Age effect on components of episodic memory and feature binding: A virtual reality study.

Objective: The aims were (1) to explore the effects of normal aging on the main aspects of episodic memory—what, where, and when,—and on feature binding in a virtual environment; (2) to explore the influence of the mode of learning, intentional versus incidental; and (3) to benchmark virtual environment findings collected with older adults against data recorded in classical neuropsychological tests. Method: We tested a population of 82 young adults and 78 older adults without dementia (they participated in a short battery of neuropsychological tests). All the participants drove a car in an urban virtual environment composing of 9 turns and specific areas. Half of the participants were told to drive through the virtual town; the other half were asked to drive and to memorize the environment (itinerary, elements, etc.). All aspects of episodic memory were then assessed (what, where, when, and binding). Results: The older participants had less recollection of the spatiotemporal context of events than the younger with intentional encoding (p p p p     

A user parameter-free approach for mining robust sequential classification rules

Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural data and complex relationships. To support graph classification, all existing methods require that training graphs should be relevant (or belong) to the target class, but cannot integrate graphs irrelevant to the class of interest into the learning process. In this paper, we study a new universum graph classification framework which leverages additional “non-example” graphs to help improve the graph classification accuracy. We argue that although universum graphs do not belong to the target class, they may contain meaningful structure patterns to help enrich the feature space for graph representation and classification. To support universum graph classification, we propose a mathematical programming algorithm, ugBoost, which integrates discriminative subgraph selection and margin maximization into a unified framework to fully exploit the universum. Because informative subgraph exploration in a universum setting requires the search of a large space, we derive an upper bound discriminative score for each subgraph and employ a branch-and-bound scheme to prune the search space. By using the explored subgraphs, our graph classification model intends to maximize the margin between positive and negative graphs and minimize the loss on the universum graph examples simultaneously. The subgraph exploration and the learning are integrated and performed iteratively so that each can be beneficial to the other. Experimental results and comparisons on real-world dataset demonstrate the performance of our algorithm.     

Digital Imaging: A Unified Topological Framework

In this article, a tractable modus operandi is proposed to model a (binary) digital image (i.e., an image defined on ? ⁿ and equipped with a standard pair of adjacencies) as an image defined in the space ( $\mathbb{F}^{n}$ ) of cubical complexes. In particular, it is shown that all the standard pairs of adjacencies (namely the (4,8) and (8,4)-adjacencies in ?², the (6,18), (18,6), (6,26), and (26,6)-adjacencies in ?³, and more generally the (2n,3 ⁿ ?1) and (3 ⁿ ?1,2n)-adjacencies in ? ⁿ ) can then be correctly modelled in $\mathbb{F}^{n}$ . Moreover, it is established that the digital fundamental group of a digital image in ? ⁿ is isomorphic to the fundamental group of its corresponding image in $\mathbb{F}^{n}$ , thus proving the topological correctness of the proposed approach. From these results, it becomes possible to establish links between topology-oriented methods developed either in classical digital spaces (? ⁿ ) or cubical complexes ( $\mathbb{F}^{n}$ ), and to potentially unify some of them.     

Splitting and merging of packet traffic: Measurement and modelling

This paper concerns the modelling of Internet packet traffic. In previous work we showed that a Bartlett–Lewis point process, as a model of packet arrivals on backbone links, enjoys strong physical backing and can predict key features. It is based on the surprising empirical observation that flows can often be considered independent for the purpose of modelling packet arrival times. We extend this work in two ways by using a unique dataset obtained from an experiment where all the packets crossing a backbone router are captured. First, this enables an examination of the validity of the fundamental assumptions underlying the model across several links, covering a large range of bandwidths and utilization levels. Second, we extend the model from links to a network node, by examining the merging and splitting properties of the (sub)streams through the router, and mapping these to the merging and splitting properties of the model. We show how the model can, in most cases, capture the observed multiplexing and demultiplexing behaviour of the router, opening up the possibility of its use for understanding traffic flows in networks. We show that failures in the model cannot be accounted for simply through considering utilisation levels, and explain how they can in fact be used as a detector of upstream bottlenecks and traffic shaping.     

The Marsh cone: a test or a rheological apparatus?

The present study is concerned with the Marsh cone, a workability test used for specification and quality control of cement pastes and grouts. It is demonstrated here that, under several consistency and geometry conditions, the flow time reflecting “fluidity” may be calculated from the plastic viscosity and yield stress in the case of a Bingham fluid and from the cone geometry. A relation between the behaviour parameters and the flow time of fresh pastes tested is derived and experimentally validated. A practical application of these results is suggested. Lastly, the concept of a new test apparatus based on two cones is presented and tested on a single cement paste.