Min-Soo Kim

TL;DR: A density-based clustering method that efficiently finds temporally smoothed local clusters of high quality by using a cost embedding technique and optimal modularity and a mapping method based on information theory that makes sequences of smoothedLocal clusters as close as possible to data-inherent quasi l-KKs.

TL;DR: This paper proposes a general, disk-based graph engine called TurboGraph to process billion-scale graphs very efficiently by using modern hardware on a single PC and proposes a novel parallel execution model, called pin-and-slide, which is the first truly parallel graph engine that exploits full parallelism including multi-core parallelism and FlashSSD IO parallelism.

TL;DR: The n-gram/2L index as mentioned in this paper is a two-level inverted index that eliminates the redundancy of the position information that exists in the N-gram inverted index, which has two major advantages: language-neutral and error-tolerant.

TL;DR: A fast and scalable graph processing method GTS is proposed that handles even RMAT32 (64 billion edges) very efficiently only by using a single machine and consistently and significantly outperforms the major distributed graph processing methods, GraphX, Giraph, and PowerGraph, and the state-of-the-art GPU-based method TOTEM.

TL;DR: It is shown using western blotting, real-time PCR, and single as well as double immunoassays that ORs and OR-associated proteins are co-expressed in diverse tissues, suggesting OMP expression is an indicator of potential OR-mediated chemoreception in non-olfactory tissues.