Paul Morris

Bio: Paul Morris is an academic researcher from University of Sheffield. The author has contributed to research in topics: Fractional flow reserve & Coronary artery disease. The author has an hindex of 49, co-authored 252 publications receiving 10739 citations. Previous affiliations of Paul Morris include Johns Hopkins University & Center for Complex Systems and Brain Sciences.

On the density of solutions in equilibrium points for the queens problem

Abstract: There has been recent interest in applying hillclimbing or iterative improvement methods to constraint satisfaction problems. An important issue for such methods is the likelihood of encountering a non-solution equilibrium (locally optimal) point. We present analytic techniques for determining the relative densities of solutions and equilibrium points with respect to these algorithms. The analysis explains empirically observed data for the n-queens problem, and provides insight into the potential effectiveness of these methods for other problems.

Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data

Abstract: The quality of biodiversity data publicly accessible via aggregators such as GBIF (Global Biodiversity Information Facility), the ALA (Atlas of Living Australia), iDigBio (Integrated Digitized Biocollections), and OBIS (Ocean Biogeographic Information System) is often questioned, especially by the research community. The Data Quality Interest Group, established by Biodiversity Information Standards (TDWG) and GBIF, has been engaged in four main activities: developing a framework for the assessment and management of data quality using a fitness for use approach; defining a core set of standardised tests and associated assertions based on Darwin Core terms; gathering and classifying user stories to form contextual-themed use cases, such as ‡ § | ¶ # ¤

Levels of Polyamines and Kinetic Characterization of Their Uptake in the Soybean Pathogen Phytophthora sojae

Abstract: Polyamines are ubiquitous biologically active aliphatic cations that are at least transiently available in the soil from decaying organic matter. Our objectives in this study were to characterize polyamine uptake kinetics in Phytophthora sojae zoospores and to quantify endogenous polyamines in hyphae, zoospores, and soybean roots. Zoospores contained 10 times more free putrescine than spermidine, while hyphae contained only 4 times as much free putrescine as spermidine. Zoospores contained no conjugated putrescine, but conjugated spermidine was present. Hyphae contained both conjugated putrescine and spermidine at levels comparable to the hyphal free putrescine and spermidine levels. In soybean roots, cadaverine was the most abundant polyamine, but only putrescine efflux was detected. The selective efflux of putrescine suggests that the regulation of polyamine availability is part of the overall plant strategy to influence microbial growth in the rhizosphere. In zoospores, uptake experiments with [1,4- 14 C]putrescine and [1,4- 14 C]spermidine confirmed the existence of high-affinity polyamine transport for both polyamines. Putrescine uptake was reduced by high levels of exogenous spermidine, but spermidine uptake was not reduced by exogenous putrescine. These observations suggest that P. sojae zoospores express at least two high-affinity polyamine transporters, one that is spermidine specific and a second that is putrescine specific or putrescine preferential. Disruption of polyamine uptake or metabolism has major effects on a wide range of cellular activities in other organisms and has been proposed as a potential control strategy for Phytophthora. Inhibition of polyamine uptake may be a means of reducing the fitness of the zoospore along with subsequent developmental stages that precede infection.

MAPGEN Planner: Mixed-Initiative Activity Planning for the Mars Exploration Rover Mission

Abstract: This document describes the Mixed initiative Activity Plan Generation system MAPGEN. This system is one of the critical tools in the Mars Exploration Rover mission surface operations, where it is used to build activity plans for each of the rovers, each Martian day. The MAPGEN system combines an existing tool for activity plan editing and resource modeling, with an advanced constraint-based reasoning and planning framework. The constraint-based planning component provides active constraint and rule enforcement, automated planning capabilities, and a variety of tools and functions that are useful for building activity plans in an interactive fashion. In this demonstration, we will show the capabilities of the system and demonstrate how the system has been used in actual Mars rover operations. In contrast to the demonstration given at ICAPS 03, significant improvement have been made to the system. These include various additional capabilities that are based on automated reasoning and planning techniques, as well as a new Constraint Editor support tool. The Constraint Editor (CE) as part of the process for generating these command loads, the MAPGEN tool provides engineers and scientists an intelligent activity planning tool that allows them to more effectively generate complex plans that maximize the science return each day. The key to the effectiveness of the MAPGEN tool is an underlying constraint-based planning and reasoning engine.

Misunderstandings and omissions in textbook accounts of effect sizes

Abstract: There have been frequent attempts in psychology to reduce the reliance on null hypothesis significance testing (NHST) as the criterion for establishing the importance of results. Many authorities now recommend the reporting of effect sizes (ESs) as a supplement or alternative to NHST. However, there is extensive specialist literature highlighting problems associated with the use and interpretation of ESs. A review of the coverage of ESs in over 100 textbooks on statistical analysis in behavioural science revealed widespread neglect of ESs and the relevant critical issues that have widespread coverage in the more specialist literature. For example, many textbooks claim that ESs should be interpreted as a simple measure of the practical real-world importance of a result despite the fact that ESs are profoundly influenced by features of design and analysis strategy. We seek to highlight areas of misunderstanding about ESs found in the pedagogical literature in the light of the more specialist literature and make recommendations to researchers for the appropriate use and interpretation of ESs. This is critical as statistics textbooks have a crucial role in the education of researchers.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).