Abstract:
  We introduce a top-down approach to discourse parsing that is conceptually
simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By
framing the task as a sequence labelling problem where the goal is to
iteratively segment a document into individual discourse units, we are able to
eliminate the decoder and reduce the search space for splitting points. We
explore both traditional recurrent models and modern pre-trained transformer
models for the task, and additionally introduce a novel dynamic oracle for
top-down parsing. Based on the Full metric, our proposed LSTM model sets a new
state-of-the-art for RST parsing.


Abstract:
  Recent promises of generative deep learning lately brought interest to its
potential uses in neural engineering. In this paper we firstly review recently
emerging studies on generating artificial electroencephalography (EEG) signals
with deep neural networks. Subsequently, we present our feasibility experiments
on generating condition-specific multichannel EEG signals using conditional
variational autoencoders. By manipulating real resting-state EEG epochs, we
present an approach to synthetically generate time-series multichannel signals
that show spectro-temporal EEG patterns which are expected to be observed
during distinct motor imagery conditions.


Abstract:
  Flow structure stability of a steady radial thermocapillary flow from the
local heat source in cylindrical geometry has been studied numerically. The up
boundary of the liquid was partially covered by the stationary film of an
insoluble surfactant, pushed to the wall of the cavity. The calculations are
fulfilled on the base of interfacial hydrodynamics equations by using
mathematical package Comsol Multiphysics. It is shown that the effect of a
radial flow symmetry breakdown is appeared as a result of convective
instability, so that the initial poloidal rotation of the liquid transforms
into the azimuthal plane. The system of inclined steady volumetric vortices is
formed under the film of surfactant. The inclination angle of the plane of
rotation in dependence on the intensity of radial thermocapillary flow is
studied. It is shown that the azimuthal vorticity can be regulated by the power
of point-heat source and by free surface area. The increase of heating
intensity leads to the growth of the azimuthal vorticity. Thus, the initially
radially symmetric toroidal flow is divided into two parts with the origination
of vortices in the azimuthal plane under the film


Abstract:
  We investigate lower bounds for the variance in arithmetic progressions of
certain multiplicative functions "close" to $1$. Specifically, we consider
$\alpha_N$-fold divisor functions, when $\alpha_N$ is a sequence of positive
real numbers approaching $1$ in a suitable way or $\alpha_N=1$, and the
indicator of $y$-smooth numbers, for suitably large parameters $y$. As a
corollary, we will strengthen a previous author's result on the first subject
and obtain matching lower bounds to some Barban-Davenport-Halberstam type
theorems for $y$-smooth numbers. Incidentally, we will also find a lower bound
for the variance in arithmetic progressions of the prime factors counting
functions $\omega(n)$ and $\Omega(n)$.


Abstract:
  We study the link between Wick, anti-Wick and analytic kernel operators on
the Bargmann transform side. We find classes of kernels, e.g. $\wideparen
{\mathcal B} _s$, whose corresponding operators agree with the sets of linear
and continuous operators on $\mathcal A _s$, the images of Pilipovi{\'c} under
the Bargmann transform. We show that in several situations, the sets of Wick,
anti-Wick and kernel operators with symbols and kernels in $\wideparen
{\mathcal B} _s$ agree. We also show some ring, module and composition
properties for $\wideparen {\mathcal B} _s$, and similarly for other spaces
related to $\wideparen {\mathcal B} _s$.


Abstract:
  Clinical trials are an instrument for making informed decisions based on
evidence from well-designed experiments. Here we consider adaptive designs
mainly from the perspective of multi-arm Phase II clinical trials, in which one
or more experimental treatments are compared to a control. Treatment allocation
of individual trial participants is assumed to take place according to a fixed
block randomization, albeit with an important twist: The performance of each
treatment arm is assessed after every measured outcome, in terms of the
posterior distribution of a corresponding model parameter. Different treatments
arms are then compared to each other, according to pre-defined criteria and
using the joint posterior as the basis for such assessment. If a treatment is
found to be sufficiently clearly inferior to the currently best candidate, it
can be closed off either temporarily or permanently from further participant
accrual. The latter possibility provides a method for adaptive treatment
selection, including early stopping of the trial. The main development in the
paper is in terms of binary outcomes, but some extensions, notably for handling
time-to-event data, are discussed as well. The presentation is to a large
extent comparative and expository.


Abstract:
  We describe an automated analysis method to quantify the detailed growth
dynamics of a population of bacilliform bacteria. We propose an innovative
approach to frame-sequence tracking of deformable-cell motion by the automated
minimization of a new, specific cost functional. This minimization is
implemented by dedicated Boltzmann machines (stochastic recurrent neural
networks). Automated detection of cell divisions is handled similarly by
successive minimizations of two cost functions, alternating the identification
of children pairs and parent identification. We validate this automatic cell
tracking algorithm using recordings of simulated cell colonies that closely
mimic the growth dynamics of \emph{E. coli} in microfluidic traps. On a batch
of 1100 image frames, cell registration accuracies per frame ranged from 94.5\%
to 100\%, with a high average. Our initial tests using experimental image
sequences of \emph{E. coli} colonies also yield convincing results, with a
registration accuracy ranging from 90\% to 100\%.


Abstract:
  In this paper, the 2-adic complexity of a class of balanced Whiteman
generalized cyclotomic sequences of period $pq$ is considered. Through
calculating the determinant of the circulant matrix constructed by one of these
sequences, we derive a lower bound on the 2-adic complexity of the
corresponding sequence, which further expands our previous work (Zhao C, Sun Y
and Yan T. Study on 2-adic complexity of a class of balanced generalized
cyclotomic sequences. Journal of Cryptologic Research,6(4):455-462, 2019). The
result shows that the 2-adic complexity of this class of sequences is large
enough to resist the attack of the rational approximation algorithm(RAA) for
feedback with carry shift registers(FCSRs), i.e., it is in fact lower bounded
by $pq-p-q-1$, which is far larger than one half of the period of the
sequences. Particularly, the 2-adic complexity is maximal if suitable
parameters are chosen.


Abstract:
  A lot of online marketing campaigns aim to promote user interaction. The
average treatment effect (ATE) of campaign strategies need to be monitored
throughout the campaign. A/B testing is usually conducted for such needs,
whereas the existence of user interaction can introduce interference to normal
A/B testing. With the help of link prediction, we design a network A/B testing
method LinkLouvain to minimize graph interference and it gives an accurate and
sound estimate of the campaign's ATE. In this paper, we analyze the network A/B
testing problem under a real-world online marketing campaign, describe our
proposed LinkLouvain method, and evaluate it on real-world data. Our method
achieves significant performance compared with others and is deployed in the
online marketing campaign.


Abstract:
  We present a tool-supported approach for the synthesis, verification and
validation of the control software responsible for the safety of the
human-robot interaction in manufacturing processes that use collaborative
robots. In human-robot collaboration, software-based safety controllers are
used to improve operational safety, e.g., by triggering shutdown mechanisms or
emergency stops to avoid accidents. Complex robotic tasks and increasingly
close human-robot interaction pose new challenges to controller developers and
certification authorities. Key among these challenges is the need to assure the
correctness of safety controllers under explicit (and preferably weak)
assumptions. Our controller synthesis, verification and validation approach is
informed by the process, risk analysis, and relevant safety regulations for the
target application. Controllers are selected from a design space of feasible
controllers according to a set of optimality criteria, are formally verified
against correctness criteria, and are translated into executable code and
validated in a digital twin. The resulting controller can detect the occurrence
of hazards, move the process into a safe state, and, in certain circumstances,
return the process to an operational state from which it can resume its
original task. We show the effectiveness of our software engineering approach
through a case study involving the development of a safety controller for a
manufacturing work cell equipped with a collaborative robot.


Abstract:
  Machine Learning (ML) algorithms are susceptible to adversarial attacks and
deception both during training and deployment. Automatic reverse engineering of
the toolchains behind these adversarial machine learning attacks will aid in
recovering the tools and processes used in these attacks. In this paper, we
present two techniques that support automated identification and attribution of
adversarial ML attack toolchains using Co-occurrence Pixel statistics and
Laplacian Residuals. Our experiments show that the proposed techniques can
identify parameters used to generate adversarial samples. To the best of our
knowledge, this is the first approach to attribute gradient based adversarial
attacks and estimate their parameters. Source code and data is available at:
https://github.com/michael-goebel/ei_red


Abstract:
  The evolution of social media platforms have empowered everyone to access
information easily. Social media users can easily share information with the
rest of the world. This may sometimes encourage spread of fake news, which can
result in undesirable consequences. In this work, we train models which can
identify health news related to COVID-19 pandemic as real or fake. Our models
achieve a high F1-score of 98.64%. Our models achieve second place on the
leaderboard, tailing the first position with a very narrow margin 0.05% points.


Abstract:
  We analyze the morphological evolution of open clusters and provide shape
parameters for 265 open clusters. The results show that the overall shape of
sample clusters becomes more elliptical as they grow older, while their core
remains circular or slightly trend to circularize. There is a negative
correlation of the ellipticities with the number of members of the sample
clusters. A significant negative correlation between the overall ellipticities
and masses is also detected for the sample clusters with log(age/year) $\geq$
8, suggesting that the overall shapes of the clusters are possibly influenced
by the number of members and masses, in addition to the external forces and the
surrounding environment. For most young sample clusters, the radial
stratification degree of the short axis direction is greater than that of the
long, implying that the radial stratification degree in the two directions
within the young sample cluster may be unevenly affected by an internal
evolutionary process. Older sample clusters exhibit lower stratification in the
tangential direction, which possibly means those clusters may continue to
survive for a long time at a low level of stratification. Our analysis shows
that the overall shape of the sample clusters may be more susceptible to the
influence of Galactic tides toward the Galactic center than the shear forces
embedded in Galactic differential rotation. By analyzing the distribution of
the ages and number of members of star clusters, we suggest that NGC 6791 may
originate from superclusters.


