Factoring out ordered sections to expose thread-level parallelism

With the rise of multi-core processors, researchers are taking a new look at extending the applicability auto-parallelization techniques. In this paper, we identify a dependence pattern on which autoparallelization currently fails. This dependence pattern occurs for ordered sections, i.e. code fragments in a loop that must be executed atomically and in original program order. We discuss why these ordered sections prohibit current auto-parallelizers from working and we present a technique to deal with them. We experimentally demonstrate the efficacy of the technique, yielding significant overall program speedups

XMM-Newton observations expose AGN in apparently normal galaxies

We have performed a detailed analysis of 3 optically normal galaxies extracted from the XMM Bright Serendipitous Source Sample. Thanks to the good statistics of the XMM-Newton data, we have unveiled the presence of an AGN in all of them. In particular, we detect both X-ray obscured (N_H>10^{22} cm^{-2}) and unobscured (N_H<10^{22} cm^{-2}) AGN with intrinsic 2--10 keV luminosities in the range between 10^{42} -- 10^{43} erg s^{-1}. We find that the X-ray and optical properties of the sources discussed here could be explained assuming a standard AGN hosted by galaxies with magnitudes M_R<M^*, taking properly into account the absorption associated with the AGN, the optical faintness of the nuclear emission with respect to the host galaxy, and the inadequate set--up and atmospheric conditions during the optical spectroscopic observations. Our new spectroscopic observations have revealed the expected AGN features also in the optical band. These results clearly show that optical spectroscopy sometimes can be inefficient in revealing the presence of an AGN, which instead is clearly found from an X-ray spectroscopic investigation. This remarks the importance of being careful in proposing the identification of X-ray sources (especially at faint fluxes) when only low quality optical spectra are in hand. This is particularly important for faint surveys (such as those with XMM-Newton and Chandra), in which optically dull but X-ray active objects are being found in sizeable numbers.Comment: Accepted for publication on A&A; 11 pages, 8 figure

Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs

Given a graph where vertices represent alternatives and arcs represent pairwise comparison data, the statistical ranking problem is to find a potential function, defined on the vertices, such that the gradient of the potential function agrees with the pairwise comparisons. Our goal in this paper is to develop a method for collecting data for which the least squares estimator for the ranking problem has maximal Fisher information. Our approach, based on experimental design, is to view data collection as a bi-level optimization problem where the inner problem is the ranking problem and the outer problem is to identify data which maximizes the informativeness of the ranking. Under certain assumptions, the data collection problem decouples, reducing to a problem of finding multigraphs with large algebraic connectivity. This reduction of the data collection problem to graph-theoretic questions is one of the primary contributions of this work. As an application, we study the Yahoo! Movie user rating dataset and demonstrate that the addition of a small number of well-chosen pairwise comparisons can significantly increase the Fisher informativeness of the ranking. As another application, we study the 2011-12 NCAA football schedule and propose schedules with the same number of games which are significantly more informative. Using spectral clustering methods to identify highly-connected communities within the division, we argue that the NCAA could improve its notoriously poor rankings by simply scheduling more out-of-conference games.Comment: 31 pages, 10 figures, 3 table

Revealing the unseen: how to expose cloud usage while protecting user privacy

Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncertainty forces users and researchers to reverse engineer the inner workings of cloud systems in order to understand and optimize the conditions their applications operate. At Massachusetts Open Cloud (MOC), as a public cloud operator, we'd like to expose the utilization of our physical infrastructure to stop this wasteful effort. Mindful that such exposure can be used maliciously for gaining insight into other user's workloads, in this position paper we argue for the need for an approach that balances openness of the cloud overall with privacy for each tenant inside of it. We believe that this approach can be instantiated via a novel combination of several security and privacy technologies. We discuss the potential benefits, implications of transparency for cloud systems and users, and technical challenges/possibilities.Accepted manuscrip

Behaviour-based Knowledge Systems: An Epigenetic Path from Behaviour to Knowledge

In this paper we expose the theoretical background underlying our current research. This consists in the development of behaviour-based knowledge systems, for closing the gaps between behaviour-based and knowledge-based systems, and also between the understandings of the phenomena they model. We expose the requirements and stages for developing behaviour-based knowledge systems and discuss their limits. We believe that these are necessary conditions for the development of higher order cognitive capacities, in artificial and natural cognitive systems