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176
Completely Derandomized SelfAdaptation in Evolution Strategies
 Evolutionary Computation
, 2001
"... This paper puts forward two useful methods for selfadaptation of the mutation distribution  the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the selfadapta ..."
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Cited by 534 (58 self)
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This paper puts forward two useful methods for selfadaptation of the mutation distribution  the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the selfadaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding.
Image Categorization by Learning and Reasoning with Regions
 Journal of Machine Learning Research
, 2004
"... Designing computer programs to automatically categorize images using lowlevel features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends MultipleInstance Learning (MIL), and its application to the problem of regionbased image cat ..."
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Cited by 191 (11 self)
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Designing computer programs to automatically categorize images using lowlevel features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends MultipleInstance Learning (MIL), and its application to the problem of regionbased image categorization. Images are viewed as bags, each of which contains a number of instances corresponding to regions obtained from image segmentation. The standard MIL problem assumes that a bag is labeled positive if at least one of its instances is positive; otherwise, the bag is negative.
Visual Models of Plants Interacting with Their Environment
, 1996
"... Interaction with the environment is a key factor affecting the development of plants and plant ecosystems. In this paper we introduce a modeling framework that makes it possible to simulate and visualize a wide range of interactions at the level of plant architecture. This framework extends the form ..."
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Cited by 153 (17 self)
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Interaction with the environment is a key factor affecting the development of plants and plant ecosystems. In this paper we introduce a modeling framework that makes it possible to simulate and visualize a wide range of interactions at the level of plant architecture. This framework extends the formalism of Lindenmayer systems with constructs needed to model bidirectional information exchange between plants and their environment. We illustrate the proposed framework with models and simulations that capture the development of tree branches limited by collisions, the colonizing growth of clonal plants competing for space in favorable areas, the interaction between roots competing for water in the soil, and the competition within and between trees for access to light. Computer animation and visualization techniques make it possible to better understand the modeled processes and lead to realistic images of plants within their environmental context. CR categories: F.4.2 [Mathematical Logi...
On estimation of the wavelet variance
 Biometrika
, 1995
"... The wavelet variance provides a scalebased decomposition of the process variance for a time series or random field. It has seen increasing use in geophysics, astronomy, genetics, hydrology, medical imaging, oceanography, soil science, signal processing and texture analysis. In practice, however, da ..."
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Cited by 66 (7 self)
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The wavelet variance provides a scalebased decomposition of the process variance for a time series or random field. It has seen increasing use in geophysics, astronomy, genetics, hydrology, medical imaging, oceanography, soil science, signal processing and texture analysis. In practice, however, data collected in the form of a time series or random field often suffer from various types of contamination. We discuss the difficulties and limitations of existing contamination models (pure replacement models, additive outliers, level shift models and innovation outliers that hide themselves in the original time series) for robust nonparametric estimates of secondorder statistics. We then introduce a new model based upon the idea of scalebased multiplicative contamination. This model supposes that contamination can occur and affect data at certain scales and thus arises naturally in multiscale processes and in the wavelet variance context. For this new contamination model, we develop a full Mestimation theory for the wavelet variance and derive its large sample theory when the underlying time series or random field is Gaussian. Our approach treats the wavelet variance as a scale parameter and offers protection against contamination that operates additively on the log of squared wavelet coefficients and acts independently at different scales.
Evolving Better Representations through Selective Genome Growth
 In Proceedings of the IEEE World Congress on Computational Intelligence
, 1994
"... The choice of how to represent the search space for a genetic algorithm (GA) is critical to the GA's performance. Representations are usually engineered by hand and fixed for the duration of the GA run. Here a new method is described in which the degrees of freedom of the representation  i.e ..."
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Cited by 59 (4 self)
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The choice of how to represent the search space for a genetic algorithm (GA) is critical to the GA's performance. Representations are usually engineered by hand and fixed for the duration of the GA run. Here a new method is described in which the degrees of freedom of the representation  i.e. the genes  are increased incrementally. The phenotypic effects of the new genes are randomly drawn from a space of different functional effects. Only those genes that initially increase fitness are kept. The genotypephenotype map that results from this selection during the construction of the genome allows better adaptation. This effect is illustrated with the NK landscape model. The resulting genotypephenotype maps are much less epistatic than unselected maps would be, having extremely low values of "K"  the number of fitness components affected by each gene. Moreover, these maps are exquisitely tuned to the specifics of the epistatic fitness function, creating adaptive landscapes that ...
Stochastic Dynamics: Simulating the Effects of Turbulence on Flexible Structures
, 1996
"... This paper addresses the problem of realistically simulating the motion of treebranches subjected to turbulence. Since the resulting motion is random in nature, we model it as a stochastic process. We synthesize this process directly by filtering a white noise in the Fourier domain. The filter is c ..."
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Cited by 53 (1 self)
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This paper addresses the problem of realistically simulating the motion of treebranches subjected to turbulence. Since the resulting motion is random in nature, we model it as a stochastic process. We synthesize this process directly by filtering a white noise in the Fourier domain. The filter is constructed by performing a modal analysis of the tree. We use a sophisticated numerical technique which is able to compute the first few significant modes of large trees. The main advantage of our technique over previous methods is that we are able to compute complicated motions without the need to integrate dynamical equations over time. Consequently, treemotions can be viewed and manipulated in realtime by a user. Our technique can further be extended to other flexible structures such as twodimensional plates.
Numerical Methods for Neuronal Modeling
 In Methods in Neuronal Modeling
, 1989
"... Introduction In this chapter we will discuss some practical and technical aspects of numerical methods that can be used to solve the equations that neuronal modelers frequently encounter. We will consider numerical methods for ordinary differential equations (ODEs) and for partial differential equa ..."
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Cited by 33 (1 self)
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Introduction In this chapter we will discuss some practical and technical aspects of numerical methods that can be used to solve the equations that neuronal modelers frequently encounter. We will consider numerical methods for ordinary differential equations (ODEs) and for partial differential equations (PDEs) through examples. A typical case where ODEs arise in neuronal modeling is when one uses a single lumpedsoma compartmental model to describe a neuron. Arguably the most famous PDE system in neuronal modeling is the phenomenological model of the squid giant axon due to Hodgkin and Huxley. The difference between ODEs and PDEs is that ODEs are equations in which the rate of change of an unknown function of a single variable is prescribed, usually the derivative with respect to time. In contrast, PDEs involve the rates of change of the solution with respect to two or more independent variables, such as time and space. The numerical methods we will discuss for both ODEs and
Analysis of Subtidal Coastal Sea Level Fluctuations Using Wavelets
, 1997
"... this paper, we demonstrate that wavelet analysis based on the maximal overlap discrete wavelet transform (MODWT) is an e#ective technique for quantifying the nonstationary characteristics of subtidal sea level fluctuations as measured by the tide gauge at Crescent City, California. This site is of p ..."
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Cited by 30 (5 self)
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this paper, we demonstrate that wavelet analysis based on the maximal overlap discrete wavelet transform (MODWT) is an e#ective technique for quantifying the nonstationary characteristics of subtidal sea level fluctuations as measured by the tide gauge at Crescent City, California. This site is of particular interest because Crescent City has a welldocumented history of tsunami inundation in which the maximum heights depend strongly of the heights of subtidal sea level (Petrauskas and Borgman, 1971; Lander and Lockridge, 1989; Mofjeld et al., 1997a). The ocean near Crescent City is also subject to the highest seasonal wind forcing along the U.S. West Coast (Strub et al., 1987b). The period of interest is 19801991, which includes the major 198283 El Nino/Southern Oscillation event and several other interannual events. The primary scientific purpose of this paper is to characterize the subtidal sea level fluctuations at Crescent City as they relate to coastal hazards. These characterizations can then be used to improve maps and forecasts of coastal inundation (Mofjeld et al., 1997a, b). The MODWT of a time series leads to two types of analyses. The first is an additive decomposition known as multiresolution analysis, which breaks up the series into a number of "details" and a single "smooth." Each detail is a time series describing variations at a particular time scale, while the smooth describes the low frequency variations. We use the details from the Crescent City series to study its temporal behavior at the synoptic (110 days) and intraseasonal scales and use the smooth to study its seasonal and longer scale fluctuations. Stacked plots of the details and smooth provide an e#ective means of exploring the relationships between sea level fluctuations at di#erent t...
Korea
 In Michael Michaely, Armeane Choksi, and Demetris Papageorgiou, eds., Liberalizing Foreign Trade
, 1991
"... After a serial study on the therapeutic efficacy of the powdery silkworm for diabetics was positively resulted in, many powdery silkworm products were on the market in Korea. Up to now, however, no causal method is available to discriminate the strain of silkworms that is a major ingredient for ma ..."
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Cited by 28 (2 self)
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After a serial study on the therapeutic efficacy of the powdery silkworm for diabetics was positively resulted in, many powdery silkworm products were on the market in Korea. Up to now, however, no causal method is available to discriminate the strain of silkworms that is a major ingredient for manufacturing powdery silkworm, even though the quality of the powdery silkworm differs greatly by source and origin of strains. We previously were successful in identifying 25 silkworm strains kept in Korea using nine intronic sequences. In this study, we tested the utility of the nine intronic sequences to distinguish the most widely reared silkworm strains originated from Korea and China. Two intron regions, PTTH Intron3 and PTTH Intron3, showed a substantial sequence divergence (mean sequence divergence of 3.13 % in PTTH Intron3 and 4.99 % in PTTH Intron3). These two intronic sequences provided no identical sequences among the seven strains tested. Thus, these sequences each along can be used to discriminate the seven strains tested in this study. Furthermore, other intron regions, except for VDP Intron4 allowed us to discriminate 2~4 strains by strainspecific unique insertion/deletion or substitution.