Abstract:
  Distributional Reinforcement Learning (RL) maintains the entire probability
distribution of the reward-to-go, i.e. the return, providing more learning
signals that account for the uncertainty associated with policy performance,
which may be beneficial for trading off exploration and exploitation and policy
learning in general. Previous works in distributional RL focused mainly on
computing the state-action-return distributions, here we model the state-return
distributions. This enables us to translate successful conventional RL
algorithms that are based on state values into distributional RL. We formulate
the distributional Bellman operation as an inference-based auto-encoding
process that minimises Wasserstein metrics between target/model return
distributions. The proposed algorithm, BDPG (Bayesian Distributional Policy
Gradients), uses adversarial training in joint-contrastive learning to estimate
a variational posterior from the returns. Moreover, we can now interpret the
return prediction uncertainty as an information gain, which allows to obtain a
new curiosity measure that helps BDPG steer exploration actively and
efficiently. We demonstrate in a suite of Atari 2600 games and MuJoCo tasks,
including well known hard-exploration challenges, how BDPG learns generally
faster and with higher asymptotic performance than reference distributional RL
algorithms.


Abstract:
  Covariate shift arises when the labelled training (source) data is not
representative of the unlabelled (target) data due to systematic differences in
the covariate distributions. A supervised model trained on the source data
subject to covariate shift may suffer from poor generalization on the target
data. We propose a novel, statistically principled and theoretically justified
method to improve learning under covariate shift conditions, based on
propensity score stratification, a well-established methodology in causal
inference. We show that the effects of covariate shift can be reduced or
altogether eliminated by conditioning on propensity scores. In practice, this
is achieved by fitting learners on subgroups ("strata") constructed by
partitioning the data based on the estimated propensity scores, leading to
balanced covariates and much-improved target prediction. We demonstrate the
effectiveness of our general-purpose method on contemporary research questions
in observational cosmology, and on additional benchmark examples, matching or
outperforming state-of-the-art importance weighting methods, widely studied in
the covariate shift literature. We obtain the best reported AUC (0.958) on the
updated "Supernovae photometric classification challenge" and improve upon
existing conditional density estimation of galaxy redshift from Sloan Data Sky
Survey (SDSS) data.


Abstract:
  We prove that a valued field of positive characteristic $p$ that has only
finitely many distinct Artin-Schreier extensions (which is a property of
infinite NTP$_2$ fields) is dense in its perfect hull. As a consequence, it is
a deeply ramified field and has $p$-divisible value group and perfect residue
field. Further, we prove a partial analogue for valued fields of mixed
characteristic and observe an open problem about 1-units in this setting.
Finally, we fill a gap that occurred in a proof in an earlier paper in which we
first introduced a classification of Artin-Schreier defect extensions.


Abstract:
  There is a growing demand for high-resolution (HR) medical images in both the
clinical and research applications. Image quality is inevitably traded off with
the acquisition time for better patient comfort, lower examination costs, dose,
and fewer motion-induced artifacts. For many image-based tasks, increasing the
apparent resolution in the perpendicular plane to produce multi-planar
reformats or 3D images is commonly used. Single image super-resolution (SR) is
a promising technique to provide HR images based on unsupervised learning to
increase resolution of a 2D image, but there are few reports on 3D SR. Further,
perceptual loss is proposed in the literature to better capture the textual
details and edges than using pixel-wise loss functions, by comparing the
semantic distances in the high-dimensional feature space of a pre-trained 2D
network (e.g., VGG). However, it is not clear how one should generalize it to
3D medical images, and the attendant implications are still unclear. In this
paper, we propose a framework called SOUP-GAN: Super-resolution Optimized Using
Perceptual-tuned Generative Adversarial Network (GAN), in order to produce
thinner slice (e.g., high resolution in the 'Z' plane) medical images with
anti-aliasing and deblurring. The proposed method outperforms other
conventional resolution-enhancement methods and previous SR work on medical
images upon both qualitative and quantitative comparisons. Specifically, we
examine the model in terms of its generalization for various SR ratios and
imaging modalities. By addressing those limitations, our model shows promise as
a novel 3D SR interpolation technique, providing potential applications in both
clinical and research settings.


Abstract:
  We construct characteristic identities for the split (polarized) Casimir
operators of the simple Lie algebras in defining (minimal fundamental) and
adjoint representations. By means of these characteristic identities, for all
simple Lie algebras we derive explicit formulae for invariant projectors onto
irreducible subrepresentations in T^{\otimes 2} in two cases, when T is the
defining and the adjoint representation. In the case when T is the defining
representation, these projectors and the split Casimir operator are used to
explicitly write down invariant solutions of the Yang-Baxter equations. In the
case when T is the adjoint representation, these projectors and characteristic
identities are considered from the viewpoint of the universal description of
the simple Lie algebras in terms of the Vogel parameters.


Abstract:
  Recent years have seen the proliferation of disinformation and misinformation
online, thanks to the freedom of expression on the Internet and to the rise of
social media. Two solutions were proposed to address the problem: (i) manual
fact-checking, which is accurate and credible, but slow and non-scalable, and
(ii) automatic fact-checking, which is fast and scalable, but lacks
explainability and credibility. With the accumulation of enough manually
fact-checked claims, a middle-ground approach has emerged: checking whether a
given claim has previously been fact-checked. This can be made automatically,
and thus fast, while also offering credibility and explainability, thanks to
the human fact-checking and explanations in the associated fact-checking
article. This is a relatively new and understudied research direction, and here
we focus on claims made in a political debate, where context really matters.
Thus, we study the impact of modeling the context of the claim: both on the
source side, i.e., in the debate, as well as on the target side, i.e., in the
fact-checking explanation document. We do this by modeling the local context,
the global context, as well as by means of co-reference resolution, and
reasoning over the target text using Transformer-XH. The experimental results
show that each of these represents a valuable information source, but that
modeling the source-side context is more important, and can yield 10+ points of
absolute improvement.


Abstract:
  To maintain blockchain-based services with ensuring its security, it is an
important issue how to decide a mining reward so that the number of miners
participating in the mining increases. We propose a dynamical model of
decision-making for miners using an evolutionary game approach and analyze the
stability of equilibrium points of the proposed model. The proposed model is
described by the 1st-order differential equation. So, it is simple but its
theoretical analysis gives an insight into the characteristics of the
decision-making. Through the analysis of the equilibrium points, we show the
transcritical bifurcations and hysteresis phenomena of the equilibrium points.
We also design a controller that determines the mining reward based on the
number of participating miners to stabilize the state that all miners
participate in the mining. Numerical simulation shows that there is a trade-off
in the choice of the design parameters.


Abstract:
  A Cayley (di)graph $Cay(G,S)$ of a group $G$ with respect to $S$ is said to
be normal if the right regular representation of $G$ is normal in the
automorphism group of $Cay(G,S)$, and is called a CI-(di)graph if there is
$\alpha\in Aut(G)$ such that $S^\alpha=T$, whenever $Cay(G,S)\cong Cay(G,T)$
for a Cayley (di)graph $Cay(G,T)$. A finite group $G$ is called a DCI-group or
a NDCI-group if all Cayley digraphs or normal Cayley digraphs of $G$ are
CI-digraphs, and is called a CI-group or a NCI-group if all Cayley graphs or
normal Cayley graphs of $G$ are CI-graphs, respectively.
  Motivated by a conjecture proposed by \'Ad\'am in 1967, CI-groups and
DCI-groups have been actively studied during the last fifty years by many
researchers in algebraic graph theory. It takes about thirty years to obtain
the classification of cyclic CI-groups and DCI-groups, and recently, the first
two authors, among others, classified cyclic NCI-groups and NDCI-groups. Even
though there are many partial results on dihedral CI-groups and DCI-groups,
their classification is still elusive. In this paper, we prove that a dihedral
group of order $2n$ is a NCI-group or a NDCI-group if and only if $n=2,4$ or
$n$ is odd. As a direct consequence, we have that if a dihedral group $D_{2n}$
of order $2n$ is a DCI-group then $n=2$ or $n$ is odd-square-free, and that if
$D_{2n}$ is a CI-group then $n=2,9$ or $n$ is odd-square-free, throwing some
new light on classification of dihedral CI-groups and DCI-groups.


Abstract:
  Samples were examined using a superconducting (Nb) neutron imaging system
employing a delay-line technique which in previous studies was shown to have
high spatial resolution. We found excellent correspondence between neutron
transmission and scanning electron microscope (SEM) images of Gd islands with
sizes between 15 and 130 micrometer which were thermally-sprayed onto a Si
substrate. Neutron transmission images could be used to identify tiny voids in
a thermally-sprayed continuous Gd2O3 film on a Si substrate which could not be
seen in SEM images. We also found that neutron transmission images revealed
pattern formations, mosaic features and co-existing dendritic phases in Wood's
metal samples with constituent elements Bi, Pb, Sn and Cd. These results
demonstrate the merits of the current-biased kinetic inductance detector
(CB-KID) system for practical studies in materials science. Moreover, we found
that operating the detector at a more optimal temperature (7.9 K) appreciably
improved the effective detection efficiency when compared to previous studies
conducted at 4 K. This is because the effective size of hot-spots in the
superconducting meanderline planes increases with temperature, which makes
particle detections more likely.


Abstract:
  In this paper, we study a mobile edge computing (MEC) system in which the
mobile device is assisted by a base station (BS) and a cooperative node. The
mobile device has sequential tasks to complete, whereas the cooperative node
assists the mobile device on both task offloading and task computation. In
specific, two cases are investigated, which are 1) the cooperative node has no
tasks to complete itself, and 2) the cooperative node has tasks to complete
itself. Our target is to minimize the total energy consumption of the mobile
device and the cooperative node through optimizing the transmit duration in
task offloading, CPU frequency in task computing along with the task index to
offload in the sequential tasks. In the first case, we decompose the
mixed-integer non-convex problem into two levels. In the lower level problem,
thanks to the convexity, Karush-Kuhn-Tucker (KKT) conditions are utilized to
simplify the problem, which is then solved with bisection search. In the upper
level problem, to find solution of the task index to offload, rather than
utilizing traversal method, we develop a monotonic condition to simplify the
searching process. In the second case, in order to guarantee the successful
computation of the mobile device and cooperative node, the uploading
transmission is classified into three schemes. Within each scheme, the
non-convex problem is decomposed. In the lower level problem, semi-closed
solution is found by Lagrangian dual method. In the upper level problem,
traversal method is applied to find the optimal offloading index.


Abstract:
  We provide a generalization of quantum polar codes to quantum channels with
qudit-input, achieving the symmetric coherent information of the channel. Our
scheme relies on a channel combining and splitting construction, where a
two-qudit unitary randomly chosen from a unitary 2-design is used to combine
two instances of a qudit-input channel. The inputs to the synthesized bad
channels are frozen by sharing EPR pairs between the sender and the receiver,
so our scheme is entanglement assisted. Using the fact that the generalized
two-qudit Clifford group forms a unitary 2-design, we conclude that the channel
combining operation can be chosen from this set. Moreover, we show that
polarization also happens for a much smaller subset of two-qudit Cliffords,
which is not a unitary 2-design. Finally, we show how to decode the proposed
quantum polar codes on Pauli qudit channels.


Abstract:
  Using a sample of $1.31\times10^9$ $J/\psi$ events collected with the BESIII
detector at the electron-positron collider BEPCII, we analyse the full $J/\psi
\to$ $\Xi^-\overline{\Xi}^+$, $\Xi^-\to \Lambda \pi^-$, $\Lambda\to p\pi^-$,
$\overline{\Xi}^+\to\overline{\Lambda}\pi^+$,
$\overline{\Lambda}\to\overline{p}\pi^+$ decay chain. A new method, exploiting
the fact that the $\Xi^-\overline{\Xi}^+$ pair is entangled and sequentially
decaying, and where the complete decay chains are reconstructed, is applied for
the first time. This enables precision measurements of the decay parameters for
the $\Xi^-\to\Lambda\pi^-$ decay ($\alpha_{\Xi}$, $\phi_{\Xi}$) as well as the
$\overline{\Xi}^+\to\overline{\Lambda}\pi^+$ decay ($\overline{\alpha}_{\Xi}$,
$\overline{\phi}_{\Xi}$). From the decay parameters, two independent CP tests
were performed, quantified by the observables $A_{\rm CP}^{\Xi}$ and $\Delta
\phi_\Xi$. Our results, $A_{\rm CP}^{\Xi}$ = $(6.0\pm13.4\pm5.6)\times10^{-3}$
and $\Delta \phi_\Xi= (-4.8\pm13.7\pm2.9)\times10^{-3}~{\rm rad}$, are
consistent with CP symmetry. Furthermore, our method enables a separation of
strong and weak $\Xi\to\Lambda\pi$ decay amplitudes. This results in the first
direct measurement of the weak phase difference for any baryon decay. The
result is found to be $(\xi_{P} - \xi_{S}) = (1.2\pm3.4\pm0.8)\times10^{-2}$
rad and is one of the most precise tests of CP symmetry for strange baryons.
The strong phase difference is measured to be $(\delta_P - \delta_S) =
(-4.0\pm3.3\pm1.7)\times10^{-2}$ rad. In addition, we provide an independent
measurement of the recently debated $\Lambda$ decay parameter,
$\alpha_{\Lambda} = 0.757 \pm 0.011 \pm 0.008 $. The
$\Lambda\overline{\Lambda}$ asymmetry is measured to be $A_{\rm CP}^{\Lambda} =
(-3.7\pm11.7\pm9.0)\times10^{-3}$.


Abstract:
  In data processing and machine learning, an important challenge is to recover
and exploit models that can represent accurately the data. We consider the
problem of recovering Gaussian mixture models from datasets. We investigate
symmetric tensor decomposition methods for tackling this problem, where the
tensor is built from empirical moments of the data distribution. We consider
identifiable tensors, which have a unique decomposition, showing that moment
tensors built from spherical Gaussian mixtures have this property. We prove
that symmetric tensors with interpolation degree strictly less than half their
order are identifiable and we present an algorithm, based on simple linear
algebra operations, to compute their decomposition. Illustrative
experimentations show the impact of the tensor decomposition method for
recovering Gaussian mixtures, in comparison with other state-of-the-art
approaches.


Abstract:
  A machine learning algorithm for partitioning the nuclear vibrational space
into subspaces is introduced. The subdivision criterion is based on Liouville's
theorem, i.e. best preservation of the unitary of the reduced dimensionality
Jacobian determinant within each subspace along a probe full-dimensional
classical trajectory. The algorithm is based on the idea of evolutionary
selection and it is implemented through a probability graph representation of
the vibrational space partitioning. We interface this customized version of
genetic algorithms with our divide-and-conquer semiclassical initial value
representation method for calculation of molecular power spectra. First, we
benchmark the algorithm by calculating the vibrational power spectra of two
model systems, for which the exact subspace division is known. Then, we apply
it to the calculation of the power spectrum of methane. Exact calculations and
full-dimensional semiclassical spectra of this small molecule are available and
provide an additional test of the accuracy of the new approach. Finally, the
algorithm is applied to the divide-and-conquer semiclassical calculation of the
power spectrum of 12-atom trans-N-Methylacetamide.


Abstract:
  We demonstrate the design, fabrication and use of a dual electrode PMUT
(Piezoelectric Micromachined Ultrasound Transducer) integrated with a
microfluidic channel as a fluid density sensor in both static and dynamic
density-change conditions. The dual electrode configuration makes the PMUT
resonator a self-contained resonant peak-shift sensor and the microfluidic
integration makes this system a versatile fluid density sensing platform that
can be used with extremely low volumes of fluids in various industrial and
healthcare applications. The density measurements carried out here under
flowing fluid conditions demonstrate the potential of this system as a
real-time fluid density monitoring system. We include results of density
measurements in the range of 1020 - 1090 kg/m3 that corresponds to the human
blood density variation generally due to the change in its hemoglobin content.
The sensitivity of the sensor-26.3 Hz/(kg/m3)-is good enough to reliably detect
even 1% change in the hemoglobin content of the human blood. Thus, this system
could potentially be used also as a hemoglobin measurement sensor in healthcare
applications.


Abstract:
  We show that the gravitational waves generated by the perturbations of
general relativistic black holes can be considered as a direct probe of the
existence of dark sector interactions. Working within the framework of
Horndeski theory and linear perturbations, we show that dark sector
interactions effectively reduce to an interaction charge that influences both
scalar and tensor waveforms. Furthermore, we show that the total dark matter
field, including the effects of dark sector interactions, satisfies a
conservation equation embodying the equivalence principle. We exploit this
realization to setup the Regge-Wheeler equation and the coupled Zerilli and
scalar wave equations for a Schwarzschild-(anti) de Sitter black hole. We then
present numerical integration of the coupled even-parity wave equations for the
case of a dark matter particle falling straight down into a Schwarzschild black
hole.


Abstract:
  The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely wide
neural nets trained under least squares loss by gradient descent. However,
despite its importance, the super-quadratic runtime of kernel methods limits
the use of NTK in large-scale learning tasks. To accelerate kernel machines
with NTK, we propose a near input sparsity time algorithm that maps the input
data to a randomized low-dimensional feature space so that the inner product of
the transformed data approximates their NTK evaluation. Our transformation
works by sketching the polynomial expansions of arc-cosine kernels.
Furthermore, we propose a feature map for approximating the convolutional
counterpart of the NTK, which can transform any image using a runtime that is
only linear in the number of pixels. We show that in standard large-scale
regression and classification tasks a linear regressor trained on our features
outperforms trained Neural Nets and Nystrom approximation of NTK kernel.


Abstract:
  This paper presents novel reconfigurable architectures for reducing the
latency of recurrent neural networks (RNNs) that are used for detecting
gravitational waves. Gravitational interferometers such as the LIGO detectors
capture cosmic events such as black hole mergers which happen at unknown times
and of varying durations, producing time-series data. We have developed a new
architecture capable of accelerating RNN inference for analyzing time-series
data from LIGO detectors. This architecture is based on optimizing the
initiation intervals (II) in a multi-layer LSTM (Long Short-Term Memory)
network, by identifying appropriate reuse factors for each layer. A
customizable template for this architecture has been designed, which enables
the generation of low-latency FPGA designs with efficient resource utilization
using high-level synthesis tools. The proposed approach has been evaluated
based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.
Experimental results show that with balanced II, the number of DSPs can be
reduced up to 42% while achieving the same IIs. When compared to other
FPGA-based LSTM designs, our design can achieve about 4.92 to 12.4 times lower
latency.


Abstract:
  We design and implement Zooid, a domain specific language for certified
multiparty communication, embedded in Coq and implemented atop our
mechanisation framework of asynchronous multiparty session types (the first of
its kind). Zooid provides a fully mechanised metatheory for the semantics of
global and local types, and a fully verified end-point process language that
faithfully reflects the type-level behaviours and thus inherits the global
types properties such as deadlock freedom, protocol compliance, and liveness
guarantees.


Abstract:
  In 2015, Felsner, Trotter, and Wiechert showed that posets with outerplanar
cover graphs have bounded dimension. We generalise this result to posets with
$k$-outerplanar cover graphs. Namely, we show that posets with $k$-outerplanar
cover graph have dimension $\mathcal{O}(k^3)$. As a consequence, we show that
every poset with a planar cover graph and height $h$ has dimension
$\mathcal{O}(h^3)$. This improves the previously best known bound of
$\mathcal{O}(h^6)$ by Kozik, Micek and Trotter.


Abstract:
  A first-order Electroweak Phase Transition (EWPT) could explain the observed
baryon-antibaryon asymmetry and its dynamics could yield a detectable
gravitational wave signature, while the underlying physics would be within the
reach of colliders. The Standard Model, however, predicts a crossover
transition. We therefore study the EWPT in the Standard Model Effective Field
Theory (SMEFT) including dimension-six operators. A first-order EWPT has
previously been shown to be possible in the SMEFT. Phenomenology studies have
focused on scenarios with a tree-level barrier between minima, which requires a
negative Higgs quartic coupling and a new physics scale low enough to raise
questions about the validity of the EFT approach. In this work we stress that a
first-order EWPT is also possible when the barrier between minima is generated
radiatively, the quartic coupling is positive, the scale of new physics is
higher, and there is good agreement with experimental bounds. Our calculation
is done in a consistent, gauge-invariant way, and we carefully analyze the
scaling of parameters necessary to generate a barrier in the potential. We
perform a global fit in the relevant parameter space and explicitly find the
points with a first-order transition that agree with experimental data. We also
briefly discuss the prospects for probing the allowed parameter space using
di-Higgs production in colliders.


Abstract:
  Time-varying parameter (TVP) regressions commonly assume that time-variation
in the coefficients is determined by a simple stochastic process such as a
random walk. While such models are capable of capturing a wide range of dynamic
patterns, the true nature of time variation might stem from other sources, or
arise from different laws of motion. In this paper, we propose a flexible TVP
VAR that assumes the TVPs to depend on a panel of partially latent covariates.
The latent part of these covariates differ in their state dynamics and thus
capture smoothly evolving or abruptly changing coefficients. To determine which
of these covariates are important, and thus to decide on the appropriate state
evolution, we introduce Bayesian shrinkage priors to perform model selection.
As an empirical application, we forecast the US term structure of interest
rates and show that our approach performs well relative to a set of competing
models. We then show how the model can be used to explain structural breaks in
coefficients related to the US yield curve.


Abstract:
  Graphs are naturally used to describe the structures of various real-world
systems in biology, society, computer science etc., where subgraphs or motifs
as basic blocks play an important role in function expression and information
processing. However, existing research focuses on the basic statistics of
certain motifs, largely ignoring the connection patterns among them. Recently,
a subgraph network (SGN) model is proposed to study the potential structure
among motifs, and it was found that the integration of SGN can enhance a series
of graph classification methods. However, SGN model lacks diversity and is of
quite high time complexity, making it difficult to widely apply in practice. In
this paper, we introduce sampling strategies into SGN, and design a novel
sampling subgraph network model, which is scale-controllable and of higher
diversity. We also present a hierarchical feature fusion framework to integrate
the structural features of diverse sampling SGNs, so as to improve the
performance of graph classification. Extensive experiments demonstrate that, by
comparing with the SGN model, our new model indeed has much lower time
complexity (reduced by two orders of magnitude) and can better enhance a series
of graph classification methods (doubling the performance enhancement).


Abstract:
  Background: Stochastic resonance (SR) refers to a faint signal being enhanced
with the addition of white noise. Previous studies have found that vestibular
perceptual thresholds are lowered with noisy galvanic vestibular stimulation
(i.e., "in-channel" SR). Auditory white noise has been shown to improve tactile
and visual thresholds, suggesting "cross-modal" SR. Objective: We aimed to
study the cross-modal impact of noisy galvanic vestibular stimulation (nGVS)
(n=9 subjects) on visual and auditory thresholds. Methods: We measured auditory
and visual perceptual thresholds of human subjects across a swath of different
nGVS levels in order to determine if a subject-specific best nGVS level
elicited a reduction in thresholds as compared the no noise condition (sham).
Results: We found an 18% improvement in visual thresholds (p = 0.026). Among
the 7 of 9 subjects with reduced thresholds, the average improvement was 26%.
Subjects with higher (worse) visual thresholds with no stimulation (sham)
improved more than those with lower thresholds (p = 0.005). Auditory thresholds
were unchanged by vestibular stimulation. Conclusions: These results are the
first demonstration of cross-modal improvement with nGVS, indicating galvanic
vestibular white noise can produce cross-modal improvements in some sensory
channels, but not all.


Abstract:
  In this paper, we construct a lightweight, high-precision and high-speed
object tracking using a trained CNN. Conventional methods with trained CNNs use
VGG16 network which requires powerful computational resources. Therefore, there
is a problem that it is difficult to apply in low computation resources
environments. To solve this problem, we use MobileNetV3, which is a CNN for
mobile terminals.Based on Feature Map Selection Tracking, we propose a new
architecture that extracts effective features of MobileNet for object tracking.
The architecture requires no online learning but only offline learning. In
addition, by using features of objects other than tracking target, the features
of tracking target are extracted more efficiently. We measure the tracking
accuracy with Visual Tracker Benchmark and confirm that the proposed method can
perform high-precision and high-speed calculation even in low computation
resource environments.


Abstract:
  Glaucoma is a severe eye disease that is known to deteriorate optic never
fibers, causing cup size to increase, which could result in permanent loss of
vision. Glaucoma is the second leading cause of blindness after cataract, but
glaucoma being more dangerous as it is not curable. Early diagnoses and
treatment of glaucoma can help to slow the progression of glaucoma and its
damages. For the detection of glaucoma, the Cup to Disc ratio (CDR) provides
significant information. The CDR depends heavily on the accurate segmentation
of cup and disc regions. In this paper, we have proposed a modified M-Net with
bidirectional convolution long short-term memory (LSTM), based on joint cup and
disc segmentation. The proposed network combines features of encoder and
decoder, with bidirectional LSTM. Our proposed model segments cup and disc
regions based on which the abnormalities in cup to disc ratio can be observed.
The proposed model is tested on REFUGE2 data, where our model achieves a dice
score of 0.92 for optic disc and an accuracy of 98.99% in segmenting cup and
disc regions


Abstract:
  We study the constraint equations for a class of scalar-tensor effective
field theories of gravity, including the operators up to $4$ derivatives in the
action ($4\partial$ST). We extend the conformal transverse traceless and
conformal thin sandwich methods of General Relativity to rewrite the constraint
equations of the scalar-tensor theory as a set of elliptic partial differential
equations. It is shown that, at weak coupling, a unique solution exists to the
corresponding elliptic boundary value problems on asymptotically Euclidean
initial slices under similar conditions as in the case of General Relativity.
Furthermore, we find a generalization of the Bowen-York solution for
$4\partial$ST theories, too. These results demonstrate that standard methods
for constructing initial data in General Relativity are applicable (with
minimal modification) to weakly coupled $4\partial$ST theories.


Abstract:
  Six binary-merger progenitors of Supernova 1987A (SN 1987A) with properties
close to those of the blue supergiant Sanduleak -69 202 are exploded by
neutrino heating and evolved until long after shock breakout in three
dimensions (3D), and continued for light-curve calculations in spherical
symmetry. Our results confirm previous findings for single-star progenitors:
(1) 3D neutrino-driven explosions with SN 1987A-like energies synthesize Ni-56
masses consistent with the radioactive light-curve tail; (2) hydrodynamic
models mix hydrogen inward to minimum velocities below 40 km/s compatible with
spectral observations of SN 1987A; and (3) for given explosion energy the
efficiency of outward radioactive Ni-56 mixing depends mainly on high growth
factors of Rayleigh-Taylor instabilities at the (C+O)/He and He/H composition
interfaces and a weak interaction of fast plumes with the reverse shock
occurring below the He/H interface. All binary-merger models possess
presupernova radii matching the photometric radius of Sanduleak -69 202 and a
structure of the outer layers allowing them to reproduce the observed initial
luminosity peak in the first about 7 days. Models that mix about 0.5 Msun of
hydrogen into the He-shell and exhibit strong outward mixing of Ni-56 with
maximum velocities exceeding the 3000 km/s observed for the bulk of ejected
Ni-56 have light-curve shapes in good agreement with the dome of the SN 1987A
light curve. A comparative analysis of the best representatives of our 3D
neutrino-driven explosion models of SN 1987A based on single-star and
binary-merger progenitors reveals that only one binary model fulfills all
observational constraints, except one.


Abstract:
  The stranglehold of low temperatures on fascinating quantum phenomena in
one-dimensional quantum magnets has been challenged recently by the discovery
of anomalous spin transport at high temperatures. Whereas both regimes have
been investigated separately, no study has attempted to reconcile them. For
instance, the paradigmatic quantum Heisenberg spin-$1/2$ chain falls at low
temperature within the Tomonaga-Luttinger liquid framework, while its
high-temperature dynamics is superdiffusive and relates to the
Kardar-Parisi-Zhang universality class in $1+1$ dimensions. This Letter aims at
reconciling the two regimes. Building on large-scale matrix product state
simulations, we find that they are connected by a temperature-dependent
spatiotemporal crossover. As the temperature $T$ is reduced, we show that the
onset of superdiffusion takes place at longer length and timescales $\propto
1/T$. This prediction has direct consequences for experiments including nuclear
magnetic resonance: it is consistent with earlier measurements on the nearly
ideal Heisenberg $S=1/2$ chain compound Sr$_2$CuO$_3$, yet calls for new and
dedicated experiments.


Abstract:
  This project takes the factors of keyword selection behavior as the research
object. Qualitative analysis methods such as interview and grounded theory were
used to construct causal influence path model. Combined with computer
simulation technology such as multi-agent simulation experiment method was used
to study the factors of keyword selection from two dimensions of individual to
group. The research was carried out according to the path of factor analysis at
individual level macro situation simulation optimization of scientific research
data management. Based on the aforementioned review of existing researches and
explanations of keywords selection, this study adopts a qualitative research
design to expand the explanation, and macro simulation based on the results of
qualitative research. There are two steps in this study, one is do interview
with authors and then design macro simulation according the deductive and
qualitative content analysis results.


Abstract:
  Assessing the quality of arguments and of the claims the arguments are
composed of has become a key task in computational argumentation. However, even
if different claims share the same stance on the same topic, their assessment
depends on the prior perception and weighting of the different aspects of the
topic being discussed. This renders it difficult to learn topic-independent
quality indicators. In this paper, we study claim quality assessment
irrespective of discussed aspects by comparing different revisions of the same
claim. We compile a large-scale corpus with over 377k claim revision pairs of
various types from kialo.com, covering diverse topics from politics, ethics,
entertainment, and others. We then propose two tasks: (a) assessing which claim
of a revision pair is better, and (b) ranking all versions of a claim by
quality. Our first experiments with embedding-based logistic regression and
transformer-based neural networks show promising results, suggesting that
learned indicators generalize well across topics. In a detailed error analysis,
we give insights into what quality dimensions of claims can be assessed
reliably. We provide the data and scripts needed to reproduce all results.


Abstract:
  The generation of high-fidelity polarization-entangled photon pairs, to date,
has been demonstrated on specific spatial modes or over relatively narrow
apertures. We put forward and demonstrate an experimental scheme to extend the
temporal and spatial indistinguishability of polarization-entangled photons
over wide emission angles, which can be applied to cover the whole SPDC cone.
Over such wide angular extent, while the time-delay map is almost flat which
renders the conventional compensation via a birefringent element an appropriate
approach, the relative-phase map -- verified as a quadratic function --
necessitates a tunable compensation paradigm. Here, to do so, we employ a
phase-only two-dimensional spatial light modulator (2D SLM) loaded by the
complementary of the relative phase map to equalize the phase variations for
one third of the noncollinear spontaneous parametric down conversion (SPDC)
emission. After eliminating the temporal and spatial distinguishability over
the 2D SLM area, a 97% polarization visibility is verified for the entangled
photon pairs scattered widely across the SPDC cone.


Abstract:
  Let $(p_k)_{k\in\mathbb{N}}$ be a discrete probability distribution for which
the counting function $x\mapsto \#\{k\in\mathbb{N}: p_k\geq 1/x\}$ belongs to
the de Haan class $\Pi$. Consider a deterministic weighted branching process
generated by $(p_k)_{k\in\mathbb{N}}$. A nested Karlin's occupancy scheme is
the sequence of Karlin balls-in-boxes schemes in which boxes of the $j$th
level, $j=1,2,\ldots$ are identified with the $j$th generation individuals and
the hitting probabilities of boxes are identified with the corresponding
weights. The collection of balls is the same for all generations, and each ball
starts at the root and moves along the tree of the deterministic weighted
branching process according to the following rule: transition from a mother box
to a daughter box occurs with probability given by the ratio of the daughter
and mother weights.
  Assuming there are $n$ balls, denote by $\mathcal{K}_n(j)$ the number of
occupied (ever hit) boxes in the $j$th level. For each $j\in\mathbb{N}$, we
prove a functional limit theorem for the vector-valued process
$(\mathcal{K}^{(1)}_{\lfloor e^{T+u}\rfloor},\ldots, \mathcal{K}^{(j)}_{\lfloor
e^{T+u}\rfloor})_{u\in\mathbb{R}}$, properly normalized and centered, as
$T\to\infty$. The limit is a vector-valued process whose components are
independent stationary Gaussian processes. An integral representation of the
limit process is obtained.


Abstract:
  Polarization is implicated in the erosion of democracy and the progression to
violence, which makes the polarization properties of large algorithmic content
selection systems (recommender systems) a matter of concern for peace and
security. While algorithm-driven social media does not seem to be a primary
driver of polarization at the country level, it could be a useful intervention
point in polarized societies. This paper examines algorithmic depolarization
interventions with the goal of conflict transformation: not suppressing or
eliminating conflict but moving towards more constructive conflict. Algorithmic
intervention is considered at three stages: which content is available
(moderation), how content is selected and personalized (ranking), and content
presentation and controls (user interface). Empirical studies of online
conflict suggest that the exposure diversity intervention proposed as an
antidote to "filter bubbles" can be improved and can even worsen polarization
under some conditions. Using civility metrics in conjunction with diversity in
content selection may be more effective. However, diversity-based interventions
have not been tested at scale and may not work in the diverse and dynamic
contexts of real platforms. Instead, intervening in platform polarization
dynamics will likely require continuous monitoring of polarization metrics,
such as the widely used "feeling thermometer." These metrics can be used to
evaluate product features, and potentially engineered as algorithmic
objectives. It may further prove necessary to include polarization measures in
the objective functions of recommender algorithms to prevent optimization
processes from creating conflict as a side effect.


Abstract:
  The conflict between individual and collective interests is in the heart of
every social dilemmas established by evolutionary game theory. We cannot avoid
these conflicts but sometimes we may choose which interaction framework to use
as a battlefield. For instance some people like to be part of a larger group
while other persons prefer to interact in a more personalized, individual way.
Both attitudes can be formulated via appropriately chosen traditional games. In
particular, the prisoner's dilemma game is based on pair interaction while the
public goods game represents multi-point interactions of group members. To
reveal the possible advantage of a certain attitude we extend these models by
allowing players not simply to change their strategies but also let them to
vary their attitudes for a higher individual income. We show that both
attitudes could be the winner at a specific parameter value. Interestingly,
however, the subtle interplay between different states may result in a
counterintuitive evolutionary outcome where the increase of the multiplication
factor of public goods game drives the population to a fully defector state. We
point out that the accompanying pattern formation can only be understood via
the multipoint or multi-player interactions of different microscopic states
where the vicinity of a particular state may influence the relation of two
other competitors.


Abstract:
  Black-box optimization is a very active area of research, with many new
algorithms being developed every year. This variety is needed, on the one hand,
since different algorithms are most suitable for different types of
optimization problems. But the variety also poses a meta-problem: which
algorithm to choose for a given problem at hand? Past research has shown that
per-instance algorithm selection based on exploratory landscape analysis (ELA)
can be an efficient mean to tackle this meta-problem. Existing approaches,
however, require the approximation of problem features based on a significant
number of samples, which are typically selected through uniform sampling or
Latin Hypercube Designs. The evaluation of these points is costly, and the
benefit of an ELA-based algorithm selection over a default algorithm must
therefore be significant in order to pay off. One could hope to by-pass the
evaluations for the feature approximations by using the samples that a default
algorithm would anyway perform, i.e., by using the points of the default
algorithm's trajectory.
  We analyze in this paper how well such an approach can work. Concretely, we
test how accurately trajectory-based ELA approaches can predict the final
solution quality of the CMA-ES after a fixed budget of function evaluations. We
observe that the loss of trajectory-based predictions can be surprisingly small
compared to the classical global sampling approach, if the remaining budget for
which solution quality shall be predicted is not too large. Feature selection,
in contrast, did not show any advantage in our experiments and rather led to
worsened prediction accuracy. The inclusion of state variables of CMA-ES only
has a moderate effect on the prediction accuracy.


Abstract:
  Consider first passage percolation with identical and independent weight
distributions and first passage time ${\rm T}$. In this paper, we study the
upper tail large deviations $\mathbb{P}({\rm T}(0,nx)>n(\mu+\xi))$, for $\xi>0$
and $x\neq 0$ with a time constant $\mu$ and a dimension $d$, for weights that
satisfy a tail assumption $ \beta_1\exp{(-\alpha t^r)}\leq \mathbb
P(\tau_e>t)\leq \beta_2\exp{(-\alpha t^r)}.$ When $r\leq 1$ (this includes the
well-known Eden growth model), we show that the upper tail large deviation
decays as $\exp{(-(2d\xi +o(1))n)}$. When $1< r\leq d$, we find that the rate
function can be naturally described by a variational formula, called the
discrete p-Capacity, and we study its asymptotics. For $r<d$, we show that the
large deviation event ${\rm T}(0,nx)>n(\mu+\xi)$ is described by a localization
of high weights around the origin. The picture changes for $r\geq d$ where the
configuration is not anymore localized.


Abstract:
  We present the study of a proof-of-concept integrated device that can be used
as a nonlinear broadband isolator. The device is based on the asymmetric
loading of a highly-confining silicon-slot photonic coupler with graphene
layers, whose ultrafast and low-threshold saturable absorption can be exploited
for nonreciprocal transmission between the cross-ports of the coupler. The
structure is essentially a non-Hermitian system, whose exceptional points are
briefly discussed. The nonlinear device is modeled with a coupled Schrodinger
equation system whose validity is checked by full-vector finite element-based
beam-propagation method simulations in CW. The numerically computed performance
reveals a nonreciprocal intensity range (NRIR) in the vicinity of 100 mW peak
power with a bandwidth spanning tens of nanometers, from CW down to ps-long
pulses. Finally, the combination of saturable absorption and self-phase
modulation (Kerr effect) in graphene is studied, indicating the existence of
two NRIR with opposite directionality.


Abstract:
  We present the results on the physical properties of filaments and dense
cores in IC 5146, as a part of the TRAO FUNS project. We carried out On-The-Fly
mapping observations using the Taeduk Radio Astronomy Observatory (TRAO) 14m
telescope covering about 1 square degree of the area of IC 5146 using various
molecular lines. We identified 14 filaments (24 in total, including
sub-filaments) from the C$^{18}$O (1-0) data cube and 22 dense cores from the
$\rm N_{2}H^{+}$ (1-0) data. We examined the filaments' gravitational
criticality, turbulence properties, accretion rate from filaments to dense
cores, and relative evolutionary stages of cores. Most filaments in IC 5146 are
gravitationally supercritical within the uncertainty, and most dense cores are
formed in them. We found that dense cores in the hubs show a systemic velocity
shift of ~0.3 km/s between the $\rm N_{2}H^{+}$ and C$^{18}$O gas. Besides,
these cores are subsonic or transonic, while the surrounding filament gas is
transonic or supersonic, indicating that the cores in the hubs are likely
formed by the turbulence dissipation in the colliding turbulent filaments and
the merging is still ongoing. We estimated the mass accretion rate of $15 -
35~M_{\odot}~\rm Myr^{-1}$ from the filaments to the dense cores, and the
required time scales to collect the current core mass are consistent with the
lifetime of the dense cores. The structures of filaments and dense cores in the
hub can form by a collision of turbulent converging flows, and mass flow along
the filaments to the dense cores may play an important role in forming dense
cores.


Abstract:
  Software architecture refers to the high-level abstraction of a system
including the configuration of the involved elements and the interactions and
relationships that exist between them. Source codes can be easily built by
referring to the software architectures. However, the reverse process i.e.
derivation of the software architecture from the source code is a challenging
task. Further, such an architecture consists of multiple layers, and
distributing the existing elements into these layers should be done accurately
and efficiently. In this paper, a novel approach is presented for the recovery
of layered architectures from Java-based software systems using the concept of
ego networks. Ego networks have traditionally been used for social network
analysis, but in this paper, they are modified in a particular way and tuned to
suit the mentioned task. Specifically, a dependency network is extracted from
the source code to create an ego network. The ego network is processed to
create and optimize ego layers in a particular structure. These ego layers when
integrated and optimized together give the final layered architecture. The
proposed approach is evaluated in two ways: on static versions of three
open-source software, and a continuously evolving software system. The
distribution of nodes amongst the proposed layers and the committed violations
are observed on both class level and package level. The proposed method is seen
to outperform the existing standard approaches over multiple performance
metrics. We also carry out the analysis of variation in the results concerning
the change in the node selection strategy and the frequency. The empirical
observations show promising signs for recovering software architecture layers
from source codes using this technique and also extending it further to other
languages and software.


Abstract:
  We prove that there is a unique nonnegative and diagram-preserving
quasiprobability representation of the stabilizer subtheory in all odd
dimensions, namely Gross's discrete Wigner function. This representation is
equivalent to Spekkens' epistemically restricted toy theory, which is
consequently singled out as the unique noncontextual ontological model for the
stabilizer subtheory. Strikingly, the principle of noncontextuality is powerful
enough (at least in this setting) to single out one particular classical
realist interpretation. Our result explains the practical utility of Gross's
representation, e.g. why (in the setting of the stabilizer subtheory)
negativity in this particular representation implies generalized contextuality,
and hence sheds light on why negativity of this particular representation is a
resource for quantum computational speedup. It also allows us to prove that
generalized contextuality is a necessary resource for universal quantum
computation in the state injection model. In all even dimensions, we prove that
there does not exist any nonnegative and diagram-preserving quasiprobability
representation of the stabilizer subtheory, and, hence, that the stabilizer
subtheory is contextual in all even dimensions. Together, these results
constitute a complete characterization of the (non)classicality of all
stabilizer subtheories.


Abstract:
  Deploying transformer models in practice is challenging due to their
inference cost, which scales quadratically with input sequence length. To
address this, we present a novel Learned Token Pruning (LTP) method which
adaptively removes unimportant tokens as an input sequence passes through
transformer layers. In particular, LTP prunes tokens with an attention score
below a threshold value which is learned for each layer during training. Our
threshold-based method allows the length of the pruned sequence to vary
adaptively based on the input sequence, and avoids algorithmically expensive
operations such as top-k token selection. We extensively test the performance
of LTP on GLUE tasks and show that our method outperforms the prior
state-of-the-art token pruning methods by up to ~2.5% higher accuracy with the
same amount of FLOPs. In particular, LTP achieves up to 2.1x FLOPs reduction
with less than 1% accuracy drop, which results in up to 1.9x and 2.0x
throughput improvement on Intel Haswell CPUs and NVIDIA V100 GPUs,
respectively. Furthermore, we demonstrate that LTP is more robust than prior
methods to variations on input sentence lengths. Our code has been developed in
PyTorch and has been open-sourced.


Abstract:
  Neural networks have achieved tremendous empirical success in many areas. It
has been observed that a randomly initialized neural network trained by
first-order methods is able to achieve near-zero training loss, although its
loss landscape is non-convex and non-smooth. There are few theoretical
explanations for this phenomenon. Recently, some attempts have been made to
bridge this gap between practice and theory by analyzing the trajectories of
gradient descent~(GD) and heavy-ball method~(HB) in an over-parameterized
regime. In this work, we make further progress by considering Nesterov's
accelerated gradient method~(NAG) with a constant momentum parameter. We
analyze its convergence for an over-parameterized two-layer fully connected
neural network with ReLU activation. Specifically, we prove that the training
error of NAG converges to zero at a non-asymptotic linear convergence rate
$(1-\Theta(1/\sqrt{\kappa}))^t$ after $t$ iterations, where $\kappa > 1$ is
determined by the initialization and the architecture of the neural network.
Besides, we present a comparison between NAG and the existing convergence
results of GD and HB. Our theoretical results show that NAG achieves an
acceleration over GD and its convergence rate is comparable to HB. Furthermore,
the numerical experiments validate the correctness of our theoretical analysis.


Abstract:
  IFOSMONDI iterative algorithm for implicit co-simulation of coupled physical
systems (introduced by the authors in july 2019 during the Simultech
conference, p.176-186) enables us to solve the nonlinear coupling function
while keeping the smoothness of interfaces without introducing a delay.
Moreover, it automatically adapts the size of the steps between data exchanges
among the systems according to the difficulty of the solving of the coupling
constraint. The latter was solved by a fixed-point algorithm in the original
implementation whereas this paper introduces the JFM version (standing for
Jacobian-Free Methods). Most implementations of Newton-like methods require a
jacobian matrix which can be difficult to compute in the co-simulation context,
except in the case where the interfaces are represented by a Zero-Order-Hold
(ZOH). As far as IFOSMONDI coupling algorithm uses Hermite interpolation for
smoothness enhancement (up to Third-Order-Hold), we propose hereafter a new
formulation of the non-linear coupling function including both the values and
the time-derivatives of the coupling variables. This formulation is well
designed for solving the coupling through jacobian-free Newton type methods.
Consequently, successive function evaluations consist in multiple simulations
of the systems on a co-simulation time step using rollback. The
orchestrator-workers structure of the algorithm enables us to combine the PETSc
framework on the orchestrator side for the non-linear Newton-type solvers with
the parallel integrations of the systems on the workers side thanks to MPI
processes. Different nonlinear methods will be compared to one another and to
the original fixed-point implementation on a newly proposed 2-systems academic
test-case (mass-spring-damper type) with direct feedthrough on both sides.


Abstract:
  To quantify the dependence between two random vectors of possibly different
dimensions, we propose to rely on the properties of the 2-Wasserstein distance.
We first propose two coefficients that are based on the Wasserstein distance
between the actual distribution and a reference distribution with independent
components. The coefficients are normalized to take values between 0 and 1,
where 1 represents the maximal amount of dependence possible given the two
multivariate margins. We then make a quasi-Gaussian assumption that yields two
additional coefficients rooted in the same ideas as the first two. These
different coefficients are more amenable for distributional results and admit
attractive formulas in terms of the joint covariance or correlation matrix.
Furthermore, maximal dependence is proved to occur at the covariance matrix
with minimal von Neumann entropy given the covariance matrices of the two
multivariate margins. This result also helps us revisit the RV coefficient by
proposing a sharper normalisation. The two coefficients based on the
quasi-Gaussian approach can be estimated easily via the empirical covariance
matrix. The estimators are asymptotically normal and their asymptotic variances
are explicit functions of the covariance matrix, which can thus be estimated
consistently too. The results extend to the Gaussian copula case, in which case
the estimators are rank-based. The results are illustrated through theoretical
examples, Monte Carlo simulations, and a case study involving
electroencephalography data.


Abstract:
  We extend non-emtpyness and irreducibility of Hassett divisors to the moduli
spaces of $M$-polarizable cubic fourfolds for higher rank lattices $M$, which
in turn provides a systematic approach for describing the irreducible
components of intersection of Hassett divisors. We show that Fermat cubic
fourfold is contained in every Hassett divisor, which yields a new proof of
Hassett's existence theorem of special cubic fourfolds. We obtain an algorithm
to determine the irreducible components of the intersection of any two Hassett
divisors and we give new examples of rational cubic fourfolds. Moreover, we
derive a numerical criterion for the algebraic cohomology of a cubic fourfold
having an associated K3 surface and answer a question of Laza by realizing
infinitely many rank $11$ lattices as the algebraic cohomologies of cubic
fourfolds having no associated K3 surfaces.


Abstract:
  Motivated by a possible convergence of terrestrial limbless locomotion
strategies ultimately determined by interfacial effects, we show how both 3D
gait alterations and locomotory adaptations to heterogeneous terrains can be
understood through the lens of local friction modulation. Via an
`everything-is-friction' modeling approach, compounded by 3D simulations, the
emergence and disappearance of a range of locomotory behaviors observed in
nature is systematically explained in relation to inhabited environments. Our
approach also simplifies the treatment of terrain heterogeneity, whereby even
solid obstacles may be seen as high friction regions, which we confirm against
experiments of snakes `diffracting' while traversing rows of posts [1], similar
to optical waves. We further this optic analogy by illustrating snake
refraction, reflection and lens focusing. We use these insights to engineer
surface friction patterns and demonstrate passive snake navigation in complex
topographies. Overall, our study outlines a unified view that connects active
and passive 3D mechanics with heterogeneous interfacial effects to explain a
broad set of biological observations, and potentially inspire engineering
design.


Abstract:
  We consider the problem of allocating multiple indivisible items to a set of
networked agents to maximize the social welfare subject to network
externalities. Here, the social welfare is given by the sum of agents'
utilities and externalities capture the effect that one user of an item has on
the item's value to others. We first provide a general formulation that
captures some of the existing models as a special case. We then show that the
social welfare maximization problem benefits some nice diminishing or
increasing marginal return properties. That allows us to devise polynomial-time
approximation algorithms using the Lovasz extension and multilinear extension
of the objective functions. Our principled approach recovers or improves some
of the existing algorithms and provides a simple and unifying framework for
maximizing social welfare subject to network externalities.


Abstract:
  Let $X$ be one of the $28$ Atkin-Lehner quotients of a curve $X_0(N)$ such
that $X$ has genus $2$ and its Jacobian variety $J$ is absolutely simple. We
show that the Shafarevich-Tate group of $J/\mathbb{Q}$ is trivial. This
verifies the strong BSD conjecture for $J$.


Abstract:
  Conversational machine reading (CMR) tools have seen a rapid progress in the
recent past. The current existing tools rely on the supervised learning
technique which require labeled dataset for their training. The supervised
technique necessitates that for every new rule text, a manually labeled dataset
must be created. This is tedious and error prone. This paper introduces and
demonstrates how unsupervised learning technique can be applied in the
development of CMR. Specifically, we demonstrate how unsupervised learning can
be used in rule extraction and entailment modules of CMR. Compared to the
current best CMR tool, our developed framework reports 3.3% improvement in
micro averaged accuracy and 1.4 % improvement in macro averaged accuracy.


Abstract:
  We have optimized the growth of superconducting TaN thin films on \ch{SiO2}
substrates via dc magnetron sputtering and extract a maximum superconducting
transition temperature of $T_{\mathrm{c}}=5$ K as well as a maximum critical
field $\mu_0H_{\mathrm{c2}}=(13.8\pm0.1)$ T. To investigate the impact of
spin-orbit interaction in superconductor/ferromagnet heterostructures, we then
analyze the magnetization dynamics of both normal state and superconducting
TaN/\ch{Ni80Fe20}(Permalloy, Py)-bilayers as a function of temperature using
broadband ferromagnetic resonance (bbFMR) spectroscopy. The phase sensitive
detection of the microwave transmission signal is used to quantitatively
extract the inverse current-induced torques of the bilayers. The results are
compared to our previous study on NbN/Py-bilayers. In the normal state of TaN,
we detect a positive damping-like current-induced torque $\sigma_{\mathrm{d}}$
from the inverse spin Hall effect (iSHE) and a small field-like torque
$\sigma_{\mathrm{f}}$ attributed to the inverse Rashba-Edelstein effect (iREE)
at the TaN/Py-interface. In the superconducting state of TaN, we detect a
negative $\sigma_{\mathrm{d}}$ attributed to the quasiparticle mediated inverse
spin Hall effect (QMiSHE) and the unexpected manifestation of a large positive
field-like $\sigma_{\mathrm{f}}$ of unknown origin matching our previous
results for NbN/Py-bilayers.


Abstract:
  We work with lattice walks in $\mathbb{Z}^{r+1}$ using step set $\{\pm
1\}^{r+1}$ that finish with $x_{r+1} = 0$. We further impose conditions of
avoiding backtracking (i.e. $[v,-v]$) and avoiding consecutive steps (i.e.
$[v,v]$) each possibly combined with restricting to the half-space $x_{r+1}
\geq 0$. We find in all cases the generating functions for such walks are
algebraic and give explicit formulas for them. We also find polynomial
recurrences for their coefficients. From the generating functions we find the
asymptotic enumeration of each family of walks considered. The enumeration in
special cases includes central binomial coefficients and Catalan numbers as
well as relations to enumeration of another family of walks previously studied
for which we provide bijection.


Abstract:
  A common developmental process, called branching morphogenesis, generates the
epithelial trees in a variety of organs, including the lungs, kidneys, and
glands. How branching morphogenesis can create epithelial architectures of very
different shapes and functions remains elusive. In this review, we compare
branching morphogenesis and its regulation in lungs and kidneys and discuss the
role of signaling pathways, the mesenchyme, the extracellular matrix, and the
cytoskeleton as potential organ-specific determinants of branch position,
orientation, and shape. Identifying the determinants of branch and organ shape
and their adaptation in different organs may reveal how a highly conserved
developmental process can be adapted to different structural and functional
frameworks and should provide important insights into epithelial morphogenesis
and developmental disorders.


Abstract:
  Frames are redundant system which are useful in the reconstruction of certain
classes of spaces. Duffin and Schaeffer introduced frames for Hilbert spaces,
while addressing some deep problems in non harmonic Fourier series. The dual of
a frame (Hilbert) always exists and can be obtained in a natural way. In this
paper we introduce the notion of $\Omega_0$-type duality of retro
$(\Omega,\mu)$-frames are given. Necessary and sufficient conditions for the
existence of the dual of retro $(\Omega,\mu)$-frames are obtained. A special
class of retro $(\Omega,\mu)$-frames which always admit a dual frame is
discussed.


Abstract:
  We develop a general formalism to study laser operation in active micro-ring
resonators supporting two counterpropagating modes. Our formalism is based on
the coupled-mode equations of motion for the field amplitudes in the two
counterpropagating modes and a linearized analysis of the small perturbations
around the steady state. We show that the devices including an additional
S-shaped waveguide establishing an unidirectional coupling between both modes
-- the so-called Taiji resonators (TJR) -- feature a preferred chirality on the
laser emission and can ultimately lead to unidirectional lasing even in the
presence of sizable backscattering. The efficiency of this mode selection
process is further reinforced by the Kerr nonlinearity of the material. This
stable unidirectional laser operation can be seen as an effective breaking of
$\mathcal{T}$-reversal symmetry dynamically induced by the breaking of the
$\mathcal{P}$-symmetry of the underlying device geometry. This mechanism
appears as a promising building block to ensure non-reciprocal behaviors in
integrated photonic networks and topological lasers without the need for
magnetic elements.


Abstract:
  Turbulent winds and gusts fluctuate on a wide range of timescales from
milliseconds to minutes and longer, a range that overlaps the timescales of
avian flight behavior, yet the importance of turbulence to avian behavior is
unclear. By combining wind speed data with the measured accelerations of a
golden eagle (Aquila chrysaetos) flying in the wild, we show that the eagle's
accelerations can be explained by a linear interaction with turbulence for
timescales between about 1/2 and 10 s. These timescales are comparable to those
of typical eagle behaviors, corresponding to between about 1 and 25 wingbeats,
and to those of turbulent gusts both larger than the eagle's wingspan and
smaller than large-scale atmospheric phenomena such as convection cells. The
eagle's accelerations exhibit power spectra and intermittent activity
characteristic of turbulence, and increase in proportion to the turbulence
intensity. Intermittency results in accelerations that are occasionally several
times stronger than gravity, and much larger than the ones we experience while
driving, for instance. These imprints of turbulence on the bird's movements
need to be further explored to understand the energetics of birds and other
volant lifeforms, to improve our own methods of flying through ceaselessly
turbulent environments, and to engage airborne wildlife as distributed probes
of the changing conditions in the atmosphere.


Abstract:
  We present results from a spectroscopically blind search for associated and
intervening HI 21-cm and OH 18-cm absorption lines towards 88 AGNs at $2\le
z\le5$ using the upgraded Giant Metrewave Radio Telescope (uGMRT). The sample
of AGNs with 1.4 GHz spectral luminosity in the range, $10^{27 - 29.3}$ W/Hz,
is selected using mid-infrared colors and closely resembles the distribution of
the underlying quasar population. The search for associated or proximate
absorption, defined to be within 3000 km/s of the AGN redshift, led to one HI
21-cm absorption detection (M1540-1453; $z_{abs}$= 2.1139). This is only the
fourth known absorption at $z>2$. The detection rate ($1.6^{+3.8}_{-1.4}$%)
suggests low covering factor of cold neutral medium (CNM; T$\sim$100 K)
associated with these powerful AGNs. The intervening absorption line search,
with a sensitivity to detect CNM in damped Ly$\alpha$ systems (DLAs), has
comoving absorption path lengths of $\Delta$X = 130.1 and 167.7 for HI and OH,
respectively. The corresponding number of absorber per unit comoving path
lengths are $\le$0.014 and $\le$0.011, respectively. The former is at least 4.5
times lower than that of DLAs and consistent with the CNM cross-section
estimated using H$_2$ and CI absorbers at $z>2$. Our AGN sample is optically
fainter compared to the quasars used to search for DLAs in the past. In our
optical spectra obtained using the Southern African Large Telescope (SALT) and
the Nordic Optical Telescope (NOT), we detect 5 intervening (redshift
path$\sim9.3$) and 2 proximate DLAs. This is slightly excessive compared to the
statistics based on optically selected quasars. The non-detection of HI 21-cm
absorption from these DLAs suggests small CNM covering fraction around galaxies
at $z>2$.


Abstract:
  Balanced steady-state free precession (bSSFP) imaging enables high scan
efficiency in MRI, but differs from conventional sequences in terms of elevated
sensitivity to main field inhomogeneity and nonstandard T2/T1-weighted tissue
contrast. To address these limitations, multiple bSSFP images of the same
anatomy are commonly acquired with a set of different RF phase-cycling
increments. Joint processing of phase-cycled acquisitions serves to mitigate
sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions
were also leveraged to estimate relaxation parameters based on explicit signal
models. While effective, these model-based methods often involve a large number
of acquisitions (N~10-16), degrading scan efficiency. Here, we propose a new
constrained ellipse fitting method (CELF) for parameter estimation with
improved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on
the elliptical signal model framework for complex bSSFP signals; and it
introduces geometrical constraints on ellipse properties to improve estimation
efficiency, and dictionary-based identification to improve estimation accuracy.
CELF generates maps of T1, T2, off-resonance and on-resonant bSSFP signal by
employing a separate B1 map to mitigate sensitivity to flip angle variations.
Our results indicate that CELF can produce accurate off-resonance and
banding-free bSSFP maps with as few as N=4 acquisitions, while estimation
accuracy for relaxation parameters is notably limited by biases from
microstructural sensitivity of bSSFP imaging.


Abstract:
  This paper develops a version of dependent type theory in which isomorphism
is handled through a direct generalization of the 1939 definitions of Bourbaki.
More specifically we generalize the Bourbaki definition of structure from
simple type signatures to dependent type signatures. Both the original Bourbaki
notion of isomorphism and its generalization given here define an isomorphism
between two structures $N$ and $N'$ to consist of bijections between their
sorts that transport the structure of $N$ to the structure of $N'$. Here
transport is defined by commutativity conditions stated with set-theoretic
equality. This differs from the dependent type theoretic treatments of
isomorphism given in the groupoid model and homotopy type theory where no
analogously straightforward set-theoretic definition of transport is specified.
The straightforward definition of transport also leads to a straightforward
constructive proof (constructive content) for the validity of the substitution
of isomorphics -- something that is difficult in the groupoid model or homotopy
type theory.


Abstract:
  Chiral exceptional points (CEPs) have been shown to emerge in traveling wave
resonators via asymmetric back scattering from two or more nano-scatterers.
Here, we provide a new perspective on the formation of CEPs based on the
coupled oscillator model. Our approach provides an intuitive understanding for
the modal coalescence that signals the emergence of CEPs, and emphasizes the
role played by dissipation throughout this process. In doing so, our model also
unveils an otherwise unexplored connection between CEPs and other types of
exceptional points associated with parity-time symmetric photonic arrangements.
In addition, our model also explains qualitative results observed in recent
experimental work involving CEPs. Importantly, the tight-binding nature of our
approach allows us to extend the notion of CEP to discrete photonics setups
that consist coupled resonator and waveguide arrays, thus opening new avenues
for exploring the exotic features of CEPs in conjunction with other interesting
physical effects such as nonlinearities and topological protections.


Abstract:
  Graphs are fundamental mathematical structures used in various fields to
model statistical and physical relationships between data, signals, and
processes. In some applications, such as data processing in graphs that
represent physical networks, the initial network topology is known. However,
disconnections of edges in the network change the topology and may affect the
signals and processes over the network. In this paper, we consider the problem
of edge disconnection identification in networks by using concepts from graph
signal processing (GSP). We assume that the graph signals measured over the
vertices of the network can be represented as white noise that has been
filtered on the graph topology by a smooth graph filter. We develop the
likelihood ratio test (LRT) to detect a specific set of edge disconnections.
Then, we provide the maximum likelihood (ML) decision rule for identifying
general scenarios of edge disconnections in the network. It is shown that the
sufficient statistics of the LRT and ML decision rule are the graph frequency
energy levels in the graph spectral domain. However, the ML decision rule leads
to a high-complexity exhaustive search over the edges in the network and is
practically infeasible. Thus, we propose a low-complexity greedy method that
identifies a single disconnected edge at each iteration. Moreover, by using the
smoothness of the considered graph filter, we suggest a local implementation of
the decision rule, which is based solely on the measurements at neighboring
vertices. Simulation results demonstrate that the proposed methods outperform
existing detection and identification methods on a synthetic dataset and for
line outage identification in power systems from the IEEE 118-bus test case.


