dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper presents a computational platform for dynamic security assessment (DSA) of large electricity grids, developed as part of the iTesla project. It leverages High Performance Computing (HPC) to analyze large power systems, with many scenarios and possible contingencies, thus paving the way for pan-European operational stability analysis. The results of the DSA are summarized by decision trees of 11 stability indicators. The platform's workflow and parallel implementation architecture is described in detail, including the way commercial tools are integrated into a plug-in architecture. A case study of the French grid is presented, with over 8000 scenarios and 1980 contingencies. Performance data of the case study (using 10,000 parallel cores) is analyzed, including task timings and data flows.
g. weekly) to ensure security rules reflect the up-to-date underlying system reality. This paper presents the iTesla offline DSA platform, describing its structure, implementation and a sample of the results obtained. The platform is based on a number of mature commercial tools, which are interfaced with custom code for interoperability and to provide specific functionality. In particular, we describe how the platform has been designed to efficiently leverage HPC resources in order carry out dynamic security analysis at an unprecedented scale. Results from a pilot case study on the French transmission network are presented and analyzed. About 7,160 operating points have been generated to explore different demand and renewables production scenarios. Each of these operating points has been simulated against a list of 1980 contingencies, resulting in a total of over 14 million dynamic simulations.
II. DSA FOR LARGE-SCALE SYSTEMS The iTesla DSA platform consists of two main components; an online and an offline module. The offline module is to be executed regularly (e. g. on a weekly basis) to reflect expected system conditions. It aims at exploring the network state-space to infer the separation between stable and unstable states using data mining techniques and high-fidelity dynamic simulations. More precisely, in the iTesla offline module, the system stability boundary is sampled using Monte Carlo techniques to generate a large number of realistic operating points, similar to what has been observed historically. Each of these operating points can then be analyzed in detail, to assess their robustness against a list of credible contingencies. Subsequently, machine learning techniques are used to compress the outcomes of the simulations into a set of rules describing the stability domain in terms of pre-fault state variables.
A. Stochastic sampling The first step to identifying relevant operating states is to sample the stochastic variables that are outside of the TSO's control such as loads and injections of intermittent sources. The aim is to construct a statistical model that approximates the multivariate probability distribution of all stochastic variables on the basis of large historical datasets of on-site measurements. There are several challenges to building such a multivariate model due to the high data dimensionality, the complexity of marginal distributions as well as the non-linearities of the dependence structure. To this end, a novel approach based on copulas, dimension reduction and clustering techniques has been developed in iTesla (more information in  [13] ). The model serves as the engine that generates the thousands of scenarios required for the Monte Carlo approximation of the system stability boundary.
The performance of the DTs, measured in terms of the misclassification error on unseen datasets, can be tuned using a plethora of user-defined parameters related to the DT training process, such as depth and leaf purity. Furthermore, given that any pre-fault state variable is a potential classifying variable, a variable selection step precedes the training process in order to identify variables which are likely to have classifying power, considerably reducing training times. In the iTesla platform, two distinct types of DTs are trained for each contingency and post-fault indicator; a 'reduced' and a 'full' tree. Full DTs are trained on the full set of state variables. On the other hand, reduced DTs are trained solely on real power injections. The 'full' DTs are used in online contingency filtering, while 'reduced' DTs are embedded in a linear optimization problem to delineate the stable pre-fault system boundaries and determine whether the system is recoverable through corrective actions.
A. Hardware setup and Platform Implementation In recognition of iTesla's significance in pioneering largescale DSA, the project was granted access to the petascale HPC facility Curie (CEA, France)  [16]  by the Partnership for Advanced Computing in Europe (PRACE)  [4]. The offline module was run on Curie's thin nodes: 5,040 nodes equipped with two 8-core Intel Xeon E5-2680 CPUs (80,640 cores in total) and 64GB of RAM, running Linux CentOS 6 and communicating over an Infiniband QDR network. Under the access arrangements, up to 10,000 cores were used simultaneously in blocks of 24 hours (wall clock time). Figure  1  depicts the functional HPC implementation of the iTesla offline platform. A Master-Workers architecture is used, and task parallelism is implemented using a hybrid MPI/thread implementation.
One node acts as the master and communicates with the 16 worker threads (one per CPU core) on each of the 624 worker nodes by means of the Message Passing Interface (MPI) protocol  [15]. A single MPI task is spawned on each worker node at job start and it dispatches tasks to the node's 16 cores by creating threads. In Figure  1 , unit tasks (programs or scripts) are indicated by dark grey blocks and MPI communication is indicated by solid arrows. The dark grey blocks are organized according to their functional units described in Section II. The different data storage sources required for each task are also shown. For example, the statistical model to be used for generating stochastic scenarios is loaded from central storage before commencing the batch sampling process. The computed DTs, the final output of the offline DSA workflow, are stored in a dedicated database for later use in order to support online operation.
