A generalized 1D particle transport method for convection diffusion reaction model

This work is devoted to the development of numerical method to deal with convection diffusion dominated problem with reaction term, non - stiff chemical reaction and stiff chemical reaction. The technique is based on the unifying Eulerian - Lagrangian schemes (particle transport method) under the framework of operator splitting method. In the computational domain, the particle set is assigned to solve the convection reaction subproblem along the characteristic curves created by convective velocity. At each time step, convection, diffusion and reaction terms are solved separately by assuming that, each phenomenon occurs separately in a sequential fashion. Moreover, adaptivities and projection techniques are used to add particles in the regions of high gradients (steep fronts) and discontinuities and transfer a solution from particle set onto grid point respectively. The numerical results show that, the particle transport method has improved the solutions of CDR problems. Nevertheless, the method is time consumer when compared with other classical technique e.g., method of lines. Apart from this advantage, the particle transport method can be used to simulate problems that involve movingsteep/smooth fronts such as separation of two or more elements in the system.

Fedora 4.0 Deep Dive

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Glädje i vårdandets värld

The present study is made in the context of basic research within the field of caring science. The overall aim is to uncover and make joy visible as an idea in the world of caring. The core of caring has historically always been to alleviate suffering and to serve life and health in a spirit of love and mercy. This study has a comprehensive direction focusing on history of ideas and culminates in a pattern of ideas contenting joy in the world of caring. Knowledge formation is based on creating understanding, wholeness and meaning with regard to the knowledge related to a context. For that a hermeneutical approach is used throughout the study. In order to understand joy more deeply, the original idea, the essence and expression, the concept of 'joy' and the related concepts of 'glad' and 'light' are examined in etymological dictionaries and in Swedish, English and Latin dictionaries. To support the interpretation classical texts containing philosophers’ thoughts about joy are used. Joy as an idea glimpses forth and is presented in the form of seven-fold pattern of ideas. Through the meaning-nuances of synonyms a realization of joy could be discerned and anchored in the heart. The seven-fold pattern form the background and represent a guide for the hermeneutic reading of joy, as it appears in the stories about caring for the years 1900–1933. The historical sources consist of the trade magazine Svensk sjukskötersketidning, books containing stories about caring, archival materials and textbooks on nursing. The result culminates in the seven-fold pattern of ideas contenting what makes joy active as caring. The true heart's pure joy - love, joy is a proof of love. The ardent heart's deep joy - joy of living, joy inspires and generates strength. The bearing heart's radiant joy - generosity, joy is a gift to the other with the promise of help. The inviting heart's sparkling joy – communion, joy invites communion. The elated heart's exhilarated joy - integration, joy enables the human to forget his or her suffering and approach to what he or she wants to be. The atmospheric heart's solemn joy - dignifying, joy creates a mood and an atmosphere where people perceive themselves dignified. The peaceful heart's great joy - rescuing, a joy turns out when the human has received what may be requested of what is good, is eluded from what is evil and is contented with his or her living lot. It is hoped that this basic research will open up for a vision that can contribute to joys further attention in the world of caring and be articulated there.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.