dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  B. Kawohl  for very useful discussions on the topic of this paper, in particular we thank him for having suggested the idea of Proposition 2. 11. This work started while CB was at the  Institut Elie Cartan Nancy  supported by the  ANR CNRS  project  GAOS (Geometric Analysis of Optimal Shapes ), and the research group  INRIA CORIDA  (Contrôle robuste infini-dimensionnel et applications). The research of CB is supported by the  Fir Project 2013 "Geometrical and Qualitative Aspects of PDEs ". The work of GC was partially done during her "délégation CNRS" at  University of Lorraine. AH is supported by the project  ANR-12-BS01-0007-01-OPTIFORM Optimisation de  formes financed by the  French Agence Nationale de la Recherche (ANR). The three authors have been supported by the  Fir Project 2013 "Geometrical and Qualitative Aspects of PDEs " in their visitings.
Acknowledgements The authors thank the  ECOS-Nord program   C12M01  and the project " IsoGalois "  ANR-13-JS01-0002-01. They also thank the " Universidad Nacional de Colombia" (project  HERMES code 37243 ) and the " Université de Rennes 1" (Actions Internationales 2016 ) for supporting this reseach, and also the  Centre Henri Lebesgue   ANR-11-LABX-0020-01  for creating an attractive mathematical environment. The authors thank  Juan Diego Vélez  for his help with the final redaction of the manuscript and the anonymous referees who gave relevant contributions to improve the paper.
These raiders (Ivan Boesky, C Boone Pickens, Carl Icahn, etc. ) used this new funding tool to maximum effect to earn huge gains. Many companies they attacked either had to pay off the raiders handsomely to stop their attack or be taken over only to be dismantled and have their pieces sold off separately. When the corporations chose to defend themselves against these raids, they had to convince mutual funds and/or pension funds owning large chunks of their shares to side with them and not sell out to the raiders. To the extent that these institutional investors sided with management, they did so under the condition that the firm restructure in order to significantly raise share (3) The Compustat Execu Comp database shows the top-manager ("CEO")-to-worker compensation ratio, including options exercised, to have risen in the US from 29 in 1978 to a peak of 343.
To assess the predictability of aggregate market fears in 1987 and 2008, in this work, we apply time-dependent, entropic measures of the financial market crisis risk from  [16, 21]. The underlying variables that are used in constructing two different time-varying entropies (Tsallis and approximate entropy) are extracted from a dataset for put and call financial options written on the S&P 500 stock market index. We track aggregate market expectations through the entropic lens ahead and during each of the two crises. The main objective is to study and discuss the applicability of entropies on the 1987 crash that was at its heart more of a market microstructural nature, relative to the fundamental economic crisis of 2008. The results demonstrate that the distribution-free, approximate entropy approach is more appropriate to predict the decline of the S&P 500 stock market index in 2008, while the Tsallis entropy that involves q-Gaussianity is more useful for predicting the 1987 crash.
Data The data are provided by DeltaNeutral and represent the daily S&P 500 index European option prices, taken from the Chicago Board Options Exchange. Call and put options across different strike prices and maturities are considered for 1987 and 2008. In 2008, there are 173,426 put options and 173,403 call options in the sample, i. e. , on average, about 690 put or call options are traded daily. The data for 1987 were taken from  [16] , and the sample is smaller with roughly 8000 pairs of put and call options for which we extract the deepest out-of-the-money options. Since it is one of the deepest and the most liquid option markets in the United States, the S&P 500 index option market is sufficiently close to the theoretical setting of the Black-Scholes model. We calculate the underlying variable (daily skewness premium), and then, by using a moving-window technique, we sequentially estimate the corresponding time-dependent Tsallis entropy for each moving window.
