Topic models can yield insight into how depressed and non-depressed individuals use language differently. In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.

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A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation we designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. We used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed-loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications.

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) [1] and the inductive learning algorithm (IL) [2]. Results to date suggest that, given the same training data, the IL learned faster than the ALN, while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified.

In Exp I, 6 18-32 yr old males performed a discrete tracking task under conditions of separate and simultaneous saccadic eye tracking and manual tracking. Both saccadic and motor latencies were dependent on direct probability, the rate of gain of information being significantly greater for the eye system. In neither case was the rate of gain affected by simultaneous performance. No significant increase in mean saccadic latency was observed during simultaneous performance, although a small increase was suggested by the results for the motor reaction times. Considered with data on error responses, results indicate substantial independence in information processing for the 2 systems. On the other hand, interdependence, most likely on the input side, was suggested by high correlations between the residual errors of the 2 systems. Exp II, using 4 21-26 yr old males, confirmed the dependence of saccadic latencies on direction information and further indicated that they were independent of extent uncertainty. (20 ref)

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A general study is made of two basic integrity constraints on relations: functional and multivalued dependencies. The latter are studied via an equivalent concept: decompositions. A model is constructed for any possible combination of functional dependencies and decompositions. The model embodies some decompositions as unions of relations having different schemata of functional dependencies. This suggests a new, stronger integrity constraint, the degenerate decomposition. More generally, the theory demonstrates the importance of using the union operation in database design and of allowing different schemata on the operands of a union. Techniques based on the union lead to a method for solving the problem of membership of a decomposition in the closure of a given set of functional dependencies and decompositions. The concept of antiroot is introduced as a tool for describing families of decompositions, and its fundamental importance for database design is indicated.