Abstract:
  The properties of modified Hayward black hole space-time can be investigated
through analyzing the particle geodesics. By means of a detailed analysis of
the corresponding effective potentials for a massive particle, we find all
possible orbits which are allowed by the energy levels. The trajectories of
orbits are plotted by solving the equation of orbital motion numerically. We
conclude that whether there is an escape orbit is associated with $b$ (angular
momentum). The properties of orbital motion are related to $b$, $\alpha$
($\alpha$ is associated with the time delay) and $\beta$ ($\beta$ is related to
1-loop quantum corrections). There are no escape orbits when $b$ $<$ $4.016M$,
$\alpha$ = 0.50 and $\beta$ = 1.00. For fixed $\alpha$ = 0.50 and $\beta$ =
1.00, if $b$ $<$ $3.493M$, there only exist unstable orbits. Comparing with the
regular Hayward black hole, we go for a reasonable speculation by mean of the
existing calculating results that the introduction of the modified term makes
the radius of the innermost circular orbit (ISCO) and the corresponding angular
momentum larger.


Abstract:
  We present Hubble Space Telescope imaging of a pre-explosion counterpart to
SN 2019yvr obtained 2.6 years before its explosion as a type Ib supernova (SN
Ib). Aligning to a post-explosion Gemini-S/GSAOI image, we demonstrate that
there is a single source consistent with being the SN 2019yvr progenitor
system, the second SN Ib progenitor candidate after iPTF13bvn. We also analyzed
pre-explosion Spitzer/IRAC imaging, but we do not detect any counterparts at
the SN location. SN 2019yvr was highly reddened, and comparing its spectra and
photometry to those of other, less extinguished SNe Ib we derive
$E(B-V)=0.51\substack{+0.27\\-0.16}$ mag for SN 2019yvr. Correcting photometry
of the pre-explosion source for dust reddening, we determine that this source
is consistent with a $\log(L/L_{\odot}) = 5.3 \pm 0.2$ and $T_{\mathrm{eff}} =
6800\substack{+400\\-200}$ K star. This relatively cool photospheric
temperature implies a radius of 320$\substack{+30\\-50} R_{\odot}$, much larger
than expectations for SN Ib progenitor stars with trace amounts of hydrogen but
in agreement with previously identified SN IIb progenitor systems. The
photometry of the system is also consistent with binary star models that
undergo common envelope evolution, leading to a primary star hydrogen envelope
mass that is mostly depleted but seemingly in conflict with the SN Ib
classification of SN 2019yvr. SN 2019yvr had signatures of strong circumstellar
interaction in late-time ($>$150 day) spectra and imaging, and so we consider
eruptive mass loss and common envelope evolution scenarios that explain the SN
Ib spectroscopic class, pre-explosion counterpart, and dense circumstellar
material. We also hypothesize that the apparent inflation could be caused by a
quasi-photosphere formed in an extended, low-density envelope or circumstellar
matter around the primary star.


Abstract:
  Precise localization of polyp is crucial for early cancer screening in
gastrointestinal endoscopy. Videos given by endoscopy bring both richer
contextual information as well as more challenges than still images. The
camera-moving situation, instead of the common camera-fixed-object-moving one,
leads to significant background variation between frames. Severe internal
artifacts (e.g. water flow in the human body, specular reflection by tissues)
can make the quality of adjacent frames vary considerately. These factors
hinder a video-based model to effectively aggregate features from neighborhood
frames and give better predictions. In this paper, we present Spatial-Temporal
Feature Transformation (STFT), a multi-frame collaborative framework to address
these issues. Spatially, STFT mitigates inter-frame variations in the
camera-moving situation with feature alignment by proposal-guided deformable
convolutions. Temporally, STFT proposes a channel-aware attention module to
simultaneously estimate the quality and correlation of adjacent frames for
adaptive feature aggregation. Empirical studies and superior results
demonstrate the effectiveness and stability of our method. For example, STFT
improves the still image baseline FCOS by 10.6% and 20.6% on the comprehensive
F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets,
respectively, and outperforms the state-of-the-art video-based method by 3.6%
and 8.0%, respectively. Code is available at
\url{https://github.com/lingyunwu14/STFT}.


Abstract:
  Recently, a careful canonical quantisation of the theory of closed bosonic
tensionless strings has resulted in the discovery of three separate vacua and
hence three different quantum theories that emerge from this single classical
tensionless theory. In this note, we perform lightcone quantisation with the
aim of determination of the critical dimension of these three inequivalent
quantum theories. The satisfying conclusion of a rather long and tedious
calculation is that one of vacua does not lead to any constraint on the number
of dimensions, while the other two give $D=26$. This implies that all three
quantum tensionless theories can be thought of as consistent sub-sectors of
quantum tensile bosonic closed string theory.


Abstract:
  The Poisson gauge algebra is a semi-classical limit of complete
non-commutative gauge algebra. In the present work we formulate the Poisson
gauge theory which is a dynamical field theoretical model having the Poisson
gauge algebra as a corresponding algebra of gauge symmetries. The proposed
model is designed to investigate the semi-classical features of the full
non-commutative gauge theory with coordinate dependent non-commutativity
$\Theta^{ab}(x)$, especially whose with a non-constant rank. We derive the
expression for the covariant derivative of matter field. The commutator
relation for the covariant derivatives defines the Poisson field strength which
is covariant under the Poisson gauge transformations and reproduces the
standard $U(1)$ field strength in the commutative limit. We derive the
corresponding Bianchi identities. The field equations for the gauge and the
matter fields are obtained from the gauge invariant action. We consider
different examples of linear in coordinates Poisson structures
$\Theta^{ab}(x)$, as well as non-linear ones, and obtain explicit expressions
for all proposed constructions. Our model is unique up to invertible field
redefinitions and coordinate transformations.


Abstract:
  In stochastic dynamic environments, team stochastic games have emerged as a
versatile paradigm for studying sequential decision-making problems of fully
cooperative multi-agent systems. However, the optimality of the derived
policies is usually sensitive to the model parameters, which are typically
unknown and required to be estimated from noisy data in practice. To mitigate
the sensitivity of the optimal policy to these uncertain parameters, in this
paper, we propose a model of "robust" team stochastic games, where players
utilize a robust optimization approach to make decisions. This model extends
team stochastic games to the scenario of incomplete information and meanwhile
provides an alternative solution concept of robust team optimality. To seek
such a solution, we develop a learning algorithm in the form of a Gauss-Seidel
modified policy iteration and prove its convergence. This algorithm, compared
with robust dynamic programming, not only possesses a faster convergence rate,
but also allows for using approximation calculations to alleviate the curse of
dimensionality. Moreover, some numerical simulations are presented to
demonstrate the effectiveness of the algorithm by generalizing the game model
of social dilemmas to sequential robust scenarios.


Abstract:
  Traditional toxicity detection models have focused on the single utterance
level without deeper understanding of context. We introduce CONDA, a new
dataset for in-game toxic language detection enabling joint intent
classification and slot filling analysis, which is the core task of Natural
Language Understanding (NLU). The dataset consists of 45K utterances from 12K
conversations from the chat logs of 1.9K completed Dota 2 matches. We propose a
robust dual semantic-level toxicity framework, which handles utterance and
token-level patterns, and rich contextual chatting history. Accompanying the
dataset is a thorough in-game toxicity analysis, which provides comprehensive
understanding of context at utterance, token, and dual levels. Inspired by NLU,
we also apply its metrics to the toxicity detection tasks for assessing
toxicity and game-specific aspects. We evaluate strong NLU models on CONDA,
providing fine-grained results for different intent classes and slot classes.
Furthermore, we examine the coverage of toxicity nature in our dataset by
comparing it with other toxicity datasets.


Abstract:
  A novel formulation of the hyperspectral broadband phase retrieval is
developed for the scenario where both object and modulation phase masks are
spectrally varying. The proposed algorithm is based on a complex domain version
of the alternating direction method of multipliers (ADMM) and Spectral
Proximity Operators (SPO) derived for Gaussian and Poissonian observations.
Computations for these operators are reduced to the solution of sets of cubic
(for Gaussian) and quadratic (for Poissonian) algebraic equations. These
proximity operators resolve two problems. Firstly, the complex domain spectral
components of signals are extracted from the total intensity observations
calculated as sums of the signal spectral intensities. In this way, the
spectral analysis of the total intensities is achieved. Secondly, the noisy
observations are filtered, compromising noisy intensity observations and their
predicted counterparts. The ability to resolve the hyperspectral broadband
phase retrieval problem and to find the spectrum varying object are essentially
defined by the spectral properties of object and image formation operators. The
simulation tests demonstrate that the phase retrieval in this formulation can
be successfully resolved.


Abstract:
  We investigate the interaction between compactness principles and guessing
principles in the Radin forcing extensions \cite{MR670992}. In particular, we
show that in any Radin forcing extension with respect to a measure sequence on
$\kappa$, if $\kappa$ is weakly compact, then $\diamondsuit(\kappa)$ holds,
answering a question raised in \cite{MR3960897}. This provides contrast with a
well-known theorem of Woodin \cite{CummingsWoodin}, who showed in a certain
Radin extension over a suitably prepared ground model relative to the existence
of large cardinals, the diamond principle fails at a strongly inaccessible
Mahlo cardinal. Refining the analysis of the Radin extensions, we consistently
demonstrate a scenario where a compactness principle, stronger than the
diagonal stationary reflection principle, holds yet the diamond principle fails
at a strongly inaccessible cardinal, improving a result from \cite{MR3960897}.


Abstract:
  In general-purpose particle detectors, the particle-flow algorithm may be
used to reconstruct a comprehensive particle-level view of the event by
combining information from the calorimeters and the trackers, significantly
improving the detector resolution for jets and the missing transverse momentum.
In view of the planned high-luminosity upgrade of the CERN Large Hadron
Collider (LHC), it is necessary to revisit existing reconstruction algorithms
and ensure that both the physics and computational performance are sufficient
in an environment with many simultaneous proton-proton interactions (pileup).
Machine learning may offer a prospect for computationally efficient event
reconstruction that is well-suited to heterogeneous computing platforms, while
significantly improving the reconstruction quality over rule-based algorithms
for granular detectors. We introduce MLPF, a novel, end-to-end trainable,
machine-learned particle-flow algorithm based on parallelizable,
computationally efficient, and scalable graph neural networks optimized using a
multi-task objective on simulated events. We report the physics and
computational performance of the MLPF algorithm on a Monte Carlo dataset of top
quark-antiquark pairs produced in proton-proton collisions in conditions
similar to those expected for the high-luminosity LHC. The MLPF algorithm
improves the physics response with respect to a rule-based benchmark algorithm
and demonstrates computationally scalable particle-flow reconstruction in a
high-pileup environment.


Abstract:
  A multilayer network depicts different types of interactions among the same
set of nodes. For example, protease networks consist of five to seven layers,
where different layers represent distinct types of experimentally confirmed
molecule interactions among proteins. In a multilayer protease network, the
co-expression layer is obtained through the meta-analysis of transcriptomic
data from various sources and platforms. While in some researches the
co-expression layer is in turn represented as a multilayered network, a
fundamental problem is how to obtain a single-layer network from the
corresponding multilayered network. This process is called multilayer network
aggregation. In this work, we propose a maximum a posteriori estimation-based
algorithm for multilayer network aggregation. The method allows to aggregate a
weighted multilayer network while conserving the core information of the
layers. We evaluate the method through an unweighted friendship network and a
multilayer gene co-expression network. We compare the aggregated gene
co-expression network with a network obtained from conflated datasets and a
network obtained from averaged weights. The Von Neumann entropy is adopted to
compare the mixedness of the three networks, and, together with other network
measurements, shows the effectiveness of the proposes method.


Abstract:
  Over the past two decades machine learning has permeated almost every realm
of technology. At the same time, many researchers have begun using category
theory as a unifying language, facilitating communication between different
scientific disciplines. It is therefore unsurprising that there is a burgeoning
interest in applying category theory to machine learning. We aim to document
the motivations, goals and common themes across these applications. We touch on
gradient-based learning, probability, and equivariant learning.


Abstract:
  In this paper, we propose CHOLAN, a modular approach to target end-to-end
entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two
transformer-based models integrated sequentially to accomplish the EL task. The
first transformer model identifies surface forms (entity mentions) in a given
text. For each mention, a second transformer model is employed to classify the
target entity among a predefined candidates list. The latter transformer is fed
by an enriched context captured from the sentence (i.e. local context), and
entity description gained from Wikipedia. Such external contexts have not been
used in the state of the art EL approaches. Our empirical study was conducted
on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical
results suggest that CHOLAN outperforms state-of-the-art approaches on standard
datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.


Abstract:
  In this paper we prove that the Cauchy problem of the Muskat equation is
wellposed locally in time for any initial data in $\dot C^1(\mathbb{R}^d)\cap
L^2(\mathbb{R}^d)$.


Abstract:
  For the need of measurements focused in condensed matter physics and
especially Bernoulli effect in superconductors we have developed an active
resonator with dual operational amplifiers. A tunable high-Q resonator is
performed in the schematics of the the General Impedance Converter (GIC). In
the framework of frequency dependent open-loop gain of operational amplifiers,
a general formula of the frequency dependence of the impedance of GIC is
derived. The explicit formulas for the resonance frequency and Q-factor include
as immanent parameter the crossover frequency of the operational amplifier.
Voltage measurements of GIC with a lock-in amplifier perfectly agree with the
derived formulas. A table reveals that electrometer operational amplifiers are
the best choice to build the described resonator.


Abstract:
  Clinical SPECT-MPI images of 345 patients acquired from a dedicated cardiac
SPECT in list-mode format were retrospectively employed to predict normal-dose
images from low-dose data at the half, quarter, and one-eighth-dose levels. A
generative adversarial network was implemented to predict non-gated normal-dose
images in the projection space at the different reduced dose levels.
Established metrics including the peak signal-to-noise ratio (PSNR), root mean
squared error (RMSE), and structural similarity index metrics (SSIM) in
addition to Pearson correlation coefficient analysis and derived parameters
from Cedars-Sinai software were used to quantitatively assess the quality of
the predicted normal-dose images. For clinical evaluation, the quality of the
predicted normal-dose images was evaluated by a nuclear medicine specialist
using a seven-point (-3 to +3) grading scheme. By considering PSNR, SSIM, and
RMSE quantitative parameters among the different reduced dose levels, the
highest PSNR (42.49) and SSIM (0.99), and the lowest RMSE (1.99) were obtained
at the half-dose level in the reconstructed images. Pearson correlation
coefficients were measured 0.997, 0.994, and 0.987 for the predicted
normal-dose images at the half, quarter, and one-eighth-dose levels,
respectively. Regarding the normal-dose images as the reference, the
Bland-Altman plots sketched for the Cedars-Sinai selected parameters exhibited
remarkably less bias and variance in the predicted normal-dose images compared
with the low-dose data at the entire reduced dose levels. Overall, considering
the clinical assessment performed by a nuclear medicine specialist, 100%, 80%,
and 11% of the predicted normal-dose images were clinically acceptable at the
half, quarter, and one-eighth-dose levels, respectively.


Abstract:
  Classically transmission conditions between subdomains are optimized for a
simplified two subdomain decomposition to obtain optimized Schwarz methods for
many subdomains. We investigate here if such a simplified optimization suffices
for the magnetotelluric approximation of Maxwell's equation which leads to a
complex diffusion problem. We start with a direct analysis for 2 and 3
subdomains, and present asymptotically optimized transmission conditions in
each case. We then optimize transmission conditions numerically for 4, 5 and 6
subdomains and observe the same asymptotic behavior of optimized transmission
conditions. We finally use the technique of limiting spectra to optimize for a
very large number of subdomains in a strip decomposition. Our analysis shows
that the asymptotically best choice of transmission conditions is the same in
all these situations, only the constants differ slightly. It is therefore
enough for such diffusive type approximations of Maxwell's equations, which
include the special case of the Laplace and screened Laplace equation, to
optimize transmission parameters in the simplified two subdomain decomposition
setting to obtain good transmission conditions for optimized Schwarz methods
for more general decompositions.


Abstract:
  Given a prediction task, understanding when one can and cannot design a
consistent convex surrogate loss, particularly a low-dimensional one, is an
important and active area of machine learning research. The prediction task may
be given as a target loss, as in classification and structured prediction, or
simply as a (conditional) statistic of the data, as in risk measure estimation.
These two scenarios typically involve different techniques for designing and
analyzing surrogate losses. We unify these settings using tools from property
elicitation, and give a general lower bound on prediction dimension. Our lower
bound tightens existing results in the case of discrete predictions, showing
that previous calibration-based bounds can largely be recovered via property
elicitation. For continuous estimation, our lower bound resolves on open
problem on estimating measures of risk and uncertainty.


Abstract:
  Serverless computing is the latest paradigm in cloud computing, offering a
framework for the development of event driven, pay-as-you-go functions in a
highly scalable environment. While these traits offer a powerful new
development paradigm, they have also given rise to a new form of cyber-attack
known as Denial of Wallet (forced financial exhaustion). In this work, we
define and identify the threat of Denial of Wallet and its potential attack
patterns. Also, we demonstrate how this new form of attack can potentially
circumvent existing mitigation systems developed for a similar style of attack,
Denial of Service. Our goal is twofold. Firstly, we will provide a concise and
informative overview of this emerging attack paradigm. Secondly, we propose
this paper as a starting point to enable researchers and service providers to
create effective mitigation strategies. We include some simulated experiments
to highlight the potential financial damage that such attacks can cause and the
creation of an isolated test bed for continued safe research on these attacks.


Abstract:
  Motivated by the immutable nature of Ethereum smart contracts and of their
transactions, quite many approaches have been proposed to detect defects and
security problems before smart contracts become persistent in the blockchain
and they are granted control on substantial financial value.
  Because smart contracts source code might not be available, static analysis
approaches mostly face the challenge of analysing compiled Ethereum bytecode,
that is available directly from the official blockchain. However, due to the
intrinsic complexity of Ethereum bytecode (especially in jump resolution),
static analysis encounters significant obstacles that reduce the accuracy of
exiting automated tools.
  This paper presents a novel static analysis algorithm based on the symbolic
execution of the Ethereum operand stack that allows us to resolve jumps in
Ethereum bytecode and to construct an accurate control-flow graph (CFG) of the
compiled smart contracts. EtherSolve is a prototype implementation of our
approach. Experimental results on a significant set of real world Ethereum
smart contracts show that EtherSolve improves the accuracy of the execrated
CFGs with respect to the state of the art available approaches.
  Many static analysis techniques are based on the CFG representation of the
code and would therefore benefit from the accurate extraction of the CFG. For
example, we implemented a simple extension of EtherSolve that allows to detect
instances of the re-entrancy vulnerability.


Abstract:
  Significant galaxy mergers throughout cosmic time play a fundamental role in
theories of galaxy evolution. The widespread usage of human classifiers to
visually assess whether galaxies are in merging systems remains a fundamental
component of many morphology studies. Studies that employ human classifiers
usually construct a control sample, and rely on the assumption that the bias
introduced by using humans will be evenly applied to all samples. In this work,
we test this assumption and develop methods to correct for it. Using the
standard binomial statistical methods employed in many morphology studies, we
find that the merger fraction, error, and the significance of the difference
between two samples are dependent on the intrinsic merger fraction of any given
sample. We propose a method of quantifying merger biases of individual human
classifiers and incorporate these biases into a full probabilistic model to
determine the merger fraction and the probability of an individual galaxy being
in a merger. Using 14 simulated human responses and accuracies, we are able to
correctly label a galaxy as ''merger'' or ''isolated'' to within 1\% of the
truth. Using 14 real human responses on a set of realistic mock galaxy
simulation snapshots our model is able to recover the pre-coalesced merger
fraction to within 10\%. Our method can not only increase the accuracy of
studies probing the merger state of galaxies at cosmic noon, but also can be
used to construct more accurate training sets in machine learning studies that
use human classified data-sets.


Abstract:
  We present predictions for the gluon-fusion Higgs $p_T$ spectrum at third
resummed and fixed order (N$^3$LL$'+$N$^3$LO) including fiducial cuts as
required by experimental measurements at the Large Hadron Collider. Integrating
the spectrum, we predict for the first time the total fiducial cross section to
third order (N$^3$LO) and improved by resummation. The N$^3$LO correction is
enhanced by cut-induced logarithmic effects and is not reproduced by the
inclusive N$^3$LO correction times a lower-order acceptance. These are the
highest-order predictions of their kind achieved so far at a hadron collider.


Abstract:
  We introduce a random differential operator, that we call the
$\mathtt{CS}_\tau$ operator, whose spectrum is given by the $\mbox{Sch}_\tau$
point process introduced by Kritchevski, Valk\'o and Vir\'ag (2012) and whose
eigenvectors match with the description provided by Rifkind and Vir\'ag (2018).
This operator acts on $\mathbf{R}^2$-valued functions from the interval $[0,1]$
and takes the form: $$ 2 \begin{pmatrix} 0 & -\partial_t \\ \partial_t & 0
\end{pmatrix} + \sqrt{\tau} \begin{pmatrix} d\mathcal{B} + \frac1{\sqrt 2}
d\mathcal{W}_1 & \frac1{\sqrt 2} d\mathcal{W}_2\\ \frac1{\sqrt 2}
d\mathcal{W}_2 & d\mathcal{B} - \frac1{\sqrt 2} d\mathcal{W}_1\end{pmatrix}\,,
$$ where $d\mathcal{B}$, $d\mathcal{W}_1$ and $d\mathcal{W}_2$ are independent
white noises. Then, we investigate the high part of the spectrum of the
Anderson Hamiltonian $\mathcal{H}_L := -\partial_t^2 + dB$ on the segment
$[0,L]$ with white noise potential $dB$, when $L\to\infty$. We show that the
operator $\mathcal{H}_L$, recentred around energy levels $E \sim L/\tau$ and
unitarily transformed, converges in law as $L\to\infty$ to $\mathtt{CS}_\tau$
in an appropriate sense. This allows to answer a conjecture of Rifkind and
Vir\'ag (2018) on the behavior of the eigenvectors of $\mathcal{H}_L$. Our
approach also explains how such an operator arises in the limit of
$\mathcal{H}_L$. Finally we show that at higher energy levels, the Anderson
Hamiltonian matches (asymptotically in $L$) with the unperturbed Laplacian
$-\partial_t^2$. In a companion paper, it is shown that at energy levels much
smaller than $L$, the spectrum is localized with Poisson statistics: the
present paper therefore identifies the delocalized phase of the Anderson
Hamiltonian.


Abstract:
  We study the problem of controlling the free surface, by fluid jets on the
boundary, for a two dimensional solid container in the context of the gravity
waves and the sloshing problem. By using conformal maps and the
Dirichlet--Neumann operator, the problem is formulated as a second order
evolutionary equation on the free surface involving a self-adjoint operator. We
present then the appropriate Sobolev spaces where having solutions for the
system and study the exact controllability through an observability inequality
for the adjoint problem.


Abstract:
  While state-of-the-art NLP models have been achieving the excellent
performance of a wide range of tasks in recent years, important questions are
being raised about their robustness and their underlying sensitivity to
systematic biases that may exist in their training and test data. Such issues
come to be manifest in performance problems when faced with out-of-distribution
data in the field. One recent solution has been to use counterfactually
augmented datasets in order to reduce any reliance on spurious patterns that
may exist in the original data. Producing high-quality augmented data can be
costly and time-consuming as it usually needs to involve human feedback and
crowdsourcing efforts. In this work, we propose an alternative by describing
and evaluating an approach to automatically generating counterfactual data for
data augmentation and explanation. A comprehensive evaluation on several
different datasets and using a variety of state-of-the-art benchmarks
demonstrate how our approach can achieve significant improvements in model
performance when compared to models training on the original data and even when
compared to models trained with the benefit of human-generated augmented data.


Abstract:
  The family of graphynes, novel two-dimensional semiconductors with various
and fascinating chemical and physical properties, has attracted great interest
from both science and industry. Currently, the focus of graphynes is on
graphdiyne, or graphyne-2. In this work, we systematically study the effect of
acetylene, i.e., carbon-carbon triple bond, links on the electronic and optical
properties of a series of graphynes (graphyne-n, where n = 1-5, the number of
acetylene bonds) using the ab initio calculations. We find an even-odd pattern,
i.e., n = 1, 3, 5 and n = 2, 4 having different features, which has not be
discovered in studying graphyne or graphdyine only. It is found that as the
number of acetylene bonds increases, the electron effective mass increases
continuously in the low energy range because of the flatter conduction band
induced by the longer acetylene links. Meanwhile, longer acetylene links result
in larger redshift of the imaginary part of the dielectric function, loss
function, and extinction coefficient. In this work, we propose an effective
method to tune and manipulate both the electronic and optical properties of
graphynes for the applications in optoelectronic devices and photo-chemical
catalysis.


Abstract:
  Most deep learning models are data-driven and the excellent performance is
highly dependent on the abundant and diverse datasets. However, it is very hard
to obtain and label the datasets of some specific scenes or applications. If we
train the detector using the data from one domain, it cannot perform well on
the data from another domain due to domain shift, which is one of the big
challenges of most object detection models. To address this issue, some
image-to-image translation techniques have been employed to generate some fake
data of some specific scenes to train the models. With the advent of Generative
Adversarial Networks (GANs), we could realize unsupervised image-to-image
translation in both directions from a source to a target domain and from the
target to the source domain. In this study, we report a new approach to making
use of the generated images. We propose to concatenate the original 3-channel
images and their corresponding GAN-generated fake images to form 6-channel
representations of the dataset, hoping to address the domain shift problem
while exploiting the success of available detection models. The idea of
augmented data representation may inspire further study on object detection and
other applications.


Abstract:
  Probing optical excitations with high resolution is important for
understanding their dynamics and controlling their interaction with other
photonic elements. This can be done using state-of-the-art electron
microscopes, which provide the means to sample optical excitations with
combined meV--sub-nm energy--space resolution. For reciprocal photonic systems,
electrons traveling in opposite directions produce identical signals, while
this symmetry is broken in nonreciprocal structures. Here, we theoretically
investigate this phenomenon by analyzing electron energy-loss spectroscopy
(EELS) and cathodoluminescence (CL) in structures consisting of magnetically
biased InAs as an instance of gyrotropic nonreciprocal material. We find that
the spectral features associated with excitations of InAs films depend on the
electron propagation direction in both EELS and CL, and can be tuned by varying
the applied magnetic field within a relatively modest sub-tesla regime. The
magnetic field modifies the optical field distribution of the sampled
resonances, and this in turn produces a direction-dependent coupling to the
electron. The present results pave the way to the use of electron microscope
spectroscopies to explore the near-field characteristics of nonreciprocal
systems with high spatial resolution.


Abstract:
  In this work, we consider the target detection problem in a sensing
architecture where the radar is aided by a reconfigurable intelligent surface
(RIS), that can be modeled as an array of sub-wavelength small reflective
elements capable of imposing a tunable phase shift to the impinging waves and,
ultimately, of providing the radar with an additional echo of the target. A
theoretical analysis is carried out for closely- and widely-spaced (with
respect to the target) radar and RIS and for different beampattern
configurations, and some examples are provided to show that large gains can be
achieved by the considered detection architecture.


Abstract:
  In this note we study analytically and numerically the existence and
stability of standing waves for one dimensional nonlinear Schr\"odinger
equations whose nonlinearities are the sum of three powers. Special attention
is paid to the curves of non-existence and curves of stability change on the
parameter planes.


Abstract:
  Quantum reservoir computing (QRC) and quantum extreme learning machines
(QELM) are two emerging approaches that have demonstrated their potential both
in classical and quantum machine learning tasks. They exploit the quantumness
of physical systems combined with an easy training strategy, achieving an
excellent performance. The increasing interest in these unconventional
computing approaches is fueled by the availability of diverse quantum platforms
suitable for implementation and the theoretical progresses in the study of
complex quantum systems. In this review article, recent proposals and first
experiments displaying a broad range of possibilities are reviewed when quantum
inputs, quantum physical substrates and quantum tasks are considered. The main
focus is the performance of these approaches, on the advantages with respect to
classical counterparts and opportunities.


Abstract:
  We consider the problem of scheduling maintenance for a collection of
machines under partial observations when the state of each machine deteriorates
stochastically in a Markovian manner. We consider two observational models:
first, the state of each machine is not observable at all, and second, the
state of each machine is observable only if a service-person visits them. The
agent takes a maintenance action, e.g., machine replacement, if he is chosen
for the task. We model both problems as restless multi-armed bandit problem and
propose the Whittle index policy for scheduling the visits. We show that both
models are indexable. For the first model, we derive a closed-form expression
for the Whittle index. For the second model, we propose an efficient algorithm
to compute the Whittle index by exploiting the qualitative properties of the
optimal policy. We present detailed numerical experiments which show that for
multiple instances of the model, the Whittle index policy outperforms myopic
policy and can be close-to-optimal in different setups.


Abstract:
  By using a novel technique that establishes a correspondence between general
relativity and metric-affine theories based on the Ricci tensor, we are able to
set stringent constraints on the free parameter of Born-Infeld gravity from the
ones recently obtained for Born-Infeld electrodynamics by using light-by-light
scattering data from ATLAS. We also discuss how these gravity theories plus
matter fit within an effective field theory framework.


Abstract:
  We apply a quantum teleportation protocol based on the Hayden-Preskill
thought experiment to quantify how scrambling a given quantum evolution is. It
has an advantage over the direct measurement of out-of-time ordered correlators
when used to diagnose the information scrambling in the presence of decoherence
effects stemming from a noisy quantum device. We demonstrate the protocol by
applying it to two physical systems: Ising spin chain and SU(2) lattice
Yang-Mills theory. To this end, we numerically simulate the time evolution of
the two theories in the Hamiltonian formalism. The lattice Yang-Mills theory is
implemented with a suitable truncation of Hilbert space on the basis of the
Kogut-Susskind formalism. On a two-leg ladder geometry and with the lowest
nontrivial spin representations, it can be mapped to a spin chain, which we
call it Yang-Mills-Ising model and is also directly applicable to future
digital quantum simulations. We find that the Yang-Mills-Ising model shows the
signal of information scrambling at late times.


Abstract:
  Zernike polynomials are one of the most widely used mathematical descriptors
of optical aberrations in the fields of imaging and adaptive optics. Their
mathematical orthogonality as well as isomorphisms with experimentally
observable aberrations make them a very powerful tool in solving numerous
problems in beam optics. However, Zernike aberrations show cross-coupling
between individual modes when used in combination with Gaussian beams, an
effect that has not been extensively studied. Here we propose a novel framework
that is capable of explaining the fundamental cross-compensation of Zernike
type aberrations, both in low-aberration and high-aberration regimes. Our
approach is based on analysing the coupling between Zernike modes and different
classes of Laguerre-Gauss modes which allows investigating aberrated beams not
only on a single plane but also during their 3D propagation.


Abstract:
  Earth's modern atmosphere is highly oxygenated and is a remotely detectable
signal of its surface biosphere. However, the lifespan of oxygen-based
biosignatures in Earth's atmosphere remains uncertain, particularly for the
distant future. Here we use a combined biogeochemistry and climate model to
examine the likely timescale of oxygen-rich atmospheric conditions on Earth.
Using a stochastic approach, we find that the mean future lifespan of Earth's
atmosphere with oxygen levels more than 1% of the present atmospheric level is
1.08+-0.14 billion years. The model projects that a deoxygenation of the
atmosphere, with atmospheric oxygen dropping sharply to levels reminiscnet of
the Archaean Earth, will most probably be triggered before the inception of
moist greenhouse conditions in Earth's climate system and before the extensive
loss of surface water from the atmosphere. We find that future deoxygenation is
an inevitable consequence of increasing solar fluxes, whereas its precise
timing is modulated by the exchange flux of reducing power between the mantle
and the ocean-atmosphere-crust system. Our results suggest that the planetary
carbonate-silicate cycle will tend to lead to terminally CO2-limited biospheres
and rapid atmospheric deoxygenation, emphasizing the need for robust
atmospheric biosignatures applicable to weakly oxygenated and anoxic exoplanet
atmospheres and highlighting the potential importance of atmospheric organic
haze during the terminal stages of planetary habitability.


Abstract:
  We prove two theorems about the Malcev Lie algebra associated to the Torelli
group of a surface of genus $g$: stably, it is Koszul and the kernel of the
Johnson homomorphism consists only of trivial $Sp_{2g}(Z)$-representations
lying in the centre.


Abstract:
  A wide variety of use case templates supports different variants to link a
use case with its associated requirements. Regardless of the linking, a reader
must process the related information simultaneously to understand them. Linking
variants are intended to cause a specific reading behavior in which a reader
interrelates a use case and its associated requirements. Due to the effort to
create and maintain links, we investigated the impact of different linking
variants on the reading behavior in terms of visual effort and the intended way
of interrelating both artifacts. We designed an eye tracking study about
reading a use case and requirements. We conducted the study twice each with 15
subjects as a baseline experiment and as a repetition. The results of the
baseline experiment, its repetition, and their joint analysis are consistent.
All investigated linking variants cause comparable visual effort. In all cases,
reading the single artifacts one after the other is the most frequently
occurring behavior. Only links embedded in the fields of a use case description
significantly increase the readers' efforts to interrelate both artifacts. None
of the investigated linking variants impedes reading a use case and
requirements. However, only the most detailed linking variant causes readers to
process related information simultaneously.


Abstract:
  Nowadays new technologies, and especially artificial intelligence, are more
and more established in our society. Big data analysis and machine learning,
two sub-fields of artificial intelligence, are at the core of many recent
breakthroughs in many application fields (e.g., medicine, communication,
finance, ...), including some that are strongly related to our day-to-day life
(e.g., social networks, computers, smartphones, ...). In machine learning,
significant improvements are usually achieved at the price of an increasing
computational complexity and thanks to bigger datasets. Currently, cutting-edge
models built by the most advanced machine learning algorithms typically became
simultaneously very efficient and profitable but also extremely complex. Their
complexity is to such an extent that these models are commonly seen as
black-boxes providing a prediction or a decision which can not be interpreted
or justified. Nevertheless, whether these models are used autonomously or as a
simple decision-making support tool, they are already being used in machine
learning applications where health and human life are at stake. Therefore, it
appears to be an obvious necessity not to blindly believe everything coming out
of those models without a detailed understanding of their predictions or
decisions. Accordingly, this thesis aims at improving the interpretability of
models built by a specific family of machine learning algorithms, the so-called
tree-based methods. Several mechanisms have been proposed to interpret these
models and we aim along this thesis to improve their understanding, study their
properties, and define their limitations.


Abstract:
  Upon investigating whether the variation of the antineutron-nucleus
annihilation cross-sections at very low energies satisfy Bethe-Landau's power
law of $\sigma_{\rm ann} (p) \propto 1/p^{\alpha}$ behavior as a function of
the antineutron momentum $p$, we uncover unexpected regular oscillatory
structures in the low antineutron energy region from 0.001 to 10 MeV, with
small amplitudes and narrow periodicity in the logarithm of the antineutron
energies, for large-$A$ nuclei such as Pb and Ag. Subsequent semiclassical
analyses of the $S$ matrices reveal that these oscillations are pocket
resonances that arise from quasi-bound states inside the pocket and the
interference between the waves reflecting inside the optical potential pockets
with those from beyond the potential barriers, implicit in the nuclear Ramsauer
effect. They are the continuation of bound states in the continuum.
Experimental observations of these pocket resonances will provide vital
information on the properties of the optical model potentials and the nature of
the antineutron annihilation process.


Abstract:
  We revisit Allendoerfer-Weil's formula for the Euler characteristic of
embedded hypersurfaces in constant sectional curvature manifolds, first taking
some time to re-prove it while demonstrating techniques of [2] and then
applying it to gain new understanding of isoparametric hypersurfaces.


Abstract:
  Neural data compression has been shown to outperform classical methods in
terms of $RD$ performance, with results still improving rapidly. At a high
level, neural compression is based on an autoencoder that tries to reconstruct
the input instance from a (quantized) latent representation, coupled with a
prior that is used to losslessly compress these latents. Due to limitations on
model capacity and imperfect optimization and generalization, such models will
suboptimally compress test data in general. However, one of the great strengths
of learned compression is that if the test-time data distribution is known and
relatively low-entropy (e.g. a camera watching a static scene, a dash cam in an
autonomous car, etc.), the model can easily be finetuned or adapted to this
distribution, leading to improved $RD$ performance. In this paper we take this
concept to the extreme, adapting the full model to a single video, and sending
model updates (quantized and compressed using a parameter-space prior) along
with the latent representation. Unlike previous work, we finetune not only the
encoder/latents but the entire model, and - during finetuning - take into
account both the effect of model quantization and the additional costs incurred
by sending the model updates. We evaluate an image compression model on
I-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate
that full-model adaptation improves $RD$ performance by ~1 dB, with respect to
encoder-only finetuning.


Abstract:
  We give the characterization of the embeddings between weighted Tandori and
Ces\`{a}ro function spaces using the combination of duality arguments for
weighted Lebesgue spaces and weighted Tandori spaces with estimates for the
iterated integral operators.


Abstract:
  In the dynamical models of gamma-ray burst (GRB) afterglows, the uniform
assumption of the shocked region is known as provoking total energy
conservation problem. In this work we consider shocks originating from
magnetized ejecta, extend the energy-conserving hydrodynamical model of Yan et
al. (2007) to the MHD limit by applying the magnetized jump conditions from
Zhang & Kobayashi (2005). Compared with the non-conservative models, our
Lorentz factor of the whole shocked region is larger by a factor
$\lesssim\sqrt{2}$. The total pressure of the forward shocked region is higher
than the reversed shocked region, in the relativistic regime with a factor of
about 3 in our interstellar medium (ISM) cases while ejecta magnetization
degree $\sigma<1$, and a factor of about 2.4 in the wind cases. For $\sigma\le
1$, the non-conservative model loses $32-42$% of its total energy for ISM
cases, and for wind cases $25-38$%, which happens specifically in the forward
shocked region, making the shock synchrotron emission from the forward shock
less luminous than expected. Once the energy conservation problem is fixed, the
late time light curves from the forward shock become nearly independent of the
ejecta magnetization. The reverse shocked region doesn't suffer from the energy
conservation problem since the changes of the Lorentz factor are recompensed by
the changes of the shocked particle number density. The early light curves from
the reverse shock are sensitive to the magnetization of the ejecta, thus are an
important probe of the magnetization degree.


Abstract:
  We associate a deformation of Heisenberg algebra to the suitably normalized
Yang $R$-matrix and we investigate its properties. Moreover, we construct new
examples of quantum vertex algebras which possess the same representation
theory as the aforementioned deformed Heisenberg algebra.


Abstract:
  Recently discovered intrinsic antiferromagnetic topological insulator
MnBi$_2$Te$_4$ presents an exciting platform for realization of the quantum
anomalous Hall effect and a number of related phenomena at elevated
temperatures. An important characteristic making this material attractive for
applications is its predicted large magnetic gap at the Dirac point (DP).
However, while the early experimental measurements reported on large DP gaps, a
number of recent studies claimed to observe a gapless dispersion of the
MnBi$_2$Te$_4$ Dirac cone. Here, using micro($\mu$)-laser angle-resolved
photoemission spectroscopy, we study the electronic structure of 15 different
MnBi$_2$Te$_4$ samples, grown by two different chemists groups. Based on the
careful energy distribution curves analysis, the DP gaps between 15 and 65 meV
are observed, as measured below the N\'eel temperature at about 10-16 K. At
that, roughly half of the studied samples show the DP gap of about 30 meV,
while for a quarter of the samples the gaps are in the 50 to 60 meV range.
Summarizing the results of both our and other groups, in the currently
available MnBi$_2$Te$_4$ samples the DP gap can acquire an arbitrary value
between a few and several tens of meV. Further, based on the density functional
theory, we discuss a possible factor that might contribute to the reduction of
the DP gap size, which is the excess surface charge that can appear due to
various defects in surface region. We demonstrate that the DP gap is influenced
by the applied surface charge and even can be closed, which can be taken
advantage of to tune the MnBi$_2$Te$_4$ DP gap size.


