dataesr/scientific-paragraphs-categorization huggingface dataset:
The CIDOC-CRM contains the most basic relationships to describe what happened in the past at a human scale, i. e. people and things meeting in space-time, parts and wholes, use, influence and reference. More detailed kinds of discourse require extensions (See Box). Having defined the ARIADNE Reference Model (RM), integration is accomplished by creating an advanced knowledge base (target, aggregation database) based on the common reference model. The integrated knowledge base is the aggregation of several existing archaeological databases that were transformed by mapping their individual schemata (source schemata) into the ARIADNE RM (target schema). The mapping process was supported by the X3ML Mapping Framework (See Box), ensuring the integrity of the initial data and preserving their initial 'meaning'.
The experiment has been described here in some detail, since it is key to an important future development, namely the creation of knowledge aggregations where researchers can find answers to research questions spanning several datasets. The experiment has had encouraging results, and will form an important item for the future. ARIADNE has also begun to tackle the sharing of services, making services on visual data and on reference resources accessible through the infrastructure. Nonetheless, it is clear that much remains to be done before archaeology has a mature research infrastructure. o Data integration needs to be undertaken more systematically, by making the available tools and resources available to the community through a state-of-the-art Virtual Research Environment, where domain experts can convene and collaborate to develop the necessary transformation rules and to apply those rules to create integrated data.
As a digital infrastructure for archaeological research ARIADNE brings together and integrates existing archaeological research data infrastructures so that researchers can use the various distributed datasets and technologies' (ESFRI 2016, 52, 175). With the inclusion of Heritage Science within the 2016 ESFRI Roadmap, and the strong engagement of ARIADNE with the nascent European Research Infrastructure for Heritage Science (E-RIHS), the foundations have been laid to place archaeology at the forefront of European research infrastructures, ensuring a sustainable future for the ARIADNE portal and services. http://www. ics. forth. gr/isl/3M ARIADNE 2014a The Way Forward to Digital Archaeology in Europe, November 2014. http://www. ariadneinfrastructure. eu/Media/Files/Ariadne-Booklet-The-Way-Forward-to-Deliverable D2.
The latter accounts for the fact that in reality the birefringence will vary in the sections where it is assumed constant, which will lead to a random phase relationship between the two polarization components  [32]. The differential group delay (DGD) of each section is selected randomly from a Gaussian distribution. This way artifacts in the wavelength domain caused by a fixed delay for all sections are avoided  [35]. Within each scattering section, the equation (1) without PMD terms is solved using standard split-step Fourier integration. To speed up the simulations, we employ a CUDA/C++ based implementation with a MEX interface to Matlab. The resulting DGD of the fiber is Maxwell distributed  [36] , p ∆t (∆t) = 32 π 2 ∆t 2 ∆t 3 exp - 4∆t 2 π ∆t 2. (23) For the Maxwell distribution, the mean is related to the root-mean-square (RMS) delay by ∆t 3π/8 = ∆t 2.
After four washes in PBS with 0. 1% triton and a rinse in PBS, the tissues were mounted in Slowfade medium containing DAPI (Invitrogen) to visualize the DNA. Microscopy. Live imaging of Nbs and pupae was performed at room temperature with either an Axio-observer inverted microscope (Carl Zeiss) equipped with a ×100 oil Plan-Apochromat objective lens (N/A 1. 4), a spinning disk (CSUX-A1, Yokogawa), and an EMCCD Evolve camera (Princeton Instrument, Roper Scientific) or a Ti-DH inverted microscope (Nikon) equipped with a ×100 oil Plan-Apochromat objective lens (N/A 1. 45), a spinning disk (CSU-W1-T1, Yokogawa), and a sCMOS camera (Roper Scientific). Images were acquired with Metamorph software (Molecular Device). Twelve to 20 Z of 0. 5 µm steps were acquired every 20 s except in Fig. 3a -f, where one z plane was acquired every second (a-e) or every 5 sec (f).
