Design and Development of Software Tools for Bio-PEPA

This paper surveys the design of software tools for the Bio-PEPA process algebra. Bio-PEPA is a high-level language for modelling biological systems such as metabolic pathways and other biochemical reaction networks. Through providing tools for this modelling language we hope to allow easier use of a range of simulators and model-checkers thereby freeing the modeller from the responsibility of developing a custom simulator for the problem of interest. Further, by providing mappings to a range of different analysis tools the Bio-PEPA language allows modellers to compare analysis results which have been computed using independent numerical analysers, which enhances the reliability and robustness of the results computed.

Accelerating advances in landscape connectivity modelling with the ConScape library

Increasingly precise spatial data (e.g. high-resolution imagery from remote sensing) allow for improved representations of the landscape network for assessing the combined effects of habitat loss and connectivity declines on biodiversity. However, evaluating large landscape networks presents a major computational challenge both in terms of working memory and computation time. We present the ConScape (i.e. “connected landscapes”) software library implemented in the high-performance open-source Julia language to compute metrics for connected habitat and movement flow on high-resolution landscapes. The combination of Julia's ‘just-in-time’ compiler, efficient algorithms and ‘landmarks’ to reduce the computational load allows ConScape to compute landscape ecological metrics—originally developed in metapopulation ecology (such as ‘metapopulation capacity’ and ‘probability of connectivity’)—for large landscapes. An additional major innovation in ConScape is the adoption of the randomized shortest paths framework to represent connectivity along the continuum from optimal to random movements, instead of only those extremes. We demonstrate ConScape's potential for using large datasets in sustainable land planning by modelling landscape connectivity based on remote-sensing data paired with GPS tracking of wild reindeer in Norway. To guide users, we discuss other applications, and provide a series of worked examples to showcase all ConScape's functionalities in Supplementary Material. Built by a team of ecologists, network scientists and software developers, ConScape is able to efficiently compute landscape metrics for high-resolution landscape representations to leverage the availability of large data for sustainable land use and biodiversity conservation. As a Julia implementation, ConScape combines computational efficiency with a transparent code base, which facilitates continued innovation through contributions from the rapidly growing community of landscape and connectivity modellers using Julia. circuitscape, conefor, ecological networks, least-cost path, metapopulation, random walk, randomized shortest pathspublishedVersio

Stochastic models for quality of service of component connectors

The intensifying need for scalable software has motivated modular development and using systems distributed over networks to implement large-scale applications. In Service-oriented Computing, distributed services are composed to provide large-scale services with a specific functionality. In this way, reusability of existing services can be increased. However, due to the heterogeneity of distributed software systems, software composition is not easy and requires additional mechanisms to impose some form of a coordination on a distributed software system. Besides functional correctness, a composed service must satisfy various quantitative requirements for its clients, which are generically called its quality of service (QoS). Particularly, it is tricky to obtain the overall QoS of a composed service even if the QoS information of its constituent distributed services is given. In this thesis, we propose Stochastic Reo to specify software composition with QoS aspects and its compositional semantic models. They are also used as intermediate models to generate their corresponding stochastic models for practical analysis. Based on this, we have implemented the tool Reo2MC. Using Reo2MC, we have modeled and analyzed an industrial software, the ASK system. Its analysis results provided the best cost-effective resource utilization and some suggestions to improve the performance of the system.UBL - phd migration 201

Robotic workcell analysis and object level programming

For many years robots have been programmed at manipulator or joint level without any real thought to the implementation of sensing until errors occur during program execution. For the control of complex, or multiple robot workcells, programming must be carried out at a higher level, taking into account the possibility of error occurrence. This requires the integration of decision information based on sensory data.Aspects of robotic workcell control are explored during this work with the object of integrating the results of sensor outputs to facilitate error recovery for the purposes of achieving completely autonomous operation.Network theory is used for the development of analysis techniques based on stochastic data. Object level programming is implemented using Markov chain theory to provide fully sensor integrated robot workcell control

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

NetSquid, a NETwork Simulator for QUantum Information using Discrete events

In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Because detailed architecture proposals are generally too complex for mathematical analysis, it is natural to employ numerical simulation. Here we introduce NetSquid, the NETwork Simulator for QUantum Information using Discrete events, a discrete-event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer and its control plane up to the application level. We study several use cases to showcase NetSquid's power, including detailed physical layer simulations of repeater chains based on nitrogen vacancy centres in diamond as well as atomic ensembles. We also study the control plane of a quantum switch beyond its analytically known regime, and showcase NetSquid's ability to investigate large networks by simulating entanglement distribution over a chain of up to one thousand nodes.Comment: NetSquid is freely available at https://netsquid.org; refined main text section

Symblicit Exploration and Elimination for Probabilistic Model Checking

Binary decision diagrams can compactly represent vast sets of states, mitigating the state space explosion problem in model checking. Probabilistic systems, however, require multi-terminal diagrams storing rational numbers. They are inefficient for models with many distinct probabilities and for iterative numeric algorithms like value iteration. In this paper, we present a new "symblicit" approach to checking Markov chains and related probabilistic models: We first generate a decision diagram that symbolically collects all reachable states and their predecessors. We then concretise states one-by-one into an explicit partial state space representation. Whenever all predecessors of a state have been concretised, we eliminate it from the explicit state space in a way that preserves all relevant probabilities and rewards. We thus keep few explicit states in memory at any time. Experiments show that very large models can be model-checked in this way with very low memory consumption

Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde

On the use of MTBDDs for performability analysis and verification of stochastic systems

AbstractThis paper describes how to employ multi-terminal binary decision diagrams (MTBDDs) for the construction and analysis of a general class of models that exhibit stochastic, probabilistic and non-deterministic behaviour. It is shown how the notorious problem of state space explosion can be circumvented by compositionally constructing symbolic (i.e. MTBDD-based) representations of complex systems from small-scale components. We emphasise, however, that compactness of the representation can only be achieved if heuristics are applied with insight into the structure of the system under investigation. We report on our experiences concerning compact representation, performance analysis and verification of performability properties