Chemisorption of atomic aluminum on Si(111): Evidence for an adsorbate-induced relaxation based on ab initio cluster-model calculations

Interaction models of atomic Al with Si4H9, Si4H7, and Si6H9 clusters have been studied to simulate Al chemisorption on the Si(111) surface in the atop, fourfold atop, and open sites. Calculations were carried out using nonempirical pseudopotentials in the framework of the ab initio Hartree-Fock procedure. Equilibrium bond distances, binding energies for adsorption, and vibrational frequencies of the adatoms are calculated. Several basis sets were used in order to show the importance of polarization effects, especially in the binding energies. Final results show the importance of considering adatom-induced relaxation effects to specify the order of energy stabilities for the three different sites, the fourfold atop site being the preferred one, in agreement with experimental findings.

Designing and testing of a sensor based on a magnetoresistive manganese perovskite thick film

In this paper we report on the growth of thick films of magnetoresistive La2/3Sr1/3MnO3 by using spray and screen printing techniques on various substrates (Al2O3 and ZrO2). The growth conditions are explored in order to optimize the microstructure of the films. The films display a room-temperature magnetoresistance of 0.0012%/Oe in the 1 kOe field region. A magnetic sensor is described and tested.

Social reciprocity measures based on dyadic discrepancies: Methodological review and software development

This paper examines statistical analysis of social reciprocity at group, dyadic, and individual levels. Given that testing statistical hypotheses regarding social reciprocity can be also of interest, a statistical procedure based on Monte Carlo sampling has been developed and implemented in R in order to allow social researchers to describe groups and make statistical decisions.

mGPT: a probabilistic planner based on heuristic search

We describe the version of the GPT planner to be used in the planning competition. This version, called mGPT, solves mdps specified in the ppddllanguage by extracting and using different classes of lower bounds, along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations of the mdp where alternativeprobabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms, on the other hand, use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state with the greedy policy.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Parameterization of written signatures based on EFD

In this work we propose a method to quantify written signatures from digitalized images based on the use of Elliptical Fourier Descriptors (EFD). As usually signatures are not represented as a closed contour, and being that a necessary condition in order to apply EFD, we have developed a method that represents the signatures by means of a set of closed contours. One of the advantages of this method is that it can reconstruct the original shape from all the coefficients, or an approximated shape from a reduced set of them finding the appropriate number of EFD coefficients required for preserving the important information in each application. EFD provides accurate frequency information, thus the use of EFD opens many possibilities. The method can be extended to represent other kind of shapes.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

Selective ablation of photovoltaic materials with UV laser sources for monolithic interconnection of devices based on a-Si:H

Lasers are essential tools for cell isolation and monolithic interconnection in thin-film-silicon photovoltaic technologies. Laser ablation of transparent conductive oxides (TCOs), amorphous silicon structures and back contact removal are standard processes in industry for monolithic device interconnection. However, material ablation with minimum debris and small heat affected zone is one of the main difficulty is to achieve, to reduce costs and to improve device efficiency. In this paper we present recent results in laser ablation of photovoltaic materials using excimer and UV wavelengths of diode-pumped solid-state (DPSS) laser sources. We discuss results concerning UV ablation of different TCO and thin-film silicon (a-Si:H and nc-Si:H), focussing our study on ablation threshold measurements and process-quality assessment using advanced optical microscopy techniques. In that way we show the advantages of using UV wavelengths for minimizing the characteristic material thermal affection of laser irradiation in the ns regime at higher wavelengths. Additionally we include preliminary results of selective ablation of film on film structures irradiating from the film side (direct writing configuration) including the problem of selective ablation of ZnO films on a-Si:H layers. In that way we demonstrate the potential use of UV wavelengths of fully commercial laser sources as an alternative to standard backscribing process in device fabrication.

Differences based on fine motor behaviour in Parkinson's patients compared to an age matched control group in proprioceptive and visuo-proprioceptive test conditions

Personality differences based on fine motor precision performance were studied in early stage Parkinson's patients and an age-matched control group under two different test conditions: proprioceptive + visual information and proprioceptive information alone. A comparative data analysis for deviations of three measured movement types (transversal, frontal and sagittal) was done for both hands (dominant and non-dominant) with relation to personality dimensions. There were found significant differences between the two groups in decision making dimension and emotionality. After splitting the data for gender subgroups, some significant differences were found for men but not for women. The differences in fine motor task performance varied, being better in some directions for the Parkinson"s patients and worse in others. The findings may suggest that medication has both positive and negative effects on motor performance and provoke personality changes, being more pronounced in men.

Models of social networks based on social distance attachment

We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP.

Molecular phylogenetics and temporal diversification in the genus Aeromonas based on the sequences of five housekeeping genes

Several approaches have been developed to estimate both the relative and absolute rates of speciation and extinction within clades based on molecular phylogenetic reconstructions of evolutionary relationships, according to an underlying model of diversification. However, the macroevolutionary models established for eukaryotes have scarcely been used with prokaryotes. We have investigated the rate and pattern of cladogenesis in the genus Aeromonas (γ-Proteobacteria, Proteobacteria, Bacteria) using the sequences of five housekeeping genes and an uncorrelated relaxed-clock approach. To our knowledge, until now this analysis has never been applied to all the species described in a bacterial genus and thus opens up the possibility of establishing models of speciation from sequence data commonly used in phylogenetic studies of prokaryotes. Our results suggest that the genus Aeromonas began to diverge between 248 and 266 million years ago, exhibiting a constant divergence rate through the Phanerozoic, which could be described as a pure birth process.