dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The primary data source used to parameterise IFM-CAP is individual farmlevel data from the FADN database  (FADN, 2015) , complemented by other external EU-wide data sources such as the Farm Structure Survey (FSS), the CAPRI database and Eurostat (see Figure  A  in the online Appendix). Most of the latter external data sources serve as an input (i. e. prior information) in the estimations rather than for model parameterisation (for more details, see  Louhichi et al. , 2015). All 83,292 farms represented in the FADN sample for the year 2012 are included in the model. To improve the model parameterisation, past observations  (2007) (2008) (2009) (2010) (2011) (2012)  on yields, prices and input costs for these farms were also exploited. For each farm, the following variables are derived from the FADN: levels (hectares or number of animal heads), yields, product prices for all crop and animal activities, available farmland (utilised agricultural area (UAA), arable land and grassland), rental prices, and coupled and decoupled subsidies.
15 he EFA measure is the most challenging measure to model, as there are no data available for capturing different eligible land elements. Because of missing data in FADN, only the following four elements of EFA are considered in IFM-CAP: fallow land (including voluntary set-aside), afforested area, catch crops and nitrogen-fixing crops. Fallow land and nitrogen-fixing crops are endogenous activities in the IFM-CAP model. Forests and catch crops are not endogenously modelled in IFM-CAP and, thus, their areas are set as equal to the base year level. The EU regulation specifies the list of crops that can be considered catch crops/green cover or nitrogen-fixing crops. Given that in the IFM-CAP model some minor activities are aggregated, they cannot be exactly mapped to this list of eligible crops.
For example, the only EU-wide information available for the total farm population is the data on CAP greening implementation collected under the CAP regulation No. 640/2014  (EU, 2014a (EU, , 2014b)). According to these data, 32 per cent of farms and 63 per cent of farm area were subject to CAP greening measures in the EU-28 in 2015. These figures are lower than our results of 55 per cent of farms and 86 per cent of area subject to CAP greening, respectively. Vanni and Cardillo (2013)  used the total farm population data for Italy (i. e. the 2007 Agricultural Census) and estimated that CAP greening affects only around 7 per cent of Italian farms, which is lower than our estimate of 10 per cent. The findings of studies based on farm sample data (e. g. FADN) depend on the regional coverage, but in general they tend to be similar in magnitude to our results.
In addition, we expect this phenomenon to remain of limited practical relevance. A second potential caveat to our analysis is that market feedback effects (output price changes) are not taken into account (see, for example, van  Zeijts et al. , 2011). This is, however, not a major drawback, given the limited EU-wide production effect of CAP greening. Third, certain crops are defined in the model as an aggregation of a set of individual crops (e. g. 'other cereals'), which may lead to a slight overestimation or underestimation of the simulated impacts, depending on farm production structure and the greening measure in question. Fourth, FADN includes only commercial farms; small non-commercial farms are underrepresented in the database, which may lead to an overestimation of the simulated impacts, as small farms are exempt from the greening measures.
Introduction Political discourse analysis  (Van Dijk, 1998 ) is a branch of discourse analysis that aims at expliciting from speeches or debates the salient features of political discourses. From that point of view, a presidential election provides interesting datasets to study. Indeed, it is a major political event in a country and gives rise to many political meetings where candidates discuss personally selected societal problems and detail their own solutions. In that context, the identification of the favourite topics of candidates as well as how they evolve throughout the campaign is a crucial task. In  (Savoy, 2010) , the author presents an analysis of the evolution of topics in political speeches by comparing the words that are overused or underused by Obama and McCain during the 2008 US presidential campaign.
Preprocessing The dataset was preprocessed as follows. First, the sentences that did not correspond to a candi- a S1 t 1 c S2 t 2 b, d S3 t 3 b S4 t 4 a S5 t 5 a, b S6 t 6 a, b, c, e S7 t 7 E1 E2 E3 E4 Figure  1 : A speech sequence and a 4-segmentation. The recurrent topics are {a, b}. date utterance (journalists questions, introduction by another speaker. ) were removed. Next, the sentences were tokenized and the tokens associated with some Part-of-Speech (POS) tags were kept. Precisely, nouns, adjectives and foreign words were kept while verbs and personal nouns were removed. While removing verbs can lead to a loss of semantic information, we found that it allows for the discovery of more interpretable topics. This choice of removing verbs has previously been made for topic modeling in political texts (Zirn and Stuckenschmidt, 2014).
Personal nouns were discarded to remove all references to interviewers or other politicians. We considered keeping some proper nouns (the ones of both campaigners and of some other politicians) but it added noise in the topic modeling step, without providing additional relevant information. Finally, remaining tokens were lemmatized and stop words were removed. We used the WordNet lemmatizer  (Miller and Fellbaum, 1998)  and the list of stop words from the nltk library 3   (Bird et al. , 2009). The final dataset contained 6240 different lemma.
, 2010) , and has been observed in other herbivores in dryland regions  (Ambrose and DeNiro, 1986; Hartman, 2011; Murphy and Bowman, 2006). In hyraceum samples, the narrowly defined feeding range of the hyraxes (<60 m from the latrine;  Sale, 1965) , and the accumulation rates of the middens (~20-60 years/sample in most cases) enforces a spatio-temporal averaging that reduces the δ 15 N variability observed in modern ecosystem studies  (Carr et al. , 2016) , and provides a more reliable index of past water variability  (Carr et al. , 2016; Chase et al. , 2012). Results from each of the different Seweweekspoort rock hyrax middens overlap such that they can be combined to create a 22,300-year composite records for the sites. To achieve this, the stable isotope data were combined using Local Regression (LOESS) curve fitting of the combined datasets (Figures  S2  and S3 ).
cal kBP) Within the broad trends of deglacial aridification in the southern Cape and increasing humidity in the SRZ, significant millennial-scale anomalies are evident in the regional records, particularly in the transitional zone between the WRZ and N-SRZ (Figure  1 , 2). Despite the strong orbital-scale similarity between the Seweweekspoort δ 15 N record and the EDML sea-salt sodium record  (Fischer et al. , 2007)  (Figure  2e , i), comparison of the detrended datasets (i. e. , removing orbitalscale deglacial trends with a cubic polynomial; Figure  4 ) indicates that at millennial time scales this relationship is in fact largely antiphase. During the last glacial-interglacial transition (LGIT; ~18-11. 7 cal kBP) and early Holocene, for example, two prominent episodes of wetter conditions occur at Seweweekspoort, at 15.
2 cal kBP  (Chevalier and Chase, 2015) (Figure  2, 4 ). This association of wetter conditions in the southern Cape during millennial-scale periods of warmth, and an in-phase response with the SRZ (Figure  4 ), may be perceived as being contrary to the paradigm of a coeval inverse relationship between the WRZ and SRZ. To explore the mechanisms behind such episodes of millennial variability, we analysed the periodicity of the dominant signals in the detrended southern Cape and SC-SRZ datasets (Figure  4 ), and their relationship with records indicative of over-arching temperate and tropical systems/drivers (i. e. EDML sea-salt sodium flux and N-SRZ precipitation respectively). To assess correlations through time, we calculated measures of correlation between the records using 2500 to 4500-year windows at 500-year intervals (Figure  5 ).
In support, the primary datasets considered in this paper are compared, and strong relationships are shown between general trends in records from the southern-central summer rainfall zone (b) and the southern Cape at Seweweekspoort (c) and records reflecting changes in tropical (northern summer rainfall zone summer precipitation (PWetQ) anomalies; as reconstructed in  Chevalier and Chase, 2015)  and temperate systems (sea salt sodium concentrations from the EPICA DML ice core in Antarctica;  Fischer et al. , 2007). Holmgren et al. , 2003) , the CD154-10-06P marine core Fe/K record  (d, not detrended; Simon et al. , 2015) , the Seweweekspoort rock hyrax midden δ 15 N record and sea salt sodium concentrations from the EPICA DML ice core in Antarctica (f;  Fischer et al. , 2007).