Abstract:
  Society is changing, has always changed, and will keep changing. However,
changes are becoming faster and what used to happen between generations, now
happens in the same generation. Computing Science is one of the reasons for
this speed and permeates, basically, every other knowledge area. This paper
(written in Portugu\^es) describes, briefly, the worldwide initiatives to
introduce Computing Science teaching in schools. As the paper's main
conclusion, it is essential to introduce Computing Science and Computational
Thinking for kids before they enter into a university.


Abstract:
  Innovations in neural architectures have fostered significant breakthroughs
in language modeling and computer vision. Unfortunately, novel architectures
often result in challenging hyper-parameter choices and training instability if
the network parameters are not properly initialized. A number of
architecture-specific initialization schemes have been proposed, but these
schemes are not always portable to new architectures. This paper presents
GradInit, an automated and architecture agnostic method for initializing neural
networks. GradInit is based on a simple heuristic; the norm of each network
layer is adjusted so that a single step of SGD or Adam with prescribed
hyperparameters results in the smallest possible loss value. This adjustment is
done by introducing a scalar multiplier variable in front of each parameter
block, and then optimizing these variables using a simple numerical scheme.
GradInit accelerates the convergence and test performance of many convolutional
architectures, both with or without skip connections, and even without
normalization layers. It also improves the stability of the original
Transformer architecture for machine translation, enabling training it without
learning rate warmup using either Adam or SGD under a wide range of learning
rates and momentum coefficients. Code is available at
https://github.com/zhuchen03/gradinit.


Abstract:
  High-level understanding of stories in video such as movies and TV shows from
raw data is extremely challenging. Modern video question answering (VideoQA)
systems often use additional human-made sources like plot synopses, scripts,
video descriptions or knowledge bases. In this work, we present a new approach
to understand the whole story without such external sources. The secret lies in
the dialog: unlike any prior work, we treat dialog as a noisy source to be
converted into text description via dialog summarization, much like recent
methods treat video. The input of each modality is encoded by transformers
independently, and a simple fusion method combines all modalities, using soft
temporal attention for localization over long inputs. Our model outperforms the
state of the art on the KnowIT VQA dataset by a large margin, without using
question-specific human annotation or human-made plot summaries. It even
outperforms human evaluators who have never watched any whole episode before.
Code is available at https://engindeniz.github.io/dialogsummary-videoqa


Abstract:
  The effect of radiative heat transfer on the entropy generation in a
two-phase non-isothermal fluid flow between two infinite horizontal parallel
plates under the influence of a constant pressure gradient and transverse
non-invasive magnetic field have been explored. Both the fluids are considered
to be viscous, incompressible, immiscible, Newtonian, and electrically
conducting. The governing equations in Cartesian coordinate are solved
analytically with the help of appropriate boundary conditions to obtain the
velocity and temperature profile inside the channel. Application of transverse
magnetic field is found to reduce the throughput and the temperature
distribution of the fluids in a pressure-driven flow. The temperature and fluid
flow inside the channel can also be non-invasively altered by tuning the
magnetic field intensity, the temperature difference between the channel walls
and the fluids, and several intrinsic fluid properties. The entropy generation
due to the heat transfer, magnetic field, and fluid flow irreversibilities can
be controlled by altering the Hartmann number, radiation parameter, Brinkmann
number, filling ratio, and the ratios of fluid viscosities, thermal and
electrical conductivities. The surfaces of the channel wall are found to act as
a strong source of entropy generation and heat transfer irreversibility. The
rate of heat transfer at the channel walls can also be tweaked by the magnetic
field intensity, temperature differences, and fluid properties. The proposed
strategies in the present study can be of significance in the design and
development of gen-next microscale reactors, micro heat exchangers, and energy
harvesting devices.


Abstract:
  For any regular Courant algebroid $E$ over a smooth manifold $M$ with
characteristic distribution $F$ and ample Lie algebroid $A_E$, we prove that
there exists a canonical homological vector field on the graded manifold
$A_E[1] \oplus (TM/F)^\ast[2]$ such that the associated dg manifold
$\mathcal{M}_E$, which we call the minimal model of the Courant algebroid $E$,
encodes all cohomological data of $E$. Thereby, the standard cohomology
$H^\bullet_{\operatorname{st}}(E)$ of $E$ can be identified with the cohomology
$H^\bullet(\mathcal{M}_E)$ of the function space on $\mathcal{M}_E$. To compute
it, we find a natural transgression map $[d_T] \colon
H^{\bullet}_{\operatorname{CE}}\big(A_E; S^{\diamond}(TM/F[-2])\big) \to
H^{\bullet+3}_{\CE}\big(A_E; S^{\diamond-1}(TM/F[-2])\big)$ from which we
construct a spectral sequence which converges to
$H^\bullet_{\operatorname{st}}(E)$. Moreover, we give applications to
generalized exact Courant algebroids and those arising from regular Lie
algebroids .


Abstract:
  For optimal power flow problems with chance constraints, a particularly
effective method is based on a fixed point iteration applied to a sequence of
deterministic power flow problems. However, a priori, the convergence of such
an approach is not necessarily guaranteed. This article analyses the
convergence conditions for this fixed point approach, and reports numerical
experiments including for large IEEE networks.


Abstract:
  We consider QCD radiative corrections to the associated production of a
heavy-quark pair ($Q{\bar Q}$) with a generic colourless system $F$ at hadron
colliders. We discuss the resummation formalism for the production of the
$Q{\bar Q}F$ system at small values of its total transverse momentum $q_T$. The
perturbative expansion of the resummation formula leads to the explicit
ingredients that can be used to apply the $q_T$ subtraction formalism to
fixed-order calculations for this class of processes. We use the $q_T$
subtraction formalism to perform a fully differential perturbative computation
for the production of a top-antitop quark pair and a Higgs boson. At
next-to-leading order we compare our results with those obtained with
established subtraction methods and we find complete agreement. We present, for
the first time, the results for the flavour off-diagonal partonic channels at
the next-to-next-to-leading order.


Abstract:
  A strong edge coloring of a graph $G$ is a proper edge coloring of $G$ such
that every color class is an induced matching. The minimum number of colors
required is termed the strong chromatic index. In this paper, we determine the
exact value of the strong chromatic index of all unitary Cayley graphs. Our
investigations reveal an underlying product structure from which the unitary
Cayley graphs emerge. We then go on to give tight bounds for the strong
chromatic index of the Cartesian product of two trees, including an exact
formula for the product in the case of stars. Further, we give bounds for the
strong chromatic index of the product of a tree with a cycle. For any tree,
those bounds may differ from the actual value only by not more than a small
additive constant (at most 2 for even cycles and at most 5 for odd cycles),
moreover they yield the exact value when the length of the cycle is divisible
by $4$.


Abstract:
  The complex matrix representation for a quaternion matrix is used in this
paper to find necessary and sufficient conditions for the existence of an
$H$-selfadjoint $m$th root of a given $H$-selfadjoint quaternion matrix. In the
process, when such an $H$-selfadjoint $m$th root exists, its construction is
also given.


Abstract:
  Decentralized network theories focus on achieving consensus and in speeding
up the rate of convergence to consensus. However, network cohesion (i.e.,
maintaining consensus) during transitions between consensus values is also
important when transporting flexible structures. Deviations in the robot
positions due to loss of cohesion when moving flexible structures from one
position to another, such as uncuredcomposite aircraft wings, can cause large
deformations, which in turn, can result in potential damage. The major
contribution of this work is to develop a decentralized approach to transport
flexible objects in a cohesive manner using local force measurements, without
the need for additional communication between the robots. Additionally,
stability conditions are developed for discrete-time implementation of the
proposed cohesive transition approach, and experimental results are presented,
which show that the proposed cohesive transportation approach can reduce the
relative deformations by 85% when compared to the case without it.


Abstract:
  In this paper, we present a new mathematical model for pandemics called
SUTRA. The acronym stands for Susceptible, Undetected, Tested (positive), and
Removed Approach. A novel feature of our model is that it allows estimation of
parameters from reported infection data, unlike most other models that estimate
parameter values from other considerations. This gives the model the ability to
predict the future trajectory well, as long as parameters do not change. In
addition, it is possible to quantify how the model parameter values were
affected by various interventions to control the pandemic, and/or the arrival
of new mutants. We have applied our model to analyze and predict the
progression of the COVID-19 pandemic in several countries. We present our
predictions for two countries: India and US. In both cases, the model-computed
trajectory closely matches actual one. Moreover, our predictions were used by
entities such as the Reserve Bank of India to formulate policy.


Abstract:
  The classical Jordan curve theorem for digital curves asserts that the Jordan
curve theorem remains valid in the Khalimsky plane. Since the Khalimsky plane
is a quotient space of $\mathbb R^2$ induced by a tiling of squares, it is
natural to ask for which other tilings of the plane it is possible to obtain a
similar result. In this paper we prove a Jordan curve theorem which is valid
for every locally finite tiling of $\mathbb R^2$. As a corollary of our result,
we generalize some classical Jordan curve theorems for grids of points,
including Rosenfeld's theorem.


Abstract:
  The use of statistical methods in sport analytics has gained a rapidly
growing interest over the last decade, and nowadays is common practice. In
particular, the interest in understanding and predicting an athlete's
performance throughout his/her career is motivated by the need to evaluate the
efficacy of training programs, anticipate fatigue to prevent injuries and
detect unexpected of disproportionate increases in performance that might be
indicative of doping. Moreover, fast evolving data gathering technologies
require up to date modelling techniques that adapt to the distinctive features
of sports data. In this work, we propose a hierarchical Bayesian model for
describing and predicting the evolution of performance over time for shot put
athletes. To account for seasonality and heterogeneity in recorded results, we
rely both on a smooth functional contribution and on a linear mixed effect
model with heteroskedastic errors to represent the athlete-specific
trajectories. The resulting model provides an accurate description of the
performance trajectories and helps specifying both the intra- and
inter-seasonal variability of measurements. Further, the model allows for the
prediction of athletes' performance in future seasons. We apply our model to an
extensive real world data set on performance data of professional shot put
athletes recorded at elite competitions.


Abstract:
  We analyze dispersion relations of magnons in ferromagnetic nanostructures
with uniaxial anisotropy taking into account inertial terms, i.e. magnetic
nutation. Inertial effects are parametrized by damping-independent parameter
$\beta$, which allows for an unambiguous discrimination of inertial effects
from Gilbert damping parameter $\alpha$. The analysis of magnon dispersion
relation shows its two branches are modified by the inertial effect, albeit in
different ways. The upper nutation branch starts at $\omega=1/ \beta$, the
lower branch coincides with FMR in the long-wavelength limit and deviates from
the zero-inertia parabolic dependence $\simeq\omega_{FMR}+Dk^2$ of the exchange
magnon. Taking a realistic experimental geometry of magnetic thin films,
nanowires and nanodiscs, magnon eigenfrequencies, eigenvectors and $Q$-factors
are found to depend on the shape anisotropy. The possibility of phase-matched
magneto-elastic excitation of nutation magnons is discussed and the condition
was found to depend on $\beta$, exchange stiffness $D$ and the acoustic
velocity.


Abstract:
  In this paper, we present a neat yet effective transformer-based framework
for visual grounding, namely TransVG, to address the task of grounding a
language query to the corresponding region onto an image. The state-of-the-art
methods, including two-stage or one-stage ones, rely on a complex module with
manually-designed mechanisms to perform the query reasoning and multi-modal
fusion. However, the involvement of certain mechanisms in fusion module design,
such as query decomposition and image scene graph, makes the models easily
overfit to datasets with specific scenarios, and limits the plenitudinous
interaction between the visual-linguistic context. To avoid this caveat, we
propose to establish the multi-modal correspondence by leveraging transformers,
and empirically show that the complex fusion modules (\eg, modular attention
network, dynamic graph, and multi-modal tree) can be replaced by a simple stack
of transformer encoder layers with higher performance. Moreover, we
re-formulate the visual grounding as a direct coordinates regression problem
and avoid making predictions out of a set of candidates (\emph{i.e.}, region
proposals or anchor boxes). Extensive experiments are conducted on five widely
used datasets, and a series of state-of-the-art records are set by our TransVG.
We build the benchmark of transformer-based visual grounding framework and make
the code available at \url{https://github.com/djiajunustc/TransVG}.


Abstract:
  Modular transformations of string theory are shown to play a crucial role in
the discussion of discrete flavor symmetries in the Standard Model. They
include CP transformations and provide a unification of CP with traditional
flavor symmetries within the framework of the "eclectic flavor" scheme. The
unified flavor group is non-universal in moduli space and exhibits the
phenomenon of "Local Flavor Unification", where different sectors of the theory
(like quarks and leptons) can be subject to different flavor structures.


Abstract:
  The cost of using a blockchain infrastructure as well as the time required to
search and retrieve information from it must be considered when designing a
decentralized application. In this work, we examine a comprehensive set of data
management approaches for Ethereum applications and assess the associated cost
in gas as well as the retrieval performance. More precisely, we analyze the
storage and retrieval of various-sized data, utilizing smart contract storage.
In addition, we study hybrid approaches by using IPFS and Swarm as storage
platforms along with Ethereum as a timestamping proof mechanism. Such schemes
are especially effective when large chunks of data have to be managed.
Moreover, we present methods for low-cost data handling in Ethereum, namely the
event-logs, the transaction payload, and the almost surprising exploitation of
unused function arguments. Finally, we evaluate these methods on a
comprehensive set of experiments.


Abstract:
  Air pollutants, such as particulate matter, negatively impact human health.
Most existing pollution monitoring techniques use stationary sensors, which are
typically sparsely deployed. However, real-world pollution distributions vary
rapidly with position and the visual effects of air pollution can be used to
estimate concentration, potentially at high spatial resolution. Accurate
pollution monitoring requires either densely deployed conventional point
sensors, at-a-distance vision-based pollution monitoring, or a combination of
both.
  The main contribution of this paper is that to the best of our knowledge, it
is the first publicly available, high temporal and spatial resolution air
quality dataset containing simultaneous point sensor measurements and
corresponding images. The dataset enables, for the first time, high spatial
resolution evaluation of image-based air pollution estimation algorithms. It
contains PM2.5, PM10, temperature, and humidity data. We evaluate several
state-of-art vision-based PM concentration estimation algorithms on our dataset
and quantify the increase in accuracy resulting from higher point sensor
density and the use of images. It is our intent and belief that this dataset
can enable advances by other research teams working on air quality estimation.
Our dataset is available at
https://github.com/implicitDeclaration/HVAQ-dataset/tree/master.


Abstract:
  In this paper, important concepts from finite group theory are translated to
localities, in particular to linking localities. Here localities are group-like
structures associated to fusion systems which were introduced by Chermak.
Linking localities (by Chermak also called proper localities) are special kinds
of localities which correspond to linking systems. Thus they contain the
algebraic information that is needed to study $p$-completed classifying spaces
of fusion systems as generalizations of $p$-completed classifying spaces of
finite groups.
  Because of the group-like nature of localities, there is a natural notion of
partial normal subgroups. Given a locality $\mathcal{L}$ and a partial normal
subgroup $\mathcal{N}$ of $\mathcal{L}$, we show that there is a largest
partial normal subgroup $\mathcal{N}^\perp$ of $\mathcal{L}$ which, in a
certain sense, commutes elementwise with $\mathcal{N}$ and thus morally plays
the role of a "centralizer" of $\mathcal{N}$ in $\mathcal{L}$. This leads to a
nice notion of the generalized Fitting subgroup $F^*(\mathcal{L})$ of a linking
locality $\mathcal{L}$. Building on these results we define and study special
kinds of linking localities called regular localities. It turns out that there
is a theory of components of regular localities akin to the theory of
components of finite groups. The main concepts we introduce and work with in
the present paper (in particular $\mathcal{N}^\perp$ in the special case of
linking localities, $F^*(\mathcal{L})$, regular localities and components of
regular localities) were already introduced and studied in a preprint by
Chermak. However, we give a different and self-contained approach to the
subject where we reprove Chermak's theorems and also show several new results.


Abstract:
  Double-descent curves in neural networks describe the phenomenon that the
generalisation error initially descends with increasing parameters, then grows
after reaching an optimal number of parameters which is less than the number of
data points, but then descends again in the overparameterised regime. Here we
use a neural network Gaussian process (NNGP) which maps exactly to a fully
connected network (FCN) in the infinite width limit, combined with techniques
from random matrix theory, to calculate this generalisation behaviour, with a
particular focus on the overparameterised regime. An advantage of our NNGP
approach is that the analytical calculations are easier to interpret. We argue
that neural network generalization performance improves in the
overparameterised regime precisely because that is where they converge to their
equivalent Gaussian process.


Abstract:
  Quantum computing has been attracting tremendous efforts in recent years. One
prominent application is to perform quantum simulations of electron
correlations in large molecules and solid-state materials, where orbital
degrees of freedom are crucial to quantitatively model electronic properties.
Electron orbitals unlike quantum spins obey crystal symmetries, making the
atomic orbital in optical lattices a natural candidate to emulate electron
orbitals. Here, we construct atom-orbital qubits by manipulating $s$- and
$d$-orbitals of atomic Bose-Einstein condensation in an optical lattice.
Noise-resilient quantum gate operations are achieved by performing holonomic
quantum control, which admits geometrical protection. We find it is critical to
eliminate the orbital leakage error in the system. The gate robustness is
tested by varying the intensity of the laser forming the lattice. Our work
opens up wide opportunities for atom-orbital based quantum information
processing, of vital importance to programmable quantum simulations of
multi-orbital physics in molecules and quantum materials.


Abstract:
  The x-vector architecture has recently achieved state-of-the-art results on
the speaker verification task. This architecture incorporates a central layer,
referred to as temporal pooling, which stacks statistical parameters of the
acoustic frame distribution. This work proposes to highlight the significant
effect of the temporal pooling content on the training dynamics and task
performance. An evaluation with different pooling layers is conducted, that is,
including different statistical measures of central tendency. Notably, 3rd and
4th moment-based statistics (skewness and kurtosis) are also tested to complete
the usual mean and standard-deviation parameters. Our experiments show the
influence of the pooling layer content in terms of speaker verification
performance, but also for several classification tasks (speaker, channel or
text related), and allow to better reveal the presence of external information
to the speaker identity depending on the layer content.


Abstract:
  We give some Korovkin-type theorems on convergence and estimates of rates of
approximations of nets of functions, satisfying suitable axioms, whose
particular cases are filter/ideal convergence, almost convergence and
triangular A-statistical convergence, where A is a non-negative summability
method. Furthermore, we give some applications to Mellin-type convolution and
bivariate Kantorovich-type discrete operators.


Abstract:
  We construct an agent-based SEIR model to simulate COVID-19 spread at a
16000-student mostly non-residential urban university during the Fall 2021
Semester. We find that mRNA vaccine coverage above 80% makes it possible to
safely reopen to in-person instruction. If vaccine coverage is 100%, then our
model indicates that facemask use is not necessary. Our simulations with
vaccine coverage below 70% exhibit a right-skew for total infections over the
semester, which suggests that high levels of infection are not exceedingly rare
with campus social connections the main transmission route. Less effective
vaccines or incidence of new variants may require additional intervention such
as screening testing to reopen safely.


Abstract:
  A big challenge in current biology is to understand the exact
self-organization mechanism underlying complex multi-physics coupling
developmental processes. With multiscale computations of from subcellular gene
expressions to cell population dynamics that is based on first principles, we
show that cell cycles can self-organize into periodic stripes in the
development of E. coli populations from one single cell, relying on the moving
graded nutrient concentration profile, which provides directing positional
information for cells to keep their cycle phases in place. Resultantly, the
statistical cell cycle distribution within the population is observed to
collapse to a universal function and shows a scale invariance. Depending on the
radial distribution mode of genetic oscillations in cell populations, a
transition between gene patterns is achieved. When an inhibitor-inhibitor gene
network is subsequently activated by a gene-oscillatory network, cell
populations with zebra stripes can be established, with the positioning
precision of cell-fate-specific domains influenced by cells' speed of free
motions. Such information may provide important implications for understanding
relevant dynamic processes of multicellular systems, such as biological
development.


Abstract:
  The Perdew-Zunger self-interaction correction(PZ-SIC) improves the
performance of density functional approximations(DFAs) for the properties that
involve significant self-interaction error(SIE), as in stretched bond
situations, but overcorrects for equilibrium properties where SIE is
insignificant. This overcorrection is often reduced by LSIC, local scaling of
the PZ-SIC to the local spin density approximation(LSDA). Here we propose a new
scaling factor to use in an LSIC-like approach that satisfies an additional
important constraint: the correct coefficient of atomic number Z in the
asymptotic expansion of the exchange-correlation(xc) energy for atoms. LSIC and
LSIC+ are scaled by functions of the iso-orbital indicator z{\sigma}, which
distinguishes one-electron regions from many-electron regions. LSIC+ applied to
LSDA works better for many equilibrium properties than LSDA-LSIC and the
Perdew, Burke, and Ernzerhof(PBE) generalized gradient approximation(GGA), and
almost as well as the strongly constrained and appropriately normed(SCAN)
meta-GGA. LSDA-LSIC and LSDA-LSIC+, however, both fail to predict interaction
energies involving weaker bonds, in sharp contrast to their earlier successes.
It is found that more than one set of localized SIC orbitals can yield a nearly
degenerate energetic description of the same multiple covalent bond, suggesting
that a consistent chemical interpretation of the localized orbitals requires a
new way to choose their Fermi orbital descriptors. To make a locally
scaled-down SIC to functionals beyond LSDA requires a gauge transformation of
the functional's energy density. The resulting SCAN-sdSIC, evaluated on
SCAN-SIC total and localized orbital densities, leads to an acceptable
description of many equilibrium properties including the dissociation energies
of weak bonds.


Abstract:
  A sizable $\cos 4\phi$ azimuthal asymmetry in exclusive di-pion production
near $\rho^0$ resonance peak in ultraperipheral heavy ion collisions recently
has been reported by STAR collaboration. We show that both elliptic gluon
Wigner distribution and final state soft photon radiation can give rise to this
azimuthal asymmetry. The fact that the QED effect alone severely underestimates
the observed asymmetry might signal the existence of the nontrivial correlation
in quantum phase distribution of gluons.


Abstract:
  We study random compact subsets of R^3 which can be described as "random
Menger sponges". We use those random sets to construct a pair of compact sets A
and B in R^3 which are of the same positive measure, such that A can be covered
by finitely many translates of B, B can be covered by finitely many translates
of A, and yet A and B are not equidecomposable. Furthermore, we construct the
first example of a compact subset of R^3 of positive measure which is not a
domain of expansion. This answers a question of Adrian Ioana.


Abstract:
  Workflow decision making is critical to performing many practical workflow
applications. Scheduling in edge-cloud environments can address the high
complexity of workflow applications, while decreasing the data transmission
delay between the cloud and end devices. However, due to the heterogeneous
resources in edge-cloud environments and the complicated data dependencies
between the tasks in a workflow, significant challenges for workflow scheduling
remain, including the selection of an optimal tasks-servers solution from the
possible numerous combinations. Existing studies are mainly done subject to
rigorous conditions without fluctuations, ignoring the fact that workflow
scheduling is typically present in uncertain environments. In this study, we
focus on reducing the execution cost of workflow applications mainly caused by
task computation and data transmission, while satisfying the workflow deadline
in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are
adopted to represent the task processing time and data transferring time. A
cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle
Swarm Optimization (ADPSO) algorithm is proposed, which employs the operators
of Genetic Algorithm (GA). This strategy introduces the randomly two-point
crossover operator, neighborhood mutation operator, and adaptive multipoint
mutation operator of GA to effectively avoid converging on local optima. The
experimental results show that our strategy can effectively reduce the workflow
execution cost in uncertain edge-cloud environments, compared with other
benchmark solutions.


Abstract:
  While classical spin systems in random networks have been intensively
studied, much less is known about quantum magnets in random graphs. Here, we
investigate interacting quantum spins on small-world networks, building on
mean-field theory and extensive quantum Monte Carlo simulations. Starting from
one-dimensional (1D) rings, we consider two situations: all-to-all interacting
and long-range interactions randomly added. The effective infinite dimension of
the lattice leads to a magnetic ordering at finite temperature $T_\mathrm{c}$
with mean-field criticality. Nevertheless, in contrast to the classical case,
we find two distinct power-law behaviors for $T_\mathrm{c}$ versus the average
strength of the extra couplings. This is controlled by a competition between a
characteristic length scale of the random graph and the thermal correlation
length of the underlying 1D system, thus challenging mean-field theories. We
also investigate the fate of a gapped 1D spin chain against the small-world
effect.


Abstract:
  Bitcoin is built on a blockchain, an immutable decentralised ledger that
allows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins
are associated with alpha-numeric addresses and are transferred via
transactions. Each transaction is composed of a set of input addresses
(associated with unspent outputs received from previous transactions) and a set
of output addresses (to which Bitcoins are transferred). Despite Bitcoin was
designed with anonymity in mind, different heuristic approaches exist to detect
which addresses in a specific transaction belong to the same entity. By
applying these heuristics, we build an Address Correspondence Network: in this
representation, addresses are nodes are connected with edges if at least one
heuristic detects them as belonging to the same entity. %addresses are nodes
and edges are drawn between addresses detected as belonging to the same entity
by at least one heuristic. %nodes represent addresses and edges model the
likelihood that two nodes belong to the same entity %In this network, connected
components represent sets of addresses controlled by the same entity. In this
paper, we analyse for the first time the Address Correspondence Network and
show it is characterised by a complex topology, signalled by a broad, skewed
degree distribution and a power-law component size distribution. Using a
large-scale dataset of addresses for which the controlling entities are known,
we show that a combination of external data coupled with standard community
detection algorithms can reliably identify entities. The complex nature of the
Address Correspondence Network reveals that usage patterns of individual
entities create statistical regularities; and that these regularities can be
leveraged to more accurately identify entities and gain a deeper understanding
of the Bitcoin economy as a whole.


Abstract:
  Federated learning plays an important role in the process of smart cities.
With the development of big data and artificial intelligence, there is a
problem of data privacy protection in this process. Federated learning is
capable of solving this problem. This paper starts with the current
developments of federated learning and its applications in various fields. We
conduct a comprehensive investigation. This paper summarize the latest research
on the application of federated learning in various fields of smart cities.
In-depth understanding of the current development of federated learning from
the Internet of Things, transportation, communications, finance, medical and
other fields. Before that, we introduce the background, definition and key
technologies of federated learning. Further more, we review the key
technologies and the latest results. Finally, we discuss the future
applications and research directions of federated learning in smart cities.


Abstract:
  We report a combined experimental and theoretical study of the PdSe2-xTex
system. With increasing Te fraction, structural evolutions, first from an
orthorhombic phase (space group Pbca) to a monoclinic phase (space group C2/c)
and then to a trigonal phase (space group P-3m1), are observed accompanied with
clearly distinct electrical transport behavior. The monoclinic phase (C2/c) is
a completely new polymorphic phase and is discovered within a narrow range of
Te composition (0.3 \leq x \leq 0.8). This phase has a different packing
sequence from all known transition metal dichalcogenides to date. Electronic
calculations and detailed transport analysis of the new polymorphic
PdSe1.3Te0.7 phase are presented. In the trigonal phase region,
superconductivity with enhanced critical temperature is also observed within a
narrow range of Te content (1.0 \leq x \leq 1.2). The rich phase diagram, new
polymorphic structure as well as abnormally enhanced superconductivity could
further stimulate more interest to explore new types of polymorphs and
investigate their transport and electronic properties in the transition metal
dichalcogenides family that are of significant interest.


Abstract:
  Although the frequency-division duplex (FDD) massive multiple-input
multiple-output (MIMO) system can offer high spectral and energy efficiency, it
requires to feedback the downlink channel state information (CSI) from users to
the base station (BS), in order to fulfill the precoding design at the BS.
However, the large dimension of CSI matrices in the massive MIMO system makes
the CSI feedback very challenging, and it is urgent to compress the feedback
CSI. To this end, this paper proposes a novel dilated convolution based CSI
feedback network, namely DCRNet. Specifically, the dilated convolutions are
used to enhance the receptive field (RF) of the proposed DCRNet without
increasing the convolution size. Moreover, advanced encoder and decoder blocks
are designed to improve the reconstruction performance and reduce computational
complexity as well. Numerical results are presented to show the superiority of
the proposed DCRNet over the conventional networks. In particular, the proposed
DCRNet can achieve almost the state-of-the-arts (SOTA) performance with much
lower floating point operations (FLOPs). The open source code and checkpoint of
this work are available at https://github.com/recusant7/DCRNet.


Abstract:
  This paper considers the approximation of a monomial $x^n$ over the interval
$[-1,1]$ by a lower-degree polynomial. This polynomial approximation can be
easily computed analytically and is obtained by truncating the analytical
Chebyshev series expansion of $x^n$. The error in the polynomial approximation
in the supremum norm has an exact expression with an interesting probabilistic
interpretation. We use this interpretation along with concentration
inequalities to develop a useful upper bound for the error.


Abstract:
  In this letter, we present experimental data demonstrating spin wave
interference detection using spin Hall effect (ISHE). Two coherent spin waves
are excited in a yttrium-iron garnet (YIG) waveguide by continuous microwave
signals. The initial phase difference between the spin waves is controlled by
the external phase shifter. The ISHE voltage is detected at a distance of 2 mm
and 4 mm away from the spin wave generating antennae by an attached Pt layer.
Experimental data show ISHE voltage oscillation as a function of the phase
difference between the two interfering spin waves. This experiment demonstrates
an intriguing possibility of using ISHE in spin wave logic circuit converting
spin wave phase into an electric signal


Abstract:
  Initial powder mixtures of Cu, Fe and Co are exposed to severe plastic
deformation by high-pressure torsion to prepare solid solutions. A broad range
of compositions is investigated, whereas this study aims at the synthesis of
soft magnetic materials and therefore at the formation of a homogeneous and
nanocrystalline microstructure. For intermediate ferromagnetic contents,
high-pressure torsion at room temperature yields single-phase supersaturated
solid solutions. For higher ferromagnetic contents, two consecutive steps of
high-pressure torsion deformation at different temperatures yield the desired
nanocrystalline microstructure. Depending on the Co-to-Fe-ratio, either a
single-phase supersaturated solid solution or a nanocomposite forms. The
composite exhibits an enhanced magnetic moment, indicating the formation of a
(Fe,Co)-alloy upon severe plastic deformation. Soft magnetic properties are
verified for large Co-to-Fe-ratios and this microstructure is found to remain
stable up to 400 {\deg}C.