1987 Crash In this subsection, we analyse the daily S&P 500 index European put and call option prices from the Chicago Board Options Exchange. The dataset (this is the dataset used in  [27] ) contains options across different strike prices and maturities over the period from January 1987-December 1988. The estimate of the long-memory parameter ( q) for the deepest out-of-the-money options is 1. 53 (0. 04) (the number in parentheses is the bootstrap standard error; we use one leave-out bootstrap with replacement for a window size of K = 120 observations). We first investigate the evolution of Tsallis entropy based on the skewness premium for the deepest out-of-the-money options. These findings are the most striking where an early indication of the lack of belief dispersion surfaced on 25 August 1987 (Figure  3 ), two months prior to the crash.
Conclusions This paper utilizes a time-dependent entropy approach in order to obtain an early indication of the 1987 and 2008 financial crises. We extract the entropy from the daily skewness premia for the put and call European options that were traded in 1987 and 2008. The results show that the entropic methodology performs well in tracking aggregate market expectations and generating early warning signals of excessive shifts in market sentiment; it represents a dominant and viable alternative to classical sentiment indicators in technical analysis. Consequently, we envision the usage of entropic sentiment indicators for the analysis of real-time financial market data as part of services such as Bloomberg Terminals and Thomson Reuters Eikon platforms. In light of our findings, we consider entropic indicators to be more general and informative than the existing sentiment indicators (e.
Empirical application In this section, we consider the relationship between log employment (ln E ), log output (ln Q) and an indicator of (log) capital investment (ln K ) across S = N i=1 M i = 255 NUTS2 regions nested within N = 25 countries of the EU. ln Q is measured by gross value added, or GVA, and ln K is gross fixed capital formation, or GFCF. These annual regional data series are based on Cambridge Econometrics' European Regional Economic Data Base. As an illustration, Fig. 1  shows the distribution of log employment in the year 2010 across the 255 regions. Similar maps but with varying regional employment levels covering the period 1999-2010 constitute the dependent variable. Our model endeavours to explain the spatio-temporal variation in ln E as a function of ln Q and ln K organized on the same basis as Fig.
Such a discontinuity is necessary for the mechanical step-change in density that we are interested in, but it is not sufficient: the density must also continue to be different further above from the threshold. As a result, we place greater weight on results from the polynomial approach. In order to apply this empirical approach, we require large datasets that contain detailed information on the earnings of individuals, otherwise classical measurement error may lead us to make type I or II errors. We also require an accurate measure of the base that SSCs are levied on, so that we can correctly identify the earnings of individuals relative to the contribution ceiling; typically gross employment earnings, as SSCs are usually not levied on self-employment or savings income. The papers in this volume-which we now summarise-use administrative (or quasi-administrative) data that fulfils both these criteria.
They find that neither employers nor employees shift a substantial part of their SSC burden. These results are consistent over the whole time period and in robustness checks corroborating previous findings. A small trend towards a slight increase in the SSC burden falling on employees is found, but its small magnitude and the statistical uncertainty attached to it lead the authors to the conclusion that their results are consistent with economic incidence coinciding with statutory incidence. Adam et al. (2017)  investigate the incidence of National Insurance contributions (NICs) in the UK, exploiting the ceiling that applied to employee and employer  contributions between 1975 and 1985, and to employee contributions only between 1986 and 2007. Using data from the New Earnings Survey Panel Dataset, a mandatory survey of British employers' payroll records, they show there is no dip at the ceiling in either period and that the full economic incidence of NICs on one side of the market can be rejected.
As with other papers in this volume, they fail to reject that the distribution of gross earnings is smooth around the threshold, consistent with economic mirroring statutory incidence. Finally, Bosch and Micevska-Scharf (2017) use a very rich Dutch administrative dataset for the period 2006-2012. Their data contain information on both the labour cost of firms and the gross earnings of employees. This means that they can directly test for discontinuities in the distribution of both, whereas other countries are restricted to examining only gross earnings. Doing so, they find that the distribution of gross pay is smooth but that there is a positive discontinuity in the distribution of labour cost, confirming that the economic burden of employer SSCs seems to fall on employers, and that the absence of discontinuities in the gross earnings distribution is not simply due to measurement error.
