Connection Strategies in Associative Memory Models

“The original publication is available at www.springerlink.com”. Copyright Springer.The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.Peer reviewe

Measuring the International Dimension of Output Volatility

This paper studies output fluctuations in a panel of OECD economies with the aim to decompose the evolution in output volatility into domestic and international factors. To this end we use a factor-augmented dynamic panel model with both domestic and international shocks and spillovers between countries through trade linkages. Changes in the volatility of output growth can be due to a time-varying sensitivity to these shocks, changes in the propagation mechanism or shifts in the variances of shocks. We explicitly model cross-sectional dependence in the variance equation by specifying a common factor structure in the volatility of domestic shocks. The results show that while the size of international shocks and spillovers does not decrease in most countries, the volatilities of domestic shocks share a clear common decreasing trend. Hence, the 'Great Moderation' appears to be mainly driven by a decline in the volatility of domestic shocks rather than smaller international shocks

Parsimonious Catchment and River Flow Modelling

It is increasingly the case that models are being developed as “evolving” products rather than\ud one-off application tools, such that auditable modelling versus ad hoc treatment of models becomes a\ud pivotal issue. Auditable modelling is particularly vital to “parsimonious modelling” aimed at meeting\ud specific modelling requirements. This paper outlines various contributory factors and aims to seed\ud proactively a research topic by inextricably linking value/risk management to parsimonious modelling.\ud Value management in modelling may be implemented in terms of incorporating “enough detail” into a\ud model so that the synergy among the constituent units of the model captures that of the real system. It is a\ud problem of diminishing returns, since further reductions in the constituent units will create an\ud unacceptable difference between the model and the real system; conversely, any further detail will add to\ud the cost of modelling without returning any significant benefit. The paper also defines risk management\ud in relation to modelling. It presents a qualitative framework for value/risk management towards\ud parsimonious modelling by the categorisation of “modelling techniques” in terms of “control volume.

Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union

Group-Lasso on Splines for Spectrum Cartography

The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finitely-parameterized (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that ``better'' explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Simulated tests corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced.Comment: Submitted to IEEE Transactions on Signal Processin

Short-run lats rate movements: impact of foreign currency shocks via trade and financial markets

This paper investigates the short-run dynamic impact of foreign currency shocks on the deviations of Latvian lats vis-à-vis US dollar market spot rate from the parity set via lats' peg to SDR for the period from 1994 to 2000. The analysis is based on the standard theoretical model of dynamic cost adjustment, from which empirical models of the autoregressive distributed-lags form are derived. Reduction of several versions of such general models leads to a number of parsimonious and data congruent models. Our main findings from the modelling experiment are: Cross-currency shocks produce extensive impact on the net rate of lats, especially those shocks from the neighbouring transition economies, such as Estonia and Lithuania; These shocks may not be original, and may well act as transmission ports of other foreign currency shocks; The Russian crisis of August 1998 has exerted massive devaluation pressure on lats; The shocks are found to be transmittable via either trade and financial linkages, with the financial channel being the most contagious; Model configurations are found, however, neither unique nor definitely invariant, suggesting that it might be necessary to maintain several models in practice to fulfil different purposes in policy analyses and economic forecasting

A lesson from robotics: Modeling infants as autonomous agents

While computational models are playing an increasingly important role in developmental psychology, at least one lesson from robotics is still being learned: modeling epigenetic processes often requires simulating an embodied, autonomous organism. This paper first contrasts prevailing models of infant cognition with an agent-based approach. A series of infant studies by Baillargeon (1986; Baillargeon & DeVos, 1991) is described, and an eye-movement model is then used to simulate infants' visual activity in this study. I conclude by describing three behavioral predictions of the eyemovement model, and discussing the implications of this work for infant cognition research