Abstract:
  Redundant storage maintains the performance of distributed systems under
various forms of uncertainty. This paper considers the uncertainty in node
access and download service. We consider two access models under two download
service models. In one access model, a user can access each node with a fixed
probability, and in the other, a user can access a random fixed-size subset of
nodes. We consider two download service models. In the first (small file)
model, the randomness associated with the file size is negligible. In the
second (large file) model, randomness is associated with both the file size and
the system's operations. We focus on the service rate of the system. For a
fixed redundancy level, the systems' service rate is determined by the
allocation of coded chunks over the storage nodes. We consider quasi-uniform
allocations, where coded content is uniformly spread among a subset of nodes.
The question we address asks what the size of this subset (spreading) should
be. We show that in the small file model, concentrating the coded content to a
minimum-size subset is universally optimal. For the large file model, the
optimal spreading depends on the system parameters. These conclusions hold for
both access models.


Abstract:
  Thanks to the increasing growth of computational power and data availability,
the research in machine learning has advanced with tremendous rapidity.
Nowadays, the majority of automatic decision making systems are based on data.
However, it is well known that machine learning systems can present problematic
results if they are built on partial or incomplete data. In fact, in recent
years several studies have found a convergence of issues related to the ethics
and transparency of these systems in the process of data collection and how
they are recorded. Although the process of rigorous data collection and
analysis is fundamental in the model design, this step is still largely
overlooked by the machine learning community. For this reason, we propose a
method of data annotation based on Bayesian statistical inference that aims to
warn about the risk of discriminatory results of a given data set. In
particular, our method aims to deepen knowledge and promote awareness about the
sampling practices employed to create the training set, highlighting that the
probability of success or failure conditioned to a minority membership is given
by the structure of the data available. We empirically test our system on three
datasets commonly accessed by the machine learning community and we investigate
the risk of racial discrimination.


Abstract:
  Light scattering is the main limitation for optical imaging. However, light
can be focused through or inside turbid media by spatially shaping the incident
wavefront. Wavefront shaping is ultimately limited by the available photon
budget. We developed a new `dual reference' wavefront shaping algorithm that
optimally uses the available light. Our method allows for multi-target
wavefront shaping, making it suitable for transmission matrix measurements or
transmitting images. We experimentally confirmed the improvement of the focus
intensity compared to existing methods.


Abstract:
  We present unsupervised parameter learning in a Gaussian variational
inference setting that combines classic trajectory estimation for mobile robots
with deep learning for rich sensor data, all under a single learning objective.
The framework is an extension of an existing system identification method that
optimizes for the observed data likelihood, which we improve with modern
advances in batch trajectory estimation and deep learning. Though the framework
is general to any form of parameter learning and sensor modality, we
demonstrate application to feature and uncertainty learning with a deep network
for 3D lidar odometry. Our framework learns from only the on-board lidar data,
and does not require any form of groundtruth supervision. We demonstrate that
our lidar odometry performs better than existing methods that learn the full
estimator with a deep network, and comparable to state-of-the-art ICP-based
methods on the KITTI odometry dataset. We additionally show results on lidar
data from the Oxford RobotCar dataset.


Abstract:
  Mutual learning, in which multiple networks learn by sharing their knowledge,
improves the performance of each network. However, the performance of ensembles
of networks that have undergone mutual learning does not improve significantly
from that of normal ensembles without mutual learning, even though the
performance of each network has improved significantly. This may be due to the
relationship between the knowledge in mutual learning and the individuality of
the networks in the ensemble. In this study, we propose an ensemble method
using knowledge transfer to improve the accuracy of ensembles by introducing a
loss design that promotes diversity among networks in mutual learning. We use
an attention map as knowledge, which represents the probability distribution
and information in the middle layer of a network. There are many ways to
combine networks and loss designs for knowledge transfer methods. Therefore, we
use the automatic optimization of knowledge-transfer graphs to consider a
variety of knowledge-transfer methods by graphically representing conventional
mutual-learning and distillation methods and optimizing each element through
hyperparameter search. The proposed method consists of a mechanism for
constructing an ensemble in a knowledge-transfer graph, attention loss, and a
loss design that promotes diversity among networks. We explore optimal ensemble
learning by optimizing a knowledge-transfer graph to maximize ensemble
accuracy. From exploration of graphs and evaluation experiments using the
datasets of Stanford Dogs, Stanford Cars, and CUB-200-2011, we confirm that the
proposed method is more accurate than a conventional ensemble method.


Abstract:
  Automatic International Classification of Diseases (ICD) coding is defined as
a kind of text multi-label classification problem, which is difficult because
the number of labels is very large and the distribution of labels is
unbalanced. The label-wise attention mechanism is widely used in automatic ICD
coding because it can assign weights to every word in full Electronic Medical
Records (EMR) for different ICD codes. However, the label-wise attention
mechanism is computational redundant and costly. In this paper, we propose a
pseudo label-wise attention mechanism to tackle the problem. Instead of
computing different attention modes for different ICD codes, the pseudo
label-wise attention mechanism automatically merges similar ICD codes and
computes only one attention mode for the similar ICD codes, which greatly
compresses the number of attention modes and improves the predicted accuracy.
In addition, we apply a more convenient and effective way to obtain the ICD
vectors, and thus our model can predict new ICD codes by calculating the
similarities between EMR vectors and ICD vectors. Extensive experiments show
the superior performance of our model. On the public MIMIC-III dataset and
private Xiangya dataset, our model achieves micro f1 of 0.583 and 0.806,
respectively, which outperforms other competing models. Furthermore, we verify
the ability of our model in predicting new ICD codes. The case study shows how
pseudo label-wise attention works, and demonstrates the effectiveness of pseudo
label-wise attention mechanism.


Abstract:
  We have carried out a 3D ideal-MHD (Magnetohydrodynamic) simulation to study
the evolution of laser generated plasma plume in a moderate external magnetic
field (0.13 T) oriented perpendicular to the flow direction of the plasma
plume. The simulation shows that the plasma plume pushes the external magnetic
field lines outward in the direction of the expansion. This leads to
compression and bending of the magnetic field lines.The force resulting from
the change in shape and the density of magnetic field lines opposes the
expansion of the plume. An elliptic layer of shocked plasma is formed at the
plasma/external field interface leaving a cavity in the plume core due to the
outward expansion and the inertia of the plume. As the plasma pressure drops
due to expansion, the imbalance between the magnetic energy and the internal
energy results in the collapse of the cavity. These observations have striking
similarities with the observations of the experiments [Phys. Plasmas 24, 033511
(2017)] performed recently to study the plasma plume expansion in the presence
of an external transverse magnetic field. This similarity indicates that the
physical mechanisms dominantly governing the plasma plume expansion in the
moderate magnetic field are aptly described in the ideal MHD regime. The
studies thus show that the laser generated plasma plume can be utilized to
carry out interesting experiments on MHD phenomena in a simple laboratory set
up.


Abstract:
  The perturbative approach was adopted to develop a temperature-dependent
version of non-relativistic quantum mechanics in the limit of low-enough
temperatures. A generalized, self-consistent Hamiltonian was therefore
constructed for an arbitrary quantum-mechanical system in a way that the
ground-state Hamiltonian turned out to be just a limiting case at absolute
zero. The weak-coupling term connecting the system of interest and its
immediate environment was accordingly treated as the perturbation. Applying the
obtained generalized Hamiltonian to some typical quantum systems with exact
zero-temperature solutions, including the free particle in a box, the free
particle in vacuum, and the harmonic oscillator, up to the first order of
self-consistency, therefore corrected their associated Hamiltonians, energy
spectrums, and wavefunctions to be consistent with the low-temperature limit.
Further investigation revealed some kind of quantum tunneling effect by a
residual probability for the free particle in a box, as a chief consequence of
thermally coupling to the reservoir. The possible effects of thermal
environment on the main properties of the wavefunctions were also thoroughly
examined and discussed.


Abstract:
  We continue our endeavor to investigate lepton number violating (LNV)
processes at low energy in the framework of effective field theory (EFT). In
this work we study the LNV tau decays $\tau^+\rightarrow \ell^-P_i^{+}P_j^{+}$,
where $\ell=e,~\mu$ and $P^+_{i,j}$ are the lowest-lying charged pseudoscalars
$\pi^+,~K^+$. We analyze the dominant contributions in a series of EFTs from
high to low energy scales, namely, the standard model effective field theory
(SMEFT), the low-energy effective field theory (LEFT), and the chiral
perturbation theory ($\chi$PT). The decay branching ratios are expressed in
terms of the Wilson coefficients of dimension-five and -seven operators in
SMEFT and hadronic low energy constants. These Wilson coefficients involve the
first and second generations of quarks and all generations of leptons and thus
cannot be explored in low energy processes such as nuclear neutrinoless double
decay or LNV kaon decays. Unfortunately, the current experimental upper limits
on the branching ratios are too weak to set useful constraints on those
coefficients. Or, if we assume the new physics scale is larger than 1 TeV, the
branching ratios are well below the current experimental bounds. We also
estimate hadronic uncertainties incurred in applying $\chi$PT to $\tau$ decays
by computing one-loop chiral logarithms and attempt to improve convergence of
chiral perturbation by employing dispersion relations in the short-distance
part of the decay amplitudes.


Abstract:
  Decision support systems have become increasingly popular in the domain of
agriculture. With the development of automated machine learning, agricultural
experts are now able to train, evaluate and make predictions using cutting edge
machine learning (ML) models without the need for much ML knowledge. Although
this automated approach has led to successful results in many scenarios, in
certain cases (e.g., when few labeled datasets are available) choosing among
different models with similar performance metrics is a difficult task.
Furthermore, these systems do not commonly allow users to incorporate their
domain knowledge that could facilitate the task of model selection, and to gain
insight into the prediction system for eventual decision making. To address
these issues, in this paper we present AHMoSe, a visual support system that
allows domain experts to better understand, diagnose and compare different
regression models, primarily by enriching model-agnostic explanations with
domain knowledge. To validate AHMoSe, we describe a use case scenario in the
viticulture domain, grape quality prediction, where the system enables users to
diagnose and select prediction models that perform better. We also discuss
feedback concerning the design of the tool from both ML and viticulture
experts.


Abstract:
  Main challenges in long-tailed recognition come from the imbalanced data
distribution and sample scarcity in its tail classes. While techniques have
been proposed to achieve a more balanced training loss and to improve tail
classes data variations with synthesized samples, we resort to leverage readily
available unlabeled data to boost recognition accuracy. The idea leads to a new
recognition setting, namely semi-supervised long-tailed recognition. We argue
this setting better resembles the real-world data collection and annotation
process and hence can help close the gap to real-world scenarios. To address
the semi-supervised long-tailed recognition problem, we present an alternate
sampling framework combining the intuitions from successful methods in these
two research areas. The classifier and feature embedding are learned separately
and updated iteratively. The class-balanced sampling strategy has been
implemented to train the classifier in a way not affected by the pseudo labels'
quality on the unlabeled data. A consistency loss has been introduced to limit
the impact from unlabeled data while leveraging them to update the feature
embedding. We demonstrate significant accuracy improvements over other
competitive methods on two datasets.


Abstract:
  Let $F^{2,m}(\mathbb{C})$ denote the Fock-Sobolev space of complex plane. In
this paper, we complete characterize the semi-commutator
$(T_f,T_{\overline{g}}]$ on $F^{2,m}(\mathbb{C})(m>0)$ is zero, where $f$ and
$g$ are functions in the set of all finite linear combinations of kernel
functions. We obtain $(T_f,T_{\overline{g}}]=0$ if and only if at least one of
$f$ and $g$ is a constant function. The result is different from the result of
Bauer, Choe, and Koo. (J. Funct. Anal., 268 (2015): 3017-3060.)


Abstract:
  Aim: This study is looking for a common pathogenicity between SARS-CoV-2 and
plasmodium species, in individuals with certain HLA serotypes. Methods: 1-)
Tblastx searches of SARS-CoV-2 are performed by limiting searches to plasmodium
species that infect human. 2-) Aligned sequences in the respective organisms'
proteomes are searched with blastp. 3-) Binding predictions of the identified
SARS-CoV-2 peptide to MHC class I supertype representatives are performed. 4-)
Blastp searches of predicted-epitopes that bind strongly to the identified HLA
allele are performed by limiting searches to human and to the plasmodium
species. 5-) Peptides with minimum 60 % identity to the predicted-epitopes are
found in results. 6-) Peptides among those, which bind strongly to the same HLA
allele, are predicted. 7-) Step-4 is repeated by limiting searches to human,
for peptides sourced by limiting searches to plasmodium species at step-4. 8-)
Step-5 and 6 are performed with results of 7. Results: CFLGYFCTCYFGLFC peptide
of SARS-CoV-2 has the highest identity to P. vivax. Its GYFCTCYFGLF and
YFCTCYFGLF parts are predicted to bind strongly to HLA-A*24:02. Results
obtained only for peptides homologous to YFCTCYFGLF, as follows: YYCARRFGLF,
YYCHCPFGVF, and YYCQQYFFLF are potential HLA-A*24:02 epitopes in the human
proteome. Among FFYTFYFELF, YFVACLFILF, and YFPTITFHLF peptides in the
plasmodium species' proteomes with strong binding affinity to HLA-A*24:02, only
FFYTFYFELF of P. falciparum is homologous to the potential HLA-A*24:02 epitope
YFYLFSLELF in the human proteome. Conclusion: Immune responses to the
identified-peptides with similar sequences and strong binding affinities to
HLA-A*24:02 may lead to autoimmune response risk in individuals with
HLA-A*24:02 serotypes, upon getting infected with SARS-CoV-2 or P. falciparum.


Abstract:
  The purpose of this paper is to study the existence and uniqueness of
solutions to a system of Stochastic Differential Equations (SDEs). The
coordinates are bounded by zero and one, and repulse each other according to a
Coulombian like interaction force. We show the existence of strong and pathwise
unique solutions to the system until the first multiple collision at zero or
one, and give a sufficient condition on the parameters of the SDEs for this
multiple collision not to occur in finite time.


Abstract:
  Since the end of 2019, the SARS-CoV-2 virus known as COVID-19 has spread
rapidly around the world, forcing many governments to impose restrictive
blocking or lockdown to combat the pandemic. With locomotion restriction of
people in almost of countries of the world, workers and students needed to keep
their activities at home. As a result, people's behavior, habits, and the way
they started using the Internet changed significantly. Like professionals of
offices, the younger played an important role in this behavior, especially in
the type of resources used by them. As result, the characterization and traffic
of communication networks were affected in some way. In this perspective
article, we join from many available studies about the COVID-19 effect at
networks and investigate the effects on the Internet traffic of using services
such as video streaming, video conferencing, and gaming during 2020's months of
the pandemic.


Abstract:
  We propose a feature generator backbone composed of an ensemble of
convolutional neuralnetworks (CNNs) to improve the recently emerging Vision
Transformer (ViT) models. We tackled the RSNA intracranial hemorrhage
classification problem, i.e., identifying various hemorrhage types from
computed tomography (CT) slices. We show that by gradually stacking several
feature maps extracted using multiple Xception CNNs, we can develop a
feature-rich input for the ViT model. Our approach allowed the ViT model to pay
attention to relevant features at multiple levels. Moreover, pretraining the n
CNNs using various paradigms leads to a diverse feature set and further
improves the performance of the proposed n-CNN-ViT. We achieved a test accuracy
of 98.04% with a weighted logarithmic loss value of 0.0708. The proposed
architecture is modular and scalable in both the number of CNNs used for
feature extraction and the size of the ViT.


Abstract:
  Refractory masonry (refractories) is exposed to in-service loads of different
types. To rationalise the masonry design and failure analysis, differences of
failure under cyclic and monotonic loading were studied. For samples of silica
refractories tested in wedge splitting set-up global failure parameters and
crack trajectories were assessed. Under cyclic loading, higher fracture energy
and lower brittleness at failure were seen. Cracks of different modes had
similar non-linearity and branching. However, the size and microstructural
characteristics of the fracture process zone was different. In addition, higher
energy dissipation during cyclic loading is promoted by repetitive friction
events along the crack trajectory.


Abstract:
  We present a way to transfer maximally- or partially-entangled states of n
single-photon-state (SPS) qubits onto n coherent-state (CS) qubits, by
employing 2n microwave cavities coupled to a superconducting flux qutrit. The
two logic states of a SPS qubit here are represented by the vacuum state and
the single-photon state of a cavity, while the two logic states of a CS qubit
are encoded with two coherent states of a cavity. Because of using only one
superconducting qutrit as the coupler, the circuit architecture is
significantly simplified. The operation time for the state transfer does not
increase with the increasing of the number of qubits. When the dissipation of
the system is negligible, the quantum state can be transferred in a
deterministic way since no measurement is required. Furthermore, the
higher-energy intermediate level of the coupler qutrit is not excited during
the entire operation and thus decoherence from the qutrit is greatly
suppressed. As a specific example, we numerically demonstrate that the
high-fidelity transfer of a Bell state of two SPS qubits onto two CS qubits is
achievable within the present-day circuit QED technology. Finally, it is worthy
to note that when the dissipation is negligible, entangled states of n CS
qubits can be transferred back onto n SPS qubits by performing reverse
operations. This proposal is quite general and can be extended to accomplish
the same task, by employing a natural or artificial atom to couple 2n microwave
or optical cavities.


Abstract:
  This paper is concerned with the development, analysis and numerical
realization of a novel variational model for the regularization of inverse
problems in imaging. The proposed model is inspired by the architecture of
generative convolutional neural networks; it aims to generate the unknown from
variables in a latent space via multi-layer convolutions and non-linear
penalties, and penalizes an associated cost. In contrast to conventional
neural-network-based approaches, however, the convolution kernels are learned
directly from the measured data such that no training is required. The present
work provides a mathematical analysis of the proposed model in a function space
setting, including proofs for regularity and existence/stability of solutions,
and convergence for vanishing noise. Moreover, in a discretized setting, a
numerical algorithm for solving various types of inverse problems with the
proposed model is derived. Numerical results are provided for applications in
inpainting, denoising, deblurring under noise, super-resolution and JPEG
decompression with multiple test images.


Abstract:
  We study the front of the solution to the F-KPP equation with randomized
non-linearity. Under suitable assumptions on the randomness involving spatial
mixing behavior and boundedness, we show that the front of the solution lags at
most logarithmically in time behind the front of the solution of the
corresponding linearized equation, i.e. the parabolic Anderson model. This can
be interpreted as a partial generalization of Bramson's findings for the
homogeneous setting. Building on this result, we establish functional central
limit theorems for the fronts of the solutions to both equations.


Abstract:
  The young (50-400 Myr) A3V star $\beta$ Leo is a primary target to study the
formation history and evolution of extrasolar planetary systems as one of the
few stars with known hot ($\sim$1600$^\circ$K), warm ($\sim$600$^\circ$K), and
cold ($\sim$120$^\circ$K) dust belt components. In this paper, we present deep
mid-infrared measurements of the warm dust brightness obtained with the Large
Binocular Telescope Interferometer (LBTI) as part of its exozodiacal dust
survey (HOSTS). The measured excess is 0.47\%$\pm$0.050\% within the central
1.5 au, rising to 0.81\%$\pm$0.026\% within 4.5 au, outside the habitable zone
of $\beta$~Leo. This dust level is 50 $\pm$ 10 times greater than in the solar
system's zodiacal cloud. Poynting-Robertson drag on the cold dust detected by
Spitzer and Herschel under-predicts the dust present in the habitable zone of
$\beta$~Leo, suggesting an additional delivery mechanism (e.g.,~comets) or an
additional belt at $\sim$5.5 au. A model of these dust components is provided
which implies the absence of planets more than a few Saturn masses between
$\sim$5 au and the outer belt at $\sim$40 au. We also observationally constrain
giant planets with the LBTI imaging channel at 3.8~$\mu$m wavelength. Assuming
an age of 50 Myr, any planet in the system between approximately 5 au to 50 au
must be less than a few Jupiter masses, consistent with our dust model. Taken
together, these observations showcase the deep contrasts and detection
capabilities attainable by the LBTI for both warm exozodiacal dust and giant
exoplanets in or near the habitable zone of nearby stars.


