The merged longest common increasing subsequence (MLCIS) problem represents a generalized variant by combining the merged longest common subsequence (merged LCS, MLCS) problem and the longest increasing subsequence (LIS) problem. Given a pair of numeric sequences A and B along with a target numeric sequence T , the MLCIS problem aims to identify the longest common subsequence that is increasing in both the merged sequence E ( A , B ) and the target sequence T . Here, E ( A , B ) represents a new sequence constructed from arbitrarily merging A and B while maintaining their original orders. In this paper, we propose two algorithms for solving the MLCIS problem: dynamic programming and diagonal. The dynamic programming algorithm has a time complexity of O( mnr ), where m , n and r denote the lengths of sequences A , B and T , respectively. The time complexity of the diagonal algorithm is O( ( L + 1 ) ( r − L + 1 ) ( m + n ) ), where L denotes the length of the MLCIS answer. In general, as the experimental results show, the diagonal algorithm is more efficient than the dynamic programming algorithm in practice. Furthermore, the diagonal algorithm is very efficient when L is either very small or L is close to r , which coincides with the theoretical time complexity.

The paper studies how to improve the trading decision of Taiwan stocks with the information of US stock market. Our method first aligns the trading days between Taiwan and US stock markets. Next, the similarity between the portfolio index (PI, constructed from 100 Taiwan stocks) and one of the US stock indices, the Dow Jones Industrial Average (DJIA), NASDAQ composite index (NASDAQ), or Standard & Poor’s 500 (S&P 500), is computed, respectively. The trading signals of PI or each US stock index are generated by the method of Lee et al. Finally, the consensus signals of PI are determined by the majority vote scheme with the weighted functions, calculated from the similarity. The testing period of PI starts from 2000/1/4 to 2017/12/29, totally 4480 days. As the experimental results show, the index combination (PI, DJIA, NASDAQ) with the weighted function W(4) is considered to be the best combination for trading PI. Its average annualized return (cumulative return) achieves 15.03% (1170.42%), which is better than the method of Lee et al. 13.88% (947.65%), and the buy-and-hold strategy 9.85% (442.90%).

Abstract In this paper, we first find out some good trading strategies from the historical series and apply them in the future. The profitable strategies are trained out by the gene expression programming (GEP), which involves some well-known stock technical indicators as features. Our data set collects the 100 stocks with the top capital from the listed companies in the Taiwan stock market. Accordingly, we build a new series called portfolio index as the investment target. For each trading day, we search for some similar template intervals from the historical data and pick out the pertained trading strategies from the strategy pool. These strategies are validated by the return during a few days before the trading day to check whether each of them is suitable or not. Then these suitable strategies decide the buying or selling consensus signal with the majority vote on the trading day. The training period is from 1996/1/6 to 2012/12/28, and the testing period is from 2000/1/4 to 2012/12/28. Two simulation experiments are performed. In experiment 1, the best average accumulated return is 548.97% (average annualized return is 15.47%). In experiment 2, we increase the diversity of trading strategies with more training. The best average accumulated return is increased to 685.31% (average annualized return is 17.18%). These two results are much better than that of the buy-and-hold strategy, whose return is 287.00%.

Essential proteins affect the cellular life deeply, but it is extreme time-consuming and labor-intensive to discriminate them experimentally. The goal of this paper is to identify the features which are crucial for discriminating protein essentiality and build learning machines for prediction. We first collect features from a variety of sources. Then we adopt a backward feature selection method and use the selected features to build SVM predictors. The cross validations are conducted on the originally imbalanced data set as well as the down-sampling balanced data set. The performance of these feature subsets are then subject to the statistical test to confirm their significance. For the imbalanced data set, our best values of F-measure and MCC are 0.549 and 0.495, respectively. For balanced data set, our best values of F-measure and MCC of our models are 0.770 and 0.545, respectively. The results are superior to all previous results under various performance measures.

In this paper, we generalize the inclusion constrained longest common subsequence (CLCS) problem to the hybrid CLCS problem which is the combination of the sequence inclusion CLCS and the string inclusion CLCS, called the sequential sub string constrained longest common subsequence (SSCLCS) problem. In the SSCLCS problem, we are given two strings A and B of lengths m and n, respectively, formed by alphabet Σ and a constraint sequence C formed by ordered strings (C1, C2, C3, · · · Cl) with total length τ. We are to find the longest common subsequence D of A and B containing C1,C2,C3, · · · , Clas substrings and the order of C's are retained. This problem have two variants that the strings in C may or may not overlap. We proposed algorithms with O(mnl + (m+ n)(|Σ| + r)) and O(mnr + (m+ n)|Σ|) time for the two variants of the problem. For the special case with one or two constraints, our algorithms runs in O(mn+(m+n)(|Σ|+r)) and O(mnr +(m+n)|Σ|) time, which are an order faster than the algorithm proposed by Chen and Chao [1].

In a 1-fair alternator of a network of concurrent processors, no processor executes the critical step twice when one or more other processors have not executed the critical step yet. In this paper, two algorithms are proposed to solve the coloring (1-fair alternator design) problem on the de Bruijn network. The first one uses 2 lceillog2krceil +1 colors to color the k-ary de Bruijn graph with two digits, while the second one uses p + 1 only colors, where (lfloor(p-1)/2rfloorp-1lfloorp/2rfloorp. The second coloring method is optimal when k =lfloorp/2rfloorp. Furthermore, the extension of our coloring method can be applied to the k-ary de Bruijn graph with three or more digits

In this paper, we study the properties of the bus-based hypercube, denoted as U(n,b), which is a kind of multiple-bus networks (MBN). U(n,b) consists of 2/sup n/ processors and 2/sup b/ buses, where 0 /spl les/ b /spl les/ n - 1, and each processor is connected to either /spl lceil/(b+2)/2/spl rceil/ or /spl lceil/(b+1)/2/spl rceil/ buses. We show that the diameter of U(n,b) is /spl lceil/(b-1)/2/spl rceil/ if b /spl ges/ 2. We also present an algorithm to select the best neighbor processor via which we can obtain one shortest routing path. In U(n,b), we show that if there exist some faults, the fault diameter DF(n,b,f) /spl les/ b+1, where f is the sum of bus faults and processor faults and 0 /spl les/ f /spl les/ /spl lceil/(b+3)/2/spl rceil/. Furthermore, we also show that the bus fault diameter DB(n,b,f) /spl les/ b/-2/spl rfloor/ - 3, where 0 /spl les/ f /spl les/ /spl lceil/(b-1)/2/spl rceil/ and f is the number of bus faults. These results improve significantly the previous result that DB(n,b,f) /spl les/ b - 2f + 1, where f is the number of bus faults.

In this paper, we study the routing problem for the undirected binary de Bruijn interconnection network. Researchers have never proposed a shortest path routing algorithm on the undirected binary de Bruijn network. We first propose a shortest path routing algorithm, whose time complexity in the binary de Bruijn network of 2m nodes is O(m2). Then, based on our shortest path routing algorithm, we propose two fault-tolerant routing schemes. It is assumed that at most one node fails in the network. In our schemes, two node-disjoint paths are found. Our first fault-tolerant routing algorithm guarantees that one of the two paths is the shortest path, and the other is of length at most m + log2 m + 4. Our second algorithm can find two node-disjoint paths with lengths at most m and m + 4, respectively, if the shortest path is not required in the fault-tolerant routing. © 2000 John Wiley & Sons, Inc.

Some common guidelines that can be used to design parallel algorithms under the single-channel broadcast communication model are presented. Several graph problems are solved, including topological ordering, the connected component problem, breadth-first search, and depth-first search. If an ideal conflict resolution scheme is used, all of the algorithms require O(n) time by using n processors. Under such a situation, the algorithms are all optimal. If a realistic conflict resolution is used, the algorithms require O(n log n) time by using n/log n processors. For both cases, all of the algorithms achieve optimal speedups.>