y-Randomization is a tool used in validation of QSPR/QSAR models, whereby the performance of the original model in data description (r2) is compared to that of models built for permuted (randomly shuffled) response, based on the original descriptor pool and the original model building procedure. We compared y-randomization and several variants thereof, using original response, permuted response...

We use a microscopic model of population dynamics, a modified version of the well known Penna model, to study some aspects of microevolution. This research is motivated by recent reports on the effect of selective hunting on the gene pool of bighorn sheep living in the Ram Mountain region, in Canada. Our model finds a sharp transition in the structure of the gene pool as some threshold for the ...

Over the past decade, several security issues with Linux Random Number Generator (LRNG) on PCs and Androids have emerged. The main problem involves the process of entropy harvesting, particularly at boot time. An entropy source in the input pool of LRNG is not transferred into the non-blocking output pool if the entropy counter of the input pool is less than 192 bits out of 4098 bits. Because t...

Numerical Methods for the Valuation of Synthetic Collateralized Debt Obligations Xiaofang Ma Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 A Collateralized Debt Obligation (CDO) is a credit derivative that creates fixed income securities, which are known as tranches. A CDO is called a synthetic CDO if the risky assets in the underlying pool are credit d...

This paper presents a technique for performing dynamic goal oriented buuer pool management for database management systems. To dynamically adjust the buuer pool sizes for the multiple buuer pools provided by database management systems is a complex constrained optimization problem. In the goal oriented approach, the user speciies each buuer pool's random access response time goal and the total ...

This paper is concerned with pool-based active learning for deep neural networks. Motivated by coreset dataset compression ideas, we present a novel active learning algorithm that queries consecutive points from the pool using farthest-first traversals in the space of neural activation over a representation layer. We show consistent and overwhelming improvement in sample complexity over passive...

Selective inactivation of a target gene by antisense mechanisms is an important biological tool to delineate specific functions of the gene product. Approaches mediated by ribozymes and RNA-cleaving DNA enzymes (DNA enzymes) are more attractive because of their ability to catalytically cleave the target RNA. DNA enzymes have recently gained a lot of importance because they are short DNA molecul...

Although identification of active motifs in large random sequence pools is central to RNA in vitro selection, no systematic computational equivalent of this process has yet been developed. We develop a computational approach that combines target pool generation, motif scanning and motif screening using secondary structure analysis for applications to 10(12)-10(14)-sequence pools; large pool siz...

Random s-intersection graphs have recently received much interest in a wide range of application areas. Broadly speaking, a random s-intersection graph is constructed by first assigning each vertex a set of items in some random manner, and then putting an undirected edge between all pairs of vertices that share at least s items (the graph is called a random intersection graph when s = 1). A spe...