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.

Caffeine as a source of misattribution in police-offender confrontations: an experiment with the fire arms training system

Abstract: Based on theories of emotions and attributional processes (Schachter, 1964; Zillman, 1978, 1983), this study hypothesized that caffeine consumption would lead to an underestimation of the offender's aggression, less aggressive feelings toward the offender, and decreased willingness to shoot at the offender. To test these hypotheses, 52 police officers in Holland ingested 150 mg of either caffeine or vitamin C and then faced a videotaped Fire Arms Training System simulated scenario. In order to investigate police officers' shooting behavior, the researcher observed the police officers' behavior by scoring the videotapes. Three different types of behavior emerged: "not shooting," "shooting in time," and "shooting too late" (shooting at the offender after he had made his stabbing movement). The officer's impression of the offender was measured with a questionnaire, as was the officer's tendency to shoot. The findings support the hypotheses. The offender made a less aggressive impression on the officers who had consumed caffeine; caffeine consumption resulted in officers' decreased aggressive feelings toward the aggressive offender; and police officers were less likely to shoot at the offender as a result of caffeine consumption.

Managing Network Access to a Distributed Database

Abstract: : This paper describes a program, FAM--for File Access Manager--, used to access to data distributed over a computer network. FAM is part of a system which allows a casual user to express queries in a restricted subset of English, about a database of fourteen files stored redundantly on several Datacomputers. FAM is responsible for the data distribution aspects of the whole system: it establishes the network connections, decides which files in redundant groups to use, opens and closes them as needed. This paper presents some of the techniques which are used and discusses the limitations of this particular approach.

Efficient Logic Programs: A Research Proposal

Abstract: Author(s): Conery, John S.; Morris, Paul H.; Kibler, Dennis F. | Abstract: The goal of the proposed research is to develop methods for efficient implementation of logic programs. There are two areas we wish to investigate, both of which are continuations of research conducted by members of the UCI dataflow architecture group. One aspect of the proposed research involves development of a non-von Neumann architecture for parallel execution of logic programs; preliminary work in this area is reported by Conery [9]. The second area invovles transformation of high level logic specifications into efficient Prolog and/or procedural language programs, and is based on work by Morris [20].

Suspicious for Malignancy Diagnoses on Pleural Effusion Cytology.

Abstract: Objectives A definitive diagnosis of malignancy may not be possible in pleural effusions. We report our experience with the diagnosis of suspicious for malignancy (SFM) in pleural effusion. Methods A search for pleural effusions diagnosed as SFM (2008-2018) was performed. Patient records and pathology reports were reviewed. Specimens were subdivided into groups depending on volume ( 400 mL). Diagnoses of malignant pleural effusion (MPE) served as controls. Results We identified 90 patients, with a mean age of 60.6 years. Diagnoses included suspicious for involvement by carcinoma/adenocarcinoma in 64.4%, leukemia/lymphoma in 15.6%, melanoma in 2.2%, sarcoma in 3.3%, germ cell tumor in 1.1%, and not otherwise specified in 13.3%. Immunostains were performed in 47.8% and considered inconclusive in 24%. Average sample volume was 419 mL. There was a statistically significant difference between the SFM vs MPE groups for volumes greater than 75 mL (P = .001, χ 2 test), with SFM having increased proportion of volumes greater than 400 mL, compared with the MPE group. There was no statistically significant difference in mean overall survival when the groups were compared (P = .49). Conclusions Samples with low cellularity, scant cell blocks, and inconclusive immunostains may contribute to a suspicious category diagnosis in pleural effusions.

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.).