Nomad, a naval message understanding system

We are building systems to automatically analyze Navy messages. Such messages typically are terse and use many abbreviations and Navy jargon. As a result, they are more difficult to understand than everyday English.The NOMAD system interacts with a message sender to ensure that only unambiguous and reasonably correct messages are generated. The VOX system will allow a human tutor to interactively extend the knowledge base of NOMAD

Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH

A new stochastic volatility model, called A-LMSV, is proposed to cope simultaneously with leverage effect and long-memory in volatility. Its statistical properties are derived and compared with the properties of the FIEGARCH model. It is shown that the dependence of the autocorrelations of squares on the parameters measuring the asymmetry and the persistence is different in both models. The kurtosis and autocorrelations of squares do not depend on the asymmetry in the A-LMSV model while they increase with the asymmetry in the FIEGARCH model. Furthermore, the autocorrelations of squares increase with the persistence in the A-LMSV model and decrease in the FIEGARCH model. On the other hand, if the correlation between returns and future volatilities is negative, the autocorrelations of absolute returns increase with the magnitude of the asymmetry in the FIEGARCH model while they decrease in the A-LMSV model. Finally, the cross-correlations between squares and original observations are, in general, larger in absolute value in the FIEGARCH model than in the A-LMSV model. The results are illustrated by fitting both models to represent the dynamic evolution of volatilities of daily returns of the S&P500 and DAX indexes.Publicad

The Design of a Low Noise, Multi-Channel Recording System for Use in Implanted Peripheral Nerve Interfaces

In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits. This paper introduces formal analyses of the performance of two commonly used filter networks. To achieve a manageable set of design equations, the state equations of the complete system are simplified. The derived equations help the designer in the task of creating an interface network for specific applications. The noise, crosstalk and common-mode rejection ratio (CMRR) of the recording system are computed as a function of electrode impedance, filter component values and amplifier specifications. The effect of electrode mismatches as an inherent part of any multi-electrode system is also discussed, using measured data taken from a MEC implanted in a sheep. The accuracy of these analyses is then verified by simulations of the complete system. The results indicate good agreement between analytic equations and simulations. This work highlights the critical importance of understanding the effect of interface circuits on the performance of neural recording systems

Urinary Bladder Innervation Within the Sacral Roots of a Sheep

Managing the urinary bladder after spinal cord injury is of primary importance because neurogenic dysfunction leads to life-threatening complications. Sacral Anterior Root Stimulators that control the bladder have been available for many years, however, these devices cannot sense the fullness of the bladder or detect the onset of reflex voiding. In order to address this fundamental limitation, this paper explores the possibility of recording the neural signals that encode bladder fullness from the sacral roots in sheep using extra-neural books. Stimulation of and recording from six roots (S1, S2 and S3 bilaterally) shows that efferent and afferent pathways seem to be co-located within roots, but also that simultaneous recording from multiple roots may be useful to enhance overall signal quality

Urinary bladder innervation within the sacral roots of a sheep

Managing the urinary bladder after spinal cord injury is of primary importance because neurogenic dysfunction leads to life-threatening complications. Sacral Anterior Root Stimulators that control the bladder have been available for many years, however, these devices cannot sense the fullness of the bladder or detect the onset of reflex voiding. In order to address this fundamental limitation, this paper explores the possibility of recording the neural signals that encode bladder fullness from the sacral roots in sheep using extra-neural books. Stimulation of and recording from six roots (S1, S2 and S3 bilaterally) shows that efferent and afferent pathways seem to be co-located within roots, but also that simultaneous recording from multiple roots may be useful to enhance overall signal quality. </p

Aggregation and memory of models of changing volatility

In this paper we study the effect of contemporaneous aggregation of an arbitrarily large number of covariance stationary processes featuring short memory dynamic conditional heteroskedasticity, when heterogeneity is allowed for across units. We look at the memory properties of the limit aggregate. General conditions for long memory heteroskedasticity are obtained. More specific results relative to certain stochastic volatility models are also developed, providing some examples of how long memory heteroskedasticity can be obtained by aggregation. (c) 2006 Elsevier B.V. All rights reserved

Granger causality and the inverse Ising problem

We study Ising models for describing data and show that autoregressive methods may be used to learn their connections, also in the case of asymmetric connections and for multi-spin interactions. For each link the linear Granger causality is two times the corresponding transfer entropy (i.e. the information flow on that link) in the weak coupling limit. For sparse connections and a low number of samples, the L1 regularized least squares method is used to detect the interacting pairs of spins. Nonlinear Granger causality is related to multispin interactions.Comment: 6 pages and 8 figures. Revised version in press on Physica