Funding. E. W. L. was supported by the  Leverhulme Trust  ( RPG-2014-353 ). S. S. , R. L. and N. S. C. were supported by the  Economic and Social Research Council  ( ES/M008460/1 ). Acknowledgements. This work was funded by the  Economic and Social Research Council ,  United Kingdom  (Grant code:  ES/M008460/1 ). We thank  Ljerka Ostojić  for her comments on a draft of the manuscript.
Neural-based learning algorithms, which in most cases implement a lengthy iterative convergence procedure, are often hardly adapted to very sparse input data, both due to practical issues concerning time and memory usage, and to the inherent difficulty of learning in high dimensional space. However, the description of many real-world data sets is sparse by nature, and learning algorithms must circumvent this barrier. This paper proposes adaptations of the standard and the batch versions of the Self-Organizing Map algorithm, specifically fine-tuned for high dimensional sparse data, with parallel implementation efficiency in mind. We extensively evaluate the performance of both adaptations on a set of experiments carried out on several real and artificial large benchmark datasets of sparse format from the LIBSVM Data: Classification.
INTRODUCTION The Self-Organizing Map (SOM) algorithm  (Kohonen, 1982 ) is an unsupervised neural network (NN) model that maps input data from a high-dimensional vector space onto an ordered two-dimensional surface. This mapping preserves topological relations in the data space. SOM extracts the characteristic features of possibly complex non linear relations between categories implicit in the original data space. These relations would otherwise stay hidden from the researcher's eye due to the dimensionality and sparsity of noisy samples. Thanks to this property, and despite a highly time-consuming iterative convergence procedure, SOM is widely applied to visual inspection and clustering of huge data sets of many kinds. Applying NN to Large Datasets. In data-mining applications 1  , the amount of generated data quickly becomes prohibitive, if not intractable.
Obviously, the hugeness of the data mass is unavoidable, because of the nature of the task itself. Using NNs for raw data inspection, one has no warranty that the original data space has been rigorously sampled. Apart from handmade carefully calibrated clean benchmark datasets, in real world applications samples remain noisy with spatial and temporal variations. We cannot know in advance to which extent the possible states of the characteristic features of the underlying processes are represented with sufficient resolution. The only way to overcome this problem is to try and apprehend the inner variability of the data space through a reasonably wide amount of samples. The widest the amount of data, the best one may compensate for the inherent lack of precision of the sampling methods applied to data spaces.
It becomes easier to do so, as data storage becomes more affordable, and data sets constantly increase in size and complexity and tend to reach considerable volumes. But, for such large-scale data processing, NNs training typically takes days to weeks  (Wittek, 2013). On the other hand, generalization of multi-core CPU make parallel computation resources more interesting. Open source specialized machine learning libraries on GPU now offer a wide access to a growing number of learning algorithms (Torch, Theano, GPUMLib. ). Implementations are available for multi-core CPU and GPU, either locally on a single computer or such GPU as the Nvidia Tesla, Titan or GeForce GTX, or on cluster computing systems on GPU (Nvidia) or CPU grids (Spark MLlib). Reducing SOM Time Costs. The computational cost of the SOM algorithm is highly dependent on the number of input vectors in the database, but the algorithmic complexity is mainly concerned by the dimensionality of the input vectors.
We thus measure the speed performance of three different SOM implementations. Regardless of result precision, maps are identically-configured to avoid unwanted parametric influence effects, while focusing on speed performance improvements brought by our modifications. We evaluate the execution time evolution of the batch versions, with respect to increasing parallelization levels, by varying the number of cores and threads devoted to the calculations. We also report results from a series of extensive experiments over nine artificial and real datasets, with vectors varying in number, size and densities. Training result accuracy is investigated with the usual average quantization error, and by mean of recall and precision measure following majority-voting calibration of the maps. The remainder of this paper is organized as follows.
