9,265 research outputs found

    Early Social-Emotional Functioning and Public Health: The Relationship Between Kindergarten Social Competence and Future Wellness

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    A new 20-year study shows a link between children's social skills in kindergarten and their well-being in early adulthood.Researchers from Pennsylvania State and Duke Universities analyzed what happened to nearly 800 kindergarteners from four locations after their teachers measured their social competency skills in 1991. The children were evaluated on a range of social behaviors, such as whether they resolve peer problems, listen to others, share materials, cooperate, and are helpful. Each student then received a composite score representing his or her overall level of positive social skills/behavior, on a scale from ("not at all") to 4 ("very well"). The research team monitored these students and the positive and negative milestones each obtained until they turned 25.Using a variety of data sources, including official records; reports from parents; and self-reporting by the participants, researchers recorded whether the students obtained high school diplomas, college degrees, and full-time jobs. They also kept track of whether students developed a criminal record or substance abuse problems, among other negative outcomes

    A study into annotation ranking metrics in geo-tagged image corpora

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    Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task

    Visual and geographical data fusion to classify landmarks in geo-tagged images

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    High level semantic image recognition and classification is a challenging task and currently is a very active research domain. Computers struggle with the high level task of identifying objects and scenes within digital images accurately in unconstrained environments. In this paper, we present experiments that aim to overcome the limitations of computer vision algorithms by combining them with novel contextual based features to describe geo-tagged imagery. We adopt a machine learning based algorithm with the aim of classifying classes of geographical landmarks within digital images. We use community contributed image sets downloaded from Flickr and provide a thorough investigation, the results of which are presented in an evaluation section

    Analyzing image-text relations for semantic media adaptation and personalization

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    Progress in semantic media adaptation and personalisation requires that we know more about how different media types, such as texts and images, work together in multimedia communication. To this end, we present our ongoing investigation into image-text relations. Our idea is that the ways in which the meanings of images and texts relate in multimodal documents, such as web pages, can be classified on the basis of low-level media features and that this classification should be an early processing step in systems targeting semantic multimedia analysis. In this paper we present the first empirical evidence that humans can predict something about the main theme of a text from an accompanying image, and that this prediction can be emulated by a machine via analysis of low- level image features. We close by discussing how these findings could impact on applications for news adaptation and personalisation, and how they may generalise to other kinds of multimodal documents and to applications for semantic media retrieval, browsing, adaptation and creation

    Automated annotation of landmark images using community contributed datasets and web resources

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    A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases

    Semi-automatic semantic enrichment of raw sensor data

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    One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

    Topology of the Spin-polarized Charge Density in bcc and fcc Iron

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    We investigate the topology of the spin-polarized charge density in bcc and fcc iron. While the total spin-density is found to possess the topology of the non-magnetic prototypical structures, in some cases the spin-polarized densities are characterized by unique topologies; for example, the spin-polarized charge densities of bcc and high-spin fcc iron are atypical of any known for non-magnetic materials. In these cases, the two spin-densities are correlated: the spin-minority electrons have directional bond paths with deep minima in the minority density, while the spin-majority electrons fill these holes, reducing bond directionality. The presence of two distinct spin topologies suggests that a well-known magnetic phase transition in iron can be fruitfully reexamined in light of these topological changes. We show that the two phase changes seen in fcc iron (paramagnetic to low-spin and low-spin to high-spin) are different. The former follows the Landau symmetry-breaking paradigm and proceeds without a topological transformation, while the latter also involves a topological catastrophe.Comment: 5 pages, 3 figures. Phys. Rev. Lett. (in press

