title:
Degree-Preserving Randomized Response for Graph Neural Networks under
  Local Differential Privacy

abstract:
  Differentially private GNNs (Graph Neural Networks) have been recently
studied to provide high accuracy in various tasks on graph data while strongly
protecting user privacy. In particular, a recent study proposes an algorithm to
protect each user's feature vector in an attributed graph with LDP (Local
Differential Privacy), a strong privacy notion without a trusted third party.
However, this algorithm does not protect edges (friendships) in a social graph
or protect user privacy in unattributed graphs. It remains open how to strongly
protect edges with LDP while keeping high accuracy in GNNs.
  In this paper, we propose a novel LDP algorithm called the DPRR
(Degree-Preserving Randomized Response) to provide LDP for edges in GNNs. Our
DPRR preserves each user's degree hence a graph structure while providing edge
LDP. Technically, we use Warner's RR (Randomized Response) and strategic edge
sampling, where each user's sampling probability is automatically tuned to
preserve the degree information. We prove that the DPRR approximately preserves
the degree information under edge LDP. We focus on graph classification as a
task of GNNs and evaluate the DPRR using two social graph datasets. Our
experimental results show that the DPRR significantly outperforms Warner's RR
and provides accuracy close to a non-private algorithm with a reasonable
privacy budget, e.g., epsilon=1.


title:
Stationary states to a free boundary transmission problem for an
  electrostatically actuated plate

abstract:
  A two-dimensional free boundary transmission problem arising in the modeling
of an electrostatically actuated plate is considered and a representation
formula for the derivative of the associated electrostatic energy with respect
to the deflection of the plate is derived. The latter paves the way for the
construction of energy minimizers and also provides the Euler-Lagrange equation
satisfied by these minimizers. A by-product is the monotonicity of the
electrostatic energy with respect to the deflection.


title: Consistent Cross Validation with stable learners

abstract:
  This paper investigates the efficiency of different cross-validation (CV)
procedures under algorithmic stability with a specific focus on the K-fold. We
derive a generic upper bound for the risk estimation error applicable to a wide
class of CV schemes. This upper bound ensures the consistency of the
leave-one-out and the leave-p-out CV but fails to control the error of the
K-fold. We confirm this negative result with a lower bound on the K-fold error
which does not converge to zero with the sample size. We thus propose a
debiased version of the K-fold which is consistent for any uniformly stable
learner. We apply our results to the problem of model selection and demonstrate
empirically the usefulness of the promoted approach on real world datasets.


title:
Optimal Feedback Controls of Stochastic Linear Quadratic Control
  Problems in Infinite Dimensions with Random Coefficients

abstract:
  It is a longstanding unsolved problem to characterize the optimal feedback
controls for general linear quadratic optimal control problem of stochastic
evolution equation with random coefficients. A solution to this problem is
given in [21] under some assumptions which can be verified for interesting
concrete models, such as controlled stochastic wave equations, controlled
stochastic Schr\"odinger equations, etc. More precisely, the authors establish
the equivalence between the existence of optimal feedback operator and the
solvability of the corresponding operator-valued, backward stochastic Riccati
equations. However, their result cannot cover some important stochastic partial
differential equations, such as stochastic heat equations, stochastic stokes
equations, etc. A key contribution of the current work is to relax the
$C_0$-group assumption of unbounded linear operator $A$ in [21] and using
contraction semigroup assumption instead. Therefore, our result can be well
applicable in the linear quadratic problem of stochastic parabolic partial
differential equations. To this end, we introduce a suitable notion to the
aforementioned Riccati equation, and some delicate techniques which are even
new in the finite dimensional case.


title: A harmonic framework for stepsize selectionin gradient methods

abstract:
  We study the use of inverse harmonic Rayleigh quotients with target for the
stepsize selection in gradient methods for nonlinear unconstrained optimization
problems. This provides not only an elegant and flexible framework to
parametrize and reinterpret existing stepsize schemes, but also gives
inspiration for new flexible and tunable families of steplengths. In
particular, we analyze and extend the adaptive Barzilai-Borwein method to a new
family of stepsizes. While this family exploits negative values for the target,
we also consider positive targets. We present a convergence analysis for
quadratic problems extending results by Dai and Liao (2002), and carry out
experiments outlining the potential of the approaches.


title:
No-signalling constrains quantum computation with indefinite causal
  structure

abstract:
  Quantum processes with indefinite causal structure emerge when we wonder
which are the most general evolutions, allowed by quantum theory, of a set of
local systems which are not assumed to be in any particular causal order. These
processes can be described within the framework of higher-order quantum theory
which, starting from considering maps from quantum transformations to quantum
transformations, recursively constructs a hierarchy of quantum maps of
increasingly higher order. In this work, we develop a formalism for quantum
computation with indefinite causal structures; namely we characterize the
computational structure of higher order quantum maps. Taking an axiomatic
approach, the rules of this computation are identified as the most general
compositions of higher order maps which are compatible with the mathematical
structure of quantum theory. We provide a mathematical characterization of the
admissible composition for arbitrary higher order quantum maps. We prove that
these rules, which have a computational and information-theoretic nature, are
determined by the more physical notion of the signalling relations between the
quantum systems of the higher order quantum maps.


title:
Polarized $Z$ cross sections in Higgsstrahlung for the determination of
  anomalous $ZZH$ couplings

abstract:
  The production of a Higgs boson in association with a $Z$ at an
electron-positron collider is one of the cleanest methods for the measurement
of the couplings of the Higgs boson. In view of the large production cross
section at energies a little above the threshold, it seems feasible to make a
more detailed study of the process by measuring the cross sections for
polarized $Z$ in order to measure possible anomalous $ZZH$ couplings. We show
that certain combinations of cross sections in $e^+e^- \to ZH$ with different
$Z$ polarizations help to enhance or isolate the effect of one of the two kinds
of $ZZH$ couplings possible on general grounds of CP and Lorentz invariance.
These combinations can be useful to get information on the $ZZH$ coupling in
the specific contexts of an effective field theory, two-Higgs-doublet models,
and composite Higgs models, in a relatively model-independent fashion. We find
in particular that the longitudinal helicity fraction of the $Z$ is expected to
be insensitive to anomalous couplings, and would be close to its value in the
standard model in the scenarios we consider. We also discuss the sensitivity of
the proposed measurements to the anomalous couplings.


title: Totally symmetric sets in the general linear group

abstract:
  A totally symmetric set is a finite subset of a group for which any
permutation of the elements can be realized by conjugation in the ambient
group. Such sets are rigid under homomorphisms, and so exert a great deal of
control over the algebraic structure. In this paper we introduce a more general
perspective on total symmetry, and formulate a notion of "irreducibility" for
totally symmetric sets in the general linear group. We classify irreducible
totally symmetric sets, as well as those of maximal cardinality.


title: I/O-Optimal Algorithms for Symmetric Linear Algebra Kernels

abstract:
  In this paper, we consider two fundamental symmetric kernels in linear
algebra: the Cholesky factorization and the symmetric rank-$k$ update (SYRK),
with the classical three nested loops algorithms for these kernels. In
addition, we consider a machine model with a fast memory of size $S$ and an
unbounded slow memory. In this model, all computations must be performed on
operands in fast memory, and the goal is to minimize the amount of
communication between slow and fast memories. As the set of computations is
fixed by the choice of the algorithm, only the ordering of the computations
(the schedule) directly influences the volume of communications.We prove lower
bounds of $\frac{1}{3\sqrt{2}}\frac{N^3}{\sqrt{S}}$ for the communication
volume of the Cholesky factorization of an $N\times N$ symmetric positive
definite matrix, and of $\frac{1}{\sqrt{2}}\frac{N^2M}{\sqrt{S}}$ for the SYRK
computation of $\mat{A}\cdot\transpose{\mat{A}}$, where $\mathbf{A}$ is an
$N\times M$ matrix. Both bounds improve the best known lower bounds from the
literature by a factor $\sqrt{2}$.In addition, we present two out-of-core,
sequential algorithms with matching communication volume: \TBS for SYRK, with a
volume of $\frac{1}{\sqrt{2}}\frac{N^2M}{\sqrt{S}} + \bigo{NM\log N}$, and \LBC
for Cholesky, with a volume of $\frac{1}{3\sqrt{2}}\frac{N^3}{\sqrt{S}} +
\bigo{N^{5/2}}$. Both algorithms improve over the best known algorithms from
the literature by a factor $\sqrt{2}$, and prove that the leading terms in our
lower bounds cannot be improved further. This work shows that the operational
intensity of symmetric kernels like SYRK or Cholesky is intrinsically higher
(by a factor $\sqrt{2}$) than that of corresponding non-symmetric kernels (GEMM
and LU factorization).


title: Bipyramid Volume, Mahler Measure and Some $\mathbb{Z}^2$-periodic Links

abstract:
  Champanerkar, Kofman and Lal\'{i}n conjectured an inequality between
bipyramid volume of links and Mahler measure of associated dimer models induced
from alternating links on torus. Hyperbolic volume and Mahler measure can be
related for isoradial graphs, which allows us to confirm the conjecture for two
examples. By exploiting a connection between perfect matchings of dimer models
and spanning trees on lattices, five more examples are calculated.


title:
Large data global well-posedness and scattering for the focusing cubic
  nonlinear Schr\"odinger equation on $\mathbb{R}^2\times\mathbb{T}$

abstract:
  We consider the focusing cubic nonlinear Schr\"odinger equation
\begin{align}\label{CNLSS} i\partial_t U+\Delta U=-|U|^2U\quad\text{on
$\mathbb{R}^2\times\mathbb{T}$}.\tag{3NLS} \end{align} Different from the 3D
Euclidean case, the \eqref{CNLSS} is mass-critical and non-scale-invariant on
the waveguide manifold $\mathbb{R}^2\times\mathbb{T}$, hence the underlying
analysis becomes more subtle and challenging. We formulate thresholds using the
2D Euclidean ground state of the focusing cubic NLS and show that solutions of
\eqref{CNLSS} lying below the thresholds are global and scattering in time. The
proof relies on several new established Gagliardo-Nirenberg inequalities, whose
best constants are formulated in term of the 2D Euclidean ground state. It is
also worth noting the interesting fact that the thresholds for global
well-posedness and scattering do not coincide. To the author's knowledge, this
paper also gives the first large data scattering result for focusing NLS on
product spaces.


title:
Maximum principle for time-fractional parabolic equations with a
  reaction coefficient of arbitrary sign

abstract:
  We consider time-fractional parabolic equations with a Caputo time derivative
of order $\alpha\in(0,1)$. For such equations, we give an elementary proof of
the weak maximum principle under no assumptions on the sign of the reaction
coefficient. This proof is also extended for weak solutions, as well as for
various types of boundary conditions and variable-coefficient variable-order
multiterm time-fractional parabolic equations.


title:
Tracking environmental policy changes in the Brazilian Federal Official
  Gazette

abstract:
  Even though most of its energy generation comes from renewable sources,
Brazil is one of the largest emitters of greenhouse gases in the world, due to
intense farming and deforestation of biomes such as the Amazon Rainforest,
whose preservation is essential for compliance with the Paris Agreement. Still,
regardless of lobbies or prevailing political orientation, all government legal
actions are published daily in the Brazilian Federal Official Gazette (BFOG, or
"Di\'ario Oficial da Uni\~ao" in Portuguese). However, with hundreds of decrees
issued every day by the authorities, it is absolutely burdensome to manually
analyze all these processes and find out which ones can pose serious
environmental hazards. In this paper, we present a strategy to compose
automated techniques and domain expert knowledge to process all the data from
the BFOG. We also provide the Government Actions Tracker, a highly curated
dataset, in Portuguese, annotated by domain experts, on federal government acts
about the Brazilian environmental policies. Finally, we build and compared four
different NLP models on the classfication task in this dataset. Our best model
achieved a F1-score of $0.714 \pm 0.031$. In the future, this system should
serve to scale up the high-quality tracking of all oficial documents with a
minimum of human supervision and contribute to increasing society's awareness
of government actions.


title:
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and
  Automatic Curriculum Learning

abstract:
  Multi-task learning by robots poses the challenge of the domain knowledge:
complexity of tasks, complexity of the actions required, relationship between
tasks for transfer learning. We demonstrate that this domain knowledge can be
learned to address the challenges in life-long learning. Specifically, the
hierarchy between tasks of various complexities is key to infer a curriculum
from simple to composite tasks. We propose a framework for robots to learn
sequences of actions of unbounded complexity in order to achieve multiple
control tasks of various complexity. Our hierarchical reinforcement learning
framework, named SGIM-SAHT, offers a new direction of research, and tries to
unify partial implementations on robot arms and mobile robots. We outline our
contributions to enable robots to map multiple control tasks to sequences of
actions: representations of task dependencies, an intrinsically motivated
exploration to learn task hierarchies, and active imitation learning. While
learning the hierarchy of tasks, it infers its curriculum by deciding which
tasks to explore first, how to transfer knowledge, and when, how and whom to
imitate.


title:
Reducing volume and shape errors in front tracking by
  divergence-preserving velocity interpolation and parabolic fit vertex
  positioning

abstract:
  Volume conservation and shape preservation are two well-known issues related
to the advection and remeshing in front tracking. To address these issues, this
paper proposes a divergence-preserving velocity interpolation method and a
parabolic fit vertex positioning method for remeshing operations for
three-dimensional front tracking. Errors in preserving the divergence of the
velocity field when interpolating the velocity from the fluid mesh to the
vertices of the triangles of the front are a primary reason for volume
conservation errors when advecting the front. The proposed interpolation method
preserves the discrete divergence of the fluid velocity by construction and is
compared in this work with other known interpolation methods in divergence-free
and non-divergence-free test cases, with respect to volume conservation and
shape preservation of the front. The presented interpolation method conserves
the volume and shape up to an order of magnitude better than the conventionally
used interpolation methods and is within the range of higher order
interpolation methods at lower computational cost. Additionally, the parabolic
fit vertex positioning method for remeshing operations locally approximates the
front with a smooth polynomial surface, improving volume conservation and shape
preservation by an order of magnitude compared to conventional remeshing
algorithms.


title:
Explicit Predictor-Corrector Method for Nonlinear Acoustic Waves Excited
  by a Moving Wave Emitting Boundary

abstract:
  We present an explicit finite difference time domain method to solve the
lossless Westervelt equation for a moving wave emitting boundary in one
dimension and in spherical symmetry. The approach is based on a coordinate
transformation between a moving physical domain and a fixed computational
domain. This allows to simulate the combined effects of wave profile distortion
due to the constitutive nonlinearity of the medium and the nonlinear Doppler
modulation of a pressure wave due to the acceleration of the wave emitting
boundary. A predictor-corrector method is employed to enhance the numerical
stability of the method in the presence of shocks and grid motion. It is
demonstrated that the method can accurately predict the Doppler shift of
nonlinear wave distortion and the amplitude modulation caused by an oscillating
motion of the wave emitting boundary. The novelty of the presented methodology
lies in its capability to reflect the Doppler shift of the rate of nonlinear
wave profile distortion and shock attenuation for finite amplitude acoustic
waves emitted from an accelerating boundary.


title: Dual Approaches to Express the Generalized Degree of Polarimetric Purity

abstract:
  The degree of polarimetric purity is an invariant dimensionless quantity that
characterizes the closeness of a polarization state of a wave to a pure state
and is related to the Von Neumann entropy. The polarimetric purity of a plane
wave characterized by the second-order statistics (i.e., the covariance matrix)
is uniquely described by the degree of polarization. However, the 2D formalism
is only applicable when the wave propagation direction is fixed. This
assumption is typical in optical and radar polarimetric measurements.
Therefore, one must consider all the components to describe the general state
of wave polarization. Starting from Samson and Barakat, several different
concepts have been proposed in the literature to describe the 3D degree of
polarization. We discuss two new ways of achieving such description: by the
Coefficient of Variation and by a Direct Sum Decomposition.


title:
Approximate Nearest Neighbor Search under Neural Similarity Metric for
  Large-Scale Recommendation

abstract:
  Model-based methods for recommender systems have been studied extensively for
years. Modern recommender systems usually resort to 1) representation learning
models which define user-item preference as the distance between their
embedding representations, and 2) embedding-based Approximate Nearest Neighbor
(ANN) search to tackle the efficiency problem introduced by large-scale corpus.
While providing efficient retrieval, the embedding-based retrieval pattern also
limits the model capacity since the form of user-item preference measure is
restricted to the distance between their embedding representations. However,
for other more precise user-item preference measures, e.g., preference scores
directly derived from a deep neural network, they are computationally
intractable because of the lack of an efficient retrieval method, and an
exhaustive search for all user-item pairs is impractical. In this paper, we
propose a novel method to extend ANN search to arbitrary matching functions,
e.g., a deep neural network. Our main idea is to perform a greedy walk with a
matching function in a similarity graph constructed from all items. To solve
the problem that the similarity measures of graph construction and user-item
matching function are heterogeneous, we propose a pluggable adversarial
training task to ensure the graph search with arbitrary matching function can
achieve fairly high precision. Experimental results in both open source and
industry datasets demonstrate the effectiveness of our method. The proposed
method has been fully deployed in the Taobao display advertising platform and
brings a considerable advertising revenue increase. We also summarize our
detailed experiences in deployment in this paper.


title: On the L$_\infty$ structure of Poisson gauge theory

abstract:
  The Poisson gauge theory is a semi-classical limit of full non-commutative
gauge theory. In this work we construct an L$_\infty^{full}$ algebra which
governs both the action of gauge symmetries and the dynamics of the Poisson
gauge theory, including the gauge covariant objects like the Poisson field
strength and the gauge covariant derivative of matter field. We derive the
minimal set of non-vanishing $\ell$-brackets and prove that they satisfy the
corresponding homotopy relations. {On the one hand,} it provides new explicit
non-trivial examples of L$_\infty$ algebras. {On the other hand,} it can be
useful for bootstrapping the full non-commutative gauge theory. In addition we
show that the derivation properties of $\ell$-brackets on L$_\infty^{full}$
with respect to the truncated product on the exterior algebra are satisfied
only for the canonical non-commutativity. In general, L$_\infty^{full}$ does
not have a structure of P$_\infty$ algebra.


title:
Physical based compact model of Y-Flash memristor for neuromorphic
  computation

abstract:
  Y-Flash memristors utilize the mature technology of single polysilicon
floating gate non-volatile memories (NVM). It can be operated in a two-terminal
configuration similar to the other emerging memristive devices, i.e., resistive
random-access memory (RRAM), phase-change memory (PCM), etc. Fabricated in
production complementary metal-oxide-semiconductor (CMOS) technology, Y-Flash
memristors allow excellent repro-ducibility reflected in high neuromorphic
products yields. Working in the subthreshold region, the device can be
programmed to a large number of fine-tuned intermediate states in an analog
fashion and allows low readout currents (1 nA ~ 5 $\mu$ A). However, currently,
there are no accurate models to describe the dynamic switching in this type of
memristive device and account for multiple operational configurations. In this
paper, we provide a physical-based compact model that describes Y-Flash
memristor performance both in DC and AC regimes, and consistently describes the
dynamic program and erase operations. The model is integrated into the
commercial circuit design tools and is ready to be used in applications related
to neuromorphic computation.


title: Analyse scientom\'etrique du domaine de l'infectiologie de 2000 \`a 2020

abstract:
  Research on infectious diseases constitutes a transversal scientific field. A
specific corpus is designed by combining a controlled language (Medline MeSH
thesaurus) and the categorization of journals (Web of Science). From this
global corpus, the article characterizes the publications from the top 20
countries publishing in the field and evolutions between 2000 and 2020. Topic
maps show the research themes within the field of infectious diseases both in
the world and in France. The explosion of publications on Covid-19 in 2020 has
a quite visible impact on the topic map in infectious diseases and changes the
position of some countries in this field of research. The conclusion points to
issues for further research as more complete data will become available on the
Covid-19 period.


title:
Spin-mixing enhanced proximity effect in aluminum-based
  superconductor-semiconductor hybrids

abstract:
  In superconducting quantum circuits, aluminum is one of the most widely used
materials. It is currently also the superconductor of choice for the
development of topological qubits. In this application, however, aluminum-based
devices suffer from poor magnetic field compatibility. In this article, we
resolve this limitation by showing that adatoms of heavy elements (e.g.
platinum) increase the critical field of thin aluminum films by more than a
factor of two. Using tunnel junctions, we show that the increased field
resilience originates from spin-orbit scattering introduced by Pt. We exploit
this property in the context of the superconducting proximity effect in
semiconductor-superconductor hybrids, where we show that InSb nanowires
strongly coupled to Al/Pt films can maintain superconductivity up to 7T. The
two-electron charging effect, a fundamental requirement for topological quantum
computation, is shown to be robust against the presence of heavy adatoms.
Additionally, we use non-local spectroscopy in a three-terminal geometry to
probe the bulk of hybrid devices, showing that it remains free of sub-gap
states. Finally, we demonstrate that semiconductor states which are
proximitized by Al/Pt films maintain their ability to Zeeman-split in an
applied magnetic field. Combined with the chemical stability and well-known
fabrication routes of aluminum, Al/Pt emerges as the natural successor to
Al-based systems and is a compelling alternative to other superconductors,
whenever high-field resilience is required.


title: Spatially Hyperbolic Gravitating Sources in $\Lambda$-Dominated Era

abstract:
  This study focuses on the impact of the cosmological constant on
hyperbolically symmetric matter configurations in static background. We extend
the work of Herrera \cite{herrera2021hyperbolically} and describe the
influences of such repulsive character on a few realistic features of
hyperbolical anisotropic fluids. After describing Einstein-$\Lambda$ equations
of motion, we elaborated the corresponding mass function along with its
conservation laws. In our study, besides observing negative energy density, we
notice the formation of Minkowskian core as matter content is compelled itself
not to follow inward motion near the axis of symmetry. Three families of
solution are found in $\Lambda$-dominated epoch. First is calculated by keeping
zero value to Weyl scalar, while the second solution maintains zero complexity
in the subsequent changes of the hyperbolical compact object. However, the last
model encompasses stiff fluid within the self-gravitating system. Such type of
theoretical setup suggests its direct link to study a few particular quantum
scenarios where negative behavior of energy density is noticed at $\Lambda$
dominated regime.


title: Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization

abstract:
  Cross-modal retrieval aims to search for data with similar semantic meanings
across different content modalities. However, cross-modal retrieval requires
huge amounts of storage and retrieval time since it needs to process data in
multiple modalities. Existing works focused on learning single-source compact
features such as binary hash codes that preserve similarities between different
modalities. In this work, we propose a jointly learned deep hashing and
quantization network (HQ) for cross-modal retrieval. We simultaneously learn
binary hash codes and quantization codes to preserve semantic information in
multiple modalities by an end-to-end deep learning architecture. At the
retrieval step, binary hashing is used to retrieve a subset of items from the
search space, then quantization is used to re-rank the retrieved items. We
theoretically and empirically show that this two-stage retrieval approach
provides faster retrieval results while preserving accuracy. Experimental
results on the NUS-WIDE, MIR-Flickr, and Amazon datasets demonstrate that HQ
achieves boosts of more than 7% in precision compared to supervised neural
network-based compact coding models.


title:
A Dynamic Model of a Skydiver With Validation in Wind Tunnel and Free
  Fall

abstract:
  An innovative approach of gaining insight into motor skills involved in human
body flight is proposed. The key idea is the creation of a model autonomous
system capable of virtually performing skydiving maneuvers. A dynamic skydiver
model and simulator is developed, comprising biomechanical, aerodynamic, and
kinematic models, dynamic equations of motion, and a virtual reality
environment. Limb relative orientations, and resulting inertial body angular
position and velocity are measured in skydiving experiments in a vertical wind
tunnel and in free fall. These experimental data are compared with
corresponding simulation data to tune and verify the model for basic skydiving
maneuvers. The model is further extended to reconstruct advanced aerial
maneuvers, such as transitions between stable equilibria. The experimental data
are used to estimate skydiver's conscious inputs as a function of time, via an
Unscented Kalman Filter modified for this purpose.


title:
A new smoothing method for nonlinear complementarity problems involving
  P0 function

abstract:
  In this paper, we propose a smoothing method to solve nonlinear
complementarity problems involving P 0-functions. We propose a nonparametric
algorithm to solve the nonlinear corresponding system of equations and prove
some global and local convergence results. We also present several numerical
experiments and applications that show the efficiency of our approach. Our main
contribution relies in the fact that the regularization parameter r is
considered as a variable and we do not need any complicated strategy to update
it.


title: Geodesic Quantum Walks

abstract:
  We propose a new family of discrete-spacetime quantum walks capable to
propagate on any arbitrary triangulations. Moreover we also extend and
generalize the duality principle introduced by one of the authors, linking
continuous local deformations of a given triangulation and the inhomogeneity of
the local unitaries that guide the quantum walker. We proved that in the formal
continuous limit, in both space and time, this new family of quantum walks
converges to the (1+2)D massless Dirac equation on curved manifolds. We believe
that this result has relevance in both modelling/simulating quantum transport
on discrete curved structures, such as fullerene molecules or dynamical causal
triangulation, and in addressing fast and efficient optimization problems in
the context of the curved space optimization methods.


title:
Edge Data Based Trailer Inception Probabilistic Matrix Factorization for
  Context-Aware Movie Recommendation

abstract:
  The rapid growth of edge data generated by mobile devices and applications
deployed at the edge of the network has exacerbated the problem of information
overload. As an effective way to alleviate information overload, recommender
system can improve the quality of various services by adding application data
generated by users on edge devices, such as visual and textual information, on
the basis of sparse rating data. The visual information in the movie trailer is
a significant part of the movie recommender system. However, due to the
complexity of visual information extraction, data sparsity cannot be remarkably
alleviated by merely using the rough visual features to improve the rating
prediction accuracy. Fortunately, the convolutional neural network can be used
to extract the visual features precisely. Therefore, the end-to-end neural
image caption (NIC) model can be utilized to obtain the textual information
describing the visual features of movie trailers. This paper proposes a trailer
inception probabilistic matrix factorization model called Ti-PMF, which
combines NIC, recurrent convolutional neural network, and probabilistic matrix
factorization models as the rating prediction model. We implement the proposed
Ti-PMF model with extensive experiments on three real-world datasets to
validate its effectiveness. The experimental results illustrate that the
proposed Ti-PMF outperforms the existing ones.


title:
Thermoelectric properties of inversion symmetry broken Weyl
  semimetal-Weyl superconductor hybrid junctions

abstract:
  We theoretically investigate the thermoelectric properties (electronic
contribution) of a hybrid structure comprising of an inversion symmetry broken
Weyl semimetal (WSM) proximitized to a bulk $s$-wave superconductor (WSM-Weyl
Superconductor (WSC) junction), employing the Blonder-Tinkham-Klapwijk
formulation for non-interacting electrons. Our study unfolds interesting
features for various relevant physical quantities such as the thermal
conductance, the thermoelectric coefficient and the corresponding figure of
merit. We also explore the effects of an interfacial insulating (I) barrier
(WSM-I-WSC set-up) on the thermoelectric response in the thin barrier limit.
Further, we compute the ratio of the thermal to the electrical conductance in
different temperature regimes and find that the Wiedemann-Franz law is violated
for small temperatures (below critical temperature $T_{c}$) near the Weyl
points while it saturates to the Lorentz number, away from the Weyl points, at
all temperatures irrespective of the barrier strength. We compare and contrast
this behaviour with other Dirac material heterostructures and provide a
detailed analysis of the thermal transport. Our study can facilitate the
fabrication of mesoscopic thermoelectric devices based on WSMs.


title: Virtual cycles on projective completions and quantum Lefschetz formula

abstract:
  For a compact quasi-smooth derived scheme M with (-1)-shifted cotangent
bundle N, there are at least two ways to localise the virtual cycle of N to M
via torus and cosection localisations, introduced by Jiang-Thomas. We produce
virtual cycles on both the projective completion and projectivisation and show
the ones on the former push down to Jiang-Thomas cycles and the one on the
latter computes the difference. Using similar ideas we give an expression for
the difference of the quintic and t-twisted quintic GW invariants of
Guo-Janda-Ruan.


title:
Fourier ptychography multi-parameter neural network with composite
  physical priori optimization

abstract:
  Fourier ptychography microscopy(FP) is a recently developed computational
imaging approach for microscopic super-resolution imaging. By turning on each
light-emitting-diode (LED) located on different position on the LED array
sequentially and acquiring the corresponding images that contain different
spatial frequency components, high spatial resolution and quantitative phase
imaging can be achieved in the case of large field-of-view. Nevertheless, FPM
has high requirements for the system construction and data acquisition
processes, such as precise LEDs position, accurate focusing and appropriate
exposure time, which brings many limitations to its practical applications. In
this paper, inspired by artificial neural network, we propose a Fourier
ptychography multi-parameter neural network (FPMN) with composite physical
prior optimization. A hybrid parameter determination strategy combining
physical imaging model and data-driven network training is proposed to recover
the multi layers of the network corresponding to different physical parameters,
including sample complex function, system pupil function, defocus distance, LED
array position deviation and illumination intensity fluctuation, etc. Among
these parameters, LED array position deviation is recovered based on the
features of brightfield to darkfield transition low-resolution images while the
others are recovered in the process of training of the neural network. The
feasibility and effectiveness of FPMN are verified through simulations and
actual experiments. Therefore FPMN can evidently reduce the requirement for
practical applications of FPM.


title: Rethinking the Zigzag Flattening for Image Reading

abstract:
  Sequence ordering of word vector matters a lot to text reading, which has
been proven in natural language processing (NLP). However, the rule of
different sequence ordering in computer vision (CV) was not well explored,
e.g., why the "zigzag" flattening (ZF) is commonly utilized as a default option
to get the image patches ordering in vision transformers (ViTs). Notably, when
decomposing multi-scale images, the ZF could not maintain the invariance of
feature point positions. To this end, we investigate the Hilbert fractal
flattening (HF) as another method for sequence ordering in CV and contrast it
against ZF. The HF has proven to be superior to other curves in maintaining
spatial locality, when performing multi-scale transformations of dimensional
space. And it can be easily plugged into most deep neural networks (DNNs).
Extensive experiments demonstrate that it can yield consistent and significant
performance boosts for a variety of architectures. Finally, we hope that our
studies spark further research about the flattening strategy of image reading.


title:
VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie
  Recommendation

abstract:
  Sparsity of user-to-item rating data becomes one of challenging issues in the
recommender systems, which severely deteriorates the recommendation
performance. Fortunately, context-aware recommender systems can alleviate the
sparsity problem by making use of some auxiliary information, such as the
information of both the users and items. In particular, the visual information
of items, such as the movie poster, can be considered as the supplement for
item description documents, which helps to obtain more item features. In this
paper, we focus on movie recommender system and propose a probabilistic matrix
factorization based recommendation scheme called visual recurrent convolutional
matrix factorization (VRConvMF), which utilizes the textual and multi-level
visual features extracted from the descriptive texts and posters respectively.
We implement the proposed VRConvMF and conduct extensive experiments on three
commonly used real world datasets to validate its effectiveness. The
experimental results illustrate that the proposed VRConvMF outperforms the
existing schemes.


title: On The Low Speed Limits of Lorentz's Transformation

abstract:
  This article contains a digest of the theory of electromagnetism and a review
of the transformation between inertial frames, especially under low speed
limits. The covariant nature of the Maxwell's equations is explained using the
conventional language. We show that even under low speed limits, the
relativistic effects should not be neglected to get a self-consistent theory of
the electromagnetic fields, unless the intrinsic dynamics of these fields has
been omitted completely. The quasi-static limits, where the relativistic
effects can be partly neglected are also reviewed, to clarify some common
misunderstandings and imprecise use of the theory in presence of moving media
and other related situations. The discussion presented in this paper provide a
clear view of why classical electromagnetic theory is relativistic in its
essence.


title: Egg-speriments: Stretch, crack, and spin

abstract:
  Eggs are key ingredients in our kitchens because of their nutritional values
and functional properties such as foaming, emulsifying and gelling, offering a
wide variety of culinary achievements. They also constitute ideal objects to
illustrate a myriad of scientific concepts. In this article, we focus on
several experiments (egg-speriments) that involve the singular properties of
the liquids contained inside the eggshell, especially the egg white. We first
characterize the rheology of an egg white in a rotational rheometer for
constant and oscillatory shear stresses revealing its shear-thinning behavior
and visco-elastic properties. Then, we measure the tendency of the fluid to
generate very long filaments when stretched that we relate to the shear modulus
of the material. Second, we explore the anisotropic crack pattern that forms on
a thin film of egg white after it is spread on a surface and let dried. The
anisotropy results from the long protein chains present in the egg white which
are straightened during film deposition. Finally, we consider the "spin test"
that permits to distinguish between raw and hard-boiled eggs. To do so, we
measure the residual rotation of a spinning raw egg after a short stop which
reflects the continuation of the internal flow. These observations are
interpreted in terms of viscous damping of the internal flow consistently with
the measurements deduced from rheology.


title:
Stochastic Modeling of Inhomogeneities in the Aortic Wall and
  Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate

abstract:
  Inhomogeneities in the aortic wall can lead to localized stress
accumulations, possibly initiating dissection. In many cases, a dissection
results from pathological changes such as fragmentation or loss of elastic
fibers. But it has been shown that even the healthy aortic wall has an inherent
heterogeneous microstructure. Some parts of the aorta are particularly
susceptible to the development of inhomogeneities due to pathological changes,
however, the distribution in the aortic wall and the spatial extent, such as
size, shape, and type, are difficult to predict. Motivated by this observation,
we describe the heterogeneous distribution of elastic fiber degradation in the
dissected aortic wall using a stochastic constitutive model. For this purpose,
random field realizations, which model the stochastic distribution of degraded
elastic fibers, are generated over a non-equidistant grid. The random field
then serves as input for a uni-axial extension test of the pathological aortic
wall, solved with the finite-element (FE) method. To include the microstructure
of the dissected aortic wall, a constitutive model developed in a previous
study is applied, which also includes an approach to model the degradation of
inter-lamellar elastic fibers. Then to assess the uncertainty in the output
stress distribution due to this stochastic constitutive model, a convolutional
neural network, specifically a Bayesian encoder-decoder, was used as a
surrogate model that maps the random input fields to the output stress
distribution obtained from the FE analysis. The results show that the neural
network is able to predict the stress distribution of the FE analysis while
significantly reducing the computational time. In addition, it provides the
probability for exceeding critical stresses within the aortic wall, which could
allow for the prediction of delamination or fatal rupture.


title:
Stationary axisymmetric Einstein-Vlasov bifurcations of the Kerr
  spacetime

abstract:
  We construct a one-parameter family of stationary axisymmetric and
asymptotically flat spacetimes solutions to the Einstein-Vlasov system
bifurcating from the Kerr spacetime. The constructed solutions have the
property that the spatial support of the matter is a finite, axisymmetric shell
located away from the black hole. Our proof is mostly based on the analysis of
the set of trapped timelike geodesics for stationary axisymmetric spacetimes
close to Kerr, where the geodesic flow is not necessarily integrable. Moreover,
the analysis of the Einstein field equations relies on the modified Carter
Robinson theory developed by Chodosh and Shlapentokh-Rothman. This provides the
first construction of black hole solutions to the Einstein-Vlasov system in the
axisymmetric case and generalises the construction already done in the
spherically symmetric case.


title:
Weak solutions to triangular cross diffusion systems modeling chemotaxis
  with local sensing

abstract:
  New estimates and global existence results are provided for a class of
systems of cross diffusion equations arising from the modeling of chemotaxis
with local sensing, possibly featuring a growth term of logistic-type as well.
For sublinear non-increasing motility functions, convergence to the spatially
homogeneous steady state is shown, a dedicated Lyapunov functional being
constructed for that purpose.


title: Expected local topology of random complex submanifolds

abstract:
  Let $n\geq 2$ and $r\in \{1, \cdots, n-1\}$ be integers, $M$ be a compact
smooth K\''ahler manifold of complex dimension $n$, $E$ be a holomorphic vector
bundle with complex rank $r$ and equipped with an hermitian metric $h_E$, and
$L$ be an ample holomorphic line bundle over $M$ equipped with a metric $h$
with positive curvature form. For any $d\in \mathbb{N}$ large enough, we
endorse the space of holomorphic sections $H^0(M,E\otimes L^d)$ with the
natural Gaussian measure associated to $h_E$ , $h$ and its curvature form. Let
$U\subset M$ be an open subset with smooth boundary. We prove that the average
of the $(n-r)$-th Betti number of the vanishing locus in $U$ of a random
section $s$ of $H^0(M,E\otimes L^d)$ is asymptotic to ${n-1 \choose r-1}
d^n\int_U c_1(L)^n$ for large $d$. On the other hand, the average of the other
Betti numbers are $o(d^n)$. The first asymptotic recovers the classical
deterministic global algebraic computation. Moreover, such a discrepancy in the
order of growth of these averages is new and constrasts with all known other
smooth Gaussian models, in particular the real algebraic one. We prove a
similar result for the affine complex Bargmann-Fock model.


title:
A self-adaptive RIS that estimates and shapes fading rich-scattering
  wireless channels

abstract:
  We present a framework for operating a self-adaptive RIS inside a fading
rich-scattering wireless environment. We model the rich-scattering wireless
channel as being double-parametrized by (i) the RIS, and (ii) dynamic
perturbers (moving objects, etc.). Within each coherence time, first, the
self-adaptive RIS estimates the status of the dynamic perturbers (e.g., the
perturbers' orientations and locations) based on measurements with an auxiliary
wireless channel. Then, second, using a learned surrogate forward model of the
mapping from RIS configuration and perturber status to wireless channel, an
optimized RIS configuration to achieve a desired functionality is obtained. We
demonstrate our technique using a physics-based end-to-end model of
RIS-parametrized communication with adjustable fading (PhysFad) for the example
objective of maximizing the received signal strength indicator. Our results
present a route toward convergence of RIS-empowered localization and sensing
with RIS-empowered channel shaping beyond the simple case of operation in free
space without fading.


title:
The Random Rouse block copolymer: Spectral Distribution of Randomly
  Segmented Tridiagonal quasi-Toeplitz Matrices

abstract:
  The Rouse bead-spring model is the simplest non-trivial model of polymers.
The key feature of this model is that it is analytically solvable. The
many-body dynamics of the polymer can be projected to orthonormal modes through
a straightforward Fourier transform, thus simplifying the dynamics of the
polymer to independent single-mode Langevin dynamics. This simplifying feature
is lost in the case of Random block copolymers when the sizes and damping
coefficients of the blocks are chosen from random distributions. Calculating
the Rouse modes for the random block polymer involves a diagonalization of a
randomly segmented tridiagonal matrix which are quasi-Toeplitz in nature. The
boundary conditions resulting from the coupling of random segments make the
diagonalization analytically non-trivial. We calculate the exact eigenvalues
and the spectral distribution in the continuum limit for the above class of
random matrices.

categories: cond-mat.stat-mech cond-mat.dis-nn cond-mat.soft math-ph math.MP


title: Effective neutrino number shift from keV-vacuum neutrinophilic 2HDM

abstract:
  If the Dirac neutrino masses are generated by a new scalar doublet with a
vacuum at keV order, there would be rare hope to probe the framework in
low-energy flavor physics, such as the lepton-flavor violating processes.
However, the predicted neutrino Yukawa couplings being around the order of
electronic Yukawa can realize a purely thermal Dirac leptogenesis, and thus
render a more direct link between the high- and the low-energy CP violation.
Despite its inert property in the low-energy flavor processes, the keV-vacuum
induced Dirac neutrino model can generate a significant shift of the effective
neutrino number, which can in turn be probed by the big-bang nucleosynthesis
and cosmic microwave background epochs. We show that such a keV-vacuum induced
Dirac neutrino model is already constrained by the current observations and
will be probed with forecast sensitivity, serving therefore as a complementary
avenue to test the Dirac leptogenesis.


title:
PointSCNet: Point Cloud Structure and Correlation Learning Based on
  Space Filling Curve-Guided Sampling

abstract:
  Geometrical structures and the internal local region relationship, such as
symmetry, regular array, junction, etc., are essential for understanding a 3D
shape. This paper proposes a point cloud feature extraction network named
PointSCNet, to capture the geometrical structure information and local region
correlation information of a point cloud. The PointSCNet consists of three main
modules: the space-filling curve-guided sampling module, the information fusion
module, and the channel-spatial attention module. The space-filling
curve-guided sampling module uses Z-order curve coding to sample points that
contain geometrical correlation. The information fusion module uses a
correlation tensor and a set of skip connections to fuse the structure and
correlation information. The channel-spatial attention module enhances the
representation of key points and crucial feature channels to refine the
network. The proposed PointSCNet is evaluated on shape classification and part
segmentation tasks. The experimental results demonstrate that the PointSCNet
outperforms or is on par with state-of-the-art methods by learning the
structure and correlation of point clouds effectively.


title:
Four-Family ${\cal N}=1$ Supersymmetric Pati-Salam Models from
  Intersecting D6-Branes

abstract:
  We investigate the construction of four-family ${\cal N}=1$ supersymmetric
Pati-Salam models from Type IIA $\mathbb{T}^6/(\mathbb{Z}_2 \times
\mathbb{Z}_2)$ orientifold with intersecting D6-branes. Utilizing the
deterministic algorithm introduced in Ref.
\cite{heCompleteSearchSupersymmetric2021}, we obtain $274$ types of models with
three rectangular tori and distinct gauge coupling relations at string scale,
while $6$ types of models with two rectangular tori and one titled torus. In
both cases, there exists a class of models with gauge coupling unification at
string scale. In particular, for the models with two rectangular tori, one
tilted torus and gauge coupling unification, the gaugino condensations are
allowed, and thus supersymmetry breaking and moduli stabilization are possible
for further phenomenological study.


title: AutoClassWeb: a simple web interface for Bayesian clustering

abstract:
  Objective: Data clustering is a common exploration step in the omics era,
notably in genomics and proteomics where many genes or proteins can
bequantified from one or more experiments. Bayesian clustering is a powerful
algorithm that can classify several thousands of genes or proteins. AutoClass
C, its original implementation, handles missing data, automatically determines
the best number of clusters but is not user-friendly.Results: We developed an
online tool called AutoClassWeb, which provides an easy-to-use web interface
for Bayesian clustering with AutoClass. Input data are entered as TSV files.
Results are provided in formats that ease further analyses with spreadsheet
programs or with programming languages, such as Python or R. AutoClassWeb is
implemented in Python and is published under the 3-Clauses BSD license. The
source code is available athttps://github.com/pierrepo/autoclassweb along with
a detailed documentation.


title: Priority Algorithms with Advice for Disjoint Path Allocation Problems

abstract:
  We analyze the Disjoint Path Allocation problem (DPA) in the priority
framework. Motivated by the problem of traffic regulation in communication
networks, DPA consists of allocating edge-disjoint paths in a graph. While
online algorithms for DPA have been thoroughly studied in the past, we extend
the analysis of this optimization problem by considering the more powerful
class of priority algorithms. Like an online algorithm, a priority algorithm
receives its input only sequentially and must output irrevocable decisions for
individual input items before having seen the input in its entirety. However,
in contrast to the online setting, a priority algorithm may choose an order on
the set of all possible input items and the actual input is then presented
according to this order. A priority algorithm is a natural model for the
intuitively well-understood concept of a greedy algorithm. Apart from analyzing
the classical priority setting, we also consider priority algorithms with
advice. Originally conceived to study online algorithms from an
information-theoretic point of view, the concept of advice has recently been
extended to the priority framework. In this paper, we analyze the classical
variant of the DPA problem on the graph class of paths, the related problem of
Length-Weighted DPA, and finally, DPA on the graph class of trees. We show
asymptotically matching upper and lower bounds on the advice necessary for
optimality in LWDPA and generalize the known optimality result for DPA on paths
to trees with maximal degree at most 3. On trees with maximal degree greater
than 3, we prove matching upper and lower bounds on the approximation ratio in
the advice-free priority setting. Finally, we present upper and lower bounds on
the advice necessary to achieve optimality on such trees.


title:
Length partition of random multicurves on large genus hyperbolic
  surfaces

abstract:
  We study the length statistics of the components of a random multicurve on a
surface of genus $g \geq 2$. For each fixed genus, the existence of such
statistics follows from the work of M.~Mirzakhani, F.~Arana-Herrera and M.~Liu.
We prove that as the genus $g$ tends to infinity the statistics converge in law
to the Poisson--Dirichlet distribution of parameter $\theta=1/2$. In
particular, as the genus tends to infinity the mean length of the three longest
components converge respectively to $75.8\%$, $17.1\%$ and $4.9\%$ of the total
length.


title: Global smooth solutions for triangular reaction-cross diffusion systems

abstract:
  For a class of reaction cross-diffusion systems of two equations with a
cross-diffusion term in the first equation and with self-diffusion terms, we
prove that the unique local smooth solution given by Amann theorem is actually
global. This class of systems arises in Population dynamics, and extends the
triangular Shigesada-Kawasaki-Teramoto system when general power-laws growth
are considered in the reaction and diffusion rates.


title: $S$-integral quadratic forms and homogeneous dynamics

abstract:
  Let $S = \{ \infty \} \cup S_f$ be a finite set of places of $\mathbb{Q}$,
and consider the ring $\mathbb{Z}_S$ of $S$-integers. Using Homogeneous
Dynamics, we prove two new quantitative results about quadratic forms. The
first is a criteria to decide if any two given integral quadratic forms are
$\mathbb{Z}_S$-equivalent. The second gives an explicit finite generating set
of any $S$-integral orthogonal group. These extend earlier results of H. Li and
G. Margulis for $S = \{ \infty\}$.


title: Brownian continuum random tree conditioned to be large

abstract:
  We consider a Feller diffusion (Zs, s $\ge$ 0) (with diffusion coefficient
$\sqrt$ 2$\beta$ and drift $\theta$ $\in$ R) that we condition on {Zt = at},
where at is a deterministic function, and we study the limit in distribution of
the conditioned process and of its genealogical tree as t $\rightarrow$
+$\infty$. When at does not increase too rapidly, we recover the standard
size-biased process (and the associated genealogical tree given by the Kesten's
tree). When at behaves as $\alpha$$\beta$ 2 t 2 when $\theta$ = 0 or as
$\alpha$ e 2$\beta$|$\theta$|t when $\theta$ = 0, we obtain a new process whose
distribution is described by a Girsanov transformation and equivalently by a
SDE with a Poissonian immigration. Its associated genealogical tree is
described by an infinite discrete skeleton (which does not satisfy the
branching property) decorated with Brownian continuum random trees given by a
Poisson point measure. As a by-product of this study, we introduce several sets
of trees endowed with a Gromovtype distance which are of independent interest
and which allow here to define in a formal and measurable way the decoration of
a backbone with a family of continuum random trees.


title: Entanglement entropy of near-extremal black hole

abstract:
  We study how the entanglement entropy of the Hawking radiation derived using
island recipe for the Reissner-Nordstr\"om black hole behaves as the black hole
mass decreases. A general answer to the question essentially depends not only
on the character of decreasing of the mass but also on decreasing of the
charge. We assume the specific relationship between the charge and mass
$Q^2=GM^2[1-\left(\frac{M}{\mu}\right)^{2\nu} ]$, which we call the constraint
equation. We discuss whether it is possible to have a constraint so that the
entanglement entropy does not have an explosion at the end of evaporation, as
happens in the case of thermodynamic entropy and the entanglement entropy for
the Schwarzschild black hole. We show that for some special scaling parameters,
the entanglement entropy of radiation does not explode as long as the mass of
the evaporating black hole exceeds the Planck mass.


title: The Horn cone associated with symplectic eigenvalues

abstract:
  In this note, we show that the Horn cone associated with symplectic
eigenvalues admits the same inequalities as the classical Horn cone, except
that the equality corresponding to Tr(C) = Tr(A)+Tr(B) is replaced by the
inequality corresponding to Tr(C) $\ge$ Tr(A)+Tr(B).


title: A Self-Supervised Descriptor for Image Copy Detection

abstract:
  Image copy detection is an important task for content moderation. We
introduce SSCD, a model that builds on a recent self-supervised contrastive
training objective. We adapt this method to the copy detection task by changing
the architecture and training objective, including a pooling operator from the
instance matching literature, and adapting contrastive learning to
augmentations that combine images.
  Our approach relies on an entropy regularization term, promoting consistent
separation between descriptor vectors, and we demonstrate that this
significantly improves copy detection accuracy. Our method produces a compact
descriptor vector, suitable for real-world web scale applications. Statistical
information from a background image distribution can be incorporated into the
descriptor.
  On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline
copy detection models and self-supervised architectures designed for image
classification by huge margins, in all settings. For example, SSCD out-performs
SimCLR descriptors by 48% absolute. Code is available at
https://github.com/facebookresearch/sscd-copy-detection.


title:
New mass estimates for massive binary systems: a probabilistic approach
  using polarimetric radiative transfer

abstract:
  Understanding the evolution of massive binary stars requires accurate
estimates of their masses. This understanding is critically important because
massive star evolution can potentially lead to gravitational wave sources such
as binary black holes or neutron stars. For Wolf-Rayet stars with optically
thick stellar winds, their masses can only be determined with accurate
inclination angle estimates from binary systems which have spectroscopic $M
\sin i$ measurements. Orbitally-phased polarization signals can encode the
inclination angle of binary systems, where the Wolf-Rayet winds act as
scattering regions.
  We investigated four Wolf-Rayet + O star binary systems, WR 42, WR 79, WR
127, and WR 153, with publicly available phased polarization data to estimate
their masses. To avoid the biases present in analytic models of polarization
while retaining computational expediency, we used a Monte Carlo radiative
transfer model accurately emulated by a neural network. We used the emulated
model to investigate the posterior distribution of parameters of our four
systems. Our mass estimates calculated from the estimated inclination angles
put strong constraints on existing mass estimates for three of the systems, and
disagrees with the existing mass estimates for WR 153. We recommend a concerted
effort to obtain polarization observations that can be used to estimate the
masses of Wolf-Rayet binary systems and increase our understanding of their
evolutionary paths.


title:
Strong Converse for Privacy Amplification against Quantum Side
  Information

abstract:
  We establish a one-shot strong converse bound for privacy amplification
against quantum side information using trace distance as a security criterion.
This strong converse bound implies that in the independent and identical
scenario, the trace distance exponentially converges to one in every finite
blocklength when the rate of the extracted randomness exceeds the quantum
conditional entropy. The established one-shot bound has an application to
bounding the information leakage of classical-quantum wiretap channel coding
and private communication over quantum channels. That is, the trace distance
between Alice and Eavesdropper's joint state and its decoupled state vanishes
as the rate of randomness used in hashing exceeds the quantum mutual
information. On the other hand, the trace distance converges to one when the
rate is below the quantum mutual information, resulting in an exponential
strong converse. Our result also leads to an exponential strong converse for
entropy accumulation, which complements a recent result by Dupuis
[arXiv:2105.05342]. Lastly, our result and its applications apply to the
moderate deviation regime. Namely, we characterize the asymptotic behaviors of
the trace distances when the associated rates approach the fundamental
thresholds with speeds slower than $O(1/\sqrt{n})$.


title:
A variational technique of mollification applied to backward heat
  conduction problems

abstract:
  This paper addresses a backward heat conduction problem with fractional
Laplacian and time-dependent coefficient in an unbounded domain. The problem
models generalized diffusion processes and is well-known to be severely
ill-posed. We investigate a simple and powerful variational regularization
technique based on mollification. Under classical Sobolev smoothness
conditions, we derive order-optimal convergence rates between the exact
solution and regularized approximation in the practical case where both the
data and the operator are noisy. Moreover, we propose an order-optimal
a-posteriori parameter choice rule based on the Morozov principle. Finally, we
illustrate the robustness and efficiency of the regularization technique by
some numerical examples including image deblurring.


title: Yields: The Galapagos Syndrome Of Cryptofinance

abstract:
  In this chapter structures that generate yield in cryptofinance will be
analyzed and related to leverage. While the majority of crypto-assets do not
have intrinsic yields in and of themselves, similar to cash holdings of fiat
currency, revolutionary innovation based on smart contracts, which enable
decentralised finance, does generate return. Examples include lending or
providing liquidity to an automated market maker on a decentralised exchange,
as well as performing block formation in a proof of stake blockchain. On
centralised exchanges, perpetual and finite duration futures can trade at a
premium or discount to the spot market for extended periods with one side of
the transaction earning a yield. Disparities in yield exist between products
and venues as a result of market segmentation and risk profile differences.
Cryptofinance was initially shunned by legacy finance and developed
independently. This led to curious and imaginative adaptions, reminiscent of
Darwin's finches, including stable coins for dollar transfers, perpetuals for
leverage, and a new class of exchanges for trading and investment.


title: Spectroscopic properties of 4He within a multiphonon approach

abstract:
  Bulk and spectroscopic properties of 4He are studied within an equation of
motion phonon method. Such a method generates a basis of n-phonon (n = 0, 1, 2,
3...) states composed of tensor products of particle-hole Tamm-Dancoff phonons
and then solves the full eigenvalue problem in such a basis. The method does
not rely on any approximation and is free of any contamination induced by the
center of mass, in virtue of a procedure exploiting the singular value
decomposition of rectangular matrices. Two potentials, both derived from the
chiral effective field theory, are adopted in a self-consistent calculation
performed within a space including up to three phonons. The latter basis states
are treated under a simplifying assumption. A comparative analysis with the
experimental data points out the different performances of the two potentials.
It shows also that the calculation succeeds only partially in the description
of the spectroscopic properties and suggests a recipe for further improvements.


title: Greedy approximation algorithms for sparse collections

abstract:
  We describe a greedy algorithm that approximates the Carleson constant of a
collection of general sets. The approximation has a logarithmic loss in a
general setting, but is optimal up to a constant with only mild geometric
assumptions. The constructive nature of the algorithm gives additional
information about the almost-disjoint structure of sparse collections.
  As applications, we give three results for collections of axis-parallel
rectangles in every dimension. The first is a constructive proof of the
equivalence between Carleson and sparse collections, first shown by H\"anninen.
The second is a structure theorem proving that every collection $\mathcal{E}$
can be partitioned into $\mathcal{O}(N)$ sparse subfamilies where $N$ is the
Carleson constant of $\mathcal{E}$. We also give examples showing that such a
decomposition is impossible when the geometric assumptions are dropped. The
third application is a characterization of the Carleson constant involving only
$L^{1,\infty}$ estimates.


title:
Protective Coating Interfaces for perovskite Solar Cell Materials: A
  first Principles Study

abstract:
  The protection of halide perovskites is important for the performance and
stability of emergent perovskite-based optoelectronic technologies. In this
work, we investigate the potential inorganic protective coating materials ZnO,
SrZrO3, and ZrO2 for the CsPbI3perovskite. The optimal interface registries are
identified with Bayesian optimization. We then use semi-local
density-functional theory (DFT) to determine the atomic structure at the
interfaces of each coating material with the clean CsI-terminated surface and
three reconstructed surface models with added PbI2and CsI complexes. For the
final structures, we explore the level alignment at the interface with hybrid
DFT calculations. Our analysis of the level alignment at the coating-substrate
interfaces reveals no detrimental mid-gap states, but substrate-dependent
valence and conduction band offsets. While ZnO and SrZrO3act as insulators on
CsPbI3, ZrO2 might be suitable as electron transport layer with the right
interface engineering.


title: Sparse bounds for pseudo-multipliers associated to Grushin operators, II

abstract:
  In this article, we establish pointwise sparse domination results for Grushin
pseudo-multipliers corresponding to various symbol classes, as a continuation
of our investigation initiated in [BBGG21]. As a consequence, we deduce
quantitative weighted estimates for these pseudo-multipliers.


title: Ground state energy of a Bose gas in the Gross-Pitaevskii regime

abstract:
  We review some rigorous estimates for the ground state energy of dilute Bose
gases. We start with Dyson's upper bound, which provides the correct leading
order asymptotics for hard spheres. Afterwards, we discuss a rigorous version
of Bogoliubov theory, which recently led to an estimate for the ground state
energy in the Gross-Pitaevskii regime, valid up to second order, for particles
interacting through integrable potentials. Finally, we explain how these ideas
can be combined to establish a new upper bound, valid to second order, for the
energy of hard spheres in the Gross-Pitaeavskii limit. Here we only sketch the
main ideas, details will appear elsewhere.


title:
Radiation Hard 3D Silicon Pixel Sensors for use in the ATLAS Detector at
  the HL-LHC

abstract:
  The High Luminosity LHC (HL-LHC) upgrade requires the planned Inner Tracker
(ITk) of the ATLAS detector to tolerate extremely high radiation doses.
Specifically, the innermost parts of the pixel system will have to withstand
radiation fluences above $1\times10^{16}$ $n_{eq}cm^{-2}$. Novel 3D silicon
pixel sensors offer a superior radiation tolerance compared to conventional
planar pixel sensors, and are thus excellent candidates for the innermost parts
of the ITk. This paper presents studies of 3D pixel sensors with pixel size $50
\times 50$ $\mu m^2$ mounted on the RD53A prototype readout chip. Following a
description of the design and fabrication steps, Test Beam results are
presented for unirradiated as well as heavily irradiated sensors. For particles
passing at perpendicular incidence, it is shown that average efficiencies above
96% are reached for sensors exposed to fluences of $1\times10^{16}$
$n_{eq}cm^{-2}$ when biased to 80 $V$.


title: Computational Pattern Making from 3D Garment Models

abstract:
  We propose a method for computing a sewing pattern of a given 3D garment
model. Our algorithm segments an input 3D garment shape into patches and
computes their 2D parameterization, resulting in pattern pieces that can be cut
out of fabric and sewn together to manufacture the garment. Unlike the general
state-of-the-art approaches for surface cutting and flattening, our method
explicitly targets garment fabrication. It accounts for the unique properties
and constraints of tailoring, such as seam symmetry, the usage of darts, fabric
grain alignment, and a flattening distortion measure that models woven fabric
deformation, respecting its anisotropic behavior. We bootstrap a recent patch
layout approach developed for quadrilateral remeshing and adapt it to the
purpose of computational pattern making, ensuring that the deformation of each
pattern piece stays within prescribed bounds of cloth stress. While our
algorithm can automatically produce the sewing patterns, it is fast enough to
admit user input to creatively iterate on the pattern design. Our method can
take several target poses of the 3D garment into account and integrate them
into the sewing pattern design. We demonstrate results on both skintight and
loose garments, showcasing the versatile application possibilities of our
approach.


title:
Hierarchical equations of motion approach to hybrid fermionic and
  bosonic environments: Matrix product state formulation in twin space

abstract:
  We extend the twin-space formulation of the hierarchical equations of motion
approach in combination with the matrix product state representation
(introduced in J. Chem. Phys. 150, 234102, [2019]) to nonequilibrium scenarios
where the open quantum system is coupled to a hybrid fermionic and bosonic
environment. The key ideas used in the extension are a reformulation of the
hierarchical equations of motion for the auxiliary density matrices into a
time-dependent Schr\"odinger-like equation for an augmented multi-dimensional
wave function as well as a tensor decomposition into a product of low-rank
matrices. The new approach facilitates accurate simulations of non-equilibrium
quantum dynamics in larger and more complex open quantum systems. The
performance of the method is demonstrated for a model of a molecular junction
exhibiting current-induced mode-selective vibrational excitation.


title: Compact Michelson interferometers with subpicometer sensitivity

abstract:
  The network of interferometric gravitational-wave observatories has
successfully detected tens of astrophysical signals since 2015. In this paper,
we experimentally investigate compact sensors that have the potential to
improve the sensitivity of gravitational-wave detectors to intermediate-mass
black holes. We use only commercial components, such as sensing heads and
lasers, to assemble the setup and demonstrate its subpicometer precision. The
setup consists of a pair of Michelson interferferometers that use deep
frequency modulation techniques to obtain a linear, relative displacement
readout over multiple interference fringes. We implement a laser-frequency
stabilisation scheme to achieve a sensitivity of 0.3\,$\text{pm} /
\sqrt{\text{Hz}}$ above 0.1\,Hz. The device has also the potential to improve
other experiments, such as torsion balances and commercial seismometers.