For Figs. 2d, k and 4i, the GMC elongation indices were calculated over time. For Supplementary Figs. 4c-f, spindle length and total cell length were normalized with the diameter of the cell at metaphase (c-d), and measured over time (c-d) or at maximal cell elongation (e-f) as shown in Supplementary Figs. 4b. To generate the kymographs of myosin dynamics at the cortex in Figs. 2c, 3b, 4e, and 6b and Supplementary Figs. 4a  and 6c , we wrote a custom script in ImageJ. First, we drew a freehand line of 2 pixels thickness along the cortex from the apical Nb pole to the basal GMC pole on a single z plane at each time point starting 2 min after AO. The script converted the freehand lines onto straight lines centered on the contractile ring position. To calculate the rate of GFP::NLS nuclear accumulation presented in Fig.
Acknowledgements We thank  Michael Murray  ( University of Melbourne, Australia ),  Michel Gho  ( Laboratoire de Biologie du developpement, Paris, France ), alérie  Doye  ( Institut Jacques Monod, Paris, France ),  Valérie Doye  ( Institut Jacques Monod, Paris, France ),  Roger Karess  ( Institute Jacques Monod, Paris, France ) and  Hugo Bellen  ( Duncan Neurological Research Institute, Houston, USA ) for sharing their reagents and technical expertise. We also thank  Derek McCusker  ( European Institute of Chemistry and Biology, Pessac, France ) and all the team members for critical reading of the manuscript. E. M. , L. B. , and C. L. were supported by  ANR-12-PDOC-0020-01  ( ARC2-ChromSCeD ). E. M. and C. L. were also supported by  Centre National de la Recherche Scientifique (CNRS). M. -C.
This sub-procedure is called whenever is moved from one period to the next period to its right. It is responsible for computing the first position of in the new period, for updating the Parikh matrix according to the new position, and for adding new relevant problematic columns that need to be visited in the L zone, starting the new period. It is described in 4. 3. Moving between positions (within a period). This is the basic step that updates the Parikh matrix according to the new position of or r. It is described in 4. 4. The rest of this section is organized as follows. We start in 4. 1 with a description of the data structures used in the algorithm. We continue with a description of each one of the sub-procedures for and r moves. In Subsection 4. 5 we describe the procedure that finds the leftmost k-MAR that contains anc.
Data Structures We start with describing the data structures used in the main procedure. As mentioned above, we use the Parikh matrix in order to count the number of modified letters in the substring T +1,r-1. The Parikh matrix contains only O(k) columns, which are the problematic columns in T. In addition, the following auxiliary lists are used throughout the algorithm. LeftList and RightList are sorted lists of problematic columns that need to be visited in L and R, respectively. Both lists contain a subset of the problematic columns in T. Each list is implemented as a balanced search tree, where the keys to the records are column positions. These lists are both initialized whenever or r is moved to a new zone, and in addition, are updated as and r increase. Observe that for a problematic column in L, there might be a situation in which the letter in L starts as the only winner of its column, and as and r increase, the letter becomes a loser in its column.
We want to visit this column in L only in the positions where the letter becomes a loser (or one of more than one winners in the column). Therefore, we need to add this problematic column to LeftList only when it is relevant. In order to support this kind of update, an additional data structure is used. For each period i in the (k + 1)-periods of L, we keep a list, newColumnList i , that contains problematic columns that need to be added to LeftList starting period i. Each newColumnList i is a doubly linked list. We keep an array of O(k) pointers, such that for every problematic column in T , we have a pointer to the specific list that it is contained in, or N ull if it is not contained in any newColumnList. Then, we can find the list a column is contained in, and update the lists (insert and delete columns) in constant time.
Our mixed traffic simulator is developed using the C language and the Open Graphics Library (OpenGL) library for graphical interface. Fig. 3  shows the structure of mixed traffic simulator. This simulator has inputs that are used by its different modules to save the output of the simulation in different formats such as video and/or data required for post-processing. The inputs of the simulator are the following three: -'XML File' in which some parameters can be set: the simulation number, the initial vehicle number, the road length and the minimum and maximum percentage of automated vehicles; -'Screen' -displays the architecture and the scenario; -'Video' -to save the simulation; -'File. txt or. bin' -saves the data needed for postprocessing. Fig. 4  shows the position of the vehicles in a platoon.
The IDM simulation model with the parameters given by Table  3  is applied to simulate the fully automated traffic using our developed microscopic traffic simulator. The presented parameter values in this table are originated from an identification step using experimental data and used to validate the IDM with the fundamental diagram by  Kesting et al. (2008). At each simulation step, the simulated microscopic traffic data such as the position, the velocity and the acceleration of each vehicle are computed. Then, the density and the velocity are computed according to Eqs (  17 ) and (  18 ) respectively. Simulation parameters are given in Table  4. In this table, the jam distance s n 0 is set to zero in order to have the maximum density equal to 1. To study the sensitivity of macroscopic parameters (velocity and density) according to the microscopic parameters (a n , b n , T n ), traffic simulation is performed made for each of the maximum acceleration a n values (the other microscopic parameters are constants), the desired deceleration b n (the other microscopic parameters are constants) and the safety time headway T n (the other microscopic parameters are constants).
This framework aims to provide a new useful method and try to standardize the comparison protocol of several SHC & DHW closed systems. Moreover, the developed tools offer an interesting database of sorption chillers where we can found construction parameters such as: the brand, the nominal power and the nominal COP … etc. The mentioned list gathers around fifty models for a nominal power raging from 10 to about 200 kW. The second data base includes a solar panel list which comprises around two hundred models. Both lists enable engineers and planners to have a quick access to relevant information required for selection and design tasks without dealing with the tedious manufacturers data sheet. The PISTACHE tool uses hourly calculations resulting in monthly and annual energy balances  [13] [14] [15].
PISTACHE description PISTACHE (Pre-sIzing Tool for SolArCooling and Heating systEms) software has been developed to help designers, planners and HVAC professionals to quickly assess SHC & DHW plant performances using sorption chillers. In this way, our tool performs quick and easy calculations for solar installation cooling, heating and DHW production. It helps the user to pre-size the installation and provides both energy balances and annual performance indicators. The tool is composed of a user interface to upload meteorological data and building loads, to enter parameters and to choose the main component characteristics. It also includes a commercial list of sorption chillers and solar collectors available in the market and a step by step help file as described in previous works  [13, 15, 17].
These tools involve time-consuming modeling adaptations and require a good command of the software that make their use for pre-sizing simulations irrelevant. Recent software specifically developed for the solar cooling concern tools like: INSEL  [23] , Polysun  [24] , TRANSOL  [25] , ODIRSOL  [26]  and PISTACHE. It should be highlighted that PISTACHE tool is the only software which is free, easy to use, with a large database of commercial components and validated with experimental data unlike ODIRSOL.
The automatic pre-sizing functions are fully described by Le Denn et al. [17]  and cover the sorption chiller nominal capacity, the solar thermal collector area, the hot and cold storage volumes and their heat loss time constants (C r ). Two chiller technologies are available: absorption and adsorption. After choosing, the user selects an existing chiller from a list established by Boudéhenn et al. [27]. A generic chiller with a defined nominal cooling capacity and thermal coefficient of performance (COP th ) can also be used. After selecting the solar collector technology (flat plate or vacuum tubes), the user selects a model from the database or a generic one as defined in task 32 of IEA SHC report  [28]. The storage volumes and the corresponding storage heat loss time constants are defined by the user or by using an automatic function  [17].
Description of the methodology used for the validation The validation phase aims to ascertain input parameters of PISTACHE (storage tank heat time constant and cooling power coefficients) from an initial experimental period named "season 1″ and to validate predicted PISTACHE results with a second experimental period named "season 2″. The validation covers two different configurations: cooling mode only and, both heating and cooling modes. Parameters are ascertained using the generic optimization program GenOpt developed by Wetter  [31]. It identifies PISTACHE parameters which minimize the associated objective function defined as: To complete the process, PISTACHE and GenOpt should be coupled as depicted in Fig. 5  which was inspired by the GenOpt user manual  [31]  and previous work carried out by Bigot  [32]  and Tamasauskas  [33].
6 ) by relative errors expressed as: = - ⋅ Err Qi Qi Qi (%) 100 PIST exp exp  (16)  Fig. 6  denotes relevance accuracy between experimental data and PISTACHE predictions. Solar energy supplied to the hot storage (Q1) has been estimated at 15843 kWh by PISTACHE while the thermal heat energy supplied to the sorption machine (Q6) and the thermal cooling energy supplied by the evaporator (Q7) have been evaluated at 13139 kWh and 7313 kWh respectively. In fact, the relative errors from the parameter identification phase are evaluated at 0. 13% for Q1, 0. 24% for Q6 and finally 1. 98% for Q7. In the histogram of the validation phase, we note that relative errors are higher than those obtained in the identification phase. For the second season, PISTACHE predicted captured solar energy to be about 25887 kWh, the absorbed thermal energy of the sorption machine to be about 20268 kWh and a thermal cooling energy of 11108 kWh; leading to relative errors of 9.
Validation with "SONNENKRAFT" data Like the previous case study, validation of the PISTACHE tool uses the following assumptions (Table  4 ): Both identification and validation phases follow the previous RAFSOL study steps mentioned above, whereas the only requirement for the SONNENKRAFT study is to consider thermal energy supplied for heating (Q3′). Using SONNENKRAFT first season meteorological and building loads inputs, the parameter identification process gives the next set of parameters (Table  5 ). The parameters identified are subsequently used to validate PISTACHE using the second season SONNENKRAFT experimental data. The results obtained are compared in Fig. 7. As confirmed by the results in Fig. 7 ,the energy values were accurately predicted for the identification inducing low relative errors namely: 4.
Conclusions and perspectives The presented word is a case study experimental validation of the new pre-sizing tool so-called PISTACHE. Two operating modes has been investigated: a simple cooling mode corresponding to the RAFSOL installation and, both cooling and heating modes corresponding to the SONNENKRAFT plant. The validation process presented in this paper completes the previous work done by Le Denn et al. [13]. The first part of the validation consisted of a study to determine parameters. This phase aimed to minimize the objective function in order to superimpose the real experimental data on PISTACHE's predictions for each installation. Utilizing the first season's meteorological and load input files, the identification phase was performed by the GenOpt program wherein a new set of internal parameters were defined for PISTACHE.
Acknowledgements This work represents the continuation and the embodiment of the previous project, ORASOL. It is part of the  "MEGAPICS" R & D project  supported by the  French National Research Agency (ANR)  through the " Habitat intelligent et solaire photovoltaïque" program  ( n°ANR-09-HABISOL-007 ). The authors wish to acknowledge all the partners of both MEGAPICS and ORASOL projects.
Some of those experiments have been recently revisited. In particular, a full characterization of ZONA-1 and ZONA-3, two different cores loaded in the ERMINE V campaign, has been done, with much attention paid to possible sources of errors. It includes detailed geometric descriptions, energy profiles of the direct and adjoint fluxes and spectral indices obtained thanks to Monte Carlo calculations and compared to a reference fast core configuration. Sample oscillation experiments of separated fission products such as 103 Rh or 99 Tc, which were part of the ERMINE V program, have been simulated using recently-developed options in the TRIPOLI-4 code and compared to the experimental values. The present paper describes the corresponding results. The findings motivate indepth studies for designing optimized coupled-core conditions in ZEPHYR, a new ZPR which will replace MINERVE and will provide integral data to meet the needs of Gen-III and Gen-IV reactors.
Generalities The ERMINE campaigns stand as a special program in the history of the MINERVE reactor. Indeed, it represents the only fast spectrum experiments ever been conducted in this facility. However, they took place between 1966 (ERMINE I) and 1985 (ERMINE VI) in order to support the design and operation of both PHENIX and SUPERPHENIX. The measurements covered several needs associated to fast reactors: Doppler effect, capture of structure materials, fission products and irradiated fuels. The ERMINE-V campaign (1976-1980) used four core configurations to get the integral capture cross sections of fission products and irradiated samples thanks to the oscillation technique  [8]  and irradiation measurements. In this paper we are focusing on two particular cores, ZONA1 and ZONA3. The fast zones were limited to the central Aluminum ring of 35 cm radius where the fissile fuels were 36 inches high (approximatively 92 cm).
Development of the WRF-CHIMERE coupled version The CHIMERE2016a release included preliminary technical changes for the development of an online-coupled version of CHIMERE. CHIMERE preprocessors (for the calculation of emissions in particular) were included in its core. Indeed, in the case of an online simulation not all input data are known, prior to the simulation, for the entire simulation period. In particular, in the case of a WRF-CHIMERE online coupling, meteorological fields are received at each time step of a simulation, thus preventing the precomputation of meteorology-dependent variables such as mineral dust emission or biogenic emission fluxes. Furthermore, CHIMERE held a master-worker pattern where the master process performed all input/output operations. A more efficient pattern was implemented in which each worker performs parallel input/output operations, using the parallel-netcdf library  (Li et al.
Physical map construction in silico and candidate gene prediction To construct a physical map of Pi57(t) locus, all molecular markers used for gene mapping were landed on the IRGSP1. 0 pseudomolecule of reference cv. Nipponbare released by IRGSP through BLASTN search (https://www. ncbi. nlm. nih. gov/Blast. cgi). Subsequently, the physical map spanning Pi57(t) locus was constructed based on the reference genomic sequence of Nipponbare. The candidate R genes in the target region were predicted based on the annotation information by Rice Genome Annotation Project (http://rice. plantbiology. msu. edu/), GEN-SCAN (http://genes. mit. edu) and FGENSH (http://www. softberry. com/) software.
Acknowledgments We are grateful to  Dr. Khay S ,  Dr. Douangboupha B ,  Dr. Somrith A  and  Dr. Chung NH  for the provision of blast isolates. This research was supported by grants from  National Natural Science Foundation of China  ( 31360462 ,  31560493 ),  Yunnan Provincial Science and Technology Department  ( 2014IA009 ), the  Special Fund for Agro-Scientific Research  in the Public Interest (201303129),  Scientific Observing  and  Experimental Station of Crops Pests in Kunming, Ministry of Agriculture, China.
Acknowledgements We acknowledge financial support of C. N. from the  UnivEarthS LabEx programme of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ), the  French National Research Agency  ( ANR-12-BS05-001-03/EXO-DUNES ), and of P. S. from the  Russian Science Foundation , Project  16-17-00093.
Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models Minimal Information standard (PDX-MI) for reporting on the generation, quality assurance and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models.
Although many academic and commercial sources of PDX models have emerged in recent years, the size of the resources and the processes for creating and characterizing PDX models is quite variable. Crucial information about tumors, host strains, transplant and quality assurance processes are inconsistently presented in both the scientific literature and in database resources, limiting the ability of researchers to find relevant models and associated data. A standardized data exchange format is needed to foster the ability of researchers to identify appropriate PDX models and share information about them. As developers of NCI-funded informatics resources, we obtained the internal standards developed by four independent PDX model resources (the EurOPDX consortium  (5) , the IMODI consortium (France), the Patient Derived Models Repository at NCI-Frederick, and The Jackson Laboratory PDX Resource  (8).