dataesr/scientific-paragraphs-categorization huggingface dataset:
M. M. is supported by  Spanish MINECO  (grants  FPA-2009-08958 ,  FPA-2009-09017 ,  FPA2012-31880 ,  FPA2012-34694 , consolider-ingenio 2010 grant  CSD-2008-0037  and " Centro de Excelencia Severo Ochoa" program   SEV-2012-0249 ) and by  Comunidad Autonoma de Madrid  ( HEP-HACOS  project  S2009/ESP-1473 ). P. A. N. M. and M. M. acknowledge partial support from the  European Union  ( FP7 Marie Curie -ITN actions PITN-GA-2009-237920 "UNILHC"). M. M. and T. S. acknowledge partial support from the  European Union   FP7 ITN INVISI-BLES (Marie Curie Actions ,  PITN-GA-2011-289442 ).
Serializer and LVDS driver The ALPIDE chip also needs to be equipped with a serializer and LVDS driver to send the data offchip. Prototypes of the LVDS driver were submitted for fabrication, and first measurement results -8 - have become available. Figure  11  shows an eye-diagram at 1 Gb/s of the output applying a pseudo random binary sequence for a bias current of 2 mA. A jitter below 10% of the unit interval has been measured illustrating the driver can exceed 1 Gb/s. The LVDS driver still needs to be combined with a serializer. Recent investigation has shown data rate requirements should not exceed 1 Gb/s.
Conclusions The ALPIDE development towards a monolithic pixel sensor for the upgrade of the ALICE Inner Tracking System is described. As an alternative to approaches based on a rolling shutter architecture, this development aims to reduce power consumption and integration time by an order of magnitude below the ALICE specifications, which would be quite beneficial in terms of material budget and background. The approach is based on an in-pixel binary front-end combined with a hit-driven architecture. Analog power consumption (∼ 40 nW/pixel or < 10 mW/cm 2 ) has been limited by optimizing the Q/C of the sensor. Digital power consumption is minimized using a priority encoder for reading the hit pixels. Preliminary first estimates of an upper limit on digital power are around 10mW/cm 2. If the ALPIDE approach is successful, data transmission off-chip could be the dominant power contributor, but if data rates can remain well below 1Gbit/s for a 1.
As a consequence, the liquid fuel may be injected at supercritical conditions. Generally, the chamber pressure is above the critical pressure of the fuel (p > p c ) while the injection temperature is subcritical (T < T c ). For example, the critical properties of decane which is commonly used to simulate kerosene, are p c = 2. 1 MPa and T c = 617. 7 K. Such a state is called compressed §uids (or transcritical state  [2] ). After injection, the fuel heats up and its temperature exceeds its critical value (T > T c ). In both regimes, the §uid is neither a liquid nor a gas, but has liquid-like density along with gas-like properties; beyond the critical point (p c , T c ), there is no distinction between gaseous and liquid phases. These points are summarized in Fig. 1. Most of the current understanding of turbulence and mixing is the result of atmospheric-pressure studies and numerous numerical and experimental databases are available.
CORIA Solver The CORIA Lab recently developed a new version of SiTCom solver called SiTCom-B (Simulation of Turbulent Combustion with Billions of points)  [2832]  which is mainly designed to perform DNS and highly resolved LES on thousands of processors. It is a ¦nite-volume code that solves the unsteady compressible reacting NavierStokes equations system on Cartesian meshes. The SiTCom-B solver features the following characteristics: forth-order central difference schemes plus arti¦cial dissipation term of the second and fourth order from Tatsumi et al. [33] , RungeKutta time-discretization (third and forth orders), full multispecies formulation, realistic thermodynamic (CHEMKIN), realistic transport properties  [11] , complex chemistry, tabulated chemistry, ideal or real gas EoS, NSCBC (NavierStokes Characteristic Boundary Conditions) boundary treatment  [31]  and immersed boundary method.
In this paper we present a mining project about extracting knowledge from public documents concerning air pollution. Our collection contains annual reports about air quality, acid rains, climatological conditions in the large area of Mexico City. These reports contain reliable data and are generated by the Department of Environment, they are in a printable format (. pdf file) with number of pages, table of content, textual information, numerical information in tables, images. For a human being it is impossible to read the whole collection during a relatively short period (a few days or weeks) and understand the content of them. An automatic box of tools able to extract knowledge, to quick retrieve important term, to answer some exact questions about precise climate parameters would be an important help for lecturers.
A first version of this project was presented in  [9]. Some related works about mining in air pollution area are brievely presented in the following section. In section 3 we will present the collection of documents and its properties; section 4 contains the description of the whole knowledge extraction application which is based on data and text mining, the application is composed mainly of three modules : integration and indexing, mining, visualization -information retrieval. Section 5 is dedicated for some important details about the cleaning, integration and indexing process, because during this iterative process the used indexes are fulfilled and the two other modules will use these indexes and by a feedback the mining processes will extract useful data integrated in indexes too. In the last section we will finish with the perspectives of our project and with a conclusion about the possible generalization of our work to other collections of printable documents in other domain.
Related works Around the world various scientific projects have been done about mining in the air pollution domain. All of these works treat data and not text and very often the mining process comes after other types of treatments. Richards, Ma and al. in  [15]  and in  [11]  have presented a long and ambitious project in London, their project is based on a high throughput sensors distributed architecture and high grid computing abilities. Their first objective was to generate a real-time monitoring and mapping of the whole city of London, the second objective was to analyze and to mine the collected data sets. The mining processes used SOM and hierarchical methods, even distributed hierarchical algorithms. Lia and Shue in  [10]  had presented a complete application used in Taiwan and the most analyzed indicator was suspended particulate PM10.
The clustering methods and SOM were done and the clusters obtained were useful for gouvernemental decisions. Also air pollution data set are used as validation data set for innovant data mining techniques. Sahu and Baker  [16]  had worked on ozone pollution in New York state and had implemented a Bayesian model. Also Temiyasathit and al. [18]  had used a collection on air quality in Dallas Fort Worth (DFW) area in a aim to realize multiscale and functional data analysis and also have purposed a spatial predictor based on kriging. Soft association rules were obtained from a quality air data set from Kuala Lumpur in  [8]. We can see that all these mining works were done on detailed data sets, containing all or part of the evolution of the main polluants as carbon monoxide (CO), lead, nitrogen dioxide (N O 2 ), ozone (O 3 ).
Complete Process of Knowledge Extraction and Mining Our project is now in work and it is composed of three main parts: • integration and indexing • mining processes • retrieval and visualization information and knowledge The figure  2  shows the structure of our final knowledge extraction and data mining application. Its structure  The initial format of the documents is not adapted for our task, so we converted. pdf format into. ebook format and than with XSLT processor in a more useful. XML format. The documents collected will be transformed in various manner and three indexes will be fulfilled: • an XML database (implemented with eXist 3  ) and an upper built mechanism allowing annotation • a textual index (implemented with Lucene 4  ) • a datawarehouse which contains the almost numerical values extract from document contents.
Integration and indexing documents After downloading all the documents, we have to covert all of them into a XML format using conversion software (like Adobe) and than to construct a XSLT processor  [7]  to transform XML ebook format into an other one more appropriate and able to differentiate the structure of the document. The paper  [4]  who describe a. pdf to. xml conversion was very helpful. The choice of XML  [1]  as central format of document representation is justified by the large capacities to present the XML document and parts of it, to preserve the structure of initial document, to allow easily future changes. On the other hand the rich structure of the documents must be preserved because it is interesting to exploit for the text mining process  [2]. An other exploration of the collection is to realize xdiff-like  [19]  comparison between paires of document in the aim to find common parts like bibliography, acknowledgements, technical description of physical mesures.
