dataesr/scientific-paragraphs-categorization huggingface dataset:
Both patients had the same baseline characteristics. In the step of creating the data, types of variables must be appropriate and coherent with the dataset used for estimation. Firstly, we prepared the data for the bivariate model setting: R> datapredj_longi <-data. frame(id = 0, year = 0, tumor. size = 0, + treatment = 0, age = 0, who. PS = 0, prev. resection = 0) R> datapredj_longi$treatment <-factor(datapredj_longi$treatment, + levels = 1 : 2) R> datapredj_longi$age <-factor(datapredj_longi$age, levels = 1 : 3) R> datapredj_longi$who. PS <-factor(datapredj_longi$who. PS, levels = 1 : 3) R> datapredj_longi$prev. resection <-factor(datapredj_longi$prev. resection, + levels = 1 : 2) For Patient 1 we assumed 5 measurements indicating increasing tumor burden: R> datapredj_longi[1, ] <-c(1, 0, 1.
Conclusions Joint models are now a well recognized statistical tool for a complex analysis of correlated data. They provide unbiased estimates comparing to the univariate models. The package frailtypack provides several functions for applications of the joint models for a longitudinal outcome, recurrent events and a terminal event. Methods of estimation applied in the package produce reliable results proven by simulations for all the implemented models  (Rondeau et al. (2007) ,  Mazroui et al. (2012) ,  Król et al. (2016) ). Moreover, each function was furnished with several options for better adjustment of a model and in tools helping the diagnostic of a model (residuals, individual predictions of random effects) and comparison with other models (LCV, AIC). The increasing interest in individual predictions of death in the clinical perspective motivated the implementation of model-based dynamic predictions.
Acknowledgments The authors thank the  Fédération Francophone de Cancérologie Digestive and Gustave Roussy  for sharing the data of the FFCD 2000-05 trial supported by an unrestricted Grant from  Sanofi. The authors thank as well the  IReSP  for financing the project AAP 2012  Plan Cancer MULTIPLE , the  Institut National du Cancer  for funding the research of  A. Mauguen  the  École des Hautes Études en Santé Publique  for funding the research of  A. Król.
UniEuk is an open, inclusive, community-based and expert-driven international initiative to build a flexible, adaptive universal taxonomic framework for eukaryotes. It unites three complementary modules, EukRef, EukBank, and EukMap, which use phylogenetic markers, environmental metabarcoding surveys, and expert knowledge to inform the taxonomic framework. The UniEuk taxonomy is directly implemented in the European Nucleotide Archive at EMBL-EBI, ensuring its broad use and long-term preservation as a reference taxonomy for eukaryotes.
g. Mah e et al. 2017; de Vargas et al. 2015). This discovery increases the challenge of studying the full complexity of protists, but, at the same time, provides an exceptional opportunity to unite and strengthen the field of modern protistology. For this to occur, it is paramount to construct a universal taxonomy for eukaryotes, a common language that will help unify the field and connect the deluge of new molecular-genetic datasets with each other and with the centuries of accumulated morphological, physiological, life history, and ecological information on these organisms. UniEuk (http://www. unieuk. org/) is an open, inclusive, community-based and expert-driven international initiative to build a flexible, adaptive universal taxonomic framework for eukaryotes that represents the views of the research community.
ebi. ac. uk/ena), with the long-term goal of becoming the reference taxonomy in all INSDC genetic data repositories. UniEuk was launched in May 2016 with initial funding from the Gordon and Betty Moore Foundation (http:// www. moore. org) and the International Society of Protistologists. During the first year, the project's taxonomy and database coordinators and members of the UniEuk Steering, Advisory, and Technical Committees (http://www. unie uk. org/people/) designed the three main complementary and interconnected modules for direct community interaction-EukRef, EukBank, and EukMap-that together constitute the core of the UniEuk system and are necessary to build the universal taxonomic framework (below and Fig. 1 ). They also established a baseline version of the UniEuk taxonomic framework, starting from existing systems (e.
g. Adl et al. 2012) , and integrating the most up-todate information from phylogenomic evidence (e. g. Burki et al. 2016). Lastly, they proposed a set of guidelines for naming environmental genetic lineages prior to their morphological characterization. (1) EukRef (Fig. 1B ): The EukRef module allows integration into the UniEuk system of all preexisting phylogenetic information on eukaryotic diversity derived from Sanger-sequenced DNA markers of described taxa and environmental clones (beginning with 18S rDNA sequences longer than 500 bp). EukRef uses a standardized, open-source bioinformatics pipeline to generate homogenous, high-quality curation of 18S rDNA sequences available in the INSDC databases. EukRef outputs include, on a lineage-specific basis, taxonomically curated 18S rDNA sequences with corresponding sequence alignments and phylogenetic trees.
In addition to being a direct source of information for the UniEuk taxonomic framework, EukRef outputs represent standalone community resources shared through partner 18S rDNA reference databases SILVA  (Quast et al. 2013 ) and PR 2  (Guillou et al. 2013 ). EukRef has thus far largely engaged PhD students and postdocs who learned how to use the pipeline during multiday workshops, progressively expanding to include all eukaryotic clades. Information on EukRef and on past and future workshops can be found at http://www. eukref. org/. (2) EukBank (Fig. 1C ): The EukBank module allows integration into the UniEuk system of the enormous and largely nameless genetic information on eukaryotic diversity obtained from high-throughput metabarcoding (HTM) surveys of the Earth's ecosystems. Combining an ultra-fast algorithm generating stable clusters of amplicons (Mah e et al.
2015) and state-of-the-art methods of phylogenetic placement  (Berger et al. 2011 ), EukBank will absorb and reduce the complexity of eukaryotic HTM datasets, and analyze them phylogenetically. Datasets from all planetary biomes will be incorporated, starting with the V4 18S rDNA marker  (Pawlowski et al. 2012 ). EukBank is centralized at ENA, providing the community with a protocol and direct assistance for the submission of HTM datasets and their critical metadata to the repository. EukBank will allow monitoring of total eukaryotic diversity (e. g. saturation, phylogeny) across biomes, as well as identification and preliminary naming of novel eukaryotic lineages of ecological and/or phylogenetic relevance. These will be integrated into the UniEuk taxonomic framework, thus highlighting parts of the tree of eukaryotic life warranting deeper investigation.
EukBank is aimed at all scientists who have generated eukaryotic HTM datasets and are interested in discovering how these contribute to a growing global perspective on eukaryotic diversity. (3) EukMap (Fig. 1D ): The EukMap module allows the community to directly interact with and inform the growing universal taxonomic framework. EukMap is a user-friendly representation of the UniEuk taxonomic framework, publicly navigable, where each node/taxon is associated with standardized features (name, contextual data, links to representative pictures, etc. ). EukMap will integrate curated genetic information from EukRef and EukBank, and represents a community hub to centralize, safeguard, and promote our current global knowledge on eukaryotic diversity, taxonomy, and ecology. The output of EukMap (the actual UniEuk taxonomy) will be directly applied to ENA at EMBL-EBI, with regular versioning.
It will provide continuous feedback to the other UniEuk modules and partner reference gene databases for optimized and standardized taxonomic annotation of environmental sequence data. It will also be useable as a stand-alone summary of the collective knowledge on eukaryotic diversity and evolutionary history for scientific, educational, or public outreach purposes. EukMap is aimed at all researchers, from students to professors, with expertise on eukaryotic taxonomy, ecology, and evolution. The taxonomic framework as a whole is freely navigable for all visitors. Once registered into the UniEuk system, community members will be able to propose changes and addition of missing taxa or contextual information (including images) at any taxonomic level, and participate in group-specific discussions to reach agreement on which changes should be adopted in official releases of the UniEuk taxonomy.
Lead taxonomy experts and the project taxonomy and database coordinators will be in charge of moderating these discussions and implementing decisions. Further information on UniEuk's vision, goals, and organization can be found at http://www. unieuk. org/, together with use-case scenarios and FAQs, a news section, and a registration page. We encourage all scientists with expertise in protist taxonomy, ecology, or evolution to join the growing UniEuk community (Box 1). The deluge of emerging eukaryotic genetic data makes this the right time to build a common, eco-morpho-genetic and organism-centered language for protistology. With its complementary, yet independent modules (all with stand-alone outputs benefitting end-users in the protistology community and beyond; see Fig. 1 ), we believe that UniEuk will generate the necessary momentum to address this challenge.
ACKNOWLEDGMENTS This study was funded by the  Gordon and Betty Moore Foundation  through grant  GBMF5257 , the  International Society of Protistologists , and the  French Government "Investissements d'Avenir" program OCEANOMICS  ( ANR-11-BTBR-0008 ). We sincerely thank all members of the  UniEuk committees  for their critical input, feedback, and contributions-in the Advisory Council:  Sandra Baldauf ,  Sonya Dyhrman ,  Laura Katz ,  Connie Lovejoy ,  Alexandra Worden ,  John Archibald ,  David Bass ,  David Caron ,  Patrick Keeling , and  Jan Pawlowski ; in the Scientific and  Technical Advisory Board :  Linda Amaral Zettler ,  Claire Gachon ,  Laure Guillou ,  Line Le Gall ,  Laura Wegener Parfrey ,  St ephane Audic ,  Matthew Brown ,  Micah Dunthorn ,  Enrique Lara , Fr ed eric Mah e,  Ramon Massana , and  Alexandros Stamatakis ; and in the Steering Committee:  Juliet Brodie ,  Virginia Edgcomb ,  Eunsoo Kim ,  Pelin Yilmaz ,  Sina Adl ,  Guy Cochrane ,  Colomban de Vargas ,  Javier del Campo ,  Stefan Geisen ,  Frank Oliver Gl€ ockner , and  Alastair Simpson.
4 Oil DIC objective or with a DeltaVision OMX SIM (General electric) in wide-field mode. Super Resolution Microscopy for CLEM. SIM images were taken with a DeltaVision OMX SIM with an Olympus Plan ApochromatN 60x oil objective with a numerical aperture of 1. 42. Tokuyasu or Epon sections were imaged with immersion oil with a refractive index of 1. 516. Stacks with a step-size of 125 nm and a whole z-volume of 3 µm were acquired and reconstructed with the SoftWoRx software package (SoftWoRx, Germiston, South Africa). Image quality was evaluated by checking the k0 angle shift, which is an indicator of SIM reconstruction quality and should be below 2 pixels and by checking the shift in linewidth (Daniel J. White, OMX SIM Application Specialist, Life Sciences, Cell Technologies, GE Healthcare, personal communication).
Afterwards they were stained with 1% lead citrate. Following post-staining 15 nm colloidal gold particles were absorbed into the grids for 30 s and used as fiducial markers. Dual tilt series ranging from -64° to +64° were acquired with a Tecnai F30 electron microscope (FEI) with an acceleration voltage of 300 kV. Tomograms were reconstructed with the IMOD software package (version 4. 7)  71. Correlation of FLM and TEM Images. Fluorescence and TEM images were correlated using the AMIRA software (FEI) with the LandmarkWarp function. For correlation of images of Tokuyasu sections cell nuclei were used as landmarks and the corresponding points were set manually in the fluorescent and TEM images. For correlation of images of Epon sections fluorescent and electron dense beads were used as landmarks and also set manually.
We also counted the number of fluorescently labeled SGs found within MGBs for the different SG ages. Measurements of insulin SG diameters were performed in FIJI. Statistical analysis was done in Excel and Motion tracking software. ANOVA, Student's t-test and Bayesian probability analysis were used to assess significance. Fit models to experimental data. The theoretical models were fitted to experimental data by mathematical tools of Motiontracking 72 (http://motiontracking. mpi-cbg. de). The models, results of fit and uncertainty estimations  39  are presented below: Model 1: τ = ⋅     -      y a t exp ( 1 ) where y is fraction of labeled granules as function of time. Result of fit: Model 2: τ =           ≤ ∆ ⋅      - -∆       > ∆ y a t a t t , exp ( ) , (2) where y is fraction of labeled granules as function of time.
Acknowledgements We thank  Dr. Claes Wollheim  for the gift of INS-1 cells,  Dr. Marino Zerial  for the gift of GFP-hLC3,  Dr. Hassan Mziaut  for the gift of ICA512-CCF-SNAP,  Dr. Ewa Paluch  for the gift of pLifeact-mCherry,  Weihua Leng  for the gift of fluorescent beads,  Nicolas Brouilly  for discussion and technical advice,  Daniela Vorkel  for technical assistance,  Daniel J. White  for advice for SIM and image quality check,  Bert Nitzsche ,  Jan Peychl  and  Britta Schroth-Diez  for advice for FLM,  Peter Hoboth  and all other members of the  Solimena lab  for advice and discussion,  Katja Pfriem  for excellent administrative assistance. This work was supported by the  German Center for Diabetes Research  (DZD e. V. ), which is financed by the  German Ministry for Education and Research , and the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no.
The object with the smallest error is then chosen as the solution of our problem. This algorithm can be summarized by: the Eq. 10. argmin 0 x n ∑ k=1 6 ∑ i=1 |I i (k) -f i (T (k), O x )| |I i (k)| (10) Figure  14  shows 24 experiments performed with an conductive ellipsoid which size was 33 × 16 mm. The real ellipses are displayed in red and the estimated ellipses in blue. Each results has been obtained offline using data collected by the robot (following a straight line trajectory) at different 6 distances 50, 60, 70, 80, 90, 100mm and with 4 different object orientations. As it can be seen the localization and the shape can be estimated at a short distance. As we increase the distance with the object the recognition is unfortunately getting worst as the signal noise ratio decreases. It has to be noted that for the presented results the resolution for the localization and the shape estimate was 2mm and the angle resolution was 0.
The massive f ne-grain reconf guration capability of FPGA technologies is ideal to implement the high number of pattern detection units needed for these intensive data mining applications. We have thus designed and implemented an IP that features high-density FPGA occupation and high working frequency. We provide detailed description of the IP internal microarchitecture and its actual implementation and optimization for the targeted FPGA resources. We validate our architecture by developing a co-designed implementation of the Apriori Frequent Itemset Mining (FIM) algorithm, and perform numerous experiments against existing hardware and software solutions. We demonstrate that FIM hardware acceleration is particularly eff cient for large and low-density datasets (i. e. long-tailed datasets).
INTRODUCTION Extracting information from huge collections of unstructured data has taken an increasing importance in the last decades. This mining process takes place in a context in which an increasing amount of data sources are data streams, i. e. data is produced by a continuous and uninterruptible source, leading to a potentially unbounded amount of data. Many algorithms have been developed to perform batch extraction of patterns (e. g. sets or sequences of items) from f nite datasets, including frequent pattern mining, emerging pattern mining, association rules mining, etc. However most of these algorithms require the ability to store the entire dataset in memory to scan it repeatedly for f ltering and data structure transformation with unconstrained processing time. This is notably the case for mining frequent itemsets and sequences.
1994] , Eclat  [Zaki et al. 1997 ], FP-Growth  [Han et al. 2000 ], and LCM  [Uno et al. 2003 ] are representative of the evolution of the domain. A large body of work has been done at optimizing software implementations  [Borgelt 2003 ], but the required CPU processing power remains very high. As such, the currently known software implementations of these algorithms cannot be used on high throughput, low latency streams, with a reasonable power budget. Apart from LCM, these algorithms feature a high level of fine-grained parallelism and hardware acceleration approaches have been studied. In this work, we evaluate our hardware accelerator with a co-designed implementation of the Apriori algorithm, as it is representative of generate-and-test algorithms. The rest of the paper is organized as follows.
Section 2 presents the context of this work by introducing data mining concepts and presenting the characteristics of the main Frequent Itemset Mining algorithms. Section 3 introduces the most relevant works that target hardware acceleration of these algorithms, and outlines their strengths and weaknesses. Section 4 details the specification of a novel hardware architecture aiming at accelerating pattern matching and support counting on FPGA, and Section 5 details its integration with the Apriori algorithm. The implementation is tested and compared to previously published software and hardware/software results in Section 6. Finally, Section 7 summarizes our work and draws some perspectives for enhancement.
Definition of the Frequent Itemset Mining Problem A dataset D is a collection of transactions over a set of items I. We assume the existence of a strict ordering on I. A transaction T ∈ D of size n is denoted T = {i 1 ,. , i n } where T ⊆ I. Table  1a  provides an example dataset of 6 transactions where I = {a, b, c, d}. An itemset P is a subset of I. A transaction T is an occurrence of P if T contains P , i. e. P ⊆ T. The number of occurrences of an itemset in D is called its support, denoted sup D (P ). In our example dataset, sup D ({a, b}) = 3. Frequent Itemset Mining (FIM) consists of finding all itemsets whose support in D is at least ε, along with their exact support. The list of frequent itemsets in our sample dataset for ε = 2 is given in Table  1b.
Most notable algorithms Apriori. Agrawal et al. defined the problem of FIM and proposed the Apriori algorithm to solve it  [Agrawal et al. 1994 ]. Apriori enumerates itemsets by increasing length, also called breadth-first exploration, finding all frequent itemsets of size K (noted K-itemset) before considering (K + 1)itemsets. Apriori follows the generate-and-test approach. (K +1)-itemset P are generated by adding one frequent item e to the frequent K-itemset P (i. e. generate phase). P is then a candidate itemset, and to decide whether it is actually frequent or not, it is necessary to count the number of dataset transactions that do contain P (i. e. test phase). This operation is named support counting and it is the most time-consuming part of Apriori, as it requires scanning through D for each candidate.
The interest of Apriori comes from the fact that for any (K + 1)-candidate P to be frequent, all the K-itemsets included in P must also be frequent. This property is known as the anti-monotony of the support of itemsets. By building the list of (K + 1)-candidates from the list of actually frequent K-itemsets, the very costly support counting operation is not launched on itemsets which are known to be infrequent. While a dataset D potentially contains 2 |I| different itemsets, Apriori avoids in practice exponential complexity by exploiting this property to prune the search space. Eclat. Zaki et al. proposed  Eclat as an alternative to Apriori  [Zaki 2000 ]. Contrary to Apriori, Eclat performs a depth-first exploration of itemsets, and computes the supersets of an itemset before other itemsets of the same size.
Eclat uses a vertical representation of the dataset: instead of storing transactions as arrays of items, it computes for each item the list of transactions it belongs to. The transaction list of an itemset P is obtained by intersecting the list of transactions of the parent itemsets of P , and its support is the number of entries it contains. This vertical representation is effectively obtained by a transposition of the dataset structure and it must be done on the whole dataset before starting frequent itemset mining. Contrary to Apriori, Eclat does not store candidates nor previously found frequent itemsets, which makes it a generally fast and memory-efficient algorithm. FP-Growth. Han et al. introduced the FP-Growth algorithm  [Han et al. 2000 ]. It consists of transforming the dataset into a frequent pattern tree (FP-tree, a tree that contains all itemsets that exists in the dataset).
In such a tree, each node represents an item, and the path from the root node to any other node represents an itemset. Each node also contains a counter that is the number of transactions that include the corresponding itemset. Once this is done, the tree can be recursively scanned and the counters checked to decide whether the corresponding itemset is frequent. Software implementations of FP-Growth are generally faster than Apriori but the FP-tree's high memory usage limits its application to large datasets. Uno et al. [Uno et al. 2005] , performs a depth-first itemsets enumeration similar to Eclat. It maintains both a horizontal (transaction based) and vertical (item based) representation of the dataset. The main innovation of LCM is a dataset reduction operation, which creates a significantly compressed version of the dataset before enumerating the supersets of an itemset.
Apriori Baker and Prasanna devised an FPGA-based acceleration for Apriori  [Baker and Prasanna 2005; Baker and Prasanna 2006]. The systolic array architecture is used for every step of the algorithm: finding pairs of parent itemsets to generate candidates, eliminating candidates using anti-monotony, and computing support of candidates. The hardware proposed in  [Baker and Prasanna 2005]  is able to load 560 candidates in a Virtex-II Pro XC2VP100 FPGA, and support is evaluated by streaming the dataset. An updated version of the support counting unit groups similar candidates around small CAMs used for item recoding. This enables to overall load more candidates, which reduces the number of iterations through the dataset  [Baker and Prasanna 2006 ]. However they do not indicate the CPU cost of efficiently grouping candidates in order to make the best use of these CAMs.
Unfortunately the results in  [Baker and Prasanna 2005; Baker and Prasanna 2006]  were based on wrong estimations of the number of candidate itemsets. This has also been noted by  [Thoni and Strey 2009]  who re-implemented the technique proposed in  [Baker and Prasanna 2006]. Although in  [Thoni and Strey 2009]  no CAM-based item recoding is implemented, the obtained execution time and FPGA resource utilization per candidate are notably higher. In  [Wen et al. 2008]  the FPGA-based platform, HAPPI, is proposed. They a use hardware hash table to limit CPU-FPGA communications and synchronizations. This seems to be very effective on the small synthetic datasets they used but scalability is probably limited due to the hardware nature of their hash table. GPU acceleration of Apriori is proposed in  [Nikam and Meshram 2014].