Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

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Sylvia Jane Annatje Sumarauw

2007-06-01

Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

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A. Tajari Siahmarzkooh

2016-12-01

Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

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Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

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Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Iluminismo e absolutismo no modelo jurídico-penal de Cesare Beccaria

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Alexander de Castro

2008-09-01

Full Text Available Cesare Beccaria, tido como o autor que,elaborando um sistema de direito penal com base emprincípios iluministas, criou as bases do modernodireito penal de cunho liberal, possuía, entretanto,vínculos muito profundos com o absolutismo austríaco.Assim, no presente trabalho, analisaremosalguns pontos da tessitura política em que Beccariaproduziu a obra Dei Delitti e delle Pene, procurandoaprofundar a compreensão sobre o modo como oIluminismo, no contexto de formação do absolutismohabsbúrgico, funcionaria na elaboração do modelojurídico-penal do jurista italiano.Abstract: Cesare Beccaria, known as the authorwho, elaborating a system of criminal law basedon illuminists principles, has created the bases ofthe modern liberal criminal law, had, however, verydeep bonds with the Austrian absolutism. Thus,in the present paper, we will analyze some pointsof the politic structure where Beccaria producedhis work Dei Delitti e delle Pene, seeking to examinethe understanding about the way Illuminism,in the context of the Habsburg absolutism’sformation, would operate in the elaboration ofthe Italian jurist’s legal criminal model.

Refractory reverse amblyopia with atropine penalization

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Preeti Ajit Patil

2010-01-01

Full Text Available Pharmacological penalization with atropine has been shown to be equally effective as conventional occlusion therapy in the treatment of amblyopia in children. Reverse amblyopia of the sound eye with atropine penalization has been reported before, but is more common in cases where the effect is augmented with optical penalization and is mostly reversible. We report a case of reverse amblyopia with atropine penalization, in a 4-year-old girl, which was refractory to treatment. This report highlights the need for strict monitoring of the vision in the sound eye and regular follow-up in children undergoing amblyopia treatment.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

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Gulnaz Ahmed

2017-02-01

Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

A Tutela Penal dos Direitos Humanos

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Paulo Cesar Correa Borges

2012-04-01

Full Text Available A proteção penal dos direitos humanos tem dois aspectos decorrentes do garantismo penal: 1 limite para a persecução penal; 2 objetividade jurídica das normas incriminadoras. O conceito de direitos humanos para fins de tutela penal prescinde da sua classificação geracional, mas determina o reconhecimento de sua historicidade e, principalmente, a sua construção a partir das mobilizações e movimentos sociais. A vulnerabilidade dos grupos humanos que são difusa e sistematicamente discriminados ou violados deve ser o critério para a definição do objeto jurídico da norma incriminadora, para manter coerência e viabilizar a aplicação do princípio da complementariedade entre a repressão interna e a persecução internacional, compatibilizando o Direito Penal interno e o Internacional.

Bayesian Analysis for Penalized Spline Regression Using WinBUGS

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Ciprian M. Crainiceanu

2005-09-01

Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

The Rise and Fall of Supermax: How the US Prison Model and Ultra Punitive Penal Policy Travelled to Colombia

OpenAIRE

de Dardel, Julie; Söderström, Ola

2016-01-01

In the context of the US anti-narcotic program, ‘Plan Colombia’, during the first decade of the 21st century, special agents of the US Federal Bureau of Prisons (BOP) took position in the heart of the Colombian penitentiary administration. Their task was to lead a profound reform of the sector, based on the US ultra-punitive penal regime and its ‘supermax’ housing units. Based on extensive fieldwork with prison architects, inmates and other actors in the penal systems of the US and Colombia, ...

GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING

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Christopher Ouma Onyango

2010-09-01

Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.

Juridical-penal aspects of the cesium-137 accident

International Nuclear Information System (INIS)

Soares, Carolina Chaves

1997-01-01

The study of the juridical-penal aspects of the Cesium-137 accident, has, as a base, the police inquiry and the penal lawsuit concerning to the episode. Due to the lack of a law which typified activities related with radioisotope material as crime, the responsible were sentenced according to the penalties of body injury crime and homicide. Among the 10 investigated people, only 5 were condemned by the Judiciary and only 4 serve the sentence. (author)

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

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Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Race Making in a Penal Institution.

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Walker, Michael L

2016-01-01

This article provides a ground-level investigation into the lives of penal inmates, linking the literature on race making and penal management to provide an understanding of racial formation processes in a modern penal institution. Drawing on 135 days of ethnographic data collected as an inmate in a Southern California county jail system, the author argues that inmates are subjected to two mutually constitutive racial projects--one institutional and the other microinteractional. Operating in symbiosis within a narrative of risk management, these racial projects increase (rather than decrease) incidents of intraracial violence and the potential for interracial violence. These findings have implications for understanding the process of racialization and evaluating the effectiveness of penal management strategies.

The rise and fall of supermax: how the US prison model and ultra-punitive penal policy travelled to Colombia

OpenAIRE

Julie de Dardel et Ola Söderström

2015-01-01

In the context of the US anti narcotic program ‘Plan Colombia’ during the first decade of the 21st century special agents of the US Federal Bureau of Prisons (BOP) took position in the heart of the Colombian penitentiary administration. Their task was to lead a profound reform of the sector based on the US ultra punitive penal regime and its ‘supermax’ housing units. Based on extensive fieldwork with prison architects inmates and other actors in the penal systems of the US and Colombia this p...

An Iterative Brinkman penalization for particle vortex methods

DEFF Research Database (Denmark)

Walther, Jens Honore; Hejlesen, Mads Mølholm; Leonard, A.

2013-01-01

We present an iterative Brinkman penalization method for the enforcement of the no-slip boundary condition in vortex particle methods. This is achieved by implementing a penalization of the velocity field using iteration of the penalized vorticity. We show that using the conventional Brinkman...... condition. These are: the impulsively started flow past a cylinder, the impulsively started flow normal to a flat plate, and the uniformly accelerated flow normal to a flat plate. The iterative penalization algorithm is shown to give significantly improved results compared to the conventional penalization...

Penal Policies In Bulgaria And Poland

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Simona Mihaiu

2016-12-01

Full Text Available The contemporary european studies underline the necessity of adoption of an european model, in order to assure the compliance with the fundamental rights and liberties of people deprived of liberty. But the visions of the model favour one of the two opposite tendencies, which are invariable present in theoretical debates and penal politics

Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization

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Liu, Jin; Huang, Jian; Ma, Shuangge

2013-01-01

Summary In cancer diagnosis studies, high-throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the “large d, small n” feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single datasets can be unsatisfactory. A cost-effective remedy is to conduct integrative analysis of multiple heterogeneous datasets. In this article, we investigate composite penalization methods for estimation and marker selection in integrative analysis. The proposed methods use the minimax concave penalty (MCP) as the outer penalty. Under the homogeneity model, the ridge penalty is adopted as the inner penalty. Under the heterogeneity model, the Lasso penalty and MCP are adopted as the inner penalty. Effective computational algorithms based on coordinate descent are developed. Numerical studies, including simulation and analysis of practical cancer datasets, show satisfactory performance of the proposed methods. PMID:24578589

Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

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Gang, Grace J. [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (Canada); Stayman, J. Webster; Zbijewski, Wojciech [Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (United States); Siewerdsen, Jeffrey H., E-mail: jeff.siewerdsen@jhu.edu [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 (United States)

2014-08-15

Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP

PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction

International Nuclear Information System (INIS)

Stayman, J Webster; Dang, Hao; Ding, Yifu; Siewerdsen, Jeffrey H

2013-01-01

Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data. (paper)

LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction

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Ma, Ming; Wang, Huafeng; Liu, Yan; Zhang, Hao; Gu, Xianfeng; Liang, Zhengrong

2014-03-01

Cone-beam computed tomography (CBCT) has attracted growing interest of researchers in image reconstruction. The mAs level of the X-ray tube current, in practical application of CBCT, is mitigated in order to reduce the CBCT dose. The lowering of the X-ray tube current, however, results in the degradation of image quality. Thus, low-dose CBCT image reconstruction is in effect a noise problem. To acquire clinically acceptable quality of image, and keep the X-ray tube current as low as achievable in the meanwhile, some penalized weighted least-squares (PWLS)-based image reconstruction algorithms have been developed. One representative strategy in previous work is to model the prior information for solution regularization using an anisotropic penalty term. To enhance the edge preserving and noise suppressing in a finer scale, a novel algorithm combining the local binary pattern (LBP) with penalized weighted leastsquares (PWLS), called LBP-PWLS-based image reconstruction algorithm, is proposed in this work. The proposed LBP-PWLS-based algorithm adaptively encourages strong diffusion on the local spot/flat region around a voxel and less diffusion on edge/corner ones by adjusting the penalty for cost function, after the LBP is utilized to detect the region around the voxel as spot, flat and edge ones. The LBP-PWLS-based reconstruction algorithm was evaluated using the sinogram data acquired by a clinical CT scanner from the CatPhan® 600 phantom. Experimental results on the noiseresolution tradeoff measurement and other quantitative measurements demonstrated its feasibility and effectiveness in edge preserving and noise suppressing in comparison with a previous PWLS reconstruction algorithm.

Comparative Analysis for Robust Penalized Spline Smoothing Methods

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Bin Wang

2014-01-01

Full Text Available Smoothing noisy data is commonly encountered in engineering domain, and currently robust penalized regression spline models are perceived to be the most promising methods for coping with this issue, due to their flexibilities in capturing the nonlinear trends in the data and effectively alleviating the disturbance from the outliers. Against such a background, this paper conducts a thoroughly comparative analysis of two popular robust smoothing techniques, the M-type estimator and S-estimation for penalized regression splines, both of which are reelaborated starting from their origins, with their derivation process reformulated and the corresponding algorithms reorganized under a unified framework. Performances of these two estimators are thoroughly evaluated from the aspects of fitting accuracy, robustness, and execution time upon the MATLAB platform. Elaborately comparative experiments demonstrate that robust penalized spline smoothing methods possess the capability of resistance to the noise effect compared with the nonrobust penalized LS spline regression method. Furthermore, the M-estimator exerts stable performance only for the observations with moderate perturbation error, whereas the S-estimator behaves fairly well even for heavily contaminated observations, but consuming more execution time. These findings can be served as guidance to the selection of appropriate approach for smoothing the noisy data.

Possible world based consistency learning model for clustering and classifying uncertain data.

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Liu, Han; Zhang, Xianchao; Zhang, Xiaotong

2018-06-01

Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

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Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

2018-01-01

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author

A user credit assessment model based on clustering ensemble for broadband network new media service supervision

Science.gov (United States)

Liu, Fang; Cao, San-xing; Lu, Rui

2012-04-01

This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

A Penalization Approach for Tomographic Reconstruction of Binary Axially Symmetric Objects

International Nuclear Information System (INIS)

Abraham, R.; Bergounioux, M.; Trelat, E.

2008-01-01

We propose a variational method for tomographic reconstruction of blurred and noised binary images based on a penalization process of a minimization problem settled in the space of bounded variation functions. We prove existence and/or uniqueness results and derive a penalized optimality system. Numerical simulations are provided to demonstrate the relevance of the approach

La Reforma del Código Penal en Nicaragua

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María Asunción Moreno Castillo

2000-08-01

Full Text Available La redacción de un nuevo Código Penal podría calificarse de urgente necesidad si tenemos en cuenta que nuestras normas penales no se adecuan a las exigencias jurídicas, sociales y políticas de un Estado Social Democrático y de Derecho, pues el Código Penal vigente (1974 no es más que un texto parchado por todas partes, e incluso en alguna ocasión doblemente parchado, que no aguanta más remiendos. De ahí la necesidad de la redacción y aprobación de un texto penal que obedezca a las nuevas tendencias político criminales y postulados del Derecho penal moderno.

pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data.

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Sun, Hokeun; Wang, Ya; Chen, Yong; Li, Yun; Wang, Shuang

2017-06-15

DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network-based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time. In this article, we proposed a penalized Exponential Tilt Model (pETM) using network-based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. By combining the strength of the two models we previously developed, we demonstrated the superior power and better performance of the pETM method through simulations and the applications to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. The developed pETM method identifies many cancer-related methylation loci that were missed by our previously developed method that considers correlations among nearby methylation loci but not variance signals. The R package 'pETM' is publicly available through CRAN: http://cran.r-project.org . sw2206@columbia.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Numerical discretization-based estimation methods for ordinary differential equation models via penalized spline smoothing with applications in biomedical research.

Science.gov (United States)

Wu, Hulin; Xue, Hongqi; Kumar, Arun

2012-06-01

Differential equations are extensively used for modeling dynamics of physical processes in many scientific fields such as engineering, physics, and biomedical sciences. Parameter estimation of differential equation models is a challenging problem because of high computational cost and high-dimensional parameter space. In this article, we propose a novel class of methods for estimating parameters in ordinary differential equation (ODE) models, which is motivated by HIV dynamics modeling. The new methods exploit the form of numerical discretization algorithms for an ODE solver to formulate estimating equations. First, a penalized-spline approach is employed to estimate the state variables and the estimated state variables are then plugged in a discretization formula of an ODE solver to obtain the ODE parameter estimates via a regression approach. We consider three different order of discretization methods, Euler's method, trapezoidal rule, and Runge-Kutta method. A higher-order numerical algorithm reduces numerical error in the approximation of the derivative, which produces a more accurate estimate, but its computational cost is higher. To balance the computational cost and estimation accuracy, we demonstrate, via simulation studies, that the trapezoidal discretization-based estimate is the best and is recommended for practical use. The asymptotic properties for the proposed numerical discretization-based estimators are established. Comparisons between the proposed methods and existing methods show a clear benefit of the proposed methods in regards to the trade-off between computational cost and estimation accuracy. We apply the proposed methods t an HIV study to further illustrate the usefulness of the proposed approaches. © 2012, The International Biometric Society.

3D Building Models Segmentation Based on K-Means++ Cluster Analysis

Science.gov (United States)

Zhang, C.; Mao, B.

2016-10-01

3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model) 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid) and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS

Directory of Open Access Journals (Sweden)

C. Zhang

2016-10-01

Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

Penalized Maximum Likelihood Estimation for univariate normal mixture distributions

International Nuclear Information System (INIS)

Ridolfi, A.; Idier, J.

2001-01-01

Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM algorithm is derived, which is still explicit and which intrinsically assures that the estimates are not singular. Numerical evidence of the latter property is put forward with a test

Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data

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Sungho Won

2015-01-01

Full Text Available Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called “large P and small N” problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration.

Penalized estimation for competing risks regression with applications to high-dimensional covariates

DEFF Research Database (Denmark)

Ambrogi, Federico; Scheike, Thomas H.

2016-01-01

of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95: (1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily...... Research 19: (1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25: (7), 890-896). The aim of this work is to consider how to do penalized regression in the presence...... implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according...

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Science.gov (United States)

Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

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Tomoki Tokuda

Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Science.gov (United States)

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data.

Science.gov (United States)

Rahman, M Shafiqur; Sultana, Mahbuba

2017-02-23

When developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commonly even if sample size is large but there is sufficient number of strong predictors. In the presence of separation, even if one develops the model, it produces overfitted model with poor predictive performance. Firth-and logF-type penalized regression methods are popular alternative to MLE, particularly for solving separation-problem. Despite the attractive advantages, their use in risk prediction is very limited. This paper evaluated these methods in risk prediction in comparison with MLE and other commonly used penalized methods such as ridge. The predictive performance of the methods was evaluated through assessing calibration, discrimination and overall predictive performance using an extensive simulation study. Further an illustration of the methods were provided using a real data example with low prevalence of outcome. The MLE showed poor performance in risk prediction in small or sparse data sets. All penalized methods offered some improvements in calibration, discrimination and overall predictive performance. Although the Firth-and logF-type methods showed almost equal amount of improvement, Firth-type penalization produces some bias in the average predicted probability, and the amount of bias is even larger than that produced by MLE. Of the logF(1,1) and logF(2,2) penalization, logF(2,2) provides slight bias in the estimate of regression coefficient of binary predictor and logF(1,1) performed better in all aspects. Similarly, ridge performed well in discrimination and overall predictive performance but it often produces underfitted model and has high rate of convergence failure (even the rate is higher than that

Model-based Clustering of High-Dimensional Data in Astrophysics

Science.gov (United States)

Bouveyron, C.

2016-05-01

The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

Science.gov (United States)

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

Science.gov (United States)

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Proyecto de reforma del Proceso Penal

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Beatriz Scapusio

2014-04-01

Full Text Available El Anteproyecto de Código de Proceso PenalExpositora: Beatriz ScapusioLa Imputabilidad Aportes dogmáticos para un tema clave de la responsablidad penalExpositor: Dardo Preza RestucciaEstructuras procesales en el proceso de conocimiento y en el proceso de ejecuciónExpositora: Raquel Landeira

A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

KAUST Repository

Lee, Seokho

2013-01-31

We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a simple bicluster structure and the combination of multiple layers is able to reveal complicated, multiple biclusters. The method allows for non-pure biclusters, and can simultaneously identify the 1-prevalent blocks and 0-prevalent blocks. A computationally efficient algorithm is developed and guidelines are provided for specifying the tuning parameters, including initial values of model parameters, the number of layers, and the penalty parameters. Missing-data imputation can be handled in the EM framework. The method is tested using synthetic and real datasets and shows good performance. © 2013 Springer Science+Business Media New York.

Poverty Control and the Penal System

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Fernanda Kilduff

2010-01-01

Full Text Available To reflect on the criminalization and penalization of poverty, this article analyzes the neoconservative turn in criminal policy, as an expression of recent changes under contemporary capitalism. In a context characterized by a regression in social policies, the paper discusses the expansion of the penal system as a strategy used by capitalist States to contain and administer in a criminalizing form the growing and increasingly complex manifestations of the “social question” linked to an objective situation of massive and structural unemployment. To conclude the debate, it presents some elements to reflect on the historic function of bourgeois penal law and to analyze its fundamental role in current imperialist strategy.

Criminal responsibility of psychophats in the light of the brazilian penal code Imputabilidade penal dos psicopatas à luz do Código Penal Brasileiro

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Fernanda Eloise Schmidt Ferreira Feguri

2012-05-01

Full Text Available Justifies the choice of this theme, before the controversy in doctrine and jurisprudence as to what a psychopath is being treated before Article 26 § only the Brazilian Penal Code, and also explain why such barbaric crimes in recidivism. At the beginning of the work, it was demonstrated the concept of crime and the disciplines surrounding the criminal law, followed by the concept of a psychopathic, through history, reporting some types of personality disorders, and when and how did the origin of psychopath and how people who had this disorder were treated in the antiquity. It was then explained what becomes of guilt, how takes its implementation, trought accountability, nonimputability sticking to Article 26 § only the Brazilian Penal Code. Following this came to the institute’s measure of security, through its concept, assumptions and methods, including the application, term and termination of dangerousness being still emphasize on expert opinions. Finally, it was reported that cases involving people who have a type of personality disorder, being described succinctly as they are seen to justice and society. Concluding the study, one comes to the conclusion that such individuals are not they amnestied of the semi-liability provided for in Article 26, § only the Brazilian Penal Code Article 26, § only the Brazilian Penal Code. For what can be seen, with latest research is that the benefit of diminished criminal responsibility, given to those agents who do not have full mental unfair, the fact that some people can stay in jail for long as these recourse to psychiatry may be out of hospitals, if any, within three years, by the grace of the laws that protect them. Thus, endorse, this work, that the arrest or other criminal penalty is more honest, more fair, less socially discriminatory, that the sanction psychiatric.Justifica-se a escolha do presente tema, ante a polêmica na doutrina e na jurisprudência, quanto à forma que um psicopata vem

MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

Science.gov (United States)

Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

artículo 9 del Código Penal

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Sebastián Felipe Sánchez Zapata

2014-01-01

Full Text Available El artículo 9 del Código Penal colombiano define, como norma rectora del sistema penal, la conducta punible. Este texto, cuestionando la recepción acrítica de doctrinas extranjeras al ordenamiento jurídico (y también códi - gos, normas, etc., expone algunos de los puntos más trascendentales de la teoría del delito, entre ellos, el concepto de conducta, desvalor de acción y re - sultado, relación de causalidad e imputación, ubicación sistemática del dolo y el tratamiento penal de los imputables a través de las interpretaciones de la doctrina penal colombiana.

Aspects of nuclear penal liability

International Nuclear Information System (INIS)

Faria, N.M. de; Cruz, A.S.C. da

1986-01-01

Topics are treated with reference to articles of the Law 6.453 of october 17, 1977, relating to the nuclear penal liability. At the same time, the Penal Code disposes on illicits which may involve nuclear activity. With regard to the Jurisdiction, mention is made to the Federal Justice competence, due to the constitutional disposal. On the international field, the Convention on Physic Protection on Nuclear Material Transport disposes on illicit fact in which nuclear material may be involved. (Author) [pt

Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering

OpenAIRE

Xiang, Sijia; Yao, Weixin

2017-01-01

In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...

Iterative Brinkman penalization for remeshed vortex methods

DEFF Research Database (Denmark)

Hejlesen, Mads Mølholm; Koumoutsakos, Petros; Leonard, Anthony

2015-01-01

We introduce an iterative Brinkman penalization method for the enforcement of the no-slip boundary condition in remeshed vortex methods. In the proposed method, the Brinkman penalization is applied iteratively only in the neighborhood of the body. This allows for using significantly larger time...

Mapping the Conditions of Penal Hope

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David Brown

2013-11-01

Full Text Available This article examines the conditions of penal optimism behind suggestions that the penal expansionism of the last three decades may be at a ‘turning point’. The article proceeds by outlining David Green’s suggested catalysts of penal reform and considers how applicable they are in the Australian context. Green’s suggested catalysts are: the cycles and saturation thesis; shifts in the dominant conception of the offender; the GFC and budgetary constraints; the drop in crime; the emergence of the prisoner re-entry movement; apparent shifts in public opinion; the influence of evangelical Christian ideas and the Right on Crime initiative. The article then considers a number of other possible catalysts or forces: the role of trade unions; the role of courts; the emergence of recidivism as a political issue; the influence of ’evidence based’/’what works’’ discourse; and the emergence of justice reinvestment (JR. The article concludes with some comments about the capacity of criminology and criminologists to contribute to penal reductionism, offering an optimistic assessment for the prospects of a reflexive criminology that engages in and engenders a wider politics around criminal justice issues.

A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior.

Science.gov (United States)

Shaffer, Anne; Whitehead, Monica; Davis, Molly; Morelen, Diana; Suveg, Cynthia

2017-10-15

In a diverse community sample of mothers (N = 108) and their preschool-aged children (M age  = 3.50 years), this study conducted person-oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self-report indicators. A model-based cluster analysis was applied to five indicators of maternal ER: maternal self-report, observed negative affect in a parent-child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model-based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed. © 2017 Family Process Institute.

Age-specific distributions from coarse-count data: An epidemiological and demographic application of a penalized composite link model

DEFF Research Database (Denmark)

Rizzi, Silvia

as realizations of a Poisson process. The latent unobserved distribution with higher resolution is assumed to be smooth and can be estimated from the composite data via maximum likelihood. In the second study the penalized composite link model for ungrouping is compared to alternative well known ungrouping...

Co-clustering models, algorithms and applications

CERN Document Server

Govaert, Gérard

2013-01-01

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixture

El concepto de terrorismo en derecho internacional penal

OpenAIRE

Valdés Tomàs, Clàudia

2017-01-01

Este trabajo consiste en el estudio de la incidencia del concepto de terrorismo en la represión efectiva del delito de terrorismo en el marco del derecho internacional. Por ello, ha sido necesario dividir el trabajo en una primera parte sobre el estudio de la definición del terrorismo en derecho penal internacional y una segunda parte sobre su estudio en derecho internacional penal, específicamente en el contexto de la Corte Penal Internacional (CPI). En la primera parte analizaremos las def...

Collaborative filtering recommendation model based on fuzzy clustering algorithm

Science.gov (United States)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

Science.gov (United States)

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

La legitimidad del derecho penal

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Francisco Bernate-Ochoa

2010-03-01

Full Text Available El interrogante sobre la legitimidad del derecho penal se ha convertido en un asunto medular dentro de los estudios contemporáneos sobre la materia, dado que hoy en día se reconoce que a partir de la solución a este interrogante teórico se puede asumir la tarea de construir un sistema. El panorama contemporáneo nos ofrece dos soluciones al respecto: por una parte, se sostiene que la legitimación del derecho penal emana de la Constitución, y se prescinde de una construcción sistémica del delito en aras de la obtención de consecuencias acordes con lo planteado en la Carta Política. Por otra, desde una propuesta -de corte normativista- se entiende que la legitimación del derecho penal debe encontrarse en la sociedad, y a partir del entendimiento de ésta se encuentra la necesidad de aquél, lo cual ejerce una influencia en la construcción del sistema del delito.

