Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.

Gap analysis of intrusion detection in smart grids

Given the recent emergence of the smart grid and smart grid related technologies, their security is a prime concern. Intrusion detection provides a second line of defense. However, conventional intrusion detection systems (IDSs) are unable to adequately address the unique requirements of the smart grid. This paper presents a gap analysis of contemporary IDSs from a smart grid perspective. This paper highlights the lack of adequate intrusion detection within the smart grid and discusses the limitations of current IDSs approaches. The gap analysis identifies current IDSs as being unsuited to smart grid application without significant changes to address smart grid specific requirements.

Closing the gap between bandit and full-information online optimization : high-probability regret bound

We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

Purification and characterization of heparan sulfate from human primary osteoblasts

Heparan sulfate (HS) is a linear, highly variable, highly sulfated glycosaminoglycan sugar whose biological activity largely depends on internal sulfated domains that mediate specific binding to an extensive range of proteins. In this study we employed anion exchange chromatography, molecular sieving and enzymatic cleavage on HS fractions purified from three compartments of cultured osteoblasts-soluble conditioned media, cell surface, and extracellular matrix (ECM). We demonstrate that the composition of HS chains purified from the different compartments is structurally non-identical by a number of parameters, and that these differences have significant ramifications for their ligand-binding properties. The HS chains purified of conditioned medium had twice the binding affinity for FGF2 when compared with either cell surface or ECM HS. In contrast, similar binding of BMP2 to the three types of HS was observed. These results suggest that different biological compartments of cultured cells have structurally and functionally distinct HS species that help to modulate the flow of HS-dependent factors between the ECM and the cell surface.