dataesr/scientific-paragraphs-categorization huggingface dataset:
The next question is do we encounter this case regularly in our database? In our sample, we count 165 cases of countries in recession over the period. But for these 165 cases of falling GDP, we have information about the gross disposable income for 112 cases only. Of these 112 recessions, we know that gross disposable income increase in 50 cases (automatic stabilizers), thus allowing for 24 cases of increased consumption (and 26 cases of decreased consumption, presumably because of precautionary saving). Of the 112 recessions, we have 62 cases where the fall in GDP is accompanied by a fall in gross disposable income. In this sub-sample, we have 49 cases of decreased consumption, but 13 cases of increased consumption. In the end, we have 37 cases out of 112 recessions where consumption increases, meaning that our proxy multipliers diverge from the true ones.
-For the 24 cases involving increased consumption thanks to increased gross disposable income, our proxy multipliers are responsible for basing the propensity to consume on GDP instead of on gross disposable income. But, we make our choice in order to draw on a larger database, including recessions before 1995 (gross disposable income data are unavailable prior to this date). -For the remaining 13 awkward cases, a careful look shows that eight of them depend on the "thickness of the line": the growth rates of consumption are not very far above zero and/or the growth rates of gross disposable income are barely below zero, and/or the GDP growth rates do not fall by much in recession. The remaining five cases are questionable. One is attributable to the simultaneous integration of households and non-profit institutions serving households (NPISHs) in our consumption measures  (Ireland, 2010).
When looking at household consumption alone, we see that consumption declines along with gross disposable income. The other four cases may be associated with countries experiencing credit bubbles  (Sweden, 1981; Romania, 1998 and 2010; Latvia, 2010). Although it takes us beyond the scope of this paper, we are dissatisfied with the work done in constructing our database. How can we define the marginal propensity to consume theoretically and how should we measure it empirically? With consumption smoothing during a recession, the marginal propensity to consume-defined as the increment in consumption divided by the increment in income-may become negative. Whereas consumption smoothing obviously indicates behavior designed to support consumption, it reduces the marginal propensity to consume, and consequently leads to a lower value for the multiplier.
AMECO is the Annual Macroeconomic database of the European Commission (available at: http://ec. europa. eu/economy_finance/db_indicators/ameco/index_en. htm). We use the following series to build our multipliers: Gross domestic product at 2010 market prices (OVGD) for real output (Y), Private final consumption expenditure at 2010 prices (OCPH) for real consumption (C), and Imports of goods and services at 2010 prices (OMGS) for real imports (M). Data were extracted in April 2015.
g. , solar collectors in a ground floor apartment) or because the action was already done by the household (e. g. , eating less meat in a vegetarian household). In cases where participants chose to stick with their Sim1 result throughout Sim2, their response counted only once. Furthermore, some actions were mutually exclusive such as eating 30% more vegetarian food, eating 60% more vegetarian food, and becoming a vegetarian. Altogether, data from 309 households were collected and one observation was removed from the sample due to missing data. The repartition by countries was as follows: Germany n = 106 + 1 missing; France n = 70; Norway n = 58; and Sweden n = 74. All calculations and analysis were performed using the software R v. 3. 2. 1 (R foundation for Statistical Computing, Vienna, Austria).
Discussion of Methods This study adds to previous research on CO 2 e mitigation since it (a) analyses household consumption and its associated CO 2 e emissions in the particular context of emission trajectories compatible with the 1. 5 • C goal; (b) uses the techniques of "serious gaming" (see Section 2) to understand how households may change their consumption in order to achieve specified CO 2 e reduction targets; and (c) differ between an initial voluntary and a subsequent forced scenario that indicate the amount of policy support the two groups of actions need to receive for initiating effective household mitigation behavior. By following individual households through these steps of interventions, data has been collected that provides insight into mitigation behavior unique of its kind.
Funding: This paper was written in the framework of the  JPI Funded project HOPE (Household preferences  for reducing greenhouse gas emissions) www. hope-project. net. The presented research project is supported by national funding bodies under the umbrella of the  Joint Program Initiative (JPI) Climate , a Pan-European intergovernmental initiative. In particular, the following national funding agencies are funding this research: The  French National Research Agency  ( ANR-14-JCLI-0001-03 ), the  German Federal Ministry of Education and Research  ( 01UV1414A ), the  Research Council of Norway  ( 244 , 905/E10 ) and the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning  ( 214-2014-1717 ).
1%-1. 2%) of the reference GDP, similar to the findings of other macro-economic assessments (Vandyck et al 2016). In the 2 • C scenario the respective cost is 1. 2% (0. 5%-4. 5%). To put this number into context we calculate GDP costs from the public IPCC AR5 WGIII Scenario database 15  , using results on GDP impacts for 450 ppm scenarios  16  , and find that costs are of comparable magnitude, equal to 1. 6% (0. 7%-6%) of reference GDP. Further, in accordance with the IPCC methods of presenting mitigation costs (IPCC 2014), we compare private consumption losses, reported by AIM/CGE, GEM-E3-ICCS, IMACLIM and MESSAGE-GLOBIOM models. We find that the scale of impacts is similar to that of GDP costs, namely a loss of 1. 2% (0. 4%-2. 5%) of reference consumption in our 2 • C scenario and a loss of 1.
How might this impact on clinical practice? ► Public policy has a tremendous effect on the profession and practice of medical oncology. National health systems are created through public health policies. The way that health systems are structured, the services that they offer, the competencies and training requirements of their workforce, the quality standards of their treatment of patients, and the robustness of cancer registry data are all public policy issues aimed at improving patient outcomes. The global policy recommendations set out in this paper can harmonise efforts across the globe to address critical issues in cancer management with sustainable solutions. The ESMO Leaders Generation Programme was launched in 2016 and gathered outstanding early career oncologists from all over Europe.
Acknowledgements The 2016 ESMO Leaders Generation Programme participants would like to acknowledge the valuable contribution and support to this paper by the  WHO  staff, the  ESMO Leadership , the  ESMO Global Policy Committee , the  ESMO Public Policy Steering Committee  and to  Nicola Latino  for reviewing the paper. The authors also thank  Prof Christoph Zielinski  who founded the  ESMO Leaders Generation Programme , and  Lone Kristoffersen  and  Katharine Fumasoli  for organising the programme.
One of the decisive advantages of using patents as sources of data stems from the fact that exclusive commercial rights to an invention are granted to applicants provided that they publish a complete description of the patented invention allowing it to be replicated by others once the exclusive rights expire. In order to assess innovativeness, patent offices map the claimed novel features of each invention to the technological fields it impacts through a standard classification system and collect the above information into dedicated databases. This leads both to an extensive coverage of the innovation spectrum and a high degree of standardization that allows large scale analysis. The rest of paper is organized as follows. Section 2 defines the technological network based on patent classification codes; Section 3 summarizes the features of autocatalytic networks; Section 4 reports the results of our study of the autocatalytic structure of the technology network; Section 5 concludes.
Connecting regions and technological fields Our analysis relies on the patent data contained in PATSTAT  [28] , a comprehensive database collecting information about applications filed at national and regional patent offices all around the world. PATSTAT contains several tables linking over fifty million patent applications to information such as the filing date of applications, the patent families 2  they belong to, and their technological content as encoded by the International Patent Classification (IPC) codes attributed to the patent claims by patent office examiners. IPC codes define a hierarchical classification consisting of five levels (sections, classes, subclasses, groups, subgroups), which includes 8 codes at the coarsest level (sections) and over 70 thousand codes at the bottom of the classification tree.
We associate patents to the location of their assignees through Orbis, a commercial database of firm level data maintained by the Bureau van Dijk, which collects the list of patent PATSTAT identifiers of the applications filed by companies that have been active in patenting at some point in time. We match the technological codes associated to patent families with the firm-level data so to unambiguously localize firms geographically through their country of residence and their postal code. This allows us to construct a geographical matrix attributing IPC codes to regions through the patent portfolios and the geographical locations of the companies  3. We observe different regions worldwide across time to uncover the effects that innovation in a field produces on other fields within the same region.
For the purpose of the analysis, we need to transform each W(t) into a presence-absence matrix. In line with the literature, we assign a value of 1 to a location-technology pair if the value within the corresponding cell is compatible with a measure of revealed advantage. This allows us to reduce noise and avoid overstating the relevance of technological fields in those regions in which they play only a marginal role. In particular, we use revealed comparative advantage  [29]  to produce a matrix M(t) in which M r,i (t) is recorded as a presence (M r,i (t) = 1) if in the corresponding W(t) we have that W r,i i W r,i > r W r,i r,i W r,i , (1) and an absence (M r,i (t) = 0) otherwise. Due to time lags between patent filing and data publication by patent offices, the version of PATSTAT we employ (2014a) contains reliable data up to 2011, after which coverage falls sharply.
Autocatalytic structure and database hierarchy In this section, we turn to the hierarchical structure of the database to investigates its role in the autocatalytic structure of the technology network. As mentioned in Section 2. 1, PATSTAT   adopts the International Patents Classification (IPC)  8  , which has a tree-like structure consisting of eight sections at the root, which branch out into the progressively finer-grained classes, subclasses, and so on. For the present analysis, we use classes and subclasses 9  as nodes of the technology network, but we are also interested in understanding how the hierarchy induced by the IPC maps to the ACS. To this end, we employ the IPC sections • HUMAN NECESSITIES (A) • PERFORMING OPERATIONS; TRANSPORTING (B) • CHEMISTRY; METALLURGY (C) • TEXTILES; PAPER (D) • FIXED CONSTRUCTIONS (E) • MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING (F) • PHYSICS (G) • ELECTRICITY (H) to cluster the nodes and see how sections map onto the structure of the technology network.
Conclusions This study is a first step to uncover the cumulative causation processes driving technological change by detecting an autocatalytic structure of patent databases. Our results can be summarized in three main points. First, the technology landscape described by the network of patent technology codes is characterized by a clear autocatalytic structure that has grown over the years to encompass most technology classes. Second, the classes that are involved in the autocatalytic set perform better in terms of innovativeness as measured by the rate of growth in the number of patents containing them. Finally, the autocatalytic structure of the technology network is more evident and stronger than the hierarchical structure of the database, in that as many links connect classes from different sections as they do classes from the same section.
Data collection The PNEUMOCOST study was an observational multicentre study, the primary goal of which was to evaluate the costs of hospital stays and follow-up over a 12 month period in patients with documented CAPP in France. Informed consent of patients was required, and the Consultative Committee for the Treatment of Information in Health Research (CCTIRS) and the French Data Protection Authority (CNIL) approved the study. Overall, 41 medical units in 38 public hospitals around France included patients. A total of 524 adult patients aged 18 years and older who were hospitalized for a confirmed CAPP were enrolled prospectively. Given the patterns of missing values for the utility index, a final sample of 269 individuals was used for the descriptive and econometric analyses. Pneumonia was confirmed at admission by X-ray and streptococcus pneumoniae confirmed with microbiological sampling.
Patient data were: age, sex, diagnostic criteria for Pneumococcal CAPP, microbiological identification of S. pneumoniae, employment status, background and lifestyle, risk factors, vaccination profile, Charlson score  [11] , Port score  [12]. The Charlson comorbidity index is based on the identification of the number of comorbidities of patients, using the International Classification of Diseases. The Port Score is the standard Pneumonia severity index used to predict mortality and morbidity in patients with pneumonia. It combines both comorbid conditions and clinical and biological parameters recorded during the inpatient stay. Parameters were collected during the hospital stay with an electronic Case Report Form (e-CRF). Investigators validated each questionnaire once it was completed. Ex post validation of completeness was counterchecked by the partner CRO.
Data relative to the hospital stay itself were mode of admission, admissions in intensive care and/or other intermediate and step down care units, length of stay and billing data for the cost analysis, and were documented ex post using the mandatory discharge abstract system for French hospitals. After hospital discharge, the partner CRO called patients to carry out the survey on resource use and costs, as well as quality of life during follow-up. Health Related Quality of life (HrQoL) was measured using the three-level EQ-5D questionnaire, for which a French value set was available. The survey was carried out by phone interviews at Month 1, 3, 6 and 12 after discharge. The EQ-5D states were converted into utility values using standard French tariffs  [13]. The severity of the initial CAPP episode was measured using the Port score and information on comorbidities captured with the Charlson index.
For those who survived over a year, utility information was fully available for 256 of them, leading to a final sample size of 269 individuals (48% of the patients enrolled). Table  1  provides with descriptive statistics for the sample that was selected for the econometric analysis as well as for those excluded because of incomplete data on health states and utility. It confirms that individuals with at least one missing value for utilities are significantly different from people that responded to all phone calls during follow-up. Patients in the analysis sample (n = 269) were more likely to be women (p = 0. 056), to consume alcohol (p = 0. 025), had a lower probability of having the worst level of the Port index (p < 0. 001) or the worst level of the Charlson index (p = 0. 03). Moreover, concerning hospital stays, the sample used in the econometric analysis showed shorter ALOS (p = 0.
Since 84. 5% were admitted through the emergency room before being transferred to another ward, eligible patients were transferred directly from ER to ICUs and lost for inclusion, since it was impossible for an unknown percentage of very severe patients to collect informed consent at admission and to record initial clinical observations Moreover, patients were lost to follow-up and thus their vital status was unknown. For the same reason, investigators could not collect QoL data at admission, and inpatient QoL was not recorded. Data at discharge, or shortly after discharge was not recorded either, to minimize both investigator and patient load. Further research on the relationship between variations in health utility measures and medical expenditure could be an interesting subject for future analyses, and thus contribute to a better understanding of medical determinants of quality of life in patients with acute conditions such as pneumonia.
Conclusion This study provides robust estimations of QoL and utility data for patients hospitalized for an acute episode of pneumococcal CAPP. Our results suggest that the impact of an acute episode of CAPP on QoL will last 6 months for most patients, but that a small number of patients, especially the eldest, may not recover fully from the initial episode. Since our analysis was focused on patients with full utility data, the 6 months to recovery applies particularly to patients with lower severity presentations. Not surprisingly, age, severity of the initial episode, previous history of pneumonia, history of smoking and active smoking are major determinants of QoL in CAPP patients and should deserve special attention after discharge. Since we collected data on patient and disease characteristics, our data can be used to document cost-effectiveness studies related to the prevention or treatment of pneumonia, while adjusting for different patient profiles.
Acknowledgements This project is the fruit of the generous and constructive contribution of all the researchers in the hospital centers below:  Dr. Chantal Belleguic ,  Dr. Stéphane Jouneau , Hôpital Pontchaillou; Rennes,  Dr. Daniel Benhamou ,  Hôpital Bois-Guillaume ,  CHU Rouen ;  Pr François Bricaire ,  Dr. Loïc Epelboin ,  Hôpital de la Pitié-Salpêtrière Paris ;  Pr Christian Chidiac ,  Dr. Joseph Koffi ,.
Introduction Nowadays huge time series can be considered due to the availability of effective low-cost sensors, the wide deployment of remote sensing systems, internet based measure networks, etc. However, collected data are often incomplete for various reasons such as sensor errors, transmission problems, incorrect measurements, bad weather conditions (outdoor sensors), for manual maintenance, etc. This is particularly the case for marine samples  [1]  that we consider in this paper. For example, the MAREL-Carnot database characterizes sea water in the eastern English Channel, in France  [2]. The data contain nineteen time series that are measured by sensors every 20 minutes as nitrate, fluorescence, phosphate, pH, and so on. The analysis of these data with remarkable size and shape allows sea biologists to reveal events such as algal blooms, understand phytoplankton processes  [3]  in detail, or detect sea pollution and so on.
But the data have a lot of missing values: 62. 2% for phosphate,  59. 9% for nitrate, 27. 22% for pH, etc. , and the size of missing data varies from one-third hour to several months. Most proposed models for multivariate time series analysis often have difficulties processing incomplete datasets, despite their powerful techniques. They usually require complete data. Then the question is how can missing values be dealt with? Ignoring or deleting is a simple way to solve this drawback. But serious problems regularly arise when applying this solution. This is prominent in time series data where the considered values depend on the previous ones. Furthermore, an analysis based on the systematic differences between observed and unobserved data leads to biased and unreliable results  [4]. Thus, it is important to propose a new technique to estimate the missing values.
The imputation technique is a conventional method to handle incompleteness problems  [5]. Considering imputation methods for multivariate time series, taking advantage of the correlations between variables is commonly applied to predict lacking data  [6] [7] [8] [9] [10] [11]. This means that relations permit using the values of available features to estimate the missing values of other features. However, considering multivariate datasets having low/noncorrelations (for instance the MAREL-Carnot dataset), the observed values of full variables cannot be utilized to complete attributes containing missing values. To handle missing data in this case, we must employ the observed values of the unique variable with the missing data to compute the incomplete values. Therefore the proposed method has to manage the high level of uncertainty of this kind of signal.
Methods Based on Fuzzy Similarity Measure. Indeed similarity-based approaches are a promising tool for time series analysis. However, many of these techniques rely on parameter tuning, and they may have shortcomings due to dependencies between variables. The objective of this study is to fill large missing values in uncorrelated multivariate time series. Thus, we have to deal with a high level of uncertainty. Mikalsen et al. [39]  proposed using GMM (Gaussian mixture models) and cluster kernel to deal with uncertainty. Their method needs ensemble learning with numerous learning datasets that are not available in our case at the moment (marine data). So we have chosen to model this global uncertainty using fuzzy sets (FS) introduced by Zadeh  [40]. These techniques consider that measurements have inherent vagueness rather than randomness.
Further, an important point of our approach is that each incomplete signal is processed as two separated time series, one time series before the considered gap and one time series after this gap. This allows increasing the search space for similar values. Moreover, applying the proposed process (one by one), FSMUMI makes it possible to handle the problem of wholly missing variables (missing data at the same index in the all variables). The proposed model is described in Algorithm 1 and is mainly divided into three phases: (i) The first phase: Building queries (cf. 1 in Figure  2 ) For each incomplete signal and each -gap, two referenced databases are extracted from the original time series and two query windows are built to retrieve similar windows. The data before the gap (noted ) and the data after this gap (denoted ) are considered as two separated time series.
We noted  is the subsequence before the gap and  is the respective subsequence after the gap. These query windows have the same size  as the gap. (ii) The second phase: Finding the most similar windows (cf. 2 and 3 in Figure  2 ) For the  database, we build sliding reference windows (noted ) of size. From these  windows, we retrieve the most similar window () to the  query using the new similarity measure  as previously defined in Section 3. 1. Details are in the following: We first find the threshold, which allows considering two windows to be similar. For each increment  ℎℎ, we compute a  similarity measure between a sliding window  and the query. The ℎℎ is the maximum value obtained from the all  calculated (Step a: in Algorithm 1). We then find the most similar window to the query. For each increment similar window   , a  of a  sliding reference and the query  is estimated.