B. Module implementation The iTesla offline platform performs a sequence of complex operations to sample and analyze power system states. Rather than developing code from scratch, the platform incorporates a number of mature commercial tools (EUROSTAG, Knitro, Xpress, Matlab) and interfaces them with custom code in a variety of languages (java, C++, Matlab, AMPL). The stochastic sampling module has been implemented in Matlab. It consists of three tasks that are run sequentially to generate a batch of sampled operating states. The sampler operates most efficiently in batch mode, taking advantage of the vectorization speed-up. Note that the parameterization of the statistical model may take place outside the HPC platform. Overall, there are two important considerations in its implementation.
The steady state analysis module consists of four serial tasks. The 'topology initialization' task is first carried out. The optimization problem is implemented in AMPL and employs the Xpress solver. This task also makes use a topology prediction model implemented in Java, which has been previously executed outside the HPC platform. The optimizer decides on substation topologies while taking into account grid modeling considerations and any dependence with the sampled stochastic variables. The second task named 'starting point initialization' is responsible for setting up all control variables; it is implemented in AMPL and uses the Knitro solver. The third 'load flow' task re-executes the preceding step with the computed control decision variables as input for validation purposes. The final 'stabilization' task involves a steady-state simulation engine so as to ensure pre-fault system stability and initialize the time-domain simulation to be executed thereafter.
Changes included faster initialization, removing the GUI and extending the API. The API is used to calculate stability indices and stop the simulation once a diagnostic is available. For the post-fault impact assessment, eleven indices have been implemented in Matlab and integrated with the simulation engine using a C interface. Once the dynamic simulation and impact assessment tasks are complete, they result in a database filled with one record per sampled pre-fault condition. Each record contains all pre-fault steady-state variables and a symbolic value (stable/unstable) for each post-fault indicator and contingency. Security rules are then to be produced in the form of decision trees; one tree is created for each contingency-indicator pair. Note that the DT training process also involves a data cleaning stage, where each state variable is scanned for missing values; state variables with too many missing values are excluded from the training.
As each DT can be trained independently, the training process can be efficiently parallelized and dispatched to an HPC platform. To this end, the input data set is prepared and packaged as a memory mapped file that is dispatched to each node of the HPC platform. The use of a memory mapped file reduces the CPU and memory footprint on each processing node, thereby limiting the performance needs strictly to the DT training algorithm. Each node produces a DT for which a text file representation is committed to a dedicated centralized database upon process termination.
The second run, carried out afterwards, was run on 1,000 cores to train and test all decision trees. This process took a little over 9 hours to complete. For each run, the simulation outcomes are stored in two databases. The first is the results database. For the first run this contains the parameters describing each initial condition, and the resulting security indices for each contingency. For the second run, the results database stores the computed decision trees. The second database, the performance database, stores data on all tasks executed on the HPC platform (task descriptors, timings, message sizes, task exit codes etc. ) for performance profiling. In this section the performance data are described in detail, and the power system analysis results are summarized.
Note that CPU time is split between the time spent performing the ascribed task and time expended on input/output (I/O) transfers. Data transfers are divided into input, output and temporary locally generated files. The French system was modeled using 3,808 TSO-external stochastic variables; real and reactive power demand across all load buses (3,716 variables), real and reactive power across all cross-border lines (64 variables) and real power and voltage for all intermittent generation plants (28 variables). A statistical model consisting of 37 clusters has been parameterized on 2,976 historical observations (15-minute measurements from January 2013). Each invocation of the iTesla dispatching module schedules the generation of a batch of 128 samples. Subsequently, an instance of the sampling algorithm is launched for each cluster-model to generate the stochastic realizations.
Automatic Modular Design More recently,  Francesca et al. (2014)  proposed an automatic design method, called AutoMoDe, that provides a white-box alternative to evolutionary robotics. The robot controllers designed using AutoMoDe are behavior-based and have the form of a probabilistic finite-state machine. Robot controllers are obtained by combining a set of predefined modules (e. g. , random walk, phototaxis) using an optimization process that, similarly to evolutionary robotics, is driven by an objective function defined by the designer for each specific scenario. Using AutoMoDe,  Francesca et al. (2014)  designed an aggregation strategy for the same scenario as in  Garnier et al. (2008 Garnier et al. ( , 2009)) ,  Campo et al. (2010a). In their experimental setup, the swarm needs to select one of two equally good aggregation spots.
