dataesr/scientific-paragraphs-categorization huggingface dataset:
46 eV) shown in Fig. 3d  is in good agreement with the experimental data, which have a broader spectrum. We now study the spectrum of the emitted field. To proceed, we collect the signal emitted toward the substrate and send it to a spectrometer (Andor Shamrock 750i). The data are shown in Fig. 4a-c  and the theoretical simulations are shown in Fig. 4 (d-f ) for three different bias voltages 1. 4, 1. 5 and 1. 6 V. Note that the calculations account for the detector responsivity, responsible for the decay of the signal at low energy. We observe two spectral peaks at 1. 2 and 1. 5 eV that are well recovered by the model. All the theoretical emission spectra are corrected by the quantum efficiency of the spectrometer responsible for the decay at low energies. g, h Theoretical magnetic field distribution  54  (H y in the X-Z plane) of the two gap modes of a 132-nm-wide antenna corresponding to the lower photon energy at 1.
Methods Devices fabrication. The ASPEIT tunnel junctions are fabricated on a standard coverslip (VWR, 0. 15 mm thick). The bottom Al electrode is fabricated by UV photolithography via a mask aligner (MJB4). An Ebeam evaporator (MEB 550, Plassys) is employed to do the Al deposition. After the lift-off, a dry thermal oxidation process in a furnace is used to generate an ultrathin AlOx layer. The Au top electrode is fabricated by using an electron-beam lithography (Nanobeam NB4) system. All the detailed fabrication processes can be found in Supplementary Note 1. Electrical characterization. The electrical characterization of our junction is performed via a source meter (Agilent B2902A). The IV measurements are conducted by a Quick IV Measurement software provided by keysight technologies website.
Acknowledgements This work was supported by a public grant by the  French National Research Agency (ANR)  project ( ANR-15-CE24-0020 ). The authors thank the C2N nanofabrication platform at  Paris-Sud University. C. Z. thanks the Ph. D financial support from the  chair Safran-IOGS on Ultimate Photonics. J. -J. G. thanks the support of  Institut Universitaire de France. The authors acknowledge enlighting discussions with  Julien Gabelli. C. Z. acknowledges technical guidance from  Jean-Rene Coudevylle.
Rapidly evolving microbes are a challenge to model because of the volatile, complex, and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. The pipeline is openly available on GitHub (https://github. com/rasmushenningsson/DISSEQT. jl, accessed 23 June 2019) and Synapse (https://www. synapse. org/ #!Synapse: syn11425758, accessed 23 June 2019), covering the entire workflow from read alignment to visualization of results. Our pipeline is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype-phenotype maps.
Overview of the DISSEQT pipeline The DISSEQT pipeline (Fig. 1 , top panel) is designed for reproducibility and openness, from the ground up, using modern software solutions. The source code is openly available in GitHub and all software dependencies are open source. The software can either be installed locally or run directly from Docker images with all required software preinstalled. Running from Docker images simplifies setup and improves reproducibility since differences between local runtime environments are eliminated. The overview described here is detailed in Section 4. The DISSEQT pipeline has three steps, serving different purposes. (1) Establishing a model for sequence space. (2) Reducing noise to make the model robust. (3) Visualization and phenotype prediction. First, the raw reads for each sample (mutant swarm) were aligned iteratively until the consensus sequence converged and both automatic and manual quality controls were performed.
Empirical fitness values were used to create fitness landscapes for prediction, using the population representations from Step 2, and for visualization, after an additional nonlinear dimension reduction step vital for getting a useful representation in 2d. The sequence space model created by the DISSEQT pipeline is also intended to be used as input to other software packages, e. g. for clustering and regression. or experimental samples-essentially, to monitor and analyze evolving populations before significant changes to consensus sequences occur. To do so, we generated samples of mutant swarms, bearing differences in minority variants. We used four genetically trackable virus populations that derived from the same infectious clone wild type Coxsackie virus B3. Within the capsid-coding region of wild type virus, 117 Serine and Leucine codons are represented by all six codons for each amino acid.
Data availability Data is available through GitHub (https://github. com/ras mushenningsson/DISSEQT. jl, accessed 23 June 2019) and Synapse (https://www. synapse. org/#!Synapse: syn11425758, accessed 23 June 2019). Synapse DOI: 10. 7303/syn11639899 and GitHub DOI: 10. 5281/zenodo. 3344557.
Purpose -The purpose of this paper is to present the use of a free code computational tool, Patent2net, in the search of patents for the implementation of distance learning aimed at Continuing Medical Education. Design/methodology/approach -This technical report is based on the extraction, organization and availability, in the format of graphs and dynamic tables, and also based on information in other patents on the subject, made available in the Espacenet database. Findings -As a result, it was possible to identify a Chinese patent, free for reproduction in Brazil, which describes an e-learning system that simulates 3D scenarios for training nursing teams. Research limitations/implications -The paper has used one unique patent database, but containing more than 100m documents. Practical implications -The selected patent can contribute to the improvement of care and behavioral techniques of the health professionals.
However,  Storopoli (2016)  reported that there are few studies related to patents and distance learning (DL), and this number is further reduced when trying to identify DL patents for continuing medical education  (Hassanzadeh, Kanaani, & Elahi, 2012; Hung, 2012). Thus, to address this shortage, the present study addresses the following question: "How can we increase and disseminate knowledge in DL patents in the area of continuing medical education using a data mining tool?" In order to answer this question, this paper uses a qualitative research methodwith a descriptive characterto demonstrate how information about DL patents related to continuing medical education can be extracted, organized and made available, in the form of graphs and dynamic tables. In this context, the purpose of this paper is to demonstrate how the Patent2net computational tool collects and organizes the massive amounts of information related to DL patentswhich is focused on continuing medical educationthus reinforcing its importance as an example of an Open Patent Service (OPS) for the whole society.
The automation of the collection and organization of the patent information demonstrates that researchers, as well as any member of society, interested in knowing and/or evaluating technical information present in patent documents, can have access to new technologies. Furthermore, there is, currently, an enormous amount of underutilized data in the databases that make a manual search of these documents nearly impossible, which justifies the use of mining data tools, such as Patent2net. Thus, through the use of this computational tool, information related to patents, previously deposited only in the deep web, becomes easily accessible in dynamic search interfaces available on the surface web, contributing to the expansion of technical and scientific knowledge, which in turn can bring a series of benefits to the nations or corporations that make use of them, promoting the creation of practical improvements in various aspects of daily life.
For this, in addition to this Introduction, the section on Theoretical Framework is presented, which discusses the importance of studying patents in the technological and managerial scope, the models of computational tools used for the search of patents, and the process of data mining of patents using computational tools, as well as an emphasis on the Patent2net tool being used to identify DL patents related to health and education. Next, the section entitled Research methodology is presented, in which the basic search mechanisms and procedures using the Patent2net data mining program are discussed, as well as its application to this work. In the Description and Analysis of Results section, the layout of the web pages generated after the treatment of the patent documents, which were extracted from a worldwide database, Espacenet, with more than 110m patent documents are shown.
Patent study Patents are important sources of information related to new technologies and innovations that, oftentimes, result in social improvements and increases in financial performance, ensure unspoken knowledge of a nation or organization and generate a sustainable competitive advantage in the market  (Ferraz et al. , 2016). For this reason, the organization of patent databases allows a large amount of constantly updated technological information to remain available for consultation and analysis throughout society  (França, 1997). This fact allows for the dissemination of information  (Corrêa & Gomes, 2013; Simões et al. , 2002) , with the knowledge of the inventors and their organizations evidently respected, since these documents become available in full after the legal period of 18 months of confidentiality, from patent filing to disclosure  (Quoniam et al.
, 2014). It is worth mentioning that databases allow the visualization of patents granted and protected by law, patents that have been denied and which are in turn free to be improved and used by others  (Ferraz et al. , 2016) , as well as patents that were granted but whose protection was not extended to all countries, making their replication legally permissible in these places  (Barbieri & Álvares, 2005). Identifying patents, both denied and granted, but not extended to other countries for any reason, may lead to a review of technologies and the motivation of incremental innovations on any subject  (Serafini, Paixao, Oliveira-Junior, Santos, & Da Silva, 2012). Additionally, the analysis of these patents brings with it a variety of technological information, often presented superficially in scientific articles  (Simões et al.
For this reason, search engines have been coupled with patent databases such as Patentscope  (Bonino et al. , 2010) , Espacenet  (Arslan et al. , 2006)  and Google Patent Search  (Aoki & Schiff, 2008). According to  Bonino, Ciaramella, and Corno (2010) , Patentscope is a search engine offered by the World Intellectual Property Organization (WIPO), which allows anyone access to more than 45m patents in the world, evaluating the content of each one according to their interest. Google Patent Search, available since 2006, can unify the search for patents in three databases: WIPO, the European Patent Office (EPO) and the United States Patent and Trademark Office, with a total of 8. 5m patents  (Aoki & Schiff, 2008). Finally, Espacenet, an EPO database, is a computational search tool that grants access to more than 110m patents  (Arslan et al.
For this to be possible, data miners collect, unify and organize data that have correlations or similarities  (Ernst, 2003; Larose, 2014; Tseng et al. , 2007) , for example, in the various patent databases, making it possible to conduct studies related to technological innovations 40 REGE 27,1  (França, 1997; Serafini et al. , 2012; Simões et al. , 2002) , to patents deposited by Institutions of Higher Education  (Melo et al. , 2014)  and to the management of the intellectual property protection system, with special emphasis on patents (Matias-Pereira, 2011), among others. Ferraz et al. (2016)  cite some examples of these miners, such as Intellixir (www. interllixir. com), Matheo Patent (www. matheo-software. com), Patent Integration (https://patent-i. com), Patent Inspiration (www. patentinspiration.
The use of Patent2net as an Open Patent Service The use of patents as an object of study in scientific work has gained relevance in recent years due to its importance for the development of innovations  (Corrêa & Gomes, 2013; Ferraz et al. , 2016; Melo et al. , 2014; Quoniam et al. , 2014; Simões et al. , 2002). These scientific works have contributed to the increase in the quality of postgraduate courses in Brazil which, by means of a quantitative and qualitative analysis of the content of patents, make an important contribution to society  (Dantas, 2004). Such scientific relevance is only possible when one takes into account the transparency of academic research, as well as its free access to the entire community  (Ferraz, Quoniam, & Maccari, 2014). Patent2net, a software available for Linux and Windows operating systems, functions as a patent document mining program that is available in the EPO database, called Espacenet  (Carvalho, Storopoli, & Quoniam, 2014).
The main function of Patent2net is to extract the information contained in Espacenet, which in turn is the only patent database that provides access to its Application Programming Interface (API), a necessary security key for the data mining process  (Mazzieri, Quoniam, & Santos, 2016). Without this key, the mining of data allocated to Espacenet would become impossible  (Ferraz, Quoniam, Pimenta, Mena-Chalco, & Nigro, 2015). Thus, Patent2net is a tool that provides access to a large volume of patent information, in a fast and organized manner, facilitating patent document analysis  (Ferraz et al. , 2016; Mazzieri et al. , 2016; Quoniam et al. , 2014). Mazzieri, Quoniam, and Santos (2016, p. 92 ) state that, "the choice of Espacenet to integrate with Patent2net is due to the availability of the API provided by Espacenet -Open Patent Service," which is a large advantage, enabling free access to millions of documents.
Research methodology This work presents a particularly quantitative and descriptive approach, when the indicators related to patents are described, although in some moments there are qualitative analyses with a technometric basis, when the contents of patents are evaluated. Furthermore, this work describes the way in which the computational tool Patent2net 42 REGE 27,1 performs the extraction, organization and presentation of the information related to healthoriented DL patents, deposited in the Espacenet database. The computational basis related to the operation of the application is not deeply discussed in this paper, although they can be found in the seminal work of  Reymond and Quoniam (2016). Briefly, according to  Ferraz et al. (2016) , Patent2net executes several computational modules sequentially.
For this, the user registers with Espacenet, which allows the user to access the database. Thereafter, an alphanumeric key is released that allows data mining. This alphanumeric key is the previously described API, which liberates either the free use or payment mode of the OPS, depending on the end user requirements  (Mazzieri et al. , 2016). To build the search strategy, the Thesaurus Dictionary (www. thesaurus. com) was initially used, applying the term "distance learning. " The Publish or Perish computational tool (https:// harzing. com/resources/publish-or-perish) was also used to find the most cited articles in the literature, and to identify new synonyms that could contribute to the construction of the search. The identified keywords were tested one by one on the Espacenet base itself (https:// worldwide.
Conclusions This paper presents the tools and platforms available for fast and efficient extraction, organization and presentation of patent information, and highlights the organization and presentation of data made by Patent2net based on Espacenet, which discovered patent documents in DL, focused on Continuing Medical Education. The results presented in this paper demonstrate how the Patent2net computational tool can increase the dissemination of knowledge by extracting and organizing data from millions of patent documents, in a direct and user-friendly manner. Through this analysis, in which the utility of this computational tool was justified, it is possible to note the social responsibility of the present research, since it presents to the scientific community a free tool that allows access to the technologies, previously ignored, to any interested party, for the promotion of economic, technological and social development.
Corroborating with the results of this research and with its contribution to the academic and scientific community, in this study a patent was presented for continuing medical education focused on teaching technologies with the use of 3D simulation, which is an area health training lacks  (Landmann, Havron, Patel, Thompson, & Lees, 2016). This Taiwanese patent does not have extended protection in Brazil, based on international patent law, and can be freely replicated in the country without the need for any rights payments, meeting all legal requirements governing the subject of industrial and intellectual property. Among the limitations presented in this work are the use of a single patent database, Espacenet, for the presentation of the results. This has restricted the demonstration of other search platforms which, although they have a lower patent volume compared to the more than 110m documents available at Espacenet, could broaden the focus of this work.