A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model

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Tao Chen

2015-08-01

Full Text Available In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Comparing implementations of penalized weighted least-squares sinogram restoration

International Nuclear Information System (INIS)

Forthmann, Peter; Koehler, Thomas; Defrise, Michel; La Riviere, Patrick

2010-01-01

Purpose: A CT scanner measures the energy that is deposited in each channel of a detector array by x rays that have been partially absorbed on their way through the object. The measurement process is complex and quantitative measurements are always and inevitably associated with errors, so CT data must be preprocessed prior to reconstruction. In recent years, the authors have formulated CT sinogram preprocessing as a statistical restoration problem in which the goal is to obtain the best estimate of the line integrals needed for reconstruction from the set of noisy, degraded measurements. The authors have explored both penalized Poisson likelihood (PL) and penalized weighted least-squares (PWLS) objective functions. At low doses, the authors found that the PL approach outperforms PWLS in terms of resolution-noise tradeoffs, but at standard doses they perform similarly. The PWLS objective function, being quadratic, is more amenable to computational acceleration than the PL objective. In this work, the authors develop and compare two different methods for implementing PWLS sinogram restoration with the hope of improving computational performance relative to PL in the standard-dose regime. Sinogram restoration is still significant in the standard-dose regime since it can still outperform standard approaches and it allows for correction of effects that are not usually modeled in standard CT preprocessing. Methods: The authors have explored and compared two implementation strategies for PWLS sinogram restoration: (1) A direct matrix-inversion strategy based on the closed-form solution to the PWLS optimization problem and (2) an iterative approach based on the conjugate-gradient algorithm. Obtaining optimal performance from each strategy required modifying the naive off-the-shelf implementations of the algorithms to exploit the particular symmetry and sparseness of the sinogram-restoration problem. For the closed-form approach, the authors subdivided the large matrix

Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization

Science.gov (United States)

Liu, Jin; Huang, Jian; Ma, Shuangge

2012-01-01

Genome-wide association studies have been extensively conducted, searching for markers for biologically meaningful outcomes and phenotypes. Penalization methods have been adopted in the analysis of the joint effects of a large number of SNPs (single nucleotide polymorphisms) and marker identification. This study is partly motivated by the analysis of heterogeneous stock mice dataset, in which multiple correlated phenotypes and a large number of SNPs are available. Existing penalization methods designed to analyze a single response variable cannot accommodate the correlation among multiple response variables. With multiple response variables sharing the same set of markers, joint modeling is first employed to accommodate the correlation. The group Lasso approach is adopted to select markers associated with all the outcome variables. An efficient computational algorithm is developed. Simulation study and analysis of the heterogeneous stock mice dataset show that the proposed method can outperform existing penalization methods. PMID:23272092

MEDIASI PENAL SEBAGAI ALTERNATIF PENYELESAIAN PERKARA PENCURIAN RINGAN (STUDI DI POLRES MALANG KOTA

Directory of Open Access Journals (Sweden)

James Hasudungan Hutajulu

2016-02-01

  Key words: penal mediation, minor theft case, an alternative penal settlement   Abstrak Penelitian mengenai pelaksanaan mediasi penal pada tindak pidana pencurian ringan oleh Polres Malang Kota bertujuan untuk mengetahui dan menganalisis digunakannya mediasi penal serta menganalisis pelaksanaan mediasi penal sebagai alternatif penyelesaian perkara. Adapun metode yang digunakan adalah yuridis sosiologis atau jenis penelitian hukum sosiologis atau penelitian lapangan. Hasil penelitian yang diperoleh antara lain: Polres Malang Kota melakukan mediasi penal dengan alasan agar tercipta rasa keadilan terhadap para saksi sehingga masyarakat puas atas pelayanan yang dilakukan penyidik. Selain itu, langkah-langkah yang dilakukan dalam penerapan mediasi penal ini adalah mempertemukan para pihak, penyidik menyaksikan pengembalian barang yang dicuri oleh pelaku, membantu membuat surat kesepakatan bersama, menerima surat pencabutan perkara serta melakukan gelar perkara.   Kata kunci: mediasi penal, pencurian ringan, alternatif penyelesaian perkara

Spatial cluster modelling

CERN Document Server

Lawson, Andrew B

2002-01-01

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

A density-based clustering model for community detection in complex networks

Science.gov (United States)

Zhao, Xiang; Li, Yantao; Qu, Zehui

2018-04-01

Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

Penalized regression procedures for variable selection in the potential outcomes framework.

Science.gov (United States)

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L

2015-05-10

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple 'impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data, and imputation are drawn. A difference least absolute shrinkage and selection operator algorithm is defined, along with its multiple imputation analogs. The procedures are illustrated using a well-known right-heart catheterization dataset. Copyright © 2015 John Wiley & Sons, Ltd.

La Corte Penal Internacional: abriendo caminos

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Alfredo Etcheberry

2009-01-01

Full Text Available A partir del análisis de las decisiones de la Corte Penal Internacional en el Caso Lubanga y la solicitud del Fiscal de detener al Presidente en ejercicio de Sudán, los autores analizan los desafíos y perspectivas del trabajo de la Corte Penal Internacional, particularmente en lo relativo a cómo se ha configurado su labor después de una etapa inicial dedicada a esclarecer su rol y constitución.

Penal stress and its manifestations in the convicts, suspects and accused persons

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Melnikova D.V.

2015-08-01

Full Text Available The paper is devoted to penal stress and its manifestations at the convicts, suspects and accused persons. This topic has been poorly studied. It is necessary to identify the groups of people especially most in need of prevention and correction of stress state and to define the target effects. The paper presents a theoretical analysis of the concepts of biological and psychological stress. Our theoretical work is mainly devoted to phenomenon of penal stress and factors affecting its formation. We suggested the definition of penal stress concept on the basis of the analyzed literature. The sample included 69 male persons (31 from predetention center, 38 from penal colony, aged 19 to 47 years old. Experimental psychological method of research was mainly used. In the practical part of the paper presents data on the prevalence of the penal stress in predetention centers and penal colonies. In addition, we have studied the relationship of penal stress with punishment stage, the crime characteristics of subjects, individual psychological characteristics, current state. The study allows us to reveal the groups of people in need of the prevention and correction of the penal stress state. We identified some target corrective action also.

Survival associated pathway identification with group Lp penalized global AUC maximization

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Liu Zhenqiu

2010-08-01

Full Text Available Abstract It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group Lp penalized global AUC summary maximization. Unlike LASSO, Lp (p 1. We first extend Lp for individual gene identification to group Lp penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS. This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.

EN BUSCA DE OTRO DERECHO PENAL

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Geovana Andrea Vallejo Jiménez

2011-05-01

Full Text Available Este texto pretende describir algunos de los problemas contemporáneos del derecho penal en Colombia, así como las orientaciones que han asumido las líneas de investigación de los grupos de las universidades Eafit, de Antioquia y de Medellín en la búsqueda de soluciones a estos problemas, y la propuesta que se tiene a partir de la fundamentación de línea de investigación en Derecho Penal en la Institución Universitaria de Envigado.

Cultura Política republicana e o Código Penal de 1890 * Cultura Política republicana y el Código Penal de 1890

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PAULO HENRIQUE MIOTTO DONADELI

2014-12-01

Full Text Available Resumo: O presente artigo buscar estabelecer o conceito de Cultura Política, para compreender a dinâmica da Cultura Política Republicana que se instaurou no Brasil no final do século XIX, como meio de analisar as novas tendências penais da Primeira República (1889-1930, que se codificam no Código Penal de 1890. Ao analisar as condições e contradições desse diploma penal, o artigo convida a fazer uma reflexão sobre a sociedade da época, mostrando que a lei penal é o resultado dos interesses e conflitos que permeia a implantação e consolidação de uma cultural política republicana. A tese central do artigo é mostrar que a elite republicana aproveitou das concepções penais para implantar e justificar mecanismos de repressão e de controle do crime, como uma das formas de dominação e manutenção de poder.Palavras-chave: Cultura Política; República Velha; Direito Penal.Resumen: En este trabajo se busca establecer el concepto de cultura política, para entender la dinámica de la cultura política republicana en Brasil que surgieron a finales del siglo XIX, como una forma de analizar las nuevas tendencias criminales de la Primera República (1889-1930, que están codificadas en el Código Penal de 1890. Al analizar las condiciones y contradicciones de esta ley penal, el artículo llama a una reflexión sobre la sociedad de la época, lo que demuestra que el derecho penal es el resultado de intereses y conflictos que se respira en la implementación y consolidación de una cultura política republicana. La tesis central del artículo es mostrar que la élite republicana aprovechó concepciones penales para implementar y justificar los mecanismos de represión y control de la delincuencia, como una forma de dominación y mantenimiento del poder.Palabras clave: Cultura Política; República Vieja; Derecho Penal.

Sobre la internacionalización de la justicia penal o el derecho penal como instrumento de guerra

OpenAIRE

Ariza Zapata, Daniel

2009-01-01

Hablar de Derecho Penal es, sin lugar a dudas, una labor compleja en la medida en que, como suele ser habitual en el ámbito jurídico, no resulta sencillo delimitar su horizonte de proyección. Dado que la ciencia jurídica se expresa por medio de convenciones lingüísticas, de ella pueden predicarse las mismas falencias y dificultades de que adolecen los términos y las palabras, razón suficiente para advertir que, si se quiere abordar un problema esencial del Derecho Penal en cualquier espacio a...

Nuclear clustering - a cluster core model study

International Nuclear Information System (INIS)

Paul Selvi, G.; Nandhini, N.; Balasubramaniam, M.

2015-01-01

Nuclear clustering, similar to other clustering phenomenon in nature is a much warranted study, since it would help us in understanding the nature of binding of the nucleons inside the nucleus, closed shell behaviour when the system is highly deformed, dynamics and structure at extremes. Several models account for the clustering phenomenon of nuclei. We present in this work, a cluster core model study of nuclear clustering in light mass nuclei

Justicia penal en el Estado arbitrario. La reforma procesal penal durante el nacionalsocialismo, de Javier Llobet Rodríguez

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Camilo Ernesto Bernal Sarmiento

2012-09-01

Full Text Available La obra del profesor Dr. Javier Llobet Rodríguez, catedrático de la Universidad de Costa Rica y abogado litigante, se inserta en una corriente de trabajos surgidos en los últimos diez años en Alemania, España y Latinoamérica relacionados con la identificación del papel que pudieron haber cumplido el derecho penal y la criminología, lo mismo que sus académicos y operadores judiciales, en el proceso de justificación, legitimación formal y aplicación práctica de la barbarie del régimen nacional socialista que lideró Adolf Hitler. En línea de continuidad con sus trabajos de investigación sobre la presunción de inocencia, la detención preventiva y las garantías procesales, Llobet nos propone en Justicia penal en el Estado arbitrario, la reforma procesal penal durante el nacionalsocialismo, analizar la importancia de las garantías del debido proceso estudiando para ello su antítesis, ejemplificada en la actuación meramente policial y la reforma procesal penal que se llevo a cabo durante el nacionalsocialismo   

ANÁLISE CRÍTICA DO DIREITO PENAL DO INIMIGO DE GÜNTHER JAKOBS

OpenAIRE

Pilati, Rachel Cardoso

2009-01-01

Este artigo tem como objetivo analisar criticamente o Direito Penal do inimigo, verificando sua compatibilidade com o Estado Democrático de Direito e o princípio penal do fato. A primeira parte traz um panorama da política criminal atual no Brasil, situa a teoria do Direito Penal do inimigo nesse contexto, e explica a teoria de Jakobs. No segundo tópico, a teoria de Jakobs é analisada criticamente.Palavras-chave: Direito Penal. Inimigo. Jakobs. Estado Democrático de Direito. Princípio Penal d...

Teoría de la inflación penal

OpenAIRE

Carrasco Jiménez, Edison

2016-01-01

[ES]La tesis en cuestión tiene por objeto la proposición de un modelo teórico, explicativo y crítico sobre la presencia del derecho penal en la vida civil, fundado en un concepto de derecho que integre tanto lo formal como lo informal, y sobre todo, en el concepto de inflación penal. Dicha teoría se asienta sobre la hipótesis: Los aumentos o disminuciones de la legislación penal estatal y positiva se encuentran directamente relacionados(as) y dependientes a causas endógenas y exógenas. Son ca...

CONTRADICTORIALITATEA ÎN CORAPORT CU ALTE PRINCIPII ALE PROCESULUI PENAL

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Lucia RUSU

2016-03-01

Full Text Available În legătură cu reformarea sistemului judiciar şi schimbările intervenite în viaţa social-politică a statului nostru, prin­cipiul contradictorialităţii a obţinut o nouă rezonanţă din considerentul că reforma judiciară şi de drept este legată direct de contradictorialitate. Reforma legii procesual penale trebuie să fie fundamentată pe o temelie teoretică solidă. Contra­dictorialitatea, însă, în calitate de noţiune juridică, este insuficient cercetată în doctrina dreptului procesual penal. La ziua de azi, specialişti notorii în domeniul dreptului procesual penal analizează şi studiază importanţa fundamentelor şi principiilor de bază ale procesului penal şi, în primul rând, contradictorialitatea acestuia. Legea procesual penală a Republicii Moldova cunoaşte o evoluţie şi dezvoltate în sensul democratizării şi lărgirii începuturilor contradictoriale în înfăptuirea justiţiei. Aceasta e şi firesc, deoarece contradictorialitatea are o importanţă enormă pentru întregul sistem al procesului penal, determinând în mare parte statutul juridic şi raporturile dintre participanţii la procesul penal, precum şi relaţiile juridice stabilite între participanţii la acest proces şi instanţa de judecată. CONTRADICTION AND ITS CORRELATION WITH OTHER PRINCIPLES OF THE CRIMINAL PROCEEDINGIn connection with the judiciary system reforming and changes in socio-political life of our state, the adversarial principle has gained a new resonance on the grounds that the judicial and legal reform is directly linked to adversariality. The reform of the criminal procedure law must be based on solid theoretical foundation. However, adversariality, as legal concept, is not enough investigated in the doctrine of the criminal procedure law. Currently, notorious specialists in the field of criminal procedure law examine and study the importance of fundamentals and basic principles of the criminal process and

El consentimiento en materia penal

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Camilo Iván Machado Rodríguez

2012-12-01

Full Text Available Se plantea un estudio de las diferentes posturas dogmáticas que se han suscitado en la doctrina jurídico-penal en torno a la relevancia del instituto del consentimiento, como su regulación, recorriendo las legislaciones penales española, alemana, italiana y colombiana. Así mismo, se toman en consideración las recientes posturas, en donde se entiende el consentimiento como un supuesto de autopuesta en peligro, o como uno de heteropuesta en peligro de la víctima. Se propone un entendimiento de la figura desde la óptica de la teoría de la imputación objetiva, y para ello se incluye el consentimiento como causa de ausencia de la imputación objetiva.

On the decennium of penal order procedure in Serbia

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Brkić Snežana

2011-01-01

Full Text Available In this paper, the author defines the notion and explains the penal order procedure and its general characteristics. It is one of the special simplified criminal proceedings, which has got special basis and special structure. This procedural form is ultimately aimed at the rationalization of the criminal procedure. It is achieved by avoiding the main hearing in the trial proceeding. The author presents the evolution of the penal order in Serbia from 2001 to 2011. He points to some legal innovations in this field during that decennium. He compares old and new legal provisions about penal order and finds some differences. There is a constant tendency to expanding the area of criminal offences which can be judged in this procedural form. New legal provisions are, in general, better than previous. However, the practice has shown that application of penal order is too small. The previous practice does not live up to expectations of theory and legislator.

Kebijakan Penal Mengenai Kriminalisasi Di Bidang Keuangan

OpenAIRE

Luthan, Salman

2009-01-01

Research of penal policy on criminalization in financial affairs discusses two main problems: regulating policy on criminal offences, and regulating policy on criminal sanction for financial offences. The regulating policy on criminal offences shows an increase of various conducts that have been criminalized by legislator as a consequence of social change, economic and trade globalization, as well as technological development. Justification of criminalization base on liberal individualiatic t...

Neuro-fuzzy system modeling based on automatic fuzzy clustering

Institute of Scientific and Technical Information of China (English)

Yuangang TANG; Fuchun SUN; Zengqi SUN

2005-01-01

A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

A eutanásia na visão do garantismo penal

OpenAIRE

Reicher, Regina Maria

2005-01-01

Num cotejo entre a eutanásia e o garantismo de Luigi Ferrajoli, quanto à proibição penal, os princípios da lesividade e necessidade, e o direito penal mínimo, este estudo faz uma projeção sobre as principais conseqüências desse comportamento em matéria penal, notadamente quanto à sua repercussão sobre o princípio da dignidade da pessoa humana, que é corolário lógico do Estado Democrático de Direito. Não se justifica, no modelo de direito penal mínimo propugnado pelo garantismo, a penalizaç...

Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model

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Qingyun Du

2016-05-01

Full Text Available A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, semantic reasoning based on a defined ontology and its relationships is primarily intended to overcome the lack of knowledge of the relevant geospatial data. Better constraints on the geographical knowledge yield more reasonable clustering results. This article uses an ontology to describe the four types of semantic constraints for geographical backgrounds: “No Constraints”, “Constraints”, “Cannot-Link Constraints”, and “Must-Link Constraints”. This paper also reports the implementation of a prototype clustering program. Based on the proposed approach, DBSCAN can be applied with both obstacle and non-obstacle constraints as a semi-supervised clustering algorithm and the clustering results are displayed on a digital map.

Improving stability of prediction models based on correlated omics data by using network approaches.

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Renaud Tissier

Full Text Available Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1 network construction, 2 clustering to empirically derive modules or pathways, and 3 building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

A mixture model-based approach to the clustering of microarray expression data.

Science.gov (United States)

McLachlan, G J; Bean, R W; Peel, D

2002-03-01

This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets. EMMIX-GENE is available at http://www.maths.uq.edu.au/~gjm/emmix-gene/

Construção e identidade da dogmática penal: do garantismo prometido ao garantimo prisioneiro

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Vera Regina Pereira de Andrade

2008-09-01

Full Text Available Tratamos, neste artigo, da construção eidentidade da Dogmática Penal enquanto paradigmade Ciência Penal dominante na modernidade. E ofazemos através de três passos, a saber: demarcandosuas matrizes e modelos fundacionais (num primeiromomento em nível do saber e, num segundomomento, em nível da relação saber-poder; demarcandoo que é a Dogmática Penal desde suaauto-imagem (desde uma escuta à voz dospenalistas que protagonizam e compartilham seuparadigma para, ao final, problematizá-la em nívelfuncional, apontando a contradição entre suasfunções declaradas (garantismo prometido e asfunções realmente cumpridas na modernidade(garantismo prisioneiro. A Dogmática aparece,nesta perspectiva, como um protagonismo decisivono processo de instrumentalização e legitimaçãodo controle penal moderno e da ordem social queele co-constitui.Abstract: This article deals with the constructionand identity of the Penal Dogmatic, as a paradigmof the Penal Science, dominant in modernity. Thisis done in three steps: delineating their originsand foundational models (at a first moment at thelevel of knowledge and, in a second, in aknowledge power moment determining what isPenal Dogmatic, since its self image (since alistening to a voice of penalists whom have carriedout and shared their paradigm, to, at the end,transform it into a problem at a functional level,pointing out the contradictions between thedeclared functions (promise guarantism and thereal functions promised in modernity (prisonguarantism. The Dogmatic appears, in thisperspective, as a decisive carry out in the processof instrumentalizating and legitimating of themodern penal control, and of the social order,which it constructed.

Hierarchical modeling of cluster size in wildlife surveys

Science.gov (United States)

Royle, J. Andrew

2008-01-01

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

Fitting Latent Cluster Models for Networks with latentnet

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Pavel N. Krivitsky

2007-12-01

Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

Projection-based curve clustering

International Nuclear Information System (INIS)

Auder, Benjamin; Fischer, Aurelie

2012-01-01

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat a l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the 'true' ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem. (authors)

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

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Yihang Yin

2015-08-01

Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

Science.gov (United States)

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

A robust regression based on weighted LSSVM and penalized trimmed squares

International Nuclear Information System (INIS)

Liu, Jianyong; Wang, Yong; Fu, Chengqun; Guo, Jie; Yu, Qin

2016-01-01

Least squares support vector machine (LS-SVM) for nonlinear regression is sensitive to outliers in the field of machine learning. Weighted LS-SVM (WLS-SVM) overcomes this drawback by adding weight to each training sample. However, as the number of outliers increases, the accuracy of WLS-SVM may decrease. In order to improve the robustness of WLS-SVM, a new robust regression method based on WLS-SVM and penalized trimmed squares (WLSSVM–PTS) has been proposed. The algorithm comprises three main stages. The initial parameters are obtained by least trimmed squares at first. Then, the significant outliers are identified and eliminated by the Fast-PTS algorithm. The remaining samples with little outliers are estimated by WLS-SVM at last. The statistical tests of experimental results carried out on numerical datasets and real-world datasets show that the proposed WLSSVM–PTS is significantly robust than LS-SVM, WLS-SVM and LSSVM–LTS.

LA CARGA DINÁMICA PROBATORIA Y SU REPERCUSIÓN EN EL PROCESO PENAL DESDE LAS REGLAS DE MALLORCA Y LA TEORÍA DEL GARANTISMO PENAL

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Sebastián Betancourt Restrepo

2010-01-01

Full Text Available El presente artículo pretende hacer algunos cuestionamientos en torno a la implementación de la teoría de la carga dinámica de la prueba en la actuación penal, a partir de los elementos brindados por las Reglas de Mallorca y la teoría del garantismo penal, defendida por el profesor Luigi Ferrajoli en Italia, y en Argentina por el profesor Adolfo Alvarado Velloso, desde la perspectiva de una sistemática procesal penal, reconocedora de garantías universales como el debido proceso.

LA CARGA DINÁMICA PROBATORIA Y SU REPERCUSIÓN EN EL PROCESO PENAL DESDE LAS REGLAS DE MALLORCA Y LA TEORÍA DEL GARANTISMO PENAL

OpenAIRE

Sebastián Betancourt Restrepo

2010-01-01

El presente artículo pretende hacer algunos cuestionamientos en torno a la implementación de la teoría de la carga dinámica de la prueba en la actuación penal, a partir de los elementos brindados por las Reglas de Mallorca y la teoría del garantismo penal, defendida por el profesor Luigi Ferrajoli en Italia, y en Argentina por el profesor Adolfo Alvarado Velloso, desde la perspectiva de una sistemática procesal penal, reconocedora de garantías universales como el debido proceso.

Parameters of oscillation generation regions in open star cluster models

Science.gov (United States)

Danilov, V. M.; Putkov, S. I.

2017-07-01

We determine the masses and radii of central regions of open star cluster (OCL) models with small or zero entropy production and estimate the masses of oscillation generation regions in clustermodels based on the data of the phase-space coordinates of stars. The radii of such regions are close to the core radii of the OCL models. We develop a new method for estimating the total OCL masses based on the cluster core mass, the cluster and cluster core radii, and radial distribution of stars. This method yields estimates of dynamical masses of Pleiades, Praesepe, and M67, which agree well with the estimates of the total masses of the corresponding clusters based on proper motions and spectroscopic data for cluster stars.We construct the spectra and dispersion curves of the oscillations of the field of azimuthal velocities v φ in OCL models. Weak, low-amplitude unstable oscillations of v φ develop in cluster models near the cluster core boundary, and weak damped oscillations of v φ often develop at frequencies close to the frequencies of more powerful oscillations, which may reduce the non-stationarity degree in OCL models. We determine the number and parameters of such oscillations near the cores boundaries of cluster models. Such oscillations points to the possible role that gradient instability near the core of cluster models plays in the decrease of the mass of the oscillation generation regions and production of entropy in the cores of OCL models with massive extended cores.

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

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Qianqian Ba

2017-01-01

Full Text Available Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM, an information value model improved by an analytic hierarchy process (IVM-AHP and our new improved model. Approximately 70% (5905 of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530 were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

La Intervención del Derecho Penal en Los Fenómenos Migratorios

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Thamara Duarte Cunha Medeiros

2016-06-01

Full Text Available El presente artículo tiene por objetivo analisar la legitimidad de la intervención penal en los actuales flujos migratórios. La premisa principal considera que la utilización del derecho penal como instrumento de gestión para controlar flujos migratório ratifica el discurso del miedo hacia la inmingración y fomenta la criminalidad organizada que se ha estructurado frente a las políticas tolerancia cero contra la inmigración, especialmente, la trata de personas y el contrabando de migrantes. En este sentido, el analisis y la delimitación del bien jurídico “ política migratória” sugiere una reflexión a la luz de las bases principiológicas del Derecho Penal y despierta cuestiones que implican en una redefinición del derecho de punir en la sociedad del riesgo.

Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression.

Science.gov (United States)

Candel, Math J J M; Van Breukelen, Gerard J P

2010-06-30

Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.

Prácticas de control socio-penal dispositivo psi pericial y adolescentes mujeres en el Sistema Penal Juvenil Uruguayo /

OpenAIRE

López Gallego, Laura

2016-01-01

La presente tesis doctoral "Prácticas de Control Socio-Penal. Dispositivo Psi Pericial y Adolescentes Mujeres en el Sistema Penal Juvenil Uruguayo" se sitúa en una perspectiva analítica que articula postulados de la Psicología Social Crítica y las Epistemologías Feministas, junto con aportes criminológicos provenientes de la Criminología Crítica y la Criminología Feminista. Los itinerarios de investigación producidos en esta tesis doctoral se descomponen en dos proyectos de investigación. Uno...

Flexible and efficient genome tiling design with penalized uniqueness score

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Du Yang

2012-12-01

Full Text Available Abstract Background As a powerful tool in whole genome analysis, tiling array has been widely used in the answering of many genomic questions. Now it could also serve as a capture device for the library preparation in the popular high throughput sequencing experiments. Thus, a flexible and efficient tiling array design approach is still needed and could assist in various types and scales of transcriptomic experiment. Results In this paper, we address issues and challenges in designing probes suitable for tiling array applications and targeted sequencing. In particular, we define the penalized uniqueness score, which serves as a controlling criterion to eliminate potential cross-hybridization, and a flexible tiling array design pipeline. Unlike BLAST or simple suffix array based methods, computing and using our uniqueness measurement can be more efficient for large scale design and require less memory. The parameters provided could assist in various types of genomic tiling task. In addition, using both commercial array data and experiment data we show, unlike previously claimed, that palindromic sequence exhibiting relatively lower uniqueness. Conclusions Our proposed penalized uniqueness score could serve as a better indicator for cross hybridization with higher sensitivity and specificity, giving more control of expected array quality. The flexible tiling design algorithm incorporating the penalized uniqueness score was shown to give higher coverage and resolution. The package to calculate the penalized uniqueness score and the described probe selection algorithm are implemented as a Perl program, which is freely available at http://www1.fbn-dummerstorf.de/en/forschung/fbs/fb3/paper/2012-yang-1/OTAD.v1.1.tar.gz.

¿ES LEGÍTIMA LA PROTECCIÓN PENAL DE LOS DERECHOS MORALES DE AUTOR?

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César Alejandro Osorio Moreno

2010-11-01

Full Text Available En el contexto del derecho penal moderno y desde una perspectiva comparada entre el derecho penal español y el derecho penal colombiano en cuanto a la protección penal de los derechos morales de autor, se pone en discusión si es necesaria la intervención penal o no, a partir de cuestionar si en verdad se protege un bien jurídico relevante que ameritaría el mecanismo de control social más grave del que dispone el Estado que es el derecho penal, o cómo se planteará, si a partir de la protección de los derechos patrimoniales de autor se cobijan los morales tal como acontece en España en la actualidad.

EL TERRORISMO EN EL CÓDIGO PENAL COLOMBIANO

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Henry Torres Vásquez

2009-06-01

Full Text Available En el presente artículo se intenta de manera general abordar el terrorismo desde el punto de vista puramente legal, doctrinal y jurisprudencial. En Colombia es necesario y en ello radica este aporte, generar discusión jurídica respecto a la implementación de los delitos de terrorismo. Aquí se pretende puntualizar los tipos penales nuestros y se estudian algunos aspectos imbricados en los mismos, sin que ello sea un aspecto formal y dogmático, sino que alcontrario, es un análisis a la luz de las actuales interpretaciones sobre el terrorismo, lo cual no obsta para hacer algunos muy breves comentarios al respecto. Los elementos constitutivos de los bienes jurídicos Seguridad Pública y Derecho internacional Humanitario son analizados junto a los ingredientes normativos de los dos tipos penales estipulados en nuestro código penal.

FORMATION OF A INNOVATION REGIONAL CLUSTER MODEL

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G. S. Merzlikina

2015-01-01

Full Text Available Summary. As a result of investigation of science and methodical approaches related problems of building and development of innovation clusters there were some issues in functional assignments of innovation and production clusters. Because of those issues, article’s authors differ conceptions of innovation cluster and production cluster, as they explain notion of innovation-production cluster. The main goal of this article is to reveal existing organizational issues in cluster building and its successful development. Based on regional clusters building analysis carried out there was typical practical structure of cluster members interaction revealed. This structure also have its cons, as following: absence cluster orientation to marketing environment, lack of members’ prolonged relations’ building and development system, along with ineffective management of information, financial and material streams within cluster, narrow competence difference and responsibility zones between cluster members, lack of transparence of cluster’s action, low environment changes adaptivity, hard to use cluster members’ intellectual property, and commercialization of hi-tech products. When all those issues listed above come together, it reduces life activity of existing models of innovative cluster-building along with practical opportunity of cluster realization. Because of that, authors offer an upgraded innovative-productive cluster building model with more efficient business processes management system, which includes advanced innovative cluster structure, competence matrix and subcluster responsibility zone. Suggested model differs from other ones by using unified innovative product development control center, which also controls production and marketing realization.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

Science.gov (United States)

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization

Science.gov (United States)

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Shia, BenChang; Ma, Shuangge

2015-01-01

In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms “classic” meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance. PMID:24395534

Evolutionary-Hierarchical Bases of the Formation of Cluster Model of Innovation Economic Development

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Yuliya Vladimirovna Dubrovskaya

2016-10-01

Full Text Available The functioning of a modern economic system is based on the interaction of objects of different hierarchical levels. Thus, the problem of the study of innovation processes taking into account the mutual influence of the activities of these economic actors becomes important. The paper dwells evolutionary basis for the formation of models of innovation development on the basis of micro and macroeconomic analysis. Most of the concepts recognized that despite a big number of diverse models, the coordination of the relations between economic agents is of crucial importance for the successful innovation development. According to the results of the evolutionary-hierarchical analysis, the authors reveal key phases of the development of forms of business cooperation, science and government in the domestic economy. It has become the starting point of the conception of the characteristics of the interaction in the cluster models of innovation development of the economy. Considerable expectancies on improvement of the national innovative system are connected with the development of cluster and network structures. The main objective of government authorities is the formation of mechanisms and institutions that will foster cooperation between members of the clusters. The article explains that the clusters cannot become the factors in the growth of the national economy, not being an effective tool for interaction between the actors of the regional innovative systems.

Cluster-specific small airway modeling for imaging-based CFD analysis of pulmonary air flow and particle deposition in COPD smokers

Science.gov (United States)

Haghighi, Babak; Choi, Jiwoong; Choi, Sanghun; Hoffman, Eric A.; Lin, Ching-Long

2017-11-01

Accurate modeling of small airway diameters in patients with chronic obstructive pulmonary disease (COPD) is a crucial step toward patient-specific CFD simulations of regional airflow and particle transport. We proposed to use computed tomography (CT) imaging-based cluster membership to identify structural characteristics of airways in each cluster and use them to develop cluster-specific airway diameter models. We analyzed 284 COPD smokers with airflow limitation, and 69 healthy controls. We used multiscale imaging-based cluster analysis (MICA) to classify smokers into 4 clusters. With representative cluster patients and healthy controls, we performed multiple regressions to quantify variation of airway diameters by generation as well as by cluster. The cluster 2 and 4 showed more diameter decrease as generation increases than other clusters. The cluster 4 had more rapid decreases of airway diameters in the upper lobes, while cluster 2 in the lower lobes. We then used these regression models to estimate airway diameters in CT unresolved regions to obtain pressure-volume hysteresis curves using a 1D resistance model. These 1D flow solutions can be used to provide the patient-specific boundary conditions for 3D CFD simulations in COPD patients. Support for this study was provided, in part, by NIH Grants U01-HL114494, R01-HL112986 and S10-RR022421.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Science.gov (United States)

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Single-cluster dynamics for the random-cluster model

NARCIS (Netherlands)

Deng, Y.; Qian, X.; Blöte, H.W.J.

2009-01-01

We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those

¿Es el plagio una conducta reprimida por el derecho penal?

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Ernesto Rengifo García

2010-11-01

Full Text Available ¿Será que el mensaje de la Sala Penal de la Corte Suprema es el de no activar la última ratio del sistema legal con conductas que deben ser materia de la jurisdicción civil, pero eso sí dependiendo de quién las acometa o ejecute? (se recuerda que el caso en análisis envuelve a una profesora universitaria y ¿será que la dificultad teórica y práctica de adecuación de la conducta a los tipos penales relacionados con el derecho de autor, hace que el derecho penal no sea el instrumento institucional más idóneo para reprimir la infracción a los derechos de esa particular área jurídica? La respuesta a estos interrogantes deben ser el comienzo para pensar o repensar si ese expansionismo del derecho penal que ha llegado al campo de la propiedad intelectual vale la pena revisarlo y mantenerlo. Señalaba Cesare Becaría: “Lo que impide el crimen, no es la cantidad de reprimendas penales, sino la garantía de su punición”. Si no hay certeza o garantía en el castigo, ¿valdrá la pena recurrir a la aplicación judicial de esos tipos penales?, este artículo busca abrir la discusión en este punto.

¿Es el plagio una conducta reprimida por el derecho penal?

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Ernesto Rengifo García

2010-11-01

Full Text Available ¿Será que el mensaje de la Sala Penal de la Corte Suprema es el de no activar la ultima ratio del sistema legal con conductas que deben ser materia de la jurisdicción civil, pero eso sí dependiendo de quien las acometa o ejecute? (se recuerda que el caso en análisis envuelve a una profesora universitaria, y ¿será que la dificultad teórica y práctica de adecuación de la conducta a los tipos penales relacionados con el derecho de autor hará que el derecho penal no sea el instrumento institucional más idóneo para reprimir la infracción a los derechos de esa particular área jurídica? La respuesta a estos interrogantes deben ser el comienzo para pensar o repensar si ese expansionismo del derecho penal que ha llegado al campo de la propiedad intelectual vale la pena revisarlo y mantenerlo. Señalaba Cesare Becaría: “Lo que impide el crimen, no es la cantidad de reprimendas penales, sino la garantía de su punición”. Si no hay certeza o garantía en el castigo, ¿valdrá la pena recurrir a la aplicación judicial de esos tipos penales? Este artículo busca abrir la discusión en este punto.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

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Rosemary M McCloskey

2017-11-01

Full Text Available Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP, which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85% and specificity (91% than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46% as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where

Ontology-based topic clustering for online discussion data

Science.gov (United States)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

El pensamiento alemán en el derecho penal argentino

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Guido L. Croxatto

2014-01-01

Full Text Available El objetivo de este ensayo es abordar desde una perspectiva histórica la influencia del pensamiento alemán (la filosofía alemana, la dogmática alemana en el derecho penal argentino, rescatando la forma en que distintos teóricos alemanes fueron leídos y receptados en Argentina. Se pretende pensar la codificación penal argentina como un diálogo – muchas veces como una mera recepción acrítica – entre los juristas y codificadores latinoamericanos y los pensadores alemanes, en sus distintas vertientes y etapas históricas. Se analizan distintos debates –como la polémica entre el causalismo y el finalismo- tanto por la forma en que se sucedieron en Alemania, como por la forma en que se dieron en Argentina (el debate causalismo-finalismo se sucedió en los complejos años setenta en Argentina, tomando en cuenta sobretodo los contextos políticos en que surgieron esas polémicas; qué significado tuvieron estas discusiones – y qué significaba asumir determinadas posiciones – en función de los contextos políticos donde se evidenciaron. De este modo se espera trazar un panorama de la discusión actual en el derecho penal argentino, haciendo énfasis en el análisis histórico de las problemáticas que aún enfrenta el Derecho Penal, tratando de generar, al mismo tiempo, un pensamiento penal crítico, consciente – pero no rehén – de las influencias provenientes de Europa, es decir, un pensamiento penal crítico que tome en cuenta las particulares condiciones sociales e históricas de la región en que se aplica el Derecho.

A Penalization-Gradient Algorithm for Variational Inequalities

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Abdellatif Moudafi

2011-01-01

Full Text Available This paper is concerned with the study of a penalization-gradient algorithm for solving variational inequalities, namely, find x̅∈C such that 〈Ax̅,y-x̅〉≥0 for all y∈C, where A:H→H is a single-valued operator, C is a closed convex set of a real Hilbert space H. Given Ψ:H→R  ∪  {+∞} which acts as a penalization function with respect to the constraint x̅∈C, and a penalization parameter βk, we consider an algorithm which alternates a proximal step with respect to ∂Ψ and a gradient step with respect to A and reads as xk=(I+λkβk∂Ψ-1(xk-1-λkAxk-1. Under mild hypotheses, we obtain weak convergence for an inverse strongly monotone operator and strong convergence for a Lipschitz continuous and strongly monotone operator. Applications to hierarchical minimization and fixed-point problems are also given and the multivalued case is reached by replacing the multivalued operator by its Yosida approximate which is always Lipschitz continuous.

Cluster model of the nucleus

International Nuclear Information System (INIS)

Horiuchi, H.; Ikeda, K.

1986-01-01

This article reviews the development of the cluster model study. The stress is put on two points; one is how the cluster structure has come to be regarded as a fundamental structure in light nuclei together with the shell-model structure, and the other is how at present the cluster model is extended to and connected with the studies of the various subjects many of which are in the neighbouring fields. The authors the present the main theme with detailed explanations of the fundamentals of the microscopic cluster model which have promoted the development of the cluster mode. Examples of the microscopic cluster model study of light nuclear structure are given

Soft Sensor Modeling Based on Multiple Gaussian Process Regression and Fuzzy C-mean Clustering

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Xianglin ZHU

2014-06-01

Full Text Available In order to overcome the difficulties of online measurement of some crucial biochemical variables in fermentation processes, a new soft sensor modeling method is presented based on the Gaussian process regression and fuzzy C-mean clustering. With the consideration that the typical fermentation process can be distributed into 4 phases including lag phase, exponential growth phase, stable phase and dead phase, the training samples are classified into 4 subcategories by using fuzzy C- mean clustering algorithm. For each sub-category, the samples are trained using the Gaussian process regression and the corresponding soft-sensing sub-model is established respectively. For a new sample, the membership between this sample and sub-models are computed based on the Euclidean distance, and then the prediction output of soft sensor is obtained using the weighting sum. Taking the Lysine fermentation as example, the simulation and experiment are carried out and the corresponding results show that the presented method achieves better fitting and generalization ability than radial basis function neutral network and single Gaussian process regression model.

On the shell model connection of the cluster model

International Nuclear Information System (INIS)

Cseh, J.; Levai, G.; Kato, K.

2000-01-01

Complete text of publication follows. The interrelation of basic nuclear structure models is a longstanding problem. The connection between the spherical shell model and the quadrupole collective model has been studied extensively, and symmetry considerations proved to be especially useful in this respect. A collective band was interpreted in the shell model language long ago as a set of states (of the valence nucleons) with a specific SU(3) symmetry. Furthermore, the energies of these rotational states are obtained to a good approximation as eigenvalues of an SU(3) dynamically symmetric shell model Hamiltonian. On the other hand the relation of the shell model and cluster model is less well explored. The connection of the harmonic oscillator (i.e. SU(3)) bases of the two approaches is known, but it was established only for the unrealistic harmonic oscillator interactions. Here we investigate the question: Can an SU(3) dynamically symmetric interaction provide a similar connection between the spherical shell model and the cluster model, like the one between the shell and collective models? In other words: whether or not the energy of the states of the cluster bands, defined by a specific SU(3) symmetries, can be obtained from a shell model Hamiltonian (with SU(3) dynamical symmetry). We carried out calculations within the framework of the semimicroscopic algebraic cluster model, in which not only the cluster model space is obtained from the full shell model space by an SU(3) symmetry-dictated truncation, but SU(3) dynamically symmetric interactions are also applied. Actually, Hamiltonians of this kind proved to be successful in describing the gross features of cluster states in a wide energy range. The novel feature of the present work is that we apply exclusively shell model interactions. The energies obtained from such a Hamiltonian for several bands of the ( 12 C, 14 C, 16 O, 20 Ne, 40 Ca) + α systems turn out to be in good agreement with the experimental

To What Extent are Domestic Penal Laws Retroactive for Crime ...

African Journals Online (AJOL)

Nafiisah

Hard or soft law most countries have at least a piece of statutory enactment, which provides for the non-retroactivity of penal law. Non retroactivity of penal laws also forms part of fundamental rights of the citizens in the Constitution, the supreme law of the country of the Republic of Mauritius which provide in its section 10(4).

Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations

KAUST Repository

Cao, Jiguo; Huang, Jianhua Z.; Wu, Hulin

2012-01-01

Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.

Direito penal na era global: garantismo positivo ajustado ao garantismo negativo

OpenAIRE

Gomes, Thiago Quintas

2009-01-01

Esta dissertação faz análise da complexa sociedade do pós-industrial, hedonista, de massa e relações objetivas, denominada sociedade de risco, e qual deve ser a postura do direito penal diante dos novos interesses, difusos e coletivos, de um mundo globalizado. O direito penal deve estar preparado para lidar com esta nova realidade, o que significa a adoção de uma nova dogmática jurídica-penal na proteção dos bens meta-individuais (direitos humanos de 3ª dimensão), porém conformada com o...

Responsabilidad penal de las personas jurídicas

OpenAIRE

Montes Castro, Claudia Marcela

2013-01-01

La responsabilidad penal de las personas jurídicas es un tema que adquiere cada vez mayor relevancia en una sociedad que sufre constantes cambios, y en la que se perfeccionan cada vez más las formas de cometer delitos. En el presente trabajo se realiza el estudio sobre la evolución de la figura de la responsabilidad penal de las personas jurídicas, abarcando desde el derecho romano hasta nuestros días. En el desarrollo del mismo, se expone el recorrido a través de las diferentes alternativas ...

Maritime environmental penal law. International and German legislation

International Nuclear Information System (INIS)

Eller, Jan Frederik

2017-01-01

The book on maritime environmental penal law discusses the following issues: part I: introduction into the importance of oceanic environment and its thread, requirement of protective measures,; part II: focus of the study and terminology: oceanic pollution, maritime environmental legislation, international legislation; part 3: international legislative regulations concerning the protection of maritime environment: avoidance of environmental pollution, maritime legislative agreements, existing protective institutions; part 4: state penal power concerning maritime environmental protection; part 5: statutory offense according to German legislation; perspectives for regulations concerning criminal acts on sea.

Price Formation Based on Particle-Cluster Aggregation

Science.gov (United States)

Wang, Shijun; Zhang, Changshui

In the present work, we propose a microscopic model of financial markets based on particle-cluster aggregation on a two-dimensional small-world information network in order to simulate the dynamics of the stock markets. "Stylized facts" of the financial market time series, such as fat-tail distribution of returns, volatility clustering and multifractality, are observed in the model. The results of the model agree with empirical data taken from historical records of the daily closures of the NYSE composite index.

SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods

Energy Technology Data Exchange (ETDEWEB)

Storm, Emma; Weniger, Christoph [GRAPPA, Institute of Physics, University of Amsterdam, Science Park 904, 1090 GL Amsterdam (Netherlands); Calore, Francesca, E-mail: e.m.storm@uva.nl, E-mail: c.weniger@uva.nl, E-mail: francesca.calore@lapth.cnrs.fr [LAPTh, CNRS, 9 Chemin de Bellevue, BP-110, Annecy-le-Vieux, 74941, Annecy Cedex (France)

2017-08-01

We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (∼> 10{sup 5}) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that are motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |ℓ|<90{sup o} and | b |<20{sup o}, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.

A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

Directory of Open Access Journals (Sweden)

Yuxian Zhang

2015-01-01

Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

On the shell-model-connection of the cluster model

International Nuclear Information System (INIS)

Cseh, J.

2000-01-01

Complete text of publication follows. The interrelation of basic nuclear structure models is a longstanding problem. The connection between the spherical shell model and the quadrupole collective model has been studied extensively, and symmetry considerations proved to be especially useful in this respect. A collective band was interpreted in the shell model language long ago [1] as a set of states (of the valence nucleons) with a specific SU(3) symmetry. Furthermore, the energies of these rotational states are obtained to a good approximation as eigenvalues of an SU(3) dynamically symmetric shell model Hamiltonian. On the other hand the relation of the shell model and cluster model is less well explored. The connection of the harmonic oscillator (i.e. SU(3)) bases of the two approaches is known [2] but it was established only for the unrealistic harmonic oscillator interactions. Here we investigate the question: Can an SU(3) dynamically symmetric interaction provide a similar connection between the spherical shell model and the cluster model, like the one between the shell and collective models? In other words: whether or not the energy of the states of the cluster bands, defined by a specific SU(3) symmetries, can be obtained from a shell model Hamiltonian (with SU(3) dynamical symmetry). We carried out calculations within the framework of the semimicroscopic algebraic cluster model [3,4] in order to find an answer to this question, which seems to be affirmative. In particular, the energies obtained from such a Hamiltonian for several bands of the ( 12 C, 14 C, 16 O, 20 Ne, 40 Ca) + α systems turn out to be in good agreement with the experimental values. The present results show that the simple and transparent SU(3) connection between the spherical shell model and the cluster model is valid not only for the harmonic oscillator interactions, but for much more general (SU(3) dynamically symmetric) Hamiltonians as well, which result in realistic energy spectra. Via

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

Science.gov (United States)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models.

Science.gov (United States)

Krivitsky, Pavel N; Handcock, Mark S; Raftery, Adrian E; Hoff, Peter D

2009-07-01

Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets. We also apply it to two simulated network datasets with the same, highly skewed, degree distribution, but very different network behavior: one unstructured and the other with transitivity and clustering. Models based on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but our model does.

Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.

Science.gov (United States)

Cheung, Y M; Leung, W M; Xu, L

1997-01-01

We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.

Environmental protection - Penal Law. Umweltschutz-Strafrecht

Energy Technology Data Exchange (ETDEWEB)

Sack, H J

1980-01-01

The 18th Amendment of the Penal Law - Law on the Abatement of Environmental Delinquency - (18. StrAendG) has now been passed. It has been promulgated on March 28, 1980 and has come into force on July 1, 1980. Through this amendment, a large number of the provisions of the environmental law regarding sanctions has been incorporated into the Penal Code. Persons concerned with environmental protection and pollution control will also in future need such a textbook with comments as a guide to the most important provisions on sanctions and fixed penalties. The 18th Amendment of the Penal Code does not cover all the provisions on sanctions to be applied in the field of environmental protection, a number of regulations still remains part of other, special laws. The same applies to the provisions on penalties which are laid down in a variety of individual laws and regulations, as a comprehensive code of environmental laws still remains to be established. This first part of the textbook in loose-leaf form deals mainly with the new provisions of sections 311d, 311e, 324, and 325. The other facts of the 18th Amendment will be discussed in the second part. As the regulations have, for the most part, not been completly revised or newly inserted, parts 1/3 of the first edition of this textbook can still be used as a help in analysing the existing provisions.

A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation

Directory of Open Access Journals (Sweden)

Liming Tang

2014-01-01

Full Text Available The fuzzy C means clustering algorithm with spatial constraint (FCMS is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

Fundamentos de la detención preventiva en el procedimiento penal colombiano

Directory of Open Access Journals (Sweden)

Leonardo Cruz Bolívar

2012-12-01

Full Text Available La contribución se ocupa de analizar en concreto los principios que rigen la privación preventiva de la libertad en el procedimiento penal, desde el punto de vista constitucional, legal y de instrumentos internacionales, buscando armonizar los criterios que utilizan los jueces de garantías al momento de resolver la solicitud del fiscal en esta materia. El principio de proporcionalidad constitucional, los elementos desarrollados legalmente y los pronunciamientos del sistema interamericano de derechos humanos dan base a las interpretaciones que se proponen en el artículo, buscando proteger la libertad, sin renunciar a las necesidades básicas de la justicia y la sociedad que requieren un proceso penal eficiente y que responda a la realidad nacional.

Cooperación internacional en materia penal en el MERCOSUR: el cibercrimen

OpenAIRE

Santiago Deluca; Enrique Del Carril

2017-01-01

En el Mercosur no existe un derecho penal común, no obstante observarse una creciente corriente orientada a la adopción de normas generales de política criminal tendiente a combatir diversos actos delictivos. Ello se cristaliza en la creación de normas de cooperación internacional en materia penal, con el objeto de lograr la asimilación y adecuación “macro” de las legislaciones penales de los Estados Parte. Es justo reconocer que en este trabajo no se pretende elaborar un corpus iuris por ...

A first packet processing subdomain cluster model based on SDN

Science.gov (United States)

Chen, Mingyong; Wu, Weimin

2017-08-01

For the current controller cluster packet processing performance bottlenecks and controller downtime problems. An SDN controller is proposed to allocate the priority of each device in the SDN (Software Defined Network) network, and the domain contains several network devices and Controller, the controller is responsible for managing the network equipment within the domain, the switch performs data delivery based on the load of the controller, processing network equipment data. The experimental results show that the model can effectively solve the risk of single point failure of the controller, and can solve the performance bottleneck of the first packet processing.

Garantismo penal para quem? O discurso penal liberal drente à sua desconstrução pela criminologia

Directory of Open Access Journals (Sweden)

Marisa Helena D`Arbo Alves de Freitas

2017-05-01

Full Text Available http://dx.doi.org/10.5007/2177-7055.2017v38n75p129 O presente artigo versa sobre a efetividade das garantias individuais e o reflexo dessa problemática no acesso à justiça penal brasileira. Apesar de o discurso idealista-garantista apresentar os instrumentos possíveis para defesa dos acusados e para isonomia de tratamento aos sujeitos do processo, a realidade processual indica que esse discurso é meramente retórico e limitado ao plano teórico-abstrato, com dificuldades de se concretizar no plano prático-reformista. O trabalho é bibliográfico e desenvolve conceitos relativos à seletividade e as formas de controle do poder punitivo. Para abordagem do tema, partiu-se da perspectiva crítica do garantismo penal proposto por Luigi Ferrajoli.

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

La protección penal del medio ambiente. Análisis del art. 338 del Código Penal colombiano sobre minería ilegal

Directory of Open Access Journals (Sweden)

Sebastián Felipe Sánchez Zapata

2016-03-01

Full Text Available El estudio de la criminalidad medio-ambiental comprende todo un elenco de problemas jurídico-penales de muy difícil solución. Basta acudir a las dificultades que sobresalen al momento de definir el bien jurídico protegido, el desvalor de acción y resultado, las leyes penales en blanco, la relación de causalidad, etc. Un acercamiento, aunque sea superficial, a las dificultades ínsitas de estas categorías revela el uso que se está dando, en nuestro contexto, a la retórica apuesta del legislador por proteger concretos objetos materiales como los yacimientos mineros, las aguas y el material de arrastre. El texto, desde una perspectiva penal, expone la realidad de la  minería ilegal en Colombia y las formas institucionales de reacción contra ella.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

Xu, Zhiqiang

2017-02-16

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke

2017-01-01

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

Localismo e non-neutralità culturale del diritto penale ‘sotto tensione’ per effetto dell’immigrazione

Directory of Open Access Journals (Sweden)

Fabio Basile

2011-05-01

penale: “il diritto penale è fortemente impregnato di cultura”. - 3. Conclusioni: le implicazioni di ‘localismo’ e ‘non-neutralità culturale’ del diritto penale in ordine al fenomeno dei reati ‘culturalmente motivati’ commessi dagli immigrati.

Likelihood-based inference for clustered line transect data

DEFF Research Database (Denmark)

Waagepetersen, Rasmus Plenge; Schweder, Tore

The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

Likelihood-based inference for clustered line transect data

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Schweder, Tore

2006-01-01

The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

mPLR-Loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction.

Science.gov (United States)

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2015-03-15

Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers' convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer). Copyright © 2014 Elsevier Inc. All rights reserved.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

Science.gov (United States)

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

Política de despenalización como medio eficaz para una justicia penal justa

Directory of Open Access Journals (Sweden)

Rogelio Barba Álvarez

2008-01-01

Full Text Available El actual sistema de justicia penal en México se encuentra en una grave crisis, derivada de la falta de una adecuada política criminal y técnica legislativa. La política de despenalización serviría para resolver muchos de los problemas que se originan por la pésima justicia penal que actualmente impera en México. Por este medio se podría resolver el problema de la hipertrofia legislativa, la antinomia de tipos penales, el fortalecimiento de un subsistema sancionatorio más benévolo que aquel basado en la amenaza penal, y sobre todo de la unificación de la legislación penal en nuestro país, para lograr la anhelada justicia justa.

Cluster Correlation in Mixed Models

Science.gov (United States)

Gardini, A.; Bonometto, S. A.; Murante, G.; Yepes, G.

2000-10-01

We evaluate the dependence of the cluster correlation length, rc, on the mean intercluster separation, Dc, for three models with critical matter density, vanishing vacuum energy (Λ=0), and COBE normalization: a tilted cold dark matter (tCDM) model (n=0.8) and two blue mixed models with two light massive neutrinos, yielding Ωh=0.26 and 0.14 (MDM1 and MDM2, respectively). All models approach the observational value of σ8 (and hence the observed cluster abundance) and are consistent with the observed abundance of damped Lyα systems. Mixed models have a motivation in recent results of neutrino physics; they also agree with the observed value of the ratio σ8/σ25, yielding the spectral slope parameter Γ, and nicely fit Las Campanas Redshift Survey (LCRS) reconstructed spectra. We use parallel AP3M simulations, performed in a wide box (of side 360 h-1 Mpc) and with high mass and distance resolution, enabling us to build artificial samples of clusters, whose total number and mass range allow us to cover the same Dc interval inspected through Automatic Plate Measuring Facility (APM) and Abell cluster clustering data. We find that the tCDM model performs substantially better than n=1 critical density CDM models. Our main finding, however, is that mixed models provide a surprisingly good fit to cluster clustering data.

Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

Directory of Open Access Journals (Sweden)

Yimei Wang

2018-04-01

Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

AS TRÊS DIMENSÕES DA PROPORCIONALIDADE NO DIREITO PENAL

OpenAIRE

Sebástian Borges de Albuquerque Mello

2015-01-01

O presente artigo analisa as manifestações do princípio da proporcionalidade, relacionando-o com diversos princípios fundamentais do Direito Penal. Assim, relaciona-se necessidade com intervenção mínima, adequação com adequação social e a proporcionalidade em sentido estrito com a compatibilização harmônica e axiológica das penas dentro do sistema jurídico-penal.

La prolusione di rocco e le dottrine del processo penale

OpenAIRE

Renzo Orlandi

2015-01-01

L’articolo analizza le conseguenze del pensiero giuridico di Arturo Rocco, dal 1910 nella dottrina di procedura penale, ossia, il metodo tecnico-giuridico (esegesi, sistematica e critica). La dogmatica di procedura pena- le inizia con la monografia di Giovanni Conso, nel 1955. L’insoddisfazione con il tecnicismo giuridico e con la mancanza di maturità scientifica si è manifestata con Carnelutti nel 1946 (Cerenterola), sottolineando le peculiarità strutturali del procedimento penale. In questa...

Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

Science.gov (United States)

Shen, Chung-Wei; Chen, Yi-Hau

2018-03-13

We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. © 2018, The International Biometric Society.

Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis.

Science.gov (United States)

Boser, Quinn A; Valevicius, Aïda M; Lavoie, Ewen B; Chapman, Craig S; Pilarski, Patrick M; Hebert, Jacqueline S; Vette, Albert H

2018-04-27

Quantifying angular joint kinematics of the upper body is a useful method for assessing upper limb function. Joint angles are commonly obtained via motion capture, tracking markers placed on anatomical landmarks. This method is associated with limitations including administrative burden, soft tissue artifacts, and intra- and inter-tester variability. An alternative method involves the tracking of rigid marker clusters affixed to body segments, calibrated relative to anatomical landmarks or known joint angles. The accuracy and reliability of applying this cluster method to the upper body has, however, not been comprehensively explored. Our objective was to compare three different upper body cluster models with an anatomical model, with respect to joint angles and reliability. Non-disabled participants performed two standardized functional upper limb tasks with anatomical and cluster markers applied concurrently. Joint angle curves obtained via the marker clusters with three different calibration methods were compared to those from an anatomical model, and between-session reliability was assessed for all models. The cluster models produced joint angle curves which were comparable to and highly correlated with those from the anatomical model, but exhibited notable offsets and differences in sensitivity for some degrees of freedom. Between-session reliability was comparable between all models, and good for most degrees of freedom. Overall, the cluster models produced reliable joint angles that, however, cannot be used interchangeably with anatomical model outputs to calculate kinematic metrics. Cluster models appear to be an adequate, and possibly advantageous alternative to anatomical models when the objective is to assess trends in movement behavior. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prime note sulla tutela penale dei culti nei Paesi dell’Est Europa

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Giovanni Cimbalo

2011-05-01

Full Text Available Testo della relazione tenuta al Convegno “La Carta e la Corte” (Ferrara, 27 ottobre 2007 destinata alla pubblicazione negli Atti. SOMMARIO: 1. Alcune considerazioni preliminari sullo status delle Confessioni religiose nei paesi dell’Est Europa - 2. I nuovi orientamenti del diritto penale nell’Est Europa - 3. Le norme statali in materia di tutela penale dei culti e del sentimento religioso. 3. Le norme penali relative ai culti e a al sentimento religioso prima del 1992 nei Paesi dell’Est Europa - 4. Tipologie e tecniche legislative di tutela penale dei culti dopo il 1992 nei Paesi dell’Est Europa - 5. Alcune sommarie considerazioni.

Voting-based consensus clustering for combining multiple clusterings of chemical structures

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Saeed Faisal

2012-12-01

Full Text Available Abstract Background Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results The cumulative voting-based aggregation algorithm (CVAA, cluster-based similarity partitioning algorithm (CSPA and hyper-graph partitioning algorithm (HGPA were examined. The F-measure and Quality Partition Index method (QPI were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA was the method of choice among consensus clustering methods.

Globular cluster metallicity scale: evidence from stellar models

International Nuclear Information System (INIS)

Demarque, P.; King, C.R.; Diaz, A.

1982-01-01

Theoretical giant branches have been constructed to determine their relative positions for metallicities in the range -2.3 0 )/sub 0,g/ based on these models is presented which yields good agreement over the observed range of metallicities for galactic globular clusters and old disk clusters. The metallicity of 47 Tuc and M71 given by this calibration is about -0.8 dex. Subject headings: clusters, globular: stars: abundances: stars: interiors

Juridical-penal aspects of the cesium-137 accident; Implicacoes juridico-penais do acidente com o cesio-137

Energy Technology Data Exchange (ETDEWEB)

Soares, Carolina Chaves [Goias Univ., Goiania, GO (Brazil)

1997-12-31

The study of the juridical-penal aspects of the Cesium-137 accident, has, as a base, the police inquiry and the penal lawsuit concerning to the episode. Due to the lack of a law which typified activities related with radioisotope material as crime, the responsible were sentenced according to the penalties of body injury crime and homicide. Among the 10 investigated people, only 5 were condemned by the Judiciary and only 4 serve the sentence. (author) 5 refs.; e-mail: mariliag at netline.com.br

MOCK OBSERVATIONS OF BLUE STRAGGLERS IN GLOBULAR CLUSTER MODELS

International Nuclear Information System (INIS)

Sills, Alison; Glebbeek, Evert; Chatterjee, Sourav; Rasio, Frederic A.

2013-01-01

We created artificial color-magnitude diagrams of Monte Carlo dynamical models of globular clusters and then used observational methods to determine the number of blue stragglers in those clusters. We compared these blue stragglers to various cluster properties, mimicking work that has been done for blue stragglers in Milky Way globular clusters to determine the dominant formation mechanism(s) of this unusual stellar population. We find that a mass-based prescription for selecting blue stragglers will select approximately twice as many blue stragglers than a selection criterion that was developed for observations of real clusters. However, the two numbers of blue stragglers are well-correlated, so either selection criterion can be used to characterize the blue straggler population of a cluster. We confirm previous results that the simplified prescription for the evolution of a collision or merger product in the BSE code overestimates their lifetimes. We show that our model blue stragglers follow similar trends with cluster properties (core mass, binary fraction, total mass, collision rate) as the true Milky Way blue stragglers as long as we restrict ourselves to model clusters with an initial binary fraction higher than 5%. We also show that, in contrast to earlier work, the number of blue stragglers in the cluster core does have a weak dependence on the collisional parameter Γ in both our models and in Milky Way globular clusters

Incorporation of punishable offences against the law on protection of the environment into the penal code

International Nuclear Information System (INIS)

Moehrenschlager, M.

1979-01-01

The government bill on the sixteenth act amending the penal legislation - act on the prevention of environmental offences - is presented, with comments being given on the most important stipulations. Paragraph 328 of the penal code deals with the unauthorized handling of nuclear fuels and represents an almost complete adoption of Paragraph 45 of the Atomic Energy law. The prohibition of the operation of nuclear facilities laid down in Paragraph 45, par. 1, No.4 of the Atomic Energy law, has been incorporated into the factural characteristics of illegal operation of facilities (Paragraph 327, par. 1,3 No. 1 of the penal code). The illegal handling of other radioactive substances has been dealt with in regard to some important cases: specifically for the case of illegal waste disposal (Paragraph 326, par. 1 No. 2, par. 2 of the penal code), and for the case of irregular transport leading to concrete danger or nuisance (Paragraph 330, par. 1 No. 4 of the penal code), as well as for the case of release of ionizing radiation having any such consequences (Paragraph 330, par. 1 No. 2, item c of the penal code). According to Paragraph 324 of the penal code, radioactive contamination of waters is a punishable offence. The rule of qualification of Paragraph 330 par. 1 of the penal code is to be applied to all these facts. (orig./HP) 891 HP/orig.- 892 MB [de

Text Clustering Algorithm Based on Random Cluster Core

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Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Derecho penal y castigo. Una excusa para la protección de los derechos humanos en la sociedad del riesgo

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Yennesit Palacios-Valencia

2013-01-01

Full Text Available Este escrito visibiliza la actual tendencia del derecho penal, donde los derechos humanos son utilizados como excusa para legitimar medidas extremas de seguridad ante la moda del “peligro”, soportando así, la base jurídica del modelo de derecho penal de la seguridad ciudadana. Esta tendencia permite activar la justicia penal en diferentes ámbitos cuando se trata de perseguir a los “enemigos”, lo que, en ocasiones, trae como consecuencia directa violentar los derechos humanos, con la creación constante de normativa que paradójicamente también vulnera los mismos por la lógica del castigo. Estos argumentos permitirán concluir, que se acude a un debilita- miento de las garantías penales en detrimento de los derechos humanos, los cuales son utilizados, bien, como herramientas para impulsar procesos emancipadores, o, como instrumentos para manipular el poder y justificar actos de barbarie por la exageración del poder punitivo.

Environmental protection - Penal Law. 2nd ed.

International Nuclear Information System (INIS)

Sack, H.J.

1980-01-01

The 18th Amendment of the Penal Law - Law on the Abatement of Environmental Delinquency - (18. StrAendG) has now been passed. It has been promulgated on March 28, 1980 and has come into force on July 1, 1980. Through this amendment, a large number of the provisions of the environmental law regarding sanctions has been incorporated into the Penal Code. Persons concerned with environmental protection and pollution control will also in future need such a textbook with comments as a guide to the most important provisions on sanctions and fixed penalties. The 18th Amendment of the Penal Code does not cover all the provisions on sanctions to be applied in the field of environmental protection, a number of regulations still remains part of other, special laws. The same applies to the provisions on penalties which are laid down in a variety of individual laws and regulations, as a comprehensive code of environmental laws still remains to be established. This first part of the textbook in loose-leaf form deals mainly with the new provisions of sections 311d, 311e, 324, and 325. The other facts of the 18th Amendment will be discussed in the second part. As the regulations have, for the most part, not been completly revised or newly inserted, parts 1/3 of the first edition of this textbook can still be used as a help in analysing the existing provisions. (orig./HP) [de

Normalization based K means Clustering Algorithm

OpenAIRE

Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika

2015-01-01

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...

Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

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Yeo‐Chan Yoon

2017-08-01

Full Text Available In this paper, we propose a fine‐grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high‐performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K‐means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.

Proceso penal, privacidad y autodeterminación informativa en la persecución penal de la delincuencia organizada. Un análisis desde la perspectiva del derecho procesal penal alemán

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Angélica Romero Sánchez

2015-08-01

Full Text Available El surgimiento y fortalecimiento de la delincuencia organizada en las últimas décadas del siglo XX determinan un cambio en las herramientas de investigación penal y su regulación en muchos sistemas jurídicos. Estos instrumentos, apoyados en el desarrollo de nuevas tecnologías, poseen características que los diferencian de las clásicas medidas de investigación. En el presente artículo se describe esta transformación en el proceso penal alemán (I, analizando el contexto en que se ha originado la regulación de estos nuevos instrumentos de investigación penal de la delincuencia organizada (A, estudiando sus características especiales (B y deduciendo las características generales (C. Posteriormente se reseña, desde la perspectiva de la doctrina y jurisprudencia alemanas, el significado de dos libertades fundamentales afectadas con la ejecución de tales mecanismos (II: el derecho a la privacidad (A y el derecho a la autodeterminación informativa (B. Cómo conciliar el uso de estas medidas de investigación y su afectación a las garantías fundamentales mencionadas, constituye el tercer apartado del artículo (III. En este se examinan los diferentes presupuestos desarrollados por la doctrina y la jurisprudencia alemanas, para legitimar este tipo de intervenciones. Seguidamente (IV se describe, de manera general, la estructura desarrollada por el legislador alemán para la regulación de las medidas de investigación aquí tratadas, teniendo en cuenta los presupuestos señalados en el acápite precedente. Finalmente, las conclusiones están dirigidas a reflexionar acerca de la importancia de una regulación estructurada de aquellas medidas de investigación, de carácter secreto y utilizadas en la persecución penal de determinados tipos de criminalidad, como la delincuencia organizada, cuyo empleo acarrea graves injerencias en derechos fundamentales.

Progressive Exponential Clustering-Based Steganography

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Li Yue

2010-01-01

Full Text Available Cluster indexing-based steganography is an important branch of data-hiding techniques. Such schemes normally achieve good balance between high embedding capacity and low embedding distortion. However, most cluster indexing-based steganographic schemes utilise less efficient clustering algorithms for embedding data, which causes redundancy and leaves room for increasing the embedding capacity further. In this paper, a new clustering algorithm, called progressive exponential clustering (PEC, is applied to increase the embedding capacity by avoiding redundancy. Meanwhile, a cluster expansion algorithm is also developed in order to further increase the capacity without sacrificing imperceptibility.

A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

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Jingyuan Zhao

2012-01-01

Full Text Available We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD penalty (Fan and Li, 2001 and Jeffrey’s Prior penalty (Firth, 1993, a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008. The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005 and the LASSO-patternsearch algorithm (Shi et al. 2007.

Scalable Density-Based Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2011-01-01

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

Science.gov (United States)

Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

2013-01-01

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

Science.gov (United States)

Zhu, Ying; Tan, Tuck Lee

2016-04-15

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects. Copyright © 2016 Elsevier B.V. All rights reserved.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

Science.gov (United States)

Zhu, Ying; Tan, Tuck Lee

2016-04-01

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

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Moumita Saha

2016-01-01

Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

Simulating star clusters with the AMUSE software framework. I. Dependence of cluster lifetimes on model assumptions and cluster dissolution modes

International Nuclear Information System (INIS)

Whitehead, Alfred J.; McMillan, Stephen L. W.; Vesperini, Enrico; Portegies Zwart, Simon

2013-01-01

We perform a series of simulations of evolving star clusters using the Astrophysical Multipurpose Software Environment (AMUSE), a new community-based multi-physics simulation package, and compare our results to existing work. These simulations model a star cluster beginning with a King model distribution and a selection of power-law initial mass functions and contain a tidal cutoff. They are evolved using collisional stellar dynamics and include mass loss due to stellar evolution. After studying and understanding that the differences between AMUSE results and results from previous studies are understood, we explored the variation in cluster lifetimes due to the random realization noise introduced by transforming a King model to specific initial conditions. This random realization noise can affect the lifetime of a simulated star cluster by up to 30%. Two modes of star cluster dissolution were identified: a mass evolution curve that contains a runaway cluster dissolution with a sudden loss of mass, and a dissolution mode that does not contain this feature. We refer to these dissolution modes as 'dynamical' and 'relaxation' dominated, respectively. For Salpeter-like initial mass functions, we determined the boundary between these two modes in terms of the dynamical and relaxation timescales.

A cluster expansion model for predicting activation barrier of atomic processes

International Nuclear Information System (INIS)

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit

2013-01-01

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEB results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog

RELICS: Strong Lens Models for Five Galaxy Clusters from the Reionization Lensing Cluster Survey

Science.gov (United States)

Cerny, Catherine; Sharon, Keren; Andrade-Santos, Felipe; Avila, Roberto J.; Bradač, Maruša; Bradley, Larry D.; Carrasco, Daniela; Coe, Dan; Czakon, Nicole G.; Dawson, William A.; Frye, Brenda L.; Hoag, Austin; Huang, Kuang-Han; Johnson, Traci L.; Jones, Christine; Lam, Daniel; Lovisari, Lorenzo; Mainali, Ramesh; Oesch, Pascal A.; Ogaz, Sara; Past, Matthew; Paterno-Mahler, Rachel; Peterson, Avery; Riess, Adam G.; Rodney, Steven A.; Ryan, Russell E.; Salmon, Brett; Sendra-Server, Irene; Stark, Daniel P.; Strolger, Louis-Gregory; Trenti, Michele; Umetsu, Keiichi; Vulcani, Benedetta; Zitrin, Adi

2018-06-01

Strong gravitational lensing by galaxy clusters magnifies background galaxies, enhancing our ability to discover statistically significant samples of galaxies at {\\boldsymbol{z}}> 6, in order to constrain the high-redshift galaxy luminosity functions. Here, we present the first five lens models out of the Reionization Lensing Cluster Survey (RELICS) Hubble Treasury Program, based on new HST WFC3/IR and ACS imaging of the clusters RXC J0142.9+4438, Abell 2537, Abell 2163, RXC J2211.7–0349, and ACT-CLJ0102–49151. The derived lensing magnification is essential for estimating the intrinsic properties of high-redshift galaxy candidates, and properly accounting for the survey volume. We report on new spectroscopic redshifts of multiply imaged lensed galaxies behind these clusters, which are used as constraints, and detail our strategy to reduce systematic uncertainties due to lack of spectroscopic information. In addition, we quantify the uncertainty on the lensing magnification due to statistical and systematic errors related to the lens modeling process, and find that in all but one cluster, the magnification is constrained to better than 20% in at least 80% of the field of view, including statistical and systematic uncertainties. The five clusters presented in this paper span the range of masses and redshifts of the clusters in the RELICS program. We find that they exhibit similar strong lensing efficiencies to the clusters targeted by the Hubble Frontier Fields within the WFC3/IR field of view. Outputs of the lens models are made available to the community through the Mikulski Archive for Space Telescopes.

A penalization approach to linear programming duality with application to capacity constrained transport

OpenAIRE

Korman, Jonathan; McCann, Robert J.; Seis, Christian

2013-01-01

A new approach to linear programming duality is proposed which relies on quadratic penalization, so that the relation between solutions to the penalized primal and dual problems becomes affine. This yields a new proof of Levin's duality theorem for capacity-constrained optimal transport as an infinite-dimensional application.

La justicia del menor: edades penales, realidades y expectativas

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Félix PANTOJA GARCIA

1997-01-01

Full Text Available La ley orgánica 4/92, reguladora de la competencia y el procedimiento de los Juzgados de Menores, otorga al Ministerio Fiscal una serie de competencias que, sin duda alguna, pueden calificarse como de protección de menores. Si bien la ley orgánica referida llene como objetivo establecer un procedimiento para aquellos menores de 16 años y mayores de 12 que han infringido las leyes penales (Código Penal y Leyes Penales especiales y, en consecuencia, expresa el reproche social de sus conductas, no cabe duda de que la pretensión procesal que se desprende de una interpretación sistemática de la Ley es evidentemente educativa, por lo que la responsabilidad atribuida al Fiscal, en cuanto que postula ante el Juzgado la imposición de una medida, es necesariamente protectora. El anteproyecto de Ley de Justicia Juvenil avanza en el camino indicado, y así se manifiesta claramente en su Exposición de Motivos; se trata de “una ley sancionadora de naturaleza primordialmente educativa y no propiamente penal”.

Penal reform in Africa: The case of prison chaplaincy

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Abraham K. Akih

2017-08-01

Full Text Available Penal reform is a challenge across the world. In Africa, those who are incarcerated are especially vulnerable and often deprived of basic human rights. Prison conditions are generally dire, resources are limited, and at times undue force is used to control inmates. The public attitude towards offenders is also not encouraging. Reform efforts include finding alternative ways of sentencing such as community service, making use of halfway houses and reducing sentences. These efforts have not yet yielded the desired results. The four principles of retribution, deterrence, incapacitation and rehabilitation guide penal practice in Africa. Retribution and rehabilitation stand in tension. Deterrence and incapacitation aim at forcing inmates to conform to the social order. The article argues that prison chaplaincy can make a valuable contribution to restoring the dignity and humanity of those who are incarcerated. Chaplaincy can contribute to improving attitudes and practices in the penal system and society. In addition to the social objective of rehabilitation, prison ministry can, on a spiritual level, also facilitate repentance, forgiveness and reconciliation. The aim is the holistic restoration of human beings.

Penal symbolism in Serbia in the first half of the 19th century

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Todorović Miljana

2011-01-01

Full Text Available The author explores a scarce and unusual phenomenon for the 19th century Serbia, of the emphasized nexus between crime and penalty. The author marks that special, symbolical relation of penalty, on the one hand, and sanction on the other, as 'penal symbolism'. This term refers to penalization which reflects the ties between crime and punishment by copying crime in terms of modus or place of execution, or by 'punishing' those body parts which partook in committing a crime. The author classifies the examples of preserved judgments and legislations containing penal symbolism to those referring to modus or place of execution of death penalty, those which are examples of penal symbolism related to other sanctions, and those which are examples of the symbolic talion. The author raises the questions of the origin of this phenomenon, as well as of its justification, and aims at providing answers by reconstructing legal and social framework of Serbia in the first half of the 19th century. With this objective in mind, she discusses the development of Criminal Law and its basic features, as well as the development of judiciary, the systematic institutionalization of the network of criminal courts, and, especially, the composition thereof. In the conclusion, the author rejects the possibility that penal symbolism is a product of legal transplantation or that of the continuity of Serbian medieval law. She asserts that the scarcity of material criminal law sources led to judging by 'justice and fairness', and that those facts created conditions for the primitive sense of justice to find its way into judgments and legislations as penal symbolism.

Funciones del Fiscal en el Sistema Procesal Penal Acusatorio Ecuatoriano

OpenAIRE

Guillén, Oscar Medardo

2006-01-01

El art.219 de la Constitución Política vigente, define el nuevo marco legal, para el ejercicio de la acción penal de instancia pública, del Ministerio Público, en concordancia con el Nuevo Código de Procedimiento Penal y la Ley Orgánica del Ministerio Público, instrumentos jurídicos que han permitido el cambio del sistema conocido como inquisitivo , caracterizado por la concentración de funciones en el juez, para dar paso al sistema acusatorio oral que se ha dicho es mas humano, democrático y...

El pensamiento alemán en el derecho penal argentino

OpenAIRE

Guido L. Croxatto; Eugenio R. Zaffaroni

2014-01-01

El objetivo de este ensayo es abordar desde una perspectiva histórica la influencia del pensamiento alemán (la filosofía alemana, la dogmática alemana) en el derecho penal argentino, rescatando la forma en que distintos teóricos alemanes fueron leídos y receptados en Argentina. Se pretende pensar la codificación penal argentina como un diálogo – muchas veces como una mera recepción acrítica – entre los juristas y codificadores latinoamericanos y los pensado...

Sistema de atribución de responsabilidad penal a las personas jurídicas

OpenAIRE

Pérez Arias, Jacinto

2013-01-01

La presente tesis aborda el análisis del modelo de atribución de responsabilidad penal de las personas jurídica en el ordenamiento jurídico español, establecido mediante LO 5/2010 de Reforma del Código Penal. Además de unas breves notas sobre la evolución legislativa, histórica y de derecho comparado, se estudian los problemas mas relevantes de la responsabilidad penal corporativa, como la incapacidad de acción y culpabilidad de la persona jurídica, aspectos diferenciadores entre el derecho a...

Determining characteristic principal clusters in the “cluster-plus-glue-atom” model

International Nuclear Information System (INIS)

Du, Jinglian; Wen, Bin; 2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" data-affiliation=" (M2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" >Melnik, Roderick; Kawazoe, Yoshiyuki

2014-01-01

The “cluster-plus-glue-atom” model can easily describe the structure of complex metallic alloy phases. However, the biggest obstacle limiting the application of this model is that it is difficult to determine the characteristic principal cluster. In the case when interatomic force constants (IFCs) inside the cluster lead to stronger interaction than the interaction between the clusters, a new rule for determining the characteristic principal cluster in the “cluster-plus-glue-atom” model has been proposed on the basis of IFCs. To verify this new rule, the alloy phases in Cu–Zr and Al–Ni–Zr systems have been tested, and our results indicate that the present new rule for determining characteristic principal clusters is effective and reliable

Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

Directory of Open Access Journals (Sweden)

Lingli Jiang

2011-01-01

Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches.

Science.gov (United States)

Zhang, Bo; Liu, Wei; Zhang, Zhiwei; Qu, Yanping; Chen, Zhen; Albert, Paul S

2017-08-01

Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected. We also evaluate the performance of the within-cluster resampling procedure and thoroughly investigate the validity of it in modeling correlated data with informative cluster sizes. We show that within-cluster resampling is a valid alternative to joint modeling for cluster-specific covariates, but it is invalid for time-dependent covariates. The two methods are applied to a developmental toxicity study that investigated the effect of exposure to diethylene glycol dimethyl ether.

HDclassif : An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

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Laurent Berge

2012-01-01

Full Text Available This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA. In a similar manner, the associated clustering method iscalled high dimensional data clustering (HDDC and uses the expectation-maximization algorithm for inference. In order to correctly t the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

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Lei Qin

2014-05-01

Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.

Adding-point strategy for reduced-order hypersonic aerothermodynamics modeling based on fuzzy clustering

Science.gov (United States)

Chen, Xin; Liu, Li; Zhou, Sida; Yue, Zhenjiang

2016-09-01

Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

Las negociaciones en el proceso penal: del procedimiento inquisitivo a la prisionización masiva

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Julio César Montañez Ruiz

2013-12-01

Full Text Available La presente investigación plantea la problemática de la prisionización masiva que generan las negociaciones en el sistema procesal penal de tendencia acusatoria, con marcada influencia norteamericana; aquí se sostiene que ese mecanismo, mediante el cual la Fiscalía busca la declaratoria masiva de culpabilidad de los imputados en plan de descongestionar el sistema represivo penal, tiene las caraterísticas más inquisitivas de un sistema de enjuiciamiento penal.

CMS penalizes 758 hospitals for safety incidents

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Robbins RA

2015-12-01

Full Text Available No abstract available. Article truncated after 150 words. The Centers for Medicare and Medicaid Services (CMS is penalizing 758 hospitals with higher rates of patient safety incidents, and more than half of those were also fined last year, as reported by Kaiser Health News (1. Among the hospitals being financially punished are some well-known institutions, including Yale New Haven Hospital, Medstar Washington Hospital Center in DC, Grady Memorial Hospital, Northwestern Memorial Hospital in Chicago, Indiana University Health, Brigham and Womens Hospital, Tufts Medical Center, University of North Carolina Hospital, the Cleveland Clinic, Hospital of the University of Pennsylvania, Parkland Health and Hospital, and the University of Virginia Medical Center (Complete List of Hospitals Penalized 2016. In the Southwest the list includes Banner University Medical Center in Tucson, Ronald Reagan UCLA Medical Center, Stanford Health Care, Denver Health Medical Center and the University of New Mexico Medical Center (for list of Southwest hospitals see Appendix 1. In total, CMS ...

La cuestión de la eficacia del procedimiento penal ambiental: Esquema de un elemento óptimo

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Jônica Marques Coura Aragão

2016-06-01

ídico-legal, subsiste la necesidad de la participación de la ciudadanía ética en el ápice del proceso, como vía legítima para implementarse justicia penal, ambiental y social; tal propósito es bastante para alterar a propia representación de la relación procesal, formal y materialmente tiendo como presupuesto lógico la base común representada por todo Estado Democrático de Derecho.

Information Clustering Based on Fuzzy Multisets.

Science.gov (United States)

Miyamoto, Sadaaki

2003-01-01

Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

Cluster radioactive decay within the preformed cluster model using relativistic mean-field theory densities

International Nuclear Information System (INIS)

Singh, BirBikram; Patra, S. K.; Gupta, Raj K.

2010-01-01

We have studied the (ground-state) cluster radioactive decays within the preformed cluster model (PCM) of Gupta and collaborators [R. K. Gupta, in Proceedings of the 5th International Conference on Nuclear Reaction Mechanisms, Varenna, edited by E. Gadioli (Ricerca Scientifica ed Educazione Permanente, Milano, 1988), p. 416; S. S. Malik and R. K. Gupta, Phys. Rev. C 39, 1992 (1989)]. The relativistic mean-field (RMF) theory is used to obtain the nuclear matter densities for the double folding procedure used to construct the cluster-daughter potential with M3Y nucleon-nucleon interaction including exchange effects. Following the PCM approach, we have deduced empirically the preformation probability P 0 emp from the experimental data on both the α- and exotic cluster-decays, specifically of parents in the trans-lead region having doubly magic 208 Pb or its neighboring nuclei as daughters. Interestingly, the RMF-densities-based nuclear potential supports the concept of preformation for both the α and heavier clusters in radioactive nuclei. P 0 α(emp) for α decays is almost constant (∼10 -2 -10 -3 ) for all the parent nuclei considered here, and P 0 c(emp) for cluster decays of the same parents decrease with the size of clusters emitted from different parents. The results obtained for P 0 c(emp) are reasonable and are within two to three orders of magnitude of the well-accepted phenomenological model of Blendowske-Walliser for light clusters.

Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions

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Wiemels Joseph

2008-09-01

Full Text Available Abstract Background Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner. Results We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age. Conclusion Our proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

Science.gov (United States)

Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

2018-05-03

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

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Farhan Aadil

2018-05-01

Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Membership determination of open clusters based on a spectral clustering method

Science.gov (United States)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

Efficient clustering aggregation based on data fragments.

Science.gov (United States)

Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Microgrids Real-Time Pricing Based on Clustering Techniques

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Hao Liu

2018-05-01

Full Text Available Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

Managing distance and covariate information with point-based clustering

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Peter A. Whigham

2016-09-01

Full Text Available Abstract Background Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley’s K and applied to the problem of clustering with deliberate self-harm (DSH, is presented. Methods Point-based Monte-Carlo simulation of Ripley’s K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years’ emergency hospital presentations (n = 136 in a New Zealand town (population ~50,000. Study area was defined by residential (housing land parcels representing a finite set of possible point addresses. Results Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Conclusions Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley’s K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for

Modeling blue stragglers in young clusters

International Nuclear Information System (INIS)

Lu Pin; Deng Licai; Zhang Xiaobin

2011-01-01

A grid of binary evolution models are calculated for the study of a blue straggler (BS) population in intermediate age (log Age = 7.85–8.95) star clusters. The BS formation via mass transfer and merging is studied systematically using our models. Both Case A and B close binary evolutionary tracks are calculated for a large range of parameters. The results show that BSs formed via Case B are generally bluer and even more luminous than those produced by Case A. Furthermore, the larger range in orbital separations of Case B models provides a probability of producing more BSs than in Case A. Based on the grid of models, several Monte-Carlo simulations of BS populations in the clusters in the age range are carried out. The results show that BSs formed via different channels populate different areas in the color magnitude diagram (CMD). The locations of BSs in CMD for a number of clusters are compared to our simulations as well. In order to investigate the influence of mass transfer efficiency in the models and simulations, a set of models is also calculated by implementing a constant mass transfer efficiency, β = 0.5, during Roche lobe overflow (Case A binary evolution excluded). The result shows BSs can be formed via mass transfer at any given age in both cases. However, the distributions of the BS populations on CMD are different.

Domestic violence and Special Criminal Courts: analysis from the legal feminism and penal criticism perspectives

OpenAIRE

Campos, Carmen Hein de; Carvalho, Salo de

2006-01-01

Este artigo pretende demonstrar a possibilidade de análise crítica da Lei 9.099/95 a partir de dois discursos considerados marginais no campo do direito penal: o feminismo jurídico e o garantismo penal. Considerando a vítima no momento do crime e o autor do fato durante o processo penal, esses discursos interagem, procurando construir um diálogo para demonstrar a ineficácia da lei em ambas as perspectivas.This article aims at demonstrating the possibility of criticism about the criminal law (...

A novel model for Time-Series Data Clustering Based on piecewise SVD and BIRCH for Stock Data Analysis on Hadoop Platform

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Ibgtc Bowala

2017-06-01

Full Text Available With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.

Modeling familial clustered breast cancer using published data

NARCIS (Netherlands)

Jonker, MA; Jacobi, CE; Hoogendoorn, WE; Nagelkerke, NJD; de Bock, GH; van Houwelingen, JC

2003-01-01

The purpose of this research was to model the familial clustering of breast cancer and to provide an accurate risk estimate for individuals from the general population, based on their family history of breast and ovarian cancer. We constructed a genetic model as an extension of a model by Claus et

A second order penalized direct forcing for hybrid Cartesian/immersed boundary flow simulations

International Nuclear Information System (INIS)

Introini, C.; Belliard, M.; Fournier, C.

2014-01-01

In this paper, we propose a second order penalized direct forcing method to deal with fluid-structure interaction problems involving complex static or time-varying geometries. As this work constitutes a first step toward more complicated problems, our developments are restricted to Dirichlet boundary condition in purely hydraulic context. The proposed method belongs to the class of immersed boundary techniques and consists in immersing the physical domain in a Cartesian fictitious one of simpler geometry on fixed grids. A penalized forcing term is added to the momentum equation to take the boundary conditions around/inside the obstacles into account. This approach avoids the tedious task of re-meshing and allows us to use fast and accurate numerical schemes. In contrary, as the immersed boundary is described by a set of Lagrangian points that does not generally coincide with those of the Eulerian grid, numerical procedures are required to reconstruct the velocity field near the immersed boundary. Here, we develop a second order linear interpolation scheme and we compare it to a simpler model of order one. As far as the governing equations are concerned, we use a particular fractional-step method in which the penalized forcing term is distributed both in prediction and correction equations. The accuracy of the proposed method is assessed through 2-D numerical experiments involving static and rotating solids. We show in particular that the numerical rate of convergence of our method is quasi-quadratic. (authors)

Dynamic Characteristics Analysis and Stabilization of PV-Based Multiple Microgrid Clusters

DEFF Research Database (Denmark)

Zhao, Zhuoli; Yang, Ping; Wang, Yuewu

2018-01-01

-based multiple microgrid clusters. A detailed small-signal model for PV-based microgrid clusters considering local adaptive dynamic droop control mechanism of the voltage-source PV system is developed. The complete dynamic model is then used to access and compare the dynamic characteristics of the single...... microgrid and interconnected microgrids. In order to enhance system stability of the PV microgrid clusters, a tie-line flow and stabilization strategy is proposed to suppress the introduced interarea and local oscillations. Robustly selecting of the key control parameters is transformed to a multiobjective......As the penetration of PV generation increases, there is a growing operational demand on PV systems to participate in microgrid frequency regulation. It is expected that future distribution systems will consist of multiple microgrid clusters. However, interconnecting PV microgrids may lead to system...

Seguridad ciudadana y respuesta penal

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Beatriz Scapusio Minvielle

2015-10-01

Full Text Available El concepto de seguridad ciudadana será empleado en esta comunicación en su sentido restringido, vinculado al sentimiento de confianza de la población de no verse expuesta a hechos de violencia física, tengan origen éstos en actos individuales o sociales.  (...Contenido: Diferentes expresiones de la seguridad ciudadana. La respuesta penal. Evaluación global del problema. El desafío dogmático. Conclusiones

La prolusione di rocco e le dottrine del processo penale

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Renzo Orlandi

2015-03-01

Full Text Available L’articolo analizza le conseguenze del pensiero giuridico di Arturo Rocco, dal 1910 nella dottrina di procedura penale, ossia, il metodo tecnico-giuridico (esegesi, sistematica e critica. La dogmatica di procedura pena- le inizia con la monografia di Giovanni Conso, nel 1955. L’insoddisfazione con il tecnicismo giuridico e con la mancanza di maturità scientifica si è manifestata con Carnelutti nel 1946 (Cerenterola, sottolineando le peculiarità strutturali del procedimento penale. In questa prospettiva, il testo sottolinea la conferenza di Franco Cordero nel 1964 (Lecce, così come le lezioni da James Goldschmidt. Inoltre, negli anni sessanta, divennero importanti diritti fondamentali, di fronte alla Costituzione democratica, con un nuovo orientamento dottrinale. Il diritto processuale penale è stato considerato come diritto costituzionale applicato. Si segnalano gli insegnamenti di Amodio, Amato, Chiavario, Ferrua, Grevi, Iluminati, Massa e Nobili. Successivamente, l’articolo mette in evidenza il CPP del 1988, l’internazionalizzazione dei sistemi giuridici, l’importanza del diritto comparato e dei diritti fondamentali. Si conclude con il nuovo ordine mondiale dei diplomi internazionali e tribunali sovranazionali, con nuove esigenze, oltre il tecnicismo giuridico.

La eficacia de la Prueba Indiciaria en el Proceso Penal Ecuatoriano

OpenAIRE

Pérez Medina, Lenin

2007-01-01

La eficacia de la prueba Indiciaria en el Proceso Penal Ecuatoriano, a tomado una nueva dimensión en nuestro sistema procesal, ya que a partir de la vigencia del nuevo Código de Procedimiento Penal, se ha orientado a garantizar la aplicación del debido proceso, en todos los parámetros que la constitución establece, en el presente trabajo estudiamos diferentes tópicos como presunción , sospecha , conjetura, vestigio, huella y rastro donde podría aparecer la prueba indiciaria, si...

Examination of influential observations in penalized spline regression

Science.gov (United States)

Türkan, Semra

2013-10-01

In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.

Robust MST-Based Clustering Algorithm.

Science.gov (United States)

Liu, Qidong; Zhang, Ruisheng; Zhao, Zhili; Wang, Zhenghai; Jiao, Mengyao; Wang, Guangjing

2018-06-01

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

Riemannian multi-manifold modeling and clustering in brain networks

Science.gov (United States)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Transfrontier environmental protection and German penal law. Grenzueberschreitende Umweltbelastungen und deutsches Strafrecht

Energy Technology Data Exchange (ETDEWEB)

Forkel, H.W.

1988-01-22

The author investigates the problem of how far German penal law is valid in case of transfrontier environmental pollution. He distinguishes between cases in which the interests of Germany and the neighbour state are congruent, and cases in which they are not congruent. According to the author, German law should be applied in cases where the other country has no environmental penal legislation, and where the emissions exceed the limits set by German and foreign law. (orig./HSCH).

A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China

Directory of Open Access Journals (Sweden)

Mingwu Wang

2014-01-01

Full Text Available The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole.

Emergence of clustering in an acquaintance model without homophily

International Nuclear Information System (INIS)

Bhat, Uttam; Krapivsky, P L; Redner, S

2014-01-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks. (paper)

Emergence of clustering in an acquaintance model without homophily

Science.gov (United States)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Aerosol cluster impact and break-up: model and implementation

International Nuclear Information System (INIS)

Lechman, Jeremy B.

2010-01-01

In this report a model for simulating aerosol cluster impact with rigid walls is presented. The model is based on JKR adhesion theory and is implemented as an enhancement to the granular (DEM) package within the LAMMPS code. The theory behind the model is outlined and preliminary results are shown. Modeling the interactions of small particles is relevant to a number of applications (e.g., soils, powders, colloidal suspensions, etc.). Modeling the behavior of aerosol particles during agglomeration and cluster dynamics upon impact with a wall is of particular interest. In this report we describe preliminary efforts to develop and implement physical models for aerosol particle interactions. Future work will consist of deploying these models to simulate aerosol cluster behavior upon impact with a rigid wall for the purpose of developing relationships for impact speed and probability of stick/bounce/break-up as well as to assess the distribution of cluster sizes if break-up occurs. These relationships will be developed consistent with the need for inputs into system-level codes. Section 2 gives background and details on the physical model as well as implementations issues. Section 3 presents some preliminary results which lead to discussion in Section 4 of future plans.

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

OpenAIRE

S. Parkash  Kumar; K. S. Ramaswami

2011-01-01

Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using...

ALGUNOS APUNTES SOBRE LAS RAZONES DE LA REFORMA DEL PROCEDIMIENTO PENAL EN AMÉRICA LATINA

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Pierre Gilles Bélanger

2010-01-01

Full Text Available Actualmente en Latinoamérica, la mayor parte de los países han modificado sus códigos penales con el objeto de tener una mejor salud procesal. En esta transición, hace falta ayuda internacional real, apoyo, colaboración e intercambio de ideas, programas, experiencias, grupos de trabajo, capacitación a nivel bilateral y multilateral dirigida por expertos para los actores en el sistema penal y para la sociedad. Todos los países de Latinoamérica, han tenido éxitos y todos tienen fallas en las reformas procesales desarrolladas en materia penal. Parece entonces oportuno, empezar el dialogo y la colaboración para contribuir a una mejor comprensión y edificación de los sistemas penales y su evolución. Para ello se requiere compartir las experiencias que han funcionado en otros países, adaptándolas al marco jurídico de cada contexto en particular. Por un lado, esto ayuda a mejorar la comprensión operacional del procedimiento penal y por otro lado, el respeto de las garantías jurídicas establecidas en las Cartas Magnas de cada país y por ende estabilidad institucional, seguridad y desarrollo económico para la sociedad.

A TUTELA PENAL DO MEIO AMBIENTE E SUA (INCOMPATIBILIDADE COM A INTERVENÇÃO MÍNIMA

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Jamille Clara Alves Adamczyk

2017-06-01

Full Text Available Considerando que o Direito Penal deve atuar como última via e para tutelar os bens jurídicos mais relevantes, propõe-se a analisar se a imputação da responsabilidade penal ambiental iria de encontro com a premissa da intervenção mínima. Assim, tem-se como objetivo do presente estudo analisar se a responsabilização penal no âmbito do direito ambiental viola o princípio da intervenção mínima.

Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

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Mi-Yuan Shan

2013-01-01

Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

Cluster-cluster correlations in the two-dimensional stationary Ising-model

International Nuclear Information System (INIS)

Klassmann, A.

1997-01-01

In numerical integration of the Cahn-Hillard equation, which describes Oswald rising in a two-phase matrix, N. Masbaum showed that spatial correlations between clusters scale with respect to the mean cluster size (itself a function of time). T. B. Liverpool showed by Monte Carlo simulations for the Ising model that the analogous correlations have a similar form. Both demonstrated that immediately around each cluster there is some depletion area followed by something like a ring of clusters of the same size as the original one. More precisely, it has been shown that the distribution of clusters around a given cluster looks like a sinus-curve decaying exponentially with respect to the distance to a constant value

Reflection on penal policy in nuclear matters

International Nuclear Information System (INIS)

Cisse, A.

1996-01-01

This document expresses ethical reflexions as far as nuclear energy development is concerned. The potential diversion of the peaceful use of nuclear energy results in the necessity of a criminal policy which would control the nuclear regulations. For each potential nuclear infringement, systems of laws are established either to prevent damages or to penalize them. (TEC)

Penal managerialism from within: implications for theory and research.

Science.gov (United States)

Cheliotis, Leonidas K

2006-01-01

Unlike the bulk of penological scholarship dealing with managerialist reforms, this article calls for greater theoretical and research attention to the often pernicious impact of managerialism on criminal justice professionals. Much in an ideal-typical fashion, light is shed on: the reasons why contemporary penal bureaucracies endeavor systematically to strip criminal justice work of its inherently affective nature; the structural forces that ensure control over officials; the processes by which those forces come into effect; and the human consequences of submission to totalitarian bureaucratic milieus. It is suggested that the heavy preoccupation of present-day penality with the predictability and calculability of outcomes entails the atomization of professionals and the dehumanization of their work. This is achieved through a kaleidoscope of direct and indirect mechanisms that naturalize and/or legitimate acquiescence.

Explanatory and Predictor Relationships Between Forgiving Behaviors, Social Anxiety Levels and Values of Convict-Prisoners in Penal Institutions

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Sidika Isler

2016-05-01

Full Text Available The purpose of the present research is revealing the correlations between social anxiety, forgiveness and values among convict-prisoners in penal institutions. Relational screening model was adopted in the present research. Relational screening is used to reveal the relationships between two or more variables, and cause-and-effect relationships. The universe of the present research consists of convicts and prisoners in Konya penal institutions in 2013-2014 years. The work group of the present research consists of 680 volunteer convicts and prisoners selected randomly among these. The data collection tool in research value scale, the scale of forgiveness and social anxiety scale was used. The results obtained from this study; The findings of the present research showed that the most important independent variable that affected values was forgiveness, In addition, the most important variable that affects social anxiety in the tested model is values variable and Additionally, second most important variable that affects social anxiety indirectly in the tested model is forgiveness variable.

Feminicidio y derecho penal: herramientas para su mejor aplicación

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Jhoanna Caterine Prieto Moreno

2012-01-01

Full Text Available El numeral 11 del artículo 104 del Código Penal colombiano introdujo como causal de agravación del homicidio aquella acción que se comete contra una mujer en razón a su condición de género, pero que ello no llega a ser suficiente en razón al desconocimiento que se tiene por parte del aparato jurisdiccional penal para su debida aplicación, en razón a que no se tiene claridad sobre las motivaciones que llevaron al victimario a cometer dicho homicidio (feminicidio.

Modelling baryonic effects on galaxy cluster mass profiles

Science.gov (United States)

Shirasaki, Masato; Lau, Erwin T.; Nagai, Daisuke

2018-06-01

Gravitational lensing is a powerful probe of the mass distribution of galaxy clusters and cosmology. However, accurate measurements of the cluster mass profiles are limited by uncertainties in cluster astrophysics. In this work, we present a physically motivated model of baryonic effects on the cluster mass profiles, which self-consistently takes into account the impact of baryons on the concentration as well as mass accretion histories of galaxy clusters. We calibrate this model using the Omega500 hydrodynamical cosmological simulations of galaxy clusters with varying baryonic physics. Our model will enable us to simultaneously constrain cluster mass, concentration, and cosmological parameters using stacked weak lensing measurements from upcoming optical cluster surveys.

Modelling Baryonic Effects on Galaxy Cluster Mass Profiles

Science.gov (United States)

Shirasaki, Masato; Lau, Erwin T.; Nagai, Daisuke

2018-03-01

Gravitational lensing is a powerful probe of the mass distribution of galaxy clusters and cosmology. However, accurate measurements of the cluster mass profiles are limited by uncertainties in cluster astrophysics. In this work, we present a physically motivated model of baryonic effects on the cluster mass profiles, which self-consistently takes into account the impact of baryons on the concentration as well as mass accretion histories of galaxy clusters. We calibrate this model using the Omega500 hydrodynamical cosmological simulations of galaxy clusters with varying baryonic physics. Our model will enable us to simultaneously constrain cluster mass, concentration, and cosmological parameters using stacked weak lensing measurements from upcoming optical cluster surveys.

Sparse Adaptive Channel Estimation Based on lp-Norm-Penalized Affine Projection Algorithm

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Yingsong Li

2014-01-01

Full Text Available We propose an lp-norm-penalized affine projection algorithm (LP-APA for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating an lp-norm into the cost function of the conventional affine projection algorithm (APA to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a zero attractor to its iterations. The simulation results, which are obtained from a sparse channel estimation, demonstrate that the proposed LP-APA can efficiently improve channel estimation performance in terms of both the convergence speed and steady-state performance when the channel is exactly sparse.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

Science.gov (United States)

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

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Musaab Riyadh

2017-01-01

Full Text Available The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

The penalization of migrants: irregularity and prison in the construction of the neoliberal State

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Ignacio González Sánchez

2016-06-01

Full Text Available In this paper the punitive processes affecting foreigners in Spain are analyzed. After a brief contextualization of the migratory process, and its problematization in terminology related to insecurity and crime, attention is paid to two ways of penalization: the administrative one, linked with laws for foreigners, and the penal one, reinforced by the precarious situation of these collectives. Later, an analysisi of their effects of the material —related to the labor market—, symbolic —in social categorizations— and disciplinary –in the conformation of subjectivities- is provided. The pertinence of studying processes of penalization to discern broader social dynamics and the pertince of giving more importance to these processes in studies about migrant populations is here proposed.

Generating clustered scale-free networks using Poisson based localization of edges

Science.gov (United States)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

Science.gov (United States)

Liu, Jianjun; Kan, Jianquan

2018-04-01

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.

Science.gov (United States)

Becker, Natalia; Toedt, Grischa; Lichter, Peter; Benner, Axel

2011-05-09

Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.The penalized SVM

Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

Science.gov (United States)

Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

2006-01-01

This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

BioCluster: Tool for Identification and Clustering of Enterobacteriaceae Based on Biochemical Data

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Ahmed Abdullah

2015-06-01

Full Text Available Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC and the Improved Hierarchical Clustering (IHC, a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

A BASE IDEOLÓGICA DO DIREITO PENAL DO INIMIGO (GÜNTER JAKOBS: SOCIEDADE DE RISCO E SEUS EFEITOS NO ESTADO DEMOCRÁTICO DE DIREITO

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Ramiro Anzit Guerrero

2016-09-01

Full Text Available O direito penal do inimigo, proposto por Günther Jacobs, representa a antítese do direito penal garantista, de cunho liberal, idealizado e desenvolvido a partir da Revolução Francesa. Contudo, para que seja possível analisá-lo a partir do texto constitucional brasileiro (proposta do presente trabalho faz-se necessária a compreensão de suas características latentes, o que significa mergulhar a fundo no pensamente de seu artífice, Günther Jacobs.

Topic modeling for cluster analysis of large biological and medical datasets.

Science.gov (United States)

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

Fuzzy Clustering Methods and their Application to Fuzzy Modeling

DEFF Research Database (Denmark)

Kroszynski, Uri; Zhou, Jianjun

1999-01-01

Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...

INFRACŢIUNI CE IMPLICĂ SEMNE DE EXTREMISM RELIGIOS: ASPECTE DE DREPT PENAL

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Alexe JEFLEA

2017-01-01

Full Text Available În articol sunt abordate unele probleme ce ţin de clarificarea conceptului de extremism religios în legea penală astăzi în vigoare a Republicii Moldova. Cercetarea legii penale în materia identificării şi interpretării extremismului religios începe cu definirea acestui fenomen. S-a precizat că la baza extremismului religios se poziţionează stârnirea discordiei religioase. Autorii au demonstrat că sintagma „extremism religios”, fiind o noţiune criminologică şi politică, nu se regăseşte în nicio normă a Codului penal din Republica Moldova. S-a constatat că mai multe componenţe de infrac­ţiuni implică, într-un fel sau altul, religia. Nu toate aceste fapte sunt de conotaţie extremistă, însă toate au la bază ura sau intoleranţa religioasă ca motiv special al săvârşirii mai multor fapte penale ce implică semne de extremism religios. Totodată, s-a arătat că legea penală a Republicii Moldova nu acoperă toate formele de extremism, lista acestora fiind de fapt mai largă. Prin urmare, autorii au demonstrat necesitatea introducerii unor prevederi noi care ar incrimina şi alte fapte cu caracter extremist.Criminal offences which involve signs of religious extremism: aspects of criminal lawThis scientific research is dedicated to some questions which are linked to the clarification of the concept of religious extremism in the light of criminal legislation of theRepublicofMoldova. Exploration of the criminal legislation in the matter of identification and interpretation of the religious extremism begins from the definition of this phenomenon. There have been summarized that namely the provocation of religious disagreement constitutes the basis on the religious extremism. The authors have demonstrated that the expression “religious extremism” being a criminological and political concept, is absent in the modern criminal legislation of theRepublicofMoldova. There have been observed that a lot of criminal

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

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Qingming Zhan

2017-08-01

Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Science.gov (United States)

Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

2018-01-01

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

Reseñas y Recensiones: Lenis, Karin. (2014). El sistema de responsabilidad penal de menores. Un estudio de las legislaciones de España y Colombia desde la teoría del Derecho Penal del Enemigo.

OpenAIRE

Muñetones Rozo, Ingrid Bibiana

2015-01-01

Reseñas y Recensiones: Reseñas y Recensiones: Lenis, Karin. (2014). El sistema de responsabilidad penal de menores. Un estudio de las legislaciones de España y Colombia desde la teoría del Derecho Penal del Enemigo. Bogotá: Grupo Editorial Ibañez, 644 p.

Automated detection of microcalcification clusters in digital mammograms based on wavelet domain hidden Markov tree modeling

International Nuclear Information System (INIS)

Regentova, E.; Zhang, L.; Veni, G.; Zheng, J.

2007-01-01

A system is designed for detecting microcalcification clusters (MCC) in digital mammograms. The system is intended for computer-aided diagnostic prompting. Further discrimination of MCC as benign or malignant is assumed to be performed by radiologists. Processing of mammograms is based on the statistical modeling by means of wavelet domain hidden markov trees (WHMT). Segmentation is performed by the weighted likelihood evaluation followed by the classification based on spatial filters for a single microcalcification (MC) and a cluster of MC detection. The analysis is carried out on FROC curves for 40 mammograms from the mini-MIAS database and for 100 mammograms with 50 cancerous and 50 benign cases from DDSM database. The designed system is capable to detect 100% of true positive cases in these sets. The rate of false positives is 2.9 per case for mini-MIAS dataset; and 0.01 for the DDSM images. (orig.)

[Penal institutions in Porto Azzuro].

Science.gov (United States)

Ciccotti, R

1976-01-01

Prior to describing the environmental and human situation of the Porto Azzurro penal Institutions, the Author devotes a chapter to the historical part and gives an outline of the most important events that, in 1600 or thereabouts, led to the construction of the Fort S. Giacoma (where the penitentiary is presently sited), which, after a number of vicissitudes and transfers of title, was finally destined for use, in 1900, as a prison establishment. The subsequent and, in particular, the recent building re-structurations, have radically changed the penitentiary in order to make it more in line with the functions required by the present prison policy. After describing the establishment's setup, which includes many institutes, the Author passes on to consider the problems of personnel and, in particular, of the military staff. In the central chapter on intramural life, he makes an in-depth review of the methods and means of treatment, some of which are proper of this particular environment. In illustrating such convicts' activities, as working, leisure time, education, relationship with the extramural world, the Author lays stress on the always present objective: that directed at helping convicts re-enter social life. In conjunction with health services and religious services, the Author deals at length with the delicate moment of the "audience", viewed as a therapeutic means indispensiable for establishing a valid re-educational contribution. The discipline and discharges conclude the description of the Porto Azzurro penal Institutions, whose environment and various treatment methods in use before the implementation of the new penitentiary System, are dealt with in detail by the Author.

Teoría del error, antecedentes y panorama de su regulación en el nuevo Código Penal Militar (Ley 1407 de 2010)

OpenAIRE

Velásquez Cuervo, Janneth Patricia

2013-01-01

Se analiza en este articulo la teoría del error, sus antecedentes, el desarrollo a través de la dogmática penal con base en los distintos esquemas de la teoría del delito, para terminar en la regulación del error en el nuevo Código Penal Militar (ley 1407 de 2010) aplicable en todos los actos relacionados con el servicio cometidos por los miembros de la Fuerza Pública.

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

KAUST Repository

Chermak, Edrisse; De Donato, Renato; Lensink, Marc F.; Petta, Andrea; Serra, Luigi; Scarano, Vittorio; Cavallo, Luigi; Oliva, Romina

2016-01-01

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

KAUST Repository

Chermak, Edrisse

2016-11-15

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers\\' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

Penalized feature selection and classification in bioinformatics

OpenAIRE

Ma, Shuangge; Huang, Jian

2008-01-01

In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classific...

Cluster management.

Science.gov (United States)

Katz, R

1992-11-01

Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

Responsabilidades en Prevención de riesgos laborales; Especial referencia a la responsabilidad penal

OpenAIRE

Fernández Fernández, Cristina

2012-01-01

En este proyecto fin de grado se abordan las responsabilidades empresariales de prevención de riesgos laborales; civiles, administrativos, penales y recargo de prestaciones por incumplimiento de la normativa de prevención de riesgos laborales, con referencias jurisprudenciales y normativa vigente. Se hace especial referencia a la responsabilidad penal detallando los distintos tipos de delito; de peligro y de resultado, con sus distintas modalidades dolosa e imprudente, en su...

Towards Accurate Modelling of Galaxy Clustering on Small Scales: Testing the Standard ΛCDM + Halo Model

Science.gov (United States)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron K.; Scoccimarro, Roman; Piscionere, Jennifer A.; Wibking, Benjamin D.

2018-04-01

Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models statistically. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the "accurate" regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. With our forward-modelling approach, we can incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard ΛCDM + halo model against the clustering of SDSS DR7 galaxies. Specifically, we use the projected correlation function, group multiplicity function and galaxy number density as constraints. We find that while the model fits each statistic separately, it struggles to fit them simultaneously. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir halos) matches the clustering of low luminosity galaxies, but exhibits a 2.3σ tension with the clustering of luminous galaxies, thus providing evidence that the "standard" halo model needs to be extended. This work opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.

Classical Music Clustering Based on Acoustic Features

OpenAIRE

Wang, Xindi; Haque, Syed Arefinul

2017-01-01

In this paper we cluster 330 classical music pieces collected from MusicNet database based on their musical note sequence. We use shingling and chord trajectory matrices to create signature for each music piece and performed spectral clustering to find the clusters. Based on different resolution, the output clusters distinctively indicate composition from different classical music era and different composing style of the musicians.

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

Science.gov (United States)

Li, Chaoshun; Zhou, Jianzhong

2014-09-01

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Topics in modelling of clustered data

CERN Document Server

Aerts, Marc; Ryan, Louise M; Geys, Helena

2002-01-01

Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and social science studies. It focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods. The authors motivate and illustrate all aspects of these models in a variety of real applications. They discuss several variations and extensions, including individual-level covariates and combined continuous and discrete outcomes. Flexible modelling with fractional and local polynomials, omnibus lack-of-fit tests, robustification against misspecification, exact, and bootstrap inferential procedures all receive extensive treatment. The application...

Testing dark energy and dark matter cosmological models with clusters of galaxies

Energy Technology Data Exchange (ETDEWEB)

Boehringer, Hans [Max-Planck-Institut fuer Extraterrestrische Physik, Garching (Germany)

2008-07-01

Galaxy clusters are, as the largest building blocks of our Universe, ideal probes to study the large-scale structure and to test cosmological models. The principle approach und the status of this research is reviewed. Clusters lend themselves for tests in serveral ways: the cluster mass function, the spatial clustering, the evolution of both functions with reshift, and the internal composition can be used to constrain cosmological parameters. X-ray observations are currently the best means of obtaining the relevant data on the galaxy cluster population. We illustrate in particular all the above mentioned methods with our ROSAT based cluster surveys. The mass calibration of clusters is an important issue, that is currently solved with XMM-Newton and Chandra studies. Based on the current experience we provide an outlook for future research, especially with eROSITA.

Variable selection in multivariate calibration based on clustering of variable concept.

Science.gov (United States)

Farrokhnia, Maryam; Karimi, Sadegh

2016-01-01

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached. Copyright © 2015 Elsevier B.V. All rights reserved.

Smoothing of X-ray diffraction data and K (alpha)2 elimination using penalized likelihood and the composite link model

NARCIS (Netherlands)

De Rooi, J.J.; Van der Pers, N.M.; Hendrikx, R.W.A.; Delhez, R.; Bottger, A.J.; Eilers, P.H.C.

2014-01-01

X-ray diffraction scans consist of series of counts; these numbers obey Poisson distributions with varying expected values. These scans are often smoothed and the K2 component is removed. This article proposes a framework in which both issues are treated. Penalized likelihood estimation is used to

Sex offender punishment and the persistence of penal harm in the U.S.

Science.gov (United States)

Leon, Chrysanthi S

2011-01-01

The U.S. has dramatically revised its approach to punishment in the last several decades. In particular, people convicted of sex crimes have experienced a remarkable expansion in social control through a wide-range of post-conviction interventions. While this expansion may be largely explained by general punishment trends, there appear to be unique factors that have prevented other penal reforms from similarly modulating sex offender punishment. In part, this continuation of a "penal harm" approach to sex offenders relates to the past under-valuing of sexual victimization. In the "bad old days," the law and its agents sent mixed messages about sexual violence and sexual offending. Some sexual offending was mere nuisance, some was treatable, and a fraction "deserved" punishment equivalent to other serious criminal offending. In contrast, today's sex offender punishment schemes rarely distinguish formally among gradations of harm or dangerousness. After examining incarceration trends, this article explores the historical context of the current broad brush approach and reviews the unintended consequences. Altogether, this article reinforces the need to return to differentiation among sex offenders, but differentiation based on science and on the experience-based, guided discretion of experts in law enforcement, corrections, and treatment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

Science.gov (United States)

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm

Science.gov (United States)

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K -means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Performance of penalized maximum likelihood in estimation of genetic covariances matrices

Directory of Open Access Journals (Sweden)

Meyer Karin

2011-11-01

Full Text Available Abstract Background Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that "borrow strength" from the phenotypic covariance matrix are considered. Methods An extensive simulation study was carried out to investigate the reduction in average 'loss', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored. Results It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy. Conclusions Penalized maximum likelihood estimation provides the means to 'make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should

Crime and Punishment: Are Copyright Violators Ever Penalized?

Science.gov (United States)

Russell, Carrie

2004-01-01

Is there a Web site that keeps track of copyright Infringers and fines? Some colleagues don't believe that copyright violators are ever penalized. This question was asked by a reader in a question and answer column of "School Library Journal". Carrie Russell is the American Library Association's copyright specialist, and she will answer selected…

La inimputabilidad y el tratamiento del disminuido psíquico en el proceso penal

Directory of Open Access Journals (Sweden)

José Manuel Rojas Salas

2013-12-01

Full Text Available La Constitución Política colombiana proscribe cualquier tipo de responsabilidad objetiva; en consecuencia, es necesario que la persona a la que se sancione con una pena haya actuado con culpabilidad, cosa que no sucede con los inimputables, personas que no pueden comprender la ilicitud de su conducta o determinarse de acuerdo con dicha comprensión, por lo que el Código Penal establece dos regímenes diferenciados de responsabilidad penal: uno para imputables y otro para inimputables, para quienes no se prevén penas sino medidas de seguridad. Por el contrario, la Ley 906 de 2004 no contempla un tratamiento jurídico diferenciado para quienes no tienen la capacidad de comprender o decidir voluntariamente sobre sus derechos en el proceso penal, por lo que se propone un nuevo enfoque al respecto.

Semantic based cluster content discovery in description first clustering algorithm

International Nuclear Information System (INIS)

Khan, M.W.; Asif, H.M.S.

2017-01-01

In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase. (author)

An ant colony based resilience approach to cascading failures in cluster supply network

Science.gov (United States)

Wang, Yingcong; Xiao, Renbin

2016-11-01

Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant's spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony's social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.

Weighted voting-based consensus clustering for chemical structure databases

Science.gov (United States)

Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

2014-06-01

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

Euthanasia in the Broader Framework of Dutch Penal Policies

NARCIS (Netherlands)

Groenhuijsen, M.S.; van Laanen, F.; Groenhuijsen, M.S.; van Laanen, F.

2006-01-01

The authors have regarded euthanasia in the broader framework of Dutch penal policies. They present euthanasia as a typical example of the pragmatic - rather than dogmatic - way the Dutch try to tackle difficult moral problems in connection with the criminal justice system. Definitions, statutory

Evolución del Derecho penal en el ámbito internacional. Pluralismo y garantismo jurídico-penal como criterios orientadores || Evolution Of The Supranational Criminal Law: Pluralism And Protection Of The Civil Liberties As Guiding Criteria

Directory of Open Access Journals (Sweden)

Jorge Correcher Mira

2013-12-01

Full Text Available RESUMEN La realidad social internacional presenta un nuevo paradigma que debe ser asumido por el Derecho penal. El contexto social internacional, marcado por la globalización a nivel mundial y el proceso de integración europea en el ámbito comunitario, supone una modificación de las líneas clásicas de recepción de las normas penales, demandando un tratamiento supraestatal del sistema penal. En este trabajo, se analiza desde una perspectiva crítica las propuestas de internacionalización del Derecho penal, en la medida que éstas no han seguido nociones como el pluralismo jurídico y el carácter garantista inherente al Derecho penal.   ABSTRACT The social international reality presents a new paradigm that must be taken up office for the Criminal law. The social international context, marked by the globalization worldwide and the process of European integration in the European area, supposes a modification of the classic lines of receipt of the Criminal law, demanding a supranational treatment of the Criminal System. In this work, the offers of internationalize the Criminal Law will be analyzed from a critical point of view, cause these have not followed notions as the juridical pluralism and the protection of civil liberties inherent in the Criminal Law.

Evolución del Derecho penal en el ámbito internacional. Pluralismo y garantismo jurídico-penal como criterios orientadores || Evolution Of The Supranational Criminal Law: Pluralism And Protection Of The Civil Liberties As Guiding Criteria

Directory of Open Access Journals (Sweden)

Jorge Correcher Mira

2013-12-01

Full Text Available RESUMEN La realidad social internacional presenta un nuevo paradigma que debe ser asumido por el Derecho penal. El contexto social internacional, marcado por la globalización a nivel mundial y el proceso de integración europea en el ámbito comunitario, supone una modificación de las líneas clásicas de recepción de las normas penales, demandando un tratamiento supraestatal del sistema penal. En este trabajo, se analiza desde una perspectiva crítica las propuestas de internacionalización del Derecho penal, en la medida que éstas no han seguido nociones como el pluralismo jurídico y el carácter garantista inherente al Derecho penal.   ABSTRACT The social international reality presents a new paradigm that must be taken up office for the Criminal law. The social international context, marked by the globalization worldwide and the process of European integration in the European area, supposes a modification of the classic lines of receipt of the Criminal law, demanding a supranational treatment of the Criminal System. In this work, the offers of internationalize the Criminal Law will be analyzed from a critical point of view, cause these have not followed notions as the juridical pluralism and the protection of civil liberties inherent in the Criminal Law.  

A Hybrid Double-Layer Master-Slave Model For Multicore-Node Clusters

International Nuclear Information System (INIS)

Liu Gang; Schmider, Hartmut; Edgecombe, Kenneth E

2012-01-01

The Double-Layer Master-Slave Model (DMSM) is a suitable hybrid model for executing a workload that consists of multiple independent tasks of varying length on a cluster consisting of multicore nodes. In this model, groups of individual tasks are first deployed to the cluster nodes through an MPI based Master-Slave model. Then, each group is processed by multiple threads on the node through an OpenMP based All-Slave approach. The lack of thread safety of most MPI libraries has to be addressed by a judicious use of OpenMP critical regions and locks. The HPCVL DMSM Library implements this model in Fortran and C. It requires a minimum of user input to set up the framework for the model and to define the individual tasks. Optionally, it supports the dynamic distribution of task-related data and the collection of results at runtime. This library is freely available as source code. Here, we outline the working principles of the library and on a few examples demonstrate its capability to efficiently distribute a workload on a distributed-memory cluster with shared-memory nodes.

Clustering Multivariate Time Series Using Hidden Markov Models

Directory of Open Access Journals (Sweden)

Shima Ghassempour

2014-03-01

Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

27 CFR 24.148 - Penal sums of bonds.

Science.gov (United States)

2010-04-01

... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Penal sums of bonds. 24.148 Section 24.148 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE BUREAU... Vinegar Plant Bond, TTB F 5510.2 Not less than the tax on all wine on hand, in transit, or unaccounted for...

La protección penal a niños y adolescentes víctimas de violencia familiar

OpenAIRE

Salazar Mariño, Carlos Rosendo

2014-01-01

La presente investigación titulada "La Protección Penal a Niños y Adolescentes Víctimas de Violencia Familiar" describe los problemas que presentan el sistema de administración de justicia penal respecto a la protección de los derechos de los niños, niñas y adolescentes que sufren violencia en el hogar. Tiene como objetivo principal analizar y describir la protección penal de Jos derechos de los niños, niñas y adolescentes víctimas de violencia familiar, conocer cómo se aplica los principios ...

[Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

Science.gov (United States)

Luo, Yi; Zhang, Tao; Li, Xiao-song

2016-05-01

To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

Running and rotating: modelling the dynamics of migrating cell clusters

Science.gov (United States)

Copenhagen, Katherine; Gov, Nir; Gopinathan, Ajay

Collective motion of cells is a common occurrence in many biological systems, including tissue development and repair, and tumor formation. Recent experiments have shown cells form clusters in a chemical gradient, which display three different phases of motion: translational, rotational, and random. We present a model for cell clusters based loosely on other models seen in the literature that involves a Vicsek-like alignment as well as physical collisions and adhesions between cells. With this model we show that a mechanism for driving rotational motion in this kind of system is an increased motility of rim cells. Further, we examine the details of the relationship between rim and core cells, and find that the phases of the cluster as a whole are correlated with the creation and annihilation of topological defects in the tangential component of the velocity field.

Constraints on Ωm and σ8 from the potential-based cluster temperature function

Science.gov (United States)

Angrick, Christian; Pace, Francesco; Bartelmann, Matthias; Roncarelli, Mauro

2015-12-01

The abundance of galaxy clusters is in principle a powerful tool to constrain cosmological parameters, especially Ωm and σ8, due to the exponential dependence in the high-mass regime. While the best observables are the X-ray temperature and luminosity, the abundance of galaxy clusters, however, is conventionally predicted as a function of mass. Hence, the intrinsic scatter and the uncertainties in the scaling relations between mass and either temperature or luminosity lower the reliability of galaxy clusters to constrain cosmological parameters. In this article, we further refine the X-ray temperature function for galaxy clusters by Angrick et al., which is based on the statistics of perturbations in the cosmic gravitational potential and proposed to replace the classical mass-based temperature function, by including a refined analytic merger model and compare the theoretical prediction to results from a cosmological hydrodynamical simulation. Although we find already a good agreement if we compare with a cluster temperature function based on the mass-weighted temperature, including a redshift-dependent scaling between mass-based and spectroscopic temperature yields even better agreement between theoretical model and numerical results. As a proof of concept, incorporating this additional scaling in our model, we constrain the cosmological parameters Ωm and σ8 from an X-ray sample of galaxy clusters and tentatively find agreement with the recent cosmic microwave background based results from the Planck mission at 1σ-level.

Modeling Transfer of Knowledge in an Online Platform of a Cluster

OpenAIRE

Schmidt, Danilo Marcello; Böttcher, Lena; Wilberg, Julian; Kammerl, Daniel; Lindemann, Udo

2016-01-01

Dealing with knowledge as a relevant resource and factor for production has become increasingly important in the course of globalization. This work focuses on questions about transferring knowledge when many companies work together in a cluster of enterprises. We developed a model of this transfer based on the theory of clusters from the New Institutional Economics’ point of view and based on existing theories about knowledge and knowledge transfer. This theoretical construct is evaluated and...

Clustering gene expression time series data using an infinite Gaussian process mixture model.

Science.gov (United States)

McDowell, Ian C; Manandhar, Dinesh; Vockley, Christopher M; Schmid, Amy K; Reddy, Timothy E; Engelhardt, Barbara E

2018-01-01

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

Clustering gene expression time series data using an infinite Gaussian process mixture model.

Directory of Open Access Journals (Sweden)

Ian C McDowell

2018-01-01

Full Text Available Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP, which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

Quark cluster model in the three-nucleon system

International Nuclear Information System (INIS)

Osman, A.

1986-11-01

The quark cluster model is used to investigate the structure of the three-nucleon systems. The nucleon-nucleon interaction is proposed considering the colour-nucleon clusters and incorporating the quark degrees of freedom. The quark-quark potential in the quark compound bag model agrees with the central force potentials. The confinement potential reduces the short-range repulsion. The colour van der Waals force is determined. Then, the probability of quark clusters in the three-nucleon bound state systems are numerically calculated using realistic nuclear wave functions. The results of the present calculations show that quarks cluster themselves in three-quark systems building the quark cluster model for the trinucleon system. (author)

Model-based and design-based inference goals frame how to account for neighborhood clustering in studies of health in overlapping context types.

Science.gov (United States)

Lovasi, Gina S; Fink, David S; Mooney, Stephen J; Link, Bruce G

2017-12-01

Accounting for non-independence in health research often warrants attention. Particularly, the availability of geographic information systems data has increased the ease with which studies can add measures of the local "neighborhood" even if participant recruitment was through other contexts, such as schools or clinics. We highlight a tension between two perspectives that is often present, but particularly salient when more than one type of potentially health-relevant context is indexed (e.g., both neighborhood and school). On the one hand, a model-based perspective emphasizes the processes producing outcome variation, and observed data are used to make inference about that process. On the other hand, a design-based perspective emphasizes inference to a well-defined finite population, and is commonly invoked by those using complex survey samples or those with responsibility for the health of local residents. These two perspectives have divergent implications when deciding whether clustering must be accounted for analytically and how to select among candidate cluster definitions, though the perspectives are by no means monolithic. There are tensions within each perspective as well as between perspectives. We aim to provide insight into these perspectives and their implications for population health researchers. We focus on the crucial step of deciding which cluster definition or definitions to use at the analysis stage, as this has consequences for all subsequent analytic and interpretational challenges with potentially clustered data.

Cluster-based control of a separating flow over a smoothly contoured ramp

Science.gov (United States)

Kaiser, Eurika; Noack, Bernd R.; Spohn, Andreas; Cattafesta, Louis N.; Morzyński, Marek

2017-12-01

The ability to manipulate and control fluid flows is of great importance in many scientific and engineering applications. The proposed closed-loop control framework addresses a key issue of model-based control: The actuation effect often results from slow dynamics of strongly nonlinear interactions which the flow reveals at timescales much longer than the prediction horizon of any model. Hence, we employ a probabilistic approach based on a cluster-based discretization of the Liouville equation for the evolution of the probability distribution. The proposed methodology frames high-dimensional, nonlinear dynamics into low-dimensional, probabilistic, linear dynamics which considerably simplifies the optimal control problem while preserving nonlinear actuation mechanisms. The data-driven approach builds upon a state space discretization using a clustering algorithm which groups kinematically similar flow states into a low number of clusters. The temporal evolution of the probability distribution on this set of clusters is then described by a control-dependent Markov model. This Markov model can be used as predictor for the ergodic probability distribution for a particular control law. This probability distribution approximates the long-term behavior of the original system on which basis the optimal control law is determined. We examine how the approach can be used to improve the open-loop actuation in a separating flow dominated by Kelvin-Helmholtz shedding. For this purpose, the feature space, in which the model is learned, and the admissible control inputs are tailored to strongly oscillatory flows.

Modeling the formation of globular cluster systems in the Virgo cluster

International Nuclear Information System (INIS)

Li, Hui; Gnedin, Oleg Y.

2014-01-01

The mass distribution and chemical composition of globular cluster (GC) systems preserve fossil record of the early stages of galaxy formation. The observed distribution of GC colors within massive early-type galaxies in the ACS Virgo Cluster Survey (ACSVCS) reveals a multi-modal shape, which likely corresponds to a multi-modal metallicity distribution. We present a simple model for the formation and disruption of GCs that aims to match the ACSVCS data. This model tests the hypothesis that GCs are formed during major mergers of gas-rich galaxies and inherit the metallicity of their hosts. To trace merger events, we use halo merger trees extracted from a large cosmological N-body simulation. We select 20 halos in the mass range of 2 × 10 12 to 7 × 10 13 M ☉ and match them to 19 Virgo galaxies with K-band luminosity between 3 × 10 10 and 3 × 10 11 L ☉ . To set the [Fe/H] abundances, we use an empirical galaxy mass-metallicity relation. We find that a minimal merger ratio of 1:3 best matches the observed cluster metallicity distribution. A characteristic bimodal shape appears because metal-rich GCs are produced by late mergers between massive halos, while metal-poor GCs are produced by collective merger activities of less massive hosts at early times. The model outcome is robust to alternative prescriptions for cluster formation rate throughout cosmic time, but a gradual evolution of the mass-metallicity relation with redshift appears to be necessary to match the observed cluster metallicities. We also affirm the age-metallicity relation, predicted by an earlier model, in which metal-rich clusters are systematically several billion younger than their metal-poor counterparts.

Cluster-cluster clustering

International Nuclear Information System (INIS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

Clustering consumers based on trust, confidence and giving behaviour: data-driven model building for charitable involvement in the Australian not-for-profit sector.

Science.gov (United States)

de Vries, Natalie Jane; Reis, Rodrigo; Moscato, Pablo

2015-01-01

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not

Clustering consumers based on trust, confidence and giving behaviour: data-driven model building for charitable involvement in the Australian not-for-profit sector.

Directory of Open Access Journals (Sweden)

Natalie Jane de Vries

Full Text Available Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN followed by a feature saliency method (the CM1 score. A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not

New spatial clustering-based models for optimal urban facility location considering geographical obstacles

Science.gov (United States)

Javadi, Maryam; Shahrabi, Jamal

2014-03-01

The problems of facility location and the allocation of demand points to facilities are crucial research issues in spatial data analysis and urban planning. It is very important for an organization or governments to best locate its resources and facilities and efficiently manage resources to ensure that all demand points are covered and all the needs are met. Most of the recent studies, which focused on solving facility location problems by performing spatial clustering, have used the Euclidean distance between two points as the dissimilarity function. Natural obstacles, such as mountains and rivers, can have drastic impacts on the distance that needs to be traveled between two geographical locations. While calculating the distance between various supply chain entities (including facilities and demand points), it is necessary to take such obstacles into account to obtain better and more realistic results regarding location-allocation. In this article, new models were presented for location of urban facilities while considering geographical obstacles at the same time. In these models, three new distance functions were proposed. The first function was based on the analysis of shortest path in linear network, which was called SPD function. The other two functions, namely PD and P2D, were based on the algorithms that deal with robot geometry and route-based robot navigation in the presence of obstacles. The models were implemented in ArcGIS Desktop 9.2 software using the visual basic programming language. These models were evaluated using synthetic and real data sets. The overall performance was evaluated based on the sum of distance from demand points to their corresponding facilities. Because of the distance between the demand points and facilities becoming more realistic in the proposed functions, results indicated desired quality of the proposed models in terms of quality of allocating points to centers and logistic cost. Obtained results show promising

Making Abortion Safer in Rwanda: Operationalization of the Penal ...

African Journals Online (AJOL)

Penal code was revised in Rwanda in 2012 allowing legal termination of pregnancy resulting from rape, incest, forced marriage, or on medical grounds. An evaluation was conducted to assess women's access to abortion services as part of an ongoing program to operationalize the new exemptions for legal abortion.

Clustering economies based on multiple criteria decision making techniques

Directory of Open Access Journals (Sweden)

Mansour Momeni

2011-10-01

Full Text Available One of the primary concerns on many countries is to determine different important factors affecting economic growth. In this paper, we study some factors such as unemployment rate, inflation ratio, population growth, average annual income, etc to cluster different countries. The proposed model of this paper uses analytical hierarchy process (AHP to prioritize the criteria and then uses a K-mean technique to cluster 59 countries based on the ranked criteria into four groups. The first group includes countries with high standards such as Germany and Japan. In the second cluster, there are some developing countries with relatively good economic growth such as Saudi Arabia and Iran. The third cluster belongs to countries with faster rates of growth compared with the countries located in the second group such as China, India and Mexico. Finally, the fourth cluster includes countries with relatively very low rates of growth such as Jordan, Mali, Niger, etc.

Tratamiento de la delincuencia organizada en España: en particular, tras la reforma penal del 2010

Directory of Open Access Journals (Sweden)

José Luis De la Cuesta Arzamendi

2013-04-01

Full Text Available La reforma operada por la Ley Orgánica 5/2010 en el Código Penal español ha supuesto, en relación con la delincuencia organizada, una completa reestructuración de los tipos penales relativos a las organizaciones y grupos criminales, con lo que se supera la insatisfactoria situación anterior de ausencia de un concepto penal de organización criminal, que aparece ya legalmente establecido. No pocos son los problemas técnicos que suscita la nueva regulación, tanto en lo que se refiere a la extensión de los tipos penales como por el solapamiento de estos con otras modalidades delictivas y actos punibles (v. gr., los delitos de asociación ilícita, que continúan vigentes. Los defectos de técnica legislativa se extienden, igualmente, al campo de las sanciones y demás consecuencias jurídicas del delito, respecto de las que se confirma la tendencia endurecedora del ordenamiento jurídico español, el cual también se caracteriza por la progresiva introducción de regulaciones procesales específicas, dirigidas a favorecer la persecución penal. La presente contribución pasa revista a esta nueva situación normativa, que se analiza en la línea de la dogmática jurídico-penal, al poner de relieve sus contradicciones e insuficiencias desde el prisma políticocriminal en un área de la mayor importancia criminológica y para la seguridad de los ciudadanos.

A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

Science.gov (United States)

Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

2017-01-01

An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

Substructures in DAFT/FADA survey clusters based on XMM and optical data

Science.gov (United States)

Durret, F.; DAFT/FADA Team

2014-07-01

The DAFT/FADA survey was initiated to perform weak lensing tomography on a sample of 90 massive clusters in the redshift range [0.4,0.9] with HST imaging available. The complementary deep multiband imaging constitutes a high quality imaging data base for these clusters. In X-rays, we have analysed the XMM-Newton and/or Chandra data available for 32 clusters, and for 23 clusters we fit the X-ray emissivity with a beta-model and subtract it to search for substructures in the X-ray gas. This study was coupled with a dynamical analysis for the 18 clusters with at least 15 spectroscopic galaxy redshifts in the cluster range, based on a Serna & Gerbal (SG) analysis. We detected ten substructures in eight clusters by both methods (X-rays and SG). The percentage of mass included in substructures is found to be roughly constant with redshift, with values of 5-15%. Most of the substructures detected both in X-rays and with the SG method are found to be relatively recent infalls, probably at their first cluster pericenter approach.

Channel Parameter Estimation for Scatter Cluster Model Using Modified MUSIC Algorithm

Directory of Open Access Journals (Sweden)

Jinsheng Yang

2012-01-01

Full Text Available Recently, the scatter cluster models which precisely evaluate the performance of the wireless communication system have been proposed in the literature. However, the conventional SAGE algorithm does not work for these scatter cluster-based models because it performs poorly when the transmit signals are highly correlated. In this paper, we estimate the time of arrival (TOA, the direction of arrival (DOA, and Doppler frequency for scatter cluster model by the modified multiple signal classification (MUSIC algorithm. Using the space-time characteristics of the multiray channel, the proposed algorithm combines the temporal filtering techniques and the spatial smoothing techniques to isolate and estimate the incoming rays. The simulation results indicated that the proposed algorithm has lower complexity and is less time-consuming in the dense multipath environment than SAGE algorithm. Furthermore, the estimations’ performance increases with elements of receive array and samples length. Thus, the problem of the channel parameter estimation of the scatter cluster model can be effectively addressed with the proposed modified MUSIC algorithm.

El nuevo tratamiento penal del blanqueo de capitales en el derecho brasileño (ley 12.683/2012)

OpenAIRE

Regis Prado, Luiz

2013-01-01

Análisis de los impactos causados por la nueva ley (12.683/2012) sobre el tratamiento penal del blanqueo de capitales This is an analysis of the impacts caused by the law 12.683/2012 concerning the penal treatment of money laudering

Crimen organizado: concepto y posibilidad de tipificación delante del contexto de la expansión del derecho penal

Directory of Open Access Journals (Sweden)

André Luis Callegari

2010-12-01

Full Text Available El presente artículo aborda las dificultades encontradas por el Derecho penal brasileño para la calificación del crimen organizado. En primer lugar se emprende un análisis del panorama actual de la política criminal delante del proceso de expansión del Derecho penal, más específicamente en sus aspectos simbólico y punitivista/eficientista, que, en conjunto, resultan en un modelo de intervención penal que posee vínculos bastante estrechos con el derecho penal del enemigo defendido por el penalista alemán GÜNTHER JAKOBS. En un segundo momento se estudia el tratamiento dispensado por el Derecho penal contemporáneo –en especial el brasileño– en el fenómeno de las organizaciones criminales delante de este contexto de expansión punitiva. En un tercer momento se hacen consideraciones sobre el proyecto de ley nº 150/2006, que busca la calificación penal del crimen organizado en Brasil y dispone sobre la investigación criminal, medios de obtención de prueba, crímenes correlatos y procedimiento criminal a ser aplicado a la referida modalidad delictiva.

Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models.

Science.gov (United States)

Liu, Jingxia; Colditz, Graham A

2018-05-01

There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency (RE) of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a set of correlated data is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which the "working correlation structure" is introduced and the association pattern depends on a vector of association parameters denoted by ρ. In this paper, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in CRTs. The variances of the estimator of the treatment effect are derived for the different types of outcome. RE is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal cluster sizes. We discuss a commonly used structure in CRTs-exchangeable, and derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster size distributions through simulation studies. We propose an adjusted sample size due to efficiency loss. Additionally, we also propose an optimal sample size estimation based on the GEE models under a fixed budget for known and unknown association parameter (ρ) in the working correlation structure within the cluster. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

On the applicability of the jellium model to the description of alkali clusters

International Nuclear Information System (INIS)

Matveentsev, A.; Lyalin, A.; Solovyov, I.A.; Solovyov, A.V.; Greiner, W.

2003-01-01

This work is devoted to the elucidation of the applicability of the jellium model to the description of alkali cluster properties. We compare the jellium model results with those derived within ab initio theoretical approaches and with experiments. On the basis of Hartree–Fock and local-density approximations we have calculated the binding energies per atom, ionization potentials, deformation parameters and optimized values of the Wigner–Seitz radii for neutral and singly charged sodium clusters with the number of atoms N ≤ 20. The characteristics calculated within the framework of the deformed jellium model are compared with the results derived from ab initio simulations of cluster electronic and ionic structure based on density functional theory and systematic post Hartree–Fock many-body perturbation theory accounting for all electrons in the system. The comparison performed demonstrates the great role of the cluster shape deformations in the formation cluster properties and quite reasonable level of applicability of the deformed jellium model. This elucidates the similarities of atomic cluster physics with the physics of atomic nuclei. (author)

On the use of a penalized least squares method to process kinematic full-field measurements

International Nuclear Information System (INIS)

Moulart, Raphaël; Rotinat, René

2014-01-01

This work is aimed at exploring the performances of an alternative procedure to smooth and differentiate full-field displacement measurements. After recalling the strategies currently used by the experimental mechanics community, a short overview of the available smoothing algorithms is drawn up and the requirements that such an algorithm has to fulfil to be applicable to process kinematic measurements are listed. A comparative study of the chosen algorithm is performed including the 2D penalized least squares method and two other commonly implemented strategies. The results obtained by penalized least squares are comparable in terms of quality to those produced by the two other algorithms, while the penalized least squares method appears to be the fastest and the most flexible. Unlike both the other considered methods, it is possible with penalized least squares to automatically choose the parameter governing the amount of smoothing to apply. Unfortunately, it appears that this automation is not suitable for the proposed application since it does not lead to optimal strain maps. Finally, it is possible with this technique to perform the derivation to obtain strain maps before smoothing them (while the smoothing is normally applied to displacement maps before the differentiation), which can lead in some cases to a more effective reconstruction of the strain fields. (paper)

Structure based alignment and clustering of proteins (STRALCP)

Science.gov (United States)

Zemla, Adam T.; Zhou, Carol E.; Smith, Jason R.; Lam, Marisa W.

2013-06-18

Disclosed are computational methods of clustering a set of protein structures based on local and pair-wise global similarity values. Pair-wise local and global similarity values are generated based on pair-wise structural alignments for each protein in the set of protein structures. Initially, the protein structures are clustered based on pair-wise local similarity values. The protein structures are then clustered based on pair-wise global similarity values. For each given cluster both a representative structure and spans of conserved residues are identified. The representative protein structure is used to assign newly-solved protein structures to a group. The spans are used to characterize conservation and assign a "structural footprint" to the cluster.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING

Directory of Open Access Journals (Sweden)

Beta Estri Adiana

2018-04-01

Full Text Available Intense competition in the business field motivates a small and medium enterprises (SMEs to manage customer services to the maximal. Improve of customer royalty by grouping cunstomers into some of groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be performed by data mining approach with clustering method. The main purpose of this paper is customer segmentation and measure their loyalty to a SME’s product. Using CRISP-DM method which consist of six phases, namely business understanding, data understanding, data preparatuin, modeling, evaluation and deployment. The K-Means algorithm is used for cluster formation and RapidMiner as a tool used to evaluate the result of clusters. Cluster formation is based on RFM (recency, frequency, monetary analysis. Davies Bouldin Index (DBI is used to find the optimal number of clusters (k. The customers are divided into 3 clusters, total of customer in first cluster is 30 customers who entered in typical customer category, the second cluster there are 8 customer whho entered in superstar customer and 89 customers in third cluster is dormant cluster category.

A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles.

Science.gov (United States)

Zhang, Lin; Meng, Jia; Liu, Hui; Huang, Yufei

2012-01-01

DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters. The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

Delito de Bigamia e Intervención Mínima: ¿Es el Matrimonio, Todavía, un Bien Jurídico-Penal?

Directory of Open Access Journals (Sweden)

Gerson Faustino Rosa

2016-10-01

Full Text Available Este artículo trata de los delitos contra el matrimonio, y del delito de bigamia como figura central, que no es compatible con la función actual del sistema penal, criticándose los bienes que deberían ser objeto de protección por otras áreas del derecho. Se trata de la política criminal sobre la planificación familiar, criticándose la intervención del Estado en los asuntos familiares. Se destacan los principios de intervención mínima, fragmentaridad, subsidiariedad y proporcionalidade. Se analiza el tipo penal del art. 235 del Código Penal, criticándose dicha penalización, dado que es un tipo penal subsidiario, perfectamente prescindible por el ordenamiento jurídico-penal.

A Postneoliberal Turn? Variants of the Recent Penal Policy in Argentina

Directory of Open Access Journals (Sweden)

Maximo Sozzo

2017-03-01

Full Text Available This paper analysed the connection between the emergence and consolidation of a postneoliberal political program and alliance – Kirchnerism – and penal policies in Argentina. Three key moments are identified in this recent period. After the experience of an intense punitive turn during the 1990s and early 2000s, Kirchnerist political alliances tried to deploy a progressive political discourse and agenda on penal issues. Nevertheless, this initially coincided with a strong wave of penal populism ‘from below’ that continued the precedent trend towards increasing punitiviness.  Since 2005, and for a brief moment, this tendency stopped. However, after that and during the presidencies of Fernandez de Kirchner a more volatile and contradictory scenario was generated. The incarceration rate between 2002 and 2014 in Argentina grew substantially as did the rate of convictions. Meanwhile the percentage of suspended sentences as part of the total convictions and the percentage of custodial sanctions both fell. Especially in relation to incarceration, these levels of change are not as stark as those of the preceding decades. However, the trends persist. Therefore, the question of how to transcend the dynamics of the punitive turn remains a pending and urgent political subject. The article argues the importance of analysing why a punitive turn is interrupted and presents an explanation of it.

Problems of causality in environmental penal law. The relevance of causality problems on the environmental sector from the view of penal law. Kausalitaetsprobleme im Umweltstrafrecht. Die strafrechtliche Relevanz der Schwierigkeiten naturwissenschaftlicher Kausalfeststellung im Umweltbereich

Energy Technology Data Exchange (ETDEWEB)

Kleine-Cosack, E.

1988-01-01

The 'classic' elements of an offence against human health or property are not applicable in environmental law, owing to problems of causality. The new environmental penal law therefore focuses on the 'capability' of any act to damage human health, animal health, vegetation, water, air, or soil. It remarks doubtful whether this approach is more efficient. Further, there is still the problem of assessing damage. The book discusses causality problems in environmental penal law. Causality in a given case is discussed from the view of general causality laws and problems of proof. Other possible causes of damage must be excluded. The author discusses: Interdependences between scientific and penal causality, the problems of successful and potential offences, the relationship between individual and universal objects of legal protection, and procedural issues (e.g. the binding effect of experts' opinions on a given subject). (orig./HSCH).

Clustering of European winter storms: A multi-model perspective

Science.gov (United States)

Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter

2016-04-01

The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak

El garantismo y el punitivismo en el Código Orgánico Integral Penal

OpenAIRE

Sebastián Cornejo Aguiar

2016-01-01

El objetivo del presente artículo es determinar la necesidad de la existencia de una parte punitivista y una garantista dentro del Código Orgánico Integral Penal, partiendo del estudio de la actualización doctrinaria en materia penal dentro del ordenamiento jurídico ecuatoriano, considerando que al ser el Ecuador un Estado constitucional de derechos y justicia, nos inspira a la construcción de mecanismos que tengan como fundamento y fin la tutela de las libertades del individuo, frente a las ...

A model of photon cell killing based on the spatio-temporal clustering of DNA damage in higher order chromatin structures.

Directory of Open Access Journals (Sweden)

Lisa Herr

Full Text Available We present a new approach to model dose rate effects on cell killing after photon radiation based on the spatio-temporal clustering of DNA double strand breaks (DSBs within higher order chromatin structures of approximately 1-2 Mbp size, so called giant loops. The main concept of this approach consists of a distinction of two classes of lesions, isolated and clustered DSBs, characterized by the number of double strand breaks induced in a giant loop. We assume a low lethality and fast component of repair for isolated DSBs and a high lethality and slow component of repair for clustered DSBs. With appropriate rates, the temporal transition between the different lesion classes is expressed in terms of five differential equations. These allow formulating the dynamics involved in the competition of damage induction and repair for arbitrary dose rates and fractionation schemes. Final cell survival probabilities are computable with a cell line specific set of three parameters: The lethality for isolated DSBs, the lethality for clustered DSBs and the half-life time of isolated DSBs. By comparison with larger sets of published experimental data it is demonstrated that the model describes the cell line dependent response to treatments using either continuous irradiation at a constant dose rate or to split dose irradiation well. Furthermore, an analytic investigation of the formulation concerning single fraction treatments with constant dose rates in the limiting cases of extremely high or low dose rates is presented. The approach is consistent with the Linear-Quadratic model extended by the Lea-Catcheside factor up to the second moment in dose. Finally, it is shown that the model correctly predicts empirical findings about the dose rate dependence of incidence probabilities for deterministic radiation effects like pneumonitis and the bone marrow syndrome. These findings further support the general concepts on which the approach is based.

Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

KAUST Repository

Liang, Faming

2013-06-01

This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.

Radiobiological analyse based on cell cluster models

International Nuclear Information System (INIS)

Lin Hui; Jing Jia; Meng Damin; Xu Yuanying; Xu Liangfeng

2010-01-01

The influence of cell cluster dimension on EUD and TCP for targeted radionuclide therapy was studied using the radiobiological method. The radiobiological features of tumor with activity-lack in core were evaluated and analyzed by associating EUD, TCP and SF.The results show that EUD will increase with the increase of tumor dimension under the activity homogeneous distribution. If the extra-cellular activity was taken into consideration, the EUD will increase 47%. Under the activity-lack in tumor center and the requirement of TCP=0.90, the α cross-fire influence of 211 At could make up the maximum(48 μm)3 activity-lack for Nucleus source, but(72 μm)3 for Cytoplasm, Cell Surface, Cell and Voxel sources. In clinic,the physician could prefer the suggested dose of Cell Surface source in case of the future of local tumor control for under-dose. Generally TCP could well exhibit the effect difference between under-dose and due-dose, but not between due-dose and over-dose, which makes TCP more suitable for the therapy plan choice. EUD could well exhibit the difference between different models and activity distributions,which makes it more suitable for the research work. When the user uses EUD to study the influence of activity inhomogeneous distribution, one should keep the consistency of the configuration and volume of the former and the latter models. (authors)

Beyond Hydrodynamic Modeling of AGN Heating in Galaxy Clusters

Science.gov (United States)

Yang, Hsiang-Yi Karen

Clusters of galaxies hold a unique position in hierarchical structure formation - they are both powerful cosmological probes and excellent astrophysical laboratories. Accurate modeling of the cluster properties is crucial for reducing systematic uncertainties in cluster cosmology. However, theoretical modeling of the intracluster medium (ICM) has long suffered from the "cooling-flow problem" - clusters with short central times or cool cores (CCs) are predicted to host massive inflows of gas that are not observed. Feedback from active galactic nuclei (AGN) is by far the most promising heating mechanism to counteract radiative cooling. Recent hydrodynamic simulations have made remarkable progress reproducing properties of the CCs. However, there remain two major questions that cannot be probed using purely hydrodynamic models: (1) what are the roles of cosmic rays (CRs)? (2) how is the existing picture altered when the ICM is modeled as weakly collisional plasma? We propose to move beyond limitations of pure hydrodynamics and progress toward a complete understanding of how AGN jet-inflated bubbles interact with their surroundings and provide heat to the ICM. Our objectives include: (1) understand how CR-dominated bubbles heat the ICM; (2) understand bubble evolution and sound-wave dissipation in the ICM with different assumptions of plasma properties, e.g., collisionality of the ICM, with or without anisotropic transport processes; (3) Develop a subgrid model of AGN heating that can be adopted in cosmological simulations based on state-of-the-art isolated simulations. We will use a combination of analytical calculations and idealized simulations to advance our understanding of each individual physical process. We will then perform the first three-dimensional (3D) magnetohydrodynamic (MHD) simulations of self-regulated AGN feedback with relevant CR and anisotropic transport processes in order to quantify the amount and distribution of heating from the AGN. Our

Alcance de la protección de los sistemas naturales y las bases naturales de la vida humana. Análisis de la legislación penal española y paraguaya

OpenAIRE

Balbuena Soto, Lorena Beatriz

2012-01-01

La catástrofe del Prestige ocurrida en España, en el año 2003, motivó a inquirir sobre el alcance del Derecho penal en la protección del medio ambiente, y llevó a confrontar con el alcance del Derecho penal en la protección de medio ambiente en Paraguay. Precisamente, en la confrontación social y jurídica de ambas realidades se constata la existencia del bien jurídico penal protegido ante las intervenciones del hombre en un sistema natural (CPE), sobre el cual el sistema tecnológico – postind...

Green Clustering Implementation Based on DPS-MOPSO

Directory of Open Access Journals (Sweden)

Yang Lu

2014-01-01

Full Text Available A green clustering implementation is proposed to be as the first method in the framework of an energy-efficient strategy for centralized enterprise high-density WLANs. Traditionally, to maintain the network coverage, all of the APs within the WLAN have to be powered on. Nevertheless, the new algorithm can power off a large proportion of APs while the coverage is maintained as the always-on counterpart. The proposed algorithm is composed of two parallel and concurrent procedures, which are the faster procedure based on K-means and the more accurate procedure based on Dynamic Population Size Multiple Objective Particle Swarm Optimization (DPS-MOPSO. To implement green clustering efficiently and accurately, dynamic population size and mutational operators are introduced as complements for the classical MOPSO. In addition to the function of AP selection, the new green clustering algorithm has another new function as the reference and guidance for AP deployment. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% up to 90% of energy consumption can be saved while the original network coverage is maintained during periods when few users are online or when the traffic load is low.

Model-based and design-based inference goals frame how to account for neighborhood clustering in studies of health in overlapping context types

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Gina S. Lovasi

2017-12-01

Full Text Available Accounting for non-independence in health research often warrants attention. Particularly, the availability of geographic information systems data has increased the ease with which studies can add measures of the local “neighborhood” even if participant recruitment was through other contexts, such as schools or clinics. We highlight a tension between two perspectives that is often present, but particularly salient when more than one type of potentially health-relevant context is indexed (e.g., both neighborhood and school. On the one hand, a model-based perspective emphasizes the processes producing outcome variation, and observed data are used to make inference about that process. On the other hand, a design-based perspective emphasizes inference to a well-defined finite population, and is commonly invoked by those using complex survey samples or those with responsibility for the health of local residents. These two perspectives have divergent implications when deciding whether clustering must be accounted for analytically and how to select among candidate cluster definitions, though the perspectives are by no means monolithic. There are tensions within each perspective as well as between perspectives. We aim to provide insight into these perspectives and their implications for population health researchers. We focus on the crucial step of deciding which cluster definition or definitions to use at the analysis stage, as this has consequences for all subsequent analytic and interpretational challenges with potentially clustered data.

Validating clustering of molecular dynamics simulations using polymer models

Directory of Open Access Journals (Sweden)

Phillips Joshua L

2011-11-01

Full Text Available Abstract Background Molecular dynamics (MD simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our

K­MEANS CLUSTERING FOR HIDDEN MARKOV MODEL

NARCIS (Netherlands)

Perrone, M.P.; Connell, S.D.

2004-01-01

An unsupervised k­means clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as

Cluster-based global firms' use of local capabilities

DEFF Research Database (Denmark)

Andersen, Poul Houman; Bøllingtoft, Anne

2011-01-01

Purpose – Despite growing interest in clusters role for the global competitiveness of firms, there has been little research into how globalization affects cluster-based firms’ (CBFs) use of local knowledge resources and the combination of local and global knowledge used. Using the cluster......’s knowledge base as a mediating variable, the purpose of this paper is to examine how globalization affected the studied firms’ use of local cluster-based knowledge, integration of local and global knowledge, and networking capabilities. Design/methodology/approach – Qualitative case studies of nine firms...... in three clusters strongly affected by increasing global division of labour. Findings – The paper suggests that globalization has affected how firms use local resources and combine local and global knowledge. Unexpectedly, clustered firms with explicit procedures and established global fora for exchanging...

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

Science.gov (United States)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Aporía de la política criminal del exhibicionismo penal en México

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Alan Jair García Flores

2017-01-01

Full Text Available El presente artículo tiene como fin ulterior, esgrimir las líneas esenciales de la política criminal de exhibicionismo penal contra los presuntos responsables de la comisión de delitos, circunstancia que vulnera el esquema garantista del sistema penal mexicano adoptado a través de la reforma constitucional de 2008 y degrada el constructo social del Estado Democrático de Derecho , robustecido mediante la reforma constitucional en materia de derechos humanos de 2011. A lo larg o de los siguientes apartados, se esgrime un estudio sobre los derechos fundamentales lesionados por la práctica estatal del exhibicionismo penal, hecho que ha significado violaciones tanto al debido proceso legal como a la dignidad y honor de los gobernad os quienes deberían ser los principales sujetos de protección del Estado mexicano.

Alpha cluster model and spectrum of 16O

International Nuclear Information System (INIS)

Bauhoff, W.; Schultheis, H.; Schultheis, R.

1983-01-01

The structure of 16 O is studied in the alpha cluster model with parity and angular-momentum projection for several nucleon-nucleon interactions. The method differs from previous studies in that the states of positive and negative parity are determined without the customary restriction of the variational space to cluster positions with certain assumed symmetries. It is demonstrated that the alpha cluster model of 16 O is capable of explaining most of the experimental T = O levels up to about 15 MeV excitation. A shell-model analysis of the excited cluster-model states shows the necessity of including a very large number of shells. The evidence for the recently proposed tetrahedral symmetry of some excited states is also discussed

El Ministerio Público y la “Atención Primaria” de la Conflictividad Penal

OpenAIRE

Mendaña, Ricardo J.; Arias Salgado, Alicia

2008-01-01

El artículo revisa la aplicación práctica de los conceptos pertinentes a la mejora de la justicia penal. La adopción de nuevos paradigmas y líneas de acción para intervenir frente a los conflictos penales implica situar la mejora de la atención primaria como una estrategia de intervención superior a las actividades de modelos tradicionales de respuesta judicial. This article looks at the practical application of relevant concepts when improving crime justice. The adoption of new paradigms ...

MEMBANGUN POLITIK KRIMINAL PADA PERTAMBANGAN BATUBARA YANG MENYEJAHTERAKAN MASYARAKAT MELALUI SARANA NON-PENAL

Directory of Open Access Journals (Sweden)

Arif Firmansyah

2016-04-01

Full Text Available In Article 33 paragraph (3 of the Constitution of 1945, states earth water and natural resources contained therein controlled by the state and used for the welfare of the people. The realization of such mastery by delegating the authority to manage the natural resources of the state to the company is to provide state Mining Permit or Special Mining Permit. In protecting and overseeing the company that is engaged in coal mining government passed Law Number 4 of 2009 on Mineral and Coal Mining. In Article 162 of Law Number 4 of 2009 states that every person who impede or interfere mining activities from business license holders of mining and business permit of the mining specifically penalized by fines or imprisonment. The article shows a process of criminalization an action (criminal policy, which aim to protect the companies that already have a Mining Permit, but the criminal policy is contrary to the purpose of the criminal policy is an effort for the welfare of society and policies the protection of society, the existence of Article 162 of Law Minerals coal and coal mining communities can impede convicted. In the case of the counteraction form caused they want to protect the environment or their ancestral lands from exploration activities. So it is activity is not uncommon form of criminal policy by means of criminal law that gives rise to new conflicts. Therefore the criminal policy should be shifted from penal facilities to non-penal policy more accommodating community participation, so that the purpose of the criminal policy, namely the welfare of society and protect the community can be realized.Keywords: Political Criminal, Mining, Non-Penal

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

Science.gov (United States)

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Feature selection model based on clustering and ranking in pipeline for microarray data

Directory of Open Access Journals (Sweden)

Barnali Sahu

2017-01-01

Full Text Available Most of the available feature selection techniques in the literature are classifier bound. It means a group of features tied to the performance of a specific classifier as applied in wrapper and hybrid approach. Our objective in this study is to select a set of generic features not tied to any classifier based on the proposed framework. This framework uses attribute clustering and feature ranking techniques in pipeline in order to remove redundant features. On each uncovered cluster, signal-to-noise ratio, t-statistics and significance analysis of microarray are independently applied to select the top ranked features. Both filter and evolutionary wrapper approaches have been considered for feature selection and the data set with selected features are given to ensemble of predefined statistically different classifiers. The class labels of the test data are determined using majority voting technique. Moreover, with the aforesaid objectives, this paper focuses on obtaining a stable result out of various classification models. Further, a comparative analysis has been performed to study the classification accuracy and computational time of the current approach and evolutionary wrapper techniques. It gives a better insight into the features and further enhancing the classification accuracy with less computational time.

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Energy Technology Data Exchange (ETDEWEB)

Harms, Joseph; Wang, Tonghe; Petrongolo, Michael; Zhu, Lei, E-mail: leizhu@gatech.edu [Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Niu, Tianye [Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (China); Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016 (China)

2016-05-15

Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high- and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan{sup ©}600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise

Quark cluster model and confinement

International Nuclear Information System (INIS)

Koike, Yuji; Yazaki, Koichi

2000-01-01

How confinement of quarks is implemented for multi-hadron systems in the quark cluster model is reviewed. In order to learn the nature of the confining interaction for fermions we first study 1+1 dimensional QED and QCD, in which the gauge field can be eliminated exactly and generates linear interaction of fermions. Then, we compare the two-body potential model, the flip-flop model and the Born-Oppenheimer approach in the strong coupling lattice QCD for the meson-meson system. Having shown how the long-range attraction between hadrons, van der Waals interaction, shows up in the two-body potential model, we discuss two distinct attempts beyond the two-body potential model: one is a many-body potential model, the flip-flop model, and the other is the Born-Oppenheimer approach in the strong coupling lattice QCD. We explain how the emergence of the long-range attraction is avoided in these attempts. Finally, we present the results of the application of the flip-flop model to the baryon-baryon scattering in the quark cluster model. (author)

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

International Nuclear Information System (INIS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Energy Technology Data Exchange (ETDEWEB)

Nedialkova, Lilia V.; Amat, Miguel A. [Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States); Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544 (United States); Hummer, Gerhard, E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main (Germany)

2014-09-21

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Modeling sports highlights using a time-series clustering framework and model interpretation

Science.gov (United States)

Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay

2005-01-01

In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.

Modeling and clustering users with evolving profiles in usage streams

KAUST Repository

Zhang, Chongsheng; Masseglia, Florent; Zhang, Xiangliang

2012-01-01

Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.

Modeling and clustering users with evolving profiles in usage streams

KAUST Repository

Zhang, Chongsheng

2012-09-01

Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users\\' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users\\' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.

Ecosystem health pattern analysis of urban clusters based on emergy synthesis: Results and implication for management

International Nuclear Information System (INIS)

Su, Meirong; Fath, Brian D.; Yang, Zhifeng; Chen, Bin; Liu, Gengyuan

2013-01-01

The evaluation of ecosystem health in urban clusters will help establish effective management that promotes sustainable regional development. To standardize the application of emergy synthesis and set pair analysis (EM–SPA) in ecosystem health assessment, a procedure for using EM–SPA models was established in this paper by combining the ability of emergy synthesis to reflect health status from a biophysical perspective with the ability of set pair analysis to describe extensive relationships among different variables. Based on the EM–SPA model, the relative health levels of selected urban clusters and their related ecosystem health patterns were characterized. The health states of three typical Chinese urban clusters – Jing-Jin-Tang, Yangtze River Delta, and Pearl River Delta – were investigated using the model. The results showed that the health status of the Pearl River Delta was relatively good; the health for the Yangtze River Delta was poor. As for the specific health characteristics, the Pearl River Delta and Yangtze River Delta urban clusters were relatively strong in Vigor, Resilience, and Urban ecosystem service function maintenance, while the Jing-Jin-Tang was relatively strong in organizational structure and environmental impact. Guidelines for managing these different urban clusters were put forward based on the analysis of the results of this study. - Highlights: • The use of integrated emergy synthesis and set pair analysis model was standardized. • The integrated model was applied on the scale of an urban cluster. • Health patterns of different urban clusters were compared. • Policy suggestions were provided based on the health pattern analysis

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

Science.gov (United States)

Pankratius, V.; Gowanlock, M.; Blair, D. M.

2015-12-01

Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

Cluster Physics with Merging Galaxy Clusters

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Sandor M. Molnar

2016-02-01

Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Energy spectra of vibron and cluster models in molecular and nuclear systems

Science.gov (United States)

Jalili Majarshin, A.; Sabri, H.; Jafarizadeh, M. A.

2018-03-01

The relation of the algebraic cluster model, i.e., of the vibron model and its extension, to the collective structure, is discussed. In the first section of the paper, we study the energy spectra of vibron model, for diatomic molecule then we derive the rotation-vibration spectrum of 2α, 3α and 4α configuration in the low-lying spectrum of 8Be, 12C and 16O nuclei. All vibrational and rotational states with ground and excited A, E and F states appear to have been observed, moreover the transitional descriptions of the vibron model and α-cluster model were considered by using an infinite-dimensional algebraic method based on the affine \\widehat{SU(1,1)} Lie algebra. The calculated energy spectra are compared with experimental data. Applications to the rotation-vibration spectrum for the diatomic molecule and many-body nuclear clusters indicate that there are solvable models and they can be approximated very well using the transitional theory.

Teoria da adequação econômica da conduta: significado econômico da conduta em face da tutela penal antitruste

OpenAIRE

Luiz da Silva, Ivan

2009-01-01

Essa tese tem por objetivo o desenvolvimento da teoria da adequação econômica da conduta no direito penal econômico. Vale-se de uma abordagem interdisciplinar abrangendo a Economia, o direito econômico e o direito penal. Para alcançar o desiderato a investigação analisou a intervenção do direito penal em face da atividade econômica e, em especial, os fundamentos da tutela penal antitruste, para fins de estabelecer os contornos teóricos necessários à aplicação das premissas fund...

Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

Science.gov (United States)

Yau, Christopher; Holmes, Chris

2011-07-01

We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.

TRIBUNALES PENALES INTERNACIONALES AD HOC DEL POST-GUERRA FRÍA: CAMBIANDO PARADIGMAS EN EL TRATAMIENTO DE CUESTIONES DE GÉNERO

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Camila Soares Lippi

2011-12-01

Full Text Available Analizaremos como la jurisprudencia de los Tribunales Penales Internacionales ad hoc del post-Guerra Fría representa un cambio de paradigmas en cuestiones de género en el Derecho Penal Internacional y en el Derecho Internacional Humanitario. Para eso, partimos del concepto de género, o sea, las construcciones sociales cuanto a los roles masculino y femenino, como categoría central de análisis. Analizamos, primeramente, el post-Segunda Guerra Mundial. Ese momento histórico, ejemplificado por los Tribunales de Núremberg y de Tokio y por los Convenios de Ginebra de 1949. Después analizase los Tribunales Penales Internacionales ad hoc del post-Guerra Fría. Finalmente, analizamos la definición de género en el Estatuto de Roma, que instituye el Tribunal Penal Internacional.

Derecho Constitucional, Derechos Humanos y Código Penal en Nicaragua. Una interrelación necesaria

Directory of Open Access Journals (Sweden)

Rafael Chamorro Fletes

2017-08-01

Full Text Available El nuevo código penal de Nicaragua se ubica dentro del engranaje del ordenamiento jurídico de nuestro país. Todo el ordenamiento se subordina a la Constitución y, por lo tanto, los principios, derechos y obligaciones que en ella se prescriben, informan y limitan las disposiciones de dicho código. Solo en ese caso estaremos en presencia de un Estado democrático de derecho y contaremos con un código penal democrático.

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [123I]FP-CIT SPECT

International Nuclear Information System (INIS)

Huertas-Fernandez, I.; Benitez-Rivero, S.; Jesus, S.; Caceres-Redondo, M.T.; Martin-Rodriguez, J.F.; Carrillo, F.; Garcia-Gomez, F.J.; Marin-Oyaga, V.A.; Lojo, J.A.; Garcia-Solis, D.; Mir, P.

2015-01-01

The study's objective was to develop diagnostic predictive models using data from two commonly used [ 123 I]FP-CIT SPECT assessment methods: region-of-interest (ROI) analysis and whole-brain voxel-based analysis. We included retrospectively 80 patients with vascular parkinsonism (VP) and 164 patients with Parkinson's disease (PD) who underwent [ 123 I]FP-CIT SPECT. Nuclear-medicine specialists evaluated the scans and calculated bilateral caudate and putamen [ 123 I]FP-CIT uptake and asymmetry indices using BRASS software. Statistical parametric mapping (SPM) was used to compare the radioligand uptake between the two diseases at the voxel level. Quantitative data from these two methods, together with potential confounding factors for dopamine transporter availability (sex, age, disease duration and severity), were used to build predictive models following a tenfold cross-validation scheme. The performance of logistic regression (LR), linear discriminant analysis and support vector machine (SVM) algorithms for ROI data, and their penalized versions for SPM data (penalized LR, penalized discriminant analysis and SVM), were assessed. Significant differences were found in the ROI analysis after covariate correction between VP and PD patients in [ 123 I]FP-CIT uptake in the more affected side of the putamen and the ipsilateral caudate. Age, disease duration and severity were also found to be informative in feeding the statistical model. SPM localized significant reductions in [ 123 I]FP-CIT uptake in PD with respect to VP in two specular clusters comprising areas corresponding to the left and right striatum. The diagnostic predictive accuracy of the LR model using ROI data was 90.3 % and of the SVM model using SPM data was 90.4 %. The predictive models built with ROI data and SPM data from [ 123 I]FP-CIT SPECT provide great discrimination accuracy between VP and PD. External validation of these methods is necessary to confirm their applicability across centres. (orig.)

Crímenes de “guerra sucia”: derecho penal internacional y jurisdicciones de la memoria

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Peter Rush

2014-07-01

Full Text Available Argentina es una comunidad asediada por experiencias indescriptibles de sufrimiento e injusticia que no cesan de regresar en fragmentos e imágenes. Es un país poseído por la guerra sucia y sus desaparecidos. Es como si esta comunidad política no hubiese superado su situación traumática; como si dicha comunidad estuviese obligada a enfrentarla como una de sus tareas inmediatas. Este ensayo considera las relaciones entre la memoria del derecho, el crimen y la guerra sucia desde la doble mirada del cine y del derecho. La primera parte reconstruye la memoria narrativa y el contexto socio jurídico de la escena contemporánea de la memoria en Argentina. En este punto, se concentra sobre cómo los procesos judiciales han perfilado las prácticas de reconocimiento de experiencias traumáticas, de injusticia y sufrimiento. La segunda parte está relacionada con la película El secreto de sus ojos, dirigida por Juan José Campanella, filmada en Buenos Aires y que describe la vida del derecho después de la atrocidad. Mi argumento es que esta película ofrece elementos para la reflexión a partir de lo que llamo una “jurisdicción de la memoria”, gracias a que la película conecta el trabajo de la memoria en Argentina al funcionamiento de la jurisdicción penal. Después de reconstruir dos formas de vivir una vida vacía, una vida vivida con el trauma de un caso penal, la coda del ensayo se concentra en los vestigios de una jurisdicción penal de la memoria. Si las decisiones son constitutivas de la justicia penal internacional en tiempos de transición, entonces la jurisdicción penal de la memoria, objeto del presente ensayo, puede ser pensada en sus propios términos: desde sus propios géneros de representación y sus propias taxonomías. Una ética del testimonio y una lógica de la memoria han de permanecer, por lo tanto, vigilantes después de la barbarie generalizada. Por ello es posible afirmar que es este el legado que, hoy por hoy

SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

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Xiaoying Li

2018-01-01

Full Text Available Aberrant expression of microRNAs (miRNAs can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP. The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

Recursive Cluster Elimination (RCE for classification and feature selection from gene expression data

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Showe Louise C

2007-05-01

Full Text Available Abstract Background Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE rather than recursive feature elimination (RFE. We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE. Results We have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs, a supervised machine learning classification method, to identify and score (rank those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA with recursive feature elimination (SVM-RFE and PDA-RFE are used to remove genes based on their individual discriminant weights. Conclusion SVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

Science.gov (United States)

Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

2011-01-19

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an

GraphCrunch 2: Software tool for network modeling, alignment and clustering

Directory of Open Access Journals (Sweden)

Hayes Wayne

2011-01-01

Full Text Available Abstract Background Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. Results We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL" for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other

Cluster-Based Adaptation Using Density Forest for HMM Phone Recognition

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

2014-01-01

The dissimilarity between the training and test data in speech recognition systems is known to have a considerable effect on the recognition accuracy. To solve this problem, we use density forest to cluster the data and use maximum a posteriori (MAP) method to build a cluster-based adapted Gaussian...... mixture models (GMMs) in HMM speech recognition. Specifically, a set of bagged versions of the training data for each state in the HMM is generated, and each of these versions is used to generate one GMM and one tree in the density forest. Thereafter, an acoustic model forest is built by replacing...... the data of each leaf (cluster) in each tree with the corresponding GMM adapted by the leaf data using the MAP method. The results show that the proposed approach achieves 3:8% (absolute) lower phone error rate compared with the standard HMM/GMM and 0:8% (absolute) lower PER compared with bagged HMM/GMM....

Towards the Availability of the Distributed Cluster Rendering System: Automatic Modeling and Verification

DEFF Research Database (Denmark)

Wang, Kemin; Jiang, Zhengtao; Wang, Yongbin

2012-01-01

, whenever the number of node-n and related parameters vary, we can create the PRISM model file rapidly and then we can use PRISM model checker to verify ralated system properties. At the end of this study, we analyzed and verified the availability distributions of the Distributed Cluster Rendering System......In this study, we proposed a Continuous Time Markov Chain Model towards the availability of n-node clusters of Distributed Rendering System. It's an infinite one, we formalized it, based on the model, we implemented a software, which can automatically model with PRISM language. With the tool...

Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model

KAUST Repository

Liu, Bo

2016-02-03

An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior distribution are first integrated forward with the dynamical model for forecasting. A GM representation of the forecast distribution is then constructed from the forecast particles. Once an observation becomes available, the forecast GM is updated according to Bayes’ rule. This leads to (i) a Kalman filter-like update of the particles, and (ii) a Particle filter-like update of their weights, generalizing the ensemble Kalman filter update to non-Gaussian distributions. We focus on investigating the impact of the clustering strategy on the behavior of the filter. Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size). Numerical experiments are performed using a two-dimensional reactive contaminant transport model in which the contaminant concentration and the heterogenous hydraulic conductivity fields are estimated within a confined aquifer using solute concentration data. The experimental results suggest that the performance of the GM filter is sensitive to the choice of the GM model. In particular, increasing the size of the GM does not necessarily result in improved performances. In this respect, the best results are obtained with the proposed adaptive clustering scheme.

LA CONCILIACIÓN PREPROCESAL EN EL SISTEMA PENAL ACUSATORIO Y SUS PRINCIPALES APORTES

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Dayana Becerra

2009-01-01

Full Text Available La aproximación que se efectúa en el presente estudio, inicia con un acercamiento al concepto de conciliación como mecanismo alternativo de resolución de conflictos, estableciendo los rasgos generales de esta figura; para posteriormente analizar el antecedente inmediato de la conciliación preprocesal en la ley 600 de 2.000, que abrió camino para concederle en materia penal a este mecanismo mayor importancia. Subsiguientemente se profundiza en la conciliación preprocesal consagrada expresamente en la ley 906 de 2004, y se analizan los cambios generados, para así conocer sus características, y determinar sus principales aportes, en el marco del sistema penal acusatorio.

SANCTIONING DUPLICATION IN ADMINISTRATIVE AND PENAL AREAS

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José Manuel Cabrera Delgado

2014-12-01

Full Text Available This article provides a first approach from the point of view of jurisprudence, to the recurring problem of concurrency sanctions in cases where further intervention of the courts has become necessary for administrative action. In this regard, the main judgments of both the Constitutional Court and the Supreme Court is, that have shaped the decisions that must be applied from the administrative level, in particular by educational inspectors, when it is foreseeable that it can produce a duplication of disciplinary procedures in the two areas, penal and administrative.

Clustering disaggregated load profiles using a Dirichlet process mixture model

International Nuclear Information System (INIS)

Granell, Ramon; Axon, Colin J.; Wallom, David C.H.

2015-01-01

Highlights: • We show that the Dirichlet process mixture model is scaleable. • Our model does not require the number of clusters as an input. • Our model creates clusters only by the features of the demand profiles. • We have used both residential and commercial data sets. - Abstract: The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called ‘Chinese restaurant process’ method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships

Maritime environmental penal law. International and German legislation; Maritimes Umweltstrafrecht. Voelkerrechtliche Grundlagen und deutsches Recht

Energy Technology Data Exchange (ETDEWEB)

Eller, Jan Frederik

2017-07-01

The book on maritime environmental penal law discusses the following issues: part I: introduction into the importance of oceanic environment and its thread, requirement of protective measures,; part II: focus of the study and terminology: oceanic pollution, maritime environmental legislation, international legislation; part 3: international legislative regulations concerning the protection of maritime environment: avoidance of environmental pollution, maritime legislative agreements, existing protective institutions; part 4: state penal power concerning maritime environmental protection; part 5: statutory offense according to German legislation; perspectives for regulations concerning criminal acts on sea.

Vibronic coupling in molecular crystals: A Franck-Condon Herzberg-Teller model of H-aggregate fluorescence based on quantum chemical cluster calculations

Energy Technology Data Exchange (ETDEWEB)

Wykes, M., E-mail: mikewykes@gmail.com; Parambil, R.; Gierschner, J. [Madrid Institute for Advanced Studies, IMDEA Nanoscience, Calle Faraday 9, Campus Cantoblanco, 28049 Madrid (Spain); Beljonne, D. [Laboratory for Chemistry of Novel Materials, University of Mons, Place du Parc 20, 7000 Mons (Belgium)

2015-09-21

Here, we present a general approach to treating vibronic coupling in molecular crystals based on atomistic simulations of large clusters. Such clusters comprise model aggregates treated at the quantum chemical level embedded within a realistic environment treated at the molecular mechanics level. As we calculate ground and excited state equilibrium geometries and vibrational modes of model aggregates, our approach is able to capture effects arising from coupling to intermolecular degrees of freedom, absent from existing models relying on geometries and normal modes of single molecules. Using the geometries and vibrational modes of clusters, we are able to simulate the fluorescence spectra of aggregates for which the lowest excited state bears negligible oscillator strength (as is the case, e.g., ideal H-aggregates) by including both Franck-Condon (FC) and Herzberg-Teller (HT) vibronic transitions. The latter terms allow the adiabatic excited state of the cluster to couple with vibrations in a perturbative fashion via derivatives of the transition dipole moment along nuclear coordinates. While vibronic coupling simulations employing FC and HT terms are well established for single-molecules, to our knowledge this is the first time they are applied to molecular aggregates. Here, we apply this approach to the simulation of the low-temperature fluorescence spectrum of para-distyrylbenzene single-crystal H-aggregates and draw comparisons with coarse-grained Frenkel-Holstein approaches previously extensively applied to such systems.

Cluster-based DBMS Management Tool with High-Availability

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Jae-Woo Chang

2005-02-01

Full Text Available A management tool which is needed for monitoring and managing cluster-based DBMSs has been little studied. So, we design and implement a cluster-based DBMS management tool with high-availability that monitors the status of nodes in a cluster system as well as the status of DBMS instances in a node. The tool enables users to recognize a single virtual system image and provides them with the status of all the nodes and resources in the system by using a graphic user interface (GUI. By using a load balancer, our management tool can increase the performance of a cluster-based DBMS as well as can overcome the limitation of the existing parallel DBMSs.

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

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Yajie Zou

2017-01-01

Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

Environmental penal law - sword of Damocles above public officials?

International Nuclear Information System (INIS)

Fuehren, K.H.

1987-01-01

An office-holder is subject to punishableness according to art. 324 seq. Penal Code under the same conditions as a citizen. If the office-holder does not intervene in case of environmental delicts, the omission of a required measure is concerned. The constitutional supplement of art. 20 Fundamental Law will have for consequence a further aggravation of the punishableness of office-holders. (CW) [de

Sistema penal e violência de gênero: análise sociojurídica da Lei 11.340/06 Penal System and Gender-based violence: a sociojuridical analysis of the Law N. 11.340/06

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Rodrigo Ghiringhelli de Azevedo

2008-04-01

Full Text Available O presente artigo parte da reflexão acerca do papel da sociologia jurídica na compreensão do funcionamento da atividade legislativa, para analisar a racionalidade e os efeitos prováveis da entrada em vigor da Lei nº 11.340/06 (Lei Maria da Penha. Conclui-se que, ao invés de avançar e desenvolver mecanismos alternativos para a administração de conflitos, possivelmente mais eficazes para alcançar o objetivo de redução da violência, mais uma vez recorreu-se ao mito da tutela penal, neste caso ela própria uma manifestação da mesma cultura que se pretende combater.The present article makes a reflection upon the role of the Sociology of Law on understanding the operation of the legislative activity, in order to analyze rationality and the probable effects of Law N. 11.340/06 (Maria da Penha Law. One concluded that, instead of moving forward and developing alternative mechanisms for the administration of conflicts, possibly more effective to reduce violence, once more, one recurred to the myth of the penal protection, in this case, a manifestation of the same culture one intended to combat.

El garantismo y el punitivismo en el Código Orgánico Integral Penal

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Sebastián Cornejo Aguiar

2016-12-01

Full Text Available El objetivo del presente artículo es determinar la necesidad de la existencia de una parte punitivista y una garantista dentro del Código Orgánico Integral Penal, partiendo del estudio de la actualización doctrinaria en materia penal dentro del ordenamiento jurídico ecuatoriano, considerando que al ser el Ecuador un Estado constitucional de derechos y justicia, nos inspira a la construcción de mecanismos que tengan como fundamento y fin la tutela de las libertades del individuo, frente a las variadas formas del ejercicio arbitrario del poder punitivo del Estado, en donde las garantías y principios establecidos en el Código Orgánico Integral Penal, deben ser considerados como filtros de contención del poder punitivo que impiden que dicho poder se desborde y destruya todo a su paso. Seguidamente se analiza el significado del garantismo y punitivismo. Por último, se toca la relación existente entre garantismo y punitivismo, concluyendo que el garantismo es una corriente que proporciona ideas sustanciales para transformar el procedimiento judicial impidiendo así la arbitrariedad del poder punitivo.

Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

KAUST Repository

Cheng, Guang; Zhou, Lan; Huang, Jianhua Z.

2014-01-01

We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

An algebraic model for three-cluster giant molecules

International Nuclear Information System (INIS)

Hess, P.O.; Bijker, R.; Misicu, S.

2001-01-01

After an introduction to the algebraic U(7) model for three bodies, we present a relation of a geometrical description of three-cluster molecule to the algebraic U(7) model. Stiffness parameters of oscillations between each of two clusters are calculated and translated to the model parameter values of the algebraic model. The model is applied to the trinuclear system l32 Sn+ α + ll6 Pd which occurs in the ternary cold fission of 252 Cf. (Author)

Exactly soluble models for surface partition of large clusters

International Nuclear Information System (INIS)

Bugaev, K.A.; Bugaev, K.A.; Elliott, J.B.

2007-01-01

The surface partition of large clusters is studied analytically within a framework of the 'Hills and Dales Model'. Three formulations are solved exactly by using the Laplace-Fourier transformation method. In the limit of small amplitude deformations, the 'Hills and Dales Model' gives the upper and lower bounds for the surface entropy coefficient of large clusters. The found surface entropy coefficients are compared with those of large clusters within the 2- and 3-dimensional Ising models

Os indesejáveis no Direito Penal moderno

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Patrícia Graziela Gonçalves

2010-02-01

Full Text Available

Este trabalho propõe a fazer uma breve aborfagem da obra "O inimigo no Direito Penal", de Eugênio Raul Zafforni, um dos maiores penalistas da América Latina. Neste obra, o autor ressalta que o poder punitivo sempre classificou e reconheceu um hostis, um estranho ou indesejável, sobre o qual se aplicou um tratamento discriminatório, neutralizante e eliminatório, negando-lhe a sua condição de pessoa e considerando-o em função da sua condição de coisa ou ente perigoso. E mais, tanto as leis quanto a doutrina legitimam esse tratamento, baseadas em saberes pretensamente empíricos sobre a conduta humana. Tal doutrina-penal contradiz os princípios constitucionais do Estado de Direito e mais se aproxima do modelo de Estado absoluto.

The selected models of the mesostructure of composites percolation, clusters, and force fields

CERN Document Server

Herega, Alexander

2018-01-01

This book presents the role of mesostructure on the properties of composite materials. A complex percolation model is developed for the material structure containing percolation clusters of phases and interior boundaries. Modeling of technological cracks and the percolation in the Sierpinski carpet are described. The interaction of mesoscopic interior boundaries of the material, including the fractal nature of interior boundaries, the oscillatory nature of it interaction and also the stochastic model of the interior boundaries’ interaction, the genesis, structure, and properties are discussed. One of part of the book introduces the percolation model of the long-range effect which is based on the notion on the multifractal clusters with transforming elements, and the theorem on the field interaction of multifractals is described. In addition small clusters, their characteristic properties and the criterion of stability are presented.

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

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Dao-Wei Bi

2007-07-01

Full Text Available A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures

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Shaoyi Liang

2017-09-01

Full Text Available Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC. Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency.

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

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Caiping Zhang

2013-05-01

Full Text Available Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters and a nonparametric part [involving the open-circuit voltage (OCV]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

Science.gov (United States)

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing

Una apuesta analítica del funcionamiento del dispositivo psi pericial en el campo penal Uma aposta analitica do funcionamento do dispositivo psi pericial inserido no campo penal An analysis of the psy expert assemblage in the penal field

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Laura López Gallego