High performance numerical modeling of ultra-short laser pulse propagation based on multithreaded parallel hardware

The focus of this study is development of parallelised version of severely sequential and iterative numerical algorithms based on multi-threaded parallel platform such as a graphics processing unit. This requires design and development of a platform-specific numerical solution that can benefit from the parallel capabilities of the chosen platform. Graphics processing unit was chosen as a parallel platform for design and development of a numerical solution for a specific physical model in non-linear optics. This problem appears in describing ultra-short pulse propagation in bulk transparent media that has recently been subject to several theoretical and numerical studies. The mathematical model describing this phenomenon is a challenging and complex problem and its numerical modeling limited on current modern workstations. Numerical modeling of this problem requires a parallelisation of an essentially serial algorithms and elimination of numerical bottlenecks. The main challenge to overcome is parallelisation of the globally non-local mathematical model. This thesis presents a numerical solution for elimination of numerical bottleneck associated with the non-local nature of the mathematical model. The accuracy and performance of the parallel code is identified by back-to-back testing with a similar serial version.

Enhanced text spacing improves reading performance in individuals with macular disease

The search by many investigators for a solution to the reading problems encountered by individuals with no central vision has been long and, to date, not very fruitful. Most textual manipulations, including font size, have led to only modest gains in reading speed. Previous work on spatial integrative properties of peripheral retina suggests that 'visual crowding' may be a major factor contributing to inefficient reading. Crowding refers to the fact that juxtaposed targets viewed eccentrically may be difficult to identify. The purpose of this study was to assess the combined effects of line spacing and word spacing on the ability of individuals with age-related macular degeneration (ARMD) to read short passages of text that were printed with either high (87.5%) or low contrast (17.5%) letters. Low contrast text was used to avoid potential ceiling effects and to mimic a possible reduction in letter contrast with light scatter from media opacities. For both low and high contrast text, the fastest reading speeds we measured were for passages of text with double line and double word spacing. In comparison with standard single spacing, double word/line spacing increased reading speed by approximately 26% with high contrast text (p < 0.001), and by 46% with low contrast text (p < 0.001). In addition, double line/word spacing more than halved the number of reading errors obtained with single spaced text. We compare our results with previous reading studies on ARMD patients, and conclude that crowding is detrimental to reading and that its effects can be reduced with enhanced text spacing. Spacing is particularly important when the contrast of the text is reduced, as may occur with intraocular light scatter or poor viewing conditions. We recommend that macular disease patients should employ double line spacing and double-character word spacing to maximize their reading efficiency. © 2013 Blackmore-Wright et al.

Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

Founders' human capital and the performance of UK new technology based firms

This paper investigates the role of entrepreneurs' general and specific human capital on the performance of UK new technology based firms using a resource based approach to the entrepreneurship theory. The effect of entrepreneurial human capital on the performance of NTBFs is investigated using data derived from a survey of 412 firms operating in both high-tech manufacturing and the services sectors. According to the resource based theory it is found that specific human capital is more important for the performance of NTBFs in relation to general. More specifically individual entrepreneurs or entrepreneurial teams with high levels of formal business education, commercial, managerial or same sector experience are found to have created better performing NTBFs. Finally it is found that the performance of a NTBF can improve through the combination of heterogeneous but complementary skills, including, for example, technical education and commercial experience or managerial technical and managerial commercial experience. © 2010 Springer Science+Business Media, LLC.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Performance analysis of IEEE 802.11 cognitive radio ad hoc networks

IEEE 802.11 standard is the dominant technology for wireless local area networks (WLANs). In the last two decades, the Distributed coordination function (DCF) of IEEE 802.11 standard has become the one of the most important media access control (MAC) protocols for mobile ad hoc networks (MANETs). The DCF protocol can also be combined with cognitive radio, thus the IEEE 802.11 cognitive radio ad hoc networks (CRAHNs) come into being. There were several literatures which focus on the modeling of IEEE 802.11 CRAHNs, however, there is still no thorough and scalable analytical models for IEEE 802.11 CRAHNs whose cognitive node (i.e., secondary user, SU) has spectrum sensing and possible channel silence process before the MAC contention process. This paper develops a unified analytical model for IEEE 802.11 CRAHNs for comprehensive MAC layer queuing analysis. In the proposed model, the SUs are modeled by a hyper generalized 2D Markov chain model with an M/G/1/K model while the primary users (PUs) are modeled by a generalized 2D Markov chain and an M/G/1/K model. The performance evaluation results show that the quality-of-service (QoS) of both the PUs and SUs can be statistically guaranteed with the suitable settings of duration of channel sensing and silence phase in the case of under loading.

Improving performance of a permeable reactive barrier in the degradation of trichloroethylene using ultrasound

The impact of ultrasound on improving the performance of a granular iron Permeable Reactive Barrier (PRB) in the degradation of Trichloroethylene (TCE) was evaluated. Two treatment columns made of clear Plexiglas with a height of 1ft and a diameter of 2 inches and filled with granular iron were used. One was fitted with 25Khz ultrasound probes. A solution of TCE was run through at constant flow rate. Samples obtained from the column at different residence times before and after sonication were analyzed for concentrations of TCE and used to generate concentration profiles to obtain rate constants, which were compared. An improvement of 23.4% in the reaction rate of TCE degradation was observed after sonication of the iron media suggesting that ultrasound may contribute to improving the performance of PRBs in the degradation of TCE in contaminated groundwater.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

Sorting Test, Tower Test, and BRIEF-SR do not predict school performance of healthy adolescents in preuniversity education

Executive functions (EF) such as self-monitoring, planning, and organizing are known to develop through childhood and adolescence. They are of potential importance for learning and school performance. Earlier research into the relation between EF and school performance did not provide clear results possibly because confounding factors such as educational track, boy-girl differences, and parental education were not taken into account. The present study therefore investigated the relation between executive function tests and school performance in a highly controlled sample of 173 healthy adolescents aged 12–18. Only students in the pre-university educational track were used and the performance of boys was compared to that of girls. Results showed that there was no relation between the report marks obtained and the performance on executive function tests, notably the Sorting Test and the Tower Test of the Delis-Kaplan Executive Functions System (D-KEFS). Likewise, no relation was found between the report marks and the scores on the Behavior Rating Inventory of Executive Function—Self-Report Version (BRIEF-SR) after these were controlled for grade, sex, and level of parental education. The findings indicate that executive functioning as measured with widely used instruments such as the BRIEF-SR does not predict school performance of adolescents in preuniversity education any better than a student's grade, sex, and level of parental education.

