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We propose a type of phase transition in quantum many-body systems, which occurs in highly excited quantum many-body scar states, while the rest of the spectrum is largely unaffected. Such scar state phase transitions can be realized by embedding a matrix product state, known to undergo a phase transition, as a scar state into the thermal spectrum of a parent Hamiltonian. We find numerically that the mechanism for the scar state phase transition involves the formation or presence of low-entropy states at energies similar to the scar state in the vicinity of the phase transition point., Comment: 5 pages, 2 figures

This report describes the state of the art in verifiable computation. The problem being solved is the following: The Verifiable Computation Problem (Verifiable Computing Problem) Suppose we have two computing agents. The first agent is the verifier, and the second agent is the prover. The verifier wants the prover to perform a computation. The verifier sends a description of the computation to the prover. Once the prover has completed the task, the prover returns the output to the verifier. The output will contain proof. The verifier can use this proof to check if the prover computed the output correctly. The check is not required to verify the algorithm used in the computation. Instead, it is a check that the prover computed the output using the computation specified by the verifier. The effort required for the check should be much less than that required to perform the computation. This state-of-the-art report surveys 128 papers from the literature comprising more than 4,000 pages. Other papers and books were surveyed but were omitted. The papers surveyed were overwhelmingly mathematical. We have summarised the major concepts that form the foundations for verifiable computation. The report contains two main sections. The first, larger section covers the theoretical foundations for probabilistically checkable and zero-knowledge proofs. The second section contains a description of the current practice in verifiable computation. Two further reports will cover (i) military applications of verifiable computation and (ii) a collection of technical demonstrators. The first of these is intended to be read by those who want to know what applications are enabled by the current state of the art in verifiable computation. The second is for those who want to see practical tools and conduct experiments themselves., Comment: 54 pages

Establishing digital twins is a non-trivial endeavour especially when users face significant challenges in creating them from scratch. Ready availability of reusable models, data and tool assets, can help with creation and use of digital twins. A number of digital twin frameworks exist to facilitate creation and use of digital twins. In this paper we propose a digital twin framework to author digital twin assets, create digital twins from reusable assets and make the digital twins available as a service to other users. The proposed framework automates the management of reusable assets, storage, provision of compute infrastructure, communication and monitoring tasks. The users operate at the level of digital twins and delegate rest of the work to the digital twin as a service framework., Comment: 8 pages, 6 figures. Accepted at Digital Twin 2023

A digital twin (DT) monitors states of the physical twin (PT) counterpart and provides a number of benefits such as advanced visualizations, fault detection capabilities, and reduced maintenance cost. It is the ability to be able to detect the states inside the DT that enable such benefits. In order to estimate the desired states of a PT, we propose the use of a Kalman Filter (KF). In this tutorial, we provide an introduction and detailed derivation of the KF. We demonstrate the use of KF to monitor and anomaly detection through an incubator system. Our experimental result shows that KF successfully can detect the anomaly during monitoring., Comment: 14 pages

The VDM-PlantUML Plugin enables translations between the text based UML tool PlantUML and VDM++ and has been released as a part of the VDM VSCode extension. This enhances already extensive feature-set of VDM VSCode with support for UML. The link between VDM and UML is thoroughly described with a set of translation rules that serve as the base of the implementation of the translation plugin. This is however still an early rendition of the plugin with limited usability due to the loss of information between translations and a lack of workflow optimisations, which we plan to solve in the future.

For recursive functions general principles of induction needs to be applied. Instead of verifying them directly using the Vienna Development Method Specification Language (VDM-SL), we suggest a translation to Isabelle/HOL. In this paper, the challenges of such a translation for recursive functions are presented. This is an extension of an existing translation and a VDM mathematical toolbox in Isabelle/HOL enabling support for recursive functions.

The game of chess is well-known and widely played all over the world. However, the rules for playing it are rather complex since there are different types of pieces and the ways they are allowed to move depend upon the type of the piece. In this paper we discuss alternative paradigms that can be used for modelling the rule of the chess game using VDM++ and show what we believe is the best model. It is also illustrated how this model can be connected to a standard textual notation for the moves in a chess game. This can be used to combine the formal model to a more convenient interface.

Digital twin technology is an essential approach to managing the lifecycle of industrial products. Among the many approaches used to manage digital twins, co-simulation has proven to be a reliable one. There have been multiple attempts to create collaborative and sustainable platforms for management of digital twins. This paper compares two such platforms, namely the HUBCAP and the DIGITbrain. Both these platforms have been and continue to be used among a stable group of researchers and industrial product manufacturers of digital twin technologies. This comparison of the HUBCAP and the DIGITbrain platforms is illustrated with an example use case of industrial factory to be used for manufacturing of agricultural robots.

