Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

Robots move : bootstrapping the development of object representations using sensorimotor coordination

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.

Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph

The challenge of persistent appearance-based navigation and mapping is to develop an autonomous robotic vision system that can simultaneously localize, map and navigate over the lifetime of the robot. However, the computation time and memory requirements of current appearance-based methods typically scale not only with the size of the environment but also with the operation time of the platform; also, repeated revisits to locations will develop multiple competing representations which reduce recall performance. In this paper we present a solution to the persistent localization, mapping and global path planning problem in the context of a delivery robot in an office environment over a one-week period. Using a graphical appearance-based SLAM algorithm, CAT-Graph, we demonstrate constant time and memory loop closure detection with minimal degradation during repeated revisits to locations, along with topological path planning that improves over time without using a global metric representation. We compare the localization performance of CAT-Graph to openFABMAP, an appearance-only SLAM algorithm, and the path planning performance to occupancy-grid based metric SLAM. We discuss the limitations of the algorithm with regard to environment change over time and illustrate how the topological graph representation can be coupled with local movement behaviors for persistent autonomous robot navigation.

Soil N2O and CO2 emissions from cotton in Australia under varying irrigation management

Irrigation is known to stimulate soil microbial carbon and nitrogen turnover and potentially the emissions of nitrous oxide (N2O) and carbon dioxide (CO2). We conducted a study to evaluate the effect of three different irrigation intensities on soil N2O and CO2 fluxes and to determine if irrigation management can be used to mitigate N2O emissions from irrigated cotton on black vertisols in South-Eastern Queensland, Australia. Fluxes were measured over the entire 2009/2010 cotton growing season with a fully automated chamber system that measured emissions on a sub-daily basis. Irrigation intensity had a significant effect on CO2 emission. More frequent irrigation stimulated soil respiration and seasonal CO2 fluxes ranged from 2.7 to 4.1 Mg-C ha−1 for the treatments with the lowest and highest irrigation frequency, respectively. N2O emission happened episodic with highest emissions when heavy rainfall or irrigation coincided with elevated soil mineral N levels and seasonal emissions ranged from 0.80 to 1.07 kg N2O-N ha−1 for the different treatments. Emission factors (EF = proportion of N fertilizer emitted as N2O) over the cotton cropping season, uncorrected for background emissions, ranged from 0.40 to 0.53 % of total N applied for the different treatments. There was no significant effect of the different irrigation treatments on soil N2O fluxes because highest emission happened in all treatments following heavy rainfall caused by a series of summer thunderstorms which overrode the effect of the irrigation treatment. However, higher irrigation intensity increased the cotton yield and therefore reduced the N2O intensity (N2O emission per lint yield) of this cropping system. Our data suggest that there is only limited scope to reduce absolute N2O emissions by different irrigation intensities in irrigated cotton systems with summer dominated rainfall. However, the significant impact of the irrigation treatments on the N2O intensity clearly shows that irrigation can easily be used to optimize the N2O intensity of such a system.

Supplementary Information : Hogan, Holland, Holloway, Petit and Read : Read Classification for Next Generation Sequencing, ESANN 2013, April 2013

This item provides supplementary materials for the paper mentioned in the title, specifically a range of organisms used in the study. The full abstract for the main paper is as follows: Next Generation Sequencing (NGS) technologies have revolutionised molecular biology, allowing clinical sequencing to become a matter of routine. NGS data sets consist of short sequence reads obtained from the machine, given context and meaning through downstream assembly and annotation. For these techniques to operate successfully, the collected reads must be consistent with the assumed species or species group, and not corrupted in some way. The common bacterium Staphylococcus aureus may cause severe and life-threatening infections in humans,with some strains exhibiting antibiotic resistance. In this paper, we apply an SVM classifier to the important problem of distinguishing S. aureus sequencing projects from alternative pathogens, including closely related Staphylococci. Using a sequence k-mer representation, we achieve precision and recall above 95%, implicating features with important functional associations.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Usability and security of gaze-based graphical grid passwords

We present and analyze several gaze-based graphical password schemes based on recall and cued-recall of grid points; eye-trackers are used to record user's gazes, which can prevent shoulder-surfing and may be suitable for users with disabilities. Our 22-subject study observes that success rate and entry time for the grid-based schemes we consider are comparable to other gaze-based graphical password schemes. We propose the first password security metrics suitable for analysis of graphical grid passwords and provide an in-depth security analysis of user-generated passwords from our study, observing that, on several metrics, user-generated graphical grid passwords are substantially weaker than uniformly random passwords, despite our attempts at designing schemes to improve quality of user-generated passwords.

Modelling word activation in semantic networks : three scaled entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being over-estimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation.

Does Identifying Ambushers as Non-Sponsors Help or Hurt Legitimate Sponsors? Memory and Attitudinal Consequences

Two experiments examine outcomes for sponsor and ambusher brands within sponsorship settings. It is demonstrated that although making consumers aware of the presence of ambusher brands can reduce subsequent event recall to competitor cues, recall to sponsor cues can also suffer. Attitudinal effects are also considered.

Featureless visual processing for SLAM in changing outdoor environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.

Neutrophil activation, antioxidant supplements and exercise-induced oxidative stress

Neutrophils produce free radicals known as reactive oxygen species (ROS), which assist in the clearance of damaged host tissue. Tissue damage may occur during exercise due to muscle damage, thermal stress and ischaemia/reperfusion. When produced in excess, neutrophil-derived ROS may overwhelm the body's endogenous antioxidant defence mechanisms, and this can lead to oxidative stress. There is increasing evidence for links between oxidative stress and a variety of pathological disorders such as cardiovascular diseases, cancer, chronic inflammatory diseases and post-ischaemic organ injury. A small number of studies have investigated whether there is a link between neutrophil activation and oxidative stress during exercise. In this review, we have summarised the findings of these studies. Exercise promotes the release of neutrophils into the circulation, and some evidence suggests that neutrophils mobilised after exercise have an enhanced capacity to generate some forms of ROS when stimulated in vitro. Neutrophil activation during exercise may challenge endogenous antioxidant defence mechanisms, but does not appear to increase lipid markers of oxidative stress to any significant degree, at least in the circulation. Antioxidant supplements such as N-acetylcysteine are effective at attenuating increases in the capacity of neutrophils to generate ROS when stimulated in vitro, whereas vitamin E reduces tissue infiltration of neutrophils during exercise. Free radicals generated during intense exercise may lead to DNA damage in leukocytes, but it is unknown if this damage is the result of neutrophil activation. Exercise enhances the expression of inducible haem (heme)-oxygenase (HO-1) in neutrophils after exercise, however, it is uncertain whether oxidative stress is the stimulus for this response.

Changes in neutrophil surface receptor expression, degranulation, and respiratory burst activity after moderate- and high-intensity exercise

Intense exercise stimulates the systemic release of a variety of factors that alter neutrophil surface receptor expression and functional activity. These alterations may influence resistance to infection after intense exercise. The aim of this study was to examine the influence of exercise intensity on neutrophil receptor expression, degranulation (measured by plasma and intracellular myeloperoxidase concentrations), and respiratory burst activity. Ten well-trained male runners ran on a treadmill for 60 min at 60% [moderate-intensity exercise (MI)] and 85% maximal oxygen consumption [high-intensity exercise (HI)]. Blood was drawn immediately before and after exercise and at 1 h postexercise. Immediately after HI, the expression of the neutrophil receptor CD16 was significantly below preexercise values (P < 0.01), whereas MI significantly reduced CD35 expression below preexercise values (P < 0.05). One hour after exercise at both intensities, there was a significant decline in CD11b expression (P < 0.05) and a further decrease in CD16 expression compared with preexercise values (P < 0.01). CD16 expression was lower 1 h after HI than 1 h after MI (P < 0.01). Immediately after HI, intracellular myeloperoxidase concentration was less than preexercise values (P < 0.01), whereas plasma myeloperoxidase concentration was greater (P < 0.01), indicating that HI stimulated neutrophil degranulation. Plasma myeloperoxidase concentration was higher immediately after HI than after MI (P < 0.01). Neutrophil respiratory burst activity increased after HI (P < 0.01). In summary, both MI and HI reduced neutrophil surface receptor expression. Although CD16 expression was reduced to a greater extent after HI, this reduction did not impair neutrophil degranulation and respiratory burst activity.

