title:
Density Functional description of spin, lattice, and spin-lattice
  dynamics in antiferromagnetic and paramagnetic phases at finite temperatures

abstract:
  Describing the (a) electronic and magnetic properties (EMP) of
antiferromagnetic or paramagnetic phases of compounds generally requires the
knowledge of (b) the spin configurations and lattice structure (SCLS) of such
phases at a given temperature. Indeed, studying the coupling between (a) and
(b) has been an outstanding challenge in the theory of magnetism. The
traditional approach to electronic phases of matter has generally focused on
solving the problem of EMP regarding the SCLS as a spectator degree of freedom
(DOF). Yet, it has been recognized that EMP of a compound generally respond
self-consistently to changes in SCLS and vice versa. We construct here a
practical, density functional theory (DFT)-based approach that provides the
SCLS as a function of temperature, involving the description of spin, lattice,
and spin-lattice dynamics of different magnetic phases. We distinguish three
Levels of dynamics: (I) dynamics of the spin DOF treated via noncollinear
Heisenberg Monte-Carlo with exchange energies from DFT, (II) dynamics of the
lattice DOF treated by ab initio molecular dynamics (AIMD) employing a fixed
spin configuration from Level I at the simulated temperature, and (III)
coupling of spin and lattice dynamics via Landau-Lifshitz-Gilbert spin dynamics
combined with AIMD. Such SCLS at each of the three levels are used as inputs to
DFT supercell calculations, providing the EMP at each temperature. The results
of this sequence include electronic band structures, band gaps, density of
states, as well as the statistical distribution of local moments and the
short-range order parameters, each as a function of temperature. Using NiO as a
test case, we address the separability of the DOF in magnetic insulators for a
minimal description of electronic and magnetic properties, demonstrating that
inclusion of spin dynamics and, to some level, lattice dynamics is enough to
explain the EMP.


title:
Analysis of an unsteady quasi-capillary channel flow with Time Resolved
  PIV and RBF-based super resolution

abstract:
  We investigate the behaviour of accelerating contact lines in an unsteady
quasi-capillary channel flow. The configuration consists of a liquid column
that moves along a vertical 2D channel, open to the atmosphere and driven by a
controlled pressure head. Both advancing and receding contact lines were
analyzed to test the validity of classic models for dynamic wetting and to
study the flow field near the interface. The operating conditions are
characterized by a large acceleration, thus dominated by inertia. The shape of
the moving meniscus was retrieved using Laser-Induced Fluorescence (LIF)-based
image processing while the flow field near was analyzed via Time-Resolved
Particle Image Velocimetry (TR-PIV). The TR-PIV measurements were enhanced in
the post-processing, using a combination of Proper Orthogonal Decomposition
(POD) and Radial Basis Functions (RBF) to achieve super-resolution of the
velocity field. Large counter-rotating vortices were observed, and their
evolution was monitored in terms of the maximum intensity of the Q-field. The
results show that classic contact angle laws based on interface velocity cannot
describe the evolution of the contact angle at a macroscopic scale. Moreover,
the impact of the interface dynamics on the flow field is considerable and
extends several capillary lengths below the interface.


title: On Lipschitz Normally Embedded singularities

abstract:
  Any subanalytic germ $(X,0) \subset (\mathbb R^n,0)$ is equipped with two
natural metrics: its outer metric, induced by the standard Euclidean metric of
the ambient space, and its inner metric, which is defined by measuring the
shortest length of paths on the germ $(X,0)$. The germs for which these two
metrics are equivalent up to a bilipschitz homeomorphism, which are called
Lipschitz Normally Embedded, have attracted a lot of interest in the last
decade. In this survey we discuss many general facts about Lipschitz Normally
Embedded singularities, before moving our focus to some recent developments on
criteria, examples, and properties of Lipschitz Normally Embedded complex
surfaces. We conclude the manuscript with a list of open questions which we
believe to be worth of interest.


title:
Large perpendicular magnetic anisotropy of transition metal dimers
  driven by polarization switching of two-dimensional ferroelectric In2Se3
  substrate

abstract:
  Large perpendicular magnetic anisotropy (MA) is highly desirable for
realizing atomic-scale magnetic data storage which represents the ultimate
limit of the density of magnetic recording. In this work, we studied the MA of
transition metal dimers Co-Os, Co-Co and Os-Os adsorbed on two-dimensional
ferroelectric In2Se3 (In2Se3-CoOs, In2Se3-OsCo, In2Se3-CoCo and In2Se3-OsOs) by
first-principles calculations. It is found that the Co-Os dimer in In2Se3-CoOs
has large total perpendicular magnetic anisotropy energy (MAE) of ~ 40 meV. In
particular, the MAE arising from Os atom is up to ~ 60 meV. The large MAE is
attributed to the high spin-orbit coupling constant and the onefold
coordination of Os atom. In addition, the MA of the dimers can be tuned by the
polarization reversal of In2Se3. When the polarization is upward, the easy-axis
directions of MA in In2Se3-OsCo, In2Se3-CoCo and In2Se3-OsOs are all in-plane,
while the directions become perpendicular as the polarization is switched to
downward. For the In2Se3-CoOs, switching polarization from upward to downward
enhance the perpendicular MA from ~ 20 meV to ~ 40 meV. Based on the
second-order perturbation theory, we confirm that the exchange splitting of
dxy/dx2-y2 and dxz/dyz orbitals as well as the occupation of dz2 orbital at the
vicinity of Fermi level play important roles in the changes of MA with the
reversal of FE polarization of In2Se3.


title:
Improved Combinatorial Approximation Algorithms for MAX CUT in Sparse
  Graphs

abstract:
  The Max-Cut problem is a fundamental NP-hard problem, which is attracting
attention in the field of quantum computation these days. Regarding the
approximation algorithm of the Max-Cut problem, algorithms based on
semidefinite programming have achieved much better approximation ratios than
combinatorial algorithms. Therefore, filling the gap is an interesting topic as
combinatorial algorithms also have some merits. In sparse graphs, there is a
linear-time combinatorial algorithm with the approximation ratio
$\frac{1}{2}+\frac{n-1}{4m}$ [Ngoc and Tuza, Comb. Probab. Comput. 1993], which
is known as the Edwards-Erd\H{o}s bound. In subcubic graphs, the combinatorial
algorithm by Bazgan and Tuza [Discrete Math. 2008] has the best approximation
ratio $\frac{5}{6}$ that runs in $O(n^2)$ time. Based on the approach by Bazgan
and Tuza, we introduce a new vertex decomposition of graphs, which we call
tree-bipartite decomposition. With the decomposition, we present a linear-time
$(\frac{1}{2}+\frac{n-1}{2m})$-approximation algorithm for the Max-Cut problem.
As a derivative, we also present a linear-time $\frac{5}{6}$-approximation
algorithm in subcubic graphs, which solves an open problem in their paper.


title:
Error of the Galerkin scheme for a semilinear subdiffusion equation with
  time-dependent coefficients and nonsmooth data

abstract:
  We investigate the error of the (semidiscrete) Galerkin method applied to a
semilinear subdiffusion equation in the presence of a nonsmooth initial data.
The diffusion coefficient is allowed to depend on time. It is well-known that
in such parabolic problems the spatial error increases when time decreases. The
rate of this time-dependency is a function of the fractional parameter and the
regularity of the initial condition. We use the energy method to find optimal
bounds on the error under weak and natural assumptions on the diffusivity.
First, we prove the result for the linear problem and then use the "frozen
nonlinearity" technique coupled with various generalizations of Gronwall
inequality to carry the result to the semilinear case. The paper ends with
numerical illustrations supporting the theoretical results.


title: Second order conditions to decompose smooth functions as sums of squares

abstract:
  We consider the problem of decomposing a regular non-negative function as a
sum of squares of functions which preserve some form of regularity. In the same
way as decomposing non-negative polynomials as sum of squares of polynomials
allows to derive methods in order to solve global optimization problems on
polynomials, decomposing a regular function as a sum of squares allows to
derive methods to solve global optimization problems on more general functions.
As the regularity of the functions in the sum of squares decomposition is a key
indicator in analyzing the convergence and speed of convergence of optimization
methods, it is important to have theoretical results guaranteeing such a
regularity. In this work, we show second order sufficient conditions in order
for a $p$ times continuously differentiable non-negative function to be a sum
of squares of $p-2$ differentiable functions. The main hypothesis is that,
locally, the function grows quadratically in directions which are orthogonal to
its set of zeros. The novelty of this result, compared to previous works is
that it allows sets of zeros which are continuous as opposed to discrete, and
also applies to manifolds as opposed to open sets of $\R^d$. This has
applications in problems where manifolds of minimizers or zeros typically
appear, such as in optimal transport, and for minimizing functions defined on
manifolds.


title:
Efficient NIZKs and Signatures from Commit-and-Open Protocols in the
  QROM

abstract:
  Commit-and-open Sigma-protocols are a popular class of protocols for
constructing non-interactive zero-knowledge arguments and digital-signature
schemes via the Fiat-Shamir transformation. Instantiated with hash-based
commitments, the resulting non-interactive schemes enjoy tight
online-extractability in the random oracle model. Online extractability
improves the tightness of security proofs for the resulting digital-signature
schemes by avoiding lossy rewinding or forking-lemma based extraction.
  In this work, we prove tight online extractability in the quantum random
oracle model (QROM), showing that the construction supports post-quantum
security. First, we consider the default case where committing is done by
element-wise hashing. In a second part, we extend our result to Merkle-tree
based commitments. Our results yield a significant improvement of the provable
post-quantum security of the digital-signature scheme Picnic.
  Our analysis makes use of a recent framework by Chung et al.
[arXiv:2010.11658] for analysing quantum algorithms in the QROM using purely
classical reasoning. Therefore, our results can to a large extent be understood
and verified without prior knowledge of quantum information science.


title:
On Inhibition of Rayleigh--Taylor Instability by a Horizontal Magnetic
  Field in Non-resistive MHD Fluids: the Viscous Case

abstract:
  It is still open whether the phenomenon of inhibition of Rayleigh--Taylor
(RT) instability by a horizontal magnetic field can be mathematically verified
for a non-resistive \emph{viscous} magnetohydrodynamic (MHD) fluid in a
two-dimensional (2D) horizontal slab domain, since it was roughly proved in the
linearized case by Wang in \cite{WYC}. In this paper, we prove such inhibition
phenomenon by the (nonlinear) inhomogeneous, incompressible, \emph{viscous
case} with \emph{Navier (slip) boundary condition}. More precisely, we show
that there is a critical number of field strength $m_{\mm{C}}$, such that if
the strength $|m|$ of a horizontal magnetic field is bigger than $m_{\mm{C}}$,
then the small perturbation solution around the magnetic RT equilibrium state
is {algebraically} stable in time. In addition, we also provide a nonlinear
instability result for the case $|m|\in[0, m_{\mm{C}})$. The instability result
presents that a horizontal magnetic field can not inhibit the RT instability,
if it's strength is too small.


title:
Impulse null approximate controllability for heat equation with dynamic
  boundary conditions

abstract:
  The main purpose of this article is to prove a logarithmic convexity estimate
for the solution of a linear heat equation subject to dynamic boundary
conditions in a bounded convex domain. As an application, we prove the
impulsive null approximate controllability for an impulsive heat equation with
dynamic boundary conditions.


title: On the Benefits of Large Learning Rates for Kernel Methods

abstract:
  This paper studies an intriguing phenomenon related to the good
generalization performance of estimators obtained by using large learning rates
within gradient descent algorithms. First observed in the deep learning
literature, we show that a phenomenon can be precisely characterized in the
context of kernel methods, even though the resulting optimization problem is
convex. Specifically, we consider the minimization of a quadratic objective in
a separable Hilbert space, and show that with early stopping, the choice of
learning rate influences the spectral decomposition of the obtained solution on
the Hessian's eigenvectors. This extends an intuition described by Nakkiran
(2020) on a two-dimensional toy problem to realistic learning scenarios such as
kernel ridge regression. While large learning rates may be proven beneficial as
soon as there is a mismatch between the train and test objectives, we further
explain why it already occurs in classification tasks without assuming any
particular mismatch between train and test data distributions.


title:
Missing Value Estimation using Clustering and Deep Learning within
  Multiple Imputation Framework

abstract:
  Missing values in tabular data restrict the use and performance of machine
learning, requiring the imputation of missing values. The most popular
imputation algorithm is arguably multiple imputations using chains of equations
(MICE), which estimates missing values from linear conditioning on observed
values. This paper proposes methods to improve both the imputation accuracy of
MICE and the classification accuracy of imputed data by replacing MICE's linear
conditioning with ensemble learning and deep neural networks (DNN). The
imputation accuracy is further improved by characterizing individual samples
with cluster labels (CISCL) obtained from the training data. Our extensive
analyses involving six tabular data sets, up to 80% missingness, and three
missingness types (missing completely at random, missing at random, missing not
at random) reveal that ensemble or deep learning within MICE is superior to the
baseline MICE (b-MICE), both of which are consistently outperformed by CISCL.
Results show that CISCL plus b-MICE outperforms b-MICE for all percentages and
types of missingness. Our proposed DNN based MICE and gradient boosting MICE
plus CISCL (GB-MICE-CISCL) outperform seven other baseline imputation
algorithms in most experimental cases. The classification accuracy on the data
imputed by GB-MICE is improved by proposed GB-MICE-CISCL imputed data across
all missingness percentages. Results also reveal a shortcoming of the MICE
framework at high missingness (>50%) and when the missing type is not random.
This paper provides a generalized approach to identifying the best imputation
model for a data set with a missingness percentage and type.


title:
Distributed approach for the indoor deployment of wireless connected
  objects by the hybridization of the Voronoi diagram and the Genetic Algorithm

abstract:
  IoT data collection networks have recently become one of the important
research areas due to their fundamental role and wide application in many
domains. The establishment of networks of objects is based essentially on the
deployment of connected objects to process the collected data and transmit them
to the various locations. Subsequently, a large number of nodes must be
adequately deployed to achieve complete coverage. This manuscript introduces a
distributed approach, which combines the Voronoi Diagram and the Genetic
algorithm(VD-GA), to maximize the coverage of a region of interest. The Voronoi
diagram is used to divide region into cells and generate initial solutions that
present the positions of the deployed IoT objects. Then, a genetic algorithm is
executed in parallel in several nodes to improve these positions. The developed
VD-GA approach was evaluated on an experimental environment by prototyping on a
real testbed utilizing M5StickC nodes equipped with ESP32 processor. The
experiments show that the distributed approach provided better degree of
coverage, RSSI, lifetime and number of neighboring objects than those given by
the original algorithms in terms of the suggested distributed Genetic-Voronoi
algorithm outperforms the centralized one in terms of speed of computing. .


title: On the Robustness of CountSketch to Adaptive Inputs

abstract:
  CountSketch is a popular dimensionality reduction technique that maps vectors
to a lower dimension using randomized linear measurements. The sketch supports
recovering $\ell_2$-heavy hitters of a vector (entries with $v[i]^2 \geq
\frac{1}{k}\|\boldsymbol{v}\|^2_2$). We study the robustness of the sketch in
adaptive settings where input vectors may depend on the output from prior
inputs. Adaptive settings arise in processes with feedback or with adversarial
attacks. We show that the classic estimator is not robust, and can be attacked
with a number of queries of the order of the sketch size. We propose a robust
estimator (for a slightly modified sketch) that allows for quadratic number of
queries in the sketch size, which is an improvement factor of $\sqrt{k}$ (for
$k$ heavy hitters) over prior work.


title: The Engel graph of a finite group

abstract:
  For a finite group $G,$ we investigate the direct graph $\Gamma(G),$ whose
vertices are the non-hypercentral elements of $G$ and where there is an edge
$x\mapsto y$ if and only if $[x,_ny]=1$ for some $n \in \mathbb N.$ We prove
that $\Gamma(G)$ is always weakly connected and is strongly connected if
$G/Z_{\infty}(G)$ is neither Frobenius nor almost simple.


title:
Non-existence of nonnegative separate variable solutions to a porous
  medium equation with spatially dependent nonlinear source

abstract:
  The non-existence of nonnegative compactly supported classical solutions to
$$- \Delta V(x) - |x|^\sigma V(x) + \frac{V^{1/m}(x)}{m-1} = 0, \qquad
x\in\mathbb{R}^N,$$ with $m>1$, $\sigma>0$, and $N\ge 1$, is proven for
$\sigma$ sufficiently large. More precisely, in dimension $N\geq4$, the optimal
lower bound on $\sigma$ for non-existence is identified, namely
$$\sigma\geq\sigma_c := \frac{2(m-1)(N-1)}{3m+1},$$ while, in dimensions
$N\in\{1,2,3\}$, the lower bound derived on $\sigma$ improves previous ones
already established in the literature. A by-product of this result is the
non-existence of nonnegative compactly supported separate variable solutions to
a porous equation medium equation with spatially dependent superlinear source.


title:
Automated Creation and Human-assisted Curation of Computable Scientific
  Models from Code and Text

abstract:
  Scientific models hold the key to better understanding and predicting the
behavior of complex systems. The most comprehensive manifestation of a
scientific model, including crucial assumptions and parameters that underpin
its usability, is usually embedded in associated source code and documentation,
which may employ a variety of (potentially outdated) programming practices and
languages. Domain experts cannot gain a complete understanding of the
implementation of a scientific model if they are not familiar with the code.
Furthermore, rapid research and development iterations make it challenging to
keep up with constantly evolving scientific model codebases. To address these
challenges, we develop a system for the automated creation and human-assisted
curation of a knowledge graph of computable scientific models that analyzes a
model's code in the context of any associated inline comments and external
documentation. Our system uses knowledge-driven as well as data-driven
approaches to identify and extract relevant concepts from code and equations
from textual documents to semantically annotate models using domain
terminology. These models are converted into executable Python functions and
then can further be composed into complex workflows to answer different forms
of domain-driven questions. We present experimental results obtained using a
dataset of code and associated text derived from NASA's Hypersonic Aerodynamics
website.


title: Some remarks on sharply 2-transitive groups and near-domains

abstract:
  A sharply 2-transitive permutation group of characteristic 0 whose point
stabiliser has an abelian subgroup of finite index splits. More generally, a
near-domain of characteristic 0 with a multiplicative subgroup of finite index
avoiding all multipliers $d_{a,b}$ must be a near-field. In particular this
answers question 12.48 b) of the Kourovka Notebook in characteristic 0.


title:
The bound of Lyapunov exponent in Einstein-Maxwell-Dilaton-Axion black
  holes

abstract:
  In this paper, we investigate the influence of the angular momentum of a
charged particle on the bound for the Lyapunov exponent of the chaos in
non-extreme and extreme Einstein-Maxwell-Dilaton-Axion black holes. The
derivation of the exponent is relied on the effective potential of the
particle. We find that when the angular momentum takes the specific values, the
bound is violated at the certain distances from the event horizons. This
violation always exists when the rotation parameter of the black holes is large
enough and the rotation direction of the particle is opposite to that of the
black holes. In the near-horizon regions, the violation of the bound is
dependent on the rotation directions of the extreme black hole and particle,
and independent on the value of the angular momentum of the particle in the
non-extreme black hole.


title: Entanglement between a muon spin and $I>\frac{1}{2}$ nuclear spins

abstract:
  We report on the first example of quantum coherence between the spins of
muons and quadrupolar nuclei. We observe this effect in vanadium intermetallic
compounds which adopt the A15 crystal structure, and whose members include all
technologically dominant superconductors. The entangled states are extremely
sensitive to the local structural and electronic environments through the
electric field gradient at the quadrupolar nuclei. This case-study demonstrates
that positive muons can be used as a quantum sensing tool to probe also
structural and charge related phenomena in materials, even in the absence of
magnetic order.


title: Periodic geodesics for contact sub-Riemannian 3D manifolds

abstract:
  The goal of this paper is to study periodic geodesics for sub-Riemannian
metrics on a contact 3D-manifold.We develop two rather independent subjects:1)
The existence of closed geodesics spiraling around periodic Reeb orbits for a
generic metric.2) The precise study of the periodic geodesics for a right
invariant metric on a quotient of SL2(R)


title: Subgradient sampling for nonsmooth nonconvex minimization

abstract:
  Risk minimization for nonsmooth nonconvex problems naturally leads to
firstorder sampling or, by an abuse of terminology, to stochastic subgradient
descent. We establish the convergence of this method in the path-differentiable
case, and describe more precise results under additional geometric assumptions.
We recover and improve results from Ermoliev-Norkin by using a different
approach: conservative calculus and the ODE method. In the definable case, we
show that first-order subgradient sampling avoids artificial critical point
with probability one and applies moreover to a large range of risk minimization
problems in deep learning, based on the backpropagation oracle. As byproducts
of our approach, we obtain several results on integration of independent
interest, such as an interchange result for conservative derivatives and
integrals, or the definability of set-valued parameterized integrals.


title:
Influence of hydrogen vacancy interactions on natural and artificial
  ageing of an AlMgSi alloy

abstract:
  The influence of hydrogen on the structural evolutions of an Al-Mg-Si alloy
during natural and artificial ageing was investigated experimentally. The aim
of the study was a better understanding of interactions between hydrogen and
crystalline defects and especially vacancies. Experimental data demonstrate
that during natural ageing in hydrogen environment, the hardening response is
delayed. This is attributed to a slower recovery of excess vacancies linked to
a lower mobility. To confirm and quantify the influence of hydrogen on the
vacancy migration energy, artificial ageing was carried out in conditions where
the vacancy concentration is constant. Hence, long-time annealing treatments
were carried out to investigate the influence of hydrogen on the coarsening of
rod-shaped precipitates. Using transmission electron microscopy and atom probe
tomography, it was demonstrated that the precipitate volume fraction and
composition are unchanged under H2 atmosphere but the coarsening kinetic is
significantly reduced. This leads to a delayed softening, in good agreement
with theoretical estimates. Thus, even a low concentration of hydrogen in solid
solution significantly affects the mobility of alloying elements in the
aluminium matrix. This is the result of hydrogen-vacancy interactions that lead
to an increase of the vacancy migration energy. Based on classical coarsening
theories, it was possible to demonstrate that this increase is of about 5% for
a concentration of hydrogen close to the vacancy concentration.


title: Solving The Travelling Salesmen Problem using HNN and HNN-SA algorithms

abstract:
  In this case study, the renowned Travelling Salesmen problem has been
studied. Travelling Salesman problem is a most demanding computational problem
in Computer Science. The Travelling Salesmen problem has been solved by two
different ways using Hopfield Network. The main theory of the problem is to
find distance and connectedness between nodes in a graph having edges between
the nodes. The basic algorithm used for this problem is Djikstra's Algorithm.
But till now , a number of such algorithms have evolved. Among them(some other
algorithms) , are distinct and have been proved to solve the travelling
salesmen problem by graph theory.


title:
A Blockchain Cloud Computing Middleware for Academic Manuscript
  Submission

abstract:
  One of the most important tasks in scientific publishing is the articles'
evaluation via the editorial board and the reviewers' community. Additionally,
in scientific publishing great concern exists regarding the peer-review process
and how it can be further optimised to decrease the time from submission to the
first decision, as well as increase the objectivity of the reviewers' remarks
ensuring that no bias or human error exists in the reviewing process. In order
to address this issue, our article suggests a novice cloud framework for
manuscript submission based on blockchain technology that further enhances the
anonymity between authors and reviewers alike. Our method covers the whole
spectrum of current submission systems capabilities, but it also provides a
decentralised solution using open-source tools such as Java Spring that enhance
the anonymity of the reviewing process.


title: Reduction and reconstruction of multisymplectic Lie systems

abstract:
  A Lie system is a non-autonomous system of first-order ordinary differential
equations describing the integral curves of a non-autonomous vector field
taking values in a finite-dimensional real Lie algebra of vector fields, a
so-called Vessiot--Guldberg Lie algebra. In this work, multisymplectic
structures are applied to the study of the reduction of Lie systems through
their Lie symmetries. By using a momentum map, we perform a reduction and
reconstruction procedure of multisymplectic Lie systems, which allows us to
solve the original problem by analysing several simpler multisymplectic Lie
systems. Conversely, we study how reduced multisymplectic Lie systems allow us
to retrieve the form of the multisymplectic Lie system that gave rise to them.
Our results are illustrated with examples occurring in physics, mathematics,
and control theory.


title:
Predictive simulation of single-leg landing scenarios for ACL injury
  risk factors evaluation

abstract:
  The Anterior Cruciate Ligament rupture is a very common knee injury during
sport activities. Landing after a jump is one of the most prominent human body
movement scenarios that can lead to such an injury. The landing - related
Anterior Cruciate Ligament (ACL) injury risk factors have been in the spotlight
of research interest. Computational modeling and simulation allows for studying
musculoskeletal systems. Specifically, predictive simulation approaches offer
researchers the opportunity to predict and study new biological motions without
the demands of acquiring experimental data. In this thesis, we present a
pipeline that aims to predict and identify key parameters of interest that are
related to ACL injury during single - leg landings. We examined the following
conditions of single - leg landing: a) initial landing height, b) hip internal
and external rotation, c) lumbar forward - backward leaning, d) lumbar medial -
lateral bending, e) lumbar internal - external rotation, f) muscle forces
permutations and g) effort goal weight. Identified on related research studies,
we evaluated the following risk factors: vertical Ground Reaction Force, knee
joint Anterior force, knee joint Abduction moment, and Quadricep / Hamstring
force ratio. Our study clearly demonstrated that ACL injury is a rather
complicated mechanism with many associated risk factors which are evidently
correlated. Nevertheless, our results were mostly in agreement with other
research studies regarding the ACL risk factors. Despite the limitations
regarding the adopted modeling assumptions, our pipeline clearly showcased
promising potential of predictive simulations to evaluate different aspects of
complicated phenomena, such as the ACL injury.


title: A Minimal Deductive System for RDFS with Negative Statements

abstract:
  The triple language RDFS is designed to represent and reason with
\emph{positive} statements only (e.g."antipyretics are drugs").
  In this paper we show how to extend RDFS to express and reason with various
forms of negative statements under the Open World Assumption (OWA). To do so,
we start from $\rho df$, a minimal, but significant RDFS fragment that covers
all essential features of RDFS, and then extend it to $\rho df_\bot^\neg$,
allowing express also statements such as "radio therapies are non drug
treatments", "Ebola has no treatment", or "opioids and antipyretics are
disjoint classes". The main and, to the best of our knowledge, unique features
of our proposal are: (i) $\rho df_\bot^\neg$ remains syntactically a triple
language by extending $\rho df$ with new symbols with specific semantics and
there is no need to revert to the reification method to represent negative
triples; (ii) the logic is defined in such a way that any RDFS reasoner/store
may handle the new predicates as ordinary terms if it does not want to take
account of the extra capabilities; (iii) despite negated statements, every
$\rho df_\bot^\neg$ knowledge base is satisfiable; (iv) the $\rho df_\bot^\neg$
entailment decision procedure is obtained from $\rho df$ via additional
inference rules favouring a potential implementation; and (v) deciding
entailment in $\rho df_\bot^\neg$ ranges from P to NP.


title: GENOME: A GENeric methodology for Ontological Modelling of Epics

abstract:
  Ontological knowledge modelling of epics, though being an established
research arena backed by concrete multilingual and multicultural works, still
suffer from two key shortcomings. Firstly, all epic ontological models
developed till date have been designed following ad-hoc methodologies, most
often, combining existing general purpose ontology development methodologies.
Secondly, none of the ad-hoc methodologies consider the potential reuse of
existing epic ontological models for enrichment, if available. The paper
presents, as a unified solution to the above shortcomings, the design and
development of GENOME - the first dedicated methodology for iterative
ontological modelling of epics, potentially extensible to works in different
research arenas of digital humanities in general. GENOME is grounded in
transdisciplinary foundations of canonical norms for epics, knowledge modelling
best practices, application satisfiability norms and cognitive generative
questions. It is also the first methodology (in epic modelling but also in
general) to be flexible enough to integrate, in practice, the options of
knowledge modelling via reuse or from scratch. The feasibility of GENOME is
validated via a first brief implementation of ontological modelling of the
Indian epic - Mahabharata by reusing an existing ontology. The preliminary
results are promising, with the GENOME-produced model being both ontologically
thorough and performance-wise competent


title:
Discrete unified gas kinetic scheme for the conservative Allen-Cahn
  equation

abstract:
  In this paper, the discrete unified gas kinetic scheme (DUGKS) with an
improved microflux across the cell interface for the conservative Allen-Cahn
equation (CACE) is proposed. In the context of DUGKS, the recovered kinetic
equation from the flux evaluation with linear reconstruction in the previous
DUGKS is analyzed. It is found that the calculated microflux across the cell
interface is only the solution to the target kinetic equation with first order
accuracy, which can result in an inaccurate CACE since the force term is
involved or the first moment of the collision model has no conservation
property. To correctly recover the kinetic equation up to the second order
accuracy, the value of the distribution function that will propagate along the
characteristic line with ending point at the cell interface is appropriated by
the parabolic reconstruction instead of the linear reconstruction. To validate
the accuracy of the present DUGKS for the CACE, several benchmark problems,
including the diagonal translation of a circular interface, the rotation of a
Zaleska disk and the deformation of a circular interface, have been simulated.
  Numerical results show that the present DUGKS scheme is able to capture the
interface with improved accuracy when compared with the previous DUGKS.


title: Machine learning-enabled high-entropy alloy discovery

abstract:
  High-entropy alloys are solid solutions of multiple principal elements,
capable of reaching composition and feature regimes inaccessible for dilute
materials. Discovering those with valuable properties, however, relies on
serendipity, as thermodynamic alloy design rules alone often fail in
high-dimensional composition spaces. Here, we propose an active-learning
strategy to accelerate the design of novel high-entropy Invar alloys in a
practically infinite compositional space, based on very sparse data. Our
approach works as a closed-loop, integrating machine learning with
density-functional theory, thermodynamic calculations, and experiments. After
processing and characterizing 17 new alloys (out of millions of possible
compositions), we identified 2 high-entropy Invar alloys with extremely low
thermal expansion coefficients around 2*10-6 K-1 at 300 K. Our study thus opens
a new pathway for the fast and automated discovery of high-entropy alloys with
optimal thermal, magnetic and electrical properties.


