Rank/Select dictionaries are data structures for an ordered set S ⊂ {0, 1, . . . , n − 1} to compute rank(x, S) (the number of elements in S which are no greater than x), and select(i, S) (the i-th smallest element in S), which are the fundamental components of succinct data structures of strings, trees, graphs, etc. In those data structures, however, only asymptotic behavior has been considered and their performance for real data is not satisfactory. In this paper, we propose novel four Rank/Select dictionaries, esp, recrank, vcode and sdarray, each of which is small if the number of elements in S is small, and indeed close to nH 0 (S) (H 0 (S) ≤ 1 is the zero-th order empirical entropy of S) in practice, and its query time is superior to the previous ones. Experimental results reveal the characteristics of our data structures and also show that these data structures are superior to existing implementations in both size and query time.

SUMMARYEstimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.

Abstract. To compute Burrows-Wheeler Transform (BWT), one usually builds a suffix array (SA) first, and then obtains BWT using SA, which requires much redundant working space. In previous studies to compute BWT directly [5,12], one constructs BWT incrementally, which requires O(n log n) time where n is the length of the input text. We present an algorithm for computing BWT directly in linear time by modifying the suffix array construction algorithm based on induced sorting [15]. We show that the working space is O(n log σ log log σ n) for any σ where σ is the alphabet size, which is the smallest among the known linear time algorithms.

This paper presents techniques to apply semi-CRFs to Named Entity Recognition tasks with a tractable computational cost. Our framework can handle an NER task that has long named entities and many labels which increase the computational cost. To reduce the computational cost, we propose two techniques: the first is the use of feature forests, which enables us to pack feature-equivalent states, and the second is the introduction of a filtering process which significantly reduces the number of candidate states. This framework allows us to use a rich set of features extracted from the chunk-based representation that can capture informative characteristics of entities. We also introduce a simple trick to transfer information about distant entities by embedding label information into nonentity labels. Experimental results show that our model achieves an F-score of 71.48% on the JNLPBA 2004 shared task without using any external resources or post-processing techniques.