Abstract:
  The early diagnosis and screening of glaucoma are important for patients to
receive treatment in time and maintain eyesight. Nowadays, deep learning (DL)
based models have been successfully used for computer-aided diagnosis (CAD) of
glaucoma from retina fundus images. However, a DL model pre-trained using a
dataset from one hospital center may have poor performance on a dataset from
another new hospital center and therefore its applications in the real scene
are limited. In this paper, we propose a self-adaptive transfer learning (SATL)
strategy to fill the domain gap between multicenter datasets. Specifically, the
encoder of a DL model that is pre-trained on the source domain is used to
initialize the encoder of a reconstruction model. Then, the reconstruction
model is trained using only unlabeled image data from the target domain, which
makes the encoder in the model adapt itself to extract useful high-level
features both for target domain images encoding and glaucoma classification,
simultaneously. Experimental results demonstrate that the proposed SATL
strategy is effective in the domain adaptation task between one private and two
public glaucoma diagnosis datasets, i.e. pri-RFG, REFUGE, and LAG. Moreover,
the proposed strategy is completely independent of the source domain data,
which meets the real scene application and the privacy protection policy.


Abstract:
  Let $G=\prod_{i=1}^{r} G_i$ be a product of simple real algebraic groups of
rank one and $\Gamma$ an Anosov subgroup of $G$ with respect to a minimal
parabolic subgroup. For each $v$ in the interior of a positive Weyl chamber,
let $\mathcal R_v\subset\Gamma\backslash G$ denote the Borel subset of all
points with recurrent $\exp (\mathbb R_+ v)$-orbits. For a maximal
horospherical subgroup $N$ of $G$, we show that the $N$-action on ${\mathcal
R}_v$ is uniquely ergodic if $r={rank}(G)\le 3$ and $v$ belongs to the interior
of the limit cone of $\Gamma$, and that there exists no $N$-invariant {Radon}
measure on $\mathcal R_v$ otherwise.


Abstract:
  Axion-like particles (ALPs) are predicted in some well-motivated theories
beyond the Standard Model. The TeV gamma-rays from active galactic nuclei (AGN)
suffer attenuation by the pair production interactions with the cosmic
background light (EBL/CMB) during its travel to the earth. The attenuation can
be circumvented through photon-ALP conversions in the AGN and Galaxy
magnetic-field, and a flux enhancement is expected to arise in the observed
spectrum. In this work, we study the potential of the AGN gamma-ray spectrum
for energy up to above 100\,TeV to probe ALP-parameter space at around $\mu$eV,
where the coupling $g_{a\gamma}$ is so far relatively weakly constrained.
  We find the nearby and bright sources, Mrk\,501, IC\,310 and M\,87, are
suitable for our objective. Assuming an intrinsic spectrum exponential cutoff
energy, we extrapolate the observed spectra of these sources up to above
100\,TeV by the models with/without ALPs. For $g_{a\gamma}\gtrsim
2\times$$10^{-11} \rm GeV^{-1}$ with $m_{a}\lesssim0.5\,\mu$eV, the flux at
around 100\,TeV predicted by the ALP model can be enhanced more than an order
of magnitude than that from the standard absorption, and could be detected by
LHAASO. Our result is subject to the uncertainty from the intrinsic cutoff
energy and the AGN lobe (or plume) magnetic-field. For an optimistic
estimation, the constraint can be improved to $g_{a\gamma}\gtrsim
2\times$$10^{-11} \rm GeV^{-1}$ with $m_{a}\lesssim1\,\mu$eV. This require
further observations on these sources by the forthcoming CTA, LHAASO, SWGO and
so on.


Abstract:
  If the strange quark were lighter, QCD phase transition could have been first
order. This implies that QCD may have quantum critical points as the Higgs vev
$v_h$ is varied from its Standard Model value. We show that inflationary
quantum evolution of $v_h$ with the relaxion can drive our universe toward
those critical points, realizing the weak scale close to the observed value
while explaining its closeness to $\Lambda_{\rm QCD}$. We first explore quantum
critical points of $N_f=3$ QCD, parameterized by $v_h$ at $T=0$, and present a
basic model for the weak scale. It results in a sharply localized probability
distribution of the weak scale, which is critical not to the crossover at zero
but to the quantum transition at ${\sim}\Lambda_{\rm QCD}$.


Abstract:
  Traffic control optimization is a challenging task for various traffic
centers around the world and the majority of existing approaches focus only on
developing adaptive methods under normal (recurrent) traffic conditions.
Optimizing the control plans when severe incidents occur still remains an open
problem, especially when a high number of lanes or entire intersections are
affected.
  This paper aims at tackling this problem and presents a novel methodology for
optimizing the traffic signal timings in signalized urban intersections, under
non-recurrent traffic incidents. With the purpose of producing fast and
reliable decisions, we combine the fast running Machine Learning (ML)
algorithms and the reliable Genetic Algorithms (GA) into a single optimization
framework. As a benchmark, we first start with deploying a typical GA algorithm
by considering the phase duration as the decision variable and the objective
function to minimize the total travel time in the network. We fine tune the GA
for crossover, mutation, fitness calculation and obtain the optimal parameters.
Secondly, we train various machine learning regression models to predict the
total travel time of the studied traffic network, and select the best
performing regressor which we further hyper-tune to find the optimal training
parameters. Lastly, we propose a new algorithm BGA-ML combining the GA
algorithm and the extreme-gradient decision-tree, which is the best performing
regressor, together in a single optimization framework. Comparison and results
show that the new BGA-ML is much faster than the original GA algorithm and can
be successfully applied under non-recurrent incident conditions.


Abstract:
  Machine learning classifiers rely on loss functions for performance
evaluation, often on a private (hidden) dataset. In a recent line of research,
label inference was introduced as the problem of reconstructing the ground
truth labels of this private dataset from just the (possibly perturbed)
cross-entropy loss function values evaluated at chosen prediction vectors
(without any other access to the hidden dataset). In this paper, we formally
study the necessary and sufficient conditions under which label inference is
possible from \emph{any} (noisy) loss function value. Using tools from
analytical number theory, we show that a broad class of commonly used loss
functions, including general Bregman divergence-based losses and multiclass
cross-entropy with common activation functions like sigmoid and softmax, it is
possible to design label inference attacks that succeed even for arbitrary
noise levels and using only a single query from the adversary. We formally
study the computational complexity of label inference and show that while in
general, designing adversarial prediction vectors for these attacks is
co-NP-hard, once we have these vectors, the attacks can also be carried out
through a lightweight augmentation to any neural network model, making them
look benign and hard to detect. The observations in this paper provide a deeper
understanding of the vulnerabilities inherent in modern machine learning and
could be used for designing future trustworthy ML.


Abstract:
  The stability of high-end piezo-acoustic drop-on-demand (DOD) inkjet printing
is sometimes compromised by the entrainment of an air bubble inside the ink
channel. Here, bubble pinch-off from an acoustically driven meniscus is studied
in an optically transparent DOD printhead as a function of the driving
waveform. We show that bubble pinch-off follows from low-amplitude higher-order
meniscus oscillations on top of the global high-amplitude meniscus motion that
drives droplet formation. In a certain window of control parameters, phase
inversion between the low and high frequency components leads to the enclosure
of an air cavity and bubble pinch-off. Although phenomenologically similar,
bubble pinch-off is not a result of capillary wave interaction such as observed
in drop impact on a liquid pool. Instead, we reveal geometrical flow focusing
as the mechanism through which at first, an outward jet is formed on the
retracted concave meniscus. When the subsequent high-frequency pressure wave
hits the now toroidal-shaped meniscus, it accelerates the toroidal ring outward
resulting in the formation of an air cavity that can pinch off. The critical
control parameters for pinch off are the pulse timing and amplitude. To cure
the bubble entrainment problem, the threshold for bubble pinch-off can be
increased by suppressing the high frequency acoustic waves through appropriate
waveform design. The present work therefore aids the improvement of the
stability of inkjet printers through a physical understanding of meniscus
instabilities.


Abstract:
  Dirac/Weyl semimetals hosting linearly-dispersing bands have received recent
attention for potential thermoelectric applications, since their
ultrahigh-mobility carriers could generate large thermoelectric and Nernst
power factors. To optimize these efficiencies, the Fermi energy needs to be
chemically controlled in a wide range, which is generally difficult in bulk
materials because of disorder effects from the substituted ions. Here it is
shown that the Fermi energy is tunable across the Dirac point for layered
magnet EuMnBi$_2$ by partially substituting Gd$^{3+}$ for Eu$^{2+}$ in the
insulating block layer, which dopes electrons into the Dirac fermion layer
without degrading the mobility. Clear quantum oscillation observed even in the
doped samples allows us to quantitatively estimate the Fermi energy shift and
optimize the power factor (exceeding 100 $\mu$W/K$^2$cm at low temperatures) in
combination with the first-principles calculation. Furthermore, it is shown
that Nernst signal steeply increases with decreasing carrier density beyond a
simple theoretical prediction, which likely originates from the field-induced
gap reduction of the Dirac band due to the exchange interaction with the Eu
moments. Thus, the magnetic block layer provides high controllability for the
Dirac fermions in EuMnBi$_2$, which would make this series of materials an
appealing platform for novel transport phenomena.


Abstract:
  Infarction- or ischaemia-induced cardiac fibrosis can be arrythmogenic. We
use mathematcal models for diffuse fibrosis ($\mathcal{DF}$), interstitial
fibrosis ($\mathcal{IF}$), patchy fibrosis ($\mathcal{PF}$), and compact
fibrosis ($\mathcal{CF}$) to study patterns of fibrotic cardiac tissue that
have been generated by new mathematical algorithms. We show that the fractal
dimension $\mathbb{D}$, the lacunarity $\mathcal{L}$, and the Betti numbers
$\beta_0$ and $\beta_1$ of such patterns are \textit{fibrotic-tissue markers}
that can be used to characterise the arrhythmogenicity of different types of
cardiac fibrosis. We hypothesize, and then demonstrate by extensive \textit{in
silico} studies of detailed mathematical models for cardiac tissue, that the
arrhytmogenicity of fibrotic tissue is high when $\beta_0$ is large and the
lacunarity parameter $b$ is small.


