International Growth as Integration of R&D Activities. Evidence from Large Multinational Companies

Corporate R&D internationalization has been analyzed predominantly in terms of the geographic diversification of multinational companies' research laboratories, and only to a lesser extent as a process involving the development of resources and capabilities within organizations and as a means of favoring international integration. This paper analyzes the relation between these two dimensions of internationalization, both of which are relevant for study of the multinational growth of R&D activities. Examination of the literature together with in-depth case reports of two large multinational clusters provides evidence in support of the following statements: · R&D internationalization can be seen as a "gradual" process that takes shape through the formation of specific resources and capabilities, which are developed within individual organizations but are designed to achieve integration both with foreign organizational units of the multinational cluster itself and also with other national innovation systems; · Multinational R&D follows a strategy that is characterized by a strong inter-relation between the formation of foreign research activities and the character of the integration process; · Corporate strategies may correspond to highly diverse and at times even contrasting R&D internationalization models, as shown by the emblematic case analyses presented here. The presence of these different models limits the scope of any general interpretation of the determinants and implications of R&D internationalization.-

Focal Spot, Winter 2006/2007

https://digitalcommons.wustl.edu/focal_spot_archives/1104/thumbnail.jp

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system

True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154106/1/kampfner1998.pd

Personal Learning Environments For Business Organizations

This exploratory, mixed-methods case study investigated supervisor/manager-level employees in a hospital health care organization to examine how they created and used personal learning environments (PLEs), what internet/Web 2.0 technologies were used to solve work-related problems (or for professional development), and what strategies were engaged to meet learning goals. Research questions addressed: what internet/Web 2.0 technologies were used to find and retrieve information, build networks, collaborate, and create and share knowledge; what triggered employees to use internet/Web 2.0 technologies to solve work-related problems; how they evaluated information found; how they determined completion of learning goals; how much confidence they had in their in their abilities to locate, analyze, and use information; what actions they took; and what types of learning activities they engaged in. Results indicated that the work environment influences decisions employees made regarding use of internet/Web 2.0 technologies. Almost 40% of survey participants reported that they did not use social network sites. Two factors played an inhibitory role: (1) perceptions of lack of organizational support for use of these technologies and (2) concern over accidental violation of confidentiality rules specific to the healthcare industry. The majority of study participants were confident in their abilities to find, critically analyze, and apply information they found (an important requisite for success in a PLE). Participants rated “traditional” technologies of online courses and Webcasts as having the most credible information. In general, learning needs for interviewees were stimulated when they needed more information to answer questions. Participants judged the quality of their learning based on a sense of accomplishment and on the end result, as well on opinions of others (e.g., co-workers and supervisors) or on a set of industry standards. The top six learning activities listed were: accessing email, accessing the internet, reading information on the internet or social media sites, seeking consultation, participating in webinars, and online courses offered by the company. The nature of participants’ PLEs, as defined in this study, were in early stages of development, both in the variety and complexity of the tools/technologies being employed, and in the learning strategies used