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Big Data

Bart Baesens, Wouter Verbeke, Cristián Bravo
No abstract text is available yet for this article.
February 24, 2017: Big Data
Philip N Howard, Gillian Bolsover
No abstract text is available yet for this article.
February 16, 2017: Big Data
Nathan Ratliff, Franziska Meier, Daniel Kappler, Stefan Schaal
It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data points mapping observed accelerations to the torques used to generate them. Unfortunately, if the initial tracking error is bad, such learning processes may train substantially off-distribution to predict well on actual observed acceleration rather than the desired accelerations...
December 2016: Big Data
Jeannette Bohg, Matei Ciocarlie, Javier Civera, Lydia E Kavraki
No abstract text is available yet for this article.
December 2016: Big Data
Yongqiang Huang, Matteo Bianchi, Minas Liarokapis, Yu Sun
Data sets is crucial not only for model learning and evaluation but also to advance knowledge on human behavior, thus fostering mutual inspiration between neuroscience and robotics. However, choosing the right data set to use or creating a new data set is not an easy task, because of the variety of data that can be found in the related literature. The first step to tackle this issue is to collect and organize those that are available. In this work, we take a significant step forward by reviewing data sets that were published in the past 10 years and that are directly related to object manipulation and grasping...
December 2016: Big Data
Juan Pablo Mendoza, Reid Simmons, Manuela Veloso
Autonomous robots often rely on models of their sensing and actions for intelligent decision making. However, when operating in unconstrained environments, the complexity of the world makes it infeasible to create models that are accurate in every situation. This article addresses the problem of using potentially large and high-dimensional sets of robot execution data to detect situations in which a robot model is inaccurate-that is, detecting context-dependent model inaccuracies in a high-dimensional context space...
December 2016: Big Data
Adrian Boteanu, Aaron St Clair, Anahita Mohseni-Kabir, Carl Saldanha, Sonia Chernova
This work seeks to leverage semantic networks containing millions of entries encoding assertions of commonsense knowledge to enable improvements in robot task execution and learning. The specific application we explore in this project is object substitution in the context of task adaptation. Humans easily adapt their plans to compensate for missing items in day-to-day tasks, substituting a wrap for bread when making a sandwich, or stirring pasta with a fork when out of spoons. Robot plan execution, however, is far less robust, with missing objects typically leading to failure if the robot is not aware of alternatives...
December 2016: Big Data
Matthias Plappert, Christian Mandery, Tamim Asfour
Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input. However, although there have been years of research in this area, no standardized and openly available data set exists to support the development and evaluation of such systems. We, therefore, propose the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset, which is large, open, and extensible...
December 2016: Big Data
Vasant Dhar
No abstract text is available yet for this article.
September 2016: Big Data
Evangelos E Papalexakis, Christos Faloutsos
Multiaspect data are ubiquitous in modern Big Data applications. For instance, different aspects of a social network are the different types of communication between people, the time stamp of each interaction, and the location associated to each individual. How can we jointly model all those aspects and leverage the additional information that they introduce to our analysis? Tensors, which are multidimensional extensions of matrices, are a principled and mathematically sound way of modeling such multiaspect data...
September 2016: Big Data
Brittany Megan Bogle, Sanjay Mehrotra
Synthetic data are becoming increasingly important mechanisms for sharing data among collaborators and with the public. Multiple methods for the generation of synthetic data have been proposed, but many have short comings with respect to maintaining the statistical properties of the original data. We propose a new method for fully synthetic data generation that leverages linear and integer mathematical programming models in order to match the moments of the original data in the synthetic data. This method has no inherent disclosure risk and does not require parametric or distributional assumptions...
September 2016: Big Data
Steven Thompson, Stephen Varvel, Maciek Sasinowski, James P Burke
Big data and advances in analytical processes represent an opportunity for the healthcare industry to make better evidence-based decisions on the value generated by various tests, procedures, and interventions. Value-based reimbursement is the process of identifying and compensating healthcare providers based on whether their services improve quality of care without increasing cost of care or maintain quality of care while decreasing costs. In this article, we motivate and illustrate the potential opportunities for payers and providers to collaborate and evaluate the clinical and economic efficacy of different healthcare services...
September 2016: Big Data
Vasant Dhar, Nandan Nilekani, Shankar Maruwada, Nagaraju Pappu
No abstract text is available yet for this article.
September 2016: Big Data
Michal Ozery-Flato, Liat Ein-Dor, Naama Parush-Shear-Yashuv, Ranit Aharonov, Hani Neuvirth, Martin S Kohn, Jianying Hu
The availability of electronic health records creates fertile ground for developing computational models of various medical conditions. We present a new approach for detecting and analyzing patients with unexpected responses to treatment, building on machine learning and statistical methodology. Given a specific patient, we compute a statistical score for the deviation of the patient's response from responses observed in other patients having similar characteristics and medication regimens. These scores are used to define cohorts of patients showing deviant responses...
September 2016: Big Data
(no author information available yet)
No abstract text is available yet for this article.
June 2016: Big Data
Jennifer G Stadler, Kipp Donlon, Jordan D Siewert, Tessa Franken, Nathaniel E Lewis
The digitization of a patient's health record has profoundly impacted medicine and healthcare. The compilation and accessibility of medical history has provided clinicians an unprecedented, holistic account of a patient's conditions, procedures, medications, family history, and social situation. In addition to the bedside benefits, this level of information has opened the door for population-level monitoring and research, the results of which can be used to guide initiatives that are aimed at improving quality of care...
June 2016: Big Data
Pilar Rey-Del-Castillo, Jesús Cardeñosa
The availability of copious data about many human, social, and economic phenomena is considered an opportunity for the production of official statistics. National statistical organizations and other institutions are more and more involved in new projects for developing what is sometimes seen as a possible change of paradigm in the way statistical figures are produced. Nevertheless, there are hardly any systems in production using Big Data sources. Arguments of confidentiality, data ownership, representativeness, and others make it a difficult task to get results in the short term...
June 2016: Big Data
Dan McGinn, David Birch, David Akroyd, Miguel Molina-Solana, Yike Guo, William J Knottenbelt
This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time...
June 2016: Big Data
Venkata Satagopam, Wei Gu, Serge Eifes, Piotr Gawron, Marek Ostaszewski, Stephan Gebel, Adriano Barbosa-Silva, Rudi Balling, Reinhard Schneider
Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing...
June 2016: Big Data
Bart Buelens
Sunspots, colder areas that are visible as dark spots on the surface of the Sun, have been observed for centuries. Their number varies with a period of ∼11 years, a phenomenon closely related to the solar activity cycle. Recently, observation records dating back to 1749 have been reassessed, resulting in the release of a time series of sunspot numbers covering 266 years of observations. This series is analyzed using circular analysis to determine the periodicity of the occurrence of solar maxima. The circular analysis is combined with spiral graphs to provide a single visualization, simultaneously showing the periodicity of the series, the degree to which individual cycle lengths deviate from the average period, and differences in levels reached during the different maxima...
June 2016: Big Data
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