Design methodology and productivity improvement in high speed VLSI circuits

2017 Spring.Includes bibliographical references.To view the abstract, please see the full text of the document

Assessment of the agriculture supply chain risks for investments of agricultural small and mediumsized enterprises (SMEs) using the decision support model

A key challenge in responding to the emerging challenges in agri-food supply chains is encouraging continued new investment. This is related to the recognition that agricultural production is often a lengthy process requiring ongoing investments that may not produce expected returns for a prolonged period, thereby being highly sensitive tomarket risks. Agricultural productions are generally susceptible to different serious risks such as crop diseases, weather conditions, and pest infections. Many practitioners in this domain, particularly small and medium-sized enterprises (SMEs), have shifted toward digitalization to address such problems. To help with this situation, the current paper develops an integrated decision-making framework, with the Pythagorean fuzzy sets (PFSs), the method for removal effects of criteria (MEREC), the ranksum (RS) and the gained and Lost dominance score (GLDS) termed as PF-MEREC-RS-GLDS approach. In this approach, the PF-MEREC-RS method is applied to compute the subjective and objective weights of the main risks to assess the agriculture supply chain for investments of SMEs, and the PF-GLDS model is used to assess the preferences of enterprises over different the main risks to assess of the agriculture supply chain for investments of SMEs. An empirical case study is taken to evaluate the main risks to assess the agriculture supply chain for SME investments. Also, comparison and sensitivity investigation are made to show the superiority of the developed framework

Introducing conventional human resources practices as part of civil service reform in Qatar 2006-2016

Qatar in the Arabian Gulf is one of many states worldwide trying to improve governance. In 2008, Qatar introduced various ‘human resources management (HRM) practices to improve management of employees. However, there is a growing belief that importing undiluted systems based on other cultures may potentially erode local Arab culture significantly and harmfully. The research project aimed to evaluate if Government Ministries in Qatar can use principally Western HRM theory and practice to manage employees successfully while still allowing them to preserve and strengthen Arab and Islamic values and identity. Some months into the project which commenced in 2006, the State initiated further major reforms and introduced new Ministers and top executive teams in each of 13 newly created Ministries. This created much additional noise in the data making it difficult to separate the effects of wider reforms from those caused by new HRM practices. Given the difficulties of using more conventional statistical analysis techniques, research then adopted a Mixed-Methods Exploratory Sequential Research Design the research completed extensive and detailed research into HRM systems in place in each Ministry. It also collected data and information about perceptions of executives about HR reforms, leadership and management style and other salient factors. The research reached eleven important findings. Among these, the findings showed the people management systems bore much closer resemblance to classic personnel management system. This negated any likely benefits of introducing HRM. The findings also found considerable differences between the national culture of Qatar and that of the West, from where the State drew many of its new ideas for reform. Adoption of such culturally dissimilar systems had the potential to offset efforts to preserve the Gulf’s highly distinctive culture. The work also make practical recommendations with which reform efforts could be improved, though not at the expense of local culture. The thesis completes with recommendations for further research

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language processing and computer vision, largely attributed to deep learning, a special class of machine learning models. Deep learning arguably surpasses traditional approaches by learning the relevant features from raw data through a series of computational layers. This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process. In particular, we ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality-i.e. the difficulty of generally learning functions in high dimensions due to the exponentially increasing need for data points with increased dimensionality? Is it their ability to learn relevant representations of the data by exploiting their structure? How do different architectures exploit different data structures? In order to address these questions, we push forward the idea that the structure of the data can be effectively characterized by its invariances-i.e. aspects that are irrelevant for the task at hand. Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models. These simplified models allow us to investigate and interpret the complex behaviors we observe in deep learning systems, offering insights into their inner workings, with the far-reaching goal of bridging the gap between theory and practice.Comment: PhD Thesis @ EPF