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Workshops of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017

作者： Robertson, Justus Amos-Binks, Adam Young, R. Michael Liquid Narrative Group Department of Computer Science NC State University RaleighNC27695 United States Liquid Narrative Group School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

Strong story interactive narratives (IN) are stories that branch based on participant actions where all branches conform to a set of predefined constraints. However, participants in these systems may create branches where the constraints no longer hold. Strong story experience management, the process of generating IN trees, can be viewed as a game where the experience management agent wins if the story constraints hold during gameplay and loses if they are broken. In domains where the player has incomplete information of the story world, the experience manager can take action by shifting the player between alternate states that are consistent with the player's observations in order to maximize the probability that constraints will hold. This process is called superposition manipulation. In this paper we present a method of estimating the number of goal states reachable from different states in order to make informed decisions during superposition manipulation. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Information management

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Planning Graphs for Efficient Generation of Desirable Narrative Trajectories

Planning Graphs for Efficient Generation of Desirable Narrat...

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Workshops of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017

作者： Amos-Binks, Adam Potts, Colin Young, R. Michael Liquid Narrative Group Department of Computer Science NC State University RaleighNC27695 United States Liquid Narrative Group School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

A goal of Experience Managers (EM) is to guide users through a space of narrative trajectories, or story branches, in an Interactive Narrative (IN). When a user performs an action that deviates from the intended trajectory, the EM uses a mediation strategy called accommodation to transition the user to a new desirable trajectory. However, generating the trajectory options then selecting the appropriate one is computationally expensive and at odds with the low-latency needs of an IN. We define three desirable properties (exemplar trajectories, narrative-theoretic comparison, and efficiency) that general solutions would possess and demonstrate how our plan-based Intention Dependency Graph addresses them. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Trajectories

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Surgical gesture classification using Dynamic Time Warping and affine velocity 39

Surgical gesture classification using Dynamic Time Warping a...

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39th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2017

作者： Cifuentes, Jenny Pham, Minh Tu Moreau, Richard Prieto, Flavio Boulanger, Pierre Program of Electrical Engineering Faculty of Engineering Universidad de la Salle Colombia Univ Lyon INSA CNRS Ampere Lab. VilleurbanneF-69100 France Grupo de Automatica GAUNAL Universidad Nacional de Colombia Departamento de Ingenieria Mecanica y Mecatronica Colombia Advanced Man-Machine Interface Laboratory University of Alberta Department of Computing Science Canada

ISBN: (纸本)9781509028092

Minimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determined by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS. © 2017 IEEE.

关键词： Trajectory Heuristic algorithms Surgery Three-dimensional displays Classification algorithms Instruments Kinematics

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Eavesdropper’s ability to attack a free-space quantum-key-distribution receiver in atmospheric turbulence

arXiv

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arXiv 2019年

作者： Chaiwongkhot, Poompong Kuntz, Katanya B. Zhang, Yanbao Huang, Anqi Bourgoin, Jean-Philippe Sajeed, Shihan Lütkenhaus, Norbert Jennewein, Thomas Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada NTT Basic Research Laboratories NTT Corporation AtsugiKanagawa Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation AtsugiKanagawa Japan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Aegis Quantum WaterlooON Canada Department of Electrical and Computer Engineering University of Toronto M5S 3G4 Canada Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada Russian Quantum Center Skolkovo Moscow143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum key distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air’s varying index of refraction. We experimentally investigate the impact turbulence has on Eve’s attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver, or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve’s attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Atmospheric turbulence

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Parameter analysis of a bee-inspired algorithm to solve the traveling salesman problem 15

Parameter analysis of a bee-inspired algorithm to solve the ...

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15th IASTED International Conference on Intelligent Systems and Control, ISC 2016

作者： Masutti, Thiago A.S. De Castro, Leandro Nunes Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University São Paulo Brazil

ISBN: (纸本)9780889869837

The Traveling Salesman Problem (TSP) is extensively used to test new algorithms that tackle combinatorial optimiza-tion problems. Bee inspired algorithms are receiving a con-siderable attention from the Swarm Intelligence field. This paper presents the TSPoptBees, a bee inspired algorithm to solve the TSP. A vast discussion is made in regards to the parameter analysis of the proposed algorithm, showing trends on varying certain parameters and proposing default values for them. The quality is assessed using instances from the TSPLIB and the results show the algorithm is able to provide good quality solutions.

关键词： Traveling salesman problem

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Autonomous Bayesian neural models for fault diagnosis in transmission lines

Autonomous Bayesian neural models for fault diagnosis in tra...

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2017 IEEE Manchester PowerTech, Powertech 2017

作者： Ferreira, V.H. Lopes, D.S. Fortes, M.Z. Gomes, S. Zanghi, R. Telecommunications and Electrical Engineering Program Fluminense Federal University - UFF Niterói Brazil Institute of Computing Fluminense Federal University - UFF Niterói Brazil

ISBN: (纸本)9781509042371

Fault diagnosis constitutes a problem in electric power systems with relevant economic impact for operators and stakeholders. Artificial neural networks have been proposed in the literature to deal with this problem in a significant number of applications. However, most proposals are based in ad-hoc structure specification and model regularization, which compromises the direct application of the algorithms to other transmission systems. Given the above, this paper will present the application of Autonomous Bayesian Neural models to fault diagnosis in electric power system transmission lines. This modeling strategy includes automatic and analytical procedures for input selection, model specification and regularization without the need of a validation set. The system performance will be analyzed considering data from a real transmission line of the Brazilian interconnected system. © 2017 IEEE.

关键词： Failure analysis

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Publisher Correction: Landscape-scale benefits of protected areas for tropical biodiversity

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Nature 2024年第7996期625卷 E28页

作者： Jedediah F Brodie Jayasilan Mohd-Azlan Cheng Chen Oliver R Wearn Mairin C M Deith James G C Ball Eleanor M Slade David F R P Burslem Shu Woan Teoh Peter J Williams An Nguyen Jonathan H Moore Scott J Goetz Patrick Burns Patrick Jantz Christopher R Hakkenberg Zaneta M Kaszta Sam Cushman David Coomes Olga E Helmy Glen Reynolds Jon Paul Rodríguez Walter Jetz Matthew Scott Luskin Division of Biological Sciences University of Montana Missoula MT USA. jedediah.brodie@umontana.edu. Wildlife Biology Program University of Montana Missoula MT USA. jedediah.brodie@umontana.edu. Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak Kota Samarahan Malaysia. jedediah.brodie@umontana.edu. Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak Kota Samarahan Malaysia. Department of Forest Resources Management University of British Columbia Vancouver British Columbia Canada. Biodiversity Research Centre University of British Columbia Vancouver British Columbia Canada. Fauna and Flora International-Vietnam Programme Hanoi Vietnam. Institute for the Oceans and Fisheries University of British Columbia Vancouver British Columbia Canada. Department of Plant Sciences and Conservation Research Institute University of Cambridge Cambridge UK. Asian School of the Environment Nanyang Technological University Singapore Singapore. School of Biological Sciences University of Aberdeen Aberdeen UK. Wildlife Biology Program University of Montana Missoula MT USA. Division of Biological Sciences University of Montana Missoula MT USA. Leibniz Institute for Zoo and Wildlife Research Berlin Germany. School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China. School of Environmental Sciences University of East Anglia Norwich UK. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA. Department of Biological Sciences Northern Arizona University Flagstaff AZ USA. Wildlife Conservation Research Unit Department of Biology University of Oxford Oxford UK. School of Forestry Northern Arizona University Flagstaff AZ USA. Aldo Leopold Wilderness Research Institute United States Department of Agriculture Forest Service Rocky Mountain Research Station Missoula MT USA. The South East Asia Rainforest Research Partn

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Author Correction: Landscape-scale benefits of protected areas for tropical biodiversity

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Nature 2024年第8009期628卷 E5页

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