Turing learning: : A metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.Comment: camera-ready versio

Evidence for mechanical and chemical alteration of iron‐nickel meteorites on Mars: Process insights for Meridiani Planum

The weathering of meteorites found on Mars involves chemical and physical processes that can provide clues to climate conditions at the location of their discovery. Beginning on sol 1961, the Opportunity rover encountered three large iron meteorites within a few hundred meters of each other. In order of discovery, these rocks have been assigned the unofficial names Block Island, Shelter Island, and Mackinac Island. Each rock presents a unique but complimentary set of features that increase our understanding of weathering processes at Meridiani Planum. Significant morphologic characteristics interpretable as weathering features include (1) a large pit in Block Island, lined with delicate iron protrusions suggestive of inclusion removal by corrosive interaction; (2) differentially eroded kamacite and taenite lamellae in Block Island and Shelter Island, providing relative timing through crosscutting relationships with deposition of (3) an iron oxide–rich dark coating; (4) regmaglypted surfaces testifying to regions of minimal surface modification, with other regions in the same meteorites exhibiting (5) large‐scale, cavernous weathering (in Shelter Island and Mackinac Island). We conclude that the current size of the rocks is approximate to their original postfall contours. Their morphology thus likely results from a combination of atmospheric interaction and postfall weathering effects. Among our specific findings is evidence supporting (1) at least one possible episode of aqueous acidic exposure for Block Island; (2) ripple migration over portions of the meteorites; (3) a minimum of two separate episodes of wind abrasion; alternating with (4) at least one episode of coating‐forming chemical alteration, most likely at subzero temperatures

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Spin-forbidden reactions: computational insight into mechanisms and kinetics

status: publishe