scholarly journals Discussing Street and Neighborhood Relations from Children’s Paintings: The Case of Sütlüce and Örnektepe Neighborhoods

2019 ◽  
pp. 516-542
Author(s):  
Yasemin Çakırer Özservet
Keyword(s):  
Neighborhood Relations

Cell-cell-neighborhood relations in tissue sections-A quantitative model for tissue cytometry

2009 ◽  
Vol 75A (4) ◽  
pp. 356-361 ◽  
Author(s):  
N. Händel ◽  
A. Brockel ◽  
M. Heindl ◽  
E. Klein ◽  
H. H. Uhlig
Keyword(s):  
Quantitative Model ◽  
Tissue Sections ◽  
Neighborhood Relations ◽  
Cell Cell

scholarly journals Orders Induced by Segments in Floorplans and $(2 - 14 - 3, 3 - 41 - 2)$-Avoiding Permutations

10.37236/2607 ◽  
2013 ◽  
Vol 20 (2) ◽  
Author(s):  
Andrei Asinowski ◽  
Gill Barequet ◽  
Mireille Bousquet-Mélou ◽  
Toufik Mansour ◽  
Ron Y. Pinter
Keyword(s):  
The Sixties ◽  
Neighborhood Relations ◽  
Forbidden Patterns ◽  
Special Case

A floorplan is a tiling of a rectangle by rectangles. There are natural ways to order the elements - rectangles and segments - of a floorplan. Ackerman, Barequet and Pinter studied a pair of orders induced by neighborhood relations between rectangles, and obtained a natural bijection between these pairs and $(2 - 41 - 3, 3 - 14 - 2)$-avoiding permutations, also known as (reduced) Baxter permutations.In the present paper, we first perform a similar study for a pair of orders induced by neighborhood relations between segments of a floorplan. We obtain a natural bijection between these pairs and another family of permutations, namely $(2 - 14 - 3, 3 - 41 - 2)$-avoiding permutations.Then, we investigate relations between the two kinds of pairs of orders - and, correspondingly, between $(2 - 41 - 3, 3 - 14 - 2)$- and $(2 - 14 - 3, 3 - 41 - 2)$-avoiding permutations. In particular, we prove that the superposition of both permutations gives a complete Baxter permutation (originally called $w$-admissible by Baxter and Joichi in the sixties). In other words, $(2 - 14 - 3, 3 - 41 - 2)$-avoiding permutations are the hidden part of complete Baxter permutations. We enumerate these permutations. To our knowledge, the characterization of these permutations in terms of forbidden patterns and their enumeration are both new results.Finally, we also study the special case of the so-called guillotine floorplans.

scholarly journals The Role of Intergenerational Neighborhood Relations for Precarious Aging During the COVID-19 Pandemic in Germany

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 531-531
Author(s):  
Anna Wanka
Keyword(s):  
Older Adults ◽  
Qualitative Study ◽  
Age Groups ◽  
Later Life ◽  
Risk Groups ◽  
Living Environment ◽  
Mixed Methods Study ◽  
Neighborhood Relations ◽  
Do So

Abstract Throughout the Covid-19 pandemic, the immediate living environment has significantly gained importance - particularly for people framed as ‘risk-groups’, such as older adults. Effects of contact restrictions to contain the spread of the virus have affected inequalities, uncertainties and loneliness in later life differently depending on the intergenerational relations, informal infrastructures of provisioning and networks of solidarity given in a certain neighborhood. The paper presents findings from a recent mixed-methods study in Frankfurt, Germany, combining a quantitative survey (n=1.000) with a longitudinal qualitative study (n=60). Results show how intergenerational neighborhood relations can play a crucial role in mediating risks of pandemic precariousness in later life, but also how older adults themselves significantly contributing to neighborhood networks of provisioning. Strengthening such very local relations is key to protecting all age groups from the effects of crises beyond the pandemic, and, in conclusion, ways to do so are being discussed.

scholarly journals Neighborhood Attentional Memory Networks for Recommendation Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tianlong Gu ◽  
Hongliang Chen ◽  
Chenzhong Bin ◽  
Liang Chang ◽  
Wei Chen
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Implicit Feedback ◽  
Learning Techniques ◽  
Ranking Score ◽  
Memory Modules ◽  
Output Module ◽  
Neighborhood Relations ◽  
Real World Datasets

Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users’ and items’ neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users’ neighborhood relations and items’ neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods.

Automatic Bug Triage in Software Systems Using Graph Neighborhood Relations for Feature Augmentation

2020 ◽  
Vol 7 (5) ◽  
pp. 1288-1303
Author(s):  
Iyad Alazzam ◽  
Ahmed Aleroud ◽  
Zainab Al Latifah ◽  
George Karabatis
Keyword(s):  
Software Systems ◽  
Neighborhood Relations ◽  
Feature Augmentation

scholarly journals New Online Streaming Feature Selection Based on Neighborhood Rough Set for Medical Data

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1635
Author(s):  
Dingfei Lei ◽  
Pei Liang ◽  
Junhua Hu ◽  
Yuan Yuan
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Rough Set Theory ◽  
Imbalanced Data ◽  
Computation Method ◽  
Feature Subset ◽  
Neighborhood Rough Set ◽  
Neighborhood Relation ◽  
Neighborhood Relations ◽  
Online Streaming

Not all features in many real-world applications, such as medical diagnosis and fraud detection, are available from the start. They are formed and individually flow over time. Online streaming feature selection (OSFS) has recently attracted much attention due to its ability to select the best feature subset with growing features. Rough set theory is widely used as an effective tool for feature selection, specifically the neighborhood rough set. However, the two main neighborhood relations, namely k-neighborhood and neighborhood, cannot efficiently deal with the uneven distribution of data. The traditional method of dependency calculation does not take into account the structure of neighborhood covering. In this study, a novel neighborhood relation combined with k-neighborhood and neighborhood relations is initially defined. Then, we propose a weighted dependency degree computation method considering the structure of the neighborhood relation. In addition, we propose a new OSFS approach named OSFS-KW considering the challenge of learning class imbalanced data. OSFS-KW has no adjustable parameters and pretraining requirements. The experimental results on 19 datasets demonstrate that OSFS-KW not only outperforms traditional methods but, also, exceeds the state-of-the-art OSFS approaches.

Sign in / Sign up

Export Citation Format

Share Document