CAMORRA: a C++ library for recursive computation of particle scattering amplitudes

We present a new Monte Carlo tool that computes full tree-level matrix elements in high-energy physics. The program accepts user-defined models and has no restrictions on the process multiplicity. To achieve acceptable performance, CAMORRA evaluates the matrix elements in a recursive way by combining off-shell currents. Furthermore, CAMORRA can be used to compute amplitudes involving continuous color and helicity final states.Comment: 22 page

Transfer learning by supervised pre-training for audio-based music classification

Very few large-scale music research datasets are publicly available. There is an increasing need for such datasets, because the shift from physical to digital distribution in the music industry has given the listener access to a large body of music, which needs to be cataloged efficiently and be easily browsable. Additionally, deep learning and feature learning techniques are becoming increasingly popular for music information retrieval applications, and they typically require large amounts of training data to work well. In this paper, we propose to exploit an available large-scale music dataset, the Million Song Dataset (MSD), for classification tasks on other datasets, by reusing models trained on the MSD for feature extraction. This transfer learning approach, which we refer to as supervised pre-training, was previously shown to be very effective for computer vision problems. We show that features learned from MSD audio fragments in a supervised manner, using tag labels and user listening data, consistently outperform features learned in an unsupervised manner in this setting, provided that the learned feature extractor is of limited complexity. We evaluate our approach on the GTZAN, 1517-Artists, Unique and Magnatagatune datasets

Parallel one-versus-rest SVM training on the GPU

Linear SVMs are a popular choice of binary classifier. It is often necessary to train many different classifiers on a multiclass dataset in a one-versus-rest fashion, and this for several values of the regularization constant. We propose to harness GPU parallelism by training as many classifiers as possible at the same time. We optimize the primal L2-loss SVM objective using the conjugate gradient method, with an adapted backtracking line search strategy. We compared our approach to liblinear and achieved speedups of up to 17 times on our available hardware

