Neighborhood Size Research Articles

A New Single-Parameter Bees Algorithm.

Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA1, a Bees Algorithm with just one parameter. BA1 eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA1 simplifies the tuning process and increases efficiency. BA1 has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.

Local Mean Suppression Filter for Effective Background Identification in Fluorescence Images.

We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub.

Community detection using Jaya optimization algorithm based on deep learning methods and KNN graph-based clustering

Purpose In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph. Design/methodology/approach To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method. Findings The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks. Originality/value This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

Rapid motion planning of manipulator in three-dimensional space under multiple scenes

Most working scenes of industrial robots are static scenes and from a previous static scene to a current static scene is a multi-scene. Finding optimal paths with limited time is difficult for motion planning in a high-dimensional space or in multiple scenes. The low efficiency of motion planning of rapidly-exploring random tree star in high-dimensional spaces, low adaptability of global replanning to multiple scenes are addressed by proposing the local replanning based on goal dynamically-guiding rapidly-exploring random tree star (LR-GD-RRT*). The algorithm contributes to fast path tree exploration and multi-scene motion planning. For path tree exploration, sampling points heuristically generating and new nodes growth by goal dynamically-guiding are proposed to reduce the blind and ineffective searches. Moreover, dynamic adjustment of size of new node neighborhood according to density of the obstacles is proposed to search for more neighbor nodes to optimize the path and also to reduce ineffective computation to improve efficiency. For multi-scene, three steps of trimming, re-exploration, and reconnection for local replanning are proposed. For path tree exploration, simulations in 2D plane, 3D space, and the manipulator show that GD-RRT* improves convergence speed, shortens path length and search time, compared with RRT*. For multi-scene, simulations in 3D space and with the manipulator show that the local replanning of the current scene has both lower path cost and higher planning efficiency compared with the global replanning of the previous scene. Motion of the six-degree-of-freedom robot end in a real scene also verifies the effectiveness of the LR-GD-RRT*.

ANS-SCMC: A matrix completion method based on adaptive neighbourhood similarity and sparse constraints for predicting microbe-disease associations.

The use of matrix completion methods to predict the association between microbes and diseases can effectively improve treatment efficiency. However, the similarity measures used in the existing methods are often influenced by various factors such as neighbourhood size, choice of similarity metric, or multiple parameters for similarity fusion, making it challenging. Additionally, matrix completion is currently limited by the sparsity of the initial association matrix, which restricts its predictive performance. To address these problems, we propose a matrix completion method based on adaptive neighbourhood similarity and sparse constraints (ANS-SCMC) for predict microbe-disease potential associations. Adaptive neighbourhood similarity learning dynamically uses the decomposition results as effective information for the next learning iteration by simultaneously performing local manifold structure learning and decomposition. This approach effectively preserves fine local structure information and avoids the influence of weight parameters directly involved in similarity measurement. Additionally, the sparse constraint-based matrix completion approach can better handle the sparsity challenge in the association matrix. Finally, the algorithm we proposed has achieved significantly higher predictive performance in the validation compared to several commonly used prediction methods proposed to date. Furthermore, in the case study, the prediction algorithm achieved an accuracy of up to 80% for the top 10 microbes associated with type 1 diabetes and 100% for Crohn's disease respectively.

Neighborhood in Chinese lexicon: A megastudy analysis of lexical decision and naming of two-character Chinese words.

The present study examines the impact of neighborhood size (number of other two-character words sharing the same character at the same position) on Chinese lexical processing, along with its joint effects with variables such as character frequency, word frequency, and semantic transparency. Previous factorial experiments have yielded conflicting results that are difficult to reconcile with existing models (Li et al., 2015, 2017). To provide high-powered tests for these theoretically important effects on visual word recognition, we leveraged the megastudy approach and used linear mixed-effect analyses to investigate lexical decision and naming responses to a large pool of two-character Chinese words (N > 17,000) sourced from Tse et al.'s (2017, 2023) database. In all analyses we controlled for extraneous orthographic (e.g., stroke count), phonological (e.g., consistency), and semantic (e.g., transparency) variables. In addition to evaluating Li et al.'s (2015, 2017) models, we also investigated whether the parallel dual-route mechanism, which entails lexical access via whole-word or character decomposition-then-composition, could account for neighborhood size effect and its interactions in lexical decision and naming. Finally, we discuss the implications of our findings on the specificity of lexical effects with regard to character position and lexical processing task. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stagnation Detection in Highly Multimodal Fitness Landscapes

Stagnation detection has been proposed as a mechanism for randomized search heuristics to escape from local optima by automatically increasing the size of the neighborhood to find the so-called gap size, i. e., the distance to the next improvement. Its usefulness has mostly been considered in simple multimodal landscapes with few local optima that could be crossed one after another. In multimodal landscapes with a more complex location of optima of similar gap size, stagnation detection suffers from the fact that the neighborhood size is frequently reset to 1 without using gap sizes that were promising in the past. In this paper, we investigate a new mechanism called radius memory which can be added to stagnation detection to control the search radius more carefully by giving preference to values that were successful in the past. We implement this idea in an algorithm called SD-RLSm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\ ext {m}}$$\\end{document} and show compared to previous variants of stagnation detection that it yields speed-ups for linear functions under uniform constraints and the minimum spanning tree problem. Moreover, its running time does not significantly deteriorate on unimodal functions and a generalization of the Jump benchmark. Finally, we present experimental results carried out to study SD-RLSm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\ ext {m}}$$\\end{document} and compare it with other algorithms.

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

Optimizing bathymetric position index (BPI) calculation: An analysis of parameters and recommendations for the selection of their optimal values

The present research paper addresses a critical gap in existing literature concerning the absence of a standardized methodology for parameter selection in the computation of the Bathymetric Position Index (BPI) values. The BPI is a measure of where a georeferenced location, with a defined depth, is relative to the neighbouring seascape, and it plays a significant role in characterizing benthic terrain for modelling and classification. Arguably, the two most important parameters when calculating the BPI are the size and the shape of the neighbourhood of analysis. With regards to the radius parameter, which defines the size of the neighbourhood, the optimal radius value for calculating the BPI must be carefully chosen, considering both the size of the target morphology and the scale factor, which is equal to the radius in map units multiplied by the cell size. It is recommended that the optimal radius value should closely match the size of the target morphology. Tests were performed using an annular neighbourhood shape and they have revealed that the outer radius is the most influential factor in the BPI calculation. Further experimentations and comparisons between circular and annular shapes have indicated that the use of different shapes has no significant impact on the results. The study has found no substantial correlation between the BPI values and other examined terrain variables, such as depth, slope, and curvature. This lack of correlation may be attributed to the BPI values accounting for the specific neighbourhood size, while for the studied variables the default window size was used, which is a considerably smaller scale than the ones used in most BPI calculations. In conclusion, this research highlights the importance of parameter selection in BPI calculations and provides valuable insights into the optimal radius choice and the negligible impact of neighbourhood shape. The findings also shed light on the unique nature of BPI values and their relationship with other geospatial variables.

A convex level-set method with multiplicative-additive model for image segmentation

The existing active contour models (ACMs) based on bias field (BF) correction mostly rely on a single BF assumption and lack in-depth discussion on the convexity of the energy functional, often leading to the problem of local minima. To address this issue, this paper introduces a dual BF and proposes a convex level-set (LS) method based on multiplicative-additive (MA) model to achieve global minima. Firstly, a MA model is adopted as the fidelity term, and a kernel function is introduced to adjust the size of the intensity inhomogeneous neighborhood, enhancing the adaptability to intensity inhomogeneity. Then, the convex LS function is embedded in the variational framework to ensure convexity of each variable in the energy functional. This transformation turns the segmentation problem into a convex optimization problem. By introducing the total variation regularization term to smooth the LS function, the model's resistance to noise is effectively enhanced. Finally, by minimizing the proposed energy functional, image segmentation and BF correction are successfully achieved. Experimental results validate the global minima property of our model, while also demonstrating good flexibility in the initial contour. The proposed model achieves superior segmentation results compared to other classical ACMs on various types of images.

Evaluation of Probabilistic Forecasts of Binary Events with the Neighborhood Brier Divergence Skill Score

Abstract A procedure for evaluating the quality of probabilistic forecasts of binary events has been developed. This is based on a two-step procedure: pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood in order to obtain frequencies at the neighborhood length scale and then to calculate the Brier divergence for these neighborhood frequencies. This score allows the comparison of a probabilistic forecast and observations at the neighborhood length scale, and therefore, the rewarding of event forecasts shifted from the location of the observed event by a distance smaller than the neighborhood size. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. The neighborhood Brier divergence skill score (BDnSS) measures the performance of the probabilistic forecast against the sample climatology. BDnSS and its decomposition have been used for idealized and real cases in order to show the utility of neighborhoods when comparing at different scales the performances of ensemble forecasts between themselves or with deterministic forecasts or of deterministic forecasts between themselves. Significance Statement A pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood, is performed in order to obtain frequencies at the neighborhood scale. The Brier divergence is then calculated for these neighborhood frequencies to compare a probabilistic forecast and observations at the neighborhood scale. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. This uncertainty term is used to define the neighborhood Brier divergence skill score which is an alternative to the popular fractions skill score, with a more appropriate denominator.

Three-dimensional information measurement and reconstruction for micro-hole array based on the modified shape from focus

The geometrical characteristics of the micro-holes and the consistency of spatial distribution among the micro-holes in the micro-hole array fabricated by laser beam drilling (LBD) directly influence the cooling performance, reliability, and longevity of the workpiece. It is of great significance to establish the relationship between the LBD processing parameters and the characteristics of the micro-hole array through the three-dimensional (3D) information measurement and reconstruction of the micro-hole array, as well as to guide and improve the LBD processing technology. However, due to the small size and high aspect ratio of the micro-holes in the micro-hole array, the 3D information measurement and reconstruction of the micro-hole array face challenges. The conventional micro-hole measurement methods suffer from low efficiency, high measurement costs, and potentially destructive measurement processes. Furthermore, the conventional micro-hole measurement methods face challenges in simultaneously obtaining the geometrical characteristics of the micro-holes and the spatial distribution among the micro-holes in the micro-hole array. Therefore, a non-contact and non-destructive 3D information measurement and reconstruction method for the micro-hole array based on the modified shape from focus is proposed. A novel focus measure operator based on grayscale difference and adaptive neighborhood size is proposed to accurately evaluate the focus value and obtain the initial focus volume matrix. A novel image processing operator based on the gradient information of the image sequence is proposed to enhance the initial focus volume matrix. The experimental results indicate that the proposed focus measure operator, combined with the novel image processing operator, is more suitable and reliable for detecting the micro-holes due to its better unimodality, higher contrast, and narrower peak width than the conventional focus measure operator. The optimal focus position of each image point is obtained using the modified Gaussian curve fitting method. On this basis, the 3D information measurement and reconstruction of the micro-hole array are realized. By comparing the results with the conventional micro-hole measurement method, the feasibility and accuracy of the proposed method have been confirmed to be an effective measurement method for the micro-hole array.

Optimizing variable selection and neighbourhood size in the K-nearest neighbour algorithm

The K-nearest neighbour (KNN) algorithm is one of the well-known classifiers applied in various research areas. The input requirement includes a set of variables, the choice of the neighbourhood size (K) and the distance metric which are typically selected on experimenting with the data. The first two are usually decided sequentially in previous studies. This paper proposes a mixed integer linear program for simultaneous variable selection and determination of neighbourhood size which has not been expored in past research. When ties occur in the variable selection and/or the choice of K, an optimization approach helps search for the best combination of decisions for a given objective. Two distance metrics, Euclidean and Hassanat, are incorporated into the model for comparison. The proposed model adopts accuracy and recall as separate objective functions to determine the best combination of the two decisions in binary classification problems. Computational experiments are designed with ten publicly available datasets. Results from the proposed models are compared with the ensemble approach KNN using Hassanat distance. Three larger problems are also tested with the optimal K generated by the model. An effective set of variables and neighbourhood size decided by optimization can facilitate solving classification problems. Model limitations are also discussed.

Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low-quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate performance improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.

Contagion, fast and low: Modeling social influence in socio-ecological systems

The concept of contagion defines how attitudes, emotions, or behaviors spread within a specific group. Traditional models of social contagion account for network characteristics that define the spread of simple or complex ideas based on the degree of exposure of susceptible agents to their affected “contact neighborhood”. However, these models understate two principal characteristics of real-world dynamics: (i) the competition among ideas of varying degrees of complexity (as observed in voting models) and (ii) the feedback-driven evolution of contagion complexity. Certain network features (e.g., network size, topology, neighborhood size) might favor the diffusion of competing simple or complex ideas, depending on their distinct characteristics. Furthermore, the dominant behavior disseminated may alter the agent’s operational environment, thereby modifying the cost of influencing additional agents. For instance, in co-evolutionary games such as the tragedy of the commons, encouraging agents to collaborate in the context of abundant and untapped resources requires a costly and complex contagion; conversely, in situations of resource depletion, prompting agents to reduce excessive exploitation is less costly—in line with the pathway of simple contagion. In this report, we develop an agent-based model that incorporates all of these aspects.

