Abstract:
  Background: Blog posts are frequently used by software practitioners to share
information about their practice. Blog posts therefore provide a potential
source of evidence for software engineering (SE) research. The use of blog
posts as evidence for research appears contentious amongst some SE researchers.
Objective: To better understand the actual and perceived value of blog posts as
evidence for SE research, and to develop guidance for SE researchers on the use
of blog posts as evidence. Method: We further analyse responses from a
previously conducted attitudinal survey of 44 software engineering researchers.
We conduct a heatmap analysis, simple statistical analysis, and a thematic
analysis. Results: We find no clear consensus from respondents on researchers'
attitudes to the credibility of blog posts, or on a standard set of criteria to
evaluate blog-post credibility. We show that some of the responses to the
survey exhibit characteristics similar to the content of blog posts, e.g.,
asserting prior beliefs as claims, with no citations and little supporting
rationale. We illustrate our insights with ~60 qualitative examples from the
survey ~40% of the total responses. We complement our quantitative and
qualitative analyses with preliminary checklists to guide SE researchers.
Conclusion: Blog posts are relevant to research because they are written by
software practitioners describing their practice and experience. But evaluating
the credibility of blog posts, so as to select the higher-quality content,
remains an ongoing challenge. The quantitative and qualitative results, with
the proposed checklists, are intended to stimulate reflection and action in the
research community on the role of blog posts as evidence in software
engineering research. Finally, our findings on researchers' attitudes to blog
posts also provide more general insights into researchers' values for SE
research.


Abstract:
  We study the collective dynamics of identical phase oscillators on globally
coupled networks whose interactions are asymmetric and mediated by positive and
negative couplings. We split the set of oscillators into two interconnected
subpopulations. In this setup, oscillators belonging to the same group interact
via symmetric couplings while the interaction between subpopulations occurs in
an asymmetric fashion. By employing the dimensional reduction scheme of the
Ott-Antonsen (OA) theory, we verify the existence of traveling-wave and
$\pi$-states, in addition to the classical fully synchronized and incoherent
states. Bistability between all collective states is reported. Analytical
results are generally in excellent agreement with simulations; for some
parameters and initial conditions, however, we numerically detect chimera-like
states which are not captured by the OA theory.


Abstract:
  We present a new hybrid, local search algorithm for quantum approximate
optimization of constrained combinatorial optimization problems. We focus on
the Maximum Independent Set problem and demonstrate the ability of quantum
local search to solve large problem instances on quantum devices with few
qubits. The quantum local search algorithm iteratively finds independent sets
over carefully constructed neighborhoods and combines these solutions to obtain
a global solution. We compare the performance of this algorithm on 3-regular
graphs with up to 100 nodes against the well known classical
Boppana-Halld\'{o}rsson algorithm for the Maximum Independent Set problem.


Abstract:
  This paper proposes an efficient autonomous valet parking system utilizing
only cameras which are the most widely used sensor. To capture more information
instantaneously and respond rapidly to changes in the surrounding environment,
fisheye cameras which have a wider angle of view compared to pinhole cameras
are used. Accordingly, visual simultaneous localization and mapping is used to
identify the layout of the parking lot and track the location of the vehicle.
In addition, the input image frames are converted into around view monitor
images to resolve the distortion of fisheye lens because the algorithm to
detect edges are supposed to be applied to images taken with pinhole cameras.
The proposed system adopts a look up table for real time operation by
minimizing the computational complexity encountered when processing AVM images.
The detection rate of each process and the success rate of autonomous parking
were measured to evaluate performance. The experimental results confirm that
autonomous parking can be achieved using only visual sensors.


Abstract:
  A local UV cutoff $\Lambda(x)$ transforming under Weyl rescalings allows to
construct Weyl invariant kinetic terms for scalar fields including Wilsonian
cutoff functions. First we consider scalar fields in curved space-time with
local bare couplings of any canonical dimension, and anomalous dimensions which
describe their dependence on the UV cutoff. The local component of the UV
cutoff plays the role of an additional coupling, albeit with a trivial constant
$\beta$ function. This approach allows to derive Weyl consistency conditions
for the corresponding anomalous dimensions which assume the form of an exact
gradient flow. For renormalizable theories the Weyl consistency conditions are
initially of the form of an approximate gradient flow for the $\beta$
functions, and we derive conditions under which it becomes the form of an exact
gradient flow.


Abstract:
  Quantum computers have the potential to speed up certain computational tasks.
A possibility this opens up within the field of machine learning is the use of
quantum techniques that may be inefficient to simulate classically but could
provide superior performance in some tasks. Machine learning algorithms are
ubiquitous in particle physics and as advances are made in quantum machine
learning technology there may be a similar adoption of these quantum
techniques. In this work a quantum support vector machine (QSVM) is implemented
for signal-background classification. We investigate the effect of different
quantum encoding circuits, the process that transforms classical data into a
quantum state, on the final classification performance. We show an encoding
approach that achieves an average Area Under Receiver Operating Characteristic
Curve (AUC) of 0.848 determined using quantum circuit simulations. For this
same dataset the best classical method tested, a classical Support Vector
Machine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of
0.793. Using a reduced version of the dataset we then ran the algorithm on the
IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As
further improvements to the error rates and availability of quantum computers
materialise, they could form a new approach for data analysis in high energy
physics.


Abstract:
  Structural features are important features in a geometrical graph. Although
there are some correlation analysis of features based on covariance, there is
no relevant research on structural feature correlation analysis with graph
neural networks. In this paper, we introuduce graph feature to feature
(Fea2Fea) prediction pipelines in a low dimensional space to explore some
preliminary results on structural feature correlation, which is based on graph
neural network. The results show that there exists high correlation between
some of the structural features. An irredundant feature combination with
initial node features, which is filtered by graph neural network has improved
its classification accuracy in some graph-based tasks. We compare differences
between concatenation methods on connecting embeddings between features and
show that the simplest is the best. We generalize on the synthetic geometric
graphs and certify the results on prediction difficulty between structural
features.


Abstract:
  We study elliptic and parabolic problems governed by the singular elliptic
operators Delta_x+c\yD_y-b\y^2 on the half-space R^{N+1}_+.


Abstract:
  A variety of emergent phenomena has been enabled by interface engineering in
the complex oxides heterostructures. While extensive attention has been
attracted to LaMnO3 (LMO) thin films for observing the control of
functionalities at its interface with substrate, the nature of the magnetic
phases in the thin film is, however,controversial. Here, it is reported that
the ferromagnetism in 2 and 5 unit cells thick LMO films epitaxially deposited
(001)-SrTiO3 substrates ferromagnetic/ferromagnetic coupling in 8 and
10-unit-cell ones, and a striking ferromagnetic/antiferromagnetic pinning
effect with apparent positive exchange bias in 15 and 20-unit-cell ones are
observed. This novel phenomenon in both 15 and 20-unit-cell films indicates a
coexistence of three magnetic orderings in a single LMO film.The
high-resolution scanning transmission electron microscopy suggests a P21/n to
Pbnm symmetry transition from interface to surface, with the spatial
stratification of MnO6 octahedral morphology, corresponding to different
magnetic orderings. These results should shed some new lights on manipulating
the functionality of oxides by interface engineering.


Abstract:
  The ground truth used for training image, video, or speech quality prediction
models is based on the Mean Opinion Scores (MOS) obtained from subjective
experiments. Usually, it is necessary to conduct multiple experiments, mostly
with different test participants, to obtain enough data to train quality models
based on machine learning. Each of these experiments is subject to an
experiment-specific bias, where the rating of the same file may be
substantially different in two experiments (e.g. depending on the overall
quality distribution). These different ratings for the same distortion levels
confuse neural networks during training and lead to lower performance. To
overcome this problem, we propose a bias-aware loss function that estimates
each dataset's biases during training with a linear function and considers it
while optimising the network weights. We prove the efficiency of the proposed
method by training and validating quality prediction models on synthetic and
subjective image and speech quality datasets.


Abstract:
  Automatic speaker verification (ASV) is a well developed technology for
biometric identification, and has been ubiquitous implemented in
security-critic applications, such as banking and access control. However,
previous works have shown that ASV is under the radar of adversarial attacks,
which are very similar to their original counterparts from human's perception,
yet will manipulate the ASV render wrong prediction. Due to the very late
emergence of adversarial attacks for ASV, effective countermeasures against
them are limited. Given that the security of ASV is of high priority, in this
work, we propose the idea of "voting for the right answer" to prevent risky
decisions of ASV in blind spot areas, by employing random sampling and voting.
Experimental results show that our proposed method improves the robustness
against both the limited-knowledge attackers by pulling the adversarial samples
out of the blind spots, and the perfect-knowledge attackers by introducing
randomness and increasing the attackers' budgets.


Abstract:
  People's visual experiences of the world are easy to carve up and examine
along natural language boundaries, e.g., by category labels, attribute labels,
etc. However, it is more difficult to elicit detailed visuospatial information
about what a person attends to, e.g., the specific shape of a tree. Paying
attention to the shapes of things not only feeds into well defined tasks like
visual category learning, but it is also what enables us to differentiate
similarly named objects and to take on creative visual pursuits, like
poetically describing the shape of a thing, or finding shapes in the clouds or
stars. We use a new data collection method that elicits people's prioritized
attention to shapes during visual photo inspection by asking them to trace
important parts of the image under varying time constraints. Using data
collected via crowdsourcing over a set of 187 photographs, we examine changes
in patterns of visual attention across individuals, across image types, and
across time constraints.


Abstract:
  In a recent work we presented a recursive algorithm to compute the matrix
elements of a generic Gaussian transformation in the photon-number basis. Its
purpose was to evolve a quantum state by building the transformation matrix and
subsequently computing the matrix-vector product. Here we present a faster
algorithm that computes the final state without having to generate the full
transformation matrix first. With this algorithm we bring the time complexity
of computing the Gaussian evolution of an $N$-dimensional $M$-mode state from
$O(MN^{2M})$ to $O(M(N^2/2)^M)$, which is an exponential improvement in the
number of modes. In the special case of high squeezing, the evolved state can
be approximated with complexity $O(MN^{M})$. Our new algorithm is
differentiable, which means we can use it in conjunction with gradient-based
optimizers for circuit optimization tasks. We benchmark our algorithm by
optimizing circuits to produce single photons, Gottesman-Kitaev-Preskill states
and NOON states, showing that it is up to one order of magnitude faster than
the state of the art.


