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We introduce and analyze quasi-local mass using Hamiltonian methods. It is based on multipole decomposition for surfaces that are topological spheres. Based on the above model, tests were performed for Kerr spacetime for two arbitrary choices of surfaces: rigid spheres and round spheres., Comment: 35 pages, no figures

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:016

In this paper, we study some of the properties of the $G \to 0$ limit of the Kerr-Newman solution of Einstein-Maxwell equations. Noting Carter's observation of the near equality between the $g = 2$ gyromagnetic ratio in the Kerr-Newman solution and that of the electron, we discuss additional such coincidences relating to the Kerr-Newman multipoles and properties of the electron. In contrast to the Coulomb field, this spinning Maxwell field has a finite Lagrangian. Moreover, by evaluating the Lagrangian for the superposition of two such Kerr-Newman electromagnetic fields on a flat background, we are able to find their interaction potential. This yields a correction to the Coulomb interaction due to the spin of the field., Comment: 30 pages, 11 figures, 3 tables

We develop new data structures and algorithms for checking verification queries in NetKAT, a domain-specific language for specifying the behavior of network data planes. Our results extend the techniques obtained in prior work on symbolic automata and provide a framework for building efficient and scalable verification tools. We present KATch, an implementation of these ideas in Scala, featuring an extended set of NetKAT operators that are useful for expressing network-wide specifications, and a verification engine that constructs a bisimulation or generates a counter-example showing that none exists. We evaluate the performance of our implementation on real-world and synthetic benchmarks, verifying properties such as reachability and slice isolation, typically returning a result in well under a second, which is orders of magnitude faster than previous approaches. Our advancements underscore NetKAT's potential as a practical, declarative language for network specification and verification.

We construct electromagnetic field with non-trivial topological properties on de Sitter background. The field is closely related with Hopf fibration. We analyze energy, angular momentum and topological charges for this solution. The paper is a generalization of CQG \textbf{35} (2018), no. 24, 245010 to de Sitter spacetime.

Solana has quickly emerged as a popular platform for building decentralized applications (DApps), such as marketplaces for non-fungible tokens (NFTs). A key reason for its success are Solana's low transaction fees and high performance, which is achieved in part due to its stateless programming model. Although the literature features extensive tooling support for smart contract security, current solutions are largely tailored for the Ethereum Virtual Machine. Unfortunately, the very stateless nature of Solana's execution environment introduces novel attack patterns specific to Solana requiring a rethinking for building vulnerability analysis methods. In this paper, we address this gap and propose FuzzDelSol, the first binary-only coverage-guided fuzzing architecture for Solana smart contracts. FuzzDelSol faithfully models runtime specifics such as smart contract interactions. Moreover, since source code is not available for the large majority of Solana contracts, FuzzDelSol operates on the contract's binary code. Hence, due to the lack of semantic information, we carefully extracted low-level program and state information to develop a diverse set of bug oracles covering all major bug classes in Solana. Our extensive evaluation on 6049 smart contracts shows that FuzzDelSol's bug oracles find bugs with a high precision and recall. To the best of our knowledge, this is the largest evaluation of the security landscape on the Solana mainnet., Comment: This paper will appear on the ACM CCS 2023 in November 2023

We analyse the Noether charges for scalar and Maxwell fields on light cones on a de Sitter, Minkowski, and anti-de Sitter backgrounds. Somewhat surprisingly, under natural asymptotic conditions all charges for the Maxwell fields on both the de Sitter and anti-de Sitter backgrounds are finite. On the other hand, one needs to renormalise the charges for the conformally-covariant scalar field when the cosmological constant does not vanish. In both cases well-defined renormalised charges, with well-defined fluxes, are obtained. Again surprisingly, a Hamiltonian analysis of a suitably rescaled scalar field leads to finite charges, without the need to renormalise. Last but not least, we indicate natural phase spaces where the Poisson algebra of charges is well defined., Comment: 79 pages, 1 figure

