Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

SwarmPS: Rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that ∼104–105 particle projections are required to attain a 3 Å resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present SwarmPS, a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (∼102), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. SwarmPS is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

A semi-local method for iterative depth-map refinement

We present an iterative hierarchical algorithm for multi-view stereo. The algorithm attempts to utilise as much contextual information as is available to compute highly accurate and robust depth maps. There are three novel aspects to the approach: 1) firstly we incrementally improve the depth fidelity as the algorithm progresses through the image pyramid; 2) secondly we show how to incorporate visual hull information (when available) to constrain depth searches; and 3) we show how to simultaneously enforce the consistency of the depth-map by continual comparison with neighbouring depth-maps. We show that this approach produces highly accurate depth-maps and, since it is essentially a local method, is both extremely fast and simple to implement.

Molecular structure of the phosphate mineral brazilianite NaAl3(PO4)2(OH)4 : a semi precious jewel

The phosphate mineral brazilianite NaAl3(PO4)2(OH)4 is a semi precious jewel. There are almost no minerals apart from brazilianite which are used in jewellery. Vibrational spectroscopy was used to characterize the mol. structure of brazilianite. Brazilianite is composed of chains of edge-sharing Al-O octahedra linked by P-O tetrahedra, with Na located in cavities of the framework. An intense sharp Raman band at 1019 cm-1 is attributed to the PO43- sym. stretching mode. Raman bands at 973 and 988 cm-1 are assigned to the stretching vibrations of the HOPO33- units. The IR spectra compliment the Raman spectra but show greater complexity. Multiple Raman bands are obsd. in the PO43- and HOPO33- bending region. This observation implies that both phosphate and hydrogen phosphate units are involved in the structure. Raman OH stretching vibrations are found at 3249, 3417 and 3472 cm-1. These peaks show that the OH units are not equiv. in the brazilianite structure. Vibrational spectroscopy is useful for increasing the knowledge of the mol. structure of brazilianite.

Three carrier ambiguity resolution : distance-independent performance demonstrated using semi-generated triple frequency GPS signals

In spite of significant research in the development of efficient algorithms for three carrier ambiguity resolution, full performance potential of the additional frequency signals cannot be demonstrated effectively without actual triple frequency data. In addition, all the proposed algorithms showed their difficulties in reliable resolution of the medium-lane and narrow-lane ambiguities in different long-range scenarios. In this contribution, we will investigate the effects of various distance-dependent biases, identifying the tropospheric delay to be the key limitation for long-range three carrier ambiguity resolution. In order to achieve reliable ambiguity resolution in regional networks with the inter-station distances of hundreds of kilometers, a new geometry-free and ionosphere-free model is proposed to fix the integer ambiguities of the medium-lane or narrow-lane observables over just several minutes without distance constraint. Finally, the semi-simulation method is introduced to generate the third frequency signals from dual-frequency GPS data and experimentally demonstrate the research findings of this paper.

Prediction of the wash-off of traffic related semi- and non-volatile organic compounds from urban roads under climate change influenced rainfall characteristics

Traffic generated semi and non volatile organic compounds (SVOCs and NVOCs) pose a serious threat to human and ecosystem health when washed off into receiving water bodies by stormwater. Climate change influenced rainfall characteristics makes the estimation of these pollutants in stormwater quite complex. The research study discussed in the paper developed a prediction framework for such pollutants under the dynamic influence of climate change on rainfall characteristics. It was established through principal component analysis (PCA) that the intensity and durations of low to moderate rain events induced by climate change mainly affect the wash-off of SVOCs and NVOCs from urban roads. The study outcomes were able to overcome the limitations of stringent laboratory preparation of calibration matrices by extracting uncorrelated underlying factors in the data matrices through systematic application of PCA and factor analysis (FA). Based on the initial findings from PCA and FA, the framework incorporated orthogonal rotatable central composite experimental design to set up calibration matrices and partial least square regression to identify significant variables in predicting the target SVOCs and NVOCs in four particulate fractions ranging from >300-1 μm and one dissolved fraction of <1 μm. For the particulate fractions range >300-1 μm, similar distributions of predicted and observed concentrations of the target compounds from minimum to 75th percentile were achieved. The inter-event coefficient of variations for particulate fractions of >300-1 μm were 5% to 25%. The limited solubility of the target compounds in stormwater restricted the predictive capacity of the proposed method for the dissolved fraction of <1 μm.

Visible and near infrared spectroscopy of Hayabusa re-entry using semi-autonomous tracking

A ground-based tracking camera and co-aligned slit-less spectrograph were used to measure the spectral signature of visible radiation emitted from the Hayabusa capsule as it entered into the Earth's atmosphere in June 2010. Good quality spectra were obtained that showed the presence of radiation from the heat shield of the vehicle and the shock-heated air in front of the vehicle. An analysis of the black body nature of the radiation concluded that the peak average temperature of the surface was about (3100±100) K.

A view from the edge of the forest : recent progress in understanding the relationships of the insect orders

Research over the last two decades has significantly increased our understanding of the evolutionary position of the insects among other arthropods, and the relationships among the insect Orders. Many of these insights have been established through increasingly sophisticated analyses of DNA sequence data from a limited number of genes. Recent results have established the relationships of the Holometabola, but relationships among the hemimetabolous orders have been more difficult to elucidate. A strong consensus on the relationships among the Palaeoptera (Ephemeroptera and Odonata) and their relationship to the Neoptera has not emerged with all three possible resolutions supported by different data sets. While polyneopteran relationships generally have resisted significant resolution, it is now clear that termites, Isoptera, are nested within the cockroaches, Blattodea. The newly discovered order Mantophasmatodea is difficult to place with the balance of studies favouring Grylloblattodea as sister-group. While some studies have found the paraneopteran orders (Hemiptera, Thysanoptera, Phthiraptera and Psocoptera) monophyletic, evidence suggests that parasitic lice (Phthiraptera) have evolved from groups within the book and bark lice (Psocoptera), and may represent parallel evolutions of parasitism within two major louse groups. Within Holometabola, it is now clear that Hymenoptera are the sister to the other orders, that, in turn are divided into two clades, the Neuropteroidea (Coleoptera, Neuroptera and relatives) and the Mecopterida (Trichoptera, Lepidoptera, Diptera and their relatives). The enigmatic order Strepsiptera, the twisted wing insects, have now been placed firmly near Coleoptera, rejecting their close relationship to Diptera that was proposed some 15years ago primarily based on ribosomal DNA data. Phylogenomic-scale analyses are just beginning to be focused on the relationships of the insect orders, and this is where we expect to see resolution of palaeopteran and polyneopteran relationships. Future research will benefit from greater coordination between intra and inter-ordinal analyses. This will maximise the opportunities for appropriate outgroup choice at the intraordinal level and provide the background knowledge for the interordinal analyses to span the maximum phylogenetic scope within groups.

Spatial and temporal clusters of Barmah Forest virus disease in Queensland, Australia

Objective To identify the spatial and temporal clusters of Barmah Forest virus (BFV) disease in Queensland in Australia, using geographical information systems (GIS) and spatial scan statistic (SaTScan). Methods We obtained BFV disease cases, population and statistical local areas boundary data from Queensland Health and Australian Bureau of Statistics respectively during 1992-2008 for Queensland. A retrospective Poisson-based analysis using SaTScan software and method was conducted in order to identify both purely spatial and space-time BFV disease high-rate clusters. A spatial cluster size of a proportion of the population and a 200km circle radius and varying time windows from 1 month to 12 months were chosen (for the space-time analysis). Results The spatial scan statistic detected a most likely significant purely spatial cluster (including 23 SLAs) and a most likely significant space-time cluster (including 24 SLAs) in approximately the same location. Significant secondary clusters were also identified from both the analyses in several locations. Conclusions This study provides evidence of the existence of statistically significant BFV disease clusters in Queensland, Australia. The study also demonstrated the relevance and applicability of SaTScan in analysing on-going surveillance data to identify clusters to facilitate the development of effective BFV disease prevention and control strategies in Queensland, Australia.

Phylogeography of Echinocladius martini Cranston (Diptera: Chironomidae) in closed forest streams of eastern Australia

The eastern Australian rainforests have experienced several cycles of range contraction and expansion since the late Miocene that are closely correlated with global glaciation events. Together with ongoing aridification of the continent, this has resulted in current distributions of native closed forest that are highly fragmented along the east coast. Several closed forest endemic taxa exhibit patterns of population genetic structure that are congruent with historical isolation of populations in discrete refugia and reflect evolutionary histories dramatically affected by vicariance. Currently, limited data are available regarding the impact of these past climatic fluctuations on freshwater invertebrate taxa. The non-biting midge species Echinocladius martini Cranston is distributed along the east coast and inhabits predominantly montane streams in closed forest habitat. Phylogeographic structure in E. martini was resolved here at a continental scale by incorporating data from a previous pilot study and expanding the sampling design to encompass populations in the Wet Tropics of north-eastern Queensland, south-east Queensland, New South Wales and Victoria. Patterns of phylogeographic structure revealed several deeply divergent mitochondrial lineages from central and south-eastern Australia that were previously unrecognised and were geographically endemic to closed forest refugia. Estimated divergence times were congruent with late Miocene onset of rainforest contractions across the east coast of Australia. This suggested that dispersal and gene flow among E. martini populations isolated in refugia has been highly restricted historically. Moreover, these data imply, in contrast to existing preconceptions about freshwater invertebrates, that this taxon may be acutely susceptible to habitat fragmentation.

Socio-environmental variability and Barmah Forest virus disease transmission : a review of epidemiological evidence and future prospects

Barmah Forest Virus (BFV) disease is the most rapidly emerging mosquito-borne disease in Australia. BFV transmission depends on factors such as climate, virus, vector and the human population. However, the impact of climatic and social factors on BFV remains to be determined. This paper provided an overview of current research and discusses the future research directions on the BFV transmission. These research findings could be regarded as an impetus towards BFV prevention and control strategies.

Semi-parametric forecasting of realized volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.