dataesr/scientific-paragraphs-categorization huggingface dataset:
The Image Annotation Task Since 2010, ImageCLEF has run a scalable concept image annotation task to promote research into the annotation of images using large-scale, noisy web page data in a weakly-supervised fashion. The main motivation for the task comes from the large number of mixed-modality data (e. g. web page text and images) which can be gathered cheaply from the Internet. Such data can potentially be exploited for image annotation. Thus, the main goal of the challenge is to encourage creative ideas of using such noisy web page data to improve various image annotation tasks: localizing different concepts depicted in images, generating descriptions of the scenes, and text-to-image retrieval.
Past Editions The Scalable Concept Image Annotation task is a continuation of the general image annotation and retrieval task that has been held every year at ImageCLEF since its very first edition in 2003. In the first editions the focus was on retrieving images relevant to given (multilingual) queries from a web collection, while from 2006 onwards annotation tasks were also held, initially aimed at object detection, but more recently also covering semantic concepts. In its current form, the 2016 Scalable Concept Image Annotation task  [9]  is its fifth edition, having been organized in 2012  [32] , 2013  [34] , 2014  [33] , and 2015  [8]. In the 2015 edition  [8] , the image annotation task was expanded to concept localization and also natural language sentential description of images. In the 2016 edition, we further  introduced a text illustration 'teaser' task  3  , to evaluate systems that analyze a text document and select the best illustration for the text from a large collection of images provided.
Likewise there are other resources available that can help to determine the relationships between text and semantic concepts, such as dictionaries or ontologies. The goal of this task is to evaluate different strategies to deal with the noisy data so that it can be reliably used for annotating, localizing, generating natural sentences and retrieving images from practically any topic. As in the 2015 task, external training data such as ImageNet ILSVRC2015 and MSCOCO are allowed and encouraged. However, in contrast to previous years, in this edition participants are expected to produce two sets of related results: 1. One approach using only externally trained data; 2. The second approach using both external data and the noisy web data of 510,123 web pages. The motivation for this is to encourage participants to utilize the provided 510,123 web pages to improve the performance of systems trained using external data.
This also distinguishes the ImageCLEF image annotation task from other similar image annotation challenges. This year's challenge comprises four subtasks: 1. Subtask 1: Image Annotation and Localization. This subtask required participants to develop a system that receives as input an image and produces as output a prediction of which concepts are present in that image, selected from a predefined list of concepts, and where they are located within the image. Participants were requested to annotate and localize concepts in all 510,123 images. 2. Subtask 2: Natural Language Caption Generation. This subtask required the participants to develop a system that receives as input an image and produces as annotation a sentential, textual description of the visual content depicted in the image. Again, the test set is all 510,123 images.
The concepts this year (for the main subtasks) were retained from the 2015 edition. They were chosen to be visual objects that are localizable and that are useful for generating textual descriptions of visual content of images, including animated objects (person, dogs), inanimated objects (houses, cars) and scenes (city, mountains). The noisy dataset used in this task was based on the 2015 edition with 500,000 documents. In the 2016 edition, the dataset was augmented with approximately 10,123 new image-document pairs from a subset of the BreakingNews dataset  [24]  which we developed, bringing the total number of documents to approximately 510,123. However, the subset of the data used for evaluating the three main subtasks remains the same, thus making the evaluation process comparable to the 2015 edition.
The current crowdsourcing-based hand labelling of the ground truth is found to not achieve this and so a recall measure is not evaluated. Two teams participated in Subtask 2 to generate natural language image descriptions. The subtask was evaluated using the Meteor evaluation metric  [4]. Table  7  shows the Meteor scores for the best run for each participant. ICTisia achieved the better Meteor score of 0. 1837 by fine-tuning on the state-of-the-art joint CNN-LSTM image captioning system. UAIC who also participated last year improved their score with 0. 0934 compared to their performance from last year (0. 0813), using a template-based approach to the problem. Table  7 : Results for Subtask 2: Natural Language Caption Generation. Team Meteor ICTisia 0. 1837 ± 0. 0847 UAIC 0. 0934 ± 0. 0249 Subtask 3 on content selection was also represented by two teams.
selected such that key challenges were included: words broken between lines, queries with words not seen in the training data, queries with repeated words, and queries with zero relevant results. The dataset used in the task was a subset of pages from unpublished manuscripts written by the philosopher and reformer, Jeremy Bentham, that were digitised and transcribed under the Transcribe Bentham project  [3]. All of the provided data for the task and scripts for computing the evaluation measures and the baseline system are publicly available and citable  [35]. These kinds of evaluations related to handwriting recognition are normally organised in conjunction with more specialised conferences such as ICDAR and ICFHR. The reason for organising it at CLEF was that most of the research done up to now in this area does not address all challenges from the perspective of information retrieval.
Participation and Results There was considerable interest in the task. Over twenty groups signed the EUA, and based on the data server logs, the test queries (only useful if there was an intention of submitting results) were downloaded from 9 countries: Germany, India, Israel, Japan, Mexico, Morocco, Tunisia, Turkey and USA. In the end, only four groups submitted results and three of them submitted a working notes paper describing their system. Table  10  presents for each group that participated, the best result both for the development and test sets, and including only the segment based performance measures, i. e. , does not consider the predicted word bounding boxes. The assessment uses the Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG), measured both globally or as the mean (preceded by a lower-case m) for all evaluated queries.
Acknowledgements The general coordination and the handwritten retrieval task have been supported by the  European Union  ( EU) Horizon 2020 grant READ (Recognition and Enrichment of Archival Documents ) (Ref:  674943 ),  EU  project  HIMANIS (JPICH programme, Spanish grant Ref :  PCIN-2015-068 ) and  MINECO/FEDER ,  UE  under project  TIN2015-70924-C2-1-R. The image annotation task is co-organized by the  VisualSense (ViSen) consortium  under the  ERA-NET CHIST-ERA D2K 2011  Programme, jointly supported by  UK EPSRC  Grants  EP/K01904X/1  and  EP/K019082/1 ,  French ANR  Grant  ANR-12-CHRI-0002-04  and  Spanish MINECO  Grant  PCIN-2013-047.
4. Teaser task: Text Illustration. The teaser task is designed to evaluate the performance of methods for text-to-image matching. Participants were asked to develop a system to analyse a given text document and find the best illustration for it from a set of all available images. The 510,123 dataset was split into 310,123 and 200,000 documents for training and testing re- spectively. At test time, participants were provided as input 180,000 text documents extracted from a subset of the test documents as queries, and the goal is to select the best illustration for each text from the 200,000 test images.
Future limits In this subsection we briefly discuss the expected future limits on the flavour diagonal and flavour violating couplings of the Higgs boson in the lepton sector. • h → e + e -: Ref. [80]  discusses the present and future bounds on the Y ee Yukawa coupling. The ACME bound on the EDM puts a strong constraint on the imaginary part of this Yukawa, i. e. < 1. 7 × 10 -2 times the SM electron Yukawa, Y e. The deviations of the real part are far less constrained. The authors find that the constraint coming from 8 TeV LHC data is the strongest. A limit κ e < 611 can be derived, where κ e is a multiplicative complex parameter to the SM Yukawa, which parametrises the deviation from the SM Yukawa coupling. Noting that for the 14 TeV run, the production cross-section of the Higgs will increase by a factor of ∼ 2.
Acknowledgments We thank  Cédric Delaunay ,  Shilpi Jain ,  Tanumoy Mandal  and  Emanuele Re  for useful discussions and technical help during various phases of this work. SB acknowledges the support of the  Indo French LIA THEP (Theoretical high Energy Physics)  of the  CNRS. The work of BB is supported by  Department of Science and Technology, Government of INDIA  under the Grant Agreement numbers  IFA13-PH-75  ( INSPIRE Faculty Award ). MM would like to thank  IISER Mohali, India  and the  DST INSPIRE Faculty  award  INSPIRE-15-0074. MM and MS acknowledge support by the  Royal Society International Exchange   2015/R2 program.
Standardized statistical procedures for "IMGT clonotype (AA)" diversity or expression comparisons have recently been described, however, no tool was yet available. IMGT/StatClonotype, a new IMGT ® tool, evaluates and visualizes statistical significance of pairwise comparisons of IMGT clonotype (AA) diversity or expression, per V (variable), D (diversity), and J (joining) gene of a given IG or TR group, from NGS IMGT/HighV-QUEST statistical output. IMGT/ StatClonotype tool is incorporated in the R package "IMGTStatClonotype," with a userfriendly interface. IMGT/StatClonotype is downloadable at IMGT ®1 for users to evaluate pairwise comparison of IG and TR NGS statistical output from IMGT/HighV-QUEST and to visualize, on their web browser, the statistical significance of IMGT clonotype (AA) diversity or expression, per gene, the comparative analysis of CDR-IMGT and the V-D-J associations, in immunoprofiles from normal or pathological immune responses.
IMGT ® has recently defined a standardized procedure for evaluating the statistical significance of pairwise comparisons between differences in proportions of the IMGT clonotype (AA) diversity or expression, per gene of a given IG or TR group  (9) , from IMGT/HighV-QUEST statistical output. To make available the results issued from this standardized procedure and for a comparative analysis of CDR-IMGT and V-D-J associations, IMGT ® developed a new tool, IMGT/StatClonotype, incorporated in the R package "IMGTStatClonotype, " with a user-friendly interface. IMGT/StatClonotype performs pairwise comparison of IG and TR NGS results, from the IMGT/HighV-QUEST statistical output. IMGT/StatClonotype is described here for the first time, using as an example, a set of B cell NGS sequences. IMGT/StatClonotype is downloadable at http://www.
IMGt/statClonotype tool IMGT/StatClonotype is an IMGT ® tool, incorporated in the R (10) package "IMGTStatClonotype, " which performs evaluation and visualization of pairwise comparisons of IMGT clonotype diversity or expression, per V (variable), D (diversity), and J (joining) gene of a given IG or TR group, from NGS IMGT/HighV-QUEST statistical output, through a user-friendly web interface implemented using Shiny framework  (11)  in users' own browser. Comparative analysis is performed per IMGT gene and, for the first time, per IMGT allele (for genes with significant differences). Additional functionalities include analysis of CDR-IMGT length and CDR-IMGT AA properties per IMGT AA classes and heatmaps of V-D-J associations.
"IMGtstatClonotype" R package The "IMGTStatClonotype" R package is downloadable at IMGT ® , at the IMGT/StatClonotype web page, see text footnote 1. The installation mode is fully described in the IMGT/StatClonotype Documentation. The package manual, the "IMGTStatClonotype" source package for users familiarized with R and example sets for testing the tool are also available at the IMGT/StatClonotype web page.
data sets Two sets, out of six sets from Ref. (12) , were chosen as examples for illustrating the features offered by IMGT/StatClonotype. Sets "S1" and "S2" correspond, respectively, to 43,558 reads of IgD + and 28,142 reads of IgD -memory B cells isolated from a healthy female subject, obtained using the Roche GLFLX 454 technology. Sequencing data are available in the NCBI Sequence Read Archive under the accession code SRP037774 ("S1": SRX470417 and "S2": SRX470416.
AVAILABILItY ANd FUtURe dIReCtIoNs The package "IMGTStatClonotype" is freely available as a downloadable standalone package under the LGPL license at IMGT ® , see text footnote 1, with full documentation and a convivial interface on the user's web browser. "IMGTStatClonotype" is installable under Windows, Linux, and Mac OSx operating systems. All R package dependencies (reshape2 (28), data. table (29) , ggplot2  (30) , gridExtra  (31) , DT  (32) , shiny  (11) , shinyjs  (33) , plotly  (34) , and d3heatmap  (35) ) are available from Comprehensive R Archive Network (CRAN) except for the package multtest  (36)  available from Bioconductor only. For each new feature added to IMGT/HighV-QUEST, our intent is to advance the development of IMGT/StatClonotype tool in terms of analysis processing and visualization.
FUNdING IMGT ® is currently supported by the Centre National de la Recherche Scientifique (CNRS); the Ministère de l'Enseignement Supérieur et de la Recherche (MESR); the Montpellier University, France; the Agence Nationale de la Recherche (ANR) Labex MabImprove [ANR-10-LABX-5301]; BioCampus Montpellier; Région Languedoc-Roussillon (Grand Plateau Technique pour la Recherche (GPTR)). This work was granted access to the HPC@ LR and to the High Performance Computing (HPC) resources of the Centre Informatique National de l'Enseignement Supérieur (CINES) and to Très Grand Centre de Calcul (TGCC) of the Commissariat l'Energie Atomique et aux Energies Alternatives The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
06884 nm). The sample was inserted into a quartz capillary of outside diameter 1. 0 mm and wall thickness 0. 01 mm. For desorption the capillary was continuously pumped with a mechanical pump. Two heating coils near the capillary were used to increase the temperature. Integration of 2-D data to 1-D data was performed using Fit2d software  6. Neutron powder diffraction (NPD) experiments were carried out on the C2 high-resolution neutron powder diffractometer at the NRU reactor at the Chalk River Laboratories. The instrument is equipped with an 800-wire position-sensitive detector covering a range of 80°. Neutron wavelength of 0. 13287 nm was selected using an Si(531) monochromator. NPD patterns of as-cast and desorbed samples were registered. Also, in situ desorption neutron diffraction were recorded.
