MEG Decoding Across Subjects

Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach "decoding across subjects". In this work, we address the problem of decoding across subjects for magnetoencephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one

Design Patterns Barriers to Social Entrepreneurship: An Application of Grounded Theory

Social entrepreneurship involves social value creation activities and like many change-oriented activities does not take place in a vacuum. Rather, it develops within a complex context of political, economic, and social changes and on the local and global levels. Although, some countries have introduced laws for social supports, they are inefficient and unpractical and there are still many obstacles in the path of social entrepreneurs that need to be dealt with.Therefore, the present research seeks to answer the following question: what are the barriers to entrepreneurship in Iran? For this purpose, the qualitative research method has been employed using the Grounded Theory method. Moreover, explorative interviews were conducted with 15 key experts who had biological and practical experiences related to the research subject.The pattern derived from the current research shows that the main obstacle that hinders the development of social entrepreneurship is the attenuation of individuals’ active participation in altruistic social responsibilities which is due to contextual factors and causative relationships. However, social responsibility can be fostered in individuals by implementing a series of initiatives and strategies, as a result of which, entrepreneurial activities begin to develop. Keywords: Social Entrepreneurship, Social Capital, Grounded Theory, Barriers, Iran

E-mail Spam Filtering by A New Hybrid Feature Selection Method Using Chi2 as Filter and Random Tree as Wrapper

The purpose of this research is presenting a machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this regard, for reducing the error rate and increasing the efficiency, the hybrid architecture on feature selection has been used. Features used in these systems, are the body of text messages. Proposed system of this research has used the combination of two filtering models, Filter and Wrapper, with Chi Squared (Chi2) filter and Random Tree wrapper as feature selectors. In addition, Multinomial Naïve Bayes (MNB) classifier, Discriminative Multinomial Naïve Bayes (DMNB) classifier, Support Vector Machine (SVM) classifier and Random Forest classifier are used for classification. Finally, the output results of this classifiers and feature selection methods are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 99%

Skill assessment and optimization of the third generation wave models for applications in Gulf of Mexico

Numerical phase-averaged wave models are the best option to obtain the spatial and temporal distribution of the wave energy over a large domain, such as the Gulf of Mexico. Parallel implementation of unstructured SWAN and WAVEWATCH-III were engaged in this research to evaluate the performance of third generation wave models for different conditions. Met-ocean data from a network of NDBC buoys and WAVCIS stations were used to assess the predictive skills of the wave models. Deep water wave energy dissipation formulations were carefully analyzed and modified to improve the accuracy of the bulk wave parameters. Moreover, the importance of the assumptions for choosing the high frequency cut-off and the slope of the power law for the frequency tail were highlighted by several simulations using SWAN and WAVEWATCH-III. The results show that previous underestimation of wave period reported from the WAM-3 formulation of SWAN was partially attributed to the different assumptions used on the high frequency end of the spectrum. When waves propagate to shallow water, several other processes affect the wave spectrum such as dissipation of wave energy by bed friction in non-cohesive environments. The wave model with an optimized set of coefficients for the Gulf of Mexico was used to skill assess two widely used bed friction formulations. Simulation results showed that the incorporation of sediment information in an eddy viscosity formulation led to more accurate wave hindcast than the JONSWAP formulation. The computation cost required to use the proposed formulation increased by less than 4%. The turbid plume exiting the Atchafalaya Bay system significantly influences the wave spectrum of western Louisiana coast. Using extended deployments during low and high discharge periods of the Atchafalaya River, meteorological, hydrodynamic and bottom boundary layer parameters were monitored from Tiger and Trinity Shoals. These datasets were used to evaluate the mud-wave interaction in SWAN. The numerical algorithm to solve the complex dispersion equation of SWAN was optimized. Moreover, the model was extended to incorporate the damping term in non-stationary simulations. The results show that without including the mud-effects, the high frequency waves were overestimated close to Tiger Shoal during northerly winds

Stability challenges and solutions in current-mode controlled power electronic converters

This dissertation focuses on stability issues in single-staged and multi-staged current controlled power electronic converters. Most current-mode control (CMC) approaches suffer from sub-harmonic oscillations. An external ramp is usually added to solve this problem. However, to guarantee stability this ramp has to be designed for the worst possible case which consequently over damps the response. Adaptive slope compensation (ASC) methods are the solution for this problem. In paper 1 of this dissertation, first three ASC methods will be investigated and analyzed through their small signal models. Then, through simulation analyses and experimental test of a variable-input voltage converter the results will be validated. Two of the methods studies in the first paper are peak CMC methods and the last one is called the projected cross point control (PCPC) approach. This method is relatively new. Therefore, a detailed discussion of the principles of operation of PCPC will be presented in paper 2. In addition, the small signal model of PCPC is developed and discussed through simulation and experimental analyses in the second paper of this dissertation. Peak, average, and hysteresis CMC schemes are used for comparison. In paper 3, the stability issues which arise in multistage converters will be addressed. A solid state transformer (SST) as an example of a multistage converter will be studied. A comprehensive small signal modeling will be conducted which helps for stability analysis of SST. Time domain simulations in Computer Aided Design software (PSCAD) are presented which validates the frequency domain analysis --Abstract, page iv