Different Numbers Of Neighbors Research Articles

Inverse radius weighting and its python package “IRWPy”: A new topography-informed interpolation to enhance geological interpretations

The extent and volume of geochemical sampling are inherently constrained by numerous factors, including budgetary limitations, analytical costs, and access restrictions. These constraints result in sampling networks that vary in density and regularity, dividing regions into sampled and unsampled areas. The creation of continuous geochemical maps is essential for geochemical prospectivity, which aims to identify geochemical anomalies, distinguish between background levels and anomalies, and define mineralization patterns. Therefore, it is necessary to interpolate data from sampled areas to estimate values in unsampled regions. Although several interpolation models exist, including Inverse Distance Weighting and various kriging methods, Inverse Distance Weighting is often used in two-dimensional ground modeling because, unlike kriging methods, Inverse Distance Weighting has no smoothing effect on edges. Inverse Distance Weighting’s reliance solely on horizontal Euclidean distances between samples overlooks critical factors such as topography and the ensuing effects on dilution, transportation, and element mobility, rendering it less effective over varied elevations. This study introduces Inverse Radius Weighting, a new interpolation technique that incorporates both Euclidean and elevation fluctuations, therefore Pythagorean distance, to help with better geological interpretations. We assessed the efficacy of Inverse Radius Weighting compared to Inverse Distance Weighting across three elements with varying mobility (Arsenic − highly mobile, Copper − moderately mobile, and Barium − nearly immobile), using different numbers of neighbors and by comparing three different evaluation measures, namely R2, Mean Absolute Error and Mean Absolute Percentage Error. By evaluating the spatial uncertainty of the generated maps and selecting the configurations with the least uncertainty as the final maps, our analysis reveals an improvement in correlation between interpolated and actual values with Inverse Haversine Radius Weighting. With this advancement, Inverse Haversine Radius Weighting overcomes the limitations of traditional Inverse Distance Weighting by accounting for elevation and its associated effects, thereby paving the way for more accurate and geochemically meaningful interpolation in geochemical prospectivity mapping.

Classification of EMG signals with CNN features and voting ensemble classifier

Electromyography (EMG) signals are primarily used to control prosthetic hands. Classifying hand gestures efficiently with EMG signals presents numerous challenges. In addition to overcoming these challenges, a successful combination of feature extraction and classification approaches will improve classification accuracy. In the current work, convolutional neural network (CNN) features are used to reduce the redundancy problems associated with time and frequency domain features to improve classification accuracy. The features from the EMG signal are extracted using a CNN and are fed to the ‘k’ nearest neighbor (KNN) classifier with a different number of neighbors ( 1 N N , 3 N N , 5 N N , and 7 N N ) . It results in an ensemble of classifiers that are combined using a hard voting-based classifier. Based on the benchmark Ninapro DB4 database and CapgMyo database, the proposed framework obtained 91.3 % classification accuracy on CapgMyo and 89.5 % on Ninapro DB4.

A vector projection similarity-based method for miRNA-disease association prediction

[S U M M A R Y] Many miRNA-disease association prediction models incorporate Gaussian interaction profile kernel similarity (GIPS). However, the GIPS fails to consider the specificity of the miRNA-disease association matrix, where matrix elements with a value of 0 represent miRNA and disease relationships that have not been discovered yet. To address this issue and better account for the impact of known and unknown miRNA-disease associations on similarity, we propose a method called vector projection similarity-based method for miRNA-disease association prediction (VPSMDA). In VPSMDA, we introduce three projection rules and combined with logistic functions for the miRNA-disease association matrix and propose a vector projection similarity measure for miRNAs and diseases. By integrating the vector projection similarity matrix with the original one, we obtain the improved miRNA and disease similarity matrix. Additionally, we construct a weight matrix using different numbers of neighbors to reduce the noise in the similarity matrix. In performance evaluation, both LOOCV and 5-fold CV experiments demonstrate that VPSMDA outperforms seven other state-of-the-art methods in AUC. Furthermore, in a case study, VPSMDA successfully predicted 10, 9, and 10 out of the top 10 associations for three important human diseases, respectively, and these predictions were confirmed by recent biomedical resources.

