title:
Hellinger-Bhattacharyya cross-validation for shape-preserving
  multivariate wavelet thresholding

abstract:
  The benefits of the wavelet approach for density estimation are well
established in the literature, especially when the density to estimate is
irregular or heterogeneous in smoothness. However, wavelet density estimates
are typically not bona fide densities. In Aya-Moreno et al (2018), a
`shape-preserving' wavelet density estimator was introduced, including as main
step the estimation of the square-root of the density. A natural concept
involving square-root of densities is the Hellinger distance - or equivalently,
the Bhattacharyya affinity coefficient. In this paper, we deliver a fully
data-driven version of the above 'shape-preserving' wavelet density estimator,
where all user-defined parameters, such as resolution level or thresholding
specifications, are selected by optimising an original leave-one-out version of
the Hellinger-Bhattacharyya criterion. The theoretical optimality of the
proposed procedure is established, while simulations show the strong practical
performance of the estimator. Within that framework, we also propose a novel
but natural 'jackknife thresholding' scheme, which proves superior to other,
more classical thresholding options.


title:
A Sampling Control Framework and Applications to Robust and Adaptive
  Control

abstract:
  In this paper, we propose a novel sampling control framework based on the
emulation technique where the sampling error is regarded as an auxiliary input
to the emulated system. Utilizing the supremum norm of sampling error, the
design of periodic sampling and event-triggered control law renders the error
dynamics bounded-input-bounded-state (BIBS), and when coupled with system
dynamics, achieves global or semi-global stabilization. The proposed framework
is then extended to tackle the event-triggered and periodic sampling
stabilization for a system where only partial state is available for feedback
and the system is subject to parameter uncertainties. The proposed framework is
further extended to solve two classes of event-triggered adaptive control
problems where the emulated closed-loop system does not admit an input-to-state
stability (ISS) Lyapunov function. For the first class of systems with linear
parameterized uncertainties, even-triggered global adaptive stabilization is
achieved without the global Lipschitz condition on nonlinearities as often
required in the literature. For the second class of systems with uncertainties
whose bound is unknown, the event-triggered adaptive (dynamic) gain controller
is designed for the first time. Finally, theoretical results are verified by
two numerical examples.


title: Introduction to Quantization of Conformal Gravity

abstract:
  A method for consistent quantization of conformal gravity treating conformal
symmetry in a very controllable way is presented. First, we discuss local
conformal symmetry in the framework of gravitational interactions, where we
view it as an example of a general gauge theory. We also present some early
attempts at quantization of conformal gravity and use the generalized framework
of covariant quantization due to Faddeev and Popov. Some salient issues such as
the need for conformal gauge-fixing, an issue with conformal third ghosts, and
discontinuities in conformal gravity are studied as well. Finally, we provide
some explanations of the original ad hoc methods of computation valid at the
first quantum loop level in conformal gravity.

categories: hep-th gr-qc hep-ph math-ph math.MP quant-ph


title:
Equine radiograph classification using deep convolutional neural
  networks

abstract:
  Purpose: To assess the capability of deep convolutional neural networks to
classify anatomical location and projection from a series of 48 standard views
of racehorse limbs.
  Materials and Methods: 9504 equine pre-import radiographs were used to train,
validate, and test six deep learning architectures available as part of the
open source machine learning framework PyTorch.
  Results: ResNet-34 achieved a top-1 accuracy of 0.8408 and the majority (88%)
of misclassification was because of wrong laterality. Class activation maps
indicated that joint morphology drove the model decision.
  Conclusion: Deep convolutional neural networks are capable of classifying
equine pre-import radiographs into the 48 standard views including moderate
discrimination of laterality independent of side marker presence.


title: One-Way Matching of Datasets with Low Rank Signals

abstract:
  We study one-way matching of a pair of datasets with low rank signals. Under
a stylized model, we first derive information-theoretic limits of matching. We
then show that linear assignment with projected data achieves fast rates of
convergence and sometimes even minimax rate optimality for this task. The
theoretical error bounds are corroborated by simulated examples. Furthermore,
we illustrate practical use of the matching procedure on two single-cell data
examples.

categories: math.ST cs.IT cs.LG math.IT q-bio.QM stat.TH


title: A Digital Twin Framework for Cyber Security in Cyber-Physical Systems

abstract:
  Currently, most of the research in digital twins focuses on simulation and
optimization. Digital twins are especially useful for critical systems.
However, digital twins can also be used for safety and cyber security. The idea
of this paper is motivated by the limitations of cyber security in
Cyber-Physical Systems (CPSs). We introduce an efficient synchronization
approach to maintain the state between the virtual environment and the physical
environment. In this case, we can receive prompt feedback by conducting
security analysis in the virtual domain. Thus, helping to enhance the cyber
security of CPSs, we propose a digital twin-based framework. Based on the
approach, the security of the CPSs can be protected by the digital twin system.
Moreover, the proposed architecture has also been optimized to meet the
security requirements and maintain less network burden for CPSs


title: Surface-knot families with arbitrarily large triple point number

abstract:
  Analogous to a classical knot diagram, a surface-link can be generically
projected to 3-space and given crossing information to create a broken sheet
diagram. The triple point number of a surface-link is the minimal number of
triple points among all broken sheet diagrams that lift to that surface-link.
This paper generalizes a family of Oshiro to show that there are non-split
surface-links of arbitrarily many trivial components whose triple point number
can be made arbitrarily large.


title:
Where in the World is this Image? Transformer-based Geo-localization in
  the Wild

abstract:
  Predicting the geographic location (geo-localization) from a single
ground-level RGB image taken anywhere in the world is a very challenging
problem. The challenges include huge diversity of images due to different
environmental scenarios, drastic variation in the appearance of the same
location depending on the time of the day, weather, season, and more
importantly, the prediction is made from a single image possibly having only a
few geo-locating cues. For these reasons, most existing works are restricted to
specific cities, imagery, or worldwide landmarks. In this work, we focus on
developing an efficient solution to planet-scale single-image geo-localization.
To this end, we propose TransLocator, a unified dual-branch transformer network
that attends to tiny details over the entire image and produces robust feature
representation under extreme appearance variations. TransLocator takes an RGB
image and its semantic segmentation map as inputs, interacts between its two
parallel branches after each transformer layer, and simultaneously performs
geo-localization and scene recognition in a multi-task fashion. We evaluate
TransLocator on four benchmark datasets - Im2GPS, Im2GPS3k, YFCC4k, YFCC26k and
obtain 5.5%, 14.1%, 4.9%, 9.9% continent-level accuracy improvement over the
state-of-the-art. TransLocator is also validated on real-world test images and
found to be more effective than previous methods.


title:
Memory Efficient Model Based Deep Learning Reconstructions for High
  Spatial Resolution 3D Non-Cartesian Acquisitions

abstract:
  Objective: Model based deep learning (MBDL) has been challenging to apply to
the reconstruction of 3D non-Cartesian MRI acquisitions due to extreme GPU
memory demand (>250 GB using traditional backpropagation) primarily because the
entire volume is needed for data-consistency steps embedded in the model. The
goal of this work is to develop and apply a memory efficient method called
block-wise learning that combines gradient checkpointing with patch-wise
training to allow for fast and high-quality 3D non-Cartesian reconstructions
using MBDL. Approach: Block-wise learning applied to a single unroll decomposes
the input volume into smaller patches, gradient checkpoints each patch, passes
each patch iteratively through a neural network regularizer, and then rebuilds
the full volume from these output patches for data-consistency. This method is
applied across unrolls during training. Block-wise learning significantly
reduces memory requirements by tying GPU memory to user selected patch size
instead of the full volume. This algorithm was used to train a MBDL
architecture to reconstruct highly undersampled, 1.25mm isotropic, pulmonary
magnetic resonance angiography volumes with matrix sizes varying from 300-450 x
200-300 x 300-450 on a single GPU. We compared block-wise learning
reconstructions against L1 wavelet compressed reconstructions and proxy ground
truth images. Main results: MBDL with block-wise learning significantly
improved image quality relative to L1 wavelet compressed sensing while
simultaneously reducing average reconstruction time 38x. Significance:
Block-wise learning allows for MBDL to be applied to high spatial resolution,
3D non-Cartesian datasets with improved image quality and significant
reductions in reconstruction time relative to traditional iterative methods


title:
Indoor 3-Dimensional Visible Light Positioning: Error Metric and LED
  Layout Optimization

abstract:
  We consider 3-dimensional (3D) visible light positioning (VLP) based on
smartphone camera in an indoor scenario. Based on the positioning model in the
quantized pixel-domain, we characterize the 3D normalized positioning error
metric (NPEM) through the partial derivative of the positioning function, and
evaluate the NPEM for horizontal and non-horizontal receiver camera positions.
Moreover, under horizontal receiver terminal position, we explore the
relationship between the NPEM and the light-emitting diode (LED) cell layout,
approximate the relationship between the NPEM and the number of LEDs captured
by the camera, and evaluate the approximation accuracy according to the
simulated positioning error. Based on the approximation results, we optimize
the LED transmitter cell layout to minimize NPEM assuming structured square
cell layouts with certain distance parameters.


title:
First-principles study of ferroelectric
  Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_2$/Si gate stack

abstract:
  The gate defect of the ferroelectric HfO$_2$-based Si transistor (Si FeFET)
plays a dominant role in its reliability issue. The first-principles
calculations are an effective method for the atomic-scale understanding of gate
defects. However, the first-principles study on the defects of FeFET gate
stacks, i.e., metal/orthorhombic-Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si
structure, has not been reported so far. The key reason is the construction of
metal/orthorhombic-Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si gate stack models.
Here, we use the Hf$_{0.5}$Zr$_{0.5}$O$_2$(130) high-index crystal face as the
orthorhombic ferroelectric layer and construct a robust atomic structure of the
orthorhombic-Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_2$/Si gate stack. The insulating
interfaces in the orthorhombic-Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_2$/Si gate
structure, achieved by the compensation of interfacial dangling bonds,
guarantee its high structural stability. The band offset reveals the property
of type-I band alignment. Furthermore, the oxygen vacancy is more favorable to
form compared with other defects such as oxygen interstitial and Hf/Zr vacancy.
The charge transition levels of oxygen vacancy defects are mainly localized
near the Si conduction band minimum (CBM) and valence band maximum (VBM). The
oxygen vacancy defects are responsible for charge trapping/de-trapping in Si
FeFET. This robust gate structure constructed in this work paves the way to
carry out the first-principles study of ferroelectric HfO$_2$-based Si FeFET.


title:
Structural and electronic properties of the random alloy
  ZnSe$_x$S$_{1-x}$

abstract:
  In this article we employ density functional theory in the generalized
gradient approximation to investigate the structural and electronic properties
of the solid solution alloy $\text{Zn}\text{Se}_x\text{S}_{1-x}$ in the
wurtzite structure. We analyzed the character of the bond lengths and angles at
the atomic scale, using a supercell approach that does not impose any
constraint on the crystal potential. We show that the bond lengths of pristine
ZnS and ZnSe compounds are almost preserved between nearest neighbors, which is
different from what would be anticipated if Vegard's law were valid at the
atomic level. We also show that bond lengths start behaving in accordance to
Vegard's law from the third shell of nearest neighbors onward, which in turn
determines the average lattice parameters of the alloys determined by
diffraction experiments. Fundamental building blocks around the anions are
identified and are shown to be non-rigid but still volume preserving. Finally,
the geometrical analysis is connected to the trend exhibited by the electronic
structure, and in particular by the band gap. The latter is found to exhibit a
small deviation from the linearity with respect to the Se concentration, in
accordance to available experimental data. By assuming a quadratic dependence,
we can extract a bowing parameter and analyze various contributions to it with
various calculations under selected constraints. The structural deformation in
response to the doping process is shown to be the driving force behind the
deviation from linearity. The difference in stiffness between ZnS and ZnSe is
showed to play a key role in the asymmetric behavior of the bowing parameter
observed in the S-rich and Se-rich regions.


title:
Gaussian fluctuations of a nonlinear stochastic heat equation in
  dimension two

abstract:
  We study the Gaussian fluctuations of a nonlinear stochastic heat equation in
spatial dimension two. The equation is driven by a Gaussian multiplicative
noise. The noise is white in time, smoothed in space at scale $\varepsilon$,
and tuned logarithmically by a factor $\frac{1}{\sqrt{\log \varepsilon^{-1}}}$
in its strength. We prove that, after centering and rescaling, the solution
random field converges in distribution to an Edwards-Wilkinson limit as
$\varepsilon \downarrow 0$. The tool we used here is the Malliavin-Stein's
method. We also give a functional version of this result.


title: Vision-Language Pre-Training for Boosting Scene Text Detectors

abstract:
  Recently, vision-language joint representation learning has proven to be
highly effective in various scenarios. In this paper, we specifically adapt
vision-language joint learning for scene text detection, a task that
intrinsically involves cross-modal interaction between the two modalities:
vision and language, since text is the written form of language. Concretely, we
propose to learn contextualized, joint representations through vision-language
pre-training, for the sake of enhancing the performance of scene text
detectors. Towards this end, we devise a pre-training architecture with an
image encoder, a text encoder and a cross-modal encoder, as well as three
pretext tasks: image-text contrastive learning (ITC), masked language modeling
(MLM) and word-in-image prediction (WIP). The pre-trained model is able to
produce more informative representations with richer semantics, which could
readily benefit existing scene text detectors (such as EAST and PSENet) in the
down-stream text detection task. Extensive experiments on standard benchmarks
demonstrate that the proposed paradigm can significantly improve the
performance of various representative text detectors, outperforming previous
pre-training approaches. The code and pre-trained models will be publicly
released.


title: Generalized weighted Hardy's inequalities with compact perturbations

abstract:
  In the present paper we shall study a variational problem relating the
weighted Hardy inequalities with sharp missing terms. As weights we treat
non-doubling functions of the distance to the boundary of bounded domain.


title:
Por Qu\'e N\~ao Utiliser Alla Spr{\aa}k? Mixed Training with Gradient
  Optimization in Few-Shot Cross-Lingual Transfer

abstract:
  The current state-of-the-art for few-shot cross-lingual transfer learning
first trains on abundant labeled data in the source language and then
fine-tunes with a few examples on the target language, termed target-adapting.
Though this has been demonstrated to work on a variety of tasks, in this paper
we show some deficiencies of this approach and propose a one-step mixed
training method that trains on both source and target data with
\textit{stochastic gradient surgery}, a novel gradient-level optimization.
Unlike the previous studies that focus on one language at a time when
target-adapting, we use one model to handle all target languages simultaneously
to avoid excessively language-specific models. Moreover, we discuss the
unreality of utilizing large target development sets for model selection in
previous literature. We further show that our method is both development-free
for target languages, and is also able to escape from overfitting issues. We
conduct a large-scale experiment on 4 diverse NLP tasks across up to 48
languages. Our proposed method achieves state-of-the-art performance on all
tasks and outperforms target-adapting by a large margin, especially for
languages that are linguistically distant from the source language, e.g., 7.36%
F1 absolute gain on average for the NER task, up to 17.60% on Punjabi.


title:
Density functional theory calculations for investigation of atomic
  structures of 4H-SiC/SiO$_2$ interface after NO annealing

abstract:
  We propose the atomic structures of the 4H-SiC/SiO$_2$ interface for the $a$,
$m$, C, and Si faces after NO annealing. Our proposed structures preferentially
form at the topmost layers of the SiC side of the interface, which agrees with
the experimental finding of secondary-ion mass spectrometry, that is, the N
atoms accumulate at the interface. In addition, the areal N-atom density is on
the order of 10$^{14}$ atom/cm$^2$ for each plane, which is also consistent
with the experimental result. Moreover, the electronic structure of the
interface after NO annealing, in which the CO bonds are removed and the nitride
layer only at the interface is inserted, is free from gap states, although some
interface models before NO annealing include the gap states arising from the CO
bonds near the valence band edge of the bandgap. Our results imply that NO
annealing can contribute to the reduction in the density of interface defects
by forming the nitride layer.


title:
Quasiperiodic Energy Release and Jets at the Base of Solar Coronal
  Plumes

abstract:
  Coronal plumes are long, ray-like, open structures, which have been
considered as possible sources for the solar wind. Their origin in the largely
unipolar coronal holes has long been a mystery. Earlier spectroscopic and
imaging observations revealed blue-shifted plasma and propagating disturbances
(PDs) in plumes that are widely interpreted in terms of flows and/or
propagating slow-mode waves, but these interpretations (flows vs waves) remain
under debate. Recently we discovered an important clue about plume internal
structure: dynamic filamentary features called plumelets, which account for
most of the plume emission. Here we present high-resolution observations from
the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) and the
Interface Region Imaging Spectrograph (IRIS) that revealed numerous,
quasiperiodic, tiny jets (so-called jetlets) associated with transient
brightening, flows, and plasma heating at the chromospheric footpoints of the
plumelets. By analogy to larger coronal jets, these jetlets are most likely
produced within the plume base by magnetic reconnection between closed and open
flux at stressed 3D null points. The jetlet-associated brightenings are in
phase with plumelet-associated PDs, and vary with a period of about 3 to 5
minutes, which is remarkably consistent with the photospheric/chromospheric
p-mode oscillation. This reconnection at the open-closed boundary in the
chromosphere/transition region is likely modulated or driven by local
manifestations of the global p-mode waves. The jetlets extend upward to become
plumelets, contribute mass to the solar wind, and may be sources of the
switchbacks recently detected by the Parker Solar Probe.


title:
Extended Analysis of "How Child Welfare Workers Reduce Racial
  Disparities in Algorithmic Decisions"

abstract:
  This is an extended analysis of our paper "How Child Welfare Workers Reduce
Racial Disparities in Algorithmic Decisions," which looks at racial disparities
in the Allegheny Family Screening Tool, an algorithm used to help child welfare
workers decide which families the Allegheny County child welfare agency (CYF)
should investigate. On April 27, 2022, Allegheny County CYF sent us an updated
dataset and pre-processing steps. In this extended analysis of our paper, we
show the results from re-running all quantitative analyses in our paper with
this new data and pre-processing. We find that our main findings in our paper
were robust to changes in data and pre-processing. Particularly, the Allegheny
Family Screening Tool on its own would have made more racially disparate
decisions than workers, and workers used the tool to decrease those algorithmic
disparities. Some minor results changed, including a slight increase in the
screen-in rate from before to after the implementation of the AFST reported our
paper.


title:
Multiple Degradation and Reconstruction Network for Single Image
  Denoising via Knowledge Distillation

abstract:
  Single image denoising (SID) has achieved significant breakthroughs with the
development of deep learning. However, the proposed methods are often
accompanied by plenty of parameters, which greatly limits their application
scenarios. Different from previous works that blindly increase the depth of the
network, we explore the degradation mechanism of the noisy image and propose a
lightweight Multiple Degradation and Reconstruction Network (MDRN) to
progressively remove noise. Meanwhile, we propose two novel Heterogeneous
Knowledge Distillation Strategies (HMDS) to enable MDRN to learn richer and
more accurate features from heterogeneous models, which make it possible to
reconstruct higher-quality denoised images under extreme conditions. Extensive
experiments show that our MDRN achieves favorable performance against other SID
models with fewer parameters. Meanwhile, plenty of ablation studies demonstrate
that the introduced HMDS can improve the performance of tiny models or the
model under high noise levels, which is extremely useful for related
applications.


title:
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak
  Supervision

abstract:
  Automatic extraction of product attributes from their textual descriptions is
essential for online shopper experience. One inherent challenge of this task is
the emerging nature of e-commerce products -- we see new types of products with
their unique set of new attributes constantly. Most prior works on this matter
mine new values for a set of known attributes but cannot handle new attributes
that arose from constantly changing data. In this work, we study the attribute
mining problem in an open-world setting to extract novel attributes and their
values. Instead of providing comprehensive training data, the user only needs
to provide a few examples for a few known attribute types as weak supervision.
We propose a principled framework that first generates attribute value
candidates and then groups them into clusters of attributes. The candidate
generation step probes a pre-trained language model to extract phrases from
product titles. Then, an attribute-aware fine-tuning method optimizes a
multitask objective and shapes the language model representation to be
attribute-discriminative. Finally, we discover new attributes and values
through the self-ensemble of our framework, which handles the open-world
challenge. We run extensive experiments on a large distantly annotated
development set and a gold standard human-annotated test set that we collected.
Our model significantly outperforms strong baselines and can generalize to
unseen attributes and product types.


title:
Pore-resolved simulations of turbulent boundary layer flow over
  permeable and impermeable sediment beds

abstract:
  Pore-resolved direct numerical simulations of turbulent open channel flow are
performed comparing the structure and dynamics of turbulence over impermeable
rough and smooth walls to a porous sediment bed at permeability Reynolds number
($Re_K$) of 2.6, representative of aquatic beds. Four configurations are
investigated; namely, (i) permeable bed with randomly packed sediment grains,
(ii) an impermeable-wall with full layer of roughness elements matching the top
layer of the sediment bed, (iii) an impermeable-wall with half layer of
roughness elements , and (iv) a smooth wall. A double-averaging methodology is
used to compute the mean velocity, Reynolds stresses, form-induced stresses,
and turbulent kinetic energy budget. It is observed that the mean velocity,
Reynolds stresses, and form-induced pressure-velocity correlations representing
upwelling and downwelling fluxes are similar in magnitude for the permeable-bed
and impermeable-full layer wall cases. However, for the impermeable-half layer
case, the wall blocking effect results in higher streamwise and lower
wall-normal stresses compared to the permeable-bed case. Bed roughness
increases Reynolds shear stress whereas permeability has minimal influence. Bed
permeability, in contrast to Reynolds stresses, significantly influences
form-induced shear stress. Bed shear stress statistics show that probability of
extreme events increases in permeable bed and rough wall cases as compared to
the smooth wall. Findings suggest that bed permeability can have significant
impact on modeling of hyporheic exchange and its effect on bed shear and
pressure fluctuations are better captured by considering at least the top layer
of sediment.


title: Graph Polynomial for Colored Embedded Graphs: A Topological Approach

abstract:
  We study finite graphs embedded in oriented surfaces by associating a
polynomial to it. The tools used in developing a theory of such graph
polynomials are algebraic topological while the polynomial itself is inspired
from ideas arising in physics. We also analyze a variant of these polynomials
for colored embedded graphs. This is used to describe the change in the
polynomial under basic graph theoretic operations. We conclude with several
applications of this polynomial including detection of certain classes of
graphs and the connection of this polynomial with topological entanglement
entropy.


title:
Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging
  Structural Regularities from Visual SLAM

abstract:
  Feature-based visual simultaneous localization and mapping (SLAM) methods
only estimate the depth of extracted features, generating a sparse depth map.
To solve this sparsity problem, depth completion tasks that estimate a dense
depth from a sparse depth have gained significant importance in robotic
applications like exploration. Existing methodologies that use sparse depth
from visual SLAM mainly employ point features. However, point features have
limitations in preserving structural regularities owing to texture-less
environments and sparsity problems. To deal with these issues, we perform depth
completion with visual SLAM using line features, which can better contain
structural regularities than point features. The proposed methodology creates a
convex hull region by performing constrained Delaunay triangulation with depth
interpolation using line features. However, the generated depth includes
low-frequency information and is discontinuous at the convex hull boundary.
Therefore, we propose a mesh depth refinement (MDR) module to address this
problem. The MDR module effectively transfers the high-frequency details of an
input image to the interpolated depth and plays a vital role in bridging the
conventional and deep learning-based approaches. The Struct-MDC outperforms
other state-of-the-art algorithms on public and our custom datasets, and even
outperforms supervised methodologies for some metrics. In addition, the
effectiveness of the proposed MDR module is verified by a rigorous ablation
study.


title:
Energy Minimization for Federated Asynchronous Learning on
  Battery-Powered Mobile Devices via Application Co-running

abstract:
  Energy is an essential, but often forgotten aspect in large-scale federated
systems. As most of the research focuses on tackling computational and
statistical heterogeneity from the machine learning algorithms, the impact on
the mobile system still remains unclear. In this paper, we design and implement
an online optimization framework by connecting asynchronous execution of
federated training with application co-running to minimize energy consumption
on battery-powered mobile devices. From a series of experiments, we find that
co-running the training process in the background with foreground applications
gives the system a deep energy discount with negligible performance slowdown.
Based on these results, we first study an offline problem assuming all the
future occurrences of applications are available, and propose a dynamic
programming-based algorithm. Then we propose an online algorithm using the
Lyapunov framework to explore the solution space via the energy-staleness
trade-off. The extensive experiments demonstrate that the online optimization
framework can save over 60% energy with 3 times faster convergence speed
compared to the previous schemes.


title: Eyes on the Prize: Improved Perception for Robust Dynamic Grasping

abstract:
  This paper is concerned with perception challenges for robust grasping in the
presence of clutter and unpredictable relative motion between robot and object.
Traditional perception systems developed for static grasping are unable to
provide feedback during the final phase of a grasp due to sensor minimum range,
occlusion, and a limited field of view. A multi-camera eye-in-hand perception
system is presented that has advantages over commonly used camera
configurations. We quantitatively evaluate the performance on a real robot with
an image-based visual servoing grasp controller and show a significantly
improved success rate on a dynamic grasping task. A fully reproducible
open-source testing system is described to encourage benchmarking of dynamic
grasping system performance.


title:
A scheme of hybrid privacy protection and utility levels for medical
  data

abstract:
  Healthcare data contains sensitive information, and it is challenging to
persuade healthcare data owners to share their information for research
purposes without any privacy assurance. The proposed hybrid medical data
privacy protection scheme explores the possibility of providing adaptive
privacy protection and data utility levels. The evaluation result demonstrates
that the scheme can provide adaptive privacy and data utility levels, and the
data holder can choose their preferred risk level and data utility through the
scheme. The evaluation results on the heart disease and diabetes data
demonstrate that the scheme can provide a wide range of adaptive privacy
protection and data utility levels to meet different privacy protection and
data utility requirements.


title:
Analysis of A New Time Filter Algorithm for The Unsteady Stokes/Darcy
  Model

abstract:
  This paper first presents a first order ${\theta}$-scheme which is one
parameter family of Linear Multistep Methods for the unsteady Stokes-Darcy
model. Then, on the basis of this scheme, we use a time filter algorithm to
increase the convergence order to the second order with almost no increasing
the amount of computation, from which we get a new efficient algorithm.
Finally, we theoretically analyze the stabilities and error estimations of
coupled and decoupled schemes respectively, and we also do some numerical
experiments to verify the effectiveness, convergence and efficiency.


title: Elements of high order in finite fields specified by binomials

abstract:
  Let $F_q$ be a field with $q$ elements, where $q$ is a power of a prime
number $p\geq 5$. For any integer $m\geq 2$ and $a\in F_q^*$ such that the
polynomial $x^m-a$ is irreducible in $F_q[x]$, we combine two different methods
to construct explicitly elements of high order in the field $F_q[x]/\langle
x^m-a\rangle $. Namely, we find elements with multiplicative order of at least
$5^{\sqrt[3]{m/2}}$, which is better than previously obtained bound for such
family of extension fields.


title:
Autonomous In-Situ Soundscape Augmentation via Joint Selection of Masker
  and Gain

abstract:
  The selection of maskers and playback gain levels in a soundscape
augmentation system is crucial to its effectiveness in improving the overall
acoustic comfort of a given environment. Traditionally, the selection of
appropriate maskers and gain levels has been informed by expert opinion, which
may not representative of the target population, or by listening tests, which
can be time-consuming and labour-intensive. Furthermore, the resulting static
choices of masker and gain are often inflexible to the dynamic nature of
real-world soundscapes. In this work, we utilized a deep learning model to
perform joint selection of the optimal masker and its gain level for a given
soundscape. The proposed model was designed with highly modular building
blocks, allowing for an optimized inference process that can quickly search
through a large number of masker and gain combinations. In addition, we
introduced the use of feature-domain soundscape augmentation conditioned on the
digital gain level, eliminating the computationally expensive waveform-domain
mixing process during inference time, as well as the tedious pre-calibration
process required for new maskers. The proposed system was validated on a
large-scale dataset of subjective responses to augmented soundscapes with more
than 440 participants, ensuring the ability of the model to predict combined
effect of the masker and its gain level on the perceptual pleasantness level.


title: GIT for $\hat U$-actions on algebraic $\mathbb C$-schemes

abstract:
  We extend the $\hat U$-theorem for projective varieties proved in
arXiv:1601.00340 and arXiv:1607.04181 to projective schemes over $\mathbb C$.
The condition "ss=s" required in arXiv:1601.00340 and arXiv:1607.04181 is
reformulated algebraically for our setting. We also describe a blowing up for
the condition "ss=s". Different from the sequence of blowing ups in
arXiv:1607.04181 , we can get "ss=s" in one blowing up.


title: Weak-Key Analysis for BIKE Post-Quantum Key Encapsulation Mechanism

abstract:
  The evolution of quantum computers poses a serious threat to contemporary
public-key encryption (PKE) schemes. To address this impending issue, the
National Institute of Standards and Technology (NIST) is currently undertaking
the Post-Quantum Cryptography (PQC) standardization project intending to
evaluate and subsequently standardize the suitable PQC scheme(s). One such
attractive approach, called Bit Flipping Key Encapsulation (BIKE), has made to
the final round of the competition. Despite having some attractive features,
the IND-CCA security of the BIKE depends on the average decoder failure rate
(DFR), a higher value of which can facilitate a particular type of side-channel
attack. Although the BIKE adopts a Black-Grey-Flip (BGF) decoder that offers a
negligible DFR, the effect of weak-keys on the average DFR has not been fully
investigated. Therefore, in this paper, we first perform an implementation of
the BIKE scheme, and then through extensive experiments show that the weak-keys
can be a potential threat to IND-CCA security of the BIKE scheme and thus need
attention from the research community prior to standardization. We also propose
a key-check algorithm that can potentially supplement the BIKE mechanism and
prevent users from generating and adopting weak keys to address this issue.


