Loop Quantum Gravity a la Aharonov-Bohm

The state space of Loop Quantum Gravity admits a decomposition into orthogonal subspaces associated to diffeomorphism equivalence classes of spin-network graphs. In this paper I investigate the possibility of obtaining this state space from the quantization of a topological field theory with many degrees of freedom. The starting point is a 3-manifold with a network of defect-lines. A locally-flat connection on this manifold can have non-trivial holonomy around non-contractible loops. This is in fact the mathematical origin of the Aharonov-Bohm effect. I quantize this theory using standard field theoretical methods. The functional integral defining the scalar product is shown to reduce to a finite dimensional integral over moduli space. A non-trivial measure given by the Faddeev-Popov determinant is derived. I argue that the scalar product obtained coincides with the one used in Loop Quantum Gravity. I provide an explicit derivation in the case of a single defect-line, corresponding to a single loop in Loop Quantum Gravity. Moreover, I discuss the relation with spin-networks as used in the context of spin foam models.Comment: 19 pages, 1 figure; v2: corrected typos, section 4 expanded

True heterotopic bone in the paralyzed patient

In past years the clinical and radiologic presentation of true heterotopic bone in the paralyzed patient has been confused with osteomyelitis, neoplasm, trauma, and thrombophlebitis. We reviewed 376 paralyzed patients' roentgenographic files and found 78 patients with soft tissue ossification unassociated with infection, neoplasm, or underlying fractures, which we called true heterotopic bone. From this population the usual spectrum of radiologic findings is described, so that the radiologist may separate roentgenographically a group of patients from other types of ectopic ossification.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46816/1/256_2004_Article_BF00347167.pd

A Synthesis of Tagging Studies Examining the Behaviour and Survival of Anadromous Salmonids in Marine Environments

This paper synthesizes tagging studies to highlight the current state of knowledge concerning the behaviour and survival of anadromous salmonids in the marine environment. Scientific literature was reviewed to quantify the number and type of studies that have investigated behaviour and survival of anadromous forms of Pacific salmon (Oncorhynchus spp.), Atlantic salmon (Salmo salar), brown trout (Salmo trutta), steelhead (Oncorhynchus mykiss), and cutthroat trout (Oncorhynchus clarkii). We examined three categories of tags including electronic (e.g. acoustic, radio, archival), passive (e.g. external marks, Carlin, coded wire, passive integrated transponder [PIT]), and biological (e.g. otolith, genetic, scale, parasites). Based on 207 papers, survival rates and behaviour in marine environments were found to be extremely variable spatially and temporally, with some of the most influential factors being temperature, population, physiological state, and fish size. Salmonids at all life stages were consistently found to swim at an average speed of approximately one body length per second, which likely corresponds with the speed at which transport costs are minimal. We found that there is relatively little research conducted on open-ocean migrating salmonids, and some species (e.g. masu [O. masou] and amago [O. rhodurus]) are underrepresented in the literature. The most common forms of tagging used across life stages were various forms of external tags, coded wire tags, and acoustic tags, however, the majority of studies did not measure tagging/handling effects on the fish, tag loss/failure, or tag detection probabilities when estimating survival. Through the interdisciplinary application of existing and novel technologies, future research examining the behaviour and survival of anadromous salmonids could incorporate important drivers such as oceanography, tagging/handling effects, predation, and physiology

How robust are community-based plant bioindicators? Empirical testing of the relationship between Ellenberg values and direct environmental measures in woodland communities

There are several community-based bioindicator systems that use species presence or abundance data as proxies for environmental variables. One example is the Ellenberg system, whereby vegetation data are used to estimate environmental soil conditions. Despite widespread use of Ellenberg values in ecological research, the correlation between bioindicated values and actual values is often an implicit assumption rather than based on empirical evidence. Here, we correlate unadjusted and UK-adjusted Ellenberg values for soil moisture, pH, and nitrate in relation to direct environmental measures for 50 woodland sites in the UK, which were subject to repeat sampling. Our results show the accuracy of Ellenberg values is parameter specific; pH values were a good proxy for direct environmental measures but this was not true for soil moisture, when relationships were weak and non-significant. For nitrates, there were important seasonal differences, with a strong positive logarithmic relationship in the spring but a non-significant (and negative) correlation in summer. The UK-adjusted values were better than, or equivalent to, Ellenberg’s original ones, which had been quantified originally for Central Europe, in all cases. Somewhat surprisingly, unweighted values correlated with direct environmental measures better than did abundance-weighted ones. This suggests that the presence of rare plants can be highly important in accurate quantification of soil parameters and we recommend using an unweighted approach. However, site profiles created only using rare plants were inferior to profiles based on the whole plant community and thus cannot be used in isolation. We conclude that, for pH and nitrates, the Ellenberg system provides a useful estimate of actual conditions, but recalibration of moisture values should be considered along with the effect of seasonality on the efficacy of the system

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation