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    Mineral dust in central Asia: 18-month lidar measurements in Tajikistan during the central Asian dust experiment (CADEX)
    (Les Ulis : EDP Sciences, 2018) Hofer, Julian; Althausen, Dietrich; Abdullaev, Sabur F.; Makhmudov, Abduvosit; Nazarov, Bakhron I.; Schettler, Georg; Fomba, K.Wadinga; Müller, Konrad; Heinold, Bernd; Baars, Holger; Engelmann, Ronny; Ansmann, Albert; Nicolae, D.; Makoto, A.; Vassilis, A.; Balis, D.; Behrendt, A.; Comeron, A.; Gibert, F.; Landulfo, E.; McCormick, M.P.; Senff, C.; Veselovskii, I.; Wandinger, U.
    Tajikistan is often affected by atmospheric mineral dust. The direct and indirect radiative effects of dust play a sensitive role in the climate system in Central Asia. The Central Asian Dust Experiment (CADEX) provides first lidar measurements in Tajikistan. The autonomous multiwavelength polarization Raman lidar PollyXT was operated for 1.5 years (2015/16) in Dushanbe. In spring, lofted layers of long-range transported dust and in summer/ autumn, lower laying dust from local or regional sources with large optical thicknesses occurred.
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    Lidar Ice nuclei estimates and how they relate with airborne in-situ measurements
    (Les Ulis : EDP Sciences, 2018) Marinou, Eleni; Amiridis, Vassilis; Ansmann, Albert; Nenes, Athanasios; Balis, Dimitris; Schrod, Jann; Binietoglou, Ioannis; Solomos, Stavros; Mamali, Dimitra; Engelmann, Ronny; Baars, Holger; Kottas, Michael; Tsekeri, Alexandra; Proestakis, Emmanouil; Kokkalis, Panagiotis; Goloub, Philippe; Cvetkovic, Bojan; Nichovic, Slobodan; Mamouri, Rodanthi; Pikridas, Michael; Stavroulas, Iasonas; Keleshis, Christos; Sciare, Jean
    By means of available ice nucleating particle (INP) parameterization schemes we compute profiles of dust INP number concentration utilizing Polly-XT and CALIPSO lidar observations during the INUIT-BACCHUS-ACTRIS 2016 campaign. The polarization-lidar photometer networking (POLIPHON) method is used to separate dust and non-dust aerosol backscatter, extinction, mass concentration, particle number concentration (for particles with radius > 250 nm) and surface area concentration. The INP final products are compared with aerosol samples collected from unmanned aircraft systems (UAS) and analyzed using the ice nucleus counter FRIDGE.
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    Wild fire aerosol optical properties measured by lidar at Haifa, Israel
    (Les Ulis : EDP Sciences, 2018) Heese, Birgit; Hofer, Julian; Baars, Holger; Engelmann, Ronny; Althausen, Dietrich; Schechner, Yoav Y.; Nicolae, D.; Makoto, A.; Vassilis, A.; Balis, D.; Behrendt, A.; Comeron, A.; Gibert, F.; Landulfo, E.; McCormick, M.P.; Senff, C.; Veselovskii, I.; Wandinger, U.
    Optical properties of fresh biomass burning aerosol were measured by lidar during the wild fires in Israel in November 2016. A single-wavelength lidar Polly was operated at the Technion Campus at Haifa. The detector with originally two channels at 532 and 607 nm was recently upgraded with a cross- and a co-polarised channel at 532 nm, and a rotational Raman channel at 530.2 nm. Preliminary results show high particle depolarisation ratios probably caused by soil dust and large fly-ash particles.
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    Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
    (New York,NY,United States : Association for Computing Machinery, 2022) Alam, Mehwish; Iana, Andreea; Grote, Alexander; Ludwig, Katharina; Müller, Philipp; Paulheim, Heiko; Laforest, Frédérique; Troncy, Raphael; Médini, Lionel; Herman, Ivan
    News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.