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  1. A longstanding goal of learner modeling and educational data min-ing is to improve the domain model of knowledge that is used to make inferences about learning and performance. In this report we present a tool for finding domain models that is built into an exist-ing modeling framework, logistic knowledge tracing (LKT). LKT allows the flexible specification of learner models in logistic re-gression by allowing the modeler to select whatever features of the data are relevant to prediction. Each of these features (such as the count of prior opportunities) is a function computed for a compo-nent of data (such as amore »student or knowledge component). In this context, we have developed the “autoKC” component, which clus-ters knowledge components and allows the modeler to compute features for the clustered components. For an autoKC, the input component (initial KC or item assignment) is clustered prior to computing the feature and the feature is a function of that cluster. Another recent new function for LKT, which allows us to specify interactions between the logistic regression predictor terms, is com-bined with autoKC for this report. Interactions allow us to move beyond just assuming the cluster information has additive effects to allow us to model situations where a second factor of the data mod-erates a first factor.« less
    Free, publicly-accessible full text available July 1, 2023
  2. Objective: Mortality-trends from alcoholic liver disease (ALD) have recently increased and they differ by various factors in the U.S. However, these trends have only been analyzed using univariate models and in reality they may be influenced by various factors. We thus examined trends in age-standardized mortality from ALD among U.S. adults for 1999-2017, using multivariable piecewise log-linear models. Methods: We collected mortality-data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research database, using the Underlying Cause of Death. Results: We identified 296,194 deaths from ALD and 346,386 deaths indirectly attributable to ALD during the periodmore »from 1999-2017. The multivariable-adjusted, age-standardized ALD mortality was stable during 1999-2006 (annual percentage change [APC]=-2.24, P=0.24), and increased during 2006-2017 (APC=3.18, P<0.006). Their trends did not differ by sex, race, age or urbanization. Subgroup analyses revealed upward multivariable-adjusted, age-standardized mortality-trends in alcoholic fatty liver (APC=4.64, P<0.001), alcoholic hepatitis (APC=4.38, P<0.001), and alcoholic cirrhosis (APC=5.33, P<0.001), but downward mortality-trends in alcoholic hepatic failure (APC=-1.63, P=0.006) and unspecified ALD (APC=-0.86, P=0.013). Strikingly, non-alcoholic cirrhosis also had an upward multivariable-adjusted, age-standardized mortality-trend (APC=0.69, P=0.046). By contrast, recent mortality-trends were stable for all cause of deaths (APC=-0.39, P=0.379) and downward for malignant neoplasms excluding liver cancer (APC=-2.82, P<0.001), infections (APC=-2.60, P<0.001), cardiovascular disease (APC=-0.69, P=0.044) and respiratory disease (APC=-0.56, P=0.002). The adjusted mortality with ALD as a contributing cause of death also had an upward trend during 2000-2017 (APC=5.47, P<0.001). Strikingly, common comorbidities of ALD, including hepatocellular carcinoma, cerebrovascular and ischemic heart cardiovascular diseases and sepsis, had upward trends during the past 14 to 16 years. Conclusions: ALD had an upward multivariable-adjusted, age-standardized mortality-trend among U.S. adults, without significant differences by sex, race, age or urbanization. Three ALD subtypes (alcoholic fatty liver, alcoholic hepatitis and alcoholic cirrhosis) and non-alcoholic cirrhosis had upward morality-trends, while other ALD subtypes and other causes of death did not.« less
    Free, publicly-accessible full text available February 28, 2023
  3. Abstract Piezoelectric surface acoustic waves (SAWs) are powerful for investigating and controlling elementary and collective excitations in condensed matter. In semiconductor two-dimensional electron systems SAWs have been used to reveal the spatial and temporal structure of electronic states, produce quantized charge pumping, and transfer quantum information. In contrast to semiconductors, electrons trapped above the surface of superfluid helium form an ultra-high mobility, two-dimensional electron system home to strongly-interacting Coulomb liquid and solid states, which exhibit non-trivial spatial structure and temporal dynamics prime for SAW-based experiments. Here we report on the coupling of electrons on helium to an evanescent piezoelectric SAW.more »We demonstrate precision acoustoelectric transport of as little as ~0.01% of the electrons, opening the door to future quantized charge pumping experiments. We also show SAWs are a route to investigating the high-frequency dynamical response, and relaxational processes, of collective excitations of the electronic liquid and solid phases of electrons on helium.« less
    Free, publicly-accessible full text available December 1, 2022
  4. The success of supervised learning requires large-scale ground truth labels which are very expensive, time- consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D point cloud features, this paper presents a novel and effective self-supervised learning approach to jointly learn both 2D image features and 3D point cloud features by exploiting cross-modality and cross-view correspondences without using any human annotated labels. Specifically, 2D image features of rendered images from different views are extracted by a 2Dmore »convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network. Two types of features are fed into a two-layer fully connected neural network to estimate the cross-modality correspondence. The three networks are jointly trained (i.e. cross-modality) by verifying whether two sampled data of different modalities belong to the same object, meanwhile, the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views (i.e. cross-view). The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition, 3D shape recognition, multi-view 2D shape retrieval, 3D shape retrieval, and 3D part-segmentation. Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method.« less
  5. Free, publicly-accessible full text available June 1, 2023