Distributed execution of bigraphical reactive systems

The bigraph embedding problem is crucial for many results and tools about bigraphs and bigraphical reactive systems (BRS). Current algorithms for computing bigraphical embeddings are centralized, i.e. designed to run locally with a complete view of the guest and host bigraphs. In order to deal with large bigraphs, and to parallelize reactions, we present a decentralized algorithm, which distributes both state and computation over several concurrent processes. This allows for distributed, parallel simulations where non-interfering reactions can be carried out concurrently; nevertheless, even in the worst case the complexity of this distributed algorithm is no worse than that of a centralized algorithm

A CSP implementation of the bigraph embedding problem

2openopenM. Miculan;M. PeressottiMiculan, Marino; Peressotti, Marc

Source-tracking cadmium in New Zealand agricultural soils: a stable isotope approach

Cadmium (Cd) is a toxic heavy metal, which is accumulated by plants and animals and therefore enters the human food chain. In New Zealand (NZ), where Cd mainly originates from the application of phosphate fertilisers, stable isotopes can be used to trace the fate of Cd in soils and potentially the wider environment due to the limited number of sources in this setting. Prior to 1997, extraneous Cd added to soils in P fertilisers was essentially limited to a single source, the small pacific island of Nauru. Analysis of Cd isotope ratios (ɛ114/110Cd) in Nauru rock phosphate, pre-1997 superphosphate fertilisers, and Canterbury (Lismore Stony Silt Loam) topsoils (Winchmore Research Farm) has demonstrated their close similarity with respect to ɛ114/110Cd. We report a consistent ɛ114/110Cd signature in fertiliser-derived Cd throughout the latter twentieth century. This finding is useful because it allows the application of mixing models to determine the proportions of fertiliser-derived Cd in the wider environment. We believe this approach has good potential because we also found the ɛ114/110Cd in fertilisers to be distinct from unfertilised Canterbury subsoils. In our analysis of the Winchmore topsoil series (1949-2015), the ɛ114/110Cd remained quite constant following the change from Nauru to other rock phosphate sources in 1997, despite a corresponding shift in fertiliser ɛ114/110Cd at this time. We can conclude that to the present day, the Cd in topsoil at Winchmore still mainly originates from historical phosphate fertilisers. One implication of this finding is that the current applications of P fertiliser are not resulting in further Cd accumulation. We aim to continue our research into Cd fate, mobility and transformations in the NZ environment by applying Cd isotopes in soils and aquatic environments across the country

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Learning high-speed flight in the wild

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit their capabilities. Autonomous operation with onboard sensing and computation has been limited to low speeds. State-of-the-art methods generally separate the navigation problem into subtasks: sensing, mapping, and planning. Although this approach has proven successful at low speeds, the separation it builds upon can be problematic for high-speed navigation in cluttered environments. The subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline. Here, we propose an end-to-end approach that can autonomously fly quadrotors through complex natural and human-made environments at high speeds with purely onboard sensing and computation. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. This direct mapping drastically reduces processing latency and increases robustness to noisy and incomplete perception. The sensorimotor mapping is performed by a convolutional network that is trained exclusively in simulation via privileged learning: imitating an expert with access to privileged information. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments that were never experienced during training: dense forests, snow-covered terrain, derailed trains, and collapsed buildings. Our work demonstrates that end-to-end policies trained in simulation enable high-speed autonomous flight through challenging environments, outperforming traditional obstacle avoidance pipelines