Search

Privacy preserving data mining for medical information is an important issue to guarantee confidentiality of integrated multiple data sets. In this paper, we propose a secured scheme to estimate related risk of cancers accurately and effectively in a privacy-preserving way. We study models to configure the appropriate set of attributes to reduce risk of identity of an individual from being determined. We examine the proposed privacy preserving protocol for encrypted hypothesis test, using actual cohort data supplied by National Cancer Center.

In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie and Holland give a theoretically important characterization of the marginal Rasch model. Because their representation of the marginal Rasch model does not involve any latent trait, nor any specific distribution of a latent trait, it opens up the possibility for constructing a Markov chain - Monte Carlo method for Bayesian inference for the marginal Rasch model that does not rely on data augmentation. Such an approach would be highly efficient as its computational cost does not depend on the number of respondents, which makes it suitable for large-scale educational measurement. In this paper, such an approach will be developed and its operating characteristics illustrated with simulated data.

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data. Electronic supplementary material The online version of this article (doi:10.1007/s12021-015-9260-y) contains supplementary material, which is available to authorized users.

The impact of the ICT sector in worldwide power consumption is an increasing concern, motivating the research community to devote an important effort to define novel energy efficient networking solutions. Despite file distribution is responsible for a major portion of the current Internet traffic, little effort has been dedicated to address the issue of its energy efficiency so far. Most of the previous literature focuses on optimizing the download time of file distribution schemes (e.g. centralized server-based or distributed peer-to-peer solutions) while it is yet unclear how to optimize file distribution schemes from the point of view of energy consumed. In this paper, we present a general modelling framework to analyze the energy consumption of file distribution systems. First, we show that the general problem of minimizing energy consumption in file distribution is NP-hard. Then, for restricted versions of the problem, we establish theoretical bounds to minimal energy consumption. Furthermore, we define a set of optimal algorithms for a variety of system settings, which exploit the service capabilities of hosts in a P2P fashion. We show that our schemes are capable of reducing at least 50 % of the energy consumed by traditional (yet largely used) centralized distribution schemes even when considering effects such as network congestion and heterogeneous access speed across nodes. Supported in part by Ministerio de Economia y Competitividad grant TEC2014- 55713-R, the DRONEXT project (TEC2014-58964-C2-1-R), Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894, co- funded by FSE & FEDER), and BRADE Project (P2013/ICE-2958), NSF of China grant 61520106005, and European Commission H2020 grants ReCred and NOTRE.

Correlated multivariate ordinal data can be analysed with structural equation models. Parameter estimation has been tackled in the literature using limited-information methods including three-stage least squares and pseudo-likelihood estimation methods such as pairwise maximum likelihood estimation. In this paper, two likelihood ratio test statistics and their asymptotic distributions are derived for testing overall goodness-of-fit and nested models, respectively, under the estimation framework of pairwise maximum likelihood estimation. Simulation results show a satisfactory performance of type I error and power for the proposed test statistics and also suggest that the performance of the proposed test statistics is similar to that of the test statistics derived under the three-stage diagonally weighted and unweighted least squares. Furthermore, the corresponding, under the pairwise framework, model selection criteria, AIC and BIC, show satisfactory results in selecting the right model in our simulation examples. The derivation of the likelihood ratio test statistics and model selection criteria under the pairwise framework together with pairwise estimation provide a flexible framework for fitting and testing structural equation models for ordinal as well as for other types of data. The test statistics derived and the model selection criteria are used on data on ‘trust in the police’ selected from the 2010 European Social Survey. The proposed test statistics and the model selection criteria have been implemented in the R package lavaan.

In this paper we explore the problem of achieving efficient packet transmission over unreliable links with worst case occurrence of errors. In such a setup, even an omniscient offline scheduling strategy cannot achieve stability of the packet queue, nor is it able to use up all the available bandwidth. Hence, an important first step is to identify an appropriate metric for measuring the efficiency of scheduling strategies in such a setting. To this end, we propose a relative throughput metric which corresponds to the long term competitive ratio of the algorithm with respect to the optimal. We then explore the impact of the error detection mechanism and feedback delay on our measure. We compare instantaneous error feedback with deferred error feedback, that requires a faulty packet to be fully received in order to detect the error. We propose algorithms for worst-case adversarial and stochastic packet arrival models, and formally analyze their performance. The relative throughput achieved by these algorithms is shown to be close to optimal by deriving lower bounds on the relative throughput of the algorithms and almost matching upper bounds for any algorithm in the considered settings. Our collection of results demonstrate the potential of using instantaneous feedback to improve the performance of communication systems in adverse environments. © Springer International Publishing 2013. 8179 LNCS 261 273 Conference code: 102178 Cited By :4

The final publication is available at Springer via http://dx.doi.org/10.1007/s00453-015-0041-7 The new dual-pivot Quicksort by Vladimir Yaroslavskiy-used in Oracle's Java runtime library since version 7-features intriguing asymmetries. They make a basic variant of this algorithm use less comparisons than classic single-pivot Quicksort. In this paper, we extend the analysis to the case where the two pivots are chosen as fixed order statistics of a random sample. Surprisingly, dual-pivot Quicksort then needs more comparisons than a corresponding version of classic Quicksort, so it is clear that counting comparisons is not sufficient to explain the running time advantages observed for Yaroslavskiy's algorithm in practice. Consequently, we take a more holistic approach and give also the precise leading term of the average number of swaps, the number of executed Java Bytecode instructions and the number of scanned elements, a new simple cost measure that approximates I/O costs in the memory hierarchy. We determine optimal order statistics for each of the cost measures. It turns out that the asymmetries in Yaroslavskiy's algorithm render pivots with a systematic skew more efficient than the symmetric choice. Moreover, we finally have a convincing explanation for the success of Yaroslavskiy's algorithm in practice: compared with corresponding versions of classic single-pivot Quicksort, dual-pivot Quicksort needs significantly less I/Os, both with and without pivot sampling.

A robust positive correlation between height and intelligence, as measured by IQ tests, has been established in the literature. This paper makes several contributions toward establishing the causes of this association. First, we extend the standard bivariate ACE model to account for assortative mating. The more general theoretical framework provides several key insights, including formulas to decompose a cross-trait genetic correlation into components attributable to assortative mating and pleiotropy and to decompose a cross-trait within-family correlation. Second, we use a large dataset of male twins drawn from Swedish conscription records and examine how well genetic and environmental factors explain the association between (i) height and intelligence and (ii) height and military aptitude, a professional psychogologist’s assessment of a conscript’s ability to deal with wartime stress. For both traits, we find suggestive evidence of a shared genetic architecture with height, but we demonstrate that point estimates are very sensitive to assumed degrees of assortative mating. Third, we report a significant within-family correlation between height and intelligence \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\hat{\rho}=0.10),$$\end{document} suggesting that pleiotropy might be at play.

Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew’s correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.

This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.

This paper introduces a deterministic model to plan the physician requirements for outpatient clinics to achieve service targets for the appointment lead-times of patients. The Ministry of Health of Singapore has established targets for the median, 95th percentile, and 100th percentile of appointment lead-times for patients, since long appointment postponements are regarded as being unacceptable for health care services. The study is to match the capacity of the healthcare providers to the patient demand for a re-entry system, subject to restrictions on the appointment lead-times for patients. We propose a mixed-integer programming model for planning capacity with the minimization of the maximum required capacity as its objective. In the model we assume a finite planning horizon, deterministic arrivals, multiple types of patients, identical physicians, and dependent demand between types of patients. We solve this model with a Branch and Cut algorithm. We test the model with numerical experiments using real data from the chosen specialty at the outpatient clinic of the studied hospital. The results show the value of the proposed model via a systematic push-pull mechanism in scheduling patients' requests to minimize the objective. The clinic should use one of the appointment lead-time targets to determine the patients' appointment dates. Finally, from the sensitivity analyses we demonstrate that the objective is negatively correlated with first-visit patients' appointment lead-time targets, the discharge rates, and the re-visit patients' mean appointment lead-time; we find a positive correlation between the first-visit patients' mean appointment lead-time and the appointment lead-time targets.

The evaluation of the carotid artery wall is essential for the diagnosis of cardiovascular pathologies or for the assessment of a patient’s cardiovascular risk. This paper presents a completely user-independent algorithm, which automatically extracts the far double line (lumen–intima and media–adventitia) in the carotid artery using an Edge Flow technique based on directional probability maps using the attributes of intensity and texture. Specifically, the algorithm traces the boundaries between the lumen and intima layer (line one) and between the media and adventitia layer (line two). The Carotid Automated Ultrasound Double Line Extraction System based on Edge-Flow (CAUDLES-EF) is characterized and validated by comparing the output of the algorithm with the manual tracing boundaries carried out by three experts. We also benchmark our new technique with the two other completely automatic techniques (CALEXia and CULEXsa) we previously published. Our multi-institutional database consisted of 300 longitudinal B-mode carotid images with normal and pathologic arteries. We compared our current new method with previous methods, and showed the mean and standard deviation for the three methods: CALEXia, CULEXsa, and CAUDLES-EF as 0.134 ± 0.088, 0.074 ± 0.092, and 0.043 ± 0.097 mm, respectively. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed a uniform behavior over the entire database. Regarding the Figure of Merit (FoM), CALEXia and CULEXsa showed the values of 84.7% and 91.5%, respectively, while our new approach, CAUDLES-EF, performed the best at 94.8%, showing a good improvement compared to previous methods.

This paper belongs to a series of applications of the Baccetti et al. formula (1) to the submicroscopical mathematical examination of human spermatozoa used for assisted reproduction. The present experiment concerns IVF, a technique requiring careful evaluation of sperm quality to predict the success of the program. Our results demonstrate that the sperm submicroscopic characters introduced in the formula are clearly correlated with the result of IVF. In fact the two numbers concerning sperm quality (percentage of spermatozoa free from structural defects and total number in the ejaculate of spermatozoa free from defects) obtained in successful and unsuccessful IVF groups, showed a large difference. The t distribution in both cases reached a significance of 0.005. The synthetic parameters obtained are therefore a good tool in the prediction of sperm power in in vitro insemination techniques. The most important characteristics seem to be the quality of the acrosomal complex, the status of the chromatin, the shape of mitochondria, the axonemal pattern, and the membrane integrity. All these characteristics are expressed with largely different means in successful and unsuccessful ejaculates (t distribution significant at 0.005). All these data confirm that submicroscopic mathematical diagnosis offers a convincing evaluation of sperm structure and function, involving all organelles, including acrosome function and cell motility. It is also demonstrated that sperm quality is a major factor in the success of IVF and that it is clearly revealed by the integrity of the majority of the sperm organelles.

The interference graph for a procedure in Static Single Assignment (SSA) Form is chordal. Since the k-colorability problem can be solved in polynomial-time for chordal graphs, this result has generated interest in SSA-based heuristics for spilling and coalescing. Since copies can be folded during SSA construction, instances of the coalescing problem under SSA have fewer affinities than traditional methods. This paper presents Optimistic Chordal Coloring (OCC), a coalescing heuristic for chordal graphs. OCC was evaluated on interference graphs from embedded/multimedia benchmarks: in all cases, OCC found the optimal solution, and ran, on average, 2.30x faster than Iterated Register Coalescing.