Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of Preliminary Centroids Using Improved K-Means Clustering Algorithm

The emergence of modern technology has enforced to collect the scientific data in a large quantity and those data are getting amassed in different databases. An organized analysis of data is very essential to obtain useful information from swiftly growing data repositories. Cluster analysis is one of the major data mining methods and the k-means clustering algorithm is widely used for many practical applications. But the original kmeans algorithm is computationally expensive and the quality of the resulting clusters substantially relies on the choice of initial centroids. Fast and high quality clustering is one of the most important tasks in the modern era of information processing wherein people rely heavily on search engines. With the huge amount of available data and with an aim to creating better quality clusters, scores of algorithms having qualitycomplexity trade-offs have been proposed. However, the kmeans algorithm proposed during late 1970’s still enjoys a respectable position in the list of clustering algorithms. It is considered to be one of the most fundamental algorithms of data mining. It is basically an iterative algorithm. In each iteration, it requires finding the distance between each data object and centroid of each cluster. Considering the hugeness of modern databases, this task in itself snowballs into a tedious task. This paper proposes an improvement on the classic kmeans algorithm to produce more accurate clusters. The proposed algorithm comprises of a O(n logn) heuristic method, based on sorting and partitioning the input data, for finding the initial centroids in accordance with the data distribution. Experimental results show that the proposed algorithm produces better clusters in less computation time.
Implementation and Application of PSF-Based EPI Distortion Correction to High Field Animal Imaging

The purpose of this work is to demonstrate the functionality and performance of a PSF-based geometric distortion correction for high-field functional animal EPI. The EPI method was extended to measure the PSF and a postprocessing chain was implemented in Matlab for offline distortion correction. The correction procedure was applied to phantom and in vivo imaging of mice and rats at 9.4T using different SE-EPI and DWI-EPI protocols. Results show the significant improvement in image quality for single- and multishot EPI. Using a reduced FOV in the PSF encoding direction clearly reduced the acquisition time for PSF data by an acceleration factor of 2 or 4, without affecting the correction quality.
Improvements in speaker adaptation using weighted training

Regardless of the distribution of the adaptation data in the testing environment, model-based adaptation methods that have so far been reported in the literature incorporate the adaptation data undiscriminately in reducing the mismatch between the training and testing environments. When the amount of data is small and the parameter tying is extensive, adaptation based on outlier data can be detrimental to the performance of the recognizer. The distribution of the adaptation data plays a critical role on the adaptation performance. In order to maximally improve the recognition rate in the testing environment using only a small amount of adaptation data, supervised weighted training is applied to the structural maximum a posterior (SMAP) algorithm. We evaluate the performance of the proposed weighted SMAP (WSMAP) and SMAP on TIDIGITS corpus. The proposed WSMAP has been found to perform better for a small amount of data. The general idea of incorporating the distribution of the adaptation data is applicable to other adaptation algorithms.
A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography

X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing norm of the patch basis functions coefficients, and a coefficient multiplying the norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography.
Analysis of Variability-Tolerance in Chip-Multiprocessors

Spatially-correlated intra-die process variations cause significant core-to-core frequency variations in chip-multiprocessors. An analytical model of chip-multiprocessor throughput is extended to model designs using frequency islands. The improved variabilitytolerance of such designs over globally-clocked ones is quantified across a range of core counts and sizes under constant die area. The benefits are highest for designs consisting of many small cores, with the throughput of a globally-clocked design with 70 small cores increasing by 8.8% in the move to frequency islands.
SQL/MapReduce: A practical approach to self-describing, polymorphic, and parallelizable user-defined functions

A user-defined function (UDF) is a powerful database feature that allows users to customize database functionality. Though useful, present UDFs have numerous limitations, including install-time specification of input and output schema and poor ability to parallelize execution. We present a new approach to implementing a UDF, which we call SQL/MapReduce (SQL/MR), that overcomes many of these limitations. We leverage ideas from the MapReduce programming paradigm to provide users with a straightforward API through which they can implement a UDF in the language of their choice. Moreover, our approach allows maximum flexibility as the output schema of the UDF is specified by the function itself at query plan-time. This means that a SQL/MR function is polymorphic. It can process arbitrary input because its behavior as well as output schema are dynamically determined by information available at query plan-time, such as the function's input schema and arbitrary user-provided parameters. This also increases reusability as the same SQL/MR function can be used on inputs with many different schemas or with different user-specified parameters. In this paper we describe the motivation for this new approach to UDFs as well as the implementation within Aster Data Systems' nCluster database. We demonstrate that in the context of massively parallel, shared-nothing database systems, this model of computation facilitates highly scalable computation within the database. We also include examples of new applications that take advantage of this novel UDF framework.
Irregular repeat accumulate codes with few iterations for the binary adder channel

Iterative interference cancellation is a powerful technique for design of multiuser decoders. Careful encoder and decoder design is essential for reducing implementation complexity. When used in conjunction with iteratively-decodeable sparse graph codes, there is a choice to be made between the number of multiuser interference cancellation stages and the number of iterations used for decoding each user's code. Too few decoder iterations may increase the number of interference cancellation steps, whereas too many may superfluous, or even inhibit the convergence behaviour of the cancellation loop. In this paper we design non-systematic irregular repeat-accumulate codes for multiple-access channels where the number of cancellation stages and number of decoder iterations at each stage are fixed. The design goal is minimal bit error rate. Our formulation is based on extrinsic information transfer, and introduces the number of iterations directly into the curve fitting problem. For clear development of concepts, we restrict attention to the binary adder channel, where our approach is exact. Generalisation to other sparse graph codes and other multiple-access channels is straightforward, subject to the usual approximations.
More than one road leads to Rome: A narrative review and meta-analysis of physical activity intervention effects on cognition in youth

A growing body of research indicates that physical activity (PA) positively impacts cognitive function in youth. However, not all forms of PA benefit cognition equally. The purpose of this review was to determine the effect of different types of chronic PA interventions on cognition in children and adolescents. A systematic search of electronic databases and examination of the reference lists of relevant studies resulted in the identification of 28 studies. Seven categories of PA were identified, based on all possible combinations of three types of PA (aerobic, motor skill, cognitively engaging), and four comparison groups (no treatment, academic, traditional physical education (PE), aerobic). Effect sizes were calculated based on means and SDs at the post-test using Hedge’s g formula, which includes a correction for small sample bias. Each study was only entered once in each intervention-comparator category. Full data were provided from 21 studies (28 effect sizes; n = 2042 intervention; n = 2002 comparison group). Overall, chronic PA interventions had a significant small-to-moderate effect on cognition (0.46). Moderate significant positive effects were identified when PA interventions were compared to no treatment (0.86) or academic content (0.57). A non-significant effect was noted when PA interventions were compared to traditional PE (0.09) or aerobic group (0.80). However, high heterogeneity in pooled effect sizes suggests that important differences in the qualitative characteristics of the PA intervention and comparison interventions may exist. Effect sizes based on comparisons between different types of PA interventions and comparison groups are discussed in order to identify possible directions for future investigations. We conclude that chronic PA interventions have a positive impact on cognitive function in youth, but more systematic research is needed in this area.
Long Hours and Longings: Australian Children's Views of Fathers' Work and Family Time

Using two waves of paired data from a population sample of 10- to 13-year-old Australian children (5,711 father–child observations), the authors consider how the hours, schedules, intensity, and flexibility of fathers' jobs are associated with children's views about fathers' work and family time. A third of the children studied considered that their father works too much, one eighth wished that he did not work at all, and one third wanted more time with him or did not enjoy time together. Logistic regression modeling revealed that working on weekends, being time pressured, being unable to vary start and stop times, and working long hours generated negative views in children about fathers' jobs and time together. The time dilemmas generated by fathers' work devotions and demands are salient to and subjectively shared by their children.
The application of adaptive beam tracing and managed DirectX for the visualisation and auralisation of virtual environments

The paper outlines the generation of virtual environments using the author's own adaptive beam tracing algorithm for the prediction of room acoustics. Details of this novel algorithm were first published in the Journal of Acoustics Society of America in 1999. Essentially the algorithm simulates the propagation of sound within an environment using reflected beams whose shape is governed by the shape of reflecting surfaces. Hence the adaptive beam tracing avoids spurious and missing reflections or large computation times associated with other methods whilst providing the prediction of both specular and diffuse sound propagation. The current implementation uses the new managed DirectX API for simulating visually and aurally virtual environments and explores possibility of real time acoustic rendering using predicted acoustics in conjunction with the 3D rendering classes offered by managed DirectX.
Interval Arithmetic: an efficient implementation and an application to computational geometry

We discuss interval techniques for speeding up the exact evaluation of geometric predicates and describe a C++ implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floating point filter for the computation of geometric predicates. We validate our approach experimentally, comparing it with other static, dynamic and semi-static filters.
Managing geoenergy-induced seismicity with society

Contemporary and future low-carbon energy supply requires using the deep underground. Ample evidence shows that onshore oil and gas extraction, associated wastewater injection, deep geothermal systems, and carbon capture and storage can induce damaging seismicity. While induced seismicity exhibits symptoms of a potentially controversial issue, research today has been limited to seismic hazard and risk. Here we argue for the need of social scientific insights to inform risk communication and processes of managing geoenergy-induced seismicity with society.
Collective action : a regulatory focus perspective

In this dissertation I investigate how individuals respond to collective disadvantage from the perspective of regulatory focus theory. Regulatory focus theory distinguishes between two motivational systems: promotion focus, the system in charge of the approach of positive end-states, and prevention focus, the system in charge of the avoidance of negative end-states. In 7 studies I investigate the effects of promotion and prevention focus on 1) low status group members’ choice between seeking individual or group status improvement, 2) their level of commitment to collective status improvement and 3) the extent to which they see hostile forms of collective action (e.g. riots, sabotage) as justified. The results showed that activation of the prevention system (compared to activation of the promotion system) causes members of low status groups 1) to prefer collective status improvement over individual status improvement, 2) to commit to collective action when they value its goal, even when the likelihood of achieving this goal is low and 3) to come to see hostile forms of collective action as justified in order to reach group status improvement. I conclude that activation of the prevention system is more conducive to collective action than activation of the promotion focus.
Design Issues and Analysis of Experiments in Nanomanufacturing

The idea of making " nanostructures " that comprise just one or a few atoms has great appeal, both as a scientific challenge and for practical reasons. In recent years, scientists have learned various techniques for building nanostructures, but they have only just begun to investigate their properties and potential applications (Scientific American (2002)). In a nanoscale environment, the effects of design parameters on product characteristics usually cannot be known purely from phenomenological models and therefore, nanotechnology design often requires data collection and analysis. There is a lot of superb nanoscience in the literature and in research labs, but there is a gap between it and nanotechnology (Chan (2003)). Often the experimentation used to build up the background of nanoscience, while methodical (e.g., changing one key factor at a time while keeping others constant), is not efficient. Through our interdisciplinary collaborations, we have identified the importance of statistically-based design of experiments (DOE) to the research and development of nanomanufacturing. Drawing from these interactions as well as the literature, this chapter addresses three cases that closely link DOE with the needs of nanomanufacturing. In particular, Section 17.2 focuses on split-plot designs in a nanoscale milling process. Often in practice, due to the physical constraints in the nanomanufacturing process, situations arise when it is impossible to execute a completely randomized experiment and we must resort to a split-plot design. We
Managing Digital Rights Using JSON

