title:
A Unified Multi-Task Learning Framework of Real-Time Drone Supervision
  for Crowd Counting

abstract:
  In this paper, a novel Unified Multi-Task Learning Framework of Real-Time
Drone Supervision for Crowd Counting (MFCC) is proposed, which utilizes an
image fusion network architecture to fuse images from the visible and thermal
infrared image, and a crowd counting network architecture to estimate the
density map. The purpose of our framework is to fuse two modalities, including
visible and thermal infrared images captured by drones in real-time, that
exploit the complementary information to accurately count the dense population
and then automatically guide the flight of the drone to supervise the dense
crowd. To this end, we propose the unified multi-task learning framework for
crowd counting for the first time and re-design the unified training loss
functions to align the image fusion network and crowd counting network. We also
design the Assisted Learning Module (ALM) to fuse the density map feature to
the image fusion encoder process for learning the counting features. To improve
the accuracy, we propose the Extensive Context Extraction Module (ECEM) that is
based on a dense connection architecture to encode multi-receptive-fields
contextual information and apply the Multi-domain Attention Block (MAB) for
concerning the head region in the drone view. Finally, we apply the prediction
map to automatically guide the drones to supervise the dense crowd. The
experimental results on the DroneRGBT dataset show that, compared with the
existing methods, ours has comparable results on objective evaluations and an
easier training process.


title:
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning
  based Deep Neural Networks

abstract:
  In recent years, Deep Learning models have shown a great performance in
complex optimization problems. They generally require large training datasets,
which is a limitation in most practical cases. Transfer learning allows
importing the first layers of a pre-trained architecture and connecting them to
fully-connected layers to adapt them to a new problem. Consequently, the
configuration of the these layers becomes crucial for the performance of the
model. Unfortunately, the optimization of these models is usually a
computationally demanding task. One strategy to optimize Deep Learning models
is the pruning scheme. Pruning methods are focused on reducing the complexity
of the network, assuming an expected performance penalty of the model once
pruned. However, the pruning could potentially be used to improve the
performance, using an optimization algorithm to identify and eventually remove
unnecessary connections among neurons. This work proposes EvoPruneDeepTL, an
evolutionary pruning model for Transfer Learning based Deep Neural Networks
which replaces the last fully-connected layers with sparse layers optimized by
a genetic algorithm. Depending on its solution encoding strategy, our proposed
model can either perform optimized pruning or feature selection over the
densely connected part of the neural network. We carry out different
experiments with several datasets to assess the benefits of our proposal.
Results show the contribution of EvoPruneDeepTL and feature selection to the
overall computational efficiency of the network as a result of the optimization
process. In particular, the accuracy is improved reducing at the same time the
number of active neurons in the final layers.


title:
BeeHIVE: Behavioral Biometric System based on Object Interactions in
  Smart Environments

abstract:
  The lack of standard input interfaces in the Internet of Things (IoT)
ecosystems presents a challenge in securing such infrastructures. To tackle
this challenge, we introduce a novel behavioral biometric system based on
naturally occurring interactions with objects in smart environments. This
biometric leverages existing sensors to authenticate users without requiring
any hardware modifications of existing smart home devices. The system is
designed to reduce the need for phone-based authentication mechanisms, on which
smart home systems currently rely. It requires the user to approve transactions
on their phone only when the user cannot be authenticated with high confidence
through their interactions with the smart environment.
  We conduct a real-world experiment that involves 13 participants in a company
environment, using this experiment to also study mimicry attacks on our
proposed system. We show that this system can provide seamless and unobtrusive
authentication while still staying highly resistant to zero-effort, video, and
in-person observation-based mimicry attacks. Even when at most 1% of the
strongest type of mimicry attacks are successful, our system does not require
the user to take out their phone to approve legitimate transactions in more
than 80% of cases for a single interaction. This increases to 92% of
transactions when interactions with more objects are considered.


title:
The Soft Compiler: A Web-Based Tool for the Design of Modular Pneumatic
  Circuits for Soft Robots

abstract:
  Developing soft circuits from individual soft logic gates poses a unique
challenge: with increasing numbers of logic gates, the design and
implementation of circuits leads to inefficiencies due to mathematically
unoptimized circuits and wiring mistakes during assembly. It is therefore
practically important to introduce design tools that support the development of
soft circuits. We developed a web-based graphical user interface, the Soft
Compiler, that accepts a user-defined robot behavior as a truth table to
generate a mathematically optimized circuit diagram that guides the assembly of
a soft fluidic circuit. We describe the design and experimental verification of
three soft circuits of increasing complexity, using the Soft Compiler as a
design tool and a novel pneumatic glove as an input interface. In one example,
we reduce the size of a soft circuit from the original 11 logic gates to 4
logic gates while maintaining circuit functionality. The Soft Compiler is a
web-based design tool for fluidic, soft circuits and published under
open-source MIT License.


title:
Design and Performance of the GERDA Low-Background Cryostat for
  Operation in Water

abstract:
  In searching for the neutrinoless double-beta decay of $^{76}$Ge the
GERmanium Detector Array (GERDA) experiment at the INFN Laboratori Nazionali
del Gran Sasso has achieved an unprecedented low background of well below
10$^{-3}$ cts/(keV$\cdot$kg$\cdot$yr) in the region of interest. It has taken
advantage of the first realization of a novel shielding concept based on a
large cryostat filled with a liquid noble gas that is immersed in a water tank.
The germanium detectors are operated without encapsulation in liquid argon.
Argon and water shield the environmental background from the laboratory and the
cryostat construction materials to a negligible level. The same approach has
been adopted in the meantime by various experiments. This paper provides an
overview of the design and operating experience of the 64 m$^3$ liquid argon
cryostat and its associated infrastructure. The discussion includes the
challenging safety issues associated with the operation of a large cryostat in
a water tank.


title:
Different Formation Scenarios of Counter-rotating Stellar Disks in
  Nearby Galaxies

abstract:
  Using the integral field unit (IFU) data from Mapping Nearby Galaxies at
Apache Point Observatory (MaNGA) survey, we select a sample of 101 galaxies
with counter-rotating stellar disks and regularly-rotating ionized gas disk. We
classify the 101 galaxies into four types based on the features of their
stellar velocity fields. The relative fractions and stellar population age
radial gradients of the four types are different in blue cloud (BC), green
valley (GV) and red sequence (RS) populations. We suggest different formation
scenarios for the counter-rotating stellar disks, and the key factors in the
formation of counter-rotating stellar disks include: (1) the abundance of
pre-existing gas in progenitor, (2) the efficiency in angular momentum
consumption.


title:
Compaction band localization in geomaterials: a mechanically consistent
  failure criterion

abstract:
  Compaction bands play a key role in the deformation processes of porous rocks
and explain different aspects of physical processes in geological formations.
The state-of-the-art description of the localized strains that lead to
compaction banding has limitations from the mechanical point of view. Thus, we
describe the phenomenon using a consistent axiomatic formulation. We build a
viscoplastic model using minimal assumptions; we base our model on six
principles to study compaction band localization triggered by viscous effects.
We analyze different stress states to determine the conditions that trigger
compaction bands. Laboratory experiments show that a material undergoes
different localizations depending on the confinement pressure; thus, we perform
a series of numerical experiments that reproduce these phenomena under varying
triaxial compression conditions. These simulations use a simple viscoplastic
constitutive model for creep based on Perzyna's viscoplasticity and show how
confinement changes the localization type for different triaxial tests. Our
analysis allows us to describe this transition, band periodicity and spacing,
and their dependence on the material parameters.


title:
Neutron diffraction evidence of the 3-dimensional structure of Ba2MnTeO6
  and misidentification of the triangular layers within the face-centred cubic
  lattice

abstract:
  Ba2MnTeO6 was first characterised using X-ray diffraction and reported to
show a small distortion1 from the idealised cubic perovskite which displays
face-centred cubic arrangement of Mn2+. A recent report has asserted that this
leads to a layered configuration of Mn2+ that serves as an example of a
triangular lattice, i.e. a 2D structure containing discreet layers.2 Here we
show how neutron scattering gives great confidence in establishing the crystal
structure being an undistorted cubic phase and how this can be mis-assigned as
a triangular layered structure. This has profound implications for the
understanding of the magnetic properties of the system.


title: MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation

abstract:
  A knowledge graph (KG) consists of a set of interconnected typed entities and
their attributes. Recently, KGs are popularly used as the auxiliary information
to enable more accurate, explainable, and diverse user preference
recommendations. Specifically, existing KG-based recommendation methods target
modeling high-order relations/dependencies from long connectivity user-item
interactions hidden in KG. However, most of them ignore the cold-start problems
(i.e., user cold-start and item cold-start) of recommendation analytics, which
restricts their performance in scenarios when involving new users or new items.
Inspired by the success of meta-learning on scarce training samples, we propose
a novel meta-learning based framework called MetaKG, which encompasses a
collaborative-aware meta learner and a knowledge-aware meta learner, to capture
meta users' preference and entities' knowledge for cold-start recommendations.
The collaborative-aware meta learner aims to locally aggregate user preferences
for each user preference learning task. In contrast, the knowledge-aware meta
learner is to globally generalize knowledge representation across different
user preference learning tasks. Guided by two meta learners, MetaKG can
effectively capture the high-order collaborative relations and semantic
representations, which could be easily adapted to cold-start scenarios.
Besides, we devise a novel adaptive task scheduler which can adaptively select
the informative tasks for meta learning in order to prevent the model from
being corrupted by noisy tasks. Extensive experiments on various cold-start
scenarios using three real data sets demonstrate that our presented MetaKG
outperforms all the existing state-of-the-art competitors in terms of
effectiveness, efficiency, and scalability.


title: Testing Linearity for Network Autoregressive Models

abstract:
  A quasi-score linearity test for continuous and count network autoregressive
models is developed. We establish the asymptotic distribution of the test when
the network dimension is fixed or increasing, under the null hypothesis of
linearity and Pitman's local alternatives. When the parameters are
identifiable, the test statistic approximates a chi-square and noncentral
chi-square asymptotic distribution, respectively. These results still hold true
when the parameters tested belong to the boundary of their space. When we deal
with non-identifiable parameters, a suitable test is proposed and its
asymptotic distribution is established when the network dimension is fixed.
Since, in general, critical values of such test cannot be tabulated, the
empirical computation of the p-values is implemented using a feasible bound.
Bootstrap approximations are also provided. Moreover, consistency and
asymptotic normality of the quasi maximum likelihood estimator is established
for continuous and count nonlinear network autoregressions, under standard
smoothness conditions. A simulation study and two data examples complement this
work.


title:
Topological Defects Induced High-Spin Quartet State in Truxene-Based
  Molecular Graphenoids

abstract:
  Topological defects in graphene materials introduce exotic properties which
are absent in their defect-free counterparts with both fundamental importance
and technological implications. Although individual topological defects have
been widely studied, collective magnetic behaviors originating from
well-organized multiple topological defects remain a great challenge. Here, we
studied the collective magnetic properties originating from three pentagon
topological defects in truxene-based molecular graphenoids by using scanning
tunneling microscopy and non-contact atomic force microscopy. Unpaired $\pi$
electrons are introduced into the aromatic topology of truxene molecular
graphenoids one by one by dissociating hydrogen atoms at the pentagon defects
via atom manipulation. Scanning tunneling spectroscopy measurements together
with density functional theory calculations suggest that the unpaired electrons
are ferromagnetically coupled, forming a collective high-spin quartet state of
S=3/2. Our work demonstrates that the collective spin ordering can be realized
through engineering regular patterned topological defects in molecular
graphenoids, providing a new platform for designer one-dimensional
ferromagnetic spin chains and two-dimensional ferromagnetic networks.


title:
Comparative Study Between Distance Measures On Supervised Optimum-Path
  Forest Classification

abstract:
  Machine Learning has attracted considerable attention throughout the past
decade due to its potential to solve far-reaching tasks, such as image
classification, object recognition, anomaly detection, and data forecasting. A
standard approach to tackle such applications is based on supervised learning,
which is assisted by large sets of labeled data and is conducted by the
so-called classifiers, such as Logistic Regression, Decision Trees, Random
Forests, and Support Vector Machines, among others. An alternative to
traditional classifiers is the parameterless Optimum-Path Forest (OPF), which
uses a graph-based methodology and a distance measure to create arcs between
nodes and hence sets of trees, responsible for conquering the nodes, defining
their labels, and shaping the forests. Nevertheless, its performance is
strongly associated with an appropriate distance measure, which may vary
according to the dataset's nature. Therefore, this work proposes a comparative
study over a wide range of distance measures applied to the supervised
Optimum-Path Forest classification. The experimental results are conducted
using well-known literature datasets and compared across benchmarking
classifiers, illustrating OPF's ability to adapt to distinct domains.


title:
Stability of Stationary Subsonic Compressible Euler Flows with
  Mass-Additions in Two-Dimensional Straight Ducts

abstract:
  We show existence, uniqueness and stability for a family of stationary
subsonic compressible Euler flows with mass-additions in two-dimensional
rectilinear ducts, subjected to suitable time-independent multi-dimensional
boundary conditions at the entrances and exits.The stationary subsonic Euler
equations consist a quasi-linear system of elliptic-hyperbolic composite-mixed
type, while addition-of-mass destructs the usual methods based upon
conservation of mass and Lagrangian coordinates to separate the elliptical and
hyperbolic modes of the system. We establish a new decomposition and nonlinear
iteration scheme to overcome this major difficulty. It reveals that
mass-additions introduce very strong interactions in the elliptic and
hyperbolic modes, and lead to a class of second-order elliptic equations with
multiple integral nonlocal terms. The linearized problem is solved by studying
algebraicand analytical properties of infinite weakly coupled boundary-value
problems of ordinary differential equations, each with multiple nonlocal terms,
after applications of Fourier analysis methods.


title: Class Density and Dataset Quality in High-Dimensional, Unstructured Data

abstract:
  We provide a definition for class density that can be used to measure the
aggregate similarity of the samples within each of the classes in a
high-dimensional, unstructured dataset. We then put forth several candidate
methods for calculating class density and analyze the correlation between the
values each method produces with the corresponding individual class test
accuracies achieved on a trained model. Additionally, we propose a definition
for dataset quality for high-dimensional, unstructured data and show that those
datasets that met a certain quality threshold (experimentally demonstrated to
be > 10 for the datasets studied) were candidates for eliding redundant data
based on the individual class densities.


title: Learning Optical Flow with Adaptive Graph Reasoning

abstract:
  Estimating per-pixel motion between video frames, known as optical flow, is a
long-standing problem in video understanding and analysis. Most contemporary
optical flow techniques largely focus on addressing the cross-image matching
with feature similarity, with few methods considering how to explicitly reason
over the given scene for achieving a holistic motion understanding. In this
work, taking a fresh perspective, we introduce a novel graph-based approach,
called adaptive graph reasoning for optical flow (AGFlow), to emphasize the
value of scene/context information in optical flow. Our key idea is to decouple
the context reasoning from the matching procedure, and exploit scene
information to effectively assist motion estimation by learning to reason over
the adaptive graph. The proposed AGFlow can effectively exploit the context
information and incorporate it within the matching procedure, producing more
robust and accurate results. On both Sintel clean and final passes, our AGFlow
achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming
state-of-the-art approaches by 11.2% and 13.6%, respectively.


title:
On Solving Robust Log-Optimal Portfolio: A Supporting Hyperplane
  Approximation Approach

abstract:
  A {log-optimal} portfolio is any portfolio that maximizes the expected
logarithmic growth (ELG) of an investor's wealth. This maximization problem
typically assumes that the information of the true distribution of returns is
known to the trader in advance. However, in practice, the return distributions
are indeed {ambiguous}; i.e., the true distribution is unknown to the trader or
it is partially known at best. To this end, a {distributional robust
log-optimal portfolio problem} formulation arises naturally. While the problem
formulation takes into account the ambiguity on return distributions, the
problem needs not to be tractable in general. To address this, in this paper,
we propose a {supporting hyperplane approximation} approach that allows us to
reformulate a class of distributional robust log-optimal portfolio problems
into a linear program, which can be solved very efficiently. Our framework is
flexible enough to allow {transaction costs}, {leverage and shorting},
{survival trades}, and {diversification considerations}. In addition, given an
acceptable approximation error, an efficient algorithm for rapidly calculating
the optimal number of hyperplanes is provided. Some empirical studies using
historical stock price data are also provided to support our theory.


title:
The application of Evolutionary and Nature Inspired Algorithms in Data
  Science and Data Analytics

abstract:
  In the past 30 years, scientists have searched nature, including animals and
insects, and biology in order to discover, understand, and model solutions for
solving large-scale science challenges. The study of bionics reveals that how
the biological structures, functions found in nature have improved our modern
technologies. In this study, we present our discovery of evolutionary and
nature-inspired algorithms applications in Data Science and Data Analytics in
three main topics of pre-processing, supervised algorithms, and unsupervised
algorithms. Among all applications, in this study, we aim to investigate four
optimization algorithms that have been performed using the evolutionary and
nature-inspired algorithms within data science and analytics. Feature selection
optimization in pre-processing section, Hyper-parameter tuning optimization,
and knowledge discovery optimization in supervised algorithms, and clustering
optimization in the unsupervised algorithms.


title:
Quantum spin liquids of Rydberg excitations in a honeycomb lattice
  induced by density-dependent Peierls phases

abstract:
  We show that the nonlinear transport of bosonic excitations in a
two-dimensional honeycomb lattice of spin-orbit coupled Rydberg atoms gives
rise to disordered quantum phases which are candidates for quantum spin
liquids. As recently demonstrated in [Lienhard et al. Phys. Rev. X, 10, 021031
(2020)] the spin-orbit coupling breaks time-reversal and chiral symmetries and
leads to a tunable density-dependent complex hopping of the hard-core bosons or
equivalently to complex XY spin interactions. Using exact diagonalization (ED)
we numerically investigate the phase diagram resulting from the competition
between density-dependent and direct transport terms. In mean-field
approximation there is a phase transition from a quasi-condensate to a
120{\deg} phase when the amplitude of the complex hopping exceeds that of the
direct one. In the full model a new phase with a finite spin gap emerges close
to the mean-field critical point as a result of quantum fluctuations induced by
the density-dependence of the complex hopping. We show that this phase is a
genuine disordered one, has a non-vanishing spin chirality and is characterized
by a non-trivial many-body Chern number. ED simulations of small lattices with
up to 28 lattice sites point to a non-degenerate ground state and thus to a
bosonic integer-quantum Hall (BIQH) phase, protected by U(1) symmetry. The
Chern number of C = 1, which is robust to disorder, is however different from
the even Chern numbers found in BIQH phases. For very strong, nonlinear
hoppings of opposite sign we find another disordered regime with vanishing spin
gap. This phase also has a large spin chirality and could be a gapless
spin-liquid but lies outside the parameter regime accessible in the Rydberg
system.


title: Targeted Trojan-Horse Attacks on Language-based Image Retrieval

abstract:
  When a retrieval system expands data, its database is at risk of being
attacked. In this paper, we introduce the concept of targeted Trojan-horse
(TTH) attacks for language-based image retrieval (LBIR), the first keyword-wise
targeted attack against the database of the retrieval system. Specifically,
given a specific keyword, TTH generates a QR-code patch that can be applied to
a set of different images to gain the targeted Trojan-horse images, which
closes to the target keyword in the common space of cross-modal matching of
retrieval model. With Uploading the generated TTH images to the database, TTH
images will rank high in a normal search, even though the images are completely
irrelevant to the query. We evaluate the attacks on standard language-based
image retrieval benchmarks (i.e. Flickr30k and MSCOCO) and compare the results
retrieved with and without the Trojan-horse images.


title: Casimir cosmology

abstract:
  In 1998 astronomers discovered that the expansion of the universe is
accelerating. Somehow, something must have made gravity repulsive on
cosmological scales. This something was called dark energy; it is described by
Einstein's cosmological constant; and it amounts to about 70% of the total mass
of the universe. It has been conjectured that the cosmological constant is a
form of vacuum energy, but its prediction from quantum field theory has failed
by many orders of magnitude, until recently. Informed by empirical evidence on
Casimir forces, Lifshitz theory has not only produced the correct order of
magnitude, but is quantitatively consistent with the astronomical data.
Moreover, the theory appears to resolve the tension between the measured and
the predicted Hubble constant. There is therefore a good chance that Casimir
physics explains dark energy. This article introduces cosmology for
practitioners of vacuum forces as part of "The State of the Quantum Vacuum:
Casimir Physics in the 2020s" edited by K. A. Milton. It may also be
interesting for other physicists and engineers who wish to have a concise
introduction to cosmology.


title:
Measurement of $\psi(2S)$ nuclear modification at backward and forward
  rapidity in $p$$+$$p$, $p$$+$Al, and $p$$+$Au collisions at
  $\sqrt{s_{_{NN}}}=200$\,GeV

abstract:
  Suppression of the $J/\psi$ nuclear-modification factor has been seen as a
trademark signature of final-state effects in large collision systems for
decades. In small systems, the nuclear modification was attributed to
cold-nuclear-matter effects until the observation of strong differential
suppression of the $\psi(2S)$ state in $p/d$$+$$A$ collisions suggested the
presence of final-state effects. Results of $J/\psi$ and $\psi(2S)$
measurements in the dimuon decay channel are presented here for $p$$+$$p$,
$p$$+$Al, and $p$$+$Au collision systems at $\sqrt{s_{_{NN}}}=200$\,GeV. The
results are predominantly shown in the form of the nuclear-modification factor,
$R_{pA}$, the ratio of the $\psi(2S)$ invariant yield per nucleon-nucleon
collision in collisions of proton on target nucleus to that in $p$$+$$p$
collisions. Measurements of the $J/\psi$ and $\psi(2S)$ nuclear-modification
factor are compared with shadowing and transport-model predictions, as well as
to complementary measurements at Large-Hadron-Collider energies.


title:
The operational foundations of PT-symmetric and quasi-Hermitian quantum
  theory

abstract:
  PT-symmetric quantum theory was originally proposed with the aim of extending
standard quantum theory by relaxing the Hermiticity constraint on Hamiltonians.
However, no such extension has been formulated that consistently describes
states, transformations, measurements and composition, which is a requirement
for any physical theory. We aim to answer the question of whether a consistent
physical theory with PT-symmetric observables extends standard quantum theory.
We answer this question within the framework of general probabilistic theories,
which is the most general framework for physical theories. We construct the set
of states of a system that result from imposing PT-symmetry on the set of
observables, and show that the resulting theory allows only one trivial state.
We next consider the constraint of quasi-Hermiticity on observables, which
guarantees the unitarity of evolution under a Hamiltonian with unbroken
PT-symmetry. We show that such a system is equivalent to a standard quantum
system. Finally, we show that if all observables are quasi-Hermitian as well as
PT-symmetric, then the system is equivalent to a real quantum system. Thus our
results show that neither PT-symmetry nor quasi-Hermiticity constraints are
sufficient to extend standard quantum theory consistently.


title: Backtrack Tie-Breaking for Decision Trees: A Note on Deodata Predictors

abstract:
  A tie-breaking method is proposed for choosing the predicted class, or
outcome, in a decision tree. The method is an adaptation of a similar technique
used for deodata predictors.


title: NFT Wash Trading: Quantifying suspicious behaviour in NFT markets

abstract:
  The smart contract-based markets for non-fungible tokens (NFTs) on the
Ethereum blockchain have seen tremendous growth in 2021, with trading volumes
peaking at 3.5b in September 2021. This dramatic surge has led to industry
observers questioning the authenticity of on-chain volumes, given the absence
of identity requirements and the ease with which agents can control multiple
addresses. We examine potentially illicit trading patterns in the NFT markets
from January 2018 to mid-November 2021, gathering data from the 52 largest
collections by volume. Our findings indicate that within our sample 3.93% of
addresses, processing a total of 2.04% of sale transactions, trigger suspicions
of market abuse. Flagged transactions contaminate nearly all collections and
may have inflated the authentic trading volumes by as much as 149,5m for the
period. Most flagged transaction patterns alternate between a few addresses,
indicating a predisposition for manual trading. We submit that the results
presented here may serve as a viable lower bound estimate for NFT wash trading
on Ethereum. Even so, we argue that wash trading may be less common than what
industry observers have previously estimated. We contribute to the emerging
discourse on the identification and deterrence of market abuse in the
cryptocurrency markets.


title: Offline Reinforcement Learning for Mobile Notifications

abstract:
  Mobile notification systems have taken a major role in driving and
maintaining user engagement for online platforms. They are interesting
recommender systems to machine learning practitioners with more sequential and
long-term feedback considerations. Most machine learning applications in
notification systems are built around response-prediction models, trying to
attribute both short-term impact and long-term impact to a notification
decision. However, a user's experience depends on a sequence of notifications
and attributing impact to a single notification is not always accurate, if not
impossible. In this paper, we argue that reinforcement learning is a better
framework for notification systems in terms of performance and iteration speed.
We propose an offline reinforcement learning framework to optimize sequential
notification decisions for driving user engagement. We describe a
state-marginalized importance sampling policy evaluation approach, which can be
used to evaluate the policy offline and tune learning hyperparameters. Through
simulations that approximate the notifications ecosystem, we demonstrate the
performance and benefits of the offline evaluation approach as a part of the
reinforcement learning modeling approach. Finally, we collect data through
online exploration in the production system, train an offline Double Deep
Q-Network and launch a successful policy online. We also discuss the practical
considerations and results obtained by deploying these policies for a
large-scale recommendation system use-case.


title: Mapping DNN Embedding Manifolds for Network Generalization Prediction

abstract:
  Understanding Deep Neural Network (DNN) performance in changing conditions is
essential for deploying DNNs in safety critical applications with unconstrained
environments, e.g., perception for self-driving vehicles or medical image
analysis. Recently, the task of Network Generalization Prediction (NGP) has
been proposed to predict how a DNN will generalize in a new operating domain.
Previous NGP approaches have relied on labeled metadata and known distributions
for the new operating domains. In this study, we propose the first NGP approach
that predicts DNN performance based solely on how unlabeled images from an
external operating domain map in the DNN embedding space. We demonstrate this
technique for pedestrian, melanoma, and animal classification tasks and show
state of the art NGP in 13 of 15 NGP tasks without requiring domain knowledge.
Additionally, we show that our NGP embedding maps can be used to identify
misclassified images when the DNN performance is poor.


title: COVID-19 Hospitalizations Forecasts Using Internet Search Data

abstract:
  As the COVID-19 spread over the globe and new variants of COVID-19 keep
occurring, reliable real-time forecasts of COVID-19 hospitalizations are
critical for public health decision on medical resources allocations such as
ICU beds, ventilators, and personnel to prepare for the surge of COVID-19
pandemics. Inspired by the strong association between public search behavior
and hospitalization admission, we extended previously-proposed influenza
tracking model, ARGO (AutoRegression with GOogle search data), to predict
future 2-week national and state-level COVID-19 new hospital admissions.
Leveraging the COVID-19 related time series information and Google search data,
our method is able to robustly capture new COVID-19 variants' surges, and
self-correct at both national and state level. Based on our retrospective
out-of-sample evaluation over 12-month comparison period, our method achieves
on average 15\% error reduction over the best alternative models collected from
COVID-19 forecast hub. Overall, we showed that our method is flexible,
self-correcting, robust, accurate, and interpretable, making it a potentially
powerful tool to assist health-care officials and decision making for the
current and future infectious disease outbreak.


title: Maximum Likelihood Uncertainty Estimation: Robustness to Outliers

abstract:
  We benchmark the robustness of maximum likelihood based uncertainty
estimation methods to outliers in training data for regression tasks. Outliers
or noisy labels in training data results in degraded performances as well as
incorrect estimation of uncertainty. We propose the use of a heavy-tailed
distribution (Laplace distribution) to improve the robustness to outliers. This
property is evaluated using standard regression benchmarks and on a
high-dimensional regression task of monocular depth estimation, both containing
outliers. In particular, heavy-tailed distribution based maximum likelihood
provides better uncertainty estimates, better separation in uncertainty for
out-of-distribution data, as well as better detection of adversarial attacks in
the presence of outliers.


title: Bubble nuclei with shape coexistence in even-even isotopes of Hf to Hg

abstract:
  The shape of a nucleus is one of fundamental nuclear properties. We perform a
systematic investigation of bubble nuclei that also exhibit shape coexistence
in Hf, W, Os, Pt and Hg even-even isotopes using the deformed relativistic
Hartree-Bogoliubov theory in continuum. For a systematic study, we first
consider nuclear bubble structures and shape coexistence separately. We confirm
that deformations and pairing correlations hinder bubble structures by
comparing our results with those from relativistic continuum Hartree-Bogoliubov
theory that assumes spherical symmetry in nuclei. We then predict candidate
isotopes with both bubble structure and shape coexistence. We observe that the
depletion fraction factor that characterizes bubble structure is mostly smaller
in oblate deformation than in prolate, while some isotopes such as $^{206}$Os
have bubble structures both in oblate and prolate deformations. We compare the
proton single-particle energy levels for the candidates of shape coexistence
both with only prolate bubble structure and with prolate and oblate bubble
structures.


title:
A parallel algorithm for minimum weight set cover with small
  neighborhood property

abstract:
  This paper studies the minimum weight set cover (MinWSC) problem with a {\em
small neighborhood cover} (SNC) property proposed by Agarwal {\it et al.} in
\cite{Agarwal.}. A parallel algorithm for MinWSC with $\tau$-SNC property is
presented, obtaining approximation ratio $\tau(1+3\varepsilon)$ in
$O(L\log_{1+\varepsilon}\frac{n^3}{\varepsilon^2}+ 4\tau^{3}2^\tau L^2\log n)$
rounds, where $0< \varepsilon <\frac{1}{2}$ is a constant, $n$ is the number of
elements, and $L$ is a parameter related to SNC property. Our results not only
improve the approximation ratio obtained in \cite{Agarwal.}, but also answer
two questions proposed in \cite{Agarwal.}.


title:
Detecting Preformed-Pair Current through Nonequilibrium Noise in the
  BCS--BEC Crossover

abstract:
  We theoretically propose a method to identify the tunneling current carrier
in interacting fermions from nonequilibrium noise in the
Bardeen-Cooper-Schrieffer to Bose--Einstein condensate crossover. The
noise-to-current ratio, the Fano factor, can be a crucial probe for the current
carrier. Bringing strongly-correlated fermions into contact with a dilute
reservoir produces a tunneling current in between. The associated Fano factor
increases from one to two as the interaction becomes stronger, reflecting the
formation of the preformed Cooper pairs or bound molecules.

categories: cond-mat.quant-gas cond-mat.mes-hall cond-mat.str-el cond-mat.supr-con


title: Bankruptcy Prediction via Mixing Intra-Risk and Spillover-Risk

abstract:
  Bankruptcy risk prediction for Small and Medium-sized Enterprises (SMEs) is a
crucial step for financial institutions to make the loan decision and identify
region economics's early warning. However, previous studies in both finance and
AI research fields only consider either the intra-risk or the spillover-risk,
ignoring their interactions and their combinatorial effect for simplicity. This
paper for the first time considers both risks simultaneously and their joint
effect in bankruptcy prediction. Specifically, we first propose an enterprise
intra-risk encoder with LSTM based on enterprise risk statistical significance
indicators from its basic business information and litigation information for
its intra-risk learning. Afterward, we propose an enterprise spillover-risk
encoder based on enterprise relational information from the enterprise
knowledge graph for its spillover-risk embedding. In particular, the
spillover-risk encoder is equipped with both the newly proposed Hyper-Graph
Neural Networks (Hyper-GNNs) and Heterogeneous Graph Neural Networks
(Heter-GNNs), which is able to model spillover risk from two different aspects,
i.e. common risk factors based on hyperedges and direct diffusion risk from the
neighbors, respectively. With the two kinds of encoders, a unified framework is
designed to simultaneously capture intra-risk and spillover-risk for bankruptcy
prediction. To evaluate our model, we collect multi-sources SMEs real-world
data and build a novel benchmark dataset SMEsD. We provide open access to the
dataset, which is expected to promote the financial risk analysis research
further. Experiments on SMEsD against nine SOTA baselines demonstrate the
effectiveness of the proposed model for bankruptcy prediction.


title: Unsupervised Source Separation via Self-Supervised Training

abstract:
  We introduce two novel unsupervised (blind) source separation methods, which
involve self-supervised training from single-channel two-source speech mixtures
without any access to the ground truth source signals. Our first method employs
permutation invariant training (PIT) to separate artificially-generated
mixtures of the original mixtures back into the original mixtures, which we
named mixture permutation invariant training (MixPIT). We found this
challenging objective to be a valid proxy task for learning to separate the
underlying sources. We improve upon this first method by creating mixtures of
source estimates and employing PIT to separate these new mixtures in a cyclic
fashion. We named this second method cyclic mixture permutation invariant
training (MixCycle), where cyclic refers to the fact that we use the same model
to produce artificial mixtures and to learn from them continuously. We show
that MixPIT outperforms a common baseline (MixIT) on our small dataset
(SC09Mix), and they have comparable performance on a standard dataset
(LibriMix). Strikingly, we also show that MixCycle surpasses the performance of
supervised PIT by being data-efficient, thanks to its inherent data
augmentation mechanism. To the best of our knowledge, no other purely
unsupervised method is able to match or exceed the performance of supervised
training.


title: Multilevel Delayed Acceptance MCMC

abstract:
  We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a
hierarchy of models of increasing complexity to efficiently generate samples
from an unnormalized target distribution. Broadly, the method rewrites the
Multilevel MCMC approach of Dodwell et al. (2015) in terms of the Delayed
Acceptance (DA) MCMC of Christen & Fox (2005). In particular, DA is extended to
use a hierarchy of models of arbitrary depth, and allow subchains of arbitrary
length. We show that the algorithm satisfies detailed balance, hence is ergodic
for the target distribution. Furthermore, multilevel variance reduction is
derived that exploits the multiple levels and subchains, and an adaptive
multilevel correction to coarse-level biases is developed. Three numerical
examples of Bayesian inverse problems are presented that demonstrate the
advantages of these novel methods. The software and examples are available in
PyMC3.


title: Fuglede-Kadison determinants over free groups and Lehmer's constants

abstract:
  Lehmer's famous problem asks whether the set of Mahler measures of
polynomials with integer coefficients admits a gap at 1. In 2019, L\"uck
extended this question to Fuglede-Kadison determinants of a general group, and
he defined the Lehmer's constants of the group to measure such a gap.
  In this paper, we compute new values for Fuglede-Kadison determinants over
non-cyclic free groups. As a consequence, we partially answer L\"uck's
question, as we provide the new upper bound $\frac{2}{\sqrt{3}}$ for Lehmer's
constants of all torsionfree groups which have non-cyclic free subgroups.
  Our proofs use relations between Fuglede-Kadison determinants and random
walks on Cayley graphs, as well as works of Bartholdi and Dasbach-Lalin.
  Furthermore, we study several fundamental groups of small hyperbolic
3-manifolds, and we show that all of their Lehmer's constants are bounded by
even smaller values than $\frac{2}{\sqrt{3}}$. For this, we use relations
between Fuglede-Kadison determinants, $L^2$-torsions and hyperbolic volumes.
  Finally, we describe a method for computing upper approximations of
Fuglede-Kadison determinants and we apply it for the group of the figure-eight
knot complement.


title:
$H^{1+\alpha}$ estimates for the fully nonlinear parabolic thin obstacle
  problem

abstract:
  We study the regularity of the viscosity solution to the fully nonlinear
parabolic thin obstacle problem. In particular, we prove that the solution is
local $H^{1+\alpha}$ on each side of the smooth obstacle, for some small
$\alpha>0.$ Following the method which was first introduced for the harmonic
case by Caffarelli in 1979, we extend the results of Fern\'{a}ndez-Real (2016)
who treated the fully nonlinear elliptic case. Our results also extend those of
Chatzigeorgiou (2019) in two ways. First, we do not assume solutions nor
operators to be symmetric. Second, our estimates are local, in the sense that
do not rely on the boundary data.


title: BIQ2021: A Large-Scale Blind Image Quality Assessment Database

abstract:
  The assessment of the perceptual quality of digital images is becoming
increasingly important as a result of the widespread use of digital multimedia
devices. Smartphones and high-speed internet are just two examples of
technologies that have multiplied the amount of multimedia content available.
Thus, obtaining a representative dataset, which is required for objective
quality assessment training, is a significant challenge. The Blind Image
Quality Assessment Database, BIQ2021, is presented in this article. By
selecting images with naturally occurring distortions and reliable labeling,
the dataset addresses the challenge of obtaining representative images for
no-reference image quality assessment. The dataset consists of three sets of
images: those taken without the intention of using them for image quality
assessment, those taken with intentionally introduced natural distortions, and
those taken from an open-source image-sharing platform. It is attempted to
maintain a diverse collection of images from various devices, containing a
variety of different types of objects and varying degrees of foreground and
background information. To obtain reliable scores, these images are
subjectively scored in a laboratory environment using a single stimulus method.
The database contains information about subjective scoring, human subject
statistics, and the standard deviation of each image. The dataset's Mean
Opinion Scores (MOS) make it useful for assessing visual quality. Additionally,
the proposed database is used to evaluate existing blind image quality
assessment approaches, and the scores are analyzed using Pearson and Spearman's
correlation coefficients. The image database and MOS are freely available for
use and benchmarking.


title: Group Fairness Is Not Derivable From Justice: a Mathematical Proof

abstract:
  We argue that an imperfect criminal law procedure cannot be group-fair, if
'group fairness' involves ensuring the same chances of acquittal or convictions
to all innocent defendants independently of their morally arbitrary features.
We show mathematically that only a perfect procedure (involving no mistake), a
non-deterministic one, or a degenerate one (everyone or no one is convicted)
can guarantee group fairness, in the general case. Following a recent proposal,
we adopt a definition of group fairness, requiring that individuals who are
equal in merit ought to have the same statistical chances of obtaining
advantages and disadvantages, in a way that is statistically independent of any
of their feature that does not count as merit. We explain by mathematical
argument that the only imperfect procedures offering an a-priori guarantee of
fairness in relation to all non-merit trait are lotteries or degenerate ones
(i.e., everyone or no one is convicted). To provide a more intuitive point of
view, we exploit an adjustment of the well-known ROC space, in order to
represent all possible procedures in our model by a schematic diagram. The
argument seems to be equally valid for all human procedures, provided they are
imperfect. This clearly includes algorithmic decision-making, including
decisions based on statistical predictions, since in practice all statistical
models are error prone.


title: Robust Hybrid Learning With Expert Augmentation

abstract:
  Hybrid modelling reduces the misspecification of expert models by combining
them with machine learning (ML) components learned from data. Like for many ML
algorithms, hybrid model performance guarantees are limited to the training
distribution. Leveraging the insight that the expert model is usually valid
even outside the training domain, we overcome this limitation by introducing a
hybrid data augmentation strategy termed \textit{expert augmentation}. Based on
a probabilistic formalization of hybrid modelling, we show why expert
augmentation improves generalization. Finally, we validate the practical
benefits of augmented hybrid models on a set of controlled experiments,
modelling dynamical systems described by ordinary and partial differential
equations.


title:
Engineering Interlayer Hybridization in Energy Space via Dipolar
  Overlayers

abstract:
  The interlayer hybridization (IH) of van der Waals (vdW) materials is thought
to be mostly associated with the unignorable interlayer overlaps of
wavefunctions ($t$) in real space. Here, we develop a more fundamental
understanding of IH by introducing a new physical quantity, the IH admixture
ratio ${\alpha}$. Consequently, an exotic strategy of IH engineering in energy
space can be proposed, i.e., instead of changing t as commonly used, ${\alpha}$
can be effectively tuned in energy space by changing the onsite energy
difference ($2{\Delta}$) between neighboring-layer states. In practice, this is
feasible via reshaping the electrostatic potential of the surface by deposing a
dipolar overlayer, e.g., crystalline ice. Our first-principles calculations
unveil that IH engineering via adjusting $2{\Delta}$ can greatly tune
interlayer optical transitions in transition-metal dichalcogenide bilayers,
switch different types of Dirac surface states in Bi$_2$Se$_3$ thin films, and
control magnetic phase transition of charge density waves in 1H/1T-TaS$_2$
bilayers, opening new opportunities to govern the fundamental optoelectronic,
topological, and magnetic properties of vdW systems beyond the traditional
interlayer-distance or twisting engineering.


title:
Tomography of the environment of the COSMOS/AzTEC-3 submillimeter galaxy
  at z=5.3 revealed by Lyalpha and MUSE observations

abstract:
  We study the members of the protocluster around AzTEC3 submillimeter galaxy
at z=5.3. We analyzed the data from the MUSE instrument in an area of 1.4x1.4
arcmin^2 around AzTEC3 and derived information on the Lya line in emission. We
compared the Lya profile of various regions of the environment with the zELDA
radiative transfer model, revealing the neutral gas distribution and
kinematics. We identified 10 Lya emitting sources, including 2 regions with
extended emission: one embedding AzTEC3 and LBG3, a star-forming galaxy located
12 kpc north of the SMG and another toward LBG-1, a star-forming galaxy located
90 kpc to the southeast. The sources appear distributed in an elongated
configuration of about 70'' in extent. The number of sources confirms the
overdensity around AzTEC3. For the AzTEC3+LBG3 system, the Lya emission appears
redshifted and more spatially extended than the [CII] line emission. Similarly,
the Lya line spectrum is broader in velocity than [CII] for LBG1. In the former
spectrum, the Lya emission is elongated to the north of LBG3 and to the south
of AzTEC3, where a faint Lya emitting galaxy is also located. The elongated
structures could resemble tidal features due to the interaction of the two
galaxies with AzTEC3. Also, we find a bridge of gas, revealed by the Lya
emission between AzTEC3 and LBG3. The Lya emission toward LBG1 embeds its three
components. The HI kinematics support the idea of a merger of the three
components. Given the availability of CO and [CII] observations from previous
campaigns, and our Lya information, we find evidence of starburst-driven
phenomena and interactions around AzTEC-3. The stellar mass of the galaxies of
the overdensity and the Lya luminosity of the HI nebula associated with AzTEC-3
imply a dark matter halo of 10^12 Msun at z=5.3 that could evolve into a
cluster of 2x10^14 Msun at z=0.


title:
GraphDCA -- a Framework for Node Distribution Comparison in Real and
  Synthetic Graphs

abstract:
  We argue that when comparing two graphs, the distribution of node structural
features is more informative than global graph statistics which are often used
in practice, especially to evaluate graph generative models. Thus, we present
GraphDCA - a framework for evaluating similarity between graphs based on the
alignment of their respective node representation sets. The sets are compared
using a recently proposed method for comparing representation spaces, called
Delaunay Component Analysis (DCA), which we extend to graph data. To evaluate
our framework, we generate a benchmark dataset of graphs exhibiting different
structural patterns and show, using three node structure feature extractors,
that GraphDCA recognizes graphs with both similar and dissimilar local
structure. We then apply our framework to evaluate three publicly available
real-world graph datasets and demonstrate, using gradual edge perturbations,
that GraphDCA satisfyingly captures gradually decreasing similarity, unlike
global statistics. Finally, we use GraphDCA to evaluate two state-of-the-art
graph generative models, NetGAN and CELL, and conclude that further
improvements are needed for these models to adequately reproduce local
structural features.


title:
Computer assisted discharging procedure on planar graphs: application to
  2-distance coloring

abstract:
  Using computational techniques we provide a framework for proving results on
subclasses of planar graphs via discharging method. The aim of this paper is to
apply these techniques to study the 2-distance coloring of planar subcubic
graphs. Applying these techniques we show that every subcubic planar graph $G$
of girth at least 8 has 2-distance chromatic number at most 6.


title:
Integration of Clinical, Biological, and Computational Perspectives to
  Support Cerebral Autoregulatory Informed Clinical Decision Making Decomposing
  Cerebral Autoregulation using Mechanistic Timescales to Support Clinical
  Decision-Making

abstract:
  Adequate brain perfusion is required for proper brain function and life.
Maintaining optimal brain perfusion to avoid secondary brain injury is one of
the main concerns of neurocritical care. Cerebral autoregulation is responsible
for maintaining optimal brain perfusion despite pressure derangements.
Knowledge of cerebral autoregulatory function should be a key factor in
clinical decision-making, yet it is often insufficiently and incorrectly
applied. Multiple physiologic mechanisms impact cerebral autoregulation, each
of which operate on potentially different and incompletely understood
timescales confounding conclusions drawn from observations. Because of such
complexities, clinical conceptualization of cerebral autoregulation has been
distilled into practical indices defined by multimodal neuromonitoring, which
removes mechanistic information and limits decision options. The next step
towards cerebral autoregulatory-informed clinical decision-making is to
quantify cerebral autoregulation mechanistically, which requires decomposing
cerebral autoregulation into its fundamental processes and partitioning those
processes into the timescales at which each operates. In this review, we
scrutinize biologically, clinically, and computationally focused literature to
build a timescales-based framework around cerebral autoregulation. This new
framework will allow us to quantify mechanistic interactions and directly infer
which mechanism(s) are functioning based only on current monitoring equipment,
paving the way for a new frontier in cerebral autoregulatory-informed clinical
decision-making.


title:
Dark matter, supernova neutrinos and other backgrounds in direct dark
  matter searches. The ANDES laboratory prospects

abstract:
  Dark Matter particles can be detected directly via their elastic scattering
with nuclei. Next generation experiments can eventually find physical evidences
about dark matter candidates. With this motivation in mind, we have calculated
the expected signals of dark matter particles in xenon detectors. The
calculations were performed by considering different masses and parameters
within the minimal supersymmetric standard model. Since the detectors can also
detect neutrinos, we have analyzed the supernova neutrino signal including a
sterile neutrino in the formalism. Using this 3+1 scheme, we make predictions
for both the normal and inverse mass hierarchy. In order to perform a study of
the response of planned direct-detection experiments, to be located in ANDES
(Agua Negra Deep Experimental Site), we have calculated the neutrino
contributions to the background by taken into account reactor's neutrinos and
geoneutrinos at the site of the lab. As a test detector, we take a Xenon1T-like
array.


title: Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

abstract:
  Combinatorial optimisation problems are ubiquitous in artificial
intelligence. Designing the underlying models, however, requires substantial
expertise, which is a limiting factor in practice. The models typically consist
of hard and soft constraints, or combine hard constraints with an objective
function. We introduce a novel setting for learning combinatorial optimisation
problems from contextual examples. These positive and negative examples show -
in a particular context - whether the solutions are good enough or not. We
develop our framework using the MAX-SAT formalism as it is simple yet powerful
setting having these features. We study the learnability of MAX-SAT models. Our
theoretical results show that high-quality MAX-SAT models can be learned from
contextual examples in the realisable and agnostic settings, as long as the
data satisfies an intuitive "representativeness" condition. We also contribute
two implementations based on our theoretical results: one leverages ideas from
syntax-guided synthesis while the other makes use of stochastic local search
techniques. The two implementations are evaluated by recovering synthetic and
benchmark models from contextual examples. The experimental results support our
theoretical analysis, showing that MAX-SAT models can be learned from
contextual examples. Among the two implementations, the stochastic local search
learner scales much better than the syntax-guided implementation while
providing comparable or better models.


title: Discrete Anomaly Matching for the Pouliot Type Dualities

abstract:
  We compute the 't Hooft anomalies of discrete symmetries in the Pouliot type
dual theories and check their anomaly matching conditions. The Pouliot type
dual theories we will consider in this paper are two dual pairs; the dual pair
of ${\cal N}=1$ supersymmetric theories of a $Spin(7)$ gauge theory with
spinors and a $SU(N_f-4)$ gauge theory with a symmetric tensor, fundamentals
and singlets, and the other dual pair of ${\cal N}=1$ supersymmetric theories
of a $Spin(10)$ gauge theory with a spinor and vectors and a $SU(N_f-5)$ gauge
theory with a symmetric tensor, fundamentals and singlets. We will show that
the both pairs satisfy the discrete anomaly matching conditions.


title: Designing active colloidal folders

abstract:
  Can active forces be exploited to drive the consistent collapse of an active
polymer into a folded structure? In this paper we introduce and perform
numerical simulations of a simple model of active colloidal folders, and show
that a judicious inclusion of active forces into a stiff colloidal chain can
generate designable and reconfigurable two dimensional folded structures. The
key feature is to organize the forces perpendicular to the chain backbone
according to specific patterns (sequences). We characterize the physical
properties of this model and perform, using a number of numerical techniques,
an in-depth statistical analysis of structure and dynamics of the emerging
conformations. We discovered a number of interesting features, including the
existence of a direct correspondence between the sequence of the active forces
and the structure of folded conformations, and we discover the existence of an
ensemble of highly mobile compact structures capable of moving from
conformation to conformation. Finally, akin to protein design problems, we
discuss a method that is capable of designing specific target folds by sampling
over sequences of active forces.


title:
Influence of the formation of clusters on the effective elastic
  properties of platelet reinforced polymers

abstract:
  The effect of the clusterization on the effective properties of a composite
material reinforced by MXene or graphene platelets is studied using the finite
element method with periodic representative volume element (RVE). A hybrid
2D/3D finite element mesh is used to reduce the computational complexity of the
numerical model. Several realizations of an RVE were generated with increasing
volume fractions of inclusions, resulting in progressive clusterization of the
platelets. Numerically obtained effective properties of the composite are
compared with analytical predictions by the Mori-Tanaka method and Halpin-Tsai
equations, and the limits of the applicability of the analytical models are
established. A two-step homogenization scheme is proposed to increase the
accuracy and stability of the effective properties of an RVE with a relatively
small number of inclusions. Simple scaling relations are proposed to generalize
numerical results to platelets with other aspect ratios.


title:
Validity of tests for time-to-event endpoints in studies with the Pocock
  and Simon covariate-adaptive randomization

abstract:
  In the presence of prognostic covariates, inference about the treatment
effect with time-to-event endpoints is mostly conducted via the stratified
log-rank test or the score test based on the Cox proportional hazards model. In
their ground-breaking work Ye and Shao (2020) have demonstrated theoretically
that when the model is misspecified, the robust score test (Wei and Lin, 1989)
as well as the unstratified log-rank test are conservative in trials with
stratified randomization. This fact, however, was not established for the
Pocock and Simon covariate-adaptive allocation other than through simulations.
In this paper, we expand the results of Ye and Shao to a more general class of
randomization procedures and show, in part theoretically, in part through
simulations, that the Pocock and Simon covariate-adaptive allocation belongs to
this class. We also advance the search for the correlation structure of the
normalized within-stratum imbalances with minimization by describing the
asymptotic correlation matrix for the case of equal prevalence of all strata.
We expand the robust tests proposed by Ye and Shao for stratified randomization
to minimization and examine their performance trough simulations.


title:
Influence maximization under limited network information: Seeding
  high-degree neighbors

abstract:
  The diffusion of information, norms, and practices across a social network
can be initiated by compelling a small number of seed individuals to adopt
first. Strategies proposed in previous work either assume full network
information or large degree of control over what information is collected.
However, privacy settings on the Internet and high non-response in surveys
often severely limit available connectivity information. Here we propose a
seeding strategy for scenarios with limited network information: Only the
degrees and connections of some random nodes are known. This new strategy is a
modification of "random neighbor sampling" and seeds the highest-degree
neighbors of randomly selected nodes. In simulations of a linear threshold
model on a range of synthetic and real-world networks, we find that this new
strategy outperforms other seeding strategies, including high-degree seeding
and clustered seeding.


title: The temporal limits of predicting fault failure

abstract:
  Machine learning models using seismic emissions can predict instantaneous
fault characteristics such as displacement in laboratory experiments and slow
slip in Earth. Here, we address whether the acoustic emission (AE) from
laboratory experiments contains information about near-future frictional
behavior. The approach uses a convolutional encoder-decoder containing a
transformer layer. We use as input progressively larger AE input time windows
and progressively larger output friction time windows. The attention map from
the transformer is used to interpret which regions of the AE contain hidden
information corresponding to future frictional behavior. We find that very
near-term predictive information is indeed contained in the AE signal, but
farther into the future the predictions are progressively worse. Notably,
information for predicting near future frictional failure and recovery are
found to be contained in the AE signal. This first effort predicting future
fault frictional behavior with machine learning will guide efforts for
applications in Earth.


title: Searching for white dwarf variables in TESS data

abstract:
  A sample of 4 thousands Gaia sources with apparent $G$-band magnitudes of
$<17$ and trigonometric parallaxes of $>3.33$ milli-arcseconds were
cross-matched to the Transiting Exoplanet Survey high level science products
and the Full-Frame Images in order to extract the light curves of possible
white dwarf variables. Most of the targets in the sample were observed in at
least 27-day observing cycle with 30-minutes cadence. Based on Lomb-Scargle
periodogram constructed from de-trended light curves, more than 600 sources
have indication of periodic variability. This paper presents the early results
from the identification.


title: Speech Emotion Recognition using Self-Supervised Features

abstract:
  Self-supervised pre-trained features have consistently delivered state-of-art
results in the field of natural language processing (NLP); however, their
merits in the field of speech emotion recognition (SER) still need further
investigation. In this paper we introduce a modular End-to- End (E2E) SER
system based on an Upstream + Downstream architecture paradigm, which allows
easy use/integration of a large variety of self-supervised features. Several
SER experiments for predicting categorical emotion classes from the IEMOCAP
dataset are performed. These experiments investigate interactions among
fine-tuning of self-supervised feature models, aggregation of frame-level
features into utterance-level features and back-end classification networks.
The proposed monomodal speechonly based system not only achieves SOTA results,
but also brings light to the possibility of powerful and well finetuned
self-supervised acoustic features that reach results similar to the results
achieved by SOTA multimodal systems using both Speech and Text modalities.


title:
Inference from Sampling with Response Probabilities Estimated via
  Calibration

abstract:
  A solution to control for nonresponse bias consists of multiplying the design
weights of respondents by the inverse of estimated response probabilities to
compensate for the nonrespondents. Maximum likelihood and calibration are two
approaches that can be applied to obtain estimated response probabilities. The
paper develops asymptotic properties of the resulting estimator when
calibration is applied. A logistic regression model for the response
probabilities is postulated and missing at random data is supposed. The author
shows that the estimators with the response probabilities estimated via
calibration are asymptotically equivalent to unbiased estimators and that a
gain in efficiency is obtained when estimating the response probabilities via
calibration as compared to the estimator with the true response probabilities.


title: Verification-Aided Deep Ensemble Selection

abstract:
  Deep neural networks (DNNs) have become the technology of choice for
realizing a variety of complex tasks. However, as highlighted by many recent
studies, even an imperceptible perturbation to a correctly classified input can
lead to misclassification by a DNN. This renders DNNs vulnerable to strategic
input manipulations by attackers, and also prone to oversensitivity to
environmental noise.
  To mitigate this phenomenon, practitioners apply joint classification by an
ensemble of DNNs. By aggregating the classification outputs of different
individual DNNs for the same input, ensemble-based classification reduces the
risk of misclassifications due to the specific realization of the stochastic
training process of any single DNN. However, the effectiveness of a DNN
ensemble is highly dependent on its members not simultaneously erring on many
different inputs.
  In this case study, we harness recent advances in DNN verification to devise
a methodology for identifying ensemble compositions that are less prone to
simultaneous errors, even when the input is adversarially perturbed --
resulting in more robustly-accurate ensemble-based classification.
  Our proposed framework uses a DNN verifier as a backend, and includes
heuristics that help reduce the high complexity of directly verifying
ensembles. More broadly, our work puts forth a novel universal objective for
formal verification that can potentially improve the robustness of real-world,
deep-learning-based systems across a variety of application domains.


title: Covert backscatter communication with directional MIMO

abstract:
  We study a backscatter communication protocol over a AWGN channel, where a
transmitter illuminates a tag with a directional multi-antenna. The tag
performs load modulation on the signal while hiding its physical presence from
a warden. We show that, if the transmitter-to-tag channel is inaccessible to
the warden, then $\Theta(n)$ reliable and covert bits can be transmitted over
$n$ channel usages. This overcomes the square-root law for covert
communication. This paper provides the first evidence for practical
implementation of covert backscatter communication, with potential applications
in IoT security.


title:
Triangle Singularity in the Production of $T_{cc}^+$(3875) and a Soft
  Pion

abstract:
  The double-charm tetraquark meson $T_{cc}^+(3875)$ can be produced in
high-energy proton-proton collisions by the creation of the charm mesons
$D^{*+} D^0$ at short distances followed by their binding into $T_{cc}^+$. The
$T_{cc}^+$ can also be produced by the creation of $D^{*+} D^{*+}$ at short
distances followed by their rescattering into $T_{cc}^+ \pi^+$. A charm-meson
triangle singularity produces a narrow peak in the $T_{cc}^+ \pi^+$ invariant
mass distribution 6.1 MeV above the threshold with a width of about 1 MeV. Well
beyond the peak, the differential cross section decreases with the invariant
kinetic energy $E$ of $T_{cc}^+ \pi^+$ as $E^{-1/2}$. The fraction of
$T_{cc}^+$ that are accompanied by $\pi^+$ with $E< m_\pi$ is estimated to be
roughly 3%. The fraction of $T_{cc}^+$ events with $T_{cc}^+ \pi^+$ in the
narrow peak from the triangle singularity could be comparable.


title: GLPU: A Geometric Approach For Lidar Pointcloud Upsampling

abstract:
  In autonomous driving, lidar is inherent for the understanding of the 3D
environment. Lidar sensors vary in vertical resolutions, where a denser
pointcloud depicts a more detailed environment, albeit at a significantly
higher cost. Pointcloud upsampling predicts high-resolution pointclouds from
sparser ones to bridge this performance gap at a lower cost. Although many
upsampling frameworks have achieved a robust performance, a fair comparison is
difficult as they were tested on different datasets and metrics. In this work,
we first conduct a consistent comparative study to benchmark the existing
algorithms on the KITTI dataset. Then, we observe that there are three common
factors that hinder the performance: an inefficient data representation, a
small receptive field, and low-frequency losses. By leveraging the scene
geometry, a new self-supervised geometric lidar pointcloud upsampling (GLPU)
framework is proposed to address the aforementioned limitations. Our
experiments demonstrate the effectiveness and superior performance of GLPU
compared to other techniques on the KITTI benchmark.


title: Nanoscale electromagnetism with the boundary element method

abstract:
  In Yang et al. [Nature 576, 248 (2019)], the authors introduced a general
theoretical framework for nanoscale electromagnetism based on Feibelman
parameters. Here quantum effects of the optically excited electrons at the
interface between two materials are lumped into two complex-valued and
frequency-dependent parameters, which can be incorporated into modified
boundary conditions for Maxwell's equations, the so-called mesoscopic boundary
conditions. These modifications can in principle be implemeted in any Maxwell
solver, although the technicalities can be subtle and depend on the chosen
computational approach. In this paper we show how to implement the mesoscopic
boundary conditions in a boundary element method approach, based on a Galerkin
scheme with Raviart-Thomas shape elements for the representation of the
tangential electromagnetic fields at the boundary. We demonstrate that the
results of our simulations are in perfect agreement with Mie theory including
Feibelman parameters, and that for typical simulation scenarios the
computational overhead is usually small.


title:
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time
  Series Forecasting

abstract:
  End-to-end data-driven machine learning methods often have exuberant
requirements in terms of quality and quantity of training data which are often
impractical to fulfill in real-world applications. This is specifically true in
time series domain where problems like disaster prediction, anomaly detection,
and demand prediction often do not have a large amount of historical data.
Moreover, relying purely on past examples for training can be sub-optimal since
in doing so we ignore one very important domain i.e knowledge, which has its
own distinct advantages. In this paper, we propose a novel knowledge fusion
architecture, Knowledge Enhanced Neural Network (KENN), for time series
forecasting that specifically aims towards combining strengths of both
knowledge and data domains while mitigating their individual weaknesses. We
show that KENN not only reduces data dependency of the overall framework but
also improves performance by producing predictions that are better than the
ones produced by purely knowledge and data driven domains. We also compare KENN
with state-of-the-art forecasting methods and show that predictions produced by
KENN are significantly better even when trained on only 50\% of the data.


title:
Efficient approximation of cardiac mechanics through reduced order
  modeling with deep learning-based operator approximation

abstract:
  Reducing the computational time required by high-fidelity, full order models
(FOMs) for the solution of problems in cardiac mechanics is crucial to allow
the translation of patient-specific simulations into clinical practice. While
FOMs, such as those based on the finite element method, provide valuable
information of the cardiac mechanical function, up to hundreds of thousands
degrees of freedom may be needed to obtain accurate numerical results. As a
matter of fact, simulating even just a few heartbeats can require hours to days
of CPU time even on powerful supercomputers. In addition, cardiac models depend
on a set of input parameters that we could let vary in order to explore
multiple virtual scenarios. To compute reliable solutions at a greatly reduced
computational cost, we rely on a reduced basis method empowered with a new
deep-learning based operator approximation, which we refer to as Deep-HyROMnet
technique. Our strategy combines a projection-based POD-Galerkin method with
deep neural networks for the approximation of (reduced) nonlinear operators,
overcoming the typical computational bottleneck associated with standard
hyper-reduction techniques. This method is shown to provide reliable
approximations to cardiac mechanics problems outperforming classical
projection-based ROMs in terms of computational speed-up of orders of
magnitude, and enhancing forward uncertainty quantification analysis otherwise
unaffordable.


title: Tube-Balloon Logic for the Exploration of Fluidic Control Elements

abstract:
  The control of pneumatically driven soft robots typically requires
electronics. Microcontrollers are connected to power electronics that switch
valves and pumps on and off. As a recent alternative, fluidic control methods
have been introduced, in which soft digital logic gates permit multiple
actuation states to be achieved in soft systems. Such systems have demonstrated
autonomous behaviors without the use of electronics. However, fluidic
controllers have required complex fabrication processes. To democratize the
exploration of fluidic controllers, we developed tube-balloon logic circuitry,
which consists of logic gates made from straws and balloons. Each tube-balloon
logic device takes a novice five minutes to fabricate and costs $0.45.
Tube-balloon logic devices can also operate at pressures of up to 200 kPa and
oscillate at frequencies of up to 15 Hz. We configure the tube-balloon logic
device as NOT-, NAND-, and NOR-gates and assemble them into a three-ring
oscillator to demonstrate a vibrating sieve that separates sugar from rice.
Because tube-balloon logic devices are low-cost, easy to fabricate, and their
operating principle is simple, they are well suited for exploring fundamental
concepts of fluidic control schemes while encouraging design inquiry for
pneumatically driven soft robots


title: Is Local $H_0$ At Odds With Dark Energy EFT?

abstract:
  Local $H_0$ determinations currently fall in a window between $H_0 \sim 70$
km/s/Mpc (TRGB) and $H_0 \sim 76$ km/s/Mpc (Tully-Fisher). In contrast, BAO
data calibrated in an early $\Lambda$CDM universe are largely consistent with
Planck-$\Lambda$CDM, $H_0 \sim 67.5$ km/s/Mpc. Employing a generic two
parameter family of evolving equations of state (EoS) for dark energy (DE)
$w_{\textrm{DE}}(z)$ and mock BAO data, we demonstrate that if i)
$w_{\textrm{DE}}(z=0) < -1$ and ii) integrated DE density less than
$\Lambda$CDM, then $H_0$ increases. EoS that violate these conditions at best
lead to modest $H_0$ increases within $1 \sigma$. Tellingly, Quintessence and
K-essence satisfy neither condition, whereas coupled Quintessence can only
satisfy ii). Beyond these seminal DE Effective Field Theories (EFTs), we turn
to explicit examples. Working model agnostically in an expansion in powers of
redshift $z$, we show that Brans-Dicke/$f(R)$ and Kinetic Gravity Braiding
models within the Horndeski class can lead to marginal and modest increases in
$H_0$, respectively. We confirm that as far as increasing $H_0$ is concerned,
no DE EFT model can outperform the phenomenological two parameter family of the
DE models. Evidently, the late universe may no longer be large enough to
accommodate $H_0$, BAO and DE described by EFT.


title:
Context-Aware Discrimination Detection in Job Vacancies using
  Computational Language Models

abstract:
  Discriminatory job vacancies are disapproved worldwide, but remain
persistent. Discrimination in job vacancies can be explicit by directly
referring to demographic memberships of candidates. More implicit forms of
discrimination are also present that may not always be illegal but still
influence the diversity of applicants. Explicit written discrimination is still
present in numerous job vacancies, as was recently observed in the Netherlands.
Current efforts for the detection of explicit discrimination concern the
identification of job vacancies containing potentially discriminating terms
such as "young" or "male". However, automatic detection is inefficient due to
low precision: e.g. "we are a young company" or "working with mostly male
patients" are phrases that contain explicit terms, while the context shows that
these do not reflect discriminatory content.
  In this paper, we show how machine learning based computational language
models can raise precision in the detection of explicit discrimination by
identifying when the potentially discriminating terms are used in a
discriminatory context. We focus on gender discrimination, which indeed suffers
from low precision when filtering explicit terms. First, we created a data set
for gender discrimination in job vacancies. Second, we investigated a variety
of computational language models for discriminatory context detection. Third,
we evaluated the capability of these models to detect unforeseen discriminating
terms in context. The results show that machine learning based methods can
detect explicit gender discrimination with high precision and help in finding
new forms of discrimination. Accordingly, the proposed methods can
substantially increase the effectiveness of detecting job vacancies which are
highly suspected to be discriminatory. In turn, this may lower the
discrimination experienced at the start of the recruitment process.


title:
Minima of shear viscosity and thermal conductivity coefficients of
  classical fluids

abstract:
  The shear viscosity and thermal conductivity coefficients of various liquids
exhibit minima along certain trajectories on the phase diagram. These minima
arise due to the crossover between the momentum and energy transport mechanisms
in gas-like and liquid-like regimes. We demonstrate that the magnitudes of the
minima are quasi-universal in appropriately reduced units, especially for the
viscosity coefficients. Results presented in support of this observation
concern the transport properties of three simple model systems with different
pairwise interaction potentials (hard spheres, Lennard-Jones, and Coulomb) as
well as seven important real atomic and molecular liquids (Ne, Ar, Kr, Xe,
CH$_4$, CO$_2$, and N$_2$). The minima in viscosity and thermal conductivity
represent useful reference points for fluid transport properties.

categories: cond-mat.soft cond-mat.mtrl-sci physics.chem-ph physics.flu-dyn


title:
Optimal vaccination at high reproductive numbers: sharp transitions and
  counter-intuitive allocations

abstract:
  Optimization of vaccine allocations among different segments of a
heterogeneous population is important for enhancing the effectiveness of
vaccination campaigns in reducing the burden of epidemics. Intuitively, it
would seem that allocations designed to minimize infections should prioritize
those with the highest risk of being infected and infecting others. This
prescription is well supported by vaccination theory, e.g., when the
vaccination campaign aims to reach herd immunity. In this work, we show,
however, that for vaccines providing partial protection (leaky vaccines) and
for sufficiently high values of the basic reproduction number, intuition is
overturned: the optimal allocation for minimizing the number of infections
prioritizes the vaccination of those who are least likely to be infected.
Furthermore, we show that this phenomenon occurs at a range of basic
reproduction numbers relevant for the currently circulating strains of
SARS-CoV-19. The work combines numerical investigations, asymptotic analysis
for a general model, and complete mathematical analysis in a simple two-group
model. The results point to important considerations in managing vaccination
campaigns for infections with high transmissibility.


title:
Singlet-triplet mixing in the order parameter of the noncentrosymmetric
  superconductor Ru$_{7}$B$_{3}$

abstract:
  One of the key effects which is predicted to arise in superconductors without
a centre of inversion is the mixing of singlet and triplet order parameters,
which are no longer good quantum numbers on their own due to parity. We have
probed the gap structure in the noncentrosymmetric superconductor Ru$_7$B$_3$,
through small-angle neutron diffraction from the vortex lattice, in order to
search for the proposed mixed order parameter. We find that the measured
temperature dependence of the vortex-lattice form factor is well characterised
by a model constructed to describe the effects of broken inversion symmetry on
the superconducting state, indicating the presence of a mixed singlet-triplet
gap and confirming the theoretical predictions.


title: An array-oriented Python interface for FastJet

abstract:
  Analysis on HEP data is an iterative process in which the results of one step
often inform the next. In an exploratory analysis, it is common to perform one
computation on a collection of events, then view the results (often with
histograms) to decide what to try next. Awkward Array is a Scikit-HEP Python
package that enables data analysis with array-at-a-time operations to implement
cuts as slices, combinatorics as composable functions, etc. However, most C++
HEP libraries, such as FastJet, have an imperative, one-particle-at-a-time
interface, which would be inefficient in Python and goes against the grain of
the array-at-a-time logic of scientific Python. Therefore, we developed
fastjet, a pip-installable Python package that provides FastJet C++ binaries,
the classic (particle-at-a-time) Python interface, and the new array-oriented
interface for use with Awkward Array.
  The new interface streamlines interoperability with scientific Python
software beyond HEP, such as machine learning. In one case, adopting this
library along with other array-oriented tools accelerated HEP analysis code by
a factor of 20. It was designed to be easily integrated with libraries in the
Scikit-HEP ecosystem, including Uproot (file I/O), hist (histogramming), Vector
(Lorentz vectors), and Coffea (high-level glue). We discuss the design of the
fastjet Python library, integrating the classic interface with the array
oriented interface and with the Vector library for Lorentz vector operations.
The new interface was developed as open source.

categories: hep-ex cs.PL physics.comp-ph physics.data-an


title:
Characterization of a continuous muon source for the Muon-Induced X-ray
  Emission (MIXE) Technique

abstract:
  The toolbox for material characterization has never been richer than today.
Great progress with all kinds of particles and interaction methods provide
access to nearly all properties of an object under study. However, a
tomographic analysis of the subsurface region remains still a challenge today.
In this regard, the Muon-Induced X-ray Emission (MIXE) technique has seen
rebirth fueled by the availability of high intensity muon beams. We report here
a study conducted at the Paul Scherrer Institute (PSI). It demonstrates that
the absence of any beam time-structure leads to low pile-up events and a high
signal-to-noise ratio (SNR) with less than one hour acquisition time per sample
or data point. This performance creates the perspective to open this technique
to a wider audience for the routine investigation of non-destructive and
depth-sensitive elemental compositions, for example in rare and precious
samples. Using a hetero-structured sample of known elements and thicknesses, we
successfully detected the characteristic muonic X-rays, emitted during the
capture of a negative muon by an atom, and the gamma-rays resulting from the
nuclear capture of the muon, characterizing the capabilities of MIXE at PSI.
This sample emphasizes the quality of a continuous beam, and the exceptional
SNR at high rates. Such sensitivity will enable totally new statistically
intense aspects in the field of MIXE, e.g. elemental 3D-tomography and chemical
analysis. Therefore, we are currently advancing our proof-of-concept
experiments with the goal of creating a full fledged permanently operated user
station to make MIXE available to the wider scientific community as well as
industry.


title: Symplectic resolutions, symplectic duality, and Coulomb branches

abstract:
  Symplectic resolutions are an exciting new frontier of research in
representation theory. One of the most fascinating aspects of this study is
symplectic duality: the observation that these resolutions come in pairs with
matching properties. The Coulomb branch construction allows us to produce and
study many of these dual pairs. These notes survey much recent work in this
area including quantization, categorification, and enumerative geometry. We
particularly focus on ADE quiver varieties and affine Grassmannian slices.


title:
Impact of Network Centrality and Income on Slowing Infection Spread
  after Outbreaks

abstract:
  The COVID-19 pandemic has shed light on how the spread of infectious diseases
worldwide are importantly shaped by both human mobility networks and
socio-economic factors. Few studies, however, have examined the interaction of
mobility networks with socio-spatial inequalities to understand the spread of
infection. We introduce a novel methodology, called the Infection Delay Model,
to calculate how the arrival time of an infection varies geographically,
considering both effective distance-based metrics and differences in regions'
capacity to isolate -- a feature associated with socioeconomic inequalities. To
illustrate an application of the Infection Delay Model, this paper integrates
household travel survey data with cell phone mobility data from the S\~ao Paulo
metropolitan region to assess the effectiveness of lockdowns to slow the spread
of COVID-19. Rather than operating under the assumption that the next pandemic
will begin in the same region as the last, the model estimates infection delays
under every possible outbreak scenario, allowing for generalizable insights
into the effectiveness of interventions to delay a region's first case. The
model sheds light on how the effectiveness of lockdowns to slow the spread of
disease is influenced by the interaction of mobility networks and
socio-economic levels. We find that a negative relationship emerges between
network centrality and the infection delay after lockdown, irrespective of
income. Furthermore, for regions across all income and centrality levels,
outbreaks starting in less central locations were more effectively slowed by a
lockdown. Using the Infection Delay Model, this paper identifies and quantifies
a new dimension of disease risk faced by those most central in a mobility
network.


title:
Pair of primitive elements in quadratic form with prescribed trace over
  a finite field

abstract:
  In this article, we establish a sufficient condition for the existence of
primitive element $\alpha\in \Fm$ is such that $f(\alpha)$ is also primitive
element of $\Fm$ and $Tr_{\Fm/\F}(\alpha)=\beta$, for any prescribed
$\beta\in\F$, where $f(x)= ax^2 + bx + c\in \Fm(x)$ such that $b^2-4ac\neq 0$.
We conclude that, for $m\geq 5$ there is only one exceptional pair $(q,m)$
which is $(2,6)$.


title:
Prospects for studies of $D^{*0} \to \mu^+\mu^-$ and $B_{(s)}^{*0} \to
  \mu^+\mu^-$ decays

abstract:
  Weak decays of the vector $D^{*0}$ and $B_{(s)}^{*0}$ mesons to the $\mumu$
final state provide novel potential to test the Standard Model of particle
physics. Such processes have extremely small branching fractions as the vector
mesons are able to decay through electromagnetic and (for the $D^{*0}$ meson)
strong interactions. Nonetheless, the production of copious quantities of these
particles in LHC collisions, and the ability to exploit experimental techniques
that can suppress background to low levels, provides good potential to reach
interesting sensitivity. The possibility to reconstruct these processes as part
of the decay chain of $B^-$ or $B_c^+$ mesons appears particularly attractive
due to the clean experimental signature of the displaced vertex. Indeed,
published LHCb data on $\Bm \to \pim\mumu$ decays already implies a stringent
limit on the branching fraction of $D^{*0} \to \mu^+\mu^-$. Estimates are made
on the achievable sensitivity to $D^{*0} \to \mu^+\mu^-$ and $B_{(s)}^{*0} \to
\mu^+\mu^-$ decays with the LHCb experiment.


title: Edge-based fever screening system over private 5G

abstract:
  Edge computing and 5G have made it possible to perform analytics closer to
the source of data and achieve super-low latency response times, which is not
possible with centralized cloud deployment. In this paper, we present a novel
fever-screening system, which uses edge machine learning techniques and
leverages private 5G to accurately identify and screen individuals with fever
in real-time. Particularly, we present deep-learning based novel techniques for
fusion and alignment of cross-spectral visual and thermal data streams at the
edge. Our novel Cross-Spectral Generative Adversarial Network (CS-GAN)
synthesizes visual images that have the key, representative object level
features required to uniquely associate objects across visual and thermal
spectrum. Two key features of CS-GAN are a novel, feature-preserving loss
function that results in high-quality pairing of corresponding cross-spectral
objects, and dual bottleneck residual layers with skip connections (a new,
network enhancement) to not only accelerate real-time inference, but to also
speed up convergence during model training at the edge. To the best of our
knowledge, this is the first technique that leverages 5G networks and limited
edge resources to enable real-time feature-level association of objects in
visual and thermal streams (30 ms per full HD frame on an Intel Core i7-8650
4-core, 1.9GHz mobile processor). To the best of our knowledge, this is also
the first system to achieve real-time operation, which has enabled fever
screening of employees and guests in arenas, theme parks, airports and other
critical facilities. By leveraging edge computing and 5G, our fever screening
system is able to achieve 98.5% accuracy and is able to process about 5X more
people when compared to a centralized cloud deployment.


title: Network Coding Multicast Key-Capacity

abstract:
  For a multi-source multi-terminal noiseless network, the key-dissemination
problem involves the task of multicasting a secret key K from the network
sources to its terminals. As in secure multicast network-coding, in the
key-dissemination problem the source nodes have access to independent
randomness and, as the network is noiseless, the resulting key K is a function
of the sources' information. However, different from traditional forms of
multicast, in key-dissemination the key K need not consist of source messages,
but rather may be any function of the information generated at the sources, as
long as it is shared by all terminals. Allowing the shared key K to be a
mixture of source information grants a flexibility to the communication process
which gives rise to the potential of increased key-rates when compared to
traditional secure multicast. The multicast key-capacity is the supremum of
achievable key-rates, subject to the security requirement that the shared key
is not revealed to an eavesdropper with predefined eavesdropping capabilities.
The key-dissemination problem (termed also, secret key-agreement) has seen
significant studies over the past decades in memoryless network structures. In
this work, we initiate the study of key-dissemination in the context of
noiseless networks, i.e., network coding. In this context, we study
similarities and differences between traditional secure-multicast and the more
lenient task of key-dissemination.


title: High-frequency homogenization of nonstationary periodic equations

abstract:
  We consider an elliptic differential operator $A_\varepsilon = - \frac{d}{dx}
g(x/\varepsilon) \frac{d}{dx} + \varepsilon^{-2} V(x/\varepsilon)$,
$\varepsilon > 0$, with periodic coefficients acting in $L_2(\mathbb{R})$. For
the nonstationary Schr\"{o}dinger equation with the Hamiltonian $A_\varepsilon$
and for the hyperbolic equation with the operator $A_\varepsilon$, analogs of
homogenization problems, related to the edges of the spectral bands of the
operator $A_\varepsilon$, are studied (the so called high-frequency
homogenization). For the solutions of the Cauchy problems for these equations
with special initial data, approximations in $L_2(\mathbb{R})$-norm for small
$\varepsilon$ are obtained.


title: vol2Brain: A new online Pipeline for whole Brain MRI analysis

abstract:
  Automatic and reliable quantitative tools for MR brain image analysis are a
very valuable resources for both clinical and research environments. In the
last years, this field has experienced many advances with successful techniques
based on label fusion and more recently deep learning. However, few of them
have been specifically designed to provide a dense anatomical labelling at
multiscale level and to deal with brain anatomical alterations such as white
matter lesions. In this work, we present a fully automatic pipeline (vol2Brain)
for whole brain segmentation and analysis which densely labels (N>100) the
brain while being robust to the presence of white matter lesions. This new
pipeline is an evolution of our previous volBrain pipeline that extends
significantly the number of regions that can be analyzed. Our proposed method
is based on a fast multiscale multi-atlas label fusion technology with
systematic error correction able to provide accurate volumetric information in
few minutes. We have deployed our new pipeline within our platform volBrain
(www.volbrain.upv.es) which has been already demonstrated to be an efficient
and effective manner to share our technology with users worldwide


title: A "power" conjugate equation in the symmetric group

abstract:
  First we consider the solutions of the general "cubic" equation
a_{1}x^{r1}a_{2}x^{r2}a_{3}x^{r3}=1
  (with r1,r2,r3 in {1,-1}) in the symmetric group S_{n}. In certain cases this
equation can be rewritten as aya^{-1}=y^{2} or as aya^{-1}=y^{-2}, where a in
S_{n} depends on the a_{i}'s and the new unknown permutation y in S_{n} is a
product of x (or x^{-1}) and one of the permutations a_{i}^{1} and a_{i}^{-1}.
Using combinatorial arguments and some basic number theoretical facts, we
obtain results about the solutions of the so-called power conjugate equation
aya^{-1}=y^{e} in S_{n}, where e is an integer exponent. Under certain
conditions, the solutions are exactly the solutions of y^{e-1}=1 in the
centralizer of a.