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The spin-orbit obliquity of a planetary system constraints its formation history. A large obliquity may either indicate a primordial misalignment between the star and its gaseous disk or reflect the effect of different mechanisms tilting planetary systems after formation. Observations and statistical analysis suggest that system of planets with sizes between 1 and 4 R$_{\oplus}$ have a wide range of obliquities ($\sim0-30^{\circ}$), and that single- and multi-planet transiting have statistically indistinguishable obliquity distributions. Here, we revisit the ``breaking the chains'' formation model with focus in understanding the origin of spin-orbit obliquities. This model suggests that super-Earths and mini-Neptunes migrate close to their host stars via planet-disk gravitational interactions, forming chain of planets locked in mean-motion resonances. After gas-disk dispersal, about 90-99\% of these planetary systems experience dynamical instabilities, which spread the systems out. Using synthetic transit observations, we show that if planets are born in disks where the disk angular momentum is virtually aligned with the star's rotation spin, their final obliquity distributions peak at about $\sim$5 degrees or less, and the obliquity distributions of single and multi-planet transiting systems are statistically distinct. By treating the star-disk alignment as a free-parameter, we show that the obliquity distributions of single and multi-planet transiting systems only become statistically indistinguishable if planets are assumed to form in primordially misaligned natal disks with a ``tilt'' distribution peaking at $\gtrsim$10-20 deg. We discuss the origin of these misalignments in the context of star formation and potential implications of this scenario for formation models., Comment: 11 pages, 9 figures, 2 tables. Accepted for publication in MNRAS

The different magnetic behaviors of LaCoO$_3$ films grown on LaAlO$_3$ and SrTiO$_3$ are related to the Co-O-Co bond angles and the constraints imposed on the Co-O bond lengths by the substrate geometries. Long-range magnetic order occurs below T ~ 90 K when the Co-O-Co bond angle is greater than 163 degrees, consistent with the behavior of bulk and nanoparticles forms of LaCoO$_3$. A LaAlO$_3$ substrate prevents magnetic long-range order at low temperatures near the film-substrate interface and collinear antiferromagnetic sublattices away from the interface. At low temperatures, the antiferromagnetically ordered sublattices are non-collinear in films grown on SrTiO$_3$ substrates, leading to a significant net moment., Comment: 28 pages, 8 figures, 6 supplementary figures

The present paper introduces a mathematical model for studying dynamic grain growth. In particular, we show how characteristic measurements, grain volumes, centroids, and central second-order moments at discrete moments in time can be turned quickly into a continuous description of the grain growth process in terms of geometric diagrams (which largely generalize the well-known Voronoi and Laguerre tessellations). We evaluate the computational behavior of our algorithm on real-world data., 21 pages, 4 tables, 6 figures

This article deals with large-eddy simulations of 3D incompressible laryngeal flow followed by acoustic simulations of human phonation of five cardinal english vowels /u, i, \textipa{A}, o, {\ae}/. The flow and aeroacoustic simulations were performed in OpenFOAM and in-house code openCFS, respectively. Given the large variety of scales in the flow and acoustics, the simulation is separated into two steps: (1) computing the flow in the larynx using the finite volume method on a fine 2.2M grid followed by (2) computing the sound sources separately and wave propagation to the radiation zone around the mouth using the finite element method on a coarse 33k acoustic grid. The numerical results showed that the anisotropic minimum dissipation model, which is not well known since it is not available in common CFD software, predicted stronger sound pressure levels at higher harmonics and especially at first two formants than the wall-adapting local eddy-viscosity model. We implemented the model as a new open library in OpenFOAM and deployed the model on turbulent flow in the larynx with positive impact on the quality of simulated vowels. Numerical simulations are in very good agreement with positions of formants from measurements., Comment: 15 pages, 20 figures, 20 audio files in att

The heating mechanisms of solar white-light flares remain unclear. We present an X1.0 white-light flare on 2022 October 2 (SOL2022-10-02T20:25) observed by the Chinese \ha\ Solar Explorer (CHASE) that provides two-dimensional spectra in the visible light for the full solar disk with a seeing-free condition. The flare shows a prominent enhancement of $\sim$40\% in the photospheric \fe\ line at 6569.2 \AA, and the nearby continuum also exhibits a maximum enhancement of $\sim$40\%. For the continuum near the \fe\ line at 6173 \AA\ from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), it is enhanced up to $\sim$20\%. At the white-light kernels, the \fe\ line at 6569.2 \AA\ has a symmetric Gaussian profile that is still in absorption and the H$\alpha$ line at 6562.8 \AA\ displays a very broad emission profile with a central reversal plus a red or blue asymmetry. The white-light kernels are co-spatial with the microwave footpoint sources observed by the Expanded Owens Valley Solar Array (EOVSA) and the time profile of the white-light emission matches that of the hard X-ray emission above 30 keV from the Gamma-ray Burst Monitor (GBM) on Fermi. These facts indicate that the white-light emission is qualitatively related to a nonthermal electron beam. We also perform a radiative hydrodynamic simulation with the electron beam parameters constrained by the hard X-ray observations from Fermi/GBM. The result reveals that the white-light enhancement cannot be well explained by a pure electron-beam heating together with its induced radiative backwarming but may need additional heating sources such as \alfven\ waves., Comment: 15 pages, 5 figures, and 1 table

We develop a finite volume method for Maxwell's equations in materials whose electromagnetic properties vary in space and time. We investigate both conservative and non-conservative numerical formulations. High-order methods accurately resolve fine structures that develop due to the varying material properties. Numerical examples demonstrate the effectiveness of the proposed method in handling temporal variation and its efficiency relative to traditional 2nd-order FDTD., Comment: 15 pages, 8 figures

The paper deals with the construction of the asymptotic soliton-like and the asymptotic peakon-like solutions to the modified Camassa-Holm equation with variable coefficicents and a singular perturbation. This equation is a generalization of the well known modified Camassa-Holm equation (\ref{CHE_cons_mod}) which is integrable system and in addition to the soliton solutions the equation has the peakon solutions. The novelty of the ideas of this paper lies in the development of a technique for constructing asymptotic peakon-like solutions. In the paper a general scheme of finding asymptotic approximation of any order is presented and accuracy of the asymptotic approximation is found. The obtained results are illustrated by examples both the soliton-like and the peakon-like solutions. For the examples the equations for the phase function as well as the main and the first terms of the soliton-like and peakon-like solutions are found. Moreover, for different values of a small parameter the graphs that demonstrate kind of the solutions are presented. The considered examples demonstrate that for an adequate description of the wave process it is enough obtain the main and the first terms of correspond asymptotic solutions. The results also confirm that the proposed technique can be used for constructing asymptotic wave-like solutions of other equations., 33 pages and 12 figures

This paper presents an octree construction method, called Cornerstone, that facilitates global domain decomposition and interactions between particles in mesh-free numerical simulations. Our method is based on algorithms developed for 3D computer graphics, which we extend to distributed high performance computing (HPC) systems. Cornerstone yields global and locally essential octrees and is able to operate on all levels of tree hierarchies in parallel. The resulting octrees are suitable for supporting the computation of various kinds of short and long range interactions in N-body methods, such as Barnes-Hut and the Fast Multipole Method (FMM). While we provide a CPU implementation, Cornerstone may run entirely on GPUs. This results in significantly faster tree construction compared to execution on CPUs and serves as a powerful building block for the design of simulation codes that move beyond an offloading approach, where only numerically intensive tasks are dispatched to GPUs. With data residing exclusively in GPU memory, Cornerstone eliminates data movements between CPUs and GPUs. As an example, we employ Cornerstone to generate locally essential octrees for a Barnes-Hut treecode running on almost the full LUMI-G system with up to 8 trillion particles.

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art approaches only consider average wait times and focus on high-load scenarios where the arrival rate of tasks approaches the limit of what can be handled by the robots while keeping the queue of unserviced tasks bounded, i.e., stable. To ensure stability, these approaches repeatedly compute minimum distance tours over a set of newly arrived tasks. This paper is aimed at addressing the missing policies for moderate-load scenarios, where quality of service can be improved by prioritizing long-waiting tasks. We introduce a novel DVRP policy based on a cost function that takes the $p$-norm over accumulated wait times and show it guarantees stability even in high-load scenarios. We demonstrate that the proposed policy outperforms the state-of-the-art in both mean and $95^{th}$ percentile wait times in moderate-load scenarios through simulation experiments in the Euclidean plane as well as using real-world data for city scale service requests., Accepted for publication in IEEE Robotics and Automation Letters (RA-L)

We discuss the use of the Discrete Element Method (DEM) to simulate the dynamics of granular systems made up of elements with nontrivial geometries. The DEM simulator is GPU accelerated and can handle elements whose shape is defined as the union with overlap of diverse sets of spheres with user-specified radii. The simulator can also handle complex materials since each sphere in an element can have its own Young's modulus $E$, Poisson ratio $\nu$, friction coefficient $\mu$, and coefficient of restitution CoR. To demonstrate the simulator, we produce a "digital simulant" (DS), a replica of the GRC-1 lunar simulant. The DS follows an element size distribution similar but not identical to that of GRC-1. We validate the predictive attributes of the simulator via several numerical experiments: repose angle, cone penetration, drawbar pull, and rover incline-climbing tests. Subsequently, we carry out a sensitivity analysis to gauge how the slope vs. slip curves change when the element shape, element size, and friction coefficient change. The paper concludes with a VIPER rover simulation that confirms a recently proposed granular scaling law. The simulation involves more than 11 million elements composed of more than 34 million spheres of different radii. The simulator works in the Chrono framework and utilizes two GPUs concurrently. The GPU code for the simulator and all numerical experiments discussed are open-source and available on GitHub for reproducibility studies and unfettered use and distribution., Comment: Main text 28 pages, including 27 figures. Submitted to Engineering with Computers

Peace means order, and war brings disorder and chaos to any society. But order and disorder are not only observed in wars, in many systems they are the dominant property. Understanding order and disorder enables us to understand the structure of systems. Order and disorder are also part of the Lagrange Principle, and as statistics is valid in all systems, we may regard Lagrange statistics as a mathematical basis of system science. Two systems out of natural and social science are compared: materials of trillions of atoms and politics of millions of people. Lagrange statistics leads to three phases of homogeneous systems: in materials we have the states: solid, liquid, gas, depending on two Lagrange parameters, temperature T (the mean energy of atoms) and pressure p. In politics we have three states: autocratic, democratic, global, depending on two Lagrange parameters, standard of living T (the mean capital of people) and political pressure p. The three phases of each system are compared in the p-T phase diagram: Different phases of one system cannot coexist as nearest neighbors: Water will dissolve ice by exchange of atoms and heat. This leads to the present climate crisis. Democracies will dissolve autocracies by exchange of goods, ideas, and people like guest workers. This is the peaceful history of the EU and has led to the aggressive reaction of Russia in the GDR, Hungary, \v{C}SR, and now in the Ukraine. At the end of war peaceful coexistence will not be possible between Russia and Ukraine. Only separation by a new Iron Curtain guaranteed by NATO can lead to a long-time armistice., Comment: 14 pages, 12 figures

Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeRF) were proposed to directly encode any point in 2D/3D Euclidean space as a high-dimensional vector, and has been successfully applied to various geospatial prediction and generative tasks. However, all current 2D and 3D location encoders are designed to model point distances in Euclidean space. So when applied to large-scale real-world GPS coordinate datasets, which require distance metric learning on the spherical surface, both types of models can fail due to the map projection distortion problem (2D) and the spherical-to-Euclidean distance approximation error (3D). To solve these problems, we propose a multi-scale location encoder called Sphere2Vec which can preserve spherical distances when encoding point coordinates on a spherical surface. We developed a unified view of distance-reserving encoding on spheres based on the DFS. We also provide theoretical proof that the Sphere2Vec preserves the spherical surface distance between any two points, while existing encoding schemes do not. Experiments on 20 synthetic datasets show that Sphere2Vec can outperform all baseline models on all these datasets with up to 30.8% error rate reduction. We then apply Sphere2Vec to three geo-aware image classification tasks - fine-grained species recognition, Flickr image recognition, and remote sensing image classification. Results on 7 real-world datasets show the superiority of Sphere2Vec over multiple location encoders on all three tasks. Further analysis shows that Sphere2Vec outperforms other location encoder models, especially in the polar regions and data-sparse areas because of its nature for spherical surface distance preservation. Code and data are available at https://gengchenmai.github.io/sphere2vec-website/., 30 Pages, 16 figures. Accepted to ISPRS Journal of Photogrammetry and Remote Sensing

This paper describes the design and C99 implementation of a free and open-source Low-Density Parity-Check (LDPC) codes encoder and decoder focused primarily on the Quasi-Cyclic LDPC (QCLDPC) codes utilized in the IEEE 802.11ax-2021 (Wi-Fi 6) and IEEE 802.16-2017 (WiMAX) standards. The encoder is designed in two variants: the first one universal, the other a minimal memory usage design. The decoder provides a single- and multi- threaded implementation of the layered singlescan min-sum LDPC decoding algorithm both for floating point and fixed-point arithmetic. Both encoder and decoder are directly callable from MATLAB using the provided MEX wrappers but are designed to be simply used in any C project. A comparison of throughput and error performance with the recent commercial closed-source MEX implementation of an LDPC encoder and decoder introduced in MATLAB R2021b Communications Toolbox is provided. Source code portability to alternative nonx86 architectures is facilitated by using only the standard C99 constructs, GNU tools, and POSIX libraries. The implementation maintains low-memory requirements, enabling its deployment in a constrained-architecture in the context of Internet of Things. All source codes are freely available on GitHub under a permissive BSD license., 18 pages, 2 figures, Sources available on GitHub: https://github.com/talenik/YALDPC Published in: https://www.paneurouni.com/veda/vedecke-casopisy/aplikacie-informacnych-technologii

$\chi$iplot is an HTML5-based system for interactive exploration of data and machine learning models. A key aspect is interaction, not only for the interactive plots but also between plots. Even though $\chi$iplot is not restricted to any single application domain, we have developed and tested it with domain experts in quantum chemistry to study molecular interactions and regression models. $\chi$iplot can be run both locally and online in a web browser (keeping the data local). The plots and data can also easily be exported and shared. A modular structure also makes $\chi$iplot optimal for developing machine learning and new interaction methods., Comment: 5 pages, 1 figure, accepted to the demo track of ECML PKDD 2023, https://github.com/edahelsinki/xiplot

Water droplets are a perspective highly abundant phase-change material to realize tunable optical lenses. We demonstrated for the first time that freezing mesoscale water droplet could be use as tunable optical lens, such that freezing becomes an asset despite both the low absolute values of the refractive indices of the shell and core materials and their optical contrast. It was shown that the dielectric shell of mesoscale water droplet in the form of solid ice allows controlling both the maximum field intensity and the focus position of the formed photonic nanojet. The formation of ice with air bubbles during the freezing of a water droplet is appropriate for a dynamic increase in the range of change of the focal position compared to solid ice. The proposed concept of a tunnelable spherical lens based on a freezing water drop can be used for microscopy, optical traping in "green" mesotronics., 8 pages, 5 figures

Physics and equality constrained artificial neural networks (PECANN) are grounded in methods of constrained optimization to properly constrain the solution of partial differential equations (PDEs) with their boundary and initial conditions and any high-fidelity data that may be available. To this end, adoption of the augmented Lagrangian method within the PECANN framework is paramount for learning the solution of PDEs without manually balancing the individual loss terms in the objective function used for determining the parameters of the neural network. Generally speaking, ALM combines the merits of the penalty and Lagrange multiplier methods while avoiding the ill conditioning and convergence issues associated singly with these methods . In the present work, we apply our PECANN framework to solve forward and inverse problems that have an expanded and diverse set of constraints. We show that ALM with its conventional formulation to update its penalty parameter and Lagrange multipliers stalls for such challenging problems. To address this issue, we propose an adaptive ALM in which each constraint is assigned a unique penalty parameter that evolve adaptively according to a rule inspired by the adaptive subgradient method. Additionally, we revise our PECANN formulation for improved computational efficiency and savings which allows for mini-batch training. We demonstrate the efficacy of our proposed approach by solving several forward and PDE-constrained inverse problems with noisy data, including simulation of incompressible fluid flows with a primitive-variables formulation of the Navier-Stokes equations up to a Reynolds number of 1000.

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified, 32 pages, 5 figures, 4 tables

Combining measurements of many production and decay channels of the observed Higgs boson allows for the highest possible measurement precision for the properties of the Higgs boson and its interactions. These combined measurements are interpreted in various ways; specific scenarios of physics beyond the Standard Model (SM) are tested, as well as a generic extension in the framework of the Standard Model Effective Field Theory. The latest highlight results of these measurements and their interpretations performed by the ATLAS Collaboration are discussed., 6 pages ; presented at DIS2023, XXX International Workshop on Deep-Inelastic Scattering and Related Subjects, 27-31 March 2023

Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy.

Under the most common experimental conditions, the adsorption of proteins to solid surfaces is an spontaneous process that leads to a rather compact layer of randomly oriented molecules. Due to the importance of this process for the development of catalytic surfaces, a number of existing computational and experimental approaches try to predict and control the orientation of such molecules. However, and despite their own advantages, these tend to be either too expensive computationally, or oversimplified, undermining their ability to predict the most appropriate experimental conditions to maximize the catalytic activity of adsorbed proteins. To address this current need, we present an efficient computational approach to model the behavior of proteins near surfaces in the presence of an external electric field, based on continuum electrostatics. Our model can not only estimate the overall affinity of the protein with the surface, but also their most likely orientation as a function of the potential applied. In this way, a rational selection of the potential can be performed to maximize the accessibility of the protein's active site to the solvent. The model relies on the Poisson-Boltzmann equation and was implemented in an extension of the code PyGBe that includes an external electric field, and renders the electrostatic component of the solvation free energy. To demonstrate the feasibility of this technique, we investigate the adsorption of trypsin onto a carbon electrode under potentiostatic conditions both numerically and experimentally. We found that even though the adsorption process is largely dominated by hydrophobic effects, the orientation of trypsin can be controlled through an external potential, influencing the position of the active sites, and resulting in an important change in the catalytic activity of the surface., 34 pages, 11 figures

VWSIM is a circuit simulator for rapid, single-flux, quantum (RSFQ) circuits. The simulator is designed to model and simulate primitive-circuit devices such as capacitors, inductors, Josephson Junctions, and can be extended to simulate other circuit families, such as CMOS. Circuit models can be provided in the native VWSIM netlist format or as SPICE-compatible netlists, which are flattened and transformed into symbolic equations that can be manipulated and simulated. Written in the ACL2 logic, VWSIM provides logical guarantees about each of the circuit models it simulates. Note, our matrix solving and evaluation routines use Common Lisp floating-point numbers, and work is ongoing to admit these models into ACL2. We currently use VWSIM to help us design self-timed, RSFQ-based circuits. Our eventual goal is to prove properties of RSFQ circuit models. The ACL2-based definition of the VWSIM simulator offers a path for specifying and verifying RSFQ circuit models., In Proceedings ACL2 2022, arXiv:2205.11103

The quasi-molecular mechanism of recombination, recently suggested by Kereselidze et al., is a non-standard process where an electron and two neighboring protons in the early universe directly form an ionized hydrogen molecule in a highly excited state, which then descends to lower levels or dissociates. It has been suggested that the increased binding energy due to the participation of a second proton may lead to an earlier cosmic recombination that alleviates the Hubble tension. Revisiting the quasi-molecular channel of recombination in more details, we find that the original work significantly overestimated the probability of finding a pair of adjacent protons in the relevant epoch ($z\sim $ a few thousand). Our new estimation suggests that the quasi-molecular mechanism of recombination cannot be the primary cause of the Hubble tension., Comment: 4 pages, 2 figures

One of the challenges of phase measuring deflectometry is to retrieve the wavefront from objects that present discontinuities or non-differentiable gradient fields. Here, we propose the integration of such gradient fields based on an L p -norm minimization problem. The solution of this problem results in a nonlinear partial differential equation, which can be solved with a fast and well-known numerical method and does not depend on external parameters. Numerical reconstructions on both synthetic and experimental data are presented that demonstrate the capability of the proposed method.

Background and Objective: This study aims to evaluate the dynamic effect of non-Newtonian cerebral arterial circulation on electrical conductivity distribution (ECD) in a realistic multi-compartment head model. It addresses the importance and challenges associated with electrophysiological modalities, such as transcranial electrical stimulation, electro-magnetoencephalography, and electrical impedance tomography. Factors such as electrical conductivity's impact on forward modeling accuracy, complex vessel networks, data acquisition limitations (especially in MRI), and blood flow phenomena are considered. Methods: The Navier-Stokes equations (NSEs) govern the non-Newtonian flow model used in this study. The solver comprises two stages: the first solves the pressure field using a dynamical pressure-Poisson equation derived from NSEs, and the second updates the velocity field using Leray regularization and the pressure distribution from the first stage. The Carreau-Yasuda model establishes the connection between blood velocity and viscosity. Blood concentration in microvessels is approximated using Fick's law of diffusion, and conductivity mapping is obtained via Archie's law. The head model used corresponds to an open 7 Tesla MRI dataset, differentiating arterial vessels from other structures. Results: The results suggest the establishment of a dynamic model of cerebral blood flow for arterial and microcirculation. Blood pressure and conductivity distributions are obtained through numerically simulated pulse sequences, enabling approximation of blood concentration and conductivity within the brain. Conclusions: This model provides an approximation of dynamic blood flow and corresponding ECD in different brain regions. The advantage lies in its applicability with limited a priori information about blood flow and compatibility with arbitrary head models that distinguish arteries., 13 pages; 8 figures; 2 table

High-fidelity scale-resolving simulations of turbulent flows quickly become prohibitively expensive, especially at high Reynolds numbers. As a remedy, we may use multifidelity models (MFM) to construct predictive models for flow quantities of interest (QoIs), with the purpose of uncertainty quantification, data fusion and optimization. For numerical simulation of turbulence, there is a hierarchy of methodologies ranked by accuracy and cost, which include several numerical/modeling parameters that control the predictive accuracy and robustness of the resulting outputs. Compatible with these specifications, the present hierarchical MFM strategy allows for simultaneous calibration of the fidelity-specific parameters in a Bayesian framework as developed by Goh et al. 2013. The purpose of the multifidelity model is to provide an improved prediction by combining lower and higher fidelity data in an optimal way for any number of fidelity levels; even providing confidence intervals for the resulting QoI. The capabilities of our multifidelity model are first demonstrated on an illustrative toy problem, and it is then applied to three realistic cases relevant to engineering turbulent flows. The latter include the prediction of friction at different Reynolds numbers in turbulent channel flow, the prediction of aerodynamic coefficients for a range of angles of attack of a standard airfoil, and the uncertainty propagation and sensitivity analysis of the separation bubble in the turbulent flow over periodic hills subject to the geometrical uncertainties. In all cases, based on only a few high-fidelity data samples (typically direct numerical simulations, DNS), the multifidelity model leads to accurate predictions of the QoIs accompanied with an estimate of confidence. The result of the UQ and sensitivity analyses are also found to be accurate compared to the ground truth in each case., 22 pages

In this work, we introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals. Three methods are developed for this task: a Bayesian inference approach for inferring the spectral acoustics characteristics, a neural-physical model which equips a neural network with forward and backward physical losses, and the non-linear least squares approach which serves as benchmark. The inferred propagation coefficient leads to the room impulse response (RIR) quantity which can be used for relocalisation with uncertainty. The simplicity and efficiency of this framework is empirically validated on simulated data.

Motions of a material point along a set of parabolas are studied, taking into account the forces of Coulomb friction. The obtained results are compared with similar motions along the cycloid. The analysis is carried out using numerical calculations in the Mathcad program., 12 pages, 7 figures

In this paper, we use Yukawa theory to calculate differential and total cross-sections for elastic and inelastic scattering in nucleon-nucleon interactions. We start from the fundamental Lagrangian and derive the $T$-matrix and hence the invariant scattering matrix leading towards the differential and total cross section. We perform calculations utilizing two types of Yukawa interaction terms: 1) scalar current, and 2) pseudoscalar current. We also carry out calculations in a laboratory frame. Calculated results using scalar current exhibits the exact features of elastic and inelastic scattering. The results agree very well with the differential cross-sections for experimental data. The pseudoscalar current calculations do not offer reasonable results and features of the NN interactions. The simplicity of the theory makes it all the more impressive, thus it can be used in place of more complicated field theories to describe NN interactions., 24 pages, 9 figures

This comprehensive treatise is written for the special occasion of the author's 70th birthday. It presents his lifelong endeavors and reflections with original reasoning and re-interpretations of the most critical and misleading issues in thermodynamics; since now, we have the advantage to look at the historical developments more comprehensively and objectively than the pioneers. Starting from Carnot (grandfather of thermodynamics-to-be) to Kelvin and Clausius (fathers of thermodynamics), and other followers, the most relevant issues are critically examined and put in historical and contemporary perspective. From original reasoning of energy forcing and displacement to logical proofs of the fundamental laws, to ubiquity of thermal motion and heat, and indestructibility of entropy, including the new concept of "thermal roughness" and the inevitability of dissipative irreversibility, to "dissecting Carnot true reversible-equivalency" and critical concept of "Carnot-Clausius heat-work equivalency (CCHWE)" regarding the interchangeability of heat and work, and to demonstrating "no hope" for the "challengers" of the Second Law of thermodynamics, among others, are offered. It is hoped that the novel contributions presented will enhance comprehension and resolve some of the fundamental issues, as well as promote collaboration and future progress., This comprehensive treatise is written for the special occasion of the author's 70th birthday, to be submitted for review and publication. It presents his lifelong endeavors and reflections with original reasoning and re-interpretations of the most critical and misleading issues in thermodynamics, and especially the Second Law of thermodynamics and entropy

In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perform trajectory tracking in a coordinated way when the sampling time of communications is much larger than the sampling time of low-level controllers, disrupting theoretical convergence guarantees of standard control design in continuous time. Given a desired trajectory in configuration space which is precomputed offline, the proposed controller receives configuration measurements, possibly via wireless, to re-compute velocity references for the robots, which are tracked by a low-level controller. We propose joint design of a sampled proportional feedback plus a novel continuous-time feedforward that linearizes the dynamics around the reference trajectory: this method is amenable to distributed communication implementation where only one broadcast transmission is needed per sample. Also, we provide closed-form expressions for instability and stability regions and convergence rate in terms of proportional gain $k$ and sampling period $T$. We test the proposed control strategy via numerical simulations in the scenario of cooperative aerial manipulation of a cable-suspended load using a realistic simulator (Fly-Crane). Finally, we compare our proposed controller with centralized approaches that adapt the feedback gain online through smart heuristics, and show that it achieves comparable performance., Comment: 23 pages (main article: 14 pages; appendix: 9 pages), 18 figures; accepted for publication on Automatica; final accepted version

Nowadays, explosive synchronization is a well documented phenomenon occurring in networks when the node frequency and its degree are correlated. This first-order transition, which may coexists with classical synchronization, has been recently causally linked to some pathological brain states like epilepsy and fibromyalgia. It is then intriguing how most of neuronal systems can operate in normal conditions avoiding explosive synchronization. Here, we have discovered that synchronization in networks where the oscillators are coupled via degree-biased Laplacian operators, naturally controls the transition from explosive to standard synchronization in neuronal-like systems. We prove analytically that explosive synchronization emerges when using this theoretical setting in star-like (neuronal) networks. As soon as this star-like network is topologically converted to a network containing cycles, e.g., via synaptic connections to other neurons, the explosive synchronization gives rise to classical synchronization. This allows us to hypothesize that such topological control of explosive synchronization could be a mechanism for the brain to naturally work in normal, non-pathological, conditions., Comment: 13 pages, 11 figures

Visual-based defect detection is a crucial but challenging task in industrial quality control. Most mainstream methods rely on large amounts of existing or related domain data as auxiliary information. However, in actual industrial production, there are often multi-batch, low-volume manufacturing scenarios with rapidly changing task demands, making it difficult to obtain sufficient and diverse defect data. This paper proposes a parallel solution that uses a human-machine knowledge hybrid augmentation method to help the model extract unknown important features. Specifically, by incorporating experts' knowledge of abnormality to create data with rich features, positions, sizes, and backgrounds, we can quickly accumulate an amount of data from scratch and provide it to the model as prior knowledge for few-data learning. The proposed method was evaluated on the magnetic tile dataset and achieved F1-scores of 60.73%, 70.82%, 77.09%, and 82.81% when using 2, 5, 10, and 15 training images, respectively. Compared to the traditional augmentation method's F1-score of 64.59%, the proposed method achieved an 18.22% increase in the best result, demonstrating its feasibility and effectiveness in few-data industrial defect detection., 24 pages, 15 figures

In this review we concentrate on the work of K. Alex M\"uller in connection with his activities on oxide perovskites and ferroelectrics which were central to his research career long before he successfully discovered the first high temperature superconductor (HTSC) together with J. G. Bednorz in 1986. Not accidentally, but taking his long experience in perovskite ferroelectrics into account, the first HTSC was an oxide perovskite which had never been considered before to be superconducting., Comment: 8 pages, 4 figures

The conceptual design of eVTOL aircraft is a high-dimensional optimization problem that involves large numbers of continuous design parameters. Therefore, eVTOL design method would benefit from numerical optimization algorithms capable of systematically searching these high-dimensional parameters spaces, using comprehensive and multidisciplinary models of the aircraft. By leveraging recent progress in sensitivity analysis methods, a computational framework called the Comprehensive Aircraft high-Dimensional DEsign Environment (CADDEE) has been developed for large-scale multidisciplinary design optimization (MDO) of electric air taxis. CADDEE uses a geometry-centric approach that propagates geometry changes in a differentiable manner to meshes for physics-based models of arbitrary fidelity level. The paper demonstrates the capabilities of this new aircraft design tool, by presenting large-scale MDO results for NASA's Lift+Cruise eVTOL concept. MDO with over 100 design variables, 17 constraints, and low-fidelity predictive models for key disciplines is demonstrated with an optimization time of less than one hour with a desktop computer. The results show a reduction in gross weight of 11.4% and suggest that CADDEE can be valuable in the conceptual design and optimization of eVTOL aircraft., 21 pages, 15 figures

Diffusion models have been widely adopted in image generation, producing higher-quality and more diverse samples than generative adversarial networks (GANs). We introduce a latent diffusion model (LDM) for precipitation nowcasting - short-term forecasting based on the latest observational data. The LDM is more stable and requires less computation to train than GANs, albeit with more computationally expensive generation. We benchmark it against the GAN-based Deep Generative Models of Rainfall (DGMR) and a statistical model, PySTEPS. The LDM produces more accurate precipitation predictions, while the comparisons are more mixed when predicting whether the precipitation exceeds predefined thresholds. The clearest advantage of the LDM is that it generates more diverse predictions than DGMR or PySTEPS. Rank distribution tests indicate that the distribution of samples from the LDM accurately reflects the uncertainty of the predictions. Thus, LDMs are promising for any applications where uncertainty quantification is important, such as weather and climate., 18 pages, 6 figures. Submitted for publication

Weak interaction of temporal cavity solitons due to gain saturation and recovery in a delay differential model of a long cavity semiconductor laser is studied numerically and analytically using an asymptotic approach. It is shown that in addition to the usual soliton repulsion leading to a harmonic mode-locking regimes a soliton attraction is also possible in a laser with nonzero linewidth enhancement factor. It is shown numerically that this attraction can lead either to a pulse merging or to a pulse bound state formation., 8 pages, 6 figures

Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types., 17 pages, 3 figures (main text); 13 pages, 10 figures, 1 table (supplement). Accepted for publication in Geophysical Research Letters

Non-Newtonian fluids have a viscosity that varies with applied stress. Elastoviscoplastic fluids, the elastic, viscous and plastic properties of which are interconnected in a non-trivial way, belong to this category. We have performed numerical simulations to investigate turbulence in elastoviscoplastic fluids at very high Reynolds-number values, as found in landslides and lava flows, focusing on the effect of plasticity. We find that the range of active scales in the energy spectrum reduces when increasing the fluid plasticity; when plastic effects dominate, a new scaling range emerges between the inertial range and the dissipative scales. An extended self-similarity analysis of the structure functions reveals that intermittency is present and grows with the fluid plasticity. The enhanced intermittency is caused by the non-Newtonian dissipation rate, which also exhibits an intermittent behaviour. These findings have relevance to catastrophic events in natural flows, such as landslides and lava flows, where the enhanced intermittency results in stronger extreme events, which are thus more destructive and difficult to predict., 12 pages and 10 figures, including supplemental material

We propose an algorithm for an optimal adaptive selection of points from the design domain of input random variables that are needed for an accurate estimation of failure probability and the determination of the boundary between safe and failure domains. The method is particularly useful when each evaluation of the performance function g(x) is very expensive and the function can be characterized as either highly nonlinear, noisy, or even discrete-state (e.g., binary). In such cases, only a limited number of calls is feasible, and gradients of g(x) cannot be used. The input design domain is progressively segmented by expanding and adaptively refining mesh-like lock-free geometrical structure. The proposed triangulation-based approach effectively combines the features of simulation and approximation methods. The algorithm performs two independent tasks: (i) the estimation of probabilities through an ingenious combination of deterministic cubature rules and the application of the divergence theorem and (ii) the sequential extension of the experimental design with new points. The sequential selection of points from the design domain for future evaluation of g(x) is carried out through a new learning function, which maximizes instantaneous information gain in terms of the probability classification that corresponds to the local region. The extension may be halted at any time, e.g., when sufficiently accurate estimations are obtained. Due to the use of the exact geometric representation in the input domain, the algorithm is most effective for problems of a low dimension, not exceeding eight. The method can handle random vectors with correlated non-Gaussian marginals. The estimation accuracy can be improved by employing a smooth surrogate model. Finally, we define new factors of global sensitivity to failure based on the entire failure surface weighted by the density of the input random vector., 42 pages, 24 figures

The Wright function, which arises in the theory of the space-time fractional diffusion equation, is an interesting mathematical object which has diverse connections with other special and elementary functions. The Wright function provides a unified treatment of several classes of special functions, such as the Gaussian, Airy, Bessel, and Error functions, etc. The manuscript demonstrates an algorithm for symbolical representation in terms of finite sums of hypergeometric (HG) functions and polynomials. The HG functions are then represented by known elementary or other special functions, wherever possible. The algorithm is programmed in the open-source computer algebra system Maxima and can be used to for testing numerical algorithms for the evaluation of the Wright function., 6 pages, 3 figures

A method is presented that allows to reduce a problem described by differential equations with initial and boundary conditions to the problem described only by differential equations. The advantage of using the modified problem for physics-informed neural networks (PINNs) methodology is that it becomes possible to represent the loss function in the form of a single term associated with differential equations, thus eliminating the need to tune the scaling coefficients for the terms related to boundary and initial conditions. The weighted loss functions respecting causality were modified and new weighted loss functions based on generalized functions are derived. Numerical experiments have been carried out for a number of problems, demonstrating the accuracy of the proposed methods., 25 pages, 7 figures, 6 tables

RooFit is a toolkit for statistical modeling and fitting used by most experiments in particle physics. Just as data sets from next-generation experiments grow, processing requirements for physics analysis become more computationally demanding, necessitating performance optimizations for RooFit. One possibility to speed-up minimization and add stability is the use of Automatic Differentiation (AD). Unlike for numerical differentiation, the computation cost scales linearly with the number of parameters, making AD particularly appealing for statistical models with many parameters. In this paper, we report on one possible way to implement AD in RooFit. Our approach is to add a facility to generate C++ code for a full RooFit model automatically. Unlike the original RooFit model, this generated code is free of virtual function calls and other RooFit-specific overhead. In particular, this code is then used to produce the gradient automatically with Clad. Clad is a source transformation AD tool implemented as a plugin to the clang compiler, which automatically generates the derivative code for input C++ functions. We show results demonstrating the improvements observed when applying this code generation strategy to HistFactory and other commonly used RooFit models. HistFactory is the subcomponent of RooFit that implements binned likelihood models with probability densities based on histogram templates. These models frequently have a very large number of free parameters and are thus an interesting first target for AD support in RooFit., 6 pages, 5 figures, 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research

Agent-based modelling constitutes a versatile approach to representing and simulating complex systems. Studying large-scale systems is challenging because of the computational time required for the simulation runs: scaling is at least linear in system size (number of agents). Given the inherently modular nature of MABSs, parallel computing is a natural approach to overcoming this challenge. However, because of the shared information and communication between agents, parellelization is not simple. We present a protocol for shared-memory, parallel execution of MABSs. This approach is useful for models that can be formulated in terms of sequential computations, and that involve updates that are localized, in the sense of involving small numbers of agents. The protocol has a bottom-up and asynchronous nature, allowing it to deal with heterogeneous computation in an adaptive, yet graceful manner. We illustrate the potential performance gains on exemplar cultural dynamics and disease spreading MABSs., 12 pages, 3 figures; work presented at the 24th International Workshop on Multi-Agent-Based Simulation

Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is {generally} not applicable, {despite of some recent promising advances}. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning., 6 pages, 5 figures, Regular paper ACC conference 2023

Past work exploring adversarial vulnerability have focused on situations where an adversary can perturb all dimensions of model input. On the other hand, a range of recent works consider the case where either (i) an adversary can perturb a limited number of input parameters or (ii) a subset of modalities in a multimodal problem. In both of these cases, adversarial examples are effectively constrained to a subspace $V$ in the ambient input space $\mathcal{X}$. Motivated by this, in this work we investigate how adversarial vulnerability depends on $\dim(V)$. In particular, we show that the adversarial success of standard PGD attacks with $\ell^p$ norm constraints behaves like a monotonically increasing function of $\epsilon (\frac{\dim(V)}{\dim \mathcal{X}})^{\frac{1}{q}}$ where $\epsilon$ is the perturbation budget and $\frac{1}{p} + \frac{1}{q} =1$, provided $p > 1$ (the case $p=1$ presents additional subtleties which we analyze in some detail). This functional form can be easily derived from a simple toy linear model, and as such our results land further credence to arguments that adversarial examples are endemic to locally linear models on high dimensional spaces., Comment: Comments welcome! V2: minor edits for clarity

A formula for the local acoustical admittance in a conical waveguide with viscous and thermal losses given by Nederveen in \emph{Acoustical Aspects of Woodwind Instruments} (1969) is rewritten as an impedance transmission matrix. Based on a self-consistent approximation for the cone, it differs from other one-dimensional transmission matrices used in musical acoustics, which implicitly include the loss model of a cylinder. The resonance frequencies of air columns calculated with this new transmission matrix are in better agreement with more comprehensive models. Even for long cones with a slight taper, there is no need to discretize along the axis., 7 pages, 2 figures

Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models -- variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors -- in detecting anomalies in flight-operations data of commercial flights consisting of multivariate time series. We devised two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. The DVAE with RBM prior, using a relatively simple -- and classically or quantum-mechanically enhanceable -- sampling technique for the evolution of the RBM's negative phase, performed better than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection tasks. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum annealer or gate-model device., 25 pages, 7 figures, 3 tables, appendix, supplementary material

Recent years have seen development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure. However, little research has been done on how these models understand chemical structure. To address this, we investigated the relationship between the learning progress of SMILES and chemical structure using a representative NLP model, the Transformer. The results suggest that while the Transformer learns partial structures of molecules quickly, it requires extended training to understand overall structures. Consistently, the accuracy of molecular property predictions using descriptors generated from models at different learning steps was similar from the beginning to the end of training. Furthermore, we found that the Transformer requires particularly long training to learn chirality and sometimes stagnates with low translation accuracy due to misunderstanding of enantiomers. These findings are expected to deepen understanding of NLP models in chemistry., 20 pages, 6 figures

Ground state structures found in nature are in many cases of high symmetry. But structure prediction methods typically render only a small fraction of high symmetry structures. Especially for large crystalline unit cells there are many low energy defect structures. For this reason methods have been developed where either preferentially high symmetry structures are used as input or where the whole structural search is done within a certain symmetry group. In both cases it is necessary to specify the correct symmetry group beforehand. However it can in general not be predicted which symmetry group is the correct one leading to the ground state. For this reason we introduce a potential energy biasing scheme that favors symmetry and where it is not necessary to specify any symmetry group beforehand. On this biased potential energy surface, high symmetry structures will be found much faster than on an unbiased surface and independently of the symmetry group to which they belong. For our two test cases, a $C_{60}$ fullerene and bulk silicon carbide, we get a speedups of 25 and 63. In our data we also find a clear correlation between the similarity of the atomic environments and the energy. In low energy structures all the atoms of a species tend to have similar environments., 8 pages, 4 figures, 1 table

Analytically, without magnetostatic approximation, the problem of electromagnetic wave propagation along arbitrary direction in a tangentially magnetized bihyrotropic layer has been solved. It is found that one can bring the Maxwell equations for this problem to the fourth order differential equation and the obtained biquadratic characteristic equation determines two different wave numbers kx21 and kx22 describing the wave distribution over the layer thickness. The dispersion equation describing wave propagation in the bihyrotropic layer was obtained for the case of real kx21 and kx22 values. It is shown that in a ferrite plate, which is a special case of a bihyrotropic layer, three types of wave distribution over the plate thickness can take place: surface-surface (when kx21 and kx22 are real numbers), volume-surface (kx21 is imaginary and kx22 is real) and volume-volume distribution (kx21 and kx22 are imaginary numbers). Characteristics of the surface spin wave in ferrite plate are investigated. It is found that dependences of the wave numbers kx21 and kx22 on the wave vector orientation are significantly different from the similar magnetostatic dependence for a large part of the wave spectrum., 30 pages, 5 figures