title:
Extension operators for some ultraholomorphic classes defined by
  sequences of rapid growth

abstract:
  While the asymptotic Borel mapping, sending a function into its series of
asymptotic expansion in a sector, is known to be surjective for arbitrary
openings in the framework of ultraholomorphic classes associated with sequences
of rapid growth, there is no general procedure to construct extension operators
in this case. We do provide such operators in complex sectors for some
particular classes considered by S.~Pilipovi{\'c}, N.~Teofanov and F.~Tomi{\'c}
in the ultradifferentiable setting. Although these classes are, in their words,
"beyond Gevrey regularity", in some cases they keep the property of stability
under differentiation, which is crucial for our technique, based on formal
Borel- and truncated Laplace-like transforms with suitable kernels.


title: Toric non-Gorenstein loci

abstract:
  We describe the non-Gorenstein loci of normal toric varieties. In the case of
Hibi rings a combinatorial description is provided in terms of the underlying
partially ordered set. As a non-toric application we compute the dimensions of
the non-Gorenstein loci of the first secant variety of Segre varieties.


title: Flexible Portrait Image Editing with Fine-Grained Control

abstract:
  We develop a new method for portrait image editing, which supports
fine-grained editing of geometries, colors, lights and shadows using a single
neural network model. We adopt a novel asymmetric conditional GAN architecture:
the generators take the transformed conditional inputs, such as edge maps,
color palette, sliders and masks, that can be directly edited by the user; the
discriminators take the conditional inputs in the way that can guide
controllable image generation more effectively. Taking color editing as an
example, we feed color palettes (which can be edited easily) into the
generator, and color maps (which contain positional information of colors) into
the discriminator. We also design a region-weighted discriminator so that
higher weights are assigned to more important regions, like eyes and skin.
Using a color palette, the user can directly specify the desired colors of
hair, skin, eyes, lip and background. Color sliders allow the user to blend
colors in an intuitive manner. The user can also edit lights and shadows by
modifying the corresponding masks. We demonstrate the effectiveness of our
method by evaluating it on the CelebAMask-HQ dataset with a wide range of
tasks, including geometry/color/shadow/light editing, hand-drawn sketch to
image translation, and color transfer. We also present ablation studies to
justify our design.


title: Optimization of Polynomials with Sparsity Encoded in a Few Linear Forms

abstract:
  We consider polynomials of a few linear forms and show how exploit this type
of sparsity for optimization on some particular domains like the Euclidean
sphere or a polytope. Moreover, a simple procedure allows to detect this form
of sparsity and also allows to provide an approximation of any polynomial by
such sparse polynomials.


title:
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View
  Stereo

abstract:
  Learning-based multi-view stereo (MVS) has by far centered around 3D
convolution on cost volumes. Due to the high computation and memory consumption
of 3D CNN, the resolution of output depth is often considerably limited.
Different from most existing works dedicated to adaptive refinement of cost
volumes, we opt to directly optimize the depth value along each camera ray,
mimicking the range (depth) finding of a laser scanner. This reduces the MVS
problem to ray-based depth optimization which is much more light-weight than
full cost volume optimization. In particular, we propose RayMVSNet which learns
sequential prediction of a 1D implicit field along each camera ray with the
zero-crossing point indicating scene depth. This sequential modeling, conducted
based on transformer features, essentially learns the epipolar line search in
traditional multi-view stereo. We also devise a multi-task learning for better
optimization convergence and depth accuracy. Our method ranks top on both the
DTU and the Tanks \& Temples datasets over all previous learning-based methods,
achieving overall reconstruction score of 0.33mm on DTU and f-score of 59.48%
on Tanks & Temples.


title:
PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking
  Models

abstract:
  Neural ranking models (NRMs) have shown remarkable success in recent years,
especially with pre-trained language models. However, deep neural models are
notorious for their vulnerability to adversarial examples. Adversarial attacks
may become a new type of web spamming technique given our increased reliance on
neural information retrieval models. Therefore, it is important to study
potential adversarial attacks to identify vulnerabilities of NRMs before they
are deployed.
  In this paper, we introduce the Adversarial Document Ranking Attack (ADRA)
task against NRMs, which aims to promote a target document in rankings by
adding adversarial perturbations to its text. We focus on the decision-based
black-box attack setting, where the attackers have no access to the model
parameters and gradients, but can only acquire the rank positions of the
partial retrieved list by querying the target model. This attack setting is
realistic in real-world search engines. We propose a novel Pseudo
Relevance-based ADversarial ranking Attack method (PRADA) that learns a
surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients
for finding the adversarial perturbations.
  Experiments on two web search benchmark datasets show that PRADA can
outperform existing attack strategies and successfully fool the NRM with small
indiscernible perturbations of text.


title: Quantum information processing in cavities: A review

abstract:
  Processing of information and computation undergoing a paradigmatic shift
since the realization of the enormous potential of quantum features to perform
these tasks. Coupled cavity array is one of the well-studied systems to carry
out these tasks. It is a versatile platform to build quantum networks for
distributed information processing and communication. Cavities have the salient
feature of retaining photons for longer, thereby enabling them to travel
coherently through the array without losing them in dissipation. Several
research groups have successfully demonstrated the coupling of cavities and
implemented various quantum information protocols. These advancements pave the
way for implementing cavity arrays for large scale quantum communications and
computations. This article reviews theoretical proposals and discusses a few
pertinent experimental realizations of quantum information tasks in cavities.


title:
Temperature-dependent second-harmonic generation from color centers in
  diamond

abstract:
  Under infrared ultrashort pulse laser stimulation, we investigated
temperature-dependent second-harmonic generation (SHG) from nitrogen-vacancy
(NV)-introduced bulk diamond. The SHG intensity decreases in the temperature
range of 20-300 $^{\circ}C$, due to phase mismatching caused by refractive
index modification. We discovered that optical phonon scattering outperforms
acoustic phonon one in NV diamond by fitting the temperature dependence of SHG
intensity using a model based on the bandgap change via the deformation
potential interaction. This study presents an efficient and viable way for
creating diamond-based nonlinear optical temperature sensing.


title:
IMOT: General-Purpose, Fast and Robust Estimation for Spatial Perception
  Problems with Outliers

abstract:
  Spatial perception problems are the fundamental building blocks of robotics
and computer vision. However, in many real-world situations, they inevitably
suffer from the issue of outliers, which hinders traditional solvers from
making correct estimates. In this paper, we present a novel, general-purpose
robust estimator IMOT (Iterative Multi-layered Otsu's Thresholding) using
standard non-minimal solvers to rapidly reject outliers for spatial perception
problems. First, we propose a new outlier-robust iterative optimizing framework
where in each iteration all the measurement data are separated into two groups
according to the residual errors and only the group with lower residual errors
can be preserved for estimation in the next iteration. Second, we introduce and
employ the well-known Otsu's method (from image processing) to conduct
thresholding on the residual errors so as to obtain the best separation
(grouping) statistically which maximizes the between-class variance. Third, to
enhance robustness, we design a multi-layered Otsu's thresholding approach in
combination with our framework to sift out the true inliers from outliers that
might even occupy the majority of measurements. We test our robust estimator
IMOT on 5 different spatial perception problems including: rotation averaging,
rotation search, point cloud registration, category-level registration, and
SLAM. Experiments show that IMOT is robust against 70%--90% of outliers and can
typically converge in only 3--10 iterations, being 3--125 times faster than
existing robust estimators: GNC and ADAPT. Moreover, IMOT is able to return
robust results even without noise bound information.


title: Introducing the MeVCube concept: a CubeSat for MeV observations

abstract:
  Despite the impressive progress achieved both by X-ray and gamma-ray
observatories in the last few decades, the energy range between
$\sim200\;\mathrm{keV}$ and $\sim50\;\mathrm{MeV}$ remains poorly explored.
COMPTEL, on-board the Compton Gamma-Ray Observatory (CGRO, $1991$-$2000$),
opened the MeV gamma-ray band as a new window to astronomy, performing the
first all-sky survey in the energy range from $0.75$ to $30\;\mathrm{MeV}$.
More than $20$ years after the de-orbit of CGRO, no successor mission is yet
operating. Over the past years many concepts have been proposed, for new
observatories exploring different configurations and imaging techniques; a
selection of the most recent ones includes AMEGO, ETCC, GECCO and COSI. We
propose here a novel concept for a Compton telescope based on the CubeSat
standard, named MeVCube, with the advantages of small cost and relatively short
development time. The scientific payload is based on two layers of pixelated
Cadmium-Zinc-Telluride (CdZnTe) detectors, coupled with low-power read-out
electronics (ASIC, VATA450.3). The performance of the read-out electronics and
CdZnTe custom designed detectors have been measured extensively at DESY. The
performance of the telescope is accessed through simulations: despite a small
effective area limited to a few $\mathrm{cm}^{2}$, MeVCube can reach an angular
resolution of $1.5^{\circ}$ and a sensitivity comparable to the one achieved by
the last generation of large-scale satellites like COMPTEL and INTEGRAL.
Combined with a large field-of-view and a moderate cost, MeVCube can be a
powerful instrument for transient observations and searches of electromagnetic
counterparts of gravitational wave events.


title: Price Optimal Routing in Public Transportation

abstract:
  With the development of fast routing algorithms for public transit the
optimization of criteria other than arrival time has moved into the spotlight.
Due to the often intricate nature of fare regulations, the price of a journey,
while certainly being one of the most interesting criteria, is seldom
considered. In this work, we present a novel framework to mathematically
describe the fare systems of local public transit companies. The model allows
us to solve the price-sensitive earliest arrival problem (PSEAP) even if prices
depend on a number of parameters and non-linear conditions. We base our
approach on conditional fare networks (CFN). A CFN consists of a ticket graph
modeling tickets and the relations between them and a ticket transition
function defined over a partially ordered monoid and a set of fare events.
Typically, shortest path algorithms rely on the subpath optimality property
which is usually lost when dealing with intricate fare systems. We show how
subpath optimality can be restored by relaxing domination rules for tickets
depending on the structure of the ticket graph. An exemplary model for the fare
system of Mitteldeutsche Verkehrsbetriebe (MDV) is provided. PSEAP is NP-hard,
even when considering a single-criterion version that optimizes for price only.
Instances on finite monoids are, however, polynomial time solvable when the
monoid is considered part of the encoding length. By integrating our framework
in the multi-criteria RAPTOR algorithm, we provide an algorithm for PSEAP and
assess its performance on data obtained from MDV. We introduce two simple
speed-up techniques that in combination with the recently introduced
Tight-BMRAPTOR pruning scheme allow us to solve PSEAP in mere milliseconds on
our data set.


title:
Algorithms for Bayesian network modeling and reliability inference of
  complex multistate systems: Part II-Dependent systems

abstract:
  In using the Bayesian network (BN) to construct the complex multistate
system's reliability model as described in Part I, the memory storage
requirements of the node probability table (NPT) will exceed the random access
memory (RAM) of the computer. However, the proposed inference algorithm of Part
I is not suitable for the dependent system. This Part II proposes a novel
method for BN reliability modeling and analysis to apply the compression idea
to the complex multistate dependent system. In this Part II, the dependent
nodes and their parent nodes are equivalent to a block, based on which the
multistate joint probability inference algorithm is proposed to calculate the
joint probability distribution of a block's all nodes. Then, based on the
proposed multistate compression algorithm of Part I, the dependent multistate
inference algorithm is proposed for the complex multistate dependent system.
The use and accuracy of the proposed algorithms are demonstrated in case 1.
Finally, the proposed algorithms are applied to the reliability modeling and
analysis of the satellite attitude control system. The results show that both
Part I and Part II's proposed algorithms make the reliability modeling and
analysis of the complex multistate system feasible.


title:
Coherent Control of Collective Spontaneous Emission through
  Self-interference

abstract:
  As one of the central topics in quantum optics, collective spontaneous
emission such as superradiance has been realized in a variety of systems. This
work proposes an innovative scheme to coherently control collective emission
rates via a self-interference mechanism in a nonlinear waveguide setting. The
self-interference is made possible by photon backward scattering incurred by
quantum scatterers in a waveguide working as quantum switches. Whether the
interference is constructive or destructive is found to depend strongly on the
distance between the scatterers and the emitters. The interference between two
propagation pathways of the same photon leads to controllable superradiance and
subradiance, with their collective decay rates much enhanced or suppressed
(also leading to hyperradiance or population trapping). Furthermore, the
self-interference mechanism is manifested by an abrupt change in the emission
rates in real time. An experimental setup based on superconducting transmission
line resonators and transmon qubits is further proposed to realize controllable
collective emission rates.


title: Channel Acquisition for HF Skywave Massive MIMO-OFDM Communications

abstract:
  In this paper, we investigate channel acquisition for high frequency (HF)
skywave massive multiple-input multiple-output (MIMO) communications with
orthogonal frequency division multiplexing (OFDM) modulation. We first
introduce the concept of triple beams (TBs) in the space-frequency-time (SFT)
domain and establish a TB based channel model using sampled triple steering
vectors. With the established channel model, we then investigate the optimal
channel estimation and pilot design for pilot segments. Specifically, we find
the conditions that allow pilot reuse among multiple user terminals (UTs),
which significantly reduces pilot overhead. Moreover, we propose a channel
prediction method for data segments based on the estimated TB domain channel.
To reduce the complexity, we are able to formulate the channel estimation as a
sparse signal recovery problem due to the channel sparsity in the TB domain and
then obtain the channel by the proposed constrained Bethe free energy
minimization (CBFEM) based channel estimation algorithm, which can be
implemented with low complexity by exploiting the structure of the TB matrix
together with the chirp z-transform (CZT). Simulation results demonstrate the
superior performance of the proposed channel acquisition approach.


title: Retrieval Study of Brown Dwarfs Across the L-T Sequence

abstract:
  A large suite of 228 atmospheric retrievals is performed on a curated sample
of 19 brown dwarfs spanning the L0 to T8 spectral types using the open-source
Helios-r2 retrieval code, which implements the method of short characteristics
for radiative transfer and a finite-element description of the
temperature-pressure profile. Surprisingly, we find that cloud-free and cloudy
(both gray and non-gray) models are equally consistent with the archival SpeX
data from the perspective of Bayesian model comparison. Only upper limits for
cloud properties are inferred if log-uniform priors are assumed, but the cloud
optical depth becomes constrained if a uniform prior is used.
  Water is detected in all 19 objects and methane is detected in all of the T
dwarfs, but no obvious trend exists across effective temperature. As carbon
monoxide is only detected in a handful of objects, the inferred
carbon-to-oxygen ratios are unreliable. The retrieved radius generally
decreases with effective temperature, but the values inferred for some T dwarfs
are implausibly low and may indicate missing physics or chemistry in the
models. For the early L dwarfs, the retrieved surface gravity depends on
whether the gray or non-gray cloud model is preferred. Future data are
necessary for constraining cloud properties and the vertical variation of
chemical abundances, the latter of which is needed for distinguishing between
the chemical instability versus traditional cloud interpretation of the L-T
transition.


title:
An improvement on Schmidt's bound on the number of number fields of
  bounded discriminant and small degree

abstract:
  We prove an improvement on Schmidt's upper bound on the number of number
fields of degree n and absolute discriminant less than X for 6 <= n <= 94. We
carry this out by improving and applying a uniform bound on the number of monic
integer polynomials, having bounded height and discriminant divisible by a
large square, that we proved in a previous work.


title: Assessment on LSPU-SPCC Students Readiness towards M-learning

abstract:
  Today, the use of technology is a powerful advantage in every field in the
society. With the advent of development in information and communications
technology (ICT), the process of learning and acquiring new knowledge had
undergone a shift marked by a transition from desktop computing to the
widespread use of mobile technology. In light of the COVID-19 pandemic, the
Commission on Higher Education said that colleges and universities following
the new school calendar will no longer require students to attend face-to-face
classes. One of the state universities that had been affected by this
inevitable situation is the Laguna State Polytechnic University. This study
aims to determine the readiness of the students in shifting to m-learning.
Specifically, it aims to determine the availability of mobile devices,
equipment readiness, technological skills readiness and psychological
readiness. A survey-based methodology was used to obtain the data and
descriptive statistics to analyze the results. It was determined that almost
all of the students own mobile devices, are fully equipped with applications,
have high technological skills and are quite ready in terms of psychological
readiness.


title: Structure of magnetized strange quark star in perturbative QCD

abstract:
  We have performed the leading order perturbative calculation to obtain the
equation of state (EoS) of the strange quark matter (SQM) at zero temperature
under the magnetic field B = 10^18 G. The SQM comprises two massless quark
flavors (up and down) and one massive quark flavor(strange). Consequently, we
have used the obtained EoS to calculate the maximum gravitational mass and the
corresponding radius of the magnetized strange quark star (SQS). We have
employed two approaches, including the regular perturbation theory (RPT) and
the background perturbation theory (BPT). In RPT the infrared (IR) freezing
effect of the coupling constant has not been accounted for, while this effect
has been included in BPT. We have obtained the value of the maximum
gravitational mass to be more than three times the solar mass. The validity of
isotropic structure calculations for SQS has also been investigated. Our
results show that the threshold magnetic field from which an anisotropic
approach begins to be significant lies in the interval 2*10^18G < B < 3*10^18G.
Furthermore, we have computed the redshift, compactness and Buchdahl-Bondi
bound of the SQS to show that this compact object cannot be a black hole.


title:
Efficient, Uncertainty-based Moderation of Neural Networks Text
  Classifiers

abstract:
  To maximize the accuracy and increase the overall acceptance of text
classifiers, we propose a framework for the efficient, in-operation moderation
of classifiers' output. Our framework focuses on use cases in which F1-scores
of modern Neural Networks classifiers (ca.~90%) are still inapplicable in
practice. We suggest a semi-automated approach that uses prediction
uncertainties to pass unconfident, probably incorrect classifications to human
moderators. To minimize the workload, we limit the human moderated data to the
point where the accuracy gains saturate and further human effort does not lead
to substantial improvements. A series of benchmarking experiments based on
three different datasets and three state-of-the-art classifiers show that our
framework can improve the classification F1-scores by 5.1 to 11.2% (up to
approx.~98 to 99%), while reducing the moderation load up to 73.3% compared to
a random moderation.


title:
Logistics in the Sky: A Two-phase Optimization Approach for the Drone
  Package Pickup and Delivery System

abstract:
  The application of drones in the last-mile distribution is a research hotspot
in recent years. Different from the previous urban distribution mode that
depends on trucks, this paper proposes a novel package pick-up and delivery
mode and system in which multiple drones collaborate with automatic devices.
The proposed mode uses free areas on the top of residential buildings to set
automatic devices as delivery and pick-up points of packages, and employs
drones to transport packages between buildings and depots. Integrated
scheduling problem of package drop-pickup considering m-drone, m-depot,
m-customer is crucial for the system. We propose a simulated-annealing-based
two-phase optimization approach (SATO) to solve this problem. In the first
phase, tasks are allocated to depots for serving, such that the initial problem
is decomposed into multiple single depot scheduling problems with m-drone. In
the second phase, considering the drone capability constraints and task demand
constraints, we generate the route planning scheme for drones in each depot.
Concurrently, an improved variable neighborhood descent algorithm (IVND) is
designed in the first phase to reallocate tasks, and a local search algorithm
(LS) are proposed to search the high-quality solution in the second phase.
Finally, extensive experiments and comparative studies are conducted to test
the effectiveness of the proposed approach. Experiments indicate that the
proposed SATO-IVND can reduce the cost by more than 14% in a reasonable time
compared with several other peer algorithms.


title:
On a Bellman function associated with the Chang--Wilson--Wolff theorem:
  a case study

abstract:
  In this paper we estimate the tail of distribution (i.e., the measure of the
set $\{f\ge x\}$) for those functions $f$ whose dyadic square function is
bounded by a given constant. In particular we get a bit better estimate than
the estimate following from the Chang--Wilson--Wolf theorem. In the paper we
investigate the Bellman function corresponding to the problem. A curious
structure of this function is found: it has jumps of the first derivative at a
dense subset of interval $[0,1]$ (where it is calculated exactly), but it is of
$C^\infty$-class for $x>\sqrt3$ (where it is calculated up to a multiplicative
constant).
  An unusual feature of the paper consists in the usage of computer
calculations in the proof. Nevertheless, all the proofs are quite rigorous,
since only the integer arithmetic was assigned to computer.


