Mutation Region Detection for Closely Related Individuals without a Known Pedigree

Wenji Ma; Yong Yang; Zhi-Zhong Chen; Lusheng Wang

doi:10.1109/tcbb.2011.134

Nineteenth-Century Scottish Demography From Linked Censuses and Civil Registers: A ‘Sets of Related Individuals’ Approach

History and Computing ◽

10.3366/hac.2002.14.1-2.61 ◽

2002 ◽

Vol 14 (1-2) ◽

pp. 61-86 ◽

Cited By ~ 11

Author(s):

Alice Reid ◽

Ros Davies ◽

Eilidh Garrett

Keyword(s):

Nineteenth Century ◽

Related Individuals

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Improving the Computational Cost for Copied Region Detection in Forensic Images

Journal of Science and Technology Issue on Information and Communications Technology ◽

10.31130/jst.2016.28 ◽

2016 ◽

Vol 2 (1) ◽

pp. 55

Author(s):

Tu Huynh-Kha ◽

Thuong Le-Tien ◽

Synh Ha ◽

Khoa Huynh-Van

Keyword(s):

Wavelet Transform ◽

Euclidean Distance ◽

Research Work ◽

Computational Cost ◽

Correlation Coefficients ◽

Zernike Moments ◽

Computational Time ◽

Discrete Wavelet ◽

Feature Vectors ◽

Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

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Judgments of Moral Responsibility in Tissue Donation Cases

10.31234/osf.io/276ng ◽

2020 ◽

Author(s):

James R. Beebe

Keyword(s):

Moral Responsibility ◽

Significant Role ◽

Primary Factor ◽

Tissue Donation ◽

Potential Donor ◽

Sperm Donor ◽

Perceived Burden ◽

Related Individuals ◽

Unique Ability

If a person requires a tissue donation in order to survive, many philosophers argue that whatever moral responsibility a biological relative may have to donate to the person in need will be grounded at least partially, if not entirely, in the biological relations the potential donor bears to the recipient. Such views tend to ignore the role played by a potential donor’s unique ability to help the person in need and the perceived burden of the donation type in underwriting such judgments. If, for example, a sperm donor is judged to have a significant moral responsibility to donate tissue to a child conceived with his sperm, we argue that such judgments will largely be grounded in the presumed unique ability of the sperm donor to help the child due to the compatibility of his tissues with those of the recipient. In this paper, we report the results of two main studies and three supplementary studies designed to investigate the comparative roles that biological relatedness, unique ability to help, and donation burden play in generating judgments of moral responsibility in tissue donation cases. We found that the primary factor driving individuals’ judgments about the moral responsibility of a potential donor to donate tissue to someone in need was the degree to which a donor was in a unique ability to help. We observed no significant role for biological relatedness as such. Biologically related individuals were deemed to have a significant moral responsibility to donate tissue only when they are one of a small number of people who have a relatively unique capacity to help. We also found that people are less inclined to think individuals have a moral responsibility to donate tissue when the donation is more costly to make. We bring these results into dialogue with contemporary disputes concerning the ethics of tissue donation.

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Judgments of moral responsibility in tissue donation cases

10.31234/osf.io/rvsnc ◽

2020 ◽

Author(s):

James R. Beebe

Keyword(s):

Moral Responsibility ◽

Tissue Donation ◽

Potential Donor ◽

Sperm Donor ◽

Biological Relationship ◽

Related Individuals ◽

Unique Ability

If a person requires an organ or tissue donation to survive, many philosophers argue that whatever moral responsibility a biological relative may have to donate to the person in need will be grounded at least partially, if not entirely, in biological relations the potential donor bears to the recipient. We contend that such views ignore the role that a potential donor’s unique ability to help the person in need plays in underwriting such judgments. If, for example, a sperm donor is judged to have a significant moral responsibility to donate tissue to a child conceived with his sperm, we think this will not be due to the fact that the donor stands in a close biological relationship to the recipient. Rather, we think such judgments will largely be grounded in the presumed unique ability of the sperm donor to help the child due to the compatibility of his tissues and organs with those of the recipient. In this paper, we report the results of two studies designed to investigate the comparative roles that biological relatedness and unique ability play in generating judgments of moral responsibility in tissue donation cases. We found that biologically related individuals are deemed to have a significant moral responsibility to donate tissue only when they are one of a small number of people who have the capacity to help.

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Efficient 3D mesh salient region detection using local homogeneity measure

IET Image Processing ◽

10.1049/iet-ipr.2017.0598 ◽

2018 ◽

Vol 12 (9) ◽

pp. 1663-1672 ◽

Cited By ~ 1

Author(s):

Abdul Rahman El Sayed ◽

Abdallah El Chakik ◽

Hassan Alabboud ◽

Adnan Yassine

Keyword(s):

Salient Region Detection ◽

3D Mesh ◽

Local Homogeneity ◽

Salient Region ◽

Region Detection

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Understanding public opinion to the introduction of minimum unit pricing in Scotland: a qualitative study using Twitter

BMJ Open ◽

10.1136/bmjopen-2019-029690 ◽

2019 ◽

Vol 9 (6) ◽

pp. e029690 ◽

Cited By ~ 2

Author(s):

Laurence Astill Wright ◽

Su Golder ◽

Adam Balkham ◽

J McCambridge

Keyword(s):

Public Opinion ◽

Thematic Analysis ◽

Media Analysis ◽

Alcohol Policy ◽

Social Media Analysis ◽

Policy Issues ◽

Related Individuals ◽

Positive Sentiment ◽

Twitter Users ◽

Unit Pricing

ObjectivesOn 1 May 2018 minimum unit pricing (MUP) of alcohol was introduced in Scotland. This study used Twitter posts to quantify sentiment expressed online during the introduction of MUP, conducted a thematic analysis of these perceptions and analysed which Twitter users were associated with which particular sentiments.Design and settingThis qualitative social media analysis captured all tweets relating to MUP during the 2 weeks after the introduction of the policy. These tweets were assessed using a mixture of human and machine coding for relevance, sentiment and source. A thematic analysis was conducted.Participants74 639 tweets were collected over 14 days. Of these 53 574 were relevant to MUP.ResultsStudy findings demonstrate that opinion on the introduction of MUP in Scotland was somewhat divided, as far as is discernible on Twitter, with a slightly higher proportion of positive posts (35%) than negative posts (28%), with positive sentiment stronger in Scotland itself. Furthermore, 55% of positive tweets/retweets were originally made by health or alcohol policy-related individuals or organisations. Thematic analysis of tweets showed some evidence of misunderstanding around policy issues.ConclusionsIt is possible to appreciate the divided nature of public opinion on the introduction of MUP in Scotland using Twitter, the nature of the sentiment around it and the key actors involved. It will be possible to later study how this changes when the policy becomes more established.

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A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽

10.3390/f12060768 ◽

2021 ◽

Vol 12 (6) ◽

pp. 768

Author(s):

Jin Pan ◽

Xiaoming Ou ◽

Liang Xu

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Learning Strategy ◽

Early Stage ◽

Fire Detection ◽

Surveillance Systems ◽

Region Detection ◽

Decision Network ◽

Fire Smoke ◽

Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

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