Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Spiking Dynamics during Perceptual Grouping in the Laminar Circuits of Visual Cortex

Grouping of collinear boundary contours is a fundamental process during visual perception. Illusory contour completion vividly illustrates how stable perceptual boundaries interpolate between pairs of contour inducers, but do not extrapolate from a single inducer. Neural models have simulated how perceptual grouping occurs in laminar visual cortical circuits. These models predicted the existence of grouping cells that obey a bipole property whereby grouping can occur inwardly between pairs or greater numbers of similarly oriented and co-axial inducers, but not outwardly from individual inducers. These models have not, however, incorporated spiking dynamics. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity to inducer configurations occur despite irregularities in spike timing across all the interacting cells. Other models have demonstrated spiking dynamics in laminar neocortical circuits, but not how perceptual grouping occurs. The current model begins to unify these two modeling streams by implementing a laminar cortical network of spiking cells whose intracellular temporal dynamics interact with recurrent intercellular spiking interactions to quantitatively simulate data from neurophysiological experiments about perceptual grouping, the structure of non-classical visual receptive fields, and gamma oscillations.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001); Defense Advanced Research Project Agency (HR001-09-C-0011

Influence of climatic variations and competitive interactions on the productivity of mountain forests in Italy

Tree growth is influenced by multiple factors including, climate and competition processes. Climate change has a strong impact on growth of trees and can cause negative impacts on forests, especially in the Mediterranean basin. However, tree growth can also be influenced by competitive interactions, through the use and absorption of resources within tree communities. To quantify the level of competition between trees, competition indices are used, which are normally computed over small areas. Predicting competitive interactions over larger areas can be very important and light detection and ranging (lidar) data, could be the suitable tool. Based on these considerations, the main objective of the thesis was to identify and study the influence of climatic variations and competitive interactions on the growth of three important forest species, European beech (Fagus sylvatica L.), Norway spruce (Picea abies L.) and silver fir (Abies alba Mill.). The work is structured into three chapters, in which the first analyzes the influence of climate and extreme events on the radial growth of beech and silver fir in mixed and pure plots along a latitudinal gradient in Italy. In the second chapter the competitive interactions in mixed and pure populations of European beech and silver fir, located at the limits of their distribution range (southern Italy) are analyzed. In the third chapter, instead, was to estimate the competition dynamics for individual trees of Norway spruce and silver fir, located in the municipality of Lavarone (Trentino), and to identify the relationship between competitive interactions and tree aboveground biomass. Overall, results highlighted the response of trees under to climate and competition processes in mountain forests in Italy. In particular, the results of the first work showed a different response only at the regional level for the maximum temperatures. In Trentino the temperatures in winter, for silver fir, and summer, for both species, had a lesser negative impact on radial growth of trees compared to southern sites. Despite this, the results obtained from the correlations (radial growthdrought indices) and from principal component analysis have shown that no plot was sensitive to summer drought. Results are important to implement operational techniques that increase species adaptation to climate change. In the second work showed that the basal area increment, under the negative influence of high competition levels and slope terrains, varied between stands. In this sense, higher competitive interactions have been observed in Molise than in Calabria. Finally, in the third work showed that lidar metrics could be used to predict the competition indices of individual trees. In addition, biomass was observed to decrease as competition increased. The results of the three works showed that for the choice of sustainable forestry options it is necessary to consider the conditions of the site where these species are found and the structure of the forest stands, in terms of density and arrangement of the trees. Furthermore, it has been found that the use of remote sensing techniques (e.g. lidar) can be very useful in the forestry field, since they can provide information on larger areas

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

The relationship between concentric hip abductor strength 1 and the performance of the Y-balance test (YBT)

Side lying hip abduction is an action used during manual muscle testing and is also prescribed as a rehabilitation exercise to improve dynamic single leg stability. Little is known about the functional cross-over of this activity. The aims of this study was to investigate the relationship between concentric hip abductor strength and performance of the Y-Balance test (YBT). Forty-five recreational gym users (27 male age 26.2 (8.4) years, 18 female age 27.4 (7.5) years) had dynamic single leg stability and concentric hip abductor peak torque assessed in the non-dominant limb using a YBT and isokinetic dynamometry, respectively. All components of the YBT had a moderate association with concentric hip abductor torque which were greater in the posteromedial (r=0.574, P<0.001) and posterolateral (r=0.657, P<0.001) directions compared to the anterior direction (r=0.402, P=0.006). Greater concentric hip abductor strength is associated with greater scores on components of the YBT, particularly the posterior reaches

Understanding the Psychophysiological Mechanisms Underlying Context-Dependent Gains and Losses Among Smokers

https://openworks.mdanderson.org/sumexp21/1181/thumbnail.jp

CNS Technology Website Administrative Guide (Version 1.0)

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624)

Methods and Apparatus for Autonomous Robotic Control

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements