The origin of Proterozoic massif-type anorthosites: Evidence from interactions between crustal xenoliths and basaltic magma

Plagioclase-rich reaction zones occur around numerous aluminous crustal xenoliths within a suite of Palaeogene sub-volcanic basic sheets on the Isle of Mull, NW Scotland. The xenoliths consist of a glassy core, containing mullite needles, generated from the melting of pelitic source rocks. Thick plagioclase mantles grew at the interface between the aluminous liquid and the enclosing basaltic magma and provide a high-level analogue for the petrogenesis of Proterozoic massif-type anorthosites. Similar interactions between mantle-derived basic magmas ponded at the base of the crust and relatively Al-rich lower crustal lithologies would result in the precipitation of large volumes of plagioclase. Anorthosite massifs were then emplaced at higher crustal levels as crystal-rich mushes within relatively juvenile Proterozoic crust. The model negates the need to crystallize large volumes of mafic minerals prior to the production of plagioclase-saturated liquids, and also accounts for the significant influence of crustal sources on the isotopic signatures of all members of the anorthosite suite

Timing of deposition, orogenesis and glaciation within the Dalradian rocks of Scotland: constraints from U-Pb zircon ages

The stratigraphical and structural continuity of the Late Proterozoic Dalradian rocks of the Scottish Highlands is re-examined in the light of new U-Pb zircon ages on the tuffs belonging to the Tayvallich Volcanic Formation (601 ñ 4 Ma), and on the late Grampian 'Newer Gabbros' (470 ñ 9 Ma) of Insch and Morven-Cabrach in Aberdeenshire. These age data, together with the existing 590 ñ 2 Ma age for the Ben Vuirich Granite, provide key radiometric constraints on the evolution of the Dalradian block, and the implications arising from these ages are critically assessed. Three main conclusions are drawn. (1) The entire Caledonian orogeny, although short-lived, is unlikely to have affected sediments of Arenig age and a break probably occurs between those Dalradian sediments of late Proterozoic (<600 Ma) age and the Ordovician rocks of the Highland Border Complex. (2) A period of crustal thickening probably affected some Dalradian rocks prior to 590 Ma. Such an event is indicated by both the polymetamorphic histories of the lower parts of the Dalradian pile and the contact metamorphic assemblages within the aureole of the Ben Vuirich Granite, which are incompatible with sedimentary thicknesses. (3) Age constraints on global Late Proterozoic glacial activity also suggest that the Dalradian stratigraphy is broken into discrete smaller units. Models involving continuous deposition of Dalradian sediments from pre-750 Ma to 470 Ma are rejected

Intelligence as inference or forcing Occam on the world

We propose to perform the optimization task of Universal Artificial Intelligence (UAI) through learning a reference machine on which good programs are short. Further, we also acknowledge that the choice of reference machine that the UAI objective is based on is arbitrary and, therefore, we learn a suitable machine for the environment we are in. This is based on viewing Occam’s razor as an imperative instead of as a proposition about the world. Since this principle cannot be true for all reference machines, we need to find a machine that makes the principle true. We both want good policies and the environment to have short implementations on the machine. Such a machine is learnt iteratively through a procedure that generalizes the principle underlying the Expectation-Maximization algorithm

Fuzzy cluster validation using the partition negentropy criterion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04277-5_24Proceedings of the 19th International Conference, Limassol, Cyprus, September 14-17, 2009We introduce the Partition Negentropy Criterion (PNC) for cluster validation. It is a cluster validity index that rewards the average normality of the clusters, measured by means of the negentropy, and penalizes the overlap, measured by the partition entropy. The PNC is aimed at finding well separated clusters whose shape is approximately Gaussian. We use the new index to validate fuzzy partitions in a set of synthetic clustering problems, and compare the results to those obtained by the AIC, BIC and ICL criteria. The partitions are obtained by fitting a Gaussian Mixture Model to the data using the EM algorithm. We show that, when the real clusters are normally distributed, all the criteria are able to correctly assess the number of components, with AIC and BIC allowing a higher cluster overlap. However, when the real cluster distributions are not Gaussian (i.e. the distribution assumed by the mixture model) the PNC outperforms the other indices, being able to correctly evaluate the number of clusters while the other criteria (specially AIC and BIC) tend to overestimate it.This work has been partially supported with funds from MEC BFU2006-07902/BFI, CAM S-SEM-0255-2006 and CAM/UAM project CCG08-UAM/TIC-442

Models and model value in stochastic programming

Finding optimal decisions often involves the consideration of certain random or unknown parameters. A standard approach is to replace the random parameters by the expectations and to solve a deterministic mathematical program. A second approach is to consider possible future scenarios and the decision that would be best under each of these scenarios. The question then becomes how to choose among these alternatives. Both approaches may produce solutions that are far from optimal in the stochastic programming model that explicitly includes the random parameters. In this paper, we illustrate this advantage of a stochastic program model through two examples that are representative of the range of problems considered in stochastic programming. The paper focuses on the relative value of the stochastic program solution over a deterministic problem solution.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44253/1/10479_2005_Article_BF02031741.pd

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York