Unsupervised Condition Change Detection In Large Diesel Engines

DEFF Research Database (Denmark)

Pontoppidan, Niels Henrik; Larsen, Jan

2003-01-01

This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investig...... be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.......This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can...

Development of partitioning method : cold experiment with partitioning test facility in NUCEF (I)

International Nuclear Information System (INIS)

Yamaguchi, Isoo; Morita, Yasuji; Kondo, Yasuo

1996-03-01

A test facility in which about 1.85 x 10 14 Bq of high-level liquid waste can be treated has been completed in 1994 at Nuclear Fuel Cycle Safety Engineering Research Facility (NUCEF) for research and development of Partitioning Method. The outline of the partitioning test facility and support equipments for it which were design terms, constructions, arrangements, functions and inspections were given in JAERI-Tech 94-030. The present report describes the results of the water transfer test and partitioning tests, which are methods of precipitation by denitration, oxalate precipitation, solvent extraction, and adsorption with inorganic ion exchanger, using nitric acid to master operation method of the test facility. As often as issues related to equipments occurred during the tests, they were improved. As to issues related to processes such as being stopped up of columns, their measures of solution were found by testing in laboratories. They were reflected in operation of the Partitioning Test Facility. Their particulars and improving points were described in this report. (author)

Application of unsupervised learning methods in high energy physics

Energy Technology Data Exchange (ETDEWEB)

Koevesarki, Peter; Nuncio Quiroz, Adriana Elizabeth; Brock, Ian C. [Physikalisches Institut, Universitaet Bonn, Bonn (Germany)

2011-07-01

High energy physics is a home for a variety of multivariate techniques, mainly due to the fundamentally probabilistic behaviour of nature. These methods generally require training based on some theory, in order to discriminate a known signal from a background. Nevertheless, new physics can show itself in ways that previously no one thought about, and in these cases conventional methods give little or no help. A possible way to discriminate between known processes (like vector bosons or top-quark production) or look for new physics is using unsupervised machine learning to extract the features of the data. A technique was developed, based on the combination of neural networks and the method of principal curves, to find a parametrisation of the non-linear correlations of the data. The feasibility of the method is shown on ATLAS data.

Unsupervised action classification using space-time link analysis

DEFF Research Database (Denmark)

Liu, Haowei; Feris, Rogerio; Krüger, Volker

2010-01-01

In this paper we address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which matches the performance of traditional unsupervised action categorization metho...

An unsupervised method for summarizing egocentric sport videos

Science.gov (United States)

Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

2015-12-01

People are getting more interested to record their sport activities using head-worn or hand-held cameras. This type of videos which is called egocentric sport videos has different motion and appearance patterns compared with life-logging videos. While a life-logging video can be defined in terms of well-defined human-object interactions, notwithstanding, it is not trivial to describe egocentric sport videos using well-defined activities. For this reason, summarizing egocentric sport videos based on human-object interaction might fail to produce meaningful results. In this paper, we propose an unsupervised method for summarizing egocentric videos by identifying the key-frames of the video. Our method utilizes both appearance and motion information and it automatically finds the number of the key-frames. Our blind user study on the new dataset collected from YouTube shows that in 93:5% cases, the users choose the proposed method as their first video summary choice. In addition, our method is within the top 2 choices of the users in 99% of studies.

Unsupervised learning algorithms

CERN Document Server

Aydin, Kemal

2016-01-01

This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...

Unsupervised Performance Evaluation of Image Segmentation

Directory of Open Access Journals (Sweden)

Chabrier Sebastien

2006-01-01

Full Text Available We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

A method for unsupervised change detection and automatic radiometric normalization in multispectral data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton John

2011-01-01

Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in North...... to carry out the analyses is available from the authors' websites....

Concept formation knowledge and experience in unsupervised learning

CERN Document Server

Fisher, Douglas H; Langley, Pat

1991-01-01

Concept Formation: Knowledge and Experience in Unsupervised Learning presents the interdisciplinary interaction between machine learning and cognitive psychology on unsupervised incremental methods. This book focuses on measures of similarity, strategies for robust incremental learning, and the psychological consistency of various approaches.Organized into three parts encompassing 15 chapters, this book begins with an overview of inductive concept learning in machine learning and psychology, with emphasis on issues that distinguish concept formation from more prevalent supervised methods and f

A novel partitioning method for block-structured adaptive meshes

Science.gov (United States)

Fu, Lin; Litvinov, Sergej; Hu, Xiangyu Y.; Adams, Nikolaus A.

2017-07-01

We propose a novel partitioning method for block-structured adaptive meshes utilizing the meshless Lagrangian particle concept. With the observation that an optimum partitioning has high analogy to the relaxation of a multi-phase fluid to steady state, physically motivated model equations are developed to characterize the background mesh topology and are solved by multi-phase smoothed-particle hydrodynamics. In contrast to well established partitioning approaches, all optimization objectives are implicitly incorporated and achieved during the particle relaxation to stationary state. Distinct partitioning sub-domains are represented by colored particles and separated by a sharp interface with a surface tension model. In order to obtain the particle relaxation, special viscous and skin friction models, coupled with a tailored time integration algorithm are proposed. Numerical experiments show that the present method has several important properties: generation of approximately equal-sized partitions without dependence on the mesh-element type, optimized interface communication between distinct partitioning sub-domains, continuous domain decomposition which is physically localized and implicitly incremental. Therefore it is particularly suitable for load-balancing of high-performance CFD simulations.

A novel partitioning method for block-structured adaptive meshes

Energy Technology Data Exchange (ETDEWEB)

Fu, Lin, E-mail: lin.fu@tum.de; Litvinov, Sergej, E-mail: sergej.litvinov@aer.mw.tum.de; Hu, Xiangyu Y., E-mail: xiangyu.hu@tum.de; Adams, Nikolaus A., E-mail: nikolaus.adams@tum.de

2017-07-15

We propose a novel partitioning method for block-structured adaptive meshes utilizing the meshless Lagrangian particle concept. With the observation that an optimum partitioning has high analogy to the relaxation of a multi-phase fluid to steady state, physically motivated model equations are developed to characterize the background mesh topology and are solved by multi-phase smoothed-particle hydrodynamics. In contrast to well established partitioning approaches, all optimization objectives are implicitly incorporated and achieved during the particle relaxation to stationary state. Distinct partitioning sub-domains are represented by colored particles and separated by a sharp interface with a surface tension model. In order to obtain the particle relaxation, special viscous and skin friction models, coupled with a tailored time integration algorithm are proposed. Numerical experiments show that the present method has several important properties: generation of approximately equal-sized partitions without dependence on the mesh-element type, optimized interface communication between distinct partitioning sub-domains, continuous domain decomposition which is physically localized and implicitly incremental. Therefore it is particularly suitable for load-balancing of high-performance CFD simulations.

Ocean surface partitioning strategies using ocean colour remote Sensing: A review

Science.gov (United States)

Krug, Lilian Anne; Platt, Trevor; Sathyendranath, Shubha; Barbosa, Ana B.

2017-06-01

The ocean surface is organized into regions with distinct properties reflecting the complexity of interactions between environmental forcing and biological responses. The delineation of these functional units, each with unique, homogeneous properties and underlying ecosystem structure and dynamics, can be defined as ocean surface partitioning. The main purposes and applications of ocean partitioning include the evaluation of particular marine environments; generation of more accurate satellite ocean colour products; assimilation of data into biogeochemical and climate models; and establishment of ecosystem-based management practices. This paper reviews the diverse approaches implemented for ocean surface partition into functional units, using ocean colour remote sensing (OCRS) data, including their purposes, criteria, methods and scales. OCRS offers a synoptic, high spatial-temporal resolution, multi-decadal coverage of bio-optical properties, relevant to the applications and value of ocean surface partitioning. In combination with other biotic and/or abiotic data, OCRS-derived data (e.g., chlorophyll-a, optical properties) provide a broad and varied source of information that can be analysed using different delineation methods derived from subjective, expert-based to unsupervised learning approaches (e.g., cluster, fuzzy and empirical orthogonal function analyses). Partition schemes are applied at global to mesoscale spatial coverage, with static (time-invariant) or dynamic (time-varying) representations. A case study, the highly heterogeneous area off SW Iberian Peninsula (NE Atlantic), illustrates how the selection of spatial coverage and temporal representation affects the discrimination of distinct environmental drivers of phytoplankton variability. Advances in operational oceanography and in the subject area of satellite ocean colour, including development of new sensors, algorithms and products, are among the potential benefits from extended use, scope and

Dominant partition method. [based on a wave function formalism

Science.gov (United States)

Dixon, R. M.; Redish, E. F.

1979-01-01

By use of the L'Huillier, Redish, and Tandy (LRT) wave function formalism, a partially connected method, the dominant partition method (DPM) is developed for obtaining few body reductions of the many body problem in the LRT and Bencze, Redish, and Sloan (BRS) formalisms. The DPM maps the many body problem to a fewer body one by using the criterion that the truncated formalism must be such that consistency with the full Schroedinger equation is preserved. The DPM is based on a class of new forms for the irreducible cluster potential, which is introduced in the LRT formalism. Connectivity is maintained with respect to all partitions containing a given partition, which is referred to as the dominant partition. Degrees of freedom corresponding to the breakup of one or more of the clusters of the dominant partition are treated in a disconnected manner. This approach for simplifying the complicated BRS equations is appropriate for physical problems where a few body reaction mechanism prevails.

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

Science.gov (United States)

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

Science.gov (United States)

Azcorra, A; Chiroque, L F; Cuevas, R; Fernández Anta, A; Laniado, H; Lillo, R E; Romo, J; Sguera, C

2018-05-03

Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

Approximation methods for the partition functions of anharmonic systems

International Nuclear Information System (INIS)

Lew, P.; Ishida, T.

1979-07-01

The analytical approximations for the classical, quantum mechanical and reduced partition functions of the diatomic molecule oscillating internally under the influence of the Morse potential have been derived and their convergences have been tested numerically. This successful analytical method is used in the treatment of anharmonic systems. Using Schwinger perturbation method in the framework of second quantization formulism, the reduced partition function of polyatomic systems can be put into an expression which consists separately of contributions from the harmonic terms, Morse potential correction terms and interaction terms due to the off-diagonal potential coefficients. The calculated results of the reduced partition function from the approximation method on the 2-D and 3-D model systems agree well with the numerical exact calculations

A conjugate gradient method for the spectral partitioning of graphs

NARCIS (Netherlands)

Kruyt, Nicolaas P.

1997-01-01

The partitioning of graphs is a frequently occurring problem in science and engineering. The spectral graph partitioning method is a promising heuristic method for this class of problems. Its main disadvantage is the large computing time required to solve a special eigenproblem. Here a simple and

Unsupervised classification of variable stars

Science.gov (United States)

Valenzuela, Lucas; Pichara, Karim

2018-03-01

During the past 10 years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric data sets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data come from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query-based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire data set of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

Directory of Open Access Journals (Sweden)

Lei Ma

2016-09-01

Full Text Available Object-based change detection (OBCD has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.

Directory of Open Access Journals (Sweden)

Alex Xijie Lu

Full Text Available Despite the importance of characterizing genes that exhibit subcellular localization changes between conditions in proteome-wide imaging experiments, many recent studies still rely upon manual evaluation to assess the results of high-throughput imaging experiments. We describe and demonstrate an unsupervised k-nearest neighbours method for the detection of localization changes. Compared to previous classification-based supervised change detection methods, our method is much simpler and faster, and operates directly on the feature space to overcome limitations in needing to manually curate training sets that may not generalize well between screens. In addition, the output of our method is flexible in its utility, generating both a quantitatively ranked list of localization changes that permit user-defined cut-offs, and a vector for each gene describing feature-wise direction and magnitude of localization changes. We demonstrate that our method is effective at the detection of localization changes using the Δrpd3 perturbation in Saccharomyces cerevisiae, where we capture 71.4% of previously known changes within the top 10% of ranked genes, and find at least four new localization changes within the top 1% of ranked genes. The results of our analysis indicate that simple unsupervised methods may be able to identify localization changes in images without laborious manual image labelling steps.

An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.

Science.gov (United States)

Lu, Alex Xijie; Moses, Alan M

2016-01-01

Despite the importance of characterizing genes that exhibit subcellular localization changes between conditions in proteome-wide imaging experiments, many recent studies still rely upon manual evaluation to assess the results of high-throughput imaging experiments. We describe and demonstrate an unsupervised k-nearest neighbours method for the detection of localization changes. Compared to previous classification-based supervised change detection methods, our method is much simpler and faster, and operates directly on the feature space to overcome limitations in needing to manually curate training sets that may not generalize well between screens. In addition, the output of our method is flexible in its utility, generating both a quantitatively ranked list of localization changes that permit user-defined cut-offs, and a vector for each gene describing feature-wise direction and magnitude of localization changes. We demonstrate that our method is effective at the detection of localization changes using the Δrpd3 perturbation in Saccharomyces cerevisiae, where we capture 71.4% of previously known changes within the top 10% of ranked genes, and find at least four new localization changes within the top 1% of ranked genes. The results of our analysis indicate that simple unsupervised methods may be able to identify localization changes in images without laborious manual image labelling steps.

Unsupervised Feature Subset Selection

DEFF Research Database (Denmark)

Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

2003-01-01

This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

New parallel SOR method by domain partitioning

Energy Technology Data Exchange (ETDEWEB)

Xie, Dexuan [Courant Inst. of Mathematical Sciences New York Univ., NY (United States)

1996-12-31

In this paper, we propose and analyze a new parallel SOR method, the PSOR method, formulated by using domain partitioning together with an interprocessor data-communication technique. For the 5-point approximation to the Poisson equation on a square, we show that the ordering of the PSOR based on the strip partition leads to a consistently ordered matrix, and hence the PSOR and the SOR using the row-wise ordering have the same convergence rate. However, in general, the ordering used in PSOR may not be {open_quote}consistently ordered{close_quotes}. So, there is a need to analyze the convergence of PSOR directly. In this paper, we present a PSOR theory, and show that the PSOR method can have the same asymptotic rate of convergence as the corresponding sequential SOR method for a wide class of linear systems in which the matrix is {open_quotes}consistently ordered{close_quotes}. Finally, we demonstrate the parallel performance of the PSOR method on four different message passing multiprocessors (a KSR1, the Intel Delta, an Intel Paragon and an IBM SP2), along with a comparison with the point Red-Black and four-color SOR methods.

Unsupervised text mining methods for literature analysis: a case study for Thomas Pynchon's V.

Directory of Open Access Journals (Sweden)

Christos Iraklis Tsatsoulis

2013-08-01

Full Text Available We investigate the use of unsupervised text mining methods for the analysis of prose literature works, using Thomas Pynchon's novel 'V'. as a case study. Our results suggest that such methods may be employed to reveal meaningful information regarding the novel’s structure. We report results using a wide variety of clustering algorithms, several distinct distance functions, and different visualization techniques. The application of a simple topic model is also demonstrated. We discuss the meaningfulness of our results along with the limitations of our approach, and we suggest some possible paths for further study.

An unsupervised strategy for biomedical image segmentation

Directory of Open Access Journals (Sweden)

Roberto Rodríguez

2010-09-01

Full Text Available Roberto Rodríguez1, Rubén Hernández21Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, MexicoAbstract: Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.Keywords: segmentation, mean shift, unsupervised segmentation, entropy

Nested partitions method, theory and applications

CERN Document Server

Shi, Leyuan

2009-01-01

There is increasing need to solve large-scale complex optimization problems in a wide variety of science and engineering applications, including designing telecommunication networks for multimedia transmission, planning and scheduling problems in manufacturing and military operations, or designing nanoscale devices and systems. Advances in technology and information systems have made such optimization problems more and more complicated in terms of size and uncertainty. Nested Partitions Method, Theory and Applications provides a cutting-edge research tool to use for large-scale, complex systems optimization. The Nested Partitions (NP) framework is an innovative mix of traditional optimization methodology and probabilistic assumptions. An important feature of the NP framework is that it combines many well-known optimization techniques, including dynamic programming, mixed integer programming, genetic algorithms and tabu search, while also integrating many problem-specific local search heuristics. The book uses...

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Science.gov (United States)

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm

Directory of Open Access Journals (Sweden)

Jinqi Zhao

2017-12-01

Full Text Available In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSAR has become a key tool for change detection. Change detection methods include both unsupervised and supervised methods. Supervised change detection, which needs some human intervention, is generally ineffective and impractical. Due to this limitation, unsupervised methods are widely used in change detection. The traditional unsupervised methods only use a part of the polarization information, and the required thresholding algorithms are independent of the multi-temporal data, which results in the change detection map being ineffective and inaccurate. To solve these problems, a novel method of change detection using a test statistic based on the likelihood ratio test and the improved Kittler and Illingworth (K&I minimum-error thresholding algorithm is introduced in this paper. The test statistic is used to generate the comparison image (CI of the multi-temporal PolSAR images, and improved K&I using a generalized Gaussian model simulates the distribution of the CI. As a result of these advantages, we can obtain the change detection map using an optimum threshold. The efficiency of the proposed method is demonstrated by the use of multi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method is effective and highly accurate.

Unsupervised Classification Using Immune Algorithm

OpenAIRE

Al-Muallim, M. T.; El-Kouatly, R.

2012-01-01

Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...

Segmentation of fluorescence microscopy cell images using unsupervised mining.

Science.gov (United States)

Du, Xian; Dua, Sumeet

2010-05-28

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

Unsupervised Learning and Generalization

DEFF Research Database (Denmark)

Hansen, Lars Kai; Larsen, Jan

1996-01-01

The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy ...... with supervised learning. The empirical and analytical estimates are compared for principal component analysis and for K-means clustering based density estimation......The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy...

Application of cluster analysis and unsupervised learning to multivariate tissue characterization

International Nuclear Information System (INIS)

Momenan, R.; Insana, M.F.; Wagner, R.F.; Garra, B.S.; Loew, M.H.

1987-01-01

This paper describes a procedure for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characterization. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons two objectives are sought: a) How well does the clustering method group the data?; b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data

Teacher and learner: Supervised and unsupervised learning in communities.

Science.gov (United States)

Shafto, Michael G; Seifert, Colleen M

2015-01-01

How far can teaching methods go to enhance learning? Optimal methods of teaching have been considered in research on supervised and unsupervised learning. Locally optimal methods are usually hybrids of teaching and self-directed approaches. The costs and benefits of specific methods have been shown to depend on the structure of the learning task, the learners, the teachers, and the environment.

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

Science.gov (United States)

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object

An unsupervised adaptive strategy for constructing probabilistic roadmaps

KAUST Repository

Tapia, L.

2009-05-01

Since planning environments are complex and no single planner exists that is best for all problems, much work has been done to explore methods for selecting where and when to apply particular planners. However, these two questions have been difficult to answer, even when adaptive methods meant to facilitate a solution are applied. For example, adaptive solutions such as setting learning rates, hand-classifying spaces, and defining parameters for a library of planners have all been proposed. We demonstrate a strategy based on unsupervised learning methods that makes adaptive planning more practical. The unsupervised strategies require less user intervention, model the topology of the problem in a reasonable and efficient manner, can adapt the sampler depending on characteristics of the problem, and can easily accept new samplers as they become available. Through a series of experiments, we demonstrate that in a wide variety of environments, the regions automatically identified by our technique represent the planning space well both in number and placement.We also show that our technique has little overhead and that it out-performs two existing adaptive methods in all complex cases studied.© 2009 IEEE.

Unsupervised Power Profiling for Mobile Devices

DEFF Research Database (Denmark)

Kjærgaard, Mikkel Baun; Blunck, Henrik

Today, power consumption is a main limitation for mobile phones. To minimize the power consumption of popular and traditionally power-hungry location-based services requires knowledge of how individual phone features consume power, so that those features can be utilized intelligently for optimal...... power savings while at the same time maintaining good quality of service. This paper proposes an unsupervised API-level method for power profiling mobile phones based on genetic algorithms. The method enables accurate profiling of the power consumption of devices and thereby provides the information...

Unsupervised Power Profiling for Mobile Devices

DEFF Research Database (Denmark)

Kjærgaard, Mikkel Baun; Blunck, Henrik

2011-01-01

Today, power consumption is a main limitation for mobile phones. To minimize the power consumption of popular and traditionally power-hungry location-based services requires knowledge of how individual phone features consume power, so that those features can be utilized intelligently for optimal...... power savings while at the same time maintaining good quality of service. This paper proposes an unsupervised API-level method for power profiling mobile phones based on genetic algorithms. The method enables accurate profiling of the power consumption of devices and thereby provides the information...

Unsupervised Assessment of Subcutaneous and Visceral Fat by MRI

DEFF Research Database (Denmark)

Jørgensen, Peter Stanley; Larsen, Rasmus; Wraae, Kristian

2009-01-01

This paper presents a. method for unsupervised assessment of visceral and subcutaneous adipose tissue in the abdominal region by MRI. The identification of the subcutaneous and the visceral regions were achieved by dynamic programming constrained by points acquired from an active shape model...

Unsupervised Image Segmentation

Czech Academy of Sciences Publication Activity Database

Haindl, Michal; Mikeš, Stanislav

2014-01-01

Roč. 36, č. 4 (2014), s. 23-23 R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : unsupervised image segmentation Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2014/RO/haindl-0434412.pdf

Further Stable methods for the calculation of partition functions

International Nuclear Information System (INIS)

Wilson, B G; Gilleron, F; Pain, J

2007-01-01

The extension to recursion over holes of the Gilleron and Pain method for calculating partition functions of a canonical ensemble of non-interacting bound electrons is presented as well as a generalization for the efficient computation of collisional line broadening

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

Science.gov (United States)

Verhoeven, Thibault; Schmid, Konstantin; Müller, Klaus-Robert; Tangermann, Michael; Kindermans, Pieter-Jan

2017-01-01

Objective Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means. Method We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task. Results Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP. PMID:28407016

Unsupervised classification of multivariate geostatistical data: Two algorithms

Science.gov (United States)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Unsupervised land cover change detection: meaningful sequential time series analysis

CSIR Research Space (South Africa)

Salmon, BP

2011-06-01

Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

Separation of soil respiration: a site-specific comparison of partition methods

Science.gov (United States)

Comeau, Louis-Pierre; Lai, Derrick Y. F.; Jinglan Cui, Jane; Farmer, Jenny

2018-06-01

Without accurate data on soil heterotrophic respiration (Rh), assessments of soil carbon (C) sequestration rate and C balance are challenging to produce. Accordingly, it is essential to determine the contribution of the different sources of the total soil CO2 efflux (Rs) in different ecosystems, but to date, there are still many uncertainties and unknowns regarding the soil respiration partitioning procedures currently available. This study compared the suitability and relative accuracy of five different Rs partitioning methods in a subtropical forest: (1) regression between root biomass and CO2 efflux, (2) lab incubations with minimally disturbed soil microcosm cores, (3) root exclusion bags with hand-sorted roots, (4) root exclusion bags with intact soil blocks and (5) soil δ13C-CO2 natural abundance. The relationship between Rh and soil moisture and temperature was also investigated. A qualitative evaluation table of the partition methods with five performance parameters was produced. The Rs was measured weekly from 3 February to 19 April 2017 and found to average 6.1 ± 0.3 Mg C ha-1 yr-1. During this period, the Rh measured with the in situ mesh bags with intact soil blocks and hand-sorted roots was estimated to contribute 49 ± 7 and 79 ± 3 % of Rs, respectively. The Rh percentages estimated with the root biomass regression, microcosm incubation and δ13C-CO2 natural abundance were 54 ± 41, 8-17 and 61 ± 39 %, respectively. Overall, no systematically superior or inferior Rs partition method was found. The paper discusses the strengths and weaknesses of each technique with the conclusion that combining two or more methods optimizes Rh assessment reliability.

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

Science.gov (United States)

Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

2016-01-01

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

Optimisation-Based Solution Methods for Set Partitioning Models

DEFF Research Database (Denmark)

Rasmussen, Matias Sevel

The scheduling of crew, i.e. the construction of work schedules for crew members, is often not a trivial task, but a complex puzzle. The task is complicated by rules, restrictions, and preferences. Therefore, manual solutions as well as solutions from standard software packages are not always su......_cient with respect to solution quality and solution time. Enhancement of the overall solution quality as well as the solution time can be of vital importance to many organisations. The _elds of operations research and mathematical optimisation deal with mathematical modelling of di_cult scheduling problems (among...... other topics). The _elds also deal with the development of sophisticated solution methods for these mathematical models. This thesis describes the set partitioning model which has been widely used for modelling crew scheduling problems. Integer properties for the set partitioning model are shown...

Modeling Visit Behaviour in Smart Homes using Unsupervised Learning

NARCIS (Netherlands)

Nait Aicha, A.; Englebienne, G.; Kröse, B.

2014-01-01

Many algorithms on health monitoring from ambient sensor networks assume that only a single person is present in the home. We present an unsupervised method that models visit behaviour. A Markov modulated multidimensional non-homogeneous Poisson process (M3P2) is described that allows us to model

Clustervision: Visual Supervision of Unsupervised Clustering.

Science.gov (United States)

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

Semi-supervised and unsupervised extreme learning machines.

Science.gov (United States)

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Comparative analysis of clustering methods for gene expression time course data

Directory of Open Access Journals (Sweden)

Ivan G. Costa

2004-01-01

Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

Partitioning in P-T concept

International Nuclear Information System (INIS)

Zhang Peilu; Qi Zhanshun; Zhu Zhixuan

2000-01-01

Comparison of dry- and water-method for partitioning fission products and minor actinides from the spent fuels, and description of advance of dry-method were done. Partitioning process, some typical concept and some results of dry-method were described. The problems fond in dry-method up to now were pointed out. The partitioning study program was suggested

Unsupervised method for automatic construction of a disease dictionary from a large free text collection.

Science.gov (United States)

Xu, Rong; Supekar, Kaustubh; Morgan, Alex; Das, Amar; Garber, Alan

2008-11-06

Concept specific lexicons (e.g. diseases, drugs, anatomy) are a critical source of background knowledge for many medical language-processing systems. However, the rapid pace of biomedical research and the lack of constraints on usage ensure that such dictionaries are incomplete. Focusing on disease terminology, we have developed an automated, unsupervised, iterative pattern learning approach for constructing a comprehensive medical dictionary of disease terms from randomized clinical trial (RCT) abstracts, and we compared different ranking methods for automatically extracting con-textual patterns and concept terms. When used to identify disease concepts from 100 randomly chosen, manually annotated clinical abstracts, our disease dictionary shows significant performance improvement (F1 increased by 35-88%) over available, manually created disease terminologies.

Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings

OpenAIRE

Aldarmaki, Hanan; Mohan, Mahesh; Diab, Mona

2017-01-01

Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector ...

Automated and unsupervised detection of malarial parasites in microscopic images

Directory of Open Access Journals (Sweden)

Purwar Yashasvi

2011-12-01

Full Text Available Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis and prone to human error (leading to erroneous diagnosis, even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. Method A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. Results The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Conclusion Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method

A physically based catchment partitioning method for hydrological analysis

Science.gov (United States)

Menduni, Giovanni; Riboni, Vittoria

2000-07-01

We propose a partitioning method for the topographic surface, which is particularly suitable for hydrological distributed modelling and shallow-landslide distributed modelling. The model provides variable mesh size and appears to be a natural evolution of contour-based digital terrain models. The proposed method allows the drainage network to be derived from the contour lines. The single channels are calculated via a search for the steepest downslope lines. Then, for each network node, the contributing area is determined by means of a search for both steepest upslope and downslope lines. This leads to the basin being partitioned into physically based finite elements delimited by irregular polygons. In particular, the distributed computation of local geomorphological parameters (i.e. aspect, average slope and elevation, main stream length, concentration time, etc.) can be performed easily for each single element. The contributing area system, together with the information on the distribution of geomorphological parameters provide a useful tool for distributed hydrological modelling and simulation of environmental processes such as erosion, sediment transport and shallow landslides.

Unsupervised classification of operator workload from brain signals

Science.gov (United States)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Remote photoplethysmography system for unsupervised monitoring regional anesthesia effectiveness

Science.gov (United States)

Rubins, U.; Miscuks, A.; Marcinkevics, Z.; Lange, M.

2017-12-01

Determining the level of regional anesthesia (RA) is vitally important to both an anesthesiologist and surgeon, also knowing the RA level can protect the patient and reduce the time of surgery. Normally to detect the level of RA, usually a simple subjective (sensitivity test) and complicated quantitative methods (thermography, neuromyography, etc.) are used, but there is not yet a standardized method for objective RA detection and evaluation. In this study, the advanced remote photoplethysmography imaging (rPPG) system for unsupervised monitoring of human palm RA is demonstrated. The rPPG system comprises compact video camera with green optical filter, surgical lamp as a light source and a computer with custom-developed software. The algorithm implemented in Matlab software recognizes the palm and two dermatomes (Medial and Ulnar innervation), calculates the perfusion map and perfusion changes in real-time to detect effect of RA. Seven patients (aged 18-80 years) undergoing hand surgery received peripheral nerve brachial plexus blocks during the measurements. Clinical experiments showed that our rPPG system is able to perform unsupervised monitoring of RA.

[On the partition of acupuncture academic schools].

Science.gov (United States)

Yang, Pengyan; Luo, Xi; Xia, Youbing

2016-05-01

Nowadays extensive attention has been paid on the research of acupuncture academic schools, however, a widely accepted method of partition of acupuncture academic schools is still in need. In this paper, the methods of partition of acupuncture academic schools in the history have been arranged, and three typical methods of"partition of five schools" "partition of eighteen schools" and "two-stage based partition" are summarized. After adeep analysis on the disadvantages and advantages of these three methods, a new method of partition of acupuncture academic schools that is called "three-stage based partition" is proposed. In this method, after the overall acupuncture academic schools are divided into an ancient stage, a modern stage and a contemporary stage, each schoolis divided into its sub-school category. It is believed that this method of partition can remedy the weaknesses ofcurrent methods, but also explore a new model of inheritance and development under a different aspect through thedifferentiation and interaction of acupuncture academic schools at three stages.

Partitioning sparse rectangular matrices for parallel processing

Energy Technology Data Exchange (ETDEWEB)

Kolda, T.G.

1998-05-01

The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.

Correlates of Unsupervised Bathing of Infants: A Cross-Sectional Study

Directory of Open Access Journals (Sweden)

Tinneke M. J. Beirens

2013-03-01

Full Text Available Drowning represents the third leading cause of fatal unintentional injury in infants (0–1 years. The aim of this study is to investigate correlates of unsupervised bathing. This cross-sectional study included 1,410 parents with an infant. Parents completed a questionnaire regarding supervision during bathing, socio-demographic factors, and Protection Motivation Theory-constructs. To determine correlates of parents who leave their infant unsupervised, logistic regression analyses were performed. Of the parents, 6.2% left their child unsupervised in the bathtub. Parents with older children (OR 1.24; 95%CI 1.00–1.54 were more likely to leave their child unsupervised in the bathtub. First-time parents (OR 0.59; 95%CI 0.36–0.97 and non-Western migrant fathers (OR 0.18; 95%CI 0.05–0.63 were less likely to leave their child unsupervised in the bathtub. Furthermore, parents who perceived higher self-efficacy (OR 0.57; 95%CI 0.47–0.69, higher response efficacy (OR 0.34; 95%CI 0.24–0.48, and higher severity (OR 0.74; 95%CI 0.58–0.93 were less likely to leave their child unsupervised. Since young children are at great risk of drowning if supervision is absent, effective strategies for drowning prevention should be developed and evaluated. In the meantime, health care professionals should inform parents with regard to the importance of supervision during bathing.

Unsupervised laparoscopic appendicectomy by surgical trainees is safe and time-effective.

Science.gov (United States)

Wong, Kenneth; Duncan, Tristram; Pearson, Andrew

2007-07-01

Open appendicectomy is the traditional standard treatment for appendicitis. Laparoscopic appendicectomy is perceived as a procedure with greater potential for complications and longer operative times. This paper examines the hypothesis that unsupervised laparoscopic appendicectomy by surgical trainees is a safe and time-effective valid alternative. Medical records, operating theatre records and histopathology reports of all patients undergoing laparoscopic and open appendicectomy over a 15-month period in two hospitals within an area health service were retrospectively reviewed. Data were analysed to compare patient features, pathology findings, operative times, complications, readmissions and mortality between laparoscopic and open groups and between unsupervised surgical trainee operators versus consultant surgeon operators. A total of 143 laparoscopic and 222 open appendicectomies were reviewed. Unsupervised trainees performed 64% of the laparoscopic appendicectomies and 55% of the open appendicectomies. There were no significant differences in complication rates, readmissions, mortality and length of stay between laparoscopic and open appendicectomy groups or between trainee and consultant surgeon operators. Conversion rates (laparoscopic to open approach) were similar for trainees and consultants. Unsupervised senior surgical trainees did not take significantly longer to perform laparoscopic appendicectomy when compared to unsupervised trainee-performed open appendicectomy. Unsupervised laparoscopic appendicectomy by surgical trainees is safe and time-effective.

Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

Science.gov (United States)

Salman, S. S.; Abbas, W. A.

2018-05-01

The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

Supervised and unsupervised condition monitoring of non-stationary acoustic emission signals

DEFF Research Database (Denmark)

Sigurdsson, Sigurdur; Pontoppidan, Niels Henrik; Larsen, Jan

2005-01-01

condition changes across load changes. In this paper we approach this load interpolation problem with supervised and unsupervised learning, i.e. model with normal and fault examples and normal examples only, respectively. We apply non-linear methods for the learning of engine condition changes. Both...

The prediction of blood-tissue partitions, water-skin partitions and skin permeation for agrochemicals.

Science.gov (United States)

Abraham, Michael H; Gola, Joelle M R; Ibrahim, Adam; Acree, William E; Liu, Xiangli

2014-07-01

There is considerable interest in the blood-tissue distribution of agrochemicals, and a number of researchers have developed experimental methods for in vitro distribution. These methods involve the determination of saline-blood and saline-tissue partitions; not only are they indirect, but they do not yield the required in vivo distribution. The authors set out equations for gas-tissue and blood-tissue distribution, for partition from water into skin and for permeation from water through human skin. Together with Abraham descriptors for the agrochemicals, these equations can be used to predict values for all of these processes. The present predictions compare favourably with experimental in vivo blood-tissue distribution where available. The predictions require no more than simple arithmetic. The present method represents a much easier and much more economic way of estimating blood-tissue partitions than the method that uses saline-blood and saline-tissue partitions. It has the added advantages of yielding the required in vivo partitions and being easily extended to the prediction of partition of agrochemicals from water into skin and permeation from water through skin. © 2013 Society of Chemical Industry.

Classification of behavior using unsupervised temporal neural networks

International Nuclear Information System (INIS)

Adair, K.L.

1998-03-01

Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem

Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

Science.gov (United States)

Schouten, Kim; van der Weijde, Onne; Frasincar, Flavius; Dekker, Rommert

2018-04-01

Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an -score of 67%. The second method is a supervised variant that outperforms existing methods with an -score of 84%.

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

Science.gov (United States)

Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

2015-01-01

Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

Binary recursive partitioning: background, methods, and application to psychology.

Science.gov (United States)

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

An unsupervised MVA method to compare specific regions in human breast tumor tissue samples using ToF-SIMS.

Science.gov (United States)

Bluestein, Blake M; Morrish, Fionnuala; Graham, Daniel J; Guenthoer, Jamie; Hockenbery, David; Porter, Peggy L; Gamble, Lara J

2016-03-21

Imaging time-of-flight secondary ion mass spectrometry (ToF-SIMS) and principal component analysis (PCA) were used to investigate two sets of pre- and post-chemotherapy human breast tumor tissue sections to characterize lipids associated with tumor metabolic flexibility and response to treatment. The micron spatial resolution imaging capability of ToF-SIMS provides a powerful approach to attain spatially-resolved molecular and cellular data from cancerous tissues not available with conventional imaging techniques. Three ca. 1 mm(2) areas per tissue section were analyzed by stitching together 200 μm × 200 μm raster area scans. A method to isolate and analyze specific tissue regions of interest by utilizing PCA of ToF-SIMS images is presented, which allowed separation of cellularized areas from stromal areas. These PCA-generated regions of interest were then used as masks to reconstruct representative spectra from specifically stromal or cellular regions. The advantage of this unsupervised selection method is a reduction in scatter in the spectral PCA results when compared to analyzing all tissue areas or analyzing areas highlighted by a pathologist. Utilizing this method, stromal and cellular regions of breast tissue biopsies taken pre- versus post-chemotherapy demonstrate chemical separation using negatively-charged ion species. In this sample set, the cellular regions were predominantly all cancer cells. Fatty acids (i.e. palmitic, oleic, and stearic), monoacylglycerols, diacylglycerols and vitamin E profiles were distinctively different between the pre- and post-therapy tissues. These results validate a new unsupervised method to isolate and interpret biochemically distinct regions in cancer tissues using imaging ToF-SIMS data. In addition, the method developed here can provide a framework to compare a variety of tissue samples using imaging ToF-SIMS, especially where there is section-to-section variability that makes it difficult to use a serial hematoxylin

Unsupervised progressive elastic band exercises for frail geriatric inpatients objectively monitored by new exercise-integrated technology

DEFF Research Database (Denmark)

Rathleff, Camilla Rams; Bandholm, T.; Spaich, Erika Geraldina

2017-01-01

the amount of supervised training, and unsupervised training could possibly supplement supervised training thereby increasing the total exercise dose during admission. A new valid and reliable technology, the BandCizer, objectively measures the exact training dosage performed. The purpose was to investigate...... feasibility and acceptability of an unsupervised progressive strength training intervention monitored by BandCizer for frail geriatric inpatients. Methods: This feasibility trial included 15 frail inpatients at a geriatric ward. At hospitalization, the patients were prescribed two elastic band exercises...... of 2-min pauses and a time-under-tension of 8 s. The feasibility criterion for the unsupervised progressive exercises was that 33% of the recommended number of sets would be performed by at least 30% of patients. In addition, patients and staff were interviewed about their experiences...

The evaluation of the equilibrium partitioning method using sensitivity distributions of species in water and soil or sediment

NARCIS (Netherlands)

Beelen P van; Verbruggen EMJ; Peijnenburg WJGM; ECO

2002-01-01

The equilibrium partitioning method (EqP-method) can be used to derive environmental quality standards (like the Maximum Permissible Concentration or the intervention value) for soil or sediment, from aquatic toxicity data and a soil/water or sediment/water partitioning coefficient. The validity of

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation

Directory of Open Access Journals (Sweden)

Dongmei Huang

2017-09-01

Full Text Available Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

Science.gov (United States)

Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

2017-09-21

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

Unsupervised Categorization in a Sample of Children with Autism Spectrum Disorders

Science.gov (United States)

Edwards, Darren J.; Perlman, Amotz; Reed, Phil

2012-01-01

Studies of supervised Categorization have demonstrated limited Categorization performance in participants with autism spectrum disorders (ASD), however little research has been conducted regarding unsupervised Categorization in this population. This study explored unsupervised Categorization using two stimulus sets that differed in their…

COMPUTING VERTICES OF INTEGER PARTITION POLYTOPES

Directory of Open Access Journals (Sweden)

A. S. Vroublevski

2015-01-01

Full Text Available The paper describes a method of generating vertices of the polytopes of integer partitions that was used by the authors to calculate all vertices and support vertices of the partition polytopes for all n ≤ 105 and all knapsack partitions of n ≤ 165. The method avoids generating all partitions of n. The vertices are determined with the help of sufficient and necessary conditions; in the hard cases, the well-known program Polymake is used. Some computational aspects are exposed in more detail. These are the algorithm for checking the criterion that characterizes partitions that are convex combinations of two other partitions; the way of using two combinatorial operations that transform the known vertices to the new ones; and employing the Polymake to recognize a limited number (for small n of partitions that need three or more other partitions for being convexly expressed. We discuss the computational results on the numbers of vertices and support vertices of the partition polytopes and some appealing problems these results give rise to.

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Directory of Open Access Journals (Sweden)

Xiangbing Zhou

2018-04-01

Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

A Family of Trigonometrically-fitted Partitioned Runge-Kutta Symplectic Methods

International Nuclear Information System (INIS)

Monovasilis, Th.; Kalogiratou, Z.; Simos, T. E.

2007-01-01

We are presenting a family of trigonometrically fitted partitioned Runge-Kutta symplectic methods of fourth order with six stages. The solution of the one dimensional time independent Schroedinger equation is considered by trigonometrically fitted symplectic integrators. The Schroedinger equation is first transformed into a Hamiltonian canonical equation. Numerical results are obtained for the one-dimensional harmonic oscillator and the exponential potential

An Improved Unsupervised Modeling Methodology For Detecting Fraud In Vendor Payment Transactions

National Research Council Canada - National Science Library

Rouillard, Gregory

2003-01-01

...) vendor payment transactions through Unsupervised Modeling (cluster analysis) . Clementine Data Mining software is used to construct unsupervised models of vendor payment data using the K-Means, Two Step, and Kohonen algorithms...

Partition wall structure in spent fuel storage pool and construction method for the partition wall

International Nuclear Information System (INIS)

Izawa, Masaaki

1998-01-01

A partitioning wall for forming cask pits as radiation shielding regions by partitioning inside of a spent fuel storage pool is prepared by covering both surface of a concrete body by shielding metal plates. The metal plate comprises opposed plate units integrated by welding while sandwiching a metal frame as a reinforcing material for the concrete body, the lower end of the units is connected to a floor of a pool by fastening members, and concrete is set while using the metal plate of the units as a frame to form the concrete body. The shielding metal plate has a double walled structure formed by welding a lining plate disposed on the outer surface of the partition wall and a shield plate disposed to the inner side. Then the term for construction can be shortened, and the capacity for storing spent fuels can be increased. (N.H.)

Gait Partitioning Methods: A Systematic Review

Directory of Open Access Journals (Sweden)

Juri Taborri

2016-01-01

Full Text Available In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments.

Gait Partitioning Methods: A Systematic Review

Science.gov (United States)

Taborri, Juri; Palermo, Eduardo; Rossi, Stefano; Cappa, Paolo

2016-01-01

In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments. PMID:26751449

Semi-supervised clustering methods.

Science.gov (United States)

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

Application of partition chromatography method for separation and analysis of actinium radionuclides

International Nuclear Information System (INIS)

Sinitsina, G.S.; Shestakova, I.A.; Shestakov, B.I.; Plyushcheva, N.A.; Malyshev, N.A.; Belyatskij, A.F.; Tsirlin, V.A.

1979-01-01

The method of partition chromatography is considered with the use of different extractants for the extraction of actinium-227, actinium-225 and actinium-228. It is advisable to extract actinium-227 from the irradiated radium with the help of D2FGFK. The use of 2DEGFK allows us to separate actinium-227 from alkaline and alkaline-earth elements. Amines have a higher radiative stability. An express-method has been developed for the identification of actinium-227 with TOA by its intrinsic α-emission in nonequilibrium preparations of irradiated radium-226 of small activity. Actinium-225 is extracted from uranium-233 with due regard for the fact that U, Th, and Ac are extracted differently by TBP from HNO 3 solutions. With the help of the given procedure one can reach the purifying coefficient of 10 4 . Actinium-228 is extracted from the radiummesothorium preparations by a deposition of decay products, including polonium-210 on the iron hydroxyde. Actinium-228 extraction from the mixture of radium radionuclides is performed by the partition chromatography method on D2EGFK. All the procedures for separation of actinium isotopes by the above methods are described

Bayesian feature weighting for unsupervised learning, with application to object recognition

OpenAIRE

Carbonetto , Peter; De Freitas , Nando; Gustafson , Paul; Thompson , Natalie

2003-01-01

International audience; We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.

Supervised versus unsupervised categorization: two sides of the same coin?

Science.gov (United States)

Pothos, Emmanuel M; Edwards, Darren J; Perlman, Amotz

2011-09-01

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions.

Dimensionality reduction with unsupervised nearest neighbors

CERN Document Server

Kramer, Oliver

2013-01-01

This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...

Automated age-related macular degeneration classification in OCT using unsupervised feature learning

Science.gov (United States)

Venhuizen, Freerk G.; van Ginneken, Bram; Bloemen, Bart; van Grinsven, Mark J. J. P.; Philipsen, Rick; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

2015-03-01

Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...

Data.gov (United States)

National Aeronautics and Space Administration — Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Abstract: This article describes the results of applying four...

An Association-Oriented Partitioning Approach for Streaming Graph Query

Directory of Open Access Journals (Sweden)

Yun Hao

2017-01-01

Full Text Available The volumes of real-world graphs like knowledge graph are increasing rapidly, which makes streaming graph processing a hot research area. Processing graphs in streaming setting poses significant challenges from different perspectives, among which graph partitioning method plays a key role. Regarding graph query, a well-designed partitioning method is essential for achieving better performance. Existing offline graph partitioning methods often require full knowledge of the graph, which is not possible during streaming graph processing. In order to handle this problem, we propose an association-oriented streaming graph partitioning method named Assc. This approach first computes the rank values of vertices with a hybrid approximate PageRank algorithm. After splitting these vertices with an adapted variant affinity propagation algorithm, the process order on vertices in the sliding window can be determined. Finally, according to the level of these vertices and their association, the partition where the vertices should be distributed is decided. We compare its performance with a set of streaming graph partition methods and METIS, a widely adopted offline approach. The results show that our solution can partition graphs with hundreds of millions of vertices in streaming setting on a large collection of graph datasets and our approach outperforms other graph partitioning methods.

Coding Partitions

Directory of Open Access Journals (Sweden)

Fabio Burderi

2007-05-01

Full Text Available Motivated by the study of decipherability conditions for codes weaker than Unique Decipherability (UD, we introduce the notion of coding partition. Such a notion generalizes that of UD code and, for codes that are not UD, allows to recover the ``unique decipherability" at the level of the classes of the partition. By tacking into account the natural order between the partitions, we define the characteristic partition of a code X as the finest coding partition of X. This leads to introduce the canonical decomposition of a code in at most one unambiguouscomponent and other (if any totally ambiguouscomponents. In the case the code is finite, we give an algorithm for computing its canonical partition. This, in particular, allows to decide whether a given partition of a finite code X is a coding partition. This last problem is then approached in the case the code is a rational set. We prove its decidability under the hypothesis that the partition contains a finite number of classes and each class is a rational set. Moreover we conjecture that the canonical partition satisfies such a hypothesis. Finally we consider also some relationships between coding partitions and varieties of codes.

An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning

Directory of Open Access Journals (Sweden)

Xiangchun Yu

2018-01-01

Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.

The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

Science.gov (United States)

Masalmah, Yahya M.; Vélez-Reyes, Miguel

2007-04-01

The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

Directory of Open Access Journals (Sweden)

Jun He

2017-07-01

Full Text Available Artificial intelligence (AI techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN and support vector machine (SVM. The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

Science.gov (United States)

He, Jun; Yang, Shixi; Gan, Chunbiao

2017-07-04

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

Specialization processes in on-line unsupervised learning

NARCIS (Netherlands)

Biehl, M.; Freking, A.; Reents, G.; Schlösser, E.

1998-01-01

From the recent analysis of supervised learning by on-line gradient descent in multilayered neural networks it is known that the necessary process of student specialization can be delayed significantly. We demonstrate that this phenomenon also occurs in various models of unsupervised learning. A

Semi-supervised clustering methods

Science.gov (United States)

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

Unsupervised Document Embedding With CNNs

OpenAIRE

Liu, Chundi; Zhao, Shunan; Volkovs, Maksims

2017-01-01

We propose a new model for unsupervised document embedding. Leading existing approaches either require complex inference or use recurrent neural networks (RNN) that are difficult to parallelize. We take a different route and develop a convolutional neural network (CNN) embedding model. Our CNN architecture is fully parallelizable resulting in over 10x speedup in inference time over RNN models. Parallelizable architecture enables to train deeper models where each successive layer has increasin...

Partition functions with spin in AdS2 via quasinormal mode methods

International Nuclear Information System (INIS)

Keeler, Cynthia; Lisbão, Pedro; Ng, Gim Seng

2016-01-01

We extend the results of http://dx.doi.org/10.1007/JHEP06(2014)099, computing one loop partition functions for massive fields with spin half in AdS 2 using the quasinormal mode method proposed by Denef, Hartnoll, and Sachdev http://dx.doi.org/10.1088/0264-9381/27/12/125001. We find the finite representations of SO(2,1) for spin zero and spin half, consisting of a highest weight state |h〉 and descendants with non-unitary values of h. These finite representations capture the poles and zeroes of the one loop determinants. Together with the asymptotic behavior of the partition functions (which can be easily computed using a large mass heat kernel expansion), these are sufficient to determine the full answer for the one loop determinants. We also discuss extensions to higher dimensional AdS 2n and higher spins.

Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

Science.gov (United States)

Liu, Wuying; Wang, Lin

2018-03-01

The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

Unsupervised learning of facial emotion decoding skills.

Science.gov (United States)

Huelle, Jan O; Sack, Benjamin; Broer, Katja; Komlewa, Irina; Anders, Silke

2014-01-01

Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practice without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear, and sadness) was shown in each clip. Although no external information about the correctness of the participant's response or the sender's true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practice effects often observed in cognitive tasks.

Classification algorithms using adaptive partitioning

KAUST Repository

Binev, Peter; Cohen, Albert; Dahmen, Wolfgang; DeVore, Ronald

2014-01-01

© 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.

Classification algorithms using adaptive partitioning

KAUST Repository

Binev, Peter

2014-12-01

© 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.

flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.

Science.gov (United States)

Ge, Yongchao; Sealfon, Stuart C

2012-08-01

For flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. The former is computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot be applied directly to high-dimensional data as the computational time and memory become unmanageable and the estimated histogram is unreliable. An algorithm without these two problems would be very useful. In this article, we combine ideas from the finite mixture model and histogram spatial exploration. This new algorithm, which we call flowPeaks, can be applied directly to high-dimensional data and identify irregular shape clusters. The algorithm first uses K-means algorithm with a large K to partition the cell population into many small clusters. These partitioned data allow the generation of a smoothed density function using the finite mixture model. All local peaks are exhaustively searched by exploring the density function and the cells are clustered by the associated local peak. The algorithm flowPeaks is automatic, fast and reliable and robust to cluster shape and outliers. This algorithm has been applied to flow cytometry data and it has been compared with state of the art algorithms, including Misty Mountain, FLOCK, flowMeans, flowMerge and FLAME. The R package flowPeaks is available at https://github.com/yongchao/flowPeaks. yongchao.ge@mssm.edu Supplementary data are available at Bioinformatics online.

Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

Directory of Open Access Journals (Sweden)

Angel Mur

2016-04-01

Full Text Available In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

Shadow detection and removal in RGB VHR images for land use unsupervised classification

Science.gov (United States)

Movia, A.; Beinat, A.; Crosilla, F.

2016-09-01

Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

Unsupervised Retinal Vessel Segmentation Using Combined Filters.

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Wendeson S Oliveira

Full Text Available Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

A novel method to augment extraction of mangiferin by application of microwave on three phase partitioning.

Science.gov (United States)

Kulkarni, Vrushali M; Rathod, Virendra K

2015-06-01

This work reports a novel approach where three phase partitioning (TPP) was combined with microwave for extraction of mangiferin from leaves of Mangifera indica . Soxhlet extraction was used as reference method, which yielded 57 mg/g in 5 h. Under optimal conditions such as microwave irradiation time 5 min, ammonium sulphate concentration 40% w/v, power 272 W, solute to solvent ratio 1:20, slurry to t -butanol ratio 1:1, soaking time 5 min and duty cycle 50%, the mangiferin yield obtained was 54 mg/g by microwave assisted three phase partitioning extraction (MTPP). Thus extraction method developed resulted into higher extraction yield in a shorter span, thereby making it an interesting alternative prior to down-stream processing.

Present status of partitioning developments

International Nuclear Information System (INIS)

Nakamura, Haruto; Kubota, Masumitsu; Tachimori, Shoichi

1978-09-01

Evolution and development of the concept of partitioning of high-level liquid wastes (HLLW) in nuclear fuel reprocessing are reviewed historically from the early phase of separating useful radioisotopes from HLLW to the recent phase of eliminating hazardous nuclides such as transuranium elements for safe waste disposal. Since the criteria in determining the nuclides for elimination and the respective decontamination factors are important in the strategy of partitioning, current views on the criteria are summarized. As elimination of the transuranium is most significant in the partitioning, various methods available of separating them from fission products are evaluated. (auth.)

Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

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Suzuki Motoyuki

2009-01-01

Full Text Available Abstract We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the "query relevance." Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29% was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%.

Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

Directory of Open Access Journals (Sweden)

Akinori Ito

2009-01-01

Full Text Available We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the “query relevance.” Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29% was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%.

Unsupervised daily routine and activity discovery in smart homes.

Science.gov (United States)

Jie Yin; Qing Zhang; Karunanithi, Mohan

2015-08-01

The ability to accurately recognize daily activities of residents is a core premise of smart homes to assist with remote health monitoring. Most of the existing methods rely on a supervised model trained from a preselected and manually labeled set of activities, which are often time-consuming and costly to obtain in practice. In contrast, this paper presents an unsupervised method for discovering daily routines and activities for smart home residents. Our proposed method first uses a Markov chain to model a resident's locomotion patterns at different times of day and discover clusters of daily routines at the macro level. For each routine cluster, it then drills down to further discover room-level activities at the micro level. The automatic identification of daily routines and activities is useful for understanding indicators of functional decline of elderly people and suggesting timely interventions.

Unsupervised online classifier in sleep scoring for sleep deprivation studies.

Science.gov (United States)

Libourel, Paul-Antoine; Corneyllie, Alexandra; Luppi, Pierre-Hervé; Chouvet, Guy; Gervasoni, Damien

2015-05-01

This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings. The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method. © 2015 Associated Professional Sleep Societies, LLC.

Performance of some supervised and unsupervised multivariate techniques for grouping authentic and unauthentic Viagra and Cialis

Directory of Open Access Journals (Sweden)

Michel J. Anzanello

2014-09-01

Full Text Available A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, the performance of several methods belonging to two different classes of multivariate techniques–supervised and unsupervised techniques–were compared. The supervised techniques (ST are the k-Nearest Neighbor (KNN, Support Vector Machine (SVM, Probabilistic Neural Networks (PNN and Linear Discriminant Analysis (LDA; the unsupervised techniques are the k-Means CA and the Fuzzy C-Means (FCM. The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.

Multiple Attribute Group Decision-Making Methods Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Partitioned Bonferroni Mean Aggregation Operators.

Science.gov (United States)

Yin, Kedong; Yang, Benshuo; Li, Xuemei

2018-01-24

In this paper, we investigate multiple attribute group decision making (MAGDM) problems where decision makers represent their evaluation of alternatives by trapezoidal fuzzy two-dimensional uncertain linguistic variable. To begin with, we introduce the definition, properties, expectation, operational laws of trapezoidal fuzzy two-dimensional linguistic information. Then, to improve the accuracy of decision making in some case where there are a sort of interrelationship among the attributes, we analyze partition Bonferroni mean (PBM) operator in trapezoidal fuzzy two-dimensional variable environment and develop two operators: trapezoidal fuzzy two-dimensional linguistic partitioned Bonferroni mean (TF2DLPBM) aggregation operator and trapezoidal fuzzy two-dimensional linguistic weighted partitioned Bonferroni mean (TF2DLWPBM) aggregation operator. Furthermore, we develop a novel method to solve MAGDM problems based on TF2DLWPBM aggregation operator. Finally, a practical example is presented to illustrate the effectiveness of this method and analyses the impact of different parameters on the results of decision-making.

Unsupervised learning of facial emotion decoding skills

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Jan Oliver Huelle

2014-02-01

Full Text Available Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practise without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear and sadness was shown in each clip. Although no external information about the correctness of the participant’s response or the sender’s true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practise effects often observed in cognitive tasks.

The effect of different evapotranspiration methods on portraying soil water dynamics and ET partitioning in a semi-arid environment in Northwest China

OpenAIRE

Yu, L.; Zeng, Yijian; Su, Zhongbo; Cai, H.; Zheng, Z.

2016-01-01

Different methods for assessing evapotranspiration (ET) can significantly affect the performance of land surface models in portraying soil water dynamics and ET partitioning. An accurate understanding of the impact a method has is crucial to determining the effectiveness of an irrigation scheme. Two ET methods are discussed: one is based on reference crop evapotranspiration (ET0) theory, uses leaf area index (LAI) for partitioning into soil evaporation and transpiration, and...

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

Directory of Open Access Journals (Sweden)

Guoqi Qian

2016-01-01

Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.

A novel unsupervised spike sorting algorithm for intracranial EEG.

Science.gov (United States)

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

A novel method to augment extraction of mangiferin by application of microwave on three phase partitioning

Directory of Open Access Journals (Sweden)

Vrushali M. Kulkarni

2015-06-01

Full Text Available This work reports a novel approach where three phase partitioning (TPP was combined with microwave for extraction of mangiferin from leaves of Mangifera indica. Soxhlet extraction was used as reference method, which yielded 57 mg/g in 5 h. Under optimal conditions such as microwave irradiation time 5 min, ammonium sulphate concentration 40% w/v, power 272 W, solute to solvent ratio 1:20, slurry to t-butanol ratio 1:1, soaking time 5 min and duty cycle 50%, the mangiferin yield obtained was 54 mg/g by microwave assisted three phase partitioning extraction (MTPP. Thus extraction method developed resulted into higher extraction yield in a shorter span, thereby making it an interesting alternative prior to down-stream processing.

The Benefits of Adaptive Partitioning for Parallel AMR Applications

Energy Technology Data Exchange (ETDEWEB)

Steensland, Johan [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Advanced Software Research and Development

2008-07-01

Parallel adaptive mesh refinement methods potentially lead to realistic modeling of complex three-dimensional physical phenomena. However, the dynamics inherent in these methods present significant challenges in data partitioning and load balancing. Significant human resources, including time, effort, experience, and knowledge, are required for determining the optimal partitioning technique for each new simulation. In reality, scientists resort to using the on-board partitioner of the computational framework, or to using the partitioning industry standard, ParMetis. Adaptive partitioning refers to repeatedly selecting, configuring and invoking the optimal partitioning technique at run-time, based on the current state of the computer and application. In theory, adaptive partitioning automatically delivers superior performance and eliminates the need for repeatedly spending valuable human resources for determining the optimal static partitioning technique. In practice, however, enabling frameworks are non-existent due to the inherent significant inter-disciplinary research challenges. This paper presents a study of a simple implementation of adaptive partitioning and discusses implied potential benefits from the perspective of common groups of users within computational science. The study is based on a large set of data derived from experiments including six real-life, multi-time-step adaptive applications from various scientific domains, five complementing and fundamentally different partitioning techniques, a large set of parameters corresponding to a wide spectrum of computing environments, and a flexible cost function that considers the relative impact of multiple partitioning metrics and diverse partitioning objectives. The results show that even a simple implementation of adaptive partitioning can automatically generate results statistically equivalent to the best static partitioning. Thus, it is possible to effectively eliminate the problem of determining the

Partition functions with spin in AdS{sub 2} via quasinormal mode methods

Energy Technology Data Exchange (ETDEWEB)

Keeler, Cynthia [Niels Bohr International Academy, Niels Bohr Institute,University of Copenhagen, Blegdamsvej 17, DK 2100, Copenhagen (Denmark); Lisbão, Pedro [Department of Physics, University of Michigan,Ann Arbor, MI-48109 (United States); Ng, Gim Seng [Department of Physics, McGill University,Montréal, QC H3A 2T8 (Canada)

2016-10-12

We extend the results of http://dx.doi.org/10.1007/JHEP06(2014)099, computing one loop partition functions for massive fields with spin half in AdS{sub 2} using the quasinormal mode method proposed by Denef, Hartnoll, and Sachdev http://dx.doi.org/10.1088/0264-9381/27/12/125001. We find the finite representations of SO(2,1) for spin zero and spin half, consisting of a highest weight state |h〉 and descendants with non-unitary values of h. These finite representations capture the poles and zeroes of the one loop determinants. Together with the asymptotic behavior of the partition functions (which can be easily computed using a large mass heat kernel expansion), these are sufficient to determine the full answer for the one loop determinants. We also discuss extensions to higher dimensional AdS{sub 2n} and higher spins.

A competition in unsupervised color image segmentation

Czech Academy of Sciences Publication Activity Database

Haindl, Michal; Mikeš, Stanislav

2016-01-01

Roč. 57, č. 9 (2016), s. 136-151 ISSN 0031-3203 R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : Unsupervised image segmentation * Segmentation contest * Texture analysis Subject RIV: BD - Theory of Information Impact factor: 4.582, year: 2016 http://library.utia.cas.cz/separaty/2016/RO/haindl-0459179.pdf

VLSI PARTITIONING ALGORITHM WITH ADAPTIVE CONTROL PARAMETER

Directory of Open Access Journals (Sweden)

P. N. Filippenko

2013-03-01

Full Text Available The article deals with the problem of very large-scale integration circuit partitioning. A graph is selected as a mathematical model describing integrated circuit. Modification of ant colony optimization algorithm is presented, which is used to solve graph partitioning problem. Ant colony optimization algorithm is an optimization method based on the principles of self-organization and other useful features of the ants’ behavior. The proposed search system is based on ant colony optimization algorithm with the improved method of the initial distribution and dynamic adjustment of the control search parameters. The experimental results and performance comparison show that the proposed method of very large-scale integration circuit partitioning provides the better search performance over other well known algorithms.

Partition function zeros of the one-dimensional Potts model: the recursive method

International Nuclear Information System (INIS)

Ghulghazaryan, R G; Ananikian, N S

2003-01-01

The Yang-Lee, Fisher and Potts zeros of the one-dimensional Q-state Potts model are studied using the theory of dynamical systems. An exact recurrence relation for the partition function is derived. It is shown that zeros of the partition function may be associated with neutral fixed points of the recurrence relation. Further, a general equation for zeros of the partition function is found and a classification of the Yang-Lee, Fisher and Potts zeros is given. It is shown that the Fisher zeros in a nonzero magnetic field are located on several lines in the complex temperature plane and that the number of these lines depends on the value of the magnetic field. Analytical expressions for the densities of the Yang-Lee, Fisher and Potts zeros are derived. It is shown that densities of all types of zeros of the partition function are singular at the edge singularity points with the same critical exponent

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

Science.gov (United States)

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Content Discovery from Composite Audio : An unsupervised approach

NARCIS (Netherlands)

Lu, L.

2009-01-01

In this thesis, we developed and assessed a novel robust and unsupervised framework for semantic inference from composite audio signals. We focused on the problem of detecting audio scenes and grouping them into meaningful clusters. Our approach addressed all major steps in a general process of

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

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Jiayi Wu

Full Text Available Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM. We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Science.gov (United States)

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Unique Path Partitions

DEFF Research Database (Denmark)

Bessenrodt, Christine; Olsson, Jørn Børling; Sellers, James A.

2013-01-01

We give a complete classification of the unique path partitions and study congruence properties of the function which enumerates such partitions.......We give a complete classification of the unique path partitions and study congruence properties of the function which enumerates such partitions....

Partition method and experimental validation for impact dynamics of flexible multibody system

Science.gov (United States)

Wang, J. Y.; Liu, Z. Y.; Hong, J. Z.

2018-06-01

The impact problem of a flexible multibody system is a non-smooth, high-transient, and strong-nonlinear dynamic process with variable boundary. How to model the contact/impact process accurately and efficiently is one of the main difficulties in many engineering applications. The numerical approaches being used widely in impact analysis are mainly from two fields: multibody system dynamics (MBS) and computational solid mechanics (CSM). Approaches based on MBS provide a more efficient yet less accurate analysis of the contact/impact problems, while approaches based on CSM are well suited for particularly high accuracy needs, yet require very high computational effort. To bridge the gap between accuracy and efficiency in the dynamic simulation of a flexible multibody system with contacts/impacts, a partition method is presented considering that the contact body is divided into two parts, an impact region and a non-impact region. The impact region is modeled using the finite element method to guarantee the local accuracy, while the non-impact region is modeled using the modal reduction approach to raise the global efficiency. A three-dimensional rod-plate impact experiment is designed and performed to validate the numerical results. The principle for how to partition the contact bodies is proposed: the maximum radius of the impact region can be estimated by an analytical method, and the modal truncation orders of the non-impact region can be estimated by the highest frequency of the signal measured. The simulation results using the presented method are in good agreement with the experimental results. It shows that this method is an effective formulation considering both accuracy and efficiency. Moreover, a more complicated multibody impact problem of a crank slider mechanism is investigated to strengthen this conclusion.

OCL-BASED TEST CASE GENERATION USING CATEGORY PARTITIONING METHOD

Directory of Open Access Journals (Sweden)

A. Jalila

2015-10-01

Full Text Available The adoption of fault detection techniques during initial stages of software development life cycle urges to improve reliability of a software product. Specification-based testing is one of the major criterions to detect faults in the requirement specification or design of a software system. However, due to the non-availability of implementation details, test case generation from formal specifications become a challenging task. As a novel approach, the proposed work presents a methodology to generate test cases from OCL (Object constraint Language formal specification using Category Partitioning Method (CPM. The experiment results indicate that the proposed methodology is more effective in revealing specification based faults. Furthermore, it has been observed that OCL and CPM form an excellent combination for performing functional testing at the earliest to improve software quality with reduced cost.

Experiments and Recommendations for Partitioning Systems of Equations

Directory of Open Access Journals (Sweden)

Mafteiu-Scai Liviu Octavian

2014-06-01

Full Text Available Partitioning the systems of equations is a very important process when solving it on a parallel computer. This paper presents some criteria which leads to more efficient parallelization, that must be taken into consideration. New criteria added to preconditioning process by reducing average bandwidth are pro- posed in this paper. These new criteria lead to a combination between preconditioning and partitioning of systems equations, so no need two distinct algorithms/processes. In our proposed methods - where the preconditioning is done by reducing the average bandwidth- two directions were followed in terms of partitioning: for a given preconditioned system determining the best partitioning (or one as close and the second consist in achieving an adequate preconditioning, depending on a given/desired partitioning. A mixed method it is also proposed. Experimental results, conclusions and recommendations, obtained after parallel implementation of conjugate gradient on IBM BlueGene /P supercomputer- based on a synchronous model of parallelization- are also presented in this paper.

The Partition of Multi-Resolution LOD Based on Qtm

Science.gov (United States)

Hou, M.-L.; Xing, H.-Q.; Zhao, X.-S.; Chen, J.

2011-08-01

The partition hierarch of Quaternary Triangular Mesh (QTM) determine the accuracy of spatial analysis and application based on QTM. In order to resolve the problem that the partition hierarch of QTM is limited by the level of the computer hardware, the new method that Multi- Resolution LOD (Level of Details) based on QTM will be discussed in this paper. This method can make the resolution of the cells varying with the viewpoint position by partitioning the cells of QTM, selecting the particular area according to the viewpoint; dealing with the cracks caused by different subdivisions, it satisfies the request of unlimited partition in part.

THE PARTITION OF MULTI-RESOLUTION LOD BASED ON QTM

Directory of Open Access Journals (Sweden)

M.-L. Hou

2012-08-01

Full Text Available The partition hierarch of Quaternary Triangular Mesh (QTM determine the accuracy of spatial analysis and application based on QTM. In order to resolve the problem that the partition hierarch of QTM is limited by the level of the computer hardware, the new method that Multi- Resolution LOD (Level of Details based on QTM will be discussed in this paper. This method can make the resolution of the cells varying with the viewpoint position by partitioning the cells of QTM, selecting the particular area according to the viewpoint; dealing with the cracks caused by different subdivisions, it satisfies the request of unlimited partition in part.

Monotonicity Conditions for Multirate and Partitioned Explicit Runge-Kutta Schemes

KAUST Repository

Hundsdorfer, Willem; Mozartova, Anna; Savcenco, Valeriu

2013-01-01

of partitioned Runge-Kutta methods. It will also be seen that the incompatibility of consistency and mass-conservation holds for ‘genuine’ multirate schemes, but not for general partitioned methods.

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

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Kwanhyo Kim

2016-09-01

Full Text Available Recently, the usage of the automotive Electronic Control Unit (ECU and its software in cars is increasing. Therefore, as the functional complexity of such software increases, so does the likelihood of software-related faults. Therefore, it is important to ensure the reliability of ECU software in order to ensure automobile safety. For this reason, systematic testing methods are required that can guarantee software quality. However, it is difficult to locate a fault during testing with the current ECU development system because a tester performs the black-box testing using a Hardware-in-the-Loop (HiL simulator. Consequently, developers consume a large amount of money and time for debugging because they perform debugging without any information about the location of the fault. In this paper, we propose a method for localizing the fault utilizing memory information during black-box testing. This is likely to be of use to developers who debug automotive software. In order to observe whether symbols stored in the memory have been updated, the memory is partitioned by a memory map and the stack, thus the fault candidate region is reduced. A memory map method has the advantage of being able to finely partition the memory, and the stack method can partition the memory without a memory map. We validated these methods by applying these to HiL testing of the ECU for a body control system. The preliminary results indicate that a memory map and the stack reduce the possible fault locations to 22% and 19% of the updated memory, respectively.

Unsupervised spike sorting based on discriminative subspace learning.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

Individualized unsupervised exercise programs and chest physiotherapy in children with cystic fibrosis

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Bogdan ALMĂJAN-GUȚĂ

2013-12-01

Full Text Available Traditionally, physiotherapy for cystic fibrosis focused mainly on airway clearance (clearing mucus from the lungs. This still makes up a large part of daily treatment, but the role of the physiotherapist in cystic fibrosis has expanded to include daily exercise, inhalation therapy, posture awareness and, for some, the management of urinary incontinence. The purpose of this study is to demonstrate the necessity and the efficiency of various methods of chest physiotherapy and individualized unsupervised exercise program, in the improvement of body composition and physical performance. This study included 12 children with cystic fibrosis, with ages between 8-13 years. Each subject was evaluated in terms of body composition, effort capacity and lower body muscular performance, at the beginning of the study and after 12 months.The intervention consisted in classic respiratory clearance and physiotherapy techniques (5 times a week and an individualized unsupervised exercise program (3 times a week. After 12 months we noticed a significant improvement of the measured parameters: body weight increased from 32.25±5.5 to 33.53±5.4 kg (p <0.001, skeletal muscle mass increased from a mean of 16.04±4.1 to 17.01±4.2 (p<0.001, the fitness score, increased from a mean of 71±3.8 points to73±3.8, (p<0.001 and power and force also registered positive evolutions (from 19.3±2.68 to 21.65±2.4 W/kg and respectively 19.68±2.689 to 20.81±2.98 N/kg.The association between physiotherapy procedures and an individualized (after a proper clinical assessment unsupervised exercise program, proved to be an effective, relatively simple and accessible (regardless of social class intervention.

Best friends' interactions and substance use: The role of friend pressure and unsupervised co-deviancy.

Science.gov (United States)

Tsakpinoglou, Florence; Poulin, François

2017-10-01

Best friends exert a substantial influence on rising alcohol and marijuana use during adolescence. Two mechanisms occurring within friendship - friend pressure and unsupervised co-deviancy - may partially capture the way friends influence one another. The current study aims to: (1) examine the psychometric properties of a new instrument designed to assess pressure from a youth's best friend and unsupervised co-deviancy; (2) investigate the relative contribution of these processes to alcohol and marijuana use; and (3) determine whether gender moderates these associations. Data were collected through self-report questionnaires completed by 294 Canadian youths (62% female) across two time points (ages 15-16). Principal component analysis yielded a two-factor solution corresponding to friend pressure and unsupervised co-deviancy. Logistic regressions subsequently showed that unsupervised co-deviancy was predictive of an increase in marijuana use one year later. Neither process predicted an increase in alcohol use. Results did not differ as a function of gender. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Supervised and Unsupervised Self-Testing for HIV in High- and Low-Risk Populations: A Systematic Review

Science.gov (United States)

Pant Pai, Nitika; Sharma, Jigyasa; Shivkumar, Sushmita; Pillay, Sabrina; Vadnais, Caroline; Joseph, Lawrence; Dheda, Keertan; Peeling, Rosanna W.

2013-01-01

Background Stigma, discrimination, lack of privacy, and long waiting times partly explain why six out of ten individuals living with HIV do not access facility-based testing. By circumventing these barriers, self-testing offers potential for more people to know their sero-status. Recent approval of an in-home HIV self test in the US has sparked self-testing initiatives, yet data on acceptability, feasibility, and linkages to care are limited. We systematically reviewed evidence on supervised (self-testing and counselling aided by a health care professional) and unsupervised (performed by self-tester with access to phone/internet counselling) self-testing strategies. Methods and Findings Seven databases (Medline [via PubMed], Biosis, PsycINFO, Cinahl, African Medicus, LILACS, and EMBASE) and conference abstracts of six major HIV/sexually transmitted infections conferences were searched from 1st January 2000–30th October 2012. 1,221 citations were identified and 21 studies included for review. Seven studies evaluated an unsupervised strategy and 14 evaluated a supervised strategy. For both strategies, data on acceptability (range: 74%–96%), preference (range: 61%–91%), and partner self-testing (range: 80%–97%) were high. A high specificity (range: 99.8%–100%) was observed for both strategies, while a lower sensitivity was reported in the unsupervised (range: 92.9%–100%; one study) versus supervised (range: 97.4%–97.9%; three studies) strategy. Regarding feasibility of linkage to counselling and care, 96% (n = 102/106) of individuals testing positive for HIV stated they would seek post-test counselling (unsupervised strategy, one study). No extreme adverse events were noted. The majority of data (n = 11,019/12,402 individuals, 89%) were from high-income settings and 71% (n = 15/21) of studies were cross-sectional in design, thus limiting our analysis. Conclusions Both supervised and unsupervised testing strategies were highly acceptable

Quantum mechanical fragment methods based on partitioning atoms or partitioning coordinates.

Science.gov (United States)

Wang, Bo; Yang, Ke R; Xu, Xuefei; Isegawa, Miho; Leverentz, Hannah R; Truhlar, Donald G

2014-09-16

atoms for capping dangling bonds, and we have shown that they can greatly improve the accuracy. Finally we present a new approach that goes beyond QM/MM by combining the convenience of molecular mechanics with the accuracy of fitting a potential function to electronic structure calculations on a specific system. To make the latter practical for systems with a large number of degrees of freedom, we developed a method to interpolate between local internal-coordinate fits to the potential energy. A key issue for the application to large systems is that rather than assigning the atoms or monomers to fragments, we assign the internal coordinates to reaction, secondary, and tertiary sets. Thus, we make a partition in coordinate space rather than atom space. Fits to the local dependence of the potential energy on tertiary coordinates are arrayed along a preselected reaction coordinate at a sequence of geometries called anchor points; the potential energy function is called an anchor points reactive potential. Electrostatically embedded fragment methods and the anchor points reactive potential, because they are based on treating an entire system by quantum mechanical electronic structure methods but are affordable for large and complex systems, have the potential to open new areas for accurate simulations where combined QM/MM methods are inadequate.

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

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Sealfon Stuart C

2010-10-01

Full Text Available Abstract Background There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments. Results To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment. Conclusions

Analog memristive synapse in spiking networks implementing unsupervised learning

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Erika Covi

2016-10-01

Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

Science.gov (United States)

Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

2016-01-01

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO 2 -based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

Unsupervised classification of major depression using functional connectivity MRI.

Science.gov (United States)

Zeng, Ling-Li; Shen, Hui; Liu, Li; Hu, Dewen

2014-04-01

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional connectivity patterns and showed that a maximum margin clustering-based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group-level clustering consistency of 92.5% and an individual-level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering-based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Copyright © 2013 Wiley Periodicals, Inc.

Development of the four group partitioning process at JAERI

International Nuclear Information System (INIS)

Kubota, Masumitsu; Morita, Yasuji; Yamaguchi, Isoo; Yamagishi, Isao; Fujiwara, T.; Watanabe, Masayuki; Mizoguchi, Kenichi; Tatsugae, Ryozo

1999-01-01

At JAERI, development of a partitioning method started about 24 years ago. From 1973 to 1984, a partitioning process was developed for separating elements in HLLW into 3 groups; TRU, Sr-Cs and others. The partitioning process consisted of three steps; solvent extraction of U and Pu with TBP, solvent extraction of Am and Cm with DIDPA, and adsorption of Sr and Cs with inorganic ion exchangers. The process was demonstrated with real HLLW. Since 1985, a four group partitioning process has been developed, in which a step for separating the Tc-PGM group was developed in addition to the three group separation. Effective methods for separating TRU, especially Np, and Tc have been developed. In this paper, the flow sheet of the four group partitioning and the results of tests with simulated and real HLLW in NUCEF hot-cell are shown. (J.P.N.)

Unsupervised Feature Selection for Interval Ordered Information Systems%区间序信息系统的无监督特征选择

Institute of Scientific and Technical Information of China (English)

闫岳君; 代建华

2017-01-01

目前已有很多针对单值信息系统的无监督特征选择方法,但针对区间值信息系统的无监督特征选择方法却很少.针对区间序信息系统,文中提出模糊优势关系,并基于此关系扩展模糊排序信息熵和模糊排序互信息,用于评价特征的重要性.再结合一种综合考虑信息量和冗余度的无监督最大信息最小冗余(UmIMR)准则,构造无监督特征选择方法.最后通过实验证明文中方法的有效性.%There are a number of unsupervised feature selection methods proposed for single-valued information systems, but little research focuses on unsupervised feature selection of interval-valued information systems. In this paper, a fuzzy dominance relation is proposed for interval ordered information systems. Then, fuzzy rank information entropy and fuzzy rank mutual information are extended to evaluate the importance of features. Consequently, an unsupervised feature selection method is designed based on an unsupervised maximum information and minimum redundancy ( UmImR ) criterion. In the UmImR criterion, the amount of information and redundancy are taken into account. Experimental results demonstrate the effectiveness of the proposed method.

Information-Based Approach to Unsupervised Machine Learning

Science.gov (United States)

2013-06-19

samples with large fitting error. The above optimization problem can be reduced to a quadratic program (Mangasarian & Musicant , 2000), which can be...recognition. Technicheskaya Kibernetica, 3. in Russian. Mallows, C. L. (1973). Some comments on CP . Technometrics, 15, 661–675. Mangasarian, O. L., & Musicant ...to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summa- rization is a typical application

The effects of an unsupervised water exercise program on low back pain and sick leave among healthy pregnant women - A randomised controlled trial

DEFF Research Database (Denmark)

Backhausen, Mette G; Tabor, Ann; Albert, Hanne

2017-01-01

BACKGROUND: Low back pain is highly prevalent among pregnant women, but evidence of an effective treatment are still lacking. Supervised exercise-either land or water based-has shown benefits for low back pain, but no trial has investigated the evidence of an unsupervised water exercise program...... on low back pain. We aimed to assess the effect of an unsupervised water exercise program on low back pain intensity and days spent on sick leave among healthy pregnant women. METHODS: In this randomised, controlled, parallel-group trial, 516 healthy pregnant women were randomly assigned to either...... unsupervised water exercise twice a week for a period of 12 weeks or standard prenatal care. Healthy pregnant women aged 18 years or older, with a single fetus and between 16-17 gestational weeks were eligible. The primary outcome was low back pain intensity measured by the Low Back Pain Rating scale at 32...

On the partitioning method and the perturbation quantum theory - discrete spectra

International Nuclear Information System (INIS)

Logrado, P.G.

1982-05-01

Lower and upper bounds to eigenvalues of the Schroedinger equation H Ψ = E Ψ (H = H 0 + V) and the convergence condition, in Schonberg's perturbation theory, are presented. These results are obtained using the partitioning technique. It is presented for the first time a perturbation treatment obtained when the reference function in the partitioning technique is chosen to be a true eigenfunction Ψ. The convergence condition and upper and lower bounds for the true eigenvalues E are derived in this formulation. The concept of the reaction and wave operators is also discussed. (author)

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

Science.gov (United States)

Goldstein, Markus; Uchida, Seiichi

2016-01-01

Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

Unsupervised neural networks for solving Troesch's problem

International Nuclear Information System (INIS)

Raja Muhammad Asif Zahoor

2014-01-01

In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. (interdisciplinary physics and related areas of science and technology)

Modeling water and hydrogen networks with partitioning regeneration units

Directory of Open Access Journals (Sweden)

W.M. Shehata

2015-03-01

Full Text Available Strict environment regulations in chemical and refinery industries lead to minimize resource consumption by designing utility networks within industrial process plants. The present study proposed a superstructure based optimization model for the synthesis of water and hydrogen networks with partitioning regenerators without mixing the regenerated sources. This method determines the number of partitioning regenerators needed for the regeneration of the sources. The number of the regenerators is based on the number of sources required to be treated for recovery. Each source is regenerated in an individual partitioning regenerator. Multiple regeneration systems can be employed to achieve minimum flowrate and costs. The formulation is linear in the regenerator balance equations. The optimized model is applied for two systems, partitioning regeneration systems of the fixed outlet impurity concentration and partitioning regeneration systems of the fixed impurity load removal ratio (RR for water and hydrogen networks. Several case studies from the literature are solved to illustrate the ease and applicability of the proposed method.

Unsupervised Two-Way Clustering of Metagenomic Sequences

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Shruthi Prabhakara

2012-01-01

Full Text Available A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.

Novel medium-throughput technique for investigating drug-cyclodextrin complexation by pH-metric titration using the partition coefficient method.

Science.gov (United States)

Dargó, Gergő; Boros, Krisztina; Péter, László; Malanga, Milo; Sohajda, Tamás; Szente, Lajos; Balogh, György T

2018-05-05

The present study was aimed to develop a medium-throughput screening technique for investigation of cyclodextrin (CD)-active pharmaceutical ingredient (API) complexes. Dual-phase potentiometric lipophilicity measurement, as gold standard technique, was combined with the partition coefficient method (plotting the reciprocal of partition coefficients of APIs as a function of CD concentration). A general equation was derived for determination of stability constants of 1:1 CD-API complexes (K 1:1,CD ) based on solely the changes of partition coefficients (logP o/w N -logP app N ), without measurement of the actual API concentrations. Experimentally determined logP value (-1.64) of 6-deoxy-6[(5/6)-fluoresceinylthioureido]-HPBCD (FITC-NH-HPBCD) was used to estimate the logP value (≈ -2.5 to -3) of (2-hydroxypropyl)-ß-cyclodextrin (HPBCD). The results suggested that the amount of HPBCD can be considered to be inconsequential in the octanol phase. The decrease of octanol volume due to the octanol-CD complexation was considered, thus a corrected octanol-water phase ratio was also introduced. The K 1:1,CD values obtained by this developed method showed a good accordance with the results from other orthogonal methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Unsupervised information extraction by text segmentation

CERN Document Server

Cortez, Eli

2013-01-01

A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors' approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a

Fat polygonal partitions with applications to visualization and embeddings

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Mark de Berg

2013-12-01

Full Text Available Let T be a rooted and weighted tree, where the weight of any node is equal to the sum of the weights of its children. The popular Treemap algorithm visualizes such a tree as a hierarchical partition of a square into rectangles, where the area of the rectangle corresponding to any node in T is equal to the weight of that node. The aspect ratio of the rectangles in such a rectangular partition necessarily depends on the weights and can become arbitrarily high.We introduce a new hierarchical partition scheme, called a polygonal partition, which uses convex polygons rather than just rectangles. We present two methods for constructing polygonal partitions, both having guarantees on the worst-case aspect ratio of the constructed polygons; in particular, both methods guarantee a bound on the aspect ratio that is independent of the weights of the nodes.We also consider rectangular partitions with slack, where the areas of the rectangles may differ slightly from the weights of the corresponding nodes. We show that this makes it possible to obtain partitions with constant aspect ratio. This result generalizes to hyper-rectangular partitions in ℝd. We use these partitions with slack for embedding ultrametrics into d-dimensional Euclidean space:  we give a polylog(Δ-approximation algorithm for embedding n-point ultrametrics into ℝd with minimum distortion, where Δ denotes the spread of the metric. The previously best-known approximation ratio for this problem was polynomial in n. This is the first algorithm for embedding a non-trivial family of weighted-graph metrics into a space of constant dimension that achieves polylogarithmic approximation ratio.

European Europart integrated project on actinide partitioning

International Nuclear Information System (INIS)

Madic, C.; Hudson, M.J.

2005-01-01

This poster presents the objectives of EUROPART, a scientific integrated project between 24 European partners, mostly funded by the European Community within the FP6. EUROPART aims at developing chemical partitioning processes for the so-called minor actinides (MA) contained in nuclear wastes, i.e. from Am to Cf. In the case of dedicated spent fuels or targets, the actinides to be separated also include U, Pu and Np. The techniques considered for the separation of these radionuclides belong to the fields of hydrometallurgy and pyrometallurgy, as in the previous FP5 programs named PARTNEW and PYROREP. The two main axes of research within EUROPART will be: The partitioning of MA (from Am to Cf) from high burn-up UO x fuels and multi-recycled MOx fuels; the partitioning of the whole actinide family for recycling, as an option for advanced dedicated fuel cycles (and in connection with the studies to be performed in the EUROTRANS integrated project). In hydrometallurgy, the research is organised into five Work Packages (WP). Four WP are dedicated to the study of partitioning methods mainly based on the use of solvent extraction methods, one WP is dedicated to the development of actinide co-conversion methods for fuel or target preparation. The research in pyrometallurgy is organized into four WP, listed hereafter: development of actinide partitioning methods, study of the basic chemistry of trans-curium elements in molten salts, study of the conditioning of the wastes, some system studies. Moreover, a strong management team will be concerned not only with the technical and financial issues arising from EUROPART, but also with information, communication and benefits for Europe. Training and education of young researchers will also pertain to the project. EUROPART has also established collaboration with US DOE and Japanese CRIEPI. (authors)

Partitioning of monomethylmercury between freshwater algae and water.

Science.gov (United States)

Miles, C J; Moye, H A; Phlips, E J; Sargent, B

2001-11-01

Phytoplankton-water monomethylmercury (MeHg) partition constants (KpI) have been determined in the laboratory for two green algae Selenastrum capricornutum and Cosmarium botrytis, the blue-green algae Schizothrix calcicola, and the diatom Thallasiosira spp., algal species that are commonly found in natural surface waters. Two methods were used to determine KpI, the Freundlich isotherm method and the flow-through/dialysis bag method. Both methods yielded KpI values of about 10(6.6) for S. capricornutum and were not significantly different. The KpI for the four algae studied were similar except for Schizothrix, which was significantly lower than S. capricornutum. The KpI for MeHg and S. capricornutum (exponential growth) was not significantly different in systems with predominantly MeHgOH or MeHgCl species. This is consistent with other studies that show metal speciation controls uptake kinetics, but the reactivity with intracellular components controls steady-state concentrations. Partitioning constants determined with exponential and stationary phase S. capricornutum cells at the same conditions were not significantly different, while the partitioning constant for exponential phase, phosphorus-limited cells was significantly lower, suggesting that P-limitation alters the ecophysiology of S. capricornutum sufficiently to impact partitioning, which may then ultimately affect mercury levels in higher trophic species.

A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification

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Yongjun Piao

2015-01-01

Full Text Available Ensemble data mining methods, also known as classifier combination, are often used to improve the performance of classification. Various classifier combination methods such as bagging, boosting, and random forest have been devised and have received considerable attention in the past. However, data dimensionality increases rapidly day by day. Such a trend poses various challenges as these methods are not suitable to directly apply to high-dimensional datasets. In this paper, we propose an ensemble method for classification of high-dimensional data, with each classifier constructed from a different set of features determined by partitioning of redundant features. In our method, the redundancy of features is considered to divide the original feature space. Then, each generated feature subset is trained by a support vector machine, and the results of each classifier are combined by majority voting. The efficiency and effectiveness of our method are demonstrated through comparisons with other ensemble techniques, and the results show that our method outperforms other methods.

Partitioning of unstructured meshes for load balancing

International Nuclear Information System (INIS)

Martin, O.C.; Otto, S.W.

1994-01-01

Many large-scale engineering and scientific calculations involve repeated updating of variables on an unstructured mesh. To do these types of computations on distributed memory parallel computers, it is necessary to partition the mesh among the processors so that the load balance is maximized and inter-processor communication time is minimized. This can be approximated by the problem, of partitioning a graph so as to obtain a minimum cut, a well-studied combinatorial optimization problem. Graph partitioning algorithms are discussed that give good but not necessarily optimum solutions. These algorithms include local search methods recursive spectral bisection, and more general purpose methods such as simulated annealing. It is shown that a general procedure enables to combine simulated annealing with Kernighan-Lin. The resulting algorithm is both very fast and extremely effective. (authors) 23 refs., 3 figs., 1 tab

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

Science.gov (United States)

Wendel, Jochen; Buttenfield, Barbara P.; Stanislawski, Larry V.

2016-01-01

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

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Chen, Jinying; Yu, Hong

2017-04-01

Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, EHR notes contain abundant medical jargon that can be difficult for patients to comprehend. One way to help patients is to reduce information overload and help them focus on medical terms that matter most to them. Targeted education can then be developed to improve patient EHR comprehension and the quality of care. The aim of this work was to develop FIT (Finding Important Terms for patients), an unsupervised natural language processing (NLP) system that ranks medical terms in EHR notes based on their importance to patients. We built FIT on a new unsupervised ensemble ranking model derived from the biased random walk algorithm to combine heterogeneous information resources for ranking candidate terms from each EHR note. Specifically, FIT integrates four single views (rankers) for term importance: patient use of medical concepts, document-level term salience, word co-occurrence based term relatedness, and topic coherence. It also incorporates partial information of term importance as conveyed by terms' unfamiliarity levels and semantic types. We evaluated FIT on 90 expert-annotated EHR notes and used the four single-view rankers as baselines. In addition, we implemented three benchmark unsupervised ensemble ranking methods as strong baselines. FIT achieved 0.885 AUC-ROC for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FIT for identifying important terms from EHR notes was 0.813 AUC-ROC. Both performance scores significantly exceeded the corresponding scores from the four single rankers (P<0.001). FIT also outperformed the three ensemble rankers for most metrics. Its performance is relatively insensitive to its parameter. FIT can automatically identify EHR terms important to patients. It may help develop future interventions

Bilingual Lexical Interactions in an Unsupervised Neural Network Model

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Zhao, Xiaowei; Li, Ping

2010-01-01

In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

Phylogenetic relationships in Asarum: Effect of data partitioning and a revised classification.

Science.gov (United States)

Sinn, Brandon T; Kelly, Lawrence M; Freudenstein, John V

2015-05-01

Generic boundaries and infrageneric relationships among the charismatic temperate magnoliid Asarum sensu lato (Aristolochiaceae) have long been uncertain. Previous molecular phylogenetic analyses used either plastid or nuclear loci alone and varied greatly in their taxonomic implications for the genus. We analyzed additional molecular markers from the nuclear and plastid genomes, reevaluated the possibility of a derived loss of autonomous self-pollination, and investigated the topological effects of matrix-partitioning-scheme choice. We sequenced seven plastid regions and the nuclear ITS1-ITS2 region of 58 individuals representing all previously recognized Asarum s.l. segregate genera and the monotypic genus Saruma. Matrices were partitioned using common a priori partitioning schemes and PartitionFinder. Topologies that were recovered using a priori partitioning of matrices differed from those recovered using a PartitionFinder-selected scheme, and by analysis method. We recovered six monophyletic groups that we circumscribed into three subgenera and six sections. Putative fungal mimic characters served as synapomorphies only for subgenus Heterotropa. Subgenus Geotaenium, a new subgenus, was recovered as sister to the remainder of Asarum by ML analyses of highly partitioned datasets. Section Longistylis, also newly named, is sister to section Hexastylis. Our analyses do not unambiguously support a single origin for all fungal-mimicry characters. Topologies recovered through the analysis of PartitionFinder-optimized matrices can differ drastically from those inferred from a priori partitioned matrices, and by analytical method. We recommend that investigators evaluate the topological effects of matrix partitioning using multiple methods of phylogenetic reconstruction. © 2015 Botanical Society of America, Inc.

Phase Grouping Line Extraction Algorithm Using Overlapped Partition

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WANG Jingxue

2015-07-01

Full Text Available Aiming at solving the problem of fracture at the discontinuities area and the challenges of line fitting in each partition, an innovative line extraction algorithm is proposed based on phase grouping using overlapped partition. The proposed algorithm adopted dual partition steps, which will generate overlapped eight partitions. Between the two steps, the middle axis in the first step coincides with the border lines in the other step. Firstly, the connected edge points that share the same phase gradients are merged into the line candidates, and fitted into line segments. Then to remedy the break lines at the border areas, the break segments in the second partition steps are refitted. The proposed algorithm is robust and does not need any parameter tuning. Experiments with various datasets have confirmed that the method is not only capable of handling the linear features, but also powerful enough in handling the curve features.

Partitioning of fissile and radio-toxic materials from spent nuclear fuel

International Nuclear Information System (INIS)

Bychkov, A.V.; Skiba, O.V.; Kormilitsyn, M.V.

2007-01-01

Full text of publication follows. The term ''partitioning'' means separation of one group of radwaste components from another. Such technological approaches are mainly applied to extraction of long-lived fission products (Tc, I) and minor actinides (Np, Am, Cm) from the waste arising from spent nuclear fuel reprocessing. Transmutation of the extracted minor actinides should be performed in a reactor or some accelerated systems. The combination of these technologies, partitioning and transmutation (P and T), will reduce the radiotoxicity of radwaste. In recent decades, partitioning has been directly linked to spent fuel reprocessing. Therefore, the basic investigations have been focused on the partitioning of liquid wastes arising from the PUREX process. These subjects have been the most developed ones, but the processes of fine aqueous separation generates an extra amount of liquid waste. This fact has an effect on the nuclear fuel cycle economy. Therefore, some other advanced compact methods have also been studied. These are dry methods involving molten chlorides and fluorides, the methods based on a supercritical movable phase, etc. The report provides a brief review of information on the basic partitioning process flow-sheets developed in France, Japan, Russia and other countries. Recent approaches to partitioning have been mostly directed towards radio-toxic hazard reduction and ecology. In the future, partitioning should be closely bound up with reprocessing and other spent nuclear fuel management processes. Reprocessing/partitioning should also be aimed at solving the problems of safety (non-proliferation) and economy in a closed fuel cycle. It is necessary to change a future ''technological philosophy'' of reprocessing and partitioning. The basic spent fuel components (U, Pu, Th) are to be extracted only for recycling in a closed nuclear fuel cycle. If these elements are regarded as a waste, additional expenses are required for transmutation. If we consider

A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection.

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Ceccarelli, Michele; d'Acierno, Antonio; Facchiano, Angelo

2009-10-15

Mass spectrometry spectra, widely used in proteomics studies as a screening tool for protein profiling and to detect discriminatory signals, are high dimensional data. A large number of local maxima (a.k.a. peaks) have to be analyzed as part of computational pipelines aimed at the realization of efficient predictive and screening protocols. With this kind of data dimensions and samples size the risk of over-fitting and selection bias is pervasive. Therefore the development of bio-informatics methods based on unsupervised feature extraction can lead to general tools which can be applied to several fields of predictive proteomics. We propose a method for feature selection and extraction grounded on the theory of multi-scale spaces for high resolution spectra derived from analysis of serum. Then we use support vector machines for classification. In particular we use a database containing 216 samples spectra divided in 115 cancer and 91 control samples. The overall accuracy averaged over a large cross validation study is 98.18. The area under the ROC curve of the best selected model is 0.9962. We improved previous known results on the problem on the same data, with the advantage that the proposed method has an unsupervised feature selection phase. All the developed code, as MATLAB scripts, can be downloaded from http://medeaserver.isa.cnr.it/dacierno/spectracode.htm.

Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition

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Jui-Yuan Su

2015-04-01

Full Text Available In this paper we present a novel unsupervised approach to detecting and segmenting objects as well as their constituent symmetric parts in an image. Traditional unsupervised image segmentation is limited by two obvious deficiencies: the object detection accuracy degrades with the misaligned boundaries between the segmented regions and the target, and pre-learned models are required to group regions into meaningful objects. To tackle these difficulties, the proposed approach aims at incorporating the pair-wise detection of symmetric patches to achieve the goal of segmenting images into symmetric parts. The skeletons of these symmetric parts then provide estimates of the bounding boxes to locate the target objects. Finally, for each detected object, the graphcut-based segmentation algorithm is applied to find its contour. The proposed approach has significant advantages: no a priori object models are used, and multiple objects are detected. To verify the effectiveness of the approach based on the cues that a face part contains an oval shape and skin colors, human objects are extracted from among the detected objects. The detected human objects and their parts are finally tracked across video frames to capture the object part movements for learning the human activity models from video clips. Experimental results show that the proposed method gives good performance on publicly available datasets.

PosQ: Unsupervised Fingerprinting and Visualization of GPS Positioning Quality

DEFF Research Database (Denmark)

Kjærgaard, Mikkel Baun; Weckemann, Kay

. This paper proposes PosQ, a system for unsupervised fingerprinting and visualization of GPS positioning quality. PosQ provides quality maps to position-based applications and visual overlays to users and managers to reveal the positioning quality in a local environment. The system reveals the quality both...

A novel method for the determination of adsorption partition coefficients of minor gases in a shale sample by headspace gas chromatography.

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Zhang, Chun-Yun; Hu, Hui-Chao; Chai, Xin-Sheng; Pan, Lei; Xiao, Xian-Ming

2013-10-04

A novel method has been developed for the determination of adsorption partition coefficient (Kd) of minor gases in shale. The method uses samples of two different sizes (masses) of the same material, from which the partition coefficient of the gas can be determined from two independent headspace gas chromatographic (HS-GC) measurements. The equilibrium for the model gas (ethane) was achieved in 5h at 120°C. The method also involves establishing an equation based on the Kd at higher equilibrium temperature, from which the Kd at lower temperature can be calculated. Although the HS-GC method requires some time and effort, it is simpler and quicker than the isothermal adsorption method that is in widespread use today. As a result, the method is simple and practical and can be a valuable tool for shale gas-related research and applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Inside-sediment partitioning of PAH, PCB and organochlorine compounds and inferences on sampling and normalization methods

International Nuclear Information System (INIS)

Opel, Oliver; Palm, Wolf-Ulrich; Steffen, Dieter; Ruck, Wolfgang K.L.

2011-01-01

Comparability of sediment analyses for semivolatile organic substances is still low. Neither screening of the sediments nor organic-carbon based normalization is sufficient to obtain comparable results. We are showing the interdependency of grain-size effects with inside-sediment organic-matter distribution for PAH, PCB and organochlorine compounds. Surface sediment samples collected by Van-Veen grab were sieved and analyzed for 16 PAH, 6 PCB and 18 organochlorine pesticides (OCP) as well as organic-matter content. Since bulk concentrations are influenced by grain-size effects themselves, we used a novel normalization method based on the sum of concentrations in the separate grain-size fractions of the sediments. By calculating relative normalized concentrations, it was possible to clearly show underlying mechanisms throughout a heterogeneous set of samples. Furthermore, we were able to show that, for comparability, screening at <125 μm is best suited and can be further improved by additional organic-carbon normalization. - Research highlights: → New method for the comparison of heterogeneous sets of sediment samples. → Assessment of organic pollutants partitioning mechanisms in sediments. → Proposed method for more comparable sediment sampling. - Inside-sediment partitioning mechanisms are shown using a new mathematical approach and discussed in terms of sediment sampling and normalization.

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

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Youkyung Han

2017-01-01

Full Text Available Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

Two modified symplectic partitioned Runge-Kutta methods for solving the elastic wave equation

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Su, Bo; Tuo, Xianguo; Xu, Ling

2017-08-01

Based on a modified strategy, two modified symplectic partitioned Runge-Kutta (PRK) methods are proposed for the temporal discretization of the elastic wave equation. The two symplectic schemes are similar in form but are different in nature. After the spatial discretization of the elastic wave equation, the ordinary Hamiltonian formulation for the elastic wave equation is presented. The PRK scheme is then applied for time integration. An additional term associated with spatial discretization is inserted into the different stages of the PRK scheme. Theoretical analyses are conducted to evaluate the numerical dispersion and stability of the two novel PRK methods. A finite difference method is used to approximate the spatial derivatives since the two schemes are independent of the spatial discretization technique used. The numerical solutions computed by the two new schemes are compared with those computed by a conventional symplectic PRK. The numerical results, which verify the new method, are superior to those generated by traditional conventional methods in seismic wave modeling.

Towards Statistical Unsupervised Online Learning for Music Listening with Hearing Devices

DEFF Research Database (Denmark)

Purwins, Hendrik; Marchini, Marco; Marxer, Richard

of sounds into phonetic/instrument categories and learning of instrument event sequences is performed jointly using a Hierarchical Dirichlet Process Hidden Markov Model. Whereas machines often learn by processing a large data base and subsequently updating parameters of the algorithm, humans learn...... and their respective transition counts. We propose to use online learning for the co-evolution of both CI user and machine in (re-)learning musical language. [1] Marco Marchini and Hendrik Purwins. Unsupervised analysis and generation of audio percussion sequences. In International Symposium on Computer Music Modeling...... categories) as well as the temporal context horizon (e.g. storing up to 2-note sequences or up to 10-note sequences) is adaptable. The framework in [1] is based on two cognitively plausible principles: unsupervised learning and statistical learning. Opposed to supervised learning in primary school children...

Integrating the Supervised Information into Unsupervised Learning

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Ping Ling

2013-01-01

Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

Unsupervised grammar induction of clinical report sublanguage

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Kate Rohit J

2012-10-01

Full Text Available Abstract Background Clinical reports are written using a subset of natural language while employing many domain-specific terms; such a language is also known as a sublanguage for a scientific or a technical domain. Different genres of clinical reports use different sublaguages, and in addition, different medical facilities use different medical language conventions. This makes supervised training of a parser for clinical sentences very difficult as it would require expensive annotation effort to adapt to every type of clinical text. Methods In this paper, we present an unsupervised method which automatically induces a grammar and a parser for the sublanguage of a given genre of clinical reports from a corpus with no annotations. In order to capture sentence structures specific to clinical domains, the grammar is induced in terms of semantic classes of clinical terms in addition to part-of-speech tags. Our method induces grammar by minimizing the combined encoding cost of the grammar and the corresponding sentence derivations. The probabilities for the productions of the induced grammar are then learned from the unannotated corpus using an instance of the expectation-maximization algorithm. Results Our experiments show that the induced grammar is able to parse novel sentences. Using a dataset of discharge summary sentences with no annotations, our method obtains 60.5% F-measure for parse-bracketing on sentences of maximum length 10. By varying a parameter, the method can induce a range of grammars, from very specific to very general, and obtains the best performance in between the two extremes.

Partition functions in even dimensional AdS via quasinormal mode methods

International Nuclear Information System (INIS)

Keeler, Cynthia; Ng, Gim Seng

2014-01-01

In this note, we calculate the one-loop determinant for a massive scalar (with conformal dimension Δ) in even-dimensional AdS d+1 space, using the quasinormal mode method developed in http://dx.doi.org/10.1088/0264-9381/27/12/125001 by Denef, Hartnoll, and Sachdev. Working first in two dimensions on the related Euclidean hyperbolic plane H 2 , we find a series of zero modes for negative real values of Δ whose presence indicates a series of poles in the one-loop partition function Z(Δ) in the Δ complex plane; these poles contribute temperature-independent terms to the thermal AdS partition function computed in http://dx.doi.org/10.1088/0264-9381/27/12/125001. Our results match those in a series of papers by Camporesi and Higuchi, as well as Gopakumar et al. http://dx.doi.org/10.1007/JHEP11(2011)010 and Banerjee et al. http://dx.doi.org/10.1007/JHEP03(2011)147. We additionally examine the meaning of these zero modes, finding that they Wick-rotate to quasinormal modes of the AdS 2 black hole. They are also interpretable as matrix elements of the discrete series representations of SO(2,1) in the space of smooth functions on S 1 . We generalize our results to general even dimensional AdS 2n , again finding a series of zero modes which are related to discrete series representations of SO(2n,1), the motion group of H 2n .

Supervised and Unsupervised Speaker Adaptation in the NIST 2005 Speaker Recognition Evaluation

National Research Council Canada - National Science Library

Hansen, Eric G; Slyh, Raymond E; Anderson, Timothy R

2006-01-01

Starting in 2004, the annual NIST Speaker Recognition Evaluation (SRE) has added an optional unsupervised speaker adaptation track where test files are processed sequentially and one may update the target model...

Unsupervised feature learning for autonomous rock image classification

Science.gov (United States)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Countering oversegmentation in partitioning-based connectivities

NARCIS (Netherlands)

Ouzounis, Georgios K.; Wilkinson, Michael H.F.

2005-01-01

A new theoretical development is presented for handling the over-segmentation problem in partitioning-based connected openings. The definition we propose treats singletons generated with the earlier method, as elements of a larger connected component. Unlike the existing formalism, this new method

High-throughput determination of octanol/water partition coefficients using a shake-flask method and novel two-phase solvent system.

Science.gov (United States)

Morikawa, Go; Suzuka, Chihiro; Shoji, Atsushi; Shibusawa, Yoichi; Yanagida, Akio

2016-01-05

A high-throughput method for determining the octanol/water partition coefficient (P(o/w)) of a large variety of compounds exhibiting a wide range in hydrophobicity was established. The method combines a simple shake-flask method with a novel two-phase solvent system comprising an acetonitrile-phosphate buffer (0.1 M, pH 7.4)-1-octanol (25:25:4, v/v/v; AN system). The AN system partition coefficients (K(AN)) of 51 standard compounds for which log P(o/w) (at pH 7.4; log D) values had been reported were determined by single two-phase partitioning in test tubes, followed by measurement of the solute concentration in both phases using an automatic flow injection-ultraviolet detection system. The log K(AN) values were closely related to reported log D values, and the relationship could be expressed by the following linear regression equation: log D=2.8630 log K(AN) -0.1497(n=51). The relationship reveals that log D values (+8 to -8) for a large variety of highly hydrophobic and/or hydrophilic compounds can be estimated indirectly from the narrow range of log K(AN) values (+3 to -3) determined using the present method. Furthermore, log K(AN) values for highly polar compounds for which no log D values have been reported, such as amino acids, peptides, proteins, nucleosides, and nucleotides, can be estimated using the present method. The wide-ranging log D values (+5.9 to -7.5) of these molecules were estimated for the first time from their log K(AN) values and the above regression equation. Copyright © 2015 Elsevier B.V. All rights reserved.

Many-body formalism for fermions: The partition function

Science.gov (United States)

Watson, D. K.

2017-09-01

The partition function, a fundamental tenet in statistical thermodynamics, contains in principle all thermodynamic information about a system. It encapsulates both microscopic information through the quantum energy levels and statistical information from the partitioning of the particles among the available energy levels. For identical particles, this statistical accounting is complicated by the symmetry requirements of the allowed quantum states. In particular, for Fermi systems, the enforcement of the Pauli principle is typically a numerically demanding task, responsible for much of the cost of the calculations. The interplay of these three elements—the structure of the many-body spectrum, the statistical partitioning of the N particles among the available levels, and the enforcement of the Pauli principle—drives the behavior of mesoscopic and macroscopic Fermi systems. In this paper, we develop an approach for the determination of the partition function, a numerically difficult task, for systems of strongly interacting identical fermions and apply it to a model system of harmonically confined, harmonically interacting fermions. This approach uses a recently introduced many-body method that is an extension of the symmetry-invariant perturbation method (SPT) originally developed for bosons. It uses group theory and graphical techniques to avoid the heavy computational demands of conventional many-body methods which typically scale exponentially with the number of particles. The SPT application of the Pauli principle is trivial to implement since it is done "on paper" by imposing restrictions on the normal-mode quantum numbers at first order in the perturbation. The method is applied through first order and represents an extension of the SPT method to excited states. Our method of determining the partition function and various thermodynamic quantities is accurate and efficient and has the potential to yield interesting insight into the role played by the Pauli

Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction.

Science.gov (United States)

Nie, Feiping; Xu, Dong; Tsang, Ivor Wai-Hung; Zhang, Changshui

2010-07-01

We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F(0) = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F(0). Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.

Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

Science.gov (United States)

Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results

Machine learning in APOGEE. Unsupervised spectral classification with K-means

Science.gov (United States)

Garcia-Dias, Rafael; Allende Prieto, Carlos; Sánchez Almeida, Jorge; Ordovás-Pascual, Ignacio

2018-05-01

Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic Evolution Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra, which is perfect for testing such alternatives. Aims: Our research applies an unsupervised classification scheme based on K-means to the massive APOGEE data set. We explore whether the data are amenable to classification into discrete classes. Methods: We apply the K-means algorithm to 153 847 high resolution spectra (R ≈ 22 500). We discuss the main virtues and weaknesses of the algorithm, as well as our choice of parameters. Results: We show that a classification based on normalised spectra captures the variations in stellar atmospheric parameters, chemical abundances, and rotational velocity, among other factors. The algorithm is able to separate the bulge and halo populations, and distinguish dwarfs, sub-giants, RC, and RGB stars. However, a discrete classification in flux space does not result in a neat organisation in the parameters' space. Furthermore, the lack of obvious groups in flux space causes the results to be fairly sensitive to the initialisation, and disrupts the efficiency of commonly-used methods to select the optimal number of clusters. Our classification is publicly available, including extensive online material associated with the APOGEE Data Release 12 (DR12). Conclusions: Our description of the APOGEE database can help greatly with the identification of specific types of targets for various applications. We find a lack of obvious groups in flux space, and identify limitations of the K-means algorithm in dealing with this kind of data. Full Tables B.1-B.4 are only available at the CDS via

Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

Science.gov (United States)

Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

2017-12-01

The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

Choosing the best partition of the output from a large-scale simulation

Energy Technology Data Exchange (ETDEWEB)

Challacombe, Chelsea Jordan [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Casleton, Emily Michele [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-26

Data partitioning becomes necessary when a large-scale simulation produces more data than can be feasibly stored. The goal is to partition the data, typically so that every element belongs to one and only one partition, and store summary information about the partition, either a representative value plus an estimate of the error or a distribution. Once the partitions are determined and the summary information stored, the raw data is discarded. This process can be performed in-situ; meaning while the simulation is running. When creating the partitions there are many decisions that researchers must make. For instance, how to determine once an adequate number of partitions have been created, how are the partitions created with respect to dividing the data, or how many variables should be considered simultaneously. In addition, decisions must be made for how to summarize the information within each partition. Because of the combinatorial number of possible ways to partition and summarize the data, a method of comparing the different possibilities will help guide researchers into choosing a good partitioning and summarization scheme for their application.

Perceptual approach for unsupervised digital color restoration of cinematographic archives

Science.gov (United States)

Chambah, Majed; Rizzi, Alessandro; Gatta, Carlo; Besserer, Bernard; Marini, Daniele

2003-01-01

The cinematographic archives represent an important part of our collective memory. We present in this paper some advances in automating the color fading restoration process, especially with regard to the automatic color correction technique. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. There are some advantages in a perceptual approach: mainly its robustness and its local filtering properties, that lead to more effective results. The resulting technique, is not just an application of ACE on movie images, but an enhancement of ACE principles to meet the requirements in the digital film restoration field. The presented preliminary results are satisfying and promising.

Development of a high-order finite volume method with multiblock partition techniques

Directory of Open Access Journals (Sweden)

E. M. Lemos

2012-03-01

Full Text Available This work deals with a new numerical methodology to solve the Navier-Stokes equations based on a finite volume method applied to structured meshes with co-located grids. High-order schemes used to approximate advective, diffusive and non-linear terms, connected with multiblock partition techniques, are the main contributions of this paper. Combination of these two techniques resulted in a computer code that involves high accuracy due the high-order schemes and great flexibility to generate locally refined meshes based on the multiblock approach. This computer code has been able to obtain results with higher or equal accuracy in comparison with results obtained using classical procedures, with considerably less computational effort.

Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers.

Science.gov (United States)

Moon, Myungjin; Nakai, Kenta

2018-04-01

Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box-Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.

Estimation of octanol/water partition coefficient and aqueous solubility of environmental chemicals using molecular fingerprints and machine learning methods

Science.gov (United States)

Octanol/water partition coefficient (logP) and aqueous solubility (logS) are two important parameters in pharmacology and toxicology studies, and experimental measurements are usually time-consuming and expensive. In the present research, novel methods are presented for the estim...

Unsupervised Learning of Spatiotemporal Features by Video Completion

OpenAIRE

Nallabolu, Adithya Reddy

2017-01-01

In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per...

Unsupervised grammar induction of clinical report sublanguage.

Science.gov (United States)

Kate, Rohit J

2012-10-05

Clinical reports are written using a subset of natural language while employing many domain-specific terms; such a language is also known as a sublanguage for a scientific or a technical domain. Different genres of clinical reports use different sublaguages, and in addition, different medical facilities use different medical language conventions. This makes supervised training of a parser for clinical sentences very difficult as it would require expensive annotation effort to adapt to every type of clinical text. In this paper, we present an unsupervised method which automatically induces a grammar and a parser for the sublanguage of a given genre of clinical reports from a corpus with no annotations. In order to capture sentence structures specific to clinical domains, the grammar is induced in terms of semantic classes of clinical terms in addition to part-of-speech tags. Our method induces grammar by minimizing the combined encoding cost of the grammar and the corresponding sentence derivations. The probabilities for the productions of the induced grammar are then learned from the unannotated corpus using an instance of the expectation-maximization algorithm. Our experiments show that the induced grammar is able to parse novel sentences. Using a dataset of discharge summary sentences with no annotations, our method obtains 60.5% F-measure for parse-bracketing on sentences of maximum length 10. By varying a parameter, the method can induce a range of grammars, from very specific to very general, and obtains the best performance in between the two extremes.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

Science.gov (United States)

Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

2017-11-01

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

Science.gov (United States)

Hu, Wei; Lv, Jiancheng; Liu, Dongbo; Chen, Yao

2018-04-01

Cardiovascular disease seriously threatens the health of many people. It is usually diagnosed during cardiac auscultation, which is a fast and efficient method of cardiovascular disease diagnosis. In recent years, deep learning approach using unsupervised learning has made significant breakthroughs in many fields. However, to our knowledge, deep learning has not yet been used for heart sound classification. In this paper, we first use the average Shannon energy to extract the envelope of the heart sounds, then find the highest point of S1 to extract the cardiac cycle. We convert the time-domain signals of the cardiac cycle into spectrograms and apply principal component analysis whitening to reduce the dimensionality of the spectrogram. Finally, we apply a two-layer autoencoder to extract the features of the spectrogram. The experimental results demonstrate that the features from the autoencoder are suitable for heart sound classification.

Safety-Critical Partitioned Software Architecture: A Partitioned Software Architecture for Robotic

Science.gov (United States)

Horvath, Greg; Chung, Seung H.; Cilloniz-Bicchi, Ferner

2011-01-01

The flight software on virtually every mission currently managed by JPL has several major flaws that make it vulnerable to potentially fatal software defects. Many of these problems can be addressed by recently developed partitioned operating systems (OS). JPL has avoided adopting a partitioned operating system on its flight missions, primarily because doing so would require significant changes in flight software design, and the risks associated with changes of that magnitude cannot be accepted by an active flight project. The choice of a partitioned OS can have a dramatic effect on the overall system and software architecture, allowing for realization of benefits far beyond the concerns typically associated with the choice of OS. Specifically, we believe that a partitioned operating system, when coupled with an appropriate architecture, can provide a strong infrastructure for developing systems for which reusability, modifiability, testability, and reliability are essential qualities. By adopting a partitioned OS, projects can gain benefits throughout the entire development lifecycle, from requirements and design, all the way to implementation, testing, and operations.

Unsupervised categorization with individuals diagnosed as having moderate traumatic brain injury: Over-selective responding.

Science.gov (United States)

Edwards, Darren J; Wood, Rodger

2016-01-01

This study explored over-selectivity (executive dysfunction) using a standard unsupervised categorization task. Over-selectivity has been demonstrated using supervised categorization procedures (where training is given); however, little has been done in the way of unsupervised categorization (without training). A standard unsupervised categorization task was used to assess levels of over-selectivity in a traumatic brain injury (TBI) population. Individuals with TBI were selected from the Tertiary Traumatic Brain Injury Clinic at Swansea University and were asked to categorize two-dimensional items (pictures on cards), into groups that they felt were most intuitive, and without any learning (feedback from experimenter). This was compared against categories made by a control group for the same task. The findings of this study demonstrate that individuals with TBI had deficits for both easy and difficult categorization sets, as indicated by a larger amount of one-dimensional sorting compared to control participants. Deficits were significantly greater for the easy condition. The implications of these findings are discussed in the context of over-selectivity, and the processes that underlie this deficit. Also, the implications for using this procedure as a screening measure for over-selectivity in TBI are discussed.

LHCb: Optimising query execution time in LHCb Bookkeeping System using partition pruning and partition wise joins

CERN Multimedia

Mathe, Z

2013-01-01

The LHCb experiment produces a huge amount of data which has associated metadata such as run number, data taking condition (detector status when the data was taken), simulation condition, etc. The data are stored in files, replicated on the Computing Grid around the world. The LHCb Bookkeeping System provides methods for retrieving datasets based on their metadata. The metadata is stored in a hybrid database model, which is a mixture of Relational and Hierarchical database models and is based on the Oracle Relational Database Management System (RDBMS). The database access has to be reliable and fast. In order to achieve a high timing performance, the tables are partitioned and the queries are executed in parallel. When we store large amounts of data the partition pruning is essential for database performance, because it reduces the amount of data retrieved from the disk and optimises the resource utilisation. This research presented here is focusing on the extended composite partitioning strategy such as rang...

An Unsupervised Online Spike-Sorting Framework.

Science.gov (United States)

Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

2016-08-01

Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

Schinus terebinthifolius countercurrent chromatography (Part III): Method transfer from small countercurrent chromatography column to preparative centrifugal partition chromatography ones as a part of method development.

Science.gov (United States)

das Neves Costa, Fernanda; Hubert, Jane; Borie, Nicolas; Kotland, Alexis; Hewitson, Peter; Ignatova, Svetlana; Renault, Jean-Hugues

2017-03-03

Countercurrent chromatography (CCC) and centrifugal partition chromatography (CPC) are support free liquid-liquid chromatography techniques sharing the same basic principles and features. Method transfer has previously been demonstrated for both techniques but never from one to another. This study aimed to show such a feasibility using fractionation of Schinus terebinthifolius berries dichloromethane extract as a case study. Heptane - ethyl acetate - methanol -water (6:1:6:1, v/v/v/v) was used as solvent system with masticadienonic and 3β-masticadienolic acids as target compounds. The optimized separation methodology previously described in Part I and II, was scaled up from an analytical hydrodynamic CCC column (17.4mL) to preparative hydrostatic CPC instruments (250mL and 303mL) as a part of method development. Flow-rate and sample loading were further optimized on CPC. Mobile phase linear velocity is suggested as a transfer invariant parameter if the CPC column contains sufficient number of partition cells. Copyright © 2017 Elsevier B.V. All rights reserved.

Six weeks of unsupervised Nintendo Wii Fit gaming is effective at improving balance in independent older adults.

Science.gov (United States)

Nicholson, Vaughan Patrick; McKean, Mark; Lowe, John; Fawcett, Christine; Burkett, Brendan

2015-01-01

To determine the effectiveness of unsupervised Nintendo Wii Fit balance training in older adults. Forty-one older adults were recruited from local retirement villages and educational settings to participate in a six-week two-group repeated measures study. The Wii group (n = 19, 75 ± 6 years) undertook 30 min of unsupervised Wii balance gaming three times per week in their retirement village while the comparison group (n = 22, 74 ± 5 years) continued with their usual exercise program. Participants' balance abilities were assessed pre- and postintervention. The Wii Fit group demonstrated significant improvements (P balance, lateral reach (left and right), and gait speed compared with the comparison group. Reported levels of enjoyment following game play increased during the study. Six weeks of unsupervised Wii balance training is an effective modality for improving balance in independent older adults.

Function approximation using combined unsupervised and supervised learning.

Science.gov (United States)

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Directory of Open Access Journals (Sweden)

Muhammad Ali

2017-11-01

Full Text Available Current transformer (CT saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL methods have brought a subversive revolution in the field of artificial intelligence (AI. This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

Axiomatic method of partitions in the theory of Noebeling spaces. I. Improvement of partition connectivity

International Nuclear Information System (INIS)

Ageev, S M

2007-01-01

The Noebeling space N k 2k+1 , a k-dimensional analogue of the Hilbert space, is considered; this is a topologically complete separable (that is, Polish) k-dimensional absolute extensor in dimension k (that is, AE(k)) and a strongly k-universal space. The conjecture that the above-listed properties characterize the Noebeling space N k 2k+1 in an arbitrary finite dimension k is proved. In the first part of the paper a full axiom system of the Noebeling spaces is presented and the problem of the improvement of a partition connectivity is solved on its basis. Bibliography: 29 titles.

Polymers as reference partitioning phase: polymer calibration for an analytically operational approach to quantify multimedia phase partitioning

DEFF Research Database (Denmark)

Gilbert, Dorothea; Witt, Gesine; Smedes, Foppe

2016-01-01

Polymers are increasingly applied for the enrichment of hydrophobic organic chemicals (HOCs) from various types of samples and media in many analytical partitioning-based measuring techniques. We propose using polymers as a reference partitioning phase and introduce polymer-polymer partitioning......-air) and multimedia partition coefficients (lipid-water, air-water) were calculated by applying the new concept of a polymer as reference partitioning phase and by using polymer-polymer partition coefficients as conversion factors. The present study encourages the use of polymer-polymer partition coefficients...

Hashing for Statistics over K-Partitions

DEFF Research Database (Denmark)

Dahlgaard, Soren; Knudsen, Mathias Baek Tejs; Rotenberg, Eva

2015-01-01

In this paper we analyze a hash function for k-partitioning a set into bins, obtaining strong concentration bounds for standard algorithms combining statistics from each bin. This generic method was originally introduced by Flajolet and Martin [FOCS'83] in order to save a factor Ω(k) of time per...... concentration bounds on the most popular applications of k-partitioning similar to those we would get using a truly random hash function. The analysis is very involved and implies several new results of independent interest for both simple and double tabulation, e.g. A simple and efficient construction...

Polyacrylate–water partitioning of biocidal compounds: Enhancing the understanding of biocide partitioning between render and water

DEFF Research Database (Denmark)

Bollmann, Ulla E.; Ou, Yi; Mayer, Philipp

2014-01-01

-N-octylisothiazolinone). The correlation of the polyacrylate-water partition constants with the octanol-water partition constants is significant, but the polyacrylate-water partition constants were predominantly below octanol-water partition constants (Kow). The comparison with render-water distribution constants showed that estimating...

On the Evaluation of Outlier Detection and One-Class Classification Methods

DEFF Research Database (Denmark)

Swersky, Lorne; Marques, Henrique O.; Sander, Jörg

2016-01-01

It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of oneclass classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies ...

Validation of a free software for unsupervised assessment of abdominal fat in MRI.

Science.gov (United States)

Maddalo, Michele; Zorza, Ivan; Zubani, Stefano; Nocivelli, Giorgio; Calandra, Giulio; Soldini, Pierantonio; Mascaro, Lorella; Maroldi, Roberto

2017-05-01

To demonstrate the accuracy of an unsupervised (fully automated) software for fat segmentation in magnetic resonance imaging. The proposed software is a freeware solution developed in ImageJ that enables the quantification of metabolically different adipose tissues in large cohort studies. The lumbar part of the abdomen (19cm in craniocaudal direction, centered in L3) of eleven healthy volunteers (age range: 21-46years, BMI range: 21.7-31.6kg/m 2 ) was examined in a breath hold on expiration with a GE T1 Dixon sequence. Single-slice and volumetric data were considered for each subject. The results of the visceral and subcutaneous adipose tissue assessments obtained by the unsupervised software were compared to supervised segmentations of reference. The associated statistical analysis included Pearson correlations, Bland-Altman plots and volumetric differences (VD % ). Values calculated by the unsupervised software significantly correlated with corresponding supervised segmentations of reference for both subcutaneous adipose tissue - SAT (R=0.9996, psoftware is capable of segmenting the metabolically different adipose tissues with a high degree of accuracy. This free add-on software for ImageJ can easily have a widespread and enable large-scale population studies regarding the adipose tissue and its related diseases. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

Energy Technology Data Exchange (ETDEWEB)

Pinar, Ali [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Wake Forest Univ., Winston-Salem, MA (United States); Ballard, Grey [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mahoney, Michael [Univ. of California, Berkeley, CA (United States)

2018-01-01

Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

Automatic microseismic event picking via unsupervised machine learning

Science.gov (United States)

Chen, Yangkang

2018-01-01

Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

A partition function approximation using elementary symmetric functions.

Directory of Open Access Journals (Sweden)

Ramu Anandakrishnan

Full Text Available In statistical mechanics, the canonical partition function [Formula: see text] can be used to compute equilibrium properties of a physical system. Calculating [Formula: see text] however, is in general computationally intractable, since the computation scales exponentially with the number of particles [Formula: see text] in the system. A commonly used method for approximating equilibrium properties, is the Monte Carlo (MC method. For some problems the MC method converges slowly, requiring a very large number of MC steps. For such problems the computational cost of the Monte Carlo method can be prohibitive. Presented here is a deterministic algorithm - the direct interaction algorithm (DIA - for approximating the canonical partition function [Formula: see text] in [Formula: see text] operations. The DIA approximates the partition function as a combinatorial sum of products known as elementary symmetric functions (ESFs, which can be computed in [Formula: see text] operations. The DIA was used to compute equilibrium properties for the isotropic 2D Ising model, and the accuracy of the DIA was compared to that of the basic Metropolis Monte Carlo method. Our results show that the DIA may be a practical alternative for some problems where the Monte Carlo method converge slowly, and computational speed is a critical constraint, such as for very large systems or web-based applications.

Artificial immune kernel clustering network for unsupervised image segmentation

Institute of Scientific and Technical Information of China (English)

Wenlong Huang; Licheng Jiao

2008-01-01

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach

Directory of Open Access Journals (Sweden)

Sebastian Böttcher

2018-03-01

Full Text Available Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user’s physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods.

Cost efficient CFD simulations: Proper selection of domain partitioning strategies

Science.gov (United States)

Haddadi, Bahram; Jordan, Christian; Harasek, Michael

2017-10-01

Computational Fluid Dynamics (CFD) is one of the most powerful simulation methods, which is used for temporally and spatially resolved solutions of fluid flow, heat transfer, mass transfer, etc. One of the challenges of Computational Fluid Dynamics is the extreme hardware demand. Nowadays super-computers (e.g. High Performance Computing, HPC) featuring multiple CPU cores are applied for solving-the simulation domain is split into partitions for each core. Some of the different methods for partitioning are investigated in this paper. As a practical example, a new open source based solver was utilized for simulating packed bed adsorption, a common separation method within the field of thermal process engineering. Adsorption can for example be applied for removal of trace gases from a gas stream or pure gases production like Hydrogen. For comparing the performance of the partitioning methods, a 60 million cell mesh for a packed bed of spherical adsorbents was created; one second of the adsorption process was simulated. Different partitioning methods available in OpenFOAM® (Scotch, Simple, and Hierarchical) have been used with different numbers of sub-domains. The effect of the different methods and number of processor cores on the simulation speedup and also energy consumption were investigated for two different hardware infrastructures (Vienna Scientific Clusters VSC 2 and VSC 3). As a general recommendation an optimum number of cells per processor core was calculated. Optimized simulation speed, lower energy consumption and consequently the cost effects are reported here.

A new 3-D ray tracing method based on LTI using successive partitioning of cell interfaces and traveltime gradients

Science.gov (United States)

Zhang, Dong; Zhang, Ting-Ting; Zhang, Xiao-Lei; Yang, Yan; Hu, Ying; Qin, Qian-Qing

2013-05-01

We present a new method of three-dimensional (3-D) seismic ray tracing, based on an improvement to the linear traveltime interpolation (LTI) ray tracing algorithm. This new technique involves two separate steps. The first involves a forward calculation based on the LTI method and the dynamic successive partitioning scheme, which is applied to calculate traveltimes on cell boundaries and assumes a wavefront that expands from the source to all grid nodes in the computational domain. We locate several dynamic successive partition points on a cell's surface, the traveltimes of which can be calculated by linear interpolation between the vertices of the cell's boundary. The second is a backward step that uses Fermat's principle and the fact that the ray path is always perpendicular to the wavefront and follows the negative traveltime gradient. In this process, the first-arriving ray path can be traced from the receiver to the source along the negative traveltime gradient, which can be calculated by reconstructing the continuous traveltime field with cubic B-spline interpolation. This new 3-D ray tracing method is compared with the LTI method and the shortest path method (SPM) through a number of numerical experiments. These comparisons show obvious improvements to computed traveltimes and ray paths, both in precision and computational efficiency.

Predicting solute partitioning in lipid bilayers: Free energies and partition coefficients from molecular dynamics simulations and COSMOmic

Science.gov (United States)

Jakobtorweihen, S.; Zuniga, A. Chaides; Ingram, T.; Gerlach, T.; Keil, F. J.; Smirnova, I.

2014-07-01

Quantitative predictions of biomembrane/water partition coefficients are important, as they are a key property in pharmaceutical applications and toxicological studies. Molecular dynamics (MD) simulations are used to calculate free energy profiles for different solutes in lipid bilayers. How to calculate partition coefficients from these profiles is discussed in detail and different definitions of partition coefficients are compared. Importantly, it is shown that the calculated coefficients are in quantitative agreement with experimental results. Furthermore, we compare free energy profiles from MD simulations to profiles obtained by the recent method COSMOmic, which is an extension of the conductor-like screening model for realistic solvation to micelles and biomembranes. The free energy profiles from these molecular methods are in good agreement. Additionally, solute orientations calculated with MD and COSMOmic are compared and again a good agreement is found. Four different solutes are investigated in detail: 4-ethylphenol, propanol, 5-phenylvaleric acid, and dibenz[a,h]anthracene, whereby the latter belongs to the class of polycyclic aromatic hydrocarbons. The convergence of the free energy profiles from biased MD simulations is discussed and the results are shown to be comparable to equilibrium MD simulations. For 5-phenylvaleric acid the influence of the carboxyl group dihedral angle on free energy profiles is analyzed with MD simulations.

Predicting solute partitioning in lipid bilayers: Free energies and partition coefficients from molecular dynamics simulations and COSMOmic

International Nuclear Information System (INIS)

Jakobtorweihen, S.; Ingram, T.; Gerlach, T.; Smirnova, I.; Zuniga, A. Chaides; Keil, F. J.

2014-01-01

Quantitative predictions of biomembrane/water partition coefficients are important, as they are a key property in pharmaceutical applications and toxicological studies. Molecular dynamics (MD) simulations are used to calculate free energy profiles for different solutes in lipid bilayers. How to calculate partition coefficients from these profiles is discussed in detail and different definitions of partition coefficients are compared. Importantly, it is shown that the calculated coefficients are in quantitative agreement with experimental results. Furthermore, we compare free energy profiles from MD simulations to profiles obtained by the recent method COSMOmic, which is an extension of the conductor-like screening model for realistic solvation to micelles and biomembranes. The free energy profiles from these molecular methods are in good agreement. Additionally, solute orientations calculated with MD and COSMOmic are compared and again a good agreement is found. Four different solutes are investigated in detail: 4-ethylphenol, propanol, 5-phenylvaleric acid, and dibenz[a,h]anthracene, whereby the latter belongs to the class of polycyclic aromatic hydrocarbons. The convergence of the free energy profiles from biased MD simulations is discussed and the results are shown to be comparable to equilibrium MD simulations. For 5-phenylvaleric acid the influence of the carboxyl group dihedral angle on free energy profiles is analyzed with MD simulations

The complex formation-partition and partition-association models of solvent extraction of ions

International Nuclear Information System (INIS)

Siekierski, S.

1976-01-01

Two models of the extraction process have been proposed. In the first model it is assumed that the partitioning neutral species is at first formed in the aqueous phase and then transferred into the organic phase. The second model is based on the assumption that equivalent amounts of cations are at first transferred from the aqueous into the organic phase and then associated to form a neutral molecule. The role of the solubility parameter in extraction and the relation between the solubility of liquid organic substances in water and the partition of complexes have been discussed. The extraction of simple complexes and complexes with organic ligands has been discussed using the first model. Partition coefficients have been calculated theoretically and compared with experimental values in some very simple cases. The extraction of ion pairs has been discussed using the partition-association model and the concept of single-ion partition coefficients. (author)

Development of partitioning method: confirmation of behavior of technetium in 4-Group Partitioning Process by a small scale experiment

International Nuclear Information System (INIS)

Morita, Yasuji; Yamaguchi, Isoo; Fujiwara, Takeshi; Kubota, Masumitsu; Mizoguchi, Kenichi

1998-08-01

The separation behavior of Tc in the whole of 4-Group Partitioning Process was examined by a flask-scale experiment using simulated high-level liquid waste containing a macro amount of Tc, in order to confirm the reproducibility of the results obtained in previous studies on the Tc behavior at each step of the process. The 4-Group Partitioning Process consists of pre-treatment step, extraction step with diisodecylphosphoric acid (DIDPA), adsorption step with active carbon or precipitation step by denitration for the separation of Tc and platinum group metals (PGM), and adsorption step with inorganic ion exchangers. The present study deals with the behavior of Tc and other elements at all the above steps and additional step for Tc dissolution from the precipitate formed by the denitration. At the pre-treatment step, the ratio of Tc precipitated was very low (about 0.2%) at both operations of heating-denitration and colloid removal. Tc was not extracted with DIDPA and was contained quantitatively in the raffinate from the extraction step. Batch adsorption with active carbon directly from the raffinate showed that distribution coefficient of Tc was more than 100ml/g, which is high enough for the separation. It also revealed much effect of coexisting Mo on the Tc adsorption. At the precipitation step by denitration, 98.2% of Tc were precipitated. At the Tc dissolution from the precipitate with H 2 O 2 , 84.2% of Tc were selectively dissolved in a single operation. Tc was not adsorbed with inorganic ion exchangers. From these results, composition of Tc product from the partitioning process was estimated. The weight ratio of Tc in the Tc product can be increased to about 50% at least. Main contaminating elements are Cr, Ni, Sr, Ba, Mo and Pd. Process optimization to decrease their contamination should be performed in a next study. (J.P.N.)

Unsupervised Language Acquisition

Science.gov (United States)

de Marcken, Carl

1996-11-01

This thesis presents a computational theory of unsupervised language acquisition, precisely defining procedures for learning language from ordinary spoken or written utterances, with no explicit help from a teacher. The theory is based heavily on concepts borrowed from machine learning and statistical estimation. In particular, learning takes place by fitting a stochastic, generative model of language to the evidence. Much of the thesis is devoted to explaining conditions that must hold for this general learning strategy to arrive at linguistically desirable grammars. The thesis introduces a variety of technical innovations, among them a common representation for evidence and grammars, and a learning strategy that separates the ``content'' of linguistic parameters from their representation. Algorithms based on it suffer from few of the search problems that have plagued other computational approaches to language acquisition. The theory has been tested on problems of learning vocabularies and grammars from unsegmented text and continuous speech, and mappings between sound and representations of meaning. It performs extremely well on various objective criteria, acquiring knowledge that causes it to assign almost exactly the same structure to utterances as humans do. This work has application to data compression, language modeling, speech recognition, machine translation, information retrieval, and other tasks that rely on either structural or stochastic descriptions of language.

Estimating extinction using unsupervised machine learning

Science.gov (United States)

Meingast, Stefan; Lombardi, Marco; Alves, João

2017-05-01

Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, Pnicer, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction toward single sources is determined by fitting Gaussian mixture models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities, rather than a single value. Pnicer effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, Pnicer offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. The Pnicer software package also offers access to the well-established Nicer technique in a simple unified interface and is capable of building extinction maps including the Nicest correction for cloud substructure. Pnicer is offered to the community as an open-source software solution and is entirely written in Python.

Quantum Mechanical Single Molecule Partition Function from PathIntegral Monte Carlo Simulations

Energy Technology Data Exchange (ETDEWEB)

Chempath, Shaji; Bell, Alexis T.; Predescu, Cristian

2006-10-01

An algorithm for calculating the partition function of a molecule with the path integral Monte Carlo method is presented. Staged thermodynamic perturbation with respect to a reference harmonic potential is utilized to evaluate the ratio of partition functions. Parallel tempering and a new Monte Carlo estimator for the ratio of partition functions are implemented here to achieve well converged simulations that give an accuracy of 0.04 kcal/mol in the reported free energies. The method is applied to various test systems, including a catalytic system composed of 18 atoms. Absolute free energies calculated by this method lead to corrections as large as 2.6 kcal/mol at 300 K for some of the examples presented.

Unsupervised progressive elastic band exercises for frail geriatric inpatients objectively monitored by new exercise-integrated technology-a feasibility trial with an embedded qualitative study

DEFF Research Database (Denmark)

Rathleff, C R; Bandholm, T; Spaich, E G

2017-01-01

feasibility and acceptability of an unsupervised progressive strength training intervention monitored by BandCizer for frail geriatric inpatients. Methods: This feasibility trial included 15 frail inpatients at a geriatric ward. At hospitalization, the patients were prescribed two elastic band exercises......Background: Frailty is a serious condition frequently present in geriatric inpatients that potentially causes serious adverse events. Strength training is acknowledged as a means of preventing or delaying frailty and loss of function in these patients. However, limited hospital resources challenge...... the amount of supervised training, and unsupervised training could possibly supplement supervised training thereby increasing the total exercise dose during admission. A new valid and reliable technology, the BandCizer, objectively measures the exact training dosage performed. The purpose was to investigate...

A passive dosing method to determine fugacity capacities and partitioning properties of leaves

DEFF Research Database (Denmark)

Bolinius, Damien Johann; Macleod, Matthew; McLachlan, Michael S.

2016-01-01

The capacity of leaves to take up chemicals from the atmosphere and water influences how contaminants are transferred into food webs and soil. We provide a proof of concept of a passive dosing method to measure leaf/polydimethylsiloxane partition ratios (Kleaf/PDMS) for intact leaves, using...... polychlorinated biphenyls (PCBs) as model chemicals. Rhododendron leaves held in contact with PCB-loaded PDMS reached between 76 and 99% of equilibrium within 4 days for PCBs 3, 4, 28, 52, 101, 118, 138 and 180. Equilibrium Kleaf/PDMS extrapolated from the uptake kinetics measured over 4 days ranged from 0...... the variability in sorptive capacities of leaves that would improve descriptions of uptake of chemicals by leaves in multimedia fate models....

Unsupervised Learning of Action Primitives

DEFF Research Database (Denmark)

Baby, Sanmohan; Krüger, Volker; Kragic, Danica

2010-01-01

and scale, the use of the object can provide a strong invariant for the detection of motion primitives. In this paper we propose an unsupervised learning approach for action primitives that makes use of the human movements as well as the object state changes. We group actions according to the changes......Action representation is a key issue in imitation learning for humanoids. With the recent finding of mirror neurons there has been a growing interest in expressing actions as a combination meaningful subparts called primitives. Primitives could be thought of as an alphabet for the human actions....... In this paper we observe that human actions and objects can be seen as being intertwined: we can interpret actions from the way the body parts are moving, but as well from how their effect on the involved object. While human movements can look vastly different even under minor changes in location, orientation...

Mesh Partitioning Algorithm Based on Parallel Finite Element Analysis and Its Actualization

Directory of Open Access Journals (Sweden)

Lei Zhang

2013-01-01

Full Text Available In parallel computing based on finite element analysis, domain decomposition is a key technique for its preprocessing. Generally, a domain decomposition of a mesh can be realized through partitioning of a graph which is converted from a finite element mesh. This paper discusses the method for graph partitioning and the way to actualize mesh partitioning. Relevant softwares are introduced, and the data structure and key functions of Metis and ParMetis are introduced. The writing, compiling, and testing of the mesh partitioning interface program based on these key functions are performed. The results indicate some objective law and characteristics to guide the users who use the graph partitioning algorithm and software to write PFEM program, and ideal partitioning effects can be achieved by actualizing mesh partitioning through the program. The interface program can also be used directly by the engineering researchers as a module of the PFEM software. So that it can reduce the application of the threshold of graph partitioning algorithm, improve the calculation efficiency, and promote the application of graph theory and parallel computing.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

Science.gov (United States)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

A new modeling and solution approach for the number partitioning problem

Directory of Open Access Journals (Sweden)

Bahram Alidaee

2005-01-01

Full Text Available The number partitioning problem has proven to be a challenging problem for both exact and heuristic solution methods. We present a new modeling and solution approach that consists of recasting the problem as an unconstrained quadratic binary program that can be solved by efficient metaheuristic methods. Our approach readily accommodates both the common two-subset partition case as well as the more general case of multiple subsets. Preliminary computational experience is presented illustrating the attractiveness of the method.

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

Czech Academy of Sciences Publication Activity Database

Scarpa, G.; Gaetano, R.; Haindl, Michal; Zerubia, J.

2009-01-01

Roč. 18, č. 8 (2009), s. 1830-1843 ISSN 1057-7149 R&D Projects: GA ČR GA102/08/0593 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : Classification * texture analysis * segmentation * hierarchical image models * Markov process Subject RIV: BD - Theory of Information Impact factor: 2.848, year: 2009 http://library.utia.cas.cz/separaty/2009/RO/haindl-hierarchical multiple markov chain model for unsupervised texture segmentation.pdf

Data mining with unsupervised clustering using photonic micro-ring resonators

Science.gov (United States)

McAulay, Alastair D.

2013-09-01

Data is commonly moved through optical fiber in modern data centers and may be stored optically. We propose an optical method of data mining for future data centers to enhance performance. For example, in clustering, a form of unsupervised learning, we propose that parameters corresponding to information in a database are converted from analog values to frequencies, as in the brain's neurons, where similar data will have close frequencies. We describe the Wilson-Cowan model for oscillating neurons. In optics we implement the frequencies with micro ring resonators. Due to the influence of weak coupling, a group of resonators will form clusters of similar frequencies that will indicate the desired parameters having close relations. Fewer clusters are formed as clustering proceeds, which allows the creation of a tree showing topics of importance and their relationships in the database. The tree can be used for instance to target advertising and for planning.

Unsupervised color image segmentation using a lattice algebra clustering technique

Science.gov (United States)

Urcid, Gonzalo; Ritter, Gerhard X.

2011-08-01

In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Development of long-lived radionuclide partitioning technology

International Nuclear Information System (INIS)

Lee, Eil Hee; Kwon, S. G.; Yang, H. B.

2001-04-01

This project was aimed at the development of an optimal process that could get recovery yields of 99% for Am and Np and 90% for Tc from a simulated radioactive waste and the improvements of unit processes. The performed works are summarized, as follows. 1) The design and the establishment of a laboratory-scale partitioning process were accomplished, and the interfacial conditions between each unit process were determined. An optimal flow diagram for long-lived radionuclide partitioning process was suggested. 2) In improvements of unit processes, a) Behaviors of the co-extraction and sequential separation for residual U, Np and Tc(/Re) by chemical and electrochemical methods were examined. b) Conditions for co-extraction of Am/RE, and selective stripping of Am with metal containing extractant and a mixed extractant were decided. c) Characteristics of adsorption and elution by ion exchange chromatography and extraction chromatography methods were analysed. d) The simulation codes for long-lived radionuclide partitioning were gathered. and reaction equations were numerically formulated. 3) An existing γ-lead cell was modified the α-γ cells for treatment of long-lived radioactive materials. 4) As the applications of new separation technologies, a) Behaviors of photo reductive precipitation for Am/RE were investigated, b) Conditions for selective extraction and stripping of Am with pyridine series extractants were established. All results will be used as the fundamental data for establishment of partitioning process and radiochemical test of long-lived radionuclides recovery technology to be performed in the next stage

An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

Science.gov (United States)

Bhardwaj, Kaushal; Patra, Swarnajyoti

2018-04-01

Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

Science.gov (United States)

Hunt, B.; Sheppard, D. G.; Wetterer, C. J.

There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.

"K"-Balance Partitioning: An Exact Method with Applications to Generalized Structural Balance and Other Psychological Contexts

Science.gov (United States)

Brusco, Michael; Steinley, Douglas

2010-01-01

Structural balance theory (SBT) has maintained a venerable status in the psychological literature for more than 5 decades. One important problem pertaining to SBT is the approximation of structural or generalized balance via the partitioning of the vertices of a signed graph into "K" clusters. This "K"-balance partitioning problem also has more…

Canonical partition functions: ideal quantum gases, interacting classical gases, and interacting quantum gases

Science.gov (United States)

Zhou, Chi-Chun; Dai, Wu-Sheng

2018-02-01

In statistical mechanics, for a system with a fixed number of particles, e.g. a finite-size system, strictly speaking, the thermodynamic quantity needs to be calculated in the canonical ensemble. Nevertheless, the calculation of the canonical partition function is difficult. In this paper, based on the mathematical theory of the symmetric function, we suggest a method for the calculation of the canonical partition function of ideal quantum gases, including ideal Bose, Fermi, and Gentile gases. Moreover, we express the canonical partition functions of interacting classical and quantum gases given by the classical and quantum cluster expansion methods in terms of the Bell polynomial in mathematics. The virial coefficients of ideal Bose, Fermi, and Gentile gases are calculated from the exact canonical partition function. The virial coefficients of interacting classical and quantum gases are calculated from the canonical partition function by using the expansion of the Bell polynomial, rather than calculated from the grand canonical potential.

EUROPART: an European integrated project on actinide partitioning

International Nuclear Information System (INIS)

Madic, C.; Baron, P.; Hudson, M.J.

2006-01-01

Full text of publication follows: The EUROPART project is a scientific integrated project between 24 European partners, from 10 countries, mostly funded by the European Community within the FP6, together with CRIEPI from Japan and ANSTO from Australia. EUROPART aims at developing chemical partitioning processes for the so-called minor actinides (MA) contained in nuclear wastes, i.e. from Am to Cf. In the case of the treatment of dedicated spent fuels or targets, the actinides to be separated also include U, Pu and Np. The techniques considered for the separation of these radionuclides belong to the fields of hydrometallurgy and pyrometallurgy, as in the previous European FP5 programs named PARTNEW, CALIXPART and PYROREP, respectively. The two main axes of research within EUROPART are: 1/ the partitioning of MA (from Am to Cf) from wastes issuing from the reprocessing of high burn-up UOX fuels and multi-recycled MOX fuels, 2/ the partitioning of the whole actinide family of elements for recycling, as an option for advanced dedicated fuel cycles (this work will be connected to the studies to be performed within the EUROTRANS European integrated project). In hydrometallurgy, the research is organized in five Work Packages (WP). Four are dedicated to the study of partitioning methods mainly based on the use of solvent extraction methods and of solid extractants, one WP is dedicated to the development of actinide co-conversion methods for fuel or target preparations. The research in pyrometallurgy is organized into four WPs, listed hereafter: (i) study of the basic chemistry of transuranium elements and of some fission products in molten salts (chlorides, fluorides), (ii) development of actinide partitioning methods, (iii) study of the conditioning of the salt wastes, (iv) system studies. Moreover, a strong management team is concerned not only with the technical and financial issues arising from EUROPART, but also with information, communication and benefits for Europe

Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks

OpenAIRE

Zhelezniak, Vitalii; Busbridge, Dan; Shen, April; Smith, Samuel L.; Hammerla, Nils Y.

2018-01-01

Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space, in which semantically close symbols are mapped to representations that are close under a similarity measure induced by the model's objective function. In addition, we present a straightforward procedure that, without any retraining or architectura...

Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System

Science.gov (United States)

Lucena, Caroline Vieira; Lacerda, Marcelo; Caldas, Rafael; De Lima Neto, Fernando Buarque

2018-01-01

There is a direct relationship between the prevalence of musculoskeletal disorders of the temporomandibular joint and orofacial disorders. A well-elaborated analysis of the jaw movements provides relevant information for healthcare professionals to conclude their diagnosis. Different approaches have been explored to track jaw movements such that the mastication analysis is getting less subjective; however, all methods are still highly subjective, and the quality of the assessments depends much on the experience of the health professional. In this paper, an accurate and non-invasive method based on a commercial low-cost inertial sensor (MPU6050) to measure jaw movements is proposed. The jaw-movement feature values are compared to the obtained with clinical analysis, showing no statistically significant difference between both methods. Moreover, We propose to use unsupervised paradigm approaches to cluster mastication patterns of healthy subjects and simulated patients with facial trauma. Two techniques were used in this paper to instantiate the method: Kohonen’s Self-Organizing Maps and K-Means Clustering. Both algorithms have excellent performances to process jaw-movements data, showing encouraging results and potential to bring a full assessment of the masticatory function. The proposed method can be applied in real-time providing relevant dynamic information for health-care professionals. PMID:29651365

Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System.

Science.gov (United States)

Lucena, Caroline Vieira; Lacerda, Marcelo; Caldas, Rafael; De Lima Neto, Fernando Buarque; Rativa, Diego

2018-01-01

There is a direct relationship between the prevalence of musculoskeletal disorders of the temporomandibular joint and orofacial disorders. A well-elaborated analysis of the jaw movements provides relevant information for healthcare professionals to conclude their diagnosis. Different approaches have been explored to track jaw movements such that the mastication analysis is getting less subjective; however, all methods are still highly subjective, and the quality of the assessments depends much on the experience of the health professional. In this paper, an accurate and non-invasive method based on a commercial low-cost inertial sensor (MPU6050) to measure jaw movements is proposed. The jaw-movement feature values are compared to the obtained with clinical analysis, showing no statistically significant difference between both methods. Moreover, We propose to use unsupervised paradigm approaches to cluster mastication patterns of healthy subjects and simulated patients with facial trauma. Two techniques were used in this paper to instantiate the method: Kohonen's Self-Organizing Maps and K-Means Clustering. Both algorithms have excellent performances to process jaw-movements data, showing encouraging results and potential to bring a full assessment of the masticatory function. The proposed method can be applied in real-time providing relevant dynamic information for health-care professionals.

Incentives and recent proposals for partitioning and transmutation in the United States

International Nuclear Information System (INIS)

Donovan, T.J.

1995-05-01

Partitioning and transmutation (P-T) is perhaps the most elegant means of high level waste disposal. Currently, the cost of fuel obtained from reprocessing spent fuel exceeds the cost of fuel obtained by mining. This has resulted in the once through fuel cycle dominating the US nuclear industry. Despite this fact P-T continues to be examined and debated by the US as well as abroad. The US first seriously considered P-T between approximately 1976 and 1982 but rejected the concept in favor of reprocessing. More recently, since about 1989, as a result of the once through fuel cycle and the growing problems of waste disposal, studies concerning P-T have resumed. This essay will seek to outline the incentives and goals of partitioning and transmutation as it would apply to the disposal of spent fuel in the US. Recent proposals by various US national laboratories for implementing partitioning and transmutation as a high level waste management and disposal device will also be discussed. The review will seek to examine the technical concepts utilized in each of the proposals and their feasibility. The major focus of this essay will be the transmutation methods themselves, while the partitioning methods will be discussed only briefly. This is because of the fact that partitioning methods fall under reprocessing as an already fairly well established and accepted technology while feasible methods for transmutation are still being advanced

Unsupervised sub-categorization for object detection: fInding cars from a driving vehicle

NARCIS (Netherlands)

Wijnhoven, R.G.J.; With, de P.H.N.

2011-01-01

We present a novel algorithm for unsupervised subcategorization of an object class, in the context of object detection. Dividing the detection problem into smaller subproblems simplifies the object vs. background classification. The algorithm uses an iterative split-and-merge procedure and uses both

Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents

Directory of Open Access Journals (Sweden)

Nicole eCichon

2014-05-01

Full Text Available Flexible communication within the brain, which relies on oscillatory activity, is not confined to adult neuronal networks. Experimental evidence has documented the presence of discontinuous patterns of oscillatory activity already during early development. Their highly variable spatial and time-frequency organization has been related to region specificity. However, it might be equally due to the absence of unitary criteria for classifying the early activity patterns, since they have been mainly characterized by visual inspection. Therefore, robust and unbiased methods for categorizing these discontinuous oscillations are needed for increasingly complex data sets from different labs. Here, we introduce an unsupervised detection and classification algorithm for the discontinuous activity patterns of rodents during early development. For this, firstly time windows with discontinuous oscillations vs. epochs of network silence were identified. In a second step, the major features of detected events were identified and processed by principal component analysis for deciding on their contribution to the classification of different oscillatory patterns. Finally, these patterns were categorized using an unsupervised cluster algorithm. The results were validated on manually characterized neonatal spindle bursts, which ubiquitously entrain neocortical areas of rats and mice, and prelimbic nested gamma spindle bursts. Moreover, the algorithm led to satisfactory results for oscillatory events that, due to increased similarity of their features, were more difficult to classify, e.g. during the pre-juvenile developmental period. Based on a linear classification, the optimal number of features to consider increased with the difficulty of detection. This algorithm allows the comparison of neonatal and pre-juvenile oscillatory patterns in their spatial and temporal organization. It might represent a first step for the unbiased elucidation of activity patterns

Unsupervised learning via self-organization a dynamic approach

CERN Document Server

Kyan, Matthew; Jarrah, Kambiz; Guan, Ling

2014-01-01

To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

Science.gov (United States)

Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature

Science.gov (United States)

Iwahashi, J.; Pike, R.J.

2007-01-01

An iterative procedure that implements the classification of continuous topography as a problem in digital image-processing automatically divides an area into categories of surface form; three taxonomic criteria-slope gradient, local convexity, and surface texture-are calculated from a square-grid digital elevation model (DEM). The sequence of programmed operations combines twofold-partitioned maps of the three variables converted to greyscale images, using the mean of each variable as the dividing threshold. To subdivide increasingly subtle topography, grid cells sloping at less than mean gradient of the input DEM are classified by designating mean values of successively lower-sloping subsets of the study area (nested means) as taxonomic thresholds, thereby increasing the number of output categories from the minimum 8 to 12 or 16. Program output is exemplified by 16 topographic types for the world at 1-km spatial resolution (SRTM30 data), the Japanese Islands at 270??m, and part of Hokkaido at 55??m. Because the procedure is unsupervised and reflects frequency distributions of the input variables rather than pre-set criteria, the resulting classes are undefined and must be calibrated empirically by subsequent analysis. Maps of the example classifications reflect physiographic regions, geological structure, and landform as well as slope materials and processes; fine-textured terrain categories tend to correlate with erosional topography or older surfaces, coarse-textured classes with areas of little dissection. In Japan the resulting classes approximate landform types mapped from airphoto analysis, while in the Americas they create map patterns resembling Hammond's terrain types or surface-form classes; SRTM30 output for the United States compares favorably with Fenneman's physical divisions. Experiments are suggested for further developing the method; the Arc/Info AML and the map of terrain classes for the world are available as online downloads. ?? 2006 Elsevier

PAQ: Partition Analysis of Quasispecies.

Science.gov (United States)

Baccam, P; Thompson, R J; Fedrigo, O; Carpenter, S; Cornette, J L

2001-01-01

The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.

Evaluating unsupervised thesaurus-based labeling of audiovisual content in an archive production environment

NARCIS (Netherlands)

de Boer, V.; Ordelman, Roeland J.; Schuurman, Josefien

2016-01-01

In this paper we report on a two-stage evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results for given requirements with respect to archival quality, authority and service levels to external

Evaluating Unsupervised Thesaurus-based Labeling of Audiovisual Content in an Archive Production Environment

NARCIS (Netherlands)

de Boer, Victor; Ordelman, Roeland J.F.; Schuurman, Josefien

In this paper we report on a two-stage evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results for given requirements with respect to archival quality, authority and service levels to external

Partitioning planning studies: Preliminary evaluation of metal and radionuclide partitioning the high-temperature thermal treatment systems

International Nuclear Information System (INIS)

Liekhus, K.; Grandy, J.; Chambers, A.

1997-03-01

A preliminary study of toxic metals and radionuclide partitioning during high-temperature processing of mixed waste has been conducted during Fiscal Year 1996 within the Environmental Management Technology Evaluation Project. The study included: (a) identification of relevant partitioning mechanisms that cause feed material to be distributed between the solid, molten, and gas phases within a thermal treatment system; (b) evaluations of existing test data from applicable demonstration test programs as a means to identify and understand elemental and species partitioning; and, (c) evaluation of theoretical or empirical partitioning models for use in predicting elemental or species partitioning in a thermal treatment system. This preliminary study was conducted to identify the need for and the viability of developing the tools capable of describing and predicting toxic metals and radionuclide partitioning in the most applicable mixed waste thermal treatment processes. This document presents the results and recommendations resulting from this study that may serve as an impetus for developing and implementing these predictive tools

Partitioning planning studies: Preliminary evaluation of metal and radionuclide partitioning the high-temperature thermal treatment systems

Energy Technology Data Exchange (ETDEWEB)

Liekhus, K.; Grandy, J.; Chambers, A. [and others

1997-03-01

A preliminary study of toxic metals and radionuclide partitioning during high-temperature processing of mixed waste has been conducted during Fiscal Year 1996 within the Environmental Management Technology Evaluation Project. The study included: (a) identification of relevant partitioning mechanisms that cause feed material to be distributed between the solid, molten, and gas phases within a thermal treatment system; (b) evaluations of existing test data from applicable demonstration test programs as a means to identify and understand elemental and species partitioning; and, (c) evaluation of theoretical or empirical partitioning models for use in predicting elemental or species partitioning in a thermal treatment system. This preliminary study was conducted to identify the need for and the viability of developing the tools capable of describing and predicting toxic metals and radionuclide partitioning in the most applicable mixed waste thermal treatment processes. This document presents the results and recommendations resulting from this study that may serve as an impetus for developing and implementing these predictive tools.

Effect of early supervised progressive resistance training compared to unsupervised home-based exercise after fast-track total hip replacement applied to patients with preoperative functional limitations

DEFF Research Database (Denmark)

Mikkelsen, L R; Mechlenburg, I; Søballe, K

2014-01-01

OBJECTIVE: To examine if 2 weekly sessions of supervised progressive resistance training (PRT) in combination with 5 weekly sessions of unsupervised home-based exercise is more effective than 7 weekly sessions of unsupervised home-based exercise in improving leg-extension power of the operated leg...... 10 weeks after total hip replacement (THR) in patients with lower pre-operative function. METHOD: A total of 73 patients scheduled for THR were randomised (1:1) to intervention group (IG, home based exercise 5 days/week and PRT 2 days/week) or control group (CG, home based exercise 7 days...... of the operated leg, at the primary endpoint 10 weeks after surgery in THR patients with lower pre-operative function. TRIAL REGISTRATION: NCT01214954....

Estimating the Partition Function Zeros by Using the Wang-Landau Monte Carlo Algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Seung-Yeon [Korea National University of Transportation, Chungju (Korea, Republic of)

2017-03-15

The concept of the partition function zeros is one of the most efficient methods for investigating the phase transitions and the critical phenomena in various physical systems. Estimating the partition function zeros requires information on the density of states Ω(E) as a function of the energy E. Currently, the Wang-Landau Monte Carlo algorithm is one of the best methods for calculating Ω(E). The partition function zeros in the complex temperature plane of the Ising model on an L × L square lattice (L = 10 ∼ 80) with a periodic boundary condition have been estimated by using the Wang-Landau Monte Carlo algorithm. The efficiency of the Wang-Landau Monte Carlo algorithm and the accuracies of the partition function zeros have been evaluated for three different, 5%, 10%, and 20%, flatness criteria for the histogram H(E).

Factored Translation with Unsupervised Word Clusters

DEFF Research Database (Denmark)

Rishøj, Christian; Søgaard, Anders

2011-01-01

Unsupervised word clustering algorithms — which form word clusters based on a measure of distributional similarity — have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word...... clusters as factors in statistical machine translation. Although some of the language pairs in this work clearly benefit from the factor augmentation, there is no consistent improvement in translation accuracy across the board. For all language pairs, the word clusters clearly improve translation for some...... proportion of the sentences in the test set, but has a weak or even detrimental effect on the rest. It is shown that if one could determine whether or not to use a factor when translating a given sentence, rather substantial improvements in precision could be achieved for all of the language pairs evaluated...

The Euler–Riemann gases, and partition identities

International Nuclear Information System (INIS)

Chair, Noureddine

2013-01-01

The Euler theorem in partition theory and its generalization are derived from a non-interacting quantum field theory in which each bosonic mode with a given frequency is equivalent to a sum of bosonic mode whose frequency is twice (s-times) as much, and a fermionic (parafermionic) mode with the same frequency. Explicit formulas for the graded parafermionic partition functions are obtained, and the inverse of the graded partition function (IGPPF), turns out to be bosonic (fermionic) partition function depending on the parity of the order s of the parafermions. It is also shown that these partition functions are generating functions of partitions of integers with restrictions, the Euler generating function is identified with the inverse of the graded parafermionic partition function of order 2. As a result we obtain new sequences of partitions of integers with given restrictions. If the parity of the order s is even, then mixing a system of parafermions with a system whose partition function is (IGPPF), results in a system of fermions and bosons. On the other hand, if the parity of s is odd, then, the system we obtain is still a mixture of fermions and bosons but the corresponding Fock space of states is truncated. It turns out that these partition functions are given in terms of the Jacobi theta function θ 4 , and generate sequences in partition theory. Our partition functions coincide with the overpartitions of Corteel and Lovejoy, and jagged partitions in conformal field theory. Also, the partition functions obtained are related to the Ramond characters of the superconformal minimal models, and in the counting of the Moore–Read edge spectra that appear in the fractional quantum Hall effect. The different partition functions for the Riemann gas that are the counter parts of the Euler gas are obtained by a simple change of variables. In particular the counter part of the Jacobi theta function is (ζ(2t))/(ζ(t) 2 ) . Finally, we propose two formulas which brings

Hawk: A Runtime System for Partitioned Objects

NARCIS (Netherlands)

Ben Hassen, S.; Bal, H.E.; Tanenbaum, A.S.

1997-01-01

Hawk is a language-independent runtime system for writing data-parallel programs using partitioned objects. A partitioned object is a multidimensional array of elements that can be partitioned and distributed by the programmer. The Hawk runtime system uses the user-defined partitioning of objects

Uncertain Henry's law constants compromise equilibrium partitioning calculations of atmospheric oxidation products

Directory of Open Access Journals (Sweden)

C. Wang

2017-06-01

Full Text Available Gas–particle partitioning governs the distribution, removal, and transport of organic compounds in the atmosphere and the formation of secondary organic aerosol (SOA. The large variety of atmospheric species and their wide range of properties make predicting this partitioning equilibrium challenging. Here we expand on earlier work and predict gas–organic and gas–aqueous phase partitioning coefficients for 3414 atmospherically relevant molecules using COSMOtherm, SPARC Performs Automated Reasoning in Chemistry (SPARC, and poly-parameter linear free-energy relationships. The Master Chemical Mechanism generated the structures by oxidizing primary emitted volatile organic compounds. Predictions for gas–organic phase partitioning coefficients (KWIOM/G by different methods are on average within 1 order of magnitude of each other, irrespective of the numbers of functional groups, except for predictions by COSMOtherm and SPARC for compounds with more than three functional groups, which have a slightly higher discrepancy. Discrepancies between predictions of gas–aqueous partitioning (KW/G are much larger and increase with the number of functional groups in the molecule. In particular, COSMOtherm often predicts much lower KW/G for highly functionalized compounds than the other methods. While the quantum-chemistry-based COSMOtherm accounts for the influence of intra-molecular interactions on conformation, highly functionalized molecules likely fall outside of the applicability domain of the other techniques, which at least in part rely on empirical data for calibration. Further analysis suggests that atmospheric phase distribution calculations are sensitive to the partitioning coefficient estimation method, in particular to the estimated value of KW/G. The large uncertainty in KW/G predictions for highly functionalized organic compounds needs to be resolved to improve the quantitative treatment of SOA formation.

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

Directory of Open Access Journals (Sweden)

Yubo Jiao

2013-01-01

Full Text Available Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

Unsupervised behaviour-specific dictionary learning for abnormal event detection

DEFF Research Database (Denmark)

Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

2015-01-01

the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

Analysis of load balance in hybrid partitioning | Talib | Botswana ...

African Journals Online (AJOL)

In information retrieval systems, there are three types of index partitioning schemes - term partitioning, document partitioning, and hybrid partitioning. The hybrid-partitioning scheme combines both term and document partitioning schemes. Term partitioning provides high concurrency, which means that queries can be ...

Monotonicity Conditions for Multirate and Partitioned Explicit Runge-Kutta Schemes

KAUST Repository

Hundsdorfer, Willem

2013-01-01

Multirate schemes for conservation laws or convection-dominated problems seem to come in two flavors: schemes that are locally inconsistent, and schemes that lack mass-conservation. In this paper these two defects are discussed for one-dimensional conservation laws. Particular attention will be given to monotonicity properties of the multirate schemes, such as maximum principles and the total variation diminishing (TVD) property. The study of these properties will be done within the framework of partitioned Runge-Kutta methods. It will also be seen that the incompatibility of consistency and mass-conservation holds for ‘genuine’ multirate schemes, but not for general partitioned methods.

Development of a partitioning method for the management of high-level liquid waste

International Nuclear Information System (INIS)

Kubota, M.; Dojiri, S.; Yamaguchi, I.; Morita, Y.; Yamagishi, I.; Kobayashi, T.; Tani, S.

1989-01-01

Fundamental studies especially focused on the separation of neptunium and technetium have been carried out to construct the advanced partitioning process of fractioning elements in a high-level liquid waste into four groups: transuranium elements, technetium-noble metals, strontium-cesium, and other elements. For the separation of neptunium by solvent extraction, DIDPA proved excellent for extracting Np(V), and its extraction rate was accelerated by hydrogen peroxide. Np(V) was found to be also separated quantitatively as precipitate with oxalic acid. For the separation of technetium, the denitration with formic acid was effective in precipitating it along with noble metals, and the adsorption with activated carbon was also effective for quantitative separation. Through these fundamental studies, the advanced partitioning process is presented as the candidate to be examined with an actual high-level liquid waste

Estimation of Partition Coefficients of Benzene, Toluene, Ethylbenzene, and ρ-Xylene by Consecutive Extraction with Solid Phase Microextraction

International Nuclear Information System (INIS)

Eom, In Yong

2011-01-01

The results show that the partition coefficients of the BTEX compound can be estimated using the SPME method under the consecutive extraction mode. The proposed technique is much simpler than previously reported methods. Its novelty is that it eliminated the calibration step in the GC/FID, i. e., liquid injection method. The use of the autosampler for the SPME fiber can facilitate the adoption of consecutive extractions; thus, it allows estimation of the partition coefficients of various analytes. Recently, GC/MS has increasingly been used in analytical laboratories; this may facilitate the identification of an unknown analyte as well as the computation of the corresponding partition coefficients with the proposed method. It is very important to use partition coefficients of organic pollutants to predict their fate in the environment. A liquid-liquid extraction technique was used to determine the partition coefficients of organic compounds between water and organic solvent. The concentration of the target compounds must be determined after equilibrium is established between the two phases. The partition coefficients can be estimated using the capacity factors of HPLC and GC

Human Rights and Peace Audit on Partition in South Asia - Phase I ...

International Development Research Centre (IDRC) Digital Library (Canada)

Human Rights and Peace Audit on Partition in South Asia - Phase I ... the South Asia Forum for Human Rights (SAFHR) to examine the efficacy of partition as a method ... Call for new OWSD Fellowships for Early Career Women Scientists now open ... IWRA/IDRC webinar on climate change and adaptive water management.

A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

Directory of Open Access Journals (Sweden)

Xite Wang

2017-01-01

Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

Unsupervised Bayesian linear unmixing of gene expression microarrays.

Science.gov (United States)

Bazot, Cécile; Dobigeon, Nicolas; Tourneret, Jean-Yves; Zaas, Aimee K; Ginsburg, Geoffrey S; Hero, Alfred O

2013-03-19

This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores

Partitioning of TRU elements from Chinese HLLW

International Nuclear Information System (INIS)

Song Chongli; Zhu Yongjun

1994-04-01

The partitioning of TRU elements from the Chinese HLLW is feasible. The required D.F. values for producing a waste suitable for land disposal are given. The TRPO process developed in China could be used for this purpose. The research and development of the TRPO process is summarized and the general flowsheet is given. The Chinese HLLW has very high salt concentration. It causes the formation of third phase when contacted with TRPO extractant. The third phase would disappear by diluting the Chinese HLLW to 2∼3 times before extraction. The preliminary experiment shows very attractive results. The separation of Sr and Cs from the Chinese HLLW is also possible. The process is being studied. The partitioning of TRU elements and long lived ratio-nuclides from the Chinese HLLW provides an alternative method for its disposal. The partitioning of the Chinese HLLW could greatly reduce the waste volume, that is needed to be vitrified and to be disposed in to the deep repository, and then would drastically save the overall waste disposal cost

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

Science.gov (United States)

Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

2016-09-01

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

An automatic taxonomy of galaxy morphology using unsupervised machine learning

Science.gov (United States)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

A New Method to Quantify the Isotopic Signature of Leaf Transpiration: Implications for Landscape-Scale Evapotranspiration Partitioning Studies

Science.gov (United States)

Wang, L.; Good, S. P.; Caylor, K. K.

2010-12-01

Characterizing the constituent components of evapotranspiration is crucial to better understand ecosystem-level water budgets and water use dynamics. Isotope based evapotranspiration partitioning methods are promising but their utility lies in the accurate estimation of the isotopic composition of underlying transpiration and evaporation. Here we report a new method to quantify the isotopic signature of leaf transpiration under field conditions. This method utilizes a commercially available laser-based isotope analyzer and a transparent leaf chamber, modified from Licor conifer leaf chamber. The method is based on the water mass balance in ambient air and leaf transpired air. We verified the method using “artificial leaves” and glassline extracted samples. The method provides a new and direct way to estimate leaf transpiration isotopic signatures and it has wide applications in ecology, hydrology and plant physiology.

Helium-air exchange flows through partitioned opening and two-opening

International Nuclear Information System (INIS)

Kang, T. I.

1997-01-01

This paper describes experimental investigations of helium-air exchange flows through partitioned opening and two-opening. Such exchange flows may occur following rupture accident of stand pipe in high temperature engineering test reactor. A test vessel with the two types of small opening on top of test cylinder is used for experiments. An estimation method of mass increment is developed to measure the exchange flow rate. Upward flow of the helium and downward flow of the air in partitioned opening system interact out of entrance and exit of the opening. Therefore, an experiment with two-opening system is made to investigate effect of the fluids interaction of partitioned opening system. As a result of comparison of the exchange flow rates between two types of the opening system, it is demonstrated that the exchange flow rate of the two-opening system is larger than that of the partitioned opening system because of absence of the effect of fluids interaction. (author)

An Efficient Technique for Hardware/Software Partitioning Process in Codesign

Directory of Open Access Journals (Sweden)

Imene Mhadhbi

2016-01-01

Full Text Available Codesign methodology deals with the problem of designing complex embedded systems, where automatic hardware/software partitioning is one key issue. The research efforts in this issue are focused on exploring new automatic partitioning methods which consider only binary or extended partitioning problems. The main contribution of this paper is to propose a hybrid FCMPSO partitioning technique, based on Fuzzy C-Means (FCM and Particle Swarm Optimization (PSO algorithms suitable for mapping embedded applications for both binary and multicores target architecture. Our FCMPSO optimization technique has been compared using different graphical models with a large number of instances. Performance analysis reveals that FCMPSO outperforms PSO algorithm as well as the Genetic Algorithm (GA, Simulated Annealing (SA, Ant Colony Optimization (ACO, and FCM standard metaheuristic based techniques and also hybrid solutions including PSO then GA, GA then SA, GA then ACO, ACO then SA, FCM then GA, FCM then SA, and finally ACO followed by FCM.

The impact of aerosol composition on the particle to gas partitioning of reactive mercury.

Science.gov (United States)

Rutter, Andrew P; Schauer, James J

2007-06-01

A laboratory system was developed to study the gas-particle partitioning of reactive mercury (RM) as a function of aerosol composition in synthetic atmospheric particulate matter. The collection of RM was achieved by filter- and sorbent-based methods. Analyses of the RM collected on the filters and sorbents were performed using thermal extraction combined with cold vapor atomic fluorescence spectroscopy (CVAFS), allowing direct measurement of the RM load on the substrates. Laboratory measurements of the gas-particle partitioning coefficients of RM to atmospheric aerosol particles revealed a strong dependence on aerosol composition, with partitioning coefficients that varied by orders of magnitude depending on the composition of the particles. Particles of sodium nitrate and the chlorides of potassium and sodium had high partitioning coefficients, shifting the RM partitioning toward the particle phase, while ammonium sulfate, levoglucosan, and adipic acid caused the RM to partition toward the gas phase and, therefore, had partitioning coefficients that were lower by orders of magnitude.

A brief history of partitions of numbers, partition functions and their modern applications

Science.gov (United States)

Debnath, Lokenath

2016-04-01

'Number rules the universe.' The Pythagoras 'If you wish to forsee the future of mathematics our course is to study the history and present conditions of the science.' Henri Poincaré 'The primary source (Urqell) of all mathematics are integers.' Hermann Minkowski This paper is written to commemorate the centennial anniversary of the Mathematical Association of America. It deals with a short history of different kinds of natural numbers including triangular, square, pentagonal, hexagonal and k-gonal numbers, and their simple properties and their geometrical representations. Included are Euclid's and Pythagorean's main contributions to elementary number theory with the main contents of the Euclid Elements of the 13-volume masterpiece of mathematical work. This is followed by Euler's new discovery of the additive number theory based on partitions of numbers. Special attention is given to many examples, Euler's theorems on partitions of numbers with geometrical representations of Ferrers' graphs, Young's diagrams, Lagrange's four-square theorem and the celebrated Waring problem. Included are Euler's generating functions for the partitions of numbers, Euler's pentagonal number theorem, Gauss' triangular and square number theorems and the Jacobi triple product identity. Applications of the theory of partitions of numbers to different statistics such as the Bose- Einstein, Fermi- Dirac, Gentile, and Maxwell- Boltzmann statistics are briefly discussed. Special attention is given to pedagogical information through historical approach to number theory so that students and teachers at the school, college and university levels can become familiar with the basic concepts of partitions of numbers, partition functions and their modern applications, and can pursue advanced study and research in analytical and computational number theory.

Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

NARCIS (Netherlands)

S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

2000-01-01

textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level

International Nuclear Information System (INIS)

Tunga, M. Alper; Demiralp, Metin

2011-01-01

Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.

Nonequilibrium thermodynamics of restricted Boltzmann machines

Science.gov (United States)

Salazar, Domingos S. P.

2017-08-01

In this work, we analyze the nonequilibrium thermodynamics of a class of neural networks known as restricted Boltzmann machines (RBMs) in the context of unsupervised learning. We show how the network is described as a discrete Markov process and how the detailed balance condition and the Maxwell-Boltzmann equilibrium distribution are sufficient conditions for a complete thermodynamics description, including nonequilibrium fluctuation theorems. Numerical simulations in a fully trained RBM are performed and the heat exchange fluctuation theorem is verified with excellent agreement to the theory. We observe how the contrastive divergence functional, mostly used in unsupervised learning of RBMs, is closely related to nonequilibrium thermodynamic quantities. We also use the framework to interpret the estimation of the partition function of RBMs with the annealed importance sampling method from a thermodynamics standpoint. Finally, we argue that unsupervised learning of RBMs is equivalent to a work protocol in a system driven by the laws of thermodynamics in the absence of labeled data.

Nonequilibrium thermodynamics of restricted Boltzmann machines.

Science.gov (United States)

Salazar, Domingos S P

2017-08-01

In this work, we analyze the nonequilibrium thermodynamics of a class of neural networks known as restricted Boltzmann machines (RBMs) in the context of unsupervised learning. We show how the network is described as a discrete Markov process and how the detailed balance condition and the Maxwell-Boltzmann equilibrium distribution are sufficient conditions for a complete thermodynamics description, including nonequilibrium fluctuation theorems. Numerical simulations in a fully trained RBM are performed and the heat exchange fluctuation theorem is verified with excellent agreement to the theory. We observe how the contrastive divergence functional, mostly used in unsupervised learning of RBMs, is closely related to nonequilibrium thermodynamic quantities. We also use the framework to interpret the estimation of the partition function of RBMs with the annealed importance sampling method from a thermodynamics standpoint. Finally, we argue that unsupervised learning of RBMs is equivalent to a work protocol in a system driven by the laws of thermodynamics in the absence of labeled data.

New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds.

Science.gov (United States)

Burant, Aniela; Thompson, Christopher; Lowry, Gregory V; Karamalidis, Athanasios K

2016-05-17

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set.

Handling Data Skew in MapReduce Cluster by Using Partition Tuning

Directory of Open Access Journals (Sweden)

Yufei Gao

2017-01-01

Full Text Available The healthcare industry has generated large amounts of data, and analyzing these has emerged as an important problem in recent years. The MapReduce programming model has been successfully used for big data analytics. However, data skew invariably occurs in big data analytics and seriously affects efficiency. To overcome the data skew problem in MapReduce, we have in the past proposed a data processing algorithm called Partition Tuning-based Skew Handling (PTSH. In comparison with the one-stage partitioning strategy used in the traditional MapReduce model, PTSH uses a two-stage strategy and the partition tuning method to disperse key-value pairs in virtual partitions and recombines each partition in case of data skew. The robustness and efficiency of the proposed algorithm were tested on a wide variety of simulated datasets and real healthcare datasets. The results showed that PTSH algorithm can handle data skew in MapReduce efficiently and improve the performance of MapReduce jobs in comparison with the native Hadoop, Closer, and locality-aware and fairness-aware key partitioning (LEEN. We also found that the time needed for rule extraction can be reduced significantly by adopting the PTSH algorithm, since it is more suitable for association rule mining (ARM on healthcare data.

A Partitioning and Bounded Variable Algorithm for Linear Programming

Science.gov (United States)

Sheskin, Theodore J.

2006-01-01

An interesting new partitioning and bounded variable algorithm (PBVA) is proposed for solving linear programming problems. The PBVA is a variant of the simplex algorithm which uses a modified form of the simplex method followed by the dual simplex method for bounded variables. In contrast to the two-phase method and the big M method, the PBVA does…

Practice-Oriented Evaluation of Unsupervised Labeling of Audiovisual Content in an Archive Production Environment

NARCIS (Netherlands)

de Boer, Victor; Ordelman, Roeland J.F.; Schuurman, Josefien

In this paper we report on an evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results given requirements with respect to archival quality, authority and service levels to external users. We

Mapping Pesticide Partition Coefficients By Electromagnetic Induction

Science.gov (United States)

A potential method for reducing pesticide leaching is to base application rates on the leaching potential of a specific chemical and soil combination. However, leaching is determined in part by the partitioning of the chemical between the soil and soil solution, which varies across a field. Standard...

Cylindric partitions, {{\\boldsymbol{ W }}}_{r} characters and the Andrews-Gordon-Bressoud identities

Science.gov (United States)

Foda, O.; Welsh, T. A.

2016-04-01

We study the Andrews-Gordon-Bressoud (AGB) generalisations of the Rogers-Ramanujan q-series identities in the context of cylindric partitions. We recall the definition of r-cylindric partitions, and provide a simple proof of Borodin’s product expression for their generating functions, that can be regarded as a limiting case of an unpublished proof by Krattenthaler. We also recall the relationships between the r-cylindric partition generating functions, the principal characters of {\\hat{{sl}}}r algebras, the {{\\boldsymbol{ M }}}r r,r+d minimal model characters of {{\\boldsymbol{ W }}}r algebras, and the r-string abaci generating functions, providing simple proofs for each. We then set r = 2, and use two-cylindric partitions to re-derive the AGB identities as follows. Firstly, we use Borodin’s product expression for the generating functions of the two-cylindric partitions with infinitely long parts, to obtain the product sides of the AGB identities, times a factor {(q;q)}∞ -1, which is the generating function of ordinary partitions. Next, we obtain a bijection from the two-cylindric partitions, via two-string abaci, into decorated versions of Bressoud’s restricted lattice paths. Extending Bressoud’s method of transforming between restricted paths that obey different restrictions, we obtain sum expressions with manifestly non-negative coefficients for the generating functions of the two-cylindric partitions which contains a factor {(q;q)}∞ -1. Equating the product and sum expressions of the same two-cylindric partitions, and canceling a factor of {(q;q)}∞ -1 on each side, we obtain the AGB identities.

AN UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND KI FROM MULTI-TEMPORAL AND FULL POLARIMETRIC SAR IMAGES

Directory of Open Access Journals (Sweden)

J. Q. Zhao

2016-06-01

Full Text Available Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.

Purification of biomaterials by phase partitioning

Science.gov (United States)

Harris, J. M.

1984-01-01

A technique which is particularly suited to microgravity environments and which is potentially more powerful than electrophoresis is phase partitioning. Phase partitioning is purification by partitioning between the two immiscible aqueous layers formed by solution of the polymers poly(ethylene glycol) and dextran in water. This technique proved to be very useful for separations in one-g but is limited for cells because the cells are more dense than the phase solutions thus tend to sediment to the bottom of the container before reaching equilibrium with the preferred phase. There are three phases to work in this area: synthesis of new polymers for affinity phase partitioning; development of automated apparatus for ground-based separations; and design of apparatus for performing simple phase partitioning space experiments, including examination of mechanisms for separating phases in the absence of gravity.

Modeling Language and Cognition with Deep Unsupervised Learning:A Tutorial Overview

OpenAIRE

Marco eZorzi; Marco eZorzi; Alberto eTestolin; Ivilin Peev Stoianov; Ivilin Peev Stoianov

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cog...

Modeling language and cognition with deep unsupervised learning: a tutorial overview

OpenAIRE

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cog...

Betweenness-based algorithm for a partition scale-free graph

International Nuclear Information System (INIS)

Zhang Bai-Da; Wu Jun-Jie; Zhou Jing; Tang Yu-Hua

2011-01-01

Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom—up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top—down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches. (interdisciplinary physics and related areas of science and technology)

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Directory of Open Access Journals (Sweden)

Yansheng Li

2016-08-01

Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

Science.gov (United States)

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

The partition coefficients of 133Xe between blood and bone

International Nuclear Information System (INIS)

Lahtinen, T.; Karjalainen, P.; Vaeaenaenen, A.; Lahtinen, R.; Alhava, E.M.

1981-01-01

The partition coefficients of 133 Xe between blood and haematopoietic bone marrow and homogenised bone have been determined in vitro. The partition coefficient lambda 1 corresponding to haematopoietic marrow was 0.95 ml g -1 while that corresponding to homogenised bone was a function of age, lambda 2 = 3.11 + 0.049(age)(ml g -1 ). These data can be used for calculating regional blood flow in healthy human femur by means of a simple 133 Xe radionuclide method. (author)

Water isotope partitioning and ecohydrologic separation in mixed conifer forest explored with a centrifugation water extraction method

Science.gov (United States)

Bowers, W.; Mercer, J.; Pleasants, M.; Williams, D. G.

2017-12-01

Isotopic partitioning of water within soil into tightly and loosely bound fractions has been proposed to explain differences between isotopic water sources used by plants and those that contribute to streams and ground water, the basis for the "two water worlds" hypothesis. We examined the isotope ratio values of water in trees, bulk soil, mobile water collected from soil lysimeters, stream water, and GW at three different hillslopes in a mixed conifer forest in southeastern Wyoming, USA. Hillslopes differed in aspect and topographic position with corresponding differences in surface energy balance, snowmelt timing, and duration of soil moisture during the dry summer. The isotopic results support the partitioning of water within the soil; trees apparently used a different pool of water for transpiration than that recovered from soil lysimeters and the source was not resolved with the isotopic signature of the water that was extracted from bulk soil via cryogenic vacuum distillation. Separating and measuring the isotope ratios values in these pools would test the assumption that the tightly bound water within the soil has the same isotopic signature as the water transpired by the trees. We employed a centrifugation approach to separate water within the soil held at different tensions by applying stepwise increases in rotational velocity and pressures to the bulk soil samples. Effluent and the remaining water (cryogenically extracted) at each step were compared. We first applied the centrifugation method in a simple lab experiment using sandy loam soil and separate introductions of two isotopically distinct waters. We then applied the method to soil collected from the montane hillslopes. For the lab experiment, we predicted that effluents would have distinct isotopic signatures, with the last effluent and extracted water more closely representing the isotopic signature of the first water applied. For our field samples, we predicted that the isotopic signature of the

Plane partition vesicles

International Nuclear Information System (INIS)

Rensburg, E J Janse van; Ma, J

2006-01-01

We examine partitions and their natural three-dimensional generalizations, plane partitions, as models of vesicles undergoing an inflation-deflation transition. The phase diagrams of these models include a critical point corresponding to an inflation-deflation transition, and exhibits multicritical scaling in the vicinity of a multicritical point located elsewhere on the critical curve. We determine the locations of the multicritical points by analysing the generating functions using analytic and numerical means. In addition, we determine the numerical values of the multicritical scaling exponents associated with the multicritical scaling regimes in these models

Determination of air-loop volume and radon partition coefficient for measuring radon in water sample.

Science.gov (United States)

Lee, Kil Yong; Burnett, William C

A simple method for the direct determination of the air-loop volume in a RAD7 system as well as the radon partition coefficient was developed allowing for an accurate measurement of the radon activity in any type of water. The air-loop volume may be measured directly using an external radon source and an empty bottle with a precisely measured volume. The partition coefficient and activity of radon in the water sample may then be determined via the RAD7 using the determined air-loop volume. Activity ratios instead of absolute activities were used to measure the air-loop volume and the radon partition coefficient. In order to verify this approach, we measured the radon partition coefficient in deionized water in the temperature range of 10-30 °C and compared the values to those calculated from the well-known Weigel equation. The results were within 5 % variance throughout the temperature range. We also applied the approach for measurement of the radon partition coefficient in synthetic saline water (0-75 ppt salinity) as well as tap water. The radon activity of the tap water sample was determined by this method as well as the standard RAD-H 2 O and BigBottle RAD-H 2 O. The results have shown good agreement between this method and the standard methods.

Determination of air-loop volume and radon partition coefficient for measuring radon in water sample

International Nuclear Information System (INIS)

Kil Yong Lee; Burnett, W.C.

2013-01-01

A simple method for the direct determination of the air-loop volume in a RAD7 system as well as the radon partition coefficient was developed allowing for an accurate measurement of the radon activity in any type of water. The air-loop volume may be measured directly using an external radon source and an empty bottle with a precisely measured volume. The partition coefficient and activity of radon in the water sample may then be determined via the RAD7 using the determined air-loop volume. Activity ratios instead of absolute activities were used to measure the air-loop volume and the radon partition coefficient. In order to verify this approach, we measured the radon partition coefficient in deionized water in the temperature range of 10-30 deg C and compared the values to those calculated from the well-known Weigel equation. The results were within 5 % variance throughout the temperature range. We also applied the approach for measurement of the radon partition coefficient in synthetic saline water (0-75 ppt salinity) as well as tap water. The radon activity of the tap water sample was determined by this method as well as the standard RAD-H 2 O and BigBottle RAD-H 2 O. The results have shown good agreement between this method and the standard methods. (author)

The importance of applying an appropriate data partitioning

CERN Document Server

Dimitrov, Gancho; The ATLAS collaboration

2015-01-01

In this presentation are described specific technical solutions put in place in various database applications of the ATLAS experiment at LHC where we make use of several partitioning techniques available in Oracle 11g. With the broadly used range partitioning and its option of automatic interval partitioning we add our own logic in PLSQL procedures and scheduler jobs to sustain data sliding windows in order to enforce various data retention policies. We also make use of the new Oracle 11g reference partitioning in the ATLAS Nightly Build System to achieve uniform data segmentation. However the most challenging was to segment the data of the new ATLAS Distributed Data Management system, which resulted in tens of thousands list type partitions and sub-partitions. Partition and sub-partition management, index strategy, statistics gathering and queries execution plan stability are important factors when choosing an appropriate physical model for the application data management. The so-far accumulated knowledge wi...

The Train Driver Recovery Problem - a Set Partitioning Based Model and Solution Method

DEFF Research Database (Denmark)

Rezanova, Natalia Jurjevna; Ryan, David

2010-01-01

The need to recover a train driver schedule occurs during major disruptions in the daily railway operations. Based on data from the Danish passenger railway operator DSB S-tog A/S, a solution method to the train driver recovery problem (TDRP) is developed. The TDRP is formulated as a set...... branching strategy using the depth-first search of the Branch & Bound tree. The LP relaxation of the TDRP possesses strong integer properties. We present test scenarios generated from the historical real-life operations data of DSB S-tog A/S. The numerical results show that all but one tested instances...... partitioning problem. We define a disruption neighbourhood by identifying a small set of drivers and train tasks directly affected by the disruption. Based on the disruption neighbourhood, the TDRP model is formed and solved. If the TDRP solution provides a feasible recovery for the drivers within...

40 CFR 799.6756 - TSCA partition coefficient (n-octanol/water), generator column method.

Science.gov (United States)

2010-07-01

... using the CLogP3 computer program in paragraph (e)(9) of this section. 4 Hawker and Connell (1988... (B) Constant temperature bath with circulation pump-bath and capable of controlling temperature to 25...-partition coefficient correlation. Environmental Science and Technology 14:1227-1229 (1980). (2) Bruggemann...

Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Directory of Open Access Journals (Sweden)

Pradipta Maji

Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Simple Method to Determine the Partition Coefficient of Naphthenic Acid in Oil/Water

DEFF Research Database (Denmark)

Bitsch-Larsen, Anders; Andersen, Simon Ivar

2008-01-01

The partition coefficient for technical grade naphthenic acid in water/n-decane at 295 K has been determined (K-wo = 2.1 center dot 10(-4)) using a simple experimental technique with large extraction volumes (0.09 m(3) of water). Furthermore, nonequilibrium values at different pH values...

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

Science.gov (United States)

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning

NARCIS (Netherlands)

Twellmann, T.; Meyer-Bäse, A.; Lange, O.; Foo, S.; Nattkemper, T.W.

2008-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition

Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables

Science.gov (United States)

Goodwell, Allison E.; Kumar, Praveen

2017-07-01

Information theoretic measures can be used to identify nonlinear interactions between source and target variables through reductions in uncertainty. In information partitioning, multivariate mutual information is decomposed into synergistic, unique, and redundant components. Synergy is information shared only when sources influence a target together, uniqueness is information only provided by one source, and redundancy is overlapping shared information from multiple sources. While this partitioning has been applied to provide insights into complex dependencies, several proposed partitioning methods overestimate redundant information and omit a component of unique information because they do not account for source dependencies. Additionally, information partitioning has only been applied to time-series data in a limited context, using basic pdf estimation techniques or a Gaussian assumption. We develop a Rescaled Redundancy measure (Rs) to solve the source dependency issue, and present Gaussian, autoregressive, and chaotic test cases to demonstrate its advantages over existing techniques in the presence of noise, various source correlations, and different types of interactions. This study constitutes the first rigorous application of information partitioning to environmental time-series data, and addresses how noise, pdf estimation technique, or source dependencies can influence detected measures. We illustrate how our techniques can unravel the complex nature of forcing and feedback within an ecohydrologic system with an application to 1 min environmental signals of air temperature, relative humidity, and windspeed. The methods presented here are applicable to the study of a broad range of complex systems composed of interacting variables.

LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning

Directory of Open Access Journals (Sweden)

Ruibang Luo

Full Text Available Linked-read sequencing, using highly-multiplexed genome partitioning and barcoding, can span hundreds of kilobases to improve de novo assembly, haplotype phasing, and other applications. Based on our analysis of 14 datasets, we introduce LRSim that simulates linked-reads by emulating the library preparation and sequencing process with fine control over variants, linked-read characteristics, and the short-read profile. We conclude from the phasing and assembly of multiple datasets, recommendations on coverage, fragment length, and partitioning when sequencing genomes of different sizes and complexities. These optimizations improve results by orders of magnitude, and enable the development of novel methods. LRSim is available at https://github.com/aquaskyline/LRSIM. Keywords: Linked-read, Molecular barcoding, Reads partitioning, Phasing, Reads simulation, Genome assembly, 10X Genomics

Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis.

Science.gov (United States)

Lacroix, André; Hortobágyi, Tibor; Beurskens, Rainer; Granacher, Urs

2017-11-01

Balance and resistance training can improve healthy older adults' balance and muscle strength. Delivering such exercise programs at home without supervision may facilitate participation for older adults because they do not have to leave their homes. To date, no systematic literature analysis has been conducted to determine if supervision affects the effectiveness of these programs to improve healthy older adults' balance and muscle strength/power. The objective of this systematic review and meta-analysis was to quantify the effectiveness of supervised vs. unsupervised balance and/or resistance training programs on measures of balance and muscle strength/power in healthy older adults. In addition, the impact of supervision on training-induced adaptive processes was evaluated in the form of dose-response relationships by analyzing randomized controlled trials that compared supervised with unsupervised trials. A computerized systematic literature search was performed in the electronic databases PubMed, Web of Science, and SportDiscus to detect articles examining the role of supervision in balance and/or resistance training in older adults. The initially identified 6041 articles were systematically screened. Studies were included if they examined balance and/or resistance training in adults aged ≥65 years with no relevant diseases and registered at least one behavioral balance (e.g., time during single leg stance) and/or muscle strength/power outcome (e.g., time for 5-Times-Chair-Rise-Test). Finally, 11 studies were eligible for inclusion in this meta-analysis. Weighted mean standardized mean differences between subjects (SMD bs ) of supervised vs. unsupervised balance/resistance training studies were calculated. The included studies were coded for the following variables: number of participants, sex, age, number and type of interventions, type of balance/strength tests, and change (%) from pre- to post-intervention values. Additionally, we coded training according

AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA

Directory of Open Access Journals (Sweden)

A. Alizade Naeini

2014-10-01

Full Text Available K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF and Random methods are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.

Software usage in unsupervised digital doorway computing environments in disadvantaged South African communities: Focusing on youthful users

CSIR Research Space (South Africa)

Gush, K

2011-01-01

Full Text Available Digital Doorways provide computing infrastructure in low-income communities in South Africa. The unsupervised DD terminals offer various software applications, from entertainment through educational resources to research material, encouraging...

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Science.gov (United States)

Sarparandeh, Mohammadali; Hezarkhani, Ardeshir

2017-12-01

The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) - including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and self-organizing map (SOM) - were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed

A standardised individual unsupervised water exercise intervention for healthy pregnant women. A qualitative feasibility study

DEFF Research Database (Denmark)

Backhausen, Mette G; Katballe, Malene; Hansson, Helena

2014-01-01

INTRODUCTION: Low back pain during pregnancy is common and associated with sick leave. Studies suggest that exercise may reduce low back pain during pregnancy. Before carrying out a randomised controlled trail with individual water exercise as intervention a qualitative feasibility study was done....... OBJECTIVE: To explore women's views and experiences of the acceptability and benefits of and possible barriers to the standardised individual unsupervised water exercise intervention. MATERIALS AND METHODS: Eleven women were interviewed after participating in a water exercise intervention. Content analysis...... was used. RESULTS: Four main categories emerged: motivation to participate, attitudes towards the exercise programme, perception of benefits, and acceptability of supportive components. The women had a desire to stay physically active during pregnancy and found water exercise a suitable, type of exercise...

A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes

Science.gov (United States)

Sato, Daisuke; Hanaoka, Shouhei; Nomura, Yukihiro; Takenaga, Tomomi; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

2018-02-01

Purpose: The target disorders of emergency head CT are wide-ranging. Therefore, people working in an emergency department desire a computer-aided detection system for general disorders. In this study, we proposed an unsupervised anomaly detection method in emergency head CT using an autoencoder and evaluated the anomaly detection performance of our method in emergency head CT. Methods: We used a 3D convolutional autoencoder (3D-CAE), which contains 11 layers in the convolution block and 6 layers in the deconvolution block. In the training phase, we trained the 3D-CAE using 10,000 3D patches extracted from 50 normal cases. In the test phase, we calculated abnormalities of each voxel in 38 emergency head CT volumes (22 abnormal cases and 16 normal cases) for evaluation and evaluated the likelihood of lesion existence. Results: Our method achieved a sensitivity of 68% and a specificity of 88%, with an area under the curve of the receiver operating characteristic curve of 0.87. It shows that this method has a moderate accuracy to distinguish normal CT cases to abnormal ones. Conclusion: Our method has potentialities for anomaly detection in emergency head CT.

A geometric toolbox for tetrahedral finite element partitions

NARCIS (Netherlands)

Brandts, J.; Korotov, S.; Křížek, M.; Axelsson, O.; Karátson, J.

2011-01-01

In this work we present a survey of some geometric results on tetrahedral partitions and their refinements in a unified manner. They can be used for mesh generation and adaptivity in practical calculations by the finite element method (FEM), and also in theoretical finite element (FE) analysis.

Experimental partition determination of octanol-water coefficients of ...

African Journals Online (AJOL)

An electrochemical method based on square wave voltammetry was developed for the measurement of octanol-water partition coefficient, LogP, for ten ferrocene derivatives. Measured LogP values ranged over two orders of magnitude, between 2.18 for 1- ferrocenylethanol and 4.38 for ferrocenyl-2-nitrophenyl.

Gentile statistics and restricted partitions

Indian Academy of Sciences (India)

The partition function of Gentile statistics also has the property that it nicely interpolates between the ... We now construct the partition function for such a system which also incorporates the property of interpolation ... As in [4], we however keep s arbitrary even though for s > 2 there are no quadratic. Hamiltonian systems.

The importance of having an appropriate data segmentation (partitioning)

CERN Document Server

Dimitrov, Gancho; The ATLAS collaboration

2014-01-01

In this presentation will be shown real life examples from database applications in the ATLAS experiment @ LHC where we make use of many Oracle partitioning techniques available in Oracle 11g. With the broadly used range partitioning and its option of automatic interval partitioning we add our own logic in PLSQL for sustaining data sliding windows in order to enforce various data retention policies. We also make use of the reference partitioning in some use cases, however the most challenging was to segment the data of a large bookkeeping system which resulted in tens of thousands list partitions and list sub-partitions. Partition and sub-partition management, index strategy, statistics gathering and queries execution plan stability are important factors when choosing an appropriate for the use case data management model. The gained experience with all of those will be shared with the audience.

Utilization of tritiated water dilution technique in determination of nitrogen partitioning in cashmere goats

International Nuclear Information System (INIS)

Wang Linfeng; Yang Gaiqing; Liu Ping; Zhang Shijun

2010-01-01

In order to investigate nitrogen partitioning in local cashmere goats, six Inner Mogolia White Cashmere goats between 2 to 2.5 years old were used to determine the nitrogen partitioning in cashmere goats. The total retained nitrogen (TN) in body, distribution of body nitrgen and hair nitrogen were measured by general digestive and metabolism method combined with tritiated water dilution technique. Results showed that the combined methods were ideal for determining body nitrgen (BN) and hair nitrogen (fur nitrogen, FN) of Cashmere goats. There were obvious significance between BN and FN in different seasons. In telogen, BN and FN partitioning was 75.7% ± 0.62% and 24.3% ± 0.62%, respectively. Whereas, it changed to 66.6% ± 2.2% and 33.4% ± 2.2% in anagen. BN partitioning decreased when the season changed from telogen to anagen, while FN partitioning increased, which indicated that more nitrogen substance was partitioned to body growth in telogen, and more nitrogen substance was distribute to cashmere growth in anagen. These transformation were related to the changing of photoperiod and some hormones, such as melatonin (MT), prolactin (PRL) and IGF-I. It could be concluded that tritiated water dilution technique can be used to detect body protein content as well as BN, combining general digestive and metabolism experiment, FN partitoning can be determined. BN and FN partitoning varied with the season in cashmere goats because of hormones changing. (authors)

Generating Milton Babbitt's all-partition arrays

OpenAIRE

Bemman, Brian; Meredith, David

2016-01-01

In most of Milton Babbitt's (1916–2011) works written since the early 1960s, both the pitch and rhythmic content is organized according to a highly constrained structure known as the all-partition array. The all-partition array provides a framework that ensures that as many different forms of a tone row as possible (generated by any combination of transposition, inversion or reversal) are expressed 'horizontally' and that each integer partition of 12 whose cardinality is no greater than the n...

Lift of dilogarithm to partition identities

International Nuclear Information System (INIS)

Terhoeven, M.

1992-11-01

For the whole set of dilogarithm identities found recently using the thermodynamic Bethe-Ansatz for the ADET series of purely elastic scattering theories we give partition identities which involve characters of those conformal field theories which correspond to the UV-limits of the scattering theories. These partition identities in turn allow to derive the dilogarithm identities using modular invariance and a saddle point approximation. We conjecture on possible generalizations of this correspondance, namely, a lift from dilogarithm to partition identities. (orig.)

Unsupervised Tensor Mining for Big Data Practitioners.

Science.gov (United States)

Papalexakis, Evangelos E; Faloutsos, Christos

2016-09-01

Multiaspect data are ubiquitous in modern Big Data applications. For instance, different aspects of a social network are the different types of communication between people, the time stamp of each interaction, and the location associated to each individual. How can we jointly model all those aspects and leverage the additional information that they introduce to our analysis? Tensors, which are multidimensional extensions of matrices, are a principled and mathematically sound way of modeling such multiaspect data. In this article, our goal is to popularize tensors and tensor decompositions to Big Data practitioners by demonstrating their effectiveness, outlining challenges that pertain to their application in Big Data scenarios, and presenting our recent work that tackles those challenges. We view this work as a step toward a fully automated, unsupervised tensor mining tool that can be easily and broadly adopted by practitioners in academia and industry.

Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition

OpenAIRE

Huang, Furong; Anandkumar, Animashree

2016-01-01

Unsupervised text embeddings extraction is crucial for text understanding in machine learning. Word2Vec and its variants have received substantial success in mapping words with similar syntactic or semantic meaning to vectors close to each other. However, extracting context-aware word-sequence embedding remains a challenging task. Training over large corpus is difficult as labels are difficult to get. More importantly, it is challenging for pre-trained models to obtain word-...

Unsupervised progressive elastic band exercises for frail geriatric inpatients objectively monitored by new exercise-integrated technology-a feasibility trial with an embedded qualitative study.

Science.gov (United States)

Rathleff, C R; Bandholm, T; Spaich, E G; Jorgensen, M; Andreasen, J

2017-01-01

Frailty is a serious condition frequently present in geriatric inpatients that potentially causes serious adverse events. Strength training is acknowledged as a means of preventing or delaying frailty and loss of function in these patients. However, limited hospital resources challenge the amount of supervised training, and unsupervised training could possibly supplement supervised training thereby increasing the total exercise dose during admission. A new valid and reliable technology, the BandCizer, objectively measures the exact training dosage performed. The purpose was to investigate feasibility and acceptability of an unsupervised progressive strength training intervention monitored by BandCizer for frail geriatric inpatients. This feasibility trial included 15 frail inpatients at a geriatric ward. At hospitalization, the patients were prescribed two elastic band exercises to be performed unsupervised once daily. A BandCizer Datalogger enabling measurement of the number of sets, repetitions, and time-under-tension was attached to the elastic band. The patients were instructed in performing strength training: 3 sets of 10 repetitions (10-12 repetition maximum (RM)) with a separation of 2-min pauses and a time-under-tension of 8 s. The feasibility criterion for the unsupervised progressive exercises was that 33% of the recommended number of sets would be performed by at least 30% of patients. In addition, patients and staff were interviewed about their experiences with the intervention. Four (27%) out of 15 patients completed 33% of the recommended number of sets. For the total sample, the average percent of performed sets was 23% and for those who actually trained ( n  = 12) 26%. Patients and staff expressed a general positive attitude towards the unsupervised training as an addition to the supervised training sessions. However, barriers were also described-especially constant interruptions. Based on the predefined criterion for feasibility, the

Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2016-01-01

To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, even when using bounded positive definite kernels. First, we propose robust kernel covariance operator (robust kernel CO) and robust kernel crosscovariance operator (robust kern...

Determination of partition behavior of organic surrogates between paperboard packaging materials and air.

Science.gov (United States)

Triantafyllou, V I; Akrida-Demertzi, K; Demertzis, P G

2005-06-03

The suitability of recycled paperboard packaging materials for direct food contact applications is a major area of investigation. Chemical contaminants (surrogates) partitioning between recycled paper packaging and foods may affect the safety and health of the consumer. The partition behavior of all possible organic compounds between cardboards and individual foodstuffs is difficult and too time consuming for being fully investigated. Therefore it may be more efficient to determine these partition coefficients indirectly through experimental determination of the partitioning behavior between cardboard samples and air. In this work, the behavior of organic pollutants present in a set of two paper and board samples intended to be in contact with foods was studied. Adsorption isotherms have been plotted and partition coefficients between paper and air have been calculated as a basis for the estimation of their migration potential into food. Values of partition coefficients (Kpaper/air) from 47 to 1207 were obtained at different temperatures. For the less volatile surrogates such as dibutyl phthalate and methyl stearate higher Kpaper/air values were obtained. The adsorption curves showed that the more volatile substances are partitioning mainly in air phase and increasing the temperature from 70 to 100 degrees C their concentrations in air (Cair) have almost doubled. The analysis of surrogates was performed with a method based on solvent extraction and gas chromatographic-flame ionization detection (GC-FID) quantification.

Using DEDICOM for completely unsupervised part-of-speech tagging.

Energy Technology Data Exchange (ETDEWEB)

Chew, Peter A.; Bader, Brett William; Rozovskaya, Alla (University of Illinois, Urbana, IL)

2009-02-01

A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schuetze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to estimate emission- and transition-probability matrices which can then be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the correlation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging.

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

DEFF Research Database (Denmark)

Fraccaro, Marco; Kamronn, Simon Due; Paquet, Ulrich

2017-01-01

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework...... for unsupervised learning of sequential data that disentangles two latent representations: an object’s representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate...

Data Partitioning Technique for Improved Video Prioritization

Directory of Open Access Journals (Sweden)

Ismail Amin Ali

2017-07-01

Full Text Available A compressed video bitstream can be partitioned according to the coding priority of the data, allowing prioritized wireless communication or selective dropping in a congested channel. Known as data partitioning in the H.264/Advanced Video Coding (AVC codec, this paper introduces a further sub-partition of one of the H.264/AVC codec’s three data-partitions. Results show a 5 dB improvement in Peak Signal-to-Noise Ratio (PSNR through this innovation. In particular, the data partition containing intra-coded residuals is sub-divided into data from: those macroblocks (MBs naturally intra-coded, and those MBs forcibly inserted for non-periodic intra-refresh. Interactive user-to-user video streaming can benefit, as then HTTP adaptive streaming is inappropriate and the High Efficiency Video Coding (HEVC codec is too energy demanding.

EXTENSION OF FORMULAS FOR PARTITION FUNCTIONS

African Journals Online (AJOL)

Ladan et al.

2Department of Mathematics, Ahmadu Bello University, Zaria. ... 2 + 1 + 1. = 1 + 1 + 1 + 1. Partition function ( ). Andrew and Erikson (2004) stated that the ..... Andrews, G.E., 1984, The Theory of Partitions, Cambridge ... Pure Appl. Math.

The partition dimension of cycle books graph

Science.gov (United States)

Santoso, Jaya; Darmaji

2018-03-01

Let G be a nontrivial and connected graph with vertex set V(G), edge set E(G) and S ⊆ V(G) with v ∈ V(G), the distance between v and S is d(v,S) = min{d(v,x)|x ∈ S}. For an ordered partition ∏ = {S 1, S 2, S 3,…, Sk } of V(G), the representation of v with respect to ∏ is defined by r(v|∏) = (d(v, S 1), d(v, S 2),…, d(v, Sk )). The partition ∏ is called a resolving partition of G if all representations of vertices are distinct. The partition dimension pd(G) is the smallest integer k such that G has a resolving partition set with k members. In this research, we will determine the partition dimension of Cycle Books {B}{Cr,m}. Cycle books graph {B}{Cr,m} is a graph consisting of m copies cycle Cr with the common path P 2. It is shown that the partition dimension of cycle books graph, pd({B}{C3,m}) is 3 for m = 2, 3, and m for m ≥ 4. pd({B}{C4,m}) is 3 + 2k for m = 3k + 2, 4 + 2(k ‑ 1) for m = 3k + 1, and 3 + 2(k ‑ 1) for m = 3k. pd({B}{C5,m}) is m + 1.

Quantum Dilogarithms and Partition q-Series

Science.gov (United States)

Kato, Akishi; Terashima, Yuji

2015-08-01

In our previous work (Kato and Terashima, Commun Math Phys. arXiv:1403.6569, 2014), we introduced the partition q-series for mutation loop γ—a loop in exchange quiver. In this paper, we show that for a certain class of mutation sequences, called reddening sequences, the graded version of partition q-series essentially coincides with the ordered product of quantum dilogarithm associated with each mutation; the partition q-series provides a state-sum description of combinatorial Donaldson-Thomas invariants introduced by Keller.

A non-conventional watershed partitioning method for semi-distributed hydrological modelling: the package ALADHYN

Science.gov (United States)

Menduni, Giovanni; Pagani, Alessandro; Rulli, Maria Cristina; Rosso, Renzo

2002-02-01

The extraction of the river network from a digital elevation model (DEM) plays a fundamental role in modelling spatially distributed hydrological processes. The present paper deals with a new two-step procedure based on the preliminary identification of an ideal drainage network (IDN) from contour lines through a variable mesh size, and the further extraction of the actual drainage network (AND) from the IDN using land morphology. The steepest downslope direction search is used to identify individual channels, which are further merged into a network path draining to a given node of the IDN. The contributing area, peaks and saddles are determined by means of a steepest upslope direction search. The basin area is thus partitioned into physically based finite elements enclosed by irregular polygons. Different methods, i.e. the constant and variable threshold area methods, the contour line curvature method, and a topologic method descending from the Hortonian ordering scheme, are used to extract the ADN from the IDN. The contour line curvature method is shown to provide the most appropriate method from a comparison with field surveys. Using the ADN one can model the hydrological response of any sub-basin using a semi-distributed approach. The model presented here combines storm abstraction by the SCS-CN method with surface runoff routing as a geomorphological dispersion process. This is modelled using the gamma instantaneous unit hydrograph as parameterized by river geomorphology. The results are implemented using a project-oriented software facility for the Analysis of LAnd Digital HYdrological Networks (ALADHYN).

Intuitionistic uncertain linguistic partitioned Bonferroni means and their application to multiple attribute decision-making

Science.gov (United States)

Liu, Zhengmin; Liu, Peide

2017-04-01

The Bonferroni mean (BM) was originally introduced by Bonferroni and generalised by many other researchers due to its capacity to capture the interrelationship between input arguments. Nevertheless, in many situations, interrelationships do not always exist between all of the attributes. Attributes can be partitioned into several different categories and members of intra-partition are interrelated while no interrelationship exists between attributes of different partitions. In this paper, as complements to the existing generalisations of BM, we investigate the partitioned Bonferroni mean (PBM) under intuitionistic uncertain linguistic environments and develop two linguistic aggregation operators: intuitionistic uncertain linguistic partitioned Bonferroni mean (IULPBM) and its weighted form (WIULPBM). Then, motivated by the ideal of geometric mean and PBM, we further present the partitioned geometric Bonferroni mean (PGBM) and develop two linguistic geometric aggregation operators: intuitionistic uncertain linguistic partitioned geometric Bonferroni mean (IULPGBM) and its weighted form (WIULPGBM). Some properties and special cases of these proposed operators are also investigated and discussed in detail. Based on these operators, an approach for multiple attribute decision-making problems with intuitionistic uncertain linguistic information is developed. Finally, a practical example is presented to illustrate the developed approach and comparison analyses are conducted with other representative methods to verify the effectiveness and feasibility of the developed approach.

A statistical mechanical approach to restricted integer partition functions

Science.gov (United States)

Zhou, Chi-Chun; Dai, Wu-Sheng

2018-05-01

The main aim of this paper is twofold: (1) suggesting a statistical mechanical approach to the calculation of the generating function of restricted integer partition functions which count the number of partitions—a way of writing an integer as a sum of other integers under certain restrictions. In this approach, the generating function of restricted integer partition functions is constructed from the canonical partition functions of various quantum gases. (2) Introducing a new type of restricted integer partition functions corresponding to general statistics which is a generalization of Gentile statistics in statistical mechanics; many kinds of restricted integer partition functions are special cases of this restricted integer partition function. Moreover, with statistical mechanics as a bridge, we reveal a mathematical fact: the generating function of restricted integer partition function is just the symmetric function which is a class of functions being invariant under the action of permutation groups. Using this approach, we provide some expressions of restricted integer partition functions as examples.

Unsupervised Segmentation Methods of TV Contents

Directory of Open Access Journals (Sweden)

Elie El-Khoury

2010-01-01

Full Text Available We present a generic algorithm to address various temporal segmentation topics of audiovisual contents such as speaker diarization, shot, or program segmentation. Based on a GLR approach, involving the ΔBIC criterion, this algorithm requires the value of only a few parameters to produce segmentation results at a desired scale and on most typical low-level features used in the field of content-based indexing. Results obtained on various corpora are of the same quality level than the ones obtained by other dedicated and state-of-the-art methods.

Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

Science.gov (United States)

Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

2017-09-01

This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert

Analysis On Land Cover In Municipality Of Malang With Landsat 8 Image Through Unsupervised Classification

Science.gov (United States)

Nahari, R. V.; Alfita, R.

2018-01-01

Remote sensing technology has been widely used in the geographic information system in order to obtain data more quickly, accurately and affordably. One of the advantages of using remote sensing imagery (satellite imagery) is to analyze land cover and land use. Satellite image data used in this study were images from the Landsat 8 satellite combined with the data from the Municipality of Malang government. The satellite image was taken in July 2016. Furthermore, the method used in this study was unsupervised classification. Based on the analysis towards the satellite images and field observations, 29% of the land in the Municipality of Malang was plantation, 22% of the area was rice field, 12% was residential area, 10% was land with shrubs, and the remaining 2% was water (lake/reservoir). The shortcoming of the methods was 25% of the land in the area was unidentified because it was covered by cloud. It is expected that future researchers involve cloud removal processing to minimize unidentified area.

Partition functions. I. Improved partition functions and thermodynamic quantities for normal, equilibrium, and ortho and para molecular hydrogen

Science.gov (United States)

Popovas, A.; Jørgensen, U. G.

2016-11-01

Context. Hydrogen is the most abundant molecule in the Universe. Its thermodynamic quantities dominate the physical conditions in molecular clouds, protoplanetary disks, etc. It is also of high interest in plasma physics. Therefore thermodynamic data for molecular hydrogen have to be as accurate as possible in a wide temperature range. Aims: We here rigorously show the shortcomings of various simplifications that are used to calculate the total internal partition function. These shortcomings can lead to errors of up to 40 percent or more in the estimated partition function. These errors carry on to calculations of thermodynamic quantities. Therefore a more complicated approach has to be taken. Methods: Seven possible simplifications of various complexity are described, together with advantages and disadvantages of direct summation of experimental values. These were compared to what we consider the most accurate and most complete treatment (case 8). Dunham coefficients were determined from experimental and theoretical energy levels of a number of electronically excited states of H2. Both equilibrium and normal hydrogen was taken into consideration. Results: Various shortcomings in existing calculations are demonstrated, and the reasons for them are explained. New partition functions for equilibrium, normal, and ortho and para hydrogen are calculated and thermodynamic quantities are reported for the temperature range 1-20 000 K. Our results are compared to previous estimates in the literature. The calculations are not limited to the ground electronic state, but include all bound and quasi-bound levels of excited electronic states. Dunham coefficients of these states of H2 are also reported. Conclusions: For most of the relevant astrophysical cases it is strongly advised to avoid using simplifications, such as a harmonic oscillator and rigid rotor or ad hoc summation limits of the eigenstates to estimate accurate partition functions and to be particularly careful when

Unsupervised versus Supervised Identification of Prognostic Factors in Patients with Localized Retroperitoneal Sarcoma: A Data Clustering and Mahalanobis Distance Approach

Directory of Open Access Journals (Sweden)

Rita De Sanctis

2018-01-01

Full Text Available The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS patients by univariate and multivariate statistical techniques. A phase I-II study on localized RPS treated with high-dose ifosfamide and radiotherapy followed by surgery (ISG-STS 0303 protocol demonstrated that chemo/radiotherapy was safe and increased the 3-year relapse-free survival (RFS with respect to historical controls. Of 70 patients, twenty-six developed local, 10 distant, and 5 combined relapse. Median disease-free interval (DFI was 29.47 months. According to a discriminant function analysis, DFI, histology, relapse pattern, and the first treatment approach at relapse had a statistically significant prognostic impact. Based on scientific literature and clinical expertise, clinicopathological data were analyzed using both a supervised and an unsupervised classification method to predict the prognosis, with similar sample sizes (66 and 65, resp., in casewise approach and 70 in mean-substitution one. This is the first attempt to predict patients’ prognosis by means of multivariate statistics, and in this light, it looks noticable that (i some clinical data have a well-defined prognostic value, (ii the unsupervised model produced comparable results with respect to the supervised one, and (iii the appropriate combination of both models appears fruitful and easily extensible to different clinical contexts.

Effects of Single Nucleotide Polymorphism Marker Density on Haplotype Block Partition

Directory of Open Access Journals (Sweden)

Sun Ah Kim

2016-12-01

Full Text Available Many researchers have found that one of the most important characteristics of the structure of linkage disequilibrium is that the human genome can be divided into non-overlapping block partitions in which only a small number of haplotypes are observed. The location and distribution of haplotype blocks can be seen as a population property influenced by population genetic events such as selection, mutation, recombination and population structure. In this study, we investigate the effects of the density of markers relative to the full set of all polymorphisms in the region on the results of haplotype partitioning for five popular haplotype block partition methods: three methods in Haploview (confidence interval, four gamete test, and solid spine, MIG++ implemented in PLINK 1.9 and S-MIG++. We used several experimental datasets obtained by sampling subsets of single nucleotide polymorphism (SNP markers of chromosome 22 region in the 1000 Genomes Project data and also the HapMap phase 3 data to compare the results of haplotype block partitions by five methods. With decreasing sampling ratio down to 20% of the original SNP markers, the total number of haplotype blocks decreases and the length of haplotype blocks increases for all algorithms. When we examined the marker-independence of the haplotype block locations constructed from the datasets of different density, the results using below 50% of the entire SNP markers were very different from the results using the entire SNP markers. We conclude that the haplotype block construction results should be used and interpreted carefully depending on the selection of markers and the purpose of the study.

Dynamics of vacuum-sealed, double-leaf partitions

Science.gov (United States)

Kavanaugh, Joshua Stephen

The goal of this research is to investigate the feasibility and potential effectiveness of using vacuum-sealed, double-leaf partitions for applications in noise control. Substantial work has been done previously on double-leaf partitions where the acoustics of the inner chamber and mechanical vibrations of structural supports are passively and actively controlled. The work presented here is unique in that the proposed system aims to eliminate the need for active acoustic control of transmitted acoustic energy by removing all the air between the two panels of the double partition. Therefore, the only remaining energy paths would be along the boundary and at the points where there are intermediate structural supports connecting the two panels. The eventual goal of the research is to develop a high-loss double-leaf partition that simplifies active control by removing the need for control of the air cavity and channeling all the energy into discrete structural paths. The work presented here is a first step towards the goal of designing a high-loss, actively-controlled double-leaf partition with an air-evacuated inner chamber. One experiment is conducted to investigate the effects of various levels of vacuum on the response of a double-leaf partition whose panels are mechanically coupled only at the boundary. Another experiment is conducted which investigates the effect of changing the stiffness of an intermediate support coupling the two panels of a double-leaf partition in which a vacuum has been applied to the inner cavity. The available equipment was able to maintain a 99% vacuum between the panels. Both experiments are accompanied by analytical models used to investigate the importance of various dynamic parameters. Results show that the vacuum-sealed system shows some potential for increased transmission loss, primarily by the changing the natural frequencies of the double-leaf partition.

Hanging out with Which Friends? Friendship-Level Predictors of Unstructured and Unsupervised Socializing in Adolescence

Science.gov (United States)

Siennick, Sonja E.; Osgood, D. Wayne

2012-01-01

Companions are central to explanations of the risky nature of unstructured and unsupervised socializing, yet we know little about whom adolescents are with when hanging out. We examine predictors of how often friendship dyads hang out via multilevel analyses of longitudinal friendship-level data on over 5,000 middle schoolers. Adolescents hang out…

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Deep unsupervised learning on a desktop PC: A primer for cognitive scientists

Directory of Open Access Journals (Sweden)

Alberto eTestolin

2013-05-01

Full Text Available Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programming parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low-cost graphic cards (GPUs without any specific programming effort, thanks to the use of high-level programming routines (available in MATLAB or Python. We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging

Energy Technology Data Exchange (ETDEWEB)

Dunlavy, Daniel; Peter A. Chew

2016-05-01

This reports describes extensions of DEDICOM (DEcomposition into DIrectional COMponents) data models [3] that incorporate bound and linear constraints. The main purpose of these extensions is to investigate the use of improved data models for unsupervised part-of-speech tagging, as described by Chew et al. [2]. In that work, a single domain, two-way DEDICOM model was computed on a matrix of bigram fre- quencies of tokens in a corpus and used to identify parts-of-speech as an unsupervised approach to that problem. An open problem identi ed in that work was the com- putation of a DEDICOM model that more closely resembled the matrices used in a Hidden Markov Model (HMM), speci cally through post-processing of the DEDICOM factor matrices. The work reported here consists of the description of several models that aim to provide a direct solution to that problem and a way to t those models. The approach taken here is to incorporate the model requirements as bound and lin- ear constrains into the DEDICOM model directly and solve the data tting problem as a constrained optimization problem. This is in contrast to the typical approaches in the literature, where the DEDICOM model is t using unconstrained optimization approaches, and model requirements are satis ed as a post-processing step.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

Constraint Programming Approach to the Problem of Generating Milton Babbitt's All-partition Arrays

DEFF Research Database (Denmark)

Tanaka, Tsubasa; Bemman, Brian; Meredith, David

2016-01-01

elements and corresponding to a distinct integer partition of 12. Constraint programming (CP) is a tool for solving such combinatorial and constraint satisfaction problems. In this paper, we use CP for the first time to formalize this problem in generating an all-partition array. Solving the whole...... of this problem is difficult and few known solutions exist. Therefore, we propose solving two sub-problems and joining these to form a complete solution. We conclude by presenting a solution found using this method. Our solution is the first we are aware of to be discovered automatically using a computer......Milton Babbitt (1916–2011) was a composer of twelve-tone serial music noted for creating the all-partition array. One part of the problem in generating an all-partition array requires finding a covering of a pitch-class matrix by a collection of sets, each forming a region containing 12 distinct...

Partitions in languages and parallel computations

Energy Technology Data Exchange (ETDEWEB)

Burgin, M S; Burgina, E S

1982-05-01

Partitions of entries (linguistic structures) are studied that are intended for parallel data processing. The representations of formal languages with the aid of such structures is examined, and the relationships are considered between partitions of entries and abstract families of languages and automata. 18 references.

Scheduling Driven Partitioning of Heterogeneous Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

1998-01-01

In this paper we present an algorithm for system level hardware/software partitioning of heterogeneous embedded systems. The system is represented as an abstract graph which captures both data-flow and the flow of control. Given an architecture consisting of several processors, ASICs and shared...... busses, our partitioning algorithm finds the partitioning with the smallest hardware cost and is able to predict and guarantee the performance of the system in terms of worst case delay....

1-loop partition function in AdS{sub 3}/CFT{sub 2}

Energy Technology Data Exchange (ETDEWEB)

Chen, Bin [Department of Physics and State Key Laboratory of Nuclear Physics and Technology,Peking University,5 Yiheyuan Rd, Beijing 100871 (China); Collaborative Innovation Center of Quantum Matter,5 Yiheyuan Rd, Beijing 100871 (China); Center for High Energy Physics, Peking University,5 Yiheyuan Rd, Beijing 100871 (China); Wu, Jie-qiang [Department of Physics and State Key Laboratory of Nuclear Physics and Technology,Peking University,5 Yiheyuan Rd, Beijing 100871 (China)

2015-12-16

The 1-loop partition function of the handlebody solutions in the AdS{sub 3} gravity have been derived some years ago using the heat kernel techniques and the method of images. In the semiclassical limit, such partition function should correspond to the order O(c{sup 0}) part in the partition function of dual conformal field theory(CFT) on the boundary Riemann surface. The higher genus partition function could be computed by the multi-point functions in the Riemann sphere via sewing prescription. In the large central charge limit, the CFT is effectively free in the sense that to the leading order of c the multi-point function is further simplified to be a summation over the products of two-point functions of single-particle states. Correspondingly in the bulk, the graviton is freely propagating without interaction. Furthermore the product of the two-point functions may define the links, each of which is in one-to-one correspondence with the conjugacy class of the Schottky group of the Riemann surface. Moreover, the value of a link is determined by the multiplier of the element in the conjugacy class. This allows us to reproduce exactly the gravitational 1-loop partition function. The proof can be generalized to the higher spin gravity and its dual CFT.

Partition functions for supersymmetric black holes

NARCIS (Netherlands)

Manschot, J.

2008-01-01

This thesis presents a number of results on partition functions for four-dimensional supersymmetric black holes. These partition functions are important tools to explain the entropy of black holes from a microscopic point of view. Such a microscopic explanation was desired after the association of a

Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem

KAUST Repository

Lellmann, Jan

2012-11-09

We consider a variational convex relaxation of a class of optimal partitioning and multiclass labeling problems, which has recently proven quite successful and can be seen as a continuous analogue of Linear Programming (LP) relaxation methods for finite-dimensional problems. While for the latter several optimality bounds are known, to our knowledge no such bounds exist in the infinite-dimensional setting. We provide such a bound by analyzing a probabilistic rounding method, showing that it is possible to obtain an integral solution of the original partitioning problem from a solution of the relaxed problem with an a priori upper bound on the objective. The approach has a natural interpretation as an approximate, multiclass variant of the celebrated coarea formula. © 2012 Springer Science+Business Media New York.

Aspects of system modelling in Hardware/Software partitioning

DEFF Research Database (Denmark)

Knudsen, Peter Voigt; Madsen, Jan

1996-01-01

This paper addresses fundamental aspects of system modelling and partitioning algorithms in the area of Hardware/Software Codesign. Three basic system models for partitioning are presented and the consequences of partitioning according to each of these are analyzed. The analysis shows...... the importance of making a clear distinction between the model used for partitioning and the model used for evaluation It also illustrates the importance of having a realistic hardware model such that hardware sharing can be taken into account. Finally, the importance of integrating scheduling and allocation...

Generating Milton Babbitt's all-partition arrays

DEFF Research Database (Denmark)

Bemman, Brian; Meredith, David

2016-01-01

In most of Milton Babbitt's (1916–2011) works written since the early 1960s, both the pitch and rhythmic content is organized according to a highly constrained structure known as the all-partition array. The all-partition array provides a framework that ensures that as many different forms...

A New Model for Optimal Mechanical and Thermal Performance of Cement-Based Partition Wall.

Science.gov (United States)

Huang, Shiping; Hu, Mengyu; Huang, Yonghui; Cui, Nannan; Wang, Weifeng

2018-04-17

The prefabricated cement-based partition wall has been widely used in assembled buildings because of its high manufacturing efficiency, high-quality surface, and simple and convenient construction process. In this paper, a general porous partition wall that is made from cement-based materials was proposed to meet the optimal mechanical and thermal performance during transportation, construction and its service life. The porosity of the proposed partition wall is formed by elliptic-cylinder-type cavities. The finite element method was used to investigate the mechanical and thermal behaviour, which shows that the proposed model has distinct advantages over the current partition wall that is used in the building industry. It is found that, by controlling the eccentricity of the elliptic-cylinder cavities, the proposed wall stiffness can be adjusted to respond to the imposed loads and to improve the thermal performance, which can be used for the optimum design. Finally, design guidance is provided to obtain the optimal mechanical and thermal performance. The proposed model could be used as a promising candidate for partition wall in the building industry.

A New Model for Optimal Mechanical and Thermal Performance of Cement-Based Partition Wall

Directory of Open Access Journals (Sweden)

Shiping Huang

2018-04-01

Full Text Available The prefabricated cement-based partition wall has been widely used in assembled buildings because of its high manufacturing efficiency, high-quality surface, and simple and convenient construction process. In this paper, a general porous partition wall that is made from cement-based materials was proposed to meet the optimal mechanical and thermal performance during transportation, construction and its service life. The porosity of the proposed partition wall is formed by elliptic-cylinder-type cavities. The finite element method was used to investigate the mechanical and thermal behaviour, which shows that the proposed model has distinct advantages over the current partition wall that is used in the building industry. It is found that, by controlling the eccentricity of the elliptic-cylinder cavities, the proposed wall stiffness can be adjusted to respond to the imposed loads and to improve the thermal performance, which can be used for the optimum design. Finally, design guidance is provided to obtain the optimal mechanical and thermal performance. The proposed model could be used as a promising candidate for partition wall in the building industry.

Learning phacoemulsification. Results of different teaching methods.

Directory of Open Access Journals (Sweden)

Hennig Albrecht

2004-01-01

Full Text Available We report the learning curves of three eye surgeons converting from sutureless extracapsular cataract extraction to phacoemulsification using different teaching methods. Posterior capsule rupture (PCR as a per-operative complication and visual outcome of the first 100 operations were analysed. The PCR rate was 4% and 15% in supervised and unsupervised surgery respectively. Likewise, an uncorrected visual acuity of > or = 6/18 on the first postoperative day was seen in 62 (62% of patients and in 22 (22% in supervised and unsupervised surgery respectively.

An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information

Science.gov (United States)

Snyder, Robin M.

2015-01-01

The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and…

Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

Science.gov (United States)

Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

2017-12-01

Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

Partitioning sources of variation in vertebrate species richness

Science.gov (United States)

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species

Applying the partitioned multiobjective risk method (PMRM) to portfolio selection.

Science.gov (United States)

Reyes Santos, Joost; Haimes, Yacov Y

2004-06-01

The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model

Development of long-lived radionuclide partitioning technology

Energy Technology Data Exchange (ETDEWEB)

Yoo, Jae Hyung; Lee, Eil Hee; Kim, Kwang Wook; Yang, Han Beom; Chung, Dong Yong; Lim, Jae Kwan; Shin, Young Jun; Kim, Heung Ho; Kown, Sun Gil; Kim, Young Hwan; Hwang, Doo Seung

1996-07-01

This study has been focused on the development of unit processes for partitioning in the 1st stage, and experimentally carried out to examine the separation characteristics and operation conditions on the following unit processes. (1) Removal of a small amount of uranium by extraction with TBP, (2) Removal of Zr and Mo and destruction of nitric acid by uranium by denitration with formic acid, (3) Co-precipitation of Am, Np and RE oxalic acid, (4) Dissolution and destruction of oxalate by hydrogen peroxide, (5) Co-extraction of Am, Np and RE by nitric acid, (8) Back-extraction of Np by oxalic acid, (9) Adsorption and elution of Cs and Sr by zeolite, and (10) Advanced separation of radionuclide by electrochemical REDOX method. The results obtained from each unit process will be use as the basic materials for the establishment of optimal partitioning and design of process equipment. (author). 46 refs., 54 tabs., 222 figs.

Matrix string partition function

CERN Document Server

Kostov, Ivan K; Kostov, Ivan K.; Vanhove, Pierre

1998-01-01

We evaluate quasiclassically the Ramond partition function of Euclidean D=10 U(N) super Yang-Mills theory reduced to a two-dimensional torus. The result can be interpreted in terms of free strings wrapping the space-time torus, as expected from the point of view of Matrix string theory. We demonstrate that, when extrapolated to the ultraviolet limit (small area of the torus), the quasiclassical expressions reproduce exactly the recently obtained expression for the partition of the completely reduced SYM theory, including the overall numerical factor. This is an evidence that our quasiclassical calculation might be exact.

SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

Directory of Open Access Journals (Sweden)

X. Han

2016-06-01

Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

Equilibrium thermodynamics of the partitioning of non-steroidal anti-inflammatory drugs into human erythrocyte ghost membranes

International Nuclear Information System (INIS)

Omran, Ahmed A.

2013-01-01

Graphical abstract: Bar diagram representing thermodynamic parameters obtained for the partitioning of NSAIDs into human erythrocyte ghost membranes at physiological pH; 7.4. Highlights: • Partition coefficients of NSAIDs into HEG membranes were determined. • Thermodynamic parameters were evaluated and successfully analyzed. • Partitioning of NSAIDs into HEG membranes was exothermic. • Partitioning of NSAIDs into HEG is spontaneous with negative free energy values. • Identical partitioning enthalpy–entropy driven compensation mechanism was shown. -- Abstract: In this work,second derivative spectrophotometry was applied for determining the partition coefficients (K p s) of four non-steroidal anti-inflammatory drugs (NSAIDs; flufenamic, meclofenamic, mefenamic and niflumic acids) into human erythrocyte ghost (HEG) membranes over a temperature range from (283.2 to 313.2) K. The proposed method allowed the evaluation and direct analyses of thermodynamic parameters; enthalpy (ΔH W→M ), Gibbs energy (ΔG W→M ) and entropy (ΔS W→M ) changes of the partitioning of NSAIDs into HEG membranes. The partitioning of NSAIDs between polar aqueous phase and non-polar lipid bilayer HEG membrane phase was exothermic with negative (ΔH W→M ) which compensated for the changes in (ΔS W→M ). The negative values of (ΔG W→M ) revealed that the partitioning of NSAIDs into HEG, owing to their transfer from polar aqueous phase and non-polar HEG phase is spontaneous. The enthalpy–entropy correlation analysis resulted in a good linearity that suggests an identical partitioning enthalpy–entropy driven compensation mechanism for the studied NSAIDs

Abiotic partitioning of clothianidin under simulated rice field conditions.

Science.gov (United States)

Mulligan, Rebecca A; Parikh, Sanjai J; Tjeerdema, Ronald S

2015-10-01

Clothianidin is registered for pre- and post-flood application in Californian rice fields for control of the rice seed midge, Cricotopus sylvestris, and the rice water weevil, Lissorhoptrus oryzophilus. The objective was to characterize air-water and soil-water partitioning of clothianidin under simulated Californian rice field conditions. Clothianidin was confirmed to be non-volatile (from water) via the gas purge method, as no loss from the aqueous phase was observed at 22 and 37 °C; an upper-limit KH value was calculated at 2.9 × 10(-11) Pa m(3) mol(-1) (20 °C). Soil-water partitioning was determined by the batch equilibrium method using four soils collected from rice fields in the Sacramento Valley, and sorption affinity (Kd ), sorbent capacity, desorption and organic-carbon-normalized distribution (Koc ) were determined. Values for pH, cation exchange capacity and organic matter content ranged from 4.5 to 6.6, from 5.9 to 37.9 and from 1.25 to 1.97% respectively. The log Koc values (22 and 37 °C) ranged from 2.6 to 2.7, while sorption capacity was low at 22 °C and decreased further at 37 °C. Hysteresis was observed in soils at both temperatures, suggesting that bound residues do not readily desorb. Soil-water and air-water partitioning will not significantly reduce offsite transport of clothianidin from flooded rice fields via drainage. © 2014 Society of Chemical Industry.

Surface mapping via unsupervised classification of remote sensing: application to MESSENGER/MASCS and DAWN/VIRS data.

Science.gov (United States)

D'Amore, M.; Le Scaon, R.; Helbert, J.; Maturilli, A.

2017-12-01

Machine-learning achieved unprecedented results in high-dimensional data processing tasks with wide applications in various fields. Due to the growing number of complex nonlinear systems that have to be investigated in science and the bare raw size of data nowadays available, ML offers the unique ability to extract knowledge, regardless the specific application field. Examples are image segmentation, supervised/unsupervised/ semi-supervised classification, feature extraction, data dimensionality analysis/reduction.The MASCS instrument has mapped Mercury surface in the 400-1145 nm wavelength range during orbital observations by the MESSENGER spacecraft. We have conducted k-means unsupervised hierarchical clustering to identify and characterize spectral units from MASCS observations. The results display a dichotomy: a polar and equatorial units, possibly linked to compositional differences or weathering due to irradiation. To explore possible relations between composition and spectral behavior, we have compared the spectral provinces with elemental abundance maps derived from MESSENGER's X-Ray Spectrometer (XRS).For the Vesta application on DAWN Visible and infrared spectrometer (VIR) data, we explored several Machine Learning techniques: image segmentation method, stream algorithm and hierarchical clustering.The algorithm successfully separates the Olivine outcrops around two craters on Vesta's surface [1]. New maps summarizing the spectral and chemical signature of the surface could be automatically produced.We conclude that instead of hand digging in data, scientist could choose a subset of algorithms with well known feature (i.e. efficacy on the particular problem, speed, accuracy) and focus their effort in understanding what important characteristic of the groups found in the data mean. [1] E Ammannito et al. "Olivine in an unexpected location on Vesta's surface". In: Nature 504.7478 (2013), pp. 122-125.

Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

Directory of Open Access Journals (Sweden)

Ahmed R. Abas

2012-07-01

Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.

Prediction of Partition Coefficients of Organic Compounds for SPME/PDMS

Directory of Open Access Journals (Sweden)

Liao Hsuan-Yu

2016-01-01

Full Text Available The partition coefficients of 51 organic compounds between SPME/PDMS and gas were compiled from the literature sources in this study. The effect of physicochemical properties and descriptors on the partitioning process of partition coefficients was explicated by the correlation analysis. The PDMS-gas partition coefficients were well correlated to the molecular weight of organic compounds (r = 0.832, p < 0.05. An empirical model, consisting of the molecular weight and the polarizability, was developed to appropriately predict the partition coefficients of organic compounds. The empirical model for estimating the PDMS-gas partition coefficient will contribute to the practical applications of the SPME technique.

Predicting protein complexes using a supervised learning method combined with local structural information.

Science.gov (United States)

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

Directory of Open Access Journals (Sweden)

Yoonsik Shim

2016-10-01

Full Text Available We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP. The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

Science.gov (United States)

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Radionuclide Partitioning in an Underground Nuclear Test Cavity

Energy Technology Data Exchange (ETDEWEB)

Rose, T P; Hu, Q; Zhao, P; Conrado, C L; Dickerson, R; Eaton, G F; Kersting, A B; Moran, J E; Nimz, G; Powell, B A; Ramon, E C; Ryerson, F J; Williams, R W; Wooddy, P T; Zavarin, M

2009-01-09

In 2004, a borehole was drilled into the 1983 Chancellor underground nuclear test cavity to investigate the distribution of radionuclides within the cavity. Sidewall core samples were collected from a range of depths within the re-entry hole and two sidetrack holes. Upon completion of drilling, casing was installed and a submersible pump was used to collect groundwater samples. Test debris and groundwater samples were analyzed for a variety of radionuclides including the fission products {sup 99}Tc, {sup 125}Sb, {sup 129}I, {sup 137}Cs, and {sup 155}Eu, the activation products {sup 60}Co, {sup 152}Eu, and {sup 154}Eu, and the actinides U, Pu, and Am. In addition, the physical and bulk chemical properties of the test debris were characterized using Scanning Electron Microscopy (SEM) and Electron Microprobe measurements. Analytical results were used to evaluate the partitioning of radionuclides between the melt glass, rubble, and groundwater phases in the Chancellor test cavity. Three comparative approaches were used to calculate partitioning values, though each method could not be applied to every nuclide. These approaches are based on: (1) the average Area 19 inventory from Bowen et al. (2001); (2) melt glass, rubble, and groundwater mass estimates from Zhao et al. (2008); and (3) fission product mass yield data from England and Rider (1994). The U and Pu analyses of the test debris are classified and partitioning estimates for these elements were calculated directly from the classified Miller et al. (2002) inventory for the Chancellor test. The partitioning results from this study were compared to partitioning data that were previously published by the IAEA (1998). Predictions of radionuclide distributions from the two studies are in agreement for a majority of the nuclides under consideration. Substantial differences were noted in the partitioning values for {sup 99}Tc, {sup 125}Sb, {sup 129}I, and uranium. These differences are attributable to two factors

Variable length adjacent partitioning for PTS based PAPR reduction of OFDM signal

International Nuclear Information System (INIS)

Ibraheem, Zeyid T.; Rahman, Md. Mijanur; Yaakob, S. N.; Razalli, Mohammad Shahrazel; Kadhim, Rasim A.

2015-01-01

Peak-to-Average power ratio (PAPR) is a major drawback in OFDM communication. It leads the power amplifier into nonlinear region operation resulting into loss of data integrity. As such, there is a strong motivation to find techniques to reduce PAPR. Partial Transmit Sequence (PTS) is an attractive scheme for this purpose. Judicious partitioning the OFDM data frame into disjoint subsets is a pivotal component of any PTS scheme. Out of the existing partitioning techniques, adjacent partitioning is characterized by an attractive trade-off between cost and performance. With an aim of determining effects of length variability of adjacent partitions, we performed an investigation into the performances of a variable length adjacent partitioning (VL-AP) and fixed length adjacent partitioning in comparison with other partitioning schemes such as pseudorandom partitioning. Simulation results with different modulation and partitioning scenarios showed that fixed length adjacent partition had better performance compared to variable length adjacent partitioning. As expected, simulation results showed a slightly better performance of pseudorandom partitioning technique compared to fixed and variable adjacent partitioning schemes. However, as the pseudorandom technique incurs high computational complexities, adjacent partitioning schemes were still seen as favorable candidates for PAPR reduction

Variable length adjacent partitioning for PTS based PAPR reduction of OFDM signal

Energy Technology Data Exchange (ETDEWEB)

Ibraheem, Zeyid T.; Rahman, Md. Mijanur; Yaakob, S. N.; Razalli, Mohammad Shahrazel; Kadhim, Rasim A. [School of Computer and Communication Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis (Malaysia)

2015-05-15

Peak-to-Average power ratio (PAPR) is a major drawback in OFDM communication. It leads the power amplifier into nonlinear region operation resulting into loss of data integrity. As such, there is a strong motivation to find techniques to reduce PAPR. Partial Transmit Sequence (PTS) is an attractive scheme for this purpose. Judicious partitioning the OFDM data frame into disjoint subsets is a pivotal component of any PTS scheme. Out of the existing partitioning techniques, adjacent partitioning is characterized by an attractive trade-off between cost and performance. With an aim of determining effects of length variability of adjacent partitions, we performed an investigation into the performances of a variable length adjacent partitioning (VL-AP) and fixed length adjacent partitioning in comparison with other partitioning schemes such as pseudorandom partitioning. Simulation results with different modulation and partitioning scenarios showed that fixed length adjacent partition had better performance compared to variable length adjacent partitioning. As expected, simulation results showed a slightly better performance of pseudorandom partitioning technique compared to fixed and variable adjacent partitioning schemes. However, as the pseudorandom technique incurs high computational complexities, adjacent partitioning schemes were still seen as favorable candidates for PAPR reduction.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

In unsupervised classification, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...

Unsupervised/supervised learning concept for 24-hour load forecasting

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Babic, B [Electrical Power Industry of Serbia, Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science

1993-07-01

An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

Unsupervised Classification of Surface Defects in Wire Rod Production Obtained by Eddy Current Sensors

Directory of Open Access Journals (Sweden)

Sergio Saludes-Rodil

2015-04-01

Full Text Available An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors.

Analytical model for macromolecular partitioning during yeast cell division

International Nuclear Information System (INIS)

Kinkhabwala, Ali; Khmelinskii, Anton; Knop, Michael

2014-01-01

Asymmetric cell division, whereby a parent cell generates two sibling cells with unequal content and thereby distinct fates, is central to cell differentiation, organism development and ageing. Unequal partitioning of the macromolecular content of the parent cell — which includes proteins, DNA, RNA, large proteinaceous assemblies and organelles — can be achieved by both passive (e.g. diffusion, localized retention sites) and active (e.g. motor-driven transport) processes operating in the presence of external polarity cues, internal asymmetries, spontaneous symmetry breaking, or stochastic effects. However, the quantitative contribution of different processes to the partitioning of macromolecular content is difficult to evaluate. Here we developed an analytical model that allows rapid quantitative assessment of partitioning as a function of various parameters in the budding yeast Saccharomyces cerevisiae. This model exposes quantitative degeneracies among the physical parameters that govern macromolecular partitioning, and reveals regions of the solution space where diffusion is sufficient to drive asymmetric partitioning and regions where asymmetric partitioning can only be achieved through additional processes such as motor-driven transport. Application of the model to different macromolecular assemblies suggests that partitioning of protein aggregates and episomes, but not prions, is diffusion-limited in yeast, consistent with previous reports. In contrast to computationally intensive stochastic simulations of particular scenarios, our analytical model provides an efficient and comprehensive overview of partitioning as a function of global and macromolecule-specific parameters. Identification of quantitative degeneracies among these parameters highlights the importance of their careful measurement for a given macromolecular species in order to understand the dominant processes responsible for its observed partitioning

Parallel Processing of Big Point Clouds Using Z-Order Partitioning

Science.gov (United States)

Alis, C.; Boehm, J.; Liu, K.

2016-06-01

As laser scanning technology improves and costs are coming down, the amount of point cloud data being generated can be prohibitively difficult and expensive to process on a single machine. This data explosion is not only limited to point cloud data. Voluminous amounts of high-dimensionality and quickly accumulating data, collectively known as Big Data, such as those generated by social media, Internet of Things devices and commercial transactions, are becoming more prevalent as well. New computing paradigms and frameworks are being developed to efficiently handle the processing of Big Data, many of which utilize a compute cluster composed of several commodity grade machines to process chunks of data in parallel. A central concept in many of these frameworks is data locality. By its nature, Big Data is large enough that the entire dataset would not fit on the memory and hard drives of a single node hence replicating the entire dataset to each worker node is impractical. The data must then be partitioned across worker nodes in a manner that minimises data transfer across the network. This is a challenge for point cloud data because there exist different ways to partition data and they may require data transfer. We propose a partitioning based on Z-order which is a form of locality-sensitive hashing. The Z-order or Morton code is computed by dividing each dimension to form a grid then interleaving the binary representation of each dimension. For example, the Z-order code for the grid square with coordinates (x = 1 = 012, y = 3 = 112) is 10112 = 11. The number of points in each partition is controlled by the number of bits per dimension: the more bits, the fewer the points. The number of bits per dimension also controls the level of detail with more bits yielding finer partitioning. We present this partitioning method by implementing it on Apache Spark and investigating how different parameters affect the accuracy and running time of the k nearest neighbour algorithm

Unsupervised image matching based on manifold alignment.

Science.gov (United States)

Pei, Yuru; Huang, Fengchun; Shi, Fuhao; Zha, Hongbin

2012-08-01

This paper challenges the issue of automatic matching between two image sets with similar intrinsic structures and different appearances, especially when there is no prior correspondence. An unsupervised manifold alignment framework is proposed to establish correspondence between data sets by a mapping function in the mutual embedding space. We introduce a local similarity metric based on parameterized distance curves to represent the connection of one point with the rest of the manifold. A small set of valid feature pairs can be found without manual interactions by matching the distance curve of one manifold with the curve cluster of the other manifold. To avoid potential confusions in image matching, we propose an extended affine transformation to solve the nonrigid alignment in the embedding space. The comparatively tight alignments and the structure preservation can be obtained simultaneously. The point pairs with the minimum distance after alignment are viewed as the matchings. We apply manifold alignment to image set matching problems. The correspondence between image sets of different poses, illuminations, and identities can be established effectively by our approach.

Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem

KAUST Repository

Lellmann, Jan; Lenzen, Frank; Schnö rr, Christoph

2012-01-01

We consider a variational convex relaxation of a class of optimal partitioning and multiclass labeling problems, which has recently proven quite successful and can be seen as a continuous analogue of Linear Programming (LP) relaxation methods

Cooperative mobile agents search using beehive partitioned structure and Tabu Random search algorithm

Science.gov (United States)

Ramazani, Saba; Jackson, Delvin L.; Selmic, Rastko R.

2013-05-01

In search and surveillance operations, deploying a team of mobile agents provides a robust solution that has multiple advantages over using a single agent in efficiency and minimizing exploration time. This paper addresses the challenge of identifying a target in a given environment when using a team of mobile agents by proposing a novel method of mapping and movement of agent teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, mobile agents have an efficient travel path while performing searches due to this partitioning approach. Second, we use a team of mobile agents that move in a cooperative manner and utilize the Tabu Random algorithm to search for the target. Due to the ever-increasing use of robotics and Unmanned Aerial Vehicle (UAV) platforms, the field of cooperative multi-agent search has developed many applications recently that would benefit from the use of the approach presented in this work, including: search and rescue operations, surveillance, data collection, and border patrol. In this paper, the increased efficiency of the Tabu Random Search algorithm method in combination with hexagonal partitioning is simulated, analyzed, and advantages of this approach are presented and discussed.

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

Science.gov (United States)

Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

2018-01-11

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

Linearization of non-commuting operators in the partition function

International Nuclear Information System (INIS)

Ahmed, M.

1983-06-01

A generalization of the Stratonovich-Hubbard scheme for evaluating the grand canonical partition function is given. The scheme involves linearization of products of non-commuting operators using the functional integral method. The non-commutivity of the operators leads to an additional term which can be absorbed in the single-particle Hamiltonian. (author)

Dynamic Load Balancing for PIC code using Eulerian/Lagrangian partitioning

OpenAIRE

Sauget, Marc; Latu, Guillaume

2017-01-01

This document presents an analysis of different load balance strategies for a Plasma physics code that models high energy particle beams with PIC method. A comparison of different load balancing algorithms is given: static or dynamic ones. Lagrangian and Eulerian partitioning techniques have been investigated.

Alternative measures of lipophilicity: from octanol-water partitioning to IAM retention.

Science.gov (United States)

Giaginis, Costas; Tsantili-Kakoulidou, Anna

2008-08-01

This review describes lipophilicity parameters currently used in drug design and QSAR studies. After a short historical overview, the complex nature of lipophilicity as the outcome of polar/nonpolar inter- and intramolecular interactions is analysed and considered as the background for the discussion of the different lipophilicity descriptors. The first part focuses on octanol-water partitioning of neutral and ionisable compounds, evaluates the efficiency of predictions and provides a short description of the experimental methods for the determination of distribution coefficients. A next part is dedicated to reversed-phase chromatographic techniques, HPLC and TLC in lipophilicity assessment. The two methods are evaluated for their efficiency to simulate octanol-water and the progress achieved in the refinement of suitable chromatographic conditions, in particular in the field of HPLC, is outlined. Liposomes as direct models of biological membranes are examined and phospolipophilicity is compared to the traditional lipophilicity concept. Difficulties associated with liposome-water partitioning are discussed. The last part focuses on Immobilised Artificial Membrane (IAM) chromatography as an alternative which combines membrane simulation with rapid measurements. IAM chromatographic retention is compared to octanol-water and liposome-water partitioning as well as to reversed-phase retention and its potential to predict biopartitioning and biological activities is discussed.

Spatial partitions systematize visual search and enhance target memory.

Science.gov (United States)

Solman, Grayden J F; Kingstone, Alan

2017-02-01

Humans are remarkably capable of finding desired objects in the world, despite the scale and complexity of naturalistic environments. Broadly, this ability is supported by an interplay between exploratory search and guidance from episodic memory for previously observed target locations. Here we examined how the environment itself may influence this interplay. In particular, we examined how partitions in the environment-like buildings, rooms, and furniture-can impact memory during repeated search. We report that the presence of partitions in a display, independent of item configuration, reliably improves episodic memory for item locations. Repeated search through partitioned displays was faster overall and was characterized by more rapid ballistic orienting in later repetitions. Explicit recall was also both faster and more accurate when displays were partitioned. Finally, we found that search paths were more regular and systematic when displays were partitioned. Given the ubiquity of partitions in real-world environments, these results provide important insights into the mechanisms of naturalistic search and its relation to memory.

Topological string partition functions as polynomials

International Nuclear Information System (INIS)

Yamaguchi, Satoshi; Yau Shingtung

2004-01-01

We investigate the structure of the higher genus topological string amplitudes on the quintic hypersurface. It is shown that the partition functions of the higher genus than one can be expressed as polynomials of five generators. We also compute the explicit polynomial forms of the partition functions for genus 2, 3, and 4. Moreover, some coefficients are written down for all genus. (author)

Partitioning of selected antioxidants in mayonnaise

DEFF Research Database (Denmark)

Jacobsen, Charlotte; Schwarz, K.; Stockmann, H.

1999-01-01

This study examined partitioning of alpha-, beta-, and gamma- tocopherol and six polar antioxidants (Trolox, ferulic acid, caffeic acid, propyl gallate, gallic acid, and catechin) in mayonnaise. Partitioning of antioxidants between different phases was determined after separation of mayonnaise...... acid and catechin) to 83% (Trolox). Accordingly, proportions of 6% (Trolox) to 80% (gallic acid and catechin) were found in the aqueous phase. Similar trends were observed after dialysis. After ultracentrifugation, large proportions of polar antioxidants were found in the "emulsion phase...... by either (a) centrifugation + ultracentrifugation or (b) centrifugation + dialysis. Antioxidants partitioned in accordance with their chemical structure and polarity: Tocopherols were concentrated in the oil phase (93-96%), while the proportion of polar antioxidants in the oil phase ranged from 0% (gallic...

Partitioning of resveratrol between pentane and DMSO

DEFF Research Database (Denmark)

Shen, Chen; Stein, Paul C.; Klösgen-Buchkremer, Beate Maria

2015-01-01

Partitioning of trans-3,5,4′-trihydroxy-stilbene (resveratrol) between n-pentane and DMSO was investigated as a contribution to understand the interaction between resveratrol and biomembranes. In order to determine the partition coefficient P* of resveratrol between pentane and DMSO, resveratrol ...

High Pressure/Temperature Metal Silicate Partitioning of Tungsten

Science.gov (United States)

Shofner, G. A.; Danielson, L.; Righter, K.; Campbell, A. J.

2010-01-01

The behavior of chemical elements during metal/silicate segregation and their resulting distribution in Earth's mantle and core provide insight into core formation processes. Experimental determination of partition coefficients allows calculations of element distributions that can be compared to accepted values of element abundances in the silicate (mantle) and metallic (core) portions of the Earth. Tungsten (W) is a moderately siderophile element and thus preferentially partitions into metal versus silicate under many planetary conditions. The partitioning behavior has been shown to vary with temperature, silicate composition, oxygen fugacity, and pressure. Most of the previous work on W partitioning has been conducted at 1-bar conditions or at relatively low pressures, i.e. pressure. Predictions based on extrapolation of existing data and parameterizations suggest an increased pressured dependence on metal/ silicate partitioning of W at higher pressures 5. However, the dependence on pressure is not as well constrained as T, fO2, and silicate composition. This poses a problem because proposed equilibration pressures for core formation range from 27 to 50 GPa, falling well outside the experimental range, therefore requiring exptrapolation of a parametereized model. Higher pressure data are needed to improve our understanding of W partitioning at these more extreme conditions.

Partition function for a singular background

International Nuclear Information System (INIS)

McKenzie-Smith, J.J.; Naylor, W.

2005-01-01

We present a method for evaluating the partition function in a varying external field. Specifically, we look at the case of a non-interacting, charged, massive scalar field at finite temperature with an associated chemical potential in the background of a delta-function potential. Whilst we present a general method, valid at all temperatures, we only give the result for the leading order term in the high temperature limit. Although the derivative expansion breaks down for inhomogeneous backgrounds we are able to obtain the high temperature expansion, as well as an analytic expression for the zero point energy, by way of a different approximation scheme, which we call the local Born approximation (LBA)

Partition function for a singular background

Energy Technology Data Exchange (ETDEWEB)

McKenzie-Smith, J.J. [Financial Risk Management Ltd, 15 Adam Street, London WC2N 6AH (United Kingdom)]. E-mail: julian.mckenzie-smith@frmhedge.com; Naylor, W. [Yukawa Institute for Theoretical Physics, Kyoto University, Kyoto 606-8502 (Japan)]. E-mail: naylor@yukawa.kyoto-u.ac.jp

2005-03-17

We present a method for evaluating the partition function in a varying external field. Specifically, we look at the case of a non-interacting, charged, massive scalar field at finite temperature with an associated chemical potential in the background of a delta-function potential. Whilst we present a general method, valid at all temperatures, we only give the result for the leading order term in the high temperature limit. Although the derivative expansion breaks down for inhomogeneous backgrounds we are able to obtain the high temperature expansion, as well as an analytic expression for the zero point energy, by way of a different approximation scheme, which we call the local Born approximation (LBA)

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

Science.gov (United States)

Doran, G.

2013-01-01

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Error Analysis of Explicit Partitioned Runge–Kutta Schemes for Conservation Laws

KAUST Repository

Hundsdorfer, Willem

2014-08-27

An error analysis is presented for explicit partitioned Runge–Kutta methods and multirate methods applied to conservation laws. The interfaces, across which different methods or time steps are used, lead to order reduction of the schemes. Along with cell-based decompositions, also flux-based decompositions are studied. In the latter case mass conservation is guaranteed, but it will be seen that the accuracy may deteriorate.

Error Analysis of Explicit Partitioned Runge–Kutta Schemes for Conservation Laws

KAUST Repository

Hundsdorfer, Willem; Ketcheson, David I.; Savostianov, Igor

2014-01-01

An error analysis is presented for explicit partitioned Runge–Kutta methods and multirate methods applied to conservation laws. The interfaces, across which different methods or time steps are used, lead to order reduction of the schemes. Along with cell-based decompositions, also flux-based decompositions are studied. In the latter case mass conservation is guaranteed, but it will be seen that the accuracy may deteriorate.

Assessing the potential for isotopic partitioning of soil respiration at research sites in Nova Scotia and Newfoundland

Energy Technology Data Exchange (ETDEWEB)

Risk, D.; Kellman, L.; Black, M. [Saint Francis Xavier Univ., Antigonish, NS (Canada). Environmental Sciences Research Centre

2005-07-01

The stable isotope ratios of carbon and oxygen in different tree species were studied with respect to different tissues, at different points within the tree, through soil profiles and in carbon dioxide respired from laboratory incubations. Although isotopic methods of partitioning autotrophic and heterotrophic soil respiration have been used with some success, stable isotopic methods are complicated by the fact that carbon isotope fractionations are small in natural systems, and radiocarbon techniques are time and equipment intensive. Studies that use isotopic analysis opportunistically, such as in C3/C4 transitional systems, have proven to be the most successful. Previously unexploited opportunities have the potential to be used for stable isotope-based partitioning in natural systems if the autotrophic/heterotrophic process distribution in the profile is well understand and if there is good process resolution and concurrent analyses using physical partitioning methods such as trenches. This study explored the different paths of opportunity in terms of background isotopic characterization that is being carried out for an existing network of carbon flux research sites in eastern Nova Scotia and in western Newfoundland. The new continuous flow-isotope ratio mass spectrometer (CF-IRMS) at the Environmental Earth Sciences Laboratory at St. Francis Xavier University was used for the isotopic analyses. The isotopic information will be evaluated for potential partitioning opportunities, considering the combination of approaches that will give the best chances of success. Isotopic partitioning trials will take place at suitable sites.

Unsupervised exercise in survivors of human papillomavirus related head and neck cancer: how many can go it alone?

Science.gov (United States)

Bauml, Joshua; Kim, Jiyoung; Zhang, Xiaochen; Aggarwal, Charu; Cohen, Roger B; Schmitz, Kathryn

2017-08-01

Patients with human papillomavirus (HPV)-related head and neck cancer (HNC) have a better prognosis relative to other types of HNC, making survivorship an emerging and critical issue. Exercise is a core component of survivorship care, but little is known about how many survivors of HPV-related HNC can safely be advised to start exercising on their own, as opposed to needing further evaluation or supervised exercise. We utilized guidelines to identify health issues that would indicate value of further evaluation prior to being safely prescribed unsupervised exercise. We performed a retrospective chart review of 150 patients with HPV-related HNC to assess health issues 6 months after completing definitive therapy. Patients with at least one health issue were deemed appropriate to receive further evaluation prior to prescription for unsupervised exercise. We utilized logistic regression to identify clinical and demographic factors associated with the need for further evaluation, likely performed by outpatient rehabilitation clinicians. In this cohort of patients, 39.3% could safely be prescribed unsupervised exercise 6 months after completing definitive therapy. On multivariable regression, older age, BMI >30, and receipt of radiation were associated with an increased likelihood for requiring further evaluation or supervised exercise. Over half of patients with HPV-related HNC would benefit from referral to physical therapy or an exercise professional for further evaluation to determine the most appropriate level of exercise supervision, based upon current guidelines. Development of such referral systems will be essential to enhance survivorship outcomes for patients who have completed treatment.

Why partition nuclear waste

International Nuclear Information System (INIS)

Cohen, J.J.

1976-01-01

A cursory review of literature dealing with various separatory processes involved in the handling of high-level liquid nuclear waste discloses that, for the most part, discussion centers on separation procedures and methodology for handling the resulting fractions, particularly the actinide wastes. There appears to be relatively little discussion on the incentives or motivations for performing these separations in the first place. Discussion is often limited to the assumption that we must separate out ''long-term'' from our ''short-term'' management problems. This paper deals with that assumption and devotes primary attention to the question of ''why partition waste'' rather than the question of ''how to partition waste'' or ''what to do with the segregated waste.''

Development of partitioning process: purification of DIDPA

Energy Technology Data Exchange (ETDEWEB)

Watanabe, Masayuki; Morita, Yasuji; Kubota, Masumitsu [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1998-04-01

The partitioning process has developed and demonstrated that the solvent extraction with diisodecylphosphoric acid (DIDPA) can successfully separate transuranium elements from a high-level liquid waste. In the solvent extraction, DIDPA is decomposed by radiolysis and hydrolysis. The main degradation product is monoisodecyl phosphoric acid (MIDPA). Ethylene glycol has been used for removing the product by a solvent extraction method. However this method has two drawbacks that two phases separate slowly and the used ethylene glycol is not regeneratable. First it was found that the addition of acetone or methanol with 20 volume % improved the phase separation. Then a new purification method was developed by using an aqueous solution of methanol or acetone. The new purification method is as excellent as the ethylene glycol method for the removal of MIDPA. (author)

A Timing-Driven Partitioning System for Multiple FPGAs

Directory of Open Access Journals (Sweden)

Kalapi Roy

1996-01-01

Full Text Available Field-programmable systems with multiple FPGAs on a PCB or an MCM are being used by system designers when a single FPGA is not sufficient. We address the problem of partitioning a large technology mapped FPGA circuit onto multiple FPGA devices of a specific target technology. The physical characteristics of the multiple FPGA system (MFS pose additional constraints to the circuit partitioning algorithms: the capacity of each FPGA, the timing constraints, the number of I/Os per FPGA, and the pre-designed interconnection patterns of each FPGA and the package. Existing partitioning techniques which minimize just the cut sizes of partitions fail to satisfy the above challenges. We therefore present a timing driven N-way partitioning algorithm based on simulated annealing for technology-mapped FPGA circuits. The signal path delays are estimated during partitioning using a timing model specific to a multiple FPGA architecture. The model combines all possible delay factors in a system with multiple FPGA chips of a target technology. Furthermore, we have incorporated a new dynamic net-weighting scheme to minimize the number of pin-outs for each chip. Finally, we have developed a graph-based global router for pin assignment which can handle the pre-routed connections of our MFS structure. In order to reduce the time spent in the simulated annealing phase of the partitioner, clusters of circuit components are identified by a new linear-time bottom-up clustering algorithm. The annealing-based N-way partitioner executes four times faster using the clusters as opposed to a flat netlist with improved partitioning results. For several industrial circuits, our approach outperforms the recursive min-cut bi-partitioning algorithm by 35% in terms of nets cut. Our approach also outperforms an industrial FPGA partitioner by 73% on average in terms of unroutable nets. Using the performance optimization capabilities in our approach we have successfully partitioned the

Combinatorics and complexity of partition functions

CERN Document Server

Barvinok, Alexander

2016-01-01

Partition functions arise in combinatorics and related problems of statistical physics as they encode in a succinct way the combinatorial structure of complicated systems. The main focus of the book is on efficient ways to compute (approximate) various partition functions, such as permanents, hafnians and their higher-dimensional versions, graph and hypergraph matching polynomials, the independence polynomial of a graph and partition functions enumerating 0-1 and integer points in polyhedra, which allows one to make algorithmic advances in otherwise intractable problems. The book unifies various, often quite recent, results scattered in the literature, concentrating on the three main approaches: scaling, interpolation and correlation decay. The prerequisites include moderate amounts of real and complex analysis and linear algebra, making the book accessible to advanced math and physics undergraduates. .

Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement.

Science.gov (United States)

Moen, Hans; Peltonen, Laura-Maria; Koivumäki, Mikko; Suhonen, Henry; Salakoski, Tapio; Ginter, Filip; Salanterä, Sanna

2018-01-01

We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled text - a clinical corpus and a general domain corpus. A joint semantic word-space model is created for the purpose of extracting easier to understand alternatives for words considered difficult to understand by laymen. Two domain experts evaluate the tool and inter-rater agreement is calculated. When having the tool suggest ten alternatives to each difficult word, it suggests acceptable lay words for 55.51% of them. This and future manual evaluation will serve to further improve performance, where also supervised machine learning will be used.

Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover

Science.gov (United States)

Li, Mengmeng; Bijker, Wietske; Stein, Alfred

2015-04-01

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a Binary Partition Tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pléiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction.

Development of partitioning method

International Nuclear Information System (INIS)

Kubota, Kazuo; Dojiri, Shigeru; Kubota, Masumitsu

1988-10-01

The literature survey was carried out on the amount of natural resources, behaviors in reprocessing process and in separation and recovery methods of the platinum group elements and technetium which are contained in spent fuel. The essential results are described below. (1) The platinum group elements, which are contained in spent fuel, are quantitatively limited, compared with total demand for them in Japan. And estimated separation and recovery cost is rather high. In spite of that, development of these techniques is considered to be very important because the supply of these elements is almost from foreign resources in Japan. (2) For recovery of these elements, studies of recovery from undisolved residue and from high level liquid waste (HLLW) also seem to be required. (3) As separation and recovery methods, following techniques are considered to be effective; lead extraction, liquid metal extraction, solvent extraction, ion-exchange, adsorption, precipitation, distillation, electrolysis or their combination. (4) But each of these methods has both advantages and disadvantages. So development of such processes largely depends on future works. (author) 94 refs

The influence of hydrogen bonding on partition coefficients

Science.gov (United States)

Borges, Nádia Melo; Kenny, Peter W.; Montanari, Carlos A.; Prokopczyk, Igor M.; Ribeiro, Jean F. R.; Rocha, Josmar R.; Sartori, Geraldo Rodrigues

2017-02-01

This Perspective explores how consideration of hydrogen bonding can be used to both predict and better understand partition coefficients. It is shown how polarity of both compounds and substructures can be estimated from measured alkane/water partition coefficients. When polarity is defined in this manner, hydrogen bond donors are typically less polar than hydrogen bond acceptors. Analysis of alkane/water partition coefficients in conjunction with molecular electrostatic potential calculations suggests that aromatic chloro substituents may be less lipophilic than is generally believed and that some of the effect of chloro-substitution stems from making the aromatic π-cloud less available to hydrogen bond donors. Relationships between polarity and calculated hydrogen bond basicity are derived for aromatic nitrogen and carbonyl oxygen. Aligned hydrogen bond acceptors appear to present special challenges for prediction of alkane/water partition coefficients and this may reflect `frustration' of solvation resulting from overlapping hydration spheres. It is also shown how calculated hydrogen bond basicity can be used to model the effect of aromatic aza-substitution on octanol/water partition coefficients.

Compactified webs and domain wall partition functions

Energy Technology Data Exchange (ETDEWEB)

Shabbir, Khurram [Government College University, Department of Mathematics, Lahore (Pakistan)

2017-04-15

In this paper we use the topological vertex formalism to calculate a generalization of the ''domain wall'' partition function of M-strings. This generalization allows calculation of partition function of certain compactified webs using a simple gluing algorithm similar to M-strings case. (orig.)

Finding reproducible cluster partitions for the k-means algorithm.

Science.gov (United States)

Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Chambers, Simon J

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

Reconstruction of a piecewise constant conductivity on a polygonal partition via shape optimization in EIT

Science.gov (United States)

Beretta, Elena; Micheletti, Stefano; Perotto, Simona; Santacesaria, Matteo

2018-01-01

In this paper, we develop a shape optimization-based algorithm for the electrical impedance tomography (EIT) problem of determining a piecewise constant conductivity on a polygonal partition from boundary measurements. The key tool is to use a distributed shape derivative of a suitable cost functional with respect to movements of the partition. Numerical simulations showing the robustness and accuracy of the method are presented for simulated test cases in two dimensions.

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

Science.gov (United States)

de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

2015-06-01

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

On the relativistic partition function of ideal gases

International Nuclear Information System (INIS)

Sinyukov, Yu.M.

1983-01-01

The covariant partition function method for ideal Boltzmann and Bose gases is developed within quantum field theory. This method is a basis to describe the statistical and thermodynamical properties of the gases in canonical, grand canonical and pressure ensembles in an arbitrary inertial system. It is shown that when statistical systems are described relativistically it is very important to take into account the boundary conditions. This is due to the fact that an equilibrium system is not closed mechanically. The results may find application in hadron physics. (orig.)

Multiaxial creep-fatigue life analysis using strainrange partitioning

International Nuclear Information System (INIS)

Manson, S.S.; Halford, G.R.

1976-01-01

Strain-Range Partitioning is a recently developed method for treating creep-fatigue interaction at elevated temperature. Most of the work to date has been on uniaxially loaded specimens, whereas practical applications often involve load multiaxiality. This paper shows how the method can be extended to treat multiaxiality through a set of rules for combining the strain components in the three principal directions. Closed hysteresis loops, as well as plastic and creep strain ratcheting, are included. An application to hold-time tests in torsion is used to illustrate the approach

Structural analysis of the ParR/parC plasmid partition complex

DEFF Research Database (Denmark)

Møller-Jensen, Jakob; Ringgaard, Simon; Mercogliano, Christopher P

2007-01-01

Accurate DNA partition at cell division is vital to all living organisms. In bacteria, this process can involve partition loci, which are found on both chromosomes and plasmids. The initial step in Escherichia coli plasmid R1 partition involves the formation of a partition complex between the DNA...

Interfacial thermodynamics and electrochemistry of protein partitioning in two-phase systems

NARCIS (Netherlands)

Fraaije, J.G.E.M.

1987-01-01

The subject of this thesis is protein partition between an aqueous salt solution and a surface or an apolair liquid and the concomitant co-partition of small ions. The extent of co-partitioning determines the charge regulation in the protein partitioning process.

Chapters 2 and 3

Partitioning of organochlorine pesticides from water to polyethylene passive samplers

International Nuclear Information System (INIS)

Hale, Sarah E.; Martin, Timothy J.; Goss, Kai-Uwe; Arp, Hans Peter H.; Werner, David

2010-01-01

The mass transfer rates and equilibrium partitioning behaviour of 14 diverse organochlorine pesticides (OCP) between water and polyethylene (PE) passive samplers, cut from custom made PE sheets and commercial polyethylene plastic bags, were quantified. Overall mass transfer coefficients, k O , estimated PE membrane diffusion coefficients, D PE , and PE-water partitioning coefficients, K PE-water, are reported. In addition, the partitioning of three polycyclic aromatic hydrocarbons (PAHs) from water to PE is quantified and compared with literature values. K PE-water values agreed mostly within a factor of two for both passive samplers and also with literature values for the reference PAHs. As PE is expected to exhibit similar sorption behaviour to long-chain alkanes, PE-water partitioning coefficients were compared to hexadecane-water partitioning coefficients estimated with the SPARC online calculator, COSMOtherm and a polyparameter linear free energy relationship based on the Abraham approach. The best correlation for all compounds tested was with COSMOtherm estimated hexadecane-water partitioning coefficients. - The partitioning of organochlorine pesticides between single phase polyethylene passive samplers and water is quantified.

Partition coefficients of methylated DNA bases obtained from free energy calculations with molecular electron density derived atomic charges.

Science.gov (United States)

Lara, A; Riquelme, M; Vöhringer-Martinez, E

2018-05-11

Partition coefficients serve in various areas as pharmacology and environmental sciences to predict the hydrophobicity of different substances. Recently, they have also been used to address the accuracy of force fields for various organic compounds and specifically the methylated DNA bases. In this study, atomic charges were derived by different partitioning methods (Hirshfeld and Minimal Basis Iterative Stockholder) directly from the electron density obtained by electronic structure calculations in a vacuum, with an implicit solvation model or with explicit solvation taking the dynamics of the solute and the solvent into account. To test the ability of these charges to describe electrostatic interactions in force fields for condensed phases, the original atomic charges of the AMBER99 force field were replaced with the new atomic charges and combined with different solvent models to obtain the hydration and chloroform solvation free energies by molecular dynamics simulations. Chloroform-water partition coefficients derived from the obtained free energies were compared to experimental and previously reported values obtained with the GAFF or the AMBER-99 force field. The results show that good agreement with experimental data is obtained when the polarization of the electron density by the solvent has been taken into account, and when the energy needed to polarize the electron density of the solute has been considered in the transfer free energy. These results were further confirmed by hydration free energies of polar and aromatic amino acid side chain analogs. Comparison of the two partitioning methods, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS), revealed some deficiencies in the Hirshfeld-I method related to the unstable isolated anionic nitrogen pro-atom used in the method. Hydration free energies and partitioning coefficients obtained with atomic charges from the MBIS partitioning method accounting for polarization by the implicit solvation model

Time and Space Partitioning the EagleEye Reference Misson

Science.gov (United States)

Bos, Victor; Mendham, Peter; Kauppinen, Panu; Holsti, Niklas; Crespo, Alfons; Masmano, Miguel; de la Puente, Juan A.; Zamorano, Juan

2013-08-01

We discuss experiences gained by porting a Software Validation Facility (SVF) and a satellite Central Software (CSW) to a platform with support for Time and Space Partitioning (TSP). The SVF and CSW are part of the EagleEye Reference mission of the European Space Agency (ESA). As a reference mission, EagleEye is a perfect candidate to evaluate practical aspects of developing satellite CSW for and on TSP platforms. The specific TSP platform we used consists of a simulated LEON3 CPU controlled by the XtratuM separation micro-kernel. On top of this, we run five separate partitions. Each partition runs its own real-time operating system or Ada run-time kernel, which in turn are running the application software of the CSW. We describe issues related to partitioning; inter-partition communication; scheduling; I/O; and fault-detection, isolation, and recovery (FDIR).

Prediction of Partition Coefficients of Organic Compounds for SPME/PDMS

OpenAIRE

Liao Hsuan-Yu; Huang Miao-Ling; Lu Yu-Ting; Chao Keh-Ping

2016-01-01

The partition coefficients of 51 organic compounds between SPME/PDMS and gas were compiled from the literature sources in this study. The effect of physicochemical properties and descriptors on the partitioning process of partition coefficients was explicated by the correlation analysis. The PDMS-gas partition coefficients were well correlated to the molecular weight of organic compounds (r = 0.832, p < 0.05). An empirical model, consisting of the molecular weight and the polarizability, was ...

Total internal partition sums for molecular species in the 2000 edition of the HITRAN database

International Nuclear Information System (INIS)

Fischer, J.; Gamache, R.R.; Goldman, A.; Rothman, L.S.; Perrin, A.

2003-01-01

Total internal partition sums (TIPS) are calculated for all molecular species in the 2000 HITRAN database. In addition, the TIPS for 13 other isotopomers/isotopologues of ozone and carbon dioxide are presented. The calculations address the corrections suggested by Goldman et al. (JQSRT 66 (2000) 455). The calculations consider the temperature range 70-3000 K to be applicable to a variety of remote sensing needs. The method of calculation for each molecular species is stated and comparisons with data from the literature are discussed. A new method of recall for the partition sums, Lagrange 4-point interpolation, is developed. This method, unlike previous versions of the TIPS code, allows all molecular species to be considered

Molecular Descriptors Family on Structure Activity Relationships 6. Octanol-Water Partition Coefficient of Polychlorinated Biphenyls

Directory of Open Access Journals (Sweden)

Lorentz JÄNTSCHI

2006-01-01

Full Text Available Octanol-water partition coefficient of two hundred and six polychlorinated biphenyls was model by the use of an original method based on complex information obtained from compounds structure. The regression analysis shows that best results are obtained in four-varied model (r2 = 0.9168. The prediction ability of the model was studied through leave-one-out analysis (r2cv(loo = 0.9093 and in training and test sets analysis. Modeling the octanol-water partition coefficient of polychlorinated biphenyls by integration of complex structural information provide a stable and performing four-varied model, allowing us to make remarks about relationship between structure of polychlorinated biphenyls and associated octanol-water partition coefficients.

OCTANOL/WATER PARTITION COEFFICIENTS AND WATER SOLUBILITIES OF PHTHALATE ESTERS

Science.gov (United States)

Measurements of the octanol/water partition coefficients (K-ow) and water solubilities of di-n-octyl phthalate (DnOP) and di-n-decyl phthalate (DnDP) by the slow-stirring method are reported. The water solubility was also measured for di-n-hexyl phthalate (DnHP). The log K-ow val...

Automated Potentiometric Titrations in KCl/Water-Saturated Octanol: Method for Quantifying Factors Influencing Ion-Pair Partitioning

Science.gov (United States)

2009-01-01

The knowledge base of factors influencing ion pair partitioning is very sparse, primarily because of the difficulty in determining accurate log PI values of desirable low molecular weight (MW) reference compounds. We have developed a potentiometric titration procedure in KCl/water-saturated octanol that provides a link to log PI through the thermodynamic cycle of ionization and partitioning. These titrations have the advantage of being independent of the magnitude of log P, while maintaining a reproducibility of a few hundredths of a log P in the calculated difference between log P neutral and log P ion pair (diff (log PN − I)). Simple model compounds can be used. The titration procedure is described in detail, along with a program for calculating pKa′′ values incorporating the ionization of water in octanol. Hydrogen bonding and steric factors have a greater influence on ion pairs than they do on neutral species, yet these factors are missing from current programs used to calculate log PI and log D. In contrast to the common assumption that diff (log PN − I) is the same for all amines, they can actually vary more than 3 log units, as in our examples. A major factor affecting log PI is the ability of water and the counterion to approach the charge center. Bulky substituents near the charge center have a negative influence on log PI. On the other hand, hydrogen bonding groups near the charge center have the opposite effect by lowering the free energy of the ion pair. The use of this titration method to determine substituent ion pair stabilization values (IPS) should bring about more accurate log D calculations and encourage species-specific QSAR involving log DN and log DI. This work also brings attention to the fascinating world of nature’s highly stabilized ion pairs. PMID:19265385

Automated potentiometric titrations in KCl/water-saturated octanol: method for quantifying factors influencing ion-pair partitioning.

Science.gov (United States)

Scherrer, Robert A; Donovan, Stephen F

2009-04-01

The knowledge base of factors influencing ion pair partitioning is very sparse, primarily because of the difficulty in determining accurate log P(I) values of desirable low molecular weight (MW) reference compounds. We have developed a potentiometric titration procedure in KCl/water-saturated octanol that provides a link to log P(I) through the thermodynamic cycle of ionization and partitioning. These titrations have the advantage of being independent of the magnitude of log P, while maintaining a reproducibility of a few hundredths of a log P in the calculated difference between log P neutral and log P ion pair (diff (log P(N - I))). Simple model compounds can be used. The titration procedure is described in detail, along with a program for calculating pK(a)'' values incorporating the ionization of water in octanol. Hydrogen bonding and steric factors have a greater influence on ion pairs than they do on neutral species, yet these factors are missing from current programs used to calculate log P(I) and log D. In contrast to the common assumption that diff (log P(N - I)) is the same for all amines, they can actually vary more than 3 log units, as in our examples. A major factor affecting log P(I) is the ability of water and the counterion to approach the charge center. Bulky substituents near the charge center have a negative influence on log P(I). On the other hand, hydrogen bonding groups near the charge center have the opposite effect by lowering the free energy of the ion pair. The use of this titration method to determine substituent ion pair stabilization values (IPS) should bring about more accurate log D calculations and encourage species-specific QSAR involving log D(N) and log D(I). This work also brings attention to the fascinating world of nature's highly stabilized ion pairs.

Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy.

Science.gov (United States)

McGuire, Jimmy A; Witt, Christopher C; Altshuler, Douglas L; Remsen, J V

2007-10-01

Hummingbirds are an important model system in avian biology, but to date the group has been the subject of remarkably few phylogenetic investigations. Here we present partitioned Bayesian and maximum likelihood phylogenetic analyses for 151 of approximately 330 species of hummingbirds and 12 outgroup taxa based on two protein-coding mitochondrial genes (ND2 and ND4), flanking tRNAs, and two nuclear introns (AK1 and BFib). We analyzed these data under several partitioning strategies ranging between unpartitioned and a maximum of nine partitions. In order to select a statistically justified partitioning strategy following partitioned Bayesian analysis, we considered four alternative criteria including Bayes factors, modified versions of the Akaike information criterion for small sample sizes (AIC(c)), Bayesian information criterion (BIC), and a decision-theoretic methodology (DT). Following partitioned maximum likelihood analyses, we selected a best-fitting strategy using hierarchical likelihood ratio tests (hLRTS), the conventional AICc, BIC, and DT, concluding that the most stringent criterion, the performance-based DT, was the most appropriate methodology for selecting amongst partitioning strategies. In the context of our well-resolved and well-supported phylogenetic estimate, we consider the historical biogeography of hummingbirds using ancestral state reconstructions of (1) primary geographic region of occurrence (i.e., South America, Central America, North America, Greater Antilles, Lesser Antilles), (2) Andean or non-Andean geographic distribution, and (3) minimum elevational occurrence. These analyses indicate that the basal hummingbird assemblages originated in the lowlands of South America, that most of the principle clades of hummingbirds (all but Mountain Gems and possibly Bees) originated on this continent, and that there have been many (at least 30) independent invasions of other primary landmasses, especially Central America.

Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data

Science.gov (United States)

Niezgoda, Stephen R.; Glover, Jared

2013-11-01

The estimation of orientation distribution functions (ODFs) from discrete orientation data, as produced by electron backscatter diffraction or crystal plasticity micromechanical simulations, is typically achieved via techniques such as the Williams-Imhof-Matthies-Vinel (WIMV) algorithm or generalized spherical harmonic expansions, which were originally developed for computing an ODF from pole figures measured by X-ray or neutron diffraction. These techniques rely on ad-hoc methods for choosing parameters, such as smoothing half-width and bandwidth, and for enforcing positivity constraints and appropriate normalization. In general, such approaches provide little or no information-theoretic guarantees as to their optimality in describing the given dataset. In the current study, an unsupervised learning algorithm is proposed which uses a finite mixture of Bingham distributions for the estimation of ODFs from discrete orientation data. The Bingham distribution is an antipodally-symmetric, max-entropy distribution on the unit quaternion hypersphere. The proposed algorithm also introduces a minimum message length criterion, a common tool in information theory for balancing data likelihood with model complexity, to determine the number of components in the Bingham mixture. This criterion leads to ODFs which are less likely to overfit (or underfit) the data, eliminating the need for a priori parameter choices.

Construction of Scaling Partitions of Unity

Directory of Open Access Journals (Sweden)

Ole Christensen

2017-11-01

Full Text Available Partitions of unity in ℝd formed by (matrix scales of a fixed function appear in many parts of harmonic analysis, e.g., wavelet analysis and the analysis of Triebel-Lizorkin spaces. We give a simple characterization of the functions and matrices yielding such a partition of unity. For expanding matrices, the characterization leads to easy ways of constructing appropriate functions with attractive properties like high regularity and small support. We also discuss a class of integral transforms that map functions having the partition of unity property to functions with the same property. The one-dimensional version of the transform allows a direct definition of a class of nonuniform splines with properties that are parallel to those of the classical B-splines. The results are illustrated with the construction of dual pairs of wavelet frames.

Trace element partitioning between plagioclase and melt: An investigation of the impact of experimental and analytical procedures

Science.gov (United States)

Nielsen, Roger L.; Ustunisik, Gokce; Weinsteiger, Allison B.; Tepley, Frank J.; Johnston, A. Dana; Kent, Adam J. R.

2017-09-01

Quantitative models of petrologic processes require accurate partition coefficients. Our ability to obtain accurate partition coefficients is constrained by their dependence on pressure temperature and composition, and on the experimental and analytical techniques we apply. The source and magnitude of error in experimental studies of trace element partitioning may go unrecognized if one examines only the processed published data. The most important sources of error are relict crystals, and analyses of more than one phase in the analytical volume. Because we have typically published averaged data, identification of compromised data is difficult if not impossible. We addressed this problem by examining unprocessed data from plagioclase/melt partitioning experiments, by comparing models based on that data with existing partitioning models, and evaluated the degree to which the partitioning models are dependent on the calibration data. We found that partitioning models are dependent on the calibration data in ways that result in erroneous model values, and that the error will be systematic and dependent on the value of the partition coefficient. In effect, use of different calibration datasets will result in partitioning models whose results are systematically biased, and that one can arrive at different and conflicting conclusions depending on how a model is calibrated, defeating the purpose of applying the models. Ultimately this is an experimental data problem, which can be solved if we publish individual analyses (not averages) or use a projection method wherein we use an independent compositional constraint to identify and estimate the uncontaminated composition of each phase.

Is the gas-particle partitioning in alpha-pinene secondary organic aerosol reversible?

Science.gov (United States)

Grieshop, Andrew P.; Donahue, Neil M.; Robinson, Allen L.

2007-07-01

This paper discusses the reversibility of gas-particle partitioning in secondary organic aerosol (SOA) formed from α-pinene ozonolysis in a smog chamber. Previously, phase partitioning has been studied quantitatively via SOA production experiments and qualitatively by perturbing temperature and observing particle evaporation. In this work, two methods were used to isothermally dilute the SOA: an external dilution sampler and an in-chamber technique. Dilution caused some evaporation of SOA, but repartitioning took place on a time scale of tens of minutes to hours-consistent with an uptake coefficient on the order of 0.001-0.01. However, given sufficient time, α-pinene SOA repartitions reversibly based on comparisons with data from conventional SOA yield experiments. Further, aerosol mass spectrometer (AMS) data indicate that the composition of SOA varies with partitioning. These results suggest that oligomerization observed in high-concentration laboratory experiments may be a reversible process and underscore the complexity of the kinetics of formation and evaporation of SOA.

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

Directory of Open Access Journals (Sweden)

Theis Fabian J

2010-10-01

Full Text Available Abstract Background Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. Results Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. Conclusions In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning

PARALLEL PROCESSING OF BIG POINT CLOUDS USING Z-ORDER-BASED PARTITIONING

Directory of Open Access Journals (Sweden)

C. Alis

2016-06-01

Full Text Available As laser scanning technology improves and costs are coming down, the amount of point cloud data being generated can be prohibitively difficult and expensive to process on a single machine. This data explosion is not only limited to point cloud data. Voluminous amounts of high-dimensionality and quickly accumulating data, collectively known as Big Data, such as those generated by social media, Internet of Things devices and commercial transactions, are becoming more prevalent as well. New computing paradigms and frameworks are being developed to efficiently handle the processing of Big Data, many of which utilize a compute cluster composed of several commodity grade machines to process chunks of data in parallel. A central concept in many of these frameworks is data locality. By its nature, Big Data is large enough that the entire dataset would not fit on the memory and hard drives of a single node hence replicating the entire dataset to each worker node is impractical. The data must then be partitioned across worker nodes in a manner that minimises data transfer across the network. This is a challenge for point cloud data because there exist different ways to partition data and they may require data transfer. We propose a partitioning based on Z-order which is a form of locality-sensitive hashing. The Z-order or Morton code is computed by dividing each dimension to form a grid then interleaving the binary representation of each dimension. For example, the Z-order code for the grid square with coordinates (x = 1 = 012, y = 3 = 112 is 10112 = 11. The number of points in each partition is controlled by the number of bits per dimension: the more bits, the fewer the points. The number of bits per dimension also controls the level of detail with more bits yielding finer partitioning. We present this partitioning method by implementing it on Apache Spark and investigating how different parameters affect the accuracy and running time of the k nearest

Probabilistic Decision Based Block Partitioning for Future Video Coding

KAUST Repository

Wang, Zhao

2017-11-29

In the latest Joint Video Exploration Team development, the quadtree plus binary tree (QTBT) block partitioning structure has been proposed for future video coding. Compared to the traditional quadtree structure of High Efficiency Video Coding (HEVC) standard, QTBT provides more flexible patterns for splitting the blocks, which results in dramatically increased combinations of block partitions and high computational complexity. In view of this, a confidence interval based early termination (CIET) scheme is proposed for QTBT to identify the unnecessary partition modes in the sense of rate-distortion (RD) optimization. In particular, a RD model is established to predict the RD cost of each partition pattern without the full encoding process. Subsequently, the mode decision problem is casted into a probabilistic framework to select the final partition based on the confidence interval decision strategy. Experimental results show that the proposed CIET algorithm can speed up QTBT block partitioning structure by reducing 54.7% encoding time with only 1.12% increase in terms of bit rate. Moreover, the proposed scheme performs consistently well for the high resolution sequences, of which the video coding efficiency is crucial in real applications.

Chaos synchronization basing on symbolic dynamics with nongenerating partition.

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Wang, Xingyuan; Wang, Mogei; Liu, Zhenzhen

2009-06-01

Using symbolic dynamics and information theory, we study the information transmission needed for synchronizing unidirectionally coupled oscillators. It is found that when sustaining chaos synchronization with nongenerating partition, the synchronization error will be larger than a critical value, although the required coupled channel capacity can be smaller than the case of using a generating partition. Then we show that no matter whether a generating or nongenerating partition is in use, a high-quality detector can guarantee the lead of the response oscillator, while the lag responding can make up the low precision of the detector. A practicable synchronization scheme basing on a nongenerating partition is also proposed in this paper.

Aqueous two-phase (polyethylene glycol + sodium sulfate) system for caffeine extraction: Equilibrium diagrams and partitioning study

International Nuclear Information System (INIS)

Araujo Sampaio, Daniela de; Mafra, Luciana Igarashi; Yamamoto, Carlos Itsuo; Forville de Andrade, Eriel; Oberson de Souza, Michèle; Mafra, Marcos Rogério; Castilhos, Fernanda de

2016-01-01

Highlights: • Binodal curves of PEG (400, 4000 and 6000) + Na_2SO_4 ATPS were determined. • Tie-lines were experimentally determined for aqueous (PEG 400 + Na_2SO_4) system. • Influence of caffeine on LLE of aqueous (PEG 400 + Na_2SO_4) system was investigated. • Partitioning of caffeine in aqueous (PEG 400 + Na_2SO_4) system was investigated. • Caffeine partition showed to be dependent on temperature and TLL. - Abstract: Environmental friendly methods for liquid–liquid extraction have been taken into account due to critical conditions and ecotoxicological effects potentially produced by organic solvents applied in traditional methods. Liquid–liquid extraction using aqueous two phase systems (ATPSs) presents advantages when compared to traditional liquid–liquid extraction. (Polyethylene glycol (PEG) + sodium sulfate + water) ATPS was applied to study partition of caffeine. Binodal curves for ATPSs composed of PEG of different molecular weights (400 g · mol"−"1, 4000 g · mol"−"1 and 6000 g · mol"−"1) sodium sulfate + water were determined by cloud point method at three different temperatures (293.15, 313.15 and 333.15) K. Liquid–liquid equilibrium (LLE) data (tie-lines, slope of the tie-line and tie-lines length) were obtained applying a gravimetric method proposed by Merchuck and co-workers at the same temperatures for aqueous (PEG 400 + sodium sulfate) and aqueous (PEG 400 + sodium sulfate + caffeine) systems. Reliability of the experimental tie-line (TL) data was evaluated using the equations reported by Othmer–Tobias and satisfactory linearity was obtained. Concerning to aqueous (PEG + sodium sulfate) system, the results pointed out that the higher PEG molecular weight the largest is the heterogeneous region. Moreover, temperature showed not to be relevant on binodal curves behavior, but it influenced on tie-line slopes. Partitioning of caffeine in aqueous (PEG 400 + sodium sulfate) system was investigated at different temperatures

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

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Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to

On the application of the partition of unity method for nonlocal response of low-dimensional structures

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Natarajan, Sundararajan

2014-12-01

The main objectives of the paper are to (1) present an overview of nonlocal integral elasticity and Aifantis gradient elasticity theory and (2) discuss the application of partition of unity methods to study the response of low-dimensional structures. We present different choices of approximation functions for gradient elasticity, namely Lagrange intepolants, moving least-squares approximants and non-uniform rational B-splines. Next, we employ these approximation functions to study the response of nanobeams based on Euler-Bernoulli and Timoshenko theories as well as to study nanoplates based on first-order shear deformation theory. The response of nanobeams and nanoplates is studied using Eringen's nonlocal elasticity theory. The influence of the nonlocal parameter, the beam and the plate aspect ratio and the boundary conditions on the global response is numerically studied. The influence of a crack on the axial vibration and buckling characteristics of nanobeams is also numerically studied.

Study and modeling of the evolution of gas-liquid partitioning of hydrogen sulfide in model solutions simulating winemaking fermentations.

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Mouret, Jean-Roch; Sablayrolles, Jean-Marie; Farines, Vincent

2015-04-01

The knowledge of gas-liquid partitioning of aroma compounds during winemaking fermentation could allow optimization of fermentation management, maximizing concentrations of positive markers of aroma and minimizing formation of molecules, such as hydrogen sulfide (H2S), responsible for defects. In this study, the effect of the main fermentation parameters on the gas-liquid partition coefficients (Ki) of H2S was assessed. The Ki for this highly volatile sulfur compound was measured in water by an original semistatic method developed in this work for the determination of gas-liquid partitioning. This novel method was validated and then used to determine the Ki of H2S in synthetic media simulating must, fermenting musts at various steps of the fermentation process, and wine. Ki values were found to be mainly dependent on the temperature but also varied with the composition of the medium, especially with the glucose concentration. Finally, a model was developed to quantify the gas-liquid partitioning of H2S in synthetic media simulating must to wine. This model allowed a very accurate prediction of the partition coefficient of H2S: the difference between observed and predicted values never exceeded 4%.

Solving Large-Scale TSP Using a Fast Wedging Insertion Partitioning Approach

Directory of Open Access Journals (Sweden)

Zuoyong Xiang

2015-01-01

Full Text Available A new partitioning method, called Wedging Insertion, is proposed for solving large-scale symmetric Traveling Salesman Problem (TSP. The idea of our proposed algorithm is to cut a TSP tour into four segments by nodes’ coordinate (not by rectangle, such as Strip, FRP, and Karp. Each node is located in one of their segments, which excludes four particular nodes, and each segment does not twist with other segments. After the partitioning process, this algorithm utilizes traditional construction method, that is, the insertion method, for each segment to improve the quality of tour, and then connects the starting node and the ending node of each segment to obtain the complete tour. In order to test the performance of our proposed algorithm, we conduct the experiments on various TSPLIB instances. The experimental results show that our proposed algorithm in this paper is more efficient for solving large-scale TSPs. Specifically, our approach is able to obviously reduce the time complexity for running the algorithm; meanwhile, it will lose only about 10% of the algorithm’s performance.

Open software tools for eddy covariance flux partitioning

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Agro-ecosystem management and assessment will benefit greatly from the development of reliable techniques for partitioning evapotranspiration (ET) into evaporation (E) and transpiration (T). Among other activities, flux partitioning can aid in evaluating consumptive vs. non-consumptive agricultural...

Partitioning a macroscopic system into independent subsystems

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Delle Site, Luigi; Ciccotti, Giovanni; Hartmann, Carsten

2017-08-01

We discuss the problem of partitioning a macroscopic system into a collection of independent subsystems. The partitioning of a system into replica-like subsystems is nowadays a subject of major interest in several fields of theoretical and applied physics. The thermodynamic approach currently favoured by practitioners is based on a phenomenological definition of an interface energy associated with the partition, due to a lack of easily computable expressions for a microscopic (i.e. particle-based) interface energy. In this article, we outline a general approach to derive sharp and computable bounds for the interface free energy in terms of microscopic statistical quantities. We discuss potential applications in nanothermodynamics and outline possible future directions.

Rational Variety Mapping for Contrast-Enhanced Nonlinear Unsupervised Segmentation of Multispectral Images of Unstained Specimen

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Kopriva, Ivica; Hadžija, Mirko; Popović Hadžija, Marijana; Korolija, Marina; Cichocki, Andrzej

2011-01-01

A methodology is proposed for nonlinear contrast-enhanced unsupervised segmentation of multispectral (color) microscopy images of principally unstained specimens. The methodology exploits spectral diversity and spatial sparseness to find anatomical differences between materials (cells, nuclei, and background) present in the image. It consists of rth-order rational variety mapping (RVM) followed by matrix/tensor factorization. Sparseness constraint implies duality between nonlinear unsupervised segmentation and multiclass pattern assignment problems. Classes not linearly separable in the original input space become separable with high probability in the higher-dimensional mapped space. Hence, RVM mapping has two advantages: it takes implicitly into account nonlinearities present in the image (ie, they are not required to be known) and it increases spectral diversity (ie, contrast) between materials, due to increased dimensionality of the mapped space. This is expected to improve performance of systems for automated classification and analysis of microscopic histopathological images. The methodology was validated using RVM of the second and third orders of the experimental multispectral microscopy images of unstained sciatic nerve fibers (nervus ischiadicus) and of unstained white pulp in the spleen tissue, compared with a manually defined ground truth labeled by two trained pathophysiologists. The methodology can also be useful for additional contrast enhancement of images of stained specimens. PMID:21708116

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

Energy Technology Data Exchange (ETDEWEB)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

2017-07-14

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

Off-diagonal series expansion for quantum partition functions

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Hen, Itay

2018-05-01

We derive an integral-free thermodynamic perturbation series expansion for quantum partition functions which enables an analytical term-by-term calculation of the series. The expansion is carried out around the partition function of the classical component of the Hamiltonian with the expansion parameter being the strength of the off-diagonal, or quantum, portion. To demonstrate the usefulness of the technique we analytically compute to third order the partition functions of the 1D Ising model with longitudinal and transverse fields, and the quantum 1D Heisenberg model.

Creep-fatigue life prediction for different heats of Type 304 stainless steel by linear-damage rule, strain-range partitioning method, and damage-rate approach

International Nuclear Information System (INIS)

Maiya, P.S.

1978-07-01

The creep-fatigue life results for five different heats of Type 304 stainless steel at 593 0 C (1100 0 F), generated under push-pull conditions in the axial strain-control mode, are presented. The life predictions for the various heats based on the linear-damage rule, strain-range partitioning method, and damage-rate approach are discussed. The appropriate material properties required for computation of fatigue life are also included

A layer model of ethanol partitioning into lipid membranes.

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Nizza, David T; Gawrisch, Klaus

2009-06-01

The effect of membrane composition on ethanol partitioning into lipid bilayers was assessed by headspace gas chromatography. A series of model membranes with different compositions have been investigated. Membranes were exposed to a physiological ethanol concentration of 20 mmol/l. The concentration of membranes was 20 wt% which roughly corresponds to values found in tissue. Partitioning depended on the chemical nature of polar groups at the lipid/water interface. Compared to phosphatidylcholine, lipids with headgroups containing phosphatidylglycerol, phosphatidylserine, and sphingomyelin showed enhanced partitioning while headgroups containing phosphatidylethanolamine resulted in a lower partition coefficient. The molar partition coefficient was independent of a membrane's hydrophobic volume. This observation is in agreement with our previously published NMR results which showed that ethanol resides almost exclusively within the membrane/water interface. At an ethanol concentration of 20 mmol/l in water, ethanol concentrations at the lipid/water interface are in the range from 30-15 mmol/l, corresponding to one ethanol molecule per 100-200 lipids.

Sharing-Aware Horizontal Partitioning for Exploiting Correlations during Query Processing

DEFF Research Database (Denmark)

Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian Søndergaard

2010-01-01

Optimization of join queries based on average selectivities is suboptimal in highly correlated databases. In such databases, relations are naturally divided into partitions, each partition having substantially different statistical characteristics. It is very compelling to discover such data...... partitions during query optimization and create multiple plans for a given query, one plan being optimal for a particular combination of data partitions. This scenario calls for the sharing of state among plans, so that common intermediate results are not recomputed. We study this problem in a setting...

Industrial scale-plant for HLW partitioning in Russia

International Nuclear Information System (INIS)

Dzekun, E.G.; Glagolenko, Y.V.; Drojko, E.G.; Kurochkin, A.I.

1996-01-01

Radiochemical plant of PA > at Ozersk, which was come on line in December 1948 originally for weapon plutonium production and reoriented on the reprocessing of spent fuel, till now keeps on storage HLW of the military program. Application of the vitrification method since 1986 has not essentially reduced HLW volumes. So, as of September 1, 1995 vitrification installations had been processed 9590 m 3 HLW and 235 MCi of radionuclides was included in glass. However only 1100 m 3 and 20.5 MCi is part of waste of the military program. The reason is the fact, that the technology and equipment of vitrification were developed for current waste of Purex-process, for which low contents of corrosion-dangerous impurity to materials of vitrification installation is characteristic of. With reference to HLW, which are growing at PA > in the course of weapon plutonium production, the program of Science-Research Works includes the following main directions of work. Development of technology and equipment of installations for immobilising HLW with high contents of impurity into a solid form at induction melter. Application of High-temperature Adsorption Method for sorption of radionuclides from HLW on silica gel. Application of Partitioning Method of radionuclides from HLW, based on extraction cesium and strontium into cobalt dicarbollyde or crown-ethers, but also on recovery of cesium radionuclides by sorption on inorganic sorbents. In this paper the results of work on creation of first industrial scale-plant for partitioning HLW by the extraction and sorption methods are reported

Vertical partitioning of relational OLTP databases using integer programming

DEFF Research Database (Denmark)

Amossen, Rasmus Resen

2010-01-01

A way to optimize performance of relational row store databases is to reduce the row widths by vertically partition- ing tables into table fractions in order to minimize the number of irrelevant columns/attributes read by each transaction. This pa- per considers vertical partitioning algorithms...... for relational row- store OLTP databases with an H-store-like architecture, meaning that we would like to maximize the number of single-sited transactions. We present a model for the vertical partitioning problem that, given a schema together with a vertical partitioning and a workload, estimates the costs...... applied to the TPC-C benchmark and the heuristic is shown to obtain solutions with costs close to the ones found using the quadratic program....

Metric Structures on Fibered Manifolds Through Partitions of Unity

Directory of Open Access Journals (Sweden)

Hulya Kadioglu

2016-05-01

Full Text Available The notion of partitions of unity is extremely useful as it allows one to extend local constructions on Euclidean patches to global ones. It is widely used in many fields in mathematics. Therefore, prolongation of this useful tool to another manifold may help constructing many geometric structures. In this paper, we construct a partition of unity on a fiber bundle by using a given partition of unity on the base manifold. On the other hand we show that the converse is also possible if it is a vector bundle. As an application, we define a Riemannian metric on the fiber bundle by using induced partition of unity on the fiber bundle.

Comparison of salting-out and sugaring-out liquid-liquid extraction methods for the partition of 10-hydroxy-2-decenoic acid in royal jelly and their co-extracted protein content.

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