Trajectories in a space with a spherically symmetric dislocation

We consider a new type of defect in the scope of linear elasticity theory, using geometrical methods. This defect is produced by a spherically symmetric dislocation, or ball dislocation. We derive the induced metric as well as the affine connections and curvature tensors. Since the induced metric is discontinuous, one can expect ambiguity coming from these quantities, due to products between delta functions or its derivatives, plaguing a description of ball dislocations based on the Geometric Theory of Defects. However, exactly as in the previous case of cylindric defect, one can obtain some well-defined physical predictions of the induced geometry. In particular, we explore some properties of test particle trajectories around the defect and show that these trajectories are curved but can not be circular orbits.Comment: 11 pages, 3 figure

Boolean networks with reliable dynamics

We investigated the properties of Boolean networks that follow a given reliable trajectory in state space. A reliable trajectory is defined as a sequence of states which is independent of the order in which the nodes are updated. We explored numerically the topology, the update functions, and the state space structure of these networks, which we constructed using a minimum number of links and the simplest update functions. We found that the clustering coefficient is larger than in random networks, and that the probability distribution of three-node motifs is similar to that found in gene regulation networks. Among the update functions, only a subset of all possible functions occur, and they can be classified according to their probability. More homogeneous functions occur more often, leading to a dominance of canalyzing functions. Finally, we studied the entire state space of the networks. We observed that with increasing systems size, fixed points become more dominant, moving the networks close to the frozen phase.Comment: 11 Pages, 15 figure

Network of recurrent events for the Olami-Feder-Christensen model

We numerically study the dynamics of a discrete spring-block model introduced by Olami, Feder and Christensen (OFC) to mimic earthquakes and investigate to which extent this simple model is able to reproduce the observed spatiotemporal clustering of seismicty. Following a recently proposed method to characterize such clustering by networks of recurrent events [Geophys. Res. Lett. {\bf 33}, L1304, 2006], we find that for synthetic catalogs generated by the OFC model these networks have many non-trivial statistical properties. This includes characteristic degree distributions -- very similar to what has been observed for real seismicity. There are, however, also significant differences between the OFC model and earthquake catalogs indicating that this simple model is insufficient to account for certain aspects of the spatiotemporal clustering of seismicity.Comment: 11 pages, 16 figure

Human Activity Recognition using Max-Min Skeleton-based Features and Key Poses

Human activity recognition is still a very challenging research area, due to the inherently complex temporal and spatial patterns that characterize most human activities. This paper proposes a human activity recognition framework based on random forests, where each activity is classified requiring few training examples (i.e. no frame-by-frame activity classification). In a first approach, a simple mechanism that divides each action sequence into a fixed-size window is employed, where max-min skeleton-based features are extracted. In the second approach, each window is delimited by a pair of automatically detected key poses, where static and max-min dynamic features are extracted, based on the determined activity example. Both approaches are evaluated using the Cornell Activity Dataset [1], obtaining relevant overall average results, considering that these approaches are fast to train and require just a few training examples. These characteristics suggest that the proposed framework can beuseful for real-time applications, where the activities are typicallywell distinctive and little training time is required, or to be integrated in larger and sophisticated systems, for a first quick impression/learning of certain activitie

Is heart rate variability affected by distinct motor imagery strategies

Although some studies have reported significant changes in autonomic responses according to the perspective-taking during motor imagery [first person perspective (1P) and third person perspective (3P)], investigations on how the strategies adopted to mentally simulate a given movement affect the heart rate variability (HRV) seem so far unexplored. Twenty healthy subjects mentally simulated the movement of middle-finger extension in 1P and 3P, while electrocardiogram was recorded. After each task, the level of easiness was self-reported. Motor imagery ability was also assessed through the revised version of Movement Imagery Questionnaire (MIQ-R) and a mental chronometry index. The traditional measures of HRV in the time- and frequency-domain were compared between 1P and 3P tasks by using Student's t-test for dependent samples. The MIQ-R results showed that subjects had the same facility to imagine movements in 1P or 3P. The mental chronometry index revealed a similar temporal course only between 1P and execution, while the 3P strategy had a shorter duration. Additionally, the subjective report was similar between the experimental tasks. Regarding the HRV measures, the low frequency component, in log-transformed unit, was significantly higher (p=0.017) in 1P than 3P, suggesting a higher activity of the sympathetic system during 1P. This log-transformed HRV parameter seems to be more sensitive than normalized values for the assessment of the motor imagery ability, together with questionnaires, scales and mental chronometry

Adaptive business intelligence in healthcare - A platform for optimising surgeries

Adaptive Business Intelligence (ABI) combines predictive with prospective analytics in order to give support to the decision making process. Surgery scheduling in hospital operating rooms is a high complex task due to huge volume of surgeries and the variety of combinations and constraints. This type of activity is critical and is often associated to constant delays and significant rescheduling. The main task of this work is to provide an ABI based platform capable of estimating the time of the surgeries and then optimising the scheduling (minimizing the waste of resources). Combining operational data with analytical tools this platform is able to present complex and competitive information to streamline surgery scheduling. A case study was explored using data from a portuguese hospital. The best achieved relative absolute error attained was 6.22%. The paper also shows that the approach can be used in more general applications.This work has been supported by FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Renormalization Ambiguities and Conformal Anomaly in Metric-Scalar Backgrounds

We analyze the problem of the existing ambiguities in the conformal anomaly in theories with external scalar field in curved backgrounds. In particular, we consider the anomaly of self-interacting massive scalar field theory and of Yukawa model in the massless conformal limit. In all cases the ambiguities are related to finite renormalizations of a local non-minimal terms in the effective action. We point out the generic nature of this phenomenon and provide a general method to identify the theories where such an ambiguity can arise.Comment: RevTeX, 10 pages, no figures. Small comment and two references added. Accepted for publication in Physical Review