[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121660-en":3,"doc-seo-121660-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121660,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Anomalous Diffusion Characterization using Machine Learning Methods - PhD Dissertation","PhD dissertation on characterizing anomalously diffusing trajectories through machine learning, with emphasis on deep learning architectures for short and noisy particle data. Presents the ConvLSTM framework and details the method architecture and training rationale. Introduces the AnDi Challenge with datasets and evaluation protocols, compares competing methods, and summarizes achieved results. Extends the approach to the ValenciaIA4Covid setting by building predictors for COVID-19 case dynamics and reporting outcomes, followed by related journal articles and conclusions.","Anomalous Diffusion Characterization using Machine Learning Methods  \nPhD DISSERTATION  \nAuthor `Oscar Garibo i Orts  \nVal`encia, February 2023  \nAdvisors  \nDr. Jos´e Alberto Conejero Casares Dr. Miguel ´Angel Garc´ıa March  \nvii  \nA ma mare i mon pare, per educar-me en llibertat. A ma germana, Joel i Le´on, per fer gran la fam´ılia. A Guillem, t’estime. A l’altra fam´ılia, la que tries, perqu`e us ho teniu guanyat.  \nTable of Contents  \nAbstract ......................................................................... 1  \nResumen ........................................................................ 2  \nResum ........................................................................... 3  \n1 Introduction ................................................................. 4  \n1.1 Motivation ............................................................... 4  \n1.2 Thesis contributions ....................................................... 5  \n1.3 Outline .................................................................. 7  \n2 ConvLSTM .................................................................. 8  \n2.1 Machine learning .......................................................... 8  \n2.2 Deep learning ............................................................. 10  \n2.3 Architecture of the method ................................................. 13  \n3 AnDi Challenge ............................................................. 14  \n3.1 Data set .................................................................. 16  \n3.2 The Challenge: Evaluation .................................................. 19  \n3.3 The Challenge: Methods .................................................... 20  \n3.4 Our results ............................................................... 22  \n3.5 Corollary ................................................................. 24  \n4 ValenciaIA4Covid ........................................................... 25  \n4.1 Data ..................................................................... 26  \n4.2 Predictors of COVID-19 cases ............................................... 27  \n4.3 Results................................................................... 33  \n5 Journal article (i) ............................................................ 36  \nGaribo-i-Orts, `O, Baeza-Bosca, A., Garc´ıa-March, M.A., Conejero, J.A. (2021) . Efficient RNN methods for anomalously diffusing trajectories] Efficient recurrent  \nneural network methods for anomalously diffusing single particle short and noisy trajectories. J. Phys. A: Math. Theor. , 54: 504002 ............................. 36  \n5.1 Introduction .............................................................. 36  \n5.2 Description of the method .................................................. 40  \n5.3 Results ................................................................... 41  \n5.4 Conclusions ............................................................... 48  \n6 Journal article (ii) ........................................................... 52  \nGaribo-i-Orts, O., Firbas, N., Sebastia, L, Conejero, J.A. (2021) . Gramian Angular  \nFields for leveraging pre-trained computer vision models with anomalous diffusion trajectories. Submitted to Phys. Rev. E....................................... 52  \nAbstract ...................................................................... 52  \n6.1 Introduction .............................................................. 53  \nTable of Contents ix  \n6.2 Anomalous Diffusion ....................................................... 54  \n6.3 Gramian Angular Fields .................................................... 56  \n6.4 Methodology .............................................................. 59  \n6.5 Results ................................................................... 60  \n6.6 Conclusions ............................................................... 68  \n7 Journal article (iii) .............................................","cbCaitYkoFSSwJEE","https://ap.wps.com/l/cbCaitYkoFSSwJEE","pdf",49217285,1,113,"English","en",105,"# Introduction\n## Motivation\n## Thesis contributions\n## Outline\n# ConvLSTM\n## Machine learning\n## Deep learning\n## Architecture of the method\n# AnDi Challenge\n## Data set\n## The Challenge: Evaluation\n## The Challenge: Methods\n## Our results\n## Corollary\n# ValenciaIA4Covid\n## Data\n## Predictors of COVID-19 cases\n## Results\n# Journal article (i)\n## Introduction\n## Description of the method\n## Results\n## Conclusions\n# Journal article (ii)\n## Introduction\n## Anomalous Diffusion\n## Gramian Angular Fields\n## Methodology\n## Results\n## Conclusions\n# Journal article (iii)\n## Introduction\n## Architecture of the method\n## A general model for inferring µ and ν parameters\n## Conclusions\n# Journal article (iv)\n## Introduction\n## Methodology\n## Results\n## Conclusions\n# Journal article (v)\n## Introduction\n## Methodology\n## Results: Bifurcation diagrams\n## Analysis and conclusions\n# Concluding remarks and recommendations\n## Concluding remarks\n## Recommendations","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses the characterization of anomalously diffusing trajectories using machine learning methods, particularly for short and noisy single-particle data.\"},{\"question\":\"Which core modeling approach is presented?\",\"answer\":\"The dissertation presents ConvLSTM, describing machine learning and deep learning components and the architecture of the method.\"},{\"question\":\"How is the work evaluated and applied beyond trajectory characterization?\",\"answer\":\"Evaluation is organized through the AnDi Challenge with datasets and comparison of methods, and the approach is further applied in the ValenciaIA4Covid setting to build predictors for COVID-19 case dynamics.\"}]","Anomalous Diffusion Characterization using Machine Learning Methods - 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