Abstract:
  Let n be a positive integer and t a non-zero integer. We consider the
elliptic curve over Q given by E : y 2 = x 3 + tx 2 -- n 2 (t + 3n 2)x + n 6.
It is a special case of an elliptic surface studied recently by Bettin, David
and Delaunay [2] and it generalizes Washington's family. The point (0, n 3)
belongs to E(Q) and we obtain some results about its nondivisibility in E(Q).
Our work extends to this two-parameter family of elliptic curves a previous
study of Duquesne (mainly stated for n = 1 and t > 0).


Abstract:
  The dataset presented provides high-resolution images of real, filled out
bank checks containing various complex backgrounds, and handwritten text and
signatures in the respective fields, along with both pixel-level and
patch-level segmentation masks for the signatures on the checks. The images of
bank checks were obtained from different sources, including other publicly
available check datasets, publicly available images on the internet, as well as
scans and images of real checks. Using the GIMP graphics software, pixel-level
segmentation masks for signatures on these checks were manually generated as
binary images. An automated script was then used to generate patch-level masks.
The dataset was created to train and test networks for extracting signatures
from bank checks and other similar documents with very complex backgrounds.


Abstract:
  Automated driving is now possible in diverse road and traffic conditions.
However, there are still situations that automated vehicles cannot handle
safely and efficiently. In this case, a Transition of Control (ToC) is
necessary so that the driver takes control of the driving. Executing a ToC
requires the driver to get full situation awareness of the driving environment.
If the driver fails to get back the control in a limited time, a Minimum Risk
Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g.,
decelerating to full stop). The execution of ToCs requires some time and can
cause traffic disruption and safety risks that increase if several vehicles
execute ToCs/MRMs at similar times and in the same area. This study proposes to
use novel C-ITS traffic management measures where the infrastructure exploits
V2X communications to assist Connected and Automated Vehicles (CAVs) in the
execution of ToCs. The infrastructure can suggest a spatial distribution of
ToCs, and inform vehicles of the locations where they could execute a safe stop
in case of MRM. This paper reports the first field operational tests that
validate the feasibility and quantify the benefits of the proposed
infrastructure-assisted ToC and MRM management. The paper also presents the CAV
and roadside infrastructure prototypes implemented and used in the trials. The
conducted field trials demonstrate that infrastructure-assisted traffic
management solutions can reduce safety risks and traffic disruptions.


Abstract:
  Commonly, machine learning models minimize an empirical expectation. As a
result, the trained models typically perform well for the majority of the data
but the performance may deteriorate in less dense regions of the dataset. This
issue also arises in generative modeling. A generative model may overlook
underrepresented modes that are less frequent in the empirical data
distribution. This problem is known as complete mode coverage. We propose a
sampling procedure based on ridge leverage scores which significantly improves
mode coverage when compared to standard methods and can easily be combined with
any GAN. Ridge leverage scores are computed by using an explicit feature map,
associated with the next-to-last layer of a GAN discriminator or of a
pre-trained network, or by using an implicit feature map corresponding to a
Gaussian kernel. Multiple evaluations against recent approaches of complete
mode coverage show a clear improvement when using the proposed sampling
strategy.


Abstract:
  Recently, deep generative models for molecular graphs are gaining more and
more attention in the field of de novo drug design. A variety of models have
been developed to generate topological structures of drug-like molecules, but
explorations in generating three-dimensional structures are still limited.
Existing methods have either focused on low molecular weight compounds without
considering drug-likeness or generate 3D structures indirectly using atom
density maps. In this work, we introduce Ligand Neural Network (L-Net), a novel
graph generative model for designing drug-like molecules with high-quality 3D
structures. L-Net directly outputs the topological and 3D structure of
molecules (including hydrogen atoms), without the need for additional atom
placement or bond order inference algorithm. The architecture of L-Net is
specifically optimized for drug-like molecules, and a set of metrics is
assembled to comprehensively evaluate its performance. The results show that
L-Net is capable of generating chemically correct, conformationally valid, and
highly druglike molecules. Finally, to demonstrate its potential in
structure-based molecular design, we combine L-Net with MCTS and test its
ability to generate potential inhibitors targeting ABL1 kinase.


Abstract:
  Information management has enter a completely new era, quantum era. However,
there exists a lack of sufficient theory to extract truly useful quantum
information and transfer it to a form which is intuitive and straightforward
for decision making. Therefore, based on the quantum model of mass function, a
fortified dual check system is proposed to ensure the judgment generated
retains enough high accuracy. Moreover, considering the situations in real
life, everything takes place in an observable time interval, then the concept
of time interval is introduced into the frame of the check system. The proposed
model is very helpful in disposing uncertain quantum information in this paper.
And some applications are provided to verify the rationality and correctness of
the proposed method.


Abstract:
  In this paper we prove upper and lower bounds on the minimal spherical
dispersion. In particular, we see that the inverse $N(\varepsilon,d)$ of the
minimal spherical dispersion is, for fixed $\varepsilon>0$, up to logarithmic
terms linear in the dimension $d$. We also derive upper and lower bounds on the
expected dispersion for points chosen independently and uniformly at random
from the Euclidean unit sphere.


Abstract:
  We prove global well-posedness for a coupled Dirac--Klein-Gordon (DKG) system
in $1+2$ dimensions under the assumption of small, compact, high-regularity
data. We reveal hidden structure within the Klein-Gordon part of the system,
which allows us to treat the nonlinearities which are below-critical in two
spatial dimensions. Furthermore, we provide the first asymptotic decay results
for the DKG system in $1+2$ dimensions.


Abstract:
  The autonomous automotive industry is one of the largest and most
conventional projects worldwide, with many technology companies effectively
designing and orienting their products towards automobile safety and accuracy.
These products are performing very well over the roads in developed countries.
But can fail in the first minute in an underdeveloped country because there is
much difference between a developed country environment and an underdeveloped
country environment. The following study proposed to train these Artificial
intelligence models in environment space in an underdeveloped country like
Pakistan. The proposed approach on image classification uses convolutional
neural networks for image classification for the model. For model pre-training
German traffic signs data set was selected then fine-tuned on Pakistan's
dataset. The experimental setup showed the best results and accuracy from the
previously conducted experiments. In this work to increase the accuracy, more
dataset was collected to increase the size of images in every class in the data
set. In the future, a low number of classes are required to be further
increased where more images for traffic signs are required to be collected to
get more accuracy on the training of the model over traffic signs of Pakistan's
most used and popular roads motorway and national highway, whose traffic signs
color, size, and shapes are different from common traffic signs.


Abstract:
  Internet of Things Driven Data Analytics (IoT-DA) has the potential to excel
data-driven operationalisation of smart environments. However, limited research
exists on how IoT-DA applications are designed, implemented, operationalised,
and evolved in the context of software and system engineering life-cycle. This
article empirically derives a framework that could be used to systematically
investigate the role of software engineering (SE) processes and their
underlying practices to engineer IoT-DA applications. First, using existing
frameworks and taxonomies, we develop an evaluation framework to evaluate
software processes, methods, and other artefacts of SE for IoT-DA. Secondly, we
perform a systematic mapping study to qualitatively select 16 processes (from
academic research and industrial solutions) of SE for IoT-DA. Thirdly, we apply
our developed evaluation framework based on 17 distinct criterion (a.k.a.
process activities) for fine-grained investigation of each of the 16 SE
processes. Fourthly, we apply our proposed framework on a case study to
demonstrate development of an IoT-DA healthcare application. Finally, we
highlight key challenges, recommended practices, and the lessons learnt based
on framework's support for process-centric software engineering of IoT-DA. The
results of this research can facilitate researchers and practitioners to
engineer emerging and next-generation of IoT-DA software applications.


Abstract:
  The IoT consists of a lot of devices such as embedded systems, wireless
sensor nodes (WSNs), control systems, etc. It is essential for some of these
devices to protect information that they process and transmit. The issue is
that an adversary may steal these devices to gain a physical access to the
device. There is a variety of ways that allows to reveal cryptographic keys.
One of them are optical Fault Injection attacks. We performed successful
optical Fault Injections into different type of gates, in particular INV, NAND,
NOR, FF. In our work we concentrate on the selection of the parameters
configured by an attacker and their influence on the success of the Fault
Injections.


Abstract:
  Liquid-liquid phase separation (LLPS) is currently of great interest in cell
biology. LLPS is an example of what is called an emergent phenomenon -- an idea
that comes from condensed-matter physics. Emergent phenomena have the
characteristic feature of having a switch-like response. I show that the Hill
equation of biochemistry can be used as a simple model of strongly cooperative,
switch-like, behaviour. One result is that a switch-like response requires
relatively few molecules, even ten gives a strongly switch-like response. Thus
if a biological function enabled by LLPS relies on LLPS to provide a
switch-like response to a stimulus, then condensates large enough to be visible
in optical microscopy are not needed.


Abstract:
  Ensemble methods are generally regarded to be better than a single model if
the base learners are deemed to be "accurate" and "diverse." Here we
investigate a semi-supervised ensemble learning strategy to produce
generalizable blind image quality assessment models. We train a multi-head
convolutional network for quality prediction by maximizing the accuracy of the
ensemble (as well as the base learners) on labeled data, and the disagreement
(i.e., diversity) among them on unlabeled data, both implemented by the
fidelity loss. We conduct extensive experiments to demonstrate the advantages
of employing unlabeled data for BIQA, especially in model generalization and
failure identification.