38637907
Average sampling in spline subspaces
We show that every function in a spline subspace is uniquely determined and can be reconstructed by its local averages near certain points. Regular and irregular average sampling theorems for spline subspaces are obtained.
8720012
The convergence of sampling series based on multiresolution analysis
The sampling theory says that a function may be determined by its sampled values at some certain points provided the function satisfies some certain conditions. In this paper, we consider the convergence of the sampling series for bounded functions with the sampling function from some multiresolution analysis. We also present a convergence theorem on the sampling series with irregular sampling points and give the approximation error.
43804396
Reconstruction of band-limited signals from local averages
The sampling theorem says that every band-limited signal is uniquely determined by its sampled values provided the sampling points satisfy certain conditions. However, sampled values obtained in practice may not be the exact values of a signal at sampling points, but only averages of the signal near these points. Grochenig (1992) proved that band-limited signals can be reconstructed exactly from local averages if the sampling density is large enough. We study the reconstruction of band-limited signals from local averages with symmetric averaging functions. We study the aliasing error arising when a non-band-limited signal is reconstructed from local averages and give explicit error bounds. Since the classical "point sampling" can be viewed as a limiting case of average sampling, we indeed give new aliasing error bounds for both regular and irregular sampling.
5687277
A self-adaptive scheduling algorithm of on-demand broadcasts
In mobile wireless systems data on air can be accessed by a large number of mobile users. Many of these applications such as wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems of on-demand broadcast environments. Traditionally, the response time of the requests has been used as a performance measure. In this paper we consider the performance as the average cost of request composed of three kinds of costs—access time cost, tuning time cost, and cost of handling failure request. Our main contribution is a self-adaptive scheduling algorithm named LDFC, which computes the delay cost of data item as the priority for broadcast. It performs well compared with some previous algorithms in this context.
18703573
Ant colony optimization for routing and load-balancing: survey and new directions
Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigating stagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include: evaporation, aging, pheromone smoothing and limiting, privileged pheromone laying and pheromone-heuristic control. The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNet and its extensions, as well as ASGA and SynthECA. Discussions on new directions include an ongoing work of the authors in applying multiple ant colony optimization in load-balancing.
8057684
A Scalable Parallel Algorithm for Global Optimization Based on Seed-Growth Techniques
Global optimization requires huge computations for complex objective functions. Conventional global optimization based on stochastic and probability algorithms can not guarantee an actual global optimum with finite searching iteration. A numerical implementation of the scalable parallel Seed-Growth (SG) algorithm is introduced for global optimization of two-dimensional multi-extremal functions. The proposed parallel SG algorithm is characterized by a parallel phase that exploits the local optimum neighborhood features of the objective function assigned to each processor. The seeds are located at the optimum and inner neighborhood points. Seeds grow towards nearby grids and attach flags to them until reaching the boundary points in each dimension. When all grids in the subspace assigned to each CPU have been searched, the local optimum neighborhood boundaries are determined. As the definition domain is completely divided into different subdomains, the global optimal solution of each CPU is found. A coordination phase follows which, by a synchronous interaction scheme, optimizes the partial results obtained by the parallel phase. The actual global optimum in the total definition space can be determined. Numerical examples demonstrate the high efficiency, global searching ability, robustness and stability of this method.
18180354
Study of coordinative gene expression at the biological process level
MOTIVATION
Cellular processes are not isolated groups of events. Nevertheless, in most microarray analyses, they tend to be treated as standalone units. To shed light on how various parts of the interlocked biological processes are coordinated at the transcription level, there is a need to study the between-unit expressional relationship directly.


RESULTS
We approach this issue by constructing an index of correlation function to convey the global pattern of coexpression between genes from one process and genes from the entire genome. Processes with similar signatures are then identified and projected to a process-to-process association graph. This top-down method allows for detailed gene-level analysis between linked processes to follow up. Using the cell-cycle gene-expression profiles for Saccharomyces cerevisiae, we report well-organized networks of biological processes that would be difficult to find otherwise. Using another dataset, we report a sharply different network structure featuring cellular responses under environmental stress.


SUPPLEMENTARY INFORMATION
http://kiefer.stat.ucla.edu/lap2/download/KL_supplement.pdf.
10198702
Global Optimization with Multi-grid Seed-Growth Parameter Space Division Algorithm