PRISM: a tool for automatic verification of probabilistic systems

Probabilistic model checking is an automatic formal verification technique for analysing quantitative properties of systems which exhibit stochastic behaviour. PRISM is a probabilistic model checking tool which has already been successfully deployed in a wide range of application domains, from real-time communication protocols to biological signalling pathways. The tool has recently undergone a significant amount of development. Major additions include facilities to manually explore models, Monte-Carlo discrete-event simulation techniques for approximate model analysis (including support for distributed simulation) and the ability to compute cost- and reward-based measures, e.g. "the expected energy consumption of the system before the first failure occurs". This paper presents an overview of all the main features of PRISM. More information can be found on the website: www.cs.bham.ac.uk/~dxp/prism

Designing sustainable medical devices

Stakeholders in the medical device manufacturing industry are becoming more concerned about the environmental impact of their products and processes. The consumers are also becoming more aware of the negative impact that manufacturers can have on the environment. Government initiatives continue to increase environmental awareness through the development of new policy and legislation, encouraging industry to become more accountable for the environmental impact of their products and operations. The ISO 14001 standard, Environmental Management Systems-Requirements with Guidance for Use, sets guidelines to enable businesses to recognize the environmental effects of their products and processes. Departments can use the tool to set targets to lower the environmental impact and identify areas of high environmental concern when designing, purchasing, and marketing products. Research in these areas will be used to develop the environmental scoring tool to aid in the design of future sustainable medical devices

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Identifying Nearby UHECR Accelerators using UHE (and VHE) Photons

Ultra-high energy photons (UHE, E > 10^19 eV) are inevitably produced during the propagation of 10^20 eV protons in extragalactic space. Their short interaction lengths (<20 Mpc) at these energies, combined with the impressive sensitivity of the Pierre Auger Observatory detector to these particles, makes them an ideal probe of nearby ultra-high-energy cosmic ray (UHECR) sources. We here discuss the particular case of photons from a single nearby (within 30 Mpc) source in light of the possibility that such an object might be responsible for several of the UHECR events published by the Auger collaboration. We demonstrate that the photon signal accompanying a cluster of a few > 6x10^19 eV UHECRs from such a source should be detectable by Auger in the near future. The detection of these photons would also be a signature of a light composition of the UHECRs from the nearby source.Comment: 4 pages, 2 figures, accepted for publication in PR

Space rescue operations. Volume 2: technical discussion

Contingency planning, rescue vehicle requirements, operational considerations, and system analysis for space rescue operations - Vol.

Bio-linguistic transition and Baldwin effect in an evolutionary naming-game model

We examine an evolutionary naming-game model where communicating agents are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of a model control parameter a poorly communicating group of linguistically unskilled agents transforms into almost perfectly communicating group with large learning abilities. When learning ability is kept fixed, the transition appears to be continuous. Genetic imprinting of the learning abilities proceeds via Baldwin effect: initially unskilled communicating agents learn a language and that creates a niche in which there is an evolutionary pressure for the increase of learning ability.Our model suggests that when linguistic (or cultural) processes became intensive enough, a transition took place where both linguistic performance and biological endowment of our species experienced an abrupt change that perhaps triggered the rapid expansion of human civilization.Comment: 7 pages, minor changes, accepted in Int.J.Mod.Phys.C, proceedings of Max Born Symp. Wroclaw (Poland), Sept. 2007. Java applet is available at http://spin.amu.edu.pl/~lipowski/biolin.html or http://www.amu.edu.pl/~lipowski/biolin.htm

Rare earth cinnamates and their corrosion inhibition mechanisms for AS1020 steel

Speciation of the inhibitors lanthanum 2-hydroxy cinnamate and lanthanum 3-hydroxy cinnamate in solution has been evaluated and compared to the speciation of lanthanum 4-hydroxy cinnamate. The results have been correlated with corrosion inhibition efficiency for AS1020 steel in an aqueous chloride solution using a combination of analytical tools such as nuclear magnetic resonance (NMR) spectroscopy, electrospray mass spectrometry (ESMS), potentiodynamic polarisation

H.E.S.S. observations of galaxy clusters

Clusters of galaxies, the largest gravitationally bound objects in the universe, are expected to contain a significant population of hadronic and leptonic cosmic rays. Potential sources for these particles are merger and accretion shocks, starburst driven galactic winds and radio galaxies. Furthermore, since galaxy clusters confine cosmic ray protons up to energies of at least 1 PeV for a time longer than the Hubble time they act as storehouses and accumulate all the hadronic particles which are accelerated within them. Consequently clusters of galaxies are potential sources of VHE (> 100 GeV) gamma rays. Motivated by these considerations, promising galaxy clusters are observed with the H.E.S.S. experiment as part of an ongoing campaign. Here, upper limits for the VHE gamma ray emission for the Abell 496 and Coma cluster systems are reported.Comment: Contribution to the 30th ICRC, Merida Mexico, July 200

Content Based Image Retrieval by Convolutional Neural Networks

Hamreras S., Benítez-Rochel R., Boucheham B., Molina-Cabello M.A., López-Rubio E. (2019) Content Based Image Retrieval by Convolutional Neural Networks. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer.In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech