dataesr/scientific-paragraphs-categorization huggingface dataset:
Most often, the output space is a two (or three) dimensional Euclidean space in order to allow an intuitive exploration. Indeed, most of the time, such algorithms aim at providing a better understanding of the dataset. In the present paper, we focus on cases where the output space is bidimensional. The proposed solution is unfortunately hardly extendable to tridimensional maps. The present paper in organized as follow: the current section 1 introduces multidimensional scaling concept. The second section explains why non-linear multidimensional scalings are often hard to read for users that are non-familiar with such methods. Section 3 propose a simple and intuitive solution that seems to help the users to infer from non-linear maps. Section 4 is dedicated to conclusions and perspectives.
Misunderstanding of a display originally made to be intuitive In the following, "naïve reader" refer to a person that:  does not have been exposed to data maps reached with multidimensional scaling;  does not currently understand the concept of "data map" and need explanation to infer from it;  and (obviously) does not know how it is calculated. As stated in section 1, the main purpose of NL-MDS is the display of a dataset so as to give an intuitive insight on the data structure, even for a naive reader. Complex configurations in a high dimensional space is expected to become easily observable. The algorithm's result can be seen as a "map" of the data and, by extension, NL-MDS methods are often called "mapping methods". As a geographical map, its orientation is no more than a convention (just as placing north at the top of a geographical map).
The circular background positioning may be done "by hand". The smallest circle that contains all the data can also be calculated (it should be noticed that the Jung's theorem  [14]  gives a maximum bound for its radius). The following 2-steps algorithm may be used in that goal: 1) The first step determines the smallest convex polygon that contains every data. The first point of the polygon is one of the ends of the maximum distances in the output space. Then, a straight line between the last-chosen-point for the polygon and every ones of not-already-chosen-points is drawn. The point associated to the line that does separate the dataset in two parts (that is to say, every points lie in the same side of the line) is chosen to complete the polygon. This stage is repeated until that every points lie inside the closed polygon.
The first step's complexity is O(N) where N is the number of items in the dataset. 2) The second step determines the smallest encompassing circle (i. e. the smallest circle that contains the dataset). As this encompassing circle is also the smallest circle that contains the summits of the polygon determined in step 1, all other items are ignored in this step. Each combination of 3 summits is now considered. The associated circumcircle of the corresponding triangle can be easily calculated (the circumcenter is positioned at the crossing of the three perpendicular bisectors of the triangle). If every other summits fall inside the circumcircle (i. e. every distances to the center are inferior or equal to the circle radius), the circumcircle is candidate for being the smallest encompassing circle.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsenergylett. 8b01504. Materials and methods, device results, additional STEM-HAADF and EDX analyses, XPS and HAXPES results, photoluminescence spectra, and additional SEM and XRD analyses (PDF) The data that support the findings of this study are available from the corresponding author on reasonable request, and at https://www. repository. cam. ac. uk/ handle/1810/283234.
■ ACKNOWLEDGMENTS M. A. -J. thanks  Nava Technology Limited ,  Cambridge Materials Limited , and  EPSRC  (Grant Number:  EP/M005143/1 ) for their funding and technical support. The authors thank the  Engineering and Physical Sciences Research Council  ( EPSRC ) for support. Z. A. -G. acknowledges funding from a  Winton Studentship  and an  ICON Studentship  from the  Lloyd's Register Foundation. S. D. S acknowledges the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (HYPER-ION , Grant Agreement Number  756962 ) and the  Royal Society and Tata Group  ( UF150033 ). HZB is acknowledged for allocation of synchrotron radiation beamtime. We thank  Dr Gabriel Man  for his help with plotting the HAXPES data. G. D. , S. C. , and C. D. acknowledge funding from  ERC  under grant number  25961976 PHOTO EM  and financial support from the  EU  under grant number  77 312483 ESTEEM2.
Measurements Water surface elevations were measured at data rate f s 5 40 Hz using sidewall-mounted resistive wave gauges at 19 locations along the deeper part of the wave flume and along the shoaling zone (Figure  1a ). In the breaking and inner surf zones, measurements of dynamic pressure were obtained at f s 5 40 Hz with pressure transducers deployed from the flume side-walls and from a mobile measuring frame. The pressure measurements were converted to water surface elevation using linear wave theory. More details on the water surface measurements are provided by  van der A et al. (2017). Velocities were measured at 12 cross-shore locations along the bar using a two-component laser Doppler anemometer (LDA) and two acoustic Doppler velocimeters (ADV) deployed from a measurement frame attached to a carriage on top of the flume.
The two-component backscatter LDA system (Dantec FiberFlow) consisted of a 14 mm diameter submersible transducer probe with 50 mm focal length powered by a 300 mW argon-ion air-cooled laser. The LDA measured the u and w velocity components in an ellipsoidal shaped measurement volume of 115 lm maximum diameter and 2 mm length in y direction. The data rate of the LDA depends on seeding particle density and velocity magnitude and therefore varied throughout the wave cycle and per location. For the present experiment, f s typically varied between 100 and 600 Hz. The three-component ADVs (Nortek Vectrino) measured velocities at outer-flow elevations with f s 5 100 Hz. The ADVs were orientated horizontally (side-looking). Their cylindrical shaped measurement volume was approximately 6 mm in diameter and 2.
Additional detailed WBL LDA velocity measurements were made at seven cross-shore locations (x 5 51. 0, 55. 0, 56. 0, 57. 0, 58. 1, 60. 2, and 63. 0 m), at 14 elevations (f 5 1, 2,  3, 4, 5, 7, 10, 14, 19, 26, 36, 51, 70 , and 94 mm) (Figure  1b ). To facilitate these near-bed measurements the LDA was orientated at an angle of 8. 98 to the horizontal and the bed level was found by slowly lowering the frame until the LDA's focal point intersected the top of the bed, the intersection point being determined by a sharp increase in the backscattered light intensity. The vertical positioning has an estimated accuracy of 6 0. 5 mm. Data acquisition was started on a 40 Hz analogue trigger pulse, corresponding to a 1/40 5 0. 025 s accuracy in timing between different LDA acquisitions. To improve this accuracy, the lowest ADV measurement (always at free-stream elevation) was recorded by the LDA internal acquisition system at the same f s as the LDA.
Consequently, slight differences between experimental runs may lead to large errors in the estimated velocity gradients. Therefore, data points that deviated, by sign, from the overal trend of d u/dz or dw/dz at each cross-shore location were removed (25 out of 104 samples) prior to evaluation of equation (  10 ). The predominant effect of removing these runs is a reduction in scatter in P(f); the effect on depth-averaged b P is limited (<10%). The estimates of P are insensitive to the applied velocity transformation prior to evaluating equation (  10 ), but the transformation does lead to a redistribution between the P s and P n terms. Turbulent dissipation rates E were calculated based on autospectra of LDA-measured u, using two methods proposed for surf zone conditions. The well-established method of  Trowbridge and Elgar (2001)  for calculating time-averaged E has been applied to outer-flow velocities from the present experimental campaign  (van der A et al.
The authors acknowledge the technical staff at the University of Aberdeen for help in preparing for the experiments and the CIEMLAB staff for assistance in conducting the experiments. The analysis presented benefited from the feedback from two anonymous reviewers and from helpful discussions with fellow SINBAD researchers. Data presented in this paper can be accessed through the 4TU data repository (https://doi. org/10. 4121/uuid:753f1d84-36e5-47fa-b74b-55c288545b9b). Journal of Geophysical Research: Oceans 10. 1002/2017JC013411.
Signature features of legumes include biological nitrogen fixation in symbiotic association with bacteria and mycorrhiza, geotropic peg and pod development in groundnut, and vernalization in chickpea, each of which is of economic and scientific interest. Legumes form an important component of the human diet, provide animal feed, and replenish soil fertility through biological nitrogen fixation. In addition, legumes can recover unavailable forms of soil phosphorus, possibly lower the emission of greenhouse gases, and are prospective assets in future cropping systems  (Stagnari et al. , 2017). A recent review gives a good summary of the many virtues of food legumes and sets an agenda for their improvement in the decades to come  (Sinclair and Vadez, 2012; Foyer et al. , 2016). According to United States Census Bureau International Database, the global population is predicted to approach 9.
High-density and precise phenotyping Understanding the complex relationships between genotype and phenotype is a major challenge in plant sciences in view of the multi-scale nature of phenotypes and of the large phenotypic plasticity displayed by plants subjected to varying environmental conditions that results in different values of phenotypic variables and different rankings of genotypes in different experiments. It requires (i) capturing information on physiological traits and performance of large numbers of plants, together with their environment, (ii) analysing and organizing the resulting datasets, and (iii) developing models able to disentangle and simulate plant behavior in a range of scenarios  (Tardieu et al. , 2017). Good and relevant phenotyping starts with a detailed understanding of plant biological processes underlying tolerance/resistance to well-defined constraints.
plant-phenotyping. eu), European Plant Phenotyping Infrastructure (EMPHASIS), and International Plant Phenotyping Network (IPPN; https://www. plant-phenotyping. org/). These infrastructure networks enable access to the necessary tools for phenotyping, in particular robot-assisted image capture  (Cooper et al. , 2009; Fiorani and Schurr, 2013) , statistical designs and models for extracting relevant physiological variables from raw data  (Cabrera-Bosquet et al. , 2016) , and specialized information systems managing large datasets originating from phenotyping experiments  (Tardieu et al. , 2017). GrowScreen-PaGe is a non-invasive, high-throughput phenotyping system developed at the Institute of Biosciences   Phenotyping platform (and institute or company).
, 2015). The University of Western Australia, Australia has developed a semi-hydroponic phenotyping system that examines architecture of deep root system and is amenable for phenotyping large sets of plants in a relatively small space  (Chen et al. , 2011). The salient features of several high throughput phenotyping platforms, such as PHENOPSIS  (Bresson et al. , 2015; Granier et al. , 2006) , PhenoArch  (Cabrera-Bosquet et al. , 2016) , DEEPER PHENOSCOPE  (Bresson et al. , 2015) , and RADIX (Le  Marié et al. , 2016) , are summarized in Table  2. Robotic-assisted imaging platforms and computer visionassisted analysis tools have also been developed for precise phenotyping of physiological growth, development, and other phenotypic properties  (Fahlgren et al. , 2015a,b). The data processing software that handles high throughput phenotyping for 2D, 3D, and anatomy cross sections of root have been extensively reviewed by  Kuijken et al.
(2015). The availability of automated high throughput phenotyping platforms enables testing of progenies from large germplasm populations across a range of target environments in terms of their impact on yield. The efficiency of these platforms, in this context, depends on the heritability and correlation of the traits with agronomically relevant breeding targets. Proper identification of the key constraints for a particular production ecology and an understanding of the biological processes can now be achieved with increasing scale and speed using existing platforms and designing new ones. Multiple challenges lie ahead for high throughput phenotyping such as (i) targeting the development of phenotyping protocols and technology specifically for the target traits of their breeding product profiles; (ii) development of precise protocols amenable for high throughput phenotyping for agronomically important traits; (iii) developing appropriate technology for collecting, collating, and analysing data; (iv) storage and processing of huge datasets collected during high throughput phenotyping; and (v) integration and deployment of high throughput phenotyping in breeding pipelines.
Genomic selection Genomic selection (GS), proposed by  Meuwissen et al. (2001) , is a promising breeding approach for simultaneous improvement of complex traits, especially those with low heritability. GS uses genomic signatures or dense marker profiles of individuals as well as parents for predicting the breeding values. The use of GS for accelerating genetic gains, based on genomic-estimated breeding values predicted using genome-wide dense markers, has been deployed extensively in dairy cattle  (Van Raden et al. , 2009). García-Ruiz et al. (2016) reported a positive impact of GS on cattle breeding through drastic reduction of generation interval and increased selection intensity for low heritable traits. Initially, computer simulations and parametric and non-parametric statistical models were used on maize and wheat datasets to study the prediction accuracies in real plant breeding scenarios  (Bernardo and Yu, 2007; de los Campos et al.
Integrated Breeding Platform (http://www. integratedbreeding. net) is one such initiative that is helping breeding programs to modernize and improving their breeding efficiency. For instance, the Breeding Management System of Integrated Breeding Platform is being used extensively at ICRISAT and its partner breeding programs. Similarly, B4R (http://bbi. irri. org/home) is a program for managing breeding data that has been developed and is being used by IRRI. The volume of data generated throughout the breeding process has increased significantly due to the involvement of high throughput genotyping and phenotyping technologies. Data volume now poses a significant challenge in compilation, processing, and interpretation. Available tools engaged today may no longer be suitable for handling high volume data in the future, thereby necessitating development of improved tools/software that can drive science-based breeding at a faster pace with user-friendly tools and databases.
In parallel, it is also possible to continue to undertake MAS or MABC in the ruling varieties for introgressing major genes/QTLs that have become deficient during their longterm cultivation in farmers' fields. While undertaking GAB approaches, it is also essential that we improve our operational efficiency with appropriate analytical and decision support tools. For instance, appropriate experimental design for testing a segregating population/germplasm collection is very important. Similarly, having phenotypic data for different breeding populations/germplasm collections in databases and using these databases for planning a new set of experiments is critical. Deployment of breeding databases such as BMS and B4R in breeding programs needs to be accelerated. Use of tablets and mobile-based 'Field Book' programs will reduce error and turnaround time for data-driven breeding decisions.
Acknowledgements The authors from ICRISAT are thankful to the  Bill & Melinda Gates Foundation  ( Tropical Legumes I, II & III ),  United States Agency for International Development (USAID) ,  MARS Chocolate Inc. ,  Indian Council of Agricultural Research (ICAR) , and  Department of Biotechnology (DBT) of Government of India. The work reported in this article was undertaken as a part of the  CGIAR Research Program  on  Grain Legumes  and  Dryland Cereals  (GLDC). ICRISAT is a member of the CGIAR.
7 (50 nm lateral size and 3 nm widthconsidering individualized particles) and φ is the volume fraction of the filler. The weight fraction was transformed into the volume fraction by using the densities of NR and graphite (for NR, the density was d = 0. 93 g•cm -3 and for NC, d = 2. 1 g•cm -3 , close to that of graphite). The theoretical and experimental values are in rather good agreement (cf. Table  1  and inset of Figure  8 ). Piezoresistive Properties. The dependence of the electrical properties with mechanical deformation was determined by measuring the change in resistance as the samples were elongated by applying force in a tensile deformation. Figure  9  shows the change in resistance as a function of strain for the composites with 10 wt % NC. Data were fitted according to the model used by Knite et al.
Acknowledgements This work was financially supported by the  NSFC  (Grant No. 21571115 ), the  Natural Science Foundation of Shandong Province  (Nos. JQ201803  and  ZR2017MB061 ),  Young Scholars Program of Shandong University  ( 2015WLJH24 ), and the  Fundamental Research Funds of Shandong University  ( 104. 205. 2. 5  and  2015JC045 ). M. K. is funded by the  CNRS-France.
Data availability. The X-ray crystallographic coordinates for structures reported in this article have been deposited at the Cambridge Crystallographic Data Centre, under deposition number CCDC: 1815301-1815304, 1815357 and 1815305 for SD/ Ag7-SD/Ag12. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif.
Data accessibility. Computer code is available from Dryad Digital Repository https://doi. org/10. 5061/dryad. 474j412 [48]. Scenarios considered include the X-shredder being present in the same released males as the YLE (b,d,f ), or in additional males that carry a wild-type Y (c,e,g), and an X-shredder that causes 100% transmission of the Y regardless of the genotype of the Y (b,c), or only of those carrying the YLE (d,e). (f,g) shows dynamics for a non-functional X-shredder (no impact on Y transmission). In all cases there is a single 10% release in generation 0 of males carrying the YLE; in the 'separate' scenarios there is an additional 10% release of males carrying the X-shredder. Black dashed lines shows abundance of females over time; blue line the frequency of the YLE in males; red line the frequency of the X-shredder in males; and green line the correlation coefficient among males between the presence of the YLE and of the X-shredder (heterozygotes and homozygotes combined).
Results and Discussion The differential thermal analysis (DTA) for elastomer matrix filled with different CB volume fractionreported in Figure  1. TheDTA curves show a small peak endothermic between 75°C and 98°C. Melting peak indicates the melting of crystals part on the composite. The peaks have become broader and lower with increasing the filler volume fraction indicating the effect of CB on the thermal stability of the matrix. This result agree with the data obtained from the thermogravimetric analysis (TGA)  [9]. Raman Spectroscopy was used to assess the affinity between the CB particles and the EBA elastomeric matrix. The Raman spectra of the CB/EBA composite samples are reported in the Figure  2  with twotypical peaks located approximately at 1331 and 1580cm -1 that are assigned to the disordered graphite structure (D band) and tangential stretching mode of carbon-carbon bands(G Band).
(2017). Often, the source term is estimated using a network of static sensors deployed in a region. In inverse modeling process, the adjoint source-receptor relationship and concentrations and meteorological datasets are required for the STE. The adjoint source-receptor relationship is defined by an inverse computation of the atmospheric transport dispersion model  (Pudykiewicz, 1998). This relationship is often affected by the nonlinearities in the flow-field by building effects in complex scenarios arising in urban environments, where the backward and forward dispersion concentrations will not match. Various inversion methods can be classified in two major categories: probabilistic and deterministic. The probabilistic category treats source parameters as the random variables associated to the probabilities distribution.
The experiment consists of 63 releases of a flammable gas (propylene C 3 H 6 ) that is not dangerous or harmful in quantities and could be released through the dissemination system into the open atmosphere  (Biltoft, 2001). Different wind conditions (direction, speed, atmospheric stability) as well as different positions for gas emissions (inside or outside the MUST urban canopy at different heights) were considered. These gas emissions were carried out under stable, very stable, and neutral stability conditions. In this study, 20 trials in various atmospheric stability conditions are selected and the meteorological variables are taken from an analysis of meteorological and micro-meteorological observations in  Yee and Biltoft (2004)  (Table  1 ). It is noted that the errors related to meteorological data can affects the accuracy of the source term estimation  (Zhang et al.
Results and Discussion The calculations were performed by coupling the SA algorithm to a deterministic renormalization inversion algorithm and the CFD adjoint fields to optimize the monitoring network in an urban environment of the MUST field experiment. The network optimization process consists of finding the best set of sensors that leads to the lowest cost function. In this study, two optimal monitoring networks were formed to have the sizes reduced by one-third (∼ 13 sensors) and one-fourth (10 sensors) respectively compared to the initial network of 40 sensors at 1. 6 m above the ground surface. The optimization calculations were performed using Matlab on a computer with configuration "Intel ® Core TM i7-4790 CPU @ 3. 60 GHz and 16 GB RAM". The averaged computational time for optimization of one 10 sensors network was ≈ 2.
Acknowledgements. The authors would like to thank  Dr. Marcel Konig  of  Leibniz Institute for Tropospheric Research  and the  Defense Threat Reduction Agency (DTRA)  for providing access to the  MUST  field experiment dataset. The authors gratefully acknowledge  Fluidyn France  for use of the CFD model fluidyn-PANACHE. We also thank to  Dr. Claude Souprayen  from  Fluidyn France  for useful discussions. 15  Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-6Manuscript under review for journal  Geosci. Model Dev. Discussion started: 9 March 2018 c Author(s) 2018. CC BY 4. 0 License.