Conditioning bounds for traveltime tomography in layered media

This paper revisits the problem of recovering a smooth, isotropic, layered wave speed profile from surface traveltime information. While it is classic knowledge that the diving (refracted) rays classically determine the wave speed in a weakly well-posed fashion via the Abel transform, we show in this paper that traveltimes of reflected rays do not contain enough information to recover the medium in a well-posed manner, regardless of the discretization. The counterpart of the Abel transform in the case of reflected rays is a Fredholm kernel of the first kind which is shown to have singular values that decay at least root-exponentially. Kinematically equivalent media are characterized in terms of a sequence of matching moments. This severe conditioning issue comes on top of the well-known rearrangement ambiguity due to low velocity zones. Numerical experiments in an ideal scenario show that a waveform-based model inversion code fits data accurately while converging to the wrong wave speed profile

Bodies, technologies and action possibilities: when is an affordance?

Borrowed from ecological psychology, the concept of affordances is often said to offer the social study of technology a means of re-framing the question of what is, and what is not, ‘social’ about technological artefacts. The concept, many argue, enables us to chart a safe course between the perils of technological determinism and social constructivism. This article questions the sociological adequacy of the concept as conventionally deployed. Drawing on ethnographic work on the ways technological artefacts engage, and are engaged by, disabled bodies, we propose that the ‘affordances’ of technological objects are not reducible to their material constitution but are inextricably bound up with specific, historically situated modes of engagement and ways of life

Large-scale zero-shot learning in the wild: classifying zoological illustrations

In this paper we analyse the classification of zoological illustrations. Historically, zoological illustrations were the modus operandi for the documentation of new species, and now serve as crucial sources for long-term ecological and biodiversity research. By employing computational methods for classification, the data can be made amenable to research. Automated species identification is challenging due to the long-tailed nature of the data, and the millions of possible classes in the species taxonomy. Success commonly depends on large training sets with many examples per class, but images from only a subset of classes are digitally available, and many images are unlabelled, since labelling requires domain expertise. We explore zero-shot learning to address the problem, where features are learned from classes with medium to large samples, which are then transferred to recognise classes with few or no training samples. We specifically explore how distributed, multi-modal background knowledge from data providers, such as the Global Biodiversity Information Facility (GBIF), iNaturalist, and the Biodiversity Heritage Library (BHL), can be used to share knowledge between classes for zero-shot learning. We train a prototypical network for zero-shot classification, and introduce fused prototypes (FP) and hierarchical prototype loss (HPL) to optimise the model. Finally, we analyse the performance of the model for use in real-world applications. The experimental results are encouraging, indicating potential for use of such models in an expert support system, but also express the difficulty of our task, showing a necessity for research into computer vision methods that are able to learn from small samples.Computer Systems, Imagery and Medi

Assessment of PNGV fuels infrastructure. Phase 1 report: Additional capital needs and fuel-cycle energy and emissions impacts

This report presents the methodologies and results of Argonne`s assessment of additional capital needs and the fuel-cycle energy and emissions impacts of using six different fuels in the vehicles with tripled fuel economy (3X vehicles) that the Partnership for a New Generation of Vehicles is currently investigating. The six fuels included in this study are reformulated gasoline, low-sulfur diesel, methanol, ethanol, dimethyl ether, and hydrogen. Reformulated gasoline, methanol, and ethanol are assumed to be burned in spark-ignition, direct-injection engines. Diesel and dimethyl ether are assumed to be burned in compression-ignition, direct-injection engines. Hydrogen and methanol are assumed to be used in fuel-cell vehicles. The authors have analyzed fuels infrastructure impacts under a 3X vehicle low market share scenario and a high market share scenario. The assessment shows that if 3X vehicles are mass-introduced, a considerable amount of capital investment will be needed to build new fuel production plants and to establish distribution infrastructure for methanol, ethanol, dimethyl ether, and hydrogen. Capital needs for production facilities will far exceed those for distribution infrastructure. Among the four fuels, hydrogen will bear the largest capital needs. The fuel efficiency gain by 3X vehicles translates directly into reductions in total energy demand, fossil energy demand, and CO{sub 2} emissions. The combination of fuel substitution and fuel efficiency results in substantial petroleum displacement and large reductions in emissions of nitrogen oxide, carbon monoxide, volatile organic compounds, sulfur oxide, and particulate matter of size smaller than 10 microns