Collaborative Urban Transportation:Recent Advances in Theory and Practice

2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)

Many algorithms for sequence analysis rely on word matching or word statistics. Often, these approaches can be improved if binary patterns representing match and don't-care positions are used as a filter, such that only those positions of words are considered that correspond to the match positions of the patterns. The performance of these approaches, however, depends on the underlying patterns. Herein, we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set. We propose a modified hill-climbing algorithm to optimize pattern sets for database searching, read mapping and alignment-free sequence comparison of nucleic-acid sequences; our implementation of this algorithm is called rasbhari. Depending on the application at hand, rasbhari can either minimize the overlap complexity of pattern sets, maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison. We show that, for database searching, rasbhari generates pattern sets with slightly higher sensitivity than existing approaches. In our Spaced Words approach to alignment-free sequence comparison, pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used. Finally, we used rasbhari to generate patterns for short read classification with CLARK-S. Here too, the sensitivity of the results could be improved, compared to the default patterns of the program. We integrated rasbhari into Spaced Words; the source code of rasbhari is freely available at http://rasbhari.gobics.de/.

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rasbhari : Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison

Optimal spaced seeds were introduced by the theoretical computer science community to bioinformatics to effectively increase homology search sensitivity. They are now serving thousands of homology search queries daily. While dozens of papers have been published on optimal spaced seeds since their invention, many fundamental questions still remain unanswered. In this paper, we settle several open questions in this area. Specifically, we prove that when the length of a non-uniformly spaced seed is bounded by an exponential function of the seed weight, the seed outperforms strictly the traditional consecutive seed in both (i) the average number of non-overlapping hits and (ii) the asymptotic hit probability. Then, we study the computation of the hit probability of a spaced seed, solving three more open questions: (iii) hit probability computation in a uniform homologous region is NP-hard and (iv) it admits a PTAS; (v) the asymptotic hit probability is computable in exponential time in seed length, independent of the homologous region length.

Superiority and Complexity of the Spaced Seeds.

Optimizing ride-sharing operations in smart sustainable cities: Challenges and the need for agile algorithms

Using the ECO method, we give succession rules for p-generalized Fibonacci and Lucas sequences. We provide sets of pattern-avoiding permuta- tions which are enumerated by the p-generalized Fibonacci and Lucas sequences. Finally we give generating algorithms in Constant Amortized Time for these sets. Mathematics Subject Classifications (2000). 05A05, 05A15, 69R05

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ECO-Generation for p-generalized Fibonacci and Lucas permutations

Group Steiner Problem (GSP) is an important generalization of some basic NP-hard problems. Many complex real-world applications require solving the GSP in graphs modeling the topology of the given problem, such as: the design of Very Large Scale Integration (VLSI) circuits, the design of a minimal length irrigation network, and routing problems for wireless sensor networks. We show our design of a new algorithm based on an Ant Colony Optimization model to solve the GSP in general graphs. Our experimental results show that our method strongly outperforms the best other heuristic methods for GSP.

An ant colony optimization algorithm for solving Group Steiner Problem

This paper introduces a new fully time-dependent model of a public transportation system in the urban context that allows sharing a taxi between one passenger and parcels with speed widows consideration. The model contains many real-life case features and is presented by a mathematical formulation. We study both static and dynamic scenarios in comparison to traditional strategies, i.e., the direct delivery model. Moreover, we classify speed windows by different zones and congestion levels during a day in the urban context. Different speed windows induce the dynamic graph model for road networks and make the problem much more difficult to solve. Because of the complex model, the preprocessing steps on data as well as on dynamic graphs are very important. We use a greedy algorithm to initiate the solution and then use some local search techniques to improve the solution quality. The experimental data set is recorded by Tokyo-Musen Taxi company. The data set includes more than 20000 requests per day, more than 4500 used taxis per day and more than 130000 crossing points on the Tokyo map. Experimental results are analyzed on various factors such as the total benefit, the accumulating traveling time during the day, the number of used taxis and the number of shared requests.

A time-dependent model with speed windows for share-a-ride problems: A case study for Tokyo transportation

In homology search, finding optimal multiple spaced seeds in genomic DNA sequences is NP-hard but even finding good ones is very difficult. The exponential-time algorithm PatternHunter use optimal spaced seeds to increase both the sensitivity and the speed of homology search. The overlap complexity measure based on the overlaps between hits of a multiple seed are well correlated with sensitivity but is computable in polynomial time. Based on overlap complexity, we have improved polynomial-time algorithms to provide better multiple seeds. Our experimental results shows that these improvements significantly run faster and make better quality of spaced seeds than previous algorithms in almost all test cases.

An improvement of the overlap complexity in the spaced seed searching problem between genomic DNAs

Machine translation is one of the most challenging topics in natural language processing. The common approaches to machine translation base on either statistical or rule-based methods. Rule-based translation analyzes sentence structures, requires extensive lexicons with morphological, syntactic, semantic information, and large sets of manually created rules. Statistics-based translation faces the challenge of collecting bilingual text corpora, which is particularly difficult for low resource language pairs as English-Vietnamese. This research aims at building state-of-the-art English-Vietnamese machine translation. Our contribution includes: (1) an enormous effort in collecting training dataset, (2) adaptation of current neural machine for English-Vietnamese translation, (3) an experimental result suggested the unnecessary of Vietnamese word segmentation as a common pre-processing step. Our model achieves a highest BLEU score in comparison with other researches.

Phan-Thuan Do

Papers

ECO-Generation for p-generalized Fibonacci and Lucas permutations

An ant colony optimization algorithm for solving Group Steiner Problem

A time-dependent model with speed windows for share-a-ride problems: A case study for Tokyo transportation

An improvement of the overlap complexity in the spaced seed searching problem between genomic DNAs

Towards State-of-the-art English-Vietnamese Neural Machine Translation