Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework

Type-Constrained Representation Learning in Knowledge Graphs

Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type-constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data

A Multivariate Surface-Based Analysis of the Putamen in Premature Newborns: Regional Differences within the Ventral Striatum

Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus) between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity disorder and attention-related learning disabilities in preterm neonates. © 2013 Shi et al

V559A and N822I double KIT mutant melanoma with predictable response to imatinib?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88125/1/j.1755-148X.2010.00822.x.pd

Analysis of Extended Threshold Wavelength Photoresponse in Nonsymmetrical p-GaAs/AlGaAs Heterostructure Photodetectors

We analyze the extended threshold wavelength photoresponse beyond the standard threshold limit (λt = 1.24/Δ, where Δ is the activation energy) in nonsymmetrical pGaAs/AlGaAs heterostructure photodetectors with a barrier energy offset. We propose that hot-cold hole carrier interactions in the p-GaAs absorber are responsible for the threshold wavelength extension. Experimental results are analyzed by considering a quasi-Fermi distribution of hot holes at a hot hole temperature (TH), which is much higher than the lattice temperature (TL). The experimental photoresponse is fitted using an escape cone model, modified with a quasi-Fermi level (EquasiF). The simulated results are found to be in good agreement with experimental data, justifying the model used

Men in Macau SAR have higher prevalence in metabolic syndrome and among related metabolic components: a cross-sectional Macau Health Survey

Background Macau has recently experienced expansive socioeconomic growth, leading to lifestyle changes that could have contributed to the development of certain diseases. Little information exists on the prevalence of metabolic syndrome (MetS) and associated risk factors. This information is important, since the management of MetS is tightly connected with prevention of cardiovascular diseases in the population. Methods This study is based on the cross-sectional Macau Health Survey 2006. Information on anthropometry, physical measurements, socio-demographics, laboratory tests and life-style habits was collected by trained health professionals from a random sub-population sample, aged 18-44 (32.6 ± 8.3). Body Mass Index (BMI) cut-offs were based on WHO criteria for Asian population. The prevalence of MetS, as defined by the International Diabetes Federation was calculated and the associated lifestyle factors were analysed. Results Among Macau’s adults (n = 1592), the age-adjusted prevalence of MetS was over two times higher in men (10.5%) than in woman (3.7%), (p <0.01). 15.8% were overweight (BMI ≥23 < 25) and 18.8% were obese (BMI ≥25). Man had significantly higher risk profile in almost all components of MetS (p <0.001), except the waist circumference and HDL. BMI, age and education were significantly related to MetS in both genders (p <0.001). Conclusions We found significant gender differences in MetS among the 18 – 44 year old population of Macau, which should be addressed separately in the gender-specific preventive strategies.published_or_final_versio

Automatic summarization of rushes video using bipartite graphs

In this paper we present a new approach for automatic summarization of rushes, or unstructured video. Our approach is composed of three major steps. First, based on shot and sub-shot segmentations, we filter sub-shots with low information content not likely to be useful in a summary. Second, a method using maximal matching in a bipartite graph is adapted to measure similarity between the remaining shots and to minimize inter-shot redundancy by removing repetitive retake shots common in rushes video. Finally, the presence of faces and motion intensity are characterised in each sub-shot. A measure of how representative the sub-shot is in the context of the overall video is then proposed. Video summaries composed of keyframe slideshows are then generated. In order to evaluate the effectiveness of this approach we re-run the evaluation carried out by TRECVid, using the same dataset and evaluation metrics used in the TRECVid video summarization task in 2007 but with our own assessors. Results show that our approach leads to a significant improvement on our own work in terms of the fraction of the TRECVid summary ground truth included and is competitive with the best of other approaches in TRECVid 2007

Genetic Classification of Populations using Supervised Learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On

Budget Processes: Theory and Experimental Evidence

This paper studies budget processes, both theoretically and experimentally. We compare the outcomes of bottom-up and top-down budget processes. It is often presumed that a top-down budget process leads to a smaller overall budget than a bottom-up budget process. Ferejohn and Krehbiel (1987) showed theoretically that this need not be the case. We test experimentally the theoretical predictions of their work. The evidence from these experiments lends strong support to their theory, both at the aggregate and the individual subject level