Abstract:
  This paper describes the development needed to support the functional and
teaching requirements of iRead, a 4-year EU-funded project which produced an
award-winning serious game utilising lexical and syntactical game content. The
main functional requirement was that the game should retain different profiles
for each student, encapsulating both the respective language model (which
language features should be taught/used in the game first, before moving on to
more advanced ones) and the user model (mastery level for each feature, as
reported by the student's performance in the game). In addition to this,
researchers and stakeholders stated additional requirements related to learning
objectives and strategies to make the game more interesting and successful;
these were implemented as a set of selection rules which take into account not
only the mastery level for each feature, but also respect the priorities set by
teachers, helping avoid repetition of content and features, and maintaining a
balance between new content and revision of already mastered features to give
students the sense of progress, while also reinforcing learning.


Abstract:
  Surface ion traps are among the most promising technologies for scaling up
quantum computing machines, but their complicated multi-electrode geometry can
make some tasks, including compensation for stray electric fields, challenging
both at the level of modeling and of practical implementation. Here we
demonstrate the compensation of stray electric fields using a gradient descent
algorithm and a machine learning technique, which trained a deep learning
network. We show automated dynamical compensation tested against induced
electric charging from UV laser light hitting the chip trap surface. The
results show improvement in compensation using gradient descent and the machine
learner over manual compensation. This improvement is inferred from an increase
of the fluorescence rate of 78% and 96% respectively, for a trapped
$^{171}$Yb$^+$ ion driven by a laser tuned to -7.8 MHz of the
$^2$S$_{1/2}\leftrightarrow^2$P$_{1/2}$ Doppler cooling transition at 369.5 nm.


Abstract:
  Active learning is usually applied to acquire labels of informative data
points in supervised learning, to maximize accuracy in a sample-efficient way.
However, maximizing the accuracy is not the end goal when the results are used
for decision-making, for example in personalized medicine or economics. We
argue that when acquiring samples sequentially, separating learning and
decision-making is sub-optimal, and we introduce an active learning strategy
which takes the down-the-line decision problem into account. Specifically, we
introduce a novel active learning criterion which maximizes the expected
information gain on the posterior distribution of the optimal decision. We
compare our targeted active learning strategy to existing alternatives on both
simulated and real data, and show improved performance in decision-making
accuracy.


Abstract:
  We show that there is a red-blue colouring of $[N]$ with no blue 3-term
arithmetic progression and no red arithmetic progression of length $e^{C(\log
N)^{3/4}(\log \log N)^{1/4}}$. Consequently, the two-colour van der Waerden
number $w(3,k)$ is bounded below by $k^{b(k)}$, where $b(k) = c \big(
\frac{\log k}{\log\log k} \big)^{1/3}$. Previously it had been speculated,
supported by data, that $w(3,k) = O(k^2)$.


Abstract:
  We prove that, for $g\geq19$ the mapping class group of a nonorientable
surface of genus $g$, $\textrm{Mod}(N_g)$, can be generated by two elements,
one of which is of order $g$. We also prove that for $g\geq26$,
$\textrm{Mod}(N_g)$ can be generated by three involutions if $g\geq26$.


Abstract:
  Advances in both instrumentation and data analysis software are now enabling
the first ultra-high-resolution microcalorimeter gamma spectrometers designed
for implementation in nuclear facilities and analytical laboratories. With
approximately ten times better energy resolution than high-purity germanium
detectors, these instruments can overcome important uncertainty limits.
Microcalorimeter gamma spectroscopy is intended to provide nondestructive
isotopic analysis capabilities with sufficient precision and accuracy to reduce
the need for sampling, chemical separations, and mass spectrometry to meet
safeguards and security goals. Key milestones were the development of the SOFIA
instrument (Spectrometer Optimized for Facility Integrated Applications) and
the SAPPY software (Spectral Analysis Program in PYthon). SOFIA is a compact
instrument that combines advances in large multiplexed transition-edge sensor
arrays with optimized cryogenic performance to overcome many practical
limitations of previous systems. With a 256-pixel multiplexed detector array
capable of 5000 counts per second, measurement time can be comparable to
high-purity germanium detectors. SAPPY was developed to determine isotopic
ratios in data from SOFIA and other microcalorimeter instruments with an
approach similar to the widely-used FRAM software. SAPPY provides a flexible
framework with rigorous uncertainty analysis for both microcalorimeter and HPGe
data, allowing direct comparison. We present current results from the SOFIA
instrument, preliminary isotopic analysis using SAPPY, and describe how the
technology is being used to explore uncertainty limits of nondestructive
isotopic characterization, inform safeguards models, and extract improved
nuclear data including gamma-ray branching ratios.


Abstract:
  This dissertation studies the quantum anomalous effects on the description of
high energy electrodynamics. We argue that on the temperatures comparable to
the electroweak scale, characteristic for the early Universe and objects like
neutron stars, the description of electromagnetic fields in conductive plasmas
needs to be extended to include the effects of chiral anomaly. It is
demonstrated that chiral effects can have a significant influence on the
evolution of magnetic fields, tending to produce exponential amplification,
creation of magnetic helicity from initially non-helical fields, and can lead
to an inverse energy transfer. We further discuss the modified
magnetohydrodynamic equations around the electroweak transition. The obtained
solutions demonstrate that the asymmetry between right-handed and left-handed
charged fermions of negligible mass typically grows with time when approaching
the electroweak crossover from higher temperatures, until it undergoes a fast
decrease at the transition, and then eventually gets damped at lower
temperatures in the broken phase. At the same time, the dissipation of magnetic
fields gets slower due to the chiral effects. We furthermore report some first
analytical attempts in the study of chiral magnetohydrodynamic turbulence.
Using the analysis of simplified regimes and qualitative arguments, it is shown
that anomalous effects can strongly support turbulent inverse cascade and lead
to a faster growth of the correlation length, when compared to the evolution
predicted by the non-chiral magnetohydrodynamics. Finally, the discussion of
relaxation towards minimal energy states in the chiral magnetohydrodynamic
turbulence is also presented.


Abstract:
  The Covid-19 pandemic introduces new challenges and constraints for return to
work business planning. We describe a space allocation problem that
incorporates social distancing constraints while optimising the number of
available safe workspaces in a return to work scenario. We propose and
demonstrate a graph based approach that solves the optimisation problem via
modelling as a bipartite graph of disconnected components over a graph of
constraints. We compare results obtained with a constrained random walk and a
linear programming approach.


Abstract:
  We provide a new analysis technique to measure the effect of the isotropic
polarization rotation, induced by e.g. the isotropic cosmic birefringence from
axion-like particles and a miscalibration of CMB polarization angle, via mode
coupling in the cosmic microwave background (CMB). Several secondary effects
such as gravitational lensing and CMB optical-depth anisotropies lead to mode
coupling in observed CMB anisotropies, i.e., non-zero off-diagonal elements in
the observed CMB covariance. To derive the mode coupling, however, we usually
assume no parity violation in the observed CMB anisotropies. We first derive a
new contribution to the CMB mode coupling arising from parity violation in
observed CMB. Since the isotropic polarization rotation leads to parity
violation in the observed CMB anisotropies, we then discuss the use of the new
mode coupling for constraining the isotropic polarization angle. We find that
constraints on the isotropic polarization angle by measuring the new
mode-coupling contribution are comparable to that using the $EB$ cross-power
spectrum in future high-sensitivity polarization experiments such as CMB-S4 and
LiteBIRD. Thus, this technique can be used to cross-check results obtained by
the use of the $EB$ cross-power spectrum.


Abstract:
  What breathes life into an embodied agent or avatar? While body motions such
as facial expressions, speech and gestures have been well studied, relatively
little attention has been applied to subtle changes due to underlying
physiology. We argue that subtle pulse signals are important for creating more
lifelike and less disconcerting avatars. We propose a method for animating
blood flow patterns, based on a data-driven physiological model that can be
used to directly augment the appearance of synthetic avatars and
photo-realistic faces. While the changes are difficult for participants to
"see", they significantly more frequently select faces with blood flow as more
anthropomorphic and animated than faces without blood flow. Furthermore, by
manipulating the frequency of the heart rate in the underlying signal we can
change the perceived arousal of the character.


Abstract:
  During software evolution, inexperienced developers may introduce design
anti-patterns when they modify their software systems to fix bugs or to add new
functionalities based on changes in requirements. Developers may also use
design patterns to promote software quality or as a possible cure for some
design anti-patterns. Thus, design patterns and design anti-patterns are
introduced, removed, and mutated from one another by developers.
  Many studies investigated the evolution of design patterns and design
anti-patterns and their impact on software development. However, they
investigated design patterns or design anti-patterns in isolation and did not
consider their mutations and the impact of these mutations on software quality.
Therefore, we report our study of bidirectional mutations between design
patterns and design anti-patterns and the impacts of these mutations on
software change- and fault-proneness.
  We analyzed snapshots of seven Java software systems with diverse sizes,
evolution histories, and application domains. We built Markov models to capture
the probability of occurrences of the different design patterns and design
anti-patterns mutations. Results from our study show that (1) design patterns
and design anti-patterns mutate into other design patterns and/or design
anti-patterns. They also show that (2) some change types primarily trigger
mutations of design patterns and design anti-patterns (renaming and changes to
comments, declarations, and operators), and (3) some mutations of design
anti-patterns and design patterns are more faulty in specific contexts. These
results provide important insights into the evolution of design patterns and
design anti-patterns and its impact on the change- and fault-proneness of
software systems.


Abstract:
  In order to study the phenomenon of regional economic development and urban
expansion from the perspective of night-light remote sensing images,
researchers use NOAA-provided night-light remote sensing image data (data from
1992 to 2013) along with ArcGIS software to process image information, obtain
the basic pixel information data of specific areas of the image, and analyze
these data from the space-time domain for presentation of the trend of regional
economic development in China in recent years, and tries to explore the
urbanization effect brought by the rapid development of China's economy.
Through the analysis and study of the data, the results show that the
urbanization development speed in China is still at its peak, and has great
development potential and space. But at the same time, people also need to pay
attention to the imbalance of regional development.


Abstract:
  Specific heat and linear thermal expansivity are fundamental thermal dynamics
and have been proven as interesting relaxing quantities to investigate in glass
transition and glassy state. However, their possibility has much less been
exploited compared to mechanical and dielectric susceptibilities due to the
limited spectroscopy bandwidth. This work reports on simultaneous spectroscopy
of the two by making use of ultrafast time-resolved thermal lens (TL)
spectroscopy. Detailed modeling of the thermoelastic transients of a relaxing
system subjected to ultrashort laser heating is presented to describe the TL
response. The model has been applied to analyze a set of experimentally
recorded TL waveforms, allowing the determination of relaxation strength and
relaxation frequency from sub-kilohertz to sub-100 MHz and in a wide
temperature range from 200-280 K.


Abstract:
  This paper proposes an efficient video summarization framework that will give
a gist of the entire video in a few key-frames or video skims. Existing video
summarization frameworks are based on algorithms that utilize computer vision
low-level feature extraction or high-level domain level extraction. However,
being the ultimate user of the summarized video, humans remain the most
neglected aspect. Therefore, the proposed paper considers human's role in
summarization and introduces human visual attention-based summarization
techniques. To understand human attention behavior, we have designed and
performed experiments with human participants using electroencephalogram (EEG)
and eye-tracking technology. The EEG and eye-tracking data obtained from the
experimentation are processed simultaneously and used to segment frames
containing useful information from a considerable video volume. Thus, the frame
segmentation primarily relies on the cognitive judgments of human beings. Using
our approach, a video is summarized by 96.5% while maintaining higher precision
and high recall factors. The comparison with the state-of-the-art techniques
demonstrates that the proposed approach yields ceiling-level performance with
reduced computational cost in summarising the videos.


Abstract:
  Let $\mathbf{X}\in\mathbb{C}^{m\times n}$ ($m\geq n$) be a random matrix with
independent rows each distributed as complex multivariate Gaussian with zero
mean and {\it single-spiked} covariance matrix $\mathbf{I}_n+ \eta
\mathbf{u}\mathbf{u}^*$, where $\mathbf{I}_n$ is the $n\times n$ identity
matrix, $\mathbf{u}\in\mathbb{C}^{n\times n}$ is an arbitrary vector with a
unit Euclidean norm, $\eta\geq 0$ is a non-random parameter, and $(\cdot)^*$
represents conjugate-transpose. This paper investigates the distribution of the
random quantity $\kappa_{\text{SC}}^2(\mathbf{X})=\sum_{k=1}^n
\lambda_k/\lambda_1$, where $0<\lambda_1<\lambda_2<\ldots<\lambda_n<\infty$ are
the ordered eigenvalues of $\mathbf{X}^*\mathbf{X}$ (i.e., single-spiked
Wishart matrix). This random quantity is intimately related to the so called
{\it scaled condition number} or the Demmel condition number (i.e.,
$\kappa_{\text{SC}}(\mathbf{X})$) and the minimum eigenvalue of the fixed trace
Wishart-Laguerre ensemble (i.e., $\kappa_{\text{SC}}^{-2}(\mathbf{X})$). In
particular, we use an orthogonal polynomial approach to derive an exact
expression for the probability density function of
$\kappa_{\text{SC}}^2(\mathbf{X})$ which is amenable to asymptotic analysis as
matrix dimensions grow large. Our asymptotic results reveal that, as
$m,n\to\infty$ such that $m-n$ is fixed and when $\eta$ scales on the order of
$1/n$, $\kappa_{\text{SC}}^2(\mathbf{X})$ scales on the order of $n^3$. In this
respect we establish simple closed-form expressions for the limiting
distributions.


Abstract:
  Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with
small number of radio-frequency (RF) chains have limited multiplexing gain.
Spatial path index modulation (SPIM) is helpful in improving this gain by
utilizing additional signal bits modulated by the indices of spatial paths. In
this paper, we introduce model-based and model-free frameworks for beamformer
design in multi-user SPIM-MIMO systems. We first design the beamformers via
model-based manifold optimization algorithm. Then, we leverage federated
learning (FL) with dropout learning (DL) to train a learning model on the local
dataset of users, who estimate the beamformers by feeding the model with their
channel data. The DL randomly selects different set of model parameters during
training, thereby further reducing the transmission overhead compared to
conventional FL. Numerical experiments show that the proposed framework
exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods
and mmWave-MIMO, which relies on the strongest propagation path. Furthermore,
the proposed FL approach provides at least 10 times lower transmission overhead
than the centralized learning techniques.


Abstract:
  Out-of-town recommendation is designed for those users who leave their
home-town areas and visit the areas they have never been to before. It is
challenging to recommend Point-of-Interests (POIs) for out-of-town users since
the out-of-town check-in behavior is determined by not only the user's
home-town preference but also the user's travel intention. Besides, the user's
travel intentions are complex and dynamic, which leads to big difficulties in
understanding such intentions precisely. In this paper, we propose a
TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The
proposed TRAINOR framework distinguishes itself from existing out-of-town
recommenders in three aspects. First, graph neural networks are explored to
represent users' home-town check-in preference and geographical constraints in
out-of-town check-in behaviors. Second, a user-specific travel intention is
formulated as an aggregation combining home-town preference and generic travel
intention together, where the generic travel intention is regarded as a mixture
of inherent intentions that can be learned by Neural Topic Model (NTM). Third,
a non-linear mapping function, as well as a matrix factorization method, are
employed to transfer users' home-town preference and estimate out-of-town POI's
representation, respectively. Extensive experiments on real-world data sets
validate the effectiveness of the TRAINOR framework. Moreover, the learned
travel intention can deliver meaningful explanations for understanding a user's
travel purposes.


Abstract:
  Cavity optomechanical systems have become a popular playground for studies of
controllable nonlinear interactions between light and motion. Owing to the
large speed of light, realizing cavity optomechanics in the microwave frequency
range requires cavities up to several mm in size, hence making it hard to embed
several of them on the same chip. An alternative scheme with much smaller
footprint is provided by magnomechanics, where the electromagnetic cavity is
replaced by a magnet undergoing ferromagnetic resonance, and the optomechanical
coupling originates from magnetic shape anisotropy. Here, we consider the
magnomechanical interaction occurring in a suspended magnetic beam -- a scheme
in which both magnetic and mechanical modes physically overlap and can also be
driven individually. We show that a sizable interaction can be produced if the
beam has some initial static deformation, as is often the case due to unequal
strains in the constituent materials. We also show how the magnetism affects
the magnetomotive detection of the vibrations, and how the magnomechanics
interaction can be used in microwave signal amplification. Finally, we discuss
experimental progress towards realizing the scheme.


Abstract:
  This paper studies offline Imitation Learning (IL) where an agent learns to
imitate an expert demonstrator without additional online environment
interactions. Instead, the learner is presented with a static offline dataset
of state-action-next state transition triples from a potentially less
proficient behavior policy. We introduce Model-based IL from Offline data
(MILO): an algorithmic framework that utilizes the static dataset to solve the
offline IL problem efficiently both in theory and in practice. In theory, even
if the behavior policy is highly sub-optimal compared to the expert, we show
that as long as the data from the behavior policy provides sufficient coverage
on the expert state-action traces (and with no necessity for a global coverage
over the entire state-action space), MILO can provably combat the covariate
shift issue in IL. Complementing our theory results, we also demonstrate that a
practical implementation of our approach mitigates covariate shift on benchmark
MuJoCo continuous control tasks. We demonstrate that with behavior policies
whose performances are less than half of that of the expert, MILO still
successfully imitates with an extremely low number of expert state-action pairs
while traditional offline IL method such as behavior cloning (BC) fails
completely. Source code is provided at https://github.com/jdchang1/milo.


Abstract:
  This position paper examines potential pitfalls on the way towards achieving
human-AI co-creation with generative models in a way that is beneficial to the
users' interests. In particular, we collected a set of nine potential pitfalls,
based on the literature and our own experiences as researchers working at the
intersection of HCI and AI. We illustrate each pitfall with examples and
suggest ideas for addressing it. Reflecting on all pitfalls, we discuss and
conclude with implications for future research directions. With this
collection, we hope to contribute to a critical and constructive discussion on
the roles of humans and AI in co-creative interactions, with an eye on related
assumptions and potential side-effects for creative practices and beyond.


Abstract:
  We give an explicit description of the generator of finitely presented
objects of the coslice of a locally finitely presentable category under a given
object, as consisting of all pushouts of finitely presented maps under this
object. Then we prove that the comma category under the direct image part of a
morphism of locally finitely presentable category is still locally finitely
presentable, and we give again an explicit description of its generator of
finitely presented objects. We finally deduce that 2-category $\LFP$ has comma
objects computed in $\Cat$.


Abstract:
  Genome-wide association studies (GWAS) have identified thousands of genetic
variants associated with complex traits. Many complex traits are found to have
shared genetic etiology. Genetic covariance is defined as the underlying
covariance of genetic values and can be used to measure the shared genetic
architecture. The data of two outcomes may be collected from the same group or
different groups of individuals and the outcomes can be of different types or
collected based on different study designs. This paper proposes a unified
approach to robust estimation and inference for genetic covariance of general
outcomes that may be associated with genetic variants nonlinearly. We provide
the asymptotic properties of the proposed estimator and show that our proposal
is robust under certain model mis-specification. Our method under linear
working models provides robust inference for the narrow-sense genetic
covariance, even when both linear models are mis-specified. Various numerical
experiments are performed to support the theoretical results. Our method is
applied to an outbred mice GWAS data set to study the overlapping genetic
effects between the behavioral and physiological phenotypes. The real data
results demonstrate the robustness of the proposed method and reveal
interesting genetic covariance among different mice developmental traits.


Abstract:
  We study unirationality of a Del Pezzo surface of degree two over a given
(non algebraically closed) field, under the assumption that it admits at least
one rational double point over an algebraic closure of the base field. As
corollaries of our main results, we find that over a finite field, it is
unirational if the cardinality of the field is greater than or equal to nine
and we also find that over an infinite field, which is not necessarily perfect,
it is unirational if and only if the rational points are Zariski dense over the
field.


Abstract:
  Input perturbation methods occlude parts of an input to a function and
measure the change in the function's output. Recently, input perturbation
methods have been applied to generate and evaluate saliency maps from
convolutional neural networks. In practice, neutral baseline images are used
for the occlusion, such that the baseline image's impact on the classification
probability is minimal. However, in this paper we show that arguably neutral
baseline images still impact the generated saliency maps and their evaluation
with input perturbations. We also demonstrate that many choices of
hyperparameters lead to the divergence of saliency maps generated by input
perturbations. We experimentally reveal inconsistencies among a selection of
input perturbation methods and find that they lack robustness for generating
saliency maps and for evaluating saliency maps as saliency metrics.


Abstract:
  We introduce a novel fine-grained dataset and benchmark, the Danish Fungi
2020 (DF20). The dataset, constructed from observations submitted to the Atlas
of Danish Fungi, is unique in its taxonomy-accurate class labels, small number
of errors, highly unbalanced long-tailed class distribution, rich observation
metadata, and well-defined class hierarchy. DF20 has zero overlap with
ImageNet, allowing unbiased comparison of models fine-tuned from publicly
available ImageNet checkpoints. The proposed evaluation protocol enables
testing the ability to improve classification using metadata -- e.g. precise
geographic location, habitat, and substrate, facilitates classifier calibration
testing, and finally allows to study the impact of the device settings on the
classification performance. Experiments using Convolutional Neural Networks
(CNN) and the recent Vision Transformers (ViT) show that DF20 presents a
challenging task. Interestingly, ViT achieves results superior to CNN baselines
with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and
12% respectively. A simple procedure for including metadata into the decision
process improves the classification accuracy by more than 2.95 percentage
points, reducing the error rate by 15%. The source code for all methods and
experiments is available at https://sites.google.com/view/danish-fungi-dataset.


Abstract:
  In the present work, the European option pricing SWIFT method is extended for
Heston model calibration. The computation of the option price gradient is
simplified thanks to the knowledge of the characteristic function in closed
form. The proposed calibration machinery appears to be extremely fast, in
particular for a single expiry and multiples strikes, outperforming the
state-of-the-art method we compare with. Further, the a priori knowledge of
SWIFT parameters makes possible a reliable and practical implementation of the
presented calibration method. A wide set of stress, speed and convergence
numerical experiments is carried out, with deep in-the-money, at-the-money and
deep out-of-the-money options for very short and very long maturities.


Abstract:
  We note that a strongly minimal Steiner $k$-Steiner system $(M,R)$ from
(Baldwin-Paolini 2020) can be `coordinatized' in the sense of (Gantner-Werner
1975) by a quasigroup if $k$ is a prime-power. But for the basic construction
this coordinatization is never definable in $(M,R)$. Nevertheless, by refining
the construction, if $k$ is a prime power there is a $(2,k)$-variety of
quasigroups which is strongly minimal and definably coordinatizes a Steiner
$k$-system.


Abstract:
  The chromosphere is a partially ionized layer of the solar atmosphere, the
transition between the photosphere where the gas motion is determined by the
gas pressure and the corona dominated by the magnetic field. We study the
effect of partial ionization for 2D wave propagation in a gravitationally
stratified, magnetized atmosphere with properties similar to the solar
chromosphere. We adopt an oblique uniform magnetic field in the plane of
propagation with strength suitable for a quiet sun region. The theoretical
model used is a single fluid magnetohydrodynamic approximation, where
ion-neutral interaction is modeled by the ambipolar diffusion term. Magnetic
energy can be converted into internal energy through the dissipation of the
electric current produced by the drift between ions and neutrals. We use
numerical simulations where we continuously drive fast waves at the bottom of
the atmosphere. The collisional coupling between ions and neutrals decreases
with the decrease of the density and the ambipolar effect becomes important.
Fast waves excited at the base of the atmosphere reach the equipartition layer
and reflect or transmit as slow waves. While the waves propagate through the
atmosphere and the density drops, the waves steepen into shocks. The main
effect of ambipolar diffusion is damping of the waves. We find that for the
parameters chosen in this work, the ambipolar diffusion affects the fast wave
before it is reflected, with damping being more pronounced for waves which are
launched in a direction perpendicular to the magnetic field. Slow waves are
less affected by ambipolar effects. The damping increases for shorter periods
and larger magnetic field strengths. Small scales produced by the nonlinear
effects and the superposition of different types of waves created at the
equipartition height are efficiently damped by ambipolar diffusion.


Abstract:
  In this paper we obtain a parametric solution of the hitherto unsolved
diophantine equation $(x_1^5+x_2^5)(x_3^5+x_4^5)=(y_1^5+y_2^5)(y_3^5+y_4^5)$.
Further, we show, using elliptic curves, that there exist infinitely many
parametric solutions of the aforementioned diophantine equation, and they can
be effectively computed.


Abstract:
  Topological Dirac semimetals are a class of semimetals that host
symmetry-protected Dirac points near the Fermi level, which arise due to a band
inversion of the conduction and valence bands. In this work, we study the less
explored class of \emph{noncentrosymmetric} topological Dirac semimetals in
three dimensions. We identify the noncentrosymmetric crystallographic point
groups required to stabilize fourfold degenerate band crossings and derive
model Hamiltonians for all distinct types of band inversions allowed by
symmetry. Using these model Hamiltonians, which emphasize the physical nature
of the allowed couplings, we establish the generic electronic phase diagram
noncentrosymmetric Dirac semimetals and show that it generically includes
phases with coexistent Weyl point nodes or Weyl line nodes. In particular, for
one specific type of band inversion in sixfold symmetric systems we show that
Weyl line nodes are always present. Based on first-principles calculations, we
predict that BiPd$_2$O$_4$ is a noncentrosymmetric Dirac semimetal under 20 Gpa
pressure and hosts topological type-II Dirac points on the fourfold rotation
axis. Furthermore, we propose that the hexagonal polar alloy
LiZnSb$_{x}$Bi$_{1-x}$ realizes a Dirac semimetal with coexistent Weyl points.
Interestingly, the emergence and location of the Weyl points is highly tunable
and can be controlled by the alloy concentration $x$. More generally, our
results not only establish band-inverted noncentrosymmetric systems as a broad
and versatile class of topological semimetals, but also provide a framework for
studying the quantum nonlinear Hall effect and nonlinear optical properties in
the Dirac semimetals.


Abstract:
  The ability to engineer the properties of quantum optical states is essential
for quantum information processing applications. Here, we demonstrate tunable
control of spatial correlations between photon pairs produced by spontaneous
parametric down-conversion. By shaping the spatial pump beam profile in a
type-I collinear configuration, we tailor the spatial structure of coincidences
between photon pairs entangled in high dimensions, without effect on intensity.
The results highlight fundamental aspects of spatial coherence and hold
potential for the development of quantum technologies based on high-dimensional
spatial entanglement.


Abstract:
  The arithmetic of N, Z, Q, R can be extended to a graph arithmetic where N is
the semiring of finite simple graphs and where Z and Q are integral domains,
culminating in a Banach algebra R. A single network completes to the Wiener
algebra. We illustrate the compatibility with topology and spectral theory.
Multiplicative linear functionals like Euler characteristic, the Poincare
polynomial or the zeta functions can be extended naturally. These functionals
can also help with number theoretical questions. The story of primes is a bit
different as the integers are not a unique factorization domain, because there
are many additive primes. Most graphs are multiplicative primes.


Abstract:
  An ultrafast laser based on coherent beam combination of four ytterbium-doped
step-index fiber amplifiers is presented. The system delivers an average power
of 3.5 kW and a pulse duration of 430 fs at 80 MHz repetition rate. The beam
quality is excellent (M2<1.24x1.10) and the relative intensity noise is as low
as 1% in the frequency span from 1 Hz to 1 MHz. The system is turn-key operable
as it features an automated spatial and temporal alignment of the
interferometric amplification channels.


Abstract:
  The recently described pushframe imager, a parallelized single pixel camera
capturing with a pushbroom-like motion, is intrinsically suited to both
remote-sensing and compressive sampling. It optically applies a 2D mask to the
imaged scene, before performing light integration along a single spatial axis,
but previous work has not made use of the architecture's potential for taking
measurements sparsely. In this paper we develop a strongly performing static
binarized noiselet compressive sampling mask design, tailored to pushframe
hardware, allowing both a single exposure per motion time-step, and retention
of 2D correlations in the scene. Results from simulated and real-world captures
are presented, with performance shown to be similar to that of immobile -- and
hence inappropriate for satellite use -- whole-scene imagers. A particular
feature of our sampling approach is that the degree of compression can be
varied without altering the pattern, and we demonstrate the utility of this for
efficiently storing and transmitting multi-spectral images.


Abstract:
  When network products and services become more valuable as their userbase
grows (network effects), this tendency can become a major determinant of how
they compete with each other in the market and how the market is structured.
Network effects are traditionally linked to high market concentration,
early-mover advantages, and entry barriers, and in the cryptoasset market they
have been used as a valuation tool too. The recent resurgence of Bitcoin has
been partly attributed to network effects too. We study the existence of
network effects in six cryptoassets from their inception to obtain a high-level
overview of the application of network effects in the cryptoasset market. We
show that contrary to the usual implications of network effects, they do not
serve to concentrate the cryptoasset market, nor do they accord any one
cryptoasset a definitive competitive advantage, nor are they consistent enough
to be reliable valuation tools. Therefore, while network effects do occur in
cryptoasset networks, they are not a defining feature of the cryptoasset market
as a whole.


Abstract:
  We consider electronic and magnetic properties of chromium, a well-known
itinerant antiferromagnet, by a combination of density functional theory (DFT)
and dynamical mean-field theory (DMFT). We find that electronic correlation
effects in chromium, in contrast to its neighbours in the periodic table, are
weak, leading to the quasiparticle mass enhancement factor ${m^*/m \approx
1.2}$. Our results for local spin-spin correlation functions and distribution
of weigths of atomic configurations indicate that the local magnetic moments
are not formed. Similarly to previous results of DFT at ambient pressure, the
non-uniform magnetic susceptibility as a function of momentum possesses close
to the wave vector ${{\mathbf Q}_{\rm H}=(0,0,2\pi/a)}$ ($a$ is the lattice
constant) sharp maxima, corresponding to Kohn anomalies. We find that these
maxima are preserved by the interaction and are not destroyed by pressure. Our
calculations qualitatively capture a decrease of the N\'eel temperature with
pressure and a breakdown of itinerant antiferomagnetism at pressure of $\sim$9
GPa in agreement with experimental data, although the N\'eel temperature is
significantly overestimated because of the mean-field nature of DMFT.


Abstract:
  While most existing segmentation methods usually combined the powerful
feature extraction capabilities of CNNs with Conditional Random Fields (CRFs)
post-processing, the result always limited by the fault of CRFs . Due to the
notoriously slow calculation speeds and poor efficiency of CRFs, in recent
years, CRFs post-processing has been gradually eliminated. In this paper, an
improved Generative Adversarial Networks (GANs) for image semantic segmentation
task (semantic segmentation by GANs, Seg-GAN) is proposed to facilitate further
segmentation research. In addition, we introduce Convolutional CRFs (ConvCRFs)
as an effective improvement solution for the image semantic segmentation task.
Towards the goal of differentiating the segmentation results from the ground
truth distribution and improving the details of the output images, the proposed
discriminator network is specially designed in a full convolutional manner
combined with cascaded ConvCRFs. Besides, the adversarial loss aggressively
encourages the output image to be close to the distribution of the ground
truth. Our method not only learns an end-to-end mapping from input image to
corresponding output image, but also learns a loss function to train this
mapping. The experiments show that our method achieves better performance than
state-of-the-art methods.


Abstract:
  We propose a relativistic quantum Otto cycle between an entangled state of
two qubits and their composite excited (or ground) state whose efficiency can
be greater than the usual single qubit quantum Otto engine. The hot and cold
reservoirs are constructed by providing uniform accelerations to these qubits
along with the interaction between the background field and individual qubits.
The efficiency, as measured from one of the qubits' frame, not only depends on
the energy gap of the states but also the relative acceleration between them.
For lower acceleration of our observer's qubit compared to the other one, the
cycle is more efficient than the single qubit quantum Otto engine. Furthermore,
a complete protocol to construct such a cycle is being provided.


Abstract:
  In recent years, trends towards studying simulated games have gained momentum
in the fields of artificial intelligence, cognitive science, psychology, and
neuroscience. The intersections of these fields have also grown recently, as
researchers increasing study such games using both artificial agents and human
or animal subjects. However, implementing games can be a time-consuming
endeavor and may require a researcher to grapple with complex codebases that
are not easily customized. Furthermore, interdisciplinary researchers studying
some combination of artificial intelligence, human psychology, and animal
neurophysiology face additional challenges, because existing platforms are
designed for only one of these domains. Here we introduce Modular
Object-Oriented Games, a Python task framework that is lightweight, flexible,
customizable, and designed for use by machine learning, psychology, and
neurophysiology researchers.


Abstract:
  We use a hybrid k dot p theory - tight binding (HkpTB) model to describe
interlayer coupling simultaneously in both Bernal and twisted graphene
structures. For Bernal-aligned interfaces, HkpTB is parametrized using the full
Slonczewski-Weiss-McClure (SWMcC) Hamiltonian of graphite, which is then used
to refine the commonly used minimal model for twisted interfaces, by deriving
additional terms that reflect all details of the full SWMcC model of graphite.
We find that these terms introduce some electron-hole asymmetry in the band
structure of twisted bilayers, but in twistronic multilayer graphene, they
produce only a subtle change of moire miniband spectra, confirming the broad
applicability of the minimal model for implementing the twisted interface
coupling in such systems.


Abstract:
  What will the future of UAV cellular communications be? In this tutorial
article, we address such a compelling yet difficult question by embarking on a
journey from 5G to 6G and sharing a large number of realistic case studies
supported by original results. We start by overviewing the status quo on UAV
communications from an industrial standpoint, providing fresh updates from the
3GPP and detailing new 5G NR features in support of aerial devices. We then
show the potential and the limitations of such features. In particular, we
demonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection
and interference challenges, we showcase encouraging mmWave coverage
evaluations in both urban and suburban/rural settings, and we examine the
peculiarities of direct device-to-device communications in the sky. Moving on,
we sneak a peek at next-generation UAV communications, listing some of the use
cases envisioned for the 2030s. We identify the most promising 6G enablers for
UAV communication, those expected to take the performance and reliability to
the next level. For each of these disruptive new paradigms (non-terrestrial
networks, cell-free architectures, artificial intelligence, reconfigurable
intelligent surfaces, and THz communications), we gauge the prospective
benefits for UAVs and discuss the main technological hurdles that stand in the
way. All along, we distil our numerous findings into essential takeaways, and
we identify key open problems worthy of further study.


Abstract:
  Composite minimization is a powerful framework in large-scale convex
optimization, based on decoupling of the objective function into terms with
structurally different properties and allowing for more flexible algorithmic
design. In this work, we introduce a new algorithmic framework for
complementary composite minimization, where the objective function decouples
into a (weakly) smooth and a uniformly convex term. This particular form of
decoupling is pervasive in statistics and machine learning, due to its link to
regularization.
  The main contributions of our work are summarized as follows. First, we
introduce the problem of complementary composite minimization in general normed
spaces; second, we provide a unified accelerated algorithmic framework to
address broad classes of complementary composite minimization problems; and
third, we prove that the algorithms resulting from our framework are
near-optimal in most of the standard optimization settings. Additionally, we
show that our algorithmic framework can be used to address the problem of
making the gradients small in general normed spaces. As a concrete example, we
obtain a nearly-optimal method for the standard $\ell_1$ setup (small gradients
in the $\ell_\infty$ norm), essentially matching the bound of Nesterov (2012)
that was previously known only for the Euclidean setup. Finally, we show that
our composite methods are broadly applicable to a number of regression
problems, leading to complexity bounds that are either new or match the best
existing ones.


Abstract:
  The formation of the most massive quasars observed at high redshifts requires
extreme inflows of gas down to the length scales of the central compact object.
Here, we estimate the maximum inflow rate allowed by gravity down to the
surface of supermassive stars, the possible progenitors of these supermassive
black holes. We use the continuity equation and the assumption of free-fall to
derive maximum allowed inflow rates for various density profiles. We apply our
approach to the mass-radius relation of rapidly accreting supermassive stars to
estimate an upper limit to the accretion rates allowed during the formation of
these objects. We find that the maximum allowed rate $\dot M_{\rm max}$ is
given uniquely by the compactness of the accretor. For the compactness of
rapidly accreting supermassive stars, $\dot M_{\rm max}$ is related to the
stellar mass $M$ by a power-law $\dot M_{\rm max}\propto M^{3/4}$. The rates of
atomically cooled halos (0.1 -- 10 M$_\odot$ yr$^{-1}$) are allowed as soon as
$M\gtrsim1$ M$_\odot$. The largest rates expected in galaxy mergers
($10^4-10^5$ M$_\odot$ yr$^{-1}$) become accessible once the accretor is
supermassive ($M\gtrsim10^4$ M$_\odot$). These results suggest that
supermassive stars can accrete up to masses $>10^6$ M$_\odot$ before they
collapse via the general-relativistic instability. At such masses, the collapse
is expected to lead to the direct formation of a supermassive black hole even
within metal-rich gas, resulting in a black hole seed that is significantly
heavier than in conventional direct collapse models for atomic cooling halos.


Abstract:
  We give a new proof of the uniformization theorem of the leaves of a
lamination by surfaces of hyperbolic conformal type. We use a laminated version
of the Ricci flow to prove the existence of a laminated Riemannian metric
(smooth on the leaves, transversaly continuous) with leaves of constant
Gaussian curvature equal to -1, which is conformally equivalent to the original
metric.


Abstract:
  How to handle gender with machine learning is a controversial topic. A
growing critical body of research brought attention to the numerous issues
transgender communities face with the adoption of current automatic gender
recognition (AGR) systems. In contrast, we explore how such technologies could
potentially be appropriated to support transgender practices and needs,
especially in non-Western contexts like Japan. We designed a virtual makeup
probe to assist transgender individuals with passing, that is to be perceived
as the gender they identify as. To understand how such an application might
support expressing transgender individuals gender identity or not, we
interviewed 15 individuals in Tokyo and found that in the right context and
under strict conditions, AGR based systems could assist transgender passing.


Abstract:
  The background magnetic-field formalism of Lattice QCD has been used recently
to calculate the magnetic polarizability of the charged pion. These $n_f = 2 +
1$ numerical simulations are electro-quenched, such that the virtual sea-quarks
of the QCD vacuum do not interact with the background field. To understand the
impact of this, we draw on partially quenched chiral perturbation theory. In
this case, the leading term proportional to $1/M_\pi$ arises at tree level from
$\mathcal{L}_4$. To describe the results from lattice QCD, while maintaining
the exact leading terms of chiral perturbation theory, we introduce a Pad\'e
approximant designed to reproduce the slow variation observed in the lattice
QCD results. Two-loop contributions are introduced to assess the systematic
uncertainty associated with higher-order terms of the expansion. Upon
extrapolation, the magnetic polarizability of the charged pion at the physical
pion mass is found to be $\beta_{\pi^\pm}=-1.70\,(14)_{\rm stat}(25)_{\rm
syst}\times 10^{-4}$ fm$^3$, in good agreement with the recent experimental
measurement.


Abstract:
  In this paper, we focus on the fairness issues regarding unsupervised outlier
detection. Traditional algorithms, without a specific design for algorithmic
fairness, could implicitly encode and propagate statistical bias in data and
raise societal concerns. To correct such unfairness and deliver a fair set of
potential outlier candidates, we propose Deep Clustering based Fair Outlier
Detection (DCFOD) that learns a good representation for utility maximization
while enforcing the learnable representation to be subgroup-invariant on the
sensitive attribute. Considering the coupled and reciprocal nature between
clustering and outlier detection, we leverage deep clustering to discover the
intrinsic cluster structure and out-of-structure instances. Meanwhile, an
adversarial training erases the sensitive pattern for instances for fairness
adaptation. Technically, we propose an instance-level weighted representation
learning strategy to enhance the joint deep clustering and outlier detection,
where the dynamic weight module re-emphasizes contributions of likely-inliers
while mitigating the negative impact from outliers. Demonstrated by experiments
on eight datasets comparing to 17 outlier detection algorithms, our DCFOD
method consistently achieves superior performance on both the outlier detection
validity and two types of fairness notions in outlier detection.


Abstract:
  Presence of large-scale surface magnetic field in early-type stars leads to
several unique electromagnetic phenomena producing radiation over X-ray to
radio bands. Among them, the rarest type of emission is electron cyclotron
maser emission (ECME) observed as periodic, circularly polarized radio pulses.
The phenomenon was first discovered in the hot magnetic star CU Vir. Past
observations of this star led to the consensus that the star produces only
right circularly polarized ECME, suggesting that only one magnetic hemisphere
takes part in the phenomenon. Here we present the first ultra-wideband (0.4$-$4
GHz) study of this star using the upgraded Giant Metrewave Radio telescope and
the Karl G. Jansky Very Large Array, which led to the surprising discovery of
ECME of both circular polarizations up to around 1.5 GHz. The GHz observations
also allowed us to infer that the upper ECME cut-off frequency is at $\gtrsim
5\,\mathrm{GHz}$. The sub-GHz observation led to the unexpected observation of
more than two pairs of ECME pulses per rotation cycle. In addition, we report
the discovery of a `giant pulse', and transient enhancements, which are
potentially the first observational evidence of `centrifugal breakout' of
plasma from the innermost part of the stellar magnetosphere. The stark contrast
between the star's behavior at GHz and sub-GHz frequencies could either be due
to propagation effects, a manifestation of varying magnetic field topology as a
function of height, or a signature of an additional `ECME engine'.


Abstract:
  Sub-Terahertz frequencies (frequencies above 100 GHz) have the potential to
satisfy the unprecedented demand on data rate on the order of hundreds of Gbps
for sixth-generation (6G) wireless communications and beyond. Accurate beam
tracking and rapid beam selection are increasingly important since antenna
arrays with more elements generate narrower beams to compensate for additional
path loss within the first meter of propagation distance at sub-THz
frequencies. Realistic channel models for above 100 GHz are needed, and should
include spatial consistency to model the spatial and temporal channel evolution
along the user trajectory. This paper introduces recent outdoor urban microcell
(UMi) propagation measurements at 142 GHz along a 39 m $\times$ 12 m
rectangular route (102 m long), where each consecutive and adjacent receiver
location is 3 m apart from each other. The measured power delay profiles and
angular power spectrum at each receiver location are used to study spatial
autocorrelation properties of various channel parameters such as shadow fading,
delay spread, and angular spread along the track. Compared to the correlation
distances reported in the 3GPP TR 38.901 for frequencies below 100 GHz, the
measured correlation distance of shadow fading at 142 GHz (3.8 m) is much
shorter than the 10-13 m as specified in 3GPP; the measured correlation
distances of delay spread and angular spread at 142 GHz (both 12 m) are
comparable to the 7-10 m as specified in 3GPP. This result may guide the
development of a statistical spatially consistent channel model for frequencies
above 100 GHz in the UMi street canyon environment.


Abstract:
  We derive a general expression for the absorptive part of the one-loop photon
polarization tensor in a strongly magnetized quark-gluon plasma at nonzero
baryon chemical potential. To demonstrate the application of the main result in
the context of heavy-ion collisions, we study the effect of a nonzero baryon
chemical potential on the photon emission rate. The rate and the ellipticity of
photon emission are studied numerically as a function the transverse momentum
(energy) for several values of temperature and chemical potential. When the
chemical potential is small compared to the temperature, the rates of the quark
and antiquark splitting processes (i.e., $q\rightarrow q +\gamma$ and
$\bar{q}\rightarrow \bar{q} +\gamma$, respectively) are approximately the same.
However, the quark splitting gradually becomes the dominant process with
increasing the chemical potential. We also find that increasing the chemical
potential leads to a growing total photon production rate but has only a small
effect on the ellipticity of photon emission. The quark-antiquark annihilation
($q+\bar{q}\rightarrow \gamma$) also contributes to the photon production, but
its contribution remains relatively small for a wide range of temperatures and
chemical potentials investigated.


Abstract:
  Topic trajectory information provides crucial insight into the dynamics of
topics and their evolutionary relationships over a given time. Also, this
information can help to improve our understanding on how new topics have
emerged or formed through a sequential or interrelated events of emergence,
modification and integration of prior topics. Nevertheless, the implementation
of the existing methods for topic trajectory identification is rarely available
as usable software. In this paper, we present TopicTracker, a platform for
topic trajectory identification and visualisation. The key of Topic Tracker is
that it can represent the three facets of information together, given two kinds
of input: a time-stamped topic profile consisting of the set of the underlying
topics over time, and the evolution strength matrix among them: evolutionary
pathways of dynamic topics, evolution states of the topics, and topic
importance. TopicTracker is a publicly available software implemented using the
R software.


Abstract:
  Mechanical strength of amyloid beta fibrils has been known to be correlated
with neuronal cell death. Here, we resorted to steered molecular dynamics (SMD)
simulations to mechanically stretch a single S-shape amyloid beta Abeta11-42
dodecamer fibril in vacuum. It was found that the weakest sites at which the
fibril was ruptured due to mechanical extension were exclusively at the
interfaces of alanine and glutamic acid distributed throughout the fibril. It
was also revealed that the free energy required to unfold the fibril to form a
long linear conformation is equivalent to ~ 210 eV, being several thousand
times larger than thermal voltage at room temperature. As a consequence, within
solution a larger free energy is needed for such a maximal stretching based on
the fact that amyloid beta fibrils are structurally more stable in solution due
to the interplay between their hydrophobic cores and solution's entropy.


Abstract:
  Multivariate functional data can be intrinsically multivariate like movement
trajectories in 2D or complementary like precipitation, temperature, and wind
speeds over time at a given weather station. We propose a multivariate
functional additive mixed model (multiFAMM) and show its application to both
data situations using examples from sports science (movement trajectories of
snooker players) and phonetic science (acoustic signals and articulation of
consonants). The approach includes linear and nonlinear covariate effects and
models the dependency structure between the dimensions of the responses using
multivariate functional principal component analysis. Multivariate functional
random intercepts capture both the auto-correlation within a given function and
cross-correlations between the multivariate functional dimensions. They also
allow us to model between-function correlations as induced by e.g.\ repeated
measurements or crossed study designs. Modeling the dependency structure
between the dimensions can generate additional insight into the properties of
the multivariate functional process, improves the estimation of random effects,
and yields corrected confidence bands for covariate effects. Extensive
simulation studies indicate that a multivariate modeling approach is more
parsimonious than fitting independent univariate models to the data while
maintaining or improving model fit.


Abstract:
  In this paper we prove the existence and uniqueness of strong solution of the
incompressible Navier-Stokes equations with damping $\alpha
(e^{\beta|u|^2}-1)u$.


Abstract:
  The hypothesis that strange quark matter is the true ground state of matter
has been investigated for almost four decades, but only a few works have
explored the dynamics of binary systems of quark stars. This is partly due to
the numerical challenges that need to be faced when modelling the large
discontinuities at the surface of these stars. We here present a novel
technique in which the EOS of a quark star is suitably rescaled to produce a
smooth change of the specific enthalpy across a very thin crust. The
introduction of the crust has been carefully tested by considering the
oscillation properties of isolated quark stars, showing that the response of
the simulated quark stars matches accurately the perturbative predictions.
Using this technique, we have carried out the first fully general-relativistic
simulations of the merger of quark-star binaries finding several important
differences between quark-star binaries and hadronic-star binaries with the
same mass and comparable tidal deformability. In particular, we find that
dynamical mass loss is significantly suppressed in quark-star binaries. In
addition, quark-star binaries have merger and post-merger frequencies that obey
the same quasi-universal relations derived from hadron stars if expressed in
terms of the tidal deformability, but not when expressed in terms of the
average stellar compactness. Hence, it may be difficult to distinguish the two
classes of stars if no information on the stellar radius is available. Finally,
differences are found in the distributions in velocity and entropy of the
ejected matter, for which quark-stars have much smaller tails. Whether these
differences in the ejected matter will leave an imprint in the electromagnetic
counterpart and nucleosynthetic yields remains unclear, calling for the
construction of an accurate model for the evaporation of the ejected quarks
into nucleons.


Abstract:
  We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic
segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The
VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations
provided by human experts. A set of state-of-the-art segmentation models are
trained on 196 images from the VinDr-RibCXR to segment and label 20 individual
ribs. Our best performing model obtains a Dice score of 0.834 (95% CI,
0.810--0.853) on an independent test set of 49 images. Our study, therefore,
serves as a proof of concept and baseline performance for future research.


Abstract:
  Incentive mechanism design is crucial for enabling federated learning. We
deal with clustering problem of agents contributing to federated learning
setting. Assuming agents behave selfishly, we model their interaction as a
stable coalition partition problem using hedonic games where agents and
clusters are the players and coalitions, respectively. We address the following
question: is there a family of hedonic games ensuring a Nash-stable coalition
partition? We propose the Nash-stable set which determines the family of
hedonic games possessing at least one Nash-stable partition, and analyze the
conditions of non-emptiness of the Nash-stable set. Besides, we deal with the
decentralized clustering. We formulate the problem as a non-cooperative game
and prove the existence of a potential game.


Abstract:
  Magnetic resonance imaging (MRI) acquisition, reconstruction, and
segmentation are usually processed independently in the conventional practice
of MRI workflow. It is easy to notice that there are significant relevances
among these tasks and this procedure artificially cuts off these potential
connections, which may lead to losing clinically important information for the
final diagnosis. To involve these potential relations for further performance
improvement, a sequential multi-task joint learning network model is proposed
to train a combined end-to-end pipeline in a differentiable way, aiming at
exploring the mutual influence among those tasks simultaneously. Our design
consists of three cascaded modules: 1) deep sampling pattern learning module
optimizes the $k$-space sampling pattern with predetermined sampling rate; 2)
deep reconstruction module is dedicated to reconstructing MR images from the
undersampled data using the learned sampling pattern; 3) deep segmentation
module encodes MR images reconstructed from the previous module to segment the
interested tissues. The proposed model retrieves the latently interactive and
cyclic relations among those tasks, from which each task will be mutually
beneficial. The proposed framework is verified on MRB dataset, which achieves
superior performance on other SOTA methods in terms of both reconstruction and
segmentation.


Abstract:
  Clustering is one of the most fundamental tasks in machine learning.
Recently, deep clustering has become a major trend in clustering techniques.
Representation learning often plays an important role in the effectiveness of
deep clustering, and thus can be a principal cause of performance degradation.
In this paper, we propose a clustering-friendly representation learning method
using instance discrimination and feature decorrelation. Our
deep-learning-based representation learning method is motivated by the
properties of classical spectral clustering. Instance discrimination learns
similarities among data and feature decorrelation removes redundant correlation
among features. We utilize an instance discrimination method in which learning
individual instance classes leads to learning similarity among instances.
Through detailed experiments and examination, we show that the approach can be
adapted to learning a latent space for clustering. We design novel
softmax-formulated decorrelation constraints for learning. In evaluations of
image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy
of 81.5% and 95.4%, respectively. We also show that the softmax-formulated
constraints are compatible with various neural networks.


