dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, the distribution of the different Types of phages is similar to that observed in the learning phage database. Some phage subfamilies are enriched. For example, at least 7 N4-like phages  [60]  are now found within Type 3 neck phages, and they all lack Hc3, thus confirming that the head-closure protein of N4-like phages is highly divergent. In the case of Type 1 phages, the distribution within Clusters is also similar to that observed for the learning set: 89% of these phages could be assigned to one of the previously defined Clusters. We conclude that the classification constructed on the 299 Aclame phage genomes is relevant for a global classification of most of the 624 newly analyzed genomes. The Virfam server is thus a robust tool for the classification of novel tailed phages and the identification of their structural organization at the head-to-tail interface.
Robustness of the proposed classification We first successfully detected a large set of structural proteins present in 299 phages, comprising 92% of the Siphoviridae, 86% of the Myoviridae and 98% of the Podoviridae of our learning database (Additional file 2: Table  S1 ). We identified a Type and thus a neck architecture for these phages, and proposed a classification within phages of the same Type and thus with the same neck structure. This approach enabled classification of 91% of the Caudovirales and unclassified phages of the learning database and 93% of the Caudovirales and unclassified phages of the NCBI database (Additional file 3: Table  S2 ). The higher success of our procedure in the case of the NCBI database is linked to an overrepresentation of Type 2 phages, which are more closely related and thus easier to detect.
Interestingly, we find that the resulting phage classification is correlated to the targeted bacteria phylogenetic tree: if Type 1 phages infect a very large number of hosts spanning the whole bacterial phylogenetic tree and in Figure  4  Typical analysis of a phage that is unclassified in the NCBI database, as provided by the Virfam Webserver. The Virfam webserver can be used to identify the head-neck-tail module in any bacteriophage genome from the set of ordered protein sequences. In the output, the Type and Cluster inferred from the detected superfamilies will be returned together with (A) a graphical representation of the components identified in the genome, (B) a clustering of the query phage with respect to those of same Type in the Aclame database, (C) a detailed report from the HHsearch analysis providing the corresponding alignments, (D) a warning in case unusual inter-gene distances are detected, (E) a list of the most similar proteins present in the Aclame database with a connexion to the corresponding phage and its pre-computed head-neck-tail module analysis page.
As regards Tc1 and Tc2, secondary structure prediction and our HHsearch calculations again predict that they share a common structural core. Moreover, recent determination of the three-dimensional structure of a Tc2 protein Figure  5  Interest of using a multi-protein analysis for phage classification when Portal sequence diverged more than other proteins of the head-neck-tail module. Four pairwise comparisons between the head-neck-tail modules of phages whose Portal proteins have significantly diverged with respect to other proteins of the head-neck-tail module. (A) Phage phi 4795, assigned to Type 1 Cluster 3, has a Portal sharing less than 25% identity with any other protein in Aclame database. Its TermL, Ne1, Tc1 and MTP proteins present a significantly higher conservation profile. In particular, compared with HK97, Ne1 and Tc1 share 76% and 49% identity with their homologs in phi 4795, respectively, while Portal only shares 22%.
(B) Phage CJW1, assigned to Type 1 Cluster 4, has a Portal sharing less than 23% identity with any other protein in Aclame database. Proteins such as Hc1 or MTP share a higher conservation profile. In particular, they share 37% and 34% identity with Ne1 and Tc1 of phage Omega, respectively while the Portal only shares 18%. (C) Phage PaP3, assigned to Type 3, has a Portal sharing less than 17% identity with any of the other proteins of Aclame database. In contrast, its TermL diverged to a lesser extent sharing 35% identity with that of phage ST64T. (D) RM 378 is among the most divergent phages assigned to Type 2. Only an Ad2 protein could be detected when searching for its head-to-tail connection proteins. Although its Portal shares at most 22% identity with the closest Portal of other phages of Aclame, its Ad2 protein could be recognized with higher identity (27% identity with KVP40) providing stronger support for the assignment of this phage to the Type 2 category.
Given the high detection rate of the neck genes provided by Virfam, most new phages can now be positioned within a "head-neck-tail module based" classification. Outside this specific module it is clear that each phage has also evolved and adapted to its specific environment. Versatility and reshuffling of gene functions in phage genomes, which often confuse a phylogenetic classification of phages, may have been less intense in the head-neck-tail module. This module may thus serve as a probe for further exploring the evolution of phages. Our methodology enables classification and comparison of viromes. Terminases and major tail proteins were previously proposed as phage markers. Unlike these single gene approaches, our characterisation of the phage headneck-tail module is not hindered when one of the genes is particularly divergent, and this gene can even be detected, provided its genome position is canonical.
Neck modules detected through the Web server in phages classified in the NCBI database but not in Aclame Percentages are calculated within the Siphoviridae, Myoviridae and Podoviridae subfamilies described by the NCBI. Total percentage corresponds to the number of assigned phages (560) compared to the total number of Caudovirales and unclassified phages of the analyzed NCBI database (624-19 non tailed bacteriophages =605).
Acknowledgements We thank  Ariane Toussaint  for very fruitful discussions,  Jessica Andréani  for carefully rereading the manuscript and both anonymous reviewers for their insightful comments. This work was supported by the  French Infrastructure for Integrated Structural Biology  [ ANR-10-INSB-05-01 ] and by the  French National Research Agency [ANR  grant " DNA Gating " to S. Z. J. and  P. T. ,  ANR  grant "Dynamophage" to R. G. and M. A. P. ].
The agent's strategy should be configurable through high-level instructions, by a strategy specification language. Those instructions should be prompted by a graphical user interface to allow for users without programming skills to define a customized Texas Hold'em Poker agent. This work has been divided into the following goals: ─ Creation of a language of concepts that includes key notions behind poker strategies and agent behavior -PokerLang; ─ Build a graphical user interface for this language -PokerBuilderwhich eases the creation of new PokerLang files; ─ Automatic generation of PokerLang strategies from human player logsthis has the purpose of imitating good players' strategies, if enough data is available; ─ Development of a Poker agent that follows the language specification; ─ Evaluate the interface usability and the performance of the developed Poker agent.
PokerBuilder After defining the high-level language, the next phase of this work was to build a simple graphical application which allows for users to easily define new PokerLang strategies. PokerBuilder is an Adobe Flex application that allows users to define the strategy's rules using the concepts of the language previously introduced, and set the behavior of a poker agent. With a smooth interface and simple features, PokerBuilder is accessible to any user that understands the main concepts of poker. One of the purposes of this work was to make a very practical application, even usable for users only familiarized with the most basic computer usage. For the implementation of the language of concepts, PokerBuilder is divided in four major classes: Strategy, Tactic, Rule and Property (Fig. 1 ).
The interface begins with an instance of the Strategy Class that creates instances of all other classes depending on what the user is creating. PokerBuilder gives the user two different views to create rules: Strategy View and Tactic View. The figures on the appendix section demonstrate this software's graphical user interface. The software includes a strategy builder (Fig. 5 ) to permit the user to create sets of tactics. To create a rule, the user must select its name, various evaluators and/or predictors and the corresponding action. Fig. 6  indicates an example of the interface used to select the evaluators, predictors and actions. Finally, if the user wants to select a pre-defined action (see section 4. 3); he or she can use the interface shown in Fig. 7  to personalize them.
However, since the hand strength is a continuous measure, its distribution has to be discretized. Let us analyze a distribution of hand strength values extracted from a particular collection of game logs, provided by Pedro Reis (see Fig. 2 ). As expected, the frequency of high values of hand strength is higher on later rounds (right hand side of Fig. 2 ). This happens because the players successively give up weaker hands. Since the distributions are rather distinct, we differentiate them during the inferring process: when inferring evaluators on Pre-Flop rounds we use the distribution on the left, and for other rounds we use the distribution on the right. Fig. 2. Hand strength relative distribution observed from the dataset. On the left, the distribution on the Pre-Flop round and on the right the distribution on the Post-Flop round.
