title: Simulation Studies of a Novel Muography Detector for the Great Pyramids

abstract:
  Muography is an imaging technique that can be used to examine the interior
structure of large-size objects. The technique is based on measurements of the
absorption of cosmic-ray muons passing through the object under study.
Muographic imaging was already successfully applied to the discovery of a new
void inside the Great Pyramid of Khufu at Giza, Egypt. With the aim of studying
the Pyramid of Khafre, the second largest pyramid at Giza, a new muography
detector is currently being constructed. In this paper we report on the
development of a corresponding new simulation framework of the Great Pyramids
and the detector setup. This simulation will serve as a basis to develop the
data and image reconstruction algorithms to be used during our future muography
campaign in the Pyramid of Khafre, and will allow us to study the relevant
experimental conditions at the site.


title: State of AI Ethics Report (Volume 6, February 2022)

abstract:
  This report from the Montreal AI Ethics Institute (MAIEI) covers the most
salient progress in research and reporting over the second half of 2021 in the
field of AI ethics. Particular emphasis is placed on an "Analysis of the AI
Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI
Design and Governance", "Laws and Regulations", "Trends", and other areas
covered in the "Outside the Boxes" section. The two AI spotlights feature
application pieces on "Constructing and Deconstructing Gender with AI-Generated
Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a
Michelin Star Restaurant?". Given MAIEI's mission to democratize AI,
submissions from external collaborators have featured, such as pieces on the
"Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using
"Representation and Imagination for Preventing AI Harms". The report is a
comprehensive overview of what the key issues in the field of AI ethics were in
2021, what trends are emergent, what gaps exist, and a peek into what to expect
from the field of AI ethics in 2022. It is a resource for researchers and
practitioners alike in the field to set their research and development agendas
to make contributions to the field of AI ethics.


title:
Mirror-symmetry protected topological phase in a zigzag ladder with
  staggered potential

abstract:
  We study spinless fermions on a zigzag ladder subjected to staggered on-site
potential along both of its legs. Three insulating phases, including a
charge-density-wave at half-filling, and two dimer insulators at quarter and
three-quarter fillings, are identified. The two dimer insulators admit
topological phases at opposite signs of the on-site potential, protected by the
underlying mirror-symmetry. The phases are characterized by a quantized value
of the Zak phase as well as of the entanglement entropy. By performing a
projection to the lower and upper two bands separately we show that the system
reveals an emergent chiral symmetry. Using density-matrix renormalization group
simulations, the topological phases are shown to be remarkably robust against
any amount of nearest-neighbor inter-leg as well as intra-leg interactions. Our
results are supported by quantum Monte-Carlo simulations in applicable limits
of the ladder.


title: Mathematical Cookbook for Snapshot Compressive Imaging

abstract:
  The author intends to provide you with a beautiful, elegant, user-friendly
cookbook for mathematics in Snapshot Compressive Imaging (SCI). Currently, the
cookbook is composed of introduction and conventional optimization, using
regularization-based optimization algorithms for SCI. The latest releases are
strongly recommended! For any other questions, suggestions, or comments, feel
free to email the author.


title: An Automated Analysis Framework for Trajectory Datasets

abstract:
  Trajectory datasets of road users have become more important in the last
years for safety validation of highly automated vehicles. Several naturalistic
trajectory datasets with each more than 10.000 tracks were released and others
will follow. Considering this amount of data, it is necessary to be able to
compare these datasets in-depth with ease to get an overview. By now, the
datasets' own provided information is mainly limited to meta-data and
qualitative descriptions which are mostly not consistent with other datasets.
This is insufficient for users to differentiate the emerging datasets for
application-specific selection. Therefore, an automated analysis framework is
proposed in this work. Starting with analyzing individual tracks, fourteen
elementary characteristics, so-called detection types, are derived and used as
the base of this framework. To describe each traffic scenario precisely, the
detections are subdivided into common metrics, clustering methods and anomaly
detection. Those are combined using a modular approach. The detections are
composed into new scores to describe three defined attributes of each track
data quantitatively: interaction, anomaly and relevance. These three scores are
calculated hierarchically for different abstract layers to provide an overview
not just between datasets but also for tracks, spatial regions and individual
situations. So, an objective comparison between datasets can be realized.
Furthermore, it can help to get a deeper understanding of the recorded
infrastructure and its effect on road user behavior. To test the validity of
the framework, a study is conducted to compare the scores with human
perception. Additionally, several datasets are compared.


title: Inclusive Study Group Formation At Scale

abstract:
  The student peer-group is one of the most important influences on student
development. Group work is essential for creating positive learning
experiences, especially in remote-learning where student interactions are more
challenging. While the benefits of study groups are established, students from
underrepresented communities often face challenges in finding social support
for their education when compared with those from majority groups. We present a
system for flexible and inclusive study group formation that can scale to
thousands of students.
  Our focus is on long-term study groups that persist throughout the semester
and beyond. Students are periodically provided opportunities to obtain a new
study group if they feel their current group is not a good fit. In contrast to
prior work that generates single-use groups, our process enables continuous
refinement of groups for each student, which in turn informs our algorithm for
future iterations.
  We trialed our approach in a 1000+ student introductory Electrical
Engineering and Computer Science course that was conducted entirely online
during the COVID-19 pandemic. We found that of all students matched to study
groups through our algorithm, a large majority felt comfortable asking
questions (78%) and sharing ideas (74%) with their group. Students from
underrepresented backgrounds were more likely to request software-matching for
study groups when compared with students from majority groups. However,
underrepresented students that we did match into study groups had group
experiences that were just as successful as students from' majority groups.
Students in engaged, regularly participating study groups had more positive
results across all other indicators of the study group experience, and certain
positive group experiences were associated with higher exam scores overall.


title:
Consistent lensing and clustering in a low-$S_8$ Universe with BOSS, DES
  Year 3, HSC Year 1 and KiDS-1000

abstract:
  We evaluate the consistency between lensing and clustering probes of
large-scale structure based on measurements of projected galaxy clustering from
BOSS combined with overlapping galaxy-galaxy lensing from three surveys: DES
Y3, HSC Y1, and KiDS-1000. An intra-lensing-survey study finds good agreement
between these lensing data. We model the observations using the Dark Emulator
and fit the data at two fixed cosmologies: Planck, with $S_8=0.83$, and a
Lensing cosmology with $S_8=0.76$. For a joint analysis limited to scales with
$R>5.25h^{-1}$Mpc, we find that both cosmologies provide an acceptable fit to
the data. Full utilisation of the small-scale clustering and lensing
measurements is hindered by uncertainty in the impact of baryon feedback and
assembly bias, which we account for with a reasoned theoretical error budget.
We incorporate a systematic scaling parameter for each redshift bin, $A$, that
decouples the lensing and clustering to capture any inconsistency. When a wide
range of scales ($0.15<R<60h^{-1}$Mpc) are incorporated, we find different
results for the consistency of clustering and lensing between the two
cosmologies. Limiting the analysis to the bins for which the impact of the
selection of the lens sample is expected to be minimal, for the low-$S_8$
Lensing cosmology, the measurements are consistent with $A$=1; $A=0.91\pm0.04$
using DES+KiDS and $A=0.97\pm0.06$ using HSC. For the Planck cosmology case, we
find a discrepancy: $A=0.79\pm0.03$ using DES+KiDS and $A=0.84\pm0.05$ using
HSC. We demonstrate that a kSZ-based estimate for baryonic effects alleviates
some of the discrepancy in the Planck cosmology. This analysis demonstrates the
statistical power of these small-scale measurements, but also indicates that
caution is still warranted given current uncertainties in modelling baryonic
effects, assembly bias, and selection effects in the foreground sample.


title:
Artificial Intelligence-Based Analytics for Impacts of COVID-19 and
  Online Learning on College Students' Mental Health

abstract:
  COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), was first
found in Wuhan, China late in the December of 2019. Not long after that the
virus spread worldwide and was declared a pandemic by the World Health
Organization in March 2020. This caused many changes around the world and in
the United States. One of these changes was the shift towards online learning.
In this paper, we seek to understand how the COVID-19 pandemic and online
learning impact college students' emotional wellbeing. To do this we use
several machine learning and statistical models to analyze data collected by
the Faculty of Public Administration at the University of Ljubljana, Slovenia
in conjunction with an international consortium of universities, other higher
education institutions and students' associations. Our results indicate that
learning modality (face-to-face, online synchronous, online asynchronous, etc.)
is the main predictor of students' emotional wellbeing, followed by financial
security. Factors such as satisfaction with their university's and government's
handling of the pandemic are also important predictors.


title: Open access to orbit and runaway space debris growth

abstract:
  As Earth's orbits fill with satellites and debris, debris-producing
collisions between orbiting bodies become more likely. Runaway space debris
growth, known as Kessler Syndrome, may render Earth's orbits unusable for
centuries. We present a dynamic physico-economic model of Earth orbit use under
rational expectations with endogenous collision risk and Kessler Syndrome. When
satellites can be destroyed in collisions with debris and other satellites, the
open-access equilibrium manifold allows for multiple steady states. When debris
can collide to produce more debris, at least one steady state may be a tipping
point and Kessler Syndrome can occur along equilibrium paths. We show open
access is increasingly and inefficiently likely to cause Kessler Syndrome as
satellites become more profitable. Calibrated simulations reveal Kessler
Syndrome is expected to occur in low-Earth orbit around 2048 under recent
historical sectoral growth trends, and may occur as early as 2035 if the space
economy grows consistent with projections by major investment banks. These
results highlight the urgent need for modeling and policy approaches which
incorporate open access and positive feedbacks in debris growth.