OpenMP OpenMP  (Dagum and Menon, 1998)  provides a shared-memory multiprocessing paradigm easily applicable to C/C++ or Fortran code with special directives, without modifying the code semantics. Thanks to this simplicity, we were able to parallelize our batch version without significant changes in the source code. A major issue we have encountered is to find a proper management of the processor cache, which has a very significant impact to performances on modern processors, by avoiding multiple access to memory. For this reason, the loop order has been modified for certain portions of code, without changing the underlying algorithm. The resulting algorithm is shown in Algorithm 2. The outer loops (lines 5 and 12) are set on the codebook, and the inner loops (lines 7 and 15) on the data. With OpenMP, using the omp for directive on the outer loop is equivalent to use network partitioning, and on the inner loop, it is equivalent to use data partitioning.
PERFORMANCE EVALUATION To evaluate the performance of our implementations, we have trained several networks with the same configuration parameters on various datasets and measured their relative performance, using the following parameters: • 30 × 40 rectangular unit grids for all the networks. • t max = 10 × N samples (or K epochs = 10 for the batch version). • rectangular neighborhood limits with the radius r(t) decreasing linearly from 15 to 0. 5. • Gaussian neighborhood function, with σ (t) = 0. 3 × r(t). • α(t) = 1 -( t /t max ) if applicable.
Datasets We have selected several large datasets of sparse format from  (Chang and Lin, 2006)  to evaluate the performance of the two approaches on true examples. rcv1: Reuters corpus dataset, multiclass  (Lewis et al. , 2004). news20: netnews dataset, normalized  (Lang, 1995). sector: text categorization dataset, normalized  (Mc-Callum and Nigam, 1998). mnist: MNIST database of handwritten digits  (Le-Cun et al. , 1998). usps: subset of CEDAR handwritten database  (Hull, 1994). protein: bioinformatic dataset  (Wang, 2002). dna: recognizing splice-junction of primate gene sequences  (Noordewier et al. , 1991). satimage: classification of satellite images  (King et al. , 1995). letter: character recognition dataset  (Frey and Slate, 1991). The detailed properties of these datasets are given in Table  1.
Parallel Comparison on Batch Algorithm We measured the performance of the parallel implementations of the batch algorithm in terms of execution time, with various levels of parallelism. For this test, we have used the sector, news20, mnist and usps training datasets. The first two are very sparse and sufficiently large to evaluate the optimization effect on sparse data, and the last two are intended to observe the implementation behavior on mostly dense data. Tests were conducted on a multicore computer with 4 sockets of 6 cores Intel Xeon E5-4610 at 2. 40 GHz (2 threads at 1. 2 GHz per core). Several runs were made with different number of cores assigned to the computation. Results shown in Figure  1  demonstrate that: Somoclu speed is correlated with the total input vectors dimension, while Sparse-BSom speed is closely correlated with the number of non-zero values.
Serial Comparison of Optimized Versions We carried out experiments to compare our optimized approaches to each other. To this end, we have selected datasets with various densities and executed our implementations using the same parameters. As stated before the standard version cannot be parallelized efficiently, so we compared single threaded versions in these tests. Results are shown in Figure  2. Sparse-BSom performs better than Sparse-Som on very sparse data, which is easy to explain, because this last algorithm involves more calculations, and for this reason has a larger constant factor in its time complexity. Less clear is the reason why Sparse-Som performs better on dense data. One possible interpretation is that it is related to the different memory access management in both algorithms.
Methodology Various metrics can be used to analyze the maps without human labelling, the most common one is the average quantization error  (Kohonen et al. , 1996)  defined as Q = ∑ N i=1 x i -w c N (9) where w c is the best match unit for x i. Since SOM can be used in a supervised manner to classify input vectors, one can also use standard evaluation metrics (recall, precision). Because our datasets are all multi-class, we calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). We have used the following evaluation method for all datasets: 1. train the SOM network with the training part of the dataset. 2. perform units calibration with the associated labels (each unit is labeled according to the majority of the data it matches).
