A compositional analysis of broadcasting embedded systems

This work takes as its starting point D Kendall's CANdle/bCANdle algebraic framework for formal modelling and specification of broadcasting embedded systems based on CAN networks. Checking real-time properties of such systems is beset by problems of state-space explosion and so a scheme is given for recasting systems specified in Kendall's framework as parallel compositions of timed automata; a CAN network channel is modelled as an automaton. This recasting is shown to be bi-similar to the original bCANdle model. In the recast framework,"compositionality" theorems allow one to infer that a model of a system is simulated by some abstraction of the model, and hence that properties of the model expressible in ACTL can be inferred from analogous properties of the abstraction. These theorems are reminiscent of "assume-guarantee" reasoning allowing one to build simulations component-wise although, unfortunately, components participating in a "broadcast" are required to be abstracted "atomically". Case studies are presented to show how this can be used in practice, and how systems which take impossibly long to model-check can tackled by compositional methods. The work is of broader interest also, as the models are built as UPPAAL systems and the compositionality theorems apply to any UPPAAL system in which the components do not share local variables. The method could for instance extend to systems using some network other than CAN, provided it can be modelled by timed automata. Possibilities also exist for building it into an automated tool, complementing other methods such as counterexample- guided abstraction refinement

A Flexible Laboratory Environment Supporting Honeypot Deployment for Teaching Real-World Cybersecurity Skills

In the practical study of cybersecurity, students benefit greatly from having full control of physical equipment and services. However, this presents far too great a risk to security to be permitted on university campus networks. This paper describes an approach, used successfully at Northumbria University, in which students have control of an off-campus network laboratory, with a dedicated connection to the Internet. The laboratory is flexible enough to allow the teaching of general purpose networking and operating systems courses, while also supporting the teaching of cybersecurity through the safe integration of honeypot devices. In addition, the paper gives an analysis of honeypot architectures and presents two in detail. One of these offers students the opportunity to study cybersecurity attacks and defences at very low cost. It has been developed as a stand-alone device that also can be integrated safely into the laboratory environment for the study of more complex scenarios. The main contributions of this paper are the design and implementation of: an off-campus, physical network laboratory; a small, low-cost, configurable platform for use as a “lightweight” honeypot; and a laboratory-based, multi-user honeypot for large-scale, concurrent, cybersecurity experiments. The paper outlines how the laboratory environment has been successfully deployed within a university setting to support the teaching and learning of cybersecurity. It highlights the type of experiments and projects that have been supported and can be supported in the future

A compositional analysis of broadcasting embedded systems

This work takes as its starting point D Kendall's CANdle/bCANdle algebraic framework for formal modelling and specification of broadcasting embedded systems based on CAN networks. Checking real-time properties of such systems is beset by problems of state-space explosion and so a scheme is given for recasting systems specified in Kendall's framework as parallel compositions of timed automata; a CAN network channel is modelled as an automaton. This recasting is shown to be bi-similar to the original bCANdle model. In the recast framework,"compositionality" theorems allow one to infer that a model of a system is simulated by some abstraction of the model, and hence that properties of the model expressible in ACTL can be inferred from analogous properties of the abstraction. These theorems are reminiscent of "assume-guarantee" reasoning allowing one to build simulations component-wise although, unfortunately, components participating in a "broadcast" are required to be abstracted "atomically". Case studies are presented to show how this can be used in practice, and how systems which take impossibly long to model-check can tackled by compositional methods. The work is of broader interest also, as the models are built as UPPAAL systems and the compositionality theorems apply to any UPPAAL system in which the components do not share local variables. The method could for instance extend to systems using some network other than CAN, provided it can be modelled by timed automata. Possibilities also exist for building it into an automated tool, complementing other methods such as counterexample- guided abstraction refinement.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Distinct hippocampal engrams control extinction and relapse of fear memory

Learned fear often relapses after extinction, suggesting that extinction training generates a new memory that coexists with the original fear memory; however, the mechanisms governing the expression of competing fear and extinction memories remain unclear. We used activity-dependent neural tagging to investigate representations of fear and extinction memories in the dentate gyrus. We demonstrate that extinction training suppresses reactivation of contextual fear engram cells while activating a second ensemble, a putative extinction engram. Optogenetic inhibition of neurons that were active during extinction training increased fear after extinction training, whereas silencing neurons that were active during fear training reduced spontaneous recovery of fear. Optogenetic stimulation of fear acquisition neurons increased fear, while stimulation of extinction neurons suppressed fear and prevented spontaneous recovery. Our results indicate that the hippocampus generates a fear extinction representation and that interactions between hippocampal fear and extinction representations govern the suppression and relapse of fear after extinction.We thank J. Dunsmoor for comments on the manuscript. A.F.L. was supported by NIH F31 MH111243 and NIH T32 MH106454. S.L.S. was supported by PD/BD/128076/2016 from the Portuguese Foundation for Science and Technology. Research supported by NIH DP5 OD017908 and New York Stem Cell Science (NYSTEM) C-029157 to C.A.D., NIH R01 MH102595 and NIH R21 EY026446 to M.R.

Infidelity of SARS-CoV Nsp14-Exonuclease Mutant Virus Replication Is Revealed by Complete Genome Sequencing

Most RNA viruses lack the mechanisms to recognize and correct mutations that arise during genome replication, resulting in quasispecies diversity that is required for pathogenesis and adaptation. However, it is not known how viruses encoding large viral RNA genomes such as the Coronaviridae (26 to 32 kb) balance the requirements for genome stability and quasispecies diversity. Further, the limits of replication infidelity during replication of large RNA genomes and how decreased fidelity impacts virus fitness over time are not known. Our previous work demonstrated that genetic inactivation of the coronavirus exoribonuclease (ExoN) in nonstructural protein 14 (nsp14) of murine hepatitis virus results in a 15-fold decrease in replication fidelity. However, it is not known whether nsp14-ExoN is required for replication fidelity of all coronaviruses, nor the impact of decreased fidelity on genome diversity and fitness during replication and passage. We report here the engineering and recovery of nsp14-ExoN mutant viruses of severe acute respiratory syndrome coronavirus (SARS-CoV) that have stable growth defects and demonstrate a 21-fold increase in mutation frequency during replication in culture. Analysis of complete genome sequences from SARS-ExoN mutant viral clones revealed unique mutation sets in every genome examined from the same round of replication and a total of 100 unique mutations across the genome. Using novel bioinformatic tools and deep sequencing across the full-length genome following 10 population passages in vitro, we demonstrate retention of ExoN mutations and continued increased diversity and mutational load compared to wild-type SARS-CoV. The results define a novel genetic and bioinformatics model for introduction and identification of multi-allelic mutations in replication competent viruses that will be powerful tools for testing the effects of decreased fidelity and increased quasispecies diversity on viral replication, pathogenesis, and evolution

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

A New Metric for the Analysis of Swarms using Potential Fields

There are many metrics defined for the analysis of swarm coordination algorithms. These metrics are usually based upon the distances between agents, the distance between agents and a fixed point, or the resultant vectors that potential field effects produce. This paper examines a distance-based metric that measures a swarm’s overall structure using inter-agent distances. More importantly, it introduces a new metric that identifies a swarm’s state based upon the resultant magnitude of the vectors produced by the agent interactions that create the agent distribution within the swarm’s structure. The algorithms used to implement the swarming feature are based upon cohesion and repulsion vectors between an agent and its neighbors. In comparing and contrasting the two metrics, we find that the cohesion/repulsion metric offers a number of advantages over the distance metric. In particular, the cohesion/repulsion metric allows the identification of the essential characteristic of a swarm as “expanding,” “stable,” or “contracting.” These states cannot be identified using a distance-based metric. Practical swarming applications where the new metric can be applied advantageously include area-filling and reconnaissance

Void Reduction in Self-Healing Swarms

Swarms consist of many agents that interact according to a simple set of rules, giving rise to emergent global behaviours. In this paper, we consider swarms of mobile robots or drones. Swarms can be tolerant of faults that may occur for many reasons, such as resource exhaustion, component failure, or disruption from an external event. The loss of agents reduces the size of a swarm, and may create an irregular structure in the swarm topology. A swarm’s structure can also be irregular due to initial conditions, or the existence of an obstacle. These changes in the structure or size of a swarm do not stop it from functioning, but may adversely affect its efficiency or effectiveness. In this paper, we describe a self-healing mechanism to counter the effect of agent loss or structural irregularity. This method is based on the reduction of concave regions at swarm perimeter regions. Importantly, this method requires no expensive communication infrastructure, relying only on agent proximity information. We illustrate the application of our method to the problem of surrounding an oil slick, and show that void reduction is necessary for full and close containment, before concluding with a brief discussion of its potential uses in other domains

Targeted Projection Pursuit Tool for Gene Expression Visualisation

A tool is introduced that uses a novel technique to enable users to explore two-dimensional views of high dimensional gene expression data sets. Unlike other such tools, the interface is intuitive and efficient, allowing the user to easily select views that meet their requirements. The tool is tested on publicly available gene expression data sets and demonstrated to find views that show the seperation of gene expression data sets into classes more effectively than standard dimension-reduction methods