dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 5  illustrates a function x i (t), where values above Ψ j are interpreted as δ i by an observer program Θ({x i }, {δ i }); that is, when x i (t) enters [Ψ j , +∞). For example, considering timed data in a database of the form "yymmddhhmmss, message" where yymmddhhmmss is a time stamp and message is a value determined by a monitoring program Θ(X,Δ); a message "080313132225, high" will be considered a timed observation (δ i , t k ) where t k = 080313132225 and δ i = high; that is to say, (δ i , t k ) = (high, 080313132225). In particular, the observer program Θ({x i }, {high}) has written this observation indicating that the function x i (t) entered a particular interval at time 080313132225; e. g. , values above Ψ j are interpreted by the observer program as a high level of x i (t). TOT establishes that the existence of a timed observation (δ i , t k ) in a database allows to infer that the mentioned observation has been recorded by a program Θ({x i }, {δ i }), perhaps unknown, which implements the abstract logical sentence described in Expr.
(2) , for a set Γ of arbitrary time instants. ∀t k ∈Γ; θ x i ; δ i ; t k ð Þ ∈Θ⇒ δ i ; t k ð Þ∈Ω ð2Þ This expression associates the set Θ of all the assignations to a ternary predicate θ(x θ , δ θ , t θ ) with the set Ω of all the timed observations carried out by the program Θ({x i }, {δ i }) (i. e. , the database). A timed observation (δ i , t k ) is then interpreted as the Fig. 5. Spatial segmentation of the x i (t) function of time. The symbol ≜ in the figure denotes rewriting or "correspond to" establishing that a timed observation (δ i , t k ) can also be written like o(t k ). logical consequence of the assignation of the values x i , δ i and t k to the mentioned ternary predicate; that is, θ(x i , δ i , t k ). This means that the timed observation (δ i , t k ) was recorded when the program Θ({x i }, {δ i }) assigned the values x i , δ i and t k to the predicate θ(x θ , δ θ , t θ ).
Given the sentences (1) and (2), the general meaning "is" can be always provided to the predicate θ so that the timed observation (δ i , t k ) is interpreted as "at time t k , x i is δ i ". Considering that x i is associated with a function x i (t), the meaning "equal" can also be attributed to the predicate θ, which leads to the following abuse of language: θ(x i , δ i , t k ) means Equal(x i , δ i , t k ); i. e. , x i (t k ) = δ i. Thus, this theory considers that a message contained in a database is a timed observation (δ i , t k ) written by a program Θ(X, Δ) which observes a time function x i (t) and implements the abstract Expr. (2). In our example, (high, t k ) indicates that a program Θ({x i }, {high}), observing a time function x i (t) and defining implicitly a predicate θ(x θ , δ θ , t θ ), has considered θ(x i , high, t k ) true and then it has written the timed observation (high, t k ) in the database Ω.
Timed observation and observation class According to the concepts previously introduced, the interpretation of a timed observation (δ i , t k ) is precisely the assigned predicate θ(x i , δ i , t k ). However, it is noteworthy that the program Θ({x i }, {δ i }) might contain errors and a timed observation (δ i , t k ) could have been written in a database although the assertion θ(x i , δ i , t k ) is not really true. The notion of timed observation is established in Definition 2 along with other concepts. Definition 2. Let X(t) = {x i (t)} i = 1 …n be a finite set of time functions; let X = {x i } i = 1 …n be the corresponding finite set of variable names; let Δ = {δ j } j = 1 …m be a finite set of constant values; let Θ(X, Δ) be a program observing the evolution of the functions of X(t); let Г = {t k | t k ∈ ℜ} be a set of arbitrary time instants (i.
e. a stochastic clock); and let θ(x θ , δ θ , t θ ) be a predicate implicitly determined by Θ(X, Δ). Then, • A timed observation (δ j , t k ) ∈ Δ × Г on x i (t) is the assignation of values x i , δ j and t k to the predicate θ(x θ , δ θ , t θ ) such that θ(x i , δ j , t k ) (by definition, o(t k ) also denotes a timed observation, i. e. , o(t k ) ≜ (δ, t k )) and • A scenario is a set Ω of time-ordered sequences ω l of timed observations; that is, ω l :{1, …, r} → Δ × Г | r ∈ ℕ ∧ ∀ i, j ∈ {1, …, r}, i ≤ j, (ω(i) = o(t k ) ∧ ω(j) = o(t r ) ⇒ t k ≤ t r ) and Ω = {ω l } l = 1,…,q constitutes the set of all timed observations; i. e. , the database. Timed observations on a particular function x i (t) implicitly determine a variable x i which assumes discrete values δ j and describes the evolution of x i (t) according to the interpretation implemented in the observer program.
Knowledge interpretation Fig. 10  illustrates nine rules of domain knowledge provided by an expert; e. g. , the rule (R 1 ) establishes that if fuse is blown, fuse inspection is broken. From the analysis carried out through CommonKADS, the knowledge pieces are classified as observable features, and as visible and invisible aspects of an internal state. In addition, two types of relations between the knowledge elements are established as the figure depicts. More details about the mentioned analysis and the rule specification in Conceptual Modelling Language  [2]  can be found in Appendix A. 1. Considering the interpretation carried out through CommonKADS and the TOM4D principles introduced in Section 6, the next objective is to define a scenario model M(Ω) = bSM(Ω), FM(Ω), BM(Ω)N from the given knowledge and a set Ω of sequences of timed observations (see Definition 2) which describe certain modes of functioning of the car.
In particular, 2000 pages make up the linguistic model formulated from experts' knowledge about 175 blast furnace phenomena, where the occurrences of these phenomena are detected through applying the spatial segmentation principle depicted in Fig. 5. The real time observation of 500 high-level time functions f i (t) accounts for the behaviour of the blast furnace. The values of these functions are computed from the values of 2850 intermediate-level time functions where these latter are calculated, in turn, from real time values obtained from 1200 concrete sensors  [57]. The sensors provide raw data measured directly on the blast furnace or manually entered in the plant information system. Sachem System has been designed according to the CommonKADS methodology. The template of the Sachem problem solving method, partially given in  [35] , is based on three levels of abstraction, which are absolutely necessary in order to analyse the behaviour observed on the 500 high-level time functions f i (t) and then to produce the occurrences of the blast furnace phenomena.
Conclusions Dynamic process modelling is generally accomplished from experts' knowledge, this could be complemented from analysing and mining data which describe the process evolution. Nevertheless, although Knowledge Engineering (KE) and Knowledge Discovery in Database (KDD) are disciplines which pursue knowledge creation, they are seldom or never considered in an integral way. In this work we presented a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D), which proposes a primarily syntax-driven modelling approach where semantic content is introduced in a gradual and controlled way through the CommonKADS conceptual approach, Formal Logic and the Tetrahedron of States framework. The principles and fundamentals of TOM4D were presented along with a didactic example that illustrates the modelling approach which, we believe, makes explicit the specialists' knowledge in a way that this one can be compared with the real process.
Moreover, nowadays we are utilising this methodology and the TOM4L process in order to model human behaviour in a smart environment from gerontologists' knowledge and data, in the context of the GerHome Project of the Centre Scientifique et Technique du Bâtiment (CSTB) of Sophia Antipolis (France). [2]. These rules can be interpreted as (R 1 ) if the fuse is blown then the result of the fuse inspection is broken, (R 2 ) if the fuse is blown then the power is off, (R 7 ) if the power is off, the engine does not start, and so on. In particular, we shall limit ourselves to introduce a concise description of the interpretation from CommonKADS, carried out by the authors in  [2] , of the introduced knowledge pieces (Fig. A. 25) , and to present the domain rules specified in the Conceptual Modelling Language (CML)  [2].
In the retail sector,  Saeed et al. (2003) ,  Steinfield et al. (2005) ,  Bendoly et al. (2005) ;  Müller-Lankenau et al. (2005 , 2006)  and  Andreini (2009)  studied the retailers' websites to highlight the various ways that activities offered to the on-line consumer and the store consumer could be integrated. They have designed different models of strategy (i. e. information supporting model, database integration model, fully integrated channels model, etc. ) from the width of activities proposed online and the nature of the integration between the website and the store involved in these activities. Three integration dimensions could be identified depending on the infrastructural requirements of the activities. The first dimension which does not require infrastructure refers to information concerning one channel that is included by the retailer in another channel (simple information flows like the store address mentioned on the website).
Data collection The data was collected between September and December 2011. We employed an investigative channel-scan approach based on two scenarios: the purchase of a product and a special offer. For the first scenario, after identifying a product sold by the retailer the analyst adopted the position of a buyer and recorded all the information and activities for each phase of the buying process until the actual purchase in each of the retailer channels. For the second scenario, after identifying a special offer the analyst followed every piece of information right to the end enabling a potential customer to access the product; they identified where each point of contact would send the potential customer and 'followed' each path. Every channel was investigated twice. The results of an initial collection by the first evaluator were thoroughly double-checked by a second evaluator to eliminate ambiguities and increase inter-subjective validity.
Data analysis The data analysis resulted in a three steps process. Step 1qualification of channels The analysis was carried out retailer by retailer and began with the identification of the channels used by the retailer from the seven channels selected. Then the channels were identified: non selling or selling channel; physical or virtual channel. Step 2categorisation of activities and child orientation Next, all the flows between the channels were identified and qualified. 139 flows were identified and studied. A direction was allocated to each flow showing the path of the activity within the channel. Three categories has been defined on the basis of multichannel integration literature. Then to explore the retailer's child orientation, features were selected following a triple rationale. factors affecting children's preferences were identified on the basis of literature.
Objective: To identify patterns of perception of front-of-pack (FOP) nutrition labels and determine dietary, lifestyle and health profiles related to such patterns. Design: Cross-sectional. Participants/Setting: 28,952 French adults participating in the web-based Nutrinet-Santecohort. Outcome measures: Perception was measured using indicators of understanding and acceptability for three simple FOP labels (''green tick'', the logo of the French Nutrition and Health Program and ''simple traffic lights'' (STL)), and two detailed FOP formats (''multiple traffic lights'' (MTL) and ''color range'' logo (CR)), placed on ready-to-eat soup packages. Dietary intake data were collected using three web-based 24 h records. Statistical analyses: Associations of perception patterns with individual characteristics, including diet, lifestyle and health status, were examined using analysis of covariance and logistic regression, adjusted for socio-demographic and economic factors.
Data Collection Comprehension and acceptability of FOP labels. The specific web-based questionnaire evaluated strengths and weaknesses of each FOP label in terms of comprehension and acceptability. Acceptability was based on several criteria: attitude, liking, visual attractiveness and perceived cognitive workload needed for interpreting it. These dimensions were inspired by the framework of system acceptability developed by Nielsen and used in information and communication technology  [27]. A withinsubjects design was used and all questions concerning acceptability and understanding were repeated for each logo and for each participant. The order of presentation of the label formats was as follows: first, the Green Tick, then STL, the CR logo, the PNNS logo and finally MTL. Participants would not be able to find any logical reason for this order.
Ease of identification was evaluated by the statement ''this FOP label is easy to identify''. Finally, reliability was measured by the statement ''this FOP label provides reliable information''. These indicators were assessed by individual questions on a 5- point rating scale, from 'fully agree' to 'fully disagree'. They were classified into three categories according to data distribution. At the end of the questionnaire, we asked participants to select the most informative and reliable label. Perceived cognitive workload grouped together a set of measurements of perception of potential format defects. It was assessed by 3 indicators: complexity of understanding, time span needed for interpreting the logo and discomfort occasioned by the message. The complexity level of understanding was evaluated by the following statements: ''this FOP label is too complex for understanding'' and ''this FOP label is easy to understand''.
Perceived time span needed for interpretation was measured by the statements ''this FOP label takes too long to understand'' and ''this FOP label permits rapid understanding of the information''. Finally, perceived discomfort was measured by the statement ''this FOP label makes me uncomfortable''. These were measured using 5-point rating scale questions, from ''fully agree'' to ''fully disagree''. These three indicators were classified into three categories according to data distribution. At the end of the questionnaire, FOP label formats were compared for perceived cognitive workload. We asked participants to choose, among the five formats, the most complicated label, the one requiring the longest time span for interpretation and the label that made them feel most uncomfortable. Assessment of individual characteristics.
Dietary intake, health and socio-demographic data were collected at enrollment in the cohort. Dietary data were collected using three web-based 24 h dietary records randomly assigned over a two-week period (1 weekend day and 2 weekdays). The dietary record relies on a meal-based approach, recording all foods and beverages (type and quantity) consumed at all eating occasions  [28]. First, the participant fills in the names of all food items eaten. Next, the participant estimates portion sizes for each reported food and beverage according to standard measurements or using photographs available via the interactive interface, taken from a validated picture booklet  [29]. Consumption of fish and seafood per week was assessed by a specific frequency question. The nutritional values for energy, macronutrients and main micronutrients were estimated using published nutrient databases  [30, 31]  and completed for recent market foods and recipes.
88 was used to identify extremely underreporting subjects, and a physical activity level of 1. 55 was used to identify other underreporting participants  [34]. Within 24 h records, the participant declared whether the reported consumptions were fairly representative of his/her usual diet or strongly differed due to specific events (illness, a social event, etc. ). These comments, such as acute disease and information collected at enrolment regarding current restrictive diet or a recent loss of weight ($5 kg) were investigated to identify specific conditions that could objectively explain low energy intake. Participants who provided such information were not considered to be underreporters, whereas other underreporting participants were excluded from the analysis. In addition, erroneous quantities due to data entry errors were identified using day-and food-specific established thresholds.
According to the percentage of erroneous data in declared quantities in the record and the declaration of the subject the representativeness of the record compared to his/her usual diet, the record was deleted or corrected. Recommendations for food group servings were defined using the French recommendations of the PNNS  [36]. Adherence to PNNS recommendations was defined for each food group as follows: $3 servings per day ,6 for 'grains'; $5 servings per day for 'fruits and vegetables'; ($1 servings per day ,2 for 'meat, seafood and eggs'; $2 servings per week for seafood; and $2. 5 servings per day ,3. 5 for 'dairy products' for persons under 55 years of age and $2. 5 servings per day ,4. 5 for older individuals. Thus, percentages of subjects who met recommendations were evaluated. In addition, we calculated the PNNS Guideline Score (PNNS-GS), a dietary quality score based on adherence to these recommendations  [36].
0) in women  [37]. Data obtained using the IPAQ were computed for the metabolic equivalent task in min per week following recommendations of the IPAQ group (available at http://www. ipaq. ki. se/scoring. pdf). Recommended categories of physical activity (low, moderate, and high) were used in analyses. Normal weight, overweight and obesity were defined according to the WHO classification for BMI, as BMI ,18. 5, 25# BMI ,30 kg/ m 2 and BMI $30 kg/m 2 , respectively  [38]. Since ''acceptability'' comprised various complementary but overlapping dimensions, overall perception of FOP labels was not captured using traditional multivariate techniques. To identify patterns of perception of FOP labels by grouping subjects into distinct groups according to acceptability and understanding of FOP label formats, we used cluster analysis, for which the procedure has been described elsewhere  [18].
Cluster 4 ('favorable to the CR logo' group) was shaped by higher liking and greater attractiveness for the CR logo, with greater dislike concerning the PNNS logo. It was also characterized by a high percentage of individuals who felt that none of the labels tested, including the MTL, required a high cognitive workload. Finally, this cluster included numerous individuals who only poorly understood the MTL, whereas a higher level of understanding was found for the green tick and PNNS logos. Socio-demographic and economic profiles of the perception clusters are presented in Table  1. All associations of sociodemographic characteristics with dietary, lifestyle and health factors were significant (p,0. 0001) (data not shown). Unhealthier dietary profiles and higher prevalence of obesity, low physical activity, declared type-2 diabetes, hypercholesterolemia and hypertension were observed in men, single persons and those falling into low socio-economic categories (data not shown).
Moreover, a strength of our study was its reliance on at least three self-reported dietary records provided in the course of one year, which are recommended methods in large epidemiological studies  [47]  and which enabled a reliable estimation of usual diet  [48]. However, it should be noted that under-reporting may still persist and the measure of energy/protein intake may be biased in the absence of objective biomarkers such as doubly labeled water or urinary nitrogen although under-reporters were excluded. Another strength was that dietary and health data were collected at enrollment in the study, prior to data on understanding and acceptability of FOP labels. Thus, the cohort effect was avoided and participant answers concerning diet and health status were not biased by their participation in the survey on FOP label perception.
Acknowledgments We thank the scientists, dieticians, technicians and assistants who helped carry out the NutriNet-Santestudy, and all dedicated and conscientious volunteers. We especially thank  Victorine Viard  for development of the questionnaire and  Gwenael Monot Mohand Aı  ¨t -Oufella ,  Paul Flanzy ,  Yasmina Chelghoum ,  Veronique Gourlet ,  Nathalie Arnault  and  Laurent Bourhis  for coordinating computer aspects and data management of the NutriNet-SanteStudy. We thank Voluntis (a healthcare software company) and MXS (a software company specialising in dietary assessment tools) for developing the NutriNet-Santeweb-based interface according to our guidelines. We are grateful to  Jerri Bram  for English editing of the manuscript.
This work was supported by the  French National Research Agency (Agence Nationale de la Recherche ; http://www. agence-nationale-recherche. fr/en/project-based-funding-to-advance-french-research/) within the  PNRA 2007 program  ( ANR-07-PNRA-018 ). The Nutrinet-Santestudy is supported by the following institutions:  Ministere de la Sante  ( DGS; French Ministry of Health ),  Institut de Veille Sanitaire (InVS ; http://www. invs. sante. fr/en), Institut National de la Prevention et de l'Education pour la Sante  (INPES; http://www. inpes. sante. fr), Fondation pour la Recherche Medicale  (FRM; http://www. frm. org/), Institut de Recherche en Sante Publique (IRESP ; http://www. iresp. net/organisation/presentation-in-english/), Institut National de la Sante et de la Recherche Medicale  (INSERM; http:// english.
S. publicly and privately held firms by revenues and profits in each year. As a first step, we created a list of companies that had ranked in the top 500 at least once during the seven year period of study. Furthermore, as we implemented an event study research design, we then excluded all privately held firms and firms for which the daily stock market returns could not be obtained from the CRSP database. As a next step in constructing our dataset, we then conducted an extensive key word search in the Factiva press database for all Fortune 500 companies that met the above described criteria in order to identify CEIs. Our search terms included key words such as "carbon neutral", "carbon offset", "clean energy", "climate neutral", "energy efficiency", "green energy", "green power", "green product(s)", "pollution control", "pollution prevention", "recycling", "renewable energy", "waste reduction", "zero emission" and "zero greenhouse gas", amongst others.
g. , raw materials, fuels, etc. ; (2) initiatives concerning a firm's throughputs, e. g. , operations, workers, wastes, etc. ; and (3) initiatives concerning a firm's outputs, e. g. , products, packaging, etc. (Shrivastava, 1995a (Shrivastava, , 1995b)). Table  2  provides some examples of environmental initiative announcements in these three categories. As a next step, we matched the event study sample to Bloomberg tickers to obtain firm level financial data and we identified confounding events. More specifically, we identified confounding events for each of the 210 announcements over a one week window (i. e. , from day -3 to +3) which is in line with previous research  (McWilliams & Siegel, 1997). Confounding events included major other events such as CEO or board changes, posting of operating results, stock splits, mergers and acquisitions, strategic alliances and restructuring, amongst others.