dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, LRTs are lightweight processes whose only purpose is to execute actors. LRTs can be implemented over heterogeneous types of PEs, such as general purpose or specialized processors and accelerators. The steps of the execution scheme of SPiDER are also depicted in Figure  1. First, the GRT analyzes the PiSDF graph and performs the mapping and scheduling of the different actors composing the graph. From the resulting mapping and scheduling, the GRT creates jobs that are sent to the dedicated job queues of each LRT , which are, in turn, associated with each PE. Specifically, a job is a message that embeds all data required to execute one instance of an actor: a job ID, location of actor data and code, and the preceding actors in the graph execution. When an LRT starts the execution of an actor , it waits for the necessary data tokens to be available in the input FIFO specified by the job message, among a pool of FIFOS.
During the execution of this function call, the eventSets will be analyzed, associating each event with its corresponding PAPI component, and they will be linked to the actor being monitored. It should be noted that several actors could have the same monitoring configuration and, hence, they will share the same eventSetId to simplify the monitoring. Once system monitoring has been configured, the code instrumentation follows a start-stop strategy. By doing so, the relevant parts of the application can be independently analyzed. Although having a local start-stop monitoring strategy increases the flexibility of what can be monitored, it also could introduce a larger overhead compared to sampling monitoring strategies. Furthermore, the library has been built taking into account the possibility to just monitor the execution time of the application, to retrieve only hardware PMC information, or to profile the system based on both data sources.
• event_stop(): reads the current values of the eventSet PMCs and computes the differences of corresponding event values. It should be highlighted that, compared to the previous version of PAPIFY, the monitoring is now performed by differences instead of starting-stopping the PMCs every time, reducing the number of kernel interruptions and, thus, reducing the overhead. Finally, the store stage is composed of only one function, event_write_file(), which has as input parameter the PapifyInfo structure. Currently, the performance information is written into a. csv file. However, this isolation allows the user to define its own store function to, for example, send the data directly to a resource manager (e. g. , to be used for further processing and decision making towards self-adaptation). To sum up, the code instrumentation using eventLib is shown in Code template 1.
This example shows that the instrumentation of the code is based on eight function calls: one to initialize PAPI and eventLib libraries (line 2), two to configure the system monitoring (lines 4-5), four to control the actual monitoring (lines 7-8 and lines 12-13) and one to save the PMC data (line 15). Once the run-time behavior has been described, it can be easily understood that, currently, PAPIFY is able to monitor not only static (PREESM) dataflow applications, but also dynamic (SPiDER) ones, where a resource manager is redistributing the application workload among the available PEs at run-time. On the one hand, for the static applications, all the required eventSets will be configured during the first iteration of the application and, after that, only KeepCounting and FastSwitching states will be reached, reducing considerably the impact of the monitoring overhead.
D. PAPIFY-VIEWER Regarding the analysis of the monitoring information, to help developers in reviewing all the retrieved hardware PMC data, a visualization tool called PAPIFY-VIEWER has been included in the toolbox. PAPIFY-VIEWER displays performance data at both execution and post-execution times. It has two different views: one represents actor-PE workload distribution and the other displays actor hardware utilization. Fig. 5  depicts an example of the workload distribution. As can be seen, on the right side of the image there is a legend in which each actor is associated with a different color. Likewise, the horizontal axis represents the timeline of the application execution. In the vertical axis, each core is represented. Finally, actors execution is displayed as a set of rectangles, whose left and right limits are the starting and finishing times of each actor execution, respectively.
C. REAL-TIME MONITORING WITH PAPIFY-VIEWER As a final part of the analysis of PAPIFY toolbox, PAPIFY-VIEWER is characterized in terms of overhead. This viewer, implemented using Python, can be employed for both static and dynamic application analysis. Additionally, it can be not only used to analyze the data after the application execution, but also to study the system behavior at run-time. However, its use at run-time implies an additional overhead. As the offline usage has no impact on the application performance and both PAPIFY-VIEWER views have already been described in Section III-D, this study will be focused on the overhead at run-time. Table  3  and Table  4  present the performance and overheads associated with the use of PAPIFY-VIEWER to monitor Sobel-morpho and Stereo-matching applications at run-time, respectively.
Secondly, the Automatic Instrumentation of the application is only available in four of the tools compared. In these cases, the code required to monitor the system is included within the application while, in the rest of tools, the monitoring is performed by a standalone application running in parallel. Considering the large amount of information that is retrieved by accessing the PMCs, the use of a graphical interface to analyze the data seems to be advisable. This characteristic is only addressed by four of the tools considered in this comparison. With regard to Dataflow Oriented Monitoring, to associate the resource utilization with each part of the system execution, PAPIFY has been integrated with both PREESM and SPiDER tools. By doing so, the analysis is performed using a dataflow oriented perspective.
Slaheddine Aridhi from Texas Instruments, France. He has coauthored more than 20 articles in peer-reviewed international journals and conferences. His research interests include dataflow models of computation and associated implementation techniques for the rapid prototyping of applications running on heterogeneous MPSoCs. In particular, his Ph. D. thesis focuses on the memory characterization and optimization of dataflow applications on MPSoCs. He has been actively involved in several projects including H2020 Project (CERBERO), French ANR Projects (COMPA, ARTEFACT), U. S. NSF Project (COMPACTS-SL-MODELS), and a young researcher project funded by the French research society ''GdR ISIS'' (MORDRED), which he leads. He took part in the creation of the MTAPI standard with the Multicore Association.
This work was supported in part by the  European Union 's  Horizon 2020 Programme  through the  CERBERO under Grant   732105 , in part by the  Ministry of Economy and Competitiveness (MINECO)  of the  Spanish Government  through the PLATINO under Project  TEC2017-86722-C4-2-R , and in part by the  Universidad Politécnica de Madrid  through the  Programa Propio Predoctoral  under Contract  RR01/2016  and Contract  RR01/2015.
Porosity study The raw images were then segmented using Image J (Fiji), the coil segmentation was verified by comparing the volume calculated numerically from the scans, with the one obtained from the coils technical specification. In order to study the porous media, an artificial sac was created using the convex hull function in Matlab (MathWorks, Inc. , USA), which build an envelope around the coil as the aneurysmal sac does. This convex hull allows to study the porosity distribution within an artificial sac in a systematic way, avoiding the difficulty to define the sac at the aneurysmal neck. Figure  1  shows the steps of the method from the CT scan to the convex hull envelope. The porosity distribution and gradients within the artificial sac were then investigated following two ways: (i) firstly, porosity profiles have been computed along the three main inertial axes of the coil, (ii) secondly, a 3D map of the aneurysm's porosity was set.
Control of water deficit After six weeks of growth, plants of each genotype were randomly assigned to either a control or a drought treatment for five weeks (day 0 was 21 May 2013). Control plants were watered to 85% soil relative extractable water content (REWsoil) by the automated system for the whole experiment. REWsoil of control plants oscillated between 65 and 85% (data not shown). For drought-treated plants, REWsoil was progressively decreased to reach 20% in two weeks and then maintained at this target level for the following three weeks (Fig. 2 ). The control of the available soil water content (SWC) was based on a calibration between volumetric SWC measured by Time Domain Reflectometry (Trime Pico-32, IMKO) and pot weight. Target weights were defined individually for each pot, and were updated every day during the first two weeks to control the SWC decrease (Fig.
29 and the contigs were assembled and aligned using MEGA 5. 0  (Tamura et al. , 2011). The sequences were submitted to Genebank with the accession number: Populus_nigra_Drome_6. sqn Populus_nigra_Drome_6 MK757467. At the genomic level, a few DNA mutations were found in rbcL of the three genotypes but all of them were synonymous and provided an identical amino acid sequence. Therefore, the functional characterisation of Rubisco catalytic traits was performed only in the French genotype.
