Numerical analysis of Augmented Lagrangian algorithms in complementary elastoplasticity

The main subject of the paper is the investigation of Augmented Lagrangian algorithms and update formulas in the solution of elastoplastic problems. A stress rate formulation for elastoplastic models with internal variables and its finite increment form is employed to state the mechanical problem. In this formulation the Augmented Lagrangian is used to enforce the constraint of plastic admissibility directly on the stresses and thermodynamic forces. This is not a limitation of the Augmented Lagrangian approach, and the same framework can be built on more classical displacement formulations as well. The meaning and the derivation of various first and second order Lagrangian multipliers update formulas and iterative schemes is shown. A new diagonal iteration algorithm and the introduction of a scale factor for the Augmented Lagrangian term are proposed. Numerical examples compare the efficiency of several forms of Augmented Lagrangian algorithms and illustrate the influence of the scale factor and of the penalty parameter. Copyright © 2004 John Wiley & Sons, Ltd.     

Evaluation of Textile Bobbins Drying Processes: Experimental and Modelling Studies

Drying of textile bobbins was carried out on two kinds of laboratory-scale dryers: in a cross circulation drying rig, atmospheric pressure heating air is used, while in the through circulation drying apparatus, the heating air is pressurized before the drying process takes place. In both cases, a bobbin was fitted with seven thermocouples, placed in a plane normal to the bobbin axis but at different radial distances, in order to provide temperature data during the drying process. In cross circulation drying, plots of temperature against time indicated the presence of two moving fronts, one starting from inside the bobbin, another from the outside, while in through circulation drying, similar plots showed the presence of an evaporation front, moving from inside the bobbin to the outside. In both canes, numerical models were developed to simulate the drying processes.     

Spike train decoding without spike sorting

We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used with any of the current decoding algorithms, such as population vector or likelihood-based algorithms. Based on analytical results and an extensive simulation study, we show that our paradigm is comparable to, and sometimes more efficient than, the traditional approach based on well-isolated neurons and that it remains efficient even when all electrodes are severely corrupted by noise, a situation that would render spike sorting particularly difficult. Our paradigm will also save time and computational effort, both of which are crucially important for successful operation of real-time brain-machine interfaces. Indeed, in place of the lengthy spike-sorting task of the traditional approach, it involves an exact expectation EM algorithm that is fast enough that it could also be left to run during decoding to capture potential slow changes in the states of the neurons.     

Effects of a startling acoustic stimulus on reaction time in different parkinsonian syndromes

BACKGROUND: The functional assessment of the startle circuit is usually done by analyzing the acoustic startle response (ASR). However, a startling acoustic stimulus (SAS) also induces changes in the excitability of neural structures that can be demonstrated by studying the SAS-induced change in the behavior of certain neurophysiologic responses. OBJECTIVE: To examine the effects induced by an SAS on voluntary reaction time in patients with parkinsonian syndromes (StartReact effect) and to compare the results with those obtained in a group of age-matched healthy volunteers. METHODS: Twelve patients with idiopathic PD (IPD), seven patients with progressive supranuclear palsy (PSP), seven patients with multisystem atrophy (MSA), and seven healthy age-matched control volunteers performed a simple visual reaction time task and received SAS together with the "go" signal in random trials. RESULTS: Baseline reaction time was significantly slower in PSP patients than in control subjects and MSA patients. The SAS induced a significant shortening of the reaction time in control subjects and in patients with IPD and MSA, but not in patients with PSP. The percentage of reaction time shortening with regard to the baseline values also differed significantly between PSP patients and the other groups of subjects. The StartReact effect was consistent throughout the experiment and showed reduced habituation with repeated testing. CONCLUSIONS: The results are consistent with an abnormal function of the startle circuit in patients with PSP and agree with previous studies using the ASR. The reduced habituation of the StartReact effect favors its clinical applicability in the assessment of differences between patients with parkinsonian syndromes.     

Discrete Time Non-Homogeneous Semi-Markov Reliability Transition Credit Risk Models and the Default Distribution Functions

This paper shows how to apply discrete time non-homogeneous semi-Markov processes (DTNHSMP) with an age index to credit risk. The idea is to consider the credit risk problem as a reliability model indexed by the age and in this way, many semi-Markov results could be adapted to describe credit risk problem. The default state is seen as a “non working state”. As the semi-Markov process is a generalization of Markov process, the presented model can be seen as a more general migration model. In fact, in semi-Markov environment the distribution function (d.f.) of the waiting time before a transition can be of any type and without the strong constraints of the Markov model. Furthermore, some results on the asymptotic behavior of the presented model are given. This permits the construction of the d.f. of the default random variable for each “working state”. An example, constructed manipulating some Standard & Poor’s (S&P) data, is presented.     

Multiple instance learning for classifying students in learning management systems

In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to find efficient solutions when traditional supervised learning is used. To check the efficiency of the new proposed representation, the most popular techniques of traditional supervised learning based on single instances are compared to those based on multiple instance learning. Computational experiments show that when the problem is regarded as a multiple instance one, performance is significantly better and the weaknesses of single-instance representation are overcome.     

Spike count correlation increases with length of time interval in the presence of trial-to-trial variation

It has been observed that spike count correlation between two simultaneously recorded neurons often increases with the length of time interval examined. Under simple assumptions that are roughly consistent with much experimental data, we show that this phenomenon may be explained as being due to excess trial-to-trial variation. The resulting formula for the correlation is able to predict the observed correlation of two neurons recorded from primary visual cortex as a function of interval length.     

Thermal conductivity of Torlon between 4.2 and 300 K

Torlon is an organic polymer (polyamide-imide) which exhibits room temperature good mechanical and thermal properties and high chemical resistance. The thermal conductivity of Torlon 4203 was measured in the range of temperature 4.2-300 K. These data complete existing measurement in the temperature range 0.1-5 K. The thermal conductivity shows a linear behavior between 30 K and 250 K and a plateau, typical of many amorphous materials, around 7 K.     

Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces

Metric Access Methods (MAMs) are indexing techniques which allow working in generic metric spaces. Therefore, MAMs are specially useful for Content-Based Image Retrieval systems based on features which use non L _p norms as similarity measures. MAMs naturally allow the design of image browsers due to their inherent hierarchical structure. The Hierarchical Cellular Tree (HCT), a MAM-based indexing technique, provides the starting point of our work. In this paper, we describe some limitations detected in the original formulation of the HCT and propose some modifications to both the index building and the search algorithm. First, the covering radius, which is defined as the distance from the representative to the furthest element in a node, may not cover all the elements belonging to the node’s subtree. Therefore, we propose to redefine the covering radius as the distance from the representative to the furthest element in the node’s subtree. This new definition is essential to guarantee a correct construction of the HCT. Second, the proposed Progressive Query retrieval scheme can be redesigned to perform the nearest neighbor operation in a more efficient way. We propose a new retrieval scheme which takes advantage of the benefits of the search algorithm used in the index building. Furthermore, while the evaluation of the HCT in the original work was only subjective, we propose an objective evaluation based on two aspects which are crucial in any approximate search algorithm: the retrieval time and the retrieval accuracy. Finally, we illustrate the usefulness of the proposal by presenting some actual applications.