We introduce a computational database with calculated structural, thermodynamic, electronic, magnetic, and optical properties of 820 one-dimensional materials. The materials are systematically selected and exfoliated from experimental databases of crystal structures based on a dimensionality scoring parameter. The database is furthermore expanded by chemical element substitution in the materials. The materials are investigated in both their bulk form and as isolated one-dimensional components. We discuss the methodology behind the database, give an overview of some of the calculated properties, and look at patterns and correlations in the data. The database is furthermore applied in computational screening to identify materials, which could exhibit Majorana bound states., Comment: 17 pages, 20 figures

Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in data-driven modeling techniques, in particular neural networks have proven to provide an effective framework for solving a wide range of tasks. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

In today's world, critical infrastructure is often controlled by computing systems. This introduces new risks for cyber attacks, which can compromise the security and disrupt the functionality of these systems. It is therefore necessary to build such systems with strong guarantees of resiliency against cyber attacks. One way to achieve this level of assurance is using formal verification, which provides proofs of system compliance with desired cyber security properties. The use of Formal Methods (FM) in aspects of cyber security and safety-critical systems are reviewed in this article. We split FM into the three main classes: theorem proving, model checking and lightweight FM. To allow the different uses of FM to be compared, we define a common set of terms. We further develop categories based on the type of computing system FM are applied in. Solutions in each class and category are presented, discussed, compared and summarised. We describe historical highlights and developments and present a state-of-the-art review in the area of FM in cyber security. This review is presented from the point of view of FM practitioners and researchers, commenting on the trends in each of the classes and categories. This is achieved by considering all types of FM, several types of security and safety critical systems and by structuring the taxonomy accordingly. The article hence provides a comprehensive overview of FM and techniques available to system designers of security-critical systems, simplifying the process of choosing the right tool for the task. The article concludes by summarising the discussion of the review, focusing on best practices, challenges, general future trends and directions of research within this field., Comment: Technical Report, Long survey version

The Language Server Protocol (LSP) changed the field of Integrated Development Environments(IDEs), as it decouples core (programming) language features functionality from editor smarts, thus lowering the effort required to extend an IDE to support a language. The concept is a success and has been adopted by several programming languages and beyond. This is shown by the emergence of several LSP implementations for the many programming and specification languages (languages with a focus on modelling, reasoning, or proofs). However, for such languages LSP has been ad-hocly extended with the additional functionalities that are typically not found for programming languages and thus not supported in LSP. This foils the original LSP decoupling goal, because the move towards a new IDE requires yet another re-implementation of the ad-hoc LSP extension. In this paper we contribute with a conservative extension of LSP providing a first proposal towards a standard protocol decoupling the support of specification languages from the IDE. We hope our research attracts the larger community and motivates the need of a joint task force leading to a standardised LSP extension serving the particular needs of specification languages., Comment: In Proceedings F-IDE 2021, arXiv:2108.02369

To demystify the Digital Twin concept, we built a simple yet representative thermal incubator system. The incubator is an insulated box fitted with a heatbed, and complete with a software system for communication, a controller, and simulation models. We developed two simulation models to predict the temperature inside the incubator, one with two free parameters and one with four free parameters. Our experiments showed that the latter model was better at predicting the thermal inertia of the heatbed itself, which makes it more appropriate for further development of the digital twin. The hardware and software used in this case study are available open source, providing an accessible platform for those who want to develop and verify their own techniques for digital twins., Comment: 18 pages, 13 figures

Businesses, particularly small and medium-sized enterprises, aiming to start up in Model-Based Design (MBD) face difficult choices from a wide range of methods, notations and tools before making the significant investments in planning, procurement and training necessary to deploy new approaches successfully. In the development of Cyber-Physical Systems (CPSs) this is exacerbated by the diversity of formalisms covering computation, physical and human processes. In this paper, we propose the use of a cloud-enabled and open collaboration platform that allows businesses to offer models, tools and other assets, and permits others to access these on a pay-per-use basis as a means of lowering barriers to the adoption of MBD technology, and to promote experimentation in a sandbox environment.

