dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013)  middleware based on a software infrastructure, a service composition architecture and a compositional adaptation mechanism. In this paper we are interested in the WComp middleware. The architecture of WComp framework is organized around containers and designers (Figure  5 ). Containers manage the instantiation, designation and destruction of components. A Designer runs containers for instantiation and destruction of components or connections between components in the assembly using an adequate formalism. WComp uses the. NET framework which has a container implemented in C# language. The components in WComp are implemented with a object oriented approach in the Bean classes. The component is a self-contained class that contains properties and methods needed to communicate with other components.
7 units of time (line 2-12). Once the time advance is achieved (sigma=1) the system move to selection state "SEL" (line 13) and Lamp state change to "OFF" depending of its previous state (line 14). This results allows us to validate the strategy II and then generate the strategy Python file to be integrated in a Bean class into WComp: In the previous code, the test of the motion is considered with a conditional statement (line 2,9) that considers the last value of the array L MotionGen. 1 def Strategy2(lightGen, The test of the level of light is assumed by the light-Gen variable (line 3,10). Depending on the values of L MotionGen, lightGen and previous state variables, the power and state variables are updated (line 14).
Swarm Mapless in Robotic Experiments: Resistance to Odometry Errors Promising results were achieved with Swarm Mapless in simulation (Figures  1  and 2 ). Nevertheless, experimental implementations are necessary to ensure that swarm Mapless can be used in real turbulent environments and can handle the numerous errors encountered. We present hereafter a successful solution for implementing swarm Mapless within a robotic system, and we assess its performance in the real environment. All experiments were performed with Khepera III robots (K-Team SA, Switzerland) and several modules: Korebot II (embedded ARM processor running Linux 2. 6 at 600 MHz), KoreIOLE (acquisition board with 12 analog inputs in the 0-5 V range with 5 mV resolution), and KoreWifi (board allowing Wifi communication with the robot).
Instead, the LIDAR returns the distance to the bottom of the smoke layer so that the measured distance decreases with the smoke density (from moderate to heavy in Figure  3B ). In agreement with  Pascoal et al. (2008)  and  Starr and Lattimer (2014) , this result indicates that a LIDAR would not be capable of providing accurate range finding information in smoky environments. An alternative method for robot localization is to use odometry, which is path integration of the robot velocity sensed from its wheels. To assess the localization error, we compared robot trajectories obtained from the odometry tracking module of the Khepera III Toolbox (http://en. wikibooks. org/wiki/Khepera_III_ Toolbox) to the ground truth provided by a motion capture device (Figure  4A ). An example of trajectory is shown in Figure  4B.
Samples and nucleic acid extraction All samples were obtained from the bank of biological resources of Ambroise Paré Hospital. All surgical or fine needle biopsies were performed for routine diagnosis or evaluation of disease progression. The research was performed in compliance with the ethical principles of the Helsinki Declaration  (1964). In accordance with French ethics laws, all patients were informed that part of their samples could also be used for research purposes, and that they could refuse this. None of patients refused the use of samples for research. Tumor sample collection was declared to the French Ministry of Research (DC 2009-933) and CPP IDF 8 ethics committee approved the MelanCohort study (030209), which is registered with Clinicaltrials. gov (NCT00839410). Signed informed consent for translational research was obtained from patients still alive.
All diagnoses were confirmed by pathology review. For most of the nucleic acid quantification studies, the tumor DNA was extracted from formalin fixed paraffin embedded (FFPE) tissue. However, for mRNA extraction and high density SNP analysis, frozen samples were used. In all cases, a 4 micrometers-thick section was stained with hematoxylin & eosin and reviewed by a pathologist before extraction, to confirm the presence of melanoma and to select areas with the highest density of tumor cells for macrodissection. For all samples, tumors cell content was estimated in the percentage of tumor cells and the data was noted. To evaluate the accuracy of tumor cell content assessment, a series of 41 randomly selected samples was assessed by three independent pathologists. For each sample, serial sections or punch sampling were then used for nucleic acid extraction.
5 millions highly monodispersed droplets were generated using the Raindrop source instrument following manufacturer's instructions. The emulsions were submitted to thermocycling, starting with 2 min at 50 °C, 10 min at 95 °C, followed by 45 cycles of: 95 °C, 15 s and 60 °C, 1 min (using a 0. 6 °C/min ramp rate). After completion, the end-point fluorescence signals from each droplet were measured using the Raindrop Sense instrument. Analyses of the data were performed using the Raindrop analyst software as previously described  [20, 21]. The reference sequence was B-RAF cDNA sequence (GenBank NM_00433. 4).
The probes and the slides were denaturated separately when BRAF BAC probes were used together with commercial chromosome 7 centromere probe. Before an overnight incubation in a humidified chamber, a suppression of the repetitive sequences was performed for DNA BRAF BAC (45 min at 37 °C). After post-hybridization wash, and DAPI staining in the Vectashield® Mounting Media (Vector Laboratories, Les Ulis, France), fluorescence signals were analyzed using a Leica DM4000B microscope equipped with appropriate filters and a DFC300FX camera under the control of LAS V4. 0 software (Leica). Two independent analyses were performed.
SNP analysis DNA was extracted from frozen melanoma samples and hybridized on HumanCore BeadChip (Infinium Ilumina®, Evry, France) according to the manufacturer's instructions by IntegraGen. This array contains more than 240,000 highly-informative genome-wide tag SNP and over 20,000 high-value markers. Chromosome 13p, 14p, 15p, 21p and 22p markers are not represented in this array. Chromosome Y and X were only used to control the gender of the patient. All genome positions were based upon NCBIGRCh37/hg19 from UCSC Genome Bioinformatics. The genotyping data were normalized by the IntegraGen commercial platform and analyzed (copy number aberrations (CNA) and allele disequilibrium (AD)) using GenomeStudio software (version 1) (Illumina Inc).
The Cancer Genome Atlas Dataset For external validation of our results on an independent cohort, the skin melanoma dataset of The Cancer Genome Atlas (TCGA)(Provisional) was downloaded through the cBioPortal for Cancer Genomics website (http:// www. cbioportal. org/ date March 13th 2015). We selected "All Complete Tumors (278)" of the patient/case set and entered the BRAF gene in the TCGA (Provisional) dataset. The cBioPortal website provided the data about mutation type, amino acid change and variant allele frequency. Analysis was performed as published  [24].
Immunohistochemistry with VE1 was performed on whole slides in cases with BRAF-M% <30 % when available (n = 14/21), and none of these cases contained any tumor area with loss of BRAF mutant expression. In order to validate our results showing heterogeneous distribution of BRAF-M% in mutated melanoma tumors, we investigated the next-generation sequenced DNA mutation data of the 104 mutated skin melanomas in the TCGA database. The BRAF-M% from TCGA database ranged from 8 to 97 %, and the distribution was in keeping with our observation (Table  1 ).
Our results were further confirmed by analysis of the TCGA database. These results raise BRAF/chromosome 7 centromere interpretation of FISH analysis: 2cells with two copies of chromosome 7; 3cells with two copies of chromosome 7 but rare cells showing an increased number of chromosome 7; 4cells with chromosome 7 gains, (A) chromosome 7 in 3 or 4 copies, (B) chromosome 7 in more than 4 copies, NInot interpretable 3 Chromosome 7 genetic status presents as disequilibrium of alleles A and B (SNP analysis). NA -not available, WTwild-type, a -clonal aberration important questions concerning BRAF targeted therapies. Indeed, ATP-competitive RAF inhibitors have different effects in cells expressing wild-type or mutant BRAF forms, with up-down-regulation of the ERK signaling pathway in certain in vitro conditions  [26].
Acknowledgements The authors thank  Arthur Frohlich ,  Maxence Tondu ,  Mariama Bakari ,  Glwadys Faucher ,  Dominique Péchaud ,  Yolaine Pothin ,  Sylvie Surel  and  Nathalie Terrones  for technique contribution and/or data collection,  Dr Alain Beauchet  for statistical analysis and  Astrid Blom  for article review. This work was supported partly by grants from the  Association Vaincre le Mélanome, Ligue Contre le Cancer  ( Comité 92 WB2013-232 ), and  Association pour la Recherche et l'Enseignement en Pathologie (AREP).
Background: Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain.
Background Huge amounts of electronic biomedical documents are generated daily; for example, over one million published papers have been collected on Medline in 2013. Automatically organizing their content in dedicated databases enables advanced search and eases information retrieval for researchers in biology, medicine or other related fields. Nowadays, these data sources are mostly in the form of unstructured free text, which is complex to incorporate into databases. Hence, many text-mining research initiatives are organized around the issue of automatically extracting information from biomedical text. Efforts specifically dedicated to biomedical text are necessary because standard natural language processing tools cannot be readily applied to extract biomedical events since such texts, articles or reports involve domain-specific jargon, shorthands, such as "IL-1, -2, -3" for the Interleukin proteins  [1].
Definitions Terminology regarding biomedical events, triggers, etc. varies from one task or data set to another; in the following, we use the definitions used by the Genia (GE) task 1 of the BioNLP challenges. An event is defined as a formula constituted of two kinds of elements: an event trigger and one or several arguments. The event trigger is a text entity to be extracted, whereas arguments can be proteins, genes or other events. We define a text entity as a character string, which can be a part of word or several consecutive words. All the entity statements mentioned in this paper refer to text entity. In the data settings of the GE task, gene/protein mentions are already annotated in the text. Nine types of events, listed in Table  1 , are defined in the BioNLP GE task. These nine types may be merged into three broader categories: first, the single theme argument events (SVT); then the Binding events (BIN), which may take up to two theme arguments; and finally, regulation events (REG), which may take up to two arguments, a theme and an optional cause.
Pre-Processing Tokenization and sentence splitting have a substancial impact on the results of the dependency parsing as well as the way we handle compound words that contain protein names. We split the data in sentences using both the nltk toolkit  [15] , and the sentence splitting provided for the BioNLP GE task. As the best pipeline model TEES and the best joint model UCLEED use fine-grained tokenizations, we conjectured that finegrained tokenizations could provide relevant features. Coarse tokenizations are useful to maintain some biomedical jargon that also convey essential information. Compared to using dependency parse trees based solely on coarse tokenizations, adding features from the dependency parse trees based on fine-grained tokenizations improves the total F-score of our RUPEE model by more than 2% on the BioNLP 2013 test set.
Candidates For each sentence S, the set C S is built with a gazetteer: candidate entities are recursively added by searching first the longest token sequences (from Tokenization2) from the gazetteer. For entities with several tokens, a representative head token is selected by a heuristic based on the dependency parse. Three types of tokens are considered: the head token, its parent and child nodes in the dependency tree, and the tokens belonging to a neighboring window of the entity. The size k of the word window is a hyper-parameter of our model. Table  4  lists all features which include stems, part-of-speech (POS) tags, etc. Special care was taken to design the feature for head token since it plays an extremely important role in candidate entities. We hence employed features and heuristics to deal with compound-words, hyphens and prefixes, inspired by such tools developed in the code of the UCLEED system and based on Tokenization2.
In  [16] , event annotations are converted into pseudo-syntactic representations and the task is solved as a syntactic extraction problem by traditional statistical parsing methods. In  [31, 7, 32, 33] , some models are proposed based on the maximization of a global score taking into account the annotations of nodes and edges in a graph representing each sentence. This maximization problem is formalized as an integer linear program with consistency constraints, and solved via dual decomposition. Such joint models perform very well (winner of the BioNLP 2011 GE task), but suffer from high computing costs, as all possible combinations of words are considered as potential events. In the following, we show that RUPEE is able to reach slightly better accuracies than joint models while being computationally much cheaper.
Results In this section, we demonstrate the performance of RUPEE in the framework of the GE tasks of the BioNLP challenges. More precisely, we use the annotated data collected for these tasks and report the results returned by the evaluation servers on the test sets of the 2013 GE task, and also of the 2011 edition so as to compare to joint methods. To assess the efficiency of our modeling choices, we also implemented a pipeline counterpart system, following the structure of the TEES approach [28,29,5] but using our feature set, together with a pre-processing and a post-processing that best match our system. This pipeline system comprises four steps: Trigger classification, which assigns event types from Y to candidate entities c i ∈C S using a multi-class SVM classifier;  Edge detection, which identifies the edges between extracted triggers and proteins and between REG triggers and all the triggers; labels from Y edge = {theme, cause, None} are assigned to those pairs; Binding theme fusion, which merges several pairs labeled as Binding to create multiple arguments events, following the post-processing used for our algorithm and described in the Methods section; Regulation-cause assignment, where two predicted pairs (c i , theme: a b ), (c i , cause : a g ) may be merged into a single (c i , theme : a b , cause : a g ).
Genia shared task 2013 For the BioNLP 2013 GE task, the hyper-parameters of RUPEE have been optimized on the GE task development set (except for the regularization parameters of the SVMs, which are selected by cross-validation), after training on the corresponding training sets: token window size is 2 for candidate entities and 1 for arguments, the threshold for dependency path is 4. Using these hyper-parameter values, the final model submitted for test evaluation on the GE task server has been trained on all documents from training and development sets of BioNLP 2011 and 2013 GE tasks. Detailed descriptions of the BioNLP 2011 and 2013 GE data are respectively given in  [3]  and  [4]. Table  5  lists the detailed test F-scores, as returned by the official challenge test server (using the default approximate span & recursive matching evaluation setting).
Genia shared task 2011 The best performing methods on the BioNLP 2013 GE task were pipeline approaches, but the joint models that were performing better in the previous challenge were not competing in 2013. As these joint models are quite tricky to train, we compare RUPEE with joint models on the BioNLP 2011 GE task, where trustworthy performances have been publicly released. We train our model using the training and development sets available at the time of the challenge and we then get an evaluation on the same test data using the official test server maintained online by BioNLP organizers. Table  6  lists the results of RUPEE, its pipeline counterpart, and those of UCLEED  [7]  and TEES  [34] , which are respectively the best performing joint model and best pipeline on this task. We also added SEARN  [14] , which is a hybrid between them.
In fact, our approach builds a collection of specialized event detectors that by-pass the intermediate trigger detection problem of pipeline models. As shown in Tables  5  and 6 , this collection of specialized detectors is more accurate than the the cascade of the pipeline counterpart. We now compare the precision-recall trade-offs that can be reached by RUPEE and its pipeline counterpart. As shown in Table  3 , most classification errors are either undetected events or false detections. We adjust the detection rate by shifting the decision threshold on the None class. As before, since the test data are blinded, these curves are computed on the development set of BioNLP 2013, for models adjusted on the BioNLP 2011 and 2013 training data. Figure  5  displays the precision-recall curves for (trigger, theme) pair extraction, that is, before the post-processing that is common to the two approaches.
Training durations In this last section, we propose to illustrate the lower complexity of our approach compared to UCLEED by providing durations for training both systems on BioNLP 2011 GE. These timings do not involve preprocessing but only running cross-validation on the training set and evaluation on the development and test sets. For UCLEED, we used the code (in java & scala), which is available from  [38]  and we chose BioNLP 2011 GE because this code was primarily designed to run on it. Our code, in python, is publicly available from  [39]. Experiments were conducted on the same computer, with a quad-core Intel Xeon CPU and 16GB of RAM. Both codes are multi-threaded and used all 4 threads simultaneously. Under these conditions, UCLEED requires around 8h30min to run its 10 epochs, while our code completes training in about 30 min.
In addition, we also ran our pipeline counterpart system, which is implemented in the same language and uses the same libraries as RUPEE, on the same machine. The pipeline counterpart system took 20 min to train. Note that UCLEED might be faster by using feature caching, but we had to disable it because the 16GB RAM filled up, thereby further slowing the process. In addition to the inference cost discussed earlier, the overall computation cost also depends on the training algorithms: both UCLEED and RUPEE use Support Vector Machines, with the online-learner MIRA  [40]  for UCLEED and a dual coordinate descent method  [41]  for RUPEE. We did not conduct a more thorough analysis about the training durations since the implementation choices such as programming languages and optimization details such as stopping criteria can make large differences on the computing durations.
The stack pointer dependencies are not considered. The parallel runs ILP measures the ultimate performance an ideal parallel machine achieves when the run order only depends on the producer to consumer dependencies, excluding the stack pointer. Each instruction on the trace is run at the cycle next to the last source reception. The processor is assumed to run all the ready instructions in the same cycle with a single cycle latency (as in the sequential runs). All runs are continued until completion. For each benchmark, the 11 parallel runs vary from 1M to 1G instructions (increasing factor 2) and the sequential runs are 1G instructions long. The figure shows that sequential runs have a very low ILP (ranging from 3. 2 to 5. 6) and parallel runs have a very high ILP (ranging from 600 to 508K for dataset 11).
The difference comes from the dominating distant ILP. Moreover, when a benchmark is data parallel its parallel run ILP increases proportionally to the dataset (e. g. benchmarks 1, 2, 5, 6, 9 and 10). The sequential run ILP we have measured confirms ILP reported values (such as  [6] ). Since 50 years many successive research works on ILP were published. In 1967, Tomasulo  [3]  presented an algorithm to run floating point instructions out-of-order. He introduced register renaming which is still used in today's speculative cores to parallelize on-the-fly instructions. In 1970, Tjaden and Flynn  [4]  measured the available parallelism in a 10 instructions window. They ran their test programs at 1. 86 instructions per cycle. In 1984, Nicolau and Fisher  [5]  measured the available parallelism to feed a VLIW processor.
Data Sets We evaluate Similitude on two datasets: Twitter, and MovieLens. The former contains the feed subscriptions of 5,000 similarly-geolocated Twitter users, randomly selected from the larger dataset presented in  [8]   5. Each user has a profile containing each of her Twitter subscriptions, i. e. , each subscribed feed counts as a positive rating. The MovieLens dataset  [1]  contains 1 million movie ratings from 6038 users, each consisting of an integer value from 1 to 5. We count values 3 and above as positive ratings. We pre-process each dataset by first removing the items with less than 20 positive ratings because they are of little interest to the recommendation process. Then, we discard the users with less than five remaining ratings. After pre-processing, the Twitter dataset contains 4569 users with a mean of 105 ratings per user, while the MovieLens dataset contains 6017 users with a mean of 68 ratings per user.
Simulator and Cross Validation We measure recommendation quality using a cross-validation approach. We split the profile of each user into a visible item set containing 80% of its items, and a hidden item set containing the remaining 20%. We use the visible item set to construct the similarity-based overlay and as a data source to generate recommendations as described in Section 3. We then consider a recommendation as successful if the hidden item set contains a corresponding item. In terms of protocol parameters, we randomly associate each node with an initial neighbourhood of 10 nodes, as well as with a randomly selected similarity metric to start the refinement process. At each round, the protocol provides each node with a number of suggestions equal to the average number of items per user.