Most of industry-and academia-developed targeted sequencing panels now include these 2 genes, and one international trial (NCT01953926) has been set up to study the efficacy of neratinib on these mutations (among other mutations of the ERBBs family). The main limitation of such approach is the rarity of ERBB2 and ERBB3 activating mutations among breast cancers. Previous reports suggested that ERBB2 mutations might be enriched in a specific histological subtype, invasive lobular carcinoma (ILC)  [12, 20] , which account for 5-15 % of invasive breast tumors  [21, 22]. ILC are characterized by a proliferation of non-cohesive cancer cells and usually display low tumor grade, high hormone receptors expression and a lower rate of ERBB2 amplification than invasive breast carcinomas of non-specific type  [23].
Patients and samples This study was approved by the local ethics committee (Breast Group of Institut Curie); no informed consent was required because of the retrospective nature of the study. Using the electronic database of Institut Curie, we selected all cases diagnosed as primary breast ILC which were surgically operated between January 2005 and December 2008. Tumor biopsies and primary tumors previously exposed to neoadjuvant therapy were excluded. All slides were reviewed by two experienced breast pathologists (AVS and JCT) and cases were classified meticulously into classic, solid, pleomorphic or mixed lobular carcinomas according to the World Health Organization criteria  [32]. When present, apocrine morphologic features were described. Non-lobular carcinomas, mixed carcinomas with a ductal component above 10%, multifocal and multicentric carcinomas with non-lobular components were further excluded after pathological review.
Conversely, genetic data of 84 PAs were directly collected from the Heidelberg files. Genetic analysis of this cohort has been described previously  [39]. Furthermore, peripheral matched blood DNA from 66 patients and from 192 healthy children (median age 10 years) was also investigated. Correlation analysis between TP53 codon 72 variant and BRAF alteration were performed using previously described BRAF genetic data  [39] [40] [41]. Written informed consent was obtained from all the patients' parents or guardians, as well as for the controls. The experiments described comply with the current laws of the countries in which they were performed. The local Ethics Committee for human studies approved the research according to Institutional guidelines.
The following variables were considered into the saturated model: gender (female vs. male), age at diagnosis (≤ 24 months vs. > 24 months), site of lesion (supratentorial vs. infratentorial), additional treatments (yes vs. no) and p53 codon 72 SNP (Arg/Arg vs. Arg/Pro or Pro/Pro). Likelihood Ratio test (LR test) was used for comparisons and HR with their 95% Confidence Intervals (95% CIs) were calculated and reported. All statistical tests were 2-sided, and p values less than 0. 05 were considered to be statistically significant. The software "Statistica" (release 9. 0, StatSoft Corporation, Tulsa, OK) was used for descriptive and bivariate analyses, and Stata software, version 7 (Stata), was used for survival and multivariate analyses.
OBSERVATIONS AND DATA REDUCTION The brightest cluster galaxy (BCG) in the 2A 0335+096 galaxy cluster (RA: 03:38:40. 50, Dec: +09:58:12. 3) was observed by ALMA Cycle 1 (Program ID 2012. 1. 00837. S, PI McNamara) centred at 111. 394  GHz and 334. 169 GHz to cover the CO(1-0) and CO(3-2) lines. The CO(1-0) observation was divided into two blocks, which were observed in band 3 on July 22, 2014 and March 08, 2015. Both observations had an on-source integration time of 35 minutes. For the July 2014  (March 2015)  observation the array was configured with 33 (30) antennas with baselines of 17 -716 m (12 -280 m), each with a primary beam diameter of 56. Our observations employed the frequency division correlator mode, so had a frequency resolution of 488. 281 kHz (1. 31 km s -1 ) over a 1. 875 GHz bandwidth.
The filament falls outside of the CO(3-2) field of view. Fig. 2  shows the CO(1-0) spectrum extracted from a 17. 5 ×22 (12. 3×15. 4 kpc) box encompassing all of the observed flux. This region is similar in size to the 21. 5 IRAM 30m beam. The spectrum was well-fit by a single Gaussian component centred at -92 ± 14 km s -1 with a linewidth of 213±33 km s -1 (full width at half maximum; FWHM). Spectral fitting throughout this work used the LMFIT 2 package with one to three Gaussian components, as necessary. Each spectrum has been corrected for the response of the primary beam as well as instrumental broadening introduced by the velocity binning. Table  1  lists the best-fitting parameters for all spectra. The total CO(1-0) flux recovered by these observations is 4. 8 ± 0. 6 Jy km s -1 , which is consistent with the OVRO measurement of 7.
The RAMSES 'standard N -body' code We carry out cosmological N -body simulations using the Adaptive Mesh Refinement (AMR) code RAMSES  (Teyssier, 2002). The collisionless dark matter dynamics are evolved using the particle-mesh technique (see e. g. Hockney & Eastwood, 1988) , with gravitational accelerations computed from the gravitational potential on the mesh. The gravitational potential is calculated by solving the Poisson equation using the multi-grid method  (Guillet & Teyssier, 2011 ) for all refinement levels. Note that while we describe RAMSES as a 'standard N -body' code, it actually differs from most Tree N -body codes in an important respect: the softening is automatically adapted according to the Romeo criterion if the refinement strategy is based on reaching a critical number of particles per cell.
ACKNOWLEDGMENTS JIR would like to acknowledge support from  SNF  grant  PP00P2 128540/1. We would like to thank  Oliver Hahn ,  Aurel Schneider ,  Romain Teyssier  and  Joachim Stadel  for useful discussions. This research used the M ÖNCH supercomputer owned by  ETH Zürich , and the  Surrey Galaxy Factory machine at the University of Surrey.
For FT-IR, dry chitin was suspended uniformly between IR windows, and the spectrum (1,400 to 4,000 cm Ϫ 1 ) was recorded in a Jasco FT/IR-6100 apparatus, subtracting the spectra of air. Experiments were performed with four different batches of chitin purified from the A. fumigatus mycelial cell wall, with a final concentration of 10 g/ml. To determine the size of the chitin particles, the chitin suspension was subjected to flow cytometry (FC500 flow cytometer [Beckman Coulter]) and compared with reference beads of 0. 1 and 0. 25 m from Thermo Scientific or 3, 6, and 10 m (Flow Check Pro Fluorospheres) from Beckman Coulter. The data were analyzed using Kaluza Analysis version 1. 3 (Beckman Coulter). PBMC isolation. Venous blood was drawn in 10 ml EDTA tubes. The blood was diluted 1:1 with phosphate-buffered saline (PBS).
ACKNOWLEDGMENTS F. L. v. d. V. was supported by a  Veni grant  from the  Netherlands Organization for Scientific Research  and an  RIMLS grant  from  Radboudumc. M. G. N. was supported by a  Vici grant  from the  Netherlands Organization for Scientific Research  and by an  ERC   Consolidator grant  (no. 310372 ). J. P. L. and V. A. were supported by  Aviesan Fungi grant Aspergillus ,  ANR  grant  ASP2R2 , and  ANR-DST  grant  ANR-13-ISV3-0004-01.
FUNDING INFORMATION This work, including the efforts of Frank L van de Veerdonk, was funded by Netherlands Organisation for Scientific Research (veni). This work, including the efforts of Frank L van de Veerdonk, was funded by RIMLS grant from Radboudumc. This work, including the efforts of Mihai G Netea, was funded by Netherlands Organisation for Scientific Research (vidi). This work, including the efforts of Mihai G Netea, was funded by ERC Consolidator Grant (#310372). This work, including efforts of Jean-Paul Latge, was funded by Aviesan Fungi Grant Aspergillus. This work, including the efforts of Jean-Paul Latge, was funded by ANR grant (ASP2R2). This work, including the efforts of Vishukumar Aimanianda, was funded by ANR-DST (ANR-13-ISV3-0004-01). This work, including the efforts of Frank L van de Veerdonk, was funded by E-rare grant EURO CMC.