Partitioning Merging Research Articles

Research on Full Coverage Path Planning Based on Reinforcement Learning in Nuclear Environment

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of [Formula: see text]-learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, [Formula: see text]-learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the [Formula: see text]-learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain.

An improved partition merging algorithm to account for maximum waiting time in carpools

Nowadays, traffic problems have become an issue that needs to be solved in every populous country. Especially in large urban areas, people face traffic problems such as road congestion and high exhaust emissions due to the rapid growth of vehicles on the roads. In this context, several researchers have shown that carpooling, i.e., vehicle sharing, is an attractive solution to effectively address the traffic stress that arises when a large number of cars are traveling at the same time. The goal of our carpool scheduling problem is to reduce the number of carpools required by all users while ensuring their waiting time. Previous studies on similar topics have introduced additional static capacity constraints to simplify the problem, which limits the number of carpooling users in a vehicle. However, this does not match most real-world situations. This is because when a passenger gets off the vehicle, their seat should also be vacated for the next user. In this paper, we eliminate the static capacity constraint to enable timely user turnover during the carpooling journey. A greedy algorithm based on iterative matching and summation is proposed. In addition, we introduce the concept of user waiting time, i.e., the amount of time a user is willing to wait to be picked up. We apply our algorithm to synthetic and real-world data sets, and our experimental results show that our algorithm has better performance than existing methods.

Coconut: sortable summarizations for scalable indexes over static and streaming data series

Many modern applications produce massive streams of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the problem to the fact that existing summarizations of data series used for indexing cannot be sorted while keeping similar data series close to each other in the sorted order. To address this problem, we present Coconut, the first data series index based on sortable summarizations and the first efficient solution for indexing and querying streaming series. The first innovation in Coconut is an inverted, sortable data series summarization that organizes data series based on a z-order curve, keeping similar series close to each other in the sorted order. As a result, Coconut is able to use bulk loading and updating techniques that rely on sorting to quickly build and maintain a contiguous index using large sequential disk I/Os. We then explore prefix-based and median-based splitting policies for bottom-up bulk loading, showing that median-based splitting outperforms the state of the art, ensuring that all nodes are densely populated. Finally, we explore the impact of sortable summarizations on variable-sized window queries, showing that they can be supported in the presence of updates through efficient merging of temporal partitions. Overall, we show analytically and empirically that Coconut dominates the state-of-the-art data series indexes in terms of construction speed, query speed, and storage costs.

K-VARP: K-anonymity for varied data streams via partitioning

The Internet-of-Things (IoT) produces and transmits enormous amounts of data. Extracting valuable information from this enormous volume of data has become an important consideration for businesses and research. However, extracting information from this data without providing privacy protection puts individuals at risk. Data has to be sanitized before use, and anonymization provides solution to this problem. Since, IoT is a collection of numerous different devices, data streams from these devices tend to vary over time thus creating varied data streams. However, implementing traditional data stream anonymization approaches only provide privacy protection for data streams that have predefined and fixed attributes. Therefore, conventional methods cannot directly work on varied data streams. In this work, we propose K-VARP (K-anonymity for VARied data stream via Partitioning) to publish varied data streams. K-VARP reads the tuple and assigns them to partitions based on description, and all tuples must be anonymized before expiring. It tries to anonymize expiring tuple within a partition if its partition is eligible to produce a K-anonymous cluster. Otherwise, partition merging is applied. In K-VARP we propose a new merging criterion called R-likeness to measure similarity distance between tuple and partitions. Moreover, flexible re-using and imputation free-publication is implied in K-VARP to achieve better anonymization quality and performance. Our experiments on a real datasets show that K-VARP is efficient and effective compared to existing algorithms. K-VARP demonstrated approximately three to nine and ten to twenty percent less information loss on two real datasets, while forming a similar number of clusters within a comparable computation time.

ENGLISH

IP Address assignment is a key challenge in Ad hoc networks because it doesn’t have predefined infrastructure. Since most dynamic networks have frequent partitions with fading nodes and joining/leaving nodes, a distributed and self-managed mechanism is required by Autonomous Addressing Protocols to avoid address collisions. To reduce the control load and proactively tackle the packet losses and network partitions, a lightweight protocol that can configure mobile ad hoc nodes based on distributed address database that is stored in filters is proposed. This proposal considers merging of partitions, joining nodes and network initialization. This protocol deals with the resolving of collision of addresses, and the reduction of the control traffic, all the while balancing the load in an optimal manner

SuperPartitions: Detection and classification of orthologs

The proper detection of orthologs is crucial for evolutionary studies of genes and species. Despite large efforts to solve this problem the methodological situation appears unsettled to a large extent and the “quest for orthologs” is still an ongoing task in large-scale genome comparisons. Here, we introduce a simple operational framework for the detection of orthologs and their classification. The operational framework relies on well-established principles, optimizing their implementation for the considered purposes, and chaining components in coherent procedures: 1) We take advantage of the efficiency and simplicity of the Reciprocal Best Hit (RBH) detections, remedying (by design) the drawback concerning the limitations in terms of 1:1 detections. The procedure is based on the partitioning of Reciprocal Best Hits, with the further merging of partitions including members of the same paralogous classes (“SuperPartition of Orthologs” (SPOs)). 2) We then resort to the conservation profiles of the obtained clusters, allowing simple detection of SPOs containing duplicated members. Based on accepted evolutionary principles, such members can be further tagged as in-paralogs (co-orthologs) or out-paralogs. The method is illustrated and validated by extensive genomic analyses. The performances of the overall approach are characterized in global terms for three sets of species ( Chlamydiae, Mycobacteria, Aspergilli), showing that at least 75% of the sets of orthologs contain at most one protein from a given species. The sets including more than one protein from a given species are shown to contain in-paralogs in proportions varying from 28% to 58%. The characterizations also show that the large majority of SPOs are associated with ancestral motifs, and accordingly not prone to chaining effects that might be triggered by multi-domain proteins. Further the SPO formulation is compared to other similarity based ortholog detection methods. Beyond core common results, significant differences are observed between various methods, which can be accounted for to a large extent on conceptual grounds, relative to the different merging schemes involved. Such comparisons highlight a major advantage of the SPO approach concerning the proper clustering of associated paralogs, which appear to be often dispatched spuriously into distinct orthologous classes. Finally the perspectives for future applications and elaborations of SPO-based compositional analyses are discussed.

3D Reconstruction Based on Hexagonal Pixel’s Dense Stereo Matching

A new feature points extraction method is presented, which consider pixel as hexagonal. The method quasi increases the density of image pixel, expands the dynamic range of feature point extraction, increases the number of the features and resolves the problem of deformation of reconstruction which was leaded by lack of feature points. Firstly, the method was successful applied to sift operator of features extraction in this paper and then use dense stereo matching method to find the matching point of the image sequences. Secondly, through the RANSAC method to eliminate mistake matches, and by the camera matrix, calculate the corresponding points’ three-dimensional coordinates of space. Finally, the 3D model can be established through the partition merging triangulation method and texture mapping. Experimental results show that this method can get more accurate matches pairs and achieve a satisfactory effect of 3D reconstruction.

Estimating the Number of Clusters via System Evolution for Cluster Analysis of Gene Expression Data

The estimation of the number of clusters (NC) is one of crucial problems in the cluster analysis of gene expression data. Most approaches available give their answers without the intuitive information about separable degrees between clusters. However, this information is useful for understanding cluster structures. To provide this information, we propose system evolution (SE) method to estimate NC based on partitioning around medoids (PAM) clustering algorithm. SE analyzes cluster structures of a dataset from the viewpoint of a pseudothermodynamics system. The system will go to its stable equilibrium state, at which the optimal NC is found, via its partitioning process and merging process. The experimental results on simulated and real gene expression data demonstrate that the SE works well on the data with well-separated clusters and the one with slightly overlapping clusters.

Improved Binary Space Partition merging

This paper presents a new method for evaluating boolean set operations between Binary Space Partition (BSP) trees. Our algorithm has many desirable features, including both numerical robustness and O ( n ) output sensitive time complexity, while simultaneously admitting a straightforward implementation. To achieve these properties, we present two key algorithmic improvements. The first is a method for eliminating null regions within a BSP tree using linear programming. This replaces previous techniques based on polygon cutting and tree splitting. The second is an improved method for compressing BSP trees based on a similar approach within binary decision diagrams. The performance of the new method is analyzed both theoretically and experimentally. Given the importance of boolean set operations, our algorithms can be directly applied to many problems in graphics, CAD and computational geometry.

A scalable framework for cluster ensembles

An ensemble of clustering solutions or partitions may be generated for a number of reasons. If the data set is very large, clustering may be done on tractable size disjoint subsets. The data may be distributed at different sites for which a distributed clustering solution with a final merging of partitions is a natural fit. In this paper, two new approaches to combining partitions, represented by sets of cluster centers, are introduced. The advantage of these approaches is that they provide a final partition of data that is comparable to the best existing approaches, yet scale to extremely large data sets. They can be 100,000 times faster while using much less memory. The new algorithms are compared against the best existing cluster ensemble merging approaches, clustering all the data at once and a clustering algorithm designed for very large data sets. The comparison is done for fuzzy and hard-k-means based clustering algorithms. It is shown that the centroid-based ensemble merging algorithms presented here generate partitions of quality comparable to the best label vector approach or clustering all the data at once, while providing very large speedups.

Group communication in partitionable systems: specification and algorithms

Gives a formal specification and an implementation for a partitionable group communication service in asynchronous distributed systems. Our specification is motivated by the requirements for building applications that can continue operating without blocking in multiple concurrent partitions and can reconfigure themselves dynamically when partitions merge. The specified service guarantees liveness and excludes trivial solutions, it constitutes a useful basis for building realistic partition-aware applications, and it is implementable in practical asynchronous distributed systems where certain stability conditions hold.

Handling of Mutual Conflicts in Distributed Databases using Timestamps

In distributed database systems, it is desirable to allow read and write accesses to occur independently on replicated copies of database files in case of network partitions to increase availability. However, the system should detect mutual conflicts among the copies of the database files when sites from different partitions merge to form one partition. We present a timestampbased algorithm for the detection of both write-write and read-write conflicts for a single file in distributed databases when sites from different partitions merge. Our algorithm allows read and write operations to occur in different network partitions simultaneously. When the sites from two different partitions merge, the algorithm detects and resolves both read-write and write-write conflicts with the help of stored timestamps using some additional information. Once the conflicts have been detected, we propose some reconciliation steps for the resolution of conflicts to bring the file into some consistent state. Our algorithm does not take into account the semantics of the transactions while detecting and resolving conflicts. Our algorithm will be useful in real-time systems where timeliness of operations is more important than response time (delayed commit).

Enriched view synchrony: a programming paradigm for partitionable asynchronous distributed systems

Distributed systems constructed using off-the-shelf communication infrastructures are becoming common vehicles for doing business in many important application domains. Large geographic extent due to increased globalization, increased probability of failures, and highly dynamic loads all contribute toward a partitionable and asynchronous characterization for these systems. In this paper, we consider the problem of developing reliable applications to be deployed in partitionable asynchronous distributed systems. What makes this task difficult is guaranteeing the consistency of shared state despite asynchrony, failures, and recoveries, including the formation and merging of partitions. While view synchrony within process groups is a powerful paradigm that can significantly simplify reasoning about asynchrony and failures, it is insufficient for coping with recoveries and merging of partitions after repairs. We first give an abstract characterization for shared state management in partitionable asynchronous distributed systems and then show how views can be enriched to convey structural and historical information relevant to the group's activity. The resulting, paradigm, called enriched view synchrony, can be implemented efficiently and leads to a simple programming methodology for solving shared state management in the presence of partitions.

A scheme for increasing availability in partitioned replicated databases

We present a dynamic voting scheme that enhances the availability of data in a replicated database system in the presence of network partitions. When a network partition occurs, the distributed system splits into several groups such that nodes in one group cannot communicate with nodes in another group. In such situations, the proposed scheme maintains consistency of replicated data by imposing the following constraint: a node can update only if its current enclosing group is a majority of its previous enclosing group. Thus, the number of nodes that constitutes a majority is reduced dynamically upon successive partitions. A distinguishing aspect of the proposed scheme is that each node maintains its view of the network and keeps track of partitions that occur between successive update transactions. This technique minimizes the situations in which no group can perform an update. Another interesting feature of the proposed scheme is that it does not require failures and repairs of links and nodes to be detected instantaneously by all nodes. Different nodes may have different views of the network at any time. This introduces complications when partitions merge, and we present a merge protocol that takes this into account. We also outline a correctness proof for our proposed scheme.

On merging partitioned databases

Partitioning of a distributed data base requires either that update activity be restricted or that a strategy for conflict resolution and partition merging be used once communication is restored. The graph-theoretic approach used by Davidson follows the latter approach and can be used to show that finding an optimum solution to the general problem is NP-complete. We give several methods of reducing the size of the graphs involved. Two open subproblems are shown to be NP-complete, while an extension of a known polynomial-time subproblem is given. Simulation results are used to study both the amount of compression achieved by the graph reduction techniques and their effects on heuristics for the problem. In addition, some modifications are made to existing heuristics to improve their performance. A simple probabilistic model is developed and compared to the simulation results.