Set Of Neighbors Research Articles

Triangle Centrality

Triangle centrality is introduced for finding important vertices in a graph based on the concentration of triangles surrounding each vertex. It has the distinct feature of allowing a vertex to be central if it is in many triangles or none at all. Given a simple, undirected graph \(G=(V,E)\) with \(n=|V|\) vertices and \(m=|E|\) edges, let \(\triangle(v)\) and \(\triangle(G)\) denote the respective triangle counts of \(v\) and \(G\) . Let \(N(v)\) be the neighborhood set of \(v\) . Respectively, \(N_{\triangle}(v)\) and \(N_{\triangle}[v]=\{v\}\cup N_{\triangle}(v)\) denote the set of neighbors that are in triangles with \(v\) and the closed set including \(v\) . Then the triangle centrality for a vertex \(v\) is \(\begin{align*}TC(v)=\frac{\frac{1}{3}\sum_{u\in N_{\triangle}[v]}\triangle(u)+\sum_{w\in\{N(v)\setminus N_{\triangle}(v)\}}\triangle(w)}{\triangle(G)}.\end{align*}\) We show experimentally that triangle centrality is broadly applicable to many different types of networks. Our empirical results demonstrate that 30% of the time triangle centrality identified central vertices that differed with those found by five well-known centrality measures, which suggests novelty without being overly specialized. It is also asymptotically faster to compute on sparse graphs than all but the most trivial of these other measures. We introduce optimal algorithms that compute triangle centrality in \(O(m\overline{\delta})\) time and \(O(m+n)\) space, where \(\overline{\delta}\leq O(\sqrt{m})\) is the average degeneracy introduced by Burkhardt, Faber, and Harris (2020). In practical applications, \(\overline{\delta}\) is much smaller than \(\sqrt{m}\) so triangle centrality can be computed in nearly linear time. On a Concurrent Read Exclusive Write (CREW) Parallel Random Access Machine (PRAM), we give a near work-optimal parallel algorithm that takes \(O(\log n)\) time using \(O(m\sqrt{m})\) CREW PRAM processors. In MapReduce, we show it takes four rounds using \(O(m\sqrt{m})\) communication bits and is therefore optimal. We also derive a linear algebraic formulation of triangle centrality which can be computed in \(O(m\overline{\delta})\) time on sparse graphs.

Feeling Carcerality: How Carceral Seepage Shapes Racialized Emotions

Abstract This article examines how young people experience policing and reveals the emotional weight of the carceral state. Drawing on interviews and focus groups with over forty Black and non-Black Latinx young men in Los Angeles County, I argue that the racialized emotions the young men allude to do not stem from one individual encounter with the police or any single identifiable source. Instead, they are responses to the ongoing violence of what I describe as carceral seepage: witnessing the policing of loved ones and peers, the omnipresence of police, and the vulnerability of being criminalized across social contexts (schools, healthcare settings, neighborhood settings, etc.). Integrating theories on racialized emotions and the slow violence of policing, I use carceral seepage to show the breadth of the carceral state and demonstrate how it elicits a particular set of racialized emotional responses (or racialized emotions).

Evaluating the Influence of Basel Accord on Banking Sector of Pakistan

The papers aim to investigate how Basel III affected Pakistan's entire banking sector from 2013 to 2018. The requirements and obligations of Basel III are regarded as the most efficient tools for the banking sector. When analyzing the impact of Basel III on the banking industry, three primary considerations are made: financial size, spread, and provision for non-performing loans. Relapse investigations were utilized for checking speculation. We inferred that the general pummel of Basel III on banks is enormous, yet not all banks have totally taken on Basel III. The significance of this research lies in giving important experiences into the viability of global regulatory structures in rising economies' potential. By evaluating the Basel Accord's effect on Pakistan's banking sector, this study adds to a more extensive comprehension of how worldwide guidelines can be adjusted to neighborhood settings and how they can address explicit challenges faced by banks in emerging nations. This analysis not only reveals insight into the operational and vital changes made by Pakistani banks but also gives suggestions for policymakers to upgrade regulatory practices and monetary strength. Eventually, this research aims to illuminate future regulatory changes and work on the flexibility of Pakistan's banking sector in the face of worldwide monetary uncertainties. We propose highlighting semiannually the effects of Basel on the banking industry and conducting a comparison analysis of conventional and Islamic banks.

Combinatorial lower bounds for the Generalized Traveling Salesman Problem with Neighborhoods

In this paper, we study the Generalized Traveling Salesman Problem with Neighborhoods (GTSPN), a variant of the Traveling Salesman Problem (TSP), where the goal is to find the shortest path visiting each of the given neighborhood sets represented as a set of convex regions. The GTSPN is motivated by the sequencing problem of robotic manipulators, where an operation can be achieved from multiple locations, such as the visual inspection that can be performed from several possible views. The GTSPN formulation allows for exploiting continuous optimization to find the most suitable locations for the inspection, yielding possible solution cost reduction. Moreover, instances with overlapping high-dimensional convex regions further allow modeling neighborhood sets with complex shapes. We propose a novel approach to determine the first lower bounds to the studied GTSPN by employing the Branch-and-Bound (BB) method and the Mixed-Integer Second-Order Cone Programming (MISOCP) model for particular BB subproblems. In addition, the proposed method allows for solving the GTSPN to optima. The developed lower bound determination is further exploited in the empirical evaluation of existing heuristic approaches to the GTSPN and assesses the solution quality using the relative optimality gap. Regarding the presented results, the proposed BB-based approach provides tight lower bounds and solutions with up to 20% optimality gap for the GTSPN instances with less than 15 neighborhood sets for the given limited computational time. Furthermore, the presented results support that the proposed approach is suitable for solving high-dimensional instances of the GTSPN that can be found in inspection tasks with robotic manipulators.

A novel ranking approach for identifying crucial spreaders in complex networks based on Tanimoto Correlation

The identification and rank of crucial spreaders aim to survey the diffusion capability, which has a significant impact on controlling information spread in networks. However, most of the findings neglect the fused information of the structural hole and neighbors within two orders. In addition, they fail to focus on the effect brought by the association strength between a pair of different neighborhood sets. To solve these issues, in this work, we design a novel semi-local ranking algorithm for achieving better recognition effects, referred to as the Extended Tanimoto Correlation (ETC). We first construct a topological structure of neighborhood sets Cluster, and define HierarchyVectorofCluster based on a novel layered approach IKsD. Considering the structural hole, Tanimoto Correlation (TC) is presented to contribute to measuring the association strength between different clusters. Additionally, we explicitly extend the Tanimoto Correlation metric on semi-local information to calculate each spreader’s influence score. Simulation experiments employ capability indicators and correctness metrics to apply in various networks. Results demonstrate that our method outperforms state-of-the-art algorithms in identifying and ranking effects for good spreading influence.

Discrepancy and sparsity

We study the connections between the notions of combinatorial discrepancy and graph degeneracy. In particular, we prove that the maximum discrepancy over all subgraphs H of a graph G of the neighborhood set system of H is sandwiched between Ω(log⁡deg(G)) and O(deg(G)), where deg(G) denotes the degeneracy of G. We extend this result to inequalities relating weak coloring numbers and discrepancy of graph powers and deduce a new characterization of bounded expansion classes.Then we switch to a model theoretical point of view, introduce pointer structures, and study their relations to graph classes with bounded expansion. We deduce that a monotone class of graphs has bounded expansion if and only if all the set systems definable in this class have bounded hereditary discrepancy.Using known bounds on the VC-density of set systems definable in nowhere dense classes we also give a characterization of nowhere dense classes in terms of discrepancy.As consequences of our results, we obtain a corollary on the discrepancy of neighborhood set systems of edge colored graphs, a polynomial-time algorithm to compute ε-approximations of size O(1/ε) for set systems definable in bounded expansion classes, an application to clique coloring, and even the non-existence of a quantifier elimination scheme for nowhere dense classes.

Estimating neighborhood-based mortality risk associated with air pollution: A prospective study

Effect modification of integrated neighborhood environment on associations of air pollution with mortality remained unclear. We analyzed data from UK biobank prospective study (n = 421,650, median 12.5 years follow-up) to examine disparities of mortality risk associated with air pollution among varied neighborhood settings. Fine particulate matter (PM2.5), PM10 and nitrogen dioxide (NO2) were measured and assigned to each participants’ address. Diverse ecological and societal settings of neighborhoods were integrated with principal component analysis and categorized into disadvantaged, intermediate and advantaged levels. We estimated mortality risk associated with air pollution across diverse neighborhoods using Cox regression. We calculated community-level proportions of mortality attributable to air pollutants. There was evidence of higher all-cause and respiratory disease mortality risk associated with PM2.5 and NO2 among those in disadvantaged neighborhoods. In disadvantaged communities, air pollutants explained larger proportions of deaths and such disparities persisted over past decades. Across 2010–2021, reducing PM2.5 and NO2 to 10 μg/m3 (World Health Organization limits) would save 87,000 (52,000−120,000) and 91,000 (37,000−145,000) deaths of populations aged ≥ 40 years, with 150 000 deaths occurred in disadvantaged neighborhood settings. These findings suggested that disadvantaged neighborhoods can exacerbate mortality risk associated with air pollution.

An examination of urban green spaces usage behaviours and self-perceived mental health benefits: findings from two urban green neighbourhoods in Pakistan

ABSTRACT The present study examined urban green space users’ self-perceived mental health benefits of visiting green spaces in Pakistan – one of South Asia’s rapidly urbanizing countries. By using a quantitative-based cross-sectional survey, 384 participants were surveyed in green urban neighbourhood settings. It was found that most green space users believed that their visits to green spaces have at least some positive effects on the different parameters of their mental health, such as stress or anxiety levels, mood, energy levels, optimism levels, confidence levels, or concentration levels, among others. The study explored that the frequency and duration of green space usage and intensity of activity performed in the green space have an important role in determining the degree of the self-perceived mental health benefits of visiting green spaces, suggesting the importance of green interventions, including physical changes in the environment and social promotion activities to encourage green space usage among urban dwellers. Moreover, the study revealed low green space usage and activity intensity levels among certain sub-population groups compared to others, such as women, young adults, or the elderly, highlighting the need to ensure equity of access to green spaces for all sub-population groups and prioritize them in intervention strategies.

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection

Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists-NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted NPE (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index is introduced in a case study, amalgamating the false alarm rate and fault detection rate. The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman process dataset, highlighting its superiority over conventional approaches.

Group vertex magic labeling of some special graphs

For any additive abelian group $A$. Let $\mu$ be an element of $A$, a graph $G=(V,E)$ is said to be $A$-vertex magic graph if there exist a labeling function $f:V(G)\rightarrow A\setminus\{0\}$ such that $\omega(v)=\sum_{u\in N(v)} f(u)=\mu$ for any vertex $v$ of $G$, where $N(v)$ is the set of the open neighborhood of $v$. In this paper, we prove that the graphs such as wheel, Corona $C_{n}\odot mk$, subdivision of ladder and $t$-fold wheel for $t\neq n$ nor $n-2$ are $A$-vertex magic graphs. Also we prove that the subdivide wheel, helm and closed helm are $Z_{k}$-vertex magic graphs. However we prove that the triangular book and $t$-fold wheel for $t=n,n-2$ are group vertex magic graphs.

Distance-Based Fractional Dimension of Certain Wheel Networks

Distance-Based Fractional Dimension of Certain Wheel Networks

Walking on Real-world Terrain with an Ankle Exoskeleton in Cerebral Palsy.

Despite medical treatment focused on addressing walking disability, many millions of people with neurological conditions, like cerebral palsy (CP), struggle to maintain independent mobility. Lower limb exoskeletons and exosuits may hold potential for augmenting walking ability. However, it remains unknown whether these wearable robots are safe and beneficial for use outside of highly controlled laboratory environments, the demonstration of which is necessary for clinical translation. Here, we show that a lightweight, portable, ankle exoskeleton with an adaptable one-size-works-for-all assistance controller can improve energy efficiency and walking speed for individuals with CP spanning a wide spectrum of lower limb impairment in a multi-terrain real-world environment. Tested on an outdoor walking route with level, sloped, and stair terrain, robotic assistance resulted in a 15-18% (p = 0.013-0.026) reduction in estimated energy cost and a 7-8% (p = 0.001-0.004) increase in average walking speed across "shorter" 6-minute and "longer" 20-minute walking durations relative to unassisted walking. This study provides evidence that wearable robots may soon improve mobility in neighborhood, school, and community settings for individuals with CP.

Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring

AbstractProcess monitoring plays an essential role in ensuring the safe and efficient operation of multi‐unit plantwide industrial processes. However, complicated nonlinear dynamics of these processes pose enormous challenges to the plantwide process monitoring approaches. In this paper, a novel distributed temporal–spatial neighbourhood enhanced variational autoencoder (DTS‐VAE) monitoring scheme is proposed. The scheme selects the temporal and spatial neighbourhood sets of a current sample from a local unit. Time‐series correlation is utilized to construct temporal representative samples to obtain process dynamic characteristics. Likewise, spatial similarity is employed to construct spatial representative samples to obtain spatial patterns in the data. Then the reconstructed and current samples serve as the inputs for a variational autoencoder to extract features from the current sample and its neighbourhoods. Subsequently, a distributed monitor that considers the temporal–spatial characteristics is established for each unit. Finally, a comprehensive evaluation index is developed using Bayesian fusion strategy to improve monitoring performance. The proposed DTS‐VAE monitoring scheme was effectively verified on the Tennessee Eastman benchmark process and a wastewater treatment plant.

A method for finding a maximum value region with a minimum width in raster space

Given a grid of cells, each of which is assigned a numerical value quantifying its suitability for a certain use, one problem in geographic information science concerns the selection of a region, i.e. a connected set of cells, with a specified size that maximizes the sum of all their values. This task can be cast as a combinatorial optimization problem called the maximum value region problem, and exact and heuristic methods exist for its solution. While those solutions are guaranteed to be feasible (if not optimal), they may not be desirable for practical use if they contain too narrow segments (down to the width of a single cell). In this paper, we present a new variation of the maximum value region problem—the maximum value wide region problem—that requires a region to be at least as wide as a specified width. We offer a heuristic method for its solution which models a region as a set of neighborhoods and test its performance through computational experiments. Results demonstrate that the method generates good feasible solutions in terms of connectedness, size, width, and value, but requires more computing time than methods for maximum value regions without minimum width requirements.

MRAC-based dynamic consensus of linear systems with biased measurements over directed networks

In this paper we address the dynamic leaderless consensus control problem for generic linear systems (stable, unstable, marginally stable, etc.) interconnected over directed networks and under the influence of biased measurements. Essentially, the control problem consists in redesigning a standard distributed consensus controller which, for each system, relies on own state biased measurements and respective erroneous data received from a set of neighbors. The difficulty in such a scheme resides in the fact that the measurement bias is directly amplified by the control gain so it cannot be handled as an additive external disturbance. Our control design relies, on one hand, on the solution of a Riccati equation and, on the other, on the design of an estimator reminiscent of a model-reference-adaptive control design. The estimator successfully computes a bias estimate and completely compensates for its effect if the bias is constant—indeed, in this case, we establish exponential stability of the consensus manifold and we show that the controller provides robustness with respect to time-varying biases.

Monitoring and Evaluation of Dementia-Friendly Neighborhoods Using a Walkshed Approach: Protocol for a Scoping Review.

The number of people in society living with dementia is growing. In Canada, most people who live with dementia live at home, often in a neighborhood setting. Neighborhood environments can be a source of independence, social engagement, and well-being. They can also contain barriers that limit physical activity, social engagement, and well-being. A dementia-friendly neighborhood includes assets that support persons living with dementia and their caregivers in multiple life domains, including those that support walking within the neighborhood environment. The objectives for this scoping review are twofold. First, focusing on walkshed analysis, we aim to extend scholarly understandings of methodological practices used in the monitoring and evaluation of dementia-friendly neighborhoods. Second, we aim to provide clear and practical guidance for those working in planning, design, and public health fields to assess the neighborhood context in support of evidence-based action to improve the lives of persons living with dementia. The study design follows Arksey and O'Malley's scoping review framework and PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. We will conduct a search of peer-reviewed studies in 6 electronic databases to identify the use of Geographic Information System analysis to measure the walkshed of persons living with dementia in a community setting. As age is a primary risk factor associated with dementia, we will also include studies that focus more broadly on community-dwelling older adults aged 65 years and older. Data will be extracted, analyzed, and represented according to 3 domains. This includes study details, walkshed analysis methods, and criteria and indicators used to measure dementia-friendly neighborhoods. The results of the study and the submission of a manuscript for peer review are expected in June 2024. The results of the review are expected to contribute to an understanding of methods for monitoring and evaluating dementia-friendly neighborhoods. Expected findings will include a detailed breakdown of current parameters and routines used to conduct walkshed analysis. Findings will also convey criteria that can be operationalized in a Geographic Information System as indicators to assess barriers and facilitators to walking in a neighborhood setting. As far as we are aware, the proposed scoping review will be the first to provide comprehensive methodological or technical guidance for conducting walkshed analysis specific to persons living with dementia. Both the scalability and objective nature of walkshed analysis are likely to be of direct interest to public health practitioners, planners, and allied professionals. Clearly documenting methods used in walkshed analysis can spur increased collaboration across these disciplines to enable an evidence-informed approach to improving neighborhood environments for persons living with dementia. PRR1-10.2196/50548.

Spatial patterns of PM2.5 air pollution in Jakarta: Insights from mobile monitoring

A mobile air quality monitoring study in Jakarta was undertaken in 2022 by the Clean Air Catalyst consortium, in partnership with Google Earth Outreach and Aclima, taking air pollution measurements using four specially equipped Google Street View cars. This study aims to map street-level PM2.5 pollution in a set of neighborhoods (polygons) throughout Jakarta to evaluate the variability caused by local emission sources and to observe the exposure to air pollution in areas with different income levels. To address these aims, we monitored the concentration five days a week, 8 am to 5 pm, from September to December in eight polygons, stratified to include high and low-income areas. The cars covered a distance of 7,750 km and at least 22 visits per location. PM2.5 concentrations were measured at 1-second intervals using a light-scattering sensor. This high-resolution data collection allowed for detailed monitoring and analysis of concentrations in the study area. The measurements showed temporal variability consistent with meteorological and activity patterns in Jakarta, with higher measured concentrations in the morning compared to the afternoon at most polygons. Median concentrations varied by 2x - >5x within individual polygons, and this variability was particularly evident in the northern part of Jakarta, encompassing both high-income and low-income neighborhoods, where these areas are significantly affected by traffic and industrial activities. The results demonstrate the extent to which PM2.5 concentrations and exposures within Jakarta neighborhoods depend on proximity to local air pollution sources.

China Initiative for Multi-Domain Intervention (CHINA-IN-MUDI) to Prevent Cognitive Decline: Study Design and Progress.

Alzheimer's disease (AD), the most common type of irreversible dementia, is predicted to affect 152 million people by 2050. Evidence from large-scale preventive randomized controlled trials (RCTs) on modifiable risk variables in Europe has shown that multi-domain lifestyle treatments for older persons at high risk of dementia may be practical and effective. Given the substantial differences between the Chinese and European populations in terms of demographics and living conditions, direct adoption of the European program in China remains unfeasible. Although a RCT has been conducted in China previously, its participants were mainly from rural areas in northern China and, thus, are not representative of the entire nation.There is an urgent need to establish cohorts that represent different economic, cultural, and geographical situations in order to explore implementation strategies and evaluate the effects of early multi-domain interventions more comprehensively and accurately. We developed an integrated intervention procedure implemented in urban neighborhood settings, namely China Initiative for Multi-Domain Intervention (CHINA-IN-MUDI). CHINA-IN-MUDI is a 2-year multicenter open-label cluster-randomised controlled trial centered around a Chinese-style multi-domain intervention to prevent cognitive decline. Participants aged 60-80 years were recruited from a nationally representative study, i.e. China Healthy Aging and Dementia Study cohort. An external harmonization process was carried out to preserve the original FINGER design. Subsequently, we standardized a series of Chinese-style intervention programs to align with cultural and socioeconomic status. Additionally, we expanded the secondary outcome list to include genomic and proteomic analyses. To enhance adherence and facilitate implementation, we leveraged an e-health application. Screening commenced in July 2022. Currently, 1,965 participants have been randomized into lifestyle intervention (n = 772) and control groups (n = 1,193). Both the intervention and control groups exhibited similar baseline characteristics. Several lifestyle and vascular risk factors were present, indicating a potential window of opportunity for intervention. The intervention will be completed by 2025. This project will contribute to the evaluation of the effectiveness and safety of intervention strategies in controlling AD risk and reducing clinical events, providing a basis for public health decision-making in China.

NEW SORT OF NEIGHBOURHOOD AND DERIVED SETS IN MILLI TOPOLOGICAL SPACES

This manuscript aims to define several models in milli topological spaces. This approach extends the scope of applications in topology. Firstly, we generate a new class of milli neighbourhood of a point, milli neighbourhood of a set

ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling

Distributed computing is promising to enable large-scale graph neural network (GNN) model training. However, care is needed to avoid excessive computational and communication overheads. Sampling is promising in terms of enabling scalability, and sampling techniques have been proposed to reduce training costs. However, online sampling introduces large overheads, and while offline sampling that is done only once can eliminate such overheads, it instead introduces information loss and accuracy degradation. Thus, existing sampling techniques are unable to improve simultaneously both efficiency and accuracy, particularly at low sampling rates. We develop a distributed system, ADGNN, for full-batch based GNN training that adopts a hybrid sampling architecture to enable a trade-off between efficiency and accuracy. Specifically, ADGNN employs sampling result reuse techniques to reduce the cost associated with sampling and thus improve training efficiency. To alleviate accuracy degradation, we introduce a new metric,Aggregation Difference (AD), that quantifies the gap between sampled and full neighbor set aggregation. We present so-called AD-Sampling that aims to minimize the Aggregation Difference with an adaptive sampling frequency tuner. Finally, ADGNN employs anAD -importance-based sampling technique for remote neighbors to further reduce communication costs. Experiments on five real datasets show that ADGNN is able to outperform the state-of-the-art by up to nearly 9 times in terms of efficiency, while achieving comparable accuracy to the non-sampling methods.