Results Detailed results are shown in Table  2 : Quantization error and Table  3 : Prediction evaluation (best F-score highlighted). The experiments were run five times, and we report mean values and standard deviation for each system. It should be emphasized that no parameters optimization per dataset was performed, and that it is certainly possible to obtain better results with careful parameter tuning. For example, the network we have used (1200 units) is too large for small training datasets, which probably explains the low recall rate for the dna dataset. It seems, however, that the standard SOM version is more robust against such type of difficulty, indicating that data samples are better distributed over the network with this algorithm. The first observation we can make is that, though not exactly identical, results of both batch versions dna train 90.
9 ± 0. 6 90. 8 ± 0. 5 88. 5 ± 0. 6 88. 5 ± 0. 5 89. 3 ± 0. 6 89. 3 ± 0. 6 test 77. 7 ± 1. 5 69. 6 ± 2. 1 81. 9 ± 2. 9 30. 3 ± 1. 7 83. 9 ± 2. With regard to the quantization error, it is clear that the batch version performs better than the standard version, but this has no effect on the predictive performance. The predictive benchmark results are globally better with the standard version than with the batch version. Furthermore, the results of the Sparse-Som also seem to be more stable, and never fall much lower than the Sparse-BSom results. A significant gap occurs between the two versions for the news20 and sector datasets, which are both very sparse. However, we cannot generalize a negative impact of sparseness with the batch version, because of the counterexample with rcv1 results.
As regards the maps obtained with both our versions, we carried out an empirical qualitative analysis using various datasets. Our results confirm the current assumption that the behavior of the standard version is more stable and generally produces overall better results than the batch version. In order to ensure reliable reproducibility of our results, our complete implementation is freely available online for the research community, with its documentation, on GitHub, under the terms of the GNU General Public License (https://github. com/yoch/sparse-som).
Acknowledgements I want to thank the organizers ( Dieter Vandebroeck  and  Annick Prieur ) and the participants of the meeting of the  Network  for the Studies of Cultural Distinctions and Social Differentiation [SCUD] in September 2014 at the  Free University of Brussels  [VUB], where I presented an earlier version of this article. I am particularly indebted to the two discussants of the paper,  Mike Savage  and  Virgilio Pereira. I also want to thank the guest editor ( Dieter Vandebroeck , again!) and the anonymous reviewers of European Societies for their great help in improving the paper.
The influence of fission-fusion dynamics, i. e. , temporal variation in group size and composition, on social complexity has been studied in large-brained mammals that rely on social bonds. Little is known about birds, even though some species like ravens have recently received attention for their socio-cognitive skills and use of social bonds. While raven breeders defend territories year-round, nonbreeders roam through large areas and form groups at food sources or night roosts. We here examined the fission-fusion patterns of non-breeding ravens over years, investigating whether birds meet repeatedly either at the same or at different locations. We combined four large datasets: presenceabsence observations from two study sites (Austria, Italy) and GPS-tracking of ravens across two study areas (Austria, France).
We investigated the individuals' tendency to use temporarily stable food sources over longer time periods and quantified how this increased contact between particular birds. Furthermore, we investigated whether factors like age class, sex or breeding status affect this tendency. Since all these factors are linked with dominance status  26  , we expected them to influence how consistently individuals use a certain food source and hence repeatedly meet other ravens. We analysed four large datasets: presence-absence observations from two study sites (Austria, Italy) to calculate the percentage of co-occurrence of each individual combination at both sites and GPS-tracking of ravens across two study areas (Austria, France).
At the Italian study site only one individual could be classified as local, 17% of the birds as frequent visitors and 81% as rare visitors. Note that the food source   1. To illustrate the individual variation we calculated for the Austrian and Italian study site mean, minimum and maximum values over all individuals for: the number of consecutive days being present or absent, the total number of days present and the percentage of days present (data refer to the time between marking and their last observation). 1 Only individuals with at least 10 observations were included; N = 126 for AUT and N = 52 for ITA. in Italy is available all day and year-round, while the food in Austria is present during a short time period in the morning only. Out of 12,206 dyadic combinations (i. e. , combinations of two individuals) in Austria local ravens encountered other locals on average on 69.