Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.Forecasting ; Inflation (Finance) ; Monetary theory

Resolution enhancement for drill-core hyperspectral mineral mapping

Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling

Digital image processing of Landsat data for mapping hydrothermally altered rocks in New Mexico

Imperial Users onl

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

3D structural controls of the shear zone hosted Dugald River zinc-lead-silver deposit, Mount Isa Inlier, Australia

Pieter Creus undertook a detailed 3D structural geological study of the Dugald River Zn-Pb-Ag deposit. In the study he found that the deposit formed during two successive mineralisation events. The mineralisation model is a new style of shear-zone hosted Zinc mineralisation in the region

Assessing geologic model uncertainty : a case study comparing methods

Evaluating mineral resources requires the prior delimitation of geologically homogeneous stationary domains. The knowledge about the ore genesis and geological processes involved are translated into three dimensional models, essential for planning the production and decision-making. The mineral industry usually considers grade uncertainty for resource evaluation; however, uncertainty related to the geological boundaries are often neglected. This uncertainty, related to the location of the boundary between distinct geological domains can be one of the major sources of uncertainty in a mineral project, and should be assessed due to its potential impact on the ore tonnage, and consequently, on enterprise profitability. This study aims at presenting three different methodologies capable of generating multiple geomodel realizations and thus, assessing uncertainty. A real dataset with high geological complexity is used to illustrate the methodology. The results are compared to a deterministic model used as a reference scenario

Doctor of Philosophy

dissertationCerebral cavernous malformation (CCM), or cavernous angioma, is a common disease that can occur sporadically or familially with autosomal dominant inheritance. CCMs are vascular malformations, predominantly in the brain, consisting of dilated, thinwalled, blood-filled caverns. These lesions can lead to headaches, seizures, focal neurological deficits, and hemorrhagic stroke, but the only available treatment is surgical resection. Familial CCM has been linked to three genes: KRIT1, CCM2, or PDCD10. These genes encode structurally unrelated proteins of poorly understood function, but the three are hypothesized to work as a complex1. Generation of an animal model that faithfully recapitulates CCM disease would greatly benefit the study of the natural history and pathophysiology of CCM and the search for therapeutics. In this dissertation, I demonstrate that Pdcd10 and CCM2 signal through distinct pathways, but that loss of heterozygosity is a common genetic mechanism by which both genes lead to CCM disease. I show that Ccm2 acts to suppress the activity of the small GTPase RhoA, whereas Pdcd10 acts through the GCKIII family of kinases. Studies of knockout mice demonstrate that Ccm2 and Pdcd10 serve essential, but different, functions in the endothelium during development. I also examined Pdcd10 function using the fruitfly Drosophila melanogaster, which has a homolog for PDCD10 but not KRIT1 and CCM2. These studies revealed that Pdcd10 regulates lumen formation in the Drosophila tracheal system and genetically interacts with GCKIII, distinguishing it from Ccm2 and indicating that Pdcd10 can function independently of the other CCM genes. Despite these differences in function, loss of heterozygosity at either locus results in the highly penetrant formation of dilated, vascular caverns in mice that phenocopy human CCM histologically and radiologically. My studies led to the surprising conclusion that the CCM proteins do not share a common signaling mechanism. The distinct pathways, however, lead to a common pathology by the genetic mechanism of loss of heterozygosity. My work has also begun to elucidate the underlying biochemistry behind CCM, which may suggest potential therapies. The establishment of an animal model with highly penetrant disease, similar to human disease, is a powerful first step in developing these therapies

The forms of repetition in social and environmental reports: insights from Hume's notion of ?impressions?

This paper focuses on the use of repetition, both in narrative and visual forms, in social and environmental reports. It investigates the forms of repetition as a rhetorical device adopted by the preparer of a social and environmental report in helping the process of knowledge acquisition, as outlined by Hume (1739). Drawing from Hume?s (1739) philosophical idea of an ?impression?, and the work of Davison (2014a) we classify repetitions into ?identical?, ?similar? and ?accumulated? forms. It is argued that the rationale for distinguishing between the different forms of repetition can be linked to their different potential or intensity in acting on different stimuli with a view to enhance learning. The empirical element of this study is based on the stand-alone social and environmental reports of a sample of 86 cooperative banks in Northern Italy; the analysis of these reports indicates that repetition is widespread and that cooperative banks use all forms of repetition, albeit to a varying extent within the different reported themes. The paper contributes to the literature by offering an alternative interpretation of repetition using an interdisciplinary perspective and by providing new insights on social and environmental reporting practices in the cooperative banking sector

GeoTechnical Investigations for the Dalton Highway Innovation Project As A Case Study of the Ice-Rich Syngenetic Permafrost

INE/AUTC 11.1