title: Spin orbital reorientation transitions induced by magnetic field

abstract:
  Here we report on a new effect similar to the spin reorientation transition
(SRT) that takes place at two magnetic fields of $B_{SORT1}$ and $B_{SORT2}$.
The effect is observed in the magnetization curves of small Mn$^{3+}$ magnetic
clusters in the wurtzite GaN (being in a paramagnetic state) calculated using
crystal field model approach. The obtained results suggest that the computed
magnetic anisotropy (MA) reverses its sign on increasing $B$ across $B_{SORT}$.
Detailed analysis show however that MA is unchanged for high magnetic fields.
We show that the observed effect arises from the interplay of the crystalline
environment and the spin-orbit coupling $\lambda \hat{\textbf{S}}
\hat{\textbf{L}}$, therefore we name it spin orbital reorientation transition
(SORT). The value of $B_{SORT1}$ depends on the crystal field model parameters
and the number of ions $N$ in a given cluster, whereas $B_{SORT2}$ is
controlled mostly by the magnitude of the spin-orbit coupling $\lambda$. The
explanation of SORT is given in terms of the spin $M_S$ and orbital momentum
$M_L$ contributions to the total magnetization $M = M_S + M_L$. The similar
effect should also be present in other materials with not completely quenched
(non zero) orbital angular momentum $L$, a uniaxial magnetic anisotropy and the
positive value of $\lambda$.


title: A Multipurpose Reduced Mechanism for Ethanol Combustion

abstract:
  New multipurpose skeletal and reduced chemical-kinetic mechanisms for ethanol
combustion are developed, along the same philosophical lines followed in our
previous work on methanol. The resulting skeletal mechanism contains 66
reactions, only 19 of which are reversible, among 31 species, and the
associated reduced mechanism contains 14 overall reactions among 16 species,
obtained from the skeletal mechanism by placing \ce{CH3CHOH}, \ce{CH2CH2OH},
\ce{CH3CO}, \ce{CH2CHO}, \ce{CH2CO}, \ce{C2H3}, \ce{C2H5}, \ce{C2H6},
\ce{S-CH2}, \ce{T-CH2}, \ce{CH4}, \ce{CH2OH}, \ce{CH3O}, \ce{HCO}, and \ce{O}
in steady state. For the reduced mechanism, the steady-state relations and rate
expressions are arranged so that computations can be made sequentially without
iteration. Comparison with experimental results for autoignition, laminar
burning velocities, and counterflow flame structure and extinction, including
comparisons with the 257-step, 54-species detailed San Diego Mechanism and five
other mechanisms in the literature, support the utility of the skeletal and
reduced mechanisms, showing, for example, that, in comparison with the San
Diego mechanism, they decrease the computational cost by 70\% and 80\%,
respectively. Measures of computation times and of extents of departures from
experimental values are defined and employed in evaluating results. Besides
contributing to improvements in understanding of the mechanisms, the derived
simplifications may prove useful in a variety of computational studies.


title: A Global Survey of Technological Resources and Datasets on COVID-19

abstract:
  The application and successful utilization of technological resources in
developing solutions to health, safety, and economic issues caused by COVID-19
indicate the importance of technology in curbing COVID-19. Also, the medical
field has had to race against tie to develop and distribute the COVID-19
vaccine. This endeavour became successful with the vaccines created and
approved in less than a year, a feat in medical history. Currently, much work
is being done on data collection, where all significant factors impacting the
disease are recorded. These factors include confirmed cases, death rates,
vaccine rates, hospitalization data, and geographic regions affected by the
pandemic. Continued research and use of technological resources are highly
recommendable-the paper surveys list of packages, applications and datasets
used to analyse COVID-19.


title: Relational Artificial Intelligence

abstract:
  The impact of Artificial Intelligence does not depend only on fundamental
research and technological developments, but for a large part on how these
systems are introduced into society and used in everyday situations. Even
though AI is traditionally associated with rational decision making,
understanding and shaping the societal impact of AI in all its facets requires
a relational perspective. A rational approach to AI, where computational
algorithms drive decision making independent of human intervention, insights
and emotions, has shown to result in bias and exclusion, laying bare societal
vulnerabilities and insecurities. A relational approach, that focus on the
relational nature of things, is needed to deal with the ethical, legal,
societal, cultural, and environmental implications of AI. A relational approach
to AI recognises that objective and rational reasoning cannot does not always
result in the 'right' way to proceed because what is 'right' depends on the
dynamics of the situation in which the decision is taken, and that rather than
solving ethical problems the focus of design and use of AI must be on asking
the ethical question. In this position paper, I start with a general discussion
of current conceptualisations of AI followed by an overview of existing
approaches to governance and responsible development and use of AI. Then, I
reflect over what should be the bases of a social paradigm for AI and how this
should be embedded in relational, feminist and non-Western philosophies, in
particular the Ubuntu philosophy.


title:
Trustworthy Autonomous Systems (TAS): Engaging TAS experts in curriculum
  design

abstract:
  Recent advances in artificial intelligence, specifically machine learning,
contributed positively to enhancing the autonomous systems industry, along with
introducing social, technical, legal and ethical challenges to make them
trustworthy. Although Trustworthy Autonomous Systems (TAS) is an established
and growing research direction that has been discussed in multiple disciplines,
e.g., Artificial Intelligence, Human-Computer Interaction, Law, and Psychology.
The impact of TAS on education curricula and required skills for future TAS
engineers has rarely been discussed in the literature. This study brings
together the collective insights from a number of TAS leading experts to
highlight significant challenges for curriculum design and potential TAS
required skills posed by the rapid emergence of TAS. Our analysis is of
interest not only to the TAS education community but also to other researchers,
as it offers ways to guide future research toward operationalising TAS
education.


title:
Towards a Unified Pandemic Management Architecture: Survey, Challenges
  and Future Directions

abstract:
  The pandemic caused by SARS-CoV-2 has left an unprecedented impact on health,
economy and society worldwide. Emerging strains are making pandemic management
increasingly challenging. There is an urge to collect epidemiological,
clinical, and physiological data to make an informed decision on mitigation
measures. Advances in the Internet of Things (IoT) and edge computing provide
solutions for pandemic management through data collection and intelligent
computation. While existing data-driven architectures attempt to automate
decision-making, they do not capture the multifaceted interaction among
computational models, communication infrastructure, and the generated data. In
this paper, we perform a survey of the existing approaches for pandemic
management, including online data repositories and contact-tracing
applications. We then envision a unified pandemic management architecture that
leverages the IoT and edge computing to automate recommendations on vaccine
distribution, dynamic lockdown, mobility scheduling and pandemic prediction. We
elucidate the flow of data among the layers of the architecture, namely, cloud,
edge and end device layers. Moreover, we address the privacy implications,
threats, regulations, and existing solutions that may be adapted to optimize
the utility of health data with security guarantees. The paper ends with a
lowdown on the limitations of the architecture and research directions to
enhance its practicality.


title: Elementary conjugated fragments in acyclic polyenes with heteroatoms

abstract:
  The study is aimed at revealing the most important substructures (fragments)
of polyenes with heteroatoms determining the alteration in the conjugation
energy of the whole compound due to substitution and the relevant charge
redistribution. The systems are modelled as sets of weakly-interacting
formally-double bonds, where the formally-single bonds represent the
interaction. Expressions for total energies and populations of basis orbitals
are then derived in the form of power series with respect to two small
parameters. Analysis of these series shows that conjugated substructures
consisting of two connected formally-double bonds and containing at least s
single heteroatom play the role of fragments being sought. Nine potential
fragments of this type are considered separately that differ one from another
in the number of heteroatoms and/or their relative positions inside and are
called elementary conjugated fragments.


title:
Nonlinear tidal excitation of super-harmonic gravity waves in
  main-sequence stars in binary and exoplanetary systems

abstract:
  We study the role of nonlinear effects on tidally-excited internal gravity
waves in stellar radiation zones in exoplanetary or binary systems. We are
partly motivated to study tides due to massive short-period hot Jupiters, which
preferentially orbit stars with convective cores, for which wave breaking near
the stellar centre cannot operate. We develop a theory (and test it with
numerical calculations) for the nonlinear excitation of super-harmonic
"secondary" waves (with frequencies $2\omega_p$) by a "primary" tidal wave
(with frequency $\omega_p$) near the interface between the radiation zone and
convective envelope. These waves have the same horizontal phase speeds to
leading order, and this nonlinear effect could contribute importantly to tidal
dissipation if the secondary waves can efficiently damp the primary. We derive
criteria involving the orbital and stellar parameters required to excite these
secondary waves to large amplitudes using a local model of the
radiative/convective interface, which we convert to apply to tides in a
spherical star. We numerically evaluate the critical amplitudes required for
this new nonlinear effect to become important using stellar models, comparing
them to the "conventional" criteria for wave breaking in radiative cores and
the application of WKBJ theory near convective cores. The criteria for this new
effect are easier to satisfy than the conventional measures of nonlinearity in
$1.4$ and $2M_\odot$ stars on the main-sequence. We predict nonlinear effects
to be important even for planetary-mass companions around the latter, but this
effect is probably less important in stars with radiative cores.


title:
Phenotyping with Positive Unlabelled Learning for Genome-Wide
  Association Studies

abstract:
  Identifying phenotypes plays an important role in furthering our
understanding of disease biology through practical applications within
healthcare and the life sciences. The challenge of dealing with the
complexities and noise within electronic health records (EHRs) has motivated
applications of machine learning in phenotypic discovery. While recent research
has focused on finding predictive subtypes for clinical decision support, here
we instead focus on the noise that results in phenotypic misclassification,
which can reduce a phenotypes ability to detect associations in genome-wide
association studies (GWAS). We show that by combining anchor learning and
transformer architectures into our proposed model, AnchorBERT, we are able to
detect genomic associations only previously found in large consortium studies
with 5$\times$ more cases. When reducing the number of controls available by
50\%, we find our model is able to maintain 40\% more significant genomic
associations from the GWAS catalog compared to standard phenotype definitions.
\keywords{Phenotyping \and Machine Learning \and Semi-Supervised \and Genetic
Association Studies \and Biological Discovery}


abstract:
  A proof of the Borel completeness of torsion free abelian groups is
presented. This proof differs considerably from the approach of Paolini-Shelah.


title: Random Walks for Adversarial Meshes

abstract:
  A polygonal mesh is the most-commonly used representation of surfaces in
computer graphics; thus, a variety of classification networks have been
recently proposed. However, while adversarial attacks are wildly researched in
2D, almost no works on adversarial meshes exist. This paper proposes a novel,
unified, and general adversarial attack, which leads to misclassification of
numerous state-of-the-art mesh classification neural networks. Our attack
approach is black-box, i.e. it has access only to the network's predictions,
but not to the network's full architecture or gradients. The key idea is to
train a network to imitate a given classification network. This is done by
utilizing random walks along the mesh surface, which gather geometric
information. These walks provide insight onto the regions of the mesh that are
important for the correct prediction of the given classification network. These
mesh regions are then modified more than other regions in order to attack the
network in a manner that is barely visible to the naked eye.


title: A Note on the Koethe Conjecture

abstract:
  In this note we provide some results related to the Koethe conjecture and
exhibit that the condition R satisfies the Koethe conjecture given in [2,
theorem 2.6 ] is superfluous at least under certain conditions described in
this note.


title:
Transforming agrifood production systems and supply chains with digital
  twins

abstract:
  Digital twins can transform agricultural production systems and supply
chains, curbing greenhouse gas emissions, food waste and malnutrition. However,
the potential of these advanced virtualization technologies is yet to be
realized. Here, we consider the promise of digital twins across five typical
agrifood supply chain steps and emphasize key implementation barriers.


title: A Survey on Image Deblurring

abstract:
  With the improvement of social life quality and the real needs of daily work,
images are more and more all around us. Image blurring due to camera shake,
human movement, etc. has become the key to affecting image quality. How to
remove image blur and restore clear image has gradually become an important
research direction in the field of computer vision. After more than half a
century of unremitting efforts, the majority of scientific and technological
workers have made fruitful progress in image deblurring. This article reviews
the work of image deblurring and specifically introduces more classic image
deblurring methods, which is helpful to understand current research and look
forward to future trends. This article reviews the traditional image deblurring
methods and depth-represented image deblurring methods, and comprehensively
classifies and introduces the corresponding technical methods. This review can
provide some guidance for researchers in the field of image deblurring, and at
the same time facilitate their subsequent study and research.


title:
Teacher and Student Experiences in Online Classes During COVID-19
  Pandemic in Serbia, Bosnia and Herzegovina and Croatia

abstract:
  In March 2020, the World Health Organization declared the COVID pandemic,
which caused interruptions and delays in many activities, but most importantly,
it led to some huge changes in education. Online teaching will prove to be the
most commonly used method that should compensate for the inability to work in
the classroom and allow the educational process to continue. Of course, this
teaching method was not created in 2020, but it was only presented and
implemented in Serbia, Bosnia and Herzegovina and Croatia with the beginning of
the pandemic. In this paper, we see how these countries have faced abrupt
changes in teaching, and how this change has affected students. Online teaching
cannot be a mere transfer of analog content to digital; a different approach is
needed in the implementation of teaching as required and offered by the digital
medium, but at the same time it is necessary to preserve the basic principles
of the lecturer and the curriculum. It is a call, both for teachers and
students. Since this is a current and universal problem, we hope that the
conclusions presented are useful.


title:
IMPACT: Integrated Multi-Domain Emission Pathways For Cities Under
  Land-Use Policy, Technology Adoption, Climate Change And Grid Decarbonization

abstract:
  Increasing urbanization puts ever-increasing pressure on cities to prioritize
sustainable growth and avoid carbon lock-in, yet available modeling frameworks
and tools fall acutely short of robustly guiding such pivotal decision-making
at the local level. Financial incentives, behavioral interventions, and
mandates can drive technology adoption, while land-use zoning policies provide
the framework for development of the built environment. Often policies and
their impacts are evaluated top down, typically on a national scale, or
post-hoc on developments vis-\`a-vis different policies in the past. Such
high-level analyses and post-hoc evaluations cannot show possible developments
pathways for specific cities, and hence cannot serve as input to policymakers
at the local level such as county, municipal, or city governments. Here, we
present IMPACT pathways from a bottom-up model with residence level granularity
that integrate policy for technology adoption with zoning policy, climate
change, and grid decarbonization scenarios. With the city at the heart of the
analysis, our results show potential greenhouse gas emission pathways, identify
synergies and trade-offs between policies, and show the importance of
appropriate baselines.


title: On centrally extended Jordan derivations and related maps in rings

abstract:
  Let $R$ be a ring and $Z(R)$ be the center of $R.$ The aim of this paper is
to define the notions of centrally extended Jordan derivations and centrally
extended Jordan $\ast$-derivations, and to prove some results involving these
mappings. Precisely, we prove that if a $2$-torsion free noncommutative prime
ring $R$ admits a centrally extended Jordan derivation (resp. centrally
extended Jordan $\ast$-derivation) $\delta:R\to R$ such that \[
[\delta(x),x]\in Z(R)~~(resp.~~[\delta(x),x^{\ast}]\in
Z(R))\text{~for~all~}x\in R, \] where $'\ast'$ is an involution on $R,$ then
$R$ is an order in a central simple algebra of dimension at most 4 over its
center.


title:
In-medium $\Lambda$ isospin impurity from charge symmetry breaking in
  the ${_{\Lambda}^4}{\rm H}-{_{\Lambda}^4}{\rm He}$ mirror hypernuclei

abstract:
  The $\Lambda$ separation energies in the mirror hypernuclei
${_{\Lambda}^4}{\rm H}-{_{\Lambda}^4}{\rm He}$ exhibit large charge symmetry
breaking (CSB). Analyzing this CSB within pionless effective field theory while
using partially conserved baryon-baryon SU(3) flavor symmetry, we deduce a
$\Lambda -\Sigma^0$ induced in-medium admixture amplitude ${\cal
A}_{I=1}\approx 1.5\%$ in the dominantly isospin $I=0$ $\Lambda$ hyperon. Our
results confirm the free-space value ${\cal A}^{(0)}_{I=1}$ inferred directly
within the SU(3) baryon octet by Dalitz and von-Hippel in 1964 and reaffirmed
in a recent QCD+QED lattice calculation. Furthermore, exploring the
consequences of SU(3) flavor symmetry on the $\Lambda$-nucleon interaction, we
find that CSB is expected to impact the $S=1$ and $S=0$ spin channels in
opposite directions, with the latter dominating by an order of magnitude. These
observations explain a recent deduction of $\Lambda$-nucleon CSB strengths.


title:
Statistical method for analysis of interactions between chosen protein
  and chondroitin sulfate in an aqueous environment

abstract:
  We present the statistical method to study the interaction between a chosen
protein and another molecule (e.g., both being components of lubricin found in
synovial fluid) in a water environment. The research is performed on the
example of univariate time series of chosen features of the dynamics of mucin,
which interact with chondroitin sulfate (4 and 6) in four different saline
solutions. Our statistical approach is based on recurrence methods to analyze
chosen features of molecular dynamics. Such recurrence methods are usually
applied to reconstruct the evolution of a molecular system in its reduced phase
space, where the most important variables in the process are taken into
account. In detail, the analyzed time-series are spitted onto sub-series of
records that are expected to carry meaningful information about the system of
molecules. Elements of sub-series are splinted by the constant delay-time lag
(that is the parameter determined by statistical testing in our case), and the
length of sub-series is the embedded dimension parameter (using the Cao
method). We use the recurrent plots approach combined with the Shannon entropy
approach to analyze the robustness of the sub-series determination. We
hypothesize that the robustness of the sub-series determines some specifics of
the dynamics of the system of molecules. We analyze rather highly noised
features to demonstrate that such features lead to recurrence plots that
graphically look similar. From the recurrence plots, the Shannon entropy has
been computed. We have, however, demonstrated that the Shannon entropy value is
highly dependent on the delay time value for analyzed features. Hence,
elaboration of a more precise method of the recurrence plot analysis is
required. For this reason, we suggest the random walk method that can be
applied to analyze the recurrence plots automatically.


title:
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of
  RNN inference

abstract:
  Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn
temporal dependencies by keeping an internal state, making them ideal for
time-series problems such as speech recognition. However, the output-to-input
feedback creates distinctive memory bandwidth and scalability challenges in
designing accelerators for RNNs. We present Muntaniala, an RNN accelerator
architecture for LSTM inference with a silicon-measured energy-efficiency of
3.25$TOP/s/W$ and performance of 30.53$GOP/s$ in UMC 65 $nm$ technology. The
scalable design of Muntaniala allows running large RNN models by combining
multiple tiles in a systolic array. We keep all parameters stationary on every
die in the array, drastically reducing the I/O communication to only loading
new features and sharing partial results with other dies. For quantifying the
overall system power, including I/O power, we built Vau da Muntanialas, to the
best of our knowledge, the first demonstration of a systolic multi-chip-on-PCB
array of RNN accelerator. Our multi-die prototype performs LSTM inference with
192 hidden states in 330$\mu s$ with a total system power of 9.0$mW$ at 10$MHz$
consuming 2.95$\mu J$. Targeting the 8/16-bit quantization implemented in
Muntaniala, we show a phoneme error rate (PER) drop of approximately 3% with
respect to floating-point (FP) on a 3L-384NH-123NI LSTM network on the TIMIT
dataset.


title:
Can Online Customer Reviews Help Design More Sustainable Products? A
  Preliminary Study on Amazon Climate Pledge Friendly Products

abstract:
  Online product reviews are a valuable resource for product developers to
improve the design of their products. Yet, the potential value of customer
feedback to improve the sustainability performance of products is still to be
exploited. The present paper investigates and analyzes Amazon product reviews
to bring new light on the following question: ``What sustainable design
insights can be identified or interpreted from online product reviews?''. To do
so, the top 100 reviews, evenly distributed by star ratings, for three product
categories (laptop, printer, cable) are collected, manually annotated, analyzed
and interpreted. For each product category, the reviews of two similar products
(one with environmental certification and one standard version) are compared
and combined to come up with sustainable design solutions. In all, for the six
products considered, between 12% and 20% of the reviews mentioned directly or
indirectly aspects or attributes that could be exploited to improve the design
of these products from a sustainability perspective. Concrete examples of
sustainable design leads that could be elicited from product reviews are given
and discussed. As such, this contribution provides a baseline for future work
willing to automate this process to gain further insights from online product
reviews. Notably, the deployment of machine learning tools and the use of
natural language processing techniques to do so are discussed as promising
lines for future research.


title: DeepSensor: Deep Learning Testing Framework Based on Neuron Sensitivity

abstract:
  Despite impressive capabilities and outstanding performance, deep neural
network(DNN) has captured increasing public concern for its security problem,
due to frequent occurrence of erroneous behaviors. Therefore, it is necessary
to conduct systematically testing before its deployment to real-world
applications. Existing testing methods have provided fine-grained criteria
based on neuron coverage and reached high exploratory degree of testing. But
there is still a gap between the neuron coverage and model's robustness
evaluation. To bridge the gap, we observed that neurons which change the
activation value dramatically due to minor perturbation are prone to trigger
incorrect corner cases. Motivated by it, we propose neuron sensitivity and
develop a novel white-box testing framework for DNN, donated as DeepSensor. The
number of sensitive neurons is maximized by particle swarm optimization, thus
diverse corner cases could be triggered and neuron coverage be further improved
when compared with baselines. Besides, considerable robustness enhancement can
be reached when adopting testing examples based on neuron sensitivity for
retraining. Extensive experiments implemented on scalable datasets and models
can well demonstrate the testing effectiveness and robustness improvement of
DeepSensor.


title: Numerical investigation of a segmented-blade ion trap with biasing rods

abstract:
  We report a numerical study of a linear ion trap that has segmented blades
and biasing rods. Our system consists of radio frequency (rf) blades, dc blades
with ten separate electrodes, and two biasing rods for compensating the ions'
micromotion. After calculating the optical access for the ions, we find rf and
dc voltages that result in a stable trapping configuration of $^{171}$Yb$^{+}$
ions. We also explore the micromotion compensation with the biasing rods, and
calculate the influence of blade misalignment to the trap potential. Our work
offers quantitative understanding of the trap architecture, assisting reliable
operation of an ion-trap quantum computer.


title:
Perspectives on risk prioritization of data center vulnerabilities using
  rank aggregation and multi-objective optimization

abstract:
  Nowadays, data has become an invaluable asset to entities and companies, and
keeping it secure represents a major challenge. Data centers are responsible
for storing data provided by software applications. Nevertheless, the number of
vulnerabilities has been increasing every day. Managing such vulnerabilities is
essential for building a reliable and secure network environment. Releasing
patches to fix security flaws in software is a common practice to handle these
vulnerabilities. However, prioritization becomes crucial for organizations with
an increasing number of vulnerabilities since time and resources to fix them
are usually limited. This review intends to present a survey of vulnerability
ranking techniques and promote a discussion on how multi-objective optimization
could benefit the management of vulnerabilities risk prioritization. The
state-of-the-art approaches for risk prioritization were reviewed, intending to
develop an effective model for ranking vulnerabilities in data centers. The
main contribution of this work is to point out multi-objective optimization as
a not commonly explored but promising strategy to prioritize vulnerabilities,
enabling better time management and increasing security.


title:
The Perception of Filipinos on the Advent of Cryptocurrency and
  Non-Fungible Token (NFT) Games

abstract:
  This study aims to shed light on the rise of play-to-earn games in the
Philippines alongside cryptocurrency. The lack of research and public
understanding of its benefits and drawbacks prompted the researchers to
investigate its market. As such, the study tried to look into the risks and
benefits of crypto gaming if it would be regulated by the government, and how
market volatility influences the churn rate of crypto games. The research used
a descriptive study to determine the perception of people who are engaged in
playing a crypto-game named as Axie Infinity. The results showed that most
players spend their time playing Axie Infinity for about 1 to 4 hours a day.
Predominantly, the return of investments for playing the game will take about 1
to 3 months. It also showed that these players agreed that there is a possible
financial instability in a volatile market. With this, they have a high trust
issue in terms of price manipulation, privacy and security, and its design and
usability. Understanding the cryptocurrency market requires comprehending the
perspective of the people who are engaged in a play-to-earn game, and their
concerns are critical for any government actions aimed at regulating
self-employed income earners playing (Non-fungible Tokens) NFT games in the
Philippines.


title:
Theoretical Analysis of SIRVVD Model to Provide Insight on the Target
  Rate of COVID-19/SARS-CoV-2 Vaccination in Japan

abstract:
  The effectiveness of the first and second dose vaccinations are different for
COVID-19; therefore, a
susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model
that can represent the states of the first and second vaccination doses has
been proposed. By the previous study, we can carry out simulating the spread of
infectious disease considering the effects of the first and second doses of the
vaccination based on the SIRVVD model. However, theoretical analysis of the
SIRVVD Model is insufficient. Therefore, we obtained an analytical expression
of the infectious number, by treating the numbers of susceptible persons and
vaccinated persons as parameters. We used the solution to determine the target
rate of the vaccination for decreasing the infection numbers of the COVID-19
Delta variant (B.1.617) in Japan. Further, we investigated the target
vaccination rates for cases with strong or weak variants by comparison with the
COVID-19 Delta variant (B.1.617). This study contributes to the mathematical
development of the SIRVVD model and provides insight into the target rate of
the vaccination to decrease the number of infections.


title:
Anisotropic stars in modified gravity: An extended gravitational
  decoupling approach

abstract:
  In the work, we present investigation on decoupling gravitational sources
under the framework of $f(R,T)$ gravity. Basically the complete geometric
deformation technique has been employed here which facilitates finding exact
solutions to the anisotropic astrophysical system smoothly without imposing any
particular ansatz for deformation function. Along with this we have also used
5-dimensional Euclidean spacetime in order to describe the embedding Class I
spacetime for getting a solvable spherical physical system. The solutions thus
obtained show physically interesting as well as viable with new possibilities
to sought for. Especially, from the present investigation it is worthy to note
that the mixture $f(R,T)$ + CGD translate the scenario beyond the pure GR realm
and hence clearly helps to enhance the features of the interior astrophysical
aspects of compact stellar objects. Therefore to check the physical
acceptability and stability of the stellar system based on the obtained
solutions, we have performed a few physical tests which satisfy all the
stability criteria, including nonsingular nature of the density as well as
pressure.


title:
Federated Contrastive Learning for Dermatological Disease Diagnosis via
  On-device Learning

abstract:
  Deep learning models have been deployed in an increasing number of edge and
mobile devices to provide healthcare. These models rely on training with a
tremendous amount of labeled data to achieve high accuracy. However, for
medical applications such as dermatological disease diagnosis, the private data
collected by mobile dermatology assistants exist on distributed mobile devices
of patients, and each device only has a limited amount of data. Directly
learning from limited data greatly deteriorates the performance of learned
models. Federated learning (FL) can train models by using data distributed on
devices while keeping the data local for privacy. Existing works on FL assume
all the data have ground-truth labels. However, medical data often comes
without any accompanying labels since labeling requires expertise and results
in prohibitively high labor costs. The recently developed self-supervised
learning approach, contrastive learning (CL), can leverage the unlabeled data
to pre-train a model, after which the model is fine-tuned on limited labeled
data for dermatological disease diagnosis. However, simply combining CL with FL
as federated contrastive learning (FCL) will result in ineffective learning
since CL requires diverse data for learning but each device only has limited
data. In this work, we propose an on-device FCL framework for dermatological
disease diagnosis with limited labels. Features are shared in the FCL
pre-training process to provide diverse and accurate contrastive information.
After that, the pre-trained model is fine-tuned with local labeled data
independently on each device or collaboratively with supervised federated
learning on all devices. Experiments on dermatological disease datasets show
that the proposed framework effectively improves the recall and precision of
dermatological disease diagnosis compared with state-of-the-art methods.


title:
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian
  Approximation

abstract:
  Quantization of deep neural networks (DNN) has been proven effective for
compressing and accelerating DNN models. Data-free quantization (DFQ) is a
promising approach without the original datasets under privacy-sensitive and
confidential scenarios. However, current DFQ solutions degrade accuracy, need
synthetic data to calibrate networks, and are time-consuming and costly. This
paper proposes an on-the-fly DFQ framework with sub-second quantization time,
called SQuant, which can quantize networks on inference-only devices with low
computation and memory requirements. With the theoretical analysis of the
second-order information of DNN task loss, we decompose and approximate the
Hessian-based optimization objective into three diagonal sub-items, which have
different areas corresponding to three dimensions of weight tensor:
element-wise, kernel-wise, and output channel-wise. Then, we progressively
compose sub-items and propose a novel data-free optimization objective in the
discrete domain, minimizing Constrained Absolute Sum of Error (or CASE in
short), which surprisingly does not need any dataset and is even not aware of
network architecture. We also design an efficient algorithm without
back-propagation to further reduce the computation complexity of the objective
solver. Finally, without fine-tuning and synthetic datasets, SQuant accelerates
the data-free quantization process to a sub-second level with >30% accuracy
improvement over the existing data-free post-training quantization works, with
the evaluated models under 4-bit quantization. We have open-sourced the SQuant
framework at https://github.com/clevercool/SQuant.


title: Sequential Bayesian experimental designs via reinforcement learning

abstract:
  Bayesian experimental design (BED) has been used as a method for conducting
efficient experiments based on Bayesian inference. The existing methods,
however, mostly focus on maximizing the expected information gain (EIG); the
cost of experiments and sample efficiency are often not taken into account. In
order to address this issue and enhance practical applicability of BED, we
provide a new approach Sequential Experimental Design via Reinforcement
Learning to construct BED in a sequential manner by applying reinforcement
learning in this paper. Here, reinforcement learning is a branch of machine
learning in which an agent learns a policy to maximize its reward by
interacting with the environment. The characteristics of interacting with the
environment are similar to the sequential experiment, and reinforcement
learning is indeed a method that excels at sequential decision making.
  By proposing a new real-world-oriented experimental environment, our approach
aims to maximize the EIG while keeping the cost of experiments and sample
efficiency in mind simultaneously. We conduct numerical experiments for three
different examples. It is confirmed that our method outperforms the existing
methods in various indices such as the EIG and sampling efficiency, indicating
that our proposed method and experimental environment can make a significant
contribution to application of BED to the real world.


title: RNASeq and VariantSeq applications and Server Side of GPRO suite

abstract:
  "RNASeq" and "VariantSeq" are two cross-platform desktop applications of the
GPRO suite customized for comparative transcriptomics and sequence variant
analysis, respectively. "RNASeq" provides a protocol with two alternative
paths: (1) to perform analyses using reference genomes and GTF/GFF files and
(2) to perform analyses based on quantification of reads mapped against
references with no GTF/GFF. "VariantSeq" offers a versatile and effective
toolset for characterizing Single Point Mutations and Indels. Both applications
provide interfaces to manage tools and workflows that are based on the most
common command line software and databases for each topic. To this extent,
"RNASeq" and "VariantSeq" are both coupled to a Server-Side bioinformatic
infrastructure that provides and manages users accounts, pipelines and software
tools dependencies, databases, tracking and logging. Both applications provide
two execution modes: (1) a "Pipeline mode" to automatically run all steps of a
chosen protocol in sequence and (2) a "Step-by-Step" mode that enables a manual
execution of a subset of tasks. The Server-Side is provided as a Docker
container image that can be deployed on remote servers or on the users PC as
long as there is enough RAM and disk space.


title: Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?

abstract:
  The triplet loss with semi-hard negatives has become the de facto choice for
image-caption retrieval (ICR) methods that are optimized from scratch. Recent
progress in metric learning has given rise to new loss functions that
outperform the triplet loss on tasks such as image retrieval and representation
learning. We ask whether these findings generalize to the setting of ICR by
comparing three loss functions on two ICR methods. We answer this question
negatively: the triplet loss with semi-hard negative mining still outperforms
newly introduced loss functions from metric learning on the ICR task. To gain a
better understanding of these outcomes, we introduce an analysis method to
compare loss functions by counting how many samples contribute to the gradient
w.r.t. the query representation during optimization. We find that loss
functions that result in lower evaluation scores on the ICR task, in general,
take too many (non-informative) samples into account when computing a gradient
w.r.t. the query representation, which results in sub-optimal performance. The
triplet loss with semi-hard negatives is shown to outperform the other loss
functions, as it only takes one (hard) negative into account when computing the
gradient.


title:
A Real-time System for Detecting Landslide Reports on Social Media using
  Artificial Intelligence

abstract:
  This paper presents an online system that leverages social media data in real
time to identify landslide-related information automatically using
state-of-the-art artificial intelligence techniques. The designed system can
(i) reduce the information overload by eliminating duplicate and irrelevant
content, (ii) identify landslide images, (iii) infer geolocation of the images,
and (iv) categorize the user type (organization or person) of the account
sharing the information. The system was deployed in February 2020 online at
https://landslide-aidr.qcri.org/landslide_system.php to monitor live Twitter
data stream and has been running continuously since then to provide
time-critical information to partners such as British Geological Survey and
European Mediterranean Seismological Centre. We trust this system can both
contribute to harvesting of global landslide data for further research and
support global landslide maps to facilitate emergency response and decision
making.


title:
MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional
  Variational Autoencoder

abstract:
  The ultimate goal of various fields is to directly generate molecules with
desired properties, such as finding water-soluble molecules in drug development
and finding molecules suitable for organic light-emitting diode (OLED) or
photosensitizers in the field of development of new organic materials. In this
respect, this study proposes a molecular graph generative model based on the
autoencoder for de novo design. The performance of molecular graph conditional
variational autoencoder (MGCVAE) for generating molecules having specific
desired properties is investigated by comparing it to molecular graph
variational autoencoder (MGVAE). Furthermore, multi-objective optimization for
MGCVAE was applied to satisfy two selected properties simultaneously. In this
study, two physical properties -- logP and molar refractivity -- were used as
optimization targets for the purpose of designing de novo molecules, especially
in drug discovery. As a result, it was confirmed that among generated
molecules, 25.89% optimized molecules were generated in MGCVAE compared to
0.66% in MGVAE. Hence, it demonstrates that MGCVAE effectively produced
drug-like molecules with two target properties. The results of this study
suggest that these graph-based data-driven models are one of the effective
methods of designing new molecules that fulfill various physical properties,
such as drug discovery.


title: Understanding DDPM Latent Codes Through Optimal Transport

abstract:
  Diffusion models have recently outperformed alternative approaches to model
the distribution of natural images, such as GANs. Such diffusion models allow
for deterministic sampling via the probability flow ODE, giving rise to a
latent space and an encoder map. While having important practical applications,
such as estimation of the likelihood, the theoretical properties of this map
are not yet fully understood. In the present work, we partially address this
question for the popular case of the VP SDE (DDPM) approach. We show that,
perhaps surprisingly, the DDPM encoder map coincides with the optimal transport
map for common distributions; we support this claim theoretically and by
extensive numerical experiments.

categories: stat.ML cs.AI cs.LG cs.NA math.AP math.NA


title:
Rigorous multi-asset optimal execution with Bayesian learning of the
  drift

abstract:
  Liquidity issues have been increasingly addressed recently, especially with
regards to optimal execution of large orders. In practice, agents facing these
issues are uncertain about the future dynamics of the assets, and face the risk
of model misspecification, which could make the execution strategies non
optimal. In this paper, we address the problem of uncertainty faced by an agent
wishing to execute large orders on multiple assets. The agent only has
knowledge about the distribution of the future drift of the assets composing
her portfolio. We build on the work in [13] who proposed a model coupling
Bayesian learning and dynamic programming techniques. More precisely, in this
article, we provide a rigorous solution to the problem of portfolio optimal
execution where prices have drifted Bachelier dynamics with an unknown drift.
The agent uses Bayesian learning to update her estimation of the drift, while
she maximizes the expected exponential utility of her final wealth. We consider
the specific case where the prior is a non-degenerate multivariate Gaussian,
and the costs are quadratic. We use stochastic optimal control tools to show
how the problem of optimal execution simplifies into a system of ordinary
differential equations (ODEs) which involves a matrix Riccati ODE with
time-dependent coefficients for which classical existence theorems do not
apply. However, using a method similar to the one in [10], we provide a
rigorous solution to the problem by using a priori estimates obtained thanks to
the original control problem.


title: Audio Inpainting via $\ell_1$-Minimization and Dictionary Learning

abstract:
  Audio inpainting refers to signal processing techniques that aim at restoring
missing or corrupted consecutive samples in audio signals. Prior works have
shown that $\ell_1$- minimization with appropriate weighting is capable of
solving audio inpainting problems, both for the analysis and the synthesis
models. These models assume that audio signals are sparse with respect to some
redundant dictionary and exploit that sparsity for inpainting purposes.
Remaining within the sparsity framework, we utilize dictionary learning to
further increase the sparsity and combine it with weighted
$\ell_1$-minimization adapted for audio inpainting to compensate for the loss
of energy within the gap after restoration. Our experiments demonstrate that
our approach is superior in terms of signal-to-distortion ratio (SDR) and
objective difference grade (ODG) compared with its original counterpart.


title:
Fast Symbolic Algorithms for Omega-Regular Games under Strong Transition
  Fairness

abstract:
  We consider fixpoint algorithms for two-player games on graphs with
$\omega$-regular winning conditions, where the environment is constrained by a
strong transition fairness assumption. Strong transition fairness is a widely
occurring special case of strong fairness, which requires that any execution is
strongly fair with respect to a specified set of live edges: whenever the
source vertex of a live edge is visited infinitely often along a play, the edge
itself is traversed infinitely often along the play as well.
  We show that, surprisingly, strong transition fairness retains the
algorithmic characteristics of the fixpoint algorithms for $\omega$-regular
games -- the new algorithms can be obtained simply by replacing certain
occurrences of the controllable predecessor by a new almost sure predecessor
operator. For Rabin games with $k$ pairs, the complexity of the new algorithm
is $O(n^{k+2}k!)$ symbolic steps, which is independent of the number of live
edges in the strong transition fairness assumption. Further, we show that GR(1)
specifications with strong transition fairness assumptions can be solved with a
3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong
fairness necessarily requires increasing the alternation depth depending on the
number of fairness assumptions.
  We get symbolic algorithms for (generalized) Rabin, parity and GR(1)
objectives under strong transition fairness assumptions as well as a direct
symbolic algorithm for qualitative winning in stochastic $\omega$-regular games
that runs in $O(n^{k+2}k!)$ symbolic steps, improving the state of the art.
  Finally, we have implemented a BDD-based synthesis engine based on our
algorithm. We show on a set of synthetic and real benchmarks that our algorithm
is scalable, parallelizable, and outperforms previous algorithms by orders of
magnitude.


title:
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural
  Networks

abstract:
  CNN architectures are generally heavy on memory and computational
requirements which makes them infeasible for embedded systems with limited
hardware resources. We propose dual convolutional kernels (DualConv) for
constructing lightweight deep neural networks. DualConv combines 3$\times$3 and
1$\times$1 convolutional kernels to process the same input feature map channels
simultaneously and exploits the group convolution technique to efficiently
arrange convolutional filters. DualConv can be employed in any CNN model such
as VGG-16 and ResNet-50 for image classification, YOLO and R-CNN for object
detection, or FCN for semantic segmentation. In this paper, we extensively test
DualConv for classification since these network architectures form the
backbones for many other tasks. We also test DualConv for image detection on
YOLO-V3. Experimental results show that, combined with our structural
innovations, DualConv significantly reduces the computational cost and number
of parameters of deep neural networks while surprisingly achieving slightly
higher accuracy than the original models in some cases. We use DualConv to
further reduce the number of parameters of the lightweight MobileNetV2 by 54%
with only 0.68% drop in accuracy on CIFAR-100 dataset. When the number of
parameters is not an issue, DualConv increases the accuracy of MobileNetV1 by
4.11% on the same dataset. Furthermore, DualConv significantly improves the
YOLO-V3 object detection speed and improves its accuracy by 4.4% on PASCAL VOC
dataset.


title: A ring of spikes

abstract:
  For the Schnakenberg model, we consider a highly symmetric configuration of N
spikes whose locations are located at the vertices of a regular N-gon inside
either a unit disk or an annulus. We call such configuration a ring of spikes.
The ring radius is characterized in terms of the modified Green's function. For
a disk, we find that a ring of 9 or more spikes is always unstable with respect
to small eigenvalues. Conversely, a ring of 8 or less spikes is stable inside a
disk provided that the feed-rate $A$ is sufficiently large. More generally, for
sufficiently high feed-rate, a ring of $N$ spikes can be stabilized provided
that the annulus is thin enough. As $A$ is decreased, we show that the ring is
destabilized due to small eigenvalues first, and then due to large eigenvalues,
although both of these thresholds are separated by an asymptotically small
amount. For a ring of 8 spikes inside a disk, the instability appears to be
supercritical, and deforms the ring into a square-like configuration. For less
than 8 spikes, this instability is subcritical and results in spike death.


title:
Strong constraints on primordial black hole dark matter from 16 years of
  INTEGRAL/SPI observations

abstract:
  We present a new analysis of the diffuse soft $\gamma$-ray emission towards
the inner Galaxy as measured by the SPectrometer aboard the INTEGRAL satellite
(SPI) with 16 years of data taking. The analysis implements a spatial template
fit of SPI data and an improved instrumental background model. We characterize
the contribution of Primordial Black Holes (PBH) as dark matter (DM) candidates
evaporating into $\mathcal{O}$(1) MeV photons by including, for the first time,
the spatial distribution of their signal into the fitting procedure. No PBH
signal is detected, and we set the strongest limit on PBH DM for masses up to
$4 \times 10^{17}$ g, significantly closing in into the so-called asteroid mass
range.


title: Phase-Based Signal Representations for Scattering

abstract:
  The scattering transform is a non-linear signal representation method based
on cascaded wavelet transform magnitudes. In this paper we introduce phase
scattering, a novel approach where we use phase derivatives in a scattering
procedure. We first revisit phase-related concepts for representing
time-frequency information of audio signals, in particular, the partial
derivatives of the phase in the time-frequency domain. By putting analytical
and numerical results in a new light, we set the basis to extend the
phase-based representations to higher orders by means of a scattering
transform, which leads to well localized signal representations of large-scale
structures. All the ideas are introduced in a general way and then applied
using the STFT.


title:
Approaching the coupon collector's problem with group drawings via
  Stein's method

abstract:
  In this paper the coupon collector's problem with group drawings is studied.
Assume there are $ n $ different coupons. At each time precisely $ s $ of the $
n $ coupons are drawn, where all choices are supposed to have equal
probability. The focus lies on the fluctuations, as $n\to\infty$, of the number
$Z_{n,s}(k_n)$ of coupons that have not been drawn in the first $k_n$ drawings.
Using a size-biased coupling construction together with Stein's method for
normal approximation, a quantitative central limit theorem for $Z_{n,s}(k_n)$
is shown for the case that $k_n={n\over s}(\alpha\log(n)+x)$, where
$0<\alpha<1$ and $x\in\mathbb{R}$. The same coupling construction is used to
retrieve a quantitative Poisson limit theorem in the boundary case $\alpha=1$,
again using Stein's method.


title: Accreting neutron stars: heating of the upper layers of the inner crust

abstract:
  Neutron stars in low-mass X-ray binaries are thought to be heated up by
accretion-induced exothermic nuclear reactions in the crust. The energy release
and the location of the heating sources are important ingredients of the
thermal evolution models. Here we present thermodynamically consistent
calculations of the energy release in three zones of the stellar crust: at the
outer-inner crust interface, in the upper layers of the inner crust (up to the
density $\rho \leq 2\times 10^{12}$ g cm$^{-3}$), and in the underlying crustal
layers. We consider three representative models of thermonuclear ashes
(Superburst, Extreme rp, and Kepler ashes). The energy release in each zone is
parametrized by the pressure at the outer-inner crust interface, which encodes
all uncertainties related to the physics of the deepest inner-crust layers. Our
calculations allow us, in particular, to set new lower limits on the net energy
release (per accreted baryon): $Q\gtrsim0.28$ MeV for Extreme rp ashes and
Q~0.43-0.51 MeV for Superburst and Kepler ashes.


title: On the cartesian product of well-orderings

abstract:
  The width of a well partial ordering (wpo) is the ordinal rank of the set of
its antichains ordered by inclusion. We compute the width of wpos obtained as
cartesian products of finitely many well-orderings.


title:
The effect of inter-particle hydrodynamic and magnetic interactions in a
  magnetorheological fluid

abstract:
  A magnetorheological fluid, which consists of magnetic particles suspended in
a viscous fluid, flows freely with well-dispersed particles in a the absence of
a magnetic field, but particle aggregation results in flow cessation when a
field is applied. The mechanism of dynamical arrest is examined by analysing
interactions between magnetic particles in a magnetic field subject to a shear
flow. An isolated spherical magnetic particle undergoes a transition between a
rotating state at low magnetic field and a static orientation at high magnetic
field. The effect of interactions for spherical dipolar and polarisable
particles with static orientation is examined for a dilute viscous suspension.
There are magnetic interactions due to the magnetic field disturbance at one
particle caused by the dipole moment of another, hydrodynamic interactions due
to the antisymmetric force moment of a non-rotating particle in a shear flow,
and a modification of the magnetic field due to the particle magnetic moment
density. When there is a concentration variation, the torque balance condition
results in a disturbance to the orientation of the particle magnetic moment.
The net force and the drift velocity due to these disturbances is calculated,
and the collective motion generated is equivalent to an anisotropic diffusion
process. When the magnetic field is in the flow plane, the diffusion
coefficients in the two directions perpendicular to the field direction are
negative, implying that concentration fluctuations are unstable in these
directions. This instability could initiate field-induced dynamical arrest in a
magnetorheological fluid.


title: Generalized uncertainty principle and quantum non-locality

abstract:
  The emergence of the generalized uncertainty principle and the existence of a
non-zero minimal length are intertwined. On the other hand, the Heisenberg
uncertainty principle forms the core of the EPR paradox. Subsequently, here,
the implications of resorting to the generalized uncertainty principle (or
equally, the minimal length) instead of the Heisenberg uncertainty principle on
the quantum non-locality are investigated through focusing on the Franson
experiment in which the uncertainty relation is the backbone of understanding
and explaining the results.


title: The Blazar sequence and its Physical Understanding

abstract:
  Introduced in 1998 to attempt a first unified view of the broad-band emission
properties of blazars, the blazar sequence has been extensively used in the
past 25 years to guide observations as well as physical interpretation of the
overall emission from these galaxies. In this review, we describe the evolution
of the sequence along with the tremendous advances in the observational field,
in particular in the gamma-ray band. A new version of the sequence built on
TeV-detected objects is also presented. Two extreme classes of objects (MeV and
hard-TeV blazars) are included in the discussion, given their relevance for
future observatories. Finally, the current physical understanding at the base
of the sequence is presented along with the major criticisms to the blazar
sequence.


title: Poincar\'e inequalities and $A_p$ weights on bow-ties

abstract:
  A metric space $X$ is called a \emph{bow-tie} if it can be written as
$X=X_{+} \cup X_{-}$, where $X_{+} \cap X_{-}=\{x_0\}$ and $X_{\pm} \ne
\{x_0\}$ are closed subsets of $X$. We show that a doubling measure $\mu$ on
$X$ supports a $(q,p)$--Poincar\'e inequality on $X$ if and only if $X$
satisfies a quasiconvexity-type condition, $\mu$ supports a $(q,p)$-Poincar\'e
inequality on both $X_{+}$ and $X_{-}$, and a variational \p-capacity condition
holds. This capacity condition is in turn characterized by a sharp measure
decay condition at $x_0$.
  In particular, we study the bow-tie $X_{\mathbf{R}^n}$ consisting of the
positive and negative hyperquadrants in $\mathbf{R}^n$ equipped with a radial
doubling weight and characterize the validity of the \p-Poincar\'e inequality
on $X_{\mathbf{R}^n}$ in several ways. For such weights, we also give a general
formula for the capacity of annuli around the origin.


title: Elliptic homogenization with almost translation-invariant coefficients

abstract:
  We consider an homogenization problem for the second order elliptic equation
$-\operatorname{div}\left(a(./\varepsilon) \nabla u^{\varepsilon} \right)=f$
when the coefficient $a$ is almost translation-invariant at infinity and models
a geometry close to a periodic geometry. This geometry is characterized by a
particular discrete gradient of the coefficient $a$ that belongs to a Lebesgue
space $L^p(\mathbb{R}^d)$ for $p\in[1,+\infty[$. When $p<d$, we establish a
discrete adaptation of the Gagliardo-Nirenberg-Sobolev inequality in order to
show that the coefficient $a$ actually belongs to a certain class of periodic
coefficients perturbed by a local defect. We next prove the existence of a
corrector and we identify the homogenized limit of $u^{\varepsilon}$. When
$p\geq d$, we exhibit admissible coefficients $a$ such that $u^{\varepsilon}$
possesses different subsequences that converge to different limits in $L^2$.


title: Fundamental limitations of the eigenvalue continuation approach

abstract:
  In this work, we show that the eigenvalue continuation approach introduced
recently in [Phys. Rev. Lett. {\bf 121}, 032501 (2018)], despite its many
advantages, has some fundamental limitations which cannot be overcome when
strongly correlated many-body systems are considered. Taking as a working
example a very simple system of several fermionic particles confined in a
harmonic trap we show that the eigenvector continuation is not able to go
beyond the accuracy of the sampling states. We support this observation within
a very simple three-level model capturing directly this obstacle. Since
mentioned inaccuracy cannot be determined self-consistently within the
eigenvalue continuation approach, support from other complementary methods is
needed.


title:
An explicit construction of derived moduli stacks of Harder-Narasimhan
  filtrations

abstract:
  In this article, we give an explicit construction of the derived moduli stack
of Harder-Narasimhan filtrations on a connected projective scheme over an
algebraically closed field k of characteristic 0 by using methods by Behrend,
Ciocan-Fontanine, Hwang and Rose. Moreover, we describe the derived deformation
theory of a filtered sheave on a connected projective scheme over k and compare
our construction with a construction by Di Natale.


title:
Laser-patterned submicron Bi2Se3-WS2 pixels with tunable circular
  polarization at room temperature

abstract:
  Characterizing and manipulating the circular polarization of light is central
to numerous emerging technologies, including spintronics and quantum computing.
Separately, monolayer tungsten disulfide (WS2) is a versatile material that has
demonstrated promise in a variety of applications, including single photon
emitters and valleytronics. Here, we demonstrate a method to tune the
photoluminescence (PL) intensity (factor of x161), peak position (38.4meV
range), circular polarization (39.4% range), and valley polarization of a
Bi2Se3-WS2 2D heterostructure using a low-power laser (0.762uW) in ambient.
Changes are spatially confined to the laser spot, enabling submicron (814nm)
features, and are long-term stable (>334 days). PL and valley polarization
changes can be controllably reversed through laser exposure in vacuum, allowing
the material to be erased and reused. Atmospheric experiments and
first-principles calculations indicate oxygen diffusion modulates the exciton
radiative vs. non-radiative recombination pathways, where oxygen absorption
leads to brightening, and desorption to darkening.

categories: cond-mat.mtrl-sci cond-mat.mes-hall physics.app-ph physics.chem-ph physics.optics


title:
Beyond the Policy Gradient Theorem for Efficient Policy Updates in
  Actor-Critic Algorithms

abstract:
  In Reinforcement Learning, the optimal action at a given state is dependent
on policy decisions at subsequent states. As a consequence, the learning
targets evolve with time and the policy optimization process must be efficient
at unlearning what it previously learnt. In this paper, we discover that the
policy gradient theorem prescribes policy updates that are slow to unlearn
because of their structural symmetry with respect to the value target. To
increase the unlearning speed, we study a novel policy update: the gradient of
the cross-entropy loss with respect to the action maximizing $q$, but find that
such updates may lead to a decrease in value. Consequently, we introduce a
modified policy update devoid of that flaw, and prove its guarantees of
convergence to global optimality in $\mathcal{O}(t^{-1})$ under classic
assumptions. Further, we assess standard policy updates and our cross-entropy
policy updates along six analytical dimensions. Finally, we empirically
validate our theoretical findings.


title:
Exploiting non-linear effects in optomechanical sensors with continuous
  photon-counting

abstract:
  Optomechanical systems are rapidly becoming one of the most promising
platforms for observing quantum behaviour, especially at the macroscopic level.
Moreover, thanks to their state-of-the-art methods of fabrication, they may now
enter regimes of non-linear interactions between their constituent mechanical
and optical degrees of freedom. In this work, we show how this novel
opportunity may serve to construct a new generation of optomechanical sensors.
We consider the canonical optomechanical setup with the detection scheme being
based on time-resolved counting of photons leaking from the cavity. By
performing simulations and resorting to Bayesian inference, we demonstrate that
the non-classical correlations of the detected photons may crucially enhance
the sensor performance in real time. We believe that our work may stimulate a
new direction in the design of such devices, while our methods apply also to
other platforms exploiting non-linear light-matter interactions and photon
detection.


title: Non-iterative Filter Bank Phase (Re)Construction

abstract:
  Signal reconstruction from magnitude-only measurements presents a
long-standing problem in signal processing. In this contribution, we propose a
phase (re)construction method for filter banks with uniform decimation and
controlled frequency variation. The suggested procedure extends the recently
introduced phase-gradient heap integration and relies on a phase-magnitude
relationship for filter bank coefficients obtained from Gaussian filters.
Admissible filter banks are modeled as the discretization of certain
generalized translation-invariant systems, for which we derive the
phase-magnitude relationship explicitly. The implementation for discrete
signals is described and the performance of the algorithm is evaluated on a
range of real and synthetic signals.


title:
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement
  For Improved Iris Recognition

abstract:
  This paper presents a texture aware end-to-end trainable iris recognition
system, specifically designed for datasets like iris having limited training
data. We build upon our previous stagewise learning framework with certain key
optimization and architectural innovations. First, we pretrain a Stage-1
encoder network with an unsupervised autoencoder learning optimized with an
additional data relation loss on top of usual reconstruction loss. The data
relation loss enables learning better texture representation which is pivotal
for a texture rich dataset such as iris. Robustness of Stage-1 feature
representation is further enhanced with an auxiliary denoising task. Such
pre-training proves beneficial for effectively training deep networks on data
constrained iris datasets. Next, in Stage-2 supervised refinement, we design a
pairwise learning architecture for an end-to-end trainable iris recognition
system. The pairwise learning includes the task of iris matching inside the
training pipeline itself and results in significant improvement in recognition
performance compared to usual offline matching. We validate our model across
three publicly available iris datasets and the proposed model consistently
outperforms both traditional and deep learning baselines for both
Within-Dataset and Cross-Dataset configurations


title:
Fast Inverter Control by Learning the OPF Mapping using
  Sensitivity-Informed Gaussian Processes

abstract:
  Fast inverter control is a desideratum towards the smoother integration of
renewables. Adjusting inverter injection setpoints for distributed energy
resources can be an effective grid control mechanism. However, finding such
setpoints optimally requires solving an optimal power flow (OPF), which can be
computationally taxing in real time. This work proposes learning the mapping
from grid conditions to OPF minimizers using Gaussian processes (GPs). This
GP-OPF model predicts inverter setpoints when presented with a new instance of
grid conditions. Training enjoys closed-form expressions, and GP-OPF
predictions come with confidence intervals. To improve upon data efficiency, we
uniquely incorporate the sensitivities (partial derivatives) of the OPF mapping
into GP-OPF. This expedites the process of generating a training dataset as
fewer OPF instances need to be solved to attain the same accuracy. To further
reduce computational efficiency, we approximate the kernel function of GP-OPF
leveraging the concept of random features, which is neatly extended to
sensitivity data. We perform sensitivity analysis for the second-order cone
program (SOCP) relaxation of the OPF, whose sensitivities can be computed by
merely solving a system of linear equations. Extensive numerical tests using
real-world data on the IEEE 13- and 123-bus benchmark feeders corroborate the
merits of GP-OPF.


title:
Many-body approximations to the superfluid gap and critical temperature
  in neutron stars

abstract:
  We compute singlet pairing gaps and critical temperatures in pure neutron
matter with different many-body approximations. Medium effects tend to reduce
gaps and critical temperatures compared to the standard BCS ansatz. In the
mean-field approximation, the ratio of these two quantities remains constant
across a wide range of densities. This constant ratio is close to the universal
prediction of BCS theory, whether three-neutron interactions are included or
not. Using a more sophisticated many-body approach that incorporates the effect
of short-range correlations in pairing properties, we find that the gap to
critical temperature ratio in the low-density regime is substantially larger
than the BCS prediction, independently of the interaction. In this region, our
results are relatively close to experiments and theoretical calculations from
the unitary Fermi gas. We also find evidence for a different density dependence
of zero-temperature gaps and critical temperatures in neutron matter.


title: Self-emulsification in chemical and pharmaceutical technologies

abstract:
  The interest in the low energy self-emulsification techniques has exploded in
the recent years, driven by three main trends: by the transition to "greener"
technologies in both its aspects - less energy consumption and replacement of
the petrochemicals by natural ingredients; by the costly and maintenance
demanding equipment for nanoemulsification; and by the quest for efficient and
robust self-emulsifying formulations for oral drug delivery. Here we first
present a brief overview of the main known low-energy methods for nanoemulsion
formation, focusing on their mechanistic understanding and discussing some
recent advances in their development and applications. Next, we review three
conceptually new approaches for self-emulsification in chemical technologies,
discovered in the last several years. The colloidal features and the specific
requirements of the self-emulsifying drug-delivery systems (SEDDS) are also
discussed briefly. Finally, we summarize the current trends and the main
challenges in this vivid research area.


title: BED: A Real-Time Object Detection System for Edge Devices

abstract:
  Deploying machine learning models to edge devices has many real-world
applications, especially for the scenarios that demand low latency, low power,
or data privacy. However, it requires substantial research and engineering
efforts due to the limited computational resources and memory of edge devices.
In this demo, we present BED, an object detection system for edge devices
practiced on the MAX78000 DNN accelerator. BED integrates on-device DNN
inference with a camera and a screen for image acquisition and output
exhibition, respectively. Experiment results indicate BED can provide accurate
detection with an only 300KB tiny DNN model.


title: vue4logs -- Automatic Structuring of Heterogeneous Computer System Logs

abstract:
  Computer system log data is commonly used in system monitoring, performance
characteristic investigation, workflow modeling and anomaly detection. Log data
is inherently unstructured or semi-structured, which makes it harder to
understand the event flow or other important information of a system by reading
raw logs. The process of structuring log files first identifies the log message
groups based on the system events that triggered them, and extracts an event
template to represent the log messages of each event. This paper introduces a
novel method to extract event templates from raw system log files, by using the
vector space model commonly used in the field of Information Retrieval to
vectorize log data and group log messages into event templates based on their
vector similarity. Template extraction process is further enhanced with the use
of character and length based filters. When evaluated on publicly available
real-world log data benchmarks, this proposed method outperforms all the
available state-of-the-art systems in terms of accuracy and robustness.


title: A note on $\partial$-bilipschitz mappings in quasiconvex metric spaces

abstract:
  This paper focuses on properties of \partial-biLipschitz mappings which were
recently introduced by Bulter. We establish several characterizations for the
class of \partial-biLipschitz mappings between domains in quasiconvex metric
spaces. As an application, we show that a locally quasisymmetric equivalence
between uniform metric spaces is quasim\"obius, quantitatively.


title: Confidence Threshold Neural Diving

abstract:
  Finding a better feasible solution in a shorter time is an integral part of
solving Mixed Integer Programs. We present a post-hoc method based on Neural
Diving to build heuristics more flexibly. We hypothesize that variables with
higher confidence scores are more definite to be included in the optimal
solution. For our hypothesis, we provide empirical evidence that confidence
threshold technique produces partial solutions leading to final solutions with
better primal objective values. Our method won 2nd place in the primal task on
the NeurIPS 2021 ML4CO competition. Also, our method shows the best score among
other learning-based methods in the competition.


title: A Division Theorem for Nodal Projective Hypersurfaces

abstract:
  Let $V_{d,n}$ be the variety of equations for hypersurfaces of degree $d$ in
$\mathbb{P}^n(\mathbb{C})$ with singularities not worse than simple nodes. We
prove that the orbit map $G'=SL_{n+1}(\mathbb{C}) \to V_{n,d}$, $g\mapsto
g\cdot s_0$, $s_0\in V_{n,d}$ is surjective on the rational cohomology if
$n>1$, $d\geq 3$, and $(n,d)\neq (2,3)$. As a result, the Leray-Serre spectral
sequence of the map from $V_{n,d}$ to the homotopy quotient $(V_{n,d})_{hG'}$
degenerates at $E_2$, and so does the Leray spectral sequence of the quotient
map $V_{n,d}\to V_{n,d}/G'$ provided the geometric quotient $V_{n,d}/G'$
exists. We show that the latter is the case when $d>n+1$.


