194 research outputs found
A Brief History of Human Time - Cross-verified Dataset
No full text
This cross-verified dataset contains 2.2 million individuals, it can be used for research purposes. This dataset is linked to the following paper that should be cited directly instead of the data itself:
Morgane Laouenan, Palaash Bhargava, Jean-Benoît Eyméoud, Olivier Gergaud, Guillaume Plique, Etienne Wasmer (2022) A cross-verified database of notable people, 3500BC-2018AD, Scientific Data, June 2022.
Bibtex:
@article{bhht3,
author = {Laouenan, Morgane and Bhargava, Palaash and Eyméoud, Jean-Benoît and Gergaud, Olivier and Plique, Guillaume and Wasmer, Etienne},
title = {A cross-verified database of notable people, 3500BC-2018AD},
journal = {Scientific Data},
publisher = {Nature Publishing Group},
year = {2022},
month = {Jun},
day = {09},
volume = {9},
number = {1},
pages = {290},
issn = {2052-4463},
doi = {10.1038/s41597-022-01369-4},
url = {https://doi.org/10.1038/s41597-022-01369-4}
}
This dataset is subject to CC-BY-SA licensing.
</p
A Brief History of Human Time - Codes & Datasets
No full text
This compressed folder includes the code used for scraping and building the dataset, the intermediate datasets and the (not cross-verified) exhaustive dataset. This dataset is linked to the following paper that should be cited directly instead of the data itself:
Morgane Laouenan, Palaash Bhargava, Jean-Benoît Eyméoud, Olivier Gergaud, Guillaume Plique, Etienne Wasmer (2022) A cross-verified database of notable people, 3500BC-2018AD, Scientific Data, June 2022.
Bibtex:
@article{bhht3,
author = {Laouenan, Morgane and Bhargava, Palaash and Eyméoud, Jean-Benoît and Gergaud, Olivier and Plique, Guillaume and Wasmer, Etienne},
title = {A cross-verified database of notable people, 3500BC-2018AD},
journal = {Scientific Data},
publisher = {Nature Publishing Group},
year = {2022},
month = {Jun},
day = {09},
volume = {9},
number = {1},
pages = {290},
issn = {2052-4463},
doi = {10.1038/s41597-022-01369-4},
url = {https://doi.org/10.1038/s41597-022-01369-4}
}
The intermediate files as well as the exhaustive database are not cross-verified and should not be used directly or under the full responsibility of users.
All datasets included in this folder are subject to CC-BY-SA licensing.
</p
Dr. Biman Bagchi a bibliometric portrait
No full textAnalyses bibliometrically 226 publications [Papers Published in journals-220, thesis [others 4] by Biman Bagchi, a renowned physical chemist from India, published during 1981 to 2002. The first contribution of the author was in 1981 at the age of 27. The number of his contributions in a year peaked in 1999 and 2002 when it touched 19. The author is highly productive in as much as on average the author has produced 10 papers per year. In the byline of authorship, Bagchi occupies the first authorship position in 69 cases. His collaborator A. Chandra occupies the first authorship position in 30 papers thus becoming Bagchi's closest collaborator. The journal has been the most preferred channel of communication of the author in as much as 220 papers out of 226 have been praced in journals. J. Chem. Phys. is found to be the most preferred journal that carried 91 papers of the author, followed by Chem. Phys. Lett. (21 papers). J. Phys. Chem. (19 papers), Proc. Indian Acad. Sci. - Chem. Sci. (13 papers), and others. Of the papers, 179 received 4030 citations and 47 received no citations. It is expected that more than 20 uncited papers till 2002 will receive citations in future. Three papers of the author have received more than 200 citations each, and another three received between 100-200 citations each. The number of papers receiving 10 citations or more total 92. On four different years the scientist has received more than 300 citations and his citation rate per paper has peaked at 18.98. The article shows with a concrete example the growth, peaking and declining of citation rate. A few new terms such as citation gain, citation loss, gaining citation rate and losing citation rate have been introduced and described
Exposing and correcting the gender bias in image captioning datasets and models
No full textThe task of image captioning implicitly involves gender identification. However, due to the gender bias in data, gender identification by an image captioning model suffers. Also, due to the word-by-word prediction, the gender-activity bias in the data tends to influence the other words in the caption, resulting in the well know problem of label bias. In this work, we investigate gender bias in the COCO captioning dataset, and show that it engenders not only from the statistical distribution of genders with contexts but also from the flawed per instance annotation provided by the human annotators. We then look at the issues created by this bias in the models trained on the data. We propose a technique to get rid of the bias by splitting the task into 2 subtasks: gender-neutral image captioning and gender classification. By this decoupling, the gender-context influence can be eradicated. We train a gender neutral image captioning model, which does not exhibit the language model based bias arising from the gender and gives good quality captions. This model gives comparable results to a gendered model even when evaluating against a dataset that possesses similar bias as the training data. Interestingly, the predictions by this model on images without humans, are also visibly different from the one trained on gendered captions. For injecting gender into the captions, we train gender classifiers using cropped portions of images that contain only the person. This allows us to get rid of the context and focus on the person to predict the gender. We train bounding box based and body mask based classifiers, giving a much higher accuracy in gender prediction than an image captioning model implicitly attempting to classify the gender from the full image. By substituting the genders into the gender-neutral captions, we get the final gendered predictions. Our predictions achieve similar performance to a model trained with gender, and at the same time are devoid of gender bias. Finally, our main result is that on an anti-stereotypical dataset, our model outperforms a popular image captioning model which is trained with gender.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01The student, Shruti Bhargava, accepted the attached license on 2019-04-25 at 16:11.The student, Shruti Bhargava, submitted this Thesis for approval on 2019-04-25 at 16:20.This Thesis was approved for publication on 2019-04-26 at 08:29.DSpace SAF Submission Ingestion Package generated from Vireo submission #13927 on 2019-08-22 at 15:08:46Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2
BHARGAVA-THESIS-2019.pdf: 6140852 bytes, checksum: 4de862cc12a2bc1c2879fdebf62bac27 (MD5)
LICENSE.txt: 4212 bytes, checksum: d8a74d2c97863fcdc54052370992c353 (MD5)
Previous issue date: 2019-04-26Embargo set by: Seth Robbins for item 112223
Lift date: 2021-08-23T20:36:18Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 112223 on 2021-08-24T09:15:20Z
Improved error estimates for the Davenport-Heilbronn theorems (Analytic Number Theory and Related Topics)
No full textThis is a résumé of the preprint [BTT] of Manjul Bhargava, Frank Thorne and the author, based on the author's talk at RIMS conference
Justicia básica procedimental: herramienta de transición hacia sociedades mínimamente decentes
No full textFrom the reconstruction of the approaches of Rodrigo Uprimny, Rajeev Bhargava and Stuart Hampshire on transitional justice models, it will be posed that the minimally decent society concept, coined by the second author, sheds new lights on the discussionsA partir de la reconstrucción de los enfoques de Rodrigo Uprimny, Rajeev Bhargava y Stuart Hampshire sobre los modelos de justicia transicional, se planteará que el concepto de sociedad mínimamente decente, acuñado por el segundo autor, arroja nuevas luc
Spectral analysis of hexane leaf extract of balamkheera (Kigelia pinnata) using gas chromatography-mass spectrometry
No full text1162-1166Kigelia pinnata (Jacq.) is an important medicinal plant belonging to the family bignoniaceae. Leaf sample of the plant have been subjected to phytochemical investigation through gas chromatography – mass spectrometry (GC-MS). Twenty two phytochemical compounds have been identified in the hexane extract of K. pinnata leaves. The identification of phytochemical compounds is based on the peak area, retention time, molecular weight and molecular formula
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