Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.

Classification of instability modes in a model of aluminium reduction cells with a uniform magnetic field

A unified view on the interfacial instability in a model of aluminium reduction cells in the presence of a uniform, vertical, background magnetic field is presented. The classification of instability modes is based on the asymptotic theory for high values of parameter β, which characterises the ratio of the Lorentz force based on the disturbance current, and gravity. It is shown that the spectrum of the travelling waves consists of two parts independent of the horizontal cross-section of the cell: highly unstable wall modes and stable or weakly unstable centre, or Sele’s modes. The wall modes with the disturbance of the interface being localised at the sidewalls of the cell dominate the dynamics of instability. Sele’s modes are characterised by a distributed disturbance over the whole horizontal extent of the cell. As β increases these modes are stabilized by the field.

Identifying problematic classes in text classification

Real-world text classification tasks often suffer from poor class structure with many overlapping classes and blurred boundaries. Training data pooled from multiple sources tend to be inconsistent and contain erroneous labelling, leading to poor performance of standard text classifiers. The classification of health service products to specialized procurement classes is used to examine and quantify the extent of these problems. A novel method is presented to analyze the labelled data by selectively merging classes where there is not enough information for the classifier to distinguish them. Initial results show the method can identify the most problematic classes, which can be used either as a focus to improve the training data or to merge classes to increase confidence in the predicted results of the classifier.

Isolation and characterisation of EfeM, a periplasmic component of the putative EfeUOBM iron transporter of Pseudomonas syringae pv. syringae

The EfeM protein is a component of the putative EfeUOBM iron-transporter of Pseudomonas syringae pathovar syringae and is thought to act as a periplasmic, ferrous-iron binding protein. It contains a signal peptide of 34 amino acid residues and a C-terminal 'Peptidase_M75' domain of 251 residues. The C-terminal domain contains a highly conserved 'HXXE' motif thought to act as part of a divalent cation-binding site. In this work, the gene (efeM or 'Psyr_3370') encoding EfeM was cloned and over-expressed in Escherichia coli, and the mature protein was purified from the periplasm. Mass spectrometry confirmed the identity of the protein (M(W) 27,772Da). Circular dichroism spectroscopy of EfeM indicated a mainly alpha-helical structure, consistent with bioinformatic predictions. Purified EfeM was crystallised by hanging-drop vapor diffusion to give needle-shaped crystals that diffracted to a resolution of 1.6A. This is the first molecular study of a peptidase M75 domain with a presumed iron transport role.

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

Chemical ageing and transformation of diffusivity in semi-solid multi-component organic aerosol particles

Recent experimental evidence underlines the importance of reduced diffusivity in amorphous semi-solid or glassy atmospheric aerosols. This paper investigates the impact of diffusivity on the ageing of multi-component reactive organic particles representative of atmospheric cooking aerosols. We apply and extend the recently developed KM-SUB model in a study of a 12-component mixture containing oleic and palmitoleic acids. We demonstrate that changes in the diffusivity may explain the evolution of chemical loss rates in ageing semi-solid particles, and we resolve surface and bulk processes under transient reaction conditions considering diffusivities altered by oligomerisation. This new model treatment allows prediction of the ageing of mixed organic multi-component aerosols over atmospherically relevant time scales and conditions. We illustrate the impact of changing diffusivity on the chemical half-life of reactive components in semisolid particles, and we demonstrate how solidification and crust formation at the particle surface can affect the chemical transformation of organic aerosols.

The underlying return generating factors for REIT returns: an application of independent component analysis

Multi-factor approaches to analysis of real estate returns have, since the pioneering work of Chan, Hendershott and Sanders (1990), emphasised a macro-variables approach in preference to the latent factor approach that formed the original basis of the arbitrage pricing theory. With increasing use of high frequency data and trading strategies and with a growing emphasis on the risks of extreme events, the macro-variable procedure has some deficiencies. This paper explores a third way, with the use of an alternative to the standard principal components approach – independent components analysis (ICA). ICA seeks higher moment independence and maximises in relation to a chosen risk parameter. We apply an ICA based on kurtosis maximisation to weekly US REIT data using a kurtosis maximising algorithm. The results show that ICA is successful in capturing the kurtosis characteristics of REIT returns, offering possibilities for the development of risk management strategies that are sensitive to extreme events and tail distributions.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

Diallyl disulphide, a beneficial component of garlic oil, causes a redistribution of cell-cycle growth phases, induces apoptosis and enhances butyrate-induced apoptosis in colorectal adenocarcinoma cells (HT-29)

Colon cancer is a leading and expanding cause of death worldwide. A major contributory factor to this disease is diet composition; some components are beneficial (e.g. dietary fibre) whilst others are detrimental (e.g. alcohol). Garlic oil is a prominent dietary constituent that prevents the development of colorectal cancer. This effect is believed to be mainly due to diallyl disulphide (DADS), which selectively induces redox stress in cancerous (rather than normal) cells which leads to apoptotic cell death. However, the detailed mechanism by which DADS causes apoptosis remains unclear. We show that DADS-treatment of colonic adenocarcinoma cells (HT-29) initiates a cascade of molecular events characteristic of apoptosis. These include a decrease in cellular proliferation, translocation of phosphatidylserine to the plasma-membrane outer-layer, activation of caspase-3, genomic-DNA fragmentation and G2/M phase cell-cycle arrest. Short-chain fatty acids (SCFAs), particularly butyrate (abundantly produced in the gut by bacterial fermentation of dietary polysaccharides), enhance colonic cell integrity but, in contrast, inhibit colonic-cancer cell growth. Combining DADS with butyrate augmented the effect of butyrate on HT-29 cells. These results suggest that the anti-cancerous properties of DADS afford greater benefit when supplied with other favourable dietary factors (SCFA/polysaccharides) that likewise reduce colonic tumour development.