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  1. Free, publicly-accessible full text available May 10, 2023
  2. McCulloch, R. (Ed.)
    Varying coefficient models (VCMs) are widely used for estimating nonlinear regression functions for functional data. Their Bayesian variants using Gaussian process priors on the functional coefficients, however, have received limited attention in massive data applications, mainly due to the prohibitively slow posterior computations using Markov chain Monte Carlo (MCMC) algorithms. We address this problem using a divide-and-conquer Bayesian approach. We first create a large number of data subsamples with much smaller sizes. Then, we formulate the VCM as a linear mixed-effects model and develop a data augmentation algorithm for obtaining MCMC draws on all the subsets in parallel. Finally, wemore »aggregate the MCMC-based estimates of subset posteriors into a single Aggregated Monte Carlo (AMC) posterior, which is used as a computationally efficient alternative to the true posterior distribution. Theoretically, we derive minimax optimal posterior convergence rates for the AMC posteriors of both the varying coefficients and the mean regression function. We provide quantification on the orders of subset sample sizes and the number of subsets. The empirical results show that the combination schemes that satisfy our theoretical assumptions, including the AMC posterior, have better estimation performance than their main competitors across diverse simulations and in a real data analysis.« less
    Free, publicly-accessible full text available May 1, 2023
  3. Introduction Twitter represents a mainstream news source for the American public, offering a valuable vehicle for learning how citizens make sense of pandemic health threats like Covid-19. Masking as a risk mitigation measure became controversial in the US. The social amplifica- tion risk framework offers insight into how a risk event interacts with psychological, social, institutional, and cultural communication processes to shape Covid-19 risk perception. Methods Qualitative content analysis was conducted on 7,024 mask tweets reflecting 6,286 users between January 24 and July 7, 2020, to identify how citizens expressed Covid-19 risk per- ception over time. Descriptive statistics were computedmore »for (a) proportion of tweets using hyperlinks, (b) mentions, (c) hashtags, (d) questions, and (e) location. Results Six themes emerged regarding how mask tweets amplified and attenuated Covid-19 risk: (a) severity perceptions (18.0%) steadily increased across 5 months; (b) mask effectiveness debates (10.7%) persisted; (c) who is at risk (26.4%) peaked in April and May 2020; (d) mask guidelines (15.6%) peaked April 3, 2020, with federal guidelines; (e) political legitimiz- ing of Covid-19 risk (18.3%) steadily increased; and (f) mask behavior of others (31.6%) composed the largest discussion category and increased over time. Of tweets, 45% con- tained a hyperlink, 40% contained mentions, 33% contained hashtags, and 16.5% were expressed as a question. Conclusions Users ascribed many meanings to mask wearing in the social media information environ- ment revealing that COVID-19 risk was expressed in a more expanded range than objective risk. The simultaneous amplification and attenuation of COVID-19 risk perception on social media complicates public health messaging about mask wearing.« less
    Free, publicly-accessible full text available September 23, 2022
  4. This paper studies an optimal stochastic impulse control problem in a finite time horizon with a decision lag, by which we mean that after an impulse is made, a fixed number units of time has to be elapsed before the next impulse is allowed to be made. The continuity of the value function is proved. A suitable version of dynamic programming principle is established, which takes into account the dependence of state process on the elapsed time. The corresponding Hamilton-Jacobi-Bellman (HJB) equation is derived, which exhibits some special feature of the problem. The value function of this optimal impulse controlmore »problem is characterized as the unique viscosity solution to the corresponding HJB equation. An optimal impulse control is constructed provided the value function is given. Moreover, a limiting case with the waiting time approaching 0 is discussed.« less
  5. Achieving dexterous in-hand manipulation with robot hands is an extremely challenging problem, in part due to current limitations in hardware design. One notable bottleneck hampering the development of improved hardware for dexterous manipulation is the lack of a standardized benchmark for evaluating in-hand dexterity. In order to address this issue, we establish a new benchmark for evaluating in- hand dexterity, specifically for humanoid type robot hands: the Elliott and Connolly Benchmark. This benchmark is based on a classification of human manipulations established by Elliott and Connolly, and consists of 13 distinct in-hand manipulation patterns. We define qualitative and quantitative metricsmore »for evaluation of the benchmark, and provide a detailed testing protocol. Additionally, we introduce a dexterous robot hand - the CMU Foam Hand III - which is evaluated using the benchmark, successfully completing 10 of the 13 manipulation patterns and outperforming human hand baseline results for several of the patterns.« less
  6. Modern microwave radar technologies and systems are taking important roles in healthcare, security, and human–machine interface by remote sensing of human life activities. This paper first reviews the developments in the past decade on the sensing front-end, transponder tag, and leveraging of other wireless infrastructure such as Wi-Fi. Based on the state-of-the-art engineering technologies, several emerging applications will then be studied, including continuous authentication, behavior recognition, human-aware localization, occupancy sensing, blood pressure monitoring, and sleep medicine. As radio frequency spectrum becomes a scarce resource, the allocation and spectrum sharing of life activity sensing bandwidth with other wireless infrastructures will bemore »discussed. Several future research directions will be laid out to solve challenges for ubiquitous deployment of these sensing technologies at the human–microwave frontier.« less
  7. This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to be a binary classification problem, and one might be inclined to employ classic tools (e.g., logistic regression, support vector machines, neural networks) to tackle it. However, wildlife surveys introduce structured noise (especially under-counting) in the species observations. If unaccounted for, these observation errors systematically bias SDMs. To address the unique challenges of SDM,more »this paper proposes a framework called StatEcoNet. Specifically, this work employs a graphical generative model in statistical ecology to serve as the skeleton of the proposed computational framework and carefully integrates neural networks under the framework. The advantages of StatEcoNet over related approaches are demonstrated on simulated datasets as well as bird species data. Since SDMs are critical tools for ecological science and natural resource management, StatEcoNet may offer boosted computational and analytical powers to a wide range of applications that have significant social impacts, e.g., the study and conservation of threatened species.« less
  8. null (Ed.)
  9. In this work, we consider learning a wafer plot recognizer where only one training sample is available. We introduce an approach called Manifestation Learning to enable the learning. The underlying technology utilizes the Variational AutoEncoder (VAE) approach to construct a so-called Manifestation Space. The training sample is projected into this space and the recognition is achieved through a pre-trained model in the space. Using wafer probe test data from an automotive product line, this paper explains the learning approach, its feasibility and limitation.