Probing the Equation of State of Ultradense Matter with a Submillisecond Pulsar Search Experiment

Current ideas about the equation of state for the ultradense matter constituting neutron stars provide models with a range of neutron star radii for a given mass. This implies different estimates for the maximum angular velocity that such an object could attain. The fastest and the slowest angular velocity differ by a significant amount, depending on the equation of state adopted. In particular, the identification of a submillisecond pulsar would allow us to constrain the equation of state of dense matter. In this paper, we discuss a possible evolutionary scenario resulting in a submillisecond pulsar, taking into account current ideas about the evolution of the magnetic field of neutron stars. Pulsar luminosities and lifetimes in the submillisecond period range, derived on the basis of phenomenological considerations, suggest that the effort of searching for such an object would be worthwhile. All the pulsar searches conducted up to now have been prevented by instrumental selection effects from probing the submillisecond range. We discuss the feasibility of a submillisecond pulsar search experiment in the context of current hardware and software capabilities

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between the various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies

An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network

Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energy-efficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an exampl