Reconstruction of a stationary flow from boundary data using iterative methods

In this paper, three iterative procedures (Landweber-Fridman, conjugate gradient and minimal error methods) for obtaining a stable solution to the Cauchy problem in slow viscous flows are presented and compared. A section is devoted to the numerical investigations of these algorithms. There, we use the boundary element method together with efficient stopping criteria for ceasing the iteration process in order to obtain stable solutions.

The UK MPharm (1) - are we over reliant on traditional teaching methods?

Introduction-The design of the UK MPharm curriculum is driven by the Royal Pharmaceutical Society of Great Britain (RPSGB) accreditation process and the EU directive (85/432/EEC).[1] Although the RPSGB is informed about teaching activity in UK Schools of Pharmacy (SOPs), there is no database which aggregates information to provide the whole picture of pharmacy education within the UK. The aim of the teaching, learning and assessment study [2] was to document and map current programmes in the 16 established SOPs. Recent developments in programme delivery have resulted in a focus on deep learning (for example, through problem based learning approaches) and on being more student centred and less didactic through lectures. The specific objectives of this part of the study were (a) to quantify the content and modes of delivery of material as described in course documentation and (b) having categorised the range of teaching methods, ask students to rate how important they perceived each one for their own learning (using a three point Likert scale: very important, fairly important or not important). Material and methods-The study design compared three datasets: (1) quantitative course document review, (2) qualitative staff interview and (3) quantitative student self completion survey. All 16 SOPs provided a set of their undergraduate course documentation for the year 2003/4. The documentation variables were entered into Excel tables. A self-completion questionnaire was administered to all year four undergraduates, using a pragmatic mixture of methods, (n=1847) in 15 SOPs within Great Britain. The survey data were analysed (n=741) using SPSS, excluding non-UK students who may have undertaken part of their studies within a non-UK university. Results and discussion-Interviews showed that individual teachers and course module leaders determine the choice of teaching methods used. Content review of the documentary evidence showed that 51% of the taught element of the course was delivered using lectures, 31% using practicals (includes computer aided learning) and 18% small group or interactive teaching. There was high uniformity across the schools for the first three years; variation in the final year was due to the project. The average number of hours per year across 15 schools (data for one school were not available) was: year 1: 408 hours; year 2: 401 hours; year 3: 387 hours; year 4: 401 hours. The survey showed that students perceived lectures to be the most important method of teaching after dispensing or clinical practicals. Taking the very important rating only: 94% (n=694) dispensing or clinical practicals; 75% (n=558) lectures; 52% (n=386) workshops, 50% (n=369) tutorials, 43% (n=318) directed study. Scientific laboratory practices were rated very important by only 31% (n=227). The study shows that teaching of pharmacy to undergraduates in the UK is still essentially didactic through a high proportion of formal lectures and with high levels of staff-student contact. Schools consider lectures still to be the most cost effective means of delivering the core syllabus to large cohorts of students. However, this does limit the scope for any optionality within teaching, the scope for small group work is reduced as is the opportunity to develop multi-professional learning or practice placements. Although novel teaching and learning techniques such as e-learning have expanded considerably over the past decade, schools of pharmacy have concentrated on lectures as the best way of coping with the huge expansion in student numbers. References [1] Council Directive. Concerning the coordination of provisions laid down by law, regulation or administrative action in respect of certain activities in the field of pharmacy. Official Journal of the European Communities 1985;85/432/EEC. [2] Wilson K, Jesson J, Langley C, Clarke L, Hatfield K. MPharm Programmes: Where are we now? Report commissioned by the Pharmacy Practice Research Trust., 2005.

A quality driven framework for adaptive video streaming in mobile wireless networks

The concern over the quality of delivering video streaming services in mobile wireless networks is addressed in this work. A framework that enhances the Quality of Experience (QoE) of end users through a quality driven resource allocation scheme is proposed. To play a key role, an objective no-reference quality metric, Pause Intensity (PI), is adopted to derive a resource allocation algorithm for video streaming. The framework is examined in the context of 3GPP Long Term Evolution (LTE) systems. The requirements and structure of the proposed PI-based framework are discussed, and results are compared with existing scheduling methods on fairness, efficiency and correlation (between the required and allocated data rates). Furthermore, it is shown that the proposed framework can produce a trade-off between the three parameters through the QoE-aware resource allocation process.

Applications of Nonclassical Logic Methods for Purposes of Knowledge Discovery and Data Mining

* The work is partially supported by Grant no. NIP917 of the Ministry of Science and Education – Republic of Bulgaria.

Comparing Several Methods of Discriminant Analysis on the Case of Wine Data

2000 Mathematics Subject Classification: 62H30, 62J20, 62P12, 68T99

Initial placement methods for Haitian adult ESOL students : the NYS place test plus profile data compared to subjective assessment

Few valid and reliable placement procedures are available to assess the English language proficiency of adults who enroll in English for Speakers of Other Languages (ESOL) programs. Whereas placement material exists for children and university ESOL students, the needs of students in adult community education programs have not been adequately addressed. Furthermore, the research suggests that a number of variables, such as, native language, age, prior schooling, length of residence, and employment are related to second language acquisition. Numerous studies contribute to our understanding of the relationship of these factors to second language acquisition of Spanish-speaking students. Again, there is a void in the research investigating the factors affecting second language acquisition and consequently, appropriate placement of Haitian Creole-speaking students. This study compared a standardized instrument, the NYS Place Test, used alone and in combination with a writing sample in English, to subjective judgement of a department coordinator for initial placement of Haitian adult ESOL students in a community education program. The study also investigated whether or not consideration of student profile data improved the accuracy of the test. Finally, the study sought to determine if a relationship existed between student profile data and those who withdrew from the program or did not enter a class after registering. Analysis of the data by crosstabulation and chi-square revealed that the standardized NYS Place Test was at least as accurate as subjective department coordinator placement and that one procedure could be substituted for li other. Although the writing sample in English improved accuracy of placement by the NYS test, the results were not significant. Of the profile variables, only length of residence was found to be significantly related to accuracy of placement using the NYS Place Test. The number of incorrect placements was higher for those students who lived in the host country from twenty-five to one hundred ten months. A post hoc analysis of NYS test scores according to level showed that those learners who placed in level three also had a significantly higher incidence of incorrect placements. No significant relationship was observed between the profile variables and those who withdrew from the program or registered but did not enter a class.

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.

Radio frequency identification and time-driven activity based costing:RFID-TDABC application in warehousing

Purpose: This paper extends the use of Radio Frequency Identification (RFID) data for accounting of warehouse costs and services. Time Driven Activity Based Costing (TDABC) methodology is enhanced with the real-time collected RFID data about duration of warehouse activities. This allows warehouse managers to have accurate and instant calculations of costs. The RFID enhanced TDABC (RFID-TDABC) is proposed as a novel application of the RFID technology. Research Approach: Application of RFID-TDABC in a warehouse is implemented on warehouse processes of a case study company. Implementation covers receiving, put-away, order picking, and despatching. Findings and Originality: RFID technology is commonly used for the identification and tracking items. The use of the RFID generated information with the TDABC can be successfully extended to the area of costing. This RFID-TDABC costing model will benefit warehouse managers with accurate and instant calculations of costs. Research Impact: There are still unexplored benefits to RFID technology in its applications in warehousing and the wider supply chain. A multi-disciplinary research approach led to combining RFID technology and TDABC accounting method in order to propose RFID-TDABC. Combining methods and theories from different fields with RFID, may lead researchers to develop new techniques such as RFID-TDABC presented in this paper. Practical Impact: RFID-TDABC concept will be of value to practitioners by showing how warehouse costs can be accurately measured by using this approach. Providing better understanding of incurred costs may result in a further optimisation of warehousing operations, lowering costs of activities, and thus provide competitive pricing to customers. RFID-TDABC can be applied in a wider supply chain.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

Methods for Imputing Missing Values and Synthesizing Confidential Values for Continuous and Magnitude Data

Abstract

Continuous variable is one of the major data types collected by the survey organizations. It can be incomplete such that the data collectors need to fill in the missingness. Or, it can contain sensitive information which needs protection from re-identification. One of the approaches to protect continuous microdata is to sum them up according to different cells of features. In this thesis, I represents novel methods of multiple imputation (MI) that can be applied to impute missing values and synthesize confidential values for continuous and magnitude data.

The first method is for limiting the disclosure risk of the continuous microdata whose marginal sums are fixed. The motivation for developing such a method comes from the magnitude tables of non-negative integer values in economic surveys. I present approaches based on a mixture of Poisson distributions to describe the multivariate distribution so that the marginals of the synthetic data are guaranteed to sum to the original totals. At the same time, I present methods for assessing disclosure risks in releasing such synthetic magnitude microdata. The illustration on a survey of manufacturing establishments shows that the disclosure risks are low while the information loss is acceptable.

The second method is for releasing synthetic continuous micro data by a nonstandard MI method. Traditionally, MI fits a model on the confidential values and then generates multiple synthetic datasets from this model. Its disclosure risk tends to be high, especially when the original data contain extreme values. I present a nonstandard MI approach conditioned on the protective intervals. Its basic idea is to estimate the model parameters from these intervals rather than the confidential values. The encouraging results of simple simulation studies suggest the potential of this new approach in limiting the posterior disclosure risk.

The third method is for imputing missing values in continuous and categorical variables. It is extended from a hierarchically coupled mixture model with local dependence. However, the new method separates the variables into non-focused (e.g., almost-fully-observed) and focused (e.g., missing-a-lot) ones. The sub-model structure of focused variables is more complex than that of non-focused ones. At the same time, their cluster indicators are linked together by tensor factorization and the focused continuous variables depend locally on non-focused values. The model properties suggest that moving the strongly associated non-focused variables to the side of focused ones can help to improve estimation accuracy, which is examined by several simulation studies. And this method is applied to data from the American Community Survey.

Size-partitioned phytoplankton carbon concentrations retrieved from ocean color data, links to data in netCDF format

Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2 µm in diameter), nanophytoplankton (2-20 µm) and microphytoplankton (20-50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield - 0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.

Barriers to working with people affected by cancer: Preliminary qualitative data from a mixed methods exploration of practitioner perspectives in nutritional therapy

Introduction: Cancer is a leading cause of death worldwide. Nutrition may affect occurrence, recurrence and survival rates and many cancer patients and survivors seek individualized nutrition advice. Appropriately skilled nutritional therapy (NT) practitioners may be well-placed to safely provide this advice, but little is known of their perspectives on working with people affected by cancer. This mixed-methods study seeks to explore their views on training, barriers to practice, use of evidence, and other resources, to support the development of safe evidence-based practice. Preliminary data on barriers to practice are reported here. Methods: Two cohorts of NT practitioners were recruited from all UK registered NT practitioners, by an on-line anonymous survey. 84 cancer practitioners (CP) and 165 non-cancer practitioners (NCP) were recruited. Mixed quantitative and qualitative data was collected by the survey. Content analysis was used to analyze qualitative data on the use of evidence, barriers to practice and perceived needs for working with clients with cancer, for further exploration using interviews and focus groups. Preliminary results: For the NCP cohort, exploring themes of perceived barriers to working with people affected by cancer suggested that perceived complexity, risk and need for caution in this area of practice were important barriers. Insufficient specialist knowledge and skills also emerged as barriers. Some NCPs perceived opposition from medical practitioners and other mainstream healthcare professions as an obstacle to starting cancer practice. To overcome these barriers, specialist training emerged as most important. For the CP cohort, in exploring the skills they considered enabled them to undertake cancer work, specialist clinical and technical knowledge emerged strongly. Only 10% CP participants did not want more work with people affected by cancer. 10% CPs reported some NHS referrals, whereas most received clients by self-referral or from other practitioners. When considering barriers that impede their cancer practice, the dominant categories for CPs were hostility or opposition by mainstream oncology professionals, and lack of dialogue and engagement with them. To overcome these barriers, CPs desired engagement with oncology professionals and recognized specialist cancer NT training. For both NCPs and CPs, evidence resources, practice guidelines and practitioner support networks also emerged as potential enablers to cancer practice. Conclusions: This is the first detailed exploration of NT practitioners’ perceived barriers to working with people affected by cancer. Acquiring specialist skills and knowledge appears important to enable NCPs to start cancer work, and for CPs with these skills, the perceived barriers appear foremost in the relationship with mainstream cancer professionals. Further exploration of these themes, and other NT practitioner perspectives on working with people affected by cancer, is underway. This work will inform and support the development of professional practice, training and other resources.

Analysis of Local Search Methods for 3D Data

In questa tesi sono stati analizzati alcuni metodi di ricerca per dati 3D. Viene illustrata una panoramica generale sul campo della Computer Vision, sullo stato dell’arte dei sensori per l’acquisizione e su alcuni dei formati utilizzati per la descrizione di dati 3D. In seguito è stato fatto un approfondimento sulla 3D Object Recognition dove, oltre ad essere descritto l’intero processo di matching tra Local Features, è stata fatta una focalizzazione sulla fase di detection dei punti salienti. In particolare è stato analizzato un Learned Keypoint detector, basato su tecniche di apprendimento di machine learning. Quest ultimo viene illustrato con l’implementazione di due algoritmi di ricerca di vicini: uno esauriente (K-d tree) e uno approssimato (Radial Search). Sono state riportate infine alcune valutazioni sperimentali in termini di efficienza e velocità del detector implementato con diversi metodi di ricerca, mostrando l’effettivo miglioramento di performance senza una considerabile perdita di accuratezza con la ricerca approssimata.