bluuebunny/crossref_metadata_2025 huggingface dataset:
Psychosocial work characteristics and anxiety and depressive disorders in midlife: the effects of prior psychological distress
Authors: Stansfeld S A , Clark C , Caldwell T , Rodgers B , Power C
Published: April 2008

Objectives:

The association between work stressors and adult psychiatric diagnoses may be biased by prior psychological distress influencing perception of work or selection into unfavourable work. This study examines the extent to which the association between work stressors and adult psychiatric diagnoses is explained by associations with earlier psychological distress and whether childhood and early adulthood psychological distress influences reported midlife work characteristics.

Methods:

Follow-up at 45 years of age of 8243 participants in paid employment from the 1958 British Birth Cohort. Karasek’s work characteristics and psychiatric diagnoses (Revised Clinical Interview Schedule) were measured at 45 years. Childhood internalising and externalising problems were measured at 7, 11 and 16 and malaise at 23 and 33 years.

Results:

Internalising behaviours in childhood and early adult psychological distress predicted adverse work characteristics in mid-adulthood. High job demands (women: relative risk (RR) = 1.75, 95% CI 1.2 to 2.5; men: RR = 4.99, 95% CI 2.5 to 10.1), low decision latitude (RR = 1.46, 95% CI 1.1 to 1.9), high job strain (OR = 1.88, 95% CI 1.5 to 2.4), low work social support (RR = 1.97, 95% CI 1.5 to 2.6) and high job insecurity (OR = 1.86, 95% CI 1.4 to 2.4) were associated with mid-adulthood diagnoses. The association between work stressors and mid-adulthood diagnoses remained after adjustment for internalising behaviours and malaise at 23 and 33 years although diminished slightly in magnitude (eg, adjusted RR for support = 1.82, 95% CI 1.4 to 2.4; job strain OR = 1.78, 95% CI 1.4 to 2.3).

Conclusions:

Childhood and early adulthood psychological distress predict work characteristics in mid-adulthood but do not explain the associations of work characteristics with depressive episode and generalised anxiety disorder in midlife. Work stressors are an important source of preventable psychiatric diagnoses in midlife. Psychological distress may influence selection into less advantaged occupations with poorer working conditions that may increase the risk of future depressive and anxiety disorders.
Effects of heat stress on gas exchange and photosystem II (PSII) photochemical activity of Phillyrea angustifolia exposed to elevated CO2 and subsaturating irradiance
Authors: Vitale Luca , Arena Carmen , De Santo Amalia Virzo , D’Ambrosio Nicola
Published: April 2008

Gas exchange and chlorophyll a fluorescence measurements were performed simultaneously on leaves of Phillyrea angustifolia L. to assess the effects of heat stress (30 min at 40 °C) on photosynthesis and photosystem II (PSII) photochemical efficiency of plants grown at ambient CO2 and exposed to an elevated CO2 concentration (800 µmol·mol–1) and 300 µmol photons·m–2·s–1. No significant difference was found in the heat-induced decreases of net photosynthesis (PN), quantum yield of PSII electron transport (ΦPSII), and maximum PSII photochemical efficiency (Fv/Fm) between plants exposed to ambient and elevated CO2 concentrations, showing that elevated CO2 was not able to reduce the potential for photoinhibition at high temperatures under moderate light conditions. The heat-induced decrease of PN was higher than that of ΦPSII indicating that reductive power was more utilized in non-assimilatory processes than in CO2 fixation at both CO2 treatments. This result suggested that impairment of the Calvin cycle rather than electron transport inhibition was the main cause of the limitation in CO2 fixation.
Difficulties Associated with Predicting Depth of Freeze or Thaw
Authors: Brown W G
Published: April 2008

Calculations using the Neumann solution (as modified by Aldrich) and thermal properties of soils (obtained by Kersten) show that the frost penetration depth for the same freezing index for essentially all soils with any moisture content and for dry sand and rock varies by a factor of about 2 to 1. The extremes calculated in this way bracket the experimentally determined design curve of the US Army Corps of Engineers and give it theoretical support. The theoretical calculations and additional experimental data are used as a basis for a small alteration in the slope of the design curve. This modified design curve is recommended for field use because of (1) inherent imperfections in existing theory and (2) practical limitations to precise specification of field conditions.
Influence of cutting shoe size in self-boring pressuremeter tests in sensitive clays
Authors: Law K. T. , Eden W. J.
Published: April 2008

This paper examines the influence of the cutting shoe size of a pressuremeter apparatus in the light of soil behaviour after some unloading and loading. An oversized cutting shoe creates a gap between the borehole and the pressuremeter probe, causing a stress release with a probable consequence of overestimating the shear strength of the soil. An undersized cutting shoe imposes a certain load to the surrounding soil prior to the pressuremeter test, thus introducing errors in measuring the stress–strain relationship of the soil.To gain a quantitative idea of such an influence, Cambridge self-boring pressuremeter tests were carried out on Leda clay at two sites in the Ottawa region. It was found that in the case of an oversized cutting shoe, both the modulus and shear strength were overestimated by about 30 and 80% respectively. In the case of an undersized cutting shoe, the shear resistance was reduced at small strains. In both cases, however, the stress–strain relationship beyond a moderate strain (5%) showed little dependence on the cutting shoe size.
Excavation slope stability related to pore-water pressure variations during piling
Authors: Crooks J. H. A. , Matyas E. L. , McKay H. M.
Published: April 2008

High excess pore-water pressures were measured within a confined stratum of loose silt underlying an excavation slope during pile-driving operations at the slope toe.The range of observed pore-water pressure increase with distance is generally in excess of previously published data. This is attributed to a combination of factors including superposition of increments of excess pore-water pressures generated during driving of individual piles within a group, redistribution of excess pore-water pressures with time, the effect of soil displacement in a confined loose silt layer, and the greater disturbance associated with driving batter piles as opposed to vertical piles. A further contributing factor was considered to be the increase in pore-water pressures due to small straining within the silt layer.Because of uncertainty regarding the accuracy of pore-water pressure predictions and the presence of deformation-sensitive services at the slope crest, the observational approach was adopted during construction.Based on the results of stability analyses using observed pore-water pressures in the field, the construction procedures were modified to maintain an adequate factor of safety and to minimize slope deformation. It was recognized that the method of analysis may have been conservative because changes in total stresses along potential failure surfaces were not taken into account.
The anatomy of landslides
Authors: Cruden D. M.
Published: April 2008

The accumulation of the displaced mass of a landslide is defined as that portion of the displaced mass of a landslide that overlies the original ground surface. The depleted mass is the portion of the displaced mass that overlies the rupture surface but underlies the original ground surface.These terms are useful in discussing the dynamics of landslides. We show that knowledge of the topography and density of the displaced mass before and after sliding can lead to estimates of the coefficient of friction of the sliding mass. A section through the Frank Slide is analysed as an example.
SUPERSYMMETRY BREAKING BY CONSTANT SUPERPOTENTIALS AND O'RAIFEARTAIGH MODEL IN WARPED SPACE
Authors: UEKUSA NOBUHIRO
Published: April 2008

Supersymmetry breaking together by constant boundary superpotentials and by the O'Raifeartaigh model is studied in a warped space model. It is shown that the contribution of constant boundary superpotentials enables the moduli of chiral supermultiplets to be stabilized and that the vacuum at the stationary point has zero cosmological constant in a wide region of parameters.
HIGH CURIE TEMPERATURE OF NANOSIZED NiZn FERRITE PARTICLES SYNTHESIZED BY A COMBUSTION METHOD
Authors: VERMA SEEMA , JOY P. A.
Published: April 2008

The magnetic properties of nanocrystalline powders of Ni 0.5 Zn 0.5 Fe 2 O 4, synthesized at low temperatures, by a combustion method, have been investigated. Different powders containing crystallites of comparable average size, obtained by varying the synthesis conditions, show a large difference in the superparamagnetic blocking temperature due to the wide difference in the size distribution. The ferrite nanoparticles show large Curie temperature enhancement up to ~ 100 K, when compared to that of the bulk.
Fluctuation Theorems
Authors: Sevick E.M. , Prabhakar R. , Williams Stephen R. , Searles Debra J.
Published: April 2008

Fluctuation theorems, developed over the past 15 years, have resulted in fundamental breakthroughs in our understanding of how irreversibility emerges from reversible dynamics and have provided new statistical mechanical relationships for free-energy changes. They describe the statistical fluctuations in time-averaged properties of many-particle systems such as fluids driven to nonequilibrium states and provide some of the few analytical expressions that describe nonequilibrium states. Quantitative predictions on fluctuations in small systems that are monitored over short periods can also be made, and therefore the fluctuation theorems allow thermodynamic concepts to be extended to apply to finite systems. For this reason, we anticipate an important role for fluctuation theorems in the design of nanotechnological devices and in the understanding of biological processes. This review discusses these theorems, their physical significance, and results for experimental and model systems.
Leukocyte Telomere Length and Carotid Artery Intimal Medial Thickness
Authors: O’Donnell Christopher J. , Demissie Serkalem , Kimura Masayuki , Levy Daniel , Gardner Jeffery P. , White Charles , D’Agostino Ralph B. , Wolf Philip A. , Polak Joseph , Cupples L. Adrienne , Aviv Abraham
Published: April 2008

Background and Purpose—

Leukocyte telomere length (LTL) is relatively short in individuals who have evidence of cardiovascular disease. The purpose of this study was to examine the link between LTL and the predisposition to atherosclerosis, as determined by carotid artery intimal medial thickness (IMT) in participants of the Framingham Offspring Study.

Methods and Results—

LTL was assayed by the mean length of the terminal restriction fragments and carotid artery IMT by B-mode ultrasonography in 1062 individuals (496 men, 566 women) aged 33 to 86 years. In the whole sample, there was a significant association of age-and sex-adjusted LTL with internal carotid artery IMT (ICA-IMT) (
r
=−0.07,
P
=0.02). In sex-stratified analysis, this association remained significant for men (
r
=−0.11,
P
=0.02) but not for women (
r
=−0.04,
P
=0.36). After further adjustment for cigarette smoking and BMI, a borderline significant association persisted in men (
P
=0.06). In secondary analysis, the age-adjusted LTL was significantly (and negatively) associated with ICA-IMT (
r
=−0.28, p=0.0006) in obese (BMI >30kg/m
2
) men but not in nonobese (BMI ≤30 kg/m
2
) men. In addition, age-adjusted LTL was significantly shorter in men (6.89±0.02 kb) than women (7.01±0.02 kb;
P
<0.0009) and in current cigarette smokers (6.87±0.05 kb) than never smokers (6.99±0.03 kb;
P
=0.0006). Although there was no significant association of LTL with common carotid artery-IMT or with carotid artery stenosis, there was a significant inverse association of LTL with common carotid artery IMT in obese men.

Conclusion—

In obese men, shortened LTL is a powerful marker of increased carotid IMT. Given the public health impact of atherosclerosis and in particular the current epidemic of obesity, the associations noted in obese men warrant further confirmation.
Coarse Coding Resource-Allocating Network
Authors: Deco Gustavo , Ebmeyer Jürgen
Published: April 2008

In recent years localized receptive fields have been the subject of intensive research, due to their learning speed and efficient reconstruction of hypersurfaces. A very efficient implementation for such a network was proposed recently by Platt (1991). This resource-allocating network (RAN) allocates a new neuron whenever an unknown pattern is presented at its input layer. In this paper we introduce a new network architecture and learning paradigm. The aim of our approach is to incorporate "coarse coding" to the resource-allocating network. The network presented here provides for each input coordinate a separate layer, which consists of one-dimensional, locally tuned gaussian neurons. In the following layer multidimensional receptive fields are built by using pi-neurons. Linear neurons aggregate the outputs of the pi-neurons in order to approximate the required input-output mapping. The learning process follows the ideas of the resource-allocating network of Platt but due to the extended architecture of our network other improvements of the learning process had to be defined. Compared to the resource-allocating network a more compact network with comparable accuracy is obtained.
Learning in the Recurrent Random Neural Network
Authors: Gelenbe Erol
Published: April 2008

The capacity to learn from examples is one of the most desirable features of neural network models. We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function. The analytical properties of the model lead to a "backpropagation" type algorithm that requires the solution of a system of n linear and n nonlinear equations each time the n-neuron network "learns" a new input-output pair.
Acetylcholine and Learning in a Cortical Associative Memory
Authors: Hasselmo Michael E.
Published: April 2008

Implementing associative memory function in biologically realistic networks raises difficulties not dealt with in previous associative memory models. In particular, during learning of overlapping input patterns, recall of previously stored patterns can interfere with the learning of new patterns. Most associative memory models avoid this difficulty by ignoring the effect of previously modified connections during learning, thereby clamping activity to the patterns to be learned. Here I propose that the effects of acetylcholine in cortical structures may provide a neuropsychological mechanism for this clamping. Recent brain slice experiments have shown that acetylcholine selectively suppresses excitatory intrinsic fiber synaptic transmission within the olfactory cortex, while leaving excitatory afferent input unaffected. In a computational model of olfactory cortex, this selective suppression, applied during learning, prevents interference from previously stored patterns during the learning of new patterns. Analysis of the model shows that the amount of suppression necessary to prevent interference depends on cortical parameters such as inhibition and the threshold of synaptic modification, as well as input parameters such as the amount of overlap between the patterns being stored.
Learning Competition and Cooperation
Authors: Cho Sungzoon , Reggia James A.
Published: April 2008

Competitive activation mechanisms introduce competitive or inhibitory interactions between units through functional mechanisms instead of inhibitory connections. A unit receives input from another unit proportional to its own activation as well as to that of the sending unit and the connection strength between the two. This, plus the finite output from any unit, induces competition among units that receive activation from the same unit. Here we present a backpropagation learning rule for use with competitive activation mechanisms and show empirically how this learning rule successfully trains networks to perform an exclusive-OR task and a diagnosis task. In particular, networks trained by this learning rule are found to outperform standard backpropagation networks with novel patterns in the diagnosis problem. The ability of competitive networks to bring about context-sensitive competition and cooperation among a set of units proved to be crucial in diagnosing multiple disorders.
Constraints on Synchronizing Oscillator Networks
Authors: Cairns David E. , Baddeley Roland J. , Smith Leslie S.
Published: April 2008

