Positron-emission tomography studies of cross-modality inhibition in selective attentional tasks: closing the "mind's eye".

It is a familiar experience that we tend to close our eyes or divert our gaze when concentrating attention on cognitively demanding tasks. We report on the brain activity correlates of directing attention away from potentially competing visual processing and toward processing in another sensory modality. Results are reported from a series of positron-emission tomography studies of the human brain engaged in somatosensory tasks, in both "eyes open" and "eyes closed" conditions. During these tasks, there was a significant decrease in the regional cerebral blood flow in the visual cortex, which occurred irrespective of whether subjects had to close their eyes or were instructed to keep their eyes open. These task-related deactivations of the association areas belonging to the nonrelevant sensory modality were interpreted as being due to decreased metabolic activity. Previous research has clearly demonstrated selective activation of cortical regions involved in attention-demanding modality-specific tasks; however, the other side of this story appears to be one of selective deactivation of unattended areas.

Interval bias in 2AFC detection tasks: sorting out the artifacts

Proportion correct in two-alternative forcedchoice (2AFC) detection tasks often varies when the stimulus is presented in the first or in the second interval.Reanalysis of published data reveals that these order effects (or interval bias) are strong and prevalent, refuting the standard difference model of signal detection theory. Order effects are commonly regarded as evidence that observers use an off-center criterion under the difference model with bias. We consider an alternative difference model with indecision whereby observers are occasionally undecided and guess with some bias toward one of the response options. Whether or not the data show order effects, the two models fit 2AFC data indistinguishably, but they yield meaningfully different estimates of sensory parameters. Under indeterminacy as to which model governs 2AFC performance, parameter estimates are suspect and potentially misleading. The indeterminacy can be circumvented by modifying the response format so that observers can express indecision when needed. Reanalysis of published data collected in this way lends support to the indecision model. We illustrate alternative approaches to fitting psychometric functions under the indecision model and discuss designs for 2AFC experiments that improve the accuracy of parameter estimates, whether or not order effects are apparent in the data.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Broadening the Scope of Ethnic Identity: A Social Sustainability Solution for Pan African Communities through Ethnically Relevant Educational Programming

This conceptual study explores ethnic identity development theory in order to argue that ethnic identity development education is a means of developing broad senses of community in the African Diaspora that expand beyond a tribal, local, familial level. This study suggests that the broadening of community understanding would contribute to establishing social sustainability on regional, national and international levels within the Pan African community. Establishing such social sustainability would have direct effects on the areas of economic and environmental sustainability. One of the goals of this project is to offer suggestions for ethnically relevant education that can develop social sustainability in several places throughout the Diaspora, such as in Nigeria where ethnic conflicts are a contemporary concern.

Evaluating EmotiBlog robustness for sentiment analysis tasks

EmotiBlog is a corpus labelled with the homonymous annotation schema designed for detecting subjectivity in the new textual genres. Preliminary research demonstrated its relevance as a Machine Learning resource to detect opinionated data. In this paper we compare EmotiBlog with the JRC corpus in order to check the EmotiBlog robustness of annotation. For this research we concentrate on its coarse-grained labels. We carry out a deep ML experimentation also with the inclusion of lexical resources. The results obtained show a similarity with the ones obtained with the JRC demonstrating the EmotiBlog validity as a resource for the SA task.

Using WordNet relations and semantic classes in information retrieval tasks

In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach.

Human-computer interaction for optical music recognition tasks

The need to digitise music scores has led to the development of Optical Music Recognition (OMR) tools. Unfortunately, the performance of these systems is still far from providing acceptable results. This situation forces the user to be involved in the process due to the need of correcting the mistakes made during recognition. However, this correction is performed over the output of the system, so these interventions are not exploited to improve the performance of the recognition. This work sets the scenario in which human and machine interact to accurately complete the OMR task with the least possible effort for the user.

Robust linear semi-infinite programming duality under uncertainty

In this paper, we propose a duality theory for semi-infinite linear programming problems under uncertainty in the constraint functions, the objective function, or both, within the framework of robust optimization. We present robust duality by establishing strong duality between the robust counterpart of an uncertain semi-infinite linear program and the optimistic counterpart of its uncertain Lagrangian dual. We show that robust duality holds whenever a robust moment cone is closed and convex. We then establish that the closed-convex robust moment cone condition in the case of constraint-wise uncertainty is in fact necessary and sufficient for robust duality. In other words, the robust moment cone is closed and convex if and only if robust duality holds for every linear objective function of the program. In the case of uncertain problems with affinely parameterized data uncertainty, we establish that robust duality is easily satisfied under a Slater type constraint qualification. Consequently, we derive robust forms of the Farkas lemma for systems of uncertain semi-infinite linear inequalities.

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

Mathematical programming model for heat exchanger design through optimization of partial objectives

Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature.

Active Visual Features Based on Events to Guide Robot Manipulators in Tracking Tasks

Traditional visual servoing systems do not deal with the topic of moving objects tracking. When these systems are employed to track a moving object, depending on the object velocity, visual features can go out of the image, causing the fail of the tracking task. This occurs specially when the object and the robot are both stopped and then the object starts the movement. In this work, we have employed a retina camera based on Address Event Representation (AER) in order to use events as input in the visual servoing system. The events launched by the camera indicate a pixel movement. Event visual information is processed only at the moment it occurs, reducing the response time of visual servoing systems when they are used to track moving objects.

Comparative study of RPSALG algorithm for convex semi-infinite programming

The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of auxiliary optimization problems: the first one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement different variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.

Development and programming of Geophonino: A low cost Arduino-based seismic recorder for vertical geophones

The commercial data acquisition systems used for seismic exploration are usually expensive equipment. In this work, a low cost data acquisition system (Geophonino) has been developed for recording seismic signals from a vertical geophone. The signal goes first through an instrumentation amplifier, INA155, which is suitable for low amplitude signals like the seismic noise, and an anti-aliasing filter based on the MAX7404 switched-capacitor filter. After that, the amplified and filtered signal is digitized and processed by Arduino Due and registered in an SD memory card. Geophonino is configured for continuous registering, where the sampling frequency, the amplitude gain and the registering time are user-defined. The complete prototype is an open source and open hardware system. It has been tested by comparing the registered signals with the ones obtained through different commercial data recording systems and different kind of geophones. The obtained results show good correlation between the tested measurements, presenting Geophonino as a low-cost alternative system for seismic data recording.

The Organizational Tasks of High-Performance Gymnastic Coaches

The purpose of this study was to report the knowledge used by expert high performance gymnastic coaches in the organization of training and competition. In-depth interviews were conducted with 9 coaches who worked with male gymnasts and 8 coaches who worked with female gymnasts. Qualitative analyses showed that coaches of males and coaches of females planned training similarly, except that coaches of females appeared to emphasize esthetic and nutritional issues to a greater extent. Coaches of males revealed more concerns about the organization of gymnasts' physical conditioning. Analysis indicated that expert gymnastic coaches of males and females are constantly involved in dynamic social interactions with gymnasts, parents, and assistant coaches. Many areas of coaches' organizational work, such as dealing with the athletes' personal concerns and working with parents, are not part of the structure of coaches' training programs and emerged as crucial tasks of expert gymnastic coaches for developing elite gymnasts.

Reducing task’s redundancy – the strategy of Faculty of Pharmacy Library to centralize all scientific research

Introduction – Based on a previous project of University of Lisbon (UL) – a Bibliometric Benchmarking Analysis of University of Lisbon, for the period of 2000-2009 – a database was created to support research information (ULSR). However this system was not integrated with other existing systems at University, as the UL Libraries Integrated System (SIBUL) and the Repository of University of Lisbon (Repositório.UL). Since libraries were called to be part of the process, the Faculty of Pharmacy Library’ team felt that it was very important to get all systems connected or, at least, to use that data in the library systems. Objectives – The main goals were to centralize all the scientific research produced at Faculty of Pharmacy, made it available to the entire Faculty, involve researchers and library team, capitalize and reinforce team work with the integration of several distinct projects and reducing tasks’ redundancy. Methods – Our basis was the imported data collection from the ISI Web of Science (WoS), for the period of 2000-2009, into ULSR. All the researchers and indexed publications at WoS, were identified. A first validation to identify all the researchers and their affiliation (university, faculty, department and unit) was done. The final validation was done by each researcher. In a second round, concerning the same period, all Pharmacy Faculty researchers identified their published scientific work in other databases/resources (NOT WoS). To our strategy, it was important to get all the references and essential/critical to relate them with the correspondent digital objects. To each researcher previously identified, was requested to register all their references of the ‘NOT WoS’ published works, at ULSR. At the same time, they should submit all PDF files (for both WoS and NOT WoS works) in a personal area of the Web server. This effort enabled us to do a more reliable validation and prepare the data and metadata to be imported to Repository and to Library Catalogue. Results – 558 documents related with 122 researchers, were added into ULSR. 1378 bibliographic records (WoS + NOT WoS) were converted into UNIMARC and Dublin Core formats. All records were integrated in the catalogue and repository. Conclusions – Although different strategies could be adopted, according to each library team, we intend to share this experience and give some tips of what could be done and how Faculty of Pharmacy created and implemented her strategy.