No abstract

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The task of providing supervisory services to clinical interns, trainees, and new psychologists in rural settings is often complicated by a host of environmental and economic constraints. Given the reemergence of telecommunication applications as a means of transcending similar obstacles in service delivery, the authors discuss the use of telecommunication technology as a means of enabling the traditional supervisor-supervisee relationship in settings in which face-to-face contact is difficult if not impossible. The evolution of telesupervision is discussed, followed by an outline of an integrated model of telesupervision and the goals, benefits, and challenges associated with the use of telecommunications technology in clinical supervision.

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995.

In this paper, we discuss the use of a general learning algo rithm for the dynamic control of robot manipulators. Unlike some other learning control schemes, learning is based solely on observations of the input-output relationship of the system being controlled and is independent of control objectives. Information learned previously can be applied to new control objectives as long as similar regions of the system state space are involved. The control scheme requires no a priori knowl edge of the robot dynamics and is easy to apply to a particu lar control problem or to modify to accommodate changes in the physical system. The control scheme is computationally efficient and well suited to fixed-point implementation. The learning controller is evaluated in a series of computer simu lations involving a two-axis-articulated robot arm during simulated repetitive and nonrepetitive movements. We inves tigate the effects of varying learning algorithm parameters as well as control system performance in the presence of obser vation noise and changing manipulator payloads. The learn ing control system presented promises to provide good dy namic performance in complex situations at a reasonable cost as measured in terms of both hardware and software devel opment.