This manuscript proposes the image intra-class correlation (I2C2) coefficient as a global measure of reliability for imaging studies. The I2C2 generalizes the classic intra-class correlation (ICC) coefficient to the case when the data of interest are images, thereby providing a measure that is both intuitive and convenient. Drawing a connection with classical measurement error models for replication experiments, the I2C2 can be computed quickly, even in high-dimensional imaging studies. A nonparametric bootstrap procedure is introduced to quantify the variability of the I2C2 estimator. Furthermore, a Monte Carlo permutation is utilized to test reproducibility versus a zero I2C2, representing complete lack of reproducibility. Methodologies are applied to three replication studies arising from different brain imaging modalities and settings: Regional Analysis of VolumEs in Normalized Space (RAVENS) imaging for characterizing brain morphology, seed-voxel brain activation maps based on resting state functional MRI (fMRI), and fractional anisotropy (FA) in an area surrounding the corpus callosum via diffusion tensor imaging (DTI). Software and data are provided to ensure rapid dissemination of methods. Resting state functional MRI (fMRI) brain activation maps are found to have low reliability ranging between 0.2 to 0.4.

Peer-to-peer (P2P) systems provide a robust, scalable and decentralized way to share and publish data. However, most existing P2P systems only provide a very rudimentary query facility; they only support equality or keyword search queries over files. We believe that future P2P applications, such as resource discovery on a grid, will require more complex query functionality. As a first step towards this goal, we propose a new distributed, fault-tolerant P2P index structure for resource discovery applications called the P-tree. Ptrees efficiently evaluate range queries in addition to equality queries. We describe algorithms to maintain a P-tree under insertions and deletions of data items/peers, and evaluate its performance using both a simulation and a real distributed implementation. Our results show the efficacy of our approach.

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Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, distributed systems, databases, etc.). With the increase in data size and distribution of data, problems arise where a large number of Bloom filters are available, and all them need to be searched for potential matches. As an example, in a federated cloud environment, each cloud provider could encode the information using Bloom filters and share the Bloom filters with a central coordinator. The problem of interest is not only whether a given element is in any of the sets represented by the Bloom filters, but which of the existing sets contain the given element. This problem cannot be solved by just constructing a Bloom filter on the union of all the sets. Instead, we effectively have a multidimensional Bloom filter problem: given an element, we wish to receive a list of candidate sets where the element might be.To solve this problem, we consider 3 alternatives. Firstly, we can naively check many Bloom filters. Secondly, we propose to organize the Bloom filters in a hierarchical index structure akin to a B+ tree, that we call Bloofi. Finally, we propose another data structure that packs the Bloom filters in such a way as to exploit bit-level parallelism, which we call Flat-Bloofi.Our theoretical and experimental results show that Bloofi and Flat-Bloofi provide scalable and efficient solutions alternatives to search through a large number of Bloom filters.

New and emerging P2P applications require sophisticated range query capability and also have strict requirements on query correctness, system availability and item availability. While there has been recent work on developing new P2P range indices, none of these indices guarantee correctness and availability. In this paper, we develop new techniques that can provably guarantee the correctness and availability of P2P range indices. We develop our techniques in the context of a general P2P indexing framework that can be instantiated with most P2P index structures from the literature. As a specific instantiation, we implement P-Ring, an existing P2P range index, and show how it can be extended to guarantee correctness and availability. We quantitatively evaluate our techniques using a real distributed implementation.