Causal Dependence Tree Approximations of Joint Distributions for Multiple Random Processes

We investigate approximating joint distributions of random processes with causal dependence tree distributions. Such distributions are particularly useful in providing parsimonious representation when there exists causal dynamics among processes. By extending the results by Chow and Liu on dependence tree approximations, we show that the best causal dependence tree approximation is the one which maximizes the sum of directed informations on its edges, where best is defined in terms of minimizing the KL-divergence between the original and the approximate distribution. Moreover, we describe a low-complexity algorithm to efficiently pick this approximate distribution.Comment: 9 pages, 15 figure

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas

The South Dakota cooperative land use effort: A state level remote sensing demonstration project

Remote sensing technology can satisfy or make significant contributions toward satisfying many of the information needs of governmental natural resource planners and policy makers. Recognizing this potential, the South Dakota State Planning Bureau and the EROS Data Center together formulated the framework for an ongoing Land Use and Natural Resource Inventory and Information System Program. Statewide land use/land cover information is generated from LANDSAT digital data and high altitude photography. Many applications of the system are anticipated as it evolves and data are added from more conventional sources. The conceptualization, design, and implementation of the program are discussed

Are seasonal calving dynamics forced by buttressing from ice mélange or undercutting by melting? Outcomes from full-Stokes simulations of Store Gletscher, West Greenland

Abstract. We use a full-Stokes 2-D model (Elmer/Ice) to investigate the flow and calving dynamics of Store Gletscher, a fast flowing outlet glacier in West Greenland. Based on a new, subgrid-scale implementation of the crevasse depth calving criterion, we perform two sets of simulations; one to identify the primary forcing mechanisms and another to constrain future stability. We find that the mixture of icebergs and sea-ice, known as ice mélange or sikussak, is principally responsible for the observed seasonal advance of the ice front, whereas submarine melting plays a secondary role. Sensitivity analysis demonstrates that the glacier's calving dynamics are sensitive to seasonal perturbation, but are stable on interannual timescales due to the glacier's topographic setting. Our results shed light on the dynamics of calving glaciers while explaining why neighbouring glaciers do not necessarily respond synchronously to changes in atmospheric and oceanic forcing. This study was funded by the Natural Environment Research Council through a Ph.D. studentship (grant no. NE/K500884/1) to J. Todd and research grant (NE/K005871/1) to P. ChristoffersenThis is the final version, which can also be found on the journal's website at: http://www.the-cryosphere-discuss.net/8/3525/2014/tcd-8-3525-2014.htm

Urban land use monitoring from computer-implemented processing of airborne multispectral data

Machine processing techniques were applied to multispectral data obtained from airborne scanners at an elevation of 600 meters over central Indianapolis in August, 1972. Computer analysis of these spectral data indicate that roads (two types), roof tops (three types), dense grass (two types), sparse grass (two types), trees, bare soil, and water (two types) can be accurately identified. Using computers, it is possible to determine land uses from analysis of type, size, shape, and spatial associations of earth surface images identified from multispectral data. Land use data developed through machine processing techniques can be programmed to monitor land use changes, simulate land use conditions, and provide impact statistics that are required to analyze stresses placed on spatial systems

High-order noise filtering in nontrivial quantum logic gates

Treating the effects of a time-dependent classical dephasing environment during quantum logic operations poses a theoretical challenge, as the application of non-commuting control operations gives rise to both dephasing and depolarization errors that must be accounted for in order to understand total average error rates. We develop a treatment based on effective Hamiltonian theory that allows us to efficiently model the effect of classical noise on nontrivial single-bit quantum logic operations composed of arbitrary control sequences. We present a general method to calculate the ensemble-averaged entanglement fidelity to arbitrary order in terms of noise filter functions, and provide explicit expressions to fourth order in the noise strength. In the weak noise limit we derive explicit filter functions for a broad class of piecewise-constant control sequences, and use them to study the performance of dynamically corrected gates, yielding good agreement with brute-force numerics.Comment: Revised and expanded to include filter function terms beyond first order in the Magnus expansion. Related manuscripts available from http://www.physics.usyd.edu.au/~mbiercu