Abstract:
  Deep neural networks have been shown to be vulnerable to adversarial examples
deliberately constructed to misclassify victim models. As most adversarial
examples have restricted their perturbations to $L_{p}$-norm, existing defense
methods have focused on these types of perturbations and less attention has
been paid to unrestricted adversarial examples; which can create more realistic
attacks, able to deceive models without affecting human predictions. To address
this problem, the proposed adversarial attack generates an unrestricted
adversarial example with a limited number of parameters. The attack selects
three points on the input image and based on their locations transforms the
image into an adversarial example. By limiting the range of movement and
location of these three points and using a discriminatory network, the proposed
unrestricted adversarial example preserves the image appearance. Experimental
results show that the proposed adversarial examples obtain an average success
rate of 93.5% in terms of human evaluation on the MNIST and SVHN datasets. It
also reduces the model accuracy by an average of 73% on six datasets MNIST,
FMNIST, SVHN, CIFAR10, CIFAR100, and ImageNet. It should be noted that, in the
case of attacks, lower accuracy in the victim model denotes a more successful
attack. The adversarial train of the attack also improves model robustness
against a randomly transformed image.


Abstract:
  Specifying reward functions for robots that operate in environments without a
natural reward signal can be challenging, and incorrectly specified rewards can
incentivise degenerate or dangerous behavior. A promising alternative to
manually specifying reward functions is to enable robots to infer them from
human feedback, like demonstrations or corrections. To interpret this feedback,
robots treat as approximately optimal a choice the person makes from a choice
set, like the set of possible trajectories they could have demonstrated or
possible corrections they could have made. In this work, we introduce the idea
that the choice set itself might be difficult to specify, and analyze choice
set misspecification: what happens as the robot makes incorrect assumptions
about the set of choices from which the human selects their feedback. We
propose a classification of different kinds of choice set misspecification, and
show that these different classes lead to meaningful differences in the
inferred reward and resulting performance. While we would normally expect
misspecification to hurt, we find that certain kinds of misspecification are
neither helpful nor harmful (in expectation). However, in other situations,
misspecification can be extremely harmful, leading the robot to believe the
opposite of what it should believe. We hope our results will allow for better
prediction and response to the effects of misspecification in real-world reward
inference.


Abstract:
  Designing provably efficient algorithms with general function approximation
is an important open problem in reinforcement learning. Recently, Wang et
al.~[2020c] establish a value-based algorithm with general function
approximation that enjoys
$\widetilde{O}(\mathrm{poly}(dH)\sqrt{K})$\footnote{Throughout the paper, we
use $\widetilde{O}(\cdot)$ to suppress logarithm factors. } regret bound, where
$d$ depends on the complexity of the function class, $H$ is the planning
horizon, and $K$ is the total number of episodes. However, their algorithm
requires $\Omega(K)$ computation time per round, rendering the algorithm
inefficient for practical use. In this paper, by applying online sub-sampling
techniques, we develop an algorithm that takes
$\widetilde{O}(\mathrm{poly}(dH))$ computation time per round on average, and
enjoys nearly the same regret bound. Furthermore, the algorithm achieves low
switching cost, i.e., it changes the policy only
$\widetilde{O}(\mathrm{poly}(dH))$ times during its execution, making it
appealing to be implemented in real-life scenarios. Moreover, by using an
upper-confidence based exploration-driven reward function, the algorithm
provably explores the environment in the reward-free setting. In particular,
after $\widetilde{O}(\mathrm{poly}(dH))/\epsilon^2$ rounds of exploration, the
algorithm outputs an $\epsilon$-optimal policy for any given reward function.


Abstract:
  We report new branching fraction measurements for 199 UV and optical
transitions of Hf II. These transitions range in wavelength (wavenumber) from
2068- 6584 A (48322-15183 cm-1) and originate in 17 odd-parity upper levels
ranging in energy from 38578-53227 cm-1. The branching fractions are combined
with radiative lifetimes reported in an earlier study to produce a set of
transition probabilities and log(gf) values with accuracy ranging from 5-25%.
Comparison is made to transition probabilities from the literature where such
data exist. We use these new transition probabilities to derive improved Hf
abundances in two metal-poor stars. HD 196944 is enhanced in s-process
elements, and we derive log epsilon (Hf) = -0.72 +/- 0.03 (sigma = 0.09) from
12 Hf II lines. HD 222925 is enhanced in r-process elements, and we derive log
epsilon (Hf) = 0.32 +/- 0.03 (sigma = 0.11) from 20 Hf II lines. These
measurements greatly expand the number of potentially useful Hf II lines for
analysis in UV and optical spectra.


Abstract:
  Neural cryptography is the application of artificial neural networks in the
subject of cryptography. The functionality of this solution is based on a tree
parity machine. It uses artificial neural networks to perform secure key
exchange between network entities. This article proposes improvements to the
synchronization of two tree parity machines. The improvement is based on
learning artificial neural network using input vectors which have a wider range
of values than binary ones. As a result, the duration of the synchronization
process is reduced. Therefore, tree parity machines achieve common weights in a
shorter time due to the reduction of necessary bit exchanges. This approach
improves the security of neural cryptography


Abstract:
  This paper provides a set of cycling problems in linear programming. These
problems should be useful for researchers to develop and test new simplex
algorithms. As matter of the fact, this set of problems is used to test a
recently proposed double pivot simplex algorithm for linear programming.


Abstract:
  We consider a nonlinearly coupled electromechanical system, and develop a
quantitative theory for two-phonon cooling. In the presence of two-phonon
cooling, the mechanical Hilbert space is effectively reduced to its ground and
first excited states, thus forming a mechanical qubit. This allows for
performing quantum operations at the level of individual mechanical phonons and
preparing nonclassical mechanical states with negative Wigner functions. We
propose a scheme for performing arbitrary Bloch sphere rotations, and derive
the fidelity in the specific case of a $\pi$-pulse. We characterise detrimental
processes that reduce the coherence in the system, and demonstrate that our
scheme can be implemented in state-of-the-art electromechanical devices.


Abstract:
  Model-free reinforcement learning (RL) for legged locomotion commonly relies
on a physics simulator that can accurately predict the behaviors of every
degree of freedom of the robot. In contrast, approximate reduced-order models
are often sufficient for many model-based control strategies. In this work we
explore how RL can be effectively used with a centroidal model to generate
robust control policies for quadrupedal locomotion. Advantages over RL with a
full-order model include a simple reward structure, reduced computational
costs, and robust sim-to-real transfer. We further show the potential of the
method by demonstrating stepping-stone locomotion, two-legged in-place balance,
balance beam locomotion, and sim-to-real transfer without further adaptations.
Additional Results: https://www.pair.toronto.edu/glide-quadruped/.


Abstract:
  We find a sharp condition on the density-dependent coefficient of damping of
a one-dimensional repulsive Euler-Poisson system, which makes it possible to
suppress the formation of singularities in the solution of the Cauchy problem
with arbitrary smooth data. In the context of plasma physics, this means the
possibility of suppressing the breakdown of arbitrary oscillations of cold
plasma.


Abstract:
  Understanding of sedimentation dynamics of particles in bounded fluids is of
crucial importance for a wide variety of processes. While there is profound
knowledge base regarding the sedimentation of rigid solid particles, the
fundamental principles of sedimentation dynamics of elastic, nonheavy spheres
in bounded fluids are not well understood. Therefore, we performed
sedimentation of deformable, elastic solid spheres with particle Reynolds
numbers much smaller than 1 in a model experiment. The spheres started from
rest in a rectangular duct with a width of about 23 times the radius R of the
sphere. The particle dynamics of elastic spheres differed fundamentally from
that of rigid spheres. Elastic effects were found to take place on
comparatively large time scales, such that elastic spheres underwent four
phases of sedimentation. Phases I and II, including transient acceleration and
a short steady velocity plateau, are comparable with sedimentation of rigid
spheres. From a characteristic onset position of about 10R, deformability
effects kick in and a second acceleration appears (phase III). In the fourth
phase, the deformable spheres reach the terminal sedimentation velocity. The
softer the spheres are, the higher the terminal velocity is. In the present
setup, a terminal velocity up to 9 percent higher than the velocity for
comparable rigid spheres was reached. By means of the obtained data, insights
into the dynamics are given that could serve as basic approaches for modelling
the dynamics of elastic spheres in bounded fluids.


Abstract:
  Uncertainty quantification plays an important role in applications that
involve simulating ensembles of trajectories of dynamical systems. Conrad et
al. (Stat. Comput., 2017) proposed randomisation of deterministic time
integration methods as a strategy for quantifying uncertainty due to time
discretisation. We consider this strategy for systems that are described by
deterministic, possibly non-autonomous operator differential equations defined
on a Banach space or a Gelfand triple. We prove pathwise and expected error
bounds on the random trajectories, given an assumption on the local truncation
error of the underlying deterministic time integration and an assumption that
the absolute moments of the random variables decay with the time step. Our
analysis shows that the error analysis for differential equations in
finite-dimensional Euclidean space carries over to infinite-dimensional
settings.


Abstract:
  We introduce a new paradigm for scaling simulations with projected
entangled-pair states (PEPS) for critical strongly-correlated systems, allowing
for reliable extrapolations of PEPS data with relatively small bond dimensions
$D$. The key ingredient consists of using the effective correlation length
$\chi$ for inducing a collapse of data points, $f(D,\chi)=f(\xi(D,\chi))$, for
arbitrary values of $D$ and the environment bond dimension $\chi$. As such we
circumvent the need for extrapolations in $\chi$ and can use many distinct data
points for a fixed value of $D$. Here, we need that the PEPS has been optimized
using a fixed-$\chi$ gradient method, which can be achieved using a novel
tensor-network algorithm for finding fixed points of 2-D transfer matrices, or
by using the formalism of backwards differentiation. We test our hypothesis on
the critical 3-D dimer model, the 3-D classical Ising model, and the 2-D
quantum Heisenberg model.


Abstract:
  The Andreev spectrum of a quantum dot embedded in a hybrid
semiconductor-superconductor interferometer can be modulated by electrostatic
gating, magnetic flux through the interferometer, and Zeeman splitting from
in-plane magnetic field. We demonstrate parity transitions in the embedded
quantum dot system, and show that the Zeeman-driven transition is accompanied
by a 0-{\pi} transition in the superconducting phase across the dot. We further
demonstrate that flux through the interferometer modulates both dot parity and
0-{\pi} transitions.


Abstract:
  Capacitated lot-sizing problems (CLSPs) are important and challenging
optimization problems in production planning. Amongst the many approaches
developed for CLSPs, constructive heuristics are known to be the most intuitive
and fastest method for finding good feasible solutions for the CLSPs, and
therefore are often used as a subroutine in building more sophisticated exact
and metaheuristic approaches. Classical constructive heuristics, such as the
period-by-period heuristics and lot elimination heuristics, are first
introduced in the 1990s, and thereafter widely used in solving the CLSPs. This
paper evaluates the performance of period-by-period and lot elimination
heuristics, and improves the heuristics using perturbation techniques and
self-adaptive methods. We have also proposed a procedure for automatically
adjusting the parameters of the proposed heuristics so that the values of the
parameters can be chosen based on features of individual instances.
Experimental results show that the proposed self-adaptive randomized
period-by-period constructive heuristics are efficient and can find better
solutions with less computational time than the tabu search and lot elimination
heuristics. When the proposed constructive heuristic is used in a basic tabu
search framework, high-quality solutions with 0.88% average optimality gap can
be obtained on benchmark instances of 12 periods and 12 items, and optimality
gap within 1.2% for the instances with 24 periods and 24 items.


Abstract:
  We consider a cell-free hybrid massive multiple-input multiple-output (MIMO)
system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and
$N_r< N_a$ radio frequency (RF) chains. When $K\ll M{N_a}$, efficient uplink
channel estimation and data detection with reduced number of pilots can be
performed based on low-rank matrix completion. However, such a scheme requires
the central processing unit (CPU) to collect received signals from all APs,
which may enable the CPU to infer the private information of user locations. We
therefore develop and analyze privacy-preserving channel estimation schemes
under the framework of differential privacy (DP). As the key ingredient of the
channel estimator, two joint differentially private noisy matrix completion
algorithms based respectively on Frank-Wolfe iteration and singular value
decomposition are presented. We provide an analysis on the tradeoff between the
privacy and the channel estimation error. In particular, we show that the
estimation error can be mitigated while maintaining the same privacy level by
increasing the payload size with fixed pilot size; and the scaling laws of both
the privacy-induced and privacy-independent error components in terms of
payload size are characterized. Simulation results are provided to further
demonstrate the tradeoff between privacy and channel estimation performance.


Abstract:
  A multi-decade exploration into the theoretical foundations of artificial and
natural general intelligence, which has been expressed in a series of books and
papers and used to guide a series of practical and research-prototype software
systems, is reviewed at a moderate level of detail. The review covers
underlying philosophies (patternist philosophy of mind, foundational
phenomenological and logical ontology), formalizations of the concept of
intelligence, and a proposed high level architecture for AGI systems partly
driven by these formalizations and philosophies. The implementation of specific
cognitive processes such as logical reasoning, program learning, clustering and
attention allocation in the context and language of this high level
architecture is considered, as is the importance of a common (e.g. typed
metagraph based) knowledge representation for enabling "cognitive synergy"
between the various processes. The specifics of human-like cognitive
architecture are presented as manifestations of these general principles, and
key aspects of machine consciousness and machine ethics are also treated in
this context. Lessons for practical implementation of advanced AGI in
frameworks such as OpenCog Hyperon are briefly considered.


Abstract:
  Survival analysis is a technique to predict the times of specific outcomes,
and is widely used in predicting the outcomes for intensive care unit (ICU)
trauma patients. Recently, deep learning models have drawn increasing attention
in healthcare. However, there is a lack of deep learning methods that can model
the relationship between measurements, clinical notes and mortality outcomes.
In this paper we introduce BERTSurv, a deep learning survival framework which
applies Bidirectional Encoder Representations from Transformers (BERT) as a
language representation model on unstructured clinical notes, for mortality
prediction and survival analysis. We also incorporate clinical measurements in
BERTSurv. With binary cross-entropy (BCE) loss, BERTSurv can predict mortality
as a binary outcome (mortality prediction). With partial log-likelihood (PLL)
loss, BERTSurv predicts the probability of mortality as a time-to-event outcome
(survival analysis). We apply BERTSurv on Medical Information Mart for
Intensive Care III (MIMIC III) trauma patient data. For mortality prediction,
BERTSurv obtained an area under the curve of receiver operating characteristic
curve (AUC-ROC) of 0.86, which is an improvement of 3.6% over baseline of
multilayer perceptron (MLP) without notes. For survival analysis, BERTSurv
achieved a concordance index (C-index) of 0.7. In addition, visualizations of
BERT's attention heads help to extract patterns in clinical notes and improve
model interpretability by showing how the model assigns weights to different
inputs.


Abstract:
  We propose Partially Interpretable Estimators (PIE) which attribute a
prediction to individual features via an interpretable model, while a
(possibly) small part of the PIE prediction is attributed to the interaction of
features via a black-box model, with the goal to boost the predictive
performance while maintaining interpretability. As such, the interpretable
model captures the main contributions of features, and the black-box model
attempts to complement the interpretable piece by capturing the "nuances" of
feature interactions as a refinement. We design an iterative training algorithm
to jointly train the two types of models. Experimental results show that PIE is
highly competitive to black-box models while outperforming interpretable
baselines. In addition, the understandability of PIE is comparable to simple
linear models as validated via a human evaluation.


Abstract:
  Most of the current supervised automatic music transcription (AMT) models
lack the ability to generalize. This means that they have trouble transcribing
real-world music recordings from diverse musical genres that are not presented
in the labelled training data. In this paper, we propose a semi-supervised
framework, ReconVAT, which solves this issue by leveraging the huge amount of
available unlabelled music recordings. The proposed ReconVAT uses
reconstruction loss and virtual adversarial training. When combined with
existing U-net models for AMT, ReconVAT achieves competitive results on common
benchmark datasets such as MAPS and MusicNet. For example, in the few-shot
setting for the string part version of MusicNet, ReconVAT achieves F1-scores of
61.0% and 41.6% for the note-wise and note-with-offset-wise metrics
respectively, which translates into an improvement of 22.2% and 62.5% compared
to the supervised baseline model. Our proposed framework also demonstrates the
potential of continual learning on new data, which could be useful in
real-world applications whereby new data is constantly available.


Abstract:
  How do we formalize the challenge of credit assignment in reinforcement
learning? Common intuition would draw attention to reward sparsity as a key
contributor to difficult credit assignment and traditional heuristics would
look to temporal recency for the solution, calling upon the classic eligibility
trace. We posit that it is not the sparsity of the reward itself that causes
difficulty in credit assignment, but rather the \emph{information sparsity}. We
propose to use information theory to define this notion, which we then use to
characterize when credit assignment is an obstacle to efficient learning. With
this perspective, we outline several information-theoretic mechanisms for
measuring credit under a fixed behavior policy, highlighting the potential of
information theory as a key tool towards provably-efficient credit assignment.


Abstract:
  In traditional software programs, it is easy to trace program logic from
variables back to input, apply assertion statements to block erroneous
behavior, and compose programs together. Although deep learning programs have
demonstrated strong performance on novel applications, they sacrifice many of
the functionalities of traditional software programs. With this as motivation,
we take a modest first step towards improving deep learning programs by jointly
training a generative model to constrain neural network activations to "decode"
back to inputs. We call this design a Decodable Neural Network, or DecNN. Doing
so enables a form of compositionality in neural networks, where one can
recursively compose DecNN with itself to create an ensemble-like model with
uncertainty. In our experiments, we demonstrate applications of this
uncertainty to out-of-distribution detection, adversarial example detection,
and calibration -- while matching standard neural networks in accuracy. We
further explore this compositionality by combining DecNN with pretrained
models, where we show promising results that neural networks can be regularized
from using protected features.


Abstract:
  The Onsager Lie algebra $O$ is often used to study integrable lattice models.
The universal enveloping algebra of $O$ admits a $q$-deformation $O_q$ called
the $q$-Onsager algebra. Recently, an algebra $\mathcal O_q$ was introduced
called the alternating central extension of $O_q$. In this paper we introduce a
Lie algebra $\mathcal O$ that is roughly described by the following two
analogies: (i) $\mathcal O$ is to $O$ as $\mathcal O_q$ is to $O_q$; (ii) $O_q$
is to $O$ as $\mathcal O_q$ is to $\mathcal O$. We call $\mathcal O$ the
alternating central extension of $O$. This paper contains a comprehensive
description of $\mathcal O$.


Abstract:
  In this paper we give local and global parametric classifications of a class
of Einstein submanifolds of Euclidean space. The highlight is for submanifolds
of codimension two since in this case our assumptions are only of intrinsic
nature.


Abstract:
  Sparse level-set formulations allow practitioners to find the minimum 1-norm
solution subject to likelihood constraints. Prior art requires this constraint
to be convex. In this letter, we develop an efficient approach for nonconvex
likelihoods, using Regula Falsi root-finding techniques to solve the level-set
formulation. Regula Falsi methods are simple, derivative-free, and efficient,
and the approach provably extends level-set methods to the broader class of
nonconvex inverse problems. Practical performance is illustrated using
l1-regularized Student's t inversion, which is a nonconvex approach used to
develop outlier-robust formulations.


Abstract:
  In 2020, Cameron et al. introduced the restricted numerical range of a
digraph (directed graph) as a tool for characterizing digraphs and studying
their algebraic connectivity. In particular, digraphs with a restricted
numerical range of a single point, a horizontal line segment, and a vertical
line segment were characterized as $k$-imploding stars, directed joins of
bidirectional digraphs, and regular tournaments, respectively. In this article,
we extend these results by investigating digraphs whose restricted numerical
range is a convex polygon in the complex plane. We provide computational
methods for identifying these polygonal digraphs and show that these digraphs
can be broken into three disjoint classes: normal, restricted-normal, and
pseudo-normal digraphs, all of which are closed under the digraph complement.
We prove sufficient conditions for normal digraphs and show that the directed
join of two normal digraphs results in a restricted-normal digraph. Also, we
prove that directed joins are the only restricted-normal digraphs when the
order is square-free or twice a square-free number. Finally, we provide methods
to construct restricted-normal digraphs that are not directed joins for all
orders that are neither square-free nor twice a square-free number.


Abstract:
  The contact process is a particular case of birth-and-death processes on
infinite particle configurations. We consider the contact models on locally
compact separable metric spaces. We prove the existence of a one-parameter set
of invariant measures in the critical regime under the condition imposed on the
associated Markov jump process. This condition, roughly speaking, requires the
separation of any pair of trajectories of this jump process. The general scheme
can be applied to the contact process on the lattice in a heterogeneous and
random environments as well as to the contact process on graphs and on
manifolds.


Abstract:
  Recent astronomical data have provided the primordial deuterium abundance
with percent precision. As a result, Big Bang nucleosynthesis may provide a
constraint on the universal baryon to photon ratio that is as precise as, but
independent from, analyses of the cosmic microwave background. However, such a
constraint requires that the nuclear reaction rates governing the production
and destruction of primordial deuterium are sufficiently well known. Here, a
new measurement of the $^2$H($p,\gamma$)$^3$He cross section is reported. This
nuclear reaction dominates the error on the predicted Big Bang deuterium
abundance. A proton beam of 400-1650keV beam energy was incident on solid
titanium deuteride targets, and the emitted $\gamma$-rays were detected in two
high-purity germanium detectors at angles of 55$^\circ$ and 90$^\circ$,
respectively. The deuterium content of the targets has been obtained in situ by
the $^2$H($^3$He,$p$)$^4$He reaction and offline using the Elastic Recoil
Detection method. The astrophysical S-factor has been determined at center of
mass energies between 265 and 1094 keV, addressing the uppermost part of the
relevant energy range for Big Bang nucleosynthesis and complementary to ongoing
work at lower energies. The new data support a higher S-factor at Big Bang
temperatures than previously assumed, reducing the predicted deuterium
abundance.


Abstract:
  We present a novel adaptive host-chip modular architecture for video
acquisition to optimize an overall objective task constrained under a given bit
rate. The chip is a high resolution imaging sensor such as gigapixel focal
plane array (FPA) with low computational power deployed on the field remotely,
while the host is a server with high computational power. The communication
channel data bandwidth between the chip and host is constrained to accommodate
transfer of all captured data from the chip. The host performs objective task
specific computations and also intelligently guides the chip to optimize
(compress) the data sent to host. This proposed system is modular and highly
versatile in terms of flexibility in re-orienting the objective task. In this
work, object tracking is the objective task. While our architecture supports
any form of compression/distortion, in this paper we use quadtree
(QT)-segmented video frames. We use Viterbi (Dynamic Programming) algorithm to
minimize the area normalized weighted rate-distortion allocation of resources.
The host receives only these degraded frames for analysis. An object detector
is used to detect objects, and a Kalman Filter based tracker is used to track
those objects. Evaluation of system performance is done in terms of Multiple
Object Tracking Accuracy (MOTA) metric. In this proposed novel architecture,
performance gains in MOTA is obtained by twice training the object detector
with different system generated distortions as a novel 2-step process.
Additionally, object detector is assisted by tracker to upscore the region
proposals in the detector to further improve the performance.


Abstract:
  One of the most important lessons from the success of deep learning is that
learned representations tend to perform much better at any task compared to
representations we design by hand. Yet evolution of evolvability algorithms,
which aim to automatically learn good genetic representations, have received
relatively little attention, perhaps because of the large amount of
computational power they require. The recent method Evolvability ES allows
direct selection for evolvability with little computation. However, it can only
be used to solve problems where evolvability and task performance are aligned.
We propose Quality Evolvability ES, a method that simultaneously optimizes for
task performance and evolvability and without this restriction. Our proposed
approach Quality Evolvability has similar motivation to Quality Diversity
algorithms, but with some important differences. While Quality Diversity aims
to find an archive of diverse and well-performing, but potentially genetically
distant individuals, Quality Evolvability aims to find a single individual with
a diverse and well-performing distribution of offspring. By doing so Quality
Evolvability is forced to discover more evolvable representations. We
demonstrate on robotic locomotion control tasks that Quality Evolvability ES,
similarly to Quality Diversity methods, can learn faster than objective-based
methods and can handle deceptive problems.


Abstract:
  Owing to a reduced solar background and low propagation losses in the
atmosphere, the 2- to 2.5-$\mu$m waveband is a promising candidate for daylight
quantum communication. This spectral region also offers low losses and low
dispersion in hollow-core fibers and in silicon waveguides. We demonstrate for
the first time near-maximally entangled photon pairs at 2.1 $\mu$m that could
support device independent quantum key distribution (DIQKD) assuming
sufficiently high channel efficiencies. The state corresponds to a positive
secure-key rate (0.254 bits/pair, with a quantum bit error rate of 3.8%) based
on measurements in a laboratory setting with minimal channel loss and
transmission distance. This is promising for the future implementation of DIQKD
at 2.1 $\mu$m.


Abstract:
  Collaboration skills are important for future software engineers. In computer
science education, these skills are often practiced through group assignments,
where students develop software collaboratively. The approach that students
take in these assignments varies widely, but often involves a division of
labour. It can then be argued whether collaboration still takes place. The
discipline of computing education is especially interesting in this context,
because some of its specific features (such as the variation in entry skill
level and the use of source code repositories as collaboration platforms) are
likely to influence the approach taken within groupwork. The aim of this
research is to gain insight into the work division and allocation strategies
applied by computer science students during group assignments. To this end, we
interviewed twenty students of four universities. The thematic analysis shows
that students tend to divide up the workload to enable working independently,
with pair programming and code reviews being often employed. Motivated
primarily by grade and efficiency factors, students choose and allocate tasks
primarily based on their prior expertise and preferences. Based on our
findings, we argue that the setup of group assignments can limit student
motivation for practicing new software engineering skills, and that
interventions are needed towards encouraging experimentation and learning.


Abstract:
  Event sourced systems are increasing in popularity because they are reliable,
flexible, and scalable. In this article, we point a microscope at a software
architecture pattern that is rapidly gaining popularity in industry, but has
not received as much attention from the scientific community. We do so through
constructivist grounded theory, which proves a suitable qualitative method for
extracting architectural knowledge from practitioners. Based on the discussion
of 19 event sourced systems we explore the rationale for and the context of the
event sourcing pattern. A description of the pattern itself and its relation to
other patterns as discussed with practitioners is given. The description itself
is grounded in the experience of 25 engineers, making it a reliable source for
both new practitioners and scientists. We identify five challenges that
practitioners experience: event system evolution, the steep learning curve,
lack of available technology, rebuilding projections, and data privacy. For the
first challenge of event system evolution, we uncover five tactics and
solutions that support practitioners in their design choices when developing
evolving event sourced systems: versioned events, weak schema, upcasting,
in-place transformation, and copy-and-transform.


Abstract:
  Since its launch, the Alpha Magnetic Spectrometer-02 (AMS-02) has delivered
outstanding quality measurements of the spectra of cosmic-ray (CR) species,
$\bar{p}$, $e^{\pm}$, and nuclei (H--O, Ne, Mg, Si, Fe), which resulted in a
number of breakthroughs. The most recent AMS-02 result is the measurement of
the spectrum of CR fluorine up to $\sim$2 TV. Given its very low solar system
abundance, fluorine in CRs is thought to be mostly secondary, produced in
fragmentations of heavier species, predominantly Ne, Mg, and Si. Similar to the
best-measured secondary-to-primary boron to carbon nuclei ratio that is widely
used to study the origin and propagation of CR species, the precise fluorine
data would allow the origin of Si-group nuclei to be studied independently.
Meanwhile, the secondary origin of CR fluorine has never been tested in a wide
energy range due to the lack of accurate CR data. In this paper, we use the
first ever precise measurements of the fluorine spectrum by AMS-02 together
with ACE-CRIS and Voyager 1 data to actually test this paradigm. Our detailed
modeling shows an excess below 10 GV in the fluorine spectrum that may hint at
a primary fluorine component. We also provide an updated local interstellar
spectrum (LIS) of fluorine in the rigidity range from few MV to $\sim$2 TV. Our
calculations employ the self-consistent GalProp-HelMod framework that has
proved to be a reliable tool in deriving the LIS of CR $\bar{p}$, $e^{-}$, and
nuclei $Z\le28$.


Abstract:
  A big, diverse and balanced training data is the key to the success of deep
neural network training. However, existing publicly available datasets used in
facial landmark localization are usually much smaller than those for other
computer vision tasks. A small dataset without diverse and balanced training
samples cannot support the training of a deep network effectively. To address
the above issues, this paper presents a novel Separable Batch Normalization
(SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust
facial landmark localization. Different from the standard BN layer that uses
all the training data to calculate a single set of parameters, SepBN considers
that the samples of a training dataset may belong to different sub-domains.
Accordingly, the proposed SepBN module uses multiple sets of parameters, each
corresponding to a specific sub-domain. However, the selection of an
appropriate branch in the inference stage remains a challenging task because
the sub-domain of a test sample is unknown. To mitigate this difficulty, we
propose a novel attention mechanism that assigns different weights to each
branch for automatic selection in an effective style. As a further innovation,
the proposed CNT strategy trains a network using multiple datasets having
different facial landmark annotation systems, boosting the performance and
enhancing the generalization capacity of the trained network. The experimental
results obtained on several well-known datasets demonstrate the effectiveness
of the proposed method.


Abstract:
  This paper concerns the design of a Fourier based pseudospectral numerical
method for the model of European Option Pricing with transaction costs under
Exponential Utility derived by Davis, Panas and Zariphopoulou. Computing the
option price involves solving two stochastic optimal control problems. With a
Exponential Utility function, the dimension of the problem can be reduced, but
one has to deal with high absolute values in the objective function. In this
paper, we propose two changes of variables that reduce the impact of the
exponential growth. We propose a Fourier pseudospectral method to solve the
resulting non linear equation. Numerical analysis of the stability,
consistency, convergence and localization error of the method are included.
Numerical experiments support the theoretical results. The effect of
incorporating transaction costs is also studied.


Abstract:
  Bipartite networks are a natural representation of the interactions between
entities from two different types. The organization (or topology) of such
networks gives insight to understand the systems they describe as a whole.
Here, we rely on motifs which provide a meso-scale description of the topology.
Moreover, we consider the bipartite expected degree distribution (B-EDD) model
which accounts for both the density of the network and possible imbalances
between the degrees of the nodes. Under the B-EDD model, we prove the
asymptotic normality of the count of any given motif, considering sparsity
conditions. We also provide close-form expressions for the mean and the
variance of this count. This allows to avoid computationally prohibitive
resampling procedures. Based on these results, we define a goodness-of-fit test
for the B-EDD model and propose a family of tests for network comparisons. We
assess the asymptotic normality of the test statistics and the power of the
proposed tests on synthetic experiments and illustrate their use on ecological
data sets.


Abstract:
  In \cite{butman1976} the linear coding scheme is applied, $X_t
=g_t\Big(\Theta - {\bf E}\Big\{\Theta\Big|Y^{t-1}, V_0=v_0\Big\}\Big)$,
$t=2,\ldots,n$, $X_1=g_1\Theta$, with $\Theta: \Omega \to {\mathbb R}$, a
Gaussian random variable, to derive a lower bound on the feedback rate, for
additive Gaussian noise (AGN) channels, $Y_t=X_t+V_t, t=1, \ldots, n$, where
$V_t$ is a Gaussian autoregressive (AR) noise, and $\kappa \in [0,\infty)$ is
the total transmitter power. For the unit memory AR noise, with parameters $(c,
K_W)$, where $c\in [-1,1]$ is the pole and $K_W$ is the variance of the
Gaussian noise, the lower bound is $C^{L,B} =\frac{1}{2} \log \chi^2$, where
$\chi =\lim_{n\longrightarrow \infty} \chi_n$ is the positive root of
$\chi^2=1+\Big(1+ \frac{|c|}{\chi}\Big)^2 \frac{\kappa}{K_W}$, and the sequence
$\chi_n \triangleq \Big|\frac{g_n}{g_{n-1}}\Big|, n=2, 3, \ldots,$ satisfies a
certain recursion, and conjectured that $C^{L,B}$ is the feedback capacity.
  In this correspondence, it is observed that the nontrivial lower bound
$C^{L,B}=\frac{1}{2} \log \chi^2$ such that $\chi >1$, necessarily implies the
scaling coefficients of the feedback code, $g_n$, $n=1,2, \ldots$, grow
unbounded, in the sense that, $\lim_{n\longrightarrow\infty}|g_n| =+\infty$.
The unbounded behaviour of $g_n$ follows from the ratio limit theorem of a
sequence of real numbers, and it is verified by simulations. It is then
concluded that such linear codes are not practical, and fragile with respect to
a mismatch between the statistics of the mathematical model of the channel and
the real statistics of the channel. In particular, if the error is perturbed by
$\epsilon_n>0$ no matter how small, then $X_n =g_t\Big(\Theta - {\bf
E}\Big\{\Theta\Big|Y^{t-1}, V_0=v_0\Big\}\Big)+g_n \epsilon_n$, and
$|g_n|\epsilon_n \longrightarrow \infty$, as $n \longrightarrow \infty$.


Abstract:
  We study a model for frustrated tunneling ionization using ultrashort laser
pulses. The model is based on the strong field approximation and it employs the
saddle point approximation to predict quasiclassical trajectories that are
captured on Rydberg states. We present a classification of the saddle-point
solutions and explore their behavior as functions of angular momentum of the
final state, as well as the carrier--envelope phase (CEP) of the laser pulse.
We compare the final state population computed by the model to results obtained
by numerical propagation of the time-dependent Schr\"odinger equation (TDSE)
for the hydrogen atom. While we find qualitative agreement in the CEP
dependence of the populations in principal quantum numbers, $n$, the
populations to individual angular momentum channels, $\ell$, are found to be
inconsistent between model and TDSE. Thus, our results show that improvements
of the quasiclassical trajectories are in order for a quantitative model of
frustrated tunneling ionizaiton.


Abstract:
  Tomonaga-Luttinger liquids (TLLs) can be used to effectively describe
one-dimensional quantum many-body systems such as ultracold atoms, charges in
nanowires, superconducting circuits, and gapless spin chains. Their properties
are given by two parameters, the propagation velocity and the Luttinger
parameter. Here we study inhomogeneous TLLs where these are promoted to
functions of position and demonstrate that they profoundly affect the dynamics:
In general, besides curving the light cone, we show that propagation is no
longer ballistically localized to the light-cone trajectories, different from
standard homogeneous TLLs. Specifically, if the Luttinger parameter depends on
position, the dynamics features pronounced spreading into the light cone, which
cannot be understood via a simple superposition of waves as in the
Huygens-Fresnel principle. This is the case for ultracold atoms in a parabolic
trap, which serves as our main motivation, and we discuss possible experimental
observations in such systems.


Abstract:
  Correlation acts as a critical role in the tracking field, especially in
recent popular Siamese-based trackers. The correlation operation is a simple
fusion manner to consider the similarity between the template and the search
region. However, the correlation operation itself is a local linear matching
process, leading to lose semantic information and fall into local optimum
easily, which may be the bottleneck of designing high-accuracy tracking
algorithms. Is there any better feature fusion method than correlation? To
address this issue, inspired by Transformer, this work presents a novel
attention-based feature fusion network, which effectively combines the template
and search region features solely using attention. Specifically, the proposed
method includes an ego-context augment module based on self-attention and a
cross-feature augment module based on cross-attention. Finally, we present a
Transformer tracking (named TransT) method based on the Siamese-like feature
extraction backbone, the designed attention-based fusion mechanism, and the
classification and regression head. Experiments show that our TransT achieves
very promising results on six challenging datasets, especially on large-scale
LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively
50 fps on GPU. Code and models are available at
https://github.com/chenxin-dlut/TransT.


Abstract:
  The regular monitoring of flat-spectrum radio quasars (FSRQs) in
$\gamma$-rays by Fermi-LAT since past 12 years indicated six sources who
exhibited extreme $\gamma$-ray outbursts crossing daily flux of $10^{-5}$
photons/cm$^{2}$/s. We obtained nearly-simultaneous multi-wavelength data of
these sources in radio to $\gamma$-ray waveband from OVRO, Steward Observatory,
SMARTS, Swift-UVOT, Swift-XRT, and Fermi-LAT. The time-averaged broadband
Spectral Energy Distributions (SEDs) of these sources in quiescent states were
studied to get an idea about the underlying baseline radiation processes. We
modeled the SEDs using one-zone leptonic synchrotron and inverse-Compton
emission scenario from broken power-law electron energy distribution inside a
spherical plasma blob, relativistically moving down a conical jet. The model
takes into account inverse-Compton scattering of externally and locally
originated seed photons in the jet. The big blue bumps visible in quiescent
state SEDs helped to estimate the accretion disk luminosities and central black
hole masses. We found a correlation between the magnetic field inside the
emission region and the ratio of emission region distance to disk luminosity,
which implies that the magnetic field decreases with an increase in emission
region distance and decrease in disk luminosity, suggesting a disk-jet
connection. The high-energy index of the electron distribution was also found
to be correlated with observed $\gamma$-ray luminosity as $\gamma$-rays are
produced by high-energy particles. In most cases, kinetic power carried by
electrons can account for jet radiation power as jets become radiatively
inefficient during quiescent states.


Abstract:
  Examining a 20th-century Scandinavian legal theoretical tradition, we can
extract an ontological naturalistic, a logical empiristic, and a modern
idealistic rationale. We introduce the mathematical syntactic figure present in
the `logical empiricism' in a contemporary mathematical logic. A new formal
framework for describing explicit purchase statutes (Sweden) is gradually
developed and subsequently proposed. This new framework is based on a
many-sorted first-order logic (MFOL) approach, where the semantics are grounded
in concrete `physical' objects and situations with a legal relevance.
Specifically, we present a concrete formal syntactic translation of one of the
central statutes of Swedish legislation for the purchase of immovable property.
Additionally, we discuss the potential implications that a subsequent
development of such formalisations would have for constructing artificial
agents (e.g., software) that can be used as `co-creative' legal assistance for
solving highly complex legal issues concerning the transfer of property, among
others.


Abstract:
  Virtual try-on methods aim to generate images of fashion models wearing
arbitrary combinations of garments. This is a challenging task because the
generated image must appear realistic and accurately display the interaction
between garments. Prior works produce images that are filled with artifacts and
fail to capture important visual details necessary for commercial applications.
We propose Outfit Visualization Net (OVNet) to capture these important details
(e.g. buttons, shading, textures, realistic hemlines, and interactions between
garments) and produce high quality multiple-garment virtual try-on images.
OVNet consists of 1) a semantic layout generator and 2) an image generation
pipeline using multiple coordinated warps. We train the warper to output
multiple warps using a cascade loss, which refines each successive warp to
focus on poorly generated regions of a previous warp and yields consistent
improvements in detail. In addition, we introduce a method for matching outfits
with the most suitable model and produce significant improvements for both our
and other previous try-on methods. Through quantitative and qualitative
analysis, we demonstrate our method generates substantially higher-quality
studio images compared to prior works for multi-garment outfits. An interactive
interface powered by this method has been deployed on fashion e-commerce
websites and received overwhelmingly positive feedback.


Abstract:
  Within the framework of Einstein-Gauss-Bonnet theory in five-dimensional
spacetime ($5D$ EGB), we derive the hydrostatic equilibrium equations and solve
them numerically to obtain the neutron stars for both isotropic and anisotropic
distribution of matter. The mass-radius relations are obtained for SLy equation
of state, which describes both the solid crust and the liquid core of neutron
stars, and for a wide range of the Gauss-Bonnet coupling parameter $\alpha$.
More specifically, we find that the contribution of the Gauss-Bonnet term leads
to substantial deviations from the Einstein gravity. We also discuss that after
a certain value of $\alpha$, the theory admits higher maximum masses compared
with general relativity, however, the causality condition is violated in the
high-mass region. Finally, our results are compared with the recent
observations data on mass-radius diagram.


Abstract:
  Deep models have improved state-of-the-art for both supervised and
unsupervised learning. For example, deep embedded clustering (DEC) has greatly
improved the unsupervised clustering performance, by using stacked autoencoders
for representation learning. However, one weakness of deep modeling is that the
local neighborhood structure in the original space is not necessarily preserved
in the latent space. To preserve local geometry, various methods have been
proposed in the supervised and semi-supervised learning literature (e.g.,
spectral clustering and label propagation) using graph Laplacian
regularization. In this paper, we combine the strength of deep representation
learning with measure propagation (MP), a KL-divergence based graph
regularization method originally used in the semi-supervised scenario. The main
assumption of MP is that if two data points are close in the original space,
they are likely to belong to the same class, measured by KL-divergence of class
membership distribution. By taking the same assumption in the unsupervised
learning scenario, we propose our Deep Embedded Clustering Aided by Measure
Propagation (DECAMP) model. We evaluate DECAMP on short text clustering tasks.
On three public datasets, DECAMP performs competitively with other
state-of-the-art baselines, including baselines using additional data to
generate word embeddings used in the clustering process. As an example, on the
Stackoverflow dataset, DECAMP achieved a clustering accuracy of 79%, which is
about 5% higher than all existing baselines. These empirical results suggest
that DECAMP is a very effective method for unsupervised learning.


Abstract:
  Many physical, biological and neural systems behave as coupled oscillators,
with characteristic phase coupling across different frequencies. Methods such
as $n:m$ phase locking value and bi-phase locking value have previously been
proposed to quantify phase coupling between two resonant frequencies (e.g. $f$,
$2f/3$) and across three frequencies (e.g. $f_1$, $f_2$, $f_1+f_2$),
respectively. However, the existing phase coupling metrics have their
limitations and limited applications. They cannot be used to detect or quantify
phase coupling across multiple frequencies (e.g. $f_1$, $f_2$, $f_3$, $f_4$,
$f_1+f_2+f_3-f_4$), or coupling that involves non-integer multiples of the
frequencies (e.g. $f_1$, $f_2$, $2f_1/3+f_2/3$). To address the gap, this paper
proposes a generalized approach, named multi-phase locking value (M-PLV), for
the quantification of various types of instantaneous multi-frequency phase
coupling. Different from most instantaneous phase coupling metrics that measure
the simultaneous phase coupling, the proposed M-PLV method also allows the
detection of delayed phase coupling and the associated time lag between coupled
oscillators. The M-PLV has been tested on cases where synthetic coupled signals
are generated using white Gaussian signals, and a system comprised of multiple
coupled R\"ossler oscillators. Results indicate that the M-PLV can provide a
reliable estimation of the time window and frequency combination where the
phase coupling is significant, as well as a precise determination of time lag
in the case of delayed coupling. This method has the potential to become a
powerful new tool for exploring phase coupling in complex nonlinear dynamic
systems.


Abstract:
  Transcriptomic analysis are characterized by being not directly quantitative
and only providing relative measurements of expression levels up to an unknown
individual scaling factor. This difficulty is enhanced for differential
expression analysis. Several methods have been proposed to circumvent this lack
of knowledge by estimating the unknown individual scaling factors however, even
the most used one, are suffering from being built on hardly justifiable
biological hypotheses or from having weak statistical background. Only two
methods withstand this analysis: one based on largest connected graph component
hardly usable for large amount of expressions like in NGS, the second based on
$\log$-linear fits which unfortunately require a first step which uses one of
the methods described before.
  We introduce a new procedure for differential analysis in the context of
transcriptomic data. It is the result of pooling together several differential
analyses each based on randomly picked genes used as reference genes. It
provides a differential analysis free from the estimation of the individual
scaling factors or any other knowledge. Theoretical properties are investigated
both in term of FWER and power. Moreover in the context of Poisson or negative
binomial modelization of the transcriptomic expressions, we derived a test with
non asymptotic control of its bounds. We complete our study by some empirical
simulations and apply our procedure to a real data set of hepatic miRNA
expressions from a mouse model of non-alcoholic steatohepatitis (NASH), the
CDAHFD model. This study on real data provides new hits with good biological
explanations.


Abstract:
  The world today is experiencing an abundance of music like no other time, and
attempts to group music into clusters have become increasingly prevalent.
Common standards for grouping music were songs, artists, and genres, with
artists or songs exploring similar genres of music seen as related. These
clustering attempts serve critical purposes for various stakeholders involved
in the music industry. For end users of music services, they may want to group
their music so that they can easily navigate inside their music library; for
music streaming platforms like Spotify, companies may want to establish a solid
dataset of related songs in order to successfully provide personalized music
recommendations and coherent playlists to their users. Due to increased
competition in the streaming market, platforms are trying their best to find
novel ways of learning similarities between audio to gain competitive
advantage. Our team, comprised of music lovers with different tastes, was
interested in the same issue, and created Music-Circles, an interactive
visualization of music from the Billboard. Music-Circles links audio feature
data offered by Spotify to popular songs to create unique vectors for each
song, and calculate similarities between these vectors to cluster them. Through
interacting with Music-Circles, users can gain understandings of audio
features, view characteristic trends in popular music, and find out which music
cluster they belong to.


Abstract:
  We propose a manifold matching approach to generative models which includes a
distribution generator (or data generator) and a metric generator. In our
framework, we view the real data set as some manifold embedded in a
high-dimensional Euclidean space. The distribution generator aims at generating
samples that follow some distribution condensed around the real data manifold.
It is achieved by matching two sets of points using their geometric shape
descriptors, such as centroid and $p$-diameter, with learned distance metric;
the metric generator utilizes both real data and generated samples to learn a
distance metric which is close to some intrinsic geodesic distance on the real
data manifold. The produced distance metric is further used for manifold
matching. The two networks are learned simultaneously during the training
process. We apply the approach on both unsupervised and supervised learning
tasks: in unconditional image generation task, the proposed method obtains
competitive results compared with existing generative models; in
super-resolution task, we incorporate the framework in perception-based models
and improve visual qualities by producing samples with more natural textures.
Experiments and analysis demonstrate the feasibility and effectiveness of the
proposed framework.


Abstract:
  We introduce a two-dimensional Hele-Shaw type free boundary model for
motility of eukaryotic cells on substrates. The key ingredients of this model
are the Darcy law for overdamped motion of the cytoskeleton gel (active gel)
coupled with advection-diffusion equation for myosin density leading to
elliptic-parabolic Keller-Segel system. This system is supplemented with
Hele-Shaw type boundary conditions: Young-Laplace equation for pressure and
continuity of velocities. We first show that radially symmetric stationary
solutions become unstable and bifurcate to traveling wave solutions at a
critical value of the total myosin mass. Next we perform linear stability
analysis of these traveling wave solutions and identify the type of bifurcation
(sub- or supercritical). Our study sheds light on the mathematics underlying
instability/stability transitions in this model. Specifically, we show that
these transitions occur via generalized eigenvectors of the linearized
operator.


Abstract:
  The algebraic monoid structure of an incidence algebra is investigated. We
show that the multiplicative structure alone determines the algebra
automorphisms of the incidence algebra. We present a formula that expresses the
complexity of the incidence monoid with respect to the two sided action of its
maximal torus in terms of the zeta polynomial of the poset. In addition, we
characterize the finite (connected) posets whose incidence monoids have
complexity $\leq 1$. Finally, we determine the covering relations of the
adherence order on the incidence monoid of a star poset.


Abstract:
  In this paper, we consider a frequency-based portfolio optimization problem
with $m \geq 2$ assets when the expected logarithmic growth (ELG) rate of
wealth is used as the performance metric. With the aid of the notion called
dominant asset, it is known that the optimal ELG level is achieved by investing
all available funds on that asset. However, such an "all-in" strategy is
arguably too risky to implement in practice. Motivated by this issue, we study
the case where the portfolio weights are chosen in a rather ad-hoc manner and a
buy-and-hold strategy is subsequently used. Then we show that, if the
underlying portfolio contains a dominant asset, buy and hold on that specific
asset is asymptotically log-optimal with a sublinear rate of convergence. This
result also extends to the scenario where a trader either does not have a
probabilistic model for the returns or does not trust a model obtained from
historical data. To be more specific, we show that if a market contains a
dominant asset, buy and hold a market portfolio involving nonzero weights for
each asset is asymptotically log-optimal. Additionally, this paper also
includes a conjecture regarding the property called high-frequency maximality.
That is, in the absence of transaction costs, high-frequency rebalancing is
unbeatable in the ELG sense. Support for the conjecture, involving a lemma for
a weak version of the conjecture, is provided. This conjecture, if true,
enables us to improve the log-optimality result obtained previously. Finally, a
result that indicates a way regarding an issue about when should one to
rebalance their portfolio if needed, is also provided. Examples, some involving
simulations with historical data, are also provided along the way to illustrate
the~theory.


Abstract:
  In this paper, we develop a new classification method for manifold-valued
data in the framework of probabilistic learning vector quantization. In many
classification scenarios, the data can be naturally represented by symmetric
positive definite matrices, which are inherently points that live on a curved
Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds,
traditional Euclidean machine learning algorithms yield poor results on such
data. In this paper, we generalize the probabilistic learning vector
quantization algorithm for data points living on the manifold of symmetric
positive definite matrices equipped with Riemannian natural metric
(affine-invariant metric). By exploiting the induced Riemannian distance, we
derive the probabilistic learning Riemannian space quantization algorithm,
obtaining the learning rule through Riemannian gradient descent. Empirical
investigations on synthetic data, image data , and motor imagery EEG data
demonstrate the superior performance of the proposed method.


Abstract:
  The extreme loads experienced by the wind turbine in the extreme wind events
are critical for the evaluation of structural reliability. Hence, the load
alleviation control methods need to be designed and deployed to reduce the
adverse effects of extreme wind events. This work demonstrates that the extreme
loads are highly correlated to wind conditions such as turbulence-induced wind
shears. Based on this insight, this work proposes a turbulence-based load
alleviation control strategy for adapting the controller to changes in wind
condition. The estimation of the rotor averaged wind shear based on the rotor
loads is illustrated, and is herein used to statistically characterize the
extreme wind events for control purpose. To demonstrates the benefits,
simulations are carried out using high-fidelity aero-elastic tool and the DTU
10 MW reference turbine in normal and extreme turbulence wind conditions. The
results indicate that the proposed method can effectively decrease the
exceedance probability of the extreme loads. Meanwhile, the method can minimize
the loss of annual energy production in normal operating condition.


Abstract:
  The asymptotics of the ground state $u(r)$ of the Schr\"odinger--Newton
equation in $\mathbb{R}^3$ was determined by V. Moroz and J. van Schaftingen to
be $u(r) \sim A e^{-r}/ r^{1 - \|u\|_2^2/8\pi}$ for some $A>0$, in units that
fix the exponential rate to unity. They left open the value of $\|u\|_2^2$, the
squared $L^2$ norm of $u$. Here it is rigorously shown that $2^{1/3}3\pi^2\leq
\|u\|_2^2\leq 2^{3}\pi^{3/2}$. It is reported that numerically
$\|u\|_2^2\approx 14.03\pi$, revealing that the monomial prefactor of $e^{-r}$
increases with $r$ in a concave manner. Asymptotic results are proposed for the
Schr\"odinger--Newton equation with external $\sim - K/r$ potential, and for
the related Hartree equation of a bosonic atom or ion.


Abstract:
  In this work, we extended a stochastic model for football leagues based on
the team's potential [R. da Silva et al. Comput. Phys. Commun. \textbf{184}
661--670 (2013)] for making predictions instead of only performing a successful
characterization of the statistics on the punctuation of the real leagues. Our
adaptation considers the advantage of playing at home when considering the
potential of the home and away teams. The algorithm predicts the tournament's
outcome by using the market value or/and the ongoing team's performance as
initial conditions in the context of Monte Carlo simulations. We present and
compare our results to the worldwide known SPI predictions performed by the
"FiveThirtyEight" project. The results show that the algorithm can deliver good
predictions even with a few ingredients and in more complicated seasons like
the 2020 editions where the matches were played without fans in the stadiums.


Abstract:
  In this paper we investigate how the bootstrap can be applied to time series
regressions when the volatility of the innovations is random and
non-stationary. The volatility of many economic and financial time series
displays persistent changes and possible non-stationarity. However, the theory
of the bootstrap for such models has focused on deterministic changes of the
unconditional variance and little is known about the performance and the
validity of the bootstrap when the volatility is driven by a non-stationary
stochastic process. This includes near-integrated volatility processes as well
as near-integrated GARCH processes. This paper develops conditions for
bootstrap validity in time series regressions with non-stationary, stochastic
volatility. We show that in such cases the distribution of bootstrap statistics
(conditional on the data) is random in the limit. Consequently, the
conventional approaches to proving bootstrap validity, involving weak
convergence in probability of the bootstrap statistic, fail to deliver the
required results. Instead, we use the concept of `weak convergence in
distribution' to develop and establish novel conditions for validity of the
wild bootstrap, conditional on the volatility process. We apply our results to
several testing problems in the presence of non-stationary stochastic
volatility, including testing in a location model, testing for structural
change and testing for an autoregressive unit root. Sufficient conditions for
bootstrap validity include the absence of statistical leverage effects, i.e.,
correlation between the error process and its future conditional variance. The
results are illustrated using Monte Carlo simulations, which indicate that the
wild bootstrap leads to size control even in small samples.


Abstract:
  In automotive domain, operation of secondary tasks like accessing
infotainment system, adjusting air conditioning vents, and side mirrors
distract drivers from driving. Though existing modalities like gesture and
speech recognition systems facilitate undertaking secondary tasks by reducing
duration of eyes off the road, those often require remembering a set of
gestures or screen sequences. In this paper, we have proposed two different
modalities for drivers to virtually touch the dashboard display using a laser
tracker with a mechanical switch and an eye gaze switch. We compared
performances of our proposed modalities against conventional touch modality in
automotive environment by comparing pointing and selection times of
representative secondary task and also analysed effect on driving performance
in terms of deviation from lane, average speed, variation in perceived workload
and system usability. We did not find significant difference in driving and
pointing performance between laser tracking system and existing touchscreen
system. Our result also showed that the driving and pointing performance of the
virtual touch system with eye gaze switch was significantly better than the
same with mechanical switch. We evaluated the efficacy of the proposed virtual
touch system with eye gaze switch inside a real car and investigated acceptance
of the system by professional drivers using qualitative research. The
quantitative and qualitative studies indicated importance of using multimodal
system inside car and highlighted several criteria for acceptance of new
automotive user interface.


Abstract:
  This paper aims to provide an overview of the ethical concerns in artificial
intelligence (AI) and the framework that is needed to mitigate those risks, and
to suggest a practical path to ensure the development and use of AI at the
United Nations (UN) aligns with our ethical values. The overview discusses how
AI is an increasingly powerful tool with potential for good, albeit one with a
high risk of negative side-effects that go against fundamental human rights and
UN values. It explains the need for ethical principles for AI aligned with
principles for data governance, as data and AI are tightly interwoven. It
explores different ethical frameworks that exist and tools such as assessment
lists. It recommends that the UN develop a framework consisting of ethical
principles, architectural standards, assessment methods, tools and
methodologies, and a policy to govern the implementation and adherence to this
framework, accompanied by an education program for staff.


Abstract:
  In the formalism of generalized holographic dark energy (HDE), the
holographic cut-off is generalized to depend upon $L_\mathrm{IR} =
L_\mathrm{IR} \left( L_\mathrm{p}, \dot L_\mathrm{p}, \ddot L_\mathrm{p},
\cdots, L_\mathrm{f}, \dot L_\mathrm{f}, \cdots, a\right)$ with $L_\mathrm{p}$
and $L_\mathrm{f}$ are the particle horizon and the future horizon,
respectively (moreover $a$ is the scale factor of the universe). Based on such
formalism, in the present paper, we show that a wide class of dark energy (DE)
models can be regarded as different candidates of the generalized HDE family,
with respective cut-offs. This can be thought as a symmetry between the
generalized HDE and different DE models. In this regard, we consider several
entropic dark energy models - like Tsallis entropic DE, the R\'{e}nyi entropic
DE, and the Sharma-Mittal entropic DE - and showed that they are indeed
equivalent with the generalized HDE. Such equivalence between the entropic DE
and the generalized HDE is extended to the scenario where the respective
exponents of the entropy functions are allowed to vary with the expansion of
the universe. Besides the entropic DE models, the correspondence with the
generalized HDE is also established for the Quintessence and for the Ricci DE
models. In all the above cases, the effective equation of state (EoS) parameter
corresponds to the holographic energy density are determined, by which the
equivalence of various DE models with the respective generalized HDE models are
further confirmed. The equivalent holographic cut-offs are determined by two
ways: (1) in terms of the particle horizon and its derivatives, (2) in terms of
the future horizon horizon and its derivatives.


Abstract:
  Automated program verification is a difficult problem. It is undecidable even
for transition systems over Linear Integer Arithmetic (LIA). Extending the
transition system with theory of Arrays, further complicates the problem by
requiring inference and reasoning with universally quantified formulas. In this
paper, we present a new algorithm, Quic3, that extends IC3 to infer universally
quantified invariants over the combined theory of LIA and Arrays. Unlike other
approaches that use either IC3 or an SMT solver as a black box, Quic3 carefully
manages quantified generalization (to construct quantified invariants) and
quantifier instantiation (to detect convergence in the presence of
quantifiers). While Quic3 is not guaranteed to converge, it is guaranteed to
make progress by exploring longer and longer executions. We have implemented
Quic3 within the Constrained Horn Clause solver engine of Z3 and experimented
with it by applying Quic3 to verifying a variety of public benchmarks of array
manipulating C programs.


Abstract:
  Fission properties of the actinide nuclei are deduced from theoretical
analysis. We investigate potential energy surfaces and fission barriers and
predict the fission fragment mass-yields of actinide isotopes. The results are
compared with experimental data where available. The calculations were
performed in the macroscopic-microscopic approximation with the
Lublin-Strasbourg Drop (LSD) for the macroscopic part and the microscopic
energy corrections were evaluated in the Yukawa-folded potential. The Fourier
nuclear shape parametrization is used to describe the nuclear shape, including
the non-axial degree of freedom. The fission fragment mass-yields of considered
nuclei are evaluated within a 3D collective model using the Born-Oppenheimer
approximation.


Abstract:
  Light beams carrying orbital-angular-momentum (OAM) play an important role in
optical manipulation and communication owing to their unbounded state space.
However, it is still challenging to efficiently discriminate OAM modes with
large topological charges and thus only a small part of the OAM states have
been usually used. Here we demonstrate that neural networks can be trained to
sort OAM modes with large topological charges and unknown superpositions. Using
intensity images of OAM modes generalized in simulations and experiments as the
input data, we illustrate that our neural network has great generalization
power to recognize OAM modes of large topological charges beyond training areas
with high accuracy. Moreover, the trained neural network can correctly classify
and predict arbitrary superpositions of two OAM modes with random topological
charges. Our machine learning approach only requires a small portion of
experimental samples and significantly reduces the cost in experiments, which
paves the way to study the OAM physics and increase the state space of OAM
beams in practical applications.


