dataesr/scientific-paragraphs-categorization huggingface dataset:
EEG Recordings and Analyses The electroencephalogram (EEG) was recorded from 58 Ag/AgCl electrodes using an elastic cap (Electrocap International) with a sampling rate of 500 Hz in a frequency band ranging between 0. 5 and 100 Hz. The left mastoid was used as a reference and the right mastoid was recorded as an active channel. The data were also re-referenced off-line to an average of the left and right mastoids for further analysis. The electrodes were placed according to the international 10-10 system. The vertical and horizontal electrooculograms (EOG) were recorded for control of eye blinks and eye movements. Signals were digitally filtered offline (Butterworth zero phase filters 1-100 Hz, slope 12 dB/octave; notch filter 50 Hz). Eye movement correction was accomplished by independent component analysis  (Vigário, 1997)  using BrainVision Analyzer (Brain Products, Gilching, Germany).
As shown in Figure  1D , we marked negative deviations in the range between -π/4 and 0 in blue (coded with "-1") and positive deviations in the range between 0 and +π/4 in red (coded with "+1"). Phase difference values beyond these ranges were marked green (coded with "0") and represent non-synchronization. In the case of two channels, A and B, a blue stripe in the diagram would mean that the phase of channel B precedes that of channel A, and a red stripe would mean that the phase of channel A precedes that of channel B. We then counted the number of data points that are phase-locked separately in each of these two ranges. Before counting, successive points in the defined range (between -π/4 and +π/4) with a time interval shorter than a period of the corresponding oscillation at the given frequency (T i = 1/f i ) were discarded from the analysis.
For both similarity measures, similarity was determined by Pearson's product correlation. We used modularity analysis for (1) identification of sequences of coherent states (81 × 81 correlation matrix indicating temporal network similarity), and (2) identification of node communities remaining stable or similar across time (580 × 580 correlation matrix indicating spatial/nodal network similarity). Compared to the k-means clustering analysis, which was used for investigation of FCD (cf. Hansen et al. , 2015) , modularity analysis uses optimization algorithms to detect optimized community structures and do not require pre-specification of the number of clusters or modules  (Newman, 2004). For this calculation, the modularity optimization method for weighted networks as implemented in the Brain Connectivity Toolbox  (Rubinov and Sporns, 2010)  was used.
Network Structure and Network Dynamics For representation of network structures and network dynamics, network coupling and corresponding network structures were determined for each moving window of 2 s with a time delay of 100 ms during the 10-s time period (81 windows in total). To determine the network properties, we set the connectivity threshold to 0. 26, which was always higher than the significance level determined by the surrogate data procedure, that is, networks at this threshold level always included significant connections. At this threshold, the cost level of the networks (i. e. , the ratio of the number of actual connections divided by the maximum possible number of connections in the network) was approximately 20%, corresponding to high sparsity networks and allowing more accurate examination of the network topology.
Introduction With the emergence of the linked data initiative and the rapid development of RDF (Resource Description Format) datasets, several approaches have recently been proposed for generating text from RDF data  (Sun and Mellish, 2006; Duma and Klein, 2013; Bontcheva and Wilks, 2004; Cimiano et al. , 2013; Lebret et al. , 2016). To support the evaluation and comparison of such systems, we propose a shared task on generating text from DBPedia data. The training data will consist of Data/Text pairs where the data is a set of triples extracted from DBPedia and the text is a verbalisation of these triples. In essence, the task consists in mapping data to text. Specific subtasks include sentence segmentation (how to chunk the input data into sentences), lexicalisation (of the DBPedia properties), aggregation (how to avoid repetitions) and surface realisation (how to build a syntactically correct and natural sounding text).
Context and Motivation DBPedia is a multilingual knowledge base that was built from various kinds of structured information contained in Wikipedia  (Mendes et al. , 2012). This data is stored as RDF triples of the form (SUBJECT, PROPERTY, OBJECT) where the subject is a URI (Uniform Resource Identifier), the property is a binary relation and the object is either a URI or a literal value such as a string, a date or a number. The English version of the DBpedia knowledge base currently encompasses 6. 2M entities, 739 classes, 1,099 properties with reference values and 1,596 proper-ties with typed literal values. 1 here are several motivations for generating text from DBPedia. First, the RDF language in which DBPedia is encoded is widely used within the Linked Data framework. Many large scale datasets are encoded in this language (e.
g. , MusicBrainz 2  , FOAF 3  , LinkedGeo-Data 4  ) and official institutions 5  increasingly publish their data in this format. Being able to generate good quality text from RDF data would permit e. g. , making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBPedia or describing, comparing and relating entities present in these knowledge bases. Second, RDF data, and in particular, DBPedia, provide a framework that is both limited and arbitrarily extensible from a linguistic point of view. In the simplest case, the goal would be to verbalise a single triple. In that case, the task mainly consists in finding an appropriate "lexicalisation" for the property. The complexity of the generation task can be closely monitored however by increasing the number of input triples, using input with different shapes 6 , working with different semantic domains and/or enriching the RDF graphs with additional (e.
g. , discourse) information. We plan to produce a dataset which varies along at least some of these dimensions so as to provide a benchmark for generation that will test systems on input of various complexity. Third, there has been much work recently on applying deep learning (in particular, sequence to sequence) models to generation. The training data used by these approaches however often have limited variability. For instance,  (Wen et al. , 2015) 's data is restricted to restaurant descriptions and (Lebret et al. , 2016)'s to WikiData frames. Typically the number of attributes (property) considered by these approaches is very low (between 15 and 40) and the text to be produced have a stereotyped structure (restaurant description, biographic abstracts). By providing a more varied dataset, the WebNLG datatext corpus will permit investigating how such deep learning models perform on more varied and more linguistically complex data.
Task Description In essence, the task consists in mapping data to text. Specific subtasks include sentence segmentation (how to chunk the input data into sentences), lexicalisation (of the DBPedia properties), aggregation (how to avoid repetitions) and surface realisation (how to build a syntactically correct and natural sounding text). The following example illustrates this. ( Given the input shown in (1a), generating (1b) involves lexicalising the OCCUPATION property as the phrase worked as, using PP coordination (born in San Antonio on 1942-08-26) to avoid repeating the word born (aggregation) and verbalising the 3 triples by a single complex sentence including an apposition, a PP coordination and a transitive verb construction (sentence segmentation and surface realisation). Relation to Previous Shared Tasks Other NLG shared task evaluation challenges have been organised in the past.
These have focused on different generation subtasks overlapping with the task we propose but our task differs from them in various ways. KBGen generation challenge. The recent KBGen  (Banik et al. , 2013)  task focused on sentence generation from Knowledge Bases (KB). In particular, the task was organised around the AURA  (Gunning et al. , 2010 ) KB on the biological domain which models n-ary relations. The input data selection process targets the extraction of KB fragments which could be verbalised as a single sentence. The content selection approach was semi-automatic, starting with the manual selection of a set of KB fragments. Then, using patterns derived from those fragments, a new set of candidate KB fragments was generated which was finally manually revised. The verbalisation of the sentence sized KB fragments was generated by human subjects.
The Surface Realisation Shared Task (SR'11). The major goal of the SR'11 task  (Belz et al. , 2011)  was to provide a common ground for the comparison of surface realisers on the task of regenerating sentences in a treebank. Two different tracks are considered with different input representations. The 'shallow' input provides a dependency tree of the sentence to be generated and the 'deep' input provides a graph representation where syntactic dependencies have been replaced by semantic roles and some function words have been removed. The focus of the SR'11 task was on the linguistic realisation subtask and the broad coverage of lin-guistic phenomena. The task we propose here starts from non-linguistic KB data and puts forward other NLG subtasks. Generating Referring Expressions (GRE). The GRE shared tasks pioneered the proposed NLG challenges.
