SwePub
Sök i SwePub databas

  Extended search

Träfflista för sökning "WFRF:(Wang Ke) srt2:(2020-2024)"

Search: WFRF:(Wang Ke) > (2020-2024)

  • Result 1-10 of 175
Sort/group result
   
EnumerationReferenceCoverFind
1.
  •  
2.
  • Beal, Jacob, et al. (author)
  • Robust estimation of bacterial cell count from optical density
  • 2020
  • In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
  • Journal article (peer-reviewed)abstract
    • Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
  •  
3.
  •  
4.
  •  
5.
  • Kristan, Matej, et al. (author)
  • The first visual object tracking segmentation VOTS2023 challenge results
  • 2023
  • In: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
  • Conference paper (peer-reviewed)abstract
    • The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
  •  
6.
  • Kristan, Matej, et al. (author)
  • The Ninth Visual Object Tracking VOT2021 Challenge Results
  • 2021
  • In: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
  • Conference paper (peer-reviewed)abstract
    • The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
  •  
7.
  • Kristan, M., et al. (author)
  • The Eighth Visual Object Tracking VOT2020 Challenge Results
  • 2020
  • In: Computer Vision. - Cham : Springer International Publishing. - 9783030682378 ; , s. 547-601
  • Conference paper (peer-reviewed)abstract
    • The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ). 
  •  
8.
  • Mishra, A, et al. (author)
  • Diminishing benefits of urban living for children and adolescents' growth and development
  • 2023
  • In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 615:7954, s. 874-883
  • Journal article (peer-reviewed)abstract
    • Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified.
  •  
9.
  • Niemi, MEK, et al. (author)
  • 2021
  • swepub:Mat__t
  •  
10.
  •  
Skapa referenser, mejla, bekava och länka
  • Result 1-10 of 175
Type of publication
journal article (143)
conference paper (15)
research review (11)
Type of content
peer-reviewed (153)
other academic/artistic (16)
Author/Editor
Jonas, JB (23)
Kim, YJ (23)
Fischer, F (20)
Kisa, A (19)
Gupta, R. (18)
Ilesanmi, OS (18)
show more...
Jozwiak, JJ (18)
Arabloo, J (17)
Banach, M (17)
Fukumoto, T (17)
Hosseinzadeh, M (17)
Jha, RP (17)
Koyanagi, A (17)
Kumar, M (17)
Mansournia, MA (17)
Quintanilla, BPA (16)
Barnighausen, TW (16)
Bhattacharyya, K (16)
Bijani, A (16)
Chu, DT (16)
Eskandarieh, S (16)
Hay, SI (16)
Hossain, N (16)
Islam, SMS (16)
Jakovljevic, M (16)
Khader, YS (16)
Khan, EA (16)
Krishan, K (16)
Mestrovic, T (16)
Mirrakhimov, EM (16)
Alipour, V (15)
Aljunid, SM (15)
Bedi, N (15)
Bhutta, ZA (15)
Butt, ZA (15)
Cardenas, R (15)
Chattu, VK (15)
Dandona, R (15)
Diaz, D (15)
Filip, I (15)
Foroutan, M (15)
Ghashghaee, A (15)
Guo, YM (15)
Ibitoye, SE (15)
Irvani, SSN (15)
Kisa, S (15)
Lasrado, S (15)
Li, SS (15)
Mendoza, W (15)
Meretoja, TJ (15)
show less...
University
Karolinska Institutet (76)
Uppsala University (28)
Lund University (28)
Royal Institute of Technology (18)
Linköping University (17)
Umeå University (16)
show more...
Chalmers University of Technology (15)
Stockholm University (11)
University of Gothenburg (10)
Högskolan Dalarna (7)
Jönköping University (3)
Luleå University of Technology (2)
Örebro University (2)
Mid Sweden University (2)
Halmstad University (1)
Mälardalen University (1)
Stockholm School of Economics (1)
University of Skövde (1)
Linnaeus University (1)
Blekinge Institute of Technology (1)
Swedish University of Agricultural Sciences (1)
show less...
Language
English (175)
Research subject (UKÄ/SCB)
Natural sciences (57)
Medical and Health Sciences (49)
Engineering and Technology (34)
Social Sciences (2)
Agricultural Sciences (1)

Year

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view