Curriculum renewal for interprofessional education in health

In this preface we comment on four matters that we think bode well for the future of interprofessional education in Australia. First, there is a growing articulation, nationally and globally, as to the importance of interprofessional education and its contribution to the development of interprofessional and collaborative health practices. These practices are increasingly recognised as central to delivering effective, efficient, safe and sustainable health services. Second, there is a rapidly growing interest and institutional engagement with interprofessional education as part of pre-registration health professional education. This has changed substantially in recent years. Whilst beyond the scope of our current studies, the need for similar developments in continuing professional development (CPD) for health professionals was a consistent topic in our stakeholder consultations. Third, we observe what might be termed a threshold effect occurring in the area of interprofessional education. Projects that address matters relating to IPE are now far more numerous, visible and discussed in terms of their aggregate outcomes. The impact of this momentum is visible across the higher education sector. Finally, we believe that effective collaboration is a critical mediating process through which the rich resources of disciplinary knowledge and capability are joined to add value to existing health service provision. We trust the conceptual and practical contributions and resources presented and discussed in this report contribute to these developments.Office of Learning and Teaching Australi

Machine Learning for Classical Planning: Neural Network Heuristics, Online Portfolios, and State Space Topologies

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best-first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work

Machine learning for classical planning : neural network heuristics, online portfolios, and state space topologies

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best- first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work.Viele Alltagsprobleme können mit Hilfe der Zustandsraumsuche gelöst werden. Heuristische Suche, insbesondere die gierige Bestensuche, ist einer der erfolgreichsten Algorithmen für die Zustandsraumsuche. Wir verbessern den aktuellen Stand der Wissenschaft bezüglich heuristischer Suche auf drei Arten. Eine der wichtigsten Komponenten der heuristischen Suche ist die Heuristik. Mit einer guten Heuristik findet die Suche schnell eine Lösung. Eine gute Heuristik für ein Problem zu modellieren ist mühsam. In Teil I präsentieren wir Methoden, um automatisiert gute Heuristiken für ein Problem zu lernen. Hierfür generieren wird die Trainingsdaten mittels Zufallsbewegungen ausgehend von (Teil-) Zuständen des Problems. Wir zeigen, dass die Heuristiken, die wir für einen einzigen Zustandsraum trainieren, oft besser sind als Heuristiken, die für eine Problemklasse trainiert wurden. Weiterhin zeigen wir, dass die Qualität aller trainierten Heuristiken je nach Problemklasse stark variiert, keine Heuristik eine andere dominiert, und es nicht vorher erkennbar ist, ob eine trainierte Heuristik gut funktioniert. Wir stellen fest, dass in fast allen getesteten Problemklassen die modellbasierte Suchalgorithmen den trainierten Heuristiken überlegen sind. Lediglich in der Storage Problemklasse sind unsere Heuristiken überlegen. Oft ist es unklar, welche Heuristik oder Suchalgorithmus man für ein Problem nutzen sollte. Daher trainieren wir online Portfolios, die für ein gegebenes Problem den besten Algorithmus vorherzusagen. Die Eingabe für das online Portfolio sind bisher immer von Menschen ausgewählte Eigenschaften des Problems. In Teil II präsentieren wir neue online Portfolios, die das gesamte Problem als Eingabe bekommen. Darüber hinaus können unsere online Portfolios ihre Entscheidung einmal korrigieren. Beide Änderungen verbessern die Qualität von online Portfolios erheblich. Weiterhin zeigen wir, dass wir auch gute online Portfolios mit erklärbaren Techniken des maschinellen Lernens trainieren können. Selbst wenn wir den besten Algorithmus für ein Problem auswählen, kann es sein, dass das Problem zu schwierig ist, um in akzeptabler Zeit gelöst zu werden. In Teil III zeigen wir, wie wir von dem Verhalten einer gierigen Bestensuche auf einfachen Problemen ihr Verhalten auf schwierigeren Problemen der gleichen Problemklasse vorhersagen können. Dieses Wissen nutzen wir, um die Suche zu verbessern. Zuerst zeigen wir, wie man Fortschrittszustände erkennt. Immer wenn gierige Bestensuche einen Fortschrittszustand expandiert, wissen wir, dass es nie wieder einen Zustand mit gleichem oder höheren heuristischen Wert expandieren wird.Wir präsentieren zwei Methoden, die diesesWissen verwenden. Aufbauend auf dieser Arbeit lernen wir von einem Problem, wie man jegliches Problem der gleichen Problemklasse in eine Reihe von einfacheren Suchen aufteilen kann

Allyn, A Recommender Assistant for Online Bookstores

Treballs Finals del Grau d'Economia i Estadística. Doble titulació interuniversitària, Universitat de Barcelona i Universitat Politècnica de Catalunya. Curs: 2017-2018. Tutors: Esteban Vegas Lozano; Salvador Torra Porras(eng) Recommender Systems are information filtering engines used to estimate user preferences on items they have not seen: books, movies, restaurants or other things for which individuals have different tastes. Collaborative and Content-based Filtering have been the two popular memory-based methods to retrieve recommendations but these suffer from some limitations and might fail to provide effective recommendations. In this project we present several variations of Artificial Neural Networks, and in particular, of Autoencoders to generate model-based predictions for the users. We empirically show that a hybrid approach combining this model with other filtering engines provides a promising solution when compared to a standalone memory-based Collaborative Filtering Recommender. To wrap up the project, a chatbot connected to an e-commerce platform has been implemented so that, using Artificial Intelligence, it can retrieve recommendations to users.(cat) Els Sistemes de Recomanació són motors de filtratge de la informació que permeten estimar les preferències dels usuaris sobre ítems que no coneixen a priori. Aquests poden ser des de llibres o películes fins a restaurants o qualsevol altre element en el qual els usuaris puguin presentar gustos diferenciats. El present projecte es centra en la recomanació de llibres. Es comença a parlar dels Sistemes de Recomanació al voltant de 1990 però és durant la darrera dècada amb el boom de la informació i les dades massives que comencen a tenir major repercussió. Tradicionalment, els mètodes utilitzats en aquests sistemes eren dos: el Filtratge Col·laboratiu i el Filtratge basat en Contingut. Tanmateix, ambdós són mètodes basats en memòria, fet que suposa diverses limitacions que poden arribar a portar a no propocionar recomanacions de manera eficient o precisa. En aquest projecte es presenten diverses variacions de Xarxes Neuronals Artificials per a generar prediccions basades en models. En concret, es desenvolupen Autoencoders, una estructura particular d’aquestes que es caracteritza per tenir la mateixa entrada i sortida. D’aquesta manera, els Autoencoders aprenen a descobrir els patrons subjacents en dades molt esparses. Tots aquests models s’implementen utilitzant dos marcs de programació: Keras i Tensorflow per a R. Es mostra empíricament que un enfocament híbrid que combina aquests models amb altres motors de filtratge proporciona una solució prometedora en comparació amb un recomanador que utilitza exclusivament Filtratge Col·laboratiu. D’altra banda, s’analitzen els sistemes de recomanació des d’un punt de vista econòmic, emfatitzant especialment el seu impacte en empreses de comerç electrònic. S’analitzen els sistemes de recomanació desenvolupats per quatre empreses pioneres del sector així com les tecnologies front-end en què s’implementen. En concret, s’analitza el seu ús en chatbots, programes informàtics de missatgeria instantània que, a través de la Intel·ligència Artificial simulen la conversa humana. Per tancar el projecte, es desenvolupa un chatbot propi implementat en una aplicació de missatgeria instantània i connectat a una empresa de comerç electrònic, capaç de donar recomanacions als usuaris fent ús del sistema de recomanació híbrid dut a terme

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Curriculum renewal for interprofessional education in health

In this preface we comment on four matters that we think bode well for the future of interprofessional education in Australia. First, there is a growing articulation, nationally and globally, as to the importance of interprofessional education and its contribution to the development of interprofessional and collaborative health practices. These practices are increasingly recognised as central to delivering effective, efficient, safe and sustainable health services. Second, there is a rapidly growing interest and institutional engagement with interprofessional education as part of pre-registration health professional education. This has changed substantially in recent years. Whilst beyond the scope of our current studies, the need for similar developments in continuing professional development (CPD) for health professionals was a consistent topic in our stakeholder consultations. Third, we observe what might be termed a threshold effect occurring in the area of interprofessional education. Projects that address matters relating to IPE are now far more numerous, visible and discussed in terms of their aggregate outcomes. The impact of this momentum is visible across the higher education sector. Finally, we believe that effective collaboration is a critical mediating process through which the rich resources of disciplinary knowledge and capability are joined to add value to existing health service provision. We trust the conceptual and practical contributions and resources presented and discussed in this report contribute to these developments

Sparse Binary Features for Image Classification

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes improvements based on an efficient descriptor representation. The efficient representation is created using dimensionality reduction techniques, entropy analysis and decorrelated sampling. In a second part, the problem of image classification is tackled. The traditional approach uses machine learning algorithms to create classifiers, and some works already propose to use a compact image representation using feature extraction as preprocessing. The second contribution of this work is to show that binary features, while being very compact and low dimensional (compared to traditional representation of images), still provide a very high discriminant power. This is shown using various learning algorithms and binary descriptors. These years a scheme has been widely used to perform object recognition on images, or equivalently image classification. It is based on the concept of Bag of Visual Words. More precisely, an image is described using an unordered set of visual words, that are generally represented by feature descriptions. The last contribution of this work is to use binary features with a simple Bag of Visual Words classifier. Tests of performance for the image classification are performed on a large database of images

2015-2016, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2015-2016.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1435/thumbnail.jp

2016-2017, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2016-2017.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1436/thumbnail.jp