Abstract:
  The performance of a cross-sectional currency strategy depends crucially on
accurately ranking instruments prior to portfolio construction. While this
ranking step is traditionally performed using heuristics, or by sorting outputs
produced by pointwise regression or classification models, Learning to Rank
algorithms have recently presented themselves as competitive and viable
alternatives. Despite improving ranking accuracy on average however, these
techniques do not account for the possibility that assets positioned at the
extreme ends of the ranked list -- which are ultimately used to construct the
long/short portfolios -- can assume different distributions in the input space,
and thus lead to sub-optimal strategy performance. Drawing from research in
Information Retrieval that demonstrates the utility of contextual information
embedded within top-ranked documents to learn the query's characteristics to
improve ranking, we propose an analogous approach: exploiting the features of
both out- and under-performing instruments to learn a model for refining the
original ranked list. Under a re-ranking framework, we adapt the Transformer
architecture to encode the features of extreme assets for refining our
selection of long/short instruments obtained with an initial retrieval.
Backtesting on a set of 31 currencies, our proposed methodology significantly
boosts Sharpe ratios -- by approximately 20% over the original LTR algorithms
and double that of traditional baselines.


Abstract:
  With the surge of inexpensive computational and memory resources, neural
networks (NNs) have experienced an unprecedented growth in architectural and
computational complexity. Introducing NNs to resource-constrained devices
enables cost-efficient deployments, widespread availability, and the
preservation of sensitive data. This work addresses the challenges of bringing
Machine Learning to MCUs, where we focus on the ubiquitous ARM Cortex-M
architecture. The detailed effects and trade-offs that optimization methods,
software frameworks, and MCU hardware architecture have on key performance
metrics such as inference latency and energy consumption have not been
previously studied in depth for state-of-the-art frameworks such as TensorFlow
Lite Micro. We find that empirical investigations which measure the perceptible
metrics - performance as experienced by the user - are indispensable, as the
impact of specialized instructions and layer types can be subtle. To this end,
we propose an implementation-aware design as a cost-effective method for
verification and benchmarking. Employing our developed toolchain, we
demonstrate how existing NN deployments on resource-constrained devices can be
improved by systematically optimizing NNs to their targeted application
scenario.


Abstract:
  We propose a new quantum state reconstruction method that combines ideas from
compressed sensing, non-convex optimization, and acceleration methods. The
algorithm, called Momentum-Inspired Factored Gradient Descent (\texttt{MiFGD}),
extends the applicability of quantum tomography for larger systems. Despite
being a non-convex method, \texttt{MiFGD} converges \emph{provably} to the true
density matrix at a linear rate, in the absence of experimental and statistical
noise, and under common assumptions. With this manuscript, we present the
method, prove its convergence property and provide Frobenius norm bound
guarantees with respect to the true density matrix. From a practical point of
view, we benchmark the algorithm performance with respect to other existing
methods, in both synthetic and real experiments performed on an IBM's quantum
processing unit. We find that the proposed algorithm performs orders of
magnitude faster than state of the art approaches, with the same or better
accuracy. In both synthetic and real experiments, we observed accurate and
robust reconstruction, despite experimental and statistical noise in the
tomographic data. Finally, we provide a ready-to-use code for state tomography
of multi-qubit systems.


Abstract:
  The transition mechanism of metal-insulator in metal oxides is discussed in
detail, which is a part of the mechanism of superconductivity. Through the
study of magic angle twisted bilayer graphene superconductor and other new
findings on superconductivity, we further demonstrate that the physical
mechanism of superconductivity proposed in the Part I is the only correct way
to handle the properties of superconductivity in various materials. We propose
that superfluid helium consists of normal liquid helium mixed with high-energy
helium atoms. Based on this new model, all peculiar features discovered in
superfluid helium can be truly understood, such as its climb on the container's
wall, its fountain effect, the discontinuity of specific heat capacity at phase
transition point, as well as the maintaining mass current in ring-shaped
container. We demonstrate that the high-energy particles play a driving force
role in both superconductors and superfluid helium, and therefore dominate
their properties.


Abstract:
  Deeply virtual Compton scattering (DVCS) and timelike Compton scattering
(TCS) leading twist amplitudes are intimately related thanks to their analytic
properties as a function of $Q^2$. We exploit this feature to use Compton form
factors previously extracted from available DVCS data and derive data-driven
predictions for TCS observables to be measured in near future experiments. Our
results quantitatively illustrate the complementarity of DVCS and TCS
experiments.


Abstract:
  The vector Apodizing Phase Plate (vAPP) is a class of pupil plane coronagraph
that enables high-contrast imaging by modifying the Point Spread Function (PSF)
to create a dark hole of deep flux suppression adjacent to the PSF core. Here,
we recover the known brown dwarf HR 2562 B using a vAPP coronagraph, in
conjunction with the Magellan Adaptive Optics (MagAO) system, at a
signal-to-noise of S/N = 3.04 in the lesser studied L-band regime. The data
contained a mix of field and pupil-stabilised observations, hence we explored
three different processing techniques to extract the companion, including
Flipped Differential Imaging (FDI), a newly devised Principal Component
Analysis (PCA)-based method for vAPP data. Despite the partial
field-stabilisation, the companion is recovered sufficiently to measure a 3.94
$\mu$m narrow-band contrast of (3.05$\pm$1.00) $\times$ 10$^{-4}$
($\Delta$m$_{3.94 {\mu}m}$ = 8.79$\pm$0.36 mag). Combined with archival GPI and
SPHERE observations, our atmospheric modelling indicates a spectral type at the
L/T transition with mass M = 29$\pm$15 M$_{\text{Jup}}$, consistent with
literature results. However, effective temperature and surface gravity vary
significantly depending on the wavebands considered
(1200$\leq$T$_{\text{eff}}$(K)$\leq$1700 and 4.0$\leq$log(g)(dex)$\leq$5.0),
reflecting the challenges of modelling objects at the L/T transition.
Observations between 2.4-3.2 $\mu$m will be more effective in distinguishing
cooler brown dwarfs due to the onset of absorption bands in this region. We
explain that instrumental scattered light and wind-driven halo can be
detrimental to FDI+PCA and thus must be sufficiently mitigated to use this
processing technique. We thus demonstrate the potential of vAPP coronagraphs in
the characterisation of high-contrast substellar companions, even in
sub-optimal conditions, and provide new, complementary photometry of HR 2562 B.


Abstract:
  Exotic electromagnetic energy injection in the early Universe may alter
cosmological recombination, and ultimately cosmic microwave background (CMB)
anisotropies. Moreover, if energy injection is inhomogeneous, it may induce a
spatially-varying ionization fraction, and non-Gaussianity in the CMB. The
observability of these signals, however, is contingent upon how far the
injected particles propagate and deposit their energy into the primordial
plasma, relative to the characteristic scale of energy injection fluctuations.
In this study we inspect the spatial properties of energy deposition and
perturbed recombination resulting from an inhomogeneous energy injection of
sub-10 MeV photons, relevant to accreting primordial black holes (PBHs). We
develop a novel Monte-Carlo radiation transport code accounting for all
relevant photon interactions in this energy range, and including secondary
electron energy deposition efficiency through a new analytic approximation. For
a specified injected photon spectrum, the code outputs an
injection-to-deposition Green's function depending on time and distance from
the injection point. Combining this output with a linearized solution of the
perturbed recombination problem, we derive time- and scale-dependent
deposition-to-ionization Green's functions. We apply this general framework to
accreting PBHs, whose luminosity is strongly spatially modulated by supersonic
relative velocities between cold dark matter and baryons. We find that the
resulting spatial fluctuations of the free-electron fraction are of the same
magnitude as its mean deviation from standard recombination, from which current
CMB power spectra constraints are derived. This work suggests that the
sensitivity to accreting PBHs might be substantially improved by propagating
these inhomogeneities to CMB anisotropy power spectra and non-Gaussian
statistics, which we study in subsequent papers.


Abstract:
  Multilevel regression and poststratification (MRP) is a flexible modeling
technique that has been used in a broad range of small-area estimation
problems. Traditionally, MRP studies have been focused on non-causal settings,
where estimating a single population value using a nonrepresentative sample was
of primary interest. In this manuscript, MRP-style estimators will be evaluated
in an experimental causal inference setting. We simulate a large-scale
randomized control trial with a stratified cluster sampling design, and compare
traditional and nonparametric treatment effect estimation methods with MRP
methodology. Using MRP-style estimators, treatment effect estimates for areas
as small as 1.3$\%$ of the population have lower bias and variance than
standard causal inference methods, even in the presence of treatment effect
heterogeneity. The design of our simulation studies also requires us to build
upon a MRP variant that allows for non-census covariates to be incorporated
into poststratification.


Abstract:
  The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had
a profound impact on the diagnosis of prostate cancers (PCa), which is the most
prevalent malignancy in males in the western world, enabling a better selection
of patients for confirmation biopsy. However, analyzing these images is complex
even for experts, hence opening an opportunity for computer-aided diagnosis
systems to seize. This paper proposes a fully automatic system based on Deep
Learning that takes a prostate mpMRI from a PCa-suspect patient and, by
leveraging the Retina U-Net detection framework, locates PCa lesions, segments
them, and predicts their most likely Gleason grade group (GGG). It uses 490
mpMRIs for training/validation, and 75 patients for testing from two different
datasets: ProstateX and IVO (Valencia Oncology Institute Foundation). In the
test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for
the GGG$\geq$2 significance criterion of 0.96/1.00/0.79 for the ProstateX
dataset, and 0.95/1.00/0.80 for the IVO dataset. Evaluated at a patient level,
the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO.
Furthermore, on the online ProstateX grand challenge, the model obtained an AUC
of 0.85 (0.87 when trained only on the ProstateX data, tying up with the
original winner of the challenge). For expert comparison, IVO radiologist's
PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and
0.85/0.58 at a patient level. Additional subsystems for automatic prostate
zonal segmentation and mpMRI non-rigid sequence registration were also employed
to produce the final fully automated system. The code for the ProstateX-trained
system has been made openly available at
https://github.com/OscarPellicer/prostate_lesion_detection. We hope that this
will represent a landmark for future research to use, compare and improve upon.


Abstract:
  Gravitational waves (GWs) directly measure the luminosity distance to the
merger, which, when combined with an independent measurement of the source's
redshift, provides a novel probe of cosmology. The proposed next generation of
ground-based GW detectors, Einstein Telescope and Cosmic Explorer, will detect
tens of thousands of binary neutron stars (BNSs) out to cosmological distances
($z>2$), beyond the peak of the star formation rate (SFR), or "cosmic noon." At
these distances, it will be challenging to measure the sources' redshifts by
observing electromagnetic (EM) counterparts or statistically marginalizing over
a galaxy catalog. In the absence of an EM counterpart or galaxy catalog, Ding
et al. showed that theoretical priors on the merger redshift distribution can
be used to infer parameters in a $w$CDM cosmology. We argue that in the BNS
case, the redshift distribution will be measured by independent observations of
short gamma ray bursts (GRBs), kilonovae, and known BNS host galaxies. We show
that, in addition to measuring the background cosmology, this method can
constrain the effects of dark energy on modified GW propagation. We consider
the simple case in which the BNS rate is \textit{a priori} known to follow the
SFR. If the SFR is perfectly known, $\mathcal{O}(10,000)$ events (to be
expected within a year of observation with Cosmic Explorer) would yield a
sub-tenth percent measurement of the combination $H_0^{2.8}\Omega_M$. Fixing
$H_0$ and $\Omega_M$, this method may enable a 5\% measurement of the dark
energy equation of state parameter. Fixing the background cosmology and probing
modified GW propagation, the running of the Planck mass parameter $c_M$ may be
measured to $\pm0.02$. Although realistically, the redshift evolution of the
merger rate will be uncertain, prior knowledge of the peak redshift will
provide valuable information for standard siren analyses.


Abstract:
  We consider a projection method for time-dependent incompressible
Navier-Stokes equations with a total pressure boundary condition. The
projection method is one of the numerical calculation methods for
incompressible viscous fluids often used in engineering. In general, the
projection method needs additional boundary conditions to solve a
pressure-Poisson equation, which does not appear in the original Navier-Stokes
problem. On the other hand, many mechanisms generate flow by creating a
pressure difference, such as water distribution systems and blood circulation.
We propose a new additional boundary condition for the projection method with a
Dirichlet-type pressure boundary condition and no tangent flow. We demonstrate
stability for the scheme and establish error estimates for the velocity and
pressure under suitable norms. A numerical experiment verifies the theoretical
convergence results. Furthermore, the existence of a weak solution to the
original Navier-Stokes problem is proved by using the stability.


Abstract:
  Waiting time distributions of solar flares and {\sl coronal mass ejections
(CMEs)} exhibit power law-like distribution functions with slopes in the range
of $\alpha_{\tau} \approx 1.4-3.2$, as observed in annual data sets during 4
solar cycles (1974-2012). We find a close correlation between the waiting time
power law slope $\alpha_\tau$ and the {\sl sunspot number (SN)}, i.e.,
$\alpha_\tau$ = 1.38 + 0.01 $\times$ SN. The waiting time distribution can be
fitted with a Pareto-type function of the form $N(\tau) = N_0$ $(\tau_0 +
\tau)^{-\alpha_{\tau}}$, where the offset $\tau_0$ depends on the instrumental
sensitivity, the detection threshold of events, and pulse pile-up effects. The
time-dependent power law slope $\alpha_{\tau}(t)$ of waiting time distributions
depends only on the global solar magnetic flux (quantified by the sunspot
number) or flaring rate, independent of other physical parameters of {\sl
self-organized criticality (SOC)} or {\sl magneto-hydrodynamic (MHD)}
turbulence models. Power law slopes of $\alpha_{\tau}\approx 1.2-1.6$ were also
found in solar wind switchback events, as observed with the {\sl Parker Solar
Probe (PSP)}. We conclude that the annual variability of switchback events in
the heliospheric solar wind is modulated by flare and CME rates originating in
the photosphere and lower corona.


Abstract:
  Communication networks are important infrastructures in contemporary society.
There are still many challenges that are not fully solved and new solutions are
proposed continuously in this active research area. In recent years, to model
the network topology, graph-based deep learning has achieved the
state-of-the-art performance in a series of problems in communication networks.
In this survey, we review the rapidly growing body of research using different
graph-based deep learning models, e.g. graph convolutional and graph attention
networks, in various problems from different types of communication networks,
e.g. wireless networks, wired networks, and software defined networks. We also
present a well-organized list of the problem and solution for each study and
identify future research directions. To the best of our knowledge, this paper
is the first survey that focuses on the application of graph-based deep
learning methods in communication networks involving both wired and wireless
scenarios. To track the follow-up research, a public GitHub repository is
created, where the relevant papers will be updated continuously.


Abstract:
  One-loop correction to the power spectrum in generic single-field inflation
is calculated by using standard perturbation theory. Due to the enhancement
inversely proportional to the observed red-tilt of the spectral index of
curvature perturbation, the correction turns out to be much larger than
previously anticipated. As a result, the primordial non-Gaussianity must be
much smaller than the current observational bound in order to warrant the
validity of cosmological perturbation theory.


Abstract:
  We study the cohomology of families of $(\varphi,\Gamma)$-modules with
coefficients in pseudoaffinoid algebras. We prove that they have finite
cohomology, and we deduce an Euler characteristic formula and Tate local
duality. We classify rank-$1$ $(\varphi, \Gamma)$-modules and deduce that
triangulations of pseudorigid families of $(\varphi,\Gamma)$-modules can be
interpolated, extending a result of [KPX14]. We then apply this to study
extended eigenvarieties at the boundary of weight space, proving in particular
that the eigencurve is proper at the boundary and that Galois representations
attached to certain characteristic $p$ points are trianguline.


Abstract:
  We use two-dimensional transverse laser cooling to produce an ultracold beam
of YbF molecules. Through experiments and numerical simulations, we study how
the cooling is influenced by the polarization configuration, laser intensity,
laser detuning and applied magnetic field. The ultracold part of the beam
contains more than $2 \times 10^5$ molecules per shot and has a temperature
below 200 $\mu$K, and the cooling yields a 300-fold increase in the brightness
of the beam. The method can improve the precision of experiments that use
molecules to test fundamental physics. In particular, the beam is suitable for
measuring the electron electric dipole moment with a statistical precision
better than $10^{-30}$ e cm.


Abstract:
  Graph Convolutional Networks (GCNs) have achieved impressive empirical
advancement across a wide variety of semi-supervised node classification tasks.
Despite their great success, training GCNs on large graphs suffers from
computational and memory issues. A potential path to circumvent these obstacles
is sampling-based methods, where at each layer a subset of nodes is sampled.
Although recent studies have empirically demonstrated the effectiveness of
sampling-based methods, these works lack theoretical convergence guarantees
under realistic settings and cannot fully leverage the information of evolving
parameters during optimization. In this paper, we describe and analyze a
general doubly variance reduction schema that can accelerate any sampling
method under the memory budget. The motivating impetus for the proposed schema
is a careful analysis of the variance of sampling methods where it is shown
that the induced variance can be decomposed into node embedding approximation
variance (zeroth-order variance) during forward propagation and
layerwise-gradient variance (first-order variance) during backward propagation.
We theoretically analyze the convergence of the proposed schema and show that
it enjoys an $\mathcal{O}(1/T)$ convergence rate. We complement our theoretical
results by integrating the proposed schema in different sampling methods and
applying them to different large real-world graphs.


Abstract:
  The state space of Android apps is huge and its thorough exploration during
testing remains a major challenge. In fact, the best exploration strategy is
highly dependent on the features of the app under test. Reinforcement Learning
(RL) is a machine learning technique that learns the optimal strategy to solve
a task by trial and error, guided by positive or negative reward, rather than
by explicit supervision. Deep RL is a recent extension of RL that takes
advantage of the learning capabilities of neural networks. Such capabilities
make Deep RL suitable for complex exploration spaces such as the one of Android
apps. However, state of the art, publicly available tools only support basic,
tabular RL. We have developed ARES, a Deep RL approach for black-box testing of
Android apps. Experimental results show that it achieves higher coverage and
fault revelation than the baselines, which include state of the art RL based
tools, such as TimeMachine and Q-Testing. We also investigated qualitatively
the reasons behind such performance and we have identified the key features of
Android apps that make Deep RL particularly effective on them to be the
presence of chained and blocking activities.


Abstract:
  We propose a functional view of matrix decomposition problems on graphs such
as geometric matrix completion and graph regularized dimensionality reduction.
Our unifying framework is based on the key idea that using a reduced basis to
represent functions on the product space is sufficient to recover a low rank
matrix approximation even from a sparse signal. We validate our framework on
several real and synthetic benchmarks (for both problems) where it either
outperforms state of the art or achieves competitive results at a fraction of
the computational effort of prior work.


Abstract:
  Photoexcitation and shaping of a propagating surface plasmon polariton (SPP)
on silver and gold microstructures are well established and lead to the
discovery of the plasmonic spin-Hall effect recently. Whereas silver is often
the material of choice due to its exceptional low plasma frequency and weak
damping, similar observations have not been reported for ferromagnetic metals.
In this work, we report on propagating SPPs on Ni$_{80}$Fe$_{20}$
microstructures imaged by photoemission electron microscopy (PEEM) in
combination with a tunable femtosecond laser system at MHz repetition rate.
Circular dichroic (CD) images in threshold PEEM show clear edge-induced SPPs
with sub-micrometer wavelength and propagation length of about 3.5 $\mu$m.
Analysis of the interference patterns as well as the coupling of the optical
spin angular momentum to the observed fringe fields reveal propagation
characteristics exclusive to evanescent waves and the presence of the plasmonic
spin-Hall effect. Our work provides direct evidence that many materials with a
high plasma frequency allow for excitation and observation of propagating SPPs
at the dielectric/metal interface via CD PEEM imaging, enabling
magnetoplasmonic investigation of common ferromagnets on nanometer length and
femtosecond time scales.