The functional organization of the brain for mental imagery and image rotation: an electroencephalographic investigation

The intent of this dissertation was to reveal (by utilizing a set of cognitive tasks that are visuospatial in nature) how complex mental tasks are performed in the brain. Particular attention was devoted to the functional systems subserving the processes comprising the complex internal generation of mental images and their rotation;Utilizing Electroencephalography (more specifically, alpha power reduction), the functional system subserving the processes of mental rotation and mental image generation was identified. In this functional system, the occipital lobes are bilaterally involved in the initial encoding of visual information into a neural representation. These representations are then shunted to the parietal lobes, with the left parietal lobe specifically involved in generating the three dimensional representation of the stimuli, while actual mental rotation is mediated by left temporal lobe. All of the above subprocesses are completed under the auspices of the right frontal lobe which appears to be involved in mediating comparison and decision subcomponents of the task. In cases where stimuli need to be resampled for further analysis, the right occipital lobe played an important role;The most significant findings of this dissertation are that multiple brain locales are involved in performance of complex visuospatial tasks, and that these locales can be quite remote from one another, with some residing in left hemisphere and some in the right. It is apparent that some of the existing confusion in the literature exploring hemispheric superiority for image generation and mental rotation may be partly attributed to the mistaken expectation that one hemisphere is solely involved in performing a given spatial task. Recent advances in brain imaging technology (like the one employed here) are rapidly changing this manner of thinking

A superspace module for the FeynRules package

We describe an additional module for the Mathematica package FeynRules that allows for an easy building of any N=1 supersymmetric quantum field theory, directly in superspace. After the superfield content of a specific model has been implemented, the user can study the properties of the model, such as the supersymmetric transformation laws of the associated Lagrangian, directly in Mathematica. While the model dependent parts of the latter, i.e., the soft supersymmetry-breaking Lagrangian and the superpotential, have to be provided by the user, the model independent pieces, such as the gauge interaction terms, are derived automatically. Using the strengths of the Feynrules program, it is then possible to derive all the Feynman rules associated to the model and implement them in all the Feynman diagram calculators interfaced to FeynRules in a straightforward way.Comment: 54 pages, 9 tables, version accepted by CP

Using the partial least squares (PLS) method to establish critical success factor interdependence in ERP implementation projects

This technical research report proposes the usage of a statistical approach named Partial Least squares (PLS) to define the relationships between critical success factors for ERP implementation projects. In previous research work, we developed a unified model of critical success factors for ERP implementation projects. Some researchers have evidenced the relationships between these critical success factors, however no one has defined in a formal way these relationships. PLS is one of the techniques of structural equation modeling approach. Therefore, in this report is presented an overview of this approach. We provide an example of PLS method modelling application; in this case we use two critical success factors. However, our project will be extended to all the critical success factors of our unified model. To compute the data, we are going to use PLS-graph developed by Wynne Chin.Postprint (published version

Loo.py: From Fortran to performance via transformation and substitution rules

A large amount of numerically-oriented code is written and is being written in legacy languages. Much of this code could, in principle, make good use of data-parallel throughput-oriented computer architectures. Loo.py, a transformation-based programming system targeted at GPUs and general data-parallel architectures, provides a mechanism for user-controlled transformation of array programs. This transformation capability is designed to not just apply to programs written specifically for Loo.py, but also those imported from other languages such as Fortran. It eases the trade-off between achieving high performance, portability, and programmability by allowing the user to apply a large and growing family of transformations to an input program. These transformations are expressed in and used from Python and may be applied from a variety of settings, including a pragma-like manner from other languages.Comment: ARRAY 2015 - 2nd ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (ARRAY 2015

Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks

Diffusion reach probability between two nodes on a network is defined as the probability of a cascade originating from one node reaching to another node. An infinite number of cascades would enable calculation of true diffusion reach probabilities between any two nodes. However, there exists only a finite number of cascades and one usually has access only to a small portion of all available cascades. In this work, we addressed the problem of estimating diffusion reach probabilities given only a limited number of cascades and partial information about underlying network structure. Our proposed strategy employs node representation learning to generate and feed node embeddings into machine learning algorithms to create models that predict diffusion reach probabilities. We provide experimental analysis using synthetically generated cascades on two real-world social networks. Results show that proposed method is superior to using values calculated from available cascades when the portion of cascades is small