, 2016) , which should lead to a drastic reduction of professional errors in the healthcare field  (Landmann et al. , 2016). Finally, in relation to the subject of this research, the importance of future studies that involve DL patents focused on continuing medical education is highlighted, which is a sector that needs new methodologies to improve practices and processes  (Landmann et al. , 2016). Future studies on other tools and platforms for extraction, organization and presentation of patent data are recommended, which would allow for a deeper understanding of the subject and the differentiation by researchers of what procedures to follow, according to their goals. It is noteworthy that patent mining with Paten2net software can be applied in the search for solutions to the most different problems, including in the social sphere.
In order to not overcharge the role of the training data from k-corrective-frozen-RANS at this stage of the methodology, we select a wide spectrum of models varying in accuracy and complexity using  (20)  instead of a single one. The validation task will be performed later using a CFD solver. Following  [26]  we use ρ = [0. 01, 0. 1, 0. 2, 0. 5, 0. 7, 0. 9, 0. 95, 0. 99, 1. 0] T , which ensures that we cover a substantial range of different regularisation types. The upper limit of the regularisation weight is defined as λ max = max(|C T ∆ ∆|)/(Kρ), because for any λ > λ max all elements in Θ will be equal to zero. The entire vector λ = [λ 0 ,. , λ max ] T (22) is defined of having 100 entries between λ 0 = ξλ max with ξ = 10 -3 uniformly spaced using a log-scale as defined in  [26]. This provides a search space (λ, ρ), the elastic net, which is large enough and has an appropriate resolution.
Our efforts are based on an empirical observation, but do not guarantee a well-behaving numerical setup under all conditions. However, we have identified corrections of b ∆ ij as the only contribution which can do harm to the convergence properties for the given test cases. Therefore, if a model does not converge, we further decrease the coefficients by a factor ξ = 0. 1, for the model correcting b ∆ ij only. This ad-hoc intervention is sufficient to achieve convergence for the studied cases. Finally, the resulting coefficient vector Θ d ∆ is used to retrieve the symbolic expression of the models by a dot product with the library of candidate functions C ∆ in (  13 ) and (  14 ) M d ∆ := C T ∆ Θ d ∆ , (24) which are implemented in the open-source finite-volume code OpenFOAM  [23]. The divergence terms of the equations are discretised with linear upwinding and turbulent diffusion with 2nd order central differencing.
Cross-validation using CFD Cross-validation tests how well models identified on training data perform on unseen test cases  [30]. This assessment allows to determine the best-predictive models from a set. As stated above, the role of the frozen, training data should not be overcharged, so that we cross-validate using CFD. By doing so, we can assess the validity of SpaRTA as a tool for model discovery as well as the predictive performance of the identified models outside of their training set. The selected correction models regress b ∆ ij and R individually and can also be applied individually for predictions when implemented in the solver, i. e. a model correcting b ∆ ij can be used without a correction of R and vice-versa. This gives us 8 models per training data for PH 10595 and CD 12600 and 6 for CBFS 13700.
In general, the data of CD 12600 and CBFS 13700 provide models, which are well performing on all test cases presented. In Figure  7 , both the error and the model structure for the correction of b ∆ ij as well as for R is shown. The models are ordered according to the meansquared error on the stream-wise velocity U. In line of the discussion of Figure  6  three groups can be identified: a few models, which lead to an increased error compared to the baseline; a small group of models per test case, which are equal or similar to the baseline; and the great majority of models, which result in an improvement. It can be observed how the error in the velocity is significantly reduced once a correction of R is used. The two other error plots in Figure  7  give an indication of the relative performance of the models compared to the baseline.
Surprisingly, for these the error in τ ij increases. When the error in U is further reduced the error in τ ij also decreases, but the error in P k increases again. It can be observered, that the best models correct the velocity up to 5 times better in mean-squared error than the k-ω SST baseline model. This leaves still room for further improvement compared to the error using the frozen data sets, see Table  1. But, especially for case CBFS 13700 the result is already very close to the possible correction provided by the frozen data at least for U. For PH 10595 the models fail to fit the complex spatial structure especially within the separated shear-layer behind the hill and on the hill itself. The skin friction coefficient C f and the associated separation and reattachment points are best captured by M (1) and M (3) for PH 10595 and CBFS 13700 and systematically under-estimated with M (2) , i.
e. a shorter recirculation zone. For CD 12600 , we observe a small recirculation zone as reported in the literature, but too far downstream. However, the baseline k-ω SST drastically over-predicts this zone and the model M (2) ignores it entirely. Overall, the models M (1) and M (3) agree best with the data, which is in line with the global error on U in Table  2. The models are different in their form, but show similar error values and spatial structure across the test cases. Model M (2) tends to overestimate the magnitude of the quantities U , k and τ xy and therefore predicts smaller or no separation bubbles. This model was identified using PH 10595 as training data and, ignoring the specific structure for b ∆ ij , has the largest coefficient for correcting R, see  (26) , which leads to larger k compared to the others, which is the reason for the systematic over-prediction.