The Fate of Napster: Digital Downloading Faces an Uphill Battle

First Diamond Multimedia, then MP3.com, now Napster. The recording industry, in a flurry to protect its copyrighted material, has waged an all-out battle against the dot-coms for the future of copyrighted music on the Internet. Since A&M Records, along with several other labels which comprise the Recording Industry Association of America (RIAA), filed suit against Napster, emotions have run high in the online community. Some have heralded this technology as a much-needed alternative to the strangling grasp of the major record labels; others view it as blatant theft of property. Students, musicians, computer programmers, trade organizations, and even the US government have voiced their opinions - all perhaps sensing that the outcome of the Napster litigation will have far-reaching consequences. Not only does the current battle over the fate of peer-to-peer technology promise to reshape the face of copyright law, it will also mark the future of the music industry, emerging technologies, and business models for years to come.The following iBrief describes the emergence of Napster\u27s peer-to-peer technology, the legal proceedings to date, and Napster\u27s defensive strategy, as well as the potential technological and cultural ramifications of the Napster cause celebr

Applied Dynamic Factor Modeling In Finance

In this dissertation, I study model misspecification in applications of dynamic factor models to finance. In Chapter 1, my co-author Jacob Warren and I examine factors for volatility of equities. Historical literature on the subject decomposes volatility into a factor component and an idiosyncratic remainder. Recent work has suggested that idiosyncratic volatility of US equities data has a factor structure, with the factor highly correlated with, and possibly precisely the market volatility. In this paper we attempt to characterize the underlying factor and find that it can be decomposed into a statistical (PCA) and structural (market volatility) factor. We also show that this feature is not unique to equities, appearing in diverse sets of financial data. Lastly, we find that this dual-factor approach is slightly dominated in forecasting environments by a single statistical factor, suggesting that accurate measurement of the factors provides a direction for future work. In Chapter 2, I explore the use of dynamic factor models in yield curve forecasting and an exploration of the spanning hypothesis – that is, whether all information necessary for forecasting yields is contained in the current yield curve. Only linear tests of the spanning hypothesis are typically conducted in the literature, and the results are subject to substantial disagreement. In this paper, I explore a key modern nonlinearity, namely the zero lower bound (ZLB). I first demonstrate in simulation that only very small nonlinearities in the measurement equation are necessary to break down the assumed linear spanning relationship. Because bond yields are determined by forward-looking behavior of investors, the effect of the ZLB affects spanning results as early as 1995. New nonlinear spanning tests are found to behave appropriately. Using the full set of yields instead of truncating to a small number of principal components is quantitatively important but does not eliminate the omitted nonlinearity effect

The Fate of Napster: Digital Downloading Faces an Uphill Battle

First Diamond Multimedia, then MP3.com, now Napster. The recording industry, in a flurry to protect its copyrighted material, has waged an all-out battle against the dot-coms for the future of copyrighted music on the Internet. Since A&M Records, along with several other labels which comprise the Recording Industry Association of America (RIAA), filed suit against Napster, emotions have run high in the online community. Some have heralded this technology as a much-needed alternative to the strangling grasp of the major record labels; others view it as blatant theft of property. Students, musicians, computer programmers, trade organizations, and even the US government have voiced their opinions - all perhaps sensing that the outcome of the Napster litigation will have far-reaching consequences. Not only does the current battle over the fate of peer-to-peer technology promise to reshape the face of copyright law, it will also mark the future of the music industry, emerging technologies, and business models for years to come.The following iBrief describes the emergence of Napster\u27s peer-to-peer technology, the legal proceedings to date, and Napster\u27s defensive strategy, as well as the potential technological and cultural ramifications of the Napster cause celebr

Distance-based Analysis of Machine Learning Prediction Reliability for Datasets in Materials Science and Other Fields

Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for characterization of the difference between the predicted and training system has not been available. Here, we show that a simple metric based on Euclidean feature space distance and sampling density allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. We show that the metric effectiveness is enhanced by the decorrelation of the features using Gram-Schmidt orthogonalization. To demonstrate the generality of the method, we apply it to support vector regression models for various small data sets in materials science and other fields. Our method is computationally simple, can be used with any ML learning method and enables analysis of the sources of the ML prediction errors. Therefore, it is suitable for use as a standard technique for the estimation of ML prediction reliability for small data sets and as a tool for data set design

The Future of Database Protection in U.S. Copyright Law

In the recent British Horseracing Board case, the English High Court signaled a return to the sweat of the brow standard of copyright protection. Although recent attempts have been made in the United States to protect databases under this standard, this iBrief argues that the information economy is wise to continuing protecting this data through trade secret, State misappropriation and contract law until legislation is passed

Identification of high-reliability regions of machine learning predictions in materials science using transparent conducting oxides and perovskites as examples

Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion, and perovskite formation energy, band gap and lattice parameter as examples, we demonstrate that 1) analysis of ML results by construction of a convex hull in feature space that encloses accurately predicted systems can be used to identify regions in feature space for which ML predictions are highly reliable 2) analysis of the systems enclosed by the convex hull can be used to extract physical understanding and 3) materials that satisfy all well-known chemical and physical principles that make a material physically reasonable are likely to be similar and show strong relationships between the properties of interest and the standard features used in ML. We also show that similar to the composition-structure-property relationships, inclusion in the ML training data set of materials from classes with different chemical properties will not be beneficial and will slightly decrease the accuracy of ML prediction and that reliable results likely will be obtained by ML model for narrow classes of similar materials even in the case where the ML model will show large errors on the dataset consisting of several classes of materials. Our work suggests that analysis of the error distributions of ML predictions will be beneficial for the further development of the application of ML methods in material science

Self-Organizing Maps Algorithm for Parton Distribution Functions Extraction

We describe a new method to extract parton distribution functions from hard scattering processes based on Self-Organizing Maps. The extension to a larger, and more complex class of soft matrix elements, including generalized parton distributions is also discussed.Comment: 6 pages, 3 figures, to be published in the proceedings of ACAT 2011, 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Researc