Impact of regeneration method on stand structure prior to first thinning

Summary

Assessing Organizational Cultural Fit prior to Integration in the M&A Process

Yritysostojen määrä on historiallisen suuri 2000-luvulla, vaikka melkein puolet niistä epäonnistuu. Aineettomilla tekijöillä, kuten organisaatiokulttuureilla, on keskeinen rooli yritysostojen onnistumisissa. Myös case yritys on aktiivinen yritysostoissa ja haluaa arvioida integraatioprosessinsa tehokkuutta. Siten diplomityön tarkoituksena on luoda työkalu organisaatiokulttuurien yhteensopivuuden arvioimiseksi, jotta ostopäätöksentekoa sekä integraation suunnittelua voitaisiin tukea paremmin yrityksessä. Diplomityö vastaakin kysymyksiin, kuten miten arvioida kulttuurista yhteensopivuutta ennen integraatiota integraatioprosessin parantamiseksi sekä mitkä ovat olleet kaikkein ongelmallisimmat ja toisaalta kaikkein menestyksekkäimmät kulttuuritekijät tutkitussa integraatiossa. Kulttuurisen yhteensopivuuden arviointi tulisi nähdä prosessina osana yrityskauppaa. Prosessin tulisi alkaa kulttuurisen integraation tavoitteiden määrittämisellä sekä organisaatiokulttuurin käsitteen ymmärtämisellä. Kulttuurianalyysi tulisi suorittaa työpajan avulla. Sen tulisi käsitellä ainakin yhdeksän kulttuurin osa-aluetta: innovatiivisuus, päätöksenteko, ihmissuuntautuneisuus, kommunikaatio, kontrolli, asiakassuuntautuneisuus, ajanhallinta, identifikaatio, sekä kollektivismi. Lisäksi kuhunkin dimensioon liittyvään kysymykseen tulisi vastata pisteillä yhdestä viiteen, jolloin voidaan piirtää kulttuurisen yhteensopivuuden kuvio. Tämän jälkeen johdon tulisi keskustella tuloksista vielä kerran tarkemmin ja lopulta koota tulokset kirjalliseksi raportiksi. Tutkitussa integraatiossa parhaiten integraatiota tukivat ihmissuuntautuneisuus sekä ajanhallinta (työn ja vapaa-ajan välinen tasapaino sekä tulevaisuus-suuntautuneisuus). Haasteellisimmat kulttuuritekijät koskivat päätöksentekoa, kommunikaatiota ja kontrollia, jotka vaikuttavat olevan tyypillisiä ongelmia ison yrityksen ostaessa pienemmän yrityksen.

Winning time in land warfare : a reading of the classical strategists prior to the age of mechanization

We live in an age where rationalization and demands of efficiency taint every aspect of our lives both as individuals and as a society. Even warfare cannot escape the increased speed of human interaction. Time is a resource to be managed. It has to be optimized, saved and won in military affairs as well. The purpose of this research paper is to analyze the dogmatic texts of military thought to search for answers what the classics of strategy saw in the interrelations of temporality and warfare and if their thoughts remain meaningful in the contemporary conjunction. Since the way a society functions is reflected in the way it conducts its wars, there naturally are differences between an agrarian, industrial and information society. Theorists of different eras emphasize things specific to their times, but warfare, like any human interaction, is always bounded by temporality. Not only is the pace of warfare dependent on the progress of the society, but time permeates warfare in all its aspects. This research paper focuses on two specific topics that arose from the texts themselves; how should time be managed and manipulated in warfare and how to economize and “win” it from the enemy. A method where lengthy quotations are used to illustrate the main point of the strategists has been chosen for this research paper. While Clausewitz is the most prominent source of quotations, thoughts from ancient India and China are represented as well to prove that the combination of right force in the right place at the right time is still the way of the victorious. Tactics change in the course of time but the principles of strategy remain unaltered and are only adapted to suit new situations. While ancient and pre-modern societies had their focus on finding auspicious moments for battle in the flow of kronos-time based on divinities, portents and auguries, we can trace elements of manipulation of time in warfare from the earliest surviving texts. While time as a fourth dimension of the battlespace emerged only in the modern era, all through the history of military thought it has had a profound meaning. In the past time could be squandered, today it always has to be won. This paper asks the question “why”.

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.