When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. Multi-Task Learning (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple K...

We describe a simple and powerful tool for the management of distributed work: the Task Manager. Common tasks may be shared and manipulated independently by a number of people. They are represented as shared to-do lists at the user interface. With the help of the tool, users may organize cooperative tasks, monitor their progress, share documents and services, and exchange messages during task p...

knowledge management is the process of capturing, developing, sharing, and effectively using organizational knowledge as we known knowledge management literature emphasizes the importance of knowledge as a valuable asset for smes. this paper highlights the efficient sharing of knowledge as a way of creating core competencies in the smes that are in civil construction activities. this perspectiv...

abstract purpose: the main purpose of this study was to investigate the relationship between job characteristics of librarians of universities under authority of the ministry of health and medical education in tehran and their motivation for knowledge sharing. methodology: method of the research was analytical survey, and the data were gathered using three questionnaires of job characteristics,...

In current multi-media systems a major part of the application consists of multiple software tasks executed on a set of processors. Composing the system performance out of the tasks performance is possible only if these tasks interfere with each other in a predictable way. However, when the used memory hierarchy consists of shared caches, tasks flush each others data out of the cache in a unpre...

The effects of policy sharing between agents in a multi-agent dynamical system has not been studied extensively. I simulate a system of agents optimizing the same task using reinforcement learning, to study the effects of different population densities and policy sharing. Rich behavior emerges, dependent on the varied parameters, giving insight towards the dynamics of the system. We demonstrate...