The accuracy is the ratio between the number of cases where the agent selected an action similar to the player's original action and the total number of cases. In our experiments, to determine the weight vector, we generate its weights randomly so that   | | 1 F i i w , generate the agent and then determine its accuracy for a fixed number of iterations. The agent with better accuracy is the one that it is selected by the system. Other policies can be used to determine the weights, namely genetic algorithms with populations of agents with different weight vectors. However, it is possible to check (Table  3 ) that the random generation policy already produced agents with very good accuracies. The weighted Euclidian distance always produced agents with greater accuracy than the two other methods, with an average accuracy of ~79% for datasets with 5000 cases and 10.
Numerical procedure To asses residual stress relaxation by cyclic four-point bending loading, a numerical procedure based on a 2D finite element model is established using Abaqus-Standard software (Fig. 1 ). Since the thickness of the bending specimen is large (e= 10 mm) compared to the deformed zone, the assumption of plane strain was selected. Due to symmetry only the half of the notched bending specimen is analyzed, and it is meshed using 4020 continuum-brick elements CPE4 (4-node bilinear plane strain quadrilateral) available in Abaqus element library. The mesh is highly refined near the notch until a length of 15µm (Fig. 2 ). Loading and boundary conditions four-point bending boundary conditions are imposed on the half of the notched bending specimen (Fig. 2 ). To account for the whole of specimen in calculation, an X-symmetry condition (U1=UR2=UR3=0) is applied to the cross section of bending specimen.
, 2009 Roberts et al. , , 2014b;; von Glasow, 2010; Kelly et al. , 2013)  focused on the local volcanic chemistry within the plume in a zero or onedimensional framework. The same thermodynamic model is used in the initialisation of the atmospheric chemistry models in these studies  (Bobrowski et al. , 2007; Roberts et al. , 2009; von Glasow, 2010)  to describe the high temperature mixtures of air and volcanic gases. This model is the "HSC Chemistry" software, described in more detail by  Martin et al. (2006 Martin et al. ( , 2009) )  following initial work by  Gerlach (2004)  for a volcanic emission. The model studies initialised using HSC show a rapid increase in BrO within Figures.
, 2005). In their paper,  Aiuppa et al. (2005)  conducted filter-pack measurements of the near-vent Etna's crater plume during January and October 2004, and showed an enhancement of four orders of magnitude in the bromine concentrations within the volcanic plume relative to background air. Etna volcano is also continuously and extensively monitored by INGV (Istituto Nazionale di Geofisica e Vulcanologia), and therefore a variety of gas composition information is available. These data are used to simulate the eruption with the numerical model. The third reason is that satellite observations have been made above the Etna region, close in time of the eruption. In the Supplement of Hörmann volcano contributed an average of 2000 tonnes of SO 2 per day (from monitoring reports of  INGV-Osservatorio Etneo; available at www.
The chemistry occurring in this high temperature near-vent plume environment differs from that at ambient atmospheric temperatures, e. g. it includes thermal dissociative reactions, generating oxidised species and radicals. New chemical species that are believed to be produced in the near-vent plume include HO x , Cl x , Br x and NO x. We represent this process using a thermodynamic equilibrium model software (HSC is a thermodynamic model software (HSC; Outokumpo, Finland), following studies of  Gerlach, 2004; Martin et al. , 2006 , 2009 , and Roberts et al. , 2014b). HSC computes the equilibrium composition of the near-vent plume after mixing between emitted hot gases and ambient air. The parameters needed to compute this high-temperature gaseous composition are: (i) the temperature of the plume mixture (calculated from the temperatures of the magmatic gases and ambient temperature); (ii) the magmatic gas composition and (iii) the volumetric mixing ratio V M /V A (V M : magmatic gas volume, V A : atmospheric gas volume).
Acknowledgements. This work benefited from the financial support of the program LEFE of the  Institut National des Sciences de l'Univers of CNRS  ( Centre National de la Recherche Scientifique ), of  Météo-France and of the "Conseil Régional de la Région Midi-Pyrénées" (France). T. J. Roberts  acknowledges  LABEX VOLTAIRE (VOLatils-Terre Atmosphère Interactions -Ressources et Environnement )  ANR-10-LABX-100-01  ( 2011-20 ) and an NSINK career development allowance that enabled HSC software purchase.
INTRODUCTION Cloud Computing is a technology that uses the Internet and central remote servers to maintain data and applications. This is a natural platform to provide support to Mobile Learning (m-Learning) systems and also for the implementation of data mining techniques that allow exploring distributed knowledge databases. Cloud Computing is a computation paradigm in which the resources of an information system are offered as services through net connections with transparent scalability in such a way that the computational resources are assigned in a dynamical and accurate manner when they are strictly necessary, without the requirement of a detailed understanding of the infrastructure from the user's point of view  [1]. Software-as-a-Service (SaaS) is a type of Cloud service which provides software functionality through Internet and can help efficiently the management of Web-based applications and pedagogical data in an m-Learning context.
An adaptive technique is realized on XML-based technology which is better suited for a large content of LOs. The use of XML as a medium-neutral data format for data storage and processing allows the learning contents to be classified hierarchically, to be structured at the desired level of granularity and to be adjusted to different contexts and devices  [3]. In this paper we argue that a future Cloud-based m-Learning system should consist of a set of independent but cooperating non-monolithic Web services-based applications that integrate pedagogical data between common Learning Course Management Systems (LCMS). The rest of this paper is organized as follows. In Section 2, the Cloud-based approach for e-Learning systems is discussed. Section 3 explains the architecture of Software-as-a-Service distributed Cloud for m-Learning, and the system design issues are described including implementation details.
B. Cloud Computing Layers Cloud computing comprises of three layers: (1) infrastructure as a service (IaaS); (2) platform as a service (PaaS); (3) software as a service (SaaS)  [4]. • IaaS comprises the layer of storage, hardware, servers and networking components. Architecture scalability is achieved through virtualization, such that multiple systems or operating systems can be run at the same time on a virtual machine or across multiple machines. • PaaS offers application platform for Internet programming interface and operating environment, where users can dispose their own applications. • SaaS is a software delivery model in which program and its associated data are hosted centrally and are accessed by users using a Web browser over the Internet. It becomes a common delivery model for business applications and learning course management system.
This project enables physical machines to be virtualized and allocated on-demand for e-Learning systems. However, the security layer for this Cloud-based framework is not addressed. The project described in  [11]  presents an approach using a service platform, which utilizes a content adaptation procedure for web-based mobile services by utilizing device capability databases and generic page transformation. Their approach enables mobile devices to visualize any generic content device-specifically on their integrated browsers. The integrated mobile Web browsers support a specific set of markup languages. These differences are not discussed in function of services interoperability. The Google App Engine  [14]  provides a Java Web framework based Jetty, a servlet container, and BigTable for data storage.
The contribution is as follows: (1) the concept encompasses the creation of an XML-based and mobile browser-independent content, which is adapted to the requesting device using a device capability recognition method and XML transformation techniques according to the recent advances of media queriesbased responsive Web design; (2) personalization in m-Learning via an adaptive approach; (3) demonstration of a Web service-based architecture to an integrated Web-based learning and m-Learning environment; (4) testing of several mobile Web browsers to proof Learning Objects (LOs)  [3]  portability; (5) design of a service-based framework, as part of an e-Learning SaaS Cloud, that uses hierarchical displaying multimedia units with index extraction and content summarization; (6) description of a SaaS-based e-Learning system architecture to provide a flexible integration model in which all the learning components and applications are well defined and loosely connected and (7) deployment of multimedia services and espe-cially the presentation of multimedia content on mobile environments, which is a more challenging task due to the diversity of mobile devices and their parameters The objective of this contribution is to combine semistructured data, stored in a Native XML Database (NXDB), with structured data stored in a Relational Database (RDB) through Web services (WS) called by a Learning Course Management System (LCMS).
Thus, we provide direct data integration located at distributed sites in order to improve the achievement of learning outcomes. This approach promotes a device-independent m-Learning gateway between different mobile units and the huge number of LOs available on a plethora of LMSs. The implementation combines our Web-based Open Semantic Editor Suite (WOSES) with a set of additional services to allow different mobile units a direct access to LOs customarily designed for desktop Web browsers. The key technologies behind WOSES are: extended LOM  [7]  base semantic structure presented in Figure  1 , device-independent LOs generator, native XML database and Web services. A semantic content adaptation service is plugged. This tool uses templates to automatically and efficiently adapt content for mobile Web browsers.
In this authoring suite, binary data of multimedia content is embedded directly into XML course content. During the editing process, if the author inserts an image or any binary data into the edited content, the semantic editor will encode it using the Base64 encoding method (screenshot 1 in Figure  2 ). This single XML collection can be managed easily by providing proper XSLT transformation files. The XML content can be presented in many forms, such as HTML for desktop users or for mobile users. All these processes are managed by appropriated Cloudbased Web services. In practice, the course authors may need to modify a pedagogical content. The correct operation of a collaborative authoring system imposes the storage of learning collections, possibly in a database, for a better reuse and diffusion of these documents.
The choice of an appropriate database is essential: the authors have chosen a native XML database which allows the storage of XML documents in their native format. This choice, in opposition to that of a relational database, is explained by the nature of learning documents which are in general of narrative types: document-centric and not data-centric. If necessary, formatted HTML and PDF versions (screenshot 3 in Figure  2 ) of extracted learning content can be published in a LCMS via Web service interface.
C. The Course Content Adaptation Process M-Learning pedagogical content can be given in the form of a visual presentation as text, pictures, tables in XML, HTML format or as PDF data. Optionally, m-Learning content can be given as sound data in the form of an acoustic presentation in an MP3 or WAV format. To achieve this goal the Web servicebased OSES suite  [3]  is extended with four additional services. The first one (XICT) is able to create a hypertext index on the basis of the course tree structure (Figure  1 ). The second service represents an XML content adaptation tool (XCAT) that uses profiles (XML metadata files) for automatic content adaptation displayed on the mobile browser. Profiles are adjusted in the function of detection: (1) mobile device profile issue from WURFL software component  [18]  that maps HTTP Request headers of the HTTP client (Desktop, Mobile phone, Smartphone, Tablet, etc.
E. System Deployment In the system deployment stage, our WOSES Cloud-based service is integrated with a Web-based LCMS. The interconnection is carried out by a Web Services Management System (WSMS). So, the learning-centric data and the managementcentric data are clearly separated. LOs are developed in WOS-ES section of the eLearning Service as a Service-based Xesop system and thereafter are stored in a NXDB. The information relevant to learner personal data, learner profiles, course maps, LOs sequencing, data presentation and general user data is stored in the RDB of LCMS. The publication process of learning content is carried out by WSMS as method for external applications integration through Web services. This allows extending and integration existing LCMS systems as a Cloudbased service.
In the discussed case, content adaptation Web service-based modules make the bridge from e-Learning to m-Learning system in a simple and effective way through Apache Libcloud  [14] , an open source library that provides a systemneutral interface to Cloud provider APIs. The Java version supports Amazon EC2. For proof-of-concept our system is deployed on AmeTice (Moodle-based) e-Learning system adopted by our university. We integrated the PHP-based LCMS interface via Web services. This offers a high degree of flexibility and ease of use, in particular as SOAP libraries for PHP already exist, which leads to an easily extensible PHP and MySQL-based LCMS. For services deployment we used Apache Axis  [12]. For storing and managing LOs, we used eXist, a Java-based open source native XML database  [17].