This paper investigates the constraints placed on some synchronized oscillator models by their underlying dynamics. Phase response graphs are used to determine the phase locking behaviors of three oscillator models. These results are compared with idealized phase response graphs for single phase and multiple phase systems. We find that all three oscillators studied are best suited to operate in a single phase system and that the requirements placed on oscillatory models for operation in a multiple phase system are not compatible with the underlying dynamics of oscillatory behavior for these types of oscillator models.
Learning Mixture Models of Spatial Coherence
Authors: Becker Suzanna , Hinton Geoffrey E.
Published: April 2008

We have previously described an unsupervised learning procedure that discovers spatially coherent properties of the world by maximizing the information that parameters extracted from different parts of the sensory input convey about some common underlying cause. When given random dot stereograms of curved surfaces, this procedure learns to extract surface depth because that is the property that is coherent across space. It also learns how to interpolate the depth at one location from the depths at nearby locations (Becker and Hinton 1992b). In this paper, we propose two new models that handle surfaces with discontinuities. The first model attempts to detect cases of discontinuities and reject them. The second model develops a mixture of expert interpolators. It learns to detect the locations of discontinuities and to invoke specialized, asymmetric interpolators that do not cross the discontinuities.
Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks
Authors: Bartlett Peter L.
Published: April 2008

We show that the Vapnik-Chervonenkis dimension of the class of functions that can be computed by arbitrary two-layer or some completely connected three-layer threshold networks with real inputs is at least linear in the number of weights in the network. In Valiant's "probably approximately correct" learning framework, this implies that the number of random training examples necessary for learning in these networks is at least linear in the number of weights.
Attraction Radii in Binary Hopfield Nets are Hard to Compute
Authors: Floréen Patrik , Orponen Pekka
Published: April 2008

We prove that it is an NP-hard problem to determine the attraction radius of a stable vector in a binary Hopfield memory network, and even that the attraction radius is hard to approximate. Under synchronous updating, the problems are already NP-hard for two-step attraction radii; direct (one-step) attraction radii can be computed in polynomial time.
Theoretical Considerations for the Analysis of Population Coding in Motor Cortex
Authors: Sanger Terence D.
Published: April 2008

Recent evidence of population coding in motor cortex has led some researchers to claim that certain variables such as hand direction or force may be coded within a Cartesian coordinate system with respect to extra personal space. These claims are based on the ability to predict the rectangular coordinates of hand movement direction using a “population vector” computed from multiple cells' firing rates. I show here that such a population vector can always be found given a very general set of assumptions. Therefore the existence of a population vector constitutes only weak support for the explicit use of a particular coordinate representation by motor cortex.
Numerical Bifurcation Analysis of an Oscillatory Neural Network with Synchronous/Asynchronous Connections
Authors: Hayashi Yukio
Published: April 2008

One of the advantages of oscillatory neural networks is their dynamic links among related features; the links are based on input-dependent synchronized oscillations. This paper investigates the relations between synchronous/asynchronous oscillations and the connection architectures of an oscillatory neural network with two excitatory-inhibitory pair oscillators. Through numerical analysis, we show synchronous and asynchronous connection types over a wide parameter space for two different inputs and one connection parameter. The results are not only consistent with the classification of synchronous/asynchronous connection types in König's model (1991), but also offer a useful guideline on how to construct a network with local connections for a segmentation task.
Supervised Training of Neural Networks via Ellipsoid Algorithms
Authors: Cheung Man-Fung , Passino Kevin M. , Yurkovich Stephen
Published: April 2008

In this paper we show that two ellipsoid algorithms can be used to train single-layer neural networks with general staircase nonlinearities. The ellipsoid algorithms have several advantages over other conventional training approaches including (1) explicit convergence results and automatic determination of linear separability, (2) an elimination of problems with picking initial values for the weights, (3) guarantees that the trained weights are in some “acceptable region,” (4) certain “robustness” characteristics, and (5) a training approach for neural networks with a wider variety of activation functions. We illustrate the training approach by training the MAJ function and then by showing how to train a controller for a reaction chamber temperature control problem.
Network Amplification of Local Fluctuations Causes High Spike Rate Variability, Fractal Firing Patterns and Oscillatory Local Field Potentials
Authors: Usher Marius , Stemmler Martin , Koch Christof , Olami Zeev
Published: April 2008

We investigate a model for neural activity in a two-dimensional sheet of leaky integrate-and-fire neurons with feedback connectivity consisting of local excitation and surround inhibition. Each neuron receives stochastic input from an external source, independent in space and time. As recently suggested by Softky and Koch (1992, 1993), independent stochastic input alone cannot explain the high interspike interval variability exhibited by cortical neurons in behaving monkeys. We show that high variability can be obtained due to the amplification of correlated fluctuations in a recurrent network. Furthermore, the cross-correlation functions have a dual structure, with a sharp peak on top of a much broader hill. This is due to the inhibitory and excitatory feedback connections, which cause “hotspots” of neural activity to form within the network. These localized patterns of excitation appear as clusters or stripes that coalesce, disintegrate, or fluctuate in size while simultaneously moving in a random walk constrained by the interaction with other clusters. The synaptic current impinging upon a single neuron shows large fluctuations at many time scales, leading to a large coefficient of variation (CV) for the interspike interval statistics. The power spectrum associated with single units shows a 1/f decay for small frequencies and is flat at higher frequencies, while the power spectrum of the spiking activity averaged over many cells—equivalent to the local field potential—shows no 1/f decay but a prominent peak around 40 Hz, in agreement with data recorded from cat and monkey cortex (Gray et al. 1990; Eckhorn et al. 1993). Firing rates exhibit self-similarity between 20 and 800 msec, resulting in 1/f-like noise, consistent with the fractal nature of neural spike trains (Teich 1992).
Statistical Analysis of an Autoassociative Memory Network
Authors: Fu A. M. N.
Published: April 2008

A statistical method for analyzing the dynamic behavior of a synchronous autoassociative memory network is presented based on an extension of Amari-Maginu theory. Through computer simulations it is shown that when the memorized pattern ratio is small (r < 0.15) or large (r > 0.2), the derived theory shows good agreement.
Loading Deep Networks Is Hard
Authors: Šíma Jiří
Published: April 2008

The loading problem formulated by J. S. Judd seems to be a relevant model for supervised connectionist learning of the feedforward networks from the complexity point of view. It is known that loading general network architectures is NP-complete (intractable) when the (training) tasks are also general. Many strong restrictions on architectural design and/or on the tasks do not help to avoid the intractability of loading. Judd concentrated on the width expanding architectures with constant depth and found a polynomial time algorithm for loading restricted shallow architectures. He suppressed the effect of depth on loading complexity and left as an open prototypical computational problem the loading of easy regular triangular architectures that might capture the crux of depth difficulties. We have proven this problem to be NP-complete. This result does not give much hope for the existence of an efficient algorithm for loading deep networks.
Decorrelated Hebbian Learning for Clustering and Function Approximation
Authors: Deco G. , Obradovic D.
Published: April 2008

This paper presents a new learning paradigm that consists of a Hebbian and anti-Hebbian learning. A layer of radial basis functions is adapted in an unsupervised fashion by minimizing a two-element cost function. The first element maximizes the output of each gaussian neuron and it can be seen as an implementation of the traditional Hebbian learning law. The second element of the cost function reinforces the competitive learning by penalizing the correlation between the nodes. Consequently, the second term has an “anti-Hebbian” effect that is learned by the gaussian neurons without the implementation of lateral inhibition synapses. Therefore, the decorrelated Hebbian learning (DHL) performs clustering in the input space avoiding the “nonbiological” winner-take-all rule. In addition to the standard clustering problem, this paper also presents an application of the DHL in function approximation. A scaled piece-wise linear approximation of a function is obtained in the supervised fashion within the local regions of its domain determined by the DHL. For comparison, a standard single hidden-layer gaussian network is optimized with the initial centers corresponding to the DHL. The efficiency of the algorithm is demonstrated on the chaotic Mackey-Glass time series.
An HMM/MLP Architecture for Sequence Recognition
Authors: Cho Sung-Bae , Kim Jin H.
Published: April 2008

This paper presents a hybrid architecture of hidden Markov models (HMMs) and a multilayer perceptron (MLP). This exploits the discriminative capability of a neural network classifier while using HMM formalism to capture the dynamics of input patterns. The main purpose is to improve the discriminative power of the HMM-based recognizer by additionally classifying the likelihood values inside them with an MLP classifier. To appreciate the performance of the presented method, we apply it to the recognition problem of on-line handwritten characters. Simulations show that the proposed architecture leads to a significant improvement in generalization performance over conventional approaches to sequential pattern recognition.
Competition and Multiple Cause Models
Authors: Dayan Peter , Zemel Richard S.
Published: April 2008

If different causes can interact on any occasion to generate a set of patterns, then systems modeling the generation have to model the interaction too. We discuss a way of combining multiple causes that is based on the Integrated Segmentation and Recognition architecture of Keeler et al. (1991). It is more cooperative than the scheme embodied in the mixture of experts architecture, which insists that just one cause generate each output, and more competitive than the noisy-or combination function, which was recently suggested by Saund (1994a,b). Simulations confirm its efficacy.
Learning the Initial State of a Second-Order Recurrent Neural Network during Regular-Language Inference
Authors: Forcada Mikel L. , Carrasco Rafael C.
Published: April 2008

Recent work has shown that second-order recurrent neural networks (2ORNNs) may be used to infer regular languages. This paper presents a modified version of the real-time recurrent learning (RTRL) algorithm used to train 2ORNNs, that learns the initial state in addition to the weights. The results of this modification, which adds extra flexibility at a negligible cost in time complexity, suggest that it may be used to improve the learning of regular languages when the size of the network is small.
Learning and Generalization with Minimerror, A Temperature-Dependent Learning Algorithm
Authors: Raffin Bruno , Gordon Mirta B.
Published: April 2008

We study the numerical performances of Minimerror, a recently introduced learning algorithm for the perceptron that has analytically been shown to be optimal both on learning linearly and nonlinearly separable functions. We present its implementation on learning linearly separable boolean functions. Numerical results are in excellent agreement with the theoretical predictions.
A Spherical Basis Function Neural Network for Modeling Auditory Space
Authors: Jenison Rick L. , Fissell Kate
Published: April 2008

This paper describes a neural network for approximation problems on the sphere. The von Mises basis function is introduced, whose activation depends on polar rather than Cartesian input coordinates. The architecture of the von Mises Basis Function (VMBF) neural network is presented along with the corresponding gradient-descent learning rules. The VMBF neural network is used to solve a particular spherical problem of approximating acoustic parameters used to model perceptual auditory space. This model ultimately serves as a signal processing engine to synthesize a virtual auditory environment under headphone listening conditions. Advantages of the VMBF over standard planar Radial Basis Functions (RBFs) are discussed.
Does Extra Knowledge Necessarily Improve Generalization?
Authors: Barber David , Saad David
Published: April 2008

The generalization error is a widely used performance measure employed in the analysis of adaptive learning systems. This measure is generally critically dependent on the knowledge that the system is given about the problem it is trying to learn. In this paper we examine to what extent it is necessarily the case that an increase in the knowledge that the system has about the problem will reduce the generalization error. Using the standard definition of the generalization error, we present simple cases for which the intuitive idea of “reducivity”—that more knowledge will improve generalization—does not hold. Under a simple approximation, however, we find conditions to satisfy “reducivity.” Finally, we calculate the effect of a specific constraint on the generalization error of the linear perceptron, in which the signs of the weight components are fixed. This particular restriction results in a significant improvement in generalization performance.
Note on the Maxnet Dynamics
Authors: Sum John P. F. , Tam Peter K. S.
Published: April 2008

A simple method is presented to derive the complete solution of the Maxnet network dynamics. Besides, the exact response time of the network is deduced.
A Nonlinear Hebbian Network that Learns to Detect Disparity in Random-Dot Stereograms
Authors: Lee Christopher W. , Olshausen Bruno A.
Published: April 2008

An intrinsic limitation of linear, Hebbian networks is that they are capable of learning only from the linear pairwise correlations within an input stream. To explore what higher forms of structure could be learned with a nonlinear Hebbian network, we constructed a model network containing a simple form of nonlinearity and we applied it to the problem of learning to detect the disparities present in random-dot stereograms. The network consists of three layers, with nonlinear sigmoidal activation functions in the second-layer units. The nonlinearities allow the second layer to transform the pixel-based representation in the input layer into a new representation based on coupled pairs of left-right inputs. The third layer of the network then clusters patterns occurring on the second-layer outputs according to their disparity via a standard competitive learning rule. Analysis of the network dynamics shows that the second-layer units' nonlinearities interact with the Hebbian learning rule to expand the region over which pairs of left-right inputs are stable. The learning rule is neurobiologically inspired and plausible, and the model may shed light on how the nervous system learns to use coincidence detection in general.
Analog versus Discrete Neural Networks
Authors: DasGupta Bhaskar , Schnitger Georg
Published: April 2008

We show that neural networks with three-times continuously differentiable activation functions are capable of computing a certain family of n-bit Boolean functions with two gates, whereas networks composed of binary threshold functions require at least Ω(log n) gates. Thus, for a large class of activation functions, analog neural networks can be more powerful than discrete neural networks, even when computing Boolean functions.
Pruning with Replacement on Limited Resource Allocating Networks by F-Projections
Authors: Molina Christophe , Niranjan Mahesan
Published: April 2008

The principle of F-projection, in sequential function estimation, provides a theoretical foundation for a class of gaussian radial basis function networks known as the resource allocating networks (RAN). The ad hoc rules for adaptively changing the size of RAN architectures can be justified from a geometric growth criterion defined in the function space. In this paper, we show that the same arguments can be used to arrive at a pruning with replacement rule for RAN architectures with a limited number of units. We illustrate the algorithm on the laser time series prediction problem of the Santa Fe competition and show that results similar to those of the winners of the competition can be obtained with pruning and replacement.
Engineering Multiversion Neural-Net Systems
Authors: Partridge D. , Yates W. B.
Published: April 2008

In this paper we address the problem of constructing reliable neural-net implementations, given the assumption that any particular implementation will not be totally correct. The approach taken in this paper is to organize the inevitable errors so as to minimize their impact in the context of a multiversion system, i.e., the system functionality is reproduced in multiple versions, which together will constitute the neural-net system. The unique characteristics of neural computing are exploited in order to engineer reliable systems in the form of diverse, multiversion systems that are used together with a "decision strategy" (such as majority vote). Theoretical notions of "methodological diversity" contributing to the improvement of system performance are implemented and tested. An important aspect of the engineering of an optimal system is to overproduce the components and then choose an optimal subset. Three general techniques for choosing final system components are implemented and evaluated. Several different approaches to the effective engineering of complex multiversion systems designs are realized and evaluated to determine overall reliability as well as reliability of the overall system in comparison to the lesser reliability of component substructures.
Type I Membranes, Phase Resetting Curves, and Synchrony
Authors: Ermentrout Bard
Published: April 2008

Type I membrane oscillators such as the Connor model (Connor et al. 1977) and the Morris-Lecar model (Morris and Lecar 1981) admit very low frequency oscillations near the critical applied current. Hansel et al. (1995) have numerically shown that synchrony is difficult to achieve with these models and that the phase resetting curve is strictly positive. We use singular perturbation methods and averaging to show that this is a general property of Type I membrane models. We show in a limited sense that so called Type II resetting occurs with models that obtain rhythmicity via a Hopf bifurcation. We also show the differences between synapses that act rapidly and those that act slowly and derive a canonical form for the phase interactions.
A Large Committee Machine Learning Noisy Rules
Authors: Urbanczik R.
Published: April 2008

Statistical mechanics is used to study generalization in a tree committee machine with K hidden units and continuous weights trained on examples generated by a teacher of the same structure but corrupted by noise. The corruption is due to additive gaussian noise applied in the input layer or the hidden layer of the teacher. In the large K limit the generalization error εg as function of α, the number of patterns per adjustable parameter, shows a qualitatively similar behavior for the two cases: It does not approach its optimal value and is nonmonotonic if training is done at zero temperature. This remains true even when replica symmetry breaking is taken into account. Training at a fixed positive temperature leads, within the replica symmetric theory, to an α-k decay of εg toward its optimal value. The value of k is calculated and found to depend on the model of noise. By scaling the temperature with α, the value of k can be increased to an optimal value kopt. However, at one step of replica symmetry breaking at a fixed positive temperature εg decays as α−kopt. So, although εg will approach its optimal value with increasing sample size for any fixed K, the convergence is only uniform in K when training at a positive temperature.
How Dependencies between Successive Examples Affect On-Line Learning
Authors: Wiegerinck Wim , Heskes Tom
Published: April 2008

We study the dynamics of on-line learning for a large class of neural networks and learning rules, including backpropagation for multilayer perceptrons. In this paper, we focus on the case where successive examples are dependent, and we analyze how these dependencies affect the learning process. We define the representation error and the prediction error. The representation error measures how well the environment is represented by the network after learning. The prediction error is the average error that a continually learning network makes on the next example. In the neighborhood of a local minimum of the error surface, we calculate these errors. We find that the more predictable the example presentation, the higher the representation error, i.e., the less accurate the asymptotic representation of the whole environment. Furthermore we study the learning process in the presence of a plateau. Plateaus are flat spots on the error surface, which can severely slow down the learning process. In particular, they are notorious in applications with multilayer perceptrons. Our results, which are confirmed by simulations of a multilayer perceptron learning a chaotic time series using backpropagation, explain how dependencies between examples can help the learning process to escape from a plateau.
Antiviral NK cell responses in HIV infection: I. NK cell receptor genes as determinants of HIV resistance and progression to AIDS
Authors: Iannello Alexandre , Debbeche Olfa , Samarani Suzanne , Ahmad Ali
Published: April 2008

NK cells play an important role in controlling viral infections. They can kill virus-infected cells directly as well as indirectly via antibody-dependent, cell-mediated cytotoxicity. They need no prior sensitization and expansion for this killing. NK cells are also considered as important regulators of antiviral immune responses. They do so by secreting a multitude of soluble mediators and by directly interacting with other immune cells, e.g., dendritic cells. NK cells do not possess a single well-defined receptor to recognize antigens on target cells. Instead, they express an array of inhibitory and activating receptors and coreceptors, which bind to their cognate ligands expressed on the surface of target cells. These ligands include classical and nonclassical MHC class I antigens, MHC-like proteins, and a variety of other self- and virus-derived molecules. They may be expressed constitutively and/or de novo on the surface of virus-infected cells. NK cell receptors (NKRs) of the killer-cell Ig-like receptor (KIR) family, like their MHC class I ligands, are highly polymorphic. Several recent studies suggest that epistatic interactions between certain KIR and MHC class I genes may determine innate resistance of the host to viral infections, including HIV. In the first part of this review article, we provide an overview of the current state of knowledge of NK cell immunobiology and describe how NKR genes, alone and in combination with HLA genes, may determine genetic resistance/susceptibilty to HIV infection and the development of AIDS in humans.
New Enzymes Involved in the Mechanism of Action of Epidermal Growth Factor in a Clonal Strain of Leydig Tumor Cells
Authors: Castilla Rocío , Gadaleta Mariana , Castillo Ana Fernanda , Duarte Alejandra , Neuman Isabel , Paz Cristina , Cornejo Maciel Fabiana , Podestá Ernesto J.
Published: April 2008

The studies presented herein were designed to investigate the effect of mouse epidermal growth factor (mEGF) on arachidonic acid (AA) release in a clonal strain of cultured murine Leydig cells (designed MA-10). In MA-10 cells, mEGF promotes AA release and metabolism to lipoxygenated products to induce the steroidogenic acute regulatory (StAR) protein. However, the mechanism by which mEGF releases AA in these cells is not totally elucidated. We show that mEGF produces an increment in the mitochondrial AA content in a short-term incubation (30 min). This AA is released by the action of a mitochondrial acyl-CoA thioesterase (Acot2), as demonstrated in experiments in which Acot2 was down or overexpressed. This AA in turn regulates the StAR protein expression, indirect evidence of its metabolism to lipoxygenated products. We also show that mEGF induces the expression (mRNA and protein) of Acot2 and an acyl-CoA synthetase that provides the substrate, arachidonyl-CoA, to Acot2. This effect is also observed in another steroidogenic cell line, the adrenocortical Y1 cells. Taken together, our results show that: 1) mEGF can induce the generation of AA in a specific compartment of the cells, i.e. the mitochondria; 2) mEGF can up-regulate acyl-CoA synthetase and Acot2 mRNA and protein levels; and 3) mEGF-stimulated intramitochondrial AA release leads to StAR protein induction.
Estrogen Elicits Dorsal Root Ganglion Axon Sprouting via a Renin-Angiotensin System
Authors: Chakrabarty Anuradha , Blacklock Audrey , Svojanovsky Stanislav , Smith Peter G.
Published: April 2008

Many painful conditions occur more frequently in women, and estrogen is a predisposing factor. Estrogen may contribute to some pain syndromes by enhancing axon outgrowth by sensory dorsal root ganglion (DRG) neurons. The objective of the present study was to define mechanisms by which estrogen elicits axon sprouting. The estrogen receptor-α agonist propyl pyrazole triol induced neurite outgrowth from cultured neonatal DRG neurons, whereas the estrogen receptor-β agonist diarylpropionitrile was ineffective. 17β-Estradiol (E2) elicited sprouting from peripherin-positive unmyelinated neurons, but not larger NF200-positive myelinated neurons. Microarray analysis showed that E2 up-regulates angiotensin II (ANGII) receptor type 2 (AT2) mRNA in vitro, and studies in adult rats confirmed increased DRG mRNA and protein in vivo. AT2 plays a central role in E2-induced axon sprouting because AT2 blockade by PD123,319 eliminated estrogen-mediated sprouting in vitro. We assessed whether AT2 may be responding to locally synthesized ANGII. DRG from adult rats expressed mRNA for renin, angiotensinogen, and angiotensin converting enzyme (ACE), and protein products were present and occasionally colocalized within neurons and other DRG cells. We determined if locally synthesized ANGII plays a role in estrogen-mediated sprouting by blocking its formation using the ACE inhibitor enalapril. ACE inhibition prevented estrogen-induced neuritogenesis. These findings support the hypothesis that estrogen promotes DRG nociceptor axon sprouting by up-regulating the AT2 receptor, and that locally synthesized ANGII can induce axon formation. Therefore, estrogen may contribute to some pain syndromes by enhancing the pro-neuritogenic effects of AT2 activation by ANGII.
Contributions of Specificity Protein-1 and Steroidogenic Factor 1 to Adcy4 Expression in Y1 Mouse Adrenal Cells
Authors: Rui Xianliang , Tsao Jennivine , Scheys Joshua O. , Hammer Gary D. , Schimmer Bernard P.
Published: April 2008

The type 4 adenylyl cyclase, Adcy4, is the least abundant of five different adenylyl cyclase isoforms expressed in the Y1 mouse adrenocortical cell line and is deficient in a Y1 mutant with impaired steroidogenic factor 1 (SF1) activity. This study examines the contributions of SF1 and other DNA promoter/regulatory elements to Adcy4 expression in the Y1 cell line and its derivative Adcy4-deficient mutant. Primer extension and in silico analyses indicate that Adcy4 transcription initiates from multiple sites just downstream of a GC-rich sequence. Luciferase reporter gene assays identify a 124-bp sequence, situated 19 bp upstream of the major transcription start site and highly conserved among several mammalian species, as the major determinant of Adcy4 expression in Y1 cells and as a site of compromised activity in the Adcy4-deficient mutant. EMSAs using competitor nucleotides and specific antibodies indicate that this conserved region contains three specificity protein (Sp)-1/Sp3-binding sites and one SF1-binding site. As determined by site-specific mutagenesis, the 5′-most Sp1/Sp3-site enhances promoter activity, whereas the middle Sp1/Sp3 and SF1 sites each repress Adcy4 promoter activity. In the Adcy4-deficient mutant, mutating the SF1 site restores Adcy4 promoter activity and knocking down SF1 with small interfering RNAs increases Adcy4 expression, confirming the contribution of SF1 to the mutant phenotype. These studies demonstrate roles for Sp1/Sp3 and SF1 in Adcy4 expression in Y1 cells and establish a repressor function for SF1 in certain promoter contexts.