Predicting Academic Performance Based on Students' Blog and Microblog Posts

This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected from six student cohorts, from six consecutive installments of the Web Applications Design course, comprising of 343 students. A significant model was obtained by relying on the textual complexity and longitudinal analysis indices, applied on the English contributions of 148 students that were actively involved in the undertaken projects.

Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.

Blurred Lines: Performance Enhancement, Common Mental Disorders and Referral in the U.K. Athletic Population

Through the awareness-raising efforts of several high-profile current and former athletes, the issue of common mental disorders (CMD) in this population is gaining increasing attention from researchers and practitioners alike. Yet the prevalence is unclear and most likely, under-reported. Whilst the characteristics of the sporting environment may generate CMD within the athletic population, it also may exacerbate pre-existing conditions, and hence it is not surprising that sport psychology and sport science practitioners are anecdotally reporting increased incidences of athletes seeking support for CMDs. In a population where there are many barriers to reporting and seeking help for CMD, due in part to the culture of the high performance sporting environment, anecdotal reports suggest that those athletes asking for help are approaching personnel who they are most comfortable talking to. In some cases, this may be a sport scientist, the sport psychologist or sport psychology consultant. Among personnel in the sporting domain, there is a perception that the sport psychologist or sport psychology consultant is best placed to assist athletes seeking assistance for CMD. However, sport psychology as a profession is split by two competing philosophical perspectives; one of which suggests that sport psychologists should work exclusively with athletes on performance enhancement, and the other views the athlete more holistically and accepts that their welfare may directly impact on their performance. To add further complication, the development of the profession of sport psychology varies widely between countries, meaning that practice in this field is not always clearly defined. This article examines case studies that illustrate the blurred lines in applied sport psychology practice, highlighting challenges with the process of referral in the U.K. athletic population. The article concludes with suggestions for ensuring the field of applied sport psychology is continually evolving and reconfiguring to ensure that it continues to meet the demands of its clients.

Creative communities: shaping process through performance and play

This paper studies the use of play as a method to unlock creativity and innovation within a community of practice (a group of individuals who share a common interest and who see value in interaction to enhance their understanding). An analysis of communities of practice and the value of play informs evaluation of two case studies exploring the development of communities of practice, one within the discipline of videogames and one which bridges performing arts and videogames. The case studies provide qualitative data from which the potential of play as a method to inspire creativity and support the development of a potential community of practice is recognised. Establishing trust, disruption of process through play and reflection are key steps proposed in a ‘context provider’s framework’ for individuals or organisations to utilise in the design of activities to support creative process and innovation within a potential community of practice.

Systemizers are better code-breakers: self-reported systemizing predicts code-breaking performance in expert hackers and naïve participants

Studies on hacking have typically focused on motivational aspects and general personality traits of the individuals who engage in hacking; little systematic research has been conducted on predispositions that may be associated not only with the choice to pursue a hacking career but also with performance in either naïve or expert populations. Here, we test the hypotheses that two traits that are typically enhanced in autism spectrum disorders—attention to detail and systemizing—may be positively related to both the choice of pursuing a career in information security and skilled performance in a prototypical hacking task (i.e., crypto-analysis or code-breaking). A group of naïve participants and of ethical hackers completed the Autism Spectrum Quotient, including an attention to detail scale, and the Systemizing Quotient (Baron-Cohen et al., 2001, 2003). They were also tested with behavioral tasks involving code-breaking and a control task involving security X-ray image interpretation. Hackers reported significantly higher systemizing and attention to detail than non-hackers. We found a positive relation between self-reported systemizing (but not attention to detail) and code-breaking skills in both hackers and non-hackers, whereas attention to detail (but not systemizing) was related with performance in the X-ray screening task in both groups, as previously reported with naïve participants (Rusconi et al., 2015). We discuss the theoretical and translational implications of our findings.

Automating and informating : roles to examine in technology's impact on performance

La calidad de la información y la calidad del sistema se consideran factores claves para determinar la utilidad de los sistemas. Sin embargo, estudios anteriores han arrojado resultados mixtos. En este artículo se sostiene que la asistencia del sistema (el grado de la asistencia proporcionada por un sistema a través de sus dos funciones: la automatización e “informating”) media en esta relación y puede ayudar a explicar estos resultados mixtos. Por otra parte, se sostiene que el nivel del sistema de intervención ( grado en que la tecnología participa en la realización de las tareas) es otro factor clave para determinar su utilidad, especialmente a través de la función de automatización. Se recogieron datos de 246 usuarios de diferentes jerarquías y funciones. Los resultados muestran que la calidad de la información y el nivel de intervención del sistema explican la utilidad de ambas funciones. La calidad del sistema explica la utilidad mediante el rol del “informating”, pero no mediante el papel de la automatización. Los efectos diferenciados de los factores a través de cada rol pueden ayudar a los gerentes para establecer criterios y prioridades más eficaces en las diferentes etapas del ciclo de vida de un sistema de información.