Mental imagery of whole-body motion along the sagittal-anteroposterior axis

Whole-body motor imagery is conceptualised as a mental symbolisation directly and indirectly associated with neural oscillations similar to whole-body motor execution. Motor and somatosensory activity, including vestibular activity, is a typical corticocortical substrate of body motion. Yet, it is not clear how this neural substrate is organised when participants are instructed to imagine moving their body forward or backward along the sagittal-anteroposterior axis. It is the aim of the current study to identify the fingerprint of the neural substrate by recording the cortical activity of 39 participants via a 32 electroencephalography (EEG) device. The participants were instructed to imagine moving their body forward or backward from a first-person perspective. Principal Component Analysis (i.e. PCA) applied to the neural activity of whole-body motor imagery revealed neural interconnections mirroring between forward and backward conditions: beta pre-motor and motor oscillations in the left and right hemisphere overshadowed beta parietal oscillations in forward condition, and beta parietal oscillations in the left and right hemisphere overshadowed beta pre-motor and motor oscillations in backward condition. Although functional significance needs to be discerned, beta pre-motor, motor and somatosensory oscillations might represent specific settings within the corticocortical network and provide meaningful information regarding the neural dynamics of continuous whole-body motion. It was concluded that the evoked multimodal fronto-parietal neural activity would correspond to the neural activity that could be expected if the participants were physically enacting movement of the whole-body in sagittal-anteroposterior plane as they would in their everyday environment

Garbage collection auto-tuning for Java MapReduce on Multi-Cores

MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests

Pricing and capacity allocation under asymmetric information using Paris Metro Pricing

We consider a Paris Metro Pricing (PMP) approach for providing service to two classes of customers differentiated by their delay sensitivity. We develop a leader-follower game, where the leader is the service provider who sets the price and the customers respond by deciding whether to join or balk. We derive the customer behaviour as the Nash equilibrium of a multi-person game and obtain the revenue maximising price pairs for all combinations of arrival rates from each class to each server. We finally derive the capacity threshold in such domain and its impact on customer accessibility to the product or service. Copyright © 2009, Inderscience Publishers

Patterns of Memory Inefficiency

Large applications often suffer from excessive memory consumption. The nature of these heaps, their scale and complex interconnections, makes it difficult to find the low hanging fruit. Techniques relying on dominance or allocation tracking fail to account for sharing, and overwhelm users with small details. More fundamentally, a programmer still needs to know whether high levels of consumption are too high. We present a solution that discovers a small set of high-impact memory problems, by detecting patterns within a heap. Patterns are expressed over a novel ContainerOrContained relation, which overcomes challenges of reuse, delegation, sharing; it induces equivalence classes of objects, based on how they participate in a hierarchy of data structures. We present results on 34 applications, and case studies for nine of these. We demonstrate that eleven patterns cover most memory problems, and that users need inspect only a small number of pattern occurrences to reap large benefits