Abstract:
  Deep Neural Networks (DNNs) have been shown vulnerable to adversarial
(Test-Time Evasion (TTE)) attacks which, by making small changes to the input,
alter the DNN's decision. We propose an attack detector based on
class-conditional Generative Adversarial Networks (GANs). We model the
distribution of clean data conditioned on the predicted class label by an
Auxiliary Classifier GAN (ACGAN). Given a test sample and its predicted class,
three detection statistics are calculated using the ACGAN Generator and
Discriminator. Experiments on image classification datasets under different TTE
attack methods show that our method outperforms state-of-the-art detection
methods. We also investigate the effectiveness of anomaly detection using
different DNN layers (input features or internal-layer features) and
demonstrate that anomalies are harder to detect using features closer to the
DNN's output layer.


Abstract:
  Using the standard fluctuational electrodynamics framework, we analytically
calculate the radiative heat current between two thin metallic layers,
separated by a vacuum gap. We analyse different contributions to the heat
current (travelling or evanescent waves, transverse electric or magnetic
polarization) and reveal the crucial qualitative role played by the dc
conductivity of the metals as compared to the speed of light. For poorly
conducting metals, the heat current may be dominated by evanescent waves even
when the separation between the layers greatly exceeds the thermal photon
wavelength, and the coupling is of electrostatic nature. For well-conducting
metals, the evanescent contribution dominates at separations smaller than the
thermal wavelength and is mainly due to magnetostatic coupling, in agreement
with earlier works on bulk metals.


Abstract:
  In this paper we present an asymptotically compatible meshfree method for
solving nonlocal equations with random coefficients, describing diffusion in
heterogeneous media. In particular, the random diffusivity coefficient is
described by a finite-dimensional random variable or a truncated combination of
random variables with the Karhunen-Lo\`{e}ve decomposition, then a
probabilistic collocation method (PCM) with sparse grids is employed to sample
the stochastic process. On each sample, the deterministic nonlocal diffusion
problem is discretized with an optimization-based meshfree quadrature rule. We
present rigorous analysis for the proposed scheme and demonstrate convergence
for a number of benchmark problems, showing that it sustains the asymptotic
compatibility spatially and achieves an algebraic or sub-exponential
convergence rate in the random coefficients space as the number of collocation
points grows. Finally, to validate the applicability of this approach we
consider a randomly heterogeneous nonlocal problem with a given spatial
correlation structure, demonstrating that the proposed PCM approach achieves
substantial speed-up compared to conventional Monte Carlo simulations.


Abstract:
  Traffic congestion is a major challenge in modern urban settings. The
industry-wide development of autonomous and automated vehicles (AVs) motivates
the question of how can AVs contribute to congestion reduction. Past research
has shown that in small scale mixed traffic scenarios with both AVs and
human-driven vehicles, a small fraction of AVs executing a controlled
multiagent driving policy can mitigate congestion. In this paper, we scale up
existing approaches and develop new multiagent driving policies for AVs in
scenarios with greater complexity. We start by showing that a congestion metric
used by past research is manipulable in open road network scenarios where
vehicles dynamically join and leave the road. We then propose using a different
metric that is robust to manipulation and reflects open network traffic
efficiency. Next, we propose a modular transfer reinforcement learning
approach, and use it to scale up a multiagent driving policy to outperform
human-like traffic and existing approaches in a simulated realistic scenario,
which is an order of magnitude larger than past scenarios (hundreds instead of
tens of vehicles). Additionally, our modular transfer learning approach saves
up to 80% of the training time in our experiments, by focusing its data
collection on key locations in the network. Finally, we show for the first time
a distributed multiagent policy that improves congestion over human-driven
traffic. The distributed approach is more realistic and practical, as it relies
solely on existing sensing and actuation capabilities, and does not require
adding new communication infrastructure.


Abstract:
  In this paper, a new asymptotic preserving (AP) scheme is proposed for the
anisotropic elliptic equations. Different from previous AP schemes, the actual
one is based on first-order system least-squares for second-order partial
differential equations, and it is uniformly well-posed with respect to
anisotropic strength. The numerical computation is realized by a deep neural
network (DNN), where least-squares functionals are employed as loss functions
to determine parameters of DNN. Numerical results show that the current AP
scheme is easy for implementation and is robust to approximate solutions or to
identify anisotropic strength in various 2D and 3D tests.


Abstract:
  Battery energy storage systems (ESS) are widely used in microgrids to
complement high renewables. However, the real-time energy management of
microgrids with battery ESS is challenging in two aspects: 1) the evolution
process of battery energy level is across-time coupled; 2) uncertainties
unavoidably arise in the forecasting process for renewable generation. In this
paper, a tube-based model predictive control (MPC) approach is innovatively
proposed in accommodating the real-time energy management of microgrids with
battery ESS. Firstly, a real-time operation model of battery, including the
degradation cost and time-aware SoC range, is proposed for the battery ESS. In
particular, the battery feature shallower-cheaper is depicted and the terminal
SoC requirement is achieved. Secondly, two cascaded MPC controllers are
designed in the proposed tube-based MPC, in which reference trajectories are
generated by the nominal MPC without uncertainties, and then the ancillary MPC
steers the actual trajectories to the nominal ones upon the realization of
uncertainties. Specifically, in this paper, the battery SoC is viewed as the
state variable of the system, while the generator power output and exchange
power with the utility are seen as control variables. Lastly, numerous case
studies demonstrate the effectiveness of the proposed approach, including both
the low and high penetration level of renewables. Additional Monte Carlo
simulations of consecutive 365 days show that the competitive ratio of the
proposed approach is excellently below 1.10.


Abstract:
  The earth system is exceedingly complex and often chaotic in nature, making
prediction incredibly challenging: we cannot expect to make perfect predictions
all of the time. Instead, we look for specific states of the system that lead
to more predictable behavior than others, often termed "forecasts of
opportunity". When these opportunities are not present, scientists need
prediction systems that are capable of saying "I don't know." We introduce a
novel loss function, termed "abstention loss", that allows neural networks to
identify forecasts of opportunity for regression problems. The abstention loss
works by incorporating uncertainty in the network's prediction to identify the
more confident samples and abstain (say "I don't know") on the less confident
samples. The abstention loss is designed to determine the optimal abstention
fraction, or abstain on a user-defined fraction via a PID controller. Unlike
many methods for attaching uncertainty to neural network predictions
post-training, the abstention loss is applied during training to preferentially
learn from the more confident samples. The abstention loss is built upon a
standard computer science method. While the standard approach is itself a
simple yet powerful tool for incorporating uncertainty in regression problems,
we demonstrate that the abstention loss outperforms this more standard method
for the synthetic climate use cases explored here. The implementation of
proposed loss function is straightforward in most network architectures
designed for regression, as it only requires modification of the output layer
and loss function.


Abstract:
  Active learning has proven to be useful for minimizing labeling costs by
selecting the most informative samples. However, existing active learning
methods do not work well in realistic scenarios such as imbalance or rare
classes, out-of-distribution data in the unlabeled set, and redundancy. In this
work, we propose SIMILAR (Submodular Information Measures based actIve
LeARning), a unified active learning framework using recently proposed
submodular information measures (SIM) as acquisition functions. We argue that
SIMILAR not only works in standard active learning, but also easily extends to
the realistic settings considered above and acts as a one-stop solution for
active learning that is scalable to large real-world datasets. Empirically, we
show that SIMILAR significantly outperforms existing active learning algorithms
by as much as ~5% - 18% in the case of rare classes and ~5% - 10% in the case
of out-of-distribution data on several image classification tasks like
CIFAR-10, MNIST, and ImageNet. SIMILAR is available as a part of the DISTIL
toolkit: "https://github.com/decile-team/distil".


Abstract:
  Performance contracts used for servitized business models enable
consideration of overall life-cycle costs rather than just production costs.
However, practical implementation of performance contracts has been limited due
to challenges with performance evaluation, accountability, and financial
concepts. As a solution, this paper proposes the connection of the digital
building twin with blockchain-based smart contracts to execute
performance-based digital payments. First, we conceptualize a technical
architecture to connect blockchain to digital building twins. The digital
building twin stores and evaluates performance data in real-time while the
blockchain ensures transparency and trusted execution of automatic performance
evaluation and rewards through smart contracts. Next, we demonstrate the
feasibility of both the concept and technical architecture by integrating the
Ethereum blockchain with digital building models and sensors via the Siemens
building twin platform. The resulting prototype is the first full-stack
implementation of a performance-based smart contract in the built environment.


Abstract:
  A fundamental result of Springer says that a quadratic form over a field of
characteristic not 2 is isotropic if it is so after an odd degree extension. In
this paper we generalize Springer's theorem as follows. Let R be a an arbitrary
semilocal ring, let S be a finite R-algebra of constant odd degree, which is
{\'e}tale or generated by one element, and let q be a nonsingular R-quadratic
form whose base ring extension q S is isotropic. We show that then q is already
isotropic.


Abstract:
  Researchers and robotic development groups have recently started paying
special attention to autonomous mobile robot navigation in indoor environments
using vision sensors. The required data is provided for robot navigation and
object detection using a camera as a sensor. The aim of the project is to
construct a mobile robot that has integrated vision system capability used by a
webcam to locate, track and follow a moving object. To achieve this task,
multiple image processing algorithms are implemented and processed in
real-time. A mini-laptop was used for collecting the necessary data to be sent
to a PIC microcontroller that turns the processes of data obtained to provide
the robot's proper orientation. A vision system can be utilized in object
recognition for robot control applications. The results demonstrate that the
proposed mobile robot can be successfully operated through a webcam that
detects the object and distinguishes a tennis ball based on its color and
shape.


Abstract:
  Magnonics is now an attractive field which focuses on the dynamic
characteristics of magnons, a kind of quasiparticles in magnetic media, and
attempt to apply magnons for functional devices. In order to construct magnon
based devices, it is necessary to fabricate materials with specific and tunable
magnon bands and band gaps. Skyrmion based magnonic crystals is one of the most
suitable materials which possess periodical skyrmion structure and show
applicative magnon bands and band gaps. In this review, we provide an overview
over recent theoretical and experimental research on skyrmion based magnonic
crystals. We will firstly provide an introduction of magnonic crystals and
magnetic skyrmion. And then we will show the theoretical and experimental
progress on skyrmion based magnonic crystals and their magnon band
characteristics. At the end, we will give an outlook and perspectives of new
fascinating fields on topological nontrivial magnon modes, as well as hybrid
and quantum magnonic phenomena of skyrmion based magnonic crystals.


Abstract:
  As a result of the rapidly advancing digital transformation of teaching,
universities have started to face major competition from Massive Open Online
Courses (MOOCs). Universities thus have to set themselves apart from MOOCs in
order to justify the added value of three to five-year degree programs to
prospective students. In this paper, we show how we address this challenge at
Deggendorf Institute of Technology in ML and AI. We first share our best
practices and present two concrete courses including their unique selling
propositions: Computer Vision and Innovation Management for AI. We then
demonstrate how these courses contribute to Deggendorf Institute of
Technology's ability to differentiate itself from MOOCs (and other
universities).


Abstract:
  Many recent AI architectures are inspired by zero-sum games, however, the
behavior of their dynamics is still not well understood. Inspired by this, we
study standard gradient descent ascent (GDA) dynamics in a specific class of
non-convex non-concave zero-sum games, that we call hidden zero-sum games. In
this class, players control the inputs of smooth but possibly non-linear
functions whose outputs are being applied as inputs to a convex-concave game.
Unlike general zero-sum games, these games have a well-defined notion of
solution; outcomes that implement the von-Neumann equilibrium of the "hidden"
convex-concave game. We prove that if the hidden game is strictly
convex-concave then vanilla GDA converges not merely to local Nash, but
typically to the von-Neumann solution. If the game lacks strict convexity
properties, GDA may fail to converge to any equilibrium, however, by applying
standard regularization techniques we can prove convergence to a von-Neumann
solution of a slightly perturbed zero-sum game. Our convergence guarantees are
non-local, which as far as we know is a first-of-its-kind type of result in
non-convex non-concave games. Finally, we discuss connections of our framework
with generative adversarial networks.


Abstract:
  The HIBEAM/NNBAR program is a proposed two-stage experiment at the European
Spallation Source focusing on searches for baryon number violation via
processes in which neutrons convert to antineutrons. This paper outlines the
computing and detector simulation framework for the HIBEAM/NNBAR program. The
simulation is based on predictions of neutron flux and neutronics together with
signal and background generation. A range of diverse simulation packages are
incorporated, including Monte Carlo transport codes, neutron ray-tracing
simulation packages, and detector simulation software. The common simulation
package in which these elements are interfaced together is discussed. Data
management plans and triggers are also described.


Abstract:
  We propose a computationally feasible way of deriving the identified features
of models with multiple equilibria in pure or mixed strategies. It is shown
that in the case of Shapley regular normal form games, the identified set is
characterized by the inclusion of the true data distribution within the core of
a Choquet capacity, which is interpreted as the generalized likelihood of the
model. In turn, this inclusion is characterized by a finite set of inequalities
and efficient and easily implementable combinatorial methods are described to
check them. In all normal form games, the identified set is characterized in
terms of the value of a submodular or convex optimization program. Efficient
algorithms are then given and compared to check inclusion of a parameter in
this identified set. The latter are illustrated with family bargaining games
and oligopoly entry games.


Abstract:
  We study the dominating set reconfiguration problem with the token sliding
rule. It consists, given a graph G=(V,E) and two dominating sets D_s and D_t of
G, in determining if there exists a sequence S=<D_1:=D_s,...,D_l:=D_t> of
dominating sets of G such that for any two consecutive dominating sets D_r and
D_{r+1} with r<t, D_{r+1}=(D_r\ u) U v, where uv is an edge of G.
  In a recent paper, Bonamy et al studied this problem and raised the following
questions: what is the complexity of this problem on circular arc graphs? On
circle graphs? In this paper, we answer both questions by proving that the
problem is polynomial on circular-arc graphs and PSPACE-complete on circle
graphs.


Abstract:
  Type I X-ray bursts are thermonuclear explosions on the neutron star (NS)
surface caused by mass accretion from a companion star. Observations of X-ray
bursts provide valuable information on X-ray binary systems, e.g., binary
parameters, the chemical composition of accreted matter, and the nuclear
equation of state (EOS) of NSs. There have been several theoretical studies to
constrain the physics of X-ray bursters. However, they have mainly focused on
the burning layers above the solid crust of the NS, which brings up issues of
the treatment of NS gravitation and internal energy. In this study, focusing on
the microphysics inside NSs, we calculate a series of X-ray bursts using a
general-relativistic stellar-evolution code with several NS EOSs. We compare
the X-ray-burst models with the burst parameters of a clocked burster
associated with GS 1826-24. We find a monotonic correlation between the NS
radius and the light-curve profile. A larger radius shows a higher recurrence
time and a large peak luminosity. In contrast, the dependence of light curves
on the NS mass becomes more complicated, where the neutrino cooling suppresses
the efficiency of nuclear ignition. We also constrain the EOS and mass of GS
1826-24, i.e., stiffer EOSs, corresponding to larger NS radii, are not
preferred due to too-high peak luminosity. The EOS and the cooling and heating
of NSs are important to discuss the theoretical and observational properties of
X-ray bursts.


Abstract:
  Horava gravity breaks Lorentz symmetry by introducing a dynamical timelike
scalar field (the khronon), which can be used as a preferred time coordinate
(thus selecting a preferred space-time foliation). Adopting the khronon as the
time coordinate, the theory is invariant only under time reparametrizations and
spatial diffeomorphisms. In the infrared limit, this theory is sometimes
referred to as khronometric theory. Here, we explicitly construct a
generalization of khronometric theory, which avoids the propagation of
Ostrogradski modes as a result of a suitable degeneracy condition (although
stability of the latter under radiative corrections remains an open question).
While this new theory does not have a general-relativistic limit and does not
yield a Friedmann-Robertson-Walker-like cosmology on large scales, it still
passes, for suitable choices of its coupling constants, local tests on Earth
and in the solar system, as well as gravitational-wave tests. We also comment
on the possible usefulness of this theory as a toy model of quantum gravity, as
it could be completed in the ultraviolet into a 'degenerate Horava gravity'
theory that could be perturbatively renormalizable without imposing any
projectability condition.


Abstract:
  With the recent development of deep convolutional neural networks and
large-scale datasets, deep face recognition has made remarkable progress and
been widely used in various applications. However, unlike the existing public
face datasets, in many real-world scenarios of face recognition, the depth of
training dataset is shallow, which means only two face images are available for
each ID. With the non-uniform increase of samples, such issue is converted to a
more general case, a.k.a long-tail face learning, which suffers from data
imbalance and intra-class diversity dearth simultaneously. These adverse
conditions damage the training and result in the decline of model performance.
Based on the Semi-Siamese Training (SST), we introduce an advanced solution,
named Multi-Agent Semi-Siamese Training (MASST), to address these problems.
MASST includes a probe network and multiple gallery agents, the former aims to
encode the probe features, and the latter constitutes a stack of networks that
encode the prototypes (gallery features). For each training iteration, the
gallery network, which is sequentially rotated from the stack, and the probe
network form a pair of semi-siamese networks. We give theoretical and empirical
analysis that, given the long-tail (or shallow) data and training loss, MASST
smooths the loss landscape and satisfies the Lipschitz continuity with the help
of multiple agents and the updating gallery queue. The proposed method is out
of extra-dependency, thus can be easily integrated with the existing loss
functions and network architectures. It is worth noting that, although multiple
gallery agents are employed for training, only the probe network is needed for
inference, without increasing the inference cost. Extensive experiments and
comparisons demonstrate the advantages of MASST for long-tail and shallow face
learning.


Abstract:
  We investigated the radio spectra of two magnetars, PSR J1622$-$4950 and 1E
1547.0$-$5408, using observations from the Australia Telescope Compact Array
and the Atacama Large Millimeter/submillimeter Array taken in 2017. Our
observations of PSR J1622$-$4950 show a steep spectrum with a spectral index of
$-$1.3 $\pm$ 0.2 in the range of 5.5-45 GHz during its re-activating X-ray
outburst in 2017. By comparing the data taken at different epochs, we found
significant enhancement in the radio flux density. The spectrum of 1E
1547.0$-$5408 was inverted in the range of 43-95 GHz, suggesting a spectral
peak at a few hundred gigahertz. Moreover, we obtained the X-ray and radio data
of radio magnetars, PSR J1622$-$4950 and SGR J1745$-$2900, from literature and
found two interesting properties. First, radio emission is known to be
associated with X-ray outburst but has different evolution. We further found
that the rising time of the radio emission is much longer than that of the
X-ray during the outburst. Second, the radio magnetars may have double peak
spectra at a few GHz and a few hundred GHz. This could indicate that the
emission mechanism is different in the cm and the sub-mm bands. These two
phenomenons could provide a hint to understand the origin of radio emission and
its connection with the X-ray properties.


Abstract:
  The origins of the Galilean satellites - namely Io, Europa, Ganymede, and
Callisto - is not fully understood yet. Here we use N-body numerical
simulations to study the formation of Galilean satellites in a gaseous
circumplanetary disk around Jupiter. Our model includes the effects of pebble
accretion, gas-driven migration, and gas tidal damping and drag.
Satellitesimals in our simulations first grow via pebble accretion and start to
migrate inwards. When they reach the trap at the disk inner edge, scattering
events and collisions take place promoting additional growth. Growing
satellites eventually reach a multi-resonant configuration anchored at the disk
inner edge. Our best match to the masses of the Galilean satellites is produced
in simulations where the integrated pebble flux is 1e-3 MJ. These simulations
typically produce between 3 and 5 satellites. In our best analogues, adjacent
satellite pairs are all locked in 2:1 mean motion resonances. However, they
have also moderately eccentric orbits (0.1), unlike the current real
satellites. We propose that the Galilean satellites system is a primordial
resonant chain, similar to exoplanet systems as TRAPPIST-1, Kepler-223, and
TOI-178. Callisto was probably in resonance with Ganymede in the past but left
this configuration - without breaking the Laplacian resonance - via divergent
migration due to tidal planet-satellite interactions. These same effects
further damped the orbital eccentricities of these satellites down to their
current values (0.001). Our results support the hypothesis that Io and Europa
were born with water-ice rich compositions and lost all/most of their water
afterwards. Firmer constraints on the primordial compositions of the Galilean
satellites are crucial to distinguish formation models.


Abstract:
  An integrated clinical environment (ICE) enables the connection and
coordination of the internet of medical things around the care of patients in
hospitals. However, ransomware attacks and their spread on hospital
infrastructures, including ICE, are rising. Often the adversaries are targeting
multiple hospitals with the same ransomware attacks. These attacks are detected
by using machine learning algorithms. But the challenge is devising the
anti-ransomware learning mechanisms and services under the following
conditions: (1) provide immunity to other hospitals if one of them got the
attack, (2) hospitals are usually distributed over geographical locations, and
(3) direct data sharing is avoided due to privacy concerns. In this regard,
this paper presents a federated distributed integrated clinical environment,
aka. FedDICE. FedDICE integrates federated learning (FL), which is
privacy-preserving learning, to SDN-oriented security architecture to enable
collaborative learning, detection, and mitigation of ransomware attacks. We
demonstrate the importance of FedDICE in a collaborative environment with up to
four hospitals and four popular ransomware families, namely WannaCry, Petya,
BadRabbit, and PowerGhost. Our results find that in both IID and non-IID data
setups, FedDICE achieves the centralized baseline performance that needs direct
data sharing for detection. However, as a trade-off to data privacy, FedDICE
observes overhead in the anti-ransomware model training, e.g., 28x for the
logistic regression model. Besides, FedDICE utilizes SDN's dynamic network
programmability feature to remove the infected devices in ICE.


Abstract:
  Principal loading analysis is a dimension reduction method that discards
variables which have only a small distorting effect on the covariance matrix.
Potentially, principal loading analysis and ordinary least squares regression
coincide by construction. We contribute conditions under which both methods
intersect. Further, we provide bounds for the cut-off value in principal
loading analysis for the case of intersection. This gives a choice for such a
threshold based on the perturbation matrices.


Abstract:
  The non-trivial zeros of the Riemann zeta function are central objects in
number theory. In particular, they enable one to reproduce the prime numbers.
They have also attracted the attention of physicists working in Random Matrix
Theory and Quantum Chaos for decades. Here we present an experimental
observation of the lowest non-trivial Riemann zeros by using a trapped ion
qubit in a Paul trap, periodically driven with microwave fields. The waveform
of the driving is engineered such that the dynamics of the ion is frozen when
the driving parameters coincide with a zero of the real component of the zeta
function. Scanning over the driving amplitude thus enables the locations of the
Riemann zeros to be measured experimentally to a high degree of accuracy,
providing a physical embodiment of these fascinating mathematical objects in
the quantum realm.


Abstract:
  Globular clusters can form inside their host galaxies at high redshift when
gas densities were higher and gas-rich mergers were common. They can also form
inside lower-mass galaxies that have since been accreted and tidally disrupted,
leaving their globular cluster complement bound to higher-mass halos. We argue
that the age-metallicity-specific orbital energy relation in a galaxy's
globular cluster system can be used to identify its origin. Gas-rich mergers
should produce tightly bound systems in which metal-rich clusters are younger
than metal-poor clusters. Globular clusters formed in massive disks and then
scattered into a halo should have no relationship between age and specific
orbital energy. Accreted globular clusters should produce weakly bound systems
in which age and metallicity are correlated with each other but inversely
correlated with specific orbital energy. We use precise relative ages,
self-consistent metallicities, and space-based proper motion-informed orbits to
show that the Milky Way's metal-poor globular cluster system lies in a plane in
age-metallicity-specific orbital energy space. We find that relatively young or
metal-poor globular clusters are weakly bound to the Milky Way, while
relatively old or metal-rich globular clusters are tightly bound to the Galaxy.
While metal-rich globular clusters may be formed either in situ or ex situ, our
results suggest that metal-poor clusters formed outside of the Milky Way in
now-disrupted dwarf galaxies. We predict that this relationship between age,
metallicity, and specific orbital energy in a $L^{*}$ galaxy's globular cluster
system is a natural outcome of galaxy formation in a $\Lambda$CDM universe.


Abstract:
  Instrument-tissue interaction forces in Minimally Invasive Surgery (MIS)
provide valuable information that can be used to provide haptic perception,
monitor tissue trauma, develop training guidelines, and evaluate the skill
level of novice and expert surgeons.Force and tactile sensing is lost in many
Robot-Assisted Surgery (RAS) systems. Therefore, many researchers have focused
on recovering this information through sensing systems and estimation
algorithms.
  This article provides a comprehensive systematic review of the current force
sensing research aimed at RAS and, more generally, keyhole endoscopy, in which
instruments enter the body through small incisions. Articles published between
January 2011 and May 2020 are considered, following the Preferred Reporting
Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The
literature search resulted in 110 papers on different force estimation
algorithms and sensing technologies, sensor design specifications, and
fabrication techniques.


Abstract:
  A novel framework for statistical learning is introduced which combines ideas
from regularization and ensembling. This framework is applied to learn an
ensemble of logistic regression models for high-dimensional binary
classification. In the new framework the models in the ensemble are learned
simultaneously by optimizing a multi-convex objective function. To enforce
diversity between the models the objective function penalizes overlap between
the models in the ensemble. Measures of diversity in classifier ensembles are
used to show how our method learns the ensemble by exploiting the
accuracy-diversity trade-off for ensemble models. In contrast to other
ensembling approaches, the resulting ensemble model is fully interpretable as a
logistic regression model, asymptotically consistent, and at the same time
yields excellent prediction accuracy as demonstrated in an extensive simulation
study and gene expression data applications. The models found by the proposed
ensemble methodology can also reveal alternative mechanisms that can explain
the relationship between the predictors and the response variable. An
open-source compiled software library implementing the proposed method is
briefly discussed.


Abstract:
  This paper studies a WiFi indoor localisation technique based on using a deep
learning model and its transfer strategies. We take CSI packets collected via
the WiFi standard channel sounding as the training dataset and verify the CNN
model on the subsets collected in three experimental environments. We achieve a
localisation accuracy of 46.55 cm in an ideal $(6.5m \times 2.5m)$ office with
no obstacles, 58.30 cm in an office with obstacles, and 102.8 cm in a sports
hall $(40 \times 35m)$. Then, we evaluate the transfer ability of the proposed
model to different environments. The experimental results show that, for a
trained localisation model, feature extraction layers can be directly
transferred to other models and only the fully connected layers need to be
retrained to achieve the same baseline accuracy with non-transferred base
models. This can save 60% of the training parameters and reduce the training
time by more than half. Finally, an ablation study of the training dataset
shows that, in both office and sport hall scenarios, after reusing the feature
extraction layers of the base model, only 55% of the training data is required
to obtain the models' accuracy similar to the base models.


Abstract:
  Detecting anomalies using deep learning has become a major challenge over the
last years, and is becoming increasingly promising in several fields. The
introduction of self-supervised learning has greatly helped many methods
including anomaly detection where simple geometric transformation recognition
tasks are used. However these methods do not perform well on fine-grained
problems since they lack finer features. By combining in a multi-task framework
high-scale shape features oriented task with low-scale fine features oriented
task, our method greatly improves fine-grained anomaly detection. It
outperforms state-of-the-art with up to 31% relative error reduction measured
with AUROC on various anomaly detection problems.


Abstract:
  A crucial part of the design of an FCC-ee detector is the minimisation of the
disruption of the beam due to the presence of a large and powerful detector
magnet. Indeed, the emittance blow-up of the few meters around the interaction
point (IP) at lower energies is comparable to the emittance introduced by the
rest of the 100 km ring. Vertical emittance is the single most important factor
in achieving high performance (luminosity, in this case) in a modern e^+ e^-
storage ring such as the FCC-ee. The design adopted is the simplest possible
arrangement that can nevertheless deliver high performance: two additional
coils per IP side. The performance achieved is such that vertical emittance
blow-up will not be a limiting performance factor even in the case of a ring
with four experiments, and even in the most demanding energy regime, that of
the Z running (about 45 GeV beam energy).


Abstract:
  In their Letter[Phys. Rev. Lett. 126, 106803 (2021)], the authors found an
interesting reentrant localization phenomenon in a one-dimensional dimerized
lattice with quasiperiodic disorder, i.e., the system undergoes a second
localization transition at a higher disorder strength. They claimed that this
reentrant feature would appear in the case of staggered disorder, but not for
the uniform ones. In this Comment, we show that such reentrant feature can also
appear for the uniform disorder when larger system parameters are taken into
account.


Abstract:
  Our objective is to compute a textural loss that can be used to train texture
generators with multiple material channels typically used for physically based
rendering such as albedo, normal, roughness, metalness, ambient occlusion, etc.
Neural textural losses often build on top of the feature spaces of pretrained
convolutional neural networks. Unfortunately, these pretrained models are only
available for 3-channel RGB data and hence limit neural textural losses to this
format. To overcome this limitation, we show that passing random triplets to a
3-channel loss provides a multi-channel loss that can be used to generate
high-quality material textures.


Abstract:
  We present a characterization of finite permutation groups which contain a
transitive dihedral subgroup.


Abstract:
  The number of relativistic species, $N_{\rm eff}$, has been precisely
calculated in the standard model, and would be measured to the percent level by
CMB-S4 in future. Neutral-current non-standard interactions would affect
neutrino decoupling in the early Universe, thus modifying $N_{\rm eff}$. We
parameterize those operators up to dimension-7 in the effective field theory
framework, and then provide a complete, generic and analytical dictionary for
the collision term integrals. From precision measurements of $N_{\rm eff}$, the
most stringent constraint is obtained for the dimension-6 vector-type
neutrino-electron operator, whose scale is constrained to be above about 195
(331) GeV from Planck (CMB-S4). We find our results complementary to other
experiments like neutrino coherent scattering, neutrino oscillation, collider,
and neutrino deep inelastic scattering experiments.


Abstract:
  We consider the problem of extending and avoiding partial edge colorings of
hypercubes; that is, given a partial edge coloring $\varphi$ of the
$d$-dimensional hypercube $Q_d$, we are interested in whether there is a proper
$d$-edge coloring of $Q_d$ that agrees with the coloring $\varphi$ on every
edge that is colored under $\varphi$; or, similarly, if there is a proper
$d$-edge coloring that disagrees with $\varphi$ on every edge that is colored
under $\varphi$. In particular, we prove that for any $d\geq 1$, if $\varphi$
is a partial $d$-edge coloring of $Q_d$, then $\varphi$ is avoidable if every
color appears on at most $d/8$ edges and the coloring satisfies a relatively
mild structural condition, or $\varphi$ is proper and every color appears on at
most $d-2$ edges. We also show that the same conclusion holds if $d$ is
divisible by $3$ and every color class of $\varphi$ is an induced matching.
Moreover, for all $1 \leq k \leq d$, we characterize for which configurations
consisting of a partial coloring $\varphi$ of $d-k$ edges and a partial
coloring $\psi$ of $k$ edges, there is an extension of $\varphi$ that avoids
$\psi$.


Abstract:
  Let $H_{s,k}$ be the graph defined by intersecting $s$ triangles and $k$
cycles of odd lengths at least five in exactly one common vertex. Recently,
Hou, Qiu and Liu [Discrete Math. 341 (2018) 126--137], and Yuan [J. Graph
Theory 89 (2018), no. 1, 26--39] determined independently the maximum number of
edges in an $n$-vertex graph that does not contain $H_{s,k}$ as a subgraph. In
this paper, we determine the graphs of order $n$ that attain the maximum
spectral radius among all graphs containing no $H_{s,k}$ for $n$ large enough.


Abstract:
  The interaction between equilibrium tides and convection in stellar envelopes
is often considered important for tidal evolution in close binary and
extrasolar planetary systems. Its efficiency for fast tides has however long
been controversial, when the tidal frequency exceeds the turnover frequency of
convective eddies. Recent numerical simulations indicate that convection can
act like an effective viscosity which decays quadratically with tidal frequency
for fast tides, resulting in inefficient dissipation in many applications
involving pre- and main-sequence stars and giant planets. A new idea was
however recently proposed by Terquem (2021), who suggested Reynolds stresses
involving correlations between tidal flow components dominate the interaction
instead of correlations between convective flow components as usually assumed.
They further showed that this can potentially significantly enhance tidal
dissipation for fast tides in many applications. Motivated by the importance of
this problem for tidal dissipation in stars and planets, we directly compute
this new term using analytical arguments and global spherical simulations using
Boussinesq and anelastic hydrodynamic models. We demonstrate that the new term
proposed by Terquem vanishes identically for equilibrium tides interacting with
convection in both Boussinesq and anelastic models; it is therefore unlikely to
contribute to tidal dissipation in stars and planets.


Abstract:
  We present results on the infection risk from SARS-CoV-2 under different
scenarios based on measured particle size-dependent mask penetration, measured
total inward leakage, measured human aerosol emission for sizes from 10nm to
1mm, and re-hydration on inhalation. Well-mixed room models significantly
underestimate the risk of infection for short and direct exposure. To this end,
we estimate the upper bound for infection risk with the susceptible in the
infectious exhalation cloud or wearing masks by having the masked susceptible
inhale the entire exhalation of a masked infectious. Social distances without a
mask, even at 3m between speaking individuals results in an upper bound of 90\%
for risk of infection after a few minutes. If both wear a surgical mask, the
risk of infection for the person speaking remains below 26\% even after 60
minutes. When both the infectious and susceptible wear a well-fitting FFP2
mask, the upper bound for risk is reduced by a factor of 60 compared to
surgical masks. In both cases, face leakage is very important. For FFP2 masks,
leakage is low in the nasal region and directed upward, which can be further
reduced significantly by applying double-sided medical tape there. Considering
that the calculated upper bound greatly overestimates the risk of infection,
and the fact that with a poorly worn mask even the upper bound we calculated is
very low, we conclude that wearing a mask, even with some leakage, provides
excellent third party and self-protection.


Abstract:
  Sixth generation (6G) mobile networks may include new passive technologies,
such as ambient backscatter communication or the use of reconfigurable
intelligent surfaces, to avoid the emission of waves and the corresponding
energy consumption. On the one hand, a reconfigurable intelligent surface
improves the network performance by adding electronically controlled reflected
paths in the radio propagation channel. On the other hand, in an ambient
backscatter system, a device, named tag, communicates towards a reader by
backscattering the waves of an ambient source (such as a TV tower). However,
the tag's backscattered signal is weak and strongly interfered by the direct
signal from the ambient source. In this paper, we propose a new reconfigurable
intelligent surface assisted ambient backscatter system. The proposed surface
allows to control the reflection of an incident wave coming from the exact
source location towards the tag and reader locations (creating hot spots at
their locations), thanks to passive reflected beams from a predefined codebook.
A common phase-shift can also be applied to the beam. Thanks to these features,
we demonstrate experimentally that the performance of ambient backscatter
communications can be significantly improved.


Abstract:
  BRITE-Constellation is devoted to high-precision optical photometric
monitoring of bright stars, distributed all over the Milky Way, in red and/or
blue passbands. Photometry from space avoids the turbulent and absorbing
terrestrial atmosphere and allows for very long and continuous observing runs
with high time resolution and thus provides the data necessary for
understanding various processes inside stars (e.g., asteroseismology) and in
their immediate environment. While the first astronomical observations from
space focused on the spectral regions not accessible from the ground it soon
became obvious around 1970 that avoiding the turbulent terrestrial atmosphere
significantly improved the accuracy of photometry and satellites explicitly
dedicated to high-quality photometry were launched. A perfect example is
BRITE-Constellation, which is the result of a very successful cooperation
between Austria, Canada and Poland. Research highlights for targets distributed
nearly over the entire HRD are presented, but focus primarily on massive and
hot stars.


Abstract:
  The lensless endoscope (LE) is a promising device to acquire in vivo images
at a cellular scale. The tiny size of the probe enables a deep exploration of
the tissues. Lensless endoscopy with a multicore fiber (MCF) commonly uses a
spatial light modulator (SLM) to coherently combine, at the output of the MCF,
few hundreds of beamlets into a focus spot. This spot is subsequently scanned
across the sample to generate a fluorescent image. We propose here a novel
scanning scheme, partial speckle scanning (PSS), inspired by compressive
sensing theory, that avoids the use of an SLM to perform fluorescent imaging in
LE with reduced acquisition time. Such a strategy avoids photo-bleaching while
keeping high reconstruction quality. We develop our approach on two key
properties of the LE: (i) the ability to easily generate speckles, and (ii) the
memory effect in MCF that allows to use fast scan mirrors to shift light
patterns. First, we show that speckles are sub-exponential random fields.
Despite their granular structure, an appropriate choice of the reconstruction
parameters makes them good candidates to build efficient sensing matrices.
Then, we numerically validate our approach and apply it on experimental data.
The proposed sensing technique outperforms conventional raster scanning: higher
reconstruction quality is achieved with far fewer observations. For a fixed
reconstruction quality, our speckle scanning approach is faster than
compressive sensing schemes which require to change the speckle pattern for
each observation.


Abstract:
  For a set of positive integers $A$, let $p_A(n)$ denote the number of ways to
write $n$ as a sum of integers from $A$, and let $p(n)$ denote the usual
partition function. In the early 40s, Erd\H{o}s extended the classical
Hardy--Ramanujan formula for $p(n)$ by showing that $A$ has density $\alpha$ if
and only if $\log p_A(n) \sim \log p(\alpha n)$. Nathanson asked if Erd\H{o}s's
theorem holds also with respect to $A$'s lower density, namely, whether $A$ has
lower-density $\alpha$ if and only if $\log p_A(n) / \log p(\alpha n)$ has
lower limit $1$. We answer this question negatively by constructing, for every
$\alpha > 0$, a set of integers $A$ of lower density $\alpha$, satisfying $$
\liminf_{n \rightarrow \infty} \frac{\log p_A(n)}{\log p(\alpha n)} \geq
\left(\frac{\sqrt{6}}{\pi}-o_{\alpha}(1)\right)\log(1/\alpha)\;. $$ We further
show that the above bound is best possible (up to the $o_\alpha(1)$ term), thus
determining the exact extremal relation between the lower density of a set of
integers and the lower limit of its partition function. We also prove an
analogous theorem with respect to the upper density of a set of integers,
answering another question of Nathanson.


Abstract:
  We calculate the relevant Spencer cohomology of the minimal Poincar\'e
superalgebra in 5 spacetime dimensions and use it to define Killing spinors via
a connection on the spinor bundle of a 5-dimensional lorentzian spin manifold.
We give a definition of bosonic backgrounds in terms of this data. By imposing
constraints on the curvature of the spinor connection, we recover the field
equations of minimal (ungauged) 5-dimensional supergravity, but also find a set
of field equations for an $\mathfrak{sp}(1)$-valued one-form which we interpret
as the bosonic data of a class of rigid supersymmetric theories on curved
backgrounds. We define the Killing superalgebra of bosonic backgrounds and show
that their existence is implied by the field equations. The maximally
supersymmetric backgrounds are characterised and their Killing superalgebras
are explicitly described as filtered deformations of the Poincar\'e
superalgebra.


Abstract:
  In this paper, we consider model order reduction for bilinear systems with
non-zero initial conditions. We discuss choices of Gramians for both the
homogeneous and the inhomogeneous parts of the system individually and prove
how these Gramians characterize the respective dominant subspaces of each of
the two subsystems. Proposing different, not necessarily structure preserving,
reduced order methods for each subsystem, we establish several strategies to
reduce the dimension of the full system. For all these approaches, error bounds
are shown depending on the truncated Hankel singular values of the subsystems.
Besides the error analysis, stability is discussed. In particular, a focus is
on a new criterion for the homogeneous subsystem guaranteeing the existence of
the associated Gramians and an asymptotically stable realization of the system.


Abstract:
  We prove sharp pointwise heat kernel estimates for symmetric Markov processes
that are generated by symmetric Dirichlet forms that are local with respect to
some coordinates and nonlocal with respect to the remaining coordinates. The
main theorem is a robustness result like the famous estimate for the
fundamental solution of second order differential operators, obtained by Donald
G. Aronson. Analogous to his result, we show that the corresponding
translation-invariant process and the one given by the general Dirichlet form
share the same pointwise points.


Abstract:
  The suffix array, describing the lexicographic order of suffixes of a given
text, is the central data structure in string algorithms. The suffix array of a
length-$n$ text uses $\Theta(n \log n)$ bits, which is prohibitive in many
applications. To address this, Grossi and Vitter [STOC 2000] and,
independently, Ferragina and Manzini [FOCS 2000] introduced space-efficient
versions of the suffix array, known as the compressed suffix array (CSA) and
the FM-index. For a length-$n$ text over an alphabet of size $\sigma$, these
data structures use only $O(n \log \sigma)$ bits. Immediately after their
discovery, they almost completely replaced plain suffix arrays in practical
applications, and a race started to develop efficient construction procedures.
Yet, after more than 20 years, even for $\sigma=2$, the fastest algorithm
remains stuck at $O(n)$ time [Hon et al., FOCS 2003], which is slower by a
$\Theta(\log n)$ factor than the lower bound of $\Omega(n / \log n)$ (following
simply from the necessity to read the entire input). We break this
long-standing barrier with a new data structure that takes $O(n \log \sigma)$
bits, answers suffix array queries in $O(\log^{\epsilon} n)$ time, and can be
constructed in $O(n\log \sigma / \sqrt{\log n})$ time using $O(n\log \sigma)$
bits of space. Our result is based on several new insights into the recently
developed notion of string synchronizing sets [STOC 2019]. In particular,
compared to their previous applications, we eliminate orthogonal range queries,
replacing them with new queries that we dub prefix rank and prefix selection
queries. As a further demonstration of our techniques, we present a new
pattern-matching index that simultaneously minimizes the construction time and
the query time among all known compact indexes (i.e., those using $O(n \log
\sigma)$ bits).


Abstract:
  Neural machine translation (NMT) architectures have achieved promising
results for automatic program repair. Yet, they have the limitation of
generating low-quality patches(e.g., not compilable patches). This is because
the existing works only optimize a purely syntactic loss function based on
characters and tokens without incorporating program-specific information during
neural net weight optimization. In this paper, we proposea novel program repair
model called RewardRepair. The core novelty of RewardRepair is to improve
NMT-based program repair with a loss function based on program compilation and
test execution information, rewarding the network to produce patches that
compile and that do not overfit. We conduct several experiments to evaluate
RewardRepair showing that it is feasible and effective to use compilation and
test execution results to optimize the underlying neural repair model.


Abstract:
  Flare spot is one type of flare artifact caused by a number of conditions,
frequently provoked by one or more high-luminance sources within or close to
the camera field of view. When light rays coming from a high-luminance source
reach the front element of a camera, it can produce intra-reflections within
camera elements that emerge at the film plane forming non-image information or
flare on the captured image. Even though preventive mechanisms are used,
artifacts can appear. In this paper, we propose a robust computational method
to automatically detect and remove flare spot artifacts. Our contribution is
threefold: firstly, we propose a characterization which is based on intrinsic
properties that a flare spot is likely to satisfy; secondly, we define a new
confidence measure able to select flare spots among the candidates; and,
finally, a method to accurately determine the flare region is given. Then, the
detected artifacts are removed by using exemplar-based inpainting. We show that
our algorithm achieve top-tier quantitative and qualitative performance.


Abstract:
  We study several classes of processes associated with the tempered positive
Linnik (TPL) distribution, in both the purely absolutely-continuous and mixed
law regimes. We explore four main ramifications. Firstly, we analyze several
subordinated representations of TPL L\'evy processes; in particular we
establish a stochastic self-similarity property of positive Linnik (PL) L\'evy
processes, connecting TPL processes with negative binomial subordination.
Secondly, in finite activity regimes we show that the explicit compound Poisson
representations gives rise to innovations following novel Mittag-Leffler type
laws. Thirdly, we characterize two inhomogeneous TPL processes, namely the
Ornstein-Uhlenbeck (OU) L\'evy-driven processes with stationary distribution
and the additive process generated by a TPL law. Finally, we propose a
multivariate TPL L\'evy process based on a negative binomial mixing methodology
of independent interest. Some potential applications of the considered
processes are also outlined in the contexts of statistical anti-fraud and
financial modelling.


Abstract:
  The von Neumann graph entropy is a measure of graph complexity based on the
Laplacian spectrum. It has recently found applications in various learning
tasks driven by networked data. However, it is computational demanding and hard
to interpret using simple structural patterns. Due to the close relation
between Lapalcian spectrum and degree sequence, we conjecture that the
structural information, defined as the Shannon entropy of the normalized degree
sequence, might be a good approximation of the von Neumann graph entropy that
is both scalable and interpretable.
  In this work, we thereby study the difference between the structural
information and von Neumann graph entropy named as {\em entropy gap}. Based on
the knowledge that the degree sequence is majorized by the Laplacian spectrum,
we for the first time prove the entropy gap is between $0$ and $\log_2 e$ in
any undirected unweighted graphs. Consequently we certify that the structural
information is a good approximation of the von Neumann graph entropy that
achieves provable accuracy, scalability, and interpretability simultaneously.
This approximation is further applied to two entropy-related tasks: network
design and graph similarity measure, where novel graph similarity measure and
fast algorithms are proposed. Our experimental results on graphs of various
scales and types show that the very small entropy gap readily applies to a wide
range of graphs and weighted graphs. As an approximation of the von Neumann
graph entropy, the structural information is the only one that achieves both
high efficiency and high accuracy among the prominent methods. It is at least
two orders of magnitude faster than SLaQ with comparable accuracy. Our
structural information based methods also exhibit superior performance in two
entropy-related tasks.


Abstract:
  Transistors are key elements for enabling computational hardware in both
classical and quantum domains. Here, we propose a voltage-gated spin transistor
using itinerant electrons in the Hubbard model which acts at the level of
single electron spins. Going beyond classical spintronics, it enables the
controlling of the flow of quantum information between distant spin qubits. The
transistor has two modes of operation, open and closed, which are realized by
two different charge configurations in the gate of the transistor. In the
closed mode, the spin information between source and drain is blocked while in
the open mode we have free spin information exchange. The switching between the
modes takes place within a fraction of the operation time which allows for
several subsequent operations within the coherence time of the transistor. The
system shows good resilience against several imperfections and opens up a
practical application for quantum dot arrays.


Abstract:
  We consider a generalized riffle shuffle on the colored permutation group
$G_{p, n}$ and derive a determinantal formula for the probability of finding
descents at given positions, proof of which is based on the bijection between
the set of shuffles in question and that of non-intersecting lattice paths.


Abstract:
  We present a thread-modular abstract interpretation(TMAI) technique to verify
programs under the release-acquire (RA) memory model for safety property
violations. The main contributions of our work are: we capture the execution
order of program statements as an abstract domain, and propose a sound upper
approximation over this domain to efficiently reason over RA concurrency. The
proposed domain is general in its application and captures the ordering
relations as a first-class feature in the abstract interpretation theory. In
particular, the domain represents a set of sequences of modifications of a
global variable in concurrent programs as a partially ordered set. Under this
approximation, older sequenced-before stores of a global variable are forgotten
and only the latest stores per variable are preserved. We establish the
soundness of our proposed abstractions and implement them in a prototype
abstract interpreter called PRIORI. The evaluations of PRIORI on existing and
challenging RA benchmarks demonstrate that the proposed technique is not only
competitive in refutation, but also in verification. PRIORI shows significantly
fast analysis runtimes with higher precision compared to recent
state-of-the-art tools for RA concurrency.


Abstract:
  We construct a family of heptagon relations -- algebraic imitations of
five-dimensional Pachner move 3--4, parameterized by simplicial 3-cocycles.


Abstract:
  A quantum (e.g. noncommutative) version of the Sierpinski gasket is proposed,
on purely topological grounds. This is a $C^*$-algebra with a suitable form of
self-similarity. Properties of this $C^*$-algebra are studied, in particular
irreducible representations and extremal traces are fully described. A
self-similar Dirichlet form together with a spectral triple are also
constructed, extending results already known for the classical gasket.


Abstract:
  Biophotonic techniques are growing in rapid rhythms enabling the monitoring
of subcellular structures and non-invasive theranostic interventions in cancer
and autoimmune diseases. The integration of Biophotonics with nanotechnology
and biosensors brings a revolution in the micro- and nano-world with new
optical tools. Among them, optical tweezers revive as a potential tool for
tracking cells behavior and probing interactions forces between cells,
cells-biomolecules and cells-nanoparticles. In this review we aim to exhibit
the state-of the art advances of the Biophotonics in the diagnostic and
therapeutic field, and the role of optical tweezers.


Abstract:
  Database fingerprinting have been widely adopted to prevent unauthorized
sharing of data and identify the source of data leakages. Although existing
schemes are robust against common attacks, like random bit flipping and subset
attack, their robustness degrades significantly if attackers utilize the
inherent correlations among database entries. In this paper, we first
demonstrate the vulnerability of existing database fingerprinting schemes by
identifying different correlation attacks: column-wise correlation attack,
row-wise correlation attack, and the integration of them. To provide robust
fingerprinting against the identified correlation attacks, we then develop
mitigation techniques, which can work as post-processing steps for any
off-the-shelf database fingerprinting schemes. The proposed mitigation
techniques also preserve the utility of the fingerprinted database considering
different utility metrics. We empirically investigate the impact of the
identified correlation attacks and the performance of mitigation techniques
using real-world relational databases. Our results show (i) high success rates
of the identified correlation attacks against existing fingerprinting schemes
(e.g., the integrated correlation attack can distort 64.8\% fingerprint bits by
just modifying 14.2\% entries in a fingerprinted database), and (ii) high
robustness of the proposed mitigation techniques (e.g., with the mitigation
techniques, the integrated correlation attack can only distort $3\%$
fingerprint bits).


Abstract:
  This contribution describes the concept, main structure and goals, and some
highlighted outcomes, of the AstroCamp -- an international academic excellence
program in the field of astronomy and physics created in 2012 and organized by
Centro de Astrof\'{\i}sica da Universidade do Porto (CAUP) together with the
Paredes de Coura municipality and several national and international partners.


Abstract:
  Word alignment over parallel corpora has a wide variety of applications,
including learning translation lexicons, cross-lingual transfer of language
processing tools, and automatic evaluation or analysis of translation outputs.
The great majority of past work on word alignment has worked by performing
unsupervised learning on parallel texts. Recently, however, other work has
demonstrated that pre-trained contextualized word embeddings derived from
multilingually trained language models (LMs) prove an attractive alternative,
achieving competitive results on the word alignment task even in the absence of
explicit training on parallel data. In this paper, we examine methods to marry
the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel
text with objectives designed to improve alignment quality, and proposing
methods to effectively extract alignments from these fine-tuned models. We
perform experiments on five language pairs and demonstrate that our model can
consistently outperform previous state-of-the-art models of all varieties. In
addition, we demonstrate that we are able to train multilingual word aligners
that can obtain robust performance on different language pairs. Our aligner,
AWESOME (Aligning Word Embedding Spaces of Multilingual Encoders), with
pre-trained models is available at https://github.com/neulab/awesome-align


Abstract:
  In recent years, there has been a massive increase in the amount of Internet
of Things (IoT) devices as well as the data generated by such devices. The
participating devices in IoT networks can be problematic due to their
resource-constrained nature, and integrating security on these devices is often
overlooked. This has resulted in attackers having an increased incentive to
target IoT devices. As the number of attacks possible on a network increases,
it becomes more difficult for traditional intrusion detection systems (IDS) to
cope with these attacks efficiently. In this paper, we highlight several
machine learning (ML) methods such as k-nearest neighbour (KNN), support vector
machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF),
artificial neural network (ANN), and logistic regression (LR) that can be used
in IDS. In this work, ML algorithms are compared for both binary and
multi-class classification on Bot-IoT dataset. Based on several parameters such
as accuracy, precision, recall, F1 score, and log loss, we experimentally
compared the aforementioned ML algorithms. In the case of HTTP distributed
denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other
simulation results-based precision, recall, F1 score, and log loss metric
reveal that RF outperforms on all types of attacks in binary classification.
However, in multi-class classification, KNN outperforms other ML algorithms
with an accuracy of 99%, which is 4% higher than RF.


