24,920 research outputs found

    A system for learning statistical motion patterns

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    Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

    A system for learning statistical motion patterns

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    Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

    Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

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    Abstract. Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semantic representation is shared between an annotated auxiliary dataset and a target dataset with no annotation. A projection from a low-level feature space to the seman-tic space is learned from the auxiliary dataset and is applied without adaptation to the target dataset. In this paper we identify an inher-ent limitation with this approach. That is, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift prob-lem and propose a novel framework, transductive multi-view embedding, to solve it. It is ‘transductive ’ in that unlabelled target data points are explored for projection adaptation, and ‘multi-view ’ in that both low-level feature (view) and multiple semantic representations (views) are embedded to rectify the projection shift. We demonstrate through ex-tensive experiments that our framework (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complemen-tarity of multiple semantic representations, (3) achieves state-of-the-art recognition results on image and video benchmark datasets, and (4) en-ables novel cross-view annotation tasks.

    Hydrostatic pressure effects on the static magnetism in Eu(Fe0.925_{0.925}Co0.075_{0.075})2_{2}As2_{2}

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    The effects of hydrostatic pressure on the static magnetism in Eu(Fe0.925_{0.925}Co0.075_{0.075})2_{2}As2_{2} are investigated by complementary electrical resistivity, ac magnetic susceptibility and single-crystal neutron diffraction measurements. A specific pressure-temperature phase diagram of Eu(Fe0.925_{0.925}Co0.075_{0.075})2_{2}As2_{2} is established. The structural phase transition, as well as the spin-density-wave order of Fe sublattice, is suppressed gradually with increasing pressure and disappears completely above 2.0 GPa. In contrast, the magnetic order of Eu sublattice persists over the whole investigated pressure range up to 14 GPa, yet displaying a non-monotonic variation with pressure. With the increase of the hydrostatic pressure, the magnetic state of Eu evolves from the canted antiferromagnetic structure in the ground state, via a pure ferromagnetic structure under the intermediate pressure, finally to a possible "novel" antiferromagnetic structure under the high pressure. The strong ferromagnetism of Eu coexists with the pressure-induced superconductivity around 2 GPa. The change of the magnetic state of Eu in Eu(Fe0.925_{0.925}Co0.075_{0.075})2_{2}As2_{2} upon the application of hydrostatic pressure probably arises from the modification of the indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between the Eu2+^{2+} moments tuned by external pressure.Comment: 9 pages, 6 figure

    DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

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    In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

    A convolutional neural network for impact detection and characterization of complex composite structures

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    This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts

    Decreased levels of circulating sex hormones as a biomarker of lung cancer in male patients with solitary pulmonary nodules

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    Background: An early differentiation of malignant from benign solitary pulmonary nodules (SPNs) is essential for management and prognosis of lung cancer.Objectives: Here we investigated whether measurement of circulating sex hormones could be useful for an early detection of malignancy among patients with SPNs.Methods: We recruited 47 patients with malignant SPNs, 45 patients with benign SPNs, and 32 healthy persons. Testosterone, estradiol, and progesterone were measured. Carcinoembryonic antigen (CEA) as well as TNF-α, IL-1 and IL-6 were also measured.Results: We found that sex hormones were decreased significantly in patients with malignant SPNs, as compared to patients with benign SPNs and healthy controls (P<0.05). Sex hormones levels showed a trend to decline in patients with benign SPNs as compared to normal controls, but the difference was not statistically significant (P>0.05). CEA levels were only abnormally elevated in eight patients with lung adenocarcinoma. The inflammatory cytokines were remarkably higher in both patients than in normal controls. However, there was no statistical difference in these cytokines among patients.Conclusions: The reduced sex hormones levels seemed to be uniquely associated with lung cancer. Therefore, measurement of sex hormones may have clinical potential in the diagnosis of malignancy in patients with SPNs.Keywords: solitary pulmonary nodules (SPNs), sex hormones, lung cancer, biomarker

    Zero-Shot Learning on Semantic Class Prototype Graph

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    Hydrologic feasibility of artificial forestation in the semi-arid Loess Plateau of China

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    Hydrologic viability, in terms of moisture availability, is fundamental to ecosystem sustainability in arid and semi-arid regions. In this study, we examine the spatial distribution and after-planting variations of soil moisture content (SMC) in black locust tree (<i>Robinia pseudoacacia</i> L.) plantings in the Loess Plateau of China at a regional scale. Thirty sites (5 to 45 yr old) were selected, spanning an area of 300 km by 190 km in the northern region of the Shaanxi Province. The SMC was measured to a depth of 100 cm at intervals of 10 cm. Geographical, topographic and vegetation information was recorded, and soil organic matter was evaluated. The results show that, at the regional scale, SMC spatial variability was most highly correlated with rainfall. The negative relationship between the SMC at a depth of 20–50 cm and the stand age was stronger than at other depths, although this relationship was not significant at a 5 % level. Watershed analysis shows that the after-planting SMC variation differed depending upon precipitation. The SMC of plantings in areas receiving sufficient precipitation (e.g., mean annual precipitation (MAP) of 617 mm) may increase with stand age due to improvements in soil water-holding capacity and water-retention abilities after planting. For areas experiencing water shortages (e.g., MAP = 509 mm), evapotranspiration may cause planting soils to dry within the first 20 yr of growth. It is expected that, as arid and semi-arid plantings age, evapotranspiration will decrease, and the soil profile may gradually recover. In extremely dry areas (e.g., MAP = 352 mm), the variation in after-planting SMC with stand age was found to be negligible. The MAP can be used as an index to divide the study area into different ecological regions. Afforestation may sequentially exert positive, negative and negligible effects on SMCs with a decrease in the MAP. Therefore, future restoration measures should correspond to the local climate conditions, and the MAP should be a major consideration for the Loess Plateau. Large-scale and long-term research on the effects of restoration projects on SMCs is needed to support more effective restoration policies. The interaction between afforestation and local environmental conditions, particularly water availability to plants, should be taken into account in afforestation campaigns in arid and semi-arid areas
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