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Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

Luke M. Western, Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, Jonathan Rougier
2020 Geoscientific Model Development  
We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model.  ...  The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields.  ...  We would like to thank Alfredo Farjat and an anonymous reviewer for their helpful reviews of the manuscript. Review statement.  ... 
doi:10.5194/gmd-13-2095-2020 fatcat:md37orxfqfdftabty67hcvtwiq

Spatio-temporally efficient coding assigns functions to hierarchical structures of the visual system [article]

Duho Sihn, Sung-Phil Kim
2021 bioRxiv   pre-print
Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.  ...  Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity  ...  into the principle of spatially efficient coding.  ... 
doi:10.1101/2021.08.13.456321 fatcat:qz3v625grzf43kseos2xdhhj3q

Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation [chapter]

Yuandong Tian, C. Lawrence Zitnick, Srinivasa G. Narasimhan
2012 Lecture Notes in Computer Science  
In this paper, we propose a new hierarchical spatial model that can capture an exponential number of poses with a compact mixture representation on each part.  ...  Different from recent hierarchical models that associate each latent node to a mixture of appearance templates (like HoG), we use the hierarchical structure as a pure spatial prior avoiding the large and  ...  This research was supported in parts by an ONR Grant N00014-11-1-0295 and a Samsung Advanced Institute of Technology grant. Yuandong Tian is supported by a Microsoft PhD fellowship.  ... 
doi:10.1007/978-3-642-33715-4_19 fatcat:ngf6igs27fd2zhshs4jjoc2qqi

Spatial Concept-based Topometric Semantic Mapping for Hierarchical Path-planning from Speech Instructions [article]

Akira Taniguchi, Shuya Ito, Tadahiro Taniguchi
2022 arXiv   pre-print
The purpose of this study is to realize a hierarchical spatial representation using a topometric semantic map and planning efficient paths through human-robot interactions.  ...  We also developed approximate inference methods for path planning, where the levels of the hierarchy can influence each other.  ...  How robots can efficiently construct and utilize such hierarchical spatial representations for interaction tasks is a major challenge.  ... 
arXiv:2203.10820v1 fatcat:tsqwersvtvby5a2oeuaxanl35i

Editorial

Sinisa Todorovic, Rama Chellappa
2011 International Journal of Computer Vision  
They then propose a coarse-to-fine algorithm for efficient detection which exploits the hierarchical nature of the model.  ...  a numerical study of the bottom-up and top-down inference processes in hierarchical models using the And-Or graph as an example.  ... 
doi:10.1007/s11263-011-0420-8 fatcat:2puoqwob25dlbky7lqi275u5p4

Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

Luke M. Western, Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, Jonathan Rougier
2019 Geoscientific Model Development Discussions  
</strong> We present a method to infer spatially and spatiotemporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model.  ...  The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields.  ...  All together, this forms a Bayesian hierarchical model, from which emissions can be inferred.  ... 
doi:10.5194/gmd-2019-66 fatcat:c7kdntrapvcrrfjqv3dy2undcm

Structured Sparse Modelling with Hierarchical GP [article]

Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
2017 arXiv   pre-print
We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data.  ...  It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients.  ...  We also develop an efficient inference method based on EP. The numerical experiments show superiority of the proposed model over the current state-of-the-art.  ... 
arXiv:1704.08727v1 fatcat:abjcxoqutrak3d44w6mj5chrvy

Study on Recent Approaches for Human Action Recognition in Real Time

R. Rajitha Jasmine, Dr. K. K. Thyagharajan
2015 International Journal of Engineering Research and  
The main aim of action recognition is an automatic analysis of various actions from video data.  ...  The challenge is to recognize human actions with more accuracy and efficiency in recognition time.  ...  It efficiently computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent-child relations.  ... 
doi:10.17577/ijertv4is080577 fatcat:hikmv56t6jc5la7ipcny5u4kha

PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models [article]

Ben Seiyon Lee, Murali Haran
2021 arXiv   pre-print
PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models.  ...  Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science.  ...  In cases where it is possible to fit the full hierarchical spatial model, we show that our approach yields comparable results in terms of both inference and prediction.  ... 
arXiv:1912.02382v2 fatcat:bjsusrvsujeopfk2khuslc5ija

Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model

Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. Serpico
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.  ...  In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model.  ...  This combination ensures causality for the whole probabilistic graphical model -thus favoring efficient inference algorithms -, takes benefit from the spatial-contextual information within each layer,  ... 
doi:10.1109/igarss.2019.8898060 dblp:conf/igarss/MontaldoFHMZS19 fatcat:5qnos44q5bdcbhsct4nlgxxnp4

Grounding Abstract Spatial Concepts for Language Interaction with Robots

Rohan Paul, Jacob Arkin, Nicholas Roy, Thomas M. Howard
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
inference in the model.  ...  We introduce a probabilistic model that incorporates an expressive space of abstract spatial concepts as well as notions of cardinality and ordinality.  ...  Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2017/696 dblp:conf/ijcai/PaulARH17 fatcat:37ggvbwixzegxiqqktad4i25da

A Bayesian approach to multiscale inverse problems using the sequential Monte Carlo method

Jiang Wan, Nicholas Zabaras
2011 Inverse Problems  
Based on a hierarchically structured sparse grid, a multiscale representation of the spatial field is constructed.  ...  The method is demonstrated with the estimation of permeability in flows through porous media.  ...  Acknowledgments This research was supported by the US Department of Energy, Office of Science, Advanced Scientific Computing Research, the Computational Mathematics program of the National Science Foundation  ... 
doi:10.1088/0266-5611/27/10/105004 fatcat:diri7bvpdvcapjmqwaclswhxr4

Improved return level estimation via a weighted likelihood, latent spatial extremes model [article]

Joshua Hewitt, Miranda J. Fix, Jennifer A. Hoeting, Daniel S. Cooley
2018 arXiv   pre-print
We adopt a hierarchical Bayesian framework for inference, use simulation to show the weighted model provides improved estimates of high quantiles, and apply our model to improve return level estimates  ...  Latent spatial extremes models reduce uncertainty by exploiting spatial dependence in statistical characteristics of extreme events to borrow strength across locations.  ...  Latent spatial extremes models are a flexible and computationally efficient class of models for marginal distributions of spatial extremes and quantities derived from them, like return levels.  ... 
arXiv:1810.07318v2 fatcat:s4ld6hv65fcppkpbejpmt27jza

Hierarchical Compositional Representations of Object Structure [chapter]

Aleš Leonardis
2012 Lecture Notes in Computer Science  
of objects to perform efficient object detection.  ...  Our framework for learning a hierarchical compositional shape vocabulary for representing multiple object classes takes simple contour fragments and learns their frequent spatial configurations.  ...  of modeled object classes.  ... 
doi:10.1007/978-3-642-34166-3_3 fatcat:myz4tninbfbnthdg56bacajowm

Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks [article]

Yongming Rao, Zuyan Liu, Wenliang Zhao, Jie Zhou, Jiwen Lu
2022 arXiv   pre-print
In this paper, we present a new approach for model acceleration by exploiting spatial sparsity in visual data.  ...  We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks that require structured feature maps by formulating a more  ...  The emergence of vision Transformers offers us a new way to explore spatial sparsity for learning more efficient models.  ... 
arXiv:2207.01580v1 fatcat:jqtq6crurzcevcczzxxjgnnoh4
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