title: Surf or sleep? Understanding the influence of bedtime patterns on campus

abstract:
  Poor sleep habits may cause serious problems of mind and body, and it is a
commonly observed issue for college students due to study workload as well as
peer and social influence. Understanding its impact and identifying students
with poor sleep habits matters a lot in educational management. Most of the
current research is either based on self-reports and questionnaires, suffering
from a small sample size and social desirability bias, or the methods used are
not suitable for the education system. In this paper, we develop a general
data-driven method for identifying students' sleep patterns according to their
Internet access pattern stored in the education management system and explore
its influence from various aspects. First, we design a Possion-based
probabilistic mixture model to cluster students according to the distribution
of bedtime and identify students who are used to staying up late. Second, we
profile students from five aspects (including eight dimensions) based on
campus-behavior data and build Bayesian networks to explore the relationship
between behavioral characteristics and sleeping habits. Finally, we test the
predictability of sleeping habits. This paper not only contributes to the
understanding of student sleep from a cognitive and behavioral perspective but
also presents a new approach that provides an effective framework for various
educational institutions to detect the sleeping patterns of students.


title: Amenable Sparse Network Investigator

abstract:
  As the optimization problem of pruning a neural network is nonconvex and the
strategies are only guaranteed to find local solutions, a good initialization
becomes paramount. To this end, we present the Amenable Sparse Network
Investigator ASNI algorithm that learns a sparse network whose initialization
is compressed. The learned sparse structure found by ASNI is amenable since its
corresponding initialization, which is also learned by ASNI, consists of only
2L numbers, where L is the number of layers. Requiring just a few numbers for
parameter initialization of the learned sparse network makes the sparse network
amenable. The learned initialization set consists of L signed pairs that act as
the centroids of parameter values of each layer. These centroids are learned by
the ASNI algorithm after only one single round of training. We experimentally
show that the learned centroids are sufficient to initialize the nonzero
parameters of the learned sparse structure in order to achieve approximately
the accuracy of non-sparse network. We also empirically show that in order to
learn the centroids, one needs to prune the network globally and gradually.
Hence, for parameter pruning we propose a novel strategy based on a sigmoid
function that specifies the sparsity percentage across the network globally.
Then, pruning is done magnitude-wise and after each epoch of training. We have
performed a series of experiments utilizing networks such as ResNets,
VGG-style, small convolutional, and fully connected ones on ImageNet, CIFAR10,
and MNIST datasets.


title: CH$_3$CN deuteration in the SVS13-A Class I hot-corino. SOLIS XV

abstract:
  We studied the line emission from CH3CN and its deuterated isotopologue
CH$_2$DCN towards the prototypical Class I object SVS13-A, where the
deuteration of a large number of species has already been reported. Our goal is
to measure the CH$_3$CN deuteration in a Class I protostar, for the first time,
in order to constrain the CH$_3$CN formation pathways and the chemical
evolution from the early prestellar core and Class 0 to the evolved Class I
stages. We imaged CH2DCN towards SVS13-A using the IRAM NOEMA interferometer at
3mm in the context of the Large Program SOLIS (with a spatial resolution of
1.8"x1.2"). The NOEMA images have been complemented by the CH$_3$CN and
CH$_2$DCN spectra collected by the IRAM-30m Large Program ASAI, that provided
an unbiased spectral survey at 3mm, 2mm, and 1.3mm. The observed line emission
has been analysed using LTE and non-LTE LVG approaches. The NOEMA/SOLIS images
of CH2DCN show that this species emits in an unresolved area centered towards
the SVS13-A continuum emission peak, suggesting that methyl cyanide and its
isotopologues are associated with the hot corino of SVS13-A, previously imaged
via other iCOMs. In addition, we detected 41 and 11 ASAI transitions of
CH$_3$CN and CH2DCN, respectively, which cover upper level energies (Eup) from
13 to 442 K and from 18 K to 200 K, respectively. The derived [CH2DCN]/[CH3CN]
ratio is $\sim$9\%. This value is consistent with those measured towards
prestellar cores and a factor 2-3 higher than those measured in Class 0
protostars. Contrarily to what expected for other molecular species, the CH3CN
deuteration does not show a decrease in SVS13-A with respect to measurements in
younger prestellar cores and Class 0 protostars. Finally, we discuss why our
new results suggest that CH3CN was likely synthesised via gas-phase reactions
and frozen onto the dust grain mantles during the cold prestellar phase.


title: Leakage Inventory Model without shortages under fuzzy parameters

abstract:
  In this paper, an attempt has been made to develop a simple leakage inventory
model without shortages with instantaneous or finite production rate under
fuzzy environment. In the present day scenario, it is difficult to decide the
exact annual demand rate and hence a major difficulty is faced by a decision
maker to forecast the demand. Also, in any inventory system, goods in stock may
subject to deterioration or leakage. Deterioration of goods refers to decrease
in quality whereas quantity remains more or less the same. On the other hand,
leakage refers to loss in quantity whereas the quality remains unchanged for a
certain period of time. Leakages in the inventory system may be considered to
be very small, not detectable by the management immediately and hence it is
difficult to decide the exact leakage rate. The objective of this paper is to
consider these variable parameters and determine the optimal economic order
quantity (EOQ) to maximise the annual total profit. So, fuzzy inventory models
have been proposed considering fuzzy leakage rate and fuzzy annual demand rate
to estimate the total profit per unit time. Signed distance method is used for
defuzzification. Numerical examples are provided to support the results of the
proposed models.


title:
On the rate of convergence for the autocorrelation operator in
  functional autoregression

abstract:
  We consider the problem of estimating the autocorrelation operator of an
autoregressive Hilbertian process. By means of a Tikhonov approach, we
establish a general result that yields the convergence rate of the estimated
autocorrelation operator as a function of the rate of convergence of the
estimated lag zero and lag one autocovariance operators. The result is general
in that it can accommodate any consistent estimators of the lagged
autocovariances. Consequently it can be applied to processes under any mode of
observation: complete, discrete, sparse, and/or with measurement errors. An
appealing feature is that the result does not require delicate spectral decay
assumptions on the autocovariances but instead rests on natural source
conditions. The result is illustrated by application to important special
cases.


title:
Optimization of the Sparse Multi-Threaded Cholesky Factorization for
  A64FX

abstract:
  Sparse linear algebra routines are fundamental building blocks of a large
variety of scientific applications. Direct solvers, which are methods for
solving linear systems via the factorization of matrices into products of
triangular matrices, are commonly used in many contexts. The Cholesky
factorization is the fastest direct method for symmetric and definite positive
matrices. This paper presents selective nesting, a method to determine the
optimal task granularity for the parallel Cholesky factorization based on the
structure of sparse matrices. We propose the OPT-D-COST algorithm, which
automatically and dynamically applies selective nesting. OPT-D-COST leverages
matrix sparsity to drive complex task-based parallel workloads in the context
of direct solvers. We run an extensive evaluation campaign considering a
heterogeneous set of 60 sparse matrices and a parallel machine featuring the
A64FX processor. OPT-D-COST delivers an average performance speedup of
1.46$\times$ with respect to the best state-of-the-art parallel method to run
direct solvers.


title: Towards a Numerical Proof of Turbulence Closure

abstract:
  The development of turbulence closure models, parametrizing the influence of
small non-resolved scales on the dynamics of large resolved ones, is an
outstanding theoretical challenge with vast applicative relevance. We present a
closure, based on deep recurrent neural networks, that quantitatively
reproduces, within statistical errors, Eulerian and Lagrangian structure
functions and the intermittent statistics of the energy cascade, including
those of subgrid fluxes. To achieve high-order statistical accuracy, and thus a
stringent statistical test, we employ shell models of turbulence. Our results
encourage the development of similar approaches for 3D Navier-Stokes
turbulence.

categories: physics.flu-dyn cond-mat.stat-mech cs.LG nlin.CD physics.comp-ph


title:
Are the black hole remnants produced from binary black hole mergers in
  GWTC-3 thermodynamically stable?

abstract:
  Black hole thermodynamics has brought strong hints of a profound and
fundamental connection between gravity, thermodynamics, and quantum theory. If
the black hole does behave like a natural thermodynamic system, it should be
thermodynamically stable in a clean environment. In this paper, using the
observational data of binary black hole (BBH) mergers observed by LIGO, Virgo,
and KAGRA detectors, we check whether the black hole remnants produced from BBH
mergers in the LIGO-Virgo-KAGRA catalog GWTC-3 are thermodynamically stable.
The criterion for the thermodynamic stability is quite simple and is directly
related to the black hole's spin, which states that a thermodynamically stable
black hole remnant requires its dimensionless spin $a>a_* \simeq 0.68$. We
check the posterior distributions of final spin $a_f$ for 83 black hole
remnants in GWTC-3 and find the whole remnant population is consistent with the
thermodynamically stable black hole with $99.98\%$ probability. This is the
first verification of the thermodynamic stability of black hole remnants
produced from BBH mergers.


title: Bayesian and Randomized Clock Auctions

abstract:
  In a single-parameter mechanism design problem, a provider is looking to sell
a service to a group of potential buyers. Each buyer $i$ has a private value
$v_i$ for receiving the service and a feasibility constraint restricts which
sets of buyers can be served simultaneously. Recent work in economics
introduced clock auctions as a superior class of auctions for this problem, due
to their transparency, simplicity, and strong incentive guarantees. Subsequent
work focused on evaluating the social welfare approximation guarantees of these
auctions, leading to strong impossibility results: in the absence of prior
information regarding the buyers' values, no deterministic clock auction can
achieve a bounded approximation, even for simple feasibility constraints with
only two maximal feasible sets.
  We show that these negative results can be circumvented by using prior
information or by leveraging randomization. We provide clock auctions that give
a $O(\log\log k)$ approximation for general downward-closed feasibility
constraints with $k$ maximal feasible sets for three different information
models, ranging from full access to the value distributions to complete absence
of information. The more information the seller has, the simpler these auctions
are. Under full access, we use a particularly simple deterministic clock
auction, called a single-price clock auction, which is only slightly more
complex than posted price mechanisms. In this auction, each buyer is offered a
single price and a feasible set is selected among those who accept their
offers. In the other extreme, where no prior information is available, this
approximation guarantee is obtained using a complex randomized clock auction.
In addition to our main results, we propose a parameterization that
interpolates between single-price clock auctions and general clock auctions,
paving the way for an exciting line of future research.


title: System Safety and Artificial Intelligence

abstract:
  This chapter formulates seven lessons for preventing harm in artificial
intelligence (AI) systems based on insights from the field of system safety for
software-based automation in safety-critical domains. New applications of AI
across societal domains and public organizations and infrastructures come with
new hazards, which lead to new forms of harm, both grave and pernicious. The
text addresses the lack of consensus for diagnosing and eliminating new AI
system hazards. For decades, the field of system safety has dealt with
accidents and harm in safety-critical systems governed by varying degrees of
software-based automation and decision-making. This field embraces the core
assumption of systems and control that AI systems cannot be safeguarded by
technical design choices on the model or algorithm alone, instead requiring an
end-to-end hazard analysis and design frame that includes the context of use,
impacted stakeholders and the formal and informal institutional environment in
which the system operates. Safety and other values are then inherently
socio-technical and emergent system properties that require design and control
measures to instantiate these across the technical, social and institutional
components of a system. This chapter honors system safety pioneer Nancy
Leveson, by situating her core lessons for today's AI system safety challenges.
For every lesson, concrete tools are offered for rethinking and reorganizing
the safety management of AI systems, both in design and governance. This
history tells us that effective AI safety management requires transdisciplinary
approaches and a shared language that allows involvement of all levels of
society.


title: Simple string diagrams and n-sesquicategories

abstract:
  We define a monad $T_n^{D^s}$ whose operations are encoded by simple string
diagrams and we define n-sesquicategories as algebras over this monad. This
monad encodes the compositional structure of n-dimensional string diagrams. We
give a generators and relations description of $T_n^{D^s}$, which allows us to
describe n-sesquicategories as globular sets equipped with associative and
unital composition and whiskering operations. One can also see them as strict
n-categories without interchange laws. Finally we give an inductive
characterization of n-sesquicategories.


title: Noise-Robust and Loss-Tolerant Quantum Steering with Qudits

abstract:
  A primary requirement for a robust and unconditionally secure quantum network
is the establishment of quantum nonlocal correlations over a realistic channel.
While loophole-free tests of Bell nonlocality allow for entanglement
certification in such a device-independent setting, they are extremely
sensitive to loss and noise, which naturally arise in any practical
communication scenario. Quantum steering relaxes the strict technological
constraints of Bell nonlocality by re-framing it in an asymmetric manner, thus
providing the basis for one-sided device-independent quantum networks that can
operate under realistic conditions. Here we introduce a noise-robust and
loss-tolerant test of quantum steering designed for single detector
measurements that harnesses the advantages of high-dimensional entanglement. We
showcase the improvements over qubit-based systems by experimentally
demonstrating detection loophole-free quantum steering in 53 dimensions through
simultaneous loss and noise conditions corresponding to 14.2 dB loss equivalent
to 79 km of telecommunication fibre, and 36% of white noise. We go on to show
how the use of high dimensions counter-intuitively leads to a dramatic
reduction in total measurement time, enabling a quantum steering violation
almost two orders of magnitude faster obtained by simply doubling the Hilbert
space dimension. By surpassing the constraints imposed upon the
device-independent distribution of entanglement, our loss-tolerant,
noise-robust, and resource-efficient demonstration of quantum steering proves
itself a critical ingredient for making device-independent quantum
communication over long distances a reality.


title:
Symmetrized and non-symmetrized Asymptotic Mean Value Laplacian in
  metric measure spaces

abstract:
  The asymptotic mean value Laplacian - AMV Laplacian - extends the Laplace
operator from $\mathbb{R}^n$ to metric measure spaces through limits of
averaging integrals. The AMV Laplacian is however not a symmetric operator in
general. In this paper therefore a symmetric version of the AMV Laplacian is
considered, and focus lies on when the symmetric and non-symmetric AMV
operators coincide. Besides Riemannian and 3D contact sub-Riemannian manifolds,
we show that they are identical on a large class of metric measure spaces
including locally Ahlfors regular spaces with vanishing metric-measure
boundary. In addition, we study the context of weighted domains of
$\mathbb{R}^n$ where the two operators typically differ, and provide concrete
formulae for these operators also at points where the weight vanishes.


title: Tight universal sums of $m$-gonal numbers

abstract:
  For a positive integer $n$, the set of all integers greater than or equal to
$n$ is denoted by $\mathcal T(n)$. A sum of generalized $m$-gonal numbers $g$
is called tight $\mathcal T(n)$-universal if the set of all nonzero integers
represented by $g$ is equal to $\mathcal T(n)$. In this article, we prove the
existence of a minimal tight $\mathcal T(n)$-universality criterion set for a
sum of generalized $m$-gonal numbers for any pair $(m,n)$. To achieve this, we
introduce an algorithm giving all candidates for tight $\mathcal
T(n)$-universal sums of generalized $m$-gonal numbers for any given pair
$(m,n)$. Furthermore, we provide some experimental results on the
classification of tight $\mathcal T(n)$-universal sums of generalized $m$-gonal
numbers.


title:
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for
  Energy Harvesting Wearable IoT Devices

abstract:
  Advances in low-power electronics and machine learning techniques lead to
many novel wearable IoT devices. These devices have limited battery capacity
and computational power. Thus, energy harvesting from ambient sources is a
promising solution to power these low-energy wearable devices. They need to
manage the harvested energy optimally to achieve energy-neutral operation,
which eliminates recharging requirements. Optimal energy management is a
challenging task due to the dynamic nature of the harvested energy and the
battery energy constraints of the target device. To address this challenge, we
present a reinforcement learning-based energy management framework, tinyMAN,
for resource-constrained wearable IoT devices. The framework maximizes the
utilization of the target device under dynamic energy harvesting patterns and
battery constraints. Moreover, tinyMAN does not rely on forecasts of the
harvested energy which makes it a prediction-free approach. We deployed tinyMAN
on a wearable device prototype using TensorFlow Lite for Micro thanks to its
small memory footprint of less than 100 KB. Our evaluations show that tinyMAN
achieves less than 2.36 ms and 27.75 $\mu$J while maintaining up to 45% higher
utility compared to prior approaches.


title:
Stratified Multivariate Multiscale Dispersion Entropy for Physiological
  Signal Analysis

abstract:
  Multivariate Entropy quantification algorithms are becoming a prominent tool
for the extraction of information from multi-channel physiological time-series.
However, during the analysis of physiological signals from heterogeneous organ
systems, certain channels may overshadow the patterns of others, resulting in
information loss. Here, we introduce the framework of Stratified Entropy to
control the prioritization of each channels' dynamics based on their allocation
to respective strata, leading to a richer description of the multi-channel
signal. As an implementation of the framework, three algorithmic variations of
the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These
variations and the original algorithm are applied to synthetic and
physiological time-series, formulated from electroencephalogram, arterial blood
pressure, electrocardiogram, and nasal respiratory signals. The results of
experiments conducted on synthetic time-series indicate that the variations
successfully prioritize channels based on their strata allocation while
maintaining the low computation time of the original algorithm. Based on the
physiological time-series results, the distributions of features extracted from
healthy sleep versus sleep with obstructive sleep apnea display increased
statistical difference for certain strata allocations in the variations. This
suggests improved physiological state monitoring by the variations.
Furthermore, stratified algorithms can be modified to utilize a priori
knowledge for the stratification of channels. Thus, our research provides a
novel approach of multivariate analysis for the extraction of previously
inaccessible information from heterogeneous systems.


title:
Large Andreev bound state zero bias peaks in a weakly dissipative
  environment

abstract:
  We study Andreev bound states in hybrid InAs-Al nanowire devices. The energy
of these states can be tuned to zero by gate voltage or magnetic field,
revealing large zero bias peaks (ZBPs) near 2e^2/h in tunneling conductance.
Probing these large ZBPs using a weakly dissipative lead reveals non-Fermi
liquid temperature (T) dependence due to environmental Coulomb blockade (ECB),
an interaction effect from the lead acting on the nanowire junction. By
increasing T, these large ZBPs either show a height increase or a transition
from split peaks to a ZBP, both deviate significantly from non-dissipative
devices where a Fermi-liquid T dependence is revealed. Our result demonstrates
the competing effect between ECB and thermal broadening on Andreev bound
states.


title:
Exploring Adversarially Robust Training for Unsupervised Domain
  Adaptation

abstract:
  Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a
labeled source domain to an unlabeled target domain. UDA has been extensively
studied in the computer vision literature. Deep networks have been shown to be
vulnerable to adversarial attacks. However, very little focus is devoted to
improving the adversarial robustness of deep UDA models, causing serious
concerns about model reliability. Adversarial Training (AT) has been considered
to be the most successful adversarial defense approach. Nevertheless,
conventional AT requires ground-truth labels to generate adversarial examples
and train models, which limits its effectiveness in the unlabeled target
domain. In this paper, we aim to explore AT to robustify UDA models: How to
enhance the unlabeled data robustness via AT while learning domain-invariant
features for UDA? To answer this, we provide a systematic study into multiple
AT variants that potentially apply to UDA. Moreover, we propose a novel
Adversarially Robust Training method for UDA accordingly, referred to as
ARTUDA. Extensive experiments on multiple attacks and benchmarks show that
ARTUDA consistently improves the adversarial robustness of UDA models.


title:
Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy
  Placement Through MAP-Elites

abstract:
  Procedural Content Generation (PCG) methods are valuable tools to speed up
the game development process. Moreover, PCG may also present in games as
features, such as the procedural dungeon generation (PDG) in Moonlighter
(Digital Sun, 2018). This paper introduces an extended version of an
evolutionary dungeon generator by incorporating a MAP-Elites population. Our
dungeon levels are discretized with rooms that may have locked-door missions
and enemies within them. We encoded the dungeons through a tree structure to
ensure the feasibility of missions. We performed computational and user
feedback experiments to evaluate our PDG approach. They show that our approach
accurately converges almost the whole MAP-Elite population for most executions.
Finally, players' feedback indicates that they enjoyed the generated levels,
and they could not indicate an algorithm as a level generator.


title:
On the Application of Differential Evolution to the Analysis of X-Ray
  Spectra

abstract:
  Using methods of differential evolution (DE,) we determined the coronal
elemental abundances and the differential emission measure (DEM) distributions
for the plasma flaring on 2003 January 21. The analyses have been based on
RESIK X-ray spectra. DE belongs to the family of evolutionary algorithms. DE is
conceptually simple and easy to be implemented, so it has been applied to solve
many problems in science and engineering. In this study we apply this method in
a new context: simultaneous determination of plasma composition and DEM . In
order to increase the confidence in the results obtained using DE, we tested
the use of its algorithms by comparing the DE synthesized spectra with
respective spectra observed by RESIK. Extensive discussion of the DE method
used and the obtained physical characteristics of flaring plasma is presented.


title:
Convex-roof entanglement measures of density matrices block diagonal in
  disjoint subspaces for the study of thermal states

abstract:
  We provide a proof that entanglement of any density matrix which block
diagonal in subspaces which are disjoint in terms of the Hilbert space of one
of the two potentially entangled subsystems can simply be calculated as the
weighted average of entanglement present within each block. This is especially
useful for thermal-equilibrium states which always inherit the symmetries
present in the Hamiltonian, since block-diagonal Hamiltonians are common as are
interactions which involve only a single degree of freedom of a greater system.
We exemplify our method on a simple Hamiltonian, showing the diversity in
possible temperature-dependencies of Gibbs state entanglement which can emerge
in different parameter ranges.


title: Tight universal octagonal forms

abstract:
  Let $P_8(x)=3x^2-2x$. For positive integers $a_1,a_2,\dots,a_k$, a polynomial
of the form $a_1P_8(x_1)+a_2P_8(x_2)+\cdots+a_kP_8(x_k)$ is called an octagonal
form. For a positive integer $n$, an octagonal form is called tight $\mathcal
T(n)$-universal if it represents (over $\mathbb{z}$) every positive integer
greater than or equal to $n$ and does not represent any positive integer less
than $n$. In this article, we find all tight $\mathcal T(n)$-universal
octagonal forms for every $n\ge 2$. Furthermore, we provide an effective
criterion on tight $\mathcal T(n)$-universality of an arbirary octagonal form,
which is a generalization of "15-Theorem" of Conway and Schneeberger.


title: Masked prediction tasks: a parameter identifiability view

abstract:
  The vast majority of work in self-supervised learning, both theoretical and
empirical (though mostly the latter), have largely focused on recovering good
features for downstream tasks, with the definition of "good" often being
intricately tied to the downstream task itself. This lens is undoubtedly very
interesting, but suffers from the problem that there isn't a "canonical" set of
downstream tasks to focus on -- in practice, this problem is usually resolved
by competing on the benchmark dataset du jour.
  In this paper, we present an alternative lens: one of parameter
identifiability. More precisely, we consider data coming from a parametric
probabilistic model, and train a self-supervised learning predictor with a
suitably chosen parametric form. Then, we ask whether we can read off the
ground truth parameters of the probabilistic model from the optimal predictor.
We focus on the widely used self-supervised learning method of predicting
masked tokens, which is popular for both natural languages and visual data.
  While incarnations of this approach have already been successfully used for
simpler probabilistic models (e.g. learning fully-observed undirected graphical
models), we focus instead on latent-variable models capturing sequential
structures -- namely Hidden Markov Models with both discrete and conditionally
Gaussian observations. We show that there is a rich landscape of possibilities,
out of which some prediction tasks yield identifiability, while others do not.
Our results, borne of a theoretical grounding of self-supervised learning,
could thus potentially beneficially inform practice. Moreover, we uncover close
connections with uniqueness of tensor rank decompositions -- a widely used tool
in studying identifiability through the lens of the method of moments.


title: Most Hitchin representations are strongly dense

abstract:
  We prove that generic Hitchin representations are strongly dense: every pair
of non commuting elements in their image generate a Zariski-dense subgroup of
SL_n(R). The proof uses a theorem of Rapinchuk, Benyash-Krivetz and Chernousov,
to show that the set of Hitchin representations is Zariski-dense in the variety
of representations of a surface group in SL_n(R).


title: Cyber Human Interaction

abstract:
  Cyber human interaction is a broad term encompassing the range of
interactions that humans can have with technology. While human interaction with
fixed and mobile computers is well understood, the world is on the cusp of
ubiquitous and sustained interactions between humans and robots. While robotic
systems are intertwined with computing and computing technologies, the word
robot here describes technologies that can physically affect and in turn be
affected by their environments which includes humans. This chapter delves into
issues of cyber human interaction from the perspective of humans interacting
with a subset of robots known as assistive robots. Assistive robots are robots
designed to assist individuals with mobility or capacity limitations in
completing everyday activities, commonly called instrumental activities of
daily living. These range from household chores, eating or drinking to any
activity with which a user may need the daily assistance of a caregiver to
complete. One common type of assistive robot is the wheelchair mounted robotic
arm. This device is designed to attach to a user's wheelchair to allow him or
her to complete their activities independently. In short, these devices have
sensors that allow them to sense and process their environment with varying
levels of autonomy to perform actions that benefit and improve the well-being
of people with capability limitations or disabilities. While human robot
interaction is a popular research topic, not much research has been dedicated
with regard to individual with limitations. In this chapter, we provide an
overview of assistive robotic devices, discuss common methods of user
interaction, and the need for an adaptive compensation framework to support
potential users in regaining their functional capabilities.


title:
Indirect Optimal Control of Advection-Diffusion Fields through Robotic
  Swarms

abstract:
  In this paper, we consider the problem of optimally guiding a large-scale
swarm of underwater vehicles that is tasked with the indirect control of an
advection-diffusion environmental field. The microscopic vehicle dynamics are
governed by a stochastic differential equation with drift. The drift terms
model the self-propelled velocity of the vehicle and the velocity field of the
currents. In the mean-field setting, the macroscopic vehicle dynamics are
governed by a Kolmogorov forward equation in the form of a linear parabolic
advection-diffusion equation. The environmental field is governed by an
advection-diffusion equation in which the advection term is defined by the
fluid velocity field. The vehicles are equipped with on-board actuators that
enable the swarm to act as a distributed source in the environmental field,
modulated by a scalar control parameter that determines the local source
intensity. In this setting, we formulate an optimal control problem to compute
the vehicle velocity and actuator intensity fields that drive the environmental
field to a desired distribution within a specified amount of time. In other
words, we design optimal vector and scalar actuation fields to indirectly
control the environmental field through a distributed source, produced by the
swarm. After proving an existence result for the solution of the optimal
control problem, we discretize and solve the problem using the Finite Element
Method (FEM). The FEM discretization naturally provides an operator that
represents the bilinear way in which the controls enter into the dynamics of
the vehicle swarm and the environmental field. Finally, we show through
numerical simulations the effectiveness of our control strategy in regulating
the environmental field to zero or to a desired distribution in the presence of
a double-gyre flow field.


title: Dimension-Free Noninteractive Simulation from Gaussian Sources

abstract:
  Let $X$ and $Y$ be two real-valued random variables. Let
$(X_{1},Y_{1}),(X_{2},Y_{2}),\ldots$ be independent identically distributed
copies of $(X,Y)$. Suppose there are two players A and B. Player A has access
to $X_{1},X_{2},\ldots$ and player B has access to $Y_{1},Y_{2},\ldots$.
Without communication, what joint probability distributions can players A and B
jointly simulate? That is, if $k,m$ are fixed positive integers, what
probability distributions on $\{1,\ldots,m\}^{2}$ are equal to the distribution
of $(f(X_{1},\ldots,X_{k}),\,g(Y_{1},\ldots,Y_{k}))$ for some
$f,g\colon\mathbb{R}^{k}\to\{1,\ldots,m\}$?
  When $X$ and $Y$ are standard Gaussians with fixed correlation
$\rho\in(-1,1)$, we show that the set of probability distributions that can be
noninteractively simulated from $k$ Gaussian samples is the same for any $k\geq
m^{2}$. Previously, it was not even known if this number of samples $m^{2}$
would be finite or not, except when $m\leq 2$.
  Consequently, a straightforward brute-force search deciding whether or not a
probability distribution on $\{1,\ldots,m\}^{2}$ is within distance
$0<\epsilon<|\rho|$ of being noninteractively simulated from $k$ correlated
Gaussian samples has run time bounded by $(5/\epsilon)^{m(\log(\epsilon/2) /
\log|\rho|)^{m^{2}}}$, improving a bound of Ghazi, Kamath and Raghavendra.
  A nonlinear central limit theorem (i.e. invariance principle) of Mossel then
generalizes this result to decide whether or not a probability distribution on
$\{1,\ldots,m\}^{2}$ is within distance $0<\epsilon<|\rho|$ of being
noninteractively simulated from $k$ samples of a given finite discrete
distribution $(X,Y)$ in run time that does not depend on $k$, with constants
that again improve a bound of Ghazi, Kamath and Raghavendra.


title: Massive neutrino self-interactions with a light mediator in cosmology

abstract:
  Nonstandard self-interactions can alter the evolution of cosmological
neutrinos, mainly by damping free streaming, which should leave traces in
cosmological observables. Although overall effects are opposite to those
produced by neutrino mass and a larger $N_{\rm eff}$, they cannot be totally
canceled by these last. We harness cosmological data that includes Cosmic
Microwave Background from Plank 2018, BAO measurements, local $H_0$,
Ly-$\alpha$ and SNIa, to constrain massive neutrino self-interactions with a
very light scalar mediator. We find that the effective coupling constant, at
the 95\% C.L., should be $g_{\rm eff}< 1.94 \times 10^{-7}$ for only Planck
2018 data and $1.97\times10^{-7}$ when Planck + BAO are considered. This bound
relaxes to $2.27\times 10^{-7}$ ($2.3\times 10^{-7}$) for $H_0$
($H_0$+SNe+Ly-$\alpha$) data. Using the Planck + BAO dataset, the $H_0$ tension
lowers from 4.3$\sigma$ (for $\Lambda$CDM) to 3.2$\sigma$. The Akaike
Information Criterion penalizes the self-interacting model due to its larger
parameter space for Plank or Planck + BAO data, but favors the interacting
model when we use local $H_0$ measurements. A somewhat larger value for $H_0$
is preferred when we include the whole data pool, which comes accompanied with
a larger value of $N_{\rm eff}$ and a more constricted bound on $\Sigma m_\nu$.


title: Finite electrodynamics from T-duality

abstract:
  In this paper, we present the repercussions of Padmanabhan's propagator in
electrodynamics. This corresponds to implement T-duality effects in a U(1)
gauge theory. By formulating a nonlocal action consistent with the above
hypothesis, we derive the profile of static potentials between electric charges
via a path integral approach. Interestingly, the Coulomb potential results
regularized by a length scale proportional to the parameter
$(\alpha^\prime)^{1/2}$. Accordingly, fields are vanishing at the origin. We
also discuss an array of experimental testbeds to expose the above results. It
is interesting to observe that T-duality generates an effect of dimensional
fractalization, that resembles similar phenomena in fractional
electromagnetism. Finally, our results have also been derived with a
gauge-invariant method, as a necessary check of consistency for any
non-Maxwellian theory.


title: Learning Predictions for Algorithms with Predictions

abstract:
  A burgeoning paradigm in algorithm design is the field of algorithms with
predictions, in which algorithms are designed to take advantage of a
possibly-imperfect prediction of some aspect of the problem. While much work
has focused on using predictions to improve competitive ratios, running times,
or other performance measures, less effort has been devoted to the question of
how to obtain the predictions themselves, especially in the critical online
setting. We introduce a general design approach for algorithms that learn
predictors: (1) identify a functional dependence of the performance measure on
the prediction quality, and (2) apply techniques from online learning to learn
predictors against adversarial instances, tune robustness-consistency
trade-offs, and obtain new statistical guarantees. We demonstrate the
effectiveness of our approach at deriving learning algorithms by analyzing
methods for bipartite matching, page migration, ski-rental, and job scheduling.
In the first and last settings we improve upon existing learning-theoretic
results by deriving online results, obtaining better or more general
statistical guarantees, and utilizing a much simpler analysis, while in the
second and fourth we provide the first learning-theoretic guarantees.


title:
Self-Consistent Relaxation Theory of Collective Ion Dynamics in Yukawa
  One-Component Plasmas under Intermediate Screening Regimes

abstract:
  The self-consistent relaxation theory is employed to describe the collective
ion dynamics in strongly coupled Yukawa classical one-component plasmas. The
theory is applied to equilibrium states corresponding to intermediate screening
regimes with appropriate values of the structure and coupling parameters. The
information about the structure (the radial distribution function and the
static structure factor) and the thermodynamics of the system is sufficient to
describe collective dynamics over a wide range of spatial ranges, namely, from
the extended hydrodynamic to the microscopic dynamics scale. The main
experimentally measurable characteristics of the equilibrium collective
dynamics of ions -- the spectrum of the dynamic structure factor, the
dispersion parameters, the speed of sound and the sound attenuation -- are
determined within the framework of the theory without using any adjustable
parameters. The results demonstrate agreement with molecular dynamics
simulations. Thus a direct realization is presented of the key idea of
statistical mechanics: for the theoretical description of the collective
particle dynamics of equilibrium fluids it is sufficient to know the
interparticle interaction potential and the structural characteristics.
Comparison with alternative or complementary theoretical approaches is
provided.

categories: physics.plasm-ph cond-mat.soft cond-mat.stat-mech physics.chem-ph physics.comp-ph


title:
Multiple combined gamma kernel estimations for nonnegative data with
  Bayesian adaptive bandwidths

abstract:
  A modified gamma kernel should not be automatically preferred to the standard
gamma kernel, especially for univariate convex densities with a pole at the
origin. In the multivariate case, multiple combined gamma kernels, defined as a
product of univariate standard and modified ones, are here introduced for
nonparametric and semiparametric smoothing of unknown orthant densities with
support $[0,\infty)^d$. Asymptotical properties of these multivariate
associated kernel estimators are established. Bayesian estimation of adaptive
bandwidth vectors using multiple pure combined gamma smoothers, and in
semiparametric setup, are exactly derived under the usual quadratic function.
The simulation results and four illustrations on real datasets reveal very
interesting advantages of the proposed combined approach for nonparametric
smoothing, compare to both pure standard and pure modified gamma kernel
versions, and under integrated squared error and average log-likelihood
criteria.


title:
Unsupervised Multiple-Object Tracking with a Dynamical Variational
  Autoencoder

abstract:
  In this paper, we present an unsupervised probabilistic model and associated
estimation algorithm for multi-object tracking (MOT) based on a dynamical
variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-variable
deep generative model that can be seen as an extension of the variational
autoencoder for the modeling of temporal sequences. It is included in DVAE-UMOT
to model the objects' dynamics, after being pre-trained on an unlabeled
synthetic dataset of single-object trajectories. Then the distributions and
parameters of DVAE-UMOT are estimated on each multi-object sequence to track
using the principles of variational inference: Definition of an approximate
posterior distribution of the latent variables and maximization of the
corresponding evidence lower bound of the data likehood function. DVAE-UMOT is
shown experimentally to compete well with and even surpass the performance of
two state-of-the-art probabilistic MOT models. Code and data are publicly
available.


title: Multiphonon quantum dynamics in cavity optomechanical systems

abstract:
  The multiphonon quantum dynamics in laser-pumped cavity optomechanical
samples containing a vibrating mirror is investigated. Especially, we focus on
dispersive interaction regimes where the externally applied coherent field
frequency detuning from the optical resonator frequency is not equal to the
mirror's oscillating frequency or to its multiples. As a result, for moderately
strong couplings among the involved subsystems, the quantum dynamics of this
complex system is described by multiphonon absorption or emission processes,
respectively. Particularly, we demonstrate efficient ways to monitor the phonon
quantum dynamics via photon detection. The possibility to extract the relevant
sample parameters, for instance, the coupling strength between the mechanical
mirror and the electromagnetic field, is also discussed.


title:
Derivation of the viscoelastic stress in Stokes flows induced by
  non-spherical Brownian rigid particles through homogenization

abstract:
  We consider a microscopic model of $n$ identical axis-symmetric rigid
Brownian particles suspended in a Stokes flow. We rigorously derive in the
homogenization limit of many small particles a classical formula for the
viscoelastic stress that appears in so-called Doi models which couple a
Fokker-Planck equation to the Stokes equations. We consider both Deborah
numbers of order $1$ and very small Deborah numbers. Our microscopic model
contains several simplifications, most importantly, we neglect the time
evolution of the particle centers as well as hydrodynamic interaction for the
evolution of the particle orientations. The microscopic fluid velocity is
modeled by the Stokes equations with given torques at the particles in terms of
Stratonovitch noise. We give a meaning to this PDE in terms of an infinite
dimensional Stratonovitch integral. This requires the analysis of the shape
derivatives of the Stokes equations in perforated domains, which we accomplish
by the method of reflections.


title: DataMUX: Data Multiplexing for Neural Networks

abstract:
  In this paper, we introduce data multiplexing (DataMUX), a technique that
enables deep neural networks to process multiple inputs simultaneously using a
single compact representation. DataMUX demonstrates that neural networks are
capable of generating accurate predictions over mixtures of inputs, resulting
in increased throughput with minimal extra memory requirements. Our approach
uses two key components -- 1) a multiplexing layer that performs a fixed linear
transformation to each input before combining them to create a mixed
representation of the same size as a single input, which is then processed by
the base network, and 2) a demultiplexing layer that converts the base
network's output back into independent representations before producing
predictions for each input. We show the viability of DataMUX for different
architectures (Transformers, and to a lesser extent MLPs and CNNs) across six
different tasks spanning sentence classification, named entity recognition and
image classification. For instance, DataMUX for Transformers can multiplex up
to $20$x/$40$x inputs, achieving $11$x/$18$x increase in throughput with
minimal absolute performance drops of $<2\%$ and $<4\%$ respectively on MNLI, a
natural language inference task. We also provide a theoretical construction for
multiplexing in self-attention networks and analyze the effect of various
design elements in DataMUX.


title: Equivariant pliability of the projective space

abstract:
  We classify finite subgroups $G\subset\mathrm{PGL}_4(\mathbb{C})$ such that
$\mathbb{P}^3$ is not $G$-birational to conic bundles and del Pezzo fibrations,
and explicitly describe all $G$-Mori fibre spaces that are $G$-birational to
$\mathbb{P}^3$ for these subgroups.


title:
Distributed Transient Safety Verification via Robust Control Invariant
  Sets: A Microgrid Application

abstract:
  Modern safety-critical energy infrastructures are increasingly operated in a
hierarchical and modular control framework which allows for limited data
exchange between the modules. In this context, it is important for each module
to synthesize and communicate constraints on the values of exchanged
information in order to assure system-wide safety. To ensure transient safety
in inverter-based microgrids, we develop a set invariance-based distributed
safety verification algorithm for each inverter module. Applying Nagumo's
invariance condition, we construct a robust polynomial optimization problem to
jointly search for safety-admissible set of control set-points and design
parameters, under allowable disturbances from neighbors. We use sum-of-squares
(SOS) programming to solve the verification problem and we perform numerical
simulations using grid-forming inverters to illustrate the algorithm.


title:
Small mode volume topological photonic states in one-dimensional
  lattices with dipole--quadrupole interactions

abstract:
  We study the topological photonic states in one-dimensional (1-D) lattices
analogue to the Su-Schrieffer-Heeger (SSH) model beyond the dipole
approximation. The electromagnetic resonances of the lattices supported by
near-field interactions between the plasmonic nanoparticles are studied
analytically with coupled dipole--quadrupole method. The topological phase
transition in the bipartite lattices is determined by the change of Zak phase.
Our results reveal the contribution of quadrupole moments to the near-field
interactions and the band topology. It is found that the topological edge
states in non-trivial lattices have both dipolar and quadrupolar nature. The
quadrupolar edge states are not only orthogonal to the dipolar edge states, but
also spatially localized at different sublattices. Furthermore, the quadrupolar
topological edge states, which coexist at the same energy with the quadrupolar
flat band have shorter localization length and hence smaller mode volume than
the conventional dipolar edge states. The findings deepen our understanding in
topological systems that involve higher-order multipoles, or in analogy to the
electron wave functions in quantum systems with higher-orbital angular
momentum, and may be useful in designing topological systems for confining
light robustly and enhancing light-matter interactions.


title: Squeezed light from a levitated nanoparticle at room temperature

abstract:
  Quantum measurements of mechanical systems can produce optical squeezing via
ponderomotive forces. Its observation requires high environmental isolation and
efficient detection, typically achieved by using optical cavities and cryogenic
cooling. Here we realize these conditions by measuring the position of an
optically levitated nanoparticle at room temperature and without the overhead
of an optical cavity. We use a fast heterodyne detection to reconstruct
simultaneously orthogonal optical quadratures, and observe a noise reduction of
$9\%\pm0.5\%$ below shot noise. Our experiment offers a novel, cavity-less
platform for squeezed-light enhanced sensing. At the same time it delineates a
clear and simple strategy towards observation of stationary optomechanical
entanglement.


title: Introduction of the Market-Based Price Autocorrelation

abstract:
  This paper considers direct dependence of the market price autocorrelation on
statistical moments of the market trades as a must necessary requirement. We
regard market time-series of the trade value and volume as origin of price
time-series. That determines dependence of the market-based averaging of price
on averaging of the trade value and volume time-series. We introduce the
market-based price statistical moments as functions of the statistical moments
of trade value and volume. Moving average helps define the market-based price
statistical moments with time-lag and introduce the price time autocorrelation
as function of time-lag statistical moments of the trade value and volume.
Statistical moments of the market trade value and volume are determined by
conventional frequency-based probability measures. However, the price
statistical moments and the price autocorrelation in particular are determined
by the market-based probability measure that differs from the conventional
frequency-based price probability. That distinction leads to different
treatments of the price autocorrelation via market-based and frequency-based
approach. To assess market dependence of price statistical moments and price
autocorrelation one should revise results founded on frequency-based approach.

categories: econ.GN q-fin.EC q-fin.GN q-fin.PR q-fin.ST


title: Magnetocaloric performance of RE$_{11}$Co$_4$In$_9$ (RE = Tb, Er)

abstract:
  The magnetocaloric effect in RE$_{11}$Co$_4$In$_9$ (RE = Tb, Er) has been
studied by means of magnetometric measurements in the function of temperature
and applied magnetic field. The maximum magnetic entropy change ($-\Delta
S_{\mathrm{M}}^{\mathrm{max}}$) at magnetic flux density change
($\Delta\mu_{0}H$) 0-9 T has been determined to be 5.51
J$\cdot$kg$^{-1}\cdot$K$^{-1}$ at 47.4 K for Tb$_{11}$Co$_4$In$_9$ and 14.28
J$\cdot$kg$^{-1}\cdot$K$^{-1}$ at 12.3 K for Er$_{11}$Co$_4$In$_9$, while
temperature averaged entropy change (TEC) with 3 K span equals 5.50 and 14.14
J$\cdot$kg$^{-1}\cdot$K$^{-1}$ for RE = Tb and Er, respectively. The relative
cooling power (RCP) and refrigerant capacity (RC) equal respectively 522.1 and
391.2 J$\cdot$kg$^{-1}$ in Tb$_{11}$Co$_4$In$_9$ and 605.2 and 463.1
J$\cdot$kg$^{-1}$ in Er$_{11}$Co$_4$In$_9$.


title:
TAP equations for orthogonally invariant spin glasses at high
  temperature

abstract:
  We study the high-temperature regime of a mean-field spin glass model whose
couplings matrix is orthogonally invariant in law. The magnetization of this
model is conjectured to satisfy a system of TAP equations, originally derived
by Parisi and Potters using a diagrammatic expansion of the Gibbs free energy.
We prove that this TAP description is correct in an $L^2$ sense, in a regime of
sufficiently high temperature. Our approach is novel even in the special case
of the Sherrington-Kirkpatrick model, and is based on proving the convergence
of an Approximate Message Passing algorithm to the magnetization vector. We
establish this by a conditional second moment analysis of the free energy
restricted to a thin band around the output of the algorithm, in a system of
many "orthogonal" replicas.


title: Edge coherence in multiplex networks

abstract:
  This paper introduces a nonparametric framework for the setting where
multiple networks are observed on the same set of nodes, also known as
multiplex networks. Our objective is to provide a simple parameterization which
explicitly captures linear dependence between the different layers of networks.
For non-Euclidean observations, such as shapes and graphs, the notion of
"linear" must be defined appropriately. Taking inspiration from the
representation of stochastic processes and the analogy of the multivariate
spectral representation of a stochastic process with joint exchangeability of
Bernoulli arrays, we introduce the notion of edge coherence as a measure of
linear dependence in the graph limit space. Edge coherence is defined for pairs
of edges from any two network layers and is the key novel parameter. We
illustrate the utility of our approach by eliciting simple models such as a
correlated stochastic blockmodel and a correlated inhomogeneous graph limit
model.


title: Hadamard Inverse Function Theorem Proved by Variational Analysis

abstract:
  We present a proof of Hadamard Inverse Function Theorem by the methods of
Variational Analysis, adapting an idea of I. Ekeland and E. Sere.


title:
Redundantly amplified information suppresses quantum correlations in
  many-body systems

abstract:
  We quantify how much information about a quantum system can be simultaneously
recorded in different parts of its environment, by establishing quantitative
bounds on bipartite quantum correlations in many-body systems. Tight limits on
quantum discord dictate that independent observers who monitor environmental
fragments can eavesdrop only on amplified and redundantly disseminated - hence,
effectively classical - information about the system, i.e., information about
its unique pointer observable. We also show how the emergence of classical
objectivity is signalled by a distinctive scaling of the conditional mutual
information, bypassing hard numerical optimizations. Our results validate the
core idea of Quantum Darwinism: objective classical reality need not be
postulated and is not accidental, but rather a compelling, emergent feature of
a quantum world.


title: Rank-Sensitive Computation of the Rank Profile of a Polynomial Matrix

abstract:
  Consider a matrix $\mathbf{F} \in \mathbb{K}^{m \times n}$ of univariate
polynomials over a field~$\mathbb{K}$. We study the problem of computing the
column rank profile of $\mathbf{F}$. To this end we first give an algorithm
which improves the minimal kernel basis algorithm of Zhou, Labahn, and
Storjohann (Proceedings ISSAC 2012). We then provide a second algorithm which
computes the column rank profile of $\mathbf{F}$ with a rank-sensitive
complexity of $O\tilde{~}(r^{\omega-2} n (m+D))$ operations in $\mathbb{K}$.
Here, $D$ is the sum of row degrees of $\mathbf{F}$, $\omega$ is the exponent
of matrix multiplication, and $O\tilde{~}(\cdot)$ hides logarithmic factors.


title: Integer Conformal Dimensions for Type IIA Flux Vacua

abstract:
  We give a concise argument that supersymmetric AdS type IIA DGKT flux vacua
on general Calabi-Yaus have, interpreted holographically, integer conformal
dimensions for low-lying scalar primaries in the dual CFT. These integers are
independent of any compactification details, such as the background fluxes or
triple intersection numbers of the compact manifold. For the K\"ahler moduli
and dilaton, there is one operator with $\Delta = 10$ and $h^{1,1}_{-}$
operators with $\Delta =6$, whereas the corresponding axions have $\Delta = 11$
and $\Delta = 5$. For the complex structure moduli, the $h^{2,1}$ saxions have
$\Delta =2$, and the axions $\Delta =3$. We give a tentative discussion of the
origin of these integers and effects that would modify these results.


title:
Ionization of heavy elements and the adiabatic exponent in the solar
  plasma

abstract:
  Context. The adiabatic exponent $\Gamma_1$ is studied as a thermodynamic
quantity in the partially ionized plasma of the solar convection zone.
  Aims. The aim of this study is to understand the impact of heavy elements on
the $\Gamma_1$ profile. We calculated $\Gamma_1$ with the SAHA-S equation of
state for different chemical compositions of plasma, and we analyzed
contributions of individual elements to $\Gamma_1$.
  Methods. We studied the decrease in $\Gamma_1$ due to the ionization of heavy
elements in comparison with the value obtained for a pure hydrogen-helium
plasma. These types of differences are denoted as "Z contributions", and we
analyzed them for eight elements (C, N, O, Ne, Mg, S, Si, and Fe) as well as
for a mixture of elements corresponding to the solar chemical composition. We
compared linear combinations of individual Z contributions with the exact Z
contribution. Applying a least-squares technique to the decomposition of the
full Z contribution to a basis of individual-element contributions, we obtained
the mass fractions of the heavy elements.
  Results. The Z contribution of heavy elements can be described by a linear
combination of individual-element Z contributions with a high level of accuracy
of 5e-6 . The inverse problem of estimating the mass fractions of heavy
elements from a given $\Gamma_1$ profile was considered for the example of
solar-type mixtures. In ideal numerical simulations, the mass fractions of the
most abundant elements could be determined with a relative accuracy better than
a few tenths of a percent. In the presence of random or systematic errors in
the $\Gamma_1$ profile, abundance estimations become remarkably less accurate.
If the amplitude of the errors does not exceed 1e-4, we can expect a
determination of at least the oxygen abundance with a relative error of about
10%.


title:
New approach for analysing dynamical processes on the surface of
  photospheric vortex tubes

abstract:
  The majority of studies on multi-scale vortex motions employ a
two-dimensional geometry by using a variety of observational and numerical
data. This approach limits the understanding the nature of physical processes
responsible for vortex dynamics. Here we develop a new methodology to extract
essential information from the boundary surface of vortex tubes. 3D
high-resolution magnetoconvection MURaM numerical data has been used to analyse
photospheric intergranular velocity vortices. The Lagrangian Averaged Vorticity
Deviation (LAVD) technique was applied to define the centers of vortex
structures and their boundary surfaces based on the advection of fluid
elements. These surfaces were mapped onto a constructed envelope grid that
allows the study of the key plasma parameters as functions of space and time.
Quantities that help in understanding the dynamics of the plasma, e.g. Lorentz
force, pressure force, plasma-$\beta$ were also determined. Our results suggest
that, while density and pressure have a rather global behaviour, the other
physical quantities undergo local changes, with their magnitude and orientation
changing in space and time. At the surface, the mixing in the horizontal
direction is not efficient, leading to appearance of localized regions with
higher/colder temperatures. In addition, the analysis of the MHD Poynting flux
confirms that the majority of the energy is directed in the horizontal
direction. Our findings also indicate that the pressure and magnetic forces
that drive the dynamics of the plasma on vortex surfaces are unbalanced and
therefore the vortices do not rotate as a rigid body.


title: On the complex structure of Yang-Mills theory

abstract:
  We consider the coupled set of spectral Dyson-Schwinger equations in
Yang-Mills theory for ghost and gluon propagators, which gives us access to the
ghost and gluon spectral functions. The set-up is used for a systematic
analytic evaluation of the constraints on generalised spectral representations
in Yang-Mills theory that are most relevant for informed spectral
reconstructions. We also provide numerical results for the coupled set of
spectral functions for a large range of potential mass gaps of the gluon, and
discuss the limitations and extensions of the present work.


title: First hard X-ray imaging results by Solar Orbiter STIX

abstract:
  Context. The Spectrometer/Telescope for Imaging X-rays (STIX) is one of 6
remote sensing instruments on-board Solar Orbiter. It provides hard X-ray
imaging spectroscopy of solar flares by sampling the Fourier transform of the
incoming flux. Aims. To show that the visibility amplitude and phase
calibration of 24 out of 30 STIX sub-collimators is well advanced and that a
set of imaging methods is able to provide the first hard X-ray images of the
flaring Sun from Solar Orbiter. Methods. We applied four visibility-based image
reconstruction methods and a count-based one to calibrated STIX observations.
The resulting reconstructions are compared to those provided by an optimization
algorithm used for fitting the amplitudes of STIX visibilities. Results. When
applied to six flares with GOES class between C4 and M4 which occurred in May
2021, the five imaging methods produce results morphologically consistent with
the ones provided by the Atmospheric Imaging Assembly on-board the Solar
Dynamic Observatory (SDO/AIA) in UV wavelengths. The $\chi^2$ values and the
parameters of the reconstructed sources are comparable between methods, thus
confirming their robustness. Conclusions. This paper shows that the current
calibration of the main part of STIX sub-collimators has reached a satisfactory
level for scientific data exploitation, and that the imaging algorithms already
available in the STIX data analysis software provide reliable and robust
reconstructions of the morphology of solar flares.


title:
The Rapidly Evolving AGB Star, V Hya: ALMA finds a Multi-Ring Circus
  with High-Velocity Outflows

abstract:
  We have observed the mass-losing carbon star V Hya that is apparently
transitioning from an AGB star to a bipolar planetary nebula, at an
unprecedented angular resolution of ~0".4-0".6 with the Atacama Large
Millimeter/submillimeter Wave Array (ALMA). Our 13CO and 12CO (J=3-2 and J=2-1)
images have led to the discovery of a remarkable set of six expanding rings
within a flared, warped Disk structure Undergoing Dynamical Expansion (DUDE)
that lies in the system's equatorial plane. We also find, for the first time,
several bipolar, high-velocity outflows, some of which have parabolic
morphologies, implying wide opening angles, while one (found previously) is
clumpy and highly collimated. The latter is likely associated with the
high-velocity bullet-like ejections of ionized gas from V Hya; a possible
molecular counterpart to the oldest of the 4 bullets can be seen in the 12CO
images. We find a bright, unresolved central source of continuum emission (FWHM
size <~165 au); about 40% of this emission can be produced in a standard radio
photosphere, while the remaining 60% is likely due to thermal emission from
very large (mm-sized) grains, having mass >~10^{-5} Msun. We have used a
radiative transfer model to fit the salient characteristics of the DUDE's 13CO
and 12CO emission out to a radius of 8" (3200 au) with a flared disk of mass
1.7 x 10^{-3} Msun, whose expansion velocity increases increases very rapidly
with radius inside a central region of size ~200 au, and then more slowly
outside it, from 9.5 to 11.5 km/s. The DUDE's underlying density decreases
radially, interspersed with local increases that represent the observationally
well-characterised innermost three rings.


title:
Absolute continuity and singularity of spectra for flows $T_t\otimes
  T_{at}$

abstract:
  Answering the question of V.I. Oseledets, we present a random variable $\xi$
such that the sum $\xi(x)+a\xi(y)$ has a singular distribution for a set of
parameters $a$ dense in $(1, +\infty)$, but for another dense set of
parameters, this sum has an absolutely continuous distribution. We prove the
following assertion: given $C,D$, countable non-intersecting dense subsets of
the ray $(1,+\infty)$, there is a measure-preserving flow $T_t$ (acting on the
infinite Lebesgue space) such that automorphisms $T_1\otimes T_{c}$ have simple
singular spectra for every $c\in C$, and $T_1\otimes T_{d}$ have Lebesgue
spectra for all $d\in D$. The spectral measure of this flow plays the role of
the distribution of our random variable $\xi$.


title: Exceptional Spectral Phase in a Dissipative Collective Spin Model

abstract:
  We study a model of a quantum collective spin weakly coupled to a
spin-polarized Markovian environment and find that the spectrum is divided into
two regions that we name normal and exceptional Liouvillian spectral phases. In
the thermodynamic limit, the exceptional spectral phase displays the unique
property of being made up exclusively of second order exceptional points. As a
consequence, the evolution of any initial density matrix populating this region
is slowed down and cannot be described by a linear combination of exponential
decays. This phase is separated from the normal one by a critical line in which
the density of Liouvillian eigenvalues diverges, a phenomenon analogous to that
of excited-state quantum phase transitions observed in some closed quantum
systems. In the limit of no bath polarization, this criticality is transferred
onto the steady state, implying a dissipative quantum phase transition and the
formation of a boundary time crystal.


title: Signal Decomposition Using Masked Proximal Operators

abstract:
  We consider the well-studied problem of decomposing a vector time series
signal into components with different characteristics, such as smooth,
periodic, nonnegative, or sparse. We propose a simple and general framework in
which the components are defined by loss functions (which include constraints),
and the signal decomposition is carried out by minimizing the sum of losses of
the components (subject to the constraints). When each loss function is the
negative log-likelihood of a density for the signal component, our method
coincides with maximum a posteriori probability (MAP) estimation; but it also
includes many other interesting cases. We give two distributed optimization
methods for computing the decomposition, which find the optimal decomposition
when the component class loss functions are convex, and are good heuristics
when they are not. Both methods require only the masked proximal operator of
each of the component loss functions, a generalization of the well-known
proximal operator that handles missing entries in its argument. Both methods
are distributed, i.e., handle each component separately. We derive tractable
methods for evaluating the masked proximal operators of some loss functions
that, to our knowledge, have not appeared in the literature.


title: A reliability measure for smart surveillance systems

abstract:
  We present a reliability measure for smart surveillance systems, taking into
account the adversarial nature of intrusion. Our approach is based on
percolation theory and is a generalisation of Hamedmoghadam et al.'s
reliability measure. Specifically, our approach incorporates a customisable
cost function to allow modelling a diverse range of situations, such as access
restrictions, monitoring, and failures. We demonstrate our approach by applying
it to a digital twin of a smart building. However, challenges remain in
estimating and modelling the key parameters needed.


title:
Learning Physics-Informed Neural Networks without Stacked
  Back-propagation

abstract:
  Physics-Informed Neural Network (PINN) has become a commonly used machine
learning approach to solve partial differential equations (PDE). But, facing
high-dimensional second-order PDE problems, PINN will suffer from severe
scalability issues since its loss includes second-order derivatives, the
computational cost of which will grow along with the dimension during stacked
back-propagation. In this paper, we develop a novel approach that can
significantly accelerate the training of Physics-Informed Neural Networks. In
particular, we parameterize the PDE solution by the Gaussian smoothed model and
show that, derived from Stein's Identity, the second-order derivatives can be
efficiently calculated without back-propagation. We further discuss the model
capacity and provide variance reduction methods to address key limitations in
the derivative estimation. Experimental results show that our proposed method
can achieve competitive error compared to standard PINN training but is two
orders of magnitude faster.


title: Perfect sampling of stochastic matching models with reneging

abstract:
  In this paper, we introduce a slight variation of the Dominated Coupling From
the Past algorithm (DCFTP) of Kendall, for bounded Markov chains. It is based
on the control of a (typically non-monotonic) stochastic recursion by a
(typically monotonic) one. We show that this algorithm is particularly suitable
for stochastic matching models with bounded patience, a class of models for
which the steady state distribution of the system is in general unknown in
closed form. We first show that the Markov chain of this model can be easily
controlled by an infinite-server queue. We then investigate the particular case
where patience times are deterministic, and this control argument may fail. in
that case we resort to an ad-hoc technique that can also be seen as a control
(this time, by the arrival sequence). We then compare this algorithm to the
classical CFTP one, and show how our perfect simulation results can be used to
estimate, and compare, the loss probabilities of various systems in
equilibrium.


title:
On index divisors and monogenity of certain number fields defined by
  trinomials $x^6+ax+b$

abstract:
  For a number field $K$ defined by a trinomail $F(x) = x^6+ax+b \in
\mathbb{Z}[x]$, Jakhar and Kumar gave some necessary conditions on $a$ and $b$,
which guarantee the non-monogenity of $K$ \cite{A6}. In this paper, for every
prime integer $p$, we characterize when $p$ is a common index divisor of $K$.
In particular, if any one of these conditions holds, then $K$ is not monogenic.
In such a way our proposed results extend those of Jakhar and Kumar.


title:
High-accuracy high-mass ratio simulations for binary neutron stars and
  their comparison to existing waveform models

abstract:
  The subsequent observing runs of the advanced gravitational-wave detector
network will likely provide us with various gravitational-wave observations of
binary neutron star systems. For an accurate interpretation of these
detections, we need reliable gravitational-wave models. To test and to point
out how existing models could be improved, we perform a set of high-resolution
numerical-relativity simulations for four different physical setups with mass
ratios $q$ = $1.25$, $1.50$, $1.75$, $2.00$, and total gravitational mass $M =
2.7M_\odot$ . Each configuration is simulated with five different resolutions
to allow a proper error assessment. Overall, we find approximately 2nd order
converging results for the dominant $(2,2)$, but also subdominant $(2,1)$,
$(3,3)$, $(4,4)$ modes, while, generally, the convergence order reduces
slightly for an increasing mass ratio. Our simulations allow us to validate
waveform models, where we find generally good agreement between
state-of-the-art models and our data, and to prove that scaling relations for
higher modes currently employed for binary black hole waveform modeling also
apply for the tidal contribution. Finally, we also test if the current NRTidal
model to describe tidal effects is a valid description for high-mass ratio
systems. We hope that our simulation results can be used to further improve and
test waveform models in preparation for the next observing runs.


title:
Towards the Combination of Model Checking and Runtime Verification on
  Multi-Agent Systems

abstract:
  Multi-Agent Systems (MAS) are notoriously complex and hard to verify. In
fact, it is not trivial to model a MAS, and even when a model is built, it is
not always possible to verify, in a formal way, that it is actually behaving as
we expect. Usually, it is relevant to know whether an agent is capable of
fulfilling its own goals. One possible way to check this is through Model
Checking. Specifically, by verifying Alternating-time Temporal Logic (ATL)
properties, where the notion of strategies for achieving goals can be
described. Unfortunately, the resulting model checking problem is not decidable
in general. In this paper, we present a verification procedure based on
combining Model Checking and Runtime Verification, where sub-models of the MAS
model belonging to decidable fragments are verified by a model checker, and
runtime monitors are used to verify the rest. Furthermore, we implement our
technique and show experimental results.


title: First Law of Mechanics for Spinning Compact Binaries: Dipolar Order

abstract:
  Building upon the Noether charge formalism of Iyer and Wald, we derive a
variational formula for spacetimes admitting a Killing vector field, for a
generic energy-momentum distribution with compact support. Applying this
general result to the particular case of a binary system of spinning compact
objects moving along an exactly circular orbit, modelled using the multipolar
gravitational skeleton formalism, we derive a first law of compact binary
mechanics at dipolar order. We prove the equivalence of this new result with
the canonical Hamiltonian first law previously derived for binary systems of
spinning compact objects, for spins colinear with the orbital angular momentum.
This paper paves the way to an extension of the first law of binary mechanics
to the next quadrupolar order, thereby accounting for the spin-induced and
tidally-induced deformability of the compact bodies.


title:
Improving Molecular Contrastive Learning via Faulty Negative Mitigation
  and Decomposed Fragment Contrast

abstract:
  Deep learning has been a prevalence in computational chemistry and widely
implemented in molecule property predictions. Recently, self-supervised
learning (SSL), especially contrastive learning (CL), gathers growing attention
for the potential to learn molecular representations that generalize to the
gigantic chemical space. Unlike supervised learning, SSL can directly leverage
large unlabeled data, which greatly reduces the effort to acquire molecular
property labels through costly and time-consuming simulations or experiments.
However, most molecular SSL methods borrow the insights from the machine
learning community but neglect the unique cheminformatics (e.g., molecular
fingerprints) and multi-level graphical structures (e.g., functional groups) of
molecules. In this work, we propose iMolCLR: improvement of Molecular
Contrastive Learning of Representations with graph neural networks (GNNs) in
two aspects, (1) mitigating faulty negative contrastive instances via
considering cheminformatics similarities between molecule pairs; (2)
fragment-level contrasting between intra- and inter-molecule substructures
decomposed from molecules. Experiments have shown that the proposed strategies
significantly improve the performance of GNN models on various challenging
molecular property predictions. In comparison to the previous CL framework,
iMolCLR demonstrates an averaged 1.3% improvement of ROC-AUC on 7
classification benchmarks and an averaged 4.8% decrease of the error on 5
regression benchmarks. On most benchmarks, the generic GNN pre-trained by
iMolCLR rivals or even surpasses supervised learning models with sophisticated
architecture designs and engineered features. Further investigations
demonstrate that representations learned through iMolCLR intrinsically embed
scaffolds and functional groups that can reason molecule similarities.


title:
Radiation of Closed Strings between the Parallel Dynamical-Dressed
  Unstable Dp-Branes

abstract:
  We introduce a boundary state which is corresponding to a D$p$-brane with
tangential dynamics in the presence of the Kalb-Ramond field, a tachyonic field
and a $U(1)$ gauge potential in a special gauge. From the interaction of such
branes radiation amplitude of a general massless closed string will be
computed. The effects of the large distances of the branes on this radiation
will be studied. In the large distances of the branes, the possibility of axion
radiation will be investigated. Our calculations will be in the context of the
bosonic string theory.


title:
A Machine Learning Paradigm for Studying Pictorial Realism: Are
  Constable's Clouds More Real than His Contemporaries?

abstract:
  European artists have sought to create life-like images since the
Renaissance. The techniques used by artists to impart realism to their
paintings often rely on approaches based in mathematics, like linear
perspective; yet the means used to assess the verisimilitude of realist
paintings have remained subjective, even intuitive. An exploration of
alternative and relatively objective methods for evaluating pictorial realism
could enhance existing art historical research. We propose a
machine-learning-based paradigm for studying pictorial realism in an
explainable way. Unlike subjective evaluations made by art historians or
computer-based painting analysis exploiting inexplicable learned features, our
framework assesses realism by measuring the similarity between clouds painted
by exceptionally skillful 19th-century landscape painters like John Constable
and photographs of clouds. The experimental results of cloud classification
show that Constable approximates more consistently than his contemporaries the
formal features of actual clouds in his paintings. Our analyses suggest that
artists working in the decades leading up to the invention of photography
worked in a mode that anticipated some of the stylistic features of
photography. The study is a springboard for deeper analyses of pictorial
realism using computer vision and machine learning.


title: Max Noether Theorem for Singular Curves

abstract:
  Max Noether's Theorem asserts that if $\omega$ is the dualizing sheaf of a
nonsingular nonhyperelliptic projective curve, then the natural morphisms
$\text{Sym}^nH^0(\omega)\to H^0(\omega^n)$ are surjective for all $n\geq 1$.
The result was extended for Gorenstein curves by many different authors in
distinct ways. More recently, it was proved for curves with projectively normal
canonical models, and curves whose non-Gorenstein points are bibranch at worse.
Based on those works, we address the combinatorics of the general case and
extend the result for any integral curve.


title: Complexity of warped conformal field theory

abstract:
  Warped conformal field theories in two dimensions are exotic nonlocal,
Lorentz violating field theories characterized by Virasoro-Kac-Moody symmetries
and have attracted a lot of attention as candidate boundary duals to warped
AdS$_3$ spacetimes, thereby expanding the scope of holography beyond
asymptotically AdS spacetimes. Here we investigate WCFT$_2$s using $circuit$
$complexity$ as a tool. First we compute the holographic volume complexity (CV)
which displays a linear UV divergence structure, more akin to that of a local
CFT$_2$ and has a very complicated dependence on the Virasoro central charge
$c$ and the $U(1)$ Kac-Moody level parameter $k$. Next we consider circuit
complexity based on Virasoro-Kac-Moody symmetry gates where the complexity
functional is the geometric (group) action on coadjoint orbits of the
Virasoro-Kac-Moody group. We consider a special solution to extremization
equations for which circuit complexity scales linearly with time. In the
semiclassical limit (large $c,k$, while $c/k$ remains finite and small) both
the holographic volume complexity and circuit complexity scales with $k$.


title: Inner ideals of real simple Lie algebras

abstract:
  A classification up to automorphism of the inner ideals of the real
finite-dimensional simple Lie algebras is given, jointly with precise
descriptions in the case of the exceptional Lie algebras.


title:
Assessment of Cyber-Physical Intrusion Detection and Classification for
  Industrial Control Systems

abstract:
  The increasing interaction of industrial control systems (ICSs) with public
networks and digital devices introduces new cyber threats to power systems and
other critical infrastructure. Recent cyber-physical attacks such as Stuxnet
and Irongate revealed unexpected ICS vulnerabilities and a need for improved
security measures. Intrusion detection systems constitute a key security
technology, which typically monitor network data for detecting malicious
activities. However, a central characteristic of modern ICSs is the increasing
interdependency of physical and cyber network processes. Thus, the integration
of network and physical process data is seen as a promising approach to improve
predictability in intrusion detection for ICSs by accounting for physical
constraints and underlying process patterns. This work systematically assesses
real-time cyber-physical intrusion detection and multiclass classification,
based on a comparison to its purely network data-based counterpart and
evaluation of misclassifications and detection delay. Multiple supervised
machine learning models are applied on a recent cyber-physical dataset,
describing various cyber attacks and physical faults on a generic ICS. A key
finding is that integration of physical process data improves detection and
classification of all attack types. In addition, it enables simultaneous
processing of attacks and faults, paving the way for holistic cross-domain
cause analysis.


title:
Model Calibration of the Liquid Mercury Spallation Target using
  Evolutionary Neural Networks and Sparse Polynomial Expansions

abstract:
  The mercury constitutive model predicting the strain and stress in the target
vessel plays a central role in improving the lifetime prediction and future
target designs of the mercury targets at the Spallation Neutron Source (SNS).
We leverage the experiment strain data collected over multiple years to improve
the mercury constitutive model through a combination of large-scale simulations
of the target behavior and the use of machine learning tools for parameter
estimation. We present two interdisciplinary approaches for surrogate-based
model calibration of expensive simulations using evolutionary neural networks
and sparse polynomial expansions. The experiments and results of the two
methods show a very good agreement for the solid mechanics simulation of the
mercury spallation target. The proposed methods are used to calibrate the
tensile cutoff threshold, mercury density, and mercury speed of sound during
intense proton pulse experiments. Using strain experimental data from the
mercury target sensors, the newly calibrated simulations achieve 7\% average
improvement on the signal prediction accuracy and 8\% reduction in mean
absolute error compared to previously reported reference parameters, with some
sensors experiencing up to 30\% improvement. The proposed calibrated
simulations can significantly aid in fatigue analysis to estimate the mercury
target lifetime and integrity, which reduces abrupt target failure and saves a
tremendous amount of costs. However, an important conclusion from this work
points out to a deficiency in the current constitutive model based on the
equation of state in capturing the full physics of the spallation reaction.
Given that some of the calibrated parameters that show a good agreement with
the experimental data can be nonphysical mercury properties, we need a more
advanced two-phase flow model to capture bubble dynamics and mercury
cavitation.

categories: physics.comp-ph cs.LG physics.acc-ph stat.AP


title: Smoothness of Directed Chain Stochastic Differential Equations

abstract:
  We study the smoothness of the solution of the directed chain stochastic
differential equations, where each process is affected by its neighborhood
process in an infinite directed chain graph, introduced by Detering et al.
(2020). Because of the auxiliary process in the chain-like structure, classic
methods of Malliavin derivatives are not directly applicable. Namely, we cannot
make a connection between the Malliavin derivative and the first order
derivative of the state process. It turns out that the partial Malliavin
derivatives can be used here to fix this problem.


title: Association schemes on triples over few vertices

abstract:
  In this paper, we obtain classification results for higher-dimensional
analogues of classical association schemes called association schemes on
triples (ASTs). We present an algorithm that enumerates all ASTs on a fixed
number of vertices whose nontrivial relations are invariant under the action of
some group. Applying this algorithm to three, four, and five vertices along
with appropriate group actions yields the unique AST over three vertices, the
unique symmetric ASTs over four or five vertices, the unique AST over four
vertices with two nontrivial relations, and the unique nontrivial circulant AST
over five vertices.


title:
An exploration of an early gravity transition in light of cosmological
  tensions

abstract:
  We study a step-like transition in the value of the effective Planck mass (or
effective gravitational constant) on cosmological scales prior to
recombination. We employ CMB, BAO, and SNIa data and find they are sufficient
to strongly constrain our implementation of the Effective Field Theory of Dark
Energy and Modified Gravity, used to model the transition, to a limited
parameter space. The data prefer a $\sim 5\%$ shift in the value of the
effective Planck mass ($<10 \%$ at $2 \sigma$) prior to recombination. This
Transitional Planck Mass (TPM) model is free to undergo its transition at any
point over multiple decades of scale factor prior to recombination,
$\log_{10}(a) = -5.32^{+0.96}_{-0.72}$ (68\% CL). This lowers the sound horizon
at last scattering, which increases the Hubble constant to $71.09 \pm 0.75$ km
$\textrm{s}^{-1}\textrm{Mpc}^{-1}$ with a combination of local measurements as
prior and to $69.22^{+0.67}_{-0.86}$ km $\textrm{s}^{-1}\textrm{Mpc}^{-1}$ when
the prior is excluded. The TPM model improves $\chi^2$ with respect to
$\Lambda$CDM by $\Delta \chi^2 = -23.72$ with the $H_0$ prior and $\Delta
\chi^2 = -4.8$ without the prior. The model allows for both $H_0 > 70$
km$\textrm{s}^{-1}\textrm{Mpc}^{-1}$ and $S_8 < 0.80$ simultaneously with lower
values of $S_8$ due to a reduction in the matter density $\Omega_m$ to offset
the increase in $H_0$ relative to $\Lambda$CDM. While this is a particular
modified gravity model, studying other variants of modified gravity may be a
productive path for potentially resolving cosmological tensions, while avoiding
the need for a cosmological constant.


title:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication
  Acceleration! Finally!

abstract:
  We introduce \algname{ProxSkip} -- a surprisingly simple and provably
efficient method for minimizing the sum of a smooth ($f$) and an expensive
nonsmooth proximable ($\psi$) function. The canonical approach to solving such
problems is via the proximal gradient descent (\algname{ProxGD}) algorithm,
which is based on the evaluation of the gradient of $f$ and the prox operator
of $\psi$ in each iteration. In this work we are specifically interested in the
regime in which the evaluation of prox is costly relative to the evaluation of
the gradient, which is the case in many applications. \algname{ProxSkip} allows
for the expensive prox operator to be skipped in most iterations: while its
iteration complexity is $\cO(\kappa \log \nicefrac{1}{\varepsilon})$, where
$\kappa$ is the condition number of $f$, the number of prox evaluations is
$\cO(\sqrt{\kappa} \log \nicefrac{1}{\varepsilon})$ only. Our main motivation
comes from federated learning, where evaluation of the gradient operator
corresponds to taking a local \algname{GD} step independently on all devices,
and evaluation of prox corresponds to (expensive) communication in the form of
gradient averaging. In this context, \algname{ProxSkip} offers an effective
{\em acceleration} of communication complexity. Unlike other local
gradient-type methods, such as \algname{FedAvg}, \algname{SCAFFOLD},
\algname{S-Local-GD} and \algname{FedLin}, whose theoretical communication
complexity is worse than, or at best matching, that of vanilla \algname{GD} in
the heterogeneous data regime, we obtain a provable and large improvement
without any heterogeneity-bounding assumptions.


title:
The Anthropic (and Mis-) Principle revisited, Steven Weinberg in
  Memoriam

abstract:
  I review the role and meaning of the Anthropic Principle, particularly in its
relevance to particle physics.


title: Machine Learning Models in Stock Market Prediction

abstract:
  The paper focuses on predicting the Nifty 50 Index by using 8 Supervised
Machine Learning Models. The techniques used for empirical study are Adaptive
Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial
Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD),
Support Vector Machine (SVM) and Decision Trees (DT). Experiments are based on
historical data of Nifty 50 Index of Indian Stock Market from 22nd April, 1996
to 16th April, 2021, which is time series data of around 25 years. During the
period there were 6220 trading days excluding all the non trading days. The
entire trading dataset was divided into 4 subsets of different size-25% of
entire data, 50% of entire data, 75% of entire data and entire data. Each
subset was further divided into 2 parts-training data and testing data. After
applying 3 tests- Test on Training Data, Test on Testing Data and Cross
Validation Test on each subset, the prediction performance of the used models
were compared and after comparison, very interesting results were found. The
evaluation results indicate that Adaptive Boost, k- Nearest Neighbors, Random
Forest and Decision Trees under performed with increase in the size of data
set. Linear Regression and Artificial Neural Network shown almost similar
prediction results among all the models but Artificial Neural Network took more
time in training and validating the model. Thereafter Support Vector Machine
performed better among rest of the models but with increase in the size of data
set, Stochastic Gradient Descent performed better than Support Vector Machine.


title:
Analytic Method for Estimating Aircraft Fix Displacement from
  Gyroscope's Allan-Deviation Parameters

abstract:
  The noise and drift requirements for a navigation-grade gyroscope are widely
known, yet there is no simple analytic model of how the noise and drift of a
gyroscope influence the fix displacement error (FDE) of an inertial navigation
system (INS). This work derives simple analytical expressions for the
cross-track and along-track errors of an aircraft whose INS consists solely of
a three-axis gyroscope system with perfect knowledge of the vertical direction.
The error signal of each gyroscope is Gaussian white noise and drift modeled as
a first-order Markov random walk. These expressions provide a straightforward
mean of calculating the FDE of an aircraft as a function of the flight
duration, velocity, noise amplitude, drift amplitude, and drift's time
constant. These expressions are validated with Monte-Carlo simulations of long
flights. This model quantifies the noise-versus-drift trade-off for a gyroscope
in an inertial navigation system. It can save time when estimating the noise
and drift that a gyroscope must exhibit to satisfy a given position-error
requirement, or vice versa. They are used in particular to confirm the values,
often cited without demonstration, of the noise and drift required to meet the
maximum position error of an aircraft imposed by the Federal Aviation
Administration's required navigation performance 10 specification. Finally, it
demonstrates that using the minimum in the measured Allan deviation of a
gyroscope as a metric of the drift is incorrect, because it fails to capture
the drift's time constant. The proper metric is the maximum in the Allan
deviation.


title:
Parameter Identification of a PN-Guided Incoming Missile Using an
  Improved Multiple-Model Mechanism

abstract:
  An active defense against an incoming missile requires information of it,
including a guidance law parameter and a first-order lateral time constant. To
this end, assuming that a missile with a proportional navigation (PN) guidance
law attempts to attack an aerial target with bang-bang evasive maneuvers, a
parameter identification model based on the gated recurrent unit (GRU) neural
network is built in this paper. The analytic identification solutions for the
guidance law parameter and the first-order lateral time constant are derived.
The inputs of the identification model are available kinematic information
between the aircraft and the missile, while the outputs contain the regression
results of missile parameters. To increase the training speed and the
identification accuracy of the Model, an output processing method called
improved multiplemodel mechanism (IMMM) is proposed in this paper. The
effectiveness of IMMM and the performance of the established model are
demonstrated through numerical simulations under various engagement scenarios.


title:
Optimal Redundancy Allocation in Coherent Systems with Heterogeneous
  Dependent Components

abstract:
  This paper is concerned with the optimal number of redundant allocation to
$n$-component coherent systems consist of heterogeneous dependent components.
We assume that the system is built of $L$ groups of different components,
$L\geq 1$, where there are $n_i$ components in group $i$, and
$\sum_{i=1}^{L}n_i=n$. The problem of interest is to allocate $v_i$ active
redundant components to each component of type $i$, $i=1,\dots, L$. To get the
optimal values of $v_i$, we propose two cost-based criteria. One of them is
introduced based on the costs of renewing the failed components and the costs
of refreshing the alive ones at the system failure time. The other criterion is
proposed based on the costs of replacing the system at its failure time or at a
predetermined time $\tau$, whichever occurs first. The expressions for the
proposed functions are derived using the mixture representation of the system
reliability function based on the notion of survival signature. We assume that
a given copula function models the dependency structure between the components.
In the particular case that the system is a series-parallel structure, we
provide the formulas for the proposed cost-based functions. The results are
discussed numerically for some specific coherent systems.


title:
Towards Digital Twin Oriented Modelling of Complex Networked Systems and
  Their Dynamics: A Comprehensive Survey

abstract:
  This paper aims to provide a comprehensive critical overview on how entities
and their interactions in Complex Networked Systems (CNS) are modelled across
disciplines as they approach their ultimate goal of creating a Digital Twin
(DT) that perfectly matches the reality. We propose a new framework to
conceptually compare diverse existing modelling paradigms from different
perspectives and create unified assessment criteria to assess their respective
capabilities of reaching such an ultimate goal. Using the proposed criteria, we
also appraise how far the reviewed current state-of-the-art approaches are from
the idealised DTs. We also identify and propose potential directions and ways
of building a DT-orientated CNS based on the convergence and integration of CNS
and DT utilising a variety of cross-disciplinary techniques.


title: Playing against no-regret players

abstract:
  In increasingly different contexts, it happens that a human player has to
interact with artificial players who make decisions following decision-making
algorithms. How should the human player play against these algorithms to
maximize his utility? Does anything change if he faces one or more artificial
players? The main goal of the paper is to answer these two questions. Consider
n-player games in normal form repeated over time, where we call the human
player optimizer, and the (n -- 1) artificial players, learners. We assume that
learners play no-regret algorithms, a class of algorithms widely used in online
learning and decision-making. In these games, we consider the concept of
Stackelberg equilibrium. In a recent paper, Deng, Schneider, and Sivan have
shown that in a 2-player game the optimizer can always guarantee an expected
cumulative utility of at least the Stackelberg value per round. In our first
result, we show, with counterexamples, that this result is no longer true if
the optimizer has to face more than one player. Therefore, we generalize the
definition of Stackelberg equilibrium introducing the concept of correlated
Stackelberg equilibrium. Finally, in the main result, we prove that the
optimizer can guarantee at least the correlated Stackelberg value per round.
Moreover, using a version of the strong law of large numbers, we show that our
result is also true almost surely for the optimizer utility instead of the
optimizer's expected utility.