Labor market reform and the changing structure of wage determination in China's state sector during the 1980s

China's market-oriented labor market reform has been in place for about one and a half decades. This study uses individual data for 1981 and 1987 to examine the success of the first half of the reform program. Success is evaluated by examining changes in the wage setting structure in the state-owned sector over the reform period. Have the market reforms stimulated worker incentives by increasing the returns to human capital acquisition? Has the wage structure altered to more closely mimic that of a market economy? In 1987, there is evidence of a structural change in the system of wage determination, with slightly increased rates of return to human capital. However, changes in industrial wage differentials appear to play the dominant role. It is argued that this may be due to labor market reforms, in particular the introduction of the profit related bonus scheme.J. Comp. Econom.,December 1997,25(3), pp. 403–421. Australian National University, Canberra, ACT0200, Australia and University of Tasmania, Hobart, Tasmania, Australia, and University of Aberdeen, Old Aberdeen, Scotland AB24 3QY.

The ionic products from bredigite bioceramics induced cementogenic differentiation of periodontal ligament cells via activation of the Wnt/β-catenin signalling pathway

Periodontitis results from the destructive inflammatory reaction of the host elicited by a bacterial biofilm adhering to the tooth surface and if left untreated, may lead to the loss of the teeth and the surrounding tissues, including the alveolar bone. Cementum is a specialized calcified tissue covering the tooth root and an essential part of the periodontium which enables the attachment of the periodontal ligament to the root and the surrounding alveolar bone. Periodontal ligament cells (PDLCs) represent a promising cell source for periodontal tissue engineering. Since cementogenesis is the critical event for the regeneration of periodontal tissues, this study examined whether inorganic stimuli derived from bioactive bredigite (Ca7MgSi4O16) bioceramics could stimulate the proliferation and cementogenic differentiation of PDLCs, and further investigated the involvement of the Wnt/β-catenin signalling pathway during this process via analysing gene/protein expression of PDLCs which interacted with bredigite extracts. Our results showed that the ionic products from bredigite powder extracts led to significantly enhanced proliferation and cementogenic differentiation, including mineralization–nodule formation, ALP activity and a series of bone/cementum-related gene/protein expression (ALP, OPN, OCN, BSP, CAP and CEMP1) of PDLCs in a concentration dependent manner. Furthermore, the addition of cardamonin, a Wnt/β-catenin signalling inhibitor, reduced the pro-cementogenesis effect of the bredigite extracts, indicating the involvement of the Wnt/β-catenin signalling pathway in the cementogenesis of PDLCs induced by bredigite extracts. The present study suggests that an entirely inorganic stimulus with a specific composition of bredigite bioceramics possesses the capacity to trigger the activation of the Wnt/β-catenin signalling pathway, leading to stimulated differentiation of PDLCs toward a cementogenic lineage. The results indicate the therapeutic potential of bredigite ceramics in periodontal tissue engineering application.

A practical comparison of manual and autonomous methods for acoustic monitoring

1. Autonomous acoustic recorders are widely available and can provide a highly efficient method of species monitoring, especially when coupled with software to automate data processing. However, the adoption of these techniques is restricted by a lack of direct comparisons with existing manual field surveys. 2. We assessed the performance of autonomous methods by comparing manual and automated examination of acoustic recordings with a field-listening survey, using commercially available autonomous recorders and custom call detection and classification software. We compared the detection capability, time requirements, areal coverage and weather condition bias of these three methods using an established call monitoring programme for a nocturnal bird, the little spotted kiwi(Apteryx owenii). 3. The autonomous recorder methods had very high precision (>98%) and required <3% of the time needed for the field survey. They were less sensitive, with visual spectrogram inspection recovering 80% of the total calls detected and automated call detection 40%, although this recall increased with signal strength. The areal coverage of the spectrogram inspection and automatic detection methods were 85% and 42% of the field survey. The methods using autonomous recorders were more adversely affected by wind and did not show a positive association between ground moisture and call rates that was apparent from the field counts. However, all methods produced the same results for the most important conservation information from the survey: the annual change in calling activity. 4. Autonomous monitoring techniques incur different biases to manual surveys and so can yield different ecological conclusions if sampling is not adjusted accordingly. Nevertheless, the sensitivity, robustness and high accuracy of automated acoustic methods demonstrate that they offer a suitable and extremely efficient alternative to field observer point counts for species monitoring.