title: Stabilization of rank-deficient continuous-time switched affine systems

abstract:
  This paper treats the global stabilization problem of continuous-time
switched affine systems that have rank-deficient convex combinations of their
dynamic matrices. For these systems, the already known set of attainable
equilibrium points has higher dimensionality than in the full-rank case due to
the existence of what we define as singular equilibrium points. Our main goal
is to design a state-dependent switching function to ensure global asymptotic
stability of a chosen point inside this set with conditions expressed in terms
of linear matrix inequalities. For this class of systems, global exponential
stability is generally impossible to be guaranteed. Hence, the proposed
switching function is shown to ensure global asymptotic and local exponential
stability of the desired equilibrium point. The position control and the
velocity control with integral action of a dc motor driven by an h-bridge fed
via a boost converter are used for validation. This practical application
example is composed of eight subsystems, and all possible convex combinations
of the dynamic matrices are singular.


title: Gamma Correction in Holographic Projection

abstract:
  In a computer-generated holographic projection system, the image is generated
via diffraction of light from spatial light modulators. In this process,
several factors contribute to non-linearities between the replay field and the
target image. This article evaluates the gamma response of the overall system
experimentally, and then applies a gamma correction method, with the aim of
increasing the image quality of a holographic projection system. Both a notable
increase in replay field quality alongside a significant reduction in mean
squared error were observed, demonstrating the effectiveness of gamma
correction in holographic projection.


title:
Dynamic Realized Beta Models Using Robust Realized Integrated Beta
  Estimator

abstract:
  This paper introduces a unified parametric modeling approach for time-varying
market betas that can accommodate continuous-time diffusion and discrete-time
series models based on a continuous-time series regression model to better
capture the dynamic evolution of market betas. We call this the dynamic
realized beta (DR Beta). We first develop a non-parametric realized integrated
beta estimator using high-frequency financial data contaminated by
microstructure noises, which is robust to the stylized features, such as the
time-varying beta and the dependence structure of microstructure noises, and
construct the estimator's asymptotic properties. Then, with the robust realized
integrated beta estimator, we propose a quasi-likelihood procedure for
estimating the model parameters based on the combined high-frequency data and
low frequency dynamic structure. We also establish asymptotic theorems for the
proposed estimator and conduct a simulation study to check the performance of
finite samples of the estimator. The empirical study with the S&P 500 index and
the top 50 large trading volume stocks from the S&P 500 illustrates that the
proposed DR Beta model effectively accounts for dynamics in the market beta of
individual stocks and better predicts future market betas.


title: Orbital memory from individual Fe atoms on black phosphorus

abstract:
  Bistable valency in individual atoms presents a new approach toward
single-atom memory, as well as a building block to create tunable and
stochastic multi-well energy landscapes. Yet, this concept of orbital memory
has thus far only been observed for cobalt atoms on the surface of black
phosphorus, which are switched using tip-induced ionization. Here, we show that
individual iron atoms on the surface of black phosphorus exhibit orbital memory
using a combination of scanning tunneling microscopy and spectroscopy with ab
initio calculations based on density functional theory. Unlike cobalt, the iron
orbital memory can be switched in its non-ionized ground state. Based on
calculations, we confirm that each iron valency has a distinct magnetic moment
that is characterized by a distinguishable charge distribution due to the
different orbital population. By studying the stochastic switching of the
valency with varying tunneling conditions, we propose that the switching
mechanism is based on a two-electron tunneling process.


title:
Should I Follow AI-based Advice? Measuring Appropriate Reliance in
  Human-AI Decision-Making

abstract:
  Many important decisions in daily life are made with the help of advisors,
e.g., decisions about medical treatments or financial investments. Whereas in
the past, advice has often been received from human experts, friends, or
family, advisors based on artificial intelligence (AI) have become more and
more present nowadays. Typically, the advice generated by AI is judged by a
human and either deemed reliable or rejected. However, recent work has shown
that AI advice is not always beneficial, as humans have shown to be unable to
ignore incorrect AI advice, essentially representing an over-reliance on AI.
Therefore, the aspired goal should be to enable humans not to rely on AI advice
blindly but rather to distinguish its quality and act upon it to make better
decisions. Specifically, that means that humans should rely on the AI in the
presence of correct advice and self-rely when confronted with incorrect advice,
i.e., establish appropriate reliance (AR) on AI advice on a case-by-case basis.
Current research lacks a metric for AR. This prevents a rigorous evaluation of
factors impacting AR and hinders further development of human-AI
decision-making. Therefore, based on the literature, we derive a measurement
concept of AR. We propose to view AR as a two-dimensional construct that
measures the ability to discriminate advice quality and behave accordingly. In
this article, we derive the measurement concept, illustrate its application and
outline potential future research.


title:
Global Counterfactual Explanations: Investigations, Implementations and
  Improvements

abstract:
  Counterfactual explanations have been widely studied in explainability, with
a range of application dependent methods emerging in fairness, recourse and
model understanding. However, the major shortcoming associated with these
methods is their inability to provide explanations beyond the local or
instance-level. While some works touch upon the notion of a global explanation,
typically suggesting to aggregate masses of local explanations in the hope of
ascertaining global properties, few provide frameworks that are either reliable
or computationally tractable. Meanwhile, practitioners are requesting more
efficient and interactive explainability tools. We take this opportunity to
investigate existing global methods, with a focus on implementing and improving
Actionable Recourse Summaries (AReS), the only known global counterfactual
explanation framework for recourse.


title:
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in
  Soccer Videos

abstract:
  Tracking objects in soccer videos is extremely important to gather both
player and team statistics, whether it is to estimate the total distance run,
the ball possession or the team formation. Video processing can help automating
the extraction of those information, without the need of any invasive sensor,
hence applicable to any team on any stadium. Yet, the availability of datasets
to train learnable models and benchmarks to evaluate methods on a common
testbed is very limited. In this work, we propose a novel dataset for multiple
object tracking composed of 200 sequences of 30s each, representative of
challenging soccer scenarios, and a complete 45-minutes half-time for long-term
tracking. The dataset is fully annotated with bounding boxes and tracklet IDs,
enabling the training of MOT baselines in the soccer domain and a full
benchmarking of those methods on our segregated challenge sets. Our analysis
shows that multiple player, referee and ball tracking in soccer videos is far
from being solved, with several improvement required in case of fast motion or
in scenarios of severe occlusion.


title:
Proof of Federated Training: Accountable Cross-Network Model Training
  and Inference

abstract:
  Blockchain has widely been adopted to design accountable federated learning
frameworks; however, the existing frameworks do not scale for distributed model
training over multiple independent blockchain networks. For storing the
pre-trained models over blockchain, current approaches primarily embed a model
using its structural properties that are neither scalable for cross-chain
exchange nor suitable for cross-chain verification. This paper proposes an
architectural framework for cross-chain verifiable model training using
federated learning, called Proof of Federated Training (PoFT), the first of its
kind that enables a federated training procedure span across the clients over
multiple blockchain networks. Instead of structural embedding, PoFT uses model
parameters to embed the model over a blockchain and then applies a verifiable
model exchange between two blockchain networks for cross-network model
training. We implement and test PoFT over a large-scale setup using Amazon EC2
instances and observe that cross-chain training can significantly boosts up the
model efficacy. In contrast, PoFT incurs marginal overhead for inter-chain
model exchanges.


title:
Traveling waves with continuous profile for hyperbolic Keller-Segel
  equation

abstract:
  This work describes a hyperbolic model for cell-cell repulsion with
population dynamics. We consider the pressure produced by a population of cells
to describe their motion. We assume that cells try to avoid crowded areas and
prefer locally empty spaces far away from the carrying capacity. Here, our main
goal is to prove the existence of traveling waves with continuous profiles.
This article complements our previous results about sharp traveling waves. We
conclude the paper with numerical simulations of the PDE problem, illustrating
such a result.


title: M-center in low-energy electron irradiated 4H-SiC

abstract:
  We report on the low-energy electron irradiated 4H-SiC material studied by
means of deep-level transient spectroscopy (DLTS) and Laplace-DLTS. Electron
irradiation has introduced the following deep level defects: EH1 and EH3
previously assigned to carbon interstitial-related defects, and M-center, a
metastable defect also recently assigned to carbon interstitial defects. We
propose that EH1 and EH3 are identical to M1 and M3 and assign them to C_i^(= )
(h) and C_i^(0 ) (h), respectively. Moreover, we provide direct evidence that
Laplace-DLTS can be used as an excellent tool to distinguish otherwise
identical DLTS signals associated with S1 (VSi) and EH1 (Ci).


title:
Calibration of scanning acoustic microscopy for the differentiation
  between unstable and stable atherosclerotic plaques by X-ray fluorescence
  imaging

abstract:
  Although cardiovascular diseases are the leading cause of death globally,
non-invasive and inexpensive diagnostic tools for the identification of
associated unstable atherosclerotic plaques are not yet available. Scanning
acoustic microscopy offers a high potential to fill this critical gap in
patient care. However, convincing validation and calibration of this technique
requires high resolution maps of Ca concentrations of atherosclerotic plaques.
Here, we demonstrate that synchrotron radiation-based X-ray fluorescence
imaging with micrometer spatial resolution can provide such a gold standard.


title:
A Unified Multi-task Learning Framework for Multi-goal Conversational
  Recommender Systems

abstract:
  Recent years witnessed several advances in developing multi-goal
conversational recommender systems (MG-CRS) that can proactively attract users'
interests and naturally lead user-engaged dialogues with multiple
conversational goals and diverse topics. Four tasks are often involved in
MG-CRS, including Goal Planning, Topic Prediction, Item Recommendation, and
Response Generation. Most existing studies address only some of these tasks. To
handle the whole problem of MG-CRS, modularized frameworks are adopted where
each task is tackled independently without considering their interdependencies.
In this work, we propose a novel Unified MultI-goal conversational recommeNDer
system, namely UniMIND. In specific, we unify these four tasks with different
formulations into the same sequence-to-sequence (Seq2Seq) paradigm.
Prompt-based learning strategies are investigated to endow the unified model
with the capability of multi-task learning. Finally, the overall learning and
inference procedure consists of three stages, including multi-task learning,
prompt-based tuning, and inference. Experimental results on two MG-CRS
benchmarks (DuRecDial and TG-ReDial) show that UniMIND achieves
state-of-the-art performance on all tasks with a unified model. Extensive
analyses and discussions are provided for shedding some new perspectives for
MG-CRS.


title: Open Source HamNoSys Parser for Multilingual Sign Language Encoding

abstract:
  This paper presents our recent developments in the field of automatic
processing of sign language corpora using the Hamburg Sign Language Annotation
System (HamNoSys). We designed an automated tool to convert HamNoSys
annotations into numerical labels for defined initial features of body and hand
positions. Our proposed numerical multilabels greatly simplify the structure of
HamNoSys annotation without significant loss of gloss meaning. These numerical
multilabels can potentially be used to feed the machine learning models, which
would accelerate the development of vision-based sign language recognition. In
addition, this tool can assist experts in the annotation process to help
identify semantic errors. The code and sample annotations are publicly
available at https://github.com/hearai/parse-hamnosys.


title:
Cooling the optical-spin driven limit cycle oscillations of a levitated
  gyroscope

abstract:
  The non-conservative, azimuthal forces associated with inhomogeneous
optical-spin angular momentum play a critical role in optical trapping.
Intriguingly, birefringent microspheres can be stably levitated and rapidly
rotated in circularly polarized optical traps in ultra-high vacuum whereas
isotropic spheres are typically destabilized and expelled, even at relatively
modest pressures. Here we show that the resolution of this apparent key paradox
rests in the form of the orientationally averaged, effective forces acting on
the spinning birefringent particle. In particular, the effective azimuthal
component is heavily suppressed and highly non-linear. As a consequence,
non-conservative effects are strongly, if imperfectly, inhibited. Their
influence is apparent only at very low pressures where we observe the formation
of noisy, nano-scale limit cycles or orbits. Finally, we show how parametric
feedback can synthesize a form of dissipation, necessary to preserve limit
cycle oscillation, without introducing additional thermal fluctuations. This
allows the preparation of highly coherent, self-sustained oscillations with
effective temperatures on the order of a milliKelvin. The tailoring of
azimuthal spin forces through the material structure of a spinning,
non-spherical particle opens up new opportunities for the design of ultra
stable optical rotors. In addition, we have shown that the unique profile of
the azimuthal force, featured in this work, allows for the formation of
nano-scale limit cycles that can be stabilized and cooled. In principle, this
approach could enable the cooling of limit cycles into the quantum regime,
allowing for experimental realisation of quantum synchronization, or
alternative ways of entangling mesoscopic bodies.


title: Primitive permutation groups of degree 3p

abstract:
  This paper presents an analysis of primitive permutation groups of degree
$3p$, where $p$ is a prime number, analogous to H. Wielandt's treatment of
groups of degree $2p$. It is also intended as an example of the systematic use
of combinatorial methods as surveyed in \S6 for distilling information about a
permutation group from knowledge of the decomposition of its character. The
work is organised into three parts. Part I contains the lesser half of the
calculation, the determination of the decomposition of the permutation
character. Part II contains a survey of the combinatorial methods and, based on
these methods, the major part of the calculation. Part III ties up loose ends
left earlier in the paper and gives a tabulation of detailed numerical results.


title:
Solution of Volume Integral and Hydrodynamic Equations to Analyze
  Electromagnetic Scattering from Composite Nanostructures

abstract:
  A coupled system of volume integral and hydrodynamic equations is solved to
analyze electromagnetic scattering from nanostructures consisting of metallic
and dielectric parts. In the metallic part, the hydrodynamic equation relates
the free electron polarization current to the electric flux and effectively
"updates" the constitutive relation to enable the modeling of nonlocality. In
the metallic and the dielectric parts, the volume integral equation relates the
electric flux and the free electron polarization current to the scattered
electric field. Unknown electric flux and free electron polarization current
are expanded using Schaubert-Wilton-Glisson basis functions. Inserting these
expansions into the coupled system of the volume integral and hydrodynamic
equations and using Galerkin testing yield a matrix system in unknown expansion
coefficients. An efficient two-level iterative solver is proposed to solve this
matrix system. This approach "inverts" the discretized hydrodynamic equation
for the coefficients of the free electron polarization current and substitutes
the result in the discretized volume integral equation. Outer iterations solve
this reduced matrix system while the inner iterations invert the discretized
hydrodynamic equation at every iteration of the outer iterations. Numerical
experiments are carried out to demonstrate the accuracy, the efficiency, and
the applicability of the proposed method.


title: Sign of the Feynman Propagator and Irreversibility

abstract:
  For the interacting Feynman propagator $ \Delta_{F,int}(x,y) $ of scalar
electrodynamics, we show that the sign property, $ \operatorname{Re}
i\Delta_{F,int} \geq 0 $, hinges on the reversibility of time evolution. In
contrast, $ \operatorname{Im} i\Delta_{F,int} $ is indeterminate. When we
switch to reduced dynamics under the weak coupling approximation, the positive
semidefinite sign of $ \operatorname{Re} i\Delta_{F,int} $ is generally lost,
unless we impose severe restrictions on the Kraus operators that govern time
evolution. With another approximation, the rotating wave approximation, we may
recover the sign by restricting the test functions to exponentials under
certain conditions.


title:
Sketch guided and progressive growing GAN for realistic and editable
  ultrasound image synthesis

abstract:
  Ultrasound (US) imaging is widely used for anatomical structure inspection in
clinical diagnosis. The training of new sonographers and deep learning based
algorithms for US image analysis usually requires a large amount of data.
However, obtaining and labeling large-scale US imaging data are not easy tasks,
especially for diseases with low incidence. Realistic US image synthesis can
alleviate this problem to a great extent. In this paper, we propose a
generative adversarial network (GAN) based image synthesis framework. Our main
contributions include: 1) we present the first work that can synthesize
realistic B-mode US images with high-resolution and customized texture editing
features; 2) to enhance structural details of generated images, we propose to
introduce auxiliary sketch guidance into a conditional GAN. We superpose the
edge sketch onto the object mask and use the composite mask as the network
input; 3) to generate high-resolution US images, we adopt a progressive
training strategy to gradually generate high-resolution images from
low-resolution images. In addition, a feature loss is proposed to minimize the
difference of high-level features between the generated and real images, which
further improves the quality of generated images; 4) the proposed US image
synthesis method is quite universal and can also be generalized to the US
images of other anatomical structures besides the three ones tested in our
study (lung, hip joint, and ovary); 5) extensive experiments on three large US
image datasets are conducted to validate our method. Ablation studies,
customized texture editing, user studies, and segmentation tests demonstrate
promising results of our method in synthesizing realistic US images.


title:
Unfolding engineering metamaterials design: relaxed micromorphic
  modeling of large-scale acoustic meta-structures

abstract:
  In this paper, we present a unit cell showing a band-gap in the lower
acoustic domain. The corresponding metamaterial is made up of a periodic
arrangement of this unit cell. We rigorously show that the relaxed micromorphic
model can be used for metamaterials' design at large scales as soon as
suficiently large specimens are considered. We manufacture the metamaterial via
metal etching procedures applied to a titanium plate so as to show that its
production for realistic applications is viable. Experimental tests are also
carried out confirming that the metamaterials' response is in good agreement
with the theoretical design. In order to show that our micromorphic model opens
unprecedented possibilities in metastructural design, we conceive a finite-size
structure that is able to focus elastic energy in a confined region, thus
enabling its possible subsequent re-use. Indeed, thanks to the introduction of
a well-posed set of micromorphic boundary conditions, we can combine different
metamaterials and classical Cauchy materials in such a way that the elastic
energy produced by a source of vibrations is focused in specific collection
points. The design of this structure would have not been otherwise possible
(via e.g., direct simulations), due to the large dimensions of the
metastructure, couting hundreds of unit cells.


title:
Geometric Deep Learning to Identify the Critical 3D Structural Features
  of the Optic Nerve Head for Glaucoma Diagnosis

abstract:
  Purpose: The optic nerve head (ONH) undergoes complex and deep 3D
morphological changes during the development and progression of glaucoma.
Optical coherence tomography (OCT) is the current gold standard to visualize
and quantify these changes, however the resulting 3D deep-tissue information
has not yet been fully exploited for the diagnosis and prognosis of glaucoma.
To this end, we aimed: (1) To compare the performance of two relatively recent
geometric deep learning techniques in diagnosing glaucoma from a single OCT
scan of the ONH; and (2) To identify the 3D structural features of the ONH that
are critical for the diagnosis of glaucoma.
  Methods: In this study, we included a total of 2,247 non-glaucoma and 2,259
glaucoma scans from 1,725 subjects. All subjects had their ONHs imaged in 3D
with Spectralis OCT. All OCT scans were automatically segmented using deep
learning to identify major neural and connective tissues. Each ONH was then
represented as a 3D point cloud. We used PointNet and dynamic graph
convolutional neural network (DGCNN) to diagnose glaucoma from such 3D ONH
point clouds and to identify the critical 3D structural features of the ONH for
glaucoma diagnosis.
  Results: Both the DGCNN (AUC: 0.97$\pm$0.01) and PointNet (AUC:
0.95$\pm$0.02) were able to accurately detect glaucoma from 3D ONH point
clouds. The critical points formed an hourglass pattern with most of them
located in the inferior and superior quadrant of the ONH.
  Discussion: The diagnostic accuracy of both geometric deep learning
approaches was excellent. Moreover, we were able to identify the critical 3D
structural features of the ONH for glaucoma diagnosis that tremendously
improved the transparency and interpretability of our method. Consequently, our
approach may have strong potential to be used in clinical applications for the
diagnosis and prognosis of a wide range of ophthalmic disorders.


title:
Topological edge-states of the PT-symmetric Su-Schrieffer-Heeger model:
  An effective two-state description

abstract:
  We consider the non-Hermitian, parity-time (PT) symmetric extensions of the
one-dimensional Su-Schrieffer-Heeger (SSH) model in the topological non-trivial
configuration. We study the properties of the topologically protected edge
states, and develop an effective two-state analytical description of the system
that accurately predicts the PT-symmetry breaking point for the edge states. We
verify our analytical results by exact numerical calculations.


title: New calculations of neutron and kaon decays

abstract:
  We describe two recent developments of the radiative correction theory in
semileptonic beta decays. For neutron, a novel dispersive analysis of the
$\gamma W$-box diagram making use of neutrino scattering data leads to an
improved precision and a shifted central value of $V_{ud}$; for kaon, an
appropriate combination of meson form factors, lattice QCD inputs and chiral
perturbation theory gives a much-improved determination of the radiative
corrections in semileptonic kaon decays. These new calculations help to
unveil/sharpen a series of anomalies in the measurements of $V_{ud}$ and
$V_{us}$ that may point towards the existence of new physics.


title:
Procedural Content Generation using Neuroevolution and Novelty Search
  for Diverse Video Game Levels

abstract:
  Procedurally generated video game content has the potential to drastically
reduce the content creation budget of game developers and large studios.
However, adoption is hindered by limitations such as slow generation, as well
as low quality and diversity of content. We introduce an evolutionary
search-based approach for evolving level generators using novelty search to
procedurally generate diverse levels in real time, without requiring training
data or detailed domain-specific knowledge. We test our method on two domains,
and our results show an order of magnitude speedup in generation time compared
to existing methods while obtaining comparable metric scores. We further
demonstrate the ability to generalise to arbitrary-sized levels without
retraining.


title: Concentration of Random Feature Matrices in High-Dimensions

abstract:
  The spectra of random feature matrices provide essential information on the
conditioning of the linear system used in random feature regression problems
and are thus connected to the consistency and generalization of random feature
models. Random feature matrices are asymmetric rectangular nonlinear matrices
depending on two input variables, the data and the weights, which can make
their characterization challenging. We consider two settings for the two input
variables, either both are random variables or one is a random variable and the
other is well-separated, i.e. there is a minimum distance between points. With
conditions on the dimension, the complexity ratio, and the sampling variance,
we show that the singular values of these matrices concentrate near their full
expectation and near one with high-probability. In particular, since the
dimension depends only on the logarithm of the number of random weights or the
number of data points, our complexity bounds can be achieved even in moderate
dimensions for many practical setting. The theoretical results are verified
with numerical experiments.


title: Description and complexity of Non-markovian open quantum dynamics

abstract:
  In open quantum system theory, unitary groups over system-environment Hilbert
space corresponding to delta-function memory kernels, and thus generating
Markovian system dynamics, are specified as the solution of a quantum
stochastic differential equation. In this paper, we identify a larger class of
non-Markovian memory kernels, described by complex-valued radon measures, and
rigorously define their dynamics by constructing system-environment unitary
groups corresponding to the memory kernels. We then consider $k-$local
many-body non-Markovian systems and show that under physically reasonable
assumptions on the total variation and smoothness of the memory kernels, their
dynamics can be efficiently approximated on quantum computers thus providing a
rigorous verification of the Extended Church-Turing thesis for non-Markovian
open quantum systems.


title:
CO Emission, Molecular Gas, and Metallicity in Main-Sequence
  Star-Forming Galaxies at $z\sim2.3$

abstract:
  We present observations of CO(3-2) in 13 main-sequence $z=2.0-2.5$
star-forming galaxies at $\log(M_*/M_{\odot})=10.2-10.6$ that span a wide range
in metallicity (O/H) based on rest-optical spectroscopy. We find that
CO(3-2)/SFR decreases with decreasing metallicity, implying that the CO
luminosity per unit gas mass is lower in low-metallicity galaxies at $z\sim2$.
We constrain the CO-to-H$_2$ conversion factor ($\alpha_{\text{CO}}$) and find
that $\alpha_{\text{CO}}$ inversely correlates with metallicity at $z\sim2$. We
derive molecular gas masses ($M_{\text{mol}}$) and characterize the relations
among $M_*$, SFR, $M_{\text{mol}}$, and metallicity. At $z\sim2$,
$M_{\text{mol}}$ increases and molecular gas fraction ($M_{\text{mol}}$/$M_*$)
decrease with increasing $M_*$, with a significant secondary dependence on SFR.
Galaxies at $z\sim2$ lie on a near-linear molecular KS law that is
well-described by a constant depletion time of 700 Myr. We find that the
scatter about the mean SFR-$M_*$, O/H-$M_*$, and $M_{\text{mol}}$-$M_*$
relations is correlated such that, at fixed $M_*$, $z\sim2$ galaxies with
larger $M_{\text{mol}}$ have higher SFR and lower O/H. We thus confirm the
existence of a fundamental metallicity relation at $z\sim2$ where O/H is
inversely correlated with both SFR and $M_{\text{mol}}$ at fixed $M_*$. These
results suggest that the scatter of the $z\sim2$ star-forming main sequence,
mass-metallicity relation, and $M_{\text{mol}}$-$M_*$ relation are primarily
driven by stochastic variations in gas inflow rates. We place constraints on
the mass loading of galactic outflows and perform a metal budget analysis,
finding that massive $z\sim2$ star-forming galaxies retain only 30% of metals
produced, implying that a large mass of metals resides in the circumgalactic
medium.


title:
Flowing the Information from Shannon to Fisher: Towards the Fundamental
  Tradeoff in ISAC

abstract:
  Integrated Sensing and Communication (ISAC) is recognized as a promising
technology for the next-generation wireless networks. In this paper, we provide
a general framework to reveal the fundamental tradeoff between sensing and
communications (S&C), where a unified ISAC waveform is exploited to perform
dual-functional tasks. In particular, we define the Cramer-Rao bound (CRB)-rate
region to characterize the S&C tradeoff, and propose a pentagon inner bound of
the region. We show that the two corner points of the CRB-rate region can be
achieved by the conventional Gaussian waveform and a novel strategy referred to
as successive hypersphere coding, respectively. Moreover, we also offer our
insights into transmission approaches achieving the boundary of the CRB-rate
region, namely the Shannon-Fisher information flow.


title:
Prediction and Control of Focal Seizure Spread: Random Walk with Restart
  on Heterogeneous Brain Networks

abstract:
  Whole-brain models offer a promising method of predicting seizure spread,
which is critical for successful surgery treatment of focal epilepsy. Existing
methods are largely based on structural connectome, which ignores the effects
of heterogeneity in regional excitability of brains. In this study, we used a
whole-brain model to show that heterogeneity in nodal excitability had a
significant impact on seizure propagation in the networks, and compromised the
prediction accuracy with structural connections. We then addressed this problem
with an algorithm based on random walk with restart on graphs. We demonstrated
that by establishing a relationship between the restarting probability and the
excitability for each node, this algorithm could significantly improve the
seizure spread prediction accuracy in heterogeneous networks, and was more
robust against the extent of heterogeneity. We also strategized surgical
seizure control as a process to identify and remove the key nodes (connections)
responsible for the early spread of seizures from the focal region. Compared to
strategies based on structural connections, virtual surgery with a strategy
based on mRWER generated outcomes with a high success rate while maintaining
low damage to the brain by removing fewer anatomical connections. These
findings may have potential applications in developing personalized surgery
strategies for epilepsy.


title: On the critical $p$-Laplace equation

abstract:
  In this paper we provide the classification of positive solutions to the
critical $p-$Laplace equation on $\mathbb{R}^n$, for $1<p<n$, possibly having
infinite energy. If $n=2$, or if $n=3$ and $\frac 32<p<2$ we prove rigidity
without any further assumptions. In the remaining cases we obtain the
classification under energy growth conditions or suitable control of the
solutions at infinity. Our assumptions are much weaker than those already
appearing in the literature. We also discuss the extension of the results to
the Riemannian setting.


title:
Probing surfactant bilayer interactions by tracking optically trapped
  single nanoparticles

abstract:
  Single-particle tracking and optical tweezers are powerful techniques for
studying diverse processes at the microscopic scale. The stochastic behavior of
a microscopically observable particle contains information about its
interaction with surrounding molecules, and an optical tweezer can further
facilitate this observation with its ability to constrain the particle to an
area of interest. Although these techniques found their initial applications in
biology, they can also shed new light on colloid and interface phenomena by
unveiling nanoscale morphologies and molecular-level interactions in real time,
which have been obscured in traditional ensemble analysis. Here we demonstrate
the application of single-particle tracking and optical tweezers for studying
molecular interactions at solid-liquid interfaces. Specifically, we investigate
the behavior of surfactants at the water-glass interface by tracing their
interactions with gold nanoparticles that are optically trapped on these
molecules. We discover the underlying mechanisms governing the particle motion,
which can be explained by hydrophobic interactions, disruptions, and
rearrangements among surfactant monomers at the interfaces. Such
interpretations are further supported by statistical analysis of an individual
trajectory and comparison to theoretical predictions. Our findings provide new
insights into the surfactant dynamics in this specific system but also
illustrate the promise of single-particle tracking and optical manipulation in
studying nanoscale physics and chemistry of surfaces and interfaces.


title:
Bistability and chaos-assisted tunneling in the dissipative quantum
  systems

abstract:
  We revisit the problem of quantum bi- and multi-stability by considering the
dissipative Double Resonance Model. For a large driving frequency, this system
has a simpler phase structure than the driven dissipative nonlinear oscillator
-- the paradigm model for classical and quantum bistability. This allows us to
obtain an analytical estimate for the lifetime of quantum limit cycles. On the
other hand, for a small driving frequency, the system is much reacher than the
nonlinear oscillator. This allows us to address a novel phenomenon of
dissipation- and chaos-assisted tunneling between quantum limits cycles.


title: Option Pricing with Time-Varying Volatility Risk Aversion

abstract:
  We introduce a novel pricing kernel with time-varying variance risk aversion
that can explain key pricing kernel puzzles. When combined with the
Heston-Nandi GARCH model, the framework yields closed-form expressions for the
VIX. We also obtain closed-form expressions for option prices by proposing a
novel method that extrapolation from the closed-form VIX. We estimate the model
with S&P 500 returns and option prices and find a substantial reduction in
pricing errors by permitting time-variation in volatility risk aversion. This
reduction is seen for both option pricing and VIX pricing and both in-sample
and out-of-sample. The variance risk ratio emerges as a fundamental variable in
our framework and we show that it is closely related to economic fundamentals
and leading measures of sentiment and uncertainty


title:
Better-Than-$\frac{4}{3}$-Approximations for Leaf-to-Leaf Tree and
  Connectivity Augmentation

abstract:
  The Connectivity Augmentation Problem (CAP) together with a well-known
special case thereof known as the Tree Augmentation Problem (TAP) are among the
most basic Network Design problems. There has been a surge of interest recently
to find approximation algorithms with guarantees below $2$ for both TAP and
CAP, culminating in the currently best approximation factor for both problems
of $1.393$ through quite sophisticated techniques.
  We present a new and arguably simple matching-based method for the well-known
special case of leaf-to-leaf instances. Combining our work with prior
techniques, we readily obtain a $(\frac{4}{3}+\epsilon)$-approximation for
Leaf-to-Leaf CAP by returning the better of our solution and one of an existing
method. Prior to our work, a $\frac{4}{3}$-guarantee was only known for
Leaf-to-Leaf TAP instances on trees of height $2$. Moreover, when combining our
technique with a recently introduced stack analysis approach, which is part of
the above-mentioned $1.393$-approximation, we can further improve the
approximation factor to $1.29$, obtaining for the first time a factor below
$\frac{4}{3}$ for a nontrivial class of TAP/CAP instances.


title: The Vision of a Human-Centered Piano

abstract:
  For around 300 years, humans have been learning to play the modern piano
either with a teacher or on their own. In recent years teaching and learning
have been enhanced using augmented technologies that support novices. Other
technologies have also tried to improve different use cases with the piano,
such as composing and performing. Researchers and practitioners have showcased
several forms of augmentation, from hardware improvements, sound quality,
rendering projected visualizations to gesture-based and immersive technologies.
Today, the landscape of piano augmentations is very diverse, and it is unclear
how to describe the ideal piano and its features. In this work, we discuss how
the human-centered piano -- the piano that has been designed with humans in the
center of the design process and that effectively supports tasks performed on
it -- can support pianists. In detail, we present the three tasks of learning,
composing, and improvising in which a human-centered piano would be beneficial
for the pianist.


title:
Visualization for Epidemiological Modelling: Challenges, Solutions,
  Reflections & Recommendations

abstract:
  We report on an ongoing collaboration between epidemiological modellers and
visualization researchers by documenting and reflecting upon knowledge
constructs -- a series of ideas, approaches and methods taken from existing
visualization research and practice -- deployed and developed to support
modelling of the COVID-19 pandemic. Structured independent commentary on these
efforts is synthesized through iterative reflection to develop: evidence of the
effectiveness and value of visualization in this context; open problems upon
which the research communities may focus; guidance for future activity of this
type; and recommendations to safeguard the achievements and promote, advance,
secure and prepare for future collaborations of this kind. In describing and
comparing a series of related projects that were undertaken in unprecedented
conditions, our hope is that this unique report, and its rich interactive
supplementary materials, will guide the scientific community in embracing
visualization in its observation, analysis and modelling of data as well as in
disseminating findings. Equally we hope to encourage the visualization
community to engage with impactful science in addressing its emerging data
challenges. If we are successful, this showcase of activity may stimulate
mutually beneficial engagement between communities with complementary expertise
to address problems of significance in epidemiology and beyond.
https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/


title:
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for
  Motor Imagery Classification

abstract:
  In recent years, neural networks and especially deep architectures have
received substantial attention for EEG signal analysis in the field of
brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end
models are more favoured than traditional approaches requiring signal
transformation pre-classification. They can eliminate the need for prior
information from experts and the extraction of handcrafted features. However,
although several deep learning algorithms have been already proposed in the
literature, achieving high accuracies for classifying motor movements or mental
tasks, they often face a lack of interpretability and therefore are not quite
favoured by the neuroscience community. The reasons behind this issue can be
the high number of parameters and the sensitivity of deep neural networks to
capture tiny yet unrelated discriminative features. We propose an end-to-end
deep learning architecture called EEG-ITNet and a more comprehensible method to
visualise the network learned patterns. Using inception modules and causal
convolutions with dilation, our model can extract rich spectral, spatial, and
temporal information from multi-channel EEG signals with less complexity (in
terms of the number of trainable parameters) than other existing end-to-end
architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation
on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset,
EEG-ITNet shows up to 5.9\% improvement in the classification accuracy in
different scenarios with statistical significance compared to its competitors.
We also comprehensively explain and support the validity of network
illustration from a neuroscientific perspective. We have also made our code
open at https://github.com/AbbasSalami/EEG-ITNet


title: Restricting $q^2 l^2$ operators from $\pi^0 \rightarrow \mu e$

abstract:
  In this paper we consider semileptonic lepton flavor violating operators of
the type $q^2l^2$ in low energy effective theory (LEFT). At the chiral scale,
we match these operators to chiral perturbation theory ($\chi$PT) and place
constraints from the process $\pi^0\rightarrow \mu^+ \,\, e^-$. These bounds
are shown to depend on the chiral nature of the operators. The scalar operators
are significantly more constrained compared to the vector operators. We then
compare the limits from $\mu \to e$ conversion in Nuclei and show that the
limits on scalar operators are within an order of magnitude of the
corresponding limits from $\mu \to e$ conversion in Ti. On the other hand, the
limits on vector operators are however much weaker. Towards the end, we evolve
the LEFT operators to W-boson mass scale using RGE, and match them to the
Standard Model effective field theory (SMEFT) operators. We, then, derive the
constraints on the parameter space of Leptoquark models that could generate
these SMEFT operators at tree level.


title:
Federated Learning for Vision-based Obstacle Avoidance in the Internet
  of Robotic Things

abstract:
  Deep learning methods have revolutionized mobile robotics, from advanced
perception models for an enhanced situational awareness to novel control
approaches through reinforcement learning. This paper explores the potential of
federated learning for distributed systems of mobile robots enabling
collaboration on the Internet of Robotic Things. To demonstrate the
effectiveness of such an approach, we deploy wheeled robots in different indoor
environments. We analyze the performance of a federated learning approach and
compare it to a traditional centralized training process with a priori
aggregated data. We show the benefits of collaborative learning across
heterogeneous environments and the potential for sim-to-real knowledge
transfer. Our results demonstrate significant performance benefits of FL and
sim-to-real transfer for vision-based navigation, in addition to the inherent
privacy-preserving nature of FL by keeping computation at the edge. This is, to
the best of our knowledge, the first work to leverage FL for vision-based
navigation that also tests results in real-world settings.


title: BEHAVE: Dataset and Method for Tracking Human Object Interactions

abstract:
  Modelling interactions between humans and objects in natural environments is
central to many applications including gaming, virtual and mixed reality, as
well as human behavior analysis and human-robot collaboration. This challenging
operation scenario requires generalization to vast number of objects, scenes,
and human actions. Unfortunately, there exist no such dataset. Moreover, this
data needs to be acquired in diverse natural environments, which rules out 4D
scanners and marker based capture systems. We present BEHAVE dataset, the first
full body human- object interaction dataset with multi-view RGBD frames and
corresponding 3D SMPL and object fits along with the annotated contacts between
them. We record around 15k frames at 5 locations with 8 subjects performing a
wide range of interactions with 20 common objects. We use this data to learn a
model that can jointly track humans and objects in natural environments with an
easy-to-use portable multi-camera setup. Our key insight is to predict
correspondences from the human and the object to a statistical body model to
obtain human-object contacts during interactions. Our approach can record and
track not just the humans and objects but also their interactions, modeled as
surface contacts, in 3D. Our code and data can be found at:
http://virtualhumans.mpi-inf.mpg.de/behave


title: Unsupervised Deep Learning Meets Chan-Vese Model

abstract:
  The Chan-Vese (CV) model is a classic region-based method in image
segmentation. However, its piecewise constant assumption does not always hold
for practical applications. Many improvements have been proposed but the issue
is still far from well solved. In this work, we propose an unsupervised image
segmentation approach that integrates the CV model with deep neural networks,
which significantly improves the original CV model's segmentation accuracy. Our
basic idea is to apply a deep neural network that maps the image into a latent
space to alleviate the violation of the piecewise constant assumption in image
space. We formulate this idea under the classic Bayesian framework by
approximating the likelihood with an evidence lower bound (ELBO) term while
keeping the prior term in the CV model. Thus, our model only needs the input
image itself and does not require pre-training from external datasets.
Moreover, we extend the idea to multi-phase case and dataset based unsupervised
image segmentation. Extensive experiments validate the effectiveness of our
model and show that the proposed method is noticeably better than other
unsupervised segmentation approaches.


title: Infomechanics of Independent and Identically Distributed Particles

abstract:
  The paper moves a step towards the full integration of statistical mechanics
and information theory. Starting from the assumption that the thermodynamical
system is composed by particles whose quantized energies can be modelled as
independent and identically distributed random variables, the paper proposes an
approach whose cornerstones are the information-theoretic typical set and the
conditional equiprobability of microstates given certain macrostates of the
system. When taken together, these two concepts explain why the standard
assumption of equally probable microstates is non-necessary (if not misleading)
and show that the celebrated Boltzmann-Planck entropy is indeed a conditional
entropy with deterministic condition. Several new specific results of physical
relevance are derived from this approach, among which are the probability
distribution of the occupancy numbers of the energy levels, the probability
distribution of the total quantum number in a system of monochromatic quantum
harmonic oscillators at the thermal equilibrium, an exact formula for the ideal
gas in a container that gives the entropy of the gas also at low temperature.
These specific results are pieces of a self-consistent and unified framework
that encompasses the cases of low and high temperature, of indexed and
non-indexed particles, of small and large number of particles, of
microcanonical, canonical and grand canonical ensembles.


title:
Convex Hulls of Grassmannians and Combinatorics of Symmetric
  Hypermatrices

abstract:
  It is known that Grassmannians can be embedded into certain spaces of
matrices. We find convex hulls of the embedded Grassmannians. We also give an
existence theorem for a certain combinatorial class of hypermatrices by a
similar argument.


title: Trace-Class and Nuclear Operators

abstract:
  This paper explores the long journey from projective tensor products of a
pair of Banach spaces, passing through the definition of nuclear operators
still on the realm of projective tensor products, to the of notion of
trace-class operators on a Hilbert space, and shows how and why these concepts
(nuclear and trace-class operators, that is) agree in the end.


title:
LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation
  of Histopathological Images of Frozen Sections

abstract:
  Accurate segmentation of medical images is essential for diagnosis and
treatment of diseases. These problems are solved by highly complex models, such
as deep networks (DN), requiring a large amount of labeled data for training.
Thereby, many DNs possess task- or imaging modality specific architectures with
a decision-making process that is often hard to explain and interpret. Here, we
propose a framework that embeds existing DNs into a low-dimensional subspace
induced by the learnable explicit feature map (LEFM) layer. Compared to the
existing DN, the framework adds one hyperparameter and only modestly increase
the number of learnable parameters. The method is aimed at, but not limited to,
segmentation of low-dimensional medical images, such as color histopathological
images of stained frozen sections. Since features in the LEFM layer are
polynomial functions of the original features, proposed LEFM-Nets contribute to
the interpretability of network decisions. In this work, we combined LEFM with
the known networks: DeepLabv3+, UNet, UNet++ and MA-net. New LEFM-Nets are
applied to the segmentation of adenocarcinoma of a colon in a liver from images
of hematoxylin and eosin (H&E) stained frozen sections. LEFM-Nets are also
tested on nuclei segmentation from images of H&E stained frozen sections of ten
human organs. On the first problem, LEFM-Nets achieved statistically
significant performance improvement in terms of micro balanced accuracy and
$F_1$ score than original networks. LEFM-Nets achieved only better performance
in comparison with the original networks on the second problem. The source code
is available at https://github.com/dsitnik/lefm.


title: Colour Evolution and Infrared Physics

abstract:
  We give a complete account of how evolution equations in colour space
originate from a factorization into a hard cross section density operator and a
soft function encoding measurements and the projection on definite colours, and
we detail this formalism up to the two loop level. We demonstrate how the
evolution kernels relate to infrared subtractions, and how the resolution of
infrared singular regions conspires with the structure of observables the
algorithm should be able to predict. The latter allows us to address evolution
in different kinematic variables, including energy ordering and angular cutoffs
in non-global observables. The soft factor and its evolution resembles a
hadronization model including effects such as colour reconnection, and gives
insight into the structure of power corrections in observables which require
soft gluon evolution.


title: Are GCMs obsolete?

abstract:
  Traditional general circulation models, or GCMs -- i.e. 3D dynamical models
with unresolved terms represented in equations with tunable parameters -- have
been a mainstay of climate research for several decades, and some of the
pioneering studies have recently been recognized by a Nobel prize in Physics.
Yet, there is considerable debate around their continuing role in the future.
Frequently mentioned as limitations of GCMs are the structural error and
uncertainty across models with different representations of unresolved scales;
and the fact that the models are tuned to reproduce certain aspects of the
observed Earth. We consider these shortcomings in the context of a future
generation of models that may address these issues through substantially higher
resolution and detail, or through the use of machine learning techniques to
match them better to observations, theory, and process models. It is our
contention that calibration, far from being a weakness of models, is an
essential element in the simulation of complex systems, and contributes to our
understanding of their inner workings. Models can be calibrated to reveal both
fine-scale detail, or the global response to external perturbations. New
methods enable us to articulate and improve the connections between the
different levels of abstract representation of climate processes, and our
understanding resides in an entire hierarchy of models where GCMs will continue
to play a central role for the foreseeable future..


title:
Ultimate accuracy of frequency to power conversion by single-electron
  injection

abstract:
  We analyze theoretically the properties of the recently introduced and
experimentally demonstrated converter of frequency to power. The system is
composed of a hybrid single-electron box with normal island and superconducting
lead, and the detector of the energy flow using a thermometer on a normal metal
bolometer. Here we consider its potential for metrology. The errors in power
arise mainly from inaccuracy of injecting electrons at the precise energy equal
to the energy gap of the superconductor. We calculate the main systematic error
in form of the excess average energy of the injected electrons and its
cumulants, and due to sub-gap leakage. We demonstrate by analytic and numerical
calculations that the systematic error in detection can, in principle, be made
much smaller than the injection errors, which also, with proper choice of
system parameters, can be very small, < 1 %, at low enough temperature. Finally
we propose a simplified configuration for metrological purposes.


title:
Analytic Solution for Perturbed Keplerian Motion Under Small
  Acceleration Using Averaging Theory

abstract:
  A novel approach is developed for analytic orbit propagation based on
asymptotic expansion with respect to a small perturbative acceleration. The
method improves upon existing first order asymptotic expansions by leveraging
on linear systems and averaging theories. The solution starts with the
linearization of Gauss planetary equations with respect to both the small
perturbation and the six orbital elements. Then, an approximate solution is
obtained in terms of secular and short period components. The method is tested
on a low-thrust maneuver scenario consisting of a Keplerian orbit perturbed by
a constant tangential acceleration, for which a solution can be obtained in
terms of elliptic integrals. Results show that the positional propagation error
is about one order of magnitude smaller with respect to state-of-the-art
methods. The position accuracy for a LEO orbit, apart from pathological cases,
is typically in the range of tens of meters for a dimensionless tangential
acceleration of 1e-5 after 5 orbital periods propagation.


title: The replication of non-inferiority and equivalence studies

abstract:
  Replication studies are increasingly conducted to assess credibility of
scientific findings. Most of these replication attempts target studies with a
superiority design, but there is a lack of methodology regarding the analysis
of replication studies with alternative types of designs. In order to fill this
gap, we adapt three approaches used for superiority settings to non-inferiority
and equivalence designs: the two-trials rule, the sceptical p-value approach
and the meta-analysis criterion. While the adaptation to the non-inferiority
design is relatively straightforward, the equivalence design requires more
effort. We propose to use the 'two one-sided test' (TOST) procedure and tailor
it to the replication setting. In addition, we formulate the different criteria
in terms of a 'success interval' for the relative effect size (replication to
original). The properties of the different approaches are studied in detail. In
particular, the two-trials rule does not penalize replication effect estimates
very close to the margin for large sample sizes. The meta-analysis criterion is
convenient as it combines the two estimates into one, but can lead to
replication success in odd situations. Finally, the sceptical p-value approach
seems well-suited as it penalizes a too large increase of the replication
effect estimate as compared to the original one, while taking into account the
direction of the latter in the calculation of the success interval.


title: Webs of 3d $\mathcal{N} = 2$ dualities with D-type superpotentials

abstract:
  We study 3d $\mathcal{N}=2$ dualities arising from the compactification of 4d
$\mathcal{N}=1$ $Usp(2 n)$ SQCD with two antisymmetric rank-two tensors and
$D_{k+2}$-type superpotential, with odd $k$. The analysis is carried out by
using field theory methods and by checking the various steps on the three
sphere partition function. Most of the results are based on a conjectural
confining duality that we do not prove but that fits consistently with the web
of dualities that we obtain. Along the analysis we recover dualities already
claimed in the literature and we propose new ones. The final picture that
emerges fits with the general scheme worked out for ordinary SQCD and for
adjoint SQCD with $A_k$-type superpotentials.


title:
Isomorph Invariant Dynamic Mechanical Analysis: A Molecular Dynamics
  Study

abstract:
  We simulate dynamic mechanical analysis experiments for the Kob-Andersen
binary Lennard-Jones system. For this, the SLLOD algorithm with time-dependent
strain rates is applied to give a sinusoidally varying strain at different
densities and temperatures. The starting point is a temperature scan at a fixed
reference density. Isomorph theory predicts that for other densities
corresponding temperatures can be identified at which the mechanical properties
are unchanged when scaled appropriately. We determine the isomorphically
equivalent temperatures by analysing how particle forces change upon scaling
configurations to the new density. Loss moduli expressed in suitable reduced
units are compared for isomorphic state points. When plotted against the
unscaled temperatures, these reduced loss curves are observed to collapse
indicating the validity of isomorph theory for dynamic mechanical analysis
experiments. Two different methods to determine isomorphic temperatures are
considered. While one of them breaks down for the largest density rescalings
considered in this study, the other one is still applicable in this region. The
decorrelation of force vectors upon rescaling is investigated as a possible
origin of this effect. Our results demonstrate that the simplification of the
phase diagram entailed by isomorph theory for a wide class of system is
relevant also for the mechanical properties of glasses.


title: Finding MNEMON: Reviving Memories of Node Embeddings

abstract:
  Previous security research efforts orbiting around graphs have been
exclusively focusing on either (de-)anonymizing the graphs or understanding the
security and privacy issues of graph neural networks. Little attention has been
paid to understand the privacy risks of integrating the output from graph
embedding models (e.g., node embeddings) with complex downstream machine
learning pipelines. In this paper, we fill this gap and propose a novel
model-agnostic graph recovery attack that exploits the implicit graph
structural information preserved in the embeddings of graph nodes. We show that
an adversary can recover edges with decent accuracy by only gaining access to
the node embedding matrix of the original graph without interactions with the
node embedding models. We demonstrate the effectiveness and applicability of
our graph recovery attack through extensive experiments.


title: Latent Aspect Detection from Online Unsolicited Customer Reviews

abstract:
  Within the context of review analytics, aspects are the features of products
and services at which customers target their opinions and sentiments. Aspect
detection helps product owners and service providers to identify shortcomings
and prioritize customers' needs, and hence, maintain revenues and mitigate
customer churn. Existing methods focus on detecting the surface form of an
aspect by training supervised learning methods that fall short when aspects are
latent in reviews. In this paper, we propose an unsupervised method to extract
latent occurrences of aspects. Specifically, we assume that a customer
undergoes a two-stage hypothetical generative process when writing a review:
(1) deciding on an aspect amongst the set of aspects available for the product
or service, and (2) writing the opinion words that are more interrelated to the
chosen aspect from the set of all words available in a language. We employ
latent Dirichlet allocation to learn the latent aspects distributions for
generating the reviews. Experimental results on benchmark datasets show that
our proposed method is able to improve the state of the art when the aspects
are latent with no surface form in reviews.


