40832727
Interactive Semisupervised Learning for Microarray Analysis
5390173
A Self-supervised Learning Framework for Classifying Microarray Gene Expression Data
It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.
42610771
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
34743400
An optimal weather condition dependent approach for emission planning in urban areas
Abstract The urban air quality strongly depends on the weather conditions. When unfavorable weather conditions are forecasted, it is desirable to reduce a certain amount of pollutant emissions to maintain a given level of air quality. In this paper, we propose and demonstrate a new approach to find the optimal reduction, and we contribute to the methodology of decision support of short-term emission control. This approach is based on weather forecasts and state-of-the-art 3-dimensional numerical air-quality prediction models by solving an optimal control problem with the emission cuts as the control variables. The objective of the optimal problem is to minimize the total cost due to the emission cuts, subject to feasibility constraints, system-governing model constraints, and target constraints. When a high-resolution numerical model is used as the state constraint, the problem can become a very high dimensional one. A practical approach to solving this problem with high dimension is proposed, based on the adjoint technique. The proposed approach is demonstrated with two computational test cases in Jinan, China for the control of sulfur dioxide. The results show the capability and computational efficiency of the method and suggest a promising potential for emission planning applications based on weather forecasts.
793200
The alignment problem of visual cryptography schemes
Pixel expansion is an important parameter for Visual Cryptography Schemes (VCS). However, most papers in the literature are dedicated to reduce pixel expansion on the pixel level, i.e. to reduce the number of subpixels that represent a pixel in the original secret image. It is quite insufficient since the final size of the transparencies of the VCS is affected not only by the number of the subpixels, but also by the size of the subpixels in the transparencies. However, reducing the size of the subpixels in the transparencies results in difficulties of alignment of the transparencies. In this paper, we consider the alignment problem in VCS, and prove that in order to visually recover the original secret image, it is not necessary to align the transparencies precisely. This study is restricted to the case when only one transparency is shifted.
856762
Research on System Integrative Evaluation Method Based on Survivability
Survivability is the essential ability of a system to fulfill its mission, in a timely manner, in the presence of attacks, failures, or accidents. Survivability should be considered beyond security for information system, and quantitative analysis can assess system survivability accurately for improvement. The paper presents a new type of analysis method for computer system survivability which considers both exterior environment and interior components and their correlations. The method can describe the survivability more precisely and exactly. The paper also elaborates on the process of using this method to evaluate system survivability and its advantages.
46240388
An Efficient Heuristic Algorithm for Constructing Delay- and Degree-Bounded Application-Level Multicast Tree
In this paper we first investigate the problem of finding a delay- and degree-bounded maximum sum of nodes ALMT. We proved the problem is NP-hard, and testify its relationship with the well-studied degree-bounded minimum maximum-delay ALMT problem. We proposed a greedy algorithm FindOverlayTree with SRPF heuristic. The simulation results show that our design solution is a valid and cost-effective approach where compared to other three modified classic heuristics under several performance metrics.