A Signal processing approach for preprocessing and 3d analysis of airborne small-footprint full waveform lidar data

The extraction of structural object metrics from a next generation remote sensing modality, namely waveform light detection and ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, a number of challenges need to be addressed before structural or 3D vegetation modeling can be accomplished. These include proper processing of complex, often off-nadir waveform signals, extraction of relevant waveform parameters that relate to vegetation structure, and from a quantitative modeling perspective, 3D rendering of a vegetation object from LiDAR waveforms. Three corresponding, broad research objectives therefore were addressed in this dissertation. Firstly, the raw incoming LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. A robust signal preprocessing chain for LiDAR waveform calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification is presented. This preprocessing chain was validated using both simulated waveform data of high fidelity 3D vegetation models, which were derived via the Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling environment and real small-footprint waveform LiDAR data, collected by the Carnegie Airborne Observatory (CAO) in a savanna region of South Africa. Results showed that the preprocessing approach significantly increased our ability to recover the temporal signal resolution, and resulted in improved waveform-based vegetation biomass estimation. Secondly, a model for savanna vegetation biomass was derived using the resultant processed waveform data and by decoding the waveform in terms of feature metrics for woody and herbaceous biomass estimation. The results confirmed that small-footprint waveform LiDAR data have significant potential in the case of this application. Finally, a 3D image clustering-based waveform LiDAR inversion model was developed for 1st order (principal branch level) 3D tree reconstruction in both leaf-off and leaf-on conditions. These outputs not only contribute to the visualization of complex tree structures, but also benefit efforts related to the quantification of vegetation structure for natural resource applications from waveform LiDAR data

Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks

As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets. Despite being widely used, we find that these datasets suffer from spurious correlations, which are ignored by existing studies and lead to severe overestimation of existing rumor detection performance. The spurious correlations stem from three causes: (1) event-based data collection and labeling schemes assign the same veracity label to multiple highly similar posts from the same underlying event; (2) merging multiple data sources spuriously relates source identities to veracity labels; and (3) labeling bias. In this paper, we closely investigate three of the most popular rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and propose event-separated rumor detection as a solution to eliminate spurious cues. Under the event-separated setting, we observe that the accuracy of existing state-of-the-art models drops significantly by over 40%, becoming only comparable to a simple neural classifier. To better address this task, we propose Publisher Style Aggregation (PSA), a generalizable approach that aggregates publisher posting records to learn writing style and veracity stance. Extensive experiments demonstrate that our method outperforms existing baselines in terms of effectiveness, efficiency and generalizability.Comment: Accepted to ECML-PKDD 202

最大事後分布を用いたクラスの分布推定に基づくFew-Shot画像分類に関する研究

早大学位記番号:新9156博士(工学)早稲田大

The Why of Abandonment: Effects of Team Diversity and Leadership Type on the Disbandment and Stagnation of Online Medical Teams

Medical teams (MTs) online could provide more comprehensive and rapid services to patients through the collaboration among physicians. Numerous doctors have participated, but parts of MTs disband or stagnate after a period, so this pressing issue is in need of relief through exploring the reasons. Effects of team diversity, leadership types and their interaction on the team disbandment and stagnation were studied. This study comprehensively examined a sample of 1,071 MTs online, the total MTs on January 10, 2018, and we crawled the data from a leading OHC in China. Logistic regression was utilized. Results revealed team state would be influenced by team diversity and its interaction with leadership type, so the combination pairwise of the leadership and team diversity could reduce the abandonment possibility. Implications in theory and practice about the dealing with the abandonment crisis in online health community, and limitations are discussed

Enhancing Learners’ Critical Thinking Ability Through Pedagogical Translation in English Language Teaching

The paper explores the strategic use of pedagogical translation in English language teaching (ELT) to enhance Chinese learners’ critical thinking ability. Different from the studies on translation theories and the teaching of translation to English majors, the present study assumes that pedagogical translation is a rational activity which can effectively help learners understand the source language and target language precisely. The translation of four different word categories is discussed and analyzed by using examples. It is concluded that learners can improve their critical thinking ability and language competence through designed translation activities

THE NON-EXISTENT CHAIR SERIES: EVALUATING GENERATIVE DESIGN OUTCOMES

Generative Design has been a popular topic in the design world for a while, earlier inventions like shape grammar and space syntax generate geometrical designs with sets of rules defined by the user. The latest invention of generative design is artificial neural networks like GAN (Generative Adversarial Network), which created a new logic of generative design. Earlier inventions focused on geometrical exploration with applied rules; therefore, the generated designs are calculated results. GANs, on the other hand, because of the nature of deep learning networks - are like a black box. Since there is no way of supervising what happens within, there are levels of randomness and uncertainty. GANs are also trained with images instead of geometrical shapes or forms. Making it capable of exploring colors, image depth, as well as overall composition. In a way, it changed the logical decision-making process in design into something more spontaneous. AI also enabled a new production journey map from ideation to manufacture, introducing new design opportunities. However, when it comes to evaluating generative design, most of current work are done by developers. Which focused on statistical evaluations to calculate the similarities between the dataset and the generated images. While they are valuable for improving algorithm efficiency, it may not apply to the designs. Current evaluation methods lack empathy, especially when it comes to judging and critiquing good vs. bad design. This work aims to explore the usability and applicability of generative networks by coming up with non-statistical measurable features. This work aims to answer how realistic the generated designs need to be for them to be “viable”, and for designers to be able to recognize the object for what it is. And how the pursuit of photorealism in image generation networks may not apply to the field of design.M.S

Heavy metal induced ecophysiological function alterations in the euhalophyte Suaeda salsa

Heavy metal accumulation affects the physiological status of plants. Suaeda salsa L. is used to investigate the toxic effects of cadmium (Cd) and lead (Pb) either alone or mixtures under the static test conditions. Cd-Pb mixture exposure can decrease lignin content and weaken the increase. Mitochondrial calcium content significantly reduced at 30 µM Cd and Pb exposure. Cd-Pb mixture exposure can increase calcium content under the same concentration exposure. Soluble sugar levels noted a significant decrease in Cd, Pb and Cd-Pb mixture exposure. The accumulations of Cd, Pb in S. salsa were significantly increased with exposure time. Soluble protein (SP) in S. salsa at 30 µM concentration treatments decreased with exposure time. Heat shock protein 70 (HSP70) was enhanced lightly along with the increase of added Cd-Pb from 30 to 70 &3181;M and then decreased below the controls which present a synergistic effect. Heat shock protein 60 (HSP60) increased slightly with the increase of Cd-Pb from 30 to 110 µM, and then decreased hereafter and significantly inhibited at 150 ƒÊM (p<0.05). Moreover, Cd-Pb mixture exposure significantly increased the Rubisco activity under lower concentration and presented antagonistic effect. At the same time, the viability percent decreased as increase Cd-Pb concentration exposure (p0.05), it presents a dose-dependent manner. Mitochondrial cells treated with Cd-Pb exposure obviously reduced the reactive oxygen species (ROS) levels in mitochondrial cells.Key words: Suaeda salsa, heavy metal, ecophysiological function

Investigating the intention of purchasing private pension scheme based on an integrated FBM-UTAUT model: The case of China

The newly established private pension scheme in China has received great attention as it would be an important supplement to China’s social safety net and corporate annuity amid an aging population. It provides a way of helping to address the challenge of ensuring adequate retirement income, and the scheme is expected to grow significantly in the coming years. This study investigates factors affecting the intention of purchasing the private pension scheme using a conceptual model based on the integration of Fogg Behavioral Model (FBM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model. The questionnaire-based data from a sample of 462 respondents had been analyzed. Both exploratory factor analysis and confirmatory factor analysis were used to assess validity. The hypothesized relationships in the integrated FBM-UTAUT model were tested using structural equation modeling. The research findings indicate that anticipation, social influence, effort expectancy, performance expectancy, side benefits and facilitating conditions have significant positive impacts on intention to purchase. According to the exploratory factor analysis, the integrated FBM-UTAUT model can explain more than 70% of the total variance. Meanwhile, effort expectancy can be affected by time effort, thought effort and physical effort collectively, while performance expectancy can be affected by risk and trust. It is revealed that the integrated FBM-UTAUT model can be effective in explaining purchase intentions in a private pension scheme context, and this study is expected to offer helpful advice on the design of pension products and the reform of pension policies