Abstract:
  Blockchain technologies originate from cryptocurrencies. Thus, most
blockchain technologies assume an environment with a fast and stable network.
However, in some blockchain-based systems, e.g., supply chain management (SCM)
systems, some Internet of Things (IOT) nodes can only rely on the low-quality
network sometimes to achieve consensus. Thus, it is critical to understand the
applicability of existing consensus algorithms in such environments. We
performed a systematic mapping study to evaluate and compare existing consensus
mechanisms' capability to provide integrity and security with varying network
properties. Our study identified 25 state-of-the-art consensus algorithms from
published and preprint literature. We categorized and compared the consensus
algorithms qualitatively based on established performance and integrity metrics
and well-known blockchain security issues. Results show that consensus
algorithms rely on the synchronous network for correctness cannot provide the
expected integrity. Such consensus algorithms may also be vulnerable to
distributed-denial-of-service (DDOS) and routing attacks, given limited network
throughput. Conversely, asynchronous consensus algorithms, e.g.,
Honey-BadgerBFT, are deemed more robust against many of these attacks and may
provide high integrity in asynchrony events.


Abstract:
  The segmentation of medical images is a fundamental step in automated
clinical decision support systems. Existing medical image segmentation methods
based on supervised deep learning, however, remain problematic because of their
reliance on large amounts of labelled training data. Although medical imaging
data repositories continue to expand, there has not been a commensurate
increase in the amount of annotated data. Hence, we propose a new spatial
guided self-supervised clustering network (SGSCN) for medical image
segmentation, where we introduce multiple loss functions designed to aid in
grouping image pixels that are spatially connected and have similar feature
representations. It iteratively learns feature representations and clustering
assignment of each pixel in an end-to-end fashion from a single image. We also
propose a context-based consistency loss that better delineates the shape and
boundaries of image regions. It enforces all the pixels belonging to a cluster
to be spatially close to the cluster centre. We evaluated our method on 2
public medical image datasets and compared it to existing conventional and
self-supervised clustering methods. Experimental results show that our method
was most accurate for medical image segmentation.


Abstract:
  In this paper, we introduce a framework for segmenting instances of a common
object class by multiple active contour evolution over semantic segmentation
maps of images obtained through fully convolutional networks. The contour
evolution is cast as an energy minimization problem, where the aggregate energy
functional incorporates a data fit term, an explicit shape model, and accounts
for object overlap. Efficient solution neighborhood operators are proposed,
enabling optimization through metaheuristics such as simulated annealing. We
instantiate the proposed framework in the context of segmenting individual
fallen stems from high-resolution aerial multispectral imagery. We validated
our approach on 3 real-world scenes of varying complexity. The test plots were
situated in regions of the Bavarian Forest National Park, Germany, which
sustained a heavy bark beetle infestation. Evaluations were performed on both
the polygon and line segment level, showing that the multi-contour segmentation
can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7
percentage points (pp) in recall and 6 in precision compared to an iterative
sample consensus line segment detection was achieved. Despite the simplicity of
the applied shape parametrization, an explicit shape model incorporated into
the energy function improved the results by up to 4 pp of recall. Finally, we
show the importance of using a deep learning based semantic segmentation method
as the basis for individual stem detection. Our method is a step towards
increased accessibility of automatic fallen tree mapping, due to higher cost
efficiency of aerial imagery acquisition compared to laser scanning. The
precise fallen tree maps could be further used as a basis for plant and animal
habitat modeling, studies on carbon sequestration as well as soil quality in
forest ecosystems.


Abstract:
  Isolating neutral and charged particles from the environment is essential in
precision experiments. For decades, this has been achieved by trapping ions
with radio-frequency (rf) fields and neutral particles with optical fields.
Recently, trapping of ions by interaction with light has been demonstrated.
This might permit combining the advantages of optical trapping and ions. For
example, by superimposing optical traps to investigate ensembles of ions and
atoms in absence of any radiofrequency fields, as well as to benefit from the
versatile and scalable trapping geometries featured by optical lattices. In
particular, ions provide individual addressability, electronic and motional
degrees of freedom that can be coherently controlled and detected via high
fidelity, state-dependent operations. Their long-range Coulomb interaction is
significantly larger compared to those of neutral atoms and molecules. This
qualifies to study ultra-cold interaction and chemistry of trapped ions and
atoms, as well as to provide a novel platform for higher-dimensional
experimental quantum simulations. The aim of this topical review is to present
the current state of the art and to discuss current challenges and the
prospects of the emerging field.


Abstract:
  Let $R>1$ and let $B$ be the Euclidean $4$-ball of radius $R$ with a closed
subset ${E}$ removed. Suppose that $B$ embeds symplectically into the unit
cylinder $\mathbb{D}^2 \times \mathbb{R}^2$. By Gromov's non-squeezing theorem,
${E}$ must be non-empty. We prove that the Minkowski dimension of ${E}$ is at
least $2$, and we exhibit an explicit example showing that this result is
optimal at least for $R \leq \sqrt{2}$. We also discuss the minimum volume of
${E}$ in the case that the symplectic embedding extends, with bounded Lipschitz
constant, to the entire ball.


Abstract:
  The contextual combinatorial semi-bandit problem with linear payoff functions
is a decision-making problem in which a learner chooses a set of arms with the
feature vectors in each round under given constraints so as to maximize the sum
of rewards of arms. Several existing algorithms have regret bounds that are
optimal with respect to the number of rounds $T$. However, there is a gap of
$\tilde{O}(\max(\sqrt{d}, \sqrt{k}))$ between the current best upper and lower
bounds, where $d$ is the dimension of the feature vectors, $k$ is the number of
the chosen arms in a round, and $\tilde{O}(\cdot)$ ignores the logarithmic
factors. The dependence of $k$ and $d$ is of practical importance because $k$
may be larger than $T$ in real-world applications such as recommender systems.
In this paper, we fill the gap by improving the upper and lower bounds. More
precisely, we show that the C${}^2$UCB algorithm proposed by Qin, Chen, and Zhu
(2014) has the optimal regret bound $\tilde{O}(d\sqrt{kT} + dk)$ for the
partition matroid constraints. For general constraints, we propose an algorithm
that modifies the reward estimates of arms in the C${}^2$UCB algorithm and
demonstrate that it enjoys the optimal regret bound for a more general problem
that can take into account other objectives simultaneously. We also show that
our technique would be applicable to related problems. Numerical experiments
support our theoretical results and considerations.


Abstract:
  It has recently been argued that the geometric-optics mean path length of
rays inside a refractive object under Lambertian illumination is independent of
the scattering strength of the medium [Savo et al., Science 358, 765 (2017)].
We here show that it is in fact different in the case of zero-scattering. We
uncover and explain the role of trapped ray trajectories in creating this
unexpected discontinuity from zero- to low-scattering. This allows us to derive
new analytic results for the zero-scattering mean path length of simple
refractive shapes. This work provides a fresh perspective on the study of path
length inside refractive objects, with possible applications in for example the
study of scattering by large particles or the design of optical systems.


Abstract:
  We establish quantitative upper and lower bounds for Schr\"odinger operators
with complex potentials that satisfy some weak form of sparsity. Our first
result is a quantitative version of an example, due to S.\ Boegli (Comm. Math.
Phys., 2017, 352, 629-639), of a Schr\"odinger operator with eigenvalues
accumulating to every point of the essential spectrum. The second result shows
that the eigenvalue bounds of Frank (Bull. Lond. Math. Soc., 2011, 43, 745-750
and Trans. Amer. Math. Soc., 2018, 370, 219-240) can be improved for sparse
potentials. The third result generalizes a theorem of Klaus (Ann. Inst. H.
Poincar\'e Sect. A (N.S.), 1983, 38, 7-13) on the characterization of the
essential spectrum to the multidimensional non-selfadjoint case. The fourth
result shows that, in one dimension, the purely imaginary (non-sparse) step
potential has unexpectedly many eigenvalues, comparable to the number of
resonances. Our examples show that several known upper bounds are sharp.


Abstract:
  The solar radio flux at F10.7 cm and F30 cm is required by most models
characterizing the state of the Earth's upper atmosphere, such as the
thermosphere and ionosphere to specify satellite orbits, re-entry services,
collision avoidance maneuvers and modeling of space debris evolution. We
develop a method called RESONANCE ("Radio Emissions from the Sun: ONline
ANalytical Computer-aided Estimator") for the prediction of the 13-month
smoothed monthly mean F10.7 and F30 indices 1-24 months ahead. The prediction
algorithm includes three steps. First, we apply a 13-month optimized running
mean technique to effectively reduce the noise in the radio flux data. Second,
we provide initial predictions of the F10.7 and F30 indices using the
McNish-Lincoln method. Finally, we improve these initial predictions by
developing an adaptive Kalman filter with the error statistics identification.
The root-mean-square-error of predictions with lead times from 1 to 24 months
is 5-27 sfu for the F10.7 and 3-16 sfu for F30 index, which statistically
outperforms current algorithms in use. The proposed approach based on Kalman
filter is universal and can be applied to improve the initial predictions of a
process under study provided by any other forecasting method. Furthermore, we
present a systematic evaluation of re-entry forecast as an application to test
the performance of F10.7 predictions on past ESA re-entry campaigns for
payloads, rocket bodies, and space debris that re-entered from June 2006 to
June 2019. The test results demonstrate that the predictions obtained by
RESONANCE in general also lead to improvements in the forecasts of re-entry
epochs.


Abstract:
  We propose a framework for deformable linear object prediction. Prediction of
deformable objects (e.g., rope) is challenging due to their non-linear dynamics
and infinite-dimensional configuration spaces. By mapping the dynamics from a
non-linear space to a linear space, we can use the good properties of linear
dynamics for easier learning and more efficient prediction. We learn a locally
linear, action-conditioned dynamics model that can be used to predict future
latent states. Then, we decode the predicted latent state into the predicted
state. We also apply a sampling-based optimization algorithm to select the
optimal control action. We empirically demonstrate that our approach can
predict the rope state accurately up to ten steps into the future and that our
algorithm can find the optimal action given an initial state and a goal state.


Abstract:
  Recent measurements performed by the ATLAS and CMS collaborations on the Run
2 dataset are testing QCD with unprecedented precision. The wealth of data, an
ever-improving experimental understanding of jets and photons, and novel
measurements of jet substructure enable an improved understanding.


Abstract:
  Hierarchical text classification consists in classifying text documents into
a hierarchy of classes and sub-classes. Although artificial neural networks
have proved useful to perform this task, unfortunately they can leak training
data information to adversaries due to training data memorization. Using
differential privacy during model training can mitigate leakage attacks against
trained models, enabling the models to be shared safely at the cost of reduced
model accuracy. This work investigates the privacy-utility trade-off in
hierarchical text classification with differential privacy guarantees, and
identifies neural network architectures that offer superior trade-offs. To this
end, we use a white-box membership inference attack to empirically assess the
information leakage of three widely used neural network architectures. We show
that large differential privacy parameters already suffice to completely
mitigate membership inference attacks, thus resulting only in a moderate
decrease in model utility. More specifically, for large datasets with long
texts we observed Transformer-based models to achieve an overall favorable
privacy-utility trade-off, while for smaller datasets with shorter texts
convolutional neural networks are preferable.


Abstract:
  Image composition assessment is crucial in aesthetic assessment, which aims
to assess the overall composition quality of a given image. However, to the
best of our knowledge, there is neither dataset nor method specifically
proposed for this task. In this paper, we contribute the first composition
assessment dataset CADB with composition scores for each image provided by
multiple professional raters. Besides, we propose a composition assessment
network SAMP-Net with a novel Saliency-Augmented Multi-pattern Pooling (SAMP)
module, which analyses visual layout from the perspectives of multiple
composition patterns. We also leverage composition-relevant attributes to
further boost the performance, and extend Earth Mover's Distance (EMD) loss to
weighted EMD loss to eliminate the content bias. The experimental results show
that our SAMP-Net can perform more favorably than previous aesthetic assessment
approaches.


Abstract:
  As proposed to describe putative continuous phase transitions between two
ordered phases, the deconfined quantum critical point (DQCP) goes beyond the
prevalent Landau-Ginzburg-Wilson (LGW) paradigm since its critical theory is
not expressed in terms of the order parameters characterizing either state, but
involves fractionalized degrees of freedom and an emergent symmetry. So far,
great efforts have been spent on its equilibrium properties, but the
nonequilibrium properties therein are largely unknown. Here we study the
nonequilibrium dynamics of the DQCP via the imaginary-time evolution in the
two-dimensional (2D) J-Q$_3$ model. We discover fascinating nonequilibrium
scaling behaviors hinging on the process of fractionization and the dynamics of
emergent symmetry associated with two length scales. Our findings not only
constitute a new realm of nonequilibrium criticality in DQCP, but also offer a
controllable knob by which to investigate the dynamics in strongly correlated
systems.


Abstract:
  For deployment, neural architecture search should be hardware-aware, in order
to satisfy the device-specific constraints (e.g., memory usage, latency and
energy consumption) and enhance the model efficiency. Existing methods on
hardware-aware NAS collect a large number of samples (e.g., accuracy and
latency) from a target device, either builds a lookup table or a latency
estimator. However, such approach is impractical in real-world scenarios as
there exist numerous devices with different hardware specifications, and
collecting samples from such a large number of devices will require prohibitive
computational and monetary cost. To overcome such limitations, we propose
Hardware-adaptive Efficient Latency Predictor (HELP), which formulates the
device-specific latency estimation problem as a meta-learning problem, such
that we can estimate the latency of a model's performance for a given task on
an unseen device with a few samples. To this end, we introduce novel hardware
embeddings to embed any devices considering them as black-box functions that
output latencies, and meta-learn the hardware-adaptive latency predictor in a
device-dependent manner, using the hardware embeddings. We validate the
proposed HELP for its latency estimation performance on unseen platforms, on
which it achieves high estimation performance with as few as 10 measurement
samples, outperforming all relevant baselines. We also validate end-to-end NAS
frameworks using HELP against ones without it, and show that it largely reduces
the total time cost of the base NAS method, in latency-constrained settings.
Code is available at https://github.com/HayeonLee/HELP.


Abstract:
  Canonical quantization of gravitational systems is obstructed by the problem
of time. Due to diffeomorphism symmetry the Hamiltonian vanishes: dynamics with
respect to a background time parameter appears "frozen." Two strategies towards
the quantization of such systems are the identification of a clock degree of
freedom before quantization (deparametrization), and quantization on a
kinematical Hilbert space which is subject to constraints (Dirac quantization).
The usual canonical quantization in quantum field theory is analogous to
deparametrization. Here we introduce a frozen formalism and Dirac quantization
for a complex Klein-Gordon scalar field, and show that the resulting theory is
equivalent to usual canonical quantization. We then apply the formalism to the
group field theory formalism for quantum gravity, for which both
deparametrization and a "timeless" quantization have been proposed in past
work. We show how a frozen formalism for group field theory links between these
two existing approaches, and illustrate in particular the construction of
physical observables. We derive effective cosmological dynamics for group field
theory in the new formalism and compare these to previous work. The frozen
formalism could be extended to other approaches to quantum gravity that do not
use a preferred time parameter.


Abstract:
  Intelligent reflection surface (IRS) is emerging as a promising technique for
future wireless communications. Considering its excellent capability in
customizing the channel conditions via energy-focusing and energy-nulling, it
is an ideal technique for enhancing wireless communication security and
privacy, through the theories of physical layer security and covert
communications, respectively. In this article, we first present some results on
applying IRS to improve the average secrecy rate in wiretap channels, to enable
perfect communication covertness, and to deliberately create extra randomness
in wireless propagations for hiding active wireless transmissions. Then, we
identify multiple challenges for future research to fully unlock the benefits
offered by IRS in the context of physical layer security and covert
communications. With the aid of extensive numerical studies, we demonstrate the
necessity of designing the amplitudes of the IRS elements in wireless
communications with the consideration of security and privacy, where the
optimal values are not always $1$ as commonly adopted in the literature.
Furthermore, we reveal the tradeoff between the achievable secrecy performance
and the estimation accuracy of the IRS's channel state information (CSI) at
both the legitimate and malicious users, which presents the fundamental
resource allocation challenge in the context of IRS-aided physical layer
security. Finally, a passive channel estimation methodology exploiting deep
neural networks and scene images is discussed as a potential solution to
enabling CSI availability without utilizing resource-hungry pilots. This
methodology serves as a visible pathway to significantly improving the covert
communication rate in IRS-aided wireless networks.


Abstract:
  In a time-reversal invariant system, while the anomalous Hall effect
identically vanishes in the linear response regime due to the constraint of
time-reversal symmetry on the distribution of Berry curvature, a nonlinear Hall
effect can emerge in the second-order response regime if the inversion symmetry
is broken to allow a nonzero Berry curvature dipole (BCD) on the Fermi surface.
In this work, we study the nonlinear Hall effect of the BCD origin in
two-dimensional doped insulators and semimetals belonging to the symmetry class
AI which has spinless time-reversal symmetry. Despite that the class AI does
not host any strong topological insulator phase in two dimensions, we find that
they can still be classified as topologically obstructed insulators and trivial
insulators if putting certain constraint on the Hamiltonians. When the
insulator gets closer to the phase boundary of the two distinct phases, we find
that the BCDs will become more prominent if the doping level is located near
the band edge. Moreover, when the insulator undergoes a phase transition
between the two distinct phases, we find that the BCDs will dramatically change
their signs. For the semimetals without inversion symmetry, we find that the
BCDs will sharply reverse their signs when the doping level crosses the Dirac
points. With the shift of the locations of Dirac points in energy, the critical
doping level at which the BCDs sharply reverse their signs will accordingly
change. Our study reveals that class AI materials can also have interesting
geometrical and topological properties, and remarkable nonlinear Hall effect
can also appear in this class of materials even though the spin-orbit coupling
is negligible. Our findings broaden the scope of materials to study the
nonlinear Hall effect and provide new perspectives for the application of this
effect.


Abstract:
  We study Choquard type equation of the form $$-\Delta u +\varepsilon
u-(I_{\alpha}*|u|^p)|u|^{p-2}u+|u|^{q-2}u=0\quad in \quad {\mathbb
R}^N,\qquad\qquad(P_\varepsilon)$$ where $N\geq3$, $I_\alpha$ is the Riesz
potential with $\alpha\in(0,N)$, $p>1$, $q>2$ and $\varepsilon\ge 0$. Equations
of this type describe collective behaviour of self-interacting many-body
systems. The nonlocal nonlinear term represents long-range attraction while the
local nonlinear term represents short-range repulsion. In the first part of the
paper for a nearly optimal range of parameters we prove the existence and study
regularity and qualitative properties of positive groundstates of $(P_0)$ and
of $(P_\varepsilon)$ with $\varepsilon>0$. We also study the existence of a
compactly supported groundstate for an integral Thomas-Fermi type equation
associated to $(P_\varepsilon)$. In the second part of the paper, for
$\varepsilon\to 0$ we identify six different asymptotic regimes and provide a
characterisation of the limit profiles of the groundstates of $(P_\varepsilon)$
in each of the regimes. We also outline three different asymptotic regimes in
the case $\varepsilon\to\infty$. In one of the asymptotic regimes positive
groundstates of $(P_\varepsilon)$ converge to a compactly supported
Thomas-Fermi limit profile. This is a new and purely nonlocal phenomenon that
can not be observed in the local prototype case of $(P_\varepsilon)$ with
$\alpha=0$. In particular, this provides a justification for the Thomas-Fermi
approximation in astrophysical models of self-gravitating Bose-Einstein
condensate.


Abstract:
  We study the thermodynamics of a $d$-dimensional Schwarzschild black hole in
the canonical ensemble. This generalizes York's formalism to any number $d$ of
dimensions. The canonical ensemble, characterized by a cavity of fixed radius
$r$ and fixed temperature $T$ at the boundary, allows for two possible
solutions in thermal equilibrium, a small and a large black hole. From the
Euclidean action and the path integral approach, we obtain the free energy, the
thermodynamic energy, the pressure, and the entropy, of the black hole plus
cavity system. The entropy is given by the Bekenstein-Hawking area law. The
heat capacity shows that the smaller black hole is in unstable equilibrium and
the larger is stable. The photon sphere radius divides the stability criterion.
To study perturbations, a generalized free energy function is obtained that
allows to understand the possible phase transitions between classical hot flat
space and the black holes. The Buchdahl radius, that appears naturally in the
general relativistic study of star structure, also shows up in our context, the
free energy is zero when the cavity's radius has the Buchdahl radius value.
Then, if the cavity's radius is smaller than the Buchdahl radius classical hot
flat space can nucleate a black hole. It is also pointed out the link between
the canonical analysis performed and the direct perturbation of the path
integral. Since gravitational hot flat space is a quantum system made purely of
gravitons it is of interest to compare the free energies of quantum hot flat
space and the stable black hole to find for which ranges of $r$ and $T$ one
phase predominates over the other. Phase diagrams are displayed. The density of
states at a given energy is found. Further calculations and comments are
carried out, notably, a connection to thin shells in $d$ spacetime dimensions
which are systems that are also apt to rigorous thermodynamics.


Abstract:
  Useful information (UI) is an elusive concept in neural networks. A
quantitative measurement of UI is absent, despite the variations of UI can be
recognized by prior knowledge. The communication bandwidth of feature maps
decreases after downscaling operations, but UI flows smoothly after training
due to lower Nyquist frequency. Inspired by the low-Nyqusit-frequency nature of
UI, we propose the use of spectral roll-off points (SROPs) to estimate UI on
variations. The computation of an SROP is extended from a 1-D signal to a 2-D
image by the required rotation invariance in image classification tasks. SROP
statistics across feature maps are implemented as layer-wise useful information
estimates. We design sanity checks to explore SROP variations when UI
variations are produced by variations in model input, model architecture and
training stages. The variations of SROP is synchronizes with UI variations in
various randomized and sufficiently trained model structures. Therefore, SROP
variations is an accurate and convenient sign of UI variations, which promotes
the explainability of data representations with respect to frequency-domain
knowledge.


Abstract:
  We study linear stability of solutions to the Navier\textendash Stokes
equations with stochastic viscosity. Specifically, we assume that the viscosity
is given in the form of a~stochastic expansion. Stability analysis requires a
solution of the steady-state Navier-Stokes equation and then leads to a
generalized eigenvalue problem, from which we wish to characterize the real
part of the rightmost eigenvalue. While this can be achieved by Monte Carlo
simulation, due to its computational cost we study three surrogates based on
generalized polynomial chaos, Gaussian process regression and a shallow neural
network. The results of linear stability analysis assessment obtained by the
surrogates are compared to that of Monte Carlo simulation using a set of
numerical experiments.


Abstract:
  Low-quality face image restoration is a popular research direction in today's
computer vision field. It can be used as a pre-work for tasks such as face
detection and face recognition. At present, there is a lot of work to solve the
problem of low-quality faces under various environmental conditions. This paper
mainly focuses on the restoration of motion-blurred faces. In increasingly
abundant mobile scenes, the fast recovery of motion-blurred faces can bring
highly effective speed improvements in tasks such as face matching. In order to
achieve this goal, a deblurring method for motion-blurred facial image signals
based on generative adversarial networks(GANs) is proposed. It uses an
end-to-end method to train a sharp image generator, i.e., a processor for
motion-blurred facial images. This paper introduce the processing progress of
motion-blurred images, the development and changes of GANs and some basic
concepts. After that, it give the details of network structure and training
optimization design of the image processor. Then we conducted a motion blur
image generation experiment on some general facial data set, and used the pairs
of blurred and sharp face image data to perform the training and testing
experiments of the processor GAN, and gave some visual displays. Finally, MTCNN
is used to detect the faces of the image generated by the deblurring processor,
and compare it with the result of the blurred image. From the results, the
processing effect of the deblurring processor on the motion-blurred picture has
a significant improvement both in terms of intuition and evaluation indicators
of face detection.


Abstract:
  This paper introduces recurrent equilibrium networks (RENs), a new class of
nonlinear dynamical models for applications in machine learning, system
identification and control. The new model class has ``built in'' guarantees of
stability and robustness: all models in the class are contracting - a strong
form of nonlinear stability - and models can satisfy prescribed incremental
integral quadratic constraints (IQC), including Lipschitz bounds and
incremental passivity. RENs are otherwise very flexible: they can represent all
stable linear systems, all previously-known sets of contracting recurrent
neural networks and echo state networks, all deep feedforward neural networks,
and all stable Wiener/Hammerstein models. RENs are parameterized directly by a
vector in R^N, i.e. stability and robustness are ensured without parameter
constraints, which simplifies learning since generic methods for unconstrained
optimization can be used. The performance and robustness of the new model set
is evaluated on benchmark nonlinear system identification problems, and the
paper also presents applications in data-driven nonlinear observer design and
control with stability guarantees.


Abstract:
  Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental
cryptographic primitive in which users register into a key generation centre
and receive a public/private key pair each. After that, any subset of these
users can compute a shared key without any interaction. Nowadays, IoT devices
suffer from a high number and large size of messages exchanged in the Key
Management Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate
the airtime and latency of messages transferred between IoT devices. MP-NIKE
schemes can be realized by using multilinear maps. There are several attempts
for constructing multilinear maps based on indistinguishable obfuscation,
lattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes
are inefficient in terms of computation cost and memory overhead. Besides,
several attacks have been recently reported against CRT-based and lattice-based
multilinear maps. There is only one modular exponentiation-based MP-NIKE scheme
in the literature which has been claimed to be both secure and efficient. In
this article, we present an attack on this scheme based on the Euclidean
algorithm, in which two colluding users can obtain the shared key of any
arbitrary subgroup of users. We also propose an efficient and secure MP-NIKE
scheme. We show how our proposal is secure in the random oracle model assuming
the hardness of the root extraction modulo a composite number.


Abstract:
  The task of voice activity detection (VAD) is an often required module in
various speech processing, analysis and classification tasks. While
state-of-the-art neural network based VADs can achieve great results, they
often exceed computational budgets and real-time operating requirements. In
this work, we propose a computationally efficient real-time VAD network that
achieves state-of-the-art results on several public real recording datasets. We
investigate different training targets for the VAD and show that using the
segmental voice-to-noise ratio (VNR) is a better and more noise-robust training
target than the clean speech level based VAD. We also show that multi-target
training improves the performance further.


Abstract:
  Event detection is an important step in extracting knowledge from the video.
In this paper, we propose a deep learning approach to detect events in a soccer
match emphasizing the distinction between images of red and yellow cards and
the correct detection of the images of selected events from other images. This
method includes the following three modules: i) the variational autoencoder
(VAE) module to differentiate between soccer images and others image, ii) the
image classification module to classify the images of events, and iii) the
fine-grain image classification module to classify the images of red and yellow
cards. Additionally, a new dataset was introduced for soccer images
classification that is employed to train the networks mentioned in the paper.
In the final section, 10 UEFA Champions League matches are used to evaluate the
networks' performance and precision in detecting the events. The experiments
demonstrate that the proposed method achieves better performance than
state-of-the-art methods.


Abstract:
  We propose a novel type of Rydberg dimer, consisting of a Rydberg-state atom
bound to a distant positive ion. The molecule is formed through long-range
electric-multipole interaction between the Rydberg atom and the point-like ion.
We present potential energy curves (PECs) that are asymptotically connected
with Rydberg $nP$- or $nD$-states of rubidium or cesium. The PECs exhibit deep,
long-range wells which support many vibrational states of Rydberg-atom-ion
molecules (RAIMs). We consider photo-association of RAIMs in both the weak and
the strong optical-coupling regimes between initial and Rydberg states of the
neutral atom. Experimental considerations for the realization of RAIMs are
discussed.


Abstract:
  The conventional classification of electrolyte solutions as 'strong' or
'weak' accounts for their charge transport properties, but neglects their mass
transport properties, and is not readily applicable to highly concentrated
solutions. Here, we use the Onsager transport formalism in combination with
linear response theory to attain a more general classification taking into
account both charge and mass transport properties. To this end, we define a
molar mass transport coefficient {\Lambda}_mass, which is related to
equilibrium center-of-mass fluctuations of the mobile ions and which is the
mass-transport analogue of the molar ionic conductivity {\Lambda}_charge. Three
classes of electrolyte solution are then distinguished: (i) 'Strong'
electrolytes with 4{\Lambda}_mass \approx {\Lambda}_charge; (ii) 'weak charge
transport electrolytes' with {\Lambda}_charge << 4{\Lambda}_mass; and (iii)
'weak mass transport electrolytes' with 4{\Lambda}_mass << {\Lambda}_charge.
While classes (i) and (ii) encompass the classical 'strong' and 'weak'
electrolytes, respectively, many highly concentrated electrolytes fall into
class (iii) and thus exhibit transport properties clearly distinct from
classical 'strong' and 'weak' electrolytes.


Abstract:
  Non-pharmaceutical interventions have been critical in the fight against the
COVID-19 pandemic. However, these sanitary measures have been partially lifted
due to socioeconomic factors causing a worrisome rebound of the epidemic in
several countries. In this work, we assess the effectiveness of the mitigation
implemented to constrain the spread of SARS-CoV-2 in the Mexican territory
during 2020. We also investigate to what extent the initial deployment of the
vaccine will help to mitigate the pandemic and reduce the need for social
distancing and other mobility restrictions. Our modeling approach is based on a
simple mechanistic Kermack-McKendrick-type model. To quantify the effect of
NPIs, we perform a monthly Bayesian inference using officially published data.
The results suggest that in the absence of the sanitary measures, the
cumulative number of infections, hospitalizations, and deaths would have been
at least twice the official number. Moreover, for low vaccine coverage levels,
relaxing NPIs may dramatically increase the disease burden; therefore, safety
measures are of critical importance at the early stages of vaccination. The
simulations also suggest that it may be more desirable to employ a vaccine with
low efficacy but reach a high coverage than a vaccine with high effectiveness
but low coverage levels. This supports the hypothesis that single doses to more
individuals will be more effective than two doses for every person.


Abstract:
  We consider a nonlinear chain of coupled oscillators, which is a direct
generalization of the classical FPU lattice and exhibits, besides the usual
nearest neighbor interaction, also next-to-nearest neighbor interaction. For
the case of nearest neighbor attraction and next-to-nearest neighbor repulsion
we prove that such a lattice admits, in contrast to the classical FPU model,
moving modulating front solutions of permanent form, which have small
converging tails at infinity and can be approximated by solitary wave solutions
of the Nonlinear Schr\"odinger equation. When the associated potentials are
even, then the proof yields moving modulating pulse solutions of permanent
form, whose profiles are spatially localized. Our analysis employs the spatial
dynamics approach as developed by Iooss and Kirchg\"assner. The relevant
solutions are constructed on a five-dimensional center manifold and their
persistence is guaranteed by reversibility arguments.


Abstract:
  Typical use cases of sentiment analysis usually revolve around assessing the
probability of a text belonging to a certain sentiment and deriving insight
concerning it; little work has been done to explore further use cases derived
using those probabilities in the context of rating. In this paper, we redefine
the sentiment proportion values as building blocks for a triangle structure,
allowing us to derive variables for a new formula for classifying text given in
the form of product reviews into a group of higher and a group of lower ratings
and prove a dependence exists between the sentiments and the ratings.


Abstract:
  Object localization in 3D space is a challenging aspect in monocular 3D
object detection. Recent advances in 6DoF pose estimation have shown that
predicting dense 2D-3D correspondence maps between image and object 3D model
and then estimating object pose via Perspective-n-Point (PnP) algorithm can
achieve remarkable localization accuracy. Yet these methods rely on training
with ground truth of object geometry, which is difficult to acquire in real
outdoor scenes. To address this issue, we propose MonoRUn, a novel detection
framework that learns dense correspondences and geometry in a self-supervised
manner, with simple 3D bounding box annotations. To regress the pixel-related
3D object coordinates, we employ a regional reconstruction network with
uncertainty awareness. For self-supervised training, the predicted 3D
coordinates are projected back to the image plane. A Robust KL loss is proposed
to minimize the uncertainty-weighted reprojection error. During testing phase,
we exploit the network uncertainty by propagating it through all downstream
modules. More specifically, the uncertainty-driven PnP algorithm is leveraged
to estimate object pose and its covariance. Extensive experiments demonstrate
that our proposed approach outperforms current state-of-the-art methods on
KITTI benchmark.


Abstract:
  We find algebraic conditions on a group equivalent to the position of its
Diophantine problem in the Chomsky Hierarchy. In particular, we prove that a
finitely generated group has a context-free Diophantine problem if and only if
it is finite.


Abstract:
  Until now, the second law of thermodynamics is not formulated for a single
(deterministic and time-symmetric) microscopic trajectory. Here, such a
formulation is suggested by introducing information as a full-valuable physical
quantity defining (a) microscopic entropy and (b) information difference
between an observed process and its time reverse. (a)+(b) then form the second
law and imply that macroscopic irreversibility and mesoscopic stochasticity are
relative phenomena depending on the used information reference frame. The
results provide fundamental insights for further applications in microphysics
(informational engines, quantum computing, etc.).


Abstract:
  The complete band representations (BRs) have been constructed in the work of
topological quantum chemistry. Each BR is expressed by either a certain orbital
at a set of Wyckoff sites in realspace, or by a set of irreducible
representations in momentum space. In this work, we define unconventional
materials as the topologically trivial compounds whose occupied bands can be
expressedas a sum of elementary BRs, but not a sum of atomic-orbital-induced
BRs (aBRs). Namely, these materials possess the unconventional feature of the
mismatch between average electronic centers and atomic positions. The existence
of an essential BR at an empty site is described by nonzero real-space
invariants. The "valence" states can be derived by the aBR decomposition, and
unconventional materials are supposed to have an uncompensatedtotal "valence"
state. The high-throughput screening for unconventional materials has been
performed through the first-principles calculations. We have discovered 392
unconventional compounds with detailed information in the table of the results,
including thermoelectronic materials, higher-order topological insulators,
electrides, hydrogenstorage materials, hydrogen evolution reaction
electrocatalysts, electrodes, and superconductors. The diversity of their
interesting properties and applications would be widely studied in the future.


Abstract:
  Accurate determination of the full momentum-dependent spin susceptibility
$\chi(\mathbf{q}) $ is very important for the description of magnetism and
superconductivity. While in principle the formalism for calculating
$\chi(\mathbf{q})$ in the linear response density functional theory (DFT) is
well established, hardly any publicly available code includes this capability.
Here, we describe an alternative way to calculate the static
$\chi(\mathbf{q})$, which can be applied to most common DFT codes without
additional programming. The method combined standard fixed-spin-moment
calculations of $\chi(\mathbf{0}) $ with direct calculations of the energy of
spin spirals stabilized by an artificial Hubbard interaction. From these
calculations, $\chi_{DFT}(\mathbf{q} )$ can be extracted by inverting the RPA
formula. We apply this recipe to the recently discovered Ising
superconductivity in NbSe$_2$ monolayer, one of the most exciting findings in
superconductivity in recent years. It was proposed that spin fluctuations may
strongly affect the parity of the order parameter. Previous estimates suggested
proximity to ferromagnetism, $i.e.$, $\chi(\mathbf{q})$ peaked at
$\mathbf{q}=0$. We find that the structure of spin fluctuations is more
complicated, with the fluctuation spectrum sharply peaked at $\mathbf{q}\approx
(0.2,0)$. Such a spectrum would change the interband pairing interaction and
considerably affect the superconducting state.


Abstract:
  Many people consider news articles to be a reliable source of information on
current events. However, due to the range of factors influencing news agencies,
such coverage may not always be impartial. Media bias, or slanted news
coverage, can have a substantial impact on public perception of events, and,
accordingly, can potentially alter the beliefs and views of the public. The
main data gap in current research on media bias detection is a robust,
representative, and diverse dataset containing annotations of biased words and
sentences. In particular, existing datasets do not control for the individual
background of annotators, which may affect their assessment and, thus,
represents critical information for contextualizing their annotations. In this
poster, we present a matrix-based methodology to crowdsource such data using a
self-developed annotation platform. We also present MBIC (Media Bias Including
Characteristics) - the first sample of 1,700 statements representing various
media bias instances. The statements were reviewed by ten annotators each and
contain labels for media bias identification both on the word and sentence
level. MBIC is the first available dataset about media bias reporting detailed
information on annotator characteristics and their individual background. The
current dataset already significantly extends existing data in this domain
providing unique and more reliable insights into the perception of bias. In
future, we will further extend it both with respect to the number of articles
and annotators per article.


Abstract:
  In this paper, we discuss a few recent conjectures made by George Beck
related to the ranks and cranks of partitions. The conjectures for the rank of
a partition were proved by Andrews by using results due to Atkin and
Swinnerton-Dyer on a suitable generating function, while the conjectures
related to cranks were studied by Shane Chern using weighted partition moments.
We revisit the conjectures on the crank of a partition by decomposing the
relevant generating function and further explore connections with Apple-Lerch
series and tenth order mock theta functions.


Abstract:
  In this paper, we introduce a additive Tridiagonal and Double-Tridiagonal
codes over $\mathbb{F}_4$ and then we study the properties of the code. Also,
we find the number of additive Tridiagonal codes over $\mathbb{F}_4.$ Finally,
we study the applications of Double-Tridiagonal codes to secret sharing scheme
based on matrix projection.


Abstract:
  Images have become an integral part of online media. This has enhanced
self-expression and the dissemination of knowledge, but it poses serious
accessibility challenges. Adequate textual descriptions are rare. Captions are
more abundant, but they do not consistently provide the needed descriptive
details, and systems trained on such texts inherit these shortcomings. To
address this, we introduce the publicly available Wikipedia-based corpus
Concadia, which consists of 96,918 images with corresponding English-language
descriptions, captions, and surrounding context. We use Concadia to further
characterize the commonalities and differences between descriptions and
captions, and this leads us to the hypothesis that captions, while not
substitutes for descriptions, can provide a useful signal for creating
effective descriptions. We substantiate this hypothesis by showing that image
captioning systems trained on Concadia benefit from having caption embeddings
as part of their inputs. These experiments also begin to show how Concadia can
be a powerful tool in addressing the underlying accessibility issues posed by
image data.


Abstract:
  We study the function space characterization of the inductive bias resulting
from controlling the $\ell_2$ norm of the weights in linear convolutional
networks. We view this in terms of an induced regularizer in the function space
given by the minimum norm of weights required to realize a linear function. For
two layer linear convolutional networks with $C$ output channels and kernel
size $K$, we show the following: (a) If the inputs to the network have a single
channel, the induced regularizer for any $K$ is a norm given by a semidefinite
program (SDP) that is independent of the number of output channels $C$. (b) In
contrast, for networks with multi-channel inputs, multiple output channels can
be necessary to merely realize all matrix-valued linear functions and thus the
inductive bias does depend on $C$. Further, for sufficiently large $C$, the
induced regularizer for $K=1$ and $K=D$ are the nuclear norm and the
$\ell_{2,1}$ group-sparse norm, respectively, of the Fourier coefficients. (c)
Complementing our theoretical results, we show through experiments on MNIST and
CIFAR-10 that our key findings extend to implicit biases from gradient descent
in overparameterized networks.


Abstract:
  Loop Quantum Gravity (LQG) is a non-perturbative attempt at quantization of a
classical phase space description of gravity in terms of $SU(2)$ connections
and electric fields. As emphasized recently [1], on this phase space, classical
gravitational evolution in $time$ can be understood in terms of certain gauge
covariant generalizations of Lie derivatives with respect to a $spatial$
$SU(2)$ Lie algebra valued vector field called the Electric Shift. We present a
derivation of a quantum dynamics for Euclidean LQG which is informed by this
understanding. In addition to the physically motivated nature of the action of
the Euclidean Hamiltonian constraint so derived, the derivation implies that
the spin labels of regulating holonomies are determined by corresponding labels
of the spin network state being acted upon thus eliminating the `spin
$j$-ambiguity' pointed out by Perez. By virtue of Thiemann's seminal work, the
Euclidean quantum dynamics plays a crucial role in the construction of the
Lorentzian quantum dynamics so that our considerations also have application to
Lorentzian LQG.


