Data requirements and graph data structures for a multi-modal, multi-objective trip planner

Traffic congestion has a significant impact on the economy and environment. Encouraging the use of multimodal transport (public transport, bicycle, park’n’ride, etc.) has been identified by traffic operators as a good strategy to tackle congestion issues and its detrimental environmental impacts. A multi-modal and multi-objective trip planner provides users with various multi-modal options optimised on objectives that they prefer (cheapest, fastest, safest, etc) and has a potential to reduce congestion on both a temporal and spatial scale. The computation of multi-modal and multi-objective trips is a complicated mathematical problem, as it must integrate and utilize a diverse range of large data sets, including both road network information and public transport schedules, as well as optimising for a number of competing objectives, where fully optimising for one objective, such as travel time, can adversely affect other objectives, such as cost. The relationship between these objectives can also be quite subjective, as their priorities will vary from user to user. This paper will first outline the various data requirements and formats that are needed for the multi-modal multi-objective trip planner to operate, including static information about the physical infrastructure within Brisbane as well as real-time and historical data to predict traffic flow on the road network and the status of public transport. It will then present information on the graph data structures representing the road and public transport networks within Brisbane that are used in the trip planner to calculate optimal routes. This will allow for an investigation into the various shortest path algorithms that have been researched over the last few decades, and provide a foundation for the construction of the Multi-modal Multi-objective Trip Planner by the development of innovative new algorithms that can operate the large diverse data sets and competing objectives.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks

In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.

Identify disorders in health records using Conditional Random Fields and Metamap: AEHRC at ShARe/CLEF 2013 eHealth Evaluation Lab Task 1

The Australian e-Health Research Centre (AEHRC) recently participated in the ShARe/CLEF eHealth Evaluation Lab Task 1. The goal of this task is to individuate mentions of disorders in free-text electronic health records and map disorders to SNOMED CT concepts in the UMLS metathesaurus. This paper details our participation to this ShARe/CLEF task. Our approaches are based on using the clinical natural language processing tool Metamap and Conditional Random Fields (CRF) to individuate mentions of disorders and then to map those to SNOMED CT concepts. Empirical results obtained on the 2013 ShARe/CLEF task highlight that our instance of Metamap (after ltering irrelevant semantic types), although achieving a high level of precision, is only able to identify a small amount of disorders (about 21% to 28%) from free-text health records. On the other hand, the addition of the CRF models allows for a much higher recall (57% to 79%) of disorders from free-text, without sensible detriment in precision. When evaluating the accuracy of the mapping of disorders to SNOMED CT concepts in the UMLS, we observe that the mapping obtained by our ltered instance of Metamap delivers state-of-the-art e ectiveness if only spans individuated by our system are considered (`relaxed' accuracy).

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Bacterial kinetics-controlled shape-directed biosynthesis of silver nanoplates using Morganella psychrotolerans

We show for the first time that by controlling the growth kinetics of Morganella psychrotolerans, a silver-resistant psychrophilic bacterium, the shape anisotropy of silver nanoparticles can be achieved. This is particularly important considering that there has been no report that demonstrates a control over shape of Ag nanoparticles by controlling the growth kinetics of bacteria during biological synthesis. Additionally, we have for the first time performed electrochemistry experiments on bacterial cells after exposing them to Ag(+) ions, which provide significant new insights about mechanistic aspects of Ag reduction by bacteria. The possibility to achieve nanoparticle shape control by using a "green" biosynthesis approach is expected to open up new exciting avenues for eco-friendly, large-scale, and economically viable shape-controlled synthesis of nanomaterials.

The effect of employment while in high school on educational attainment : a conditional difference-in-differences approach

Using American panel data from the National Education Longitudinal Study of 1988, this article investigates the effect of working during grade 12 on attainment.We employ, for the first time in the related literature, a semiparametric propensity score matching approach combined with difference-in-differences. We address selection on both observables and unobservables associated with part-time work decisions, without the need for instrumental variable. Once such factors are controlled for, little to no effects on reading and math scores are found. Overall, our results therefore suggest a negligible academic cost from part-time working by the end of high school.

Hairpin RNAs derived from RNA polymerase II and polymerase III promoter-directed transgenes are processed differently in plants

RNA polymerase III (Pol III) as well as Pol II (35S) promoters are able to drive hairpin RNA (hpRNA) expression and induce target gene silencing in plants. siRNAs of 21 nt are the predominant species in a 35S Pol II line, whereas 24- and/or 22-nucleotide (nt) siRNAs are produced by a Pol III line. The 35S line accumulated the loop of the hpRNA, in contrast to full-length hpRNA in the Pol III line. These suggest that Pol II and Pol III-transcribed hpRNAs are processed by different pathways. One Pol III transgene produced only 24-nt siRNAs but silenced the target gene efficiently, indicating that the 24-nt siRNAs can direct mRNA degradation; specific cleavage was confirmed by 59 rapid amplification of cDNA ends (RACE). Both Pol II- and Pol III-directed hpRNA transgenes induced cytosine methylation in the target DNA. The extent of methylation is not correlated with the level of 21-nt siRNAs, suggesting that they are not effective inducers of DNA methylation. The promoter of a U6 transgene was significantly methylated, whereas the promoter of the endogenous U6 gene was almost free of cytosine methylation, suggesting that endogenous sequences are more resistant to de novo DNA methylation than are transgene constructs. Published by Cold Spring Harbor Laboratory Press. Copyright © 2008 RNA Society.

Expression of Caenorhabditis elegans RNA-directed RNA polymerase in transgenic Drosophila melanogaster does not affect morphological development

Drosophila melanogaster, along with all insects and the vertebrates, lacks an RdRp gene. We created transgenic strains of Drosophila melanogaster in which the rrf-1 or ego-1 RdRp genes from C. elegans were placed under the control of the yeast GAL4 upstream activation sequence. Activation of the gene was performed by crossing these lines to flies carrying the GAL4 transgene under the control of various Drosophila enhancers. RT-PCR confirmed the successful expression of each RdRp gene. The resulting phenotypes indicated that introduction of the RdRp genes had no effect on D. melanogaster morphological development. © 2010 Springer Science+Business Media B.V.

The nature and correlates of young women's peer-directed protective behavioral strategies

Objective Recently, a number of studies have identified self-employed Protective Behavioral Strategies (PBS) as effective in decreasing the level of alcohol-related harm among young people. However, much of the published research has ignored important gender differences, such as women's increased tendency to rely on PBS that are social in nature. To further the understanding of women's PBS, the current study sought to investigate the nature and correlates of the strategies young women employ to keep their friends safe when drinking (i.e., peer-directed PBS). Method A scale measuring peer-directed PBS was developed and administered in conjunction with existing measures of alcohol consumption, personal PBS, and peer attachment. Participants consisted of 422 women aged 18–30 years, recruited among psychology students and the general public. Results Exploratory factor analysis revealed two clusters of peer-directed PBS; those that were aimed at reducing intoxication among one's friends and those that were designed to minimize alcohol-related harms. Further analysis found a positive relationship between women's tendency to implement personal and peer-directed PBS and that risky drinkers were less likely to engage in personal or peer-directed PBS (either type). Conclusion Findings indicate that personal and peer-directed PBS are related behaviors that are less frequently adopted by risky drinkers.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

A stochastic neighborhood conditional autoregressive model for spatial data

A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.

Photodissociation of TEMPO-modified peptides: New approaches to radical-directed dissociation of biomolecules

Radical-directed dissociation of gas phase ions is emerging as a powerful and complementary alternative to traditional tandem mass spectrometric techniques for biomolecular structural analysis. Previous studies have identified that coupling of 2-[(2,2,6,6-tetramethylpiperidin-1-oxyl)methyl] benzoic acid (TEMPO-Bz) to the N-terminus of a peptide introduces a labile oxygen-carbon bond that can be selectively activated upon collisional activation to produce a radical ion. Here we demonstrate that structurally-defined peptide radical ions can also be generated upon UV laser photodissociation of the same TEMPO-Bz derivatives in a linear ion-trap mass spectrometer. When subjected to further mass spectrometric analyses, the radical ions formed by a single laser pulse undergo identical dissociations as those formed by collisional activation of the same precursor ion, and can thus be used to derive molecular structure. Mapping the initial radical formation process as a function of photon energy by photodissociation action spectroscopy reveals that photoproduct formation is selective but occurs only in modest yield across the wavelength range (300-220 nm), with the photoproduct yield maximised between 235 and 225 nm. Based on the analysis of a set of model compounds, structural modifications to the TEMPO-Bz derivative are suggested to optimise radical photoproduct yield. Future development of such probes offers the advantage of increased sensitivity and selectivity for radical-directed dissociation. © 2014 the Owner Societies.

Formation and Fragmentation of Unsaturated Fatty Acid M-2H+Na (-) Ions: Stabilized Carbanions for Charge-Directed Fragmentation

Fatty acids are long-chain carboxylic acids that readily produce \[M - H](-) ions upon negative ion electrospray ionization (ESI) and cationic complexes with alkali, alkaline earth, and transition metals in positive ion ESI. In contrast, only one anionic monomeric fatty acid-metal ion complex has been reported in the literature, namely \[M - 2H + (FeCl)-Cl-II](-). In this manuscript, we present two methods to form anionic unsaturated fatty acid-sodium ion complexes (i.e., \[M - 2H + Na](-)). We find that these ions may be generated efficiently by two distinct methods: (1) negative ion ESI of a methanolic solution containing the fatty acid and sodium fluoride forming an \[M - H + NaF](-) ion. Subsequent collision-induced dissociation (CID) results in the desired \[M - 2H + Na](-) ion via the neutral loss of HF. (2) Direct formation of the \[M - 2H + Na](-) ion by negative ion ESI of a methanolic solution containing the fatty acid and sodium hydroxide or bicarbonate. In addition to deprotonation of the carboxylic acid moiety, formation of \[M - 2H + Na](-) ions requires the removal of a proton from the fatty acid acyl chain. We propose that this deprotonation occurs at the bis-allylic position(s) of polyunsaturated fatty acids resulting in the formation of a resonance-stabilized carbanion. This proposal is supported by ab initio calculations, which reveal that removal of a proton from the bis-allylic position, followed by neutral loss of HX (where X = F- and -OH), is the lowest energy dissociation pathway.

Graph coloring applied to secure computation in non-Abelian groups

We study the natural problem of secure n-party computation (in the computationally unbounded attack model) of circuits over an arbitrary finite non-Abelian group (G,⋅), which we call G-circuits. Besides its intrinsic interest, this problem is also motivating by a completeness result of Barrington, stating that such protocols can be applied for general secure computation of arbitrary functions. For flexibility, we are interested in protocols which only require black-box access to the group G (i.e. the only computations performed by players in the protocol are a group operation, a group inverse, or sampling a uniformly random group element). Our investigations focus on the passive adversarial model, where up to t of the n participating parties are corrupted.