(searched for: 10.29328/journal.ijcv.1001031)
Published: 16 February 2021
International Journal of Clinical Virology, Volume 5, pp 022-031; https://doi.org/10.29328/journal.ijcv.1001031
Before actual COVID-19 pandemia coronavirus was not so dangerous like now. In December 2019 - January 2020 in Wuhan first and then in other places this coronavirus was responsible of a first wave of severe pulmonitis responsible of many deaths. Wuhan and other region involved first was high level air polluted and industrial area. New COVID-19 variant in last part of 2020 and in first month of 2021 was responsible of great diffusion of this pandemic disease. UK, South Africa and brasilian new variant show higher diffusion then the first wave of COVID-19. Aim of this work is to analyze relationship with air pollution and the possibility that mutagen substantia inside of this microenvironment can produce new variant trough an genetic pressure process. RNA viruses are normally subjected by natural mutation but some phenomena can contribute to accelerate this process and their airborne – aeresols microenvironment is relevant. Some air pollutants are recognized as mutagen factors by literature.
Published: 20 July 2017
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.
Published: 9 June 2014
Manifold Learning for 3D Shape Description and Classification; https://doi.org/10.21236/ada606874
Periodically, the US Army conducts detailed measurement surveys of its soldiers as a way to understand the impact that changes in soldier body size have for the design, fit and sizing of virtually every piece of clothing and equipment that Soldiers wear and use in combat. Recently finished US Army Anthropometric Survey (ANSUR II) has collected 3D body scan data of soldiers at the Natick Solider Center (NSC), as shown in Figure 1. By applying new techniques for shape analysis and classification to these 3D body scan data will help designers of clothing and personal protection equipment to understand and fit Army population. The overall research goal of this proposal is to create a new manifold learning framework for large-scale graph decomposition and approximation problems by low-rank approximation and guarantee computable, stable and fast optimizations for 3D shape description and classification. The PI's group has published (or accepted for publication) 1 book through Springer and 13 scientific papers partially supported by this grant. In particular, these papers are in top journals and conference proceedings such as TPAMI, IJCV, TCSVT, ICCV, AAAI, SDM, ACM MM, etc. One paper, 1 out of 384, receives the Best Paper Award in SDM 2014. The PI, Dr. Y. Raymond Fu has received the 2014 INNS Young Investigator Award, from International Neural Networks Society (INNS), 2014. Leveraged by this grant, the PI has been granted an ARO Young Investigator Program (YIP) Award and a Defense University Research Instrumentation Program (DURIP) award.