Data As illustrated in Example 1 above, the training corpus consists of (D, T ) pairs such that D is a set of DBPedia triples and T is an English text (possibly consisting of a single sentence). This corpus will be constructed in two steps by first, extracting from DBPedia content units that are both coherent and diverse and second, associating these content units with English text verbalising their content. Data To extract content units from DBPedia, we will use the content selection procedure sketched in  (Mohammed et al. , 2016). This procedure consists of two steps. First, bigram models of DBPedia properties specific to a given DBPedia category (e. g. , Astronaut) are learned from the DBPedia graphs associated with entities of that category. Second, an ILP program is used to extract from DBPedia, subtrees that maximise bigram probability.
In the third step, annotators are asked to "align" triples and sentences that is, to remove from the sentence all material that is irrelevant to express the associated triples and vice versa, to remove any triples that is not expressed by the sentence. Statistics, Schedule and Funding The WebNLG shared task will be funded by the WebNLG ANR Project 9. We aim to produce a data-text corpus of medium size (between 10K and 50K data-text pairs) bearing on at least 5 different domains and consisting of input data containing between 2 and 5 RDF triples. Ideally, training data will be made available early in 2017 and testing will be carried out in early summer (May-June 2017).
During the second session (90 min. long), participants used MetaTutor to learn about the circulatory system. Participants had exactly 60 minutes to interact with the content during which they could initiate SRL processes or do so after a PA's prompt. MetaTutor was paused when participants were watching a video, taking a survey, and during an optional 5 minutes break half-way through the session. At the end of the session, participants were given a post-test and filled a questionnaire, the Agent Response Inventory (ARI)  [7] , which included questions on their perception of the quality of PAs' prompts. All participants completed their sessions individually on a desktop computer. Data coding and scoring. Six variables were extracted from the pre-test and posttest questionnaires (two equivalent 25-item multiple choice tests on the human circulatory system), the ARI questionnaire, as well as from the system log files (cf.
PRACTICAL APPLICATIONS: COMPUTERIZED TOOLS The approach presented here needs computing tools, because, if the underlying formalization is simple, the calculations -particularly the manipulation of the complete system of inaccuracy intervals -are quite boring manually. There again, it is a work in progress, and the complete integration of the process within a single application is not finished yet. Nevertheless, two applications already exist and allow handling automatically a wide part of the process. The first one is Le Stratifiant: a stratigraphic data processing application developed since 2005, as an add-on to the Microsoft Excel software  [6] ,  [7]. Its functionalities include stratigraphic graphs generation from sets of data (units and relationships) Figure  12. "Estimated time" provided to a unit i (between an estimated beginning at the earliest eBe and an estimated end at the latest eEl) if estimated inaccuracy intervals have been valued besides the certain limits.
It contains formula to solve inequations and to reduce the intervals from known endpoints and default values chosen by the user. It is not yet integrated in Le Stratifiant and it doesn't process stratigraphic relationships (or relative time constraints); but for each unit, it detects logical faults of dating, gives the possible, certain and/or estimated time; and it provides quantified time graphs (Figure  15Chronophage  tool (formulas and macros in LibreOffice Calc free software)). These tools are free and available (in French) at: https://cours. univ-paris1. fr/fixe/03-40-doctorat-archeologieatelier-sitrada (section: "outils téléchargeables").
g. , foraging only one resource). Tournament selection with various tournament sizes (k) will be tested, from k = 1 (i. e. , mEDEA, emphasizing exploration) to k = 50 (i. e. , selection largely favoring exploitation). Figure  5  shows the results obtained with different tournament sizes (k = 1, 2, 3, 5, 10, 50), population size (100 and 500) and densities (starting from the minimal density wrt. population size). For each parameter sets, 160 independent runs are conducted (i. e. , a total of 5760 runs). Results are compiled from the data of the last generation for each run and shown as violin plots to capture the details of possibly non-uniform distributions (i. e. , full histograms, rather than box-plots). FigUre  7  | The level of specialization (at the end of evolution) within populations depending on the number of active agents.
