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.

Communicative and Anticipatory Decision-Making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

Bayesian classification of forest ecological type from satellite data

The main objective of this study was todo a statistical analysis of ecological type from optical satellite data, using Tipping's sparse Bayesian algorithm. This thesis uses "the Relevence Vector Machine" algorithm in ecological classification betweenforestland and wetland. Further this bi-classification technique was used to do classification of many other different species of trees and produces hierarchical classification of entire subclasses given as a target class. Also, we carried out an attempt to use airborne image of same forest area. Combining it with image analysis, using different image processing operation, we tried to extract good features and later used them to perform classification of forestland and wetland.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Long-term effects of maintaining ditch networks on runoff water quality

Tiivistelmä: Kunnostusojituksen pitkän ajan vaikutus valumaveden ominaisuuksiin

Effects of maintaining ditch networks on the development of Scots pine stands

Tiivistelmä: Kunnostusojituksen vaikutus rämemänniköiden kehitykseen

Discharge water quality from old ditch networks in Finnish peatland forests

Tiivistelmä: Vanhoilta metsäojitusalueilta valuvan veden ominaisuudet

Bayesian estimation of diameter distribution during harvesting

Abstract

The Possibilities of IP Networks in Strategic Partnership Development

This study focuses on the status of using the Internet in partnership development. The aim is to find out howthe parties in partnership can benefit from the available data networks (the Internet, Intranet and Extranet). The study also explains what the typical practices at the moment are and what features might be exploitable in the future. The research problem is to find out whether there are any possibilities to utilize the web more than is done at the moment. This study is a preliminary study for a more extensive study on the topic 'Information Technology in Business Relationships'.

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Mobility Management in Packet Switched Mobile Networks

Nykyisin matkaviestinverkot ovat osa jokapäiväistä elämää. Merkittävimpiä eroja kiinteiden ja matkaviestinverkkojen välillä on käyttäjän liikkuvuus, joka voidaan määritellä mahdollisuudeksi soittaa ja vastaanottaa puheluita missä ja milloin tahansa. Työ selittää termin liikkuvuus ja määrittää ongelmat, jotka täytyy ratkaista liikkuvuuden aikaansaamiseksi sekä tavat, joilla nämä ongelmat on ratkaistu matkaviestinverkoissa. Työ luo yleiskatsauksen liikkuvuuden aikaansaamisessa käytettäviin menetelmiin, joita ovat haku, sijainnin päivitys, sijainnin seuranta ja kanavan vaihto. Työ keskittyy liikkuvuuteen kolmannen sukupolven matkaviestinverkkojen paketti-kytkentäisessä osassa, esimerkkinä liikkuvuuden hallinta UMTS:ssa (Universal Mobile Telecommunications System). Erot paketti- ja piirikytkentäisen osan välillä tuodaan esille ja selitetään. Jotta käyttäjät ja heidän päätteensä voisivat liikkua, tiedon täytyy kulkea verkon eri osien välillä. Merkinanto verkkoelementtien välillä ja liikkuvuuden mahdollistavien toimenpiteiden suoritus tehdään yhteyskäytännön avulla. Työ kuvaa yhteyskäytännöt, jotka ovat osallisena liikkuvuuden tarjontaan. Painopiste on GPRS:n liikkuvuuden-hallintayhteyskäytännössä, GMM:ssä. GMM protokollan prototyypin toteutus on esitetty työn käytännön osassa.