title: Deep Constrained Least Squares for Blind Image Super-Resolution

abstract:
  In this paper, we tackle the problem of blind image super-resolution(SR) with
a reformulated degradation model and two novel modules. Following the common
practices of blind SR, our method proposes to improve both the kernel
estimation as well as the kernel-based high-resolution image restoration. To be
more specific, we first reformulate the degradation model such that the
deblurring kernel estimation can be transferred into the low-resolution space.
On top of this, we introduce a dynamic deep linear filter module. Instead of
learning a fixed kernel for all images, it can adaptively generate deblurring
kernel weights conditional on the input and yield a more robust kernel
estimation. Subsequently, a deep constrained least square filtering module is
applied to generate clean features based on the reformulation and estimated
kernel. The deblurred feature and the low input image feature are then fed into
a dual-path structured SR network and restore the final high-resolution result.
To evaluate our method, we further conduct evaluations on several benchmarks,
including Gaussian8 and DIV2KRK. Our experiments demonstrate that the proposed
method achieves better accuracy and visual improvements against
state-of-the-art methods.


title: On Polynomial Ideals And Overconvergence In Tate Algebras

abstract:
  In this paper, we study ideals spanned by polynomials or overconvergent
series in a Tate algebra. With state-of-the-art algorithms for computing Tate
Gr{\"o}bner bases, even if the input is polynomials, the size of the output
grows with the required precision, both in terms of the size of the
coefficients and the size of the support of the series. We prove that ideals
which are spanned by polynomials admit a Tate Gr{\"o}bner basis made of
polynomials, and we propose an algorithm, leveraging Mora's weak normal form
algorithm, for computing it. As a result, the size of the output of this
algorithm grows linearly with the precision. Following the same ideas, we
propose an algorithm which computes an overconvergent basis for an ideal
spanned by overconvergent series. Finally, we prove the existence of a
universal analytic Gr{\"o}bner basis for polynomial ideals in Tate algebras,
compatible with all convergence radii.


title: Controlling inventory on electric roads

abstract:
  Electric road systems (ERS) are roads that allow compatible vehicles to be
powered by grid electricity while in transit, reducing the need for stopping to
recharge electric batteries. We investigate how this technology can affect
routing and delivery decisions for hybrid heavy good vehicles (HGVs) travelling
on a ERS network to support the demand of a single product faced by a set of
retailers in the network. We introduce the Electric Roads Routing Problem,
which accounts for the costs of electricity and fuel on a ERS network,
consumption that are affected by the battery level of the vehicle in each step
of the journey, the routing decisions and the variable weight of the vehicle,
which depends on vehicle load and delivery decisions. In particular, we study a
stochastic demand version of the problem, formulating a mathematical
programming heuristic and proving its effectiveness. We use our model on a
realistic instance of the problem, showcasing the different strategies that a
vehicle may follow depending on fuel costs in relation to the costs of
electricity.


title:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium
  Learning from Offline Datasets

abstract:
  We study episodic two-player zero-sum Markov games (MGs) in the offline
setting, where the goal is to find an approximate Nash equilibrium (NE) policy
pair based on a dataset collected a priori. When the dataset does not have
uniform coverage over all policy pairs, finding an approximate NE involves
challenges in three aspects: (i) distributional shift between the behavior
policy and the optimal policy, (ii) function approximation to handle large
state space, and (iii) minimax optimization for equilibrium solving. We propose
a pessimism-based algorithm, dubbed as pessimistic minimax value iteration
(PMVI), which overcomes the distributional shift by constructing pessimistic
estimates of the value functions for both players and outputs a policy pair by
solving NEs based on the two value functions. Furthermore, we establish a
data-dependent upper bound on the suboptimality which recovers a sublinear rate
without the assumption on uniform coverage of the dataset. We also prove an
information-theoretical lower bound, which suggests that the data-dependent
term in the upper bound is intrinsic. Our theoretical results also highlight a
notion of "relative uncertainty", which characterizes the necessary and
sufficient condition for achieving sample efficiency in offline MGs. To the
best of our knowledge, we provide the first nearly minimax optimal result for
offline MGs with function approximation.


title: On a class of linearisable Abel equations

abstract:
  Applying symmetry reduction to a class of $\mathrm{SL}(2,\mathbb
R)$-invariant third-order ODEs, we obtain Abel equations whose general solution
can be parametrised by hypergeometric functions. Particular case of this
construction provides a general parametric solution to the Kudashev equation,
an ODE arising in the asymptotic analysis of a simultaneous solution to the KdV
equation and the stationary part of its higher non-autonomous symmetry.


title: Post-Training Quantization for Cross-Platform Learned Image Compression

abstract:
  It has been witnessed that learned image compression has outperformed
conventional image coding techniques and tends to be practical in industrial
applications. One of the most critical issues that need to be considered is the
non-deterministic calculation, which makes the probability prediction
cross-platform inconsistent and frustrates successful decoding. We propose to
solve this problem by introducing well-developed post-training quantization and
making the model inference integer-arithmetic-only, which is much simpler than
presently existing training and fine-tuning based approaches yet still keeps
the superior rate-distortion performance of learned image compression. Based on
that, we further improve the discretization of the entropy parameters and
extend the deterministic inference to fit Gaussian mixture models. With our
proposed methods, the current state-of-the-art image compression models can
infer in a cross-platform consistent manner, which makes the further
development and practice of learned image compression more promising.


title:
Scalable noncontextuality inequalities and certification of multiqubit
  quantum systems

abstract:
  We propose a family of noncontextuality inequalities and show that they can
be used for certification of multiqubit quantum systems. Our scheme, unlike
those based on non-locality, does not require spatial separation between the
subsystems, yet it makes use of certain compatibility relations between
measurements. Moreover, it is scalable in the sense that the number of
expectation values that are to be measured to observe contextuality scales
polynomially with the number of qubits that are being certified. In a
particular case we also show our scheme to be robust errors and experimental
imperfections. Our results seem promising as far as certification of physical
set-ups is concerned in scenarios where spatial separation between the
subsystems cannot be guaranteed.


title: Exploiting coherence for quantum thermodynamic advantage

abstract:
  The introduction of the quantum analogue of a Carnot engine based on a bath
comprising of particles with a small amount of coherence initiated an active
line of research on the harnessing of different quantum resources for the
enhancement of thermal machines beyond the standard reversible limit, with an
emphasis on non-thermal baths containing quantum coherence. In our work, we
investigate the impact of coherence on the thermodynamic tasks of a collision
model which is composed of a system interacting, in the continuous time limit,
with a series of coherent ancillas of two baths at different temperatures. Our
results show the advantages of utilising coherence as a resource in the
operation of the machine, and allows it: (i) to exhibit unconventional behavior
such as the appearance of a hybrid refrigerator, capable of simultaneous
refrigeration and generation of work, and (ii) to function as an engine or a
refrigerator with efficiencies larger than the Carnot bound. Moreover, we find
an effective upper bound to the efficiency of the thermal machine operating as
an engine in the presence of a coherent reservoir.


title:
OpenStreetMap-based LiDAR Global Localization in Urban Environment
  without a Prior LiDAR Map

abstract:
  Using publicly accessible maps, we propose a novel vehicle localization
method that can be applied without using prior light detection and ranging
(LiDAR) maps. Our method generates OSM descriptors by calculating the distances
to buildings from a location in OpenStreetMap at a regular angle, and LiDAR
descriptors by calculating the shortest distances to building points from the
current location at a regular angle. Comparing the OSM descriptors and LiDAR
descriptors yields a highly accurate vehicle localization result. Compared to
methods that use prior LiDAR maps, our method presents two main advantages: (1)
vehicle localization is not limited to only places with previously acquired
LiDAR maps, and (2) our method is comparable to LiDAR map-based methods, and
especially outperforms the other methods with respect to the top one candidate
at KITTI dataset sequence 00.


title:
An Equilibrium Model of the First-Price Auction with Strategic
  Uncertainty: Theory and Empirics

abstract:
  In many first-price auctions, bidders face considerable strategic
uncertainty: They cannot perfectly anticipate the other bidders' bidding
behavior. We propose a model in which bidders do not know the entire
distribution of opponent bids but only the expected (winning) bid and lower and
upper bounds on the opponent bids. We characterize the optimal bidding
strategies and prove the existence of equilibrium beliefs. Finally, we apply
the model to estimate the cost distribution in highway procurement auctions and
find good performance out-of-sample.


title:
HII regions in CALIFA survey: II. The relation between their physical
  properties and galaxy evolution

abstract:
  We present in here the exploration of the physical properties of the sample
of HII regions and aggregations of the last HII regions catalog of the CALIFA
survey. This sample comprises the optical spectroscopic properties of more than
~26,000 ionized regions corresponding to 924 galaxies from the Integral Field
Spectroscopy data, including the flux intensity and equivalent widths and the
properties of their underlying stellar population. In the current study we
derive a set of physical quantities for all these regions based on those
properties, including (i) the fraction of young stars; (ii) the ionization
strength (using six different estimations); (iii) the oxygen abundance (using
25 different calibrators); (iv) the nitrogen and nitrogen-to-oxygen abundance;
(v) the dust extinction and (vi) the electron density. Using this dataset we
explore how the loci in the classical diagnostic diagrams are connected with
those quantities, the radial distributions of these parameters, and the
inter-relations between themselves and with the properties of the underlying
stellar populations. We conclude that many properties of the HII regions are
tightly related to the galactic stellar evolution at the location where those
regions are observed. Those properties are modulated only as a second-order
effect by the properties of the ionizing stars and the ionized nebulae that do
not depend on the astrophysical context in which they are formed. Our results
highlight the importance of HII regions to explore the chemical evolution in
galaxies, clarifying which of their properties can be used as proxies of that
evolution.