Mixed Source

We study competitive interaction between profit-maximizing firms that sell software and complementary goods or services. In addition to tactical price competition, we allow firms to compete through business model reconfigurations. We consider three business models: the proprietary model (where all software modules offered by the firm are proprietary), the open source model (where all modules are open source), and the mixed source model (where a few modules are open). When a firm opens one of its modules, users can access and improve the source code. At the same time, however, opening a module sets up an open source (free) competitor. This hampers the firm's ability to capture value. We analyze three competitive situations: monopoly, commercial firm vs. non-profit open source project, and duopoly. We show that: (i ) firms may become 'more closed' in response to competition from an outside open source project; (ii ) firms are more likely to open substitute, rather than complementary, modules to existing open source projects; (iii) when the products of two competing firms are similar in quality, firms differentiate through choosing different business models; and (iv ) low-quality firms are generally more prone to opening some of their technologies than rms with high-quality products

The BELLFLOW system

The BELLFLOW flowcharting system was developed to meet certain Bell System standards of documentation. There are three modes of operation with the BELLFLOW system: source mode, comment mode, and mixed mode. In the source mode, all of the flowcharting information is derived directly from the source code. In the comment mode, BELLFLOW ignores the source code completely and derives the entire flowchart purely from comments imbedded in the program. In the mixed mode, the source and comment mode are combined. The mixed mode is unique to BELLFLOW and was designed to provide a self-documenting program source deck. Other features of BELLFLOW include: automatic placement, automatic line routing, paging, and generation of on and off sheet connectors

First- and Second-Order Hypothesis Testing for Mixed Memoryless Sources with General Mixture

The first- and second-order optimum achievable exponents in the simple hypothesis testing problem are investigated. The optimum achievable exponent for type II error probability, under the constraint that the type I error probability is allowed asymptotically up to epsilon, is called the epsilon-optimum exponent. In this paper, we first give the second-order epsilon-exponent in the case where the null hypothesis and the alternative hypothesis are a mixed memoryless source and a stationary memoryless source, respectively. We next generalize this setting to the case where the alternative hypothesis is also a mixed memoryless source. We address the first-order epsilon-optimum exponent in this setting. In addition, an extension of our results to more general setting such as the hypothesis testing with mixed general source and the relationship with the general compound hypothesis testing problem are also discussed.Comment: 23 page

Single channel speech music separation using nonnegative matrix factorization with sliding windows and spectral masks

A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with sliding windows and spectral masks is proposed in this work. We train a set of basis vectors for each source signal using NMF in the magnitude spectral domain. Rather than forming the columns of the matrices to be decomposed by NMF of a single spectral frame, we build them with multiple spectral frames stacked in one column. After observing the mixed signal, NMF is used to decompose its magnitude spectra into a weighted linear combination of the trained basis vectors for both sources. An initial spectrogram estimate for each source is found, and a spectral mask is built using these initial estimates. This mask is used to weight the mixed signal spectrogram to find the contributions of each source signal in the mixed signal. The method is shown to perform better than the conventional NMF approach

Open Source Licensing in Mixed Markets, or Why Open Source Software Does Not Succeed

The rivalry between developers of open source and proprietary software encourages open source developers to court users and respond to their needs. If the open source developer wants to promote her own open source standard and solutions, she may choose liberal license terms such as those of the Berkeley Software Distribution as proprietary developers will then find it easier to adopt her standard in their products. If she wants to promote the use of open source software per se, she may use more restrictive license terms such as the General Public License to discourage proprietary appropriation of her effort. I show that open source software that comes late into a market will be less likely than more innovative open source software to be compatible with proprietary software, but is also more likely to be made more accessible to inexperienced users.Open Source; Software; Standards; Compatibility; Network Effects; Duopoly; Mixed Markets; Intellectual Property; Copyright; Licensing

Single channel speech music separation using nonnegative matrix factorization and spectral masks

A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization followed by masking to separate the mixed signal. In the training stage, NMF uses the training data to train a set of basis vectors for each source. These bases are trained using NMF in the magnitude spectrum domain. After observing the mixed signal, NMF is used to decompose its magnitude spectra into a linear combination of the trained bases for both sources. The decomposition results are used to build a mask, which explains the contribution of each source in the mixed signal. Experimental results show that using masks after NMF improves the separation process even when calculating NMF with fewer iterations, which yields a faster separation process