A Comparison Between Kinetic Test Results and Natural Weathering: The Abandoned Kettara Mine Tailings Pond

More than three million tons of sulfide tailings were discarded without any protection for more than 36 years at the since-abandoned Kettara mine. The impact of these tailings as well as the coarse wastes disorderly dumped on the surface site is highlighted by the elevated sulfate levels in the wells downstream of the wastes. Kinetic tests carried out on these sulfide tailings over 10 years ago, showed similar trends in metal dissolution despite large difference in test duration (21 and 53 weeks for the weathering and humidity cell tests, respectively). 40% of the sulfate release occurred in the first 3 weeks of the humidity test. However, in this study, the elevated sulfate levels in the groundwater was shown not be linked to the tailings, where the original sulfide material is still present under an oxidized layer of 10 cm–1.2 m. Thus, kinetic test results should not be extrapolated to field scale without accounting for site-specific factors, especially particle size and climate.     

Self-organization in the basal ganglia with modulation of reinforcement signals

Self-organization is one of fundamental brain computations for forming efficient representations of information. Experimental support for this idea has been largely limited to the developmental and reorganizational formation of neural circuits in the sensory cortices. We now propose that self-organization may also play an important role in short-term synaptic changes in reward-driven voluntary behaviors. It has recently been shown that many neurons in the basal ganglia change their sensory responses flexibly in relation to rewards. Our computational model proposes that the rapid changes in striatal projection neurons depend on the subtle balance between the Hebb-type mechanisms of excitation and inhibition, which are modulated by reinforcement signals. Simulations based on the model are shown to produce various types of neural activity similar to those found in experiments.     

Population coding and decoding in a neural field: a computational study

This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further--wider than (sqrt)2 times the effective width of the turning function--the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.     

Conformal Transformation of Kernel Functions: A Data-Dependent Way to Improve Support Vector Machine Classifiers

In this paper we extend the conformal method of modifying a kernel function to improve the performance of Support Vector Machine classifiers [14, 15]. The kernel function is conformally transformed in a data-dependent way by using the information of Support Vectors obtained in primary training. We further investigate the performances of modified Gaussian Radial Basis Function and Polynomial kernels. Simulation results for two artificial data sets show that the method is very effective, especially for correcting bad kernels. This revised version was published online in August 2006 with corrections to the Cover Date.     

Adaptive method of realizing natural gradient learning for multilayer perceptrons

The natural gradient learning method is known to have ideal performances for on-line training of multilayer perceptrons. It avoids plateaus, which give rise to slow convergence of the backpropagation method. It is Fisher efficient, whereas the conventional method is not. However, for implementing the method, it is necessary to calculate the Fisher information matrix and its inverse, which is practically very difficult. This article proposes an adaptive method of directly obtaining the inverse of the Fisher information matrix. It generalizes the adaptive Gauss-Newton algorithms and provides a solid theoretical justification of them. Simulations show that the proposed adaptive method works very well for realizing natural gradient learning.     

A positive selection for plasmid loss in Saccharomyces cerevisiae using galactose-inducible growth inhibitory sequences

Counter-selections for the loss of introduced plasmid sequences are useful for gene manipulations in yeast. We have used GAL10 promoter-mediated overexpression of GIN sequences, which inhibit the growth of cells, to develop a novel counter-selection system. Yeast cells carrying a GIN sequence grow normally on glucose medium but are unable to grow on galactose medium, whereas derivatives that have lost the GIN sequence are able to grow in the presence of galactose. We constructed autonomously replicating, integrating, and disruption plasmids carrying GIN sequences and tested their use to select for loss of the plasmid. The results showed that the GIN sequences provide a selection for efficient loss of plasmids or integrated constructs from yeast during growth on galactose medium, indicating that this system can be used for plasmid shuffling, gene replacements and marker gene recycling. This counter-selection system has wide application, because any Gal+ strain and a wide variety of marker genes can be used. In addition, counter-selection systems using growth-inhibitory sequences should be applicable to other yeasts and possibly to other organisms.     

Synchronous firing and higher-order interactions in neuron pool

The stochastic mechanism of synchronous firing in a population of neurons is studied from the point of view of information geometry. Higher-order interactions of neurons, which cannot be reduced to pairwise correlations, are proved to exist in synchronous firing. In a neuron pool where each neuron fires stochastically, the probability distribution q(r) of the activity r, which is the fraction of firing neurons in the pool, is studied. When q(r) has a widespread distribution, in particular, when q(r) has two peaks, the neurons fire synchronously at one time and are quiescent at other times. The mechanism of generating such a probability distribution is interesting because the activity r is concentrated on its mean value when each neuron fires independently, because of the law of large numbers. Even when pairwise interactions, or third-order interactions, exist, the concentration is not resolved. This shows that higher-order interactions are necessary to generate widespread activity distributions. We analyze a simple model in which neurons receive common overlapping inputs and prove that such a model can have a widespread distribution of activity, generating higher-order stochastic interactions.     

Feedback control for a class of discrete event systems with critical time

This paper presents an algebraic approach for control laws synthesis of timed event graphs subjected to strict temporal constraints. This class of discrete event systems is deterministic, in the sense that its behaviour only depends on the initial marking and on the control that is applied. This behaviour can be modelled by a linear equations system in Min-Plus algebra. The temporal constraint is represented by an inequality that is also linear in the Min-Plus algebra. Then, a method for the synthesis of control laws ensuring the respect of constraints is described. We give explicit formulas characterising a control law, which ensures the validity of the temporal constraints. It is a causal feedback control, involving delays. The method is illustrated on an example.     

A separable method for incorporating imperfect fault-coverage intocombinatorial models

This paper presents a new method for incorporating imperfect FC (fault coverage) into a combinatorial model. Imperfect FC, the probability that a single malicious fault can thwart automatic recovery mechanisms, is important to accurate reliability assessment of fault-tolerant computer systems. Until recently, it was thought that the consideration of this probability necessitated a Markov model rather than the simpler (and usually faster) combinatorial model. SEA, the new approach, separates the modeling of FC failures into two terms that are multiplied to compute the system reliability. The first term, a simple product, represents the probability that no uncovered fault occurs. The second term comes from a combinatorial model which includes the covered faults that can lead to system failure. This second term can be computed from any common approach (e.g. fault tree, block diagram, digraph) which ignores the FC concept by slightly altering the component-failure probabilities. The result of this work is that reliability engineers can use their favorite software package (which ignores the FC concept) for computing reliability, and then adjust the input and output of that program slightly to produce a result which includes FC. This method applies to any system for which: the FC probabilities are constant and state-independent; the hazard rates are state-independent; and an FC failure leads to immediate system failure     

Statistical inference under multiterminal rate restrictions: adifferential geometric approach

A statistical inference problem for a two-terminal information source emitting mutually correlated signals X and Y is treated. Let sequences Xⁿ and Yⁿ of n independent observations be encoded independently of each other into message sets M_X and M_Y at rates R₁ and R ₂ per letter, respectively. This compression causes a loss of the statistical information available for testing hypotheses concerning X and Y. The loss of statistical information is evaluated as a function of the amounts R₁ and R ₂ of the Shannon information. A complete solution is given in the case of asymptotically complete data compression, R₁, R₂→0 as n→∞. It is shown that the differential geometry of the manifold of all probability distributions plays a fundamental role in this type of multiterminal problem connecting Shannon information and statistical information. A brief introduction to the dually coupled e-affine and m-affine connections together with e -flatness and m-flatness is given