Abstract:
  Video grounding aims to localize a moment from an untrimmed video for a given
textual query. Existing approaches focus more on the alignment of visual and
language stimuli with various likelihood-based matching or regression
strategies, i.e., P(Y|X). Consequently, these models may suffer from spurious
correlations between the language and video features due to the selection bias
of the dataset. 1) To uncover the causality behind the model and data, we first
propose a novel paradigm from the perspective of the causal inference, i.e.,
interventional video grounding (IVG) that leverages backdoor adjustment to
deconfound the selection bias based on structured causal model (SCM) and
do-calculus P(Y|do(X)). Then, we present a simple yet effective method to
approximate the unobserved confounder as it cannot be directly sampled from the
dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL)
to better align the text and video by maximizing the mutual information (MI)
between query and video clips, and the MI between start/end frames of a target
moment and the others within a video to learn more informative visual
representations. Experiments on three standard benchmarks show the
effectiveness of our approaches. Our code is available on GitHub:
https://github.com/nanguoshun/IVG.


Abstract:
  Based on improving the classical Bohr inequality, we get in this paper some
refined versions for a quasi-subordination family of functions, one of which is
key to build our results. By means of these investigations, for a family of
harmonic mappings defined in the unit disk $\D$, we establish an improved Bohr
inequality with refined Bohr radius under particular conditions. Along the line
of extremal problems concerning the refined Bohr radius, we derive a series of
results. % in a logical way. Here the family of harmonic mappings have the form
$f=h+\overline{g}$, where $g(0)=0$, the analytic part $h$ is bounded by 1 and
that $|g'(z)|\leq k|h'(z)|$ in $\D$ and for some $k\in[0,1]$.


Abstract:
  Recent renewed interest in multi-agent reinforcement learning (MARL) has
generated an impressive array of techniques that leverage deep reinforcement
learning, primarily actor-critic architectures, and can be applied to a limited
range of settings in terms of observability and communication. However, a
continuing limitation of much of this work is the curse of dimensionality when
it comes to representations based on joint actions, which grow exponentially
with the number of agents. In this paper, we squarely focus on this challenge
of scalability. We apply the key insight of action anonymity, which leads to
permutation invariance of joint actions, to two recently presented deep MARL
algorithms, MADDPG and IA2C, and compare these instantiations to another recent
technique that leverages action anonymity, viz., mean-field MARL. We show that
our instantiations can learn the optimal behavior in a broader class of agent
networks than the mean-field method, using a recently introduced pragmatic
domain.


Abstract:
  Let $T$ be a compact torus. We prove that, up to equivariant rational
equivalence, the category of $T$-simply connected, $T$-finite type $T$-spaces
with finitely many isotropy types is completely described by certain finite
systems of commutative differential graded algebras with consistent choices of
degree $2$ cohomology classes. We show that the algebraic systems corresponding
to finite $T$-CW-complexes are exactly those which satisfy the necessary
condition imposed by the Borel localization theorem along with certain
finiteness conditions. We derive an algebraic characterization of when an
algebra over a polyonmial ring is realized as the rational equivariant
cohomology of a finite $T$-CW-complex. As further applications we prove that
any GKM graph cohomology is realized by a finite $T$-CW-complex and classify
equivariant cohomology algebras of finite $S^1$-CW-complexes with discrete
fixed points.


Abstract:
  In Rayleigh-B\'enard convection, a fluid volume is heated from below and
cooled at the top. Due to buoyancy forces, hot fluid rises and cold fluid
sinks, leading to an overall upward heat flux that has a power-law dependence
on the imposed temperature difference. Our experiment shows that, adding
sidewall heating modifies this power-law relation, thus brings control to this
classical fluidic system. Through careful flow, temperature, and heat flux
measurements, we investigate the effects of such side-heating to
Rayleigh-B\'enard convection and rationalize our observations with simple
relations that describe how side-heating controls the overall heat transfer.
Such control-response mechanism is conceptually analogous to that of electrical
transistors, and our "thermal transistor" may serve as a potential building
block for more complex thermal-fluidic circuits.


Abstract:
  ALICE will significantly increase its Pb--Pb data taking rate from the 1\,kHz
of triggered readout in Run 2 to 50 kHz of continuous readout for LHC Run 3.
Updated tracking detectors are installed for Run 3 and a new two-phase
computing strategy is employed. In the first synchronous phase during the data
taking, the raw data is compressed for storage to an on-site disk buffer and
the required data for the detector calibration is collected. In the second
asynchronous phase the compressed raw data is reprocessed using the final
calibration to produce the final reconstruction output. Traditional CPUs are
unable to cope with the huge data rate and processing demands of the
synchronous phase, therefore ALICE employs GPUs to speed up the processing.
Since the online computing farm performs a part of the asynchronous processing
when there is no beam in the LHC, ALICE plans to use the GPUs also for this
second phase. This paper gives an overview of the GPU processing in the
synchronous phase, the full system test to validate the reference GPU
architecture, and the prospects for the GPU usage in the asynchronous phase.


Abstract:
  Quantum fluctuations are expected to lead to highly entangled spin-liquid
states in some two-dimensional spin-1/2 compounds. We have synthesized and
measured thermodynamic properties and muon relaxation rates in Cu-based
two-dimensional triangular-lattice spin liquids, one of which is the least
disordered of this kind synthesized hitherto. Its measured properties can all
be simply characterized by scale-invariant time-dependent fluctuations with a
single parameter. The specific heat divided by temperature and muon relaxation
rates are both temperature-independent at low temperatures, followed by a
logarithmic decrease with increasing temperature. Even more remarkably,
$\sim$57\% of the magnetic entropy is missing down to temperatures of
\textit{O}(10$^{-3}$) the exchange energy, independent of magnetic field up to
$g\mu_BH > k_BT$\@. These properties are intrinsic. They are evidence that
quantum fluctuations lead either to a gigantic specific heat peak from
topological singlet excitations below such temperatures, or to an extensively
degenerate topological singlet ground state. This is an ultra-quantum state of
matter.


Abstract:
  Dickson conjectured that a set of polynomials will take on infinitely many
simultaneous prime values. Later others, such as Hardy and Littlewood, gave
estimates for the number of these primes. In this article we look at this
conjecture, develop a simple heuristic and rederive these classic estimates. We
then apply them to several special forms of primes and compare the estimates
with the actual numbers.


Abstract:
  We define a probabilistic programming language for Gaussian random variables
with a first-class exact conditioning construct. We give operational,
denotational and equational semantics for this language, establishing
convenient properties like exchangeability of conditions. Conditioning on
equality of continuous random variables is nontrivial, as the exact observation
may have probability zero; this is Borel's paradox. Using categorical
formulations of conditional probability, we show that the good properties of
our language are not particular to Gaussians, but can be derived from universal
properties, thus generalizing to wider settings. We define the Cond
construction, which internalizes conditioning as a morphism, providing general
compositional semantics for probabilistic programming with exact conditioning.


Abstract:
  In this work, we present a novel methodology for texture image recognition
using a partial differential equation modeling. More specifically, we employ
the pseudo-parabolic Buckley-Leverett equation to provide a dynamics to the
digital image representation and collect local descriptors from those images
evolving in time. For the local descriptors we employ the magnitude and signal
binary patterns and a simple histogram of these features was capable of
achieving promising results in a classification task. We compare the accuracy
over well established benchmark texture databases and the results demonstrate
competitiveness, even with the most modern deep learning approaches. The
achieved results open space for future investigation on this type of modeling
for image analysis, especially when there is no large amount of data for
training deep learning models and therefore model-based approaches arise as
suitable alternatives.


Abstract:
  A new analysis of the modification of the hadronization process in the
nuclear medium for pions is presented. The effective description is condensed
in a set of medium modified fragmentation functions (nFFs) obtained at
next-to-leading order (NLO) accuracy. The study was made using the open-source
tool xFitter, conveniently modified to incorporate semi-inclusive processes.
Theoretical uncertainties for the nFFs are also provided.


Abstract:
  According to the classical view of globular clusters, stars inside globular
clusters are evolved from the same giant molecular cloud. Then their stars'
chemical compositions must be the same. But recent photometric and
spectroscopic studies of globular clusters reveal the presence of more-than-one
stellar populations inside globular clusters. This finding challenges our
classical view of globular clusters.
  In this work, we investigated the possibility of solving multiple stellar
populations problem in globular clusters using dark matter assumptions. We
showed that the presence of dark matter inside globular clusters changes the
physical parameters (e.g. chemical composition, luminosity, temperature, age,
etc.) of stars inside them. We supposed that dark matter distributed
non-uniformly inside globular clusters. It means stars in high dark matter
density environments (like the central region of globular clusters) are more
affected by the presence of dark matter. Using this assumption, we showed that
stars in different locations of globular clusters (corresponding to different
dark matter densities) follow different evolutionary paths (e.g. on
Hertzsprung-Russell diagram). We used this note to infer that the presence of
dark matter inside globular clusters can be the reason for the multiple stellar
populations.


Abstract:
  In order to process efficiently ever-higher dimensional data such as images,
sentences, or audio recordings, one needs to find a proper way to reduce the
dimensionality of such data. In this regard, SVD-based methods including PCA
and Isomap have been extensively used. Recently, a neural network alternative
called autoencoder has been proposed and is often preferred for its higher
flexibility. This work aims to show that PCA is still a relevant technique for
dimensionality reduction in the context of classification. To this purpose, we
evaluated the performance of PCA compared to Isomap, a deep autoencoder, and a
variational autoencoder. Experiments were conducted on three commonly used
image datasets: MNIST, Fashion-MNIST, and CIFAR-10. The four different
dimensionality reduction techniques were separately employed on each dataset to
project data into a low-dimensional space. Then a k-NN classifier was trained
on each projection with a cross-validated random search over the number of
neighbours. Interestingly, our experiments revealed that k-NN achieved
comparable accuracy on PCA and both autoencoders' projections provided a big
enough dimension. However, PCA computation time was two orders of magnitude
faster than its neural network counterparts.


Abstract:
  Supernovae are the dominant source of chemical enrichment of galaxies, and
they are an important source of energy to heat the interstellar medium and
accelerate cosmic rays. Our knowledge of supernovae in the Milky Way is based
mostly on the study of Galactic supernova remnants (SNRs), providing an
(incomplete) record to supernova activity over the last ~100,000 yr. Here we
report on an investigation of the spatial distribution of Galactic SNRs. Given
the limited number of SNRs it is common to assume a functional form for the
Galactocentric distribution of SNRs. However, several functional forms have
been used in the past, without much justification for the radial distribution.
For example, one often used functional form implies that no supernova activity
is present in the Galactic Centre region. However, the presence of a magnetar
and a SNR near the Galactic Centre suggest that a spatial distribution with
zero SNRs at the Galactic Centre is not realistic. In light of these concerns
we reevaluate the Galactic SNR distribution. We provide a brief outline of the
main detection biases in finding SNRs and we investigate whether or not the use
of the most common functional form is justified and how it compares to other
models for the SNR distribution. We do this by analysing the longitudinal
distribution of SNRs. We find that a simple exponential distribution is the
most consistent and simplest model for describing the radial SNR distribution
in the Galaxy and draw comparisons with the massive star formation and
metallicity distributions.


Abstract:
  Most ultraluminous X-ray sources (ULXs) are believed to be stellar mass black
holes or neutron stars accreting beyond the Eddington limit. Determining the
nature of the compact object and the accretion mode from broadband spectroscopy
is currently a challenge, but the observed timing properties provide insight
into the compact object and details of the geometry and accretion processes.
Here we report a timing analysis for an 800 ks XMM-Newton campaign on the
supersoft ultraluminous X-ray source, NGC 247 ULX-1. Deep and frequent dips
occur in the X-ray light curve, with the amplitude increasing with increasing
energy band. Power spectra and coherence analysis reveals the dipping
preferentially occurs on $\sim 5$ ks and $\sim 10$ ks timescales. The dips can
be caused by either the occultation of the central X-ray source by an optically
thick structure, such as warping of the accretion disc, or from obscuration by
a wind launched from the accretion disc, or both. This behaviour supports the
idea that supersoft ULXs are viewed close to edge-on to the accretion disc.


Abstract:
  Monocular depth estimation is an especially important task in robotics and
autonomous driving, where 3D structural information is essential. However,
extreme lighting conditions and complex surface objects make it difficult to
predict depth in a single image. Therefore, to generate accurate depth maps, it
is important for the model to learn structural information about the scene. We
propose a novel Patch-Wise EdgeConv Module (PEM) and EdgeConv Attention Module
(EAM) to solve the difficulty of monocular depth estimation. The proposed
modules extract structural information by learning the relationship between
image patches close to each other in space using edge convolution. Our method
is evaluated on two popular datasets, the NYU Depth V2 and the KITTI Eigen
split, achieving state-of-the-art performance. We prove that the proposed model
predicts depth robustly in challenging scenes through various comparative
experiments.


Abstract:
  In this paper, we have proposed a dispersive formulation of scalar-based
meshless method for time-domain analysis of electromagnetic wave propagation
through left-handed (LH) materials. Moreover, we have incorporated Berenger's
perfectly matched layer (PML) absorbing boundary condition (ABC) into the
dispersive formulation to truncate open-domain structures. In general, the LH
medium as a kind of dispersive media can be described by frequency-dependent
constitutive parameters. The most appropriate numerical techniques for analysis
of LH media are dispersive formulations of conventional numerical methods. In
comparison to the conventional grid-based numerical methods, it is proved that
meshless methods not only are strong tools for accurate approximation of
derivatives in Maxwell's equations but also can provide more flexibility in
modeling the spatial domain of problems. However, we have not seen any reports
on using dispersive forms of meshless methods for simulation of wave
propagation in metamaterials and applying any PML ABCs to dispersive
formulation of meshless method. The proposed formulation in this paper enables
us to take advantage of meshless methods in analysis of LH media. For modeling
the frequency behavior of the medium in the proposed dispersive formulation, we
have used auxiliary differential equation (ADE) method based on the relations
between electromagnetic fields intensities and current densities. Effectiveness
of the proposed formulation is verified by a numerical example; also, some
basic factors which affect the accuracy and computational cost of the
simulations are studied.


Abstract:
  For quantum search via the continuous-time quantum walk, the evolution of the
whole system is usually limited in a small subspace. In this paper, we discuss
how the symmetries of the graphs are related to the existence of such an
invariant subspace, which also suggests a dimensionality reduction method based
on group representation theory. We observe that in the one-dimensional subspace
spanned by each desired basis state which assembles the identically evolving
original basis states, we always get a trivial representation of the symmetry
group. So we could find the desired basis by exploiting the projection operator
of the trivial representation. Besides being technical guidance in this type of
problem, this discussion also suggests that all the symmetries are used up in
the invariant subspace and the asymmetric part of the Hamiltonian is very
important for the purpose of quantum search.


Abstract:
  We present a deep learning based approach to containerized application
runtime stability analysis, and an intelligent publishing algorithm that can
dynamically adjust the depth of process-level forensics published to a backend
incident analysis repository. The approach applies variational autoencoders
(VAEs) to learn the stable runtime patterns of container images, and then
instantiates these container-specific VAEs to implement stability detection and
adaptive forensics publishing. In performance comparisons using a 50-instance
container workload, a VAE-optimized service versus a conventional eBPF-based
forensic publisher demonstrates 2 orders of magnitude (OM) CPU performance
improvement, a 3 OM reduction in network transport volume, and a 4 OM reduction
in Elasticsearch storage costs. We evaluate the VAE-based stability detection
technique against two attacks, CPUMiner and HTTP-flood attack, finding that it
is effective in isolating both anomalies. We believe this technique provides a
novel approach to integrating fine-grained process monitoring and
digital-forensic services into large container ecosystems that today simply
cannot be monitored by conventional techniques


Abstract:
  We make variational estimates for the binding energies of
$^2H,~^3H,~^4He,~^{16}O,~^{40}Ca$, showing their running with the Similarity
Renormalization Group (SRG) cutoff towards the infrared region and show the
generalized Tjon lines that emerge from the calculations. We infer the SRG
evolution of the three-body contributions for the $^3H$ and $^4He$ binding
energies by computing the two-body contributions with a variational approach
assuming that four-body forces are negligible. At any given SRG cutoff, the
three-body contributions may then be inferred as being the experimental value
minus the two-body contributions. The off-shell / on-shell transition at a
critical SRG cutoff $\lambda_c$ drives the behavior of all binding energies and
this scale is the turning point of the generalized Tjon lines. Also, at
$\lambda_c$ the ratios between three-body and two-body contributions to the
binding energies, $B_{\lambda_c}(3) ~/~ B_{\lambda_c}(2)$, are the same in both
$^3H$ and $^4He$ systems and equal to $1/4$, so that $B_{\lambda_c}(2) ~=~
4~B_{\lambda_c}(3)$. All calculations are carried out with the Idaho-Salamanca
N3LO potential, which is evolved with the SRG up to the infrared fixed point
($\lambda \to 0$) in all S, P, D, F and G partial-wave channels in order to
compute the variational binding energies at several SRG cutoff scales.


Abstract:
  Products between phase-type distributed random variables and any independent,
positive and continuous random variable are studied. Their asymptotic
properties are established, and an expectation-maximization algorithm for their
effective statistical inference is derived and implemented using real-world
datasets. In contrast to discrete scaling studied in earlier literature, in the
present continuous case closed-form formulas for various functionals of the
resulting distributions are obtained, which facilitates both their analysis and
implementation. The resulting mixture distributions are very often heavy-tailed
and yet retain various properties of phase-type distributions, such as being
dense (in weak convergence) on the set of distributions with positive support.


Abstract:
  A viable approach for building large-scale quantum computers is to interlink
small-scale quantum computers with a quantum network to create a larger
distributed quantum computer. When designing quantum algorithms for such a
distributed quantum computer, one can make use of the added parallelization and
distribution abilities inherent in the system. An added difficulty to then
overcome for distributed quantum computing is that a complex control system to
orchestrate the various components is required. In this work, we aim to address
these issues. We explicitly define what it means for a quantum algorithm to be
distributed and then present various quantum algorithms that fit the
definition. We discuss potential benefits and propose a high-level scheme for
controlling the system. With this, we present our software framework called
Interlin-q, a simulation platform that aims to simplify designing and verifying
parallel and distributed quantum algorithms. We demonstrate Interlin-q by
implementing some of the discussed algorithms using Interlin-q and layout
future steps for developing Interlin-q into a control system for distributed
quantum computers.


Abstract:
  Perovskites have been the focus of attention due to their multitude of
outstanding optoelectronic properties and structural versatility.
Two-dimensional halide perovskite such as (C_6H_5C_2H_4NH_3)_2PbI_4, or simply
PEPI, forms natural multiple quantum wells with enhanced light-matter
interactions, making them attractive systems for further investigation. This
work reports tunable splitting of exciton modes in PEPI resulting from strong
light-matter interactions, manifested as multiple dips (modes) in the
reflection spectra. While the origin of the redder mode is well understood,
that for the bluer dip at room temperature is still lacking. Here, it is
revealed that the presence of the multiple modes originates from an indirect
coupling between excitons in different quantum wells. The long-range
characteristic of the mediated coupling between excitons in distant quantum
wells is also demonstrated in a structure design along with its tunability.
Moreover, a device architecture involving an end silver layer enhances the two
excitonic modes and provides further tunability. Importantly, this work will
motivate the possibility of coupling of the excitonic modes with a confined
light mode in a microcavity to produce multiple exciton-polariton modes.


Abstract:
  In a continuous-variable optical system, the Gottesman-Kitaev-Preskill (GKP)
qubit is a promising candidate for fault-tolerant quantum computation. To
implement non-Clifford operations on GKP qubits, non-Gaussian operations are
required. In this context, the implementation of a cubic phase gate by
combining nonlinear feedforward with ancillary states has been widely
researched. Recently, however, it is pointed out that the cubic phase gate is
not the most suitable for non-Clifford operations on GKP qubits. In this work,
we show that we can achieve linear optical implementation of non-Clifford
operations on GKP qubit with high fidelity by applying the nonlinear
feedforward originally developed for the cubic phase gate and using a
GKP-encoded ancillary state. Our work shows the versatility of nonlinear
feedforward technique important for optical implementation of the
fault-tolerant continuous-variable quantum computation.


