17289553
On the complexity of the minimum and maximum global snapshot problems
Deriving the minimum and maximum global snapshots is very useful for some error detection problems in distributed programs. Several researchers, e.g., Groselj (1993) and Chen and Wu (1996), have shown that the minimum and maximum global snapshot problems are linear-time reducible to the maximum constant-ratio network flow (MCNF) problem, here defined as the well-known maximum network flow problem with m=/spl Theta/(n), where m is the number of edges and n is the number of vertices in the given flow network. The authors show in a reverse way that the MCNF problem is also linear-time reducible to these global snapshot problems. Thus, one can conclude that the global snapshot problems are "as difficult as" the MCNF problem in terms of time complexity.
5417653
On the Web Data Extraction Model
2039675
On Detection of Bounded Global Predicates
Distributed programs often follow some bounded global predicates, for example, the total number of certain tokens is always the same or bounded in a range. In order to detect bounded global predicates, we can first derive the minimum and maximum global snapshots and then check if the minimum and maximum are out of the range. Recently, Chase and Garg proposed an efficient method to derive the minimum global snapshot by reducing this problem to a maximum network flow problem. A restriction of this method is that all message values (e.g., the token number in messages) must be zero and all process state values (e.g., the token number in processes) must be non-negative. In this paper, we propose an elegant technique, called normalization. By using this technique, we can remove the above restriction and also derive the minimum and maximum global snapshots at the same time.
7800069
Detection of summative global predicates
In distributed programs, we usually keep some global predicates from being satisfied to make it easy to run the programs correctly. A common type of global predicates are: the total number of certain tokens in the whole distributed system is always the same or in specific ranges. In this paper, we call this summative predicates, classified into the following four: (1) at some global state of the system, N/spl ne/K, (2) NK (or N/spl ges/K), and (4) N=K, where N is the total number of tokens and K is a constant. This paper investigates the methods of detecting various summative global predicates. The first class of summative predicates are trivial to detect by simply checking each message. For the second class of summative predicates, B. Groselj (1993) and V.K. Garg (1995) solved the problem by reducing the problem to a maximum network flow problem. In this paper, we propose an elegant technique, called normalization, to allow the second and third classes of summative predicates to be solved by also reducing the problem to a maximum network flow problem. For the fourth class of summative predicates, we prove that it is an NP-complete problem.
21260780
An efficient incremental algorithm for identifying consistent checkpoints
In a distributed system, identifying consistent checkpoints is essential for error recovery and debugging. We design an efficient incremental algorithm capable of identifying all the consistent and removable checkpoints each time a new checkpoint is reported. By doing so, the required memory space can be minimized by removing those removables. While minimizing the memory space, the algorithm requires only O(p/sup 2/M) time in total, where p is the number of processes and M is the number of checkpoints.
205888059
On the Time Complexity of Minimum and Maximum Global Snapshot Problems
Abstract Deriving the minimum and maximum global snapshots is very useful for some error detection problems in distributed programs. Several researchers, e.g., Groselj, Chen and Wu, have shown that the minimum and maximum global snapshot problems are linear-time reducible to the maximum constant-ratio network flow (MCNF) problem, here defined as the wellknown maximum network flow problem with m = Θ(n), where m is the number of edges and n is the number of vertices in the given flow network. In this paper we show in a reverse way that the MCNF problem is also linear-time reducible to these global snapshot problems. Thus, we can conclude that the global snapshot problems are “as difficult as” the MCNF problem in terms of time complexity.
12012725
An intelligent control system with a multi-objective self-exploration process
Abstract This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System—mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.
7065561
Partitioning Graphs Into Hamiltonian Ones
Abstract In this paper, we present an (optimal) linear-time algorithm that decides whether a bipartite permutation graph can be partitioned into one or more induced Hamiltonian subgraphs each of which contains at least k vertices for a given k , and if so, obtains such a partition. We then give an efficient parallelization of the algorithm. We also investigate the problem of partitioning arbitrary directed graphs into Hamiltonian ones.
16730298
Efficient moving object segmentation algorithm using background registration technique
An efficient moving object segmentation algorithm suitable for real-time content-based multimedia communication systems is proposed in this paper. First, a background registration technique is used to construct a reliable background image from the accumulated frame difference information. The moving object region is then separated from the background region by comparing the current frame with the constructed background image. Finally, a post-processing step is applied on the obtained object mask to remove noise regions and to smooth the object boundary. In situations where object shadows appear in the background region, a pre-processing gradient filter is applied on the input image to reduce the shadow effect. In order to meet the real-time requirement, no computationally intensive operation is included in this method. Moreover, the implementation is optimized using parallel processing and a processing speed of 25 QCIF fps can be achieved on a personal computer with a 450-MHz Pentium III processor. Good segmentation performance is demonstrated by the simulation results.
5161241
Protein structure alignment by deterministic annealing
MOTIVATION
Protein structure alignment is one of the most important computational problems in molecular biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and so on. From the viewpoint of computational complexity, a pairwise structure alignment is also a NP-hard problem, in contrast to the polynomial time algorithm for a pairwise sequence alignment.


RESULTS
We propose a method for solving the structure alignment problem in an accurate manner at the amino acid level, based on a mean field annealing technique. We define the structure alignment as a mixed integer-programming (MIP) problem. By avoiding complicated combinatorial computation and exploiting the special structure of the continuous partial problem, we transform the MIP into a reduced non-linear continuous optimization problem (NCOP) with a much simpler form. To optimize the reduced NCOP, a mean field annealing procedure is adopted with a modified Potts model, whose solution is generally identical to that of the MIP. There is no 'soft constraint' in our mean field model and all constraints are automatically satisfied throughout the annealing process, thereby not only making the optimization more efficient but also eliminating many unnecessary parameters that depend on problems and usually require careful tuning. A number of benchmark examples are tested by the proposed method with comparisons to several existing approaches.
4781028
Merging R-Trees: Efficient Strategies for Local Bulk Insertion
A lot of recent work has focussed on bulk loading of data into multidimensional index structures in order to efficiently construct such structures for large datasets. In this paper, we address this problem with particular focus on R-trees—which are an important class of index structures used widely in commercial database systems. We propose a new technique, which as opposed to the current technique of inserting data one by one, bulk inserts entire new datasets into an active R-tree. This technique, called STLT (for small-tree-large-tree), considers the new dataset as an R-tree itself (small tree), identifies and prepares a suitable location in the original R-tree (large tree) for insertion, and lastly performs the insert of the small tree into the large tree. Besides an analytical cost model of STLT, extensive experimental studies both on synthetic and real GIS data sets are also reported. These experiments not only compare STLT against the conventional technique, but also evaluate the suitability and limitations of STLT under different conditions, such as varying buffer sizes, ratio between existing and new data sizes, and skewness of new data with respect to the whole spatial region. We find that STLT does much better (in average, about 65%) than the existing technique for skewed datasets as well for large sizes of both the large tree and the small tree in terms of insertion time, while keeping comparable query tree quality. STLT consistently outperforms the alternate technique in all other circumstances in terms of bulk insertion time, especially, even up to 2,000% for the cases when the area of new data sets covers up to 4% of the global region covered by the existing index tree; however, at the cost of a deteriorating resulting tree quality.