dataesr/scientific-paragraphs-categorization huggingface dataset:
This result is consistent with the findings of  Sautner and Weber (2009) , who use survey data to assess the relation between individual characteristics and option exercise decisions and find no evidence that differences in diversification or riskaversion can explain the observed differences in exercise behavior. Furthermore, including the natural logarithm of the total value (in $ 0 000) of the CEO's portfolio of company stock and options at the beginning of the fiscal year as an additional control variable in the compensation regressions has no material effect on the estimation results. 23  Table  12  presents the results of these regressions. The coefficient estimates for both optimism measures LongHolder (Panel A) and HighForecast (Panel B) remain negative and statistically significant in the regressions pertaining to the CEO's option grants, bonus payments, and total compensation.
B. 2. Empirical evidence for a non-monotonic effect of CEO optimism As the previous example shows, the effect of optimism on compensation is not, in general, monotone if the agent's incentive compatibility constraint is not binding. In what follows, I thus investigate whether or not there is any evidence in the data for a non-monotonic relation between a CEO's optimism and his compensation. In particular, I examine whether or not there is evidence for a locally positive effect of optimism over a particular range of the measures LongHolder and HighForecast. That is, I investigate if for any level of optimism there is any evidence that slightly more optimistic CEOs receive higher compensation than slightly less optimistic CEOs. Overall, I do not find convincing evidence that this is the case.
Section Geometry Deterioration Time Distributions An analysis of the records for each 1/8 th mile section on the UK railway network has been carried out. The records contain two types of data. The first is the information recorded by the measurement train which passes along each section of the network at regular intervals and records the positions of the rails. This data is then processed to derive characteristics which represent the geometry quality and indicate when maintenance is required. The characteristic to best indicate the need to improve the geometry is the Standard Deviation of the average vertical alignment taken over a 35m wave-length. A second set of information indicates when maintenance (tamping) or renewal have been performed on each section. A typical plot of this data with the tamping history superimposed on the track quality data is shown in figure  2.
We are in a project driven by the inductor effect of COOPERFIC ®. The inductor tool of partnerships: It is about a strategic partnership between the FRCA LR and the Mediterranean Agronomic Institute of Montpellier. This partnership has also led other relationships with other organizations. The adoption of the COOPERFIC ® tool by six Regional Federations of Agricultural Cooperatives covering 12 administrative regions of France; basically the operation of translation has led the actors to become spokespersons of the tool in other regions. The COOPERFIC ® tool as a ground for the launch of innovative projects in relation to its environment: it is the induction of an electronic data exchange project (EDI). Territorial organizations need the tool: COOPERFIC ® has led to the creation of a "KIT study area" that is a part of the tool.
During his study of the bibliography of a theses sample (586 from Brazil and 580 from U. S. ) in the field related to technology research, Juliana Ravaschio found only 15% of theses that have patent citations  (Ravaschio, 2010). This result shows the lack on "patent" knowledge in the academic world. Lack in part, due to the use of different vocabularies and concepts, cleavage of communication. It was on this ground that the research "Automatic mapping of scientific and technical bibliographies databanks using the International Patent Classification (IPC): contribution to the rapprochement between sciences and technology" was developed by Pascal Faucompré. This research considers the IPC as a pivot system with keywords for indexing scientific databases, has set glances descriptions of academic research with an industrial description of these research  (Faucompré, 1997).
A patent granted is legally valid at a given period. Beyond this period, the patent becomes invalid. Wanise  Barroso (2003) , a patent examiner in the Patent Office in Brazil upon completion of her research, worked on the subject. She proposed the creation of a sub-base of this patent office, containing only "public domain patents" for distribution to SMEs in order to facilitate technology transfer. This was possible by querying the database on the field which informed the legal status of the patent. She showed that more than 30% of the Brazilian Patent Base became in public domain in 5-6 years and around 40% of the base was in public domain which may be used freely as technological support, since applications patent must be written to be able to reproduce what is claimed. If we assume on equal proportions in other available databases, would bring to 28 million, the number of available patents in full text and freely "reusable" in the world as "Technical and Technological Encyclopedia".
Patent Information as Knowledge Database Discovery To respond to this problematic: "how to find substances that interact with all the symptoms of a disease without anyone had made the relationship between these substances and disease?" Jean Dominique Pierret  (Pierret, 2006; Pierret & al. , 2010)  conducted research on "methodology and structure of a knowledge discovery tool based on the biomedical literature". This research led to filing four new patents based on new medical indications of molecules previously patented in order to reach the market. This proves that it is even possible to "invent" and "patent" by a recombination of existing knowledge on technical documentary barely developed. This kind of methodology could be generalized to materials, processes, etc. The experience of Jean Dominique Pierret is to our knowledge, the first creation of "patentable subject matter" starting from documentary research.
To go further in this field it will be necessary again to use the concept of "homo faber" to improve document interfaces to systematize this kind of queries. The methodology called "Diseases Physiopathology Molecules (DPM)" is part of methods grouped under the term "Knowledge Database Discovery (KDD)". This is indeed, weak signals which  Pierret (2006)   (Miguel & al. , 2010). The cost of this legal provisions exceed R $ 1. 4 billion (580 million €), with a number of pharmaceutical products distributed evolving from 15 in 1993 to 243 in 2007  (Carias & al. , 2011). Brazilian scientists were early interested in this (  2003 ), using Information Systems to find ways in order to contain costs  (Barroso & al. , 2004 ) by a joint team of Brazilian and French researchers. They were interested in the Tenofovir case and they filed an opposition on patent application in conjunction with a research team in India for the antiretroviral used in the treatment of AIDS  (Barroso & al.
It was set-in place by the Strasbourg Agreement concerning the International Patent Classification of March 24, 1971, amended September 28, 1979. IPC was developed in two "official languages: French and English", divides technology into eight sections with approximately 70,000 subdivisions described by symbols. This classification is useful to search on patent documents in the context of research on the "State of Art". A patent database, free and freely accessible online, allows to do research in this classification. Thesaurus allows browsing in the classification with 22798 English keywords and 25676 French keywords.
Methods The China SimSmoke tobacco policy model SimSmoke is a discrete-time first-order Markov process (state-transition) model of the prevalence of tobacco smoking and smoking related mortality that simulates the impact of tobacco control policies. The model begins in a baseline year, with the population divided into current, never, and former smokers by age and sex. The projected population is estimated using fertility and mortality data from the United Nations Population Division, Department of Economic and Social Affairs. 19 Smoking prevalence evolves with age and sex specific smoking initiation, cessation, and relapse rates. SimSmoke is programmed in Microsoft Excel (Office 2007 version; Microsoft, Redmond, WA, USA).
We used these data to apportion the former smokers to groups representing less than one year, one to two years, and two years or more since quitting. Owing to lack of data for two years or more since quitting, we used US survey percentages (for 3-5 years, 6-10 years, 11-15 years, and ≥15 years) to apportion the former smokers who had quit for two years or more. 22 oking rates evolve through age and sex specific initiation, cessation, and relapse rates. We assumed that cessation, initiation, and relapse rates depend only on current rates and not past behaviors (Markov assumption); that cessation and initiation rates are assumed constant over time, except when changes in policy occur; and that relapse rates are constant over time and unaffected by policies. Owing to empirical challenges in measuring initiation and cessation among younger smokers and to ensure stability of the model, we measured initiation rates at each age as the difference between the smoking rate at that age year and the rate at the previous age year, determined by year at which smoking rates begin to level off.
Because no similar data were provided in the global adult tobacco survey, we relied on the 1996 survey data for initiation and cessation rates. Based on an examination of prevalence data for 1996 and ensuing years, and 1996 data in the 1997 report showing stated ages of initiation, 2 prevalence rates increased until a specific age. Initiation in the model occurred until age 30 years for men and age 35 years for women. Cessation rates were based on published data in the 1997 report. Based on an examination of 1996 data on smokers who quit in the past year,  2  we set annual cessation rates in males equal to 2% for smokers aged 30-65 years and 3% for those ages 65 years or more. We set the cessation rates in females to 2%. We tracked cessation from age 30 years for males and age 35 years for females.
During the past 20 years, China's urban population increased from 18% to 46% of the total population. 31 A second factor is a population's baseline level of awareness of tobacco related harms. In countries with lower levels of income and general health awareness and no previous serious tobacco control campaigns, a lower base level of the knowledge of the dangers of smoking and of antismoking attitudes is expected to provide increased potential for the effectiveness of policies. The SimSmoke China base case (1996-2011) incorporated Chinese policies over the time period on tax policy, legislation on smoke-free environments, mass media anti-tobacco campaigns, bans on tobacco marketing, health warnings, cessation of treatment for tobacco dependence, and restrictions on access for youth. China SimSmoke simulated Monitor, Protect, Offer, Warn, Enforce, and Raise (MPOWER) measures, 32 a package of practical steps toward implementation of the FCTC, starting in 2012 and continuing through to 2050 (the last year projected by the model).
The aim of this contribution is to conceptualize a generic model (a site specific viticultural activity) including strategies of production (modeling by decision trees) to reproduce the impact of spatial and temporal variability of climatic conditions on grapevine growth and berry maturity using an approach that combines technologies derived from GIS, agent-based models and spatial and temporal databases. The objective is to specify a systemic and integrated modeling environment for simulating grapevine growth and berry ripening under different conditions and constraints (slope, aspect, soil type, climate variability. ) and to integrate production strategies and corresponding adaptation rules according to the evolution of these constraints.
Modeling sequence In order to relate vine management to environmental constraints, a significant knowledge base informing all biophysical and agronomic parameters of the vine as been developed. This database allows simulating the growth cycle of the vine by taking into account climate variability and agronomic practices. In order to model the growth of the vine simple algorithms based on bioclimatic indices are used. Theses indices allows to study plant response to climate variability according to grape variety  (Morlat, 2010; Tonietto 2004; Huglin, 1986). Temperature data are also used to calculate Huglin solar thermal index  (Huglin, 1978) , which is used by the 01015-p. 2 The value of the Index Huglin for a station is the cumulative value of the index at the date of September 30, the accumulation start since April 1 (northern hemisphere to the southern hemisphere, index is calculated for the period from September 30 to April 1) using the following formula: The architecture of the model is to formalize a relationship between vine growth cycle, grape variety and production method used.
Simulation The purpose of the first tests carried out in the Coteaux du Layon region (France) is to assess the capacity of grapevine growers to adapt to the considerable rise in temperature and produce sweet wine  (Bonnefoy et al. , 2009). Three production profiles were identified: conventional (traditional vine-growing), integrated (plant protection product and fertiliser restrictions), organic (precise specifications severely limiting the use of plant protection products). An extensive survey of grapevine growers helped associate calendars of specific practices with each production profile. These calendars facilitated the development of decision trees under agroclimatic constraints. Temperature and precipitation data were used to define which days were agronomically feasible for carrying out vineyard practices.
T he International Union Against Tuberculosis and Lung Disease (The Union), Médecins sans Frontières (MSF) and the Special Programme for Research and Training in Tropical Diseases (TDR) hosted at the World Health Organization, have joined forces under a programme called the Structured Operational Research and Training Initiative (SORT IT) to improve health care delivery and public health through operational research. This is done through integrated operational research and training programmes, in which participants from low-and middle-income countries are enrolled and taken through a research project from protocol development to implementation, data collection and analysis to the writing and submission of a paper to a peer-reviewed journal. 1  Between April 2009 and January 2014, we have undertaken 18 programmes (of which 14 are completed), enrolling a total of 212 participants.
Acknowledgements The authors gratefully acknowledge support from the  TCR conference Lille  for the two days of intense discussions. 2006 Marketing and  Public Policy Conference Proceedings ,  Ingrid M. Martin ,  David W. Stewart , and  Michael Kamins , eds. Chicago, IL: American Marketing Association,  54-62. World Bank  ( 2011 ), The World Development Report 2011: Conflict, Security, and  Development. Washington , DC: The  World Bank Group. World Bank  ( 2010 ), '' Poverty & Equity: Sub-Saharan Africa ,'' (accessed May 30, 2014), [available at http://povertydata. worldbank. org/poverty/region/SSA].
Dynamic process modelling is generally accomplished from experts' knowledge through Knowledge Engineering (KE); however, the obtained models are sometimes deficient for interpreting the input data flow coming from the real process evolution perceived through sensors. This shortcoming lies in specialists' tacit knowledge, difficult to elicit, and in that certain process phenomena are unknown or unforeseen to experts. An alternative to complement the modelling task is to resort to a Knowledge Discovery in Database (KDD) process. Nevertheless, most KE approaches do not address the processing of knowledge obtained from data. This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts' knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.
The problem originates principally in two issues. A first point is the expert's tacit knowledge, inherent in a human being, and the knowledge engineer's subjective interpretation; both central to KE. Another question is that certain properties, as for example those that are temporal, taking place during the process could be unknown or unforeseen by the expert. An interesting alternative to deal with these problems is to resort to processes of Knowledge Discovery in Database (KDD), which use data mining techniques in order to acquire knowledge from data when obtaining a model of these ones. This model allows complementing experts' knowledge in order to build a more suitable process model. In an ideal world, the data model could be collated with the experts' model, and even it might extend the latter, in order to define a model that better reflects the process "reality".
Different representation formalisms Given a process about which an expert has knowledge, a model M e of this process can be constructed from expert knowledge by applying KE techniques, as Fig. 1  illustrates. In turn, the process can be observed by a program which records data in a database describing its evolution over time. Thus, these data can be analysed by applying data mining techniques in a KDD process in order to obtain a model M d of the process. Ideally, if it were possible to compare easily M e and M d , both models could be combined in order to obtain a more precise process model M PR. However, relating both models presents a first difficulty which lies in the different natures of the representation formalisms used in KE and KDD. Diverse KE methodologies and tools such as Roles-Limiting Methods and Generic Tasks  [23] , and later, CommonKADS  [1, 2] , Protégé  [3] , MIKE  [4, 5] , KAMET II  [6, 7] , VITAL  [8] , etc.
which are present in a data model obtained from sensors. Thus, the Fig. 1. Building a process model from two knowledge sources. abstraction level of the expert's knowledge model is higher than that one of the data model. Therefore, the data do not have direct significance in the expert's knowledge model and the expert's concepts are meaningless in the database. Owing to this, sometimes analysing the relation between possible models obtained from these two sources is not an alternative. Consequently, a translation from data into expert's vocabulary is indispensable, but at the same time carrying out this task can be extremely complex. Our proposal aspires then to reduce the distance between experts' knowledge and data by proposing an approach which allows relating the aforesaid different abstraction levels.
Related work As established in  [21] , although creating knowledge is the central focus of both KE and KDD, knowledge creation has tended to be approached from one or the other perspective, rather than from a combined perspective. In particular, KE approaches do not address the interpretation of data models obtained through data mining techniques; and consequently, building dynamic process models from combining knowledge obtained from data with that obtained from experts is difficult. In the KE domain, some of the well-known methodologies and tools for building KBS are CommonKADS  [2, 1] , MIKE  [4, 5]  and Protégé  [3]. CommonKADS proposes a structured approach which consists in the creation of a collection of models that capture different aspects of the system to be developed, MIKE is an approach which puts emphasis on a formal and executable specification of the Expert Model.
KEMNAD  [25] , in turn, is a methodology which uses CommonKADS in the development of negotiation agents and it establishes a pattern library for the design of these agents. In  [26]  a methodological approach for the mapping of knowledge models in production rules is presented, this approach combines CommonKADS Knowledge Models with a production rule representation language. CommonKADS has also been applied to the health area; in particular  [27, 28]  resort to this methodology. The former presents the development of a knowledge-based system for diagnosing breast cancer while the latter describes a knowledge-based approach to supervised classifier design emphasising in Alzheimer diagnosis. A general model for representing temporal information through causal temporal constraint networks is presented in  [29].
In this proposal, CommonKADS is used for temporally correlating the model symbolic information in order to represent temporal knowledge. HeKatE (Hybrid Knowledge Engineering)  [30] , for its part, is a methodology for developing and designing complex rule-based systems for control and decision support. Although diverse are the KE approaches, none of them addresses the processing of knowledge obtained from data by means of a KDD process; and consequently, the gap between expert models and data models is generally wide. The objective of our proposal is precisely to propose a KE approach which contributes in this sense to bring both disciplines in order to build more suitable process models.
Observed process TOT defines a dynamic process as an arbitrarily constituted set X(t) = {x 1 (t), x 2 (t), …, x n (t)} of n functions x i (t) of continuous time t ∈ ℜ. This set implicitly defines a set X = {x 1 , x 2 , …, x n } of n variable names x i. According to this theory, a dynamic process X(t) is monitored by an abstract observer program denoted by Θ(X, Δ) which observes the functions x i (t) ∈ X(t); and then, it describes their evolution over time with a finite set Δ = {δ j } j = 1,…,m of constants δ j (i. e. a number or a string) by registering timed messages (alarms, warnings, etc. ) in a database. The program Θ(X, Δ) is considered an abstract observer because its implementation can be carried out by either a standard computer (i. e. , a monitoring program) or a human (i. e. , an expert) providing and recording the timed messages in a database.
Definition 1. An observed process is a couple (X(t), Θ(X, Δ)) where X(t) = {x i (t)} i = 1,…,n is a finite set of time functions, X = {x i } i = 1,…,n is the corresponding finite set of variable names, Δ = {δ j } j = 1,…,m is a finite set of constant values and Θ(X, Δ) is a program observing the evolution of the functions of X(t). TOT considers a message δ i at time t k in a database as a timed observation (δ i , t k ) where δ i ∈ Δ is a constant value and t k is the moment at which the observation is considered to occur. The timed observation is written by the abstract observer program Θ(X, Δ) when a function x i (t) of continuous time enters a specific interval of values. That is, the specification of the program determines that x i t k-1 ð Þb Ψ j ∧x i t k ð Þ≥Ψ j ⇒write δ i ; t k ð Þ ð Þ ð1Þ In general terms, the program registers a timed observation (δ i , t k ) in a memory whenever a particular predicate θ(x θ , δ θ , t θ ) is satisfied; in this case, the mentioned predicate is x i (t k -1 ) b Ψ j ∧ x i (t k ) ≥ Ψ j.