Meetings and Meeting Modeling in Smart Environments

In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear

A conceptual framework for studying collective reactions to events in location-based social media

Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events

Towards a Universal Modeling and Control Framework for Soft Robots

Traditional rigid-bodied robots are designed for speed, precision, and repeatability. These traits make them well suited for highly structured industrial environments, but poorly suited for the unstructured environments in which humans typically operate. Soft robots are well suited for unstructured human environments because they them to can safely interact with delicate objects, absorb impacts without damage, and passively adapt their shape to their surroundings. This makes them ideal for applications that require safe robot-human interaction, but also presents modeling and control challenges. Unlike rigid-bodied robots, soft robots exhibit continuous deformation and coupling between structure and actuation and these behaviors are not readily captured by traditional robot modeling and control techniques except under restrictive simplifying assumptions. The contribution of this work is a modeling and control framework tailored specifically to soft robots. It consists of two distinct modeling approaches. The first is a physics-based static modeling approach for systems of fluid-driven actuators. This approach leverages geometric relationships and conservation of energy to derive models that are simple and accurate enough to inform the design of soft robots, but not accurate enough to inform their control. The second is a data-driven dynamical modeling approach for arbitrary (soft) robotic systems. This approach leverages Koopman operator theory to construct models that are accurate and computationally efficient enough to be integrated into closed-loop optimal control schemes. The proposed framework is applied to several real-world soft robotic systems, enabling the successful completion of control tasks such as trajectory following and manipulating objects of unknown mass. Since the framework is not robot specific, it has the potential to become the dominant paradigm for the modeling and control of soft robots and lead to their more widespread adoption.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163062/1/bruderd_1.pd

An Overview of Models for Response Times and Processes in Cognitive Tests.

Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

An Ontological Approach to Representing the Product Life Cycle

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from The Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry