dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar trends were found in German and UK markets It is well known that some of the recent financial crises were global in nature, affecting other major economies of the world. Therefore, it is natural to ask whether same trends hold true in other major stock markets. We chose two major European markets: the DAX (Deutscher Aktienindex or German stock index), a stock market consisting of 30 major German companies trading in Frankfurt Stock Exchange and the FTSE (the Financial Times Stock Exchange 100 Index) which is a major stock market listed on the London Stock Exchange. We analysed these market data prior to crashes of 2000 and 2008. In both of these markets, there was no critical slowing down prior to either of the crashes (Figs L and M in S1 File). In addition, like in other markets, we found strong trends of increasing variance and power spectrum at low frequencies.
Our ongoing analytical and simulation work suggests that such the Shannon entropic measure is related to time series variance. Therefore, their results are broadly consistent with our analysis. However, another study claims evidence for critical slowing down prior to 2008 crash in based on housing market data  [49]. It remains unclear whether those results are supported by robust parameter scans. The aggregate market data, such as DJI, comprise of a large number of firms. Two related questions arise in the context of the relationship between macro data and its constituents: (a) Do individual constituent firms of these major markets also show similar trends? (b) What is the structure of relationship between firms, e. g. measured by cross-correlations, prior to crashes? We conducted a preliminary analysis of high frequency data of each of the 30 constituent firms of Dow Jones Index prior to 2008 crash.
Methods B: Time series analysis Data prior to crash We downloaded freely available stock market data for Dow Jones Index (DJI), S&P 500 and NASDAQ. The data correspond to daily close values of all market days. We purchased historical high frequency data (one minute) from Pi Trading company. To investigate whether stock market crashes exhibited early warning signals, we selected four major crashes, namely the 1929 crash (DJI only), the 1987 crash, the 2000 crash, and the most recent 2008 crash. Well documented crash dates correspond to 24/10/1929, 19/10/1987, 10/03/2000 and 01/10/2008, respectively. To compute the indicators, we used 1000 days (roughly corresponding to four years) prior to the crash. More specifically, we first found the date in the crash year on which the stock index was maximum; for DJI these dates were 03/09/1929, 25/08/1987, 14/01/2000 and 02/05/2008, respectively and they were all at least a month before the corresponding crash dates.
This method ensured that we were discarding data points too close to the crash that could potentially bias estimates of indicators. Moreover, if indicators were to provide a signal by this date of maximum index, one can make a stronger claim about the usefulness of early warning signals. All analyses closely follow methods described in  Dakos et al 2008 Dakos et al , 2012 [28, 29] [28, 29]. Codes are based in open source statistical software package R and are available for download from the supplementary materials and at the Github repository (https://github. com/ tee-lab/indicators-financial-meltdowns).
The following three methods are employed to address this question. In the first method, we generated time series that has the same Fourier spectrum and the amplitude as the original data. By original data, we mean the residuals of the detrended stock market index of length four years prior to the crash. In the second method, we fitted the original data set to an AR(1) model. We then generated time series from the fitted AR(1) model that also preserves the mean and the variance of the original data. In the third method, we bootstrap the original data by a random resampling with replacement. This method too preserves the mean and variance of the original data. In each of the above three methods, we generated 1000 such surrogate time series and estimated the trends of indicators using Kendall-τ.
Acknowledgments This work was supported by  DBT-IISc partnership program  and a  DBT Ramalingaswamy fellowship  to VG. SR acknowledges support from  Whitaker Institute and J. E. Cairnes College of Business, Public Policy and Law, National University of Ireland, Galway. We acknowledge constructive comments from  Marten Scheffer ,  William Brock ,  Vasilis Dakos  and  Karthik Azhagesan  on the manuscript.
When the respondents were comfortable with the context, the enumerators asked them to choose an alternative based on their preferences. Subsequently, follow-up questions were asked to determine respondents' motivations especially, when respondents chose the SQ option in order to be able to identify the protestors. Third, the respondents' socioeconomic and demographic characteristics (i. e. place of residence, gender, age, number of household members, household monthly income, formal educational attainment, occupation, and agricultural working experience) and responses to general opinion questions were recorded. This information was necessary to assess the representativeness of the sample in NR and, crucially, to use these data as explanatory variables to investigate heterogeneity in preferences.
C. T. I. R. S. ) (CPP ID RCB: 2013-A00129-36); US IRB MPM-SAH Institutional Review Board, Clearwater Florida [IRB number 2013_005]). The study was registered at the ClinicalTrials. gov registration website (http://www. clinicaltrials. gov/ct2/show/NCT01768494) and the study protocol has been published previously  [16]  as well as the main analysis  [17].
Data collection and processing We included all consecutive medical ED patients with a history of an acute infection. Patients presenting to the surgical ward and patients <18 years of age were excluded. All procedures were carried out as part of standard patient care. In all patients, we recorded type of infection, collected clinical parameters, pertinent initial vital signs, and laboratory values. Urinary tract infections comprised cystitis, pyelonephritis, prostatitis. Gastrointestinal tract infections mostly comprised gastroenteritis, biliary tract infections, and pancreatitis. Erysipelas displayed the largest part of skin infections, followed by cellulitis. Socio-demographic data were available using routinely gathered information from the hospital electronic medical system used for coding of diagnosis-related groups (DRG) codes.
Acknowledgments We thank the emergency room ( Ulrich Buergi ,  Petra Tobias  and their team), medical clinic (nursing department:  Susanne Schirlo ) and central laboratory staff ( Martha Kaeslin ,  Renate Hunziker ), the  Study Nurses of the TRIAGE study  ( Katharina Regez ,  Ursula Schild ,  Merih Guglielmetti ,  Zeljka Caldara ), and the  IT department  ( Roger Wohler ,  Kurt Amstad ,  Ralph Dahnke ,  Sabine Storost ). The TRIAGE study group includes further members from the  University Department of Internal Medicine, Kantonsspital Aarau, Switzerland (Ulrich Buergi, MD ,  Petra Tobias , RN,  Eva Grolimund, MD ,  Ursula Schild , RN,  Zeljka Caldara , RN,  Katharina Regez , RN,  Martha Kaeslin ,  Ursina Minder , RN,  Renate Hunziker , RN,  Andriy Zhydkov, MD ,  Timo Kahles, MD ,  Krassen Nedeltchev, MD ,  Petra Schäfer-Keller, PhD ) the  Clinical trials unit University hospital Basel (Stefanie von Felten, PhD ), the  Institute of Nursing Science, University Basel , Switzerland.
Funding: Thermofisher provided an unrestricted research grant for this study. PS is supported by the  Swiss National Science Foundation (SNSF Professorship ,  PP00P3_150531 / 1 ). This study was also supported by the  Schweizerische Akademie der Medizinischen Wissenschaften (SAMW). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data variables and sources of data Aggregate numbers of HIV-infected people started on ART were obtained from global reports from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Data on the estimated total population living with HIV were also obtained from the UNAIDS website. 12 These data are generated annually using the Estimation and Projection Package and Spectrum software  13  from UNAIDS based on primary collected data from census reports, antenatal clinic surveillance, population-based surveys and programme data. Data on TB case notifications, stratified by category, type of new TB and HIV status, were obtained from the WHO website. 14 o calculate TB CNRs, national population figures for 2002 and 2012 were obtained from national census reports for the respective years. 6, 15 The population figures for the period from 2002 to 2012 were based on the annual average inter-census growth rate for 2002-2012 of 1.
Analysis and statistics Data were analysed descriptively, mainly by plotting line and bar graphs of the various indicators over time from 2000 to 2013. Due to changing ART eligibility criteria over the years, ART coverage was calculated by dividing the annual total number of HIV-infected persons started and retained on ART by the annual estimated number of PLHIV. Annual TB CNRs were calculated by dividing annual TB case notifications by annual national population figures to obtain case notifications per 100 000 population. Changes in TB notification rates between 2000 and 2013 were determined by comparing the highest and lowest annual TB CNR using tests for proportions in Stata version 13. 0 (StataCorp, College Station, TX, USA). Levels of significance were set at 5%.
Estimation For a Gaussian random field, maximum full likelihood (FL) requires the evaluation of the determinant and the inverse of the np × np covariance matrix, where n is the number of space-time points and p the number of variables. The computational cost being of the order of O (np) 3 for both operations, a maximum full likelihood is unfeasible for large datasets, and even for datasets of moderate size as soon as the number of variables increases. Composite likelihood (CL) methods  (Lindsay, 1988)  have been proposed to perform efficient estimation with less time-consuming computational steps, and to guarantee good asymptotic properties. Composite likelihoods are products of smaller likelihoods defined on certain subsets of data such as marginal or conditional events, which are easy to compute.
There are 10 years of data, assumed independent. We thus simulate a sample of size 10 from a Gaussian vector with 30 × 13 × 3 = 1170 components. The elements of the 1170 × 1170 matrix are given by the multivariate space-time Matérn covariance function with unit variance for all variables, i. e. C ij (h, u) = ρ ij α|u| 2a + 1 M h (α|u| 2a + 1) b/2 ; r ij , ν ij i, j = 1,. , p. (12) Exact simulations with Cholesky decomposition are feasible, and a total of 100 repetitions are simulated. The reasonable dimension of the matrices (1170 × 1170) allows to compare WPL inference with a usual likelihood approach, referred to as the Full Likelihood (FL) approach. Parameters have been chosen to match those estimated by an exploratory analysis of the spatial and temporal margins of the dataset (see Table  3 ).
Interestingly, it is better estimated using WPL than using FL. With WPL, the median is equal to the true value and it is close to the mean. The separability parameter is estimated to be non null, for all simulated datasets but one. To complete this comparison, we compute the relative efficiency of WPL with respect to FL. For each approach, and for each of the 15 parameters θ i , with i ≤ i ≤ 15, the root mean square error, rmse i = bs 2 i + sd 2 i , is computed, where the bias is bs i = θi -θ i , and the variance is sd 2 i = 100 j=1 ( θj,iθi ) 2 /100, with θi = 100 j=1 θj,i /100. For each variable, we denote rmse FL i and rmse WPL i the root mean square error corresponding respectively to FL and to WPL. The root relative efficiency, defined as rre i = rmse FL i /rmse WPL i , is reported for each variable in Table  4.
Western France weather dataset This section illustrates the use of the Gneiting-Matérn multivariate space-time model for the analysis of a weather dataset which consists of three daily variables (solar radiation, R, temperature, T, and humidity, H) recorded at 13 stations in Western France from 2003 to 2012 (see Figure  4 ). These data are part of the French National Institute for Agricultural Research (INRA) archive of weather data. The considered domain experiences an oceanic climate, characterized by moist and cool (but not cold) winters. Due to the prevailing westerly winds, some amount of space-time interaction is thus expected, from West to East. The validation stations (indicated with stars in Figure  4 ) have been selected in the Eastern part of the domain, neither too close, nor too far from the other stations.
The first validation q q q S-E NS-E S-V NS-V 0. 46 0. 48 0. 50 0. 52 Root Mean Squared Error q q q q q q q q q q q S-E NS-E S-V NS-V 0. 36 0. 38 0. 40 Mean Absolute Error q q q q q q q q S-E NS-E S-V NS-V 0. 26 0. 27 0. 28 0. 29 and NS-D. Same simulations as in Table  1. Estimates obtained with WPL. CRPS q q q q q q S-E NS-E S-V NS-V 0 station, Le Rheu, is located near Rennes, in Brittany. The second one, Bourran, is located near Agen, in Aquitaine. The other 11 stations are used to estimate the parameters. In order to restrict ourselves to data being stationary in time, we selected data recorded in January, from 2003 to 2012. q q q q q q q q q q q 100km France Figure  4 : Location of the 13 weather stations over western France. Stars: validation stations. To model the January data Y(s, t) = {Y R (s, t), Y T (s, t), Y H (s, t)} , we consider a model that is stationary in time and non-stationary in space, namely Y i (s, t) = µ i (s) + σ i (s)Z i (s, t) i ∈ {R, T, H} (s, t) ∈ R 2 × R.
