Miracle-dz/UniFAR-FTUs huggingface dataset:
Eye modelling for personalised intraocular lens design

Approximately 20 million cataract operations are performed per year worldwide1[1], routinely removing the opaque cataractous lens and implanting an artificial intraocular lens (IOL). Modern technologies used to treat cataract are highly sophisticated and the operation involves remarkable skills; however the IOL power calculation in certain circumstances can be unsatisfactory. Currently the calculation of required IOL power is based on formulas developed from the paraxial geometrical optics equations and/or statistical analysis of retrospective cases. This work aims to improve upon the methodologies currently used to predict required IOL power, particularly for unusual eyes with extreme ocular biometry (e.g. extreme axial length or corneal shape). This work also demonstrates the usefulness of personalized eye models in investigations of IOL designs proposed to correct the higher order aberrations of the eye. Biometric data was collected on a cohort of subjects undergoing routine cataract surgery with phacoemulsification and in-the-bag implantation with an Akreos Adapt AO (Bausch and Lomb) IOL. Anterior corneal topography elevation data using Placido disc technology (Atlas 9000, Carl Zeiss Meditec) and segmented axial length data using optical low coherence reflectometry (Lenstar 900, Haag-Streit) was collected on both eyes of each subject prior to surgery. The same measurements were repeated approximately 3 weeks post-operatively with additional measurements of aberrometry (Zywave, Bausch and Lomb) and autorefraction (ARK-510A, Nidek) to quantify the refractive outcome. In vitro IOL metrology was performed on a series of IOLs (Akreos Adapt AO, Bausch and Lomb) immersed in a saline bath, using a Twyman-Green interferometer (FISBA OPTIK μPhase). From the measured ocular biometry and IOL metrology data, personalized eye models were generated and their accuracy in predicting the required IOL power is compared to standard industry formulas applied to the same sample of eyes. The personalized models were also used to study the optimal customized IOL design for an individual eye. This simulation of higher order monochromatic aberration correction allows the retinal image quality to be quantified, which is a preliminary step in investigating the potential visual benefit from customized aberration-correcting IOL designs. This work concludes that refractive outcomes for cataract patients may be improved by establishing a comprehensive methodology regarding IOL power, design and selection process. Personalised ray tracing eye models are an important tool in this regard and reduce reliance on approximations inherent in the current industry standard formulas. 2010 data projected from 1995 estimates assuming service levels remain at 1995 levels. iii
Progress Towards and Improved CFD Method : AUSM+

This paper describes recent progress towards developing a new numerical scheme, termed AUSMf: we give new polynomial representations for splitting the Mach number and pressure; we introduce the notion of employing a common speed of sound which turns out to be a critical ingredient for achieving important properties. Also, we propose an enhanced version AUSM+-W, which is especially advantageous for resolving strong colliding shocks. We show that both AUSM+ schemes have the following features: ( I ) Exact resolution of contact and shock discontinuities, (2) Positivity preserving of scalar quantity such as the density, (3) Free of "Carbuncle phenomenon", (4) Easy entension to treat other hyperbolic systems, (5) Free of oscillations at the slowly-moving shock, and (6) Computation efficiency. A wall-pressure condition, formulated to be consistent with the interior scheme, is found to dramatically remove pressure oscillations. An alternative for higher order accurate spatial discretization using flux extrapolation is suggestled. We also describe a procedure for including the proposed schemes in a centraldifferencing code, revealing that Jameson's recent CUSP is an approximate form of the present method. To confirm the capability and improvement of the present schemes, numerical results are given for a wide variety of problems with various types of complexities.
Low-latency Image Recognition withGPU-accelerated Convolutional Networksfor Web-based Services

In this work, we describe an application of convolutional networks to object classification and detection in images. The task of image based object recognition is surveyed in the first chapter. Its application in internet advertisement is one of the main motivations of this work. The architecture of the convolutional networks is described in details in the following chapter. Stochastic gradient descent is used to train the networks. We then describe the data collection and labelling process. The set of training data labelled basically decides what kind of recognizer is being built. Four binary classifiers are trained for the object types of sailboat, car, motorbike, and dog. GPU based massive parallel implementation of the convolutional networks is built. This enables us to run the convolution operations at close to 40 times faster than running on a traditional CPU. Details about how to implement the convolutional operation on NVIDIA GPUs using CUDA is discussed. In order to apply the object recognizer in a production environment where millions of images are processed daily, we have built a platform with cloud computing. We describe how large scale and low latency image processing can be achieved with such a system.
Application of Polyalphabetic Substitution cipher Using Genetic Algorithm

Several Genetic Algorithms have been developed for applications of cryptography problem; the primary distinction among all of them being the G.A. used for decryption problem and obtains the plain text. In this paper a new approach is proposed using Genetic Algorithm with cryptography. G.A. is used to obtain a best secret key in polyalphabetic substitution cipher. This key will be used then for encryption and decryption with a high level of security. The program is written in Matlab language (6.5).
Matrix Approximation for Large-scale Learning

Modern learning problems in computer vision, natural language processing, computational biology, and other areas are often based on large data sets of tens of thousands to millions of training instances. However, several standard learning algorithms, such as kernel-based algorithms, e.g., Support Vector Machines, Kernel Ridge Regression, Kernel PCA, do not easily scale to such orders of magnitude. This thesis focuses on sampling-based matrix approximation techniques that help scale kernel-based algorithms to large-scale datasets. We address several fundamental theoretical and empirical questions including: (1) What approximation should be used? We discuss two common sampling-based methods, providing novel theoretical insights regarding their suitability for various applications and experimental results motivated by this theory. Our results show that one of these methods, the Nystrom method, is superior in the context of large-scale learning. (2) Do these approximations work in practice? We show the effectiveness of approximation techniques on a variety of problems. In the largest study to-date for manifold learning, we use the Nystrom method to extract low-dimensional structure from high-dimensional data to effectively cluster face images. We also report good empirical results for Kernel Ridge Regression and Kernel Logistic Regression. (3) How should we sample columns? A key aspect of sampling-based algorithms is the distribution according to which columns are sampled. We study both fixed and adaptive sampling schemes as well as a promising ensemble technique that can be easily parallelized and generates superior approximations, both in theory and in practice. (4) How well do these approximations work in theory? We provide theoretical analyses of the Nystrom method to understand when this technique should be used. We present guarantees on approximation accuracy based on various matrix properties and analyze the effect of matrix approximation on actual kernel-based algorithms. This work has important consequences for the machine learning community since it extends to large-scale applications the benefits of kernel-based algorithms. The crucial aspect of this research, involving low-rank matrix approximation, is of independent interest within the field of linear algebra.
A comparative analysis of Romanian-English and Romanian-Spanish code-switching patterns

The present study compares Romanian-Spanish and Romanian-English code-switch patterns at a morpho-syntactic level. Spanish and Romanian have similar grammatical features and share many structural and phonological properties, while Romanian and English differ in most of these aspects. In comparing 240 code-switches from both language pairs, I address whether these differences and similarities in code-switch patterns can be explained by typological factors. The results show that switch points in both language pairs are the same at phrase level. However, different CS patterns occur at morpheme-level, with Romanian-Spanish switches showing more complex combinations between Spanish stems and Romanian inflectional or function morphemes, than in the Romanian-English data. These findings shed light on the role of feature matching between switched categories and the effect of typological similarity.
Developing a Self-Report-Based Sequential Analysis Method for Educational Technology Systems

The development of a usability evaluation method for educational systems or applications, called the self-report-based sequential analysis, is described herein. The method aims to extend the current practice by proposing self-report-based sequential analysis as a new usability method, which integrates the advantages of self-report in survey research (e.g., questionnaires and interviews) and lag sequential analysis to evaluate educational systems or applications and further understand students’ operational process patterns of behaviors toward the systems’ interfaces. An empirical study using a drill-based game as a platform was conducted to test the effectiveness of the self-report-based sequential analysis method. The results showed the critical mediators and behavioral transfer patterns among the different area of interests during the students’ overall operational process behaviors. For example, the health points and badges as rewards served as critical mediators between the rewards and feedback and health points and countdown timer, respectively. The self-report-based sequential analysis helped explore the participants’ operational process behaviors in the area of interests, and why behaviors shift on area of interests that can assist to diagnose the inappropriate arrangement of area of interests. Suggestions are provided based on the results.
Analysis of illumination conditions at the lunar south pole using parallel computing techniques

Faculty VI Planning Building Environment Department of Geodesy and Geoinformation Science Master of Science Analysis of illumination conditions at the lunar south pole using parallel computing techniques by Ramiro Marco figuera In this Master Thesis an analysis of illumination conditions at the lunar south pole using parallel computing techniques is presented. Due to the small inclination (1.54o) of the lunar rotational axis with respect to the ecliptic plane and the topography of the lunar south pole, which allows long illumination periods, the study of illumination conditions is of great importance. As a result of the large amount of computation time needed to calculate illumination conditions with sequential programming techniques, a new parallel tool was developed. Different tests are presented using different areas and periods of time. The cross-validation of the tool with TUB-Shader and ICAT is presented. Two case studies analyzing the landing site candidate for the Russian LunaResurs and the Shackleton Vicinity (SV1) landing site are presented. This tool allowed us to conduct simulations in a reduced amount of time. As an example we have reduced the computation time from a 24 hours process analyzing a 20 km by 20 km region over a 1 year period to 25 minutes for the same set up.
An overview of nonlinear identification and control with neural networks.

This chapter has introduced the basic concepts related with the use of neural net works for nonlinear systems identification, and has briefly reviewed neuro-control approaches. Several important issues for the design of data-driven models, as neural networks are, such as data acquisition and the design of excitation signals could not be covered here, but the will be discussed in Chapter 5. On purpose, important topics such as neuro-fuzzy and local linear models were not discussed, as they will be treated in other chapters of this book. Neural network modelling is an iterative process, requiring, at the present stage, substantial skills and know ledge from the designer. It is our view that the methodology presented in the example, employing multi-objective evolutionary algorithms for the design of neural models, is a suitable tool to aid the designer in this task. It incorporates input model order and structure selection, as well as parameter estimation, providing the designer with a good number of well performing models with varying degrees of complexity. It also allows the incorporation of objectives which are specific for the ultimate use of the model. Through the analysis of the results obtained in one iteration, the search space can be reduced for future iterations, therefore allowing a more refined search in promising model regions.
Eğitim Programları ve Öğretim Alanında Yapılan Doktora Tezlerinin İçerik Analizi (2009-2014)

The purpose of this research is to analyze dissertations completed in the field of Curriculum and Instruction (2009-2014) in terms of various aspects. Out of 165 dissertations, totally 121 dissertations were included in detailed analysis because 44 of them were unauthorized. In this research, the dissertations were analyzed with document analysis in terms of variables such as university, year, research topic, method, design, sample type, sample size, data collection methods and data analysis techniques. In addition, descriptive statistics such as frequencies, percentages were also used by using SPSS-18 program. As a result of the study, it was found that mostly preferred topics in dissertations in the field of Curriculum and Instruction between the dates 2009-2014 are teaching-learning approaches/models/ methods and techniques, evaluation of formal education curricula, teaching-learning strategies/styles and their instruction, evaluation of teacher education curricula and teacher development practices. Furthermore, curriculum evaluation studies were mostly descriptive in which target curricula were evaluated based on perceptions of teachers, students, academicians etc. It was also found that in most of the dissertations between the dates 2009-2014 survey/descriptive design was mostly preferred, the most used research method is mixed method, mostly preferred sampling types [target sample] are teachers and undergraduate students, the most used data collection tools are scales and interview form. Considering the current state of Curriculum and Instruction field according to the results of the research, it can suggested that dissertations should be in a way that contributes to the theory of the field.
Sensor and Data Fusion: A Tool for Information Assessment and Decision Making

This text is the latest version of "Data and Sensor Fusion Concepts and Applications" (SPIE Vol. TT35). In this edition, Dr. Klein has substantially expanded and updated the information to incorporate recent approaches to data and sensor fusion. For example, classical inference is a new topic, and the chapter on Dempster-Shafer theory is more comprehensive. The book also provides a summary of the information required to implement each of the data fusion methods.
FOCUS ON FORM INSTRUCTION: FOUNDATIONS, APPLICATIONS, AND CRITICISMS

Focus on form instruction makes up an important part of the literature on second language acquisition research. However, few works have both summarized and critically evaluated focus on form instruction. This article seeks to fill this gap. More precisely, it reviews its main principles, and points out the difficulties of implementing this relatively new pedagogical innovation in all but a limited number of instructional settings. It concludes by proposing conditions under which focus on form instruction could effectively function.
Observations Of Sea Ice Drift Off Newfoundland Using Satellite Imagery And Ice Beacons

Daily se� ice velocity fields were obtained by manually tracking ice features in 80 pairs of sequential cloud-free visible NOAA/AVHRR satellite images for the region off Labrador and Eastern Newfoundland from 1984-1986. Although cloud-free images were found for winds from all directions, the images were least likely to be cloud-free when winds were onshore. During the winters of 1985 to 1987, ice velocities were also obtained using a total of 28 Argos satellite-tracked ice beacons, deployed off the Labrador coast by helicopter. The imagery-derived velocity fields were in good agreement with these data. With light to moderate winds, the influence of the Labrador Current could clearly be seen at the shelf break. Strong topographic steering was evident around the numerous banks and saddles on the shelf. Daily geostrophic wind, which was calculated from FNOC surface pressure data on a 380 km grid, accounts for about half the variance in daily ice velocity, for both the imagery and beacon data.
Improving frame synchronization when using convolutional codes

Presents a method of improving the performance of a frame synchronizer when the data frames are convolutionally encoded and terminated. Termination of the data helps to ensure correct decoding and reduces the effective rate of the code, especially for short data sequences. The synchronizer makes use of the symbols transmitted for termination in order to increase the probability of correct frame synchronization. The author derives an optimum decision rule and several suboptimal algorithms that considerably reduce computation and complexity. Improvement of several dB in frame sync performance was found by simulations. The technique can be applied to "one shot" (e.g. packet) or continuous transmission applications. Furthermore, no changes need to be made to the frame structure, only modifications of the receiver. <<ETX>>
I-MPLS: A Transparent Micro-Mobility-enabled MPLS Framework

The paper describes I-MPLS (Integrated-Multi-Protocol Label Switching), an MPLS-based hierarchical approach to support micro-mobility. I-MPLS is based on the idea to integrate the traffic engineering and QoS capabilities of MPLS with the mobility management framework of Mobile IP. This is implemented by integrating the Mobile IP registration messages with the MPLS signalling traffic. The advantage of our approach is transparency for Mobile IP, i.e. there is no need for changes neither in the mobile node nor the home domains of the mobile nodes. I-MPLS significantly reduces the encapsulation overhead for IP-in-IP tunneling known from Mobile IP, supports fast handoffs, flexible QoS models, enhances access security and allows for the stepwise integration in existing networks.
Problem-Based Learning Associated by Action-Process-Object-Schema (APOS) Theory to Enhance Students’ High Order Mathematical Thinking Ability

The research has shown a model of learning activities that can be used to stimulate reflective abstraction in students. Reflective abstraction as a method of constructing knowledge in the Action-Process-Object-Schema theory, and is expected to occur when students are in learning activities, will be able to encourage students to make the process of formation of new mental objects, new processes and new schemes through the construction process in the form of generalization, interiorization, encapsulation, coordination and reversal. Problem-based learning that is presented through eight steps of learning has been able to enhance the mental action in students even though there is no doubt that it cannot possibly know the whole picture of a person's mental activity. All steps in the problem-based learning approach can reflect on the problems of mental action in students. Problem-based learning is appropriate to be used to improve students’ high order mathematical thinking ability because of it has been able to condition the reflective abstraction related mental actions, mental processes, mental objects and schemes in students. Computer assistance and scaffolding techniques can be further stimulus for students to take place in their mental action which corresponded to expectations.
A taxonomy of keyword pattern matching algorithms

This paper presents a taxonomy of keyword pattern matching algorithms, including the well­ known Knuth-Morris-Pratt, Aho-Corasick, Boyer-Moore, and Commentz-Walter algorithms and a number of their variants. The taxonomy is based on the idea of ordering algorithms according to their essential problem and algorithm details, and deriving all algorithms from a common starting point by adding these details in a correctness preserving way. This way of presentation not only provides a complete correctness argument of each algorithm, but also makes very clear what algorithms have in common (the details of their nearest common ancestor) and where they differ (the details added after their nearest common ancestor). Moreover, the paper provides complete derivations of the intricate precomputation algorithms, some of which either can not be found in the literature (Commentz-Walter) or are given in several different versions (Boyer-Moore).
Use of Electrical Coductivity Sensors to monitor Health Status and Quality of Milk in Dairy Goats

Intramammary infection (IMI) can adversely affect, in dairy goats, milk quality and milk yield leading to high economical losses. Although somatic cell count (SCC) and microbiological tests could be valid approaches to detect IMI, other methods of IMI early detection may be useful to detect infected animals and to improve milk quality . The aim of this study was to test a new multivariate model developed with the fuzzy logic technology and based on the milk EC - acquired on-line for each gland by dedicated sensors - and on new qualitative and quantitative indexes derived from the spectrum of the recorded signals. Results obtained showed that the fuzzy logic model tested could achive better results than those already reached in dairy goat research. Nevertheless, further experiment and more field data could be useful to reach the best possible accuracy that this multivariate approach could show.
Semantic structuring and nominalization processes: the case of agent and instrument nouns in Bantu languages

Abstract The semantic relationship between derived agent and instrument nouns patterns in significantly diverse ways across human languages. Thus, for instance, in Germanic and Romance languages a correlation is commonly found between the semantic and formal affinity of agent and instrument nouns and processes of semantic extension which result in syncretism or polysemy. In contrast, some languages not only tend to constrain formal identity among derived nouns but also disallow processes of semantic extension. This is the case with the Bantu languages, in which derived agent and instrument nouns are formed in different noun classes whose classificatory function, together with the semantic content of noun prefixes, severely constrains the processes of semantic extension. We suggest that such semantic content, inseparable from the classificatory function of Bantu noun classes, accounts for this peculiarity. The lack of such content in Germanic and Romance derivational suffixes and inflectional endings explains, at least in part, their inability to constrain the semantics of verb bases with similar effects.