Heap Defragmentation in Bounded Time

Knuth’s buddy system is an attractive algorithm for managing storage allocation, and it can be made to operate in real time. However, the is-sue of defragmentation for heaps that are managed by the buddy system has not been studied. In this paper, we present strong bounds on the amount of storage necessary to avoid defragmentation. We then present an algorithm for defragmenting buddy heaps and present experiments from applying that algorithm to real and syn-thetic benchmarks. Our algorithm is within a factor of two of optimal in terms of the time re-quired to defragment the heap so as to respond to a single allocation request. Our experiments show our algorithm to be much more efficient than extant defragmentation algorithms

The Proneural Molecular Signature Is Enriched in Oligodendrogliomas and Predicts Improved Survival among Diffuse Gliomas

The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of ‘-omic’ data on glioblastoma (GBM), resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we present an investigation of the significance of the TCGA consortium's expression classification when applied to Rembrandt gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III samples (p = 2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also predicted improved survival in this tumor type (n = 43, p = 1.25e-4). Thus, expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3, TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy

MI-Winnow: A New Multiple-Instance Learning Algorithm

We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a collection (or bag) ofd-dimensional points where each dimension corresponds to a feature. A label is provided for the bag, but not for the individual points within the bag. MI-Winnow is different from existing multipleinstance learning algorithms in several key ways. First, MI-Winnow allows each image to be converted into a bag in multiple ways to create training (and test) data that varies in both the number of dimensions per point, and in the kind of features used. Second, instead of learning a concept defined by a single point-and-scaling hypothesis, MI-Winnow allows the underlying concept to be described by combining a set of separators learned by Winnow. For content-based image retrieval applications, such a generalized hypothesis is important since there may be different ways to recognize which images are of interest. 1

Local image representations using pruned salient points with applications to CBIR

Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). The features for a salient point should represent the local characteristic of that point so that the similarity between features indicates the similarity between the salient points. Traditional uses of salient points for CBIR assign features to a salient point based on the image features of all pixels in a window around that point. However, since salient points are often on the boundary of objects, the features assigned to a salient point often involve pixels from different objects. In this paper, we propose a CBIR system that uses a novel salient point method that both reduces the number of salient points using a segmentation as a filter, and also improves the representation so that it is a more faithful representation of a single object (or portion of an object) that includes information about its surroundings. We also introduce an improved Expectation Maximization-Diverse Density (EM-DD) based multiple-instance learning algorithm. Experimental results show that our CBIR techniques improve retrieval performance by ∼5%-11 % as compared with current methods

Veritas: Combining Expert Opinions without Labeled Data

We consider a variation of the problem of combining expert opinions for the situation in which there is no ground truth to use for training. Even though we don’t have labeled data, the goal of this work is quite different from an unsupervised learning problem in which the goal is to cluster the data into different groups. Our work is motivated by the application of segmenting a lung nodule in a computed tomography (CT) scan of the human chest. The lack of a gold standard of truth is a critical problem in medical imaging. A variety of experts, both human and computer algorithms, are available that can mark which voxels are part of a nodule. The question is, how to combine these expert opinions to estimate the unknown ground truth. We present the Veritas algorithm that predicts the underlying label using the knowledge in the expert opinions even without the benefit of any labeled data for training. We evaluate Veritas using artificial data and real CT images to which a synthetic nodule has been added, providing a known ground truth. 1