FUNDiNg This work was partially supported by the  European Research Council  through the  ERC Advanced Grant  " E-SWARM: Engineering Swarm Intelligence Systems " (contract  246939 ) to MD. GV acknowledges support from the  National Science Foundation  (grant  1505048 ). EF acknowledges support from the  Fund for Scientific Research (FWO) Flanders  (grant  12N7515N ), the  DIVINA Challenge Team framework of the Labex MS2T program , supported by the  French Government  through the program "Investments for the future" and managed by the  National Agency for Research (Reference   ANR-11-IDEX-0004-02 ), and the  European Regional Development Fund (ERDF) of the Regional Council of Hauts de France. MD acknowledges support from the  Belgian F. R. S. -FNRS , of which he is a Research Director.
Procedure Twenty-eight subjects (8 female) voluntarily took part in this experiment. They were chosen from university students and staff, with ages between 20 and 34 years (mean = 23. 6; SD = 3. 4). Three volunteers were left-handed, but no differences were found, concerning handedness. All participants were naïve as to the purpose of the experiment. Participants were first informed about the general procedure, and were subsequently equipped with seventeen tracking markers, the vibrotactile device, and the head-mounted display (HMD) (Figure  1 , left). A calibration step was required to build a biomechanical model of the subject, using the ART-Human® software (Advanced Realtime Tracking GmbH). A configuration file with the bone lengths was generated and used by the XDE engine (See "Apparatus") to create a morphological avatar of the subject in the VE.
Apparatus The experiment was conducted in a square area (3 x 3 meters) at CRVM (www. crvm. eu). Subjects were equipped with an Oculus Rift DK2 device. The tracking system (ArtTrack®), using optical trackers with eight cameras, was used to monitor the subject's full-body posture and movements (Figure  1 , left). The ART-Human® software was used to reconstruct the subject's skeleton from the tracked markers' positions. This body reconstruction was used to calculate a real-time and co-localized virtual representation of the subject's body in the Unity application (Figure  1 , top right). The real-time VR system operated at 60 Hz. Moreover, visual feedback (virtual environment and self-avatar) could be augmented by vibrotactile feedback. The vibrotactile device was based on an Arduino-like microcontroller communicating via Bluetooth with the PC on which the simulation was running.
The controller addressed ten vibrators, positioned on the subject's right upper limb (Figure  2 ); with variable amplitude using Pulse-width modulation (PWM). The controller activated vibrators (DC motor with an eccentric mass, from Parallax Inc. ref. 28822). Due to Bluetooth wireless connection and the vibrator activation, there was a delay between the instruction decision and when the vibration actually occurred. However it was never noticed by the subjects. The VR loop (motion capture to sensorial rendering) was controlled by a PC. The experimental software was developed with Unity3D. It allowed experimental control, data recording and all scenario actions. The XDE physical engine developed by CEA LIST (http://www-list. cea. fr/), integrated into Unity3D, allowed realistic and real-time physical simulation and human control from ART-Human data.
Data Analysis During the experiment, several behavioral indicators and events were recorded and analyzed. To reduce noise in the data, we discarded initial failed attempts to reach the target and we kept only the final (and successful) reaching movement for each individual trial. The main behavioral metric was related to the concept of visuoproprioceptive consistency. To analyze that, we computed (for the right upper limb segments) the Root Mean Square (RMS) of the distance between the real subject's 3D positions (obtained from the tracking system) and the avatar's positions throughout each individual trial. These positions (markers and avatar) had the same spatial frame of reference and were measured at the center of segments/bones. This integrated value gave us a direct indication of the subject's behavior with respect to physical objects of the environment.
Statistical analysis For every outcome of interest, the overall effect size for each comparison was determined as a weighted mean difference of the effect size estimates obtained from extracted data and presented as mean difference ± standard deviation (SD) and 95% confidence interval (CI). Tests used to assess heterogeneity were c 2 , t 2 , and I 2 , with greater relevance given to the latter. Statistical analyses for direct comparisons and heterogeneity tests were calculated using "R" statistical computing software with the "meta" package and Review Manager (RevMan) (Version 5. 2; Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) (with the DerSimonian and Laird method used for the calculation of t), which yielded the same results. We applied random effect models in all comparisons because random effect estimates are more conservative, and for clinical reasoning, since patient groups are heterogeneous within and across studies.
Studies were stratified into sedation <24 hours (short-term sedation) and sedation >24 hours (long-term sedation). Included studies were additionally stratified into randomized controlled trials (RCTs) and non-RCTs. We conducted a primary analysis of data from 27 RCTs and a broad analysis of these 27 RCTs plus eight non-RCTs to add generalizability to the results. While RCTs provide greater internal validity and help to identify causal relationships, the restrictive exclusion criteria for patient selection may result in lower external validity. 28 urther details on eligibility criteria, search strategy, data extraction, and statistical analysis can be found in the Supplementary materials.
Composite drug costs were calculated from the costs of sedative agents and analgesic drugs. Expenses for sedation are proportional to the consumption of sedatives. Costs were calculated for each individual as the sum of loading dose and maintenance infusion costs. Analgesic drug costs were calculated similar to sedative costs. The least expensive combination of vials to reach the total dose was calculated for all drugs. Since data on country-specific hospital costs of benzodiazepines in France and morphine both in the UK and USA were missing, the lowest unit costs among those reported for the other countries were used as a proxy.
Literature search We identified 346 publications (59 in PubMed, 287 in Scopus). Manual check of previous reviews and metaanalyses yielded 17 articles. Title, abstract and full text screening led to the exclusion of 328 articles. Details on the selection process can be found in the Supplementary materials and a flow diagram of article selection is shown in Figure  S1. Data were extracted from 35 papers identified in   S1  and S2  in the Supplementary materials. Sixteen studies reported data on short-term sedation,  29, 30, 35, 37, 38, 40, 42, 43, 47, 48, 50, 51, 53, [58] [59] [60]  16 studies investigated long-term sedation,  17, 19, [31] [32] [33] [34] 39, 41, [44] [45] [46] 49, [54] [55] [56] [57]  and three studies presented data on both. 18, 36, 52  Meta-analys Results of the meta-analysis are shown in Tables  3 4 5 6 , and corresponding forest plots are shown in Figures  S2-S5  in the Supplementary materials.
Acknowledgments We thank  Fresenius Kabi Deutschland GmbH  for supporting this publication with an unrestricted educational grant to  AdRes HE&OR  for statistical analysis and modeling. We would like to acknowledge  Dr Marco Bellone  and  Dr Orietta Zaniolo ,  AdRes Health, Torino , for statistical support with the meta-analysis,  Mahmoud Hashim  from  AdRes Health , for critically reviewing the economic model, and  Antonio Aliano  from  AdRes Health , for early collaboration on the project. Furthermore, we thank  Dr Mario Pahl  and  Dr Aike Schweda ,  DBM Wissen schafft GmbH, Mühlhausen, Germany , who helped with medical writing funded by  Fresenius Kabi Deutschland GmbH.
E. D. Raimondi, P. Comon and O. J. Michel are with GIPSA-Lab, CNRS, Univ. Grenoble Alpes, 38000 Grenoble, France (e-mail: firstname. lastname@gipsa-lab. grenoble-inp. fr). R. Cabral Farias is with Université Côte d'Azur, CNRS, I3S, France. This work was supported by the ERC Grant AdG-2013-320594 "DECODA". in  [17]. A major advantage of deterministic tensor analysis involving time or frequency as diversities, lies in its ability to estimate source waveforms together with DoAs, with no need to estimate second or high order statistics from the data. Thus, tensors can deal with very short data samples. MUSIC, ESPRIT and tensor decompositions for array processing originally addressed the narrowband case. In fact, in wideband, signal subspace and steering matrices vary with frequency, thus requiring a focusing operation on the same reference subspace.
Even with the most sophisticated bioinformatics target prediction tool, the large proportion of false predictions may be prohibitive to enable further analysis. To deal with this issue, sRNA target analysis can be run from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by biologist experts who needs to confront various biological features to prioritize the target candidates. Pathway analysis tools intend to deal with these issues. Accordingly, one of the most famous software, Cytoscape  [11] , supports many features dedicated to the visualization and exploration of large networks. The addition of plugins to Cytoscape is also needed for integrating networkdedicated functionalities.
For instance, the ReNE plugin  [12]  enables to query biological databases for building a regulatory network while integrating external heterogeneous data. While transcription factors and microRNAs can be analyzed together, the main limitation lies in the fact that ReNE only supports the analysis of two eukaryotic organisms. In the annotation of gene set using enrichment strategies, the most popular plugins are Bingo and ClueGO  [13, 14]. At the moment no integrated tool provides functionalities dedicated to small RNAs to support their analysis by making use of annotations together with interaction and regulatory motif information. However, these issues need to be further investigated. In this context, we introduce the rNAV 2. 0 software, the first available release of the prototype presented in  [15]  as a proof of concept (for a comparison of its functionnalities with in addition Cytoscape, see Additional file 1).
Technical aspects rNAV 2. 0 have been mostly developed in C++ but also in Python and is provided under LGPL. The software is based on Tulip framework  [18]. Tulip is an information visualization framework for the analysis and the visualization of relational which provides a complete library for the design of interactive visualization applications. The framework also enables the development of plugins to integrate new algorithms, visual encodings, interaction tools and also domain-specific visualizations. Following Munzner's recommendation  [19] , Tulip also supports the implementation of user interface overlays and domain specific software. The Model-View-Controller architecture of Tulip allows rNAV to support multiple and synchronized views. Any interaction on a view (e. g. selection of an element) implies the automatic update of all views displaying this data.