309 research outputs found

    Lanczos exact diagonalization study of field-induced phase transition for Ising and Heisenberg antiferromagnets

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    Using an exact diagonalization treatment of Ising and Heisenberg model Hamiltonians, we study field-induced phase transition for two-dimensional antiferromagnets. For the system of Ising antiferromagnet the predicted field-induced phase transition is of first order, while for the system of Heisenberg antiferromagnet it is the second-order transition. We find from the exact diagonalization calculations that the second-order phase transition (metamagnetism) occurs through a spin-flop process as an intermediate step.Comment: 4 pages, 4 figure

    Comparing the quality of crowdsourced data contributed by expert and non-experts

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    There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but here are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future

    Limitations of Majority Agreement in Crowdsourced Image Interpretation

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    Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter-volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer-expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non-cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non-cropland) on 27% of the images, but disagreed strongly (cropland vs. non-cropland) on only 7%. Inter-volunteer disagreement increased significantly with inter-expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis-categorized if only the volunteers’ majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns

    A global dataset of crowdsourced land cover and land use reference data

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    Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general

    The Role of Citizen Science and Crowdsourcing Tools in Supporting Systems Analysis at IIASA

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    The involvement of citizens in scientific activities from data collection to hypothesis generation is referred to as citizen science. The majority of citizen involvement tends to be on the data collection side, where numerous crowdsourcing platforms have been built to involve citizens in image interpretation, online mapping and other micro-tasks that would not otherwise have been possible. There has been increasing attention directed towards how citizen-contributed data can be used for improved calibration and validation of satellite-derived products, such as land cover, as well as data for modeling purposes. This poster will provide examples of tools and applications in the area of citizen science and crowdsourcing within the Earth Observation Systems group of the IIASA Ecosystem Services Program. These tools include Geo-Wiki, mobile gaming apps such as Cropland Capture and Picture Pile, and other high-frequency mobile data collection tools. Some of the crowdsourced data have led to improved global maps of cropland, crop-type distributions and forest cover, information which is needed by economic land-use models such as the Global Biosphere Management Model and crop-growth models such as the Environmental Policy Integrated Model. Other data have the potential to help calibrate and validate these models, for example, through information on farm-level crop types and management information. These various activities, and their linkages to systems analysis work at IIASA, will be showcased on the poster

    Decreased Mitochondrial DNA Mutagenesis in Human Colorectal Cancer

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    Genome instability is regarded as a hallmark of cancer. Human tumors frequently carry clonally expanded mutations in their mitochondrial DNA (mtDNA), some of which may drive cancer progression and metastasis. The high prevalence of clonal mutations in tumor mtDNA has commonly led to the assumption that the mitochondrial genome in cancer is genetically unstable, yet this hypothesis has not been experimentally tested. In this study, we directly measured the frequency of non-clonal (random) de novo single base substitutions in the mtDNA of human colorectal cancers. Remarkably, tumor tissue exhibited a decreased prevalence of these mutations relative to adjacent non-tumor tissue. The difference in mutation burden was attributable to a reduction in C∶G to T∶A transitions, which are associated with oxidative damage. We demonstrate that the lower random mutation frequency in tumor tissue was also coupled with a shift in glucose metabolism from oxidative phosphorylation to anaerobic glycolysis, as compared to non-neoplastic colon. Together these findings raise the intriguing possibility that fidelity of mitochondrial genome is, in fact, increased in cancer as a result of a decrease in reactive oxygen species-mediated mtDNA damage
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