Glycosylated haemoglobins AIa+Ib and AIc were measured serially in 10 consecutive cases of newly discovered non-acidotic diabetes before and after diet and insulin treatment. The average concentration of Hb AIc was 11.4% in untreated diabetics as compared with 4.3% in healthy controls. With prolonged optimal regulation of blood glucose Hb AIc slowly decreased to a mean concentration of 5.5%. Th...

This study addresses the problem of learning the optimal structure of a dynamic graphical model for video content analysis given sparse data. We propose a Completed Likelihood AIC (CL-AIC) scoring function that differs from existing ones by optimising explicitly both the explanation and prediction capabilities of a model simultaneously. We demonstrate that CL-AIC is superior to existing scoring...

The Akaike (1973, 1974) information criterion, AIC, and the corrected Akaike information criterion (Hurvich and Tsai, 1989), AICc, were both designed as estimators of the expected Kullback-Leibler discrepancy between the model generating the data and a tted candidate model. AIC is justiied in a very general framework, and as a result, ooers a crude estimator of the expected discrepancy: one whi...

BACKGROUND Hepatocellular carcinoma (HCC) is a common cancer in China, an area of high hepatitis B virus (HBV) infection. Although several staging systems are available, there is no consensus on the best classification to use because multiple factors, such as etiology, clinical treatment and populations could affect the survival of HCC patients. METHODS This study analyzed 743 HBV-related Chi...

We revisit the synthesis of supervisors for partially-observed discrete-event systems from a new angle, based on the construction of a new structure called the All Inclusive Controller (or AIC). We consider control problems for safety specifications, where the legal language is a prefix-closed sublanguage of the system language. We define the AIC as a bipartite transition system that embeds all...

A key problem in video content analysis using dynamic graphical models is to learn a suitable model structure given observed visual data. We propose a completed likelihood AIC (CL-AIC) scoring function for solving the problem. CL-AIC differs from existing scoring functions in that it aims to optimise explicitly both the explanation and prediction capabilities of a model simultaneously. CL-AIC i...

The Akaike information criterion, AIC, and its corrected version, AICc are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback–Leibler information between the model generating the data and the approximating candidate model. In this paper, two new corrected variants of AIC are derived for the purpose of small sample linear...