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In this paper, we consider mobile sensor networks that use spatiotemporal Gaussian processes to predict a wide range of spatiotemporal physical phenomena. Nonparametric Gaussian process regression that is based on truncated observations is proposed for mobile sensor networks with limited memory and computational power. We first provide a theoretical(More)
In this paper, we formulate Gaussian process regression with observations under the localization uncertainty due to the resource-constrained sensor networks. In our formulation, effects of observations, measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically(More)
This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of gaussian processes introduced to model a(More)
— This paper presents an explorative navigation method using sparse Gaussian processes for mobile sensor networks. We first show that a near-optimal approximation is possible with a subset of measurements if we select the subset carefully, i.e., if the correlation between the selected measurements and the remaining measurements is small and the correlation(More)
The goal of this work is to present methodology to first evaluate the performance of an in vivo spine system and then to synthesize optimal neuromuscular control for rehabilitation interventions. This is achieved (1) by determining control system parameters such as static feedback gains and delays from experimental data, (2) by synthesizing the optimal(More)
Epithelial-mesenchymal transition (EMT) plays a fundamental role in cancer metastasis. The ubiquitin ligase FBXW7, a general tumor suppressor in human cancer, has been implicated in diverse cellular processes, however, its role in cholangiocarcinoma (CCA) metastasis has not been identified. Here, we report a crucial role of FBXW7 in CCA metastasis by(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t In this paper, a new class of Gaussian processes is proposed for(More)
In this paper, we formulate a fully Bayesian approach for spatio-temporal Gaussian process regression such that multifactorial effects of observations, measurement noise and prior distributions are all correctly incorporated in the predictive distribution. Using discrete prior probabilities and compactly supported kernels, we provide a way to design(More)
— In this paper, we formulate a full Bayesian approach for spatio-temporal Gaussian process regression under practical conditions such as measurement noise and unknown hyperparmeters (particularly, the bandwidths). Thus, multi-factorial effects of observations, measurement noise and prior distributions of hyperparameters are all correctly incorporated in(More)