Browsing for information by highlighting automatically generated annotations:a user study and evaluation

The realization of the Semantic Web is constrained by a knowledge acquisition bottleneck, i.e. the problem of how to add RDF mark-up to the millions of ordinary web pages that already exist. Information Extraction (IE) has been proposed as a solution to the annotation bottleneck. In the task based evaluation reported here, we compared the performance of users without access to annotation, users working with annotations which had been produced from manually constructed knowledge bases, and users working with annotations augmented using IE. We looked at retrieval performance, overlap between retrieved items and the two sets of annotations, and usage of annotation options. Automatically generated annotations were found to add value to the browsing experience in the scenario investigated. Copyright 2005 ACM.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

Identification of nesfatin-1 in human and murine adipose tissue:a novel depot-specific adipokine with increased levels in obesity

Nesfatin-1 is a recently identified anorexigenic peptide derived from its precursor protein, nonesterified fatty acid/nucleobindin 2 (NUCB2). Although the hypothalamus is pivotal for the maintenance of energy homeostasis, adipose tissue plays an important role in the integration of metabolic activity and energy balance by communicating with peripheral organs and the brain via adipokines. Currently no data exist on nesfatin-1 expression, regulation, and secretion in adipose tissue. We therefore investigated NUCB2/nesfatin-1 gene and protein expression in human and murine adipose tissue depots. Additionally, the effects of insulin, dexamethasone, and inflammatory cytokines and the impact of food deprivation and obesity on nesfatin-1 expression were studied by quantitative RT-PCR and Western blotting. We present data showing NUCB2 mRNA (P < 0.001), nesfatin-1 intracellular protein (P < 0.001), and secretion (P < 0.01) were significantly higher in sc adipose tissue compared with other depots. Also, nesfatin-1 protein expression was significantly increased in high-fat-fed mice (P < 0.01) and reduced under food deprivation (P < 0.01) compared with controls. Stimulation of sc adipose tissue explants with inflammatory cytokines (TNFa and IL-6), insulin, and dexamethasone resulted in a marked increase in intracellular nesfatin-1 levels. Furthermore, we present evidence that the secretion of nesfatin-1 into the culture media was dramatically increased during the differentiation of 3T3-L1 preadipocytes into adipocytes (P < 0.001) and after treatments with TNF-a, IL-6, insulin, and dexamethasone (P < 0.01). In addition, circulating nesfatin-1 levels were higher in high-fat-fed mice (P < 0.05) and showed positive correlation with body mass index in human. We report that nesfatin-1 is a novel depot specific adipokine preferentially produced by sc tissue, with obesity- and food deprivation-regulated expression.

Co-propagating cladding modes coupling assisted by tilted Bragg grating with large tilting angle

We report for the first time forward propagating cladding modes coupling by using tilted gratings. The spectral responses of these gratings were investigated and their thermal characteristics and sensitivity to environmental refractive index were evaluated.

Pragmatic algorithms for implementing geostatistics with large datasets

With the ability to collect and store increasingly large datasets on modern computers comes the need to be able to process the data in a way that can be useful to a Geostatistician or application scientist. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively for likelihood-based Geostatistics. Various methods have been proposed and are extensively used in an attempt to overcome these complexity issues. This thesis introduces a number of principled techniques for treating large datasets with an emphasis on three main areas: reduced complexity covariance matrices, sparsity in the covariance matrix and parallel algorithms for distributed computation. These techniques are presented individually, but it is also shown how they can be combined to produce techniques for further improving computational efficiency.

Approximation of photonic crystal fibres with large air holes by the step index fibre model

An equivalent step index fibre with a silica core and air cladding is used to model photonic crystal fibres with large air holes. We model this fibre for linear polarisation (we focus on the lowest few transverse modes of the electromagnetic field). The equivalent step index radius is obtained by equating the lowest two eigenvalues of the model to those calculated numerically for the photonic crystal fibres. The step index parameters thus obtained can then be used to calculate nonlinear parameters like the nonlinear effective area of a photonic crystal fibre or to model nonlinear few-mode interactions using an existing model.

Enhanced slotted aloha protocols for underwater sensor networks with large propagation delay

Recently underwater sensor networks (UWSN) attracted large research interests. Medium access control (MAC) is one of the major challenges faced by UWSN due to the large propagation delay and narrow channel bandwidth of acoustic communications used for UWSN. Widely used slotted aloha (S-Aloha) protocol suffers large performance loss in UWSNs, which can only achieve performance close to pure aloha (P-Aloha). In this paper we theoretically model the performances of S-Aloha and P-Aloha protocols and analyze the adverse impact of propagation delay. According to the observation on the performances of S-Aloha protocol we propose two enhanced S-Aloha protocols in order to minimize the adverse impact of propagation delay on S-Aloha protocol. The first enhancement is a synchronized arrival S-Aloha (SA-Aloha) protocol, in which frames are transmitted at carefully calculated time to align the frame arrival time with the start of time slots. Propagation delay is taken into consideration in the calculation of transmit time. As estimation error on propagation delay may exist and can affect network performance, an improved SA-Aloha (denoted by ISA-Aloha) is proposed, which adjusts the slot size according to the range of delay estimation errors. Simulation results show that both SA-Aloha and ISA-Aloha perform remarkably better than S-Aloha and P-Aloha for UWSN, and ISA-Aloha is more robust even when the propagation delay estimation error is large. © 2011 IEEE.

Co-propagating cladding modes coupling assisted by tilted Bragg grating with large tilting angle

We report for the first time forward propagating cladding modes coupling by using tilted gratings. The spectral responses of these gratings were investigated and their thermal characteristics and sensitivity to environmental refractive index were evaluated.

Are democratising countries ‘rewarded’ with higher levels of foreign aid?

The paper examines how flows of foreign aid have reacted to events of democratisation in developing countries. Using a panel dataset of 136 aid-receiving countries between 1980 and 2009, aid allocation regressions reveal that Western donors in general have tended to react to visible, major democratic transitions by increasing aid to the partner country, but no significant increases can be identified in the case of countries introducing smaller democratic reforms. The increases in aid flows are not sustained over time, implying that donors do not provide long-term support to nascent democracies. Also, democratisations in Sub-Saharan Africa do not seem to have been rewarded with higher levels of aid.

Individual variability analysis of fluorescence parameters measured in skin with different levels of nutritive blood flow

Fluorescence spectroscopy has recently become more common in clinical medicine. However, there are still many unresolved issues related to the methodology and implementation of instruments with this technology. In this study, we aimed to assess individual variability of fluorescence parameters of endogenous markers (NADH, FAD, etc.) measured by fluorescent spectroscopy (FS) in situ and to analyse the factors that lead to a significant scatter of results. Most studied fluorophores have an acceptable scatter of values (mostly up to 30%) for diagnostic purposes. Here we provide evidence that the level of blood volume in tissue impacts FS data with a significant inverse correlation. The distribution function of the fluorescence intensity and the fluorescent contrast coefficient values are a function of the normal distribution for most of the studied fluorophores and the redox ratio. The effects of various physiological (different content of skin melanin) and technical (characteristics of optical filters) factors on the measurement results were additionally studied.The data on the variability of the measurement results in FS should be considered when interpreting the diagnostic parameters, as well as when developing new algorithms for data processing and FS devices.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.

Cationic ring opening polymerisation (CROP) of oxetane and its derivatives using oxonium derived initiators

Several cationic initiator systems were developed and used to polymerise oxetane with two oxonium ion initiator systems being investigated in depth. The first initiator system was generated by the elimination of a chloride group from a chloro methyl ethyl ether. Adding a carbonyl co-catalyst to a carbocationic centre generated the second initiator system. It was found that the anion used to stabilise the initiator was critical to the initial rate of polymerisation of oxetane with hexafluoroantimonate resulting in the fastest polymerisations. Both initiator systems could be used at varying monomer to initiator concentrations to control the molecular number average, Mn, of the resultant polymer. Both initiator systems showed living characteristics and were used to polymerise further monomers and generate higher molecular weight material and block copolymers. Oxetane and 3,3-dimethyl oxetane can both be polymerised using either oxonium ion initiator system in a variety of DCM or DCM/1,4-dioxane solvent mixtures. The level of 1,4-dioxane does have an impact on the initial rate of polymerisation with higher levels resulting in lower initial rates of polymerisation but do tend to result in higher polydispersities. The level of oligomer formation is also reduced as the level of 1,4-dioxane is increased. 3,3-bis-bromomethyl oxetane was also polymerised but a large amount of hyperbranching was seen at the bromide site resulting in a difficult to solvate polymer system. Multifunctional initiator systems were also generated using the halide elimination reactions with some success being achieved with 1,3,5-tris-bromomethyl-2,4,6-tris-methyl-benzene derived initiator system. This offered some control over the molecular number average of the resultant polymer system.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.