Conceived and designed the experiments: J-MM, SC, and NB. Analysed the data: J-MM, SC, and NB. Performed the experiments: J-MM and SC. Coordinated the writing: NB. acKnOWleDgMenTsThis work is supported by the European Unions Horizon 2020 research and innovation programme under grant agreement No 640891, and the ERC Advanced Grant EPNet (340828). Part of the experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER, and several Universities as well as other funding bodies (see https://www. grid5000. fr). The other parts of the simulations have been done in the supercomputer MareNostrum at Barcelona Supercomputing Center -Centro Nacional de Supercomputacion (The Spanish National Supercomputing Center).
The latter involves two steps: Tier 1 processing that makes the bit-plane processing and entropy encoding based on binary AC, and Tier 2 that organizes the final bitstream. JPEG2000 considers segment and synchronization markers, which reduces the quality loss in the presence of transmission errors, but still not sufficient for severe wireless environments. An extension of JPEG2000 is proposed in Part 11 for image compression dedicated to wireless multimedia applications: JPEG2000 Wireless (JPWL)  [4]. This standard offers many tools to make the compressed bitstream more resilient against errors. One of these tools is the use of APP layer error correcting codes to protect the compressed data and the headers. The standard proposes the use of Reed Solomon (RS) codes over (2 8 ) and gives a choice between many RS codes with different error-correction capabilities.
The new standard is called HEVC for High Efficiency Video Coding. Compared to H264/AVC, HEVC doubles the data compression ratio at the same level of video quality. However, H264/AVC and HEVC do not support scalability, and their application to variable rate systems is not guaranteed. In this context, scalable extensions were proposed. The main expectation of H264 Scalable Video Coding (H264/SVC)  [5]  is to support temporal, spatial, and quality scalability with similar coding efficiency as a H264/AVC. The temporal scalability is performed by the SVC codec by introducing the concept of hierarchical B-picture coding. Interlayer prediction mechanisms are also introduced for spatial scalability, and quality scalability is performed using a medium-grain quality scalability (MGS). In this chapter, we mainly focus on scalable multimedia coding techniques, then we will consider the H264/SVC codec with quality and temporal scalability.
We also demonstrate that this is possible by using scalable techniques such as UEP, UPA, adaptive modulation, and scalable compression. Second, we demonstrate the efficiency of the proposed optimization procedure for JPWL image compressed data and for H264/SVC video compressed content. Third, we show that using soft decoding methods for the demodulator, the channel decoder, and the JPWL image decoder can provide significant quality gains while keeping the same throughput. The results are provided based on simulation results of the visual objective and subjective quality. This seminal chapter resumes many results  [42, 43]  developed in the XLIM-RESYST team of University of Poitiers, France, and some of them  [44]  were under a cooperation with the SYSCOM laboratory in Tunisia.
Context-based precoding for JPWL image transmission In this part, we suppose that the channel coding and modulation are static. However, the power allocation will be optimized. The system model treated in this part is depicted in Figure  1  and aims at transmitting the compressed data using a precoded MIMO system. After diagonalization, the equivalent channel matrix, given by the CSI, will have b virtual independent single antenna channels with different SNRs. Then, the scalable JPWL encoder will be asked to generate b quality layers that should be transmitted across the b virtual channels. Naturally, the first quality layer, which is the most important, will be assigned to the highest SNR subchannel. Then, according to their importance order, every quality layer will be assigned to a specific subchannel.
Then, the JPWL source encoder will compress the test image "Monarch" and deliver four quality layers with 0. 25 bits per pixel (bpp) each. We use an equal error protection method with a fixed RS  (37, 32)  code and a fixed M-QAM modulation with = 4. Figure  3  shows the PSNR results as a function of the receiver position for different precoding strategies. For better readability, we present the mean value over 20 samples with a sliding window for all the schemes. The presented precoding methods have the same principle with different optimization constraints. While water filling (WF) aims at maximizing the channel capacity, minimum mean square error (MMSE) tends to minimize the mean square error -² , and MBER stands for minimizing the BER. E-d min is a non-diagonal precoder that focuses on maximizing the distance between the constellation points.
Now, we run simulations to investigate the gains induced by the link adaptation technique in the case of 4 × 4 MIMO for the same realistic channel. We also assume the same channel estimation process. The reference static configuration for the test image "Monarch" 768 × 512 pixels considers a JPWL encoder generating four quality layers having each a constant rate of 0. 125 bpp. The error correcting code is a RS  (37, 32) , and the modulation is 4-QAM. The link adaptation optimized system always considers a 4 × 4 MIMO and can choose dynamically the number of subchannels to use ∈ 1, 2, 3, 4 , the modulation order for each subchannel M i ∈{4, 16, 64}, the corresponding RS encoded sequence length N i ∈{37, 38, 40, 43, 45}, and source encoding rate R Si. The power limitation is set to P 0 = 1, the objective BER to achieve is BER t = 10 -9 , and the rate is constrained by a maximum of 512 OFDM symbols per subchannel.
Soft-decoding methods for image transmission As specified in Section 2. 1, joint source channel decoding is also a good solution to improve the image/video reconstruction quality. While CBP and link adaptation focused on how to transmit efficiently the image/video compressed information, soft decoding algorithms can enhance the quality of the reconstructed data without introducing extra redundancy. The aim of this section is to improve the performance of JPWL image transmission over highly corrupted noisy channels. We focus on a system where the image is compressed with a JPEG2000 encoder, then protected by a RS code, and finally modulated and transmitted on single antenna noisy channel. Unlike conventional hard decoding methods, no decision has to be made on the received samples. The latter gives an extra information about the decision reliability, which can be exploited to further improve the system decoding performance.
Furthermore, the information seekers in the health domain also experience difficulties in expressing their information needs as search queries. CLEF eHealth aims to bring together researchers working on related information access topics and provide them with datasets to work with and validate the outcomes. The vision for the Lab is two-fold:  (1)  to develop tasks that potentially impact patient understanding of medical information and (2) to provide the community with an increasingly sophisticated dataset of clinical narrative, enriched with links to standard knowledge bases, evidence-based care guidelines, systematic reviews, and other further information, to advance the state-of-theart in multilingual information extraction and IR in health care. Furthermore, we aim to support reproducible research by encouraging participants to reflect on methods and practical steps to take to facilitate the replication of their experiments.
The subtasks within the IR challenge were similar to 2017's: ad hoc search, query variation, methods to personalize health search, and multilingual search. A new subtask was also introduced this year which required participants to classify queries with respect to the underlying query intent as detailed in  [3]. Query variations were generated based on the fact that there are multiple ways to express a single information need. Translations of the English queries into several languages were also provided. Participants were required to translate the queries back to English and use the English translation to search the collection. The remainder of this paper is structured as follows: in Sect. 2 we detail the tasks, evaluation and datasets created; in Sect. 3 we describe the submission and results for each task; and in Sect.
Text Documents Task 1. The multilingual information extraction: ICD10 coding of death certificates task challenged its participants to information extraction in written text with focus on unexplored languages corpora, specifically French, Hungarian, and Italian this year to supplement last year's task on French and English. Its data set, called the CepiDC Causes of Death Corpus, comprised free-text descriptions of causes of death as reported by physicians in the standardized causes of death forms. Each document was manually coded by experts with ICD10 per international WHO standards. Task 2. The technologically assisted reviews in empirical medicine task used the PubMed document collection for its Boolean Search challenge and a subset of PubMed documents for its challenge to make Abstract and Title Screening more effective.