title:
Waste bins location problem: A review of recent advances in the storage
  stage of the Municipal Solid Waste reverse logistic chain

abstract:
  Municipal Solid Waste systems have important economic, social, and
environmental impacts for society. Within the diverse stages of the Municipal
Solid Waste reverse logistic chain, the waste bin location problem consists in
properly locating bins in the corresponding urban area to store waste produced
by the citizens. This stage has a large impact in the overall efficiency of the
whole system. Thus, several researchers have addressed the location problem
considering different optimization criteria and approaches. This article
presents a comprehensive review of recent advances on the Waste Bins Location
Problem, with the main goal of serving as a reference point for decision-makers
in this area. The main findings indicate that several optimization criteria and
resolution approaches have been applied, but few proposals have simultaneously
optimized bins location and waste collection, or considered uncertainty of the
model parameters and integrated approaches.


title:
Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial
  Training

abstract:
  Capsule networks (CapsNets) are new neural networks that classify images
based on the spatial relationships of features. By analyzing the pose of
features and their relative positions, it is more capable to recognize images
after affine transformation. The stacked capsule autoencoder (SCAE) is a
state-of-the-art CapsNet, and achieved unsupervised classification of CapsNets
for the first time. However, the security vulnerabilities and the robustness of
the SCAE has rarely been explored. In this paper, we propose an evasion attack
against SCAE, where the attacker can generate adversarial perturbations based
on reducing the contribution of the object capsules in SCAE related to the
original category of the image. The adversarial perturbations are then applied
to the original images, and the perturbed images will be misclassified.
Furthermore, we propose a defense method called Hybrid Adversarial Training
(HAT) against such evasion attacks. HAT makes use of adversarial training and
adversarial distillation to achieve better robustness and stability. We
evaluate the defense method and the experimental results show that the refined
SCAE model can achieve 82.14% classification accuracy under evasion attack. The
source code is available at https://github.com/FrostbiteXSW/SCAE_Defense.


title: Data-to-text Generation with Variational Sequential Planning

abstract:
  We consider the task of data-to-text generation, which aims to create textual
output from non-linguistic input. We focus on generating long-form text, i.e.,
documents with multiple paragraphs, and propose a neural model enhanced with a
planning component responsible for organizing high-level information in a
coherent and meaningful way. We infer latent plans sequentially with a
structured variational model, while interleaving the steps of planning and
generation. Text is generated by conditioning on previous variational decisions
and previously generated text. Experiments on two data-to-text benchmarks
(RotoWire and MLB) show that our model outperforms strong baselines and is
sample efficient in the face of limited training data (e.g., a few hundred
instances).


title:
Fast off-the-grid sparse recovery with over-parametrized projected
  gradient descent

abstract:
  We consider the problem of recovering off-the-grid spikes from Fourier
measurements. Successful methods such as sliding Frank-Wolfe and continuous
orthogonal matching pursuit (OMP) iteratively add spikes to the solution then
perform a costly (when the number of spikes is large) descent on all parameters
at each iteration. In 2D, it was shown that performing a projected gradient
descent (PGD) from a gridded over-parametrized initialization was faster than
continuous orthogonal matching pursuit. In this paper, we propose an
off-the-grid over-parametrized initialization of the PGD based on OMP that
permits to fully avoid grids and gives faster results in 3D.


title: Logical Fallacy Detection

abstract:
  Reasoning is central to human intelligence. However, fallacious arguments are
common, and some exacerbate problems such as spreading misinformation about
climate change. In this paper, we propose the task of logical fallacy
detection, and provide a new dataset (Logic) of logical fallacies generally
found in text, together with an additional challenge set for detecting logical
fallacies in climate change claims (LogicClimate). Detecting logical fallacies
is a hard problem as the model must understand the underlying logical structure
of the argument. We find that existing pretrained large language models perform
poorly on this task. In contrast, we show that a simple structure-aware
classifier outperforms the best language model by 5.46% on Logic and 3.86% on
LogicClimate. We encourage future work to explore this task as (a) it can serve
as a new reasoning challenge for language models, and (b) it can have potential
applications in tackling the spread of misinformation.


title:
On the complexity of finding well-balanced orientations with upper
  bounds on the out-degrees

abstract:
  We show that the problem of deciding whether a given graph $G$ has a
well-balanced orientation $\vec{G}$ such that $d_{\vec{G}}^+(v)\leq \ell(v)$
for all $v \in V(G)$ for a given function $\ell:V(G)\rightarrow
\mathbb{Z}_{\geq 0}$ is NP-complete. We also prove a similar result for
best-balanced orientations. This improves a result of Bern\' ath, Iwata, Kir\'
aly, Kir\'aly and Szigeti and answers a question of Frank.


title:
A comment on "Robust stabilization of delayed neural fields with partial
  measurement and actuation" [Automatica 83 (2017) 262-274]

abstract:
  In [2], the authors study the stabilization of a class of delayed neural
fields through output proportional feedback. They provide a condition under
which the resulting closed-loop system is input-to-state stable (ISS). However,
a key assumption in that paper is the existence of an equilibrium for the
closed-loop system. We show here that such an equilibrium does exist if the
activation functions are bounded.


title:
Global Correlation and Local Information Flows in Controllable
  Non-Markovian Open Quantum Dynamics

abstract:
  In a fully-controllable experiment platform for studying non-Markovian open
quantum dynamics, we show that the non-Markovianity could be investigated from
the global and local aspects. By mixing random unitary dynamics, we demonstrate
non-Markovian and Markovian open quantum dynamics. From the global point of
view, by tuning the base frequency we demonstrate the transition from the
Markovianity to the non-Markovianity as measured by the quantum mutual
information (QMI). In a Markovian open quantum process, the QMI decays
monotonically, while it may rise temporarily in a non-Markovian process.
However, under some circumstances, it is not sufficient to globally investigate
the non-Markovianity of the open quantum dynamics. As an essential supplement,
we further utilize the quantum Fisher information (QFI) flow to locally
characterize the non-Markovianity in different channels. We demonstrate that
the QMI in combination with the QFI flow are capable of measuring the
non-Markovianity for a multi-channel open quantum dynamics.

categories: quant-ph cond-mat.stat-mech physics.chem-ph physics.optics


title: The double Fock space of type B

abstract:
  In this article we introduce the notion of a double Fock space of type B. We
will show that this new construction is compatible with combinatorics of
counting positive and negative inversions on a hyperoctahedral group.


title: A System Level Approach to Regret Optimal Control

abstract:
  We present an optimisation-based method for synthesising a dynamic regret
optimal controller for linear systems with potentially adversarial disturbances
and known or adversarial initial conditions. The dynamic regret is defined as
the difference between the true incurred cost of the system and the cost which
could have optimally been achieved under any input sequence having full
knowledge of all future disturbances for a given disturbance energy. This
problem formulation can be seen as an alternative to classical $\mathcal{H}_2$-
or $\mathcal{H}_\infty$-control. The proposed controller synthesis is based on
the system level parametrisation, which allows reformulating the dynamic regret
problem as a semi-definite problem. This yields a new framework that allows to
consider structured dynamic regret problems, which have not yet been considered
in the literature. For known pointwise ellipsoidal bounds on the disturbance,
we show that the dynamic regret bound can be improved compared to using only a
bounded energy assumption and that the optimal dynamic regret bound differs by
at most a factor of $\frac{2}{\pi}$ from the computed solution. Furthermore,
the proposed framework allows guaranteeing state and input constraint
satisfaction. Finally, we present a numerical mass-spring-damper example.


title:
A Note on "Optimum Sets of Interference-Free Sequences With Zero
  Autocorrelation Zone"

abstract:
  In this paper, a simple construction of interference-free zero correlation
zone (IF-ZCZ) sequence sets is proposed by well designed finite Zak transform
lattice tessellation. Each set is characterized by the period of sequences
$KM$, the set size $K$ and the length of zero correlation zone $M-1$, which is
optimal with respect to the Tang-Fan-Matsufuji bound. Secondly, the
transformations that keep the properties of the optimal IF-ZCZ sequence set
unchanged are given, and the equivalent relation of the optimal IF-ZCZ sequence
set is defined based on these transformations. Then, it is proved that the
general construction of the optimal IF-ZCZ sequence set proposed by Popovic is
equivalent to the simple construction of the optimal IF-ZCZ sequence set, which
indicates that the generation of the optimal IF-ZCZ sequence set can be
simplified. Moreover, it is pointed out that the alphabet size for the special
case of the simple construction of the optimal IF-ZCZ sequence set can be a
factor of the period. Finally, both the simple construction of the optimal
IF-ZCZ sequence set and its special case have sparse and highly structured Zak
spectra, which can greatly reduce the computational complexity of implementing
matched filter banks.


title: Simple mechanism rules the dynamics of volleyball

abstract:
  In volleyball games, we define a rally as the succession of events observed
since the ball is served until one of the two teams on the court scores the
point. In this process, athletes evolve in response to physical and information
constraints, spanning several spatiotemporal scales and interplaying
co-adaptively with the environment. Aiming to study the emergence of complexity
in this system, we carried out a study focused on three steps: data collection,
data analysis, and modeling. First, we collected data from 20 high-level
professional volleyball games. Then we conducted a data-driven analysis from
where we identified fundamental insights that we used to define a parsimonious
stochastic model for the dynamics of the game. On these bases, we show that it
is possible to give a closed-form expression for the probability that the
players perform n hits in a rally using only two stochastic variables. Our
results fully agree with the empirical observations and represent a new advance
in the comprehension of team-sports competition complexity and dynamics.


title:
On the Orbital Velocity of Isolated Galaxy Pairs: II Accurate MOND
  Predictions

abstract:
  Examining a catalogue of isolated galaxy pairs, a preferred orbital
intervelocity of ~150 km/s was recently reported. This discovery is difficult
to reconcile with the expectations from Newtonian numerical simulations of
cosmological structure formations. In a previous paper we have shown that a
preferred intervelocity for galaxy pairs is expected in Modified Newtonian
Dynamics (MOND). Here a detailed analysis of the MOND predictions is presented,
showing that a remarkable agreement with the observations can be achieved for a
mass to light ratio M/L~1 in solar units. This agrees with the expectations for
a typical stellar population, without requiring non-baryonic dark matter for
these systems.


title: Place-level urban-rural indices for the United States from 1930 to 2018

abstract:
  Rural-urban classifications are essential for analyzing geographic,
demographic, environmental, and social processes across the rural-urban
continuum. Most existing classifications are, however, only available at
relatively aggregated spatial scales, such as at the county scale in the United
States. The absence of rurality or urbanness measures at high spatial
resolution poses significant problems when the process of interest is highly
localized, as with the incorporation of rural towns and villages into
encroaching metropolitan areas. Moreover, existing rural-urban classifications
are often inconsistent over time, or require complex, multi-source input data
(e.g., remote sensing observations or road network data), thus, prohibiting the
longitudinal analysis of rural-urban dynamics. Here, we develop a set of
distance- and spatial-network-based methods for consistently estimating the
remoteness and rurality of places at fine spatial resolution, over long periods
of time. We demonstrate the utility of our approach by constructing indices of
urbanness for 30,000 places in the United States from 1930 to 2018 and further
test the plausibility of our results against a variety of evaluation datasets.
We call these indices the place-level urban-rural index (PLURAL) and make the
resulting datasets publicly available (https://doi.org/10.3886/E162941) so that
other researchers can conduct long-term, fine-grained analyses of urban and
rural change. In addition, due to the simplistic nature of the input data,
these methods can be generalized to other time periods or regions of the world,
particularly to data-scarce environments.


title:
Maxwell's equations for a mechano-driven, shape-deformable,
  charged-media system, slowly moving at an arbitrary velocity field v(r,t)

abstract:
  The differential form of the Maxwell's equations was first derived based on
an assumption that the media are stationary, which is the foundation for
describing the electro-magnetic coupling behavior of a system. For a general
case in which the medium has a time-dependent volume, shape and boundary and
may move at an arbitrary velocity field v(r,t) and along a general trajectory,
we derived the Maxwell's equations for a mechano-driven slow-moving media
system directly starting from their integral forms, which should be accurate
enough for describing the coupling among mechano-electro-magnetic interactions
of a general system in practice although it may not Lorentz covarance. Our key
point is from the integral expressions of the four physics laws by describing
all of the fields, the space and the time in the frame where the observation is
done. The equations should be applicable to not only moving charged solid and
soft media, but also charged fluid/liquid media, e.g., fluid electrodynamics.
General strategies for solving the Maxwell's equations for mechano-driven
slowing moving medium are presented using the perturbation theory both in time
and frequency spaces. Finally, approaches for the electrodynamics of moving
media are compared, and related discussions are given about a few interesting
questions.


title:
FinFET and Nanowire SRAM Radiation Hardness Studies using Ab initio-TCAD
  Simulation Framework

abstract:
  In this paper, we study the vulnerability of 5nm node FinFET and nanowire
(20nm gate length) and their corresponding SRAM under radiation. Ab initio
tools, SRIM, PHITS, and GEANT4, are used to find the Linear Energy Transfer
(LET) of neutron and alpha particles in Silicon and Silicon-Germanium.
Technology Computer-Aided Design (TCAD) is then used to find the most
vulnerable incident location and direction in FinFET, nanowire, and their SRAM.
Full 3D TCAD simulation, which allows the study of layout effect in SRAM, is
used. It is found that NW is about 2-3 times more robust than FinFET in terms
of flipping energy. Based on the simulation in the ab initio-TCAD framework, it
is projected that there is a possibility to design an SRAM using NW that is
immune to alpha-particle. It is also expected that SRAM can be optimized for
more robust radiation hardness if Design Technology Co-Optimization (DTCO) is
taken into consideration.


title:
Thermal spreading resistance of GaN HEMTs with heat source heating
  studied by hybrid Monte Carlo-diffusion simulations

abstract:
  Exact assessment of thermal spreading resistance is of great importance to
the thermal management of electronic devices, especially when completely
considering the heat conduction process from the nanoscale heat source to the
macroscopic scale heat sink. The existing simulation methods are either based
on convectional Fourier's law or limited to small system sizes, making it
difficult to accurately and efficiently study the cross-scale heat transfer. In
this paper, a hybrid phonon Monte Carlo-diffusion method that couples phonon
Monte Carlo (MC) method with Fourier's law by dividing the computational domain
is adopted to analyze thermal spreading resistance in ballistic-diffusive
regime. Compared with phonon MC simulation, the junction temperature of the
hybrid method has the same precision, while the time costs could be reduced up
to 2 orders of magnitude at most. Furthermore, the simulation results indicate
that the heating scheme has a remarkable impact on phonon transport. The
thermal resistance of the heat source (HS) scheme can be larger than that of
the heat flux (HF) scheme, which is opposite from the prediction of Fourier's
law. In the HS scheme, the enhanced phonon-boundary scattering counteracts the
broadening of the heat source, leading to a stronger ballistic effect as the
heat source thickness decreases. The conclusion is verified by a
one-dimensional thermal resistance model. This work has opened up an
opportunity for the fast and extensive thermal modeling of cross-scale heat
transfer in electronic devices and highlighted the influence of heating
schemes.


title: Conceptual Mathematics via Literate Programming

abstract:
  Recent developments in computer programming and in mathematics suggest that
there is a strong case for a new way of introducing programming to enhance the
learning of school mathematics. The article describes a collaboration of
mathematics and computer science teachers to solve the Josephus problem. We
demonstrate how a programming approach based on both types and functions can
make a vastly improved contribution to learning mathematics than the less
successful use of conventional computer programming in Scratch.


title: Path-Aware Graph Attention for HD Maps in Motion Prediction

abstract:
  The success of motion prediction for autonomous driving relies on integration
of information from the HD maps. As maps are naturally graph-structured,
investigation on graph neural networks (GNNs) for encoding HD maps is
burgeoning in recent years. However, unlike many other applications where GNNs
have been straightforwardly deployed, HD maps are heterogeneous graphs where
vertices (lanes) are connected by edges (lane-lane interaction relationships)
of various nature, and most graph-based models are not designed to understand
the variety of edge types which provide crucial cues for predicting how the
agents would travel the lanes. To overcome this challenge, we propose
Path-Aware Graph Attention, a novel attention architecture that infers the
attention between two vertices by parsing the sequence of edges forming the
paths that connect them. Our analysis illustrates how the proposed attention
mechanism can facilitate learning in a didactic problem where existing graph
networks like GCN struggle. By improving map encoding, the proposed model
surpasses previous state of the art on the Argoverse Motion Forecasting
dataset, and won the first place in the 2021 Argoverse Motion Forecasting
Competition.


title:
A spectral-neighbour representation for vector fields: machine-learning
  potentials including spin

abstract:
  We introduce a translational and rotational invariant local representation
for vector fields, which can be employed in the construction of
machine-learning energy models of solids and molecules. This allows us to
describe, on the same footing, the energy fluctuations due to the atomic
motion, the longitudinal and transverse excitations of the vector field, and
their mutual interplay. The formalism can then be applied to physical systems
where the total energy is determined by a vector density, as in the case of
magnetism. Our representation is constructed over the power spectrum of the
combined angular momentum describing the local atomic positions and the vector
field, and can be used in conjunction with different machine-learning schemes
and data taken from accurate ab initio electronic structure theories. We
demonstrate the descriptive power of our representation for a range of
classical spin Hamiltonian and machine-learning algorithms. In particular, we
construct energy models based on both linear Ridge regression, as in
conventional spectral neighbour analysis potentials, and gaussian
approximation. These are both built to represent a Heisenberg-type Hamiltonian
including a longitudinal energy term and spin-lattice coupling.


title: Selection, Ignorability and Challenges With Causal Fairness

abstract:
  In this paper we look at popular fairness methods that use causal
counterfactuals. These methods capture the intuitive notion that a prediction
is fair if it coincides with the prediction that would have been made if
someone's race, gender or religion were counterfactually different. In order to
achieve this, we must have causal models that are able to capture what someone
would be like if we were to counterfactually change these traits. However, we
argue that any model that can do this must lie outside the particularly well
behaved class that is commonly considered in the fairness literature. This is
because in fairness settings, models in this class entail a particularly strong
causal assumption, normally only seen in a randomised controlled trial. We
argue that in general this is unlikely to hold. Furthermore, we show in many
cases it can be explicitly rejected due to the fact that samples are selected
from a wider population. We show this creates difficulties for counterfactual
fairness as well as for the application of more general causal fairness
methods.


title:
Learning the nonlinear dynamics of soft mechanical metamaterials with
  graph networks

abstract:
  The dynamics of soft mechanical metamaterials provides opportunities for many
exciting engineering applications. Previous studies often use discrete systems,
composed of rigid elements and nonlinear springs, to model the nonlinear
dynamic responses of the continuum metamaterials. Yet it remains a challenge to
accurately construct such systems based on the geometry of the building blocks
of the metamaterial. In this work, we propose a machine learning approach to
address this challenge. A metamaterial graph network (MGN) is used to represent
the discrete system, where the nodal features contain the positions and
orientations the rigid elements, and the edge update functions describe the
mechanics of the nonlinear springs. We use Gaussian process regression as the
surrogate model to characterize the elastic energy of the nonlinear springs as
a function of the relative positions and orientations of the connected rigid
elements. The optimal model can be obtained by "learning" from the data
generated via finite element calculation over the corresponding building block
of the continuum metamaterial. Then, we deploy the optimal model to the network
so that the dynamics of the metamaterial at the structural scale can be
studied. We verify the accuracy of our machine learning approach against
several representative numerical examples. In these examples, the proposed
approach can significantly reduce the computational cost when compared to
direct numerical simulation while reaching comparable accuracy. Moreover,
defects and spatial inhomogeneities can be easily incorporated into our
approach, which can be useful for the rational design of soft mechanical
metamaterials.


title: Bounds via spectral radius-preserving row sum expansions

abstract:
  We show a simple method for constructing larger matrices but preserving the
spectral radius. This yields a sufficient criteria for two square matrices of
arbitrary dimension have the same spectral radius, a way to compare spectral
radii of two matrices, and a way to derive new upper and lower bounds on
spectral radius which give the standard row sum bounds as a special case.


title: Towards Unsupervised Domain Adaptation via Domain-Transformer

abstract:
  As a vital problem in pattern analysis and machine intelligence, Unsupervised
Domain Adaptation (UDA) studies how to transfer an effective feature learner
from a labeled source domain to an unlabeled target domain. Plenty of methods
based on Convolutional Neural Networks (CNNs) have achieved promising results
in the past decades. Inspired by the success of Transformers, some methods
attempt to tackle UDA problem by adopting pure transformer architectures, and
interpret the models by applying the long-range dependency strategy at image
patch-level. However, the algorithmic complexity is high and the
interpretability seems weak. In this paper, we propose the Domain-Transformer
(DoT) for UDA, which integrates the CNN-backbones and the core attention
mechanism of Transformers from a new perspective. Specifically, a plug-and-play
domain-level attention mechanism is proposed to learn the sample correspondence
between domains. This is significantly different from existing methods which
only capture the local interactions among image patches. Instead of explicitly
modeling the distribution discrepancy from either domain-level or class-level,
DoT learns transferable features by achieving the local semantic consistency
across domains, where the domain-level attention and manifold regularization
are explored. Then, DoT is free of pseudo-labels and explicit domain
discrepancy optimization. Theoretically, DoT is connected with the optimal
transportation algorithm and statistical learning theory. The connection
provides a new insight to understand the core component of Transformers.
Extensive experiments on several benchmark datasets validate the effectiveness
of DoT.


title: Rule-based Evolutionary Bayesian Learning

abstract:
  In our previous work, we introduced the rule-based Bayesian Regression, a
methodology that leverages two concepts: (i) Bayesian inference, for the
general framework and uncertainty quantification and (ii) rule-based systems
for the incorporation of expert knowledge and intuition. The resulting method
creates a penalty equivalent to a common Bayesian prior, but it also includes
information that typically would not be available within a standard Bayesian
context. In this work, we extend the aforementioned methodology with
grammatical evolution, a symbolic genetic programming technique that we utilise
for automating the rules' derivation. Our motivation is that grammatical
evolution can potentially detect patterns from the data with valuable
information, equivalent to that of expert knowledge. We illustrate the use of
the rule-based Evolutionary Bayesian learning technique by applying it to
synthetic as well as real data, and examine the results in terms of point
predictions and associated uncertainty.


title:
Compilation of Hazardous and Benign Material Complex Dielectric
  Constants at Millimeter Wave Frequencies for Security Applications

abstract:
  This paper compiles measured complex dielectric constants of benign and
hazardous materials used for developing algorithms for personnel scanners at
airports at 30 GHz. The materials are grouped into broad classifications of
potential threat by mapping the materials to the complex dielectric constant
plane.


title:
Anomalous bulk-boundary correspondence of topological magnon excitations
  in a spin chain

abstract:
  Bulk-boundary correspondence, connecting bulk topology to robust edge states,
is a generic feature of topological matter. Here, we discover unexpected
sub-edge states and defect edge states of magnon excitations in a dimerized
Heisenberg XXZ chain, which was supposed to be trivial within the topological
band theory. We retrieve the topological origin of these sub-edge states by
redefining a real-space representation of winding number, whose relation with
sub-edge states is termed as anomalous bulk-boundary correspondence.
Remarkably, even for single-magnon excitations, the longitudinal interaction
still play a crucial role in the emergence of anomalous topological edge
states. For multi-magnon excitations, even-magnon bound states are always
topological trivial, but odd-magnon bound states may be topological nontrivial
due to the interplay between the transverse dimerization and the longitudinal
interaction. Our work opens an avenue for exploring topological magnon
excitations in complement with the topological band theory.


title:
Thermodynamically-consistent derivation and computation of twinning and
  fracture in brittle materials by means of phase-field approaches in the
  finite element method

abstract:
  A computational method is proposed for simulating and informing on
controlling failure pattern development in anisotropic brittle solids
experiencing extreme mechanical loading. Constitutive equations are derived by
using thermodynamics in order to establish a fully coupled and transient
fracture mechanics model. A phase-field approach is developed for modeling
twinning, fracture, and fracture-induced twinning in single crystals at
nanometer length-scale. At the nanoscale, this continuum mechanics based
formulation is implemented in a monolithic computational scheme ensuring
necessary accuracy for the following problems: (i) twin evolution in 2D single
crystal magnesium and boron carbide under simple shear deformation; (ii)
crack-induced twinning for single crystal magnesium under pure mode I and mode
II loading; and (iii) study of fracture in homogeneous single crystal boron
carbide under biaxial compressive loading. The results are verified by
steady-state phase-field approach and validated by available experimental data
in the literature. The success of this computational method relies on using two
separate phase-field (order) parameters related to fracture and twinning. A
finite element method-based code is developed within the Python-based
open-source platform FEniCS. We make the code publicly available and the
developed algorithm may be extended for the study of phase transformations
under dynamic loading or thermally-activated mechanisms, where the competition
between various deformation mechanisms is accounted for within the current
comprehensive model approach.


title: Edible Resonators

abstract:
  The development of green, biodegradable electronics that degrade soon after
their function is accomplished is a significant contribution to healthcare
where edible electronics can be used for diagnosis. An important component of
such systems is a resonator. Here we present the design and development of
inductor-capacitor (LC) circuits based on a form of sugar called isomalt. Using
its low roughness and mechanical properties, we demonstrate resonators that use
capacitors, planar inductors, helical inductors and inductors with an edible
core, all fabricated from isomalt.


title: Factors of composite $4n^2 +1$ using Fermat's factorization method

abstract:
  In this article, we factor the composite $4n^2 +1$ using Fermat's
factorization method. Consequently, we characterized all proper factors of
composite $4n^2 +1$ in terms of its form. Furthermore, the composite Fermat's
number is considered in this study.


title:
A Signature-based Algorithm for Computing the Nondegenerate Locus of a
  Polynomial System

abstract:
  Polynomial system solving arises in many application areas to model
non-linear geometric properties. In such settings, polynomial systems may come
with degeneration which the end-user wants to exclude from the solution set.
The nondegenerate locus of a polynomial system is the set of points where the
codimension of the solution set matches the number of equations. Computing the
nondegenerate locus is classically done through ideal-theoretic operations in
commutative algebra such as saturation ideals or equidimensional decompositions
to extract the component of maximal codimension. By exploiting the algebraic
features of signature-based Gr\"obner basis algorithms we design an algorithm
which computes a Gr\"obner basis of the equations describing the closure of the
nondegenerate locus of a polynomial system, without computing first a Gr\"obner
basis for the whole polynomial system.


title:
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge
  Graph Completion

abstract:
  Knowledge graphs store a large number of factual triples while they are still
incomplete, inevitably. The previous knowledge graph completion (KGC) models
predict missing links between entities merely relying on fact-view data,
ignoring the valuable commonsense knowledge. The previous knowledge graph
embedding (KGE) techniques suffer from invalid negative sampling and the
uncertainty of fact-view link prediction, limiting KGC's performance. To
address the above challenges, we propose a novel and scalable Commonsense-Aware
Knowledge Embedding (CAKE) framework to automatically extract commonsense from
factual triples with entity concepts. The generated commonsense augments
effective self-supervision to facilitate both high-quality negative sampling
(NS) and joint commonsense and fact-view link prediction. Experimental results
on the KGC task demonstrate that assembling our framework could enhance the
performance of the original KGE models, and the proposed commonsense-aware NS
module is superior to other NS techniques. Besides, our proposed framework
could be easily adaptive to various KGE models and explain the predicted
results.


title:
Search for Magnetic Monopoles with ten years of the ANTARES neutrino
  telescope

abstract:
  This work presents a new search for magnetic monopoles using data taken with
the ANTARES neutrino telescope over a period of 10 years (January 2008 to
December 2017). Compared to previous ANTARES searches, this analysis uses a
run-by-run simulation strategy, with a larger exposure as well as a new
simulation of magnetic monopoles taking into account the Kasama, Yang and
Goldhaber model for their interaction cross-section with matter. No signal
compatible with the passage of relativistic magnetic monopoles is observed, and
upper limits on the flux of magnetic monopoles with $\beta$ = v/c $\geq$ 0.55,
are presented. For ultra-relativistic magnetic monopoles the flux limit is
$\sim$ 7$\times$$10^{-18}$ $\rm cm^{-2} s^{-1} sr^{-1}$.


title:
A method to reduce the Lehmer measure in a multi-term Machin-like
  formula for $\pi$

abstract:
  Previously we have proposed a new method of transforming quotients into
reciprocals of integers in the Machin-like formulas for $\pi$. As a further
development, here we show how to generate a multi-term Machin-like formula for
$\pi$ with a reduced Lehmer measure. The Mathematica codes validating these
results are presented.


title:
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured,
  Varying-Size Point Cloud Data

abstract:
  Unstructured point clouds with varying sizes are increasingly acquired in a
variety of environments through laser triangulation or Light Detection and
Ranging (LiDAR). Predicting a scalar response based on unstructured point
clouds is a common problem that arises in a wide variety of applications. The
current literature relies on several pre-processing steps such as structured
subsampling and feature extraction to analyze the point cloud data. Those
techniques lead to quantization artifacts and do not consider the relationship
between the regression response and the point cloud during pre-processing.
Therefore, we propose a general and holistic "Bayesian Nonlinear Tensor
Learning and Modeler" (ANTLER) to model the relationship of unstructured,
varying-size point cloud data with a scalar or multivariate response. The
proposed ANTLER simultaneously optimizes a nonlinear tensor dimensionality
reduction and a nonlinear regression model with a 3D point cloud input and a
scalar or multivariate response. ANTLER has the ability to consider the complex
data representation, high-dimensionality,and inconsistent size of the 3D point
cloud data.


title: Bulk-edge correspondence in the trimer Su-Schrieffer-Heeger model

abstract:
  A remarkable feature of the trimer Su-Schrieffer-Heeger (SSH3) model is that
it supports localized edge states. Although Zak's phase remains quantized for
the case of a mirror-symmetric chain, it is known that it fails to take integer
values in the absence of this symmetry and thus it cannot play the role of a
well-defined bulk invariant in the general case. Attempts to establish a
bulk-edge correspondence have been made via Green's functions or through
extensions to a synthetic dimension. Here we propose a simple alternative for
SSH3, utilizing the previously introduced sublattice Zak's phase, which also
remains valid in the absence of mirror symmetry and for non-commensurate
chains. The defined bulk quantity takes integer values, is gauge invariant, and
can be interpreted as the difference of the number of edge states between a
reference and a target Hamiltonian. Our derivation further predicts the exact
corrections for finite open chains, is straightforwadly generalizable, and
invokes a chiral-like symmetry present in this model.


