and 2(b), it is evident that the gamma and σ-stable CRM based models are characterized by late and early intra–cluster dependence, respectively. More specifically, contour lines indicating positive correlation appear earlier **for** the σ-stable CRMs, corresponding **to** higher values of the survival functions. **For** example, compare the contour lines **for** the value Σ = 1.05 in Figure 2. On the other hand, the rate of increase of the survival ratio is slower in the σ-stable case. Thus, high values of the survival ratio appear earlier when gamma CRMs are considered. As a matter of fact, in the σ-stable case, two units of the same cluster tend **to** be relatively weakly correlated in the long term. Those patterns of failures are often observed in familial associations of onset ages **for** diseases with low penetrance (Fine et al., 2003). Therefore, the parameter σ can be thought of as a dependence parameter. As suggested by our numerical study, if σ → 1, then τ → 0 and Σ → 1, capturing both local and global independence between survival times. On the other hand, a value σ < 1 reflects positive correlation between observations within and between clusters. The interpretation of σ as a parameter capturing the dependence in a cluster is supported also by the marginalization properties of the proposed σ-stable **model**, since in the marginal **model** the parameter σ affects multiplicatively the regression coefficients β. Hence, the stronger is the association between survival times in the same cluster (the smaller the value of σ), the weaker should be the effect of the individual covariates of the subjects in the cluster. Once again, the previous discussion shows how our modelling framework preserves and extends well-known results **for** the shared frailty **PH** models with gamma and positive stable distributions (Duchateau and Janssen, 2008).

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31 Lee mas

In statistical **data** analysis it is common **to** consider the regression set up in which a given response variable depends on some factors and/or covariates. The **model** selection problem mainly consists in choosing the covariates which better explain the dependent variable in a precise and hopefully fast manner. This process usually has several steps: the ﬁrst one is **to** collect considerations from an expert about the set of covariates, then the statistician derives a prior on **model** parameters and constructs a tool **to** solve the **model** selection problem. We consider the **model** selection problem in survival analy- sis when the response variable is the **time** **to** **event**. Diﬀerent terminal events can be considered, depending on the purposes of the analysis: deaths, failures in mechanical systems, divorces, discharges from hospital and so on. Survival studies include clinical trials, cohort studies (prospective and retrospective), etc.

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We propose a **Bayesian** nonparametric hierarchical **model** that includes a cluster analysis, aimed at identifying profiles or hospital behaviors that may affect the out- come at patient level. In particular, we introduce a multivariate multiple regression **model**, where the response has three mixed-type components. The components are, respectively: (1) the door **to** balloon **time** (DB), i.e. the **time** between the admission **to** the hospital and the PTCA; (2) the in-hospital survival; and (3) the survival after 60 days from admission. The first response (continuous) is essential in quantifying the efficiency of health providers, since it plays a key role in the success of the ther- apy; the second is the basic treatment success indicator, while the third concerns a 60-days period, during which the treatment effectiveness, in terms of survival and quality of life, can be truly evaluated. Note that the last two responses are binary, so that, as a whole, the multivariate response is of mixed type. It is worth noting that the information on patients’ survival after 60 days is obtained from the linkage between STEMI archive and a further administrative database concerning patient-specific vital statistics such as date of birth and death **for** general causes. The linkage between the different **data** sources was carried out by Lombardia Informatica S.p.A, the agency managing regional datawarehouses. We do not have direct access **to** the **data** sources so as **to** construct different outcomes of potential interest. Moreover we work with a singly imputed **data** set and we could not identify **data** preprocessing tools used by the Lombardia Informatica S.p.A. agency, in particular the technique used **to** impute the missing **data**. The modeling of multiple outcomes from **data** collected in STEMI Archive was previously discussed in Ieva et al. (2014), under a **semiparametric** fre- quentist bivariate probit **model**. Their aim was **to** analyze the relationship among in-hospital mortality and a treatment effectiveness outcome in the presence of con- founders, that is, variables that are associated with both covariates and response. This is a problem that poses serious limitations **to** covariate adjustment since the use of classical techniques may yield biased and inconsistent estimates. In this context, Ieva et al. (2014) proposed the use of a **semiparametric** recursive bivariate probit **model**, as an effective way **to** estimate the effect that a binary regressor has on a binary outcome in the presence of nonlinear confounder response relationships. In contrast, we focus on a joint **model** **for** the grouped outcomes. As discussed below, our aim is **to** find relevant groups of hospitals in terms of patient-specific characteristics, which may assist in further planning and policy making.

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25 Lee mas

In order **to** verify whether that is the case, the comparison will be based both on word length distribution, as well as on the frequency with which the twenty most frequent function words are used in these sentences. Before counting the number of l-lettered words and the number of times function words appear in the sentences, we have excluded from the text all citations, acronyms, capital lettered words, numbers, dates and names of persons and of cities. On top of that, we have only considered the factual, the legal basis and the final verdict, excluding from the analysis the formal paragraphs that are always repeated at the end of all sentences. These twenty most frequent function words are: de, la, que, el, en, y, a, los, se, por, del, las, no, una, con, es, o, para, su y al. Note that, different from what happens in the authorship attribution problem case, with S > 1, in the authorship verification case, with S = 1, one can not choose the list of words or features based on their discriminating power, because one only has a single candidate author. This is, in fact, the only feature that distinguishes verification studies from attribution studies, other than the number of candidate authors involved.

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195 Lee mas

by each rotor, thus there is a maximum velocity v max and Ω max . From v max , F max can be determined. The constraint on the rate of the thrust i.e. W max implies that ˙ F max exists and is a function of the vehicle attitude (R) linked by Equation 17. Hence through Equation 19, the instantaneous constraints on the control input u can be obtained. Therefore, the state and input constraints are dependent on R, T and v of the vehicle. **To** the best knowledge of the authors, there is no published work on the solution **to** the state and input constraints MPC problem that are state dependent. However, a solution **to** the linearisation of nonlinear MPC with state dependent input constraints has been proposed in (Simon et al. (2013); Deng et al. (2009)). This paper uses unconstrained inputs and states in order **to** have real- **time** solution. In addition, these input constraints are dependent on the maximum voltage of the battery through Equation 10 that is two levels down in the hierarchy. This enables the complete removal of the constraints on the MPC control input u and state x thereby transforming the problem into an unconstrained MPC problem.

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8 Lee mas

This paper illustrates the methodology **to** develop a real **time** object oriented tool (RTOOT) destined **to** design and implement SFC based applications by means of an object oriented virtual engineering environment, the HP-VEE. Arrangement of proposed RTOOT consists in developing a library which comprises a set of user objects capable **for** implementing the mathematical **model** of sequential function charts on the basis of Petri Nets, being IEC 1131-3 standard language compliance [4]

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relevance of the spatial component within the heritage elements **to** the forefront. When dea- ling with protected natural sites, the value is typically embedded in the place itself that is protected. Characteristics that make a place naturally valuable are inherently attached **to** their geographical location, and cannot be set apart from each other. A hill, a lagoon or a marshy area, are all natural features that can- not be protected separately from the place where they are located. Rather than being loca- ted somewhere, natural places are best descri- bed as locations in themselves. This could also be the case with cultural entities. Indeed, the relevance of location and place in the characte- rization of cultural features has long been claimed in such disciplines as Cultural Geo- graphy (**for** instance Claval 1995), Anthropo- logy (see Tuan 1974; or Ingold 2000) or Landscape Archaeology (**for** instance David and Thomas 2008) **to** name but a few. However, in Heritage Management, locations have been traditionally disregarded as merely contextual, and even circumstantial, attributes of objects and features. When describing cultural featu- res, such as buildings or sites, heritage experts tend **to** focus on the formal characteristics, with the spatial dimension constituting just another attribute, rather than a property. Cu- rrently, in terms of preservation, heritage ele- ments are sometimes preserved by removing them from their places of origin. Dealing with the two proposed categories of spatial objects (legal and cultural), allows us **to** make the dif- ferent nature of both aspects explicit and **to** make clear the need **for** a different process of reasoning in their creation.

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82 Lee mas

Correspondence should be addressed **to** Momiao Xiong; momiao.xiong@uth.tmc.edu Received 4 December 2014; Accepted 16 February 2015
Academic Editor: Ernesto Picardi
Copyright © 2015 L. Li and M. Xiong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. By measuring messenger RNA levels **for** all genes in a sample, RNA-seq provides an attractive option **to** characterize the global changes in transcription. RNA-seq is becoming the widely used platform **for** gene expression profiling. However, real transcription signals in the RNA-seq **data** are confounded with measurement and sequencing errors and other random biological/technical variation. **To** extract biologically useful transcription process from the RNA-seq **data**, we propose **to** use the second ODE **for** modeling the RNA-seq **data**. We use differential principal analysis **to** develop statistical methods **for** estimation of location-varying coefficients of the ODE. We validate the accuracy of the ODE **model** **to** fit the RNA-seq **data** by prediction analysis and 5-fold cross validation. **To** further evaluate the performance of the ODE **model** **for** RNA-seq **data** analysis, we used the location-varying coefficients of the second ODE as features **to** classify the normal and tumor cells. We demonstrate that even using the ODE **model** **for** single gene we can achieve high classification accuracy. We also conduct response analysis **to** investigate how the transcription process responds **to** the perturbation of the external signals and identify dozens of genes that are related **to** cancer.

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We discuss the problem of selecting among alternative parametric models within the **Bayesian** framework. **For** **model** selection problems which involve non-nested models, the common ob- jective choice of a prior on the **model** space is the uniform distribution. The same applies **to** situations where the models are nested. It is our contention that assigning equal prior probabil- ity **to** each **model** is over simplistic. Consequently, we introduce a novel approach **to** objectively determine **model** prior probabilities conditionally on the choice of priors **for** the parameters of the models. The idea is based on the notion of the worth of having each **model** within the selection process. At the heart of the procedure is the measure of this worth using the Kullback–Leibler divergence between densities from different models.

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28 Lee mas

cell proliferation. On the other hand, the mechanism of epigenetic alterations is more complicated. **For** example, DNA methylation patterns are globally disrupted in tumor cells. The cancer methylome is characterized by both global hypomethy- lation and region-specific hypermethylation at CpG islands. Hypomethylation may contribute **to** carcinogenesis via transcriptional activation of tumor-promoting genes (Wu et al., 2005), while hypermethylation at CpG islands is associated with si- lencing genes involved in growth regulation, cell cycle control, apoptosis and tumor suppression. It is even noted that hypermethylation is more likely prominent in transcriptional silencing and down-regulating pathways involved in drug resistance (chemoresistance) (Li et al., 2009). Therefore, genetic mutations and chromosomal aberrations are the central characteristics of tumor cells (Pe’er and Hacohen, 2011). In recent years, the emergence of large-scale copy number assays and methyla- tion platforms enables the possibility of tracing phenotypic differences back **to** their genetic/epigenetic source. However, only a few genetic mutations or epigenetic al- terations provide a persistent fitness advantage across multiple tumors. Such a rare **event** could leave a ’genomic footprint’ in the form of a gene expression signature (Akavia et al., 2010). Therefore, it becomes increasingly important **to** distinguish genetic/epigenetic changes that alter mRNA transcription, and thus promote can- cer progression (driver mutation) from those with no selective advantage (passenger mutation) (Pe’er and Hacohen, 2011; Akavia et al., 2010).

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137 Lee mas

scale services. It is, at the **time** of this writing, composed of 1069 nodes hosted in 494 different sites. Each Planetlab node is an Intel IA32 machine that must comply with minimum hardware requirements (i.e., 1 GHz PIII + 1 Gb RAM) running the same base software, basically a modified Linux operating system offering services **to** create virtual isolated partitions in the node, called slivers, which look **to** users as the real machine. Planetlab allows every user **to** dynamically create up **to** one sliver in every node, the set of slivers assigned **to** a user form what is called a slice. It is said that a Planetlab node can run up **to** 100 concurrent slivers. **To** test our Grid prototype, we turned Planetlab into a Grid by installing the GT3’s Grid service container in every sliver of our slice. Moreover, we implemented the worker as a simple Grid service playing the role of the parser and outputter components and deployed it on the GT3’s container of every sliver of our slice. On the other hand, we wrote a simple Java client playing the role of the master and mapping **to** the sensor and extractor components, which dispatches, using a simple list scheduling strategy, the tasks **to** the workers by calling the operations exposed by the worker Grid services.

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17 Lee mas

Accessed February 18, 2015 1:11:09 PM EST
Citable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:3992146
Terms of Use This article was downloaded from Harvard University's DASH repository, and is made available under the terms and conditions applicable **to** Other Posted Material, as set forth at

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Since nodes in model 1 do not know which of their respective neighbors are closer to the sink, the decisions at node i in this model will be based on the energy estimation at any of the [r]

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On the other hand, this thesis covers the characterization of dynamically differen- tiable brain states in Zebrafish in the context of epilepsy and epileptogenesis. Zebrafish larvae represent a valuable animal **model** **for** the study of epilepsy due **to** both their genetic and dynamical resemblance with humans [9, 10]. The fundamental premise of this research is the early apparition of subtle functional changes preceding the clinical symptoms of seizures. More generally, this idea, based on bifurcation theory, can be described by a progressive loss of resilience of the brain and ultimately, its transition from a healthy state **to** another characterizing the disease [11]. First, the morphological signatures of seizures generated by distinct pathological mechanisms are investigated. **For** this purpose, a range of mathematical biomarkers that characterizes relevant dynamical aspects of the neurophysiological signals are considered. Such mathematical markers are later used **to** address the subtle manifestations of early epileptogenic activity. Fi- nally, the feasibility of a probabilistic prediction **model** that indicates the susceptibility of seizure emergence over **time** is investigated. The existence of alternative stable sys- tem states and their sudden and dramatic changes have notably been observed in a wide range of complex systems such as in ecosystems, climate or financial markets [12, 13, 14]. Overall, the frameworks of systems identification theory, systems control theory, (non)linear **time** series analysis, dynamical bifurcation theory and machine learning constitute the foundations upon which both the reconstruction of gene regulatory networks and the investigation of brain vulnerability **to** epileptic seizure are addressed. Hereafter, the background underlying these two problematics is introduced.

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214 Lee mas

We obtain the exact posterior distribution **for** the param- eters of the INAR(p) **model** (p = 0, 1, 2, 3). (The INAR(0) corresponds **to** independent and identically distributed Pois- son **data**.) We also computed the marginal log-likelihood (evidence) **for** the models **for** comparison. The results are presented in Table 2 . There is clear evidence from the marginal log-likelihood **for** p = 2. Applying the BIC-based penalisation prior used in Enciso-Mora et al. ( 2009a ), where **for** p = 0, 1, 2, 3 the prior on INAR(p) was set proportional **to** n −p/2 gives posterior probabilities of 0.0030, 0.7494 and 0.2476, **for** the INAR(1), INAR(2) and INAR(3) models, re- spectively. The total number of categories grows rapidly with the order p of the **model**, and **to** compute {π(y|x)} us- ing Fortran95 **for** the INAR(1), INAR(2) and INAR(3) mod- els, took less than a second, 8 seconds and 45 minutes, re- spectively. It should be noted that the INAR(3) **model** was at the limits of what is computationally feasible requiring over 1500 MB of computer memory. The memory limitation is due **to** the total number of categories, and **for** smaller **data** sets, either in terms of n, or the magnitude of x t ’s, it would

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15 Lee mas

Basically, this table says if with j seats available in day i before departure date it is optimal **to** do promotion (represented with a 1) or not **to** do promotion (represented with a zero). **For** example, if we are on 8 days before the departure day and we have 24 seats available we must do promotion until there are 12 seats available. It is important that if the 12 seats are not sold that day, the policy could change next day. Continuing with the example, if only 2 seats are sold in day -8, then in day -7 it is not optimal **to** do promotion until there are 20 seats available. So the promotional policy varies depending on the number of days before departure and the number of seats available. Specifically in this result we can see that the promotion is done in the days closer **to** the departure date and when having more than ten seats available. This has sense since we want **to** maximize the number of seats sold. If in the days closer **to** departure we have a lot of chairs, something is need **to** be done. In the other hand, promotion starts when having more than 10 seats available and this can be because of the arrival rate that its mean is closer **to** ten. So we have in average that 10 people are going **to** buy at a regular fare.

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14 Lee mas

A global high prevalence of short sleeping **time**, a slight increase of sleep **time** in adolescents with UBN, and different patterns of wake activities that predict sleep deficit, depending on the presence of UBN, were found. The poor academic achievement, increased risk of accidents and adverse health outcomes associated with sleep deprivation support the view that sleep is an additional unsatisfied basic need that worsens living conditions at this age. The results may help **to** design public health policies that contribute **to** ameliorate this adverse situation. In our study the presence of UBN increased rise **time** and sleep duration. This contradicts previous literature, where a lower socioeconomic background is usually associated with less sleep duration and more sleep disruption in adolescents. Socioeconomic demographics like income, educational level, and employment status are usually associated with more delayed, shorter duration, and less consistent sleep patterns [20]. However, none of these studies focused in situations of extreme poverty. Among the factors associated with the presence of UBN that may justify these findings, the assistance **to** nearby schools probably explain the increased rise **time** and indirectly the increased sleep **time**. The association between UBN and attendance **to** neighborhood schools is as expected, since better schools tends **to** be available **for** families with higher socio–economic status through residential mobility and enrolment in private schools. Another factor that could explain the increased rise **time** is the observed lower percentage of children that assist **to** extra–curricular intellectual or physical activities. School starting **time** and full–day schooling were strong predictors of sleep deficit in adolescents with and without UBN. Starting school at the morning school is a well–recognized risk factor **for** sleep deprivation, determining less **time** spent in bed, worse sleep quality and increased daytime sleepiness which in turn leads **to** bad mood and poor performance. Unlike other school systems, in Argentina some schools have half–day schedules while others full–day schedules. It is expected that the extended day pose a higher risk of sleep deficit, because it combines an early school starting with being at school most of the day, thus preventing the possibility of taking naps or delaying bed **time**.

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7 Lee mas

seven-) dimensional [1] , and modern likelihoods introduce a host of nuisance parameters **to** combat the influence of foregrounds and systematics. **For** example the Planck likelihood [36] is in total 21-dimensional, the DES like- lihood [2] is 26-dimensional, and their combination 41-dimensional (Table I ). While samples from the posterior distribution represent a near lossless compression of the information present in this distribution, it goes without saying that visualizing a 40-dimensional object is challenging. Triangle and corner plots [37] represent marginalized views of this information and can hide hidden correlations and constraints between three or more parameters. The fear is that one could misdiagnose a dataset that has powerful constraints if Fig. 1 occurred in higher dimensions. It would be helpful if there were a number d similar **to** the Kullback-Leibler divergence D which quan- tifies the effective number of constrained parameters.

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14 Lee mas

In order **to** have better market analysis and customer relationship management, the utilization of Information Technology (IT) is a significant tool used **to** help the company. It helps business organizations **to** enhance competence and sustain continuous growth of company business (Chung et al., 2009). According **to** Kwan et al. (2005), technology can support knowledge management in business including **data** warehousing, **data** mining, the Internet and document management systems. In recent decades, **data** mining has been applied **to** a broad range of topics and areas (Hosseini et al., 2009; Ting et al., 2009; Ngai et al., 2008; Kirkos et al., 2006). Most business organizations use it **to** find the problems area and allow managers **to** make strategic decisions that will allow the business organizations **to** succeed. It is true that IT plays a significant role that helps business organizations **to** improve their performance. In order **to** increase understanding of the reasoning buying patterns of customers, many companies use automated tools **to** study the behavior of their customers. Once relevant information has been obtained, it can be used in a way that will allow the organization **to** predict the behavior of their clients. With the advent of the rapid development of information technology, the biggest challenge is not only getting important information that accumulates daily in databases, but also searching through such a huge database **to** find relevant connections. However, patterns among the **data** are not easy **to** extract. The reason **for** this is that the information must be specific and refined. **To** successfully apply **data** mining on the information obtained, a company must be able **to** understand the connections between the business strategies and the models that are created within the **data** mining programs. However, many managers do not notice the importance of **data** and the information **for** **data** analysis. Also, most managers do not understand the relationship between the **data** due **to** the lack of technical background. **For** example, a financial marketing manager does not understand the relationship between the hidden patterns and the customer’s portfolio. Therefore, it is important **to** have a sophisticated tool **to** help companies find out the relationship between different **data** (Zhang and Zhou, 2004).

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14 Lee mas