title:
Comparison of the interaction between scalar fields as dark energy with
  neutrinos and put constraint on neutrino mass in these models

abstract:
  In this paper, we considered the interaction neutrino with three scalar
fields(phantom, quintessence, and quintom model) and put constraints on the
total mass of neutrinos, and comparison the interaction constant $ \beta $ in
these models. The data were used in this paper are supernova type Ia(pantheon
catalog), CMB and BAO data. For each model, we first investigate the results
obtained from Pantheon data, then survey the CMB and BAO data, and finally, the
total data from these catalogs. It seems that using a combination of data
produces more favorable results. For combination data, we find that the total
mass of neutrino $\sum m_{\nu}< 0.121 eV$ $(95\% $ Confidence Level (C.L.) for
quintom model and $\sum m_{\nu}< 0.19 eV$ $(95\% $ Confidence Level (C.L.) for
phantom model and $\sum m_{\nu}< 0.124 eV$ $(95\% $ Confidence Level (C.L.) for
quintessence model. These results are in good agreement with the results of
Planck 2018 where the limit of the total neutrino mass is $\sum m_{\nu}<0.12
eV$ ($95\%$ C.L., TT, TE, EE+lowE+lensing+BAO)


title: ReACC: A Retrieval-Augmented Code Completion Framework

abstract:
  Code completion, which aims to predict the following code token(s) according
to the code context, can improve the productivity of software development.
Recent work has proved that statistical language modeling with transformers can
greatly improve the performance in the code completion task via learning from
large-scale source code datasets. However, current approaches focus only on
code context within the file or project, i.e. internal context. Our distinction
is utilizing "external" context, inspired by human behaviors of copying from
the related code snippets when writing code. Specifically, we propose a
retrieval-augmented code completion framework, leveraging both lexical copying
and referring to code with similar semantics by retrieval. We adopt a
stage-wise training approach that combines a source code retriever and an
auto-regressive language model for programming language. We evaluate our
approach in the code completion task in Python and Java programming languages,
achieving a state-of-the-art performance on CodeXGLUE benchmark.


title:
Boundary points, minimal $L^2$ integrals and concavity property II: on
  weakly pseudoconvex K\"ahler manifolds

abstract:
  In this article, we consider minimal $L^2$ integrals on the sublevel sets of
plurisubharmonic functions on weakly pseudoconvex K\"ahler manifolds with
Lebesgue measurable gain related to modules at boundary points of the sublevel
sets, and establish a concavity property of the minimal $L^2$ integrals. As
applications, we present a necessary condition for the concavity degenerating
to linearity, a concavity property related to modules at inner points of the
sublevel sets, an optimal support function related to the modules, a strong
openness property of the modules and a twisted version, an effectiveness result
of the strong openness property of the modules.


title:
CODA: A Real-World Road Corner Case Dataset for Object Detection in
  Autonomous Driving

abstract:
  Contemporary deep-learning object detection methods for autonomous driving
usually assume prefixed categories of common traffic participants, such as
pedestrians and cars. Most existing detectors are unable to detect uncommon
objects and corner cases (e.g., a dog crossing a street), which may lead to
severe accidents in some situations, making the timeline for the real-world
application of reliable autonomous driving uncertain. One main reason that
impedes the development of truly reliably self-driving systems is the lack of
public datasets for evaluating the performance of object detectors on corner
cases. Hence, we introduce a challenging dataset named CODA that exposes this
critical problem of vision-based detectors. The dataset consists of 1500
carefully selected real-world driving scenes, each containing four object-level
corner cases (on average), spanning 30+ object categories. On CODA, the
performance of standard object detectors trained on large-scale autonomous
driving datasets significantly drops to no more than 12.8% in mAR. Moreover, we
experiment with the state-of-the-art open-world object detector and find that
it also fails to reliably identify the novel objects in CODA, suggesting that a
robust perception system for autonomous driving is probably still far from
reach. We expect our CODA dataset to facilitate further research in reliable
detection for real-world autonomous driving. Our dataset will be released at
https://coda-dataset.github.io.


title:
Meta Ordinal Regression Forest for Medical Image Classification with
  Ordinal Labels

abstract:
  The performance of medical image classification has been enhanced by deep
convolutional neural networks (CNNs), which are typically trained with
cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal
property in nature, e.g., the development from benign to malignant tumor, CE
loss cannot take into account such ordinal information to allow for better
generalization. To improve model generalization with ordinal information, we
propose a novel meta ordinal regression forest (MORF) method for medical image
classification with ordinal labels, which learns the ordinal relationship
through the combination of convolutional neural network and differential forest
in a meta-learning framework. The merits of the proposed MORF come from the
following two components: a tree-wise weighting net (TWW-Net) and a grouped
feature selection (GFS) module. First, the TWW-Net assigns each tree in the
forest with a specific weight that is mapped from the classification loss of
the corresponding tree. Hence, all the trees possess varying weights, which is
helpful for alleviating the tree-wise prediction variance. Second, the GFS
module enables a dynamic forest rather than a fixed one that was previously
used, allowing for random feature perturbation. During training, we
alternatively optimize the parameters of the CNN backbone and TWW-Net in the
meta-learning framework through calculating the Hessian matrix. Experimental
results on two medical image classification datasets with ordinal labels, i.e.,
LIDC-IDRI and Breast Ultrasound Dataset, demonstrate the superior performances
of our MORF method over existing state-of-the-art methods.


title: Snowmass White Paper: Flavor Model Building

abstract:
  In this white paper for the Snowmass process, we summarize the role flavor
model building plays in the quest for new physics. We review approaches to
address the non-generic flavor structure of the Standard Model and discuss how
new physics models can be made compatible with the stringent constraints from
flavor changing processes that indirectly probe very high scales. We also give
an overview of the persistent anomalies in B decays and the anomalous magnetic
moment of the muon and some of their most popular new physics explanations.


title: Kondo cooling in quantum impurity systems

abstract:
  The Peltier effect is the reverse phenomenon of the Seebeck effect, and has
been observed experimentally in nanoscale junctions. However, despite its
promising applications in local cooling of nanoelectronic devices, the role of
strong electron correlations on such a phenomenon is still unclear. Here, by
analyzing the thermoelectric properties of quantum impurity systems out of
equilibrium, we unveil the essential role of electron-electron interactions and
quantum resonant states in Peltier cooling, leading to the prediction of the
Kondo cooling phenomenon. The existence of such Kondo cooling is validated by a
reverse heat current and a lowered local temperature in a model junction. The
discovery of this unconventional Peltier cooling offers a new approach toward
nano-refrigeration, and highlights the unique role of strong electron
correlations in nonequilibrium quantum systems.


title:
Securing the Classification of COVID-19 in Chest X-ray Images: A
  Privacy-Preserving Deep Learning Approach

abstract:
  Deep learning (DL) is being increasingly utilized in healthcare-related
fields due to its outstanding efficiency. However, we have to keep the
individual health data used by DL models private and secure. Protecting data
and preserving the privacy of individuals has become an increasingly prevalent
issue. The gap between the DL and privacy communities must be bridged. In this
paper, we propose privacy-preserving deep learning (PPDL)-based approach to
secure the classification of Chest X-ray images. This study aims to use Chest
X-ray images to their fullest potential without compromising the privacy of the
data that it contains. The proposed approach is based on two steps: encrypting
the dataset using partially homomorphic encryption and training/testing the DL
algorithm over the encrypted images. Experimental results on the COVID-19
Radiography database show that the MobileNetV2 model achieves an accuracy of
94.2% over the plain data and 93.3% over the encrypted data.


title: Semileptonic B_c decays to P-wave charmonia in the light-cone QCDSR

abstract:
  B_c mesons are laboratories for probing heavy quark physics. Furthermore,
they decay into charmonia and hence their decays can be used to analyze
possible exotic charmonium. In this work, the semileptonic decays of B_c mesons
into P wave charmonia are analysed using QCD sum rules (QCDSR). The
distributions amplitudes for the charmonia are taken from a quark model
computation. The obtained decay rates for the ground state and excited
charmonia are compared with the results found in the literature.


title: Computable paradoxical decompositions

abstract:
  We prove a computable version of Hall's Harem Theorem and apply it to
computable versions of Tarski's alternative theorem.


title:
Evaluating BERT-based Pre-training Language Models for Detecting
  Misinformation

abstract:
  It is challenging to control the quality of online information due to the
lack of supervision over all the information posted online. Manual checking is
almost impossible given the vast number of posts made on online media and how
quickly they spread. Therefore, there is a need for automated rumour detection
techniques to limit the adverse effects of spreading misinformation. Previous
studies mainly focused on finding and extracting the significant features of
text data. However, extracting features is time-consuming and not a highly
effective process. This study proposes the BERT- based pre-trained language
models to encode text data into vectors and utilise neural network models to
classify these vectors to detect misinformation. Furthermore, different
language models (LM) ' performance with different trainable parameters was
compared. The proposed technique is tested on different short and long text
datasets. The result of the proposed technique has been compared with the
state-of-the-art techniques on the same datasets. The results show that the
proposed technique performs better than the state-of-the-art techniques. We
also tested the proposed technique by combining the datasets. The results
demonstrated that the large data training and testing size considerably
improves the technique's performance.


title:
S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular
  Image

abstract:
  We present a novel face reconstruction method capable of reconstructing
detailed face geometry, spatially varying face reflectance from a single
monocular image. We build our work upon the recent advances of DNN-based
auto-encoders with differentiable ray tracing image formation, trained in
self-supervised manner. While providing the advantage of learning-based
approaches and real-time reconstruction, the latter methods lacked fidelity. In
this work, we achieve, for the first time, high fidelity face reconstruction
using self-supervised learning only. Our novel coarse-to-fine deep architecture
allows us to solve the challenging problem of decoupling face reflectance from
geometry using a single image, at high computational speed. Compared to
state-of-the-art methods, our method achieves more visually appealing
reconstruction.


title:
Implications of the cosmological 21-cm absorption profile for
  high-redshift star formation and deep JWST surveys

abstract:
  Apart from its anomalously large depth, the cosmological 21-cm absorption
signal measured by the EDGES collaboration also has a shape that is distinctly
different from theoretical predictions. This peculiar shape requires the
establishment of a cosmic Ly $\alpha$ background within $25\,$Myr at redshift
$z\sim 20$ and a cosmic X-ray background within $50\,$Myr at redshift $z\sim
15$. Models with non-traditional components such as super-adiabatic baryonic
cooling or an excess radio background explain the depth of the observed
profile, but still conspicuously fail to explain its shape. In this paper, we
quantify the requirements imposed by the EDGES measurement on sources of Ly
$\alpha$ and X-ray photons at cosmic dawn. In extreme cases, the Ly $\alpha$
and X-ray emissivities require to be enhanced by up to an order of magnitude
relative to traditional models. Furthermore, this enhancement needs to be
active only for a short duration. We find that under conventional assumptions
for the cosmic star formation density, standard stellar populations are
incapable of meeting these conditions. Only highly unusual models of massive
metal-free stars seem to provide a possible mechanism. Conversely, if the
sources of Ly $\alpha$ and X-ray photons are compelled to have standard
properties, the EDGES measurement puts strong demands on the cosmic star
formation rate density. This provides interesting falsifiable predictions for
high-redshift galaxy surveys enabled by JWST. We derive predictions for galaxy
UV luminosity functions and number densities, and show that a deep JWST survey
with a limiting UV magnitude of $m_\mathrm{UV,lim}=32$ would potentially be
able to rule out the predictions enforced by the EDGES measurement.


title:
Disc instability and bar formation: a detailed view from the
  IllustrisTNG

abstract:
  Assessing in a simple manner the stability of disc galaxies against bar
formation is a considerable challenge, especially for semi-analytic models of
galaxy formation. Thus far, the standard approach to address this has been the
analytical criterion originally proposed by Efstathiou, Lake, and Negroponte
(ELN-criterion), although several works have questioned its reliability. Here
we assess the ELN-criterion performance by making use of z=0 barred and
unbarred disc galaxy samples extracted from TNG100 and TNG50 cosmological
hydrodynamical simulations. We first study the evolution of these two
populations, finding that barred galaxies generally assemble earlier, are more
stellar dominated in the central region and their discs are more massive and
compact than those of unbarred galaxies. We then apply the ELN-criterion to the
two populations, finding that it is capable of correctly identifying 75% and
80% of the barred and unbarred galaxies, respectively. We find that barred
galaxies not correctly identified by the criterion are galaxies with generally
more extended discs, higher spin values and with bars assembled at later times
with respect to the average of the barred sample. We also find an indication
that the bars in these galaxies developed during the interaction with a close
neighbor (external trigger) rather than being the consequence of the secular
growth of the disc. On the other hand, we find that the unbarred galaxies that
the ELN-criterion misclassifies as barred, have stellar discs similar to those
of barred galaxies but are less stellar dominated in the central region,
possibly because of their late formation time. Taken all together, our results
indicate that the ELN-criterion can robustly describe the stability of most
disc galaxies secularly evolving, whereas additional environmental criteria
should be possibly considered to account for interaction-induced bar formation.


title:
Augmenting Document Representations for Dense Retrieval with
  Interpolation and Perturbation

abstract:
  Dense retrieval models, which aim at retrieving the most relevant document
for an input query on a dense representation space, have gained considerable
attention for their remarkable success. Yet, dense models require a vast amount
of labeled training data for notable performance, whereas it is often
challenging to acquire query-document pairs annotated by humans. To tackle this
problem, we propose a simple but effective Document Augmentation for dense
Retrieval (DAR) framework, which augments the representations of documents with
their interpolation and perturbation. We validate the performance of DAR on
retrieval tasks with two benchmark datasets, showing that the proposed DAR
significantly outperforms relevant baselines on the dense retrieval of both the
labeled and unlabeled documents.


title: CSRS: Code Search with Relevance Matching and Semantic Matching

abstract:
  Developers often search and reuse existing code snippets in the process of
software development. Code search aims to retrieve relevant code snippets from
a codebase according to natural language queries entered by the developer. Up
to now, researchers have already proposed information retrieval (IR) based
methods and deep learning (DL) based methods. The IR-based methods focus on
keyword matching, that is to rank codes by relevance between queries and code
snippets, while DL-based methods focus on capturing the semantic correlations.
However, the existing methods do not consider capturing two matching signals
simultaneously. Therefore, in this paper, we propose CSRS, a code search model
with relevance matching and semantic matching. CSRS comprises (1) an embedding
module containing convolution kernels of different sizes which can extract
n-gram embeddings of queries and codes, (2) a relevance matching module that
measures lexical matching signals, and (3) a co-attention based semantic
matching module to capture the semantic correlation. We train and evaluate CSRS
on a dataset with 18.22M and 10k code snippets. The experimental results
demonstrate that CSRS achieves an MRR of 0.614, which outperforms two
state-of-the-art models DeepCS and CARLCS-CNN by 33.77% and 18.53%
respectively. In addition, we also conducted several experiments to prove the
effectiveness of each component of CSRS.


title: An Annotation-free Restoration Network for Cataractous Fundus Images

abstract:
  Cataracts are the leading cause of vision loss worldwide. Restoration
algorithms are developed to improve the readability of cataract fundus images
in order to increase the certainty in diagnosis and treatment for cataract
patients. Unfortunately, the requirement of annotation limits the application
of these algorithms in clinics. This paper proposes a network to
annotation-freely restore cataractous fundus images (ArcNet) so as to boost the
clinical practicability of restoration. Annotations are unnecessary in ArcNet,
where the high-frequency component is extracted from fundus images to replace
segmentation in the preservation of retinal structures. The restoration model
is learned from the synthesized images and adapted to real cataract images.
Extensive experiments are implemented to verify the performance and
effectiveness of ArcNet. Favorable performance is achieved using ArcNet against
state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in
cataract patients is promoted by ArcNet. The capability of properly restoring
cataractous images in the absence of annotated data promises the proposed
algorithm outstanding clinical practicability.


title: CSN: Component-Supervised Network for Few-Shot Classification

abstract:
  The few-shot classification (FSC) task has been a hot research topic in
recent years. It aims to address the classification problem with insufficient
labeled data on a cross-category basis. Typically, researchers pre-train a
feature extractor with base data, then use it to extract the features of novel
data and recognize them. Notably, the novel set only has a few annotated
samples and has entirely different categories from the base set, which leads to
that the pre-trained feature extractor can not adapt to the novel data
flawlessly. We dub this problem as Feature-Extractor-Maladaptive (FEM) problem.
Starting from the root cause of this problem, this paper presents a new scheme,
Component-Supervised Network (CSN), to improve the performance of FSC. We
believe that although the categories of base and novel sets are different, the
composition of the sample's components is similar. For example, both cat and
dog contain leg and head components. Actually, such entity components are
intra-class stable. They have fine cross-category versatility and new category
generalization. Therefore, we refer to WordNet, a dictionary commonly used in
natural language processing, to collect component information of samples and
construct a component-based auxiliary task to improve the adaptability of the
feature extractor. We conduct experiments on two benchmark datasets
(mini-ImageNet and tiered-ImageNet), the improvements of $0.9\%$-$5.8\%$
compared with state-of-the-arts have evaluated the efficiency of our CSN.


title: Reducing the complexity of the Quantum Fourier Transform implementation

abstract:
  The quantum Fourier transform (QFT) is one of the most important quantum
operations with wide range of application from integer factoring, quantum
simulation, quantum Monte Carlo simulation for the option pricing to relatively
new topic such as quantum algorithm for solving Navier-Stokes equation of the
turbulent flow, to name a few. Major obstacle to the implementation of the QFT
is the large number of elemental quantum gates needed to build the QFT. Of
these resources needed for the implementation, the T-count and the T-depth are
the major costs. So far, the existing best implementation of approximate
quantum Fourier transform (AQFT) shows the T-count of 8nlog2(n/\varepsilon) and
the T-depth of 2nlog2(n/\varepsilon) where n is the number of qubits and
\varepsilon is the approximate error. Here, we propose the new approximation
scheme of the QFT which halves both the T-count and the T-depth of the QFT of
the best existing implementation. Our approach relies on the manipulation of Rz
gates after the QFT decomposition and the application of the quantum Karnaugh
map for systematic reduction. We report the detailed quantum circuits and the
experimental verification of the proposed scheme using IonQ quantum computer
though Amazon Braket cloud service. The quantum amplitude estimation algorithm
(QAEA) which plays an essential role in solving nonlinear partial differential
equations employs a QFT and an inverse QFT. This QAEA would be an exemplary
quantum algorithm that can get the most benefit from our proposed scheme and
can be applied not only to financial engineering but also to solving
Navier-Stokes equations for turbulent hyper sonic flow and numerical weather
prediction that may have disruptive socio-economic impact, for which little
attention is given.


title:
Exact Feature Distribution Matching for Arbitrary Style Transfer and
  Domain Generalization

abstract:
  Arbitrary style transfer (AST) and domain generalization (DG) are important
yet challenging visual learning tasks, which can be cast as a feature
distribution matching problem. With the assumption of Gaussian feature
distribution, conventional feature distribution matching methods usually match
the mean and standard deviation of features. However, the feature distributions
of real-world data are usually much more complicated than Gaussian, which
cannot be accurately matched by using only the first-order and second-order
statistics, while it is computationally prohibitive to use high-order
statistics for distribution matching. In this work, we, for the first time to
our best knowledge, propose to perform Exact Feature Distribution Matching
(EFDM) by exactly matching the empirical Cumulative Distribution Functions
(eCDFs) of image features, which could be implemented by applying the Exact
Histogram Matching (EHM) in the image feature space. Particularly, a fast EHM
algorithm, named Sort-Matching, is employed to perform EFDM in a plug-and-play
manner with minimal cost. The effectiveness of our proposed EFDM method is
verified on a variety of AST and DG tasks, demonstrating new state-of-the-art
results. Codes are available at https://github.com/YBZh/EFDM.


title:
Probing the proton structure with associated vector boson and heavy
  flavor jet production at the LHC

abstract:
  We consider the production of $Z$ bosons associated with heavy (charm and
beauty) jets at the LHC energies using two scenarios based on the transverse
momentum dependent (TMD) parton densities in a proton. The first of them
employs the Catani-Ciafaloni-Fiorani-Marchesini gluon evolution and is
implemented in the Monte-Carlo event generator PEGASUS. Here, the heavy quarks
are always produced in the hard partonic scattering. The second scheme is based
on the parton branching approach, currently implemented into the Monte-Carlo
event generator CASCADE. In this scenario, the $Z$ + jets sample is generated
and then events containing the heavy flavor jet in a final state are selected.
We compare the predictions obtained within these two TMD-based approaches to
each other, investigate their sensitivity to the TMD gluon densities in a
proton and estimate the effects coming from parton showers and double parton
scattering mechanism. Additionally, we compare our predictions with the results
of traditional (collinear) pQCD calculations performed at NLO accuracy. It is
shown that the TMD-based results agree with the LHC experimental data collected
at $\sqrt s = 8$ and $13$ TeV. We discuss the sensitivity of observables to the
quark distributions in a proton and present predictions to search for the
intrinsic charm signal in forthcoming analyses of the LHC experimental data.


title:
ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For
  Low-resource Language

abstract:
  Most of the current task-oriented dialogue systems (ToD), despite having
interesting results, are designed for a handful of languages like Chinese and
English. Therefore, their performance in low-resource languages is still a
significant problem due to the absence of a standard dataset and evaluation
policy. To address this problem, we proposed ViWOZ, a fully-annotated
Vietnamese task-oriented dialogue dataset. ViWOZ is the first multi-turn,
multi-domain tasked oriented dataset in Vietnamese, a low-resource language.
The dataset consists of a total of 5,000 dialogues, including 60,946 fully
annotated utterances. Furthermore, we provide a comprehensive benchmark of both
modular and end-to-end models in low-resource language scenarios. With those
characteristics, the ViWOZ dataset enables future studies on creating a
multilingual task-oriented dialogue system.


title: Fibonacci direct product variation tilings

abstract:
  The direct product of two Fibonacci tilings can be described as a genuine
stone inflation rule with four prototiles. This rule admits various
modifications, which lead to 48 different inflation rules, known as the direct
product variations. They all result in tilings that are measure-theoretically
isomorphic by the Halmos--von Neumann theorem. They can be described as cut and
project sets with characteristic windows in a two-dimensional Euclidean
internal space. Here, we analyse and classify them further, in particular with
respect to topological conjugacy.


title:
Confining Eutectic Gallium Indium (eGaIn) in Expired Artificial Kidneys
  to Unveil Nanoporous Conductive Wires

abstract:
  Nanoporous membranes have gained considerable interest in drug delivery1, ion
transportation2, micro/nanofluidics3, molecular sensing4, and separation
science5. Artificial kidneys, also known as dialyzers, reject pathogens and
other unwanted substances from the blood, utilize hundreds of soft and
nanoporous polymeric microtubes, and slowly become a burden to the environment
with the growing number of dialysis patients worldwide. We demonstrate the
fabrication of nanoporous conductive wires utilizing empty polysulfone
microtubes collected from expired and unused artificial kidneys, also known as
medical wastes. Injecting a fluidic, highly conductive, and room temperature
liquid alloy (eutectic gallium indium-eGaIn$_6$, 75% Ga, 25% In) into
microtubes of a twenty years old dialyzer, here, we have revealed a new class
of nanoporous and conductive functional materials. These conductive fibers
upcycle a medical waste, do not require expensive and conventional fabrication
processes, and still provide the quintessential metal-oxide/metal framework due
to the presence of the native surface oxide (i.e., Gallium Oxide, Ga2O3) of
eGaIn at the nanoconfinement (i.e., nanopores) for nano/biosensing. We
harnessed these new materials to sense and differentiate microliter volumes of
deionized (DI) water, 1M hydrochloric acid (HCl), and 95% ethanol (EtOH),
leveraging their electrical signatures. This new class of soft nanomaterials
has the potential to become the paradigm-shift platforms for the
next-generation of biomedical, bioelectronics, nanoelectronics, and sensor
devices.


title: A new look on black hole perturbations in modified gravity

abstract:
  We study the linear perturbations about a nonrotating black hole solution of
Horndeski's theory, using a systematic approach that extracts the asymptotic
behaviour of perturbations (at spatial infinity and near the horizon) directly
from the first-order radial differential system governing these perturbations
instead of finding Schr\"odinger-like equations for their dynamics. We
illustrate this method in the case of a specific black hole solution. The
knowledge of the asymptotic behaviours of the perturbations paves the way for a
numerical computation of the quasinormal modes. Finally, the asymptotic form of
the modes also signals some pathologies in the scalar sector of the solution
considered here.


title:
Stress-testing the Resilience of the Austrian Healthcare System Using
  Agent-Based Simulation

abstract:
  Patients do not access physicians at random but rather via naturally emerging
networks of patient flows between them. As retirements, mass quarantines and
absence due to sickness during pandemics, or other shocks thin out these
networks, the system might be pushed closer to a tipping point where it loses
its ability to deliver care to the population. Here we propose a data-driven
framework to quantify the regional resilience to such shocks of primary and
secondary care in Austria via an agent-based model. For each region and medical
specialty we construct detailed patient-sharing networks from administrative
data and stress-test these networks by removing increasing numbers of
physicians from the system. This allows us to measure regional resilience
indicators describing how many physicians can be removed from a certain area
before individual patients won't be treated anymore. We find that such tipping
points do indeed exist and that regions and medical specialties differ
substantially in their resilience. These systemic differences can be related to
indicators for individual physicians by quantifying how much their hypothetical
removal would stress the system (risk score) or how much of the stress from the
removal of other physicians they would be able to absorb (benefit score). Our
stress-testing framework could enable health authorities to rapidly identify
bottlenecks in access to care as well as to inspect these naturally emerging
physician networks and how potential absences would impact them.


title:
Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and
  Agile Robotic Platforms

abstract:
  Model Predictive Control (MPC) has become a popular framework in embedded
control for high-performance autonomous systems. However, to achieve good
control performance using MPC, an accurate dynamics model is key. To maintain
real-time operation, the dynamics models used on embedded systems have been
limited to simple first-principle models, which substantially limits their
representative power. In contrast, neural networks can model complex effects
purely from data. In contrast to such simple models, machine learning
approaches such as neural networks have been shown to accurately model even
complex dynamic effects, but their large computational complexity hindered
combination with fast real-time iteration loops. With this work, we present
Neural-MPC, a framework to efficiently integrate large, complex neural network
architectures as dynamics models within a model-predictive control pipeline.
Our experiments, performed in simulation and the real world on a highly agile
quadrotor platform, demonstrate up to 83% reduction in positional tracking
error when compared to state-of-the-art MPC approaches without neural network
dynamics.


title: Anatomy of scalar mediated proton decays in $SO(10)$ models

abstract:
  Realistic models based on the renormalizable grand unified theories have
varieties of scalars, many of which are capable of mediating baryon and lepton
number non-conserving processes. We identify all such scalar fields residing in
${\bf 10}$, $\overline{\bf 126}$ and ${\bf 120}$ dimensional irreducible
representations of $SO(10)$ which can induce baryon and lepton number violating
interactions at tree level. Explicitly computing their couplings with the
standard model fermions, we derive effective operators including the
possibility of mixing between the scalars stemming from a given representation.
We find that such interactions are mediated by only three sets of scalars:
$T(3,1,-1/3)$, ${\cal T} (3,1,-4/3)$ and $\mathbb{T}(3,3,-1/3)$ and their
conjugates. In the models with ${\bf 10}$ and $\overline{\bf 126}$, only the
first has appropriate couplings to mediate the proton decay. While ${\cal T}$
and $\mathbb{T}$ can induce baryon number violating interactions when ${\bf
120}$ is present, ${\cal T}$ does not contribute to the proton decay at tree
level because of its flavour antisymmetric coupling. We give general
expressions for partial widths of proton in terms of the fundamental Yukawa
couplings and use these results to explicitly compute the proton lifetime and
branching ratios for the minimal non-supersymmetric $SO(10)$ model based on
${\bf 10}$ and $\overline{\bf 126}$ Higgs. We find that the proton preferably
decays into $\overline{\nu}\, K^+$ or $\mu^+\, K^0$ and list several distinct
features of scalar mediated proton decay. If the latter dominates over the
gauge mediated contributions, the proton decay spectrum provides a direct probe
to the flavour structure of the underlying grand unified theory.


title:
Efficient Bipartite Entanglement Detection Scheme with a Quantum
  Adversarial Solver

abstract:
  The recognition of entanglement states is a notoriously difficult problem
when no prior information is available. Here, we propose an efficient quantum
adversarial bipartite entanglement detection scheme to address this issue. Our
proposal reformulates the bipartite entanglement detection as a two-player
zero-sum game completed by parameterized quantum circuits, where a two-outcome
measurement can be used to query a classical binary result about whether the
input state is bipartite entangled or not. In principle, for an $N$-qubit
quantum state, the runtime complexity of our proposal is $O(\text{poly}(N)T)$
with $T$ being the number of iterations. We experimentally implement our
protocol on a linear optical network and exhibit its effectiveness to
accomplish the bipartite entanglement detection for 5-qubit quantum pure states
and 2-qubit quantum mixed states. Our work paves the way for using near-term
quantum machines to tackle entanglement detection on multipartite entangled
quantum systems.


title:
Development of a multi-timescale method for classifying hybrid energy
  storage systems in grid applications

abstract:
  An extended use of renewable energies and a trend towards increasing energy
consumption lead to challenges such as temporal and spatial decoupling of
energy generation and consumption. This work evaluates the possible
applications and advantages of hybrid energy storage systems compared to
conventional, single energy storage applications. In a mathematical approach,
evaluation criteria such as frequency, probability of power transients, as well
as absolute power peaks are combined to identify suitable thresholds for energy
management systems on a multi-timescale basis. With experimental load profiles
from a municipal application, an airport, and an industrial application, four
categories, clustering similar roles of the VRFB and the SC, are developed.


title: Structure preserving model order reduction of port-Hamiltonian systems

abstract:
  This work proposes a structure-preserving model reduction method for linear,
time-invariant port-Hamiltonian systems. We show that a low order system of the
same type can be constructed which interpolates the original transfer function
in a given set of frequencies.


title: Optimal denoising of rotationally invariant rectangular matrices

abstract:
  In this manuscript we consider denoising of large rectangular matrices: given
a noisy observation of a signal matrix, what is the best way of recovering the
signal matrix itself? For Gaussian noise and rotationally-invariant signal
priors, we completely characterize the optimal denoiser and its performance in
the high-dimensional limit, in which the size of the signal matrix goes to
infinity with fixed aspects ratio, and under the Bayes optimal setting, that is
when the statistician knows how the signal and the observations were generated.
Our results generalise previous works that considered only symmetric matrices
to the more general case of non-symmetric and rectangular ones. We explore
analytically and numerically a particular choice of factorized signal prior
that models cross-covariance matrices and the matrix factorization problem. As
a byproduct of our analysis, we provide an explicit asymptotic evaluation of
the rectangular Harish-Chandra-Itzykson-Zuber integral in a special case.

categories: cond-mat.dis-nn cond-mat.stat-mech cs.IT math.IT math.PR


title:
Estimating the potential to prevent locally acquired HIV infections in a
  UNAIDS Fast-Track City, Amsterdam

abstract:
  Amsterdam and other UNAIDS Fast-Track cities aim for zero new HIV infections.
Utilising molecular and clinical data of the ATHENA observational HIV cohort,
our primary aims are to estimate the proportion of undiagnosed HIV infections
and the proportion of locally acquired infections in Amsterdam in 2014-2018,
both in MSM and heterosexuals and Dutch-born and foreign-born individuals.
  We located diagnosed HIV infections in Amsterdam using postcode data at time
of registration to the cohort, and estimated their date of infection using
clinical HIV data. We then inferred the proportion undiagnosed from the
estimated times to diagnosis. To determine sources of Amsterdam infections, we
used HIV sequences of people living with HIV (PLHIV) within a background of
other Dutch and international sequences to phylogenetically reconstruct
transmission chains. Frequent late diagnoses indicate that more recent
phylogenetically observed chains are increasingly incomplete, and we use a
Bayesian model to estimate the actual growth of Amsterdam transmission chains,
and the proportion of locally acquired infections.
  We estimate that 20% [95% CrI 18-22%] of infections acquired among MSM
between 2014-2018 were undiagnosed by the start of 2019, and 44% [37-50%] among
heterosexuals, with variation by place of birth. The estimated proportion of
MSM infections in 2014-2018 that were locally acquired was 68% [61-74%], with
no substantial differences by region of birth. In heterosexuals, this was 57%
[41-71%] overall, with heterogeneity by place of birth.
  The data indicate substantial potential to further curb local transmission,
in both MSM and heterosexual Amsterdam residents. In 2014-2018 the largest
proportion of local transmissions in Amsterdam are estimated to have occurred
in foreign-born MSM, who would likely benefit most from intensified
interventions.


title: Engineering the sub-Doppler force in molecular magneto-optical traps

abstract:
  Current dual-frequency magneto-optical traps for ultracold molecules are
plagued by sub-Doppler heating effects, making them vastly inferior to standard
atomic MOTs. Here we demonstrate theoretically that the sub-Doppler effects in
such a MOT can be engineered to provide cooling instead of heating. We give an
intuitive picture how to achieve such cooling and show the cooling and trapping
force results of the 16 level optical Bloch equations for the case of CaF
molecules. From three-dimensional Monte Carlo simulations we estimate the
temperature and density of our MOT to be $40 \mu K$ and $4 \times 10^8 cm^{-3}$
respectively for a molecule number of $10^5$.


title: Generative models and Bayesian inversion using Laplace approximation

abstract:
  The Bayesian approach to solving inverse problems relies on the choice of a
prior. This critical ingredient allows the formulation of expert knowledge or
physical constraints in a probabilistic fashion and plays an important role for
the success of the inference. Recently, Bayesian inverse problems were solved
using generative models as highly informative priors. Generative models are a
popular tool in machine learning to generate data whose properties closely
resemble those of a given database. Typically, the generated distribution of
data is embedded in a low-dimensional manifold. For the inverse problem, a
generative model is trained on a database that reflects the properties of the
sought solution, such as typical structures of the tissue in the human brain in
magnetic resonance (MR) imaging. The inference is carried out in the
low-dimensional manifold determined by the generative model which strongly
reduces the dimensionality of the inverse problem. However, this proceeding
produces a posterior that admits no Lebesgue density in the actual variables
and the accuracy reached can strongly depend on the quality of the generative
model. For linear Gaussian models we explore an alternative Bayesian inference
based on probabilistic generative models which is carried out in the original
high-dimensional space. A Laplace approximation is employed to analytically
derive the required prior probability density function induced by the
generative model. Properties of the resulting inference are investigated.
Specifically, we show that derived Bayes estimates are consistent, in contrast
to the approach employing the low-dimensional manifold of the generative model.
The MNIST data set is used to construct numerical experiments which confirm our
theoretical findings.


title:
Multi-Curve Translator for Real-Time High-Resolution Image-to-Image
  Translation

abstract:
  The dominant image-to-image translation methods are based on fully
convolutional networks, which extract and translate an image's features and
then reconstruct the image. However, they have unacceptable computational costs
when working with high-resolution images. To this end, we present the
Multi-Curve Translator (MCT), which not only predicts the translated pixels for
the corresponding input pixels but also for their neighboring pixels. And if a
high-resolution image is downsampled to its low-resolution version, the lost
pixels are the remaining pixels' neighboring pixels. So MCT makes it possible
to feed the network only the downsampled image to perform the mapping for the
full-resolution image, which can dramatically lower the computational cost.
Besides, MCT is a plug-in approach that utilizes existing base models and
requires only replacing their output layers. Experiments demonstrate that the
MCT variants can process 4K images in real-time and achieve comparable or even
better performance than the base models on various image-to-image translation
tasks.


title:
Self-organization of bacterial mixtures in the presence of
  quorum-sensing interactions

abstract:
  We study analytically and numerically the self-organization of mixtures of
run-and-tumble bacteria whose motilities are regulated by quorum-sensing
interactions. We show the emergence of rich spatial structures, including
static and dynamic patterns. We derive a microscopic condition for restoring
action-reaction at the macroscopic scale, which endows the system with an
effective free energy functional. The latter allows us to rationalize the
static patterns observed in our simulations and to predict the corresponding
phase diagram. In the presence of non-reciprocal interactions, we derive a
sufficient condition to observe the emergence of travelling patterns.


title:
Fundamental sequnces and fast-growing hierarchies for the
  Bachmann-Howard ordinal

abstract:
  We prove that Buchholz's system of fundamental sequences for the $\vartheta$
function enjoys various regularity conditions, including the Bachmann property.
We partially extend these results to variants of the $\vartheta$ function,
including a version without addition for countable ordinals. We conclude that
the Hardy functions based on these notation systems enjoy natural monotonicity
properties and majorize all functions defined by primitive recursion along
$\vartheta(\varepsilon_{\Omega+1})$.


title: Magnetic dipole moments of the $B_{(s)}^{(*)}B_{(s)}^{(*)}$ states

abstract:
  We systematically study the magnetic dipole moments of multiquark states. In
this study, the magnetic dipole moments of possible $B^- B^{*-}$, $B^0 B^{*-}$,
$B^- B^{*0} $, $B^0 B^{*0}$, $B_s^0 B^{*-}$, $B^- B_s^{*0}$, $B_s^{0} B^{*0}$,
$B^0 B_s^{*0}$ and $B^0_s B_s^{*0}$ states are extracted by means of light-cone
sum rules. We explore magnetic dipole moments of these states as molecular
picture with spin-parity $J^P = 1^+$. The magnetic dipole moments of hadrons
includes useful information on the distributions of charge and magnetization
their inside, which can be used to better understand their geometrical shapes
and quark-gluon organizations. The acquired results in the present study
together with the spectroscopic parameters may elucidate the future theoretical
and experimental researches on the characteristics of doubly-bottom tetraquark
states.


title: The Cheeger Cut and Cheeger Problem in Metric Graphs

abstract:
  For discrete weighted graphs there is sufficient literature about the Cheeger
cut and the Cheeger problem, but for metric graphs there are few results about
these problems. Our aim is to study the Cheeger cut and the Cheeger problem in
metric graphs. For that, we use the concept of total variation and perimeter in
metric graphs introduced in \cite{Mazon}, which takes into account the jumps at
the vertices of the functions of bounded variation. Moreover, we study the
eigenvalue problem for the minus $1$-Laplacian operator in metric graphs,
whereby we give a method to solve the optimal Cheeger cut problem.


title: Reactive Motion Generation on Learned Riemannian Manifolds

abstract:
  In recent decades, advancements in motion learning have enabled robots to
acquire new skills and adapt to unseen conditions in both structured and
unstructured environments. In practice, motion learning methods capture
relevant patterns and adjust them to new conditions such as dynamic obstacle
avoidance or variable targets. In this paper, we investigate the robot motion
learning paradigm from a Riemannian manifold perspective. We argue that
Riemannian manifolds may be learned via human demonstrations in which geodesics
are natural motion skills. The geodesics are generated using a learned
Riemannian metric produced by our novel variational autoencoder (VAE), which is
especially intended to recover full-pose end-effector states and joint space
configurations. In addition, we propose a technique for facilitating on-the-fly
end-effector/multiple-limb obstacle avoidance by reshaping the learned manifold
using an obstacle-aware ambient metric. The motion generated using these
geodesics may naturally result in multiple-solution tasks that have not been
explicitly demonstrated previously. We extensively tested our approach in task
space and joint space scenarios using a 7-DoF robotic manipulator. We
demonstrate that our method is capable of learning and generating motion skills
based on complicated motion patterns demonstrated by a human operator.
Additionally, we assess several obstacle avoidance strategies and generate
trajectories in multiple-mode settings.


title: Rigidity of complex projective spaces in Ricci shrinkers

abstract:
  In this paper, we prove that any Ricci shrinker that is sufficiently close to
$(\mathbb C P^N,g_{FS})$ in the Gromov-Hausdorff sense must itself be isometric
to $(\mathbb C P^N,g_{FS})$.


title: A Characterization of the Vector Lattice of Measurable Functions

abstract:
  Given a probability measure space $(X,\Sigma,\mu)$, it is well known that the
Riesz space $L^0(\mu)$ of equivalence classes of measurable functions $f: X \to
\mathbf{R}$ is universally complete and the constant function $\mathbf{1}$ is a
weak order unit. Moreover, the linear functional $L^\infty(\mu)\to \mathbf{R}$
defined by $f \mapsto \int f\,\mathrm{d}\mu$ is strictly positive and order
continuous. Here we show, in particular, that the converse holds true, i.e.,
any universally complete Riesz space $E$ with a weak order unit $e>0$ which
admits a strictly positive order continuous linear functional on the principal
ideal generated by $e$ is lattice isomorphic onto $L^0(\mu)$, for some
probability measure space $(X,\Sigma,\mu)$.


title:
Imaginary spin-orbital coupling in parity-time symmetric systems with
  momentum-dependent gain and loss

abstract:
  Spin-orbital coupling (SOC) and parity-time ($\mathcal{PT}$) symmetry both
have attracted paramount research interest in condensed matter physics, cold
atom physics, optics and acoustics to develop spintronics, quantum computation,
precise sensors and novel functionalities. Natural SOC is an intrinsic
relativistic effect. However, there is an increasing interest in synthesized
SOC nowadays. Here, we show that in a $\mathcal{PT}$-symmetric spin-1/2 system,
the momentum-dependent balanced gain and loss can synthesize a new type of SOC,
which we call imaginary SOC. The imaginary SOC can substantially change the
energy spectrum of the system. Firstly, we show that it can generate a pure
real energy spectrum with a double-valleys structure. Therefore, it has the
ability to generate supersolid stripe states. Especially, the imaginary SOC
stripe state can have a high contrast of one. Moreover, the imaginary SOC can
also generate a spectrum with tunable complex energy band, in which the waves
are either amplifying or decaying. Thus, the imaginary SOC would also find
applications in the engineering of $\mathcal{PT}$-symmetry-based coherent wave
amplifiers/absorbers. Potential experimental realizations of imaginary SOC are
proposed in cold atomic gases and systems of coupled waveguides constituted of
nonlocal gain and loss.


title:
Optimal selection and tracking of generalized Nash equilibria in
  monotone games

abstract:
  A fundamental open problem in monotone game theory is the computation of a
specific generalized Nash equilibrium (GNE) among all the available ones, e.g.
the optimal equilibrium with respect to a system-level objective. The existing
GNE seeking algorithms have in fact convergence guarantees toward an arbitrary,
possibly inefficient, equilibrium. In this paper, we solve this open problem by
leveraging results from fixed-point selection theory and in turn derive
distributed algorithms for the computation of an optimal GNE in monotone games.
We then extend the technical results to the time-varying setting and propose an
algorithm that tracks the sequence of optimal equilibria up to an asymptotic
error, whose bound depends on the local computational capabilities of the
agents.


title: Asymptotic analysis for a second order curved thin film

abstract:
  We consider a second order thin curved film whose behavior is governed by an
energy made up of a first order nonlinear part depending on the gradient of the
deformation augmented by a quadratic second order part depending on the tensor
of second derivatives of the deformation. We carry out a 3D-2D analysis through
an asymptotic expansion in powers of the thickness of the film as it tends to
zero.


title: Homological stability: a tool for computations

abstract:
  Homological stability has shown itself to be a powerful tool for the
computation of homology of families of groups such as general linear groups,
mapping class groups or automorphisms of free groups. We survey here tools and
techniques for proving homological stability theorems and for computing the
stable homology, and illustrate the method through the computation of the
homology of Higman-Thompson groups.


title: Graphs without rainbow triangles

abstract:
  Let F,G,H be three graphs on the same n vertices. We consider the maximum of
the sum and product of the number of their edges subject to the condition in
the title.


title:
Inverse Problems: A Deterministic Approach using Physics-Based Reduced
  Models

abstract:
  These lecture notes summarize various summer schools that I have given on the
topic of solving inverse problems (state and parameter estimation) by combining
optimally measurement observations and parametrized PDE models. After defining
a notion of optimal performance in terms of the smallest reconstruction error
that any reconstruction algorithm can achieve, the notes present practical
numerical algorithms based on nonlinear reduced models for which one can prove
that they can deliver a performance close to optimal. We also discuss
algorithms for sensor placement with the approach. The proposed concepts may be
viewed as exploring alternatives to Bayesian inversion in favor of more
deterministic notions of accuracy quantification.


title: On Delannoy paths without peaks and valleys

abstract:
  A lattice path is called \emph{Delannoy} if its every step belongs to
$\left\{N, E, D\right\}$, where $N=(0,1)$, $E=(1,0)$, and $D=(1,1)$ steps.
\emph{Peak}, \emph{valley}, and \emph{deep valley} mean $NE$, $EN$, and $EENN$
on the lattice path, respectively.
  In this paper, we find a bijection between $\mathcal{P}_{n,m}(NE, EN)$ and a
specific subset of ${\mathcal{P}_{n,m}}(D, EENN)$, where $\mathcal{P}_{n,m}(NE,
EN)$ is the set of Delannoy paths from the origin to the points $(n,m)$ without
peaks and valleys and ${\mathcal{P}_{n,m}}(D, EENN)$ is the set of Delannoy
lattice paths from the origin to the points $(n,m)$ without diagonal steps and
deep valleys. We also enumerate the number of Delannoy paths without peaks and
valleys on the restricted region $\left\{ (x,y) \in \mathbb{Z}^2 : y \ge k x
\right\}$ for a positive integer $k$.


title: Optimal mixing for two-state anti-ferromagnetic spin systems

abstract:
  We prove an optimal $\Omega(n^{-1})$ lower bound for modified log-Sobolev
(MLS) constant of the Glauber dynamics for anti-ferromagnetic two-spin systems
with $n$ vertices in the tree uniqueness regime. Specifically, this optimal MLS
bound holds for the following classes of two-spin systems in the tree
uniqueness regime:
  $\bullet$ all strictly anti-ferromagnetic two-spin systems (where both edge
parameters $\beta,\gamma<1$), which cover the hardcore models and the
anti-ferromagnetic Ising models;
  $\bullet$ general anti-ferromagnetic two-spin systems on regular graphs.
  Consequently, an optimal $O(n\log n)$ mixing time holds for these
anti-ferromagnetic two-spin systems when the uniqueness condition is satisfied.
These MLS and mixing time bounds hold for any bounded or unbounded maximum
degree, and the constant factors in the bounds depend only on the gap to the
uniqueness threshold. We prove this by showing a boosting theorem for MLS
constant for distributions satisfying certain spectral independence and
marginal stability properties.


title:
Fast Autofocusing using Tiny Transformer Networks for Digital
  Holographic Microscopy

abstract:
  The numerical wavefront backpropagation principle of digital holography
confers unique extended focus capabilities, without mechanical displacements
along z-axis. However, the determination of the correct focusing distance is a
non-trivial and time consuming issue. A deep learning (DL) solution is proposed
to cast the autofocusing as a regression problem and tested over both
experimental and simulated holograms. Single wavelength digital holograms were
recorded by a Digital Holographic Microscope (DHM) with a 10$\mathrm{x}$
microscope objective from a patterned target moving in 3D over an axial range
of 92 $\mu$m. Tiny DL models are proposed and compared such as a tiny Vision
Transformer (TViT), tiny VGG16 (TVGG) and a tiny Swin-Transfomer (TSwinT). The
experiments show that the predicted focusing distance $Z_R^{\mathrm{Pred}}$ is
accurately inferred with an accuracy of 1.2 $\mu$m in average in comparison
with the DHM depth of field of 15 $\mu$m. Numerical simulations show that all
tiny models give the $Z_R^{\mathrm{Pred}}$ with an error below 0.3 $\mu$m. Such
a prospect would significantly improve the current capabilities of computer
vision position sensing in applications such as 3D microscopy for life sciences
or micro-robotics. Moreover, all models reach state of the art inference time
on CPU, less than 25 ms per inference.


title:
Slip of submerged two-dimensional liquid-infused surfaces in the
  presence of surfactants

abstract:
  Using numerical simulations, we investigate the effects of Marangoni stresses
on the effective slip length of liquid-infused surfaces (LIS) with transverse
grooves. The surfactants are assumed soluble in the external liquid and can
adsorb onto the interfaces. Two different adsorption models are used: a
classical Frumkin model and a more advanced model that better describes the
decrease of surface tension for minuscule concentrations. The simulations show
that LIS may face even more severe effects of surfactants than previously
investigated superhydrophobic surfaces. Constructing an analytical model for
the effective slip length, we can predict the critical surfactant concentration
for which the slip length decreases significantly. This analytical model
describes both adsorptions models of LIS on a unified framework if properly
adjusted. We also advance the understanding of when surfactant advection gives
rise to highly skewed interfacial concentrations - the so-called partial
stagnant cap regime. To a good approximation, this regime can only exist below
a specific surfactant concentration given by the Marangoni number and the
strength of the surfactants.


title: An Empirical Study of Market Inefficiencies in Uniswap and SushiSwap

abstract:
  Decentralized exchanges are revolutionizing finance. With their ever-growing
increase in popularity, a natural question that begs to be asked is: how
efficient are these new markets?
  We find that nearly 30% of analyzed trades are executed at an unfavorable
rate. Additionally, we observe that, especially during the DeFi summer in 2020,
price inaccuracies across the market plagued DEXes. Uniswap and SushiSwap,
however, quickly adapt to their increased volumes. We see an increase in market
efficiency with time during the observation period. Nonetheless, the DEXes
still struggle to track the reference market when cryptocurrency prices are
highly volatile. During such periods of high volatility, we observe the market
becoming less efficient - manifested by an increased prevalence in cyclic
arbitrage opportunities.


title:
Shadows of Kerr-like black holes in $4D$ Einstein-Gauss-Bonnet gravity
  and constraints from EHT observations

abstract:
  The M87* black hole shadow observation by the Event Horizon Telescope (EHT)
has enabled us to test the modified gravity theories in the extreme-field
regime and estimating the black hole parameters. Having this assertion, we
investigate the Kerr-like rotating black holes in $4D$ Einstein-Gauss-Bonnet
(EGB) gravity and deduce their shadows. Considering the inclination angle
$\theta_0=17^o$, we show that the EGB black hole shadows are smaller and more
distorted than for the Kerr black holes. Modelling the M87* black hole as the
EGB black hole, we predict the shadow angular size $35.7888\mu as\leq
\theta_d\leq 39.6192\mu as$. The M87* black hole shadow angular size
$\theta_d=42\pm 3\mu as$, within the 1$\sigma$ region, constrains the GB
coupling parameter and the black hole spin parameter. Interestingly, the
circularity deviation of the EGB black hole shadows is smaller than the bounded
deduced for the M87* black hole.


title:
Comparison of propagation models and forward calculation methods on
  cellular, tissue and organ scale atrial electrophysiology

abstract:
  Objective: The bidomain model and the finite element method are an
established standard to mathematically describe cardiac electrophysiology, but
are both suboptimal choices for fast and large-scale simulations due to high
computational costs. We investigate to what extent simplified approaches for
propagation models (monodomain, reaction-eikonal and eikonal) and forward
calculation (boundary element and infinite volume conductor) deliver markedly
accelerated, yet physiologically accurate simulation results in atrial
electrophysiology. Methods: We compared action potential durations, local
activation times (LATs), and electrocardiograms (ECGs) for sinus rhythm
simulations on healthy and fibrotically infiltrated atrial models. Results: All
simplified model solutions yielded LATs and P waves in accurate accordance with
the bidomain results. Only for the eikonal model with pre-computed action
potential templates shifted in time to derive transmembrane voltages,
repolarization behavior notably deviated from the bidomain results. ECGs
calculated with the boundary element method were characterized by correlation
coefficients >0.9 compared to the finite element method. The infinite volume
conductor method led to lower correlation coefficients caused predominantly by
systematic overestimations of P wave amplitudes in the precordial leads.
Conclusion: Our results demonstrate that the eikonal model yields accurate LATs
and combined with the boundary element method precise ECGs compared to markedly
more expensive full bidomain simulations. However, for an accurate
representation of atrial repolarization dynamics, diffusion terms must be
accounted for in simplified models. Significance: Simulations of atrial LATs
and ECGs can be notably accelerated to clinically feasible time frames at high
accuracy by resorting to the eikonal and boundary element methods.


title:
Social Choice Around the Block: On the Computational Social Choice of
  Blockchain

abstract:
  One of the most innovative aspects of blockchain technology consists in the
introduction of an incentive layer to regulate the behavior of distributed
protocols. The designer of a blockchain system faces therefore issues that are
akin to those relevant for the design of economic mechanisms, and faces them in
a computational setting. From this perspective the present paper argues for the
importance of computational social choice in blockchain research. It identifies
a few challenges at the interface of the two fields that illustrate the strong
potential for cross-fertilization between them.


title:
Production and Polarization of $S$-Wave Quarkonia in Potential
  Nonrelativistic QCD

abstract:
  Based on the potential nonrelativistic QCD formalism, we compute the
nonrelativistic QCD long-distance matrix elements (LDMEs) for inclusive
production of $S$-wave heavy quarkonia. This greatly reduces the number of
nonperturbative unknowns and brings in a substantial enhancement in the
predictive power of the NRQCD factorization formalism. We obtain improved
determinations of the LDMEs and find cross sections and polarizations of
$J/\psi$, $\psi(2S)$, and excited $\Upsilon$ states that agree well with LHC
data. Our results may have important implications in pinning down the heavy
quarkonium production mechanism.


title:
Effects of particle roughness on the rheology and structure of capillary
  suspensions

abstract:
  We show that particle roughness leads to changes in the number, shape and
resulting capillary force of liquid bridges in capillary suspensions. We
created fluorescently labeled, raspberry-like particles with varying roughness
by electrostatically adsorbing silica nanoparticles with sizes between 40 nm
and 250 nm on silica microparticles. Rougher particles require more liquid to
fill the surface asperities before they form pendular bridges, resulting in
smaller and weaker bridges. In a system where the effective bridge volume is
adjusted, higher particle roughness leads to less clustered networks, which
show a higher yield strain for a matching storage modulus compared to the
smooth particle networks. This finding suggests that the particle-particle
frictional contacts affect the strength of capillary suspensions. Using
asymptotically nonlinear oscillatory rheology, we corroborate the non-cubical
power law scaling of the third harmonic in the shear stress response that
results from both Hertzian contacts and friction between particles connected by
capillary bridges. We demonstrate that the repulsive Hertzian contact parameter
$A$ is sensitive to the liquid bridge strength and that roughness shifts the
relative scaling of the power law exponents from adhesive-controlled friction
to load-controlled friction.


title:
Two-point function of a quantum scalar field in the interior region of a
  Kerr black hole

abstract:
  Quantum field effects on a classical background spacetime may be obtained
from the semiclassical equations of General Relativity with the expectation
value of the stress-energy tensor of the quantum field as a source. This
expectation value can be calculated from Hadamard's elementary two-point
function, which in practice is given in terms of sums of products of field
modes evaluated at two spacetime points. We derive expressions for the
two-point function for a massless scalar field in the Unruh state on a Kerr
black hole spacetime. Our main result in this paper is a novel expression valid
when the two points lie inside the black hole; we also (re-)derive, using a new
method, the known expression valid when the two points lie outside the black
hole. We achieve these expressions by finding relationships between Unruh
modes, defined in terms of the retarded Kruskal coordinate, and Eddington
modes, defined in terms of the Eddington coordinates. While our starting
expression for the two-point function is written in terms of the Unruh modes,
we give our final expression in terms of the Eddington modes, which have the
computational advantage that they decompose into factors that obey ordinary
differential equations. In an appendix we also derive expressions for the bare
mode contributions to the flux components of the stress-energy tensor for a
minimally-coupled massless scalar field inside the black hole. Our results thus
lay the groundwork for future calculations of quantum effects inside a Kerr
black hole.


title:
UniSAr: A Unified Structure-Aware Autoregressive Language Model for
  Text-to-SQL

abstract:
  Existing text-to-SQL semantic parsers are typically designed for particular
settings such as handling queries that span multiple tables, domains or turns
which makes them ineffective when applied to different settings. We present
UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits
from directly using an off-the-shelf language model architecture and
demonstrates consistently high performance under different settings.
Specifically, UniSAr extends existing autoregressive language models to
incorporate three non-invasive extensions to make them structure-aware: (1)
adding structure mark to encode database schema, conversation context, and
their relationships; (2) constrained decoding to decode well structured SQL for
a given database schema; and (3) SQL completion to complete potential missing
JOIN relationships in SQL based on database schema. On seven well-known
text-to-SQL datasets covering multi-domain, multi-table and multi-turn, UniSAr
demonstrates highly comparable or better performance to the most advanced
specifically-designed text-to-SQL models. Importantly, our UniSAr is
non-invasive, such that other core model advances in text-to-SQL can also adopt
our extensions to further enhance performance.


title:
Complex Evolutional Pattern Learning for Temporal Knowledge Graph
  Reasoning

abstract:
  A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to
different timestamps. TKG reasoning aims to predict potential facts in the
future given the historical KG sequences. One key of this task is to mine and
understand evolutional patterns of facts from these sequences. The evolutional
patterns are complex in two aspects, length-diversity and time-variability.
Existing models for TKG reasoning focus on modeling fact sequences of a fixed
length, which cannot discover complex evolutional patterns that vary in length.
Furthermore, these models are all trained offline, which cannot well adapt to
the changes of evolutional patterns from then on. Thus, we propose a new model,
called Complex Evolutional Network (CEN), which uses a length-aware
Convolutional Neural Network (CNN) to handle evolutional patterns of different
lengths via an easy-to-difficult curriculum learning strategy. Besides, we
propose to learn the model under the online setting so that it can adapt to the
changes of evolutional patterns over time. Extensive experiments demonstrate
that CEN obtains substantial performance improvement under both the traditional
offline and the proposed online settings.


title:
Symplectic tomographic probability distribution of crystallized
  Schr\"odinger cat states

abstract:
  Within the framework of the probability representation of quantum mechanics,
we study a superposition of generic Gaussian states associated to symmetries of
a regular polygon of n sides; in other words, the cyclic groups (containing the
rotational symmetries) and dihedral groups (containing the rotational and
inversion symmetries). We obtain the Wigner functions and tomographic
probability distributions (symplectic and optical tomograms) determining the
density matrices of the states explicitly as the sums of Gaussian terms. The
obtained Wigner functions demonstrate nonclassical behavior, i.e., contain
negative values, while the tomograms show a series of maxima and minima
different for each state, where the number of the critical points reflects the
order of the group defining the states. We discuss general properties of such a
generalization of normal probability distributions.


title: Emergent phase transition in Cluster Ising model with dissipation

abstract:
  We study a cluster Ising model with non-Hermitian external field which can be
exactly solved in the language of free fermions. By investigating the second
derivative of energy density and fidelity, the possible new critical points are
tentatively located. String order parameter and staggered magnetization are
then detected to reveal emergent phases of brand new characteristics. To
categorize the exotic phases and phase transitions induced by non-Hermiticity,
we calculate the variation mode of spin correlation function as well as string
parameter, which characterize the emergent phases and critical points with
different patterns of decay and critical exponents. With the help of string
order parameter and staggered magnetization, we find that there are four phases
after introducing the non-Hermiticity -- the cluster phase, the gapless phase,
the paramagnetic (PM) phase and the antiferromagnetic (AF) phase. A phase
diagram is then presented to graphically illustrate, based on two "KT-like"
phase transitions and an Ising phase transition, respectively, the generation
of three critical lines as non-Hermitian strength increases. Our theoretical
work is expected to be realized in the experiment of ultra-cold atoms, pushing
for progress in exploring novel phases and phase transitions.


title: The Ghost in the Machine has an American accent: value conflict in GPT-3

abstract:
  The alignment problem in the context of large language models must consider
the plurality of human values in our world. Whilst there are many resonant and
overlapping values amongst the world's cultures, there are also many
conflicting, yet equally valid, values. It is important to observe which
cultural values a model exhibits, particularly when there is a value conflict
between input prompts and generated outputs. We discuss how the co-creation of
language and cultural value impacts large language models (LLMs). We explore
the constitution of the training data for GPT-3 and compare that to the world's
language and internet access demographics, as well as to reported statistical
profiles of dominant values in some Nation-states. We stress tested GPT-3 with
a range of value-rich texts representing several languages and nations;
including some with values orthogonal to dominant US public opinion as reported
by the World Values Survey. We observed when values embedded in the input text
were mutated in the generated outputs and noted when these conflicting values
were more aligned with reported dominant US values. Our discussion of these
results uses a moral value pluralism (MVP) lens to better understand these
value mutations. Finally, we provide recommendations for how our work may
contribute to other current work in the field.


title: $U(1)_{T3R}$ Extension of Standard Model: A Sub-GeV Dark Matter Model

abstract:
  We present a model based on a $U(1)_{T3R}$ extension of the Standard Model.
The model addresses the mass hierarchy between the third generation and the
first two generation fermions. $U(1)_{T3R}$ is spontaneously broken at $\sim
1-10$ GeV. The model contains a sub-GeV dark matter candidate and two sub-GeV
light scalar and vector mediators. The model explains the thermal dark matter
abundance, measurements of the muon g-2 and $R_{K^{(\ast)}}$ anomalies. The
model can be probed at the LHC, FASER, dark matter experiments and various
beam-dump based neutrino facilities, e.g., COHERENT, CCM, MicroBooNE, SBND,
ICARUS, DUNE etc.


title: On Galois inertial types of elliptic curves over $\mathbb{Q}_\ell$

abstract:
  We provide a complete, explicit description of the inertial Weil--Deligne
types arising from elliptic curves over $\mathbb{Q}_\ell$ for $\ell$ prime.


title:
Scalable Penalized Regression for Noise Detection in Learning with Noisy
  Labels

abstract:
  Noisy training set usually leads to the degradation of generalization and
robustness of neural networks. In this paper, we propose using a theoretically
guaranteed noisy label detection framework to detect and remove noisy data for
Learning with Noisy Labels (LNL). Specifically, we design a penalized
regression to model the linear relation between network features and one-hot
labels, where the noisy data are identified by the non-zero mean shift
parameters solved in the regression model. To make the framework scalable to
datasets that contain a large number of categories and training data, we
propose a split algorithm to divide the whole training set into small pieces
that can be solved by the penalized regression in parallel, leading to the
Scalable Penalized Regression (SPR) framework. We provide the non-asymptotic
probabilistic condition for SPR to correctly identify the noisy data. While SPR
can be regarded as a sample selection module for standard supervised training
pipeline, we further combine it with semi-supervised algorithm to further
exploit the support of noisy data as unlabeled data. Experimental results on
several benchmark datasets and real-world noisy datasets show the effectiveness
of our framework. Our code and pretrained models are released at
https://github.com/Yikai-Wang/SPR-LNL.


title:
Engineering data-driven solutions for future mobility: perspectives and
  challenges

abstract:
  The automotive industry is currently undergoing major changes. These include
a general shift towards decarbonised mode of transportation, the implementation
of mobility as an end-to-end service, and the transition to vehicles that
increasingly rely on software and digital tools to function. Digitalisation is
expected to play a key role in shaping the future of mobility ecosystems by
fostering the integration of traditionally independent system domains in the
energy, transportation and information sectors. This report discusses
opportunities and challenges for engineering data-driven solutions that support
the requirements of future digitalised mobility systems based on three use
cases for electric vehicle public charging infrastructures, services and
security.


title:
Uncertainties and Design of Active Aerodynamic Attitude Control in Very
  Low Earth Orbit

abstract:
  This paper discusses the design and the performance achievable with active
aerodynamic attitude control in very low Earth orbit, i.e. below 450 km in
altitude. A novel real-time algorithm is proposed for selecting the angles of
deflection of aerodynamic actuators providing the closest match to the control
signal computed by a selected control law. The algorithm is based on a panel
method for the computation of the aerodynamic coefficients and relies on
approximate environmental parameters estimation and worst-case scenario
assumptions for the re-emission properties of space materials. Discussion of
results is performed by assuming two representative pointing manoeuvres, for
which momentum wheels and aerodynamic actuators are used synergistically. A
quaternion feedback PID controller implemented in discrete time is assumed to
determine the control signal at a sampling frequency of 1 Hz. The outcome of a
Monte Carlo analysis, performed for a wide range of orbital conditions, shows
that the target attitude is successfully achieved for the vast majority of the
cases, thus proving the robustness of the approach in the presence of
environmental uncertainties and realistic attitude hardware limitations.


title:
Noisy induced entanglement transition in one-dimensional random quantum
  circuits

abstract:
  Random quantum circuit is a minimally structured model to study the
entanglement dynamics of many-body quantum systems. In this paper, we
considered a one-dimensional quantum circuit with noisy Haar-random unitary
gates using density matrix operator and tensor contraction methods. It is shown
that the entanglement evolution of the random quantum circuits is properly
characterized by the logarithmic entanglement negativity. By performing exact
numerical calculations, we found that, as the physical error rate is decreased
below a critical value $p_c\approx 0.056$, the logarithmic entanglement
negativity changes from the area law to the volume law, giving rise to an
entanglement transition. The critical exponent of the correlation length can be
determined from the finite-size scaling analysis, revealing the universal
dynamic property of the noisy intermediate-scale quantum devices.


title: Parking Analytics Framework using Deep Learning

abstract:
  With the number of vehicles continuously increasing, parking monitoring and
analysis are becoming a substantial feature of modern cities. In this study, we
present a methodology to monitor car parking areas and to analyze their
occupancy in real-time. The solution is based on a combination between image
analysis and deep learning techniques. It incorporates four building blocks put
inside a pipeline: vehicle detection, vehicle tracking, manual annotation of
parking slots, and occupancy estimation using the Ray Tracing algorithm. The
aim of this methodology is to optimize the use of parking areas and to reduce
the time wasted by daily drivers to find the right parking slot for their cars.
Also, it helps to better manage the space of the parking areas and to discover
misuse cases. A demonstration of the provided solution is shown in the
following video link: https://www.youtube.com/watch?v=KbAt8zT14Tc.


title:
Training Generative Adversarial Networks for Optical Property Mapping
  using Synthetic Image Data

abstract:
  We demonstrate training of a Generative Adversarial Network (GAN) for
prediction of optical property maps (scattering and absorption) using spatial
frequency domain imaging (SFDI) image data sets generated synthetically with
free open-source 3D modelling and rendering software, Blender. The flexibility
of Blender is exploited to simulate 3 models with real-life relevance to
clinical SFDI of diseased tissue: flat samples, flat samples with spheroidal
tumours and cylindrical samples with spheroidal tumours representing imaging
inside a tubular organ e.g. the gastro-intestinal tract. In all 3 scenarios we
show the GAN provides accurate reconstruction of optical properties from single
SFDI images with mean normalised error ranging from 1-1.2% for absorption and
0.7-1.2% for scattering, resulting in visually improved contrast for tumour
spheroid structures. This compares favourably with 25% absorption error and 10%
scattering error achieved using GANs on experimental SFDI data. However, some
of this improvement is due to lower noise and availability of perfect ground
truths so we therefore cross-validate our synthetically-trained GAN with a GAN
trained on experimental data and observe visually accurate results with error
of <40% for absorption and <25% for scattering, due largely to the presence of
spatial frequency mismatch artefacts. Our synthetically trained GAN is
therefore highly relevant to real experimental samples, but provides
significant added benefits of large training datasets, perfect ground-truths
and the ability to test realistic imaging geometries, e.g. inside cylinders,
for which no conventional single-shot demodulation algorithms exist. In future
we expect that application of techniques such as domain adaptation or training
on hybrid real-synthetic datasets will create a powerful tool for fast,
accurate production of optical property maps from real clinical imaging
systems.


title:
Revisiting the role of bars in AGN fuelling with propensity score sample
  matching

abstract:
  The high luminosity displayed by an active galactic nucleus (AGN) requires
that gas be transported to the centre of the galaxy by some mechanism.
Bar-driven processes are often pointed out in this context and a number of
studies have addressed the bar-AGN connection, but with conflicting results.
Some of the inconsistencies can be explained by the different spatial- and
timescales involved in bar-driven gas inflows, accretion by the central black
hole, and AGN emission. However, the discrepant results could also be due to
sample biases, because both the AGN activity determination and the bar
detection are influenced by the method employed. We revisit the bar-AGN
connection in a sample of galaxies from SDSS, looking for evidence of the
influence of bars on AGN activity. We determine AGN activity by emission line
diagnostics and the properties of the bar were previously estimated with
\texttt{BUDDA}, which performs 2D bulge-bar-disk decomposition. Before
comparing active and inactive galaxies, we made a careful selection of the
sample to minimise selection biases. We created control samples by matching
them with the AGN sample using propensity score matching. This technique offers
an analytical approach for creating control samples given some object
parameters. We find that AGN are preferentially found in barred galaxies and
that the accretion rate is higher in barred galaxies, but only when different
M-$\sigma$ relations are used to estimate the black hole mass M$_\bullet$ in
barred and unbarred galaxies (from the central velocity dispersion $\sigma$).
On the other hand, we find no correlation between activity level and bar
strength. Altogether, our results strengthen theoretical predictions that the
bar is an important mechanism in disc galaxies, creating a gas reservoir to
feed AGN, but they also indicate that other mechanisms can play a major role,
particularly at scales <~100 pc.


title: Reality from maximizing overlap in the periodic complex action theory

abstract:
  We study the periodic complex action theory (CAT) by imposing a periodic
condition in the future-included CAT where the time integration is performed
from the past to the future, and extend a normalized matrix element of an
operator $\hat{\mathcal O}$, which is called weak value in the real action
theory, to another expression $\langle \hat{\mathcal O}
\rangle_{\mathrm{periodic}~\mathrm{time}}$. We present two theorems stating
that $\langle \hat{\mathcal O} \rangle_{\mathrm{periodic}~\mathrm{time}}$
becomes real for $\hat{\mathcal O}$ being Hermitian with regard to a modified
inner product that makes a given non-normal Hamiltonian $\hat{H}$ normal. The
first theorem holds for a given period $t_p$ in a case where the number of
eigenstates having the maximal imaginary part $B$ of the eigenvalues of
$\hat{H}$ is just one, while the second one stands for $t_p$ selected such that
the absolute value of the transition amplitude is maximized in a case where $B
\leq 0$ and $|B|$ is much smaller than the distances between any two real parts
of the eigenvalues of $\hat{H}$. The latter proven via a number-theoretical
argument suggests that, if our universe is periodic, then even the period could
be an adjustment parameter to be determined in the Feynman path integral. This
is a variant type of the maximization principle that we previously proposed.


title: Multi-Unit Diffusion Auctions with Intermediaries

abstract:
  This paper studies multi-unit auctions powered by intermediaries, where each
intermediary owns a private set of unit-demand buyers and all intermediaries
are networked with each other. Our goal is to incentivize the intermediaries to
diffuse the auction information to individuals they can reach, including their
private buyers and neighboring intermediaries, so that more potential buyers
are able to participate in the auction. To this end, we build a diffusion-based
auction framework which incorporates the strategic interaction of
intermediaries. It is showed that the classic Vickrey-Clarke-Groves (VCG)
mechanism within the framework can achieve the maximum social welfare, but it
may decrease the seller's revenue or even lead to a deficit. To overcome the
revenue issue, we propose a novel auction, called critical neighborhood
auction, which not only maximizes the social welfare, but also improves the
seller's revenue comparing to the VCG mechanism with/without intermediaries.


title: Wigner- and Marchenko-Pastur-type limits for Jacobi processes

abstract:
  We study Jacobi processes $(X_{t})_{t\ge0}$ on the compact spaces $[-1,1]^N$
and on the noncompact spaces $[1,\infty[^N$ which are motivated by the
Heckman-Opdam theory for the root systems of type BC and associated integrable
particle systems. These processes depend on three positive parameters and
degenerate in the freezing limit to solutions of deterministic dynamical
systems. In the compact case, these models tend for $t\to\infty$ to the
distributions of the $\beta$-Jacobi ensembles and, in the freezing case, to
vectors consisting of ordered zeros of one-dimensional Jacobi polynomials.
Representing these processes by stochastic differential equations, we derive
almost sure analogues of Wigner's semicircle and Marchenko-Pastur limit laws
for $N\to\infty$ for the empirical distributions of the $N$ particles on some
local scale. We there allow for arbitrary initial conditions, which enter the
limiting distributions via free convolutions These results generalize
corresponding stationary limit results in the compact case for $\beta$-Jacobi
ensembles and, in the deterministic case, for the empirical distributions of
the ordered zeros of Jacobi polynomials by Dette and Studden. The results are
also related to free limit theorems for multivariate Bessel processes,
$\beta$-Hermite and $\beta$-Laguerre ensembles, and the asymptotic empirical
distributions of the zeros of Hermite and Laguerre polynomials for
$N\to\infty$.


title:
Igeood: An Information Geometry Approach to Out-of-Distribution
  Detection

abstract:
  Reliable out-of-distribution (OOD) detection is fundamental to implementing
safer modern machine learning (ML) systems. In this paper, we introduce Igeood,
an effective method for detecting OOD samples. Igeood applies to any
pre-trained neural network, works under various degrees of access to the ML
model, does not require OOD samples or assumptions on the OOD data but can also
benefit (if available) from OOD samples. By building on the geodesic
(Fisher-Rao) distance between the underlying data distributions, our
discriminator can combine confidence scores from the logits outputs and the
learned features of a deep neural network. Empirically, we show that Igeood
outperforms competing state-of-the-art methods on a variety of network
architectures and datasets.


title: Superconducting detector magnets for high energy physics

abstract:
  Various superconducting detector solenoids for particle physics have been
developed in the world. The key technology is the aluminum-stabilized
superconducting conductor for almost all the detector magnets in particle
physics experiments. With the progress of the conductor, the coil fabrication
technology has progressed as well, such as the inner coil winding technique,
indirect cooling, transparent vacuum vessel, quench protection scheme using
pure aluminum strips and so on. The detector solenoids design study is in
progress for future big projects in Japan and Europe, that is, ILC, FCC and
CLIC, based on the technologies established over many years. The combination of
good mechanical properties and keeping a high RRR is a key point for the
development of Al-stabilized conductor. The present concern for the detector
solenoid development is to have been gradually losing the key technologies and
experiences, because large-scale detector magnets with Al-stabilized conductor
has not been fabricated after the success of CMS and ATLAS-CS in LHC.
Complementary efforts are needed to resume an equivalent level of expertise, to
extend the effort on research and to develop these technologies and apply them
to future detector magnet projects. Especially, further effort is necessary for
the industrial technology of Al-stabilized superconductor production. The
worldwide collaboration with relevant institutes and industries will be
critically important to re-realize and validate the required performances. Some
detector solenoids for mid-scale experiment wound with conventional
copper-stabilized Nb-Ti conductor require precise control of magnetic field
distribution. The development efforts are on-going in terms of the magnetic
field design technology with high precision simulation, coil fabrication
technology and control method of magnetic field distribution.


title:
Masses of vector and pseudovector hybrid mesons in a chiral symmetric
  model

abstract:
  We enlarge the chiral model, the so-called extended Linear Sigma Model
(eLSM), by including the low-lying hybrid nonet with exotic quantum numbers
$J^{PC}=1^{-+}$ and the nonet of their chiral partners with $J^{PC}=1^{+-}$ to
a global $U(3)_r \times U(3)_l$ chiral symmetry. We use the assignment of the
$\pi_1^{hyb}= \pi_1(1600)$ as input to determine the unknown parameters. Then,
we compute the lightest vector and pseudovector hybrid masses that could guide
ongoing and upcoming experiments in searching for hybrids.


abstract:
  The definition of Milnor-Witt cycle modules in [Feld, N., Milnor-Witt cycle
modules, Journal of Pure and Applied Algebra 224 (2020) 106298] can easily be
adapted over general regular base schemes. However, there are simple examples
to show that Gersten complex fails to be exact for cycle modules in general if
the base is not a field. The goal of this article is to show that, for a
restricted class of Milnor-Witt cycle modules over an excellent DVR satisfying
an extra axiom, called here as R5, the expected properties of exactness of
Gersten complex and $\mathbb{A}^1$-invariance hold. Moreover R5 is vacuously
satisfied when the base is a perfect field and it is also satisfied by $K^{MW}$
over any base. As a corollary, we obtain the strict $\mathbb{A}^1$-invariance
and the exactness of Gersten complex for $K^{MW}$ over an excellent DVR.


title: A Framework for Verifiable and Auditable Federated Anomaly Detection

abstract:
  Federated Leaning is an emerging approach to manage cooperation between a
group of agents for the solution of Machine Learning tasks, with the goal of
improving each agent's performance without disclosing any data. In this paper
we present a novel algorithmic architecture that tackle this problem in the
particular case of Anomaly Detection (or classification or rare events), a
setting where typical applications often comprise data with sensible
information, but where the scarcity of anomalous examples encourages
collaboration. We show how Random Forests can be used as a tool for the
development of accurate classifiers with an effective insight-sharing mechanism
that does not break the data integrity. Moreover, we explain how the new
architecture can be readily integrated in a blockchain infrastructure to ensure
the verifiable and auditable execution of the algorithm. Furthermore, we
discuss how this work may set the basis for a more general approach for the
design of federated ensemble-learning methods beyond the specific task and
architecture discussed in this paper.


title: A negative binomial approximation in group testing

abstract:
  We consider the problem of group testing (pooled testing), first introduced
by Dorfman. For non-adaptive testing strategies, we refer to a non-defective
item as `intruding' if it only appears in positive tests. Such items cause
mis-classification errors in the well-known COMP algorithm, and can make other
algorithms produce an error. It is therefore of interest to understand the
distribution of the number of intruding items. We show that, under Bernoulli
matrix designs, this distribution is well approximated in a variety of senses
by a negative binomial distribution, allowing us to understand the performance
of the two-stage conservative group testing algorithm of Aldridge.


title: Future Circular Hadron Collider FCC-hh: Overview and Status

abstract:
  The Future Circular Collider (FCC) study was launched as a world-wide
international collaboration hosted by CERN. Its goal is to push the field to
the next energy frontier beyond LHC, increasing by an order of magnitude the
mass of particles that could be directly produced, and decreasing by an order
of magnitude the subatomic distances to be studied. The FCC study covers two
accelerators, namely, an energy-frontier hadron collider (FCC-hh) and a highest
luminosity, high-energy lepton collider (FCC-ee). Both rings are hosted in the
same 100 km tunnel infrastructure, replicating the CERN strategy for LEP and
LHC, i.e. developing a lepton and a hadron ring sharing the same tunnel. This
paper is devoted to the FCC-hh and summarizes the key features of the FCC-hh
accelerator design, performance reach, and underlying technologies. The
material presented in this paper builds on the conceptual design report
published in 2019, and extends it, including also the progress made and the
results achieved since then.


title: On the focusing of thermal images

abstract:
  In this paper we present a new thermographic image database suitable for the
analysis of automatic focus measures. This database consists of 8 different
sets of scenes, where each scene contains one image for 96 different focus
positions. Using this database we evaluate the usefulness of six focus measures
with the goal to determine the optimal focus position. Experimental results
reveal that an accurate automatic detection of optimal focus position is
possible, even with a low computational burden. We also present an acquisition
tool able to help the acquisition of thermal images. To the best of our
knowledge, this is the first study about automatic focus of thermal images.


title:
This is not the padding you are looking for! On the ineffectiveness of
  QUIC PADDING against website fingerprinting

abstract:
  Website fingerprinting (WF) is a well-know threat to users' web privacy. New
internet standards, such as QUIC, include padding to support defenses against
WF. We study whether network-layer padding can indeed be used to construct
effective WF defenses. We confirm previous claims that network-layer padding
cannot provide good protection against powerful adversaries capable of
observing all traffic traces. In contrast to prior work, we also demonstrate
that such padding is ineffective even against adversaries with partial view of
the traffic. Network-layer padding without application input is ineffective
because it fails to hide information unique across different applications. We
show that application-layer padding solutions need to be deployed by both first
and third parties, and that they can only thwart traffic analysis in limited
situations. We identify challenges to deploy effective WF defenses and provide
recommendations to reduce these hurdles.


title:
End-to-end P300 BCI using Bayesian accumulation of Riemannian
  probabilities

abstract:
  In brain-computer interfaces (BCI), most of the approaches based on
event-related potential (ERP) focus on the detection of P300, aiming for single
trial classification for a speller task. While this is an important objective,
existing P300 BCI still require several repetitions to achieve a correct
classification accuracy. Signal processing and machine learning advances in
P300 BCI mostly revolve around the P300 detection part, leaving the character
classification out of the scope. To reduce the number of repetitions while
maintaining a good character classification, it is critical to embrace the full
classification problem. We introduce an end-to-end pipeline, starting from
feature extraction, and is composed of an ERP-level classification using
probabilistic Riemannian MDM which feeds a character-level classification using
Bayesian accumulation of confidence across trials. Whereas existing approaches
only increase the confidence of a character when it is flashed, our new
pipeline, called Bayesian accumulation of Riemannian probabilities (ASAP),
update the confidence of each character after each flash. We provide the proper
derivation and theoretical reformulation of this Bayesian approach for a
seamless processing of information from signal to BCI characters. We
demonstrate that our approach performs significantly better than standard
methods on public P300 datasets.