title:
Error Resilient Quantum Amplitude Estimation from Parallel Quantum Phase
  Estimation

abstract:
  We show how phase and amplitude estimation algorithms can be parallelized.
This can reduce the gate depth of the quantum circuits to that of a single
Grover operator with a small overhead. Further, we show that for quantum
amplitude estimation, the parallelization can lead to vast improvements in
resilience against quantum errors. The resilience is not caused by the lower
gate depth, but by the structure of the algorithm. Even in cases with errors
that make it impossible to read out the exact or approximate solutions from
conventional amplitude estimation, our parallel approach provided the correct
solution with high probability. The results on error resilience hold for the
standard version and for low depth versions of quantum amplitude estimation.
Methods presented are subject of a patent application [Quantum computing
device: Patent application EP 21207022.1].


title:
An Initialization Scheme for Meeting Separation with Spatial Mixture
  Models

abstract:
  Spatial mixture model (SMM) supported acoustic beamforming has been
extensively used for the separation of simultaneously active speakers. However,
it has hardly been considered for the separation of meeting data, that are
characterized by long recordings and only partially overlapping speech. In this
contribution, we show that the fact that often only a single speaker is active
can be utilized for a clever initialization of an SMM that employs time-varying
class priors. In experiments on LibriCSS we show that the proposed
initialization scheme achieves a significantly lower Word Error Rate (WER) on a
downstream speech recognition task than a random initialization of the class
probabilities by drawing from a Dirichlet distribution. With the only
requirement that the number of speakers has to be known, we obtain a WER of 5.9
%, which is comparable to the best reported WER on this data set. Furthermore,
the estimated speaker activity from the mixture model serves as a diarization
based on spatial information.


title: mVEM: A MATLAB Software Package for the Virtual Element Methods

abstract:
  This paper summarizes the development of mVEM, a MATLAB software package
containing efficient and easy-following codes for various virtual element
methods (VEMs) published in the literature. We explain in detail the numerical
implementation of the mixed VEMs for the Darcy problem and the
three-dimensional linear VEMs for the Poisson equation. For other model
problems, we present the construction of the discrete methods and only provide
the implementation of the elliptic projection matrices. Some mesh related
functions are also given in the package, including the mesh generation and
refinement in two or three dimensions. mVEM is free and open source software.


title:
50-W average power Ho:YAG SESAM-modelocked thin-disk oscillator at 2.1
  um

abstract:
  Ultrafast laser systems operating with high-average power in the wavelength
range from 1.9 um to 3 um are of interest for a wide range of applications for
example in spectroscopy, material processing and as drivers for secondary
sources in the XUV spectral region. In this area, laser systems based on
holmium-doped gain materials directly emitting at 2.1 um have made significant
progress over the past years, however so far only very few results were
demonstrated in power-scalable high-power laser geometries. In particular, the
thin-disk geometry is promising for directly modelocked oscillators with high
average power levels that are comparable to amplifier systems at MHz repetition
rate. In this paper, we demonstrate Semiconductor Saturable Absorber Mirror
(SESAM) modelocked Ho:YAG thin-disk lasers (TDLs) emitting at 2.1 um wavelength
with record-holding performance levels. In our highest average power
configuration, we reach 50 W of average power, with 1.13 ps pulses, 2.11 uJ of
pulse energy and ~1.9 MW of peak power. To the best of our knowledge, this
represents the highest average power, as well as the highest output pulse
energy so far demonstrated from a modelocked laser in the 2 um wavelength
region. This record performance level was enabled by the recent development of
high-power GaSb-based SESAMs with low loss, adapted for high intracavity power
and pulse energy. We also explore the limitations in terms of reaching shorter
pulse durations at high power with this gain material in the disk geometry and
using SESAM modelocking, and present first steps in this direction, with the
demonstration of 30 W of output power, with 692 fs pulses in another laser
configuration.


title:
An application of Pixel Interval Down-sampling (PID) for dense tiny
  microorganism counting on environmental microorganism images

abstract:
  This paper proposes a novel pixel interval down-sampling network (PID-Net)
for dense tiny objects (yeast cells) counting tasks with higher accuracy. The
PID-Net is an end-to-end CNN model with encoder to decoder architecture. The
pixel interval down-sampling operations are concatenated with max-pooling
operations to combine the sparse and dense features. It addresses the
limitation of contour conglutination of dense objects while counting.
Evaluation was done using classical segmentation metrics (Dice, Jaccard,
Hausdorff distance) as well as counting metrics. Experimental result shows that
the proposed PID-Net has the best performance and potential for dense tiny
objects counting tasks, which achieves 96.97% counting accuracy on the dataset
with 2448 yeast cell images. By comparing with the state-of-the-art approaches
like Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can
segment the dense tiny objects with clearer boundaries and fewer incorrect
debris, which shows the great potential of PID-Net in the task of accurate
counting tasks.


title: Abrupt transitions in turbulent thermoacoustic systems

abstract:
  Abrupt transitions to the state of thermoacoustic instability (TAI) in gas
turbine combustors are a significant challenge plaguing the development of
next-generation low-emission aircraft and power generation engines. In this
paper, we present the observation of abrupt transition in three disparate
turbulent thermoacoustic systems: an annular combustor, a swirl-stabilized
combustor, and a preheated bluff-body stabilized combustor. Using a low-order
stochastic thermoacoustic model, we show that the reported abrupt transitions
occur when an initially stable, supercritical limit cycle becomes unstable,
leading to a secondary bifurcation to a large amplitude limit cycle solution.
The states of combustion noise and intermittency observed in these turbulent
combustors are well captured by the additive stochastic noise in the model.
Through amplitude reduction, we analyze the underlying potential functions
affecting the stability of the observed dynamical states. Finally, we make use
of the Fokker-Planck equation, educing the effect of stochastic fluctuations on
subcritical and secondary bifurcation. We conclude that a high enough intensity
of stochastic fluctuations which transforms a subcritical bifurcation into an
intermittency-facilitated continuous transition may have little effect on the
abrupt nature of secondary bifurcation. Our findings imply the high likelihood
of abrupt transitions in turbulent combustors possessing higher-order
nonlinearities where turbulence intensities are disproportionate to the large
amplitude limit cycle solution.


title:
SEAByTE: A Self-adaptive Micro-service System Artifact for Automating
  A/B Testing

abstract:
  Micro-services are a common architectural approach to software development
today. An indispensable tool for evolving micro-service systems is A/B testing.
In A/B testing, two variants, A and B, are applied in an experimental setting.
By measuring the outcome of an evaluation criterion, developers can make
evidence-based decisions to guide the evolution of their software. Recent
studies highlight the need for enhancing the automation when such experiments
are conducted in iterations. To that end, we contribute a novel artifact that
aims at enhancing the automation of an experimentation pipeline of a
micro-service system relying on the principles of self-adaptation. Concretely,
we propose SEAByTE, an experimental framework for testing novel self-adaptation
solutions to enhance the automation of continuous A/B testing of a
micro-service based system. We illustrate the use of the SEAByTE artifact with
a concrete example.


title: Dynamical notions along filters

abstract:
  We study the localization along a filter of several dynamical notions. This
generalizes and extends similar localizations that have been considered in the
literature, e.g. near 0 and near an idempotent. Definitions and basic
properties of F-syndetic, piecewise F-syndetic, collectionwise F-piecewise
syndetic, F-quasi central and F-central sets and their relations with
F-uniformly recurrent points and ultrafilters are studied. We provide also the
nonstandard characterizations of some of the above notions and we prove the
partition regularity of several nonlinear equations along filters under mild
general assumptions.


title:
MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality
  Assessment

abstract:
  The acoustic environment can degrade speech quality during communication
(e.g., video call, remote presentation, outside voice recording), and its
impact is often unknown. Objective metrics for speech quality have proven
challenging to develop given the multi-dimensionality of factors that affect
speech quality and the difficulty of collecting labeled data. Hypothesizing the
impact of acoustics on speech quality, this paper presents MOSRA: a
non-intrusive multi-dimensional speech quality metric that can predict room
acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean
opinion score (MOS) for speech quality. By explicitly optimizing the model to
learn these room acoustics parameters, we can extract more informative features
and improve the generalization for the MOS task when the training data is
limited. Furthermore, we also show that this joint training method enhances the
blind estimation of room acoustics, improving the performance of current
state-of-the-art models. An additional side-effect of this joint prediction is
the improvement in the explainability of the predictions, which is a valuable
feature for many applications.


title: Concurrent learning in high-order tuners for parameter identification

abstract:
  High-order tuners are algorithms that show promise in achieving greater
efficiency than classic gradient-based algorithms in identifying the parameters
of parametric models and/or in facilitating the progress of a control or
optimization algorithm whose adaptive behavior relies on such models. For
high-order tuners, robust stability properties, namely uniform global
asymptotic (and exponential) stability, currently rely on a persistent
excitation (PE) condition. In this work, we establish such stability properties
with a novel analysis based on a Matrosov theorem and then show that the PE
requirement can be relaxed via a concurrent learning technique driven by
sampled data points that are sufficiently rich. We show numerically that
concurrent learning may greatly improve efficiency. We incorporate reset
methods that preserve the stability guarantees while providing additional
improvements that may be relevant in applications that demand highly accurate
parameter estimates at relatively low additional cost in computation.


title: Integrality and some evaluations of odd multiple harmonic sums

abstract:
  In 2015, S. Hong and C. Wang proved that none of the elementary symmetric
functions of $1,1/3,\ldots,1/(2n-1)$ is an integer when $n\geq 2$. In 2017, Kh.
Pilehrood, T. Pilehrood and R. Tauraso proved that the multiple harmonic sums
$H_n(s_1,\ldots,s_r)$ are never integers with exceptions of $H_1(s_1)=1$ and
$H_3(1,1)=1$. They also proved that the multiple harmonic star sums are never
integers when $n\geq 2$. In this paper, we consider the odd multiple harmonic
sums and the odd multiple harmonic star sums and show that none of these sums
is an integer with exception of the trivial case. Besides, we give evaluations
of the odd (alternating) multiple harmonic sums with depth one.


title:
Extended Reality for Anxiety and Depression Therapy amidst Mental
  Disorders -- A Systematic Review

abstract:
  This systematic study is aimed to investigate the implementation level of
different extended reality (XR) techniques in the care of mental disorder. We
point out some XR technologies used to deliver care for mental disorders, and
to evaluate the effectiveness of using XR systems for anxiety and depression
amidst other mental disorders. A search period of May 2017 and August 2021 was
defined to filter out articles related to the usage of virtual reality (VR),
augmented reality (AR) and mixed reality (AR) in a mental health context.
Search done on three databases namely Google Scholar, PubMED, and Association
for Computing Machinery Digital Library yielded 689 articles. Also, 10 articles
were recommended. Upon eligibility filtering, only 72 articles were found
relevant and were utilized for the study. Results show that the 72 studies were
done in only 23 countries across the globe, with the majority of studies being
reported for developed countries such as USA (20.64%) and Germany (11.11%).
Thus this could rapidly aid intervention of mental health disorder with XR.
Meanwhile, none of the studies observed was from an African country. The
majority of the articles reported that XR techniques led to significant
reduction in symptoms of anxiety or depression. The majority of studies (23,
36.51%) were published in the year 2021 of the total studies included. In a
sense, this data might be attributed to COVID-19 pandemic. Most studies (30,
47.62%) focused a population with age range of 18 to 65 years, while fewer
studies (4, 6.35%) focused on each of adolescents (10 - 19 years) and seniors
(over 64 years). Also, more studies were done experimentally (52, 82.54%)
rather than by analytical and modeling approach (5, 7.94%) as found in other XR
studies domain. This review study could aid the development of XR systems for
effective cognitive behavioral and exposure therapies of mental disorders.


title:
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial
  Action Units Detection

abstract:
  The Facial Action Coding System (FACS) encodes the action units (AUs) in
facial images, which has attracted extensive research attention due to its wide
use in facial expression analysis. Many methods that perform well on automatic
facial action unit (AU) detection primarily focus on modeling various types of
AU relations between corresponding local muscle areas, or simply mining global
attention-aware facial features, however, neglect the dynamic interactions
among local-global features. We argue that encoding AU features just from one
perspective may not capture the rich contextual information between regional
and global face features, as well as the detailed variability across AUs,
because of the diversity in expression and individual characteristics. In this
paper, we propose a novel Multi-level Graph Relational Reasoning Network
(termed MGRR-Net) for facial AU detection. Each layer of MGRR-Net performs a
multi-level (i.e., region-level, pixel-wise and channel-wise level) feature
learning. While the region-level feature learning from local face patches
features via graph neural network can encode the correlation across different
AUs, the pixel-wise and channel-wise feature learning via graph attention
network can enhance the discrimination ability of AU features from global face
features. The fused features from the three levels lead to improved AU
discriminative ability. Extensive experiments on DISFA and BP4D AU datasets
show that the proposed approach achieves superior performance than the
state-of-the-art methods.


title:
Hubbard model on the kagome lattice with time-reversal invariant flux
  and spin-orbit coupling

abstract:
  We study the Hubbard model with time-reversal invariant flux and spin-orbit
coupling and position-dependent onsite energies on the kagome lattice, using
numerical and analytical methods. In particular, we perform calculations using
real space dynamical mean-field theory (R-DMFT). To study the topological
properties of the system, we use the topological Hamiltonian approach. We
obtain a rich phase diagram: for weak and intermediate interactions, depending
on the model parameters, the system is in the band insulator, topological
insulator, or metallic phase, while for strong interactions the system is in
the Mott insulator phase. We also investigate the magnetic phases that occur in
this system. For this purpose, in addition to R-DMFT, we also use two
analytical methods: perturbation theory for large interactions and onsite
energies, and stochastic mean-field theory.


title: Detection of Dangerous Events on Social Media: A Perspective Review

abstract:
  Social media is an essential gateway of information and communication for
people worldwide. The amount of time spent and reliance of people on social
media makes it a vital resource for detecting events happening in real life.
Thousands of significant events are posted by users every hour in the form of
multimedia. Some individuals and groups target the audience to promote their
agenda among these users. Their cause can threaten other groups and individuals
who do not share the same views or have specific differences. Any group with a
definitive cause cannot survive without the support which acts as a catalyst
for their agenda. A phenomenon occurs where people are fed information that
motivates them to act on their behalf and carry out their agenda. One is
benefit results in the loss of the others by putting their lives, assets,
physical and emotional health in danger. This paper introduces a concept of
dangerous events to approach this problem and their three main types based on
their characteristics: action, scenarios, and sentiment-based dangerous events.


title: From Hopf algebra to braided $L_\infty$-algebra

abstract:
  We show that an $L_\infty$-algebra can be extended to a graded Hopf algebra
with a codifferential. Then we twist this extended $L_\infty$-algebra with a
Drinfel'd twist, simultaneously twisting its modules. Taking the
$L_\infty$-algebra as its own (Hopf) module, we obtain the recently proposed
braided $L_\infty$-algebra. The Hopf algebra morphisms are identified with the
strict $L_\infty$-morphisms, while the braided $L_\infty$-morphisms define a
more general $L_\infty$-action of twisted $L_\infty$-algebras.


title:
Improved replica bounds for the independence ratio of random regular
  graphs

abstract:
  Studying independent sets of maximum size is equivalent to considering the
hard-core model with the fugacity parameter $\lambda$ tending to infinity.
Finding the independence ratio of random $d$-regular graphs for some fixed
degree $d$ has received much attention both in random graph theory and in
statistical physics.
  For $d \geq 20$ the problem is conjectured to exhibit 1-step replica symmetry
breaking (1-RSB). The corresponding 1-RSB formula for the independence ratio
was confirmed for (very) large $d$ in a breakthrough paper by Ding, Sly, and
Sun. Furthermore, the so-called interpolation method shows that this 1-RSB
formula is an upper bound for each $d \geq 3$. For $d \leq 19$ this bound is
not tight and a full-RSB structure is expected.
  In this work we use numerical optimization to find good substituting
parameters for $r$-RSB formulas ($r=2,3,4,5$) to obtain improved rigorous upper
bounds for the independence ratio for each degree $3 \leq d \leq 19$. This is a
challenging task for multiple reasons. First, the formulas get increasingly
complicated as $r$ grows, and fast computation of the value and the derivatives
becomes difficult even for $d=3$. Second, as the parameter space grows, the
functions to minimize have many local minima, and global optimization over such
high-dimensional rugged landscapes is notoriously difficult.


title:
Non-LTE analysis of the Si II lines in iota Her with various atomic data
  sets

abstract:
  This study shows that the statistical equilibrium of Si II in the atmosphere
of a B3 IV type star iota Her is extremely sensitive to a variation in
photoionization cross-sections for the Si II levels. The difference in
abundances derived from absorption lines of Si II between applying the data
from two equal accuracy sources, namely, the Opacity Project (OP) and the NORAD
database, amounts to 0.18 dex, on average. Using the hydrogenic approximation
for photoionization cross-sections, we obtain the departure coefficients for
the Si II \eu{4s}{2}{S}{}{} level, the source function for Si II 6371 A, and
the abundance derived from this line, which are very similar to the
corresponding values computed by Takeda (2021). We suppose that close-to-solar
abundance obtained by Takeda (2021) from Si II 6371 A in iota Her is due to
using the hydrogenic photoionization cross-sections for the Si II levels.
However, emission lines of Si II observed in iota Her can only be reproduced
with the OP photoionization cross-sections. Photoionization cross-sections for
the Si II levels need further improvements.


title:
Target Confusion in End-to-end Speaker Extraction: Analysis and
  Approaches

abstract:
  Recently, end-to-end speaker extraction has attracted increasing attention
and shown promising results. However, its performance is often inferior to that
of a blind source separation (BSS) counterpart with a similar network
architecture, due to the auxiliary speaker encoder may sometimes generate
ambiguous speaker embeddings. Such ambiguous guidance information may confuse
the separation network and hence lead to wrong extraction results, which
deteriorates the overall performance. We refer to this as the target confusion
problem. In this paper, we conduct an analysis of such an issue and solve it in
two stages. In the training phase, we propose to integrate metric learning
methods to improve the distinguishability of embeddings produced by the speaker
encoder. While for inference, a novel post-filtering strategy is designed to
revise the wrong results. Specifically, we first identify these confusion
samples by measuring the similarities between output estimates and enrollment
utterances, after which the true target sources are recovered by a subtraction
operation. Experiments show that performance improvement of more than 1dB
SI-SDRi can be brought, which validates the effectiveness of our methods and
emphasizes the impact of the target confusion problem.


title: Dominant Andreev Reflection through Nonlinear Radio-Frequency Transport

abstract:
  We theoretically propose the laser-induced Andreev reflection between
two-component Fermi superfluid and normal states via spatially-uniform Rabi
couplings. By analyzing the tunneling current between the superfluid and normal
states up to the fourth order in the Rabi couplings, we find that the Andreev
current exhibits unconventional non-Ohmic transport at zero temperature.
Remarkably, the Andreev current gives the only contribution in the synthetic
junction system at zero detunings regardless of the ratio of the chemical
potential bias to the superfluid gap, which is in sharp contrast to that in the
conventional superconductor-normal metal junction. Our result may also pave a
way for understanding the black hole information paradox through the Andreev
reflection as a quantum-information mirror.

categories: cond-mat.quant-gas cond-mat.supr-con gr-qc nucl-th physics.atom-ph


title: Relativistic spin-magnetohydrodynamics

abstract:
  Starting from kinetic theory description of massive spin-1/2 particles in
presence of magnetic field, equations for relativistic dissipative
non-resistive magnetohydrodynamics are obtained in the small polarization
limit. We use a relaxation time approximation for the collision kernel in the
relativistic Boltzmann equation and calculate non-equilibrium corrections to
the phase-space distribution function of spin-polarizable particles. We obtain
multiple novel transport coefficients and show that all dissipative currents
contain coupling between spin and magnetic field at first-order in gradients.


title: Nano-scale collinear multi-Q states driven by higher-order interactions

abstract:
  Complex magnetic order arises due to the competition of different
interactions between the magnetic moments. Recently, there has been an
increased interest in such states not only to unravel the fundamental physics
involved, but also with regards to applications exploiting their unique
interplay with moving electrons. Whereas it is the Dzyaloshinskii-Moriya
interaction (DMI) that has attracted much attention because of its nature to
induce non-collinear magnetic order including magnetic-field stabilized
skyrmions, it is the frustration of exchange interactions that can drive
magnetic order down to the nano-scale. On top of that, interactions between
multiple spins can stabilize two-dimensional magnetic textures as zero-field
ground states, known as multi-Q states. Here, we introduce a two-dimensional
itinerant magnet with various competing atomic-scale magnetic phases. Using
spin-polarized scanning tunneling microscopy we observe several zero-field
uniaxial or hexagonal nano-scale magnetic states. First-principles calculations
together with an atomistic spin model reveal that these states are stabilized
by the interplay of frustrated exchange and higher-order interactions while the
DMI is weak. Unexpectedly, it is found that not only non-collinear magnetic
states arise, but that higher-order interactions can also lead to collinear
nano-scale multi-Q states.


title:
Effect of color reconnection and hadronic re-scattering on underlying
  events in pp collisions at LHC energies

abstract:
  Underlying events dominate most of the hadronic activity in p$-$p collisions
and are spanned from perturbative to non-perturbative QCD, having a sensitivity
ranging from the multi-scale to very low-x scale physics. A detailed
understanding of such events plays a crucial role in the accurate understanding
of Standard Model ()SM and Beyond Standard Model physics. The underlying event
activities has been studied within the framework of Pythia 8 Monte Carlo model,
considering the underlying events observables mean charged particle
multiplicity density , $\langle d^{2}N /d\eta d\phi \rangle$ and mean scalar
$p_T$ sum, $\langle d^{2} \sum p_{T} /d\eta d\phi \rangle$ as a function of
leading charged particle in towards, away, and transverse region of p$-$p
collisions at $\sqrt{s}$ = 2.76, 7 and 13 TeV. The towards, away, and
transverse regions have been defined on an azimuthal plane relative to leading
particle in p$-$p collisions. The energy dependence of underlying events and
their activities in the central and forward region has also been studied. The
effect of hadronic re-scattering, color reconnection, and rope hadronization
mechanism implemented in Pythia 8 has been studied in details to gain insight
into the different processes contributing to underlying events in soft sector.


title: Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval

abstract:
  This paper considers the phase retrieval (PR) problem, which aims to
reconstruct a signal from phaseless measurements such as magnitude or power
spectrograms. PR is generally handled as a minimization problem involving a
quadratic loss. Recent works have considered alternative discrepancy measures,
such as the Bregman divergences, but it is still challenging to tailor the
optimal loss for a given setting. In this paper we propose a novel strategy to
automatically learn the optimal metric for PR. We unfold a recently introduced
ADMM algorithm into a neural network, and we emphasize that the information
about the loss used to formulate the PR problem is conveyed by the proximity
operator involved in the ADMM updates. Therefore, we replace this proximity
operator with trainable activation functions: learning these in a supervised
setting is then equivalent to learning an optimal metric for PR. Experiments
conducted with speech signals show that our approach outperforms the baseline
ADMM, using a light and interpretable neural architecture.


title: Discretely Indexed Flows

abstract:
  In this paper we propose Discretely Indexed flows (DIF) as a new tool for
solving variational estimation problems. Roughly speaking, DIF are built as an
extension of Normalizing Flows (NF), in which the deterministic transport
becomes stochastic, and more precisely discretely indexed. Due to the discrete
nature of the underlying additional latent variable, DIF inherit the good
computational behavior of NF: they benefit from both a tractable density as
well as a straightforward sampling scheme, and can thus be used for the dual
problems of Variational Inference (VI) and of Variational density estimation
(VDE). On the other hand, DIF can also be understood as an extension of mixture
density models, in which the constant mixture weights are replaced by flexible
functions. As a consequence, DIF are better suited for capturing distributions
with discontinuities, sharp edges and fine details, which is a main advantage
of this construction. Finally we propose a methodology for constructiong DIF in
practice, and see that DIF can be sequentially cascaded, and cascaded with NF.


title:
Chain conditions for rings with enough idempotents with applications to
  category graded rings

abstract:
  We obtain criteria for when a ring with enough idempotents is left/right
artinian or noetherian in terms of local criteria defined by the associated
complete set of idempotents for the ring. We apply these criteria to object
unital category graded rings in general and, in particular, to the class of
skew category algebras. Thereby, we generalize results by Nastasescu-van
Oystaeyen, Bell, Park and Zelmanov from the group graded case to groupoid, and
in some cases category, gradings.


title:
On the failure of the chain rule for the divergence of Sobolev vector
  fields

abstract:
  We construct a large class of incompressible vector fields with Sobolev
regularity, in dimension $d \geq 3$, for which the chain rule problem has a
negative answer. In particular, for any renormalization map $\beta$ (satisfying
suitable assumptions) and any (distributional) renormalization defect $T$ of
the form $T = {\rm div}\, h$, where $h$ is an $L^1$ vector field, we can
construct an incompressible Sobolev vector field $u \in W^{1, \tilde p}$ and a
density $\rho \in L^p$ for which ${\rm div}\, (\rho u) =0$ but ${\rm div}\,
(\beta(\rho) u) = T$, provided $1/p + 1/\tilde p \geq 1 + 1/(d-1)$


title:
Scalable random number generation for truncated log-concave
  distributions

abstract:
  Inverse transform sampling is an exceptionally general method to generate
non-uniform-distributed random numbers, but can be rather unstable when
simulating extremely truncated distributions. Many famous probability models
share a property called log-concavity, which is not affected by truncation, so
they can all be simulated via rejection sampling using Devroye's approach. This
sampler is based on rejection and thus more stable than inverse transform, and
uses a very simple envelope whose acceptance rate is guaranteed to be at least
20\%. The aim of this paper is threefold: firstly, to warn against the risk of
wrongly simulating from truncated distributions; secondly, to motivate a more
extensive use of rejection sampling to mitigate the issues; lastly, to motivate
Devroye's automatic method as a practical standard in the case of log-concave
distributions. We illustrate the proposal by means of simulations based on some
Tweedie distributions, for their relevance in regression analysis.


title: Deep learning, stochastic gradient descent and diffusion maps

abstract:
  Stochastic gradient descent (SGD) is widely used in deep learning due to its
computational efficiency but a complete understanding of why SGD performs so
well remains a major challenge. It has been observed empirically that most
eigenvalues of the Hessian of the loss functions on the loss landscape of
over-parametrized deep networks are close to zero, while only a small number of
eigenvalues are large. Zero eigenvalues indicate zero diffusion along the
corresponding directions. This indicates that the process of minima selection
mainly happens in the relatively low-dimensional subspace corresponding to top
eigenvalues of the Hessian. Although the parameter space is very
high-dimensional, these findings seems to indicate that the SGD dynamics may
mainly live on a low-dimensional manifold. In this paper we pursue a truly data
driven approach to the problem of getting a potentially deeper understanding of
the high-dimensional parameter surface, and in particular of the landscape
traced out by SGD, by analyzing the data generated through SGD, or any other
optimizer for that matter, in order to possibly discovery (local)
low-dimensional representations of the optimization landscape. As our vehicle
for the exploration we use diffusion maps introduced by R. Coifman and
coauthors.


title:
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted
  Graph Convolutional Neural Networks

abstract:
  The minimum cost multicut problem is the NP-hard/APX-hard combinatorial
optimization problem of partitioning a real-valued edge-weighted graph such as
to minimize the total cost of the partition. While graph convolutional neural
networks (GNN) have proven to be promising in the context of combinatorial
optimization, most of them are only tailored to or tested on positive-valued
edge weights, i.e. they do not comply to the nature of the multicut problem. We
therefore adapt various GNN architectures including Graph Convolutional
Networks, Signed Graph Convolutional Networks and Graph Isomorphic Networks to
facilitate the efficient encoding of real-valued edge costs. Moreover, we
employ a reformulation of the multicut ILP constraints to a polynomial program
as loss function that allows to learn feasible multicut solutions in a scalable
way. Thus, we provide the first approach towards end-to-end trainable
multicuts. Our findings support that GNN approaches can produce good solutions
in practice while providing lower computation times and largely improved
scalability compared to LP solvers and optimized heuristics, especially when
considering large instances.


title: Algebraicity of L-values attached to Quaternionic Modular Forms

abstract:
  In this paper we prove the algebraicity of some L-values attached to
quaternionic modular forms. We follow the rather well established path of the
doubling method. Our main contribution is that we include the case where the
corresponding symmetric space is of non-tube type. We make various aspects very
explicit such as, the doubling embedding, coset decomposition, and the
definition of algebraicity of modular forms via CM points.


title: Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete

abstract:
  We consider the algorithmic problem of finding the optimal weights and biases
for a two-layer fully connected neural network to fit a given set of data
points. This problem is known as empirical risk minimization in the machine
learning community. We show that the problem is $\exists\mathbb{R}$-complete.
This complexity class can be defined as the set of algorithmic problems that
are polynomial-time equivalent to finding real roots of a polynomial with
integer coefficients. Our results hold even if the following restrictions are
all added simultaneously.
  $\bullet$ There are exactly two output neurons.
  $\bullet$ There are exactly two input neurons.
  $\bullet$ The data has only 13 different labels.
  $\bullet$ The number of hidden neurons is a constant fraction of the number
of data points.
  $\bullet$ The target training error is zero.
  $\bullet$ The ReLU activation function is used.
  This shows that even very simple networks are difficult to train. The result
offers an explanation (though far from a complete understanding) on why only
gradient descent is widely successful in training neural networks in practice.
We generalize a recent result by Abrahamsen, Kleist and Miltzow [NeurIPS 2021].
  This result falls into a recent line of research that tries to unveil that a
series of central algorithmic problems from widely different areas of computer
science and mathematics are $\exists\mathbb{R}$-complete: This includes the art
gallery problem [JACM/STOC 2018], geometric packing [FOCS 2020], covering
polygons with convex polygons [FOCS 2021], and continuous constraint
satisfaction problems [FOCS 2021].


title:
Plant root growth against a mechanical obstacle: The early growth
  response of a maize root facing an axial resistance agrees with the Lockhart
  model

abstract:
  Plant root growth is dramatically reduced in compacted soils, affecting the
growth of the whole plant. Through a model experiment coupling force and
kinematics measurements, we probed the force-growth relationship of a primary
root contacting a stiff resisting obstacle, that mimics the strongest soil
impedance variation encountered by a growing root. The growth of maize roots
just emerging from a corseting agarose gel and contacting a force sensor
(acting as an obstacle) was monitored by time-lapse imaging simultaneously to
the force.
  The evolution of the velocity field along the root was obtained from
kinematics analysis of the root texture with a PIV derived-technique. A
triangular fit was introduced to retrieve the elemental elongation rate or
strain rate. A parameter-free model based on the Lockhart law quantitatively
predicts how the force at the obstacle modifies several features of the growth
distribution (length of the growth zone, maximal elemental elongation rate,
velocity) during the first 10 minutes. These results suggest a strong
similarity of the early growth responses elicited either by a directional
stress (contact) or by an isotropic perturbation (hyperosmotic bath).


title: Miniature biplanar coils for alkali-metal-vapor magnetometry

abstract:
  Atomic spin sensors offer precision measurements using compact,
microfabricated packages, placing them in a competitive position for both
market and research applications. Performance of these sensors such as dynamic
range may be enhanced through magnetic field control. In this work, we discuss
the design of miniature coils for three-dimensional, localized field control by
direct placement around the sensor, as a flexible and compact alternative to
global approaches used previously. Coils are designed on biplanar surfaces
using a stream-function approach and then fabricated using standard
printed-circuit techniques. Application to a laboratory-scale optically pumped
magnetometer of sensitivity $\sim$20 fT/Hz$^{1/2}$ is shown. We also
demonstrate the performance of a coil set measuring $7 \times 17 \times 17$
mm$^3$ that is optimized specifically for magnetoencephalography, where
multiple sensors are operated in proximity to one another. Characterization of
the field profile using $^{87}$Rb free-induction spectroscopy and other
techniques show $>$96% field homogeneity over the target volume of a MEMS vapor
cell and a compact stray field contour of $\sim$1% at 20 mm from the center of
the cell.


title: Non-crossing convex quantile regression

abstract:
  Quantile crossing is a common phenomenon in shape constrained nonparametric
quantile regression. A recent study by Wang et al. (2014) has proposed to
address this problem by imposing non-crossing constraints to convex quantile
regression. However, the non-crossing constraints may violate an intrinsic
quantile property. This paper proposes a penalized convex quantile regression
approach that can circumvent quantile crossing while better maintaining the
quantile property. A Monte Carlo study demonstrates the superiority of the
proposed penalized approach in addressing the quantile crossing problem.


title:
Exponential ergodicity for damping Hamiltonian dynamics with
  state-dependent and non-local collisions

abstract:
  In this paper, we investigate the exponential ergodicity in a
Wasserstein-type distance for a damping Hamiltonian dynamics with
state-dependent and non-local collisions, which indeed is a special case of
piecewise deterministic Markov processes while is very popular in numerous
modelling situations including stochastic algorithms. The approach adopted in
this work is based on a combination of the refined basic coupling and the
refined reflection coupling for non-local operators. In a certain sense, the
main result developed in the present paper is a continuation of the counterpart
in \cite{BW2022} on exponential ergodicity of stochastic Hamiltonian systems
with L\'evy noises and a complement of \cite{BA} upon exponential ergodicity
for Andersen dynamics with constant jump rate functions.


title:
A Bootstrap-Assisted Self-Normalization Approach to Inference in
  Cointegrating Regressions

abstract:
  Traditional inference in cointegrating regressions requires tuning parameter
choices to estimate a long-run variance parameter. Even in case these choices
are "optimal", the tests are severely size distorted. We propose a novel
self-normalization approach, which leads to a nuisance parameter free limiting
distribution without estimating the long-run variance parameter directly. This
makes our self-normalized test tuning parameter free and considerably less
prone to size distortions at the cost of only small power losses. In
combination with an asymptotically justified vector autoregressive sieve
bootstrap to construct critical values, the self-normalization approach shows
further improvement in small to medium samples when the level of error serial
correlation or regressor endogeneity is large. We illustrate the usefulness of
the bootstrap-assisted self-normalized test in empirical applications by
analyzing the validity of the Fisher effect in Germany and the United States.


title: Silting, cosilting, and extensions of commutative ring

abstract:
  We study the transfer of (co)silting objects in derived categories of module
categories via the extension functors induced by a morphism of commutative
rings. It is proved that the extension functors preserve (co)silting objects of
(co)finite type. In many cases the bounded silting property descends along
faithfully flat ring extensions. In particular, the notion of bounded silting
complex is Zariski local.


title: The Extreme Mechanics of Viscoelastic Metamaterials

abstract:
  Mechanical metamaterials made of flexible building blocks can exhibit a
plethora of extreme mechanical responses, such as negative elastic constants,
shape-changes, programmability and memory. To date, dissipation has largely
remained overlooked for such flexible metamaterials. As a matter of fact,
extensive care has often been devoted in the constitutive materials' choice to
avoid strong dissipative effects. However, in an increasing number of
scenarios, where metamaterials are loaded dynamically, dissipation can not be
ignored. In this review, we show that the interplay between mechanical
instabilities and viscoelasticity can be crucial and can be harnessed to obtain
new functionalities. We first show that this interplay is key to understanding
the dynamical behaviour of flexible dissipative metamaterials that use buckling
and snapping as functional mechanisms. We further discuss the new opportunities
that spatial patterning of viscoelastic properties offer for the design of
mechanical metamaterials with properties that depend on loading rate.


title: Synthetic Graph Generation to Benchmark Graph Learning

abstract:
  Graph learning algorithms have attained state-of-the-art performance on many
graph analysis tasks such as node classification, link prediction, and
clustering. It has, however, become hard to track the field's burgeoning
progress. One reason is due to the very small number of datasets used in
practice to benchmark the performance of graph learning algorithms. This
shockingly small sample size (~10) allows for only limited scientific insight
into the problem.
  In this work, we aim to address this deficiency. We propose to generate
synthetic graphs, and study the behaviour of graph learning algorithms in a
controlled scenario. We develop a fully-featured synthetic graph generator that
allows deep inspection of different models. We argue that synthetic graph
generations allows for thorough investigation of algorithms and provides more
insights than overfitting on three citation datasets. In the case study, we
show how our framework provides insight into unsupervised and supervised graph
neural network models.


title:
Galactic Cosmic Rays at Mars and Venus: Temporal Variations from Hours
  to Decades Measured as the Background Signal of Onboard Micro-Channel Plates

abstract:
  A Micro-Channel Plate (MCP) is a widely used component for counting particles
in space. Using the background counts of MCPs on Mars Express and Venus Express
orbiters operated over 17 years and 8 years, respectively, we investigate the
galactic cosmic ray (GCR) characteristics in the inner solar system. The MCP
background counts at Mars and Venus on a solar cycle time scale exhibit clear
anti-correlation to the sunspot number. We conclude that the measured MCP
background contain the GCR information. The GCR characteristics measured using
the MCP background at Mars show features that are consistent with the
ground-based measurement in solar cycle 24. The time lag between the sunspot
number and the MCP background at Mars is found ~9 months. The shorter-term
background data recorded along the orbits (with a time scale of several hours)
also show evident depletion of the background counts due to the absorption of
the GCR particles by the planets. Thanks to the visible planetary size change
along an orbit, the GCR contribution to the MCP background can be separated
from the internal contribution due to the \b{eta}-decay. Our statistical
analysis of the GCR absorption signatures at Mars implies that the effective
absorption size of Mars for the GCR particles have a >100 km larger radius than
the solid Martian body.

categories: astro-ph.EP astro-ph.HE astro-ph.IM physics.space-ph


title:
Removable sets and $L^p$-uniqueness on manifolds and metric measure
  spaces

abstract:
  We study symmetric diffusion operators on metric measure spaces. Our main
question is whether or not the restriction of the operator to a suitable core
continues to be essentially self-adjoint or $L^p$-unique if a small closed set
is removed from the space. The effect depends on how large the removed set is,
and we provide characterizations of the critical size in terms of capacities
and Hausdorff dimension. As a key tool we prove a truncation result for
potentials of nonnegative functions. We apply our results to Laplace operators
on Riemannian and sub-Riemannian manifolds and on metric measure spaces
satisfying curvature dimension conditions. For non-collapsing Ricci limit
spaces with two-sided Ricci curvature bounds we observe that the self-adjoint
Laplacian is already fully determined by the classical Laplacian on the regular
part.


title:
Taking ROCKET on an Efficiency Mission: Multivariate Time Series
  Classification with LightWaveS

abstract:
  Nowadays, with the rising number of sensors in sectors such as healthcare and
industry, the problem of multivariate time series classification (MTSC) is
getting increasingly relevant and is a prime target for machine and deep
learning approaches. Their expanding adoption in real-world environments is
causing a shift in focus from the pursuit of ever-higher prediction accuracy
with complex models towards practical, deployable solutions that balance
accuracy and parameters such as prediction speed. An MTSC model that has
attracted attention recently is ROCKET, based on random convolutional kernels,
both because of its very fast training process and its state-of-the-art
accuracy. However, the large number of features it utilizes may be detrimental
to inference time. Examining its theoretical background and limitations enables
us to address potential drawbacks and present LightWaveS: a framework for
accurate MTSC, which is fast both during training and inference. Specifically,
utilizing wavelet scattering transformation and distributed feature selection,
we manage to create a solution that employs just 2.5% of the ROCKET features,
while achieving accuracy comparable to recent MTSC models. LightWaveS also
scales well across multiple compute nodes and with the number of input channels
during training. In addition, it can significantly reduce the input size and
provide insight to an MTSC problem by keeping only the most useful channels. We
present three versions of our algorithm and their results on distributed
training time and scalability, accuracy, and inference speedup. We show that we
achieve speedup ranging from 9x to 53x compared to ROCKET during inference on
an edge device, on datasets with comparable accuracy.


title: Interferometry based on quantum Kibble-Zurek mechanism

abstract:
  We propose an interferometry within the framework of quantum Kibble-Zurek
mechanism by exemplifying two prototypical quench protocols, namely the
round-trip and quarter-turn ones, on the transverse Ising and quantum $XY$
chains. Each protocol contains two linear ramps that drive the system across
quantum critical point twice. The two linear ramps arouse two respective
nonadiabatic critical dynamics that are well described by the quantum
Kibble-Zurek mechanism. However, in combination, the two critical dynamics can
interfere with each other deeply. As an effect of the interference, the
dynamical phase is exposed in the final excitation probability, which leads to
a quantum coherent many-body oscillation in the density of defects with
predictable characteristic period. Thus such an interference is available for
direct experimental observations. In the quantum $XY$ model, we show that an
interference can also arise from the interplay between two different critical
dynamics derived from a critical point and a tricritical point. Furthermore, we
demonstrate that the interference influences the dephasing of the excited
quasiparticle modes intricately by disclosing a phenomenon of multiple length
scales, diagonal and off-diagonal ones, in the defect-defect correlators. It
turns out that the dephased result relies on how the diagonal and off-diagonal
lengths are modulated by the controllable parameter in a quench protocol.


title:
Evidences of the Photosphere Emission Origin for the Gamma-ray Burst
  Prompt Emission

abstract:
  The physical origin of gamma-ray burst (GRB) prompt emission is still subject
to debate after five decades (photosphere or synchrotron). Here, firstly we
find that many observed characteristics of 15 long GRBs, which have the highest
prompt emission efficiency $\epsilon _{\gamma}$ ($\epsilon_{\gamma }\gtrsim
80\%$), strongly support the photosphere (thermal) emission origin: (1) The
relation between $E_{\text{p}}$ and $E_{\text{iso}}$ is almost
$E_{\text{p}}\propto (E_{\text{iso}})^{1/4}$ , and the dispersion is quite
small. (2) The simple power-law shape of the X-ray afterglow light curves and
the significant reverse shock signals in the optical afterglow light curves.
(3) Best-fitted by the cutoff power-law model for the time-integrated spectrum.
(4) The consistent efficiency from observation (with $E_{\text{iso}}/E_{k}$)
and the prediction of photosphere emission model (with $\eta /\Gamma $). Then,
we further investigate the characteristics of the long GRBs for two
distinguished samples ($\epsilon _{\gamma }\gtrsim 50\%$ and $\epsilon _{\gamma
}\lesssim 50\%$). It is found that the different distributions for
$E_{\text{p}}$ and $E_{\text{iso}}$, and the similar observed efficiency (from
the X-ray afterglow) and theoretically predicted efficiency (from the prompt
emission or the optical afterglow) well follow the prediction of photosphere
emission model. Also, based on the same efficiency, we derive an excellent
correlation of $\Gamma \propto
E_{\text{iso}}^{1/8}E_{\text{p}}^{1/2}/(T_{90})^{1/4}$ to estimate $\Gamma $.
Finally, the different distributions for $E_{\text{p}}$ and $E_{\text{iso}}$,
and the consistent efficiency exist for the short GRBs. Besides, we give a
natural explanation of the extended emission ($\epsilon _{\gamma }\lesssim
50\%$) and the main pulse ($\epsilon _{\gamma }\gtrsim 50\%$).


title:
On the Global Convergence of Stochastic Fictitious Play in Stochastic
  Games with Turn-based Controllers

abstract:
  This paper presents a learning dynamic with almost sure convergence guarantee
for any stochastic game with turn-based controllers (on state transitions) as
long as stage-payoffs have stochastic fictitious-play-property. For example,
two-player zero-sum and n-player potential strategic-form games have this
property. Note also that stage-payoffs for different states can have different
structures such as they can sum to zero in some states and be identical in
others. The dynamics presented combines the classical stochastic fictitious
play with value iteration for stochastic games. There are two key properties:
(i) players play finite horizon stochastic games with increasing lengths within
the underlying infinite-horizon stochastic game, and (ii) the turn-based
controllers ensure that the auxiliary stage-games (induced from the
continuation payoff estimated) have the stochastic fictitious-play-property.


title:
Manufacturing of Textured Bulk Fe-SmCo$_{5}$ Magnets by Severe Plastic
  Deformation

abstract:
  Exchange-coupling between soft- and hard-magnetic phases plays an important
role in the engineering of novel magnetic materials. To achieve exchange
coupling, a two-phase microstructure is necessary. This interface effect is
further enhanced if both phase dimensions are reduced to the nanometer scale.
At the same time, it is challenging to obtain large sample dimensions. In this
study, powder blends and ball-milled powder blends of Fe-SmCo$_{5}$ are
consolidated and are deformed by high-pressure torsion (HPT), as this technique
allows us to produce bulk magnetic materials of reasonable sizes. Additionally,
the effect of severe deformation by ball-milling and severe plastic deformation
by HPT on exchange coupling in Fe-SmCo$_{5}$ composites is investigated. Due to
the applied shear deformation, it is possible to obtain a texture in both
phases, resulting in an anisotropic magnetic behavior and an improved magnetic
performance.


title:
Direct numerical simulations of the modified Poisson-Nernst-Planck
  equations for the charging dynamics of cylindrical electrolyte-filled pores

abstract:
  Understanding how electrolyte-filled porous electrodes respond to an applied
potential is important to many electrochemical technologies. Here, we consider
a model supercapacitor of two blocking cylindrical pores on either side of a
cylindrical electrolyte reservoir. A stepwise potential difference $2\Phi$
between the pores drives ionic fluxes in the setup, which we study through the
modified Poisson-Nernst-Planck equations, solved with finite elements. We focus
our discussion on the dominant timescales with which the pores charge and how
these timescales depend on three dimensionless numbers. Next to the
dimensionless applied potential $\Phi$, we consider the ratio $R/R_b$ of the
pore's resistance $R$ to the bulk reservoir resistance $R_b$ and the ratio
$r_{p}/\lambda$ of the pore radius $r_p$ to the Debye length $\lambda$. We
compare our data to theoretical predictions by Aslyamov and Janssen ($\Phi$),
Posey and Morozumi ($R/R_b$), and Henrique, Zuk, and Gupta ($r_{p}/\lambda$).
Through our numerical approach, we delineate the validity of these theories and
the assumptions on which they were based.


title: Aligned Weight Regularizers for Pruning Pretrained Neural Networks

abstract:
  While various avenues of research have been explored for iterative pruning,
little is known what effect pruning has on zero-shot test performance and its
potential implications on the choice of pruning criteria. This pruning setup is
particularly important for cross-lingual models that implicitly learn alignment
between language representations during pretraining, which if distorted via
pruning, not only leads to poorer performance on language data used for
retraining but also on zero-shot languages that are evaluated.
  In this work, we show that there is a clear performance discrepancy in
magnitude-based pruning when comparing standard supervised learning to the
zero-shot setting. From this finding, we propose two weight regularizers that
aim to maximize the alignment between units of pruned and unpruned networks to
mitigate alignment distortion in pruned cross-lingual models and perform well
for both non zero-shot and zero-shot settings.
  We provide experimental results on cross-lingual tasks for the zero-shot
setting using XLM-RoBERTa$_{\mathrm{Base}}$, where we also find that pruning
has varying degrees of representational degradation depending on the language
corresponding to the zero-shot test set. This is also the first study that
focuses on cross-lingual language model compression.


title:
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader
  Packing and Soft Rasterization

abstract:
  Differentiable rendering (DR) enables various computer graphics and computer
vision applications through gradient-based optimization with derivatives of the
rendering equation. Most rasterization-based approaches are built on
general-purpose automatic differentiation (AD) libraries and DR-specific
modules handcrafted using CUDA. Such a system design mixes DR algorithm
implementation and algorithm building blocks, resulting in hardware dependency
and limited performance. In this paper, we present a practical
hardware-agnostic differentiable renderer called Dressi, which is based on a
new full AD design. The DR algorithms of Dressi are fully written in our
Vulkan-based AD for DR, Dressi-AD, which supports all primitive operations for
DR. Dressi-AD and our inverse UV technique inside it bring hardware
independence and acceleration by graphics hardware. Stage packing, our runtime
optimization technique, can adapt hardware constraints and efficiently execute
complex computational graphs of DR with reactive cache considering the render
pass hierarchy of Vulkan. HardSoftRas, our novel rendering process, is designed
for inverse rendering with a graphics pipeline. Under the limited
functionalities of the graphics pipeline, HardSoftRas can propagate the
gradients of pixels from the screen space to far-range triangle attributes. Our
experiments and applications demonstrate that Dressi establishes hardware
independence, high-quality and robust optimization with fast speed, and
photorealistic rendering.


title:
Anti-Spoofing Using Transfer Learning with Variational Information
  Bottleneck

abstract:
  Recent advances in sophisticated synthetic speech generated from
text-to-speech (TTS) or voice conversion (VC) systems cause threats to the
existing automatic speaker verification (ASV) systems. Since such synthetic
speech is generated from diverse algorithms, generalization ability with using
limited training data is indispensable for a robust anti-spoofing system. In
this work, we propose a transfer learning scheme based on the wav2vec 2.0
pretrained model with variational information bottleneck (VIB) for speech
anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA)
database shows that our method improves the performance of distinguishing
unseen spoofed and genuine speech, outperforming current state-of-the-art
anti-spoofing systems. Furthermore, we show that the proposed system improves
performance in low-resource and cross-dataset settings of anti-spoofing task
significantly, demonstrating that our system is also robust in terms of data
size and data distribution.


title: Loop corrections to a cosmological particle creation

abstract:
  We consider dynamics of the massive minimally coupled scalar field theory in
an expanding Friedmann-Lemaitre-Robertson-Walker universe. We consider the
standard toy model of the conformally flat space-time where the conformal
factor becomes constant at the distant past and the distant future. Employing
Schwinger-Keldysh diagrammatic technique, we compute infrared loop corrections
to the occupation number and anomalous quantum average of the scalar field and
show that these corrections are growing with time. Using these observations, we
demonstrate that the regularized stress-energy tensor at the distant future
acquires substantial quantum corrections which exceed the long known tree-level
contributions to the particle flux.


title: Quantum Statistical Mechanics of Vortices

abstract:
  The asymptotic partition function for quantized Abelian Higgs vortices at
high temperature $T$ is found to leading and subleading order, and from this
the equation of state of the vortex gas is derived, including the first quantum
correction. It is assumed that the Hamiltonian is proportional to the
Laplace--Beltrami operator on the moduli space of static $N$-vortex solutions.
The partition function is calculated using the total volume and total scalar
curvature of the moduli space.


title: Automated Machine Learning for Deep Recommender Systems: A Survey

abstract:
  Deep recommender systems (DRS) are critical for current commercial online
service providers, which address the issue of information overload by
recommending items that are tailored to the user's interests and preferences.
They have unprecedented feature representations effectiveness and the capacity
of modeling the non-linear relationships between users and items. Despite their
advancements, DRS models, like other deep learning models, employ sophisticated
neural network architectures and other vital components that are typically
designed and tuned by human experts. This article will give a comprehensive
summary of automated machine learning (AutoML) for developing DRS models. We
first provide an overview of AutoML for DRS models and the related techniques.
Then we discuss the state-of-the-art AutoML approaches that automate the
feature selection, feature embeddings, feature interactions, and system design
in DRS. Finally, we discuss appealing research directions and summarize the
survey.


title: Hamiltonian Analysis of Nonlocal F(R) Gravity Models

abstract:
  We construct a Hamiltonian for the nonlocal F(R) theory in the present work.
By this construction, we demonstrate the nature of the ghost degrees of
freedom. Finally, we find conditions that give rise to ghost-free theories


title: JShelter: Give Me My Browser Back

abstract:
  The Web is used daily by billions. Even so, users are not protected from many
threats by default. This position paper builds on previous web privacy and
security research and introduces JShelter, a webextension that fights to return
the browser to users. Moreover, we introduce a library helping with common
webextension development tasks and fixing loopholes misused by previous
research. JShelter focuses on fingerprinting prevention, limitations of rich
web APIs, prevention of attacks connected to timing, and learning information
about the computer, the browser, the user, and surrounding physical environment
and location. We discovered a loophole in the sensor timestamps that lets any
page observe the device boot time if sensor APIs are enabled in Chromium-based
browsers. JShelter provides a fingerprinting report and other feedback that can
be used by future security research and data protection authorities. Thousands
of users around the world use the webextension every day.


title: Hypernuclei and Antihypernuclei Production in Heavy-Ion Collisions

abstract:
  In these proceedings, an overview of recent hypernuclei measurements in heavy
ion collisions is presented. These results include the lifetime, $\Lambda$
binding energy, production yield, and directed flow in heavy ion collisions.
These results provide constraints on the hyperon-nucleon interaction, enhance
our understanding in hypernuclear structure, and provide insight on the
production mechanisms of hypernuclei in heavy ion collisions.


title:
Propagation of moments and sharp convergence rate for inhomogeneous
  non-cutoff Boltzmann equation with soft potentials

abstract:
  We prove the well-posedness for the non-cutoff Boltzmann equation with soft
potentials when the initial datum is close to the {\it global Maxwellian} and
has only polynomial decay at the large velocities in $L^2$ space. As a result,
we get the {\it propagation of the exponential moments} and the {\it sharp
rates} of the convergence to the {\it global Maxwellian} which seems the first
results for the original equation with soft potentials. The new ingredients of
the proof lie in localized techniques, the semigroup method as well as the
propagation of the polynomial and exponential moments in $L^2$ space.


title: The Parking Problem: A Game-Theoretic Solution

abstract:
  In this paper, we propose a game-theoretic solution to the parking problem,
by exploiting a strategic-reasoning approach for multi-agent systems.
Precisely, cars are modeled by agents interacting among them in a multi-player
game setting, whose aim is to get a free slot parking-place satisfying their
own constraints. The overall assignment is then given as a Nash equilibrium
solution. We come up with an algorithm (and its implementation in a tool) that
works in quadratic time. We give evidence of the benefits of our approach by
running our tool on a large hospital parking space.


title:
RE-EDS Using GAFF Topologies: Application to Relative Hydration
  Free-Energy Calculations for Large Sets of Molecules

abstract:
  Free-energy differences between pairs of end-states can be estimated based on
molecular dynamics (MD) simulations using standard pathway-dependent methods
such as thermodynamic integration (TI), free-energy perturbation, or Bennett's
acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS),
on the other hand, allows for the sampling of multiple end-states in a single
simulation without the specification of any pathways. In this work, we use the
RE-EDS method as implemented in GROMOS together with generalized AMBER force
field (GAFF) topologies, converted to a GROMOS-compatible format with a newly
developed GROMOS++ program amber2gromos, to compute relative hydration free
energies for a series of benzene derivatives. The results obtained with RE-EDS
are compared to the experimental data as well as calculated values from the
literature. In addition, the estimated free-energy differences in water and in
vacuum are compared to values from TI calculations carried out with GROMACS.
The hydration free energies obtained using RE-EDS for multiple molecules are
found to be in good agreement with both the experimental data and the results
calculated using other free-energy methods. While all considered free-energy
methods delivered accurate results, the RE-EDS calculations required the least
amount of total simulation time. This work serves as a validation for the use
of GAFF topologies with the GROMOS simulation package and the RE-EDS approach.
Furthermore, the performance of RE-EDS for a large set of 28 end-states is
assessed with promising results.


title:
A Study of Gender Impact in Self-supervised Models for Speech-to-Text
  Systems

abstract:
  Self-supervised models for speech processing emerged recently as popular
foundation blocks in speech processing pipelines. These models are pre-trained
on unlabeled audio data and then used in speech processing downstream tasks
such as automatic speech recognition (ASR) or speech translation (ST). Since
these models are now used in research and industrial systems alike, it becomes
necessary to understand the impact caused by some features such as gender
distribution within pre-training data. Using French as our investigation
language, we train and compare gender-specific wav2vec 2.0 models against
models containing different degrees of gender balance in their pre-training
data. The comparison is performed by applying these models to two
speech-to-text downstream tasks: ASR and ST. Our results show that the type of
downstream integration matters. We observe lower overall performance using
gender-specific pre-training before fine-tuning an end-to-end ASR system.
However, when self-supervised models are used as feature extractors, the
overall ASR and ST results follow more complex patterns, in which the balanced
pre-trained model is not necessarily the best option. Lastly, our crude
'fairness' metric, the relative performance difference measured between female
and male test sets, does not display a strong variation from balanced to
gender-specific pre-trained wav2vec 2.0 models.


title: A Variational Approach For Price Formation Models In One Dimension

abstract:
  In this paper, we study a class of first-order mean-field games (MFGs) that
model price formation. Using Poincar{\'e} Lemma, we eliminate one of the
equations and obtain a variational problem for a single function. This
variational problem offers an alternative approach for the numerical solution
of the original MFGs system. We show a correspondence between solutions of the
MFGs system and the variational problem. Moreover, we address the existence of
solutions for the variational problem using the direct method in the calculus
of variations. We end the paper with numerical results for a linear-quadratic
model.


title: The Vicomtech Spoofing-Aware Biometric System for the SASV Challenge

abstract:
  This paper describes our proposed integration system for the spoofing-aware
speaker verification challenge. It consists of a robust spoofing-aware
verification system that use the speaker verification and antispoofing
embeddings extracted from specialized neural networks. First, an integration
network, fed with the test utterance's speaker verification and spoofing
embeddings, is used to compute a spoof-based score. This score is then linearly
combined with the cosine similarity between the speaker verification embeddings
from the enrollment and test utterances, thus obtaining the final scoring
decision. Moreover, the integration network is trained using a one-class loss
function to discriminate between target trials and unauthorized accesses. Our
proposed system is evaluated in the ASVspoof19 database, exhibiting competitive
performance compared to other integration approaches. In addition, we test,
along with our integration approach, state of the art speaker verification and
antispoofing systems based on self-supervised learning, yielding
high-performance speech biometric systems.


title: Strain Effect on Air-Stability of Monolayer CrSe2

abstract:
  The discovery of two dimensional (2D) magnetic materials has brought great
research value for spintronics and data storage devices. However, their
air-stability as well as the oxidation mechanism has not been unveiled, which
limits their further applications. Here, by first-principles calculations, we
carried out a detailed study on the oxidation process of monolayer CrSe2 and
biaxial tensile strain effect. We found dissociation process of O2 on pristine
CrSe2 sheet is an endothermic reaction with a reaction energy barrier of 0.53
eV, indicating its thermodynamics stability. However, such a process becomes
exothermic under a biaxial tensile strain reaching 1%, accompanying with a
decreased reaction barrier, leading to reduced stability. These results
manifest that in-plane strain plays a significant role in modifying
air-stability in CrSe2 and shed considerable light on searching appropriate
substrate to stabilize 2D magnetic materials.


title:
Variance estimation for Sequential Monte Carlo Algorithms: a backward
  sampling approach

abstract:
  In this paper, we consider the problem of online asymptotic variance
estimation for particle filtering and smoothing. Current solutions for the
particle filter rely on the particle genealogy and are either unstable or hard
to tune in practice. We propose to mitigate these limitations by introducing a
new estimator of the asymptotic variance based on the so called backward
weights. The resulting estimator is weakly consistent and trades computational
cost for more stability and reduced variance. We also propose a more
computationally efficient estimator inspired by the PaRIS algorithm of Olsson &
Westerborn. As an application, particle smoothing is considered and an
estimator of the asymptotic variance of the Forward Filtering Backward
Smoothing estimator applied to additive functionals is provided.


title: The period isomorphism in tame geometry

abstract:
  We describe singular homology of a manifold $X$ via simplices
  $\sigma:\Delta_d\to X$ that satisfy Stokes' formula with respect to all
differential forms. The notion is geared to the case of tame geometry
(definable manifolds with respect to an o-minimal structure), where it gives a
description of the period pairing with de Rham cohomology via definable
$\sigma$'s.