Abstract:
  It is shown how the vector space $V_{8,8}$ arises from the Clifford algebra
$Cl(1,3)$ of spacetime. The latter algebra describes fundamental objects such
as strings and branes in terms of their $r$-volume degrees of freedom,
$x^{\mu_1 \mu_2 ...\mu_r}$ $\equiv x^M$, $r=0,1,2,3$, that generalizethe
concept of center of mass. Taking into account that there are sixteen $x^M$,
$M=1,2,3,...,16$, and in general $16 \times 15/2 = 120$ rotations of the form
$x'^M = {R^M}_N x^N$, we can consider $x^M$ as components of a vector $X=x^M
q_M$, where $q_M$ are generators of the Clifford algebra $Cl(8,8)$. The vector
space $V_{8,8}$ has enough room for the unification of the fundamental
particles and forces of the standard model. The rotations in $V_{8,8}\otimes
\mathbb{C}$ contain the grand unification group $SO(10)$ as a subgroup, and
also the Lorentz group $SO(1,3)$. It is shown how the Coleman-Mandula no go
theorem can be avoided. Spinors in $V_{8,8}\otimes \mathbb{C}$ are constructed
in terms of the wedge products of the basis vectors rewritten in the Witt
basis. They satisfy the massless Dirac equation in $M_{8,8}$ with the internal
part of the Dirac operator giving the non vanishing masses in four dimensions.


Abstract:
  One of the most important achievements of inflationary cosmology is to
predict a departure from scale invariance of the power spectrum for scalar
curvature cosmological fluctuations. This tilt is understood as a consequence
of a quasi de Sitter classical equation of state describing the inflationary
dark energy dominated era. Here, following previous work, we find a departure
of scale invariance for the quantum Fisher information associated to de Sitter
vacuum for scalar quantum spectator modes. This gives rise to a purely quantum
cosmological tilt with a well defined dependence on energy scale. This quantum
tilt is imprinted, in a scale dependent energy uncertainty for the spectator
modes. The effective quasi de Sitter description of this model independent
energy uncertainty uniquely sets the effective quasi de Sitter parameters at
all energy scales. In particular, in the slow-roll regime characterized by an
almost constant $\epsilon$, the quantum Fisher -- model independent --
prediction for the spectral index is $(1-n_s) = 0.0328$ ($n_s=0.9672$).
Moreover, the energy scale dependence of the quantum cosmological tilt implies
the existence of a cosmological phase transition at energies higher than the
CMB scale where the tilt goes from red into blue. This strongly suggest the
existence of a pre-inflationary phase where the effective scalaron contributes
to the spectral index as normal relativistic matter and where the corresponding
growth of the power spectrum can result in dark matter in the form of small
mass primordial black holes. The source and features of the quantum
cosmological tilt leading to these predictions are determined by the
entanglement features of the de Sitter vacuum states.


Abstract:
  Tensor recovery has recently arisen in a lot of application fields, such as
transportation, medical imaging and remote sensing. Under the assumption that
signals possess sparse and/or low-rank structures, many tensor recovery methods
have been developed to apply various regularization techniques together with
the operator-splitting type of algorithms. Due to the unprecedented growth of
data, it becomes increasingly desirable to use streamlined algorithms to
achieve real-time computation, such as stochastic optimization algorithms that
have recently emerged as an efficient family of methods in machine learning. In
this work, we propose a novel algorithmic framework based on the Kaczmarz
algorithm for tensor recovery. We provide thorough convergence analysis and its
applications from the vector case to the tensor one. Numerical results on a
variety of tensor recovery applications, including sparse signal recovery,
low-rank tensor recovery, image inpainting and deconvolution, illustrate the
enormous potential of the proposed methods.


Abstract:
  The trajectory deflection and gravitational-electromagnetic dual lensing
(GEL) of charged signal in general charged static and spherically symmetric
spacetimes are considered in this work. We showed that the perturbative
approach previously developed for neutral particles can be extended to the
electromagnetic interaction case. The deflection angle still takes a
(quasi-)series form and the finite distance effect of both the source and
observer can be taken into account. Comparing to pure gravitational case, the
apparent angles of the images in the GEL, their magnifications and time delay
all receive the electromagnetic corrections starting from the first non-trivial
order. The sign and relative size of the leading corrections are determined by
$\sim \frac{Q}{M}\frac{q}{E}$ where $M,~Q,~q,~E$ are the spacetime mass and
charge, and signal particle charge and energy respectively. It is found that
for $qQ>0$ (or $<0$), the electromagnetic interaction will decrease (or
increase) the deflection angle, and in GEL the impact parameters, apparent
angles, magnifications and total travel time for each image. The time delay is
increased for small $\beta$ and $qQ>0$, and otherwise always increased
regardless the sign of $qQ$. The results are then applied to the deflection and
GEL of charged protons in cosmic rays in Reissner-Nordstrom, charged dilaton
and charged Horndeski spacetimes.


Abstract:
  Music Information Retrieval (MIR) is a collaborative scientific study that
help to build innovative information research themes, novel frameworks, and
developing connected delivery mechanisms in addition to making the world's
massive collection of music open for everyone. Modern rock music proved to be
difficult to estimate tempo and chord recognition did not work. All of the
findings indicate that modern rock and metal music can be analysed, despite its
complexity, but that further research is needed in this area to make it useful.
Using a neural network has been one of the simplest ways of dealing with it.
The pitch class profile vector is used in the neural network method. Because
the vector only contains 12 elements of semi-tone values, it is enough for
chord recognition. Of course, there are other ways of achieving this work, most
of them depend on pitch class profiling to transform the chord into a type that
can be recognised, but the recognition process is time-consuming centred on
extremely complicated and memory-intensive methods.


Abstract:
  In this paper, we prove an optimal inequality between the potential function
of a complete Schouten soliton and the norm of its gradient. We also prove that
these metrics have bounded scalar curvature of defined sign. As an application,
we prove that the potential function of a Shrinking Schouten soliton grows
linearly and provide optimal estimates for the growth of the volume of geodesic
balls, without any further assumption.


Abstract:
  The problem of categorizing short speech sentences according to their
semantic features with high accuracy is a subject studied in natural language
processing. In this study, a data set created with samples classified in 46
different categories was used. Examples consist of sentences taken from chat
conversations between a company's customer representatives and the company's
website visitors. The primary purpose is to automatically tag questions and
requests from visitors in the most accurate way for 46 predetermined categories
for use in a chat application to generate meaningful answers to the questions
asked by the website visitors. For this, different BERT models and one GPT-2
model, pre-trained in Turkish, were preferred. The classification performances
of the relevant models were analyzed in detail and reported accordingly.


Abstract:
  During their evolution, proteins explore sequence space via an interplay
between random mutations and phenotypic selection. Here we build upon recent
progress in reconstructing data-driven fitness landscapes for families of
homologous proteins, to propose stochastic models of experimental protein
evolution. These models predict quantitatively important features of
experimentally evolved sequence libraries, like fitness distributions and
position-specific mutational spectra. They also allow us to efficiently
simulate sequence libraries for a vast array of combinations of experimental
parameters like sequence divergence, selection strength and library size. We
showcase the potential of the approach in re-analyzing two recent experiments
to determine protein structure from signals of epistasis emerging in
experimental sequence libraries. To be detectable, these signals require
sufficiently large and sufficiently diverged libraries. Our modeling framework
offers a quantitative explanation for the variable success of recently
published experiments. Furthermore, we can forecast the outcome of time- and
resource-intensive evolution experiments, opening thereby a way to
computationally optimize experimental protocols.


Abstract:
  When personal, assistive, and interactive robots make mistakes, humans
naturally and intuitively correct those mistakes through physical interaction.
In simple situations, one correction is sufficient to convey what the human
wants. But when humans are working with multiple robots or the robot is
performing an intricate task often the human must make several corrections to
fix the robot's behavior. Prior research assumes each of these physical
corrections are independent events, and learns from them one-at-a-time.
However, this misses out on crucial information: each of these interactions are
interconnected, and may only make sense if viewed together. Alternatively,
other work reasons over the final trajectory produced by all of the human's
corrections. But this method must wait until the end of the task to learn from
corrections, as opposed to inferring from the corrections in an online fashion.
In this paper we formalize an approach for learning from sequences of physical
corrections during the current task. To do this we introduce an auxiliary
reward that captures the human's trade-off between making corrections which
improve the robot's immediate reward and long-term performance. We evaluate the
resulting algorithm in remote and in-person human-robot experiments, and
compare to both independent and final baselines. Our results indicate that
users are best able to convey their objective when the robot reasons over their
sequence of corrections.


Abstract:
  We study the use of spike and slab priors for consistent estimation of the
number of change points and their locations. Leveraging recent results in the
variable selection literature, we show that an estimator based on spike and
slab priors achieves optimal localization rate in the multiple offline change
point detection problem. Based on this estimator, we propose a Bayesian change
point detection method, which is one of the fastest Bayesian methodologies, and
it is more robust to misspecification of the error terms than the competing
methods. We demonstrate through empirical work the good performance of our
approach vis-a-vis some state-of-the-art benchmarks.


Abstract:
  We consider frequency-weighted damping optimization for vibrating systems
described by a second-order differential equation. The goal is to determine
viscosity values such that eigenvalues are kept away from certain undesirable
areas on the imaginary axis. To this end, we present two complementary
techniques. First, we propose new frameworks using nonsmooth constrained
optimization problems, whose solutions both damp undesirable frequency bands
and maintain stability of the system. These frameworks also allow us to weight
which frequency bands are the most important to damp. Second, we also propose a
fast new eigensolver for the structured quadratic eigenvalue problems that
appear in such vibrating systems. In order to be efficient, our new eigensolver
exploits special properties of diagonal-plus-rank-one complex symmetric
matrices, which we leverage by showing how each quadratic eigenvalue problem
can be transformed into a short sequence of such linear eigenvalue problems.
The result is an eigensolver that is substantially faster than standard
techniques. By combining this new solver with our new optimization frameworks,
we obtain our overall algorithm for fast computation of optimal viscosities.
The efficiency and performance of our new methods are verified and illustrated
on several numerical examples.


Abstract:
  A search for production of the supersymmetric partners of the top quark, top
squarks, is presented. The search is based on proton-proton collision events
containing multiple jets, no leptons, and large transverse momentum imbalance.
The data were collected with the CMS detector at the CERN LHC at a
center-of-mass energy of 13 TeV, and correspond to an integrated luminosity of
137 fb$^{-1}$. The targeted signal production scenarios are direct and
gluino-mediated top squark production, including scenarios in which the top
squark and neutralino masses are nearly degenerate. The search utilizes novel
algorithms based on deep neural networks that identify hadronically decaying
top quarks and W bosons, which are expected in many of the targeted signal
models. No statistically significant excess of events is observed relative to
the expectation from the standard model, and limits on the top squark
production cross section are obtained in the context of simplified
supersymmetric models for various production and decay modes. Exclusion limits
as high as 1310 GeV are established at the 95% confidence level on the mass of
the top squark for direct top squark production models, and as high as 2260 GeV
on the mass of the gluino for gluino-mediated top squark production models.
These results represent a significant improvement over the results of previous
searches for supersymmetry by CMS in the same final state.


Abstract:
  We investigate the high-temperature dynamical conductivity $\sigma(\omega)$
in two one-dimensional integrable quantum lattice models: the anisotropic XXZ
spin chain and the Hubbard chain. The emphasis is on the metallic regime of
both models, where besides the ballistic component, the regular part of
conductivity might reveal a diffusive-like transport. To resolve the
low-frequency dynamics, we upgrade the microcanonical Lanczos method enabling
studies of finite-size systems with up to $L\leq 32$ sites for XXZ spin model
with the frequency resolution $\delta \omega \sim 10^{-3} J$. Results for the
XXZ chain reveal a fine structure of $\sigma(\omega)$ spectra, which originates
from the discontinuous variation of the stiffness, previously found at
commensurate values of the anisotropy parameter $\Delta$. Still, we do not find
a clear evidence for a diffusive component, at least not for commensurate
values of $\Delta$, particularly for $\Delta =0.5$, as well as for $\Delta \to
0$. Similar is the conclusion for the Hubbard model away from half-filling,
where the spectra reveal more universal behavior.


Abstract:
  We present the photometric and spectroscopic evolution of supernova (SN)
2019cad during the first $\sim100$ days from explosion. Based on the light
curve morphology, we find that SN 2019cad resembles the double-peaked type Ib/c
SN 2005bf and the type Ic PTF11mnb. Unlike those two objects, SN 2019cad also
shows the initial peak in the redder bands. Inspection of the g-band light
curve indicates the initial peak is reached in $\sim8$ days, while the r band
peak occurred $\sim15$ days post-explosion. A second and more prominent peak is
reached in all bands at $\sim45$ days past explosion, followed by and fast
decline from $\sim60$ days. During the first 30 days, the spectra of SN 2019cad
show the typical features of a type Ic SN, however, after 40 days, a blue
continuum with prominent lines of Si II ${\lambda}6355$ and C II
${\lambda}6580$ is observed again. Comparing the bolometric light curve to
hydrodynamical models, we find that SN 2019cad is consistent with a pre-SN mass
of 11 M$_{\odot}$, and an explosion energy of $3.5\times 10^{51}$ erg. The
light curve morphology can be reproduced either by a double-peaked $^{56}$Ni
distribution with an external component of 0.041 M$_{\odot}$ and an internal
component of 0.3 M$_{\odot}$ or a double-peaked $^{56}$Ni distribution plus
magnetar model (P $\sim11$ ms and B $\sim26\times 10^{14}$ G). If SN 2019cad
were to suffer from significant host reddening (which cannot be ruled out), the
$^{56}$Ni model would require extreme values, while the magnetar model would
still be feasible.


Abstract:
  The notions of transposed Hom-Poisson and Hom-pre-Lie Poisson algebras are
introduced. Their bimodules and matched pairs are defined and the relevant
properties and theorems are given. The notion of Manin triple of transposed
Hom-Poisson algebras is introduced, and its equivalence to the transposed
Hom-Poisson bialgebras is investigated. The notion of $\mathcal{O}$-operator is
exploited to illustrate the relations existing between transposed Hom-Poisson
and Hom-pre-Lie Poisson algebras.


Abstract:
  Panoptic segmentation of point clouds is a crucial task that enables
autonomous vehicles to comprehend their vicinity using their highly accurate
and reliable LiDAR sensors. Existing top-down approaches tackle this problem by
either combining independent task-specific networks or translating methods from
the image domain ignoring the intricacies of LiDAR data and thus often
resulting in sub-optimal performance. In this paper, we present the novel
top-down Efficient LiDAR Panoptic Segmentation (EfficientLPS) architecture that
addresses multiple challenges in segmenting LiDAR point clouds including
distance-dependent sparsity, severe occlusions, large scale-variations, and
re-projection errors. EfficientLPS comprises of a novel shared backbone that
encodes with strengthened geometric transformation modeling capacity and
aggregates semantically rich range-aware multi-scale features. It incorporates
new scale-invariant semantic and instance segmentation heads along with the
panoptic fusion module which is supervised by our proposed panoptic periphery
loss function. Additionally, we formulate a regularized pseudo labeling
framework to further improve the performance of EfficientLPS by training on
unlabelled data. We benchmark our proposed model on two large-scale LiDAR
datasets: nuScenes, for which we also provide ground truth annotations, and
SemanticKITTI. Notably, EfficientLPS sets the new state-of-the-art on both
these datasets.


Abstract:
  Most of the adversarial attack methods suffer from large perceptual
distortions such as visible artifacts, when the attack strength is relatively
high. These perceptual distortions contain a certain portion which contributes
less to the attack success rate. This portion of distortions, which is induced
by unnecessary modifications and lack of proper perceptual distortion
constraint, is the target of the proposed framework. In this paper, we propose
a perceptual distortion reduction framework to tackle this problem from two
perspectives. Firstly, we propose a perceptual distortion constraint and add it
into the objective function to jointly optimize the perceptual distortions and
attack success rate. Secondly, we propose an adaptive penalty factor $\lambda$
to balance the discrepancies between different samples. Since SGD and
Momentum-SGD cannot optimize our complex non-convex problem, we exploit Adam in
optimization. Extensive experiments have verified the superiority of our
proposed framework.


Abstract:
  Humans have a remarkable ability to make decisions by accurately reasoning
about future events, including the future behaviors and states of mind of other
agents. Consider driving a car through a busy intersection: it is necessary to
reason about the physics of the vehicle, the intentions of other drivers, and
their beliefs about your own intentions. If you signal a turn, another driver
might yield to you, or if you enter the passing lane, another driver might
decelerate to give you room to merge in front. Competent drivers must plan how
they can safely react to a variety of potential future behaviors of other
agents before they make their next move. This requires contingency planning:
explicitly planning a set of conditional actions that depend on the stochastic
outcome of future events. In this work, we develop a general-purpose
contingency planner that is learned end-to-end using high-dimensional scene
observations and low-dimensional behavioral observations. We use a conditional
autoregressive flow model to create a compact contingency planning space, and
show how this model can tractably learn contingencies from behavioral
observations. We developed a closed-loop control benchmark of realistic
multi-agent scenarios in a driving simulator (CARLA), on which we compare our
method to various noncontingent methods that reason about multi-agent future
behavior, including several state-of-the-art deep learning-based planning
approaches. We illustrate that these noncontingent planning methods
fundamentally fail on this benchmark, and find that our deep contingency
planning method achieves significantly superior performance. Code to run our
benchmark and reproduce our results is available at
https://sites.google.com/view/contingency-planning


Abstract:
  We present a photometric redshift (photo-$z$) estimation technique for
galaxies in the P\lowercase{an}-STARRS1 (PS1) $3\pi $ survey. Specifically, we
train and test a regression and a classification Random-Forest (RF) models
using photometric features (magnitudes, colors and moments of the radiation
intensity) from the optical PS1 data release 2 (PS1-DR2) and from the
AllWISE/unWISE infrared source catalogs. The classification RF model
($RF_{clas}$) has better performance in the local universe ($z\lesssim 0.1$),
while the second one ($RF_{reg}$) is on average better for $0.1 \lesssim
z\lesssim1$. We adopt as labels the spectroscopic redshift of the galaxies from
the Sloan Digital Sky Survey (SDSS) data release 16 (SDSS-DR16). We find that
the combination of AllWISE/unWISE and PS1-DR2 features leads to an average bias
of $\overline{\Delta z_{norm}}=1\times 10^{-3}$, a standard deviation
$\sigma(\Delta z_{norm})=0.0225$, (where $\Delta z_{norm} \equiv
(z_{phot}-z_{spec})/(1+z_{spec})$), and an outlier rate of $P_0=1.48 \%$ in the
test set for the $RF_{clas}$ model. In the low-redshift Universe ($z<0.1$) that
is of primary interest to many astronomical transient studies, our model
produces an error estimate on the inferred magnitude of an object of $\le$1 mag
in 87\% of the test sample.


Abstract:
  The aim of this paper is to obtain new inequalities for a large family of
topological indices restricted to unicyclic graphs and to characterize the set
of extremal unicyclic graphs with respect to them. This family includes
variable first Zagreb, variable sum exdeg, multiplicative second Zagreb and
Narumi-Katayama indices. Our main results provide upper and lower bounds for
these topological indices on unicyclic graphs, fixing or not the maximum degree
or the number of pendant vertices.


Abstract:
  A wide choice of cinematic lenses enables motion-picture creators to adapt
image visual-appearance to their creative vision. Such choice does not exist in
the realm of real-time computer graphics, where only one type of perspective
projection is widely used. This work provides a perspective imaging model that
in an artistically convincing manner resembles anamorphic photography lens
variety and more. It presents an asymmetrical-anamorphic azimuthal projection
map with natural vignetting and realistic chromatic aberration. The
mathematical model for this projection has been chosen such that its parameters
reflect the psycho-physiological aspects of visual perception. That enables its
use in artistic and professional environments, where specific aspects of the
photographed space are to be presented.


Abstract:
  Deep learning has successfully been leveraged for medical image segmentation.
It employs convolutional neural networks (CNN) to learn distinctive image
features from a defined pixel-wise objective function. However, this approach
can lead to less output pixel interdependence producing incomplete and
unrealistic segmentation results. In this paper, we present a fully automatic
deep learning method for robust medical image segmentation by formulating the
segmentation problem as a recurrent framework using two systems. The first one
is a forward system of an encoder-decoder CNN that predicts the segmentation
result from the input image. The predicted probabilistic output of the forward
system is then encoded by a fully convolutional network (FCN)-based context
feedback system. The encoded feature space of the FCN is then integrated back
into the forward system's feed-forward learning process. Using the FCN-based
context feedback loop allows the forward system to learn and extract more
high-level image features and fix previous mistakes, thereby improving
prediction accuracy over time. Experimental results, performed on four
different clinical datasets, demonstrate our method's potential application for
single and multi-structure medical image segmentation by outperforming the
state of the art methods. With the feedback loop, deep learning methods can now
produce results that are both anatomically plausible and robust to low contrast
images. Therefore, formulating image segmentation as a recurrent framework of
two interconnected networks via context feedback loop can be a potential method
for robust and efficient medical image analysis.


Abstract:
  We report here the first detection in the interstellar medium of the
cyanomidyl radical (HNCN). Using the Yebes 40m and the IRAM 30m telescopes, we
have targeted the doublets of the $N$=2$-$1, 4$-$3, 5$-$4, 6$-$5, and 7$-$6
transitions of HNCN toward the molecular cloud G+0.693-0.027. We have detected
three unblended lines of HNCN, these are the $N$=6$-$5 doublet and one line of
the $N$=4$-$3 transition. Additionally we present one line of the $N$=5$-$4
transition partially blended with emission from other species. The Local
Thermodynamic Equilibrium best fit to the data gives a molecular abundance of
(0.91$\pm$0.05)$\times$10$^{-10}$ with respect to H$_2$. The relatively low
abundance of this species in G+0.693-0.027, and its high reactivity, suggest
that HNCN is possibly produced by gas-phase chemistry. Our work shows that this
highly reactive molecule is present in interstellar space, and thus it
represents a plausible precursor of larger prebiotic molecules with the NCN
backbone such as cyanamide (NH$_2$CN), carbodiimide (HNCNH) and formamidine
(NH$_2$CHNH).


Abstract:
  The results from the ESA Gaia astrometric mission and deep photometric
surveys have revolutionized our knowledge of the Milky Way. There are many
ongoing efforts to search these data for stellar substructure to find evidence
of individual accretion events that built up the Milky Way and its halo. One of
these newly identified features, called Nyx, was announced as an accreted
stellar stream traveling in the plane of the disk. Using a combination of
elemental abundances and stellar parameters from the GALAH and APOGEE surveys,
we find that the abundances of the highest likelihood Nyx members are entirely
consistent with membership of the thick disk, and inconsistent with a dwarf
galaxy origin. We conclude that the postulated Nyx stream is most probably a
high-velocity component of the Milky Way's thick disk. With the growing
availability of large data sets including kinematics, stellar parameters, and
detailed abundances, the probability of detecting chance associations
increases, and hence new searches for substructure require confirmation across
as many data dimensions as possible.


Abstract:
  Bayesian neural networks are theoretically well-understood only in the
infinite-width limit, where Gaussian priors over network weights yield Gaussian
priors over network outputs. Recent work has suggested that finite Bayesian
networks may outperform their infinite counterparts, but their non-Gaussian
function space priors have been characterized only though perturbative
approaches. Here, we derive exact solutions for the function space priors for
individual input examples of a class of finite fully-connected feedforward
Bayesian neural networks. For deep linear networks, the prior has a simple
expression in terms of the Meijer $G$-function. The prior of a finite ReLU
network is a mixture of the priors of linear networks of smaller widths,
corresponding to different numbers of active units in each layer. Our results
unify previous descriptions of finite network priors in terms of their tail
decay and large-width behavior.


Abstract:
  Inpainting arbitrary missing regions is challenging because learning valid
features for various masked regions is nontrivial. Though U-shaped
encoder-decoder frameworks have been witnessed to be successful, most of them
share a common drawback of mask unawareness in feature extraction because all
convolution windows (or regions), including those with various shapes of
missing pixels, are treated equally and filtered with fixed learned kernels. To
this end, we propose our novel mask-aware inpainting solution. Firstly, a
Mask-Aware Dynamic Filtering (MADF) module is designed to effectively learn
multi-scale features for missing regions in the encoding phase. Specifically,
filters for each convolution window are generated from features of the
corresponding region of the mask. The second fold of mask awareness is achieved
by adopting Point-wise Normalization (PN) in our decoding phase, considering
that statistical natures of features at masked points differentiate from those
of unmasked points. The proposed PN can tackle this issue by dynamically
assigning point-wise scaling factor and bias. Lastly, our model is designed to
be an end-to-end cascaded refinement one. Supervision information such as
reconstruction loss, perceptual loss and total variation loss is incrementally
leveraged to boost the inpainting results from coarse to fine. Effectiveness of
the proposed framework is validated both quantitatively and qualitatively via
extensive experiments on three public datasets including Places2, CelebA and
Paris StreetView.


Abstract:
  Antibiotic resistance is a growing public health problem. To gain a
fundamental understanding of resistance evolution, a combination of systematic
experimental and theoretical approaches is required. Evolution experiments
combined with next-generation sequencing techniques, laboratory automation, and
mathematical modeling are enabling the investigation of resistance development
at an unprecedented level of detail. Recent work has directly tracked the
intricate stochastic dynamics of bacterial populations in which resistant
mutants emerge and compete. In addition, new approaches have enabled measuring
how prone a large number of genetically perturbed strains are to evolve
resistance. Based on advances in quantitative cell physiology, predictive
theoretical models of resistance are increasingly being developed. Taken
together, a new strategy for observing, predicting, and ultimately controlling
resistance evolution is emerging.


Abstract:
  The Reinforcement Learning (RL) building blocks, i.e. Q-functions and policy
networks, usually take elements from the cartesian product of two domains as
input. In particular, the input of the Q-function is both the state and the
action, and in multi-task problems (Meta-RL) the policy can take a state and a
context. Standard architectures tend to ignore these variables' underlying
interpretations and simply concatenate their features into a single vector. In
this work, we argue that this choice may lead to poor gradient estimation in
actor-critic algorithms and high variance learning steps in Meta-RL algorithms.
To consider the interaction between the input variables, we suggest using a
Hypernetwork architecture where a primary network determines the weights of a
conditional dynamic network. We show that this approach improves the gradient
approximation and reduces the learning step variance, which both accelerates
learning and improves the final performance. We demonstrate a consistent
improvement across different locomotion tasks and different algorithms both in
RL (TD3 and SAC) and in Meta-RL (MAML and PEARL).


Abstract:
  The computation of determinants plays a central role in diagrammatic Monte
Carlo algorithms for strongly correlated systems. The evaluation of large
numbers of determinants can often be the limiting computational factor
determining the number of attainable diagrammatic expansion orders. In this
work we build upon the algorithm presented in [\emph{Linear Algebra and its
applications} 419.1 (2006), 107-124] which computes all principal minors of a
matrix in $O(2^n)$ operations. We present multiple generalizations of the
algorithm to the efficient evaluation of certain subsets of all principal
minors with immediate applications to Connected Determinant Diagrammatic Monte
Carlo within the normal and symmetry-broken phases as well as Continuous-time
Quantum Monte Carlo. Additionally, we improve the asymptotic scaling of
diagrammatic Monte Carlo formulated in real-time to $O(2^n)$ and report
speedups of up to a factor $25$ at computationally realistic expansion orders.
We further show that all permanent-principal-minors, corresponding to sums of
bosonic Feynman diagrams, can be computed in $O(3^n)$, making diagrammatic
Monte Carlo for bosonic and mixed systems a viable path worth exploring.


Abstract:
  We show that the word problem for braided monoidal categories is at least as
hard as the unknotting problem. As a corollary, so is the word problem for
tricategories. We conjecture that the word problem for tricategories is
decidable.


Abstract:
  We review the basic definitions and properties of trapped surfaces and
discuss them in the context of Kerr-Vaidya line-element. Our study shows that
the apparent horizon does not exist in general for axisymmetric space-times.
The reason being the surface at which the null tangent vectors are geodesic and
the surface at which the expansion of such vectors vanishes do not coincide.
Calculation of an approximate apparent horizon for space-times that ensure its
existence seems to be the only way to get away with this problem. The
approximate apparent horizon, however, turned out to be non-unique. The choice
of the shear free null geodesics, at least in the leading order, seem to remove
this non-uniqueness. We also propose a new definition of the black hole
boundary.


Abstract:
  In this talk, we will describe a framework for assertion-based verification
(ABV) of quantum circuits by applying model checking techniques for quantum
systems developed in our previous work, in which:
  (i) Noiseless and noisy quantum circuits are modelled as operator- and
super-operator-valued transition systems, respectively, both of which can be
further represented by tensor networks.
  (ii) Quantum assertions are specified by a temporal extension of Birkhoff-von
Neumann quantum logic. Their semantics is defined based on the design decision:
they will be used in verification of quantum circuits by simulation on
classical computers or human reasoning rather than by quantum physics
experiments (e.g. testing through measurements);
  (iii) Algorithms for reachability analysis and model checking of quantum
circuits are developed based on contraction of tensor networks. We observe that
many optimisation techniques for computing relational products used in
BDD-based model checking algorithms can be generalised for contracting tensor
networks of quantum circuits.


Abstract:
  Scintillators play an important role in radiation detection and imaging.
Thallium-doped cesium iodide (CsI:Tl) and sodium iodide (NaI:Tl) are two of the
major scintillators that have been used for many applications for many decades.
In this paper we will present an improved scintillation performance of Bridgman
method grown CsI(Tl) and NaI(Tl) crystals developed by Xtallized Intelligence,
Inc. (XI, Inc.) and we will compare the performance with commercially available
CsI:Tl and NaI:Tl. In a preliminary testing using MicroFJ-60035-TSV silicon
photomultipliers (ON Semiconductor), the newly developed and improved
12.5x12.5x6 mm3 CsI:Tl crystal has shown an energy resolution of 4.8% (FWHM) at
662 keV, compared to 7.2% obtained by a commercially available CsI:Tl with a
size of 12.5x12.5x25 mm3. Energy resolution of 5.4% (FWHM) at 662 keV is
obtained for the newly improved NaI:Tl crystal, compared to 7% obtained by a
commercially available 1 in. dia. x 1 in. NaI(Tl). Comparison of the photo peak
channel locations shows that the improved CsI:Tl and NaI:Tl developed by XI,
Inc. have produced much larger signal amplitudes when compared to the
commercially available CsI(Tl) and NaI:Tl. Improved decay time constants are
presented as well.


Abstract:
  Submodular optimization has numerous applications such as crowdsourcing and
viral marketing. In this paper, we study the fundamental problem of
non-negative submodular function maximization subject to a $k$-system
constraint, which generalizes many other important constraints in submodular
optimization such as cardinality constraint, matroid constraint, and
$k$-extendible system constraint. The existing approaches for this problem
achieve the best-known approximation ratio of $k+2\sqrt{k+2}+3$ (for a general
submodular function) based on deterministic algorithmic frameworks. We propose
several randomized algorithms that improve upon the state-of-the-art algorithms
in terms of approximation ratio and time complexity, both under the
non-adaptive setting and the adaptive setting. The empirical performance of our
algorithms is extensively evaluated in several applications related to data
mining and social computing, and the experimental results demonstrate the
superiorities of our algorithms in terms of both utility and efficiency.


Abstract:
  The representative cuprate, La$_{2-x}$M$_x$CuO$_4$, with M = Sr and $x = 1/8$
is studied via first-principles calculations in the high-temperature tetragonal
(HTT), low-temperature orthorhombic (LTO), and low-temperature
less-orthorhombic (LTLO) structures. By suppressing the magnetism and
superconductivity, the LTLO phase, which has rarely been observed in
La$_{2-x}$Sr$_x$CuO$_4$, is found to be the ground state, where the structural
phase transitions, HTT$\rightarrow$LTO$\rightarrow$LTLO, can be understood via
phonon instability. While the La-O composition is identified to be responsible
for the phonon softening, the superconducting CuO$_2$ layer is dynamically
stable. The LTLO phase, which can exhibit a $\sim$20 meV splitting in the
density of states, is proposed to have an intimate relationship with the
observed pseudogap and the charge density wave giving the stripe. We argue that
at low temperatures, the superconducting LTO La$_{1.875}$Sr$_{0.125}$CuO$_4$
competes with the phonon-preferred LTLO phase by spontaneously forming the
Cooper pairs, resulting in suppressing the stripe. Therefore, the revealed LTLO
phase is indispensable for understanding La$_{2-x}$Sr$_x$CuO$_4$.


Abstract:
  Consider a set P of points in the unit square U, one of them being the
origin. For each point p in P you may draw a rectangle in U with its lower-left
corner in p. What is the maximum area such rectangles can cover without
overlapping each other? Freedman [1969] posed this problem in 1969, asking
whether one can always cover at least 50% of U. Over 40 years later, Dumitrescu
and T\'oth [2011] achieved the first constant coverage of 9.1%; since then, no
significant progress was made. While 9.1% might seem low, the authors could not
find any instance where their algorithm covers less than 50%, nourishing the
hope to eventually prove a 50% bound. While we indeed significantly raise the
algorithm's coverage to 39%, we extinguish the hope of reaching 50% by giving
points for which the coverage is below 43.3%. Our analysis studies the
algorithm's average and worst-case density of so-called tiles, which represent
the area where a given point can freely choose its maximum-area rectangle. Our
approachis comparatively general and may potentially help in analyzing related
algorithms.


Abstract:
  We propose an artificial life framework aimed at facilitating the emergence
of intelligent organisms. In this framework there is no explicit notion of an
agent: instead there is an environment made of atomic elements. These elements
contain neural operations and interact through exchanges of information and
through physics-like rules contained in the environment. We discuss how an
evolutionary process can lead to the emergence of different organisms made of
many such atomic elements which can coexist and thrive in the environment. We
discuss how this forms the basis of a general AI generating algorithm. We
provide a simplified implementation of such system and discuss what advances
need to be made to scale it up further.


Abstract:
  We present a second-order ensemble method based on a blended three-step
backward differentiation formula (BDF) timestepping scheme to compute an
ensemble of Navier-Stokes equations. Compared with the only existing
second-order ensemble method that combines the two-step BDF timestepping scheme
and a special explicit second-order Adams-Bashforth treatment of the advection
term, this method is more accurate with nominal increase in computational cost.
We give comprehensive stability and error analysis for the method. Numerical
examples are also provided to verify theoretical results and demonstrate the
improved accuracy of the method.


Abstract:
  The optical and UV variability of the majority of AGN may be related to the
reprocessing of rapidly-changing X-ray emission from a more compact region near
the central black hole. Such a reprocessing model would be characterised by
lags between X-ray and optical/UV emission due to differences in light travel
time. Observationally however, such lag features have been difficult to detect
due to gaps in the lightcurves introduced through factors such as source
visibility or limited telescope time. In this work, Gaussian process regression
is employed to interpolate the gaps in the Swift X-ray and UV lightcurves of
the narrow-line Seyfert 1 galaxy Mrk 335. In a simulation study of five
commonly-employed analytic Gaussian process kernels, we conclude that the
Matern 1/2 and rational quadratic kernels yield the most well-specified models
for the X-ray and UVW2 bands of Mrk 335. In analysing the structure functions
of the Gaussian process lightcurves, we obtain a broken power law with a break
point at 125 days in the UVW2 band. In the X-ray band, the structure function
of the Gaussian process lightcurve is consistent with a power law in the case
of the rational quadratic kernel whilst a broken power law with a breakpoint at
66 days is obtained from the Matern 1/2 kernel. The subsequent
cross-correlation analysis is consistent with previous studies and furthermore,
shows tentative evidence for a broad X-ray-UV lag feature of up to 30 days in
the lag-frequency spectrum where the significance of the lag depends on the
choice of Gaussian process kernel.


Abstract:
  Remote collaboration systems have become increasingly important in today's
society, especially during times where physical distancing is advised.
Industry, research and individuals face the challenging task of collaborating
and networking over long distances. While video and teleconferencing are
already widespread, collaboration systems in augmented, virtual, and mixed
reality are still a niche technology. We provide an overview of recent
developments of synchronous remote collaboration systems and create a taxonomy
by dividing them into three main components that form such systems:
Environment, Avatars, and Interaction. A thorough overview of existing systems
is given, categorising their main contributions in order to help researchers
working in different fields by providing concise information about specific
topics such as avatars, virtual environment, visualisation styles and
interaction. The focus of this work is clearly on synchronised collaboration
from a distance. A total of 82 unique systems for remote collaboration are
discussed, including more than 100 publications and 25 commercial systems.


Abstract:
  Starting from the construction of the free quantum scalar field of mass
$m\geq 0$ we give mathematically precise and rigorous versions of three
different approaches to computing the Casimir forces between compact obstacles.
We then prove that they are equivalent.


Abstract:
  Self-supervised representation learning is able to learn semantically
meaningful features; however, much of its recent success relies on multiple
crops of an image with very few objects. Instead of learning view-invariant
representation from simple images, humans learn representations in a complex
world with changing scenes by observing object movement, deformation, pose
variation, and ego motion. Motivated by this ability, we present a new
self-supervised learning representation framework that can be directly deployed
on a video stream of complex scenes with many moving objects. Our framework
features a simple flow equivariance objective that encourages the network to
predict the features of another frame by applying a flow transformation to the
features of the current frame. Our representations, learned from
high-resolution raw video, can be readily used for downstream tasks on static
images. Readout experiments on challenging semantic segmentation, instance
segmentation, and object detection benchmarks show that we are able to
outperform representations obtained from previous state-of-the-art methods
including SimCLR and BYOL.


Abstract:
  We consider the discrete-time quantum walk whose local dynamics is denoted by
$C$ at the perturbed region $\{0,1,\dots,M-1\}$ and free at the other
positions. We obtain the stationary state with a bounded initial state. The
initial state is set so that the perturbed region receives the inflow
$\omega^n$ at time $n$ $(|\omega|=1)$. From this expression, we compute the
scattering on the surface of $-1$ and $M$ and also compute the quantity how
quantum walker accumulates in the perturbed region; namely the energy of the
quantum walk, in the long time limit. We find a discontinuity of the energy
with respect to the frequency of the inflow.


Abstract:
  The primary mechanism governing the emergence of near-room-temperature
superconductivity in superhydrides is widely accepted to be the electron-phonon
interaction. If so, the temperature dependent resistance, R(T), in these
materials should obey the Bloch-Gr\"uneisen equation, where the power-law
exponent, p, should be equal to the exact integer value of p=5. On the other
hand, there is a well-established theoretical result that pure electron-magnon
interaction should be manifested by p=3, and p=2 is the value for pure
electron-electron interaction. Here we aimed to reveal the type of charge
carrier interaction in the layered transition metal dichalcogenides PdTe2,
high-entropy alloy (ScZrNb)0.65[RhPd]0.35, and highly-compressed elemental
boron and superhydrides H3S, LaHx, PrH9 and BaH12 by fitting the temperature
dependent resistance of these materials to the Bloch-Gr\"uneisen equation where
the power-law exponent, p, is a free-fitting parameter. In the result, we
showed that the high-entropy alloy (ScZrNb)0.65[RhPd]0.35 exhibited pure
electron-phonon mediated superconductivity with p = 4.9. Unexpectedly we
revealed that all studied superhydrides exhibit 1.8 < p < 3.2. This implies
that it is unlikely that the electron-phonon interaction is the primary
mechanism for the Cooper pairs formation in highly-compressed superhydrides and
alternative pairing mechanisms, for instance, the electron-magnon, the
electron-polaron, the electron-electron or other, should be considered as the
origin for the emergence of near-room-temperature superconductivity in these
compounds.


Abstract:
  A non-local hidden variables theory for non-relativisitic quantum theory is
presented, which gives a realist completion of quantum mechanics, in the sense
of a complete description of individual events. The proposed fundamental theory
is an extension of an energetic causal set theory, which assumes that time,
events, causal structure, momentum and energy are fundamental. But space and
the wave function are emergent.
  The beables of the theory are the views of the events, which are a subset of
their causal pasts. Thus, this theory asserts that the universe is a causal
network of events, which consists of partial views of itself as seen by looking
backwards from each event.
  The fundamental dynamics is based on an action whose potential energy is
proportional to the variety, which is a measure of the diversity of the views
of the events, while the kinetic energy is proportional to its rate of change.
The Schroedinger equation is derived to leading order in an expansion in
density of the events of the fundamental histories. To higher order, there are
computable corrections, non-linear in the wave function, from which new
physical effects may be predicted.


Abstract:
  Knowledge distillation (KD) is one of the most useful techniques for
light-weight neural networks. Although neural networks have a clear purpose of
embedding datasets into the low-dimensional space, the existing knowledge was
quite far from this purpose and provided only limited information. We argue
that good knowledge should be able to interpret the embedding procedure. This
paper proposes a method of generating interpretable embedding procedure (IEP)
knowledge based on principal component analysis, and distilling it based on a
message passing neural network. Experimental results show that the student
network trained by the proposed KD method improves 2.28% in the CIFAR100
dataset, which is higher performance than the state-of-the-art (SOTA) method.
We also demonstrate that the embedding procedure knowledge is interpretable via
visualization of the proposed KD process. The implemented code is available at
https://github.com/sseung0703/IEPKT.


Abstract:
  Financial disclosure analysis and Knowledge extraction is an important
financial analysis problem. Prevailing methods depend predominantly on
quantitative ratios and techniques, which suffer from limitations like window
dressing and past focus. Most of the information in a firm's financial
disclosures is in unstructured text and contains valuable information about its
health. Humans and machines fail to analyze it satisfactorily due to the
enormous volume and unstructured nature, respectively. Researchers have started
analyzing text content in disclosures recently. This paper covers the previous
work in unstructured data analysis in Finance and Accounting. It also explores
the state of art methods in computational linguistics and reviews the current
methodologies in Natural Language Processing (NLP). Specifically, it focuses on
research related to text source, linguistic attributes, firm attributes, and
mathematical models employed in the text analysis approach. This work
contributes to disclosure analysis methods by highlighting the limitations of
the current focus on sentiment metrics and highlighting broader future research
areas


Abstract:
  Electromagnetic energy backflow is a phenomenon occurring in regions where
the direction of the Poynting vector is opposite to that of the propagation of
the wave field. It is particularly remarkable in the nonparaxial regime and has
been exhibited in the focal region of sharply focused beams, for vector Bessel
beams, and vector-valued spatiotemporally localized waves. A detailed study is
undertaken of this phenomenon and the conditions for its appearance are
examined in detail in the case of a superposition of four plane waves in free
space, the simplest electromagnetic arrangement for the observation of negative
energy flow, as well as its comprehensive and transparent physical
interpretation. It is shown that the state of polarization of the constituent
components of the electromagnetic plane wave quartet determines whether energy
backflow takes place or not and what values the energy flow velocity assumes.
Depending on the polarization angles, the latter can assume any value from c
(the speed of light in vacuum) to -c in certain spatiotemporal regions of the
field.


Abstract:
  In this thesis, we present a flexible framework for specifying and
constructing operads which are suited to reasoning about network construction.
The data used to present these operads is called a \emph{network model}, a
monoidal variant of Joyal's combinatorial species. The construction of the
operad required that we develop a monoidal lift of the Grothendieck
construction. We then demonstrate how concepts like priority and dependency can
be represented in this framework. For the former, we generalize Green's graph
products of groups to the context of universal algebra. For the latter, we
examine the emergence of monoidal fibrations from the presence of catalysts in
Petri nets.


Abstract:
  A novel $\gamma$-ray supernova remnant (SNR) G206.9+2.3 is first reported in
this study. We arrived at this conclusion after analyzing 12.4 years of
observation data of the Fermi Large Area Telescope (Fermi-LAT). The photon flux
of the remnant was (1.19$\pm$0.59) 10$^{-9}$ cm$^{-2}$ s$^{-1}$, and its
power-law spectral index was 2.22$\pm$0.19 in the 0.2-500 GeV energy band.
Moreover, we found that the test statistic values of the global fit from the
four different energy bands were greater than 9. We identified that this was a
real $\gamma$-ray signal. Furthermore, we found that its GeV spatial location
was in good agreement with that of its radio band. Its spectral energy
distribution and light curve properties were similar to those of SNRs. We
suggest that the novel $\gamma$-ray source is a likely counterpart to SNR
G206.9+2.3. Consequently, we discuss its likely leptonic or hadronic origin.


Abstract:
  It is shown that the famous Allen -- Dynes asymtotic limit for
superconducting transition temperature in very strong coupling region
$T_{c}>\frac{1}{2\pi}\sqrt{\lambda}\Omega_0$ (where $\lambda\gg 1$ - is
Eliashberg - McMillan electron - phonon coupling constant and $\Omega_0$ - the
characteristic frequency of phonons) in antiadiabatic limit of Eliashberg
equations $\Omega_0/D\gg 1$ ($D\sim E_F$ is conduction band half-width and
$E_F$ is Fermi energy) is replaced by $T_c>(2\pi^4)^{-1/3}(\lambda
D\Omega_0^2)^{1/3}$, with the upper limit for $T_c$ given by
$T_c<\frac{2}{\pi^2}\lambda D$.


Abstract:
  While many works on Continual Learning have shown promising results for
mitigating catastrophic forgetting, they have relied on supervised training. To
successfully learn in a label-agnostic incremental setting, a model must
distinguish between learned and novel classes to properly include samples for
training. We introduce a novelty detection method that leverages network
confusion caused by training incoming data as a new class. We found that
incorporating a class-imbalance during this detection method substantially
enhances performance. The effectiveness of our approach is demonstrated across
a set of image classification benchmarks: MNIST, SVHN, CIFAR-10, CIFAR-100, and
CRIB.


Abstract:
  In a paper from 1973 R.D. Hill studied linear matrix maps
$\mathcal{L}:\mathbb{C}^{q \times q}\to\mathbb{C}^{n \times n}$ which map
Hermitian matrices to Hermitian matrices, or equivalently, preserve adjoints,
i.e., $\mathcal{L}(V^*)=\mathcal{L}(V)^*$, via representations of the form
\begin{equation*} \mathcal{L}(V)=\sum_{k,l=1}^m \mathbb{H}_{kl}\, A_l V
A_k^*,\quad V\in\mathbb{C}^{q \times q}, \end{equation*} for matrices
$A_1,\ldots,A_m \in\mathbb{C}^{n \times q}$ and continued his study of such
representations in later work, sometimes with co-authors, to completely
positive matrix maps and associated matrix reorderings. In this paper we expand
the study of such representations, referred to as Hill representations here, in
various directions. In particular, we describe which matrices $A_1,\ldots, A_m$
can appear in Hill representations (provided the number $m$ is minimal) and
determine the associated Hill matrix $\mathbb{H}=\left[\mathbb{H}_{kl}\right]$
explicitly. Also, we describe how different Hill representations of
$\mathcal{L}$ (again with $m$ minimal) are related and investigate further the
implication of $*$-linearity on the linear map $\mathcal{L}$.


Abstract:
  Seizures are one of the defining symptoms in patients with epilepsy, and due
to their unannounced occurrence, they can pose a severe risk for the individual
that suffers it. New research efforts are showing a promising future for the
prediction and preemption of imminent seizures, and with those efforts, a vast
and diverse set of features have been proposed for seizure prediction
algorithms. However, the data-driven discovery of nonsinusoidal waveforms for
seizure prediction is lacking in the literature, which is in stark contrast
with recent works that show the close connection between the waveform
morphology of neural oscillations and the physiology and pathophysiology of the
brain, and especially its use in effectively discriminating between normal and
abnormal oscillations in electrocorticographic (ECoG) recordings of epileptic
patients. Here, we explore a scalable, energy-guided waveform search strategy
on spatially-projected continuous multi-day ECoG data sets. Our work shows that
data-driven waveform learning methods have the potential to not only contribute
features with predictive power for seizure prediction, but also to facilitate
the discovery of oscillatory patterns that could contribute to our
understanding of the pathophysiology and etiology of seizures.


Abstract:
  We investigate the Lyapunov Exponents of a variation of Hodge structure which
has $G_2$ as geometric monodromy group, and discuss formulas for the sum of
positive Lyapunov Exponents of variations of Hodge structures of any weight.


Abstract:
  Affect preferences vary with user demographics, and tapping into demographic
information provides important cues about the users' language preferences. In
this paper, we utilize the user demographics, and propose EmpathBERT, a
demographic-aware framework for empathy prediction based on BERT. Through
several comparative experiments, we show that EmpathBERT surpasses traditional
machine learning and deep learning models, and illustrate the importance of
user demographics to predict empathy and distress in user responses to
stimulative news articles. We also highlight the importance of affect
information in the responses by developing affect-aware models to predict user
demographic attributes.


Abstract:
  Distantly supervised (DS) relation extraction (RE) has attracted much
attention in the past few years as it can utilize large-scale auto-labeled
data. However, its evaluation has long been a problem: previous works either
took costly and inconsistent methods to manually examine a small sample of
model predictions, or directly test models on auto-labeled data -- which, by
our check, produce as much as 53% wrong labels at the entity pair level in the
popular NYT10 dataset. This problem has not only led to inaccurate evaluation,
but also made it hard to understand where we are and what's left to improve in
the research of DS-RE. To evaluate DS-RE models in a more credible way, we
build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20,
and thoroughly evaluate several competitive models, especially the latest
pre-trained ones. The experimental results show that the manual evaluation can
indicate very different conclusions from automatic ones, especially some
unexpected observations, e.g., pre-trained models can achieve dominating
performance while being more susceptible to false-positives compared to
previous methods. We hope that both our manual test sets and novel observations
can help advance future DS-RE research.


Abstract:
  We use a three-flavor Nambu--Jona-Lasinio model to study the thermodynamics
of strange quark matter under a strong magnetic field. The model Lagrangian
features flavor SU(3) four-quark interactions and six-quark interactions that
break the UA(1) symmetry. We incorporate thermomagnetic effects in the
four-quark coupling. The model predicts magnetic catalysis at low temperatures
and inverse magnetic catalysis at temperatures close to the pseudocritical
temperature of the QCD transition, in agreement with lattice QCD results. We
compute the pressure at the mean field level and obtain the magnetization of
quark matter. We adopt the recently proposed vacuum magnetic regularization
(VMR) scheme, in that divergent quark mass independent contributions are not
subtracted, thereby avoiding unphysical results for the magnetization. We
devote special attention to the renormalized magnetization, a projected
quantity that allows for direct comparisons with lattice QCD simulations. Our
results are in very good agreement with lattice data indicating a paramagnetic
behavior for quark matter.


Abstract:
  We present ECLIPSE (Efficient Cmb poLarization and Intensity Power Spectra
Estimator), an optimized implementation of the Quadratic Maximum Likelihood
(QML) method for the estimation of the power spectra of the Cosmic Microwave
Background (CMB). This approach allows one to reduce significantly the
computational costs associated to this technique, allowing to estimate the
power spectra up to higher multipoles than previous implementations. In
particular, for a resolution of $N_\mathrm{side}=64$, $\ell_{\mathrm{max}}=192$
and a typical Galactic mask, the number of operations can be reduced by
approximately a factor of 1000 in a full analysis including intensity and
polarization with respect to an efficient direct implementation of the method.
In addition, if one is interested in studying only polarization, it is possible
to obtain the power spectra of the E and B modes with a further reduction of
computational resources without degrading the results. We also show that for
experiments observing a small fraction of the sky, the Fisher matrix becomes
singular and, in this case, the standard QML can not be applied. To solve this
problem, we have developed a binned version of the method that is unbiased and
of minimum variance. We also test the robustness of the QML estimator when the
assumed fiducial model differs from that of the sky and show the performance of
an iterative approach. Finally, we present a comparison of the results obtained
by QML and a pseudo-$C_{\ell}$ estimator (NaMaster) for a next-generation
satellite, showing that, as expected, QML produces significantly smaller errors
at low multipoles. The ECLIPSE fast QML code developed in this work will be
made publicly available.


Abstract:
  Leveraging the framework of Optimal Transport, we introduce a new family of
generative autoencoders with a learnable prior, called Symmetric Wasserstein
Autoencoders (SWAEs). We propose to symmetrically match the joint distributions
of the observed data and the latent representation induced by the encoder and
the decoder. The resulting algorithm jointly optimizes the modelling losses in
both the data and the latent spaces with the loss in the data space leading to
the denoising effect. With the symmetric treatment of the data and the latent
representation, the algorithm implicitly preserves the local structure of the
data in the latent space. To further improve the quality of the latent
representation, we incorporate a reconstruction loss into the objective, which
significantly benefits both the generation and reconstruction. We empirically
show the superior performance of SWAEs over the state-of-the-art generative
autoencoders in terms of classification, reconstruction, and generation.


Abstract:
  Modelers use automatic differentiation (AD) of computation graphs to
implement complex Deep Learning models without defining gradient computations.
Stochastic AD extends AD to stochastic computation graphs with sampling steps,
which arise when modelers handle the intractable expectations common in
Reinforcement Learning and Variational Inference. However, current methods for
stochastic AD are limited: They are either only applicable to continuous random
variables and differentiable functions, or can only use simple but high
variance score-function estimators. To overcome these limitations, we introduce
Storchastic, a new framework for AD of stochastic computation graphs.
Storchastic allows the modeler to choose from a wide variety of gradient
estimation methods at each sampling step, to optimally reduce the variance of
the gradient estimates. Furthermore, Storchastic is provably unbiased for
estimation of any-order gradients, and generalizes variance reduction
techniques to higher-order gradient estimates. Finally, we implement
Storchastic as a PyTorch library at https://github.com/HEmile/storchastic.


Abstract:
  The Gaia satellite will observe the positions and velocities of over a
billion Milky Way stars. In the early data releases, the majority of observed
stars do not have complete 6D phase-space information. In this Letter, we
demonstrate the ability to infer the missing line-of-sight velocities until
more spectroscopic observations become available. We utilize a novel neural
network architecture that, after being trained on a subset of data with
complete phase-space information, takes in a star's 5D astrometry (angular
coordinates, proper motions, and parallax) and outputs a predicted
line-of-sight velocity with an associated uncertainty. Working with a mock Gaia
catalog, we show that the network can successfully recover the distributions
and correlations of each velocity component for stars that fall within ~5 kpc
of the Sun. We also demonstrate that the network can accurately reconstruct the
velocity distribution of a kinematic substructure in the stellar halo that is
spatially uniform, even when it comprises a small fraction of the total star
count.


Abstract:
  Soiling, which consists of dust, dirt and particles accumulated on the
surface of conventional or concentrator photovoltaic modules, absorbs,
scatters, and reflects part of the incoming sunlight. Therefore, it reduces the
amount of energy converted by the semiconductor solar cells. This work focuses
on the effect of soiling on the spectral performance of multi-junction (MJ)
cells, widely used in concentrator photovoltaic (CPV) applications. Novel
indexes, useful to quantify the spectral impact of soiling are introduced, and
their meanings are discussed. The results of a one-year experimental
investigation conducted in Spain are presented and are used to discuss how
soiling impacts each of the subcells of a MJ cell, as well as the cell
current-matching. Results show that soiling affects the current balance among
the junctions, i.e. the transmittance losses have found to be around 4% higher
in the top than in the middle subcell. The spectral nature of soiling has
demonstrated to increase the annual spectral losses of around 2%. Ideal
conditions for the mitigation of soiling are also discussed and found to be in
blue-rich environments, where the higher light intensity at the shorter
wavelengths can limit the impact of soiling on the overall production of the
CPV system.


Abstract:
  We formulate sufficient conditions for the strong integrability of dressing
cosets. We provide several sigma-model backgrounds solving those conditions,
some of them are new and some of them were not so far formulated as the
dressing cosets. The new models are based on the Drinfeld doubles having the
structure of higher order jet bundles of quadratic Lie groups.


Abstract:
  With electric power systems becoming more compact and increasingly powerful,
the relevance of thermal stress especially during overload operation is
expected to increase ceaselessly. Whenever critical temperatures cannot be
measured economically on a sensor base, a thermal model lends itself to
estimate those unknown quantities. Thermal models for electric power systems
are usually required to be both, real-time capable and of high estimation
accuracy. Moreover, ease of implementation and time to production play an
increasingly important role. In this work, the thermal neural network (TNN) is
introduced, which unifies both, consolidated knowledge in the form of
heat-transfer-based lumped-parameter models, and data-driven nonlinear function
approximation with supervised machine learning. A quasi-linear
parameter-varying system is identified solely from empirical data, where
relationships between scheduling variables and system matrices are inferred
statistically and automatically. At the same time, a TNN has physically
interpretable states through its state-space representation, is end-to-end
trainable -- similar to deep learning models -- with automatic differentiation,
and requires no material, geometry, nor expert knowledge for its design.
Experiments on an electric motor data set show that a TNN achieves higher
temperature estimation accuracies than previous white-/grey- or black-box
models with a mean squared error of $3.18~\text{K}^2$ and a worst-case error of
$5.84~\text{K}$ at 64 model parameters.


Abstract:
  Recently, general salient object detection (SOD) has made great progress with
the rapid development of deep neural networks. However, task-aware SOD has
hardly been studied due to the lack of task-specific datasets. In this paper,
we construct a driving task-oriented dataset where pixel-level masks of salient
objects have been annotated. Comparing with general SOD datasets, we find that
the cross-domain knowledge difference and task-specific scene gap are two main
challenges to focus the salient objects when driving. Inspired by these
findings, we proposed a baseline model for the driving task-aware SOD via a
knowledge transfer convolutional neural network. In this network, we construct
an attentionbased knowledge transfer module to make up the knowledge
difference. In addition, an efficient boundary-aware feature decoding module is
introduced to perform fine feature decoding for objects in the complex
task-specific scenes. The whole network integrates the knowledge transfer and
feature decoding modules in a progressive manner. Experiments show that the
proposed dataset is very challenging, and the proposed method outperforms 12
state-of-the-art methods on the dataset, which facilitates the development of
task-aware SOD.


Abstract:
  Complex network analyses have provided clues to improve power-grid stability
with the help of numerical models. The high computational cost of numerical
simulations, however, has inhibited the approach, especially when it deals with
the dynamic properties of power grids such as frequency synchronization. In
this study, we investigate machine learning techniques to estimate the
stability of power-grid synchronization. We test three different machine
learning algorithms -- random forest, support vector machine, and artificial
neural network -- training them with two different types of synthetic power
grids consisting of homogeneous and heterogeneous input-power distribution,
respectively. We find that the three machine learning models better predict the
synchronization stability of power-grid nodes when they are trained with the
heterogeneous input-power distribution than the homogeneous one. With the
real-world power grids of Great Britain, Spain, France, and Germany, we also
demonstrate that the machine learning algorithms trained on synthetic power
grids are transferable to the stability prediction of the real-world power
grids, which implies the prospective applicability of machine learning
techniques on power-grid studies.


Abstract:
  We study a novel dark matter production mechanism based on the freeze-in
through semi-production, i.e. the inverse semi-annihilation processes. A
peculiar feature of this scenario is that the production rate is suppressed by
a small initial abundance of dark matter and consequently creating the observed
abundance requires much larger coupling values than for the usual freeze-in. We
provide a concrete example model exhibiting such production mechanism and study
it in detail, extending the standard formalism to include the evolution of dark
matter temperature alongside its number density and discuss the importance of
this improved treatment. Finally, we confront the relic density constraint with
the limits and prospects for the dark matter indirect detection searches. We
show that, even if it was never in full thermal equilibrium in the early
Universe, dark matter could, nevertheless, have strong enough present-day
annihilation cross section to lead to observable signals.


Abstract:
  The Spectrometer/Telescope for Imaging X-rays (STIX) is the HXR instrument
onboard Solar Orbiter designed to observe solar flares over a broad range of
flare sizes, between 4-150 keV. We report the first STIX observations of
microflares recorded during the instrument commissioning phase in order to
investigate the STIX performance at its detection limit. This first result
paper focuses on the temporal and spectral evolution of STIX microflares
occuring in the AR12765 in June 2020, and compares the STIX measurements with
GOES/XRS, SDO/AIA, and Hinode/XRT. For the observed microflares of the GOES A
and B class, the STIX peak time at lowest energies is located in the impulsive
phase of the flares, well before the GOES peak time. Such a behavior can either
be explained by the higher sensitivity of STIX to higher temperatures compared
to GOES, or due to the existence of a nonthermal component reaching down to low
energies. The interpretation is inconclusive due to limited counting statistics
for all but the largest flare in our sample. For this largest flare, the
low-energy peak time is clearly due to thermal emission, and the nonthermal
component seen at higher energies occurs even earlier. This suggests that the
classic thermal explanation might also be favored for the majority of the
smaller flares. In combination with EUV and SXR observations, STIX corroborates
earlier findings that an isothermal assumption is of limited validity. Future
diagnostic efforts should focus on multi-wavelength studies to derive
differential emission measure distributions over a wide range of temperatures
to accurately describe the energetics of solar flares. Commissioning
observations confirm that STIX is working as designed. As a rule of thumb, STIX
detects flares as small as the GOES A class. For flares above the GOES B class,
detailed spectral and imaging analyses can be performed.


Abstract:
  On a class of four-dimensional Lifshitz spacetimes with critical exponent
$z=2$, including a hyperbolic and a spherical Lifshitz topological black hole,
we consider a real Klein-Gordon field. Using a mode-decomposition, we split the
equation of motion into a radial and into an angular component. As first step,
we discuss under which conditions on the underlying parameters, we can impose
to the radial equation boundary conditions of Robin type and whether bound
state solutions do occur. Subsequently, we show that, whenever bound states are
absent, one can associate to each admissible boundary condition a ground and a
KMS state whose associated two-point correlation function is of local Hadamard
form.


Abstract:
  Occlusions pose a significant challenge to optical flow algorithms that rely
on local evidences. We consider an occluded point to be one that is imaged in
the first frame but not in the next, a slight overloading of the standard
definition since it also includes points that move out-of-frame. Estimating the
motion of these points is extremely difficult, particularly in the two-frame
setting. Previous work relies on CNNs to learn occlusions, without much
success, or requires multiple frames to reason about occlusions using temporal
smoothness. In this paper, we argue that the occlusion problem can be better
solved in the two-frame case by modelling image self-similarities. We introduce
a global motion aggregation module, a transformer-based approach to find
long-range dependencies between pixels in the first image, and perform global
aggregation on the corresponding motion features. We demonstrate that the
optical flow estimates in the occluded regions can be significantly improved
without damaging the performance in non-occluded regions. This approach obtains
new state-of-the-art results on the challenging Sintel dataset, improving the
average end-point error by 13.6% on Sintel Final and 13.7% on Sintel Clean. At
the time of submission, our method ranks first on these benchmarks among all
published and unpublished approaches. Code is available at
https://github.com/zacjiang/GMA


Abstract:
  The Alcock-Paczynski (AP) effect is a geometrical distortion in
three-dimensional observed galaxy statistics. In anticipation of precision
cosmology based on ongoing and upcoming all-sky galaxy surveys, we build an
efficient method to compute the AP-distorted correlations of galaxy number
density and peculiar velocity fields for any larger angular scale not relying
on the conventionally used plane-parallel (PP) approximation. Here, instead of
the usual Legendre polynomial basis, the correlation functions are decomposed
using tripolar spherical harmonic basis; hence, characteristic angular
dependence due to the wide-angle AP effect can be rigorously captured. By means
of this, we demonstrate the computation of the AP-distorted correlations over
the various scales. Comparing our results with the PP-limit ones, we confirm
that the errors due to the PP approximation become more remarkable as the
visual angle of separation between target galaxies, $\Theta$, enlarges, and
especially for the density auto correlation, the error exceeds $10\%$ when
$\Theta \gtrsim 30^\circ$. This highlights the importance of the analysis
beyond the PP approximation.


Abstract:
  We study features of the 3D anisotropy of galactic cosmic rays (GCR) for
1965-2014. We analyze the 27-day variations of the 2D GCR anisotropy in the
ecliptic plane, and the north-south anisotropy normal to the ecliptic plane. We
study the dependence of the 27-day variation of the 3D GCR anisotropy on the
solar cycle and solar magnetic cycle. We demonstrate that the 27-day variations
of the GCR intensity and anisotropy can be used as an important tool to study
solar wind, solar activity and heliosphere. We use the components of the 3D GCR
anisotropy found based on hourly data of neutron monitors (NMs) and muon
telescopes (MTs) using the harmonic analyses and spectrographic methods. We
correct 2D diurnal variation of the GCR intensity for the influence of the
Earth magnetic field. We derive the north-south component of the GCR anisotropy
based on the GG index calculated as the difference in GCR intensities of Nagoya
multidirectional MTs. We show that behavior of the 27-variation of the 3D
anisotropy verifies an existence of a stable long-lived active heliolongitudes
on the sun. This finding illustrates usefulness of the 27-day variation of the
GCR anisotropy as a unique proxy to study solar wind, solar activity and
heliosphere. We distinguish a tendency of the 22-year changes of the amplitudes
of the 27-day variation of the 2D anisotropy connected with the solar magnetic
cycle. We demonstrate that the amplitudes of the 27-day variation of the
north-south component of the anisotropy vary upon the 11 year solar cycle,
however, a dependence of the solar magnetic polarity hardly can be recognized.
We show that the 27-day recurrences of the $GG$ index and At component are in a
high positive correlation, and both are highly correlated with By component of
the heliospheric magnetic field.


Abstract:
  A text to image generation (T2I) model aims to generate photo-realistic
images which are semantically consistent with the text descriptions. Built upon
the recent advances in generative adversarial networks (GANs), existing T2I
models have made great progress. However, a close inspection of their generated
images reveals two major limitations: (1) The condition batch normalization
methods are applied on the whole image feature maps equally, ignoring the local
semantics; (2) The text encoder is fixed during training, which should be
trained with the image generator jointly to learn better text representations
for image generation. To address these limitations, we propose a novel
framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion
so that the text encoder can exploit better text information. Concretely, we
introduce a novel Semantic-Spatial Aware Convolution Network, which (1) learns
semantic-adaptive transformation conditioned on text to effectively fuse text
features and image features, and (2) learns a mask map in a weakly-supervised
way that depends on the current text-image fusion process in order to guide the
transformation spatially. Experiments on the challenging COCO and CUB bird
datasets demonstrate the advantage of our method over the recent
state-of-the-art approaches, regarding both visual fidelity and alignment with
input text description. Code is available at
https://github.com/wtliao/text2image.


Abstract:
  We introduce StockBabble, a conversational agent designed to support
understanding and engagement with the stock market. StockBabble's value and
novelty is in its ability to empower retail investors -- many of which may be
new to investing -- and supplement their informational needs using a
user-friendly agent. Users have the ability to query information on companies
to retrieve a general and financial overview of a stock, including accessing
the latest news and trading recommendations. They can also request charts which
contain live prices and technical investment indicators, and add shares to a
personal portfolio to allow performance monitoring over time. To evaluate our
agent's potential, we conducted a user study with 15 participants. In total,
73% (11/15) of respondents said that they felt more confident in investing
after using StockBabble, and all 15 would consider recommending it to others.
These results are encouraging and suggest a wider appeal for such agents.
Moreover, we believe this research can help to inform the design and
development of future intelligent, financial personal assistants.


Abstract:
  In many real-world scenarios, a team of agents coordinate with each other to
compete against an opponent. The challenge of solving this type of game is that
the team's joint action space grows exponentially with the number of agents,
which results in the inefficiency of the existing algorithms, e.g.,
Counterfactual Regret Minimization (CFR). To address this problem, we propose a
new framework of CFR: CFR-MIX. Firstly, we propose a new strategy
representation that represents a joint action strategy using individual
strategies of all agents and a consistency relationship to maintain the
cooperation between agents. To compute the equilibrium with individual
strategies under the CFR framework, we transform the consistency relationship
between strategies to the consistency relationship between the cumulative
regret values. Furthermore, we propose a novel decomposition method over
cumulative regret values to guarantee the consistency relationship between the
cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which
employs a mixing layer to estimate cumulative regret values of joint actions as
a non-linear combination of cumulative regret values of individual actions.
Experimental results show that CFR-MIX outperforms existing algorithms on
various games significantly.


Abstract:
  We use IGETS absolute gravitational acceleration measurement data to study
the gravitational acceleration variance. The relative variance of $\delta g /g$
in 22 years is less than $4\times 10^{-8}$. Since $\delta G /G\lessapprox\delta
g /g $, this implies the relative variance of Newtonian constant is less than
$3\times 10^{-9}$ based on an sine-like oscillation hypothesis. This limit is
at least 4 orders of magnitude better than the existing $G$ measurements. The
scattered values of reported $G$ measurements coming from different experiments
are most probably coming from systematic errors associated with these
experiments and not due to intrinsic time variation of $G$. We also find that
$\dot{ G} /G<5.61\times 10^{-10} \text{yr}^{-1}$ based on a linear hypothesis.
This is the best terrestrial result so far.


Abstract:
  Autonomous mobile robots are usually faced with challenging situations when
driving in complex environments. Namely, they have to recognize the static and
dynamic obstacles, plan the driving path and execute their motion. For
addressing the issue of perception and path planning, in this paper, we
introduce OctoPath , which is an encoder-decoder deep neural network, trained
in a self-supervised manner to predict the local optimal trajectory for the
ego-vehicle. Using the discretization provided by a 3D octree environment
model, our approach reformulates trajectory prediction as a classification
problem with a configurable resolution. During training, OctoPath minimizes the
error between the predicted and the manually driven trajectories in a given
training dataset. This allows us to avoid the pitfall of regression-based
trajectory estimation, in which there is an infinite state space for the output
trajectory points. Environment sensing is performed using a 40-channel
mechanical LiDAR sensor, fused with an inertial measurement unit and wheels
odometry for state estimation. The experiments are performed both in simulation
and real-life, using our own developed GridSim simulator and RovisLab's
Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath
in different driving scenarios, both indoor and outdoor, while benchmarking our
system against a baseline hybrid A-Star algorithm and a regression-based
supervised learning method, as well as against a CNN learning-based optimal
path planning method.


Abstract:
  The amount, size, and complexity of astronomical data-sets and databases are
growing rapidly in the last decades, due to new technologies and dedicated
survey telescopes. Besides dealing with poly-structured and complex data,
sparse data has become a field of growing scientific interest. A specific field
of Astroinformatics research is the estimation of redshifts of extra-galactic
sources by using sparse photometric observations. Many techniques have been
developed to produce those estimates with increasing precision. In recent
years, models have been favored which instead of providing a point estimate
only, are able to generate probabilistic density functions (PDFs) in order to
characterize and quantify the uncertainties of their estimates.
  Crucial to the development of those models is a proper, mathematically
principled way to evaluate and characterize their performances, based on
scoring functions as well as on tools for assessing calibration. Still, in
literature inappropriate methods are being used to express the quality of the
estimates that are often not sufficient and can potentially generate misleading
interpretations. In this work we summarize how to correctly evaluate errors and
forecast quality when dealing with PDFs. We describe the use of the
log-likelihood, the continuous ranked probability score (CRPS) and the
probability integral transform (PIT) to characterize the calibration as well as
the sharpness of predicted PDFs. We present what we achieved when using proper
scoring rules to train deep neural networks as well as to evaluate the model
estimates and how this work led from well calibrated redshift estimates to
improvements in probabilistic weather forecasting. The presented work is an
example of interdisciplinarity in data-science and illustrates how methods can
help to bridge gaps between different fields of application.


Abstract:
  We extend the single-stage stellarator coil design approach for
quasi-symmetry on axis from [Giuliani et al, 2020] to additionally take into
account coil manufacturing errors. By modeling coil errors independently from
the coil discretization, we have the flexibility to consider realistic forms of
coil errors. The corresponding stochastic optimization problems are formulated
using a risk-neutral approach and risk-averse approaches. We present an
efficient, gradient-based descent algorithm which relies on analytical
derivatives to solve these problems. In a comprehensive numerical study, we
compare the coil designs resulting from deterministic and risk-neutral
stochastic optimization and find that the risk-neutral formulation results in
more robust configurations and reduces the number of local minima of the
optimization problem. We also compare deterministic and risk-neutral approaches
in terms of quasi-symmetry on and away from the magnetic axis, and in terms of
the confinement of particles released close to the axis. Finally, we show that
for the optimization problems we consider, a risk-averse objective using the
Conditional Value-at-Risk leads to results which are similar to the
risk-neutral objective.


Abstract:
  Alinhac solved a long-standing open problem in 2001 and established that
quasilinear wave equations in two space dimensions with quadratic null
nonlinearities admit global-in-time solutions, provided that the initial data
are compactly supported and sufficiently small in Sobolev norm. In this work,
Alinhac obtained an upper bound with polynomial growth in time for the
top-order energy of the solutions. A natural question then arises whether the
time-growth is a true phenomena, despite the possible conservation of basic
energy. Analogous problems are also of central importance for Schr\"odinger
equations and the incompressible Euler equations in two space dimensions, as
studied by Bourgain, Colliander-Keel-Staffilani-Takaoka-Tao, Kiselev-Sverak,
and others. In the present paper, we establish that the top-order energy of the
solutions in Alinhac theorem remains globally bounded in time, which is
opposite to Alinhac's blowup-at-infinity conjecture.


Abstract:
  We develop a model for lead-time quotation in a Markovian make-to-order
production or service system with strategic customers who exhibit risk
aversion. Based on a CARA utility function of their net benefit, customers make
individual decisions to join the system or balk by observing the state of the
queue. The decisions of arriving customers result in a symmetric join/balk
game. Regarding the firm's strategy, the provider announces a lead-time
quotation for each state and a respective balking threshold. There is also a
fixed entrance fee and compensation rate for the part of a customer' delay
exceeding the quoted lead-time. Moreover, we consider the problem from the
point of view of a social optimizer who maximizes the total net benefit of the
system.
  We analyze the provider's and social optimizer's maximization problems and we
consider two cases regarding the class of lead-time quotation policies, i.e.,
dynamic and single. We identify the optimal entrance thresholds in each case.
Finally, through computational experiments we quantify the effect of risk
aversion on the profits and the degree of flexibility that the compensation
policy offers. It is shown that the detrimental effects of risk aversion can be
addressed more efficiently for the provider's problem compared to the social
optimizer's one. Furthermore, the profit loss when setting a single lead-time
quote is generally small compared to the optimal dynamic quotation policy.


Abstract:
  Mastitis is a billion dollar health problem for the modern dairy industry,
with implications for antibiotic resistance. The use of AI techniques to
identify the early onset of this disease, thus has significant implications for
the sustainability of this agricultural sector. Current approaches to treating
mastitis involve antibiotics and this practice is coming under ever increasing
scrutiny. Using machine learning models to identify cows at risk of developing
mastitis and applying targeted treatment regimes to only those animals promotes
a more sustainable approach. Incorrect predictions from such models, however,
can lead to monetary losses, unnecessary use of antibiotics, and even the
premature death of animals, so it is important to generate compelling
explanations for predictions to build trust with users and to better support
their decision making. In this paper we demonstrate a system developed to
predict mastitis infections in cows and provide explanations of these
predictions using counterfactuals. We demonstrate the system and describe the
engagement with farmers undertaken to build it.


Abstract:
  A dessin d'enfant, or dessin, is a bicolored graph embedded into a Riemann
surface, and the monodromy group is an algebraic invariant of the dessin
generated by rotations of edges about black and white vertices. A rational
billiards surface is a two dimensional surface that allows one to view the path
of a billiards ball as a continuous path. In this paper, we classify the
monodromy groups of dessins associated to rational triangular billiards
surfaces.


Abstract:
  We study analytically the two dimensional deformed bosonic oscillator
equations for charged particles (both spin 0 and spin 1 particles) subject to
the effect of a uniform magnetic field. We consider the presence of a minimal
uncertainty in momentum caused by the Anti-deSitter model and we use the
Nikiforov-Uvarov method to solve the system. The exact energy eigenvalues and
the corresponding wave functions are analytically obtained for both
Klein-Gordon and scalar Duffin-Kemmer-Petiau cases. For spin 1 DKP case, we
deduce the behaviour of the DKP equation and write the non-relativistic
energies where we show the fundamental role of the spin in this case. Finally,
we study the thermodynamic properties of the system.


Abstract:
  In this paper, we present a systematic investigation on simple inverse seesaw
models for neutrino masses and flavor mixing based on the modular $S^{}_4$
symmetry. Two right-handed neutrinos and three extra fermion singlets are
introduced to account for light neutrino masses through the inverse seesaw
mechanism, and to provide a keV-mass sterile neutrino as the candidate for warm
dark matter in our Universe. Considering all possible modular forms with
weights no larger than four, we obtain twelve models, among which we find one
is in excellent agreement with the observed lepton mass spectra and flavor
mixing. Moreover, we explore the allowed range of the sterile neutrino mass and
mixing angles, by taking into account the direct search of $X$-ray line and the
Lyman-$\alpha$ observations. The model predictions for neutrino mixing
parameters and the dark matter abundance will be readily testable in future
neutrino oscillation experiments and cosmological observations.


Abstract:
  We consider a stochastic individual based model where each predator searches
during a random time and then manipulates its prey or rests. The time
distributions may be non-exponential. An age structure allows to describe these
interactions and get a Markovian setting. The process is characterized by a
measure-valued stochastic differential equation. We prove averaging results in
this infinite dimensional setting and get the convergence of the slow-fast
macroscopic prey predator process to a two dimensional dynamical system. We
recover classical functional responses. We also get new forms arising in
particular when births and deaths of predators are affected by the lack of
food.


Abstract:
  In recent years, with the explosive growth of visual sensors and a large
number of related video applications in Internet of Things (IoT), massive video
data is generated by IoT devices. Since the volume of video data is far greater
than traditional data in IoT, it is challenging to ensure high Quality of
Service (QoS) for video uplinking in IoT. To address this challenge, we
integrate non-orthogonal multiple access (NOMA) and scalable video coding (SVC)
in IoT. To improve the video quality, we formulate a power allocation problem
to maximize the average QoS in the proposed integrated system. Due to that the
problem is non-convex, we transform it into a monotonic problem based on its
hidden monotonicity. Then a power allocation algorithm based on polyblock outer
approximation is proposed to solve the problem effectively. Finally, simulation
results demonstrate that the proposed algorithm outperforms existing OMA and
NOMA based schemes for video uplinking in IoT in terms of QoS and energy
efficiency.


Abstract:
  Recent experimental measurements of light absorption in few-layer black
phosphorus (BP) reveal a series of high and sharp peaks, interspersed by pairs
of lower and broader features. Here, we propose a theoretical model for these
excitonic states in few-layer black phosphorus (BP) within a continuum approach
for the in-plane degrees of freedom and a tight-binding approximation that
accounts for inter-layer couplings. This yields excitonic transitions between
different combinations of the sub-bands created by the coupled BP layers, which
leads to a series of high and low oscillator strength excitonic states,
consistent with the experimentally observed bright and dark exciton peaks,
respectively. The main characteristics of such sub-band exciton states, as well
as the possibility to control their energies and oscillator strengths via
applied electric and magnetic fields, are discussed, towards a full
understanding of the excitonic spectrum of few-layer BP and its tunability.


Abstract:
  In this paper, we have investigated a bulk viscous anisotropic Universe and
constrained its model parameters with recent $H(z)$ and Pantheon compilation
data. Using cosmic chronometric technique, we estimate the present value of
Hubble's constant as $H_{0} = 69.39 \pm 1.54~km~s^{-1}Mpc^{-1}$, $70.016 \pm
1.65~km~s^{-1}Mpc^{-1}$ and $69.36 \pm 1.42~km~s^{-1}Mpc^{-1}$ by bounding our
derived model with recent $H(z)$ data, Pantheon and joint $H(z)$ and Pantheon
data respectively. The present age of the Universe is specified as $t_0=
0.9796H_0^{-1}\sim 13.79$ Gyrs. The model favours a transitioning Universe with
the transition red-shift as $z_{t} = 0.73$. We have reconstructed the jerk
parameter using the observational data sets. From the analysis of the jerk
parameter, it is observed that, our derived model shows a marginal departure
from the concordance $\Lambda$CDM model.


Abstract:
  An one-parameter regularization freedom of the Hamiltonian constraint for
loop quantum gravity is analyzed. The corresponding spatially flat, homogenous
and isotropic model includes the two well-known models of loop quantum
cosmology as special cases. The quantum bounce nature is tenable in the
generalized cases. For positive value of the regularization parameter, the
effective Hamiltonian leads to an asymptotic de-Sitter branch of the Universe
connecting to the standard Friedmann branch by the quantum bounce. Remarkably,
by suitably choosing the value of the regularization parameter, the
observational cosmological constant can emerge at large volume limit from the
effect of quantum gravity, and the effective Newtonian constant satisfies the
experimental restrictions in the meantime.