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  1. Free, publicly-accessible full text available June 1, 2022
  2. A key goal of Next Generation Science Standards is to promote interest and exploration of natural phenomena. In preschool settings, teachers prompt exploration by asking questions, encouraging informal exploration and experimentation. To date, live or offline video observation has been the sole way to capture the quality of teacher question asking in the pre-k classroom (e.g., Sanders et al., 2016). To date, Automatic Speech Recognition (ASR) has not been used to measure the content/quality of teacher talk. Here, we used ASR to quantify preschool teachers’ use of keywords that promote student exploration and inquiry.
  3. In this paper, we develop a novel procedure for low-rank tensor regression, namely Importance Sketching Low-rank Estimation for Tensors (ISLET). The central idea behind ISLET is importance sketching, i.e., carefully designed sketches based on both the responses and low-dimensional structure of the parameter of interest. We show that the proposed method is sharply minimax optimal in terms of the mean-squared error under low-rank Tucker assumptions and under the randomized Gaussian ensemble design. In addition, if a tensor is low-rank with group sparsity, our procedure also achieves minimax optimality. Further, we show through numerical study that ISLET achieves comparable or bettermore »mean-squared error performance to existing state-of-the-art methods while having substantial storage and run-time advantages including capabilities for parallel and distributed computing. In particular, our procedure performs reliable estimation with tensors of dimension $p = O(10^8)$ and is 1 or 2 orders of magnitude faster than baseline methods.« less
  4. Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, has also arisen considerable interests from the PD community. In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing PD cases from controls. Onmore »Parkinson's Progression Markers Initiative (PPMI) cohort, our approach achieved 0.9537±0.0587 AUC, compared with 0.6443±0.0223 AUC achieved by traditional approaches such as PCA.« less
  5. Most of the Eurasian steppe grasslands, including the Balagaer River watershed located in north China, have an arid/semiarid climatic, and thus a vulnerable ecohydrologic, condition. The grass growth in such a region is critical to combat negative eco-environmental issues such as land desertification and the subsequent degradation of pasture productivity. How to predict responses of grass growth to climatic variations and human activities (e.g., grazing) is important for the utilization and protection of steppe grasslands. However, the information of such predictions is yet incomplete in existing literature. Taking the Balagaer River watershed as a test bed, this study parameterized amore »WOFOST (WOrld FOod STudies) simulation model to predict the potential plant growth as influenced by climate and human. The model is calibrated by manually adjusting various eco-physiological parameters, whose initial values were estimated using existing literature, field experiments, and remote sensing techniques. The soil-water parameters (e.g., porosity and saturated hydraulic conductivity) were determined by analyzing least-disturbed soil samples, while the physiological parameters (e.g., assimilation rate) of the dominant vegetation species of Stipa Grandis and Leymus Chinensis were determined by laboratory analyses of grass samples as well as from literature. The grazing frequency and intensity by sheep, horses, and cows were modeled as possible management scenarios. The model was driven by historical climate data recorded in a past half century at a weather station within the watershed. This study firstly expanded the WOFOST’s application to tracing dynamics of steppe grasses, while its results would likely be used to understand the threshold conditions for possibly irreversible degradation of steppe grasslands. In this presentation, we will highlight our successes, challenges, and solutions in parameterizing such a WOFOST model, and show the simulation results.« less
  6. Soil erosion by wind has been found to be negatively related to soil water content, as evidenced by that for a given area, such a soil erosion can be much less in a wet than a dry year. However, few studies have examined the functional relationship between wind erosion and soil moisture, primarily due to lack of field measured data. The objectives of this study were to: 1) measure wind erosion in field using a portable wind tunnel devised and made by the authors; and 2) use the measured data to calibrate/validate a wind erosion model previously developed by themore »authors. The study was conducted in the steppe grassland within the Balaguer watershed located in north China. As part of a larger project funded by National Science Foundation, this study focused on soil conditions with a minimal vegetation coverage to understand the functional relationship between wind erosion, soil moisture, and climate. These conditions are similar with those when the grassland degrades and ultimately becomes deserted. Field samples were analyzed in laboratory to determine the soil characteristics (e.g., moisture content, texture, hydraulic conductivity, and organic content). In this conference, we will present our portable wind tunnel, measured data, and the wind erosion model and its predictions.« less
  7. The accelerating degradation of native grasslands is becoming a threat to the world’s biome supply and has raised serious environmental concerns such as desertification and dust storm. Given that the steppe grasslands, such as those located in the Inner Mongolia Plateau of north China, have a dry climatic condition, the grass growth closely relies on available soil water, which in turn depends on precipitation prior to the growing season (in particular from May to July). However, our understanding of steppe hydrology and water consumption by grasses is incomplete. In this study, the agro-hydrologic Soil Water Plant Atmosphere (SWAP) model wasmore »used to mimic the long-term variations in soil water and vegetation growth in a typical steppe grassland of north China to further understand how alterations of hydrologic processes are related to grassland degradation. A field experiment was conducted to collect the data needed to set up the model. The SWAP model was calibrated using continuous observations of soil moisture and soil temperature at various depths for a simulation period of 2014 to 2017. The results indicated that the SWAP model can be used to simulate the responses of soil moisture and vegetation growth to climates. Moreover, this study examines the water balance and chronological variations of precipitation, evapotranspiration, soil water, and runoff. This study will add new knowledge of steppe hydrologic processes into existing literature.« less