dataesr/scientific-paragraphs-categorization huggingface dataset:
e. close to (1, 0) in Fig. 5(c ). This is because the scatter plot of the original dataset is slightly asymmetric with respect to the bisector, while the Frank copula is symmetric. C u v ( , ) is trans- formed back into the original units by inverting the cumulative marginal distributions as = d F d F ( , ) ( ( ), ( )) d 1 1. Fig. 5(d)  shows the original experimental dataset with the copula generated sample in the original dplane. Both the d ( , ) copula generated pairs and the corresponding empirical marginal distributions show a good overall match with those related to the original dataset. Finally, d ( , ) s pairs and related statistics are derived from the obtained copula generated sample d ( , ). The size of the copula-gener- ated sample is increased up to 10 7 in order to post-process the joint probability density function f d ( , ) s.
Conclusions Accurately characterizing the sedimentation velocity of natural particles is important for many aeolian modeling applications. In practice, the sedimentation velocity of natural particles may be assessed by means of direct experimental measurement, or through the definition of analytical laws as a function of the particle size. There are challenges with both approaches. On the one hand, experimental measurements requires special expertise and dedicated instrumentation not always available, especially in the context of practical engineering applications. On the other hand, there is substantial discrepancy in the predictions made by the different sedimentation velocity laws. These challenges underpin the motivation to attempt a copula-based approach to include a statistical representation of the natural variability in the data describing drag coefficients and sedimentation velocities.
The proposed statistical characterization is based on the collection of a wide dataset of experimental measurements from the literature. As a result, the data comprise different kinds of natural particles, from aeolian sand grains to volcanic particles. The distributions of the data were first compared to a well-controlled data set and model that predicts the well-known aerodynamic behavior of smooth spheres. Two well-established drag laws were fit to the data with common nonlinear regression, and their goodness of fit was assessed by reference to other well-known laws for natural particles. Despite their overall goodness of fit, they merely describe the average trend and do not provide any information about the variability of the sedimentation velocity. Hence, a copula-based regression was performed to provide the trend of high order statistics and extreme percentiles.
velocity for given values of diameter together with the original dataset. Fig. 6(b) and (c) plot the corresponding trend of the coefficient of variation (c o v. ) and skew- ness (sk) of f d ( | ) s , respectively. It is worth noting that the c o v. and sk show the same general trend: they increase with increasing d. Overall, s increases its variability up to non-negligible values (c o v. 0. 2), by preserving a.
) plots on the Re-C d plane the fitted laws resulting from the nonlinear regression, the mean value µ and i-th percentiles of the drag coefficient p C ( )i d for = i {1, 5, 25, 75, 95, 99} from the copula-based regression, and the original dataset. Fig. 7(b) plots the same curves and dataset on the ds plane by referring to quartz particles in air. Indeed, it is straightforward to pass between the two planes thanks to Eqs. (6) and (7). From the comparison of results, it emerges that µ C ( )dand µ ( ) s follow almost exactly the fitted laws of Clift and Gauvin (1971) and Bagheri and Bonadonna (2016) up to < Re 500 and < d 10 3 m, respectively. After that, they slightly depart from each other, with the relationship found by Bagheri and Bonadonna (2016) tending again to the mean value for high Re and d.
Acknowledgments The study has been partially funded by the  European Union  within the  MSCA-ITN-2016-EID research project Sand Mitigation around Railway Tracks  (SMaRT, www. smart-eid. eu). The  SMaRT  project has received funding from the  European Union   Horizon 2020 research and innovation program  under grant agreement No  721798. In particular, we acknowledge  M. Horvat ,  R. Nuca  and  L. Preziosi , members of the  SMaRT team , for the early discussions about the topic of the study within the  SMaRT innovative training programme. The study has been developed in the framework of the  Windblown Sand Modelling and Mitigation (WSMM)  joint research, development and consulting group established between  Politecnico di Torino  and  Optiflow Company. The present study benefits from a long-term effort made by several authors to measure particle sedimentation velocities: we are grateful to all of them.
Radio Channel Characteristics A 3D ray-tracing tool (RT) has been programmed in MATLAB by our research group. The tool includes the usual reflected and diffracted components and combinations of them as well as single-order diffuse scattering components. The following mechanisms were considered in order to achieve a good accuracy and a reasonable simulation time: direct ray, up to two order reflections, simple diffractions, simple diffractions plus single reflections, single reflections plus simple diffractions and single order diffuse scattering components. The Lambertian model was used to simulate the diffuse scattering; this model has only one parameter, the scattering parameter S that controls how much energy is scattered in all directions  [35]. An iterative method, as the one shown in  [21] , was applied to find the best values of the S parameter in the measured band and scenario.
Code development and checking procedures We have implemented the model equations detailed above in a numerical code in Python using the scipy package for the numerical integrator ("odeint" functions), with the range of input parameters given in § 4. 6. While the kernel of the models implementation uses dimensionless variables, dimensional inputs/outputs can be specified/returned to compare our results with field experience. It is good coding practice to cross-check the consistency of results obtained with various methods. In the following sub-sections, we have compared QT-model results obtained with the other models presented in § 3.
have analysed the impact of wind on uncertainties based on large experimental datasets for the former and an analytical model for the latter. Walker et al. (2013) recommend multipoint testing at low wind speeds (< 3 m s -1 ) when the reference pressure is 4 Pa to avoid extrapolation errors due a fixed flow exponent; however, for the same reference pressure (4 Pa), both Walker et al. (2013) and Carrié and Leprince (2016) recommend single point-testing at high wind speeds (above 5-6 m s -1 ).
SCALAR is a new platform for running real-time machine learning competitions on data streams. Following the intent of Kaggle, which serves as a platform for organizing machine learning competitions adapted for batch learning, we propose SCALAR as a novel platform explicitly designed for stream learning in real-time. SCALAR supports both classification and regression problems in the data streaming setting. It has been developed in Python, using state of the art open-source solutions: Apache Kafka, Apache Spark, gRPC, Protobuf, and Docker.
Statement of need Existing machine learning platforms for research and competitions, such as Kaggle and Ali baba Tianchi provide static data sets that users leverage to do batch training and scoring. These popular platforms, especially Kaggle, enable users to propose better solutions for a wide range of machine learning problems and applications, pushing forward the whole research community. While these platforms enable collaborative model building, they do not provide real-time settings and data streaming instead of static data sets. Inspired by the great reception and adoption that these platforms have had, SCALAR was built for real-time environments by supporting data streaming to fill this gap, helping users test and improve their methodologies in a real-time machine learning scenario.
Streaming learning setting Most of the existing platforms for data science competitions are tailored to offline learning where a static dataset is made available to the participants before the competition starts. This dataset is divided into training and test sets. The training set is used to build and train the model, which is then scored on the test set. In online learning, data arrive in a stream of records (instances) and the model needs to be trained incrementally as new instances are released. Since the data arrive at high speed, predictions have to be issued within a short time. Considering this specific setup of the data stream mining scenario (Figure  1 ), every model should use a limited amount of time and memory, process one instance at a time and inspect it only once. The model must be able to predict at any time  (Bifet et al.
MongoDB is used for unstructured data (e. g. , user's predictions, records from the stream, evaluation metrics, etc. ) and MySQL for structured data (competition information, user information). • Streaming Engine: this layer is responsible for handling the streams. From the CSV files, Kafka recreates multiple streams to be sent to multiple users. SCALAR provides a secure and independent bi-directional streaming communication between the user and the streaming engine. This allows each user to consume training and test instances and submit the respective predictions according to the competition predefined data format. The resource server is the API that handles all authenticated requests from the client application, whereas the authorization server is in charge of users' identification. The Online evaluation engine handles both the stream of instances, and the streams of participants' predictions in order to compute and update the evaluation metrics online before storing them into the dedicated database.
Numerical Experiments To test our theory, we have implemented our algorithm in MATLAB. Before displaying the results of the experiments, let us discuss a few key steps in the implementation. In the entire section, we assume that Ω = [0, 1] d for d = 1 or 2 for simplicity. Note that this is no true restriction: we can always by scaling and translation ensure that Ω ⊆ [0, 1] d , and trivially extend the measurement functions by 0 to the entirety of [0, 1] d. Evaluating X k Each iteration of the exchange algorithm requires the exact calculation of the local maximizers of A * q k exceeding 1. This is, in general, an impossible task. We resort to the following heuristic method: Given a q k , we first evaluate |A * q k | on a fixed rectangular grid G = ((n) -1 [0,. , n]) d , and determine all of the discrete peaks, i.
Video analysis 30 images were extracted for every second of video footage (videos are thus downsampled) and corrected for the optical distortion of the lens (barrel distortion). We noticed that the cameras were rotating during the recording; this was corrected manually by applying rotations to each image so that given landmarks on the floor coincide between images. We developed a home-made software in Python to detect and track the participants' hats on the basis of their colors. The algorithm proved very efficient and yielded almost no false negatives: virtually all hats were correctly tracked. Post-processing manual corrections allowed us to remove some spurious detections on the floor (false positives) and to track the obstacle. The correspondence between the hat positions in pixel coordinates and the ground coordinates of the bodies was obtained by 1) finding the affine function that provides the best fit between the pixel coordinates of a subset of detected hats and the corresponding 'body' positions (defined as the middle of the feet, as determined manually when the person stands more or less straight) ; 2) converting pixel coordinates into ground coordinates via linear interpolation between reference points.
Acknowledgements We thank all participants in the experiments and, above all, the students and researchers who took an active part in their organization:  Tom Marzin ,  Ioannis Touloupas ,  Aymeric Duigou-Majumdar , and  80-7  Proceedings of the 9 th International Conference on Pedestrian and  Evacuation Dynamics  ( PED2018 ) Lund, Sweden -August 21-23, 2018 Paper No. 80  Étienne Pinsard  for the PERCE-FOULE experiments;  Armel Cretual ,  Anthony Sorel ,  Dany Saligaut ,  Trenton Wirth ,  Gregory Dachner  for the PEDINTERACT experiments. The  PERCE - FOULE  project was funded by a grant from  French Labex PALM  ( ANR-10-LABX-0039-PALM ). PEDINTERACT experiments were funded by the  Inria SIMS  associated team as well as the  ANR PERCOLATION ANR-13-JS02-0008  project. PEDINTERACT Project  was also funded by  US National Science Foundation  grant  BCS-1431406.
Although their role in seed development needs further validation, this work underlined the considerable scope still available for the genetic improvement of 100-seed weight in soybean using candidate gene mining and subsequent marker-assisted breeding. Pea has been studied as genetic model since the Eighteenth century, with key contributions to genetics and the development of the basic principles of heredity. The pea genome is characterized by its large size (∼4. 45 gigabases) of which ∼85% is comprised of highly repetitive sequences. In this topic, Gali, Tar'an et al. , reports the construction of a sequence-based physical map of the pea genome using whole-genome profiling (WGP). This study reports a very valuable dataset that will provide a framework to obtain a reference pea genome sequence to further explore the genes governing major traits, including those influencing seed yield and seed quality.
This led to the identification of many SNPs with significant association with specific traits. In some cases, these mapped to QTLs previously associated with the specific trait. Overall, this large study generated resources that have potential use in marker-assisted selection for accelerating pea cultivar improvements. Sanderson et al. described their online legume genetic and genomic resource, KnowPulse, which they have developed to serve legume breeding and genetic research communities. The database hosts phenotypic, genotypic and genomic data for chickpea, common bean, field pea, faba bean and lentil, which can be queried by a range of visualization and data exploration tools. Built on an open-source platform, it is amenable to community collaboration, which will help to ensure its ongoing relevance and usefulness.
This kind of resource is vital for linking independent studies and deriving maximum value from costly datasets generated globally. Legumes play an important role in the sustainability of agricultural and food systems, contributing to soil fertility and environmental protection, as well as to food safety and nutrition. Under this framework, the perception of Lens culinaris producers and consumers of North America has been evaluated by Warne et al. , following agronomic, economic, and nutritional criteria. In a survey of producers, the main agroeconomic reason to introduce lentil in production systems was to diversify crop rotation in order to capitalize on dryland production and serve as a cash crop. Diversifying crop rotation improves agricultural system robustness, increasing system resistance to biotic stresses and resilience to abiotic disturbances favoring the constancy of crop productivity.
FUNDING This work was supported by  MINECO -Spanish Government  grant ref. :  BFU2016-77243-P , Ramón y Cajal RYC-2014-16536 to JJ-L and by  European Research Program MARIE CURIE  ( FP7-PEOPLE-2011-IOF ) grant ref. :  PIOF-GA-2011-301550  to JJ-L and KS; by the  Grains Research and Development Corporation , including current project # 9176622  to KS; by the  MINECO-AEI  (ERDF co-financed grant  AGL2017-83772-R ) to AC; and by the grant  ARC Industrial Transformation Research HUB IH140100013 Legumes for Sustainable Agriculture  to PS.
Determination of the material parameters The constitutive parameter identification process for the MarBN steel follows the work described in  [6, 30] , based on the cyclic SWT and DWT data over a range of test temperatures. A total of 11 parameters involved in the adopted unified viscoplastic model are separated into three groups consisting of elastic parameters, non-linear viscous parameters, and cyclic plasticity parameters. The material parameters such as Young's modulus, E, and initial yield stress, , are estimated through the 1 st cycle of LCF test data. It should be noted that the value of k, is defined as half the distance of the linear vertical region when plotting plastic strain versus stress following the approach described by Lemaitre and Chaboche  [6, 33] , while Poisson's ratio is assumed to be 0.
■ COMPUTATIONAL DETAILS Computational Method. This work presents results achieved by calculations using the VASP code (Vienna ab initio simulation package)  47, 48  that is developed for electronic structure calculations on periodic structural models and is based on Kohn-Sham formulation of DFT. Particularly, we used Perdew-Burke-Ernzerhof functional  49  and the generalized gradient approximation to calculate exchange-correlation energy. We applied the projector-augmented-wave method  50, 51  with an energy cutoff equal to 400 eV and used atomic pseudopotentials to describe the electron-ion interactions. The structural relaxations were performed with following relaxation criteria10 -5 eV for the total energy change and 10 -4 eV/Å for the forces acting on an atom, respectively. All calculations were limited to Γ-point Brillouin-zone because of using large computational cells.
However, since clinical irradiations are done with LINACS in the MV range, some studies focused on that range and have also shown significant dose enhancement. GNPs also enhance proton therapy. Gold nanoshells and nanorods absorb near infrared radiation (NIR, ~800 nm) enabling heating of tumors or lesions and their subsequent ablation (photothermal therapy) (Ahmad et al 2016). NIR is reduced by a factor of 10 in 2 cm of tissue which limits this to superficial lesions. However, a clinical trial is underway using an array of light pipes to irradiate and heat deeper prostate cancers (clinicaltrials. gov ID NCT02680535).
Current and future challenges Currently, TS MC codes are limited to simulations in water. Physics models need to consider the full deexcitation of electronically-excited heavy elements such as via long Auger cascades, simulate discrete ionization events and electron transport with low cut-off energies and benchmark these processes with updated cross section databases and scattering experiments on metallic nanostructures. In addition, more systematic experiments are necessary to describe the chemical processes with MNPs. MC codes need to be extended to the chemical stage as recent experiments point out a significant impact of nanoparticles on radiolysis and scavenging of ROS. The implementation of selected test cases orientated on experimental benchmarks promises a major step forward in understanding the indirect effect by MNPs.
This challenge is compounded by the difficulty of translating in vitro observations into in vivo systems. Here, differences in the pharmacokinetics of different preparations and their impact through the course of a full treatment schedule must also be taken into account. As a result, modelling in this area remains under-conditioned, with ad-hoc analysis of limited datasets producing models which are in reasonably good agreement with fitting data, but A c c e p t e d M a n u s c r i p t which do not translate effectively to other systems. This approach is unlikely to generate models with wider applicability to support the development and optimisation of novel MNP treatments.
Advances in science and technology to meet challenges The most efficient way to characterise a single nanoparticle preparation, remains an ad-hoc approach -that is, treating the nanoparticles as simple dose-modifying agents, using standard techniques to measure the additive and synergistic effects of the MNPs with radiation, and fitting a suitable empirical model. This has the advantage of being simple, and less experimentally demanding. Studies of the dependence of MNP radiosensitisation on irradiation type and energy may also enable a separation of "physical" and "biological" components of sensitisation. However, such ad-hoc approaches are not suitable for more general predictive tools. Instead, more general predictions require the development of relevant, systematic, mechanistic models to enable the integration of many heterogeneous datasets to produce useful predictions for a range of MNP.
Concluding Remarks While it is clear that a large portion of MNP radiosensitisation is driven by biological effects which are largely independent of the physical dose modification, modelling in this area remains a significant weakness. Designing an optimised nanoparticle therapy necessitates the development of new approaches. This may involve the development of more mechanistic descriptions of MNP-cell-radiation interactions, or machine learning and systems biology approaches to leverage heterogeneous datasets for novel insights. Regardless of the approach, there remains a pressing need for more high-quality, systematic experimental investigation of the impact of MNP across all stages of radiation response. Moreover, efforts should be made to present data in as accessible a format as possible, to enable incorporation of these disparate datasets in unified models.
of evidence demonstrating the potential of MNPs to enhance radiation response, only a very small number of agents have progressed to clinical trials. These include nanoparticle formulations of gold (CYT-6091, Cytimmune, NCT00356980; NCT00436410), gadolinium (AGuIX, NH TherAguix, NCT02820454; NCT03818386; A c c e p t e d M a n u s c r i p t NCT03308604) and hafnium oxide (NBTXR3, Nanobiotix, NCT01433068; NCT02721056; NCT02805894). Preclinical studies have provided considerable insight into the molecular mechanisms of MNP radiosensitization, yet there remains a number of considerable challenges to be addressed if the successful translation of investigative new MNPs to early phase trials is to improve.
Acknowledgments We acknowledge  Woon Yong Baek  from  PTB  and  Jussi Toppari  from  Jyväskylä University (Finland)  for contributing their ideas for measurement of radiation effects of nanoparticles using DNA origami structures,  Valeria Conte  from  INFN (Italy)  for sharing her ideas regarding microdosimetry of nanoparticles, and  Heike Nittmann  for preparing the respective illustrations.
Despite a significant body of evidence demonstrating the potential of MNPs to enhance radiation response, only a very small number of agents have progressed to clinical trials. These include nanoparticle formulations of gold (CYT-6091, Cytimmune, NCT00356980; NCT00436410), gadolinium (AGuIX, NH TherAguix, NCT02820454; NCT03818386; A c c e p t e d M a n u s c r i p t NCT03308604) and hafnium oxide (NBTXR3, Nanobiotix, NCT01433068; NCT02721056; NCT02805894). Preclinical studies have provided considerable insight into the molecular mechanisms of MNP radiosensitization, yet there remains a number of considerable challenges to be addressed if the successful translation of investigative new MNPs to early phase trials is to improve.