title:
Comparing the convected level-set and the Allen-Cahn phase-field methods
  in AMR/C simulations of two-phase flows

abstract:
  The modeling and simulation of two-phase flows is still an active research
area, mainly when surface tension is present. One way to model the different
phases is with interface capturing methods. Two well-established interface
capturing approaches are the level-set and phase-field methods. The level-set
method is known for its ability to compute the surface tension accurately, and
phase-field models satisfy the second law of thermodynamics. This paper reviews
and compares a level-set and a phase-field approach to simulate two-phase
flows. We use a modified level-set method, called convected level-set. The
difference to the standard level-set method is that the re-initialization step
is embedded in the convection equation, avoiding a separate step during the
calculation. We also apply a global mass conservation procedure to enforce the
mass balance between phases. On the other hand, the phase-field approach uses a
conservative Allen-Cahn equation with a Lagrange multiplier to conserve the
mass. The methods are implemented in libMesh, a parallel adaptive finite
element library, using the same finite element formulations, time-marching
schemes, solvers, and mesh adaptivity strategies. We present numerical
solutions for the two methods considering adaptive mesh refinement and
coarsening (AMR/C). Results are analyzed and discussed.


title: Integral Motivic Sheaves And Geometric Representation Theory

abstract:
  With representation-theoretic applications in mind, we construct a formalism
of reduced motives with integral coefficients. These are motivic sheaves from
which the higher motivic cohomology of the base scheme has been removed. We
show that reduced stratified Tate motives satisfy favorable properties
including weight and t-structures. We also prove that reduced motives on
cellular (ind-)schemes unify various approaches to mixed sheaves in
representation theory, such as Soergel-Wendt's semisimplified Hodge motives,
Achar-Riche's complexes of parity sheaves, as well as Ho-Li's recent category
of graded $\ell$-adic sheaves.


title:
MeqSilhouette v2: Spectrally-resolved polarimetric synthetic data
  generation for the Event Horizon Telescope

abstract:
  We present MeqSilhouette v2.0 (MeqSv2), a fully polarimetric, time-and
frequency-resolved synthetic data generation software for simulating millimetre
(mm) wavelength very long baseline interferometry (VLBI) observations with
heterogeneous arrays. Synthetic data are a critical component in understanding
real observations, testing calibration and imaging algorithms, and predicting
performance metrics of existing or proposed sites. MeqSv2 applies physics-based
instrumental and atmospheric signal corruptions constrained by
empirically-derived site and station parameters to the data. The new version is
capable of applying instrumental polarization effects and various other
spectrally-resolved effects using the Radio Interferometry Measurement Equation
(RIME) formalism and produces synthetic data compatible with calibration
pipelines designed to process real data. We demonstrate the various corruption
capabilities of MeqSv2 using different arrays, with a focus on the effect of
complex bandpass gains on closure quantities for the EHT at 230 GHz. We
validate the frequency-dependent polarization leakage implementation by
performing polarization self-calibration of synthetic EHT data using PolSolve.
We also note the potential applications for cm-wavelength VLBI array analysis
and design and future directions.


title:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with
  NMF

abstract:
  This paper tackles post-hoc interpretability for audio processing networks.
Our goal is to interpret decisions of a network in terms of high-level audio
objects that are also listenable for the end-user. To this end, we propose a
novel interpreter design that incorporates non-negative matrix factorization
(NMF). In particular, a carefully regularized interpreter module is trained to
take hidden layer representations of the targeted network as input and produce
time activations of pre-learnt NMF components as intermediate outputs. Our
methodology allows us to generate intuitive audio-based interpretations that
explicitly enhance parts of the input signal most relevant for a network's
decision. We demonstrate our method's applicability on popular benchmarks,
including a real-world multi-label classification task.


title: A bicycle can be balanced by stochastic optimal feedback control

abstract:
  I propose and evaluate a model for bicycle balance control. The central
concept in this model is a computational system, implemented in the central
nervous system (CNS), that not only controls but also learns a mechanical
system that exists outside the CNS. This computational system uses an internal
model to calculate optimal control actions as specified by stochastic optimal
control theory. Because the computational system depends on parameters that the
CNS must learn from interaction with the mechanical system, this model must be
robust against inaccuracies in the learned parameter values. By means of
simulations, I demonstrate that this model can balance a bicycle under
realistic conditions and is robust against some (but not all) variations in the
learned noise characteristics. The model is not robust to variations in the
learned internal model caused by a misestimation of the speed. Although the
current model makes several simplifications for the sake of computational
feasibility, it can be extended in several directions to provide a more
realistic account of human balance control.


title:
On the existence of infinitely many realization functions of non-global
  local minima in the training of artificial neural networks with ReLU
  activation

abstract:
  Gradient descent (GD) type optimization schemes are the standard instruments
to train fully connected feedforward artificial neural networks (ANNs) with
rectified linear unit (ReLU) activation and can be considered as temporal
discretizations of solutions of gradient flow (GF) differential equations. It
has recently been proved that the risk of every bounded GF trajectory converges
in the training of ANNs with one hidden layer and ReLU activation to the risk
of a critical point. Taking this into account it is one of the key research
issues in the mathematical convergence analysis of GF trajectories and GD type
optimization schemes, respectively, to study sufficient and necessary
conditions for critical points of the risk function and, thereby, to obtain an
understanding about the appearance of critical points in dependence of the
problem parameters such as the target function. In the first main result of
this work we prove in the training of ANNs with one hidden layer and ReLU
activation that for every $ a, b \in \mathbb{R} $ with $ a < b $ and every
arbitrarily large $ \delta > 0 $ we have that there exists a Lipschitz
continuous target function $ f \colon [a,b] \to \mathbb{R} $ such that for
every number $ H > 1 $ of neurons on the hidden layer we have that the risk
function has uncountably many different realization functions of non-global
local minimum points whose risks are strictly larger than the sum of the risk
of the global minimum points and the arbitrarily large $ \delta $. In the
second main result of this work we show in the training of ANNs with one hidden
layer and ReLU activation in the special situation where there is only one
neuron on the hidden layer and where the target function is continuous and
piecewise polynomial that there exist at most finitely many different
realization functions of critical points.


title:
The evolution of cooperation in the public goods game on the scale-free
  community networks under multiple strategy updating rules

abstract:
  Social networks have a scale-free property and community structure, and many
problems in life have the characteristic of public goods, such as resource
shortage. Due to different preferences of individuals, there exist individuals
who adopt heterogeneous strategies updating rules in the network. We
investigate the evolution of cooperation in the scale-free community network
with public goods games and the influence of multiple strategy updating rules.
Here, two types of strategy updating rules are considered which are pairwise
comparison rules and aspiration-driven rules. Numerical simulations are
conducted and presented corresponding results. We find that community structure
promotes the emergence of cooperation in public goods games. In the meantime,
there is a "U" shape relationship between the frequency of cooperators and the
proportion of the two strategy updating rules. With the variance in the
proportion of the two strategy updating rules, pairwise comparison rules seem
to be more sensitive. Compared with aspiration-driven rules, pairwise
comparison rules play a more important role in promoting cooperation. Our work
may be helpful to understand the evolution of cooperation in social networks.


title: High-precision Hardware Oscillators Ensemble for GNSS Attack Detection

abstract:
  A wide gamut of important applications rely on global navigation satellite
systems (GNSS) for precise time and positioning. Attackers dictating the GNSS
receiver position and time solution are a significant risk, especially due to
the inherent vulnerability of GNSS systems. A first line of defense, for a
large number of receivers, is to rely on additional information obtained
through the rich connectivity of GNSS enabled platforms. Network time can be
used for direct validation of the GNSS receiver time; but this depends on
network availability. To allow attack detection even when there are prolonged
network disconnections, we present a method based on on-board ensemble of
reference clocks. This allows the receiver to detect sophisticated attacks
affecting the GNSS time solution, independently of the specific attack
methodology. Results obtained with Chip-Scale Oven Compensated Oscillators
(CS-OCXO) are promising and demonstrate the potential of embedded ensembles of
reference clocks, detecting attacks causing modifications of the receiver time
offset as low as 0.3us, with half the detection latency compared to related
literature.


title: Reconstruction Task Finds Universal Winning Tickets

abstract:
  Pruning well-trained neural networks is effective to achieve a promising
accuracy-efficiency trade-off in computer vision regimes. However, most of
existing pruning algorithms only focus on the classification task defined on
the source domain. Different from the strong transferability of the original
model, a pruned network is hard to transfer to complicated downstream tasks
such as object detection arXiv:arch-ive/2012.04643. In this paper, we show that
the image-level pretrain task is not capable of pruning models for diverse
downstream tasks. To mitigate this problem, we introduce image reconstruction,
a pixel-level task, into the traditional pruning framework. Concretely, an
autoencoder is trained based on the original model, and then the pruning
process is optimized with both autoencoder and classification losses. The
empirical study on benchmark downstream tasks shows that the proposed method
can outperform state-of-the-art results explicitly.


title:
Learning Temporal Point Processes for Efficient Retrieval of Continuous
  Time Event Sequences

abstract:
  Recent developments in predictive modeling using marked temporal point
processes (MTPP) have enabled an accurate characterization of several
real-world applications involving continuous-time event sequences (CTESs).
However, the retrieval problem of such sequences remains largely unaddressed in
literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and
rank a relevant set of continuous-time event sequences for a given query
sequence, from a large corpus of sequences. More specifically, NEUROSEQRET
first applies a trainable unwarping function on the query sequence, which makes
it comparable with corpus sequences, especially when a relevant query-corpus
pair has individually different attributes. Next, it feeds the unwarped query
sequence and the corpus sequence into MTPP guided neural relevance models. We
develop two variants of the relevance model which offer a tradeoff between
accuracy and efficiency. We also propose an optimization framework to learn
binary sequence embeddings from the relevance scores, suitable for the
locality-sensitive hashing leading to a significant speedup in returning top-K
results for a given query sequence. Our experiments with several datasets show
the significant accuracy boost of NEUROSEQRET beyond several baselines, as well
as the efficacy of our hashing mechanism.


title: Augmentation based unsupervised domain adaptation

abstract:
  The insertion of deep learning in medical image analysis had lead to the
development of state-of-the art strategies in several applications such a
disease classification, as well as abnormality detection and segmentation.
However, even the most advanced methods require a huge and diverse amount of
data to generalize. Because in realistic clinical scenarios, data acquisition
and annotation is expensive, deep learning models trained on small and
unrepresentative data tend to outperform when deployed in data that differs
from the one used for training (e.g data from different scanners). In this
work, we proposed a domain adaptation methodology to alleviate this problem in
segmentation models. Our approach takes advantage of the properties of
adversarial domain adaptation and consistency training to achieve more robust
adaptation. Using two datasets with white matter hyperintensities (WMH)
annotations, we demonstrated that the proposed method improves model
generalization even in corner cases where individual strategies tend to fail.


title: DOME: Discrete oriented muon emission in GEANT4 simulations

abstract:
  In this study, we exhibit a number elementary strategies that might be at
disposal in diverse computational applications in the GEANT4 simulations with
the purpose of hemispherical particle sources. To further detail, we initially
generate random points on a spherical surface for a sphere of a practical
radius by employing Gaussian distributions for the three components of the
Cartesian coordinates, thereby obtaining a generating surface for the initial
positions of the corresponding particles. Since we do not require the half
bottom part of the produced spherical surface for our tomographic applications,
we take the absolute value of the vertical component in the Cartesian
coordinates by leading to a half-spherical shell, which is traditionally called
a hemisphere. Last but not least, we direct the generated particles into the
target material to be irradiated by favoring a selective momentum direction
that is based on the vector construction between the random point on the
hemispherical surface and the origin of the target material, hereby optimizing
the particle loss through the source biasing. In the end, we incorporate our
strategy by using G4ParticleGun in the GEANT4 code. Furthermore, we also
exhibit a second scheme that is based on the coordinate transformation from the
spherical coordinates to the Cartesian coordinates, thereby reducing the number
of random number generators. While we plan to exert our strategy in the
computational practices for muon scattering tomography, this source scheme
might find its straightforward applications in different neighboring fields
including but not limited to atmospheric sciences, space engineering, and
astrophysics where a 3D particle source is a necessity for the modeling goals.


title:
Queueing Systems with Some Versions of Limited Processor Sharing
  Discipline

abstract:
  The paper considers a queueing system with limited processor sharing. No more
than n jobs may be served simultaneously. This system may be used for modeling
bandwidth sharing in wireless communication systems and processes of service in
computer networks. If there are n jobs in the considered queueing system and a
new job arrives, then the arriving job is lost or the service of a job is
interrupted and this job is lost. We study two rules to choose the job to be
lost. In accordance with one of these rules, the job with the shortest
remaining length is lost. Relations are obtained between the state
probabilities of considered system and the state probabilities of the
corresponding unlimited processor sharing system. These relations allow to
compute the state probabilities for considered system if the state
probabilities for the unlimited processor sharing system are known. In the case
of Poisson arrival process, the probability that the server capacity is
exhausted is equal to the probability that a job is lost. We have obtained an
explicit formulas for the stationary state probabilities and the loss
probability for this case. These probabilities are invariant under the job
length distribution under the condition that the average value of the length is
fixed.


title: Water Waves: Nonlinear Theory

abstract:
  A novel mathematical nonlinear theory of surface gravity waves in deep water
is presented, in which analytical analysis of the classical nonlinear equations
of fluid dynamics is performed under less restrictive assumptions than those
applied by existing theories. In particular, the new theory ensures uniqueness
of the solution without the need to employ the so-called radiation condition,
and its solutions are such that the liquid always remains at rest at infinity,
and the energy supplied to the water by a source of disturbances is finite at
all times - all that in contrast with conventional approaches that operate in
terms of spatially-infinite harmonic waves. The new theory accounts for the
non-linearity of the problem, and yields solutions valid at all times, from
zero (the time of setting initial conditions) to infinity. The author describes
previously unknown patterns in wave evolution, and confirms those patterns with
the experimental results by other researchers.

categories: physics.flu-dyn math-ph math.MP physics.ao-ph


title:
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural
  Architecture Search

abstract:
  Neural networks often encounter various stringent resource constraints while
deploying on edge devices. To tackle these problems with less human efforts,
automated machine learning becomes popular in finding various neural
architectures that fit diverse Artificial Intelligence of Things (AIoT)
scenarios. Recently, to prevent the leakage of private information while enable
automated machine intelligence, there is an emerging trend to integrate
federated learning and neural architecture search (NAS). Although promising as
it may seem, the coupling of difficulties from both tenets makes the algorithm
development quite challenging. In particular, how to efficiently search the
optimal neural architecture directly from massive non-independent and
identically distributed (non-IID) data among AIoT devices in a federated manner
is a hard nut to crack. In this paper, to tackle this challenge, by leveraging
the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture
Search (FDNAS) framework that allows for hardware-friendly NAS from non- IID
data across devices. To further adapt to both various data distributions and
different types of devices with heterogeneous embedded hardware platforms,
inspired by meta-learning, a Cluster Federated Direct Neural Architecture
Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense
that each device can learn a tailored deep learning model for its particular
data distribution and hardware constraint. Extensive experiments on non-IID
datasets have shown the state-of-the-art accuracy-efficiency trade-offs
achieved by the proposed solution in the presence of both data and device
heterogeneity.


title:
Networked Online Learning for Control of Safety-Critical
  Resource-Constrained Systems based on Gaussian Processes

abstract:
  Safety-critical technical systems operating in unknown environments require
the ability to quickly adapt their behavior, which can be achieved in control
by inferring a model online from the data stream generated during operation.
Gaussian process-based learning is particularly well suited for safety-critical
applications as it ensures bounded prediction errors. While there exist
computationally efficient approximations for online inference, these approaches
lack guarantees for the prediction error and have high memory requirements, and
are therefore not applicable to safety-critical systems with tight memory
constraints. In this work, we propose a novel networked online learning
approach based on Gaussian process regression, which addresses the issue of
limited local resources by employing remote data management in the cloud. Our
approach formally guarantees a bounded tracking error with high probability,
which is exploited to identify the most relevant data to achieve a certain
control performance. We further propose an effective data transmission scheme
between the local system and the cloud taking bandwidth limitations and time
delay of the transmission channel into account. The effectiveness of the
proposed method is successfully demonstrated in a simulation.


title:
Ab initio calculation of the magnetic Gibbs free energy of materials
  using magnetically constrained supercells

abstract:
  We present a first-principles approach for the computation of the magnetic
Gibbs free energy of materials using magnetically constrained supercell
calculations. Our approach is based on an adiabatic approximation of slowly
varying local moment orientations, the so-called finite-temperature disordered
local moment picture. It describes magnetic phase transitions and how
electronic and/or magnetostructural mechanisms generate a discontinuous
(first-order) character. We demonstrate that the statistical mechanics of the
local moment orientations can be described by an affordable number of supercell
calculations containing noncollinear magnetic configurations. The applicability
of our approach is illustrated by firstly studying the ferromagnetic state in
bcc Fe. We then investigate the temperature-dependent properties of a
triangular antiferromagnetic state stabilizing in two antiperovskite systems
Mn$_3$AN (A = Ga, Ni). Our calculations provide the negative volume expansion
of these materials as well as the ab initio origin of the discontinuous
character of the phase transitions, electronic and/or magnetostructural, in
good agreement with experiment.


title:
A Survey on Technological Trends to Enhance Spectrum Efficiency in 6G
  Communications

abstract:
  The research community has already identified that, by 2030, 5G networks will
reach the capacity limits, and hence, will be inadequate to support next
generation bandwidth-hungry, ubiquitous, intelligent services, and
applications. Therefore, in view of sustaining the competitive edge of wireless
technology and stratifying the next decade's communication requirements both,
industry and research community have already begun conceptualizing the 6G
technology. This article presents a detailed survey on the recent technological
trends which address the capacity issues and enhance the spectrum-efficiency in
6G Communications. We present these trends in detail and then identify the
challenges that need solutions before the practical deployment to realize 6G
communications. Our survey article attempts to significantly contribute to
initiating future research directions in the area of spectrum-efficiency in 6G
communications.


title:
Ultra-Hot plasma of magnetic monopoles as fifth phase of matter: Bonding
  dissociation conditions of N-S magnetic in a hot plasma medium and
  superlattices of solids

abstract:
  A magnetic atom has two magnetic poles of N and S, and in normal conditions,
separation of magnetic poles cannot be happened, even in very small atomic
dimensions. Existence of magnetic dipoles is due to a fundamental property of
magnetic materials and belongs to intrinsic spin of electrons. For the
electron, N and S magnetic poles are considered, similar to a current loop. The
aim of this paper is theoretical calculate dissociation energy of N-S poles a
quantum magnetic particle with two approaches of classical and quantum
mechanics by providing a harmonic oscillator simple model to estimate
dissociation energy of the N-S poles and corresponding breakdown temperature
and internal pressure. The results showed that separation of magnetic poles
occurs in two states: (a) in an ultra-hot plasma medium with extremely high
temperatures, such as in the center of a hot star, and (b) at extremely high
pressures, such as between internal plates in complex superlattices of layered
solids. It will be shown that breakdown temperature is in order of {\theta}
=107 to 108 Kelvin. This temperature is very high and it only happens in an
ultra-hot plasma environment as fifth phase of matter. In addition, based on
this model, we calculated that the possibility of dissociation of bonds between
N and S magnetic poles for solid superlattices occurs at very high pressures
between crystal plates. According to these results, the presence of isolated
magnetic monopole in superlattices of solids under ultra-high-pressure
conditions is possible. Therefore, this model suggests that the conductivity of
magnetic monopole carries can be used in the manipulation of nanomaterials for
applications in the production of advanced devices such as new generation of
superconductors, new spin devices and magnetic-electronics advanced materials
with magnetic monopoles and super-dielectrics.


title: Fully on-chip microwave photonics system

abstract:
  Microwave photonics (MWP), harnessing the tremendous bandwidth of light to
generate, process and measure wideband microwave signals, are poised to spark a
new revolution for the information and communication fields. Within the past
decade, new opportunity for MWP has emerged driven by the advances of
integrated photonics. However, despite significant progress made in terms of
integration level, a fully on-chip MWP functional system comprising all the
necessary photonic and electronic components, is yet to be demonstrated. Here,
we break the status quo and provide a complete on-chip solution for MWP system,
by exploiting hybrid integration of indium phosphide, silicon photonics and
complementary metal-oxide-semiconductor (CMOS) electronics platforms. Applying
this hybrid integration methodology, a fully chip-based MWP microwave
instantaneous frequency measurement (IFM) system is experimentally
demonstrated. The unprecedented integration level brings great promotion to the
compactness, reliability, and performances of the overall MWP IFM system,
including a wide frequency measurement range (2-34 GHz), ultralow estimation
errors (10.85 MHz) and a fast response speed (0.3 ns). Furthermore, we deploy
the chip-scale MWP IFM system into realistic application tasks, where diverse
microwave signals with rapid-varying frequencies at X-band (8-12 GHz) are
accurately identified in real-time. This demonstration marks a milestone for
the development of integrated MWP, by providing the technology basis for the
miniaturization and massive implementations of various MWP functional systems.


title: MITI: SLAM Benchmark for Laparoscopic Surgery

abstract:
  We propose a new benchmark for evaluating stereoscopic visual-inertial
computer vision algorithms (SLAM/ SfM/ 3D Reconstruction/ Visual-Inertial
Odometry) for minimally invasive surgical (MIS) interventions in the abdomen.
Our MITI Dataset available at [https://mediatum.ub.tum.de/1621941] provides all
the necessary data by a complete recording of a handheld surgical intervention
at Research Hospital Rechts der Isar of TUM. It contains multimodal sensor
information from IMU, stereoscopic video, and infrared (IR) tracking as ground
truth for evaluation. Furthermore, calibration for the stereoscope,
accelerometer, magnetometer, the rigid transformations in the sensor setup, and
time-offsets are available. We wisely chose a suitable intervention that
contains very few cutting and tissue deformation and shows a full scan of the
abdomen with a handheld camera such that it is ideal for testing SLAM
algorithms. Intending to promote the progress of visual-inertial algorithms
designed for MIS application, we hope that our clinical training dataset helps
and enables researchers to enhance algorithms.


title:
Charge-asymmetric correlations in elastic lepton- and antilepton-proton
  scattering from real photon emission

abstract:
  Observation of charge asymmetry by comparing electron and positron, or muon
and anti-muon, scattering on a hadronic target presently serves as an
experimental tool to study two-photon exchange effects. In addition to
two-photon exchange, real photon emission also contributes to the charge
asymmetry. We present a theoretical formalism, explicit expressions, and a
numerical analysis of hard photon emission for the charge asymmetry in lepton-
and antilepton-proton scattering. Different kinematic conditions are
considered, namely, either fixed transferred momentum squared or a fixed
electron scattering angle. The infrared divergence from real photon emission is
treated by the Bardin-Shumeiko technique and canceled with the soft part of the
two-photon exchange contribution extracted and calculated using Tsai approach.
All final expressions are obtained beyond the ultrarelativistic approximation
with respect to the lepton mass that allows to evaluate numerically of the
considered effects not only for ultrarelativistic leptons (JLab) and but for
moderately relativistic (MUSE) kinematics, too.


title: BYOD Security: A Study of Human Dimensions

abstract:
  The prevalence and maturity of Bring Your Own Device (BYOD) security along
with subsequent frameworks and security mechanisms in Australian organisations
is a growing phenomenon somewhat similar to other developed nations. During the
COVID 19 pandemic, even organisations that were previously reluctant to embrace
BYOD have been forced to accept it to facilitate remote work. The aim of this
paper is to discover, through a study conducted using a survey questionnaire
instrument, how employees practice and perceive the BYOD security mechanisms
deployed by Australian businesses which can help guide the development of
future BYOD security frameworks. Three research questions are answered by this
study - What levels of awareness do Australian businesses have for BYOD
security aspects? How are employees currently responding to the security
mechanisms applied by their organisations for mobile devices? What are the
potential weaknesses in businesses IT networks that have a direct effect on
BYOD security? Overall, the aim of this research is to illuminate the findings
of these research objectives so that they can be used as a basis for building
new and strengthening existing BYOD security frameworks in order to enhance
their effectiveness against an ever-growing list of attacks and threats
targeting mobile devices in a virtually driven work force.


title:
Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive
  Features

abstract:
  Fairness-aware machine learning seeks to maximise utility in generating
predictions while avoiding unfair discrimination based on sensitive attributes
such as race, sex, religion, etc. An important line of work in this field is
enforcing fairness during the training step of a classifier. A simple yet
effective binary classification algorithm that follows this strategy is
two-naive-Bayes (2NB), which enforces statistical parity - requiring that the
groups comprising the dataset receive positive labels with the same likelihood.
In this paper, we generalise this algorithm into N-naive-Bayes (NNB) to
eliminate the simplification of assuming only two sensitive groups in the data
and instead apply it to an arbitrary number of groups.
  We propose an extension of the original algorithm's statistical parity
constraint and the post-processing routine that enforces statistical
independence of the label and the single sensitive attribute. Then, we
investigate its application on data with multiple sensitive features and
propose a new constraint and post-processing routine to enforce differential
fairness, an extension of established group-fairness constraints focused on
intersectionalities. We empirically demonstrate the effectiveness of the NNB
algorithm on US Census datasets and compare its accuracy and debiasing
performance, as measured by disparate impact and DF-$\epsilon$ score, with
similar group-fairness algorithms. Finally, we lay out important considerations
users should be aware of before incorporating this algorithm into their
application, and direct them to further reading on the pros, cons, and ethical
implications of using statistical parity as a fairness criterion.


title:
Quotient algebras of Banach operator ideals related to non-classical
  approximation properties

abstract:
  We investigate the quotient algebra $\mathfrak{A}_X^{\mathcal I}:=\mathcal
I(X)/\overline{\mathcal F(X)}^{||\cdot||_{\mathcal I}}$ for Banach operator
ideals $\mathcal I$ contained in the ideal of the compact operators, where $X$
is a Banach space that fails the $\mathcal I$-approximation property. The main
results concern the nilpotent quotient algebras $\mathfrak
A_X^{\mathcal{QN}_p}$ and $\mathfrak A_X^{\mathcal{SK}_p}$ for the quasi
$p$-nuclear operators $\mathcal{QN}_p$ and the Sinha-Karn $p$-compact operators
$\mathcal{SK}_p$. The results include the following: (i) if $X$ has cotype 2,
then $\mathfrak A_X^{\mathcal{QN}_p}=\{0\}$ for every $p\ge 1$; (ii) if $X^*$
has cotype 2, then $\mathfrak A_X^{\mathcal{SK}_p}=\{0\}$ for every $p\ge 1$;
(iii) the exact upper bound of the index of nilpotency of $\mathfrak
A_X^{\mathcal{QN}_p}$ and $\mathfrak A_X^{\mathcal{SK}_p}$ for $p\neq 2$ is
$\max\{2,\left \lceil p/2 \right \rceil\}$, where $\left \lceil p/2 \right
\rceil$ denotes the smallest $n\in\mathbb N$ such that $n\ge p/2$; (iv) for
every $p>2$ there is a closed subspace $X\subset c_0$ such that both $\mathfrak
A_X^{\mathcal{QN}_p}$ and $\mathfrak A_X^{\mathcal{SK}_p}$ contain a countably
infinite decreasing chain of closed ideals. In addition, our methods yield a
closed subspace $X\subset c_0$ such that the compact-by-approximable algebra
$\mathfrak A_X=\mathcal K(X)/\mathcal A(X)$ contains two incomparable countably
infinite chains of nilpotent closed ideals.


title: A bias-adjusted estimator in quantile regression for clustered data

abstract:
  The manuscript discusses how to incorporate random effects for quantile
regression models for clustered data with focus on settings with many but small
clusters. The paper has three contributions: (i) documenting that existing
methods may lead to severely biased estimators for fixed effects parameters;
(ii) proposing a new two-step estimation methodology where predictions of the
random effects are first computed {by a pseudo likelihood approach (the LQMM
method)} and then used as offsets in standard quantile regression; (iii)
proposing a novel bootstrap sampling procedure in order to reduce bias of the
two-step estimator and compute confidence intervals. The proposed estimation
and associated inference is assessed numerically through rigorous simulation
studies and applied to an AIDS Clinical Trial Group (ACTG) study.


title: Graphs of continuous functions and fractal dimension

abstract:
  In this paper, we show that, for any $\beta \in [1,2]$, a given strictly
positive real-valued continuous function on $[0,1]$ whose graph has upper
box-counting dimension less than or equal to $\beta $ can be decomposed as a
product of two real-valued continuous functions on $[0,1]$ whose graphs have
upper box-counting dimension equal to $\beta$. We also obtain a formula for the
upper box-counting dimension of every element of a ring of polynomials in
finite number of continuous functions on $[0,1]$ over the field $\mathbb{R}.$


title:
Visual-tactile sensing for Real-time liquid Volume Estimation in
  Grasping

abstract:
  We propose a deep visuo-tactile model for realtime estimation of the liquid
inside a deformable container in a proprioceptive way.We fuse two sensory
modalities, i.e., the raw visual inputs from the RGB camera and the tactile
cues from our specific tactile sensor without any extra sensor calibrations.The
robotic system is well controlled and adjusted based on the estimation model in
real time. The main contributions and novelties of our work are listed as
follows: 1) Explore a proprioceptive way for liquid volume estimation by
developing an end-to-end predictive model with multi-modal convolutional
networks, which achieve a high precision with an error of around 2 ml in the
experimental validation. 2) Propose a multi-task learning architecture which
comprehensively considers the losses from both classification and regression
tasks, and comparatively evaluate the performance of each variant on the
collected data and actual robotic platform. 3) Utilize the proprioceptive
robotic system to accurately serve and control the requested volume of liquid,
which is continuously flowing into a deformable container in real time. 4)
Adaptively adjust the grasping plan to achieve more stable grasping and
manipulation according to the real-time liquid volume prediction.


title: Relativistic magnetohydrodynamics with spin

abstract:
  We extend the classical phase-space distribution function to include the spin
and electromagnetic fields coupling and derive the modified constitutive
relations for charge current, energy-momentum tensor, and spin tensor. Because
of the coupling, the new tensors receive corrections to their perfect-fluid
counterparts and make the background and spin fluid equations of motion
communicate with each other. We investigate special cases which are relevant in
high-energy heavy-ion collisions, including baryon free matter and large mass
limit. Using Bjorken symmetries, we find that spin polarization increases with
increasing magnetic field for an initially positive baryon chemical potential.
The corrections derived in this framework may help to explain the splitting
observed in Lambda hyperons spin polarization measurements.


title: Reconfigurable topological states in arrays of bianisotropic particles

abstract:
  Topological photonics promotes a novel approach to resilient light
manipulation by exploiting spatio-temporal symmetries of the system and dual
symmetry of electromagnetic field. Various prospective device applications pose
the need to flexibly control robust field localization associated with
topological modes. Here we design a topological array of resonant dielectric
meta-atoms with bianisotropic response induced by a spatial symmetry reduction.
Mutual orientation of the designed meta-atoms encodes a staggered bianisotropy
pattern capable of trapping topological states in a one-dimensional array
containing a small number of particles. We show that the topological interface
state can be tailored by the rotation of coaxial bianisotropic particles
arranged in an equidistant lattice. Our experimental implementation based on
ceramic horseshoe-shaped disks demonstrates remarkable reconfigurability of
electromagnetic topological states.


title:
Mechanical properties of Nucleic Acids and the non-local Twistable
  Wormlike Chain model

abstract:
  We investigate the mechanical properties of double stranded RNA by means of
all-atom simulations and compare its elastic behavior to that of DNA.
Differently from DNA, which is characterized by a strong coupling between twist
and roll degrees of freedom, such coupling is very weak in RNA. Both nucleic
acids are characterized by couplings between distal sites, i.e. by interactions
that go beyond nearest neighbors. These non-local couplings, both in RNA and
DNA, are strong for tilt and twist degrees of freedom and weak for roll. We
introduce and analyze a simple double stranded polymer model which clarifies
the origin of the distal couplings. Overall, our results indicate that nucleic
acid mechanics is well-described by a non-local Twistable Worm Like Chain
(nlTWLC). Differently from its local counterpart, the nlTWLC is characterized
by a length-scale-dependent elasticity: nucleic acids are mechanically softer
at the scale of a few base pairs as compared to an asymptotic stiffer behavior.


title:
On Carbon Taxes Effectiveness to Induce a Clean Technology Transition:
  An Evaluation Framework Based on Optimal Strategic Capacity Planning

abstract:
  This paper studies carbon taxes effectiveness to induce a transition to
cleaner production when a firm faces different technologies and demands. To
determine carbon taxes effectiveness, we propose a framework based on a
strategic capacity planning under carbon taxes model, that consider proper
perfomance measures. The model, which is formulated as a mixed integer linear
problem (MILP), considers issues that previous work have not studied jointly,
such as machine replacement, workforce planning, and maintenance. The
effectiveness measures consider levels of clean production and periods to reach
a technological transition. Our computational experiments, based on a real
case, have shown that carbon taxes by themselves do not necessarily induce a
transition to clean production, since their effectiveness depends on the
available technology relationship and the demand magnitude.


title:
AI-empowered Joint Communication and Radar Systems with Adaptive
  Waveform for Autonomous Vehicles

abstract:
  In Joint Communication and Radar (JCR)-based Autonomous Vehicle (AV) systems,
optimizing waveform structure is one of the most challenging tasks due to
strong influences between radar and data communication functions. Specifically,
the preamble of a data communication frame is typically leveraged for the radar
function. As such, the higher number of preambles in a Coherent Processing
Interval (CPI) is, the greater radar's performance is. In contrast,
communication efficiency decreases as the number of preambles increases.
Moreover, AVs' surrounding radio environments are usually dynamic with high
uncertainties due to their high mobility, making the JCR's waveform
optimization problem even more challenging. To that end, this paper develops a
novel JCR framework based on the Markov decision process framework and recent
advanced techniques in deep reinforcement learning. By doing so, without
requiring complete knowledge of the surrounding environment in advance, the
JCR-AV can adaptively optimize its waveform structure (i.e., number of frames
in the CPI) to maximize radar and data communication performance under the
surrounding environment's dynamic and uncertainty. Extensive simulations show
that our proposed approach can improve the joint communication and radar
performance up to 46.26% compared with those of conventional methods (e.g.,
greedy policy- and fixed waveform-based approaches).


title:
Quasiperiodic Oscillations, Quasinormal Modes and Shadows of
  Bardeen-Kiselev Black Holes

abstract:
  In this article, we study the particle dynamics around a static and
spherically symmetric Bardeen-Kiselev black hole (BK BH) which is a solution of
the Einstein-non-linear Maxwell field equations along with a quintessential
field. We discuss its essential geometrical properties such as scalar
invariants and size of innermost stable circular orbits. Dynamics of test
particles around the BH is also studied. Moreover, we also computed the
fundamental frequencies of a test particle orbiting the BH in a slightly
perturbed orbit. Further, the degeneracy relations between the spin of rotating
Kerr BH and magnetic charge of the BK BH at the values of the quintessential
parameter $\omega_q=-1/3$ and in terms of the same values of innermost stable
circular orbits (ISCOs) radius, energy efficiency, twin-peaks quasiperiodic
oscillations (QPOs) frequencies and impact parameter for photon are also
discussed in detail. Finally, we have investigated the relationship between the
shadow radius and the real part of the quasinormal mode (QNM) frequency.


title:
Diversity patterns and speciation processes in a two-island system with
  continuous migration

abstract:
  Geographic isolation is a central mechanism of speciation, but perfect
isolation of populations is rare. Although speciation can be hindered if gene
flow is large, intermediate levels of migration can enhance speciation by
introducing genetic novelty in the semi-isolated populations or founding small
communities of migrants. Here we consider a two island neutral model of
speciation with continuous migration and study diversity patterns as a function
of the migration probability, population size, and the number of genes involved
in reproductive isolation (genome size). For small genomes, low levels of
migration induce speciation on the islands that otherwise would not occur.
Diversity, however, drops sharply to a single species inhabiting both islands
as the migration probability increases. For large genomes, sympatric speciation
occurs even when the islands are strictly isolated. Then species richness per
island increases with the probability of migration, but the total number of
species decreases as they become cosmopolitan. For each genome size, there is
an optimal migration intensity for each population size that maximizes the
number of species. We discuss the observed modes of speciation induced by
migration and how they increase species richness in the insular system while
promoting asymmetry between the islands and hindering endemism.


title:
Increasing and Diverging Greenhouse Gas Emissions of Urban Wastewater
  Treatment in China

abstract:
  Upgrading effluent standards of wastewater treatment plants (WWTPs) and
repairing sewerage systems leads to contradictions and synergies between water
pollution control and climate change mitigation. This affects historical
trajectories and characteristics of greenhouse gas (GHG) emissions from China's
WWTPs, which stay inadequately studied. Here we establish emissions inventories
of China's WWTPs using plant-level WWTP operational data. We find that removed
amount of chemical oxygen demand and ammonia nitrogen increased 0.8 and 1.3
times during 2009-2019, while WWTP GHG emissions increased 1.8 times, being 6
times national GHG emissions growth rate. Increasing sludge yield and
electricity intensity became primary driving factors in 2015 because of
stricter effluent standards and lower influent contaminant concentration. We
defined Functional Unit-Gini coefficient to quantify divergence of WWTP GHG
emissions, which grew from 0.20 in 2009 to 0.29 in 2019. Diversified sludge
disposal methods and energy structure increased the inequality, while upgrading
effluent standards decreased it.


title:
Using Deep Reinforcement Learning with Automatic Curriculum Learning for
  Mapless Navigation in Intralogistics

abstract:
  We propose a deep reinforcement learning approach for solving a mapless
navigation problem in warehouse scenarios. In our approach, an automation
guided vehicle is equipped with LiDAR and frontal RGB sensors and learns to
perform a targeted navigation task. The challenges reside in the sparseness of
positive samples for learning, multi-modal sensor perception with partial
observability, the demand for accurate steering maneuvers together with long
training cycles. To address these points, we propose NavACL-Q as a method for
automatic curriculum learning in combination with a distributed version of the
soft actor-critic algorithm. The performance of the learning algorithm is
evaluated exhaustively in an unseen warehouse environment to validate both
robustness and generalizability of the learned policy. Results in NVIDIA Isaac
Sim demonstrates that our trained agent significantly outperforms a map-based
navigation pipeline provided by NVIDIA Isaac Sim with an increased agent-goal
distance of 3m and wider initial relative agent-goal rotations of 45 degree.
The ablation studies also suggests that NavACL-Q greatly facilitates the
learning process with a performance gain of roughly 40% compared to training
with random starts and that the utilization of a pre-trained feature extractor
manifestly boosts the performance by approximately 60%.


title:
Generating functions for powers in finite orthogonal and symplectic
  groups

abstract:
  Let $G$ be one of the finite orthogonal or symplectic groups over a finite
field of odd order $q$ and $M\geq 2$ be an integer. We enumerate the set
$\{x^M:x\in \mathfrak{S}\}$ where $\mathfrak{S}$ is the set of any one of the
separable, semisimple, cyclic or regular matrices of $G$, in the sense of
generating functions. In this article we will be assuming $(M,q)=1$.


title:
A Comparative Study of Deep Reinforcement Learning-based Transferable
  Energy Management Strategies for Hybrid Electric Vehicles

abstract:
  The deep reinforcement learning-based energy management strategies (EMS) have
become a promising solution for hybrid electric vehicles (HEVs). When driving
cycles are changed, the neural network will be retrained, which is a
time-consuming and laborious task. A more efficient way of choosing EMS is to
combine deep reinforcement learning (DRL) with transfer learning, which can
transfer knowledge of one domain to the other new domain, making the network of
the new domain reach convergence values quickly. Different exploration methods
of DRL, including adding action space noise and parameter space noise, are
compared against each other in the transfer learning process in this work.
Results indicate that the network added parameter space noise is more stable
and faster convergent than the others. In conclusion, the best exploration
method for transferable EMS is to add noise in the parameter space, while the
combination of action space noise and parameter space noise generally performs
poorly. Our code is available at
https://github.com/BIT-XJY/RL-based-Transferable-EMS.git.


title:
Inverse spin crossover in fluorinated
  Fe(1,10-phenanthroline)$_2$(NCS)$_2$ adsorbed on Cu (001) surface

abstract:
  Density functional theory (DFT) including van der Waals weak interaction in
conjunction with the so called rotational invariant DFT+U, where $U$ is the
Hubbard interaction of the iron site, is used to show that the fluorinated spin
crossover Fe(phen$)_{2}$(NCS$)_{2}$ molecule whether in the gas phase or
adsorbed on Cu(001) surface switches from the original low spin state to the
high spin state. Using Bader electron density analysis, this inversion of the
spin-crossover is explained in terms of electron doping of the Fe-octahedron
cage which led to an increase of the Fe-N bond lengths. Consequently, the
ligand-field splitting is drastically reduced, making the high-spin ground
state more stable than the low-spin state. The calculated scanning tunneling
microscopy (STM) images in the Tersoff-Hamann approximation show a clear
distinction between the fluorinated and the unfluorinated molecule. This
theoretical prediction is awaiting future STM experimental confirmation.


title: CHEOPS geometric albedo of the hot Jupiter HD 209458b

abstract:
  We report the detection of the secondary eclipse of the hot Jupiter HD
209458b in optical/visible light using the CHEOPS space telescope. Our
measurement of 20.4 +/- 3.3 ppm translates into a geometric albedo of A_g =
0.096 +/- 0.016. The previously estimated dayside temperature of about 1500 K
implies that our geometric albedo measurement consists predominantly of
reflected starlight and is largely uncontaminated by thermal emission. This
makes the present result one of the most robust measurements of A_g for any
exoplanet. Our calculations of the bandpass-integrated geometric albedo
demonstrate that the measured value of A_g is consistent with a cloud-free
atmosphere, where starlight is reflected via Rayleigh scattering by hydrogen
molecules, and the water and sodium abundances are consistent with stellar
metallicity. We predict that the bandpass-integrated TESS geometric albedo is
too faint to detect and that a phase curve of HD 209458b observed by CHEOPS
would have a distinct shape associated with Rayleigh scattering if the
atmosphere is indeed cloud free.


title: Refinements of Franks' theorem and applications in Reeb dynamics

abstract:
  In this article, we give two refinements of Franks' theorem: For orientation
and area preserving homeomorphisms of the closed or open annulus, the existence
of $k$-periodic orbits ($(k,n_0)=1$) forces the existence of infinitely many
periodic orbits with periods prime to $n_0$. Moreover, if $f$ is reversible,
the periodic orbits above could be symmetric. Our improvements of Franks'
theorem can be applied to Reeb dynamics and celestial mechanics, for example,
we give some precise information about the symmetries of periodic orbits found
in Hofer, Wysocki and Zehnder's dichotomy theorem when the tight 3-sphere is
equipped with some additional symmetries, and also the symmetries of periodic
orbits on the energy level of H$\acute{e}$non-Heiles system in celestial
mechanics.


title:
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution
  Analysis

abstract:
  Dimensionality reduction algorithms are standard tools in a researcher's
toolbox. Dimensionality reduction algorithms are frequently used to augment
downstream tasks such as machine learning, data science, and also are
exploratory methods for understanding complex phenomena. For instance,
dimensionality reduction is commonly used in Biology as well as Neuroscience to
understand data collected from biological subjects. However, dimensionality
reduction techniques are limited by the von-Neumann architectures that they
execute on. Specifically, data intensive algorithms such as dimensionality
reduction techniques often require fast, high capacity, persistent memory which
historically hardware has been unable to provide at the same time. In this
paper, we present a re-implementation of an existing dimensionality reduction
technique called Geometric Multi-Scale Resolution Analysis (GMRA) which has
been accelerated via novel persistent memory technology called Memory Centric
Active Storage (MCAS). Our implementation uses a specialized version of MCAS
called PyMM that provides native support for Python datatypes including NumPy
arrays and PyTorch tensors. We compare our PyMM implementation against a DRAM
implementation, and show that when data fits in DRAM, PyMM offers competitive
runtimes. When data does not fit in DRAM, our PyMM implementation is still able
to process the data.


title: A micro-CT of a human skull

abstract: This is a data descriptor for an X-ray microtomography of a human skull.


title: Bounding the Frobenius norm of a q-deformed commutator

abstract:
  For two $n \times n$ complex matrices $A$ and $B$, we define the $q$-deformed
commutator as $[ A, B ]_q := A B - q BA$ for a real parameter $q$. In this
paper, we investigate a generalization of the B\"{o}ttcher-Wenzel inequality
which gives the sharp upper bound of the (Frobenius) norm of the commutator. In
our generalisation, we investigate sharp upper bounds on the $q$-deformed
commutator. This generalization can be studied in two different scenarios:
firstly bounds for general matrices, and secondly for traceless matrices. For
both scenarios, partial answers and conjectures are given for positive and
negative $q$. In particular, denoting the Frobenius norm by $||.||_F$, when
either $A$ or $B$ is normal, we prove the following inequality to be true and
sharp: $|| [ A , B ]_q||_F^2 \le \left(1+q^2 \right) ||A||_F^2 ||B||_F^2$ for
positive $q$. Also, we conjecture that the same bound is true for positive $q$
when either $A$ or $B$ is traceless. For negative $q$, we conjecture other
sharp upper bounds to be true for the generic scenarios and the scenario when
either of $A$ or $B$ is traceless. All conjectures are supported with numerics
and proved for $n=2$.


title: Monolayer Kagome Metals AV3Sb5

abstract:
  Recently, layered kagome metals AV3Sb5 (A = K, Rb, and Cs) have emerged as a
fertile platform for exploring frustrated geometry, correlations, and topology.
Here we demonstrate that AV3Sb5 can crystallize in a mono-layered form,
revealing a range of properties that render the system unique by using
first-principles and mean-field calculations. Most importantly, the
two-dimensional monolayer preserves intrinsically different symmetries from the
three-dimensional layered bulk, enforced by stoichiometry. Enrichment of the
van Hove singularities, a logarithmic divergence of electronic density of
states, consequently appears, leading to a variety of competing instabilities
such as doublets of charge density waves and s-and d-wave superconductivity. We
show that the competition between orders can be fine-tuned in the monolayer via
electron-filling of the van Hove singularities. Thus, our results suggest the
monolayer kagome metal AV3Sb5 as a promising platform for designer metallic
quantum phases.


title: Energy in the Relativistic Theory of Gravity

abstract:
  The more precise definition and the more fundamental understanding of the
concepts of time, energy, entropy and information are building upon the new,
relativistic foundation of gravity. This lecture is an attempt to explain the
basic principles that underpin this progress, by focusing on the simple but
subtle universal definition of energy. The principles are unearthed from
Einstein's theory and Noether's theorems, beneath a century of misconceptions.


title:
ReverseORC: Reverse Engineering of Resizable User Interface Layouts with
  OR-Constraints

abstract:
  Reverse engineering (RE) of user interfaces (UIs) plays an important role in
software evolution. However, the large diversity of UI technologies and the
need for UIs to be resizable make this challenging. We propose ReverseORC, a
novel RE approach able to discover diverse layout types and their dynamic
resizing behaviours independently of their implementation, and to specify them
by using OR constraints. Unlike previous RE approaches, ReverseORC infers
flexible layout constraint specifications by sampling UIs at different sizes
and analyzing the differences between them. It can create specifications that
replicate even some non-standard layout managers with complex dynamic layout
behaviours. We demonstrate that ReverseORC works across different platforms
with very different layout approaches, e.g., for GUIs as well as for the Web.
Furthermore, it can be used to detect and fix problems in legacy UIs, extend
UIs with enhanced layout behaviours, and support the creation of flexible UI
layouts.


title:
Weakly-supervised learning for image-based classification of primary
  melanomas into genomic immune subgroups

abstract:
  Determining early-stage prognostic markers and stratifying patients for
effective treatment are two key challenges for improving outcomes for melanoma
patients. Previous studies have used tumour transcriptome data to stratify
patients into immune subgroups, which were associated with differential
melanoma specific survival and potential treatment strategies. However,
acquiring transcriptome data is a time-consuming and costly process. Moreover,
it is not routinely used in the current clinical workflow. Here we attempt to
overcome this by developing deep learning models to classify gigapixel H&E
stained pathology slides, which are well established in clinical workflows,
into these immune subgroups. Previous subtyping approaches have employed
supervised learning which requires fully annotated data, or have only examined
single genetic mutations in melanoma patients. We leverage a multiple-instance
learning approach, which only requires slide-level labels and uses an attention
mechanism to highlight regions of high importance to the classification.
Moreover, we show that pathology-specific self-supervised models generate
better representations compared to pathology-agnostic models for improving our
model performance, achieving a mean AUC of 0.76 for classifying histopathology
images as high or low immune subgroups. We anticipate that this method may
allow us to find new biomarkers of high importance and could act as a tool for
clinicians to infer the immune landscape of tumours and stratify patients,
without needing to carry out additional expensive genetic tests.


title:
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through
  Rate Prediction

abstract:
  Short video has witnessed rapid growth in China and shows a promising market
for promoting the sales of products in e-commerce platforms like Taobao. To
ensure the freshness of the content, the platform needs to release a large
number of new videos every day, which makes the conventional click-through rate
(CTR) prediction model suffer from the severe item cold-start problem. In this
paper, we propose GIFT, an efficient Graph-guIded Feature Transfer system, to
fully take advantages of the rich information of warmed-up videos that related
to the cold-start video. More specifically, we conduct feature transfer from
warmed-up videos to those cold-start ones by involving the physical and
semantic linkages into a heterogeneous graph. The former linkages consist of
those explicit relationships (e.g., sharing the same category, under the same
authorship etc.), while the latter measure the proximity of multimodal
representations of two videos. In practice, the style, content, and even the
recommendation pattern are pretty similar among those physically or
semantically related videos. Besides, in order to provide the robust id
representations and historical statistics obtained from warmed-up neighbors
that cold-start videos covet most, we elaborately design the transfer function
to make aware of different transferred features from different types of nodes
and edges along the metapath on the graph. Extensive experiments on a large
real-world dataset show that our GIFT system outperforms SOTA methods
significantly and brings a 6.82% lift on click-through rate (CTR) in the
homepage of Taobao App.


title: Diaz-Metcalf Type Inequality for Sugeno and Pseudo Integrals

abstract: In this paper, we have proved Diaz-Metcolf inequality for fuzzy integrals.


title: A new LDA formulation with covariates

abstract:
  The Latent Dirichlet Allocation (LDA) model is a popular method for creating
mixed-membership clusters. Despite having been originally developed for text
analysis, LDA has been used for a wide range of other applications. We propose
a new formulation for the LDA model which incorporates covariates. In this
model, a negative binomial regression is embedded within LDA, enabling
straight-forward interpretation of the regression coefficients and the analysis
of the quantity of cluster-specific elements in each sampling units (instead of
the analysis being focused on modeling the proportion of each cluster, as in
Structural Topic Models). We use slice sampling within a Gibbs sampling
algorithm to estimate model parameters. We rely on simulations to show how our
algorithm is able to successfully retrieve the true parameter values and the
ability to make predictions for the abundance matrix using the information
given by the covariates. The model is illustrated using real data sets from
three different areas: text-mining of Coronavirus articles, analysis of grocery
shopping baskets, and ecology of tree species on Barro Colorado Island
(Panama). This model allows the identification of mixed-membership clusters in
discrete data and provides inference on the relationship between covariates and
the abundance of these clusters.


title: Mobility and the spatially heterogeneous spread of SARS-CoV-2 in Belgium

abstract:
  Given that social interactions drive the spread of infectious diseases
amongst humans, one anticipates that human mobility in Belgium affected the
spread of COVID-19 during both 2020 "waves". Measures against this spread in
turn influenced mobility patterns. In this study, we analyse and mutually
compare time series of COVID-19-related data and mobility data across Belgium's
43 arrondissements (NUTS 3 level). First, we confirm that overall mobility did
in fact change significantly over the consecutive stages of the pandemic. So
doing, we define a quantity that represents the degree of mobility between two
arrondissements: the "connectivity index". Second, we analyse spatio-temporal
COVID-19-related incidence and hospitalisation data using dynamic time warping
and time-lagged cross-correlation. This allows us to quantify time lag and
morphological similarities between localised waves. Third, by coupling those
analyses, we conclude that mobility between arrondissements is in fact
indicative of the spatio-temporal spread of COVID-19 in Belgium, as was
previously shown for other EU countries. This conclusion supports the need for
mobility analysis and/or control during a pandemic, and by extension motivates
the development of our spatially explicit Belgian metapopulation model.


title:
Some aspects of impact of the Electron Ion Collider on particle physics
  at the Energy Frontier

abstract:
  This overview describes several science cases at the Electron-Ion-Collider
(EIC) experiment which are traditional to general particle physics. It has an
emphasis on connections between future measurements at the EIC and the physics
topics explored at high-energy frontier colliders. It covers several selected
topics, such as parton density functions, multi-quarks states, correlations of
final-state particles, precision QCD measurements and forward jet physics. We
will discuss possible EIC measurements that can improve previous experimental
results obtained at high-energy (HEP) experiments.


title: Phase flip code with semiconductor spin qubits

abstract:
  The fault-tolerant operation of logical qubits is an important requirement
for realizing a universal quantum computer. Spin qubits based on quantum dots
have great potential to be scaled to large numbers because of their
compatibility with standard semiconductor manufacturing. Here, we show that a
quantum error correction code can be implemented using a four-qubit array in
germanium. We demonstrate a resonant SWAP gate and by combining controlled-Z
and controlled-$\text{S}^{-1}$ gates we construct a Toffoli-like three-qubit
gate. We execute a two-qubit phase flip code and find that we can preserve the
state of the data qubit by applying a refocusing pulse to the ancilla qubit. In
addition, we implement a phase flip code on three qubits, making use of a
Toffoli-like gate for the final correction step. Both the quality and quantity
of the qubits will require significant improvement to achieve fault-tolerance.
However, the capability to implement quantum error correction codes enables
co-design development of quantum hardware and software, where codes tailored to
the properties of spin qubits and advances in fabrication and operation can now
come together to scale semiconductor quantum technology toward universal
quantum computers.


title: Self-consistent Kohn-Sham calculations with adiabatic connection models

abstract:
  Adiabatic connection models (ACMs) are powerful tools to build accurate
exchange-correlation functionals. In this work we introduce a fully
self-consistent-field (SCF) implementation of some popular ACM based
functionals. To this purpose we also introduce new GGA approximations, beyond
the usual point-charge-plus-continuum model, for the strong correlation
functional and its first derivative, showing their robustness and reliability.
Then we assess the SCF-ACM functionals for molecular systems and for
strong-correlation problems. We find that they perform well for both the total
energy and the electronic density, being in line or superior to SCF
double-hybrid methods, and that the impact of SCF orbitals is directly
connected to the accuracy of the ACM functional form. For the H$_2$
dissociation the SCF-ACM functionals yield significant improvements, also
thanks to the use of the new GGA strong-correlation formulas.


title: Inference of Affordances and Active Motor Control in Simulated Agents

abstract:
  Flexible, goal-directed behavior is a fundamental aspect of human life. Based
on the free energy minimization principle, the theory of active inference
formalizes the generation of such behavior from a computational neuroscience
perspective. Based on the theory, we introduce an output-probabilistic,
temporally predictive, modular artificial neural network architecture, which
processes sensorimotor information, infers behavior-relevant aspects of its
world, and invokes highly flexible, goal-directed behavior. We show that our
architecture, which is trained end-to-end to minimize an approximation of free
energy, develops latent states that can be interpreted as affordance maps. That
is, the emerging latent states signal which actions lead to which effects
dependent on the local context. In combination with active inference, we show
that flexible, goal-directed behavior can be invoked, incorporating the
emerging affordance maps. As a result, our simulated agent flexibly steers
through continuous spaces, avoids collisions with obstacles, and prefers
pathways that lead to the goal with high certainty. Additionally, we show that
the learned agent is highly suitable for zero-shot generalization across
environments: After training the agent in a handful of fixed environments with
obstacles and other terrains affecting its behavior, it performs similarly well
in procedurally generated environments containing different amounts of
obstacles and terrains of various sizes at different locations. To improve and
focus model learning further, we plan to invoke active inference-based,
information-gain-oriented behavior also while learning the temporally
predictive model itself in the near future. Moreover, we intend to foster the
development of both deeper event-predictive abstractions and compact, habitual
behavioral primitives.


title: Observation of the Solar F-corona from Space

abstract:
  We present a review of the observations of the solar F-corona from space with
a special emphasis of the 25 years of continuous monitoring achieved by the
LASCO-C2 and C3 coronagraphs. Our work includes images obtained by the
navigation cameras of the Clementine spacecraft, the SECCHI/HI-1A heliospheric
imager onboard STEREO-A, and the Wide Field Imager for Solar Probe onboard the
Parker Solar Probe. The connection to the zodiacal light is considered based on
ground- and space-based observations, prominently from the past Helios, IRAS,
COBE, and IRAKI missions. The characteristic radiance profiles along the
equatorial and polar directions follow power laws in the 5{\deg}-50{\deg} range
of elongation, with constant power exponents of -2.33 and -2.55. Both profiles
connect extremely well to the corresponding standard profiles of the zodiacal
light. The LASCO equatorial profile exhibits a shoulder implying a 17% decrease
of the radiance within 10Rsun that may be explained by the disappearance of
organic materials within 0.3 AU. LASCO detected for the first time a secular
variation of the F-corona, an increase at a rate of 0.46% per year of the
integrated radiance in the LASCO-C3 FoV. This is likely the first observational
evidence of the role of collisions in the inner zodiacal cloud. A composite of
C2 and C3 images produced the LASCO reference map of the radiance of the
F-corona from 2 to 30Rsun and, by combining with ground-based measurements, the
LASCO extended map from 1 to 6 Rsun. The plane of symmetry of the inner
zodiacal cloud is strongly warped, its inclination increasing towards the
planes of the inner planets and ultimately the solar equator. In contrast, its
longitude of ascending node is found to be constant and equal to 87.6{\deg}.
LASCO did not detect any small scale structures such as putative rings
occasionally reported during solar eclipses.


title: On Computing the $k$-Shortcut Fr\'echet Distance

abstract:
  The Fr\'echet distance is a popular measure of dissimilarity for polygonal
curves. It is defined as a min-max formulation that considers all
direction-preserving continuous bijections of the two curves. Because of its
susceptibility to noise, Driemel and Har-Peled introduced the shortcut
Fr\'echet distance in 2012, where one is allowed to take shortcuts along one of
the curves, similar to the edit distance for sequences. We analyse the
parameterized version of this problem, where the number of shortcuts is bounded
by a parameter $k$. The corresponding decision problem can be stated as
follows: Given two polygonal curves $T$ and $B$ of at most $n$ vertices, a
parameter $k$ and a distance threshold $\delta$, is it possible to introduce
$k$ shortcuts along $B$ such that the Fr\'echet distance of the resulting curve
and the curve $T$ is at most $\delta$? We study this problem for polygonal
curves in the plane. We provide a complexity analysis for this problem with the
following results: (i) assuming the exponential-time-hypothesis (ETH), there
exists no algorithm with running time bounded by $n^{o(k)}$; (ii) there exists
a decision algorithm with running time in $O(kn^{2k+2}\log n)$. In contrast, we
also show that efficient approximate decider algorithms are possible, even when
$k$ is large. We present a $(3+\varepsilon)$-approximate decider algorithm with
running time in $O(k n^2 \log^2 n)$ for fixed $\varepsilon$. In addition, we
can show that, if $k$ is a constant and the two curves are $c$-packed for some
constant $c$, then the approximate decider algorithm runs in near-linear time.


title: Diffractive optical system design by cascaded propagation

abstract:
  Modern design of complex optical systems relies heavily on computational
tools. These typically utilize geometrical optics as well as Fourier optics,
which enables the use of diffractive elements to manipulate light with features
on the scale of a wavelength. Fourier optics is typically used for designing
thin elements, placed in the system's aperture, generating a shift-invariant
Point Spread Function (PSF). A major bottleneck in applying Fourier Optics in
many cases of interest, e.g. when dealing with multiple, or out-of-aperture
elements, comes from numerical complexity. In this work, we propose and
implement an efficient and differentiable propagation model based on the
Collins integral, which enables the optimization of diffraction optical systems
with unprecedented design freedom using backpropagation. We demonstrate the
applicability of our method, numerically and experimentally, by engineering
shift-variant PSFs via thin plate elements placed in arbitrary planes inside
complex imaging systems, performing cascaded optimization of multiple planes,
and designing optimal machine-vision systems by deep learning.


title:
Large, global solutions to the three-dimensional the navier-stokes
  equations without vertical viscosity

abstract:
  The three-dimensional, homogeneous, incompressible Navier-Stokes equations
are studied in the absence of viscosity in one direction. It is shown that
there are arbitrarily large initial data generating a unique global solution,
the main feature of which is that they are slowly varying in the direction
where viscosity is missing. The difficulty arises from the complete absence of
a regularising effect in this direction. The special structure of the nonlinear
term, joint with the divergence-free condition on the velocity field, is
crucial in obtaining the result.


title: Nuclear magnetic shielding in HD and HT

abstract:
  We perform a calculation of the nuclear magnetic shielding in HD and HT
molecules, with complete and perturbative accounts for nuclear masses. From the
difference in shielding, we obtain the deuteron and triton magnetic moments in
agreement with the CODATA value, with the accuracy limited only by nuclear
magnetic resonance measurements. Most importantly, our calculations indicate a
potential for improved determination of nuclear magnetic moments.


title:
A low frequency model for the aeroacoustic scattering of cylindrical
  tube rows in cross-flow

abstract:
  Heat exchanger tube rows can influence the thermoacoustic instability
behaviour of combustion systems since they act as both acoustic scatterers and
unsteady heat sinks. Therefore, with careful tuning of their thermoacoustic
properties, heat exchangers have the potential to act as passive control
devices. In this work, we focus on (only) the acoustic scattering behaviour of
heat exchanger tubes. We present a comparison of existing acoustic models for
tube rows and slits, models for the latter having the advantage of
incorporating frequency dependence. We then propose a new model that enables
the adaptation of slit models for tube rows. This model is validated against
experiments and Linearised Navier Stokes Equations (LNSE) predictions for the
transmission and reflection coefficients, including phase information. The
model predictions show very good agreement with the experimental and numerical
validations, especially for low frequencies (Strouhal number < 0.5, based on
tube radius and excitation frequency), with mean differences less than 2% for
the transmission coefficients (the reflection coefficient errors are somewhat
larger since their magnitudes are very close to zero).


title:
Self-Supervised Transformers for Unsupervised Object Discovery using
  Normalized Cut

abstract:
  Transformers trained with self-supervised learning using self-distillation
loss (DINO) have been shown to produce attention maps that highlight salient
foreground objects. In this paper, we demonstrate a graph-based approach that
uses the self-supervised transformer features to discover an object from an
image. Visual tokens are viewed as nodes in a weighted graph with edges
representing a connectivity score based on the similarity of tokens. Foreground
objects can then be segmented using a normalized graph-cut to group
self-similar regions. We solve the graph-cut problem using spectral clustering
with generalized eigen-decomposition and show that the second smallest
eigenvector provides a cutting solution since its absolute value indicates the
likelihood that a token belongs to a foreground object. Despite its simplicity,
this approach significantly boosts the performance of unsupervised object
discovery: we improve over the recent state of the art LOST by a margin of
6.9%, 8.1%, and 8.1% respectively on the VOC07, VOC12, and COCO20K. The
performance can be further improved by adding a second stage class-agnostic
detector (CAD). Our proposed method can be easily extended to unsupervised
saliency detection and weakly supervised object detection. For unsupervised
saliency detection, we improve IoU for 4.9%, 5.2%, 12.9% on ECSSD, DUTS,
DUT-OMRON respectively compared to previous state of the art. For weakly
supervised object detection, we achieve competitive performance on CUB and
ImageNet.


title:
Probing the magnetic band gap of the ferromagnetic topological insulator
  MnSb$_2$Te$_4$

abstract:
  Mn-rich MnSb$_2$Te$_4$ is a ferromagnetic topological insulator with yet the
highest Curie temperature T_C = 45-50 K. It exhibits a magnetic gap at the
Dirac point of the topological surface state that disappears above T_C. By
scanning tunneling spectroscopy, we probe this gap at different magnetic fields
and temperatures. We firstly reveal that the gap size shrinks, when an in-plane
magnetic field of up to B = 3 T is applied, but does not close completely as
the magnetization is only partially rotated in-plane. This corroborates the
magnetic origin of the gap and the complex magnetic structure. In addition, we
demonstrate significant spatiotemporal fluctuations of the gap size at
temperatures as low as T_C/2, above which the remanent magnetization indeed
decays. This temperature is close to the antiferromagnetic transition
temperature observed for bulk-type single crystals of MnSb$_2$Te$_4$,
highlighting the important role of competing magnetic orders in the formation
of the favorable ferromagnetic topological insulator. Our study, thus, provides
crucial insights into the complex magnetic gap opening of topological
insulators that is decisive for quantum anomalous Hall devices.


title: Multi-dimensional microwave sensing using graphene waveguides

abstract:
  This paper presents an electrolytically gated broadband microwave sensor
where atomically-thin graphene layers are integrated into coplanar waveguides
and coupled with microfluidic channels. The interaction between a solution
under test and the graphene surface causes material and concentration-specific
modifications of graphene's DC and AC conductivity. Moreover, wave propagation
in the waveguide is modified by the dielectric properties of materials in its
close proximity via the fringe field, resulting in a combined sensing mechanism
leading to an enhanced S-parameter response compared to metallic microwave
sensors. The possibility of further controlling the graphene conductivity via
an electrolytic gate enables a new, multi-dimensional approach merging chemical
field-effect sensing and microwave measurement methods. By controlling and
synchronizing frequency sweeps, electrochemical gating and liquid flow in the
microfluidic channel, we generate multidimensional datasets that enable a
thorough investigation of the solution under study. As proof of concept, we
functionalize the graphene surface in order to identify specific
single-stranded DNA sequences dispersed in phosphate buffered saline solution.
We achieve a limit of detection of ~1 attomole for a perfect match DNA strand
and a sensitivity of ~3 dB/decade for sub-pM concentrations. These results show
that our devices represent a new and accurate metrological tool for chemical
and biological sensing.


title: Amodal Panoptic Segmentation

abstract:
  Humans have the remarkable ability to perceive objects as a whole, even when
parts of them are occluded. This ability of amodal perception forms the basis
of our perceptual and cognitive understanding of our world. To enable robots to
reason with this capability, we formulate and propose a novel task that we name
amodal panoptic segmentation. The goal of this task is to simultaneously
predict the pixel-wise semantic segmentation labels of the visible regions of
stuff classes and the instance segmentation labels of both the visible and
occluded regions of thing classes. To facilitate research on this new task, we
extend two established benchmark datasets with pixel-level amodal panoptic
segmentation labels that we make publicly available as KITTI-360-APS and
BDD100K-APS. We present several strong baselines, along with the amodal
panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify
the performance in an interpretable manner. Furthermore, we propose the novel
amodal panoptic segmentation network (APSNet), as a first step towards
addressing this task by explicitly modeling the complex relationships between
the occluders and occludes. Extensive experimental evaluations demonstrate that
APSNet achieves state-of-the-art performance on both benchmarks and more
importantly exemplifies the utility of amodal recognition. The benchmarks are
available at http://amodal-panoptic.cs.uni-freiburg.de.


title:
Correlating hydrothermal system dynamics and eruptive activity -- A
  case-study of Piton de la Fournaise volcano, La R{\'e}union

abstract:
  Piton de la Fournaise volcano, La R{\'e}union Island, is a basaltic shield
volcano which underwent an intense cycle of eruptive activity between 1998 and
2008. Self-potential and other geophysical investigations of the volcano have
shown the existence of a well-established hydrothermal system within the summit
cone. The present study investigates the relationship between changes in the
hydrothermal system and eruptive activity at the summit cone of Piton de la
Fournaise. Here, we consider the depth of the hydrothermal activity section to
be the area where the hydrothermal flow is the most intense along its path. Ten
complete-loop self-potential surveys have been analyzed through multi-scale
wavelet tomography (MWT) to characterize depth variations of the hydrothermal
system between 1993 and 2008. Our MWT models strongly support the existence of
six main hydrothermalflow pathways associated with the main edifice structure.
Each of these pathways is part of the main hydrothermal system and is connected
to the main hydrothermal reservoir at depth. In both 2006 and 2008, around
Dolomieu crater, based on our results, the hydrothermal activity sections are
located between 2300 and 2500 m a.s.l., which correlate well with the elevation
of the observed fumarole belt within the post-2007-collapse crater wall. Our
results show that the depths of the local hydrothermal activity sections change
substantially over the investigated period. Vertical displacement of the main
potential generation area, associated withthese hydrothermal activity sections,
is observed on the order of several hundred meters at the transition between
the period of quiescence (1993--1997) and the resumption of eruptive activity
in 1998 and 2007, respectively. From 1999 to March 2008, the hydrothermal
system was consistently located at relatively shallow depths.By quantitatively
determining the vertical displacement of hydrothermal fluids over 16 years, we
identify a significant link between hydrothermal system and magmatic activity.
Hydrothermal system depth below the surface is an indicator of the activity
level (pressurization/depressurization of the volcano) within the
shallowmagmatic systems. Thus, when used in conjunction with long term volcano
monitoring, this approach can contribute substantially to detection of the
precursory signals of changes in volcanic activity.


title: Gene Expression Time Delays in Reaction-Diffusion Systems

abstract:
  Gene expression time delays, modelling the complex biological processes of
gene transcription and translation, have been shown to play an important role
in cellular dynamics. Time delays, motivated by the gene expression process,
can also greatly affect the behaviour of reaction-diffusion systems. In this
dissertation, we explore their effects on Turing pattern mechanisms. By
incorporating time delays, modelled as both a fixed parameter and as a
continuous distribution, into classical reaction-diffusion systems that exhibit
Turing instabilities, we investigate the changing behaviour of these systems.
We find that an introduction of increasing time delay increases the time taken
for spatially inhomogeneous patterns to stabilise, and the two are related
linearly. We also present results to show, through a linear stability analysis,
that an increasing time delay can act both to expand or shrink the Turing space
of a certain reaction-diffusion mechanism, depending on the placement of
time-delayed terms. Significantly, we find that modelling time delays as a
continuous distribution has a negligible impact on qualitative or quantitative
aspects of the results seen compared with a fixed time delay of the mean of the
distribution. These findings serve to highlight the importance of considering
gene expression time delays when modelling biological patterning events, as
well as requiring a complete understanding of the cellular dynamics before
attempting to apply Turing mechanisms to explain biological phenomena. The
results also suggest, at least for the distributions considered in this
dissertation, that fixed delay and distributed delay models have almost
identical dynamics. This allows one to use simpler fixed delay models rather
than the more complicated distributed delay variants.


title:
Accessibility Properties of Abnormal Geodesics in Optimal Control
  Illustrated by Two Case Studies

abstract:
  In this article, we use two case studies from geometry and optimal control of
chemical network to analyze the relation between abnormal geodesics in time
optimal control, accessibility properties and regularity of the time minimal
value function.


title:
Impact of QCD uncertainties on antiproton spectra from dark-matter
  annihilation

abstract:
  The search for dark matter through indirect detection remains a promising
avenue to decipher the nature of this elusive yet dominant component of matter
in the universe. Dark-matter particles produced in the early universe
annihilate or decay to a set of Standard-Model particles that can undergo
complex sequences of processes, including strong and electromagnetic radiation,
hadronization, and hadron decays, to produce stable particles. Antiprotons
produced in this way may leave footprints in experiments such as AMS-02.
Several groups have reported an excess of events in the antiproton flux in the
rigidity range of $10$-$20$ GV. However, the theoretical modeling of baryon
production is not straightforward and relies in part on phenomenological models
in Monte Carlo event generators. The associated theoretical uncertainties have
so far not been systematically quantified in this context. In this Letter we
assess the impact of QCD uncertainties on the spectra of antiprotons from
dark-matter annihilation. As a proof-of-principle, we show that within a
Supersymmetric scenario these uncertainties can be as large as the experimental
errors of the AMS-02 data in the region of the excess.


title:
Power distribution of oscillations in the atmosphere of a plage region:
  Joint observations with ALMA, IRIS and SDO

abstract:
  We present a statistical analysis of power distribution of oscillations in a
plage region in active region NOAA AR12651, observed jointly with ALMA (Atacama
Large Millimeter/Submillimeter Array), IRIS (Interface Region Imaging
Spectrograph), and SDO (Solar Dynamics Observatory). We employ coordinated ALMA
Band-6 (1.25 mm) brightness temperature maps, IRIS Slit-Jaw Images in 2796
{\AA} passband, and observations in six passbands (1600 {\AA}, 304 {\AA}, 131
{\AA}, 171 {\AA}, 193 {\AA} and 211 {\AA}) of AIA (Atmospheric Imaging
Assembly) onboard SDO. We perform Lomb-Scargle transforms to study the
distribution of oscillation power over the observed region by means of dominant
period maps and power maps. We study spatial association of oscillations
through the atmosphere mapped by the different passbands, with focus on the
correlation of power distribution of ALMA oscillations with others. We do not
observe any significant association of ALMA oscillations with IRIS and AIA
oscillations. While the global behavior of ALMA dominant oscillations shows
similarity with that of transition region and coronal passbands of AIA, the
ALMA dominant period maps and power maps do not show any correlation with those
from the other passbands. The spatial distribution of dominant periods and
power in different period intervals of ALMA oscillations is uncorrelated with
any other passband. We speculate the non-association of ALMA oscillations with
those of IRIS and AIA be due to significant variations in the height of
formation of the millimeter continuum observed by ALMA. Additionally, the fact
that ALMA directly maps the brightness temperature, in contrast to the
intensity observations by IRIS and AIA, can result in the very different
intrinsic nature of the ALMA oscillations compared to the IRIS and AIA
oscillations.


title:
On-chip integrated Yb3+-doped waveguide amplifiers on thin film lithium
  niobate

abstract:
  We report the fabrication and optical characterization of Yb3+-doped
waveguide amplifiers (YDWA) on the thin film lithium niobate fabricated by
photolithography assisted chemo-mechanical etching. The fabricated Yb3+-doped
lithium niobate waveguides demonstrates low propagation loss of 0.13 dB/cm at
1030 nm and 0.1 dB/cm at 1060 nm. The internal net gain of 5 dB at 1030 nm and
8 dB at 1060 nm are measured on a 4.0 cm long waveguide pumped by 976nm laser
diodes, indicating the gain per unit length of 1.25 dB/cm at 1030 nm and 2
dB/cm at 1060 nm, respectively. The integrated Yb3+-doped lithium niobate
waveguide amplifiers will benefit the development of a powerful gain platform
and are expected to contribute to the high-density integration of thin film
lithium niobate based photonic chip.


title:
An immersed Crouzeix-Raviart finite element method in 2D and 3D based on
  discrete level set functions

abstract:
  This paper is devoted to the construction and analysis of immersed finite
element (IFE) methods in three dimensions. Different from the 2D case, the
points of intersection of the interface and the edges of a tetrahedron are
usually not coplanar, which makes the extension of the original 2D IFE methods
based on a piecewise linear approximation of the interface to the 3D case not
straightforward. We address this coplanarity issue by an approach where the
interface is approximated via discrete level set functions. This approach is
very convenient from a computational point of view since in many practical
applications the exact interface is often unknown, and only a discrete level
set function is available. As this approach has also not be considered in the
2D IFE methods, in this paper we present a unified framework for both 2D and 3D
cases. We consider an IFE method based on the traditional Crouzeix-Raviart
element using integral values on faces as degrees of freedom. The novelty of
the proposed IFE is the unisolvence of basis functions on arbitrary
triangles/tetrahedrons without any angle restrictions, which is advantageous
over the IFE using nodal values as degrees of freedom. The optimal bounds for
the IFE interpolation errors are proved on shape-regular triangulations. For
the IFE method, optimal a priori error and condition number estimates are
derived with constants independent of the location of the interface with
respect to the unfitted mesh. Numerical examples supporting the theoretical
results are provided.


title: Robust Geometric Metric Learning

abstract:
  This paper proposes new algorithms for the metric learning problem. We start
by noticing that several classical metric learning formulations from the
literature can be viewed as modified covariance matrix estimation problems.
Leveraging this point of view, a general approach, called Robust Geometric
Metric Learning (RGML), is then studied. This method aims at simultaneously
estimating the covariance matrix of each class while shrinking them towards
their (unknown) barycenter. We focus on two specific costs functions: one
associated with the Gaussian likelihood (RGML Gaussian), and one with Tyler's M
-estimator (RGML Tyler). In both, the barycenter is defined with the Riemannian
distance, which enjoys nice properties of geodesic convexity and affine
invariance. The optimization is performed using the Riemannian geometry of
symmetric positive definite matrices and its submanifold of unit determinant.
Finally, the performance of RGML is asserted on real datasets. Strong
performance is exhibited while being robust to mislabeled data.


title:
SeqMapPDB: A Standalone Pipeline to Identify Representative Structures
  of Protein Sequences and Mapping Residue Indices in Real-Time at Proteome
  Scale

abstract:
  Motivation: 3D structures of proteins provide rich information for
understanding their biochemical roles. Identifying the representative protein
structures for protein sequences is essential for analysis of proteins at
proteome scale. However, there are technical difficulties in identifying the
representative structure of a given protein sequence and providing accurate
mapping of residue indices. Existing databases of mapping between structures
and sequences are usually static that are not suitable for studying proteomes
with frequent gene model revisions. They often do not provide reliable and
consistent representative structures that maximizes sequence coverage.
Furthermore, proteins isomers are usually not properly resolved. Results: To
overcome these difficulties, we have developed a computational pipeline called
SeqMapPDB to provide high-quality representative PDB structures of given
sequences. It provides mapping to structures that fully cover the sequences
when available, or to the set of partial non-overlapping structural domains
that maximally cover the query sequence. The residue indices are accurate
mapped and isomeric proteins are resolved. SeqMapPDB is efficient and can
rapidly carry out proteome-wide mapping to the selected version of reference
genomes in real-time. Furthermore, SeqMapPDB provides the flexibility of a
stand-alone pipeline for large scale mapping of in-house sequence and structure
data. Availability: Our method is available at
https://bitbucket.org/lianglabuic/seqmappdb with GNU GPL license.


title:
General model of sex distribution, mating probability and egg production
  for macroparasites with polygamous mating system

abstract:
  The reproductive habits of helminths are important for the study of the
dynamics of their transmission. For populations of parasites distributed by
Poisson or negative binomial models, these habits have already been studied.
However, there are other statistical models that describe these populations,
such as zero-inflated models, but where reproductive characteristics were not
analyzed. Using an arbitrary model for the parasite population, we model the
distribution of females and males per host, and from these we model the
different reproductive variables such as the mean number of fertile females,
the mean egg production, the mating probability, the mean fertilized egg
production. We show that these variables change due to the effects of a
negative density-dependence fecundity, a characteristic of helminth parasites.
We present the results obtained for some particular models.


title:
Time-modulated excitation for enhanced single molecule localization
  microscopy

abstract:
  Structured illumination in Single Molecule Localization Microscopy provides
new information on the position of molecules and thus improves the localization
precision compared to standard localization methods. Here, we used a
time-shifted sinusoidal excitation pattern to modulate the fluorescence signal
of the molecules whose position information is carried by the phase and
recovered by synchronous demodulation. We designed two flexible fast
demodulation systems located upstream of the camera, allowing us to overcome
the limiting camera acquisition frequency and thus to maximize the collection
of photons in the demodulation process. The temporally modulated fluorescence
signal was then sampled synchronously on the same image, repeatedly during
acquisition. This microscopy, called ModLoc, allows to experimentally improve
the localization precision by a factor of 2.4 in one direction, compared to
classical Gaussian fitting methods. A temporal study and an experimental
demonstration both show that the short lifetimes of the molecules in blinking
regimes impose a modulation frequency in the kilohertz range, which is beyond
the reach of current cameras. A demodulation system operating at these
frequencies would thus be necessary to take full advantage of this new
localization approach.