title:
Dealing with collinearity in large-scale linear system identification
  using Bayesian regularization

abstract:
  We consider the identification of large-scale linear and stable dynamic
systems whose outputs may be the result of many correlated inputs. Hence,
severe ill-conditioning may affect the estimation problem. This is a scenario
often arising when modeling complex physical systems given by the
interconnection of many sub-units where feedback and algebraic loops can be
encountered. We develop a strategy based on Bayesian regularization where any
impulse response is modeled as the realization of a zero-mean Gaussian process.
The stable spline covariance is used to include information on smooth
exponential decay of the impulse responses. We then design a new Markov chain
Monte Carlo scheme that deals with collinearity and is able to efficiently
reconstruct the posterior of the impulse responses. It is based on a variation
of Gibbs sampling which updates possibly overlapping blocks of the parameter
space on the basis of the level of collinearity affecting the different inputs.
Numerical experiments are included to test the goodness of the approach where
hundreds of impulse responses form the system and inputs correlation may be
very high.


title: A new observable for cosmic shear

abstract:
  In this paper we introduce a new observable to measure cosmic shear. We show
that if we can measure with good accuracy both, the orientation of a galaxy and
the polarisation direction of its radio emission, the angle between them is
sensitive to the foreground cosmic shear. Even if the signal-to-noise ratio for
a single measurement is expected to be rather small, the fact that all galaxies
in a given pixel are subject to the same shear can be used to overcome the
noise. An additional advantage of this observable is that the signal is not
plagued by intrinsic alignment. We estimate the SNR for the shear correlation
functions measured in this way with the future SKA II survey.


title: BeyondPlanck V. Minimal ADC Corrections for Planck LFI

abstract:
  We describe the correction procedure for Analog-to-Digital Converter (ADC)
differential non-linearities (DNL) adopted in the Bayesian end-to-end
BeyondPlanck analysis framework. This method is nearly identical to that
developed for the official LFI Data Processing Center (DPC) analysis, and
relies on the binned rms noise profile of each detector data stream. However,
rather than building the correction profile directly from the raw rms profile,
we first fit a Gaussian to each significant ADC-induced rms decrement, and then
derive the corresponding correction model from this smooth model. The main
advange of this approach is that only samples which are significantly affected
by ADC DNLs are corrected. The new corrections are only applied to data for
which there is a clear detection of the non-linearities, and for which they
perform at least comparably with the DPC corrections. Out of a total of 88 LFI
data streams (sky and reference load for each of the 44 detectors) we apply the
new minimal ADC corrections in 25 cases, and maintain the DPC corrections in 8
cases. All these correctsion are applited to 44 or 70 GHz channels, while, as
in previous analyses, none of the 30 GHz ADCs show significant evidence of
non-linearity. By comparing the BeyondPlanck and DPC ADC correction methods, we
estimate that the residual ADC uncertainty is about two orders of magnitude
below the total noise of both the 44 and 70 GHz channels, and their impact on
current cosmological parameter estimation is small. However, we also show that
non-idealities in the ADC corrections can generate sharp stripes in the final
frequency maps, and these could be important for future joint analyses with
HFI, WMAP, or other datasets. We therefore conclude that, although the existing
corrections are adequate for LFI-based cosmological parameter analysis, further
work on LFI ADC corrections is still warranted.


title:
Validation of x-ray line-profile analysis of extended defects in
  deformed crystals

abstract:
  Quantitative measurements of extended defects in crystalline materials are
important in understanding material behaviour. X-ray line profile analysis
provides a complement to direct counting in the electron microscope, but is an
indirect method and requires validation. Previous studies have focused on
comparing x-ray analysis to electron microscopy results. Instead, we use
simulated defective material with known defect content and apply line profile
analysis to calculated diffraction profiles to directly show that line profile
analysis can reliably quantify dislocations and stacking faults.


title:
Tidal star-planet interaction and its observed impact on stellar
  activity in planet-hosting wide binary systems

abstract:
  Tidal interaction between an exoplanet and its host star is a possible
pathway to transfer angular momentum between the planetary orbit and the
stellar spin. In cases where the planetary orbital period is shorter than the
stellar rotation period, this may lead to angular momentum being transferred
into the star's rotation, possibly counteracting the intrinsic stellar
spin-down induced by magnetic braking. Observationally, detecting altered
rotational states of single, cool field stars is challenging, as precise ages
for such stars are rarely available. Here we present an empirical investigation
of the rotation and magnetic activity of a sample of planet-hosting stars that
are accompanied by wide stellar companions. Without needing knowledge about the
absolute ages of the stars, we test for relative differences in activity and
rotation of the planet hosts and their co-eval companions, using X-ray
observations to measure the stellar activity levels. Employing three different
tidal interaction models, we find that host stars with planets that are
expected to tidally interact display elevated activity levels compared to their
companion stars. We also find that those activity levels agree with the
observed rotational periods for the host stars along the usual
rotation-activity relationships, implying that the effect is indeed caused by a
tidal interaction and not a purely magnetic interaction which would be expected
to affect the stellar activity, but not necessarily the rotation. We conclude
that massive, close-in planets have an impact on the stellar rotational
evolution, while the smaller, more distant planets do not have a significant
influence.


title: Geometric percolation of colloids in shear flow

abstract:
  We combine a heuristic theory of geometric percolation and the Smoluchowski
theory of colloid dynamics to predict the impact of shear flow on the
percolation threshold of hard spherical colloidal particles, and verify our
findings by means of molecular dynamics simulations. It appears that the impact
of shear flow is subtle and highly non-trivial, even in the absence of
hydrodynamic interactions between the particles. The presence of shear flow can
both increase and decrease the percolation threshold, depending on the
criterion used for determining whether or not two particles are connected and
on the P\'{e}clet number. Our approach opens up a route to quantitatively
predict the percolation threshold in nanocomposite materials that, as a rule,
are produced under non-equilibrium conditions, making comparison with
equilibrium percolation theory tenuous. Our theory can be adapted
straightforwardly for application in other types of flow field, and particles
of different shape or interacting via other than hard-core potentials.

categories: cond-mat.soft cond-mat.mtrl-sci cond-mat.stat-mech physics.chem-ph


title:
Give Me Your Attention: Dot-Product Attention Considered Harmful for
  Adversarial Patch Robustness

abstract:
  Neural architectures based on attention such as vision transformers are
revolutionizing image recognition. Their main benefit is that attention allows
reasoning about all parts of a scene jointly. In this paper, we show how the
global reasoning of (scaled) dot-product attention can be the source of a major
vulnerability when confronted with adversarial patch attacks. We provide a
theoretical understanding of this vulnerability and relate it to an adversary's
ability to misdirect the attention of all queries to a single key token under
the control of the adversarial patch. We propose novel adversarial objectives
for crafting adversarial patches which target this vulnerability explicitly. We
show the effectiveness of the proposed patch attacks on popular image
classification (ViTs and DeiTs) and object detection models (DETR). We find
that adversarial patches occupying 0.5% of the input can lead to robust
accuracies as low as 0% for ViT on ImageNet, and reduce the mAP of DETR on MS
COCO to less than 3%.


title:
Long-range interactions between membrane inclusions: Electric field
  induced giant amplification of the pairwise potential

abstract:
  The aim of this work is to revisit the phenomenological theory of the
interaction between membrane inclusions, mediated by the membrane fluctuations.
We consider the case where the inclusions are separated by distances larger
than their characteristic size. Within our macroscopic approach a physical
nature of such inclusions is not essential, however we have always in mind two
prototypes of such inclusions: proteins and RNA macromolecules. Because the
interaction is driven by the membrane fluctuations, and the coupling between
inclusions and the membrane, it is possible to change the interaction potential
by external actions affecting these factors. As an example of such external
action we consider an electric field. Under external electric field (both dc or
ac), we propose a new coupling mechanism between inclusions possessing dipole
moments (as it is the case for most protein macromolecules) and the membrane.
We found, quite unexpected and presumably for the first time, that the new
coupling mechanism yields to giant enhancement of the pairwise potential of the
inclusions. This result opens up a way to handle purposefully the interaction
energy, and as well to test of the theory set forth in our article.


title:
StretchBEV: Stretching Future Instance Prediction Spatially and
  Temporally

abstract:
  In self-driving, predicting future in terms of location and motion of all the
agents around the vehicle is a crucial requirement for planning. Recently, a
new joint formulation of perception and prediction has emerged by fusing rich
sensory information perceived from multiple cameras into a compact bird's-eye
view representation to perform prediction. However, the quality of future
predictions degrades over time while extending to longer time horizons due to
multiple plausible predictions. In this work, we address this inherent
uncertainty in future predictions with a stochastic temporal model. Our model
learns temporal dynamics in a latent space through stochastic residual updates
at each time step. By sampling from a learned distribution at each time step,
we obtain more diverse future predictions that are also more accurate compared
to previous work, especially stretching both spatially further regions in the
scene and temporally over longer time horizons. Despite separate processing of
each time step, our model is still efficient through decoupling of the learning
of dynamics and the generation of future predictions.


title: Weyl-Einstein structures on conformal solvmanifolds

abstract:
  A conformal Lie group is a conformal manifold $(M,c)$ such that $M$ has a Lie
group structure and $c$ is the conformal structure defined by a left-invariant
metric $g$ on $M$. We study Weyl-Einstein structures on conformal solvable Lie
groups and on their compact quotients. In the compact case, we show that every
conformal solvmanifold carrying a Weyl-Einstein structure is Einstein. We also
show that there are no left-invariant Weyl-Einstein structures on non-abelian
nilpotent conformal Lie groups, and classify them on conformal solvable Lie
groups in the almost abelian case. Furthermore, we determine the precise list
(up to automorphisms) of left-invariant metrics on simply connected solvable
Lie groups of dimension 3 carrying left-invariant Weyl-Einstein structures.


title:
Validating x-ray line-profile defect analysis using atomistic models of
  deformed material

abstract:
  The population of dislocation defects in a crystalline material strongly
influences its properties, so the ability to analyse this population in
experimental samples is of great utility. As a complement to direct counting in
the transmission electron microscope, quantitative analysis of x-ray
diffraction line profiles is an important tool. This is an indirect approach to
quantification and so requires careful validation of the physical models that
underly the inferential process. Here we undertake to directly evaluate the
ability of line profile analysis to quantify aspects of the dislocation and
stacking fault populations by exploiting atomistic models of deformed copper
single crystals. We directly analyse these models to determine exact details of
the defect content (our "ground truth"). We then generate theoretical line
profiles for the models and analyse them using the same procedures used in
experimental analysis. This leads to inferred measures of the defect content
which we are able to compare with the exact data. We show that line profile
analysis is able to provide sound predictions of both dislocation density and
stacking fault fraction across two orders of magnitude. We further show how the
outer cut-off radius in the mean-square strain of a dislocation distribution
invoked by Warren and Averbach corresponds to the cell size in an artificially
constructed restrictedly-random distribution of dislocations according to the
model of Wilkens. Overall, our results lend important new support to the use of
line profile analysis for the quantification of line and planar defects in
crystalline materials.


title: A non-archimedean variant of Littlewood--Paley theory for curves

abstract:
  We prove a variant of a square function estimate for the extension operator
associated to the moment curve in non-archimedean local fields. The arguments
rely on a structural analysis of congruences (sublevel sets) of univariate
polynomials over field extensions of the base field. Our analysis can be
adapted to the archimedean setting as well.


title: Audio-text Retrieval in Context

abstract:
  Audio-text retrieval based on natural language descriptions is a challenging
task. It involves learning cross-modality alignments between long sequences
under inadequate data conditions. In this work, we investigate several audio
features as well as sequence aggregation methods for better audio-text
alignment. Moreover, through a qualitative analysis we observe that semantic
mapping is more important than temporal relations in contextual retrieval.
Using pre-trained audio features and a descriptor-based aggregation method, we
build our contextual audio-text retrieval system. Specifically, we utilize
PANNs features pre-trained on a large sound event dataset and NetRVLAD pooling,
which directly works with averaged descriptors. Experiments are conducted on
the AudioCaps and CLOTHO datasets, and results are compared with the previous
state-of-the-art system. With our proposed system, a significant improvement
has been achieved on bidirectional audio-text retrieval, on all metrics
including recall, median and mean rank.


title: Intrinsic emergence of Majorana modes in Luttinger j=3/2 systems

abstract:
  We analyze theoretically two different setups for s-wave superconductivity
(SC) proximitized $j=3/2$ particles in Luttinger materials that are able to
host Majorana bound states (MBSs). First, we consider a one-dimensional SC wire
with intrinsic bulk inversion asymmetry (BIA). In contrast to wires, modeled by
a quadratic dispersion with Rashba spin-orbit coupling, there are two
topological phase transitions in our systems at finite magnetic fields. Second,
we analyze a two-dimensional Josephson junction on the Luttinger model finding
a topological region even in the absence of BIA and Rashba spin-orbit
couplings. This originates from the hybridization of the light and heavy hole
bands of the $j=3/2$ states in combination with the SC pairing. As a
consequence, both systems can be driven into a topological phase hosting MBSs.
Hence, we predict that MBSs form in any SC proximitized Josephson junction on
2D Luttinger materials by the application of magnetic field alone. This opens a
new avenue for the search of topological SC.


title: Pre-main Sequence stars in LH91

abstract:
  We study the accretion properties of pre-main sequence low-mass stars in the
LH 91 association within the Large Magellanic Clouds. Using optical multiband
photometry obtained with the Hubble Space Telescope, we identify 75 candidates
showing H$\alpha$ excess emission above the 3$\sigma$ level with equivalent
width $EW_{H\alpha}$ $\ge$ 10 \AA. We estimate the physical parameters
(effective temperature, luminosity, age, mass, accretion luminosity and mass
accretion rate) of the pre-main sequence stellar candidates. The age
distribution suggests a star formation ranging from few Myr up to $\sim$ 60 Myr
with a gap between $\sim$ 5 Myr and 10 Myr. The masses of the PMS candidates
span from 0.2 $M_{\odot}$ for the cooler objects to 1.0 $M_{\odot}$ with a
median of $\sim$ 0.80 $M_{\odot}$. The median value of the accretion luminosity
of our 75 PMS stars is about 0.12 $L_{\odot}$, the median value of the mass
accretion rate is about 4.8 $\times$ $10^{-9}$ $M_{\odot} yr^{-1}$ with higher
values for the younger population ($\sim$ 1.2 $\times$ $10^{-8}$ $M_{\odot}
yr^{-1}$), and lower values for the older candidates ($\sim$ 4.7 $\times$
$10^{-9}$ $M_{\odot} yr^{-1}$). We compare our results with the findings on LH
95, the closer region to LH 91 for which accretion properties of PMS candidates
were derived. An interesting qualitative outcome is that LH 91 seems to be in a
more evolved stage. Moreover, we find that the PMS candidates are distributed
homogeneously, without any evidence of clumps around more massive stars.


title:
Understanding the Difficulty of Training Physics-Informed Neural
  Networks on Dynamical Systems

abstract:
  Physics-informed neural networks (PINNs) seamlessly integrate data and
physical constraints into the solving of problems governed by differential
equations. In settings with little labeled training data, their optimization
relies on the complexity of the embedded physics loss function. Two fundamental
questions arise in any discussion of frequently reported convergence issues in
PINNs: Why does the optimization often converge to solutions that lack physical
behavior? And why do reduced domain methods improve convergence behavior in
PINNs? We answer these questions by studying the physics loss function in the
vicinity of fixed points of dynamical systems. Experiments on a simple
dynamical system demonstrate that physics loss residuals are trivially
minimized in the vicinity of fixed points. As a result we observe that
solutions corresponding to nonphysical system dynamics can be dominant in the
physics loss landscape and optimization. We find that reducing the
computational domain lowers the optimization complexity and chance of getting
trapped with nonphysical solutions.


title: A Point Mass Proposal Method for Bayesian State-Space Model Fitting

abstract:
  State-space models (SSMs) are often used to model time series data where the
observations depend on an unobserved latent process. However, inference on the
process parameters of an SSM can be challenging, especially when the likelihood
of the data given the parameters is not available in closed form. We focus on
the problem of model fitting within a Bayesian framework, to which a variety of
approaches have been applied, including MCMC using Bayesian data augmentation,
sequential Monte Carlo (SMC) approximation, and particle MCMC algorithms, which
combine SMC approximations and MCMC steps. However, these different methods can
be inefficient because of sample impoverishment in the sequential Monte Carlo
approximations and/or poor mixing in the MCMC steps. In this article, we
propose an approach that borrows ideas from discrete hidden Markov models
(HMMs) to provide an efficient MCMC with data augmentation approach, imputing
the latent states within the algorithm. Our approach deterministically
approximates the SSM by a discrete HMM, which is subsequently used as an MCMC
proposal distribution for the latent states. We demonstrate that the algorithm
provides an efficient alternative approach via two different case studies.


title:
Strong Allee effect synaptic plasticity rule in an unsupervised learning
  environment

abstract:
  Synaptic plasticity or the ability of a brain to changes one or more of its
functions or structures has generated and is sill generating a lot of interest
from the scientific community especially neuroscientists. These interests
especially went into high gear after empirical evidences were collected that
challenged the established paradigm that human brain structures and functions
are set from childhood and only modest changes were expected beyond. Early
synaptic plasticity rules or laws to that regard include the basic Hebbian rule
that proposed a mechanism for strengthening or weakening of synapses (weights)
during learning and memory. This rule however did not account from the fact
that weights must have bounded growth overtime. Thereafter, many other rules
were proposed to complement the basic Hebbian rule and they also possess other
desirable properties. In particular, a desirable property in synaptic
plasticity rule is that the ambient system must account for inhibition which is
often achieved if the rule used allows for a lower bound in synaptic weights.
In this paper, we propose a synaptic plasticity rule inspired from the Allee
effect, a phenomenon often observed in population dynamics. We show properties
such such as synaptic normalization, competition between weights,
de-correlation potential, and dynamic stability are satisfied. We show that in
fact, an Allee effect in synaptic plasticity can be construed as an absence of
plasticity.


title:
Spin-orbit coupling and crystal-field splitting in Ti-doped Ca2RuO4
  studied by ellipsometry

abstract:
  In Ca2RuO4, the competition of spin-orbit coupling $\zeta$ and tetragonal
crystal field splitting $\Delta_{CF}$ has been discussed controversially for
many years. The orbital occupation depends on $\Delta_{CF}/\zeta$, which allows
us to address this ratio via the optical spectral weights of the lowest
intersite Mott-Hubbard excitations. We study the optical conductivity of
Ca$_2$Ru$_{0.99}$Ti$_{0.01}$O$_4$ in the range of 0.75 - 5 eV by ellipsometry,
using the large single crystals that can be grown for small Ti concentrations.
Based on a local multiplet calculation, our analysis results in $2.4 \leq
\Delta_{CF}/\zeta \leq 4$ at 15 K. The dominant crystal field yields a ground
state close to xy orbital order but spin-orbit coupling is essential for a
quantitative description of the properties. Furthermore, we observe a
pronounced decrease of $\Delta_{CF}$ with increasing temperature, as expected
based on the reduction of octahedral distortions.


title:
HYDRA: Competing convolutional kernels for fast and accurate time series
  classification

abstract:
  We demonstrate a simple connection between dictionary methods for time series
classification, which involve extracting and counting symbolic patterns in time
series, and methods based on transforming input time series using convolutional
kernels, namely ROCKET and its variants. We show that by adjusting a single
hyperparameter it is possible to move by degrees between models resembling
dictionary methods and models resembling ROCKET. We present HYDRA, a simple,
fast, and accurate dictionary method for time series classification using
competing convolutional kernels, combining key aspects of both ROCKET and
conventional dictionary methods. HYDRA is faster and more accurate than the
most accurate existing dictionary methods, and can be combined with ROCKET and
its variants to further improve the accuracy of these methods.


title: An introduction to using dual quaternions to study kinematics

abstract:
  We advocate for the use of dual quaternions to represent poses, twists, and
wrenches.


title: Rank-based Non-dominated Sorting

abstract:
  Non-dominated sorting is a computational bottleneck in Pareto-based
multi-objective evolutionary algorithms (MOEAs) due to the runtime-intensive
comparison operations involved in establishing dominance relationships between
solution candidates. In this paper we introduce Rank Sort, a non-dominated
sorting approach exploiting sorting stability and ordinal information to avoid
expensive dominance comparisons in the rank assignment phase. Two algorithmic
variants are proposed: the first one, RankOrdinal (RO), uses ordinal rank
comparisons in order to determine dominance and requires O(N) space; the second
one, RankIntersect (RS), uses set intersections and bit-level parallelism and
requires O(N^2) space. We demonstrate the efficiency of the proposed methods in
comparison with other state of the art algorithms in empirical simulations
using the NSGA2 algorithm as well as synthetic benchmarks. The RankIntersect
algorithm is able to significantly outperform the current state of the art
offering up to 30% speed-up for many objectives. C++ implementations are
provided for all algorithms.


title: Gransformer: Transformer-based Graph Generation

abstract:
  Transformers have become widely used in modern models for various tasks such
as natural language processing and machine vision. This paper, proposes
Gransformer, an algorithm for generating graphs that takes advantage of the
transformer. We extend a simple autoregressive transformer encoder to exploit
the structural information of the graph through efficient modifications. The
attention mechanism is modified to consider the presence or absence of edges
between each pair of nodes. We also introduce a graph-based familiarity measure
that applies to both the attention and the positional coding. This
autoregressive criterion, inspired by message passing algorithms, contains
structural information about the graph. In the output layer, we also use a
masked autoencoder for density estimation to efficiently model the generation
of dependent edges. We also propose a technique to prevent the model from
generating isolated nodes. We evaluate this method on two real-world datasets
and compare it with some state-of-the-art autoregressive graph generation
methods. Experimental results have shown that the proposed method performs
comparative to these methods, including recurrent models and graph
convolutional networks.


title: Sensitivity of a collisional single-atom spin probe

abstract:
  We study the sensitivity of a collisional single-atom probe for ultracold
gases. Inelastic spin-exchange collisions map information about the gas
temperature or external magnetic field onto the quantum spin-population of
single-atom probes. We numerically investigate the steady-state sensitivity of
such single-atom probes to various observables. We find that the probe has
maximum sensitivity when sensing the energy ratio between thermal energy and
Zeeman energy in an externally applied magnetic field, while the sensitivity to
the absolute energy, i.e., the sum of kinetic and Zeeman energy, is low. We
identify the parameters yielding maximum sensitivity for a given absolute
energy, which we can relate to a direct comparison of the thermal
Maxwell-Boltzmann distribution with the Zeeman-energy splitting. We compare our
findings to experimental results from a single-atom quantum probe, showing the
expected sensitivity behavior. Our work thereby yields a microscopic
explanation for the properties and performance of this type of single-atom
quantum probe and connecting thermodynamic properties to microscopic
interaction mechanisms.


title: Derivations and loops of some evolution algebras

abstract:
  In this work we study the space of derivations of non-degenerate evolution
algebras. We improve some results obtained recently in the literature and, as a
consequence, we advance in the description of the derivations for
$n$-dimensional Volterra evolution algebras. In addition, we introduce the
notion of loop of an evolution algebra and we analyze under which conditions
the set of loops is invariant under change of basis.


title: MDsrv -- visual sharing and analysis of molecular dynamics simulations

abstract:
  Molecular dynamics simulation is a proven technique for computing and
visualizing the time-resolved motion of macromolecules at atomic resolution.
The MDsrv is a tool that streams MD trajectories and displays them
interactively in web browsers without requiring advanced skills, facilitating
interactive exploration and collaborative visual analysis. We have now enhanced
the MDsrv to further simplify the upload and sharing of MD trajectories and
improve their online viewing and analysis. With the new instance, the MDsrv
simplifies the creation of sessions, which allows the exchange of MD
trajectories with preset representations and perspectives. An important
innovation is that the MDsrv can now access and visualize trajectories from
remote datasets, which greatly expands its applicability and use, as the data
no longer needs to be accessible on a local server. In addition, initial
analyses such as sequence or structure alignments, distance measurements, or
RMSD calculations have been implemented, which optionally support visual
analysis. Finally, the MDsrv now offers a faster and more efficient
visualization of even large trajectories.


title: No CC-NSI explanation of the Gallium anomaly

abstract: We show that the Gallium anomaly can not be explained by CC-NSI.


title:
Terahertz measurements on subwavelength-size samples down to the
  tunneling limit

abstract:
  For terahertz spectroscopy on single crystals, the wavelength $\lambda$ often
is comparable to the size of the studied samples, emphasizing diffraction
effects. Using a continuous-wave terahertz spectrometer in transmission
geometry, we address the effect of the sample size on the achievable accuracy
of the optical properties, focusing in particular on the phase data. We employ
$\alpha$-lactose monohydrate as a paradigmatic example and compare data that
were measured using apertures with diameters D in the range from 10 mm to 0.2
mm. For small D, strong diffraction typically invalidates a quantitative
analysis of the transmitted amplitude at low frequencies. The phase data,
however, can be evaluated to lower frequency and show a more systematic
dependence on D. For a quantitative analysis, we employ a waveguide picture for
the description of small apertures with a cylindrical bore. For D as small as
0.2 mm, corresponding to 1/D = 50 cm$^{-1}$, a circular waveguide does not
support propagating waves below its cut-off frequency $1/\lambda_c =
\omega_c/2\pi c \approx 29$ cm$^{-1}$. Experimentally, we confirm this cut-off
for cylindrical apertures with a thickness $d_{ap} = 1$ mm. Close to
$\omega_c$, the measured phase velocity is an order of magnitude larger than
$c$, the speed of light in vacuum. The cut-off is washed out if a sample is
mounted on a thin aperture with a conical bore. In this case, the phase data of
$\alpha$-lactose monohydrate for D = 0.2 mm can quantitatively be described
down to about 10 cm$^{-1}$ if the waveguide-like properties of the aperture are
taken into account in the analysis.


title:
Common Failure Modes of Subcluster-based Sampling in Dirichlet Process
  Gaussian Mixture Models -- and a Deep-learning Solution

abstract:
  The Dirichlet Process Gaussian Mixture Model (DPGMM) is often used to cluster
data when the number of clusters is unknown. One main DPGMM inference paradigm
relies on sampling. Here we consider a known state-of-art sampler (proposed by
Chang and Fisher III (2013) and improved by Dinari et al. (2019)), analyze its
failure modes, and show how to improve it, often drastically. Concretely, in
that sampler, whenever a new cluster is formed it is augmented with two
subclusters whose labels are initialized at random. Upon their evolution, the
subclusters serve to propose a split of the parent cluster. We show that the
random initialization is often problematic and hurts the otherwise-effective
sampler. Specifically, we demonstrate that this initialization tends to lead to
poor split proposals and/or too many iterations before a desired split is
accepted. This slows convergence and can damage the clustering. As a remedy, we
propose two drop-in-replacement options for the subcluster-initialization
subroutine. The first is an intuitive heuristic while the second is based on
deep learning. We show that the proposed approach yields better splits, which
in turn translate to substantial improvements in performance, results, and
stability.


title:
Efficient Strong Privacy-Preserving Conjunctive Keyword Search Over
  Encrypted Cloud Data

abstract:
  Searchable symmetric encryption (SSE) supports keyword search over outsourced
symmetrically encrypted data. Dynamic searchable symmetric encryption (DSSE), a
variant of SSE, further enables data updating. Most prior DSSE works primarily
focus on single keyword search, and some have later expanded to support
conjunctive keyword search. These works on conjunctive DSSE primarily consider
forward and backward privacy. Ideally, the server should only learn the result
sets related to all the keywords in the conjunction. However, existing schemes
suffer from keyword pair result pattern (KPRP) leakage, making the server
reveal the partial result sets containing two of the query keywords. We propose
the first DSSE scheme to address the above concerns that achieves strong
privacy-preserving conjunctive keyword search. Specifically, our scheme can
maintain forward and backward privacy and eliminate KPRP leakage, offering a
higher level of security for DSSE. The search complexity is proportional to the
number of documents stored in the database in several existing schemes.
However, the complexity of our scheme scales with the update frequency of the
least frequent keyword in the conjunction, which is much smaller than the size
of the entire database. Besides, we devise a least frequent keyword acquisition
protocol to reduce the frequent interactions between the data owner and search
users. Finally, we analyze the security of our scheme and evaluate its
performance theoretically and experimentally. The results show that our scheme
has strong privacy preservation and efficiency.


title:
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task
  Learning

abstract:
  Multi-task learning (MTL) is a novel framework to learn several tasks
simultaneously with a single shared network where each task has its distinct
personalized header network for fine-tuning. MTL can be implemented in
federated learning settings as well, in which tasks are distributed across
clients. In federated settings, the statistical heterogeneity due to different
task complexities and data heterogeneity due to non-iid nature of local
datasets can both degrade the learning performance of the system. In addition,
tasks can negatively affect each other's learning performance due to negative
transference effects. To cope with these challenges, we propose FedGradNorm
which uses a dynamic-weighting method to normalize gradient norms in order to
balance learning speeds among different tasks. FedGradNorm improves the overall
learning performance in a personalized federated learning setting. We provide
convergence analysis for FedGradNorm by showing that it has an exponential
convergence rate. We also conduct experiments on multi-task facial landmark
(MTFL) and wireless communication system dataset (RadComDynamic). The
experimental results show that our framework can achieve faster training
performance compared to equal-weighting strategy. In addition to improving
training speed, FedGradNorm also compensates for the imbalanced datasets among
clients.


title:
Adjacent Context Coordination Network for Salient Object Detection in
  Optical Remote Sensing Images

abstract:
  Salient object detection (SOD) in optical remote sensing images (RSIs), or
RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to
the difference between optical RSIs and natural scene images (NSIs), directly
applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results.
In this paper, we propose a novel Adjacent Context Coordination Network
(ACCoNet) to explore the coordination of adjacent features in an
encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of
three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and a
decoder. As the key component of ACCoNet, ACCoM activates the salient regions
of output features of the encoder and transmits them to the decoder. ACCoM
contains a local branch and two adjacent branches to coordinate the multi-level
features simultaneously. The local branch highlights the salient regions in an
adaptive way, while the adjacent branches introduce global information of
adjacent levels to enhance salient regions. Additionally, to extend the
capabilities of the classic decoder block (i.e., several cascaded convolutional
layers), we extend it with two bifurcations and propose a
Bifurcation-Aggregation Block to capture the contextual information in the
decoder. Extensive experiments on two benchmark datasets demonstrate that the
proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation
metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and
results of our method are available at https://github.com/MathLee/ACCoNet.


title: Resilience family of receiver operating characteristic curves

abstract:
  A new semiparametric model of the ROC curve based on the resilience family or
proportional reversed hazard family is proposed which is an alternative to the
existing models. The resulting ROC curve and its summary indices (such as area
under the curve (AUC) and Youden index) have simple analytic forms. The partial
likelihood method is applied to estimate the ROC curve. Moreover, the
estimation methodologies of the resilience family of the ROC curve have been
developed based on AUC estimators exploiting Mann-Whitney statistics and the
Rojo approach. A simulation study has been carried out to assess the
performance of all considered estimators. Real data from the American National
Health and Nutrition Examination Survey (NHANES) has been analysed in detail
based on the proposed model and the usual binormal model prevalent in the
literature. Real data in the context of brain injury-related biomarkers is also
analysed in order to compare our model with the Lehmann family of the ROC
curves. Finally, we show that the proposed model may be applicable in the
misspecification scenario through a Ducheme muscular dystrophy data.


title: A more flexible counterpart of a Huang-Kotz's copula-type

abstract:
  We propose a more flexible symmetric counterpart of the Huang-Kotz's copula
of the 1st type. Both the counterpart and Huang-Kotz's copula of the 1st type
provide the same improvement of the correlation level. Moreover, the proposed
copula includes special cases of many other extensions of the
Farlie-Gumbel-Morgenstern (FGM) copula.


title:
Complete cosmological evolution model of a classical scalar field with a
  Higgs potential. III. Features of phase trajectory flows

abstract:
  The flows of phase trajectories of cosmological models based on the vacuum
classical Higgs field and their behavior on the Einstein-Higgs surface near
singular points of a dynamical system are investigated by numerical simulation.
The influence of the singular points on the process of phase flow splitting is
shown and the efficiency of the Einstein-Higgs hypersurface application is
demonstrated.


title:
Lack of temperature dependence for the filling of the metallic-like
  bands in Si(553)-Au

abstract:
  We study the temperature dependence of the metallic-like bands of Si(553)-Au
by angular-resolved photoemission spectroscopy (ARPES). First, we define a
protocol for dealing with the short-term stability due to surface contamination
and the effect of photo-voltage. After that, we extract the changes in the
band-filling and Fermi-velocity. We conclude that both quantities change only
weakly with temperature. This lack of temperature dependence can be compared to
currently proposed models for the temperature evolution of the electronic
structure of Si(553)-Au.


title: The generalized Saint Venant operator and integral moment transforms

abstract:
  In this article, we work with a generalized Saint Venant operator introduced
by Vladimir Sharafutdinov to describe the kernel of the integral moment
transforms over symmetric m-tensor fields in n-dimensional Euclidean space. We
also provide an equivalence between the injectivity question for the integral
moment transforms and the generalized Saint Venant operator over symmetric
tensor fields of the Schwartz class.


title:
Comment on "Hysteretic transition between states of a filled hexagonal
  magnetic dipole cluster"

abstract:
  In the paper "Andrew D.P. Smith, Peter T. Haugen, Boyd F. Edwards: Hysteretic
transition between states of a filled hexagonal magnetic dipole cluster,
Journal of Magnetism and Magnetic Materials 549 (2022): 168991" a hysteretic
transition between two stable arrangements of a cluster of seven dipoles is
presented. The relative strength of the center dipole in a hexagonal
arrangement serves as the bifurcation parameter. The authors clearly
demonstrate the existence of two instabilities accompanied by discontinuous
jumps of the dipole arrangement, but leave the question about the nature of
these instabilities unanswered. This comment clarifies the nature of the two
instabilities: the first one is a symmetry-breaking sub-critical bifurcation
with parabolic scaling of the magnetic potential energy difference between the
two branches, and the second one is a fold with its characteristic scaling in
the form of a semi-cubic parabola.


title:
Flexible development and evaluation of machine-learning-supported
  optimal control and estimation methods via HILO-MPC

abstract:
  Model-based optimization approaches for monitoring and control, such as model
predictive control and optimal state and parameter estimation, have been used
for decades in many engineering applications. Models describing the dynamics,
constraints, and desired performance criteria are fundamental to model-based
approaches. Thanks to recent technological advancements in digitalization,
machine learning methods such as deep learning, and computing power, there has
been an increasing interest in using machine learning methods alongside
model-based approaches for control and estimation. The number of new methods
and theoretical findings using machine learning for model-based control and
optimization is increasing rapidly. This paper outlines the basic ideas and
principles behind an easy-to-use Python toolbox that allows to quickly and
efficiently solve machine-learning-supported optimization, model predictive
control, and estimation problems. The toolbox leverages state-of-the-art
machine learning libraries to train components used to define the problem. It
allows to efficiently solve the resulting optimization problems. Machine
learning can be used for a broad spectrum of tasks, ranging from model
predictive control for stabilization, setpoint tracking, path following, and
trajectory tracking to moving horizon estimation and Kalman filtering. For
linear systems, it enables quick code generation for embedded MPC applications.
HILO-MPC is flexible and adaptable, making it especially suitable for research
and fundamental development tasks. Due to its simplicity and numerous already
implemented examples, it is also a powerful teaching tool. The usability is
underlined, presenting a series of application examples.


title: A triple coproduct of curves and knots

abstract:
  We introduce two kinds of invariants: one for stable equivalence classes of
curves on surfaces and another for long virtual knots; these are based on a
triple coproduct of curves on surfaces. It is a counterpart of a double
coproduct, known as Turaev cobracket, which induces the affine index
polynomial. We also introduce analogues of the Milnor's triple linking number.


title:
Ode to Legendre: Geometric and thermodynamic implications on curved
  statistical manifolds

abstract:
  The recent link discovered between generalized Legendre transforms and curved
(i.e. non-Euclidean) statistical manifolds suggests a fundamental reason behind
the ubiquity of R\'enyi's divergence and entropy in a wide range of physical
phenomena. However, these early findings still provide little intuition on the
nature of this relationship and its implications for physical systems. Here we
shed new light on the Legendre transform by revealing the consequences of its
deformation via symplectic geometry, complexification, and stochastic
thermodynamics. These findings reveal a novel common framework that leads to a
principled and unified understanding of physical systems that are not
well-described by classic information-theoretic quantities.


title: Dense Continuous-Time Optical Flow from Events and Frames

abstract:
  We present a method for estimating dense continuous-time optical flow.
Traditional dense optical flow methods compute the pixel displacement between
two images. Due to missing information, these approaches cannot recover the
pixel trajectories in the blind time between two images. In this work, we show
that it is possible to compute per-pixel, continuous-time optical flow by
additionally using events from an event camera. Events provide temporally
fine-grained information about movement in image space due to their
asynchronous nature and microsecond response time. We leverage these benefits
to predict pixel trajectories densely in continuous-time via parameterized
B\'ezier curves. To achieve this, we introduce multiple innovations to build a
neural network with strong inductive biases for this task: First, we build
multiple sequential correlation volumes in time using event data. Second, we
use B\'ezier curves to index these correlation volumes at multiple timestamps
along the trajectory. Third, we use the retrieved correlation to update the
B\'ezier curve representations iteratively. Our method can optionally include
image pairs to boost performance further. The proposed approach outperforms
existing image-based and event-based methods by 11.5 % lower EPE on DSEC-Flow.
Finally, we introduce a novel synthetic dataset MultiFlow for pixel trajectory
regression on which our method is currently the only successful approach.


title:
On the performance of preconditioned methods to solve \(L^p\)-norm phase
  unwrapping

abstract:
  In this paper, we analyze and evaluate suitable preconditioning techniques to
improve the performance of the $L^p$-norm phase unwrapping method. We consider
five preconditioning techniques commonly found in the literature, and analyze
their performance with different sizes of wrapped-phase maps. Keywords.- Phase
unwrapping, $L^p$-norm based method, Preconditioning techniques.


title: Differential and Integral Calculus of Sequence

abstract:
  We create a sequence version of calculus. First, we define equivalence, some
fundamental operations, differential, and integral for sequences. Then, we
propose sequence versions of identity function, power function, exponential
function, hyperbolic function, trigonometric function, and also find sequence
versions of the Maclaurin series for them. The sequence versions of exponential
function involve divergent series including Grandi's series. By using this
framework, we find a sequence version of the binomial theorem and Euler's
identity. In addition, we design new formalisms of Fibonacci sequence and its
generalizations. Last, we propose a sequence dual of factorial and Bell number,
and find sequence dual of modular property of factorial concerning prime number
(Wilson's theorem) and of Bell number concerning prime number.


title: Simplicial structures in ecological networks

abstract:
  An ecological network is a formal representation of a specific type of
interaction in a corresponding ecosystem. Such networks have traditionally been
modelled as encoding exclusively pairwise interactions among the fundamental
units of ecosystems and have been represented and analysed using
graph-theoretic methods. However, many real-world ecosystems may entertain
non-binary, polyadic relations between their units, which cannot be captured by
the pairwise interaction methods, but require higher-order interaction
framework, and consequently the corresponding ecological networks cannot be
modelled using graph-theoretic framework. This work gives a structural
definition of ecological network suitable for modelling all orders of
interactions between the fundamental units of the corresponding ecological
system, including and going beyond the pairwise interaction framework. Carbon
mediation between units of some select ecosystems are studied by modelling the
corresponding ecological networks as simplicial complexes following the
definition. The concept of graph centrality measure has been extended to
simplicial centrality, and some important centrality measures of these networks
at various structural levels of the complexes have been calculated. The
centrality measures reveal valuable structural information including
information about those vertices that are more likely to participate in
higher-order interactions, as well as inform whether there is a difference in
the ranks of vertices for these higher-order networks based on graph centrality
and simplicial centrality measures.


title:
LQoCo: Learning to Optimize Cache Capacity Overloading in Storage
  Systems

abstract:
  Cache plays an important role to maintain high and stable performance (i.e.
high throughput, low tail latency and throughput jitter) in storage systems.
Existing rule-based cache management methods, coupled with engineers' manual
configurations, cannot meet ever-growing requirements of both time-varying
workloads and complex storage systems, leading to frequent cache overloading.
In this paper, we for the first time propose a light-weight learning-based
cache bandwidth control technique, called \LQoCo which can adaptively control
the cache bandwidth so as to effectively prevent cache overloading in storage
systems. Extensive experiments with various workloads on real systems show that
LQoCo, with its strong adaptability and fast learning ability, can adapt to
various workloads to effectively control cache bandwidth, thereby significantly
improving the storage performance (e.g. increasing the throughput by 10\%-20\%
and reducing the throughput jitter and tail latency by 2X-6X and 1.5X-4X,
respectively, compared with two representative rule-based methods).


title:
Oedometric compression of a granular material: computation of energies
  involved during breakage with a discrete element modelling

abstract:
  A numerical model able to simulate the grain breakage with the discrete
element method, using the "Non-Smooth Contact Dynamics" is presented. The model
reproduces 3D grains having complex shapes and is tested in single grain and in
oedometric compressions. Numerical simulations are then carried out to evaluate
the different energies active during breakage (surface creation and
redistribution energies). The surface creation energy is estimated. Results are
closed to the ones found in the literature.


title:
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19
  Segmentation

abstract:
  Chest Computational Tomography (CT) scans present low cost, speed and
objectivity for COVID-19 diagnosis and deep learning methods have shown great
promise in assisting the analysis and interpretation of these images. Most
hospitals or countries can train their own models using in-house data, however
empirical evidence shows that those models perform poorly when tested on new
unseen cases, surfacing the need for coordinated global collaboration. Due to
privacy regulations, medical data sharing between hospitals and nations is
extremely difficult. We propose a GAN-augmented federated learning model,
dubbed ST-FL (Style Transfer Federated Learning), for COVID-19 image
segmentation. Federated learning (FL) permits a centralised model to be learned
in a secure manner from heterogeneous datasets located in disparate private
data silos. We demonstrate that the widely varying data quality on FL client
nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image
segmentation. ST-FL is a novel FL framework that is robust in the face of
highly variable data quality at client nodes. The robustness is achieved by a
denoising CycleGAN model at each client of the federation that maps arbitrary
quality images into the same target quality, counteracting the severe data
variability evident in real-world FL use-cases. Each client is provided with
the target style, which is the same for all clients, and trains their own
denoiser. Our qualitative and quantitative results suggest that this FL model
performs comparably to, and in some cases better than, a model that has
centralised access to all the training data.


title:
Spatially Resolved Mass-Metallicity Relation at z~0.26 from MUSE-Wide
  Survey

abstract:
  Aims: There is a spatially resolved star-forming main sequence (rSFMS) and
mass-metallicity relation (rMZR) of galaxies in local universe. We know that
the global mass-metallicity relation (MZR) results from the integral of rMZR,
and it will evolve with the redshift. However, the evolution of rMZR with
redshift is still unclear due to the low spatial resolution and signal-to-noise
ratio. There are currently too few observations beyond local universe, and only
simulations can reproduce the evolution of rMZR with redshift. Methods: In this
work, we select ten emission-line galaxies with an average redshift of $z\sim
0.26$ from MUSE-Wide DR1. We obtain the spatially resolved star formation rate
(SFR) and metallicity from the integral field spectroscopy (IFS), as well as
the stellar mass surface density from the 3D-HST photometry. We derive the
rSFMS and rMZR at $z\sim 0.26$ and compare them with local galaxies. Results:
We find the rSFMS of galaxies at $z\sim 0.26$ has a slope of $\sim$0.771. The
rMZR exists at $z\sim 0.26$, showing a similar shape to the local universe but
a lower average metallicity about $\sim$0.11 dex than the local one. In
addition, we also study their spatially resolved fundamental metallicity
relation (rFMR). However, there is no obvious evidence that rFMR exists at
$z\sim$0.26 and it is not an extension of rMZR at a high SFR. Conclusions:
Similar to their global versions, the rSFMS and rMZR of galaxies also evolve
with redshift. Given the fixed stellar mass, galaxies at higher redshift show
higher SFR and lower metallicity. These suggest that the evolution of the
global galaxy properties with redshift may result from integrating the
evolution of spatially resolved properties of galaxies.


title:
Data-driven reduced order modelling for patient-specific hemodynamics of
  coronary artery bypass grafts with physical and geometrical parameters

abstract:
  In this work the development of a machine learning-based Reduced Order Model
(ROM) for the investigation of hemodynamics in a patient-specific configuration
of Coronary Artery Bypass Graft (CABG) is proposed. The computational domain is
referred to left branches of coronary arteries when a stenosis of the Left Main
Coronary Artery (LMCA) occurs. The method extracts a reduced basis space from a
collection of high-fidelity solutions via a Proper Orthogonal Decomposition
(POD) algorithm and employs Artificial Neural Networks (ANNs) for the
computation of the modal coefficients. The Full Order Model (FOM) is
represented by the incompressible Navier-Stokes equations discretized using a
Finite Volume (FV) technique. Both physical and geometrical parametrization are
taken into account, the former one related to the inlet flow rate and the
latter one related to the stenosis severity. With respect to the previous works
focused on the development of a ROM framework for the evaluation of coronary
artery disease, the novelties of our study include the use of the FV method in
a patient-specific configuration, the use of a data-driven ROM technique and
the mesh deformation strategy based on a Free Form Deformation (FFD) technique.
The performance of our ROM approach is analyzed in terms of the error between
full order and reduced order solutions as well as the speedup achieved at the
online stage.


title: Overgroups of elementary groups in polyvector representations

abstract:
  We initiate the study of subgroups $H$ of the general linear group
$GL_{\binom{n}{m}}(R)$ over a commutative ring $R$ that contain the $m$-th
exterior power of an elementary group $\bigwedge^mE_n(R)$. Each such group $H$
corresponds to a uniquely defined level $(A_0,\dots,A_{m-1})$, where
$A_0,\dots,A_{m-1}$ are ideals of $R$ with certain relations. In the crucial
case of the exterior squares, we state the subgroup lattice to be standard. In
other words, for $\bigwedge^2E_n(R)$ all intermediate subgroups $H$ are
parametrized by a single ideal of the ring $R$. Moreover, we characterize
$\bigwedge^mGL_n(R)$ as the stabilizer of a system of invariant forms. This
result is classically known for algebraically closed fields, here we prove the
corresponding group scheme to be smooth over $\mathbb{Z}$. So the last result
holds over arbitrary commutative rings.


title:
Nonideal self-gravity and cosmology: the importance of correlations in
  the dynamics of the large-scale structures of the Universe

abstract:
  Inspired by the role of correlations in the statistical mechanics of nonideal
self-interacting fluids, we suggest that unresolved sub-structures (i.e.
correlations) have to be taken into account in the Virial theorem of
self-gravitating astrophysical systems. We demonstrate that their omission
leads to a missing mass problem by using the semi-analytic polytropic solutions
of the Lane-Emden equation. This problem suggests to extend the Friedmann
equations to the nonideal regime by taking into account correlations in the
dynamics of the expansion. The increase of correlations induced by the
formation of the large-scale structures could explain naturally the accelerated
expansion of the Universe in such a paradigm.


title: Learning to Mediate Disparities Towards Pragmatic Communication

abstract:
  Human communication is a collaborative process. Speakers, on top of conveying
their own intent, adjust the content and language expressions by taking the
listeners into account, including their knowledge background, personalities,
and physical capabilities. Towards building AI agents with similar abilities in
language communication, we propose Pragmatic Rational Speaker (PRS), a
framework extending Rational Speech Act (RSA). The PRS attempts to learn the
speaker-listener disparity and adjust the speech accordingly, by adding a
light-weighted disparity adjustment layer into working memory on top of
speaker's long-term memory system. By fixing the long-term memory, the PRS only
needs to update its working memory to learn and adapt to different types of
listeners. To validate our framework, we create a dataset that simulates
different types of speaker-listener disparities in the context of referential
games. Our empirical results demonstrate that the PRS is able to shift its
output towards the language that listener are able to understand, significantly
improve the collaborative task outcome.


title: Image Compression and Actionable Intelligence With Deep Neural Networks

abstract:
  If a unit cannot receive intelligence from a source due to external factors,
we consider them disadvantaged users. We categorize this as a preoccupied unit
working on a low connectivity device on the edge. This case requires that we
use a different approach to deliver intelligence, particularly satellite
imagery information, than normally employed. To address this, we propose a
survey of information reduction techniques to deliver the information from a
satellite image in a smaller package. We investigate four techniques to aid in
the reduction of delivered information: traditional image compression, neural
network image compression, object detection image cutout, and image to caption.
Each of these mechanisms have their benefits and tradeoffs when considered for
a disadvantaged user.


title: Chain-based Discriminative Autoencoders for Speech Recognition

abstract:
  In our previous work, we proposed a discriminative autoencoder (DcAE) for
speech recognition. DcAE combines two training schemes into one. First, since
DcAE aims to learn encoder-decoder mappings, the squared error between the
reconstructed speech and the input speech is minimized. Second, in the code
layer, frame-based phonetic embeddings are obtained by minimizing the
categorical cross-entropy between ground truth labels and predicted
triphone-state scores. DcAE is developed based on the Kaldi toolkit by treating
various TDNN models as encoders. In this paper, we further propose three new
versions of DcAE. First, a new objective function that considers both
categorical cross-entropy and mutual information between ground truth and
predicted triphone-state sequences is used. The resulting DcAE is called a
chain-based DcAE (c-DcAE). For application to robust speech recognition, we
further extend c-DcAE to hierarchical and parallel structures, resulting in
hc-DcAE and pc-DcAE. In these two models, both the error between the
reconstructed noisy speech and the input noisy speech and the error between the
enhanced speech and the reference clean speech are taken into the objective
function. Experimental results on the WSJ and Aurora-4 corpora show that our
DcAE models outperform baseline systems.


title:
ARPES in strongly disordered systems -- theory of electronic bands
  melting

abstract:
  It is well known that translational symmetry-breaking disorder will disrupt
ARPES spectra up to the point where they become invisible. However, a
theoretical framework to capture this phenomenon has been largely missing.
Here, based on a rigorous theory of the ARPES process, we provide this
much-needed framework. In particular, we show how the frequently used sudden
electron approximation has to be modified in this situation. Our main result is
an argument that links the photoemission line broadening with an operator
content of a disorder operator and so with the criticality of the corresponding
order-disorder phase transition. For concreteness, we focus here on the
frustrated 2D trigonal (pseudo-)spin model, with Ising order-disorder operators
behind the transition. Still, our formalism is general and can be applied in a
much broader context.


title: Bloch points in nanostrips

abstract:
  Complex magnetic materials hosting topologically non-trivial particle-like
objects such as skyrmions are under intensive research and could fundamentally
change the way we store and process data. One important class of materials are
helimagnetic materials with Dzyaloshinskii-Moriya interaction. Recently, it was
demonstrated that nanodisks consisting of two layers with opposite chirality
can host a single stable Bloch point of two different types at the interface
between the layers. Using micromagnetic simulations we show that FeGe
nanostrips consisting of two layers with opposite chirality can host multiple
coexisting Bloch points in an arbitrary combination of the two different types.
We show that the number of Bloch points that can simultaneously coexist depends
on the strip geometry and the type of the individual Bloch points. Our
simulation results allow us to predict strip geometries suitable for an
arbitrary number of Bloch points. We show an example of an 80-Bloch-point
configuration verifying the prediction.


title:
Quantitative unique continuation for elasticity and Maxwell systems with
  application to the kinematic inverse rupture problem

abstract:
  We obtain explicit estimates on the stability of the unique continuation for
a linear system of hyperbolic equations. In particular our result applies to
the elasticity and Maxwell systems. As an application, we study the kinematic
inverse rupture problem of determining the jump in displacement and the
friction force at the rupture surface, and we obtain new features on the stable
unique continuation up to the rupture surface.


title: The TerraByte Client: providing access to terabytes of plant data

abstract:
  In this paper we demonstrate the TerraByte Client, a software to download
user-defined plant datasets from a data portal hosted at Compute Canada. To
that end the client offers two key functionalities: (1) It allows the user to
get an overview on what data is available and a quick way to visually check
samples of that data. For this the client receives the results of queries to a
database and displays the number of images that fulfill the search criteria.
Furthermore, a sample can be downloaded within seconds to confirm that the data
suits the user's needs. (2) The user can then download the specified data to
their own drive. This data is prepared into chunks server-side and sent to the
user's end-system, where it is automatically extracted into individual files.
The first chunks of data are available for inspection after a brief waiting
period of a minute or less depending on available bandwidth and type of data.
The TerraByte Client has a full graphical user interface for easy usage and
uses end-to-end encryption. The user interface is built on top of a low-level
client. This architecture in combination of offering the client program
open-source makes it possible for the user to develop their own user interface
or use the client's functionality directly. An example for direct usage could
be to download specific data on demand within a larger application, such as
training machine learning models.


title:
Bisimulations for Verifying Strategic Abilities with an Application to
  the ThreeBallot Voting Protocol

abstract:
  We propose a notion of alternating bisimulation for strategic abilities under
imperfect information. The bisimulation preserves formulas of ATL$^*$ for both
the {\em objective} and {\em subjective} variants of the state-based semantics
with imperfect information, which are commonly used in the modeling and
verification of multi-agent systems. Furthermore, we apply the theoretical
result to the verification of coercion-resistance in the ThreeBallot voting
system, a voting protocol that does not use cryptography. In particular, we
show that natural simplifications of an initial model of the protocol are in
fact bisimulations of the original model, and therefore satisfy the same
ATL$^*$ properties, including coercion-resistance. These simplifications allow
the model-checking tool MCMAS to terminate on models with a larger number of
voters and candidates, compared with the initial model.


title: UKP-SQUARE: An Online Platform for Question Answering Research

abstract:
  Recent advances in NLP and information retrieval have given rise to a diverse
set of question answering tasks that are of different formats (e.g.,
extractive, abstractive), require different model architectures (e.g.,
generative, discriminative), and setups (e.g., with or without retrieval).
Despite having a large number of powerful, specialized QA pipelines (which we
refer to as Skills) that consider a single domain, model or setup, there exists
no framework where users can easily explore and compare such pipelines and can
extend them according to their needs. To address this issue, we present
UKP-SQUARE, an extensible online QA platform for researchers which allows users
to query and analyze a large collection of modern Skills via a user-friendly
web interface and integrated behavioural tests. In addition, QA researchers can
develop, manage, and share their custom Skills using our microservices that
support a wide range of models (Transformers, Adapters, ONNX), datastores and
retrieval techniques (e.g., sparse and dense). UKP-SQUARE is available on
https://square.ukp-lab.de.


title:
Implicit Neural Representations for Variable Length Human Motion
  Generation

abstract:
  We propose an action-conditional human motion generation method using
variational implicit neural representations (INR). The variational formalism
enables action-conditional distributions of INRs, from which one can easily
sample representations to generate novel human motion sequences. Our method
offers variable-length sequence generation by construction because a part of
INR is optimized for a whole sequence of arbitrary length with temporal
embeddings. In contrast, previous works reported difficulties with modeling
variable-length sequences. We confirm that our method with a Transformer
decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC
datasets in terms of realism and diversity of generated motions. Surprisingly,
even our method with an MLP decoder consistently outperforms the
state-of-the-art Transformer-based auto-encoder. In particular, we show that
variable-length motions generated by our method are better than fixed-length
motions generated by the state-of-the-art method in terms of realism and
diversity.


title: NLO JIMWLK evolution with massive quarks

abstract:
  NLO evolution of the Jalilian-Marian-Iancu-McLerran-Weigert-Leonidov-Kovner
(JIMWLK) equation with massless quarks was derived a few years ago. We make a
step further to compute the evolution kernels focusing on the effects due to
finite quark masses. To this goal, the light-cone wave function of a fast
moving dilute hadronic projectile is computed up to ${\cal O}(g^3)$ in QCD
coupling constant. Compared with the massless case, a new IR divergence
emerges, which is eventually canceled by a mass dependent counter term. Our
results extend the theoretical tools used in physics of gluon saturation and
aim at improving precision in future phenomenological applications.


title: Speech-enhanced and Noise-aware Networks for Robust Speech Recognition

abstract:
  Compensation for channel mismatch and noise interference is essential for
robust automatic speech recognition. Enhanced speech has been introduced into
the multi-condition training of acoustic models to improve their generalization
ability. In this paper, a noise-aware training framework based on two cascaded
neural structures is proposed to jointly optimize speech enhancement and speech
recognition. The feature enhancement module is composed of a multi-task
autoencoder, where noisy speech is decomposed into clean speech and noise. By
concatenating its enhanced, noise-aware, and noisy features for each frame, the
acoustic-modeling module maps each feature-augmented frame into a triphone
state by optimizing the lattice-free maximum mutual information and cross
entropy between the predicted and actual state sequences. On top of the
factorized time delay neural network (TDNN-F) and its convolutional variant
(CNN-TDNNF), both with SpecAug, the two proposed systems achieve word error
rate (WER) of 3.90% and 3.55%, respectively, on the Aurora-4 task. Compared
with the best existing systems that use bigram and trigram language models for
decoding, the proposed CNN-TDNNF-based system achieves a relative WER reduction
of 15.20% and 33.53%, respectively. In addition, the proposed CNN-TDNNF-based
system also outperforms the baseline CNN-TDNNF system on the AMI task.


title:
KM3NeT upper bounds of detection rates of solar neutrinos from
  annihilations of dark matter at the solar core

abstract:
  The Weakly Interacting Massive Particles (WIMPs) so far remain one of the
most popular candidates for dark matter. If captured gravitationally inside the
core of the Sun, these WIMPs may produce high energy neutrinos as the end
product in case they undergo self annihilations at the solar core. In this
work, we address the detectability of such neutrinos at the proposed KM3NeT
detector. Upper bounds of the detection rate for such neutrinos at KM3NeT are
computed for the case of a generic dark matter scenario and also when specific
models for particle dark matter are chosen. In this work, upper bounds of muon
event rates for different annihilating dark matter masses are computed for each
of the cases of dark matter annihilation channels (e.g. $b\bar{b}~, W^+W^-,
Z\bar{Z} $ etc). These upper bounds are also computed by considering the dark
matter scattering cross-section using upper bounds obtained from XENON1T direct
dark matter search experiment.


title: HST/WFC3 transmission spectroscopy of the cold rocky planet TRAPPIST-1h

abstract:
  TRAPPIST-1 is a nearby ultra-cool dwarf star transited by seven rocky
planets. We observed three transits of its outermost planet, TRAPPIST-1h, using
the G141 grism of the Wide Field Camera 3 instrument aboard the Hubble Space
Telescope to place constraints on its potentially cold atmosphere. In order to
deal with the effect of stellar contamination, we model TRAPPIST-1 active
regions as portions of a cooler and a hotter photosphere, and generate
multi-temperature models that we compare to the out-of-transit spectrum of the
star. Using the inferred spot parameters, we produce corrected transmission
spectra for planet h under five transit configurations and compare these data
to planetary atmospheric transmission models using the forward model CHIMERA.
Our analysis reveals that TRAPPIST-1h is unlikely to host an aerosol-free
H/He-dominated atmosphere. While the current data precision limits the
constraints we can put on the planetary atmosphere, we find that the likeliest
scenario is that of a flat, featureless transmission spectrum in the WFC3/G141
bandpass due to a high mean molecular weight atmosphere (>1000x solar), no
atmosphere, or an opaque aerosol layer, all in absence of stellar
contamination. This work outlines the limitations of modeling active
photospheric regions with theoretical stellar spectra, and those brought by our
lack of knowledge of the photospheric structure of ultracool dwarf stars.
Further characterization of the planetary atmosphere of TRAPPIST-1h would
require higher precision measurements over wider wavelengths, which will be
possible with the James Webb Space Telescope.


title: Unsupervised Image Deraining: Optimization Model Driven Deep CNN

abstract:
  The deep convolutional neural network has achieved significant progress for
single image rain streak removal. However, most of the data-driven learning
methods are full-supervised or semi-supervised, unexpectedly suffering from
significant performance drops when dealing with real rain. These data-driven
learning methods are representative yet generalize poor for real rain. The
opposite holds true for the model-driven unsupervised optimization methods. To
overcome these problems, we propose a unified unsupervised learning framework
which inherits the generalization and representation merits for real rain
removal. Specifically, we first discover a simple yet important domain
knowledge that directional rain streak is anisotropic while the natural clean
image is isotropic, and formulate the structural discrepancy into the energy
function of the optimization model. Consequently, we design an optimization
model-driven deep CNN in which the unsupervised loss function of the
optimization model is enforced on the proposed network for better
generalization. In addition, the architecture of the network mimics the main
role of the optimization models with better feature representation. On one
hand, we take advantage of the deep network to improve the representation. On
the other hand, we utilize the unsupervised loss of the optimization model for
better generalization. Overall, the unsupervised learning framework achieves
good generalization and representation: unsupervised training (loss) with only
a few real rainy images (input) and physical meaning network (architecture).
Extensive experiments on synthetic and real-world rain datasets show the
superiority of the proposed method.


title: Viterbo's spectral bound conjecture for homogeneous spaces

abstract:
  We prove a conjecture of Viterbo about the spectral distance on the space of
compact exact Lagrangian submanifolds of a cotangent bundle $T^*M$ in the case
where $M$ is a compact homogeneous space: if such a Lagrangian submanifold is
contained in the unit ball bundle of $T^*M$, its spectral distance to the zero
section is uniformly bounded. This also holds for some immersed Lagrangian
submanifolds if we take into account the length of the maximal Reeb chord.


title:
Sobolev-Gaffney type inequalities for differential forms on
  sub-Riemannian contact manifolds with bounded geometry

abstract:
  In this paper we establish a Gaffney type inequality, in $W^{\ell,p}$-Sobolev
spaces, for differential forms on sub-Riemannian contact manifolds without
boundary, having bounded geometry (hence, in particular, we have in mind
non-compact manifolds). Here $p\in]1,\infty[$ and $\ell=1,2$ depending on the
order of the differential form we are considering. The proof relies on the
structure of the Rumin's complex of differential forms in contact manifolds, on
a Sobolev-Gaffney inequality proved by Baldi-Franchi in the setting of the
Heisenberg groups and on some geometric properties that can be proved for
sub-Riemannian contact manifolds with bounded geometry.


title:
Dynamics of a colloidal particle coupled to a Gaussian field: from a
  confinement-dependent to a non-linear memory

abstract:
  The effective dynamics of a colloidal particle immersed in a complex medium
is often described in terms of an overdamped linear Langevin equation for its
velocity with a memory kernel which determines the effective (time-dependent)
friction and the correlations of fluctuations. Recently, it has been shown in
experiments and numerical simulations that this memory may depend on the
possible optical confinement the particle is subject to, suggesting that this
description does not capture faithfully the actual dynamics of the colloid,
even at equilibrium. Here, we propose a different approach in which we model
the medium as a Gaussian field linearly coupled to the colloid. The resulting
effective evolution equation of the colloidal particle features a non-linear
memory term which extends previous models and which explains qualitatively the
experimental and numerical evidence in the presence of confinement. This
non-linear term is related to the correlations of the effective noise via a
novel fluctuation-dissipation relation which we derive.


title:
Are Quantum-Classical Hybrids compatible with Ontological Cellular
  Automata?

abstract:
  Based on the concept of ontological states and their dynamical evolution by
permutations, as assumed in the Cellular Automaton Interpretation (CAI) of
quantum mechanics, we address the issue whether quantum-classical hybrids can
be described consistently in this framework. We consider chains of 'classical'
two-state Ising spins and their discrete deterministic dynamics as an
ontological model with an unitary evolution operator generated by pair exchange
interactions. A simple error mechanism is identified, which turns them into
quantum mechanical objects, chains of qubits. Consequently, an interaction
between a quantum mechanical and a 'classical' chain can be introduced and its
consequences for this quantum-classical hybrid be studied. We find that such
hybrid character of composites, generally, does not persist under interactions
and, therefore, cannot be upheld consistently, or even as a fundamental notion
as in Kopenhagen interpretation, within CAI.


title:
Clustering Aided Weakly Supervised Training to Detect Anomalous Events
  in Surveillance Videos

abstract:
  Formulating learning systems for the detection of real-world anomalous events
using only video-level labels is a challenging task mainly due to the presence
of noisy labels as well as the rare occurrence of anomalous events in the
training data. We propose a weakly supervised anomaly detection system which
has multiple contributions including a random batch selection mechanism to
reduce inter-batch correlation and a normalcy suppression block which learns to
minimize anomaly scores over normal regions of a video by utilizing the overall
information available in a training batch. In addition, a clustering loss block
is proposed to mitigate the label noise and to improve the representation
learning for the anomalous and normal regions. This block encourages the
backbone network to produce two distinct feature clusters representing normal
and anomalous events. Extensive analysis of the proposed approach is provided
using three popular anomaly detection datasets including UCF-Crime,
ShanghaiTech, and UCSD Ped2. The experiments demonstrate a superior anomaly
detection capability of our approach.


title: Anchoring Code Understandability Evaluations Through Task Descriptions

abstract:
  In code comprehension experiments, participants are usually told at the
beginning what kind of code comprehension task to expect. Describing experiment
scenarios and experimental tasks will influence participants in ways that are
sometimes hard to predict and control. In particular, describing or even
mentioning the difficulty of a code comprehension task might anchor
participants and their perception of the task itself. In this study, we
investigated in a randomized, controlled experiment with 256 participants (50
software professionals and 206 computer science students) whether a hint about
the difficulty of the code to be understood in a task description anchors
participants in their own code comprehensibility ratings. Subjective code
evaluations are a commonly used measure for how well a developer in a code
comprehension study understood code. Accordingly, it is important to understand
how robust these measures are to cognitive biases such as the anchoring effect.
Our results show that participants are significantly influenced by the initial
scenario description in their assessment of code comprehensibility. An initial
hint of hard to understand code leads participants to assess the code as harder
to understand than participants who received no hint or a hint of easy to
understand code. This affects students and professionals alike. We discuss
examples of design decisions and contextual factors in the conduct of code
comprehension experiments that can induce an anchoring effect, and recommend
the use of more robust comprehension measures in code comprehension studies to
enhance the validity of results.


title: Some Examples of Bicrossed Products with the Rapid Decay Property

abstract:
  We consider bicrossed products obtained by twisting compact semi-direct
products by a suitble finite subgroup. We give a practical criterion for the
rapid decay property and polynomial growth of the dual of such bicrossed
products under a mild restriction. Using this theory, we construct concrete new
examples of discrete quantum groups possessing the rapid decay property but do
not grow polynomially.


title: On a fourth order equation describing single-component film models

abstract:
  We study existence results for a fourth order problem describing
single-component film models assuming initial data in Wiener spaces and without
any smallness condition.


title:
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search
  Space Quantization

abstract:
  Black-box functions are broadly used to model complex problems that provide
no explicit information but the input and output. Despite existing studies of
black-box function optimization, the solution set satisfying an inequality with
a black-box function plays a more significant role than only one optimum in
many practical situations. Covering as much as possible of the solution set
through limited evaluations to the black-box objective function is defined as
the Black-Box Coverage (BBC) problem in this paper. We formalized this problem
in a sample-based search paradigm and constructed a coverage criterion with
Confusion Matrix Analysis. Further, we propose LAMBDA (Latent-Action
Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA
can focus around the solution set quickly by recursively partitioning the
search space into accepted and rejected sub-spaces. Compared with La-MCTS,
LAMBDA introduces density information to overcome the sampling bias of
optimization and obtain more exploration. Benchmarking shows, LAMBDA achieved
state-of-the-art performance among all baselines and was at most 33x faster to
get 95% coverage than Random Search. Experiments also demonstrate that LAMBDA
has a promising future in the verification of autonomous systems in virtual
tests.


title:
Incompressible limits of Patlak-Keller-Segel model and its stationary
  state

abstract:
  The incompressible limits of both the $n$-dimensional $(n\geq3)$
Patlak-Keller-Segel (PKS) model and its stationary state are investigated. We
derive a geometric free boundary problem of Hele-Shaw type for describing the
limit density which relates the compressible (PKS) model and the incompressible
(Hele-Shaw) model, and establish the complementarity relation which describes
the limit pressure by a degenerate elliptic equation in the saturated zone. To
this end, the novel components are to establish the uniform $L^3$ estimate of
the pressure gradient, the uniform Aronson-B\'enilan estimates in $L^1\& L^2\&
L^3$ settings, and the uniform $L^1$ estimate for the time derivative of
pressure. Furthermore, for the Hele-Shaw problem, we prove both the uniqueness
of solutions, meaning that the incompressible limit of the PKS model is unique,
the finite speed of propagation, and the limit energy functional. In addition,
we establish the corresponding incompressible limit of the stationary state for
the PKS model with a given mass, where, different from the case of PKS model,
we obtain the uniform bound of pressure and the uniformly bounded support of
density.


