944,771 research outputs found

    An Integrated First-Order Theory of Points and Intervals over Linear Orders (Part II)

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    There are two natural and well-studied approaches to temporal ontology and reasoning: point-based and interval-based. Usually, interval-based temporal reasoning deals with points as a particular case of duration-less intervals. A recent result by Balbiani, Goranko, and Sciavicco presented an explicit two-sorted point-interval temporal framework in which time instants (points) and time periods (intervals) are considered on a par, allowing the perspective to shift between these within the formal discourse. We consider here two-sorted first-order languages based on the same principle, and therefore including relations, as first studied by Reich, among others, between points, between intervals, and inter-sort. We give complete classifications of its sub-languages in terms of relative expressive power, thus determining how many, and which, are the intrinsically different extensions of two-sorted first-order logic with one or more such relations. This approach roots out the classical problem of whether or not points should be included in a interval-based semantics. In this Part II, we deal with the cases of all dense and the case of all unbounded linearly ordered sets.Comment: This is Part II of the paper `An Integrated First-Order Theory of Points and Intervals over Linear Orders' arXiv:1805.08425v2. Therefore the introduction, preliminaries and conclusions of the two papers are the same. This version implements a few minor corrections and an update to the affiliation of the second autho

    A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation

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    Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance

    Context-based decision-making for virtual soccer players.

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    International audienceThis article introduces a decision-making model for virtual agents evolving in dynamic and collaborative situations. Agents and humans have to collaborate in a virtual environment. In order to enhance the collaboration, the agent decision-making model is based on notions close to human ones. Those notions are context and case based reasoning. After an introduction of dynamic and collaborative situations, we present the notion of context and we give a definition adapted to our framework. The next part describes the decision making process. This one relies on the case identification thanks to a graph search algorithm. The last part of this document illustrates our purpose with an example taken from our application

    Nonprofit Organizations as Ideal Type of Socially Responsible and Impact Investors

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    Nonprofit organizations (NPOs) as mission-driven organizations could profit from investing in stocks diametrically opposed to their mission, as they serve as a perfect hedge. Earning more income from oil or tobacco companies when there is a greater need for ecological interventions or cancer research might help effectively fighting the cause. We show the flaw in this logic as in its optimal state, this strategy is at most a financial zero-sum game. However, as NPOs strive at creating net value by aiming at a most effective mission-accomplishment, socially responsible and impact investments may offer a better way of doing so. We present NPOs as an ideal type of a socially responsible and impact investor and give the corresponding formal economic reasoning. For mission-driven organizations only the combination of financial and mission-based goals allows for an effective, goal-oriented financial decision-making. The full application of this logic is what is broadly understood under the term of mission investing (MI). Based on a theoretic introduction, we present a formalized way of analyzing multidimensional tradeoffs in the case of NPOs being mission-driven investors. This formalization will supply NPOs with a tool that enables them to capture their investments’ financial and mission-based impact and therefore the full benefit of responsible and impact-driven investments

    Rationality in discovery : a study of logic, cognition, computation and neuropharmacology

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    Part I Introduction The specific problem adressed in this thesis is: what is the rational use of theory and experiment in the process of scientific discovery, in theory and in the practice of drug research for Parkinson’s disease? The thesis aims to answer the following specific questions: what is: 1) the structure of a theory?; 2) the process of scientific reasoning?; 3) the route between theory and experiment? In the first part I further discuss issues about rationality in science as introduction to part II, and I present an overview of my case-study of neuropharmacology, for which I interviewed researchers from the Groningen Pharmacy Department, as an introduction to part III. Part II Discovery In this part I discuss three theoretical models of scientific discovery according to studies in the fields of Logic, Cognition, and Computation. In those fields the structure of a theory is respectively explicated as: a set of sentences; a set of associated memory chunks; and as a computer program that can generate the observed data. Rationality in discovery is characterized by: finding axioms that imply observation sentences; heuristic search for a hypothesis, as part of problem solving, by applying memory chunks and production rules that represent skill; and finding the shortest program that generates the data, respectively. I further argue that reasoning in discovery includes logical fallacies, which are neccesary to introduce new hypotheses. I also argue that, while human subjects often make errors in hypothesis evaluation tasks from a logical perspective, these evaluations are rational given a probabilistic interpretation. Part III Neuropharmacology In this last part I discusses my case-study and a model of discovery in a practice of drug research for Parkinson’s disease. I discuss the dopamine theory of Parkinson’s disease and model its structure as a qualitative differential equation. Then I discuss the use and reasons for particular experiments to both test a drug and explore the function of the brain. I describe different kinds of problems in drug research leading to a discovery. Based on that description I distinguish three kinds of reasoning tasks in discovery, inference to: the best explanation, the best prediction and the best intervention. I further demonstrate how a part of reasoning in neuropharmacology can be computationally modeled as qualitative reasoning, and aided by a computer supported discovery system

    A Semantic Web Annotation Tool for a Web-Based Audio Sequencer

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    Music and sound have a rich semantic structure which is so clear to the composer and the listener, but that remains mostly hidden to computing machinery. Nevertheless, in recent years, the introduction of software tools for music production have enabled new opportunities for migrating this knowledge from humans to machines. A new generation of these tools may exploit sound samples and semantic information coupling for the creation not only of a musical, but also of a "semantic" composition. In this paper we describe an ontology driven content annotation framework for a web-based audio editing tool. In a supervised approach, during the editing process, the graphical web interface allows the user to annotate any part of the composition with concepts from publicly available ontologies. As a test case, we developed a collaborative web-based audio sequencer that provides users with the functionality to remix the audio samples from the Freesound website and subsequently annotate them. The annotation tool can load any ontology and thus gives users the opportunity to augment the work with annotations on the structure of the composition, the musical materials, and the creator's reasoning and intentions. We believe this approach will provide several novel ways to make not only the final audio product, but also the creative process, first class citizens of the Semantic We

    Diagnostic Reasoning Assessment Toolkit: Guided Reflection and Standardized Cases for At-Risk Final-Year Medical Students

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    Introduction: A failing diagnostic reasoning performance may represent student deficiency in a number of potential areas. However, many standard clinical skills assessments do not offer detailed assessments of diagnostic reasoning ability. This toolkit was designed to identify specific learner deficiencies with respect to diagnostic reasoning by focusing on individual student remedial work and by standardizing faculty evaluation. Methods: Educational objectives were derived from institutional patient care competency learning objectives at the Indiana University School of Medicine. Review of existing clinical skills remediation literature yielded a design that combined two learning methods: guided reflection and standardized patient cases. Results: Over the 2014-2015 academic year, 12 final-year medical students used this resource to help develop an individual remedial learning plan prior to retaking a failed standardized assessment. Students were generally satisfied with the combined guided reflection and standardized case learning methods. Discussion: Unique final-year medical student scheduling pressures, combined with a reporting time line for both institutional high-stakes OSCE remediation exams and the USMLE Step 2 Clinical Skills exam, incentivized failing students to schedule a retest on a short time line, often leaving little time for critical preparation. This resource offered an opportunity to efficiently spend limited preparation time to individualize exam preparation using a variety of faculty facilitators. The simplistic design was readily deployable to multiple faculty remediation mentors. Our institution can now provide a standardized diagnostic reasoning remedial evaluation using numerous clinical faculty based at any of our nine campuses

    Principles and Practice of Case-based Clinical Reasoning Education: A Method for Preclinical Students

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    This volume describes and explains the educational method of Case-Based Clinical Reasoning (CBCR) used successfully in medical schools to prepare students to think like doctors before they enter the clinical arena and become engaged in patient care. Although this approach poses the paradoxical problem of a lack of clinical experience that is so essential for building proficiency in clinical reasoning, CBCR is built on the premise that solving clinical problems involves the ability to reason about disease processes. This requires knowledge of anatomy and the working and pathology of organ systems, as well as the ability to regard patient problems as patterns and compare them with instances of illness scripts of patients the clinician has seen in the past and stored in memory. CBCR stimulates the development of early, rudimentary illness scripts through elaboration and systematic discussion of the courses of action from the initial presentation of the patient to the final steps of clinical management. The book combines general backgrounds of clinical reasoning education and assessment with a detailed elaboration of the CBCR method for application in any medical curriculum, either as a mandatory or as an elective course. It consists of three parts: a general introduction to clinical reasoning education, application of the CBCR method, and cases that can used by educators to try out this method
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