It thereby confirms that weighted pairwise likelihood is a reliable alternative to full likelihood when analyzing large datasets. Our approach could be generalized in several ways. First, with similar arguments to those used in Theorems 1 and 2, we could propose a wider range of flexible multivariate space-time models, including compactly supported models, and models on manifolds such as spheres. One limit of our models is that there is a unique temporal covariance for all variables. By exchanging the role of time and space, one can easily build models that are flexible in time and unique in space. Building valid multivariate space-time models that are flexible both in time and space is still an open challenge. For the time being, the analysis of models requiring different temporal covariances for different variables can be carried out using a sum of models, as in Section 5.
Bevilacqua et al. (2012)e estimated variances of all parameters for several windows d = (d S , d T ). 100 synthetic datasets simulated according to model (12). The number in brackets corresponds to the percentage of pairs used in the computation of wpl. in kms), and temporal distance d T ∈ {2, 5, 10} (in days), the estimation of the parameters is performed on each simulated repetition. Then, empirical estimates of the estimation variances are computed. Their sum is an estimate of the criterion in Eq. (11), as recommended inBevilacqua et al. (2012). Results are reported in Table1. As the spatial distance increases,.
Acknowledgments Two anonymous referees are gratefully acknowledged. The authors wish to thank  Carlo Gaetan  from the  Ca' Foscari University in Venezia (Italy)  for fruitful discussions and  Liliane Bel  from  AgroParisTech in Paris (France ) for her very careful reading of earlier versions of this manuscript. This work was supported by the  metaprogramme Adaptation of Agriculture and Forests to Climate Change (AAFCC)  of the  French National Institute for Agricultural Research (INRA). Emilio Porcu  has been supported by Proyecto Fondecyt n. 1130647 from  Chilean Ministry of Education.
Dose recommendation for renally impaired patients In the examples from Lehr Ex103 and Kleijn Ex100, similar approaches were pursued to integrate a complex database into empirical PKPD models to predict dose regimens. In the former, severely renally impaired patients treated with a reduced dose were predicted to have exposure data largely within the concentration range proven to be safe and effective in patients with atrial fibrillation and creatinine clearance above 30 mL/min. In the Kleijn example, the interaction with sugammadex and the reversal of the neuromuscular effects of rocuronium in the pediatric and elderly population was modeled. The combined model provided evidence that age, but not renal function, influenced reversal time (most important PD parameter), but not to a clinically relevant extent).
The MID3 strategic plan should be fully integrated with the related ''general'' drug discovery and clinical development plan. The MID3 plan should also identify knowledge gaps that could be adequately filled by alternative sources (scientific literature, clinical practice database, precompetitive collaborations, etc. ), thereby avoiding or mitigating the need for ''new'' data generation. The MID3 strategic plan needs to consider the balance and purpose of any new data generated. On the one hand, there will be a need to generate sufficient empirical evidence derived from appropriate study designs, whereas, on the other hand, there will be a need for emerging data to enable a thorough evaluation of influential assumptions used to fill important knowledge gaps pertinent to MID3. Only fully integrated plans can serve to highlight the interdependence between experimental conditions, data generation, evidence base generation, and MID3.
For example, MID3 may provide the important justification for the development of particular biomarkers or may highlight the benefit of adaptive design to more effectively fill the knowledge gap. An important goal for these integrated plans is to consider the trade-offs in time and expense of additional data generation to facilitate MID3 vs. the potential downstream costs of relying on untested and potentially flawed assumptions or indeed vs. an overreliance on inferences obtained principally through direct experimentation. To that end, we propose that an MID3 strategic plan should be constructed through consideration of pertinent R&D questions that commonly arise with respect to some prominent themes, as illustrated in Table  1:  • Medical need/commercial viability -R&D questions related to the understanding of medical need and areas of potential differentiation from the standard of care for a particular disease/indication.
Although empirical models derive robust characterization and description of dose-efficacy-safety relationships (which can enable specific product labelling statements) at compound level, the more mechanistic approaches will be necessary to effectively incorporate mechanism and disease/ indication level data into a broader quantitative framework. The timing of these MID3 activities is very important as deliverables must be available to inform important R&D decisions. The MID3 strategic plan should be a ''living document'' that is updated after compound, mechanism, and disease/indication level decision points. After a decision to advance a compound, at least the compound-specific components of the MID3 plan should be refreshed in order to prepare for the next decision point. Accordingly, after a decision not to advance a compound, the disease and www.
Organizational level influence Challenges. MID3 aims to enhance the extraction of inference from both existing information and data emanating from ongoing experiments. MID3 is an integrative approach that can effectively support translation across, and extrapolation beyond, a given set of experimental conditions. An inherent consequence of this integrative approach is that the necessary source data, information, expertise will reside across a broad range of personnel, departments, and institutions. However, an unintended consequence can be the potential for ''narrower'' concerns emanating from infringements of actual or perceived ''control,'' ''ownership,'' ''territory,'' and ''domain'' diminishing the likelihood that the ''broader'' organization can derive the greatest benefit. Another ''integration'' challenge surrounds how well MID3 can fit within R&D planning, processes, and timelines that are designed to service the needs of study level activities.
For the potential for MID3 to be realized, the associated outputs need to be available for specific R&D decisions and be of a quality consistent with appropriate industry and regulatory standards. This can present a significant challenge for what are predominately iterative modeling approaches and can also curtail the choice of approach taken because of both data and time constraints. Opportunities. The desired technical methodologies for MID3 are established, and companies have colleagues with the appropriate skills to deliver them. However, most companies do not fully capitalize on this opportunity as a result of organizational and/or cultural impediments. Mitigating or removing these organizational impediments has been shown to improve late-stage clinical development productivity. 31, 33 n addition, the adoption of a more realistic assessment of the merits of any given compound can bring productivity dividends to the organization.
Activity level influence Challenges. Approaches that are precedented and conservative can be considered in some quarters to be the most robust and trustworthy. In line with good clinical practice and International Conference on Harmonisation guidelines, primary data analyses are defined in clinical study protocols and statistical analysis plans in advance of study initiation and conduct. Although this approach fulfils a requirement for both statistical rigor and integrity, which is of particular importance within a confirmatory setting, prespecification will often lead to the adoption of more straightforward ''assumption-light'' analysis methods. Within a confirmatory setting, MID3 approaches are often conducted as secondary or exploratory analyses after the primary analysis has been conducted and communicated, limiting the potential for MID3 approaches to effectively influence R&D decision-making and regulatory assessment.
Appropriate and acceptable level of documentation For MID3 activities performed to support internal decisionmaking (such as go/no-go decisions), the nature and extent of documentation will ultimately be determined by the particular company. However, in order to be consistent with our good practice recommendations, we consider the following to represent a minimum level of documentation: (1) a short analysis and/or simulation plan or a memo to gain stakeholders agreement on objectives, data plan, assumptions (including their evaluation and impact), and deliverables; and (2) a memo/abbreviated report or slide presentation documenting results. At some future point in time, MID3 activities performed to support internal decision-making may be used for related compounds or for interaction with regulators that will necessitate an ''upgrade'' to the available documentation Table  3  EFPIA categorization of MID3 value for internal decision-making LOW CATEGORY IMPACT -describe -MID3 approach provides inference which has limited impact on internal decisions MID3 inference is consistent with standard reporting methods in response to strategic questions.