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Award ID contains: 1701081

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  1. ; ; ; ; ; ; (, Micromachines)
    Metallic structures can be used for the localized heating of fluid and the controlled generation of microfluidic currents. Carefully designed currents can move and trap small particles and cells. Here we demonstrate a new bi-metallic substrate that allows much more powerful micro-scale manipulation. We show that there are multiple regimes of opto-fluidic manipulation that can be controlled by an external laser power. While the lowest power does not affect even small objects, medium power can be used for efficiently capturing and trapping particles and cells. Finally, the high-power regime can be used for 3D levitation that, for the first time, has been demonstrated in this paper. Additionally, we demonstrate opto-fluidic manipulation for an extraordinarily dynamic range of masses extending eight orders of magnitude: from 80 fg nano-wires to 5.4 µg live worms. 
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  2. ; ; ; (, Sensors and Actuators B: Chemical)
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  3. ; (, Conference: 2018 IEEE International Conference on Big Data (Big Data))
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  4. ; (, Conference: 2018 IEEE International Conference on Big Data (Big Data))
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  5. ; ; ; ; (, WWW '18 Companion Proceedings of the The Web Conference 2018)
    Variety of Big data is a significant impediment for anyone who wants to search inside a large-scale structured dataset. For example, there are millions of tables available on the Web, but the most relevant search result does not necessarily match the keyword-query exactly due to a variety of ways to represent the same information. Here we describe Hybrid.AI, a learning search engine for large-scale structured data that uses automatically generated machine learning classifiers and Unified Famous Objects (UFOs) to return the most relevant search results from a large-scale Web tables corpora. We evaluate it over this corpora, collecting 99 queries and their results from users, and observe significant relevance gain. 
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  6. ; ; ; ; (, Biosensors and Bioelectronics)
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  7. ; ; ; (, IEEE Sensors Journal)
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