title:
A conservative level-set method based on a posterior mass correction
  preserving distance property for incompressible multiphase flows simulations

abstract:
  As one of the most popular interface-capturing methods, the level-set method
is inherently non-conservative, and its evolution usually leads to unphysical
mass gain/loss. In this paper, a novel conservative level set method is
developed for incompressible multiphase flows simulations. A posterior mass
correction is performed by introducing a small perturbation to the level-set
field, which is solved via the Newton method. Unlike in previous researches,
the signed distance property of the level-set function is exactly preserved
after the present mass correction. Moreover, this method can be easily
generalized from 2D to 3D. The influence for the computational efficiency is
slight as the correction does not need to be applied at every time step.
Various benchmark cases involving pure interface-evolution problems and
multiphase flows problems are considered to validate the present method. For
all cases, the accuracy and efficiency of the original method and the present
method are quantitatively compared. It is observed that, with negligibly extra
cost, the conservation error is reduced to the order of machine accuracy by the
present method, which indicates its potential applications in complex
multiphase flows simulations.


title: Poisoning Attacks and Defenses on Artificial Intelligence: A Survey

abstract:
  Machine learning models have been widely adopted in several fields. However,
most recent studies have shown several vulnerabilities from attacks with a
potential to jeopardize the integrity of the model, presenting a new window of
research opportunity in terms of cyber-security. This survey is conducted with
a main intention of highlighting the most relevant information related to
security vulnerabilities in the context of machine learning (ML) classifiers;
more specifically, directed towards training procedures against data poisoning
attacks, representing a type of attack that consists of tampering the data
samples fed to the model during the training phase, leading to a degradation in
the models accuracy during the inference phase. This work compiles the most
relevant insights and findings found in the latest existing literatures
addressing this type of attacks. Moreover, this paper also covers several
defense techniques that promise feasible detection and mitigation mechanisms,
capable of conferring a certain level of robustness to a target model against
an attacker. A thorough assessment is performed on the reviewed works,
comparing the effects of data poisoning on a wide range of ML models in
real-world conditions, performing quantitative and qualitative analyses. This
paper analyzes the main characteristics for each approach including performance
success metrics, required hyperparameters, and deployment complexity. Moreover,
this paper emphasizes the underlying assumptions and limitations considered by
both attackers and defenders along with their intrinsic properties such as:
availability, reliability, privacy, accountability, interpretability, etc.
Finally, this paper concludes by making references of some of main existing
research trends that provide pathways towards future research directions in the
field of cyber-security.


title:
End-to-End High Accuracy License Plate Recognition Based on Depthwise
  Separable Convolution Networks

abstract:
  Automatic license plate recognition plays a crucial role in modern
transportation systems such as for traffic monitoring and vehicle violation
detection. In real-world scenarios, license plate recognition still faces many
challenges and is impaired by unpredictable interference such as weather or
lighting conditions. Many machine learning based ALPR solutions have been
proposed to solve such challenges in recent years. However, most are not
convincing, either because their results are evaluated on small or simple
datasets that lack diverse surroundings, or because they require powerful
hardware to achieve a reasonable frames-per-second in real-world applications.
In this paper, we propose a novel segmentation-free framework for license plate
recognition and introduce NP-ALPR, a diverse and challenging dataset which
resembles real-world scenarios. The proposed network model consists of the
latest deep learning methods and state-of-the-art ideas, and benefits from a
novel network architecture. It achieves higher accuracy with lower
computational requirements than previous works. We evaluate the effectiveness
of the proposed method on three different datasets and show a recognition
accuracy of over 99% and over 70 fps, demonstrating that our method is not only
robust but also computationally efficient.


title: A categorical review of complete regularity

abstract:
  We use the ultrafilter-convergence axiomatics for topological spaces to
motivate in detail a gentle categorical introduction, first to Barr's Set-based
relational T-algebras, and then to Burroni's T-preorders internal to a category
C, here called T-spaces in C, for a monad T on C that substitutes the
ultrafilter monad on Set. Within these settings one finds not only the notions
of compactness and Hausdorff separation, originally due to Manes, but also that
of complete regularity. Based on a somewhat hidden result by Burroni, the main
theorem of this paper establishes an external fibrational characterization of
the category of completely regular T-spaces with its reflexive subcategory of
compact Hausdorff T-spaces, under modest assumptions on C and T.


title: Cryogenic thermo-acoustics in the SPIRAL2 LINAC

abstract:
  SPIRAL2 is a superconducting LINAC subject to cryogenic thermo-acoustic
oscillations occurring in its valves-boxes. 4 years of monitoring and
experimental investigations with thousands of datasets turned these unwanted
effects into an opportunity to study and understand thermo-acoustics in a
complex environment such as a real life accelerator. Without digging deep into
Rott's thermo-acoustics theory, thoroughly shown in other works, this paper
describes the instrumentation and the methods that prepare more advanced
modelling of these phenomena either to damp or to harness the energy of
cryogenic thermo-acoustics.


title:
On the Information-theoretic Security of Combinatorial All-or-nothing
  Transforms

abstract:
  All-or-nothing transforms (AONT) were proposed by Rivest as a message
preprocessing technique for encrypting data to protect against brute-force
attacks, and have numerous applications in cryptography and information
security. Later the unconditionally secure AONT and their combinatorial
characterization were introduced by Stinson. Informally, a combinatorial AONT
is an array with the unbiased requirements and its security properties in
general depend on the prior probability distribution on the inputs $s$-tuples.
Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has
perfect security provided that all the inputs $s$-tuples are equiprobable, and
has weak security provided that all the inputs $s$-tuples are with non-zero
probability.
  This paper aims to explore on the gap between perfect security and weak
security for combinatorial $(t,s,v)$-AONTs. Concretely, we consider the typical
scenario that all the $s$ inputs take values independently (but not necessarily
identically) and quantify the amount of information
$H(\mathcal{X}|\mathcal{Y})$ about any $t$ inputs $\mathcal{X}$ that is not
revealed by any $s-t$ outputs $\mathcal{Y}$. In particular, we establish the
general lower and upper bounds on $H(\mathcal{X}|\mathcal{Y})$ for
combinatorial AONTs using information-theoretic techniques, and also show that
the derived bounds can be attained in certain cases. Furthermore, the
discussions are extended for the security properties of combinatorial
asymmetric AONTs.


title:
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent
  Variables

abstract:
  In many anomaly detection tasks, where anomalous data rarely appear and are
difficult to collect, training with only normal data is important. Although it
is possible to manually create anomalous data using prior knowledge, they may
be subject to user bias. In this paper, we propose an Anomalous Latent variable
Generative Adversarial Network (ALGAN) in which the GAN generator produces
pseudo-anomalous data as well as fake-normal data, whereas the discriminator is
trained to distinguish between normal and pseudo-anomalous data. This differs
from the standard GAN discriminator, which specializes in classifying two
similar classes. The training dataset contains only normal data as anomalous
states are introduced in the latent variable and input them into the generator
to produce diverse pseudo-anomalous data. We compared the performance of ALGAN
with other existing methods using the MVTec-AD, Magnetic Tile Defects, and
COIL-100 datasets. The experimental results showed that the proposed ALGAN
exhibited an AUROC comparable to state-of-the-art methods while achieving a
much faster prediction time.


title: Orthorhombic charge density wave on the tetragonal lattice of EuAl4

abstract:
  EuAl4 possesses the BaAl4 crystal structure type with tetragonal symmetry
I4/mmm. It undergoes a charge-density-wave (CDW) transition at TCDW = 145 K and
it features four consecutive antiferromagnetic phase transitions below 16 K.
Here, we use single-crystal x-ray diffraction to determine incommensurately
modulated crystal structure of EuAl4 in its CDW state. The CDW is shown to be
incommensurate with modulation wave vector q = (0, 0, 0.1781(3)) at 70 K. The
symmetry of the incommensurately modulated crystal structure is orthorhombic
with superspace group Fmmm(00{\sigma})s00, where Fmmm is a subgroup of I4/mmm
of index 2. Both the lattice and the atomic coordinates of the basic structure
remain tetragonal. Symmetry breaking is entirely due to the modulation wave,
where atoms Eu and Al1 have displacements exclusively along a, while the
fourfold rotation would require equal displacement amplitudes along a and b.
The calculated band structure of the basic structure and interatomic distances
in the modulated crystal structure both indicate the aluminum atoms as location
of the CDW. The temperature dependence of the specific heat reveals an anomaly
at TCDW = 145 K of a magnitude similar to canonical CDW systems. The present
discovery of orthorhombic symmetry for the CDW state of EuAl4 leads to the
suggestion of monoclinic instead of orthorhombic symmetry for the third AFM
state.


title: On quotients of bounded homogeneous domains by unipotent discrete groups

abstract:
  We show that the quotient of any bounded homogeneous domain by a unipotent
discrete group of automorphisms is holomorphically separable. Then we give a
necessary condition for the quotient to be Stein and prove that in some cases
this condition is also sufficient.


title: A Review of Emerging Research Directions in Abstract Visual Reasoning

abstract:
  Abstract Visual Reasoning (AVR) problems are commonly used to approximate
human intelligence. They test the ability of applying previously gained
knowledge, experience and skills in a completely new setting, which makes them
particularly well-suited for this task. Recently, the AVR problems have become
popular as a proxy to study machine intelligence, which has led to emergence of
new distinct types of problems and multiple benchmark sets. In this work we
review this emerging AVR research and propose a taxonomy to categorise the AVR
tasks along 5 dimensions: input shapes, hidden rules, target task, cognitive
function, and main challenge. The perspective taken in this survey allows to
characterise AVR problems with respect to their shared and distinct properties,
provides a unified view on the existing approaches for solving AVR tasks, shows
how the AVR problems relate to practical applications, and outlines promising
directions for future work. One of them refers to the observation that in the
machine learning literature different tasks are considered in isolation, which
is in the stark contrast with the way the AVR tasks are used to measure human
intelligence, where multiple types of problems are combined within a single IQ
test.


title:
Exclusive semileptonic $B \to \pi \ell \nu_\ell$ and $B_s \to K \ell
  \nu_\ell$ decays through unitarity and lattice QCD

abstract:
  The Cabibbo-Kobayashi-Maskawa (CKM) matrix element $\vert V_{ub}\vert$ is
obtained from exclusive semileptonic $B \to \pi \ell \nu_\ell$ and $B_s \to K
\ell \nu_\ell$ decays adopting the unitarity-based dispersion matrix approach
for the determination of the hadronic form factors (FFs) in the whole
kinematical range. We use lattice computations of the relevant susceptibilities
and of the FFs in the large-$q^2$ regime in order to derive their behavior in
the low-$q^2$ region without assuming any specific momentum dependence and
without constraining their shape using experimental data. Then, we address the
extraction of $\vert V_{ub}\vert$ from the experimental data, obtaining $\vert
V_{ub}\vert = (3.62 \pm 0.47) \cdot 10^{-3}$ from $B \to \pi$ and $\vert
V_{ub}\vert = (3.77 \pm 0.48) \cdot 10^{-3}$ from $B_s \to K$, which after
averaging yield $\vert V_{ub}\vert = (3.69 \pm 0.34) \cdot 10^{-3}$. These
results are compatible with the most recent inclusive value $\vert V_{ub}
\vert_{incl} = 4.13\,(26) \cdot 10^{-3}$ at the 1$\sigma$ level. We also
present purely theoretical estimates of the ratio of the $\tau/\mu$
differential decay rates $R^{\tau/\mu}_{\pi(K)}$, the normalized
forward-backward asymmetry $\bar{\mathcal{A}}_{FB}^{\ell,\pi(K)}$ and the
normalized lepton polarization asymmetry
$\bar{\mathcal{A}}_{polar}^{\ell,\pi(K)}$.


title:
A Comprehensive Evaluation on Multi-channel Biometric Face Presentation
  Attack Detection

abstract:
  The vulnerability against presentation attacks is a crucial problem
undermining the wide-deployment of face recognition systems. Though
presentation attack detection (PAD) systems try to address this problem, the
lack of generalization and robustness continues to be a major concern. Several
works have shown that using multi-channel PAD systems could alleviate this
vulnerability and result in more robust systems. However, there is a wide
selection of channels available for a PAD system such as RGB, Near Infrared,
Shortwave Infrared, Depth, and Thermal sensors. Having a lot of sensors
increases the cost of the system, and therefore an understanding of the
performance of different sensors against a wide variety of attacks is necessary
while selecting the modalities. In this work, we perform a comprehensive study
to understand the effectiveness of various imaging modalities for PAD. The
studies are performed on a multi-channel PAD dataset, collected with 14
different sensing modalities considering a wide range of 2D, 3D, and partial
attacks. We used the multi-channel convolutional network-based architecture,
which uses pixel-wise binary supervision. The model has been evaluated with
different combinations of channels, and different image qualities on a variety
of challenging known and unknown attack protocols. The results reveal
interesting trends and can act as pointers for sensor selection for
safety-critical presentation attack detection systems. The source codes and
protocols to reproduce the results are made available publicly making it
possible to extend this work to other architectures.


title:
Domain Adaptation in Neural Machine Translation using a Qualia-Enriched
  FrameNet

abstract:
  In this paper we present Scylla, a methodology for domain adaptation of
Neural Machine Translation (NMT) systems that make use of a multilingual
FrameNet enriched with qualia relations as an external knowledge base. Domain
adaptation techniques used in NMT usually require fine-tuning and in-domain
training data, which may pose difficulties for those working with
lesser-resourced languages and may also lead to performance decay of the NMT
system for out-of-domain sentences. Scylla does not require fine-tuning of the
NMT model, avoiding the risk of model over-fitting and consequent decrease in
performance for out-of-domain translations. Two versions of Scylla are
presented: one using the source sentence as input, and another one using the
target sentence. We evaluate Scylla in comparison to a state-of-the-art
commercial NMT system in an experiment in which 50 sentences from the Sports
domain are translated from Brazilian Portuguese to English. The two versions of
Scylla significantly outperform the baseline commercial system in HTER.


title: Topology of black hole thermodynamics in Gauss-Bonnet gravity

abstract:
  Thermodynamics of black holes in anti de Sitter (AdS) spacetimes typically
contains critical points in the phase diagram, some of which correspond to the
first order transition ending in a second order one. Following the recent
proposal in [arXiv:2112.01706] on using Duan's $\phi$-mapping theory, we
classify the critical points of six dimensional charged Gauss-Bonnet black
holes in AdS spacetime. We find that the higher derivative corrections from
Gauss-Bonnet gravity do not change the topological class of critical points in
charged black holes in AdS, unlike the case of Born-Infeld corrections noted
earlier. The connection between the topological nature of critical points and
existence of first order phase transitions breaks down in a certain parameter
regime. A resolution is proposed by treating the novel and conventional
critical points as phase creation and phase annihilation points, respectively.
Examples are provided to support the proposal.


title:
The Mathematics of Evolution: The Price Equation, Natural Selection, and
  Environmental Change

abstract:
  We extend George Price's evolutionary framework to the measure-theoretic and
quantum cases, showing that all processes decompose into selective and
environmental components. We further quantify selection and environmental
change using entropy functionals: selective entropy is non-positive,
representing biological negentropy, and environmental entropy is non-negative,
representing physical entropy. We prove four Laws of Natural Selection, showing
that selection consistently acts in a manner to increase selection, but which
can be disrupted by environmental change.


title:
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric
  and Elderly Speech Recognition

abstract:
  Despite the rapid progress of automatic speech recognition (ASR) technologies
targeting normal speech in recent decades, accurate recognition of dysarthric
and elderly speech remains highly challenging tasks to date. Sources of
heterogeneity commonly found in normal speech including accent or gender, when
further compounded with the variability over age and speech pathology severity
level, create large diversity among speakers. To this end, speaker adaptation
techniques play a key role in personalization of ASR systems for such users.
Motivated by the spectro-temporal level differences between dysarthric, elderly
and normal speech that systematically manifest in articulatory imprecision,
decreased volume and clarity, slower speaking rates and increased dysfluencies,
novel spectrotemporal subspace basis deep embedding features derived using SVD
speech spectrum decomposition are proposed in this paper to facilitate
auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN/TDNN
and end-to-end Conformer speech recognition systems. Experiments were conducted
on four tasks: the English UASpeech and TORGO dysarthric speech corpora; the
English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets.
The proposed spectro-temporal deep feature adapted systems outperformed
baseline i-Vector and xVector adaptation by up to 2.63% absolute (8.63%
relative) reduction in word error rate (WER). Consistent performance
improvements were retained after model based speaker adaptation using learning
hidden unit contributions (LHUC) was further applied. The best speaker adapted
system using the proposed spectral basis embedding features produced the lowest
published WER of 25.05% on the UASpeech test set of 16 dysarthric speakers.


title: Machine-learning assisted quantum control in random environment

abstract:
  Disorder in condensed matter and atomic physics is responsible for a great
variety of fascinating quantum phenomena, which are still challenging for
understanding, not to mention the relevant dynamical control. Here we introduce
proof of the concept and analyze neural network-based machine learning
algorithm for achieving feasible high-fidelity quantum control of a particle in
random environment. To explicitly demonstrate its capabilities, we show that
convolutional neural networks are able to solve this problem as they can
recognize the disorder and, by supervised learning, further produce the policy
for the efficient low-energy cost control of a quantum particle in a
time-dependent random potential. We have shown that the accuracy of the
proposed algorithm is enhanced by a higher-dimensional mapping of the disorder
pattern and using two neural networks, each properly trained for the given
task. The designed method, being computationally more efficient than the
gradient-descent optimization, can be applicable to identify and control
various noisy quantum systems on a heuristic basis.


title:
Seeing the advantage: visually grounding word embeddings to better
  capture human semantic knowledge

abstract:
  Distributional semantic models capture word-level meaning that is useful in
many natural language processing tasks and have even been shown to capture
cognitive aspects of word meaning. The majority of these models are purely text
based, even though the human sensory experience is much richer. In this paper
we create visually grounded word embeddings by combining English text and
images and compare them to popular text-based methods, to see if visual
information allows our model to better capture cognitive aspects of word
meaning. Our analysis shows that visually grounded embedding similarities are
more predictive of the human reaction times in a large priming experiment than
the purely text-based embeddings. The visually grounded embeddings also
correlate well with human word similarity ratings. Importantly, in both
experiments we show that the grounded embeddings account for a unique portion
of explained variance, even when we include text-based embeddings trained on
huge corpora. This shows that visual grounding allows our model to capture
information that cannot be extracted using text as the only source of
information.


title:
Benchmark calculations of infinite neutron matter with realistic two-
  and three-nucleon potentials

abstract:
  We present the equation of state of infinite neutron matter as obtained from
highly-realistic Hamiltonians that include nucleon-nucleon and three-nucleon
coordinate-space potentials. We benchmark three independent many-body methods:
Brueckner-Bethe-Goldstone (BBG), Fermi hypernetted chain/single-operator chain
(FHNC/SOC), and auxiliary-field diffusion Monte Carlo (AFDMC). We find them to
provide similar equations of state when the Argonne $v_{18}$ and the Argonne
$v_{6}^\prime$ nucleon-nucleon potentials are used in combination with the
Urbana IX three-body force. Only at densities larger than about 1.5 the nuclear
saturation density ($\rho_0 = 0.16\,\rm{fm}^{-3}$) the FHNC/SOC energies are
appreciably lower than the other two approaches. The AFDMC calculations carried
out with all of the Norfolk potentials fitted to reproduce the experimental
trinucleon ground-state energies and $nd$ doublet scattering length yield
unphysically bound neutron matter, associated with the formation of neutron
droplets. Including tritium $\beta$-decay in the fitting procedure, as in the
second family of Norfolk potentials, mitigates but does not completely resolve
this problem. An excellent agreement between the BBG and AFDMC results is found
for the subset of Norfolk interactions that do not make neutron-matter
collapse, while the FHNC/SOC equations of state are moderately softer.