0, 22 mM MgCl2, 0. 4 mM RuBP and about 100 W-A units of carbonic anhydrase), and equilibrated either with nitrogen (N2) or a mixture of O2 and N2 (21:79). Assays (1. 0 ml total volume) were started by the prompt addition of 10 µL of activated leaf extract, and quenched after 1 min by the addition of 0. 2 ml of 10 M formic acid. Acid-stable 14 C was determined by liquid scintillation counting, following removal of acid-labile 14 C by evaporation. The Michaelis-Menten constant for CO2 (Kc) was determined from the fitted data as described by  Bird et al. (1982). Replicate measurements (n = 4-5) were made using independent protein preparations from different individuals. For each sample, the maximum rate of carboxylation (Kcat c ) was extrapolated from the corresponding Vmax value after allowance was made for the Rubisco active site concentration, as determined by [ 14 C]CPBP binding  (Yokota & Canvin, 1985).
Acknowledgments : We thank  Alexi Marchal ,  Josselin Groux ,  Carole Antoine ,  Nathalie Aubry  and  Billy Valdes-Fragoso  for their help with leaf surface area and growth measurements and  Christian Hossann  who performed the isotopic measurements at the  Plateforme Technique d'Ecologie Fonctionnelle (PTEF)  ( OC 081 ,  INRA Nancy, France). We acknowledge the providers of the original P. nigra genotypes 'France  6J-29 ' (INRA, Paris, France represented by  G. Pilate ) and ' Spain RIN2-new ' ( CITA,Zaragosa, Spain ,.
Statistical analysis To test for the effect of training on bee performance (number of correct choices), data from the learning phase of 100 choices were analyzed with a generalized linear mixed-effect model (GLMM) with a binomial distribution using the "glmer" package within the R environment for statistical analysis. We fitted a full model with trial number as a continuous predictor and subject as a random factor to account for repeated choices of individual bees. To determine whether the bees were able to learn to follow additional and subtraction rules, we analyzed the test data by using a GLMM including only the intercept term as fixed factor and subject as a random term. The mean proportion of "correct" choices (MPCC) recorded from the tests were used as a response variable in the model.
Profiling Section It is the heart of the logical model of OpinionML which acts as a database of all elements of OpinionML. Details about each element are added by extracting information from the given text. Only unique entities of each type are kept and information is not repeated on same entities. This information acts as a profile of an entity whether it is of type holder, target or modifier, etc. Each element is given a unique ID which can be referenced from other sections to leverage this element. In short, the profiling section is like a section where variables of a program are defined using a programming language.
OpinionML Ontology We also propose an opinion ontology (called OpinionML ontology). OpinionML Ontology aims to complement the Marl Ontology  [40]  and Onyx Ontology  [41]  by providing a simple means to describe opinion analysis processes and results using semantic technologies. Figures  2  and 3  depict the simpler versions of our proposed OpinionML ontology. Figures  4  and 5  shown (Part 1) and (Part 2) Class diagram of OpinionML ontology respectively. A more recent work on sentiment analysis using ontology called OntoSenticNet  [42]  is also worth noting. However, OntoSenticNet is a commonsense ontology which does not as such relate to sentiment ontology and hence can be ignored for comparison with OpinionML. The goals of the OpinionML ontology to achieve as a data schema are:.
• To make it enable in order to publish the raw data about opinionated text in user-generated content, • To construct a model that helps to compare the opinions automatically, coming from different sources having different polarity, topics, features etc. , • To link the opinions using contextual information expressed in concepts with other popular specialized ontologies. The detail about the terms used in OpinionML ontology are given below. These terms are classified by class (concepts) and by property (relationships, attributes). The OpinionML ontology class diagram (see Figures  4  and 5 ) shows connections between classes and properties used for describing opinions. We used protege http://protege. stanford. edu for creating ontology and Graffoo  [43]  for its visualization.
e. , polarity of modifiers) to a textual segment containing opinion (i. e. , <opinion>) and then to aggregating them on object and document level. OpinionML combines polarity and emotion components in a coherent way by representing both in a similar fashion. On comparing all three ontologies, it seems that Onyx resembles OpinionML in terms of its dealings with emotions but lacks on complete representation of opinions. We detail the advantages of OpinionML as follows. • Linked Data and Knowledge Base Support: One of the biggest advantage of using OpinionML is its support for linked data. All the entities and concepts are identified with Universal Resource Identifiers (URIs) that can be used to link to external resources like knowledge base DBpedia, etc. It will help enrich more information about entities where information is lacking.
• Ontology-Based Indexation: The support for our proposed opinion ontology facilitates the ontology-based indexation of documents which will definitely ensure the efficient processing of OpinionML documents. • Domain Independent Support: OpinionML is not limited to textual data annotation of a certain domain but it holds the capability of supporting data from all domains i. e. , product reviews, news data, tourism, social network data, etc. • Granularity Independence: Annotation schemes generally restrict their users to limit themselves to annotation of one granularity level like document, sentence, sub-sentences or passages but OpinionML gives liberty to its users to choose a granularity level of their own choice by identifying them as fragments. A fragment is supposed to be a basic semantic unit having a semantic orientation.
• Redundancy Reduction: The logical model of OpinionML helps to reduce redundancy by keeping profiles of opinion elements separately from actual annotation. Hence, annotation only keeps references of those opinion elements. This model facilitates the modifications in annotations and improves the comprehension of the annotations. • Modular Approach: OpinionML elements are equipped with rich information about the text to be annotated. This information is split into different modules to save readability and comprehension of the annotation. References are created among several elements of the OpinionML to create a link between several modules. The < Pro f iling > module acts like a database of all OpinionML elements where information about all elements is kept while in < Opinion >, a collection of several elements are combined to give some semantics of the text.
• Online Political Speeches: This part of the data collection consists of text documents describing political speeches of US presidents. • TED Talks: Second source of the data collection is supposed to be Technology, Entertainment, Design (TED) talks on several topics revolving around the theme "ideas worth spreading". • MPQA Human Rights: Third source for the used data collection is an extraction from popular MPQA opinion data collection  [46]. The "human rights" part of the mentioned data collection is used for this purpose. It is to be mentioned that MPQA original data collections consist of texts with opinion and other emotional annotations of opinions. The preparations of the data collection involve annotating the chosen data collection with OpinionML.
OpinionML Annotation For data collection to be annotated using OpinionML, the following operations are to be performed: • identifying opinionated statements within a document, • identifying (possible) phrases within a statement, • identifying opinion holders in the sentence, • identifying the object about which the opinion was (or target) made in a sentence, • identifying different aspects of target within extracted phrases, • extracting sentiment of the target from the opinion expressed in a sentence, • assigning an appropriate title of the given sentence, • identifying opinion holder at phrase level, • identifying the possible expressions at temporal level within sentences. For annotating data collection with OpinionML, we hire five persons having a computer science background in their major studies and having some experience with data annotations.
In vitro methylation reactions were performed with tetramers containing Paramecium histone H3 and with FLAG-HA double-affinity purified Ezl1 wt and Ezl1 H526A , or with tetramers only as negative controls. Under the assay conditions, Ezl1 catalyzes trimethylation of K9 and K27 (Fig. 2e , Table  1 , and Supplementary Data 1). We could detect the existence of other sites of methylation within the N-terminal part of histone H3, in particular K23 (Supplementary Data 1), but only the methylations on K9 and K27 are significantly reduced in the catalytic Ezl1 H526A mutant (Table  1 , Fig. 2e , and Supplementary Data 1). Detection of methylation on other lysines provides an explanation for the residual activity we detected when lysines 9 and 27 were mutated, either on Xenopus or Paramecium H3 (Fig.
b Histone methyltransferase activity on recombinant histone octamers (rOct) and on oligonucleosomes (rNuc). c Substrate specificity. Histone methyltransferase assays were performed with recombinant histone octamers assembled with wild-type H3 histones or with H3 histones bearing a point mutation on lysine 9 (K9A) or on lysine 27 (K27A) or on both (K9A/K27A) on the same histone H3 tail. The same reaction without addition of histone octamers was performed as a negative control. For Ezl1 wt and Ezl1 H526A , the horizontal bars indicate the mean of three biological replicates (independent nuclear extracts). Circles indicate the individual data points. d Full-length 3xFLAG-HA tagged Ezl1 wt and Ezl1 H526A proteins were immunoprecipitated using a FLAG-HA double-affinity purification from Paramecium transformed cells, depleted for the endogenous Ezl1 protein in the case of the mutant FLAG-HA-Ezl1 H526A , and used in in vitro histone methyltransferase reactions (see Supplementary Fig.
2 ). Histone methyltransferase assays were performed with recombinant histone tetramers assembled with wild-type H3 from Paramecium or with Paramecium H3 histones bearing point mutations on both K9 and K27 (K9R/K27R) on the same histone tail. The horizontal bars indicate the mean of two or three biological replicates (independent nuclear extracts) for Ezl1 wt and Ezl1 H526A , respectively. e Schematic representation of quantitative label-free mass spectrometry analysis of Paramecium histone H3. See Table  1  and Supplementary Data 1. Owing to the insertion of one residue at position 14 in the Paramecium H3 sequence (see Supplementary Fig. 2 ), positions in the Xenopus and Paramecium H3 sequences differs by one amino acid gap after that position (Supplementary Fig. 2  and Supplementary Data 1).
We selected ions with charge state from 2 + to 6 + for screening. Normalized collision energy (NCE) was set at 27 and the dynamic exclusion of 20 s. For identification, the data were searched against the UniProt Paramecium tetraurelia (downloaded on 10 September 2018 and containing 39,463 sequences) database using Mascot (version 2. 5. 1). Enzyme specificity was set to trypsin and a maximum of five-missed cleavage sites were allowed. Oxidized methionine, Nterminal acetylation and Propionylation, Methyl lysine, Dimethyl lysine, Trimethyl lysine, Propionyl lysine, Methyl-Propionyl lysine, and carbamidomethyl cysteine were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks. The resulting files were further processed using myProMS v3.
6 (https://github. com/bioinfo-pfcurie/myproms)  59 , where we fixed the false discovery rate for peptide identification to <5%. All peptide identifications were manually inspected to ensure the quality of the analysis. To quantify the modified peptides, we extracted from the MS survey of nano-LC-MS/MS raw files the extracted ion chromatogram (XIC) signal of the well characterized tryptic peptide ions using Skyline (version 4. 1) MacCoss Lab Software, Seattle, WA; https://skyline. ms/project/home/sofware/Skyline/begin. view). The peptide XIC areas were log2 transformed and the mean log2-area was normalized by the mean area of peptide A[Propionyl (N-term)]RTK[Propionyl (K)]QTAR using the R statistical computing environment version 3. 1. 0. On each peptide a linear model was used to estimate the mean and the 5% confidence interval of each condition.
The associated ratio and p-value have been computed thanks to a two-sided t-test with the Welch approximation of the number of degrees of freedom. Protein structure modeling. ClustalW 60 was used for sequence alignment of target human EZH2 (Q15910) sequence and template Ezl1 (PTET. 51. 1. G1740049) sequence. Modeller v 9. 19  61  was used for comparative homology modeling of Ezl1 SET domain using the same criteria as in ref. 62. The structure of EZH2 in interaction with the peptide substrate analog and the S-adenosylhomocysteine (SAH) cofactor (PDB ID: 5HYN) was used as a template. Pymol molecular graphics system (Version 1. 7. 2. 1 Schrödinger, LLC) was used for comparative structural analysis between reference and modeled structures. Model quality was analyzed using PROCHECK  63. RNA-seq data analysis.
This study used the Paramecium tetraurelia strain 51 MAC (ptetraurelia_mac_51. fa) and MAC + IES (ptetraurelia_mac_51_with_ies. fa) reference assemblies, P. tetraurelia gene annotation version 2  34  and P. tetraurelia IES annotation  35. TE copies larger than 100 nt and not overlapping genes were identified using the TEAnnot module of the REPET-2. 5 package 64 on the P. tetraurelia MIC contigs  24  , using a manually curated library of 61 TE consensus sequences  24. TEannot provides the percent identity between TE copies and the consensus (used as a proxy for the age of the copy). Tandem repeats not overlapping genes or TE were identified on MIC contigs larger than 500 nt using Tandem Repeat Finder  24, 65. Developmental genes were identified by RNA-seq DE analysis of wild-type P. tetraurelia strain 51  34.