Abstract:
  We study Hamilton circuits of the Cayley graphs of the Weyl groupoids of the
generalized quantum groups, or the quantum double of the Nichols algebras of
diagonal-type, with finite root systems. We prove the existence of a Hamilton
circuit for any of them and explicitly draw one of them for rank 3 and 4 cases.


Abstract:
  In [ZY2], the second author proved Perelman's assertion, namely, for an
ancient Ricci flow with bounded and nonnegative curvature operator, bounded
entropy is equivalent to noncollapsing on all scales. In this paper, we
continue this discussion. It turns out that the curvature operator
nonnegativity is not a necessary condition, and we need only to assume a
consequence of Hamilton's trace Harnack. Furthermore, we show that this
condition holds for steady Ricci solitons with nonnegative Ricci curvature.


Abstract:
  We report the direct imaging discovery of a low-mass companion to the nearby
accelerating A star, HIP 109427, with the Subaru Coronagraphic Extreme Adaptive
Optics (SCExAO) instrument coupled with the MKID Exoplanet Camera (MEC) and
CHARIS integral field spectrograph. CHARIS data reduced with reference star PSF
subtraction yield 1.1-2.4 $\mu$m spectra. MEC reveals the companion in $Y$ and
$J$ band at a comparable signal-to-noise ratio using stochastic speckle
discrimination, with no PSF subtraction techniques. Combined with complementary
follow-up $L_{\rm p}$ photometry from Keck/NIRC2, the SCExAO data favors a
spectral type, effective temperature, and luminosity of M4-M5.5, 3000-3200 $K$,
and $\log_{10}(L/L_{\rm \odot}) = -2.28^{+0.04}_{-0.04}$, respectively.
Relative astrometry of HIP 109427 B from SCExAO/CHARIS and Keck/NIRC2, and
complementary Gaia-Hipparcos absolute astrometry of the primary favor a
semimajor axis of $6.55^{+3.0}_{-0.48}$ au, an eccentricity of
$0.54^{+0.28}_{-0.15}$, an inclination of $66.7^{+8.5}_{-14}$ degrees, and a
dynamical mass of $0.280^{+0.18}_{-0.059}$ $M_{\odot}$. This work shows the
potential for extreme AO systems to utilize speckle statistics in addition to
widely-used post-processing methods to directly image faint companions to
nearby stars near the telescope diffraction limit.


Abstract:
  We study the asymptotic dynamics of the lake equations in the following two
cases, an island shrinking to a point and an emerging island. For both cases,
we derive an asymptotic lake-type equation. In the former case, the asymptotic
dynamics includes an additional Dirac mass in the vorticity. The main
mathematical difficulty is that the equations are singular when the water depth
vanishes. We provide new uniform estimates in weighted spaces for the related
stream functions which will imply the compactness result.


Abstract:
  In this article we explore the requirements for enabling high quality
optically detected magnetic resonance (ODMR) spectroscopy in a conventional
gradient force optical tweezers using nanodiamonds containing nitrogen-vacancy
(NV$^{-}$) centres. We find that modulation of the infrared (1064 nm) trapping
laser during spectroscopic measurements substantially improves the ODMR
contrast compared with continuous wave trapping. The work is significant as it
allows trapping and quantum sensing protocols to be performed in conditions
where signal contrast is substantially reduced. We demonstrate the utility of
the technique by resolving NV$^{-}$ spin projections within the ODMR spectrum.
Manipulating the orientation of the nanodiamond via the trapping laser
polarisation, we observe changes in spectral features. Theoretical modelling
then allows us to infer the crystallographic orientation of the NV$^{-}$. This
is an essential capability for future magnetic sensing applications of
optically trapped nanodiamonds.


Abstract:
  Very recently, a variety of vision transformer architectures for dense
prediction tasks have been proposed and they show that the design of spatial
attention is critical to their success in these tasks. In this work, we revisit
the design of the spatial attention and demonstrate that a carefully-devised
yet simple spatial attention mechanism performs favourably against the
state-of-the-art schemes. As a result, we propose two vision transformer
architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures
are highly-efficient and easy to implement, only involving matrix
multiplications that are highly optimized in modern deep learning frameworks.
More importantly, the proposed architectures achieve excellent performance on a
wide range of visual tasks, including image level classification as well as
dense detection and segmentation. The simplicity and strong performance suggest
that our proposed architectures may serve as stronger backbones for many vision
tasks. Our code is released at https://github.com/Meituan-AutoML/Twins .


Abstract:
  Traditional computer vision models are trained to predict a fixed set of
predefined categories. Recently, natural language has been shown to be a
broader and richer source of supervision that provides finer descriptions to
visual concepts than supervised "gold" labels. Previous works, such as CLIP,
use a simple pretraining task of predicting the pairings between images and
text captions. CLIP, however, is data hungry and requires more than 400M image
text pairs for training. We propose a data-efficient contrastive distillation
method that uses soft labels to learn from noisy image-text pairs. Our model
transfers knowledge from pretrained image and sentence encoders and achieves
strong performance with only 3M image text pairs, 133x smaller than CLIP. Our
method exceeds the previous SoTA of general zero-shot learning on ImageNet
21k+1k by 73% relatively with a ResNet50 image encoder and DeCLUTR text
encoder. We also beat CLIP by 10.5% relatively on zero-shot evaluation on
Google Open Images (19,958 classes).


Abstract:
  In this paper an approximation of the image of the closed ball of the space
$L_p$ $(p>1)$ centered at the origin with radius $r$ under Hilbert-Schmidt
integral operator $F(\cdot):L_p\rightarrow L_q$ $\displaystyle
\left(\frac{1}{p}+\frac{1}{q}=1\right)$ is presented. An error estimation for
given approximation is obtained.


Abstract:
  We study the $\delta$ Scuti -- red giant binary KIC9773821, the first
double-pulsator binary of its kind. It was observed by \textit{Kepler} during
its four-year mission. Our aims are to ascertain whether the system is bound,
rather than a chance alignment, and to identify the evolutionary state of the
red giant via asteroseismology. An extension of these aims is to determine a
dynamical mass and an age prior for a $\delta$ Sct star, which may permit mode
identification via further asteroseismic modelling. We determine spectroscopic
parameters and radial velocities (RVs) for the red giant component using
HERMES@Mercator spectroscopy. Light arrival-time delays from the $\delta$ Sct
pulsations are used with the red-giant RVs to determine that the system is
bound and to infer its orbital parameters, including the binary mass ratio. We
use asteroseismology to model the individual frequencies of the red giant to
give a mass of $2.10^{+0.20}_{-0.10}$ M$_{\odot}$ and an age of
$1.08^{+0.06}_{-0.24}$ Gyr. We find that it is a helium-burning secondary clump
star, confirm that it follows the standard $\nu_{\rm max}$ scaling relation,
and confirm its observed period spacings match their theoretical counterparts
in the modelling code MESA. Our results also constrain the mass and age of the
$\delta$ Sct star. We leverage these constraints to construct $\delta$ Sct
models in a reduced parameter space and identify four of its five pulsation
modes.


Abstract:
  We propose a novel algorithm, based on physics-informed neural networks
(PINNs) to efficiently approximate solutions of nonlinear dispersive PDEs such
as the KdV-Kawahara, Camassa-Holm andBenjamin-Ono equations. The stability of
solutions of these dispersive PDEs is leveraged to prove rigorous bounds on the
resulting error. We present several numerical experiments to demonstrate
thatPINNs can approximate solutions of these dispersive PDEs very accurately


Abstract:
  Probabilistic cellular automata (CA) provides a classic framework for
studying non-equilibrium statistical physics on a lattices. A notable example
is the Domany-Kinzel CA, which has been used to investigate the process of
directed percolation and the critical dynamics of the non-equilibrium phase
transition betweeen absorbing and percolating phases. In this work, we
construct a non-unitary Quantum Cellular Automaton that generalises the
Domany-Kinzel cellular automaton and study the resulting dynamical evolution
using the numerical simulations using the tensor network iTEBD algorithm. We
demonstrate the system undergoes the absorbing/percolating phase transition and
the addition of the Hamiltonian generates coherences, which are a distinct
feature of the quantum dynamics. A proposal for the implementation of the model
with Rydberg array is put forward, which does not require local addressing of
individual sites.


Abstract:
  The aim of this work is to create a long (410 years) series of average annual
total sunspot areas AR - physically-based index of sunspot activity. We used
telescopic observations of the AR index in 1832-1868 and 1875-2020, as well as
the relationship between AR and long series of sunspot indices SN (ISN version
2.0) and sunspot groups GN (Svalgaard and Schatten (2016) GSN version). The
Royal Greenwich observatory series after 1976 is extended by the Kislovodsk
Mountain Astronomical Station data. When reconstructing AR from SN, it is taken
into account that the function AR = f (SN) has a nonlinear systematic character
and uncertainty associated with the heterogeneity of these indices. Therefore,
in addition to modeling the most probable AR values, predictive limits of
reconstruction uncertainty are determined. In the interval 1610-1699 the
reconstruction we carried out on the basis of the GN series using the
previously proposed decomposition in pseudo-phase space method (DPS). The
resulting series NO21y is freely available online. We show that for this series
the empirical Gnevyshev-Ohl rule and Waldmeier effect are fulfilled. Wavelet
analysis reveals periodicities of 8.4-13.8 years for the main cycle (with a
sharp decrease of the period before the global Maunder and Dalton minima) and a
two-component Gleissberg cycle with typical periods of 50-60 years and 90-110
years.


Abstract:
  Off-lattice SIR models are constructed on continuum percolation clusters
defined on the Ginibre point process (GPP) and on the Poisson point process
(PPP). The static percolation transitions in the continuum percolation models
as well as the infection-spreading transitions in the SIR models, which are
regarded as the dynamic percolation transitions, are enhanced in the GPP-based
model compared with the PPP-based model. This enhancement is caused by
hyperuniformity of the GPP. On the other hand, in the extinction phase of
infection on the phase diagram, a wide parameter region is determined in which
formation of infection clusters is suppressed in the GPP-based model compared
with the PPP-based model. We think that the PPP approximates a disordered
configuration of individuals and the GPP does a configuration of citizens
adopting a strategy to keep social distancing in a city in order to avoid
contagion. The suppression of infection clusters observed in the GPP-based
model will prove that such a strategy is effective when the infectivity is
relatively small.


Abstract:
  In this work we demonstrate how to compute the one- and two-body reduced
density matrices within the spin-adapted full configuration interaction quantum
Monte Carlo (FCIQMC) method, which is based on the graphical unitary group
approach (GUGA). This allows us to use GUGA-FCIQMC as a spin-pure configuration
interaction (CI) eigensolver within the complete active space self-consistent
field (CASSCF) procedure, and hence to stochastically treat active spaces far
larger than conventional CI solvers whilst variationally relaxing orbitals for
specific spin-pure states. We apply the method to investigate the spin-ladder
in iron-sulfur dimer and tetramer model systems. We demonstrate the importance
of the orbital relaxation by comparing the Heisenberg model magnetic coupling
parameters from the CASSCF procedure to those from a CI-only procedure based on
restricted open-shell Hartree-Fock orbitals. We show that orbital relaxation
differentially stabilizes the lower spin states, thus enlarging the coupling
parameters with respect to the values predicted by ignoring orbital relaxation
effects. Moreover, we find that while CI eigenvalues are well fit by a simple
bilinear Heisenberg Hamiltonian, the CASSCF eigenvalues exhibits deviations
that necessitate the inclusion of biquadratic terms in the model Hamiltonian.


Abstract:
  Neural volume rendering became increasingly popular recently due to its
success in synthesizing novel views of a scene from a sparse set of input
images. So far, the geometry learned by neural volume rendering techniques was
modeled using a generic density function. Furthermore, the geometry itself was
extracted using an arbitrary level set of the density function leading to a
noisy, often low fidelity reconstruction. The goal of this paper is to improve
geometry representation and reconstruction in neural volume rendering. We
achieve that by modeling the volume density as a function of the geometry. This
is in contrast to previous work modeling the geometry as a function of the
volume density. In more detail, we define the volume density function as
Laplace's cumulative distribution function (CDF) applied to a signed distance
function (SDF) representation. This simple density representation has three
benefits: (i) it provides a useful inductive bias to the geometry learned in
the neural volume rendering process; (ii) it facilitates a bound on the opacity
approximation error, leading to an accurate sampling of the viewing ray.
Accurate sampling is important to provide a precise coupling of geometry and
radiance; and (iii) it allows efficient unsupervised disentanglement of shape
and appearance in volume rendering. Applying this new density representation to
challenging scene multiview datasets produced high quality geometry
reconstructions, outperforming relevant baselines. Furthermore, switching shape
and appearance between scenes is possible due to the disentanglement of the
two.


Abstract:
  Recently, V. Alexandrov proposed an intriguing sufficient condition for
rigidity, which we will call "transverse rigidity". We show that transverse
rigidity is actually equivalent to the known sufficient condition for rigidity
called "prestress stability". Indeed this leads to a novel interpretation of
the prestress condition.


Abstract:
  Climate change perceptions are fundamental for adaptation and environmental
policy support. Although Africa is one of the most vulnerable regions to
climate change, little research has focused on how climate change is perceived
in the continent. Using random forest methodology, we analyse Afrobarometer
data (N = 45,732), joint with climatic data, to explore what shapes climate
change perceptions in Africa. We include 5 different dimensions of climate
change perceptions: awareness, belief in its human cause, risk perception, need
to stop it and self-efficacy. Results indicate that perceived agriculture
conditions are crucial for perceiving climate change. Country-level factors and
long-term changes in local weather conditions are among the most important
predictors. Moreover, education level, access to information, poverty,
authoritarian values, and trust in institutions shape individual climate change
perceptions. Demographic effects -- including religion -- seem negligible.
These findings suggest policymakers and environmental communicators how to
frame climate change in Africa to raise awareness, gather public support and
induce adaptation.


Abstract:
  Kernel-based feature selection is an important tool in nonparametric
statistics. Despite many practical applications of kernel-based feature
selection, there is little statistical theory available to support the method.
A core challenge is the objective function of the optimization problems used to
define kernel-based feature selection are nonconvex. The literature has only
studied the statistical properties of the \emph{global optima}, which is a
mismatch, given that the gradient-based algorithms available for nonconvex
optimization are only able to guarantee convergence to local minima. Studying
the full landscape associated with kernel-based methods, we show that feature
selection objectives using the Laplace kernel (and other $\ell_1$ kernels) come
with statistical guarantees that other kernels, including the ubiquitous
Gaussian kernel (or other $\ell_2$ kernels) do not possess. Based on a sharp
characterization of the gradient of the objective function, we show that
$\ell_1$ kernels eliminate unfavorable stationary points that appear when using
an $\ell_2$ kernel. Armed with this insight, we establish statistical
guarantees for $\ell_1$ kernel-based feature selection which do not require
reaching the global minima. In particular, we establish model-selection
consistency of $\ell_1$-kernel-based feature selection in recovering main
effects and hierarchical interactions in the nonparametric setting with $n \sim
\log p$ samples.


Abstract:
  Let $G=(V(G),E(G))$ be a simple graph. A set $S \subseteq V(G)$ is a strong
edge geodetic set if there exists an assignment of exactly one shortest path
between each pair of vertices from $S$, such that these shortest paths cover
all the edges $E(G)$. The cardinality of a smallest strong edge geodetic set is
the strong edge geodetic number $\text{sge}(G)$ of $G$. In this paper, the
strong edge geodetic problem is studied on the Cartesian product of two paths.
The exact value of the strong edge geodetic number is computed for $P_n \,
\square \, P_2$, $P_n \, \square \, P_3$ and $P_n \, \square \, P_4$. Some
general upper bounds for $\text{sge}(P_n \, \square \, P_m)$ are also proved.


Abstract:
  We consider a two-way half-duplex decode-and-forward (DF) relaying system
with multiple pairs of single-antenna users assisted by a cell-free (CF)
massive multiple-input multiple-output (mMIMO) architecture with
multiple-antenna access points (APs). Under the practical constraint of
imperfect channel state information (CSI), we derive the achievable sum
spectral efficiency (SE) for a finite number of APs with maximum ratio (MR)
linear processing for both reception and transmission in closed-form. Notably,
the proposed CF mMIMO relaying architecture, exploiting the spatial diversity,
and providing better coverage, outperforms the conventional collocated mMIMO
deployment. Moreover, we shed light on the power-scaling laws maintaining a
specific SE as the number of APs grows. A thorough examination of the interplay
between the transmit powers per pilot symbol and user/APs takes place, and
useful conclusions are extracted. Finally, differently to the common approach
for power control in CF mMIMO systems, we design a power allocation scheme
maximizing the sum SE.


Abstract:
  In this article, we have theoretically studied the time averaged adiabatic
potential (TAAP) scheme for realizing different atom trapping geometries. It is
shown that by varying time orbiting potential (TOP) fields and radio frequency
(rf) fields parameters, controlled manipulation of trapping potentials, and
conversion from one trapping geometry to another, is possible. The proposed
trapping geometries can be useful for studying various atom-optic phenomena
such as Bose-Einstein condensation (BEC) in low dimensions, super-fluidity,
tunnelling, atom interferometry, etc.


Abstract:
  We propose an iterative algorithm for low-rank matrix completion that can be
interpreted as an iteratively reweighted least squares (IRLS) algorithm, a
saddle-escaping smoothing Newton method or a variable metric proximal gradient
method applied to a non-convex rank surrogate. It combines the favorable
data-efficiency of previous IRLS approaches with an improved scalability by
several orders of magnitude. We establish the first local convergence guarantee
from a minimal number of samples for that class of algorithms, showing that the
method attains a local quadratic convergence rate. Furthermore, we show that
the linear systems to be solved are well-conditioned even for very
ill-conditioned ground truth matrices. We provide extensive experiments,
indicating that unlike many state-of-the-art approaches, our method is able to
complete very ill-conditioned matrices with a condition number of up to
$10^{10}$ from few samples, while being competitive in its scalability.


Abstract:
  Simulating electron-ion dynamics using time-dependent density functional
theory within an Ehrenfest dynamics scheme can be done in two ways that are in
principle exact and identical: propagating time-dependent electronic Kohn-Sham
equations or propagating electronic coefficients on surfaces obtained from
linear-response. We show here that using an approximate functional leads to
qualitatively different dynamics in the two approaches. We argue that the
latter is more accurate because the functionals are evaluated on domains close
to the ground-state where current approximations perform better. We demonstrate
this on an exactly-solvable model of charge-transfer, and discuss implications
for time-resolved spectroscopy.


Abstract:
  We perform for the first time a 3D hydrodynamics simulation of the evolution
of the last minutes pre-collapse of the oxygen shell of a fast-rotating massive
star. This star has an initial mass of 38 M$_\odot$, a metallicity of
$\sim$1/50 Z$_\odot$, an initial rotational velocity of 600 km s$^{-1}$, and
experiences chemically homogeneous evolution. It has a silicon- and oxygen-rich
(Si/O) convective layer at (4.7-17)$\times 10^{8}$ cm, where oxygen-shell
burning takes place. The power spectrum analysis of the turbulent velocity
indicates the dominance of the large-scale mode ($\ell \sim 3$), which has also
been seen in non-rotating stars that have a wide Si/O layer. Spiral arm
structures of density and silicon-enriched material produced by oxygen-shell
burning appear in the equatorial plane of the Si/O shell. Non-axisymmetric,
large-scale ($m \le 3$) modes are dominant in these structures. The spiral arm
structures have not been identified in previous non-rotating 3D pre-supernova
models. Governed by such a convection pattern, the angle-averaged specific
angular momentum becomes constant in the Si/O convective layer, which is not
considered in spherically symmetrical stellar evolution models. Such spiral
arms and constant specific angular momentum might affect the ensuing explosion
or implosion of the star.