    The Regularizing Capacity of Metabolic Networks

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    Despite their topological complexity almost all functional properties of metabolic networks can be derived from steady-state dynamics. Indeed, many theoretical investigations (like flux-balance analysis) rely on extracting function from steady states. This leads to the interesting question, how metabolic networks avoid complex dynamics and maintain a steady-state behavior. Here, we expose metabolic network topologies to binary dynamics generated by simple local rules. We find that the networks' response is highly specific: Complex dynamics are systematically reduced on metabolic networks compared to randomized networks with identical degree sequences. Already small topological modifications substantially enhance the capacity of a network to host complex dynamic behavior and thus reduce its regularizing potential. This exceptionally pronounced regularization of dynamics encoded in the topology may explain, why steady-state behavior is ubiquitous in metabolism.Comment: 6 pages, 4 figure

    Archaeological Investigations Associated With The 1886 Hidalgo County Courthouse And Jail, Hidalgo County, Texas

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    EGV Architects Inc. (Client), on the behalf of the City of Hidalgo, contracted with Raba Kistner Environmental, Inc. (RKEI) to perform archaeological services in support of the on-going restoration to the Old Hidalgo County Courthouse and Jail located in Hidalgo, Hidalgo County, Texas. Services requested included the documentation of two historic cisterns present in the vicinity of the Courthouse and Old Jail Building and the mapping of foundations recently uncovered during the restoration of the Courthouse Building. The Courthouse and Jail were constructed simultaneously in 1886 by S.W. Brooks and were listed on the National Register of Historic Places (NRHP) in 1980 (THC 2016). The Courthouse originally served as the Hidalgo County Courthouse until the county seat was moved to Chapin (later Edinburg) in 1908. The original structure was two-stories, with a cupola. The roof, cupola, and entire second story were destroyed in a fire on Sunday January 18, 1920 (Le Meschacebe 1920). The building later served as an immigration and customs facility. Historic photographs and aerials depict a wall that surrounded the Jail and a probable guard house on the southeast corner within the wall. The tract of land the Courthouse and Jail currently stand contains at least two historic cisterns, one still extending above the existing ground surface (Cistern 1) and another that is currently buried below a functioning parking lot (Cistern 2). A third historic cistern, located at the northwestern corner of the courthouse, is also associated with the courthouse and jail, but is not located within the current project area. In addition to these cisterns, recent excavations for the installation of utilities also uncovered a series of shallowly-buried foundations. The network of these foundations was not been fully exposed. Their exact relationships to each other and to the standing structures on the Courthouse grounds are not well understood. The project had two principal goals. The first goal was to map the recently uncovered foundations that were discovered during the installation of utilities associated with the site. Secondly, RKEI was to collect cultural materials encountered if midden or trash deposits contemporaneous with the use of the Courthouse were revealed during the re-exposure of the foundations. A minimal number of cultural materials, consisting mostly of glass and ceramic, were observed during investigations. As a result of the project, RKEI archaeologists were able to uncover, map, photograph and profile several features within the project area. The edges of the known cistern just south of the jail (Cistern 1) ii were exposed to determine the cistern’s diameter. The buried cistern under the parking lot just east of the Jail (Cistern 2) was uncovered and profiled. The four corners of the original wall that surrounded the Jail were all exposed. The foundations of the guard house located at the southeast corner of the Jail wall were uncovered and documented. Finally, the original brick-lined storm water drains that led from both the Courthouse and Jail to the cistern extending above the existing ground surface just south of the Jail (Cistern 1) were exposed and documented. All exposed features were documented as archaeological site 41HG260. Since the project area is currently owned by a political subdivision of the state, the project fell under the Antiquities Code of Texas as administered by the Texas Historical Commission (THC). The proposed impacts to a property that is listed on the National Register had to conform to the requirements of Section 106 of the National Historic Preservation Act (NHPA) of 1966, as administered by the Texas Historical Commission. The monitoring was conducted under Texas Antiquities Permit No. 7808. Dr. Steve A. Tomka served as Principal Investigator, Mark P. Luzmoor served as Project Archaeologist, and Tomás Cruz served as field technician. Stephen Walker, Landscape Architect and volunteer on the project, aided with excavations and provided insight into the locations of features within the courthouse complex. All field records, photographs, and diagnostic cultural materials collected during investigations will be curated at the Center for Archaeological Research at the University of Texas at San Antonio
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