title:
A derivative-free approach to mixed integer constrained multiobjective
  nonsmooth optimization

abstract:
  In this work, we consider multiobjective optimization problems with both
bound constraints on the variables and general nonlinear constraints, where
objective and constraint function values can only be obtained by querying a
black box. Furthermore, we consider the case where a subset of the variables
can only take integer values. We propose a new linesearch-based solution method
and show that it converges to a set of stationary points for the problem. For
what concerns the continuous variables, we employ a strategy for the estimation
of the Pareto frontier recently proposed in the literature and which takes
advantage of dense sequences of search directions. The subset of variables that
must assume discrete values are dealt with using primitive directions
appropriately modified to take into account the presence of more than one
objective functions. Numerical results obtained with the proposed method on a
set of test problems and comparison with other solution methods show the
viability and efficiency of the proposed approach.


title: Double spending prevention of digital Euros using a web-of-trust

abstract:
  In order to provide more security on double-spending, we have implemented a
system allowing for a web-of-trust. In this paper, we explore different
approaches taken against double-spending and implement our own version to avoid
this within TrustChain as part of the ecosystem of EuroToken, the digital
version of the euro. We have used the EVA protocol as a means to transfer data
between users, building on the existing functionality of transferring money
between users. This allows the sender of EuroTokens to leave recommendations of
users based on their previous interactions with other users. This dissemination
of trust through the network allows users to make more trustworthy decisions.
Although this provides an upgrade in terms of usability, the mathematical
details of our implementation can be explored further in other research.


title: Self-Guided Learning to Denoise for Robust Recommendation

abstract:
  The ubiquity of implicit feedback makes them the default choice to build
modern recommender systems. Generally speaking, observed interactions are
considered as positive samples, while unobserved interactions are considered as
negative ones. However, implicit feedback is inherently noisy because of the
ubiquitous presence of noisy-positive and noisy-negative interactions.
Recently, some studies have noticed the importance of denoising implicit
feedback for recommendations, and enhanced the robustness of recommendation
models to some extent. Nonetheless, they typically fail to (1) capture the hard
yet clean interactions for learning comprehensive user preference, and (2)
provide a universal denoising solution that can be applied to various kinds of
recommendation models.
  In this paper, we thoroughly investigate the memorization effect of
recommendation models, and propose a new denoising paradigm, i.e., Self-Guided
Denoising Learning (SGDL), which is able to collect memorized interactions at
the early stage of the training (i.e., "noise-resistant" period), and leverage
those data as denoising signals to guide the following training (i.e.,
"noise-sensitive" period) of the model in a meta-learning manner. Besides, our
method can automatically switch its learning phase at the memorization point
from memorization to self-guided learning, and select clean and informative
memorized data via a novel adaptive denoising scheduler to improve the
robustness. We incorporate SGDL with four representative recommendation models
(i.e., NeuMF, CDAE, NGCF and LightGCN) and different loss functions (i.e.,
binary cross-entropy and BPR loss). The experimental results on three benchmark
datasets demonstrate the effectiveness of SGDL over the state-of-the-art
denoising methods like T-CE, IR, DeCA, and even state-of-the-art robust
graph-based methods like SGCN and SGL.


title:
MARF: Multiscale Adaptive-switch Random Forest for Leg Detection with 2D
  Laser Scanners

abstract:
  For the 2D laser-based tasks, e.g., people detection and people tracking, leg
detection is usually the first step. Thus, it carries great weight in
determining the performance of people detection and people tracking. However,
many leg detectors ignore the inevitable noise and the multiscale
characteristics of the laser scan, which makes them sensitive to the unreliable
features of point cloud and further degrades the performance of the leg
detector. In this paper, we propose a multiscale adaptive-switch Random Forest
(MARF) to overcome these two challenges. Firstly, the adaptive-switch decision
tree is designed to use noisesensitive features to conduct weighted
classification and noiseinvariant features to conduct binary classification,
which makes our detector perform more robust to noise. Secondly, considering
the multiscale property that the sparsity of the 2D point cloud is proportional
to the length of laser beams, we design a multiscale random forest structure to
detect legs at different distances. Moreover, the proposed approach allows us
to discover a sparser human leg from point clouds than others. Consequently,
our method shows an improved performance compared to other state-of-the-art leg
detectors on the challenging Moving Legs dataset and retains the whole pipeline
at a speed of 60+ FPS on lowcomputational laptops. Moreover, we further apply
the proposed MARF to the people detection and tracking system, achieving a
considerable gain in all metrics.


title:
Polish spaces of Banach spaces. Complexity of isometry and isomorphism
  classes

abstract:
  We study the complexities of isometry and isomorphism classes of separable
Banach spaces in the Polish spaces of Banach spaces recently introduced and
investigated by the authors in [14]. We obtain sharp results concerning the
most classical separable Banach spaces.
  We prove that the infinite-dimensional separable Hilbert space is
characterized as the unique separable infinite-dimensional Banach space whose
isometry class is closed, and also as the unique separable infinite-dimensional
Banach space whose isomorphism class is $F_\sigma$. For
$p\in\left[1,2\right)\cup\left(2,\infty\right)$, we show that the isometry
classes of $L_p[0,1]$ and $\ell_p$ are $G_\delta$-complete sets and
$F_{\sigma\delta}$-complete sets, respectively. Then we show that the isometry
class of $c_0$ is an $F_{\sigma\delta}$-complete set.
  Additionally, we compute the complexities of many other natural classes of
separable Banach spaces; for instance, the class of separable
$\mathcal{L}_{p,\lambda+}$-spaces, for $p,\lambda\geq 1$, is shown to be a
$G_\delta$-set, the class of superreflexive spaces is shown to be an
$F_{\sigma\delta}$-set, and the class of spaces with local $\Pi$-basis
structure is shown to be a $\boldsymbol{\Sigma}^0_6$-set. The paper is
concluded with many open problems and suggestions for a future research.


title:
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with
  Application to Robotic Grasping

abstract:
  The domain of robotics is challenging to apply deep reinforcement learning
due to the need for large amounts of data and for ensuring safety during
learning. Curriculum learning has shown good performance in terms of sample-
efficient deep learning. In this paper, we propose an algorithm (named GloCAL)
that creates a curriculum for an agent to learn multiple discrete tasks, based
on clustering tasks according to their evaluation scores. From the
highest-performing cluster, a global task representative of the cluster is
identified for learning a global policy that transfers to subsequently formed
new clusters, while the remaining tasks in the cluster are learned as local
policies. The efficacy and efficiency of our GloCAL algorithm are compared with
other approaches in the domain of grasp learning for 49 objects with varied
object complexity and grasp difficulty from the EGAD! dataset. The results show
that GloCAL is able to learn to grasp 100% of the objects, whereas other
approaches achieve at most 86% despite being given 1.5 times longer training
time.


title:
Electronic Structure Calculation of Graphene by Formulating a
  Relativistic Tight-Binding Model

abstract:
  A non-perturbative relativistic tight-binding (TB) approximation method
applicable to crystalline material immersed in a magnetic field was developed
in 2015. To apply this method to any material in the magnetic field, the
electronic structure of the material in absence of a magnetic field must be
calculated. In this study, we present the relativistic TB approximation method
for graphene in a zero magnetic field. The Hamiltonian and overlap matrix is
constructed considering the nearest neighbouring atomic interactions between
the $s$ and $p$ valence orbitals, where the relativistic hopping and overlap
integrals are calculated using the relativistic version of the Slater-Koster
table. The method of constructing the Hamiltonian and overlap matrix and the
resulting energy-band structure of graphene in the first Brillouin zone is
presented in this paper. It is found that there is an appearance of a small
band-gap at the $\textbf{K}$ points (also known as the spin-orbit gap) due to
the relativistic effect, whose magnitude is $25$ $\mu$eV.


title: Modeling Indirect Illumination for Inverse Rendering

abstract:
  Recent advances in implicit neural representations and differentiable
rendering make it possible to simultaneously recover the geometry and materials
of an object from multi-view RGB images captured under unknown static
illumination. Despite the promising results achieved, indirect illumination is
rarely modeled in previous methods, as it requires expensive recursive path
tracing which makes the inverse rendering computationally intractable. In this
paper, we propose a novel approach to efficiently recovering spatially-varying
indirect illumination. The key insight is that indirect illumination can be
conveniently derived from the neural radiance field learned from input images
instead of being estimated jointly with direct illumination and materials. By
properly modeling the indirect illumination and visibility of direct
illumination, interreflection- and shadow-free albedo can be recovered. The
experiments on both synthetic and real data demonstrate the superior
performance of our approach compared to previous work and its capability to
synthesize realistic renderings under novel viewpoints and illumination. Our
code and data are available at https://zju3dv.github.io/invrender/.


title: Magma-valued metric spaces

abstract:
  In this paper, first we introduce dense unital magmas and magma-valued metric
spaces. The density property of the ordered unital magmas, monoids, and
hemirings helps us to generalize a couple of classical results related to the
convergence and Cauchy property of sequences and series. For example, we prove
that the Cauchy Condensation Test holds for Cauchy complete fields. We also
introduce hemiring-valued pseudonormed rings which are a generalization of
pseudonormed rings and valuation domains. Then, we prove that
finite-dimensional algebras over hemiring-valued pseudonormed fields can be
pseudonormed which is a generalization of a result by Albert.


title:
Thermodynamically consistent concurrent material and structure
  optimization of elastoplastic multiphase hierarchical systems

abstract:
  The concept of concurrent material and structure optimization aims at
alleviating the computational discovery of optimum microstructure
configurations in multiphase hierarchical systems, whose macroscale behavior is
governed by their microstructure composition that can evolve over multiple
length scales from a few micrometers to centimeters. It is based on the split
of the multiscale optimization problem into two nested sub-problems, one at the
macroscale (structure) and the other at the microscales (material). In this
paper, we establish a novel formulation of concurrent material and structure
optimization for multiphase hierarchical systems with elastoplastic
constituents at the material scales. Exploiting the thermomechanical
foundations of elastoplasticity, we reformulate the material optimization
problem based on the maximum plastic dissipation principle such that it assumes
the format of an elastoplastic constitutive law and can be efficiently solved
via modified return mapping algorithms. We integrate continuum micromechanics
based estimates of the stiffness and the yield criterion into the formulation,
which opens the door to a computationally feasible treatment of the material
optimization problem. To demonstrate the accuracy and robustness of our
framework, we define new benchmark tests with several material scales that, for
the first time, become computationally feasible. We argue that our formulation
naturally extends to multiscale optimization under further path-dependent
effects such as viscoplasticity or multiscale fracture and damage.


title:
Construction of polynomial algebras from intermediate Casimir invariants
  of Lie algebras

abstract:
  We propose a systematic procedure to construct polynomial algebras from
intermediate Casimir invariants arising from (semisimple or non-semisimple) Lie
algebras $\mathfrak{g}$. In this approach, we deal with explicit polynomials in
the enveloping algebra of $\mathfrak{g} \oplus \mathfrak{g} \oplus
\mathfrak{g}$. We present explicit examples to show how these Lie algebras can
display different behaviours and can lead to Abelian algebras, quadratic
algebras or more complex structures involving higher order nested commutators.
Within this framework, we also demonstrate how virtual copies of the Levi
factor of a Levi decomposable Lie algebra can be used as a tool to construct
"copies" of polynomial algebras. Different schemes to obtain polynomial
algebras associated to algebraic Hamiltonians have been proposed in the
literature, among them the use of commutants of various type. The present
approach is different and relies on the construction of intermediate Casimir
invariants in the enveloping algebra $\mathcal{U}(\mathfrak{g} \oplus
\mathfrak{g} \oplus \mathfrak{g})$.


title: Embedding bordered Riemann surfaces in strongly pseudoconvex domains

abstract:
  We show that every bordered Riemann surface, $M$, with smooth boundary $bM$
admits a proper holomorphic map $M\to \Omega$ into any bounded strongly
pseudoconvex domain $\Omega$ in $\mathbb C^n$, $n>1$, extending to a smooth map
$f:\overline M\to\overline \Omega$ which can be chosen an immersion if $n\ge 3$
and an embedding if $n\ge 4$. Furthermore, $f$ can be chosen to approximate a
given holomorphic map $\overline M\to \Omega$ on compacts in $M$ and
interpolate it at finitely many given points in $M$.


title: On restricted completions of chordal and trivially perfect graphs

abstract:
  Let $G$ be a graph having a vertex $v$ such that $H = G - v$ is a trivially
perfect graph. We give a polynomial-time algorithm for the problem of deciding
whether it is possible to add at most $k$ edges to $G$ to obtain a trivially
perfect graph. This is a slight variation of the well-studied {\sc Edge
Completion}, also known as {\sc Minimum Fill-In}, problem. We also show that if
$H$ is a chordal graph, then the problem of deciding whether it is possible to
add at most $k$ edges to $G$ to obtain a chordal graph is \NP-complete.


title:
Surface Similarity Parameter: A New Machine Learning Loss Metric for
  Oscillatory Spatio-Temporal Data

abstract:
  Supervised machine learning approaches require the formulation of a loss
functional to be minimized in the training phase. Sequential data are
ubiquitous across many fields of research, and are often treated with Euclidean
distance-based loss functions that were designed for tabular data. For smooth
oscillatory data, those conventional approaches lack the ability to penalize
amplitude, frequency and phase prediction errors at the same time, and tend to
be biased towards amplitude errors. We introduce the surface similarity
parameter (SSP) as a novel loss function that is especially useful for training
machine learning models on smooth oscillatory sequences. Our extensive
experiments on chaotic spatio-temporal dynamical systems indicate that the SSP
is beneficial for shaping gradients, thereby accelerating the training process,
reducing the final prediction error, and implementing a stronger regularization
effect compared to using classical loss functions. The results indicate the
potential of the novel loss metric particularly for highly complex and chaotic
data, such as data stemming from the nonlinear two-dimensional
Kuramoto-Sivashinsky equation and the linear propagation of dispersive surface
gravity waves in fluids.


title: Second-harmonic generation from singular metasurfaces

abstract:
  We present a theoretical study of second-harmonic generation from a singular
metasurface. The singular metasurfaces strongly interact with the incident
light, where the large field enhancement forms an intense surface polarization
that generates the second-harmonic field. By using transformation optics, the
calculation of nonlinear optical response is converted from the metasurface
frame to that of a simple slab geometry, largely reducing the complexity of the
problem. In addition, the singular metasurface exhibits a weak dependence on
the incident angle of light, which can be potentially used as an all-angle
device for harmonic generations. Finally, we study the symmetry dependence of
second-harmonic generation in the far field for the singular metasurface and
show how to enhance the conversion efficiency under normal incidence by
breaking the surface inversion symmetry.


title:
N-body simulations, halo mass functions, and halo density profile in
  $f(T)$ gravity

abstract:
  We perform N-body simulations for $f(T)$ gravity using the ME-Gadget code, in
order to investigate for the first time the structure formation process in
detail. Focusing on the power-law model, and considering the model-parameter to
be consistent within 1$\sigma$ with all other cosmological datasets (such as
SNIa, BAO, CMB, CC), we show that there are clear observational differences
between $\Lambda$CDM cosmology and $f(T)$ gravity, due to the modifications
brought about the latter in the Hubble function evolution and the effective
$Newton\prime s$ constant. We extract the matter density distribution, matter
power spectrum, counts-in-cells, halo mass function and excess surface density
(ESD) around low density positions (LDPs) at present time. Concerning the
matter power spectrum we find a difference from $\Lambda$CDM scenario, which is
attributed to about 2/3 to the different expansion and to about 1/3 to the
effective gravitational constant. Additionally, we find a difference in the
cells, which is significantly larger than the Poisson error, which may be
distinguishable with weak-lensing reconstructed mass maps. Moreover, we show
that there are different massive halos with mass $M>10^{14}M_{\odot}/h$, which
may be distinguishable with statistical measurements of cluster number
counting, and we find that the ESD around LDPs is mildly different. In
conclusion, high-lighting possible smoking guns, we show that large scale
structure can indeed lead us to distinguish General Relativity and $\Lambda$CDM
cosmology from $f(T)$ gravity.


title:
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor
  Scenes

abstract:
  We propose a one-step person detector for topview omnidirectional indoor
scenes based on convolutional neural networks (CNNs). While state of the art
person detectors reach competitive results on perspective images, missing CNN
architectures as well as training data that follows the distortion of
omnidirectional images makes current approaches not applicable to our data. The
method predicts bounding boxes of multiple persons directly in omnidirectional
images without perspective transformation, which reduces overhead of pre- and
post-processing and enables real-time performance. The basic idea is to utilize
transfer learning to fine-tune CNNs trained on perspective images with data
augmentation techniques for detection in omnidirectional images. We fine-tune
two variants of Single Shot MultiBox detectors (SSDs). The first one uses
Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1
FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in
Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12
datasets, specifically on class person. Random 90-degree rotation and random
vertical flipping are used for data augmentation in addition to the methods
proposed by original SSD. We reach an average precision (AP) of 67.3 % with
moSSD and 74.9 % with resSSD onthe evaluation dataset. To enhance the
fine-tuning process, we add a subset of HDA Person dataset and a subset of
PIROPOdatabase and reduce the number of perspective images to PASCAL VOC07. The
AP rises to 83.2 % for moSSD and 86.3 % for resSSD, respectively. The average
inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD
using Nvidia Quadro P6000. Our method is applicable to other CNN-based object
detectors and can potentially generalize for detecting other objects in
omnidirectional images.


title:
Enumeration of three quadrant walks with small steps and walks on other
  M-quadrant cones

abstract:
  We address the enumeration of walks with small steps confined to a
two-dimensional cone, for example the quarter plane, three-quarter plane or the
slit plane. In the quarter plane case, the solutions for unweighted step-sets
are already well understood, in the sense that it is known precisely for which
cases the generating function is algebraic, D-finite or D-algebraic, and exact
integral expressions are known in all cases. We derive similar results in a
much more general setting: we enumerate walks on an $M$-quadrant cone for any
positive integer $M$, with weighted steps starting at any point. The main
breakthrough in this work is the derivation of an analytic functional equation
which characterises the generating function of these walks, which is analogous
to one now used widely for quarter-plane walks. In the case $M=3$, which
corresponds to walks avoiding a quadrant, we provide exact integral-expression
solutions for walks with weighted small steps which determine the generating
function ${\sf C}(x,y;t)$ counting these walks. Moreover, for each step-set and
starting point of the walk we determine whether the generating function ${\sf
C}(x,y;t)$ is algebraic, D-finite or D-algebraic as a function of $x$ and $y$.
In fact we provide results of this type for any $M$-quadrant cone, showing that
this nature is the same for any odd $M$. For $M$ even we find that the
generating functions counting these walks are D-finite in $x$ and $y$, and
algebraic if and only if the starting point of the walk is on the same axis as
the boundaries of the cone.


title: Learning Probability Distributions in Macroeconomics and Finance

abstract:
  We propose a deep learning approach to probabilistic forecasting of
macroeconomic and financial time series. Being able to learn complex patterns
from a data rich environment, our approach is useful for a decision making that
depends on uncertainty of large number of economic outcomes. Specifically, it
is informative to agents facing asymmetric dependence of their loss on outcomes
from possibly non-Gaussian and non-linear variables. We show the usefulness of
the proposed approach on the two distinct datasets where a machine learns the
pattern from data. First, we construct macroeconomic fan charts that reflect
information from high-dimensional data set. Second, we illustrate gains in
prediction of stock return distributions which are heavy tailed, asymmetric and
suffer from low signal-to-noise ratio.


title:
Ensuring accurate stain reproduction in deep generative networks for
  virtual immunohistochemistry

abstract:
  Immunohistochemistry is a valuable diagnostic tool for cancer pathology.
However, it requires specialist labs and equipment, is time-intensive, and is
difficult to reproduce. Consequently, a long term aim is to provide a digital
method of recreating physical immunohistochemical stains. Generative
Adversarial Networks have become exceedingly advanced at mapping one image type
to another and have shown promise at inferring immunostains from haematoxylin
and eosin. However, they have a substantial weakness when used with pathology
images as they can fabricate structures that are not present in the original
data. CycleGANs can mitigate invented tissue structures in pathology image
mapping but have a related disposition to generate areas of inaccurate
staining. In this paper, we describe a modification to the loss function of a
CycleGAN to improve its mapping ability for pathology images by enforcing
realistic stain replication while retaining tissue structure. Our approach
improves upon others by considering structure and staining during model
training. We evaluated our network using the Fr\'echet Inception distance,
coupled with a new technique that we propose to appraise the accuracy of
virtual immunohistochemistry. This assesses the overlap between each stain
component in the inferred and ground truth images through colour deconvolution,
thresholding and the Sorensen-Dice coefficient. Our modified loss function
resulted in a Dice coefficient for the virtual stain of 0.78 compared with the
real AE1/AE3 slide. This was superior to the unaltered CycleGAN's score of
0.74. Additionally, our loss function improved the Fr\'echet Inception distance
for the reconstruction to 74.54 from 76.47. We, therefore, describe an advance
in virtual restaining that can extend to other immunostains and tumour types
and deliver reproducible, fast and readily accessible immunohistochemistry
worldwide.


title:
An Energy Aware Clustering Scheme for 5G-enabled Edge Computing based
  IoMT Framework

abstract:
  In recent years, 5G network systems start to offer communication
infrastructure for Internet of Things (IoT) applications, especially for health
care service pro-viders. In smart health care systems, edge computing enabled
Internet of Medical Things (IoMT) is an innovative technology to provide online
health care monitor-ing facility to patients. Here, energy consumption, along
with extending the lifespan of biosensor network, is a key concern. In this
contribution, a Chicken Swarm Optimization algorithm, based on Energy Efficient
Multi-objective clus-tering scheme is applied in the context of IoMT system. An
effective fitness func-tion is designed for cluster head selection., using
multiple objectives, such as re-sidual energy, queuing delay, communication
cost, link quality and node centrali-ty. Simulated outcomes of the proposed
scheme are compared with the existing schemes in terms of parameters such as
cluster formation time, energy consump-tion, network lifetime, throughput and
propagation delay.


title:
(Fractional) Online Stochastic Matching via Fine-Grained Offline
  Statistics

abstract:
  Motivated by display advertising on the internet, the online stochastic
matching problem is proposed by Feldman, Mehta, Mirrokni, and Muthukrishnan
(FOCS 2009). Consider a stochastic bipartite graph with offline vertices on one
side and with i.i.d. online vertices on the other side. The algorithm knows the
offline vertices and the distribution of the online vertices in advance. Upon
the arrival of each online vertex, its type is realized and the algorithm
immediately and irrevocably decides how to match it. In the vertex-weighted
version of the problem, each offline vertex is associated with a weight and the
goal is to maximize the total weight of the matching.
  In this paper, we generalize the model to allow non-identical online vertices
and focus on the fractional version of the vertex-weighted stochastic matching.
We design fractional algorithms that are $0.718$-competitive and
$0.731$-competitive for non i.i.d. arrivals and i.i.d. arrivals respectively.
We also prove that no fractional algorithm can achieve a competitive ratio
better than $0.75$ for non i.i.d. arrivals. Furthermore, we round our
fractional algorithms by applying the recently developed multiway online
correlated selection by Gao et al. (FOCS 2021) and achieve $0.666$-competitive
and $0.704$-competitive integral algorithms for non i.i.d. arrivals and i.i.d.
arrivals. Our results for non i.i.d. arrivals are the first algorithms beating
the $1-1/e \approx 0.632$ barrier of the classical adversarial setting. Our
$0.704$-competitive integral algorithm for i.i.d. arrivals slightly improves
the state-of-the-art $0.701$-competitive ratio by Huang and Shu (STOC 2021).


title:
Non-intrusive implementation of Multiscale Finite Element Methods: an
  illustrative example

abstract:
  Multiscale Finite Element Methods (MsFEM) are finite element type approaches
dedicated to multiscale problems. They first compute local, oscillatory,
problem-dependent basis functions which generate a specific discretization
space, and next perform a Galerkin approximation of the problem on that space.
We investigate here how these approaches can be implemented in a non-intrusive
way, in order to facilitate their dissemination within industrial codes or non
academic environments.


title: On the Shannon capacity of sums and products of graphs

abstract:
  Let $\Theta(G)$ denote the Shannon capacity of a graph $G$. We give an
elementary proof of the equivalence, for any graphs $G$ and $H$, of the
inequalities $\Theta(G\sqcup H)>\Theta(G)+\Theta(H)$ and $\Theta(G\boxtimes
H)>\Theta(G)\Theta(H)$. This was shown independently by Wigderson and Zuiddam
[2022] using Kadison-Dubois duality and the Axiom of choice.


title: Sivers function of sea quarks in the light-cone model

abstract:
  We calculate the Sivers function of $\bar{u}$ and $\bar{d}$ quarks using the
overlap representation within the light-cone formalism. The light-cone wave
functions of the proton is obtained in terms of the $|\bar{q} q B\rangle$ Fock
states motivated by the meson-baryon fluctuation model. We consider the
final-state interaction at the level of one gluon exchange. In a simplified
scenario, the Sivers function of $\bar{u}$ and $\bar{d}$ can be expressed as
the convolution of the Sivers function of the pion inside the proton and the
unpolarized distribution of $\bar{q}$ inside the pion. The model parameters are
fixed by fitting the unpolarized sea quark distributions to the known
parameterizations. We present the numerical results for $f_1^{\perp
\bar{u}/p}(x,\boldsymbol{k}_T^2)$ and $f_1^{\perp
\bar{d}/p}(x,\boldsymbol{k}_T^2)$. The first transverse moment of the sea quark
Sivers functions in our model are find to be negative and the magnitude is
about 0.004 at most.


title: Special flow equation and GKP-Witten relation

abstract:
  We develop a framework for the reconstruction of the bulk theory dual to
conformal field theory (CFT) without any assumption by means of a flow
equation. To this end we investigate a minimal extension of the free flow
equation and find that at a special parametrization the conformal
transformation for a normalized smeared operator exactly becomes the isometry
of anti-de Sitter space (AdS). By employing this special flow equation to
O$(N)$ vector models, we explicitly show that the AdS geometry as well as the
scalar field satisfying the GKP-Witten relation concurrently emerge in this
framework.


title:
A consistent approach to the path integral formalism of quantum
  mechanics based on the maximum length uncertainty

abstract:
  We have developed a proper path integral formalism consistent with the
deformed version of the quantum mechanics which contains a maximum observable
length scale at the order of the Cosmological particle horizon, existing in
cosmology. First, we have presented the modifications to the classical
mechanics which shows non-minimal effects on the equation of motion of a
particle. Next, we have provided representation of the deformed quantum
mechanical algebra. With this algebra in hand, we have calculated the general
form of the path integral propagator in this deformed background. Finally, as a
most simple case, we have built up the explicit form of the free particle
propagator. The modifications to the free particle propagator shows some
non-trivial effects in this case, which can have some important significance.


title:
A New Refinement-Free Preconditioner for the Symmetric Formulation in
  Electroencephalography

abstract:
  Widely employed for the accurate solution of the electroencephalography
forward problem, the symmetric formulation gives rise to a first kind,
ill-conditioned operator ill-suited for complex modelling scenarios. This work
presents a novel preconditioning strategy based on an accurate spectral
analysis of the operators involved which, differently from other
Calder\'on-based approaches, does not necessitate the barycentric refinement of
the primal mesh (i.e., no dual matrix is required). The discretization of the
new formulation gives rise to a well-conditioned, symmetric, positive-definite
system matrix, which can be efficiently solved via fast iterative techniques.
Numerical results for both canonical and realistic head models validate the
effectiveness of the proposed formulation.


title: Flexible LED Index Modulation for MIMO Optical Wireless Communications

abstract:
  The limited bandwidth of optical wireless communication (OWC) front-end
devices motivates the use of multiple-input-multiple-output (MIMO) techniques
to enhance data rates. It is known that very high multiplexing gains could be
achieved by spatial multiplexing (SMX) in exchange for exhaustive detection
complexity. Alternatively, in spatial modulation (SM), a single light emitting
diode (LED) is activated per time instance where information is carried by both
the signal and the LED index. Since only an LED is active, both transmitter
(TX) and receiver (RX) complexity reduces significantly while retaining the
information transmission in the spatial domain. However, significant spectral
efficiency losses occur in SM compared to SMX. In this paper, we propose a
technique which adopts the advantages of both systems. Accordingly, the
proposed flexible LED index modulation (FLIM) technique harnesses the inactive
state of the LEDs as a transmit symbol. Therefore, the number of active LEDs
changes in each transmission, unlike conventional techniques. Moreover, the
system complexity is reduced by employing a linear minimum mean squared error
(MMSE) equalizer and an angle perturbed receiver at the RX. Numerical results
show that FLIM outperforms the reference systems by at least 6 dB in the low
and medium/high spectral efficiency regions.


title: Semi-Supervised Training to Improve Player and Ball Detection in Soccer

abstract:
  Accurate player and ball detection has become increasingly important in
recent years for sport analytics. As most state-of-the-art methods rely on
training deep learning networks in a supervised fashion, they require huge
amounts of annotated data, which are rarely available. In this paper, we
present a novel generic semi-supervised method to train a network based on a
labeled image dataset by leveraging a large unlabeled dataset of soccer
broadcast videos. More precisely, we design a teacher-student approach in which
the teacher produces surrogate annotations on the unlabeled data to be used
later for training a student which has the same architecture as the teacher.
Furthermore, we introduce three training loss parametrizations that allow the
student to doubt the predictions of the teacher during training depending on
the proposal confidence score. We show that including unlabeled data in the
training process allows to substantially improve the performances of the
detection network trained only on the labeled data. Finally, we provide a
thorough performance study including different proportions of labeled and
unlabeled data, and establish the first benchmark on the new SoccerNet-v3
detection task, with an mAP of 52.3%. Our code is available at
https://github.com/rvandeghen/SST .


title:
AlphaFold2 can predict structural and phenotypic effects of single
  mutations

abstract:
  AlphaFold2 (AF2) is a promising tool for structural biology, but is it
sufficiently accurate to predict the effect of missense mutations? We find that
structural variation between closely related 1-3 mutations) protein pairs is
correlated across experimental and AF2-predicted structures (${\sim}90000$
pairs). Analysis of ${\sim}10000$ predicted structures from three
high-throughput studies linking sequence and phenotype (fluorescence,
foldability, and catalysis) demonstrates that AF2 can predict the phenotypic
effect of missense mutations


title:
Opacity of the highly ionized lanthanides and the effect on the early
  kilonova

abstract:
  We investigate the effect of the presence of lanthanides (Z = 57- 71) on the
kilonova at t~hours after the neutron star merger for the first time. For this
purpose, we calculate the atomic structures and the opacities for selected
lanthanides: Nd (Z = 60), Sm (Z = 62), and Eu (Z = 63). We consider the
ionization degree up to tenth (XI), applicable for the ejecta at t ~ a few
hours after the merger, when the temperature is T ~ 10^5 K. We find that the
opacities for the highly ionized lanthanides are exceptionally high, reaching
k_exp~1000 cm^2/g for Eu, thanks to the energy levels being highly dense. Using
the new opacity, we perform radiative transfer simulations to show that the
early light curves become fainter by a (maximum) factor of four, in comparison
to lanthanide-free ejecta at t~0.1 day. However, the period at which the light
curves are affected is relatively brief due to the rapid time evolution of the
opacity in the outermost layer of the ejecta. We predict that for a source at a
distance of ~100 Mpc, UV brightness for lanthanide-rich ejecta shows a drop to
~21-22 mag at t~0.1 day and the UV peaks around t~0.2 day with a magnitude of
~19 mag. Future detection of such a kilonova by the existing UV satellite like
Swift or the upcoming UV satellite ULTRASAT will provide useful constraints on
the abundance in the outer ejecta and the corresponding nucleosynthesis
conditions in the neutron star mergers.


title:
Human Identity-Preserved Motion Retargeting in Video Synthesis by
  Feature Disentanglement

abstract:
  Most motion retargeting methods in human action video synthesis decompose the
input video to motion (dynamic information) and shape (static information).
However, we observe if the dynamic information is directly transferred to
another subject, it will result in unnatural synthesised motion. This
phenomenon is mainly caused by neglecting subject-dependent information in
motion. To solve the problem, we propose a novel motion retargeting method
which can combine both subject-independent (common motion content) information
from a source video and subject-dependent (individualized identity motion)
information from a target video. So it can synthesize videos with a much
natural appearance along with identity-preserved motion. In the proposed method
two encoders are employed to extract identity and motion content
representations respectively. We employ the adaptive instance normalization
(AdaIN) layer in the generator and the instance normalization (IN) layer in the
motion content encoder to synthesize the new motion. Besides, we also collected
a dataset, named $Chuang101$, with 101 subjects in total. Each subject performs
identical dancing movement, and so it is convenient for feature disentanglement
among motion and identity of each subject. Furthermore, an efficient
quantitative metric for identify information is designed by gait recognition.
The experiments show the proposed method can synthesize videos more naturally
when the subject's identity is preserved.


title:
ULF: Unsupervised Labeling Function Correction using Cross-Validation
  for Weak Supervision

abstract:
  A way to overcome expensive and time-consuming manual data labeling is weak
supervision - automatic annotation of data samples via a predefined set of
labeling functions (LFs), rule-based mechanisms that generate potentially
erroneous labels. In this work, we investigate noise reduction techniques for
weak supervision based on the principle of k-fold cross-validation. In
particular, we extend two frameworks for detecting the erroneous samples in
manually annotated data to the weakly supervised setting. Our methods profit
from leveraging the information about matching LFs and detect noisy samples
more accurately. We also introduce a new algorithm for denoising the weakly
annotated data called ULF, that refines the allocation of LFs to classes by
estimating the reliable LFs-to-classes joint matrix. Evaluation on several
datasets shows that ULF successfully improves weakly supervised learning
without using any manually labeled data.


title:
A Unified Programming Model for Heterogeneous Computing with CPU and
  Accelerator Technologies

abstract:
  This paper consists of three parts. The first part provides a unified
programming model for heterogeneous computing with CPU and accelerator (like
GPU, FPGA, Google TPU, Atos QPU, and more) technologies. To some extent, this
new programming model makes programming across CPUs and accelerators turn into
usual programming tasks with common programming languages, and relieves
complexity of programming across CPUs and accelerators. It can be achieved by
extending file managements in common programming languages, such as C/C++,
Fortran, Python, MPI, etc., to cover accelerators as I/O devices. In the second
part, we show that all types of computer systems can be reduced to the simplest
type of computer system, a single-core CPU computer system with I/O devices, by
the unified programming model. Thereby, the unified programming model can truly
build the programming of various computer systems on one API (i.e. file
managements of common programming languages), and can make programming for
various computer systems easier. In third part, we present a new approach to
coupled applications computing (like multidisciplinary simulations) by the
unified programming model. The unified programming model makes coupled
applications computing more natural and easier since it only relies on its own
power to couple multiple applications through MPI.


title: Finitistic dimensions over commutative DG-rings

abstract:
  We study the small and big finitistic projective, injective and flat
dimensions over a non-positively graded commutative noetherian DG-ring $A$ with
bounded cohomology. Our main results generalize results of Bass and
Raynaud-Gruson to this derived setting, showing that any bounded DG-module $M$
of finite flat dimension satisfies $\operatorname{proj\,dim}_A(M) \le
\dim(\mathrm{H}^0(A)) -\inf(M)$. We further construct DG-modules of prescribed
projective dimension, and deduce that the big finitistic projective dimension
satisfies the inequalities $\dim(\mathrm{H}^0(A)) - \operatorname{amp}(A) \le
\mathsf{FPD}(A) \le \dim(\mathrm{H}^0(A))$. It is further shown that this
result is optimal, in the sense that there are examples that achieve either
bound. As an application, new vanishing results for the derived Hochschild
(co)homology of homologically smooth algebras are deduced.


title:
Controlling distribution of prime sequences in discretely ordered
  principal ideal subrings of $\mathbb Q[x]$

abstract:
  We show how to construct discretely ordered principal ideal subrings of
$\mathbb Q[x]$ with various types of prime behaviour. Given any set $\mathcal
D$ consisting of finite strictly increasing sequences $(d_1,d_2,\dots, d_l)$ of
positive integers such that, for each prime integer $p$, the set $\{p\mathbb Z,
d_1+p\mathbb Z,\dots, d_l+p\mathbb Z\}$ does not contain all the cosets modulo
$p$, we can stipulate to have, for each $(d_1,\dots, d_l)\in \mathcal D$, a
cofinal set of progressions $(f, f+d_1, \dots, f+d_l)$ of prime elements in our
PID $R_\tau$. Moreover, we can simultaneously guarantee that each positive
prime $g\in R_\tau\setminus\mathbb N$ is either in a prescribed progression as
above or there is no other prime $h$ in $R_\tau$ such that $g-h\in\mathbb Z$.


title:
A Generalized Switched-Capacitor Modular Multilevel Inverter Topology
  for Multiphase Electrical Machines with Capacitor-Voltage Self-Balancing
  Capability

abstract:
  Recent research on multilevel inverters shows exciting properties, including
the potential to generate multiple output voltages and integrated voltage
boosting. However, most presented inverter topologies have a restricted number
of output voltage levels and limited output voltage boosting ratio. In
addition, balancing the voltage of capacitors in multilevel converters is very
important and should be considered in the topology or control method. This
paper describes a generalized switched-capacitor circuit topology for
multilevel dc-to-ac inverters that can be developed for the desired output
voltage levels. Also, the maximum ac output voltage can vary from values much
lower than the dc input voltage to several times of it depending on the design
requirement. High-voltage dc input and ac output could be handled using
low-voltage capacitors, which substantially decreases overall cost and volume.
The proposed topology further allows for easy expansion through stackable
circuits for multiple load phases. It has an inherent capacity for balancing
the voltage of capacitors. To validate the feasibility and practicality of this
concept, we provide circuit descriptions, control strategies, design
recommendations, and pertinent simulation findings for the suggested inverter
topology.


title: Program Analysis of Probabilistic Programs

abstract:
  Probabilistic programming is a growing area that strives to make statistical
analysis more accessible, by separating probabilistic modelling from
probabilistic inference. In practice this decoupling is difficult. No single
inference algorithm can be used as a probabilistic programming back-end that is
simultaneously reliable, efficient, black-box, and general. Probabilistic
programming languages often choose a single algorithm to apply to a given
problem, thus inheriting its limitations. While substantial work has been done
both to formalise probabilistic programming and to improve efficiency of
inference, there has been little work that makes use of the available program
structure, by formally analysing it, to better utilise the underlying inference
algorithm.
  This dissertation presents three novel techniques (both static and dynamic),
which aim to improve probabilistic programming using program analysis. The
techniques analyse a probabilistic program and adapt it to make inference more
efficient, sometimes in a way that would have been tedious or impossible to do
by hand.


title:
The High Time Resolution Universe Pulsar Survey - XVII. PSR J1325-6253,
  a low eccentricity double neutron star system from an ultra-stripped
  supernova

abstract:
  The observable population of double neutron star (DNS) systems in the Milky
Way allow us to understand the nature of supernovae and binary stellar
evolution. Until now, all DNS systems in wide orbits ($ P_{\textrm{orb}}>$
1~day) have been found to have orbital eccentricities, $e > 0.1$. In this
paper, we report the discovery of pulsar PSR J1325$-$6253: a DNS system in a
1.81 day orbit with a surprisingly low eccentricity of just $e = 0.064$.
Through 1.4 yr of dedicated timing with the Parkes radio telescope we have been
able to measure its rate of advance of periastron, $\dot{\omega}=0.138 \pm
0.002$ $\rm deg$ $\rm yr^{-1}$. If this induced $\dot{\omega}$ is solely due to
general relativity then the total mass of the system is, $M_{\rm sys} = 2.57
\pm 0.06$ M$_{\odot}$. Assuming an edge-on orbit the minimum companion mass is
constrained to be $M_\mathrm{c,min}>0.98$ M$_{\odot}$ which implies the pulsar
mass is $M_\mathrm{p,max}<1.59 $ M$_{\odot}$. Its location in the $P$-$\dot{P}$
diagram suggests that, like other DNS systems, PSR J1325$-$6253 is a recycled
pulsar and if its mass is similar to the known examples ($>1.3$ M$_\odot$),
then the companion neutron star is probably less than $\sim1.25$ M$_\odot$ and
the system is inclined at about $50^{\circ}$-$60^{\circ}$. The low eccentricity
along with the wide orbit of the system strongly favours a formation scenario
involving an ultra-stripped supernova explosion.


title:
On extension of closed complex (basic) differential forms: Hodge numbers
  and (transversely) $p$-K\"ahler structures

abstract:
  Inspired by a recent work of D. Wei--S. Zhu on the extension of closed
complex differential forms and Voisin's usage of the
$\partial\bar{\partial}$-lemma, we obtain several new theorems of deformation
invariance of Hodge numbers and reprove the local stabilities of $p$-K\"ahler
structures with the $\partial\bar{\partial}$-lemma. Our approach more concerns
about the $d$-closed extension by means of the exponential operator
$e^{\iota_\varphi}$. Furthermore, we prove the local stabilities of
transversely $p$-K\"ahler structures with mild $\partial\bar{\partial}$-lemma
by adapting the power series method to the foliated case, which strengthens the
works of A. El Kacimi Alaoui--B. Gmira and P. Ra\'zny on the local stabilities
of transversely ($1$-)K\"ahler structures.


title:
Calculation of $\beta$-decay half-lives with Skyrme
  Hartree-Fock-Bogoliubov+$pn$-QRPA and isoscalar pairing strengths optimized
  by a Bayesian method

abstract:
  For radioactive nuclear data, $\beta$ decay is one of the most important
information and is applied to various fields. However, some of the
$\beta$-decay data are not available due to experimental difficulties. From
this respect, theoretically calculated results have been embedded in the
$\beta$-decay data to compensate the missing information. To calculate the
$\beta$-decay half-lives, a proton-neutron quasi-particle random phase
approximation on top of a Skryme energy density functional is applied with an
assumption of spherical symmetry. The isoscalar pairing strength is estimated
by a Bayesian neutral network (BNN). We verify the predicted isoscalar pairing
strengths by preparing the training data and test data. It was confirmed that
the finite-range isovector pairing ensures the $\beta$-decay half-lives
insensitive to the model space, while the zero-range one was largely dependent
on it. The half-lives calculated with the BNN isoscalar pairing strengths
reproduced most of experimental data, although those of highly deformed nuclei
were underestimated. We also studied that the predictive performance on new
experimental data that were not used for the BNN training and found that they
were reproduced well. Our study demonstrates that the isoscalar pairing
strengths determined by the BNN can reproduce experimental data in the same
accuracy as other theoretical works.


title:
Character varieties and algebraic surfaces for the topology of quantum
  computing

abstract:
  It is shown that the representation theory of some finitely presented groups
thanks to their $SL_2(\mathbb{C})$ character variety is related to algebraic
surfaces. We make use of the Enriques-Kodaira classification of algebraic
surfaces and the related topological tools to make such surfaces explicit. We
study the connection of $SL_2(\mathbb{C})$ character varieties to topological
quantum computing (TQC) as an alternative to the concept of anyons. The Hopf
link $H$, whose character variety is a Del Pezzo surface $f_H$ (the trace of
the commutator), is the kernel of our view of TQC. Qutrit and two-qubit magic
state computing, derived from the trefoil knot in our previous work, may be
seen as TQC from the Hopf link. The character variety of some two-generator
Bianchi groups as well as that of the fundamental group for the singular fibers
$\tilde{E}_6$ and $\tilde{D}_4$ contain $f_H$. A surface birationally
equivalent to a $K_3$ surface is another compound of their character varieties.


title:
Formal Development of Safe Automated Driving using Differential Dynamic
  Logic

abstract:
  The challenges in providing convincing arguments for safe and correct
behavior of automated driving (AD) systems have so far hindered their
widespread commercial deployment. Conventional development approaches such as
testing and simulation are limited by non-exhaustive analysis, and can thus not
guarantee correctness in all possible scenarios. Formal methods is an approach
to provide mathematical proofs of correctness, using a model of a system, that
could be used to give the necessary arguments. This paper investigates the use
of differential dynamic logic and the deductive verification tool KeYmaera X in
the development of an AD feature. Specifically, formal models and safety proofs
of different design variants of a Decision & Control module for an in-lane AD
feature are presented. In doing so, the assumptions and invariant conditions
necessary to guarantee safety are identified, and the paper shows how such an
analysis helps during the development process in requirement refinement and
formulation of the operational design domain. Furthermore, it is shown how the
performance of the different models is formally analyzed exhaustively, in all
their allowed behaviors.


title: A Note on Edge Colorings and Trees

abstract:
  We point out some connections between existence of homogenous sets for
certain edge colorings and existence of branches in certain trees. As a
consequence, we get that any locally additive coloring (a notion introduced in
the paper) of a cardinal $\kappa$ has a homogeneous set of size $\kappa$
provided that the number of colors, $\mu$ satisfies $\mu^+<\kappa$. Another
result is that an uncountable cardinal $\kappa$ is weakly compact if and only
if $\kappa$ is regular, has the tree property and for each $\lambda,\mu<\kappa$
there exists $\kappa^*<\kappa$ such that every tree of height $\mu$ with
$\lambda$ nodes has less than $\kappa^*$ branches.


title:
Universal Prony fitting decomposition for optimized hierarchical quantum
  master equations

abstract:
  In this work, we propose the Prony fitting decomposition (PFD) as an accurate
and efficient exponential series method, applicable to arbitrary interacting
bath correlation functions. The resulting hierarchical equations of motion
(HEOM) formalism is greatly optimized, especially in extremely low temperature
regimes that would be inaccessible with other methods. For demonstration, we
calibrate the present PFD against the celebrated Pad\'e spectrum decomposition
method, followed by converged HEOM evaluations on the single-impurity Anderson
model system.


title:
Distributed Over-the-air Computing for Fast Distributed Optimization:
  Beamforming Design and Convergence Analysis

abstract:
  Distributed optimization concerns the optimization of a common function in a
distributed network, which finds a wide range of applications ranging from
machine learning to vehicle platooning. Its key operation is to aggregate all
local state information (LSI) at devices to update their states. The required
extensive message exchange and many iterations cause a communication bottleneck
when the LSI is high dimensional or at high mobility. To overcome the
bottleneck, we propose in this work the framework of distributed over-the-air
computing (AirComp) to realize a one-step aggregation for distributed
optimization by exploiting simultaneous multicast beamforming of all devices
and the property of analog waveform superposition of a multi-access channel. We
consider two design criteria. The first one is to minimize the sum AirComp
error (i.e., sum mean-squared error (MSE)) with respect to the desired
average-functional values. An efficient solution approach is proposed by
transforming the non-convex beamforming problem into an equivalent
concave-convex fractional program and solving it by nesting convex programming
into a bisection search. The second criterion, called zero-forcing (ZF)
multicast beamforming, is to force the received over-the-air aggregated signals
at devices to be equal to the desired functional values. In this case, the
optimal beamforming admits closed form. Both the MMSE and ZF beamforming
exhibit a centroid structure resulting from averaging columns of conventional
MMSE/ZF precoding. Last, the convergence of a classic distributed optimization
algorithm is analyzed. The distributed AirComp is found to accelerate
convergence by dramatically reducing communication latency. Another key finding
is that the ZF beamforming outperforms the MMSE design as the latter is shown
to cause bias in subgradient estimation.


title: Tension tuning of sound and heat transport in graphene

abstract:
  Heat transport by acoustic phonons in 2D materials is fundamentally different
from that in 3D crystals because the out-of-plane phonons propagate in a unique
way that strongly depends on tension and bending rigidity. Since in-plane and
out-of-plane phonon baths are decoupled, initial studies suggested they provide
independent pathways for heat transport and storage in 2D materials. Here, we
induce tension in freestanding graphene membranes by electrostatic force, and
use optomechanical techniques to demonstrate that it can change the rate of
heat transport by as much as 33%. Using a ballistic Debye model, we account for
these observations and extract the average bending rigidity of the flexural
acoustic phonons, which increases approximately linearly with the membrane's
areal mass density, in contrast to the cubic dependence seen in bulk
structures. Thus, we not only elucidate phononic heat transport mechanisms in
suspended 2D materials, but also provide a promising route for controlling
nanoscale heat transport by tension.


title:
Accumulate: An identity-based blockchain protocol with cross-chain
  support, human-readable addresses, and key management capabilities

abstract:
  The Accumulate Protocol ("Accumulate") is an identity-based, Delegated Proof
of Stake (DPoS) blockchain designed to power the digital economy through
interoperability with Layer-1 blockchains, integration with enterprise tech
stacks, and interfacing with the World Wide Web. Accumulate bypasses the
trilemma of security, scalability, and decentralization by implementing a
chain-of-chains architecture in which digital identities with the ability to
manage keys, tokens, data, and other identities are treated as their own
independent blockchains. This architecture allows these identities, known as
Accumulate Digital Identifiers (ADIs), to be processed and validated in
parallel over the Accumulate network. Each ADI also possesses a hierarchical
set of keys with different priority levels that allow users to manage their
security over time and create complex signature authorization schemes that
expand the utility of multi-signature transactions. A two token system provides
predictable costs for enterprise users, while anchoring all transactions to
Layer-1 blockchains provides enterprise-grade security to everyone.


title: On $n$-hereditary algebras and $n$-slice algebras

abstract:
  In this paper we show that $n$-slice algebras are exactly $n$-hereditary
algebras whose $(n+1)$-preprojective algebras are $(q+1,n+1)$-Koszul. We also
list the equivalent triangulated categories arising from the algebra
constructions related to an $n$-slice algebra. We show that higher slice
algebras of finite type appear in pair and they share the Auslander-Reiten
quiver for their higher preprojective components.


title:
Maximizing intrinsic anomalous Hall effect by controlling the Fermi
  level in simple Weyl semimetal films

abstract:
  Large intrinsic anomalous Hall effect (AHE) originating in the Berry
curvature has attracted growing attention for potential applications. Recently
proposed magnetic Weyl semimetal EuCd$_2$Sb$_{\mathrm{2}}$ provides an
excellent platform for controlling the intrinsic AHE because it only hosts a
Weyl-points related band structure near the Fermi energy. Here we report the
fabrication of EuCd$_2$Sb$_{\mathrm{2}}$ single-crystalline films and control
of their anomalous Hall effect by film technique. As also analyzed by
first-principles calculations of energy-dependent intrinsic anomalous Hall
conductivity, the obtained anomalous Hall effect shows a sharp peak as a
function of carrier density, demonstrating clear energy dependence of the
intrinsic AHE.


title:
Effect of Magnetism on Lattice Vibrations: M\"ossbauer Spectroscopic
  Evidence

abstract:
  Based on the available literature reviewed are results pertinent to the
effect of magnetism on lattice dynamics obtained by M\"ossbauer spectroscopy at
57Fe and 119Sn probe atoms. Presented and discussed are sigma-phase Fe-Cr,
Fe-V, Fe-Cr-Ni and lambda-phase (C14 Laves) NbFe2 compounds that belong to the
Frank-Kasper family of phases, metallic chromium and iron mono-arsenide, FeAs.
The common feature of all these metallic systems is itinerant character of
their magnetism. Two spectral parameters relevant to the lattice dynamics viz.
the center shift, CS, and/or the recoil-free fraction, f, are considered as
figures of merit. Their temperature dependences show anomalies at magnetic
ordering temperatures giving evidence that that vibrations in the magnetic
phase are different than those in the paramagnetic phase. The lower the
magnetic ordering temperature, the stronger the difference.


title: On Random Number Generation for Kernel Applications

abstract:
  An operating system kernel uses cryptographically secure pseudorandom number
generator for creating address space localization randomization offsets to
protect memory addresses to processes from exploration, storing users' password
securely and creating cryptographic keys. The paper proposes a CSPRNG called
KCS-PRNG which produces non-reproducible bitstreams. The proposed KCS-PRNG
presents an efficient design uniquely configured with two new non-standard and
verified elliptic curves and clock-controlled linear feedback shift registers
and a novel method to consistently generate non-reproducible random bits of
arbitrary lengths. The generated bit streams are statistically
indistinguishable from true random bitstreams and provably secure, resilient to
important attacks, exhibits backward and forward secrecy, exhibits exponential
linear complexity, large period and huge key space.


title:
Primordial magnetic fields in theories of gravity with non-minimal
  coupling between curvature and matter

abstract:
  The existence of magnetic fields in the universe is unmistakable. They are
observed at all scales from stars to galaxy clusters. However, the origin of
these fields remains enigmatic. It is believed that magnetic field seeds may
have emerged from inflation, under certain conditions. This possibility is
analised in the context of an alternative theory of gravity with non-minimal
coupling between curvature and matter. We find, through the solution of the
generalised Maxwell equations in the context of non-minimal models, that for
general slow-roll inflationary scenarios with low reheating temperatures,
$T_{RH}\simeq10^{10}$GeV, the generated magnetic fields can be made compatible
with observations at large scales, $\lambda \sim 1 Mpc$.


title: Influence of C/O Ratio on Hot Jupiter Atmospheric Chemistry

abstract:
  We have conducted laboratory experiments to study the chemistry in hot
Jupiter atmospheres with C/O ratio of 0.35. We have compared our results with
the ones obtained previously for atmospheres with a C/O ratio of 1 to
investigate the influence of the C/O ratio on the chemistry and formation of
photochemical organic aerosol. We found that the C/O ratio and the gas mixture
compositions strongly influence the pathways responsible for the formation of
CO2. Thermochemical reactions are primarily responsible for the formation of
CO2 in low C/O ratio atmospheres, while photochemistry is the dominant process
in high C/O ratio atmospheres even if the final CO2 concentration is the same
in both cases. Our results show that low C/O atmospheres at the thermochemical
equilibrium contain a higher water abundance, while high C/O atmospheres are
significantly depleted in water. However, in low C/O atmospheres, the water
abundance is not affected by UV photolysis, while our previous work
demonstrated that significant amount of water can be produced in high C/O ratio
atmospheres. This contrast in water production suggests that photochemistry
should be considered when interpreting exoplanet transit spectra. Finally, we
did not observe the formation of a detectable amount of non-volatile
photochemical aerosols in low C/O atmospheres, in contrast to our previous
study. We infer that for C/O ratio < 1, water likely inhibits organic growth
and aerosol formation, suggesting that photochemical organic aerosols are
likely to be observed in planets presenting a carbon enrichment compared to
their host stars.


title: Shedding New Light on the Language of the Dark Web

abstract:
  The hidden nature and the limited accessibility of the Dark Web, combined
with the lack of public datasets in this domain, make it difficult to study its
inherent characteristics such as linguistic properties. Previous works on text
classification of Dark Web domain have suggested that the use of deep neural
models may be ineffective, potentially due to the linguistic differences
between the Dark and Surface Webs. However, not much work has been done to
uncover the linguistic characteristics of the Dark Web. This paper introduces
CoDA, a publicly available Dark Web dataset consisting of 10000 web documents
tailored towards text-based Dark Web analysis. By leveraging CoDA, we conduct a
thorough linguistic analysis of the Dark Web and examine the textual
differences between the Dark Web and the Surface Web. We also assess the
performance of various methods of Dark Web page classification. Finally, we
compare CoDA with an existing public Dark Web dataset and evaluate their
suitability for various use cases.


title:
Spatial correlations of field observables in two half-spaces separated
  by a movable perfect mirror

abstract:
  We consider a system of two cavities separated by a reflecting boundary of
finite mass that is free to move, and bounded to its equilibrium position by a
harmonic potential. This yields an effective mirror-field interaction, as well
as an effective interaction between the field modes mediated by the movable
boundary. Two massless scalar fields are defined in each cavity. We consider
the second-order interacting ground state of the system, that contains virtual
excitations of both mirror's degrees of freedom and of the scalar fields. We
investigate the correlation functions between field observables in the two
cavities, and find that the squared scalar fields in the two cavities, in the
interacting ground state, are anti-correlated. We discuss the dependence of the
correlation on the distance of the two points considered from the mirror's
average position, and on its mass and oscillation angular frequency. These
results show a sort of communication between the two half-spaces separated by
the movable mirror, mediated by its position fluctuations. Observability of
this new phenomenon is also discussed.


title: Exploring new states of matter with a photonic emulator

abstract:
  We implement the equation of motion of the large-N Gross-Neveu model from
strong interaction physics in photonic waveguide arrays and study one of its
paradigmatic multi-fermion bound state solutions in an optical experiment. The
present study constitutes an important first step towards waveguide-based
simulations of phenomena relevant for high-energy physics.


title:
Anomalous Josephson Hall effect in doped topological insulators with the
  nematic superconductivity

abstract:
  We study the physics of the Josephson effect in nematic superconductors with
$E_u$ odd parity in the Ginzburg-Landau approach. Two-component vector
superconducting order parameter makes this effect rather unusual. We get that
the Meissner kernel has off-diagonal components. We derive current-phase
relations for different configurations of the junction, crystallographic axes
of the sample, and nematicity direction. We show that an anomalous Josephson
Hall effect can be observed in such a system without any magnetization. That
is, for definite orientations of the junction and crystal axes, a component of
the Josephson current along the junction is induced by the order parameter
phase difference across the contact. We also calculate the magnetic field
dependence of the maximum current through the junction. We find that the period
of the Fraunhofer oscillations of the maximum Josephson current depends on the
geometry of the junction, direction of the magnetic field, and nematicity
vector.


title:
Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on
  a Syntactic Task

abstract:
  Although transformer-based Neural Language Models demonstrate impressive
performance on a variety of tasks, their generalization abilities are not well
understood. They have been shown to perform strongly on subject-verb number
agreement in a wide array of settings, suggesting that they learned to track
syntactic dependencies during their training even without explicit supervision.
In this paper, we examine the extent to which BERT is able to perform
lexically-independent subject-verb number agreement (NA) on targeted syntactic
templates. To do so, we disrupt the lexical patterns found in naturally
occurring stimuli for each targeted structure in a novel fine-grained analysis
of BERT's behavior. Our results on nonce sentences suggest that the model
generalizes well for simple templates, but fails to perform
lexically-independent syntactic generalization when as little as one attractor
is present.


title: Clothes-Changing Person Re-identification with RGB Modality Only

abstract:
  The key to address clothes-changing person re-identification (re-id) is to
extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and
gait. Most current works mainly focus on modeling body shape from
multi-modality information (e.g., silhouettes and sketches), but do not make
full use of the clothes-irrelevant information in the original RGB images. In
this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine
clothes-irrelevant features from the original RGB images by penalizing the
predictive power of re-id model w.r.t. clothes. Extensive experiments
demonstrate that using RGB images only, CAL outperforms all state-of-the-art
methods on widely-used clothes-changing person re-id benchmarks. Besides,
compared with images, videos contain richer appearance and additional temporal
information, which can be used to model proper spatiotemporal patterns to
assist clothes-changing re-id. Since there is no publicly available
clothes-changing video re-id dataset, we contribute a new dataset named CCVID
and show that there exists much room for improvement in modeling spatiotemporal
information. The code and new dataset are available at:
https://github.com/guxinqian/Simple-CCReID.


title: Optimized Population Monte Carlo

abstract:
  Adaptive importance sampling (AIS) methods are increasingly used for the
approximation of distributions and related intractable integrals in the context
of Bayesian inference. Population Monte Carlo (PMC) algorithms are a subclass
of AIS methods, widely used due to their ease in the adaptation. In this paper,
we propose a novel algorithm that exploits the benefits of the PMC framework
and includes more efficient adaptive mechanisms, exploiting geometric
information of the target distribution. In particular, the novel algorithm
adapts the location and scale parameters of a set of importance densities
(proposals). At each iteration, the location parameters are adapted by
combining a versatile resampling strategy (i.e., using the information of
previous weighted samples) with an advanced optimization-based scheme. Local
second-order information of the target distribution is incorporated through a
preconditioning matrix acting as a scaling metric onto a gradient direction. A
damped Newton approach is adopted to ensure robustness of the scheme. The
resulting metric is also used to update the scale parameters of the proposals.
We discuss several key theoretical foundations for the proposed approach.
Finally, we show the successful performance of the proposed method in three
numerical examples, involving challenging distributions.


title: Implicit Sample Extension for Unsupervised Person Re-Identification

abstract:
  Most existing unsupervised person re-identification (Re-ID) methods use
clustering to generate pseudo labels for model training. Unfortunately,
clustering sometimes mixes different true identities together or splits the
same identity into two or more sub clusters. Training on these noisy clusters
substantially hampers the Re-ID accuracy. Due to the limited samples in each
identity, we suppose there may lack some underlying information to well reveal
the accurate clusters. To discover these information, we propose an Implicit
Sample Extension (\OurWholeMethod) method to generate what we call support
samples around the cluster boundaries. Specifically, we generate support
samples from actual samples and their neighbouring clusters in the embedding
space through a progressive linear interpolation (PLI) strategy. PLI controls
the generation with two critical factors, i.e., 1) the direction from the
actual sample towards its K-nearest clusters and 2) the degree for mixing up
the context information from the K-nearest clusters. Meanwhile, given the
support samples, ISE further uses a label-preserving loss to pull them towards
their corresponding actual samples, so as to compact each cluster.
Consequently, ISE reduces the "sub and mixed" clustering errors, thus improving
the Re-ID performance. Extensive experiments demonstrate that the proposed
method is effective and achieves state-of-the-art performance for unsupervised
person Re-ID. Code is available at:
\url{https://github.com/PaddlePaddle/PaddleClas}.


title: Challenges for Open-domain Targeted Sentiment Analysis

abstract:
  Since previous studies on open-domain targeted sentiment analysis are limited
in dataset domain variety and sentence level, we propose a novel dataset
consisting of 6,013 human-labeled data to extend the data domains in topics of
interest and document level. Furthermore, we offer a nested target annotation
schema to extract the complete sentiment information in documents, boosting the
practicality and effectiveness of open-domain targeted sentiment analysis.
Moreover, we leverage the pre-trained model BART in a sequence-to-sequence
generation method for the task. Benchmark results show that there exists large
room for improvement of open-domain targeted sentiment analysis. Meanwhile,
experiments have shown that challenges remain in the effective use of
open-domain data, long documents, the complexity of target structure, and
domain variances.


title:
To What Extent do Deep Learning-based Code Recommenders Generate
  Predictions by Cloning Code from the Training Set?

abstract:
  Deep Learning (DL) models have been widely used to support code completion.
These models, once properly trained, can take as input an incomplete code
component (e.g., an incomplete function) and predict the missing tokens to
finalize it. GitHub Copilot is an example of code recommender built by training
a DL model on millions of open source repositories: The source code of these
repositories acts as training data, allowing the model to learn "how to
program". The usage of such a code is usually regulated by Free and Open Source
Software (FOSS) licenses, that establish under which conditions the licensed
code can be redistributed or modified. As of Today, it is unclear whether the
code generated by DL models trained on open source code should be considered as
"new" or as "derivative" work, with possible implications on license
infringements. In this work, we run a large-scale study investigating the
extent to which DL models tend to clone code from their training set when
recommending code completions. Such an exploratory study can help in assessing
the magnitude of the potential licensing issues mentioned before: If these
models tend to generate new code that is unseen in the training set, then
licensing issues are unlikely to occur. Otherwise, a revision of these licenses
urges to regulate how the code generated by these models should be treated when
used, for example, in a commercial setting. Highlights from our results show
that ~$10% to ~0.1% of the predictions generated by a state-of-the-art DL-based
code completion tool are Type-1 clones of instances in the training set,
depending on the size of the predicted code. Long predictions are unlikely to
be cloned.


title: Gradient boosting for convex cone predict and optimize problems

abstract:
  Many problems in engineering and statistics involve both predictive
forecasting and decision-based optimization. Traditionally, predictive models
are optimized independently from the final decision-based optimization problem.
In contrast, a `smart, predict then optimize' (SPO) framework optimizes
prediction models to explicitly minimize the final downstream decision loss. In
this paper we present dboost, a gradient boosting algorithm for training
prediction model ensembles to minimize decision regret. The dboost framework
supports any convex optimization program that can be cast as convex quadratic
cone program and gradient boosting is performed by implicit differentiation of
a custom fixed-point mapping. To our knowledge, the dboost framework is the
first general purpose implementation of gradient boosting to predict and
optimize problems. Experimental results comparing with state-of-the-art SPO
methods show that dboost can further reduce out-of-sample decision regret.


title: Tropical Mirror

abstract:
  We describe the tropical curves in projective space and define the tropical
Gromov-Witten invariants. We introduce amplitudes for the higher topological
quantum mechanics with circle action on graphs and show that the amplitudes are
equal to the tropical Gromov-Witten invariants. The state-operator
correspondence in topological quantum mechanics on graphs allows to express the
sum of the amplitudes in A-type quantum mechanics as the amplitude in deformed
quantum mechanics, naturally identified as the B-type quantum mechanics. We use
the equality between the amplitudes for three observables to prove the mirror
symmetry for cohomology rings.


title:
$L^2$ Sobolev space bijectivity of the scattering-inverse scattering
  transforms related to defocusing Ablowitz-Ladik systems

abstract:
  In this paper, we establish $L^2$-Sobolev space bijectivity of the inverse
scattering transform related to the defocusing Ablowitz-Ladik system. On the
one hand, in the direct problem, based on the spectral problem, we establish
the reflection coefficient and the corespondent Riemann-Hilbert problem. And we
also prove that if the potential belongs to $l^{2,k}$ space, then the
reflection coefficient belongs to $H^k_\theta(\Sigma)$. On the other hand, in
the inverse problem, based on the Riemann-Hilbert problem, we obtain the
corespondent reconstructed formula and recover potentials from reflection
coefficients. And we also confirm that if reflection coefficients are in
$H^k_\theta(\Sigma)$, then we show that potentials also belong to $l^{2,k}$.
This study also confirm that for the initial-valued problem of defocusing
Ablowitz-Ladik equations, it the initial potential belongs to $l^{2,k}$ and
satisfying $\parallel q\parallel_\infty<1$, then the solution for $t\ne0$ also
belongs to $l^{2,k}$.


title:
Collective excitations in cigar-shaped spin-orbit coupled spin-1
  Bose-Einstein condensates

abstract:
  We theoretically study the collective excitations of a spin-orbit-coupled
spin-1 Bose-Einstein condensate with antiferromagnetic spin-exchange
interactions in a cigar-shaped trapping potential at zero and finite
temperatures using the Hartree-Fock-Bogoliubov theory with Popov approximation.
The collective modes at zero temperature are corroborated by the real-time
evolution of the ground state subjected to a perturbation suitable to excite a
density or a spin mode. We have also calculated a few low-lying modes
analytically and found a very good agreement with the numerical results. We
confirm the presence of excitations belonging to two broad categories, namely
density, and spin excitations, based on the calculation of dispersion. The
degeneracy between a pair of spin modes is broken by the spin-orbit coupling.
At finite temperature, spin and density excitations show qualitatively
different behavior as a function of temperature.


title:
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly
  Supervised Object Detection

abstract:
  Weakly supervised object detection (WSOD), which is an effective way to train
an object detection model using only image-level annotations, has attracted
considerable attention from researchers. However, most of the existing methods,
which are based on multiple instance learning (MIL), tend to localize instances
to the discriminative parts of salient objects instead of the entire content of
all objects. In this paper, we propose a WSOD framework called the Spatial
Likelihood Voting with Self-knowledge Distillation Network (SLV-SD Net). In
this framework, we introduce a spatial likelihood voting (SLV) module to
converge region proposal localization without bounding box annotations.
Specifically, in every iteration during training, all the region proposals in a
given image act as voters voting for the likelihood of each category in the
spatial dimensions. After dilating the alignment on the area with large
likelihood values, the voting results are regularized as bounding boxes, which
are then used for the final classification and localization. Based on SLV, we
further propose a self-knowledge distillation (SD) module to refine the feature
representations of the given image. The likelihood maps generated by the SLV
module are used to supervise the feature learning of the backbone network,
encouraging the network to attend to wider and more diverse areas of the image.
Extensive experiments on the PASCAL VOC 2007/2012 and MS-COCO datasets
demonstrate the excellent performance of SLV-SD Net. In addition, SLV-SD Net
produces new state-of-the-art results on these benchmarks.


title: Using Machine Learning for Particle Identification in ALICE

abstract:
  Particle identification (PID) is one of the main strengths of the ALICE
experiment at the LHC. It is a crucial ingredient for detailed studies of the
strongly interacting matter formed in ultrarelativistic heavy-ion collisions.
ALICE provides PID information via various experimental techniques, allowing
for the identification of particles over a broad momentum range (from around
100 MeV/$c$ to around 50 GeV/$c$). The main challenge is how to combine the
information from various detectors effectively. Therefore, PID represents a
model classification problem, which can be addressed using Machine Learning
(ML) solutions. Moreover, the complexity of the detector and richness of the
detection techniques make PID an interesting area of research also for the
computer science community. In this work, we show the current status of the ML
approach to PID in ALICE. We discuss the preliminary work with the Random
Forest approach for the LHC Run 2 and a more advanced solution based on Domain
Adaptation Neural Networks, including a proposal for its future implementation
within the ALICE computing software for the upcoming LHC Run 3.


title:
RankNEAT: Outperforming Stochastic Gradient Search in Preference
  Learning Tasks

abstract:
  Stochastic gradient descent (SGD) is a premium optimization method for
training neural networks, especially for learning objectively defined labels
such as image objects and events. When a neural network is instead faced with
subjectively defined labels--such as human demonstrations or annotations--SGD
may struggle to explore the deceptive and noisy loss landscapes caused by the
inherent bias and subjectivity of humans. While neural networks are often
trained via preference learning algorithms in an effort to eliminate such data
noise, the de facto training methods rely on gradient descent. Motivated by the
lack of empirical studies on the impact of evolutionary search to the training
of preference learners, we introduce the RankNEAT algorithm which learns to
rank through neuroevolution of augmenting topologies. We test the hypothesis
that RankNEAT outperforms traditional gradient-based preference learning within
the affective computing domain, in particular predicting annotated player
arousal from the game footage of three dissimilar games. RankNEAT yields
superior performances compared to the gradient-based preference learner
(RankNet) in the majority of experiments since its architecture optimization
capacity acts as an efficient feature selection mechanism, thereby, eliminating
overfitting. Results suggest that RankNEAT is a viable and highly efficient
evolutionary alternative to preference learning.


title: SIR epidemics in populations with large sub-communities

abstract:
  We investigate final outcome properties of an SIR (susceptible $\to$
infective $\to$ recovered) epidemic model defined on a population of large
sub-communities in which there is stronger disease transmission within the
communities than between them. Our analysis involves approximation of the
epidemic process by a chain of within-community large outbreaks spreading
between the communities. We derive law of large numbers and central limit type
results for the number of individuals and the number of communities affected
and the so-called severity of the outbreak. These results are valid as the size
of communities tends to infinity, with the number of communities either fixed
or also tending to infinity. The weaker between-community connections lead to
randomness even in the law of large numbers type limit. As part of our proofs
we also obtain a new result concerning the rate of convergence of the expected
fraction infected in a standard SIR epidemic to its large-population limit.


title:
Stability of Exponentially Damped Oscillations under Perturbations of
  the Mori-Chain

abstract:
  There is an abundance of evidence that some relaxation dynamics, e.g.,
exponential decays, are much more common in nature than others. Recently, there
have been attempts to trace this dominance back to a certain stability of the
prevalent dynamics versus generic Hamiltonian perturbations. In the paper at
hand, we tackle this stability issue from yet another angle, namely in the
framework of the recursion method. We investigate the behavior of various
relaxation dynamics with respect to alterations of the so-called Lanczos
coefficients. All considered scenarios are set up in order to comply with the
"universal operator growth hypothesis". Our numerical experiments suggest the
existence of stability in a larger class of relaxation dynamics consisting of
exponentially damped oscillations. Further, we propose a criterion to identify
"pathological" perturbations that lead to uncommon dynamics.


title:
Efficient and practical quantum compiler towards multi-qubit systems
  with deep reinforcement learning

abstract:
  Efficient quantum compiling tactics greatly enhance the capability of quantum
computers to execute complicated quantum algorithms. Due to its fundamental
importance, a plethora of quantum compilers has been designed in past years.
However, there are several caveats to current protocols, which are low
optimality, high inference time, limited scalability, and lack of universality.
To compensate for these defects, here we devise an efficient and practical
quantum compiler assisted by advanced deep reinforcement learning (RL)
techniques, i.e., data generation, deep Q-learning, and AQ* search. In this
way, our protocol is compatible with various quantum machines and can be used
to compile multi-qubit operators. We systematically evaluate the performance of
our proposal in compiling quantum operators with both inverse-closed and
inverse-free universal basis sets. In the task of single-qubit operator
compiling, our proposal outperforms other RL-based quantum compilers in the
measure of compiling sequence length and inference time. Meanwhile, the output
solution is near-optimal, guaranteed by the Solovay-Kitaev theorem. Notably,
for the inverse-free universal basis set, the achieved sequence length
complexity is comparable with the inverse-based setting and dramatically
advances previous methods. These empirical results contribute to improving the
inverse-free Solovay-Kitaev theorem. In addition, for the first time, we
demonstrate how to leverage RL-based quantum compilers to accomplish two-qubit
operator compiling. The achieved results open an avenue for integrating RL with
quantum compiling to unify efficiency and practicality and thus facilitate the
exploration of quantum advantages.


title:
Making Markets for Information Security: The Role of Online Platforms in
  Bug Bounty Programs

abstract:
  Security is an essential cornerstone of functioning digital marketplaces and
communities. If users doubt that data shared online will remain secure, they
will withdraw from platforms. Even when firms take these risks seriously,
security expertise is expensive and vulnerabilities are diverse in nature.
Increasingly, firms and governments are turning to bug bounty programs (BBPs)
to crowdsource their cybersecurity, in which they pay individuals for reporting
vulnerabilities in their systems. And while the use of BBPs has grown
significantly in recent years, research on the actors in this market and their
incentives remains limited. Using the lens of transaction cost economics, this
paper examines the incentives of firms and researchers (sometimes called
hackers) participating in BBPs. We study the crucial role that centralized
platforms that organize BBPs play in this emerging market. We carry out an
analysis of the HackerOne BBP platform, using a novel dataset on over 14,000
researchers reporting over 125,000 public vulnerabilities to over 500 firms
from 2014 to the end of 2021. We outline how platforms like HackerOne make a
market for information security vulnerabilities by reducing information
asymmetries and their associated transaction costs.


title:
Multivariate Overnight GARCH-It\^o Model with Applications in Large
  Volatility Matrix Estimation and Prediction

abstract:
  This paper introduces a unified multivariate overnight GARCH-It\^o model for
volatility matrix estimation and prediction both in the low- and
high-dimensional set-up. To account for whole-day market dynamics in the
financial market, the proposed model has two different instantaneous volatility
processes for the open-to-close and close-to-open periods, while each embeds
the discrete-time multivariate GARCH model structure. We call it the
multivariate overnight GARCH-It\^o (MOGI) model. Based on the connection
between the discrete-time model structure and the continuous-time diffusion
process, we propose a weighted least squares estimation procedure for
estimating model parameters with open-to-close high-frequency and close-to-open
low-frequency financial data, and establish its asymptotic theorems. We further
discuss the prediction of future vast volatility matrices and study its
asymptotic properties. A simulation study is conducted to check the finite
sample performance of the proposed estimation and prediction methods. The
empirical analysis is carried out to compare the performance of the proposed
MOGI model with other benchmark methods in portfolio allocation problems.


title:
Lombard Effect for Bilingual Speakers in Cantonese and English:
  importance of spectro-temporal features

abstract:
  For a better understanding of the mechanisms underlying speech perception and
the contribution of different signal features, computational models of speech
recognition have a long tradition in hearing research. Due to the diverse range
of situations in which speech needs to be recognized, these models need to be
generalizable across many acoustic conditions, speakers, and languages. This
contribution examines the importance of different features for speech
recognition predictions of plain and Lombard speech for English in comparison
to Cantonese in stationary and modulated noise. While Cantonese is a tonal
language that encodes information in spectro-temporal features, the Lombard
effect is known to be associated with spectral changes in the speech signal.
These contrasting properties of tonal languages and the Lombard effect form an
interesting basis for the assessment of speech recognition models. Here, an
automatic speech recognition-based ASR model using spectral or spectro-temporal
features is evaluated with empirical data. The results indicate that
spectro-temporal features are crucial in order to predict the speaker-specific
speech recognition threshold SRT$_{50}$ in both Cantonese and English as well
as to account for the improvement of speech recognition in modulated noise,
while effects due to Lombard speech can already be predicted by spectral
features.


title:
Exact and approximate determination of the Pareto set using minimal
  correction subsets

abstract:
  Recently, it has been shown that the enumeration of Minimal Correction
Subsets (MCS) of Boolean formulas allows solving Multi-Objective Boolean
Optimization (MOBO) formulations. However, a major drawback of this approach is
that most MCSs do not correspond to Pareto-optimal solutions. In fact, one can
only know that a given MCS corresponds to a Pareto-optimal solution when all
MCSs are enumerated. Moreover, if it is not possible to enumerate all MCSs,
then there is no guarantee of the quality of the approximation of the Pareto
frontier. This paper extends the state of the art for solving MOBO using MCSs.
First, we show that it is possible to use MCS enumeration to solve MOBO
problems such that each MCS necessarily corresponds to a Pareto-optimal
solution. Additionally, we also propose two new algorithms that can find a (1 +
{\varepsilon})-approximation of the Pareto frontier using MCS enumeration.
Experimental results in several benchmark sets show that the newly proposed
algorithms allow finding better approximations of the Pareto frontier than
state-of-the-art algorithms, and with guaranteed approximation ratios.


title:
On the Modeling and Analysis of Fast Conditional Handover for
  5G-Advanced

abstract:
  Conditional handover (CHO) is a state-of-the-art 3GPP handover mechanism used
in 5G networks. Although it improves mobility robustness by reducing mobility
failures, the decoupling of the handover preparation and execution phases in
CHO significantly increases the signaling overhead. For 5G-Advanced networks,
fast CHO (FCHO) is a recent 3GPP proposal that offers a practical solution
whereby the user equipment (UE) can reuse earlier target cell preparations
after each handover to autonomously execute subsequent handovers. This saves
the signaling overhead associated with the reconfiguration and repreparation of
target cells after each handover. In this paper, a comprehensive study on the
mobility performance of FCHO with respect to mobility failures and signaling
overhead in frequency range 2 (FR2) is carried out. In particular, the
performance of FCHO is compared with CHO for two different multi-panel UE
(MPUE) schemes. Results show that FCHO substantially reduces the signaling
overhead of CHO, while at the same time it also reduces mobility failures due
to faster triggering of the handover that is achieved by saving the preparation
delay.


title:
Measurement-based Admission Control in Sliced Networks: A Best Arm
  Identification Approach

abstract:
  In sliced networks, the shared tenancy of slices requires adaptive admission
control of data flows, based on measurements of network resources. In this
paper, we investigate the design of measurement-based admission control
schemes, deciding whether a new data flow can be admitted and in this case, on
which slice. The objective is to devise a joint measurement and decision
strategy that returns a correct decision (e.g., the least loaded slice) with a
certain level of confidence while minimizing the measurement cost (the number
of measurements made before committing to the decision). We study the design of
such strategies for several natural admission criteria specifying what a
correct decision is. For each of these criteria, using tools from best arm
identification in bandits, we first derive an explicit information-theoretical
lower bound on the cost of any algorithm returning the correct decision with
fixed confidence. We then devise a joint measurement and decision strategy
achieving this theoretical limit. We compare empirically the measurement costs
of these strategies, and compare them both to the lower bounds as well as a
naive measurement scheme. We find that our algorithm significantly outperforms
the naive scheme (by a factor $2-8$).


title: Robust Bayesian inference in complex models with possibility theory

abstract:
  We propose a general solution to the problem of robust Bayesian inference in
complex settings where outliers may be present. In practice, the automation of
robust Bayesian analyses is important in the many applications involving large
and complex datasets. The proposed solution relies on a reformulation of
Bayesian inference based on possibility theory, and leverages the observation
that, in this context, the marginal likelihood of the data assesses the
consistency between prior and likelihood rather than model fitness. Our
approach does not require additional parameters in its simplest form and has a
limited impact on the computational complexity when compared to non-robust
solutions. The generality of our solution is demonstrated via applications on
simulated and real data including matrix estimation and change-point detection.