Abstract:
  The rapid increase of distributed energy resources has led to the widespread
deployment of microgrids. These flexible and efficient local energy grids are
able to operate in both grid-connected mode and islanded mode; they are
interfaced to the main power system by a fast semiconductor switch and commonly
make use of inverter-interfaced generation. This paper focuses on inverter
interfaced microgrids, which present a challenge for protection as they do not
provide the high short-circuit current necessary for conventional
time-overcurrent protection. The application of admittance relaying for the
protection of inverter-interfaced microgrids is investigated as a potential
solution. The comparison of analytical and simulated results of performed four
experiments prove the suitability of admittance relaying for microgrids
protection.


Abstract:
  The task of semantic role labeling (SRL) is dedicated to finding the
predicate-argument structure. Previous works on SRL are mostly supervised and
do not consider the difficulty in labeling each example which can be very
expensive and time-consuming. In this paper, we present the first neural
unsupervised model for SRL. To decompose the task as two argument related
subtasks, identification and clustering, we propose a pipeline that
correspondingly consists of two neural modules. First, we train a neural model
on two syntax-aware statistically developed rules. The neural model gets the
relevance signal for each token in a sentence, to feed into a BiLSTM, and then
an adversarial layer for noise-adding and classifying simultaneously, thus
enabling the model to learn the semantic structure of a sentence. Then we
propose another neural model for argument role clustering, which is done
through clustering the learned argument embeddings biased towards their
dependency relations. Experiments on CoNLL-2009 English dataset demonstrate
that our model outperforms previous state-of-the-art baseline in terms of
non-neural models for argument identification and classification.


Abstract:
  This paper presents a neural-network-based solution to recover pixels
occluded by clouds in satellite images. We leverage radio frequency (RF)
signals in the ultra/super-high frequency band that penetrate clouds to help
reconstruct the occluded regions in multispectral images. We introduce the
first multi-modal multi-temporal cloud removal model. Our model uses publicly
available satellite observations and produces daily cloud-free images.
Experimental results show that our system significantly outperforms baselines
by 8dB in PSNR. We also demonstrate use cases of our system in digital
agriculture, flood monitoring, and wildfire detection. We will release the
processed dataset to facilitate future research.


Abstract:
  Empirical mode decomposition (EMD) has developed into a prominent tool for
adaptive, scale-based signal analysis in various fields like robotics, security
and biomedical engineering. Since the dramatic increase in amount of data puts
forward higher requirements for the capability of real-time signal analysis, it
is difficult for existing EMD and its variants to trade off the growth of data
dimension and the speed of signal analysis. In order to decompose
multi-dimensional signals at a faster speed, we present a novel
signal-serialization method (serial-EMD), which concatenates multi-variate or
multi-dimensional signals into a one-dimensional signal and uses various
one-dimensional EMD algorithms to decompose it. To verify the effects of the
proposed method, synthetic multi-variate time series, artificial 2D images with
various textures and real-world facial images are tested. Compared with
existing multi-EMD algorithms, the decomposition time becomes significantly
reduced. In addition, the results of facial recognition with Intrinsic Mode
Functions (IMFs) extracted using our method can achieve a higher accuracy than
those obtained by existing multi-EMD algorithms, which demonstrates the
superior performance of our method in terms of the quality of IMFs.
Furthermore, this method can provide a new perspective to optimize the existing
EMD algorithms, that is, transforming the structure of the input signal rather
than being constrained by developing envelope computation techniques or signal
decomposition methods. In summary, the study suggests that the serial-EMD
technique is a highly competitive and fast alternative for multi-dimensional
signal analysis.


Abstract:
  The magnetar SGR J1745-2900 located in the vicinity of the supermassive black
hole Sgr A$^{\star}$ was detected during its X-ray outburst with the Swifht/XRT
telescope in April 2013. For several months after its detection the source was
observed with the NuSTAR observatory, which allowed pulsations with a period
$\sim3.76$ s to be recorded. Using these observations, we have studied in
detail the dependence of the pulse profile and the pulsed fraction on the
energy and intensity of the magnetar. The pulsed fraction in the 3-5 and 5-10
keV energy bands is shown to be 40-50%, slightly increasing with decreasing
flux. We have performed phase-resolved spectroscopy for the source in the
energy band from 3 to $\sim$40 keV and show that the temperature of the
emitting regions remains fairly stable during the pulse, while their apparent
size changes significantly with phase.


Abstract:
  We relate the machinery of persistence modules to the Legendrian contact
homology theory and to Poisson bracket invariants, and use it to show the
existence of connecting trajectories of contact and symplectic Hamiltonian
flows.


Abstract:
  Deep neural networks have been the driving force behind the success in
classification tasks, e.g., object and audio recognition. Impressive results
and generalization have been achieved by a variety of recently proposed
architectures, the majority of which are seemingly disconnected. In this work,
we cast the study of deep classifiers under a unifying framework. In
particular, we express state-of-the-art architectures (e.g., residual and
non-local networks) in the form of different degree polynomials of the input.
Our framework provides insights on the inductive biases of each model and
enables natural extensions building upon their polynomial nature. The efficacy
of the proposed models is evaluated on standard image and audio classification
benchmarks. The expressivity of the proposed models is highlighted both in
terms of increased model performance as well as model compression. Lastly, the
extensions allowed by this taxonomy showcase benefits in the presence of
limited data and long-tailed data distributions. We expect this taxonomy to
provide links between existing domain-specific architectures.


Abstract:
  Recently, much progress in natural language processing has been driven by
deep contextualized representations pretrained on large corpora. Typically, the
fine-tuning on these pretrained models for a specific downstream task is based
on single-view learning, which is however inadequate as a sentence can be
interpreted differently from different perspectives. Therefore, in this work,
we propose a text-to-text multi-view learning framework by incorporating an
additional view -- the text generation view -- into a typical single-view
passage ranking model. Empirically, the proposed approach is of help to the
ranking performance compared to its single-view counterpart. Ablation studies
are also reported in the paper.


Abstract:
  This paper presents a techno-economic optimization model to analyze the
economic viability of a PV-battery system for different customer groups in
Switzerland clustered based on their annual electricity consumption, rooftop
size, annual irradiation and location. The simulations for a static investment
model are carried out for years 2020-2050 and a comprehensive sensitivity
analysis is conducted to investigate the impacts of individual parameters such
as costs, load profiles, electricity prices and tariffs, etc. Results show that
while combining PV with batteries already results in better net present values
than PV alone for some customer groups today, the payback periods fluctuate
between 2020 and 2035 due to the mixed effects of policy changes, costs and
electricity price developments. The optimal PV and battery sizes increase over
time and in 2050 the PV investment is mostly limited by the rooftop size. The
economic viability of PV-battery system investments varies between different
customer groups and the most attractive investment (i.e., that has the shortest
payback period) is mostly accessible to customer groups with higher annual
irradiation and electricity demand. In addition, investment decisions are
highly sensitive to payback periods, future costs, electricity prices and
tariff developments. Lastly, the grid impact of the PV-battery system
deployments are investigated by analyzing the residual Swiss system load
profiles. The dynamics of residual load profiles caused by the seasonal, daily
and hourly patterns of the solar generation emphasizes the need for flexible
resources with fast ramping capabilities.


Abstract:
  I simulate the evolution of a stellar system hosting two stellar populations
whose initial set up is defined according to the two main scenarios proposed
for the origin of multiple populations in Galactic globular clusters: (i)
formation of a second generation from a cooling flow of pristine+polluted gas
and (ii) accretion of polluted gas onto the proto-stellar disks of a fraction
of low-mass stars. For this purpose, Monte Carlo simulations containing from
$10^{5}$ up to $3\cdot 10^{6}$ particles have been run including the effect of
stellar evolution, binary interactions, external tidal field and a detailed
modelling of the proto-stellar disk structure. The early accretion of gas onto
proto-stellar disks is unable to produce discrete populations and to alter the
chemical composition of a significant ($>10\%$) fraction of stars unless a disk
lifetime larger ($t_{disk}\sim20~Myr$) than that predicted by models is
assumed. Moreover, in this scenario the mixing timescale of the two populations
is too short to reproduce the observed segregation of the chemically enriched
population. On the other hand, simulations run within the cooling flow scenario
can evolve after a Hubble time into stellar systems with a first-to-second
population mass ratio similar to that observed in globular clusters, provided
that an initial filling-factor $r_{h}/r_{J}>0.15$ is adopted. However, in the
weak tidal field regime a radial segregation of the second population stronger
than what observed in Milky Way globular clusters at large Galactocentric
distances is predicted. This discrepancy disappears in simulations following
eccentric orbits in a realistic axisymmetric potential.


Abstract:
  We consider the problem of jointly modeling and clustering populations of
tensors by introducing a high-dimensional tensor mixture model with
heterogeneous covariances. To effectively tackle the high dimensionality of
tensor objects, we employ plausible dimension reduction assumptions that
exploit the intrinsic structures of tensors such as low-rankness in the mean
and separability in the covariance. In estimation, we develop an efficient
high-dimensional expectation-conditional-maximization (HECM) algorithm that
breaks the intractable optimization in the M-step into a sequence of much
simpler conditional optimization problems, each of which is convex, admits
regularization and has closed-form updating formulas. Our theoretical analysis
is challenged by both the non-convexity in the EM-type estimation and having
access to only the solutions of conditional maximizations in the M-step,
leading to the notion of dual non-convexity. We demonstrate that the proposed
HECM algorithm, with an appropriate initialization, converges geometrically to
a neighborhood that is within statistical precision of the true parameter. The
efficacy of our proposed method is demonstrated through comparative numerical
experiments and an application to a medical study, where our proposal achieves
an improved clustering accuracy over existing benchmarking methods.


Abstract:
  Microarray gene expression data are often accompanied by a large number of
genes and a small number of samples. However, only a few of these genes are
relevant to cancer, resulting in signigicant gene selection challenges. Hence,
we propose a two-stage gene selection approach by combining extreme gradient
boosting (XGBoost) and a multi-objective optimization genetic algorithm
(XGBoost-MOGA) for cancer classification in microarray datasets. In the first
stage, the genes are ranked use an ensemble-based feature selection using
XGBoost. This stage can effectively remove irrelevant genes and yield a group
comprising the most relevant genes related to the class. In the second stage,
XGBoost-MOGA searches for an optimal gene subset based on the most relevant
genes's group using a multi-objective optimization genetic algorithm. We
performed comprehensive experiments to compare XGBoost-MOGA with other
state-of-the-art feature selection methods using two well-known learning
classifiers on 13 publicly available microarray expression datasets. The
experimental results show that XGBoost-MOGA yields significantly better results
than previous state-of-the-art algorithms in terms of various evaluation
criteria, such as accuracy, F-score, precision, and recall.


Abstract:
  We propose a fast partial video copy detection framework in this paper. In
this framework all frame features of the reference videos are organized in a
KNN searchable database. Instead of scanning all reference videos, the query
video segment does a fast KNN search in the global feature database. The
returned results are used to generate a short list of candidate videos. A
modified temporal network is then used to localize the copy segment in the
candidate videos. We evaluate different choice of CNN features on the VCDB
dataset. Our benchmark F1 score exceeds the state of the art by a big margin.


Abstract:
  We prove that any length metric space homeomorphic to a surface may be
decomposed into non-overlapping convex triangles of arbitrarily small diameter.
This generalizes a previous result of Alexandrov--Zalgaller for surfaces of
bounded curvature.


Abstract:
  In this paper, we propose a new approach to infer state machine models from
protocol implementations. Our method, STATEINSPECTOR, learns protocol states by
using novel program analyses to combine observations of run-time memory and
I/O. It requires no access to source code and only lightweight execution
monitoring of the implementation under test. We demonstrate and evaluate
STATEINSPECTOR's effectiveness on numerous TLS and WPA/2 implementations. In
the process, we show STATEINSPECTOR enables deeper state discovery, increased
learning efficiency, and more insightful post-mortem analyses than existing
approaches. Further to improved learning, our method led us to discover several
concerning deviations from the standards and a high impact vulnerability in a
prominent Wi-Fi implementation.


Abstract:
  In this paper, we investigate the stabilization of a linear Bresse system
with one singular local frictional damping acting in the longitudinal
displacement, under fully Dirichlet boundary conditions. First, we prove the
strong stability of our system. Next, using a frequency domain approach
combined with the multiplier method, we establish the exponential stability of
the solution if and only if the three waves have the same speed of propagation.
On the contrary, we prove that the energy of our system decays polynomially
with rates $t^{-1}$ or $t^{-\frac{1}{2}}$.


Abstract:
  We scrutinize the red dwarf component in the eclipsing binary system V471 Tau
in order to unravel relations between different activity layers from the
stellar surface through the chromosphere up to the corona. We aim at studying
how the magnetic dynamo in the late-type component is affected by the close
white dwarf companion. We use space photometry, high resolution spectroscopy
and X-ray observations from different space instruments to explore the main
characteristics of magnetic activity. From K2 photomery we find that 5-10 per
cent of the apparent surface of the red dwarf is covered by cool starspots.
From seasonal photometric period changes we estimate a weak differential
rotation. From the flare activity we derive a cumulative flare frequency
diagram which suggests that frequent flaring could have a significant role in
heating the corona. Using high resolution spectroscopy we reconstruct four
Doppler images for different epochs which reveal an active longitude, that is,
a permanent dominant spot facing the white dwarf. From short term changes in
the consecutive Doppler images we derive a weak solar-type surface differential
rotation with 0.0026 shear coefficient, similar to that provided by photometry.
The long-term evolution of X-ray luminosity reveals a possible activity cycle
length of 12.7 ys, traces of which were discovered also in the H$\alpha$
spectra. We conclude that the magnetic activity of the red dwarf component in
V471 Tau is strongly influenced by the close white dwarf companion. We confirm
the presence of a permanent dominant spot (active longitude) on the red dwarf
facing the white dwarf. The weak differential rotation of the red dwarf is very
likely the result of tidal confinement by the companion. We find that the
periodic appearance of the inter-binary H$\alpha$ emission from the vicinity of
the inner Lagrangian point is correlated with the activity cycle.


Abstract:
  Visual navigation and three-dimensional (3D) scene reconstruction are
essential for robotics to interact with the surrounding environment.
Large-scale scenes and critical camera motions are great challenges facing the
research community to achieve this goal. We raised a pose-only imaging geometry
framework and algorithms that can help solve these challenges. The
representation is a linear function of camera global translations, which allows
for efficient and robust camera motion estimation. As a result, the spatial
feature coordinates can be analytically reconstructed and do not require
nonlinear optimization. Experiments demonstrate that the computational
efficiency of recovering the scene and associated camera poses is significantly
improved by 2-4 orders of magnitude. This solution might be promising to unlock
real-time 3D visual computing in many forefront applications.


Abstract:
  We propose a simple method for automatic speech recognition (ASR) by
fine-tuning BERT, which is a language model (LM) trained on large-scale
unlabeled text data and can generate rich contextual representations. Our
assumption is that given a history context sequence, a powerful LM can narrow
the range of possible choices and the speech signal can be used as a simple
clue. Hence, comparing to conventional ASR systems that train a powerful
acoustic model (AM) from scratch, we believe that speech recognition is
possible by simply fine-tuning a BERT model. As an initial study, we
demonstrate the effectiveness of the proposed idea on the AISHELL dataset and
show that stacking a very simple AM on top of BERT can yield reasonable
performance.


Abstract:
  Expansions in non-integer bases have been extensively investigated since a
pioneering work of R\'enyi. We introduce a more general framework of
alphabet-base systems that also includes Pedicini's general alphabets and the
multiple-base expansions of Neunh\"auserer and Li. We extend the Parry type
lexicographic theory to this setup, and we improve and generalize various
former results on unique expansions.


Abstract:
  We present a novel finite element analysis of inelastic structures containing
Shape Memory Alloys (SMAs). Phenomenological constitutive models for SMAs lead
to material nonlinearities, that require substantial computational effort to
resolve. Finite element analysis methods, which rely on Gauss quadrature
integration schemes, must solve two sets of coupled differential equations: one
at the global level and the other at the local, i.e. Gauss point level. In
contrast to the conventional return mapping algorithm, which solves these two
sets of coupled differential equations separately using a nested Newton
procedure, we propose a scheme to solve the local and global differential
equations simultaneously. In the process we also derive closed-form expressions
used to update the internal/constitutive state variables, and unify the popular
closest-point and cutting plane methods with our formulas. Numerical testing
indicates that our method allows for larger thermomechanical loading steps and
provides increased computational efficiency, over the standard return mapping
algorithm.


Abstract:
  The concept of inertial frame of reference is analysed. It has been shown
that this fundamental concept of physics is not clear enough. A definition of
inertial frame of reference is proposed which expresses its key inherent
property. The definition is operational and powerful. Many other properties of
inertial frames follow from the definition or it makes them plausible. In
particular, the definition shows why physical laws obey space and time
symmetries and the principle of relativity, it resolves the problem of clock
synchronization and the role of light in it, as well as the problem of the
geometry of inertial frames.


Abstract:
  The homologous collapse from rest of a uniform density sphere under its self
gravity is a well-known toy model for the formation dynamics of astronomical
objects ranging from stars to galaxies. Equally well-known is that the
evolution of the radius with time cannot be explicitly obtained because of the
transcendental nature of the differential equation solution. Rather, both
radius and time are written parametrically in terms of the development angle
$\theta$. We here present an explicit integral solution for radius as a
function of time, exploiting methods from complex analysis recently applied to
the mathematically-similar 'geometric goat problem'. Our solution can be
efficiently evaluated using a Fast Fourier Transform and allows for arbitrary
sampling in time, with a simple Python implementation that is $\sim$$100\times$
faster than using numerical root-finding to achieve arbitrary sampling. Our
explicit solution is advantageous relative to the usual approach of first
generating a uniform grid in $\theta$, since this latter results in a
non-uniform radial or time sampling, less useful for applications such as
generation of sub-grid physics models.


Abstract:
  Users online tend to consume information adhering to their system of beliefs
and to ignore dissenting information. During the COVID-19 pandemic, users get
exposed to a massive amount of information about a new topic having a high
level of uncertainty. In this paper, we analyze two social media that enforced
opposite moderation methods, Twitter and Gab, to assess the interplay between
news consumption and content regulation concerning COVID-19. We compare the two
platforms on about three million pieces of content analyzing user interaction
with respect to news articles. We first describe users' consumption patterns on
the two platforms focusing on the political leaning of news outlets. Finally,
we characterize the echo chamber effect by modeling the dynamics of users'
interaction networks. Our results show that the presence of moderation pursued
by Twitter produces a significant reduction of questionable content, with a
consequent affiliation towards reliable sources in terms of engagement and
comments. Conversely, the lack of clear regulation on Gab results in the
tendency of the user to engage with both types of content, showing a slight
preference for the questionable ones which may account for a
dissing/endorsement behavior. Twitter users show segregation towards reliable
content with a uniform narrative. Gab, instead, offers a more heterogeneous
structure where users, independently of their leaning, follow people who are
slightly polarized towards questionable news.


Abstract:
  We consider the problem of semantic matching in product search: given a
customer query, retrieve all semantically related products from a huge catalog
of size 100 million, or more. Because of large catalog spaces and real-time
latency constraints, semantic matching algorithms not only desire high recall
but also need to have low latency. Conventional lexical matching approaches
(e.g., Okapi-BM25) exploit inverted indices to achieve fast inference time, but
fail to capture behavioral signals between queries and products. In contrast,
embedding-based models learn semantic representations from customer behavior
data, but the performance is often limited by shallow neural encoders due to
latency constraints. Semantic product search can be viewed as an eXtreme
Multi-label Classification (XMC) problem, where customer queries are input
instances and products are output labels. In this paper, we aim to improve
semantic product search by using tree-based XMC models where inference time
complexity is logarithmic in the number of products. We consider hierarchical
linear models with n-gram features for fast real-time inference.
Quantitatively, our method maintains a low latency of 1.25 milliseconds per
query and achieves a 65% improvement of Recall@100 (60.9% v.s. 36.8%) over a
competing embedding-based DSSM model. Our model is robust to weight pruning
with varying thresholds, which can flexibly meet different system requirements
for online deployments. Qualitatively, our method can retrieve products that
are complementary to existing product search system and add diversity to the
match set.