We have carried out a detailed classifier performance comparison study across the different Classifier families (referred to as Cross-classifier Comparison below) and also within each Classifier family (referred to as Intra-classifier Comparison). As far as we are aware, this is the first empirical comparison of Machine Learning Classifiers based entirely on financial market data. To clarify this point, we briefly review some of the existing Classifier Comparison literature. Readers who are unfamiliar with the Machine Learning-terminology used in this section may refer to Section 2 for clarification. STATLOG by  King at al. (1995)  is probably the best known empirical classifier comparison study for a comprehensive list of classifiers. One conclusion drawn from that study is that classifier performance can vary greatly depending on the type of dataset used, such as sector-type: Medical Sciences, Pharmaceutical Industries and others, and that researchers should rank classifiers' performance for the specific type of data set which is the subject of their study.
As mentioned, we believe ours to be the first such study for financial market data. Delgado and Amorim (2014) is a more recent contribution to the classifier comparison literature. It compared 179 classifiers from 17 families based on 121 datasets, using Maximum Accuracy as the criterion to identify the top performing classifier families. As emerged from their study, these were, in descending order of performance, the Random Forest (an example of a so-called Ensemble Classifier), the Support Vector Machine, more specifically with Gaussian or Polynomial Kernels, and the Neural Network. All of these will be presented in some detail in Section 2 below, except for the Random Forest algorithm, which in our study we replaced with another Ensemble Classifier, the Bagged Tree. For our cross-classifier performance comparison we have rank-ordered classifiers according to Expected Accuracy as estimated by K-fold Stratified Cross Validation with K = 10 (a choice which we show to be empirically justified).
An Example of a Decision Tree Table  2  shows the decision rules generated by the Decision Tree algorithm using as feature vector x := (PD 3yr , PD 5yr , σ h 3m ), for the five observable counterparties indicated by their codes in the last column of the table. The algorithm was run on data collected from the 100 days leading up to Lehman's bankruptcy on 15-Sept-2008: see Appendix A. The tree has nine nodes, labelled from 1 to 9. Depending on the values of its feature variables, a nonobservable will be led through a sequence of nodes starting from node 1, until ending up with a node associated with a single observable counterparty, to which it then is classified.
Choice of K for K-fold cross validation Kohavi (1995) recommends using Stratified Cross Validation to test Classifiers. Based on extensive datasets, it suggests that K = 10 is a good choice. This was also found by  Breiman et al. (1984) , who report that K = 10 gives satisfactory results for cross validation in their Decision Tree study. We examined the influence of K on Stratified Coss Validation for the Discriminant Analysis, the Logistic Regression and the Support Vector Machine families (See Figures 13),  17 and 11)  and also found K = 10 to be a satisfactory choice, and unless otherwise stated, all of our cross validation results for the eight classifier families are obtained with K = 10.
According to  Berndt et al. (2005) , implied volatilities have statistically significant explanatory power for CDS rates. • σ h 1m ,σ h 2m ,σ h 3m ,σ h 4m ,σ h 6m denote the historical volatilities estimated from 1-year historical equity price returns for terms of 1-month, 2-month, 3-month, 4-month and 6-month respectively. • All the data used in our study are from Bloomberg TM , which is readily available to financial institutions. In the case where PD data are not available, one can consider using EDF TM as an alternative. For the empirical part of our paper, we focused ourselves on counterparties in the US financial industry. We constructed our data sample based on the observations taken during the 100 calendar days leading to the bankruptcy of Lehman Brothers for all constituents of CDX-NA-IG coming from the financial sector.
Our top three performing classifier families were the Neural Network, the Support Vector Machine and the Bagged Tree, a result which is consistent with Machine Learning classification results using non-financial data reported in Kong et al. (1995) and Delgado and Amorim (2014). 2. In contrast with existing studies such as Kong et al. (1995) and Delgado and Amorim (2014) which compared performances of classifiers on dozens and sometimes hundreds of non-financial datasets, we specialised our comparison to financial market datasets and for the purpose of one particular problem, that of CDS Proxy construction. This ensures that the performance comparisons are like-for-like. To the best of knowledge, ours is the first comprehensive classifier comparison based entirely on financial market data. Our findings for the overall ranking of best performing classifier families are nevertheless in line with those of existing literature in this area, with some exceptions, notably the Naïve Bayes classifiers.
The  Health Services Research Unit, University of Aberdeen , receives core funding from the  Chief Scientist Office of the Scottish Government Health Directorates. The  Parker Institute, Bispebjerg and Frederiksberg Hospital , is supported by a core grant from the  Oak Foundation  ( OCAY-13-309 ). S. V. K. is funded by an  NRS   Scottish Senior Clinical Fellowship  ( SCAF/15/02 ), the  Medical Research Council  ( MC_UU_12017/13  and  MC_UU_12017/15 ), and  Chief Scientist's Office  ( SPHSU13  and  SPHSU15 ).
This article aims to investigate the origin of foreign direct investment (FDI) in Central and Eastern Europe (CEE) and its spatial distribution among CEE headquarters in national urban systems. The conceptual framework is based on discussion of the role of metropolises. To provide a broad geographical scope the ORBIS database has been used, and its value has been discussed. The analysis shows that headquarters in capital cities were still attracting the greatest amount of foreign direct investment as of 2013, even when considerations are set at the level of different types of activity sector.
The basic criterion representing an investor's influence on the management of an enterprise is ownership of at least 10% of the voting power of the enterprise, while 50% or greater ownership implies complete power in terms of decisions  (OECD 2013). The latter criterion will be considered in the article as a whole. The purpose of this paper is to identify the origin of FDI in the CEECs, and to characterise the cities there, most attractive as headquarters for foreign investors in 2013. The following questions will be addressed: who are the main investors in Central Eastern Europe and what is the state of intra-CEE relations in terms of FDI? What type of cities in the CEECs are involved in the FDI inflows and how is this related to urban patterns of the different national systems in the CEECs? And how does the location of foreign ownership vary between the different activity sectors that firms in the CEECs represent? The originality of this article lies in its answering these cognitive questions by means of the ORBIS database.
This is a rare source as it permits simultaneous identification of the exact locations in cities of the headquarters of foreign-owned companies in the CEECs and the owner firm based abroad. Other databases providing the same information are either at national level or do not exist in the CEECs 2. Moreover, it allows for work with a comparative perspective throughout the whole CEE region. In addition, it permits comparisons of these distributions of FDI within different types of activity sector to be made, as the database indicates for each firm (owner and owned company) the NACE 3  code division (Albert-Bohan 2015; Finance 2016).
Data and Methodology The analysis of foreign holder control over Central and Eastern European economic management centres and the economic attractiveness of metropolitan areas in the CEECs has been driven by the ORBIS database produced by the Brussels-based Bureau Van Dijk (BvD). The database Połączenia Świat (hereafter cited as The World's Connections), as developed by  Śleszyński (described in detail in Śleszyński 2007)  has also been used to confirm the results concerning the structure and origin underpinning the foreign ownership of Polish firms as of 2004. The Bureau Van Dijk specialises in collecting private company information, and collaborates with at least 100 sources from around the world. ORBIS is thus a very extensive global database encompassing some 200 million firms throughout the world, and around 86 million companies in Europe alone 4.
It has been updated regularly since being founded in 2002, and provides financial and geographical information about headquarters, shareholders, subsidiaries and direct ownership. Taken together, such data allow company hierarchies and parentage links to be reconstructed  5. The reliability of the database has been proven by many researchers  (Gautier 2012; Finance 2014; Albert-Bohan 2015; Rozenblat & Bellwald 2015). In the case of the work described here, the variables from the database that have been made use of -for both foreign owners and owned firms in CEEare company name, geographical coordinates and annual revenue, as well as the direct foreign-firm ownership of a given CEE firm's capital, expressed in percentage terms. ORBIS resembles all databases in having certain weaknesses. Information is not complete in the case of every firm, for example, though 50% of missing geographical coordinates have been added by the author, while annual revenues of the largest firms have been checked against information on the latter's own websites.
Another disadvantage is that ORBIS only indicates where firms are headquartered, as opposed to where their production and service activities are located. This leaves results regarding locations in cities as potentially biased, given that a different approach may be required to the study of headquarters and the real locations of production. A demonstrable example concerns Skoda Auto, which is headquartered in Prague, but has its production plant in Mlada Boleslav. For the purposes of the work described here, a STRENGTH 6 indicator was therefore constructed (Fig. 1 ), by reference to data from ORBIS as treated by  Śleszyński (2007). The STRENGTH indicator quantifies the cumulated value of invested foreign capital in a CEE firm in euros, and at the same time reflects the force of such intra-companies relations.
In the article overall, only possession of 50% or more of a CEE firm's capital by foreign companies has been considered sufficient to qualify as clear 'foreign control' of a CEEC-based firm. Analysis based on the database has entailed the use of spatial analysis methods accompanied by GIS visualisation. A division into four parts has been pursued, into EU (European Union), NON-EU, CEEC and post-communist countries outside the EU. The data has already been allocated in the database following the 'Statistical Classification of Economic Activities in the European Community' (NACE). However for present purposes the manufacturing industry sector has been 6 The STRENGTH indicator has been calculated by P. Śleszyński by multiplying the part of the shares held by foreign institutions in a given firm's capital headquartered in Poland by the size of the annual revenues of this Polish company.
Origins of the main foreign investors in the CEEC region The ORBIS database provides information on 7696 Central and Eastern European firms with foreign ownership of capital (Tab. 1) 8. Throughout the analysis of the STRENGTH indicator reflecting the amount invested by foreign companies in Central and Eastern European firms (see Fig. 1  and also  Śleszyński 2007) , it is possible to note a clear prevalence among firms owning more than 50% of the capital of CEE companies of European Union origins. This is true to the tune of 82. 4% (Tab. 2). More specifically, this intra-EU ownership is mainly seen to involve companies from The Netherlands (24. 1%), Germany (14. 4%) and France (12. 3%). The highest single example of FDI in any of the CEECs as of 2013 was that generated by Volkswagen International Finance NV (based in Amsterdam), which has a 10-billioneuro investment in Skoda Auto a.
However, * Number of firms with foreign ownership of capital equal to or greater than 50%. ** The total number includes all firms indicated in the database with missing information on annual revenues, employees and percentage of foreign ownership, which had to be excluded from analysis. NB: The ORBIS database is a very powerful tool, if within a few limits. Important work has thus been carried out by the author, with a view to the database being corrected where missing annual totals for revenue and geographical coordinates are concerned. Source: ORBIS database, BVD, 2013. as was already alluded to above, it is nothing more than direct ownership that is analysed here. Indirect ownership is in fact present in the CEEC region, as capital from Russia is quite often injected via intermediary third firms for example headquartered in the EU Member State that is Cyprus.
reveals that CEEC-based owners of Polish firms' capital are mainly from Czechia, Slovakia and Hungary, which are all adjacent to, or located not far from, Poland's borders. In contrast, no direct ownership from Romania or Bulgaria was registered in the database as of 2013. An interesting fact is that foreign CEEC-based investors located most capital (600 million euros) in Polish firms based in Kraków, as opposed to in Warsaw, as might have been expected (Fig. 2). The latter fact reflects the Slovak firm Slovnaft a. s. (headquartered in Bratislava) being the Central and Eastern European holder control over Polish economic management centres in 2013 owner of the Kraków-based Slovnaft-Polska S. A. Slovnaft a. s specialises in the manufacture of refined petroleum products, while the subsidiary in Kraków engages in the retail sale of automotive fuel.