We review two standard methods for structural classification in simulations of crystalline phases, the Common Neighbour Analysis and the Centrosymmetry Parameter. We explore the definitions and implementations of each of their common variants, and investigate their respective failure modes and classification biases. Simple modifications to both methods are proposed, which improve their robustness, interpretability, and applicability. We denote these variants the Interval Common Neighbour Analysis, and the Minimum-Weight Matching Centrosymmetry Parameter., Comment: 11 pages, 8 figures

Resolving pseudosymmetry has long presented a challenge for electron backscatter diffraction (EBSD) and has been notoriously challenging in the case of tetragonal ZrO2 in particular. In this work, a method is proposed to resolve pseudosymmetry by building upon the dictionary indexing method and augmenting it with the application of global optimization to fit accurate pattern centers, clustering of the Hough-indexed orientations to focus the dictionary in orientation space, and interpolation to improve the accuracy of the indexed solution. The proposed method is demonstrated to resolve pseudosymmetry with 100% accuracy in simulated patterns of tetragonal ZrO2, even with high degrees of binning and noise. The method is then used to index an experimental dataset, which confirms its ability to efficiently and accurately resolve pseudosymmetry in these materials. The present method can be applied to resolve pseudosymmetry in a wide range of materials, possibly even some more challenging than tetragonal ZrO2. Source code for this implementation is available online.

Bravais lattices are the most fundamental building blocks of crystallography. They are classified into groups according to their translational, rotational, and inversion symmetries. In computational analysis of Bravais lattices, fulfilment of symmetry conditions is usually determined by analysis of the metric tensor, using either a numerical tolerance to produce a binary (i.e. yes or no) classification, or a distance function which quantifies the deviation from an ideal lattice type. The metric tensor, though, is not scale-invariant, which complicates the choice of threshold and the interpretation of the distance function. Here, we quantify the distance of a lattice from a target Bravais class using strain. For an arbitrary lattice, we find the minimum-strain transformation needed to fulfil the symmetry conditions of a desired Bravais lattice type; the norm of the strain tensor is used to quantify the degree of symmetry breaking. The resulting distance is invariant to scale and rotation, and is a physically intuitive quantity. By symmetrizing to all Bravais classes, each lattice can be placed in a 14 dimensional space, which we use to create a map of the space of Bravais lattices and the transformation paths between them. A software implementation is available online under a permissive license., Comment: 12 pages, 9 figures

Accurate pattern center determination has long been a challenge for the electron backscatter diffraction (EBSD) community and is becoming critically accuracy-limiting for more recent advanced EBSD techniques. Here, we study the parameter landscape over which a pattern center must be fitted in quantitative detail and reveal that it is both sloppy and noisy, which limits the accuracy to which pattern centers can be determined. To locate the global optimum in this challenging landscape, we propose a combination of two approaches: the use of a global search algorithm and averaging the results from multiple patterns. We demonstrate the ability to accurately determine pattern centers of simulated patterns, inclusive of effects of binning and noise on the error of the fitted pattern center. We also demonstrate the ability of this method to accurately detect changes in pattern center in an experimental dataset with noisy and highly binned patterns. Source code for our pattern center fitting algorithm is available online.

Co-simulation consists of the theory and techniques to enable global simulation of a coupled system via the composition of simulators. Despite the large number of applications and growing interest in the challenges, the field remains fragmented into multiple application domains, with limited sharing of knowledge. This tutorial aims at introducing co-simulation of continuous systems, targeted at researchers new to the field.

The last decade has seen intense research in materials with reduced dimensionality. The low dimensionality leads to interesting electronic behavior due to electronic confinement and reduced screening. The investigations have to a large extent focused on 2D materials both in their bulk form, as individual layers a few atoms thick, and through stacking of 2D layers into heterostructures. The identification of low-dimensional compounds is therefore of key interest. Here, we perform a geometric analysis of material structures, demonstrating a strong clustering of materials depending on their dimensionalities. Based on the geometric analysis, we propose a simple scoring parameter to identify materials of a particular dimension or of mixed dimensionality. The method identifies spatially connected components of the materials and gives a measure of the degree of "1D-ness," "2D-ness," etc., for each component. The scoring parameter is applied to the Inorganic Crystal Structure Database and the Crystallography Open Database ranking the materials according to their degree of dimensionality. In the case of 2D materials the scoring parameter is seen to clearly separate 2D from non-2D materials and the parameter correlates well with the bonding strength in the layered materials. About 3000 materials are identified as one-dimensional, while more than 9000 are mixed-dimensionality materials containing a molecular (0D) component. The charge states of the components in selected highly ranked materials are investigated using density functional theory and Bader analysis showing that the spatially separated components have either zero charge, corresponding to weak interactions, or integer charge, indicating ionic bonding.

We introduce the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than 30 different crystal structures. Material properties are systematically calculated by state-of-the art density functional theory and many-body perturbation theory (G$_0\!$W$\!_0$ and the Bethe-Salpeter Equation for $\sim$200 materials) following a semi-automated workflow for maximal consistency and transparency. The C2DB is fully open and can be browsed online or downloaded in its entirety. In this paper, we describe the workflow behind the database, present an overview of the properties and materials currently available, and explore trends and correlations in the data. Moreover, we identify a large number of new potentially synthesisable 2D materials with interesting properties targeting applications within spintronics, (opto-)electronics, and plasmonics. The C2DB offers a comprehensive and easily accessible overview of the rapidly expanding family of 2D materials and forms an ideal platform for computational modeling and design of new 2D materials and van der Waals heterostructures., Comment: Add journal reference and DOI; Minor updates to figures and wording

Rapid robotic system development sets a demand for multi-disciplinary methods and tools to explore and compare design alternatives. In this paper, we present collaborative modeling that combines discrete-event models of controller software with continuous-time models of physical robot components. The presented co-modeling method utilized VDM for discrete-event and 20-sim for continuous-time modeling. The collaborative modeling method is illustrated with a concrete example of collaborative model development of a mobile robot animal feeding system. Simulations are used to evaluate the robot model output response in relation to operational demands. The result of the simulations provides the developers with an overview of the impacts of each solution instance in the chosen design space. Based on the solution overview the developers can select candidates that are deemed viable to be deployed and tested on an actual physical robot., Comment: 5 pages, 4 figures, conference

We show that nanoparticles can have very rich ground state chemical order. This is illustrated by determining the chemical ordering of Ag-Au 309-atom Mackay icosahedral nanoparticles. The energy of the nanoparticles is described using a cluster expansion model, and a Mixed Integer Programming (MIP) approach is used to find the exact ground state configurations for all stoichiometries. The chemical ordering varies widely between the different stoichiometries, and display a rich zoo of structures with non-trivial ordering., Comment: Revised version. New figure added, discussion expanded, some material moved into supplementary file

Orientation mapping is a widely used technique for revealing the microstructure of a polycrystalline sample. The crystalline orientation at each point in the sample is determined by analysis of the diffraction pattern, a process known as pattern indexing. A recent development in pattern indexing is the use of a brute-force approach, whereby diffraction patterns are simulated for a large number of crystalline orientations, and compared against the experimentally observed diffraction pattern in order to determine the most likely orientation. Whilst this method can robust identify orientations in the presence of noise, it has very high computational requirements. In this article, the computational burden is reduced by developing a method for nearly-optimal sampling of orientations. By using the quaternion representation of orientations, it is shown that the optimal sampling problem is equivalent to that of optimally distributing points on a four-dimensional sphere. In doing so, the number of orientation samples needed to achieve a indexing desired accuracy is significantly reduced. Orientation sets at a range of sizes are generated in this way for all Laue groups, and are made available online for easy use., Comment: 11 pages, 7 figures

The physical properties of polycrystalline materials depend on their microstructure, which is the nano-to-centimeter-scale arrangement of phases and defects in their interior. Such microstructure depends on the shape, crystallographic phase and orientation, and interfacing of the grains constituting the material. This article presents a new non-destructive 3D technique to study bulk samples with sizes in the cm range with a resolution of hundred micrometers: time-of-flight three-dimensional neutron diffraction (ToF 3DND). Compared to existing analogous X-ray diffraction techniques, ToF 3DND enables studies of samples that can be both larger in size and made of heavier elements. Moreover, ToF 3DND facilitates the use of complicated sample environments. The basic ToF 3DND setup, utilizing an imaging detector with high spatial and temporal resolution, can easily be implemented at a time-of-flight neutron beamline. The technique was developed and tested with data collected at the Materials and Life Science Experimental Facility of the Japan Proton Accelerator Complex (J-PARC) for an iron sample. We successfully reconstructed the shape of 108 grains and developed an indexing procedure. The reconstruction algorithms have been validated by reconstructing two stacked Co-Ni-Ga single crystals and by comparison with a grain map obtained by post-mortem electron backscatter diffraction (EBSD)., Comment: Submitted

It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures. One possible solution for this challenge is to use a heterogeneous model-based approach where different teams can produce their conventional models and carry out their usual mono-disciplinary analysis, but in addition, the different models can be coupled for simulation (co-simulation), allowing the study of the global behavior of the system. Due to its potential, co-simulation is being studied in many different disciplines but with limited sharing of findings. Our aim with this work is to summarize, bridge, and enhance future research in this multidisciplinary area. We provide an overview of co-simulation approaches, research challenges, and research opportunities, together with a detailed taxonomy with different aspects of the state of the art of co-simulation and classification for the past five years. The main research needs identified are: finding generic approaches for modular, stable and accurate coupling of simulation units; and expressing the adaptations required to ensure that the coupling is correct., Comment: 157 pages, about 30 figures

Plants rarely occur in isolated systems. Bacteria can inhabit either the endosphere, the region inside the plant root, or the rhizosphere, the soil region just outside the plant root. Our goal is to understand if using genomic data and media dependent metabolic model information is better for training machine learning of predicting bacterial ecological niche than media independent models or pure genome based species trees. We considered three machine learning techniques: support vector machine, non-negative matrix factorization, and artificial neural networks. In all three machine-learning approaches, the media-based metabolic models and flux balance analyses were more effective at predicting bacterial niche than the genome or PRMT models. Support Vector Machine trained on a minimal media base with Mannose, Proline and Valine was most predictive of all models and media types with an f-score of 0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that media-based metabolic modeling provides a holistic view of the metabolome, allowing machine learning algorithms to highlight the differences between and categorize endosphere and rhizosphere bacteria. There was no single media type that best highlighted differences between endosphere and rhizosphere bacteria metabolism and therefore no single enzyme, reaction, or compound that defined whether a bacteria's origin was of the endosphere or rhizosphere., Comment: 15 pages, keywords: Pseudomonas, SVM, ANN, NMF, FBA, endosphere, rhizosphere, metabolic model, machine learning, KBase Comments: (e.g.: 10 pages, 5 figures, conference or other essential info)

Successful scientific applications of large-scale molecular dynamics often rely on automated methods for identifying the local crystalline structure of condensed phases. Many existing methods for structural identification, such as Common Neighbour Analysis, rely on interatomic distances (or thresholds thereof) to classify atomic structure. As a consequence they are sensitive to strain and thermal displacements, and preprocessing such as quenching or temporal averaging of the atomic positions is necessary to provide reliable identifications. We propose a new method, Polyhedral Template Matching (PTM), which classifies structures according to the topology of the local atomic environment, without any ambiguity in the classification, and with greater reliability than e.g. Common Neighbour Analysis in the presence of thermal fluctuations. We demonstrate that the method can reliably be used to identify structures even in simulations near the melting point, and that it can identify the most common ordered alloy structures as well. In addition, the method makes it easy to identify the local lattice orientation in polycrystalline samples, and to calculate the local strain tensor. An implementation is made available under a Free and Open Source Software license., Comment: 20 pages, 14 figures. Revised version: algorithm improved slightly

Formal Methods tools will never have as many users as tools for popular programming languages and so the effort spent on constructing Integrated Development Environments (IDEs) will be orders of magnitudes lower than that of programming languages such as Java. This means newcomers to formal methods do not get the same user experience as with their favourite programming IDE. In order to improve this situation it is essential that efforts are combined so it is possible to reuse common features and thus not start from scratch every time. This paper presents the Overture platform where such a reuse philosophy is present. We give an overview of the platform itself as well as the extensibility principles that enable much of the reuse. The paper also contains several examples platform extensions, both in the form of new features and a new IDE supporting a new language., Comment: In Proceedings F-IDE 2015, arXiv:1508.03388

We demonstrate the direct photothermal probing and mapping of single plasmonic nanostructures via the temperature induced detuning of nanomechanical string resonators. Single Au nanoslits are illuminated with a low-power polarized focused laser beam ({\lambda} = 633 nm). Polarization dependent heat generation in gold nanoslits is then imaged with high sensitivity. A sensitivity of -4.1 ppm/nW with respect to the illuminated light (beam diameter 5.0pm0.8 {\mu}m) is determined for a single nanoslit (1 {\mu}m long and 53 nm wide), which equals to a total light absorption of 16%. This results in a heating of 0.5 K for an illuminance of 8 nW/{\mu}m2. Our results show that nanomechanical resonators are a unique and robust analysis tool for the low-power investigation of thermoplasmonic effects in plasmonic hot spots.

The DNA molecule is modeled by a parabola embedded chain with long-range interactions between twisted base pair dipoles. A mechanism for bubble generation is presented and investigated in two different configurations. Using random normally distributed initial conditions to simulate thermal fluctuations, a relationship between bubble generation, twist and curvature is established. An analytical approach supports the numerical results., Comment: 7 pages, 8 figures. Accepted for Phys. Rev. E (in press)