dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work was supported by the  Academy of Finland ;  Joint Program of Neurodegenerative Disorders-prevention  ( MIND-AD ) grant through the following funding organizations under the aegis of JPND-www. jpnd. eu: Finland, Suomen Akatemia  ( Academy of Finland ,  291803 ); Sweden,  Vetenskapsrådet (VR) (Swedish Research Council ,  529-2014-7503 ) France,  French National Agency for Research (ANR) ,  ANR-14-JPPS-0001-02 ];  Swedish Research Council  grant  2017-06105 ;  Juho Vainio Foundation ,  Finnish Medical Foundation ;  Finnish Social Insurance Institution; Ministry of Education and Culture   Research Grant ;  Finnish Cultural Foundation ;  Knut and Alice Wallenberg Foundation Sweden; Center for Innovative Medicine (CIMED)  at  Karolinska Institutet Sweden ; Stiftelsen Stockholms sjukhem Sweden; Konung Gustaf V:s och Drottning Victorias Frimurarstiftelse Sweden; af  Jochnick Foundation Sweden; Alzheimer's Research and Prevention Foundation ; the  European Research Council   Starting Grant  ( ERC-804371 ),  Alzheimerfonden Sweden ;  Region Stockholm  (ALF and NSV grants),  Sweden.
U. S. POINTER  trial is supported by the  Alzheimer's Association   POINTER-19-611541  and  POINTER-19-612024  (PIs:  Laura Baker ,  Mark Espeland ,  Miia Kivipelto ,  Rachel Whitmer , and ALZ PIs:  Maria C. Carrillo  and  Heather M. Snyder ), and ancillary Neuroimaging study to U. S. POINTER with  National Institutes of Health (NIH)  grant number  R01AG062689  (PI:  S Landau ). Additional support was provided by the  Wake Forest Alzheimer's Disease Core Center  ( P30AG049638-01A1 ). The MYB trial is funded by a  National Health and Medical Research Council  ( NHMRC) Boosting Dementia Research Team Grant  ( APP1095097 ). The SINGER study was supported by  Centre For Healthy Ageing, National University Health System, Singapore. The J-MINT study is supported by  Japan Agency for Medical Research and Development.
The  Canadian Thumbs Up Platform Trial  is part of the  Canadian Consortium on Neurodegen-eration in Aging (CCNA)   Phase 2 , which is funded by the  Canadian Institutes of Health Research  (CIHR # 201901CNA-417847-CAN-ABPI-32054 ) and a consortium of partners including the  Alzheimer Society of Canada. The work in India is made possible by funding from  Centre for Brain Research, Bengaluru, India. The PENSA study is funded by the  Alzheimer's Association  ( 18PTC-R-592192 ) and the  Instituto Carlos III  ( PI17/00223 ). MIND-CHINA  was supported by the  National Key R&D Program of China  from the  Ministry of Science and Technology of China  (grant no. :  2017YFC1310100 ), and additional grants were received from the  Shandong Taishan Scholar Program ,  Shandong , China; the  Sino-Sweden  joint research project grants from the  National Natural Science Foundation of China (NSFC  grant no.
:  81861138008 ) and the  Swedish Research Council  (grants no. :  2017-00740  and  2017-05819 ); and the  Joint China-Sweden Mobility Programme  grants from the  National Natural Science Foundation of China NSFC  (grant no. :  8191101618 ) and the  Swedish Foundation for International Cooperation in Research and Higher Education (STINT , grant no. :  CH2019-8320 ). GOIZ  ZAINDU  project has been partially financed by the  Health Department of the Basque Government  (File No. 2017111120 ). Paulo Caramelli holds a senior research fellowship (bolsa de produtividade em pesquisa) from  CNPq, Brazil. SUPERBRAIN was supported by the grant from the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  ( HI18C0479 ).
Funding information Swedish Research Council , Grant/Award Number:  529-2014-7503 ;  French National Agency for Research , Grant/Award Number:  ANR-14-JPPS-0001-02 ;  Swedish Research Council , Grant/Award Number:  2017-06105 ;  Juho Vainio Foundation ;  Finnish Medical Foundation ;  Finnish Social Insurance Institution; Ministry of Education and Culture   Research Grant ;  Finnish Cultural Foundation ;  Knut and Alice Wallenberg Foundation ;  Center for Innovative Medicine ;  Karolinska Institutet Sweden ; Stiftelsen Stockholms sjukhem Sweden; Konung Gustaf V:s och Drottning Victorias Frimurarstiftelse Sweden; af  Jochnick Foundation Sweden; Alzheimer's Research and Prevention Foundation ;  European Research Council Starting , Grant/Award Number:  ERC-804371 ;  Alzheimerfonden Sweden ;  Region Stockholm ;  National Institutes of Health , Grant/Award Number:  R01AG062689 ; Wake Forest Alzheimer's  Disease Core Center , Grant/Award Number:  P30AG049638-01A1 ;  National Health and Medical Research , Grant/Award Number:  APP1095097 ;  Centre For Healthy Ageing; National University Health System ;  Japan Agency for Medical Research and Development; Canadian Institutes of Health Research , Grant/Award Number: # 201901CNA-417847-CAN-ABPI-32054 ;  Centre for Brain Research; Alzheimer's Association , Grant/Award Number:  18PTC-R-592192 ;  Instituto Carlos III , Grant/Award Number:  PI17/00223 ;  National Key R&D Program of China; Ministry of Science and Technology of China , Grant/Award Number:  2017YFC1310100 ;  National Natural Science Foundation of China , Grant/Award Number:  81861138008 ;  Swedish Research Council , Grant/Award Numbers:  2017-00740 ,  2017-05819 ;  Joint China-Sweden Mobility Programme  grants from the  National Natural Science Foundation of China ;  NSFC, Grant/Award Number :  8191101618 ;  Swedish Foundation for International Cooperation ;  Research and Higher Education , Grant/Award Number:  CH2019-8320.
S. POINTER (USA) 2000 at-risk, cognitively cognitive decline plus, SINGER (Singapore) normal adults age 70 seniors, age >65 with carriers of the APOE4 allele, 60+ years 60-79 mild-to-moderate frailty and/or cognitive impairment MIND-CHINA (China) SUPERBRAIN (Korea) Can Thumbs Up (Canada) 3000 non-demented 150 adults, cognitively normal, with at least adults age 60-79 at 52 one modifiable risk villages in Western factor for cognitive Shandong province impairment 2024 non-demented adults aged 60-85 Age 60-79 the United States, the U. S. POINTER study (ClinicalTrials. gov: NCT03688126) will enroll approximately 2000 cognitively normal adults who are at an increased risk for cognitive decline and demen-tia in later life. Increased risk is defined by sedentary lifestyle, poor diet, and other factors such as suboptimal cardiovascular health sta-tus and first-degree family history of significant memory impairment.
The GOIZ-ZAINDU project is performed and funded by the CITA-Alzheimer Foundation in collaboration with the Municipality of Beasain and Osakidetza (the public Basque Health System). Another study in Spain, the "Prevention of cognitive decline after a multimodal intervention combined with epigallocatechin gal- late in APOE4 carriers with subjective cognitive decline" (PENSA study: in Catalan PENSA means "think about"; ClinicalTrials. gov: NCT03978052) is an ongoing project in Barcelona, performed in collaboration with the Hospital del Mar Medical Research Institute (IMIM) and the BarcelonaBeta Brain Research Center (BBRC). PENSA will randomize 200 individuals meeting criteria of subjective cogni- tive decline (SCD), fulfilling four SCD plus features, including sub- jects older than 60 years and APOE4 carriers.
B. Real networks To benchmark the performance of the algorithm we measured reachability values of a set of real-world networks. (a) A mobile call network  [21]  of 325 million events of over 5 million customers over a period of 120 days; (b) 258 million Twitter interactions  [25]  of over 12 million users over a period of more than 200 days; (c) air transport network of United States  [26]  with 180 112 flights between 279 airports; and (d) public transportation network of Helsinki  [27]  with 664 138 trips (defined as a vehicle moving between two consecutive stops) between 6 858 bus, metro and ferry stops. The mobile call and Twitter interactions datasets were processed as an undirected temporal network. Public transportation and Air transportation datasets were processed as directed networks with delays (duration of time between departure and arrival) taken into account.
HyperLogLog estimators for Mobile and Twitter networks use m = 2 10 registers. For other datasets m = 2 14 registers were used. Largest out-component sizes were measured with a maximum probability of misidentifying of at most 0. 01. TABLE I. Running times for real-world networks when calculating the reachability (number of unique reachable events, nodes and lifetime) from all events in the network. The δt * corresponds to a waiting time around the time at which there is a jump in the largest out-component size (see the text for details; this corresponds to the vertical line in Fig. 5 ). As the δt values around δt * is of interest for a wide range of studies, the runtime for δt = δt * would be representative of the running times for a typical study. The values for δt * are 271 seconds for air and public transport networks, 100 minutes for the Twitter network and 6.
We also report the largest possible threshold δt = ∞ leading to largest running times. For undirected temporal networks (Mobile and Twitter) taking δt to infinity does not result in a substantial increase in the running time as most of the increase in the number of event graph links are never considered due to the optimising for redundant links (see Appendix B). This, however, is not the case for directed temporal networks as the optimisation method described in Appendix B does not apply to directed events. Assuming a homogeneous distribution of events across time, the runtime   I. for event graphs constructed from directed events grows by O(δt log δt) and reaches a maximum at δt = T where T is the maximum δt between any two events in the network. For the case of instantaneous (non-delayed) events, T is equal to the measurement window of the dataset.
As previously described in Sec. III A, as δt is increased larger and larger connected structures begin to form in the event graph. The increase in size is also visible from the largest out-component size curves for the same dataset in Fig. 5. This transition period usually marks the most interesting area for further studies. Running times of the breadth-first type of algorithms are in practice dependent on the component sizes and can thus see a dramatic increase in the running times during and after the transition period. Running time plots (Fig. 5 ) show that as expected our algorithm is not sensitive to these transitions. They show that while for the case of directed networks (air and public transportation) runtime grows almost linearly as a function of δt, it grows sub-linearly for undirected networks because of the wider range of applied optimisation described in Appendix B.
ACKNOWLEDGMENTS The calculations presented in this work were performed using computer resources within the  Aalto University School of Science "Science-IT " project. Márton Karsai  acknowledges support from the  ANR  project  DataRedux  ( ANR-19-CE46-0008 ) and the ACADEMICS grant of the  IDEXLYON , project of the  Université de Lyon ,  PIA  operated by  ANR-16-IDEX-0005.
A MILP then calculates alternative distribution network designs as trade-offs between emission and cost, displayed on a Pareto front. AHP is used to weigh the two conflicting goals, which themselves only consist of quantitative factors -cost and CO2 emission. The model is based on data from the Irish dairy industry, however, there is no implementation into the managerial decision process. Irawan et al. (2018)  propose a model to select ports for offshore windfarms and design the distribution system for components. Ports are pre-selected by AHP. Subsequently, component flows are determined by linear programming. The paper uses real-life data but does not describe how the model was adopted by managers. A common missing feature in existing research is that the practical implementation of the GSCND models is not demonstrated.
Data To understand the problem and solution requirements from the case company's perspective, several interviews and workshops were conducted. Information describing product specifications, production quantities, sales, inventory capacities, handling processes, shipping fees, and transit times were collected and analysed to understand the network in detail. In close cooperation with analysts and experts from the case company, data from diverse sources was compiled and integrated, including internal IT systems for sales and accounting as well as from external providers. Transparency about product flows, costs, and capacity requirements was established, that was not available before. The company previously had no holistic visibility over the number, utilisation, and cost of distribution points.
To understand the facilities,  external data on the environment was collected, and interviews with experts were conducted. Interviewees included senior managers, internal consultants, and facility directors. An overview of the data is presented in Table  2  below. Following the CIMO-logic, the solution was validated and generalised as described by O'Keefe (2014). Design specifications were derived from the relevant literature. The utility and validity of the presented approach were evaluated by constructing and testing the solution together with the problem owner and analysing the intervention. To validate and generalise the specifications and the usefulness of the solution framework in other contexts, interviews with five top managers in the supply chain areas of multinational companies from diverse industries were conducted.
