dataesr/scientific-paragraphs-categorization huggingface dataset:
2 (with median absolute deviations and extreme values) and the statistics for ĉ1 and ĉ2 , leading to the following comments and discussion. The FNC range yields, for both c 1 and c 2 , estimates with (slightly) lower variance (compared to the estimates stemming from the A and FC ranges), thus indicating a reduced variability in estimation resulting from the use of a large range of scales for estimation. Yet, the means (and medians) of the estimates significantly differ from those computed with the A and FC ranges, thus suggesting a strong bias, resulting from the poor accounting of the inter-individual variations of the actual scaling range bounds. Table  7  shows that the average selected scaling range, from the automated selection procedure with a joint detection for c 1 and c 2 , reads  (6, 11) , with little dispersion around the lower bound, thus showing a posteriori a relative homogeneity of the database and a relatively low inter-individual variability.
The dispersion around the upper bound appears larger, yet, as already discussed in Section 4. 2, optimal MSE as a function of j 2 is flat, thus the precise determination of the upper bound j 2 turns less critical in estimation performance as the variances of the quantities over which linear regression is performed increase with scales. Moreover, the automatically selected scaling range is found to yield an average scaling range close to the FC range,  (8, 10) , as well as estimation performance which is very comparable to those obtained with the FC range. The automated scaling range selection thus performs as well as the conservative scaling range choice, yet avoiding the issues raised by a systematic visual inspection of each subject in the database: Feasibility in terms of time devoted to the task and size of the database; inter-practitioner and even intra-practitioner subjectivity, etc.
In addition, the scaling ranges selected independently for ĉ1 and ĉ2 closely match in a remarkable way for these real-world data: This is a requirement from the theory underlying the multifractal paradigm that is not always observed on empirical data, and which thus constitutes a solid evidence supporting the use of the multifractal paradigm to model HRV at least on that dataset. To finish with, the automated scaling range selection procedure yields estimates c 2 that are quasisystematically strictly negative, excluding 0 or positive estimates but for a few subjects: This constitutes another clear and solid evidence that scale invariance in HRV is better modeled by multifractal models rather than by the more standard self-similar processes, such as fractional Brownian motion (cf. e. g. ,  [13, 29]  for detailed discussions).
Also, the procedure can be extended straightforwardly to multivariate analysis or to higher dimensional field (image) analysis. At the moderate price of an increased computational cost (essentially due to the bootstrap operation), this procedure avoids the subjective and tedious (or not achievable for very large databases) task consisting of the systematic visual inspection of all subjects, while yielding comparable and even improved results. In addition to optimal estimation, the procedure also contributes to the analysis of scaling in data: By outputting the lower and upper bounds of the scaling range, it provides practitioners with indications of possible departures of scaling; also, the comparison of the scaling ranges selected from different multifractal attributes (c 1 and c 2 for instance) yields indications with respect to the validity of the multifractal paradigm underlying scale invariance analysis; further, the selected scaling range itself may constitute an information as important as the scaling exponent estimates themselves, discrimination between patients and healthy subjects may come from the change in the scaling range rather than changes in the scaling exponents.
Price and reimbursement conditions Price comparison was conducted using the wholesaler purchasing price (WPP) for Europe case-studies and the wholesale acquisition cost (WAC) for the USA case-studies. This price level was chosen because it does not include margins for wholesalers or pharmacies or value added tax (VAT). Prices for the source and target product were mainly collected from publicly available sources. All data sources and respective Web links are listed in Supplementary file. For the case-studies from the United Kingdom (UK), we assumed that WPP was discounted 12% from PPP because the pricing source did not list WPP  (4). In order to conduct the statistical analyses, all prices were extracted and used in local currency since price calculation and comparison were country specific.
In this study, we considered price and reimbursement as proxies for market access. Reimbursement conditions were collected through the evaluation of reimbursement databases and HTA evaluation reports. The same sources as those for the collection of pricing data were used and were complemented with a review of additional data sources specific to reimbursement and HTA. All data sources and respective Web links for reimbursement data and HTA are listed in Supplementary file. Attributes with potential impact on market access outcome A list of attributes (variables) with potential impact on market access outcome was developed (Table  II ). These attributes were discussed and validated by an external expert group composed of four academic experts in pricing and market access for the USA and Europe, with an academic background in clinical pharmacology (USA and France), epidemiology (UK), and methodology (Germany).
Disease-related attributes ICD-10 chapter change. Therapeutic indications for source and target products were retrieved from publicly available summaries of product characteristics (SmPCs) included in national regulatory data sources (Supplementary file). The corresponding ICD-10 code was retrieved for all case studies  (6). Due to the multitude of ICD codes for all of case studies, it was decided to evaluate only for the change in ICD chapter, which was evaluated as a dichotomous variable. Prevalence change. Prevalence information of the disease for which source and target products were indicated was extracted, to the extent possible, within the same data sources and methodological reports. Among the main sources for prevalence data were World Health Organization (WHO) health statistics data on prevalence  (7, 8)  (using WHO Region A: Americas as a proxy for the USA cases and WHO Region B: Europe for the European cases), a commercial database for epidemiological data* PatientBase † (9), and the Centre for Disease Control and Prevention database  (10)  for the USA data that was unavailable from the previous sources.
In situations where no prevalence data could be found for the studied indication due to a partial indication or subpopulation, we opted for the use of the disease as a whole as a proxy, referring to the ICD coding research. Prevalence change was assessed as a quantitative attribute (difference between prevalence for target and source product). DALY change. Disability data were sourced from WHO's Global Burden of Disease Health Statistics database  (11). The disability information collected was measured in standard disability adjusted life years (DALYs) weighted for age and using a 3% discount factor, as per the description of WHO's methodology  (11, 12). DALY change was assessed as a quantitative attribute (the difference between DALYs for target and source product). Demographic group change.
Discussion The main objective of this article was to identify the determinants for successful market access outcome for repurposed drugs, measured by the mean price change, in order to allow a prediction of market access impact when pursuing the different repurposing strategies. It was assumed that reimbursement was a proxy for market access conjointly with price level approved. Consequently no further collection of data regarding regional listing or formulary inclusion for each case per country was collected. Nonetheless, it is possible that asymmetries in access due to those listings could exist for the source and target product. We have selected several potential attributes that could be related to a change in price. It is noteworthy that we were limited by the availability of the data and had to collect information from various sources.
Most information was retrieved from HTA reports and from other reliable public and proprietary databases. However, several variables were sometimes missing, and they were collected elsewhere as often as possible. More than 60% of the reformulation and repositioning cases used for analysis were approved in Europe. This higher ratio of European case studies could have an impact on the interpretation of the aggregated repurposing case study results in Table  IV  and could affect the interpretation of each attribute weight to the price outcome because pricing and reimbursement environment and regulations are different in Europe than in the USA. Prevalence of disease was considered an important attribute for evaluation. During data extraction of prevalence information, it was found that this information was not fully available or that conflicting data existed for many indications.
Acknowledgement This study was carried within the project Floresta em Pé funded by  FFEM (Fonds Français pour l'Environnement Mondial ) and coordinated by Ibama from 2007 to 2011. In the field, we benefited from the full support of all the members of the ACOPRASA association. We are also very grateful to all the MAFLOPS staff and particularly to  Antonio Abalarde Leite  and  Roseane Siqueira  for their help and their hospitality. Finally, we also thank  Daphné Goodfellow  for her contribution in English editing.
• LCA is not adapted for a global corporate vision When companies need to establish carbon footprint report that includes its product, eco-labels on their products or engaging continuous improvement actions  [24] , they first need a reliable environmental assessment of the whole product portfolio. But, LCA is both time and resources consuming and thus not usable for global portfolio assessment. The central problem for sustainable business alignment is to analyze, to optimize and to communicate on product portfolio with the least effort  [6]. It is then necessary to find a way to perform global environmental assessments with limited resources at an acceptable quality level  [24]. The intelligent use of existing digital databases as PLM, ERP systems, design tools and a good supplier relationship may streamline the data collection to obtain explicit and tacit environmental knowledge  [5, 13].
Different interoperability approaches have been studied to connect environmental with design tools. These can be defined following three distinct points of view  [28] : integration, unification and federation. System structure will be easier to evolve on a federative way than on an integrative one, if one of the tools have to change  [23]. This connection between environmental and design tools is one of the emerging challenges that design software companies, like Dassault Systèmes, have to face  [29]. Here below are described existing software with their main functionalities and limits:.
• Planning According to the environmental strategy of the company, the objectives of the assessment are established such as carbon footprint reduction or product labeling. This will define the scope of the assessment, the relevant environmental indicators and their level of accuracy, as well as the methodology to calculate them. A preliminary analysis of existing database and tools containing product lifecycle information (transport, sales, markets…) is recommended. Applying a complete LCA on some products could help to determine the relevant lifecycle stages. The system operators (users, administrators…) and system development team are defined. According to the objectives and the available data, the assessment scope can consider a part of the product portfolio and limit the lifecycle stages included.
System specifications and development According to the input/output and the objective, the functional and technical specifications of the system are created. Modules, interfaces, referential, databases, information flow, calculation algorithm and data management are specified in order to collect all relevant data to perform SLCA at different stages of design phase. Validation is necessary on a case study before the launching for software development. The experimentation is currently in this phase. Once the software is ready it will have to be tested before the integration in real work environment.
g. , the TSP-1060 dataset). In this subsection we show, by counter-example, that the solution obtained by KHM could also be degenerate. First, we distinguish 2 types of degeneracy. We can say that the solution of the clustering problem is degenerate Type-1 if there is one, or more cluster centres without allocated entities. We can say that the solution of the clustering problem is degenerate Type-2 if there exist at least two identical cluster centres. We also define the degree d of degeneracy  [6]  as the number of empty clusters. Type-1 degeneracy of KHM. We illustrate degenerate Type-1 on the following well-known Ruspini data set  [23]  (entities are 75 points in the plane, as given in Fig. 1 ). We show that a degenerate solution of Type-1 may occur even in the first iteration of the KHM algorithm if K = 4.
0000 0. 0000 0. 0000 0. 3913 1. 0000 1. 0000 0. 0000 0. 0000 0. 3913 0. 0000 ⎞ ⎟ ⎟ ⎠. The fact is that m 34 = m 44 = 0. 3913 will cause future degeneracy. From this matrix, in fact, we can obtain a crisp clustering matrix, as follows: (a) (b) (c) (d) (e) (f) Fig. 3  KHM clustering degeneracy for dataset-2 M 1 = max j (m K H M (c j /e i )) = ⎛ ⎜ ⎜ ⎝ 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 ⎞ ⎟ ⎟ ⎠ , or M 2 = ⎛ ⎜ ⎜ ⎝ 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 ⎞ ⎟ ⎟ ⎠. By repeating the same steps in the next iteration we get: The results are shown in Fig. 3b. We see in Fig. 3b , c that all five entities are clustered in 4 groups, as desired. But in Fig. 3d , two centroid points are almost joined in one cluster. In the rest of the Fig. 3e , f, we can see clearly how they coincide. This step implies that the degeneracy in this example is considered to be of type 2.
If an improvement is obtained, k is returned to its initial value, and the new incumbent is updated (line 2). Otherwise, the next neighborhood is considered (line 3). Computer. All experiments were performed on a personal computer Intel(R) Core(TM)2 with 0. 98 GB of RAM and a speed of 2. 40 GHz. All our methods were coded in Lahey/Fujitsu FORTRAN 95. For plotting, we used MATLAB 7. 6. Test instances. We choose the following test instances: (i) Ruspini which has 75 entities in 2-dimensions  [23] ; (ii) Iris which has 150 entities in 4-dimensions; (iii) Wine which has 178 entities in 13-dimensions; (iv) Glass which has 214 entities in 9-dimensional space; (v) Breast-cancer which has 699 entities in 10-dimensions, and (vi) Image Segmentation with 2,310 entities in 19-dimensions. For more details about them, see  [5].
