Background and Objectives: Graph Processing is increasingly gaining attention during era of big data. However graph Processing applications are highly memory intensive due to nature of graphs. Processing-in-memory (PIM) is an old idea which revisited recently with the advent of technology specifically the ability to manufacture 3D stacked chipsets. PIM puts forward to enrich memory units with computational capabilities to reduce the cost of data movement between processor and memory system. This approach seems to be a way of dealing with large-scale graph Processing, considering recent advances in the field. Methods: This paper explores real-world PIM technology to improve graph Processing efficiency by reducing irregular access patterns and improving temporal locality using HMC. We propose NodeFetch, a new method to access nodes and their neighbors while Processing a graph by adding a new command to HMC system. Results: Results of our simulation on a set of real-world graphs point out that the proposed idea can achieve 3. 3x speed up in average and 69% reduction of energy consumption over the baseline PIM architecture which is HMC. Conclusion: Most of the techniques in the field of Processing-in-memory, hire methods to reduce movement of data between processor and memory. This paper proposes a method to reduce graph Processing execution time and energy consumption by reducing cache misses while Processing a graph.

A brief account of the concept of working memory is presented, followed by a more detailed description of one sub-component of the system, namely the phonological loop. The question of the functional significance of this component of working memory is discussed. Evidence suggests a minor role in language comprehension, together with a much more substantial role in the capacity to acquire novel phonological, and possibly grammatical forms. It is suggested that the phonological loop has evolved as a mechanism for language acquisition.

Background and Aim: The vestibular system contributes in the stabilization of the head and body, orientation, and gazing through the pro-cessing of sensory inputs. A wealth of evidence supports the involvement of vestibular informa-tion in higher functions, too. Methods: In this paper, we reviewed the pre-vious studies on the effect of the vestibular sys-tem on memory as one of the cognitive func-tions. Results: Clinical and laboratory findings indi-cate the association of vestibular inputs (besides postural control and oculomotor) with a variety of higher functions, especially memory func-tion. Because part of the memory function is determined by other cognitive processes i. e. att-ention capacity, emotional disturbances, and executive functions, the study of the effect of vestibular inputs on these functions provides a more accurate view of how the vestibular inputs affect memory performance. Conclusion: Although our current knowledge on vestibular-memory interaction is increasing, the exact involvement of vestibular signals in memory representations is still unclear and needs further studies to determine the theore-tical basis of vestibular involvement in memory Processing.

There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a model in which the topographical differences are quite distinguishable. In our proposed model, we have translated this topographical situation into quantities, which are attributed to the nodes. The result is an edge-weighted graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.