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No description available.

No description available.

No description available.

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The outbreak of coronavirus disease 2019 (COVID-19) has caused immense challenges to businesses and people’s lives. For the pharmaceutical industry, these challenges entail changes in demand, supply chain, consumption trends, as well as a shift towards telemedicine and changes in R&D priorities. To respond to these market changes, firms must reallocate their resources and modify their processes, which requires an agility in the firms’ structure and management practices.The aim of this work was to identify how firms can manage their operations resources and processes to adapt to sudden market change and facilitate crisis-driven digital transformation. The objective is to investigate how firms’ operations strategy and organizational attributes affect firm performance under the changing market conditions during the recent pandemic. To do this, we analyzed the operations strategy of 13 large (more than 250 employees) pharmaceutical firms in Sweden qualitatively. Using the operations strategy matrix as a guiding tool, keywords describing the decision areas and performance objectives of these firm were used for searching online published information. By reviewing annual reports, press releases and articles in trade journals, information on firms’ strategic priorities was extracted. By the use of pattern matching, the relation between firms’ strategic preferences and their ability to adapt to market changes was established. Moreover, we analyzed the relation between pharmaceutical firms’ financial performance, their organization attributes (i.e., span of control, financial resources and intellectual resources) and crisis conditions quantitatively. For this purpose, OLS regression and panel (data) analysis was used to identify significant variables that impact performance of 239 registered firms in Sweden.We found that firms focusing on market competitiveness and growth orientation in their operations strategy showed better performance during the pandemic in comparison to firms focusing only on market competitiveness. It was also noted that it takes time to see the effects of changes in strategic priorities and depends on firms’ existing agility. However, the relation between firms’ performance during the pandemic and their status within the organization and ownership structure was unclear. It was also observed that ownership structure and firms’ status within the organization had no impact on the choice of perspective on operations strategy.Similarly, organizational attributes in terms of firms’ financial resources were found to have a positive impact on financial performance, and this relation was more prominent for firms with a wide span of control (horizontal structure) than for firms with a narrow span of control (vertical structure). While a negative relation between crisis conditions and financial performance was observed for firms with a narrow span of control, no such relationship could be observed for firms with a wide span of control. Similarly, no relation between firms’ intellectual resources and their financial performance was found in this study.This work provides evidence on how firms’ operations strategy and organizational attributes affect performance, particularly during COVID-19 pandemic-driven market changes. The findings provided in this work are relevant considering that the business environment is currently changing at an increasing pace. Additionally, the increased use of artificial intelligence, big data, the internet of things, and the platform economy has led us to a new industrial revolution, in which firms with efficient use of resources and processes are endowed with increased survival chances. The results of this thesis can provide insight into how organizations can optimize their resource usage and processes to achieve organizational agility for sustainable competitive advantages during future market changes.

<p> We developed and tested procedures to rank the performance of security analysts according to the timeliness of their earning forecasts. We compared leaders and followers among analysts on various performance attributes, such as accuracy, boldness, experience, brokerage size and so on. We also use discriminant analysis and logistic regression model to examine what attributes have an effect on the classification. Further, we examined whether the timeliness of forecasts is related to their impact on stock prices. We found that the lead analysts identified by the measure of forecast timeliness have a greater impact on stock price than follower analysts. Our initial sample includes all firms on the Institutional Brokers Estimate System (I/B/E/S) database and security return data on the daily CRSP file for the years 1994 through 2003.</p> / Thesis / Master of Science (MSc)

Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and which ones do not (cannot-link). These approaches require many such constraints before achieving good clustering performance because each constraint only provides weak cues about the desired clustering. In this work, we propose to use image attributes as a modality for the user to provide more informative cues. In particular, the clustering algorithm iteratively and actively queries a user with an image pair. Instead of the user simply providing a must-link/cannot-link constraint for the pair, the user also provides an attribute-based reasoning e.g. "these two images are similar because both are natural and have still water'' or "these two people are dissimilar because one is way older than the other''. Under the guidance of this explanation, and equipped with attribute predictors, many additional constraints are automatically generated. We demonstrate the effectiveness of our approach by incorporating the proposed attribute-based explanations in three standard semi-supervised clustering algorithms: Constrained K-Means, MPCK-Means, and Spectral Clustering, on three domains: scenes, shoes, and faces, using both binary and relative attributes. / Master of Science

Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. National Cancer Institute estimated that 91,270 new case and 9,320 deaths are expected in 2018 caused by melanoma. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is dermoscopy, which leads to improved diagnose accuracy compared to traditional ABCD criteria. But reading and examining dermoscopic images is a time-consuming and complex process. Therefore, computerized analysis methods of dermoscopic images have been developed to assist the visual interpretation of dermoscopic images. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this thesis, I develop a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The approach described in the thesis achieved the Jaccard index of 0.477 on the official test dataset, which ranked 5th place in the challenge. / Master of Science / Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is called dermoscopy. It has become increasingly conveniently to use dermoscopic device to image the skin in recent years. Dermoscopic lens are available in the market for individual customer. When coupling the dermoscopic lens with smartphones, people are be able to take dermoscopic images of their skin even at home. However, reading and examining dermoscopic images is a time-consuming and complex process. It requires specialists to examine the image, extract the features, and compare with criteria to make clinical diagnosis. The time-consuming image examination process becomes the bottleneck of fast diagnosis of melanoma. Therefore, computerized analysis methods of dermoscopic images have been developed to promote the melanoma diagnosis and to increase the survival rate and save lives eventually. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. In this thesis, I developed a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The segmentation result from this approach won 5th place in a public competition. It has the potential to be utilized in clinic application in the future.