Preliminary diagnosis of ophtalmological diseases through machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

SimSight: A Virtual Machine Based Dynamic Call Graph Generator

One problem with using component-based software development approach is that once software modules are reused over generations of products, they form legacy structures that can be challenging to understand, making validating these systems difficult. Therefore, tools and methodologies that enable engineers to see interactions of these software modules will enhance their ability to make these software systems more dependable. To address this need, we propose SimSight, a framework to capture dynamic call graphs in Simics, a widely adopted commercial full-system simulator. Simics is a software system that simulates complete computer systems. Thus, it performs nearly identical tasks to a real system but at a much lower speed while providing greater execution observability. We have implemented SimSight to generate dynamic call graphs of statically and dynamically linked functions in x86/Linux environment. A case study illustrates how we can use SimSight to identify sources of software errors. We then evaluate its performance using 12 integer programs from SPEC CPU2006 benchmark suite.

An ice machine adapted into an autonomous photovoltaic system without batteries using a variable-speed drive

The adaptation of a commercially available ice machine for autonomous photovoltaic operation without batteries is presented. In this adaptation a 1040 W(p) photovoltaic array directly feeds a variable-speed drive and a 24 V(dc) source. The drive runs an induction motor coupled by belt-and-pulley to an open reciprocating compressor, while the dc source supplies a solenoid valve and the control electronics. Motor speed and refrigerant evaporation pressure are set aiming at continuously matching system power demand to photovoltaic power availability. The resulting system is a simple integration of robust, standard, readily available parts. It produces 27 kg of ice in a clear-sky day and has ice production costs around US$0.30/kg. Although a few machine features might be specific to Brazil, its technical and economical guidelines are applicable elsewhere. Copyright (C); 2010 John Wiley & Sons, Ltd.

Heuristic methods for the single machine scheduling problem with different ready times and a common due date

The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Mechanical Performance of Cylindrical and Dual-Core Pedicle Screws After Repeated Insertion

Study Design. Ex vivo study of the mechanical performance of cylindrical and dual-core pedicle screws after insertion, removal, and reinsertion in the same hole. Objective. To evaluate the effect of repeated use of same screw hole on the insertion torque and the retentive strength of the cylindrical and dual-core screws. Summary of Background Data. Insertion and removal of pedicle screws is sometimes necessary during surgical procedure to assess the integrity of the pilot-hole wall. However, this maneuver may compromise the implant-holding capacity. Methods. Sixty thoracolombar vertebrae (T13-L5), harvested from 10 healthy calves, were used to insert 2 different designs of pedicle screws: cylindrical (5.0-mm outer diameter) and dual-core screws (5.2-mm outer diameter). Three experimental groups were created on the basis of the number of insertions of the screws and 2 subgroups were established according to the core pedicle screw design (dual-core and cylindrical). The insertion torque was measured during initial insertion, second insertion, and third insertion. Pullout screw tests were performed using a universal testing machine to evaluate the pullout strength after initial insertion, second insertion, and third insertion. Results. Significant reductions of 38% in mean insertion torque and 30% in mean pullout strength of dual-core screw were observed between the initial insertion and the third insertion. The cylindrical screw observed significant reductions of 52.5% in mean insertion torque and 42.3% in mean pullout strength between the initial insertion and the third insertion. A reduction of mean insertion torque and pullout strength between the first insertion and the second insertion but without significance was also observed for both types of screws. Conclusion. Insertions and reinsertion of either cylindrical or dual-core pedicle screws have compromised insertion torque and pullout strength of the implants as measured by mechanical tests.

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation.

Analysis and design of high performance multiphase electric drives for vehicle and aircraft applications

The first part of this thesis has focused on the construction of a twelve-phase asynchronous machine for More Electric Aircraft (MEA) applications. In fact, the aerospace world has found in electrification the way to improve the efficiency, reliability and maintainability of an aircraft. This idea leads to the aircraft a new management and distribution of electrical services. In this way is possible to remove or to reduce the hydraulic, mechanical and pneumatic systems inside the aircraft. The second part of this dissertation is dedicated on the enhancement of the control range of matrix converters (MCs) operating with non-unity input power factor and, at the same time, on the reduction of the switching power losses. The analysis leads to the determination in closed form of a modulation strategy that features a control range, in terms of output voltage and input power factor, that is greater than that of the traditional strategies under the same operating conditions, and a reduction in the switching power losses.

