Essays on the Costs and Benefits of Large Shareholders in Corporate Governance (summary section only)

“Corporate governance deals with the ways in which suppliers of finance to firms assure themselves of getting a return on their investment” (Shleifer and Vishny (1997, p. 737). According to La Porta et al. (1999), research in corporate finance relevant for most countries should focus on the incentives and capabilities of controlling shareholders to treat themselves preferentially at the expense of minority shareholders. Accordingly, this thesis sets out to answer a number of research questions regarding the role of large shareholders in public firms that have received little attention in the literature so far. A common theme in the essays stems from the costs and benefits of individual large-block owners and the role of control contestability from the perspective of outside minority shareholders. The first essay empirically examines whether there are systematic performance differences between family controlled and nonfamily controlled firms in Western Europe. In contrast to the widely held view that family control penalizes firm value, the essay shows that publicly traded family firms have higher performance than comparable firms. In the second essay, we present both theoretical and empirical analysis on the effects of control contestability on firm valuation. Consistent with the theoretical model, the empirical results show that minority shareholders benefit from a more contestable control structure. The third essay explores the effects of individual large-block owners on top management turnover and board appointments in Finnish listed firms. The results indicate that firm performance is an important determinant for management and board restructurings. For certain types of turnover decisions the corporate governance structure influences the performance / turnover sensitivity. In the fourth essay, we investigate the relation between the governance structure and dividend policy in Finnish listed firms. We find evidence in support of the outcome agency model of dividends stating that lower agency conflicts should be associated with higher dividend payouts.

Cluster based core vector machine

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.

Component Inventory Costs in an Assembly Problem with Uncertain Supplier Lead-Times

We present a generic study of inventory costs in a factory stockroom that supplies component parts to an assembly line. Specifically, we are concerned with the increase in component inventories due to uncertainty in supplier lead-times, and the fact that several different components must be present before assembly can begin. It is assumed that the suppliers of the various components are independent, that the suppliers' operations are in statistical equilibrium, and that the same amount of each type of component is demanded by the assembly line each time a new assembly cycle is scheduled to begin. We use, as a measure of inventory cost, the expected time for which an order of components must be held in the stockroom from the time it is delivered until the time it is consumed by the assembly line. Our work reveals the effects of supplier lead-time variability, the number of different types of components, and their desired service levels, on the inventory cost. In addition, under the assumptions that inventory holding costs and the cost of delaying assembly are linear in time, we study optimal ordering policies and present an interesting characterization that is independent of the supplier lead-time distributions.

An SIMD machine for low-level vision

This paper presents an SIMD machine which has been tuned to execute low-level vision algorithms employing the relaxation labeling paradigm. Novel features of the design include: 1. (1) a communication scheme capable of window accessing under a single instruction. 2. (2) flexible I/O instructions to load overlapped data segments; and 3. (3) data-conditional instructions which can be nested to an arbitrary degree. A time analysis of the stereo correspondence problem, as implemented on a simulated version of the machine using the probabilistic relaxation technique, shows a speed up of almost N2 for an N × N array of PEs.

DTC Technique for Induction Machine based Integrated Starter-Generator of an Automobile

This paper mainly concentrates on the application of the direct torque control (DTC) technique for the induction machine based integrated startergenerator (ISG) for automobile applications. It also discusses in brief about the higher DC bus voltage requirements in the automobiles i.e. present 14V system vs. 42V system to meet the power requirements, modes of operation of ISG, electric machine and the drive selection for the ISG,description of DTC technique, simulation and experimental results, and implementation.

Hydropower Project Design Incorporating Submergence Costs

Submergence of land is a major impact of large hydropower projects. Such projects are often also dogged by siltation, delays in construction and heavy debt burdens-factors that are not considered in the project planning exercise. A simple constrained optimization model for the benefit~ost analysis of large hydropower projects that considers these features is proposed. The model is then applied to two sites in India. Using the potential productivity of an energy plantation on the submergible land is suggested as a reasonable approach to estimating the opportunity cost of submergence. Optimum project dimensions are calculated for various scenarios. Results indicate that the inclusion of submergence cost may lead to a substanual reduction in net present value and hence in project viability. Parameters such as project lifespan, con$truction time, discount rate and external debt burden are also of significance. The designs proposed by the planners are found to be uneconomic, whIle even the optimal design may not be viable for more typical scenarios. The concept of energy opportunity cost is useful for preliminary screening; some projects may require more detailed calculations. The optimization approach helps identify significant trade-offs between energy generation and land availability.

PERFECT Stroke : PERFormance, Effectiveness, and Costs of treatment episodes in Stroke

Stroke is a major cause of death and disability, incurs significant costs to healthcare systems, and inflicts severe burden to the whole society. Stroke care in Finland has been described in several population-based studies between 1967 and 1998, but not since. In the PERFECT Stroke study presented here, a system for monitoring the Performance, Effectiveness, and Costs of Treatment episodes in Stroke was developed in Finland. Existing nationwide administrative registries were linked at individual patient level with personal identification numbers to depict whole episodes of care, from acute stroke, through rehabilitation, until the patients went home, were admitted to permanent institutional care, or died. For comparisons in time and between providers, patient case-mix was adjusted for. The PERFECT Stroke database includes 104 899 first-ever stroke patients over the years 1999 to 2008, of whom 79% had ischemic stroke (IS), 14% intracerebral hemorrhage (ICH), and 7% subarachnoid hemorrhage (SAH). A 18% decrease in the age and sex adjusted incidence of stroke was observed over the study period, 1.8% improvement annually. All-cause 1-year case-fatality rate improved from 28.6% to 24.6%, or 0.5% annually. The expected median lifetime after stroke increased by 2 years for IS patients, to 7 years and 7 months, and by 1 year for ICH patients, to 4 years 5 months. No change could be seen in median SAH patient survival, >10 years. Stroke prevalence was 82 000, 1.5% of total population of Finland, in 2008. Modern stroke center care was shown to be associated with a decrease in both death and risk of institutional care of stroke patients. Number needed to treat to prevent these poor outcomes at one year from stroke was 32 (95% confidence intervals 26 to 42). Despite improvements over the study period, more than a third of Finnish stroke patients did not have access to stroke center care. The mean first-year healthcare cost of a stroke patient was ~20 000 , and among survivors ~10 000 annually thereafter. Only part of this cost was incurred by stroke, as the same patients cost ~5000 over the year prior to stroke. Total lifetime costs after first-ever stroke were ~85 000 . A total of 1.1 Billion , 7% of all healthcare expenditure, is used in the treatment of stroke patients annually. Despite a rapidly aging population, the number of new stroke patients is decreasing, and the patients are more likely to survive. This is explained in part by stroke center care, which is effective, and should be made available for all stroke patients. It is possible, in a suitable setting with high-quality administrative registries and a common identifier, to avoid the huge workload and associated costs of setting up a conventional stroke registry, and still acquire a fairly comprehensive dataset on stroke care and outcome.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

In-Network Computation in Random Wireless Networks: A PAC Approach to Constant Refresh Rates with Lower Energy Costs

We propose a method to compute a probably approximately correct (PAC) normalized histogram of observations with a refresh rate of Theta(1) time units per histogram sample on a random geometric graph with noise-free links. The delay in computation is Theta(root n) time units. We further extend our approach to a network with noisy links. While the refresh rate remains Theta(1) time units per sample, the delay increases to Theta(root n log n). The number of transmissions in both cases is Theta(n) per histogram sample. The achieved Theta(1) refresh rate for PAC histogram computation is a significant improvement over the refresh rate of Theta(1/log n) for histogram computation in noiseless networks. We achieve this by operating in the supercritical thermodynamic regime where large pathways for communication build up, but the network may have more than one component. The largest component however will have an arbitrarily large fraction of nodes in order to enable approximate computation of the histogram to the desired level of accuracy. Operation in the supercritical thermodynamic regime also reduces energy consumption. A key step in the proof of our achievability result is the construction of a connected component having bounded degree and any desired fraction of nodes. This construction may also prove useful in other communication settings on the random geometric graph.

Light scattering in random media with wavelength-scale structures : astronomical and industrial applications

Light scattering, or scattering and absorption of electromagnetic waves, is an important tool in all remote-sensing observations. In astronomy, the light scattered or absorbed by a distant object can be the only source of information. In Solar-system studies, the light-scattering methods are employed when interpreting observations of atmosphereless bodies such as asteroids, atmospheres of planets, and cometary or interplanetary dust. Our Earth is constantly monitored from artificial satellites at different wavelengths. With remote sensing of Earth the light-scattering methods are not the only source of information: there is always the possibility to make in situ measurements. The satellite-based remote sensing is, however, superior in the sense of speed and coverage if only the scattered signal can be reliably interpreted. The optical properties of many industrial products play a key role in their quality. Especially for products such as paint and paper, the ability to obscure the background and to reflect light is of utmost importance. High-grade papers are evaluated based on their brightness, opacity, color, and gloss. In product development, there is a need for computer-based simulation methods that could predict the optical properties and, therefore, could be used in optimizing the quality while reducing the material costs. With paper, for instance, pilot experiments with an actual paper machine can be very time- and resource-consuming. The light-scattering methods presented in this thesis solve rigorously the interaction of light and material with wavelength-scale structures. These methods are computationally demanding, thus the speed and accuracy of the methods play a key role. Different implementations of the discrete-dipole approximation are compared in the thesis and the results provide practical guidelines in choosing a suitable code. In addition, a novel method is presented for the numerical computations of orientation-averaged light-scattering properties of a particle, and the method is compared against existing techniques. Simulation of light scattering for various targets and the possible problems arising from the finite size of the model target are discussed in the thesis. Scattering by single particles and small clusters is considered, as well as scattering in particulate media, and scattering in continuous media with porosity or surface roughness. Various techniques for modeling the scattering media are presented and the results are applied to optimizing the structure of paper. However, the same methods can be applied in light-scattering studies of Solar-system regoliths or cometary dust, or in any remote-sensing problem involving light scattering in random media with wavelength-scale structures.