Forward Exchange Market Unbiasedness: The Case of the Australian Dollar Since 1984

This paper implements a new statistical approach to robust regression with nonstationary time series. The methods are presently under theoretical development in other work, and are briefly exposited here. They allow us to perform regressions in levels with nonstationary time series data, they accommodate data distributions with heavy tails and they permit serial dependence and temporal heterogeneity of unknown form in the equation errors. With these features the methods are well suited to applications with frequently sampled exchange rate data, which generally display all of these empirical characteristics. Our application is to daily data on spot and forward exchange rates between the Australian and US dollars over the period 1984-1991 following the deregulation of the Australian foreign exchange market. We find big differences between the robust and the non-robust regression outcomes and in the associated statistical tests of the hypothesis that the forward rate is an unbiased predictor of the future spot rate. The robust regression tests reject the unbiasedness hypothesis but still give the forward rate an important role as a predictor of the future spot rate.

A schematic analysis and discussion of species taken by otter trawl net by the research vessel Orion from July, 1965 to June 1966 in the Chesapeake Bay and major tributaries

From July 1965 to June 1964 the Natural Resources Institute's Research Vessel ORION took 16 minute tows with a forty (40) foot otter trawl net at 38 selected locations in Chesapeake Bay from the south of the Potomac River to Turkey Point at the head of the Bay and including some tributaries. Shallow and deep hauls were taken at most stations with depths ranging from 5 to 140 feet. A schematic summary of the 54 different species caught was compared with "Fishes of the Chesapeake Bay" by S. F. Hildebrand and W. C. Schroeder. Sixteen species including five not contained in the above references were selected for discussion. (PDF contains 21 pages

Activation functions, computational goals, and learning rules for local processors with contextual guidance

Information about context can enable local processors to discover latent variables that are relevant to the context within which they occur, and it can also guide short-term processing. For example, Becker and Hinton (1992) have shown how context can guide learning, and Hummel and Biederman (1992) have shown how it can guide processing in a large neural net for object recognition. This article studies the basic capabilities of a local processor with two distinct classes of inputs: receptive field inputs that provide the primary drive and contextual inputs that modulate their effects. The contextual predictions are used to guide processing without confusing them with receptive field inputs. The processor's transfer function must therefore distinguish these two roles. Given these two classes of input, the information in the output can be decomposed into four disjoint components to provide a space of possible goals in which the unsupervised learning of Linsker (1988) and the internally supervised learning of Becker and Hinton (1992) are special cases. Learning rules are derived from an information-theoretic objective function, and simulations show that a local processor trained with these rules and using an appropriate activation function has the elementary properties required

Short Distance Expansion from the Dual Representation of Infinite Dimensional Lie Algebras

We compute the short distance expansion of fields or operators that live in the coadjoint representation of an infinite dimensional Lie algebra by using only properties of the adjoint representation and its dual. We explicitly compute the short distance expansion for the duals of the Virasoro algebra, affine Lie Algebras and the geometrically realized N-extended supersymmetric GR Virasoro algebra.Comment: 19 pages, LaTeX twice, no figure, replacement has corrected Lie algebr

Yield Reserve Program Costs in the Virginia Coastal Plain

A proposed Yield Reserve Program designed to compensate farmers for any reduced yields resulting from nitrogen (N) application rates reduced to below recommended rates is evaluated. Assuming that farmers currently follow Extension recommendations for applying N, Yield Reserve Program participation reduces expected net revenue by $10 to$13/ha. The Yield Reserve Program reduces expected net revenue by $17 to$20/ha for farmers who apply N to maximize expected net revenue. Farmers’ costs of participation increase with lower probabilities of inadequate rainfall and higher corn prices and decline with higher N prices. The Yield Reserve Program can significantly reduce N applications to cropland, which may reduce N content of surface waters, but the costs to taxpayers and farmers will depend on how the program is implemented.compliance cost, nitrogen fertilizer, nonpoint source pollution, policy, yield response function, Agricultural and Food Policy, Crop Production/Industries,

Evaluation of Potential Attractants for Six Species of Stored- Product Psocids (Psocoptera: Liposcelididae, Trogiidae)

Psocids have emerged as worldwide pests of stored commodities during the past two decades, and are difficult to control with conventional management tactics such as chemical insecticides. Therefore, it is necessary to investigate alternative management strategies, such as the use of attractants for monitoring and controlling psocids, which can be incorporated into integrated pest management programs for psocids. Using a two-choice pitfall test, we studied the response of adults of different ages and sexes of Liposcelis entomophila (Enderlein) (Psocoptera: Liposcelididae), Liposcelis paeta Pearman, Liposcelis decolor (Pearman), Liposcelis brunnea Motschulsky, Liposcelis corrodens (Heymons), and Lepinotus reticulatus Enderlein (Psocoptera: Trogiidae) to volatiles from different potential attractants including grains, grain-based oils, brewer’s yeast, wheat germ, and commercially available kairomone lures. For all species tested, sex and age did not have a major influence on response to the different potential attractants. Brewer’s yeast most consistently elicited the strongest response for psocids, but this response frequently was not different from that to wheat germ and wheat germ oil. The percentage response to brewer’s yeast varied among the psocid species tested: L. decolor (73–78%), L. entomophila (62–73%), L. brunnea (64–68%), L. paeta (42–57%), Lep. reticulatus (40%), and L. corrodens (15–19%). Two psocids species (L. corrodens and Lep. reticulatus) had low responses to all the potential attractants evaluated compared with the other four species. These results show there is high potential for using these attractants in a psocid-monitoring program. En las ultimas dos decadas los psocidos han emergido como plagas cosmopolitas. Los pso - cidos son difıciles de controlar con metodos convencionales de control como los insecticidas quımicos. Por lo tanto, es necesario investigar estrategias de manejo alternativas, como el uso de atrayentes para monitorear y/o controlar pso cidos, que puedan ser incorporadas a un programa de manejo integrado de plagas (MIP) para psocidos. Por medio de experimentos de libre seleccion, se estudio la respuesta de adultos (diferentes edades y sexos) de Liposcelis entomophila (Enderlein) (Psocoptera: Liposcelididae), Liposcelis paeta Pearman, Liposcelis decolor (Pearman), Liposcelis brunnea Motschulsky, Liposcelis corrodens (Heymons), y Lepinotus reticulatus Enderlein (Psocoptera: Trogiidae) a volatiles de diferentes atrayentes potenciales que incluyeron granos, aceites de diferentes granos, levadura de cerveza, germen de trigo y cebos con kairomonas disponibles comercialmente. En todas las especies evaluadas, el sexo y la edad no tuvieron una mayor influencia en la respuesta a los diferentes atrayentes potenciales. La levadura de cerveza fue el material que consistentemente provoco la respuesta mas fuerte por parte de las especies de psocidos evaluadas, pero en general esta respuesta no fue diferente a la de el germen de trigo y el aceite de germen de trigo. El porcentaje de respuesta a la levadura de cerveza vario entre las especies de psocidos evaluadas: L. decolor (73–78%), L. entomophila (62–73%), L. brunnea (64–68%), L. paeta (42–57%), Lep. reticulatus (40%), y L. corrodens (15–19%). Dos species de psocidos (L. corrodens y Lep. reticulatus) tuvieron baja respuesta a los diferentes atrayentes potenciales evaluados comparados con las otras cuatro especies. Estos resultados indican un alto potencial para incorporar estos atrayentes a un programa de monitoreo de psocidos

Nonlinear computations in spiking neural networks through multiplicative synapses

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be implemented successfully in spiking neural networks, this requires supervised training and the resulting connectivity can be hard to interpret. In contrast, the required connectivity for any computation in the form of a linear dynamical system can be directly derived and understood with the spike coding network (SCN) framework. These networks also have biologically realistic activity patterns and are highly robust to cell death. Here we extend the SCN framework to directly implement any polynomial dynamical system, without the need for training. This results in networks requiring a mix of synapse types (fast, slow, and multiplicative), which we term multiplicative spike coding networks (mSCNs). Using mSCNs, we demonstrate how to directly derive the required connectivity for several nonlinear dynamical systems. We also show how to carry out higher-order polynomials with coupled networks that use only pair-wise multiplicative synapses, and provide expected numbers of connections for each synapse type. Overall, our work demonstrates a novel method for implementing nonlinear computations in spiking neural networks, while keeping the attractive features of standard SCNs (robustness, realistic activity patterns, and interpretable connectivity). Finally, we discuss the biological plausibility of our approach, and how the high accuracy and robustness of the approach may be of interest for neuromorphic computing