Decentralized Asynchronous Crash-Resilient Runtime Verification

Runtime Verification (RV) is a lightweight method for monitoring the formal specification of a system during its execution. It has recently been shown that a given state predicate can be monitored consistently by a set of crash-prone asynchronous distributed monitors, only if sufficiently many different verdicts can be emitted by each monitor. We revisit this impossibility result in the context of LTL semantics for RV. We show that employing the four-valued logic Rv-LTL will result in inconsistent distributed monitoring for some formulas. Our first main contribution is a family of logics, called Ltl2k+4, that refines Rv-Ltl incorporating 2k + 4 truth values, for each k >= 0. The truth values of Ltl2k+4 can be effectively used by each monitor to reach a consistent global set of verdicts for each given formula, provided k is sufficiently large. Our second main contribution is an algorithm for monitor construction enabling fault-tolerant distributed monitoring based on the aggregation of the individual verdicts by each monitor

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Model checking is a well-established technique for automaticallyverifying complex systems. Recently, model checkers have appearedin computer tools for the analysis of biochemical (and generegulatory) networks. We survey several such tools to assess thepotential of model checking in computational biology. Next, our overviewfocuses on direct applications of existing model checkers, as well ason algorithms for biochemical network analysis influenced by modelchecking, such as those using binary decision diagrams or Booleansatisfiability solvers. We conclude with advantages and drawbacks ofmodel checking for the analysis of biochemical networks

The 125th anniversary of the first postulation of the soil origin of endophytic bacteria – a tribute to M.L.V. Galippe

In both managed and natural ecosystems, a wide range of various non-nodulating bacteria can thrive as endophytes in the plant interior, and some can be beneficial to their hosts (Hallmann and Berg 2007; Reinhold-Hurek and Hurek 2011). Colonizationmechanisms, the ecology and functioning of these endophytic bacteria as well as their interactions with plants have been investigated (Hardoim et al. 2008; Compant et al. 2010). Although the source of colonization can also be the spermosphere, anthosphere, caulosphere, and the phyllosphere,most endophytic bacteria are derived from the soil environment (Hallmann and Berg 2007; Compant et al. 2010)

How are gene sequences analyses modifying bacterial taxonomy? The case of Klebsiella

Bacterial names are continually being changed in order to more adequately describe natural groups (the units of microbial diversity) and their relationships. The problems in Klebsiella taxonomy are illustrative and common to other bacterial genera. Like other bacteria, Klebsiella spp. were isolated long ago, when methods to identify and classify bacteria were limited. However, recently developed molecular approaches have led to taxonomical revisions in several cases or to sound proposals of novel species. [Int Microbiol 2004; 7(4):261-268

Traffic flow on realistic road networks with adaptive traffic lights

We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adaptive vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, both the mean and the fluctuation of the travel time are smallest when using the joint upstream-downstream control strategy.Comment: 41 pages, pdflate

Optimal control systems with time delay and conditions for normality

Imperial Users onl

A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis