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5.5 million Americans have Alzheimer's disease or some form of dementia and by mid-century, the number of people living with dementia in the United States is projected to grow to 13.8 million. About half of the persons with dementia currently live at home, 43% require occasional support, 47% daily support, and 10% continuous support; with most of this support provided by informal family caregivers. Caregivers face on-going significant risk throughout the ebb and flow of providing care, as care recipients may lose grasp of their understanding of their circumstances, relying heavily on caregivers for all aspects of support. This support includes prompting and cueing through the multi-step instrumental activities of daily living (IADLs) such as meal preparation and house-keeping which require complex cognitive processing and are cognitively demanding. This paper describes the preliminary pilot study work laying the groundwork for ongoing development of a cognitive assistive technology (CAT) to support IADLs in dementia homecare. Through a user-centered design methodology, an online survey for caregivers identified areas of the home with the highest levels of IADL needs, aiding in the development of an 800 sq ft mock-up apartment for preliminary testing of the mobile interface for the CAT tool. [ABSTRACT FROM AUTHOR]

In this paper, we apply the profile least-square method and inverse probability weighted method to define estimation of the error variance in partially linear varying-coefficient model when the covariates are missing at random. At the same time, we construct a jackknife estimator and jackknife empirical likelihood (JEL) statistic of the error variance, respectively. It is proved that the proposed estimators are asymptotical normality and the JEL statistic admits a limiting standard chi-square distribution. A simulation study is conducted to compare the JEL method with the normal approximation approach in terms of coverage probabilities and average interval lengths, and a comparison of the proposed estimators is done based on sample means, biases and mean square errors under different settings. Subsequently, a real data set is analyzed for illustration of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with composite quantile regression for partially linear varying-coefficient models with heteroscedasticity when data are right censored and censoring indicators are missing at random. We construct estimators of parametric regression coefficients and nonparametric varying-coefficient functions in the proposed models based on regression calibration, imputation and inverse probability weighted approaches. The asymptotic normality of the proposed estimators is proved. Meanwhile, an adaptive LASSO penalized variable selection method and its oracle property are considered. We also demonstrate the performance of the proposed estimation method and variable selection procedure through comprehensive simulations and a real-data application. [ABSTRACT FROM AUTHOR]

We present a new framework for online dense 3D reconstruction of indoor scenes by using only depth sequences. This research is particularly useful in cases with a poor light condition or in a nearly featureless indoor environment. The lack of RGB information makes long-range camera pose estimation difficult in a large indoor environment. The key idea of our research is to take advantage of the geometric prior of Manhattan scenes in each stage of the reconstruction pipeline with the specific aim to reduce the cumulative registration error and overall odometry drift in a long sequence. This idea is further boosted by local Manhattan frame growing and the local-to-global strategy that leads to implicit loop closure handling for a large indoor scene. Our proposed pipeline, namely ManhattanFusion, starts with planar alignment and local pose optimization where the Manhattan constraints are imposed to create detailed local segments. These segments preserve intrinsic scene geometry by minimizing the odometry drift even under complex and long trajectories. The final model is generated by integrating all local segments into a global volumetric representation under the constraint of Manhattan frame-based registration across segments. Our algorithm outperforms others that use depth data only in terms of both the mean distance error and the absolute trajectory error, and it is also very competitive compared with RGB-D based reconstruction algorithms. Moreover, our algorithm outperforms the state-of-the-art in terms of the surface area coverage by 10-40 percent, largely due to the usefulness and effectiveness of the Manhattan assumption through the reconstruction pipeline. [ABSTRACT FROM AUTHOR]

Key-frame extraction and object segmentation are usually implemented independently and separately due to the fact that they are on different semantic levels and involve different features. In this work, we propose a joint key-frame extraction and object segmentation method by constructing a unified feature space for both processes, where key-frame extraction is formulated as a feature selection process for object segmentation in the context of Gaussian mixture model (GMM)-based video modeling. Specifically, two divergence-based criteria are introduced for key-frame extraction. One recommends key-frame extraction that leads to the maximum pairwise interclass divergence between GMM components. The other aims at maximizing the marginal divergence that shows the intra-frame variation of the mean density. The proposed methods can extract representative key-frames for object segmentation, and some interesting characteristics of key-frames are also discussed. This work provides a unique paradigm for content-based video analysis. Index Terms--Cluster divergence, feature selection, Gaussian mixture model, key-frame extraction, object segmentation.

Video segmentation has been an important and challenging issue for many video applications. Usually there are two different video segmentation approaches, i.e., shot-based segmentation that uses a set of key-frames to represent a video shot and object-based segmentation that partitions a video shot into objects and background. Representing a video shot at different semantic levels, two segmentation processes are usually implemented separately or independently for video analysis. In this paper, we propose a new approach to combine two video segmentation techniques together. Specifically, a combined key-frame extraction and object-based segmentation method is developed based state-of-the-art video segmentation algorithms and statistical clustering approaches. On the one hand, shot-based segmentation can dramatically facilitate and enhance object-based segmentation by using key-frame extraction to select a few key-frames for statistical model training. On the other hand, object-based segmentation can be used to improve shot-based segmentation results by using model-based key-frame refinement. The proposed approach is able to integrate advantages of these two segmentation methods and provide a new combined shot-based and object-based framework for a variety of advanced video analysis tasks. Experimental results validate effectiveness and flexibility of the proposed video segmentation algorithm. Index Terms--Expectation maximization, Gaussian mixture model (GMM), key-frame extraction, object-based video segmentation, shot-based video segmentation, statistical clustering.

This paper studies an uneven two-class unsupervised classification problem of satellite imagery, i.e., the mapping of U.S. Department of Agriculture's (USDA) Conservation Reserve Program (CRP) tracts. CRP is a nationwide program that encourages farmers to plant long-term, resource conserving covers to improve soil, water, and wildlife resources. With recent payments of nearly US $1.6 billion for new enrollments (2002 signup), it is imperative to obtain accurate digital CRP maps for management and evaluation purposes. CRP mapping is a complex classification problem where both CRP and non-CRP areas are composed of various cover types. Two nonparametric machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVMs) are implemented in this work. Specifically, considering the importance of CRP classification sensitivity, a new DTC pruning method is proposed to increase recall. We also study two SVM relaxation approaches to increase recall. Moreover, a localized and parallel framework is suggested in order to efficiently deal with the large-scale CRP mapping need. Simulation results validate the applicability of the suggested framework and proposed techniques. Index Terms--Conservation reserve program, decision tree, multisource data classification, support vector machine (SVM).