Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

A Generic Storage API

We present a generic API suitable for provision of highly generic storage facilities that can be tailored to produce various individually customised storage infrastructures. The paper identifies a candidate set of minimal storage system building blocks, which are sufficiently simple to avoid encapsulating policy where it cannot be customised by applications, and composable to build highly flexible storage architectures. Four main generic components are defined: the store, the namer, the caster and the interpreter. It is hypothesised that these are sufficiently general that they could act as building blocks for any information storage and retrieval system. The essential characteristics of each are defined by an interface, which may be implemented by multiple implementing classes.Comment: Submitted to ACSC 200

Some shortcomings of long-term working memory

Within the framework of their long-term working memory theory, Ericsson and Kintsch (1995) propose that experts rapidly store information in long-term memory through two mechanisms: elaboration of long-term memory patterns and schemas and use of retrieval structures. They use chess players’ memory as one of their most compelling sources of empirical evidence. In this paper, I show that evidence from chess memory, far from supporting their theory, limits its generality. Evidence from other domains reviewed by Ericsson and Kintsch, such as medical expertise, is not as strong as claimed, and sometimes contradicts the theory outright. I argue that Ericsson and Kintsch’s concept of retrieval structure conflates three different types of memory structures that possess quite different properties. One of these types of structures—generic, general-purpose retrieval structures—has a narrower use than proposed by Ericsson and Kintsch: it applies only in domains where there is a conscious, deliberate intent by individuals to improve their memory. Other mechanisms, including specific retrieval structures, exist that permit a rapid encoding into long-term memory under other circumstances

Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

Retrieval structures and schemata: A brief reply to Ericsson and Kintsch

In their commentary, Ericsson and Kintsch address several important issues. While I am more convinced than they are about the substantial similarities shared by our two approaches, and hence their comparability, this short reply will mostly limit itself to matters of disagreement

Neural Distributed Autoassociative Memories: A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.Comment: 31 page

Localized activity profiles and storage capacity of rate-based autoassociative networks

We study analytically the effect of metrically structured connectivity on the behavior of autoassociative networks. We focus on three simple rate-based model neurons: threshold-linear, binary or smoothly saturating units. For a connectivity which is short range enough the threshold-linear network shows localized retrieval states. The saturating and binary models also exhibit spatially modulated retrieval states if the highest activity level that they can achieve is above the maximum activity of the units in the stored patterns. In the zero quenched noise limit, we derive an analytical formula for the critical value of the connectivity width below which one observes spatially non-uniform retrieval states. Localization reduces storage capacity, but only by a factor of 2~3. The approach that we present here is generic in the sense that there are no specific assumptions on the single unit input-output function nor on the exact connectivity structure.Comment: 4 pages, 4 figure

Development and application of computer software techniques to human factors task data handling problems Final report, 21 Jun. 1965 - 21 Jun. 1966

Computer software techniques applied to human factors task data handling problem