On the Evaluation of the Joint Distribution of Order Statistics

10.1016/j.csda.2008.05.005Computational Statistics and Data Analysis52125091-5099CSDA

PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries

Range aggregate queries find frequent application in data analytics. In some use cases, approximate results are preferred over accurate results if they can be computed rapidly and satisfy approximation guarantees. Inspired by a recent indexing approach, we provide means of representing a discrete point data set by continuous functions that can then serve as compact index structures. More specifically, we develop a polynomial-based indexing approach, called PolyFit, for processing approximate range aggregate queries. PolyFit is capable of supporting multiple types of range aggregate queries, including COUNT, SUM, MIN and MAX aggregates, with guaranteed absolute and relative error bounds. Experiment results show that PolyFit is faster and more accurate and compact than existing learned index structures.Comment: 13 page

Walking for transportation in Hong Kong Chinese urban elders: a cross-sectional study on what destinations matter and when

published_or_final_versio

EDN1 Lys198Asn is Associated with Diabetic Retinopathy in Type 2 Diabetes

Purpose: We tested the hypothesis that genetic variants in vasoactive and angiogenic factors regulating the retina vasculature contribute to the development of diabetic retinopathy (DR). Methods: A case-control study was performed to study the genetic association between DR and polymorphic variants of EDN1 (Lys198Asn), LTA (IVS1–80C>A, IVS1–206G>C, IVS1–252>G), eNOS (Glu298Asp), and ITGA2 (BgI II) in a Chinese population with type 2 diabetes mellitus. A well defined population with type 2 diabetes, consisting of 127 controls and 216 DR patients, was recruited. Results: A higher frequency of the Asn/Asn genotype of EDN1 was found in individuals with at least 10 years of diabetes and no retinopathy (controls) compared with DR patients with any duration of diabetes (DR: 2.3%; control: 11.0%; p=0.0002). The Asn allele was also more frequent in controls than DR patients (DR: 16.4%; control: 29.5%; p=0.007). Multiple logistic regression analysis showed that the Asn/Asn genotype was the factor most significantly associated with reduced risk of DR (odds ratio=0.19; 95% CI: 0.07-0.53; p=0.002) and with late onset of diabetes (Asn/Asn: 59 years; Lys/Lys + Lys/Asn: 53 years; p=0.02). Moreover, the Lys/Lys genotype was more common among patients with nonproliferative (75.7%) than proliferative DR (56.9%; p=0.008). The distributions of Lys198Asn alleles in hypertension did not differ from normotensive subjects. No associations between DR and polymorphisms of LTA, eNOS, or ITGA2 were detected, and there were no detectable gene-gene or gene-environmental interactions among the polymorphisms.Conclusions The Asn/Asn genotype of EDN1 was associated with a reduced risk of DR and with delayed onset of type 2 diabetes

An intelligent system for trading signal of cryptocurrency based on market tweets sentiments

The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visu�alization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals