Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Image similarity in medical images

Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al

On the application of scattering matrix measurements to detection and identification of major types of airborne aerosol particles: Volcanic ash, desert dust and pollen

This work has been funded by the excellence research program of the Andalusian Regional Government, grant number P18RT-1854, the National Plan of Scientific and Technical Research and Innovation of the Spanish Ministry of Science and Innovation, grant number RTI2018-095330-B-100 (LEONIDAS), and the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofisica de Andalucia (SEV-2017-0709) by the Spanish State Agency for Research. J.C.G.M acknowledges financial support from the Ramon y Cajal Program of the Spanish Ministry of Science and Innovation (RYC-2016-19570). JoseLuis de la Rosa, JoseAntonio Ruiz and Shi Zongbo are acknowledged for collecting the Sahara-OSN and GobiBeijing desert dust samples.Atmospheric aerosols play key roles in climate and have important impacts on human activities and health. Hence, much effort is directed towards developing methods of improved detection and discrimina- tion of different types of aerosols. Among these, light scattering-based detection of aerosol offers several advantages including applications in both in situ and remote sensing devices. In this work, new scat- tering matrix measurements for two samples of airborne desert dust collected in Spain and China are reported. The average extrapolated scattering matrices of airborne desert dust and of volcanic ash at two wavelengths have been calculated and compared with the aim of finding criteria to distinguish these two types of aerosol. Additionally, the scattering matrix of cypress pollen has been measured and extrapo- lated to explore differences with mineral dust that can be exploited in atmospheric detection. Field mea- surements of the backscattering linear depolarization ratio δL (180 °) are used to obtain information about non-sphericity and discrimination between fine and coarse aerosol. However, the average δL (180 °) for the three types of aerosols considered in this work in the visible spectral range is δL (180 °) = 0.40 ±0.05. This shows that δL (180 °) is not informative about the composition or morphology of irregular particles. By contrast, measurements of scattering matrix elements or depolarization ratios at different scattering angles may provide information about the structural differences of particles, and in particular may en- able to differentiate airborne volcanic ash from desert dust, which are otherwise similar in terms of size and optical constants. Cypress pollen shows a characteristic degree of linear polarization curve that is very different from that of polydisperse irregular mineral dust. Light scattering field instruments and re- mote sensing methods could extract more information about the characteristics of aerosol particles if modifications were introduced to measure the phase curves of several scattering matrix elements or de- polarization ratios.excellence research program of the Andalusian Regional Government P18RT-1854National Plan of Scientific and Technical Research and Innovation of the Spanish Ministry of Science and Innovation RTI2018-095330-B-100Spanish State Agency for Research SEV-2017-0709Spanish Government RYC-2016-1957

Automated Assessment of the Aftermath of Typhoons Using Social Media Texts

Disasters are one of the major threats to economics and human societies, causing substantial losses of human lives, properties and infrastructures. It has been our persistent endeavors to understand, prevent and reduce such disasters, and the popularization of social media is offering new opportunities to enhance disaster management in a crowd-sourcing approach. However, social media data is also characterized by its undue brevity, intense noise, and informality of language. The existing literature has not completely addressed these disadvantages, otherwise vast manual efforts are devoted to tackling these problems. The major focus of this research is on constructing a holistic framework to exploit social media data in typhoon damage assessment. The scope of this research covers data collection, relevance classification, location extraction and damage assessment while assorted approaches are utilized to overcome the disadvantages of social media data. Moreover, a semi-supervised or unsupervised approach is prioritized in forming the framework to minimize manual intervention. In data collection, query expansion strategy is adopted to optimize the search recall of typhoon-relevant information retrieval. Multiple filtering strategies are developed to screen the keywords and maintain the relevance to search topics in the keyword updates. A classifier based on a convolutional neural network is presented for relevance classification, with hashtags and word clusters as extra input channels to augment the information. In location extraction, a model is constructed by integrating Bidirectional Long Short-Time Memory and Conditional Random Fields. Feature noise correction layers and label smoothing are leveraged to handle the noisy training data. Finally, a multi-instance multi-label classifier identifies the damage relations in four categories, and the damage categories of a message are integrated with the damage descriptions score to obtain damage severity score for the message. A case study is conducted to verify the effectiveness of the framework. The outcomes indicate that the approaches and models developed in this study significantly improve in the classification of social media texts especially under the framework of semi-supervised or unsupervised learning. Moreover, the results of damage assessment from social media data are remarkably consistent with the official statistics, which demonstrates the practicality of the proposed damage scoring scheme

Quantifying the psychological properties of words

This thesis explores the psychological properties of words – the idea that words carry links to additional information beyond their dictionary meaning. It does so by presenting three distinct publications and an applied project, the Macroscope. The published research respectively covers: the modelling of language networks to explain lexical growth; the use of high dimensional vector representations of words to discuss language learning; and the collection of a normative dataset of single word humour ratings. The first publication outlines the use of network science in psycholinguistics. The methodology is discussed, providing clear guidelines on the application of networks when answering psychologically motivated questions. A selection of psychological studies is presented as a demonstration of use cases for networks in cognitive psychology. The second publication uses referent feature norms to represent words in a high dimensional vector space. A correlative link between referent distinctiveness and age of acquisition is proposed. The shape bias literature (the idea that children only pay attention to the shape of objects early on) is evaluated in relation to the findings. The third publication collects and shares a normative dataset of single word humour ratings. Descriptive properties of the dataset are outlined and the potential future use in the field of humour is discussed. Finally, the thesis presents the Macroscope, a collaborative project put together with Li Ying. The Macroscope is an online platform, allowing for easy analysis of the psychological properties of target words. The platform is showcased, and its full functionality is presented, including visualisation examples. Overall, the thesis aims to give researchers all that’s necessary to start working with psychological properties of words – the understanding of network science in psycholinguistics, high dimensional vector spaces, normative datasets and the applied use of all the above through the Macroscope

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Earth Resources. A continuing bibliography with indexes, issue 34, July 1982

This bibliography lists 567 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between April 1, and June 30, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

The impact of a computerized conferencing system on scientific research communities

The author is indebted to Murray Turoff for coauthoring the sections describing the EIES system and for his suppport and encouragement for this study at all stages. Mary Anne Solimine served as a research assistant, supervising the distribution, coding and tabulations of questionnaire responses. Without her diligent efforts, the study would not have been possible. Ann Marie Rabke, Joanne Garofalo, Diane Price, Duchess Brooks, Margaret Wnorowski, Christine Naegle, Sonia Khalil, and Marion Whitescarver provided valuable assistance with coding and data entry and editing tasks. Larry Landwebber was most cooperative in providing access to the Theory Net group. Alan Leurck, Thomas Moulton, and Sanjit Chinai are among those at NJIT who prepared statistical data from information on users recorded by the system monitor. Among those who have made helpful contributions to the project are Diana Crane, Kenneth Johnson, Peter and Trudy Johnson-Lenz, Elaine Kerr, Ian Mitroff, Nicholas Mullins, Ronald Rice, Julian Scher, and Barry Wellman. Initial interest in the sociology of science was inspired by the work of Robert Merton, who of course bears no responsibility for the directions taken by his student. Last but certainly not least, Fred Weingarten, formerly of the National Science Foundation, has the author\u27s gratitude for his support of research in the interdisciplinary (and therefore controversial) area of computers and society