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Mixed pixels are common in hyperspectral remote sensing images. Endmember extraction is a key step in spectral unmixing. The linear spectral mixture model (LSMM) constitutes a geometric approach that is commonly used for this purpose. This paper introduces the use of artificial bee colony (ABC) algorithms for spectral unmixing. First, the objective function(More)
Band selection (BS), which selects a subset of original bands that contain the most useful information about objects, is an important technique to reduce the dimensionality of hyperspectral data. Dimensionality reduction before hyperspectral data classification can reduce redundancy information and even improve classification accuracy. We propose BS(More)
We present Swarm-NG, a C++ library for the efficient direct integration of many n-body systems using a Graphics Processing Unit (GPU), such as NVIDIA's Tesla T10 and M2070 GPUs. While previous studies have demonstrated the benefit of GPUs for n-body simulations with thousands to millions of bodies, Swarm-NG focuses on many few-body systems, e.g., thousands(More)
In this paper, we propose a 4-D representation of RNA secondary structure. Based on this representation, we outline an approach to compute the similarities of RNA secondary structures by constructing a 3-component vector whose components are the leading eigenvalues of the L/L matrices. The examinations of similarities/dissimilarities among the secondary(More)
  • Jianwei Gao
  • 2008
This paper studies the optimal investment strategy for an investor who seeks to maximize the expected utility of the terminal wealth in a defined contribution pension plan. The portfolio consists of a risk-free asset, and a stock whose price dynamics are governed by a constant elasticity of variance (CEV) model. We derive the explicit solutions for the CARA(More)
  • Jianwei Gao
  • 2009
In this paper, we study the classical portfolio selection problem and extend the Brownian motion about the noises involved in the dynamics of wealth to a short-range fractional Brownian motion. Instead of using the classical tool of optimal control as optimization engine, we convert the stochastic optimal control problem into a non-random optimization by(More)
To characterize the influence of decision makers' psychological factors on the group decision process, this paper develops a new class of aggregation operators based on reference-dependent utility functions (RUs) in multi-attribute group decision analysis. We consider two types of RUs: S-shaped, representing decision makers who are risk-seeking for relative(More)
We focus on hybrid condition attribute reduction based on rough set. Generally, the process of attribute reduction from a large information system is time consuming. Since its computational complexity increases exponentially with the number of input variables and in multiplication with the size of data patterns, we develop a new approach to attribute(More)