dataesr/scientific-paragraphs-categorization huggingface dataset:
Main Server and Remote Clients The main server component is used to initiate the module (Figure  4 ) automatically, and once it is the server, it can only be executed on the main machine. On the other hand, the remote clients are executed in all other machines that host agents. Firstly, a server is created in a specific IP and port, and then, it subscribes itself to a database, which store all information about this module. In this way, future clients will have the possibility of accessing the database and extracting the server IP and port in order to connect to it. All machines need to have an Agent Management System (AMS) agent that is responsible for controlling the other agents that are in the same machine. Analyzing Figure  4  minutely, the first agent/client that is created is the AMS.
Besides that, after its creation, the AMS connects to the server after a proper request and verification. To make the AMS operable, it subscribes itself to the database, recording its name, its state (active or inactive) and the machine IP wherein it operates. In each machine, the module developed creates an XML file in order to save information about the hosted agents. The purpose of this file is to store information locally about the agents that are active. In this way, when it is necessary to realize the boot process (for example, when the machine or the module is restarted), the administrator does not need to start the agents manually, like the first time, when the module was initiated. Instead, the agents are automatically started after a proper verification of the XML file content. Continuing the analysis of the Figure  4 , the only difference between the AMS creation and the other agents/clients creation is that the agents/clients are not able to subscribe themselves to a database; they need to send a request to their own AMS.
Results The module for controlling the AIDA agent community was implemented in a real environment, in the Centro Hospitalar do Porto (CHP), in order to evaluate its performance. A set of three agents was created in this new module, and their activities were monitored during the period between 10 and 16 September 2013. Due one of the agents have more of a workload, the results of Agent 609 were selected to be shown in this paper. This agent, responsible for ensuring the interoperability with the information system in which are registered the data resulting from nursing practices, was scheduled to execute its activity every 10 min. Figure  5  represents the properties page from Agent 609. This page enables the user to know the agent code and its name; the last one is composed of the code and the machine name where the agent performs its activities.
With this new module integrated with the AIDA and implemented in the CHP, the AIDA platform administrators may better manage the whole platform through the monitoring of the agents that constitute it. Therefore, this module provides all the needed information to know what period and what days the agents have more functions, i. e. , that their activity is more time consuming. Thus, it is possible to schedule routine changes, updates and other operations, improving the overall performance of the platform without disrupting the proper functioning of the agents and, consequently, of the health institution. Through these graphs and all the functionalities provided by the web controller, it is possible to guarantee interoperability in the AIDA platform. For instance, the monitoring and controlling of Agent 609 ensures the correct communication between the AIDA platform and the system responsible for nursing practices, ensuring a proper interoperability procedure.
X Comparing the solution presented in this paper with others presented in Section 3 (Table  1 ), it is possible to state that our solution is endowed with great scalability and adaptability, once new agents can easily be added to the system, and it can be applied to any field (unlike most of the solutions presented). In spite of our solution not supporting mobile agents, it provides proper management and customization of the agents through an intuitive and dynamic interface. This interface owns scheduling functionalities and monitoring dashboards, which aid the system administrators to manage and to ensure interoperability in the AIDA platform. This new module was integrated with AIDA in order to ensure an efficient communication among the agents, being considered a good practice for interoperation and communication in an MAS.
The scientific community that uses intelligent agents to solve a wide range of problems in the informatics area should look into this solution as a base for the construction of an MAS, once the MAS included in this new module of AIDA was revealed to be very important for the AIDA platform implemented in the CHP. The importance of this module in the CHP was evidenced, allowing the full control of AIDA agents. This full control enables the AIDA administrators to perform better management of the platform, not overloading the workflow of AIDA and of the HIS and, therefore, ensuring the quality of the services delivered.
Conclusions This project is part of an important theme these days: interoperability. Over the past few years, it has emerged as a requirement in most health services entities. The AIDA platform has been revealed as a huge success regarding the interoperability among HIS through its main functions, the integration, the archiving and the diffusion of medical information, which are ensured by its multi-agent system  [17]. Using the agent-oriented programming, the ACL messages and an architecture client/server multi-thread with TCP/IP sockets, it was possible to develop the module for controlling the agent community of AIDA components. The results presented allow the users to analyze in a detailed way the activity of each agent. Through the web controller, dynamic graphs with the average duration and the number of executions of the agents are available.
It is possible to conclude that the module for controlling the agent community of AIDA implemented in CHP offers to AIDA a greater functionality and usability, ensuring interoperability in a reliable way with great scalability and growth capacity. This module, although a specific module of a platform, can be adapted and extended to other platforms in healthcare that use agents. The same can be used as a top layer of the management and evaluation of agents. This module represents an important contribution to the next generation of interoperability in healthcare, ensuring continuous data collection from heterogeneous data sources in order to support the decision-making process in real time. This work is essential for the proper functioning of new platforms in healthcare. As future work, new graphs will be built in order to give to the user more information about the agent activities.
We choose to use Bayesian methods to conduct these inferences, which means that we will need to specify prior probability distributions for the variables of our models. To simplify our analyses, we will sample five species: a dog, a bat, a rat, a human, and a koala. The Supplementary Material (http://dx. doi. org/ 10. 5061/dryad. nt898) presents similar analyses with a much better taxonomic sampling of 274 species. Our first attempt at modeling the distribution of the baculum assumes that all species are independent of An explicit graphical model of the distribution of a binary trait. Descriptions of the objects have been added for pedagogical purpose. The presence or absence of the binary trait is assumed to follow a Bernoulli distribution with parameter p. This parameter is equal to the probability of the presence of the baculum in an independently sampled species.
In our case, the bottom nodes have been clamped because they correspond to anatomical observations in the species of interest. We inferred the value of p in this model using a Markov chain Monte Carlo (MCMC) algorithm, and found that its value was 0. 57 with 95% highest posterior density (HPD) interval of [0. 23,0. 88] (p = 0. 48, HPD = [0. 42,0. 54] on the larger dataset, see Supplementary Material).
As for the first model, we use a Beta prior distribution with parameters and both set to 1 (p ∼ Beta(,)). We also need another parameter for the equilibrium frequency of the state 1, parameterized the same way as p. We use the dated phylogeny of dos  Reis et al. (2012) , pruned to contain only the five species of interest or the 274 species in our dataset (Supplementary Material; http://dx. doi. org/10. 5061/dryad. nt898). We assume this phylogeny is known without error (Fig. 3a ). Comparing Figure  3a  and Figure  3b  demonstrates how the structure of the phylogenetic tree (partially) forms the structure of the graphical model. The structure of the phylogenetic tree can be recovered as a central subset of the graphical model, because each node of the phylogenetic tree is a stochastic variable in our model, taking values 0/1, and depending only on its parent node, the branch length and on the parameter of the continuous time Markov process.
In the Supplementary Material (http://dx. doi. org/10. 5061/dryad. nt898), we provide scripts for performing Bayesian inference with this model and the complete dataset; it turns out there is a 50/50 chance that the ancestor of mammals had a baculum. We believe such a graphical model representation is a very powerful pedagogical construct, as it displays the entire structure of our probabilistic model. It makes it easy for a student or a reviewer to identify key assumptions made by this model. For example, although the evolutionary process is the same along all branches of the phylogenetic tree, the model is not stationary, because the root has an extra parameter for the probability of presence/absence of the baculum VOL. 63 1 1 1 0 0 0/1 0/1 0/1 0/1 dog bat rat human koala l1 l2 l6 l7 l8 l3 l4 l5 a) S 1 S 2 S 3 S 4 S 5 S 6 S 7 S 8 S 9 p α β θ x y l 1 l 2 l 3 l 4 l 5 l 6 l 7 l 8 b) FIGURE 3.
