Set Of Neighbors Research Articles

Entanglement of free fermions on Johnson graphs

Free fermions on Johnson graphs J(n, k) are considered, and the entanglement entropy of sets of neighborhoods is computed. For a subsystem composed of a single neighborhood, an analytical expression is provided by the decomposition in irreducible submodules of the Terwilliger algebra of J(n, k) embedded in two copies of su(2). For a subsystem composed of multiple neighborhoods, the construction of a block-tridiagonal operator that commutes with the entanglement Hamiltonian is presented, its usefulness in computing the entropy is stressed, and the area law pre-factor is discussed.

Adaptive multi-granularity sparse subspace clustering

Sparse subspace clustering (SSC) focuses on revealing data distribution from algebraic perspectives and has been widely applied to high-dimensional data. The key to SSC is to learn the sparsest representation and derive an adjacency graph. Theoretically, the adjacency matrix with proper block diagonal structure leads to a desired clustering result. Various generalizations have been made through imposing Laplacian regularization or locally linear embedding to describe the manifold structure based on the nearest neighborhoods of samples. However, a single set of nearest neighborhoods cannot effectively characterize local information. From the perspective of granular computing, the notion of scored nearest neighborhoods is introduced to develop multi-granularity neighborhoods of samples. The multi-granularity representation of samples is integrated with SSC to collaboratively learn the sparse representation, and an adaptive multi-granularity sparse subspace clustering model (AMGSSC) is proposed. The learned adjacency matrix has a consistent block diagonal structure at all granularity levels. Furthermore, the locally linear relationship between samples is embedded in AMGSSC, and an enhanced AMGLSSC is developed to eliminate the over-sparsity of the learned adjacency graph. Experimental results show the superior performance of both models on several clustering criteria compared with state-of-the-art subspace clustering methods.

An urban plan evaluation for park accessibility: a case in Izmir (Türkiye)

Plan evaluations about park accessibility are rare at the neighbourhood scale. Moreover, urban plans traditionally identify park accessibility with predetermined measurements that may ignore limited walking conditions of children, the elderly, women with children, and low-income groups. Alternatively, this paper considers equitable (rather than equal) park accessibility as an important goal concerning environmental justice. To guide a path to achieving this goal, it investigates how to assess and revise urban plans with parks within walking distance to social groups in the case of a plan (1/1000 scale) in Izmir (Türkiye). Deployment of the location-allocation analysis (a multi-criteria assessment methodology in Geographic Information Systems, GIS) allows this research to consider physical/geographical barriers to walkability in actual neighbourhood settings and reconfigure such barriers as contextual variables, including limited walking distances of disadvantaged groups. Ultimately, this study also contributes to how to handle spatial and demographic data deficiencies in Türkiye when measuring equitable accessibility of public facilities by walking. Results identify an uneven distribution of park accessibility even within the neighbourhood on the plan and the potential for improving park accessibility by designing some non-park public lands with park features.

Metric-Based Fractional Dimension of Rotationally-Symmetric Line Networks

The parameter of distance plays an important role in studying the properties symmetric networks such as connectedness, diameter, vertex centrality and complexity. Particularly different metric-based fractional models are used in diverse fields of computer science such as integer programming, pattern recognition, and in robot navigation. In this manuscript, we have computed all the local resolving neighborhood sets and established sharp bounds of a metric-based fractional dimension called by the local fractional metric dimension of the rotationally symmetric line networks of wheel and prism networks. Furthermore, the bounded and unboundedness of these networks is also checked under local fractional metric dimension when the order of these networks approaches to infinity. The lower and upper bounds of local fractional metric dimension of all the rotationally symmetric line networks is also analyzed by using 3D shapes.

An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost.

Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26-45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.

The orthographic/phonological neighbourhood size effect and set size.

A growing number of studies have shown that on serial recall tests, words with more orthographic/phonological neighbours are better recalled than otherwise comparable words with fewer neighbours, the so-called neighbourhood size effect. Greeno et al. replicated this result when using a large stimulus pool but found a reverse neighbourhood size effect-better recall of words with fewer rather than more neighbours-when using a small stimulus pool. We report three registered experiments that further examine the role of set size in the neighbourhood size effect. Experiment 1 used the large pool from Greeno et al. and replicated their finding of a large-neighbourhood advantage. Experiment 2 used the small pool from Greeno et al. but found no difference in recall between the large and small neighbourhood conditions. Experiment 3 also used a small pool but the small pool was randomly generated for each subject from the large pool used in Experiment 1. This resulted in a typical large neighbourhood advantage. We suggest that set size is not critical to the direction of the neighbourhood size effect, with a large neighbourhood advantage appearing with both small and large pools.

Mask-Aware Pseudo Label Denoising for Unsupervised Vehicle Re-Identification

As a significant part of Intelligent Transportation System (ITS), vehicle Re-Identification (Re-ID) aims to retrieve all target vehicle images captured from non-overlapping cameras. Though the Re-ID methods based on supervised learning have achieved rapid progress, they are still difficult to be applied in real scenarios due to the domain bias between the training set and real scenarios. Recently, methods based on unsupervised learning have been proposed to address the problem of domain bias by exploring techniques of pseudo-label generation. However, these methods suffer from pseudo-label noise. To solve this problem, we propose the Mask-Aware Pseudo Label Denoising framework (MAPLD) consisting of three key components, i.e., Mask-Aware Feature Extraction (MAFE), Adaptive Threshold Neighborhood Consistency (ATNC), and Compact Loss (CL). Firstly, the MAFE is proposed to improve the distinguishability of feature representation and widen the gap in feature space among vehicles with different IDs. Next, the ATNC is introduced to filter out pseudo-label noise of hard negative samples by comparing the image ID of the samples in their neighborhood set i.e., neighborhood consistency. Moreover, the threshold of neighborhood consistency is adaptively adjusted according to feature similarity ranking, which is robust to hyper-parameter variation. Finally, consisting of regression term and compact term, the CL is designed to drive the cluster more compact and alleviate the impact of outliers of hard positive samples. Extensive experiments on VeRi-776 and VeRi-Wild datasets demonstrate that MAPLD can generate reliable pseudo-labels and achieve superior performance in unsupervised target-only and unsupervised domain adaptation tasks.

Minus HELLO: HELLO Devoid Protocols for Energy Preservation in Mobile Ad-hoc Networks

In mobile ad-hoc networks, nodes have to transmit HELLO or Route Maintenance messages at regular intervals, and all nodes residing within its radio range, reply with an acknowledgment message informing their node identifier, current location, and radio-range. Regular transmitting these messages consume a significant amount of battery power in nodes, especially when the set of down-link neighbors does not change over time and the radio-range of the sender node is large. The present article focuses on this aspect and tries to eliminate the number of HELLO messages in existing state-of-art protocols. Also, it shortens radio-ranges of nodes whenever possible. Moreover, the required number of route re-discovery reduces. Simulation results show that the average lifetime of nodes significantly increases in proposed Minus HELLO devoid routing protocols along with a significant increase in network throughput.

On the Solubilizer of an Element in a Finite Group

The solubility graph Gamma _S(G) associated with a finite group G is a simple graph whose vertices are the elements of G, and there is an edge between two distinct vertices if and only if they generate a soluble subgroup. In this paper, we focus on the set of neighbors of a vertex x which we call the solubilizer of x in G, mathrm {Sol}_G(x), investigating both arithmetic and structural properties of this set.

A Trust Inference Method Employing Combinatorial Strategies

Most trust inference algorithms based on Q -learning and learning automata often suffer from the problem of insufficient storage space, as well as some algorithms using the shortest trust path do not fully utilize the trust information. To solve the above problems, a trust inference algorithm (DuelingDQNTrust) based on deep reinforcement learning is proposed. It uses Dueling DQN to compute Q values to reduce the required storage space and does not limit the traversal depth. Moreover, the way to find out the trusted neighbors of a user is crucial because it affects the reliability of the trust path. So three combination strategies are proposed to find users’ trusted neighbors more accurately. One strategy is to define a priority metric by combining node importance and user similarity to find the top k neighbor users of the user as the trusted neighbor set. Another two strategies consider node importance and user similarity in a different order of priority, which first uses a metric to find the top k neighbor users of the user, and then further filters these k users on another metric to determine the trusted neighbor set of the user. The evaluation of the algorithm on the FilmTrust dataset indicates that the algorithm outperforms several existing trust inference algorithms.

Formation Control for Second-Order Multi-Agent Systems with Collision Avoidance

This paper deals with the formation control problem without collisions for second-order multi-agent systems. We propose a control strategy which consists of a bounded attractive component to ensure convergence to a specific geometrical pattern and a complementary repulsive component to guarantee collision-free rearrangement. For convergence purposes, it is assumed that the communication graph contains at least a directed spanning tree. The avoidance complementary component is formed by applying repulsive vector fields with unstable focus structure. Using the well-known input-to-state stability property a control law for second-order agents is derived in a constructive manner starting from the first-order case. We consider that every agent is able to detect the presence of any other agent in the surrounding area and also can measure and share both position and velocity with his predefined set of neighbours. The resulting control law ensures the convergence to the desired geometrical pattern without collisions during the transient behaviour, as well as bounded velocities and accelerations. Numerical simulations are provided to show the performance and effectiveness of the proposed strategy.

Bilangan Dominasi-Lokasi pada Graf Hasil Kali Operasi Comb Graf Lintasan dan Graf Siklus

Dominating set of graph G is subset D⊆V(G) which for every vertex v∈V(G)\D those vertices has neighbour in D. If for every pairs of vertice x and y their neighbour set different than we called D locating-dominating set. As for the minimum cardinality of possible dominating set of G is called the locating-dominating number of G. We determine the value of the locating-dominating number for comb product path (Pn) and cycle (Cn) graph.

Proof theory for the logics of bringing-it-about: Ability, coalitions and means-end relationship

Abstract The logic of bringing-it-about (BIAT) aims to capture a notion of agency in which actions are analysed in terms of their results: ‘An agent does something’ means that the agent brings it about that something takes place. Our starting point is the basic BIAT logic as introduced by Elgesem in the ‘90s: this logic contains only a modal operator to express BIAT statements by single agents. Several extensions have been proposed by Elgesem himself and others, notably with the capability operator, coalitions of agents and means-end BIAT statements (i.e. of the form ‘the agent does B by doing A’). We first propose a variant of the neighbourhood semantics, called bi-neighbourhood semantics, for the basic BIAT logic and the mentioned extensions, in which a world is equipped by a set of pairs or neighbourhoods. Differently from the semantics defined in the literature, this reformulation is well suited for countermodel construction. We then introduce modular hypersequent calculi for all logics considered in this work. Our calculi enjoy the fundamental property of cut admissibility, from which it follows their completeness with respect to the axiomatization. Moreover, our calculi provide at the same time a decision procedure, as well as the first practical countermodel extraction procedure: from a single failed proof it is possible to build directly a finite countermodel of the formula under verification in the bi-neighbourhood semantics. By this last result, we obtain constructive proofs of the semantic completeness of the calculi and consequently of the finite model property for all logics.

Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design.

Benzoquinolines are used in many drug design projects as starting molecules subject to derivatization. This computational study aims to characterize e benzoquinone drug space to ease future drug design processes based on these molecules. The drug space is composed of all benzoquinones, which are active on topoisomerase II and ATP synthase. Topological, chemical, and bioactivity spaces are explored using computational methodologies based on virtual screening and scaffold hopping and molecular docking, respectively. Topological space is a geometrical space in which the elements composing it can be defined as a set of neighbors (which satisfy a particular axiom). In such space, a chemical space can be defined as the property space spanned by all possible molecules and chemical compounds adhering to a given set of construction principles and boundary conditions. In this chemical space, the potentially pharmacologically active molecules form the bioactivity space. Results show a poly-morphological chemical space that suggests distinct characteristics. The chemical space is correlated with properties such as steric energy, the number of hydrogen bonds, the presence of halogen atoms, and membrane permeability-related properties. Lastly, novel chemical compounds (such as oxadiazole methybenzamide and floro methylcyclohexane diene) with drug-like potential, active on TOPO II and ATP synthase have been identified.

Crime in Philadelphia: Bayesian Clustering with Particle Optimization

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled “sharing of information” between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. Supplementary materials for this article are available online.

Incentive Mechanism Design for Unicast Service Provisioning With Network Aggregative Game

We investigate the problem of distributed resource allocation in unicast communication networks with strategic/selfish users. First, through mechanism design, the centralized problem is converted to a decentralized problem that induces a network aggregative game among users. At every Nash equilibrium, this mechanism strongly implements the solution of the resource allocation problem, and it is budget balance as well. Then, we establish a relationship between Nash equilibria of the induced game and the solutions of the corresponding variational inequality problem. Next, a distributed algorithm is proposed, and finally, its converges to the Nash equilibrium of the induced game is proved under certain assumptions. Since each link can be shared among a different set of users, there is a specific connectivity graph among the users of each link. Hence, a user simultaneously utilizes multiple connectivity graphs to interact with different sets of neighbors on different links.

Feature Aggregation via Attention Mechanism for Visible-Thermal Person Re-Identification

Visible-thermal person re-identification (VT-ReID) is an image retrieval task that aims at matching the target pedestrian across the visible and thermal modalities. However, intra-class variations and cross-modality discrepancy degrade the performance of VT-ReID. Recent methods focus on extracting discriminative local features of each modality to alleviate the intra-class variations and cross-modality discrepancy, but these methods ignore semantic relations between the local features of two modalities, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the spatial relations and channel relations. In this paper, we proposed a feature aggregation module (FAM) to enhance the correlation between local features including spatial dependencies and channel dependencies. Furthermore, FAM implements cross-modality feature aggregation on the enhanced features to reduce the cross-modality discrepancy. Moreover, we also proposed near neighbor cross-modality loss (NNCLoss) to mine feature consistency between modalities by constructing a cross-modality near neighbor set, which facilitates feature alignment between two modalities. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-arts.

Hyperparameter-Free Localized Simple Multiple Kernel K-Means With Global Optimum.

The newly proposed localized simple multiple kernel k-means (SimpleMKKM) provides an elegant clustering framework which sufficiently considers the potential variation among samples. Although achieving superior clustering performance in some applications, we observe that it is required to pre-specify an extra hyperparameter, which determines the size of the localization. This greatly limits its availability in practical applications since there is a little guideline to set a suitable hyperparameter in clustering tasks. To overcome this issue, we firstly parameterize a neighborhood mask matrix as a quadratic combination of a set of pre-computed base neighborhood mask matrices, which corresponds to a group of hyperparameters. We then propose to jointly learn the optimal coefficient of these neighborhood mask matrices together with the clustering tasks. By this way, we obtain the proposed hyperparameter-free localized SimpleMKKM, which corresponds to a more intractable minimization-minimization-maximization optimization problem. We rewrite the resultant optimization as a minimization of an optimal value function, prove its differentiability, and develop a gradient based algorithm to solve it. Furthermore, we theoretically prove that the obtained optimum is the global one. Comprehensive experimental study on several benchmark datasets verifies its effectiveness, comparing with several state-of-the-art counterparts in the recent literature. The source code for hyperparameter-free localized SimpleMKKM is available at https://github.com/xinwangliu/SimpleMKKMcodes/.

The differential on operator S(Γ).

Consider a simple graph $ \Gamma = (V(\Gamma), E(\Gamma)) $ with $ n $ vertices and $ m $ edges. Let $ P $ be a subset of $ V(\Gamma) $ and $ B(P) $ the set of neighbors of $ P $ in $ V(\Gamma)\backslash P $. In the study of graphs, the concept of differential refers to a measure of how much the number of edges leaving a set of vertices exceeds the size of that set. Specifically, given a subset $ P $ of vertices, the differential of $ P $, denoted by $ \partial(P) $, is defined as $ |B(P)|-|P| $. The differential of $ \Gamma $, denoted by $ \partial(\Gamma) $, is then defined as the maximum differential over all possible subsets of $ V(\Gamma) $. Additionally, the subdivision operator $ {{\mathcal{S}}({\Gamma})} $ is defined as the graph obtained from $ \Gamma $ by inserting a new vertex on each edge of $ \Gamma $. In this paper, we present results for the differential of graphs on the subdivision operator $ {{\mathcal{S}}({\Gamma})} $ where some of these show exact values of $ \partial({{\mathcal{S}}({\Gamma})}) $ if $ \Gamma $ belongs to a classical family of graphs. We obtain bounds for $ \partial({{\mathcal{S}}({\Gamma})}) $ involving invariants of a graph such as order $ n $, size $ m $ and maximum degree $ \Delta $, and we study the realizability of the graph $ \Gamma $ for any value of $ \partial({{\mathcal{S}}({\Gamma})}) $ in the interval $ \left[n-2, \frac{n(n-1)}{2}-n+2\right] $. Moreover, we give a characterization for $ \partial({{\mathcal{S}}({\Gamma})}) $ using the notion of edge star packing.

Spectral–Spatial Prototype Learning-Based Nearest Neighbor Classifier for Hyperspectral Images

Due to the Hughes phenomenon, hyperspectral image (HSI) classification under small sample size situation is still a key challenging problem. To alleviate this issue, we propose a novel spectral–spatial prototype learning-based nearest neighbor classifier (SSPLNN) for HSI in this article. The local spectral–spatial neighbor set is first constructed for each sample based on both spectral similarity and spatial structural context to accurately explore the local spectral–spatial information. Then, a spectral–spatial prototype learning model is designed to learn a set of spectral–spatial prototypes, which can optimally utilize both the similarity and variance of samples within each spectral–spatial set and excavate the unseen spectral–spatial variations. The learned spectral–spatial prototypes offer more complementary information to improve the classification accuracy remarkably under small sample size situation. In addition, a linear discriminative projection is simultaneously learned to make each test local spectral–spatial set to be optimally classified to the same class with its nearest neighbor (NN) spectral–spatial prototype set in the projected target subspace. Finally, the NN classifier based on measuring the minimum geometric distance between the projected test spectral–spatial set and the projected spectral–spatial prototype sets is employed to determine the label. Experimental results demonstrate that the proposed SSPLNN method outperforms several well-known classification methods by a large margin on three widely analyzed HSI datasets.