Aspects of precommercial thinning in heterogeneous forests in southern Sweden

The overall objective of the work underlying this thesis was to suggest and evaluate possible strategies for the tending of young heterogeneous stands of Norway spruce, Scots pine and birch in southern Sweden. Heterogeneity was defined as variation in species composition, height distribution and spatial arrangement of the trees. The influence of stand density after precommercial thinning and timing of thinning on the diameter of the thickest branch was studied for naturally regenerated Scots pine. The branch diameter was found to decrease with increasing number of remaining stems after precommercial thinning. However, leaving very dense stands (> 3000 stems ha-1) resulted only in a minor reduction of the branch diameter. Late precommercial thinning, compared to early, reduced the branch diameter. The influence of the precommercial thinning regime on the crown ratio (living crown length/tree height) was also analysed. To be able to simulate the influence of different management options on the development of the young forest, single-tree growth models was developed for Scots pine, Norway spruce and birch. Height growth and diameter was estimated as a function of tree height, stand and site variables. Growth reduction due to competition was estimated using individual, distance independent indices as well as expressions of the overall stand density. In the third study the influence of stand structure after precommercial thinning on the development of mixtures between Norway spruce and silver birch was simulated. The aim was to identify mixtures that allowed both species to develop well until the first commercial thinning. By leaving birches with an average height slightly greater than spruce at precommercial thinning, a large proportion of competitive birches were available at first commercial thinning, at the same time as the relative diameter distribution of spruce in the mixture was equal to that of a pure spruce stand of the same density. The height difference between the species as well as the species proportion had a decisive impact on volume production. In the fourth study different precommercial thinning strategies were identified and applied to a heterogeneous stand including Scots pine, Norway spruce and birch. Stand development and economical returns over a rotation was estimated using a set of empirical models. The aim of the long-term strategies was: (i) a conifer dominated stand with focus on high production, (ii) a conifer dominated stand with focus on high timber quality, (iii) to preserve the heterogeneous stand structure, (iv) a mosaic pattern by tree species, (v) to reduce the precommercial thinning cost, without jeopardizing the future stand development. The difference in total volume production was found to be relatively small between the strategies. The lowest production was found for the strategies promoting species mixture at tree level (iii) and group level (iv). The net present value was highest for the strategy aiming at high production (ii) and lowest for the strategy aiming at preserved heterogeneity (iii). The minimal precommercial thinning (v) was a less profitable alternative, mainly because of an expensive first commercial thinning. Differences in timber quality were not considered in the simulations. The case study illustrates the possibilities for influencing the structure of a heterogeneous stand through precommercial thinning, as well as the limitations imposed by the initial stand structure

A general guide to applying machine learning to computer architecture

The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results. The purpose of this paper is to serve as a foundational base and guide to future computer architecture research seeking to make use of machine learning models for improving system efficiency. We describe a method that highlights when, why, and how to utilize machine learning models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data generation every execution quantum and parameter engineering. This is followed by a survey of a set of popular machine learning models. We discuss their strengths and weaknesses and provide an evaluation of implementations for the purpose of creating a workload performance predictor for different core types in an x86 processor. The predictions can then be exploited by a scheduler for heterogeneous processors to improve the system throughput. The algorithms of focus are stochastic gradient descent based linear regression, decision trees, random forests, artificial neural networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version

Random Prism: An Alternative to Random Forests.

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting