Clustering in preferential attachment random graphs with edge-step

We prove concentration inequality results for geometric graph properties of an instance of the Cooper–Frieze [5] preferential attachment model with edge-steps. More precisely, we investigate a random graph model that at each time $t\in \mathbb{N}$ , with probability p adds a new vertex to the graph (a vertex-step occurs) or with probability $1-p$ an edge connecting two existent vertices is added (an edge-step occurs). We prove concentration results for the global clustering coefficient as well as the clique number. More formally, we prove that the global clustering, with high probability, decays as $t^{-\gamma(p)}$ for a positive function $\gamma$ of p, whereas the clique number of these graphs is, up to subpolynomially small factors, of order $t^{(1-p)/(2-p)}$ .

GeoGraph

In many applications of graph processing, the input data is often generated from an underlying geometric point data set. However, existing high-performance graph processing frameworks assume that the input data is given as a graph. Therefore, to use these frameworks, the user must write or use external programs based on computational geometry algorithms to convert their point data set to a graph, which requires more programming effort and can also lead to performance degradation. In this paper, we present our ongoing work on the Geo- Graph framework for shared-memory multicore machines, which seamlessly supports routines for parallel geometric graph construction and parallel graph processing within the same environment. GeoGraph supports graph construction based on k-nearest neighbors, Delaunay triangulation, and b-skeleton graphs. It can then pass these generated graphs to over 25 graph algorithms. GeoGraph contains highperformance parallel primitives and algorithms implemented in C++, and includes a Python interface. We present four examples of using GeoGraph, and some experimental results showing good parallel speedups and improvements over the Higra library. We conclude with a vision of future directions for research in bridging graph and geometric data processing.

Covariance Kalman Geometric Graph Based Feature Extraction And Bernoulli Kernel Classifier For Plant Leaf Disease Prediction

As far as the agricultural domain is concerned, one of the most hot research areas of analysis is accurate prediction of leaf disease from the leaf images of a plant. The prediction of agricultural plant diseases bymeans of the image processing techniques will hence reduce the dependence on the farmers to safeguard their agricultural land and also their products. However, with the presence of noise, the leaf disease prediction is said to be hindered. To address this issue, in this paper, Covariance Kalman Geometric Graph-basedBernoulliClassifier (CKGG-BC) for Plant leaf disease prediction is proposed. The CKGG-BC method is split into three parts. To start with the plant leaf image provided as input, the Covariance Kalman Filtered Preprocessing modelintroduced for the image enhancement. Second, Geometric Graph-based Segmented Co-occurrence Feature Extraction model is applied to the preprocessed image to accurately segment the infected leaf areas and followed by which extracting the accurate infected leaf areas. Finally, Bernoulli Online Multiple Kernel Learning Classifier is applied for accurate plant leaf disease prediction with minimum classification error. The proposed method provides a significant refinement with respect to state-of-the-art methods. Even under complex background conditions, i.e., in the presence of noise, the averageaccuracy of the proposed method is said to be improved and hence paves mechanism for prediction of plant leaf disease in a significant manner. Experimentalresults exhibit the effectiveness of the proposed method in terms of computational overhead, accuracy, true positive rate and classification error respectively.

Investigation of Cylindrical Piezoelectric and Specific Multi-Channel Circular MEMS-Transducer Array Resonator of Ultrasonic Ablation

Background: A cylindrical piezoelectric element and a specific multi-channel circular microelectromechanical systems (MEMS)-transducer array of ultrasonic system were used for ultrasonic energy generation and ablation. A relatively long time is required for the heat to be conducted to the target position. Ultrasound thermal therapy has great potential for treating deep hyperplastic tissues and tumors, such as breast cancer and liver tumors. Methods: Ultrasound ablation technology produces thermal energy by heating the surface of a target, and the heat gradually penetrates to the target’s interior. Beamforming was performed to observe energy distribution. A resonance method was used to generate ablation energy for verification. Energy was generated according to the coordinates of geometric graph positions to reach the ablation temperature. Results: The mean resonance frequency of Channels 1–8 was 2.5 MHz, and the cylindrical piezoelectric ultrasonic element of Channel A was 4.2546 Ω at 5.7946 MHz. High-intensity ultrasound has gradually been applied in clinical treatment. Widely adopted, ultrasonic hyperthermia involves the use of high-intensity ultrasound to heat tissues at 42–45 °C for 30–60 minutes. Conclusion: In the ultrasonic energy method, when the target position reaches a temperature that significantly reduces the cell viability (46.9 °C), protein surface modification occurs on the surface of the target.