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Advances in predicting omics profiles from imaging data. [PDF]
Beachum AH +5 more
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Clinical potential and challenges of spatially profiling tumor-infiltrating lymphocytes in early-stage breast cancer. [PDF]
Page DB, Simanonok M, Hanes DA, Su A.
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Maritime traffic congestion identification and ship trajectory prediction using temporal graph convolutional networks. [PDF]
Zhou W, Zhang W, Sun S, Zhang Y.
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Parents’ spatial talk predicts toddlers’ spatial language gains
Danielle S. Fox +5 more
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Spatiotemporal-decoupled interactive learning for traffic flow prediction. [PDF]
Chen L, Wu Q.
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Dynamic Spatial Predicted Background
IEEE Transactions on Image Processing, 2020We present a novel method for online background modeling for static video cameras - Dynamic Spatial Predicted Background (DSPB). Our unique method employs a small subset of image pixels to predict the whole scene by exploiting pixel correlations (distant and close).
Yaniv Tocker +2 more
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Nonparametric Spatial Prediction
Statistical Inference for Stochastic Processes, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Biau, Gérard, Cadre, Benoît
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Technometrics, 2011
Under a general loss function, we develop a hypothesis test to determine whether a significant difference in the spatial predictions produced by two competing models exists on average across the entire spatial domain of interest. The null hypothesis is that of no difference, and a spatial loss differential is created based on the observed data, the two
Hering, Amanda S., Genton, Marc G.
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Under a general loss function, we develop a hypothesis test to determine whether a significant difference in the spatial predictions produced by two competing models exists on average across the entire spatial domain of interest. The null hypothesis is that of no difference, and a spatial loss differential is created based on the observed data, the two
Hering, Amanda S., Genton, Marc G.
openaire +2 more sources

