scholarly journals On the geodetic iteration number of a graph in which geodesic and monophonic convexities are equivalent

2020 ◽  
Vol 283 ◽  
pp. 142-152
Author(s):  
Marina Moscarini
Keyword(s):  
Iteration Number

Iteration number-based hierarchical gradient aggregation for distributed deep learning

2021 ◽  
Author(s):  
Danyang Xiao ◽  
Xinxin Li ◽  
Jieying Zhou ◽  
Yunfei Du ◽  
Weigang Wu
Keyword(s):  
Deep Learning ◽  
Iteration Number

scholarly journals Random Walk Based Key Nodes Discovery in Opportunistic Networks

2016 ◽  
Vol 12 (03) ◽  
pp. 28
Author(s):  
Qin Qin ◽  
Yong-qiang He
Keyword(s):  
Random Walk ◽  
Binary Tree ◽  
Opportunistic Networks ◽  
Communication Cost ◽  
Simulation Experiments ◽  
Efficiency And Effectiveness ◽  
Iteration Number ◽  
Key Nodes

In opportunistic networks, temporary nodes choose neighbor nodes to forward messages while communicating. However, traditional forward mechanisms don’t take the importance of nodes into consideration while forwarding. In this paper, we assume that each node has a status indicating its importance, and temporary nodes choose the most important neighbors to forward messages. While discovering important neighbors, we propose a binary tree random walk based algorithm. We analyze the iteration number and communication cost of the proposed algorithm, and they are much less than related works. The simulation experiments validate the efficiency and effectiveness of the proposed algorithm.

Dual Residual for Centralized Augmented Lagrangian Coordination Based on Optimality Conditions

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Meng Xu ◽  
Georges Fadel ◽  
Margaret M. Wiecek
Keyword(s):  
Optimality Conditions ◽  
Parallel Computation ◽  
Augmented Lagrangian ◽  
Numerical Tests ◽  
Wide Range ◽  
Initial Setting ◽  
Iteration Number ◽  
Update Strategy ◽  
Solution Accuracy ◽  
Augmented Lagrangian Coordination

Centralized augmented Lagrangian coordination (ALC) has drawn much attention due to its parallel computation capability, efficiency, and flexibility. The initial setting and update strategy of the penalty weights in this method are critical to its performance. The traditional weight update strategy always increases the weights and research shows that inappropriate initial weights may cause optimization failure. Making use of the Karush–Kuhn–Tucker (KKT) optimality conditions for the all-in-one (AIO) and decomposed problems, the terms “primal residual” and “dual residual” are introduced into the centralized ALC, and a new update strategy considering both residuals and thus guaranteeing the unmet optimality condition in the traditional update is introduced. Numerical tests show a decrease in the iteration number and significant improvements in solution accuracy with both calculated and fine-tuned initial weights using the new update. Additionally, the proposed approach is capable to start from a wide range of possible weights and achieve optimality, and therefore brings robustness to the centralized ALC.

scholarly journals Selection Criteria for Determination of Optimal Reconstruction Method for Cu-64 Trastuzumab Dosimetry on Siemens Inveon PET Scanner

2019 ◽  
Vol 8 (4) ◽  
pp. 512 ◽  
Author(s):  
Seonhwa Lee ◽  
Hyeongi Kim ◽  
Ye-rin Kang ◽  
Hyungwoo Kim ◽  
Jung Young Kim ◽  
...  
Keyword(s):  
Effective Dose ◽  
Selection Criteria ◽  
Absorbed Dose ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Recovery Coefficient ◽  
Optimal Reconstruction ◽  
Iteration Number

The goal of this study was to suggest criteria for the determination of the optimal image reconstruction algorithm for image-based dosimetry of Cu-64 trastuzumab PET in a mouse model. Image qualities, such as recovery coefficient (RC), spill-over ratio (SOR), and non-uniformity (NU), were measured according to National Electrical Manufacturers Association (NEMA) NU4-2008. Mice bearing a subcutaneous tumor ( 200 mm 3 , HER2 NCI N87) were injected with monoclonal antibodies (trastuzumab) with Cu-64. Preclinical mouse PET images were acquired at 4 time points after injection (2, 15, 40 and 64 h). Phantom and Cu-64 trastuzumab PET images were reconstructed using various reconstruction algorithms (filtered back projection (FBP), 3D reprojection algorithm (FBP-3DRP), 2D ordered subset expectation maximization (OSEM 2D), and OSEM 3D maximum a posteriori (OSEM3D-MAP)) and filters. The absorbed dose for the tumor and the effective dose for organs for Cu-64 trastuzumab PET were calculated using the OLINDA/EXM program with various reconstruction algorithms. Absorbed dose for the tumor ranged from 923 mGy/MBq to 1830 mGy/MBq with application of reconstruction algorithms and filters. When OSEM2D was used, the effective osteogenic dose increased from 0.0031 to 0.0245 with an increase in the iteration number (1 to 10). In the region of kidney, the effective dose increased from 0.1870 to 1.4100 when OSEM2D was used with iteration number 1 to 10. To determine the optimal reconstruction algorithms and filters, a correlation between RC and NU was plotted and selection criteria (0.9 < RC < 1.0 and < 10% of NU) were suggested. According to the selection criteria, OSEM2D (iteration 1) was chosen for the optimal reconstruction algorithm. OSEM2D (iteration 10) provided 154.7% overestimated effective dose and FBP with a Butterworth filter provided 20.9% underestimated effective dose. We suggested OSEM2D (iteration 1) for the calculation of the effective dose of Cu-64 trastuzumab on an Inveon PET scanner.

Segmentation of Fabric Defect Images Based on PCNN Model and Symmetric Tsallis Cross Entropy

2013 ◽  
Vol 760-762 ◽  
pp. 1472-1476 ◽  
Author(s):  
Hong Wan ◽  
Yi Quan Wu ◽  
Zhao Qing Cao ◽  
Zhi Long Ye
Keyword(s):  
Defect Detection ◽  
Cross Entropy ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Segmentation Methods ◽  
Otsu Method ◽  
Defect Area ◽  
Fabric Defect ◽  
Iteration Number ◽  
Strength Difference

Segmentation of defect images is an important step in the automatic fabric defect detection. In order to extract fabric defects effectively, a segmentation method of fabric defect images based on pulse coupled neural network (PCNN) model and symmetric Tsallis cross entropy is proposed. The image is segmented by PCNN according to the gray strength difference between fabric defect area and non-defect area. To guarantee that the grayscale inside the object and background is uniform after segmentation, symmetric Tsallis cross entropy is used as the image segmentation criterion to select the optimal threshold and iteration number. A large number of experimental results show that, compared with the related segmentation methods such as Otsu method, PCNN method, the method based on PCNN and cross entropy, the segmentation effect of the proposed method is the best. The texture of non-defect area is removed more completely, and the defect area is segmented more accurately.

scholarly journals Clustering for Probability Density Functions by New k-Medoids Method

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
D. Ho-Kieu ◽  
T. Vo-Van ◽  
T. Nguyen-Trang
Keyword(s):  
Probability Density ◽  
Clustering Algorithm ◽  
Real Life ◽  
Probability Density Functions ◽  
Rand Index ◽  
Computational Time ◽  
Adjusted Rand Index ◽  
Density Functions ◽  
Potential Applications ◽  
Iteration Number

This paper proposes a novel and efficient clustering algorithm for probability density functions based on k-medoids. Further, a scheme used for selecting the powerful initial medoids is suggested, which speeds up the computational time significantly. Also, a general proof for convergence of the proposed algorithm is presented. The effectiveness and feasibility of the proposed algorithm are verified and compared with various existing algorithms through both artificial and real datasets in terms of adjusted Rand index, computational time, and iteration number. The numerical results reveal an outstanding performance of the proposed algorithm as well as its potential applications in real life.

scholarly journals A Fixed Point Result with a Contractive Iterate at a Point

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 606 ◽  
Author(s):  
Badr Alqahtani ◽  
Andreea Fulga ◽  
Erdal Karapınar
Keyword(s):  
Fixed Point ◽  
Contraction Mapping ◽  
Contraction Mapping Principle ◽  
Continuity Condition ◽  
Ulam Stability ◽  
Banach Contraction ◽  
Iteration Number ◽  
The Given ◽  
Banach Contraction Mapping Principle ◽  
Banach Contraction Mapping

In this manuscript, we define generalized Kincses-Totik type contractions within the context of metric space and consider the existence of a fixed point for such operators. Kincses-Totik type contractions extends the renowned Banach contraction mapping principle in different aspects. First, the continuity condition for the considered mapping is not required. Second, the contraction inequality contains all possible geometrical distances. Third, the contraction inequality is formulated for some iteration of the considered operator, instead of the dealing with the given operator. Fourth and last, the iteration number may vary for each point in the domain of the operator for which we look for a fixed point. Consequently, the proved results generalize the acknowledged results in the field, including the well-known theorems of Seghal, Kincses-Totik, and Banach-Caccioppoli. We present two illustrative examples to support our results. As an application, we consider an Ulam-stability of one of our results.

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