title: HCMD-zero: Learning Value Aligned Mechanisms from Data

abstract:
  Artificial learning agents are mediating a larger and larger number of
interactions among humans, firms, and organizations, and the intersection
between mechanism design and machine learning has been heavily investigated in
recent years. However, mechanism design methods make strong assumptions on how
participants behave (e.g. rationality), or on the kind of knowledge designers
have access to a priori (e.g. access to strong baseline mechanisms). Here we
introduce HCMD-zero, a general purpose method to construct mechanism agents.
HCMD-zero learns by mediating interactions among participants, while remaining
engaged in an electoral contest with copies of itself, thereby accessing direct
feedback from participants. Our results on the Public Investment Game, a
stylized resource allocation game that highlights the tension between
productivity, equality and the temptation to free-ride, show that HCMD-zero
produces competitive mechanism agents that are consistently preferred by human
participants over baseline alternatives, and does so automatically, without
requiring human knowledge, and by using human data sparingly and effectively
Our detailed analysis shows HCMD-zero elicits consistent improvements over the
course of training, and that it results in a mechanism with an interpretable
and intuitive policy.


title:
Homogeneous $2$-nondegenerate CR manifolds of hypersurface type in low
  dimensions

abstract:
  In a recent paper, the author and I. Zelenko introduce the concept of
modified CR symbols for organizing local invariants of $2$-nondegenerate CR
structures. Among homogeneous structures with given modified CR symbols it
often happens that the maximally symmetric structure is unique. We show that
this phenomenon occurs for all modified symbols of homogeneous hypersurfaces in
$\mathbb{C}^4$ and classify them, obtaining (up to local equivalence) nine
maximally symmetric homogeneous $2$-nondegenerate real hypersurfaces in
$\mathbb{C}^4$. The methods used to obtain this classification are then applied
to find homogeneous hypersurfaces in higher dimensional spaces. In total $21$
locally non-equivalent maximally symmetric homogeneous $2$-nondegenerate
hypersurfaces are described in $\mathbb{C}^5$, and $40$ such hypersurfaces are
described in $\mathbb{C}^6$, of which some have been described in other works
while many are new. Two new sequences, indexed by $n$, of homogeneous
$2$-nondegenerate hypersurfaces in $\mathbb{C}^{n+1}$ are described. Notably,
all examples from one of these latter sequences can be realized as
left-invariant structures on nilpotent Lie groups.


title:
Multi-Task Conditional Imitation Learning for Autonomous Navigation at
  Crowded Intersections

abstract:
  In recent years, great efforts have been devoted to deep imitation learning
for autonomous driving control, where raw sensory inputs are directly mapped to
control actions. However, navigating through densely populated intersections
remains a challenging task due to uncertainty caused by uncertain traffic
participants. We focus on autonomous navigation at crowded intersections that
require interaction with pedestrians. A multi-task conditional imitation
learning framework is proposed to adapt both lateral and longitudinal control
tasks for safe and efficient interaction. A new benchmark called IntersectNav
is developed and human demonstrations are provided. Empirical results show that
the proposed method can achieve a success rate gain of up to 30% compared to
the state-of-the-art.


title:
ABO3 Perovskites' Formability Prediction and Crystal Structure
  Classification using Machine Learning

abstract:
  Renewable energy sources are of great interest to combat global warming, yet
promising sources like photovoltaic (PV) cells are not efficient and cheap
enough to act as an alternative to traditional energy sources. Perovskite has
high potential as a PV material but engineering the right material for a
specific application is often a lengthy process. In this paper, ABO3 type
perovskites' formability is predicted and its crystal structure is classified
using machine learning with high accuracy, which provides a fast screening
process. Although the study was done with solar-cell application in mind, the
prediction framework is generic enough to be used for other purposes.
Formability of perovskite is predicted and its crystal structure is classified
with an accuracy of 98.57% and 90.53% respectively using Random Forest after
5-fold cross-validation. Our machine learning model may aid in the accelerated
development of a desired perovskite structure by providing a quick mechanism to
get insight into the material's properties in advance.


title:
Improving the performance of fermionic neural networks with the Slater
  exponential Ansatz

abstract:
  In this work, we propose a technique for the use of fermionic neural networks
(FermiNets) with the Slater exponential Ansatz for electron-nuclear and
electron-electron distances, which provides faster convergence of target
ground-state energies due to a better description of the interparticle
interaction in the vicinities of the coalescence points. Analysis of learning
curves indicates on the possibility to obtain accurate energies with smaller
batch sizes using arguments of the bagging approach. In order to obtain even
more accurate results for the ground-state energies, we suggest an
extrapolation scheme, which estimates Monte Carlo integrals in the limit of an
infinite number of points. Numerical tests for a set of molecules demonstrate a
good agreement with the results of original FermiNets (achieved with larger
batch sizes than required by our approach) and with results of coupled-cluster
singles and doubles with perturbative triples (CCSD(T)) method, calculated in
the complete basis set (CBS) limit.


title:
On solutions of the Diophantine equation
  $F_{n_{1}}+F_{n_{2}}+F_{n_{3}}+F_{n_{4}}=2^a$

abstract:
  Let $(F_n)_{n\geq 0}$ be the Fibonacci sequence given by $F_0 = 0, F_1 = 1$
and $F_{n+2} = F_{n+1}+F_n$ for $n \geq 0$. In this paper, we solve all powers
of two which are sums of four Fibonacci numbers with a few exceptions that we
characterize.


title:
On the dynamic distinguishability of nodal quasi-particles in overdoped
  cuprates

abstract:
  La$_{1.67}$Sr$_{0.33}$CuO$_4$ is not a superconductor and its resistivity
follows a purely T$^2$ temperature dependence at very low temperatures.
La$_{1.71}$Sr$_{0.29}$CuO$_4$, on the other hand, has a superconducting ground
state together with a T-Linear term in its resistivity. The concomitant
emergence of these two features below a critical doping is mystifying. Here, I
begin by noticing that the electron-electron collision rate in the Fermi liquid
above the doping threshold is unusually large. Therefore, the scattering time
of nodal quasi-particles is close to the threshold for dynamic
indistinguishibality, which is documented in liquid $^3$He at its
zero-temperature melting pressure. Failing this requirement of Fermi-Dirac
statistics will exclude nodal electrons from the Fermi sea. Becoming classical,
they will scatter other carriers within a phase space growing linearly with
temperature.


title:
Effect of Contact Angle on Capillary Pressure and Liquid Recovery from
  Angular Pore Channels

abstract:
  We predict the volume of liquid recovered from different-shaped prismatic
channels following gas displacement. This recovery factor depends strongly on
the contact angle at which the gas-liquid interfaces meet the walls of the
channel. We show that scaling the contact angle by a critical value, dependent
on the channel shape, leads to a master curve for the recovery factor.} Our
numerical simulations with the Surface Evolver are supported by theoretical
calculations for the capillary entry pressure in a channel with a triangular or
rectangular cross-section and for the interface shape in a "scalloped"
triangular channel formed between three parallel cylinders.


title: Improved variational quantum eigensolver via quasi-dynamical evolution

abstract:
  The variational quantum eigensolver (VQE) is a hybrid quantum-classical
algorithm designed for current and near-term quantum devices. Despite its
initial success, there is a lack of understanding involving several of its key
aspects. There are problems with VQE that forbid a favourable scaling towards
quantum advantage. In order to alleviate the problems, we propose and
extensively test a quantum annealing inspired heuristic that supplements VQE.
The improved VQE enables an efficient initial state preparation mechanism, in a
recursive manner, for a quasi-dynamical unitary evolution. We conduct an
in-depth scaling analysis of finding the ground state energies with increasing
lattice sizes of the Heisenberg model, employing simulations of up to $40$
qubits that manipulate the complete state vector. For the current devices, we
further propose a benchmarking toolkit using a mean-field model and test it on
IBM Q devices. The improved VQE avoids barren plateaus, exits local minima, and
works with low-depth circuits. Realistic gate execution times estimate a longer
computational time to complete the same computation on a fully functional
error-free quantum computer than on a quantum computer emulator implemented on
a classical computer. However, our proposal can be expected to help accurate
estimations of the ground state energies beyond $50$ qubits when the complete
state vector can no longer be stored on a classical computer, thus enabling
quantum advantage.


title: On the bijective colouring of Cantor trees based on transducers

abstract:
  Given a vertex colouring of the infinite $n$-ary Cantor tree with $m$ colours
($n,m\geq 2$), the natural problem arises: may this colouring induce a
bijective colouring of the infinite paths starting at the root, i.e., that
every infinite $m$-coloured string is used for some of these paths but
different paths are not coloured identically? In other words, we ask if the
above vertex colouring may define a bijective short map between the
corresponding Cantor spaces. We show that the answer is positive if and only if
$n\geq m$, and provide an effective construction of the bijective colouring in
terms of Mealy automata and functions defined by such automata. We also show
that a finite Mealy automaton may define such a bijective colouring only in the
trivial case, i.e. $m=n$.


title: Tensor methods inside mixed oracle for min-min problems

abstract:
  In this article we consider min-min type of problems or minimization by two
groups of variables. Min-min problems may occur in case if some groups of
variables in convex optimization have different dimensions or if these groups
have different domains. Such problem structure gives us an ability to split the
main task to subproblems, and allows to tackle it with mixed oracles. However
existing articles on this topic cover only zeroth and first order oracles, in
our work we consider high-order tensor methods to solve inner problem and fast
gradient method to solve outer problem. We assume, that outer problem is
constrained to some convex compact set, and for the inner problem we consider
both unconstrained case and being constrained to some convex compact set. By
definition, tensor methods use high-order derivatives, so the time per single
iteration of the method depends a lot on the dimensionality of the problem it
solves. Therefore, we suggest, that the dimension of the inner problem variable
is not greater than 1000. Additionally, we need some specific assumptions to be
able to use mixed oracles. Firstly, we assume, that the objective is convex in
both groups of variables and its gradient by both variables is Lipschitz
continuous. Secondly, we assume the inner problem is strongly convex and its
gradient is Lipschitz continuous. Also, since we are going to use tensor
methods for inner problem, we need it to be $p$-th order Lipschitz continuous
($p > 1$). Finally, we assume strong convexity of the outer problem to be able
to use fast gradient method for strongly convex functions. We need to
emphasize, that we use superfast tensor method to tackle inner subproblem in
unconstrained case. And when we solve inner problem on compact set, we use
accelerated high-order composite proximal method.


title: Evolution equations with eventually positive solutions

abstract:
  We discuss linear autonomous evolution equations on function spaces which
have the property that a positive initial value leads to a solution which
initially changes sign, but then becomes - and stays - positive again for
sufficiently large times. This eventual positivity phenomenon has recently been
discovered for various classes of differential equations, but so far a general
theory to explain this type of behaviour exists only under additional spectral
assumptions.


title:
DQMIX: A Distributional Perspective on Multi-Agent Reinforcement
  Learning

abstract:
  In cooperative multi-agent tasks, a team of agents jointly interact with an
environment by taking actions, receiving a team reward and observing the next
state. During the interactions, the uncertainty of environment and reward will
inevitably induce stochasticity in the long-term returns and the randomness can
be exacerbated with the increasing number of agents. However, most of the
existing value-based multi-agent reinforcement learning (MARL) methods only
model the expectations of individual Q-values and global Q-value, ignoring such
randomness. Compared to the expectations of the long-term returns, it is more
preferable to directly model the stochasticity by estimating the returns
through distributions. With this motivation, this work proposes DQMIX, a novel
value-based MARL method, from a distributional perspective. Specifically, we
model each individual Q-value with a categorical distribution. To integrate
these individual Q-value distributions into the global Q-value distribution, we
design a distribution mixing network, based on five basic operations on the
distribution. We further prove that DQMIX satisfies the
\emph{Distributional-Individual-Global-Max} (DIGM) principle with respect to
the expectation of distribution, which guarantees the consistency between joint
and individual greedy action selections in the global Q-value and individual
Q-values. To validate DQMIX, we demonstrate its ability to factorize a matrix
game with stochastic rewards. Furthermore, the experimental results on a
challenging set of StarCraft II micromanagement tasks show that DQMIX
consistently outperforms the value-based multi-agent reinforcement learning
baselines.


title: The Good Shepherd: An Oracle Agent for Mechanism Design

abstract:
  From social networks to traffic routing, artificial learning agents are
playing a central role in modern institutions. We must therefore understand how
to leverage these systems to foster outcomes and behaviors that align with our
own values and aspirations. While multiagent learning has received considerable
attention in recent years, artificial agents have been primarily evaluated when
interacting with fixed, non-learning co-players. While this evaluation scheme
has merit, it fails to capture the dynamics faced by institutions that must
deal with adaptive and continually learning constituents. Here we address this
limitation, and construct agents ("mechanisms") that perform well when
evaluated over the learning trajectory of their adaptive co-players
("participants"). The algorithm we propose consists of two nested learning
loops: an inner loop where participants learn to best respond to fixed
mechanisms; and an outer loop where the mechanism agent updates its policy
based on experience. We report the performance of our mechanism agents when
paired with both artificial learning agents and humans as co-players. Our
results show that our mechanisms are able to shepherd the participants
strategies towards favorable outcomes, indicating a path for modern
institutions to effectively and automatically influence the strategies and
behaviors of their constituents.


title:
Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused
  Ultrasound Interventions with Conditional Adversarial Networks

abstract:
  Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic
ultrasound method that focuses sound through the skull to a small region
noninvasively under MRI guidance. It is clinically approved to thermally ablate
regions of the thalamus and is being explored for other therapies, such as
blood brain barrier opening and neuromodulation. To accurately target
ultrasound through the skull, the transmitted waves must constructively
interfere at the target region. However, heterogeneity of the sound speed,
density, and ultrasound attenuation in different individuals' skulls requires
patient-specific estimates of these parameters for optimal treatment planning.
CT imaging is currently the gold standard for estimating acoustic properties of
an individual skull during clinical procedures, but CT imaging exposes patients
to radiation and increases the overall number of imaging procedures required
for therapy. A method to estimate acoustic parameters in the skull without the
need for CT would be desirable. Here, we synthesized CT images from routinely
acquired T1-weighted MRI by using a 3D patch-based conditional generative
adversarial network and evaluated the performance of synthesized CT images for
treatment planning with transcranial focused ultrasound. We compared the
performance of synthetic CT to real CT images using Kranion and k-Wave acoustic
simulation. Our work demonstrates the feasibility of replacing real CT with the
MR-synthesized CT for TcMRgFUS planning.


title:
A new data augmentation method for intent classification enhancement and
  its application on spoken conversation datasets

abstract:
  Intent classifiers are vital to the successful operation of virtual agent
systems. This is especially so in voice activated systems where the data can be
noisy with many ambiguous directions for user intents. Before operation begins,
these classifiers are generally lacking in real-world training data. Active
learning is a common approach used to help label large amounts of collected
user input. However, this approach requires many hours of manual labeling work.
We present the Nearest Neighbors Scores Improvement (NNSI) algorithm for
automatic data selection and labeling. The NNSI reduces the need for manual
labeling by automatically selecting highly-ambiguous samples and labeling them
with high accuracy. This is done by integrating the classifier's output from a
semantically similar group of text samples. The labeled samples can then be
added to the training set to improve the accuracy of the classifier. We
demonstrated the use of NNSI on two large-scale, real-life voice conversation
systems. Evaluation of our results showed that our method was able to select
and label useful samples with high accuracy. Adding these new samples to the
training data significantly improved the classifiers and reduced error rates by
up to 10%.


title: Driven anti-Bragg subradiant states in waveguide quantum electrodynamics

abstract:
  We study theoretically driven quantum dynamics in periodic arrays of
two-level qubits coupled to the waveguide. We demonstrate, that strongly
subradiant eigenstates of the master equation for the density matrix emerge
under strong coherent driving for arrays with the anti-Bragg periods
$d=\lambda/4,3\lambda/4$... This happens even though are no such states at low
driving powers. Thus, our findings show that in the quantum nonlinear regime
the domain of existence of subradiant states expands beyond conventional
subwavelength structures. These driven states are directly manifested in
long-living quantum correlations between the qubit excitations.


title:
S3T: Self-Supervised Pre-training with Swin Transformer for Music
  Classification

abstract:
  In this paper, we propose S3T, a self-supervised pre-training method with
Swin Transformer for music classification, aiming to learn meaningful music
representations from massive easily accessible unlabeled music data. S3T
introduces a momentum-based paradigm, MoCo, with Swin Transformer as its
feature extractor to music time-frequency domain. For better music
representations learning, S3T contributes a music data augmentation pipeline
and two specially designed pre-processors. To our knowledge, S3T is the first
method combining the Swin Transformer with a self-supervised learning method
for music classification. We evaluate S3T on music genre classification and
music tagging tasks with linear classifiers trained on learned representations.
Experimental results show that S3T outperforms the previous self-supervised
method (CLMR) by 12.5 percents top-1 accuracy and 4.8 percents PR-AUC on two
tasks respectively, and also surpasses the task-specific state-of-the-art
supervised methods. Besides, S3T shows advances in label efficiency using only
10% labeled data exceeding CLMR on both tasks with 100% labeled data.


title:
Vibronic Resonance Along Effective Modes Mediates Selective Energy
  Transfer in Excitonically Coupled Aggregates

abstract:
  We recently proposed effective normal modes for excitonically coupled
aggregates which exactly transform the energy transfer Hamiltonian into a sum
of one-dimensional Hamiltonians along the effective normal modes. Identifying
physically meaningful vibrational motions which maximally promote vibronic
mixing suggested an interesting possibility of leveraging
vibrational-electronic resonance for mediating selective energy transfer. Here
we expand on the effective mode approach elucidating its iterative nature for
successively larger aggregates, and extend the idea of mediated energy transfer
to larger aggregates. We show that energy transfer between electronically
uncoupled but vibronically resonant donor-acceptor sites does not depend on the
intermediate site energy or the number of intermediate sites. The intermediate
sites simply mediate electronic coupling such that vibronic coupling along
specific promoter modes leads to direct donor-acceptor energy transfer
bypassing any intermediate uphill energy transfer steps. We show that interplay
between the electronic Hamiltonian and the effective mode transformation
partitions the linear vibronic coupling along specific promoter modes to
dictate the selectivity of mediated energy transfer, with a vital role of
interference between vibronic couplings and multi-particle basis states. Our
results suggest a general design principle for enhancing energy transfer
through synergistic effects of vibronic resonance and weak mediated electronic
coupling, where both effects individually do not promote efficient energy
transfer. The effective mode approach proposed here paves a facile route
towards four-wavemixing spectroscopy simulations of larger aggregates without
severely approximating resonant vibronic coupling.


title: Dynamic Relation Repairing for Knowledge Enhancement

abstract:
  Dynamic relation repair aims to efficiently validate and repair the instances
for knowledge graph enhancement (KGE), where KGE captures missing relations
from unstructured data and leads to noisy facts to the knowledge graph. With
the prosperity of unstructured data, an online approach is asked to clean the
new RDF tuples before adding them to the knowledge base. To clean the noisy RDF
tuples, graph constraint processing is a common but intractable approach. Plus,
when adding new tuples to the knowledge graph, new graph patterns would be
created, whereas the explicit discovery of graph constraints is also
intractable. Therefore, although the dynamic relation repair has an unfortunate
hardness, it is a necessary approach for enhancing knowledge graphs effectively
under the fast-growing unstructured data. Motivated by this, we establish a
dynamic repairing and enhancing structure to analyze its hardness on basic
operations. To ensure dynamic repair and validation, we introduce implicit
graph constraints, approximate graph matching, and linkage prediction based on
localized graph patterns. To validate and repair the RDF tuples efficiently, we
further study the cold start problems for graph constraint processing.
Experimental results on real datasets demonstrate that our proposed approach
can capture and repair instances with wrong relation labels dynamically and
effectively.


title: A Rule-based Operational Semantics of Graph Query Languages

abstract:
  We consider a core language of graph queries. These queries are seen as
formulas to be solved with respect to graph-oriented databases. For this
purpose, we first define a graph query algebra where some operations over
graphs and sets of graph homomorphisms are specified. Then, the notion of
pattern is introduced to represent a kind of recursively defined formula over
graphs. The syntax and formal semantics of patterns are provided. Afterwards,
we propose a new sound and complete calculus to solve patterns. This calculus,
which is based on a rewriting system, develops only one derivation per pattern
to be solved. Our calculus is generic in the sense that it can be adapted to
different kinds of graph databases provided that the notions of graph and graph
homomorphism (match) are well defined.


title: TUVOpipe: a pipeline to search for UV transients with Swift-UVOT

abstract:
  Despite the prevalence of transient-searching facilities operating across
most wavelengths, the ultraviolet (UV) transient sky remains to be
systematically studied. We have recently initiated the Transient Ultraviolet
Objects (TUVO) project, with which we search for serendipitous UV transients in
data from currently available UV instruments, with a focus on the UV/Optical
(UVOT) telescope aboard the Neil Gehrels Swift Observatory (an overview of the
TUVO project is described in a companion paper). Here we describe TUVOpipe, the
pipeline we constructed in order to find such transients in the UVOT data,
using difference image analysis. The pipeline is run daily on all new public
UVOT data (which are available 6-8 hours after the observations are performed),
so we discover transients in near real-time. This allows for follow-up
observations to be performed. From October 1, 2020, to the time of submission,
we have processed 75,183 individual UVOT images and we currently detect an
average rate of ~100 transient candidates per day. Of these daily candidates,
on average ~30 are real transients, separated by human vetting from the
remaining `bogus' transients which were not discarded automatically within the
pipeline. Most of the real transients we detect are known variable stars,
though we also detect many known active galactic nuclei and accreting white
dwarfs. TUVOpipe can additionally run in archival mode, whereby all archival
UVOT data of a given field is scoured for `historical' transients; in this mode
we also mostly find variable stars. However, some of the transients we find (in
particular in the real-time mode) represent previously unreported new
transients, or undiscovered outbursts of known transients, predominantly
outbursts from cataclysmic variables. In this paper we describe the operation
of (both modes of) TUVOpipe and some of the initial results we have so far
obtained.


title:
Inferring Network Structure with Unobservable Nodes from Time Series
  Data

abstract:
  Network structures play important roles in social, technological and
biological systems. However, the observable nodes and connections in real cases
are often incomplete or unavailable due to measurement errors, private
protection issues, or other problems. Therefore, inferring the complete network
structure is useful for understanding human interactions and complex dynamics.
The existing studies have not fully solved the problem of inferring network
structure with partial information about connections or nodes. In this paper,
we tackle the problem by utilizing time-series data generated by network
dynamics. We regard the network inference problem based on dynamical time
series data as a problem of minimizing errors for predicting states of
observable nodes and proposed a novel data-driven deep learning model called
Gumbel-softmax Inference for Network (GIN) to solve the problem under
incomplete information. The GIN framework includes three modules: a dynamics
learner, a network generator, and an initial state generator to infer the
unobservable parts of the network. We implement experiments on artificial and
empirical social networks with discrete and continuous dynamics. The
experiments show that our method can infer the unknown parts of the structure
and the initial states of the observable nodes with up to 90\% accuracy. The
accuracy declines linearly with the increase of the fractions of unobservable
nodes. Our framework may have wide applications where the network structure is
hard to obtain and the time series data is rich.


title: Information Revelation Through Signalling

abstract:
  This paper studies a Stackelberg game wherein a sender (leader) attempts to
shape the information of a less informed receiver (follower) who in turn takes
an action that determines the payoff of both players. The sender chooses
signals to maximize its own utility function while the receiver aims to
ascertain the value of a source that is privately known to the sender. It is
well known that such sender-receiver games admit a vast number of equilibria
and not all signals from the sender can be relied on as truthful. Our main
contribution is an exact characterization of the minimum number of distinct
source symbols that can be correctly recovered by a receiver in \textit{any}
equilibrium of this game; we call this quantity the \textit{informativeness} of
the sender. We show that the informativeness is given by the \textit{vertex
clique cover number} of a certain graph induced by the utility function,
whereby it can be computed based on the utility function alone without the need
to enumerate all equilibria. We find that informativeness characterizes the
existence of well-known classes of separating, pooling and semi-separating
equilibria. We also compare informativeness with the amount of information
obtained by the receiver when it is the leader and show that the
informativeness is always greater than the latter, implying that the receiver
is better off being a follower.


title: alpha_s in DIS scheme

abstract:
  Deep inelastic scattering data on $F_2$ structure function from various
fixed-target experiments were analyzed in a nonsinglet approximation in the
MSbar and DIS scheme. The study of high statistics deep inelastic scattering
data provided by BCDMS, SLAC and NMC collaborations, was carried out using a
combined analysis. The application of the DIS scheme leads to the resummation
of contributions that are important for large x values. It is found that using
the DIS scheme does not significantly change the strong coupling constant
itself but does strongly change the values of the twist-four corrections.


title: Linear resolutions and quasi-linearity of monomial ideals

abstract:
  We introduce the concept of quasi-linearity and prove it is necessary for a
monomial ideal to have a linear resolution and identify all the quasi-linear
quadratic monomial ideals. We define a strongly linear monomial for a monomial
ideal $I$ and prove that if $\mathbf{u}$ is a strongly linear monomial over $I$
then $I$ has a linear resolution (resp: is quasi-linear) if and only if
$I+\mathbf{u}\mathfrak{p}$ has a linear resolution (resp: is quasi-linear).
Here $\mathfrak{p}$ is any monomial prime ideal.


title: Two-snapshot DOA Estimation via Hankel-structured Matrix Completion

abstract:
  In this paper, we study the problem of estimating the direction of arrival
(DOA) using a sparsely sampled uniform linear array (ULA). Based on an initial
incomplete ULA measurement, our strategy is to choose a sparse subset of array
elements for measuring the next snapshot. Then, we use a Hankel-structured
matrix completion to interpolate for the missing ULA measurements. Finally, the
source DOAs are estimated using a subspace method such as Prony on the fully
recovered ULA. We theoretically provide a sufficient bound for the number of
required samples (array elements) for perfect recovery. The numerical
comparisons of the proposed method with existing techniques such as atomic-norm
minimization and off-the-grid approaches confirm the superiority of the
proposed method.


title:
Correction of calibration ringing in the context of the MTG-S IRS
  instruments

abstract:
  EUMETSAT is developing the on-ground processing chain of the infrared Fourier
transform spectrometers (IRS) on-board of the Meteosat Third Generation
sounding satellites (MTG-S). In this context, the authors have investigated the
impact of a particular type of radiometric error, called the calibration
ringing. It arises in Fourier transform spectrometers when the instrument
optical transmission varies within the domain of the spectral response
function. The expected radiometric errors are simulated in the context of the
MTG-IRS instrument in the long wave infrared (LWIR) band. A software correction
methodology is designed and its performance is assessed.


title:
Enhanced quantum tunneling in quantum Zeno dynamics freezing momentum
  direction

abstract:
  Quantum tunneling (QT) and its control is of great importance in many
physical phenomena such as nuclear fusion, high-energy particle collisions,
photoelectron ionization in high harmonic generation, or electron and proton
transfer in chemical reactions. The QT probability of a particle is seen here
to be enhanced up to unity via the quantum Zeno dynamics undergone by a
particle tunneling a potential barrier while the momentum direction of the
particle is frequently monitored.


title: A density-wave mechanism with a continuously variable wave vector

abstract:
  The origin of density waves is a vital component of our insight into
electronic quantum matters. Here, we propose an additional mosaic to the
existing mechanisms such as Fermi-surface nesting, electron-phonon coupling,
and exciton condensation. In particular, we find that certain 2D density-wave
systems are equivalent to 3D nodal-line systems in the presence of a magnetic
field, whose electronic structure takes the form of Dirac-fermion Landau levels
and allows a straightforward analysis on its optimal filling. The subsequent
minimum-energy wave vector varies over a continuous range and shows no direct
connection to the original Fermi surfaces in 2D. Also, we carry out numerical
calculations where the results on model examples support our theory. Our study
points out that we have yet attained a complete story on our understanding of
emergent density wave formalism.


title: A Less Uncertain Sampling-Based Method of Batch Bayesian Optimization

abstract:
  This paper presents a method called sampling-computation-optimization (SCO)
to design batch Bayesian optimization. SCO does not construct new
high-dimensional acquisition functions but samples from the existing one-site
acquisition function to obtain several candidate samples. To reduce the
uncertainty of the sampling, the general discrepancy is computed to compare
these samples. Finally, the genetic algorithm and switch algorithm are used to
optimize the design. Several strategies are used to reduce the computational
burden in the SCO. From the numerical results, the SCO designs were less
uncertain than those of other sampling-based methods. As for application in
batch Bayesian optimization, SCO can find a better solution when compared with
other batch methods in the same dimension and batch size. In addition, it is
also flexible and can be adapted to different one-site methods. Finally, a
complex experimental case is given to illustrate the application value and
scenario of SCO method.


title: Inferring Lexicographically-Ordered Rewards from Preferences

abstract:
  Modeling the preferences of agents over a set of alternatives is a principal
concern in many areas. The dominant approach has been to find a single
reward/utility function with the property that alternatives yielding higher
rewards are preferred over alternatives yielding lower rewards. However, in
many settings, preferences are based on multiple, often competing, objectives;
a single reward function is not adequate to represent such preferences. This
paper proposes a method for inferring multi-objective reward-based
representations of an agent's observed preferences. We model the agent's
priorities over different objectives as entering lexicographically, so that
objectives with lower priorities matter only when the agent is indifferent with
respect to objectives with higher priorities. We offer two example applications
in healthcare, one inspired by cancer treatment, the other inspired by organ
transplantation, to illustrate how the lexicographically-ordered rewards we
learn can provide a better understanding of a decision-maker's preferences and
help improve policies when used in reinforcement learning.


title:
Heat transport and cooling performance in a nanomechanical system with
  local and non local interactions

abstract:
  In the present work, we study heat transport through a one dimensional
time-dependent nanomechanical system. The microscopic model consists of coupled
chains of atoms, considering local and non-local interactions between
particles. We show that the system presents different stationary transport
regimes depending on the driving frequency, temperature gradients and the
degree of locality of the interactions. In one of these regimes, the system
operates as a phonon refrigerator, and its cooling performance is analyzed.
Based on a low frequency approach, we show that non-locality and its interplay
with dissipation cause a decrease in cooling capacity. The results are obtained
numerically by means of the Keldysh non-equilibrium Green's function formalism.


title:
The maximum number of $K_{r_1,\ldots,r_s}$ in graphs with a given
  circumference or matching number

abstract:
  Let $K_{r_1,\ldots,r_s}$ denote the complete multipartite graph with class
sizes $r_1,\ldots,r_s$ and let $K_s$ denote the complete graph of order $s$. In
2018, Luo determined the maximum number of $K_s$ in 2-connected graphs with a
given circumference. Recently, Lu, Yuan and Zhang determined the maximum number
of $K_{r_1,r_2}$ in 2-connected graphs with a given circumference, and Wang
determined the maximum number of $K_s$ or $K_{r_1,1,\ldots,1}$ in graphs with
given matching number. Motivated by these works, we determine the maximum
number of $K_{r_1,\ldots,r_s}$ in $2$-connected graphs with given circumference
and large minimum degree. The maximum number of $K_{r_1,\ldots,r_s}$ in graphs
with given matching number and large minimum degree is also given.
Consequently, we determine the maximum number of $K_{r_1,\ldots,r_s}$ in graphs
with a given circumference or matching number. We also solve the corresponding
problems for graphs with a given detour order.


title: 1-WL Expressiveness Is (Almost) All You Need

abstract:
  It has been shown that a message passing neural networks (MPNNs), a popular
family of neural networks for graph-structured data, are at most as expressive
as the first-order Weisfeiler-Leman (1-WL) graph isomorphism test, which has
motivated the development of more expressive architectures. In this work, we
analyze if the limited expressiveness is actually a limiting factor for MPNNs
and other WL-based models in standard graph datasets. Interestingly, we find
that the expressiveness of WL is sufficient to identify almost all graphs in
most datasets. Moreover, we find that the classification accuracy upper bounds
are often close to 100\%. Furthermore, we find that simple WL-based neural
networks and several MPNNs can be fitted to several datasets. In sum, we
conclude that the performance of WL/MPNNs is not limited by their
expressiveness in practice.


title: Conformal models for hypercolumns and the visual V1 cortex

abstract:
  We propose a model for the hypercolumns that combines the main points of the
symplectic model of the primary visual cortex V1 by A. Sarti, G. Citti and J.
Petitot and of the spherical model of the hypercolumns by P. Bressloff and J.
Cowan. The model by Sarti, Citti and Petitot is based on the assumption that
each Gabor filter $\text{Gab}_n$, modelling the behaviour of a simple neuron
$n$, is obtained from a fixed (``mother'') Gabor filter $\text{Gab}_{n_0}$ by a
transformation $g_n$ of an appropriate group $G$. By analogy, we propose a
model for hypercolumns, which is based on the assumption that the Gabor filters
of their simple neurons are obtained from a mother filter by the group $ G =
\operatorname{SL}_2(\mathbb C)$ of conformal transformations of the sphere
$S^2$. This is a generalisation of the spherical model by Bressloff and Cowan.
In neighbourhoods of pinwheels, we reduce our model to a version of Sarti,
Citti and Petitot's model. The presentation is preceded by a review of basic
facts on the functional architecture of the V1 cortex.


title:
Global well-posedness for three-dimensional compressible viscous
  micropolar and heat-conducting fluids with vacuum at infinity and large
  oscillations

abstract:
  We investigate global well-posedness to the Cauchy problem of
three-dimensional compressible viscous and heat-conducting micropolar fluid
equations with zero density at infinity. By delicate energy estimates, we
establish global existence and uniqueness of strong solutions under some
smallness condition depending only on the parameters appeared in the system and
the initial mass. In particular, the initial mass can be arbitrarily large.
This improves our previous work [23]. Moreover, we also generalize the result
[13] to the case that vacuum is allowed at infinity.


title:
Various Activities above Sunspot Light Bridges in IRIS Observations:
  Classification and Comparison

abstract:
  Light bridges (LBs) are among the most striking sub-structures in sunspots,
where various activities have been revealed by recent high-resolution
observations from the Interface Region Imaging Spectrograph (IRIS). According
to the variety of physical properties, we classified these activities into four
distinct categories: transient brightening (TB), intermittent jet (IJ), type-I
light wall (LW-I), and type-II light wall (LW-II). In IRIS 1400/1330 {\AA}
observations, TBs are characterized by abrupt emission enhancements, and IJs
appear as collimated plasma ejections with a width of 1-2 Mm at some LB sites.
Most observed TBs are associated with IJs and show superpositions of some
chromosphere absorption lines on enhanced and broadened wings of C II and Si IV
lines, which could be driven by intermittent magnetic reconnection in the lower
atmosphere. LW-I and LW-II are wall-shaped structures with bright fronts above
the whole LB. An LW-I has a continuous oscillating front with a typical height
of several Mm and an almost stationary period of 4-5 minutes. On the contrary,
an LW-II has a indented front with a height of over 10 Mm, which has no stable
period and is accompanied by recurrent TBs in the entire LB. These results
support that LW-IIs are driven by frequent reconnection occurring along the
whole LB due to large-scale magnetic flux emergence or intrusion, rather than
the leakage of waves producing LW-Is. Our observations reveal a highly
dynamical scenario of activities above LBs driven by different basic physical
processes, including magneto-convection, magnetic reconnection, and wave
leakage.


title: Lorentz-covariant kinetic theory for massive spin-1/2 particles

abstract:
  We construct a matrix-valued spin-dependent distribution function (MVSD) for
massive spin-1/2 fermions and study its properties under Lorentz
transformations. Such transformations result in a Wigner rotation in spin space
and in a nontrivial matrix-valued shift in space-time, which corresponds to the
side jump in the massless case. We express the vector and axial-vector
components of the Wigner function in terms of the MVSD and show that they
transform in a Lorentz-covariant manner. We then construct a manifestly
Lorentz-covariant Boltzmann equation which contains a nonlocal collision term
encoding spin-orbit coupling. Finally, we obtain the spin-dependent
distribution function in local equilibrium by demanding detailed balance.


title: Tuning of passivity-based controllers for mechanical systems

abstract:
  This manuscript describes several approaches to tune the parameters of a
class of passivity-based controllers for standard nonlinear mechanical systems.
In particular, we are interested in controllers that preserve the mechanical
system structure in closed-loop. To this end, first, we provide tuning rules
for stabilization, i.e., the rate of convergence (exponential stability) and
stability margin (input-to-state stability). Then, we provide guidelines to
remove the overshoot while prescribing the rise time. Additionally, we propose
a methodology to tune the gyroscopic-related parameters. We also provide
remarks on the damping phenomena to facilitate the practical implementation of
our approaches. We conclude this paper with experimental results obtained from
applying our tuning rules to an underactuated and a fully-actuated mechanical
configuration.


title: Poisson-Birnbaum-Saunders Regression Model for Clustered Count Data

abstract:
  The premise of independence among subjects in the same cluster/group often
fails in practice, and models that rely on such untenable assumption can
produce misleading results. To overcome this severe deficiency, we introduce a
new regression model to handle overdispersed and correlated clustered counts.
To account for correlation within clusters, we propose a Poisson regression
model where the observations within the same cluster are driven by the same
latent random effect that follows the Birnbaum-Saunders distribution with a
parameter that controls the strength of dependence among the individuals. This
novel multivariate count model is called Clustered Poisson Birnbaum-Saunders
(CPBS) regression. As illustrated in this paper, the CPBS model is analytically
tractable, and its moment structure can be explicitly obtained. Estimation of
parameters is performed through the maximum likelihood method, and an
Expectation-Maximization (EM) algorithm is also developed. Simulation results
to evaluate the finite-sample performance of our proposed estimators are
presented. We also discuss diagnostic tools for checking model adequacy. An
empirical application concerning the number of inpatient admissions by
individuals to hospital emergency rooms, from the Medical Expenditure Panel
Survey (MEPS) conducted by the United States Agency for Health Research and
Quality, illustrates the usefulness of our proposed methodology.


title:
DeepShovel: An Online Collaborative Platform for Data Extraction in
  Geoscience Literature with AI Assistance

abstract:
  Geoscientists, as well as researchers in many fields, need to read a huge
amount of literature to locate, extract, and aggregate relevant results and
data to enable future research or to build a scientific database, but there is
no existing system to support this use case well. In this paper, based on the
findings of a formative study about how geoscientists collaboratively annotate
literature and extract and aggregate data, we proposed DeepShovel, a
publicly-available AI-assisted data extraction system to support their needs.
DeepShovel leverages the state-of-the-art neural network models to support
researcher(s) easily and accurately annotate papers (in the PDF format) and
extract data from tables, figures, maps, etc. in a human-AI collaboration
manner. A follow-up user evaluation with 14 researchers suggested DeepShovel
improved users' efficiency of data extraction for building scientific
databases, and encouraged teams to form a larger scale but more tightly-coupled
collaboration.


title:
Distributed Strategies for Dynamic Coverage with Limited Sensing
  Capabilities

abstract:
  In this work, it is presented the development of a novel distributed
algorithm performing robotic coverage, clustering and dispatch around an event
in static-obstacle structured environments without relying on metric
information. Specifically, the aim is to account for the trade-off between
local communication given by bearing visibility sensors installed on each agent
involved, optimal deployment in closed unknown scenarios and focus of a group
of agents on one point of interest. The particular targets of this study can be
summarized as 1. the computation, under certain topological assumptions, of a
lower bound for the number of required agents, which are provided by a
realistic geometric model (e.g. a round shape) to emphasize physical
limitations; 2. the minimization of the number of nodes and links maintaining a
distributed approach over a connected communication graph; 3. the
identification of an activation cluster around an event with a radial
decreasing intensity, sensed by each agent; 4. the attempt to send the agents
belonging to the cluster towards the most intense point in the scenario by
minimizing a weighted isoperimetric functional.


title:
Weyl covariance, second clock effect and proper time in theories of
  symmetric teleparallel gravity

abstract:
  We give a new prescription of parallel transport of a vector tangent to a
curve which is invariant under both of a local general coordinate and a Weyl
transformations. Thus since the length of tangent vector does not change during
parallel transport along a closed curve in spacetimes with non-metricity, a
second clock effect does not appear in general, not only for the inetgrable
Weyl spacetime. We have specially motivated the problem from the point of view
of symmetric teleparallel (or Minkowski-Weyl) geometry. We also conclude that
as long as nature respects Lorentz symmetry and Weyl symmetry, one can only
develop alternative gravity models in the symmetric teleparallel spacetime
($Q\neq 0, \; T=R=0$) or the most general non-Riemannian spacetime ($Q\neq 0,
\; T\neq 0, \; R\neq 0$).


title: Diffusion Causal Models for Counterfactual Estimation

abstract:
  We consider the task of counterfactual estimation from observational imaging
data given a known causal structure. In particular, quantifying the causal
effect of interventions for high-dimensional data with neural networks remains
an open challenge. Herein we propose Diff-SCM, a deep structural causal model
that builds on recent advances of generative energy-based models. In our
setting, inference is performed by iteratively sampling gradients of the
marginal and conditional distributions entailed by the causal model.
Counterfactual estimation is achieved by firstly inferring latent variables
with deterministic forward diffusion, then intervening on a reverse diffusion
process using the gradients of an anti-causal predictor w.r.t the input.
Furthermore, we propose a metric for evaluating the generated counterfactuals.
We find that Diff-SCM produces more realistic and minimal counterfactuals than
baselines on MNIST data and can also be applied to ImageNet data. Code is
available https://github.com/vios-s/Diff-SCM.


title: On another characterization of Askey-Wilson polynomials

abstract:
  In this paper we show that the only sequences of orthogonal polynomials
$(P_n)_{n\geq 0}$ satisfying \begin{align*} \phi(x)\mathcal{D}_q
P_{n}(x)=a_n\mathcal{S}_q P_{n+1}(x) +b_n\mathcal{S}_q P_n(x) +c_n\mathcal{S}_q
P_{n-1}(x), \end{align*} ($c_n\neq 0$) where $\phi$ is a well chosen polynomial
of degree at most two, $\mathcal{D}_q$ is the Askey-Wilson operator and
$\mathcal{S}_q$ the averaging operator, are the multiple of Askey-Wilson
polynomials, or specific or limiting cases of them.


title:
Using analog scrambling circuits for automotive sensor integrity and
  authenticity

abstract:
  The automotive domain rapidly increases the embedded amount of complex and
interconnected electronics systems. A considerable proportion of them are
real-time safety-critical devices and must be protected against cybersecurity
attacks. Recent regulations impose carmakers to protect vehicles against
replacing trusted electronic hardware and manipulating the information
collected by sensors. Analog sensors are critical elements whose security is
now strictly regulated by the new UN R155 recommendation but lacks
well-developed and established solutions. This work takes a step forward in
this direction, adding integrity and authentication to automotive analog
sensors proposing a schema to create analog signatures based on a scrambling
mechanism implemented with commercial-of-the-shelf (COTS) operational
amplifiers. The proposed architecture implements a hardware secret and a
hard-to-invert exponential function to generate a signal's signature. A
prototype of the circuit was implemented and simulated on LTspice. Preliminary
results show the feasibility of the proposed schema and provide interesting
hints for further developments to increase the robustness of the approach.


title: Machine Learning Operations: A Survey on MLOps Tool Support

abstract:
  Machine Learning (ML) has become a fast-growing, trending approach in
solution development in practice. Deep Learning (DL) which is a subset of ML,
learns using deep neural networks to simulate the human brain. It trains
machines to learn techniques and processes individually using computer
algorithms, which is also considered to be a role of Artificial Intelligence
(AI). In this paper, we study current technical issues related to software
development and delivery in organizations that work on ML projects. Therefore,
the importance of the Machine Learning Operations (MLOps) concept, which can
deliver appropriate solutions for such concerns, is discussed. We investigate
commercially available MLOps tool support in software development. The
comparison between MLOps tools analyzes the performance of each system and its
use cases. Moreover, we examine the features and usability of MLOps tools to
identify the most appropriate tool support for given scenarios. Finally, we
recognize that there is a shortage in the availability of a fully functional
MLOps platform on which processes can be automated by reducing human
intervention.


title:
Entropy of Generating Series for Nonlinear Input-Output Systems and
  Their Interconnections

abstract:
  This paper has two main objectives. The first is to introduce a notion of
entropy that is well suited for the analysis of nonlinear input-output systems
that have a Chen-Fliess series representation. The latter is defined in terms
of its generating series over a noncommutative alphabet. The idea is to assign
an entropy to a generating series as an element of a graded vector space. The
second objective is to describe the entropy of generating series originating
from interconnected systems of Chen-Fliess series that arise in the context of
control theory. It is shown that one set of interconnections can never increase
entropy as defined here, while a second set has the potential to do so. The
paper concludes with a brief introduction of an entropy ultrametric space.


title: Attractors with Non-Invariant Interior and Pinheiro's Theorem A

abstract:
  This is a provisional version of an article, intended to be devoted to
properties of attractor's intertior for smooth maps (not diffeomorphisms). We
were originally motivated for this research by Pinhero's Theorem A from his
recent preprint, and in Section 3 we give a simple and straightforward proof of
this result.


title: In search of Einstein's brain

abstract:
  The author reflects on the significance of Einstein's brain and the search
for what makes it distinct.


title:
On-chip piezo-optomechanical dynamic single photon routing and rotation
  of a photonic qubit

abstract:
  Integrated photonic circuits are key components for photonic quantum
technologies and for the implementation of practical chip-based quantum
devices. Future applications demand flexible architectures which overcome
common limitations of many current devices, for instance the lack of tuneabilty
or built-in quantum light sources. Here, we report on a dynamically
reconfigurable integrated photonic circuit comprising integrated quantum dots
(QDs), a Mach-Zehnder interferometer (MZI) and surface acoustic wave (SAW)
transducers fabricated on a monolithic semiconductor platform. We demonstrate
on-chip single photon generation by the QD, initializing a photonic qubit. Two
independently applied SAWs piezo-optomechanically rotates the single photon in
the MZI or spectrally modulated the QD emission wavelength. In the MZI, SAWs
imprint a time-dependent optical phase and modulate the qubit rotation to the
output superposition state. This enables dynamic single photon routing with
frequencies exceeding one gigahertz. Finally, the combination of the dynamic
single photon control and spectral tuning of the QD realizes wavelength
multiplexing of the input state and demultiplexing at the output. Our approach
is scalable to multi-component integrated quantum photonic circuits and is
compatible with hybrid photonic architectures and other key components for
instance photonic resonators or on-chip detectors.

categories: physics.optics cond-mat.mes-hall physics.app-ph quant-ph


title:
A Lifting Approach to Learning-Based Self-Triggered Control with
  Gaussian Processes

abstract:
  This paper investigates the design of self-triggered control for networked
control systems (NCS), where the dynamics of the plant is unknown apriori. To
deal with the nature of the self-triggered control, in which state measurements
are transmitted to the controller a-periodically, we propose to lift the
continuous-time dynamics to a novel dynamical model by taking an inter-event
time as an additional input, and then, the lifted model is learned by the
Gaussian processes (GP) regression. Moreover, we propose a learning-based
approach, in which a self-triggered controller is learned by minimizing a cost
function, such that it can take inter-sample behavior into account. By
employing the lifting approach, we can utilize a gradient-based policy update
as an efficient method to optimize both control and communication policies.
Finally, we summarize the overall algorithm and provide a numerical simulation
to illustrate the effectiveness of the proposed approach.


title:
Critical points with prescribed energy for a class of functionals
  depending on a parameter: existence, multiplicity and bifurcation results

abstract:
  We look for critical points with prescribed energy for the family of even
functionals $\Phi_\mu=I_1-\mu I_2$, where $I_1,I_2$ are $C^1$ functionals on a
Banach space $X$, and $\mu \in \mathbb{R}$. For several classes of $\Phi_\mu$
we prove the existence of infinitely many couples $(\mu_{n,c}, u_{n,c})$ such
that $$\Phi'_{\mu_{n,c}}(\pm u_{n,c}) = 0 \quad \mbox{and} \quad
\Phi_{\mu_{n,c}}( \pm u_{n,c}) = c \quad \forall n \in \mathbb{N}.$$ More
generally, we analyze the structure of the solution set of the problem
$$\Phi_\mu'(u)=0, \quad \Phi_{\mu}(u)=c$$ with respect to $\mu$ and $c$. In
particular, we show that the maps $c \mapsto \mu_{n,c}$ are continuous, which
gives rise to a family of {\it energy curves} for this problem. The analysis of
these curves provide us with several bifurcation and multiplicity type results,
which are then applied to some elliptic problems. Our approach is based on the
{\it nonlinear generalized Rayleigh quotient} method developed in \cite{I1}.


title:
Twisted chiral superconductivity in photodoped frustrated Mott
  insulators

abstract:
  Recent advances in ultrafast pump-probe spectroscopy provide access to hidden
phases of correlated matter, including light-induced superconducting states,
but the theoretical understanding of these nonequilibrium phases remains
limited. Here we report how a new type of chiral superconducting phase can be
stabilized in photodoped frustrated Mott insulators. The metastable phase
features a spatially varying order parameter with a $120^\circ$ phase twist
which breaks both time-reversal and inversion symmetry. Under an external
electric pulse, the $120^\circ$ chiral superconducting state can exhibit a
second-order supercurrent perpendicular to the field in addition to a
first-order parallel response, similar to a nonlinear anomalous Hall effect.
This phase can be tuned by artificial gauge fields when the system is dressed
by high-frequency periodic driving. The mechanism revealed in this study
applies to Mott insulators on various frustrated lattices and the hidden
superconducting phase can be realized in both cold-atom quantum simulators and
correlated solids.


title:
Cell nuclei classification in histopathological images using hybrid
  OLConvNet

abstract:
  Computer-aided histopathological image analysis for cancer detection is a
major research challenge in the medical domain. Automatic detection and
classification of nuclei for cancer diagnosis impose a lot of challenges in
developing state of the art algorithms due to the heterogeneity of cell nuclei
and data set variability. Recently, a multitude of classification algorithms
has used complex deep learning models for their dataset. However, most of these
methods are rigid and their architectural arrangement suffers from
inflexibility and non-interpretability. In this research article, we have
proposed a hybrid and flexible deep learning architecture OLConvNet that
integrates the interpretability of traditional object-level features and
generalization of deep learning features by using a shallower Convolutional
Neural Network (CNN) named as $CNN_{3L}$. $CNN_{3L}$ reduces the training time
by training fewer parameters and hence eliminating space constraints imposed by
deeper algorithms. We used F1-score and multiclass Area Under the Curve (AUC)
performance parameters to compare the results. To further strengthen the
viability of our architectural approach, we tested our proposed methodology
with state of the art deep learning architectures AlexNet, VGG16, VGG19,
ResNet50, InceptionV3, and DenseNet121 as backbone networks. After a
comprehensive analysis of classification results from all four architectures,
we observed that our proposed model works well and perform better than
contemporary complex algorithms.


title:
Formal Analysis of the Sampling Behaviour of Stochastic Event-Triggered
  Control

abstract:
  Analyzing Event-Triggered Control's (ETC) sampling behaviour is of paramount
importance, as it enables formal assessment of its sampling performance and
prediction of its sampling patterns. In this work, we formally analyze the
sampling behaviour of stochastic linear periodic ETC (PETC) systems by
computing bounds on associated metrics. Specifically, we consider functions
over sequences of state measurements and intersampling times that can be
expressed as average, multiplicative or cumulative rewards, and introduce their
expectations as metrics on PETC's sampling behaviour. We compute bounds on
these expectations, by constructing appropriate Interval Markov Chains equipped
with suitable reward structures, that abstract stochastic PETC's sampling
behaviour. Our results are illustrated on a numerical example, for which we
compute bounds on the expected average intersampling time and on the
probability of triggering with the maximum possible intersampling time in a
finite horizon.


title:
Dynamical friction in globular cluster-rich ultra-diffuse galaxies: the
  case of NGC5846-UDG1

abstract:
  Ultra-diffuse galaxies that contain a large sample of globular clusters (GCs)
offer an opportunity to test the predictions of galactic dynamics theory.
NGC5846-UDG1 is an excellent example, with a high-quality sample of dozens of
GC candidates. We show that the observed distribution of GCs in NGC5846-UDG1 is
suggestive of mass segregation induced by gravitational dynamical friction. We
present simple analytic calculations, backed by a series of numerical
simulations, that naturally explain the observed present-day pattern of GC
masses and radial positions. Subject to some assumptions on the GC population
at birth, the analysis supports the possibility that NGC5846-UDG1 resides in a
massive dark matter halo. This is an example for the use of GC-rich systems as
dynamical (in addition to kinematical) tracers of dark matter.


title: Defining long words succinctly in FO and MSO

abstract:
  We consider the length of the longest word definable in FO and MSO via a
formula of size n. For both logics we obtain as an upper bound for this number
an exponential tower of height linear in n. We prove this by counting types
with respect to a fixed quantifier rank. As lower bounds we obtain for both FO
and MSO an exponential tower of height in the order of a rational power of n.
We show these lower bounds by giving concrete formulas defining word
representations of levels of the cumulative hierarchy of sets. In addition, we
consider the L\"owenheim-Skolem and Hanf numbers of these logics on words and
obtain similar bounds for these as well.


title:
MILP-based optimal day-ahead scheduling for system-centric CEMS
  supporting different types of homes and energy trading

abstract:
  Optimal day-ahead scheduling for a system-centric community energy management
system (CEMS) is proposed to provide economic benefits and user comfort of
energy management at the community level. Our proposed community includes
different types of homes and allows prosumers to trade energy locally using
mid-market rate pricing. A mathematical model of the community is constructed
and the optimization problem of this model is transformed into an MILP problem
that can be solved in a short time. By solving this MILP problem, the
optimization of the overall energy cost of the community and satisfaction of
the thermal comfort at every home are achieved. For comparison, we also
establish two different scenarios for the same community: a prosumer-centric
CEMS and no CEMS. The simulation results demonstrate that the overall energy
cost of the community with the system-centric CEMS is the smallest among the
three scenarios and is only half that of the community with the
prosumer-centric CEMS. Moreover, by using linear transformation, the
computational time of the optimization problem of the proposed system-centric
CEMS is only 118.2 s for a 500-home community, which is a short time for
day-ahead scheduling of a community.


title:
Validation and atmospheric exploration of the sub-Neptune TOI-2136b
  around a nearby M3 dwarf

abstract:
  The NASA space telescope $TESS$ is currently in the extended mission of its
all-sky search for new transiting planets. Of the thousands of candidates that
TESS is expected to deliver, transiting planets orbiting nearby M dwarfs are
particularly interesting targets since they provide a great opportunity to
characterize their atmospheres by transmission spectroscopy. We aim to validate
and characterize the new sub-Neptune sized planet candidate TOI-2136.01
orbiting a nearby M dwarf ($d = 33.36 \pm 0.02$ pc, $T_{eff} = 3373 \pm 108$ K)
with an orbital period of 7.852 days. We use TESS data, ground-based
multi-color photometry, and radial velocity measurements with the InfraRed
Doppler (IRD) instrument on the Subaru Telescope to validate the planetary
nature of TOI-2136.01 and estimate the stellar and planetary parameters. We
also conduct high-resolution transmission spectroscopy to search for helium in
its atmosphere. We confirmed that TOI-2136.01 (now named as TOI-2136b) is a
bona fide planet with a planetary radius of $R_p = 2.2 \pm 0.07$ $R_{Earth}$
and a mass of $M_p = 4.7^{+3.1}_{-2.6}$ $M_{Earth}$. We also search for helium
10830 \r{A} absorption lines and place an upper limit on the equivalent width
of $<$ 7.8 m\r{A} (95 % confidence) and on the absorption signal of $<$ 1.44 %
(95 % confidence). TOI-2136b is a sub-Neptune transiting a nearby and bright
star (J=10.8) and is a potential hycean planet, making it an excellent target
for atmospheric studies to understand the formation, evolution, and
habitability of the small planets.


title: Higher amalgamation properties in measured structures

abstract:
  Using an infinitary version of the Hypergraph Removal Lemma due to Towsner,
we prove a model-theoretic higher amalgamation result. In particular, we obtain
an independent amalgamation property which holds in structures which are
measurable in the sense of Macpherson and Steinhorn, but which is not generally
true in structures which are superstable of finite SU-rank. We use this to show
that some of Hrushovski's non-locally-modular, superstable $\omega$-categorical
structures are not MS-measurable.


title:
Path of Destruction: Learning an Iterative Level Generator Using a Small
  Dataset

abstract:
  We propose a new procedural content generation method which learns iterative
level generators from a dataset of existing levels. The Path of Destruction
method, as we call it, views level generation as repair; levels are created by
iteratively repairing from a random starting state. The first step is to
generate an artificial dataset from the original set of levels by introducing
many different sequences of mutations to existing levels. In the generated
dataset, features are observations of destroyed levels and targets are the
specific actions that repair the mutated tile in the middle of the
observations. Using this dataset, a convolutional network is trained to map
from observations to their respective appropriate repair actions. The trained
network is then used to iteratively produce levels from random starting states.
We demonstrate this method by applying it to generate unique and playable
tile-based levels for several 2D games (Zelda, Danger Dave, and Sokoban) and
vary key hyperparameters.


title:
OSegNet: Operational Segmentation Network for COVID-19 Detection using
  Chest X-ray Images

abstract:
  Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using
Machine Learning algorithms over chest X-ray (CXR) images. However, most of the
earlier studies used Deep Learning models over scarce datasets bearing the risk
of overfitting. Additionally, previous studies have revealed the fact that deep
networks are not reliable for classification since their decisions may
originate from irrelevant areas on the CXRs. Therefore, in this study, we
propose Operational Segmentation Network (OSegNet) that performs detection by
segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data
scarcity encountered in training and especially in evaluation, this study
extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121,378 CXRs
including 9258 COVID-19 samples with their corresponding ground-truth
segmentation masks that are publicly shared with the research community.
Consequently, OSegNet has achieved a detection performance with the highest
accuracy of 99.65% among the state-of-the-art deep models with 98.09%
precision.


title: Analytic Solutions for Wheeled Mobile Manipulator Supporting Forces

abstract:
  When a mobile manipulator's wheel loses contact with the ground, tipping-over
may occur, causing material damage, and in the worst case, it can put human
lives in danger. The tip-over stability of wheeled mobile manipulators must not
be overlooked at any stage of a mobile manipulator's life, starting from the
design phase, continuing through the commissioning period and extending to the
operational phase. Many tip-over stability criteria formulated throughout the
years do not explicitly consider the normal wheel loads, with most of them
relying on prescribed stability margins in terms of overturning moments. In
these formulations, it is commonly argued that overturning will occur about one
of the axes connecting adjacent manipulator's contact points with the ground.
This claim may not always be valid and is certainly restrictive. Explicit
expressions for the manipulator supporting forces provide the best insight into
relevant affecting terms which contribute to the tip-over (in)stability. They
also remove the necessity for thinking about which axis the manipulator could
tip over and simultaneously enable the formulation of more intuitive stability
margins and on-line tip-over prevention techniques. The present study presents
a general dynamics modelling approach in the Newton--Euler framework using 6D
vectors and gives normal wheel load equations in a typical 4-wheeled mobile
manipulator negotiating a slope. The given expressions are expected to become
standard in wheeled mobile manipulators and to provide a basis for effective
tip-over stability criteria and tip-over avoidance techniques. Based on the
presented results, specific improvements of the state-of-the-art criteria are
discussed.


title:
Learning Multiple Explainable and Generalizable Cues for Face
  Anti-spoofing

abstract:
  Although previous CNN based face anti-spoofing methods have achieved
promising performance under intra-dataset testing, they suffer from poor
generalization under cross-dataset testing. The main reason is that they learn
the network with only binary supervision, which may learn arbitrary cues
overfitting on the training dataset. To make the learned feature explainable
and more generalizable, some researchers introduce facial depth and reflection
map as the auxiliary supervision. However, many other generalizable cues are
unexplored for face anti-spoofing, which limits their performance under
cross-dataset testing. To this end, we propose a novel framework to learn
multiple explainable and generalizable cues (MEGC) for face anti-spoofing.
Specifically, inspired by the process of human decision, four mainly used cues
by humans are introduced as auxiliary supervision including the boundary of
spoof medium, moir\'e pattern, reflection artifacts and facial depth in
addition to the binary supervision. To avoid extra labelling cost,
corresponding synthetic methods are proposed to generate these auxiliary
supervision maps. Extensive experiments on public datasets validate the
effectiveness of these cues, and state-of-the-art performances are achieved by
our proposed method.


title:
FM-AFM with hanging fiber probe for the study of liquid / liquid
  interfaces

abstract:
  This article describes how a frequency modulation AFM using a hanging fiber
force probe made from a quartz tuning fork provides local measurements on
liquid-liquid interfaces. After detailing the manufacture and calibration of
the force probe, we provide evidence that this AFM is suitable for quantitative
measurements at the interface between two liquids. The repeatability of the
measurements allows a poly-dimethylsiloxane / water moving interface to be
monitored over several hours : the evaporation of a water droplet immersed in
poly-dimethylsiloxane is observed, and its interfacial tension evolution over
time is measured. Deformation of the interface is also observed. These
capabilities, and preliminary results on the interface between two immiscible
liquids, pave the way for interface manipulation and study of complex
fluid-fluid interfaces.


title:
The Nature of Losses from Cyber-Related Events: Risk Categories and
  Business Sectors

abstract:
  In this study we examine the nature of losses from cyber related events
across different risk categories and business sectors. Using a leading industry
dataset of cyber events, we evaluate the relationship between the frequency and
severity of individual cyber-related events and the number of affected records.
We find that the frequency of reported cyber related events has substantially
increased between 2008 and 2016. Furthermore, the frequency and severity of
losses depend on the business sector and type of cyber threat: the most
significant cyber loss event categories, by number of events, were related to
data breaches and the unauthorized disclosure of data, while cyber extortion,
phishing, spoofing and other social engineering practices showed substantial
growth rates. Interestingly, we do not find a distinct pattern between the
frequency of events, the loss severity, and the number of affected records as
often alluded to in the literature. We also analyse the severity distribution
of cyber related events across all risk categories and business sectors. This
analysis reveals that cyber risks are heavy-tailed, i.e., cyber risk events
have a higher probability to produce extreme losses than events whose severity
follows an exponential distribution. Furthermore, we find that the frequency
and severity of cyber related losses exhibits a very dynamic and time varying
nature.


title:
Graphs for torus actions on oriented manifolds with isolated fixed
  points and classification in dimension 6

abstract:
  To a torus action on a compact oriented manifold $M$ with isolated fixed
points, we associate a signed labeled multigraph encoding the fixed point data
(weights and signs at fixed points and isotropy submanifolds) of the manifold.
When the circle group acts on a 6-dimensional $M$, we classify such a
multigraph by proving that we can convert it into the empty graph by
successively applying two types of operations. In particular, this calssifies
the fixed point data of any such manifold. We prove this by showing that for
any such manifold, we can successively take equivariant connected sums at fixed
points with itself, $\mathbb{CP}^3$, and 6-dimensional analogue $Z_1$ and $Z_2$
of the Hirzebruch surfaces (and these with opposite orientations) to a fixed
point free action on a compact oriented 6-manifold. We also classify a
multigraph for a torus action on a 4-dimensional $M$.


title: Testing strong lensing subhalo detection with a cosmological simulation

abstract:
  Strong gravitational lensing offers a compelling test of the cold dark matter
paradigm, as it allows for subhaloes with masses of $\sim10^{9}$ M$_\odot$ and
below to be detected. We test commonly-used techniques for detecting dark
matter subhaloes superposed in images of strongly lensed galaxies. For the lens
we take a simulated galaxy in a $\sim10^{13}$ M$_\odot$ halo grown in a
high-resolution cosmological hydrodynamics simulation, which we view from two
different directions. To remove particle noise, we represent the projected
galaxy mass distribution by a series of analytic Gaussian profiles which
precisely capture the features of the projected galaxy. We first model the lens
mass as a broken power-law density profile (the parameterization of which
includes a core ensuring the results are unaffected by a numerical central core
in the simulated galaxy) and then search for small haloes. Of the two
projections, one has a regular elliptical shape, while the other has distinct
deviations from an elliptical shape. For the former, the broken power-law model
gives no false positives and correctly recovers the mass of the superposed
small halo, but for the latter we do find false positives and the inferred halo
mass is overestimated by a factor of $\sim4-5$. We then replace the broken
power-law model by a more complex model in which the lens mass is decomposed
into separate stellar and dark matter components. In this case, we show that we
can precisely capture the complex projected structures of the simulated galaxy
and correctly infer the input parameters of the superposed small halo. By
improving the lens galaxy mass model, we argue this enables more robust subhalo
inference that can detect lower mass dark matter subhaloes, strengthening the
technique's ability to test alternative models of dark matter which do not form
dark matter subhaloes below a cut-off mass.


title:
convex characteristics of quaternionic positive definite functions on
  abelian groups

abstract:
  This paper is concerned with the topological space of normalized
quaternion-valued positive definite functions on an arbitrary abelian group G,
especially its convex characteristics. There are two main results. Firstly, we
prove that the extreme elements in the family of such functions are exactly the
homomorphisms from G to the sphere group S, i.e., the unit 3-sphere in the
quaternion algebra. Secondly, we real a phenomenon which does not exist in the
complex setting: The compact convex set of such functions is not a Bauer
simplex except when G is of exponent less than or equal to 2. In contrast, its
complex counterpart is always a Bauer simplex, as is well known. We also
present an integral representation for such functions as an application and
some other minor interesting results.


title:
Decoupled phase modulation for circularly polarized lights via chiral
  metasurface

abstract:
  Metasurfaces are believed as one of the best candidates in nano-optical
devices, attributed to the key feasible modulation features of phase,
polarization, and local field enhancement by structural designing. However,
current methods of propagation- and geometric-phase modulation are interrelated
between two eigen spin-states. This means that when the left-handed component
phase of a beam is modulated by metasurfaces, its right-handed component phase
will change accordingly, which limits the versatility of spin-decoupled
applications. In this paper, we experimentally and numerically demonstrate a
new phase modulation pathway based on chiral V-shaped holes, which enable fully
decoupled one-handed phase modulation of the two eigen spin-states. Two
enantiomers are proposed to realize decoupled functions for the two
eign-states, e.g., the enantiomer can manipulate the left-handed component
phase of a laser beam without changes of the right-handed component. This
proposed method has significant meaning in metasurfaces, which can expand the
methods of phase engineering.


title:
Low-Dimensional High-Fidelity Kinetic Models for NOX Formation by a
  Compute Intensification Method

abstract:
  A novel compute intensification methodology to the construction of
low-dimensional, high-fidelity "compact" kinetic models for NOX formation is
designed and demonstrated. The method adapts the data intensive Machine Learned
Optimization of Chemical Kinetics (MLOCK) algorithm for compact model
generation by the use of a Latin Square method for virtual reaction network
generation. A set of logical rules are defined which construct a minimally
sized virtual reaction network comprising three additional nodes (N, NO, NO2).
This NOX virtual reaction network is appended to a pre-existing compact model
for methane combustion comprising fifteen nodes.
  The resulting eighteen node virtual reaction network is processed by the
MLOCK coded algorithm to produce a plethora of compact model candidates for NOX
formation during methane combustion. MLOCK automatically; populates the terms
of the virtual reaction network with candidate inputs; measures the success of
the resulting compact model candidates (in reproducing a broad set of gas
turbine industry-defined performance targets); selects regions of input
parameters space showing models of best performance; refines the input
parameters to give better performance; and makes an ultimate selection of the
best performing model or models.
  By this method, it is shown that a number of compact model candidates exist
that show fidelities in excess of 75% in reproducing industry defined
performance targets, with one model valid to >75% across fuel/air equivalence
ratios of 0.5-1.0. However, to meet the full fuel/air equivalence ratio
performance envelope defined by industry, we show that with this minimal
virtual reaction network, two further compact models are required.

categories: physics.chem-ph cs.DC cs.LG physics.comp-ph stat.ML


title:
Efficient computation of oriented vertex and arc colorings of special
  digraphs

abstract:
  In this paper we study the oriented vertex and arc coloring problem on edge
series-parallel digraphs (esp-digraphs) which are related to the well known
series-parallel graphs. Series-parallel graphs are graphs with two
distinguished vertices called terminals, formed recursively by parallel and
series composition. These graphs have applications in modeling series and
parallel electric circuits and also play an important role in theoretical
computer science. The oriented class of series-parallel digraphs is recursively
defined from pairs of vertices connected by a single arc and applying the
parallel and series composition, which leads to specific orientations of
undirected series-parallel graphs. Further we consider the line digraphs of
edge series-parallel digraphs, which are known as minimal series-parallel
digraphs (msp-digraphs).
  We show tight upper bounds for the oriented chromatic number and the oriented
chromatic index of edge series-parallel digraphs and minimal series-parallel
digraphs. Furthermore, we introduce first linear time solutions for computing
the oriented chromatic number of edge series-parallel digraphs and the oriented
chromatic index of minimal series-parallel digraphs.


title:
Optimal Time-Invariant Formation Tracking for a Second-Order Multi-Agent
  System

abstract:
  Given a multi-agent linear system, we formalize and solve a trajectory
optimization problem that encapsulates trajectory tracking, distance-based
formation control and input energy minimization. To this end, a numerical
projection operator Newton's method is developed to find a solution by the
minimization of a cost functional able to capture all these different tasks. To
stabilize the formation, a particular potential function has been designed,
allowing to obtain specified geometrical configurations while the barycenter
position and velocity of the system follows a desired trajectory.


title:
Linear first order differential operators and their Hutchinson-invariant
  sets

abstract:
  In this paper, we initiate the study of a new interrelation between linear
ordinary differential operators and complex dynamics which we discuss in
details in the simplest case of operators of order $1$. Namely, assuming that
such an operator $T$ has polynomial coefficients, we interpret it as a
continuous family of Hutchinson operators acting on the space of positive
powers of linear forms. Using this interpretation of $T$, we introduce its
continuously Hutchinson invariant subsets of the complex plane and investigate
a variety of their properties. In particular, we prove that for any $T$ with
non-constant coefficients, there exists a unique minimal under inclusion
invariant set $\mathrm{M}^T_{CH}$. Further, we completely characterize the
class of operators $T$ for which $\mathrm{M}^T_{CH}$ is compact and find it
explicitly for several special types of operators. In particular, we present
strong evidence that the boundary of $\mathrm{M}^T_{CH}$ is piecewise analytic
in contrast to the boundaries of classical invariant sets occurring in complex
dynamics.


title: On embeddings of extensions of almost finite actions into cubical shifts

abstract:
  For a countable amenable group $G$ and a fixed dimension $m\geq 1$, we
investigate when it is possible to embed a $G$-space $X$ into the
$m$-dimensional cubical shift $([0,1]^m)^G$. We focus our attention on systems
that arise as an extension of an almost finite $G$-action on a totally
disconnected space $Y$, in the sense of Matui and Kerr. We show that if such a
$G$-space $X$ has mean dimension less than $m/2$, then $X$ embeds into the
$(m+1)$-dimensional cubical shift. If the distinguished factor $G$-space $Y$ is
assumed to be a subshift of finite type, then this can be improved to an
embedding into the $m$-dimensional cubical shift. This result ought to be
viewed as the generalization of a theorem by Gutman-Tsukamoto for $G=\mathbb Z$
to actions of all amenable groups, and represents the first result supporting
the Lindenstrauss-Tsukamoto conjecture for actions of groups other than
$G=\mathbb{Z}^k$.


title: Non-Clairvoyant Scheduling with Predictions Revisited

abstract:
  In non-clairvoyant scheduling, the task is to find an online strategy for
scheduling jobs with a priori unknown processing requirements with the
objective to minimize the total (weighted) completion time. We revisit this
well-studied problem in a recently popular learning-augmented setting that
integrates (untrusted) predictions in online algorithm design. While previous
works used predictions on processing requirements, we propose a new prediction
model, which provides a relative order of jobs which could be seen as
predicting algorithmic actions rather than parts of the unknown input. We show
that these predictions have desired properties, admit a natural error measure
as well as algorithms with strong performance guarantees and that they are
learnable in both, theory and practice. We generalize the algorithmic framework
proposed in the seminal paper by Kumar et al. (NeurIPS'18) and present the
first learning-augmented scheduling results for weighted jobs and unrelated
machines. We demonstrate in empirical experiments the practicability and
superior performance compared to the previously suggested single-machine
algorithms.


title:
Quantitative unique continuation for parabolic equations with Neumann
  boundary conditions

abstract:
  In this paper, we establish a globally quantitative estimate of unique
continuation at one time point for solutions of parabolic equations with
Neumann boundary conditions in bounded domains. Our proof is mainly based on
Carleman commutator estimates and a global frequency function argument, which
is motivated from a recent work [5]. As an application, we obtain an
observability inequality from measurable sets in time for all solutions of the
above equations.


title:
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer
  Learning for Telepresence Robotics

abstract:
  Scene graph generation from images is a task of great interest to
applications such as robotics, because graphs are the main way to represent
knowledge about the world and regulate human-robot interactions in tasks such
as Visual Question Answering (VQA). Unfortunately, its corresponding area of
machine learning is still relatively in its infancy, and the solutions
currently offered do not specialize well in concrete usage scenarios.
Specifically, they do not take existing "expert" knowledge about the domain
world into account; and that might indeed be necessary in order to provide the
level of reliability demanded by the use case scenarios. In this paper, we
propose an initial approximation to a framework called Ontology-Guided Scene
Graph Generation (OG-SGG), that can improve the performance of an existing
machine learning based scene graph generator using prior knowledge supplied in
the form of an ontology; and we present results evaluated on a specific
scenario founded in telepresence robotics.


title:
On the Distributed Estimation from Relative Measurements: a Graph-Based
  Convergence Analysis

abstract:
  For a multi-agent system state estimation resting upon noisy measurements
constitutes a problem related to several application scenarios. Adopting the
standard least-squares approach, in this work we derive both the (centralized)
analytic solution to this issue and two distributed iterative schemes, which
allow to establish a connection between the convergence behavior of consensus
algorithm toward the optimal estimate and the theory of the stochastic matrices
that describe the network system dynamics. This study on the one hand
highlights the role of the topological links that define the neighborhood of
agent nodes, while on the other allows to optimize the convergence rate by easy
parameter tuning. The theoretical findings are validated considering different
network topologies by means of numerical simulations.


title:
Learning Bayesian Sparse Networks with Full Experience Replay for
  Continual Learning

abstract:
  Continual Learning (CL) methods aim to enable machine learning models to
learn new tasks without catastrophic forgetting of those that have been
previously mastered. Existing CL approaches often keep a buffer of
previously-seen samples, perform knowledge distillation, or use regularization
techniques towards this goal. Despite their performance, they still suffer from
interference across tasks which leads to catastrophic forgetting. To ameliorate
this problem, we propose to only activate and select sparse neurons for
learning current and past tasks at any stage. More parameters space and model
capacity can thus be reserved for the future tasks. This minimizes the
interference between parameters for different tasks. To do so, we propose a
Sparse neural Network for Continual Learning (SNCL), which employs variational
Bayesian sparsity priors on the activations of the neurons in all layers. Full
Experience Replay (FER) provides effective supervision in learning the sparse
activations of the neurons in different layers. A loss-aware reservoir-sampling
strategy is developed to maintain the memory buffer. The proposed method is
agnostic as to the network structures and the task boundaries. Experiments on
different datasets show that our approach achieves state-of-the-art performance
for mitigating forgetting.


title:
Mixed Precision Iterative Refinement with Sparse Approximate Inverse
  Preconditioning

abstract:
  With the commercial availability of mixed precision hardware, mixed precision
GMRES-based iterative refinement schemes have emerged as popular approaches for
solving sparse linear systems. Existing analyses of these approaches, however,
are all based on using a full LU factorization to construct preconditioners for
use within GMRES in each refinement step. In practical applications, inexact
preconditioning techniques, such as incomplete LU or sparse approximate
inverses, are often used for performance reasons.
  In this work, we investigate the use of sparse approximate inverse
preconditioners within GMRES-based iterative refinement. We analyze the
computation of sparse approximate inverses in finite precision and derive
constraints under which the user-specified stopping criteria will be satisfied.
We then analyze the behavior of and convergence constraints for a GMRES-based
iterative refinement scheme that uses sparse approximate inverse
preconditioning, which we call SPAI-GMRES-IR. Our numerical experiments confirm
that in some cases, sparse approximate inverse preconditioning can have an
advantage over using a full LU factorization.


title: Partial Dyck paths with Air Pockets

abstract:
  Dyck paths with air pockets are obtained from ordinary Dyck paths by
compressing maximal runs of down-steps into giant down-steps of arbitrary size.
Using the kernel method, we consider partial Dyck paths with air pockets, both,
from left to right and from right to left.


title:
TENET: Towards Self-sovereign and Fair Multi-party Computation Ecology
  Empowered by Decentralized TEE Network

abstract:
  Recently, blockchain has been adopted across an increasing range of domains,
e.g., supply chain finance, digital government. The confidentiality of smart
contracts has now become a fundamental and crucial demand for practical
applications. However, while new privacy protection techniques are emerging,
how to securely enable Multi-party Computation (MPC) in confidential smart
contracts is understudied. State-of-the-art solutions suffer on high on-chain
cost, low flexibility and security thus can hardly reach general users. This
paper proposes TENET, a pluggable and configurable confidential smart contract
framework. TENET identifies the pitfalls for supporting off-chain MPC-enabled
confidential smart contracts by leveraging the TEE network and proposes
corresponding treats, e.g., anonymously negotiate MPC among apriori-unknown
participants, atomically and fairly distribute the MPC outputs, and securely
evaluate an MPC program with constant on-chain transactions. TENET manages to
secure an MPC by only 3 transactions. In our evaluation on both examples and
real-world applications involving 2 to 11 parties, TENET costs 0.55-0.98X gas
(0.91X on average) against the state-of-the-art.


title: Offline Text-Independent Writer Identification based on word level data

abstract:
  This paper proposes a novel scheme to identify the authorship of a document
based on handwritten input word images of an individual. Our approach is
text-independent and does not place any restrictions on the size of the input
word images under consideration. To begin with, we employ the SIFT algorithm to
extract multiple key points at various levels of abstraction (comprising
allograph, character, or combination of characters). These key points are then
passed through a trained CNN network to generate feature maps corresponding to
a convolution layer. However, owing to the scale corresponding to the SIFT key
points, the size of a generated feature map may differ. As an alleviation to
this issue, the histogram of gradients is applied on the feature map to produce
a fixed representation. Typically, in a CNN, the number of filters of each
convolution block increase depending on the depth of the network. Thus,
extracting histogram features for each of the convolution feature map increase
the dimension as well as the computational load. To address this aspect, we use
an entropy-based method to learn the weights of the feature maps of a
particular CNN layer during the training phase of our algorithm. The efficacy
of our proposed system has been demonstrated on two publicly available
databases namely CVL and IAM. We empirically show that the results obtained are
promising when compared with previous works.


title:
Non-invasive chemically selective energy delivery and focusing inside a
  scattering medium guided by Raman scattering

abstract:
  Raman scattering is a chemically selective probing mechanism with diverse
applications in industry and clinical settings. Yet, most samples are optically
opaque limiting the applicability of Raman probing at depth. Here, we
demonstrate chemically selective energy deposition behind a scattering medium
by combining prior information on the chemical's spectrum with the measurement
of a spectrally resolved Raman speckle as a feedback mechanism for wavefront
shaping. We demonstrate unprecedented six-fold signal enhancement in an
epi-geometry, realizing targeted energy deposition and focusing on selected
Raman active particles.