Neighborhood Formation Research Articles

Distributed Adaptive Fault-Tolerant Formation-Containment Control With Prescribed Performance for Heterogeneous Multiagent Systems.

This article proposes a distributed adaptive fault-tolerant formation-containment control with prescribed performance for heterogeneous multiagent systems (MASs) consisting of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. First, utilizing the neighborhood formation error information, the distributed fault-tolerant formation control strategy is developed for the trajectory dynamics of each UAV to achieve the formation tracking, that is, all UAVs track the virtual leader and perform the prespecified formation configuration. Then, the adaptive fault-tolerant containment algorithm, independent of the positions of the leaders, is proposed to guarantee the UGVs converge to the convex hull formed by the leader UAVs. The adaptive estimation scheme is constructed to compensate for the unknown system parameters and actuator loss-of-effectiveness and bias faults. The formation-containment tracking performance is analyzed based on Lyapunov theory with the synchronization errors satisfying the prescribed performance. A simulation example based on UAVs-UGVs systems is adopted to verify the effectiveness of the proposed control strategy.

Making Our Neighborhoods, Making Our Selves: A Review Essay

This essay reviews Making Our Neighborhoods, Making Our Selves, in which George C. Galster provides an overview of the literatures on neighborhood formation and neighborhood effects. I see two clear ways that Making Our Neighborhoods will serve as a reference strengthening these literatures. Given the state of the literature on neighborhood effects, which is often still at the stage of testing the existence and magnitude of such effects, the author’s framework for classifying the types of heterogeneity we might observe in neighborhood effects will be a valuable tool for researchers. And since the literature on neighborhood formation approaches the issues from a disparate set of fields, the author’s presentation of individual actors shaping neighborhood dynamics while using ideas of equilibrium, belief formation, equity and efficiency, and unpriced externalities should help unify understanding of the economic approach to neighborhood formation. While the author certainly does not shy away from race, I argue that several parts of the presentation would be clearer if they were tied more directly to racial segregation. (JEL D62, D83, I31, J15, R23, R31, Z13)

Active Disturbance Rejection Formation Tracking Control for Uncertain Nonlinear Multi-Agent Systems With Switching Topology via Dynamic Event-Triggered Extended State Observer

In this paper, the time-varying formation tracking (TVFT) control problem is concerned for multi-agent systems (MASs). The primary objective is to achieve asymptotical convergence for formation tracking error subject to nonparametric and nonvanishing uncertainties. Firstly, in order to estimate lumped unmodeled dynamics and external disturbances, an event- triggered fuzzy extended state observer (FESO) inspired by our previous works is established for follower group, in which efficient nonlinear observation pattern and convenient linear numerical tractability are integrated. For rationally scheduling transmission, a dynamic event-triggered mechanism is applied to form adaptively regulating strategy. Secondly, a distributed TVFT control law is developed by utilizing neighborhood formation tracking errors. Different from conventional disturbance-tolerant methodologies, total disturbance compensation is introduced to attenuate uncertainty influence in real time. Consequently, active disturbance rejection formation tracking control architecture is systematically constructed for uncertain MASs. Moreover, considering switching topology, the controller design algorithm is presented by piecewise Lyapunov analysis. Finally, the effectiveness and advantages of the proposed event-triggered FESO-based active disturbance rejection control protocol is illustrated by an numerical example on unmanned aerial vehicle swarm system.

Learning-Based Cooperative Multiagent Formation Control With Collision Avoidance

This article presents a learning-based controller to solve the cooperative formation control problem for multiagent system (MAS) with collision avoidance. First, the consensus problem of first-order MAS is mostly solved by linear matrix inequality (LMI) without consideration of energy loss. To overcome these difficulties, an adaptive dynamic programming (ADP) technique is fit to solve the consensus problem and similar formation control problem for second-order MAS by the establishment of a performance index function. Besides, we introduce the generalized policy iteration (GPI) algorithm as a kind of ADP technique without the problem of low convergence speed and high computational complexity. Combined with previous works, it can be found that our proposed structure can be extended to high-order cases based on the structure of local neighborhood formation error and algorithm. Afterward, the convergence analysis, optimality analysis, and stability analysis are given. Neural networks (NNs) are also implemented to approximate the iterative control policies and value functions, respectively. Moreover, we realize that many collisions may occur in the formation control problem. Inspired by the idea of the artificial potential field (APF) technique, the concept of the repulsive force field is introduced based on our proposed learning-based structure to avoid collisions simply and efficiently. Finally, a simulation is provided to demonstrate the effectiveness of our proposed method.

Sacred Sites for Suburbanites: Organic Urban Growth and Neighborhood Formation at Preclassic Ceibal, Guatemala

ABSTRACT This article examines ancient Maya urbanization and neighborhood formation during the Preclassic period by focusing on evidence from five minor temple groups at Ceibal, Guatemala. During the Late Preclassic (ca. 350–75 b.c.) and Terminal Preclassic (ca. 75 b.c.–a.d. 300) periods, minor temple complexes were constructed at regular intervals in outlying areas of Ceibal. These complexes were built at different times, and each is distinct in size, shape, and construction materials. Geospatial analyses of settlement based on a lidar survey reveals residential clustering around the temples, and excavations of nearby patios indicate they were occupied contemporaneously with their associated temple. Together, the evidence suggests that local groups of people built their own temple complexes as they began to settle in new areas. Urbanization at Ceibal can be explained as a generative process involving an interplay between top-down influences from the epicenter and organic cooperation among incoming residents.

Distributed adaptive fault-tolerant formation control for heterogeneous multiagent systems under switching directed topologies

This paper studies the cooperative fault-tolerant formation control problem of tracking a dynamic leader for heterogeneous multiagent systems consisting of multipile unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults under switching directed interaction topologies. Based on local neighborhood formation information, the distributed fault-tolerant formation controllers are constructed to ensure that all follower UAVs and UGVs can accomplish the demanding formation configuration in the state space and track the dynamic leader’s trajectory. By incorporating the sliding mode control and adaptive control technique, the actuator faults and unknown parameters of follower agents can be compensated. Through the theoretical analysis, it is proved that the cooperatively semiglobally uniformly ultimately boundedness of the closed-loop system is guaranteed, and the formation tracking errors converge to a small adjustable neighborhood of the origin. A simulation example is introduced to show the validity of the proposed distributed fault-tolerant formation control algorithm.

A novel top-n recommendation method for multi-criteria collaborative filtering

Most online service providers utilize a recommender system to help their customers’ decision making process by producing referrals. If a customer requests a suggestion for a specific item, the recommender systems produce predictions for it. On the other hand, it is also possible to create top-n lists containing the products that the customer might like the most. Recommender systems’ outcomes depend on individuals’ preferences which can be provided by considering a single criterion or multiple criteria about the services or products. Therefore, there must be methods to produce predictions and top-n lists for single and multiple-criteria datasets. Although the researchers introduced several algorithms on single criterion-based ratings for producing single predictions and top-n lists, there are only methods for producing referrals for a specific item on multi-criteria data. Accordingly, this paper proposes an intuitionistic fuzzy set-based top-n recommender system method with a novel neighborhood formation process for multi-criteria datasets. The proposed method consists of two crucial points: (i) Determining the relational structure between products; (ii) Investigating user tendencies, as well as their distinctive structures and rating distributions. The rating distribution and the relational structure between the products are determined with association rule mining and entropy measure, while the attitudes and tendencies of the users during the evaluation are analyzed with intuitionistic fuzzy sets. We also adopt a single-criterion top-n method to a multi-criteria recommender system, and we employ crisp ratings instead of fuzzy ones to compare the performance of the proposed method. The measurements of serendipity, diversity, and novelty are utilized to show how the experimental results are compelling. When the experiments’ results are examined, it is concluded that our method can generate successful top-n lists.

Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study

Read-across is a data gap filling technique utilized to predict the toxicity of a target chemical using data from similar analogues. Recent efforts such as Generalized Read-Across (GenRA) facilitate automated read-across predictions for untested chemicals. GenRA makes predictions of toxicity outcomes based on “neighboring” chemicals characterized by chemical and bioactivity fingerprints. Here we investigated the impact of biological similarities on neighborhood formation and read-across performance in predicting hazard (based on repeat-dose testing outcomes from US EPA ToxRefDB v2.0). We used targeted transcriptomic data on 93 genes for 1060 chemicals in HepaRG™ cells that measure nuclear receptor activation, xenobiotic metabolism, cellular stress, cell cycle progression, and apoptosis. Transcriptomic similarity between chemicals was calculated using binary hit-calls from concentration-response data for each gene. We evaluated GenRA performance in predicting ToxRefDB v2.0 hazard outcomes using the area under the Receiver Operating Characteristic (ROC) curve (AUC) for the baseline approach (chemical fingerprints) versus transcriptomic fingerprints and a combination of both (hybrid). For all endpoints, there were significant but only modest improvements in ROC AUC scores of 0.01 (2.1%) and 0.04 (7.3%) with transcriptomic and hybrid descriptors, respectively. However, for liver-specific toxicity endpoints, ROC AUC scores improved by 10% and 17% for transcriptomic and hybrid descriptors, respectively. Our findings suggest that using hybrid descriptors formed by combining chemical and targeted transcriptomic information can improve in vivo toxicity predictions in the right context.

Neighborhood formation in St. Louis, 1930

What are the social bases of neighborhood formation in urban areas, and at what spatial scale are they most distinct from other neighborhoods? We address these questions in the case of St. Louis, Missouri, in 1930, where we can take advantage of unique geocoded census microdata on the whole population of the city that identifies who, with what background characteristics, lived where. Our analyses show that homophily by race and ethnicity was by far the strongest factor linking characteristics of persons to the composition of their neighbors. Measures of social class also were quite important, while the person’s nativity and family status were statistically significant but minor predictors. Yet while this hierarchy of social factors held for the population as a whole, their relative importance varied greatly across racial/ethnic groups. Similarity in social class to neighbors was most important for native whites, nativity counted as much or more than class for recently arriving immigrant groups including Russians, Italians, and Poles, and race/ethnicity was by far the key predictor for these groups and blacks. We also found that these patterns of homophily were clearest at the scale of individual street segment and first-order combinations of segments. They were similar but less distinct at a larger spatial scale.

Distributed Estimation and Control for Leader-Following Formations of Nonholonomic Mobile Robots

The problem of the leader-following formation control of nonholonomic mobile robots is addressed in this paper. A distributed formation control strategy using explicitly the coordination errors among robots is proposed without assuming that each follower robot knows the full state of the leader. First, a distributed estimation law is proposed for each follower robot to estimate the states, including the position, orientation, and linear velocity of the leader. The distributed formation control law is then designed based on the estimated states of the leader, and the neighborhood formation tracking error. Under some mild assumptions on the interaction graph among the leader and the follower robots, and the velocity of the leader, asymptotic convergence of formation tracking errors to zero can be achieved. Finally, some numerical simulations and experiments on a group of nonholonomic mobile robots are presented to demonstrate the effectiveness of the proposed strategy. Note to Practitioners —The motivation of this paper is to investigate a practical control strategy for the leader-following formation of multiple autonomous mobile robots subjected to nonholonomic constraints. In most of the existing leader-following formation control schemes for nonholonomic mobile robots, having access to the full state of the leader is a requirement. However, due to limitations in communication bandwidth and range, it is reasonable to assume that the information of the leader is available only to a subset of followers. Hence, this paper suggests a new distributed leader-following formation control strategy based on the distributed estimation of the leader’s states. Moreover, the coordination error between a pair of interacting robots is explicitly used in the control design to weaken the dependence on the estimated state of the leader, and enhance the decentralized nature of the proposed control scheme. The stability and convergence of the system are analyzed mathematically and the experiment using unicycles provides promising results. In ongoing research, we are addressing the issues of collision avoidance and communication delays to provide more realistic setup for the industrial applications of multivehicle systems.

La formalidad de la informalidad en la urbanización de las ciudades fronterizas de Leticia (Colombia) y Tabatinga (Brasil) en torno del límite internacional

Este artículo ofrece una aproximación a la dinámica de urbanización de Leticia y Tabatinga, dos ciudades fronterizas en la Amazonia, a partir del estudio de la morfología urbana. Se sugiere que la urbanización de estas ha estado determinada por elementos formales e informales que parecen estar directamente conectados y se asocian con la gestión de diversos agentes urbanos tanto de los suelos periurbanos, integrados de múltiples maneras al continuo de la ciudad, como de la condición fronteriza. Dicho proceso de urbanización ha generado formas que refuerzan o resisten a la división política del espacio. Por tanto, considerar las ciudades desde la transformación de su espacio físico, permite ver dos procesos: primero, las formas como cambian suelos otrora periféricos o rurales adyacentes al límite en áreas crecientemente urbanizadas con barrios formales e informales. Segundo, cómo en dichos barrios sus habitantes construyen su espacio de vivienda reforzando o cuestionado la condición fronteriza. El estudio, por tanto, se centra en las dinámicas de formación de los barrios y la expresión de la forma urbana en tres diferentes zonas de las ciudades adyacentes al límite que responden a diferentes momentos del desarrollo de las ciudades fronterizas.

Ethnic drift and white flight: A gravity model of neighborhood formation

AbstractMost research on ethnic relocation at the neighborhood level focuses on either in‐migration or out‐migration, considering characteristics of either the origin or the destination neighborhood. Gravity models consider characteristics of both the origin and destination of movers, but are mostly used to explain international or interregional migrant flows. We estimate a gravity model at the neighborhood level to identify the role of ethnic heterogeneity across space in two Dutch cities, explaining both size and ethnic composition of mover flows within cities. Our findings suggest a preference for living with the own ethnic group in an otherwise diverse neighborhood.

Introduction: Making and Unmaking Neighborhood Boundaries in Postwar U.S. Cities

This essay introduces the theme of this special issue, “Making and Unmaking Neighborhood Boundaries in Postwar U.S. Cities,” by tracing the enduring meanings of the words, neighbor, neighborliness, and neighborhood, and relating them to community, place, conduct, and the idea of dwelling, important in Henri Lefebvre’s theory of the production of space. The four case studies in this issue highlight places, where neighborhood formation and boundary making stand out in the historical production of space, and are examples of the benefits of the recent spatial turn in urban history. By examining neighborhoods in San Francisco, Atlanta, and New York City, the authors topple assumptions that prop up postwar urban history and demonstrate the relevance of historical studies of neighborhoods to the crises of the present moment (and the need for more of the same).

Nearest biclusters collaborative filtering framework with fusion

Collaborative filtering is one of the widely used recommendation technique. It provides automated and personalized suggestions to consumers for selecting variety of products by examining their preferences. However, sparsity is one of the major weaknesses of this prosperous approach. This problem inherently occurs in the system due to ever increasing number of users and items. This affects the performance of a recommender system as the accuracy of prediction decreases. Thus, there is a need for a technique that can perform efficiently under sparse environment and this work proposes one such technique. The memory based CF techniques can be user-based or item based. In both cases, the user-item rating matrix can provide only partial information to predict unknown ratings. This is due to the sparsity inherent to rating data. Hence, we propose to fuse the item-based CF and user-based CF. Subsequently, Neighborhood formation is a crucial step in Collaborative filtering technique. Therefore, this paper adopts the biclustering approach for neighborhood formation. This approach, allows a degree of overlap between biclusters (i.e. a user or item is included in more than one clusters). Therefore, a new similarity measure is proposed that obtains a bicluster that has strong partial similarity with an active users’ preferences. Experimental results demonstrate that proposed approach generates better accuracy of rate prediction compared to the tradition item-based, user-based and some state of the art approaches.

Universal and Dynamic Clustering Scheme for Energy Constrained Cooperative Wireless Sensor Networks

Energy conservation is considered to be one of the key design challenges within resource constrained wireless sensor networks that leads the researchers to investigate energy efficient protocols with some application specific challenges. Dynamic clustering is generally considered as one of the energy conservation techniques; but unbalanced distribution of cluster heads, highly variable number of sensor nodes in the clusters and high number of sensor nodes involved in event reporting tend to drain out the network energy quickly resulting premature decrease in network lifetime. In this paper, a dynamic and cooperative clustering and neighborhood formation scheme is proposed that is expected to evenly distribute energy demand from the cluster heads and optimize the number of sensor nodes involved in event reporting. Assuming multiple sensors will form a cluster, while responding to an event to report to the fusion center. However, all the sensor nodes are assuming to report the sensing parameters to a cluster-head; which are to be summarized and then report it to fusion center. The transmission of the same event data from multiple sensors within the cluster at different distances with single or multiple antennas to the cluster-head with similar antenna characteristics can be realized as multiple-input multiple-output (MIMO) channel set up as found in the literature. Such realization among clusters of MIMO channel and existence of a feedback channel between the clusters and fusion center is the key of the proposed framework. The dynamic behavior has been adopted within the framework with a proposed index derived from the received measure of the channel quality, which has been attained through the feedback channel from the fusion center. The dynamic property of the proposed framework makes it robust against time-varying behavior of the propagation environment. The proposed framework is independent of the nature of the sensing application, providing with universal behavior. From simulation results, it is observed that the proposed clustering scheme enhances network lifetime by 24.5% and 36% as compared to existing schemes e.g. DDEEC and EDDEEC respectively. Furthermore, it is validated by simulation results that the proposed framework provides a trade-off model between network lifetime and transmission reliability.

A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging

Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected photons per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in the presence of high background noise. We introduce a new approach to depth and reflectivity estimation that focuses on unmixing contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, we borrow data from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content. Algorithm performance is demonstrated on experimental data at varying levels of noise. Results show improved performance of both reflectivity and depth estimates over state-of-the-art methods, especially at low signal-to-background ratios. In particular, accurate imaging is demonstrated with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant method demonstrates the viability of rapid, long-range, and low-power LIDAR imaging.

Birds of a Feather: Social Bases of Neighborhood Formation in Newark, New Jersey, 1880

This study examines the bases of residential segregation in a late nineteenth century American city, recognizing the strong tendency toward homophily within neighborhoods. Our primary question is how ethnicity, social class, nativity, and family composition affect where people live. Segregation is usually studied one dimension at a time, but these social differences are interrelated, and thus a multivariate approach is needed to understand their effects. We find that ethnicity is the main basis of local residential sorting, while occupational standing and, to a lesser degree, family life cycle and nativity also are significant. A second concern is the geographic scale of neighborhoods: in this study, the geographic area within which the characteristics of potential neighbors matter in locational outcomes of individuals. Studies of segregation typically use a single spatial scale, often one determined by the availability of administrative data. We take advantage of a unique data set containing the address and geo-referenced location of every resident. We conclude that it is the most local scale that offers the best prediction of people's similarity to their neighbors. Adding information at larger scales minimally improves prediction of the person's location. The 1880 neighborhoods of Newark, New Jersey, were formed as individuals located themselves among similar neighbors on a single street segment.

A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback

User profiles in collaborative filtering (CF) recommendation technique are built based on ratings given by users on a set of items. The most eminent shortcoming of the CF technique is the sparsity problem. This problem refers to the low ratio of rated items by users to the total number of available items; hence the quality of recommendation will be affected. Most researchers use implicit data as a solution for sparsity problem, to decrease the dependency of CF technique on the user's rating and this term is more common in this field. The aim of this research is to aggregate evidence on state of research and practice of CF and implicit data applying systematic literature review (SLR) which is a method for evidence-based software engineering (EBSE). EBSE has the potential value for synthesizing evidence and make this evidence available to practitioners and researchers with providing the best references and appropriate software engineering solutions for sparsity problem. We executed the standard systematic literature review method using a manual search in 5 prestigious databases and 38 studies were finally included for analyzing. This paper follows manifestation of Kitchenham's SLR guidelines and describes in a great detail the process of selecting and analyzing research papers. This paper is first academic systematic literature review of CF technique along with implicit data from user behaviors and activities to aggregate existing evidence as a synthesis of best quality scientific studies. The 38 research papers are categorized into eleven application fields (movie, shopping, books, Social systems, music and others) and six data mining techniques (dimensionality reduction, association rule, heuristic methods and other). According to the review results, neighborhood formation is a relevant aspect of CF and it can be improved with the use of user-item preference matrix as implicit feedback mechanism, the most common domains of CF are in e-commerce and movie software applications.

A Racial Inequality Trap

Why has the U.S. black/white earnings gap remained around 40 percent for nearly 40 years? This paper''s answer consists of a model of skill accumulation and neighborhood formation featuring a trap: Initial racial inequality and racial preferences induce racial segregation and asymmetric skill accumulation choices that perpetuate racial inequality. Calibrated to match the U.S. distribution of race, house prices and earnings across neighborhoods, the model produces one-half of the observed racial earnings gap. Moving the economy from the trap to a racially integrated steady state implies a 15.6 percent welfare gain for black households and a 2.7 percent loss for white households.

A semantic approach to improve neighborhood formation in collaborative recommender systems

Automatic recommenders are now omnipresent in e-commerce websites, as selecting and offering to users products they may be interested in can considerably increase sales revenue. The most popular recommendation strategy is currently considered to be the collaborative filtering technique, based on offering to the user who will receive the recommendation items that were appealing to other individuals with similar preferences (the so-called neighbors). On the other hand, its principal obstacle is the sparsity problem, related to the difficulty to find overlappings in ratings when there are many items. As the product catalogue of these sites gets more and more diverse, a new problem has arisen that happens when users share likings for lots of products (for which they are reckoned to be neighbors) but they differ in products similar to the one that is being considered for recommendation. They are fake neighbors. Narrowing the range of products on which similarities between users are sought can help to avoid this, but it usually causes a worsening of the sparsity problem because the chances of finding overlappings gets lower. In this paper, a new strategy is proposed based on semantic reasoning that aims to improve the neighborhood formation in order to overcome the aforementioned fake neighborhood problem. Our approach is aimed at making more flexible the search for semantic similarities between different products, and thus not require users to rate the same products in order to be compared.