Browsing by Type "conference papers and proccedings"
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Item Assessing the biogeographical and socio-ecological representativeness of the ILTER site network(EGU Publications) Wohner, Christoph; Ohnemus, Thomas; Zacharias, Steffen; Mollenhauer, Hannes; Ellis, Erle C.; Klug, Hermann; Shibata, Hideaki; Mirtl, MichaelThe challenges posed by climate and land use change are increasingly complex, with rising and accelerating impacts on the global environmental system. Novel environmental and ecosystem research needs to properly interpret system changes and derive management recommendations across scales.Item Deep Multi-Sensor Domain Adaptation on Active and Passive Satellite Remote Sensing DataPurushotham, Sanjay; Huang, Xin; Ali, Sahara; Wang, Chenxi; Ning, Zeyu; Wang, Jianwu; Wang, Chenxi; Zhang, ZhiboItem Inexact Proximal Conjugate Subgradient Algorithm for fMRI Data Completion(European Association for Signal Processing (EURASIP), 2020) Belyaeva, Irina; Long, Qunfang; Adali, TulayTensor representations have proven useful for many problems, including data completion. A promising application for tensor completion is functional magnetic resonance imaging (fMRI) data that has an inherent four-dimensional (4D) structure and is prone to missing voxels and regions due to issues in acquisition. A key component of successful tensor completion is a rank estimation. While widely used as a convex relaxation of the tensor rank, tensor nuclear norm (TNN) imposes strong low-rank constraints on all tensor modes to be simultaneously low-rank and often leads to suboptimal solutions. We propose a novel tensor completion model in tensor train (TT) format with a proximal conjugate subgradient (PCS-TT) method for solving the nonconvex rank minimization problem by using properties of Moreau’s decomposition. PCS-TT allows the use of a wide range of robust estimators and can be used for data completion and sparse signal recovery problems. We present experimental results for data completion in fMRI, where PCS-TT demonstrates significant improvements compared with competing methods. In addition, we present results that demonstrate the advantages of considering the 4D structure of the fMRI data. as opposed to using three- and two-dimensional representations that have dominated the work on fMRI analysis.