A note on the evaluation of generative models

Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided

The ecology of technology : the co-evolution of technology and organization

In this day and age, arguing that technology is a powerful force that drives many economic processes is like preaching to the choir. Nevertheless, despite the widespread realization of the important role of technology in our modern day society, an intimate understanding of the process of technological change is still lacking. This study seeks to provide more insight into the concept of technological change by characterizing it as a socio-cultural evolutionary process of variation, selection and retention. According to this logic, variety (or novelty) is created by (random or non-random) mutations (i.e., organizations and individuals that (re-) combine existing components in novel ways). This variety is subsequently selected out by the stakeholders in the environment, such as individuals, organizations, and institutions. In other words, the variety is then retained in the structural characteristics of the environment, commonly referred to as organizational routines and technological paradigms. Finally, these structural characteristics subsequently provide the context in/from which new mutations (or variations) are created. From there, the cycle can be repeated. Because, nowadays, technology is mostly developed in an organizational context, the appropriate place to study technology and technological change is in the context of organization science, which is an academic discipline that studies all facets of organization. Even though technology deserves a central role in any organization theory, technology has not yet penetrated fully the domain of organization science. The only domain in which technology has a central role is within evolutionary economics, a school of economic thought that was influenced by evolutionary biology. Even though evolutionary economics has surely added much to our understanding of the process of technological change, in our view, this school of thought mainly concentrates its attention on idiosyncratic accounts of variety creation and their subsequent selection by the environment. Much less attention has been attributed to how the selection environment (or the structural characteristics thereof) determines the variety creation. Consequently, insights from organizational ecology, which has its center of gravity at the selection environment, can add value over and above the ones originating from evolutionary economics. The key source of inspiration of organizational ecology is bioecology, which makes it evolutionary economics’ counterpart in sociology. In this study, we therefore seek to close the evolutionary circle by developing a structural or ecological perspective of technological change. After all, holding both links between variety and selection in focus at the same time (i.e., how variety is selected by the environment, and how the selection environment facilitates and constrains the creation of variety) provides for a truly evolutionary model of technological change. Accordingly, we define our research objective as follows: Research objective: To develop an ecology of technology in organization science. Because this objective is rather vague and abstract, we formulate several research questions to provide more direction in our quest to fulfill our objective. We formulate our first research question as follows. Research question 1: What is the importance of biotechnology? Providing an answer to this research question is the subject of Chapter 2. As a means of introducing biotechnology, we first describe biotechnology’s central dogma (i.e., DNA as the building block of life). Moreover, we provide a timeline to get a certain feel of the history and evolution of biotechnology, and list numerous socio-economic trends to get an idea of the importance of biotechnology in society. These trends clearly illustrate that biotechnology drives important social and economic events. Next, we evaluate biotechnology’s position in the overall technological landscape. Our main finding is that, despite its sharply increasing societal and economic importance, biotechnology still has not yet conquered a place in the technological core of our society. Reviewing the developments within synthetic biology (in this domain, complex systems are designed by (re-)combining DNA into biological parts that represent biological functions and, as such, is the domain where all aspects of biotechnology come together), it becomes clear that biotechnology as a whole is not yet in the growth stage of technological convergence that is characterized by a stable configuration of component technologies (i.e., a dominant design). Moreover, on the basis of the future expectations of experts, we conclude that biotechnology is a strategic technology that is nowhere near its peak influence, and that we can expect the importance to increase even further over the coming years. Obviously, whether biotechnology can deliver on its promise and materialize the expectations of insiders is not certain. Even when biotechnology delivers on only a small part of the promise, though, its impact will already be gigantic. For example, consider the fact that, in a 2007 interview, Craig Venter – who is one of the most well-renowned biotechnologists today – said that, in 20 years time, synthetic genomics is going to become the standard for making anything (Aldhous, 2007). So, in conclusion, biotechnology is a technology that is still emerging and does yet not display a stable and predictable pattern of growth that characterizes mature (i.e., non-emerging) technologies. Our next research question thus is as follows. Research question 2: How to study the growth of an emerging technology? In Chapter 3, on the basis of ecological insights and principles, we develop a structural or systemic view towards technology, and hereby take into explicit account the embedded nature of technology. That is, we propose that it adds value to view technology as a system composed of a set of interdependent components (or subsystems). More specifically, by relying on density dependence theory from organizational ecology, we effectively develop a multilevel framework that can be used to empirically study emerging technologies. Moreover, we employ the concept of the technological niche from organizational ecology, with its associated dimensions of crowding (associated with processes of competition) and status (associated with processes of legitimation), and add diversity as a key dimension. Through sophisticated multivariate analysis of biotechnology patents from the United States Patent and Trademark Office (USPTO), we validate this model, which we label the ‘ecology of technology’. However, we also discover some anomalies, which point to the limitations of our model, the most important being its rather static nature. Because emerging technologies are characterized by fluid patterns of growth, a static model is a severe misrepresentation of the evolution of emerging technologies. Our next research question naturally follows from this. Research question 3: How to study the evolution of an emerging technology? On the basis of insights from evolutionary economics, Chapter 4 distinguishes between two stages of technological development, namely the stages of divergence and convergence (that connect nicely with the seed and growth stage of life cycle theory). The focal element is what is generally referred to as the deep structure (in the context of technology also commonly referred to as a dominant design) that facilitates cumulative changes by reducing uncertainty and enabling specialization and integration through standardization. The stage of divergence is characterized by the absence of a deep structure, while the stage of convergence is characterized by its presence. So, in the latter stage, there is a relatively stable configuration of the system’s component technologies that results in relatively stable and predictable patterns of growth. On the basis of these insights, we adapt our multi-level model to identify these different stages of development at the component level. More specifically, if there is a mutualistic relationship between a component and the system (i.e., if system density contributes positively to component entry), the component is argued to have a dominant design. As we are dealing with an emerging technology, our main interest lies in the transition from the initial seed stage of technological divergence (i.e., the absence of a deep structure) to a growth stage of technological convergence (i.e., the existence of a deep structure), or the creation of a deep structure. This means that we do not take into account the revolutionary transition from a stage of convergence into divergence (i.e., the maturity and decline stage in life cycle theory). Not only do we refine our predictions regarding the effects of our existing dimensions (i.e., multilevel density dependence, crowding, status, and focal diversity), but, by further taking into account the lineage of technology, we refine our dimension of diversity by adding antecedent and descendant diversity as additional dimensions to the technological niche. This results in an intricate model that can be used to study the growth and evolution of an emerging technology. We demonstrate this by an empirical investigation of biotechnology patents from the USPTO and hereby provide further support for our ‘ecology of technology’. In the light of our research objective, before we answer the question of what the precise consequences are for organizations, we ask ourselves how we can effectively integrate our findings at the organizational level of analysis. We thus formulate our next research question accordingly. Research question 4: How can we integrate technology into the theory of the organization specifictechnological niche? In Chapter 5, we use a process of logical formalization to represent the theory of the organization-specific technological niche in a formal logical language. The reason for doing so is threefold. First, this forces us to explicate all underlying assumptions and to remove any inconsistencies to make the argument logically sound. Second, this requires us to supplement the theory so that it is complete, without missing elements. Third and finally, it results in a logically sound and complete theory fragment ready for extension by integrating the insights from the study of the evolution of technology. We choose nonmonotonic logic as the language in which we represent our arguments because nonmonotonic logic is better suited for theory building, and this connects better to the current wave of formalization in non-monotonic logic in organizational ecology. On the basis of this analysis, we already make two important theoretical extensions. First, by distinguishing between crowding in technological and market space, we tie technological crowding to both competition and legitimation. To be precise, technological crowding results in competition mainly if the crowding organization is a competitor of the focal organization. Second, uncertainty mediates the relationship between the perceived and actual technological quality of the organization. More specifically, under uncertainty, the actual quality of an organization’s technology cannot be readily observed so that resource controllers have to rely on status (i.e., historic technological quality) instead. With this formalized, logically sound and complete theory fragment in hand, we can turn to the question of the organizational consequences. We thus pose our next research question as follows. Research question 5: What are the consequences of integrating several technological insights into thetheory of the organization-specific technological niche? In Chapter 6, we integrate four technological insights from Chapters 3 and 4 into our formalized theory fragment from the previous chapter. These insights are: (1) multiple technological domains exist that have (2) different stages of development, (3) different levels of uncertainty, and (4) different growth rates. On the basis of these four insights, we extend the theory of the organization-specific technological niche considerably. For crowding, we demonstrate that the effect of crowding is not only conditional upon the identity of the other organization, but also on the stage of technological development. We also add non-crowding to the mix. Regarding the effect of (non-)crowding, in the stage of divergence, multiple competing design configurations exist, and crowding (non-crowding) increases (decreases) the competitiveness of the supported design configuration, having a legitimating (competition) effect. In contrast, in the stage of convergence, crowding (non-crowding) loses its legitimating (competition) function and results in competitive (legitimation) pressure. For status, the most important consequences are that: (1) status is domain dependent, and (2) its effect is dependent upon the stage of technological development (i.e., the effect of status is higher in the stage of divergence). We also add two additional dimensions, which are (1) technological opportunities (that can be represented by the growth rate of the domain), and (2) technological diversity (measured by the distribution of activities over alternative domains). By operationalizing performance as a two-dimensional vector, we suggest that the dimensions of the technological niche are related to different performance measures in distinct temporal relationships. However, even though this theoretical extension is certainly valuable, the subsequent question is whether these extensions hold when subjected to advanced empirical tests. We therefore formulate our next research question as follows. Research question 6: Can we find proof for our extended theory of the organization-specific technological niche? In Chapter 7, we empirically test several of our theoretical extensions of the organization-specific technological niche. Our dependent variable is biotechnology innovation (i.e., the number of biotechnology patents). Through a sophisticated empirical analysis, we find strong support for our extended theory. However, we also encounter some inconsistencies and anomalies. This seems to connect to the fact that processes of competition and legitimation are more appropriately defined at lower levels of analysis (i.e., at the component instead of at the system level). Moreover, due to the dual role of a direct technological tie (i.e., it can have both a competing and a legitimating function) that forms the basis for our measure of status, status is better defined at the component level of analysis. In contrast, biotechnological quality can be aggregated to the system level without losing significance. We thus find strong support for this dimension. Furthermore, we also clearly demonstrate the importance of taking into account the different dimensions of technological diversity (i.e., antecedent, focal, and descendant), with a vital role for antecedent diversity, which logically connects with the notion of absorptive capacity. The subsequent question is what this means for the broader academic debate regarding the (co-)evolution of technology and organization. We formulate our next research question accordingly. Research question 7: What are the implications for the study of the (co-)evolution of technology and organization? In the final chapter of this dissertation, we start by stating the main contribution of this dissertation, which is that we develop a dynamic multilevel model that can be used to empirically study the evolution of an emerging technology. As this model is based on the assumption that technology can effectively be studied as a system composed of an interacting set of components, we pay explicit attention to the embedded nature of technology. Hence, when studying the evolution of technology, it is inappropriate to focus on a single level of analysis and using a multilevel perspective adds value over and above any single level study. That is, technology (e.g., biotechnology) is composed of a set of technological components (e.g., biotechnology’s component technologies) while, at the same time, being embedded in a larger technological system (i.e., technological landscape). It is precisely this multilevel nature of technology that gives it the potential to close part of the chasm in the debate between organizational adaptation (i.e., the dominant perspective in evolutionary economics) and environmental selection (i.e., the dominant perspective in organizational ecology). More specifically, by defining technology at different levels of analysis (e.g., invention, component, system, and landscape), it is possible to tie the evolution of technology to the evolution of organization at different levels of analysis (i.e., individual organization, population of organizations, community, and society). This enables studying the evolution of technology and organization in unison, and thus provides the basis for a co-evolutionary model of technology and organization. Employing a multilevel perspective to both technology and organization at the same time, and defining technology and organization as nested hierarchies tied together at multiple levels of analysis, effectively allows an analyzes of how stable configurations travels upwards in this hierarchy. After all, "it is the information about stable configurations […] that guides the process of evolution" (Simon, 1952: 473)