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1. Location-specific public broadcasts

   https://doi.org/10.1145/3498361.3538666  

   Chen, Haige; Yin, Zixin; Dhekne, Ashutosh (June 2022, MobiSys '22: Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services)

   This demonstration presents the Location-Specific Public Broadcast system, in which localization and wireless broadcasts are combined to deliver a scalable, privacy preserving, and generic solution to location-based services. Other interactive location-based systems either preload information on the user devices, which are usually bulky, difficult to update and have to be custom-made for each venue, or fetch information from cloud based on location, which sacrifices user privacy. In our system, a wireless access point continuously broadcasts information tagged by locations of interest, and the mobile devices performing passive localization select and display the information pertinent to themselves. In this case, the location-specific information is stored only on the WiFi AP, and the phone app would be ultra lightweight with only the location calculation and information filtering functionalities, which can be used in any space. We envision our solution being adopted in public places, such as museums, aquariums, etc., for location-specific information delivery purposes, like enhancing interactive experience for visitors. 

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2. Query-specific subtopic clustering

   https://doi.org/10.1145/3529372.3530923  

   Kashyapi, Sumanta; Dietz, Laura (June 2022, Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries)
3. SoyCSN: Soybean context‐specific network analysis and prediction based on tissue‐specific transcriptome data

   https://doi.org/10.1002/pld3.167  

   Wang, Juexin; Hossain, Md Shakhawat; Lyu, Zhen; Schmutz, Jeremy; Stacey, Gary; Xu, Dong; Joshi, Trupti (September 2019, Plant Direct)

   Abstract The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA‐Seq data generated in the project serve as a valuable resource for discovering tissue‐specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context‐specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context‐specific networks at both transcriptome and interactome levels. BicMix and Cross‐Conditions Cluster Detection algorithms were applied to detect modules based on co‐expression relationships across all the tissues. Soybean context‐specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen‐fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) athttp://soykb.org/SoyCSN. SoyCSN provides a useful web‐based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders. 

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4. Designing Domain Specific Computing Systems

   https://doi.org/10.1109/FCCM48280.2020.00052  

   Cabrera, Anthony M.; Chamberlain, Roger D. (May 2020, IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM))

   Domain specific computing is an idea that has been pro-posed as a path forward given the slowing of Moore’s Law and the breakdown of Dennard scaling. Two fundamental questions include: (1) how does one define a domain; and (2) how does one go about architecting hardware that performs well for that domain? We present our preliminary work towards answering these questions. 

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5. EGGS: Sparsity-Specific Code Generation

   Tang, Xuan; Schneider, Teseo; Kamil, Shoaib; Panda, Aurojit; Li, Jinyang; Panozzo, Daniele (January 2020, Computer graphics forum)