering aims to build processors in which hardware
mimics neurons and synapses in the brain for distributed and parallel
processing. Neuromorphic engineering enabled by photonics (optical physics) can
offer sub-nanosecond latencies and high bandwidth with low energies to extend
the domain of artificial intelligence and neuromorphic computing applications
to machine learning acceleration, nonlinear programming, intelligent signal
processing, etc. Photonic neural networks have been demonstrated on integrated
platforms and free-space optics depending on the class of applications being
targeted. Here, we discuss the prospects and demonstrated applications of these
photonic neural networks.


Abstract:
  The general linear model (GLM) is a widely popular and convenient tool for
estimating the functional brain response and identifying areas of significant
activation during a task or stimulus. However, the classical GLM is based on a
massive univariate approach that does not explicitly leverage the similarity of
activation patterns among neighboring brain locations. As a result, it tends to
produce noisy estimates and be underpowered to detect significant activations,
particularly in individual subjects and small groups. A recent alternative, a
cortical surface-based spatial Bayesian GLM, leverages spatial dependencies
among neighboring cortical vertices to produce more accurate estimates and
areas of functional activation. The spatial Bayesian GLM can be applied to
individual and group-level analysis. In this study, we assess the reliability
and power of individual and group-average measures of task activation produced
via the surface-based spatial Bayesian GLM. We analyze motor task data from 45
subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also
extend the model to multi-run analysis and employ subject-specific cortical
surfaces rather than surfaces inflated to a sphere for more accurate
distance-based modeling. Results show that the surface-based spatial Bayesian
GLM produces highly reliable activations in individual subjects and is powerful
enough to detect trait-like functional topologies. Additionally, spatial
Bayesian modeling enhances reliability of group-level analysis even in
moderately sized samples (n=45). The power of the spatial Bayesian GLM to
detect activations above a scientifically meaningful effect size is nearly
invariant to sample size, exhibiting high power even in small samples (n=10).
The spatial Bayesian GLM is computationally efficient in individuals and groups
and is convenient to implement with the open-source BayesfMRI R package.


Abstract:
  We prove new $L^p(\mathbb{R}^3)$ bounds on Stein's square function for
$p\geq3.25$. As an application, it improves the maximal Bochner-Riesz
conjecture to the same range of $p$.


Abstract:
  In large-scale systems there are fundamental challenges when centralised
techniques are used for task allocation. The number of interactions is limited
by resource constraints such as on computation, storage, and network
communication. We can increase scalability by implementing the system as a
distributed task-allocation system, sharing tasks across many agents. However,
this also increases the resource cost of communications and synchronisation,
and is difficult to scale.
  In this paper we present four algorithms to solve these problems. The
combination of these algorithms enable each agent to improve their task
allocation strategy through reinforcement learning, while changing how much
they explore the system in response to how optimal they believe their current
strategy is, given their past experience. We focus on distributed agent systems
where the agents' behaviours are constrained by resource usage limits, limiting
agents to local rather than system-wide knowledge. We evaluate these algorithms
in a simulated environment where agents are given a task composed of multiple
subtasks that must be allocated to other agents with differing capabilities, to
then carry out those tasks. We also simulate real-life system effects such as
networking instability. Our solution is shown to solve the task allocation
problem to 6.7% of the theoretical optimal within the system configurations
considered. It provides 5x better performance recovery over no-knowledge
retention approaches when system connectivity is impacted, and is tested
against systems up to 100 agents with less than a 9% impact on the algorithms'
performance.


Abstract:
  Topic models such as the Structural Topic Model (STM) estimate latent topical
clusters within text. An important step in many topic modeling applications is
to explore relationships between the discovered topical structure and metadata
associated with the text documents. Methods used to estimate such relationships
must take into account that the topical structure is not directly observed, but
instead being estimated itself. The authors of the STM, for instance, perform
repeated OLS regressions of sampled topic proportions on metadata covariates by
using a Monte Carlo sampling technique known as the method of composition. In
this paper, we propose two improvements: first, we replace OLS with more
appropriate Beta regression. Second, we suggest a fully Bayesian approach
instead of the current blending of frequentist and Bayesian methods. We
demonstrate our improved methodology by exploring relationships between Twitter
posts by German members of parliament (MPs) and different metadata covariates.


Abstract:
  Using miniscope recordings of calcium fluorescence signals in the CA1 region
of the hippocampus of mice, we monitor the neural activity of hippocampal
regions while the animals are freely moving in an open chamber. Using a
data-driven statistical modeling approach, the statistical properties of the
recorded data are mapped to spin-glass models with pairwise interactions.
Considering the parameter space of the model, the observed system is generally
near a critical state between two distinct phases. The close proximity to the
criticality is found to be robust against different ways of sampling and
segmentation of the measured data. By independently altering the coupling
distribution and the network structure of the statistical model, the network
structures are found to be vital to maintain the proximity to the critical
state. We further find the observed assignment of the coupling strengths makes
the net coupling at each site more balanced with slight variation, which likely
helps the maintenance of the critical state. Network analysis on the
connectivity obtained by thresholding the coupling strengths find the
connectivity of the networks to be well described by a random network model.
These results are consistent across different experiments, sampling and
segmentation choices in our analysis.


Abstract:
  The aim of this note is to explore the interplay between the eikonal
resummation in impact-parameter space and the exponentiation of infrared
divergences in momentum space for gravity amplitudes describing collisions of
massive objects. The eikonal governs the classical dynamics relevant to the
two-body problem, and its infrared properties are directly linked to the
zero-frequency limit of the gravitational wave emission spectrum and to
radiation-reaction effects. Combining eikonal and infrared exponentiations it
is possible to derive these properties at a given loop order starting from
lower-loop data. This is illustrated explicitly in $\mathcal{N}=8$ supergravity
and in general relativity by deriving the divergent part of the two-loop
eikonal from tree-level and one-loop elastic amplitudes.


Abstract:
  In the past decade, advances in electronics technology have made larger
imaging sensors available to the experimental fluid mechanics community. These
advancements have enabled the measurement of 2-component 2-dimensional (2C-2D)
velocity fields using particle image velocimetry (PIV) with much higher spatial
resolution than previously possible. However, due to the large size of the
sensor, the lens distortion needs to be taken into account as it will now have
a more significant effect on the measurement quality that must be corrected to
ensure accurate high-fidelity 2C-2D velocity field measurements. In this paper,
two dewarping models, a second-order rational function (R2) and a bicubic
polynomial (P3) are investigated with regards to 2C-2D PIV measurements of a
turbulent boundary layer (TBL) using a large imaging sensor. Two approaches are
considered and compared: (i) dewarping the images prior to the PIV
cross-correlation analysis and (ii) undertaking the PIV cross-correlation
analysis using the original recorded distorted images then followed by using
the mapping functions derived for image dewarping to provide the correct
spatial location of the velocity measurement point. The results demonstrate
that the use of P3 dewarping model to correct lens distortion yields better
results than the R2 dewarping model. Furthermore, both approaches for the P3
dewarping model yield results which are statistically indistinguishable.


Abstract:
  Bayesian models of axisymmetric plasmas using Gaussian processes and force
balance equations have been developed. These models give the full joint
posterior probability distributions over plasma current distributions and
pressure profiles given the magnetic field and pressure measurements
simultaneously. The toroidal currents such as plasma and magnetic field coil
currents are modelled as a grid of toroidal current carrying solid beams. The
plasma pressure and poloidal current flux profiles are given as a function of
the poloidal magnetic flux surface, determined by the toroidal currents.
Inference of all these physics parameters is a tomographic problem, thus, in
order to exclude unreasonable solutions, two different prior distributions have
been exploited: a Gaussian process prior and an equilibrium prior. The Gaussian
process prior constrains the plasma current distributions by their covariance
(smoothness) function whose hyperparameters have been optimally selected by
Bayesian Occam's razor. On the other hand, the equilibrium prior imposes the
magnetohydrodynamic force balance by introducing observations that the
differences between the magnetic force and the plasma pressure gradient are
almost zero at every plasma current beam. These \textit{virtual} observations
emphasise equilibrium solutions \textit{a~priori} as a part of the prior
knowledge. These models with the two different priors employ predictive models
of magnetic sensors and other plasma diagnostics in order to find all possible
solutions consistent with all the measurements. The complex, high dimensional
posterior distributions are explored by a new method based on the Gibbs
sampling scheme.


Abstract:
  The nucleon-pion-state contribution to QCD two-point and three-point
functions relevant for lattice calculations of the nucleon electromagnetic form
factors are studied in chiral perturbation theory. To leading order the results
depend on a few experimentally known low-energy constants only, and the
nucleon-pion-state contribution to the form factors can be estimated. The
nucleon-pion-state contribution to the electric form factor $G_{\rm E}(Q^2)$ is
at the +5 percent level for a source-sink separation of 2 fm, and it increases
with increasing momentum transfer $Q^2$. For the magnetic form factor the
nucleon-pion-state contribution leads to an underestimation of $G_{\rm M}(Q^2)$
by about 5 percent that decreases with increasing $Q^2$. For smaller
source-sink separations that are accessible in present-day lattice simulations
the impact is larger, although the ChPT results may not be applicable for such
small time separations. Still, a comparison with lattice data at $t\approx 1.6$
fm works reasonably well.


Abstract:
  Primordial black holes (PBHs) has been considered to form a part of dark
matter for a long time but the possibility has been poorly constrained over a
wide mass range, including the stellar mass range ($1-100~M_{\odot}$). However,
due to the discovery of merger events of black hole binaries by LIGO-Virgo
gravitational wave observatories, the interest for PBHs in the stellar mass
window has been aroused again. Fast radio bursts (FRBs) are bright radio
transients with millisecond duration and very high all-sky occurrence rate.
Lensing effect of these bursts has been proposed as one of the optimal probes
for constraining the abundance of PBHs in the stellar mass range. In this
paper, we first investigate constraints on the abundance of PBHs from the
latest $593$ FRB observations for both the monochromatic mass distribution and
three other popular extended mass distributions related to different formation
mechanisms of PBHs. It is found that constraints from currently public FRB
observations are relatively weaker than those from existing gravitational wave
detections. Furthermore, we forecast constraining power of future FRB
observations on the abundance of PBHs with different mass distributions of PBHs
and different redshift distributions of FRBs taken into account. Finally, We
find that constraints of parameter space on extended mass distributions from
$\sim10^5$ FRBs with $\overline{\Delta t}\leq1 ~\rm ms$ would be comparable
with what can be constrained from gravitational wave events. It is foreseen
that upcoming complementary multi-messenger observations will yield
considerable constraints on the possibilities of PBHs in this intriguing mass
window.


Abstract:
  Generative modelling has been a topic at the forefront of machine learning
research for a substantial amount of time. With the recent success in the field
of machine learning, especially in deep learning, there has been an increased
interest in explainable and interpretable machine learning. The ability to
model distributions and provide insight in the density estimation and exact
data likelihood is an example of such a feature. Normalizing Flows (NFs), a
relatively new research field of generative modelling, has received substantial
attention since it is able to do exactly this at a relatively low cost whilst
enabling competitive generative results. While the generative abilities of NFs
are typically explored, we focus on exploring the data distribution modelling
for Out-of-Distribution (OOD) detection. Using one of the state-of-the-art NF
models, GLOW, we attempt to detect OOD examples in the ISIC dataset. We notice
that this model under performs in conform related research. To improve the OOD
detection, we explore the masking methods to inhibit co-adaptation of the
coupling layers however find no substantial improvement. Furthermore, we
utilize Wavelet Flow which uses wavelets that can filter particular frequency
components, thus simplifying the modeling process to data-driven conditional
wavelet coefficients instead of complete images. This enables us to efficiently
model larger resolution images in the hopes that it would capture more relevant
features for OOD. The paper that introduced Wavelet Flow mainly focuses on its
ability of sampling high resolution images and did not treat OOD detection. We
present the results and propose several ideas for improvement such as
controlling frequency components, using different wavelets and using other
state-of-the-art NF architectures.


Abstract:
  Due to high annotation costs making the best use of existing human-created
training data is an important research direction. We, therefore, carry out a
systematic evaluation of transferability of BERT-based neural ranking models
across five English datasets. Previous studies focused primarily on zero-shot
and few-shot transfer from a large dataset to a dataset with a small number of
queries. In contrast, each of our collections has a substantial number of
queries, which enables a full-shot evaluation mode and improves reliability of
our results. Furthermore, since source datasets licences often prohibit
commercial use, we compare transfer learning to training on pseudo-labels
generated by a BM25 scorer. We find that training on pseudo-labels -- possibly
with subsequent fine-tuning using a modest number of annotated queries -- can
produce a competitive or better model compared to transfer learning. Yet, it is
necessary to improve the stability and/or effectiveness of the few-shot
training, which, sometimes, can degrade performance of a pretrained model.


Abstract:
  Let $\varepsilon>0$ be a small parameter. We consider the domain
$\Omega_\varepsilon:=\Omega\setminus D_\varepsilon$, where $\Omega$ is an open
domain in $\mathbb{R}^n$, and $D_\varepsilon$ is a family of small balls of the
radius $d_\varepsilon=o(\varepsilon)$ distributed periodically with period
$\varepsilon$. Let $\Delta_\varepsilon$ be the Laplace operator in
$\Omega_\varepsilon$ subject to the Robin condition ${\partial u\over \partial
n}+\gamma_\varepsilon u = 0$ with $\gamma_\varepsilon\ge 0$ on the boundary of
the holes and the Dirichlet condition on the exterior boundary. Kaizu (1985,
1989) and Brillard (1988) have shown that, under appropriate assumptions on
$d_\varepsilon$ and $\gamma_\varepsilon$, the operator $\Delta_\varepsilon$
converges in the strong resolvent sense to the sum of the Dirichlet Laplacian
in $\Omega$ and a constant potential. We improve this result deriving estimates
on the rate of convergence in terms of $L^2\to L^2$ and $L^2\to H^1$ operator
norms. As a byproduct we establish the estimate on the distance between the
spectra of the associated operators.


Abstract:
  Modern large-scale knowledge graphs, such as DBpedia, are datasets which
require large computational resources to serve and process. Moreover, they
often have longer release cycles, which leads to outdated information in those
graphs. In this paper, we present DBpedia on Demand -- a system which serves
DBpedia resources on demand without the need to materialize and store the
entire graph, and which even provides limited querying functionality.


Abstract:
  We study the $a_1a_1$ and $Za_1$ decay channels of the next-to-lightest
CP-even Higgs boson $h_2$ of the NMSSM at the LHC, where the $h_2$ is produced
in gluon fusion. It is found that while the $h_2$ discovery is impossible
through the latter channel, the former one in the $ 4\tau$ final state is a
promising channel to discover the $h_2$ with masses up to around 250 GeV at the
LHC. Such a discovery of the $h_2$ is mostly accompanied with a light $a_1$,
which is a clear evidence for distinguishing the NMSSM from the MSSM since such
a light $a_1$ is impossible in the MSSM.


Abstract:
  It is easier to hear birds than see them, however, they still play an
essential role in nature and they are excellent indicators of deteriorating
environmental quality and pollution. Recent advances in Machine Learning and
Convolutional Neural Networks allow us to detect and classify bird sounds, by
doing this, we can assist researchers in monitoring the status and trends of
bird populations and biodiversity in ecosystems. We propose a sound detection
and classification pipeline for analyzing complex soundscape recordings and
identify birdcalls in the background. Our pipeline learns from weak labels,
classifies fine-grained bird vocalizations in the wild, and is robust against
background sounds (e.g., airplanes, rain, etc). Our solution achieved 10th
place of 816 teams at the BirdCLEF 2021 Challenge hosted on Kaggle.


Abstract:
  Metal-Organic Frameworks (MOFs) have gained much interest due to their
intrinsic tunable nature. In this work, we study how linker functionalization
modifies the electronic structure of the host MOF, more specifically the
MIL-47(V)-R (R=-F, -Cl, -Br, -OH, -CH$_3$, -CF$_3$, and -OCH$_3$). It is shown
that the presence of a functional group leads to a splitting of the
$\pi$-orbital on the linker. Moreover, the upward shift of the split-off
$\pi$-band correlates well with the electron withdrawing/donating nature of the
functional groups. For halide functional groups the presence of lone-pair back
donation is corroborated by calculated Hirshfeld-I charges. In case of the
ferromagnetic configuration of the host MIL-47(V$^{\mathrm{+IV}}$) material a
half-metal to insulator transition is noted for the -Br, -OCH$_3$, and -OH
functional groups, while for the anti-ferromagnetic configuration only the
hydroxy-group results in an effective reduction of the band gap.


Abstract:
  We present a simulation showing that super-Nyquist frequencies may have
periodic amplitude and frequency modulations, even if actually stable, in time
series sampled like the Kepler data. These modulations are caused by the
barycentric time correction, which destroys the evenly spaced time
measurements, making the Nyquist frequency variable over the spacecraft orbit
around the Sun. These modulations can easily be identified in pulsating stars
from Kepler's photometric data.


Abstract:
  PNIPAm microgels synthesized via free radical polymerization (FRP) are often
considered as neutral colloids in aqueous media, although it is well known,
since the pioneering work of Pelton and coworkers [Langmuir 1989, 5, 816-818],
that the vanishing electrophoretic mobility characterizing swollen microgels
largely increases above the lower critical solution temperature (LCST) of
PNIPAm, at which microgels partially collapse. The presence of an electric
charge has been attributed to the ionic initiators that are employed when FRP
is performed in water and that stay anchored to microgel particles. Combining
dynamic light scattering (DLS), electrophoresis, transmission electron
microscopy (TEM) and atomic force microscopy (AFM) experiments, we show that
collapsed ionic PNIPAm microgels undergo large mobility reversal and reentrant
condensation when they are co-suspended with oppositely charged
polyelectrolytes (PE) or nanoparticles (NP), while their stability remains
unaffected by PE or NP addition at lower temperatures, where microgels are
swollen and their charge density is low. Our results highlight a somehow
double-faced electrostatic behavior of PNIPAm microgels due to their tunable
charge density: they behave as quasi-neutral colloids at temperature below
LCST, while they strongly interact with oppositely charged species when they
are in their collapsed state. The very similar phenomenology encountered when
microgels are surrounded by polylysine chains and silica nanoparticles points
to the general character of this twofold behavior of PNIPAm-based colloids in
water.


Abstract:
  We use semi-derived Ringel-Hall algebras of quivers with loops to realize the
whole quantum Borcherds-Bozec algebras and quantum generalized Kac-Moody
algebras.


Abstract:
  Relevant information about physical properties of the surface of airless
bodies such as porosity, particle size, or roughness can be inferred knowing
the dependence of the brightness with illumination and observing geometry.
Additionally, this knowledge is necessary to standardize or photometrically
correct data acquired under different illumination conditions. In this work we
develop a robust, automatic, and efficient photometric modeling methodology
which is tested and validated using Bennu images acquired by the camera MapCam
from the OSIRIS-REx spacecraft. It consists of a supervised machine learning
algorithm through an artificial neural network. Our system provides a more
precise modeling for all color filters than the previous procedures which are
already published, offering an improvement over this classic approach of up to
14.30%, as well as a considerable reduction in computing time.


Abstract:
  Testing is recommended for all close contacts of confirmed COVID-19 patients.
However, existing group testing methods are oblivious to the circumstances of
contagion provided by contact tracing. Here, we build upon a well-known
semi-adaptive pool testing method, Dorfman's method with imperfect tests, and
derive a simple group testing method based on dynamic programming that is
specifically designed to use the information provided by contact tracing.
Experiments using a variety of reproduction numbers and dispersion levels,
including those estimated in the context of the COVID-19 pandemic, show that
the pools found using our method result in a significantly lower number of
tests than those found using standard Dorfman's method, especially when the
number of contacts of an infected individual is small. Moreover, our results
show that our method can be more beneficial when the secondary infections are
highly overdispersed.


Abstract:
  The online recruitment matching system has been the core technology and
service platform in CareerBuilder. One of the major challenges in an online
recruitment scenario is to provide good matches between job posts and
candidates using a recommender system on the scale. In this paper, we discussed
the techniques for applying an embedding-based recommender system for the large
scale of job to candidates matching. To learn the comprehensive and effective
embedding for job posts and candidates, we have constructed a fused-embedding
via different levels of representation learning from raw text, semantic
entities and location information. The clusters of fused-embedding of job and
candidates are then used to build and train the Faiss index that supports
runtime approximate nearest neighbor search for candidate retrieval. After the
first stage of candidate retrieval, a second stage reranking model that
utilizes other contextual information was used to generate the final matching
result. Both offline and online evaluation results indicate a significant
improvement of our proposed two-staged embedding-based system in terms of
click-through rate (CTR), quality and normalized discounted accumulated gain
(nDCG), compared to those obtained from our baseline system. We further
described the deployment of the system that supports the million-scale job and
candidate matching process at CareerBuilder. The overall improvement of our job
to candidate matching system has demonstrated its feasibility and scalability
at a major online recruitment site.


Abstract:
  This paper reports a CPU-level real-time stereo matching method for surgical
images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed
method is built on the fast ''dense inverse searching'' algorithm, which
estimates the disparity of the stereo images. The overlapping image patches
(arbitrary squared image segment) from the images at different scales are
aligned based on the photometric consistency presumption. We propose a Bayesian
framework to evaluate the probability of the optimized patch disparity at
different scales. Moreover, we introduce a spatial Gaussian mixed probability
distribution to address the pixel-wise probability within the patch. In-vivo
and synthetic experiments show that our method can handle ambiguities resulted
from the textureless surfaces and the photometric inconsistency caused by the
Lambertian reflectance. Our Bayesian method correctly balances the probability
of the patch for stereo images at different scales. Experiments indicate that
the estimated depth has higher accuracy and fewer outliers than the baseline
methods in the surgical scenario.


Abstract:
  We propose a machine learning based approach for automatic 3D building
reconstruction and vectorization. Taking a single-channel photogrammetric
digital surface model (DSM) and panchromatic (PAN) image as input, we first
filter out non-building objects and refine the building shapes of input DSM
with a conditional generative adversarial network (cGAN). The refined DSM and
the input PAN image are then used through a semantic segmentation network to
detect edges and corners of building roofs. Later, a set of vectorization
algorithms are proposed to build roof polygons. Finally, the height information
from the refined DSM is added to the polygons to obtain a fully vectorized
level of detail (LoD)-2 building model. We verify the effectiveness of our
method on large-scale satellite images, where we obtain state-of-the-art
performance.


Abstract:
  We outline the Great Misalignment Problem in natural language processing
research, this means simply that the problem definition is not in line with the
method proposed and the human evaluation is not in line with the definition nor
the method. We study this misalignment problem by surveying 10 randomly sampled
papers published in ACL 2020 that report results with human evaluation. Our
results show that only one paper was fully in line in terms of problem
definition, method and evaluation. Only two papers presented a human evaluation
that was in line with what was modeled in the method. These results highlight
that the Great Misalignment Problem is a major one and it affects the validity
and reproducibility of results obtained by a human evaluation.


Abstract:
  Let $f(z,w)=(p(z),q(z,w))$ be a holomorphic skew product with a
superattracting fixed point at the origin. In the previous paper we have
succeeded to specify a dominant term of $q$ by the order of $p$ and the Newton
polygon of $q$, and to construct a B\"{o}ttcher coordinate on an invariant
wedge. By using the same idea and terminologies, we give inequalities of
attraction rates for the vertical dynamics of $f$ in this paper. The results
hold not only for the superattracting case, but for all the other cases.


Abstract:
  We consider a problem of superconductivity coexistence with the
spin-density-wave order in disordered multiband metals. It is assumed that
random variations of the disorder potential on short length scales render the
interactions between electrons to develop spatial correlations. As a
consequence, both superconducting and magnetic order parameters become
spatially inhomogeneous and are described by the universal phenomenological
quantities, whereas all the microscopic details are encoded in the correlation
function of the coupling strength fluctuations. We consider a minimal model
with two nested two-dimensional Fermi surfaces and disorder potentials which
include both intra- and inter-band scattering. The model is analyzed using the
quasiclassical approach to show that short-scale pairing-potential disorder
leads to a broadening of the coexistence region.


Abstract:
  We propose a new, exactly solvable Schr\"{o}dinger equation. The potential
partner is given by \[{
V=}-Bp\operatorname{csch}[px]^{2}-9p(B+p)\operatorname*{sech}[3px]^{2}+(B\coth[px]-3(B+p)\tanh[3px])^{2}.\]
obtained using supersymmetric method with shape invariance property having a
superpotential $W(x,A,B)=A\tanh 3px-B\coth px.$ We derive entirely the exact
solutions of this family of Schr\"{o}dinger equations with the eigenvalue given
by $E_{n}^{\left( -\right) }=(A-B)^{2}-(A-B-4np)^{2}% $ and the corresponding
eigenfunctions are determined exactly and in closed form. Schr\"{o}dinger
equations, and Sturm-Liouville equations in general, are challenging to solve
in closed form, and only a few of them are known. Therefore, in a strict
mathematical sense, discovering new solvable equations is essential in
understanding the eluded solutions' underpinnings. This result has potential
applications in nuclear physics and chemistry, and other fields of science.


Abstract:
  The rapid advancement of deep learning models that can generate and synthesis
hyper-realistic videos known as Deepfakes and their ease of access to the
general public have raised concern from all concerned bodies to their possible
malicious intent use. Deep learning techniques can now generate faces, swap
faces between two subjects in a video, alter facial expressions, change gender,
and alter facial features, to list a few. These powerful video manipulation
methods have potential use in many fields. However, they also pose a looming
threat to everyone if used for harmful purposes such as identity theft,
phishing, and scam. In this work, we propose a Convolutional Vision Transformer
for the detection of Deepfakes. The Convolutional Vision Transformer has two
components: Convolutional Neural Network (CNN) and Vision Transformer (ViT).
The CNN extracts learnable features while the ViT takes in the learned features
as input and categorizes them using an attention mechanism. We trained our
model on the DeepFake Detection Challenge Dataset (DFDC) and have achieved 91.5
percent accuracy, an AUC value of 0.91, and a loss value of 0.32. Our
contribution is that we have added a CNN module to the ViT architecture and
have achieved a competitive result on the DFDC dataset.


Abstract:
  We perform, in a manifestly $SO(n-1,1)$ [$SO(n)$] covariant fashion, the
Hamiltonian analysis of general relativity in $n$ dimensions written as a
constrained $BF$ theory. We solve the constraint on the $B$ field in a way
naturally adapted to the foliation of the spacetime that avoids explicitly the
introduction of the vielbein. This leads to a form of the action involving a
presymplectic structure, which is reduced by doing a suitable parametrization
of the connection and then, after integrating out some auxiliary fields, the
Hamiltonian form involving only first-class constraints is obtained.


Abstract:
  Recently, Kumar and Ghosh have derived Kerr-like rotating black hole
solutions in the framework of four-dimensional Einstein-Gauss-Bonnet theory of
gravity and investigated the black hole shadow. Using the steady-state
Novikov-Thorne model, we study thin accretion disk processes for such rotating
black holes including the energy flux, temperature distribution, emission
spectrum, energy conversion efficiency as well as the radius of the innermost
stable circular orbit. We also study the effects of the Gauss-Bonnet coupling
parameter $\alpha$ on these quantities. The results are compared to slowly
rotating relativistic Kerr black holes which show that for a positive
Gauss-Bonnet coupling, thin accretion disks around rotating black holes in
four-dimensional Einstein-Gauss-Bonnet gravity are hotter and more efficient
than that for Kerr black holes with the same rotation parameter $a$, while for
a negative coupling they are cooler and less efficient. Thus the accretion disk
processes may be considered as tools for testing Einstein-Gauss-Bonnet gravity
using astrophysical observations.


Abstract:
  We prove the hard Lefschetz property for pseudomanifolds and cycles in any
characteristic with respect to an appropriate Artinian reduction. The proof is
a combination of Adiprasito's biased pairing theory and a generalization of a
formula of Papadakis-Petrotou to arbitrary characteristic. In particular, we
prove the Lefschetz theorem for doubly Cohen Macaulay complexes, solving a
generalization of the g-conjecture due to Stanley. We also provide a simplified
presentation of the characteristic 2 case, and generalize it to pseudomanifolds
and cycles.


Abstract:
  In this paper we provide a formal framework for comparing the expressive
power of Behavior Trees (BTs) to other action selection architectures. Taking
inspiration from the analogous comparisons of structural programming
methodologies, we formalise the concept of `expressiveness'. This leads us to
an expressiveness hierarchy of control architectures, which includes BTs,
Decision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines
(FSMs). By distinguishing between BTs with auxiliary variables and those
without, we demonstrate the existence of a trade-off in BT design between
readability and expressiveness. We discuss what this means for BTs in practice.


Abstract:
  Data-intensive science is increasingly reliant on real-time processing
capabilities and machine learning workflows, in order to filter and analyze the
extreme volumes of data being collected. This is especially true at the energy
and intensity frontiers of particle physics where bandwidths of raw data can
exceed 100 Tb/s of heterogeneous, high-dimensional data sourced from hundreds
of millions of individual sensors. In this paper, we introduce a new
data-driven approach for designing and optimizing high-throughput data
filtering and trigger systems such as those in use at physics facilities like
the Large Hadron Collider (LHC). Concretely, our goal is to design a
data-driven filtering system with a minimal run-time cost for determining which
data event to keep, while preserving (and potentially improving upon) the
distribution of the output as generated by the hand-designed trigger system. We
introduce key insights from interpretable predictive modeling and
cost-sensitive learning in order to account for non-local inefficiencies in the
current paradigm and construct a cost-effective data filtering and trigger
model that does not compromise physics coverage.


Abstract:
  Extremely high data rates at modern synchrotron and X-ray free-electron laser
light source beamlines motivate the use of machine learning methods for data
reduction, feature detection, and other purposes. Regardless of the
application, the basic concept is the same: data collected in early stages of
an experiment, data from past similar experiments, and/or data simulated for
the upcoming experiment are used to train machine learning models that, in
effect, learn specific characteristics of those data; these models are then
used to process subsequent data more efficiently than would general-purpose
models that lack knowledge of the specific dataset or data class. Thus, a key
challenge is to be able to train models with sufficient rapidity that they can
be deployed and used within useful timescales. We describe here how specialized
data center AI (DCAI) systems can be used for this purpose through a
geographically distributed workflow. Experiments show that although there are
data movement cost and service overhead to use remote DCAI systems for DNN
training, the turnaround time is still less than 1/30 of using a locally
deploy-able GPU.


Abstract:
  In this paper we aim at presenting a concise but also comprehensive study of
time-dependent (tdependent) Hamiltonian dynamics on a locally conformal
symplectic (lcs) manifold. We present a generalized geometric theory of
canonical transformations and formulate a time-dependent geometric
Hamilton-Jacobi theory on lcs manifolds. In contrast to previous papers
concerning locally conformal symplectic manifolds, here the introduction of the
time dependency brings out interesting geometric properties, as it is the
introduction of contact geometry in locally symplectic patches. To conclude, we
show examples of the applications of our formalism, in particular, we present
systems of differential equations with time-dependent parameters, which admit
different physical interpretations as we shall point out.


Abstract:
  We investigate upper bounds on the length of cost optimal plans that are
valid for problems with 0-cost actions. We employ these upper bounds as
horizons for a SAT-based encoding of planning with costs. Given an initial
upper bound on the cost of the optimal plan, we experimentally show that this
SAT-based approach is able to compute plans with better costs, and in many
cases it can match the optimal cost. Also, in multiple instances, the approach
is successful in proving that a certain cost is the optimal plan cost.


Abstract:
  We report on the first resolved HI observations of two blue ultra-diffuse
galaxies (UDGs)using the Giant Metrewave Radio Telescope (GMRT). These
observations add to the sofar limited number of UDGs with resolved HI data. The
targets are from contrasting non-cluster environments: UDG-B1 is projected in
the outskirts of Hickson Compact Group 25 and Secco-dI-2 (SdI-2) is an isolated
UDG. These UDGs also have contrasting effective radii with Re of 3.7 kpc
(similar to the Milky Way) and 1.3 kpc respectively. SdI-2 has an unusually
large MHI/M* ratio =28.9, confirming a previous single dish HI observation.
Both galaxies display HI morphological and kinematic signatures consistent with
a recent tidal interaction, which is also supported by observations from other
wavelengths, including optical spectroscopy. Within the limits of the
observations' resolution, our analysis indicates that SdI-2 is dark
matter-dominated within its HI radius and this is also likely to be the case
for UDG-B1. Our study highlights the importance of high spatial and spectral
resolution HI observations for the study of the dark matter properties of UDGs.


Abstract:
  The H-frame (also known as H-Bot) architecture is a simple and elegant
two-axis parallel positioning system used to construct the XY stage of 3D
printers. It holds potential for high speed and excellent dynamic performance
due to the use of frame-mounted motors that reduce the moving mass of the
printer while allowing for the use of (heavy) higher torque motors. However,
the H-frame's dynamic accuracy is limited during high-acceleration and
high-speed motion due to racking -- i.e., parasitic torsional motions of the
printer's gantry due to a force couple. Mechanical solutions to the racking
problem are either costly or detract from the simplicity of the H-frame. In
this paper, we introduce a feedforward software compensation algorithm, based
on the filtered B-splines (FBS) method, that rectifies errors due to racking.
The FBS approach expresses the motion command to the machine as a linear
combination of B-splines. The B-splines are filtered through an identified
model of the machine dynamics and the control points of the B-spline based
motion command are optimized such that the tracking error is minimized. To
compensate racking using the FBS algorithm, an accurate frequency response
function of the racking motion is obtained and coupled to the H-frame's x- and
y-axis dynamics with a kinematic model. The result is a coupled linear
parameter varying model of the H-frame that is utilized in the FBS framework to
compensate racking. An approximation of the proposed racking compensation
algorithm, that decouples the x- and y-axis compensation, is developed to
significantly improve its computational efficiency with almost no loss of
compensation accuracy. Experiments on an H-frame 3D printer demonstrate a 43
percent improvement in the shape accuracy of a printed part using the proposed
algorithm compared to the standard FBS approach without racking compensation.


Abstract:
  We study the nodal set, that is the zero set, of "flat" Laplace
eigenfunctions on the standard two dimensional flat torus
$\mathbb{T}^2=\mathbb{R}^2/\mathbb{Z}^2$. We find the asymptotic law for the
nodal length of these eigenfunctions in any ball of radius larger than the
Planck-scale. In particular, we prove that the nodal set equidistributes above
Planck-scale. At Planck-scale, we show that the nodal set equidistributes
around almost every point for some eigenfunctions, but we also construct
eigenfunctions whose nodal set fails to equidistribute. The proof is based on
Bourgain's de-randomisation and the main new ingredient, which might be of
independent interest, is the integrability of arbitrarily large powers of the
logarithm of \textquotedblleft generic \textquotedblright eigenfunctions, based
on the work of Nazarov \cite{N93,Nun}, and some arithmetic ingredients called
semi-correlations.


Abstract:
  We study the quasi-periodic oscillations from the accretion disk around
rotating traversable wormholes by means of the resonance models. We investigate
the linear stability of the circular geodesic orbits in the equatorial plane
for a general class of wormhole geometries deriving analytical expressions for
the epicyclic frequencies. Since wormholes can often mimic black holes in the
astrophysical observations, we analyze the properties of the quasi-circular
oscillatory motion in comparison with the Kerr black hole. We demonstrate that
wormholes possess distinctive features, which can be observationally
significant. It is characteristic for the Kerr black hole that the orbital and
the epicyclic frequencies obey a constant ordering in the whole range of the
spin parameter. In contrast, for wormhole spacetimes we can have various types
of orderings between the frequencies in the different regions of the parametric
space. This enables the excitation of much more diverse types of resonances
including parametric and forced resonances of lower order, which could lead to
stronger observable signals. In addition, for co-rotating orbits the resonances
can be excited in a very close neighbourhood of the wormhole throat for a wide
range of values of the angular momentum, making wormholes a valuable laboratory
for testing strong gravity.


Abstract:
  Commonsense inference to understand and explain human language is a
fundamental research problem in natural language processing. Explaining human
conversations poses a great challenge as it requires contextual understanding,
planning, inference, and several aspects of reasoning including causal,
temporal, and commonsense reasoning. In this work, we introduce CIDER -- a
manually curated dataset that contains dyadic dialogue explanations in the form
of implicit and explicit knowledge triplets inferred using contextual
commonsense inference. Extracting such rich explanations from conversations can
be conducive to improving several downstream applications. The annotated
triplets are categorized by the type of commonsense knowledge present (e.g.,
causal, conditional, temporal). We set up three different tasks conditioned on
the annotated dataset: Dialogue-level Natural Language Inference, Span
Extraction, and Multi-choice Span Selection. Baseline results obtained with
transformer-based models reveal that the tasks are difficult, paving the way
for promising future research. The dataset and the baseline implementations are
publicly available at https://cider-task.github.io/cider/.


Abstract:
  The coronavirus continues to disrupt our everyday lives as it spreads at an
exponential rate. It needs to be detected quickly in order to quarantine
positive patients so as to avoid further spread. This work proposes a new
convolutional neural network (CNN) architecture called 'slow Encoding CNN. The
proposed model's best performance wrt Sensitivity, Positive Predictive Value
(PPV) found to be SP=0.67, PP=0.98, SN=0.96, and PN=0.52 on AI AGAINST COVID19
- Screening X-ray images for COVID-19 Infections competition's test data
samples. SP and PP stand for the Sensitivity and PPV of the COVID-19 positive
class, while PN and SN stand for the Sensitivity and PPV of the COVID-19
negative class.


Abstract:
  In an effort to increase spatial and temporal resolution of ultrafast
electron diffraction and microscopy, ultra-high brightness photocathodes are
actively sought to improve electron beam quality. Beam dynamics codes often
approximate the Coulomb interaction with mean-field space charge, which is a
good approximation in traditional beams. However, point-to-point Coulomb
effects, such as disorder induced heating and the Boersch effect, can not be
neglected in cold, dense beams produced by such photocathodes. In this paper,
we introduce two new numerical methods to calculate the important effects of
the photocathode image charge when using a point-to-point interaction model.
Equipped with an accurate model of the image charge, we calculate the effects
of point-to-point interactions on two high brightness photoemission beamlines
for ultrafast diffraction. The first beamline uses a 200 keV gun, whereas the
second uses a 5 MeV gun, each operating in the single-shot diffraction regime
with $10^5$ electrons/pulse. Assuming a zero photoemission temperature, it is
shown that including stochastic Coulomb effects increases the final emittance
of these beamlines by over a factor of 2 and decreases the peak transverse
phase space density by over a factor of 3 as compared to mean-field
simulations. We then introduce a method to compute the energy released by
disorder induced heating using the pair correlation function. This disorder
induced heating energy was found to scale very near the theoretical result for
stationary ultracold plasmas, and it accounts for over half of the emittance
growth above mean-field simulations.


Abstract:
  We present upper bounds on the quantum and private capacity of single-mode,
phase-insentitive Bosonic Gaussian Channels based on degradable extensions. Our
findings are state-of-the-art in the following parameter regions: low
temperature and high transmissivity for the thermal attenuator, low temperature
for additive Gaussian noise, high temperature and intermediate amplification
for the thermal amplifier.


Abstract:
  The single lap shear test is widely used to measure the strength of
dissimilar welds even though such a test brings limited understanding of the
intrinsic weld toughness. The present study proposes a numerical finite element
(FE) analysis and experimental characterization of dissimilar joints presenting
various microstructures (thickness of the intermetallic layer (IML) and
hardness profile). For this purpose, Friction Melt Bonding (FMB) and Friction
Stir Welding (FSW) were used to join aluminum AA6061 and Dual Phase steel
(DP980). The FE simulations allowed calculating the evolution of the J-integral
near this notch tip. It shows that crack initiation depends significantly on
the plastic properties of the welded metallic alloys around the notch tip and
the width of the welded zone, which both are significantly different for FSW
and FMB processes. Nevertheless, a similar weld fracture toughness J_C of
approximately 1 kJ.m-2 is estimated from the analysis for both FMB and FSW.
This is three orders of magnitude higher than the fracture toughness of the
intermetallic layer, revealing that the plastic dissipation in the Al and steel
plates around the crack tip has a major effect on the weld toughness.


Abstract:
  Recent progress in constraining the massive accretions (>1:10) experienced by
the Milky Way (MW) and the Andromeda galaxy (M31) offers an opportunity to
understand the dwarf galaxy population of the Local Group. Using zoom-in dark
matter-only simulations of MW-mass haloes and concentrating on subhaloes that
are thought to be capable of hosting dwarf galaxies, we demonstrate that the
infall of a massive progenitor is accompanied with the accretion and
destruction of a large number of subhaloes. Massive accreted progenitors do not
increase the total number of infalling subhaloes onto a MW-mass host, but
instead focus surrounding subhaloes onto the host causing a clustering in the
infall time of subhaloes. This leads to a temporary elevation in the number of
subhaloes as well as changes in their cumulative radial profile within the
virial radius of the host. Surviving associated subhaloes with a massive
progenitor have a large diversity in their orbits. We find that the star
formation quenching times of Local Group dwarf spheroidal galaxies ($10^{5}
\mathrm{M_{\odot}} \lesssim \mathrm{M}_{*} \lesssim 10^{7} \mathrm{M_{\odot}}$)
are clustered around the times of the most massive accretions suffered by the
MW and M31. Our results imply that a) the quenching time of dwarf spheroidals
is a good proxy of their infall time and b) the absence of recently quenched
satellites around M31 suggests that M33 is not on its first infall and was
accreted much earlier.


Abstract:
  We study the possibility of transferring fermions from a trivial system as
particle source to an empty system but at topological phase as a mold for
casting a stable topological insulator dynamically. We show that this can be
realized by a non-Hermitian unidirectional hopping, which connects a central
system at topological phase and a trivial flat-band system with a periodic
driving chemical potential, which scans over the valence band of the central
system. The near exceptional-point dynamics allows a unidirectional dynamical
process: the time evolution from an initial state with full-filled source
system to a stable topological insulating state approximately. The result is
demonstrated numerically by a source-assistant QWZ model and SSH chain in the
presence of random perturbation. Our finding reveals a classical analogy of
quench dynamics in quantum matter and provides a way for topological quantum
state engineering.


Abstract:
  In this work, we propose a privacy-preservation framework, TradeChain, which
decouples the trade events of participants using decentralised identities.
TradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the
following novel contributions: a) it incorporates two separate ledgers: a
public permissioned blockchain for maintaining identities and the permissioned
blockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs)
on traders' private credentials to prove multiple identities on trade ledger
and c) allows data owners to define dynamic access rules for verifying
traceability information from the trade ledger using access tokens and
Ciphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept
implementation of TradeChain is presented on Hyperledger Indy and Fabric and an
extensive evaluation of execution time, latency and throughput reveals minimal
overheads.


Abstract:
  We conduct a comparative analysis of desktop web search behaviour of users
from Germany (n=558) and Switzerland (n=563) based on a combination of web
tracking and survey data. We find that web search accounts for 13% of all
desktop browsing, with the share being higher in Switzerland than in Germany.
We find that in over 50% of cases users clicked on the first search result,
with over 97% of all clicks being made on the first page of search outputs.
Most users rely on Google when conducting searches, and users preferences for
other engines are related to their demographics. We also test relationships
between user demographics and daily number of searches, average share of search
activities among tracked events by user as well as the tendency to click on
higher- or lower-ranked results. We find differences in such relationships
between the two countries that highlights the importance of comparative
research in this domain. Further, we observe differences in the temporal
patterns of web search use between women and men, marking the necessity of
disaggregating data by gender in observational studies regarding online
information behaviour.


Abstract:
  Cell detection in histopathology images is of great value in clinical
practice. \textit{Convolutional neural networks} (CNNs) have been applied to
cell detection to improve the detection accuracy, where cell annotations are
required for network training. However, due to the variety and large number of
cells, complete annotations that include every cell of interest in the training
images can be challenging. Usually, incomplete annotations can be achieved,
where positive labeling results are carefully examined to ensure their
reliability but there can be other positive instances, i.e., cells of interest,
that are not included in the annotations. This annotation strategy leads to a
lack of knowledge about true negative samples. Most existing methods simply
treat instances that are not labeled as positive as truly negative during
network training, which can adversely affect the network performance. In this
work, to address the problem of incomplete annotations, we formulate the
training of detection networks as a positive-unlabeled learning problem.
Specifically, the classification loss in network training is revised to take
into account incomplete annotations, where the terms corresponding to negative
samples are approximated with the true positive samples and the other samples
of which the labels are unknown. To evaluate the proposed method, experiments
were performed on a publicly available dataset for mitosis detection in breast
cancer cells, and the experimental results show that our method improves the
performance of cell detection given incomplete annotations for training.


Abstract:
  The Australian Square Kilometre Array Pathfinder (ASKAP) collects images of
the sky at radio wavelengths with an unprecedented field of view, combined with
a high angular resolution and sub-millijansky sensitivities. The large quantity
of data produced is used by the ASKAP Variables and Slow Transients (VAST)
survey science project to study the dynamic radio sky. Efficient pipelines are
vital in such research, where searches often form a `needle in a haystack' type
of problem to solve. However, the existing pipelines developed among the
radio-transient community are not suitable for the scale of ASKAP datasets.
  In this paper we provide a technical overview of the new "VAST Pipeline": a
modern and scalable Python-based data pipeline for transient searches, using
up-to-date dependencies and methods. The pipeline allows source association to
be performed at scale using the Pandas DataFrame interface and the well-known
Astropy crossmatch functions. The Dask Python framework is used to parallelise
operations as well as scale them both vertically and horizontally, by means of
a cluster of workers. A modern web interface for data exploration and querying
has also been developed using the latest Django web framework combined with
Bootstrap.


Abstract:
  We present the Multilingual TEDx corpus, built to support speech recognition
(ASR) and speech translation (ST) research across many non-English source
languages. The corpus is a collection of audio recordings from TEDx talks in 8
source languages. We segment transcripts into sentences and align them to the
source-language audio and target-language translations. The corpus is released
along with open-sourced code enabling extension to new talks and languages as
they become available. Our corpus creation methodology can be applied to more
languages than previous work, and creates multi-way parallel evaluation sets.
We provide baselines in multiple ASR and ST settings, including multilingual
models to improve translation performance for low-resource language pairs.


Abstract:
  This study presents a novel pressure-based methodology for the efficient
numerical solution of a four-equation two-phase diffuse interface model. The
proposed methodology has the potential to simulate low-Mach flows with mass
transfer. In contrast to the classical conservative four-equation model
formulation, the adopted set of equations features volume fraction,
temperature, velocity and pressure as the primary variables. The model includes
the effects of viscosity, surface tension, thermal conductivity and gravity,
and has the ability to incorporate complex equations of state. Additionally, a
Gibbs free energy relaxation procedure is used to model mass transfer. A key
characteristic of the proposed methodology is the use of high performance and
scalable solvers for the solution of the Helmholtz equation for the pressure,
which drastically reduces the computational cost compared to analogous
density-based approaches. We demonstrate the capabilities of the methodology to
simulate flows with large density and viscosity ratios through extended
verification against a range of different test cases. Finally, the potential of
the methodology to tackle challenging phase change flows is demonstrated with
the simulation of three-dimensional nucleate boiling.


Abstract:
  In this work, we propose the world's first closed-loop ML-based planning
benchmark for autonomous driving. While there is a growing body of ML-based
motion planners, the lack of established datasets and metrics has limited the
progress in this area. Existing benchmarks for autonomous vehicle motion
prediction have focused on short-term motion forecasting, rather than long-term
planning. This has led previous works to use open-loop evaluation with L2-based
metrics, which are not suitable for fairly evaluating long-term planning. Our
benchmark overcomes these limitations by introducing a large-scale driving
dataset, lightweight closed-loop simulator, and motion-planning-specific
metrics. We provide a high-quality dataset with 1500h of human driving data
from 4 cities across the US and Asia with widely varying traffic patterns
(Boston, Pittsburgh, Las Vegas and Singapore). We will provide a closed-loop
simulation framework with reactive agents and provide a large set of both
general and scenario-specific planning metrics. We plan to release the dataset
at NeurIPS 2021 and organize benchmark challenges starting in early 2022.


Abstract:
  Structure from Motion (SfM) often fails to estimate accurate poses in
environments that lack suitable visual features. In such cases, the quality of
the final 3D mesh, which is contingent on the accuracy of those estimates, is
reduced. One way to overcome this problem is to combine data from a monocular
camera with that of a LIDAR. This allows fine details and texture to be
captured while still accurately representing featureless subjects. However,
fusing these two sensor modalities is challenging due to their fundamentally
different characteristics. Rather than directly fusing image features and LIDAR
points, we propose to leverage common geometric features that are detected in
both the LIDAR scans and image data, allowing data from the two sensors to be
processed in a higher-level space. In particular, we propose to find
correspondences between 3D lines extracted from LIDAR scans and 2D lines
detected in images before performing a bundle adjustment to refine poses. We
also exploit the detected and optimized line segments to improve the quality of
the final mesh. We test our approach on the recently published dataset, Newer
College Dataset. We compare the accuracy and the completeness of the 3D mesh to
a ground truth obtained with a survey-grade 3D scanner. We show that our method
delivers results that are comparable to a state-of-the-art LIDAR survey while
not requiring highly accurate ground truth pose estimates.


Abstract:
  Let $X$ be a separable Banach space and let $Q:X^*\rightarrow X$ be a linear,
bounded, non-negative and symmetric operator and let $A:D(A)\subseteq
X\rightarrow X$ be the infinitesimal generator of a strongly continuous
semigroup of contractions on $X$. We consider the abstract Wiener space
$(X,\mu_\infty,H_\infty)$ where $\mu_\infty$ is a centred non-degenerate
Gaussian measure on $X$ with covariance operator defined, at least formally, as
\begin{align*} Q_\infty=\int_0^{+\infty} e^{sA}Qe^{sA^*}ds, \end{align*} and
$H_\infty$ is the Cameron--Martin space associated to $\mu_\infty$.
  Let $H$ be the reproducing kernel Hilbert space associated with $Q$ with
inner product $[\cdot,\cdot]_H$. We assume that the operator $Q_\infty
A^*:D(A^*)\subseteq X^*\rightarrow X$ extends to a bounded linear operator
$B\in \mathcal L(H)$ which satisfies $B+B^*=-{\rm Id}_H$, where ${\rm Id}_H$
denotes the identity operator on $H$. Let $D$ and $D^2$ be the first and second
order Fr\'echet derivative operators, we denote by $D_H$ and $D^2_H$ the
closure in $L^2(X,\mu_\infty)$ of the operators $QD$ and $QD^2$ and by
$W^{1,2}_H(X,\mu_\infty)$ and and $W^{2,2}_H(X,\mu_\infty)$ their domains in
$L^2(X,\mu_\infty)$, respectively,. Furthermore, we denote by $D_{A_\infty}$
the closure of the operator $Q_\infty A^*D$ and by
$W^{1,2}_{A_\infty}(X,\mu_\infty)$ its domain in $L^2(X,\mu_\infty)$. We
characterize the domain of the operator $L$, associated to the bilinear form
\begin{align*} (u,v)\mapsto-\int_{X}[BD_Hu,D_Hv]_Hd\mu_\infty, \qquad u,v\in
W^{1,2}_H(X,\mu_\infty), \end{align*} in $L^2(X,\mu_\infty)$. More precisely,
we prove that $D(L)$ coincides, up to an equivalent remorming, with a subspace
of $W^{2,2}_H(X,\mu_\infty)\cap W^{1,2}_{A_\infty}(X,\mu_\infty)$. We stress
that we are able to treat the case when $L$ is degenerate and non-symmetric.


Abstract:
  We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an
explainable system for emotion attribution and recommendation. This system
relies on a recently introduced commonsense reasoning framework, the TCL logic,
which is based on a human-like procedure for the automatic generation of novel
concepts in a Description Logics knowledge base. Starting from an ontological
formalization of emotions based on the Plutchik model, known as ArsEmotica, the
system exploits the logic TCL to automatically generate novel commonsense
semantic representations of compound emotions (e.g. Love as derived from the
combination of Joy and Trust according to Plutchik). The generated emotions
correspond to prototypes, i.e. commonsense representations of given concepts,
and have been used to reclassify emotion-related contents in a variety of
artistic domains, ranging from art datasets to the editorial contents available
in RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian
public broadcasting company). We show how the reported results (evaluated in
the light of the obtained reclassifications, the user ratings assigned to such
reclassifications, and their explainability) are encouraging, and pave the way
to many further research directions.


Abstract:
  High dimensional data reduction techniques are provided by using partial
least squares within deep learning. Our framework provides a nonlinear
extension of PLS together with a disciplined approach to feature selection and
architecture design in deep learning. This leads to a statistical
interpretation of deep learning that is tailor made for predictive problems. We
can use the tools of PLS, such as scree-plot, bi-plot to provide model
diagnostics. Posterior predictive uncertainty is available using MCMC methods
at the last layer. Thus we achieve the best of both worlds: scalability and
fast predictive rule construction together with uncertainty quantification. Our
key construct is to employ deep learning within PLS by predicting the output
scores as a deep learner of the input scores. As with PLS our X-scores are
constructed using SVD and applied to both regression and classification
problems and are fast and scalable. Following Frank and Friedman 1993, we
provide a Bayesian shrinkage interpretation of our nonlinear predictor. We
introduce a variety of new partial least squares models: PLS-ReLU,
PLS-Autoencoder, PLS-Trees and PLS-GP. To illustrate our methodology, we use
simulated examples and the analysis of preferences of orange juice and
predicting wine quality as a function of input characteristics. We also
illustrate Brillinger's estimation procedure to provide the feature selection
and data dimension reduction. Finally, we conclude with directions for future
research.


Abstract:
  We revisit the two-player planar target-defense game initially posed by
Isaacs where a pursuer (or defender) attempts to guard a target set from an
attack by an evader (or attacker). This paper builds on existing analytical
solutions to games of defending a simple shape of target area to develop a
generalized and extended solution to the same game with a compact convex target
set with smooth boundary. Isaacs' method is applied to address the game of kind
and games of degree. A geometric solution approach is used to find the barrier
surface that demarcates the winning sets of the players. A value function
coupled with a set of optimal state feedback strategies in each winning set is
derived and proven to correspond to the saddle point solution of the game. The
proposed solutions are illustrated by means of numerical simulations.


Abstract:
  Due to their increasing spread, confidence in neural network predictions
became more and more important. However, basic neural networks do not deliver
certainty estimates or suffer from over or under confidence. Many researchers
have been working on understanding and quantifying uncertainty in a neural
network's prediction. As a result, different types and sources of uncertainty
have been identified and a variety of approaches to measure and quantify
uncertainty in neural networks have been proposed. This work gives a
comprehensive overview of uncertainty estimation in neural networks, reviews
recent advances in the field, highlights current challenges, and identifies
potential research opportunities. It is intended to give anyone interested in
uncertainty estimation in neural networks a broad overview and introduction,
without presupposing prior knowledge in this field. A comprehensive
introduction to the most crucial sources of uncertainty is given and their
separation into reducible model uncertainty and not reducible data uncertainty
is presented. The modeling of these uncertainties based on deterministic neural
networks, Bayesian neural networks, ensemble of neural networks, and test-time
data augmentation approaches is introduced and different branches of these
fields as well as the latest developments are discussed. For a practical
application, we discuss different measures of uncertainty, approaches for the
calibration of neural networks and give an overview of existing baselines and
implementations. Different examples from the wide spectrum of challenges in
different fields give an idea of the needs and challenges regarding
uncertainties in practical applications. Additionally, the practical
limitations of current methods for mission- and safety-critical real world
applications are discussed and an outlook on the next steps towards a broader
usage of such methods is given.


Abstract:
  Nowadays voice search for points of interest (POI) is becoming increasingly
popular. However, speech recognition for local POI has remained to be a
challenge due to multi-dialect and massive POI. This paper improves speech
recognition accuracy for local POI from two aspects. Firstly, a geographic
acoustic model (Geo-AM) is proposed. The Geo-AM deals with multi-dialect
problem using dialect-specific input feature and dialect-specific top layer.
Secondly, a group of geo-specific language models (Geo-LMs) are integrated into
our speech recognition system to improve recognition accuracy of long tail and
homophone POI. During decoding, specific language models are selected on demand
according to users' geographic location. Experiments show that the proposed
Geo-AM achieves 6.5%$\sim$10.1% relative character error rate (CER) reduction
on an accent testset and the proposed Geo-AM and Geo-LM totally achieve over
18.7% relative CER reduction on Tencent Map task.


Abstract:
  Time series are often complex and rich in information but sparsely labeled
and therefore challenging to model. In this paper, we propose a self-supervised
framework for learning generalizable representations for non-stationary time
series. Our approach, called Temporal Neighborhood Coding (TNC), takes
advantage of the local smoothness of a signal's generative process to define
neighborhoods in time with stationary properties. Using a debiased contrastive
objective, our framework learns time series representations by ensuring that in
the encoding space, the distribution of signals from within a neighborhood is
distinguishable from the distribution of non-neighboring signals. Our
motivation stems from the medical field, where the ability to model the dynamic
nature of time series data is especially valuable for identifying, tracking,
and predicting the underlying patients' latent states in settings where
labeling data is practically impossible. We compare our method to recently
developed unsupervised representation learning approaches and demonstrate
superior performance on clustering and classification tasks for multiple
datasets.


Abstract:
  Joint probability mass function (PMF) estimation is a fundamental machine
learning problem. The number of free parameters scales exponentially with
respect to the number of random variables. Hence, most work on nonparametric
PMF estimation is based on some structural assumptions such as clique
factorization adopted by probabilistic graphical models, imposition of low rank
on the joint probability tensor and reconstruction from 3-way or 2-way
marginals, etc. In the present work, we link random projections of data to the
problem of PMF estimation using ideas from tomography. We integrate this idea
with the idea of low-rank tensor decomposition to show that we can estimate the
joint density from just one-way marginals in a transformed space. We provide a
novel algorithm for recovering factors of the tensor from one-way marginals,
test it across a variety of synthetic and real-world datasets, and also perform
MAP inference on the estimated model for classification.


Abstract:
  Understanding and predicting the duration or "return-to-normal" time of
traffic incidents is important for system-level management and optimisation of
road transportation networks. Increasing real-time availability of multiple
data sources characterising the state of urban traffic networks, together with
advances in machine learning offer the opportunity for new and improved
approaches to this problem that go beyond static statistical analyses of
incident duration. In this paper we consider two such improvements: dynamic
update of incident duration predictions as new information about incidents
becomes available and automated interpretation of the factors responsible for
these predictions. For our use case, we take one year of incident data and
traffic state time-series from the M25 motorway in London. We use it to train
models that predict the probability distribution of incident durations,
utilising both time-invariant and time-varying features of the data. The latter
allow predictions to be updated as an incident progresses, and more information
becomes available. For dynamic predictions, time-series features are fed into
the Match-Net algorithm, a temporal convolutional hitting-time network,
recently developed for dynamical survival analysis in clinical applications.
The predictions are benchmarked against static regression models for survival
analysis and against an established dynamic technique known as landmarking and
found to perform favourably by several standard comparison measures. To provide
interpretability, we utilise the concept of Shapley values from the domain of
interpretable artificial intelligence to rank the features most relevant to the
model predictions at different time horizons. For example, the time of day is
always a significantly influential time-invariant feature, whereas the
time-series features strongly influence predictions at 5 and 60-minute
horizons.


Abstract:
  A constant increase in the need of clean energy demands more innovation from
the research community. Active and stable materials that can make use of the
solar radiation to promote different reactions are the cornerstone of emerging
technologies. Polymeric carbon nitrides that harvest solar radiation to drive
electrochemical reactions are considered solid candidates. In this respect,
polymeric carbon nitrides were prepared by the thermal polycondensation of
melamine. As-obtained materials were characterized by synchrotron radiation and
lab-based techniques. The electron band structure was fully characterized by a
combined electrochemical optoelectronic study. Electron microscopy studies
before and after the photoelectrochemical experiments showed morphological and
structure degradation. The work at-hand concludes that polymeric carbon
nitrides are prone to photoelectrochemical degradation at high overpotentials.


Abstract:
  Several counterexample models to the Nelson-Seiberg theorem have been
discovered in previous literature, with generic superpotentials respecting the
R-symmetry and non-generic R-charge assignments for chiral fields. This work
present a sufficient condition for such counterexample models: The number of
R-charge 2 fields, which is greater than the number of R-charge 0 fields, must
be less than or equal to the number of R-charge 0 fields plus the number of
independent field pairs with opposite R-charges and satisfying some extra
requirements. We give a correct count of such field pairs when there are
multiple field pairs with degenerated R-charges. These models give
supersymmetric vacua with spontaneous R-symmetry breaking, thus are
counterexamples to both the Nelson-Seiberg theorem and its extensions.


Abstract:
  Kriging is a widely recognized method for making spatial predictions. On the
sphere, popular methods such as ordinary kriging assume that the spatial
process is intrinsically homogeneous. However, intrinsic homogeneity is too
strict in many cases. This research uses intrinsic random function (IRF) theory
to relax the homogeneity assumption. A key component of modeling IRF processes
is estimating the degree of non-homogeneity. A graphical approach is proposed
to accomplish this estimation. With the ability to estimate non-homogeneity, an
IRF universal kriging procedure can be developed. Results from simulation
studies are provided to demonstrate the advantage of using IRF universal
kriging as opposed to ordinary kriging when the underlying process is not
intrinsically homogeneous.


Abstract:
  Infographics are documents designed to effectively communicate information
using a combination of textual, graphical and visual elements. In this work, we
explore the automatic understanding of infographic images by using Visual
Question Answering technique.To this end, we present InfographicVQA, a new
dataset that comprises a diverse collection of infographics along with natural
language questions and answers annotations. The collected questions require
methods to jointly reason over the document layout, textual content, graphical
elements, and data visualizations. We curate the dataset with emphasis on
questions that require elementary reasoning and basic arithmetic skills.
Finally, we evaluate two strong baselines based on state of the art multi-modal
VQA models, and establish baseline performance for the new task. The dataset,
code and leaderboard will be made available at http://docvqa.org


Abstract:
  In this chapter, we will give comprehensive examples of applying RL in
optimizing the physical layer of wireless communications by defining different
class of problems and the possible solutions to handle them. In Section 9.2, we
present all the basic theory needed to address a RL problem, i.e. Markov
decision process (MDP), Partially observable Markov decision process (POMDP),
but also two very important and widely used algorithms for RL, i.e. the
Q-learning and SARSA algorithms. We also introduce the deep reinforcement
learning (DRL) paradigm and the section ends with an introduction to the
multi-armed bandits (MAB) framework. Section 9.3 focuses on some toy examples
to illustrate how the basic concepts of RL are employed in communication
systems. We present applications extracted from literature with simplified
system models using similar notation as in Section 9.2 of this Chapter. In
Section 9.3, we also focus on modeling RL problems, i.e. how action and state
spaces and rewards are chosen. The Chapter is concluded in Section 9.4 with a
prospective thought on RL trends and it ends with a review of a broader state
of the art in Section 9.5.


Abstract:
  Multiple populations in globular clusters are usually explained by the
formation of stars out of material with a chemical composition that is polluted
to different degrees by the ejecta of short-lived, massive stars of various
type. Among other things, these polluters differ by the amount of helium they
spread in the surrounding medium. In this study we investigate whether the
present-day photometric method used to infer the helium content of multiple
populations indeed gives the true value or underestimates it by missing very
He-rich, but rare stars. We focus on the specific case of NGC6752. We compute
atmosphere models and synthetic spectra along isochrones produced for this
cluster for a very broad range of He abundances covering the predictions of
different pollution scenarios, including the extreme case of the fast-rotating
massive star (FRMS) scenario. We calculate synthetic photometry in HST filters
best suited to study the helium content. We subsequently build synthetic
clusters with various distributions of stars. We finally determine the maximum
helium mass fraction of these synthetic clusters using a method similar to that
applied to observational data. We build toy models of clusters with various
distributions of multiple populations and ensure that we are able to recover
the input maximum Y. We then build synthetic clusters with the populations
predicted by the FRMS scenario and find that while we slightly underestimate
the maximum Y value, we are still able to detect stars much more He-rich than
the current observed maximum Y. It is easier to determine the maximum Y on main
sequence stars than on red giant branch stars, but qualitatively the results
are unaffected by the sample choice. We show that in NGC6752 it is unlikely
that stars more He-rich than the current observational limit of about 0.3 are
present.


Abstract:
  We propose a data-driven online convex optimization algorithm for controlling
dynamical systems. In particular, the control scheme makes use of an initially
measured input-output trajectory and behavioral systems theory which enable it
to handle unknown discrete-time linear time-invariant systems as well as a
priori unknown time-varying cost functions. Further, only output feedback
instead of full state measurements is required for the proposed approach.
Analysis of the closed loop's performance reveals that the algorithm achieves
sublinear regret if the variation of the cost functions is sublinear. The
effectiveness of the proposed algorithm, even in the case of noisy
measurements, is illustrated by a simulation example.


Abstract:
  The paper studies categories of definable subassignments with some category
equivalences to semi-algebraic and constructible subsets of arc spaces of
algebraic varieties. These materials allow us to compare the motivic measure of
Cluckers-Loeser and the one of Denef-Loeser in certain classes of definable
subassignments.


Abstract:
  The FCAL collaboration is preparing large-scale prototypes of special
calorimeters to be used in the very forward region at future electron-positron
colliders for a precise measurement of integrated luminosity and for instant
luminosity measurement and assisting beam-tuning. LumiCal is designed as a
silicon-tungsten sandwich calorimeter with very thin sensor planes to keep the
Moli\`ere radius small, facilitating such the measurement of electron showers
in the presence of background. Dedicated front-end electronics has been
developed to match the timing and dynamic range requirements. A partially
instrumented prototype was investigated in a 1 to 5 GeV electron beam at the
DESY II synchrotron. In the recent beam tests, a multi-plane compact prototype
was equipped with thin detector planes fully assembled with readout electronics
and installed in 1 mm gaps between tungsten plates of one radiation length
thickness. High statistics data were used to perform sensor alignment, and to
measure the longitudinal and transversal shower development in the sandwich.
This talk covers the latest status of the calorimeter prototype development and
selected performance results, obtained in test beam measurements, the prospects
for the upcoming DESY test beam, as well as the expected simulation
performance.