dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The RoboDiff has been designed for high reliability and stability. This has been achieved by integrating a number of diagnostic tools in the sample changer and goniometer. Considerable effort has been made both to transfer the experience of beamline scientists to the software and to implement the ability to recover from situations that could otherwise prevent the continuation of experiments. To date, over 20 000 user samples have been loaded, characterized and collected. The 240 samples which can be stored in the HCD increase the throughput and allow MASSIF-1 to run unattended 24 h a day. The hardware and software is all owned by ESRF, making maintenance and developments very flexible. Moreover, several collaborations based on the RoboDiff project have been set up to allow dispersion of the technology all over the world.
The close links that have been designed between the RoboDiff and the ISPyB database and MXCuBE brings MASSIF-1 to the forefront of instruments for structural biology, and provide one of the missing steps in the automatic gene-to-protein pipeline. jF obs j À jF calc j = P hkl jF obs j, where F obs and F calc are the observed and calculated structure-factor amplitudes. ‡ R free = P hkl&T jF obs j À jF calc j = P hkl&T jF obs j, where F obs and F calc are the observed and calculated structure-factor amplitudes and T is the test set of data omitted from refinement (5% in this case).
We show that our method can be adapted to different underlying solvers (Gecode, Choco2, OR-tools) on different computing infrastructures (multi-core, data centers, cloud computing). The experiments cover unsatisfiable, enumeration and optimization problems, but do not cover first solution search because it makes the results hard to analyze. The same variability can be observed for optimization problems, but at a lesser extent because the optimality proof is required. EPS offers good average performance, and matches or outperforms other available parallel implementations of Gecode as well as some solvers portfolios. Moreover, we perform an in-depth analysis of the various factors that make this approach efficient as well as the anomalies that can occur. Last, we show that the decomposition is a key component for efficiency and load balancing.
In this paper, we introduce an Embarrassingly Parallel Search (EPS) method for constraint problems and show that this method often outperforms state-of-the-art parallel B&B algorithms for a number of problems on various computing infrastructures. A master decomposes the problem into many disjoint subproblems which are then solved independently by workers. Since a constraint program is not trivially embarrassingly parallel, the decomposition procedure must be carefully designed. Our approach has three advantages: it is an efficient method; it involves almost no communication, synchronization, or mutual exclusion between workers; its implementation is simple because the master and the workers rely on an underlying constraint solver but does not require to modify it. Additionally, it is deterministic under certain restrictions.
This paper integrates results from a series of publications  (Régin, Rezgui, & Malapert, 2013 , 2014; Rezgui, Régin, & Malapert, 2014). However, this paper includes novel contributions, implementations, and results. A new implementation of EPS on the top of the Java library Choco2  (Choco, 2010)  uses a new decomposition procedure. New results are given for the implementations on the top of the C++ library Gecode  (Schulte, 2006)  and OR-tools  (Perron, Nikolaj, & Vincent, 2012) , More problem's types and instances are tested. EPS is compared with other parallelizations of Gecode and with several static solvers portfolios, We perform in-depth analysis of various components, especially the decomposition procedures, as well as the anomalies that can occur. The paper is organized as follows. Section 2 presents the constraint programming background, Amdahl's law, and related work about parallel constraint solving.
Machado, Pedro, and Abreu (2013)  proposed a hierarchical work stealing scheme correlated to the cluster physical infrastructure, in order to reduce the communication overhead. A worker first tries to steal from its local node, before considering remote nodes (starting with the closest remote node). This approach achieved good scalability up to 512 cores for the n-queens and quadratic assignment problems. For constraint optimization problems, maintaining the best solution for each worker would require a large communication and synchronization overhead. But, Machado et al. observed that the scalability was lowered because the lazy dissemination of the so-far best solution, i. e. because some workers use obsolete best solution. General-purpose programming languages designed for multi-threaded parallel computing like Charm++  (Kale & Krishnan, 1993)  and Cilk++  (Leiserson, 2010; Budiu, Delling, & Werneck, 2011)  can ease the implementation of work stealing approaches.
Features characterize problem instances like number of variables, domain sizes, number of constraints, constraints arities. Many portfolios select the best candidate solvers from a pool based on static features or by learning the dynamic behaviour of solvers. The SAT portfolio iSAC  (Amadini, Gabbrielli, & Mauro, 2013)  and the CP portfolio CPHydra (O'  Mahony, Hebrard, Holland, Nugent, & O'Sullivan, 2008)  use feature selection to choose the solvers that yield the best performance. Additionally, CPHydra exploits the knowledge coming from the resolution of a training set of instances by each candidate solver. Then, given an instance, CPHydra determines the k most similar instances of the training set and determines a time limit for each candidate solver based on constraint program maximizing the number of solved instances within a global time limit of 30 minutes.
Besides, unsatisfiable instances have a practical importance, for instance in software testing, and enumeration is important for users to compare various solutions. The first set called fzn is a selection of 18 instances selected from more than 5000 instances either from the repository maintained by  Kjellerstrand (2014)  or directly from the Minizinc 1. 6 distribution written in the FlatZinc language (NICTA Optimisation Research Group, 2012). Each instance is solved in more than 500 seconds and less than 1 hour with Gecode. The selection is composed of 1 unsatisfiable, 6 enumeration, and 11 optimization instances. The set xcsp is composed of instances from the categories ACAD and REAL of XCSP 2. 1  (Roussel & Lecoutre, 2008). It consists of difficult instances that can be solved within 24 hours by Choco2  (Malapert & Lecoutre, 2014).
Implementation Details We implemented EPS method on top of three solvers: Choco2 2. 1. 5 written in Java, Gecode 4. 2. 1 and OR-tools rev. 3163 written in C++. We use two parallelism implementation technologies: Threads  (Mueller et al. , 1993; Kleiman, Shah, & Smaalders, 1996)  and MPI  (Lester, 1993; Gropp & Lusk, 1993). The typical difference between both is that threads (of the same process) run in a shared memory space, while MPI is a standardized and portable message-passing system to exchange information between processes running in separate memory spaces. Therefore, Thread technology does not handle multiple nodes of a cluster whereas MPI does. In C++, we use Threads implemented by pthreads, a POSIX library  (Mueller et al. , 1993; Kleiman et al. , 1996)  used by Unix systems. In Java, we use the standard Java Thread technology  (Hyde, 1999).
There are many implementations for MPI like OpenMPI  (Gabriel, Fagg, Bosilca, Angskun, Dongarra, Squyres, Sahay, Kambadur, Barrett, Lumsdaine, et al. , 2004 ), Intel MPI (Intel Corporation, 2015) , MPI-CH (MPI-CH Team, 2015) and MS-MPI  (Krishna, Balaji, Lusk, Thakur, & Tiller, 2010; Lantz, 2008). MPI is a standard API, so the characteristics of the machine are never taken into account. So, the machine providers like Bull, IBM or Intel provide their own MPI implementation according to the specifications of the delivered machine. Thus, the cluster provided by Bull has a custom Intel MPI 4. 0 library, but OpenMPI 1. 6. 4 is also installed, and Microsoft Azure only supports its own MS-MPI 7 library. OR-tools uses a sequential top-down decomposition and C++ Threads. Gecode uses a parallel top-down decomposition and C++ Threads or MPI technologies.
In fact, Gecode will use C++ pthread on the multi-core computer, OpenMPI on the data center, and MS-MPI on the cloud platform. Gecode and OR-tools both use the lex variable selection heuristic because the top-down decomposition requires a fixed variable ordering. Choco2 uses a bottom-up decomposition and Java Threads. In every case, the foreman schedules the jobs in FIFO to mimic as much as possible the sequential algorithm so that speedups are relevant. When needed, the master and the workers read the model from the same file. We always take the value selection heuristic which selects the smallest value whatever be the variable selection heuristic.
Execution Environments We use three execution environments that are representative of computing platforms available nowadays. Multi-core is a Dell computer with 256 GB of RAM and 4 Intel E7-4870 2. 40 GHz processors running on Scientific Linux 6. 0 (each processor has 10 cores). Data Center is the "Centre de Calcul Interactif" hosted by the "Université Nice Sophia Antipolis" which provides a cluster composed of 72 nodes (1152 cores) running on CentOS 6. 3, each node with 64 GB of RAM and 2 Intel E5-2670 2. 60 GHz processors (8 cores). The cluster is managed by OAR  (Capit, Da Costa, Georgiou, Huard, Martin, Mounie, Neyron, & Richard, 2005) , i. e. , a versatile resource and task manager. As Thread technology is limited to a single node of a cluster, Choco2 can use up to 16 physical cores whereas Gecode can use any number of nodes thanks to MPI.
Decomposition Time Figure  4 (b) gives the percentage of decompositions done within a given time. The Choco2 times are reported for all variable selection heuristics on xcsp instances. The Gecode times are reported for lex on xcsp and fzn instances. Because of the implementation differences, times reported for Choco2 and Gecode are slightly different. Indeed, the decomposition time alone is given for Gecode. The Choco2 times take also into account the estimation time, the time taken by the foreman to fill the queue of subproblems, and the time taken by workers to empty the queue. Let us also remind that subproblems only become available after the top-down decomposition is complete whereas they become available on the fly during the bottom-up decomposition. In both cases, the reported time is a lower bound on the solving time.
As outlined before, the decomposition of knights-80-5 takes more than 1100 seconds and generates too much subproblems, which forbids any speedup. The issue is lessened using the sequential decomposition of OR-tools and is resolved by the parallel top-down decomposition of Gecode. Note also that the sequential solving times of OR-tools and Gecode respectively are 20 and 40 times higher. Similarly, the long decomposition time of Choco2 for lemma-100-9-mod leads to a low speedup. However, the moderate efficiency of Choco2 and Gecode for squares-9-9 is not caused by the decomposition. Gecode and OR-tools are often more efficient and faster than Choco2. The solvers show different behaviors even when using the same variable selection heuristic because their  propagation mechanisms and decompositions differ.
Scalability up to 512 Workers Table  5  compares the Gecode implementations of EPS and work stealing (WS) for solving xcsp instances using 16 or 512 workers. EPS is faster and more efficient than the work stealing. With 16 workers, the work stealing is ranked last using the Borda score. With 512 workers, EPS is in average almost 10 times faster than the work stealing. It is also more efficient because they both parallelize the same sequential solver. On the multi-core machine, Gecode was faster than Choco2 on most instances of xcsp1. Here, the performance of Gecode are more mitigated as outlined by the Borda scores. Five instances that are not solved within the time limit by Gecode are not reported in  heuristics whereas all instances are solved in sequential or parallel when using dom/wdeg or dom/bwdeg.
Cloud Computing EPS can be deployed on the Microsoft Azure cloud platform. The available computing infrastructure is organized as follows: cluster nodes computes the application; one head node manages the cluster nodes; and proxy nodes load-balances communication between cluster nodes. On the contrary to a data center, cluster nodes may be far from each other and communication time may take longer. Proxy nodes requires 2 cores and are managed by the service provider. Here, 3 nodes of 8 cores with 56 GB of RAM memory provide 24 workers (cluster nodes) managed by MPI. Table  6  compares the Gecode implementations of EPS and work stealing for solving the fzn instances with 24 workers. Briefly, EPS is always faster than the work stealing, and therefore, more efficient because they both parallelize the same sequential solver.
Embarrassingly Distributed Search In this section, we transform with reasonable effort a parallel solver (EPS) into a distributed parallel solver (EDPS) by using the batch scheduler OAR  (Capit et al. , 2005)  provided by the data center. In fact, the batch scheduler OAR plays the foreman. The parallel Choco2 solver is modified so that the workers write the subproblems into files instead of solving them. Then, a script submits the jobs/subproblems to the OAR batch scheduler, waits for their termination, and gathers the results. OAR schedules jobs on the cluster using priority FIFO with backfilling and fair-share based priorities. Backfilling allows to start lower priority jobs without delaying highest priority jobs whereas fair-share means that no user/application is preferred in any way. The main drawback is that a new worker must be created for each subproblem.
Acknowledgments We would like to thank very much  Christophe Lecoutre ,  Laurent Perron ,  Youssef Hamadi ,  Carine Fédèle ,  Bertrand Lecun  and  Tarek Menouer  for their comments and advices which helped to improve the paper. This work has been supported by both  CNRS  and  OSEO (BPI France)  within the  ISI project  " Pajero ". This work was granted access to the  HPC  and visualization resources of " Centre de Calcul Interactif" hosted by "Université Nice Sophia Antipolis ", and also to the  Microsoft Azure Cloud. We also wish to thank the anonymous referees for their comments.
This permits the linking together of images, sounds and textual materials with hyperlinks, thereby enriching the content with relevant information. The result of the edition is displayed through a viewer interface, allowing one to experiment with the entire eTalk or to actively navigate, scroll and search inside its content. After having recorded the speech of the scholar, the software, Audacity, allows for the splitting of the speech in pieces of 2-3 sentences. Each piece of speech can be associated with its written version, a slide, images, or hyperlinks and so forth. Each piece is also quotable with a specific URL: a new kind of reference (see a presentation video 9 ). Thus, the final release of eTalks allows for the complete 'citability' of its contents: each and every portion of the researchers' talks can be precisely referred to and therefore cited, just like any traditional, paper-based scientific publication but with all the potential for plural literacies.
The concept of the eTalk was developed by Claire Clivaz (SIB, VITAL-IT) and Frédéric Kaplan (EPFL) in conjunction with an interdisciplinary team of colleagues. The core of the eTalk engine was developed in JavaScript by Frédéric Kaplan and Cyril Bornet (EPFL). The code is now available as open source on Github as a free application for further development; 10 relevant guidelines are available to interested users. 11 The eTalks is currently being further developed and disseminated by our interdisciplinary team of researchers in Digital Humanities and bioinformatics, at the Swiss Institute of Bioinformatics (Lausanne, CH: etalk. vital-it. ch). The following pattern summarizes how an eTalk is built. Four series of eTalks have thus far been published as openly accessible: twelve on funerary rituals, nine on the enhanced Human, two on the Lausanne institutional Biobank, and one on Digital Humanities.
12 The eTalks are now in development by institutional and research collaborations, notably with colleagues from the Pedagogical High School of Lausanne (HEPVaud) 13 and the ERASMUS+ #dari-ahTeach project, whose purpose is to offer a webportal by 2017 that will include digital teaching modules (see point 3). The question of DRM (digital rights management) is a constant concern for our team. Indeed, this particularly delicate point is presently not handled by the usual publishers. We are lucky enough to have Dr. Frédéric Schütz, who is also working for Wikimedia CH, in our SIB team. He has trained our team, and we are attempting to deal with the copyright question with respect to different levels of responsibility, reserving a few delicate cases for the juridical service of our institution. We check absolutely all copyrights, and attempt to implement the most accurate form of DRM administration.
We encourage the authors to use material coming from free databases, such as Wikimedia Commons, as much as possible. Copyrights are asked for whenever it is necessary. For example, sometimes we have to ask a photographer for the use of their pictures, sometimes we have to ask a book edition for the use of a front cover. Being so attentive to the copyright question resulted in the experience of a few cases which are worth sharing while keeping them anonymous. In certain cases we have had to decline the use of materials. For example, a certain journal required the payment of $3000, to be renewed every year, for the use of a mere screen shot of an article page where one could only barely read the title. As another example, a magazine was asking $2000 as a "standard base fee without exclusivity" for a video screen shot.
Those guidelines will permit a more efficient exchange between our team and the author during the creation process: such as providing image format and size requirements. With regards to the eTalk software specifically, several major improvements will be considered. Firstly, a full user-rights management system also has to be established: it will allow for the coping with of a steady increase in eTalk projects and their authors' expectations in terms of privacy and diffusion. Our team will also offer DRM training to interested people. We also aim to extend the range of media supported, such as enabling the citation of short films in eTalks, as well as the use of a stable URL system for quotation. 15 Moreover, sharing options have to be taken into consideration in order to take full advantage of social media sharing possibilities and the increase of authors' eTalk visibility.
The project team is currently developing the infrastructure and design of the modules based on the production of five modules: Introduction to Digital Humanities, Text Encoding, AudioVisual Media (AVM) and Multimodal Literacies, Retrodigitizing Dictionaries, and Ontologies and Knowledge Management. It is envisioned that these modules will be used beyond the DH community as the societal impact of a culturally-driven digital transition grows, opening up new ways of collaborating on productive theory and critical thinking  [10]. Inside of the module on AVM and Multimodal Literacies, we will develop the most efficient way of recording and editing an eTalk by oneself with some editorial help from our team.
As such, it provides solutions to flexibly address the requirements identified in Table  3 , and in the future extend our solution with new features in a more flexible manner. The solution prototype is implemented by utilizing contemporary web technologies in terms of HTML5, CSS, JavaScript and PHP as well as external libraries including: HTML DOM, Modernizr, jQuery, Web Speech API, Lens effect, TextScrolling, and contextJS (source code: https://github. com/davidsalvador/degree-project). All adaptation techniques identified in Table  3  are carefully implemented into our solution. Fig. 4 , presents the architectural overview of our prototype solution. The "Controller" and the "View" play a central role in our solution. The "Controller" usually manipulates the "Model" elements and updates the "View".
Acknowledgments. We thank all participants from Kosova, Macedonia and Sweden that attended our workshops. We thank the eHealth  Institute in Kalmar and Blekinge Institute of Technology in Karlskrona , for hosting our workshops. Furthermore, we thank the professor of  UPC -FOOT ,  Eulalia Sánchez , for her scientific, optical and optometry approach and counseling during the assessment study. Finally, we thank the  Swedish Foundation for International Cooperation in Research and Higher Education  for supporting this research.
Segregating the human cortex into distinct areas based on structural connectivity criteria is of widespread interest in neuroscience. This paper presents a groupwise connectivity-based parcellation framework for the whole cortical surface using a new high quality diffusion dataset of 79 healthy subjects. Our approach performs gyrus by gyrus to parcellate the whole human cortex. The main originality of the method is to compress for each gyrus the connectivity profiles used for the clustering without any anatomical prior information. This step takes into account the interindividual cortical and connectivity variability. To this end, we consider intersubject high density connectivity areas extracted using a surface-based watershed algorithm. A wide validation study has led to a fully automatic pipeline which is robust to variations in data preprocessing (tracking type, cortical mesh characteristics and boundaries of initial gyri), data characteristics (including number of subjects), and the main algorithmic parameters.
Introduction Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique providing in vivo information about human brain white matter fiber organization. Tractography algorithms allow the modeling of the white matter fiber organization exploring local orientation information. Cortical neurons are connected via such fibers, so that knowledge of the fiber tracts allows, in turn, the apprehension of the cortical areas according to their connectivity. The exploration of a large connectivity database, performed on macaque brains, showed that each cortical functional area has its own connectional fingerprint  (Passingham et al. , 2002). A connectivity-based structural parcellation seems there-fore appropriate to extend the knowledge of the brain's functional organization  (Behrens and Johansen-Berg, 2005; Knösche et al.
Whereas the majority of methods are performed in the volume, our approach is based on a surface-based representation of the cortex, more adapted to its spherical topology. A surface-based registration of individual cortical surfaces, followed by a geodesic smoothing of connectivity data, is a first way to overcome cortical and connectional interindividual variabilities. A second complementary way consists in performing a group analysis defining the cortical target regions with reproducible connectivity to the input region. Another aspect of this study was to validate the results on a new set of high quality data, the ARCHI database  (Duclap et al. , 2013; Assaf et al. , 2013) , composed of 79 healthy subjects. For this purpose, the method has been fully automated, parallelized and implemented in the BrainVisa framework  (Geffroy et al.