Abstract:
  Based on the current measurement of the neutron distribution radius ($R_n$)
of $^{208}$Pb through the PREX-2 data, we re-visited the recently developed G3
and IOPB-I force parameter by fine-tuning some of the specific couplings within
the relativistic mean-field model. The $\omega-\rho-$mesons coupling
$\Lambda_{\omega}$ and the $\rho-$meson coupling $g_{\rho}$ are refitted to
reproduce the experimental neutron radius of $^{208}$Pb without compromising
the bulk properties of finite nuclei and infinite nuclear matter observables.
The modified parameter sets are applied to calculate the gross properties of
finite nuclei for a few double closed-shell nuclei and further used to obtain
the various infinite nuclear matter observables at saturation. In addition to
these, the force parameters are adopted to calculate the properties of high
isospin asymmetry dense system such as neutron star matter and tested for the
validation for the constraint from GW170817 binary neutron star merger events.
The tuned forces are predicting relatively good results for finite and infinite
nuclear matter systems and the current limitation on neutron radius from
PREX-2. A systematic analysis using these two refitted parameter sets over the
nuclear chat will be communicated shortly.


Abstract:
  We consider the KZ equations over $\mathbb C$ in the case, when the
hypergeometric solutions are hyperelliptic integrals of genus $g$. Then the
space of solutions is a $2g$-dimensional complex vector space. We also consider
the same equations modulo $p^s$, where $p$ is an odd prime and $s$ is a
positive integer, and over the field $\mathbb Q_p$ of $p$-adic numbers. We
construct polynomial solutions of the KZ equations modulo $p^s$ and study the
space $\mathcal M_{p^s}$ of all constructed solutions. We show that the
$p$-adic limit of $\mathcal M_{p^s}$ as $s\to\infty$ gives us a $g$-dimensional
vector space of solutions of the KZ equations over $\mathbb Q_p$. The solutions
over $\mathbb Q_p$ are power series at a certain asymptotic zone of the KZ
equations.
  In the appendix written jointly with Steven Sperber we consider all
asymptotic zones of the KZ equations in the case $g=1$ of elliptic integrals.
The $p$-adic limit of $\mathcal M_{p^s}$ as $s\to \infty$ gives us a
one-dimensional space of solutions over $\mathbb Q_p$ at every asymptotic zone.
We apply Dwork's theory and show that our germs of solutions over $\mathbb Q_p$
defined at different asymptotic zones analytically continue into a single
global invariant line subbundle of the associated KZ connection. Notice that
the corresponding KZ connection over $\mathbb C$ does not have proper
nontrivial invariant subbundles, and therefore our invariant line subbundle is
a new feature of the KZ equations over $\mathbb Q_p$. We describe the Frobenius
transformations of solutions of the KZ equations for $g =1$ and then recover
the unit roots of the zeta functions of the elliptic curves defined by the
equations $y^2= \beta \,x(x-1)(x-\alpha)$ over the finite field $\mathbb F_p$.
Here $\alpha,\beta\in\mathbb F_p^\times, \alpha \ne 1$.


Abstract:
  This work introduces World-GAN, the first method to perform data-driven
Procedural Content Generation via Machine Learning in Minecraft from a single
example. Based on a 3D Generative Adversarial Network (GAN) architecture, we
are able to create arbitrarily sized world snippets from a given sample. We
evaluate our approach on creations from the community as well as structures
generated with the Minecraft World Generator. Our method is motivated by the
dense representations used in Natural Language Processing (NLP) introduced with
word2vec [1]. The proposed block2vec representations make World-GAN independent
from the number of different blocks, which can vary a lot in Minecraft, and
enable the generation of larger levels. Finally, we demonstrate that changing
this new representation space allows us to change the generated style of an
already trained generator. World-GAN enables its users to generate Minecraft
worlds based on parts of their creations.


Abstract:
  Software systems are designed according to guidelines and constraints defined
by business rules. Some of these constraints define the allowable or required
values for data handled by the systems. These data constraints usually
originate from the problem domain (e.g., regulations), and developers must
write code that enforces them. Understanding how data constraints are
implemented is essential for testing, debugging, and software change.
Unfortunately, there are no widely-accepted guidelines or best practices on how
to implement data constraints.
  This paper presents an empirical study that investigates how data constraints
are implemented in Java. We study the implementation of 187 data constraints
extracted from the documentation of eight real-world Java software systems.
First, we perform a qualitative analysis of the textual description of data
constraints and identify four data constraint types. Second, we manually
identify the implementations of these data constraints and reveal that they can
be grouped into 30 implementation patterns. The analysis of these
implementation patterns indicates that developers prefer a handful of patterns
when implementing data constraints and deviations from these patterns are
associated with unusual implementation decisions or code smells. Third, we
develop a tool-assisted protocol that allows us to identify 256 additional
trace links for the data constraints implemented using the 13 most common
patterns. We find that almost half of these data constraints have multiple
enforcing statements, which are code clones of different types.


Abstract:
  We propose a unifying framework for the matrix-based formulation and analysis
of discontinuous Galerkin (DG) and flux reconstruction (FR) methods for
conservation laws on general unstructured grids. Within such an algebraic
framework, the multidimensional summation-by-parts (SBP) property is used to
establish the discrete equivalence of strong and weak formulations, as well as
the conservation and energy stability properties of a broad class of DG and FR
schemes. Specifically, the analysis enables the extension of the equivalence
between the strong and weak forms of the discontinuous Galerkin collocation
spectral-element method demonstrated by Kopriva and Gassner (J Sci Comput
44:136-155, 2010) to more general nodal and modal DG formulations, as well as
to the Vincent-Castonguay-Jameson-Huynh (VCJH) family of FR methods. Moreover,
new algebraic proofs of conservation and energy stability for DG and VCJH
schemes with respect to suitable quadrature rules and discrete norms are
presented, in which the SBP property serves as a unifying mechanism for
establishing such results. Numerical experiments are provided for the
two-dimensional linear advection and Euler equations, highlighting the design
choices afforded for methods within the proposed framework and corroborating
the theoretical analysis.


Abstract:
  Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI
patients of high risks converting to Alzheimer's disease (AD), is essential for
preventing or slowing the progression of AD. Although previous studies have
shown that the fusion of multi-modal data can effectively improve the
prediction accuracy, their applications are largely restricted by the limited
availability or high cost of multi-modal data. Building an effective prediction
model using only magnetic resonance imaging (MRI) remains a challenging
research topic. In this work, we propose a multi-modal multi-instance
distillation scheme, which aims to distill the knowledge learned from
multi-modal data to an MRI-based network for MCI conversion prediction. In
contrast to existing distillation algorithms, the proposed multi-instance
probabilities demonstrate a superior capability of representing the complicated
atrophy distributions, and can guide the MRI-based network to better explore
the input MRI. To our best knowledge, this is the first study that attempts to
improve an MRI-based prediction model by leveraging extra supervision distilled
from multi-modal information. Experiments demonstrate the advantage of our
framework, suggesting its potentials in the data-limited clinical settings.


Abstract:
  Discrete abstractions have become a standard approach to assist control
synthesis under complex specifications. Most techniques for the construction of
a discrete abstraction for a continuous-time system require time-space
discretization of the concrete system, which constitutes property satisfaction
for the continuous-time system non-trivial. In this work, we aim at relaxing
this requirement by introducing a control interface. Firstly, we connect the
continuous-time uncertain concrete system with its discrete deterministic
state-space abstraction with a control interface. Then, a novel stability
notion called $\eta$-approximate controlled globally practically stable, and a
new simulation relation called robust approximate simulation relation are
proposed. It is shown that the uncertain concrete system, under the condition
that there exists an admissible control interface such that the augmented
system (composed of the concrete system and its abstraction) can be made
$\eta$-approximate controlled globally practically stable, robustly
approximately simulates its discrete abstraction. The effectiveness of the
proposed results is illustrated by two simulation examples.


Abstract:
  Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging
biomedical imaging modality to reveal frequency-dependent conductivity
distributions in biomedical applications. Conventional model-based image
reconstruction methods suffer from low spatial resolution, unconstrained
frequency correlation and high computational cost. Deep learning has been
extensively applied in solving the EIT inverse problem in biomedical and
industrial process imaging. However, most existing learning-based approaches
deal with the single-frequency setup, which is inefficient and ineffective when
extended to address the multi-frequency setup. In this paper, we present a
Multiple Measurement Vector (MMV) model based learning algorithm named MMV-Net
to solve the mfEIT image reconstruction problem. MMV-Net takes into account the
correlations between mfEIT images and unfolds the update steps of the
Alternating Direction Method of Multipliers (ADMM) for the MMV problem. The
non-linear shrinkage operator associated with the weighted l2_1 regularization
term is generalized with a cascade of a Spatial Self-Attention module and a
Convolutional Long Short-Term Memory (ConvLSTM) module to capture intra- and
inter-frequency dependencies. The proposed MMVNet was validated on our
Edinburgh mfEIT Dataset and a series of comprehensive experiments. All
reconstructed results show superior image quality, convergence performance and
noise robustness against the state of the art.


Abstract:
  Master equations describing open quantum dynamics are typically first order
differential equations. When such dynamics brings the trajectories in state
space of more than one initial state to the same point at finite instants in
time, the generator of the corresponding master equation becomes singular. The
first-order, time-local, homogeneous master equations then fail to describe the
dynamics beyond the singular point. Retaining time-locality in the master
equation necessitates a reformulation in terms of higher-order differential
equations. We formulate a method to eliminate the divergent behavior of the
generator by using a combination of higher-order derivatives of the generator
with suitable weights and illustrate it with several examples. We also present
a detailed study of the central spin model and we propose the average rate of
information inflow in non-Markovian processes as a quantity that captures a
different aspect of non-Markovian dynamics.


Abstract:
  A radiative transfer model was used to reproduce several millions of OMEGA
(Observatoire pour la Min\'eralogie, l'Eau, les Glaces et l'Activit\'e) spectra
representative of igneous terrains of Mars. This task provided the modal
composition and grain sizes at a planetary scale. The lithology can be
summarized in five mineral maps at km-scale. We found that the low albedo
equatorial regions of the Martian surface (from 60{\deg}S to 30{\deg}N) are
globally dominated by plagioclase with average abundance ~50 vol% and pyroxenes
with total averaged abundance close to 40 vol%. An evolution of the
LCP/(LCP+HCP) ratio is observed with time at the global scale, suggesting an
evolution of the degree of partial melting throughout the Martian eras. Olivine
and Martian dust are minor components of the modelled terrains. The olivine
distribution is quite different from the other minerals because it is found on
localized areas with abundance reaching 20 vol%. A statistical approach, to
classify the pixels of the abundances maps, using k-means clustering,
highlighted seven distinct mineral assemblages on the surface. This
classification illustrates that diverse mineralogical units are found in the
Noachian and Hesperian terrains, which suggests the presence of various and
complex magmatic processes at a global scale during the two oldest eras. The
chemical composition was derived from the modal composition maps. The
OMEGA-derived chemical composition is quite consistent with several distinctive
geochemical characteristics previously considered as fingerprints of the
Martian surface. A major discrepancy is in regards to the Fe content that is
significantly smaller than soil and rock analyses from GRS and in situ
measurements. The discrepancy could be partly explained by the assumptions used
for the spectral modelling or could also indicate surface alteration rinds.


Abstract:
  Panoptic segmentation unifies semantic segmentation and instance segmentation
which has been attracting increasing attention in recent years. However, most
existing research was conducted under a supervised learning setup whereas
unsupervised domain adaptive panoptic segmentation which is critical in
different tasks and applications is largely neglected. We design a domain
adaptive panoptic segmentation network that exploits inter-style consistency
and inter-task regularization for optimal domain adaptive panoptic
segmentation. The inter-style consistency leverages geometric invariance across
the same image of the different styles which fabricates certain
self-supervisions to guide the network to learn domain-invariant features. The
inter-task regularization exploits the complementary nature of instance
segmentation and semantic segmentation and uses it as a constraint for better
feature alignment across domains. Extensive experiments over multiple domain
adaptive panoptic segmentation tasks (e.g., synthetic-to-real and real-to-real)
show that our proposed network achieves superior segmentation performance as
compared with the state-of-the-art.


Abstract:
  At the Large Hadron Collider (LHC), both the ATLAS and CMS Collaborations
have been searching for light charged Higgs bosons via top (anti)quark
production and decays channels, like $pp\to t \bar{t}$ with one top (anti)quark
decaying into a charged Higgs boson and a $b$ (anti)quark, when the decay is
kinematically open (i.e., when $m_{H^\pm}\lesssim m_t$). In this paper, we
propose new searches at the LHC involving light charged Higgs bosons via their
pair production channels like $pp\to H^\pm h/A$ and $pp\to H^+ H^-$ in the
2-Higgs Doublet Model (2HDM) Type-I and -X scenarios. By focusing on the case
where the heavy $H$ state plays the role of the Standard Model (SM)-like Higgs
boson with a mass near 125 GeV, we study the aforementioned Higgs boson pair
production channels and investigate their bosonic decays, such as $H^\pm \to
W^{\pm } h$ and/or $H^\pm \to W^{\pm } A$. We demonstrate that for a light
charged Higgs boson state, with $m_{H^\pm}\lesssim m_t$, at the LHC, such
di-Higgs production and decay channels can give rise to signatures with event
rates much larger than those emerging from $pp\to t\bar{t}\to t\bar{b} H^-$ +
c.c.. We specifically study $h/A\to b\bar b$ and $\tau^+\tau^-$ decays. We,
therefore, claim that the discussed combination of new production and decay
modes can result in an alternative discovery channel for charged Higgs bosons
lighter than the top (anti)quark at the LHC within the above two 2HDM Types.
Finally, in order to motivate experimentalists in ATLAS and CMS to search for
such signatures, we propose 16 Benchmark Points (BPs) which are compatible with
both theoretical and experimental constraints.


Abstract:
  Quantum key distribution (QKD) is a promising technique for secure
communication based on quantum mechanical principles. To improve the secure key
rate of a QKD system, most studies on reconciliation primarily focused on
improving the efficiency. With the increasing performance of QKD systems, the
research priority has shifted to the improvement of both throughput and
efficiency. In this paper, we propose a high performance solution of Cascade
reconciliation, including a high-throughput-oriented framework and an
integrated-optimization-oriented scheme. Benefiting from the fully utilizing
computation and storage resources, effectively dealing with communication
delays, the integrated-optimization-oriented parameters setting, etc., an
excellent overall performance was achieved. Experimental results showed that,
the throughput of up to 570Mbps with an efficiency of 1.038 was achieved,
which, to our knowledge, was more than four times faster than any throughput
previously demonstrated. Furthermore, throughputs on real data sets were
capable of reaching up to 86Mbps even on embedded platforms. Additionally, our
solution offers good adaptability to the fluctuating communication delay and
quantum bit error rate (QBER). Based on our study, low performance (i.e. low
power-consumption and cost-effective) CPU platforms will be sufficient for
reconciliation in the existing and near-term QKD systems.


Abstract:
  In this study we follow up our recent paper (Monteiro et al. 2020) and
present a homogeneous sample of fundamental parameters of open clusters in our
Galaxy, entirely based on Gaia DR2 data. We used published membership
probability of the stars derived from Gaia DR2 data and applied our isochrone
fitting code, updated as in Monteiro et al. (2020), to GB and GR Gaia DR2 data
for member stars. In doing this we take into account the nominal errors in the
data and derive distance, age, and extinction of each cluster. This work
therefore provides parameters for 1743 open clusters and, as a byproduct, a
list of likely not physical or dubious open clusters is provided as well for
future investigations. Furthermore, it was possible to estimate the mean radial
velocity of 831 clusters (198 of which are new and unpublished so far) using
stellar radial velocities from Gaia DR2 catalog. By comparing the open cluster
distances obtained from isochrone fitting with those obtained from a maximum
likelihood estimate of individual member parallaxes, we found a systematic
offset of $(-0.05\pm0.04)$mas.


Abstract:
  Patients with long-standing diabetes often fall prey to Diabetic Retinopathy
(DR) resulting in changes in the retina of the human eye, which may lead to
loss of vision in extreme cases. The aim of this study is two-fold: (a) create
deep learning models that were trained to grade degraded retinal fundus images
and (b) to create a browser-based application that will aid in diagnostic
procedures by highlighting the key features of the fundus image. In this
research work, we have emulated the images plagued by distortions by degrading
the images based on multiple different combinations of Light Transmission
Disturbance, Image Blurring and insertion of Retinal Artifacts. InceptionV3,
ResNet-50 and InceptionResNetV2 were trained and used to classify retinal
fundus images based on their severity level and then further used in the
creation of a browser-based application, which implements the Integration
Gradient (IG) Attribution Mask on the input image and demonstrates the
predictions made by the model and the probability associated with each class.


Abstract:
  Due to its proximity to Earth, Jupiter of the Solar System serves as a unique
case study for gas-giant exoplanets. In the current study, we perform fits of
ab initio, reflective, semi-infinite, homogeneous model atmospheres to 61 phase
curves from 0.40 to 1.00 $\mu$m, obtained from the Cassini spacecraft, within a
Bayesian framework. We reproduce the previous finding that atmospheric models
using classic reflection laws (Lambertian, Rayleigh, single Henyey-Greenstein)
provide poor fits to the data. Using the double Henyey-Greenstein reflection
law, we extract posterior distributions of the single-scattering albedo and
scattering asymmetry factors and tabulate their median values and
uncertainties. We infer that the aerosols in the Jovian atmosphere are large,
irregular, polydisperse particles that produce strong forward scattering
together with a narrow backscattering lobe. The near-unity values of the
single-scattering albedos imply that multiple scattering of radiation is an
important effect. We speculate that the observed narrow backscattering lobe is
caused by coherent backscattering of radiation, which is usually associated
with Solar System bodies with solid surfaces and regolith. Our findings
demonstrate that precise, multi-wavelength phase curves encode valuable
information on the fundamental properties of cloud/haze particles. The method
described in this Letter enables single-scattering albedos and scattering
asymmetry factors to be retrieved from James Webb Space Telescope phase curves
of exoplanets.


Abstract:
  Automatic code summarization frees software developers from the heavy burden
of manual commenting and benefits software development and maintenance.
Abstract Syntax Tree (AST), which depicts the source code's syntactic
structure, has been incorporated to guide the generation of code summaries.
However, existing AST based methods suffer from the difficulty of training and
generate inadequate code summaries. In this paper, we present the Block-wise
Abstract Syntax Tree Splitting method (BASTS for short), which fully utilizes
the rich tree-form syntax structure in ASTs, for improving code summarization.
BASTS splits the code of a method based on the blocks in the dominator tree of
the Control Flow Graph, and generates a split AST for each code split. Each
split AST is then modeled by a Tree-LSTM using a pre-training strategy to
capture local non-linear syntax encoding. The learned syntax encoding is
combined with code encoding, and fed into Transformer to generate high-quality
code summaries. Comprehensive experiments on benchmarks have demonstrated that
BASTS significantly outperforms state-of-the-art approaches in terms of various
evaluation metrics. To facilitate reproducibility, our implementation is
available at https://github.com/XMUDM/BASTS.


Abstract:
  In order to face food insecurity as a global phenomenon, it is essential to
rely on measurement tools that guarantee comparability across countries.
Although the official indicators adopted by the United Nations in the context
of the Sustainable Development Goals (SDGs) and based on the Food Insecurity
Experience Scale (FIES) already embeds cross-country comparability, other
experiential scales of food insecurity currently employ national thresholds and
issues of comparability thus arise. In this work we address comparability of
food insecurity experience-based scales by presenting two different studies.
The first one involves the FIES and three national scales (ELCSA, EMSA and
EBIA) currently included in national surveys in Guatemala, Ecuador, Mexico and
Brazil. The second study concerns the adult and children versions of these
national scales. Different methods from the equating practice of the
educational testing field are explored: classical and based on the Item
Response Theory (IRT).


Abstract:
  Unsupervised learning methods have recently shown their competitiveness
against supervised training. Typically, these methods use a single objective to
train the entire network. But one distinct advantage of unsupervised over
supervised learning is that the former possesses more variety and freedom in
designing the objective. In this work, we explore new dimensions of
unsupervised learning by proposing the Progressive Stage-wise Learning (PSL)
framework. For a given unsupervised task, we design multilevel tasks and define
different learning stages for the deep network. Early learning stages are
forced to focus on lowlevel tasks while late stages are guided to extract
deeper information through harder tasks. We discover that by progressive
stage-wise learning, unsupervised feature representation can be effectively
enhanced. Our extensive experiments show that PSL consistently improves results
for the leading unsupervised learning methods.


Abstract:
  Recent advancements in radiation detection and computer vision have enabled
small unmanned aerial systems (sUASs) to produce 3D nuclear radiation maps in
real-time. Currently these state-of-the-art systems still require two
operators: one to pilot the sUAS and another operator to monitor the detected
radiation. In this work we present a system that integrates real-time 3D
radiation visualization with semi-autonomous sUAS control. Our Virtual Reality
interface enables a single operator to define trajectories using waypoints to
abstract complex flight control and utilize the semi-autonomous maneuvering
capabilities of the sUAS. The interface also displays a fused radiation
visualization and environment map, thereby enabling simultaneous remote
operation and radiation monitoring by a single operator. This serves as the
basis for development of a single system that deploys and autonomously controls
fleets of sUASs.


Abstract:
  We consider the mimetic tachyon model in the Lagrange multiplier approach. We
study both the linear and non-linear perturbations and find the perturbation
and non-gaussianity parameters in this setup. By adopting two types of the
scale factor as the power-law ($a=a_{0}\,t^{n}$) and intermediate
($a=a_{0}\exp(bt^{\beta})$) scale factors, we perform a numerical analysis on
the model which is based on Planck2018 TT, TE, EE+lowE+lensing +BAO +BK14 and
Planck2018 TTT, EEE, TTE and EET data sets. We show that the mimetic tachyon
model with both the power-law and intermediate scale factors, in some ranges of
its parameter space is instabilities-free and observationally viable. The
power-law mimetic tachyon model with $26.3<n<33.0$ and the intermediate mimetic
tachyon model with $0.116<\beta<0.130$ are consistent with observational data
and free of the ghost and gradient instabilities.


Abstract:
  We obtain the energy eigenvalues and radial wave functions of the
D-Dimensional Dirac equation in the case of spin symmetry for Woods-Saxon
potential in minimal length formalism. The radial part of the D-Dimensional
Dirac equation is solved by applied the supersymmetric quantum mechanics method
using the Pekeris approximation to deal with the centrifugal term. The behavior
of bound-state energy eigenvalues versus dimension and also quantum number is
discussed for various minimal length parameters.


Abstract:
  Web search queries can be ambiguous: is "source of the nile" meant to find
information on the actual river or on a board game of that name? We tackle this
problem by deriving entity-based query interpretations: given some query, the
task is to derive all reasonable ways of linking suitable parts of the query to
semantically compatible entities in a background knowledge base. Our suggested
approach focuses on effectiveness but also on efficiency since web search
response times should not exceed some hundreds of milliseconds. In our
approach, we use query segmentation as a pre-processing step that finds
promising segment-based "interpretation skeletons". The individual segments
from these skeletons are then linked to entities from a knowledge base and the
reasonable combinations are ranked in a final step. An experimental comparison
on a combined corpus of all existing query entity linking datasets shows our
approach to have a better interpretation accuracy at a better run time than the
previously most effective methods.


Abstract:
  The NIH 3D Print Exchange is a public and open source repository for
primarily 3D printable medical device designs with contributions from
expert-amateur makers, engineers from industry and academia, and clinicians. In
response to the COVID-19 pandemic, a collection was formed to foster
submissions of low-cost, local manufacture of personal protective equipment
(Personal Protective Equipment (PPE)). We systematically evaluated the 623
submissions in this collection to understand: what makers contributed, how they
were made, who made them, and key characteristics of their designs. Our
analysis reveals an immediate design convergence to derivatives of a few
initial designs affiliated with NIH partners (e.g., universities, the Veteran's
Health Administration, America Makes) and major for-profit groups (e.g.,
Prusa). The NIH worked to review safe and effective designs but was quickly
overloaded by derivative works. We found that the vast majority were never
reviewed (81.3%) while 10.4% of those reviewed were deemed safe for clinical
(5.6%) or community use (4.8%). Our work contributes insights into: the
outcomes of distributed, community-based, medical making; features the
community accepted as "safe" making; and how platforms can support regulated
maker activities in high-risk domains (e.g., healthcare).


Abstract:
  Nonequilibrium topological matter has been a fruitful topic of both
theoretical and experimental interest. A great variety of exotic topological
phases unavailable in static systems may emerge under nonequilibrium
situations, often challenging our physical intuitions. How to locate the
borders between different nonequilibrium topological phases is an important
issue to facilitate topological characterization and further understand phase
transition behaviors. In this work, we develop an unsupervised machine-learning
protocol to distinguish between different Floquet (periodically driven)
topological phases, by incorporating the system's dynamics within one driving
period, adiabatic deformation in the time dimension, plus the system's symmetry
all into our machine learning algorithm. Results from two rich case studies
indicate that machine learning is able to reliably reveal intricate topological
phase boundaries and can hence be a powerful tool to discover novel topological
matter afforded by the time dimension.


Abstract:
  The recent discovery of room-temperature superconductivity (RTSC) at
pressures of several megabars has led to intensive efforts to probe the origin
of superconducting (SC) electron pairs. Although the signatures of the SC phase
transition have been well established, few reports of the SC properties of
RTSCs have been published because of the diamond anvil cell (DAC) environments.
Here, we report the first direct evidence of two SC gaps in Y metal via
point-contact spectroscopy (PCS) in DAC environments, where a sharp peak at the
zero-bias voltage in the differential conductance is overlaid with a broad peak
owing to Andreev reflection. Analysis based on the Blonder-Tinkham-Klapwijk
(BTK) model reveals the existence of two SC gaps: the larger gap is 3.63 meV
and the smaller gap is 0.46 meV. The temperature dependence of the two SC gaps
is well explained by the BCS theory, indicating that two-band superconductivity
is realized in Y metal. The successful application of PCS to Y in DAC
environments is expected to guide future research on the SC gap in megabar
high-Tc superconductors.


Abstract:
  Given an integral commutative residuated lattice L=(L,\vee,\wedge), its full
twist-product (L^2,\sqcup,\sqcap) can be endowed with two binary operations
\odot and \Rightarrow introduced formerly by M. Busaniche and R. Cignoli as
well as by C. Tsinakis and A. M. Wille such that it becomes a commutative
residuated lattice. For every a in L we define a certain subset P_a(L) of L^2.
We characterize when P_a(L) is a sublattice of the full twist-product
(L^2,\sqcup,\sqcap). In this case P_a(L) together with some natural antitone
involution ' becomes a pseudo-Kleene lattice. If L is distributive then
(P_a(L),\sqcup,\sqcap,') becomes a Kleene lattice. We present sufficient
conditions for P_a(L) being a subalgebra of
(L^2,\sqcup,\sqcap,\odot,\Rightarrow) and thus for \odot and \Rightarrow being
a pair of adjoint operations on P_a(L). Finally, we introduce another pair
\odot and \Rightarrow of adjoint operations on the full twist-product of a
bounded commutative residuated lattice such that the resulting algebra is a
bounded commutative residuated lattice satisfying the double negation law and
we investigate when P_a(L) is closed under these new operations \odot and
\Rightarrow.


Abstract:
  Measurement for qubits plays a key role in quantum computation. Current
methods for classifying states of single qubit in a superconducting multi-qubit
system produce fidelities lower than expected due to the existence of
crosstalk, especially in case of frequency crowding. Here, We make the digital
signal processing (DSP) system used in measurement into a shallow neural
network and train it to be an optimal classifier to reduce the impact of
crosstalk. The experiment result shows the crosstalk-induced readout error
deceased by 100% after a 3-second optimization applied on the 6-qubit
superconducting quantum chip.


Abstract:
  Using a recently introduced tensor network method, we study the density of
states of the lattice Schwinger model, a standard testbench for lattice gauge
theory numerical techniques, but also the object of recent experimental quantum
simulations. We identify regimes of parameters where the spectrum appears to be
symmetric and displays the expected continuum properties even for finite
lattice spacing and number of sites. However, we find that for moderate system
sizes and lattice spacing of $ga\sim O(1)$, the spectral density can exhibit
very different properties with a highly asymmetric form. We also explore how
the method can be exploited to extract thermodynamic quantities.


Abstract:
  The trend of space commercialization is changing the decision-making process
for future space exploration architectures, and there is a growing need for a
new decision-making framework that explicitly considers the interactions
between the mission coordinator (i.e., government) and the commercial players.
In response to this challenge, this paper develops a framework for space
exploration and logistics decision making that considers the incentive
mechanism to stimulate commercial participation in future space infrastructure
development and deployment. By extending the state-of-the-art space logistics
design formulations from the game-theoretic perspective, we first analyze the
relationship between the mission coordinator and commercial players and then
derive the formulation for the optimal architecture design and incentive
mechanism in three different scenarios. To demonstrate and evaluate the
effectiveness of the proposed framework, we conduct a case study on lunar
habitat infrastructure design and deployment. Results show how total mission
demands and in-situ resource utilization (ISRU) system performances after
deployment may impact the cooperation among stakeholders. As an outcome of this
study, we derive an incentive-based decision-making framework that can benefit
both the mission coordinator and the commercial players from commercialization,
leading to a mutually beneficial space exploration between the government and
the industry.


Abstract:
  In this work we present the design of a new test geometry inspired by the
Tapered Double Cantilever Beam (TDCB) specimen that is shown to provide an
improved characterization of the fracture properties of brittle solids. First,
we show that our new design results in an exponential increase of the specimen
compliance with crack length, leading to an extremely stable crack growth
during the test. We determine an analytical description of this behavior, which
provides a simple procedure to extract the fracture energy without depending on
finite element calculations. Validation tests are done on
polymethylmethacrylate (PMMA) specimens. We use both finite element simulations
and our analytical model to interpret the data. We find a very good agreement
between the toughness determined by both methods. The stable nature of crack
growth in our improved TDCB specimens results in a precise control of the crack
speed. This feature is employed to go one step further and characterize the
variations of toughness with crack speed. We propose an original optimization
procedure for the determination of the material parameters characterizing the
kinetic law describing the toughness rate dependency. Overall, the approach
proposed together with the newly designed test geometry offer unprecedented
possibilities for the full and accurate characterization of the fracture
behavior of brittle materials such as rocks, sandstone, mortar etc.


Abstract:
  In the present work, two machine learning based constitutive models for
finite deformations are proposed. Using input convex neural networks, the
models are hyperelastic, anisotropic and fulfill the polyconvexity condition,
which implies ellipticity and thus ensures material stability. The first
constitutive model is based on a set of polyconvex, anisotropic and objective
invariants. The second approach is formulated in terms of the deformation
gradient, its cofactor and determinant, uses group symmetrization to fulfill
the material symmetry condition, and data augmentation to fulfill objectivity
approximately. The extension of the dataset for the data augmentation approach
is based on mechanical considerations and does not require additional
experimental or simulation data. The models are calibrated with highly
challenging simulation data of cubic lattice metamaterials, including finite
deformations and lattice instabilities. A moderate amount of calibration data
is used, based on deformations which are commonly applied in experimental
investigations. While the invariant-based model shows drawbacks for several
deformation modes, the model based on the deformation gradient alone is able to
reproduce and predict the effective material behavior very well and exhibits
excellent generalization capabilities. In addition, the models are calibrated
with transversely isotropic data, generated with an analytical polyconvex
potential. For this case, both models show excellent results, demonstrating the
straightforward applicability of the polyconvex neural network constitutive
models to other symmetry groups.


Abstract:
  In this work, we present a web-based annotation and querying tool Sangrahaka.
It annotates entities and relationships from text corpora and constructs a
knowledge graph (KG). The KG is queried using templatized natural language
queries. The application is language and corpus agnostic, but can be tuned for
special needs of a specific language or a corpus. A customized version of the
framework has been used in two annotation tasks. The application is available
for download and installation. Besides having a user-friendly interface, it is
fast, supports customization, and is fault tolerant on both client and server
side. The code is available at https://github.com/hrishikeshrt/sangrahaka and
the presentation with a demo is available at https://youtu.be/nw9GFLVZMMo.


Abstract:
  Coarse-to-fine models and cascade segmentation architectures are widely
adopted to solve the problem of large scale variations in medical image
segmentation. However, those methods have two primary limitations: the
first-stage segmentation becomes a performance bottleneck; the lack of overall
differentiability makes the training process of two stages asynchronous and
inconsistent. In this paper, we propose a differentiable two-stage network
architecture to tackle these problems. In the first stage, a localization
network (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in
the second stage, a segmentation network (S-Net) performs fine segmentation on
the recalibrated RoIs; a RoI recalibration module between L-Net and S-Net
eliminating the inconsistencies. Experimental results on the public dataset
show that our method outperforms state-of-the-art coarse-to-fine models with
negligible computation overheads.


Abstract:
  P-time event graphs (P-TEGs) are specific timed discrete-event systems, in
which the timing of events is constrained by intervals. An important problem is
to check, for all natural numbers $d$, the existence of consistent $d$-periodic
trajectories for a given P-TEG. In graph theory, the
Proportional-Inverse-Constant-Non-positive Circuit weight Problem (PIC-NCP)
consists in finding all the values of a parameter such that a particular
parametric weighted directed graph does not contain circuits with positive
weight. In a related paper, we have proposed a strongly polynomial algorithm
that solves the PIC-NCP in lower worst-case complexity compared to other
algorithms reported in literature. In the present paper, we show that the first
problem can be formulated as an instance of the second; consequently, we prove
that the same algorithm can be used to find $d$-periodic trajectories in
P-TEGs. Moreover, exploiting the connection between the PIC-NCP and max-plus
algebra we prove that, given a P-TEG, the existence of a consistent 1-periodic
trajectory of a certain period is a necessary and sufficient condition for the
existence of a consistent $d$-periodic trajectory of the same period, for any
value of $d$.


Abstract:
  A weighted point-availability time-dependent network is a list of temporal
edges, where each temporal edge has an appearing time value, a travel time
value, and a cost value. In this paper we consider the single source Pareto
problem in weighted point-availability time-dependent networks, which consists
of computing, for any destination d, all Pareto optimal pairs (t, c), where t
and c are the arrival time and the cost of a path from s to d, respectively (a
pair (t, c) is Pareto optimal if there is no path with arrival time smaller
than t and cost no worse than c or arrival time no greater than t and better
cost). We design and analyse a general algorithm for solving this problem,
whose time complexity is O(M log P), where M is the number of temporal edges
and P is the maximum number of Pareto optimal pairs for each node of the
network. This complexity significantly improves the time complexity of the
previously known solution. Our algorithm can be used to solve several different
minimum cost path problems in weighted point-availability time-dependent
networks with a vast variety of cost definitions, and it can be easily modified
in order to deal with the single destination Pareto problem. All our results
apply to directed networks, but they can be easily adapted to undirected
networks with no edges with zero travel time.


Abstract:
  Skin effect, experimentally discovered in one dimension, describes the
physical phenomenon that on an open chain, an extensive number of eigenstates
of a non-Hermitian hamiltonian are localized at the end(s) of the chain. Here
in two and higher dimensions, we establish a theorem that the skin effect
exists, if and only if periodic-boundary spectrum of the hamiltonian covers a
finite area on the complex plane. This theorem establishes the universality of
the effect, because the above condition is satisfied in almost every generic
non-Hermitian hamiltonian, and, unlike in one dimension, is compatible with all
spatial symmetries. We propose two new types of skin effect in two and higher
dimensions: the corner-skin effect where all eigenstates are localized at one
corner of the system, and the geometry-dependent-skin effect where skin modes
disappear for systems of a particular shape, but appear on generic polygons. An
immediate corollary of our theorem is that any non-Hermitian system having
exceptional points (lines) in two (three) dimensions exhibits skin effect,
making this phenomenon accessible to experiments in photonic crystals, Weyl
semimetals, and Kondo insulators.


Abstract:
  This work develops and demonstrates the integration of the SCAMP-5d vision
system into the CoppeliaSim robot simulator, creating a semi-simulated
environment. By configuring a camera in the simulator and setting up
communication with the SCAMP python host through remote API, sensor images from
the simulator can be transferred to the SCAMP vision sensor, where on-sensor
image processing such as CNN inference can be performed. SCAMP output is then
fed back into CoppeliaSim. This proposed platform integration enables rapid
prototyping validations of SCAMP algorithms for robotic systems. We demonstrate
a car localisation and tracking task using this proposed semi-simulated
platform, with a CNN inference on SCAMP to command the motion of a robot. We
made this platform available online.


Abstract:
  The formation mechanism of the W50/SS433 complex has long been a mystery. We
propose a new scenario in which the SS433 jets themselves form the W50/SS433
system. We carry out magnetohydrodynamics simulations of two-side jet
propagation using the public code CANS+. As found in previous jet studies, when
the propagating jet is lighter than the surrounding medium, the shocked plasma
flows back from the jet tip to the core. We find that the morphology of light
jets is spheroidal at early times, and afterward, the shell and wings are
developed by the broadening spherical cocoon. The morphology strongly depends
on the density ratio of the injected jet to the surrounding medium. Meanwhile,
the ratio of the lengths of the two-side jets depends only on the density
profile of the surrounding medium. We also find that most of the jet kinetic
energy is dissipated at the oblique shock formed by the interaction between the
backflow and beam flow, rather than at the jet terminal shock. The position of
the oblique shock is spatially consistent with the X-ray and TeV gamma-ray
hotspots of W50.


Abstract:
  Soft-elasticity in monodomain liquid crystal elastomers (LCEs) is promising
for impact-absorbing applications where strain energy is ideally absorbed at
constant stress. Conventionally, compressive and impact studies on LCEs have
not been performed given the notorious difficulty synthesizing sufficiently
large monodomain devices. Here we demonstrate 3D printing bulk ($>cm^3$)
monodomain LCE devices using direct ink writing and study their compressive
soft-elasticity over 8 decades of strain rate. At quasi-static rates, the
monodomain soft-elastic LCE dissipated 45% of strain energy while comparator
materials dissipated less than 20%. At strain rates up to $3000~s^{-1}$, our
soft-elastic monodomain LCE consistently performed closest to an ideal-impact
absorber. Drop testing reveals soft-elasticity as a likely mechanism for
effectively reducing the severity of impacts -- with soft elastic LCEs offering
a Gadd Severity Index 40% lower than a comparable isotropic elastomer. Lastly,
we demonstrate tailoring deformation and buckling behavior in monodomain LCEs
via the printed director orientation.


Abstract:
  Scalar field cosmologies with a generalized harmonic potential and a matter
fluid with a barotropic Equation of State (EoS) with barotropic index $\gamma$
for the Locally Rotationally Symmetric (LRS) Bianchi I and flat
Friedmann-Lema\^itre-Robertson-Walker (FLRW) metrics are investigated. Methods
from the theory of averaging of nonlinear dynamical systems are used to prove
that time-dependent systems and their corresponding time-averaged versions have
the same late-time dynamics. Therefore, the simplest time-averaged system
determines the future asymptotic behavior. Depending on the values of $\gamma$,
the late-time attractors of physical interests are flat quintessence dominated
FLRW universe and Einstein-de Sitter solution. With this approach, the
oscillations entering the system through the Klein-Gordon (KG) equation can be
controlled and smoothed out as the Hubble parameter $H$ - acting as
time-dependent perturbation parameter - tends monotonically to zero. Numerical
simulations are presented as evidence of such behavior.


Abstract:
  Let $F_n(k)$ be the generalized Fibonacci number defined by (with $F_i(k)$
abbreviated to $F_i$): $F_n = F_{n-1} + F_{n-2} + \dots + F_{n-k}$, for $n \geq
k$, and the initial values $(F_0,F_1,...,F_{k-1})$. Let $B_n(k,j)$ be $F_n(k)$
with initial values given by $F_j = 1$ and, for $i<j$ and $j<i<k$, $F_i = 0$.
This paper shows that any $F_n(k)$ can be expressed as the sum of $B_n(k,j)$s.
This paper also expresses $B_n(k,j)$ and $F_n(k)$ as finite sums, derives some
properties and evaluates their 2-adic order for a range of values of $k, j$ and
$n$ and those of $B_n(3,j)$ and $B_n(4,j)$ for most values of $j$ and $n$.


Abstract:
  Photometric measurements of the contact binary system CK Aqr are presented.
49 new times of minimum were obtained between 2005 and 2020. Along with already
published observations, this allows the derivation of an improved ephemeris.
The resulting O-C diagram shows oscillations which are interpreted as the light
time travel effect due to a third component, with a period of 8.2 years.


Abstract:
  This paper has proposed a novel approach to classify the subjects' smoking
behavior by extracting relevant regions from a given image using deep learning.
After the classification, we have proposed a conditional detection module based
on Yolo-v3, which improves model's performance and reduces its complexity. As
per the best of our knowledge, we are the first to work on this dataset. This
dataset contains a total of 2,400 images that include smokers and non-smokers
equally in various environmental settings. We have evaluated the proposed
approach's performance using quantitative and qualitative measures, which
confirms its effectiveness in challenging situations. The proposed approach has
achieved a classification accuracy of 96.74% on this dataset.


Abstract:
  Recent accelerated MRI reconstruction models have used Deep Neural Networks
(DNNs) to reconstruct relatively high-quality images from highly undersampled
k-space data, enabling much faster MRI scanning. However, these techniques
sometimes struggle to reconstruct sharp images that preserve fine detail while
maintaining a natural appearance. In this work, we enhance the image quality by
using a Conditional Wasserstein Generative Adversarial Network combined with a
novel Adaptive Gradient Balancing (AGB) technique that automates the process of
combining the adversarial and pixel-wise terms and streamlines hyperparameter
tuning. In addition, we introduce a Densely Connected Iterative Network, which
is an undersampled MRI reconstruction network that utilizes dense connections.
In MRI, our method minimizes artifacts, while maintaining a high-quality
reconstruction that produces sharper images than other techniques. To
demonstrate the general nature of our method, it is further evaluated on a
battery of image-to-image translation experiments, demonstrating an ability to
recover from sub-optimal weighting in multi-term adversarial training.


Abstract:
  We present a detailed study of the dynamics associated with the ghost sector
of quenched QCD in the Landau gauge, where the relevant dynamical equations are
supplemented with key inputs originating from large-volume lattice simulations.
In particular, we solve the coupled system of Schwinger-Dyson equations that
governs the evolution of the ghost dressing function and the ghost-gluon
vertex, using as input for the gluon propagator lattice data that have been
cured from volume and discretization artifacts. In addition, we explore the
soft gluon limit of the same system, employing recent lattice data for the
three-gluon vertex that enters in one of the diagrams defining the
Schwinger-Dyson equation of the ghost-gluon vertex. The results obtained from
the numerical treatment of these equations are in excellent agreement with
lattice data for the ghost dressing function, once the latter have undergone
the appropriate scale-setting and artifact elimination refinements. Moreover,
the coincidence observed between the ghost-gluon vertex in general kinematics
and in the soft gluon limit reveals an outstanding consistency of physical
concepts and computational schemes.


Abstract:
  The technology of network slicing, as the most characteristic feature of the
fifth generation (5G) wireless networks, manages the resources and network
functions in heterogeneous and logically isolated slices on the top of a shared
physical infrastructure, where every slice can be independently customized to
fulfill the specific requirements of its devoted service type. It enables a new
paradigm of multi-tenancy networking, where the network slices can be leased by
the mobile network operator (MNO) to tenants in form of public cloud computing
service, known as Slice-asa- Service (SlaaS). Similar to classical cloud
computing scenarios, SlaaS benefits from overbooking its resources to numerous
tenants, taking advantage of the resource elasticity and diversity, at a price
of risking overloading network resources and violating the service-level
agreements (SLAs), which stipulate the quality of service (QoS) that shall be
guaranteed to the network slices. Thus, it becomes a critical challenge to the
MNOs, accurately estimating the resource overload risk - especially under the
sophisticated network dynamics - for monitoring and enhancing the reliability
of SlaaS business.


Abstract:
  This paper applies neural network models to forecast inflation. The use of a
particular recurrent neural network, the long-short term memory model, or LSTM,
that summarizes macroeconomic information into common components is a major
contribution of the paper. Results from an exercise with US data indicate that
the estimated neural nets usually present better forecasting performance than
standard benchmarks, especially at long horizons. The LSTM in particular is
found to outperform the traditional feed-forward network at long horizons,
suggesting an advantage of the recurrent model in capturing the long-term trend
of inflation. This finding can be rationalized by the so called long memory of
the LSTM that incorporates relatively old information in the forecast as long
as accuracy is improved, while economizing in the number of estimated
parameters. Interestingly, the neural nets containing macroeconomic information
capture well the features of inflation during and after the Great Recession,
possibly indicating a role for nonlinearities and macro information in this
episode. The estimated common components used in the forecast seem able to
capture the business cycle dynamics, as well as information on prices.


Abstract:
  The article proposes a new type of AI system that does not give solutions
directly but rather points toward it, friendly prompting the user with
questions and adjusting messages. Models of AI human collaboration can be
deduced from the classic literary example of interaction between Mr. Holmes and
Dr. Watson from the stories by Conan Doyle, where the highly qualified expert
Mr. Holmes answers questions posed by Dr. Watson. Here Mr. Holmes, with his
rule-based calculations, logic, and memory management, apparently plays the
role of an AI system, and Dr. Watson is the user. Looking into the same
Holmes-Watson interaction, we find and promote another model in which the AI
behaves like Dr. Watson, who, by asking questions and acting in a particular
way, helps Holmes (the AI user) make the right decisions. We call the systems
based on this principle "Dr. Watson-type systems." The article describes the
properties of such systems and introduces two particular: Patient Management
System for intensive care physicians and Data Error Prevention System.


Abstract:
  Network models describe the brain as sets of nodes and edges that represent
its distributed organization. So far, most discoveries in network neuroscience
have prioritized insights that highlight distinct groupings and specialized
functional contributions of network nodes. Importantly, these functional
contributions are determined and expressed by the web of their
interrelationships, formed by network edges. Here, we underscore the important
contributions made by brain network edges for understanding distributed brain
organization. Different types of edges represent different types of
relationships, including connectivity and similarity among nodes. Adopting a
specific definition of edges can fundamentally alter how we analyze and
interpret a brain network. Furthermore, edges can associate into collectives
and higher-order arrangements, describe time series, and form edge communities
that provide insights into brain network topology complementary to the
traditional node-centric perspective. Focusing on the edges, and the
higher-order or dynamic information they can provide, discloses previously
underappreciated aspects of structural and functional network organization.


Abstract:
  The classical Raychaudhuri equation predicts the formation of conjugate
points for a congruence of geodesics, in a finite proper time. This in
conjunction with the Hawking-Penrose singularity theorems predicts the
incompleteness of geodesics and thereby the singular nature of practically all
spacetimes. We compute the generic corrections to the Raychaudhuri equation in
the interior of a Schwarzschild black hole, arising from modifications to the
algebra inspired by the generalized uncertainty principle (GUP) theories. Then
we study four specific models of GUP, compute their effective dynamics as well
as their expansion and its rate of change using the Raychaudhuri equation. We
show that the modification from GUP in two of these models, where such
modifications are dependent of the configuration variables, lead to finite
Kretchmann scalar, expansion and its rate, hence implying the resolution of the
singularity. However, the other two models for which the modifications depend
on the momenta still retain their singularities even in the effective regime.


Abstract:
  We present benchmark calculations of the Anderson lattice model based on the
recently-developed "ghost Gutzwiller approximation". Our analysis shows that,
in some parameters regimes, the predictions of the standard Gutzwiller
approximation can be incorrect by orders of magnitude for this model. We show
that this is caused by the inability of this method to describe simultaneously
the Mott physics and the hybridization between correlated and itinerant degrees
of freedom (whose interplay often governs the metal-insulator transition in
real materials). Finally, we show that the ghost Gutzwiller approximation
solves this problem, providing us with results in remarkable agreement with
dynamical mean field theory throughout the entire phase diagram, while being
much less computationally demanding. We provide an analytical explanation of
these findings and discuss their implications within the context of ab-initio
computation of strongly-correlated matter.


Abstract:
  We study theoretically the nonlinear optical response of Tomonaga-Luttinger
spin liquid in the context of terahertz (THz) two-dimensional coherent
spectroscopy (2DCS). Using the gapless phase of the XXZ-type spin chain as an
example, we show that its third-order nonlinear magnetic susceptibilities
$\chi^{(3)}_{+--+}$ and $\chi^{(3)}_{-++-}$ exhibit photon echo, where $\pm$
refers to the left/right-hand circular polarization with respect to the $S^z$
axis. The photon echo arises from a ``lensing'' phenomenon in which the wave
packets of fractional excitations move apart and then come back toward each
other, amounting to a refocusing of the excitations' world lines.
Renormalization group irrelevant corrections to the fixed point Hamiltonian
result in dispersion and/or damping of the wave packets, which can be
sensitively detected by lensing and consequently the photon echo. Our results
thus unveil the strength of THz-2DCS in probing the dynamical properties of the
collective excitations in a prototypical gapless many-body system.


Abstract:
  Let $f(x)\in\mathbb{Z}[x]$ be a nonconstant polynomial. Let $n, k$ and $c$ be
integers such that $n\ge 1$ and $k\ge 2$. An integer $a$ is called an
$f$-exunit in the ring $\mathbb{Z}_n$ of residue classes modulo $n$ if
$\gcd(f(a),n)=1$. In this paper, we use the principle of cross-classification
to derive an explicit formula for the number ${\mathcal N}_{k,f,c}(n)$ of
solutions $(x_1,...,x_k)$ of the congruence $x_1+...+x_k\equiv c\pmod n$ with
all $x_i$ being $f$-exunits in the ring $\mathbb{Z}_n$. This extends a recent
result of Anand {\it et al.} [On a question of $f$-exunits in
$\mathbb{Z}/{n\mathbb{Z}}$, {\it Arch. Math. (Basel)} {\bf 116} (2021),
403-409]. We derive a more explicit formula for ${\mathcal N}_{k,f,c}(n)$ when
$f(x)$ is linear or quadratic.


Abstract:
  By means of high-pressure resistivity measurements on single crystals, we
investigate the charge transport properties of Cu$_x$PdTe$_2$, notable for the
combination of topological type-II Dirac semimetallic properties with
superconductivity up to $T_c = 2.5$ K. In both cases of pristine ($x = 0$) and
intercalated ($x=0.05$) samples, we find an unconventional $T^4$ power law
behavior of the low-temperature resistivity visible up to $\sim$40 K and
remarkably stable under pressure up to 8.2 GPa. This observation is explained
by the low carrier density $n$, which strongly reduces the $k$-region available
for electron-phonon scattering, as previously reported in other low-$n$
two-dimensional systems, such as multilayer graphene and semiconductor
heterostructures. Our data analysis complemented by specific heat measurements
and supported by previous quantum oscillation studies and \textit{ab initio}
calculations suggests a scenario of one-band charge transport. Within this
scenario, our analysis yields a large value of transport electron-phonon
coupling constant $\lambda_{tr} = 1.2$ at ambient pressure that appears to be
strongly enhanced by pressure assuming a constant effective mass.


Abstract:
  The physics of Cosmic ray (CR) transport remains a key uncertainty in
assessing whether CRs can produce galaxy-scale outflows consistent with
observations. In this paper, we elucidate the physics of CR-driven galactic
winds for CR transport dominated by diffusion. A companion paper considers CR
streaming. We use analytic estimates validated by time-dependent
spherically-symmetric simulations to derive expressions for the mass-loss rate,
momentum flux, and speed of CR-driven galactic winds, suitable for
cosmological-scale or semi-analytic models of galaxy formation. For CR
diffusion coefficients $\kappa \gtrsim r_0 c_i$ where $r_0$ is the base radius
of the wind and $c_i$ is the isothermal gas sound speed, the asymptotic wind
energy flux is comparable to that supplied to CRs, and the outflow rapidly
accelerates to supersonic speeds. By contrast, for $\kappa \lesssim r_0 c_i$,
CR-driven winds accelerate more slowly and lose most of their energy to
gravity, a CR analogue of photon-tired stellar winds. Given CR diffusion
coefficients estimated using Fermi gamma-ray observations of pion decay, we
predict mass-loss rates in CR-driven galactic winds of order the star formation
rate for dwarf and disc galaxies. The dwarf galaxy mass-loss rates are small
compared to the mass-loadings needed to reconcile the stellar and dark matter
halo mass functions. For nuclear starbursts (e.g., M82, Arp 220), CR diffusion
and pion losses suppress the CR pressure in the galaxy and the strength of
CR-driven winds. We discuss the implications of our results for interpreting
observations of galactic winds and for the role of CRs in galaxy formation.


Abstract:
  Inspired by the classical Riemannian systolic inequality of Gromov we present
a combinatorial analogue providing a lower bound on the number of vertices of a
simplicial complex in terms of its edge-path systole. Similarly to the
Riemannian case, where the inequality holds under a topological assumption of
"essentiality'', our proofs rely on a combinatorial analogue of that
assumption. Under a stronger assumption, expressed in terms of cohomology
cup-length, we improve our results quantitatively. We also illustrate our
methods in the continuous setting, generalizing and improving quantitatively
the Minkowski principle of Balacheff and Karam; a corollary of this result is
the extension of the Guth--Nakamura cup-length systolic bound from manifolds to
complexes.


Abstract:
  Recently, intriguing physical properties have been unraveled in anisotropic
layered semiconductors with the in-plane anisotropy often originates directly
from the low crystallographic symmetry. However, little has been known about
the systems where the size effect dominates the anisotropy of electronic band
structures. Here, applying both experiment and theory, we show that the
anisotropic energy bands of monoclinic gallium telluride (GaTe) are determined
by a strong bulk-surface interaction rather than geometric factors. Bulk
electronic states are found to be the major contribution to the highest valence
band, whose anisotropy is yet immune to surface doping by potassium atoms.
Further analysis indicates the weakened bulk-surface interaction gives rise to
an inverse anisotropy of hole effective masses and the strong interlayer
coupling induces a direct-indirect-direct band gap transition at transfer from
mono- to few-layer GaTe. Our results thus pave the way to future applications
of anisotropic layered semiconductors in nanoelectronics and optoelectronics.


Abstract:
  It is commonly accepted that the periods of superhumps can be satisfactorily
explained within a model involving apsidal motion of the accretion disk
provided the frequency of the apsidal motion in addition to the dynamical term
includes also the pressure effects. Using a larger sample of systems with
reliable mass ratios it is shown, however, that this view is not true and the
model requires further modifications.


Abstract:
  Clouds are expected to form in a wide range of conditions in the atmosphere
of exoplanets given the large range of possible condensible species. However
this diversity might lead to very different small-scale dynamics depending on
radiative transfer in various thermal conditions: we aim at providing some
insights into these dynamical regimes. We perform an analytical linear
stability analysis of a compositional discontinuity with a heating source term
that depends on composition. We also perform idealized two-dimensional (2D)
simulations of an opacity discontinuity in a stratified medium with the code
ARK. We use a two-stream grey model for radiative transfer and explore the
brown-dwarf and earth-like regimes. We reveal the existence of a Radiative
Rayleigh-Taylor Instability (RRTI hereafter, a particular case of diabatic
Rayleigh-Taylor instability) when an opacity discontinuity is present in a
stratified medium. This instability is similar in nature to diabatic convection
and relies only on buoyancy with radiative transfer heating and cooling. When
the temperature is decreasing with height in the atmosphere, a lower-opacity
medium on top of a higher-opacity medium is dynamically unstable while a
higher-opacity medium on top of a lower-opacity medium is stable. This
stability/instability behavior is reversed if the temperature is increasing
with height. The existence of the RRTI could have important implications for
the stability of the cloud cover of a wide range of planetary atmospheres. In
our solar system, it could help explain the formation of mammatus cloud in
Earth atmospheres and the existence of Venus cloud deck. Likewise, it suggests
that stable and large scale cloud covers could be ubiquitous in strongly
irradiated exoplanets but might be more patchy in low-irradiated or isolated
objects like brown dwarfs and directly imaged exoplanets.


Abstract:
  Speech quality assessment has been a critical issue in speech processing for
decades. Existing automatic evaluations usually require clean references or
parallel ground truth data, which is infeasible when the amount of data soars.
Subjective tests, on the other hand, do not need any additional clean or
parallel data and correlates better to human perception. However, such a test
is expensive and time-consuming because crowd work is necessary. It thus
becomes highly desired to develop an automatic evaluation approach that
correlates well with human perception while not requiring ground truth data. In
this paper, we use self-supervised pre-trained models for MOS prediction. We
show their representations can distinguish between clean and noisy audios.
Then, we fine-tune these pre-trained models followed by simple linear layers in
an end-to-end manner. The experiment results showed that our framework
outperforms the two previous state-of-the-art models by a significant
improvement on Voice Conversion Challenge 2018 and achieves comparable or
superior performance on Voice Conversion Challenge 2016. We also conducted an
ablation study to further investigate how each module benefits the task. The
experiment results are implemented and reproducible with publicly available
toolkits.


Abstract:
  As one of the most important estimators in classical statistics, the
uniformly minimum variance unbiased estimator (UMVUE) has been adopted for
point estimation in many statistical studies, especially for small sample
problems. Moving beyond typical settings in the exponential distribution
family, it is usually challenging to prove the existence and further construct
such UMVUE in finite samples. For example in the ongoing Adaptive COVID-19
Treatment Trial (ACTT), it is hard to characterize the complete sufficient
statistics of the underlying treatment effect due to pre-planned modifications
to design aspects based on accumulated unblinded data. As an alternative
solution, we propose a Deep Neural Networks (DNN) guided ensemble learning
framework to construct an improved estimator from existing ones. We show that
our estimator is consistent and asymptotically reaches the minimal variance
within the class of linearly combined estimators. Simulation studies are
further performed to demonstrate that our proposed estimator has considerable
finite-sample efficiency gain. In the ACTT on COVID-19 as an important
application, our method essentially contributes to a more ethical and efficient
adaptive clinical trial with fewer patients enrolled.


Abstract:
  We provide a deterministic CONGEST algorithm to constant factor approximate
the minimum dominating set on graphs of bounded arboricity in $O(\log n)$
rounds. This improves over the well-known randomized algorithm of Lenzen and
Wattenhofer[DISC2010] by making it a deterministic algorithm.


Abstract:
  Cellular appendages conferring motility, such as flagella or cilia, are known
to synchronise their periodic beats. The origin of synchronisation is a
combination of long-range hydrodynamic interactions with physical mechanisms
allowing the phases of these biological oscillators to evolve. Two of such
mechanisms have been identified by previous work, the elastic compliance of the
periodic orbit or oscillations driven by phase-dependent biological forcing. To
help uncover the physical mechanism for hydrodynamic synchronisation most
essential overall in biology, we theoretically investigate the effect of strong
confinement on the effectiveness of hydrodynamic synchronisation. We use
minimal models where appendages are modelled as rigid spheres forced to move
along circular trajectories near a rigid surface. Strong confinement is
modelled by adding a second nearby surface, parallel to the first one, where
the distance between the surfaces is much smaller than the typical distance
between the cilia. We calculate separately the impact of confinement on the
synchronisation dynamics of the elastic compliance and the force modulation
mechanisms and compare our results to the case with no confinement. Applying
our results to the biologically-relevant situation of nodal cilia, we show that
force modulation is a mechanism that leads to phase-locked states under strong
confinement that are very similar to those without confinement as a difference
with the elastic compliance mechanism. Our results point therefore to the
robustness of force modulation for synchronisation, an important feature for
biological dynamics that suggests it could be the most essential physical
mechanism overall in arrays of nodal cilia. We further examine the distinct
situation of primary cilia and show in that case that the difference in
robustness of the mechanisms is not as pronounced but still favours the force
modulation.


Abstract:
  We prove dispersive estimate for the elastic wave equation by which we extend
the known Strichartz estimates for the classical wave equation to those for the
elastic wave equation. In particular, the endpoint Strichartz estimates are
deduced. For the purpose we diagonalize the symbols of the Lam\'e operator and
its semigroup, which also gives an alternative and simpler proofs of the
previous results on perturbed elastic wave equations. Furthermore, we obtain
uniform Sobolev inequalities for the elastic wave operator.


Abstract:
  In this paper we study circular orbits around higher dimensional rotating
Myers-Perry and pure Gauss-Bonnet (GB) black holes. It turns out that for the
former there occurs no potential well to harbour bound and thereby stable
circular orbits. The only circular orbits that could occur are all unstable and
their radius is bounded from the below by that of the photon circular orbit. On
the other hand bound and stable circular orbits do exist in pure GB/Lovelock
rotating black holes (the metric is though not an exact solution of pure
Lovelock vacuum equation but it satisfies the equation in the leading order and
has all the desired properties) in dimensions, $2N+2 \leq D \leq 4N$ (for $N=2$
pure GB in $D = 6, 7, 8$) where $N$ is the degree of Lovelock polynomial. Thus
bound and stable circular orbits could exist around higher dimensional rotating
black holes only for pure GB/Lovelock gravity. This property is a nice
discriminator between Myers-Perry and pure GB/Lovelock rotating black holes.


Abstract:
  We propose the use of the Earth as a transducer for ultralight dark-matter
detection. In particular we point out a novel signal of kinetically mixed
dark-photon dark matter: a monochromatic oscillating magnetic field generated
at the surface of the Earth. Similar to the signal in a laboratory experiment
in a shielded box (or cavity), this signal arises because the lower atmosphere
is a low-conductivity air gap sandwiched between the highly conductive interior
of the Earth below and ionosphere or interplanetary medium above. At low masses
(frequencies) the signal in a laboratory detector is usually suppressed by the
size of the detector multiplied by the dark-matter mass. Crucially, in our case
the suppression is by the radius of the Earth, and not by the (much smaller)
height of the atmosphere. We compute the size and global vectorial pattern of
our magnetic field signal, which enables sensitive searches for this signal
using unshielded magnetometers dispersed over the surface of the Earth. In
principle, the signal we compute exists for any dark photon in the mass range
$10^{-21} \text{eV}\lesssim m_{A'} \lesssim 3\times 10^{-14} \text{eV}$. We
summarize the results of our companion paper [arXiv:2108.08852], in which we
detail such a search using a publicly available dataset from the SuperMAG
Collaboration: we report no robust signal candidates and so place constraints
in the (more limited) dark-photon dark-matter mass range $2\times 10^{-18}
\text{eV} \lesssim m_{A'} \lesssim 7\times 10^{-17} \text{eV}$ (corresponding
to frequencies $6\times 10^{-4} \text{Hz}\lesssim f \lesssim 2\times 10^{-2}
\text{Hz}$). These constraints are complementary to existing astrophysical
bounds. Future searches for this signal may improve the sensitivity over a wide
range of ultralight dark-matter candidates and masses.


Abstract:
  This article introduces a novel approach to the classification of categorical
time series under the supervised learning paradigm. To construct meaningful
features for categorical time series classification, we consider two relevant
quantities: the spectral envelope and its corresponding set of optimal
scalings. These quantities characterize oscillatory patterns in a categorical
time series as the largest possible power at each frequency, or spectral
envelope, obtained by assigning numerical values, or scalings, to categories
that optimally emphasize oscillations at each frequency. Our procedure combines
these two quantities to produce an interpretable and parsimonious feature-based
classifier that can be used to accurately determine group membership for
categorical time series. Classification consistency of the proposed method is
investigated, and simulation studies are used to demonstrate accuracy in
classifying categorical time series with various underlying group structures.
Finally, we use the proposed method to explore key differences in oscillatory
patterns of sleep stage time series for patients with different sleep disorders
and accurately classify patients accordingly.


Abstract:
  We briefly explain the structure of the
Hartree-Fock-Bardeen-Cooper-Schrieffer (HFBCS) and Quasi-particle Random Phase
Approximation (QRPA) code hfbcs-qrpa, highlighting only the differences with
the code skyrme_rpa that has been previously published [G. Col\`o, L. Cao, N.
Van Giai, and L. Capelli, Comp. Phys. Comm. 184, 142 (2013)]. The code deals
with open shell spherical systems and non-spin flip, non-charge-exchange and
natural parity $(-1)^l$ excitations.


Abstract:
  Estimating the contribution from axial-vector intermediate states to hadronic
light-by-light scattering requires input on their transition form factors
(TFFs). Due to the Landau-Yang theorem, any experiment sensitive to these TFFs
needs to involve at least one virtual photon, which complicates their
measurement. Phenomenologically, the situation is best for the $f_1(1285)$
resonance, for which information is available from $e^+e^-\to e^+e^- f_1$,
$f_1\to 4\pi$, $f_1\to \rho \gamma$, $f_1\to \phi \gamma$, and $f_1\to e^+e^-$.
We provide a comprehensive analysis of the $f_1$ TFFs in the framework of
vector meson dominance, including short-distance constraints, to determine to
which extent the three independent TFFs can be constrained from the available
experimental input, a prerequisite for improved calculations of the
axial-vector contribution to hadronic light-by-light scattering. In particular,
we focus on the process $f_1\to e^+e^-$, evidence for which has been reported
recently by SND for the first time, and discuss the impact that future improved
measurements will have on the determination of the $f_1$ TFFs.


Abstract:
  We calculate the two-magnon Raman scattering spectra in antiferromagnetic
phases of several frustrated spin models defined on the honeycomb lattice.
These include the N\'{e}el antiferromagnetic phase of a $J_1$-$J_2$-$J_3$ model
and the stripe phase of the Heisenberg-Kitaev model. We show that both the
magnetic frustration and the anisotropy of interactions may significantly
affect the Raman spectra. We further discuss the implications of our results to
the magnetic excitations of the iron-based compound BaFe$_2$Se$_2$O and show
how the magnetic interactions can be extracted from fit to the Raman spectrum.


Abstract:
  The existence of a positive solution to a class of Choquard equations with
potential going at a positive limit at infinity possibly from above or
oscillating is proved. Our results include the physical case and do not require
any symmetry assumptions on the potential.


Abstract:
  Correlators of Wilson-line operators in non-abelian gauge theories are known
to exponentiate, and their logarithms can be organised in terms of collections
of Feynman diagrams called webs. In [1] we introduced the concept of Cweb, or
correlator web, which is a set of skeleton diagrams built with connected gluon
correlators, and we computed the mixing matrices for all Cwebs connecting four
or five Wilson lines at four loops. Here we complete the evaluation of
four-loop mixing matrices, presenting the results for all Cwebs connecting two
and three Wilson lines. We observe that the conjuctured column sum rule is
obeyed by all the mixing matrices that appear at four-loops. We also show how
low-dimensional mixing matrices can be uniquely determined from their known
combinatorial properties, and provide some all-order results for selected
classes of mixing matrices. Our results complete the required colour building
blocks for the calculation of the soft anomalous dimension matrix at four-loop
order.


Abstract:
  Recommender systems are gaining increasing and critical impacts on human and
society since a growing number of users use them for information seeking and
decision making. Therefore, it is crucial to address the potential unfairness
problems in recommendations. Just like users have personalized preferences on
items, users' demands for fairness are also personalized in many scenarios.
Therefore, it is important to provide personalized fair recommendations for
users to satisfy their personalized fairness demands. Besides, previous works
on fair recommendation mainly focus on association-based fairness. However, it
is important to advance from associative fairness notions to causal fairness
notions for assessing fairness more properly in recommender systems. Based on
the above considerations, this paper focuses on achieving personalized
counterfactual fairness for users in recommender systems. To this end, we
introduce a framework for achieving counterfactually fair recommendations
through adversary learning by generating feature-independent user embeddings
for recommendation. The framework allows recommender systems to achieve
personalized fairness for users while also covering non-personalized
situations. Experiments on two real-world datasets with shallow and deep
recommendation algorithms show that our method can generate fairer
recommendations for users with a desirable recommendation performance.


Abstract:
  The fast-growing techniques of measuring and fusing multi-modal biomedical
signals enable advanced motor intent decoding schemes of lowerlimb
exoskeletons, meeting the increasing demand for rehabilitative or assistive
applications of take-home healthcare. Challenges of exoskeletons motor intent
decoding schemes remain in making a continuous prediction to compensate for the
hysteretic response caused by mechanical transmission. In this paper, we solve
this problem by proposing an ahead of time continuous prediction of lower limb
kinematics, with the prediction of knee angles during level walking as a case
study. Firstly, an end-to-end kinematics prediction network(KinPreNet),
consisting of a feature extractor and an angle predictor, is proposed and
experimentally compared with features and methods traditionally used in
ahead-of-time prediction of gait phases. Secondly, inspired by the
electromechanical delay(EMD), we further explore our algorithm's capability of
compensating response delay of mechanical transmission by validating the
performance of the different sections of prediction time. And we experimentally
reveal the time boundary of compensating the hysteretic response. Thirdly, a
comparison of employing EMG signals or not is performed to reveal the EMG and
kinematic signals collaborated contributions to the continuous prediction.
During the experiments, EMG signals of nine muscles and knee angles calculated
from inertial measurement unit (IMU) signals are recorded from ten healthy
subjects. To the best of our knowledge, this is the first study of continuously
predicting lower-limb kinematics in an ahead-of-time manner based on the
electromechanical delay (EMD).


Abstract:
  Of the five exceptional groups, $\mathrm{E}_6$ is considered the most
attractive for unification due to the following reasons: (i) it contains both
$\mathrm{Spin} (10) \times \mathrm{U}(1)$ and $\mathrm{SU} (3) \times
\mathrm{SU}(3) \times \mathrm{SU}(3)$ as maximal subgroups, each of which admit
embeddings of the Standard Model; (ii) uniquely among the exceptional groups,
it admits complex representations; in particular, its 27 dimensional
fundamental representation accommodates one generation of left-handed fermions
under the usual charge assignments; (iii) all of its representations are
anomaly-free. In this master's thesis, written in the spirit of Baez and
Huerta's "The Algebra of Grand Unified Theories", we rigorously show how an
$\mathrm{E}_6$ grand unified theory is mathematically constructed. Our modest
contribution to the literature includes an explicit check that that
$\mathbb{Z}_4$ kernel of the homomorphism $\mathrm{Spin} (10) \times
\mathrm{U}(1) \to \mathrm{E}_6$ acts trivially on every fermion; we also
formulate symmetry breaking, in particular the symmetry breaking of the exotic
$\mathrm{E}_6$ fermions under $\mathrm{Spin} (10) \to \mathrm{SU}(5)$, using a
different approach than the usual Dynkin diagrams: we explicitly embedded
$\mathfrak{su}(5) \hookrightarrow \mathfrak{so}(10) \cong \mathfrak{spin} (10)$
and solve the related eigenvalue problem. Phenomenological aspects of grand
unified theories are also discussed.


Abstract:
  The rational Chow ring of the moduli space $\mathcal{M}_g$ of curves of genus
$g$ is known for $g \leq 6$. Here, we determine the rational Chow rings of
$\mathcal{M}_7, \mathcal{M}_8,$ and $\mathcal{M}_9$ by showing they are
tautological. One key ingredient is intersection theory on Hurwitz spaces of
degree $4$ and $5$ covers of $\mathbb{P}^1$, as developed by the authors in
[1]. The main focus of this paper is a detailed geometric analysis of special
tetragonal and pentagonal covers whose associated vector bundles on
$\mathbb{P}^1$ are so unbalanced that they fail to lie in the large open subset
considered in [1]. In genus $9$, we use work of Mukai [23] to present the locus
of hexagonal curves as a global quotient stack, and, using equivariant
intersection theory, we show its Chow ring is generated by restrictions of
tautological classes.