Oxytocin release and lactation performance in Syrian Shami cattle milked with and without suckling

Oxytocin (OT) release and lactation performance in primiparous Syrian Shami cows were evaluated in response to two different machine milking regimes. Six cows were milked in the presence of the calves (PC) and subsequently suckled, whereas six cows were exclusively machine milked without the presence of their calves (WC) until day 91 post partum. Milk yield and milk constituents were determined weekly. The degree of udder evacuation was determined by the succeeding removal of residual milk. PC released OT during the milking process, whereas in WC no OT release was detected throughout the milking process. Consequently, the residual milk fraction was much lower in PC than in WC (11% v. 58%, P<0.05) and daily milk yield until day 91 post partum was higher in PC than in WC (12.6+/-0.3 v. 7.1+/-0.4 kg, P<0.05). In conclusion, Syrian Shami cattle are not suitable to be exclusively machine milked without the presence of their calves.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

La performance teatral de las máquinas maravillosas : Configuraciones ambivalentes de la técnica y el teatro en los siglos XVI y XVII

El artículo explora las formas de representación y a la dimensión epistemológica de la máquina en el campo discursivo de los siglos XVI y XVII, cuando todavía no existe una diferencia fundamental entre los discursos científicos, paracientíficos, filosóficos y estéticos. A partir de las investigaciones de Jan Lazardzig, analiza el carácter paradójico de la ingeniería mecánica entre la funcionalidad y la admiración, la racionalidad y lo maravilloso dentro del contexto español en el Siglo de Oro. En este sentido, el artículo examina las formas de performance teatral de la máquina como objeto admirable y maravilloso: por un lado en el teatro de máquinas cortesano y por el otro en los libros de máquinas en la tradición del Theatrum machinarum. Analizamos así los paralelismos y las diferencias respecto a sus estructuras, sus modos de representación y sus dimensiones pragmáticas.

La performance teatral de las máquinas maravillosas : Configuraciones ambivalentes de la técnica y el teatro en los siglos XVI y XVII

El artículo explora las formas de representación y a la dimensión epistemológica de la máquina en el campo discursivo de los siglos XVI y XVII, cuando todavía no existe una diferencia fundamental entre los discursos científicos, paracientíficos, filosóficos y estéticos. A partir de las investigaciones de Jan Lazardzig, analiza el carácter paradójico de la ingeniería mecánica entre la funcionalidad y la admiración, la racionalidad y lo maravilloso dentro del contexto español en el Siglo de Oro. En este sentido, el artículo examina las formas de performance teatral de la máquina como objeto admirable y maravilloso: por un lado en el teatro de máquinas cortesano y por el otro en los libros de máquinas en la tradición del Theatrum machinarum. Analizamos así los paralelismos y las diferencias respecto a sus estructuras, sus modos de representación y sus dimensiones pragmáticas.

La performance teatral de las máquinas maravillosas : Configuraciones ambivalentes de la técnica y el teatro en los siglos XVI y XVII

El artículo explora las formas de representación y a la dimensión epistemológica de la máquina en el campo discursivo de los siglos XVI y XVII, cuando todavía no existe una diferencia fundamental entre los discursos científicos, paracientíficos, filosóficos y estéticos. A partir de las investigaciones de Jan Lazardzig, analiza el carácter paradójico de la ingeniería mecánica entre la funcionalidad y la admiración, la racionalidad y lo maravilloso dentro del contexto español en el Siglo de Oro. En este sentido, el artículo examina las formas de performance teatral de la máquina como objeto admirable y maravilloso: por un lado en el teatro de máquinas cortesano y por el otro en los libros de máquinas en la tradición del Theatrum machinarum. Analizamos así los paralelismos y las diferencias respecto a sus estructuras, sus modos de representación y sus dimensiones pragmáticas.

Automatic Categorization for Improving Spanish into Spanish Sign Language Machine Translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82.