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.

Managing Temporal Uncertainty Through Waypoint Controllability

Abstract: Simple Temporal Networks have proved useful in applications that involve metric time. However, many applications involve events whose timing is not controlled by the execution agent. A number of properties relating to overall controllability in such cases have been introduced in [Vidal and Ghallab, 1996] and [Vidal and Fargier, 1997], including Weak and Strong Controllability. We derive some new results concerning these properties. In particular, we prove the negation of Weak Controllability is NP-hard, confirming a conjecture in [Vidal and Fargier, 1997]. We also introduce a more general controllability property of which Weak and Strong Controllability are special cases. A propagation algorithm is provided for determining whether the property holds, and we identify tractable cases where the algorithm runs in polynomial time.

External calcium controls the developmental strategy of Phytophthora sojae cysts

Abstract: The effect of added calcium on the developmental fate of encysted zoospores was studied in four strains of Phytophthora sojae. Cysts from all strains were capable of producing both secondar...

Don't be stupid

Abstract: We are studying control knowledge for a general problem solver, named BLOCKHEAD. Currently the problem solver is based on negative heuristics, which has led to surprising efficiency in suitable domains. The lesson seems to be that it is easier to avoid being stupid than to try to be smart. Stupid plans are defined and a plan improvement method proposed. Analyses of stupid plans and failed plans suggest effective negative heuristics.

The "textbook Gibson": the assimilation of dissidence

Abstract: We examine how the textbooks have dealt with one of psychology's most eminent dissidents, James Gibson (1904-1979). Our review of more than a hundred textbooks, dating from the 1950s to the present, reveals fundamental and systematic misrepresentations of Gibson. Although Gibson continues to figure in most of the textbooks, his work is routinely assimilated to theoretical positions he emphatically rejected: cue theory, stimulus-response psychology, and nativism. As Gibson's one-time colleague, Ulric Neisser, pointed out, psychologists are especially prone to trying to understand new proposals "by mapping it on to some existing scheme," and warned that when "an idea is really new, that strategy fails" (Neisser, 1990, p. 749). The "Textbook Gibson" is an example of such a failure, and perhaps also of the more general importance of assimilation-"shadow history"-within the actual history of psychology.

Heterologous expression of a pleiotropic drug resistance transporter from Phytophthora sojae in yeast transporter mutants

Abstract: A system for the expression of an ATP binding cassette (ABC) transporter from the soybean pathogen Phytophthora sojae is described. Pdr1, an ABC transporter with homology to the pleiotropic drug resistance (PDR) family of transporters, was cloned by primer walking from a P. sojae genomic library. Reverse transcriptase PCR assays showed that the transcript disappeared after encystment of zoospores and was not detected in hyphal germlings in dilute salts, in hyphae growing in liquid V8 media, or in tissue extracts from infected hypocotyls. BLAST analysis of Pdr1 against the P. sojae EST database also revealed that this gene was present only in zoospore libraries. Comparison of the number of hits to Pdr1 with that of a set of housekeeping genes revealed that Pdr1 was expressed at rates two- to threefold higher than other transcripts. To test the hypothesis that Pdr1p functions as a broad substrate membrane transporter, Pdr1 was transformed into yeast mutants deficient in several drug resistance transporters. Yeast mutants transformed with Pdr1 possessed partial drug resistance against only 5 of 17 chemically distinct compounds. Thus, when expressed in yeast, this transporter has a significantly narrower substrate specificity in comparison to the yeast transporters, Pdr5p, Yorlp, and Snq2p.

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.).