Abstract:
  The phase stability and equilibria of carbon dioxide is investigated from 125
-- 325K and 1 -- 10,000 atm using extensive molecular dynamics (MD) simulations
and the Two-Phase Thermodynamics (2PT) method. We devise a direct approach for
calculating phase diagrams in general, by considering the separate chemical
potentials of the isolated phase at specific points on the P-T diagram. The
unique ability of 2PT to accurately and efficiently approximate the entropy and
Gibbs energy of liquids thus allows for assignment of phase boundaries from
relatively short ($\mathrm{\sim}$ 100ps) MD simulations. We validate our
approach by calculating the critical properties of the flexible Elementary
Physical Model 2 (FEPM2), showing good agreement with previous results. We
show, however, that the incorrect description of the short-range Pauli force
and the lack of molecular charge polarization leads to deviations from
experiments at high pressures. We thus develop a many-body, fluctuating charge
model for CO${}_{2}$, termed CO${}_{2}$-Fq, from high level quantum mechanics
(QM) calculations, that accurately captures the condensed phase vibrational
properties of the solid (including the Fermi resonance at 1378 cm${}^{-1}$) as
well as the diffusional properties of the liquid, leading to overall excellent
agreement with experiments over the entire phase diagram. This work provides an
efficient computational approach for determining phase diagrams of arbitrary
systems and underscore the critical role of QM charge reorganization physics in
molecular phase stability.


Abstract:
  K\"ahler's geometric approach in which relativistic fermion fields are
treated as differential forms is applied in three spacetime dimensions. It is
shown that the resulting continuum theory is invariant under global
U($N)\otimes$U($N)$ field transformations, and has a parity-invariant mass
term, both symmetries shared in common with staggered lattice fermions. The
formalism is used to construct a version of the Thirring model with contact
interactions between conserved Noether currents. Under reasonable assumptions
about field rescaling after quantum corrections, a more general interaction
term is derived, sharing the same symmetries but now including terms which
entangle spin and taste degrees of freedom, which exactly coincides with the
leading terms in the staggered lattice Thirring model in the long-wavelength
limit. Finally truncated versions of the theory are explored; it is found that
excluding scalar and pseudoscalar components leads to a theory of six-component
fermion fields describing particles with spin 1, with fermion and antifermion
corresponding to states with definite circular polarisation. In the UV limit
only transverse states with just four non-vanishing components propagate.
Implications for the description of dynamics at a strongly interacting
renormalisation-group fixed point are discussed.


Abstract:
  Chiral optical effects are generally quantified along some specific incident
directions of exciting waves (especially for extrinsic chiralities of achiral
structures) or defined as direction-independent properties by averaging the
responses among all structure orientations. Though of great significance for
various applications, chirality extremization (maximized or minimized) with
respect to incident directions or structure orientations have not been
explored, especially in a systematic manner. In this study we examine the
chiral responses of open photonic structures from perspectives of quasi-normal
modes and polarization singularities of their far-field radiations. The
nontrivial topology of the momentum sphere secures the existence of singularity
directions along which mode radiations are either circularly or linearly
polarized. When plane waves are incident along those directions, the
reciprocity ensures ideal maximization and minimization of optical chiralities,
for corresponding mode radiations of circular and linear polarizations
respectively. For directions of general elliptical polarizations, we have
unveiled the subtle equality of a Stokes parameter and the circular dichroism,
showing that an intrinsically chiral structure can unexpectedly exhibit no
chirality at all or even chiralities of opposite handedness for different
incident directions. The framework we establish can be applied to not only
finite scattering bodies but also infinite periodic structures, encompassing
both intrinsic and extrinsic optical chiralities. We have effectively merged
two vibrant disciplines of chiral and singular optics, which can potentially
trigger more optical chirality-singularity related interdisciplinary studies.


Abstract:
  The semantic matching capabilities of neural information retrieval can
ameliorate synonymy and polysemy problems of symbolic approaches. However,
neural models' dense representations are more suitable for re-ranking, due to
their inefficiency. Sparse representations, either in symbolic or latent form,
are more efficient with an inverted index. Taking the merits of the sparse and
dense representations, we propose an ultra-high dimensional (UHD)
representation scheme equipped with directly controllable sparsity. UHD's large
capacity and minimal noise and interference among the dimensions allow for
binarized representations, which are highly efficient for storage and search.
Also proposed is a bucketing method, where the embeddings from multiple layers
of BERT are selected/merged to represent diverse linguistic aspects. We test
our models with MS MARCO and TREC CAR, showing that our models outperforms
other sparse models


Abstract:
  We explore the physics of topological lattice models in c-QED architectures
for arbitrary coupling strength, and the possibility of using the cavity
transmission as a topological marker. For this, we develop an approach
combining the input-output formalism with Mean-Field theory, which includes
self-consistency and quantum fluctuations to first order, and allows to go
beyond the small-coupling regime. We apply our formalism to the case of a
fermionic Su-Schrieffer-Heeger (SSH) chain. Our findings confirm that the
cavity can indeed act as a quantum sensor for topological phases, where the
initial state preparation plays a crutial role. Additionally, we discuss the
persistence of topological features when the coupling strength increases, in
terms of an effective Hamiltonian, and calculate the entanglement entropy. Our
approach can be applied to other fermionic systems, opening a route to the
characterization of their topological properties in terms of experimental
observables.


Abstract:
  Hyperactive comets have high water production rates, with inferred
sublimation areas of order the surface area of the nucleus. Comets 46P/Wirtanen
and 103P/Hartley 2 are two examples of this cometary class. Based on
observations of comet Hartley 2 by the Deep Impact spacecraft, hyperactivity
appears to be caused by the ejection of water-ice grains and/or water-ice rich
chunks of nucleus into the coma. These materials increase the sublimating
surface area, and yield high water production rates. The historic close
approach of comet Wirtanen to Earth in 2018 afforded an opportunity to test
Hartley 2 style hyperactivity in a second Jupiter-family comet. We present high
spatial resolution, near-infrared spectroscopy of the inner coma of Wirtanen.
No evidence for the 1.5- or 2.0-$\mu$m water-ice absorption bands is found in
six 0.8-2.5 $\mu$m spectra taken around perihelion and closest approach to
Earth. In addition, the strong 3.0-$\mu$m water-ice absorption band is absent
in a 2.0-5.3 $\mu$m spectrum taken near perihelion. Using spectroscopic and
sublimation lifetime models we set constraints on the physical properties of
the ice grains in the coma, assuming they are responsible for the comet's
hyperactivity. We rule out pure water-ice grains of any size, given their long
lifetime. Instead, the hyperactivity of the nucleus and lack of water-ice
absorption features in our spectra can be explained either by icy grains on the
order of 1 $\mu$m in size with a small amount of low albedo dust (greater than
0.5% by volume), or large chunks containing significant amounts of water ice.


Abstract:
  Human movement disorders or paralysis lead to the loss of control of muscle
activation and thus motor control. Functional Electrical Stimulation (FES) is
an established and safe technique for contracting muscles by stimulating the
skin above a muscle to induce its contraction. However, an open challenge
remains on how to restore motor abilities to human limbs through FES, as the
problem of controlling the stimulation is unclear. We are taking a robotics
perspective on this problem, by developing robot learning algorithms that
control the ultimate humanoid robot, the human body, through electrical muscle
stimulation. Human muscles are not trivial to control as actuators due to their
force production being non-stationary as a result of fatigue and other internal
state changes, in contrast to robot actuators which are well-understood and
stationary over broad operation ranges. We present our Deep Reinforcement
Learning approach to the control of human muscles with FES, using a recurrent
neural network for dynamic state representation, to overcome the unobserved
elements of the behaviour of human muscles under external stimulation. We
demonstrate our technique both in neuromuscular simulations but also
experimentally on a human. Our results show that our controller can learn to
manipulate human muscles, applying appropriate levels of stimulation to achieve
the given tasks while compensating for advancing muscle fatigue which arises
throughout the tasks. Additionally, our technique can learn quickly enough to
be implemented in real-world human-in-the-loop settings.


Abstract:
  Let $(\kappa_n(a))_{n\geq 1}$ denote the sequence of free cumulants of a
random variable $a$ in a non-commutative probability space
$(\mathcal{A},\varphi)$. Based on some considerations on bipartite graphs, we
provide a formula to compute the cumulants $(\kappa_n(ab+ba))_{n\geq 1}$ in
terms of $(\kappa_n(a))_{n\geq 1}$ and $(\kappa_n(b))_{n\geq 1}$, where $a$ and
$b$ are freely independent. Our formula expresses the $n$-th free cumulant of
$ab+ba$ as a sum indexed by partitions in the set $\mathcal{Y}_{2n}$ of
non-crossing partitions of the form
  \[ \sigma=\{B_1,B_3,\dots, B_{2n-1},E_1,\dots,E_r\}, \quad \text{with }r\geq
0, \]
  such that $i\in B_{i}$ for $i=1,3,\dots,2n-1$ and $|E_j|$ even for $j\leq r$.
Therefore, by studying the sets $\mathcal{Y}_{2n}$ we obtain new results
regarding the distribution of $ab+ba$. For instance, the size
$|\mathcal{Y}_{2n}|$ is closely related to the case when $a,b$ are free Poisson
random variables of parameter 1. Our formula can also be expressed in terms of
cacti graphs. This graph theoretic approach suggests a natural generalization
that allows us to study quadratic forms in $k$ free random variables.


Abstract:
  Deep reinforcement learning (DRL) is applied in safety-critical domains such
as robotics and autonomous driving. It achieves superhuman abilities in many
tasks, however whether DRL agents can be shown to act safely is an open
problem. Atari games are a simple yet challenging exemplar for evaluating the
safety of DRL agents and feature a diverse portfolio of game mechanics. The
safety of neural agents has been studied before using methods that either
require a model of the system dynamics or an abstraction; unfortunately, these
are unsuitable to Atari games because their low-level dynamics are complex and
hidden inside their emulator. We present the first exact method for analysing
and ensuring the safety of DRL agents for Atari games. Our method only requires
access to the emulator. First, we give a set of 43 properties that characterise
"safe behaviour" for 30 games. Second, we develop a method for exploring all
traces induced by an agent and a game and consider a variety of sources of game
non-determinism. We observe that the best available DRL agents reliably satisfy
only very few properties; several critical properties are violated by all
agents. Finally, we propose a countermeasure that combines a bounded
explicit-state exploration with shielding. We demonstrate that our method
improves the safety of all agents over multiple properties.


Abstract:
  The phase structure of baryonic matter is investigated with focus on the role
of fluctuations beyond the mean-field approximation. The prototype test case
studied is the chiral nucleon-meson model, with added comments on the chiral
quark-meson model. Applications to the liquid-gas phase transition in nuclear
matter and extensions to dense matter are performed. The role of vacuum
fluctuations and thermal excitations is systematically explored. It is pointed
out that such fluctuations tend to stabilise the hadronic phase characterised
by spontaneously broken chiral symmetry, shifting the chiral restoration
transition to very high densities. This stabilisation effect is shown to be
further enhanced by additional dynamical fluctuations treated with functional
renormalisation group methods.


Abstract:
  Deep learning (DL) has recently attracted increasing interest to improve
object type classification for automotive radar.In addition to high accuracy,
it is crucial for decision making in autonomous vehicles to evaluate the
reliability of the predictions; however, decisions of DL networks are
non-transparent. Current DL research has investigated how uncertainties of
predictions can be quantified, and in this article, we evaluate the potential
of these methods for safe, automotive radar perception. In particular we
evaluate how uncertainty quantification can support radar perception under (1)
domain shift, (2) corruptions of input signals, and (3) in the presence of
unknown objects. We find that in agreement with phenomena observed in the
literature,deep radar classifiers are overly confident, even in their wrong
predictions. This raises concerns about the use of the confidence values for
decision making under uncertainty, as the model fails to notify when it cannot
handle an unknown situation. Accurate confidence values would allow optimal
integration of multiple information sources, e.g. via sensor fusion. We show
that by applying state-of-the-art post-hoc uncertainty calibration, the quality
of confidence measures can be significantly improved,thereby partially
resolving the over-confidence problem. Our investigation shows that further
research into training and calibrating DL networks is necessary and offers
great potential for safe automotive object classification with radar sensors.


Abstract:
  ESA's INTEGRAL space mission has achieved unique results for solar and
terrestrial physics, although spacecraft operations nominally excluded the
possibility to point at the Sun or the Earth. The Earth avoidance was, however,
exceptionally relaxed for special occultation observations of the Cosmic X-ray
Background (CXB), which on some occasions allowed the detection of strong X-ray
auroral emission. In addition, the most intense solar flares can be bright
enough to be detectable from outside the field of view of the main instruments.
This article presents for the first time the auroral observations by INTEGRAL
and reviews earlier studies of the most intense solar flares. We end by briefly
summarising the studies of the Earth's radiation belts, which can be considered
as another topic of serendipitous science with INTEGRAL.


Abstract:
  Non-invasive therapeutic ultrasound methods, such as high-intensity focused
ultrasound (HIFU), have limited access to tissue targets shadowed by bones or
presence of gas. This study demonstrates that an ultrasonically actuated
medical needle can be used to translate nanoparticles and fluids under the
action of nonlinear phenomena, potentially overcoming some limitations of HIFU.
A simulation study was first conducted to study the delivery of a tracer with
an ultrasonically actuated needle (33 kHz) inside a porous medium acting as a
model for soft tissue. The model was then validated experimentally in different
concentrations of agarose gel showing a close match with the experimental
results, when diluted soot nanoparticles (diameter < 150 nm) were employed as
delivered entity. An additional simulation study demonstrated a threefold
increase of the volume covered by the delivered agent in liver under a constant
injection rate, when compared to without ultrasound. This method, if developed
to its full potential, could serve as a cost effective way to improve safety
and efficacy of drug therapies by maximizing the concentration of delivered
entities within e.g. a small lesion, while minimizing exposure outside the
lesion.


Abstract:
  Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT
imaging. Especially, The Low Dose CT imaging is one of possible options to
protect organs of patients when conducting CT imaging. Therefore Low Dose CT
imaging can be an alternative instead of Standard dose CT imaging. However Low
Dose CT imaging has a fundamental issue with noises within results compared to
Standard Dose CT imaging. Currently, there are lots of attempts to erase the
noises. Most of methods with artificial intelligence have many parameters and
unexplained layers or a kind of black-box methods. Therefore, our research has
purposes related to these issues. Our approach has less parameters than usual
methods by having Iterative learn-able bilateral filtering approach with Deep
reinforcement learning. And we applied The Iterative learn-able filtering
approach with deep reinforcement learning to sinograms and reconstructed volume
domains. The method and the results of the method can be much more explainable
than The other black box AI approaches. And we applied the method to Helical
Cone Beam Computed Tomography(CBCT), which is the recent CBCT trend. We tested
this method with on 2 abdominal scans(L004, L014) from Mayo Clinic TCIA
dataset. The results and the performances of our approach overtake the results
of the other previous methods.


Abstract:
  In this paper, we consider the energy conservation and regularity of the weak
solution $u$ to the Navier-Stokes equations in the endpoint case. We first
construct a divergence-free field $u(t,x)$ which satisfies $\lim_{t\to
T}\sqrt{T-t}||u(t)||_{BMO}<\infty$ and $\lim_{t\to
T}\sqrt{T-t}||u(t)||_{L^\infty}=\infty$ to demonstrate that the Type II
singularity is admissible in the endpoint case $u\in L^{2,\infty}(BMO)$.
Secondly, we prove that if a suitable weak solution $u(t,x)$ satisfying
$||u||_{L^{2,\infty}([0,T];BMO(\Omega))}<\infty$ for arbitrary
$\Omega\subseteq\mathbb{R}^3$ then the local energy equality is valid on
$[0,T]\times\Omega$. As a corollary, we also prove
$||u||_{L^{2,\infty}([0,T];BMO(\mathbb{R}^3))}<\infty$ implies the global
energy equality on $[0,T]$. Thirdly, we show that as the solution $u$
approaches a finite blowup time $T$, the norm $||u(t)||_{BMO}$ must blow up at
a rate faster than $\frac{c}{\sqrt{T-t}}$ with some absolute constant $c>0$.
Furthermore, we prove that if
$||u_3||_{L^{2,\infty}([0,T];BMO(\mathbb{R}^3))}=M<\infty$ then there exists a
small constant $c_M$ depended on $M$ such that if
$||u_h||_{L^{2,\infty}([0,T];BMO(\mathbb{R}^3))}\leq c_M$ then $u$ is regular
on $(0,T]\times\mathbb{R}^3$.


Abstract:
  We propose a series-based nonparametric specification test for a regression
function when data are spatially dependent, the `space' being of a general
economic or social nature. Dependence can be parametric, parametric with
increasing dimension, semiparametric or any combination thereof, thus covering
a vast variety of settings. These include spatial error models of varying types
and levels of complexity. Under a new smooth spatial dependence condition, our
test statistic is asymptotically standard normal. To prove the latter property,
we establish a central limit theorem for quadratic forms in linear processes in
an increasing dimension setting. Finite sample performance is investigated in a
simulation study, with a bootstrap method also justified and illustrated, and
empirical examples illustrate the test with real-world data.


Abstract:
  Inelastic neutron scattering (INS) is a key method for studying magnetic
excitations in spin systems, including molecular spin clusters. The method has
significantly advanced in recent years and now permits to probe the scattering
intensity as a function of the energy transfer and the momentum-transfer vector
Q. It was recently shown that high molecular symmetry facilitates the analysis
of spectra. Point-group symmetry imposes selection rules in isotropic as well
as anisotropic spin models. Furthermore, the Q-dependence of the INS intensity
may be completely determined by the point-group symmetry of the states involved
in a transition, thereby affording a clear separation of dynamics (energies,
transition strengths) and geometrical features (Q-dependencies). The present
work addresses this issue for anisotropic spin models. We identify a number of
cases where the Q-dependence is completely fixed by the point-group symmetry.
For six- and eight-membered planar spin rings and two polyhedra (the cube and
the icosahedron) we tabulate and plot the corresponding powder-averaged
universal intensity functions. The outlined formalism straightforwardly applies
to other highly-symmetric systems and should be useful for future analyses of
INS spectra by focusing on those features that contain information on either
spin dynamics or the point-group symmetry of states.


Abstract:
  We report the detection of CH$_3$OH emission in comet 46P/Wirtanen on UT 2018
December 8 and 9 using the Atacama Compact Array (ACA), part of the Atacama
Large Millimeter/Submillimeter Array (ALMA). These interferometric measurements
of CH$_3$OH along with continuum emission from dust probed the inner coma
($<$2000 km from the nucleus) of 46P/Wirtanen approximately one week before its
closest approach to Earth ($\Delta$ = 0.089 -- 0.092 au), revealing rapidly
varying and anisotropic CH$_3$OH outgassing during five separate ACA executions
between UT 23:57 December 7 and UT 04:55 December 9, with a clear progression
in the spectral line profiles over a timescale of minutes. We present
spectrally integrated flux maps, production rates, rotational temperatures, and
spectral line profiles of CH$_3$OH during each ACA execution. The variations in
CH$_3$OH outgassing are consistent with Wirtanen's 9 hr nucleus rotational
period derived from optical and millimeter wavelength measurements and thus are
likely coupled to the changing illumination of active sites on the nucleus. The
consistent blue offset of the line center indicates enhanced CH$_3$OH
sublimation from the sunward hemisphere of the comet, perhaps from icy grains.
These results demonstrate the exceptional capabilities of the ACA for
time-resolved measurements of comets such as 46P/Wirtanen.


Abstract:
  Direct numerical simulations have been performed for heat and momentum
transfer in internally heated turbulent shear flow with constant bulk mean
velocity and temperature, $u_{b}$ and $\theta_{b}$, between parallel,
isothermal, no-slip and permeable walls. The wall-normal transpiration velocity
on the walls $y=\pm h$ is assumed to be proportional to the local pressure
fluctuations, i.e. $v=\pm \beta p/\rho$ (Jim\'enez et al., J. Fluid Mech., vol.
442, 2001, pp.89-117). The temperature is supposed to be a passive scalar, and
the Prandtl number is set to unity. Turbulent heat and momentum transfer in
permeable-channel flow for $\beta u_{b}=0.5$ has been found to exhibit distinct
states depending on the Reynolds number $Re_b=2h u_b/\nu$. At $Re_{b}\lesssim
10^4$, the classical Blasius law of the friction coefficient and its similarity
to the Stanton number, $St\approx c_{f}\sim Re_{b}^{-1/4}$, are observed,
whereas at $Re_{b}\gtrsim 10^4$, the so-called ultimate scaling, $St\sim
Re_b^0$ and $c_{f}\sim Re_b^0$, is found. The ultimate state is attributed to
the appearance of large-scale intense spanwise rolls with the length scale of
$O(h)$ arising from the Kelvin-Helmholtz type of shear-layer instability over
the permeable walls. The large-scale rolls can induce large-amplitude velocity
fluctuations of $O(u_b)$ as in free shear layers, so that the Taylor
dissipation law $\epsilon\sim u_{b}^{3}/h$ (or equivalently $c_{f}\sim Re_b^0$)
holds. In spite of strong turbulence promotion there is no flow separation, and
thus large-amplitude temperature fluctuations of $O(\theta_b)$ can also be
induced similarly. As a consequence, the ultimate heat transfer is achieved,
i.e., a wall heat flux scales with $u_{b}\theta_{b}$ (or equivalently $St\sim
Re_b^0$) independent of thermal diffusivity, although the heat transfer on the
walls is dominated by thermal conduction.


Abstract:
  This paper considers the problem of nonstationary process monitoring under
frequently varying operating conditions. Traditional approaches generally
misidentify the normal dynamic deviations as faults and thus lead to high false
alarms. Besides, they generally consider single relatively steady operating
condition and suffer from the catastrophic forgetting issue when learning
successive operating conditions. In this paper, recursive cointegration
analysis (RCA) is first proposed to distinguish the real faults from normal
systems changes, where the model is updated once a new normal sample arrives
and can adapt to slow change of cointegration relationship. Based on the
long-term equilibrium information extracted by RCA, the remaining short-term
dynamic information is monitored by recursive principal component analysis
(RPCA). Thus a comprehensive monitoring framework is built. When the system
enters a new operating condition, the RCA-RPCA model is rebuilt to deal with
the new condition. Meanwhile, elastic weight consolidation (EWC) is employed to
settle the `catastrophic forgetting' issue inherent in RPCA, where significant
information of influential parameters is enhanced to avoid the abrupt
performance degradation for similar modes. The effectiveness of the proposed
method is illustrated by a practical industrial system.


Abstract:
  In this paper, we prove that a Sasakian pseudo-metric manifold which admits
an $\eta-$Ricci soliton is an $\eta-$Einstein manifold, and if the potential
vector field of the $\eta-$Ricci soliton is not a Killing vector field then the
manifold is $\mathcal{D}-$homothetically fixed, and the vector field leaves the
structure tensor field invariant. Next, we prove that a $K-$contact
pseudo-metric manifold with a gradient $\eta-$Ricci soliton metric is
$\eta-$Einstein. Moreover, we study contact pseudo-metric manifolds admitting
an $\eta-$Ricci soliton with a potential vector field point-wise colinear with
the Reeb vector field. Finally, we study gradient $\eta-$Ricci solitons on
$(\kappa, \mu)$-contact pseudo-metric manifolds.


Abstract:
  Self-supervised learning for depth estimation possesses several advantages
over supervised learning. The benefits of no need for ground-truth depth,
online fine-tuning, and better generalization with unlimited data attract
researchers to seek self-supervised solutions. In this work, we propose a new
self-supervised framework for stereo matching utilizing multiple images
captured at aligned camera positions. A cross photometric loss, an
uncertainty-aware mutual-supervision loss, and a new smoothness loss are
introduced to optimize the network in learning disparity maps end-to-end
without ground-truth depth information. To train this framework, we build a new
multiscopic dataset consisting of synthetic images rendered by 3D engines and
real images captured by real cameras. After being trained with only the
synthetic images, our network can perform well in unseen outdoor scenes. Our
experiment shows that our model obtains better disparity maps than previous
unsupervised methods on the KITTI dataset and is comparable to supervised
methods when generalized to unseen data. Our source code and dataset are
available at https://sites.google.com/view/multiscopic.


Abstract:
  Resource Public Key Infrastructure (RPKI) is vital to the security of
inter-domain routing. However, RPKI enables Regional Internet Registries (RIRs)
to unilaterally takedown IP prefixes - indeed, such attacks have been launched
by nation-state adversaries. The threat of IP prefix takedowns is one of the
factors hindering RPKI adoption.
  In this work, we propose the first distributed RPKI system, based on
threshold signatures, that requires the coordination of a number of RIRs to
make changes to RPKI objects; hence, preventing unilateral prefix takedown. We
perform extensive evaluations using our implementation demonstrating the
practicality of our solution. Furthermore, we show that our system is scalable
and remains efficient even when RPKI is widely deployed.


Abstract:
  Dujmovi\'{c}, Joret, Micek, Morin, Ueckerdt and Wood recently in [Planar
graphs have bounded queue-number, Journal of the ACM, Volume 67, Issue 4,
Article No.: 22, August 2020] showed some attractive graph product structure
theorems for planar graphs. By using the product structure, they proved that
planar graphs have bounded queue-number $48$; in [Planar graphs have bounded
nonrepetitive chromatic number, Advances in Combinatorics, 5, 11 pp, 2020], the
authors proved that planar graphs have bounded nonrepetitive chromatic number
$768$.
  In this paper, still by using some product structure theorem, we improve the
upper bound of queue-number of planar graphs to $27$ and the non-repetitive
chromatic number to $320$. We also study powers of trees. We show a graph
product structure theorem of the $k$-th power $T^k$ of tree $T$, then use it
giving an upper bound of the nonrepetitive~chromatic~number of $T^k$. We also
give an asymptotically tight upper bound of the queue-number of $T^k$.


Abstract:
  We study sets of recurrence, in both measurable and topological settings, for
actions of $(\mathbb{N},\times)$ and $(\mathbb{Q}^{>0},\times)$. In particular,
we show that autocorrelation sequences of positive functions arising from
multiplicative systems have positive additive averages. We also give criteria
for when sets of the form $\{(an+b)^{\ell}/(cn+d)^{\ell}: n \in \mathbb{N}\}$
are sets of multiplicative recurrence, and consequently we recover two recent
results in number theory regarding completely multiplicative functions and the
Omega function.


Abstract:
  The interaction between magnetic and acoustic excitations have recently
inspired many interdisciplinary studies ranging from fundamental physics to
circuit implementation. Specifically, the exploration of their coherent
interconversion enabled via the magnetoelastic coupling opens a new playground
combining straintronics and spintronics, and provides a unique platform for
building up on-chip coherent information processing networks with miniaturized
magnonic and acoustic devices. In this Perspective, we will focus on the recent
progress of magnon-phonon coupled dynamic systems, including materials,
circuits, imaging and new physics. In particular, we highlight the unique
features such as nonreciprocal acoustic wave propagation and strong coupling
between magnons and phonons in magnetic thin-film systems, which provides a
unique platform for their coherent manipulation and transduction. We will also
review the frontier of surface acoustic wave resonators in coherent quantum
transduction and discuss how the novel acoustic circuit design can be applied
in microwave spintronics.


Abstract:
  We conjecture and verify a set of universal relations between global
parameters of hot and fast-rotating compact stars, including a relation
connecting the masses of the mass-shedding (Kepler) and static configurations.
We apply these relations to the GW170817 event by adopting the scenario in
which a hypermassive compact star remnant formed in a merger evolves into a
supramassive compact star that collapses into a black hole once the stability
line for such stars is crossed. We deduce an upper limit on the maximum mass of
static, cold neutron stars $ 2.15^{+0.10}_{-0.07}\le M^\star_{\mathrm{TOV}} \le
2.24^{+0.12}_{-0.10} $ for the typical range of entropy per baryon $2 \le S/A
\le 3$ and electron fraction $Y_e = 0.1$ characterizing the hot hypermassive
star. Our result implies that accounting for the finite temperature of the
merger remnant relaxes previously derived constraints on the value of the
maximum mass of a cold, static compact star.


Abstract:
  For distant iris recognition, a long focal length lens is generally used to
ensure the resolution ofiris images, which reduces the depth of field and leads
to potential defocus blur. To accommodate users at different distances, it is
necessary to control focus quickly and accurately. While for users in motion,
it is expected to maintain the correct focus on the iris area continuously. In
this paper, we introduced a novel rapid autofocus camera for active refocusing
ofthe iris area ofthe moving objects using a focus-tunable lens. Our end-to-end
computational algorithm can predict the best focus position from one single
blurred image and generate a lens diopter control signal automatically. This
scene-based active manipulation method enables real-time focus tracking of the
iris area ofa moving object. We built a testing bench to collect real-world
focal stacks for evaluation of the autofocus methods. Our camera has reached an
autofocus speed ofover 50 fps. The results demonstrate the advantages of our
proposed camera for biometric perception in static and dynamic scenes. The code
is available at https://github.com/Debatrix/AquulaCam.


Abstract:
  We minimize the stray electric field in a linear Paul trap quickly and
accurately, by applying interferometry pulse sequences to a trapped ion optical
qubit. The interferometry sequences are sensitive to the change of ion
equilibrium position when the trap stiffness is changed, and we use this to
determine the stray electric field. The simplest pulse sequence is a two-pulse
Ramsey sequence, and longer sequences with multiple pulses offer a higher
precision. The methods allow the stray field strength to be minimized beyond
state-of-the-art levels, with only modest experimental requirements. Using a
sequence of nine pulses we reduce the 2D stray field strength to
$(10.5\pm0.8)\,\mathrm{mV\,m^{-1}}$ in $11\,\mathrm{s}$ measurement time. The
pulse sequences are easy to implement and automate, and they are robust against
laser detuning and pulse area errors.
  We use interferometry sequences with different lengths and precisions to
measure the stray field with an uncertainty below the standard quantum limit.
This marks a real-world case in which quantum metrology offers a significant
enhancement. Also, we minimize micromotion in 2D using a single probe laser, by
using an interferometry method together with the resolved sideband method; this
is useful for experiments with restricted optical access.
  Furthermore, a technique presented in this work is related to quantum
protocols for synchronising clocks; we demonstrate these protocols here.