dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank students from the  Chair in Biogeography and Metacommunity Ecology  at  UQAR  for helpful comments on earlier versions of the manuscript. C. J. was supported by a grant from the  Ministry of Higher Education and Research of France. C. M. 's post-doctoral fellowship grants were provided by  Ressources Aquatiques Quebec  and the  NSERC  through the  Canadian Fisheries Research Network  and the  FQRNT. Financial support was provided by the  Canada Research Chair program  and a  NSERC   Discovery grant  to D. G.
Conclusions: We proposed a modelization strategy that can be used to increase the tools performances in context of chRNA classification based on a simulated data generator, that permit to continuously integrate new complex chimeric events. The pipeline incorporated a genome mutation process and simulated RNA-seq data. The reads within distinct depth were aligned and analysed by CRAC that integrates genomic location and local coverage, allowing biological predictions at the read scale. Additionally, these reads were functionally annotated and aggregated to form chRNAs events, making it possible to evaluate ML methods (classifiers) performance in both levels of reads and events. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95 % (ACC=94 %, Kappa=0.
For instance the well-known gene fusion in chronic myelogenous leukemia leading to an mRNA transcript that encompass the 5' end of the BCR gene and the 3' end of the ABL gene, producing the chRNA BCR-ABL. Most protocols used to identify chimeric transcripts rely on a reverse transcription step and the reverse transcriptase is known to switch templates, thus creating chimeric artifacts in vitro  [4]. Thus, it remains unclear what proportion of putative chimeric transcripts are true, and of these how many are translated. With the rapid evolution of genomic research, next generation sequencing (NGS) has become widely available. Sequences in databases or sequences directly from RNA-seq often contain information that leads to false results when they are analysed with bioinformatics approaches. This is partially due to the fact that the first stage of the available bioinformatic tools for finding chRNA basically rely on mapping, where reads are aligned with respect to the reference genome used by the algorithm.
Additionally, the length and depth of sequencing might be inadequate for covering all exon junctions, and given that repeated sequences are present, clearly, the alignment of sequences to specific regions is highly biased to sequencing. Next to mapping, a set of filters based on several technological or biological assumptions are applied to reduce the set of putative chRNAs. Many tools have been developed to detect chRNAs  [5] , though many complications arise when trying to provide a complete benchmark. Each one of these tools have particular constraints regarding installation, parametrization, usability, and database(s) or software(s) dependencies. Liu et al. [6]  provided an overall evaluation of thirteen strategies, suggesting the combination of three of them as a robust solution. In fact, there is a trend to explore combination of algorithms to boost the analysis of chimera detection.
However, a good combination of software requires proper criteria of selections based on metrics according to the biological question in order to choose the most suitable ones. In fact, the basic information shared in common by all these softwares is the initial sequenced reads which is distinctively explored by each of them. In other words, each software integrates its own algorithm and different external features and database to define proper margins or "rule(s)" to classify potential chRNAs. If one is able to make such abstraction (or rules) from the reads, it might be an interesting alternative to directly explore models generated from classifiers. In this work, we propose a different angle of discussion, model-oriented rather than comparing software for chRNAs detection which are not necessarily comparable because there are questions-oriented.
Indeed, the key component behind ensemble success is the concept of classifier diversity  [7]. Classifier diversity can be achieved in several ways. The most known approach is to use different training datasets to train individual classifiers. Such datasets are often obtained through resampling techniques, such as bootstrapping or bagging, where training data subsets are drawn randomly, usually with replacement, from the entire training data. Most of the available gene fusion-finders methods  [5, 10]  do not explore the potentials of applying ML techniques to this classification problem. As far as we are aware of, only two gene fusion-finders, namely EricScript  [11]  and deFuse  [12] , have explored AdaBoost. AdaBoost generates a set of hypotheses and combines them through weighted majority voting of the classes predicted by the individual hypotheses.
Feature engineering We have developed a benchmark pipeline (Fig. 1 ) using the above genome simulation procedure along with i) the generation of corresponding RNA-seq reads by Flux Simulator  [14]  and ii) labelling chRNAs classes through the use of CRAC Software  [15]. CRAC has been chosen to perform this experimental study for two main reasons. First of all, it is a splice-aware mapping software that integrates the identification of chimeric junctions with a high confidence ratio. The second reason is that CRAC's algorithm is based on the study of kmer support and location profiles which provides for every k-mer of a read its number of occurrences in the whole read collection and all its possible locations on the reference genome. These profiles are of an extremely valuable source Fig. 1  The benchmark pipeline covering mutations along the genome and simulation of RNA-seq data with Flux Simulator, followed by a chRNA labeling step with CRAC tool.
The goals of this study are to obtain a comprehensive study of mutations and gene expression in human acute myeloid leukemia (AML). All sequence data are freely available at Gene Expression Ominibus (GEO) database within the accession number GSE49642. From the previous simulation study, we observed that most of the false chRNA events belong to the "Class_1" (60 %), while the remaining false events are shared between "Class_2"(30 %) and, "Class_3" and "Class_4" together (10 %). We hypothesise that the same amount of sequence artefacts found in simulated data can be equally distributed on real data. Thus, it is expected that classifiers might be able to eliminate successfully many of these false events. Indeed, the classification results obtained after applying the pipeline highlight a significant reduction in chRNA class distribution (Fig.
The same amount of sequence artefacts found in simulated data can be equally distributed on real data (a). The classification results obtained after applying the pipeline highlight a significant reduction in chRNA class distribution (b) data generator, called SimCT (not yet published), that permit to continuously integrate new complex chimeric events. We have developed a benchmark pipeline incorporating a mutation genome process and simulated RNA-seq data to evaluate the fidelity of using classifiers to predict chRNAs. The simulated sequencing reads were aligned and annotated by CRAC. CRAC analyzes the RNA-seq data, integrating genomic location and local coverage, allowing biological predictions in one single step which is not available in any other fusion finder tools. Moreover, we enhance chRNAs classification by building models at both levels of read and event.
Forcing and model comparison data As a test of stability over a long-term run, the model was forced (i. e. run offline, independently from the atmospheric model) using above-canopy measurements. The forcing data that were used in this simulation was derived from the long-term Fluxnet measurements for the years 2002 to 2007, specifically above-canopy measurements of long-wave and shortwave radiation, temperature, humidity, wind speed, rainfall, and snowfall. The first 4 years of data, from 2002 to 2005, were used as a spin-up to charge the soil to its typical water content for the main simulation. The biomass from the spin-up was overwritten by the observed leaf biomass to impose the observed LAI profile. Soil carbon is not required in this study, which justify the short spin-up time. The years 2006 and 2007 were then used as the main part of the run.
Although the shortwave radiation was recorded at the field site in upwelling and downwelling components (using a set of directional radiometers), the long-wave radiation was not. As a consequence, the outgoing long wave was calculated using the recorded above-canopy temperature by the Stefan-Boltzmann law with an emissivity factor of 0. 96 (a standard technique for estimating this variable; e. g. Park et al. , 2008). This value is then subtracted from the net radiation, together with the two shortwave components, to obtain an estimation of the downwelling long-wave radiation with which to force the model. For the validation of the within-canopy processes, more detailed measurement data were required. For the same site there exists data from an intensive campaign of measurements made during November 2006 (Austral summer), described by  Haverd et al.
(2009). Within the canopy, profiles of temperature and potential temperature were recorded over the 30-day period and, for a number of days (7-14 November), sonic anemometers were used to measure wind speed and sensible heat flux in the vertical profile at eight heights as well. Measurements were also made over the 30-day period of the soil heat flux and the soil water content. These within-canopy data were used for validation of the modelled output, but the same above-canopy long-term data (i. e. the Fluxnet data) were used in the forcing file in all cases. No further measurements were collected specifically for this publication. The measurement data (i. e. the data both from the 1-month intensive campaign and the long-term Fluxnet measurements at the same site; Ozflux, 2013) were prepared as an ORCHIDEE forcing file, according to the criteria for gapfilling missing data  (Vuichard and Papale, 2015).
