Power Modelling for Heterogeneous Cloud-Edge Data Centers

Existing power modelling research focuses not on the method used for developing models but rather on the model itself. This paper aims to develop a method for deploying power models on emerging processors that will be used, for example, in cloud-edge data centers. Our research first develops a hardware counter selection method that appropriately selects counters most correlated to power on ARM and Intel processors. Then, we propose a two stage power model that works across multiple architectures. The key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual selection methods reported in literature, and (ii) the two stage power model can predict dynamic power more accurately on both ARM and Intel processors when compared to classic power models.Comment: 10 pages,10 figures,conferenc

Assessing similarity of feature selection techniques in high-dimensional domains

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement

Classification of ordered texture images using regression modelling and granulometric features

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images

Machine learning-guided synthesis of advanced inorganic materials

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development

Determination of amino and fatty acid composition of soybeans using near-infrared spectroscopy

Applicability of near-infrared spectroscopy for measurement of amino and fatty acid composition in whole soybeans was the main subject of three research papers included in this dissertation. The effects of type of spectrometer, calibration method, and data preprocessing techniques were also investigated.;Validation of developed amino acid calibration models resulted in r2 values ranging from 0.04 (tryptophan) to 0.91 (leucine and lysine). Most of the models were usable for research purposes and sample screening, however, no sufficient correlation was found between spectral data and concentrations of cysteine and tryptophan. It was established that the variation in predictive ability of equations was determined by how a certain amino acid correlated to reference protein. Comparison of calibration methods demonstrated that (1) performance of partial least squares and support vector machines regressions was significantly better than that of artificial neural networks, and (2) choice of preferred modeling method was spectrometerdependent.;Validation of developed fatty acid calibration equations demonstrated that (1)equations for total saturates had the highest predictive ability ( r2 = 0.91--0.94) and were usable for quality assurance applications, (2) palmitic acid models (r2 = 0.80--0.84) were usable for certain research applications, and (3) equations for stearic (r2 = 0.49--0.68), oleic (r2 = 0.76--0.81), linoleic ( r2 = 0.73--0.76), and linolenic (r 2 = 0.67--0.74) acids could be used for sample screening. The results also showed that support vector machines models produced significantly more accurate predictions than those developed with partial least squares regression. Neural networks calibrations were not significantly different from the other two methods. Reduction of number of calibration samples reduced predictive ability of all types of equations, however the rate of performance degradation of support vector machines models was the lowest.;The third study compared applicability of global and local implementations of principal component analysis compression to near-infrared calibration problems solved with the neural networks regression. Two lysine data sets were used for development of control and experimental calibrations. The results demonstrated that local principal component compression could significantly outperform its traditional global counterpart