title: Learning Adaptive Warping for Real-World Rolling Shutter Correction

abstract:
  This paper proposes the first real-world rolling shutter (RS) correction
dataset, BS-RSC, and a corresponding model to correct the RS frames in a
distorted video. Mobile devices in the consumer market with CMOS-based sensors
for video capture often result in rolling shutter effects when relative
movements occur during the video acquisition process, calling for RS effect
removal techniques. However, current state-of-the-art RS correction methods
often fail to remove RS effects in real scenarios since the motions are various
and hard to model. To address this issue, we propose a real-world RS correction
dataset BS-RSC. Real distorted videos with corresponding ground truth are
recorded simultaneously via a well-designed beam-splitter-based acquisition
system. BS-RSC contains various motions of both camera and objects in dynamic
scenes. Further, an RS correction model with adaptive warping is proposed. Our
model can warp the learned RS features into global shutter counterparts
adaptively with predicted multiple displacement fields. These warped features
are aggregated and then reconstructed into high-quality global shutter frames
in a coarse-to-fine strategy. Experimental results demonstrate the
effectiveness of the proposed method, and our dataset can improve the model's
ability to remove the RS effects in the real world.


title: The $a$-points of the Riemann zeta-function and the functional equation

abstract:
  We prove an equivalent of the Riemann hypothesis in terms of the functional
equation (in its asymmetrical form) and the $a$-points of the zeta-function,
i.e., the roots of the equation $\zeta(s)=a$, where $a$ is an arbitrary fixed
complex number.


title: Some examples of factor groupoids

abstract:
  We examine two classes of examples of Hausdorff \'etale factor groupoids; one
comes from taking a quotient space of the unit space of an AF-groupoid, and the
other comes from certain nonhomogeneous extensions of Cantor minimal systems
considered by Robin Deeley, Ian Putnam, and Karen Strung. The reduced
C$^*$-algebras of the factor groupoids are classifiable in the Elliott scheme,
and we describe their $K$-theory and traces.


title:
Failure of strong unique continuation for harmonic functions on RCD
  Spaces

abstract:
  Unique continuation of harmonic functions on $RCD$ space is a long-standing
open problem, with little known even in the setting of Alexandrov spaces. In
this paper, we establish the weak unique continuation theorem for harmonic
functions in $RCD(K,2)$ spaces and give a counterexample for strong unique
continuation in the setting of $
  RCD(K,N)$ space for any $N\geq 4$ and any $K\in \mathbb{R}$.


title: Deployment of an IoT System for Adaptive In-Situ Soundscape Augmentation

abstract:
  Soundscape augmentation is an emerging approach for noise mitigation by
introducing additional sounds known as "maskers" to increase acoustic comfort.
Traditionally, the choice of maskers is often predicated on expert guidance or
post-hoc analysis which can be time-consuming and sometimes arbitrary.
Moreover, this often results in a static set of maskers that are inflexible to
the dynamic nature of real-world acoustic environments. Overcoming the
inflexibility of traditional soundscape augmentation is twofold. First, given a
snapshot of a soundscape, the system must be able to select an optimal masker
without human supervision. Second, the system must also be able to react to
changes in the acoustic environment with near real-time latency. In this work,
we harness the combined prowess of cloud computing and the Internet of Things
(IoT) to allow in-situ listening and playback using microcontrollers while
delegating computationally expensive inference tasks to the cloud. In
particular, a serverless cloud architecture was used for inference, ensuring
near real-time latency and scalability without the need to provision computing
resources. A working prototype of the system is currently being deployed in a
public area experiencing high traffic noise, as well as undergoing public
evaluation for future improvements.


title: The eighth moment of the Riemann zeta function

abstract:
  In this article, we establish an asymptotic formula for the eighth moment of
the Riemann zeta function, assuming the Riemann hypothesis and a quaternary
additive divisor conjecture. This builds on the work of the first author on the
sixth moment of the Riemann zeta function and work of Conrey-Gonek and
Ivi\'{c}. A key input is a sharp bound for a certain shifted moment of the
Riemann zeta function, assuming the Riemann hypothesis.


title:
SideRT: A Real-time Pure Transformer Architecture for Single Image Depth
  Estimation

abstract:
  Since context modeling is critical for estimating depth from a single image,
researchers put tremendous effort into obtaining global context. Many global
manipulations are designed for traditional CNN-based architectures to overcome
the locality of convolutions. Attention mechanisms or transformers originally
designed for capturing long-range dependencies might be a better choice, but
usually complicates architectures and could lead to a decrease in inference
speed. In this work, we propose a pure transformer architecture called SideRT
that can attain excellent predictions in real-time. In order to capture better
global context, Cross-Scale Attention (CSA) and Multi-Scale Refinement (MSR)
modules are designed to work collaboratively to fuse features of different
scales efficiently. CSA modules focus on fusing features of high semantic
similarities, while MSR modules aim to fuse features at corresponding
positions. These two modules contain a few learnable parameters without
convolutions, based on which a lightweight yet effective model is built. This
architecture achieves state-of-the-art performances in real-time (51.3 FPS) and
becomes much faster with a reasonable performance drop on a smaller backbone
Swin-T (83.1 FPS). Furthermore, its performance surpasses the previous
state-of-the-art by a large margin, improving AbsRel metric 6.9% on KITTI and
9.7% on NYU. To the best of our knowledge, this is the first work to show that
transformer-based networks can attain state-of-the-art performance in real-time
in the single image depth estimation field. Code will be made available soon.


title:
Revisiting Javanese pranata mangsa: On ethnic groups and the four sample
  cities in Java

abstract:
  Pranata mangsa is a local knowledge based on a calendrical system utilized
historically by Javanese peasant farmers in conducting their agricultural
activities in cultivating plants and vegetation. It is also used by fishermen
to guide them not only in capturing fish and other aquatic animals but also in
predicting the type of seafood they might gather. Although the Javanese possess
and maintain their own calendrical system based on a combination of solar and
lunar calendars, pranata mangsa is solely based on the solar calendar and
passed on from one generation to the next verbally. Literature has confirmed
that by combining this local knowledge and scientific data, the Javanese
community has a better resilience in adapting to extreme hydrological events
due to global warming and climate change. In this comment, we provide remarks
on ethnic groups and the four sample cities in Java where the latter were
selected for the pranata mangsa study.


title:
High-Fidelity Model of Stand-Alone Diesel Electric Generator with Hybrid
  Turbine-Governor Configuration for Microgrid Studies

abstract:
  Diesel electric generators are an inherent part of remote hybrid microgrids
found in remote regions of the world that provide primary frequency response
(PFR) to restore system frequency during load or generation changes. However,
with inverter-based resources (IBR) integration into microgrids, the IBR
control provides a fast frequency response (FFR) to restore the system
frequency. Hence, supplementing PFR with FFR requires a sophisticated control
system and a high fidelity diesel electric generator model to design these
control systems. In this work, a high-fidelity model of a diesel electric
generator is developed. Its parameters are tuned using a surrogate optimization
algorithm by emulating its response during a load change to a 400 kVA
Caterpillar C-15 diesel generator, similar to those found in remote microgrids.
The diesel electric generator model consists of a synchronous machine, DC4B
excitation with V/Hz limiter, and a proposed modified IEEE GGOV1
engine-governor model (GGOV1D). The performance of the GGOV1D is compared with
simple, Woodward DEGOV, and a standard IEEE GGOV1 engine-governor model.
Results show that error in the diesel electric generator's response to load
changes using the GGOV1D model is lower with an improved frequency response
during the arresting and rebound period than the other engine-governor models.


title: Six Feet Apart: Online Payments During the COVID-19 Pandemic

abstract:
  Since the COVID-19 pandemic, businesses have faced unprecedented challenges
when trying to remain open. Because COVID-19 spreads through aerosolized
droplets, businesses were forced to distance their services; in some cases,
distancing may have involved moving business services online. In this work, we
explore digitization strategies used by small businesses that remained open
during the pandemic, and survey/interview small businesses owners to understand
preliminary challenges associated with moving online. Furthermore, we analyze
payments from 400K businesses across Japan, Australia, United States, Great
Britain, and Canada. Following initial government interventions, we observe (at
minimum for each country) a 47% increase in digitizing businesses compared to
pre-pandemic levels, with about 80% of surveyed businesses digitizing in under
a week. From both our quantitative models and our surveys/interviews, we find
that businesses rapidly digitized at the start of the pandemic in preparation
of future uncertainty. We also conduct a case-study of initial digitization in
the United States, examining finer relationships between specific government
interventions, business sectors, political orientation, and resulting
digitization shifts. Finally, we discuss the implications of rapid & widespread
digitization for small businesses in the context of usability challenges and
interpersonal interactions, while highlighting potential shifts in pre-existing
social norms.


title: Information-Theoretic Reduction of Markov Chains

abstract:
  We survey information-theoretic approaches to the reduction of Markov chains.
Our survey is structured in two parts: The first part considers Markov chain
coarse graining, which focuses on projecting the Markov chain to a process on a
smaller state space that is informative}about certain quantities of interest.
The second part considers Markov chain model reduction, which focuses on
replacing the original Markov model by a simplified one that yields similar
behavior as the original Markov model. We discuss the practical relevance of
both approaches in the field of knowledge discovery and data mining by
formulating problems of unsupervised machine learning as reduction problems of
Markov chains. Finally, we briefly discuss the concept of lumpability, the
phenomenon when a coarse graining yields a reduced Markov model.


title:
Differentially Private Load Restoration for Microgrids with Distributed
  Energy Storage

abstract:
  Distributed energy storage systems (ESSs) can be efficiently leveraged for
load restoration (LR) for a microgrid (MG) in island mode. When the ESSs are
owned by third parties rather than the MG operator (MGO), the ESS operating
setpoints may be considered as private information of their respective owners.
Therefore, efforts must be put forth to avoid the disclosure through
adversarial analysis of load setpoints. In his paper, we consider a scenario
where LR takes place in a MG by determining load and ESS power injections
through the solution of an AC optimal power flow (AC-OPF) problem. Since the
charge/discharge mode at any given time is assumed to be private, we develop a
differentially-private mechanism which restores load while maintaining privacy
of ESS mode data. The performance of the proposed mechanism is demonstrated for
a 33-bus MG.


title:
Calder\'{o}n-Zygmund operators and their commutators on generalized
  weighted Orlicz-Morrey spaces

abstract:
  In this paper, we obtain the necessary and sufficient conditions for the
weak/strong boundedness of the Calder\'{o}n-Zygmund operators in generalized
weighted Orlicz-Morrey spaces. We also study the boundedness of the commutators
of Calder\'{o}n-Zygmund operators on these spaces. Moreover, the boundedness of
Calder\'{o}n-Zygmund operators in the vector-valued setting is given.


title: Topological flatband loop states in fractal-like photonic lattices

abstract:
  Noncontractible loop states (NLSs) are recently realized topological entity
in flatband lattices, arising typically from band touching at a point where a
flat band intersects one or more dispersive bands. There exists also band
touching across a plane, where one flat band overlaps another all over the
Brillouin zone without crossing a dispersive band. Such isolated plane-touching
flat bands remain largely unexplored. For example, what are the topological
features associated with such flatband degeneracy? Here, we demonstrate for the
first time to our knowledge nontrivial NLSs and robust boundary modes in a
system with such degeneracy. Based on a tailored photonic lattice constructed
from the well-known fractal Sierpinski gasket, we theoretically analyze the
wavefunction singularities and the conditions for the existence of the NLSs. We
show that the NLSs can exist in both singular and nonsingular flat bands, as a
direct reflection of the real-space topology. Experimentally, we observe
directly such flatband NLSs in a laser-written Corbino-shaped fractal-like
lattice. This work not only leads to a deep understanding of the mechanism
behind the nontrivial flatband states, but also opens up new avenues to explore
fundamental phenomena arising from the interplay of flatband degeneracy,
fractal structures and band topology.


title:
Framework for Behavioral Disorder Detection Using Machine Learning and
  Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic

abstract:
  In this modern world, people are becoming more self-centered and unsocial. On
the other hand, people are stressed, becoming more anxious during COVID-19
pandemic situation and exhibits symptoms of behavioral disorder. To measure the
symptoms of behavioral disorder, usually psychiatrist use long hour sessions
and inputs from specific questionnaire. This process is time consuming and
sometime is ineffective to detect the right behavioral disorder. Also, reserved
people sometime hesitate to follow this process. We have created a digital
framework which can detect behavioral disorder and prescribe virtual Cognitive
Behavioral Therapy (vCBT) for recovery. By using this framework people can
input required data that are highly responsible for the three behavioral
disorders namely depression, anxiety and internet addiction. We have applied
machine learning technique to detect specific behavioral disorder from samples.
This system guides the user with basic understanding and treatment through vCBT
from anywhere any time which would potentially be the steppingstone for the
user to be conscious and pursue right treatment.


title:
Comment on "Explicit Analytical Solution for Random Close Packing in
  $d=2$ and $d=3$"

abstract:
  I comment on Zaccone, Phys. Rev. Lett. {\bf 128}, 028002 (2022) highlighting
a flaw in the derivation that led to a spurious divergent factor. This renders
the derivation of the random close packing density invalid.


title: Fast Sampling of Diffusion Models with Exponential Integrator

abstract:
  The past few years have witnessed the great success of Diffusion models~(DMs)
in generating high-fidelity samples in generative modeling tasks. A major
limitation of the DM is its notoriously slow sampling procedure which normally
requires hundreds to thousands of time discretization steps of the learned
diffusion process to reach the desired accuracy. Our goal is to develop a fast
sampling method for DMs with much less number of steps while retaining high
sample quality. To this end, we systematically analyze the sampling procedure
in DMs and identify key factors that affect the sample quality, among which the
method of discretization is most crucial. By carefully examining the learned
diffusion process, we propose Diffusion Exponential Integrator Sampler~(DEIS).
It is based on the Exponential Integrator designed for discretizing ordinary
differential equations (ODEs) and leverages a semilinear structure of the
learned diffusion process to reduce the discretization error. The proposed
method can be applied to any DMs and can generate high-fidelity samples in as
few as 10 steps. In our experiments, it takes about 3 minutes on one A6000 GPU
to generate $50k$ images from CIFAR10.
  Moreover, by directly using pre-trained DMs, we achieve the state-of-art
sampling performance when the number of score function evaluation~(NFE) is
limited, e.g., 3.37 FID and 9.74 Inception score with only 15 NFEs on CIFAR10.


title:
Towards the ground state of molecules via diffusion Monte Carlo on
  neural networks

abstract:
  Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed
significant developments in the past decades and become one of the go-to
methods when accurate ground state energy of molecules and materials is needed.
The remaining bottleneck is the limitations of the inaccurate nodal structure,
prohibiting more challenging electron correlation problems to be tackled with
DMC. In this work, we apply the neural-network based trial wavefunction in
fixed-node DMC, which allows accurate calculation of a broad range of atomic
and molecular systems of different electronic characteristics. Our method is
superior in both accuracy and efficiency compared to state-of-the-art neural
network methods using variational Monte Carlo. Overall, this computational
framework provides a new benchmark for accurate solution of correlated
electronic wavefunction and also shed light on the chemical understanding of
molecules.


title:
Symmetric finite representability of $\ell^p$-spaces in rearrangement
  invariant spaces on $[0,1]$

abstract:
  For a separable rearrangement invariant space $X$ on $[0,1]$ of fundamental
type we identify the set of all $p\in [1,\infty]$ such that $\ell^p$ is
finitely represented in $X$ in such a way that the unit basis vectors of
$\ell^p$ ($c_0$ if $p=\infty$) correspond to pairwise disjoint and
equimeasurable functions. This can be treated as a follow up of a paper by the
first-named author related to separable rearrangement invariant spaces on
$(0,\infty)$.


title:
Detector-based prediction of dose and positron-emitting isotope profiles
  in particle therapy using a deep learning approach

abstract:
  Estimating the real dose distribution during proton therapy through
experimental measurements is key to optimizing treatment planning and ensuring
treatment quality. Positron emission correlates with dose distribution, but
this correlation is non-linear and difficult to estimate in practical
situations. We propose a deep learning-based method that utilizes raw
coincidence data from readouts of palm-sized PET modules and predicts the
positron activity and dose distributions bypassing traditional reconstruction
steps. An extensive simulation dataset was generated using the GATE/Geant4 10.4
toolkit and a human CT phantom with a compact in-beam PET imaging setup for
realistic single-fraction dose delivery. Conditional generative adversarial
networks were trained to generate dose maps conditioned to the coincidence
distributions on the detector. The generated dose maps are used to validate the
range against simulated output. We evaluated the model performance through mean
relative error (MRE), absolute dose fraction difference, and shift in Bragg
peak position, and present it as a function of the detector threshold, the
number of coincidences required, and other conditions that optimize the
predictive power of the model. The results show that the model can predict the
Bragg peak position and dose for mono-energetic irradiation between 50 MeV and
122 MeV with uncertainties less than 1% and 2%, respectively, with
$\mathrm{10^5}$ coincidences acquired for five minutes post-irradiation. An
important aspect of this simulation study is the use of compact detectors
applicable in a treatment scenario, which operate on very low counts and the
demonstration of a method for direct reconstruction. Through our analysis, we
can continue to expand the scope of inverse modeling to simulate realistic data
by learning imaging and experimental physics in radiation therapy from
observations.


title:
Unsupervised Reinforcement Learning for Transferable Manipulation Skill
  Discovery

abstract:
  Current reinforcement learning (RL) in robotics often experiences difficulty
in generalizing to new downstream tasks due to the innate task-specific
training paradigm. To alleviate it, unsupervised RL, a framework that
pre-trains the agent in a task-agnostic manner without access to the
task-specific reward, leverages active exploration for distilling diverse
experience into essential skills or reusable knowledge. For exploiting such
benefits also in robotic manipulation, we propose an unsupervised method for
transferable manipulation skill discovery that ties structured exploration
toward interacting behavior and transferable skill learning. It not only
enables the agent to learn interaction behavior, the key aspect of the robotic
manipulation learning, without access to the environment reward, but also to
generalize to arbitrary downstream manipulation tasks with the learned
task-agnostic skills. Through comparative experiments, we show that our
approach achieves the most diverse interacting behavior and significantly
improves sample efficiency in downstream tasks including the extension to
multi-object, multitask problems.


title: Weak Convergence and Spectrality of Infinite Convolutions

abstract:
  Let $\{ A_k\}_{k=1}^\infty$ be a sequence of finite subsets of $\mathbb{R}^d$
satisfying that $\# A_k \ge 2$ for all integers $k \ge 1$. In this paper, we
first give a sufficient and necessary condition for the existence of the
infinite convolution $$\nu =\delta_{A_1}*\delta_{A_2} * \cdots
*\delta_{A_n}*\cdots, $$ where all sets $A_k \subseteq \mathbb{R}_+^d$ and
$\delta_A = \frac{1}{\# A} \sum_{a \in A} \delta_a$. Then we study the
spectrality of a class of infinite convolutions generated by Hadamard triples
in $\mathbb{R}$ and construct a class of singular spectral measures without
compact support. Finally we show that such measures are abundant, and the
dimension of their supports has the intermediate-value property.


title:
Task Embedding Temporal Convolution Networks for Transfer Learning
  Problems in Renewable Power Time-Series Forecast

abstract:
  Task embeddings in multi-layer perceptrons for multi-task learning and
inductive transfer learning in renewable power forecasts have recently been
introduced. In many cases, this approach improves the forecast error and
reduces the required training data. However, it does not take the seasonal
influences in power forecasts within a day into account, i.e., the diurnal
cycle. Therefore, we extended this idea to temporal convolutional networks to
consider those seasonalities. We propose transforming the embedding space,
which contains the latent similarities between tasks, through convolution and
providing these results to the network's residual block. The proposed
architecture significantly improves up to 25 percent for multi-task learning
for power forecasts on the EuropeWindFarm and GermanSolarFarm dataset compared
to the multi-layer perceptron approach. Based on the same data, we achieve a
ten percent improvement for the wind datasets and more than 20 percent in most
cases for the solar dataset for inductive transfer learning without
catastrophic forgetting. Finally, we are the first proposing zero-shot learning
for renewable power forecasts to provide predictions even if no training data
is available.


title:
Electrically Active Domain Wall Magnons in Layered van der Waals
  Antiferromagnets

abstract:
  We study theoretically domain wall (DW) magnons-elementary collective
excitations of magnetic DWs -- in easy-axis layered van der Waals (vdW)
antiferromagnets, where they behave as normal modes of coupled spin
superfluids. We uncover that, due to spin-charge coupling in vdW magnets, such
DW magnons can be activated by voltage-induced torques, thereby providing a
path for their low-dissipation and nanoscale excitation. Moreover, the
electrical activation and the number of DW magnons at a frequency can be
controlled by applying symmetry-breaking static magnetic field, adding
tunability of signal transmission by them. Our results highlight that domain
walls in vdW magnets provide a promising platform to route coherent spin
information for a broad range of explorations in spintronics and magnetism.


title: Classification in a rotational flow of two-dimensional algebras

abstract:
  In the present paper we consider a flow, which is a time depending family of
two-dimensional algebras. For any two algebras of this flow, formed at
different times, we find conditions under which they are isomorphic. We show
that the flow contains uncountably pairwise non-isomorphic algebras and there
is one commutative algebra among them. Moreover, we compare our algebras with a
known classification of 2-dimensional real algebras.


title:
Control-Theoretic Modeling of Multi-Species Water Quality Dynamics in
  Drinking Water Networks: Survey, Methods, and Test Cases

abstract:
  Chlorine is a widely used disinfectant and proxy for water quality (WQ)
monitoring in water distribution networks (WDN). Chlorine-based WQ regulation
and control aims to maintain pathogen-free water. Chlorine residual evolution
within WDN is commonly modeled using the typical single-species decay and
reaction dynamics that account for network-wide, spatiotemporal chlorine
concentrations only. Prior studies have proposed more advanced and accurate
descriptions via multi-species dynamics. This paper presents a host of novel
state-space, control-theoretic representations of multi-species water quality
dynamics. These representations describe decay, reaction, and transport of
chlorine and a fictitious reactive substance to reflect realistic complex
scenarios in WDN. Such dynamics are simulated over space- and time-discretized
grids of the transport partial differential equation and the nonlinear reaction
ordinary differential equation. To that end, this paper (i) provides a full
description on how to formulate a high fidelity model-driven state-space
representation of the multi-species water quality dynamics and (ii)
investigates the applicability and performance of different Eulerian-based
schemes (Lax-Wendroff, backward Euler, and Crank- Nicolson) and
Lagrangian-based schemes (method of characteristics) in contrast with EPANET
and its EPANET-MSX extension. Numerical case studies reveal that the
Lax-Wendroff scheme and method of characteristics outperform other schemes with
reliable results under reasonable assumptions and limitations.


title:
Quantitative Prediction of Fracture Toughness $(K_{{\rm I}c})$ of
  Polymer by Fractography Using Deep Neural Networks

abstract:
  Fracture surfaces provide various types of information about fracture. The
fracture toughness $K_{{\rm I}c}$, which represents the resistance to fracture,
can be estimated using the three-dimensional (3D) information of a fracture
surface, i.e., its roughness. However, this is time-consuming and expensive to
obtain the 3D information of a fracture surface; thus, it is desirable to
estimate $K_{{\rm I}c}$ from a two-dimensional (2D) image, which can be easily
obtained. In recent years, methods of estimating a 3D structure from its 2D
image using deep learning have been rapidly developed. In this study, we
propose a framework for fractography that directly estimates $K_{{\rm I}c}$
from a 2D fracture surface image using deep neural networks (DNNs). Typically,
image recognition using a DNN requires a tremendous amount of image data, which
is difficult to acquire for fractography owing to the high experimental cost.
To compensate for the limited data, in this study, we used the transfer
learning (TL) method, and constructed high-performance prediction models even
with a small dataset by transferring machine learning models trained using
other large datasets. We found that the regression model obtained using our
proposed framework can predict $K_{{\rm I}c}$ in the range of approximately 1-5
[MPa$\sqrt{m}$] with a standard deviation of the estimation error of
approximately $\pm$0.37 [MPa$\sqrt{m}$]. The present results demonstrate that
the DNN trained with TL opens a new route for quantitative fractography by
which parameters of fracture process can be estimated from a fracture surface
even with a small dataset. The proposed framework also enables the building of
regression models in a few hours. Therefore, our framework enables us to screen
a large number of image datasets available in the field of materials science
and find candidates that are worth expensive machine learning analysis.


title:
Leaner and Faster: Two-Stage Model Compression for Lightweight
  Text-Image Retrieval

abstract:
  Current text-image approaches (e.g., CLIP) typically adopt dual-encoder
architecture using pre-trained vision-language representation. However, these
models still pose non-trivial memory requirements and substantial incremental
indexing time, which makes them less practical on mobile devices. In this
paper, we present an effective two-stage framework to compress large
pre-trained dual-encoder for lightweight text-image retrieval. The resulting
model is smaller (39% of the original), faster (1.6x/2.9x for processing
image/text respectively), yet performs on par with or better than the original
full model on Flickr30K and MSCOCO benchmarks. We also open-source an
accompanying realistic mobile image search application.


title: Modified (hyper-)viscosity for coarse-resolution ocean models

abstract:
  We present a simple parameterization for coarse-resolution ocean models. To
replace computationally expensive high-resolution ocean models, we develop a
computationally cheap parameterization for coarse-resolution models based
solely on the modification of the viscosity term in advection equations. It is
meant to reproduce the mean quantities like pressure, velocity, or vorticity
computed from a high-resolution reference solution or using observations. We
test this new parameterization on a double-gyre quasi-geostrophic model in the
eddy-permitting regime. Our results show that the proposed scheme improves
significantly the energy statistics and the intrinsic variability on the coarse
mesh. This method shall serve as a deterministic basis model for
coarse-resolution stochastic parameterizations in future works.


title: Czech Dataset for Cross-lingual Subjectivity Classification

abstract:
  In this paper, we introduce a new Czech subjectivity dataset of 10k manually
annotated subjective and objective sentences from movie reviews and
descriptions. Our prime motivation is to provide a reliable dataset that can be
used with the existing English dataset as a benchmark to test the ability of
pre-trained multilingual models to transfer knowledge between Czech and English
and vice versa. Two annotators annotated the dataset reaching 0.83 of the
Cohen's \k{appa} inter-annotator agreement. To the best of our knowledge, this
is the first subjectivity dataset for the Czech language. We also created an
additional dataset that consists of 200k automatically labeled sentences. Both
datasets are freely available for research purposes. Furthermore, we fine-tune
five pre-trained BERT-like models to set a monolingual baseline for the new
dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing
English dataset for which we obtained results that are on par with the current
state-of-the-art results. Finally, we perform zero-shot cross-lingual
subjectivity classification between Czech and English to verify the usability
of our dataset as the cross-lingual benchmark. We compare and discuss the
cross-lingual and monolingual results and the ability of multilingual models to
transfer knowledge between languages.


title: A study of tree-based methods and their combination

abstract:
  Tree-based methods are popular machine learning techniques used in various
fields. In this work, we review their foundations and a general framework the
importance sampled learning ensemble (ISLE) that accelerates their fitting
process. Furthermore, we describe a model combination strategy called the
adaptive regression by mixing (ARM), which is feasible for tree-based methods
via ISLE. Moreover, three modified ISLEs are proposed, and their performance
are evaluated on the real data sets.


title:
A Mixed-Domain Self-Attention Network for Multilabel Cardiac
  Irregularity Classification Using Reduced-Lead Electrocardiogram

abstract:
  Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such
as atrial fibrillation, bradycardia, and other irregular complexes. While
previous studies have achieved great accomplishment classifying these
irregularities with standard 12-lead ECGs, there existed limited evidence
demonstrating the utility of reduced-lead ECGs in capturing a wide-range of
diagnostic information. In addition, classification model's generalizability
across multiple recording sources also remained uncovered. As part of the
PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC,
proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac
abnormalities from reduced-lead ECG. Our classifiers received scores of 0.602,
0.593, 0.597, 0.591, and 0.589 (ranked 54th, 37th, 38th, 38th, and 39th) for
the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden
validation set with the evaluation metric defined by the challenge.


title: An Efficient Routing Protocol for Quantum Key Distribution Networks

abstract:
  Quantum key distribution (QKD) can provide point-to-point
information-theoretic secure key services for two connected users. To broaden
the service scale of QKD technology, the research of QKD networks that can
deliver end-to-end secure key services for more users is crucial. Due to the
limited key generation capability of QKD devices, high quantum secure key
utilization is the major concern for QKD networks. Since traditional routing
techniques do not account the state of quantum secure keys on links, applying
them in QKD networks directly will result in the underutilization of quantum
secure keys. Therefore, a better routing protocol for QKD networks, especially
for large-scale QKD networks, is desperately needed. In this study, an
efficient routing protocol based on Optimized Link State Routing (OLSR), i.e.
QOLSR is proposed for QKD networks. QOLSR can considerably improve quantum key
utilization in QKD networks by designing a link-state awareness mechanism and
an optimal path selection algorithm. Simulation results demonstrate the
validity and efficiency of the proposed routing protocol QOLSR. Most
importantly, with the growth of communication traffic, the benefit becomes even
more apparent.


title:
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training
  in Image Retrieval

abstract:
  The task of privacy-preserving model upgrades in image retrieval desires to
reap the benefits of rapidly evolving new models without accessing the raw
gallery images. A pioneering work introduced backward-compatible training,
where the new model can be directly deployed in a backfill-free manner, i.e.,
the new query can be directly compared to the old gallery features. Despite a
possible solution, its improvement in sequential model upgrades is gradually
limited by the fixed and under-quality old gallery embeddings. To this end, we
propose a new model upgrade paradigm, termed Bidirectional Compatible Training
(BiCT), which will upgrade the old gallery embeddings by forward-compatible
training towards the embedding space of the backward-compatible new model. We
conduct comprehensive experiments to verify the prominent improvement by BiCT
and interestingly observe that the inconspicuous loss weight of backward
compatibility actually plays an essential role for both backward and forward
retrieval performance. To summarize, we introduce a new and valuable problem
named privacy-preserving model upgrades, with a proper solution BiCT. Several
intriguing insights are further proposed to get the most out of our method.


title:
On global randomized block Kaczmarz algorithm for solving large-scale
  matrix equations

abstract:
  The randomized Kaczmarz algorithm is one of the most popular approaches for
solving large-scale linear systems due to its simplicity and efficiency. In
this paper, we propose two classes of global randomized Kaczmarz methods for
solving large-scale linear matrix equations $AXB=C$, the global randomized
block Kaczmarz algorithm and global randomized average block Kaczmarz
algorithm. The feature of global randomized block Kaczmarz algorithm is the
fact that the current iterate is projected onto the solution space of the
sketched matrix equation at each iteration, while the global randomized average
block Kaczmarz approach is pseudoinverse-free and therefore can be deployed on
parallel computing units to achieve significant improvements in the
computational time. We prove that these two methods linearly converge in the
mean square to the minimum norm solution $X_*=A^\dag CB^\dag$ of a given linear
matrix equation. The convergence rates depend on the geometric properties of
the data matrices and their submatrices and on the size of the blocks.
Numerical results reveal that our proposed algorithms are efficient and
effective for solving large-scale matrix equations. In particular, they can
also achieve satisfying performance when applied to image deblurring problems.


title:
QRelScore: Better Evaluating Generated Questions with Deeper
  Understanding of Context-aware Relevance

abstract:
  Existing metrics for assessing question generation not only require costly
human reference but also fail to take into account the input context of
generation, rendering the lack of deep understanding of the relevance between
the generated questions and input contexts. As a result, they may wrongly
penalize a legitimate and reasonable candidate question when it (i) involves
complicated reasoning with the context or (ii) can be grounded by multiple
evidences in the context. In this paper, we propose $\textbf{QRelScore}$, a
context-aware $\underline{\textbf{Rel}}$evance evaluation metric for
$\underline{\textbf{Q}}$uestion Generation. Based on off-the-shelf language
models such as BERT and GPT2, QRelScore employs both word-level hierarchical
matching and sentence-level prompt-based generation to cope with the
complicated reasoning and diverse generation from multiple evidences,
respectively. Compared with existing metrics, our experiments demonstrate that
QRelScore is able to achieve a higher correlation with human judgments while
being much more robust to adversarial samples.


title:
Nucleon and $\Delta$ resonances in $\gamma n \to K^+\Sigma^-$
  photoproduction

abstract:
  The most recent data on beam asymmetries, $\Sigma$, and beam-target
asymmetries, $E$, from the CLAS Collaboration, together with the previous data
on differential cross sections and beam asymmetries from the CLAS and LEPS
Collaborations, for the $\gamma n \to K^+\Sigma^-$ reaction are studied based
on an effective Lagrangian approach in the tree-level Born approximation. The
$t$-channel $K$ and $K^\ast(892)$ exchanges, the $u$-channel $\Sigma$ exchange,
the interaction current, and the exchanges of $N$, $\Delta$, and their excited
states in the $s$ channel are considered in constructing the reaction
amplitudes to describe the available experimental data. The reaction mechanisms
of $\gamma n \to K^+\Sigma^-$ are analyzed, and the associated resonances'
parameters are extracted. The numerical results show that the $\Delta$ exchange
and the $N(1710)1/2^+$, $N(1880)1/2^+$, $N(1900)3/2^+$, and $\Delta(1920)3/2^+$
resonance exchanges in the $s$-channel dominate the $\gamma n \to K^+\Sigma^-$
reaction in the lower energy region, and the $t$-channel $K^\ast(892)$ exchange
plays a crucial role at forward angles in the higher energy region.


title: Maxmin Participatory Budgeting

abstract:
  Participatory Budgeting (PB) is a popular voting method by which a limited
budget is divided among a set of projects, based on the preferences of voters
over the projects. PB is broadly categorised as divisible PB (if the projects
are fractionally implementable) and indivisible PB (if the projects are
atomic). Egalitarianism, an important objective in PB, has not received much
attention in the context of indivisible PB. This paper addresses this gap
through a detailed study of a natural egalitarian rule, Maxmin Participatory
Budgeting (MPB), in the context of indivisible PB. Our study is in two parts:
(1) computational (2) axiomatic. In the first part, we prove that MPB is
computationally hard and give pseudo-polynomial time and polynomial-time
algorithms when parameterized by certain well-motivated parameters. We propose
an algorithm that achieves for MPB, additive approximation guarantees for
restricted spaces of instances and empirically show that our algorithm in fact
gives exact optimal solutions on real-world PB datasets. We also establish an
upper bound on the approximation ratio achievable for MPB by the family of
exhaustive strategy-proof PB algorithms. In the second part, we undertake an
axiomatic study of the MPB rule by generalizing known axioms in the literature.
Our study leads to the proposal of a new axiom, maximal coverage, which
captures fairness aspects. We prove that MPB satisfies maximal coverage.


title:
Numerical approximation of the stochastic Navier-Stokes equations
  through artificial compressibility

abstract:
  A constructive numerical approximation of the two-dimensional unsteady
stochastic Navier-Stokes equations of an incompressible fluid is proposed via a
pseudo-compressibility technique involving a parameter $\epsilon$. Space and
time are discretized through a finite element approximation and an Euler
method. The convergence analysis of the suggested numerical scheme is
investigated throughout this paper. It is based on a local monotonicity
property permitting the convergence toward the unique strong solution of the
Navier-Stokes equations to occur within the originally introduced probability
space. Justified optimal conditions are imposed on the parameter $\epsilon$ to
ensure convergence within the best rate.


title: Dynamical decoupling for realization of topological frequency conversion

abstract:
  The features of topological physics can manifest in a variety of physical
systems in distinct ways. Periodically driven systems, with the advantage of
high flexibility and controllability, provide a versatile platform to simulate
many topological phenomena and may lead to novel phenomena that can not be
observed in the absence of driving. Here we investigate the influence of
realistic experimental noise on the realization of a two-level system under a
two-frequency drive that induces topologically nontrivial band structure in the
two-dimensional Floquet space. We propose a dynamical decoupling scheme that
sustains the topological phase transition overcoming the influence of
dephasing. Therefore, the proposal would facilitate the observation of
topological frequency conversion in the solid state spin system, e.g. NV center
in diamond.


title:
Cooperative Aerial-Ground Multi-Robot System for Automated Construction
  Tasks

abstract:
  In this paper, we study a cooperative aerial-ground robotic team and its
application to the task of automated construction. We propose a solution for
planning and coordinating the mission of constructing a wall with a predefined
structure for a heterogeneous system consisting of one mobile robot and up to
three unmanned aerial vehicles. The wall consists of bricks of various weights
and sizes, some of which need to be transported using multiple robots
simultaneously. To that end, we use hierarchical task representation to specify
interrelationships between mission subtasks and employ effective scheduling and
coordination mechanism, inspired by Generalized Partial Global Planning. We
evaluate the performance of the method under different optimization criteria
and validate the solution in the realistic Gazebo simulation environment.


title: Metastable states and hidden phase slips in nanobridge SQUIDs

abstract:
  We fabricated an asymmetric nanoscale SQUID consisting of one nanobridge weak
link and one Dayem bridge weak link. The current phase relation of these
particular weak links is characterized by multivaluedness and linearity. While
the latter is responsible for a particular magnetic field dependence of the
critical current (so-called vorticity diamonds), the former enables the
possibility of different vorticity states (phase winding numbers) existing at
one magnetic field value. In experiments the observed critical current value is
stochastic in nature, does not necessarily coincide with the current associated
with the lowest energy state and critically depends on the measurement
conditions. In this work, we unravel the origin of the observed metastability
as a result of the phase dynamics happening during the freezing process and
while sweeping the current. Moreover, we employ special measurement protocols
to prepare the desired vorticity state and identify the (hidden) phase slip
dynamics ruling the detected state of these nanodevices. In order to gain
insights into the dynamics of the condensate and, more specifically the hidden
phase slips, we performed time-dependent Ginzburg-Landau simulations.


title: Chemical evolution during the formation of molecular clouds

abstract:
  To study the chemical evolution during the formation of molecular clouds, we
model three types of clouds with different density structures: collapsing
spherical, collapsing ellipsoidal, and static spherical profiles. The
collapsing models are better than the static models in matching the
observational characteristics in typical molecular clouds. This is mainly
because the gravity can speed up the formation of some important molecules
(e.g., H$_2$, CO, OH) by increasing the number density during collapse. The
different morphologies of prolate, oblate, and spherical clouds lead to
differences in chemical evolution, which are mainly due to their different
evolution of number density. We also study the effect of initial chemical
compositions on chemical evolution, and find that H atoms can accelerate OH
formation by two major reactions: O + H $\rightarrow$ OH in gas phase and on
dust grain surfaces, leading to the models in which hydrogen is mainly atomic
initially better match observations than the models in which hydrogen is mainly
molecular initially. Namely, to match observations, initially hydrogen must be
mostly atomic. The CO molecules are able to form even without the pre-existence
of H$_2$. We also study the influence of gas temperature, dust temperature,
intensity of interstellar radiation field and cosmic-ray ionization rate on
chemical evolution in static clouds. The static CO clouds with high dust
temperature, strong radiation field, and intensive cosmic rays are transient
due to rapid CO destruction.


title: Revisiting the Random Subset Sum problem

abstract:
  The average properties of the well-known \emph{Subset Sum Problem} can be
studied by the means of its randomised version, where we are given a target
value $z$, random variables $X_1, \ldots, X_n$, and an error parameter
$\varepsilon > 0$, and we seek a subset of the $X_i$'s whose sum approximates
$z$ up to error~$\varepsilon$. In this setup, it has been shown that, under
mild assumptions on the distribution of the random variables, a sample of size
$\mathcal{O}\left(\log (1/\varepsilon)\right)$ suffices to obtain, with high
probability, approximations for all values in $[-1/2, 1/2]$. Recently, this
result has been rediscovered outside the algorithms community, enabling
meaningful progress in other fields. In this work we present an alternative
proof for this theorem, with a more direct approach and resourcing to more
elementary tools, in the hope of disseminating it even further.


title: Morphodynamics of Active Nematic Fluid Surfaces

abstract:
  Morphodynamic equations governing the behaviour of active nematic fluids on
deformable curved surfaces are constructed for the general large deformation
limit. Emphasis is placed on the formulation of objective rates that account
for normal deformations whilst ensuring that tangential flows are Eulerian, and
the use of the surface derivative (rather than the covariant derivative) in the
nematic free energy, which elastically couples local order to out of plane
bending of the surface. Focusing on surface geometry and its dynamical
interplay with the hydrodynamics, several illustrative instabilities are then
characterised. These include where the role of the Scriven-Love number and its
nematic analogue (the spin-connection) are non-negligible, and where the active
nematic forcing can be characterised by a non-zero active analogue of the
F\"{o}ppl-von-K\'{a}rm\'{a}n number. For the former, the viscous dissipation
induced by intrinsic curvature couples the nematic director to surface
deformations, resulting in non-trivial relaxation dynamics of non-axisymmetric
perturbations to a tube. For the latter, the nematic active forcing couples to
the elastic free-energy of the texture, resulting in extensile (active
ruffling) and contractile (active pearling) instabilities under axisymmetric
perturbations to a tube, as well as a further bend instability in the nematic
texture on a tube (decoupled from surface geometry) in the contractile case.
Finally, we examine a topological defect located on a almost flat patch of
membrane and show that there exists a steady state where a combination of
defect elasticity, activity and non-negligible spin-connection drive a shape
change in the surface.


title: KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching

abstract:
  One of the strongest signals for automated matching of knowledge graphs and
ontologies are textual concept descriptions. With the rise of transformer-based
language models, text comparison based on meaning (rather than lexical
features) is available to researchers. However, performing pairwise comparisons
of all textual descriptions of concepts in two knowledge graphs is expensive
and scales quadratically (or even worse if concepts have more than one
description). To overcome this problem, we follow a two-step approach: we first
generate matching candidates using a pre-trained sentence transformer (so
called bi-encoder). In a second step, we use fine-tuned transformer
cross-encoders to generate the best candidates. We evaluate our approach on
multiple datasets and show that it is feasible and produces competitive
results.


title:
On the directional asymptotic approach in optimization theory Part A:
  approximate, M-, and mixed-order stationarity

abstract:
  We show that, for a fixed order $\gamma\geq 1$, each local minimizer of a
rather general nonsmooth optimization problem in Euclidean spaces is either
M-stationary in the classical sense (corresponding to stationarity of order
$1$), satisfies stationarity conditions in terms of a coderivative construction
of order $\gamma$, or is approximately stationary with respect to a critical
direction as well as $\gamma$ in a certain sense. By ruling out the latter case
with a constraint qualification not stronger than directional metric
subregularity, we end up with new necessary optimality conditions comprising a
mixture of limiting variational tools of order $1$ and $\gamma$. These abstract
findings are carved out for the broad class of geometric constraints. As a
byproduct, we obtain new constraint qualifications ensuring M-stationarity of
local minimizers. The paper closes by illustrating these results in the context
of standard nonlinear, complementarity-constrained, and nonlinear semidefinite
programming.


title: Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud

abstract:
  Extracting information from raw data is probably one of the central
activities of experimental scientific enterprises. This work is about a
pipeline in which a specific model is trained to provide a compact, essential
representation of the training data, useful as a starting point for
visualization and analyses aimed at detecting patterns, regularities among
data. To enable researchers exploiting this approach, a cloud-based system is
being developed and tested in the NEANIAS project as one of the ML-tools of a
thematic service to be offered to the EOSC. Here, we describe the architecture
of the system and introduce two example use cases in the astronomical context.


title:
Quadrature domains for the Helmholtz equation with applications to
  non-scattering phenomena

abstract:
  In this paper, we introduce quadrature domains for the Helmholtz equation. We
show existence results for such domains and implement the so-called partial
balayage procedure. We also give an application to inverse scattering problems,
and show that there are non-scattering domains for the Helmholtz equation at
any positive frequency that have inward cusps.


title:
Comment on "Flavored leptogenesis and neutrino mass with $A_4$ symmetry"
  [JHEP12(2021)051, arXiv:2106.06773]

abstract:
  Recently, in ref. \cite{A4Datta} Datta et al. [arXiv:2106.06773] proposed an
$A_4$ flavor symmetric model supplemented by $Z_2\times Z_3$ symmetry which can
accommodate the appropriate lepton mixing and neutrino masses via Type-I seesaw
mechanism. They have constructed a minimal model with only one $SU(2)_L$
doublet scalar and six flavons that generate a specific flavor structure,
favors the normal hierarchy of light neutrinos and narrows down the range of
Dirac CP violating phase. Taking into account the contribution of all invariant
terms, under all symmetries, is very important in the model building process,
however, in ref. \cite{A4Datta} the authors miss a Majorana mass term which
contributes to and changes the structure of the Majorana mass matrix of the
right handed neutrinos. In this comment paper, we point out the above issue and
provide a solution to fill in the missing term in ref. \cite{A4Datta}.


title: On the total versions of 1-2-3-conjecture for graphs and hypergraphs

abstract:
  In 2004, Karo\'nski, \L uczak and Thomason proposed $1$-$2$-$3$-conjecture:
For every nice graph $G$ there is an edge weighting function $
w:E(G)\rightarrow\{1,2,3\} $ such that the induced vertex coloring is proper.
After that, the total versions of this conjecture were suggested in the
literature and recently, Kalkowski et al. have generalized this conjecture to
hypergraphs. In this paper, some previously known results on the total versions
are improved. Moreover, an affirmative answer is given to the conjecture for
some well-known families of hypergraphs like complete $n$-partite hypergraphs,
paths, cycles, theta hypergraphs and some geometric planes. Also, these
hypergraphs are characterized based on the corresponding parameter.


title:
Traffic flow stability in stochastic second-order macroscopic continuum
  model

abstract:
  Second-order macroscopic continuum models have been constantly improved for a
more realistic traffic flow modeling. Recently, a series of experimental
studies have suggested that the presence of stochastic factors contributes
significantly to the emergence of traffic instabilities. Nevertheless, the
traffic flow stability of stochastic second-order macroscopic continuum model
hasn't received the attention it deserves in past studies. More importantly, we
have found that the destabilizing aspect of stochasticity is still not
correctly validated in the existing theoretical stability analysis. In this
paper, we analytically study the impact of stochasticity on traffic flow
stability for a general stochastic second-order macroscopic model by using the
direct Lyapunov method. Numerical simulations have been carried out for a
stochastic speed gradient model. Our analytical stability analysis has been
validated and the empirically observed concave growth pattern of traffic
oscillations has been reproduced. Our methodology has been proved efficient and
theoretically revealed that the presence of stochasticity indeed destabilizes
the traffic flow system which is in agreement with the last empirical findings.
Finally, a parsimonious stochastic speed gradient model has been calibrated and
validated against empirical data. It is found that the stochastic second-order
macroscopic model can successfully reproduce the empirically observed
spontaneous emergence of traffic oscillations.


title:
Temperature-sensitive spin-wave nonreciprocity induced by interlayer
  dipolar coupling in ferromagnet/X (X=paramagnet, superconductor) hybrid
  systems

abstract:
  Spin-wave (SW) spectra have theoretically been studied in a thin film of a
ferromagnet (FM) on a substrate from a paramagnet (PM) (an FM above the
critical temperature) or from superconductor (SC). A spin wave propagating in
the FM induces the dynamic magnetization and superconducting current in the
underlying PM and SC, respectively, which affect the SW propagation by their
magnetic fields. As a result of this interaction, the SW spectrum becomes
nonreciprocal to depend on the sign of the SW wave vector q. We show that the
nonreciprocal contribution to the SW spectra in FM/PM and FM/SC systems is
given by the frequency shift of
{\Delta}{\omega}(q)={\omega}(q)-{\omega}(-q)=a(T)({\tau}{\cdot}q) with
{\tau}=(n{\times}M) being the toroidal magnetic moment, M the FM magnetization,
n the unit vector normal to the FM/PM(SC) interface, and a(T) the
temperature-dependent constant of a dipole nature, whose sign depends on the
substrate type. As the {\Delta}{\omega}(T) dependence is strong at temperatures
T close to the critical temperature Tc for the FM-PM or normal metal-SC
transition, one gets a possibility to control the frequency SW nonreciprocity
with temperature variation near Tc. The dipolar mechanism we propose for SW
frequency nonreciprocity is promising for introducing this property of SW
propagation into functional devices.


title:
Short-Term Density Forecasting of Low-Voltage Load using
  Bernstein-Polynomial Normalizing Flows

abstract:
  The transition to a fully renewable energy grid requires better forecasting
of demand at the low-voltage level to increase efficiency and ensure reliable
control. However, high fluctuations and increasing electrification cause huge
forecast variability, not reflected in traditional point estimates.
Probabilistic load forecasts take future uncertainties into account and thus
allow more informed decision-making for the planning and operation of
low-carbon energy systems. We propose an approach for flexible conditional
density forecasting of short-term load based on Bernstein polynomial
normalizing flows, where a neural network controls the parameters of the flow.
In an empirical study with 363 smart meter customers, our density predictions
compare favorably against Gaussian and Gaussian mixture densities. Also, they
outperform a non-parametric approach based on the pinball loss for 24h-ahead
load forecasting for two different neural network architectures.


title: Learned Gradient of a Regularizer for Plug-and-Play Gradient Descent

abstract:
  The Plug-and-Play (PnP) framework allows integrating advanced image denoising
priors into optimization algorithms, to efficiently solve a variety of image
restoration tasks. The Plug-and-Play alternating direction method of
multipliers (ADMM) and the Regularization by Denoising (RED) algorithms are two
examples of such methods that made a breakthrough in image restoration.
However, while the former method only applies to proximal algorithms, it has
recently been shown that there exists no regularization that explains the RED
algorithm when the denoisers lack Jacobian symmetry, which happen to be the
case of most practical denoisers. To the best of our knowledge, there exists no
method for training a network that directly represents the gradient of a
regularizer, which can be directly used in Plug-and-Play gradient-based
algorithms. We show that it is possible to train a denoiser along with a
network that corresponds to the gradient of its regularizer. We use this
gradient of the regularizer in gradient-based optimization methods and obtain
better results comparing to other generic Plug-and-Play approaches. We also
show that the regularizer can be used as a pre-trained network for unrolled
gradient descent. Lastly, we show that the resulting denoiser allows for a
quick convergence of the Plug-and-Play ADMM.


title:
Thickness of the air-water interface from first-principles
  simulation-based hydrogen bond dynamics

abstract:
  The thickness of the air-water interface is determined by interface hydrogen
bond (HB) dynamics. By density functional theory-based molecular dynamics
(DFTMD) simulations, two extreme cases of the interface HB dynamics are
obtained: one underestimates the HB breaking rate constant and the other
overestimates it. The interface HB dynamics in these two cases tends to be the
same as the thickness of the air-water interface increases to 4 Angstroms. The
interface thickness is determined when the interface HB dynamics under the two
cases is converged.