Model set-up The multi-layer module that is described in this paper only calculates the energy budget. Its code was therefore integrated in the enhanced model ORCHIDEE-CAN (revision 2966), and relies on that larger model for input-output operations of drivers and simulations, as well as the calcula-tion of soil hydrology, soil heat fluxes, and photosynthesis (see Table  3  for other input). A more detailed description of how these processes are implemented in ORCHIDEE-CAN is provided in  Naudts et al. (2015). The ORCHIDEE-CAN model is capable of simulating the canopy vegetation structure prognostically, and these prognostic vegetation stands have now been linked to the multilayer energy budget calculations. In these tests, a vegetation profile was prescribed, in order to obtain a simulation as close as possible to the observed conditions.
5a-f ). Long-term measurements from above the forest and data from a short intensive field campaign were jointly used to evaluate model performance at different levels within the canopy. As was the case for the annual cycle, the sinusoidal cycles resulting from the diurnal pattern in solar angle are well matched (Fig. 6a-d ). Sensible heat flux was measured below and above the canopy and the model was able to simulate the trend of the gradient, though inexactly (Fig. 6a, b ). Latent heat flux at an equivalent height of 2 m was not recorded (Fig. 6c ). However, the match in magnitude of the measured data is not accurately simulated hour by hour (Fig. 6e ). Using the current parameters, there is a discrepancy between the measured and the modelled temperature gradients within the canopy (Fig.
Acknowledgements. J. Ryder ,  Y. Chen ,  M. J. McGrath ,  J. Otto ,  K. Naudts , and  S. Luyssaert  were funded through  ERC starting  grant  242564  (DOFOCO), and  A. Valade  was funded through  ADEME (BiCaFF). ESA CCI Landcover  also supported this work. The study benefited from an  STSM (COST ,  TERRABITES ES0805 ) offered to  J. Ryder. We thank the three anonymous reviewers for their very helpful observations and suggestions. The authors would like to thank  A. Boone  for sharing his numerical scheme of an implicit coupling of snowpack and atmosphere temperature. Plots were produced using matplotlib software (Hunter, 2007). Edited by:  D. Lawrence.
Designed with Green Ideas Many green ideas are in experiment, such as energy recovery. Data server room calories are reused to heat an atrium (Figure  5 ). Free cooling strategies using natural convection through the atrium (Figure  6 ), and forced convection in offices. Green roof: free spaces on the roof are covered by vegetal plants. Total primary energy consumption will be less than 2200 MWh / year which correspond to 110 kWh/m 2.
Multi-flow Energy Optimization Both electric and thermal energy flow must be taken into account in smart buildings since one of the main consumption in building is HVAC (heating, ventilation and air conditioning). Predictive models for both electrical and thermal equipments are then required for the optimal operation. Regarding thermal behaviour of buildings, dynamic simulation are done based on thermal properties of the skin (Figure  12 ), depending on weather conditions, occupancy and control set points. The results are the energy consumption with peak power, and occupant comfort criteria. The previous software (COMFIE) is mainly used for design stage (not in control), since occupancy is predefined by scenario. In order to make a control depending on the occupant behaviour, we are able to make a co-simulation with this software and agent based modelling occupants  (Gaaloul, 2013).
FLUXIONNAL COMPILER The source languages we focus on should offer higher-order functions and be implemented as an event-loop with a global memory. Javascript is such a language and is often implemented on top of an event-loop, like in Node. js. We developed a compiler that transforms a Node. js application into a fluxional application compliant with the execution model described in section 2. Our compiler uses the estools 6 suite to parse, manipulate and generate source code from Abstract Syntax Tree (AST). And it is tailored for -but not limited to -web applications using Express 7 , the most used Node. js web framework. The chain of compilation is described in figure  3. The compiler extracts an AST from the source with esprima. From this AST, the Analyzer step identifies the limits of the different application parts and how they relate to form a pipeline.
The two corresponding types of rupture points are start and post. Start rupture points (listeners) are on the border between the application and the outside, continuously receiving incoming user requests. An example of a start rupture point is in listing 1, between the call to app. get(), and its listener handler. These rupture points indicate the input of a data stream in the program, and the beginning of a chain of fluxions to process this stream. Post rupture points (continuations) represent a continuity in the execution flow after an asynchronous operation yielding a unique result, such as reading a file, or a database. An example of a post rupture points is in listing 1, between the call to fs. readFile(), and its continuation reply.
RELATED WORKS Splitting a task into independent parts goes back to the Actor's model, functional programming  [12]  and the following works on DataFlow leading to Flow-based Programming (FBP) and Functional Reactive programming (FRP)  [8]. Both FBP and FRP, recently got some attention in the Javascript community with NoFlo 9  , Bacon. js 10  and react  11. The execution model we presented in section 2, is inspired by works on scalability for very large systems, like the Staged Event-Driven Architecture (SEDA) by Matt Welsh  [20]  and by the MapReduce architecture  [7]. It also drew its inspiration from more recent work following SEDA like Spark  [21] , MillWheel  [1] , Naiad  [15]  and Storm  [19]. The first part of our work stands upon these thorough studies. However, we believe that it is difficult for most developers to distribute the state of an application.
In addition to that, this data is stored in different spreadsheets, databases or other media where each document adopts its own data representation. Although this heterogeneity and this diversity make the university a rich institution, they fail to facilitate the access to the knowledge on the one hand and to make the university intelligible and understandable in its signs, concepts and structures on the other. For that reason, it is mandatory to find a solution where a unified vocabulary can be adopted to make the different resources of the university understandable. In order to ensure the readability and the intelligibility of Strasbourg University, the IDEX (excellence initiative) project "Complex identities" was launched in January 2015. The main goal of the project is to enhance the readability of Strasbourg university, by proposing a unified representation of the available knowledge.
In addition to describing the different concepts related to Strasbourg university, this ontology will allow the specification of the existing links that exist between the different structures and will ease the navigation over the individuals, in order to find a certain required information. When constructing the ontology, we faced some problems. In fact, we noticed that the "graphiclexicon" is incomplete; some information is missing or some definitions are not given. Engineering procedures were implemented to recover the missing information (exhaustive research in the website of the University, interviews with the officials in charge of the university information system, or queries to the databases to which we had access). The methodology described in  (Noy and McGuinness, 2001)  has been used for the development of the ontology.
, 2014b)  and to use especially We-bVOWL which is a web implementation of VOWL  (Lohmann et al. , 2015). VOWL, the Visual Notation for OWL ontologies, is a visual language for representing ontologies. Based on graphical primitives and color scheme, VOWL is able to visualize classes, properties and datatypes. Classes are represented by circles where the size of each circle depends on the number of the individuals of the represented class. Lines are used to represent properties. Property labels and datatypes are shown in rectangles. To demonstrate its applicability, VOWL was implemented in two different tools: ProtégéVOWL  (Lohmann et al. , 2014a)  and We-bVOWL. The former is a VOWL plugin for the Protégé editor while the latter is a standalone web application. As part of this project, we used WebVOWL as our aim is to propose an application for university users.
IMPLEMENTATION As university data is stored in different spreadsheets and databases, a thorough study was handled to depict the different classes, properties as well as datatypes. We used Protégé 5. 0. 0 4  , as the most popular and widely used tool for ontology development. It is a free open-source tool developed by Standford university. It gains popularity because it offers to users a set of packages for editing and visualizing ontologies. As already mentioned, we used WebVOWL (versions 0. 5. 2) for visualization. As it is a standalone application, the OWL ontology is converted into a VOWL-JSON file proper to WebVOWL. At the time being, WebVOWL is able to visualize classes, properties and datatypes. To visualize individuals, we created a second VOWL-JSON file containing all the information related to individuals.
CONCLUSION The IDEX (excellence initiative) project "Complex Identities" is a large scale project. Its main goal is to ensure the intelligibility of the university through providing a comprehensive and structured view of its different components. In this paper, we presented our solution of creating a knowledge mapping based on ontologies. We described in detail this ontology and how it has been constructed. To visualize the ontology we used the WebVOWL tool which helps users to explore easily the represented knowledge. Although we have not been able to get access to all the existing databases, we think that given the constructed ontology and the visualization results we have been able to achieve the objectives of this project. However, an exhaustive experimentation protocol is being setup during the new academic year 2016-2017, for validation of the correctness of the enriched ontology and evaluation of the ergonomics of the proposed visualisation tools.
• An O (n) time and communications protocol Distributed and Secure Dot-Product (DSDP i ) (for i participants) which allows us to securely compute a dot product UV , against a semi-honest adversary, where one player owns a vector U and where each player knows one coefficient of V. • A parallel variant that performs the dot-product computation in parallel among the players, limits the total number of rounds. This is extended to a Parallel Distributed and Secure Matrix-Multiplication (PDSMM i ) family of protocols. • A security analysis of the DSDP protocol using a cryptographic protocol verification tool, here ProVerif  [7, 8]. This tool allows us to define countermeasures for each found attack: adapted proofs of knowledge in order to preserve privacy and a random ring order, where private inputs are protected as in a wiretap code  [25]  and where the players take random order in the protocol to preserve privacy with high probability, even against a coalition of malicious insiders.
In silico analysis of NLR protein domains To analyze a representative sample of NLR proteins from all available plant genomes, we used the NLR repertories defined by the Greenphyl database (http://www. greenphyl. org/; Conte et al. , 2008; Rouard et al. , 2011). Using the TRIBEMCL software, Greenphyl has initially produced gene family clusters for rice and Arabidopsis that have been manually curated  (Conte et al. , 2008). The protein sequences of other plant genomes were allocated to these protein clusters using BLASTP  (Rouard et al. , 2011)  and Interpro annotation was finally included for each protein. Thus, Greenphyl has the advantage of providing gene cluster families that were defined in a unified manner across the plant kingdom. We retrieved the Greenphyl protein clusters and the corresponding Interpro domains corresponding to c.
155 000 NLR proteins (GP000012, GP015056, GP015065 and GP015132) as well as > 2500 receptor-like kinases (RLKs) (GP039790). It is noteworthy that the Interpro database contains several, different, accessions for a given molecular function (see, for instance, kinase and leucine-rich repeat (LRR)). The criterion used to select canonical NLRs (Supporting Information Table  S1 ) was the presence of both the NB-ARC domain IPR002182 and an LRR domain (any of IPR001611, IPR011713, IPR003591, IPR025875, IPR006553, IPR026906, IPR008615, IPR021929, IPR013210 and IPR013101). According to the same logic, a set of 1393 canonical RLKs (Table  S2 ) was defined by the presence of an LRR domain (same Interpro domains as above) and the protein kinase domain IPR000719. Other domains were frequently found in the NB-ARC parts of the NLR proteins (AAA+ ATPase domain IPR003593) or the kinase domain of LRR-RLK proteins (IPR000719, IPR008271, IPR002290, IPR017441, IPR001245 and IPR020635); these frequently found domains were therefore considered as usual components of these proteins and not as putative decoys (see Tables  S2, S3 ).
For re-annotation of cloned R proteins, we used the Interpro database (http://www. ebi. ac. uk/interpro/search/sequence-search) or the NCBI domain (http://www. ncbi. nlm. nih. gov/Structure/ cdd/wrpsb. cgi) search tools. The gene ontology (GO) terms (http://www. ebi. ac. uk/interpro/download. html) were assigned (or manually when no GO term was available for some Interpro domains) to each Interpro domain to establish the global molecular function of decoy domains (Table  S3 ).