On the dimensionality of multi-view hyperspectral measurements of vegetation

This note investigates the structure of two data sets of highly resolved directional-reflectance of vegetation canopies, obtained with a spectroradiometer mounted on a goniometer. One canopy was a grass lawn (largely erectophile) and the other was of watercress (largely planophile). The data sets consist of radiance measurements in 356 spectral bands in visible and near-infrared wavelengths, and in either 61 or 358 different directions, but avoiding directions very close to the hot-spot. The singular value decompositions of the two-dimensional data sets are used to investigate their intrinsic dimensionality, and so also the redundancy contained in the data. A single directional function and a single spectral function together fit the data quite well in a least squares sense, with the root mean square residual amounting to one part in a hundred of the total sum of squares of the data. However, the residuals indicate that one or two further pairs of functions are needed to characterize the systematic variation of spectral reflectance with direction (or equivalently, of the angular reflectance distribution with wavelength). Three such pairs of functions are found to fit the data to better than one part in a thousand, with the residuals then showing no systematic structure. The consequences for atmospheric correction of multi-view remote sensing data are discussed.     

The effect of anisotropic reflectance on imaging spectroscopy of snow properties

How does snow's anisotropic directional reflectance affect the mapping of snow properties from imaging spectrometer data? This sensitivity study applies two spectroscopy models to synthetic images of the spectral hemispherical-directional reflectance factor (HDRF) with prescribed snow-covered area and snow grain size. The MEMSCAG model determines both sub-pixel snow-covered area and the grain size of the fractional snow cover. The Nolin/Dozier model analyzes the ice absorption feature that spans wavelength λ≅1.03 μm to estimate snow grain radius when the pixel is fully snow-covered. Retrievals of subpixel snow-covered area with MEMSCAG are progressively more sensitive to the HDRF as grain size decreases, solar zenith angle increases, and fractional snow cover increases. The model overestimates snow cover in the forward reflectance angles by up to +20% and underestimates it in the backward reflectance angles by as much as −15%. Grain size retrievals from both MEMSCAG and Nolin/Dozier are more sensitive to anisotropy as grain size and solar zenith angle increase. MEMSCAG retrievals of grain size are insensitive to snow-covered area. The largest inferred grain sizes occur around a peak in the backward reflectance angles and the smallest generally occur at the largest view angles in the forward direction. Retrievals of albedo from MEMSCAG and Nolin/Dozier are similarly sensitive to anisotropy, with albedo errors up to 5% for a 30° solar zenith angle and up to 10% at 60°. The albedo differences between the two models are less than 0.015 for all grain sizes and solar zenith angles.     

Spatial orientations and structural irregularities associated with the formation of microbands in a cold deformed Goss oriented Ni single crystal

The crystallographic nature of microband boundaries was investigated in a Goss oriented nickel single crystal following cold deformation in channel die plane strain compression. Standard electron backscatter diffraction (EBSD), three-dimensional (3-D)-EBSD and transmission electron microscopy (TEM) were used in the investigation. When viewed in the three orthogonal sections microband boundary traces were classically aligned in the transverse direction section at an acute angle from the rolling direction (RD), but appeared wavy in the normal direction (ND) section. The latter observation may lead to the conclusion that microband boundaries are non-crystallographic. 3-D EBSD was used to reconstruct actual microbands in a deformed volume that revealed significant new information about their structure. Here microband surfaces are largely planar over large distances, but frequently interrupted by local distortions and undulations due to interactions between intersecting non-coplanar microbands. The combined EBSD/TEM investigation has revealed that microband boundaries are aligned close to an active {1 1 1} slip plane (i.e. they are crystallographic), but the undulations and distortions they contain are non-crystallographic in the sense that they deviate from an active slip plane. The non-crystallographic features of microbands (as revealed by their wavy structure in the ND section) may be explained by the crystallographic oscillations of up to ±7.5° towards RD that occur during plastic deformation. Such oscillations result in varying fractions of slip on a given {1 1 1} plane, resulting in varying degrees of interaction between the two sets of non-coplanar microbands. These local and intense microband interactions result in their deviation from their active slip planes.     

Does Kripke’s Argument Against Functionalism Undermine the Standard View of What Computers Are?

Kripke’s argument against functionalism extended to physical computers poses a deep philosophical problem (not previously addressed in the literature) for understanding the standard view of what computers are. The problem puts into jeopardy the definition in the standard view that computers are physical machines for performing physical computations. Indeed, it is entirely possible that, unless this philosophical problem is resolved, we will never have a good understanding of computers and may never know just what they are.     

Efficient and scalable omniscient debugging for model transformations

This paper discusses a technique for supporting omniscient debugging for model transformations, which are used to define core operations on software and system models. Similar to software systems developed using general-purpose languages, model transformations are also subject to human error and may possess defects. Existing model-driven engineering tools provide stepwise execution to aid developers in locating and removing defects. In this paper, we describe our investigation into a technique and associated algorithms that support omniscient debugging features for model transformations. Omniscient debugging enables enhanced navigation and exploration features during a debugging session beyond those possible in a strictly stepwise execution environment. Finally, the execution time performance is comparatively evaluated against stepwise execution, and the scalability (in terms of memory usage) is empirically investigated.