Digital halftoning is a technique to convert a continuous-tone image into a binary image consisting of black and white dots. It is an important technique for printing machines and printers to output an image with few intensity levels or colors which looks similar to an input image. This paper surveys how algorithm engineering can contribute to digital halftoning or what combinatorial problems are related to digital halftoning. A common criterion on optimal digital halftoning leads to a negative result that obtaining an optimal halftoned image is NP-complete. So, there are two choices: approximation algorithm and polynomial-time algorithm with relaxed condition. Main algorithmic notions related are geometric discrepancy, matrix (or array) rounding problems, and network-flow algorithms.

It is known that all minimum cuts in an edge-weighted undirected graph with n vertices and m edges can be represented by a cactus with O(n) vertices and edges, a connected graph in which each edge is contained in an exactly one cycle. In this paper, we show that such a cactus representation can be computed in O(mn+n²log n) time and O(m) space. This improves the previously best complexity of deterministic cactus construction algorithms, and matches with the time bound of the fastest deterministic algorithm for computing a single minimum cut.

Assume that each edge e of a graph G is assigned a list (set) L(e) of colors. Then an edge-coloring of G is called an L-edge-coloring if each edge e of G is colored with a color contained in L(e). It is known that any series-parallel simple graph G has an L-edge-coloring if either (i) |L(e)| max{4,d(v),d(w)} for each edge e=vw or (ii) the maximum degree of G is at most three and |L(e)| 3 for each edge e, where d(v) and d(w) are the degrees of the ends v and w of e, respectively. In this paper we give a linear-time algorithm for finding such an L-edge-coloring of a series-parallel graph G.

Let each vertex v of a graph G have a positive integer weight ω(v). Then a multicoloring of G is to assign each vertex v a set of ω(v) colors so that any pair of adjacent vertices receive disjoint sets of colors. A partial k-tree is a graph with tree-width bounded by a fixed constant k. This paper presents an algorithm which finds a multicoloring of any given partial k-tree G with the minimum number of colors. The computation time of the algorithm is bounded by a polynomial in the number of vertices and the maximum weight of vertices in G.

This paper investigates properties of space-bounded "two-dimensional Turing machines (2-tm's)," whose input tapes are restricted to square ones, with bounded input head reversals in vertical direction. We first investigate a relationship between determinism and nondeterminism for space-bounded and input head reversal-bounded 2-tm's. We then investigate how the number of input head reversals affects the accepting power of sublinearly space-bounded 2-tm's. Finally, we investigate necessary and sufficient spaces for three-way 2-tm's to simulate four-way two-dimensional finite automata with constant input head reversals.

In this paper, we consider two types of promises for (k-)CNF formulas which can help to find a satisfying assignment of a given formula. The first promise is the Hamming distance between truth assignments. Namely, we know in advance that a k-CNF formula with n variables, if satisfiable, has a satisfying assignment with at most pn variables set to 1. Then we ask whether or not the formula is satisfiable. It is shown that for k 3 and (i) when p=n^c (-1 < c 0), the problem is NP-hard; and (ii) when p=log n/n, there exists a polynomial-time deterministic algorithm. The algorithm is based on the exponential-time algorithm recently presented by Schoning. It is also applied for coloring k-uniform hypergraphs. The other promise is the number of satisfying assignments. For a CNF formula having 2ⁿ/2^nε (0 ε < 1) satisfying assignments, we present a subexponential-time deterministic algorithm based on the inclusion-exclusion formula.

This paper investigates the interaction of mind changes and anomalies for inductive inference of recursive real-valued functions. We show that the criteria for inductive inference of recursive real-valued functions by bounding the number of mind changes and anomalies preserve the same hierarchy as that of recursive functions, if the length of each anomaly as an interval is bounded. However, we also show that, without bounding it, the hierarchy of some criteria collapses. More precisely, while the class of recursive real-valued functions inferable in the limit allowing no more than one anomaly is properly contained in the class allowing just two anomalies, the latter class coincides with the class allowing arbitrary and bounded number of anomalies.

This paper defines the distributed arrangement selection problem in a line network in a distributed context and describes the design of a strictly-time-optimal algorithm which solves the problem with a limited local memory space. The problem is regarded as a combined distributed sorting and k-selection problem, namely a problem of sorting elements that are not larger than the kth minimum element in predetermined processes. The algorithm also provides a solution to a resource allocation problem in a line network in a strictly-optimal time.

In this paper, we consider the problem of multicasting a message in k-ary n-cubes under the store-and-forward model. The objective of the problem is to minimize the size of the resultant multicast tree by keeping the distance to each destination over the tree the same as the distance in the original graph. In the following, we first propose an algorithm that grows a multicast tree in a greedy manner, in the sense that for each intermediate vertex of the tree, the outgoing edges of the vertex are selected in a non-increasing order of the number of destinations that can use the edge in a shortest path to the destination. We then evaluate the goodness of the algorithm in terms of the worst case ratio of the size of the generated tree to the size of an optimal tree. It is proved that for any k 5 and n 6, the performnance ratio of the greedy algorithm is c kn - o(n) for some constant 1/12 c 1/2.

We propose two simple algorithms based on bounded tickets for the mutual exclusion problem on the asynchronous single-writer/multi-reader shared memory model. These algorithms are modifications of the Bakery algorithm. An unattractive property of the Bakery algorithm is that the size of its shared memory is unbounded. Initially we design a provisional algorithm based on bounded tickets. It guarantees mutual exclusion in the case where a certain condition is satisfied. To remove the condition, we use an additional process that does not correspond to any user. The algorithm with the additional process is a lockout-free mutual exclusion algorithm on the asynchronous single-writer/multi-reader shared memory model. We then modify this algorithm to reduce the shared memory size with the cost of using another additional process. The maximum waiting time using each of the algorithms proposed in this paper is bounded by (n-1)c+O(nl), where n is the number of users, l is an upper bound on the time between two successive atomic steps, and c is an upper bound on the time that any user spends using the resource. The shared memory size needed by the first algorithm and the second algorithm are (n+1)(1+log (4n)) bits and n(1+log (4n-4))+2 bits, respectively.

In this paper, parallelization methods for quantum circuits are studied, where parallelization of quantum circuits means to reconstruct a given quantum circuit to one which realizes the same quantum computation with a smaller depth, and it is based on using additional bits, called ancillae, each of which is initialized to be in a certain state. We propose parallelization methods in terms of the number of available ancillae, for three types of quantum circuits. The proposed parallelization methods are more general than previous one in the sense that the methods are applicable when the number of available ancillae is fixed arbitrarily. As consequences, for the three types of n-bit quantum circuits, we show new upper bounds of the number of ancillae for parallelizing to logarithmic depth, which are 1/log n of previous upper bounds.

By a worst input (for the selection phase of Heapsort) of size n, we mean a heap of size n such that, if it is given to the selection phase of Heapsort, the number of movements of data is maximum among the numbers of movements for all heaps of size n. D. E. Knuth showed a special type of worst inputs which we will call the K-type heaps. His definition of K-type heaps was by the induction on the size n. We give one characterization of K-type worst heaps that is based on their tree structure. This characterization gives more information on the tree structure of K-type heaps than the Knuth's definition.

As pseudorandom number generators for Monte Carlo simulations, inversive linear congruential generators (ICG) have some advantages compared with traditional linear congruential generators. It has been shown that a sequence generated by an ICG has a low discrepancy even if the length of the sequence is far shorter than its period. In this paper, we formulate fractional linear congruential generators (FCG), a generalized concept of the inversive linear congruential generators. It is shown that the sequence generated by an FCG is a geometrical shift of a sequence from an ICG and satisfies the same upper bounds of discrepancy. As an application of the general formulation, we show that under certain condition, "Leap-Frog technique," a way of splitting a random number sequence to parallel sequences, can be applied to the ICG or FCG with no extra cost on discrepancy.

A term rewriting system which effectively preserves recognizability (EPR-TRS) has good mathematical properties. In this paper, a new subclass of TRSs, layered transducing TRSs (LT-TRSs) is defined and its recognizability preserving property is discussed. The class of LT-TRSs contains some EPR-TRSs, e.g., {f(x)f(g(x))} which do not belong to any of the known decidable subclasses of EPR-TRSs. Bottom-up linear tree transducer, which is a well-known computation model in the tree language theory, is a special case of LT-TRS. We present a sufficient condition for an LT-TRS to be an EPR-TRS. Also reachability and joinability are shown to be decidable for LT-TRSs.

This paper describes a stock trading system based on reinforcement learning, regarding the process of stock price changes as Markov decision process (MDP). The system adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing trading parameters, respectively. Input features of the system are devised using technical analysis and value functions are approximated by feedforward neural networks. Multiple cooperative agents are used for Q-learning to efficiently integrate global trend prediction with local trading strategy. Agents communicate with others sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on the Korean stock market show that our trading system outperforms the market average and makes appreciable profits. Furthermore, we can find that our system is superior to a system trained by supervised learning in view of risk management.