As a result, similar models will have similar structural representations. We provide in the Supplementary Material (http://dx. doi. org/10. 5061/dryad. nt898) the example of a Brownian motion model of the evolution of continuous characters to convey this point  (Felsenstein 1985) , and show here in more detail the example of a model of sequence evolution. Note that graphical model representations are not unique. In particular, the level S 1 i S 2 i S 3 i S 4 i S 5 i S 6 i S 7 i S 8 i S 9i p α β θ x y l 1 l 2 l 3 l 4 l 5 l 6 l 7 l 8 i ∈ {1, 2,. , N} FIGURE 4. A phylogenetic graphical model of N independently evolving binary characters. When sampling N different binary characters for each extant species, we assume that these characters are independent and identically distributed. Thus the model for each character is the same as in Figure  3b.
A General-Time-Reversible Model for Sequence Evolution One of the most popular models of sequence evolution is the general time reversible (GTR) substitution model  (Tavaré 1986 ). Here we give a simple example of a GTR model for a fixed, nonclock tree with fixed branch lengths. In this case, branch lengths are not defined in units of time as in the previous examples, but instead in expected numbers of substitutions and for simplicity we consider that we have some trustworthy exterior information about them. The resulting graphical model is depicted in Figure  5a , and is very similar to the previous figures for the binary and continuous characters (Fig. 4  and Supplementary Fig. S. 1; http://dx. doi. org/10. 5061/dryad. nt898). The tree sits on a plate because it is replicated for N sites.
A comparison between Figure  5  and Figure  6  shows how a tree plate can simplify the representation of a phylogenetic model, and how it interacts nicely with a plate. The example is extended in the Supplementary Material (http://dx. doi. org/10. 5061/dryad. nt898) by the commonly used mixture model for rate variation across sites, the GTR+ model  (Yang 1994 (Yang , 1996) )  (see Supplementary Fig. S. 2; http://dx. doi. org/10. 5061/ dryad. nt898). Importantly, the recursive representation of a tree plate protects it against cluttering as the size of the tree grows: no matter how many tips are included in the tree, these three classes of nodes are enough to describe most phylogenetic models. More classes are only needed when the model further distinguishes between nodes, for example when different models of sequence evolution are associated with different subtrees or when particular nodes are associated with time calibration information.
Modularization Although tree plates simplify the representation of a phylogenetic model, visualization remains a challenge for the most complex models. As an example consider the common case of a multilocus analysis using a multispecies coalescent model, an uncorrelated relaxed clock and a GTR+ substitution model, which would contain, among others, Supplementary Figure  S2  and Figure  S3  (http://dx. doi. org/10. 5061/dryad. nt898) merged together. Clearly, such a figure would be overwhelming. Ideally, one would like a method that allows one to quickly convey the bigger picture while allowing parts of special interest to be exposed in all the necessary detail. For instance, it is common practice to create new phylogenetic models by combining existing model components, possibly in new patterns, with new components.
S. 2; http://dx. doi. org/10. 5061/dryad. nt898). Alternatively, each site rate may be drawn directly from the gamma distribution, as in our example (Fig. 8b ). Other distributions than the gamma can be used for the rate variation across sites, sometimes leading to better results  (Mayrose et al. 2005). In addition to models based on simple continuous distributions, any mixture model of rates  (Pagel and Meade 2004, 2005)  would be eligible for the site rates module. For instance, a standard model considered in the literature is a mixture of invariable sites (rate zero) and gamma-distributed rates. High-level modular graphs. -We end this section by a simple example illustrating the power of high-level modular graphs in summarizing the essential structure of a large and complex model. For this example, let us consider a model where we want to simultaneously estimate a set of gene trees and the species tree into which they fold.
Simulation Simulating data from a model is essential in many applications, for instance in exploring model properties  (Huelsenbeck 1995)  or in model adequacy testing  (Bollback 2002; Brown and ElDabaje 2009; Höhna 2013). Simulations are also used to validate inference methods and to initialize MCMC runs. Since probabilistic graphical models are generative, that is, they specify how to generate data from the model, simulation is straightforward. We simply traverse the model graph from the source nodes toward the sink nodes, drawing values of each random variable conditioned on the already generated values of its parent nodes. As an example, consider the evolution of a binary character under the model presented in Figure  3. The key to the simulation is the simulation sequence of the random variables, obtained from the structure of the model graph.
As an illustrative example consider the case when a new value for the probability p of a baculum of the common ancestor of all taxa is proposed (see Fig. 3 ). The joint probability density was given in Equation (  2 ) and the computation for the full dataset contains many factors. However, the probability ratio simplifies to P(p ) P(p (t) ) = p p (t) S 9 1-p 1-p (t) 1-S 9 (8) regardless of how many taxa are included in the study. The probability ratio is clearly simpler than the joint posterior probability density and the ratio thereof and the computation is much faster. Finally, consider Gibbs sampling  (Geman and Geman 1984; Gelfand and Smith 1990) , a special case of the Metropolis-Hastings algorithm, in which the proposal distribution is the posterior probability distribution of the changed variable(s), conditional on the values of the other random variables in the model.
2012). Such model formulae are rarely used in phylogenetics today. However, they are closely related to graphical model concepts, so phylogeneticists adopting this framework are likely to find such model formulae helpful and informative summaries of their models. This, in turn, will make it easier for applied statisticians to contribute to phylogenetics. Last but not least, the adoption of graphical models would facilitate the design and development of computational phylogenetics software. There are decidedly some challenges involved in doing this, VOL. 63 particularly in finding efficient software representation of the huge PhyloGMs. However, regular plates and tree plates help identify some of the replicated structure that can be used in efficient implementation of PhyloGMs. Modularization also encourages good software engineering principles, in that it supports a natural, high-level design with exchangeable and reusable components corresponding to standard modules in PhyloGMs.
We present Oqtans, an open-source workbench for quantitative transcriptome analysis, that is integrated in Galaxy. Its distinguishing features include customizable computational workflows and a modular pipeline architecture that facilitates comparative assessment of tool and data quality. Oqtans integrates an assortment of machine learning-powered tools into Galaxy, which show superior or equal performance to state-of-the-art tools. Implemented tools comprise a complete transcriptome analysis workflow: short-read alignment, transcript identification/quantification and differential expression analysis. Oqtans and Galaxy facilitate persistent storage, data exchange and documentation of intermediate results and analysis workflows. We illustrate how Oqtans aids the interpretation of data from different experiments in easy to understand use cases.
Users can easily create their own workflows and extend Oqtans by integrating specific tools. Oqtans is available as (i) a cloud machine image with a demo instance at cloud. oqtans. org, (ii) a public Galaxy instance at galaxy. cbio. mskcc. org, (iii) a git repository containing all installed software (oqtans. org/ git); most of which is also available from (iv) the Galaxy Toolshed and (v) a share string to use along with Galaxy CloudMan.
INTRODUCTION Technological advance in large-scale sequencing has revolutionized molecular biology. Its application to profiling the transcriptome, the total complement of cellular RNA, called RNA-seq, provides an unmatched dynamic range for expression quantification and single base pair resolution for the discovery of new transcripts  (Mortazavi et al. , 2008). However, analyzing these complex data to their full potential requires computational frameworks. Here, we present Oqtans, the online platform for quantitative RNA-seq data analysis (online since 2010). Its integration into the Galaxy framework ensures transparent and reproducible computational analyses. Oqtans provides a Galaxy interface to many recently developed RNA-seq analysis tools, and this way considerably extends the standard repertoire of the Galaxy toolbox (usegalaxy.
RESULTS Oqtans provides a versatile analysis workbench for RNA-seq data comprising tools suitable for basic and advanced analysis tasks (see Supplementary Table  S1  for a current list of Oqtans tools and Supplementary Table  S2  for supported file formats). Their modular organization within the Galaxy framework allows advanced users to easily customize and extend analysis workflows. We showcase Oqtans capabilities in use cases for which all data, parameters, intermediate output and final results are made public on a Page in our Galaxy cloud instance (see oqtans. org/usecases). As a first use case, we wanted to identify annotated genes that were differentially expressed between male and female Drosophila melanogaster fruit flies [using data from  (Daines et al. , 2011) ]. This analysis requires three major steps: read alignment, quantification and enrichment analysis (Fig.
1A  and B ). The chosen Oqtans tools were combined in a workflow (Supplementary Fig. S1 ). After starting an Oqtans cloud instance in Amazon Web Service EC2 (machine image ami-65376a0c) and importing the RNA-seq read data from the NCBI short read archive, we aligned these to the reference genome. Oqtans currently offers three tools for spliced alignments of short reads, Tophat  (Trapnell et al. , 2009) , STAR  (Dobin et al. , 2013)  and PALMapper  (Jean et al. , 2010). Subsequently, we determined genes that were differentially expressed in males and females using DESeq, which tests read counts for statistically significant differences  (Anders and Huber, 2010). To determine enriched Gene Ontology (GO) terms among differentially expressed genes, we supplied the gene list to topGO  (Alexa et al.
, 2006) , which we integrated into Oqtans. Its graphical output highlights expression differences in genes annotated with the functions 'reproduction' and 'sex determination', as is expected for this comparison between male and female fruit fly transcriptomes (see Supplementary Fig. S3 ). The whole experiment excluding short read alignment requires $10 min of compute time. Duration of the alignment depends on the number and size of compute nodes that can be allocated for this task (20 min in our setup with 19 '4Â large memory' instances on Amazon Web Service). Uniquely within Oqtans and through the benefits of the Galaxy framework, we can directly compare integrated software tools on the same input data. This is of great value for a researchers who are looking for the most appropriate and accurate algorithm to analyze their newly generated data.
For instance, for de novo transcript prediction, the accuracy of the alignments is particularly important. We demonstrate this in a comparison of the accuracy of introns predicted from spliced alignments against the genome annotation generated by TopHat and PALMapper (Fig. 2A  and see Supplementary Section S3 for details). Although alignment accuracy may have a negligible effect on the detection of differentially expressed annotated genes, it becomes crucial for de novo inference of transcripts (isoforms). Owing to the high resolution provided by RNA-seq, the discovery of novel transcript isoforms from these data has been a prime analysis goal. In  Go¨rnitz et al. (2011) , the authors compared the accuracy of transcript inference by combining different read alignment programs (PALMapper, TopHat) with different transcript predictors (margin-based Transcript Identification Method, Cufflinks).
DISCUSSION As high-throughput genome and transcriptome sequencing becomes routine in many laboratories around the world, there is an increasing demand for standardized data analysis. Directly associated with this need are accessibility, transparency and persistency of analysis pipelines. As a Galaxy web server, Oqtans brings us closer to these goals  (Schultheiss, 2011)  for the important task of RNA-seq data analysis by providing easy access to state-of-the-art analysis tools to a wide audience. Importantly, while profiting from many free software development efforts, its user friendly interface abstracts from programming languages and operating systems, and thus enables even inexperienced users to rapidly analyze their RNA-seq data. The same workflow in the Galaxy instance is shown in Supplementary Figure  S1  1301.
Introduction Teaching machines how to automatically answer questions asked in natural language on any topic or in any domain has always been a long standing goal in Artificial Intelligence. With the rise of large scale structured knowledge bases (KBs), this problem, known as open-domain question answering (or open QA), boils down to being able to query efficiently such databases with natural language. These KBs, such as Freebase  [3]  encompass huge ever growing amounts of information and ease open QA by organizing a great variety of answers in a structured format. However, the scale and the difficulty for machines to interpret natural language still makes this task a challenging problem. The state-of-the-art techniques in open QA can be classified into two main classes, namely, information retrieval based and semantic parsing based.