Prior art in the expression of digital rights using XML is demonstrated to require a process of interpretation or parsing, as is characteristic with language processing, and in addition to be subject to the cross-domain scripting problem. The expression of digital rights using JSON, however, represents a novel approach, bypassing the need for language processing, and in addition, solving the cross-domain scripting problem.
A Request Skew Aware Heterogeneous Distributed Storage System Based on Cassandra

many distributed storage systems have been proposed to provide high scalability and high availability for modern web applications. However, most of those applications only aware data skew while actually request skew is also widely exist and needed to be considered as well. In this paper, we present a request skew aware heterogeneous distributed storage system based on Cassandra-a famous NoSQL database aiming to manage very large scale data without single point of failure. We improve Cassandra through two ways: 1) minimize forward request load by shifting the node where the client application connect to the one which can handle maximum number of skewed request dynamically; 2) when balancing data load among all nodes within the cluster, take their storage capacity into consideration. The results of our experiment present that we can reduce about 25% forward read request and 15% forward write request through approach 1) and balance storage utilization of each node obviously after applying 2).
Vascular permeability in cancer and infection as related to macromolecular drug delivery, with emphasis on the EPR effect for tumor-selective drug targeting

Tumor and inflammation have many common features. One hallmark of both is enhanced vascular permeability, which is mediated by various factors including bradykinin, nitric oxide (NO), peroxynitrite, prostaglandins etc. A unique characteristic of tumors, however, is defective vascular anatomy. The enhanced vascular permeability in tumors is also distinctive in that extravasated macromolecules are not readily cleared. We utilized the enhanced permeability and retention (EPR) effect of tumors for tumor selective delivery of macromolecular drugs. Consequently, such drugs, nanoparticles or lipid particles, when injected intravenously, selectively accumulate in tumor tissues and remain there for long periods. The EPR effect of tumor tissue is frequently inhomogeneous and the heterogeneity of the EPR effect may reduce the tumor delivery of macromolecular drugs. Therefore, we developed methods to augment the EPR effect without inducing adverse effects for instance raising the systemic blood pressure by infusing angiotensin II during arterial injection of SMANCS/Lipiodol. This method was validated in clinical setting. Further, benefits of utilization of NO-releasing agent such as nitroglycerin or angiotensin-converting enzyme (ACE) inhibitors were demonstrated. The EPR effect is thus now widely accepted as the most basic mechanism for tumor-selective targeting of macromolecular drugs, or so-called nanomedicine.
Human activity classification in people centric sensing exploiting sparseness measurement

Existing human activity recognition models in people centric sensing have explored different features of the mobile phone data to achieve considerable accuracy. However, the heterogeneity of such data caused by different phone carrying modes during data collection process remains a challenge. It has been observed by us that although the waveform of tri-axial accelerometer data varies in different modes, its sparseness within segmented frames tends to preserve in general. In this paper, we propose to adopt an augmented feature set by taking into account the sparseness measurement to improve the robustness of human activity classification. It has been shown in the experiment results that the sparseness features have a high importance ranking among the list. In addition, the experiment results show that the AUC measurement results of the Random Forest model can be improved both in single and mixed phone carrying modes, which justify the effectiveness of the sparseness measure in addressing the heterogeneity of tri-axial accelerometer data on mobile phones.
Chapter Seven PRACTICING WHAT WE PREACH: HELPING

Student teachers often criticize the content of their classes as too theoretical to be useful for their practicum experience. This is particularly true of survey type courses that cover a wide range of subject matter to give students some insight into the breadth of the topic, and that student teachers are often required to take. The educational psychology course on developmental theories, which I teach and which CITE students are required to take is one such course. Lack of teaching experience is often the reason that theory fails to inform students but it is not an unreasonable criticism given the considerable pressure of a one or two year preparation program for teaching. The purpose of my course is to expose students to theories of learning and cognitive development by reviewing the work of researchers like: Piaget, Vygotsky, Erickson, Skinner, Watson and Kohlberg. The thinking being, one assumes, that students will understand what, for example, constitutes a behaviorist approach to instruction as opposed to a developmental approach. Although most students I have talked to find the information presented in the course intrinsically interesting, one senses a general restlessness in having to spend the time on what seems far removed from the daily concern of planning lessons and managing children; on topics that seem esoteric rather than useful. In the past, I have found that the words ‘not useful’ and ‘not meaningful’ come up frequently when students are asked to describe their experience with the course. In preparing to teach the course in the fall of 2004, my choice was to present the course as in past years or to introduce an element that demanded STUDENT TEACHERS TURN THEORY INTO PRACTICE