We present ResilienC, a framework for resilient control of Cyber-Physical Systems subject to STL-based requirements. ResilienC utilizes a recently developed formalism for specifying CPS resiliency in terms of sets of $(\mathit{rec},\mathit{dur})$ real-valued pairs, where $\mathit{rec}$ represents the system's capability to rapidly recover from a property violation (recoverability), and $\mathit{dur}$ is reflective of its ability to avoid violations post-recovery (durability). We define the resilient STL control problem as one of multi-objective optimization, where the recoverability and durability of the desired STL specification are maximized. When neither objective is prioritized over the other, the solution to the problem is a set of Pareto-optimal system trajectories. We present a precise solution method to the resilient STL control problem using a mixed-integer linear programming encoding and an a posteriori $\epsilon$-constraint approach for efficiently retrieving the complete set of optimally resilient solutions. In ResilienC, at each time-step, the optimal control action selected from the set of Pareto-optimal solutions by a Decision Maker strategy realizes a form of Model Predictive Control. We demonstrate the practical utility of the ResilienC framework on two significant case studies: autonomous vehicle lane keeping and deadline-driven, multi-region package delivery., Comment: 11 pages, 6 figures

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background and the details of the proposed algorithms we apply them to optimization of selected variants of the problem and discuss the results., Comment: In proceedings of the Intelligent Systems IS2022 Conference, October 12-14, 2022, Warsaw, Poland

Edge computing enables the processing of data - frequently personal data - at the edge of the network. For personal data, legislation such as the European General Data Protection Regulation requires data protection by design. Hence, data protection has to be accounted for in the design of edge computing systems whenever personal data is involved. This leads to specific requirements for modeling the architecture of edge computing systems, e.g., representation of data and network properties. To the best of our knowledge, no existing modeling language fulfils all these requirements. In our previous work we showed that the commonly used UML profile UMLsec fulfils some of these requirements, and can thus serve as a starting point. The aim of this paper is to create a modeling language which meets all requirements concerning the design of the architecture of edge computing systems accounting for data protection. Thus, we extend UMLsec to satisfy all requirements. We call the resulting UML profile UMLsec4Edge. We follow a systematic approach to develop UMLsec4Edge. We apply UMLsec4Edge to real-world use cases from different domains, and create appropriate deployment diagrams and class diagrams. These diagrams show UMLsec4Edge is capable of meeting the requirements., Comment: 8 pages, 4 figures, 2 tables, published at the 48th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA)

The ExoClock project has been created with the aim of increasing the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates over an extended period, in order to produce a consistent catalogue of reliable and precise ephemerides. This work presents a homogenous catalogue of updated ephemerides for 450 planets, generated by the integration of $\sim$18000 data points from multiple sources. These sources include observations from ground-based telescopes (ExoClock network and ETD), mid-time values from the literature and light-curves from space telescopes (Kepler/K2 and TESS). With all the above, we manage to collect observations for half of the post-discovery years (median), with data that have a median uncertainty less than one minute. In comparison with literature, the ephemerides generated by the project are more precise and less biased. More than 40\% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95\%), and also the identification of missing data. The dedicated ExoClock network effectively supports this task by contributing additional observations when a gap in the data is identified. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (TTVs - Transit Timing Variations) for a sample of 19 planets. All products, data, and codes used in this work are open and accessible to the wider scientific community., Comment: Recommended for publication to ApJS (reviewer's comments implemented). Main body: 13 pages, total: 77 pages, 7 figures, 7 tables. Data available at http://doi.org/10.17605/OSF.IO/P298N

Motivated by the classical work of Halmos on functional monadic Boolean algebras we derive three basic sup-semilattice constructions, among other things the so-called powersets and powerset operators. Such constructions are extremely useful and can be found in almost all branches of modern mathematics, including algebra, logic and topology. Our three constructions give rise to four covariant and two contravariant functors and constitute three adjoint situations we illustrate in simple examples.

Resiliency is the ability to quickly recover from a violation and avoid future violations for as long as possible. Such a property is of fundamental importance for Cyber-Physical Systems (CPS), and yet, to date, there is no widely agreed-upon formal treatment of CPS resiliency. We present an STL-based framework for reasoning about resiliency in CPS in which resiliency has a syntactic characterization in the form of an STL-based Resiliency Specification (SRS). Given an arbitrary STL formula $\varphi$, time bounds $\alpha$ and $\beta$, the SRS of $\varphi$, $R_{\alpha,\beta}(\varphi)$, is the STL formula $\neg\varphi\mathbf{U}_{[0,\alpha]}\mathbf{G}_{[0,\beta)}\varphi$, specifying that recovery from a violation of $\varphi$ occur within time $\alpha$ (recoverability), and subsequently that $\varphi$ be maintained for duration $\beta$ (durability). These $R$-expressions, which are atoms in our SRS logic, can be combined using STL operators, allowing one to express composite resiliency specifications, e.g., multiple SRSs must hold simultaneously, or the system must eventually be resilient. We define a quantitative semantics for SRSs in the form of a Resilience Satisfaction Value (ReSV) function $r$ and prove its soundness and completeness w.r.t. STL's Boolean semantics. The $r$-value for $R_{\alpha,\beta}(\varphi)$ atoms is a singleton set containing a pair quantifying recoverability and durability. The $r$-value for a composite SRS formula results in a set of non-dominated recoverability-durability pairs, given that the ReSVs of subformulas might not be directly comparable (e.g., one subformula has superior durability but worse recoverability than another). To the best of our knowledge, this is the first multi-dimensional quantitative semantics for an STL-based logic. Two case studies demonstrate the practical utility of our approach., Comment: 16 pages excluding references and appendix (23 pages in total), 6 figures

We introduce the novel concept of Spatial Predictive Control (SPC) to solve the following problem: given a collection of agents (e.g., drones) with positional low-level controllers (LLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time-step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the LLC. Using a high-fidelity simulation environment, we show that SPC outperforms the most closely related class of controllers, Potential Field Controllers, on the drone flocking problem. We also show that SPC is able to cope with a potential sim-to-real transfer gap by demonstrating its performance on real hardware, namely our implementation of flocking using nine Crazyflie 2.1 drones.

We present Barrier-based Simplex (Bb-Simplex), a new, provably correct design for runtime assurance of continuous dynamical systems. Bb-Simplex is centered around the Simplex control architecture, which consists of a high-performance advanced controller that is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In Bb-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We also propose extensions to Bb-Simplex to enable its use in hybrid systems, which have multiple modes each with its own dynamics, and to support its use when only approximate dynamics (not exact dynamics) are available, for both continuous-time and hybrid dynamical systems. We consider significant applications of Bb-Simplex to microgrids featuring advanced controllers in the form of neural networks trained using reinforcement learning. These microgrids are modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that Bb-Simplex can automatically derive switching conditions for complex continuous-time and hybrid systems, the switching conditions are not overly conservative, and Bb-Simplex ensures safety even in the presence of adversarial attacks on the neural controller when only approximate dynamics (with an error bound) are available., Comment: This version includes the following new contributions. (1) We extend Bb-Simplex to hybrid systems and prove the correctness of this extension. (2) We extend Bb-Simplex to support the use of approximate dynamics. (3) We combine these two extensions of Bb-Simplex. (4) We present new experiments evaluating Bb-Simplex and its extensions using a complex model of a real microgrid

We introduce a new stochastic verification algorithm that formally quantifies the behavioral robustness of any time-continuous process formulated as a continuous-depth model. Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states. We call our algorithm GoTube. Through its construction, GoTube ensures that the bounding tube is conservative up to a desired probability and up to a desired tightness. GoTube is implemented in JAX and optimized to scale to complex continuous-depth neural network models. Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube does not accumulate overapproximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques. We show that GoTube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability on a large set of experiments. GoTube is stable and sets the state-of-the-art in terms of its ability to scale to time horizons well beyond what has been previously possible., Comment: Accepted to the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)

We analyse the canonical energy of vacuum linearised gravitational fields on light cones on a de Sitter, Minkowski, and Anti de Sitter backgrounds in Bondi gauge. We derive the associated asymptotic symmetries. When $\Lambda>0$ the energy diverges, but a renormalised formula with well defined flux is obtained. We show that the renormalised energy in the asymptotically off-diagonal gauge coincides with the quadratisation of the generalisation of the Trautman-Bondi mass proposed in [13]., Comment: minor improvements in the presentation, retypeset in EPJC preprint style

The Simplex Architecture is a runtime assurance framework where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain the safety of an autonomous cyber-physical system. In this work, we show that runtime checks can replace the requirement to statically verify safety of the baseline controller. This is important as there are many powerful control techniques, such as model-predictive control and neural network controllers, that work well in practice but are difficult to statically verify. Since the method does not use internal information about the advanced or baseline controller, we call the approach the Black-Box Simplex Architecture. We prove the architecture is safe and present two case studies where (i) model-predictive control provides safe multi-robot coordination, and (ii) neural networks provably prevent collisions in groups of F-16 aircraft, despite the controllers occasionally outputting unsafe commands.