Asymmetric game promotes the emergence of cooperation in dynamic networks

Individuals in networked social groups often exhibit asymmetry when interacting with each other due to enjoying different amounts of resources and having different numbers of neighbors. In this paper, we study the impact of asymmetric games on the evolution of cooperation in dynamic networks. By constructing linking dynamics, the evolution of network structure is analyzed, and further the coevolution of strategy and network structure is explored. Based on three different contribution modes in the public goods game, it is revealed that compared with symmetric game, asymmetric game may be more conducive to promoting the emergence and maintenance of cooperation, while the emergence of cooperation benefits from the dynamic adjustment of network structure. We investigate the influence of the adjustment rules of dynamic networks on the evolution of cooperation, and show the difference of cooperation level under different social connection preferences. By analyzing the time scale of strategy updating compared with network link adjustment, it is pointed out that faster network evolution is beneficial to cooperation. In addition, we explore the influence of some statistical characteristics of networks on the system evolution.

Diabetes mellitus prediction using machine learning within the scope of a generic framework

<span>Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models.</span>

Studying the effectiveness of deep active learning in software defect prediction

Accurate prediction of defective software modules is of great importance for prioritizing quality assurance efforts, reasonably allocating testing resources, reducing costs and improving software quality. Several studies have used machine learning to predict software defects. However, complex structures and imbalanced class distributions in software defect data make learning an effective defect prediction model challenging. In this article, two deep learning-based defect prediction models using static code metrics are proposed. In order to enhance the learning process and improve the performance of the proposed models, pool-based active learning is employed. In this regard, the possibility of using active learning to mitigate the need for a large amount of labeled data in the process of building deep learning models is investigated. To deal with imbalanced distribution of software modules between defective and non-defective classes, Near-Miss under-sampling and KNN, with different number of neighbors, are used. The reason for choosing them is their good performance in binary classification problems. Experiments are performed on two well-known, publicly available datasets, GitHub Bug Dataset and public Unified Bug Dataset for java projects. The evaluation results reveal the effectiveness of our proposed models in comparison to the traditional machine learning algorithms. In the conducted investigations on the Unified Bug Dataset, at the file level, the value of F-measure and AUC criteria have improved by 13 and 11 percent, respectively and at the class level, the values have improved by 14 and 11 percent, respectively.

Bi-directional adaptive neighborhood rough sets based attribute subset selection

In fields such as pattern recognition and computational intelligence, attribute subset selection has gradually attracted the attention of researchers as a challenging issue, particularly in big data. In rough sets community, many of the selection methods until now have been proposed based on one-directional and non-adaptive rough approximation sets, which increase the uncertainty of granular computing. Based on a series of bi-directional adaptive neighborhood rough sets models, a novel attribute subset selection method is proposed in this paper. In contrast to classical one-directional k-nearest neighborhood relations, the proposed model not only allocates different numbers of neighbors to each sample, but also takes the support information of the neighbors into account in the construction of information granularity. On the basis of set operators, two specific bi-directional neighborhood relations as well as optimistic and pessimistic rough set models are discussed in detail. Several propositions and uncertainty measures not only show the relationship between the traditional and the proposed models, but also present the intrinsic connection of three discussed models. In addition, we propose an optimal neighbor selection scheme, in which justifiable granularity is fused with evidence theory to obtain an optimal k value for each sample. Finally, in the attribute selection process, we prove non-monotonic property of the neighborhood entropy and describe an upgraded forward greedy algorithm. The proposed algorithm is compared with existing algorithms using a ten-fold cross-validation method on several UCI datasets. The experimental results show the advantages of our models in terms of computational performance and classification accuracy, respectively.

Detailed Modeling of Heterogeneous and Contention-Constrained Point-to-Point MPI Communication

The network topology of modern parallel computing systems is inherently heterogeneous, with a variety of latency and bandwidth values. Moreover, contention for the bandwidth can exist on different levels when many processes communicate with each other. Many-pair, point-to-point MP communication is thus characterized by heterogeneity and contention, even on a cluster of homogeneous multicore CPU nodes. To get a detailed understanding of the individual communication cost per MPI process, we propose a new modeling methodology that incorporates both heterogeneity and contention. First, we improve the standard max-rate model to better quantify the actually achievable bandwidth depending on the number of MPI processes in competition. Then, we make a further extension that more detailedly models the bandwidth contention when the competing MPI processes have different numbers of neighbors, with also non-uniform message sizes. Thereafter, we include more flexibility by considering interactions between intra-socket and inter-socket messaging. Through a series of experiments done on different processor architectures, we show that the new heterogeneous and contention-constrained performance models can adequately explain the individual communication cost associated with each MPI process. The largest test of realistic point-to-point MPI communication involves 8,192 processes and in total 2,744,632 simultaneous messages over 64 dual-socket AMD Epyc Rome compute nodes connected by InfiniBand, for which the overall prediction accuracy achieved is 8 4%.

A Novel Flight Dynamics Modeling Using Robust Support Vector Regression against Adversarial Attacks

<div>An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly. The trained Support Vector Regression (SVR) predicted the FDM for the entire flight envelope. Although the SVR-FDM showed excellent performance, it remained vulnerable to adversarial attacks. Hence, we modified it using an adversarial retraining defense algorithm by transforming it into a Robust SVR-FDM. For validation studies, the quality of predicted UAS-S4 FDM was evaluated based on the Root Locus diagram. The closeness of predicted eigenvalues to the original eigenvalues confirmed the high accuracy of the UAS-S4 SVR-FDM. The SVR prediction accuracy was evaluated at 216 flight conditions, for different numbers of neighbors, and a variety of kernel functions were also considered. In addition, the regression performance was analyzed based on the step response of state variables in the closed-loop control architecture. The SVR-FDM provided the shortest rise time and settling time, but it failed when adversarial attacks were imposed on the SVR. The Robust-SVR-FDM step response properties showed that it could provide more accurate results than the LLS-FDM approach while protecting the controller from adversarial attacks.</div>

Parameter-Free Conglomerate nearest Neighbor Classifier Using Mass-Ratio-Variance Outlier Factors

Classification is one important area in machine learning that labels the class of an instance via a classifier from known-class historical data. One of the popular classifiers is k-NN, which stands for “k-nearest neighbor” and requires a global parameter k to proceed. This global parameter may not be suitable for all instances. Naturally, each instance may situate on different regions of clusters such as an interior instance placed inside a cluster, a border instance placed on the outskirts, an outer instance placed faraway from any cluster, which requires a different number of neighbors. To automatically assign a different number of neighbors to each instance, the concept of scoring from the anomaly detection research is desired. The Mass-ratio-variance Outlier Factor, MOF, is selected as the scoring scheme for the number of neighbors of each instance. MOF gives the highest score to an instance placed very far from any cluster and the lowest score to an instance surrounded by other instances. This leads to the proposed classifier called the conglomerate nearest neighbor classifier, which does not require any parameter assigning the appropriate number of neighbors to each instance ordered by MOF. Experimental results show that this classifier exhibits similar accuracy to the k-nearest neighbor algorithm with the best k over the synthesized datasets. Six UCI datasets, the QSAR dataset, the German dataset, the Cancer dataset, the Wholesale dataset, the Haberman dataset, and the Glass3 dataset are used in the experiment. This method outperforms two UCI datasets, Wholesale and Glass3, and displays similar performance with respect to these six UCI datasets.

Heterogeneous Explore-Exploit Strategies on Multi-Star Networks

We investigate the benefits of heterogeneity in multi-agent explore-exploit decision making where the goal of the agents is to maximize cumulative group reward. To do so we study a class of distributed stochastic bandit problems in which agents communicate over a multi-star network and make sequential choices among options in the same uncertain environment. Typically, in multi-agent bandit problems, agents use homogeneous decision-making strategies. However, group performance can be improved by incorporating heterogeneity into the choices agents make, especially when the network graph is irregular, i.e., when agents have different numbers of neighbors. We design and analyze new heterogeneous explore-exploit strategies, using the multi-star as the model irregular network graph. The key idea is to enable center agents to do more exploring than they would do using the homogeneous strategy, as a means of providing more useful data to the peripheral agents. In the case all agents broadcast their reward values and choices to their neighbors with the same probability, we provide theoretical guarantees that group performance improves under the proposed heterogeneous strategies as compared to under homogeneous strategies. We use numerical simulations to illustrate our results and to validate our theoretical bounds.

Optimal Neighborhood Multiple Kernel Clustering with Adaptive Local Kernels

Multiple kernel clustering (MKC) algorithm aims to group data into different categories by optimally integrating information from a group of pre-specified kernels. Though demonstrating superiorities in various applications, we observe that existing MKC algorithms usually do not sufficiently consider the local density around individual data samples and excessively limit the representation capacity of the learned optimal kernel, leading to unsatisfying performance. In this paper, we propose an algorithm, called optimal neighborhood MKC with adaptive local kernels (ON-ALK), to address the two issues. In specific, we construct adaptive local kernels to sufficiently consider the local density around individual data samples, where different numbers of neighbors are discriminatingly selected on each sample. Further, the proposed ON-ALK algorithm boosts the representation of the learned optimal kernel via relaxing it into the neighborhood area of weighted combination of the pre-specified kernels. To solve the resultant optimization problem, a three-step iterative algorithm is designed and theoretically proven to be convergent. After that, we also study the generalization bound of the proposed algorithm. Extensive experiments have been conducted to evaluate the clustering performance. As indicated, the algorithm significantly outperforms state-of-the-art methods in recent literatures on six challenging benchmark datasets, verifying its advantages and effectiveness.

Multiplexed Supply of a MISO Wireless Power Transfer System for Battery-Free Wireless Sensors

Multi-input single output wireless power transmission (MISO-WPT) systems have decisive advantages concerning flexible receiver position in comparison to single coil systems. However, the supply of the primary side brings a large uncertainty in case of variable positions of the secondary side. In this paper, a compact multiplexed primary side electronic circuit is proposed, which includes only one signal generator, a passive peak detector, a communication module, and a compensation capacitor. The novel approach has been studied and evaluated for a MISO-WPT system having a 16 coils on primary side and one coil on secondary side having the double diameter. Results show that a standard microcontroller, in this case an STM32, is sufficient for the control of the whole system, so that the costs and the energy consumption are significantly reduced. An activation strategy has been proposed, which allows to determine the optimal transmitting coil for each position of the receiving coil and to switch it on. The time-to-start-charging at different positions of the receiving coil and different number of neighbors has been determined. It remains in all cases under 2.5 s.

Learning graph structure via graph convolutional networks

Graph convolutional neural networks have aroused more and more attentions on account of the ability to handle the graph-structured data defined on irregular or non-Euclidean domains. Different from the data defined on regular grids, each node in the graph-structured data has different number of neighbors, and the interactions and correlations between nodes vary at different locations, resulting in complex graph structure. However, the existing graph convolutional neural networks generally pay little attention to exploiting the graph structure information. Moreover, most existing graph convolutional neural networks employ the weight sharing strategy which lies on the statistical assumption of stationarity. This assumption is not always verified on the graph-structured data. To address these issues, we propose a method that learns Graph Structure via graph Convolutional Networks (GSCN), which introduces the graph structure parameters measuring the correlation degrees of adjacent nodes. The graph structure parameters are constantly modified the graph structure during the training phase and will help the filters of the proposed method to focus on the relevant nodes in each neighborhood. Meanwhile by combining the graph structure parameters and kernel weights, our method, which relaxes the restriction of weight sharing, is better to handle the graph-structured data of non-stationarity. In addition, the non-linear activation function ReLU and the sparse constraint are employed on the graph structure parameters to promote GSCN to focus on the important links and filter out the insignificant links in each neighborhood. Experiments on various tasks, including text categorization, molecular activity detection, traffic forecasting and skeleton-based action recognition, illustrate the validity of our method.

Interplay of frustrations, interaction length, and dilution on magnetic transitions in vector models

The research of one-dimensional Ising model with the different number of neighbors and the different sign of the exchange integral were performed. The clustered order parameter is proposed for the description of the magnetic transition in spin systems regardless of the sign of the exchange integral and the number of neighbors. The method of calculation of critical field to change the magnetic states was presented. For diluted 1D antiferromagnetic model two plateaus in magnetization behavior are observed, and explained. Influence of length of interaction plays critical role for phase transition of dipoles system. Dipoles on hexagonal lattice show the phase transition only in model of long range interaction, and there isn’t critical behavior for short range. The approach to calculation of spin system in Heisenberg model is proposed.

The influence of sampling unit size and spatial arrangement patterns on neighborhood-based spatial structure analyses of forest stands

Aim of the study: Neighborhood-based stand spatial structure parameters can quantify and characterize forest spatial structure effectively. How these neighborhood-based structure parameters are influenced by the selection of different numbers of nearest-neighbor trees is unclear, and there is some disagreement in the literature regarding the appropriate number of nearest-neighbor trees to sample around reference trees. Understanding how to efficiently characterize forest structure is critical for forest management.Area of study: Multi-species uneven-aged forests of Northern ChinaMaterial and methods: We simulated stands with different spatial structural characteristics and systematically compared their structure parameters when two to eight neighboring trees were selected.Main results: Results showed that values of uniform angle index calculated in the same stand were different with different sizes of structure unit. When tree species and sizes were completely randomly interspersed, different numbers of neighbors had little influence on mingling and dominance indices. Changes of mingling or dominance indices caused by different numbers of neighbors occurred when the tree species or size classes were not randomly interspersed and their changing characteristics can be detected according to the spatial arrangement patterns of tree species and sizes.Research highlights: The number of neighboring trees selected for analyzing stand spatial structure parameters should be fixed. We proposed that the four-tree structure unit is the best compromise between sampling accuracy and costs for practical forest management.

Упорядочение в одномерных моделях Изинга с разным количеством соседей

Упорядочение в одномерных моделях Изинга с разным количеством соседей

Mapping the mean annual precipitation of China using local interpolation techniques

Spatially explicit precipitation data are required in the research of hydrology, agriculture, ecology, and environmental sciences. In this study, two established techniques of local ordinary linear regression (OLR) and geographically weighted regression (GWR) and two new local hybrid interpolation techniques of local regression-kriging (LRK) and geographically weighted regression kriging (GWRK) were compared to predict the spatial distribution of mean annual precipitation of China. Precipitation data from 684 meteorological stations were used in the analysis, and a stepwise regression analysis was used to select six covariates, including longitude, latitude, elevation, slope, surface roughness, and river density. The four spatial prediction methods (OLR, GWR, LRK, and GWRK) were implemented with local regression techniques with different number of neighbors (50, 100, 150, and 200). The prediction accuracy was assessed at validation sites with the root mean squared deviation, mean estimation error, and R-square values. The results showed that LRK outperforms OLR and GWRK outperforms GWR, indicating that adding the kriging of regression residuals can help improve the prediction performance. GWRK gives the best prediction but the accuracy of estimation varies with the number of neighborhood points used for modeling. Although LRK is outperformed by GWRK, LRK is still recommended as a powerful and practical interpolation method given its computation efficiency. However, if LRK and GWRK are used to extrapolate prediction values, post-processing in the areal interpolation will be needed.

Visually weighted neighbor voting for image tag relevance learning

The presence of non-relevant tags in image folksonomies hampers the effective organization and retrieval of user-contributed images. In this paper, we propose to learn the relevance of user-supplied tags by means of visually weighted neighbor voting, a variant of the popular baseline neighbor voting algorithm proposed by Li et al. (IEEE Trans Multimedia 11(7):1310–1322, 2009). To gain insight into the effectiveness of baseline and visually weighted neighbor voting, we qualitatively analyze the difference in tag relevance when using a different number of neighbors, for both tags relevant and tags not relevant to the content of a seed image. Our qualitative analysis shows that tag relevance values computed by means of visually weighted neighbor voting are more stable and representative than tag relevance values computed by means of baseline neighbor voting. This is quantitatively confirmed through extensive experimentation with MIRFLICKR-25000, studying the variation of tag relevance values as a function of the number of neighbors used (for both tags relevant and tags not relevant with respect to the content of a seed image), as well as the influence of tag relevance learning on the effectiveness of image tag refinement, tag-based image retrieval, and image tag recommendation.

Upstream reciprocity in heterogeneous networks

Many mechanisms for the emergence and maintenance of altruistic behavior in social dilemma situations have been proposed. Indirect reciprocity is one such mechanism, where other-regarding actions of a player are eventually rewarded by other players with whom the original player has not interacted. The upstream reciprocity (also called generalized indirect reciprocity) is a type of indirect reciprocity and represents the concept that those helped by somebody will help other unspecified players. In spite of the evidence for the enhancement of helping behavior by upstream reciprocity in rats and humans, theoretical support for this mechanism is not strong. In the present study, we numerically investigate upstream reciprocity in heterogeneous contact networks, in which the players generally have different number of neighbors. We show that heterogeneous networks considerably enhance cooperation in a game of upstream reciprocity. In heterogeneous networks, the most generous strategy, by which a player helps a neighbor on being helped and in addition initiates helping behavior, first occupies hubs in a network and then disseminates to other players. The scenario to achieve enhanced altruism resembles that seen in the case of the Prisoner's Dilemma game in heterogeneous networks.