Reducing the Number of Falls in On Lok Participants by Enhancing Homecare Services

Falls in elderly are one of the major health concerns in the US. They comprise up to 80% of the key risk factors for injuries in the elderly in the US (Spears, Roth, Miake-Lye, Saliba, Shekelle, & Ganz, 2013). The project aims at reduction of falls among the elderly participants in On Lok program. Based on the findings of the literature review and observations conducted, the proposed intervention to address the practice gap will involve emphasizing the need for carrying the mobility devices, such as canes or walkers, along with clearing the participants’ home environment from hazards. The anticipated measure of the project success comprises of the outcomes, namely, the reduced number of falls which caused by the participants not using their mobility devices and environmental hazards at home from three to two per month based on the implemented intervention. The results of the project will be evaluated with regard to the usefulness and success of the intervention to addressing the problem and the conclusions will be drawn whether the strategy is likely to lead to healthcare service improvement for elderly population, as expected by the project goals

Effect of Earth's rotation on the trajectories of free-fall bodies in Equivalence Principle Experiment

Owing to Earth's rotation a free-fall body would move in an elliptical orbit rather than along a straight line forward to the center of the Earth. In this paper on the basis of the theory for spin-spin coupling between macroscopic rotating bodies we study violation of the equivalence principle from long-distance free-fall experiments by means of a rotating ball and a non-rotating sell. For the free-fall time of 40 seconds, the difference between the orbits of the two free-fall bodies is of the order of 10^{-9}cm which could be detected by a SQUID magnetometer owing to such a magnetometer can be used to measure displacements as small as 10^{-13} centimeters.Comment: 6 pages, 4 figure

Late Fusion Multi-view Clustering via Global and Local Alignment Maximization

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering, which could cause over-complicated optimization and intensive computational cost. In this paper, we propose late fusion MVC via alignment maximization to address these issues. To do so, we first reveal the theoretical connection of existing k-means clustering and the alignment between base partitions and the consensus one. Based on this observation, we propose a simple but effective multi-view algorithm termed LF-MVC-GAM. It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones. Such an alignment is beneficial to integrate partition level information and significantly reduce the computational complexity by sufficiently simplifying the optimization procedure. We then design another variant, LF-MVC-LAM to further improve the clustering performance by preserving the local intrinsic structure among multiple partition spaces. After that, we develop two three-step iterative algorithms to solve the resultant optimization problems with theoretically guaranteed convergence. Further, we provide the generalization error bound analysis of the proposed algorithms. Extensive experiments on eighteen multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The codes of the proposed algorithms are publicly available at https://github.com/wangsiwei2010/latefusionalignment

Coupled Multiple Kernel Learning for Supervised Classification

Multiple kernel learning (MKL) has recently received significant attention due to the fact that it is able to automatically fuse information embedded in multiple base kernels and then find a new kernel for classification or regression. In this paper, we propose a coupled multiple kernel learning method for supervised classification (CMKL-C), which comprehensively involves the intra-coupling within each kernel, inter-coupling among different kernels and coupling between target labels and real ones in MKL. Specifically, the intra-coupling controls the class distribution in a kernel space, the inter-coupling captures the co-information of base kernel matrices, and the last type of coupling determines whether the new learned kernel can make a correct decision. Furthermore, we deduce the analytical solutions to solve the CMKL-C optimization problem for highly efficient learning. Experimental results over eight UCI data sets and three bioinformatics data sets demonstrate the superior performance of CMKL-C in terms of the classification accuracy

The role of US agricultural and forest activities in global climate change mitigation

In 2005 the highest global surface temperature ever was recorded. A virtual consensus exists today among scientists that global warming is underway and that human greenhouse gas (GHG) emissions are a significant cause. Possible mitigation of climate change through reduction of net GHG emissions has become a worldwide concern. Under the United Nation’s Framework convention on Climate Change, the Kyoto Protocol was formed in 1997 and required ratifying countries to co-operate in stabilizing atmospheric GHG concentrations. The protocol took effect on February 16, 2005. The mitigation cost for reducing GHG emissions for the US economy has been argued to be high particularly through the energy sector. Agriculture and Forestry (AF) can provide some low cost strategies to help with this mitigation principally through carbon sequestration but must be competitive with mitigation costs in the rest of the economy. A general equilibrium approach is used herein to evaluate the role of AF mitigation in an economy wide setting. The results show that the AF sectors have significant mitigation potential. Higher carbon prices lead to more sequestration, less emissions, reduced consumer and total welfare, improved environmental indicators and increased producer welfare. AF mitigation increases as the carbon price increase over time. In the earlier periods, while the carbon price is low, AF emissions and sink are quite small compared to the energy sector. As carbon prices increase over time, the AF sectors mitigate about 25% of the net emissions. This verifies McCarl et al's (2001) argument that the AF sectors “may be very important in a world that requires time and technological investment to develop low-cost greenhouse gas emission offsets.” AF GHG emission mitigation is sensitive to saturation of sequestration sinks. This research finds that ignoring saturation characteristics leads to a severe overestimate of mitigation potential with estimates being inflated by as much as a factor of 6

Video anomaly detection and localization by local motion based joint video representation and OCELM

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)