These common parts will be indicated with annotations inside. xml document and during visualization these parts will be indicated with a different style. The indexing part will be for : • textual content and this step will be done with Lucene  [12]  • numerical values and all numerical values will be stored in DBMS and a great part in datamarts with a model of hypercube  [6]  • XML documents and their successive versions will be stored en a XML database (eXist). The textual indexation is the first one of the indexing step. We use TreeTagger  [17]  for stemming, but we deplore the absence of an ontology about the environnement and, more particular, an ontology or some other knowledge representations about air pollution  [13]  for Spanish. A problem we have met was the large number of abbreviations (see the figure  1  for example) The extraction of all the abbreviations will be done semi manual and using text mining like in  [3]  and  [14].
We also planned to rapidly integrate all the. xml versions of document (initial version, version with annotation for images localization or for abbreviation changes) into eXist XML database. We will also do a large tool set to annotate the documents, to extract some interesting parts, to display friendly various parts of documents, these tools will be realized using XPath, XQuery and XSLT  [7]. This database will be used for the XML mining step and also the database may be used by the visualization and information retrieval module. In the aim to extract numerical values, we also need to detect the context of a value. If the datas are in a table like in figures 1 and 3 we have to translate the abbreviations, to fix the year of publication and to explore the caption of the figure or the text around the Fig.
3 : A table inside a report with numerical values. A such table is present with actualized information in every annual report reference to this table. We have also to use a data mining for a pattern search for detecting parameter tables which repeat in every year report. We have the objective to explore (extract) at least 75% of numerical values. These values will be saved in a relational database and a set of them will be organized as datamart in hypercube organized by month, year, area, parameter  [6]. A similar approach as in  [8]  about extracting some association rules can be also possible, but we need the aid of an expert able to indicate as some thresholds of parameters like CO 2 ≥ 0. 02 or O 3 ≥ 0. 007. Our complete collection contain the values some polluants that are aggregate by month and by geographical area and also some climatic informations as temperature, wind, humidity.
Results In this section, we present results of experiments with the method described in the preceding sections. We implemented our algorithm using Matlab. All measurements were made on a Windows 7 Professional machine with 16 GB of memory and Intel Core i7-2600 processor running at 3. 40 GHz. We tested our method on the models from the Non-Rigid World Benchmark and High-resolution Tosca data set  [20] , as well as on our own synthetic embossed plate models. Fig. 13  shows some of the results we obtained. Note that these models exhibit deformations which are only approximate isometries; also each family of objects have a mesh in an initial posture, which is labeled as a mesh in a rest posture (or source mesh). The landmarks have been chosen to be a set of 100 points evenly spaced over the surface using Poisson sampling.
As shown in Table  1 , the overall time of landmark transfer is just a matter of seconds; except for the centaur model. On the centaur our method shows rather a high computation time. The main reason is that this mesh has many more geometric feature points compared to other models (e. g. 15 feature points for the centaur vs. only 8 for the cat). Since t H is a function of a number of vertices and geometric features, our technique works fast on the embossed plate, which also has many geometric features, but the number of vertices is much less compared to the centaur model. In general, the computation time is an advantage of our method. It takes about 1 minute to find full correspondence for the cat model (on Matlab platform). This has been possible because (1) the update of the geodesic distances has been made only once, and (2) full graph has been used in place of minimal graph for every vertex.
Robustness In order to measure the quality of the results of landmark transfer, we perform cross-validations by using the Tosca highresolution dataset as ground truth. We applied our techique to a mixed data set of different subjects in different postures. Our test cases evaluate the maximum and mean errors while transferring 100 Poisson sampled landmarks: on the cat models (Fig. 8(a) ), for the Centaur models (Fig. 8(b) ). The error of landmark transfer is measured as a geodesic distance deviation from the corresponding ground truth, further normalized by square root of the mesh surface area. Average error values for all data sets are evaluated as shown in Fig. 8(c ). Due to imperfect isometries, the error values vary from one posture to another (clearly visible in Fig. 8(a, b )). (Be reminded that these models are only nearly isometric.
Within each family of objects from the full Tosca dataset (11 cats, 9 dogs, 3 wolves, 8 horses, 6 centaurs, 4 gorillas, 39 human figures including one female and two male subjects), we arbitrarily selected a model as a source and computed full correspondences to the rest of the models using LTMG by treating each vertex on the source mesh as a landmark. The results of BIM benchmark and Möbius Voting were referred and reproduced, as presented in  [13]. Comparative study of correspondence errors is illustrated in Fig. 12. Overall, LTMG and BIM show better accuracy than the Möbius Voting. LTMG shows comparable accuracy to Blended Maps. Compared to LTMG, BIM produces slightly larger number of correspondences in the error range of less than 0. 03. However, in contrast to BIM, LTMG gives less outliers with errors higher than 0.
04. Additionally, LTMG's plot is noticeably steeper and converges quickly to 100% of correspondences at the error value of 0. 14 on the Tosca data set. On the contrary, BIM reaches 100% of correspondences only at the error value 0. 25 on the same set. Comparison with PLANSAC. We also compared our method to PLANSAC  [10]  and its predecessor RANSAC  [9]. Provided by  [10]  the average error score Eðf Þ for the centaur model computed with PLANSAC and RANSAC methods is 0. 032 and 0. 113 respectively. With the LTMG method applied to the same data, we observe an average matching error of 0. 027. This makes our method comparable to PLANSAC matching method, and actually better than RANSAC. Note that Tevs and co-workers  [10]  run their algorithm on Poisson sampled centaur model and provide accuracy results in ε-units, where ε is the minimum distance between two points in the discretization.
Executable We provide a sample LTMG application on the Collage authoring environment (  [21] ). At the moment, the cat data from Tosca is available for the test: one source model ("Experiment Data Item 1") and two target models. In "Experiment Code Item 1" a user can specify the landmark location by typing a vertex index. Running "Experiment Code Item 1" yields "Experiment Data Item 2", which shows the landmark on the source in red color. Given this landmark position, "Experiment Code Item 2" and "Experiment Code Item 3" invoke our algorithm and compute correspondences on target #1 and target #2, respectively. Transferred landmarks are further shown as red dots in "Experiment Data Item 4" (target #1) and "Experiment Data Item 6" (target #2), which are point cloud data in. pcd file format. Note that this implementation on Collage workbench runs in Octave, which is an interpreter and thus executes slower than a compiled version.
Acknowledgments We would like to thank  Art Tevs  and other authors of PLANSAC paper [10] for providing us with part of their codes, which allowed us to compare our landmark transfer technique to theirs. We also thank  Vladmir Kim  for having provided the ground truth correspondences [13] for the Watertight dataset. Paul Montgomery has proofread the paper. This work has been supported by the  French national  project  SHARED  ( Shape Analysis  and Registration of People Using Dynamic Data, No. 10-CHEX-014-01 ).
MODESA Algorithm MODESA, a centralized raw data convergecast scheduling, was proposed in  [2]. It takes into account the availability of multiple channels to reduce the activity period in the data gathering frame, while ensuring a fair medium access. MODESA builds the primary schedule after having collected the required information (e. g. , topology links, node demands, routing tree, conflicting nodes) at the sink. The primary schedule is then broadcast to all nodes in a multihop fashion along the routing tree. MODESA builds the multichannel schedule slot by slot. At each iteration, MODESA assigns the current slot to one or several tuples (slotnumber, sender, receiver, channel), applying the following rules: 1. Any node has a dynamic priority. The priority is equal to remP ckt * parentRcv, where remP ckt is the number of packets the node has in its buffer at the current iteration.
These results are given by the GLPK linear program (in black cells) and by Figure  9  describes the bonus slot assignment for the bonus request of node 9 asking for an additional slot. In this case, the bonus slot assignment overflows from the primary schedule, taking slots from the inactivity period (slots 10 and 11). 2 → 1 2 1 5 1 8 1 9 1 10 1 9' 1 9' 1 3 → 1 3 2 6 2 7 2 4 → 1 4 1 5 → 2 5 1 8 1 9 1 10 1 9' 1 9' 2 6 → 3 6 1 7 → 3 7 1 8 → 5 8 2 9 → 5 9 2 9' 2 9' 2 10 → 5 10 2 With the additional demand of node 9, MODESA obtains the assignment presented in Figure  10. In both examples, AMSA obtains the same schedule length as the linear program; so its bonus slot assignment is optimal for these scenarios. Moreover, the bonus slot assignment has the same number of slots as with a complete recomputation of the optimal assignment obtained with the GLPK model presented in Section  [2] , taking into account the initial and additional transmission needs.
USEB method The aim of the USEB (Unstressed Surface-Energy Balance) method is to estimate ET P in a LSM considering a non-stressed surface. This is a new means of computing ET P which has been developed in the ORCHIDEE LSM. Like the other two methods, it has been implemented in the SECHIBA module, Schématisation des EChanges Hydriques à l'Interface Biosphère-Atmosphère (de Rosnay and Polcher, 1998), which simulates physical processes between the ground, the vegetation and the atmosphere, as well as the ground's hydrological cycle. The LSM can be run coupled with the general circulation model LMDZ, which was developed by the Laboratoire de Météorologie Dynamique (LMD), or on a stand-alone mode. For this study, uncoupled simulations have been carried out. The computation time step is typically 30 min, allowing for a full representation of the diurnal cycle.
, 2010)  which includes data from the year 2000 until 2100. It is considered to be a greenhouse gas increase scenario, based on the IPCC fourth Special Report on Emissions Scenarios. FAO and the empirical approximations' estimates will use daily averages of WFD/IPSL A2 scenario except for variables which are affected by land surface properties. For these variables, daily averages diagnosed within ORCHIDEE are used. Therefore, atmospheric variables, namely, the wind speed and the maximums and minimums of both the relative humidity and the air temperature, correspond to those given by the WFD/IPSL A2 scenario. On the other hand, surface related parameters, like the net radiation, contain information from both the WFD/IPSL A2 scenario and ORCHIDEE. In addition to producing direct estimates of ET P , detailed in Sect.
For this study the approximation detailed in  Allen et al. (1998) , which uses the maximum and minimum 2 m temperatures and relative humidities, RH, has been employed. In order to compute it, daily averages of these variables have been obtained from the WFD/IPSL A2 scenario data sets. On the other hand, ET P BULK , ET P USEB and ET P Milly are computed using a specific humidity gradient. Taking advantage of ORCHIDEE, the LSM uses the Clausius-Clapeyron equation with a 30 min time step, resulting in a precise representation of the diurnal cycle. Two different gradients are computed in ORCHIDEE: the first is used in the bulk formula and Milly's method, where q s is computed with T s. The second is used for the USEB method implementation, and q s is computed by means of the virtual temperature, T w.
Acknowledgements. We gratefully acknowledge the helpful comments of  Agnès Ducharne ,  Filipe Aires ,  Kaicun Wang ,  Eleanor Blyth ,  Tim McVicar ,  Kerstin Stahl , and  Laura Kerber. The computer time was provided by the  Institut du Développement et des Resources en Informatique Scientifique (IDRIS). This research was undertaken within the  EU FP6  project  WATCH, Water and Global Change , (Contract  036946 ). Edited by: K. Stahl The publication of this article is financed by  CNRS-INSU.
Materials and methods The first part of the work was devoted to the identification of proteins that interact with Hsp27 according to information stored and shared in bioinformatic databases. This was performed using the software for systems biology Pathway Studio (Ariadne Genomics). Pathway Studio helps to interpret experimental data in the context of pathways, gene regulation networks, protein interaction maps, and to automatically update pathways with newly published facts using MedScan technology (www. elsevier. com). The Medscan reader extracts the relationship information from literature. We used the ResNet Mammalian (human, rat and mouse) database which contained the latest information extracted from the literature and.
Membranes were then blocked with 5% non-fat milk in TBS1 x buffer containing (blocking solution) and incubated under gentle agitation all night at room temperature in the presence of the primary antibodies. Then the membranes were incubated at 37°C for 30 minutes with the secondary fluorochrome-conjugated LICORantibody. Infrared fluorescence detection was then used for protein quantification. Membranes were scanned by the scanner Odyssey (LI-COR Biosciences) at 800 nm. Band volumes were quantified in the images using ImageQuant TL v 7. 0. 1. 0 software (Amersham). Protein abundance for each sample is given in arbitrary units.