Abstract:
  In this work we present the results of the survey carried out on one of the
deepest X-ray fields observed by the XMM-Newton satellite. The 1.75 Ms Ultra
Narrow Deep Field (XMM175UNDF) survey is made by 13 observations taken over 2
years with a total exposure time of 1.75 Ms (1.372 Ms after flare-filtered) in
a field of $30' \times 30' $ centered around the blazar 1ES 1553+113. We
stacked the 13 observations reaching flux limits of $4.03 \times 10^{-16} $,
$1.3 \times 10^{-15}$, and $9.8 \times 10^{-16}\, erg\, s^{-1}\, cm^{-2}$ in
the soft $(0.2 - 2\, \mathrm{keV})$, hard $(2 - 12\, \mathrm{keV})$, and full
$(0.2 - 12\, \mathrm{keV})$ bands, respectively. Using a conservative threshold
of Maximum Likelihood significance of $ML \geq 6$, corresponding to $3\sigma$,
we detected 301 point-sources for which we derived positions, fluxes in
different bands, and hardness ratios. Thanks to an optical follow-up carried
out using the 10.4m the Gran Telescopio Canarias (GTC) on the same field in the
$u'g'r'i'z'$ bands, combined with WISE/2MASS IR data; we identified 244
optical/IR counterpart candidates for our X-ray sources and estimated their
X-ray luminosities, redshift distribution, X-ray/optical $-$ X-ray/IR flux
ratios, and absolute magnitudes. Finally, we divided this subsample in 40
non-active sources and 204 AGNs, of which 139 are classified as Seyfert
galaxies and 41 as Quasars.


Abstract:
  Artificial intelligence (AI) is disrupting the medical field as advances in
modern technology allow common household computers to learn anatomical and
pathological features that distinguish between healthy and disease with the
accuracy of highly specialized, trained physicians. Computer vision AI
applications use medical imaging, such as lung chest X-Rays (LCXRs), to
facilitate diagnoses by providing second-opinions in addition to a physician's
or radiologist's interpretation. Considering the advent of the current
Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to
indirectly aid in infection containment, however generating a reliably labeled
image dataset for a novel disease is not an easy feat, nor is it of highest
priority when combating a global pandemic. Deep learning techniques such as
convolutional neural networks (CNNs) are able to select features that
distinguish between healthy and disease states for other lung pathologies; this
study aims to leverage that body of literature in order to apply image
transformations that would serve to balance the lack of COVID-19 LCXR data.
Furthermore, this study utilizes a simple CNN architecture for high-performance
multiclass LCXR classification at 94 percent accuracy.


Abstract:
  Tremendous progress has been made in visual representation learning, notably
with the recent success of self-supervised contrastive learning methods.
Supervised contrastive learning has also been shown to outperform its
cross-entropy counterparts by leveraging labels for choosing where to contrast.
However, there has been little work to explore the transfer capability of
contrastive learning to a different domain. In this paper, we conduct a
comprehensive study on the transferability of learned representations of
different contrastive approaches for linear evaluation, full-network transfer,
and few-shot recognition on 12 downstream datasets from different domains, and
object detection tasks on MSCOCO and VOC0712. The results show that the
contrastive approaches learn representations that are easily transferable to a
different downstream task. We further observe that the joint objective of
self-supervised contrastive loss with cross-entropy/supervised-contrastive loss
leads to better transferability of these models over their supervised
counterparts. Our analysis reveals that the representations learned from the
contrastive approaches contain more low/mid-level semantics than cross-entropy
models, which enables them to quickly adapt to a new task. Our codes and models
will be publicly available to facilitate future research on transferability of
visual representations.


Abstract:
  Due to the pervasiveness of image capturing devices in every-day life, images
of individuals are routinely captured. Although this has enabled many benefits,
it also infringes on personal privacy. A promising direction in research on
obfuscation of facial images has been the work in the k-same family of methods
which employ the concept of k-anonymity from database privacy. However, there
are a number of deficiencies of k-anonymity that carry over to the k-same
methods, detracting from their usefulness in practice. In this paper, we first
outline several of these deficiencies and discuss their implications in the
context of facial obfuscation. We then develop a framework through which we
obtain a formal differentially private guarantee for the obfuscation of facial
images in generative machine learning models. Our approach provides a provable
privacy guarantee that is not susceptible to the outlined deficiencies of
k-same obfuscation and produces photo-realistic obfuscated output. In addition,
we demonstrate through experimental comparisons that our approach can achieve
comparable utility to k-same obfuscation in terms of preservation of useful
features in the images. Furthermore, we propose a method to achieve
differential privacy for any image (i.e., without restriction to facial images)
through the direct modification of pixel intensities. Although the addition of
noise to pixel intensities does not provide the high visual quality obtained
via generative machine learning models, it offers greater versatility by
eliminating the need for a trained model. We demonstrate that our proposed use
of the exponential mechanism in this context is able to provide superior visual
quality to pixel-space obfuscation using the Laplace mechanism.


Abstract:
  We present a modification of the DFS graph search algorithm, suited for
finding long induced paths. We use it to give simple proofs of the following
results. We show that the induced size-Ramsey number of paths satisfies
$\hat{R}_{\mathrm{ind}}(P_n)\leq 5\cdot 10^7n$, thus giving an explicit
constant in the linear bound, improving the previous bound with a large
constant from a regularity lemma argument by Haxell, Kohayakawa and {\L}uczak.
We also provide a bound for the $k$-color version, showing that
$\hat{R}_{\mathrm{ind}}^k(P_n)=O(k^3\log^4k)n$. Finally, we present a new short
proof of the fact that the binomial random graph in the supercritical regime,
$G(n,\frac{1+\varepsilon}{n})$, contains typically an induced path of length
$\Theta(\varepsilon^2) n$.


Abstract:
  The characterization of nanostructured surfaces with sensitivity in the
sub-nm range is of high importance for the development of current and next
generation integrated electronic circuits. Modern transistor architectures for
e.g. FinFETs are realized by lithographic fabrication of complex, well ordered
nanostructures. Recently, a novel characterization technique based on X-ray
fluorescence measurements in grazing incidence geometry has been proposed for
such applications. This technique uses the X-ray standing wave field, arising
from an interference between incident and the reflected radiation, as a
nanoscale sensor for the dimensional and compositional parameters of the
nanostructure. The element sensitivity of the X-ray fluorescence technique
allows for a reconstruction of the spatial element distribution using a
finite-element method. Due to a high computational time, intelligent
optimization methods employing machine learning algorithms are essential for a
timely provision of results. Here, a sampling of the probability distributions
by Bayesian optimization is not only fast, it also provides an initial estimate
of the parameter uncertainties and sensitivities. The high sensitivity of the
method requires a precise knowledge of the material parameters in the modeling
of the dimensional shape provided that some physical properties of the material
are known or determined beforehand. The unknown optical constants were
extracted from an unstructured but otherwise identical layer system by means of
soft X-ray reflectometry. The spatial distribution profiles of the different
elements contained in the grating structure were compared to scanning electron
and atomic force microscopy and the influence of carbon surface contamination
on the modeling results were discussed.


Abstract:
  We perform computer simulations to probe the magnetic hysteresis in a
two-dimensional ($L^{}_x\times L^{}_y$) assembly of magnetic nanoparticles as a
function of dipolar interaction strength $h^{}_d$, temperature $T$, aspect
ratio $A^{}_r=L^{}_y/L^{}_x$, and the applied alternating magnetic field's
direction. In the absence of magnetic interaction ($h^{}_d\approx0$) and
thermal fluctuations ($T=0$ K), the hysteresis follows the Stoner and Wohlfarth
model, as expected. For weak dipolar interaction and substantial temperature,
the hysteresis has the dominance of superparamagnetic behaviour, irrespective
of the applied magnetic field's direction and $A^{}_r$. Interestingly, the
hysteresis curve has all the characteristics of antiferromagnetic interaction
dominance for $A^{}_r\leq6$ and considerable dipolar interaction strength
($h^{}_d>0.2)$, which is independent of applied magnetic direction. When the
magnetic field is applied along the system's shorter axis ($x$-direction), a
non-hysteresis straight line is observed with large $h^{}_d$. In the case of
the magnetic field applied along the long axis of the sample ($y$-direction),
ferromagnetic interaction dominates the hysteresis for large $h^{}_d$ and
$A^{}_r>6$. Irrespective of $h^{}_d$ and applied magnetic field's direction,
the coercive field $\mu^{}_oH^{}_c$ and remanence $M^{}_r$ are minimal for
$A^{}_r\leq6$ and significant temperature. They are found to increase with
$h^{}_d$ when $A^{}_r$ is enormous. Remarkably, the variation of hysteresis
loop area $E^{}_H$ as a function of these parameters is the same as that of the
coercive field variation. We believe that the concepts presented in this work
are relevant in various technological applications such as spintronics and
magnetic hyperthermia, in which such self-assembled nanoparticle arrays are
ubiquitous.


Abstract:
  We propose an $A_4$ modular invariant flavor model of leptons, in which both
CP and modular symmetries are broken spontaneously by the vacuum expectation
value of the modulus $\tau$. The value of the modulus $\tau$ is restricted by
the observed lepton mixing angles and lepton masses for the normal hierarchy of
neutrino masses at $3\sigma$ confidence level. The predictive Dirac CP phase
$\delta_{CP}$ is in the ranges $[0^\circ,50^\circ]$, $[170^\circ,175^\circ]$
and $[280^\circ,360^\circ]$ for ${\rm Re}\, [\tau]<0$, and
$[0^\circ,80^\circ]$, $[185^\circ,190^\circ]$ and $[310^\circ,360^\circ]$ for
${\rm Re}\,[\tau]>0$ at $3\sigma$ confidence level. The sum of three neutrino
masses is predicted in $[60, 84]$ meV, and the effective mass for the
$0\nu\beta\beta$ decay is in [0.003, 3] meV. On the other hand, there is no
allowed region of the modulus $\tau$ for the inverted hierarchy of neutrino
masses at $3\sigma$ confidence level. The modulus $\tau$ links the Dirac CP
phase to the cosmological baryon asymmetry (BAU) via the leptogenesis. Due to
the strong wash-out effect, the predictive baryon asymmetry $Y_B$ can be at
most the same order of the observed value. Then, the lightest right-handed
neutrino mass is restricted in the range of $M_1 =[1.5,\,6.5] \times 10^{13}$
GeV. We find the correlation between the predictive $Y_B$ and the Dirac CP
phase $\delta_{CP}$. Only two predictive $\delta_{CP}$ ranges,
$[5^\circ,40^\circ]$ (${\rm Re}\,[\tau]>0$) and $[320^\circ,355^\circ]$ (${\rm
Re}\,[\tau]<0$) are consistent with the BAU.


Abstract:
  This paper addresses the localization of contacts of an unknown grasped rigid
object with its environment, i.e., extrinsic to the robot.
  We explore the key role that distributed tactile sensing plays in localizing
contacts external to the robot, in contrast to the role that aggregated
force/torque measurements play in localizing contacts on the robot. When in
contact with the environment, an object will move in accordance with the
kinematic and possibly frictional constraints imposed by that contact. Small
motions of the object, which are observable with tactile sensors, indirectly
encode those constraints and the geometry that defines them.
  We formulate the extrinsic contact sensing problem as a constraint-based
estimation. The estimation is subject to the kinematic constraints imposed by
the tactile measurements of object motion, as well as the kinematic (e.g.,
non-penetration) and possibly frictional (e.g., sticking) constraints imposed
by rigid-body mechanics.
  We validate the approach in simulation and with real experiments on the case
studies of fixed point and line contacts.
  This paper discusses the theoretical basis for the value of distributed
tactile sensing in contrast to aggregated force/torque measurements. It also
provides an estimation framework for localizing environmental contacts with
potential impact in contact-rich manipulation scenarios such as assembling or
packing.


Abstract:
  Convergence-extension is a morphological process involved in the shaping of
complex biological tissues. It is driven by an intricate interplay of cellular
behaviours carefully controlled by chemical and mechanical signalling. A key
cellular process in convergence-extension is intercalation, i.e. the exchange
of cell neighbours via T1 transitions. Here we construct and analyse a model
with positive feedback between recruitment of force-generating molecules and
mechanical tension in cell junctions. The model characterises an important
class of active T1 events, which occur against locally imposed mechanical
stresses, as a function of a pulling force applied to the tissue boundary,
characteristic time scales of viscoelastic remodelling in cell junctions, and
kinetics of myosin motors. Furthermore, we measured the time scale associated
with T1 events, and used local strain, cell shapes, and polarisation of myosin
motors to show the formation of tension chains and alignment of junctions in
the direction of intercalation. Using a minimal setup of several active cells
embedded in an otherwise passive tissue, we found an optimal range of applied
external pulling forces that activate the cell's contractile machinery and
induce an active T1 transition. This initiates local convergence-extension
flows that extend the tissue perpendicular to the direction of the maximum
principal applied stress and provides an important cellular mechanism for
tissue-scale remodelling, e.g. as observed during primitive streak formation in
avian embryos.


Abstract:
  Nonlinear mechanics of solids is an exciting field that encompasses both
beautiful mathematics, such as the emergence of instabilities and the formation
of complex patterns, as well as multiple applications. Two-dimensional crystals
and van der Waals (vdW) heterostructures allow revisiting this field on the
atomic level, allowing much finer control over the parameters and offering
atomistic interpretation of experimental observations. In this work, we
consider the formation of instabilities consisting of radially-oriented
wrinkles around mono- and few-layer "bubbles" in two-dimensional vdW
heterostructures. Interestingly, the shape and wavelength of the wrinkles
depend not only on the thickness of the two-dimensional crystal forming the
bubble, but also on the atomistic structure of the interface between the bubble
and the substrate, which can be controlled by their relative orientation. We
argue that the periodic nature of these patterns emanates from an energetic
balance between the resistance of the top membrane to bending, which favors
large wavelength of wrinkles, and the membrane-substrate vdW attraction, which
favors small wrinkle amplitude. Employing the classical "Winkler foundation"
model of elasticity theory, we show that the number of radial wrinkles conveys
a valuable relationship between the bending rigidity of the top membrane and
the strength of the vdW interaction. Armed with this relationship, we use our
data to demonstrate a nontrivial dependence of the bending rigidity on the
number of layers in the top membrane, which shows two different regimes driven
by slippage between the layers, and a high sensitivity of the vdW force to the
alignment between the substrate and the membrane.


Abstract:
  Finding a good way to model probability densities is key to probabilistic
inference. An ideal model should be able to concisely approximate any
probability while being also compatible with two main operations:
multiplications of two models (product rule) and marginalization with respect
to a subset of the random variables (sum rule). In this work, we show that a
recently proposed class of positive semi-definite (PSD) models for non-negative
functions is particularly suited to this end. In particular, we characterize
both approximation and generalization capabilities of PSD models, showing that
they enjoy strong theoretical guarantees. Moreover, we show that we can perform
efficiently both sum and product rule in closed form via matrix operations,
enjoying the same versatility of mixture models. Our results open the way to
applications of PSD models to density estimation, decision theory and
inference.


Abstract:
  The main aim of this study is to introduce a 2-layered Artificial Neural
Network (ANN) for solving the Black-Scholes partial differential equation (PDE)
of either fractional or ordinary orders. Firstly, a discretization method is
employed to change the model into a sequence of Ordinary Differential Equations
(ODE). Then each of these ODEs is solved with the aid of an ANN. Adam
optimization is employed as the learning paradigm since it can add the
foreknowledge of slowing down the process of optimization when getting close to
the actual optimum solution. The model also takes advantage of fine tuning for
speeding up the process and domain mapping to confront infinite domain issue.
Finally, the accuracy, speed, and convergence of the method for solving several
types of Black-Scholes model are reported.


Abstract:
  In the consensus halving problem we are given n agents with valuations over
the interval $[0,1]$. The goal is to divide the interval into at most $n+1$
pieces (by placing at most n cuts), which may be combined to give a partition
of $[0,1]$ into two sets valued equally by all agents. The existence of a
solution may be established by the Borsuk-Ulam theorem. We consider the task of
computing an approximation of an exact solution of the consensus halving
problem, where the valuations are given by distribution functions computed by
algebraic circuits. Here approximation refers to computing a point that
$\varepsilon$-close to an exact solution, also called strong approximation. We
show that this task is polynomial time equivalent to computing an approximation
to an exact solution of the Borsuk-Ulam search problem defined by a continuous
function that is computed by an algebraic circuit.
  The Borsuk-Ulam search problem is the defining problem of the complexity
class BU. We introduce a new complexity class BBU to also capture an
alternative formulation of the Borsuk-Ulam theorem from a computational point
of view. We investigate their relationship and prove several structural results
for these classes as well as for the complexity class FIXP.


Abstract:
  We provide a lower value for the volume of a unit vector field tangent to an
antipodally Euclidean sphere $\mathbb{S}^2$ depending on the length of an
ellipse determined by the indexes of its singularities.


Abstract:
  It has been suggested that certain antiferromagnetic topological insulators
contain axion quasiparticles (AQs), and that such materials could be used to
detect axion dark matter (DM). The AQ is a longitudinal antiferromagnetic spin
fluctuation coupled to the electromagnetic Chern-Simons term, which, in the
presence of an applied magnetic field, leads to mass mixing between the AQ and
the electric field. The electromagnetic boundary conditions and transmission
and reflection coefficients are computed. A model for including losses into
this system is presented, and the resulting linewidth is computed. It is shown
how transmission spectroscopy can be used to measure the resonant frequencies
and damping coefficients of the material, and demonstrate conclusively the
existence of the AQ. The dispersion relation and boundary conditions permit
resonant conversion of axion DM into THz photons in a material volume that is
independent of the resonant frequency, which is tuneable via an applied
magnetic field. A parameter study for axion DM detection is performed,
computing boost amplitudes and bandwidths using realistic material properties
including loss. The proposal could allow for detection of axion DM in the mass
range between 1 and 10 meV using current and near future technology.


Abstract:
  We propose a concentrated thermionic emission solar cell design, which
demonstrates a high solar-to-electricity energy conversion efficiency larger
than 10\% under 600 sun, by harnessing the exceptional electrical, thermal and
radiative properties of the graphene as a collector electrode. By constructing
an analytical model that explicitly takes into account the non-Richardson
behavior of the thermionic emission current from graphene, space charge effect
in vacuum gap, and the various irreversible energy losses within the
subcomponents, we perform a detailed characterization on the conversion
efficiency limit and electrical power output characteristics of the proposed
system. We systematically model and compare the energy conversion efficiency of
various configurations of graphene-graphene and graphene-diamond and
diamond-diamond thermionic emitter, and show that utilizing diamond films as an
emitter and graphene as a collector offers the highest maximum efficiency, thus
revealing the important role of graphene in achieving high-performance
thermionic emission solar cell. A maximum efficiency of 12.8\% under 800 sun
has been revealed, which is significantly higher than several existing
solid-state solar cell designs, such as the solar-driven thermoelectric and
thermophotovoltaic converters. Our work thus opens up new avenues to advance
the efficiency limit of thermionic solar energy conversion and the development
of next-generation novel-nanomaterial-based solar energy harvesting technology.


Abstract:
  Borexino is a liquid scintillator detector located at the Laboratori
Nazionale del Gran Sasso, Italy with the main goal to measure solar neutrinos.
The experiment recently provided the first direct experimental evidence of
CNO-cycle neutrinos in the Sun, rejecting the no-CNO signal hypothesis with a
significance greater than 5$\sigma$ at 99\%C.L. The intrinsic $^{210}$Bi is an
important background for this analysis due to its similar spectral shape to
that of CNO neutrinos. $^{210}$Bi can be measured through its daughter
$^{210}$Po which can be distinguished through an event-by-event basis via pulse
shape discrimination. However, this required reducing the convective motions in
the scintillator that brought additional $^{210}$Po from peripheral sources.
This was made possible through the thermal insulation and stabilization
campaign performed between 2015 and 2016. This article will explain the
strategy and the different methods performed to extract the $^{210}$Bi upper
limit in Phase-III (Jul 2016- Feb 2020) of the experiment through the analysis
of $^{210}$Po in the cleanest region of the detector called the Low Polonium
Field.


Abstract:
  In this paper, we develop a new regularized version of the Factorization
Method for positive operators mapping a complex Hilbert Space into it's dual
space. The Factorization Method uses Picard's Criteria to define an indicator
function to image an unknown region. In most applications, the data operator is
compact which gives that the singular values can tend to zero rapidly which can
cause numerical instabilities. The regularization of the Factorization Method
presented here seeks to avoid the numerical instabilities in applying Picard's
Criteria. This method allows one to image the interior structure of an object
with little a priori information in a computationally simple and analytically
rigorous way. Here we will focus on an application of this method to diffuse
optical tomography where will prove that this method can be used to recover an
unknown subregion from the Dirichlet-to-Neumann mapping. Numerical examples
will be presented in two dimensions.


Abstract:
  Dialogue policy learning, a subtask that determines the content of system
response generation and then the degree of task completion, is essential for
task-oriented dialogue systems. However, the unbalanced distribution of system
actions in dialogue datasets often causes difficulty in learning to generate
desired actions and responses. In this paper, we propose a
retrieve-and-memorize framework to enhance the learning of system actions.
Specially, we first design a neural context-aware retrieval module to retrieve
multiple candidate system actions from the training set given a dialogue
context. Then, we propose a memory-augmented multi-decoder network to generate
the system actions conditioned on the candidate actions, which allows the
network to adaptively select key information in the candidate actions and
ignore noises. We conduct experiments on the large-scale multi-domain
task-oriented dialogue dataset MultiWOZ 2.0 and MultiWOZ 2.1. Experimental
results show that our method achieves competitive performance among several
state-of-the-art models in the context-to-response generation task.


Abstract:
  Our observations of TMC-1 with the Yebes 40 m radio telescope in the
31.0-50.3 GHz range allowed us to detect a group of unidentified lines, showing
a complex line pattern indicative of an open-shell species. {}The observed
frequencies of these lines and the similarity of the spectral pattern with that
of the 2$_{0,2}$-1$_{0,1}$ rotational transition of H$_2$CCN indicate that the
lines arise from the deuterated cyanomethyl radical, HDCCN. Using Fourier
transform microwave spectroscopy experiments combined with electric discharges,
we succeeded in producing the radical HDCCN in the laboratory and observed its
1$_{0,1}$-0$_{0,0}$ and 2$_{0,2}$-1$_{0,1}$ rotational transitions. From our
observations and assuming a rotational temperature of 5 K, we derive an
abundance ratio H$_2$CCN/HDCCN=20$\pm$4. The high abundance of the deuterated
form of H$_2$CCN is well accounted for by a standard gas-phase model, in which
deuteration is driven by deuteron transfer from the H$_2$D$^+$ molecular ion.


Abstract:
  This is a translation from Latin of 'De novo oscillationum genere', which was
motivated by Krafft's accidental observation of a suspended clock setting
itself in constant motion as a pendulum. This publication, in turn, motivated
Euler to write 'De novo genere oscillationum', in which Euler derived for the
first time, the differential equation for the (undamped) simple harmonic
oscillator under harmonic excitation.


Abstract:
  Blogs are a source of grey literature which are widely adopted by software
practitioners for disseminating opinion and experience. Analysing such articles
can provide useful insights into the state-of-practice for software engineering
research. However, there are challenges in identifying higher quality content
from the large quantity of articles available. Credibility assessment can help
in identifying quality content, though there is a lack of existing corpora.
Credibility is typically measured through a series of conceptual criteria, with
'argumentation' and 'evidence' being two important criteria.
  We create a corpus labelled for argumentation and evidence that can aid the
credibility community. The corpus consists of articles from the blog of a
single software practitioner and is publicly available.
  Three annotators label the corpus with a series of conceptual credibility
criteria, reaching an agreement of 0.82 (Fleiss' Kappa). We present preliminary
analysis of the corpus by using it to investigate the identification of claim
sentences (one of our ten labels).
  We train four systems (Bert, KNN, Decision Tree and SVM) using three feature
sets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of
0.64 using InferSent and a Linear SVM.
  Our preliminary results are promising, indicating that the corpus can help
future studies in detecting the credibility of grey literature. Future research
will investigate the degree to which the sentence level annotations can infer
the credibility of the overall document.


Abstract:
  ORCID is a scientific infrastructure created to solve the problem of author
name ambiguity. Over the years ORCID has also become a useful source for
studying academic activities reported by researchers. Our objective in this
research was to use ORCID to analyze one of these research activities: the
publication of datasets. We illustrate how the identification of datasets that
shared in researchers' ORCID profiles enables the study of the characteristics
of the researchers who have produced them. To explore the relevance of ORCID to
study data sharing practices we obtained all ORCID profiles reporting at least
one dataset in their "works" list, together with information related to the
individual researchers producing the datasets. The retrieved data was organized
and analyzed in a SQL database hosted at CWTS. Our results indicate that
DataCite is by far the most important data source for providing information
about datasets recorded in ORCID. There is also a substantial overlap between
DataCite records with other repositories (Figshare, Dryad, and Zenodo). The
analysis of the distribution of researchers producing datasets shows that the
top six countries with more data producers, also have a relatively higher
percentage of people who have produced datasets out of total researchers with
datasets than researchers in the total ORCID. By disciplines, researchers that
belong to the areas of Natural Sciences and Medicine and Life Sciences are
those with the largest amount of reported datasets. Finally, we observed that
researchers who have started their PhD around 2015 published their first
dataset earlier that those researchers that started their PhD before. The work
concludes with some reflections of the possibilities of ORCID as a relevant
source for research on data sharing practices.


Abstract:
  The new coronavirus outbreak has been officially declared a global pandemic
by the World Health Organization. To grapple with the rapid spread of this
ongoing pandemic, most countries have banned indoor and outdoor gatherings and
ordered their residents to stay home. Given the developing situation with
coronavirus, mental health is an important challenge in our society today. In
this paper, we discuss the investigation of social media postings to detect
signals relevant to depression. To this end, we utilize topic modeling features
and a collection of psycholinguistic and mental-well-being attributes to
develop statistical models to characterize and facilitate representation of the
more subtle aspects of depression. Furthermore, we predict whether signals
relevant to depression are likely to grow significantly as time moves forward.
Our best classifier yields F-1 scores as high as 0.8 and surpasses the utilized
baseline by a considerable margin, 0.173. In closing, we propose several future
research avenues.


Abstract:
  Educational technologies, and the systems of schooling in which they are
deployed, enact particular ideologies about what is important to know and how
learners should learn. As artificial intelligence technologies -- in education
and beyond -- may contribute to inequitable outcomes for marginalized
communities, various approaches have been developed to evaluate and mitigate
the harmful impacts of AI. However, we argue in this paper that the dominant
paradigm of evaluating fairness on the basis of performance disparities in AI
models is inadequate for confronting the systemic inequities that educational
AI systems (re)produce. We draw on a lens of structural injustice informed by
critical theory and Black feminist scholarship to critically interrogate
several widely-studied and widely-adopted categories of educational AI and
explore how they are bound up in and reproduce historical legacies of
structural injustice and inequity, regardless of the parity of their models'
performance. We close with alternative visions for a more equitable future for
educational AI.


Abstract:
  We prove that there is an action of the cyclic group $\mathbf{C}_2$ on the
$10$-dimensional Floyd manifold which turns it into a conjugation manifold. The
submanifold of fixed points is the $5$-dimensional Floyd manifold, whose
cohomology is isomorphic to that of the large one, scaled down by dividing the
cohomological degree by a factor two.


Abstract:
  Botnets are one of the online threats with the biggest presence, causing
billionaire losses to global economies. Nowadays, the increasing number of
devices connected to the Internet makes it necessary to analyze large amounts
of network traffic data. In this work, we focus on increasing the performance
on botnet traffic classification by selecting those features that further
increase the detection rate. For this purpose we use two feature selection
techniques, Information Gain and Gini Importance, which led to three
pre-selected subsets of five, six and seven features. Then, we evaluate the
three feature subsets along with three models, Decision Tree, Random Forest and
k-Nearest Neighbors. To test the performance of the three feature vectors and
the three models we generate two datasets based on the CTU-13 dataset, namely
QB-CTU13 and EQB-CTU13. We measure the performance as the macro averaged F1
score over the computational time required to classify a sample. The results
show that the highest performance is achieved by Decision Trees using a five
feature set which obtained a mean F1 score of 85% classifying each sample in an
average time of 0.78 microseconds.


Abstract:
  Causal reasoning is the main learning and explanation tool used by humans. AI
systems should possess causal reasoning capabilities to be deployed in the real
world with trust and reliability. Introducing the ideas of causality to machine
learning helps in providing better learning and explainable models.
Explainability, causal disentanglement are some important aspects of any
machine learning model. Causal explanations are required to believe in a
model's decision and causal disentanglement learning is important for transfer
learning applications. We exploit the ideas of causality to be used in deep
learning models to achieve better and causally explainable models that are
useful in fairness, disentangled representation, etc.


Abstract:
  Although it is widely known that Gaussian processes can be conditioned on
observations of the gradient, this functionality is of limited use due to the
prohibitive computational cost of $\mathcal{O}(N^3 D^3)$ in data points $N$ and
dimension $D$. The dilemma of gradient observations is that a single one of
them comes at the same cost as $D$ independent function evaluations, so the
latter are often preferred. Careful scrutiny reveals, however, that derivative
observations give rise to highly structured kernel Gram matrices for very
general classes of kernels (inter alia, stationary kernels). We show that in
the low-data regime $N<D$, the Gram matrix can be decomposed in a manner that
reduces the cost of inference to $\mathcal{O}(N^2D + (N^2)^3)$ (i.e., linear in
the number of dimensions) and, in special cases, to $\mathcal{O}(N^2D + N^3)$.
This reduction in complexity opens up new use-cases for inference with
gradients especially in the high-dimensional regime, where the
information-to-cost ratio of gradient observations significantly increases. We
demonstrate this potential in a variety of tasks relevant for machine learning,
such as optimization and Hamiltonian Monte Carlo with predictive gradients.


Abstract:
  Two-dimensional nanomaterials, such as graphene, have been extensively
studied because of their outstanding physical properties. Structure and
geometry optimization of nanopores on such materials is beneficial for their
performances in real-world engineering applications, like water desalination.
However, the optimization process often involves very large number of
experiments or simulations which are expensive and time-consuming. In this
work, we propose a graphene nanopore optimization framework via the combination
of deep reinforcement learning (DRL) and convolutional neural network (CNN) for
efficient water desalination. The DRL agent controls the growth of nanopore by
determining the atom to be removed at each timestep, while the CNN predicts the
performance of nanoporus graphene for water desalination: the water flux and
ion rejection at a certain external pressure. With the synchronous feedback
from CNN-accelerated desalination performance prediction, our DRL agent can
optimize the nanoporous graphene efficiently in an online manner. Molecular
dynamics (MD) simulations on promising DRL-designed graphene nanopores show
that they have higher water flux while maintaining rival ion rejection rate
compared to the normal circular nanopores. Semi-oval shape with rough edges
geometry of DRL-designed pores is found to be the key factor for their high
water desalination performance. Ultimately, this study shows that DRL can be a
powerful tool for material design.


Abstract:
  In Generalized Zero-Shot Learning (GZSL), unseen categories (for which no
visual data are available at training time) can be predicted by leveraging
their class embeddings (e.g., a list of attributes describing them) together
with a complementary pool of seen classes (paired with both visual data and
class embeddings). Despite GZSL is arguably challenging, we posit that knowing
in advance the class embeddings, especially for unseen categories, is an actual
limit of the applicability of GZSL towards real-world scenarios. To relax this
assumption, we propose Open Zero-Shot Learning (OZSL) to extend GZSL towards
the open-world settings. We formalize OZSL as the problem of recognizing seen
and unseen classes (as in GZSL) while also rejecting instances from unknown
categories, for which neither visual data nor class embeddings are provided. We
formalize the OZSL problem introducing evaluation protocols, error metrics and
benchmark datasets. We also suggest to tackle the OZSL problem by proposing the
idea of performing unknown feature generation (instead of only unseen features
generation as done in GZSL). We achieve this by optimizing a generative process
to sample unknown class embeddings as complementary to the seen and the unseen.
We intend these results to be the ground to foster future research, extending
the standard closed-world zero-shot learning (GZSL) with the novel open-world
counterpart (OZSL).


Abstract:
  Open-domain conversational search assistants aim at answering user questions
about open topics in a conversational manner. In this paper we show how the
Transformer architecture achieves state-of-the-art results in key IR tasks,
leveraging the creation of conversational assistants that engage in open-domain
conversational search with single, yet informative, answers. In particular, we
propose an open-domain abstractive conversational search agent pipeline to
address two major challenges: first, conversation context-aware search and
second, abstractive search-answers generation. To address the first challenge,
the conversation context is modeled with a query rewriting method that unfolds
the context of the conversation up to a specific moment to search for the
correct answers. These answers are then passed to a Transformer-based re-ranker
to further improve retrieval performance. The second challenge, is tackled with
recent Abstractive Transformer architectures to generate a digest of the top
most relevant passages. Experiments show that Transformers deliver a solid
performance across all tasks in conversational search, outperforming the best
TREC CAsT 2019 baseline.


Abstract:
  Interpretability methods for image classification assess model
trustworthiness by attempting to expose whether the model is systematically
biased or attending to the same cues as a human would. Saliency methods for
feature attribution dominate the interpretability literature, but these methods
do not address semantic concepts such as the textures, colors, or genders of
objects within an image. Our proposed Robust Concept Activation Vectors (RCAV)
quantifies the effects of semantic concepts on individual model predictions and
on model behavior as a whole. RCAV calculates a concept gradient and takes a
gradient ascent step to assess model sensitivity to the given concept. By
generalizing previous work on concept activation vectors to account for model
non-linearity, and by introducing stricter hypothesis testing, we show that
RCAV yields interpretations which are both more accurate at the image level and
robust at the dataset level. RCAV, like saliency methods, supports the
interpretation of individual predictions. To evaluate the practical use of
interpretability methods as debugging tools, and the scientific use of
interpretability methods for identifying inductive biases (e.g. texture over
shape), we construct two datasets and accompanying metrics for realistic
benchmarking of semantic interpretability methods. Our benchmarks expose the
importance of counterfactual augmentation and negative controls for quantifying
the practical usability of interpretability methods.


Abstract:
  In 1973, Diestel published his seminal paper `Grothendieck spaces and vector
measures' that drew a connection between Grothendieck spaces (Banach spaces for
which weak- and weak*-sequential convergences in the dual space coincide) and
vector measures. This connection was developed in his book with J. Uhl Jr.
`Vector measures'. Additionally, Diestel's paper included a section with
several open problems about the structural properties of Grothendieck spaces,
and only half of them have been solved to this day.
  The present paper aims at synthetically presenting the state of the art at
subjectively selected corners of the theory of Banach spaces with the
Grothendieck property, describing the main examples of spaces with this
property, recording the solutions to Diestel's problems, providing
generalisations/extensions or new proofs of various results concerning
Grothendieck spaces, and adding to the list further problems that we believe
are of relevance and may reinvigorate a better-structured development of the
theory.


Abstract:
  Time-dependent wave equations represent an important class of partial
differential equations (PDE) for describing wave propagation phenomena, which
are often formulated over unbounded domains. Given a compactly supported
initial condition, classical numerical methods reduce such problems to bounded
domains using artificial boundary condition (ABC). In this work, we present a
machine-learning method to solve this type of equations as an alternative to
ABCs. Specifically, the mapping from the initial conditions to the PDE solution
is represented by a neural network, trained using wave packets that are
parameterized by their band width and wave numbers. The accuracy is tested for
both the second-order wave equation and the Schrodinger equation, including the
nonlinear Schrodinger equation. We examine the accuracy from both
interpolations and extrapolations. For initial conditions lying in the training
set, the learned map has good interpolation accuracy, due to the approximation
property of deep neural networks. The learned map also exhibits some good
extrapolation accuracy. We also demonstrate the effectiveness of the method for
problems in irregular domains. Overall, the proposed method provides an
interesting alternative for finite-time simulation of wave propagation.


Abstract:
  One of the biggest challenges hindering progress in low-resource and
multilingual machine translation is the lack of good evaluation benchmarks.
Current evaluation benchmarks either lack good coverage of low-resource
languages, consider only restricted domains, or are low quality because they
are constructed using semi-automatic procedures. In this work, we introduce the
FLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from
English Wikipedia and covering a variety of different topics and domains. These
sentences have been translated in 101 languages by professional translators
through a carefully controlled process. The resulting dataset enables better
assessment of model quality on the long tail of low-resource languages,
including the evaluation of many-to-many multilingual translation systems, as
all translations are multilingually aligned. By publicly releasing such a
high-quality and high-coverage dataset, we hope to foster progress in the
machine translation community and beyond.


Abstract:
  We present the EVONANO platform for the evolution of nanomedicines with
application to anti-cancer treatments. EVONANO includes a simulator to grow
tumours, extract representative scenarios, and then simulate nanoparticle
transport through these scenarios to predict nanoparticle distribution. The
nanoparticle designs are optimised using machine learning to efficiently find
the most effective anti-cancer treatments. We demonstrate our platform with two
examples optimising the properties of nanoparticles and treatment to
selectively kill cancer cells over a range of tumour environments.


Abstract:
  In this work, the problem of predicting dropout risk in undergraduate studies
is addressed from a perspective of algorithmic fairness. We develop a machine
learning method to predict the risks of university dropout and
underperformance. The objective is to understand if such a system can identify
students at risk while avoiding potential discriminatory biases. When modeling
both risks, we obtain prediction models with an Area Under the ROC Curve (AUC)
of 0.77-0.78 based on the data available at the enrollment time, before the
first year of studies starts. This data includes the students' demographics,
the high school they attended, and their admission (average) grade. Our models
are calibrated: they produce estimated probabilities for each risk, not mere
scores. We analyze if this method leads to discriminatory outcomes for some
sensitive groups in terms of prediction accuracy (AUC) and error rates
(Generalized False Positive Rate, GFPR, or Generalized False Negative Rate,
GFNR). The models exhibit some equity in terms of AUC and GFNR along groups.
The similar GFNR means a similar probability of failing to detect risk for
students who drop out. The disparities in GFPR are addressed through a
mitigation process that does not affect the calibration of the model.


Abstract:
  The dynamics of a molecule in a magnetic field is significantly different
form its zero-field counterpart. One important difference in the presence of a
field is the Lorentz force acting on the nuclei, which can be decomposed as the
sum of the bare nuclear Lorentz force and a screening force due to the
electrons. This screening force is calculated from the Berry curvature and can
change the dynamics qualitatively. It is therefore important to include the
contributions from the Berry curvature in molecular dynamics simulations in a
magnetic field. In this work, we present a scheme for calculating the Berry
curvature numerically, by a finite-difference technique, addressing challenges
related to the arbitrary global phase of the wave function. The Berry curvature
is calculated as a function of bond distance for H$_2$ at the restricted and
unrestricted Hartree--Fock levels of theory and for CH$^{+}$ as a function of
the magnetic field strength at the restricted Hartree--Fock level of theory.
The calculations are carried out using basis sets of contracted Gaussian
functions equipped with London phase factors (London orbitals) to ensure
gauge-origin invariance. In the paper, we also interpret the Berry curvature in
terms of atomic charges and discuss its convergence in basis sets with and
without London phase factors. Calculation of the Berry curvature allows for its
inclusion in \textit{ab initio} molecular dynamics simulations in a magnetic
field.


Abstract:
  Modulation of the optical index by means of atomic and material resonances
provides a basis for controlling light propagation in natural and artificially
fabricated materials. In addition, recent advances in the tuning of
spatiotemporal couplings of ultrashort laser pulses have enabled almost
arbitrary control over the group velocity of light. Here, using femtosecond
time-resolved microscopy and numerical calculations, we investigate the
spatiotemporal dynamics of a surface plasmon polariton wave packet (SPP WP)
that interacts with a plasmonic nanocavity. The nanocavity consists of
metal-insulator-metal multilayer films that function as subwavelength meta-atom
possessing tunable discretized eigenmodes. When a chirp-induced femtosecond SPP
WP is incident on a nanocavity, only the spectral component matching the
resonance energy is transmitted. This spectral clipping effect is accompanied
by a spatial shift of the WP. The shift can be adjusted in either the positive
or negative direction by controlling the resonance energy or the chirp. If this
spatial shift is regarded as a modulation of the apparent group velocity in the
nanocavity, the range of modulation includes superluminal, subluminal, and
negative group velocities.


Abstract:
  In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000
fps with the extreme motion to the research community for video frame
interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that
first handles the VFI for 4K videos with large motion. The XVFI-Net is based on
a recursive multi-scale shared structure that consists of two cascaded modules
for bidirectional optical flow learning between two input frames (BiOF-I) and
for bidirectional optical flow learning from target to input frames (BiOF-T).
The optical flows are stably approximated by a complementary flow reversal
(CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start
at any scale of input while the BiOF-T module only operates at the original
input scale so that the inference can be accelerated while maintaining highly
accurate VFI performance. Extensive experimental results show that our XVFI-Net
can successfully capture the essential information of objects with extremely
large motions and complex textures while the state-of-the-art methods exhibit
poor performance. Furthermore, our XVFI-Net framework also performs comparably
on the previous lower resolution benchmark dataset, which shows a robustness of
our algorithm as well. All source codes, pre-trained models, and proposed
X4K1000FPS datasets are publicly available at
https://github.com/JihyongOh/XVFI.


Abstract:
  Inward migration of giant planets is predicted by hydrodynamical simulations
during the gas phase of the protoplanetary disc. The phenomenon is also invoked
to explain resonant and near-resonant exoplanetary system structures. The early
inward migration may also have affected our Solar System and sculpted its
different minor planet reservoirs. In this study we explore how the early
inward migration of the giant planets shapes the Kuiper Belt. We test different
scenarios with only Neptune and Uranus and with all the four giant planets,
including also some models with the subsequent outward planetesimal-driven
migration of Neptune after the gas dispersal. We find objects populating mean
motion resonances even when Neptune and Uranus do not migrate at all or only
migrate inwards. When the planets are fixed, planetesimals stick only
temporarily to the mean motion resonances, while inwards migration yields a new
channel to populate the resonances without invoking convergent migration. In
these cases, however, it is hard to populate mean motion resonances that do not
cross the planetesimal disc (such as 2:1 and 5:2) and there is a lack of
resonant KBOs that cross Neptune's orbit. These Neptune crossers are an
unambiguous signature of the outward migration of Neptune. The starting
position and the growth rate of Neptune matters for the contamination of the
classical Kuiper belt region from neighbouring regions. The eccentricity and
inclination space of the hot classicals and the scattered disc region become
much more populated when all the giant planets are included. The 5:2 resonance
with Neptune becomes increasingly populated with deeper inward migrations of
Neptune. The overall inclination distribution, however, is still narrower than
from observations, as is generally the case for Kuiper belt population models.


Abstract:
  The Everett interpretation of quantum mechanics divides naturally into two
parts: first, the interpretation of the structure of the quantum state, in
terms of branching, and second, the interpretation of this branching structure
in terms of probability. This is the second of two reviews of the Everett
interpretation, and focuses on probability. Branching processes are identified
as chance processes, and the squares of branch amplitudes are chances. Since
branching is emergent, physical probability is emergent as well.


Abstract:
  Long-distance transfer of quantum information in architectures based on
quantum dot spin qubits will be necessary for their scalability. One way of
achieving it is to simply move the electron between two quantum registers.
Precise control over the electron shuttling through a chain of tunnel-coupled
quantum dots is possible when interdot energy detunings are changed
adiabatically. Deterministic character of shuttling is however endangered by
coupling of the transferred electron to thermal reservoirs: sources of
fluctuations of electric fields, and lattice vibrations. We theoretically
analyse how the electron transfer between two quantum dots is affected by
electron-phonon scattering, and interaction with sources of $1/f$ and Johnson
charge noise in both detuning and tunnel coupling. The electron-phonon
scattering turns out to be irrelevant in Si quantum dots, while a competition
between the effects of charge noise and Landau-Zener effect leads to an
existence of optimal detuning sweep rate, at which probability of leaving the
electron behind is minimal. In GaAs quantum dots, on the other hand, coupling
to phonons is strong enough to make the phonon-assisted processes of interdot
transfer dominate over influence of charge noise. The probability of leaving
the electron behind depends then monotonically on detuning sweep rate, and
values much smaller than in silicon can be obtained for slow sweeps. However,
after taking into account limitations on transfer time imposed by need for
preservation of electron's spin coherence, minimal probabilities of leaving the
electron behind in both GaAs- and Si-based double quantum dots turn out to be
of the same order of magnitude. Bringing them down below $10^{-3}$ requires
temperatures $\leq \! 100$ mK and tunnel couplings above $20$ $\mu$eV.


Abstract:
  Finding shape correspondences can be formulated as an NP-hard quadratic
assignment problem (QAP) that becomes infeasible for shapes with high sampling
density. A promising research direction is to tackle such quadratic
optimization problems over binary variables with quantum annealing, which
allows for some problems a more efficient search in the solution space.
Unfortunately, enforcing the linear equality constraints in QAPs via a penalty
significantly limits the success probability of such methods on currently
available quantum hardware. To address this limitation, this paper proposes
Q-Match, i.e., a new iterative quantum method for QAPs inspired by the
alpha-expansion algorithm, which allows solving problems of an order of
magnitude larger than current quantum methods. It implicitly enforces the QAP
constraints by updating the current estimates in a cyclic fashion. Further,
Q-Match can be applied iteratively, on a subset of well-chosen correspondences,
allowing us to scale to real-world problems. Using the latest quantum annealer,
the D-Wave Advantage, we evaluate the proposed method on a subset of QAPLIB as
well as on isometric shape matching problems from the FAUST dataset.


Abstract:
  Recently, researchers have utilized neural networks to accurately solve
partial differential equations (PDEs), enabling the mesh-free method for
scientific computation. Unfortunately, the network performance drops when
encountering a high nonlinearity domain. To improve the generalizability, we
introduce the novel approach of employing multi-task learning techniques, the
uncertainty-weighting loss and the gradients surgery, in the context of
learning PDE solutions. The multi-task scheme exploits the benefits of learning
shared representations, controlled by cross-stitch modules, between multiple
related PDEs, which are obtainable by varying the PDE parameterization
coefficients, to generalize better on the original PDE. Encouraging the network
pay closer attention to the high nonlinearity domain regions that are more
challenging to learn, we also propose adversarial training for generating
supplementary high-loss samples, similarly distributed to the original training
distribution. In the experiments, our proposed methods are found to be
effective and reduce the error on the unseen data points as compared to the
previous approaches in various PDE examples, including high-dimensional
stochastic PDEs.


Abstract:
  We construct a triangle equivalence between the singularity categories of two
isolated cyclic quotient singularities of Krull dimensions two and three,
respectively. This is the first example of a singular equivalence involving
connected commutative algebras of odd and even Krull dimension. In combination
with Orlov's localization result, this gives further singular equivalences
between certain quasi-projective varieties of dimensions two and three,
respectively.


Abstract:
  Conditional Variance Estimation (CVE) is a novel sufficient dimension
reduction (SDR) method for additive error regressions with continuous
predictors and link function. It operates under the assumption that the
predictors can be replaced by a lower dimensional projection without loss of
information. In contrast to the majority of moment based sufficient dimension
reduction methods, Conditional Variance Estimation is fully data driven, does
not require the restrictive linearity and constant variance conditions, and is
not based on inverse regression. CVE is shown to be consistent and its
objective function to be uniformly convergent. CVE outperforms the mean average
variance estimation, (MAVE), its main competitor, in several simulation
settings, remains on par under others, while it always outperforms the usual
inverse regression based linear SDR methods, such as Sliced Inverse Regression.


Abstract:
  In this pedagogical work we reviewed the mathematical formalism and the
physical interpretation, based on statistical mechanics, of the meta-heuristics
called simulated annealing. Moreover, we presented the mathematical formulation
of the algorithm and why it is capable to yield the optimal solution or a good
approximated solution of a given problem. Furthermore, we described the
travelling salesman problem, showing its interpretation as a Markov Chain and
how the simulated annealing can be used to optimize it and we did its
simulations for two scenarios. Firstly, for 50 cities distributed around a
circle and we found the best solution. Finally, we applied the meta-heuristic
in a another instance, with 100 nodes random uniformly distributed in a square,
and one shows that it allows finding a good solution.


Abstract:
  We study a spin 1/2 fermion in a thick braneworld in the context of
teleparallel $f(T, B)$ gravity. Here, $f(T,B)$ is such that
$f_1(T,B)=T+k_1B^{n_1}$ and $f_2(T,B)=B+k_2T^{n_2}$, where $n_{1,2}$ and
$k_{1,2}$ are parameters that control the influence of torsion and the boundary
term. We assume Yukawa coupling, where one scalar field is coupled to a Dirac
spinor field. We show how the $n_{1,2}$ and $k_{1,2}$ parameters control the
width of the massless Kaluza-Klein mode, the breadth of non-normalized massive
fermionic modes, and the properties of the analogue quantum-potential near the
origin.


Abstract:
  The cost of annotating training data has traditionally been a bottleneck for
supervised learning approaches. The problem is further exacerbated when
supervised learning is applied to a number of correlated tasks simultaneously
since the amount of labels required scales with the number of tasks. To
mitigate this concern, we propose an active multitask learning algorithm that
achieves knowledge transfer between tasks. The approach forms a so-called
committee for each task that jointly makes decisions and directly shares data
across similar tasks. Our approach reduces the number of queries needed during
training while maintaining high accuracy on test data. Empirical results on
benchmark datasets show significant improvements on both accuracy and number of
query requests.


Abstract:
  Quantum imaging with undetected photons (QIUP) is a unique imaging technique
that does not require the detection of the light used for illuminating the
object. The technique requires a correlated pair of photons. In the existing
implementations of QIUP, the imaging is enabled by the momentum correlation
between the twin photons. We investigate the complementary scenario in which
the imaging is instead enabled by the position correlation between the two
photons. We present a general theory and show that the properties of the images
obtained in these two cases are significantly distinct.


Abstract:
  Tropical cyclones are one of the most powerful and destructive natural
phenomena on earth. Tropical storms and heavy rains can cause floods, which
lead to human lives and economic loss. Devastating winds accompanying cyclones
heavily affect not only the coastal regions, even distant areas. Our study
focuses on the intensity estimation, particularly cyclone grade and maximum
sustained surface wind speed (MSWS) of a tropical cyclone over the North Indian
Ocean. We use various machine learning algorithms to estimate cyclone grade and
MSWS. We have used the basin of origin, date, time, latitude, longitude,
estimated central pressure, and pressure drop as attributes of our models. We
use multi-class classification models for the categorical outcome variable,
cyclone grade, and regression models for MSWS as it is a continuous variable.
Using the best track data of 28 years over the North Indian Ocean, we estimate
grade with an accuracy of 88% and MSWS with a root mean square error (RMSE) of
2.3. For higher grade categories (5-7), accuracy improves to an average of
98.84%. We tested our model with two recent tropical cyclones in the North
Indian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22% and
95.23% respectively, while for MSWS, we obtained RMSE of 2.2 and 3.4 and $R^2$
of 0.99 and 0.99, respectively.


Abstract:
  We find the index of symmetry for all solvable three-dimensional Lie groups
with a left-invariant metric. When combined with the work of Reggiani on
unimodular three-dimensional Lie groups, the index of symmetry is then known
for all three-dimensional Lie groups with a left-invariant metric. Similar to
the unimodular case, for every solvable three-dimensional Lie algebra there is
at least one left-invariant metric for which the index of symmetry is positive.
Also, the index is never equal to 2.


Abstract:
  We study in this work the convergence of a sequence of asymptotically
critical self-exciting systems. In these systems, we use multivariate marked
Hawkes point processes to describe the event arrivals. Under mild assumptions
we prove the weak convergence of their rescaled density processes to a
multi-type continuous-state branching process with immigration (CBI-process).
In addition, we also provide two scaling limits for their shot noise processes
with limits being functionals of the multi-type CBI-process. Finally, we
provide a Hawkes representation for Crump-Mode-Jagers branching processes and
apply our limit results to study their asymptotic behavior. Specially, we
observe an interesting phenomenon, known as state space collapse in queueing
theory, in the population structure described by two measure-valued processes:
age-distribution process and residual-life distribution process. Indeed, under
a convergence condition on the life-length distributions, both the two rescaled
measure-valued processes converge weakly to a common measure-valued diffusion
process, which can be characterized as a lifting map of a multi-type
CBI-process associated with the limit excess life-length distribution.


Abstract:
  We tackle the problem of learning object detectors without supervision.
Differently from weakly-supervised object detection, we do not assume
image-level class labels. Instead, we extract a supervisory signal from
audio-visual data, using the audio component to "teach" the object detector.
While this problem is related to sound source localisation, it is considerably
harder because the detector must classify the objects by type, enumerate each
instance of the object, and do so even when the object is silent. We tackle
this problem by first designing a self-supervised framework with a contrastive
objective that jointly learns to classify and localise objects. Then, without
using any supervision, we simply use these self-supervised labels and boxes to
train an image-based object detector. With this, we outperform previous
unsupervised and weakly-supervised detectors for the task of object detection
and sound source localization. We also show that we can align this detector to
ground-truth classes with as little as one label per pseudo-class, and show how
our method can learn to detect generic objects that go beyond instruments, such
as airplanes and cats.


Abstract:
  We measure the galaxy 2- and 3-point correlation functions at $z=[0.5,0.7]$
and $z=[0.7, 0.9]$, from the final data release of the VIPERS survey (PDR2). We
model the two statistics including a nonlinear 1-loop model for the 2-point
function and a tree-level model for the 3-point function, and perform a joint
likelihood analysis. The entire process and nonlinear corrections are tested
and validated through the use of the 153 highly realistic VIPERS mock
catalogues, showing that they are robust down to scales as small as 10 $h^{-1}
\, \mathrm{Mpc}$. The mocks are also adopted to compute the covariance matrix
that we use for the joint 2- and 3-point analysis. Despite the limited
statistics of the two (volume-limited) sub-samples analysed, we demonstrate
that such a combination successfully breaks the degeneracy existing at 2-point
level between clustering amplitude $\sigma_8$, linear bias $b_1$ and the linear
growth rate of fluctuations $f$. For the latter, in particular, we measure
$f(z=0.61)=0.64^{+0.55}_{-0.37}$ and $f(z=0.8)=1.0\pm1.0$, while the amplitude
of clustering is found to be $\sigma_8(z=0.61)=0.50\pm 0.12$ and
$\sigma_8(z=0.8)=0.39^{+0.11}_{-0.13}$. These values are in excellent agreement
with the extrapolation of a Planck cosmology.


Abstract:
  We provide a test for the specification of a structural model without
identifying assumptions. We show the equivalence of several natural
formulations of correct specification, which we take as our null hypothesis.
From a natural empirical version of the latter, we derive a Kolmogorov-Smirnov
statistic for Choquet capacity functionals, which we use to construct our test.
We derive the limiting distribution of our test statistic under the null, and
show that our test is consistent against certain classes of alternatives. When
the model is given in parametric form, the test can be inverted to yield
confidence regions for the identified parameter set. The approach can be
applied to the estimation of models with sample selection, censored observables
and to games with multiple equilibria.


Abstract:
  Surprisal theory has provided a unifying framework for understanding many
phenomena in sentence processing (Hale, 2001; Levy, 2008a), positing that a
word's conditional probability given all prior context fully determines
processing difficulty. Problematically for this claim, one local statistic,
word frequency, has also been shown to affect processing, even when conditional
probability given context is held constant. Here, we ask whether other local
statistics have a role in processing, or whether word frequency is a special
case. We present the first clear evidence that more complex local statistics,
word bigram and trigram probability, also affect processing independently of
surprisal. These findings suggest a significant and independent role of local
statistics in processing. Further, it motivates research into new
generalizations of surprisal that can also explain why local statistical
information should have an outsized effect.


Abstract:
  Recent advances in multi-fingered robotic grasping have enabled fast
6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in
cluttered scenes, on the other hand, remains mostly unexplored due to the added
difficulty of reasoning over obstacles which greatly increases the
computational time to generate high-quality collision-free grasps. In this work
we address such limitations by introducing DDGC, a fast generative multi-finger
grasp sampling method that can generate high quality grasps in cluttered scenes
from a single RGB-D image. DDGC is built as a network that encodes scene
information to produce coarse-to-fine collision-free grasp poses and
configurations. We experimentally benchmark DDGC against the
simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and
7 real world scenes. The results show that DDGC outperforms the baseline on
synthesizing high-quality grasps and removing clutter while being 5 times
faster. This, in turn, opens up the door for using multi-finger grasps in
practical applications which has so far been limited due to the excessive
computation time needed by other methods.


Abstract:
  Opportunistic usage of spectrum owned by licensed (or primary) users is the
cornerstone on which the Cognitive Radio technology is built. Unlicensed (or
secondary) users that thus use the spectrum rely opportunistically on spectrum
sensing to determine the presence of primary user signal. In such a context, an
attacker may mimic the behavior of a primary user (PU) to deceive the secondary
users (SUs) into believing that a PU signal is present whereas it is not. Such
an attack is known as the Primary User Emulation Attack (PUEA). A malicious
user may launch a PUEA with the intention of grabbing the vacant bands for its
own transmission. Another reason may be to simply disrupt the functioning of
the Cognitive Radio Network (CRN). This work investigates the use of one-class
classification for detecting PUEA in an infrastructure-based CRN. We opine that
sensing data collected at the fusion center mainly for Collaborative Spectrum
Sensing (CSS) can be exploited to characterize a PU signal. The PU signal
features thus learned can aid in distinguishing a PU signal from a PU signal
emulation. In particular, we investigate the use of one-class classification
techniques, viz., Isolation Forest, Support Vector Machines (SVM), Minimum
Covariance Determinant(MCD) and Local Outlier Factor(LOF) for detection of PUEA
attacks. Simulation results support the validity of using one-class
classification for detection of PUEA.


Abstract:
  Free-text clinical notes detail all aspects of patient care and have great
potential to facilitate quality improvement and assurance initiatives as well
as advance clinical research. However, concerns about patient privacy and
confidentiality limit the use of clinical notes for research. As a result, the
information documented in these notes remains unavailable for most researchers.
De-identification (de-id), i.e., locating and removing personally identifying
protected health information (PHI), is one way of improving access to clinical
narratives. However, there are limited off-the-shelf de-identification systems
able to consistently detect PHI across different data sources and medical
specialties. In this abstract, we present the performance of a state-of-the art
de-id system called NeuroNER1 on a diverse set of notes from University of
Washington (UW) when the models are trained on data from an external
institution (Partners Healthcare) vs. from the same institution (UW). We
present results at the level of PHI and note types.


Abstract:
  The presence of valley states is a significant obstacle to realizing quantum
information technologies in Silicon quantum dots, as leakage into alternate
valley states can introduce errors into the computation. We use a perturbative
analytical approach to study the dynamics of exchange-coupled quantum dots with
valley degrees of freedom. We show that if the valley splitting is large and
electrons are not properly initialized to valley eigenstates, then time
evolution of the system will lead to spin-valley entanglement. Spin-valley
entanglement will also occur if the valley splitting is small and electrons are
not initialized to the same valley state. Additionally, we show that for small
valley splitting, spin-valley entanglement does not affect measurement
probabilities of two-qubit systems; however, systems with more qubits will be
affected. This means that two-qubit gate fidelities measured in two-qubit
systems may miss the effects of valley degrees of freedom. Our work shows how
the existence of valleys may adversely affect multiqubit fidelities even when
the system temperature is very low.


Abstract:
  Context: Post-release user feedback plays an integral role in improving
software quality and informing new features. Given its growing importance,
feedback concerning security enhancements is particularly noteworthy. In
considering the rapid uptake of Android we have examined the scale and severity
of Android security threats as reported by its stakeholders. Objective: We
systematically mine Android issue logs to derive insights into stakeholder
perceptions and experiences in relation to certain Android security issues.
Method: We employed contextual analysis techniques to study issues raised
regarding confidentiality and privacy in the last three major Android releases,
considering covariance of stakeholder comments, and the level of consistency in
user preferences and priorities. Results: Confidentiality and privacy concerns
varied in severity, and were most prevalent over Jelly Bean releases. Issues
raised in regard to confidentiality related mostly to access, user credentials
and permission management, while privacy concerns were mainly expressed about
phone locking. Community users also expressed divergent preferences for new
security features, ranging from more relaxed to very strict. Conclusion:
Strategies that support continuous corrective measures for both old and new
Android releases would likely maintain stakeholder confidence. An approach that
provides users with basic default security settings, but with the power to
configure additional security features if desired, would provide the best
balance for Android's wide cohort of stakeholders.


Abstract:
  Prophet inequalities and secretary problems have been extensively studied in
recent years due to their elegance, connections to online algorithms,
stochastic optimization, and mechanism design problems in game theoretic
settings. Rubinstein and Singla developed a notion of combinatorial prophet
inequalities in order to generalize the standard prophet inequality setting to
combinatorial valuation functions such as submodular and subadditive functions.
For non-negative submodular functions they demonstrated a constant factor
prophet inequality for matroid constraints. Along the way they showed a variant
of the correlation gap for non-negative submodular functions.
  In this paper we revisit their notion of correlation gap as well as the
standard notion of correlation gap and prove much tighter and cleaner bounds.
Via these bounds and other insights we obtain substantially improved constant
factor combinatorial prophet inequalities for both monotone and non-monotone
submodular functions over any constraint that admits an Online Contention
Resolution Scheme. In addition to improved bounds we describe efficient
polynomial-time algorithms that achieve these bounds.


Abstract:
  Space-time (ST) wave packets are pulsed optical beams endowed with precise
spatio-temporal structure by virtue of which they exhibit unique and useful
characteristics, such as propagation invariance and tunable group velocity. We
study in detail here, and in two accompanying papers, the refraction of ST wave
packets at planar interfaces between non-dispersive, homogeneous, isotropic
dielectrics. We formulate a law of refraction that determines the change in the
ST wave-packet group velocity across such an interface as a consequence of a
newly identified optical refractive invariant that we call 'the spectral
curvature'. Because the spectral curvature vanishes in conventional optical
fields where the spatial and temporal degrees of freedom are separable, these
phenomena have not been observed to date. We derive the laws of refraction for
baseband, X-wave, and sideband ST wave packets that reveal fascinating
refractive phenomena, especially for the former class of wave packets. We
predict theoretically, and confirm experimentally in the accompanying papers,
refractive phenomena such as group-velocity invariance (ST wave packets whose
group velocity does not change across the interface), anomalous refraction
(group-velocity increase in higher-index media), group-velocity inversion
(change in the sign of the group velocity upon refraction but not its
magnitude), and the dependence of the group velocity of the refracted ST wave
packet on the angle of incidence.


Abstract:
  Selberg's central limit theorem states that the values of $\log|\zeta(1/2+i
\tau)|$, where $\tau$ is a uniform random variable on $[T,2T]$, is distributed
like a Gaussian random variable of mean $0$ and standard deviation
$\sqrt{\frac{1}{2}\log \log T}$. It was conjectured by Radziwi{\l}{\l} that
this breaks down for values of order $\log\log T$, where a multiplicative
correction $C_k$ would be present at level $k\log\log T$, $k>0$. This constant
should be equal to the leading asymptotic for the $2k^{th}$ moment of $\zeta$,
as first conjectured by Keating and Snaith using random matrix theory. In this
paper, we provide numerical and theoretical evidence for this conjecture. We
propose that this correction has a significant effect on the distribution of
the maximum of $\log|\zeta|$ in intervals of size $(\log T)^\theta$,
$\theta>0$. The precision of the prediction enables the numerical detection of
$C_k$ even for low $T$'s of order $T=10^8$. A similar correction appears in the
large deviations of the Keating-Snaith central limit theorem for the logarithm
of the characteristic polynomial of a random unitary matrix, as first proved by
F\'eray, M\'eliot and Nikeghbali.


Abstract:
  We find all analytic SU(2) Yang-Mills solutions on de Sitter space by
reducing the field equations to Newton's equation for a particle in a
particular 3d potential and solving the latter in a special case. In contrast,
Maxwell's equations on de Sitter space can be solved in generality, by
separating them in hysperspherical coordinates. Employing a well-known
conformal map between (half of) de Sitter space and (the future half of)
Minkowski space, the Maxwell solutions are mapped to a complete basis of
rational electromagnetic knot configurations. We discuss some of their
properties and illustrate the construction method with two nontrivial examples
given by rational functions of increasing complexity. The material is partly
based on [1,2].


Abstract:
  The high-redshift quasar PMN J0909+0354 ($z=3.288$) is known to have a
pc-scale compact jet structure, based on global 5-GHz very long baseline
interferometry (VLBI) observations performed in 1992. Its kpc-scale structure
was studied with the Karl G. Jansky Very Large Array (VLA) in the radio and the
Chandra space telescope in X-rays. Apart from the north-northwestern jet
component seen in both the VLA and Chandra images at $2.3''$ separation from
the core, there is another X-ray feature at $6.48''$ in the northeastern (NE)
direction. To uncover more details and possibly structural changes in the inner
jet, we conducted new observations at 5 GHz using the European VLBI Network
(EVN) in 2019. These data confirm the northward direction of the one-sided
inner jet already suspected from the 1992 observations. A compact core and
multiple jet components were identified that can be traced up to $\sim0.25$ kpc
projected distance towards the north, while the structure becomes more and more
diffuse. A comparison with arcsec-resolution imaging with the VLA shows that
the radio jet bends by $\sim30^\circ$ between the two scales. The direction of
the pc-scale jet as well as the faint optical counterpart found for the
newly-detected X-ray point source (NE) favors the nature of the latter as a
background or foreground object in the field of view. However, the extended
($\sim160$ kpc) emission around the positions of the quasar core and NE
detected by the Wide-field Infrared Survey Explorer (WISE) in the mid-infrared
might suggest physical interaction of the two objects.


Abstract:
  A powerful new perspective in the analysis of absolute Galois groups has
recently emerged from the study of Galois modules related to classical
parameterizing spaces of certain Galois extensions. The recurring trend in
these decompositions is their stunning simplicity: almost all summands are free
over some quotient ring. The non-free summands which appear are exceptional not
only because they are different in form, but because they play the key role in
controlling arithmetic conditions that allow the remaining summands to be
easily described. In this way, these exceptional summands are the lynchpin for
a bevy of new properties of absolute Galois groups that have been gleaned from
these surprising decompositions.
  In one such recent decomposition, a remarkable new exceptional summand was
discovered which exhibited interesting properties that have not been seen
before. The exceptional summand is drawn from a particular finite family that
has not yet been investigated. The main goal of this paper is to introduce this
family of modules and verify their indecomposability. We believe this module
will be of interest to people working in Galois theory, representation theory,
combinatorics, and general algebra. The analysis of these modules includes some
interesting new tools, including analogs of $p$-adic expansions.


Abstract:
  Model-free deep reinforcement learning has achieved great success in many
domains, such as video games, recommendation systems and robotic control tasks.
In continuous control tasks, widely used policies with Gaussian distributions
results in ineffective exploration of environments and limited performance of
algorithms in many cases. In this paper, we propose a density-free off-policy
algorithm, Generative Actor-Critic(GAC), using the push-forward model to
increase the expressiveness of policies, which also includes an entropy-like
technique, MMD-entropy regularizer, to balance the exploration and
exploitation. Additionnally, we devise an adaptive mechanism to automatically
scale this regularizer, which further improves the stability and robustness of
GAC. The experiment results show that push-forward policies possess desirable
features, such as multi-modality, which can improve the efficiency of
exploration and asymptotic performance of algorithms obviously.


Abstract:
  In this note, we derive bounds on the median bias of univariate M-estimators
under mild regularity conditions. These requirements are not sufficient to
imply convergence in distribution of the M-estimators. We also discuss median
bias of some multivariate M-estimators.


Abstract:
  Based upon the resistor analogy and using the ideal loop gas
approximation(ILGA) it is shown that only pending loops reduce the modulus of
an otherwise perfect network made of monodisperse strands and junctions of
identical functionality. Thus, the cycle rank of the network with pending
structures removed (cyclic and branched) is sufficient to characterize modulus,
if the resistor analogy can be employed. It is further shown that it is
impossible to incorporate finite cycles into a polymer network such that
individual network strands are at equilibrium conformations while maintaining
simultaneously a force balance at the junctions. Therefore, the resistor
analogy provides only an approximation for the phantom modulus of networks
containing finite loops. Improved approaches to phantom modulus can be
constructed from considering a force balance at the junctions, which requires
knowledge of the distribution of cross-link fluctuations in imperfect networks.
Assuming loops with equilibrium conformations and a force balance at all loop
junctions, a lower bound estimate for the phantom modulus,
$G_{\text{ph}}\approx\left(\xi-c_{\text{f}}L_{1}\right)kT/V$ is obtained within
the ILGA for end-linked model networks and in the limit of $L_{1}\ll\xi$. Here,
$L_{1}$ is the number of primary ("pending") loops, $\xi$ the cycle rank of the
network, $k$ the Boltzmann constant, $V$ the volume of the sample, and $T$ the
absolute temperature. $c_{\text{f}}$ is a functionality dependent coefficient
that is $\approx2.56$ for junction functionality $f=3$ and $\approx3.06$ for
$f=4$, while it converges quickly towards $\approx4.2$ in the limit of large
$f$. Further corrections to phantom modulus beyond finite loops are addressed
briefly.


Abstract:
  Genetic association data from national biobanks and large-scale association
studies have provided new prospects for understanding the genetic evolution of
complex traits and diseases in humans. In turn, genomes from ancient human
archaeological remains are now easier than ever to obtain, and provide a direct
window into changes in frequencies of trait-associated alleles in the past.
This has generated a new wave of studies aiming to analyse the genetic
component of traits in historic and prehistoric times using ancient DNA, and to
determine whether any such traits were subject to natural selection. In humans,
however, issues about the portability and robustness of complex trait inference
across different populations are particularly concerning when predictions are
extended to individuals that died thousands of years ago, and for which little,
if any, phenotypic validation is possible. In this review, we discuss the
advantages of incorporating ancient genomes into studies of trait-associated
variants, the need for models that can better accommodate ancient genomes into
quantitative genetic frameworks, and the existing limits to inferences about
complex trait evolution, particularly with respect to past populations.


Abstract:
  We report on large-scale simulations of intrachain exciton dynamics in
poly(para-phenylene vinylene). Our coarse-grained model describes Frenkel
exciton coupling to both fast, quantized C-C bond vibrations and slow,
classical torsional modes. We also incorporate system-bath interactions. The
dynamics are simulated using the Time Evolution Block Decimation method, which
avoids the failures of the Ehrenfest approximation to describe decoherence
processes and nonadiabatic interstate conversion. System-bath interactions are
modeled using quantum trajectories and Lindblad quantum jump operators. We find
that following photoexcitation, the quantum mechanical entanglement of the
exciton and C-C bond phonons causes exciton-site decoherence. Next, system-bath
interactions cause the stochastic collapse of high-energy delocalized excitons
onto chromophores. Finally, torsional relaxation causes additional
exciton-density localization. We relate these dynamical processes to the
predicted fluorescence depolarization and extract the timescales corresponding
to them.


Abstract:
  High power operation in extreme fast charging significantly increases the
risk of internal faults in Electric Vehicle batteries which can lead to
accelerated battery failure. Early detection of these faults is crucial for
battery safety and widespread deployment of fast charging. In this setting, we
propose a real-time {detection} framework for battery voltage and thermal
faults. A major challenge in battery fault detection arises from the effect of
uncertainties originating from sensor inaccuracies, nominal aging, or
unmodelled dynamics. Inspired by physics-based learning, we explore a detection
paradigm that combines physics-based models, model-based detection observers,
and data-driven learning techniques to address this challenge. Specifically, we
construct the {detection} observers based on an experimentally identified
electrochemical-thermal model, and subsequently design the observer tuning
parameters following Lyapunov's stability theory. Furthermore, we utilize
Gaussian Process Regression technique to learn the model and measurement
uncertainties which in turn aid the {detection} observers in distinguishing
faults and uncertainties. Such uncertainty learning essentially helps
suppressing their effects, potentially enabling early detection of faults. We
perform simulation and experimental case studies on the proposed fault
{detection} scheme verifying the potential of physics-based learning in early
detection of battery faults.


Abstract:
  A large repulsion between particles in a quantum system can lead to their
localization, as it happens for the electrons in Mott insulating materials.
This paradigm has recently branched out into a new quantum state, the
orbital-selective Mott insulator, where electrons in some orbitals are
predicted to localize, while others remain itinerant. We provide a direct
experimental realization of this phenomenon, that we extend to a more general
flavour-selective localization. By using an atom-based quantum simulator, we
engineer SU(3) Fermi-Hubbard models breaking their symmetry via a tunable
coupling between flavours, observing an enhancement of localization and the
emergence of flavour-dependent correlations. Our realization of
flavour-selective Mott physics opens the path to the quantum simulation of
multicomponent materials, from superconductors to topological insulators.


Abstract:
  Delivering fast responses to retrospective queries on video datasets is
difficult due to the large number of frames to consider and the high costs of
running convolutional neural networks (CNNs) on each one. A natural solution is
to perform a subset of the necessary computations ahead of time, as video is
ingested. However, existing ingest-time systems require knowledge of the
specific CNN that will be used in future queries -- a challenging requisite
given the evergrowing space of CNN architectures and training
datasets/methodologies.
  This paper presents Boggart, a retrospective video analytics system that
delivers ingest-time speedups in a model-agnostic manner. Our underlying
insight is that traditional computer vision (CV) algorithms are capable of
performing computations that can be used to accelerate diverse queries with
wide-ranging CNNs. Building on this, at ingest-time, Boggart carefully employs
a variety of motion tracking algorithms to identify potential objects and their
trajectories across frames. Then, at query-time, Boggart uses several novel
techniques to collect the smallest sample of CNN results required to meet the
target accuracy: (1) a clustering strategy to efficiently unearth the
inevitable discrepancies between CV- and CNN-generated outputs, and (2) a set
of accuracy-preserving propagation techniques to safely extend sampled results
along each trajectory. Across many videos, CNNs, and queries Boggart
consistently meets accuracy targets while using CNNs sparingly (on 3-54% of
frames).


Abstract:
  The Transformer architecture deeply changed the natural language processing,
outperforming all previous state-of-the-art models. However, well-known
Transformer models like BERT, RoBERTa, and GPT-2 require a huge compute budget
to create a high quality contextualised representation. In this paper, we study
several efficient pre-training objectives for Transformers-based models. By
testing these objectives on different tasks, we determine which of the ELECTRA
model's new features is the most relevant. We confirm that Transformers
pre-training is improved when the input does not contain masked tokens and that
the usage of the whole output to compute the loss reduces training time.
Moreover, inspired by ELECTRA, we study a model composed of two blocks; a
discriminator and a simple generator based on a statistical model with no
impact on the computational performances. Besides, we prove that eliminating
the MASK token and considering the whole output during the loss computation are
essential choices to improve performance. Furthermore, we show that it is
possible to efficiently train BERT-like models using a discriminative approach
as in ELECTRA but without a complex generator, which is expensive. Finally, we
show that ELECTRA benefits heavily from a state-of-the-art hyper-parameters
search.


Abstract:
  Since prior work has identified that cultural differences influence user
design preferences and interaction methods, as well as emphasizing the need to
reflect on the appropriateness of popular HCI principles, we believe that it is
equally important to apply this inquiry to digital accessibility and how
accessibility fits within the design process around the world. Our long-term
plan is to build upon work in this area by investigating how digital designers
in different parts of the world consider accessibility and whether current
accessibility resources (often developed in the west) meet or conflict with
their approach to design.


Abstract:
  Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to
characterise the microstructure of the nervous tissue, e.g. to delineate brain
white matter connections in a non-invasive manner via fibre tracking. Magnetic
Resonance Imaging (MRI) in high spatial resolution would play an important role
in visualising such fibre tracts in a superior manner. However, obtaining an
image of such resolution comes at the expense of longer scan time. Longer scan
time can be associated with the increase of motion artefacts, due to the
patient's psychological and physical conditions. Single Image Super-Resolution
(SISR), a technique aimed to obtain high-resolution (HR) details from one
single low-resolution (LR) input image, achieved with Deep Learning, is the
focus of this study. Compared to interpolation techniques or sparse-coding
algorithms, deep learning extracts prior knowledge from big datasets and
produces superior MRI images from the low-resolution counterparts. In this
research, a deep learning based super-resolution technique is proposed and has
been applied for DW-MRI. Images from the IXI dataset have been used as the
ground-truth and were artificially downsampled to simulate the low-resolution
images. The proposed method has shown statistically significant improvement
over the baselines and achieved an SSIM of $0.913\pm0.045$.


Abstract:
  We develop a model to establish the interconnection between galaxies and
their dark matter halos. We use Principal Component Analysis (PCA) to reduce
the dimensionality of both the mass assembly histories of halos/subhalos and
the star formation histories of galaxies, and Gradient Boosted Decision Trees
(GBDT) to transform halo/subhalo properties into galaxy properties. We use two
sets of hydrodynamic simulations to motivate our model architecture and to
train the transformation. We then apply the two sets of trained models to dark
matter only (DMO) simulations to show that the transformation is reliable and
statistically accurate. The model trained by a high-resolution hydrodynamic
simulation, or by a set of such simulations implementing the same physics of
galaxy formation, can thus be applied to large DMO simulations to make "mock"
copies of the hydrodynamic simulation. The model is both flexible and
interpretable, which paves the way for future applications in which we will
constrain the model using observations at different redshifts simultaneously
and explore how galaxies form and evolve in dark matter halos empirically.


Abstract:
  Highly charged liquid droplets are unstable above the critical charge
squared-to-volume ratio given by the Rayleigh limit. The instability leads to
ion ejection from jets formed on the droplet's surface. Despite the many
experiments that have been performed to capture the jet formation the precise
fission mechanism has not yet been observed because of its brief transient
nature. Here, we present the first atomistic simulations that reveal the
mechanism of Rayleigh fission. We demonstrate that ion ejection takes place
through a drop's deformation from a spherical into a distinct shape that
contains a conical protrusion. We assert that the latter state is a free energy
minimum along an order parameter that measures the degree of droplet
asphericity. The charged droplet's long-time evolution proceeds by alternating
between the two minima above and below the critical value that are reached
through solvent evaporation and ion ejection, respectively. For the first time,
this mechanism allows one to explain the nature of the progeny droplets and the
percentage of charge lost during fission. We determine that the cone half-angle
is close to the value predicted from the solution of the electrostatic equation
for the dielectric liquid. It is found that the conical deformation is
independent of the effect of electrohydrodynamic forces reported in
experiments. Contrary to the experimental observations of two diametrically
opposite jets for droplets suspended in the electric field, we found that a
single jet is formed at the Rayleigh limit. This indicates that super-charged
droplet states may have been detected in the experiments.