Convergence Results for the EM Approach to Mixtures of Experts Architectures

The Expectation-Maximization (EM) algorithm is an iterative approach to maximum likelihood parameter estimation. Jordan and Jacobs (1993) recently proposed an EM algorithm for the mixture of experts architecture of Jacobs, Jordan, Nowlan and Hinton (1991) and the hierarchical mixture of experts architecture of Jordan and Jacobs (1992). They showed empirically that the EM algorithm for these architectures yields significantly faster convergence than gradient ascent. In the current paper we provide a theoretical analysis of this algorithm. We show that the algorithm can be regarded as a variable metric algorithm with its searching direction having a positive projection on the gradient of the log likelihood. We also analyze the convergence of the algorithm and provide an explicit expression for the convergence rate. In addition, we describe an acceleration technique that yields a significant speedup in simulation experiments.

Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Comparative eye tracking of experts and novices in web single sign-on

Security indicators in web browsers alert users to the presence of a secure connection between their computer and a web server; many studies have shown that such indicators are largely ignored by users in general. In other areas of computer security, research has shown that technical expertise can decrease user susceptibility to attacks. In this work, we examine whether computer or security expertise affects use of web browser security indicators. Our study takes place in the context of web-based single sign-on, in which a user can use credentials from a single identity provider to login to many relying websites; single sign-on is a more complex, and hence more difficult, security task for users. In our study, we used eye trackers and surveyed participants to examine the cues individuals use and those they report using, respectively. Our results show that users with security expertise are more likely to self-report looking at security indicators, and eye-tracking data shows they have longer gaze duration at security indicators than those without security expertise. However, computer expertise alone is not correlated with recorded use of security indicators. In survey questions, neither experts nor novices demonstrate a good understanding of the security consequences of web-based single sign-on.

Low temperature pretreatment of sugarcane bagasse at atmospheric pressure using mixtures of ethylene carbonate and ethylene glycol

A low temperature lignocellulose pretreatment process was developed using acid-catalysed mixtures of alkylene carbonate and alkylene glycol. Pretreatment of sugarcane bagasse with mixtures of ethylene carbonate (EC) and ethylene glycol (EG) was more effective than that with mixtures of propylene carbonate (PC) and propylene glycol (PG). These mixtures were more effective than the individual components in making bagasse cellulose more amenable to cellulase digestion. Glucan digestibilities of ≥87% could be achieved with a wide range of EC to EG ratios from 9:1 to 1:1 (w/w). Pretreatment of bagasse by the EC/EG mixture with a ratio of 4:1 in the presence of 1.2% H2SO4 at 90 °C for 30 min led to the highest glucan enzymatic digestibility of 93%. The high glucan digestibilities obtained under these acidic conditions were due to (a) the ability of alkylene carbonate to cause significant biomass size reduction, (b) the ability of alkylene glycol to cause biomass defibrillation, (c) the ability of alkylene carbonate and alkylene glycol to remove xylan and lignin, and (d) the magnified above attributes in the mixtures of alkylene carbonate and alkylene glycol.

Combining passive sampling and toxicity testing for evaluation of mixtures of polar organic chemicals in sewage treatment plant effluent

Effluent from sewage treatment plants has been associated with a range of pollutant effects. Depending on the influent composition and treatment processes the effluent may contain a myriad of different chemicals which makes monitoring very complex. In this study we aimed to monitor relatively polar organic pollutant mixtures using a combination of passive sampling techniques and a set of biochemistry based assays covering acute bacterial toxicity (Microtox™), phytotoxicity (Max-I-PAM assay) and genotoxicity (umuC assay). The study showed that all of the assays were able to detect effects in the samples and allowed a comparison of the two plants as well as a comparison between the two sampling periods. Distinct improvements in water quality were observed in one of the plants as result of an upgrade to a UV disinfection system, which improved from 24× sample enrichment required to induce a 50% response in the Microtox™ assay to 84×, from 30× sample enrichment to induce a 50% reduction in photosynthetic yield to 125×, and the genotoxicity observed in the first sampling period was eliminated. Thus we propose that biochemical assay techniques in combination with time integrated passive sampling can substantially contribute to the monitoring of polar organic toxicants in STP effluents.

Electron Mean Energy, Secondary Ionization Coefficients And (E/P)Crit In Binary-Mixtures Of Sf6-N2 And Ccl2f2-N2

Measurements of the ratio of diffusion coefficient to mobility (D/ mu ) of electrons in SF6-N2 and CCl2F2-N2 mixtures over the range 80