A mood-based music classification and exploration system

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 89-93).Mood classification of music is an emerging domain of music information retrieval. In the approach presented here features extracted from an audio file are used in combination with the affective value of song lyrics to map a song onto a psychologically based emotion space. The motivation behind this system is the lack of intuitive and contextually aware playlist generation tools available to music listeners. The need for such tools is made obvious by the fact that digital music libraries are constantly expanding, thus making it increasingly difficult to recall a particular song in the library or to create a playlist for a specific event. By combining audio content information with context-aware data, such as song lyrics, this system allows the listener to automatically generate a playlist to suit their current activity or mood.by Owen Craigie Meyers.S.M

Machine Learning Methods for Autonomous Classification and Decision Making

This thesis focuses on developing machine learning methods for autonomous classification and decision making, especially on two case studies: traffic speed prediction and cancer bone segmentation. For traffic speed prediction, the convolutional neural network (CNN) achieves state-of-the-art results in complex traffic networks. However, the pooling layers cause the loss of information within the data. This thesis proposes an efficient capsule network for traffic speed prediction. The proposed capsule network replaces the pooling layer with capsules connected by dynamic routing and encodes the features and probability of those features showing on the local region. The proposed capsule network provides outperformed results compared to state-of-the-art CNNs. However, the CNN and capsule network (CapsNet) are parametric models and the uncertainty is, thus, not analysed. Two Gaussian process (GP) frameworks are proposed for traffic speed prediction, equipping the CNN with the ability to quantify uncertainty. The first framework proposes to equate a state-of-the-art CNN with a shallow GP. The proposed approach is evaluated and the uncertainty is analysed by applying the confidence interval. In addition, the impact of the noise is investigated by adding a different level of noise. The second framework is a novel deep kernel CNN-GP framework with spatio-temporal kernels, allowing it to abstract high-level features and consider both time and space. The proposed CNN-GP framework is validated and evaluated using CO2 concentration and traffic prediction for the short-term and long-term. An efficient uniform error bound is proposed and evaluated with simulated and real data. For cancer bone segmentation, machine learning methods are proposed to segment bone lesions in cancer-induced bone disease from Micro Computed Tomography (µCT) images, which brings a new perspective of dealing with bone caner segmentation. The performances are evaluated and their effectiveness is compared. Due to the limited number of datasets and the lack of labelled lesions within the dataset, an approach to generate simulated data is proposed. With an enhanced dataset, a generative adversarial network is proposed to reconstruct the bone with a lesion to a healthy bone. Consequently, the location of the lesion can be obtained by subtracting the original image from the reconstructed image

Modeling Stock Option Contracts - Evidence from Spain

Pocs temes han generat tant debat en matèria de govern corporatiu com el de la remuneració de directius. Aquesta recerca analitza una pràctica tan controvertida com estesa en la contractació executiva, tal com és la concessió al directiu d'opcions sobre accions de l'empresa. Són les opcions sobre accions la resposta al desafiament d'alinear eficientment els incentius del directiu amb els de l'accionista? La clau radica en el disseny del contracte. Aquest estudi pretén contribuir a llançar llum sobre aquesta controvèrsia, a través d'una anàlisi sistemàtica del disseny dels plans d'opcions sobre accions de les empreses de major liquiditat i capitalització del mercat espanyol, representades en l'índex borsari Ibex 35. Les variables de disseny objecto d'anàlisi són el preu d'exercici, el període d'espera, el venciment del contracte, l'actualització del preu d'exercici i les restriccions a la venda posterior de les accions. Sobre aquests plans s'apliquen les teories de contractació òptima i extracció de rendes, per identificar desviaments del paradigma d'alineació d'incentius. Per avaluar l'eficiència en aquesta alineació d'incentius que es persegueix amb el contracte d'opció, es vincula el disseny de les variables a dalt esmentades amb la tornada ajustada per risc de les empreses que concedeixen opcions, a través d'una anàlisi de dades de panell.Pocos temas han generado tanto debate en materia de gobierno corporativo como el de la remuneración de directivos. Esta investigación analiza una práctica tan controvertida como extendida en la contratación ejecutiva, tal como es la concesión al directivo de opciones sobre acciones de la empresa. ¿Son las opciones sobre acciones la respuesta al desafío de alinear eficientemente los incentivos del directivo con los del accionista? La clave radica en el diseño del contrato. Este estudio pretende contribuir a arrojar luz sobre dicha controversia, a través de un análisis sistemático del diseño de los planes de opciones sobre acciones de las empresas de mayor liquidez y capitalización del mercado español, representadas en el índice bursátil Ibex 35. Las variables de diseño objeto de análisis son el precio de ejercicio, el período de espera, el vencimiento del contrato, la actualización del precio de ejercicio y las restricciones a la venta posterior de las acciones. Sobre dichos planes se aplican las teorías de contratación óptima y extracción de rentas, para identificar desvíos del paradigma de alineación de incentivos. Para evaluar la eficiencia en esta alineación de incentivos que se persigue con el contrato de opción, se vincula el diseño de las variables arriba mencionadas con el retorno ajustado por riesgo de las empresas que conceden opciones, a través de un análisis de datos de panel.Few issues in modern corporate governance have received as much attention lately as executive compensation. This research deals with a highly controversial yet widespread practice in executive pay: stock options plans. Are stock options the answer to efficiently align incentives, bridging the gap between cash-flow rights and control rights? A design that delivers that goal proves crucial. This study aims to contribute to the current debate on such a heated corporate governance issue by presenting a systematic analysis of stock option design in Spanish largest and most liquid companies, out of the entire population of the Ibex 35 stock market index. The specific design variables to be examined are strike price, vesting period, maturity, repricing and trading restrictions. A mix of the optimal contracting and the rent-extracting approaches are applied to explore for significant deviations from the incentive-alignment paradigm. Finally, panel data analysis is conducted to identify potential relationships between the above mentioned variables and risk-adjusted returns for Ibex 35 firms with stock option plans