A four port network adder-subtractor module, for surface plasmon polariton (SPP) waves based on a ring resonator filter is proposed. The functionality of module is achieved by the phase difference manipulation of guided SPPs through different arms connected to the ring resonator. The module is designed using the concepts of a basic two-port device proposed in this paper. It is shown that two po...

Reaching to grasp is of fundamental importance to primate motor behavior and requires coordinating hand preshaping with limb transport and grasping. We aimed to clarify the role of cerebellar output via the magnocellular red nucleus (RNm) to the control of reaching to grasp. Rubrospinal fibers originating from RNm constitute one pathway by which cerebellar output influences spinal circuitry dir...

The ongoing fragmentation of work has resulted in a narrowing of tasks into smaller pieces that can be sent outside the organization and, in many instances, around the world. This trend is shifting the boundaries of organizations and leading to increased outsourcing. Though the consolidation of volume may lead to productivity improvement, little is known about how this shift toward outsourcing ...

Drawing on Robinson’s cognition hypothesis, the study attempted to examine how task conditions influence EFL learners’ oral performance and whether learners’ individual differences in terms of tolerance of ambiguity and self-efficacy mediate the effects of such conditions. To this end, 62 Iranian intermediate EFL learners from private language institutes in Tehran performed four dyadic decision...

The development of computers, sensors and other technical instruments changed scientific research. Current scientific experiments often contain a whole range of computational activities, e.g: capturing and saving huge amounts of data in databases or flat files, analysis of data with software and data visualization [1]. The complexity of these data intensive experiments led to new challenges. De...

In this paper, we propose a machine-learning solution to problems consisting of many similar prediction tasks. Each of the individual tasks has a high risk of overrtting. We combine two types of knowledge transfer between tasks to reduce this risk: multi-task learning and hierarchical Bayesian modeling. Multi-task learning is based on the assumption that there exist features typical to the task...