Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex spatial maps consist of tree-like neurons interconnected by synapses. Motif analysis is a widely used method for identifying recurring subgraph patterns in connectomes. These motifs, thus, potentially represent fundamental units of information processing. However, existing computational tools often oversimplify neurons as mere nodes in a graph, disregarding their intricate morphologies. In this paper, we introduce MoMo, a novel interactive visualization framework for analyzing neuron morphology-aware motifs in large connectome graphs. First, we propose an advanced graph data structure that integrates both neuronal morphology and synaptic connectivity. This enables highly efficient, parallel subgraph isomorphism searches, allowing for interactive morphological motif queries. Second, we develop a sketch-based interface that facilitates the intuitive exploration of morphology-based motifs within our new data structure. Users can conduct interactive motif searches on state-of-the-art connectomes and visualize results as interactive 3D renderings. We present a detailed goal and task analysis for motif exploration in connectomes, incorporating neuron morphology. Finally, we evaluate MoMo through case studies with four domain experts, who asses the tool's usefulness and effectiveness in motif exploration, and relevance to real-world neuroscience research. The source code for MoMo is available here.

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that the Contour algorithm with two or higher order operators can identify all connected components of an undirected graph within$\mathcal{O} (\log d_{max})$iterations, with each iteration involving$\mathcal{O}(m)$work, where$d_{max}$represents the largest diameter among all components in the given graph, and$m$is the total number of edges in the graph. Importantly, each iteration is highly parallelizable, making use of the efficient minimum-mapping operator applied to all edges. To further enhance its practical performance, we optimize the Contour algorithm through asynchronous updates, early convergence checking, eliminating atomic operations, and choosing more efficient mapping operators. Our implementation of the Contour algorithm has been integrated into the open-source framework Arachne. Arachne extends Arkouda for large-scale interactive graph analytics, providing a Python API powered by the high-productivity parallel language Chapel. Experimental results on both real-world and synthetic graphs demonstrate the superior performance of our proposed Contour algorithm compared to state-of-the-art large-scale parallel algorithm FastSV and the fastest shared memory algorithm ConnectIt. On average, Contour achieves a speedup of 7.3x and 1.4x compared to FastSV and ConnectIt, respectively. All code for the Contour algorithm and the Arachne framework is publicly available on GitHub11https://githuh.comJBears-R-Us/arkouda-njit, ensuring transparency and reproducibility of our work.

In this paper a structure for providing and offering service in NGN network is proposed. This structure, which is named application server, is a specified unit and it is not a tool. Actually, an application server is a set of different parameters providing connection between various parts, which are dealing to network problems, such as third parties, developers and etc. This structure is designed modular and it has many advantages in NGN network. Service providing is the most important task which this structure has. This structure decrease the expense used for adding new services. Also in this structure adding new services is independent of deeper layer protocols. The implementation method for this unit will be proposed in this paper. In this method, adding new service to application server is easy and it doesn't need to any particular technical knowledge.