An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Non-spectral interferences due to the presence of sulfuric acid in inductively coupled plasma mass spectrometry

Results of a systematic study concerning non-spectral interferences from sulfuric acid containing matrices on a large number of elements in inductively coupled plasma–mass spectrometry (ICP-MS) are presented in this work. The signals obtained with sulfuric acid solutions of different concentrations (up to 5% w w− 1) have been compared with the corresponding signals for a 1% w w− 1− nitric acid solution at different experimental conditions (i.e., sample uptake rates, nebulizer gas flows and r.f. powers). The signals observed for 128Te+, 78Se+ and 75As+ were significantly higher when using sulfuric acid matrices (up to 2.2-fold for 128Te+ and 78Se+ and 1.8-fold for 75As+ in the presence of 5 w w-1 sulfuric acid) for the whole range of experimental conditions tested. This is in agreement with previously reported observations. The signal for 31P+ is also higher (1.1-fold) in the presence of sulfuric acid. The signal enhancements for 128Te+, 78Se+, 75As+ and 31P+ are explained in relation to an increase in the analyte ion population as a result of charge transfer reactions involving S+ species in the plasma. Theoretical data suggest that Os, Sb, Pt, Ir, Zn and Hg could also be involved in sulfur-based charge transfer reactions, but no experimental evidence has been found. The presence of sulfuric acid gives rise to lower ion signals (about 10–20% lower) for the other nuclides tested, thus indicating the negative matrix effect caused by changes in the amount of analyte loading of the plasma. The elemental composition of a certified low-density polyethylene sample (ERM-EC681K) was determined by ICP-MS after two different sample digestion procedures, one of them including sulfuric acid. Element concentrations were in agreement with the certified values, irrespective of the acids used for the digestion. These results demonstrate that the use of matrix-matched standards allows the accurate determination of the tested elements in a sulfuric acid matrix.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Spectrally tunable light source based on light-emitting diodes for custom lighting solutions

This study describes a novel spectral LED-based tunable light source used for customized lighting solutions, especially for the reconstruction of CIE (Commission Internationale de l’Éclairage) standard illuminants. The light source comprises 31 spectral bands ranging from 400 to 700 nm, an integrating cube and a control board with a 16-bit resolution. A minimization algorithm to calculate the weighting values for each channel was applied to reproduce illuminants with precision. The differences in spectral fitting and colorimetric parameters showed that the reconstructed spectra were comparable to the standard, especially for the D65, D50, A and E illuminants. Accurate results were also obtained for illuminants with narrow peaks such as fluorescents (F2 and F11) and a high-pressure sodium lamp (HP1). In conclusion, the developed spectral LED-based light source and the minimization algorithm are able to reproduce any CIE standard illuminants with a high spectral and colorimetric accuracy able to advance available custom lighting systems useful in the industry and other fields such as museum lighting.

Radiolarian-based paleotemperatures of ODP Site 177-1089 in the Atlantic Sector of the Southern Ocean

Two cores, Site 1089 (ODP Leg 177) and PS2821-1, recovered from the same location (40°56'S; 9°54'E) at the Subtropical Front (STF) in the Atlantic Sector of the Southern Ocean, provide a high-resolution climatic record, with an average temporal resolution of less than 600 yr. A multi-proxy approach was used to produce an age model for Core PS2821-1, and to correlate the two cores. Both cores document the last climatic cycle, from Marine Isotopic Stage 6 (MIS 6, ca. 160 kyr BP, ka) to present. Summer sea-surface temperatures (SSSTs) have been estimated, with a standard error of ca. +/-1.16°C, for the down core record by using Q-mode factor analysis (Imbrie and Kipp method). The paleotemperatures show a 7°C warming at Termination II (last interglacial, transition from MIS 6 to MIS 5). This transition from glacial to interglacial paleotemperatures (with maximum temperatures ca. 3°C warmer than present at the core location) occurs earlier than the corresponding shift in delta18O values for benthic foraminifera from the same core; this suggests a lead of Southern Ocean paleotemperature changes compared to the global ice-volume changes, as indicated by the benthic isotopic record. The climatic evolution of the record continues with a progressive temperature deterioration towards MIS 2. High-frequency, millennial-scale climatic instability has been documented for MIS 3 and part of MIS 4, with sudden temperature variations of almost the same magnitude as those observed at the transitions between glacial and interglacial times. These changes occur during the same time interval as the Dansgaard-Oeschger cycles recognized in the delta18Oice record of the GRIP and GISP ice cores from Greenland, and seem to be connected to rapid changes in the STF position in relation to the core location. Sudden cooling episodes ('Younger Dryas (YD)-type' and 'Antarctic Cold Reversal (ACR)-type' of events) have been recognized for both Termination I (ACR-I and YD-I events) and II (ACR-II and YD-II events), and imply that our core is located in an optimal position in order to record events triggered by phenomena occurring in both hemispheres. Spectral analysis of our SSST record displays strong analogies, particularly for high, sub-orbital frequencies, to equivalent records from Vostok (Antarctica) and from the Subtropical North Atlantic ocean. This implies that the climatic variability of widely separated areas (the Antarctic continent, the Subtropical North Atlantic, and the Subantarctic South Atlantic) can be strongly coupled and co-varying at millennial time scales (a few to 10-ka periods), and eventually induced by the same triggering mechanisms. Climatic variability has also been documented for supposedly warm and stable interglacial intervals (MIS 1 and 5), with several cold events which can be correlated to other Southern Ocean and North Atlantic sediment records.

Urea based osmoregulation and endocrine control in elasmobranch fish with special reference to euryhalinity

Since the landmark contributions of Homer Smith and co-workers in the 1930s there has been a considerable advance in our knowledge regarding the osmoregulatory strategy of elasmobranch fish. Smith recognised that urea was retained in the body fluids as part of the 'osmoregulatory ballast' of elasmobranch fish so that body fluid osmolality is raised to a level that is iso- or slightly hyper-osmotic to that of the surrounding medium. From studies at that time he also postulated that many marine dwelling elasmobranchs were not capable of adaptation to dilute environments. However, more recent investigations have demonstrated that, at least in some species, this may not be the case. Gradual acclimation of marine dwelling elasmobranchs to varying environmental salinities under laboratory conditions has demonstrated that these fish do have the capacity to acclimate to changes in salinity through independent regulation of Na+, Cl- and urea levels. This suggests that many of the presumed stenohaline marine elasmobranchs could in fact be described as partially euryhaline. The contributions of Thomas Thorson in the 1970s demonstrated the osmoregulatory strategy of a fully euryhaline elasmobranch, the bull shark, Carcharhinus leucas, and more recent investigations have examined the mechanisms behind this strategy in the euryhaline elasmobranch, Dasyatis sabina. Both partially euryhaline and fully euryhaline species utilise the same physiological processes to control urea, Na+ and Cl- levels within the body fluids. The role of the gills, kidney, liver, rectal gland and drinking process is discussed in relation to the endocrine control of urea, Na+ and Cl- levels as elasmobranchs acclimate to different environmental salinities. (C) 2003 Elsevier Inc. All rights reserved.

Microspectrophotometry of visual pigments and oil droplets in a marine bird, the wedge-tailed shearwater Puffinus pacificus: Topographic variations in photoreceptor spectral characteristics

Microspectrophotometric examination of the retina of a procellariiform marine bird, the wedge-tailed shearwater Puffinus pacificus, revealed the presence of five different types of vitamin A(1)-based visual pigment in seven different types of photoreceptor. A single class of rod contained a medium-wavelength sensitive visual pigment with a wavelength of maximum absorbance (lambda(max)) at 502 nm. Four different types of single cone contained visual pigments maximally sensitive in either the violet (VS, lambda(max) 406 nm), short (SWS, lambda(max) 450 nm), medium (MWS, lambda(max) 503 nm) or long (LWS, lambda(max) 566 nm) spectral ranges. In the peripheral retina, the SWS, MWS and LWS single cones contained pigmented oil droplets in their inner segments with cut-off wavelengths (lambda(cut)) at 445 (C-type), 506 (Y-type) and 562 nm (R-type), respectively. The VS visual pigment was paired with a transparent (T-type) oil droplet that displayed no significant absorption above at least 370 run. Both the principal and accessory members of the double cone pair contained the same 566 nm lambda(max) visual pigment as the LWS single cones but only the principal member contained an oil droplet, which had a lambda(cut) at 413 nm. The retina had a horizontal band or 'visual streak' of increased photoreceptor density running across the retina approximately 1.5 mm dorsal to the top of the pecten. Cones in the centre of the horizontal streak were smaller and had oil droplets that were either transparent/colourless or much less pigmented than at the periphery. It is proposed that the reduction in cone oil droplet pigmentation in retinal areas associated with high visual acuity is an adaptation to compensate for the reduced photon capture ability of the narrower photoreceptors found there. Measurements of the spectral transmittance of the ocular media reveal that wavelengths down to at least 300 nm would be transmitted to the retina.

The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar

In broader catchment scale investigations, there is a need to understand and ultimately exploit the spatial variation of agricultural crops for an improved economic return. In many instances, this spatial variation is temporally unstable and may be different for various crop attributes and crop species. In the Australian sugar industry, the opportunity arose to evaluate the performance of 231 farms in the Tully Mill area in far north Queensland using production information on cane yield (t/ha) and CCS ( a fresh weight measure of sucrose content in the cane) accumulated over a 12-year period. Such an arrangement of data can be expressed as a 3-way array where a farm x attribute x year matrix can be evaluated and interactions considered. Two multivariate techniques, the 3-way mixture method of clustering and the 3-mode principal component analysis, were employed to identify meaningful relationships between farms that performed similarly for both cane yield and CCS. In this context, farm has a spatial component and the aim of this analysis was to determine if systematic patterns in farm performance expressed by cane yield and CCS persisted over time. There was no spatial relationship between cane yield and CCS. However, the analysis revealed that the relationship between farms was remarkably stable from one year to the next for both attributes and there was some spatial aggregation of farm performance in parts of the mill area. This finding is important, since temporally consistent spatial variation may be exploited to improve regional production. Alternatively, the putative causes of the spatial variation may be explored to enhance the understanding of sugarcane production in the wet tropics of Australia.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

An evaluation of potential mechanism-based inactivation of human drug metabolizing cytochromes P450 by monoamine oxidase inhibitors, including isoniazid

To characterize potential mechanism-based inactivation (MBI) of major human drug-metabolizing cytochromes P450 (CYP) by monoamine oxidase (MAO) inhibitors, including the antitubercular drug isoniazid. Human liver microsomal CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A activities were investigated following co- and preincubation with MAO inhibitors. Inactivation kinetic constants (K-I and k(inact)) were determined where a significant preincubation effect was observed. Spectral studies were conducted to elucidate the mechanisms of inactivation. Hydrazine MAO inhibitors generally exhibited greater inhibition of CYP following preincubation, whereas this was less frequent for the propargylamines, and tranylcypromine and moclobemide. Phenelzine and isoniazid inactivated all CYP but were most potent toward CYP3A and CYP2C19. Respective inactivation kinetic constants (K-I and k(inact)) for isoniazid were 48.6 mu M and 0.042 min(-1) and 79.3 mu M and 0.039 min(-1). Clorgyline was a selective inactivator of CYP1A2 (6.8 mu M and 0.15 min(-1)). Inactivation of CYP was irreversible, consistent with metabolite-intermediate complexation for isoniazid and clorgyline, and haeme destruction for phenelzine. With the exception of phenelzine-mediated CYP3A inactivation, glutathione and superoxide dismutase failed to protect CYP from inactivation by isoniazid and phenelzine. Glutathione partially slowed (17%) the inactivation of CYP1A2 by clorgyline. Alternate substrates or inhibitors generally protected against CYP inactivation. These data are consistent with mechanism-based inactivation of human drug-metabolizing CYP enzymes and suggest that impaired metabolic clearance may contribute to clinical drug-drug interactions with some MAO inhibitors.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.