Abstract:
  A non-equilibrium model for laser-induced plasmas is used to describe how
nano-second temporal mode-beating affects plasma kernel formation and growth in
quiescent air. The chemically reactive Navier-Stokes equations describe the
hydrodynamics, and non-equilibrium effects are modeled based on a
two-temperature model. Inverse Bremsstrahlung and multiphoton ionization are
self-consistently taken into account via a coupled solution of the equations
governing plasma dynamics and beam propagation and attenuation (i.e., Radiative
Transfer Equation). This strategy, despite the additional challenges it may
bring, allows to minimize empiricism and enables for more accurate simulations
since it does not require an artificial plasma seed to trigger breakdown. The
benefits of this methodology are demonstrated by the good agreement between the
predicted and the experimental plasma boundary evolution and absorbed energy.
The same goes for the periodic plasma kernel structures which, as suggested by
experiments and confirmed by the simulations discussed here, are linked to the
modulating frequency.


Abstract:
  We introduce a neural relighting algorithm for captured indoors scenes, that
allows interactive free-viewpoint navigation. Our method allows illumination to
be changed synthetically, while coherently rendering cast shadows and complex
glossy materials. We start with multiple images of the scene and a 3D mesh
obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is
well-explained as the sum of a view-independent diffuse component and a
view-dependent glossy term concentrated around the mirror reflection direction.
We design a convolutional network around input feature maps that facilitate
learning of an implicit representation of scene materials and illumination,
enabling both relighting and free-viewpoint navigation. We generate these input
maps by exploiting the best elements of both image-based and physically-based
rendering. We sample the input views to estimate diffuse scene irradiance, and
compute the new illumination caused by user-specified light sources using path
tracing. To facilitate the network's understanding of materials and synthesize
plausible glossy reflections, we reproject the views and compute mirror images.
We train the network on a synthetic dataset where each scene is also
reconstructed with MVS. We show results of our algorithm relighting real indoor
scenes and performing free-viewpoint navigation with complex and realistic
glossy reflections, which so far remained out of reach for view-synthesis
techniques.


Abstract:
  The temperature dependence of quantum Hall conductivities is studied in the
context of the AdS/CMT paradigm using a model with a bulk theory consisting of
(3+1)-dimensional Einstein-Maxwell action coupled to a dilaton and an axion,
with a negative cosmological constant. We consider a solution which has a
Lifshitz like geometry with a dyonic black-brane in the bulk. There is an
$Sl(2,R)$ action in the bulk corresponding to electromagnetic duality, which
maps between classical solutions, and is broken to $Sl(2,Z)$ by Dirac
quantisation of dyons. This bulk $Sl(2,Z)$ action translates to an action of
the modular group on the 2-dimensional transverse conductivities. The
temperature dependence of the infra-red conductivities is then linked to
modular forms via gradient flow and the resulting flow diagrams show remarkable
agreement with existing experimental data on the temperature flow of both
integral and fractional quantum Hall conductivities.


Abstract:
  Accurate characterisation of small defects remains a challenge in
non-destructive testing (NDT). In this paper, a principle-component
parametric-manifold mapping approach is applied to single-frequency
eddy-current defect characterisation problems for surface breaking defects in a
planar half-space. A broad 1-8 MHz frequency-range FE-circuit model &
calibration approach is developed & validated to simulate eddy-current scans of
surface-breaking notch defects. This model is used to generate parametric
defect databases for surface breaking defects in an aluminium planar half-space
and defect characterisation of experimental measurements performed.
Parametric-manifold mapping was conducted in N-dimensional principle component
space, reducing the dimensionality of the characterisation problem. In a study
characterising slot depth, the model & characterisation approach is shown to
accurately invert the depth with greater accuracy than a simple amplitude
inversion method with normalised percentage characterisation errors of 38% and
17% respectively measured at 2.0 MHz across 5 slot depths between 0.26 - 2.15
mm. The approach is used to characterise the depth of a sloped slot
demonstrating good accuracy up to ~2.0 mm in depth over a broad range of
sub-resonance frequencies, indicating applications in geometric feature
inversion. Finally the technique is applied to finite rectangular notch defects
of surface extents smaller than the diameter of the inspection coil
(sub-aperture) over a range of frequencies. The results highlight the
limitations in characterising these defects and indicate how the inherent
instabilities in resonance can severely limit characterisation at these
frequencies.


Abstract:
  In this work, we estimate how much bulk viscosity driven by Urca processes is
likely to affect the gravitational wave signal of a neutron star coalescence.
In the late inspiral, we show that bulk viscosity affects the binding energy at
fourth post-Newtonian (PN) order. Even though this effect is enhanced by the
square of the gravitational compactness, the coefficient of bulk viscosity is
likely too small to lead to observable effects in the waveform during the late
inspiral, when only considering the orbital motion itself. In the post-merger,
however, the characteristic time-scales and spatial scales are different,
potentially leading to the opposite conclusion. We post-process data from a
state-of-the-art equal-mass binary neutron star merger simulation to estimate
the effects of bulk viscosity (which was not included in the simulation
itself). In that scenario, we find that bulk viscosity can reach high values in
regions of the merger. We compute several estimates of how much it might
directly affect the global dynamics of the considered merger scenario, and find
that it could become significant. Even larger effects could arise in different
merger scenarios or in simulations that include non-linear effects. This
assessment is reinforced by a quantitative comparison with relativistic
heavy-ion collisions where such effects have been explored extensively.


Abstract:
  Large-scale trademark retrieval is an important content-based image retrieval
task. A recent study shows that off-the-shelf deep features aggregated with
Regional-Maximum Activation of Convolutions (R-MAC) achieve state-of-the-art
results. However, R-MAC suffers in the presence of background clutter/trivial
regions and scale variance, and discards important spatial information. We
introduce three simple but effective modifications to R-MAC to overcome these
drawbacks. First, we propose the use of both sum and max pooling to minimise
the loss of spatial information. We also employ domain-specific unsupervised
soft-attention to eliminate background clutter and unimportant regions.
Finally, we add multi-resolution inputs to enhance the scale-invariance of
R-MAC. We evaluate these three modifications on the million-scale METU dataset.
Our results show that all modifications bring non-trivial improvements, and
surpass previous state-of-the-art results.


Abstract:
  This is the first one in a series of papers classifying the factorizations of
almost simple groups with nonsolvable factors. In this paper we deal with
almost simple linear groups.


Abstract:
  In the crowded environment of bio-inspired population-based metaheuristics,
the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately
gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp
colonies, which are displaced in long chains following a leader, this algorithm
seems to provide an interesting optimization performance. However, the original
work was characterized by some conceptual and mathematical flaws, which
influenced all ensuing papers on the subject. In this manuscript, we perform a
critical review of SSO, highlighting all the issues present in the literature
and their negative effects on the optimization process carried out by this
algorithm. We also propose a mathematically correct version of SSO, named
Amended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. We
benchmarked the performance of ASSO on a set of tailored experiments, showing
that it is able to achieve better results than the original SSO. Finally, we
performed an extensive study aimed at understanding whether SSO and its
variants provide advantages compared to other metaheuristics. The experimental
results, where SSO cannot outperform simple well-known metaheuristics, suggest
that the scientific community can safely abandon SSO.


Abstract:
  Neural network architectures in natural language processing often use
attention mechanisms to produce probability distributions over input token
representations. Attention has empirically been demonstrated to improve
performance in various tasks, while its weights have been extensively used as
explanations for model predictions. Recent studies (Jain and Wallace, 2019;
Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot
generally be considered as a faithful explanation (Jacovi and Goldberg, 2020)
across encoders and tasks. In this paper, we seek to improve the faithfulness
of attention-based explanations for text classification. We achieve this by
proposing a new family of Task-Scaling (TaSc) mechanisms that learn
task-specific non-contextualised information to scale the original attention
weights. Evaluation tests for explanation faithfulness, show that the three
proposed variants of TaSc improve attention-based explanations across two
attention mechanisms, five encoders and five text classification datasets
without sacrificing predictive performance. Finally, we demonstrate that TaSc
consistently provides more faithful attention-based explanations compared to
three widely-used interpretability techniques.


Abstract:
  Phishing attacks have evolved and increased over time and, for this reason,
the task of distinguishing between a legitimate site and a phishing site is
more and more difficult, fooling even the most expert users. The main proposals
focused on addressing this problem can be divided into four approaches:
List-based, URL based, content-based, and hybrid. In this state of the art, the
most recent techniques using web content-based and hybrid approaches for
Phishing Detection are reviewed and compared.


Abstract:
  Context: The energy-limited (EL) atmospheric escape approach is used to
estimate mass-loss rates for a broad range of planets that host
hydrogen-dominated atmospheres as well as for performing atmospheric evolution
calculations. Aims: We aim to study the applicability range of the EL
approximation. Methods: We revise the EL formalism and its assumptions. We also
compare its results with those of hydrodynamic simulations, employing a grid
covering planets with masses, radii, and equilibrium temperatures ranging
between 1 $M_{\oplus}$ and 39 $M_{\oplus}$, 1 $R_{\oplus}$ and 10 $R_{\oplus}$,
and 300 and 2000 K, respectively. Results: Within the grid boundaries, we find
that the EL approximation gives a correct order of magnitude estimate for
mass-loss rates for about 76% of the planets, but there can be departures from
hydrodynamic simulations by up to three orders of magnitude in individual
cases. Furthermore, we find that planets for which the mass-loss rates are
correctly estimated by the EL approximation to within one order of magnitude
have intermediate gravitational potentials as well as low-to-intermediate
equilibrium temperatures and irradiation fluxes of extreme ultraviolet and
X-ray radiation. However, for planets with low or high gravitational
potentials, or high equilibrium temperatures and irradiation fluxes, the
approximation fails in most cases. Conclusions: The EL approximation should not
be used for planetary evolution calculations that require computing mass-loss
rates for planets that cover a broad parameter space. In this case, it is very
likely that the EL approximation would at times return mass-loss rates of up to
several orders of magnitude above or below those predicted by hydrodynamic
simulations. For planetary atmospheric evolution calculations, interpolation
routines or approximations based on grids of hydrodynamic models should be used
instead.


Abstract:
  Recently, much work has been done to investigate Galois module structure of
local field extensions, particularly through the use of Galois scaffolds. Given
a totally ramified $p$-extension of local fields $L/K$, a Galois Scaffold gives
us a $K$-basis for $K[G]$ whose effect on the valuation of elements of $L$ is
easy to determine. In 2013, N.P. Byott and G.G. Elder gave sufficient
conditions for the existence of Galois scaffolds for cyclic extensions of
degree $p^2$ in characteristic $p$. We take their work and adapt it to cyclic
extensions of degree $p^2$ in characteristic $0$.


Abstract:
  The discoveries of high-temperature superconductivity in H3S and LaH10 have
excited the search for superconductivity in compressed hydrides. In contrast to
rapidly expanding theoretical studies, high-pressure experiments on hydride
superconductors are expensive and technically challenging. Here we
experimentally discover superconductivity in two new phases,Fm-3m-CeH10 (SC-I
phase) and P63/mmc-CeH9 (SC-II phase) at pressures that are much lower (<100
GPa) than those needed to stabilize other polyhydride superconductors.
Superconductivity was evidenced by a sharp drop of the electrical resistance to
zero, and by the decrease of the critical temperature in deuterated samples and
in an external magnetic field. SC-I has Tc=115 K at 95 GPa, showing expected
decrease on further compression due to decrease of the electron-phonon coupling
(EPC) coefficient {\lambda} (from 2.0 at 100 GPa to 0.8 at 200 GPa). SC-II has
Tc = 57 K at 88 GPa, rapidly increasing to a maximum Tc ~100 K at 130 GPa, and
then decreasing on further compression. This maximum of Tc is due to a maximum
of {\lambda} at the phase transition from P63/mmc-CeH9 into a symmetry-broken
modification C2/c-CeH9. The pressure-temperature conditions of synthesis affect
the actual hydrogen content, and the actual value of Tc. Anomalously low
pressures of stability of cerium superhydrides make them appealing for studies
of superhydrides and for designing new superhydrides with even lower pressures
of stability.


Abstract:
  We consider a linear non-local heat equation in a bounded domain
$\Omega\subset\mathbb{R}^d$, $d\geq 1$, with Dirichlet boundary conditions,
where the non-locality is given by the presence of an integral kernel.
Motivated by several applications in biological systems, in the present paper
we study some optimal control problems from a theoretical and numerical point
of view. In particular, we will employ the classical low-regret approach of
J.-L. Lions for treating the problem of incomplete data and provide a simple
computational implementation of the method. The effectiveness of the results
are illustrated by several examples.


Abstract:
  Gardner conjectured that if two bounded measurable sets $A,B \subset
\mathbb{R}^n$ are equidecomposable by a set of isometries $\Gamma$ generating
an amenable group then $A$ and $B$ admit a measurable equidecomposition by all
isometries. Cie\'sla and Sabok asked if there is a measurable equidecomposition
using isometries only in the group generated by $\Gamma$. We answer this
question negatively.


Abstract:
  Given a Riemannian manifold $M,$ and an open interval $I\subset\mathbb{R},$
we characterize nontrivial totally umbilical hypersurfaces of the product
$M\times I$ -- as well as of warped products $I\times_\omega M$ -- as those
which are local graphs built on isoparametric families of totally umbilical
hypersurfaces of $M.$ By means of this characterization, we fully extend to
$\mathbb{S}^n\times\mathbb{R}$ and $\mathbb{H}^n\times\mathbb{R}$ the results
by Souam and Toubiana on the classification of totally umbilical hypersurfaces
of $\mathbb{S}^2\times\mathbb{R}$ and $\mathbb{H}^2\times\mathbb{R}.$ It is
also shown that an analogous classification holds for arbitrary warped products
$I\times_\omega\mathbb{S}^n$ and $I\times_\omega\mathbb{H}^n.$


Abstract:
  Zero-shot learning, the task of learning to recognize new classes not seen
during training, has received considerable attention in the case of 2D image
classification. However, despite the increasing ubiquity of 3D sensors, the
corresponding 3D point cloud classification problem has not been meaningfully
explored and introduces new challenges. In this paper, we identify some of the
challenges and apply 2D Zero-Shot Learning (ZSL) methods in the 3D domain to
analyze the performance of existing models. Then, we propose a novel approach
to address the issues specific to 3D ZSL. We first present an inductive ZSL
process and then extend it to the transductive ZSL and Generalized ZSL (GZSL)
settings for 3D point cloud classification. To this end, a novel loss function
is developed that simultaneously aligns seen semantics with point cloud
features and takes advantage of unlabeled test data to address some known
issues (e.g., the problems of domain adaptation, hubness, and data bias). While
designed for the particularities of 3D point cloud classification, the method
is shown to also be applicable to the more common use-case of 2D image
classification. An extensive set of experiments is carried out, establishing
state-of-the-art for ZSL and GZSL on synthetic (ModelNet40, ModelNet10, McGill)
and real (ScanObjectNN) 3D point cloud datasets.


Abstract:
  We prove that it is consistent that Club Stationary Reflection and the
Special Aronszajn Tree Property simultaneously hold on $\omega_2$, thereby
contributing to the study of the tension between compactness and incompactness
in set theory. The poset which produces the final model follows the collapse of
a weakly compact cardinal first with an iteration of club adding (with
anticipation) and second with an iteration specializing Aronszajn trees.
  In the first part of the paper, we prove a general theorem about specializing
Aronszajn trees after forcing with what we call $\mathcal{F}_{WC}$-Strongly
Proper posets. This type of poset, of which the Levy collapse is a degenerate
example, uses systems of exact residue functions to create many strongly
generic conditions. We prove a new result about stationary set preservation by
quotients of this kind of poset; as a corollary, we show that the original
Laver-Shelah model satisfies a strong stationary reflection principle, though
it fails to satisfy the full Club Stationary Reflection. In the second part, we
show that the composition of collapsing and club adding (with anticipation) is
an $\mathcal{F}_{WC}$-Strongly Proper poset. After proving a new result about
Aronszajn tree preservation, we show how to obtain the final model.


Abstract:
  In this article, we prove Talenti's comparison theorem for Poisson equation
on complete noncompact Riemannian manifold with nonnegative Ricci curvature.
Furthermore, we obtain the Faber-Krahn inequality for the first eigenvalue of
Dirichlet Laplacian, $L^1$- and $L^\infty$-moment spectrum, especially
Saint-Venant theorem for torsional rigidity and a reverse H\"older inequality
for eigenfunctions of Dirichlet Laplacian.


Abstract:
  IoT systems have been facing increasingly sophisticated technical problems
due to the growing complexity of these systems and their fast deployment
practices. Consequently, IoT managers have to judiciously detect failures
(anomalies) in order to reduce their cyber risk and operational cost. While
there is a rich literature on anomaly detection in many IoT-based systems,
there is no existing work that documents the use of ML models for anomaly
detection in digital agriculture and in smart manufacturing systems. These two
application domains pose certain salient technical challenges. In agriculture
the data is often sparse, due to the vast areas of farms and the requirement to
keep the cost of monitoring low. Second, in both domains, there are multiple
types of sensors with varying capabilities and costs. The sensor data
characteristics change with the operating point of the environment or machines,
such as, the RPM of the motor. The inferencing and the anomaly detection
processes therefore have to be calibrated for the operating point.
  In this paper, we analyze data from sensors deployed in an agricultural farm
with data from seven different kinds of sensors, and from an advanced
manufacturing testbed with vibration sensors. We evaluate the performance of
ARIMA and LSTM models for predicting the time series of sensor data. Then,
considering the sparse data from one kind of sensor, we perform transfer
learning from a high data rate sensor. We then perform anomaly detection using
the predicted sensor data. Taken together, we show how in these two application
domains, predictive failure classification can be achieved, thus paving the way
for predictive maintenance.


Abstract:
  Ultraperipheral collisions of high energy protons are a source of
approximately real photons colliding with each other. Photon fusion can result
in production of yet unknown charged particles in very clean events. The
cleanliness of such an event is due to the requirement that the protons survive
during the collision. Finite sizes of the protons reduce the probability of
such outcome compared to point-like particles. We calculate the survival
factors and cross sections for the production of heavy charged particles at the
Large Hadron Collider.


Abstract:
  Animals are capable of extreme agility, yet understanding their complex
dynamics, which have ecological, biomechanical and evolutionary implications,
remains challenging. Being able to study this incredible agility will be
critical for the development of next-generation autonomous legged robots. In
particular, the cheetah (acinonyx jubatus) is supremely fast and maneuverable,
yet quantifying its whole-body 3D kinematic data during locomotion in the wild
remains a challenge, even with new deep learning-based methods. In this work we
present an extensive dataset of free-running cheetahs in the wild, called
AcinoSet, that contains 119,490 frames of multi-view synchronized high-speed
video footage, camera calibration files and 7,588 human-annotated frames. We
utilize markerless animal pose estimation to provide 2D keypoints. Then, we use
three methods that serve as strong baselines for 3D pose estimation tool
development: traditional sparse bundle adjustment, an Extended Kalman Filter,
and a trajectory optimization-based method we call Full Trajectory Estimation.
The resulting 3D trajectories, human-checked 3D ground truth, and an
interactive tool to inspect the data is also provided. We believe this dataset
will be useful for a diverse range of fields such as ecology, neuroscience,
robotics, biomechanics as well as computer vision.


Abstract:
  We consider gauged U(1) extensions of the standard model of particle physics
with three right-handed sterile neutrinos and a singlet scalar. The neutrinos
obtain mass via the type I seesaw mechanism. We compute the one loop
corrections to the elements of the tree level mass matrix of the light
neutrinos and show explicitly the cancellation of the gauge dependent terms. We
present a general formula for the gauge independent, finite one-loop
corrections for arbitrary number new U(1) groups, new complex scalars and
sterile neutrinos. We estimate the size of the corrections relative to the tree
level mass matrix in a particular extension, the super-weak model.


Abstract:
  Reliably assessing the error in an estimated vehicle position is integral for
ensuring the vehicle's safety in urban environments. Many existing approaches
use GNSS measurements to characterize protection levels (PLs) as probabilistic
upper bounds on the position error. However, GNSS signals might be reflected or
blocked in urban environments, and thus additional sensor modalities need to be
considered to determine PLs. In this paper, we propose a novel approach for
computing PLs by matching camera image measurements to a LiDAR-based 3D map of
the environment. We specify a Gaussian mixture model probability distribution
of position error using deep neural network-based data-driven models and
statistical outlier weighting techniques. From the probability distribution, we
compute the PLs by evaluating the position error bound using numerical
line-search methods. Through experimental validation with real-world data, we
demonstrate that the PLs computed from our method are reliable bounds on the
position error in urban environments.


Abstract:
  \begin{abstract} By following the paradigm of the global quantisation,
instead of the analysis under changes of coordinates, in this work we establish
a global analysis for the explicit computation of the Dixmier trace and the
Wodzicki residue of (elliptic and subelliptic) pseudo-differential operators on
compact Lie groups. The regularised determinant for the Dixmier trace is also
computed. We obtain these formulae in terms of the global symbol of the
corresponding operators. In particular, our approach links the Dixmier trace
and Wodzicki residue to the representation theory of the group. Although we
start by analysing the case of compact Lie groups, we also compute the Dixmier
trace and its regularised determinant on arbitrary closed manifolds $M$, for
the class of invariant pseudo-differential operators in terms of their
matrix-valued symbols. This analysis includes e.g. the family of positive and
elliptic pseudo-differential operators on $M$.


Abstract:
  Recently, a universal formula for the Nicolai map in terms of a coupling flow
functional differential operator was found. We present the full perturbative
expansion of this operator in Yang-Mills theories where supersymmetry is
realized off-shell. Given this expansion, we develop a straightforward method
to compute the explicit Nicolai map to any order in the gauge coupling. Our
work extends the previously known construction method from the Landau gauge to
arbitrary gauges and from the gauge hypersurface to the full gauge-field
configuration space. As an example, we present the map in the axial gauge to
the second order.


Abstract:
  In this paper, we investigate dynamic resource scheduling (i.e., joint user,
subchannel, and power scheduling) for downlink multi-channel non-orthogonal
multiple access (MC-NOMA) systems over time-varying fading channels.
Specifically, we address the weighted average sum rate maximization problem
with quality-of-service (QoS) constraints. In particular, to facilitate fast
resource scheduling, we focus on developing a very low-complexity algorithm. To
this end, by leveraging Lagrangian duality and the stochastic optimization
theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby
the original problem is decomposed into a series of subproblems, one for each
time slot. Accordingly, resource scheduling works in an online manner by
solving one subproblem per time slot, making it more applicable to practical
systems. Then, we further develop a heuristic joint subchannel assignment and
power allocation (Joint-SAPA) algorithm with very low computational complexity,
called Joint-SAPA-LCC, that solves each subproblem. Finally, through
simulation, we show that our Joint-SAPA-LCC algorithm provides good performance
comparable to the existing Joint-SAPA algorithms despite requiring much lower
computational complexity. We also demonstrate that our opportunistic MC-NOMA
scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works
well while satisfying given QoS requirements.


Abstract:
  The charmonium-like exotic states $Y(4230)$ and the less known $Y(4320)$,
produced in $e^+e^-$ collisions, are sources of positive parity exotic hadrons
in association with photons or pseudoscalar mesons. We analyze the radiative
and pion decay channels in the compact tetraquark scheme, with a method that
proves to work equally well in the most studied $D^*\to \gamma/\pi+D$ decays.
The decay of the vector $Y$ into a pion and a $Z_c$ state requires a flip of
charge conjugation and isospin that is described appropriately in the formalism
used. Rates however are found to depend on the fifth power of pion momentum
which would make the final states $\pi Z_c(4020)$ strongly suppressed with
respect to $\pi Z_c(3900)$. The agreement with BES III data would be improved
considering the $\pi Z_c(4020)$ events to be fed by the tail of the $Y(4320)$
resonance under the $Y(4230)$. These results should renovate the interest in
further clarifying the emerging experimental picture in this mass region.


Abstract:
  We propose a fully convolutional multi-person pose estimation framework using
dynamic instance-aware convolutions, termed FCPose. Different from existing
methods, which often require ROI (Region of Interest) operations and/or
grouping post-processing, FCPose eliminates the ROIs and grouping
post-processing with dynamic instance-aware keypoint estimation heads. The
dynamic keypoint heads are conditioned on each instance (person), and can
encode the instance concept in the dynamically-generated weights of their
filters. Moreover, with the strong representation capacity of dynamic
convolutions, the keypoint heads in FCPose are designed to be very compact,
resulting in fast inference and making FCPose have almost constant inference
time regardless of the number of persons in the image. For example, on the COCO
dataset, a real-time version of FCPose using the DLA-34 backbone infers about
4.5x faster than Mask R-CNN (ResNet-101) (41.67 FPS vs. 9.26FPS) while
achieving improved performance. FCPose also offers better speed/accuracy
trade-off than other state-of-the-art methods. Our experiment results show that
FCPose is a simple yet effective multi-person pose estimation framework. Code
is available at: https://git.io/AdelaiDet


Abstract:
  We discuss an approach to the computer assisted proof of the existence of
branches of stationary and periodic solutions for dissipative PDEs, using the
Brussellator system with diffusion and Dirichlet boundary conditions as an
example, We also consider the case where the branch of periodic solutions
emanates from a branch of stationary solutions through a Hopf bifurcation.


Abstract:
  This article presents a randomized matrix-free method for approximating the
trace of $f({\bf A})$, where ${\bf A}$ is a large symmetric matrix and $f$ is a
function analytic in a closed interval containing the eigenvalues of ${\bf A}$.
Our method uses a combination of stochastic trace estimation (i.e.,
Hutchinson's method), Chebyshev approximation, and multilevel Monte Carlo
techniques. We establish general bounds on the approximation error of this
method by extending an existing error bound for Hutchinson's method to
multilevel trace estimators. Numerical experiments are conducted for common
applications such as estimating the log-determinant, nuclear norm, and Estrada
index, and triangle counting in graphs. We find that using multilevel
techniques can substantially reduce the variance of existing single-level
estimators.


Abstract:
  Computational Social Science (CSS), aiming at utilizing computational methods
to address social science problems, is a recent emerging and fast-developing
field. The study of CSS is data-driven and significantly benefits from the
availability of online user-generated contents and social networks, which
contain rich text and network data for investigation. However, these
large-scale and multi-modal data also present researchers with a great
challenge: how to represent data effectively to mine the meanings we want in
CSS? To explore the answer, we give a thorough review of data representations
in CSS for both text and network. Specifically, we summarize existing
representations into two schemes, namely symbol-based and embedding-based
representations, and introduce a series of typical methods for each scheme.
Afterwards, we present the applications of the above representations based on
the investigation of more than 400 research articles from 6 top venues involved
with CSS. From the statistics of these applications, we unearth the strength of
each kind of representations and discover the tendency that embedding-based
representations are emerging and obtaining increasing attention over the last
decade. Finally, we discuss several key challenges and open issues for future
directions. This survey aims to provide a deeper understanding and more
advisable applications of data representations for CSS researchers.


Abstract:
  In this paper, we develop a safe decision-making method for self-driving cars
in a multi-lane, single-agent setting. The proposed approach utilizes deep
reinforcement learning (RL) to achieve a high-level policy for safe tactical
decision-making. We address two major challenges that arise solely in
autonomous navigation. First, the proposed algorithm ensures that collisions
never happen, and therefore accelerate the learning process. Second, the
proposed algorithm takes into account the unobservable states in the
environment. These states appear mainly due to the unpredictable behavior of
other agents, such as cars, and pedestrians, and make the Markov Decision
Process (MDP) problematic when dealing with autonomous navigation. Simulations
from a well-known self-driving car simulator demonstrate the applicability of
the proposed method


Abstract:
  We present a simple lithographic method for fabrication of microresonator
devices at the optical fiber surface. First, we undress the predetermined
surface areas of a fiber segment from the polymer coating with a focused CO2
laser beam. Next, using the remaining coating as a mask, we etch the fiber in a
hydrofluoric acid solution. Finally, we completely undress the fiber segment
from coating to create a chain of silica bottle microresonators with nanoscale
radius variation (SNAP microresonators). We demonstrate the developed method by
fabrication of a chain of five 1 mm long and 30 nm high microresonators at the
surface of a 125 micron diameter optical fiber and a single 0.5 mm long and 291
nm high microresonator at the surface of a 38 micron diameter fiber. As another
application, we fabricate a rectangular 5 mm long SNAP microresonator at the
surface of a 38 micron diameter fiber and investigate its performance as a
miniature delay line. The propagation of a 100 ps pulse with 1 ns delay, 0.035c
velocity, and negligible dispersion is demonstrated. In contrast to the
previously developed approaches in SNAP technology, the developed method allows
the introduction of much larger fiber radius variation ranging from nanoscale
to microscale.


Abstract:
  This study follows many classical approaches to multi-object tracking (MOT)
that model the problem using dynamic graphical data structures, and adapts this
formulation to make it amenable to modern neural networks. Our main
contributions in this work are the creation of a framework based on dynamic
undirected graphs that represent the data association problem over multiple
timesteps, and a message passing graph neural network (MPNN) that operates on
these graphs to produce the desired likelihood for every association therein.
We also provide solutions and propositions for the computational problems that
need to be addressed to create a memory-efficient, real-time, online algorithm
that can reason over multiple timesteps, correct previous mistakes, update
beliefs, and handle missed/false detections. To demonstrate the efficacy of our
approach, we only use the 2D box location and object category ID to construct
the descriptor for each object instance. Despite this, our model performs on
par with state-of-the-art approaches that make use of additional sensors, as
well as multiple hand-crafted and/or learned features. This illustrates that
given the right problem formulation and model design, raw bounding boxes (and
their kinematics) from any off-the-shelf detector are sufficient to achieve
competitive tracking results on challenging MOT benchmarks.


Abstract:
  The model-based investing using financial factors is evolving as a principal
method for quantitative investment. The main challenge lies in the selection of
effective factors towards excess market returns. Existing approaches, either
hand-picking factors or applying feature selection algorithms, do not
orchestrate both human knowledge and computational power. This paper presents
iQUANT, an interactive quantitative investment system that assists equity
traders to quickly spot promising financial factors from initial
recommendations suggested by algorithmic models, and conduct a joint refinement
of factors and stocks for investment portfolio composition. We work closely
with professional traders to assemble empirical characteristics of "good"
factors and propose effective visualization designs to illustrate the
collective performance of financial factors, stock portfolios, and their
interactions. We evaluate iQUANT through a formal user study, two case studies,
and expert interviews, using a real stock market dataset consisting of 3000
stocks times 6000 days times 56 factors.


Abstract:
  This paper describes considerations behind the organisation of a third
semester BSc education. The project aims to facilitate a feedback-oriented
environment using assessment for learning and for incremental measure of
learner progress [Vleuten et al, 2012, "A model for programmatic assessment fit
for purpose"]. Learning outcomes encourage higher order cognitive skills,
following [Biggs & Tang, 2011,"Teaching for quality learning at university:
what the student does"]. Embracing [Dochy et al. 2018, "Creating Impact Through
Future Learning: The High Impact Learning that Lasts (HILL) Model"], several
mechanisms encourage focus and motivation.


Abstract:
  Persistent homology is typically computed through persistent cohomology.
While this generally improves the running time significantly, it does not
facilitate extraction of homology representatives. The mentioned
representatives are geometric manifestations of the corresponding holes and
often carry desirable information.
  We propose a new method of extraction of persistent homology representatives
using cohomology. In a nutshell, we first compute persistent cohomology and use
the obtained information to significantly improve the running time of the
direct persistent homology computations. This algorithm applied to Rips
filtrations generally computes persistent homology representatives much faster
than the standard methods.


Abstract:
  The proof of the relative consistency of the axiom of choice has been
mechanized using Isabelle/ZF. The proof builds upon a previous mechanization of
the reflection theorem. The heavy reliance on metatheory in the original proof
makes the formalization unusually long, and not entirely satisfactory: two
parts of the proof do not fit together. It seems impossible to solve these
problems without formalizing the metatheory. However, the present development
follows a standard textbook, Kunen's Set Theory, and could support the
formalization of further material from that book. It also serves as an example
of what to expect when deep mathematics is formalized.


Abstract: A short history of Russian researches in Chinese astronomy in 19-20 centuries


Abstract:
  This paper introduces the SemEval-2021 shared task 4: Reading Comprehension
of Abstract Meaning (ReCAM). This shared task is designed to help evaluate the
ability of machines in representing and understanding abstract concepts. Given
a passage and the corresponding question, a participating system is expected to
choose the correct answer from five candidates of abstract concepts in a
cloze-style machine reading comprehension setup. Based on two typical
definitions of abstractness, i.e., the imperceptibility and nonspecificity, our
task provides three subtasks to evaluate the participating models.
Specifically, Subtask 1 aims to evaluate how well a system can model concepts
that cannot be directly perceived in the physical world. Subtask 2 focuses on
models' ability in comprehending nonspecific concepts located high in a
hypernym hierarchy given the context of a passage. Subtask 3 aims to provide
some insights into models' generalizability over the two types of abstractness.
During the SemEval-2021 official evaluation period, we received 23 submissions
to Subtask 1 and 28 to Subtask 2. The participating teams additionally made 29
submissions to Subtask 3. The leaderboard and competition website can be found
at https://competitions.codalab.org/competitions/26153. The data and baseline
code are available at
https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning.


Abstract:
  We study the popular distributed consensus method over networks composed of a
number of densely connected clusters with a sparse connection between them. In
these cluster networks, the method often constitutes two-time-scale dynamics,
where the internal nodes within each cluster reach consensus quickly relative
to the aggregate nodes across clusters. Our main contribution is to provide the
rate of the distributed consensus method, which characterize explicitly the
impacts of the internal and external graphs on the performance of this method.
Our main result shows that this rate converges exponentially and only scales
with a few number of nodes, which is relatively small to the size of the
network.
  The key technique in our analysis is to consider a Lyapunov function which
captures the impacts of different time-scale dynamics on the convergence of the
method. Our approach avoids using model reduction, which is the typical way
according to singular perturbation theory and relies on relatively simple
definitions of the slow and fast variables. In addition, Lyapunov analysis
allows us to derive the rate of distributed consensus methods over cluster
networks, which is missing from the existing works using singular perturbation
theory. We illustrate our theoretical results by a number of numerical
simulations over different cluster networks.


Abstract:
  Classification of galactic morphologies is a crucial task in galactic
astronomy, and identifying fine structures of galaxies (e.g., spiral arms,
bars, and clumps) is an essential ingredient in such a classification task.
However, seeing effects can cause images we obtain to appear blurry, making it
difficult for astronomers to derive galaxies' physical properties and, in
particular, distant galaxies. Here, we present a method that converts blurred
images obtained by the ground-based Subaru Telescope into quasi Hubble Space
Telescope (HST) images via machine learning. Using an existing deep learning
method called generative adversarial networks (GANs), we can eliminate seeing
effects, effectively resulting in an image similar to an image taken by the
HST. Using multiple Subaru telescope image and HST telescope image pairs, we
demonstrate that our model can augment fine structures present in the blurred
images in aid for better and more precise galactic classification. Using our
first of its kind machine learning-based deblurring technique on space images,
we can obtain up to 18% improvement in terms of CW-SSIM (Complex Wavelet
Structural Similarity Index) score when comparing the Subaru-HST pair versus
SeeingGAN-HST pair. With this model, we can generate HST-like images from
relatively less capable telescopes, making space exploration more accessible to
the broader astronomy community. Furthermore, this model can be used not only
in professional morphological classification studies of galaxies but in all
citizen science for galaxy classifications.


Abstract:
  Loneliness does not only have emotional implications on a person but also on
his/her well-being. The study of loneliness has been challenging and largely
inconclusive in findings because of the several factors that might correlate to
the phenomenon. We present one approach to predicting this event by discovering
patterns of language associated with loneliness. Our results show insights and
promising directions for mining text from instant messaging to predict
loneliness.


Abstract:
  The [CII] deficit, which describes the observed decrease in the ratio of
[CII] 158 micron emission to continuum infrared emission in galaxies with high
star formation surface densities, places a significant challenge to the
interpretation of [CII] detections from across the observable universe. In an
attempt to further decode the cause of the [CII] deficit, the [CII] and dust
continuum emission from 18 Local Volume galaxies has been split based on
conditions within the interstellar medium where it originated. This is
completed using the Key Insights in Nearby Galaxies: a Far-Infrared Survey with
Herschel (KINGFISH) and Beyond the Peak (BtP) surveys and the wide-range of
wavelength information, from UV to far-infrared emission lines, available for a
selection of star-forming regions within these samples. By comparing these
subdivided [CII] emissions to isolated infrared emission and other properties,
we find that the thermalization (collisional de-excitation) of the [CII] line
in HII regions plays a significant role in the deficit observed in our sample.


Abstract:
  We describe recent work on the physics of the Higgs boson at future muon
colliders. Starting from the low energy muon collider at the Higgs boson pole
we extend our discussion to the multi-TeV muon collider and outline the physics
case for such machines about the properties of the Higgs boson and physics
beyond the Standard Model that can be possibly discovered.


Abstract:
  A certain class of neutrino mass models predicts long-lived particles whose
electric charge is four or three times larger than that of protons. Such
particles, if they are light enough, may be produced at the LHC and detected.
We investigate the possibility of observing those long-lived multi-charged
particles with the MoEDAL detector, which is sensitive to long-lived particles
with low velocities ($\beta$) and a large electric charge ($Z$) with $\Theta
\equiv \beta/Z \lesssim 0.15$. We demonstrate that multi-charged scalar
particles with a large $Z$ give three-fold advantage for MoEDAL; reduction of
$\Theta$ due to strong interactions with the detector, and enhancement of the
photon-fusion process, which not only increases the production cross-section
but also lowers the average production velocity, reducing $\Theta$ further. To
demonstrate the performance of MoEDAL on multi-charged long-lived particles,
two concrete neutrino mass models are studied. In the first model, the new
physics sector is non-coloured and contains long-lived particles with electric
charges 2, 3 and 4. A model-independent study finds MoEDAL can expect more than
1 signal event at the HL-LHC ($L = 300$ fb$^{-1}$) if these particles are
lighter than 600, 1100 and 1430 GeV, respectively. These compare with the
current ATLAS limits 650, 780 and 920 GeV for $L = 36$ fb$^{-1}$. The second
model has a coloured new physics sector, which possesses long-lived particles
with electric charges 4/3, 7/3 and 10/3. The corresponding MoEDAL's mass
reaches at the HL-LHC are 1400, 1650 and 1800 GeV, respectively, which compare
with the current CMS limits 1450, 1480 and 1510 GeV for $L = 36$ fb$^{-1}$. In
a model-specific study we explore the parameter space of neutrino mass
generation models and identify the regions that can be probed with MoEDAL at
the end of Run-3 and the High-Luminosity LHC.


Abstract:
  We investigate the linearized KdV equation on a metric tree consisting of
three different types of bonds: incoming unbounded root, two finite bonds, and
four outgoing unbounded bonds. Under natural assumptions at the vertices, we
obtain the uniqueness of a solution. To show the existence we use the theory of
potentials and reduce the problem to a system of linear algebraic equations. We
show that the latter is uniquely solvable under conditions of the uniqueness
theorem. Also, we show that the system we consider can be used to model wave
propagation in pipelines.


Abstract:
  We present an optimisation problem defined using a measure preserving action
by a group G on a compact probability space X. We show that this problem cannot
be solved in any way measurable with respect to a finitely additive measure
that extends the original probability measure and such that the group G remains
measure preserving. The proof involves a computer-performed analysis of a
stochastic process. On the other hand, there are some non-measurable solutions
that induce some paradoxical decompositions of the space X. Additionally, we
show that, for some small positive epsilon, epsilon-stability also cannot be
satisfied in such a measurable way. By epsilon-stability, we mean that the
expected gains from local deviations considered independently cannot exceed
epsilon.


Abstract:
  The amalgamation of different generations of mobile cellular networks around
the globe has resulted in diverse data speed experiences for end users. At
present there are no defined mechanisms in place for a subscriber of one mobile
network operator (MNO) to use the services of a WiFi provider. Cellular and
Data Service providers also have no standardized procedures to securely
interact with each other, and to allow their subscribers to use third party
services on a pay-as-you-go basis. This paper proposes a blockchain-based
offloading framework that allows a subscriber of a mobile operator to
temporarily use another MNO or WiFi provider's higher speed network. Smart
contracts allow diverse entities such as MNOs, Brokers and WiFi Providers to
automatically execute mutual agreements to enable the utilization of third
party infrastructure in a secure and controlled manner. To test the proposed
framework, the offloading of a subscriber from 3G/4G/4G-LTE/5G networks to a
fixed broadband WiFi network was carried out and the results analyzed. The
offloading framework was implemented using the ns-3 network simulator, and the
Ethereum blockchain smart contract features were used for the settlement of
invoices.


Abstract:
  In representation learning, there has been recent interest in developing
algorithms to disentangle the ground-truth generative factors behind a dataset,
and metrics to quantify how fully this occurs. However, these algorithms and
metrics often assume that both representations and ground-truth factors are
flat, continuous, and factorized, whereas many real-world generative processes
involve rich hierarchical structure, mixtures of discrete and continuous
variables with dependence between them, and even varying intrinsic
dimensionality. In this work, we develop benchmarks, algorithms, and metrics
for learning such hierarchical representations.


Abstract:
  Between January 2017 and January 2021, thousands of local news sources in the
United States reported on over 42,000 protests about topics such as civil
rights, immigration, guns, and the environment. Given the vast number of local
journalists that report on protests daily, extracting these events as
structured data to understand temporal and geographic trends can empower civic
decision-making. However, the task of extracting events from news articles
presents well known challenges to the NLP community in the fields of domain
detection, slot filling, and coreference resolution.
  To help improve the resources available for extracting structured data from
news stories, our contribution is three-fold. We 1) release a manually labeled
dataset of news article URLs, dates, locations, crowd size estimates, and 494
discrete descriptive tags corresponding to 42,347 reported protest events in
the United States between January 2017 and January 2021; 2) describe the
semi-automated data collection pipeline used to discover, sort, and review the
144,568 English articles that comprise the dataset; and 3) benchmark a
long-short term memory (LSTM) low dimensional classifier that demonstrates the
utility of processing news articles based on syntactic structures, such as
paragraphs and sentences, to count the number of reported protest events.


Abstract:
  It is generally accepted that CMEs result from eruptions of magnetic flux
ropes, which are dubbed as magnetic clouds in interplanetary space. The
composition (including the ionic charge states and elemental abundances) is
determined prior to and/or during CME eruptions in the solar atmosphere, and
does not alter during magnetic cloud propagation to 1 AU and beyond. It has
been known that the composition is not uniform within a cross section
perpendicular to magnetic cloud axis, and the distribution of ionic charge
states within a cross section provides us an important clue to investigate the
formation and eruption processes of flux ropes due to the freeze-in effect. The
flux rope is a three dimensional magnetic structure intrinsically, and it
remains unclear whether the composition is uniform along the flux rope axis as
most magnetic clouds are only detected by one spacecraft. In this paper we
report a magnetic cloud that was observed by ACE near 1 AU on 1998 March 4--6
and Ulysses near 5.4 AU on March 24--28 sequentially. At these times, both
spacecraft were located around the ecliptic plane, and the latitudinal and
longitudinal separations between them were $\sim$2.2$^{\circ}$ and
$\sim$5.5$^{\circ}$, respectively. It provides us an excellent opportunity to
explore the axial inhomogeneity of flux rope composition, as both spacecraft
almost intersected the cloud center at different sites along its axis. Our
study shows that the average values of ionic charge states exhibit significant
difference along the axis for carbon, and the differences are relatively slight
but still obvious for charge states of oxygen and iron, as well as the
elemental abundances of iron and helium. Besides the means, the composition
profiles within the cloud measured by both spacecraft also exhibit some
discrepancies. We conclude that the inhomogeneity of composition exists along
the cloud axis.


Abstract:
  While learning-based control techniques often outperform classical controller
designs, safety requirements limit the acceptance of such methods in many
applications. Recent developments address this issue through so-called
predictive safety filters, which assess if a proposed learning-based control
input can lead to constraint violations and modifies it if necessary to ensure
safety for all future time steps. The theoretical guarantees of such predictive
safety filters rely on the model assumptions and minor deviations can lead to
failure of the filter putting the system at risk. This paper introduces an
auxiliary soft-constrained predictive control problem that is always feasible
at each time step and asymptotically stabilizes the feasible set of the
original safety filter, thereby providing a recovery mechanism in
safety-critical situations. This is achieved by a simple constraint tightening
in combination with a terminal control barrier function. By extending
discrete-time control barrier function theory, we establish that the proposed
auxiliary problem provides a `predictive' control barrier function. The
resulting algorithm is demonstrated using numerical examples.


Abstract:
  We revisit D3-branes at toric CY$_3$ singularities with orientifolds and
their description in terms of dimer models. We classify orientifold actions on
the dimer through smooth involutions of the torus. In particular, we describe
new orientifold projections related to maps on the dimer without fixed points,
leading to Klein bottles. These new orientifolds lead to novel $\mathcal{N}=1$
SCFT's that resemble, in many aspects, non-orientifolded theories. For
instance, we recover the presence of fractional branes and some of them trigger
a cascading RG-flow \`a la Klebanov-Strassler. The remaining involutions lead
to non-supersymmetric setups, thus exhausting the possible orientifolds on
dimers.


Abstract:
  Domain walls arise in theories where there is spontaneous symmetry breaking
of a discrete symmetry such as $\mathbb{Z}_{N}$ and are a feature of many BSM
models. In this work we consider the possibility of detecting domain walls
through their optical effects and specify three different methods of coupling
domain walls to the photon. We consider the effects of these couplings in the
context of gravitational wave detectors, such as LIGO, and examine the
sensitivity of these experiments to domain wall effects. In cases where
gravitational wave detectors are not sensitive we examine our results in the
context of axion experiments and show how effects of passing domain walls can
be detected at interferometers searching for an axion signal.


Abstract:
  We performed $^{59}$Co nuclear magnetic and quadrupole resonance (NMR and
NQR) measurements under pressure on a single-crystalline CeCoSi, which
undergoes an unresolved phase transition at $T_0$. The NQR spectra clearly
showed that the phase transition at $T_0$ is nonmagnetic, but any symmetry
lowering at the Co site was not seen irrespective of the feature of
second-order phase transition. By contrast, the NMR spectra were split by the
induced magnetic field perpendicular to the external magnetic field. These
results show that the phase below $T_0$ is not a simple paramagnetic state but
is most likely electric multipolar ordered state of Ce $4f$ electrons. The
development of the Kondo effect by applying pressure is thought to be crucial
to stabilize this state and to show novel features beyond commonality of
tetragonal Ce-based systems.


Abstract:
  Conflicting interpretations of experimental data preclude the understanding
of the quantum magnetic state of spin-orbit coupled $d^2$ double perovskites.
Whether the ground state is a Janh-Teller-distorted order of quadrupoles or the
hitherto elusive octupolar order remains debated. We resolve this uncertainty
through direct calculations of all-rank inter-site exchange interactions and
inelastic neutron scattering cross-section for the $d^2$ double perovskite
series Ba$_2M$OsO$_6$ ($M$= Ca, Mg, Zn). Using advanced many-body first
principles methods we show that the ground state is formed by ferro-ordered
octupoles coupled by superexchange interactions within the ground-state $E_g$
doublet. Computed ordering temperature of the single second-order
phase-transition is consistent with experimentally observed material-dependent
trends. Minuscule distortions of the parent cubic structure are shown to
qualitatively modify the structure of gaped magnetic excitations.


Abstract:
  We report on a nanomechanical engineering method to monitor matter growth in
real time via e-beam electromechanical coupling. This method relies on the
exceptional mass sensing capabilities of nanomechanical resonators. Focused
electron beam induced deposition (FEBID) is employed to selectively grow
platinum particles at the free end of singly clamped nanotube cantilevers. The
electron beam has two functions: it allows both to grow material on the
nanotube and to track in real time the deposited mass by probing the
noise-driven mechanical resonance of the nanotube. On the one hand, this
detection method is highly effective as it can resolve mass deposition with a
resolution in the zeptogram range; on the other hand, this method is simple to
use and readily available to a wide range of potential users, since it can be
operated in existing commercial FEBID systems without making any modification.
The presented method allows to engineer hybrid nanomechanical resonators with
precisely tailored functionality. It also appears as a new tool for studying
growth dynamics of ultra-thin nanostructures, opening new opportunities for
investigating so far out-of-reach physics of FEBID and related methods.


Abstract:
  We investigate localised bulging or necking in an incompressible,
hyperelastic cylindrical tube under axial stretching and surface tension. Three
cases are considered in which the tube is subjected to different constraints.
In case 1 the inner and outer surfaces are traction-free and under surface
tension, whilst in cases 2 and 3 the inner and outer surfaces (respectively)
are fixed to prevent radial displacement and surface tension. However, each
free surface in these latter two cases is still under surface tension. We first
state the analytical bifurcation conditions for localisation and then validate
them numerically whilst determining whether localisation is preferred over
bifurcation into periodic modes. It is shown that bifurcation into a localised
solution is unattainable in case 1 but possible and favourable in cases 2 and
3. In contrast, in case 1 any bifurcation must necessarily take the form of a
periodic mode with a non-zero wave number. Our results are validated using
Finite Element Method (FEM) simulations.


Abstract:
  The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses
on the object and style like other artwork researches. Such study has benefited
from the renewed interest by the machine learning community in culturally
important topics, leading to interdisciplinary works including collections of
images, quantitative approaches, and machine learning-based creativities. They,
however, have several drawbacks, and it remains challenging to integrate these
works into a comprehensive view. To bridge this gap, we propose a holistic
approach We first present a large-scale Ukiyo-e dataset with coherent semantic
labels and geometric annotations, then show its value in a quantitative study
of Ukiyo-e paintings' object using these labels and annotations. We further
demonstrate the machine learning methods could help style study through soft
color decomposition of Ukiyo-e, and finally provides joint insights into object
and style by composing sketches and colors using colorization. Dataset
available at https://github.com/rois-codh/arc-ukiyoe-faces


Abstract:
  In this paper, we investigate the ratio of the numbers of odd and even cycles
in outerplanar graphs. We verify that the ratio generally diverges to infinity
as the order of a graph diverges to infinity. We also give sharp estimations of
the ratio for several classes of outerplanar graphs, and obtain a constant
upper bound of the ratio for some of them. Furthermore, we consider similar
problems in graphs with some pairs of forbidden subgraphs/minors, and propose a
challenging problem concerning claw-free graphs.


Abstract:
  Point defects in crystalline materials often occur in multiple charge states.
Although many experimental methods to study and explore point defects are
available, techniques to explore the non-equilibrium dynamics of the charge
states of these defects at ultrafast (sub-nanosecond) time scales have not been
discussed before. We present results from ultrafast optical-pump
supercontinuum-probe spectroscopy measurements on $\beta$-Ga$_2$O$_3$. The
study of point defects in $\beta$-Ga$_2$O$_3$ is essential for its
establishment as a material platform for high-power electronics and deep-UV
optoelectronics. Use of a supercontinuum probe allows us to obtain the
time-resolved absorption spectra of material defects under non-equilibrium
conditions with picosecond time resolution. The probe absorption spectra shows
defect absorption peaks at two energies, $\sim$2.2 eV and $\sim$1.63 eV, within
the 1.3-2.5 eV probe energy bandwidth. The strength of the absorption
associated with each peak is time-dependent and the spectral weight shifts from
the lower energy peak to the higher energy peak with pump-probe delay. Further,
maximum defect absorption is seen for probe polarized along the crystal c-axis.
The time-dependent probe absorption spectra and the observed dynamics for all
probe wavelengths at all pump-probe delays can be fit with a set of rate
equations for a single multi-level defect. Based on first-principles
calculations within hybrid density functional theory we attribute the observed
absorption features to optical transitions from the valence band to different
charge states of Gallium vacancies. Our results demonstrate that broadband
ultrafast supercontinuum spectroscopy can be a useful tool to explore charge
states of defects and defect dynamics in semiconductors.


Abstract:
  We examine the one-dimensional Gross-Pitaevskii lattice at zero temperature
in the presence of uncorrelated disorder. We obtain analytical expressions for
the thermodynamic properties of the ground state field and compare them with
numerical simulations both in the weak and strong interaction regimes. We
analyze weak excitations above the ground state and compute the localization
properties of Bogoliubov-de Gennes modes. In the long-wavelength limit, these
modes delocalize in accordance with the extended nature of the ground state.
For strong interactions, we observe and derive a divergence of their
localization length at finite energy due to an effective correlated disorder
induced by the weak ground state field fluctuations. We derive effective strong
interaction field equations for the excitations and generalize to higher
dimensions.


Abstract:
  We define a variant of real-analytic polylogarithms that are single-valued
and that satisfy ''clean'' functional relations that do not involve any
products of lower weight functions. We discuss the basic properties of these
functions and, for depths one and two, we present some explicit formulas and
results. We also give explicit formulas for the single-valued and clean
single-valued version attached to the Nielsen polylogarithms $S_{n,2}(x)$, and
we show how the clean single-valued functions give new evaluations of multiple
polylogarithms at certain algebraic points.


Abstract:
  We associate with each convex optimization problem, posed on some locally
convex space, with infinitely many constraints indexed by the set T, and a
given non-empty family H of finite subsets of T, a suitable Lagrangian-Haar
dual problem. We obtain necessary and sufficient conditions for H-reducibility,
that is, equivalence to some subproblem obtained by replacing the whole index
set T by some element of H. Special attention is addressed to linear
optimization, infinite and semi-infinite, and to convex problems with a
countable family of constraints. Results on zero H-duality gap and on
H-(stable) strong duality are provided. Examples are given along the paper to
illustrate the meaning of the results.


Abstract:
  Online real estate platforms have become significant marketplaces
facilitating users' search for an apartment or a house. Yet it remains
challenging to accurately appraise a property's value. Prior works have
primarily studied real estate valuation based on hedonic price models that take
structured data into account while accompanying unstructured data is typically
ignored. In this study, we investigate to what extent an automated visual
analysis of apartment floor plans on online real estate platforms can enhance
hedonic rent price appraisal. We propose a tailored two-staged deep learning
approach to learn price-relevant designs of floor plans from historical price
data. Subsequently, we integrate the floor plan predictions into hedonic rent
price models that account for both structural and locational characteristics of
an apartment. Our empirical analysis based on a unique dataset of 9174 real
estate listings suggests that current hedonic models underutilize the available
data. We find that (1) the visual design of floor plans has significant
explanatory power regarding rent prices - even after controlling for structural
and locational apartment characteristics, and (2) harnessing floor plans
results in an up to 10.56% lower out-of-sample prediction error. We further
find that floor plans yield a particularly high gain in prediction performance
for older and smaller apartments. Altogether, our empirical findings contribute
to the existing research body by establishing the link between the visual
design of floor plans and real estate prices. Moreover, our approach has
important implications for online real estate platforms, which can use our
findings to enhance user experience in their real estate listings.


Abstract:
  HDR reconstruction is an important task in computer vision with many
industrial needs. The traditional approaches merge multiple exposure shots to
generate HDRs that correspond to the physical quantity of illuminance of the
scene. However, the tedious capturing process makes such multi-shot approaches
inconvenient in practice. In contrast, recent single-shot methods predict a
visually appealing HDR from a single LDR image through deep learning. But it is
not clear whether the previously mentioned physical properties would still
hold, without training the network to explicitly model them. In this paper, we
introduce the physical illuminance constraints to our single-shot HDR
reconstruction framework, with a focus on spherical panoramas. By the proposed
physical regularization, our method can generate HDRs which are not only
visually appealing but also physically plausible. For evaluation, we collect a
large dataset of LDR and HDR images with ground truth illuminance measures.
Extensive experiments show that our HDR images not only maintain high visual
quality but also top all baseline methods in illuminance prediction accuracy.


Abstract:
  We analyze the weak-field limit of General Relativity with matter and its
possible quantisations. This analysis aims towards a predictive quantum theory
to provide a first-principles description of gravitational effects in
macroscopic quantum systems. This includes recently proposed experiments on the
generation of (Newtonian) gravitational forces from quantum distributions of
matter, and phenomena like gravity-induced entanglement, gravitational cat
states, gravity-induced Rabi oscillations, and quantum causal orderings of
events. Our main results include: (i) The demonstration that these phenomena do
not involve true gravitational degrees of freedom. (ii) We show that, unlike
full general relativity, weak gravity with matter is a parameterised field
theory, i.e., a theory obtained by promoting spacetime coordinates to
`dynamical' variables. (iii) Quantisation via gauge-fixing leads to an
effective field theory that account for some phenomena, but at the price of
gauge dependence that manifests more strongly on spacetime observables. This
ambiguity is a manifestation of the problem of time that persists even in weak
gravity. (iv) A consistent quantisation of parameterised field theories is
essential for a predictive and spacetime covariant theory of weak gravity that
describes gravitational effects in macroscopic quantum systems. We also discuss
the implication of our results to gravitational decoherence theories, the
notion of locality in gravity vis-a-vis quantum information theory, and the
intriguing possibility that proposed solutions to the problem of time can be
tested in weak-gravity quantum experiments.


Abstract:
  We determine all local smooth or formal CR maps from the unit sphere
$\mathbb{S}^3\subset \mathbb{C}^2$ into the tube $\mathcal{T}:= \mathcal{C}
\times i\mathbb{R}^3 \subset \mathbb{C}^3$ over the future light cone
$\mathcal{C}:= \left\{x\in \mathbb{R}^3\colon x_1^2+x_2^2 = x_3^2, \ x_3 >
0\right\}$. This result leads to a complete classification of proper
holomorphic maps from the unit ball in $\mathbb{C}^2$ into Cartan's classical
domain of type IV in $\mathbb{C}^3$ that extend smoothly to some boundary
point. Up to composing with CR automorphisms of the source and target, the
classification consists of four algebraic maps. Two maps among them were known
earlier in the literature, which were shown to be ``rigid'' in the higher
dimensional case in a recent paper by Xiao and Yuan. Two newly discovered
quadratic polynomial maps provide counterexamples to a conjecture appeared in
the same paper for the case of dimension two.


Abstract:
  Purpose: Grating-based imaging (GBI) and edge-illumination (EI) are two
promising types of XPCI as the conventional x-ray sources can be directly
utilized. For GBI and EI systems, the phase-stepping acquisition with multiple
exposures at a constant fluence is usually adopted in the literature. This
work, however, attempts to challenge such a constant fluence concept during the
phase-stepping process and proposes a fluence adaptation mechanism for dose
reduction. Method: Recently, analytic multi-order moment analysis has been
proposed to improve the computing efficiency. In these algorithms, multiple
contrasts can be calculated by summing together the weighted phase-stepping
curves (PSCs) with some kernel functions, which suggests us that the raw data
at different steps have different contributions for the noise in retrieved
contrasts. Based on analytic retrieval formulas and the Gaussian noise model
for detected signals, we derived an optimal adaptive fluence distribution,
which is proportional to the absolute weighting kernel functions and the root
of original sample PSCs acquired under the constant fluence. Results: To
validate our analyses, simulations and experiments are conducted for GBI and EI
systems. Simulated results demonstrate that the dose reduction ratio between
our proposed fluence distributions and the typical constant one can be about
20% for the phase contrast, which is consistent with our theoretical
predictions. Although the experimental noise reduction ratios are a little
smaller than the theoretical ones, synthetic and real experiments both observe
better noise performance by our proposed method. Our simulated results also
give out the effective ranges of the parameters of the PSCs, such as the
visibility in GBI, the standard deviation and the mean value in EI, providing a
guidance for the use of our proposed approach in practice.


Abstract:
  This paper proposes two algorithms to maximize the minimum array power gain
in a wide-beam mainlobe by solving the power gain pattern synthesis (PGPS)
problem with and without sidelobe constraints. Firstly, the nonconvex PGPS
problem is transformed into a nonconvex linear inequality optimization problem
and then converted to an augmented Lagrangian problem by introducing auxiliary
variables via the Alternating Direction Method of Multipliers (ADMM) framework.
Next,the original intractable problem is converted into a series of nonconvex
and convex subproblems. The nonconvex subproblems are solved by dividing their
solution space into a finite set of smaller ones, in which the solution would
be obtained pseudoanalytically. In such a way, the proposed algorithms are
superior to the existing PGPS-based ones as their convergence can be
theoretically guaranteed with a lower computational burden. Numerical examples
with both isotropic element pattern (IEP) and active element pattern (AEP)
arrays are simulated to show the effectiveness and superiority of the proposed
algorithms by comparing with the related existing algorithms.


Abstract:
  The Renyi entropy is an important measure of the information, it is proposed
by Renyi in the context of entropy theory as a generalization of Shannon
entropy. We study in detail this measure of multivariate autoregressive moving
average (ARMA) control systems. The characteristic function of output process
is represented from the terms of its residual characteristic functions. Simple
expression to compute the Renyi entropy for the output process of these systems
is derived. In addition, we investigate the covariance matrix for finding the
upper bound of the entropy. Finally, we present three separate examples that
serve to illustrate the behavior of information in a multivariate ARMA control
system where the control and noise distributed as Gaussian, Cauchy and Laplace
processes.


Abstract:
  This contribution to "Solar Magnetic Variability and Climate" reviews
small-scale magnetic features on the solar surface, in particular the
strong-field but tiny magnetic concentrations that constitute network and plage
and represent most magnetism outside sunspots and filaments. Where these are
mostly of the same polarity, as in active-region plage, their occurrence varies
with the activity variations measured by the sunspot number, but when they
appear bipolar-mixed on small scales they can also result from granular-scale
dynamo action that does not vary with the cycle.


Abstract:
  Learning node representations on temporal graphs is a fundamental step to
learn real-word dynamic graphs efficiently. Real-world graphs have the nature
of continuously evolving over time, such as changing edges weights, removing
and adding nodes and appearing and disappearing of edges, while previous graph
representation learning methods focused generally on static graphs. We present
ConvDySAT as an enhancement of DySAT, one of the state-of-the-art dynamic
methods, by augmenting convolution neural networks with the self-attention
mechanism, the employed method in DySAT to express the structural and temporal
evolution. We conducted single-step link prediction on a communication network
and rating network, Experimental results show significant performance gains for
ConvDySAT over various state-of-the-art methods.


Abstract:
  The amount of remote sensing (RS) data has increased at an unexpected scale,
due to the rapid progress of earth-observation and the growth of satellite RS
and sensor technologies. Traditional relational databases attend their limit to
meet the needs of high-resolution and large-scale RS Big Data management. As a
result, massive RS data management is currently one of the most imperative
topics. To address this problem, this paper describes a distributed
architecture for big RS data storage based on a unified metadata file, pyramid
model, and Hilbert curve for data composition and indexing using NoSQL
databases (i.e, Apache Hbase). In this paper, a Hadoop-based framework in
AzureInsight cloud platform is designed to manage massive RS data in a parallel
and distributed way. Experimental results prove that our method has the
potential to overcome the weakness of traditional methods. The proposed model
is suitable for massive high-resolution image data management.


Abstract:
  For the first time, we present the simultaneous detection and
characterization of three distinct phases at $>10^5$ K in $z=0$ absorption,
using deep $\it{Chandra}$ observations toward Mrk 421. The extraordinarily high
signal-to-noise ratio ($\geqslant60$) of the spectra has allowed us to detect a
$\it{hot}$ phase of the Milky Way circumgalactic medium (CGM) at
3.2$^{+1.5}_{-0.5}\times$ 10$^7$ K, coexisting with a $\textit{warm-hot}$ phase
at 1.5$\pm$0.1$\times$10$^6$ K and a $\textit{warm}$ phase at
3.0$\pm$0.4$\times$10$^5$ K. The $\textit{warm-hot}$ phase is at the virial
temperature of the Galaxy, and the $\textit{warm}$ phase may have cooled from
the $\textit{warm-hot}$ phase, but the super-virial $\textit{hot}$ phase
remains a mystery. We find that [C/O] in the $\textit{warm}$ and
$\textit{warm-hot}$ phases, [Mg/O] in the $\textit{warm-hot}$ phase and [Ne/O]
in the $\textit{hot}$ phase are super-solar, and the $\textit{hot}$ and the
$\textit{warm-hot}$ phases are $\alpha-$enhanced. Non-thermal line broadening
is evident in the $\textit{warm-hot}$ and the $\textit{hot}$ phases and it
dominates the total line broadening. Our results indicate that the $>10^5$ K
CGM is a complex ecosystem. It provides insights on the thermal and chemical
history of the Milky Way CGM, and theories of galaxy evolution.


Abstract:
  A theorem of Brady and Meier states that a right-angled Artin group is a
duality group if and only if the flag complex of the defining graph is
Cohen--Macaulay. We use this to give an example of a RAAG with the property
that its outer automorphism group is not a virtual duality group. This gives a
partial answer to a question of Vogtmann. In an appendix, Br\"uck describes how
he used a computer-assisted search to find further examples.


Abstract:
  Crashing ocean waves, cappuccino froths and microfluidic bubble crystals are
examples of foamy flows. Foamy flows are critical in numerous natural and
industrial processes and remain notoriously difficult to compute as they
involve coupled, multiscale physical processes. Computations need to resolve
the interactions of the bubbles with the fluid and complex boundaries, while
capturing the drainage and rupture of the microscopic liquid films at their
interface. We present a novel multilayer simulation framework (Multi-VOF) that
advances the state of the art in simulation capabilities of foamy flows. The
framework introduces a novel scheme for the distinct handling of multiple
neighboring bubbles and a new regularization method that produces sharp
interfaces and removes spurious fragments. Multi-VOF is verified and validated
with experimental results and complemented with open source, efficient scalable
software. We demonstrate capturing of bubble crystalline structures in
realistic microfluidics devices and foamy flows involving tens of thousands of
bubbles in a waterfall. The present multilayer framework extends the classical
volume-of-fluid methodology and allows for unprecedented large scale,
predictive simulations of flows with multiple interfaces.


Abstract:
  Modern Superconducting QUantum Interference Devices (SQUIDs) are commonly
fabricated from either Al or Nb electrodes, with an in-situ oxidation process
to create a weak link between them. However, common problems of such planar
nano- and micro-SQUIDs are hysteretic current-voltage curves, and a shallow
flux modulation depth. Here, we demonstrate the formation of both Josephson
junctions and SQUIDs using a dry transfer technique to stack and
deterministically misalign flakes of NbSe$_{2}$; allowing one to overcome these
issues. The Josephson dynamics of the resulting twisted NbSe$_{2}$-NbSe$_{2}$
junctions are found to be sensitive to the misalignment angle of the
crystallographic axes. A single lithographic process was then implemented to
shape the Josephson junction into a SQUID geometry with typical loop areas of
$\simeq$ 25 $\mu m^{2}$ and weak links $\simeq$ 600 nm wide. These devices
display large stable current and voltage modulation depths of up to $\Delta
I_{c} \simeq$ 75$\%$ and $\Delta V \simeq$ 1.4 mV respectively.


Abstract:
  We demonstrate how to obtain the analytic continuation of formulae for the
Lerch transcendent function, $\Phi(e^m,k,b)$, and the polylogarithm,
$\mathrm{Li}_{k}(e^{m})$, that only hold at the positive integers. The result
are alternative expressions for these functions and their partial sums valid in
the complex half-plane, $\Re{(k)}>0$.


Abstract:
  Women represent less than 24% of employees in the software development
industry and experience various types of prejudice and bias. Despite various
efforts to increase diversity and multi-gendered participation, women are even
more underrepresented in Open Source Software (OSS) projects. In my PhD, I
investigate the following question: How can OSS communities increase women's
participation in their projects? I will identify different OSS career pathways
and develop a holistic view of women's motivations to join or leave OSS, as
well as their definitions of success. Based on this empirical investigation, I
will work together with the Linux Foundation to design attraction and retention
strategies focused on women. Before and after implementing the strategies, I
will conduct empirical studies to evaluate the state of the practice and
understand the implications of the strategies.


Abstract:
  We focus on coherent direction of arrival estimation of wideband sources
based on spatial sparsity. This area of research is encountered in many
applications such as passive radar, sonar, mining, and communication problems,
in which an increasing attention has been devoted to improving the estimation
accuracy and robustness to noise. By the development of super-resolution
algorithms, narrowband direction of arrival estimation based on gridless sparse
algorithms and atomic norm minimization has already been addressed. In this
paper, a superresolution based method is proposed for coherent direction of
arrival estimation of multiple wideband sources. First, unlike the conventional
coherent methods, we develop a new focusing method to map the subband with the
largest center frequency to the other ones, which leads to an accurate method
with no requirement for initial estimates for DOAs. Then, we introduce an
atomic norm problem by defining a new set of atoms and exploiting the signal
joint sparsity of different frequency subbands in a continuous spatial domain.
This problem is then cast as a semidefinite program, which leads to
implementing a new coherent direction of arrival estimation method with higher
resolution and more robustness to noise. Our method needs only one single
snapshot for each frequency subband, leading to a small number of snapshots for
the received wideband signal compared to the other coherent DOA techniques.
Numerical simulations show the outperformance of the proposed method compared
to the conventional ones.


Abstract:
  The problem of finding the number of ordered commuting tuples of elements in
a finite group is equivalent to finding the size of the solution set of the
system of equations determined by the commutator relations that impose
commutativity among any pair of elements from an ordered tuple. We consider
this type of systems for the case of ordered triples and express the size of
the solution set in terms of the irreducible characters of the group. The
obtained formulas are natural extensions of Frobenius' character formula that
calculates the number of ways a group element is a commutator of an ordered
pair of elements in a finite group. We discuss how our formulas can be used to
study the probability distributions afforded by these systems of equations, and
we show explicit calculations for dihedral groups.


Abstract:
  There are no known failures of Bounded Negativity in characteristic 0. In the
light of recent work showing the Bounded Negativity Conjecture fails in
positive characteristics for rational surfaces, we propose new characteristic
free conjectures as a replacement. We also develop bounds on numerical
characteristics of curves constraining their negativity. For example, we show
that the $H$-constant of a rational curve $C$ with at most $9$ singular points
satisfies $H(C)>-2$ regardless of the characteristic.


Abstract:
  International library standards require cataloguers to tediously input
Romanization of their catalogue records for the benefit of library users
without specific language expertise. In this paper, we present the first
reported results on the task of automatic Romanization of undiacritized Arabic
bibliographic entries. This complex task requires the modeling of Arabic
phonology, morphology, and even semantics. We collected a 2.5M word corpus of
parallel Arabic and Romanized bibliographic entries, and benchmarked a number
of models that vary in terms of complexity and resource dependence. Our best
system reaches 89.3% exact word Romanization on a blind test set. We make our
data and code publicly available.


Abstract:
  We report experimental and computational studies of thermal transport
properties in hexagonal boron nitride (hBN) encapsulated molybdenum disulfide
(MoS2) structure using refined optothermal Raman techniques, and reveal very
high interfacial thermal conductance between hBN and MoS2. By studying the
Raman shift of hBN and MoS2 in suspended and substrate-supported thin films
under varying laser power and temperature, we calibrate lateral (in-plane)
thermal conductivity of hBN and MoS2 and the vertical interfacial thermal
conductance in the hBN/MoS2/hBN heterostructure as well as the interfaces
between heterostructure and substrate. Crucially, we have found that
interfacial thermal conductance between hBN and encapsulated MoS2 is 74MW/m2K
and 72MW/m2K in supported and suspended films, respectively, which are
significantly higher than interfacial thermal conductance between MoS2 and
other substrates. Molecular dynamics (MD) computations conducted in parallel
have shown consistent results. This work provides clear evidence of
significantly efficient heat dissipation in hBN/MoS2/hBN heterostructures and
sheds light on building novel hBN encapsulated nanoelectronics with efficient
thermal management.


Abstract:
  Convolutional neural network (CNN) based architectures, such as Mask R-CNN,
constitute the state of the art in object detection and segmentation. Recently,
these methods have been extended for model-based segmentation where the network
outputs the parameters of a geometric model (e.g. an ellipse) directly. This
work considers objects whose three-dimensional models can be represented as
ellipsoids. We present a variant of Mask R-CNN for estimating the parameters of
ellipsoidal objects by segmenting each object and accurately regressing the
parameters of projection ellipses. We show that model regression is sensitive
to the underlying occlusion scenario and that prediction quality for each
object needs to be characterized individually for accurate 3D object
estimation. We present a novel ellipse regression loss which can learn the
offset parameters with their uncertainties and quantify the overall geometric
quality of detection for each ellipse. These values, in turn, allow us to fuse
multi-view detections to obtain 3D ellipsoid parameters in a principled
fashion. The experiments on both synthetic and real datasets quantitatively
demonstrate the high accuracy of our proposed method in estimating 3D objects
under heavy occlusions compared to previous state-of-the-art methods.


Abstract:
  We study constrained reinforcement learning (CRL) from a novel perspective by
setting constraints directly on state density functions, rather than the value
functions considered by previous works. State density has a clear physical and
mathematical interpretation, and is able to express a wide variety of
constraints such as resource limits and safety requirements. Density
constraints can also avoid the time-consuming process of designing and tuning
cost functions required by value function-based constraints to encode system
specifications. We leverage the duality between density functions and Q
functions to develop an effective algorithm to solve the density constrained RL
problem optimally and the constrains are guaranteed to be satisfied. We prove
that the proposed algorithm converges to a near-optimal solution with a bounded
error even when the policy update is imperfect. We use a set of comprehensive
experiments to demonstrate the advantages of our approach over state-of-the-art
CRL methods, with a wide range of density constrained tasks as well as standard
CRL benchmarks such as Safety-Gym.


Abstract:
  Grice's Cooperative Principle (1975) describes the implicit maxims that guide
conversation between humans. As humans begin to interact with non-human
dialogue systems more frequently and in a broader scope, an important question
emerges: what principles govern those interactions? The present study addresses
this question by evaluating human-AI interactions using Grice's four maxims; we
demonstrate that humans do, indeed, apply these maxims to interactions with AI,
even making explicit references to the AI's performance through a Gricean lens.
Twenty-three participants interacted with an American English-speaking Alexa
and rated and discussed their experience with an in-lab researcher. Researchers
then reviewed each exchange, identifying those that might relate to Grice's
maxims: Quantity, Quality, Manner, and Relevance. Many instances of explicit
user frustration stemmed from violations of Grice's maxims. Quantity violations
were noted for too little but not too much information, while Quality
violations were rare, indicating trust in Alexa's responses. Manner violations
focused on speed and humanness. Relevance violations were the most frequent,
and they appear to be the most frustrating. While the maxims help describe many
of the issues participants encountered, other issues do not fit neatly into
Grice's framework. Participants were particularly averse to Alexa initiating
exchanges or making unsolicited suggestions. To address this gap, we propose
the addition of human Priority to describe human-AI interaction. Humans and AIs
are not conversational equals, and human initiative takes priority. We suggest
that the application of Grice's Cooperative Principles to human-AI interactions
is beneficial both from an AI development perspective and as a tool for
describing an emerging form of interaction.


Abstract:
  End-to-end delay is a critical attribute of quality of service (QoS) in
application domains such as cloud computing and computer networks. This metric
is particularly important in tandem service systems, where the end-to-end
service is provided through a chain of services. Service-rate control is a
common mechanism for providing QoS guarantees in service systems. In this
paper, we introduce a reinforcement learning-based (RL-based) service-rate
controller that provides probabilistic upper-bounds on the end-to-end delay of
the system, while preventing the overuse of service resources. In order to have
a general framework, we use queueing theory to model the service systems.
However, we adopt an RL-based approach to avoid the limitations of
queueing-theoretic methods. In particular, we use Deep Deterministic Policy
Gradient (DDPG) to learn the service rates (action) as a function of the queue
lengths (state) in tandem service systems. In contrast to existing RL-based
methods that quantify their performance by the achieved overall reward, which
could be hard to interpret or even misleading, our proposed controller provides
explicit probabilistic guarantees on the end-to-end delay of the system. The
evaluations are presented for a tandem queueing system with non-exponential
inter-arrival and service times, the results of which validate our controller's
capability in meeting QoS constraints.


Abstract:
  During the last decade or so, there has been an insurgence in the deep
learning community to solve health-related issues, particularly breast cancer.
Following the Camelyon-16 challenge in 2016, several researchers have dedicated
their time to build Convolutional Neural Networks (CNNs) to help radiologists
and other clinicians diagnose breast cancer. In particular, there has been an
emphasis on Ductal Carcinoma in Situ (DCIS); the clinical term for early-stage
breast cancer. Large companies have given their fair share of research into
this subject, among these Google Deepmind who developed a model in 2020 that
has proven to be better than radiologists themselves to diagnose breast cancer
correctly.
  We found that among the issues which exist, there is a need for an
explanatory system that goes through the hidden layers of a CNN to highlight
those pixels that contributed to the classification of a mammogram. We then
chose an open-source, reasonably successful project developed by Prof. Shen,
using the CBIS-DDSM image database to run our experiments on. It was later
improved using the Resnet-50 and VGG-16 patch-classifiers, analytically
comparing the outcome of both. The results showed that the Resnet-50 one
converged earlier in the experiments.
  Following the research by Montavon and Binder, we used the DeepTaylor
Layer-wise Relevance Propagation (LRP) model to highlight those pixels and
regions within a mammogram which contribute most to its classification. This is
represented as a map of those pixels in the original image, which contribute to
the diagnosis and the extent to which they contribute to the final
classification. The most significant advantage of this algorithm is that it
performs exceptionally well with the Resnet-50 patch classifier architecture.


Abstract:
  We present a new tensor network algorithm for calculating the partition
function of interacting quantum field theories in 2 dimensions. It is based on
the Tensor Renormalization Group (TRG) protocol, adapted to operate entirely at
the level of fields. This strategy was applied in Ref.[1] to the much simpler
case of a free boson, obtaining an excellent performance. Here we include an
arbitrary self-interaction and treat it in the context of perturbation theory.
A real space analogue of the Wilsonian effective action and its expansion in
Feynman graphs is proposed. Using a $\lambda \phi^4$ theory for benchmark, we
evaluate the order $\lambda$ correction to the free energy. The results show a
fast convergence with the bond dimension, implying that our algorithm captures
well the effect of interaction on entanglement.


Abstract:
  Quantifying the composition of the material in protoplanetary disks is
paramount to determining the potential for exoplanetary systems to produce and
support habitable environments. A key complex organic molecule (COM) to detect
is methanol (CH3OH). CH3OH primarily forms at low temperatures via the
hydrogenation of CO ice on the surface of icy dust grains and is a necessary
basis for the formation of more complex species like amino acids and proteins.
We report the detection of CH3OH in a disk around a young, luminous A-type star
HD100546. This disk is warm and therefore does not host a significant CO ice
reservoir. We argue that the CH3OH cannot form in situ, and hence, this disk
has likely inherited COMs rich ice from an earlier cold dark cloud phase. This
is strong evidence that at least some of the organic material survives the disk
formation process and can then be incorporated into forming planets, moons and
comets. Therefore, crucial pre-biotic chemical evolution already takes place in
dark star-forming clouds.