This paper describes a highly-portable multilingual question answering system on multiple relational databases. We apply techniques which were verified on open-domain text-based question answering, such as semantic category and pattern-based grammars, into natural language interfaces to relational databases. Lexico-semantic pattern (LSP) and multi-level grammars achieve portability of languages, domains, and DB management systems. The LSP-based linguistic processing does not require deep analysis that sacrifices robustness and flexibility, but can handle delicate natural language questions. To maximize portability, we drive three dependency factors into the following two parts: language-dependent part into front linguistic analysis, and domain-dependent and database-dependent parts into backend SQL query generation. We also support session-based dialog by preserving SQL queries created from previous user's question, and then re-generating new SQL query for the successive questions. Experiments with 779 queries generate only constraint-missing errors, which can be easily corrected by adding new terms, of 2.25% for English and 5.67% for Korean.

This paper presents an efficient Hybrid Learning Algorithm (HLA) for Radial Basis Function Neural Network (RBFNN). The HLA combines the gradient method and the linear least squared method for adjusting the RBF parameters and connection weights. The number of hidden neurons and their characteristics are determined using an unsupervised clustering procedure, and are used as input parameters to the learning algorithm. We demonstrate that the HLA, while providing faster convergence in training phase, is also less sensitive to training and testing patterns. The proposed HLA in conjunction with RBFNN is used as a classifier in a face recognition system to show the usefulness of the learning algorithm. The inputs to the RBFNN are the feature vectors obtained by combining shape information and Pseudo Zernike Moment (PZM). Simulation results on the Olivetti Research Laboratory (ORL) database and comparison with other algorithms indicate that the HLA yields excellent recognition rate with less hidden neurons in human face recognition.

We consider the routing for a multicast in a WDM all-optical network. We prove a min-max theorem on the number of wavelengths necessary for routing a multicast. Based on the min-max theorem, we propose an efficient on-line algorithm for routing a multicast.

For an uncertain discrete event system (DES) modeled as a set of some possible nondeterministic automata, we address robust supervisory control problems. Based on language models, this paper presents the existence conditions of a robust nonblocking (RN) supervisor that guarantees the absence of blocked states in a closed-loop system. We show that an RN supervisor achieves both a given language specification and the nonblocking characteristics of any nondeterministic automata of the set.

In this paper, we propose a corpus-based lip-sync algorithm for natural face animation. For this purpose, we constructed a Korean audio-visual (AV) corpus. Based on this AV corpus, we propose a concatenation method of AV units, which is similar to a corpus-based text-to-speech system. For our AV corpus, lip-related parameters were extracted from every video-recorded facial shot which of speaker reads the given texts selected from newspapers. The spoken utterances were labeled with HTK and such prosodic information as duration, pitch and intensity was extracted as lip-sync parameters. Based on the constructed AV corpus, basic synthetic units are set by CVC-syllable units. For the best concatenation performance, based on the phonetic environment distance and the prosodic distance, the best path is estimated by a general Viterbi search algorithm. From the computer simulation results, we found that the information concerned with not only duration but also pitch and intensity is useful to enhance the lip-sync performance. And the reconstructed lip parameters have almost equal values to those of the original parameters.

To increase the discriminating ability of the speech feature based on linear predictive coding (LPC) and increase its noise robustness, an SNR-dependent arcsin transform is applied to the autocorrelation sequence (ACS) of each analysis frame in a speech signal. Moreover, each component in the ACS is also weighted by the normalized reciprocal of the average magnitude difference function (AMDF) for emphasizing its peak structure. Experimental results for the task of Mandarin digit recognition indicate that the LPC speech feature employing the proposed scheme is more robust than some widely used LPC-based approaches over a wide range of SNR values.

In fractal image coding, for each range block, the best matching domain block is identified, and information from the best matching domains and range blocks are transmitted to the decoder for image reconstruction. In this paper, the similarity between range blocks and domain blocks is evaluated according to their centers of gravity. The number of searched domain blocks are reduced by limiting the candidates for the best matching domain blocks to those domain blocks whose similarity to the range block are high. Using simulation experiments, the number of candidates for the best matching domain blocks were reduced to about 10-23% of the current method. Thus, our proposed method had significantly reduced the number of searched domain blocks below the current method and at the same time it turns out that degradation of the reconstructed image was seldom observed.