Effects of defoliation height on regrowth of timothy and meadow fescue in the generative and vegetative phases of growth

Selostus: Leikkuukorkeuden vaikutus timotein ja nurminadan jälkikasvuun generatiivisessa ja vegetatiivisessa kasvuvaiheessa

Developing and Applying Generative Market Intelligence within Multinational Companies

Tutkielman tarkoitus on kehittää monikansallisille yrityksille tuottavan markkinaälyn malli, jonka avulla yritykset pystyvät käsittelemään muuttuvasta ja globalisoituvasta markkinaympäristöstä aiheutuvaa epävarmuutta. Malli koostuu pääosin kolmesta käsitteestä: markkinainformaation prosessoinnista, markkinasuuntautuneisuudesta ja organisationaalisesta oppimisesta. Tutkimuksessa osoitetaan, kuinka näiden samanaikainen soveltaminen johtaa synergiaetuihin. Lähdeaineistona käytettiin alan kirjallisuutta. Lisäksi haastateltiin neljää johtajaa monikansallisista yrityksistä. Käytännössä markkinaälyn soveltamisen haasteet liittyvät lähinnä markkinainformaation prosessoinnin asenteellisiin ja psykologisiin aspekteihin. Ihmisten tulisi ymmärtää, että koko yritys hyötyy heidän halukkuudestaan tiedon tuottamiseen ja jakamiseen. Lisäksi tietoa itsessään voimavarana tulisi kunnioittaa

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.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Future of Internet News Portals after the Case of Delfi

The thesis analyzes liability of Internet news portals for third-party defamatory comments. After the case of Delfi AS v. Estonia, decided by the Grand Chamber of the European Court of Human Rights on 16 June 2015, a portal can be held liable for user-generated unlawful comments. The thesis aims at exploring consequences of the case of Delfi for Internet news portals’ business model. The model is described as a mixture of two modes of information production: traditional industrial information economy and new networked information economy. Additionally, the model has a generative comment environment. I name this model “the Delfian model”. The thesis analyzes three possible strategies which portals will likely apply in the nearest future. I will discuss these strategies from two perspectives: first, how each strategy can affect the Delfian model and, second, how changes in the model can, in their turn, affect freedom of expression. The thesis is based on the analysis of case law, legal, and law and economics literature. I follow the law and technology approach in the vein of ideas developed by Lawrence Lessig, Yochai Benkler and Jonathan Zittrain. The Delfian model is researched as an example of a local battle between industrial and networked information economy modes. The thesis concludes that this local battle is lost because the Delfian model has to be replaced with a new walled-garden model. Such a change can seriously endanger freedom of expression.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.