Glaucoma is now viewed not just a disease of the eye but also a disease of the brain. The prognosis of glaucoma critically depends on how early the disease can be detected. However, early glaucomatous loss of the laminar functions in the human lateral geniculate nucleus (LGN) and superior colliculus (SC) remains difficult to detect and poorly understood. Using functional MRI, we measured neural signals from different layers of the LGN and SC, as well as from the early visual cortices (V1, V2 and MT), in patients with early-stage glaucoma and normal controls. Compared to normal controls, early glaucoma patients showed more reduction of response to transient achromatic stimuli than to sustained chromatic stimuli in the magnocellular layers of the LGN, as well as in the superficial layer of the SC. Magnocellular responses in the LGN were also significantly correlated with the degree of behavioral deficits to the glaucomatous eye. Finally, early glaucoma patients showed no reduction of fMRI response in the early visual cortex. These findings demonstrate that 'large cells' in the human LGN and SC suffer selective loss of response to transient achromatic stimuli at the early stage of glaucoma. Hum Brain Mapp 37:558-569, 2016. © 2015 Wiley Periodicals, Inc.

Mining concept drifting data streams is a defining challenge for data mining research. Recent years have seen a large body of work on detecting changes and building prediction models from stream data, with a vague understanding on the types of the concept drifting and the impact of different types of concept drifting on the mining algorithms. In this paper, we first categorize concept drifting into two scenarios: Loose Concept Drifting (LCD) and Rigorous Concept Drifting (RCD), and then propose solutions to handle each of them separately. For LCD data streams, because concepts in adjacent data chunks are sufficiently close to each other, we apply kernel mean matching (KMM) method to minimize the discrepancy of the data chunks in the kernel space. Such a minimization process will produce weighted instances to build classifier ensemble and handle concept drifting data streams. For RCD data streams, because genuine concepts in adjacent data chunks may randomly and rapidly change, we propose a new Optimal Weights Adjustment (OWA) method to determine the optimum weight values for classifiers trained from the most recent (up-to-date) data chunk, such that those classifiers can form an accurate classifier ensemble to predict instances in the yet-tocome data chunk. Experiments on synthetic and real-world datasets will show that weighted instance approach is preferable when the concept drifting is mainly caused by the changing of the class prior probability; whereas the weighted classifier approach is preferable when the concept drifting is mainly triggered by the changing of the conditional probability.

Suspended piping systems often suffer from severe damages when subjected to seismic excitation. Due to the high flexibility of the piping systems, reducing their displacement is important for the prevention of damage during times of disaster. A solution to protecting piping systems during heavy excitation is the use of the emerging pounding tuned mass damper (PTMD) technology. In particular, the single-sided PTMD combines the advantages of the tuned mass damper (TMD) and the impact damper, including the benefits of a simple design and rapid, efficient energy dissipation. In this paper, two single-sided PTMDs (spring steel-type PTMD and simple pendulum-type PTMD) were designed and fabricated. The dampers were tested and compared with the traditional TMD for mitigating free vibration and forced vibration. In the free vibration experiment, both PTMDs suppressed vibrations much faster than the TMD. For the forced vibration test, the frequency response of the piping system was obtained for three conditions: without control, with TMD control, and with PTMD control. These novel results demonstrate that the single-sided PTMD is a cost-effective method for efficiently and passively mitigating the vibration of suspended piping systems. Thus, the single-sided PTMD will be an important tool for increasing the resilience of structures as well as for improving the safety of their occupants.

Robust and rapid image dense matching is the key to large-scale three-dimensional (3D) reconstruction for multiple Unmanned Aerial Vehicle (UAV) images. However, the following problems must be addressed: (1) the amount of UAV image data is very large, but ordinary computer memory is limited; (2) the patch-based multi-view stereo-matching algorithm (PMVS) does not work well for narrow-baseline cases, and its computing efficiency is relatively low, and thus, it is difficult to meet the UAV photogrammetry's requirements of convenience and speed. This paper proposes an Image-grouping and Self-Adaptive Patch-based Multi-View Stereo-matching algorithm (IG-SAPMVS) for multiple UAV imagery. First, multiple UAV images were grouped reasonably by a certain grouping strategy. Second, image dense matching was performed in each group and included three processes.(1) Initial feature-matching consists of two steps: The first was feature point detection and matching, which made some improvements to PMVS, according to the characteristics of UAV imagery. The second was edge point detection and matching, which aimed to control matching propagation during the expansion process; (2) The second process was matching propagation based on the self-adaptive patch. Initial patches were built that were centered by the obtained 3D seed points, and these were repeatedly expanded. The patches were prevented from crossing the discontinuous terrain by using the edge constraint, and the extent size and shape of the patches could automatically adapt to the terrain relief; (3) The third process was filtering the erroneous matching points. Taken the overlap problem between each group of 3D dense point clouds into account, the matching results were merged into a whole. Experiments conducted on three sets of typical UAV images with different texture features demonstrate that the proposed algorithm can address a large amount of UAV image data almost without computer memory restrictions, and the processing efficiency is significantly better than that of the PMVS algorithm and the matching accuracy is equal to that of the state-of-the-art PMVS algorithm.

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Detecting outliers from big data plays an important role in network security. Previous outlier detection algorithms are generally incapable of handling big data. In this paper we present an parallel outlier detection method for big data, based on a new parallel auto-encoder method. Specifically, we build a replicator model of the input data to obtain the representation of sample data. Then, the replicator model is used to measure the replicability of test data, where records having higher reconstruction errors are classified as outliers. Experimental results show the performance of the proposed parallel algorithm.

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