title: Free actions of groups on separated graph C*-algebras

abstract:
  In this paper we study free actions of groups on separated graphs and their
C*-algebras, generalizing previous results involving ordinary (directed)
graphs.
  We prove a version of the Gross-Tucker theorem for separated graphs yielding
a characterization of free actions on separated graphs via a skew product of
the (orbit) separated graph by a group labeling function. Moreover, we describe
the C*-algebras associated to these skew products as crossed products by
certain coactions coming from the labeling function on the graph. Our results
deal with both the full and the reduced C*-algebras of separated graphs.
  To prove our main results we use several techniques that involve certain
canonical conditional expectations defined on the C*-algebras of separated
graphs and their structure as amalgamated free products of ordinary graph
C*-algebras. Moreover, we describe Fell bundles associated with the coactions
of the appearing labeling functions. As a byproduct of our results, we deduce
that the C*-algebras of separated graphs always have a canonical Fell bundle
structure over the free group on their edges.


title:
Two Higgs doublets, Effective Interactions and a Strong First-Order
  Electroweak Phase Transition

abstract:
  It is well-known that type II two Higgs doublet models (2HDMs) can struggle
to facilitate a strong first-order electroweak phase transition in the early
universe whilst remaining theoretically appealing scenarios for many reasons.
We analyse this apparent shortfall from the perspective of additional new
physics. Starting from a consistent dimension-6 effective field theory Higgs
potential extension, we identify the Higgs potential extensions that provide
the necessary additional contributions required to achieve a strong first-order
electroweak phase transition and trace their phenomenological implications for
the Large Hadron Collider. In passing, we critically assess the reliability of
the dimension-6 approximation depending on the expected 2HDM phenomenology. In
particular, we focus on the role of Higgs pair production (resonant and
non-resonant) and interference effects expected in top final states, which are
the prime candidates of 2HDM exotics discoveries.


title: JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices

abstract:
  Will the Opportunity Zones (OZ) program, America's largest new place-based
policy in decades, generate neighborhood change? We compare single-family
housing price growth in OZs with price growth in areas that were eligible but
not included in the program. We also compare OZs to their nearest geographic
neighbors. Our most credible estimates rule out price impacts greater than 0.5
percentage points with 95% confidence, suggesting that, so far, home buyers
don't believe that this subsidy will generate major neighborhood change. OZ
status reduces prices in areas with little employment, perhaps because buyers
think that subsidizing new investment will increase housing supply. Mixed
evidence suggests that OZs may have increased residential permitting.


title:
Shift Equivalence Testing of Polynomials and Symbolic Summation of
  Multivariate Rational Functions

abstract:
  The Shift Equivalence Testing (SET) of polynomials is deciding whether two
polynomials $p(x_1, \ldots, x_m)$ and $q(x_1, \ldots, x_m)$ satisfy the
relation $p(x_1 + a_1, \ldots, x_m + a_m) = q(x_1, \ldots, x_m)$ for some $a_1,
\ldots, a_m$ in the coefficient field. The SET problem is one of basic
computational problems in computer algebra and algebraic complexity theory,
which was reduced by Dvir, Oliverira and Shpilka in 2014 to the Polynomial
Identity Testing (PIT) problem. This paper presents a general scheme for
designing algorithms to solve the SET problem which includes
Dvir-Oliverira-Shpilka's algorithm as a special case. With the algorithms for
the SET problem over integers, we give complete solutions to two challenging
problems in symbolic summation of multivariate rational functions, namely the
rational summability problem and the existence problem of telescopers for
multivariate rational functions. Our approach is based on the structure of
isotropy groups of polynomials introduced by Sato in 1960s. Our results can be
used to detect the applicability of the Wilf-Zeilberger method to multivariate
rational functions.


title: Manually Acquiring Targets from Multiple Viewpoints Using Video Feedback

abstract:
  Objective: The effect of camera viewpoint was studied when performing
visually obstructed psychomotor targeting tasks. Background: Previous research
in laparoscopy and robotic teleoperation found that complex perceptual-motor
adaptations associated with misaligned viewpoints corresponded to degraded
performance in manipulation. Because optimal camera positioning is often
unavailable in restricted environments, alternative viewpoints that might
mitigate performance effects are not obvious. Methods: A virtual
keyboard-controlled targeting task was remotely distributed to workers of
Amazon Mechanical Turk. The experiment was performed by 192 subjects for a
static viewpoint with independent parameters of target direction, Fitts' law
index of difficulty, viewpoint azimuthal angle (AA), and viewpoint polar angle
(PA). A dynamic viewpoint experiment was also performed by 112 subjects in
which the viewpoint AA changed after every trial. Results: AA and target
direction had significant effects on performance for the static viewpoint
experiment. Movement time and travel distance increased while AA increased
until there was a discrete improvement in performance for 180{\deg}. Increasing
AA from 225{\deg} to 315{\deg} linearly decreased movement time and distance.
There were significant main effects of current AA and magnitude of transition
for the dynamic viewpoint experiment. Orthogonal direction and no-change
viewpoint transitions least affected performance. Conclusions: Viewpoint
selection should aim to minimize associated rotations within the manipulation
plane when performing targeting tasks whether implementing a static or dynamic
viewing solution. Because PA rotations had negligible performance effects, PA
adjustments may extend the space of viable viewpoints. Applications: These
results can inform viewpoint-selection for visual feedback during psychomotor
tasks.


title:
Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue
  Representations Incrementally Encode Shared Knowledge

abstract:
  Cognitively plausible visual dialogue models should keep a mental scoreboard
of shared established facts in the dialogue context. We propose a theory-based
evaluation method for investigating to what degree models pretrained on the
VisDial dataset incrementally build representations that appropriately do
scorekeeping. Our conclusion is that the ability to make the distinction
between shared and privately known statements along the dialogue is moderately
present in the analysed models, but not always incrementally consistent, which
may partially be due to the limited need for grounding interactions in the
original task.


title: Appending Information Reconciliation for Quantum Key Distribution

abstract:
  Information reconciliation (IR), which corrects the errors in the sifted
keys, directly determines the secure key rate and the link distance of quantum
key distribution (QKD) systems. In this article, we propose an appending
information reconciliation (AIR) scheme based on polar codes, which achieves
high efficiency and ultra-low failure probability simultaneously, by gradually
disclosing the bit values of the polarized channels with high error
probability. The experimental results show that the efficiency of the proposed
AIR scheme is closer to the Shannon limit, compared with the state-of-the-art
implemented polar codes-based IR schemes, with the overall failure probability
around 1E-8, especially when performed with smaller block sizes. Moreover, the
efficiency of the proposed AIR scheme is 1.046, when the block size is 1 Gb and
the quantum bit error rate of 0.02. Therefore, the proposed AIR scheme can
further eradicate the performance gap between theory and implementation for QKD
systems.


title: The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark

abstract:
  We present Visuelle 2.0, the first dataset useful for facing diverse
prediction problems that a fast-fashion company has to manage routinely.
Furthermore, we demonstrate how the use of computer vision is substantial in
this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing
products of Nuna Lie, a famous Italian company with hundreds of shops located
in different areas within the country. In particular, we focus on a specific
prediction problem, namely short-observation new product sale forecasting
(SO-fore). SO-fore assumes that the season has started and a set of new
products is on the shelves of the different stores. The goal is to forecast the
sales for a particular horizon, given a short, available past (few weeks),
since no earlier statistics are available. To be successful, SO-fore approaches
should capture this short past and exploit other modalities or exogenous data.
To these aims, Visuelle 2.0 is equipped with disaggregated data at the
item-shop level and multi-modal information for each clothing item, allowing
computer vision approaches to come into play. The main message that we deliver
is that the use of image data with deep networks boosts performances obtained
when using the time series in long-term forecasting scenarios, ameliorating the
WAPE by 8.2% and the MAE by 7.7%. The dataset is available at:
https://humaticslab.github.io/forecasting/visuelle.


title:
Automatic generation of complementary auxiliary basis sets (CABS) for
  explicitly correlated methods

abstract:
  Explicitly correlated calculations, aside from the orbital basis set,
typically require three auxiliary basis sets: JK (Coulomb-exchange fitting),
RI-MP2 (resolution of the identity MP2), and CABS (complementary auxiliary
basis set). If unavailable for the orbital basis set and chemical elements of
interest, the first two can be auto-generated on the fly using existing
algorithms, but not the third. In this paper, we present a quite simple
algorithm named autoCABS; a Python implementation under a free software license
is offered at Github. For the cc-pVnZ-F12 (n=D,T,Q,5) and the W4-08
thermochemical benchmark, we demonstrate that autoCABS-generated CABS basis
sets are comparable in quality to purpose-optimized OptRI basis sets from the
literature, and that the quality difference becomes entirely negligible as n
increases.


title: Planting Undetectable Backdoors in Machine Learning Models

abstract:
  Given the computational cost and technical expertise required to train
machine learning models, users may delegate the task of learning to a service
provider. We show how a malicious learner can plant an undetectable backdoor
into a classifier. On the surface, such a backdoored classifier behaves
normally, but in reality, the learner maintains a mechanism for changing the
classification of any input, with only a slight perturbation. Importantly,
without the appropriate "backdoor key", the mechanism is hidden and cannot be
detected by any computationally-bounded observer. We demonstrate two frameworks
for planting undetectable backdoors, with incomparable guarantees.
  First, we show how to plant a backdoor in any model, using digital signature
schemes. The construction guarantees that given black-box access to the
original model and the backdoored version, it is computationally infeasible to
find even a single input where they differ. This property implies that the
backdoored model has generalization error comparable with the original model.
Second, we demonstrate how to insert undetectable backdoors in models trained
using the Random Fourier Features (RFF) learning paradigm or in Random ReLU
networks. In this construction, undetectability holds against powerful
white-box distinguishers: given a complete description of the network and the
training data, no efficient distinguisher can guess whether the model is
"clean" or contains a backdoor.
  Our construction of undetectable backdoors also sheds light on the related
issue of robustness to adversarial examples. In particular, our construction
can produce a classifier that is indistinguishable from an "adversarially
robust" classifier, but where every input has an adversarial example! In
summary, the existence of undetectable backdoors represent a significant
theoretical roadblock to certifying adversarial robustness.


title:
Non-equilibrium Phonon Thermal Resistance at MoS2/Oxide and
  Graphene/Oxide Interfaces

abstract:
  Accurate measurements and physical understanding of thermal boundary
resistance (R) of two-dimensional (2D) materials are imperative for effective
thermal management of 2D electronics and photonics. In previous studies, heat
dissipation from 2D material devices was presumed to be dominated by phonon
transport across the interfaces. In this study, we find that in addition to
phonon transport, thermal resistance between non-equilibrium phonons in the 2D
materials could play a critical role too when the 2D material devices are
internally self-heated, either optically or electrically. We accurately measure
R of oxide/MoS2/oxide and oxide/graphene/oxide interfaces for three oxides
(SiO2, HfO2, Al2O3) by differential time-domain thermoreflectance (TDTR). Our
measurements of R across these interfaces with external heating are 2-to-4
times lower than previously reported R of the similar interfaces measured by
Raman thermometry with internal self-heating. Using a simple model, we show
that the observed discrepancy can be explained by an additional internal
thermal resistance (Rint) between non-equilibrium phonons present during Raman
measurements. We subsequently estimate that for MoS2 and graphene, Rint is
about 31 and 22 m2 K/GW, respectively. The values are comparable to the thermal
resistance due to finite phonon transmission across interfaces of 2D materials
and thus cannot be ignored in the design of 2D material devices. Moreover, the
non-equilibrium phonons also lead to a different temperature dependence than
that by phonon transport. As such, our work provides important insights into
physical understanding of heat dissipation in 2D material devices.


title:
Arithmetic level raising for certain quaternionic unitary Shimura
  variety

abstract:
  In this article we prove an arithmetic level raising theorem for the
symplectic group of degree four in the ramified case. This result is a key step
towards the Beilinson-Bloch-Kato conjecture for certain Rankin-Selberg motives
associated to orthogonal groups within the framework of the Gan-Gross-Prasad
conjecture. The theorem itself can be also viewed as an analogue of the Ihara's
lemma or the Tate conjecture for special fibers of Shimura varieties at
ramified characteristics. The proof relies heavily on the description of the
supersingular locus of certain quaternionic unitary Shimura variety which is
closely related to the classical Siegel threefold.


title:
Long range entanglement in quantum dot systems under the Fermi-Hubbard
  approach

abstract:
  In the present paper we are interested in analyzing the pairwise entanglement
in quantum dots, as ququart systems, naturally described by the Fermi-Hubbard
model. Using the lower bound of concurrence we show the effect of the Coulomb
interaction on the pairwise entanglement and creating entanglement within the
pairs. Specifically, it is shown that the range of entanglement can be extended
to the third neighboring site for a system size of $L=4$, while for $L>4$ the
range could be created and extended to the third neighboring site by means of
the Coulomb interaction. A rigorous description of the pairs is given in terms
of a local half filled state associated to each pair with an electron number
$N=2$ and a spin $S=0$. A thorough study of this state provides a proper
explanation related to the pairwise entanglement, namely its amount and its
behavior under the effect of the Coulomb interaction as well as the system's
size.


title:
Impact of centre vortex removal on the Landau-gauge quark propagator in
  dynamical QCD

abstract:
  The important role of centre vortices in dynamical chiral symmetry breaking
and corresponding dynamical mass generation has been demonstrated in pure gauge
studies of the Landau-gauge quark propagator. We present initial results of our
investigation into the impact of centre vortex removal on the Landau-gauge
propagator on dynamical gauge fields. To ensure sensitivity to the topology of
the gauge fields, the propagator is computed with overlap fermions. Upon
removal of centre vortices we find that dynamical mass generation is
suppressed. The quark renormalisation function remains remarkably flat except
in the deep infrared where it exhibits significant suppression. These effects
become more prominent at lighter quark masses.


title:
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral
  Denoising Package

abstract:
  As with any physical instrument, hyperspectral cameras induce different kinds
of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial
step for analyzing hyperspectral images (HSIs). Conventional computational
methods rarely use GPUs to improve efficiency and are not fully open-source.
Alternatively, deep learning-based methods are often open-source and use GPUs,
but their training and utilization for real-world applications remain
non-trivial for many researchers. Consequently, we propose HyDe: the first
open-source, GPU-accelerated Python-based, hyperspectral image denoising
toolbox, which aims to provide a large set of methods with an easy-to-use
environment. HyDe includes a variety of methods ranging from low-rank
wavelet-based methods to deep neural network (DNN) models. HyDe's interface
dramatically improves the interoperability of these methods and the performance
of the underlying functions. In fact, these methods maintain similar HSI
denoising performance to their original implementations while consuming nearly
ten times less energy. Furthermore, we present a method for training DNNs for
denoising HSIs which are not spatially related to the training dataset, i.e.,
training on ground-level HSIs for denoising HSIs with other perspectives
including airborne, drone-borne, and space-borne. To utilize the trained DNNs,
we show a sliding window method to effectively denoise HSIs which would
otherwise require more than 40 GB. The package can be found at:
\url{https://github.com/Helmholtz-AI-Energy/HyDe}.


title:
Few Observation Binary Orbit Solver (FOBOS) from two (or more)
  astrometric observations

abstract:
  We have developed a new, fast method of estimating the orbital properties of
a binary or triple system using as few as two epochs of astrometric data. FOBOS
(Few Observation Binary Orbit Solver) uses a flat prior brute force Monte Carlo
method to produce probability density functions of the likely orbital
parameters. We test the code on fake observations and show that it can (fairly
often) constrain the semi-major axis to within a factor of 2-3, and the
inclination to within $\sim$20$^{\circ}$ from only two astrometric
observations. We also show that the 68 and 95 per cent confidence intervals are
statistically reliable. Applying this method to triple systems allows the
relative inclination of the secondary and tertiary star orbits to be
constrained. FOBOS can usually find a statistically significant number of
possible matches in CPU minutes for binary systems, and CPU hours for triple
systems.


title: Gravitational wave memory and its tail in cosmology

abstract:
  We study gravitational wave memory effect in the FRW cosmological model with
matter and cosmological constant. Since the background is curved, gravitational
radiation develops a tail part arriving after the main signal that travels
along the past light cone of the observer. First we discuss first order
gravitational wave sourced by a binary system, and find that the tail only
gives a negligible memory, in accord with previous results. Then we study the
nonlinear memory effect coming from induced gravitational radiation sourced by
first order gravitational radiation propagating over cosmological distances. In
the light cone part of the induced gravitational wave we find a novel term
missed in previous studies of the cosmological memory effect. Furthermore, we
show that the induced gravitational wave has a tail part that slowly
accumulates after the light cone part has passed and grows to a sizeable
magnitude over a cosmological timescale. This tail part of the memory effect
will be a new component in the stochastic gravitational wave background.


title: Gibbs partitions: a comprehensive phase diagram

abstract:
  We study Gibbs partition models, also known as composition schemes. Our main
results comprehensively describe their phase diagram, including a phase
transition from the convergent case described in Stufler (2018, Random
Structures \& Algorithms) to a new dense regime characterized by a linear
number of components with fluctuations of smaller order quantified by an
$\alpha$-stable law for $1< \alpha \le 2$. We prove a functional scaling limit
for a process whose jumps correspond to the component sizes and discuss
applications to extremal component sizes. At the transition we observe a
mixture of the two asymptotic shapes. We also treat extended composition
schemes and prove a local limit theorem in an dilute regime with the limiting
law being related to an $\alpha$-stable law for $0< \alpha < 1$.


title:
Cutting rules on a cylinder and simplified diagrammatic approach to $CP$
  violation in quantum kinetic theory

abstract:
  We briefly explain a novel diagrammatic method for including thermal
corrections in $CP$ asymmetric reaction rates entering the quantum Boltzmann
equation. In thermal equilibrium, the asymmetries have to cancel precisely due
to the $S$-matrix unitarity and $CPT$ invariance. Such cancelations are easy to
track in zero-temperature leading-order calculations. However, accounting for
medium effects requires modification of the on-shell part of propagators and,
consequently, the $CP$ violating rates. In the literature, the correct form of
the statistical factors in the asymmetry source term has been obtained
employing the real-time-formalism of non-equilibrium field theory in a certain
approximation. We demonstrate that the same results can be obtained by summing
an infinite sequence of zero-temperature asymmetries. Those are derived from
cuttings of forward diagrams drawn on a cylindrical surface, while thermal
corrections come from windings of their propagators. The procedure is entirely
diagrammatic, and the $CPT$ and unitarity cancelations are formulated for
thermally corrected reaction rate asymmetries. The simplification achieved is
the primary focus of our work. The aspect of infrared finiteness in
higher-order corrections will also be discussed.


title: Repulsive Fermi and Bose Polarons in Quantum Gases

abstract:
  Polaron quasiparticles are formed when a mobile impurity is coupled to the
elementary excitations of a many-particle background. In the field of ultracold
atoms, the study of the associated impurity problem has attracted a growing
interest over the last fifteen years. Polaron quasiparticle properties are
essential to our understanding of a variety of paradigmatic quantum many-body
systems realized in ultracold atomic gases and in the solid state, from
imbalanced Bose-Fermi and Fermi-Fermi mixtures to fermionic Hubbard models. In
this topical review, we focus on the so-called repulsive polaron branch, which
emerges as an excited many-body state in systems with underlying attractive
interactions such as ultracold atomic mixtures, and is characterized by an
effective repulsion between the impurity and the surrounding medium. We give a
brief account of the current theoretical and experimental understanding of
repulsive polaron properties, for impurities embedded in both fermionic and
bosonic media, and we highlight open issues deserving future investigations.


title: The average Euler-genus of the vertex-amalgamation of signed graphs

abstract:
  In this paper, we first generalize a theorem for counting the number of faces
of an oriented embedding of a graph that passing through a given cut-edge set
[S. Stahl, Trans. Amer. Math. Soc. 259 (1980), 129--145] to all surfaces. Then
we extend Stahl's bounds for the average genus of the vertex-amalgamation of
graphs [S. Stahl, Discrete Math. 142 (1995), 235--245] to signed graphs.


title: Cross-Image Relational Knowledge Distillation for Semantic Segmentation

abstract:
  Current Knowledge Distillation (KD) methods for semantic segmentation often
guide the student to mimic the teacher's structured information generated from
individual data samples. However, they ignore the global semantic relations
among pixels across various images that are valuable for KD. This paper
proposes a novel Cross-Image Relational KD (CIRKD), which focuses on
transferring structured pixel-to-pixel and pixel-to-region relations among the
whole images. The motivation is that a good teacher network could construct a
well-structured feature space in terms of global pixel dependencies. CIRKD
makes the student mimic better structured semantic relations from the teacher,
thus improving the segmentation performance. Experimental results over
Cityscapes, CamVid and Pascal VOC datasets demonstrate the effectiveness of our
proposed approach against state-of-the-art distillation methods. The code is
available at https://github.com/winycg/CIRKD.


title: Non-autonomous hybrid stochastic systems with delays

abstract:
  The aim of this paper is to study the dynamical behavior of non-autonomous
stochastic hybrid systems with delays. By general Krylov-Bogolyubov's method,
we first obtain the sufficient conditions for the existence of an evolution
system of measures of the non-autonomous stochastic system and also give some
easily verifiable conditions. We then prove a sufficient condition for
convergence of evolution systems of measures as the delay approaches zero. As
an application of the abstract theory, we first prove the existence of
evolution systems of measures for stochastic system with time-vary delays,
which comes from feedback control problem based on discrete-time state
observations. Furthermore, when observation interval goes to zero, we show
every limit point of a sequence of evolution system of measures of the
non-autonomous stochastic system must be a evolution system of measures of the
limiting system.


title:
Trust2Vec: Large-Scale IoT Trust Management System based on Signed
  Network Embeddings

abstract:
  A trust management system (TMS) is an integral component of any IoT network.
A reliable trust management system must guarantee the network security, data
integrity, and act as a referee that promotes legitimate devices, and punishes
any malicious activities. Trust scores assigned by TMSs reflect devices'
reputations, which can help predict the future behaviours of network entities
and subsequently judge the reliability of different network entities in IoT
networks. Many TMSs have been proposed in the literature, these systems are
designed for small-scale trust attacks, and can deal with attacks where a
malicious device tries to undermine TMS by spreading fake trust reports.
However, these systems are prone to large-scale trust attacks. To address this
problem, in this paper, we propose a TMS for large-scale IoT systems called
Trust2Vec, which can manage trust relationships in large-scale IoT systems and
can mitigate large-scale trust attacks that are performed by hundreds of
malicious devices. Trust2Vec leverages a random-walk network exploration
algorithm that navigates the trust relationship among devices and computes
trust network embeddings, which enables it to analyze the latent network
structure of trust relationships, even if there is no direct trust rating
between two malicious devices. To detect large-scale attacks, suck as
self-promoting and bad-mouthing, we propose a network embeddings community
detection algorithm that detects and blocks communities of malicious nodes. The
effectiveness of Trust2Vec is validated through large-scale IoT network
simulation. The results show that Trust2Vec can achieve up to 94\% mitigation
rate in various network scenarios.


title:
Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural
  Network

abstract:
  Atmospheric turbulence distorts visual imagery and is always problematic for
information interpretation by both human and machine. Most well-developed
approaches to remove atmospheric turbulence distortion are model-based.
However, these methods require high computation and large memory preventing
their feasibility of real-time operation. Deep learning-based approaches have
hence gained more attention but currently work efficiently only on static
scenes. This paper presents a novel learning-based framework offering short
temporal spanning to support dynamic scenes. We exploit complex-valued
convolutions as phase information, altered by atmospheric turbulence, is
captured better than using ordinary real-valued convolutions. Two concatenated
modules are proposed. The first module aims to remove geometric distortions
and, if enough memory, the second module is applied to refine micro details of
the videos. Experimental results show that our proposed framework efficiently
mitigate the atmospheric turbulence distortion and significantly outperforms
the existing methods.


title:
Observable adjustments in single-index models for regularized
  M-estimators

abstract:
  We consider observations $(X,y)$ from single index models with unknown link
function, Gaussian covariates and a regularized M-estimator $\hat\beta$
constructed from convex loss function and regularizer. In the regime where
sample size $n$ and dimension $p$ are both increasing such that $p/n$ has a
finite limit, the behavior of the empirical distribution of $\hat\beta$ and the
predicted values $X\hat\beta$ has been previously characterized in a number of
models: The empirical distributions are known to converge to proximal operators
of the loss and penalty in a related Gaussian sequence model, which captures
the interplay between ratio $p/n$, loss, regularization and the data generating
process. This connection between$(\hat\beta,X\hat\beta)$ and the corresponding
proximal operators require solving fixed-point equations that typically involve
unobservable quantities such as the prior distribution on the index or the link
function.
  This paper develops a different theory to describe the empirical distribution
of $\hat\beta$ and $X\hat\beta$: Approximations of $(\hat\beta,X\hat\beta)$ in
terms of proximal operators are provided that only involve observable
adjustments. These proposed observable adjustments are data-driven, e.g., do
not require prior knowledge of the index or the link function. These new
adjustments yield confidence intervals for individual components of the index,
as well as estimators of the correlation of $\hat\beta$ with the index. The
interplay between loss, regularization and the model is thus captured in a
data-driven manner, without solving the fixed-point equations studied in
previous works. The results apply to both strongly convex regularizers and
unregularized M-estimation. Simulations are provided for the square and
logistic loss in single index models including logistic regression and 1-bit
compressed sensing with 20\% corrupted bits.


title:
Role of Polarons in Single-Atom Catalysts: Case Study of Me1 [Au1, Pt1,
  and Rh1] on TiO2(110)

abstract:
  The local environment of metal-oxide supported single-atom catalysts plays a
decisive role in the surface reactivity and related catalytic properties. The
study of such systems is complicated by the presence of point defects on the
surface, which are often associated with the localization of excess charge in
the form of polarons. This can affect the stability, the electronic
configuration, and the local geometry of the adsorbed adatoms. In this work,
through the use of density functional theory and surface-sensitive experiments,
we study the adsorption of Rh1, Pt1, and Au1 metals on the reduced TiO2(110)
surface; a prototypical polaronic material. A systematic analysis of the
adsorption configurations and oxidation states of the adsorbed metals reveals
different types of couplings between adsorbates and polarons. As confirmed by
scanning tunneling microscopy measurements, the favored Pt1 and Au1 adsorption
at oxygen vacancy sites is associated with a strong electronic charge transfer
from polaronic states to adatom orbitals, which results in a reduction of the
adsorbed metal. In contrast, the Rh1 adatoms interact weakly with the excess
charge, which leaves the polarons largely unaffected. Our results show that an
accurate understanding of the properties of single-atom catalysts on oxide
surfaces requires a careful account of the interplay between adatoms, vacancy
sites, and polarons.


title:
Presentations for wreath products involving symmetric inverse monoids
  and categories

abstract:
  Wreath products involving symmetric inverse monoids/semigroups/categories
arise in many areas of algebra and science, and presentations by generators and
relations are crucial tools in such studies. The current paper finds such
presentations for $M\wr\mathcal I_n$, $M\wr\operatorname{Sing}(\mathcal I_n)$
and $M\wr\mathcal I$. Here $M$ is an arbitrary monoid, $\mathcal I_n$ is the
symmetric inverse monoid, $\operatorname{Sing}(\mathcal I_n)$ its singular
ideal, and $\mathcal I$ is the symmetric inverse category.


title:
Measurement of energy differential spectrum of cosmic-ray muons below
  400 MeV

abstract:
  Recent applications of cosmic-ray muons require accurate modeling of their
flux at low-energy. However, no measurement has been reported below 400 MeV.
Therefore, we developed a full-absorption muon energy spectrometer to obtain
energy differential flux below 400 MeV. Because our main detector can measure
muon energies below 75 MeV, an energy degradation method is adopted (using 5-
and 20-cm thick lead blocks) to shift the sensitive energy range. Three
measurements were performed (in the normal mode and the two energy degrading
modes) for 10 and 11 days each. The measurement results were compared with an
analytical cosmic-ray model, PARMA (the particle and heavy ion transport code
system-based analytical radiation model in the atmosphere) and we found that
the model can precisely predict the lower energy part of the flux.


title:
Benchmarking an improved statistical adiabatic channel model for
  competing inelastic and reactive processes

abstract:
  Inelastic collisions and elementary chemical reactions proceeding through the
formation and subsequent decay of an intermediate collision complex, with an
associated deep well on the potential energy surface, pose a challenge for
accurate fully quantum mechanical approaches, such as the close-coupling
method. In this study, we report on the theoretical prediction of
temperature-dependent state-to-state rate coefficients for these complex-mode
processes, using a statistical quantum method. This statistical adiabatic
channel model is benchmarked by a direct comparison using accurate rate
coefficients from the literature for a number of systems (H2 + H+, HD + H+, SH+
+ H, and CH+ + H) of interest in astrochemistry and astrophysics. For all of
the systems considered, an error of less than factor 2 was found, at least for
the dominant transitions and at low temperatures, which is sufficiently
accurate for applications in the above mentioned disciplines.


title:
$\beta^{2}$ corrections to spherical EDF calculations for
  root-mean-square charge radii

abstract:
  Root-mean-square charge radii are discussed in terms of spherical Energy
Density Functional (EDF) models corrected for quadrupole deformations.
Comparisons between experiment and theory are made for the absolute radii of
all even-even nuclei, for the isotonic shift between cadmium and tin isotopes,
the isotopic shifts of the calcium isotopes and the isotonic shift for nuclei
with $ N=28 $. We conclude that the data are well described in this approach,
except for the sharp rise just after the neutron magic numbers.