Abstract:
  Cross-modal retrieval methods build a common representation space for samples
from multiple modalities, typically from the vision and the language domains.
For images and their captions, the multiplicity of the correspondences makes
the task particularly challenging. Given an image (respectively a caption),
there are multiple captions (respectively images) that equally make sense. In
this paper, we argue that deterministic functions are not sufficiently powerful
to capture such one-to-many correspondences. Instead, we propose to use
Probabilistic Cross-Modal Embedding (PCME), where samples from the different
modalities are represented as probabilistic distributions in the common
embedding space. Since common benchmarks such as COCO suffer from
non-exhaustive annotations for cross-modal matches, we propose to additionally
evaluate retrieval on the CUB dataset, a smaller yet clean database where all
possible image-caption pairs are annotated. We extensively ablate PCME and
demonstrate that it not only improves the retrieval performance over its
deterministic counterpart but also provides uncertainty estimates that render
the embeddings more interpretable. Code is available at
https://github.com/naver-ai/pcme


Abstract:
  The recent opening of gravitational wave astronomy has shifted the debate
about black hole mimickers from a purely theoretical arena to a
phenomenological one. In this respect, missing a definitive quantum gravity
theory, the possibility to have simple, meta-geometries describing in a compact
way alternative phenomenologically viable scenarios is potentially very
appealing. A recently proposed metric by Simpson and Visser is exactly an
example of such meta-geometry describing, for different values of a single
parameter, different non-rotating black hole mimickers. Here, we employ the
Newman--Janis procedure to construct a rotating generalisation of such
geometry. We obtain a stationary, axially symmetric metric that depends on
mass, spin and an additional real parameter $\ell$. According to the value of
such parameter, the metric may represent a rotating traversable wormhole, a
rotating regular black hole with one or two horizons, or three more limiting
cases. By studying the internal and external rich structure of such solutions,
we show that the obtained metric describes a family of interesting and simple
regular geometries providing viable Kerr black hole mimickers for future
phenomenological studies.


Abstract:
  The mechanisms that drive metal-to-insulator transitions (MIT) in correlated
solids are not fully understood. For example, the perovskite (PV) SrCoO3 is a
FM metal while the oxygen-deficient (n-doped) brownmillerite (BM) SrCoO2.5 is
an anti-ferromagnetic (AFM) insulator. Given the magnetic and structural
transitions that accompany the MIT, the driver for such a MIT transition is
unclear. We also observe that the perovskite metals LaNiO3, SrFeO3, and SrCoO3
also undergo MIT when n-doped via high-to-low valence compositional changes.
Also, pressurizing the insulating BM SrCoO2.5 phase, drives a gap closing.
Using DFT and correlated diffusion Monte Carlo approaches we demonstrate that
the ABO3 perovskites most prone to MIT are self hole-doped materials,
reminiscent of a negative charge-transfer system. Upon n-doping away from the
negative-charge transfer metallic phase, an underlying charge-lattice (or
e-phonon) coupling drives the system to a bond-disproportionated gapped state,
thereby achieving ligand hole passivation at certain sites only, leading to
charge-disproportionated states. The size of the gap opened is correlated with
the size of the hole-filling at these ligand sites. This suggests that the
interactions driving the gap opening to realize a MIT even in correlated metals
is the charge-transfer energy, but it couples with the underlying phonons to
enable the transition to the insulating phase. Other orderings (magnetic,
charge, etc.) driven by weaker interactions are secondary and may assist gap
openings at small dopings, but its the charge-transfer energy that
predominantly determines the bandgap, with a negative energy preferring the
metallic phase. This n-doping can be achieved by modulations in stoichiometry
or composition or pressure. Hence, controlling the amount of the ligand-hole is
key in controlling MIT. We compare our predictions to experiments where
possible.


Abstract:
  A single-spin qubit placed near the surface of a conductor acquires an
additional contribution to its $1/T_1$ relaxation rate due to magnetic noise
created by electric current fluctuations in the material. We analyze this
technique as a wireless probe of superconductivity in atomically thin two
dimensional materials. At temperatures $T \lesssim T_c$, the dominant
contribution to the qubit relaxation rate is due to transverse electric current
fluctuations arising from quasiparticle excitations. We demonstrate that this
method enables detection of metal-to-superconductor transitions, as well as
investigation of the symmetry of the superconducting gap function, through the
noise scaling with temperature. We show that scaling of the noise with
sample-probe distance provides a window into the non-local quasi-static
conductivity of superconductors, both clean and disordered. At low temperatures
the quasiparticle fluctuations get suppressed, yet the noise can be substantial
due to resonant contributions from collective longitudinal modes, such as
plasmons in monolayers and Josephson plasmons in bilayers. Potential
experimental implications are discussed.


Abstract:
  Data-driven dimensionality reduction methods such as proper orthogonal
decomposition (POD) and dynamic mode decomposition (DMD) have proven to be
useful for exploring complex phenomena within fluid dynamics and beyond. A
well-known challenge for these techniques is posed by the continuous
symmetries, e.g. translations and rotations, of the system under consideration
as drifts in the data dominate the modal expansions without providing an
insight into the dynamics of the problem. In the present study, we address this
issue for the pressure-driven flow in a rectangular channel by formulating a
continuous symmetry reduction method that eliminates the translations
simultaneously in the streamwise and spanwise directions. As an application, we
consider turbulence in a minimal flow unit at a Reynolds number (based on the
centerline velocity and half-channel height) Re = 2000 and compute the
symmetry-reduced dynamic mode decomposition (SRDMD) of sliding data windows of
varying durations. SRDMD of channel flow reveals episodes of turbulent time
evolution that can be approximated by a low-dimensional linear expansion.


Abstract:
  We propose a binary representation of categorical values using a linear map.
This linear representation preserves the neighborhood structure of categorical
values. In the context of evolutionary algorithms, it means that every
categorical value can be reached in a single mutation. The linear
representation is embedded into standard metaheuristics, applied to the problem
of Sudoku puzzles, and compared to the more traditional direct binary encoding.
It shows promising results in fixed-budget experiments and empirical cumulative
distribution functions with high dimension instances, and also in fixed-target
experiments with small dimension instances.


Abstract:
  Current dynamic networks and dynamic pruning methods have shown their
promising capability in reducing theoretical computation complexity. However,
dynamic sparse patterns on convolutional filters fail to achieve actual
acceleration in real-world implementation, due to the extra burden of indexing,
weight-copying, or zero-masking. Here, we explore a dynamic network slimming
regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good
hardware-efficiency via dynamically adjusting filter numbers of networks at
test time with respect to different inputs, while keeping filters stored
statically and contiguously in hardware to prevent the extra burden. Our DS-Net
is empowered with the ability of dynamic inference by the proposed
double-headed dynamic gate that comprises an attention head and a slimming head
to predictively adjust network width with negligible extra computation cost. To
ensure generality of each candidate architecture and the fairness of gate, we
propose a disentangled two-stage training scheme inspired by one-shot NAS. In
the first stage, a novel training technique for weight-sharing networks named
In-place Ensemble Bootstrapping is proposed to improve the supernet training
efficacy. In the second stage, Sandwich Gate Sparsification is proposed to
assist the gate training by identifying easy and hard samples in an online way.
Extensive experiments demonstrate our DS-Net consistently outperforms its
static counterparts as well as state-of-the-art static and dynamic model
compression methods by a large margin (up to 5.9%). Typically, DS-Net achieves
2-4x computation reduction and 1.62x real-world acceleration over ResNet-50 and
MobileNet with minimal accuracy drops on ImageNet. Code release:
https://github.com/changlin31/DS-Net .


Abstract:
  We extract the imaginary part of the heavy-quark potential using
classical-statistical simulations of real-time Yang-Mills dynamics in classical
thermal equilibrium. The $r$-dependence of the imaginary part of the potential
is extracted by measuring the temporal decay of Wilson loops of spatial length
$r$. We compare our results to continuum expressions obtained using hard
thermal loop theory and to semi-analytic lattice perturbation theory
calculations using the hard classical loop formalism. We find that, when
plotted as a function of $m_D r$, where $m_D$ is the hard classical loop Debye
mass, the imaginary part of the heavy-quark potential shows little sensitivity
to the lattice spacing at small $m_D r \lesssim 1$ and agrees well with the
semi-analytic hard classical loop result. For large quark-antiquark
separations, we quantify the magnitude of the non-perturbative long-range
corrections to the imaginary part of the heavy-quark potential. We present our
results for a wide range of temperatures, lattice spacings, and lattice
volumes. This work sets the stage for extracting the imaginary part of the
heavy-quark potential in an expanding non-equilibrium Yang Mills plasma.


Abstract:
  The millihertz gravitational-wave frequency band is expected to contain a
rich symphony of signals with sources ranging from galactic white dwarf
binaries to extreme mass ratio inspirals. Many of these gravitational-wave
signals will not be individually resolvable. Instead, they will incoherently
add to produce stochastic gravitational-wave confusion noise whose frequency
content will be governed by the dynamics of the sources. The angular structure
of the power of the confusion noise will be modulated by the distribution of
the sources across the sky. Measurement of this structure can yield important
information about the distribution of sources on galactic and extra-galactic
scales, their astrophysics and their evolution over cosmic timescales.
Moreover, since the confusion noise is part of the noise budget of LISA,
mapping it will also be essential for studying resolvable signals. In this
paper, we present a Bayesian algorithm to probe the angular distribution of the
stochastic gravitational-wave confusion noise with LISA using a spherical
harmonic basis. We develop a technique based on Clebsch-Gordan coefficients to
mathematically constrain the spherical harmonics to yield a non-negative
distribution, making them optimal for expanding the gravitational-wave power
and amenable to Bayesian inference. We demonstrate these techniques using a
series of simulations and analyses, including recovery of simulated distributed
and localized sources of gravitational-wave power. We also apply this method to
map the gravitational-wave foreground from galactic white-dwarfs using a
simplified model of the galactic white dwarf distribution.


Abstract:
  Although AI holds promise for improving human decision making in societally
critical domains, it remains an open question how human-AI teams can reliably
outperform AI alone and human alone in challenging prediction tasks (also known
as complementary performance). We explore two directions to understand the gaps
in achieving complementary performance. First, we argue that the typical
experimental setup limits the potential of human-AI teams. To account for lower
AI performance out-of-distribution than in-distribution because of distribution
shift, we design experiments with different distribution types and investigate
human performance for both in-distribution and out-of-distribution examples.
Second, we develop novel interfaces to support interactive explanations so that
humans can actively engage with AI assistance. Using virtual pilot studies and
large-scale randomized experiments across three tasks, we demonstrate a clear
difference between in-distribution and out-of-distribution, and observe mixed
results for interactive explanations: while interactive explanations improve
human perception of AI assistance's usefulness, they may reinforce human biases
and lead to limited performance improvement. Overall, our work points out
critical challenges and future directions towards enhancing human performance
with AI assistance.


Abstract:
  The $\gamma$-metric is a static, axially-symmetric singular solution of the
vacuum Einstein's equations without an event horizon. This is a two-parameter
family of solutions, generic values of one of which (called $\gamma$) measure
the deviation from spherical symmetry. Here, we first study the shadow cast by
this geometry, in order to constrain the $\gamma$-metric from observations. We
find that for $\gamma < 1/2$, there are, in principle, no shadows cast. On the
other hand, shadows cast for all values of $\gamma \geq 1/2$ are consistent
with observations of M$87^*$ by the Event Horizon Telescope. We also study
images of thin accretion disks in the $\gamma$-metric background. In situations
where the $\gamma$-metric possesses light rings, these qualitatively mimic
Schwarzschild black holes with the same ADM mass, while in the absence of such
rings, they are drastically different from the black hole case.


Abstract:
  By strengthening one of the hypotheses of a well-known sufficient condition
for the hypercyclicity of linear operators in Banach spaces, we arrive at a
sufficient condition for linear chaos and reveal consequences of the latter for
inverses, powers, multiples, and spectral properties. Extending the results,
familiar for bounded linear operators, we also show that the hypercyclicity of
unbounded linear operators subject to the sufficient condition for
hypercyclicity is inherited by their bounded inverses, powers, and unimodular
multiples and that necessary conditions for linear hypercyclicity stretch to
the unbounded case.


Abstract:
  Many researchers have developed VR systems for people with visual impairments
by using various audio feedback techniques. However, there has been much less
study of collaborative VR systems in which people with visual impairments and
people with able-body can participate together. Therefore, we developed a VR
showdown game which is similar to a real Showdown game in which two players can
play together in the same virtual environment. We incorporate auditory distance
perception using the HRTF (Head Related Transform Function) based on a spatial
position in VR. We developed two modes in the showdown game. One is the PVA
(Player vs. Agent) mode in which people with visual impairments can play alone
and the PVP (Player vs. Player) mode in which people with visual impairments
can play with another player in the network environment. We conducted our user
studies by comparing the performances of people with visual impairments and
people with able-body. The user study results show that people with visual
impairments won 67.6% of the games when competing against people with
able-body. This paper reports an example of a collaborative VR system for
people with visual impairments and also design guideline for developing VR
systems for people with visual impairments.


Abstract:
  Typical fact verification models use retrieved written evidence to verify
claims. Evidence sources, however, often change over time as more information
is gathered and revised. In order to adapt, models must be sensitive to subtle
differences in supporting evidence. We present VitaminC, a benchmark infused
with challenging cases that require fact verification models to discern and
adjust to slight factual changes. We collect over 100,000 Wikipedia revisions
that modify an underlying fact, and leverage these revisions, together with
additional synthetically constructed ones, to create a total of over 400,000
claim-evidence pairs. Unlike previous resources, the examples in VitaminC are
contrastive, i.e., they contain evidence pairs that are nearly identical in
language and content, with the exception that one supports a given claim while
the other does not. We show that training using this design increases
robustness -- improving accuracy by 10% on adversarial fact verification and 6%
on adversarial natural language inference (NLI). Moreover, the structure of
VitaminC leads us to define additional tasks for fact-checking resources:
tagging relevant words in the evidence for verifying the claim, identifying
factual revisions, and providing automatic edits via factually consistent text
generation.


Abstract:
  Intrinsically disordered proteins (IDPs) constitute a broad set of proteins
with few uniting and many diverging properties. IDPs-and intrinsically
disordered regions (IDRs) interspersed between folded domains-are generally
characterized as having no persistent tertiary structure; instead they
interconvert between a large number of different and often expanded structures.
IDPs and IDRs are involved in an enormously wide range of biological functions
and reveal novel mechanisms of interactions, and while they defy the common
structure-function paradigm of folded proteins, their structural preferences
and dynamics are important for their function. We here discuss open questions
in the field of IDPs and IDRs, focusing on areas where machine learning and
other computational methods play a role. We discuss computational methods aimed
to predict transiently formed local and long-range structure, including methods
for integrative structural biology. We discuss the many different ways in which
IDPs and IDRs can bind to other molecules, both via short linear motifs, as
well as in the formation of larger dynamic complexes such as biomolecular
condensates. We discuss how experiments are providing insight into such
complexes and may enable more accurate predictions. Finally, we discuss the
role of IDPs in disease and how new methods are needed to interpret the
mechanistic effects of genomic variants in IDPs.


Abstract:
  We study the problem of distributed multi-robot coverage over an unknown,
nonuniform sensory field. Modeling the sensory field as a realization of a
Gaussian Process and using Bayesian techniques, we devise a policy which aims
to balance the tradeoff between learning the sensory function and covering the
environment. We propose an adaptive coverage algorithm called Deterministic
Sequencing of Learning and Coverage (DSLC) that schedules learning and coverage
epochs such that its emphasis gradually shifts from exploration to exploitation
while never fully ceasing to learn. Using a novel definition of coverage regret
which characterizes overall coverage performance of a multi-robot team over a
time horizon $T$, we analyze DSLC to provide an upper bound on expected
cumulative coverage regret. Finally, we illustrate the empirical performance of
the algorithm through simulations of the coverage task over an unknown
distribution of wildfires.


Abstract:
  Leaf hydraulic networks play an important role not only in fluid transport
but also in maintaining whole-plant water status through transient
environmental changes in soil-based water supply or air humidity. Both water
potential and hydraulic resistance vary spatially throughout the leaf transport
network, consisting of xylem, stomata and water-storage cells, and portions of
leaf areas far from the leaf base can be disproportionately disadvantaged under
water stress. Besides the suppression of transpiration and reduction of water
loss caused by stomatal closure, the leaf capacitance of water storage, which
can also vary locally, is thought to be crucial for the maintenance of leaf
water status. In order to study the fluid dynamics in these networks, we
develop a spatially explicit, capacitive model which is able to capture the
local spatiotemporal changes of water potential and flow rate. In
electrical-circuit analogues described by Ohm's law, we implement linear
capacitors imitating water storage, and we present both analytical calculations
of a uniform one-dimensional model and numerical simulation methods for general
spatially explicit network models, and their relation to conventional
lumped-element models. Calculation and simulation results are shown for the
uniform model, which mimics key properties of a monocotyledonous grass leaf. We
illustrate water status of a well-watered leaf, and the lowering of water
potential and transpiration rate caused by excised water source or reduced air
humidity. We show that the time scales of these changes under water stress are
hugely affected by leaf capacitance and resistances to capacitors, in addition
to stomatal resistance. Through this modeling of a grass leaf, we confirm the
presence of uneven water distribution over leaf area, and also discuss the
importance of considering the spatial variation of leaf hydraulic traits in
plant biology.


Abstract:
  Conventional stochastic rounding (CSR) is widely employed in the training of
neural networks (NNs), showing promising training results even in low-precision
computations. We introduce an improved stochastic rounding method, that is
simple and efficient. The proposed method succeeds in training NNs with 16-bit
fixed-point numbers and provides faster convergence and higher classification
accuracy than both CSR and deterministic rounding-to-the-nearest method.


Abstract:
  We propose a novel infection spread model based on a random connection graph
which represents connections between $n$ individuals. Infection spreads via
connections between individuals and this results in a probabilistic cluster
formation structure as well as a non-i.i.d. (correlated) infection status for
individuals. We propose a class of two-step sampled group testing algorithms
where we exploit the known probabilistic infection spread model. We investigate
the metrics associated with two-step sampled group testing algorithms. To
demonstrate our results, for analytically tractable exponentially split cluster
formation trees, we calculate the required number of tests and the expected
number of false classifications in terms of the system parameters, and identify
the trade-off between them. For such exponentially split cluster formation
trees, for zero-error construction, we prove that the required number of tests
is $O(\log_2n)$. Thus, for such cluster formation trees, our algorithm
outperforms any zero-error non-adaptive group test, binary splitting algorithm,
and Hwang's generalized binary splitting algorithm. Our results imply that, by
exploiting probabilistic information on the connections of individuals, group
testing can be used to reduce the number of required tests significantly even
when infection rate is high, contrasting the prevalent belief that group
testing is useful only when infection rate is low.


Abstract:
  We develop a novel model of the magnetized spin-1 Bose-Einstein condensate
(BEC) of neutral atoms, using the method of many-particle quantum hydrodynamic
(QHD) and propose an original derivation of the system of continual equations.
We consider bosons with a spin-spin interaction and a short range interaction
in the first order in the interaction radius, on the of basis of the
self-consistent field approximation of the QHD equations. We demonstrate that
the dynamics of the fluid velocity and magnetization is determined by a
nontrivial modification of the Euler and Landau-Lifshitz equation, and show
that a nontrivial modification of the spin density evolution equation contains
the spin torque effect that arises from the self-interactions between spins of
the bosons. The properties of the dispersion spectrum of collective excitations
are described. We obtain the new contribution of the self-interaction of spins
in the spin wave spectrum together with the influence of an external magnetic
field and spin-spin interactions between polarized particles. The anisotropic
spin wave instability is predicted.


Abstract:
  Recommendation and ranking systems are known to suffer from popularity bias;
the tendency of the algorithm to favor a few popular items while
under-representing the majority of other items. Prior research has examined
various approaches for mitigating popularity bias and enhancing the
recommendation of long-tail, less popular, items. The effectiveness of these
approaches is often assessed using different metrics to evaluate the extent to
which over-concentration on popular items is reduced. However, not much
attention has been given to the user-centered evaluation of this bias; how
different users with different levels of interest towards popular items are
affected by such algorithms. In this paper, we show the limitations of the
existing metrics to evaluate popularity bias mitigation when we want to assess
these algorithms from the users' perspective and we propose a new metric that
can address these limitations. In addition, we present an effective approach
that mitigates popularity bias from the user-centered point of view. Finally,
we investigate several state-of-the-art approaches proposed in recent years to
mitigate popularity bias and evaluate their performances using the existing
metrics and also from the users' perspective. Our experimental results using
two publicly-available datasets show that existing popularity bias mitigation
techniques ignore the users' tolerance towards popular items. Our proposed
user-centered method can tackle popularity bias effectively for different users
while also improving the existing metrics.


Abstract:
  In this study, we improve the topological complexity computations on digital
images with introducing the digital topological complexity computations of a
surjective and digitally continuous map between digital images. We also reveal
differences in topological complexity computations of maps between digital
images and topological spaces. Moreover, we emphasize the importance of the
adjacency relation on the domain and the range of a digital map in these
computations.


Abstract:
  We introduce a non-local $L^2$-gradient flow for the Willmore energy of
immersed surfaces which preserves the isoperimetric ratio. For initial data
with energy below an explicit threshold, we show long-time existence and
convergence to a Helfrich immersion.


Abstract:
  We find a novel confinement mechanism in the two-flavor dense quark matter
proposed recently, that consists of the 2SC condensates and the $P$-wave
diquark condensates of $d$-quarks. This quark matter exhibiting color
superconductivity as well as superfluidity is classified into two phases;
confined and deconfined phases of vortices. We establish that the criterion of
the confinement is color neutrality of Aharonov-Bohm (AB) phases: vortices
exhibiting color non-singlet AB phases are confined by the so-called AB defects
to form color-singlet bound states. In the deconfined phase, the most stable
vortices are non-Abelian Alice strings, which are superfluid vortices with
fractional circulation and non-Abelian color magnetic fluxes therein,
exhibiting color non-singlet AB phases. On the other hand, in the confined
phase, these non-Abelian vortices are confined to either a baryonic or mesonic
bound state in which constituent vortices are connected by AB defects. The
baryonic bound state consists of three non-Abelian Alice strings with different
color magnetic fluxes with the total flux canceled out connected by a domain
wall junction, while the mesonic bound state consists of two non-Abelian Alice
strings with the same color magnetic fluxes connected by a single domain wall.
Interestingly, the latter contains a color magnetic flux in its core, but this
can exist because of color neutrality of its AB phase.


Abstract:
  We investigate the effect of longitudinal and transverse calorimeter
segmentation on event-by-event software compensation for hadronic showers. To
factorize out sampling and electronics effects, events are simulated in which a
single charged pion is shot at a homogenous lead glass calorimeter, split into
longitudinal and transverse segments of varying size. As an approximation of an
optimal reconstruction, a neural network-based energy regression is trained.
The architecture is based on blocks of convolutional kernels customized for
shower energy regression using local energy densities; biases at the edges of
the training dataset are mitigated using a histogram technique. With this
approximation, we find that a longitudinal and transverse segment size less
than or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is
necessary to achieve an optimal energy measurement. In addition, an intrinsic
energy resolution of $8\%/\sqrt{E}$ for pion showers is observed.


Abstract:
  Combining the representations of the words that make up a sentence into a
cohesive whole is difficult, since it needs to account for the order of words,
and to establish how the words present relate to each other. The solution we
propose consists in iteratively adjusting the context. Our algorithm starts
with a presumably erroneous value of the context, and adjusts this value with
respect to the tokens at hand. In order to achieve this, representations of
words are built combining their symbolic embedding with a positional encoding
into single vectors. The algorithm then iteratively weighs and aggregates these
vectors using our novel second-order attention mechanism. Our models report
strong results in several well-known text classification tasks.


Abstract:
  Place/Transition Petri nets with inhibitor arcs (PTI nets for short), which
are a well-known Turing-complete, distributed model of computation, are
equipped with a decidable, behavioral equivalence, called pti-place
bisimilarity, that conservatively extends place bisimilarity defined over
Place/Transition nets (without inhibitor arcs). We prove that pti-place
bisimilarity is sensible, as it respects the causal semantics of PTI nets.


Abstract:
  We present a resource-planning game for the cybersecurity of network control
systems (NCS). The NCS is assumed to be operating in closed-loop using a linear
state-feedback $\mathcal{H}_2$ controller. A zero-sum, two-player Stackelberg
game (SG) is developed between an attacker and a defender for this NCS. The
attacker aims to disable communication of selected nodes and thereby render the
feedback gain matrix to be sparse, leading to degradation of closed-loop
performance. The defender, on the other hand, aims to prevent this loss by
investing in the protection of targeted nodes. Both players trade their
$\mathcal{H}_2$-performance objectives for the costs of their actions. The
standard backward induction method is modified to determine a cost-based
Stackelberg equilibrium (CBSE) that provides the optimal performance payoffs to
both players while saving their individual costs. We analyze the dependency of
a CBSE on the relative budgets of the players as well as on the "important"
network nodes, which must be preserved to achieve a reliable
$\mathcal{H}_2$-performance. Additionally, we consider NCS models with
uncertainties and develop robust, long-term security investment games based on
this cost-based SG. The proposed games are validated using an example of
wide-area control of electric power systems, where it is demonstrated that
reliable and robust defense is feasible unless the defender is much more
resource-limited than the attacker.


Abstract:
  In the past few years, numerous deep learning methods have been proposed to
address the task of segmenting salient objects from RGB images. However, these
approaches depending on single modality fail to achieve the state-of-the-art
performance on widely used light field salient object detection (SOD) datasets,
which collect large-scale natural images and provide multiple modalities such
as multi-view, micro-lens images and depth maps. Most recently proposed light
field SOD methods have acquired improving detecting accuracy, yet still predict
rough objects' structures and perform slow inference speed. To this end, we
propose CMA-Net, which consists of two novel cascaded mutual attention modules
aiming at fusing the high level features from the modalities of all-in-focus
and depth. Our proposed CMA-Net outperforms 30 SOD methods on two widely
applied light field benchmark datasets. Besides, the proposed CMA-Net is able
to inference at the speed of 53 fps. Extensive quantitative and qualitative
experiments illustrate both the effectiveness and efficiency of our CMA-Net.


Abstract:
  We address a system of equations modeling an incompressible fluid interacting
with an elastic body. We prove the local existence when the initial velocity
belongs to the space $H^{s}$, where $s>3/2$ and the initial structure velocity
is in $H^{s-1/2}$.


Abstract:
  Quantum devices can be used to solve constrained combinatorial optimization
(COPT) problems thanks to the use of penalization methods to embed the COPT
problem's constraints in its objective to obtain a quadratic unconstrained
binary optimization (QUBO) reformulation of the COPT. However, the particular
way in which this penalization is carried out, affects the value of the penalty
parameters, as well as the number of additional binary variables that are
needed to obtain the desired QUBO reformulation. In turn, these factors
substantially affect the ability of quantum computers to efficiently solve
these constrained COPT problems. This efficiency is key towards the goal of
using quantum computers to solve constrained COPT problems more efficiently
than with classical computers. Along these lines, we consider an important
constrained COPT problem; namely, the maximum $k$-colorable subgraph (M$k$CS)
problem, in which the aim is to find an induced $k$-colorable subgraph with
maximum cardinality in a given graph. This problem arises in channel assignment
in spectrum sharing networks, VLSI design, human genetic research, and
cybersecurity. We derive two QUBO reformulations for the M$k$CS problem, and
fully characterize the range of the penalty parameters that can be used in the
QUBO reformulations. Further, one of the QUBO reformulations of the M$k$CS
problem is obtained without the need to introduce additional binary variables.
To illustrate the benefits of obtaining and characterizing these QUBO
reformulations, we benchmark different QUBO reformulations of the M$k$CS
problem by performing numerical tests on D-Wave's quantum annealing devices.
These tests also illustrate the numerical power gained by using the latest
D-Wave's quantum annealing device.


Abstract:
  Self-training is a competitive approach in domain adaptive segmentation,
which trains the network with the pseudo labels on the target domain. However
inevitably, the pseudo labels are noisy and the target features are dispersed
due to the discrepancy between source and target domains. In this paper, we
rely on representative prototypes, the feature centroids of classes, to address
the two issues for unsupervised domain adaptation. In particular, we take one
step further and exploit the feature distances from prototypes that provide
richer information than mere prototypes. Specifically, we use it to estimate
the likelihood of pseudo labels to facilitate online correction in the course
of training. Meanwhile, we align the prototypical assignments based on relative
feature distances for two different views of the same target, producing a more
compact target feature space. Moreover, we find that distilling the already
learned knowledge to a self-supervised pretrained model further boosts the
performance. Our method shows tremendous performance advantage over
state-of-the-art methods. We will make the code publicly available.


Abstract:
  Density matrix quantum Monte Carlo (DMQMC) is a recently-developed method for
stochastically sampling the $N$-particle thermal density matrix to obtain
exact-on-average energies for model and \emph{ab initio} systems. We report a
systematic numerical study of the sign problem in DMQMC based on simulations of
atomic and molecular systems. In DMQMC, the density matrix is written in an
outer product basis of Slater determinants and has a size of space which is the
square of the number of Slater determinants. In principle this means DMQMC
needs to sample a space which scales in the system size, $N$, as
$\mathcal{O}[(\exp(N))^2]$. In practice, there is a system-dependent critical
walker population ($N_c$) which must be exceeded in order to remove the sign
problem, and this imposes limitations by way of storage and computer time. We
establish that $N_c$ for DMQMC is the square of $N_c$ for FCIQMC. By contrast,
the minimum $N_c$ in the interaction picture modification of DMQMC (IP-DMQMC)
only is directly proportionate to the $N_c$ for FCIQMC. We find that this comes
from the asymmetric propagation of IP-DMQMC compared to the symmetric
propagation of canonical DMQMC. An asymmetric mode of propagation is
prohibitively expensive for DMQMC because it has a much greater stochastic
error. Finally, we find that the equivalence between IP-DMQMC and FCIQMC seems
to extend to the initiator approximation, which is often required to study
larger basis sets and other systems. This suggests IP-DMQMC offers a way to
ameliorate the cost of moving between a Slater determinant space and an outer
product basis.


Abstract:
  In this paper, we study the existence of global Yamabe flow on asymptotically
flat (in short, AF or ALE) manifolds. Note that the ADM mass is preserved in
dimensions 3,4 and 5. We present a new general local existence of Yamabe flow
on a complete Riemannian manifold with the initial metric quasi-isometric to a
background metric of bounded scalar curvature. Asymptotic behaviour of the
Yamabe flow on ALE manifolds is also addressed provided the initial scalar
curvature is non-negative and there is a bounded subsolution to the
corresponding Poisson equation. We also present a maximum principle for a very
general parabolic equations on the complete Riemannian manifolds.


Abstract:
  The present article revisits the Diffusion Operator Integral (DOI) variance
reduction technique originally proposed in Heath and Platen (2002) and extends
its theoretical concept to the pricing of American-style options under
(time-homogeneous) L\'evy stochastic differential equations. The resulting Jump
Diffusion Operator Integral (JDOI) method can be combined with numerous Monte
Carlo based stopping-time algorithms, including the ubiquitous least-squares
Monte Carlo (LSMC) algorithm of Longstaff and Schwartz (cf. Carriere (1996),
Longstaff and Schwartz (2001)). We exemplify the usefulness of our theoretical
derivations under a concrete, though very general jump-diffusion stochastic
volatility dynamics and test the resulting LSMC based version of the JDOI
method. The results provide evidence of a strong variance reduction when
compared with a simple application of the LSMC algorithm and proves that
applying our technique on top of Monte Carlo based pricing schemes provides a
powerful way to speed-up these methods.


Abstract:
  Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by
implicit equations, have been becoming more and more attractive recently. In
this paper, we investigate an emerging question: can an implicit equilibrium
model's equilibrium point be regarded as the solution of an optimization
problem? To this end, we first decompose DNNs into a new class of unit layer
that is the proximal operator of an implicit convex function while keeping its
output unchanged. Then, the equilibrium model of the unit layer can be derived,
named Optimization Induced Equilibrium Networks (OptEq), which can be easily
extended to deep layers. The equilibrium point of OptEq can be theoretically
connected to the solution of its corresponding convex optimization problem with
explicit objectives. Based on this, we can flexibly introduce prior properties
to the equilibrium points: 1) modifying the underlying convex problems
explicitly so as to change the architectures of OptEq; and 2) merging the
information into the fixed point iteration, which guarantees to choose the
desired equilibrium point when the fixed point set is non-singleton. We show
that deep OptEq outperforms previous implicit models even with fewer
parameters. This work establishes the first step towards the
optimization-guided design of deep models.


Abstract:
  Secure integer comparison has been a popular research topic in cryptography,
both for its simplicity to describe and for its applications. The aim is to
enable two parties to compare their inputs without revealing the exact value of
those inputs.
  In this paper, we highlight three-party integer comparison (TPIC), where a
\emph{judge}, with no private input, wants to know the comparison result, while
two \emph{competitors} hold secret integers to do privacy-preserving
comparison. The judge actively obtains the result rather than passively waiting
for it sent by a competitor. We give two TPIC constructions considering
\emph{Mixed adversaries}, who have with different capabilities. One is secure
against a semi-honest adversary with low computation and communication cost,
while the other is secure against a malicious adversary.
  Basing on TPIC, we present multi-party comparisons through concrete
applications, including a joint bidding scheme and a practical auction. Brief
security proofs and analysis for the applications are presented. In comparison,
our auction scheme is more efficient with lower cost, making it feasible in
practice rather than a theoretical design. All the comparisons and application
schemes run on top of blockchain requiring a constant number of rounds.


Abstract:
  In this work we investigate the parallel scalability of the numerical method
developed in Guthrey and Rossmanith [The regionally implicit discontinuous
Galerkin method: Improving the stability of DG-FEM, SIAM J. Numer. Anal.
(2019)]. We develop an implementation of the regionally-implicit discontinuous
Galerkin (RIDG) method in DoGPack, which is an open source C++ software package
for discontinuous Galerkin methods. Specifically, we develop and test a hybrid
OpenMP and MPI parallelized implementation of DoGPack with the goal of
exploring the efficiency and scalability of RIDG in comparison to the popular
strong stability-preserving Runge-Kutta discontinuous Galerkin (SSP-RKDG)
method. We demonstrate that RIDG methods are able to hide communication latency
associated with distributed memory parallelism, due to the fact that almost all
of the work involved in the method is highly localized to each element,
producing a localized prediction for each region. We demonstrate the enhanced
efficiency and scalability of the of the RIDG method and compare it to SSP-RKDG
methods and show extensibility to very high order schemes. The two-dimensional
scaling study is performed on machines at the Institute for Cyber-Enabled
Research at Michigan State University, using up to 1440 total cores on Intel(R)
Xeon(R) Gold 6148 CPU @ 2.40GHz CPUs. The three dimensional scaling study is
performed on Livermore Computing clusters at at Lawrence Livermore National
Laboratory, using up to 28672 total cores on Intel Xeon CLX-8276L CPUs with
Omni-Path interconnects.


Abstract:
  We discuss relations between different notions of ranks for multilinear
forms. In particular we show that the Schmidt and the analytic ranks for
trilinear forms are essentially proportional.


Abstract:
  The study of multi-type Protein-Protein Interaction (PPI) is fundamental for
understanding biological processes from a systematic perspective and revealing
disease mechanisms. Existing methods suffer from significant performance
degradation when tested in unseen dataset. In this paper, we investigate the
problem and find that it is mainly attributed to the poor performance for
inter-novel-protein interaction prediction. However, current evaluations
overlook the inter-novel-protein interactions, and thus fail to give an
instructive assessment. As a result, we propose to address the problem from
both the evaluation and the methodology. Firstly, we design a new evaluation
framework that fully respects the inter-novel-protein interactions and gives
consistent assessment across datasets. Secondly, we argue that correlations
between proteins must provide useful information for analysis of novel
proteins, and based on this, we propose a graph neural network based method
(GNN-PPI) for better inter-novel-protein interaction prediction. Experimental
results on real-world datasets of different scales demonstrate that GNN-PPI
significantly outperforms state-of-the-art PPI prediction methods, especially
for the inter-novel-protein interaction prediction.


Abstract:
  In this paper, we consider numerical approximation to periodic measure of a
time periodic stochastic differential equations (SDEs) under weakly dissipative
condition. For this we first study the existence of the periodic measure
$\rho_t$ and the large time behaviour of $\mathcal{U}(t+s,s,x) :=
\mathbb{E}\phi(X_{t}^{s,x})-\int\phi d\rho_t,$ where $X_t^{s,x}$ is the
solution of the SDEs and $\phi$ is a test function being smooth and of
polynomial growth at infinity. We prove $\mathcal{U}$ and all its spatial
derivatives decay to 0 with exponential rate on time $t$ in the sense of
average on initial time $s$. We also prove the existence and the geometric
ergodicity of the periodic measure of the discretized semi-flow from the
Euler-Maruyama scheme and moment estimate of any order when the time step is
sufficiently small (uniform for all orders). We thereafter obtain that the weak
error for the numerical scheme of infinite horizon is of the order $1$ in terms
of the time step. We prove that the choice of step size can be uniform for all
test functions $\phi$. Subsequently we are able to estimate the average
periodic measure with ergodic numerical schemes.


Abstract:
  Perturbative supersymmetry breaking on the landscape of string vacua is
expected to favor large soft terms as a power-law or log distribution, but
tempered by an anthropic veto of inappropriate vacua or vacua leading to too
large a value for the derived weak scale -- a violation of the atomic
principle. Indeed, scans of such vacua yield a statistical prediction for light
Higgs boson mass m_h~ 125 GeV with sparticles (save possibly light higgsinos)
typically beyond LHC reach. In contrast, models of dynamical SUSY breaking
(DSB) -- with a hidden sector gauge coupling g^2 scanned uniformly -- lead to
gaugino condensation and a uniform distribution of soft parameters on a log
scale. Then soft terms are expected to be distributed as $m_{\rm soft}^{-1}$
favoring small values. A scan of DSB soft terms generally leads to $m_h\ll 125$
GeV and sparticle masses usually below LHC limits. Thus, the DSB landscape
scenario seems excluded from LHC search results. An alternative is that the
exponential suppression of the weak scale is set anthropically on the landscape
via the atomic principle.


Abstract:
  We propose an approach to saddle point optimization relying only on oracles
that solve minimization problems approximately. We analyze its convergence
property on a strongly convex--concave problem and show its linear convergence
toward the global min--max saddle point. Based on the convergence analysis, we
develop a heuristic approach to adapt the learning rate. An implementation of
the developed approach using the (1+1)-CMA-ES as the minimization oracle,
namely Adversarial-CMA-ES, is shown to outperform several existing approaches
on test problems. Numerical evaluation confirms the tightness of the
theoretical convergence rate bound as well as the efficiency of the learning
rate adaptation mechanism. As an example of real-world problems, the suggested
optimization method is applied to automatic berthing control problems under
model uncertainties, showing its usefulness in obtaining solutions robust to
uncertainty.


Abstract:
  Chest X-ray (CXR) is the most typical diagnostic X-ray examination for
screening various thoracic diseases. Automatically localizing lesions from CXR
is promising for alleviating radiologists' reading burden. However, CXR
datasets are often with massive image-level annotations and scarce lesion-level
annotations, and more often, without annotations. Thus far, unifying different
supervision granularities to develop thoracic disease detection algorithms has
not been comprehensively addressed. In this paper, we present OXnet, the first
deep omni-supervised thoracic disease detection network to our best knowledge
that uses as much available supervision as possible for CXR diagnosis. We first
introduce supervised learning via a one-stage detection model. Then, we inject
a global classification head to the detection model and propose dual attention
alignment to guide the global gradient to the local detection branch, which
enables learning lesion detection from image-level annotations. We also impose
intra-class compactness and inter-class separability with global prototype
alignment to further enhance the global information learning. Moreover, we
leverage a soft focal loss to distill the soft pseudo-labels of unlabeled data
generated by a teacher model. Extensive experiments on a large-scale chest
X-ray dataset show the proposed OXnet outperforms competitive methods with
significant margins. Further, we investigate omni-supervision under various
annotation granularities and corroborate OXnet is a promising choice to
mitigate the plight of annotation shortage for medical image diagnosis.


Abstract:
  In system identification, estimating parameters of a model using limited
observations results in poor identifiability. To cope with this issue, we
propose a new method to simultaneously select and estimate sensitive parameters
as key model parameters and fix the remaining parameters to a set of typical
values. Our method is formulated as a nonlinear least squares estimator with
L1-regularization on the deviation of parameters from a set of typical values.
First, we provide consistency and oracle properties of the proposed estimator
as a theoretical foundation. Second, we provide a novel approach based on
Levenberg-Marquardt optimization to numerically find the solution to the
formulated problem. Third, to show the effectiveness, we present an application
identifying a biomechanical parametric model of a head position tracking task
for 10 human subjects from limited data. In a simulation study, the variances
of estimated parameters are decreased by 96.1% as compared to that of the
estimated parameters without L1-regularization. In an experimental study, our
method improves the model interpretation by reducing the number of parameters
to be estimated while maintaining variance accounted for (VAF) at above 82.5%.
Moreover, the variances of estimated parameters are reduced by 71.1% as
compared to that of the estimated parameters without L1-regularization. Our
method is 54 times faster than the standard simplex-based optimization to solve
the regularized nonlinear regression.


Abstract:
  In this paper we continue the stability analysis of the model for coinfection
with density dependent susceptible population introduced in the 1st part of the
paper. We look for coexistence equilibrium points, their stability and
dependence on the carrying capacity $K$. Two sets of parameter value are
determined, each giving rise to different scenarios for the equilibrium branch
parametrized by $K$. In both scenarios the branch includes coexistence points
implying that both coinfection and single infection of both diseases can exist
together in a stable state. There are no simple explicit expression for these
equilibrium points and we will require a more delicate analysis of these points
with a new bifurcation technique adapted to such epidemic related problems. The
first scenario is described by the branch of stable equilibrium points which
includes a section of coexistence points starting at a bifurcation equilibrium
point with zero second single infections and finishing at another bifurcation
point with zero first single infections. In the second scenario the branch also
includes a section of coexistence equilibrium points with the same type of
starting point but the branch stays inside the positive cone after this. The
coexistence equilibrium points are stable at the start of the section. It stays
stable as long as the product of $K$ and the rate $\bar \gamma$ of coinfection
resulting from two single infections is small but, after this it can reach a
Hopf bifurcation and periodic orbits will appear.


Abstract:
  Mike Lockwood and Mathew Owens discuss how eclipse observations are aiding
the development of a climatology of near-Earth space


Abstract:
  Compound ion distributions, fi(v), have been measured by NASA's
Magnetospheric Multi-Scale Mission (MMS) and have been found in reconnection
simulations. A complex distribution, fi(v), consisting, for example, of
essentially disjoint pieces will be called a multi-beam distribution and
modeled as a sum of "beams," fi(v) = f1(v) + ... +fN(v). Velocity moments of
fi(v) are taken beam by beam and summed. Such multi-beam moments of fi(v) have
advantages over the customary standard velocity moments of fi(v), forwhich
there is only one mean flow velocity. For example, the standard thermal energy
momentof a pair of equal and opposite cold particle beams is non-zero even
though each beam has zero thermal energy. We therefore call this thermal energy
pseudo-thermal. By contrast, a multi-beam moment of two or more beams has no
pseudo-thermal energy. We develop three different ways of decomposing into a
sum and finding multi-beam moments for both a multi-beam fi(v) measured by MMS
in the dayside magnetosphere during reconnection and a multi-beam fi(v) found
in a PIC simulation of magnetotail reconnection. The three methods are: A
visual method in which the velocity centroid of each beam is estimated and its
density determined self-consistently; A k-means method in which particles in a
particle-representation of fi(v) are sorted into a minimum energy configuration
of N (= k) clusters; A nonlinear least squares method based on a fit to a sum
of N kappa functions.
  Multi-beam energy moments are calculated and compared with standard moments
for the thermal energy density, pressure tensor, thermal energy flux (heat plus
enthalpy fluxes), bulk kinetic energy density, RAM pressure and bulk kinetic
energy flux. Applying this new formalism to real data demonstrates in detail
how multi-beam techniques may provide significant insight into the properties
of observed space plasmas.


Abstract:
  Various algorithms in reinforcement learning exhibit dramatic variability in
their convergence rates and ultimate accuracy as a function of the problem
structure. Such instance-specific behavior is not captured by existing global
minimax bounds, which are worst-case in nature. We analyze the problem of
estimating optimal $Q$-value functions for a discounted Markov decision process
with discrete states and actions and identify an instance-dependent functional
that controls the difficulty of estimation in the $\ell_\infty$-norm. Using a
local minimax framework, we show that this functional arises in lower bounds on
the accuracy on any estimation procedure. In the other direction, we establish
the sharpness of our lower bounds, up to factors logarithmic in the state and
action spaces, by analyzing a variance-reduced version of $Q$-learning. Our
theory provides a precise way of distinguishing "easy" problems from "hard"
ones in the context of $Q$-learning, as illustrated by an ensemble with a
continuum of difficulty.


Abstract:
  We study the dependent type theory CaTT, introduced by Finster and Mimram,
which presents the theory of weak $\omega$-categories, following the idea that
type theories can be considered as presentations of generalized algebraic
theories. Our main contribution is a formal proof that the models of this type
theory correspond precisely to weak $\omega$-categories, as defined by
Maltsiniotis, by generalizing a definition proposed by Grothendieck for weak
$\omega$-groupoids: Those are defined as suitable presheaves over a
cat-coherator, which is a category encoding structure expected to be found in
an $\omega$-category. This comparison is established by proving the initiality
conjecture for the type theory CaTT, in a way which suggests the possible
generalization to a nerve theorem for a certain class of dependent type
theories


Abstract:
  Differentiable particle filters provide a flexible mechanism to adaptively
train dynamic and measurement models by learning from observed data. However,
most existing differentiable particle filters are within the bootstrap particle
filtering framework and fail to incorporate the information from latest
observations to construct better proposals. In this paper, we utilize
conditional normalizing flows to construct proposal distributions for
differentiable particle filters, enriching the distribution families that the
proposal distributions can represent. In addition, normalizing flows are
incorporated in the construction of the dynamic model, resulting in a more
expressive dynamic model. We demonstrate the performance of the proposed
conditional normalizing flow-based differentiable particle filters in a visual
tracking task.


Abstract:
  As Facial Expression Recognition (FER) systems become integrated into our
daily lives, these systems need to prioritise making fair decisions instead of
aiming at higher individual accuracy scores. Ranging from surveillance systems
to diagnosing mental and emotional health conditions of individuals, these
systems need to balance the accuracy vs fairness trade-off to make decisions
that do not unjustly discriminate against specific under-represented
demographic groups. Identifying bias as a critical problem in facial analysis
systems, different methods have been proposed that aim to mitigate bias both at
data and algorithmic levels. In this work, we propose the novel usage of
Continual Learning (CL), in particular, using Domain-Incremental Learning
(Domain-IL) settings, as a potent bias mitigation method to enhance the
fairness of FER systems while guarding against biases arising from skewed data
distributions. We compare different non-CL-based and CL-based methods for their
classification accuracy and fairness scores on expression recognition and
Action Unit (AU) detection tasks using two popular benchmarks, the RAF-DB and
BP4D datasets, respectively. Our experimental results show that CL-based
methods, on average, outperform other popular bias mitigation techniques on
both accuracy and fairness metrics.


Abstract:
  Accurate modeling of the electronic structure of warm dense matter is a
challenging problem whose solution would allow a better understanding of
material properties like equation of state, opacity, and conductivity, with
resulting applications from astrophysics to fusion energy research. Here we
explore the real-space Green's function method as a technique for solving the
Kohn-Sham density functional theory equations under warm dense matter
conditions. We find the method to be tractable and accurate throughout the
density and temperature range of interest, in contrast to other approaches.
Good agreement on equation of state is found when comparing to other methods,
where they are thought to be accurate.


Abstract:
  If $f$ is an entire function and $a$ is a complex number, $a$ is said to be
an asymptotic value of $f$ if there exists a path $\gamma$ from $0$ to infinity
such that $f(z) - a$ tends to $0$ as $z$ tends to infinity along $\gamma$. The
Denjoy--Carleman--Ahlfors Theorem asserts that if $f$ has $n$ distinct
asymptotic values, then the rate of growth of $f$ is at least order $n/2$, mean
type. A long-standing problem asks whether this conclusion holds for entire
functions having $n$ distinct asymptotic (entire) functions, each of growth at
most order $1/2$, minimal type. In this paper conditions on the function $f$
and associated asymptotic paths are obtained that are sufficient to guarantee
that $f$ satisfies the conclusion of the Denjoy--Carleman--Ahlfors Theorem. In
addition, for each positive integer $n$, an example is given of an entire
function of order $n$ having $n$ distinct, prescribed asymptotic functions,
each of order less than $1/2$.


Abstract:
  Taking the recently reported non-zero rotation angle of the cosmic microwave
background (CMB) linear polarization $\beta=0.35\pm0.14{\rm\, deg}$ as the hint
for a pseudo Nambu-Goldstone boson quintessence dark energy (DE), we study the
electroweak (EW) axion quintessence DE model where the axion mass is generated
by the EW instantons. We find that the observed value of $\beta$ implies a
non-trivial $U(1)$ electromagnetic anomaly coefficient ($c_{\gamma}$), once the
current constraint on the DE equation of state is also taken into account. With
the aid of the hypothetical high energy structure of the model inspired by the
experimentally inferred $c_{\gamma}$, the model is shown to be able to make
prediction for the current equation of state ($w_{\rm DE,0}$) of the
quintessence DE. This is expected to make our scenario distinguishable in
comparison with the cosmological constant ($w=-1$) and testable in future when
the error in the future measurement of $w_{\rm DE,0}$ is reduced to
$\mathcal{O}(1)\%$ level ($\delta w=\mathcal{O}(10^{-2})$).


Abstract:
  Graph Convolution Networks (GCNs) manifest great potential in recommendation.
This is attributed to their capability on learning good user and item
embeddings by exploiting the collaborative signals from the high-order
neighbors. Like other GCN models, the GCN based recommendation models also
suffer from the notorious over-smoothing problem - when stacking more layers,
node embeddings become more similar and eventually indistinguishable, resulted
in performance degradation. The recently proposed LightGCN and LR-GCN alleviate
this problem to some extent, however, we argue that they overlook an important
factor for the over-smoothing problem in recommendation, that is, high-order
neighboring users with no common interests of a user can be also involved in
the user's embedding learning in the graph convolution operation. As a result,
the multi-layer graph convolution will make users with dissimilar interests
have similar embeddings. In this paper, we propose a novel Interest-aware
Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order
graph convolution inside subgraphs. The subgraph consists of users with similar
interests and their interacted items. To form the subgraphs, we design an
unsupervised subgraph generation module, which can effectively identify users
with common interests by exploiting both user feature and graph structure. To
this end, our model can avoid propagating negative information from high-order
neighbors into embedding learning. Experimental results on three large-scale
benchmark datasets show that our model can gain performance improvement by
stacking more layers and outperform the state-of-the-art GCN-based
recommendation models significantly.


Abstract:
  Because manufacturing processes evolve fast, and since production visual
aspect can vary significantly on a daily basis, the ability to rapidly update
machine vision based inspection systems is paramount. Unfortunately, supervised
learning of convolutional neural networks requires a significant amount of
annotated images for being able to learn effectively from new data.
Acknowledging the abundance of continuously generated images coming from the
production line and the cost of their annotation, we demonstrate it is possible
to prioritize and accelerate the annotation process. In this work, we develop a
methodology for learning actively, from rapidly mined, weakly (i.e. partially)
annotated data, enabling a fast, direct feedback from the operators on the
production line and tackling a big machine vision weakness: false positives. We
also consider the problem of covariate shift, which arises inevitably due to
changing conditions during data acquisition. In that regard, we show
domain-adversarial training to be an efficient way to address this issue.


Abstract:
  A family of original formulae for computing number PI and its proof are
presented. An algorithm is proposed to validate the results of this new
algorithm.


Abstract:
  Time-lapse fluorescent microscopy (TLFM) combined with predictive
mathematical modelling is a powerful tool to study the inherently dynamic
processes of life on the single-cell level. Such experiments are costly,
complex and labour intensive. A complimentary approach and a step towards in
silico experimentation, is to synthesise the imagery itself. Here, we propose
Multi-StyleGAN as a descriptive approach to simulate time-lapse fluorescence
microscopy imagery of living cells, based on a past experiment. This novel
generative adversarial network synthesises a multi-domain sequence of
consecutive timesteps. We showcase Multi-StyleGAN on imagery of multiple live
yeast cells in microstructured environments and train on a dataset recorded in
our laboratory. The simulation captures underlying biophysical factors and time
dependencies, such as cell morphology, growth, physical interactions, as well
as the intensity of a fluorescent reporter protein. An immediate application is
to generate additional training and validation data for feature extraction
algorithms or to aid and expedite development of advanced experimental
techniques such as online monitoring or control of cells.
  Code and dataset is available at
https://git.rwth-aachen.de/bcs/projects/tp/multi-stylegan.


Abstract:
  Microservices have become a popular architectural style for data-driven
applications, given their ability to functionally decompose an application into
small and autonomous services to achieve scalability, strong isolation, and
specialization of database systems to the workloads and data formats of each
service. Despite the accelerating industrial adoption of this architectural
style, an investigation of the state of the practice and challenges
practitioners face regarding data management in microservices is lacking. To
bridge this gap, we conducted a systematic literature review of representative
articles reporting the adoption of microservices, we analyzed a set of popular
open-source microservice applications, and we conducted an online survey to
cross-validate the findings of the previous steps with the perceptions and
experiences of over 120 experienced practitioners and researchers.
  Through this process, we were able to categorize the state of practice of
data management in microservices and observe several foundational challenges
that cannot be solved by software engineering practices alone, but rather
require system-level support to alleviate the burden imposed on practitioners.
We discuss the shortcomings of state-of-the-art database systems regarding
microservices and we conclude by devising a set of features for
microservice-oriented database systems.


Abstract:
  We review recent studies of the operator product expansion of the plaquette
and of the associated determination of the gluon condensate. One first needs
the perturbative expansion to orders high enough to reach the asymptotic regime
where the renormalon behavior sets in. The divergent perturbative series is
formally regulated using the principal value prescription for its Borel
integral. Subtracting the perturbative series truncated at the minimal term, we
obtain the leading non-perturbative correction of the operator product
expansion, i.e. the gluon condensate, with superasymptotic accuracy. It is then
explored how to increase such precision within the context of the
hyperasymptotic expansion. The results fully confirm expectations from
renormalons and the operator product expansion.


Abstract:
  We propose and demonstrate that thermal laser evaporation can be applied to
all solid, non-radioactive elements in the periodic table. By depositing thin
films, we achieve growth rates exceeding 1 angstrom/s with output laser powers
less than 500 W, using identical beam parameters for many different elements.
The source temperature is found to vary linearly with laser power within the
examined power range. High growth rates are possible using free-standing
sources for most of the elements tested, eliminating the need for crucibles.


Abstract:
  Magnetic skyrmions are topologically stable nanoscale particle-like objects
that were discovered in 2009. Since that time, intense research interest has
led to the identification of numerous compounds that support skyrmions over a
range of conditions spanning cryogenic to room temperatures. Skyrmions can be
set into motion under various types of driving, and the combination of their
size, stability, and dynamics makes them ideal candidates for numerous
applications. Skyrmions represent a new class of system in which the energy
scales of the skyrmion-skyrmion interactions, sample disorder, temperature, and
drive can compete. A growing body of work indicates that the static and dynamic
states of skyrmions can be influenced strongly by pinning or disorder in the
sample; thus, an understanding of such effects is essential for the eventual
use of skyrmions in applications. In this article we review the current state
of knowledge regarding individual skyrmions and skyrmion assemblies interacting
with quenched disorder or pinning. We outline the microscopic mechanisms for
skyrmion pinning, including the repulsive and attractive interactions that can
arise from impurities, grain boundaries, or nanostructures. This is followed by
descriptions of depinning phenomena, sliding states over disorder, the effect
of pinning on the skyrmion Hall angle, the competition between thermal and
pinning effects, the control of skyrmion motion using ordered potential
landscapes such as one- or two-dimensional periodic asymmetric substrates, the
creation of skyrmion diodes, and skyrmion ratchet effects. We highlight the
distinctions arising from internal modes and the strong gyroscopic or Magnus
forces that cause the dynamical states of skyrmions to differ from those of
other systems with pinning. We also discuss future directions and open
questions related to the pinning and dynamics in skyrmion systems.


Abstract:
  The Municipality of Rondon\'opolis possesses several touristic attractions
such as a great diversity of waterfalls and little beaches located in the
surroundings of the urban area, which attract tourists from various locations.
Aiming to understand how ecotourism can contribute to the conservation of water
resources in the leisure areas, as well as their potential development of
touristic activities in those places. The procedures included the use of
various techniques subsidized in remote sensing and geoprocessing tools that
allowed the analysis and spatial distribution of tourism activities of the main
leisure areas. The spatial distribution of the waterfalls and its surroundings,
we observe the biophysical characters such as: the endemic vegetation, the
cachoeiras, the waterfalls, the rocky outcrops, rivers, little beaches and
espraiados. The results showed a correct perception of respondents on existing
inter-relationships between ecotourism practices and the sustainable use of
water resources. In conclusion though, a long way must be performed in order to
prevent the economic benefits of ecotourism generate an inappropriate
exploitation of natural resources, causing environmental problems, particularly
to water resources in the surroundings.


Abstract:
  We present a search for continuous gravitational waves emitted by neutron
stars in binary systems conducted on data from the early third observing run of
the Advanced LIGO and Advanced Virgo detectors using the semicoherent,
GPU-accelerated, BinarySkyHough pipeline. The search analyzes the most
sensitive frequency band of the LIGO detectors, 50 - 300 Hz. Binary orbital
parameters are split into four regions, comprising orbital periods of 3 - 45
days and projected semimajor axes of 2 - 40 light-seconds. No detections are
reported. We estimate the sensitivity of the search using simulated continuous
wave signals, achieving the most sensitive results to date across the analyzed
parameter space.


Abstract:
  Twisted spin waves attracted research attentions lately and the orbital
angular momentum they carry may be utilized in communication and computing
technologies. In this work, we manifest by micromagnetic simulations that
twisted spin wave modes naturally exist in thick ferromagnetic disks. The
twisted spin waves can be readily stimulated using rotating magnetic field when
it is tuned to the eigenfrequency of corresponding modes. We analytically
derive dispersion relation of the twisted spin waves and the results agree well
with the numerical studies. Lastly, we demonstrate that the topological charge
of twisted spin waves can be controlled by the exciting field.