Abstract:
  The existence of multiple solutions to AC optimal power flow (ACOPF) problems
has been noted for decades. Existing solvers are generally successful in
finding local solutions, which are stationary points but may not be globally
optimal. In this paper, we propose a simple iterative approach to find globally
optimal solutions to ACOPF problems. First, we call an existing solver for the
ACOPF problem. From the solution and the associated dual variables, we form a
partial Lagrangian. Then we optimize this partial Lagrangian and use its
solution as a warm start to call the solver again for the ACOPF problem. By
repeating this process, we can iteratively improve the solution quality, moving
from local solutions to global ones. We show the effectiveness our algorithm on
standard IEEE networks. The simulation results show that our algorithm can
escape from local solutions to achieve global optimums within a few iterations.


Abstract:
  GRO J1008-57, as a Be/X-ray transient pulsar, is considered to have the
highest magnetic field in known neutron star X-ray binary systems.
Observational data of the X-ray outbursts in GRO J1008-57 from 2017 to 2020
were collected by the Insight-HXMT satellite. In this work, the spin period of
the neutron star in GRO J1008-57 was determined to be about 93.28 seconds in
August 2017, 93.22 seconds in February 2018, 93.25 seconds in June 2019 and
93.14 seconds in June 2020. GRO J1008-57 evolved in the spin-up process with a
mean rate of $-(2.10\pm 0.05)\times$10$^{-4}$ s/d from 2009 -- 2018, and turned
into a spin down process with a rate of $(6.7\pm 0.6)\times$10$^{-5}$ s/d from
Feb 2018 to June 2019. During the type II outburst of 2020, GRO J1008-57 had
the spin-up torque again. During the torque reversals, the pulse profiles and
continuum X-ray spectra did not change significantly, and the cyclotron
resonant scattering feature around 80 keV was only detected during the
outbursts in 2017 and 2020. Based on the observed mean spin-up rate, we
estimated the inner accretion disk radius in GRO J1008-57 (about 1 - 2 times of
the Alfv\'{e}n radius) by comparing different accretion torque models of
magnetic neutron stars. During the spin-down process, the magnetic torque
should dominate over the matter accreting inflow torque, and we constrained the
surface dipole magnetic field $B\geq 6\times 10^{12}$ G for the neutron star in
GRO J1008-57, which is consistent with the magnetic field strength obtained by
cyclotron line centroid energy.


Abstract:
  Question answering (QA) systems provide a way of querying the information
available in various formats including, but not limited to, unstructured and
structured data in natural languages. It constitutes a considerable part of
conversational artificial intelligence (AI) which has led to the introduction
of a special research topic on Conversational Question Answering (CQA), wherein
a system is required to understand the given context and then engages in
multi-turn QA to satisfy the user's information needs. Whilst the focus of most
of the existing research work is subjected to single-turn QA, the field of
multi-turn QA has recently grasped attention and prominence owing to the
availability of large-scale, multi-turn QA datasets and the development of
pre-trained language models. With a good amount of models and research papers
adding to the literature every year recently, there is a dire need of arranging
and presenting the related work in a unified manner to streamline future
research. This survey, therefore, is an effort to present a comprehensive
review of the state-of-the-art research trends of CQA primarily based on
reviewed papers from 2016-2021. Our findings show that there has been a trend
shift from single-turn to multi-turn QA which empowers the field of
Conversational AI from different perspectives. This survey is intended to
provide an epitome for the research community with the hope of laying a strong
foundation for the field of CQA.


Abstract:
  The data clustering problem consists in dividing a data set into prescribed
groups of homogeneous data. This is a NP-hard problem that can be relaxed in
the spectral graph theory, where the optimal cuts of a graph are related to the
eigenvalues of graph $1$-Laplacian. In this paper, we firstly give new
notations to describe the paths, among critical eigenvectors of the graph
$1$-Laplacian, realizing sets with prescribed genus.
  We introduce the pseudo-orthogonality to characterize $m_3(G)$, a special
eigenvalue for the graph $1$-Laplacian. Furthermore, we use it to give an upper
bound for the third graph Cheeger constant $h_3(G)$, that is $h_3(G) \le
m_3(G)$. This is a first step for proving that the $k$-th Cheeger constant is
the minimum of the $1$-Laplacian Raylegh quotient among vectors that are
pseudo-orthogonal to the vectors realizing the previous $k-1$ Cheeger
constants.
  Eventually, we apply these results to give a method and a numerical algorithm
to compute $m_3(G)$, based on a generalized inverse power method.


Abstract:
  An atomic monoid is length-factorial if each two distinct factorizations of
any element have distinct factorization lengths. We provide a characterization
of length-factorial Krull monoids in terms of their class groups and the
distribution of prime divisors in the classes.


Abstract:
  Matter distribution around clusters is highly anisotropic from their being
the nodes of the cosmic web. Clusters' shape and the number of filaments they
are connected to, i.e., their connectivity, should reflect their level of
anisotropic matter distribution and must be, in principle, related to their
physical properties. We investigate the influence of the dynamical state and
the formation history on both the morphology and local connectivity of about
2400 groups and clusters of galaxies from the large hydrodynamical simulation
IllustrisTNG at z=0. We find that the mass of groups and clusters mainly
influences the geometry of the matter distribution: massive halos are
significantly more elliptical, and more connected to the cosmic web than
low-mass ones. Beyond the mass-driven effect, ellipticity and connectivity are
correlated and are imprints of the growth rate of groups and clusters. Both
anisotropy measures appear to trace different dynamical states, such that
unrelaxed groups and clusters are more elliptical and more connected than
relaxed ones. This relation between matter anisotropies and dynamical state is
the sign of different accretion histories. Relaxed groups and clusters are
mostly formed long time ago, and slowly accreting matter at the present time.
They are rather spherical and weakly connected to their environment, mostly
because they had enough time to relax and, hence, lost the connection with
their preferential directions of accretion and merging. In contrast,
late-formed unrelaxed objects are highly anisotropic with large connectivities
and ellipticities. These groups and clusters are in formation phase and must be
strongly affected by the infalling of materials from filaments.


Abstract:
  We derive a series of quantitative bulk-boundary correspondences for 3D
bosonic and fermionic symmetry-protected topological (SPT) phases under the
assumption that the surface is gapped, symmetric and topologically ordered,
i.e., a symmetry-enriched topological (SET) state. We consider those SPT phases
that are protected by the mirror symmetry and continuous symmetries that form a
group of $U(1)$, $SU(2)$ or $SO(3)$. In particular, the fermionic cases
correspond to a crystalline version of 3D topological insulators and
topological superconductors in the famous ten-fold-way classification, with the
time-reversal symmetry replaced by the mirror symmetry and with strong
interaction taken into account. For surface SETs, the most general interplay
between symmetries and anyon excitations is considered. Based on the previously
proposed dimension reduction and folding approaches, we re-derive the
classification of bulk SPT phases and define a \emph{complete} set of bulk
topological invariants for every symmetry group under consideration, and then
derive explicit expressions of the bulk invariants in terms of surface
topological properties (such as topological spin, quantum dimension) and
symmetry properties (such as mirror fractionalization, fractional charge or
spin). These expressions are our quantitative bulk-boundary correspondences.
Meanwhile, the bulk topological invariants can be interpreted as \emph{anomaly
indicators} for the surface SETs which carry 't Hooft anomalies of the
associated symmetries whenever the bulk is topologically non-trivial. Hence,
the quantitative bulk-boundary correspondences provide an easy way to compute
the 't Hooft anomalies of the surface SETs. Moreover, our anomaly indicators
are complete. Our derivations of the bulk-boundary correspondences and anomaly
indicators are explicit and physically transparent.


Abstract:
  We establish general "collapse to the mean" principles that provide
conditions under which a law-invariant functional reduces to an expectation. In
the convex setting, we retrieve and sharpen known results from the literature.
However, our results also apply beyond the convex setting. We illustrate this
by providing a complete account of the "collapse to the mean" for quasiconvex
functionals. In the special cases of consistent risk measures and Choquet
integrals, we can even dispense with quasiconvexity. In addition, we relate the
"collapse to the mean" to the study of solutions of a broad class of
optimisation problems with law-invariant objectives that appear in mathematical
finance, insurance, and economics. We show that the corresponding quantile
formulations studied in the literature are sometimes illegitimate and require
further analysis.


Abstract:
  Most of the knowledge in materials science literature is in the form of
unstructured data such as text and images. Here, we present a framework
employing natural language processing, which automates text and image
comprehension and precision knowledge extraction from inorganic glasses'
literature. The abstracts are automatically categorized using latent Dirichlet
allocation (LDA), providing a way to classify and search semantically linked
publications. Similarly, a comprehensive summary of images and plots are
presented using the 'Caption Cluster Plot' (CCP), which provides direct access
to the images buried in the papers. Finally, we combine the LDA and CCP with
the chemical elements occurring in the manuscript to present an 'Elemental
map', a topical and image-wise distribution of chemical elements in the
literature. Overall, the framework presented here can be a generic and powerful
tool to extract and disseminate material-specific information on
composition-structure-processing-property dataspaces, allowing insights into
fundamental problems relevant to the materials science community and
accelerated materials discovery.


Abstract:
  We study a novel task, Video Question-Answer Generation (VQAG), for
challenging Video Question Answering (Video QA) task in multimedia. Due to
expensive data annotation costs, many widely used, large-scale Video QA
datasets such as Video-QA, MSVD-QA and MSRVTT-QA are automatically annotated
using Caption Question Generation (CapQG) which inputs captions instead of the
video itself. As captions neither fully represent a video, nor are they always
practically available, it is crucial to generate question-answer pairs based on
a video via Video Question-Answer Generation (VQAG). Existing video-to-text
(V2T) approaches, despite taking a video as the input, only generate a question
alone. In this work, we propose a novel model Generator-Pretester Network that
focuses on two components: (1) The Joint Question-Answer Generator (JQAG) which
generates a question with its corresponding answer to allow Video Question
"Answering" training. (2) The Pretester (PT) verifies a generated question by
trying to answer it and checks the pretested answer with both the model's
proposed answer and the ground truth answer. We evaluate our system with the
only two available large-scale human-annotated Video QA datasets and achieves
state-of-the-art question generation performances. Furthermore, using our
generated QA pairs only on the Video QA task, we can surpass some supervised
baselines. We apply our generated questions to Video QA applications and
surpasses some supervised baselines using generated questions only. As a
pre-training strategy, we outperform both CapQG and transfer learning
approaches when employing semi-supervised (20%) or fully supervised learning
with annotated data. These experimental results suggest the novel perspectives
for Video QA training.


Abstract:
  A nonsingular rational curve $C$ in a complex manifold $X$ whose normal
bundle is isomorphic to $${\mathcal O}_{{\mathbb P}^1}(1)^{\oplus p} \oplus
{\mathcal O}_{{\mathbb P}^1}^{\oplus q}$$ for some nonnegative integers $p$ and
$q$ is called an unbendable rational curve on $X$. Associated with it is the
variety of minimal rational tangents (VMRT) at a point $x \in C,$ which is the
germ of submanifolds ${\mathcal C}^C_x \subset {\mathbb P} T_x X$ consisting of
tangent directions of small deformations of $C$ fixing $x$. Assuming that there
exists a distribution $D \subset TX$ such that all small deformations of $C$
are tangent to $D$, one asks what kind of submanifolds of projective space can
be realized as the VMRT ${\mathcal C}^C_x \subset {\mathbb P} D_x$. When $D
\subset TX$ is a contact distribution, a well-known necessary condition is that
${\mathcal C}_x^C$ should be Legendrian with respect to the induced contact
structure on ${\mathbb P} D_x$. We prove that this is also a sufficient
condition: we construct a complex manifold $X$ with a contact structure $D
\subset TX$ and an unbendable rational curve $C \subset X$ such that all small
deformations of $C$ are tangent to $D$ and the VMRT ${\mathcal C}^C_x \subset
{\mathbb P} D_x$ at some point $x\in C$ is projectively isomorphic to an
arbitrarily given Legendrian submanifold. Our construction uses the geometry of
contact lines on the Heisenberg group and a technical ingredient is the
symplectic geometry of distributions the study of which has originated from
geometric control theory.


Abstract:
  The classifying space for the framed Haefliger structures of codimension $q$
and class $C^r$ is $(2q-1)$-connected, for $1\le r\le\infty$. The corollaries
deal with the existence of foliations, with the homology and the perfectness of
the diffeomorphism groups, with the existence of foliated products, and of
foliated bundles.


Abstract:
  We present an astrometric study of the proper motions (PMs) in the core of
the globular cluster NGC 6441. The core of this cluster has a high density and
observations with current instrumentation are very challenging. We combine
ground-based, high-angular-resolution NACO@VLT images with Hubble Space
Telescope ACS/HRC data and measure PMs with a temporal baseline of 15 yr for
about 1400 stars in the centermost 15 arcseconds of the cluster. We reach a PM
precision of $\sim$30 $\mu$as yr$^{-1}$ for bright, well-measured stars.
  Our results for the velocity dispersion are in good agreement with other
studies and extend already-existing analyses of the stellar kinematics of NGC
6441 to its centermost region never probed before. In the innermost arcsecond
of the cluster, we measure a velocity dispersion of (19.1 $\pm$ 2.0) km
s$^{-1}$ for evolved stars. Because of its high mass, NGC 6441 is a promising
candidate for harbouring an intermediate-mass black hole (IMBH). We combine our
measurements with additional data from the literature and compute dynamical
models of the cluster. We find an upper limit of $M_{\rm IMBH} < 1.32 \times
10^4\,\textrm{M}_\odot$ but we can neither confirm nor rule out its presence.
We also refine the dynamical distance of the cluster to $12.74^{+0.16}_{-0.15}$
kpc.
  Although the hunt for an IMBH in NGC 6441 is not yet concluded, our results
show how future observations with extremely-large telescopes will benefit from
the long temporal baseline offered by existing high-angular-resolution data.


Abstract:
  In recent years, thanks to the continuous surveys performed by INTEGRAL and
Swift satellites, our knowledge of the hard X-ray/soft gamma-ray sky has
greatly improved. As a result it is now populated with about 2000 sources, both
Galactic and extra-galactic, mainly discovered by IBIS and BAT instruments.
Many different follow-up campaigns have been successfully performed by using a
multi-wavelength approach, shedding light on the nature of a number of these
new hard X-ray sources. However, a fraction are still of a unidentified nature.
This is mainly due to the lack of lower energy observations, which usually
deliver a better constrained position for the sources, and the unavailability
of the key observational properties, needed to obtain a proper physical
characterization. Here we report on the classification of two poorly studied
Galactic X-ray transients IGR J20155+3827 and Swift J1713.4-4219, for which the
combination of new and/or archival X-ray and Optical/NIR observations have
allowed us to pinpoint their nature. In particular, thanks to XMM\Newton
archival data together with new optical spectroscopic and archival Optical/NIR
photometric observations, we have been able to classify IGR J20155+3827 as a
distant HMXB. The new INTEGRAL and Swift data collected during the 2019 X-ray
outburst of Swift J1713.4-4219, in combination with the archival optical/NIR
observations, suggest a LMXB classification for this source.


Abstract:
  We calculate the centrality dependence for coherent photoproduction of very
low-$p_{T}$ $J/\psi$ at Relativistic Heavy Ion Collider (RHIC) and Large Hadron
Collider (LHC) energies within the impact parameter dependent saturated color
dipole model. By using the large equivalent photon fluxes, we present the
differential cross section of very low-$p_{T}$ $J/\psi$ produced by coherent
photonuclear in peripheral heavy-ion collisions. The numerical results
demonstrate that our calculation are agree with $J/\psi$ data in peripheral
heavy ion collisions at Relativistic Heavy Ion Collider (RHIC) energies.


Abstract:
  In this article we generalize a classical result by Passman on primeness of
unital strongly group graded rings to the class of nearly epsilon-strongly
group graded rings which are not necessarily unital. Using this result, we
obtain (i) a characterization of prime $s$-unital strongly group graded rings,
and, in particular, of infinite matrix rings and of group rings over $s$-unital
rings, thereby generalizing a well-known result by Connell; (ii)
characterizations of prime $s$-unital partial skew group rings and of prime
unital partial crossed products; (iii) a generalization of the well-known
characterizations of prime Leavitt path algebras, by Larki and by
Abrams-Bell-Rangaswamy.


Abstract:
  Although there is a rapidly growing literature on dynamic connectivity
methods, the primary focus has been on separate network estimation for each
individual, which fails to leverage common patterns of information. We propose
novel graph-theoretic approaches for estimating a population of dynamic
networks that are able to borrow information across multiple heterogeneous
samples in an unsupervised manner and guided by covariate information.
Specifically, we develop a Bayesian product mixture model that imposes
independent mixture priors at each time scan and uses covariates to model the
mixture weights, which results in time-varying clusters of samples designed to
pool information. The computation is carried out using an efficient
Expectation-Maximization algorithm. Extensive simulation studies illustrate
sharp gains in recovering the true dynamic network over existing dynamic
connectivity methods. An analysis of fMRI block task data with behavioral
interventions reveal sub-groups of individuals having similar dynamic
connectivity, and identifies intervention-related dynamic network changes that
are concentrated in biologically interpretable brain regions. In contrast,
existing dynamic connectivity approaches are able to detect minimal or no
changes in connectivity over time, which seems biologically unrealistic and
highlights the challenges resulting from the inability to systematically borrow
information across samples.


Abstract:
  The recently introduced topological quantum chemistry (TQC) framework has
provided a description of universal topological properties of all possible band
insulators in all space groups based on crystalline unitary symmetries and time
reversal. While this formalism filled the gap between the mathematical
classification and the practical diagnosis of topological materials, an obvious
limitation is that it only applies to weakly interacting systems-which can be
described within band theory. It is an open question to which extent this
formalism can be generalized to correlated systems that can exhibit symmetry
protected topological phases which are not adiabatically connected to any band
insulator. In this work we address the many facettes of this question by
considering the specific example of a Hubbard diamond chain. This model
features a Mott insulator, a trivial insulating phase and an obstructed atomic
limit phase. Here we discuss the nature of the Mott insulator and determine the
phase diagram and topology of the interacting model with infinite density
matrix renormalization group calculations, variational Monte Carlo simulations
and with many-body topological invariants. We then proceed by considering a
generalization of the TQC formalism to Green's functions combined with the
concept of topological Hamiltonian to identify the topological nature of the
phases, using cluster perturbation theory to calculate the Green's functions.
The results are benchmarked with the above determined phase diagram and we
discuss the applicability and limitations of the approach and its possible
extensions.


Abstract:
  Today's advanced Reinforcement Learning algorithms produce black-box
policies, that are often difficult to interpret and trust for a person. We
introduce a policy distilling algorithm, building on the CN2 rule mining
algorithm, that distills the policy into a rule-based decision system. At the
core of our approach is the fact that an RL process does not just learn a
policy, a mapping from states to actions, but also produces extra
meta-information, such as action values indicating the quality of alternative
actions. This meta-information can indicate whether more than one action is
near-optimal for a certain state. We extend CN2 to make it able to leverage
knowledge about equally-good actions to distill the policy into fewer rules,
increasing its interpretability by a person. Then, to ensure that the rules
explain a valid, non-degenerate policy, we introduce a refinement algorithm
that fine-tunes the rules to obtain good performance when executed in the
environment. We demonstrate the applicability of our algorithm on the Mario AI
benchmark, a complex task that requires modern reinforcement learning
algorithms including neural networks. The explanations we produce capture the
learned policy in only a few rules, that allow a person to understand what the
black-box agent learned. Source code:
https://gitlab.ai.vub.ac.be/yocoppen/svcn2


Abstract:
  Despite the considerable success of neural networks in security settings such
as malware detection, such models have proved vulnerable to evasion attacks, in
which attackers make slight changes to inputs (e.g., malware) to bypass
detection. We propose a novel approach, \emph{Fourier stabilization}, for
designing evasion-robust neural networks with binary inputs. This approach,
which is complementary to other forms of defense, replaces the weights of
individual neurons with robust analogs derived using Fourier analytic tools.
The choice of which neurons to stabilize in a neural network is then a
combinatorial optimization problem, and we propose several methods for
approximately solving it. We provide a formal bound on the per-neuron drop in
accuracy due to Fourier stabilization, and experimentally demonstrate the
effectiveness of the proposed approach in boosting robustness of neural
networks in several detection settings. Moreover, we show that our approach
effectively composes with adversarial training.


Abstract:
  Underwater object detection for robot picking has attracted a lot of
interest. However, it is still an unsolved problem due to several challenges.
We take steps towards making it more realistic by addressing the following
challenges. Firstly, the currently available datasets basically lack the test
set annotations, causing researchers must compare their method with other SOTAs
on a self-divided test set (from the training set). Training other methods lead
to an increase in workload and different researchers divide different datasets,
resulting there is no unified benchmark to compare the performance of different
algorithms. Secondly, these datasets also have other shortcomings, e.g., too
many similar images or incomplete labels. Towards these challenges we introduce
a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark,
based on the collection and re-annotation of all relevant datasets. DUO
contains a collection of diverse underwater images with more rational
annotations. The corresponding benchmark provides indicators of both efficiency
and accuracy of SOTAs (under the MMDtection framework) for academic research
and industrial applications, where JETSON AGX XAVIER is used to assess detector
speed to simulate the robot-embedded environment.


Abstract:
  The escaping atmospheres of hydrogen driven by stellar X-ray and extreme
Ultraviolet (XUV) have been detected around some exoplanets by the excess
absorption of Ly$\alpha$ in far ultraviolet band. In the optical band the
excess absorption of H$\alpha$ is also found by the ground-based instruments.
However, it is not certain so far if the escape of the atmosphere driven by XUV
can result in such absorption. Here we present the XUV driven hydrodynamic
simulation coupled with the calculation of detailed level population and the
process of radiative transfer for WASP-121b. Our fiducial model predicts a mass
loss rate of $\sim$1.28$\times$10$^{12}$g/s for WASP-121b. Due to the high
temperature and Ly$\alpha$ intensity predicted by the fiducial model, many
hydrogen atoms are populated into the first excited state. As a consequence,
the transmission spectrum of H$\alpha$ simulated by our model is broadly
consistent with the observation. Comparing with the absorption of H$\alpha$ at
different observation times, the stellar XUV emission varies in the range of
0.5-1.5 times fiducial value, which may reflect the variation of the stellar
activity. Finally, we find that the supersonic regions of the planetary wind
contribute a prominent portion to the absorption of H$\alpha$ by comparing the
equivalent width of H$\alpha$, which hints that a transonic outflow of the
upper atmosphere driven by XUV irradiation of the host star can be detected by
the ground-based telescope and the H$\alpha$ can be a good indicator of
escaping atmosphere.


Abstract:
  We propose a method to non-rigidly align a three-dimensional (3D) volumetric
image with a two-dimensional (2D) planar image representing a projection of the
deformed volume. The application in mind comes from biological studies in which
2D intravital microscopy videos of living tissue are recorded, after which the
tissue is excised and a more detailed 3D volume microscopy is performed.
Coregistration of both data sets allows to combine the temporal (but 2D)
information with more detailed spatial 3D information.
  Our approach is variational and uses a hyperelastic deformation
regularization, as is appropriate for biological material. As a particular
feature, the out of plane deformation is estimated based on the out of focus
blur inside the 2D microscopy image. The approach becomes computationally
feasible through the use of a coarse-to-fine optimization strategy and higher
order optimization methods.


Abstract:
  Assigning geospatial objects with specific categories at the pixel level is a
fundamental task in remote sensing image analysis. Along with rapid development
in sensor technologies, remotely sensed images can be captured at multiple
spatial resolutions (MSR) with information content manifested at different
scales. Extracting information from these MSR images represents huge
opportunities for enhanced feature representation and characterisation.
However, MSR images suffer from two critical issues: 1) increased scale
variation of geo-objects and 2) loss of detailed information at coarse spatial
resolutions. To bridge these gaps, in this paper, we propose a novel
scale-aware neural network (SaNet) for semantic segmentation of MSR remotely
sensed imagery. SaNet deploys a densely connected feature network (DCFFM)
module to capture high-quality multi-scale context, such that the scale
variation is handled properly and the quality of segmentation is increased for
both large and small objects. A spatial feature recalibration (SFRM) module is
further incorporated into the network to learn intact semantic content with
enhanced spatial relationships, where the negative effects of information loss
are removed. The combination of DCFFM and SFRM allows SaNet to learn
scale-aware feature representation, which outperforms the existing multi-scale
feature representation. Extensive experiments on three semantic segmentation
datasets demonstrated the effectiveness of the proposed SaNet in
cross-resolution segmentation.


Abstract:
  Recent advances in automatic speech recognition (ASR) have achieved accuracy
levels comparable to human transcribers, which led researchers to debate if the
machine has reached human performance. Previous work focused on the English
language and modular hidden Markov model-deep neural network (HMM-DNN) systems.
In this paper, we perform a comprehensive benchmarking for end-to-end
transformer ASR, modular HMM-DNN ASR, and human speech recognition (HSR) on the
Arabic language and its dialects. For the HSR, we evaluate linguist performance
and lay-native speaker performance on a new dataset collected as a part of this
study. For ASR the end-to-end work led to 12.5%, 27.5%, 33.8% WER; a new
performance milestone for the MGB2, MGB3, and MGB5 challenges respectively. Our
results suggest that human performance in the Arabic language is still
considerably better than the machine with an absolute WER gap of 3.5% on
average.


Abstract:
  We consider the reconstruction problem of video snapshot compressive imaging
(SCI), which captures high-speed videos using a low-speed 2D sensor (detector).
The underlying principle of SCI is to modulate sequential high-speed frames
with different masks and then these encoded frames are integrated into a
snapshot on the sensor and thus the sensor can be of low-speed. On one hand,
video SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On
the other hand, applying SCI to large-scale problems (HD or UHD videos) in our
daily life is still challenging and one of the bottlenecks lies in the
reconstruction algorithm. Exiting algorithms are either too slow (iterative
optimization algorithms) or not flexible to the encoding process (deep learning
based end-to-end networks). In this paper, we develop fast and flexible
algorithms for SCI based on the plug-and-play (PnP) framework. In addition to
the PnP-ADMM method, we further propose the PnP-GAP (generalized alternating
projection) algorithm with a lower computational workload. We first employ the
image deep denoising priors to show that PnP can recover a UHD color video with
30 frames from a snapshot measurement. Since videos have strong temporal
correlation, by employing the video deep denoising priors, we achieve a
significant improvement in the results. Furthermore, we extend the proposed PnP
algorithms to the color SCI system using mosaic sensors, where each pixel only
captures the red, green or blue channels. A joint reconstruction and
demosaicing paradigm is developed for flexible and high quality reconstruction
of color video SCI systems. Extensive results on both simulation and real
datasets verify the superiority of our proposed algorithm.


Abstract:
  The radio spectra of main-sequence stars remain largely unconstrained due to
the lack of observational data to inform stellar atmosphere models. As such,
the dominant emission mechanisms at long wavelengths, how they vary with
spectral type, and how much they contribute to the expected brightness at a
given radio wavelength are still relatively unknown for most spectral types. We
present radio continuum observations of Altair, a rapidly rotating A-type star.
We observed Altair with NOEMA in 2018 and 2019 at 1.34 mm, 2.09 mm, and 3.22 mm
and with the VLA in 2019 at 6.7 mm and 9.1 mm. In the radio spectra, we see a
brightness temperature minimum at millimeter wavelengths followed by a steep
rise to temperatures larger than the optical photosphere, behavior that is
unexpected for A-type stars. We use these data to produce the first
sub-millimeter to centimeter spectrum of a rapidly rotating A-type star
informed by observations. We generated both PHOENIX and KINICH-PAKAL model
atmospheres and determine the KINICH-PAKAL model better reproduces Altair's
radio spectrum. The synthetic spectrum shows a millimeter brightness
temperature minimum followed by significant emission over that of the
photosphere at centimeter wavelengths. Together, these data and models show how
the radio spectrum of an A-type star can reveal the presence of a chromosphere,
likely induced by rapid rotation, and that a Rayleigh Jean's extrapolation of
the stellar photosphere is not an adequate representation of a star's radio
spectrum.


Abstract:
  In this early draft, we present an end-to-end decentralized protocol for the
secure and privacy preserving workflow of vaccination, vaccination status
verification, and adverse reactions or symptoms reporting. The proposed system
improves the efficiency, privacy, equity, and effectiveness of the existing
manual system while remaining interoperable with its capabilities. We also
discuss various security concerns and alternate methodologies based on the
proposed protocols.


Abstract:
  Despite the dependency of electric vehicle (EV) fleets on charging station
availability, charging infrastructure remains limited in many cities. Three
contributions are made. First, we propose an EV-to-charging station user
equilibrium (UE) assignment model with a M/D/C queue approximation as a
nondifferentiable nonlinear program. Second, to address the
non-differentiability of the queue delay function, we propose an original
solution algorithm based on the derivative-free Method of Successive Averages.
Computational tests with a toy network show that the model converges to a UE. A
working code in Python is provided free on Github with detailed test cases.
Third, the model is applied to the large-scale case study of New York City
Department of Citywide Administrative Services (NYC DCAS) fleet and EV charging
station configuration as of July 8, 2020, which includes unique, real data for
563 Level 2 chargers and 4 Direct Current Fast Chargers (DCFCs) and 1484 EVs
distributed over 512 Traffic Analysis Zones. The arrival rates of the
assignment model are calibrated in the base scenario to fit an observed average
utilization ratio of 7.6% in NYC. The model is then applied to compare charging
station investment policies of DCFCs to Level 2 charging stations based on two
alternative criteria. Results suggest a policy based on selecting locations
with high utilization ratio instead of with high queue delay.


Abstract:
  Underwater images suffer from color casts and low contrast due to wavelength-
and distance-dependent attenuation and scattering. To solve these two
degradation issues, we present an underwater image enhancement network via
medium transmission-guided multi-color space embedding, called Ucolor.
Concretely, we first propose a multi-color space encoder network, which
enriches the diversity of feature representations by incorporating the
characteristics of different color spaces into a unified structure. Coupled
with an attention mechanism, the most discriminative features extracted from
multiple color spaces are adaptively integrated and highlighted. Inspired by
underwater imaging physical models, we design a medium transmission (indicating
the percentage of the scene radiance reaching the camera)-guided decoder
network to enhance the response of the network towards quality-degraded
regions. As a result, our network can effectively improve the visual quality of
underwater images by exploiting multiple color spaces embedding and the
advantages of both physical model-based and learning-based methods. Extensive
experiments demonstrate that our Ucolor achieves superior performance against
state-of-the-art methods in terms of both visual quality and quantitative
metrics.


Abstract:
  Using a special complex K3 surface and its blow up, we construct a smooth
complex projective rational surface with infinitely many mutually
non-isomorphic real forms. This gives a negative answer to a long standing open
question if a smooth complex projective rational surface has only finitely many
non-isomorphic real forms or not.


Abstract:
  Refinement types decorate types with assertions that enable automatic
verification. Like assertions, refinements are limited to binders that are in
scope, and hence, cannot express higher-order specifications. Ghost variables
circumvent this limitation but are prohibitively tedious to use as the
programmer must divine and explicate their values at all call-sites. We
introduce Implicit Refinement Types which turn ghost variables into implicit
pair and function types, in a way that lets the refinement typechecker
automatically synthesize their values at compile time. Implicit Refinement
Types further take advantage of refinement type information, allowing them to
be used as a lightweight verification tool, rather than merely as a technique
to automate programming tasks. We evaluate the utility of Implicit Refinement
Types by showing how they enable the modular specification and automatic
verification of various higher-order examples including stateful protocols,
access control, and resource usage.


Abstract:
  Direct Multisearch (DMS) is a Derivative-free Optimization class of
algorithms suited for computing approximations to the complete Pareto front of
a given Multiobjective Optimization problem. It has a well-supported
convergence analysis and simple implementations present a good numerical
performance, both in academic test sets and in real applications. Recently,
this numerical performance was improved with the definition of a search step
based on the minimization of quadratic polynomial models, corresponding to the
algorithm BoostDMS.
  In this work, we propose and numerically evaluate the performance of
parallelization strategies for this solver, applied to the search and to the
poll steps. The final parallelized version not only considerably decreases the
computational time required for solving a Multiobjective Optimization problem,
but also increases the quality of the computed approximation to the Pareto
front. Extensive numerical results will be reported in an academic test set and
in a chemical engineering application.


Abstract:
  We consider a class of 2d $\sigma$-models on products of group spaces that
provide new examples of a close connection between integrability and stability
under the RG flow. We first study the integrable $G \times G$ model derived
from the affine Gaudin construction (for which the 1-loop $\beta$-functions
were found in arXiv:2010.07879) and show that its condition of integrability is
preserved also by the 2-loop RG flow. We then investigate the RG flow in the
gauged $G \times G /H$ model, in particular the integrable $T^{1,1}$ model
found in arXiv:2010.05573. We also construct a new class of integrable $G
\times G /H$ models in the case when the subgroup $H$ is abelian. In the
simplest case of $G=SU_2$, $H=U_1$ this leads to an integrable $\sigma$-model
on the $T^{1,q}$ space (with a particular $B$-field). This model is also shown
to be stable under the 2-loop RG flow, and we relate this property to its
invariance under T-duality in an isometric $U_1$ direction. This $T^{1,q}$
model may be interpreted as an integrable deformation of the GMM model (of two
coupled WZW theories with generic levels) away from the conformal point.


Abstract:
  The interlayer coupling mediated by fermions in ferromagnets brings about
parallel and anti-parallel magnetization orientations of two magnetic layers,
resulting in the giant magnetoresistance, which forms the foundation in
spintronics and accelerates the development of information technology. However,
the interlayer coupling mediated by another kind of quasi-particle, boson, is
still lacking. Here we demonstrate such a static interlayer coupling at room
temperature in an antiferromagnetic junction Fe2O3/Cr2O3/Fe2O3, where the two
antiferromagnetic Fe2O3 layers are functional materials and the
antiferromagnetic Cr2O3 layer serves as a spacer. The N\'eel vectors in the top
and bottom Fe2O3 are strongly orthogonally coupled, which is bridged by a
typical bosonic excitation (magnon) in the Cr2O3 spacer. Such an orthogonally
coupling exceeds the category of traditional collinear interlayer coupling via
fermions in ground state, reflecting the fluctuating nature of the magnons, as
supported by our magnon quantum well model. Besides the fundamental
significance on the quasi-particle-mediated interaction, the strong coupling in
an antiferromagnetic magnon junction makes it a realistic candidate for
practical antiferromagnetic spintronics and magnonics with ultrahigh-density
integration.


Abstract:
  We study a broad class of quantum process discrimination problems that can
handle many optimization strategies such as the Bayes, Neyman-Pearson, and
unambiguous strategies, where each process can consist of multiple time steps
and can have an internal memory. Given a collection of candidate processes, our
task is to find a discrimination strategy, which may be adaptive and/or
entanglement-assisted, that maximizes a given objective function subject to
given constraints. Our problem can be formulated as a convex problem. Its
Lagrange dual problem with no duality gap and necessary and sufficient
conditions for an optimal solution are derived. We also show that if a problem
has a certain symmetry and at least one optimal solution exists, then there
also exists an optimal solution with the same type of symmetry. A minimax
strategy for a process discrimination problem is also discussed. As
applications of our results, we provide some problems in which an adaptive
strategy is not necessary for optimal discrimination. We also present an
example of single-shot channel discrimination for which an analytical solution
can be obtained.


Abstract:
  Hyperbolic ordinal embedding (HOE) represents entities as points in
hyperbolic space so that they agree as well as possible with given constraints
in the form of entity i is more similar to entity j than to entity k. It has
been experimentally shown that HOE can obtain representations of hierarchical
data such as a knowledge base and a citation network effectively, owing to
hyperbolic space's exponential growth property. However, its theoretical
analysis has been limited to ideal noiseless settings, and its generalization
error in compensation for hyperbolic space's exponential representation ability
has not been guaranteed. The difficulty is that existing generalization error
bound derivations for ordinal embedding based on the Gramian matrix do not work
in HOE, since hyperbolic space is not inner-product space. In this paper,
through our novel characterization of HOE with decomposed Lorentz Gramian
matrices, we provide a generalization error bound of HOE for the first time,
which is at most exponential with respect to the embedding space's radius. Our
comparison between the bounds of HOE and Euclidean ordinal embedding shows that
HOE's generalization error is reasonable as a cost for its exponential
representation ability.


Abstract:
  Electron spin resonance (ESR) pulsed dipolar spectroscopy (PDS) is used
effectively in measuring nano-meter range distances for protein structure
prediction. The current global approach in extracting the distance distribution
from time domain PDS signal has multiple limitations. We present a parameter
free global method, which is more efficacious and less sensitive to signal
noise compared to the current method.


Abstract:
  This work re-implements the OpenAI Gym multi-goal robotic manipulation
environment, originally based on the commercial Mujoco engine, onto the
open-source Pybullet engine. By comparing the performances of the Hindsight
Experience Replay-aided Deep Deterministic Policy Gradient agent on both
environments, we demonstrate our successful re-implementation of the original
environment. Besides, we provide users with new APIs to access a joint control
mode, image observations and goals with customisable camera and a built-in
on-hand camera. We further design a set of multi-step, multi-goal, long-horizon
and sparse reward robotic manipulation tasks, aiming to inspire new
goal-conditioned reinforcement learning algorithms for such challenges. We use
a simple, human-prior-based curriculum learning method to benchmark the
multi-step manipulation tasks. Discussions about future research opportunities
regarding this kind of tasks are also provided.


Abstract:
  Compositional generalization is the capacity to recognize and imagine a large
amount of novel combinations from known components. It is a key in human
intelligence, but current neural networks generally lack such ability. This
report connects a series of our work for compositional generalization, and
summarizes an approach. The first part contains concepts and properties. The
second part looks into a machine learning approach. The approach uses
architecture design and regularization to regulate information of
representations. This report focuses on basic ideas with intuitive and
illustrative explanations. We hope this work would be helpful to clarify
fundamentals of compositional generalization and lead to advance artificial
intelligence.


Abstract:
  A multiring ([Mar3]) is a kind of ring where is allowed the sum of two
elements to be anon-empty subset of the structure instead of just one element
-and an hyperring is a multiring with a strong distributive property. Thus a
reduced hyperring where the prime spec is a Boolean topological space is called
von Neumann regular hyperring (vNH). It is possible to associate to every such
object a structural presheaf in the same way it is made with rings but there
are some vNH such that this presheaf is not a sheaf. In this sense, we give a
first-order characterization of vNH with a structural sheaf (geometric vNH or
just GvNH) and how to transform a vNH in aGvNH -in fact, this transformation
shows that the category GvNH is a reflexive subcategory of vNH. We also build a
von Neumann regular hull for multirings and use this to give applications for
algebraic theory of quadratic forms. More precisely, we work with Real Reduced
Multiring (RRM, [Mar3]) -also known as Real Semigroup (RS, [DP1])-, a special
kind of multirings that is useful to explore the real structure of rings, and
show that a von Neumann hull of a RRM is again a RRM. This gives a
generalization of sheafs arguments present in [DM4].


Abstract:
  Current vision and language tasks usually take complete visual data (e.g.,
raw images or videos) as input, however, practical scenarios may often consist
the situations where part of the visual information becomes inaccessible due to
various reasons e.g., restricted view with fixed camera or intentional vision
block for security concerns. As a step towards the more practical application
scenarios, we introduce a novel task that aims to describe a video using the
natural language dialog between two agents as a supplementary information
source given incomplete visual data. Different from most existing
vision-language tasks where AI systems have full access to images or video
clips, which may reveal sensitive information such as recognizable human faces
or voices, we intentionally limit the visual input for AI systems and seek a
more secure and transparent information medium, i.e., the natural language
dialog, to supplement the missing visual information. Specifically, one of the
intelligent agents - Q-BOT - is given two semantic segmented frames from the
beginning and the end of the video, as well as a finite number of opportunities
to ask relevant natural language questions before describing the unseen video.
A-BOT, the other agent who has access to the entire video, assists Q-BOT to
accomplish the goal by answering the asked questions. We introduce two
different experimental settings with either a generative (i.e., agents generate
questions and answers freely) or a discriminative (i.e., agents select the
questions and answers from candidates) internal dialog generation process. With
the proposed unified QA-Cooperative networks, we experimentally demonstrate the
knowledge transfer process between the two dialog agents and the effectiveness
of using the natural language dialog as a supplement for incomplete implicit
visions.


Abstract:
  In 2014, a union of German research organisations established Projekt DEAL, a
national-level project to negotiate licensing agreements with large scientific
publishers. Negotiations between DEAL and Elsevier began in 2016, and broke
down without a successful agreement in 2018; in this time, around 200 German
research institutions cancelled their license agreements with Elsevier, leading
Elsevier to restrict journal access at those institutions from July 2018
onwards. We investigated the effect of these access restrictions on
researchers' publishing and citing behaviours from a bibliometric perspective,
using a dataset of ~410,000 articles published by researchers at the affected
DEAL institutions between 2012-2020. We further investigated these effects with
respect to the timing of contract cancellations with Elsevier, research
disciplines, collaboration patterns, and article open-access status. We find
evidence for a decrease in Elsevier's market share of articles from DEAL
institutions, from a peak of 25.3% in 2015 to 20.6% in 2020, with the largest
year-on-year market share decreases occurring in 2019 (-1.1%) and 2020 (-1.6%)
following the implementation of access restrictions. We also observe
year-on-year decreases in the proportion of citations made from articles
published by authors at DEAL institutions to articles in Elsevier journals
post-2018, although the decrease is smaller (-0.4% in 2019 and -0.6% in 2020)
than changes in publishing volume. We conclude that Elsevier access
restrictions have led to some reduced willingness of researchers at DEAL
institutions to publish their research in Elsevier journals, but that
researchers are not strongly affected in their ability to cite Elsevier
articles, with the implication that researchers use a variety of other methods
(e.g. interlibrary loans, sharing between colleagues, or "shadow libraries") to
access scientific literature.


Abstract:
  Preventable or undiagnosed visual impairment and blindness affect billion of
people worldwide. Automated multi-disease detection models offer great
potential to address this problem via clinical decision support in diagnosis.
In this work, we proposed an innovative multi-disease detection pipeline for
retinal imaging which utilizes ensemble learning to combine the predictive
capabilities of several heterogeneous deep convolutional neural network models.
Our pipeline includes state-of-the-art strategies like transfer learning, class
weighting, real-time image augmentation and Focal loss utilization.
Furthermore, we integrated ensemble learning techniques like heterogeneous deep
learning models, bagging via 5-fold cross-validation and stacked logistic
regression models. Through internal and external evaluation, we were able to
validate and demonstrate high accuracy and reliability of our pipeline, as well
as the comparability with other state-of-the-art pipelines for retinal disease
prediction.


Abstract:
  In this paper, we analyze the performance of orthogonal time frequency space
(OTFS) modulation with antenna selection at the receiver, where $n_s$ out of
$n_r$ receive antennas with maximum channel Frobenius norms in the
delay-Doppler (DD) domain are selected. Single-input multiple-output OTFS
(SIMO-OTFS), multiple-input multiple-output OTFS (MIMO-OTFS), and space-time
coded OTFS (STC-OTFS) systems with receive antenna selection (RAS) are
considered. We consider these systems without and with phase rotation. Our
diversity analysis results show that, with no phase rotation, SIMO-OTFS and
MIMO-OTFS systems with RAS are rank deficient, and therefore they do not
extract the full receive diversity as well as the diversity present in the DD
domain. Also, Alamouti coded STC-OTFS system with RAS and no phase rotation
extracts the full transmit diversity, but it fails to extract the DD diversity.
On the other hand, SIMO-OTFS and STC-OTFS systems with RAS become full-ranked
when phase rotation is used, because of which they extract the full spatial as
well as the DD diversity present in the system. Also, when phase rotation is
used, MIMO-OTFS systems with RAS extract the full DD diversity, but they do not
extract the full receive diversity because of rank deficiency. Simulation
results are shown to validate the analytically predicted diversity performance.


Abstract:
  In this work, we outline a cross-domain assurance process for safety-relevant
software in embedded systems. This process aims to be applied in various
different application domains and in conjunction with any development
methodology. With this approach we plan to reduce the growing effort for safety
assessment in embedded systems by reusing safety analysis techniques and tools
for the product development in different domains.


Abstract:
  Gamma-ray spectral data were collected from sensors mounted to traffic
signals around Northern Virginia. The data were collected over a span of
approximately fifteen months. A subset of the data were analyzed manually and
subsequently used to train machine-learning models to facilitate the evaluation
of the remaining 50k anomalous events identified in the dataset. We describe
the analysis approach used here and discuss the results in terms of
radioisotope classes and frequency patterns over day-of-week and time-of-day
spans. Data from this work has been archived and is available for future and
ongoing research applications.


Abstract:
  Magnetic clouds (MCs) are transient structures containing large-scale
magnetic flux ropes from solar eruptions. The twist of magnetic field lines
around the rope axis reveals information about flux rope formation processes
and geoeffectivity. During propagation, MC flux ropes may erode via
reconnection with the ambient solar wind. Any erosion reduces the magnetic flux
and helicity of the ropes, and changes their cross-sectional twist profiles.
This study relates twist profiles in MC flux ropes observed at 1 AU to the
amount of erosion undergone by the MCs in interplanetary space. The twist
profiles of two well-identified MC flux ropes associated with the clear
appearance of post eruption arcades in the solar corona are analysed. To infer
the amount of erosion, the magnetic flux content of the ropes in the solar
atmosphere is estimated, and compared to estimates at 1 AU. The first MC shows
a monotonically decreasing twist from the axis to periphery, while the second
displays high twist at the axis, rising twist near the edges, and lower twist
in between. The first MC displays a larger reduction in magnetic flux between
the Sun and 1 AU, suggesting more erosion, than that seen in the second MC. In
the second cloud, rising twist at the rope edges may have been due to an
envelope of overlying coronal field lines with relatively high twist, formed by
reconnection beneath the erupting flux rope in the low corona. This high-twist
envelope remained almost intact from the Sun to 1 AU due to the low erosion
levels. In contrast, the high-twist envelope of the first cloud may have been
entirely peeled away via erosion by the time it reaches 1 AU.


Abstract:
  We propose a method to define quasiprobability distributions for general
spin-$j$ systems of dimension $n=2j+1$, where $n$ is a prime or power of prime.
The method is based on a complete set of orthonormal commuting operators
related to Mutually Unbiased Bases which enable (i) a parameterisation of the
density matrix and (ii) construction of measurement operators that can be
physically realised. As a result we geometrically characterise the set of
states for which the quasiprobability distribution is non-negative, and can be
viewed as a joint distribution of classical random variables assuming values in
a finite set of outcomes. The set is an $(n^2-1)$-dimensional convex polytope
with $n+1$ vertices as the only pure states, $n^{n+1}$ number of higher
dimensional faces, and $n^3(n+1)/2$ edges.


Abstract:
  Deep neural networks (DNNs) represent the mainstream methodology for
supervised speech enhancement, primarily due to their capability to model
complex functions using hierarchical representations. However, a recent study
revealed that DNNs trained on a single corpus fail to generalize to untrained
corpora, especially in low signal-to-noise ratio (SNR) conditions. Developing a
noise, speaker, and corpus independent speech enhancement algorithm is
essential for real-world applications. In this study, we propose a
self-attending recurrent neural network(SARNN) for time-domain speech
enhancement to improve cross-corpus generalization. SARNN comprises of
recurrent neural networks (RNNs) augmented with self-attention blocks and
feedforward blocks. We evaluate SARNN on different corpora with nonstationary
noises in low SNR conditions. Experimental results demonstrate that SARNN
substantially outperforms competitive approaches to time-domain speech
enhancement, such as RNNs and dual-path SARNNs. Additionally, we report an
important finding that the two popular approaches to speech enhancement:
complex spectral mapping and time-domain enhancement, obtain similar results
for RNN and SARNN with large-scale training. We also provide a challenging
subset of the test set used in this study for evaluating future algorithms and
facilitating direct comparisons.


Abstract:
  In this paper, we propose a $\mu$-mode integrator for computing the solution
of stiff evolution equations. The integrator is based on a $d$-dimensional
splitting approach and uses exact (usually precomputed) one-dimensional matrix
exponentials. We show that the action of the exponentials, i.e. the
corresponding batched matrix-vector products, can be implemented efficiently on
modern computer systems. We further explain how $\mu$-mode products can be used
to compute spectral transforms efficiently even if no fast transform is
available. We illustrate the performance of the new integrator by solving,
among the others, three-dimensional linear and nonlinear Schr\"odinger
equations, and we show that the $\mu$-mode integrator can significantly
outperform numerical methods well established in the field. We also discuss how
to efficiently implement this integrator on both multi-core CPUs and GPUs.
Finally, the numerical experiments show that using GPUs results in performance
improvements between a factor of $10$ and $20$, depending on the problem.