This paper introduces a variable step-size adaptive algorithm for blind source separation. From frequency characteristics of mixed input signals, we need to adjust the convergence speed regularly in each frequency bin. This algorithm varies a step-size according to the magnitude of input at each frequency bin. This guarantee of the regular convergence in each frequency bin would become more eff...

We present a 1 − 1 √ k -competitive algorithm for the online stochastic generalized assignment problem under the assumption that no item takes up more than 1 k fraction of the capacity of any bin. Items arrive online; each item has a value and a size; upon arrival, an item can be placed in a bin or discarded; the objective is to maximize the total value of the placement. Both value and size of ...

Bin packing is an NP-hard [10] classical combinatorial optimization problem. The objective is to pack a set of items into a minimum number of unit-size bins such that the total size of the items in a bin does not exceed the bin capacity. The problem has been studied extensively both in the offline and online settings [7, 8, 5]. In the online setting [11, 12], items may arrive at arbitrary time;...

MsatAllele is a computer package built on R to visualize and bin the raw microsatellite allele size distributions. The method is based on the creation of an R database with exported files from the open-source electropherogram peak-reading program STRAND. Contrary to other binning programs, in this program, the bin limits are not fixed and are automatically defined by the data stored in the data...

Neurophysiologists often use histograms to explore patterns of activity in neural spike trains. The bin size selected to construct a histogram is crucial: too large bin widths result in coarse histograms, too small bin widths expand unimportant detail. Peri-stimulus time (PST) histograms of simulated nerve fibers were studied in the current article. This class of histograms gives information ab...

We study a variety of NP-hard bin packing problems under a divisibility constraint that generalizes the often encountered situation in which all item sizes are powers of 2. For ordinary one-dimensional bin packing, we show that First Fit Decreasing produces optimal packings under this restriction, and that if in addition the largest item size divides the bin capacity, then even the less powerfu...

In the dual bin packing problem, the objective is to assign items of given size to the largest possible number of bins, subject to the constraint that the total size of the items assigned to any bin is at least equal to 1. We carry out a probabilistic analysis of this problem under the assumption that the items are drawn independently from the uniform distribution on [0,1], and reveal the conne...

Given a set of m identical bins of size 1, the online input consists of a (potentially, infinite) stream of items in (0, 1]. Each item is to be assigned to a bin upon arrival. The goal is to cover all bins, that is, to reach a situation where a total size of items of at least 1 is assigned to each bin. The cost of an algorithm is the sum of all used items at the moment when the goal is first fu...

Usually, for bin packing problems, we try to minimize the number of bins used or in the case of the dual bin packing problem, maximize the number or total size of accepted items. This paper presents results for the opposite problems, where we would like to maximize the number of bins used or minimize the number or total size of accepted items. We consider off-line and on-line variants of the pr...