8326762
A Space Efficient Algorithm for Sequence Alignment with Inversions
A dynamic programming algorithm to find an optimal alignment for a pair of DNA sequences has been described by Schoniger and Waterman. The alignments use not only substitutions, insertions, and deletions of single nucleotides, but also inversions, which are the reversed complements, of substrings of the sequences. With the restriction that the inversions are pairwise non-intersecting, their proposed algorithm runs in O(n2m2) time and consumes O(n2m2) space, where n and m are the lengths of the input sequences respectively. We develop a space efficient algorithm to compute such an optimal alignment which consumes only O(nm) space within the same amount of time. Our algorithm enables the computation for a pair of DNA sequences of length up to 10,000 to be carried out on an ordinary desktop computer. Simulation study is conducted to verify some biological facts about gene shuffling across species.
3221081
Consistency and Random Constraint Satisfaction Models
In this paper, we study the possibility of designing non-trivial random CSP models by exploiting the intrinsic connection between structures and typical-case hardness. We show that constraint consistency, a notion that has been developed to improve the efficiency of CSP algorithms, is in fact the key to the design of random CSP models that have interesting phase transition behavior and guaranteed exponential resolution complexity without putting much restriction on the parameter of constraint tightness or the domain size of the problem. We propose a very flexible framework for constructing problem instances with interesting behavior and develop a variety of concrete methods to construct specific random CSP models that enforce different levels of constraint consistency. 
 
A series of experimental studies with interesting observations are carried out to illustrate the effectiveness of introducing structural elements in random instances, to verify the robustness of our proposal, and to investigate features of some specific models based on our framework that are highly related to the behavior of backtracking search algorithms.
45061344
Space Complexity of Estimation of Distribution Algorithms
In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.
181464
An Analysis of Phase Transition in NK Landscapes
In this paper, we analyze the decision version of the NK landscape model from the perspective of threshold phenomena and phase transitions under two random distributions, the uniform probability model and the fixed ratio model. For the uniform probability model, we prove that the phase transition is easy in the sense that there is a polynomial algorithm that can solve a random instance of the problem with the probability asymptotic to 1 as the problem size tends to infinity. For the fixed ratio model, we establish several upper bounds for the solubility threshold, and prove that random instances with parameters above these upper bounds can be solved polynomially. This, together with our empirical study for random instances generated below and in the phase transition region, suggests that the phase transition of the fixed ratio model is also easy.
11821573
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
There has been great interest in identifying tractable subclasses of NP complete problems and designing efficient algorithms for these tractable classes. Constraint satisfaction and Bayesian network inference are two examples of such problems that are of great importance in AI and algorithms. In this paper we study, under the frameworks of random constraint satisfaction problems and random Bayesian networks, a typical tractable subclass characterized by the treewidth of the problems. We show that the property of having a bounded treewidth for CSPs and Bayesian network inference problem has a phase transition that occurs while the underlying structures of problems are still sparse. This implies that algorithms making use of treewidth based structural knowledge only work efficiently in a limited range of random instance.
2287582
Resolution complexity of random constraint satisfaction problems: Another half of the story
Let C"n","m^2^,^k^,^t be a random constraint satisfaction problem (CSP) on n binary variables, where m constraints are selected uniformly at random from all the possible k-ary constraints each of which contains exactly t tuples of the values as its restrictions. We establish an upper bound on the constraint tightness threshold for C"n","m^2^,^k^,^t to have an exponential resolution complexity. The upper bound partly answers the open problem regarding the CSP resolution complexity with the tightness between the existing upper and lower bounds [D. Mitchell, Resolution complexity of random constraints, in: Proceedings Principles and Practices of Constraint Programming-CP 2002, Springer, Berlin, 2002, pp. 295-309].
20003562
Mapping Web Usage Patterns to MDP Model and Mining with Reinforcement Learning
For many web usage mining applications, it is crucial to compare navigation paths of different users. This paper presents a reinforcement learning based method for mining the sequential usage patterns of user behaviors. In detail, the temporal data set about every user is constructed from the web log file, and then the navigation paths of the users are modelled using the extended Markov decision process. The proposed method could learn the dynamical sequential usage patterns on-line.
975623
Face Recognition by Combining Kernel Associative Memory and Gabor Transforms
Kernel associative memory (KAM) has previously been proposed as an efficient scheme for face recognition. In this paper, a hybrid method of combining KAM and Gabor wavelet transform is proposed. In this method, face images of each person are first decomposed into their spatial/frequency domains by Gabor transforms, which are then modelled by a KAM. While Gabor properties of orientation selectivity and spatial frequency selectivity provide discriminating features, KAM offers the means to capture the important intra-class variations. Experimental results obtained on two standard face databases demonstrated that the proposed method consistently improved the system performance
17160551
Pattern discovery on character sets and real-valued data: linear bound on irredundant motifs and an efficient polynomial time algorithm
Given an input sequence of data, a motif is a repeating pattern, possibly interspersed with "dont care" characters. The data could be a sequence of characters or sets of characters or even real values. In the first two cases, the number of motifs could potentially be exponential in the size of the input sequence and in the third case there could be infinite number of motifs (assuming two real numbers are equal if they are within some specified 5 > 0 of each other). However, it has been shown in some motif based applications, such as multiple sequence aligz~nent [Par98, PFR99], that a small subset of these motifs capture all the relevant information. Also, from an information-theoretic viewpoint, the special motifs represent the sets with low co-relation, hence of higher informational value. In this paper, we show, for any sequence with n characters, there exists only a linear (or no more than 3n) number of these special motifs and every other motif can be constructed from this set. We term these special motifs as irredundant motifs. We also present an efficient polynomial time algorithm to generate these motifs. This bound on the number of useful motifs ~ves validation to motif-based approaches, since the total number of irredundant motifs does not explode. This result is potentially of significance to most applications that use pattern discovery as the basic engine such as data mining, clustering, matching and others.
18106801
Mining Protein Sequences for Motifs
We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.
27306380
A case for exploiting self-similarity of network traffic in TCP congestion control
Analytical and empirical studies have shown the self-similar traffic can have detrimental impact on network performance including amplified queuing delay and packet loss ratio. On the flip side, the ubiquity of scale-invariant burstiness observed across diverse networking contexts can be exploited to better design resource control algorithms. In this paper, we explore the issueof exploiting the self-similar characteristics of network traffic in TCP congestion control. We show that the correlation structure present in long-range dependent traffic can be detected on-line and used to predict the future traffic. We then devise a novel scheme, called TCP with traffic prediction (TCP-TP), that exploits the prediction result to infer, in the context of AIMD steady state dynamics, the optimal operational point at which a TCP connection should operate. Through analytical reasoning, we show that the impact of prediction errors on fairness is minimal. We also conduct ns-2 simulation and freeBSD, 4.1-based implementation studies to validate the design and to demonstrate the performance improvement in terms of packet loss ratio and throughput attained by connections.