dataesr/scientific-paragraphs-categorization huggingface dataset:
The hardware side of the video-game industry is dominated by Sony (PlayStation 4, PlayStation Vita), Microsoft (Xbox One) and Nintendo (WiiU, Nintendo DS), which control about 88% of the global market. Sony accounts for about £5. 1 bn in terms of unit sales, followed by Microsoft (£4 bn) and Nintendo (£3. 8 bn). Similarly, the software side of the video-game industry is characterised by heavy concentration, with seven large publishing companies representing about 64% of the global software market  (Euromonitor, 2014). However, the rest of software market is more fragmented, with several smaller publishing and distributing companies operating from different locations, developing and commercialising video-game titles  (Lee, 2012). These companies produce videogames (and related copyrights) either using in-house studios or by outsourcing, through acquisitions of or in partnership with independent third-party studios.
They were essentially independent programmers who provided the basis for the foundations of major video-game developer companies such as Codemasters (founded in 1986), or small studios such as Interactive Studio (founded in 1991, renamed Blitz Games in 1999) and Optimus Software  (founded in 1988 , renamed Atomic Planet in 2000). Differently from what happened in the US and Japan, where videogame companies were already significantly investing in marketing and branding in both platform-consoles and published titles, the customer basis for UK video-game industry developed more informally using computer magazines and fanzines often available in local software stores, where gamers exchanged information on how to win games and developers shared programming codes  (Izushi and Aoyama, 2006). Trade shows and conventions also provided a platform for exchange between amateur programmers and professional developers, with a number companies and spin-offs starting from encounters made during these events  (Izushi and Aoyama, 2006).
Methodology and data selection While the UK video games industry is recognised as being worldclass and highly innovative, it is still difficult to obtain hard data about its economic performance. The main challenges related to finding complete and reliable information about the businesses operating in the industry are mostly due to the lack of proper and complete Standard Industrial Classification (SIC) codes, which are frequently unable to capture the variety and peculiarities of video-game businesses in a consistent manner. Therefore, in order to investigate survivability of companies in the UK video-game industry, the authors develop their analysis in two phases. In the first phase, they compile a dataset with information related to 3576 businesses operating in the industry between 2009 and 2014.
Data gathered for generating the dataset were filtered from the FAME database, which provides sectoral information of the vast majority of UK registered companies captured by using the Standard Industrial Classification (SIC). The SIC classifies business establishments and other standard units by the type of economic activity in which these are engaged, using a four-digits code to identify companies. The UK government introduced a fifth digit to better identify a range of economic activities, including those associated with companies operating in the video-games industry. However, the SIC system implies that a company's corresponding code is selected directly by the company, meaning that a number of businesses elude the count. As a result, the sample analysed in this study does not capture the full population of UK video-game companies.
Non parametric statistical testing (Mann-Whitney U test) is used to verify whether companies' life expectancy, measured by average age, is statistically different between two groups of companies, namely publishers and developers. Results indicate that the two groups differ significantly in terms of life expectancy, providing preliminary statistical evidence of resource partitioning in the video-games industry and hinting publishers acting as generalists and developers as specialists in the market. In the second phase, the authors examine the presence of cause-effect relationships between selected variables and companies' survivability. These relationships are explored by developing and performing an explanatory analysis on a sample of 1925 companies, extracted from the main dataset by filtering companies with regard to year of market entry within the period 2009-2014.
Concluding remarks The study presented in this paper explored and examined the companies operating within the UK video-game industry with regard to their levels of survivability in the industry. Using a unique dataset of video-game companies founded between 2009 and 2014, the authors developed a set of hierarchical logistic regressions to investigate the effects of a range of variables such as industry concentration, market size and density have on companies' survival rates. In particular, the locational dimension of the video-game industry was explored by introducing an extra regionally-related variable into the models, associated with the number of video-game university programmes locally available. In addition, companies were selected by type to investigate potential effects associated with the intrinsic organisational structure on the models' predictive powers.
It is crucial to take mean age differences of the populations under study into account: Populations with a somewhat older mean age should have higher rates of obesity without this necessarily reflecting a true (age-adjusted) difference. Whatever the explanation, the data indicate that more individuals are likely to be crossing the obesity threshold of a BMI of 30 kg/m 2 and that people remain for longer periods within the BMI 30+ category. The data clearly indicate that the total 'amount' of obesity in Europe is increasing -either as a function of an ageing population or from a factor that drives people from overweight to obesity; this seems to be operating in all EU countries -and to approximately the same degree (with the exception of Italy). Concerning the collection of reliable data, the Childhood Obesity Surveillance Initiative (COSI) is gathering consistent data on a national basis and provides a uniform basis for comparisons.
Introduction Patents have been increasingly used as an indicator of innovation activity over the last decades, and the recent availability of global patent databases such as the European Patent Office's Worldwide Patent Statistical Database PATSTAT has only reinforced this trend. However, it has long been acknowledged that using patent statistics to measure the productivity of research and development activities is not without limitations  (Pavitt 1982 (Pavitt , 1985;; Griliches 1990 ). Some innovations are never patented, while others are protected by several patents. Mere patent counts tell little about the market value of patented innovations, which varies considerably. Furthermore, patent design and patenting practices differ between countries. Moreover, the propensity to patent (e. g.
Based on these patterns, we revisit the interpretation of statistics on patent families, and propose new international patent-based indicators enabling more rigorous cross-country comparisons of innovation performances at the national and sector levels. Our approach is based on a statistical analysis of the global PATSTAT database between 1993 and 2010, supplemented by interviews with IP attorneys and companies and by a review of patent law in the European, American and Japanese patent systems. Our interviews suggest that patent families should be viewed from a dynamic perspective, taking into account the maturation process of innovations. Applicants indeed face a tradeoff between the pre-emption of patent protection at an early stage of the innovation development, and the fine-tuning of patent protection as the innovation matures and its market potential becomes clearer.
Patents as innovation indicators Patents are one of the main indicators used to assess the productivity of innovation systems. In contrast to R&D spending, patents do not measure inputs but outputs of the R&D process. Compared with academic publications-an alternative indicator of innovative output-they are more oriented towards the industry and cover a broader scope of technologies  (Freeman 1982; Grupp 1997 Grupp , 1998;; Frietsch and Schmoch 2006). Especially in high-technology areas, patents can help to assess present and future competitiveness of companies, sectors, or economies, since they indicate a potential for opening new markets or gaining market shares with new products  (Frietsch and Schmoch 2006; Schmoch 2004 ). In addition, patent data are now widely available, making it possible to generate large datasets for statistical analyses without having to conduct costly interviews or surveys.
As a consequence, survey-based measures have been mostly used to show that patent value is correlated with a number of features of the patents that are readily available in patent databases. Some value indicators are observable as soon as the patent is published. Those include the number of claims, which provides an indication of the legal breadth of patent protection and signals the complexity of a patent. Tong and Frame (1994)  show that patents weighted by their claims are positively linked to other measures of national research performance, while  Lanjouw and Schankerman (2001)  show that a patent is more likely to be litigated if it has more claims. The number of IPC classes mentioned on the patent application is another measure of the scope or breadth of a patent. As inventions are considered to be a combination of existing ideas, the wider set of ideas, the more valuable the patent (Guellec and van Pottelsberghe de la Potterie 2000).
Harhoff et al. (2003)  and  Harhoff and Reitzig (2004)  confirm that oppositions and the value of patents are positively related and only 8% of all EPO patents-likely the highest-value ones-are opposed. Unfortunately, information on opposition and litigation is not yet consistently available for all patent offices in global databases such as PATSTAT. The number of citations made to patents (or forward citations), initially proposed by  Narin et al. (1987)  and later popularized by  Trajtenberg (1990) , is one of the most frequent value indicators used in the literature. It is based on the fact that inventors are required to mention prior art, implying that the more a patent is cited, the more relevant it is to subsequent inventors. The number of citation-weighted patents a firm files is strongly correlated with measures of firm value based on financial market data  (Harhoff et al.
1999; Lanjouw and Schankerman 2001; Hall et al. 2005; Moser et al. 2012). Interestingly,  Hall et al. (2005)  show that self-citations (i. e. citations made by a firm to its own patents) are actually more valued by the market than other citations. A general drawback of patent citations is that they can be used strategically by applicants  (Abrams et al. 2013) , introducing noise in the measure. From a practical point of view however, the wide availability of this measure in patent databases constitutes a major advantage. Finally, patent family size, introduced by  Putnam (1996) , refers to the number of countries in which the applicant has sought protection for a given invention. More specifically, patent families include all the patents claiming the same priority (or set of priorities), and are usually thought of as the set of patents protecting the same invention in different countries (for a very complete review of the definition of patent families, see  Martinez 2010).
A look inside patents families In this section, we take a historical look at the use of domestic (i. e. second domestic filings) and foreign extensions in the major patent offices over the past decades. The source of our data is the Worldwide Patent Statistical Database (PATSTAT, October 2016 edition), developed by the European Patent Office (EPO). PATSTAT was first published in 2006 and is updated bi-annually. The richness of the database comes from its unique geographical coverage: it gathers data from almost all of the world's patent offices and contains over 87 million patent documents. Consequently, it is the first truly global patent database available to the research community. It should be noted that only applications that are maintained until the publication of 18 months after the priority filing date are stored.
The data reveal that some patents have several hundreds of continuations (the maximum recorded in PATSTAT for a single DOCDB family being 468 continuations). Moreover, the distribution is skewed. Among patents having continuations, roughly 70% of them have only one continuation and 15% have three or more continuations. This distribution seems similar to that of the value of patents as suggested by various other value indicators  (Scherer 1965; Pakes and Schankerman 1984; Pakes 1986; Griliches 1990 ). Alongside continuations, US patent families also include continuations-in-part and divisional applications, although used less frequently. 19 ivisional applications are relatively less frequent at the EPO with a patent family having on average 0. 07 divisional at the EPO and 0. 03 at the JPO. Note that PATSTAT does not have a complete information on divisionals filed at the JPO  (Martinez 2010 ), but 17 According to the PATSTAT Data Catalog, applications sharing the same DOCDB family identifier ''most probably share exactly the same priorities (Paris Convention or technical relation) as in table TLS204_PRIOR_APPLN, TLS205_TECH_REL and tls216_APPLN_CONTN.
Acknowledgements Financial support has come from the  Economic and Social Research Council  through the  Centre for Climate Change Economics and Policy , the  Grantham Foundation for the Protection of the Environment  and the  Swiss National Science Foundation  under the Sinergia programme, Project No  CRSII1_147612. Antoine Dechezlepre  ˆtre gratefully acknowledges the support of the  ESRC  under the  ESRC Postdoctoral Fellowship Scheme (Award   PTA-026-27-2756 ).
Some citations may obviously be missing from the PATSTAT database. We do not know of any investigation of the extent of this potential problem. Generally speaking, since most citations come from domestic patents and we focus on focal patents filed at EPO, USPTO and JPO for which the data coverage in Patstat is good, we do not worry too much about this issue. In the presence of classical measurement error, this would lead to attenuation bias, and our point estimates should be seen as a lower bound of the true estimates. Scientometrics (2017) 111:793-828.
, 2012). Maps and databases were often not well archived or centralized and as a result it is difficult nowadays to obtain accurate information on the localization and characteristics of the buried pipes and hydraulic equipment. The manhole covers and inlet grates which are surface elements and thus indicators of the location of these networks, can be observed on aerial images. Hence, we can rely on the automatic detection of these objects to provide an estimation of the underground utility networks' position  (Pasquet et al. , 2016). Some works have aimed to automatically detect manhole covers based on MLS (Mobile Laser Scanning). This has the advantage of providing a feedback about the current road conditions and thus avoiding accidents whether it is for intelligent transportation systems, moving vehicles or pedestrians  (Yu et al.
The Deep Convolutional Neural Network Deep learning models have demonstrated high performance when used in various technical fields, such as computer vision and speech recognition, because of their capabilities in learning hierarchical deep features from large amounts of data. Namely, the deep convolutional neural network has shown exceptional performance in computer vision tasks such as classification  (Szegedy et al. , 2015). The well-known AlexNet architecture  (Krizhevsky et al. , 2012)  is used in the present study. More sophisticated networks like GoogleNet  (Szegedy et al. , 2015)  have been envisaged. Tests have been carried out with this network, but the results obtained were not conclusive and very time consuming. As GoogleNet requires 256×256 images, it is necessary to resize all the 40×40 thumbnails of the training database.
Boosting database In order to improve the performance of the CNN, we have tested boosting, a greedy technique for ensemble learning  (Li et al. , 2005). The idea is to train a new network that learns to fix the errors of the previous one. Once a network is trained on the Prades-Le-Lez dataset, it is applied to the Prades-Le-Lez entire image and all the false positives i. e. objects incorrectly detected as manhole covers are added to the "other objects" category in a newly created training database. A new network is then trained with this dataset. In this paper, we employ boosting in a straightforward manner, working iteratively with the same network. Several iterations of boosting have been carried out and have improved the results, as shown in section 4.
Training and Test protocol Deep learning algorithms require very large datasets for the training phase. To overcome the lack of images (only 605 thumbnails in the training dataset) and improve performance, a number of data augmentation techniques can be used to enlarge the size of the dataset. In this work, we employ the Keras library  (Chollet, 2015)  to increase the training database size by combining several data augmentation methods such as rotation, translation, horizontal flip, vertical and horizontal shift. From the combination of all transformations, we can obtain fifty images for the dataset from a single ground-truth image. Concerning the "other objects" category, we first choose, ten times more randomly extracted thumbnails from the dataset image and make sure that the thumbnails do not contain manholes.
Results and discussion Four experimental networks have been applied on the aerial images: 1) the original AlexNet network with the default parameters presented in  (Krizhevsky et al. , 2012) , 2) the boosted version of the network, 3) the boosted version with the cleaned database and 4) the customized version of AlexNet with the cleaned database i. e. with the parameters presented in Figure  4. We trained the original AlexNet network on a database which contains the thumbnails of all the manhole covers identified in the operator's database. This network yields a huge number of detections with 1575 thumbnails classified as manhole covers and only 49% of real manhole covers detected. Most of the detections being false positives, the precision is hardly better than 3%. The second experiment consists in boosting this network, as explained before, by adding all the false positive detections obtained on Prades-Le-Lez with the original network to the "other objects" category, and training it again.
Five boosting iterations have been done. At the fifth iteration, the number of detections has greatly reduced, with 136 thumbnails classified as "manhole  covers". The recall also slightly decreased to 43% while precision reached 31. 6%. The analysis of these results shows that several real manhole covers whose locations were reported in the operator's database were hardly visible on the image, as they were covered by cars, shadows or vegetation. Consequently, a third test was carried out with a cleaned training database from which 296 thumbnails corresponding to manhole covers that were not entirely visible on the image were retrieved. To avoid the detection of other patterns classically encountered near manhole covers such as pedestrian crossings or pavements, we also added the immediate surroundings of the manhole covers to the "other objects" category.
This yielded better results as the number of correctly detected manhole covers increased up to 51%. The total number of detected objects increased also as it reached 162 compared to 136 with the previous network. However this increase did not deteriorate the precision which reached 32. 48% because most of the added objects were true positives, i. e. manhole covers. Several tests were performed to customize the network by modifying some parameters. Best results were obtained with the values given in Figure  4. Having noted a better recall with the cleaned database, the customized network was trained using only the cleaned database. The results obtained are significantly better than in the previous two cases. Indeed, for a recall of 50% the precision is of 68. 49%. In addition, when using this network the number of false positives becomes lower than the number of manhole covers that are detected.
The customized network using the cleaned database yields the best results with a precision of 75% for a recall of 49%. Although better recall scores have been reached using the first network (66%), the corresponding precision is lower due to the large number of false positives. This renders the first network less interesting than the remaining three. In the case of the customized network, even if the recall does not exceed 61%, the precision can exceed 70%. For a given recall, the customized network produces better precision scores than other networks. The customized network detects almost 50% of the manhole covers in the aerial images. These results are better than those obtained by  (Pasquet et al. , 2016) , using a machine-learning SVM approach jointly with a low level approach. In their case, for a precision of 66% the recall is only of 45% with a simpler database and numerous preprocessing steps, whereas for the same precision value we obtain a recall of 54% without any additional preprocessing.
CONCLUSION In this work, we have put forward a procedure that automatically detects manhole covers using aerial RGB images. We have trained it on the Prades-le-Lez database and tested it on the Gigean dataset. Four different network configurations were tested and compared in terms of recall, precision and F-measure. The preliminary results are encouraging and indicate that the model can efficiently detect these small objects in very high resolution aerial images with a recall that is higher than 50% and an average precision of 60%. To improve its performance, we are currently working on the combination of an information on the circular shape of the objects. Adding information on the geometrical shape of the objects may help in reducing false detection for objects that have low contrast with their surroundings.
The Prades-Le-Lez database consists of 6 20000 × 20000 pixel georeferenced images. The exact position of the 605 manhole covers of the wastewater network was provided by Montpellier Métropole, the local operator. The Gigean dataset consists of one image of 17749 × 18361 pixels, and the position of 101 mahnole covers provided by CCBT, the local operator.
Prevention of hepatic encephalopathy (HE) Baveno VI's recommendations do not include a specific strategy to prevent HE in the context of variceal bleeding. However, data from trials are now available. A prospective randomized controlled trial (RCT) included 70 patients admitted for variceal bleeding with no signs of HE. Patients were allocated to receive lactulose or placebo  [53]. The proportion of patients developing HE was significantly lower in the lactulose group (14% vs 40%, p ¼ 0. 03). Rifaximin is a non-absorbable antibiotic used in the prevention of recurrence of HE. Its efficiency to prevent HE in the context of variceal bleeding has been found to be similar to that of lactulose in a prospective RCT  [54]. There were fewer digestive side effects with rifaximin.