Automatic event detection in microblogs using incremental machine learning

The global popularity of microblogs has led to an increasing accumulation of large volumes of text data on microblogging platforms such as Twitter. These corpora are untapped resources to understand social expressions on diverse subjects. Microblog analysis aims to unlock the value of such expressions by discovering insights and events of significance hidden among swathes of text. Besides velocity; diversity of content, brevity, absence of structure and time-sensitivity are key challenges in microblog analysis. In this paper, we propose an unsupervised incremental machine learning and event detection technique to address these challenges. The proposed technique separates a microblog discussion into topics to address the key problem of diversity. It maintains a record of the evolution of each topic over time. Brevity, time-sensitivity and unstructured nature are addressed by these individual topic pathways which contribute to generate a temporal, topic-driven structure of a microblog discussion. The proposed event detection method continuously monitors these topic pathways using multiple domain-independent event indicators for events of significance. The autonomous nature of topic separation, topic pathway generation, new topic identification and event detection, appropriates the proposed technique for extensive applications in microblog analysis. We demonstrate these capabilities on tweets containing #microsoft and tweets containing #obama

Draft genome sequence of Colletotrichum acutatum sensu lato (Colletotrichum fioriniae)

In addition to its economic impact, Colletotrichum acutatum sensu lato is an interesting model for molecular investigations due to the diversity of host-determined specialization and reproductive lifestyles within the species complex. The pathogen Colletotrichum fioriniae forms part of this species complex and causes anthracnose in a wide range of crops and wild plants worldwide. Some members of this species have also been reported to be entomopathogenic. Here, we report the draft genome sequence of a heterothallic reference isolate of C. fioriniae (strain PJ7). This sequence provides a range of new resources that serve as a useful platform for further research in the field

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM

Multi-objective decision analytics for short-notice bushfire evacuation: An Australian case study

This paper develops a multi-objective optimisation model to compute resource allocation,shelter assignment and routing options to evacuate late evacuees from affected areas to shelters.Three bushfire scenarios are analysed to incorporate constraints of restricted time-window and potential road disruptions.Capacity and number of rescue vehicles and shelters are other constraints that are identical in all scenarios.The proposed mathematical model is solved by ?-constraint approach.Objective functions are simultaneously optimised to maximise the total number of evacuees and assigned rescue vehicles and shelters.We argue that this model provides a scenario-based decision-making platform to aid minimise resource utilisation and maximise coverage of late evacuees

Burden of diabetes-related foot disease in North Queensland, Australia

Chanika Alahakoon investigated the burden of Diabetes-related foot disease (DFD) in North Queensland, Australia. She found that the readmission rate following DFD was around 50% and that Aboriginal and Torres Strait Islander people had significant distal vessel disease with a higher risk of major amputation. Prevention of DFD through offloading footwear is recommended for those with DFD

Exploring Phenotypic Diversity and Quantitative Trait Loci Mapping for Root Architecture, Freezing Tolerance, Chilling Fulfillment, and Photoperiod Traits in Grapevine Populations

Grapevine (Vitis species) is one of the most valuable fruit crops widely cultivated throughout the United States of America (USA). Grape and wine industries in the northern USA have been expanding rapidly with the demand for quality wine grapes that can be grown in Northern cold climate regions. As most popular cultivars are freezing sensitive, the development of new cultivars for the region continues through breeding. In this study, we evaluated grapevine root system architecture, freezing tolerance, and bud break in different genetic backgrounds in natural or controlled environments. The dissertation research objectives were to explore trait phenotypic variation and identify quantitative trait loci (QTL). A mapping population of 266 F2 genotypes a self of a F1 (16_9_2) from a cross between V. riparia ‘Manitoba 37’ and ‘Seyval’ cross was grown in the greenhouse for the root study. Sixteen root system architecture (RSA) traits were measured to study natural variations of F2 root morphology. Several trait-specific significant phenotype-genotype associations were identified. Forty-two QTLs were detected for root system architecture with hotspots on chromosomes 1, 9, 11, 13, and 19. Enriched pathways identified common (Cell division) and specific (lateral root development) genetic mechanisms suggesting complex genetic control encompassing morphology traits. Freezing tolerance, identified by low temperature exotherms, of field grown F2 grapevine was evaluated using digital thermal analysis for six winter seasons. Significant correlation was detected between freezing tolerance and monthly temperature. Eight significant QTLs were identified for freezing tolerance traits, one each on chromosomes 4 and 8 and two QTLs on chromosomes 2, 13, and 14. QTLs on chromosomes 4 and 14 were associated with enriched genetic pathways for grapevine freezing tolerance. Colocation of freezing tolerance and long-term winter endurance QTLs was detected on chromosome (chr) 14 suggesting interrelated genetic control. An enriched circadian pathway under freezing tolerance QTL on chr13 indicates the potential impact of photoperiod on initiation of freezing tolerance of grapevine. Chilling fulfillment was measured in F2 grapevine buds after different amounts of natural chilling in the field. Bud break was measured for four weeks under optimal forcing conditions in the laboratory after sampling. Chilling fulfillment, measured over six winter seasons, was associated with 53 QTLs across 14 chromosomes. Colocalization of chilling fulfillment QTLs were identified on chromosomes 3, 8, and 18. A pattern of QTL emerging with chilling fulfillment was identified suggesting that biochemical pathways related to meristem activation were initiated during chilling fulfillment and dormancy release. A total of 143 genotypes in a Riesling × Cabernet Sauvignon F1 grapevine population was studied under 12 chilling (168 to 2016 by 168 chilling hours) and two photoperiod treatments (13 hours (h) and 24h). Increased chilling and chilling fulfillment increase the rate of bud break and reduce the time for initiation of bud break. Longer (24h) photoperiod increased the rate of bud break and replaced the insufficient chilling. QTL mapping identified 55 QTL (26 and 29 QTLs for the 13h and 24h photoperiods respectively). Two major QTL were observed on chr2 (for 13h and 24h photoperiods) and chr10 (for 24h photoperiod). Root system architecture, freezing tolerance and chilling fulfillment studies identified several QTLs with significantly enriched pathways within the loci. These enriched pathways provide candidate genes that may be used to further dissect the mechanisms underlying root system characters and winter sustainability in grapevine

AN APPLICATION OF INFORMATION SEEKING ANXIETY SCALE TO A UNIVERSITY LIBRARY: A CASE STUDY AT UNIVERSITY OF PERADENIYA, SRI LANKA

Libraries and their users are nervous of falling behind with the rapid rate of technological change. All the forms of academic-related anxiety, and frustrations associated when searching information. Most of the research studies have shown that, majority of the undergraduates do not use the library resources in an expected level due to variety of anxieties.Therefore, the main aim of this paper is to assess and apply the Information seeking Anxiety Scale (ISAS) among university library users at University of Peradeniya and determines the suitability of application of this scale to a Sri Lankan university students relating to seeking of information.A survey research method was applied. Close-ended questionnaire was used as the research tool and data was collected from undergraduates in 2013/2014 academic year.The ISAS developed and validated by Mohammadamin, Abrizah and Karim, (2012) was taken as the base construct for this study. The overall a=0.902 of the original scale indicated and the Sri Lankan study increases up to 0.937. It indicates more or less similar results of the original study and it has been proved that, this scale can be applied to the Sri Lankan university context too. All the students are suffering with different types of anxieties related to the seeking of information. Therefore, it is better to have a year-based user education program for them to develop their skills to use resources without getting anxious

Recurrent Event Data Analysis with Mismeasured Covariates

Consider a study with n units wherein every unit is monitored for the occurrence of an event that can recur with random end of monitoring. At each recurrence, p concomitant variables associated to the event recurrence are recorded with q (q ≤ p) collected with errors. Of interest in this dissertation is the estimation of the regression parameters of event time regression models accounting for the covariates. To circumvent the problem of bias and consistency associated with model\u27s parameter estimation in the presence of measurement errors, we propose inference for corrected estimating functions with well-behaved roots under additive measurement errors model. We consider two types of failure time regression models: one with additive effects and the other with multiplicative effects on the pure event history. We show that estimation is essentially unbiased under the corrected profile likelihood for recurrent events, in comparison to biased estimations under a likelihood function that ignores correction in both cases. We propose methods for obtaining estimators of error variance and discuss the property of the estimators. We further investigate the case of misspecified error models under the multiplicative regression model and show that the resulting estimators under misspecification converge to a value different from that of the true parameter--thereby providing a basis for bias assessment. In both cases, simulation studies indicate that the asymptotic properties of the regression parameters closely approximate its finite sample properties. We demonstrate the foregoing correction methods on an open source rhDNase dataset which was gathered in a clinical setting Abstract, p. i