Abstract:
  Product quantization (PQ) is a widely used technique for ad-hoc retrieval.
Recent studies propose supervised PQ, where the embedding and quantization
models can be jointly trained with supervised learning. However, there is a
lack of appropriate formulation of the joint training objective; thus, the
improvements over previous non-supervised baselines are limited in reality. In
this work, we propose the Matching-oriented Product Quantization (MoPQ), where
a novel objective Multinoulli Contrastive Loss (MCL) is formulated. With the
minimization of MCL, we are able to maximize the matching probability of query
and ground-truth key, which contributes to the optimal retrieval accuracy.
Given that the exact computation of MCL is intractable due to the demand of
vast contrastive samples, we further propose the Differentiable Cross-device
Sampling (DCS), which significantly augments the contrastive samples for
precise approximation of MCL. We conduct extensive experimental studies on four
real-world datasets, whose results verify the effectiveness of MoPQ. The code
is available at https://github.com/microsoft/MoPQ.


Abstract:
  We show that a conjecture of Putman-Wieland, which posits the nonexistence of
finite orbits for higher Prym representations of the mapping class group, is
equivalent to the existence of surface-by-surface and surface-by-free groups
which do not virtually algebraically fiber.


Abstract:
  Children in a large number of international and cross-cultural families in
and outside of the US learn and speak more than one language. However, parents
often struggle to acquaint their young children with their local language if
the child spends majority of time at home and with their spoken language if
they go to daycare or school. By reviewing relevant literature about the role
of screen media content in young children's language learning, and interviewing
a subset of parents raising multilingual children, we explore the potential of
designing conversational user interfaces which can double as an assistive
language aid.We present a preliminary list of objectives to guide the the
design of conversational user interfaces dialogue for young children's
bilingual language acquisition.


Abstract:
  Inelastic scattering experiments are key methods for mapping the full
dispersion of fundamental excitations of solids in the ground as well as
non-equilibrium states. A quantitative analysis of inelastic scattering in
terms of phonon excitations requires identifying the role of multi-phonon
processes. Here, we develop an efficient first-principles methodology for
calculating the all-phonon quantum mechanical structure factor of solids. We
demonstrate our method by obtaining excellent agreement between measurements
and calculations of the diffuse scattering patterns of black phosphorus,
showing that multi-phonon processes play a substantial role. The present
approach constitutes a step towards the interpretation of static and
time-resolved electron, X-ray, and neutron inelastic scattering data.


Abstract:
  We introduce a family of Generalized Continuous Maxwell Demons (GCMDs)
operating on idealized single-bit equilibrium devices that combine the
single-measurement Szilard and the Continuous Maxwell Demon protocols. We
derive the cycle-distributions for extracted work, information-content and
time, and compute the power and information-to-work efficiency fluctuations for
the different models. We show that the efficiency at maximum power is maximal
for an opportunistic protocol of continuous-type in the dynamical regime
dominated by rare events. We also extend the analysis to finite-time work
extracting protocols by mapping them to a three-state GCMD. We show that
dynamical finite-time correlations in this model increase the
information-to-work conversion efficiency, underlining the role of temporal
correlations in optimizing information-to-energy conversion.


Abstract:
  The UN-Habitat estimates that over one billion people live in slums around
the world. However, state-of-the-art techniques to detect the location of slum
areas employ high-resolution satellite imagery, which is costly to obtain and
process. As a result, researchers have started to look at utilising free and
open-access medium resolution satellite imagery. Yet, there is no clear
consensus on which data preparation and machine learning approaches are the
most appropriate to use with such imagery data. In this paper, we evaluate two
techniques (multi-spectral data and grey-level co-occurrence matrix feature
extraction) on an open-access dataset consisting of labelled Sentinel-2 images
with a spatial resolution of 10 meters. Both techniques were paired with a
canonical correlation forests classifier. The results show that the grey-level
co-occurrence matrix performed better than multi-spectral data for all four
cities. It had an average accuracy for the slum class of 97% and a mean
intersection over union of 94%, while multi-spectral data had 75% and 64% for
the respective metrics. These results indicate that open-access satellite
imagery with a resolution of at least 10 meters may be suitable for keeping
track of development goals such as the detection of slums in cities.


Abstract:
  Legal English is a sublanguage that is important for everyone but not for
everyone to understand. Pretrained models have become best practices among
current deep learning approaches for different problems. It would be a waste or
even a danger if these models were applied in practice without knowledge of the
sublanguage of the law. In this paper, we raise the issue and propose a trivial
solution by introducing BERTLaw a legal sublanguage pretrained model. The
paper's experiments demonstrate the superior effectiveness of the method
compared to the baseline pretrained model


Abstract:
  In this article, we first introduced the inflated unit Lindley distribution
considering zero or/and one inflation scenario and studied its basic
distributional and structural properties. Both the distributions are shown to
be members of exponential family with full rank. Different parameter estimation
methods are discussed and supporting simulation studies to check their efficacy
are also presented. Proportion of students passing the high school leaving
examination for the schools across the state of Manipur in India for the year
2020 are then modeled using the proposed distributions and compared with the
inflated beta distribution to justify its benefits.


Abstract:
  Readability assessment is the task of evaluating the reading difficulty of a
given piece of text. Although research on computational approaches to
readability assessment is now two decades old, there is not much work on
synthesizing this research. This article is a brief survey of contemporary
research on developing computational models for readability assessment. We
identify the common approaches, discuss their shortcomings, and identify some
challenges for the future. Where possible, we also connect computational
research with insights from related work in other disciplines such as education
and psychology.


Abstract:
  Event-based sensors have the potential to optimize energy consumption at
every stage in the signal processing pipeline, including data acquisition,
transmission, processing and storage. However, almost all state-of-the-art
systems are still built upon the classical Nyquist-based periodic signal
acquisition. In this work, we design and validate the Polygonal Approximation
Sampler (PAS), a novel circuit to implement a general-purpose event-based
sampler using a polygonal approximation algorithm as the underlying sampling
trigger. The circuit can be dynamically reconfigured to produce a coarse or a
detailed reconstruction of the analog input, by adjusting the error threshold
of the approximation. The proposed circuit is designed at the Register Transfer
Level and processes each input sample received from the ADC in a single clock
cycle. The PAS has been tested with three different types of archetypal signals
captured by wearable devices (electrocardiogram, accelerometer and respiration
data) and compared with a standard periodic ADC. These tests show that
single-channel signals, with slow variations and constant segments (like the
used single-lead ECG and the respiration signals) take great advantage from the
used sampling technique, reducing the amount of data used up to 99% without
significant performance degradation. At the same time, multi-channel signals
(like the six-dimensional accelerometer signal) can still benefit from the
designed circuit, achieving a reduction factor up to 80% with minor performance
degradation. These results open the door to new types of wearable sensors with
reduced size and higher battery lifetime.


Abstract:
  Space information networks (SIN) are facing an ever-increasing thirst for
high-speed and high-capacity seamless data transmission due to the integration
of ground, air, and space communications. However, this imposes a new paradigm
on the architecture design of the integrated SIN. Recently, reconfigurable
intelligent surfaces (RISs) and mobile edge computing (MEC) are the most
promising techniques, conceived to improve communication and computation
capability by reconfiguring the wireless propagation environment and
offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap
the double benefits of computation and communication. In this article, we
propose an RIS-assisted collaborative MEC architecture for SIN and discuss its
implementation. Then we present its potential benefits, major challenges, and
feasible applications. Subsequently, we study different cases to evaluate the
system data rate and latency. Finally, we conclude with a list of open issues
in this research area.


Abstract:
  The presence of stable, compact circumbinary discs of gas and dust around
post-asymptotic giant branch (post-AGB) binary systems has been well
established. We focus on one such system: IRAS 08544-4431. We present an
interferometric multi-wavelength analysis of the circumstellar environment of
IRAS 08544-4431. The aim is to constrain different contributions to the total
flux in the H, K, L, and N-bands in the radial direction. The data from
VLTI/PIONIER, VLTI/GRAVITY, and VLTI/MATISSE range from the near-infrared,
where the post-AGB star dominates, to the mid-infrared, where the disc
dominates. We fitted two geometric models to the visibility data to reproduce
the circumbinary disc: a ring with a Gaussian width and a flat disc model with
a temperature gradient. The flux contributions from the disc, the primary star
(modelled as a point-source), and an over-resolved component are recovered
along with the radial size of the emission, the temperature of the disc as a
function of radius, and the spectral dependencies of the different components.
The trends of all visibility data were well reproduced with the geometric
models. The near-infrared data were best fitted with a Gaussian ring model
while the mid-infrared data favoured a temperature gradient model. This implies
that a vertical structure is present at the disc inner rim, which we attribute
to a rounded puffed-up inner rim. The N-to-K size ratio is 2.8, referring to a
continuous flat source, analogues to young stellar objects. By combining
optical interferometric instruments operating at different wavelengths we can
resolve the complex structure of circumstellar discs and study the
wavelength-dependent opacity profile. A detailed radial, vertical, and
azimuthal structural analysis awaits a radiative transfer treatment in 3D to
capture all non-radial complexity.


Abstract:
  We report on the development and extensive characterization of co-sputtered
tantala-zirconia thin films, with the goal to decrease coating Brownian noise
in present and future gravitational-wave detectors. We tested a variety of
sputtering processes of different energies and deposition rates, and we
considered the effect of different values of cation ratio $\eta =$ Zr/(Zr+Ta)
and of post-deposition heat treatment temperature $T_a$ on the optical and
mechanical properties of the films. Co-sputtered zirconia proved to be an
efficient way to frustrate crystallization in tantala thin films, allowing for
a substantial increase of the maximum annealing temperature and hence for a
decrease of coating mechanical loss. The lowest average coating loss was
observed for an ion-beam sputtered sample with $\eta = 0.485 \pm 0.004$
annealed at 800 $^{\circ}$C, yielding $\overline{\varphi} = 1.8 \times
10^{-4}$. All coating samples showed cracks after annealing. Although in
principle our measurements are sensitive to such defects, we found no evidence
that our results were affected. The issue could be solved, at least for
ion-beam sputtered coatings, by decreasing heating and cooling rates down to 7
$^{\circ}$C/h. While we observed as little optical absorption as in the
coatings of current gravitational-wave interferometers (0.5 parts per million),
further development will be needed to decrease light scattering and avoid the
formation of defects upon annealing.


Abstract:
  Beginning with the Everett-DeWitt many-worlds interpretation of quantum
mechanics, there have been a series of proposals for how the state vector of a
quantum system might split at any instant into orthogonal branches, each of
which exhibits approximately classical behavior. Here we propose a
decomposition of a state vector into branches by finding the minimum of a
measure of the mean squared quantum complexity of the branches in the branch
decomposition. In a non-relativistic formulation of this proposal, branching
occurs repeatedly over time, with each branch splitting successively into
further sub-branches among which the branch followed by the real world is
chosen randomly according to the Born rule. In a Lorentz covariant version, the
real world is a single random draw from the set of branches at asymptotically
late time, restored to finite time by sequentially retracing the set of
branching events implied by the late time choice. The complexity measure
depends on a parameter $b$ with units of volume which sets the boundary between
quantum and classical behavior. The value of $b$ is, in principle, accessible
to experiment.


Abstract:
  We prove existence, uniqueness and non-negativity of solutions of certain
integral equations describing the density of states $u(z)$ in the spectral
theory of soliton gases for the one dimensional integrable focusing Nonlinear
Schr\"{o}dinger Equation (fNLS) and for the Korteweg de Vries (KdV) equation.
Our proofs are based on ideas and methods of potential theory. In particular,
we show that the minimizing (positive) measure for certain energy functional is
absolutely continuous and its density $u(z)\geq 0$ solves the required integral
equation. In a similar fashion we show that $v(z)$, the temporal analog of
$u(z)$, is the difference of densities of two absolutely continuous measures.
Together, integral equations for $u,v$ represent nonlinear dispersion relation
for the fNLS soliton gas. We also discuss smoothness and other properties of
the obtained solutions. Finally, we obtain exact solutions of the above
integral equations in the case of a KdV condensate and a bound state fNLS
condensate. Our results is a first step towards a mathematical foundation for
the spectral theory of soliton and breather gases, which appeared in work of El
and Tovbis, Phys. Rev. E, 2020. It is expected that the presented ideas and
methods will be useful for studying similar classes of integral equation
describing, for example, breather gases for the fNLS, as well as soliton gases
of various integrable systems.


Abstract:
  The second law of thermodynamics is asymmetric with respect to time as it
says that the entropy of the universe must have been lower in the past and will
be higher in the future. How this time-asymmetric law arises from the
time-symmetric equations of motion has been the subject of extensive discussion
in the scientific literature. The currently accepted resolution of the problem
is to assume that the universe began in a low entropy state for an unknown
reason. But the probability of this happening by chance is exceedingly small,
if all microstates are assigned equal a-priori probabilities. In this paper, I
explore another possible explanation, which is that our observations of the
time-asymmetric increase of entropy could simply be the result of the way we
assign a-priori probabilities differently to past and future events.


Abstract:
  Recently, there have been several papers that discuss the extension of the
Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by
Huang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function,
which has been defined in terms of the parameter $\tau$. The parameter $\tau$
can take values in $[ -1,1]$. The existing Pin-SVM model requires to solve the
same optimization problem for all values of $\tau$ in $[ -1,1]$. In this paper,
we improve the existing Pin-SVM model for the binary classification task. At
first, we note that there is major difficulty in Pin-SVM model (Huang et al.
[1]) for $ -1 \leq \tau < 0$. Specifically, we show that the Pin-SVM model
requires the solution of different optimization problem for $ -1 \leq \tau <
0$. We further propose a unified model termed as Unified Pin-SVM which results
in a QPP valid for all $-1\leq \tau \leq 1$ and hence more convenient to use.
The proposed Unified Pin-SVM model can obtain a significant improvement in
accuracy over the existing Pin-SVM model which has also been empirically
justified by extensive numerical experiments with real-world datasets.


Abstract:
  The presence of non-zero helicity in intergalactic magnetic fields is a
smoking gun for their primordial origin since they have to be generated by
processes that break CP invariance. As an experimental signature for the
presence of helical magnetic fields, an estimator $Q$ based on the triple
scalar product of the wave-vectors of photons generated in electromagnetic
cascades from, e.g., TeV blazars, has been suggested previously. We propose to
apply deep learning to helicity classification employing Convolutional Neural
Networks and show that this method outperforms the $Q$ estimator.


Abstract:
  Being able to segment unseen classes not observed during training is an
important technical challenge in deep learning, because of its potential to
reduce the expensive annotation required for semantic segmentation. Prior
zero-label semantic segmentation works approach this task by learning
visual-semantic embeddings or generative models. However, they are prone to
overfitting on the seen classes because there is no training signal for them.
In this paper, we study the challenging generalized zero-label semantic
segmentation task where the model has to segment both seen and unseen classes
at test time. We assume that pixels of unseen classes could be present in the
training images but without being annotated. Our idea is to capture the latent
information on unseen classes by supervising the model with self-produced
pseudo-labels for unlabeled pixels. We propose a consistency regularizer to
filter out noisy pseudo-labels by taking the intersections of the pseudo-labels
generated from different augmentations of the same image. Our framework
generates pseudo-labels and then retrain the model with human-annotated and
pseudo-labelled data. This procedure is repeated for several iterations. As a
result, our approach achieves the new state-of-the-art on PascalVOC12 and
COCO-stuff datasets in the challenging generalized zero-label semantic
segmentation setting, surpassing other existing methods addressing this task
with more complex strategies.


Abstract:
  We consider a diffusion given by a small noise perturbation of a dynamical
system driven by a potential function with a finite number of local minima. The
classical results of Freidlin and Wentzell show that the time this diffusion
spends in the domain of attraction of one of these local minima is
approximately exponentially distributed and hence the diffusion should behave
approximately like a Markov chain on the local minima. By the work of Bovier
and collaborators, the local minima can be associated with the small
eigenvalues of the diffusion generator. In Part I of this work, by applying a
Markov mapping theorem, we used the eigenfunctions of the generator to couple
this diffusion to a Markov chain whose generator has eigenvalues equal to the
eigenvalues of the diffusion generator that are associated with the local
minima and established explicit formulas for conditional probabilities
associated with this coupling. The fundamental question now becomes to relate
the coupled Markov chain to the approximate Markov chain suggested by the
results of Freidlin and Wentzel. In this paper, we take up this question and
provide a complete analysis of this relationship in the special case of a
double-well potential in one dimension.


Abstract:
  To reconcile the two experimental findings on La_{2-x}Sr_{x}CuO_4, namely,
Fermi surface (FS) observed by angle-resolved photoemission spectroscopy and
sharp incommensurate magnetic peaks by neutron scattering, we propose a picture
that a quasi-one-dimensional FS (q-1dFS) is realized in each CuO_{2} plane
whose q-1d direction alternates along the c-axis.


Abstract:
  We consider the problem of finding the subset of order statistics that
contains the most information about a sample of random variables drawn
independently from some known parametric distribution. We leverage
information-theoretic quantities, such as entropy and mutual information, to
quantify the level of informativeness and rigorously characterize the amount of
information contained in any subset of the complete collection of order
statistics. As an example, we show how these informativeness metrics can be
evaluated for a sample of discrete Bernoulli and continuous Uniform random
variables. Finally, we unveil how our most informative order statistics
framework can be applied to image processing applications. Specifically, we
investigate how the proposed measures can be used to choose the coefficients of
the L-estimator filter to denoise an image corrupted by random noise. We show
that both for discrete (e.g., salt-pepper noise) and continuous (e.g., mixed
Gaussian noise) noise distributions, the proposed method is competitive with
off-the-shelf filters, such as the median and the total variation filters, as
well as with wavelet-based denoising methods.


Abstract:
  In [6] the authors gave a generalization of the concept of Igusa-Todorov
algebra and proved that those algebras, named Lat-Igusa-Todorov (or LIT for
short), satisfy the finitistic dimension conjecture. In this paper we explore
the scope of that generalization and give conditions for a triangular matrix
algebra to be LIT in terms of the algebras and the module used in its
definition.


Abstract:
  Classical machine learning (ML) provides a potentially powerful approach to
solving challenging quantum many-body problems in physics and chemistry.
However, the advantages of ML over more traditional methods have not been
firmly established. In this work, we prove that classical ML algorithms can
efficiently predict ground state properties of gapped Hamiltonians in finite
spatial dimensions, after learning from data obtained by measuring other
Hamiltonians in the same quantum phase of matter. In contrast, under widely
accepted complexity theory assumptions, classical algorithms that do not learn
from data cannot achieve the same guarantee. We also prove that classical ML
algorithms can efficiently classify a wide range of quantum phases of matter.
Our arguments are based on the concept of a classical shadow, a succinct
classical description of a many-body quantum state that can be constructed in
feasible quantum experiments and be used to predict many properties of the
state. Extensive numerical experiments corroborate our theoretical results in a
variety of scenarios, including Rydberg atom systems, 2D random Heisenberg
models, symmetry-protected topological phases, and topologically ordered
phases.


Abstract:
  We investigate a mechanism for a super-massive black hole at the center of a
galaxy to wander in the nucleus region. A situation is supposed in which the
central black hole tends to move by the gravitational attractions from the
nearby molecular clouds in a nuclear bulge but is braked via the dynamical
frictions by the ambient stars there. We estimate the approximate kinetic
energy of the black hole in an equilibrium between the energy gain rate through
the gravitational attractions and the energy loss rate through the dynamical
frictions, in a nuclear bulge composed of a nuclear stellar disk and a nuclear
stellar cluster as observed from our Galaxy. The wandering distance of the
black hole in the gravitational potential of the nuclear bulge is evaluated to
get as large as several 10 pc, when the black hole mass is relatively small.
The distance, however, shrinks as the black hole mass increases and the
equilibrium solution between the energy gain and loss disappears when the black
hole mass exceeds an upper limit. As a result, we can expect the following
scenario for the evolution of the black hole mass: When the black hole mass is
smaller than the upper limit, mass accretion of the interstellar matter in the
circum-nuclear region, causing the AGN activities, makes the black hole mass
larger. However, when the mass gets to the upper limit, the black hole loses
the balancing force against the dynamical friction and starts spiraling
downward to the gravity center. From simple parameter scaling, the upper mass
limit of the black hole is found to be proportional to the bulge mass and this
could explain the observed correlation of the black hole mass with the bulge
mass.