Application After collecting the data described earlier, we proceeded to build the cost-minimising MILP using MS Excel. As the model was too complex for the built-in solver, we used two more powerful solvers: Gurobi and COIN-CBC. The second step was to lay out and execute the AHP. Beforehand, management pre-selected seven DCs as possible candidates for the future setup. Pre-selection was done purely on size, as the company's overall strategy was to concentrate distribution in fewer, larger facilities. Because the number of decision alternatives was not larger than nine, AHP could be used for both structuring the decision problem, as well as for assessing the alternatives. Had there been more than nine candidates, it would have been still possible for AHP to structure the decision, but another method not based on pairwise comparison, such as Weighted Scoring, would subsequently have been used to rate the alternatives.
After the AHP structure was ready, the decision alternatives had to be rated according to the defined criteria. The necessary knowledge was dispersed to such a high degree that a group AHP  (Dyer and Forman 1992)  was infeasible. Instead, data regarding the facilities collected earlier from IT systems and expert interviews was coded and compiled in a two-dimensional matrix, which summarised all information describing each facility along each sub-criterion of the AHP. The information matrix was presented to managers for review and discussion. Based on this, pairwise comparisons between all decision alternatives within all sub-criteria were conducted, producing scores that were aggregated into an overall preference score for each potential location. For executing the AHP we used the software 'PriEsT'  (Siraj, Mikhailov, and Keane 2015).
The methods used rely heavily on data. Missing information must be replaced by assumptions and estimations, which can be imprecise. The MILP is linear and static, representing 'a sensible trade-off between realism, scope, complexity, and solvability'  (Melo, Nickel, and Saldanhada-Gama 2009). The AHP limits the decision alternatives to 9 items to ensure consistency  (Saaty 1980). As a strategic tool, it is unsuitable for operational decisionmaking with a short time horizon, because it simplifies or excludes several operational parameters. The model's effectiveness cannot be proven by large datasets and multiple applications, as the employed methodology focussed on a specific field problem and the subsequent generalisation of findings. Nevertheless, the main contribution of the study is not the model itself, but to describe how a solution can be effectively constructed and implemented to support the managerial decision process for GSCND.
(ii) The semantic model recognises and endows the meaning of the elements of heritage. (iii) Parametric model defines and alters the shape of the elements by parameters  [10] [11] [12]. (iv) Information model builds a database with the attached attribute, material, and relationship information about the entity and its components as well as the 3D geometry. Besides, a knowledge model is considered, which pays more attention to heterogeneous semantic details of the heritage instead of geometric aspect. In literature, related review papers from different views for built heritage modelling are available. Chane et al. [13]  surveyed the imaging systems and their applications for the study of cultural heritage. Remondino  [14]  reviewed the actual optical 3D measurement sensors and 3D modelling techniques, with their limitations and potentialities, requirements and specifications on heritage recording.
Information and knowledge management 3D computer graphics, photogrammetry and laser scanning paid attention to the documentation of the geometric aspect of built heritage. On the contrary, GIS and ontology provide the enhanced relative database for the management and analysis of the semantic, attribute and relationships among the sub-elements composing heritage. GIS "lets us visualise, question, analyse and interpret data to understand relationships, patterns and trends"  [46] , which focuses on managing the spatial relationship and semantic infor-mation  [47, 48]. Ontology is used to formally represent knowledge and rules of a particular domain to facilitate computer processing, reasoning, knowledge sharing and re-use  [49] [50] [51].
Geographic information system GIS pays attention to managing, querying and analysing spatial, attribute, and relationship information among elements. As a tool for heritage site management, GIS can establish databases for archaeological sites and monuments at regional and national levels  [52, 53]. GIS provides tools to query the data for preservation purposes and to realise advanced analysis  [54]. Also, 3D WebGIS is widely adopted for 3D built heritage models to be visited on websites, which benefits the product management and popularity. In this manner, a user-centred system is obtained to extend the interoperability and exchange of data to reach more potential users  [55] [56] [57]. For example, a 3D WebGIS for archaeological research, called the MayaArch3D project  [58] , offers a system architecture for managing 3D models and their associated data so that researchers can view them on the Internet in real-time, analyse them, and compare them with other models.
BIM software is firstly used to construct the parametric elements regarding the historical buildings based on documentation. The application of inverse engineering, which maps the parametric elements to the reality-based data, makes it possible to refine the parameters and obtain a current-state diagnosis  [80, 81]. Therefore, an HBIM project needs to organise the protocol created simultaneously or interactively by analysing documentation-and reality-based data. That is, how to fit the standard shapes and BIM components into the 3D point cloud are essential processing that could be performed via manual interpretation (currently) and 3D structure similarity measurement  [82].
Literature analysis On the basis of the Web of Science TM Core Collection, we indexed the publications with combinations of key terms and analysed the tendency of each technique. To limit the broad scope to 3D built heritage modelling, structured queries on Web of Science using combinations of key terms and their synonyms (heritage/ ḧistoric* building* ÄND 3D) were conducted, yielding a total of 2848 records until the year of 2019. Then, to analyse the transformation from geometric modelling to semantic-and information-enriched heritage modelling, typical publications utilizing BIM techniques are marked. Due to the applied query method, the review excludes research currently underway that is not available in the mentioned databases, journals or conferences that are not included in the Web of Science TM Core Collection, or studies that have not been published in English yet.
What types of techniques are popular within certain time span We firstly take related key terms (Table  1 ) to monitor the publications on heritage documentation using different techniques. Two periods are considered, including all years in the database (from 1975 to 2019) and 2010s. The number of publications can reflect the focuses of the researchers in the field of heritage documentation. The result shows heritage documentation draws increasing attention, considering that 87% of the works are published in the 2010s. A rate (Eq. 1) is calculated by the ratio of publication numbers during the 2010s among the total publications, which are dedicated to mirroring the change of the hotspot issues. That is, the higher the value is, the more attention has been paid in recent days. As shown in Fig.
Roles of BIM techniques in built heritage We selected about 60 typical articles related to build heritage modelling utilizing BIM techniques (Table  2 ). Via the research statistics, current BIM in heritage modelling has focused on semantic modelling by historical materials with the BIM concept or reality-based data with the as-built concept. Documentation materials, including 2D drawings  [83] , 2D floor plans  [84] , archival documents  [85]  and architectural pattern books  [86] , are used to re-create the heritage by parameterizing the new elements in the BIM library. Yet some firstly build a virtual or geometric model using computer graphics software and just improve it to a parametric model using BIM software  [71, 87]. Laser scanning has been widely used to reconstruct existing heritage and import it into BIM software to build the elements as a reference.
Generate complex element by the combination with computer graphics It is usually a manually time-consuming concept in BIM software to parametrically model the historical elements, and they are limited in their ability to create complicated irregular geometry. That is, the time complexity and geometric accuracy for parameterizing the complex and irregular historic structure elements are the main challenges. Thus far, there exist mainly two ways to parametrically model the irregular elements not included in the BIM library. The first one is the manual modelling of the elements in a BIM software platform directly (e. g. Revit  8  and ArchiCAD 9  ). The main drawback is its low efficiency and how hard it is to ensure geometric accuracy. The other is the resort to programming, including the Geometric Descrip-tion Language (GDL) of ArchiCAD library parts and Autodesk Dynamo visual programming for Revit.
The parametric GDL and Dynamo elements could be completely described as 2D symbols, 3D geometric models, attribute specifications and additional spatial relationships in the BIM platform. Both the computer graphics and the original BIM concept for built heritage modelling have formed documentation-based parametric modelling using historical materials, such as historical documents, bibliographic references, photographs and drawings. BIM goes further than computer graphics to improve the virtual reconstruction of an information-rich model by semanticizing the elements. Currently, BIM is increasingly paying attention to irregularly shaped modelling by introducing computer graphics modelling techniques. Some works build the irregular structures with the help of computer graphics software, such as Rhino 3D  [106, 107]  or Sketchup  [108] , which could address any irregularly shaped object compared to BIM platforms  [99].