title:
Internet of Drones Simulator: Design, Implementation, and Performance
  Evaluation

abstract:
  The Internet of Drones (IoD) is a networking architecture that stems from the
interplay between Unmanned Aerial Vehicles (UAVs) and wireless communication
technologies. Networked drones can unleash disruptive scenarios in many
application domains. At the same time, to really capitalize their potential,
accurate modeling techniques are required to catch the fine details that
characterize the features and limitations of UAVs, wireless communications, and
networking protocols. To this end, the present contribution proposes the
Internet of Drones Simulator (IoD-Sim), a comprehensive and versatile open
source tool that addresses the many facets of the IoD. IoD-Sim is a Network
Simulator 3 (ns-3)-based simulator organized in a 3-layer stack, composed by
(i) the Underlying Platform, which provides the telecommunication primitives
for different standardized protocol stacks, (ii) the Core, that implements all
the fundamental features of an IoD scenario, and (iii) the Simulation
Development Platform, mainly composed by a set of tools that speeds up the
graphical design for every possible use-case. In order to prove the huge
potential of this proposal, three different scenarios are presented and
analyzed from both a software perspective and a telecommunication standpoint.
The peculiarities of this open-source tool are of interest for researchers in
academia, as they will be able to extend to model upcoming specifications,
including, but not limited to, mobile networks and satellite communications.
Still, it will certainly be of relevance in industry to accelerate the design
phase, thus improving the time-to-market of IoD-based services.


title:
Voltage-Controlled Skyrmionic Interconnect with Multiple Magnetic
  Information Carriers

abstract:
  Magnetic skyrmions have been in the spotlight since their observation in
technologically relevant systems at room temperature. More recently, there has
been increasing interest in additional quasiparticles that may exist as
stable/metastable spin textures in magnets, such as the skyrmionium and the
anti-skyrmionite (i.e., a double-antiskyrmion-skyrmion pair) that have distinct
topological characteristics. The next challenge and opportunity, at the same
time, is to investigate the use of multiple magnetic quasiparticles as
information carriers in a single device for next generation nanocomputing. In
this paper, we propose a spintronic interconnect device where multiple
sequences of information signals are encoded and transmitted simultaneously by
skyrmions, skyrmioniums, and anti-skyrmionites. The proposed spintronic
interconnect device can be pipelined via voltage-controlled-magnetic-anisotropy
(VCMA) gated synchronisers that behave as intermediate registers. We
demonstrate theoretically that the interconnect throughput and transmission
energy can be effectively tuned by the VCMA gate voltage and appropriate
electric current pulses. By carefully adjusting the device structure
characteristics, our spintronic interconnect device exhibits comparable energy
efficiency with copper interconnects in mainstream CMOS technologies. This
study provides fresh insight into the possibilities of skyrmionic devices in
future spintronic applications.


title: Effective and Efficient Core Decomposition in Signed Networks

abstract:
  With the proliferation of mobile technology and IT development, people can
use social network services at any place and anytime. Among many social network
mining problems, identifying cohesive subgraphs attract many attentions from
different fields due to its numerous applications. Among many cohesive subgraph
models, k-core is the most widely used model due to its simple and intuitive
structure. In this paper, we formulate (p,n)-core in signed networks by
extending k-core. (p,n)-core simultaneously guarantees the sufficient internal
positive edges and deficient internal negative edges. We formally prove that
finding an exact (p,n)-core is NP-hard. Hence, we propose three efficient and
effective algorithms to find a solution. Using real-world and synthetic
networks, we demonstrate the superiority of our proposed algorithms.


title:
On the probability of a Condorcet winner among a large number of
  alternatives

abstract:
  Consider $2k-1$ voters, each of which has a preference ranking between $n$
given alternatives. An alternative $A$ is called a Condorcet winner, if it wins
against every other alternative $B$ in majority voting (meaning that for every
other alternative $B$ there are at least $k$ voters who prefer $A$ over $B$).
The notion of Condorcet winners has been studied intensively for many decades,
yet some basic questions remain open. In this paper, we consider a model where
each voter chooses their ranking randomly according to some probability
distribution among all rankings. One may then ask about the probability to have
a Condorcet winner with these randomly chosen rankings (which, of course,
depends on $n$ and $k$, and the underlying probability distribution on the set
of rankings). In the case of the uniform probability distribution over all
rankings, which has received a lot of attention and is often referred to as the
setting of an "impartial culture", we asymptotically determine the probability
of having a Condorcet winner for a fixed number $2k-1$ of voters and $n$
alternatives with $n\to \infty$. This question has been open for around fifty
years. While some authors suggested that the impartial culture should exhibit
the lowest possible probability of having a Condorcet winner, in fact the
probability can be much smaller for other distributions. We determine, for all
values of $n$ and $k$, the smallest possible probability of having a Condorcet
winner (and give an example of a probability distribution over all rankings
which achieves this minimum possible probability).


title: Searching for Network Width with Bilaterally Coupled Network

abstract:
  Searching for a more compact network width recently serves as an effective
way of channel pruning for the deployment of convolutional neural networks
(CNNs) under hardware constraints. To fulfill the searching, a one-shot
supernet is usually leveraged to efficiently evaluate the performance
\wrt~different network widths. However, current methods mainly follow a
\textit{unilaterally augmented} (UA) principle for the evaluation of each
width, which induces the training unfairness of channels in supernet. In this
paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet)
to address this issue. In BCNet, each channel is fairly trained and responsible
for the same amount of network widths, thus each network width can be evaluated
more accurately. Besides, we propose to reduce the redundant search space and
present the BCNetV2 as the enhanced supernet to ensure rigorous training
fairness over channels. Furthermore, we leverage a stochastic complementary
strategy for training the BCNet, and propose a prior initial population
sampling method to boost the performance of the evolutionary search. We also
propose the first open-source width benchmark on macro structures named
Channel-Bench-Macro for the better comparison of width search algorithms.
Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that
our method can achieve state-of-the-art or competing performance over other
baseline methods. Moreover, our method turns out to further boost the
performance of NAS models by refining their network widths. For example, with
the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.53\% Top-1
accuracy on ImageNet dataset, surpassing the performance of original setting by
0.65\%.


title: The Nonlinear Schr\"odinger-Airy equation in weighted Sobolev spaces

abstract:
  We study the persistence property of the solution for the nonlinear
Schr\"odinger-Airy equation with initial data in the weighted Sobolev space
$H^{1/4}(\mathbb{R})\cap L^2(|x|^{2m}dx)$, $0<m \leq 1/8$, via the contraction
principle.


title:
Stabilizing Adversarially Learned One-Class Novelty Detection Using
  Pseudo Anomalies

abstract:
  Recently, anomaly scores have been formulated using reconstruction loss of
the adversarially learned generators and/or classification loss of
discriminators. Unavailability of anomaly examples in the training data makes
optimization of such networks challenging. Attributed to the adversarial
training, performance of such models fluctuates drastically with each training
step, making it difficult to halt the training at an optimal point. In the
current study, we propose a robust anomaly detection framework that overcomes
such instability by transforming the fundamental role of the discriminator from
identifying real vs. fake data to distinguishing good vs. bad quality
reconstructions. For this purpose, we propose a method that utilizes the
current state as well as an old state of the same generator to create good and
bad quality reconstruction examples. The discriminator is trained on these
examples to detect the subtle distortions that are often present in the
reconstructions of anomalous data. In addition, we propose an efficient generic
criterion to stop the training of our model, ensuring elevated performance.
Extensive experiments performed on six datasets across multiple domains
including image and video based anomaly detection, medical diagnosis, and
network security, have demonstrated excellent performance of our approach.


title:
Beyond the thin-film equation: multi-component lattice Boltzmann
  simulations for film rupture

abstract:
  Under the condition of partial surface wettability, thin liquid films can be
destabilized by small perturbations and rupture into droplets. The thin film
equation (TFE) is one of the most popular theoretical approaches to describe
this mechanism, but it is limited in principle to small equilibrium contact
angles and ratios $\epsilon=h/L$ of film height $h$ and lateral size $L$. Here,
we use a multi-component lattice Boltzmann (LB) approach to investigate
systematically the stability of thin films in contact with a flat surface for
large equilibrium contact angles and finite heights. The dispersion law
predicted by the TFE turns out to be in very good agreement for angles as large
as 130$^\circ$. The requirement of a small height is instead a more strict
condition for the validity of the TFE, and agreement with LB results is found
only for $\epsilon\lesssim\times10^{-3}$. The presence of two fluid components
allows for a much richer phenomenology than in the single component case, which
reflects into the possibility of tuning the dewetting dynamics over a wide
range of time scales.


title: Digital Fingerprinting of Microstructures

abstract:
  Finding efficient means of fingerprinting microstructural information is a
critical step towards harnessing data-centric machine learning approaches. A
statistical framework is systematically developed for compressed
characterisation of a population of images, which includes some classical
computer vision methods as special cases. The focus is on materials
microstructure. The ultimate purpose is to rapidly fingerprint sample images in
the context of various high-throughput design/make/test scenarios. This
includes, but is not limited to, quantification of the disparity between
microstructures for quality control, classifying microstructures, predicting
materials properties from image data and identifying potential processing
routes to engineer new materials with specific properties. Here, we consider
microstructure classification and utilise the resulting features over a range
of related machine learning tasks, namely supervised, semi-supervised, and
unsupervised learning.
  The approach is applied to two distinct datasets to illustrate various
aspects and some recommendations are made based on the findings. In particular,
methods that leverage transfer learning with convolutional neural networks
(CNNs), pretrained on the ImageNet dataset, are generally shown to outperform
other methods. Additionally, dimensionality reduction of these CNN-based
fingerprints is shown to have negligible impact on classification accuracy for
the supervised learning approaches considered. In situations where there is a
large dataset with only a handful of images labelled, graph-based label
propagation to unlabelled data is shown to be favourable over discarding
unlabelled data and performing supervised learning. In particular, label
propagation by Poisson learning is shown to be highly effective at low label
rates.