Abstract:
  Previous work has argued that atomic gas mass estimates of galaxies from 21
cm HI emission are systematically low due to a cold opaque atomic gas
component. If true, this opaque component necessitates a ~35% correction factor
relative to the mass from assuming optically-thin HI emission. These mass
corrections are based on fitting HI spectra with a single opaque component
model that produces a distinct "top-hat" shaped line profile. Here, we
investigate this issue using deep, high spectral resolution HI VLA observations
of M31 and M33 to test if these top-hat profiles are instead superpositions of
multiple HI components along the line-of-sight. We fit both models and find
that >80% of the spectra strongly prefer a multi-component Gaussian model while
<2% prefer the single opacity-corrected component model. This strong preference
for multiple components argues against previous findings of lines-of-sight
dominated by only cold HI. Our findings are enabled by the improved spectral
resolution (0.42 km/s), whereas coarser spectral resolution blends multiple
components together. We also show that the inferred opaque atomic ISM mass
strongly depends on the goodness-of-fit definition and is highly uncertain when
the inferred spin temperature has a large uncertainty. Finally, we find that
the relation of the HI surface density with the dust surface density and
extinction has significantly more scatter when the inferred HI opacity
correction is applied. These variations are difficult to explain without
additionally requiring large variations in the dust properties. Based on these
findings, we suggest that the opaque HI mass is best constrained by HI
absorption studies.


Abstract:
  The theoretical parametrisation of mixing and time-dependent $CP$ violation
for $D^0$decays into two hadrons is reviewed, along with the experimental
methods to measure them. While these phenomena are usually neglected for $D^0
\to K^- \pi^+$ decays, this approximation is not always justified at the
current level of experimental precision. In particular, it is shown to produce
a bias on the measurement of the parameter $y_{CP}$, when this is performed by
relying on $D^0 \to K^- \pi^+$ decays as normalisation channel, whose size is
around 40% of the precision of the current world average. The sensitivity to
$CP$ violation in the mixing achievable by studying $D^0 \to K^- \pi^+$ and
untagged $D \to K^- \pi^+$ decays, where $D$ stands for either of $D^0$ or
$\bar{D}^0$ mesons, is also estimated.


Abstract:
  The unique optical properties of phase change materials (PCMs) can be
exploited to develop efficient reconfigurable photonic devices. Here, we
design, model, and compare the performance of programmable 1X2 optical couplers
based on: Ge$_2$Sb$_2$Te$_5$, Ge$_2$Sb$_2$Se$_4$Te$_1$, Sb$_2$Se$_3$, and
Sb$_2$S$_3$ PCMs. Once programmed, these devices are passive, which can reduce
the overall energy consumed compared to thermo-optic or electro-optic
reconfigurable devices. Of all the PCMs studied, our ellipsometry refractive
index measurements show that Sb$_2$S$_3$ has the lowest absorption in the
telecommunications wavelength band. Moreover, Sb$_2$S$_3$-based couplers show
the best overall performance, with the lowest insertion losses in both the
amorphous and crystalline states. We show that by growth crystallization tuning
at least four different coupling ratios can be reliably programmed into the
Sb$_2$S$_3$ directional couplers. We used this effect to design a 2-bit
tuneable Sb$_2$S$_3$ directional coupler with a dynamic range close to 32 dB.
The bit-depth of the coupler appears to be limited by the crystallization
stochasticity.


Abstract:
  Programmable photonic circuits of reconfigurable interferometers can be used
to implement arbitrary operations on optical modes, facilitating a flexible
platform for accelerating tasks in quantum simulation, signal processing, and
artificial intelligence. A major obstacle to scaling up these systems is static
fabrication error, where small component errors within each device accrue to
produce significant errors within the circuit computation. Mitigating this
error usually requires numerical optimization dependent on real-time feedback
from the circuit, which can greatly limit the scalability of the hardware. Here
we present a deterministic approach to correcting circuit errors by locally
correcting hardware errors within individual optical gates. We apply our
approach to simulations of large scale optical neural networks and infinite
impulse response filters implemented in programmable photonics, finding that
they remain resilient to component error well beyond modern day process
tolerances. Our results highlight a new avenue for scaling up programmable
photonics to hundreds of modes within current day fabrication processes.


Abstract:
  We consider the 2D Boussinesq equations with viscous but without thermal
dissipation and observe that in any neighborhood of Couette flow and
hydrostatic balance (with respect to local norms) there are time-dependent
traveling wave solutions of the form $\omega=-1+f(t)\cos(x-ty)$, $\theta=\alpha
y + g(t)\sin(x-ty)$. As our main result we show that the linearized equations
around these waves for $\alpha=0$ exhibit echo chains and norm inflation
despite viscous dissipation of the velocity. Furthermore, we construct initial
data in a critical Gevrey 3 class, for which temperature and vorticity diverge
to infinity in Sobolev regularity as $t\rightarrow \infty$ but for which the
velocity still converges.


Abstract:
  It has been postulated and observed in practice that for prediction problems
in which covariate data can be naturally partitioned into clusters, ensembling
algorithms based on suitably aggregating models trained on individual clusters
often perform substantially better than methods that ignore the clustering
structure in the data. In this paper, we provide theoretical support to these
empirical observations by asymptotically analyzing linear least squares and
random forest regressions under a linear model. Our main results demonstrate
that the benefit of ensembling compared to training a single model on the
entire data, often termed 'merging', might depend on the underlying bias and
variance interplay of the individual predictors to be aggregated. In
particular, under both fixed and high dimensional linear models, we show that
merging is asymptotically superior to optimal ensembling techniques for linear
least squares regression due to the unbiased nature of least squares
prediction. In contrast, for random forest regression under fixed dimensional
linear models, our bounds imply a strict benefit of ensembling over merging.
Finally, we also present numerical experiments to verify the validity of our
asymptotic results across different situations.


Abstract:
  Although knowledge bases play an important role in many domains (including in
archives, where they are sometimes used for entity extraction and semantic
annotation tasks), it is challenging to build knowledge bases by hand. This is
owing to a number of factors: Knowledge bases must be accurate, up-to-date,
comprehensive, and as flexible and as efficient as possible. These requirements
mean a large undertaking, in the form of extensive work by subject matter
experts (such as scientists, programmers, archivists, and other information
professionals). Even when successfully engineered, manually built knowledge
bases are typically one-off, use-case-specific, non-standardized,
hard-to-maintain solutions. We present a scalable, flexible, and extensible
architecture for knowledge base construction frameworks. As a work in progress,
we extend a specific framework to address some of its design limitations. The
contributions presented in this short paper can shed a light on the suitability
of using AKBC frameworks for computational use cases in this domain and provide
future directions for building improved AKBC frameworks.


Abstract:
  This paper introduces NVCache, an approach that uses a non-volatile main
memory (NVMM) as a write cache to improve the write performance of legacy
applications. We compare NVCache against file systems tailored for NVMM
(Ext4-DAX and NOVA) and with I/O-heavy applications (SQLite, RocksDB). Our
evaluation shows that NVCache reaches the performance level of the existing
state-of-the-art systems for NVMM, but without their limitations: NVCache does
not limit the size of the stored data to the size of the NVMM, and works
transparently with unmodified legacy applications, providing additional
persistence guarantees even when their source code is not available.


Abstract:
  We obtain exact formulas for the time-dependence of a few physical
observables for the open XX spin chain with Lindbladian dynamics. Our analysis
is based on the fact that the Lindblad equation for an arbitrary open quadratic
system of $N$ fermions is explicitly solved in terms of diagonalization of a
$4N\times4N$ matrix called structure matrix by following the scheme of the
third quantization. We mainly focus on the time-dependence of magnetization and
spin current. We observe that the spatio-temporal dependence of magnetization
and spin current show the light-cone structure. Moreover, for a fixed site, we
observe specific behaviors which are not seen in the closed XX spin chain. Near
the center of the chain, these quantities exhibit an exponential approach to
the steady state values and the decay time is given by the Liouvillian gap. The
contribution of the corresponding state becomes smaller for sites near a
boundary, where we observe a plateau regime where magnetization does not change
over a duration of time. Using the fact that the time derivative of
magnetization is written as a product of the Bessel function and exponential
function, we can explain various properties of the plateau regime analytically.


Abstract:
  In this work I investigate the finite temperature Casimir effect due to a
massive and charged scalar field that breaks Lorentz invariance in a CPT-even,
aether-like way. I study the cases of Dirichlet and mixed (Dirichlet-Neumann)
boundary conditions on a pair of parallel plates. I will not examine the case
of Neumann boundary conditions since it produces the same results as Dirichlet
boundary conditions. The main tool used in this investigation is the
$\zeta$-function technique that allows me to obtain the Helmoltz free energy
and Casimir pressure in the presence of a uniform magnetic field perpendicular
to the plates. Three cases of Lorentz asymmetry are studied: timelike,
spacelike and perpendicular to the magnetic field, spacelike and parallel to
the magnetic field. Asymptotic cases of small plate distance, high temperature,
strong magnetic field, and large mass will be considered for each of the three
types of Lorentz asymmetry and each of the two types of boundary conditions
examined. In all these cases simple and very accurate analytic expressions of
the thermal corrections to the Casimir energy and pressure are obtained and I
discover that these corrections strongly depend on the direction of the unit
vector that produces the breaking of the Lorentz symmetry.


Abstract:
  In the field of animal behavior and behavioral ecology, many standardized
methods to observe animal behavior were established in the last decades. While
the protocols remained similar, behavioral researchers can take advantage of
technological advancements to enter observations directly onto a handheld
computer (phone, tablet, etc.), saving time and potentially increasing fidelity
of recordings. However, we now have the choice between many different platforms
for recording behavioral observations. Our challenge is choosing the most
appropriate platform that fits a particular study question, research design,
budget, and desired amount of preparatory time. Here, we review six low-cost
software applications for handheld computers that are available for real-time
entry of behavioral observations: Animal Behaviour Pro, Animal Observer, BORIS,
CyberTracker, Prim8, and ZooMonitor. We discuss the preliminary decisions that
have to be made about the study design, and we assess the six applications by
providing the advantages and disadvantages of each platform and an overall
application comparison. Our goal is to help researchers make calculated
decisions about what behavioral observation platform is best for their study
system and question.