Conclusions In this paper, we consider the KHMCP and an ALT type of heuristic (ALT) to solve it. We show that the KHM clustering heuristic for solving KHMCP suffers from degeneracy, i. e. , that some clusters could become or remain empty (without entities) during the execution of the heuristic. We distinguish two types of degenerate solutions, and provide an efficient procedure which removes degeneracy immediately when it appears. Moreover, this new routine is used as a local search within a recent variable neighborhood search (VNS+) heuristic, which appears to represent the current state-of-the-art. Extensive computational analysis on some usual data sets from the literature confirms that degeneracy could seriously damage the solution qualities of both KHM and VNS-KHM. Future research may include: (i) application of our approach to investigating effectiveness of ALT procedures for solving k-Heronian, Generalized k-Heronian mean  [11]  and k-logarithmic mean clustering  [10] ; (ii) the development of a general statement regarding degeneracy in ALT iterative procedures; (iii) the design of different methods for correcting degenerate solutions for ALT methods; (iv) an investigation of the relation between initial solution methods of K -means and KHM with degeneracy, i.
Acknowledgments The research of E. Carrizosa is partially supported by Grants  MTM2009-14039  ( Ministerio de Educación y Ciencia, Spain)  and  FQM329  ( Junta de Andalucía, Spain ). Part of this research was done while  N. Mladenović  was visiting the  Instituto de Matemáticas de la Universidad de Sevilla  (Grant  SAB2009-0144 ,  Ministerio de Educación y Ciencia, Spain).
502 3 2 62 0. 25 250 KHM+ 19,052. 13 2 0. 85 300 KHM 23,265. 06 57. 817 2 2 78 0. 20 300 KHM+ 14,741. 80 2 1. 09 (d) Dataset: Image Segmentation-2310 100 KHM 32,480,866 0. 197 104 2 1 14. 75 100 KHM+ 32,416,846 131 16. 67 200 KHM 31,760,192 0. 017 157 2 1 38. 18 200 KHM+ 31,754,658 150 40. 87 300 KHM 30,295,272 0. 023 105 2 3 37. 68 300 KHM+ 30,288,258 91 38. 03 400 KHM 29,596,908 0. 968 105 2 6 49. 70 400 KHM+ 29,313,292 102 48. 68 500 KHM 28,287,296 0. 419 50 2 10 29. 28 500 KHM+ 28,169,312 43 26. 18 (ii) The degeneracy type depends on the instance considered. There is no instance with both types of degeneracy: Wine-178 exhibits only type 1 and the other instances only type 2 degeneracy. (iii) The number of clusters without entity (the maximum degree of degeneracy) can be more than 40 % of the total number of clusters K (see the Breast Cancer-699 instance).
Exploiting open data in the web community is an established movement that is growing these recent years. Government public data is probably the most common and visible part of the later phenomena. What about companies and business data? Even if the kickoff was slow, forward-thinking companies and businesses are embracing semantic technologies to manage their corporate information. The availability of various sources, be they internal or external, the maturity of semantic standards and frameworks, the emergence of big data technologies for managing huge volumes of data have fostered the companies to migrate their internal information systems from traditional silos of corporate data into semantic business data hubs. In other words, the shift from conventional enterprise information management into Linked Opened Data compliant paradigm is a strong trend in enterprise roadmaps.
Introduction Linked Data, Open Data and Linked Open Data (LOD) are three concepts that are very popular nowadays in the semantic community. Various initiatives, like openspending. org, are gaining ground to promote the openness of data for more transparency of institutions. But what is the difference between these three concepts? Linked Data refers to the way of structuring the data and creates relationships between them. Open Data similar to open-source, opens content to make it available to citizens, developers, etc. for use with as limited restrictions as possible  (legal, technological, financial, license). Linked Open Data, that we refer to as LOD, is the combination of both: to structure data and to make it available for others to be reused. The LOD paradigm democratized the approach of opening data sources and interlinking content from various locations to express semantic connections like similarity or equivalence relationships for example.
Why Should My Company Assets Go Linked Open Data? The benefit of the adoption of Linked Data technologies in enterprises is multidimensional: • address the problem of data heterogeneity and integration within the business; • create value chains inside and across companies; • meaning on data enables search for relevant information; • increase value of existing data and create new insights using bi and predictive analytics techniques; • Linked Data is an add-on technology which means no need to change the existing infrastructure and models; • get a competitive advantage by being an earlier adaptor of LOD technologies. These benefits are better detailed in Fig. 3  taken from Deloitte report "Open data: Driving growth, ingenuity and innovation" 1.
To comply with the openness criterion of LOD paradigm, publishing data is a major recommendation in the "LODification" process of corporate data. To do so, a licensing scheme must be released to define how the opened data can be reused and exploited by third-party users, applications and services. Considering the company interest, a compromise must be found to open as much data as possible and maintaining a good balance between keeping strategic enterprise data confidential, like the know-how for example, and the rest of data open. Lot of reusable licensing schemes can be considered. Last but not least, the opened data license scheme must guarantee the reuse principle of data by third-party applications with as few technical, financial and legal restrictions as possible. One way of achieving these goals is to provide rich metadata descriptions of the opened data with appropriate vocabularies, like DCAT 3  , VoID 4  , DublinCore 5  , etc.
Prefix duplicate property names with the name of the source entity to make them unique. • Use xml support for data-typing. Simple built-in xml Schema datatypes such as xsd:date and xsd:integer are useful to supply schemas with information on property ranges. • The meaning of a class or property can be explicated by adding an "rdfs:comment", preferably containing a definition from the original documentation. If documentation is available online, "rdfs:seeAlso" or "rdfs:isDefinedBy" statements can be used to link to the original documentation and/or definition. Domain specific data, can be modelled with vocabularies like Org 6  or GoodRelations 7. Only when existing vocabularies do not cover ones needs new schemas should be developed. Data sets that will be published on the web should be described with metadata vocabularies such as VoiD, so that people can learn what the data is about from just looking at its content.
Use HTTP/DNS Based URIs A uri is a mechanism that can be used for identifying different objects and concepts. Some of these objects and concepts may have a physical existence like books for example with ISBN, web page with URL locations. Other concepts are abstract and represent conceptual things like ontology concepts or data items. Different schemes of uris exist for representing a resource: uris based on dns (Domain Name Server) names, ark (Archival Resource Key) and uris based on names and ids like isbn (International Standard Book Number), doi (Digital Object Identifiers), Barcodes, etc. Some of the schemes described above can be inadequate to implement basic Linked Open Data features like publishing, referencing and interlinking. Therefore, it is strongly recommended to use uris based on http protocol and dns names (like urls and ark) to ensure visibility, accessibility and reuse of business items in external applications and to third party users.
Publishing Pattern for Relational Data Relational databases (rdb) are the core asset in the existing state-of-art of data management and will remain a prevalent source of data in enterprises. Therefore the interest of the research community 11,12 has gathered around the development of mapping approaches and techniques in moving from rdb to rdf data. These approaches will enable businesses to: • Integrate their rdb with another structured source in rdb, xls, csv, etc. (or unstructured html, pdf, etc. ) source, so they must convert rdb to rdf and assume any other structured (or unstructured) source can also be in rdf. • Integrate their rdb with existing rdf on the web (Linked Data), so they must convert to rdf and then be able to link and integrate. • Make their rdb data to be available for sparql or other rdf-based querying, and/or for others to integrate with other data sources (structured, rdf, unstructured).