Accurate Integration of Aerosol Predictions by Smoothing on a Manifold

Authors: Shuai Zheng, James Kwok

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental experimental results on both synthetic and real-world data sets show that it significantly outperforms the state-of-the-art.
Researcher Affiliation Academia Shuai Zheng James T. Kwok Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong {szhengac, jamesk}@cse.ust.hk
Pseudocode No The paper describes its methods using mathematical formulations and prose, but does not include any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide an explicit statement or link for open-source code related to the methodology described in this paper.
Open Datasets Yes ground-based AERONET data (aeronet.gsfc.nasa.gov/cgi-bin/combined_data_access_new) and five satellite measurements (disc.sci.gsfc.nasa.gov/aerosols/services/mapss)
Dataset Splits No The paper describes the use of 'labeled' and 'unlabeled' locations for its semi-supervised learning task, but does not explicitly define a separate 'validation' dataset split for hyperparameter tuning or model selection in the conventional sense of training/validation/test splits.
Hardware Specification No The paper does not provide any specific details about the hardware used for running the experiments.
Software Dependencies No The paper does not provide specific software dependency details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment.
Experiment Setup Yes The ground truth y vector is generated from (10), with u = 0.1, σ2 = 0.01 and α = 1. The number of satellites K is 5, and their measurements are sampled using (3), with Σ = diag([0.01, 0.02, 0.03, 0.04, 0.05]). To simulate missing data, we remove each satellite measurement randomly with probability p = 0.5.