Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

The effects of a changing pollution climate on throughfall deposition and cycling in a forested area in southern England

This study compares two sets of measurements of the composition of bulk precipitation and throughfall at a site in southern England with a 20-year gap between them. During this time, SO2 emissions from the UK fell by 82%, NOx emissions by 35% and NH3 emissions by 7%. These reductions were partly reflected in bulk precipitation, with deposition reductions of 56% in SO4,38% in NO3, 32% in NH4, and 73% in H+. In throughfall under Scots pine, the effects were more dramatic, with an 89% reduction in SO4 deposition and a 98% reduction in H+ deposition. The mean pH under these trees increased from 2.85 to 4.30. Nitrate and ammonium deposition in throughfall increased slightly, however. In the earlier period, the Scots pines were unable to neutralise the high flux of acidity associated with sulphur deposition, even though this was not a highly polluted part of the UK, and deciduous trees (oak and birch) were only able to neutralise it in summer when the leaves were present. In the later period, the sulphur flux had reduced to the point where the acidity could be neutralised by all species — the neutralisation mechanism is thus likely to be largely leaching of base cations and buffering substances from the foliage. The high fluxes are partly due to the fact that these are 60–80 year old trees growing in an open forest structure. The increase in NO3 and NH4 in throughfall in spite of decreased deposition seems likely due to a decrease in foliar uptake, perhaps due to the increasing nitrogen saturation of the catchment soils. These changes may increase the rate of soil microbial activity as nitrogen increases and acidity declines, with consequent effects on water quality of the catchment drainage stream.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.

Evaluation of cumulative externality: environmental economics, oligopoly and the private provision of public goods

This paper evaluates environmental externality when the structure of the externality is cumulative. The evaluation exercise is based on the assumption that the agents in question form conjectural variations. A number of environments are encompassed within this classification and have received due attention in the literature. Each of these heterogeneous environments, however, possesses considerable analytical homogeneity and permit subscription to a general model treatment. These environments include environmental externality, oligopoly and the analysis of the private provision of public goods. We highlight the general analytical approach by focusing on this latter context, in which debate centers around four issues: the existence of free-riding, the extent to which contributions are matched equally across individuals, the nature of conjectures consistent with equilibrium, and the allocative inefficiency of alternative regimes. This paper resolves each of these issues, with the following conclusions: A consistent-conjectures equilibrium exists in the private provision of public goods. It is the monopolistic-conjectures equilibrium. Agents act identically, contributing positive amounts of the public good in an efficient allocation of resources. There is complete matching of contributions among agents, no free-riding, and the allocation is independent of the number of members within the community. Thus the Olson conjecture—that inefficiency is exacerbated by community size—has no foundation in a consistent-conjectures, cumulative-externality, context (212 words).

The curious case of the camelthorn: competition, coexistence and nest-site limitation in a multispecies mutualism

Myrmecophyte plants house ants in domatia in exchange for protection from herbivores. Ant-myrmecophyte mutualisms exhibit two general patterns due to competition between ants for plant occupancy: i) domatia nest-sites are a limiting resource and ii) each individual plant hosts one ant species at a time. However, individual camelthorn trees (Vachellia erioloba) typically host two to four ant species simultaneously, often coexisting in adjacent domatia on the same branch. Such fine-grain spatial coexistence brings into question the conventional wisdom on ant-myrmecophyte mutualisms. Camelthorn ants appear not to be nest-site limited, despite low abundance of suitable domatia, and have random distributions of nest-sites within and across trees. These patterns suggest a lack of competition between ants for domatia and contrast strongly with other ant-myrmecophyte systems. Comparison of this unusual case with others suggests that spatial scale is crucial to coexistence or competitive exclusion involving multiple ant species. Furthermore, coexistence may be facilitated when co-occurring ant species diverge strongly on at least one niche axis. Our conclusions provide recommendations for future ant-myrmecophyte research, particularly in utilising multispecies systems to further our understanding of mutualism biology.

Reproducibility of the online Food4Me food-frequency questionnaire for estimating dietary intakes across Europe

Background: Accurate dietary assessment is key to understanding nutrition-related outcomes and is essential for estimating dietary change in nutrition-based interventions. Objective: The objective of this study was to assess the pan-European reproducibility of the Food4Me food-frequency questionnaire (FFQ) in assessing the habitual diet of adults. Methods: Participantsfromthe Food4Me study, a 6-mo,Internet-based, randomizedcontrolled trial of personalized nutrition conducted in the United Kingdom, Ireland, Spain, Netherlands, Germany, Greece, and Poland were included. Screening and baseline data (both collected before commencement of the intervention) were used in the present analyses, and participants were includedonly iftheycompleted FFQs at screeningand at baselinewithin a 1-mo timeframebeforethe commencement oftheintervention. Sociodemographic (e.g., sex andcountry) andlifestyle[e.g.,bodymass index(BMI,inkg/m2)and physical activity] characteristics were collected. Linear regression, correlation coefficients, concordance (percentage) in quartile classification, and Bland-Altman plots for daily intakes were used to assess reproducibility. Results: In total, 567 participants (59% female), with a mean 6 SD age of 38.7 6 13.4 y and BMI of 25.4 6 4.8, completed bothFFQswithin 1 mo(mean 6 SD: 19.26 6.2d).Exact plus adjacent classification oftotal energy intakeinparticipants was highest in Ireland (94%) and lowest in Poland (81%). Spearman correlation coefficients (r) in total energy intake between FFQs ranged from 0.50 for obese participants to 0.68 and 0.60 in normal-weight and overweight participants, respectively. Bland-Altman plots showed a mean difference between FFQs of 210 kcal/d, with the agreement deteriorating as energy intakes increased. There was little variation in reproducibility of total energy intakes between sex and age groups. Conclusions: The online Food4Me FFQ was shown to be reproducible across 7 European countries when administered within a 1-mo period to a large number of participants. The results support the utility of the online Food4Me FFQ as a reproducible tool across multiple European populations. This trial was registered at clinicaltrials.gov as NCT01530139.

Evaluating the annual nature of juvenile rings in Bolivian tropical rainforest trees

Knowledge on juvenile tree growth is crucial to understand how trees reach the canopy in tropical forests. However, long-term data on juvenile tree growth are usually unavailable. Annual tree rings provide growth information for the entire life of trees and their analysis has become more popular in tropical forest regions over the past decades. Nonetheless, tree ring studies mainly deal with adult rings as the annual character of juvenile rings has been questioned. We evaluated whether juvenile tree rings can be used for three Bolivian rainforest species. First, we characterized the rings of juvenile and adult trees anatomically. We then evaluated the annual nature of tree rings by a combination of three indirect methods: evaluation of synchronous growth patterns in the tree- ring series, (14)C bomb peak dating and correlations with rainfall. Our results indicate that rings of juvenile and adult trees are defined by similar ring-boundary elements. We built juvenile tree-ring chronologies and verified the ring age of several samples using (14)C bomb peak dating. We found that ring width was correlated with rainfall in all species, but in different ways. In all, the chronology, rainfall correlations and (14)C dating suggest that rings in our study species are formed annually.

Toward a resolution of Campanulid phylogeny, with special reference to the placement of Dipsacales

Broad-scale phylogenetic analyses of the angiosperms and of the Asteridae have failed to confidently resolve relationships among the major lineages of the campanulid Asteridae (i.e., the euasterid II of APG II, 2003). To address this problem we assembled presently available sequences for a core set of 50 taxa, representing the diversity of the four largest lineages (Apiales, Aquifoliales, Asterales, Dipsacales) as well as the smaller ""unplaced"" groups (e.g., Bruniaceae, Paracryphiaceae, Columelliaceae). We constructed four data matrices for phylogenetic analysis: a chloroplast coding matrix (atpB, matK, ndhF, rbcL), a chloroplast non-coding matrix (rps16 intron, trnT-F region, trnV-atpE IGS), a combined chloroplast dataset (all seven chloroplast regions), and a combined genome matrix (seven chloroplast regions plus 18S and 26S rDNA). Bayesian analyses of these datasets using mixed substitution models produced often well-resolved and supported trees. Consistent with more weakly supported results from previous studies, our analyses support the monophyly of the four major clades and the relationships among them. Most importantly, Asterales are inferred to be sister to a clade containing Apiales and Dipsacales. Paracryphiaceae is consistently placed sister to the Dipsacales. However, the exact relationships of Bruniaceae, Columelliaceae, and an Escallonia clade depended upon the dataset. Areas of poor resolution in combined analyses may be partly explained by conflict between the coding and non-coding data partitions. We discuss the implications of these results for our understanding of campanulid phylogeny and evolution, paying special attention to how our findings bear on character evolution and biogeography in Dipsacales.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Identification of genes associated with local aggressiveness and metastatic behavior in soft tissue tumors

Soft tissue tumors represent a group of neoplasia with different histologic and biological presentations varying from benign, locally confined to very aggressive and metastatic tumors. The molecular mechanisms responsible for such differences are still unknown. The understanding of these molecular alterations mechanism will be critical to discriminate patients who need systemic treatment from those that can be treated only locally and could also guide the development of new drugs` against this tumors. Using 102 tumor samples representing a large spectrum of these tumors, we performed expression profiling and defined differentially expression genes that are likely to be involved in tumors that are locally aggressive and in tumors with metastatic potential. We described a set of 12 genes (SNRPD3, MEGF9, SPTAN-1, AFAP1L2, ENDOD1, SERPIN5, ZWINTAS, TOP2A, UBE2C, ABCF1, MCM2, and ARL6IP5) showing opposite expression when these two conditions were compared. These genes are mainly related to cell-cell and cell-extracellular matrix interactions and cell proliferation and might represent helpful tools for a more precise classification and diagnosis as well as potential drug targets.

Nuclear magnetic resonance characterization of metabolite disorder in orange trees caused by citrus sudden death disease

Citrus sudden death (CSD) is a new disease of sweet orange and mandarin trees grafted on Rangpur lime and Citrus volkameriana rootstocks. It was first seen in Brazil in 1999, and has since been detected in more than four million trees. The CSD causal agent is unknown and the current hypothesis involves a virus similar to Citrus tristeza virus or a new virus named Citrus sudden death-associated virus. CSD symptoms include generalized foliar discoloration, defoliation and root death, and, in most cases, it can cause tree death. One of the unique characteristics of CSD disease is the presence of a yellow stain in the rootstock bark near the bud union. This region also undergoes profound anatomical changes. In this study, we analyse the metabolic disorder caused by CSD in the bark of sweet orange grafted on Rangpur lime by nuclear magnetic resonance (NMR) spectroscopy and imaging. The imaging results show the presence of a large amount of non-functional phloem in the rootstock bark of affected plants. The spectroscopic analysis shows a high content of triacylglyceride and sucrose, which may be related to phloem blockage close to the bud union. We also propose that, without knowing the causal CSD agent, the determination of oil content in rootstock bark by low-resolution NMR can be used as a complementary method for CSD diagnosis, screening about 300 samples per hour.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.