Supplementary Material for “Ensemble Diffusion for Retrieval”

An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in s...

ارائه الگوریتمی مبتنی بر یادگیری جمعی به منظور یادگیری رتبه‌بندی در بازیابی اطلاعات

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking ...

Mechanisms of the 2014-2016 Delayed Super El Niño Captured by Simple Dynamical Models (Supplementary Material)

The present supplementary material is organized as follows. Section 1 details the formulation of the ENSO model used in the present article. Section 2 provides additional examples of delayed, super and moderate El Niño events in the model. Section 3 provides additional details on the ensemble runs including the evolution of ensemble mean and standard deviation. Section 4 repeats results from th...

ArrayMining: A modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization - Supplementary Material

Selecting climate simulations for impact studies based on multivariate patterns of climate change

In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorologic...