scholarly journals Decentralised Scalable Search for a Hazardous Source in Turbulent Conditions

2020 ◽  
Author(s):  
Branko Ristic ◽  
Christopher Gilliam
Keyword(s):  
Scalable Search

scholarly journals Efficient and scalable search on scale-free P2P networks

2009 ◽  
Vol 2 (2) ◽  
pp. 98-108 ◽  
Author(s):  
Lu Liu ◽  
Jie Xu ◽  
Duncan Russell ◽  
Paul Townend ◽  
David Webster
Keyword(s):  
P2p Networks ◽  
Scale Free ◽  
Scalable Search

scholarly journals CellAtlasSearch: a scalable search engine for single cells

2018 ◽  
Vol 46 (W1) ◽  
pp. W141-W147 ◽  
Author(s):  
Divyanshu Srivastava ◽  
Arvind Iyer ◽  
Vibhor Kumar ◽  
Debarka Sengupta
Keyword(s):  
Search Engine ◽  
Single Cells ◽  
Scalable Search

METASET: An Automated Data Selection Method for Scalable Data-Driven Design of Metamaterials

2020 ◽  
Author(s):  
Yu-Chin Chan ◽  
Faez Ahmed ◽  
Liwei Wang ◽  
Wei Chen
Keyword(s):  
Point Processes ◽  
Data Driven ◽  
Combinatorial Search ◽  
Determinantal Point Processes ◽  
Unique Challenge ◽  
Mechanical Metamaterials ◽  
Unit Cells ◽  
Property Space ◽  
Physical Simulations ◽  
Scalable Search

Abstract Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to accelerate the process. However, the dependence on data induces a unique challenge: An imbalanced dataset containing more of certain shapes or physical properties than others can be detrimental to the efficacy of the approaches and any models built on those sets. In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning. To select such subsets, we propose METASET, a methodology that 1) uses similarity metrics and positive semi-definite kernels to jointly measure the closeness of unit cells in both shape and property space, and 2) incorporates Determinantal Point Processes for efficient subset selection. Moreover, METASET allows the trade-off between shape and property diversity so that subsets can be tuned for various applications. Through the design of 2D metamaterials with target displacement profiles, we demonstrate that smaller, diverse subsets can indeed improve the search process as well as structural performance. We also apply METASET to eliminate inherent overlaps in a dataset of 3D unit cells created with symmetry rules, distilling it down to the most unique families. Our diverse subsets are provided publicly for use by any designer.

Scalable search-based image annotation

2008 ◽  
Vol 14 (4) ◽  
pp. 205-220 ◽  
Author(s):  
Changhu Wang ◽  
Feng Jing ◽  
Lei Zhang ◽  
Hong-Jiang Zhang
Keyword(s):  
Image Annotation ◽  
Scalable Search
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