Cavity approach for the approximation of spectral density of graphs with heterogeneous structures.

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|h_{max}^{3}t) and O(|E|h_{max}^{2}t), respectively, where |E| is the number of edges, h_{max} is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

Granular structure evaluation and selection based on justifiable granularity principle

Granular structures are fundamental components of human granulation intelligence and different views or scales of granulation result in different granular structures. Therefore, the evaluation and selection of optimal granular structures can lay the foundation for problem-solving. Information granules are basic components of granular structures. The principle of justifiable granularity presents a coherent method for designing information granules. Therefore, this study performs granular structure evaluation and selection based on the justifiable granularity principle. First, it proposes a new evaluation criterion for granular structures that considers the coverage and specificity of all information granules in a granular structure. Thereafter, coverage and specificity are evaluated based on a core sample of the information granule. Subsequently, a detailed formulation is provided to compute the significance of the granular structure according to the proposed evaluation criterion. Finally, a general framework for granular structure selection is presented, and a detailed algorithm for selecting the optimal granular structure with the aid of the justifiable granularity principle is provided. The proposed method is employed to determine the optimal attribute and select the optimal neighborhood size for neighborhood classifiers. Experiments and analyses have demonstrated the necessity, reasonableness, and effectiveness of the proposed method.

Set size and the orthographic/phonological neighbourhood size effect in serial recognition: The importance of randomization.

The neighbourhood size effect refers to the finding of better memory for words with more orthographic/phonological neighbours than otherwise comparable words with fewer neighbours. Although many studies have replicated this result with serial recall, only one has used serial recognition. Greeno et al. (2022) found no neighbourhood size effect when a large stimulus pool was used and a reverse effect-better performance for small neighbourhood words-when a small stimulus pool was used. We reexamined these results but made two methodological changes. First, for the large pool, we randomly generated lists for each subject rather than creating one set of lists that all subjects experienced. Second, for the small pool, we randomly generated a small pool for each subject rather than using one small pool for all subjects. In both cases, we observed a neighbourhood size effect consistent with results from the serial recall literature. Implications for methodology and theoretical accounts of both the neighbourhood size effect and serial recognition are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Factors Relating to Kanji Reading Accuracy in Kun‐readings: An Item‐Level Analysis

AbstractThis study sought to obtain comprehensive data on the difficulty level of kun‐reading (native Japanese reading) of Japanese kanji (Chinese characters adapted for Japanese) learned in junior high and high school. Drawing from an item‐based analysis, it explored the word variables related to the difficulty level of kun‐reading. A reading test comprising 645 kun‐readings of common kanji learned in junior high and high school was administered to 123 university students. Tobit regressions were conducted for the total, okurigana (phonetic kana letters suffix written after kanji), and single kanji items, using the word variables as independent variables and the reading accuracy as the dependent variable. Significant positive contributions were observed for imageability and word frequency for all item categories, kun‐reading ratio for the single kanji items, and okurigana ratio for the total items. A significant negative contribution was observed for okurigana neighborhood size for the okurigana items. The results highlighted the need for education focusing on the meanings of kanji characters, and for a language policy for the use of the okurigana.

Multi-scale feature learning via residual dynamic graph convolutional network for hyperspectral image classification

ABSTRACT Thanks to the relevant spectral-spatial information, hyperspectral images (HSIs) have been widely exploited in Earth observation. Recently, graph convolutional networks (GCNs) have attracted increasing attention in HSI classification due to their advantages in processing non-Euclidean structure data. Unlike convolutional neural networks (CNNs), which perform convolution operations on regular square regions, GCNs can directly work on graph structure data to extract the relationships among adjacent land covers. However, extracting meaningful and deep discriminative spectral-spatial features from HSIs is still a challenging task. In this article, a novel multi-scale feature learning via residual dynamic graph convolutional network is designed for HSI classification, which can extract large-scale contextual spatial structures at superpixel-level graph and local spectral-spatial information at pixel-level, significantly improving the performance of HSI classification. Unlike from the existing GCN-based methods that operate on a graph with a fixed neighbourhood size, multiple graphs with diverse neighbourhood scales are built to comprehensively leverage spectral-spatial information and relationship at multiple scales, and these graphs are dynamically updated to generate more discriminative features (via dynamic GCN) during the convolution process. Moreover, to fully use the multi-scale features extracted from HSIs, a multi-scale feature fusion module is developed to emphasize important features and suppress irrelevant ones. Extensive experiments carried on three benchmark data sets demonstrate the superiority of the proposed approach over other state-of-the-art methods.