Inter-firm R&D networks in pharmaceutical biotechnology : what determines firm's centrality-based partnering capability?

This paper analyses the inter-firm R&D network formed in the pharmaceutical biotechnology industry during the 1990s from different perspectives: theoretical network formation, firm's structural positions and its collaborations at the entire network level, and the determinants for firm's centrality-based partnering capability. The results indicate that pharmaceutical biotechnology industry has experienced a significant evolutional change in size and structure during 1991-1998. By considering individual structural positions, the descriptive statistics show that in the 1990s, established pharmaceutical companies developed into dominant star players with multiple partnerships while holding central roles in the R&D network. In the network analysis that emphasized aggregate network level, the degree-based and betweenness-based network centralization were not high implying that the distribution of overall positional advantages in the pharmaceutical biotechnology industry is, to a large degree, not unequal and even though most firms in this sector are linked to the R&D network, some of them are more active than others. The current analysis also shows that firm's efficiency, firm's dependency on its complementary resources and firm's experiences at managing partnerships are important determinants for firm's centrality-based partnering capability, which has important managerial implications for understanding firm's strategic partnering behaviour

Recognition of Nonideal Iris Images Using Shape Guided Approach and Game Theory

Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The main objective of this thesis is to develop a nonideal iris recognition system by using active contour methods, Genetic Algorithms (GAs), shape guided model, Adaptive Asymmetrical Support Vector Machines (AASVMs) and Game Theory (GT). In this thesis, the proposed iris recognition method is divided into two phases: (1) cooperative iris recognition, and (2) noncooperative iris recognition. While most state-of-the-art iris recognition algorithms have focused on the preprocessing of iris images, recently, important new directions have been identified in iris biometrics research. These include optimal feature selection and iris pattern classification. In the first phase, we propose an iris recognition scheme based on GAs and asymmetrical SVMs. Instead of using the whole iris region, we elicit the iris information between the collarette and the pupil boundary to suppress the effects of eyelid and eyelash occlusions and to minimize the matching error. In the second phase, we process the nonideal iris images that are captured in unconstrained situations and those affected by several nonideal factors. The proposed noncooperative iris recognition method is further divided into three approaches. In the first approach of the second phase, we apply active contour-based curve evolution approaches to segment the inner/outer boundaries accurately from the nonideal iris images. The proposed active contour-based approaches show a reasonable performance when the iris/sclera boundary is separated by a blurred boundary. In the second approach, we describe a new iris segmentation scheme using GT to elicit iris/pupil boundary from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm to form a unified approach, which is robust to noise and poor localization and less affected by weak iris/sclera boundary. Finally, to further improve the segmentation performance, we propose a variational model to localize the iris region belonging to the given shape space using active contour method, a geometric shape prior and the Mumford-Shah functional. The verification and identification performance of the proposed scheme is validated using four challenging nonideal iris datasets, namely, the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and the WVU Nonideal, plus the non-homogeneous combined dataset. We have conducted several sets of experiments and finally, the proposed approach has achieved a Genuine Accept Rate (GAR) of 97.34% on the combined dataset at the fixed False Accept Rate (FAR) of 0.001% with an Equal Error Rate (EER) of 0.81%. The highest Correct Recognition Rate (CRR) obtained by the proposed iris recognition system is 97.39%

Untangling Neoliberalism’s Gordian Knot: Cancer Prevention and Control Services for Rural Appalachian Populations

In eastern Kentucky, as in much of central Appalachia, current local storylines narrate the frictions and contradictions involved in the structural transition from a post-WWII Fordist industrial economy and a Keynesian welfare state to a Post-Fordist service economy and Neoliberal hollow state, starving for energy to sustain consumer indulgence (Jessop, 1993; Harvey, 2003; 2005). Neoliberalism is the ideological force redefining the “societal infrastructure of language” that legitimates this transition, in part by redefining the key terms of democracy and citizenship, as well as valorizing the market, the individual, and technocratic innovation (Chouliaraki & Fairclough, 1999; Harvey, 2005). This project develops a perspective that understands cancer prevention and control in Appalachiaas part of the structural transition that is realigning community social ties in relation to ideological forces deployed as “commonsense” storylines that “lubricate” frictions that complicates the transition

Deep Neural Networks on Genetic Motif Discovery: the Interpretability and Identifiability Issues

Deep neural networks have made great success in a wide range of research fields and real-world applications. However, as a black-box model, the drastic advances in the performance come at the cost of model interpretability. This becomes a big concern especially for domains that are safety-critical or have ethical and legal requirements (e.g., avoiding algorithmic discrimination). In other situations, interpretability might be able to help scientists gain new ``knowledge'' that is learnt by the neural networks (e.g., computational genomics), and neural network based genetic motif discovery is such a field. It naturally leads us to another question: Can current neural network based motif discovery methods identify the underlying motifs from the data? How robust and reliable is it? In other words, we are interested in the motif identifiability problem. In this thesis, we first conduct a comprehensive review of the current neural network interpretability research, and propose a novel unified taxonomy which, to the best of our knowledge, provides the most comprehensive and clear categorisation of the existing approaches. Then we formally study the motif identifiability problem in the context of neural network based motif discovery i.e., if we only have access to the predictive performance of a neural network, which is a black-box, how well can we recover the underlying ``true'' motifs by interpreting the learnt model). Systematic controlled experiments show that although accurate models tend to recover the underlying motifs better, the motif identifiability (a measure of the similarity between true motifs and learnt motifs) still varies in a large range. Also, the over-complexity (without overfitting) of a high-accuracy model (e.g., using 128 kernels while 16 kernels are already good enough) may be harmful to the motif identifiability. We thus propose a robust neural network based motif discovery workflow addressing above issues, which is verified on both synthetic and real-world datasets. Finally, we propose probabilistic kernels in place of conventional convolutional kernels and study whether it would be better to directly learn probabilistic motifs in the neural networks rather than post hoc interpretation. Experiments show that although probabilistic kernels have some merits (e.g., stable output), their performance is not comparable to classic convolutional kernels under the same network setting (the number of kernels)

Análisis de datos etnográficos, antropológicos y arqueológicos: una aproximación desde las humanidades digitales y los sistemas complejos

La llegada de las Ciencias de la Computación, el Big Data, el Análisis de Datos, el Aprendizaje Automático y la Minería de Datos ha modificado la manera en que se hace ciencia en todos los campos científicos, dando lugar, a su vez, a la aparición de nuevas disciplinas tales como la Mecánica Computacional, la Bioinformática, la Ingeniería de la Salud, las Ciencias Sociales Computacionales, la Economía Computacional, la Arqueología Computacional y las Humanidades Digitales –entre otras. Cabe destacar que todas estas nuevas disciplinas son todavía muy jóvenes y están en continuo crecimiento, por lo que contribuir a su avance y consolidación tiene un gran valor científico. En esta tesis doctoral contribuimos al desarrollo de una nueva línea de investigación dedicada al uso de modelos formales, métodos analíticos y enfoques computacionales para el estudio de las sociedades humanas tanto actuales como del pasado.El Ministerio de Ciencia e Innovación • Proyecto SimulPast – “Transiciones sociales y ambientales: simulando el pasado para entender el comportamiento humano” (CSD2010-00034 CONSOLIDER-INGENIO 2010). • Proyecto CULM – “Modelado del cultivo en la prehistoria” (HAR2016-77672-P). • Red de Excelencia SimPastNet – “Simular el pasado para entender el comportamiento humano” (HAR2017-90883-REDC). • Red de Excelencia SocioComplex – “Sistemas Complejos Socio-Tecnológicos” (RED2018-102518-T). La Consejería de Educación de la Junta de Castilla y León • Subvención a la línea de investigación “Entendiendo el comportamiento humano, una aproximación desde los sistemas complejos y las humanidades digitales” dentro del programa de apoyo a los grupos de investigación reconocidos (GIR) de las universidades públicas de Castilla y León (BDNS 425389

Econometrics of network models

View of road and industry from Cumbala Hill.GrayscaleSorensen Safety Negatives, Binder: Asia

The State of the Parties 2018 (Eight Edition)

The State of the Parties 2018 brings together leading scholars of parties, elections, and interest groups to provide an indispensable overview of American political parties today. The 2016 presidential election was extraordinary, especially the unexpected nomination and election of Donald Trump to the White House. What role did political parties play in these events? How did the party organizations fare? What are the implications for the future? Scholars and practitioners from throughout the United States explore the current state of American party organizations, constituencies and resources at the national, state and local level.https://ideaexchange.uakron.edu/state_of_the_parties8/1000/thumbnail.jp