Local Proximity for Enhanced Visibility in Haze

2020 ◽  
Vol 29 ◽  
pp. 2478-2491 ◽  
Author(s):  
Srimanta Mandal ◽  
A. N. Rajagopalan
Keyword(s):  
Author(s):  
Bornali Phukon ◽  
Akash Anil ◽  
Sanasam Ranbir Singh ◽  
Priyankoo Sarmah

WordNets built for low-resource languages, such as Assamese, often use the expansion methodology. This may result in missing lexical entries and missing synonymy relations. As the Assamese WordNet is also built using the expansion method, using the Hindi WordNet, it also has missing synonymy relations. As WordNets can be visualized as a network of unique words connected by synonymy relations, link prediction in complex network analysis is an effective way of predicting missing relations in a network. Hence, to predict the missing synonyms in the Assamese WordNet, link prediction methods were used in the current work that proved effective. It is also observed that for discovering missing relations in the Assamese WordNet, simple local proximity-based methods might be more effective as compared to global and complex supervised models using network embedding. Further, it is noticed that though a set of retrieved words are not synonyms per se, they are semantically related to the target word and may be categorized as semantic cohorts.


1967 ◽  
Vol 169 (2) ◽  
pp. 275-281 ◽  
Author(s):  
Solomon Leader

2017 ◽  
Vol 98 (3) ◽  
pp. 993-1009 ◽  
Author(s):  
Jason Turcotte ◽  
Ashley Kirzinger ◽  
Johanna Dunaway ◽  
Kirby Goidel

2021 ◽  
Vol 11 (5) ◽  
pp. 2371
Author(s):  
Junjian Zhan ◽  
Feng Li ◽  
Yang Wang ◽  
Daoyu Lin ◽  
Guangluan Xu

As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes the network structure and node content into low-dimensional embeddings. On one hand, our model captures the local proximity and proximities at any distance of a network by exploiting a high-order proximity indicator named Rooted Pagerank. On the other hand, our method learns the data distribution of each node representation while circumvents the side effect its sampling process causes on learning a robust embedding through adversarial training. On benchmark datasets, we demonstrate that our method performs competitively compared with state-of-the-art models.


2014 ◽  
Vol 173 ◽  
pp. 294-307 ◽  
Author(s):  
A. Di Concilio ◽  
C. Guadagni

2019 ◽  
Vol 3 (2) ◽  
pp. 24
Author(s):  
Mohammed Soheeb Khan ◽  
Vassilis Charissis ◽  
Sophia Sakellariou

The hip joint is highly prone to traumatic and degenerative pathologies resulting in irregular locomotion. Monitoring and treatment depend on high-end technology facilities requiring physician and patient co-location, thus limiting access to specialist monitoring and treatment for populations living in rural and remote locations. Telemedicine offers an alternative means of monitoring, negating the need for patient physical presence. In addition, emerging technologies, such as virtual reality (VR) and immersive technologies, offer potential future solutions through virtual presence, where the patient and health professional can meet in a virtual environment (a virtual clinic). To this end, a prototype asynchronous telemedicine VR gait analysis system was designed, aiming to transfer a full clinical facility within the patients’ local proximity. The proposed system employs cost-effective alternative motion capture combined with the system’s immersive 3D virtual gait analysis clinic. The user interface and the tools in the application offer health professionals asynchronous, objective, and subjective analyses. This paper investigates the requirements for the design of such a system and discusses preliminary comparative data of its performance evaluation against a high-fidelity gait analysis clinical application.


2018 ◽  
Vol 4 (11) ◽  
pp. eaau0695 ◽  
Author(s):  
Venhar Celik Ozgen ◽  
Wentao Kong ◽  
Andrew E. Blanchard ◽  
Feng Liu ◽  
Ting Lu

In microbial communities, social interactions such as competition occur ubiquitously across multiple spatial scales from local proximity to remote distance. However, it remains unclear how such a spatial variation of interaction contributes to the structural development of microbial populations. Here, we developed synthetic consortia, biophysical theory, and simulations to elucidate the role of spatial interference scale in governing ecosystem organization during range expansion. For consortia with unidirectional interference, we discovered that, at growing fronts, the extinction time of toxin-sensitive species is reciprocal to the spatial interference scale. In contrast, for communities with bidirectional interference, their structures diverge into distinct monoculture colonies under different initial conditions, with the corresponding separatrix set by the spatial scale of interference. Near the separatrix, ecosystem development becomes noise-driven and yields opposite structures. Our results establish spatial interaction scale as a key determinant for microbial range expansion, providing insights into microbial spatial organization and synthetic ecosystem engineering.


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