dataesr/scientific-paragraphs-categorization huggingface dataset:
All data and materials have been made publicly available via Open Science Framework and can be accessed at https://osf. io/gjc5b/. Experiment 1 was preregistered at https://osf. io/gjc5b/registrations/. Experiment 3 was preregistered at https://osf. io/43uwf/registrations/. The complete Open Practices Disclosure for this article can be found at https://osf. io/v5x9s/.
Introduction Categories (understood as types of collective identities for broad classes of objects or of agents) are the most basic cognitive tools, and are key to the use of language, the construction of knowledge and identity, and the formation of agents' evaluations and decisions. The literature on categorization is expanding rapidly, motivated by-and in connection with-the theories and methodologies of a wide range of fields in the social sciences and AI. For instance, in linguistics, categories are central to the mechanisms of grammar generation  [7] ; in AI, classification techniques are core to pattern recognition, data mining, text mining and knowledge discovery in databases; in sociology, categories are used to explain the construction of social identity  [21] ; in management science, categories are used to predict how products and producers will be perceived and evaluated by consumers and investors  [20, 24, 37, 31, 1].
Formal Concept Analysis. A very different approach, Formal Concept Analysis (FCA)  [14] , is a method of data analysis based on Birkhoff's representation theory of complete lattices  [8]. In FCA, databases are represented as formal contexts, i. e. structures (A, X , I) such that A and X are sets, and I ⊆ A × X is a binary relation. Intuitively, A is understood as a collection of objects, X as a collection of features, and for any object a and feature x, the tuple (a, x) belongs to I exactly when object a has feature x. Every formal context (A, X , I) can be associated with the collection of its formal concepts, i. e. the tuples (B,Y ) such that B ⊆ A, Y ⊆ X , and B × Y is a maximal rectangle included in I. The set B is the extent of the formal concept (B,Y ), and Y is its intent. Because of maximality, the extent of a formal concept uniquely identifies and is identified by its intent.
The formulas so generated do not denote states of affairs (to which a truth-value can be assigned), but categories or concepts. In this modal language, as usual, it is easy to distinguish the 'objective' or factual information (stored in the database), encoded in the formulas of the modal-free fragment of the language, and the agents' subjective interpretation of the 'objective' information, encoded in formulas in which modal operators occur. In this language, we can talk about e. g. the category that according to Alice is the category that according to Bob is the category of Western movies. This makes it possible to define fixed points of these regressions, similarly to the way in which common knowledge is defined in classical epistemic logic  [10]. Intuitively, these fixed points represent the stabilization of a process of social interaction; for instance, the consensus reached by a group of agents regarding a given category.
Models for this logic are formal contexts (A, X , I) enriched with an extra relation R i ⊆ A × X for each agent (intuitively, for every object a ∈ A and every feature x ∈ X , we read aR i x as 'object a has feature x according to agent i'. Hence, while the relation I represents reality as is recorded in the database represented by the formal context (A, X , I), each relation R i represents as usual the subjective view of the corresponding agent i about objects and their features, and is used to interpret i -formulas. This logic arises and has been studied in the context of unified correspondence theory  [5] , and allows one to relate, via Sahlqvist-type results, sentences in the first-order language of enriched formal contexts (expressing low-level, concrete conditions about objects and features) with inequalities ϕ ≤ ψ, where ϕ and ψ are formulas in the modal language above, expressing high-level, abstract relations about categories and how they are perceived and understood by different agents.
By an L -logic, we understand any extension of L with L -axioms ϕ ⊢ ψ. Interpretation in enriched formal contexts. Let us discuss the structures which play the role of Kripke frames. 2  An enriched formal context is a tuple F = (P, {R i | i ∈ Ag}) such that P = (A, X , I) is a formal context, and R i ⊆ A × X for every i ∈ Ag, satisfying certain additional properties which guarantee that their associated modal operators are well defined (cf. Definition 2). As mentioned above, formal contexts represent databases of market products (the elements of the set A), relevant features (the elements of the set X ), and an incidence relation I ⊆ A × X (so that aIx reads: "market product a has feature x"). In addition, enriched formal contexts contain information about the epistemic attitudes of individual agents, so that aR i x reads: "market product a has feature x according to agent i", for any i ∈ Ag.
and by the set of movie-features Y (i. e. the set of features which all western movies have). The elements of B are the members of category p in M; the elements of Y describe category p in M. The set B (resp. Y ) is the extension (resp. the description) of p in M, and sometimes we will denote it[[p]]   and B   × Y is maximal rectangle contained in I. For example, if p is the category-label denoting western movies, and P is a given database of movies (stored in A) and movie-features (stored in X ), then V interprets the category-label p in the model M = (F,V ) as the formal concept (i. e. semantic category) V (p) = (B,Y ), specified by the set of movies B (i. e. the set of western movies of the database) M (resp. ([p]) M ) or [[p]] (resp. ([p] )) when it does not cause confusion. Alternatively, we write:.
The analysed period runs from 2008 to 2013 (i. e. from the beginning of the economic crisis to the last year of data availability, for all the data sources consulted). The first step was to map the provincial traffic flows of each port. For this purpose, the database of the Spanish Customs Statistics and the directory of Spanish exporting-importing companies of the Chamber of Commerce were used. The former provides information about flows of foreign trade generated at each province, which can be linked to the Spanish port serving as gateway. The latter provides information about the location of firms generating those trade flows. A drawback of the directory of Spanish exporting-importing companies is that it does not include information on the main mode of transport used by them. For that reason, only flows to American and Asian countries were used to assess the evolution of port traffic distribution.
Therefore, both Coslada and the PLAZA terminal are operated by consortia owned by a major port terminal operator at Valencia and Bilbao. It is however Barcelona that has had the recent significant rail traffic growth. Another interesting change from a few years ago is that the terminal of Azuqueca de Henares, northeast of Madrid, has not managed to maintain its traffic with the port of Barcelona. Data on the shares of each inland container terminal reveals that each of them is dominated by a single port. At the Madrid Coslada terminal in 2014, Valencia contributed 89% of a total of 125,000 TEUs, with fewer than a thousand containers from the other two ports. This is a slight change from 2008, when Valencia contributed 82% of 60,000 TEU, but the difference was that in 2008 the other ports contributed a few thousand containers each.
At TMZ, 92% of their current container traffic of over 250,000 TEU comes from the port of Barcelona and the remainder from Bilbao. In the case of Plaza, a container traffic of around 60,000 TEU is all with the port of Bilbao (although it has several trains for other destinations carrying non-unitised cargo). Increasing interest has been shown to the possibility of using logistics platforms to consolidate traffic, but little empirical data is available due to commercial sensitivity. What does seem apparent is little evidence of such platforms being used to support the growth of rail, as they cannot overcome traditional challenges such as the need to share information and align shipments between several different shippers. Therefore, the co-location of a large intermodal terminal with the logistics platform at Plaza has not led to increased use of rail and only about 2% of traffic from the site uses the rail terminal.
Discussion and conclusion The research question of this paper was to what extent the development of inland terminals and container rail shuttles has influenced the ability of Spanish ports to compete for distant hinterlands. While commercial data availability limits the extent to which this question can be answered in its entirety, a number of conclusions have been drawn. In the Spanish case, it is clear that the development of rail services has not been necessary to expand contestable port hinterlands, as Valencia has grown its share of both Madrid and Zaragoza while experiencing a declining share in overall rail volumes. While Valencia has increased its use of rail to Madrid, doubling its container throughput to the Coslada terminal in six years, this has merely maintained its share of rail on that route.
The concentrations of atmospheric greenhouse gases were lowered to their LGM values derived from ice core data (185 ppm for CO 2 , 350 ppb for CH 4 and 200 ppb for N 2 O) and the ice sheets are prescribed according to the product developed for PMIP3  (Abe-Ouchi et al. , 2015). The model is run for several hundred years until the response to the LGM forcing in terms of surface climate variables is stabilised  (Kageyama et al. , 2013a, b). For this research, we extracted 50 years of monthly mean data (temperature 2m above the surface, precipitation, sea level pressure and relative humidity) from 15 the stabilised part of the simulation. Next, we downscaled the data, calculated their average climatology and their temporal (interannual) variability.
GAMs were calibrated using the mgcv package  (Wood, 2006)  in R, and the number of knots was determined automatically using generalised cross-validation. 30 Using a combination of geographical and physical predictor variables has been shown to improve spatial downscaling results Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2017-254 Manuscript under review for journal Geosci. Model Dev. Discussion started: 8 November 2017 c Author(s) 2017. CC BY 4. 0 License. (Vrac et al. , 2007). Using GAMs on climate variables requires the predictor and dependent variables to have the same spatial scale. The present-day dependent variables (precipitation and temperature) are at a fine spatial scale. The elevation variable is also at a fine spatial scale, whereas the predictor climate variables generated by the GCM are at a coarser scale.
Large-scale predictor variables. We used the data from a CMIP5 historical simulation run with the IPSL-CM5A-LR model to produce the predictor climate variables used for the calibration of the GAMs. We calculated monthly climatological averages from the simulation outputs for the period from 1960 to 1990, i. e. the same years as for the CRU data (see above). The predictor variables we used are: temperature (T), precipitation (P), atmospheric pressure at sea level (SLP) and relative humidity (RH). The variables were 20 spatially interpolated to match the spatial resolution of CRU data, which is 10'; each grid-point in the CRU data therefore matches a value for each of the predictor variables. Three interpolation methods were tested: bilinear, bicubic and kriging (Figs. S2-S4 ). For the kriging method, we used the "krig" function from the vacumm python package (http://relay.
Fine-scale predictor variables. We extracted present-day elevation data from the CRU dataset gridded at the same fine-scale spatial resolution as the dependent variables. We computed the advective (Aco) and diffusive (Dco) continentalities, following  Vrac et al. (2007). Dco is bounded between 0 and 1, and corresponds to the shortest distance to the ocean. A low value means that distance to the ocean is small, and vice versa. Aco is also bounded between 0 and 1, and takes the direction and intensity of prevailing winds into account along with the distance to the ocean. Both variables are used to account for the fact that an air mass becomes more continental as it travels across land. Since Dco and the Aco proved to be highly correlated but Aco provided the best performance in the models, we only selected Aco for the present analysis (Figs.
Evaluation data (palynological data and vertebrate remains) 25 To evaluate the performance of the SDM, we compared our temperature and precipitation outputs to local climate variables estimated on the basis of pollen and vertebrate fossils from 29 test locations (Table  S1 , Fig. S8 ). For each of our 29 test sites, we estimated the mean, minimum, and maximum temperature and precipitation rate on a monthly basis over the course of the 50 downscaled, simulated years, and compared the ranges of downscaled, simulated values to the ranges of values reconstructed using the palynological data and vertebrate remains. 30 Reconstruction of local temperature and precipitation values were obtained from pollen data reported in an independent study using inverse vegetation modelling for 10 sites located in the Iberian peninsula  (Wu et al.
Analysis by metric was additionally adjusted for the other metrics. Models were then stratified by sex, age (tertiles), and study site; multiplicative terms were included in each model, and p values for interaction of <0. 10 were considered significant. HRs per 1 additional metric at the ideal level were also provided. Finally, models were adjusted for timedependent functional disability and depressive symptoms to evaluate their potential mediating effect. Analyses for the behavioral and the biological CVH were also conducted. The proportionality assumption of Cox regression analysis was verified graphically. The following sensitivity analyses were performed to investigate the robustness of the association between CVH and combined CHD or stroke. First, missing global CVH status was imputed: 1) as being poor (worst-case scenario); and 2) using multiple imputations by fully conditional specification with SAS Proc MI and MIAnalyze (SAS Institute, Cary, North Carolina)  (23).
Second, to investigate reverse causality, the analysis was repeated after excluding deaths that occurred in the first 2 years of follow-up. Third, the competing effect of death on the association between CVH and vascular events was investigated using the Fine and Gray method as mentioned earlier in the text  (22). Fourth, definite hospitalized angina cases were excluded from adjudicated CHD events. Last, using the total CVH score (range 0 to 14), we quantified the association of average (9 to 11) and optimal CVH (12 to 14) versus inadequate CVH (0 to 8) with all-cause mortality, CHD, and stroke. All analyses were 2-sided and a p value <0. 05 was considered as statistically significant except for the study of interaction. SAS software version 9. 4 (SAS Institute) was employed.
Uncertain Qualitative Criteria 'The rigor of numbers has its limitations: each quantitative data must be interpreted and complemented by qualitative appreciations',  62  even if such evaluation is more difficult. 63 For the ECtHR, quality criteria have taken a back seat to numbers and have changed considerably over the years, giving the impression of fumbling and short-term vision or even lack of vision dependent on the States' desires and diktat. Yet, numerous studies could have helped in establishing and strengthening a clear approach. Three main forms of quality criteria can be broadly distinguished: those assessing the quality of the functioning of justice as a whole (including the independence and impartiality of the courts, but also the quality of the service provided to users of the justice system, as the ECtHR fulfils a public service), the quality of the judiciary process (compliance with procedural safeguards, etc.
Data collection, variables and data sources A routine questionnaire for each patient was filled out by psychologists and included socio demographic variables (including age, sex, nationality, vulnerability type) and mental health care information. The latter included types of traumatic event(s), if the event(s) involved violence, type and location of physical trauma (if any), country where the incident(s) took place, perpetrators of violence and categories of mental health symptoms. Psychologists entered these data on a dedicated pro forma which was then transferred into a standardized data base (Microsoft Excel). For the purpose of this study, traumatic events were classified into violent and non-violent events. A violent event included one or more the following: physical or sexual violence by State authorities or communities, incarceration/ kidnapping, family violence, and ill treatment-by State authorities, smugglers or others.
