Context Modeling for Ranking and Tagging Bursty Features in Text Streams

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI

Numerical Investigation of Flow and Heat Transfer in Rectangular Microchannels with and without Semi-Elliptical Protrusions

Micro-cooling is a growing trend in the field of turbine blade cooling. Technical difficulties in the experiments of large-aspect-ratio rectangular microchannels that are commonly used in the turbine blades cause the rareness of related literature. In this study, the flow characteristics and heat transfer performance of the microchannels with and without semi-ellipsoidal protrusions, whose height is 0.6 mm and width is 9 mm, are numerically investigated. In the microchannel without protrusions, when 2214 3760, it is worth noting that from the sidewall to the middle of the channel, Nu first decreases and then increases. In the microchannel with protrusions, when Re 3815, the flow is all turbulent. The protrusions enhance the irreversibility of heat transfer and friction. The performance evaluation criteria (PEC) increases first and then decreases with Re and the maximum value is 1.80 at Re = 2004. In this work, the details that are difficult to obtain in experiments are fully analyzed to provide suggestions for the design of micro-cooling structures in gas turbine blades

Berberine Improves Insulin Sensitivity by Inhibiting Fat Store and Adjusting Adipokines Profile in Human Preadipocytes and Metabolic Syndrome Patients

Berberine is known to inhibit the differentiation of 3T3-L1 cells in vitro, improve glycemic control, and attenuate dyslipidemia in clinical study. The aim of this study was to investigate the effects of berberine on preadipocytes isolated from human omental fat and in metabolic syndrome patients treated with berberine for 3 months. We have shown that treatment with 10 μM berberine resulted in a major inhibition of human preadipocyte differentiation and leptin and adiponectin secretion accompanied by downregulation of PPARγ2, C/EBPα, adiponectin, and leptin mRNA expression. After 3 months of treatment, metabolic syndrome patients showed decrease in their BMI (31.5 ± 3.6 versus 27.4 ± 2.4 kg/m2) and leptin levels (8.01 versus 5.12 μg/L), as well as leptin/adiponectin ratio and HOMA-IR. These results suggest that berberine improves insulin sensitivity by inhibiting fat store and adjusting adipokine profile in human preadipocytes and metabolic syndrome patients

A General SIMD-based Approach to Accelerating Compression Algorithms

Compression algorithms are important for data oriented tasks, especially in the era of Big Data. Modern processors equipped with powerful SIMD instruction sets, provide us an opportunity for achieving better compression performance. Previous research has shown that SIMD-based optimizations can multiply decoding speeds. Following these pioneering studies, we propose a general approach to accelerate compression algorithms. By instantiating the approach, we have developed several novel integer compression algorithms, called Group-Simple, Group-Scheme, Group-AFOR, and Group-PFD, and implemented their corresponding vectorized versions. We evaluate the proposed algorithms on two public TREC datasets, a Wikipedia dataset and a Twitter dataset. With competitive compression ratios and encoding speeds, our SIMD-based algorithms outperform state-of-the-art non-vectorized algorithms with respect to decoding speeds