Linkage problem, distribution estimation, and Bayesian networks

This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. The algorithm is settled into the context of genetic and evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian Optimization Algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The BOA identifies, reproduces, and mixes building blocks up to a specified order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm, but it is not essential. First experiments were done with additively decomposable problems with both nonoverlapping as well as overlapping building blocks. The proposed algorithm is able to solve all but one of the tested problems in linear or close to linear time with respect to the problem size. Except for the maximal order of interactions to be covered, the algorithm does not use any prior knowledge about the problem. The BOA represents a step toward alleviating the problem of identifying and mixing building blocks correctly to obtain good solutions for problems with very limited domain information.     

The role of spatial location in remembering and forgetting peripheral words.

This study examined whether spatial location mediates intentional forgetting of peripherally presented words. Using an item-method directed forgetting paradigm, words were presented in peripheral locations at study. A recognition test presented all words at either the same or a different location relative to study. Results showed that while recognition of Remember words was unaffected by test location, when Forget words were presented in the same location at test as at study, recognition accuracy was significantly greater than when presented in a different location. Experiment 2 showed that the speed to localize a previously studied word was faster when it was presented in the same rather than a different study-test location but that the magnitude of this spatial priming was unaffected by memory instruction. We suggest that the location of peripherally presented words is represented in memory and can aid the retrieval of poorly encoded words. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Suomi NPP/VIIRS: improving drought watch,crop loss prediction,and food security

Since 1990, the role of satellite observations for climate and land services increased considerably, especially with the introduction in 2011 of the new generation of NOAA operational satellites, called Suomi NPOSS Polar-Orbiting Partnership (S-NPP). S-NPP will continue as the Joint Polar Satellite System (JPSS) for the next two decades. The Visible Infrared Imaging Radiometer Suite (VIIRS) on the S-NPP spacecraft is accommodating the best technical and scientific features of its predecessors and has several new important features. S-NPP and JPSS, in addition to data collection, will address the impacts of climate and weather on industries, water, energy, population health, and other resources and activities. This article discusses how these operational satellites improve early drought detection, monitoring its features (intensity, duration, area, etc.) and prediction of agricultural losses; how fast the Earth’s natural resources deteriorate; and whether the current warm climate intensifies droughts and increases its area and duration. These climate services have already become available to the global community (http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/index.php). The S-NPP/VIIRS data permits its users to enhance long-term environmental data records, thereby improving the ability to estimate global warming, land-cover changes, and better monitoring of environmental resources.     

Object-based automatic terrain shadow removal from SNPP/VIIRS flood maps

Terrain shadow is a big challenge to land products such as flood extent and snow cover from moderate-resolution optical satellite data. Because terrain shadows share similar spectral features with floodwaters, they can be easily detected as floodwaters by flood detection algorithms based on spectral features in visible, near-infrared, and short-wave infrared channels, which decreases the accuracy of flood detection substantially. However, because terrain shadows appear in mountainous areas with large surface roughness while floodwaters accumulate in low-lying areas with small surface roughness, analysis on surface roughness between terrain shadows and floodwaters can be very effective to distinguish one from the other. Root-mean-square height, internal height difference, and external height difference are used as principal quantitative surface roughness parameters in this study and calculated upon water objects that are clustered from a group of adjacent water pixels. This object-based method is applied in terrain shadow removal from SNPP/VIIRS (Suomi National Polar Orbit Partnership/Visible Infrared Imager Radiometer Suite) near-real-time flood maps and shows promising results according to the tests with 10,000+ VIIRS granules across global areas. Quantitative evaluation in the northwest of the USA also indicates that more than 99% terrain shadow pixels could be removed from VIIRS flood maps by this method, which significantly improves the accuracy of near-real-time flood detection from SNPP/VIIRS imagery.     

Approximating the average daily surface albedo with respect to soil roughness and latitude

The present study explores the diurnal variations in blue-sky albedo (α) of soils under clear sky conditions with respect to surface roughness. Three roughness levels of ploughed and unploughed soil surfaces, developed from the same loessial material, were examined. The relation between α of the surfaces and the solar zenith angle, determined during the experiment, enabled us to predict the diurnal α variation of the surfaces throughout the year at a given latitude, between 75° S and 75° N. The optimal time (T _O) for measuring the soil albedo by an instantaneous observation was considered as the best represented time for the daily averaged value within an error lower than ±2%. It was found that the T _O, falling at different times depending on the soil surface roughness, limits the possibilities of data achievement by remote-sensing satellites along one of their sun-synchronous orbits.     

Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data

Epidemic malaria cases and satellite-based vegetation health (VH) indices were investigated to be used as predictors of malaria vector activities in Bangladesh. The VH indices were derived from radiances, measured by the Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) afternoon polar orbiting satellites. Two indices characterizing moisture and thermal conditions were investigated using correlation and regression analysis applied to the number of malaria cases recorded in the entire Bangladesh region and three administrative divisions (Chittagong, Sylhet and Dhaka) during 1992–2001. It is shown that during the cooler months (November to March), when mosquitoes are less active, the correlation between number of malaria cases and two investigated indices was near zero. From April, when the mosquito activity season starts, the correlation increased, reaching a maximum value of 0.5–0.8 by the middle of the high season (June to July), reducing thereafter to zero by the beginning of the cool season in November. Following these results, regressional equations for the number of malaria cases as a function of VH indices were built and tested independently. They showed that, in the main malaria administrative division (Chittagong) and the entire Bangladesh region, the regressional equations can be used for early prediction of malaria development.     

The gambler's ruin problem, genetic algorithms, and the sizing of populations.

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problem-the gambler's ruin-the model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.