214,138 research outputs found
Modeling of predictive human movement coordination patterns for applications in computer graphics
The planning of human body movements is highly predictive. Within a sequence of actions, the anticipation of a
final task goal modulates the individual actions within the overall pattern of motion. An example is a sequence of
steps, which is coordinated with the grasping of an object at the end of the step sequence. Opposed to this property
of natural human movements, real-time animation systems in computer graphics often model complex activities by
a sequential concatenation of individual pre-stored movements, where only the movement before accomplishing
the goal is adapted. We present a learning-based technique that models the highly adaptive predictive movement
coordination in humans, illustrated for the example of the coordination of walking and reaching. The proposed
system for the real-time synthesis of human movements models complex activities by a sequential concatenation
of movements, which are approximated by the superposition of kinematic primitives that have been learned from
trajectory data by anechoic demixing, using a step-wise regression approach. The kinematic primitives are then
approximated by stable solutions of nonlinear dynamical systems (dynamic primitives) that can be embedded
in control architectures. We present a control architecture that generates highly adaptive predictive full-body
movements for reaching while walking with highly human-like appearance. We demonstrate that the generated
behavior is highly robust, even in presence of strong perturbations that require the insertion of additional steps
online in order to accomplish the desired task
Investigating urban soundscapes of the COVID-19 lockdown: A predictive soundscape modeling approach
The unprecedented lockdowns resulting from COVID-19 in spring 2020 triggered changes in human activities in public spaces. A predictive modeling approach was developed to characterize the changes in the perception of the sound environment when people could not be surveyed. Building on a database of soundscape questionnaires (N = 1,136) and binaural recordings (N = 687) collected in 13 locations across London and Venice during 2019, new recordings (N = 571) were made in the same locations during the 2020 lockdowns. Using these 30-s-long recordings, linear multilevel models were developed to predict the soundscape pleasantness ( R2=0.85) and eventfulness ( R2=0.715) during the lockdown and compare the changes for each location. The performance was above average for comparable models. An online listening study also investigated the change in the sound sources within the spaces. Results indicate (1) human sounds were less dominant and natural sounds more dominant across all locations; (2) contextual information is important for predicting pleasantness but not for eventfulness; (3) perception shifted toward less eventful soundscapes and to more pleasant soundscapes for previously traffic-dominated locations but not for human- and natural-dominated locations. This study demonstrates the usefulness of predictive modeling and the importance of considering contextual information when discussing the impact of sound level reductions on the soundscape
Predictive Non-equilibrium Social Science
Non-Equilibrium Social Science (NESS) emphasizes dynamical phenomena, for
instance the way political movements emerge or competing organizations
interact. This paper argues that predictive analysis is an essential element of
NESS, occupying a central role in its scientific inquiry and representing a key
activity of practitioners in domains such as economics, public policy, and
national security. We begin by clarifying the distinction between models which
are useful for prediction and the much more common explanatory models studied
in the social sciences. We then investigate a challenging real-world predictive
analysis case study, and find evidence that the poor performance of standard
prediction methods does not indicate an absence of human predictability but
instead reflects (1.) incorrect assumptions concerning the predictive utility
of explanatory models, (2.) misunderstanding regarding which features of social
dynamics actually possess predictive power, and (3.) practical difficulties
exploiting predictive representations.Comment: arXiv admin note: substantial text overlap with arXiv:1212.680
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportunities to
study the mechanisms that explain group success. While a growing body of
research investigates community success through a single measure -- typically,
the number of members -- we argue that there are multiple ways of measuring
success. Here, we present a systematic study to understand the relations
between these success definitions and test how well they can be predicted based
on community properties and behaviors from the earliest period of a community's
lifetime. We identify four success measures that are desirable for most
communities: (i) growth in the number of members; (ii) retention of members;
(iii) long term survival of the community; and (iv) volume of activities within
the community. Surprisingly, we find that our measures do not exhibit very high
correlations, suggesting that they capture different types of success.
Additionally, we find that different success measures are predicted by
different attributes of online communities, suggesting that success can be
achieved through different behaviors. Our work sheds light on the basic
understanding of what success represents in online communities and what
predicts it. Our results suggest that success is multi-faceted and cannot be
measured nor predicted by a single measurement. This insight has practical
implications for the creation of new online communities and the design of
platforms that facilitate such communities.Comment: To appear at The Web Conference 201
Human Computation and Convergence
Humans are the most effective integrators and producers of information,
directly and through the use of information-processing inventions. As these
inventions become increasingly sophisticated, the substantive role of humans in
processing information will tend toward capabilities that derive from our most
complex cognitive processes, e.g., abstraction, creativity, and applied world
knowledge. Through the advancement of human computation - methods that leverage
the respective strengths of humans and machines in distributed
information-processing systems - formerly discrete processes will combine
synergistically into increasingly integrated and complex information processing
systems. These new, collective systems will exhibit an unprecedented degree of
predictive accuracy in modeling physical and techno-social processes, and may
ultimately coalesce into a single unified predictive organism, with the
capacity to address societies most wicked problems and achieve planetary
homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added
references to page 1 and 3, and corrected typ
Identification of delivery models for the provision of predictive genetic testing in Europe: protocol for a multicentre qualitative study and a systematic review of the literature
Introduction: The appropriate application of genomic technologies in healthcare is surrounded by many concerns. In particular, there is a lack of evidence on what constitutes an optimal genetic service delivery model, which depends on the type of genetic test and healthcare context considered. The present project aims to identify, classify, and evaluate delivery models for the provision of predictive genetic testing in Europe and in selected Anglophone extra-European countries (the USA, Canada, Australia, and New Zealand). It also sets out to survey the European public health community’s readiness to incorporate public health genomics into their practice.
Materials and equipment: The project consists of (i) a systematic review of published literature and selected country websites, (ii) structured interviews with health experts on the genetic service delivery models in their respective countries, and (iii) a survey of European Public Health Association (EUPHA) members’ knowledge and attitudes toward genomics applications in clinical practice. The inclusion criteria for the systematic review are that articles be published in the period 2000–2015; be in English or Italian; and be from European countries or from Canada, the USA, Australia, or New Zealand. Additional policy documents will be retrieved from represented countries’ government-affiliated websites. The results of the research will be disseminated through the EUPHA network, the Italian Network for Genomics in Public Health (GENISAP), and seminars and workshops.
Expected impact of the study on public health: The transfer of genomic technologies from research to clinical application is influenced not only by several factors inherent to research goals and delivery of healthcare but also by external and commercial interests that may cause the premature introduction of genetic tests in the public and private sectors. Furthermore, current genetic services are delivered without a standardized set of process and outcome measures, which makes the evaluation of healthcare services difficult. The present study will identify and classify delivery models and, subsequently, establish which are appropriate for the provision of predictive genetic testing in Europe by comparing sets of process and outcome measures. In this way, the study will provide a basis for future recommendations to decision makers involved in the financing, delivery, and consumption of genetic services
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