title: Dissipative processes at the acoustic horizon

abstract:
  A transonic fluid flow creates an acoustic hole that is the hydrodynamic
analogue of a gravitational black hole. Acoustic holes emit a detectable
thermal radiation of phonons at a characteristic Hawking temperature. We show
that viscosity arises from the thermal phonon emission associated to the
horizon entropy, shedding light on its geometrical origin and on how viscosity
acts as an effective coupling between background (metric) perturbations and the
acoustic horizon. The celebrated Kovtun, Son and Starinets (KSS) universal
lower bound $\eta /s = 1 / 4 \pi $ of the shear viscosity to entropy density
ratio, readily follows, and is extended to the longitudinal bulk viscosity.


title:
Designing Ultra-Flat Bands in Twisted Bilayer Materials at Large Twist
  Angles without specific degree

abstract:
  Inter-twisted bilayers of two-dimensional (2D) materials can host low-energy
flat bands, which offer opportunity to investigate many intriguing physics
associated with strong electron correlations. In the existing systems,
ultra-flat bands only emerge at very small twist angles less than a few
degrees, which poses challenge for experimental study and practical
applications. Here, we propose a new design principle to achieve low-energy
ultra-flat bands with increased twist angles. The key condition is to have a 2D
semiconducting material with large energy difference of band edges controlled
by stacking. We show that the interlayer interaction leads to defect-like
states under twisting, which forms a flat band in the semiconducting band gap
with dispersion strongly suppressed by the large energy barriers in the moire
superlattice even for large twist angles. We explicitly demonstrate our idea in
bilayer alpha-In2Se3 and bilayer InSe. For bilayer alpha-In2Se3, we show that a
twist angle -13.2 degree is sufficient to achieve the band flatness comparable
to that of twist bilayer graphene at the magic angle -1.1 degree. In addition,
the appearance of ultra-flat bands here is not sensitive to the twist angle as
in bilayer graphene, and it can be further controlled by external gate fields.
Our finding provides a new route to achieve ultra-flat bands other than
reducing the twist angles and paves the way towards engineering such flat bands
in a large family of 2D materials.


title: Unrestricted virtual braids and crystallographic braid groups

abstract:
  We show that the crystallographic braid group $B_n/[P_n,P_n]$ embeds
naturally in the group of unrestricted virtual braids $UVB_n$, we give new
proofs of known results about the torsion elements of $B_n/[P_n,P_n]$, and we
characterise the torsion elements of $UVB_n$.


title: Forecasting US Inflation Using Bayesian Nonparametric Models

abstract:
  The relationship between inflation and predictors such as unemployment is
potentially nonlinear with a strength that varies over time, and prediction
errors error may be subject to large, asymmetric shocks. Inspired by these
concerns, we develop a model for inflation forecasting that is nonparametric
both in the conditional mean and in the error using Gaussian and Dirichlet
processes, respectively. We discuss how both these features may be important in
producing accurate forecasts of inflation. In a forecasting exercise involving
CPI inflation, we find that our approach has substantial benefits, both overall
and in the left tail, with nonparametric modeling of the conditional mean being
of particular importance.


title: Inkorrect: Online Handwriting Spelling Correction

abstract:
  We introduce Inkorrect, a data- and label-efficient approach for online
handwriting (Digital Ink) spelling correction - DISC. Unlike previous work, the
proposed method does not require multiple samples from the same writer, or
access to character level segmentation. We show that existing automatic
evaluation metrics do not fully capture and are not correlated with the human
perception of the quality of the spelling correction, and propose new ones that
correlate with human perception. We additionally surface an interesting
phenomenon: a trade-off between the similarity and recognizability of the
spell-corrected inks. We further create a family of models corresponding to
different points on the Pareto frontier between those two axes. We show that
Inkorrect's Pareto frontier dominates the points that correspond to prior work.


title: A review on geometric constraint solving

abstract:
  This paper presents a comprehensive review of geometric constraint solving in
parametric computer-aided design (CAD), with the major focus on its advances in
the last 15 years. Geometric constraint solving can date back to the very first
CAD prototype, Sketchpad, in the 1960s, but serious research studies were
carried out only after parametric CAD was introduced in the late 1980s. In the
following 30-year history of GCS research, two development stages may be
identified: (1) the first 15 years (late 1980s - mid 2000s) were primarily
devoted to geometric constraint decomposition for well-constrained systems or
those with only structural constraint dependencies; and (2) the second 15 years
(late 2000s - now) have seen research efforts shifted towards classification
criteria and decomposition algorithms for general constraint systems (with and
without non-structural constraint dependencies). Most existing reviews focused
on the first 15 years. The problem researched in the second 15 years is,
however, equally important, considering that a manually specified constraint
system usually contains under- and over-constrained parts, and that such parts
must be correctly detected and resolved before numerical solving can work. In
this regard, this review paper covers both stages and will discusses what has
already been made possible for handling general constraint systems, what
developments can be expected in the near future, and which areas remain
problematic.


title:
The debiased compositional distribution of MITHNEOS: Global match
  between the near-Earth and main-belt asteroid populations and excess of
  D-type Near-Earth Objects

abstract:
  We report 491 new near-infrared spectroscopic measurements of 420 near-Earth
objects (NEOs) collected on the NASA InfraRed Telescope Facility (IRTF) as part
of the MIT-Hawaii NEO Spectroscopic Survey (MITHNEOS). These measurements were
combined with previously published data (Binzel et al. 2019) and bias-corrected
to derive the intrinsic compositional distribution of the overall NEO
population, as well as of subpopulations coming from various escape routes
(ERs) in the asteroid belt and beyond. The resulting distributions reflect well
the overall compositional gradient of the asteroid belt, with decreasing
fractions of silicate-rich (S- and Q-type) bodies and increasing fractions of
carbonaceous (B-, C-, D- and P-type) bodies as a function of increasing ER
distance from the Sun. The close compositional match between NEOs and their
predicted source populations validates dynamical models used to identify ERs
and argues against any strong composition change with size in the asteroid belt
between ~5 km down to ~100 m. A notable exception comes from the over-abundance
of D-type NEOs from the 5:2J and, to a lesser extend, the 3:1J and nu6 ERs,
hinting at the presence of a large population of small D-type asteroids in the
main belt. Alternatively, this excess may indicate preferential spectral
evolution from D-type surfaces to C- and P-types as a consequence of space
weathering, or to the fact that D-type objects fragment more often than other
spectral types in the NEO space. No further evidence for the existence of
collisional families in the main belt, below the detection limit of current
main-belt surveys, was found in this work.


title:
Connecting Asteroids and Meteorites with visible and near-infrared
  spectroscopy

abstract:
  We identify spectral similarities between asteroids and meteorites. We
identify spectral matches between 500 asteroid spectra and over 1,000 samples
of RELAB meteorite spectra over 0.45-2.5 microns. We reproduce many major and
previously known meteorite-asteroid connections and find possible new, more
rare or less-established connections. Well-established connections include:
ordinary chondrites (OC) with S-complex asteroids; pristine CM carbonaceous
chondrites with Ch-type asteroids and heated CMs with C-type asteroids; HED
meteorites with V-types; enstatite chondrites with Xc-type asteroids; CV
meteorites with K-type asteroids; Brachinites, Pallasites and R chondrites with
olivine-dominated A-type asteroids.
  We find a trend from Q, Sq, S, Sr to Sv correlates with LL to H, with Q-types
matching predominately to L and LL ordinary chondrites, and Sr and Sv matching
predominantly with L and H ordinary chondrites. Ordinary chondrite samples that
match to the X-complex, all measurements of slabs and many labeled as dark or
black (shocked) OCs. We find carbonaceous chondrite samples having spectral
slopes large enough to match D-type asteroid spectra.
  In many cases the asteroid type to meteorite type links are not unique. While
there are well established matches between an asteroid class and meteorite
class, there are less common but still spectrally compatible matches between
many asteroid types and meteorite types. This result emphasizes the diversity
of asteroid and meteorite compositions and highlights the degeneracy of
classification by spectral features alone. Recent and upcoming spacecraft
missions will shed light on the compositions of many of the asteroid classes,
particularly those without diagnostic features, (C-, B-, X-, and D-types), with
measurements of Ceres, Ryugu, Bennu, Psyche, and C-, P-, and D-types as part of
the Lucy mission.


title: Computational Code-Based Privacy in Coded Federated Learning

abstract:
  We propose a privacy-preserving federated learning (FL) scheme that is
resilient against straggling devices. An adaptive scenario is suggested where
the slower devices share their data with the faster ones and do not participate
in the learning process. The proposed scheme employs code-based cryptography to
ensure \emph{computational} privacy of the private data, i.e., no device with
bounded computational power can obtain information about the other devices'
data in feasible time. For a scenario with 25 devices, the proposed scheme
achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively,
for an accuracy of 95\% on the MNIST dataset compared with conventional
mini-batch FL.


title: OUR-GAN: One-shot Ultra-high-Resolution Generative Adversarial Networks

abstract:
  We propose OUR-GAN, the first one-shot ultra-high-resolution (UHR) image
synthesis framework that generates non-repetitive images with 4K or higher
resolution from a single training image. OUR-GAN generates a visually coherent
image at low resolution and then gradually increases the resolution by
super-resolution. Since OUR-GAN learns from a real UHR image, it can synthesize
large-scale shapes with fine details while maintaining long-range coherence,
which is difficult with conventional generative models that generate large
images based on the patch distribution learned from relatively small images.
OUR-GAN applies seamless subregion-wise super-resolution that synthesizes 4k or
higher UHR images with limited memory, preventing discontinuity at the
boundary. Additionally, OUR-GAN improves visual coherence maintaining diversity
by adding vertical positional embeddings to the feature maps. In experiments on
the ST4K and RAISE datasets, OUR-GAN exhibited improved fidelity, visual
coherency, and diversity compared with existing methods. The synthesized images
are presented at https://anonymous-62348.github.io.


title:
Differential equation and probability inspired graph neural networks for
  latent variable learning

abstract:
  Probabilistic theory and differential equation are powerful tools for the
interpretability and guidance of the design of machine learning models,
especially for illuminating the mathematical motivation of learning latent
variable from observation. State estimation and subspace learning are two
classical problems in latent variable learning. State estimation solves optimal
value for latent variable (i.e. state) from noised observation. Subspace
learning maps high-dimensional features on low-dimensional subspace to capture
efficient representation. Graphs are widely applied for modeling latent
variable learning problems, and graph neural networks implement deep learning
architectures on graphs. Inspired by probabilistic theory and differential
equations, this paper proposes graph neural networks to solve state estimation
and subspace learning problems. This paper conducts theoretical studies, and
adopts empirical studies on several tasks, including text classification,
protein classification, stock prediction and state estimation for robotics.
Experiments illustrate that the proposed graph neural networks are superior to
the current methods. Source code of this paper is available at
https://github.com/zshicode/Latent-variable-GNN.


title:
Analysis of scattered higher dimensional data using generalized Fourier
  interpolation

abstract:
  A method based on orthogonal function series interpolation of the square root
probability density to analyze higher dimensional scattered data is presented.
The method is targeted for the use-case when the model and/or data are
available only as discrete events. While fast and efficient algorithms are well
known for pseudo-spectral (grid node based) methods, this work focuses on a
spectral (non grid based) approach. A typical application is the extraction of
physics model parameters from events detected in high energy particle
collisions. Several examples are provided and the performance is compared to
existing conventional procedures. In some cases the method can be shown to
behave as an optimal observable of the data, exemplified by the ability to
approach the Cramer-Rao bound.


title: A Mutually Reinforced Framework for Pretrained Sentence Embeddings

abstract:
  The lack of labeled data is a major obstacle to learning high-quality
sentence embeddings. Recently, self-supervised contrastive learning (SCL) is
regarded as a promising way to address this problem. However, the existing
works mainly rely on hand-crafted data annotation heuristics to generate
positive training samples, which not only call for domain expertise and
laborious tuning, but are also prone to the following unfavorable cases: 1)
trivial positives, 2) coarse-grained positives, and 3) false positives. As a
result, the self-supervision's quality can be severely limited in reality. In
this work, we propose a novel framework InfoCSE to address the above problems.
Instead of relying on annotation heuristics defined by humans, it leverages the
sentence representation model itself and realizes the following iterative
self-supervision process: on one hand, the improvement of sentence
representation may contribute to the quality of data annotation; on the other
hand, more effective data annotation helps to generate high-quality positive
samples, which will further improve the current sentence representation model.
In other words, the representation learning and data annotation become mutually
reinforced, where a strong self-supervision effect can be derived. Extensive
experiments are performed based on three benchmark datasets, where notable
improvements can be achieved against the existing SCL-based methods.


title:
The interaction of the active nucleus with the host galaxy interstellar
  medium

abstract:
  Imaging X-ray spectroscopy of nearby AGNs, mostly with Chandra, has shown
that extended soft (<2.5 keV) emission-line dominated X-ray biconical
structures, of kiloparsec scale, are widespread in highly absorbed Compton
Thick (CT) AGNs. The X-ray emission is complex, requiring both photoionized and
shock-ionized gas. It originates from high ionization regions and is surrounded
by cocoons of low ionization narrow line emission regions (LINERS). Bicone 3-6
keV continuum and 6.4 keV Fe Kalpha emission has been detected, contrary to the
standard AGN model expectation that would confine this hard emission to the
pc-size nuclear absorbing torus. Extended emission in the cross-cone direction,
also requires modifications to the AGN standard model. A porous torus, with a
significant fraction of escaping AGN continuum, and/or jet interaction with ISM
creating a blow-back towards the nuclear region seem to be required. Here we
discuss these results and their implications for both the AGN model and our
understanding of AGN feedback. The finding of hot and highly photoionized gas
on 10s parsecs to several kiloparsec scales demonstrates that all three
feedback mechanisms are at work: radiation affects the inner molecular clouds
of the host on a ~1 kpc scale; shocks of relativistic jets with the host ISM a
few kpc from the central AGN; and photoionization of the ISM via winds on
scales from pc to multiple kpc. These results demonstrate that X-rays are
needed to develop a complete picture of AGN/host interaction along with radio
continuum, mm and sub-mm molecular line emission, and optical/near-IR emission
lines.


title: RestainNet: a self-supervised digital re-stainer for stain normalization

abstract:
  Color inconsistency is an inevitable challenge in computational pathology,
which generally happens because of stain intensity variations or sections
scanned by different scanners. It harms the pathological image analysis
methods, especially the learning-based models. A series of approaches have been
proposed for stain normalization. However, most of them are lack flexibility in
practice. In this paper, we formulated stain normalization as a digital
re-staining process and proposed a self-supervised learning model, which is
called RestainNet. Our network is regarded as a digital restainer which learns
how to re-stain an unstained (grayscale) image. Two digital stains, Hematoxylin
(H) and Eosin (E) were extracted from the original image by Beer-Lambert's Law.
We proposed a staining loss to maintain the correctness of stain intensity
during the restaining process. Thanks to the self-supervised nature, paired
training samples are no longer necessary, which demonstrates great flexibility
in practical usage. Our RestainNet outperforms existing approaches and achieves
state-of-the-art performance with regard to color correctness and structure
preservation. We further conducted experiments on the segmentation and
classification tasks and the proposed RestainNet achieved outstanding
performance compared with SOTA methods. The self-supervised design allows the
network to learn any staining style with no extra effort.


title:
Structure of the super-Eddington outflow and itsimpact on the
  cosmological scale

abstract:
  It is one of the biggest issues in black hole (BH) astrophysics how to
precisely evaluate BH feedback to its environments. Aiming at studying the
unique gas dynamics of super-Eddington flow around supermassive black hole
(SMBH) seeds at high redshift, we carried out axisymmetric two dimensional
radiation hydrodynamic simulations by a nested simulation-box method. Here we
divide the simulation box into the inner zone at $(2 - 3 \times 10^3)
r_{\rm{Sch}}$ (with $r_{\rm Sch}$ being the Schwarzschild radius) and the outer
zone at $(2\times 10^{3} - 3\times 10^6) r_{\rm{Sch}}$, with smooth connection
of the physical quantities, such as gas density, velocity, and radiation
energy. We start the calculation by injecting mass through the outer boundary
of the inner zone at a constant rate of
$\dot{M}_{\rm{inj}}=10^3L_{\rm{Edd}}/c^2$, where $L_{\rm{Edd}}$ is the
Eddington luminosity and $c$ is the speed of light. A powerful outflow is
generated in the innermost region and it propagates from the inner zone to the
outer zone. The outflows are characterized by a velocity of 0.02$c$ (0.7$c$)
and density of $10^{-17}$ ($10^{-19}$) g cm$^{-3}$ for near the edge-on
(face-on) direction. The outflow is gradually accelerated as it travels by
accepting radiation-pressure force. The final mass outflow rate at the
outermost boundary is $\dot{M}_{\rm{out}}\sim 0.3 \times \dot{M}_{\rm{inj}}$.
By extrapolating the outflow structure to a further larger scale, we find that
the momentum and energy fluxes at $r \sim 0.1$ pc are $\sim 10-100
L_{\rm{Edd}}/c $ and $\sim 0.1-10 L_{\rm{Edd}}$, respectively. Moreover, we
find that the impacts are highly anisotropic in the sense that larger impacts
are given towards the face-on direction than in the edge-on direction. These
results indicate that the BH feedback will more efficiently work on the
interstellar medium than that assumed in the cosmological simulations.


title: Parameter estimation and model reduction for retinal laser treatment

abstract:
  Laser photocoagulation is one of the most frequently used treatment
approaches for retinal diseases such as diabetic retinopathy and macular edema.
The use of model-based control, such as Model Predictive Control (MPC),
enhances a safe and effective treatment by guaranteeing temperature bounds. In
general, real-time requirements for model-based control designs are not met
since the temperature distribution in the eye fundus is governed by a heat
equation with a nonlinear parameter dependency. This issue is circumvented by
representing the model by a lower-dimensional system which well-approximates
the original model, including the parametric dependency. We combine a
global-basis approach with the discrete empirical interpolation method, tailor
its hyperparameters to laser photocoagulation, and show its superiority in
comparison to a recently proposed method based on Taylor-series approximation.
Its effectiveness is measured in computation time for MPC. We further present a
case study to estimate the range of absorption parameters in porcine eyes, and
by means of a theoretical and numerical sensitivity analysis we show that the
sensitivity of the temperature increase is higher with respect to the
absorption coefficient of the retinal pigment epithelium (RPE) than of the
choroid's.


title:
La matematica armonia dei suoni naturali. Ovvero, l'armonica matematica
  dei suoni naturali

abstract:
  The collaboration of mathematics in the two musical systems of Pythagorean
sounds and of equal sounds is evident and opportune for generating the elements
and managing their relationships. The only essential notion for a rational
intervention in the two sound worlds is that of distance between sounds. The
scale of natural sounds does not seem to dialogue directly with a purely
mathematical module that articulates its definition and is generally presented
as a manual adjustment of the ancient Pythagorean sounds. The model of
proportions that formed and inspired the architecture of the sixteenth century,
an era in which the natural scale emerges, provides a clear idea for
systematically adjusting natural sounds; on the other hand, the generation of
sounds based and started exclusively on the respect of the proportions proposes
interesting natural sound systems. The object is not so much to illustrate the
harmonic device of the already formed natural scale, as to implement the
formation of sound elements that respond to the unitary principle of proportion
and combine harmoniously.


title: DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training

abstract:
  A standard hardware bottleneck when training deep neural networks is GPU
memory. The bulk of memory is occupied by caching intermediate tensors for
gradient computation in the backward pass. We propose a novel method to reduce
this footprint by selecting and caching part of intermediate tensors for
gradient computation. Our Intermediate Tensor Drop method (DropIT) adaptively
drops components of the intermediate tensors and recovers sparsified tensors
from the remaining elements in the backward pass to compute the gradient.
Experiments show that we can drop up to 90% of the elements of the intermediate
tensors in convolutional and fully-connected layers, saving 20% GPU memory
during training while achieving higher test accuracy for standard backbones
such as ResNet and Vision Transformer. Our code is available at
https://github.com/ChenJoya/dropit.


title:
A natural class of cellular automata containing fractional
  multiplication automata, Rule 30, and others

abstract:
  We define the class of rapidly left expansive cellular automata, which
contains fractional multiplication automata, Wolfram's Rule 30, and many
others. The definition has been shaped by a proposition of Jen on aperiodicity
of columns in space-time diagrams of certain cellular automata, which
generalizes to this new class. We also present results that originate from the
theory of distribution modulo 1.