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

Generative models of the human connectome.

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

The grammar of psychosis

This thesis is concerned with the philosophical grammar of certain psychiatric concepts, which play a central role in delineating the field of psychiatric work. The concepts studied are ‘psychosis’, ‘delusion’, ‘person’, ‘understanding’ and ‘incomprehensibility’. The purpose of this conceptual analysis is to provide a more perspicuous view of the logic of these concepts, how psychiatric work is constituted in relation to them, and what this tells us about the relationships between the conceptual and the empirical in psychiatric concepts. The method used in the thesis is indebted primarily to Ludwig Wittgenstein’s conception of philosophy, where we are urged to look at language uses in relation to practices in order to obtain a clearer overview of practices of interest; this will enable us to resolve the conceptual problems related to these practices. This questioning takes as its starting point the concept of psychosis, a central psychiatric concept during the twentieth century. The conceptual analysis of ‘psychosis’ shows that the concept is logically dependent on the concepts of ‘understanding’ and ‘person’. Following the lead found in this analysis, the logic of person-concepts in psychiatric discourse is analysed by a detailed textual analysis of a psychiatric journal article. The main finding is the ambiguous uses of ‘person’, enabling a specifically psychiatric form of concern in human affairs. The grammar of ‘understanding’ is then tackled from the opposite end, by exploring the logic of the concept of ‘incomprehensibility’. First, by studying the DSM-IV definition of delusion it is shown that its ambiguities boil down to the question of whether psychiatric practice is better accounted for in terms of the grammar of ‘incorrectness’ or ‘incomprehensibility’. Second, the grammar of ‘incomprehensibility’ is further focused on by introducing the distinction between positive and negative conceptions of ‘incomprehensibility’. The main finding is that this distinction has wide-ranging implications for our understanding of psychiatric concepts. Finally, some of the findings gained in these studies are ‘put into practice’ in studying the more practical question of the conceptual and ethical problems associated with the concept of ‘prodromal symptom of schizophrenia’ and the agenda of early detection and intervention in schizophrenia more generally.

Comprehension of passives in Persian speaking children with ASD : an investigation of grammar in autism

Most research on the underlying causes of social and communicative impairment in autism spectrum disorders (ASD) has been devoted to pragmatic aspects of language. The present research is exploring the syntactic knowledge as a probable underlying mechanism of language deficit in ASD. Three groups comprising high-functioning ASD, low-functioning ASD, and typically developing 5-year-old Persian-speaking children were tested on comprehension of passive sentences. Results suggest that while low-functioning children with ASD might be impaired in the area of grammar, high-functioning children with ASD are not. The new results are compared to those of two recent studies on comprehension of passives in Greek-speaking and English-speaking subjects with ASD (Perovic et al., 2007; Terzi, et al., to appear).

Defining the art of grammar: Ancient perceptions of γραμματική and grammatical

The subject of this dissertation, which belongs to the field of Classical Philology, are the definitions of the art of grammar found in Greek and Latin sources from the Classical era to the second century CE. Definitions survive from grammarians, philosophers, and general scholars. I have examined these definitions from two main points of view: how they are formed, and how they reflect the development of the art itself. Defining formed part of dialectic, in practice also of rhetoric, and was perceived as important from the Classical era onwards. Definitions of grammar seem to have become established as part of preliminary discussions, located at the beginning of grammatical manuals (tékhnai, artes). These discussions included certain principal notions of the art; in addition to the definition, a list of the parts of the art was also typically included. These lists were formed by two different methods: division (diaíresis, divisio) and partition (merismós, partitio). Many of the grammarians may actually have been unfamiliar with these methods, unlike the two most important scholars of the Late Republic, Varro and Cicero. Significant attention was devoted to the question whether the art of grammar is based on lógos or empeiría. This epistemological question had its roots in medical theories, which were prominent in Alexandria. In the history of the concept of grammatiké or grammatica, three stages become evident. In the Classical era, the Greek term is used to refer to a very concrete art of letters (grámmata); from the Hellenistic era onwards it refers to the art developed by the Alexandrian scholars, a matter of textual and literary criticism. Towards the end of the Hellenistic era, the grammarian also becomes involved with the question of correct language, which gradually begins to appear in the definitions as well.

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.

Fonthill Literary Journal, Fonthill Grammar School Lyceum, n.d.

The Fonthill Grammar School was established in 1856. The school closed in 1876 when it failed to receive community support for a new building. The editors of the journal were two individuals with the last names Ray, and Wiggins, unfortunately their first names are not included in any of the submissions to the journal.

Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.

County Grammar School Beamsville - Vignette

A vignette of the County Grammar School located in Beamsville.

Elementary French; the Essentials of French grammar with exercises

UANL