Neighborhood Relations Research Articles

Cytoarchitectonic Analysis and 3D Maps of the Mesial Piriform Region in the Human Brain

The mesial piriform region plays a central role in olfaction. Its small size and complex geometry, however, make it a difficult target in functional neuroimaging studies, while histological maps often represent schematic drawings, which are not compatible with requirements for modern imaging. To bridge this gap, cytoarchitectonic analysis and mapping of the region was performed in serial histological sections over their full extent in 10 postmortem brains. The temporobasal areas PirTBd and PirTBv and temporal areas PirTu and PirTit were identified and analyzed. Probabilistic cytoarchitectonic maps of the piriform areas in MNI reference space and high-resolution maps of the amygdala-piriform region on the BigBrain model were calculated as part of the Julich-Brain. Differences in the cytoarchitectonic “texture” of the region were quantified based on the Gray Level Co-Occurrence Matrix. Results showed that allocortical areas were not consistently associated with the rostral Limen insulae, although it was often suggested as a landmark in neuroimaging protocols. PirTu was associated with the uncal tip. PirTit was the largest area, reaching to the temporal pole, with a “temporal” (caudal) and a “temporopolar” (rostral) part having complex neighborhood relationships. The probabilistic maps reflect interindividual variability; they are openly available via the digital EBRAINS platform to serve as an anatomical reference for studies related to olfaction.

Solving the medical student scheduling problem using simulated annealing

AbstractWe consider the medical student scheduling (MSS) problem, which consists of assigning medical students to internships of different disciplines in various hospitals during the academic year to fulfill their educational and clinical training. The MSS problem takes into account, among other constraints and objectives, precedences between disciplines, student preferences, waiting periods, and hospital changes. We developed a local search technique, based on a combination of two different neighborhood relations and guided by a simulated annealing procedure. Our search method has been able to find the optimal solution for all instances of the dataset proposed by Akbarzadeh and Maenhout (Comput Oper Res 129: 105209, 2021b), in a much shorter runtime than their technique. In addition, we propose a novel dataset in order to test our technique on a more challenging ground. For this new dataset, which is publicly available along with our source code for inspection and future comparisons, we report the experimental results and a sensitivity analysis.

Goal-conflict identification based on local search and fast boundary-condition verification based on incremental satisfiability filter

Identifying boundary conditions (BCs) is of fundamental importance for goal-conflict analysis. BCs are able to capture particular combinations of circumstances that make some special conflicts, namely goal divergences, in which the goals of the requirement cannot be satisfied as a whole. Unfortunately, existing approaches only handle small-scale cases within a reasonable running time and do not consider how to search more general BCs because of the search space explosion and the high computational cost of verifying BCs. In this paper, we observed that the BCs identified by the state-of-the-art (SOTA) approach share similarities in structure. It inspires us to develop a BC identification approach, named LOGION, based on local search, to better exploit the similarity in structures. Specifically, we utilize the structural similarity of BCs to construct the neighborhood relation of our local search algorithm. Besides, we design strengthening and weakening operators of formulae to capture the semantics of more general BCs in local searching. In order to boost the BC verification, we propose a lasso-based incremental satisfiability filter (LISF). The primary intuition behind LISF is using trace checking as a light-weight pre-checking, and if it is not successful, to call the satisfiability checking. We conduct extensive experiments to evaluate LOGION, comparing against SOTA approaches. Experimental results show that LOGION produces about 1 order of magnitude more general BCs than the SOTA approaches. It is also shown that LISF significantly boosts the speed, especially for large-scale cases (up to about 1 order of magnitude faster on average).

Assessment of Residential Satisfaction using Gap Analysis: A Case Study in Naypyitaw City, Myanmar

This research paper conducts an evaluation of residential satisfaction within government-provided four-storied apartment complexes situated in civil-servant housing areas in Naypyitaw City, Myanmar. As the construction of these apartment buildings continues to address the housing demands of civil servants, comprehending the determinants of residents' contentment becomes paramount. The study encompasses data collected from 500 households through structured questionnaires employing a multi-cluster sampling approach. These questionnaires utilize a five-point Likert Scale format to gauge residents' levels of importance and satisfaction. Gap Analysis is employed to scrutinize the gathered data, ultimately identifying seven pivotal attributes that hold the potential to substantially enhance residential satisfaction. These attributes encompass ceiling height, apartment layout plan, room and corridor dimensions, quantity of rooms, staircase design, neighborhood relations, and proximity to police stations. Based on these revelations, recommendations are tendered, advocating the modification and reevaluation of these attributes in forthcoming housing developments. This underscores the imperative need for creating a livable city by ensuring the provision of essential services in closed proximity to residential areas.

Enhanced Invariant Feature Joint Learning via Modality-Invariant Neighbor Relations for Cross-Modality Person Re-Identification

Enhanced Invariant Feature Joint Learning via Modality-Invariant Neighbor Relations for Cross-Modality Person Re-Identification

공동주택 공동체 활성화 사업이 주민의 사회적 자본 형성과 주거만족에 미치는 영향 탐색

This study intends to explore the structural relationship between participation in community revitalization projects in apartment complexes and the formation of social capital among residents and their relationships with each other and the impact of formed social capital on residential satisfaction. The case study is the Lifelong Learning Village School of Chamisul Apartment in Siheung-si, Gyeonggi-do. Focus Group Interview (FGI) has been conducted with 6 main participants. Social capital includs participation experience, trust, network, and norms, and residential satisfaction included safety, education, culture, environment, and neighborhood relationships. The results show that participation in community revitalization projects influences the formation of social capital in terms of participation experience, trust, network, and norm among community members, and participation experience influences network and trust. The social capital is found to affect housing satisfaction, although there are differences by some factors.

银发一族智慧社区养老服务参与意愿影响因素研究

With the accelerating global aging population, solving eldercare issues has become a significant challenge, and smart community elderly care services based on big data and internet technology have emerged as a response. To further analyze the factors influencing the elderly’s willingness to participate in smart community elderly care services, this study first reviews relevant literature from the perspectives of smart community elderly care and willingness to participate, providing a theoretical basis for this paper. Next, to ensure the typicality of the research, Weifang City is selected as the study’s experimental site. Questionnaires were distributed to collect data on the elderly’s willingness to participate in smart community elderly care services, and univariate analysis was conducted for factor screening. The screened results were then analyzed using binary logistic regression. The results are mainly analyzed from three aspects: individual factors such as age, type of household registration, and occupation; family internal factors such as living situation, family support for participating in community elderly care, and external factors such as neighborhood relationships and exposure to privacy. These factors influence the elderly’s willingness to participate in smart community elderly care. Finally, through discussion, the study identifies existing problems and proposes measures to improve the elderly’s willingness to participate in smart community elderly care, such as providing demand-oriented services, updating elderly care concepts, and creating a favorable external environment.

Any-Stereo: Arbitrary Scale Disparity Estimation for Iterative Stereo Matching

Due to unaffordable computational costs, the regularized disparity in iterative stereo matching is typically maintained at a lower resolution than the input. To regress the full resolution disparity, most stereo methods resort to convolutions to decode a fixed-scale output. However, they are inadequate for recovering vital high-frequency information lost during downsampling, limiting their performance on full-resolution prediction. In this paper, we introduce AnyStereo, an accurate and efficient disparity upsampling module with implicit neural representation for the iterative stereo pipeline. By modeling the disparity as a continuous representation over 2D spatial coordinates, subtle details can emerge from the latent space at arbitrary resolution. To further complement the missing information and details in the latent code, we propose two strategies: intra-scale similarity unfolding and cross-scale feature alignment. The former unfolds the neighbor relationships, while the latter introduces the context in high-resolution feature maps. The proposed AnyStereo can seamlessly replace the upsampling module in most iterative stereo models, improving their ability to capture fine details and generate arbitrary-scale disparities even with fewer parameters. With our method, the iterative stereo pipeline establishes a new state-of-the-art performance. The code is available at https://github.com/Zhaohuai-L/Any-Stereo.

The Local, the Uncanny, the Queer. Neighbourhood in Recent Polish Women’sLiterature

The article presents a diverse spectrum of neighbour relations as depicted in recent Polishwomen’s literature. Representations of neighbourhood in family sagas, herstorical and autoethnographicnarratives, neo-post-settlement novels and pathographies are discussed (novels by JoannaBator, Dominika Buczak, Agnieszka Kuchmister, Dorota Kotas, Aleksandra Zielińska). The articlealso address the problem of neighbourhood in a post-anthropocentric perspective, transcendinginterpersonal relations.

Multi-level graph regularized robust multi-modal feature selection for Alzheimer’s disease classification

Multi-modal classification has demonstrated its superiority over conventional single-modal based methods on Alzheimer’s disease (AD) diagnosis, and multi-modal feature selection has attracted increasing attention. However, most previous approaches use a fixed affinity matrix to describe the local neighborhood relations among samples, and only consider the intra-modal similarity while ignoring the inter-modal similarity. Besides, they generally treat all samples equally and neglect the negative influence of noise and outliers. For solving these problems, this paper proposes a new multi-level Graph regularized Robust Multi-modal Feature Selection method called GRMFS that simultaneously performs noise-robust feature selection and adaptive multi-level similarity preservation. On the one hand, GRMFS introduces an ɛ-capped ℓ2-norm loss into regression framework to improve the robustness against outliers, which adaptively assigns a weight to each sample. On the other hand, to explore the intrinsic multi-modal local structures, GRMFS simultaneously learns intra-modal and inter-modal local similarities, and preserves them in subspace to guide feature selection. Experiments on real AD database illustrate the advantages of our proposed in identifying disease status compared with other approaches.

Development of multilevel mesh adaptivity for global variance reduction in Monte Carlo code cosRMC

The mesh-based weight window method is a widely used global variance reduction technique for Monte Carlo particle transport calculation. In order to meet the fine voxel division required for high-quality weight window distribution, the uniform structured mesh division will lead to large memory footprint pressure. This work focused on the research of the multilevel adaptive mesh algorithm, using a recursive algorithm to adjust the voxel resolution adaptively according to the material of geometry. Then the adaptive mesh algorithm was applied to the weight window generator based on the response matrix method by establishing the neighbor relationship of adaptive mesh. And an optimization method for memory allocation of the response matrix solver was proposed, effectively reducing the memory footprint of the response matrix solver in parallel computing. In the calculation of the CFETR model, the calculation efficiency under the weight window based on adaptive mesh was increased by 53.3 % compared with the weight window based on uniform mesh, and the proportion of voxels with a statistical error less than 10 % reached 93.33 %, which verified the effectiveness of the multilevel adaptive mesh algorithm.

Neighborhood safety perception and depressive symptoms in China: a moderated mediation relationship.

Despite rich data on neighborhood safety perception's role in shaping depressive symptoms, a comprehensive view of this dynamic interplay remains a frontier. This study seeks to unravel the intricate interplay of neighborhood safety perception and depressive symptoms, utilizing the lens of social safety theory. Employing the 2016 and 2020 waves of the China Family Panel Studies (CFPS), this study utilized ordered logistic regression (ologit) for statistical analysis. The approach encompassed descriptive analysis of variables, Spearman's correlation analyses to explore associations between variables, and a moderated mediation analysis. Sensitivity analyses were also conducted to affirm the robustness of findings against model assumptions and data processing techniques. The study uncovered a significant negative correlation between neighborhood safety perception and depressive symptoms (Direct effect = - 0.338, Z = - 2.564, p = 0.010). A key finding was the steeper slope of the relationship between neighborhood safety perception and neighborhood relation perception among individuals with a higher perception of neighborhood environment quality. As neighborhood environment quality perception increases, the strength of the mediated negative impact on depressive symptoms intensifies (- 0.102 > - 0.132 > - 0.162). This study offers a comprehensive moderated mediation model that establishes a novel connection between neighborhood safety perception and depressive symptoms. It integrates the complexities of social safety theory and social information processes, revealing key strategies such as enhancing perceived neighborhood environment quality and neighborhood relationship quality.

The association between neighborhood social and built environment on loneliness among young adults with cancer.

Young adults with cancer (YAs, aged 18-39) are at increased risk of experiencing loneliness due to their unique challenges of coping with a cancer diagnosis and treatment during young adulthood. Understanding factors that impact loneliness is critical to improving survivorship outcomes for this vulnerable YA population. Neighborhoods are key determinants of health. However, little is known about how such neighborhood characteristics are associated with loneliness among YA survivors. YA survivors (N = 181) drawn from the National Institutes of Health All of Us Research Program completed measures of neighborhood social environment (e.g., shared values), aspects of their neighborhood built environment (e.g., access to transit, recreational activities), and loneliness. Two total scores were calculated with higher scores reflecting higher neighborhood social cohesion and higher neighborhood walkability/bikeability (i.e., built environment). Hierarchical linear regression examined associations between the social and built environment on loneliness. Higher levels of neighborhood social cohesion (β = - 0.28, 95% confidence interval (CI) = - 0.44, - 0.11) and neighborhood walkability/bikeability (β = - 0.15, 95% CI = - 0.31, - 0.006) were significantly associated with lower levels of loneliness. Findings suggest that living within a cohesive social environment with neighborhood walkability/bikeability to built environment amenities such as green space, grocery stores, and public transportation is protective against loneliness among YA survivors. More longitudinal research is necessary to understand the dynamic changes in loneliness among YA survivors living in diverse social and built environments. YA survivors may benefit from cultivating neighbor relationships and living within neighborhoods with walkability/bikeability.

Improved topology identification for distribution network with relatively balanced power supplies

AbstractHaving correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

Deep Neighborhood-aware Proxy Hashing with Uniform Distribution Constraint for Cross-modal Retrieval

Cross-modal retrieval methods based on hashing have gained significant attention in both academic and industrial research. Deep learning techniques have played a crucial role in advancing supervised cross-modal hashing methods, leading to significant practical improvements. Despite these achievements, current deep cross-modal hashing still encounters some underexplored limitations. Specifically, most of the available deep hashing usually utilizes pair-wise or triplet-wise strategies to promote the separation of the inter-classes by calculating the relative similarities between samples, weakening the compactness of intra-class data from different modalities, which could generate ambiguous neighborhoods. In this article, the Deep Neighborhood-aware Proxy Hashing (DNPH) framework is proposed to learn a discriminative embedding space with the original neighborhood relation preserved. By introducing learnable shared category proxies, the neighborhood-aware proxy loss is proposed to project the heterogeneous data into a unified common embedding, in which the sample is pulled closer to the corresponding category proxy and is pushed away from other proxies, capturing small within-class scatter and big between-class scatter. To enhance the quality of the obtained binary codes, the uniform distribution constraint is developed to make each hash bit independently obey the discrete uniform distribution. In addition, the discrimination loss is designed to preserve modality-specific semantic information of samples. Extensive experiments are performed on three benchmark datasets to prove that our proposed DNPH framework achieves comparable or even better performance compared with the state-of-the-art cross-modal retrieval applications. The corresponding code implementation of our DNPH framework is as follows: https://github.com/QinLab-WFU/OUR-DNPH .

Hierarchical relationship modeling in multi-agent reinforcement learning for mixed cooperative–competitive environments

In multi-agent reinforcement learning (MARL), information fusion through relationship modeling can effectively learn behavior strategies. However, the high dynamics among heterogeneous interactive agents in mixed cooperative–competitive environments pose difficulties for relational modeling. Traditional MARL solutions concatenate all agents’ states based on the global relationship, which is unrealistic and unscalable under large-scale conditions. Other methods fuse local information by modeling neighbor relationships, but local features lead to suboptimal strategies. From this perspective, we propose a novel relational information fusion approach for mixed tasks to fuse local and global features by modeling heterogeneous relationships through a hierarchical graph. During training, remote agents’ global features are fused through second-order graph representation to help strategy optimization. During decision making, the practicality and scalability of strategy are improved by fusing neighbor agents’ local features through first-order graph representation. Our approach consistently outperforms the state-of-the-art MARL methods in several multi-agent tasks, such as the Predator–Prey and Soccer Games. In particular, it achieves an 83.7% win rate, which is 11.5% higher than baselines in the 4 vs. 4 Soccer Game, and can scale from 4 vs. 4 to 13 vs. 13 in the Predator–Prey Game while maintaining good performance.

Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism

The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighbor-enriched subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction.

Research on factors influencing Chinese parents' support for death education: a cross-sectional survey.

This study aims to explore the factors influencing Chinese parents' attitudes toward death education. Given the current lack of such education in China, this research is particularly significant. Death education is vital for shaping the values of young people and alleviating mental health issues, such as depression and suicidal tendencies. By identifying these influencing factors, this study seeks to provide guidance for policymakers and educators in promoting the development and widespread adoption of death education. To do so, a national cross-sectional quota sample of 12,435 Chinese parents was used. Borrowing from social-ecological theory, the researchers carried out multiple stepwise regression analyses to examine the individual, family, and social-level factors that shape the supportive attitudes of Chinese parents toward death education. The findings revealed that at the individual level, parent (β = 0.04, p < 0.001), education level (β = 0.07, p < 0.001), and religious belief (β = -0.02, p < 0.05) were significant predictors of Chinese parents' support for death education. Meanwhile, at the family and social level, average monthly household income (β = 0.07, p < 0.001), family health (β = 0.03, p < 0.05), family communication (β = 0.02, p < 0.05), social support (β = 0.15, p < 0.001), neighborhood relations (β = 0.11, p < 0.001), and social network size (β = 0.05, p < 0.001) were significant predictors of Chinese parents' supportive attitudes toward death education. Based on these findings, it is suggested that the relevant development, planning, publicity, and public welfare groups and government departments should promote death education, provide more social support, and encourage neighborhood harmony. As higher education and average monthly household income were found to significantly impact the support, the government should improve access to higher education and actively work to increase residents' income to facilitate the development of death education.

A Method for Enhancing the Simulation Continuity of the Snesim Algorithm in 2D Using Multiple Search Trees

Multiple-point geostatistics (MPS) has more advantages than two-point geostatistics in reproducing the continuity of geobodies in subsurface reservoir modeling. For fluvial reservoir modeling, the more continuous a channel, the more consistent it is with geological knowledge in general, and fluvial continuity is also of paramount importance when simulating fluid flow. Based on the pixel-based MPS algorithm Snesim, this study proposes a method that utilizes multiple search trees (MSTs) to enhance simulation continuity in 2D fluvial reservoir modeling. The objective of the MST method is to capture complete data events from a training image (TI), which aims to achieve enhanced continuity in fluvial reservoir sublayer modeling. By resorting to search neighborhoods based on their proximity to the central node of the data template, multiple data templates that correspond to the MSTs will be generated. Here, four data templates were generated by arranging the relative search neighborhood coordinates in ascending and descending order with respect to the central node. Parallel computing was tried for the construction of the search trees. This work calculated the conditional probability distribution function (CPDF) of the simulating nodes by averaging the CPDFs derived from the MSTs, and double retrieval was employed to filter out the search trees that possessed an inaccurate local CPDF for the simulating nodes. In addition, the connected component labeling (CCL) method was introduced to evaluate the simulation continuity in MPS. The results indicated that the MST method can enhance the simulation continuity of the Snesim algorithm by reproducing the fine connectivity of channel facies in 2D fluvial reservoir modeling.

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

Due to the heterogeneity of data distribution in real life and the spatial autocorrelation among spatial instances, traditional spatial co-location pattern mining methods tend to ignore valuable information specific to local regions. To address the limitation, regional co-location pattern mining has been proposed to find patterns that may be hidden within local regions. In this paper, a fuzzy regional co-location pattern mining framework based on efficient density peak clustering and maximal fuzzy grid cliques is presented. By incorporating a grid-splitting method and fuzzy theory, an efficient density peak clustering algorithm is proposed to divide the global area into distinct local regions. Furthermore, we propose a method to materialize the neighbor relationships between instances based on the maximal fuzzy grid cliques, and parallelize the clustering process to improve the algorithm efficiency. Experimental results show that the proposed algorithm can not only reduce the time consumption by about 40%, but also mine meaningful patterns with tighter instance distributions.