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(searched for: 10.29328/journal.ijcv.1001032)
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Mishra Kp, Mishra Priyanka, Singh Ak, Singh Sb
International Journal of Clinical Virology, Volume 5, pp 032-036; https://doi.org/10.29328/journal.ijcv.1001032

Abstract:
Corona Virus Disease-2019 (COVID-19) has become one of the most serious diseases in the history of mankind. It has captured the entire world and solutions are yet to be discovered to fight this global crisis. The outcomes of COVID-19 are influenced by a variety of pre-existing factors. The secondary microbial infections are one of the prominent ones that are major contributors for Antimicrobial Resistance (AMR) as they warrant the use of antimicrobial medications. The present review aimed at exploring the potential relationship between AMR under such circumstances and COVID-19 related outcomes. The published literature across the globe has delineated that the impact of COVID-19 may have worsened by a great degree due to the presence of secondary infections majorly bacterial ones. The consequences of COVID-19 have been fatal and a significant proportion can be a major attributor to AMR, either directly or indirectly. Although, there is a dearth of studies that can establish a very strong and direct relationship between AMR and negative COVID-19 outcomes so in-depth research on this topic is required to further explain this relationship in detail.
, Soma Shirakabe, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin'ichiro Morita, Toshiyuki Yabe, Yoshihiro Kanehara, et al.
Published: 20 July 2017
Abstract:
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.
Yun R. Fu
Manifold Learning for 3D Shape Description and Classification; https://doi.org/10.21236/ada606874

Abstract:
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.
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