Abstract:
  Relativistic p+A collisions may produce droplets of quark gluon plasma (QGP)
that quickly develop a toroidal vortex structure similar to that of an
expanding smoke ring. We present viscous relativistic hydrodynamic calculations
of ultra-central p+A collisions and develop an experimental observable to probe
the structure, correlating the polarization and momentum of hyperons emitted
from the collision. The effect is robust against changes in the definition of
vorticity used to calculate the polarization. Experiments at RHIC and LHC may
test the existence and strength of the vortex toroids, bringing new evidence to
bear on the question of collectivity in the smallest QGP droplets.


Abstract:
  We show that unconventional boundary phenomena occur in the $3Q$ charge bond
orders on the hexagonal lattices. At the Van Hove singularity with three
nesting momenta, $3Q$ orders can trigger a $\text{C}_6$-symmetric insulator
under bond modulations. On the kagome lattice, in-gap corner states appear in
the energy spectrum and carry fractional corner charge $-2e/3$. Such corner
phenomena originate from the corner filling anomaly and indicate a higher-order
topological insulator. The in-gap corner states are also observed on the
triangular lattice. The honeycomb lattice does not support fractional corner
charges, while in-gap edge states are observed. We discuss possible indications
to the experimentally uncovered charge bond orders in the kagome metals
$\text{AV}_3\text{Sb}_5$ with $\text{A}=\text{K},\text{Rb},\text{Cs}$. With
layer stacking along the out-of-plane direction, the corner states can
constitute the hinge states with fractional charge densities.


Abstract:
  We investigate the quantum thermodynamic cycle of a quantum heat engine
carrying out an Otto thermodynamic cycle. We use the thermal properties of a
moving heat bath with relativistic velocity with respect to the cold bath. As a
working medium, we use a two-level system and a harmonic oscillator that
interact with a moving heat bath and a static cold bath. In the current work,
the quantum heat engine is studied in the high and low temperatures regime.
Using quantum thermodynamics and the theory of open quantum system we obtain
the total produced work, the efficiency and the efficiency at maximum power.
The maximum efficiency of the Otto quantum heat engine depends only on the
ratio of the minimum and maximum energy gaps. On the contrary, the efficiency
at maximum power and the extracted work decreases with the velocity since the
motion of the heat bath has an energy cost for the quantum heat engine.
Furthermore, the efficiency at maximum power depends on the nature of the
working medium.


Abstract:
  In this paper, we introduce a Lorentzian Anosov family (LAfamily) up to a
sequence of distributions of null vectors. We prove for each p Mi, where Mi is
a Lorentzian manifold for i Z the tangent space Mi at p has a unique splitting
and this splitting varies continuously on a sequence via the distance function
created by a unique torsion-free semi-Riemannian connection. We present three
examples of LA-families. Also, we define Lorentzian shadowing property of type
I and II and prove some results related to this property.


Abstract:
  We present a quasi-static elasticity model that accounts for damage evolution
based on the ideas of Kachanov 1958 and and Rabotnov 1968. We analyze the
resulting strongly nonlinear system of differential equations in view of
well-posedness. The specific feature is that the displacements are connected to
the damage evolution equation via a Nemytskii- or superposition- operator. The
novelty in this work is that we present an inverse problem in a parameter
identification setting, in which we are able to identify the shape of this
Nemytskii-operator. From the material modelling point of view, the relation of
material damage and displacements is modelled by the shape of this operator. We
establish the Fr\'echet-derivative of the forward operator as well as the
adjoint of the derivative and characterize both via systems of linear
differential equations. We prove ill-posedness of the inverse problem and
provide a sufficient condition for the classical nonlinear Landweber method to
converge.


Abstract:
  This paper presents a physical interface for collaborative mobile
manipulators in industrial manufacturing and logistics applications. The
proposed work builds on our earlier MOCA-MAN interface, through which an
operator could be physically coupled to a mobile manipulator to be assisted in
performing daily activities. The previous interface was based on a magnetic
clamp attached to one arm of the user for the coupling stage, and a bracelet
based on EMG sensors on the other arm for human-robot communication via
gestures. The new interface instead presents the following additions: i) An
industrial-like design that allows the worker to couple/decouple easily and to
operate mobile manipulators locally; ii) A simplistic communication channel via
a simple buttons board that allows controlling the robot with one hand only;
iii) The interface offers enhanced loco-manipulation capabilities that do not
compromise the worker mobility. Besides, an experimental evaluation with six
human subjects is carried out to analyze the enhanced locomotion and
flexibility of the proposed interface in terms of mobility constraint,
usability, and physical load reduction.


Abstract:
  Intelligent systems are increasingly part of our everyday lives and have been
integrated seamlessly to the point where it is difficult to imagine a world
without them. Physical manifestations of those systems on the other hand, in
the form of embodied agents or robots, have so far been used only for specific
applications and are often limited to functional roles (e.g. in the industry,
entertainment and military fields). Given the current growth and innovation in
the research communities concerned with the topics of robot navigation,
human-robot-interaction and human activity recognition, it seems like this
might soon change. Robots are increasingly easy to obtain and use and the
acceptance of them in general is growing. However, the design of a socially
compliant robot that can function as a companion needs to take various areas of
research into account. This paper is concerned with the navigation aspect of a
socially-compliant robot and provides a survey of existing solutions for the
relevant areas of research as well as an outlook on possible future directions.


Abstract:
  We describe the non-minimal Standard Model, consisting of minimalistic
extensions of the Standard Model, which for all we know is the theory of the
universe, able to describe all of the universe from the beginning of time.
Extensions discussed are an extra neutrino and a new Higgs model. We introduce
the principle of global relativity and discuss how the theory can be largely
derived from this principle. One is led to the unification of forces into SU(5)
and a form of dark matter. We discuss the limitations of the theory, showing
that it is not the theory of everything. However we argue that it is the only
part, that is within conceivable reach of physical experiment or astronomical
observations. It is argued, that at the Planck scale the universe is
effectively three-dimensional.


Abstract:
  NLP has a significant role in advancing healthcare and has been found to be
key in extracting structured information from radiology reports. Understanding
recent developments in NLP application to radiology is of significance but
recent reviews on this are limited. This study systematically assesses recent
literature in NLP applied to radiology reports. Our automated literature search
yields 4,799 results using automated filtering, metadata enriching steps and
citation search combined with manual review. Our analysis is based on 21
variables including radiology characteristics, NLP methodology, performance,
study, and clinical application characteristics. We present a comprehensive
analysis of the 164 publications retrieved with each categorised into one of 6
clinical application categories. Deep learning use increases but conventional
machine learning approaches are still prevalent. Deep learning remains
challenged when data is scarce and there is little evidence of adoption into
clinical practice. Despite 17% of studies reporting greater than 0.85 F1
scores, it is hard to comparatively evaluate these approaches given that most
of them use different datasets. Only 14 studies made their data and 15 their
code available with 10 externally validating results. Automated understanding
of clinical narratives of the radiology reports has the potential to enhance
the healthcare process but reproducibility and explainability of models are
important if the domain is to move applications into clinical use. More could
be done to share code enabling validation of methods on different institutional
data and to reduce heterogeneity in reporting of study properties allowing
inter-study comparisons. Our results have significance for researchers
providing a systematic synthesis of existing work to build on, identify gaps,
opportunities for collaboration and avoid duplication.


Abstract:
  Two boron-rich chalcogenides B12S and B12Se isostructural to
{\alpha}-rhombohedral boron were synthesized by chemical reaction of the
elements at high-pressure - high-temperature conditions. The crystal structures
and stoichiometries of both compounds were confirmed by Rietveld refinement and
elemental analysis. The experimental Raman spectra of B12S and B12Se were
investigated for the first time. All observed Raman bands have been attributed
to the theoretically calculated phonon modes, and the mode assignment has been
performed.


Abstract:
  We show that causality is violated in the early stages of state-of-the-art
heavy-ion hydrodynamic simulations. Up to $30\%$ of the initial fluid cells
violate nonlinear causality constraints while the rest are uncertain. Only
after 2-3 fm$/c$ of evolution do we find that $\sim 50\%$ of the fluid cells
are definitely causal. Inclusion of pre-equilibrium evolution significantly
reduces the number of acausal cells, but it does not eliminate them. Our
findings suggest that relativistic causality places stringent constraints on
the available model parameter space of heavy-ion collision simulations.


Abstract:
  Deep learning algorithms have made many breakthroughs and have various
applications in real life. Computational resources become a bottleneck as the
data and complexity of the deep learning pipeline increases. In this paper, we
propose optimized deep learning pipelines in multiple aspects of training
including time and memory. OpTorch is a machine learning library designed to
overcome weaknesses in existing implementations of neural network training.
OpTorch provides features to train complex neural networks with limited
computational resources. OpTorch achieved the same accuracy as existing
libraries on Cifar-10 and Cifar-100 datasets while reducing memory usage to
approximately 50%. We also explore the effect of weights on total memory usage
in deep learning pipelines. In our experiments, parallel encoding-decoding
along with sequential checkpoints results in much improved memory and time
usage while keeping the accuracy similar to existing pipelines. OpTorch python
package is available at available at https://github.com/cbrl-nuces/optorch


Abstract:
  We propose a Hamiltonian approach to reproduce the relevant elements of the
centuries-old Subak irrigation system in Bali, showing a cluster-size power-law
distribution. The resulting system presents two equilibria. The first balance
is between energy and entropy contributions. The second lies in the energy
contribution: a local Potts-type interaction and a long-range
anti-ferromagnetic one without attenuation. Finite-size scaling analysis shows
that as a result of the second balance, the transition balancing energy and
entropy contributions for low values of the antiferromagnetic contribution is
absorbed by the transition for high values of that contribution as the system
size increases. The system thus presents only a weak-first order phase
transition at the thermodynamic limit. This extends the Hamiltonian framework
to a new domain of coupled human-environmental interactions.


Abstract:
  The remarkable advancements in Deep Learning (DL) algorithms have fueled
enthusiasm for using Artificial Intelligence (AI) technologies in almost every
domain; however, the opaqueness of these algorithms put a question mark on
their applications in safety-critical systems. In this regard, the
`explainability' dimension is not only essential to both explain the inner
workings of black-box algorithms, but it also adds accountability and
transparency dimensions that are of prime importance for regulators, consumers,
and service providers. eXplainable Artificial Intelligence (XAI) is the set of
techniques and methods to convert the so-called black-box AI algorithms to
white-box algorithms, where the results achieved by these algorithms and the
variables, parameters, and steps taken by the algorithm to reach the obtained
results, are transparent and explainable. To complement the existing literature
on XAI, in this paper, we take an `engineering' approach to illustrate the
concepts of XAI. We discuss the stakeholders in XAI and describe the
mathematical contours of XAI from engineering perspective. Then we take the
autonomous car as a use-case and discuss the applications of XAI for its
different components such as object detection, perception, control, action
decision, and so on. This work is an exploratory study to identify new avenues
of research in the field of XAI.


Abstract:
  We propose a Dirac neutrino portal dark matter scenario by minimally
extending the particle content of the Standard Model (SM) with three right
handed neutrinos ($\nu_R$), a Dirac fermion dark matter candidate ($\psi$) and
a complex scalar ($\phi$), all of which are singlets under the SM gauge group.
An additional $\mathbb{Z}_4$ symmetry has been introduced for the stability of
dark matter candidate $\psi$ and also ensuring the Dirac nature of light
neutrinos at the same time. Both the right handed neutrinos and the dark matter
thermalise with the SM plasma due to a new Yukawa interaction involving
$\nu_R$, $\psi$ and $\phi$ while the latter maintains thermal contact via the
Higgs portal interaction. The decoupling of $\nu_R$ occurs when $\phi$ loses
its kinetic equilibrium with the SM plasma and thereafter all three
$\mathbb{Z}_4$ charged particles form an equilibrium among themselves with a
temperature $T_{\nu_R}$. The dark matter candidate $\psi$ finally freezes out
within the dark sector and preserves its relic abundance. We have found that in
the present scenario, some portion of low mass dark matter
($M_{\psi}\lesssim10$ GeV) is already excluded by the Planck 2018 data for
keeping $\nu_R$s in the thermal bath below a temperature of 600 MeV and thereby
producing an excess contribution to $N_{\rm eff}$. The next generation
experiments like CMB-S4, SPT-3G etc. will have the required sensitivities to
probe the entire model parameter space of this minimal scenario, especially the
low mass range of $\psi$ where direct detection experiments are still not
capable enough for detection.


Abstract:
  We study Hibridizable Discontinuous Galerkin (HDG) discretizations for a
class of non-linear interior elliptic boundary value problems posed in curved
domains where both the source term and the diffusion coefficient are
non-linear. We consider the cases where the non-linear diffusion coefficient
depends on the solution and on the gradient of the solution. To sidestep the
need for curved elements, the discrete solution is computed on a polygonal
subdomain that is not assumed to interpolate the true boundary, giving rise to
an unfitted computational mesh. We show that, under mild assumptions on the
source term and the computational domain, the discrete systems are well posed.
Furthermore, we provide a priori error estimates showing that the discrete
solution will have optimal order of convergence as long as the distance between
the curved boundary and the computational boundary remains of the same order of
magnitude as the mesh parameter.


Abstract:
  Let $\mu$ be an $n$-dimensional finite positive measure on $\mathbb{R}^m$. We
obtain a $T1$ condition sufficient for the boundedness of Calder\'{o}n-Zygmund
operators on $\textrm{RBMO}(\mu)$, the regular BMO space of Tolsa.


Abstract:
  Transverse (Hall-effect) and Goos--H\"{a}nchen shifts of light beams
reflected/refracted at planar interfaces are important wave phenomena, which
can be significantly modified and enhanced by the presence of intrinsic orbital
angular momentum (OAM) in the beam. Recently, optical spatiotemporal vortex
pulses (STVPs) carrying a purely transverse intrinsic OAM were predicted
theoretically and generated experimentally. Here we consider the reflection and
refraction of such pulses at a planar isotropic interface. We find
theoretically and confirm numerically novel types of the OAM-dependent
transverse and longitudinal pulse shifts. Remarkably, the longitudinal shifts
can be regarded as time delays, which appear, in contrast to the well-known
Wigner time delay, without temporal dispersion of the reflection/refraction
coefficients. Such time delays allow one to realize OAM-controlled slow
(subluminal) and fast (superluminal) pulse propagation without medium
dispersion. These results can have important implications in various problems
involving scattering of localized vortex states carrying transverse OAM.


Abstract:
  Segmentation of enhancing tumours or lesions from MRI is important for
detecting new disease activity in many clinical contexts. However, accurate
segmentation requires the inclusion of medical images (e.g., T1 post contrast
MRI) acquired after injecting patients with a contrast agent (e.g.,
Gadolinium), a process no longer thought to be safe. Although a number of
modality-agnostic segmentation networks have been developed over the past few
years, they have been met with limited success in the context of enhancing
pathology segmentation. In this work, we present HAD-Net, a novel offline
adversarial knowledge distillation (KD) technique, whereby a pre-trained
teacher segmentation network, with access to all MRI sequences, teaches a
student network, via hierarchical adversarial training, to better overcome the
large domain shift presented when crucial images are absent during inference.
In particular, we apply HAD-Net to the challenging task of enhancing tumour
segmentation when access to post-contrast imaging is not available. The
proposed network is trained and tested on the BraTS 2019 brain tumour
segmentation challenge dataset, where it achieves performance improvements in
the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods
(U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained
student network and (d) a non-hierarchical version of the network (AD-Net), in
terms of Dice scores for enhancing tumour (ET). The network also shows
improvements in tumour core (TC) Dice scores. Finally, the network outperforms
both the baseline student network and AD-Net in terms of uncertainty
quantification for enhancing tumour segmentation based on the BraTs 2019
uncertainty challenge metrics. Our code is publicly available at:
https://github.com/SaverioVad/HAD_Net


Abstract:
  With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we
propose the general molecule optimization framework, Molecular Neural Assay
Search (MONAS), consisting of three components: a property predictor which
identifies molecules with specific desirable properties, an energy model which
approximates the statistical similarity of a given molecule to known training
molecules, and a molecule search method. In this work, these components are
instantiated with graph neural networks (GNNs), Deep Energy Estimator Networks
(DEEN) and Monte Carlo tree search (MCTS), respectively. This implementation is
used to identify 120K molecules (out of 40-million explored) which the GNN
determined to be likely SARS-CoV-1 inhibitors, and, at the same time, are
statistically close to the dataset used to train the GNN.


Abstract:
  We revisit some ideas of K.-M.~Perfekt who has provided an elegant framework
to detect the biduality between function or sequence spaces defined in terms of
some $o$- resp.\ $O$-condition. We present new proofs under somewhat weaker
assumptions than before and apply the result to Lipschitz spaces.


Abstract:
  Automatic multiple-choice question generation (MCQG) is a useful yet
challenging task in Natural Language Processing (NLP). It is the task of
automatic generation of correct and relevant questions from textual data.
Despite its usefulness, manually creating sizeable, meaningful and relevant
questions is a time-consuming and challenging task for teachers. In this paper,
we present an NLP-based system for automatic MCQG for Computer-Based Testing
Examination (CBTE).We used NLP technique to extract keywords that are important
words in a given lesson material. To validate that the system is not perverse,
five lesson materials were used to check the effectiveness and efficiency of
the system. The manually extracted keywords by the teacher were compared to the
auto-generated keywords and the result shows that the system was capable of
extracting keywords from lesson materials in setting examinable questions. This
outcome is presented in a user-friendly interface for easy accessibility.


Abstract:
  We discuss a particular non-relativistic limit of NS-NS gravity that can be
taken at the level of the action and equations of motion, without imposing any
geometric constraints by hand. This relies on the fact that terms that diverge
in the limit and that come from the Vielbein in the Einstein-Hilbert term and
from the kinetic term of the Kalb-Ramond two-form field cancel against each
other. This cancelling of divergences is the target space analogue of a similar
cancellation that takes place at the level of the string sigma model between
the Vielbein in the kinetic term and the Kalb-Ramond field in the Wess-Zumino
term. The limit of the equations of motion leads to one equation more than the
limit of the action, due to the emergence of a local target space scale
invariance in the limit. Some of the equations of motion can be solved by scale
invariant geometric constraints. These constraints define a so-called
Dilatation invariant String Newton-Cartan geometry.


Abstract:
  We show existence of a natural rational structure on periodic cyclic
homology, conjectured by L. Katzarkov, M. Kontsevich, T. Pantev, for several
classes of dg-categories, including proper connective $\mathbb{C}$-dg-algebras
and dg-categories of local systems. The main ingredient is derived nilpotent
invariance of A. Blanc's semi-topological K-theory, which we establish along
the way.


Abstract:
  We obtain the complete and independent bases of effective operators at mass
dimension 5, 6, 7, 8, 9 in both standard model effective field theory with
light sterile right-handed neutrinos ($\nu$SMEFT) and low energy effective
field theory with light sterile neutrinos ($\nu$LEFT). These theories provide
systematical parametrizations on all possible Lorentz-invariant physical
effects involving in the Majorana/Dirac neutrinos, with/without the lepton
number violations. In the $\nu$SMEFT, we find that there are 2 (18), 29 (1614),
80 (4206), 323 (20400), 1358 (243944) independent operators with sterile
neutrinos included at the dimension 5, 6, 7, 8, 9 for one (three) generation of
fermions, while 24, 5223, 3966, 25425, 789426 independent operators in the
$\nu$LEFT.


Abstract:
  Unsupervised health monitoring has gained much attention in the last decade
as the most practical real-time structural health monitoring (SHM) approach.
Among the proposed unsupervised techniques in the literature, there are still
obstacles to robust and real-time health monitoring. These barriers include
loss of information from dimensionality reduction in feature extraction steps,
case-dependency of those steps, lack of a dynamic clustering, and detection
results' sensitivity to user-defined parameters. This study introduces an
unsupervised real-time SHM method with a mixture of low- and high-dimensional
features without a case-dependent extraction scheme. Both features are used to
train multi-ensembles of Generative Adversarial Networks (GAN) and one-class
joint Gaussian distribution models (1-CG). A novelty detection system of
limit-state functions based on GAN and 1-CG models' detection scores is
constructed. The Resistance of those limit-state functions (detection
thresholds) is tuned to user-defined parameters with the GAN-generated data
objects by employing the Monte Carlo histogram sampling through a
reliability-based analysis. The tuning makes the method robust to user-defined
parameters, which is crucial as there is no rule for selecting those parameters
in a real-time SHM. The proposed novelty detection framework is applied to two
standard SHM datasets to illustrate its generalizability: Yellow Frame (twenty
damage classes) and Z24 Bridge (fifteen damage classes). All different damage
categories are identified with low sensitivity to the initial choice of
user-defined parameters with both introduced dynamic and static baseline
approaches with few or no false alarms.


Abstract:
  Existing public person Re-Identification~(ReID) datasets are small in modern
terms because of labeling difficulty. Although unlabeled surveillance video is
abundant and relatively easy to obtain, it is unclear how to leverage these
footage to learn meaningful ReID representations. In particular, most existing
unsupervised and domain adaptation ReID methods utilize only the public
datasets in their experiments, with labels removed. In addition, due to small
data sizes, these methods usually rely on fine tuning by the unlabeled training
data in the testing domain to achieve good performance. Inspired by the recent
progress of large-scale self-supervised image classification using contrastive
learning, we propose to learn ReID representation from large-scale unlabeled
surveillance video alone. Assisted by off-the-shelf pedestrian detection tools,
we apply the contrastive loss at both the image and the tracklet levels.
Together with a principal component analysis step using camera labels freely
available, our evaluation using a large-scale unlabeled dataset shows far
superior performance among unsupervised methods that do not use any training
data in the testing domain. Furthermore, the accuracy improves with the data
size and therefore our method has great potential with even larger and more
diversified datasets.


Abstract:
  We study H\'{e}non maps with biholomorphic escaping sets. We show that if $H$
and $F$ are two H\'{e}non maps of degree $d$ with biholomorphic escaping sets,
then there exist complex numbers $\alpha,\beta$ and $\gamma$ with
$\alpha^{d+1}=\beta$ and $\beta^{d-1}=\gamma^{d-1}=1$ such that with the
following linear automorphisms $$ B(x,y)=(\gamma \alpha \beta^{-1}x,
\alpha^{-1}y) \quad\text{and}\quad L(x,y)=(\gamma^{-1}\beta x,\beta y ) $$one
has $$ F\equiv L \circ B \circ H \circ B. $$


Abstract:
  Deep Neural Networks (DNNs) are capable of solving complex problems in
domains related to embedded systems, such as image and natural language
processing. To efficiently implement DNNs on a specific FPGA platform for a
given cost criterion, e.g. energy efficiency, an enormous amount of design
parameters has to be considered from the topology down to the final hardware
implementation. Interdependencies between the different design layers have to
be taken into account and explored efficiently, making it hardly possible to
find optimized solutions manually. An automatic, holistic design approach can
improve the quality of DNN implementations on FPGA significantly. To this end,
we present a cross-layer design space exploration methodology. It comprises
optimizations starting from a hardware-aware topology search for DNNs down to
the final optimized implementation for a given FPGA platform. The methodology
is implemented in our Holistic Auto machine Learning for FPGAs (HALF)
framework, which combines an evolutionary search algorithm, various
optimization steps and a library of parametrizable hardware DNN modules. HALF
automates both the exploration process and the implementation of optimized
solutions on a target FPGA platform for various applications. We demonstrate
the performance of HALF on a medical use case for arrhythmia detection for
three different design goals, i.e. low-energy, low-power and high-throughput
respectively. Our FPGA implementation outperforms a TensorRT optimized model on
an Nvidia Jetson platform in both throughput and energy consumption.


Abstract:
  COVID-19 has resulted in a public health global crisis. The pandemic control
necessitates epidemic models that capture the trends and impacts on infectious
individuals. Many exciting models can implement this but they lack practical
interpretability. This study combines the epidemiological and network theories
and proposes a framework with causal interpretability in response to this
issue. This framework consists of an extended epidemic model in interconnected
networks and a dynamic structure that has major human mobility. The networked
causal analysis focuses on the stochastic processing mechanism. It highlights
the social infectivity as the intervention estimator between the observable
effect (the number of daily new cases) and unobservable causes (the number of
infectious persons). According to an experiment on the dataset for Tokyo
metropolitan areas, the computational results indicate the propagation features
of the symptomatic and asymptomatic infectious persons. These new
spatiotemporal findings can be beneficial for policy decision making.


Abstract:
  In low-power wide-area networks (LPWANs), various trade-offs among the
bandwidth, data rates, and energy per bit have different effects on the quality
of service under different propagation conditions (e.g. fading and multipath),
interference scenarios, multi-user requirements, and design constraints. Such
compromises, and the manner in which they are implemented, further affect other
technical aspects, such as system's computational complexity and power
efficiency. At the same time, this difference in trade-offs also adds to the
technical flexibility in addressing a broader range of IoT applications. This
paper addresses a physical layer LPWAN approach based on the Aggregate Spread
Pulse Modulation (ASPM) and provides a brief assessment of its properties in
additive white Gaussian noise (AWGN) channel. In the binary ASPM the control of
the quality of service is performed through the change in the spectral
efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary
encoding in ASPM further enables controlling service quality through changing
the energy per bit (in about an order of magnitude range) as an additional
trade-off parameter. Such encoding is especially useful for improving the
ASPM's energy per bit performance, thus increasing its range and overall energy
efficiency, and making it more attractive for use in LPWANs for IoT
applications.


Abstract:
  Sampling from a complex distribution $\pi$ and approximating its intractable
normalizing constant Z are challenging problems. In this paper, a novel family
of importance samplers (IS) and Markov chain Monte Carlo (MCMC) samplers is
derived. Given an invertible map T, these schemes combine (with weights)
elements from the forward and backward Orbits through points sampled from a
proposal distribution $\rho$. The map T does not leave the target $\pi$
invariant, hence the name NEO, standing for Non-Equilibrium Orbits. NEO-IS
provides unbiased estimators of the normalizing constant and self-normalized IS
estimators of expectations under $\pi$ while NEO-MCMC combines multiple NEO-IS
estimates of the normalizing constant and an iterated sampling-importance
resampling mechanism to sample from $\pi$. For T chosen as a discrete-time
integrator of a conformal Hamiltonian system, NEO-IS achieves state-of-the art
performance on difficult benchmarks and NEO-MCMC is able to explore highly
multimodal targets. Additionally, we provide detailed theoretical results for
both methods. In particular, we show that NEO-MCMC is uniformly geometrically
ergodic and establish explicit mixing time estimates under mild conditions.


Abstract:
  As software projects are very diverse, each software development process must
be adjusted to the needs of the project and the corresponding development team.
Frequently, we find different methods and practices combined in a so-called
hybrid development method. Research has shown that these hybrid methods evolve
over time and are devised based on experience. However, when devising a hybrid
method, the methods and practices used should cover the whole software project
with its different phases including, among others, project management,
requirements analysis, quality management, risk management, and implementation.
In this paper, we analyze which methods and practices are used in which phase
of a software project. Based on an initial survey with 27 practitioners, we
provide a mapping of methods and practices to different project phases and vice
versa. Despite the preliminary nature of our study and the small sample size,
we observe three remarkable aspects: (1) there are discrepancies between the
intended use of methods and practices according to literature and the real use
in practice, (2) practices are used more consistently than methods, and (3)
parts of the software lifecycle such as maintenance and evolution are hardly
covered by widely distributed methods and practices. Consequently, when
devising a development process, it is worth a thought whether all phases of the
software lifecycle are addressed or not.


Abstract:
  Contextual Bandits find important use cases in various real-life scenarios
such as online advertising, recommendation systems, healthcare, etc. However,
most of the algorithms use flat feature vectors to represent context whereas,
in the real world, there is a varying number of objects and relations among
them to model in the context. For example, in a music recommendation system,
the user context contains what music they listen to, which artists create this
music, the artist albums, etc. Adding richer relational context representations
also introduces a much larger context space making exploration-exploitation
harder. To improve the efficiency of exploration-exploitation knowledge about
the context can be infused to guide the exploration-exploitation strategy.
Relational context representations allow a natural way for humans to specify
knowledge owing to their descriptive nature. We propose an adaptation of
Knowledge Infused Policy Gradients to the Contextual Bandit setting and a novel
Knowledge Infused Policy Gradients Upper Confidence Bound algorithm and perform
an experimental analysis of a simulated music recommendation dataset and
various real-life datasets where expert knowledge can drastically reduce the
total regret and where it cannot.


Abstract:
  Transforming a causal system from a given initial state to a desired target
state is an important task permeating multiple fields including control theory,
biology, and materials science. In causal models, such transformations can be
achieved by performing a set of interventions. In this paper, we consider the
problem of identifying a shift intervention that matches the desired mean of a
system through active learning. We define the Markov equivalence class that is
identifiable from shift interventions and propose two active learning
strategies that are guaranteed to exactly match a desired mean. We then derive
a worst-case lower bound for the number of interventions required and show that
these strategies are optimal for certain classes of graphs. In particular, we
show that our strategies may require exponentially fewer interventions than the
previously considered approaches, which optimize for structure learning in the
underlying causal graph. In line with our theoretical results, we also
demonstrate experimentally that our proposed active learning strategies require
fewer interventions compared to several baselines.


Abstract:
  We review some important results by Gross, Hacking, Keel, and Kontsevich on
cluster algebra theory, namely, the column sign-coherence of $C$-matrices and
the Laurent positivity, both of which were conjectured by Fomin and Zelevinsky.
We digest and reconstruct the proofs of these conjectures by Gross et al. still
based on their scattering diagram method, however, without relying on toric
geometry. At the same time, we also give a detailed account of the
correspondence between the notions of cluster patterns and scattering diagrams.
Most of the results in this text are found in or translated from the known
results in the literature. However, the approach, the construction of logic and
proofs, and the overall presentation are new. Also, as an application of the
results and the techniques in the text, we show that there is a one-to-one
correspondence between $g$-vectors and cluster variables in cluster patterns
with arbitrary coefficients.


Abstract:
  We show that for any sequence $(f_n)_{n\ge n_0}$ of positive integers
satisfying $\sum_{n=n_0}^\infty f_n/2^n \le 1/4$ and $f_n\le f_{n+1}\le 2f_n$,
there exists an infinite antichain $\mathcal{F}$ of finite subsets of
$\mathbb{N}$ such that $|\mathcal{F}\cap 2^{[n]}| \geq f_n$ for all $n\ge n_0$.
It follows that for any $\varepsilon>0$ there exists an antichain
$\mathcal{F}\subseteq 2^{\mathbb{N}}$ such that $$\liminf_{n \to \infty}
|\mathcal{F}\cap 2^{[n]}| \cdot \left(\frac{2^n}{n\log^{1+\varepsilon}
n}\right)^{-1} > 0.$$ This resolves a problem of Sudakov, Tomon and Wagner in a
strong form, and is essentially tight.


Abstract:
  Low-carbon electricity is a key enabler in combating climate change.
Decarbonising the power sector is now at the centre of global and European
policies. As the IPCC highlights, pathways where the power sector rapidly
decarbonises by 2030 have higher chances of keeping global warming below
1.5$^\circ$C. The electricity sector should be fully decarbonised by 2050 to
meet either the 1.5$^\circ$C or 2$^\circ$C targets. This means that EU policy
efforts should focus on supporting a maximum reduction of emissions per unit of
electricity by 2030 and net-zero emissions by 2050. Reaching these targets is
one of the most pressing questions EU policymakers face today. In light of the
COVID-19 crisis, EU policies should guide a cost-effective, reliable and
environmentally sound transition of the power sector, benefiting EU research
and innovation and its citizens. This meta-analysis provides a novel view on
historical data and compares data from modelling scenarios identified in the
literature. It assesses the current and future role of nuclear energy in
decarbonizing the EU power sector, while reviewing socio-economic implications
that could arise if limited public support nearly excludes nuclear fission
electricity from the future EU power mix. This work highlights relevant
socio-economic policy implications and actionable policy recommendations.


Abstract:
  We investigate the strongly lensed (\mu x10-100) Lyman continuum (LyC)
galaxy, dubbed Sunburst, at z=2.37, taking advantage of a new accurate model of
the lens. A characterization of the intrinsic (delensed) properties of the
galaxy yields a size of ~3 sq.kpc, a luminosity Muv=-20.3,and a stellar mass
M~10^9 Msun;16% of the ultraviolet light is located in a 3 Myr old
gravitationally-bound young massive star cluster (YMC) with an effective radius
of Re~8 pc and a dynamical mass of ~10^7 Msun (similar to the stellar mass),
from which LyC radiation is detected (\lambda < 912A). The inferred outflowing
gas velocity (>300 km/s) exceeds the escape velocity of the star cluster. The
resulting escape fraction of the ionizing radiation emerging from the Sunburst
galaxy is >6-12%, whilst it is >46-93% if inferred from the YMC. 12 additional
likely star clusters with 3<Re<20 pc are identified in the galaxy from which we
derive a cluster formation efficiency \Gamma>~30%, which is consistent with the
high \Gamma derived in local galaxies experiencing extreme gas physical
conditions. The presence of the YMC influences the morphology (nucleation),
photometry (photometric jumps) and spectroscopic output (nebular emission) of
the entire galaxy. The de-lensed LyC and UV (1600A) magnitudes of the YMC are
~30.6 and ~26.9, whilst the galaxy has m1600~24.8. A relatively large
rest-frame equivalent width of EWrest(Hb+[OIII]4959-5007)~450A emerges from the
galaxy with the YMC contributing to ~30%. If O-type stars are mainly forged in
star clusters, then such engines were the key ionizing agents during
reionization and the increasing occurrence of high EW lines (Hb+[OIII])
observed at z>6.5 might be an indirect signature of a high \Gamma at
reionization.Future facilities (like VLT/MAVIS or ELT), will probe bound
clusters on moderately magnified (\mu<5-10) galaxies across cosmic epochs up to
reionization[ABRIDGED]


Abstract:
  The reduction and control of particulate matter generated by fossil fuel
combustion are among the main issues for actual and future combustion devices
due to the increasingly stringent emission regulations. Recently, various fuels
have been investigated as a potential substitute or additive for diesel and
gasoline. This work focuses on how oxymethylene ether-3 (OME3), the smallest
promising OME compound, affects carbon particulate formation when blended with
ethylene in burner-stabilized premixed flames at different equivalence ratios.
Particle size distribution (PSD) and Laser Induced Fluorescence (LIF) and
Incandescence (LII) along with numerical (Conditional Quadrature Method of
Moments - CQMOM, based on D'Anna physico-chemical soot model) investigations
were conducted to study particle formation and growth in pure ethylene and
ethylene/OME3 flames. The soot volume fraction and PSD indicate a reduction in
the total number and the size of the soot particles at all equivalence ratios,
while the number of small nanoparticles remains almost unchanged. The CQMOM
model is able to predict similar trends for the soot volume fraction and, using
the entropy maximization concept, the general shape of the PSD for both pure
ethylene and OME3-blended flames, compared to the experimental measurements.
Further, carbon particulate matter was thermophoretically sampled in the
highest equivalence ratio conditions and spectroscopically analyzed. The soot
structure was investigated using UV-Visible and Raman spectroscopy, finding a
slightly higher aromaticity for the pure ethylene soot. FTIR analysis showed
that carbon particulate matter produced from an OME3-doped flame contained
larger amounts of oxygen, mainly in the form of C=O.


Abstract:
  Recent studies on lattice QCD have shown the emergence of large symmetries at
high temperature. This includes not only the restoration $SU(n_F)_L \times
SU(n_F)_R$, but also the effective emergence of an unexpected symmetry group,
namely $SU(2)_{CS}$, which contains $U(1)_A$ as subgroup. At the same time, at
high $T$, a gap in Dirac spectrum appears. As it is argued in several works of
\textit{L. Glozman et al.}, there should be a connection between a gap in the
Dirac spectrum and the presence of $SU(2)_{CS}$.In this paper, we analyze
whether the quark propagator can be invariant under $SU(n_F)_L \times
SU(n_F)_R$ and $SU(2)_{CS}$ transformations, in case of a gap in the Dirac
spectrum, and consequently the invariance of hadron correlators, giving the
condition for a quark propagator to be invariant under $SU(2)_{CS}$.


Abstract:
  Although deep learning (DL) has received much attention in accelerated MRI,
recent studies suggest small perturbations may lead to instabilities in
DL-based reconstructions, leading to concern for their clinical application.
However, these works focus on single-coil acquisitions, which is not practical.
We investigate instabilities caused by small adversarial attacks for multi-coil
acquisitions. Our results suggest that, parallel imaging and multi-coil CS
exhibit considerable instabilities against small adversarial perturbations.


Abstract:
  The paper presents an innovative method for tourist route planning inside a
destination. The necessity of reorganizing the tourist routes within a
destination comes as an immediate response to the Covid-19 crisis. The
implementation of the method inside tourist destinations can be an important
advantage in transforming a destination into a safer destination in times of
Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay
length has been accelerated while the epidemic became a pandemic. Moreover, the
wariness for future pandemics has brought to the spotlight the issue of
overcrowded attractions inside a destination at certain moments. The method
proposed in this paper proposes a backtracking algorithm, more precisely an
adaptation of the travelling salesman problem. The method presented aims to
facilitate the navigation inside a destination and to revive certain
less-visited sightseeing spots inside a destination while facilitating the
social distancing measures imposed by Covid-19.


Abstract:
  Knowledge distillation is a widely used paradigm for inheriting information
from a complicated teacher network to a compact student network and maintaining
the strong performance. Different from image classification, object detectors
are much more sophisticated with multiple loss functions in which features that
semantic information rely on are tangled. In this paper, we point out that the
information of features derived from regions excluding objects are also
essential for distilling the student detector, which is usually ignored in
existing approaches. In addition, we elucidate that features from different
regions should be assigned with different importance during distillation. To
this end, we present a novel distillation algorithm via decoupled features
(DeFeat) for learning a better student detector. Specifically, two levels of
decoupled features will be processed for embedding useful information into the
student, i.e., decoupled features from neck and decoupled proposals from
classification head. Extensive experiments on various detectors with different
backbones show that the proposed DeFeat is able to surpass the state-of-the-art
distillation methods for object detection. For example, DeFeat improves
ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50
based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Our implementation
is available at https://github.com/ggjy/DeFeat.pytorch.


Abstract:
  It is well known that the mathematical structure underlying renormalization
in perturbative quantum field theory is based on a Hopf algebra of Feynman
diagrams. A precondition for this is locality of the field theory.
Consequently, one might suspect that non-local field theories such as matrix or
tensor field theories cannot benefit from a similar algebraic understanding.
Here I show that, on the contrary, the renormalization and perturbative
diagramatics of a broad class of such field theories is based in the same way
on a Hopf algebra. These theories are characterized by interaction vertices
with graphs as external structure leading to Feynman diagrams which can be
summed up under the concept of "2-graphs". From the renormalization
perspective, such graph-like interactions are as much local as point-like
interactions. They differ in combinatorial details as I exemplify with the
central identity for the perturbative series of combinatorial correlation
functions. This sets the stage for a systematic study of perturbative
renormalization as well as non-perturbative aspects, e.g. Dyson-Schwinger
equations, for a number of combinatorially non-local field theories with
possible applications to quantum gravity, statistical models and more.


Abstract:
  Previous studies of the stellar mean metallicity and [Mg/Fe] values of
massive elliptical (E)~galaxies suggest that their stars were formed in a very
short timescale which cannot be reconciled with estimates from stellar
population synthesis (SPS) studies and with hierarchical-assembly. Applying the
previously developed chemical evolution code, GalIMF, which allows an
environment-dependent stellar initial mass function (IMF) to be applied in the
integrated galaxy initial mass function (IGIMF) theory instead of an invariant
canonical IMF, the star formation timescales (SFT) of E galaxies are
re-evaluated. The code's uniqueness lies in it allowing the galaxy-wide IMF and
associated chemical enrichment to evolve as the physical conditions in the
galaxy change. The calculated SFTs become consistent with the independent SPS
results if the number of type Ia supernovae (SNIa) per unit stellar mass
increases for more massive E~galaxies. This is a natural outcome of galaxies
with higher star-formation rates producing more massive star clusters, spawning
a larger number of SNIa progenitors per star. The calculations show E~galaxies
with a stellar mass $\approx 10^{9.5} M_\odot$ to have had the longest mean
SFTs of $\approx2\,$Gyr. The bulk of more massive E~galaxies were formed faster
(SFT$\,\approx 1\,$Gyr) leading to domination by M~dwarf stars and larger
dynamical mass-to-light ratios as observed, while lower-mass galaxies tend to
lose their gas supply more easily due to their shallower potential and
therefore also have similarly-short mean SFTs. This work achieves, for the
first time, consistency of the SFTs for early-type galaxies between
chemical-enrichment and SPS modelling and leads to an improved understanding of
how the star formation environment may affect the total number of SNIa per unit
stellar mass formed.


Abstract:
  Tree-based machine learning techniques, such as Decision Trees and Random
Forests, are top performers in several domains as they do well with limited
training datasets and offer improved interpretability compared to Deep Neural
Networks (DNN). However, while easier to train, they are difficult to optimize
for fast inference without accuracy loss in von Neumann architectures due to
non-uniform memory access patterns. Recently, we proposed a novel analog, or
multi-bit, content addressable memory(CAM) for fast look-up table operations.
Here, we propose a design utilizing this as a computational primitive for rapid
tree-based inference. Large random forest models are mapped to arrays of analog
CAMs coupled to traditional analog random access memory (RAM), and the unique
features of the analog CAM enable compression and high performance. An
optimized architecture is compared with previously proposed tree-based model
accelerators, showing improvements in energy to decision by orders of magnitude
for common image classification tasks. The results demonstrate the potential
for non-volatile analog CAM hardware in accelerating large tree-based machine
learning models.


Abstract:
  The principal aim of this paper is to employ Bessel-type operators in proving
the inequality \begin{align*} \int_0^\pi dx \, |f'(x)|^2 \geq
\dfrac{1}{4}\int_0^\pi dx \, \dfrac{|f(x)|^2}{\sin^2
(x)}+\dfrac{1}{4}\int_0^\pi dx \, |f(x)|^2,\quad f\in H_0^1 ((0,\pi)),
\end{align*} where both constants $1/4$ appearing in the above inequality are
optimal. In addition, this inequality is strict in the sense that equality
holds if and only if $f \equiv 0$. This inequality is derived with the help of
the exactly solvable, strongly singular, Dirichlet-type Schr\"{o}dinger
operator associated with the differential expression \begin{align*}
\tau_s=-\dfrac{d^2}{dx^2}+\dfrac{s^2-(1/4)}{\sin^2 (x)}, \quad s \in
[0,\infty), \; x \in (0,\pi). \end{align*}
  The new inequality represents a refinement of Hardy's classical inequality
\begin{align*} \int_0^\pi dx \, |f'(x)|^2 \geq \dfrac{1}{4}\int_0^\pi dx \,
\dfrac{|f(x)|^2}{x^2}, \quad f\in H_0^1 ((0,\pi)), \end{align*} it also
improves upon one of its well-known extensions in the form \begin{align*}
\int_0^\pi dx \, |f'(x)|^2 \geq \dfrac{1}{4}\int_0^\pi dx \,
\dfrac{|f(x)|^2}{d_{(0,\pi)}(x)^2}, \quad f\in H_0^1 ((0,\pi)), \end{align*}
where $d_{(0,\pi)}(x)$ represents the distance from $x \in (0,\pi)$ to the
boundary $\{0,\pi\}$ of $(0,\pi)$.


Abstract:
  We address how to robustly interpret natural language refinements (or
critiques) in recommender systems. In particular, in human-human recommendation
settings people frequently use soft attributes to express preferences about
items, including concepts like the originality of a movie plot, the noisiness
of a venue, or the complexity of a recipe. While binary tagging is extensively
studied in the context of recommender systems, soft attributes often involve
subjective and contextual aspects, which cannot be captured reliably in this
way, nor be represented as objective binary truth in a knowledge base. This
also adds important considerations when measuring soft attribute ranking. We
propose a more natural representation as personalized relative statements,
rather than as absolute item properties. We present novel data collection
techniques and evaluation approaches, and a new public dataset. We also propose
a set of scoring approaches, from unsupervised to weakly supervised to fully
supervised, as a step towards interpreting and acting upon soft attribute based
critiques.


Abstract:
  We establish the conjecture of Reiner and Yong for an explicit combinatorial
formula for the expansion of a Grothendieck polynomial into the basis of
Lascoux polynomials. This expansion is a subtle refinement of its symmetric
function version due to Buch, Kresch, Shimozono, Tamvakis, and Yong, which
gives the expansion of stable Grothendieck polynomials indexed by permutations
into Grassmannian stable Grothendieck polynomials. Our expansion is the
K-theoretic analogue of that of a Schubert polynomial into Demazure characters,
whose symmetric analogue is the expansion of a Stanley symmetric function into
Schur functions. Our expansions extend to flagged Grothendieck polynomials.


Abstract:
  In the style of Lindstr\"om's theorem for classical first-order logic, this
article characterizes propositional bi-intuitionistic logic as the maximal
(with respect to expressive power) abstract logic satisfying a certain form of
compactness, the Tarski union property and preservation under bi-asimulations.
Since bi-intuitionistic logic introduces new complexities in the intuitionistic
setting by adding the analogue of a backwards looking modality, the present
paper constitutes a non-trivial modification of previous work done by the
authors for intuitionistic logic in: G. Badia and G. Olkhovikov. A Lindstr\"om
theorem for intuitionistic propositional logic. Notre Dame Journal of Formal
Logic, 61 (1): 11-30 (2020).


Abstract:
  We propose a new architecture to approximately learn incentive compatible,
revenue-maximizing auctions from sampled valuations. Our architecture uses the
Sinkhorn algorithm to perform a differentiable bipartite matching which allows
the network to learn strategyproof revenue-maximizing mechanisms in settings
not learnable by the previous RegretNet architecture. In particular, our
architecture is able to learn mechanisms in settings without free disposal
where each bidder must be allocated exactly some number of items. In
experiments, we show our approach successfully recovers multiple known optimal
mechanisms and high-revenue, low-regret mechanisms in larger settings where the
optimal mechanism is unknown.


Abstract:
  Background. Many mutation reduction strategies, which aim to reduce the
number of mutants, have been proposed. Problem. It is important to measure the
ability of a mutation reduction strategy to maintain test suite effectiveness
evaluation. However, existing evaluation indicators are unable to measure the
"order-preserving ability". Objective. We aim to propose evaluation indicators
to measure the "order-preserving ability" of a mutation reduction strategy,
which is important but missing in our community. Method. Given a test suite on
a Software Under Test (SUT) with a set of original mutants, we leverage the
test suite to generate a group of test suites that have a partial order
relationship in fault detecting potential. When evaluating a reduction
strategy, we first construct two partial order relationships among the
generated test suites in terms of mutation score, one with the original mutants
and another with the reduced mutants. Then, we measure the extent to which the
two partial order relationships are consistent. The more consistent the two
partial order relationships are, the stronger the Order Preservation (OP) of
the mutation reduction strategy is, and the more effective the reduction
strategy is. Furthermore, we propose Effort-aware Relative Order Preservation
(EROP) to measure how much gain a mutation reduction strategy can provide
compared with a random reduction strategy. Result. The experimental results
show that OP and EROP are able to efficiently measure the "order-preserving
ability" of a mutation reduction strategy. As a result, they have a better
ability to distinguish various mutation reduction strategies compared with the
existing evaluation indicators. Conclusion. We suggest, for the researchers,
that OP and EROP should be used to measure the effectiveness of a mutant
reduction strategy.


Abstract:
  Released pollutants and poisonous chemicals in the highly-populated urban
areas can spread via wind flow and affect the health, safety, and wellbeing of
those close to the source and others downstream. Being able to predict the
transport of airborne contaminants in such urban areas could help stakeholders
to plan emergency responses. Airflow due to the wind in urban areas with
closely packed buildings results in complicated aerodynamics around buildings
that, in turn, can lead to intricate contaminant transport phenomena. We
conduct high-resolution large-eddy simulation (LES) of airflow in Southern
Manhattan, a highly-populated area in New York City, due to a dominant wind
blowing from south to north. The LES results show complex flow dynamics around
the buildings of various heights and, consequently, complicated contaminant
transport processes from a source-point which is located in front of the New
York Stock Exchange building at 30 m above ground. The flow tends to align with
the streets that are fairly parallel to the wind direction creating
high-velocity core regions, while the airflow loses its momentum markedly
farther downwind in the urban area. For as long as the source of contamination
exists, the pollution plume rapidly travels through the streets reaching
downwind areas. When the source is removed, contaminants that are trapped in
the low-momentum urban area linger to exit the flow domain.


Abstract:
  The recent boom in computational chemistry has enabled several projects aimed
at discovering useful materials or catalysts. We acknowledge and address two
recurring issues in the field of computational catalyst discovery. First,
calculating macro-scale catalyst properties is not straight-forward when using
ensembles of atomic-scale calculations (e.g., density functional theory). We
attempt to address this issue by creating a multiscale model that estimates
bulk catalyst activity using adsorption energy predictions from both density
functional theory and machine learning models. The second issue is that many
catalyst discovery efforts seek to optimize catalyst properties, but
optimization is an inherently exploitative objective that is in tension with
the explorative nature of early-stage discovery projects. In other words: why
invest so much time finding a "best" catalyst when it is likely to fail for
some other, unforeseen problem? We address this issue by relaxing the catalyst
discovery goal into a classification problem: "What is the set of catalysts
that is worth testing experimentally?" Here we present a catalyst discovery
method called myopic multiscale sampling, which combines multiscale modeling
with automated selection of density functional theory calculations. It is an
active classification strategy that seeks to classify catalysts as "worth
investigating" or "not worth investigating" experimentally. Our results show a
~7-16 times speedup in catalyst classification relative to random sampling.
These results were based on offline simulations of our algorithm on two
different datasets: a larger, synthesized dataset and a smaller, real dataset.


Abstract:
  Young neutron stars (NSs) have magnetic fields $B$ in the range
$10^{12}-10^{15}$ G, believed to be generated by dynamo action at birth. We
argue that such a dynamo is actually too inefficient to explain the strongest
of these fields. Dynamo action in the mature star is also unlikely. Instead we
propose a promising new precession-driven dynamo and examine its basic
properties, as well as arguing for a revised mean-field approach to NS dynamos.
The precession-driven dynamo could also play a role in field generation in
main-sequence stars.


Abstract:
  This paper develops a method of biologically guided deep learning for
post-radiation FDG-PET image outcome prediction based on pre-radiation images
and radiotherapy dose information. Based on the classic reaction-diffusion
mechanism, a novel biological model was proposed using a partial differential
equation that incorporates spatial radiation dose distribution as a
patient-specific treatment information variable. A 7-layer
encoder-decoder-based convolutional neural network (CNN) was designed and
trained to learn the proposed biological model. As such, the model could
generate post-radiation FDG-PET image outcome predictions with possible
time-series transition from pre-radiotherapy image states to post-radiotherapy
states. The proposed method was developed using 64 oropharyngeal patients with
paired FDG-PET studies before and after 20Gy delivery (2Gy/daily fraction) by
IMRT. In a two-branch deep learning execution, the proposed CNN learns specific
terms in the biological model from paired FDG-PET images and spatial dose
distribution as in one branch, and the biological model generates post-20Gy
FDG-PET image prediction in the other branch. The proposed method successfully
generated post-20Gy FDG-PET image outcome prediction with breakdown
illustrations of biological model components. Time-series FDG-PET image
predictions were generated to demonstrate the feasibility of disease response
rendering. The developed biologically guided deep learning method achieved
post-20Gy FDG-PET image outcome predictions in good agreement with ground-truth
results. With break-down biological modeling components, the outcome image
predictions could be used in adaptive radiotherapy decision-making to optimize
personalized plans for the best outcome in the future.


Abstract:
  We develop a thermal description for photon modes within the context of
bouncing universe. Within this study, we start with a Lorentz-breaking
dispersion relation which accounts for modified Friedmann equations with a
bounce solution. We calculate the spectral radiance, the entropy, the Helmholtz
free energy, the heat capacity, and the mean energy. Nevertheless, the latter
two ones turn out to contribute only in a trivial manner. Furthermore, one
intriguing aspect gives rise to: for some configurations of $\eta$, $l_{P}$,
and $T$, the thermodynamical behavior of the system seems to indicate
instability. However, despite of showing this particularity, the solutions of
the thermodynamic functions in general turn out to agree with the literature,
e.g., the second law of thermodynamics is maintained. More so, all the results
are derived \textit{analytically} and three different regimes of temperature of
the universe are also considered, i.e., the inflationary era, the electroweak
epoch and the cosmic microwave background.


Abstract:
  The success of state-of-the-art machine learning is essentially all based on
different variations of gradient descent algorithms that minimize some version
of a cost or loss function. A fundamental limitation, however, is the need to
train these systems in either supervised or unsupervised ways by exposing them
to typically large numbers of training examples. Here, we introduce a
fundamentally novel conceptual approach to machine learning that takes
advantage of a neurobiologically derived model of dynamic signaling,
constrained by the geometric structure of a network. We show that MNIST images
can be uniquely encoded and classified by the dynamics of geometric networks
with nearly state-of-the-art accuracy in an unsupervised way, and without the
need for any training.


Abstract:
  We study the convergence rate of Bregman gradient methods for convex
optimization in the space of measures on a d-dimensional manifold. Under basic
regularity assumptions, we show that the suboptimality gap at iteration k is in
O(log(k)k^{--1}) for multiplicative updates, while it is in O(k^{--q/(d+q)})
for additive updates for some q = {1, 2, 4} determined by the structure of the
objective function. Our flexible proof strategy, based on approximation
arguments, allows to painlessly cover all Bregman Proximal Gradient Methods
(PGM) and their acceleration (APGM) under various geometries such as the
hyperbolic entropy and L^p divergences. We also prove the tightness of our
analysis with matching lower bounds and confirm the theoretical results with
numerical experiments on low dimensional problems. Note that all these
optimization methods must additionally pay the computational cost of
discretization, which can be exponential in d.


Abstract:
  We present a Mathematica program package MagneticTB, which can generate the
tight-binding model for arbitrary magnetic space group. The only input
parameters in MagneticTB are the (magnetic) space group number and the orbital
information in each Wyckoff positions. Some useful functions including getting
the matrix expression for symmetry operators, manipulating the energy band
structure by parameters and interfacing with other software are also developed.
MagneticTB can help to investigate the physical properties in both magnetic and
non-magnetic system, especially for topological properties.


Abstract:
  Abuse on the Internet is an important societal problem of our time. Millions
of Internet users face harassment, racism, personal attacks, and other types of
abuse across various platforms. The psychological effects of abuse on
individuals can be profound and lasting. Consequently, over the past few years,
there has been a substantial research effort towards automated abusive language
detection in the field of NLP. In this position paper, we discuss the role that
modeling of users and online communities plays in abuse detection.
Specifically, we review and analyze the state of the art methods that leverage
user or community information to enhance the understanding and detection of
abusive language. We then explore the ethical challenges of incorporating user
and community information, laying out considerations to guide future research.
Finally, we address the topic of explainability in abusive language detection,
proposing properties that an explainable method should aim to exhibit. We
describe how user and community information can facilitate the realization of
these properties and discuss the effective operationalization of explainability
in view of the properties.


Abstract:
  We propose easily verifiable necessary and sufficient conditions for the
linearizability of two-input systems by an endogenous dynamic feedback with a
dimension of at most two.


Abstract:
  We analyze the $L^1$-mixing of a generalization of the Averaging process
introduced by Aldous. The process takes place on a growing sequence of graphs
which we assume to be finite-dimensional, in the sense that the random walk on
those geometries satisfies a family of Nash inequalities. As a byproduct of our
analysis, we provide a complete picture of the total variation mixing of a
discrete dual of the Averaging process, which we call Binomial Splitting
process. A single particle of this process is essentially the random walk on
the underlying graph. When several particles evolve together, they interact by
synchronizing their jumps when placed on neighboring sites. We show that, given
$k$ the number of particles and $n$ the (growing) size of the underlying graph,
the system exhibits cutoff in total variation if $k\to\infty$ and $k=O(n^2)$.
Finally, we exploit the duality between the two processes to show that the
Binomial Splitting satisfies a version of Aldous' spectral gap identity,
namely, the relaxation time of the process is independent of the number of
particles.


Abstract:
  Nous montrons la propri\'et\'e du trou spectral pour la famille des graphes
de Cayley obtenus par r\'eduction modulo $q$ d'un sous-groupe de
$\mathrm{SL}_d(\mathbb{Z})$ dont l'adh\'erence de Zariski est un
$\mathbb{Q}$-groupe simple.
  --
  We show a spectral gap property for the family of Cayley graphs obtained by
reduction modulo $q$ of a subgroup of $\mathrm{SL}_d(\mathbb{Z})$ whose Zariski
closure is a simple $\mathbb{Q}$-group.


Abstract:
  Diffusion is a fundamental graph procedure and has been a basic building
block in a wide range of theoretical and empirical applications such as graph
partitioning and semi-supervised learning on graphs. In this paper, we study
computationally efficient diffusion primitives beyond random walk.
  We design an $\widetilde{O}(m)$-time randomized algorithm for the
$\ell_2$-norm flow diffusion problem, a recently proposed diffusion model based
on network flow with demonstrated graph clustering related applications both in
theory and in practice. Examples include finding locally-biased low conductance
cuts. Using a known connection between the optimal dual solution of the flow
diffusion problem and the local cut structure, our algorithm gives an
alternative approach for finding such cuts in nearly linear time.
  From a technical point of view, our algorithm contributes a novel way of
dealing with inequality constraints in graph optimization problems. It adapts
the high-level algorithmic framework of nearly linear time Laplacian system
solvers, but requires several new tools: vertex elimination under constraints,
a new family of graph ultra-sparsifiers, and accelerated proximal gradient
methods with inexact proximal mapping computation.


Abstract:
  Information verification is quite a challenging task, this is because many
times verifying a claim can require picking pieces of information from multiple
pieces of evidence which can have a hierarchy of complex semantic relations.
Previously a lot of researchers have mainly focused on simply concatenating
multiple evidence sentences to accept or reject claims. These approaches are
limited as evidence can contain hierarchical information and dependencies. In
this research, we aim to verify facts based on evidence selected from a list of
articles taken from Wikipedia. Pretrained language models such as XLNET are
used to generate meaningful representations and graph-based attention and
convolutions are used in such a way that the system requires little additional
training to learn to verify facts.


Abstract:
  We have performed a systematical analysis of lepton and quark masses models
based on $\Gamma_4\cong S_4$ modular symmetry with gCP symmetry. We have
considered both cases that neutrinos are Majorana particles and Dirac
particles. All possible nontrivial representation assignments of matter fields
are considered, and the most general form of fermion mass matrices are given.
The phenomenologically viable models with the lowest number of free parameters
together with the results of fit are presented. We find out nine lepton models
with seven real free parameters including the real and imaginary parts of
modulus for Majorana neutrinos, which can accommodate the lepton masses and
neutrino oscillation data. The prediction for leptogenesis is studied in an
example lepton model. The observed baryon asymmetry as well as lepton masses
and mixing angles can be explained. For Dirac neutrinos, four lepton models
with five real free couplings are compatible with experimental data. Ten quark
models containing seven couplings are found to be able to accommodate the
hierarchical quark masses and mixing angles and CP violation phase.
Furthermore, the $S_4$ modular symmetry can provide a unified description of
lepton and quark flavor structure, and a benchmark model is presented.


Abstract:
  MicroRNAs (miRNAs) are endogenous small non-coding RNAs that play an
important role in post-transcriptional gene regulation. However, the
experimental determination of miRNA sequence and structure is both expensive
and time-consuming. Therefore, computational and machine learning-based
approaches have been adopted to predict novel microRNAs. With the involvement
of data science and machine learning in biology, multiple research studies have
been conducted to find microRNAs with different computational methods and
different miRNA features. Multiple approaches are discussed in detail
considering the learning algorithm/s used, features considered, dataset/s used
and the criteria used in evaluations. This systematic review focuses on the
machine learning methods developed for miRNA identification in plants. This
will help researchers to gain a detailed idea about past studies and identify
novel paths that solve drawbacks occurred in past studies. Our findings
highlight the need for plant-specific computational methods for miRNA
identification.


Abstract:
  Various measures have been taken in different countries to mitigate the
Covid-19 epidemic. But, throughout the world, many citizens don't understand
well how these measures are taken and even question the decisions taken by
their government. Should the measures be more (or less) restrictive? Are they
taken for a too long (or too short) period of time? To provide some
quantitative elements of response to these questions, we consider the
well-known SEIR model for the Covid-19 epidemic propagation and propose a
pragmatic model of the government decision-making operation. Although simple
and obviously improvable, the proposed model allows us to study the tradeoff
between health and economic aspects in a pragmatic and insightful way. Assuming
a given number of phases for the epidemic and a desired tradeoff between health
and economic aspects, it is then possible to determine the optimal duration of
each phase and the optimal severity level for each of them. The numerical
analysis is performed for the case of France but the adopted approach can be
applied to any country. One of the takeaway messages of this analysis is that
being able to implement the optimal 4-phase epidemic management strategy in
France would have led to 1.05 million infected people and a GDP loss of 231
billion euro instead of 6.88 million of infected and a loss of 241 billion
euro. This indicates that, seen from the proposed model perspective, the
effectively implemented epidemic management strategy is good economically,
whereas substantial improvements might have been obtained in terms of health
impact. Our analysis indicates that the lockdown/severe phase should have been
more severe but shorter, and the adjustment phase occurred earlier. Due to the
natural tendency of people to deviate from the official rules, updating
measures every month over the whole epidemic episode seems to be more
appropriate.


Abstract:
  A key challenge on the path to developing agents that learn complex
human-like behavior is the need to quickly and accurately quantify
human-likeness. While human assessments of such behavior can be highly
accurate, speed and scalability are limited. We address these limitations
through a novel automated Navigation Turing Test (ANTT) that learns to predict
human judgments of human-likeness. We demonstrate the effectiveness of our
automated NTT on a navigation task in a complex 3D environment. We investigate
six classification models to shed light on the types of architectures best
suited to this task, and validate them against data collected through a human
NTT. Our best models achieve high accuracy when distinguishing true human and
agent behavior. At the same time, we show that predicting finer-grained human
assessment of agents' progress towards human-like behavior remains unsolved.
Our work takes an important step towards agents that more effectively learn
complex human-like behavior.


Abstract:
  3D point-cloud-based perception is a challenging but crucial computer vision
task. A point-cloud consists of a sparse, unstructured, and unordered set of
points. To understand a point-cloud, previous point-based methods, such as
PointNet++, extract visual features through hierarchically aggregation of local
features. However, such methods have several critical limitations: 1) Such
methods require several sampling and grouping operations, which slow down the
inference speed. 2) Such methods spend an equal amount of computation on each
points in a point-cloud, though many of points are redundant. 3) Such methods
aggregate local features together through downsampling, which leads to
information loss and hurts the perception performance. To overcome these
challenges, we propose a novel, simple, and elegant deep learning model called
YOGO (You Only Group Once). Compared with previous methods, YOGO only needs to
sample and group a point-cloud once, so it is very efficient. Instead of
operating on points, YOGO operates on a small number of tokens, each of which
summarizes the point features in a sub-region. This allows us to avoid
computing on the redundant points and thus boosts efficiency.Moreover, YOGO
preserves point-wise features by projecting token features to point features
although the computation is performed on tokens. This avoids information loss
and can improve point-wise perception performance. We conduct thorough
experiments to demonstrate that YOGO achieves at least 3.0x speedup over
point-based baselines while delivering competitive classification and
segmentation performance on the ModelNet, ShapeNetParts and S3DIS datasets.


Abstract:
  Collaborative inference has recently emerged as an intriguing framework for
applying deep learning to Internet of Things (IoT) applications, which works by
splitting a DNN model into two subpart models respectively on
resource-constrained IoT devices and the cloud. Even though IoT applications'
raw input data is not directly exposed to the cloud in such framework,
revealing the local-part model's intermediate output still entails privacy
risks. For mitigation of privacy risks, differential privacy could be adopted
in principle. However, the practicality of differential privacy for
collaborative inference under various conditions remains unclear. For example,
it is unclear how the calibration of the privacy budget epsilon will affect the
protection strength and model accuracy in presence of the state-of-the-art
reconstruction attack targeting collaborative inference, and whether a good
privacy-utility balance exists. In this paper, we provide the first systematic
study to assess the effectiveness of differential privacy for protecting
collaborative inference in presence of the reconstruction attack, through
extensive empirical evaluations on various datasets. Our results show
differential privacy can be used for collaborative inference when confronted
with the reconstruction attack, with insights provided about privacyutility
trade-offs. Specifically, across the evaluated datasets, we observe there
exists a suitable privacy budget range (particularly 100<=epsilon<=200 in our
evaluation) providing a good tradeoff between utility and privacy protection.
Our key observation drawn from our study is that differential privacy tends to
perform better in collaborative inference for datasets with smaller intraclass
variations, which, to our knowledge, is the first easy-toadopt practical
guideline.


Abstract:
  White dwarfs which exhibit transit signatures of planetary debris and
accreted planetary material provide exceptional opportunities to probe the
material composition and dynamical structure of planetary systems. Although
previous theoretical work investigating the role of minor body disruption
around white dwarfs has focussed on spherical bodies, Solar System asteroids
can be more accurately modelled as triaxial ellipsoids. Here we present an
analytical framework to identify the type of disruption (tidal fragmentation,
total sublimation or direct impact) experienced by triaxial asteroids
approaching white dwarfs on extremely eccentric (e \sim 1) orbits. This
framework is then used to identify the outcomes for simplified Main belt
analogues of 100 bodies across five different white dwarf temperatures. We also
present an empirical relationship between cooling age and effective temperature
for both DA and DB white dwarfs to identify the age of the white dwarfs
considered here. We find that using a purely spherical shape model can
underestimate the physical size and radial distance at which an asteroid is
subjected to complete sublimation, and these differences increase with greater
elongation of the body. Contrastingly, fragmentation always occurs in the
largest semi-axis of a body and so can be modelled by a sphere of that radius.
Both fragmentation and sublimation are greatly affected by the body's material
composition, and hence by the composition of their progenitor asteroid belts.
The white dwarf temperature, and hence cooling age, can affect the expected
debris distribution: higher temperatures sublimate large elongated asteroids,
and cooler temperatures accommodate more direct impacts.


Abstract:
  Multilayer perceptrons (MLPs) have been successfully used to represent 3D
shapes implicitly and compactly, by mapping 3D coordinates to the corresponding
signed distance values or occupancy values. In this paper, we propose a novel
positional encoding scheme, called Spline Positional Encoding, to map the input
coordinates to a high dimensional space before passing them to MLPs, for
helping to recover 3D signed distance fields with fine-scale geometric details
from unorganized 3D point clouds. We verified the superiority of our approach
over other positional encoding schemes on tasks of 3D shape reconstruction from
input point clouds and shape space learning. The efficacy of our approach
extended to image reconstruction is also demonstrated and evaluated.


Abstract:
  Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is
the pivotal step to KGs integration, also known as \emph{entity alignment}
(EA). However, most existing EA methods are inefficient and poor in
scalability. A recent summary points out that some of them even require several
days to deal with a dataset containing 200,000 nodes (DWY100K). We believe
over-complex graph encoder and inefficient negative sampling strategy are the
two main reasons. In this paper, we propose a novel KG encoder -- Dual
Attention Matching Network (Dual-AMN), which not only models both intra-graph
and cross-graph information smartly, but also greatly reduces computational
complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to
smoothly select hard negative samples with reduced loss shift. The experimental
results on widely used public datasets indicate that our method achieves both
high accuracy and high efficiency. On DWY100K, the whole running process of our
method could be finished in 1,100 seconds, at least 10* faster than previous
work. The performances of our method also outperform previous works across all
datasets, where Hits@1 and MRR have been improved from 6% to 13%.


Abstract:
  We consider the problem of quantifying uncertainty for the estimation error
of the leading eigenvector from Oja's algorithm for streaming principal
component analysis, where the data are generated IID from some unknown
distribution. By combining classical tools from the U-statistics literature
with recent results on high-dimensional central limit theorems for quadratic
forms of random vectors and concentration of matrix products, we establish a
$\chi^2$ approximation result for the $\sin^2$ error between the population
eigenvector and the output of Oja's algorithm. Since estimating the covariance
matrix associated with the approximating distribution requires knowledge of
unknown model parameters, we propose a multiplier bootstrap algorithm that may
be updated in an online manner. We establish conditions under which the
bootstrap distribution is close to the corresponding sampling distribution with
high probability, thereby establishing the bootstrap as a consistent
inferential method in an appropriate asymptotic regime.


Abstract:
  We reconsider the issue of the search for a nonzero electric dipole form
factor (EDM) $d_\tau(s)$ using optimal observables in $\tau^+\tau^-$ production
by $e^+ e^-$ collisions in the center-of-mass energy range from the $\tau$-pair
threshold to about $\sqrt{s} \sim 15$ GeV. We discuss the general formalism of
optimal observables and apply it to two $CP$-odd observables that are sensitive
to the real and imaginary part of $d_\tau(s)$, respectively. We compute the
expectation values and covariances of these optimal $CP$ observables for
  $\tau$-pair production at $\sqrt{s}=10.58$ GeV with subsequent decays of
$\tau^\pm$ into major leptonic or semihadronic modes. For the $\tau$ decays to
two pions and three charged pions we take the full kinematic information of the
hadronic system into account. Assuming that the Belle II experiment at the KEKB
accelerator will eventually
  analyze data corresponding to an integrated luminosity of 50 ab$^{-1}$ and
applying acceptance cuts on the final-state pions we find that 1~s.d.
sensitivities $\delta {\rm Re} d_\tau = 6.8 \times 10^{-20}$ e cm and $\delta
{\rm} Im d_\tau = 4.0 \times 10^{-20}$ e cm can be obtained with events where
both $\tau$'s decay semihadronically.
  In the ideal case where no cuts on the final-state particles are applied we
find with 50 ab$^{-1}$ at $\sqrt{s}=10.58$ GeV corresponding to $4.5 \times
10^{10}$ $\tau^+ \tau^-$ events the 1 s.d. sensitivities $\delta {\rm Re}
d_\tau = 5.8 \times 10^{-20}$ e cm and $\delta {\rm} Im d_\tau = 3.2 \times
10^{-20}$ e cm, again for events where both $\tau$ leptons decay
semihadronically. Furthermore, we analyze the potential magnitude of the $\tau$
EDM form factor in the type-II two-Higgs doublet extension and in two scalar
leptoquark extensions of the Standard Model.


Abstract:
  Motivated by the success of Transformers in natural language processing (NLP)
tasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to
the vision domain. However, pure Transformer architectures often require a
large amount of training data or extra supervision to obtain comparable
performance with convolutional neural networks (CNNs). To overcome these
limitations, we analyze the potential drawbacks when directly borrowing
Transformer architectures from NLP. Then we propose a new
\textbf{Convolution-enhanced image Transformer (CeiT)} which combines the
advantages of CNNs in extracting low-level features, strengthening locality,
and the advantages of Transformers in establishing long-range dependencies.
Three modifications are made to the original Transformer: \textbf{1)} instead
of the straightforward tokenization from raw input images, we design an
\textbf{Image-to-Tokens (I2T)} module that extracts patches from generated
low-level features; \textbf{2)} the feed-froward network in each encoder block
is replaced with a \textbf{Locally-enhanced Feed-Forward (LeFF)} layer that
promotes the correlation among neighboring tokens in the spatial dimension;
\textbf{3)} a \textbf{Layer-wise Class token Attention (LCA)} is attached at
the top of the Transformer that utilizes the multi-level representations.
  Experimental results on ImageNet and seven downstream tasks show the
effectiveness and generalization ability of CeiT compared with previous
Transformers and state-of-the-art CNNs, without requiring a large amount of
training data and extra CNN teachers. Besides, CeiT models also demonstrate
better convergence with $3\times$ fewer training iterations, which can reduce
the training cost significantly\footnote{Code and models will be released upon
acceptance.}.


Abstract:
  Simulating quantum many-body systems is a highly demanding task since the
required resources grow exponentially with the dimension of the system. In the
case of fermionic systems, this is even harder since nonlocal interactions
emerge due to the antisymmetric character of the fermionic wave function. Here,
we introduce a digital-analog quantum algorithm to simulate a wide class of
fermionic Hamiltonians including the paradigmatic Fermi-Hubbard model. These
digital-analog methods allow quantum algorithms to run beyond digital versions
via an efficient use of coherence time. Furthermore, we exemplify our
techniques with a low-connected architecture for realistic digital-analog
implementations of specific fermionic models.


Abstract:
  An important characteristic of the Dilaton cosmological model is the
Gasperini-Veneziano duality transformation which follows from the existence of
the $O\left( d,d\right) $ symmetry. In this study, we consider the equivalent
Dilaton theory in teleparallel dark energy with the $O\left( d,d\right) $
symmetry, while the equivalent teleparallel-duality transformation is
presented. The classical solution of the field equations is derived. Finally
the Wheeler-DeWitt equation of quantum cosmology is discussed.


Abstract:
  Gender disparity in science is one of the most focused debating points among
authorities and the scientific community. Over the last few decades, numerous
initiatives have endeavored to accelerate gender equity in academia and
research society. However, despite the ongoing efforts, gaps persist across the
world, and more measures need to be taken. Using social network analysis,
natural language processing, and machine learning, in this study, we
comprehensively analyzed gender-specific patterns in the highly
interdisciplinary and evolving field of artificial intelligence for the period
of 2000-2019. Our findings suggest an overall increasing rate of mixed-gender
collaborations. From the observed gender-specific collaborative patterns, the
existence of disciplinary homophily at both dyadic and team levels is
confirmed. However, a higher preference was observed for female researchers to
form homophilous collaborative links. Our core-periphery analysis indicated a
significant positive association between having diverse collaboration and
scientific performance and experience. We found evidence in support of
expecting the rise of new female superstar researchers in the artificial
intelligence field.


Abstract:
  Machine Learning (ML) has been embraced as a powerful tool by the financial
industry, with notable applications spreading in various domains including
investment management. In this work, we propose a full-cycle data-driven
investment robo-advising framework, consisting of two ML agents. The first
agent, an inverse portfolio optimization agent, infers an investor's risk
preference and expected return directly from historical allocation data using
online inverse optimization. The second agent, a deep reinforcement learning
(RL) agent, aggregates the inferred sequence of expected returns to formulate a
new multi-period mean-variance portfolio optimization problem that can be
solved using deep RL approaches. The proposed investment pipeline is applied on
real market data from April 1, 2016 to February 1, 2021 and has shown to
consistently outperform the S&P 500 benchmark portfolio that represents the
aggregate market optimal allocation. The outperformance may be attributed to
the the multi-period planning (versus single-period planning) and the
data-driven RL approach (versus classical estimation approach).


Abstract:
  Recently, we presented a new Two-Bar Charts Packing Problem (2-BCPP), in
which it is necessary to pack two-bar charts (2-BCs) in a unit-height strip of
minimum length. The problem is a generalization of the Bin Packing Problem and
2-D Vector Packing Problem. Earlier, we have proposed several polynomial
approximation algorithms. In particular, when each 2-BC has at least one bar of
height more than 1/2, we have proposed a 3/2--approximation polynomial
algorithm. This paper proposes an $O(n^3)$--time 16/11--approximation algorithm
for packing 2-BCs when at least one bar of each BC has a height not less than
1/2 and an $O(n^{2.5})$--time 5/4--approximation algorithm for packing
non-increasing or non-decreasing 2-BCs when each 2-BC has at least one bar
which height is more than 1/2, where $n$ is the number of 2-BCs.


Abstract:
  A new regime of proton-driven plasma wakefield acceleration is discovered, in
which the plasma nonlinearity increases the phase velocity of the excited wave
compared to that of the protons. If the beam charge is much larger than
minimally necessary to excite a nonlinear wave, there is sufficient freedom in
choosing the longitudinal plasma density profile to make the wave speed close
to the speed of light. This allows electrons or positrons to be accelerated to
about 200 GeV with a 400 GeV proton driver.


Abstract:
  We consider the chiral model of twisted bilayer graphene introduced by
Tarnopolsky-Kruchkov-Vishwanath (TKV). TKV have proved that for inverse twist
angles $\alpha$ such that the effective Fermi velocity at the moir\'e $K$ point
vanishes, the chiral model has a perfectly flat band at zero energy over the
whole Brillouin zone. By a formal expansion, TKV found that the Fermi velocity
vanishes at $\alpha \approx .586$. In this work, we give a proof that the Fermi
velocity vanishes for at least one $\alpha$ between $.57$ and $.61$ by
rigorously justifying TKV's formal expansion of the Fermi velocity over a
sufficiently large interval of $\alpha$ values. The idea of the proof is to
project the TKV Hamiltonian onto a finite dimensional subspace, and then expand
the Fermi velocity in terms of explicitly computable linear combinations of
modes in the subspace, while controlling the error. The proof relies on two
propositions whose proofs are computer-assisted, i.e., numerical computation
together with worst-case estimates on the accumulation of round-off error which
show that round-off error cannot possibly change the conclusion of the
computation. The propositions give a bound below on the spectral gap of the
projected Hamiltonian, an Hermitian $80 \times 80$ matrix whose spectrum is
symmetric about $0,$ and verify that two real 18th order polynomials, which
approximate the numerator of the Fermi velocity, take values with definite sign
when evaluated at specific values of $\alpha$. Together with TKV's work our
result proves existence of at least one perfectly flat band of the chiral
model.


Abstract:
  Knowledge distillation (KD), transferring knowledge from a cumbersome teacher
model to a lightweight student model, has been investigated to design efficient
neural architectures. Generally, the objective function of KD is the
Kullback-Leibler (KL) divergence loss between the softened probability
distributions of the teacher model and the student model with the temperature
scaling hyperparameter tau. Despite its widespread use, few studies have
discussed the influence of such softening on generalization. Here, we
theoretically show that the KL divergence loss focuses on the logit matching
when tau increases and the label matching when tau goes to 0 and empirically
show that the logit matching is positively correlated to performance
improvement in general. From this observation, we consider an intuitive KD loss
function, the mean squared error (MSE) between the logit vectors, so that the
student model can directly learn the logit of the teacher model. The MSE loss
outperforms the KL divergence loss, explained by the difference in the
penultimate layer representations between the two losses. Furthermore, we show
that sequential distillation can improve performance and that KD, particularly
when using the KL divergence loss with small tau, mitigates the label noise.
The code to reproduce the experiments is publicly available online at
https://github.com/jhoon-oh/kd_data/.


Abstract:
  We study the stochastic viscous nonlinear wave equations (SvNLW) on $\mathbb
T^2$, forced by a fractional derivative of the space-time white noise $\xi$. In
particular, we consider SvNLW with the singular additive forcing
$D^\frac{1}{2}\xi$ such that solutions are expected to be merely distributions.
By introducing an appropriate renormalization, we prove local well-posedness of
SvNLW. By establishing an energy bound via a Yudovich-type argument, we also
prove global well-posedness of the defocusing cubic SvNLW. Lastly, in the
defocusing case, we prove almost sure global well-posedness of SvNLW with
respect to certain Gaussian random initial data.


Abstract:
  Tomography is a widely used tool for analyzing microstructures in three
dimensions (3D). The analysis, however, faces difficulty because the
constituent materials produce similar grey-scale values. Sometimes, this
prompts the image segmentation process to assign a pixel/voxel to the wrong
phase (active material or pore). Consequently, errors are introduced in the
microstructure characteristics calculation. In this work, we develop a
filtering algorithm called PerSplat based on topological persistence (a
technique used in topological data analysis) to improve segmentation quality.
One problem faced when evaluating filtering algorithms is that real image data
in general are not equipped with the `ground truth' for the microstructure
characteristics. For this study, we construct synthetic images for which the
ground-truth values are known. On the synthetic images, we compare the pore
tortuosity and Minkowski functionals (volume and surface area) computed with
our PerSplat filter and other methods such as total variation (TV) and
non-local means (NL-means). Moreover, on a real 3D image, we visually compare
the segmentation results provided by our filter against TV and NL-means. The
experimental results indicate that PerSplat provides a significant improvement
in segmentation quality.