Culture and Cognitive Theory: Toward a Reformulation

In a provocative and important recent article Anthony Marsella (1998) makes an eloquent plea for the forging of a new metadiscipline of psychology that he labels global-community psychology. Marsella argues that we need a radical rethinking of the fundamental premises of psychology, rooted as they are in Western cultural traditions. Features of an emergent global-community psychology include an emphasis on multicultural and multidisciplinary approaches to human behavior that draw attention to the importance of context and meaning in human lives. Marsella's call for a global-community psychology reflects, in part, a growing body of literature that demonstrates the importance of cultural factors in a diver-sity of psychological domains such as cognition, emotion, social behavior, and psychopathology

Prefetching and clustering techniques for network based storage.

The usage of network-based applications is increasing, as network speeds increase, and the use of streaming applications, e.g BBC iPlayer, YouTube etc., running over network infrastructure is becoming commonplace. These applications access data sequentially. However, as processor speeds and the amount of memory available increase, the rate at which streaming applications access data is now faster than the rate at which the blocks can be fetched consecutively from network storage. In addition to sequential access, the system also needs to promptly satisfy demand misses in order for applications to continue their execution. This thesis proposes a design to provide Quality-Of-Service (QoS) for streaming applications (sequential accesses) and demand misses, such that, streaming applications can run without jitter (once they are started) and demand misses can be satisfied in reasonable time using network storage. To implement the proposed design in real time, the thesis presents an analytical model to estimate the average time taken to service a demand miss. Further, it defines and explores the operational space where the proposed QoS could be provided. Using database techniques, this region is then encapsulated into an autonomous algorithm which is verified using simulation. Finally, a prototype Experimental File System (EFS) is designed and implemented to test the algorithm on a real test-bed

Low strain, long life creep fatigue of AF2-1DA and INCO 718

Two aircraft turbine disk alloys, GATORIZED AF2-DA and INCO 718 were evaluated for their low strain long life creep-fatigue behavior. Static (tensile and creep rupture) and cyclic properties of both alloys were characterized. The cntrolled strain LCF tests were conducted at 760 C (1400 F) and 649 C (1200 F) for AF2-1DA and INCO 718, respectively. Hold times were varied for tensile, compressive and tensile/compressive strain dwell (relaxation) tests. Stress (creep) hold behavior of AF2-1DA was also evaluated. Generally, INCO 718 exhibited more pronounced reduction in cyclic life due to hold than AF2-1DA. The percent reduction in life for both alloys for strain dwell tests was greater at low strain ranges (longer life regime). Changing hold time from 0 to 0.5, 2.0 and 15.0 min. resulted in corresponding reductions in life. The continuous cycle and cyclic/dwell initiation failure mechanism was predominantly transgranular for AF2-1DA and intergranular for INCO 718

Modelling network memory servers with parallel processors, break-downs and repairs.

This paper presents an analytical method for the performability evaluation of a previously reported network memory server attached to a local area network. To increase the performance and availability of the proposed system, an additional server is added to the system. Such systems are prone to failures. With this in mind, a mathematical model has been developed to analyse the performability of the proposed system with break-downs and repairs. Mean queue lengths and the probability of job losses for the LAN feeding the network memory server is calculated and presented

Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean space, this region $X_{inf}$, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling $X_{inf}$ directly. State-of-the-art approaches to sampling $X_{inf}$ in such domains such as Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional state space. This may cause the planning algorithm to spend most of its time trying to produces samples in $X_{inf}$ rather than explore it. In this paper, we suggest an alternative approach to produce samples in the informed set $X_{inf}$ for a wide range of settings. Our main insight is to recast this problem as one of sampling uniformly within the sub-level-set of an implicit non-convex function. This recasting enables us to apply Monte Carlo sampling methods, used very effectively in the Machine Learning and Optimization communities, to solve our problem. We show for a wide range of scenarios that using our sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems