The indirect amygdala-dorsal striatum pathway mediates conditioned freezing: Insights on emotional memory networks

The dorsal striatum (DS) is involved in various forms of learning and memory such as procedural learning, habit learning, reward-association and emotional learning. We have previously reported that bilateral DS lesions disrupt tone fear conditioning (TFC), but not contextual fear conditioning (CFC) [Ferreira TL, Moreira KM, Ikeda DC, Bueno OFA, Oliveira MGM (2003) Effects of dorsal striatum lesions in tone fear conditioning and contextual fear conditioning. Brain Res 987:17-24]. To further elucidate the participation of DS in emotional learning, in the present study, we investigated the effects of bilateral pretest (postraining) electrolytic DS lesions on TFC. Given the well-acknowledged role of the amygdala in emotional learning, we also examined a possible cooperation between DS and the amygdala in TFC, by using asymmetrical electrolytic lesions, consisting of a unilateral lesion of the central amygdaloid nucleus (CeA) combined to a contralateral DS lesion. The results show that pre-test bilateral DS lesions disrupt TFC responses, suggesting that DS plays a role in the expression of TFC. More importantly, rats with asymmetrical pre-training lesions were impaired in TFC, but not in CFC tasks. This result was confirmed with muscimol asymmetrical microinjections in DS and CeA, which reversibly inactivate these structures. On the other hand, similar pretest lesions as well as unilateral electrolytic lesions of CeA and DS in the same hemisphere did not affect TFC. Possible anatomical substrates underlying the observed effects are proposed. Overall, the present results underscore that other routes, aside from the well-established CeA projections to the periaqueductal gray, may contribute to the acquisition/consolidation of the freezing response associated to a TFC task. It is suggested that CeA may presumably influence DS processing via a synaptic relay on dopaminergic neurons of the substantia nigra compacta and retrorubral nucleus. The present observations are also in line with other studies showing that TFC and CFC responses are mediated by different anatomical networks. (C) 2008 IBRO. Published by Elsevier Ltd. All rights reserved.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Using metrics from complex networks to evaluate machine translation

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Translation, Communication and 'Learning' : Experiences from teaching a master program in Viet Nam

In Vietnam, as in other Asian countries, co-operation with foreign universities plays an important role for the development of higher education. This paper is based on personal experiences from teaching a Swedish Master Programme in Education Science at Vietnam National University in Hanoi. Using theories developed by Lev Vygotsky and Donald Schon, the programme is explored as an inter-cultural learning process. Three aspects are focused upon. Firstly, the fact that communication between students and teachers is conducted with the help of translators who support both teachers and students in their attempt to understand and make themselves understood. Secondly, the expressed need to connect the ideas and techniques which are studied in the programme to the students´ professional worlds. Thirdly, the need to construct a framework wherein the students can inquire into their own situations and to encourage them to try new and more productive ways to deal with problems they are confronted with.

Scanlation - What Fan Translators of Manga Learn in the Informal Learning Environment

The present paper discusses two pilot studies carried out to see the possibility of the fan community of manga (Japanese comics), in which fan translators translate the original Japanese manga into English (which is called scanlation), functioning as an informal learning environment for the Japanese language learning and translator training. Two pilot studies consist of a) comparison of the original Japanese version with the scanlation and official translation, and b) comparison of the original Japanese version with two different versions of scanlation to see the translators’ level of Japanese language and the overall translation quality. The results show that in scanlation versions, there were numbers of inaccuracies which would prevent them to be treated as professional translation. Some of these errors are clearly caused by insufficient understanding of Japanese language by the translator. However, the pilot studies also suggested some interesting features of fan translation, such as the treatment of cultural references. The two pilot studies indicate that it is desirable to conduct further studies with more data, in order to confirm the results of present studies, and to see the possible relationship between the types of trnalsation errors found in scanlation and the particular type of Japanese language (informal, conversational) that could be learned from manga.

Fan Culture as an Informal Learning Environment : Presentation of a NGL project

Fan culture is a subculture that has developed explosively on the internet over the last decades. Fans are creating their own films, translations, fiction, fan art, blogs, role play and also various forms that are all based on familiar popular culture creations like TV-series, bestsellers, anime, manga stories and games. In our project, we analyze two of these subculture genres, fan fiction and scanlation. Amateurs, and sometimes professional writers, create new stories by adapting and developing existing storylines and characters from the original. In this way, a "network" of texts occurs, and writers step into an intertextual dialogue with established writers such as JK Rowling (Harry Potter) and Stephanie Meyer (Twilight). Literary reception and creation then merge into a rich reciprocal creative activity which includes comments and feedback from the participators in the community. The critical attitude of the fans regarding quality and the frustration at waiting for the official translation of manga books led to the development of scanlation, which is an amateur translation of manga distributed on the internet.  Today, young internet users get involved in conceptual discussions of intertextuality and narrative structures through fan activity. In the case of scanlation, the scanlators practice the skills and techniques of translating in an informal environment. This phenomenon of participatory culture has been observed by scholars and it is concluded that they contribute to the development of a student’s literacy and foreign language skills. Furthermore, there is no doubt that the fandom related to Japanese cultural products such as manga, anime and videogames is one of the strong motives for foreign students to start learning Japanese. This is something to take into pedagogical consideration when we develop web-based courses. Fan fiction and fan culture make it ​​possible to have an intensive transcultural dialogue between participators throughout the world and is of great interest when studying the interaction between formal and informal learning that puts the student in focus

LEARNING STUDY: ROZVOJ ŠKOLY SKRZE KOLEGIÁLNÍ UČENÍ UČITELŮ : Learning study: school development through collegial learning of teachers

The aim of this article is to describe how the Learning Study method (LS) was implemented in a Swedish upper secondary school, as well as how the principals and the teachers involved perceived this to affect teaching at, and the development of, the school. It is an empirical study that was conducted as an action research project over a period of three years. The project to implement the LS method was based on the assumption that proper training is the result of collegial activity that occurs when teachers learn from each other. The teachers in this study were, in general, positive about using the LS method. It created opportunities to meet and talk about teaching skills, developed better professional relationships between colleagues, and offered a systematic method for planning, implementing and monitoring teaching. However, working together requires that time be set aside to allow for implementation of the LS method. This is crucial, as the LS method is a rather expensive way to make school development work. This places heavy demands on principals to create the necessary conditions for the implementation of the LS method.

The Nurse Professional Competence (NPC) scale : self-reported competence among nursing students on the point of graduation

BACKGROUND: International organisations, e.g. WHO, stress the importance of competent registered nurses (RN) for the safety and quality of healthcare systems. Low competence among RNs has been shown to increase the morbidity and mortality of inpatients. OBJECTIVES: To investigate self-reported competence among nursing students on the point of graduation (NSPGs), using the Nurse Professional Competence (NPC) Scale, and to relate the findings to background factors. METHODS AND PARTICIPANTS: The NPC Scale consists of 88 items within eight competence areas (CAs) and two overarching themes. Questions about socio-economic background and perceived overall quality of the degree programme were added. In total, 1086 NSPGs (mean age, 28.1 [20-56]years, 87.3% women) from 11 universities/university colleges participated. RESULTS: NSPGs reported significantly higher scores for Theme I "Patient-Related Nursing" than for Theme II "Organisation and Development of Nursing Care". Younger NSPGs (20-27years) reported significantly higher scores for the CAs "Medical and Technical Care" and "Documentation and Information Technology". Female NSPGs scored significantly higher for "Value-Based Nursing". Those who had taken the nursing care programme at upper secondary school before the Bachelor of Science in Nursing (BSN) programme scored significantly higher on "Nursing Care", "Medical and Technical Care", "Teaching/Learning and Support", "Legislation in Nursing and Safety Planning" and on Theme I. Working extra paid hours in healthcare alongside the BSN programme contributed to significantly higher self-reported scores for four CAs and both themes. Clinical courses within the BSN programme contributed to perceived competence to a significantly higher degree than theoretical courses (93.2% vs 87.5% of NSPGs). SUMMARY AND CONCLUSION: Mean scores reported by NSPGs were highest for the four CAs connected with patient-related nursing and lowest for CAs relating to organisation and development of nursing care. We conclude that the NPC Scale can be used to identify and measure aspects of self-reported competence among NSPGs.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Variáveis internas que levam à manutenção de alunos em uma rede de ensino de idiomas

Este estudo procura entender as variáveis que possam determinar a fidelidade dos alunos a uma rede de ensino de idiomas (a empresa Y), líder no mercado há 50 anos. Partindo da hipótese de que a motivação do professor possa exercer um efeito positivo com relação à fidelidade dos alunos, são abordadas as questões referentes à motivação profunda do profissional, através do conceito de flow, teoricamente descrito e analisado por Mihalyi Csikszentmihalyi (1991, 1992, 1994, 1996, 1997). Numa perspectiva sócio-histórica, são trazidas informações do universo pessoal e profissional do professor, referentes a seu perfil psicológico, sua formação e seu desenvolvimento. Questões relativas a metodologias de ensino de língua estrangeira e a teorias de aprendizagem são também analisadas, com o objetivo de melhor situar e surtir melhor compreensão dos resultados da pesquisa. Coletaram-se os dados por meio de um questionário aplicado em 99 professores de 26 escolas da empresa Y (localizadas nos estados de Santa Catarina e Rio Grande do Sul) e sua análise foi feita a partir de testes de correlação das variáveis pertinentes ao modelo teórico. O problema principal estudado na pesquisa é a existência, a freqüência e a profundidade de atividades autotélicas, geradoras de flow no contexto dos professores da empresa Y, testando a seguinte hipótese: quanto maior o flow, maior a fidelidade dos alunos. Na ótica de Csikszentmihalyi, a atividade autotélica é uma atividade que, por si só, gera prazer, satisfação, recompensa, e que atende às necessidades profundas do indivíduo, levando-o à experiência máxima (ou ao flow).

Windows of opportunities and knowledge networks: implications for the catch-up in developing countries

This document represents a doctoral thesis held under the Brazilian School of Public and Business Administration of Getulio Vargas Foundation (EBAPE/FGV), developed through the elaboration of three articles. The research that resulted in the articles is within the scope of the project entitled “Windows of opportunities and knowledge networks: implications for catch-up in developing countries”, funded by Support Programme for Research and Academic Production of Faculty (ProPesquisa) of Brazilian School of Public and Business Administration (EBAPE) of Getulio Vargas Foundation.