Abstract:
  The propagating intensity disturbances (PIDs) in plumes are still poorly
understood and their identity (magnetoacoustic waves or flows) remains an open
question. We investigate PIDs in five plumes located in the northern polar
coronal hole observed during the 2017 total solar eclipse. Three plumes are
associated with coronal bright points, jets and macrospicules at their base
(active plumes) and the other two plumes are not (quiet plumes). The electron
temperature at the base of the plumes is obtained from the filter ratio of
images taken with the X-ray Telescope on board Hinode and the passband ratio
around 400 nm from an eclipse instrument, the Diagnostic Coronagraph Experiment
(DICE). The phase speed (v_r), frequency (omega), and wavenumber (k) of the
PIDs in the plumes are obtained by applying a Fourier transformation to the
space-time (r-t plane) plots in images taken with the Atmospheric Imaging
Assembly (AIA) in three different wavelength channels (171 A, 193 A, and 211
A). We found that the PIDs in the higher temperature AIA channels, 193 and 211
A, are faster than that of the cooler AIA 171 A channel. This tendency is more
significant for the active plumes than the quiet ones. The observed speed ratio
(~1.3) between the AIA 171 and 193 A channels is similar to the theoretical
value (1.25) of a slow magnetoacoustic wave. Our results support the idea that
PIDs in plumes represent a superposition of slow magnetoacoustic waves and
plasma outflows that consist of dense cool flows and hot coronal jets.


Abstract:
  In order to increase the resilience of distribution systems against
high-impact low-probability (HILP) events, it is important to prioritize assets
damaged by these events so that the lost loads, especially sensitive and
important loads, can be recovered faster. For this reason, this paper discusses
the prioritization of electricity supply lines for scheduling and prioritizing
repair actions. To this end, the economic value of distribution system lines
has been considered as a criterion representing the sensitivity of the network
to hurricanes. The modeling is based on value, in which the load value,
lifetime-based failure probability of the poles, fragility curve, duration of
line repair by the maintenance team, and the topology factor have been
considered. This is so that the significance of the demand side, the failure
extent and accessibility of the lines, the importance of time, and the network
configuration are considered. The results provide an order of line priority for
fault resolution, in which the topology factor has a larger effect. This
modeling has been tested on an IEEE 33-bus network.


Abstract:
  This paper proposes a blind estimation method based on the modulation
transfer function and Schroeder model for estimating reverberation time in
seven-octave bands. Therefore, the speech transmission index and five
room-acoustic parameters can be estimated.


Abstract:
  Spectral clustering is one of the most popular clustering methods. However,
how to balance the efficiency and effectiveness of the large-scale spectral
clustering with limited computing resources has not been properly solved for a
long time. In this paper, we propose a divide-and-conquer based large-scale
spectral clustering method to strike a good balance between efficiency and
effectiveness. In the proposed method, a divide-and-conquer based landmark
selection algorithm and a novel approximate similarity matrix approach are
designed to construct a sparse similarity matrix within extremely low cost.
Then clustering results can be computed quickly through a bipartite graph
partition process. The proposed method achieves the lower computational
complexity than most existing large-scale spectral clustering. Experimental
results on ten large-scale datasets have demonstrated the efficiency and
effectiveness of the proposed methods. The MATLAB code of the proposed method
and experimental datasets are available at
https://github.com/Li-Hongmin/MyPaperWithCode.


Abstract:
  Given a set $S$ of $n$ distinct keys, a function $f$ that bijectively maps
the keys of $S$ into the range $\{0,\ldots,n-1\}$ is called a minimal perfect
hash function for $S$. Algorithms that find such functions when $n$ is large
and retain constant evaluation time are of practical interest; for instance,
search engines and databases typically use minimal perfect hash functions to
quickly assign identifiers to static sets of variable-length keys such as
strings. The challenge is to design an algorithm which is efficient in three
different aspects: time to find $f$ (construction time), time to evaluate $f$
on a key of $S$ (lookup time), and space of representation for $f$. Several
algorithms have been proposed to trade-off between these aspects. In 1992, Fox,
Chen, and Heath (FCH) presented an algorithm at SIGIR providing very fast
lookup evaluation. However, the approach received little attention because of
its large construction time and higher space consumption compared to other
subsequent techniques. Almost thirty years later we revisit their framework and
present an improved algorithm that scales well to large sets and reduces space
consumption altogether, without compromising the lookup time. We conduct an
extensive experimental assessment and show that the algorithm finds functions
that are competitive in space with state-of-the art techniques and provide
$2-4\times$ better lookup time.


Abstract:
  Interior-point methods for linear programming problems require the repeated
solution of a linear system of equations. Solving these linear systems is
non-trivial due to the severe ill-conditioning of the matrices towards
convergence. This issue can be alleviated by incorporating suitable
regularization terms in the linear programming problem. Regularization also
allows us to efficiently handle rank deficient constraint matrices. We provide
a convergence analysis of a regularized inexact interior-point method. The term
`inexact' refers to the fact that we do not need to compute the true solution
of the linear system of equations, only an approximation thereof. The
formulation of the algorithm is sufficiently general such that specialized
linear algebra routines developed in other work on inexact interior-point
methods can also be incorporated in our regularized framework. In this work, we
exploit the inexactness by using a mixed-precision solver for the linear system
of equations. More specifically, we perform a Cholesky factorization in IEEE
single precision and use it as a preconditioner for the Conjugate Gradient
method. Numerical experiments illustrate the benefits of this approach applied
to linear programming problems with a dense constraint matrix.


Abstract:
  The semi-random graph process is a single player game in which the player is
initially presented an empty graph on $n$ vertices. In each round, a vertex $u$
is presented to the player independently and uniformly at random. The player
then adaptively selects a vertex $v$, and adds the edge $uv$ to the graph. For
a fixed monotone graph property, the objective of the player is to force the
graph to satisfy this property with high probability in as few rounds as
possible.
  We focus on the problem of constructing a perfect matching in as few rounds
as possible. In particular, we present an adaptive strategy for the player
which achieves a perfect matching in $\beta n$ rounds, where the value of
$\beta < 1.206$ is derived from a solution to some system of differential
equations. This improves upon the previously best known upper bound of
$(1+2/e+o(1)) \, n < 1.736 \, n$ rounds. We also improve the previously best
lower bound of $(\ln 2 + o(1)) \, n > 0.693 \, n$ and show that the player
cannot achieve the desired property in less than $\alpha n$ rounds, where the
value of $\alpha > 0.932$ is derived from a solution to another system of
differential equations. As a result, the gap between the upper and lower bounds
is decreased roughly four times.


Abstract:
  Although the use of RGB photometry has exploded in the last decades due to
the advent of high-quality and inexpensive digital cameras equipped with
Bayer-like color filter systems, there is surprisingly no catalogue of bright
stars that can be used for calibration purposes. Since due to their excessive
brightness, accurate enough spectrophotometric measurements of bright stars
typically cannot be performed with modern large telescopes, we have employed
historical 13-color medium-narrow-band photometric data, gathered with quite
reliable photomultipliers, to fit the spectrum of 1346 bright stars using
stellar atmosphere models. This not only constitutes a useful compilation of
bright spectrophotometric standards well spread in the celestial sphere, the
UCM library of spectrophotometric spectra, but allows the generation of a
catalogue of reference RGB magnitudes, with typical random uncertainties $\sim
0.01$ mag. For that purpose, we have defined a new set of spectral sensitivity
curves, computed as the median of 28 sets of empirical sensitivity curves from
the literature, that can be used to establish a standard RGB photometric
system. Conversions between RGB magnitudes computed with any of these sets of
empirical RGB curves and those determined with the new standard photometric
system are provided. Even though particular RGB measurements from single
cameras are not expected to provide extremely accurate photometric data, the
repeatability and multiplicity of observations will allow access to a large
amount of exploitable data in many astronomical fields, such as the detailed
monitoring of light pollution and its impact on the night sky brightness, or
the study of meteors, solar system bodies, variable stars, and transient
objects. In addition, the RGB magnitudes presented here make the sky an
accessible and free laboratory for the calibration of the cameras themselves.


Abstract:
  Generalizing our recent joint paper with Vasily Pestun (arXiv:2001.04929), we
construct a family of $SO(2r),Sp(2r),SO(2r+1)$ rational Lax matrices,
polynomial in the spectral parameter, parametrized by the divisors on the
projective line with coefficients being dominant integral coweights of
associated Lie algebras. To this end, we provide the RTT realization of the
antidominantly shifted extended Drinfeld Yangians of $\mathfrak{so}_{2r},
\mathfrak{sp}_{2r}, \mathfrak{so}_{2r+1}$, and of their coproduct
homomorphisms. This establishes some of the recent conjectures in the physics
literature by Costello-Gaiotto-Yagi (arXiv:2103.01835) in the classical types.


Abstract:
  Deep deterministic policy gradient (DDPG) based car-following strategy can
break through the constraints of the differential equation model due to the
ability of exploration on complex environments. However, the car-following
performance of DDPG is usually degraded by unreasonable reward function design,
insufficient training and low sampling efficiency. In order to solve this kind
of problem, a hybrid car-following strategy based on DDPG and cooperative
adaptive cruise control (CACC) is proposed. Firstly, the car-following process
is modeled as markov decision process to calculate CACC and DDPG simultaneously
at each frame. Given a current state, two actions are obtained from CACC and
DDPG, respectively. Then an optimal action, corresponding to the one offering a
larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a
rule is designed to ensure that the change rate of acceleration is smaller than
the desired value. Therefore, the proposed strategy not only guarantees the
basic performance of car-following through CACC, but also makes full use of the
advantages of exploration on complex environments via DDPG. Finally, simulation
results show that the car-following performance of proposed strategy is
improved significantly as compared with that of DDPG and CACC in the whole
state space.