All other events were classified as being non-violent. Testimonies of traumatic events and physical violence were collected as part of the routine clerking, transcribed and translated the same day into English and included in the clinical files. Cross-validation of data was done by comparing details in the standardized database with clinical files. Information on registered arrivals of migrants/refugees to Serbia were sourced from the United Nations High Commissioner for Refugees  [3]. This information was used to verify if there was an association between border closures and numbers of arrivals in Serbia.
Statistical analysis Trends in violent events seen by month in MSF mental health clinics in Serbia were standardized per 100 mental health consultations. This information along with numbers of migrant/refugee arrivals in Serbia was expressed graphically for the period October 2015 to June 2016. Data on arrivals in Serbia were only available from UNHCR as from October 2015. Descriptive statistics (numbers, proportions, medians and inter-quartile intervals) were used to report results. Linear trends in violent events (as a proportion of all traumatic events per month) were examined using the chi-square test for linear trend. The level of significance was set at P ≤ 0. 05 with 95% confidence intervals. Selected testimonies of violent events were reported verbatim after removing any identifiers.
As member States of the United Nations recently gathered in New York for a summit (on 19 th September 2016) to agree on a "coordinated and humane approach" to the migrant and refugee issue, these data provide a sobering reality check of the considerable gap between rhetoric and action that needs to be bridged  [15]. The study strengths are that recording of traumatic events (including violence) were done according to standardized guidelines, multiple sites were included and data were encoded by trained and experienced psychologists. In addition every psychologist was accompanied by a cultural mediator(s) who spoke the languages of the migrants/refugees and were from similar cultural backgrounds. The use of cultural mediators is crucial as it enhanced dialogue, trust and trans-cultural understanding, and has been recommended as a way to bridge ethno-cultural barriers of communication  [9, 16].
We also used a triangulation design for linking quantitative and qualitative information and reporting was in accordance with STROBE guidelines  [17]. There are some study limitations. First, data on traumatic events came from interviews conducted through mobile mental health clinics. As many individuals may not have had access to such clinics, we may have underreported the real situation. Selection bias is very likely. Population based surveys would be the best way of determining the actual prevalence of traumatic events. Second, and understandably, there were variations in the numbers of individuals attending mental health clinics by month. To assess trends, we thus standardized violent events by 100 mental health consultations. Finally, as it is difficult to differentiate between migrants and refugees in transit situations, we considered them as one group "migrants/refugees".
B. Test Sequences and Configurations A set of video sequences, from different categories, has been selected from MPEG database 1. The choice of these videos is mainly based on the color content, movement and texture. Five original sequences, of 10 seconds duration each, have been used in the experiment, as given in "Table  V ". An example frame of the used sequences is given in "Fig. 4 ". These sequences are processed with the HEVC encoder/decoder reference software. Different configurations are then tested (computation skipping, computation approximation, with LD and RA). Two quality levels, based on Quantization Parameter (QP), are considered: QP=27 and QP=32. Finally, we define three filter categories, adapting their computational complexity: low, middle, intermediate, and legacy (reference), respectively  [7].
C. Participants Sixteen healthy naive subjects have been invited to participate to these experiments. There were 11 male and 5 female with ages ranging between 21 and 43. Visual acuity has been checked by FrACT (Freiburg Visual Acuity Test) and it was around 1 with or without correction. All subjects have been screened using the Ishihara compatible color vision test for color blindness. In order to ensure reliable scores, all participants have been gratified. 1 This dataset is used by JCTVC MPEG Team in HEVC test conditions for compression.
C. Energy Consumption As noticed above, the proposed-oriented methods allow a computation complexity reduction without affecting the perceived quality. For energy measurements, an octa-core Exynos 5410 SoC has been used. This System on Chip (SoC) is based on the big. LITTLE configuration including a cluster of 4 ARM Cortex-A15 cores and a cluster of 4 ARM Cortex-A7 cores. Power sensors are embedded in this SoC allowing power measurements during the processing. For these experiments, power measurements are done after each frame processing and averaged over the complete video decoding process. The computation is done only on the 4 ARM Cortex-A15 cores. Thus, the thread scheduling has no impact on consumption measurements. Finally, the standard "OnDemand" DVFS governor from Linux have been used to reduce static power consumption during the experiments.
IFM-CAP is a static positive programming model applied to each individual farm (83,292 farms) in the Farm Accountancy Data Network (FADN) to guarantee the highest possible representativeness of the EU agricultural sector. It assumes that farmers maximise expected utility given yield levels, product prices and production subsidies, subject to resource endowments (arable land, grassland and feed) and policy constraints, such as CAP greening restrictions. Farmers' expected utility is defined following the meanvariance (E-V) approach  (Markowitz, 2014)  with a constant absolute risk aversion (CARA) specification  (Pratt, 1964). Following this approach, it is assumed that farmers select a production plan that minimises the variance in income caused by a set of stochastic variables for a given expected income level  (Hazell and Norton, 1986).