dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Data used in preparing this article were obtained from the  Alzheimer's Disease Neuroimaging Initiative  (ADNI) database (adni. loni. usc. edu). As such, many investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List. pdf. Full acknowledgements are provided in the Supplementary Information.
In this study, we produced several language models based on different parameters using Word2Vec as implemented in the Python Gensim package and evaluated each of these using both simple distance measures and Gap Score. The second part of the study considers the most similar words according to the best scoring Word-2Vec models and a different semantic-extraction algorithm, which is similar to that used by  Bullinaria and Levy. 7  We chose these two algorithms because Word2Vec is widely considered to be one of the most effective algorithms for discovering word relationships and the algorithms that Bullinaria and Levy used produced the highest published accuracy on the TOEFL Synonym Questions task as reported by the ACL Wiki (see link above). We will analyze the patterns of these three models to discover what light the similarities and differences between these two lists shed on the type of semantic information returned by the two different algorithms.
This result in a Gap Score is positive if the indomain words are more similar to each other but is negative if the out-of-domain words fit better. Also the distance of the Gap Score from 0 reflects the difference between the in-domain and out-of-domain words. If the in-domain words are significantly more similar to the X most similar words, then the score will be significantly above 0, whereas if the out-of-domain words are significantly more similar to the same X words, then the score will be significantly below 0. The code to carry out the gapscore algorithm was written in Python and, along with thorough documentation on its use, is openly available on Github at <https://github. com/hipster-philology/param-bias>.
2. 3 Evaluation Procedure Once one has one or more vector-space language models of the corpus under investigation, the next task is to evaluate how these models performed. As semantic categories, we have used the semantic sub-domains from the Louw-Nida lexicon, which can be found online. 17 This online data represents the domains and sub-domains of the printed edition of this lexicon18 and is based on the theoretical work done by the authors of the lexicon. 19 When we say "sub-domains", we mean the collections of words represented by, e. g. , domain "1A Universe, Creation" as opposed to using the whole primary domain, e. g. , "1 Geographical Objects and Features". And we have only included sub-domains that have at least 10 words whose primary meaning belongs to that sub-domain. In the online version of the Louw-Nida lexicon, the primary meaning of a word is represented either by a "Gloss" that has no letter before it (for words with only a single gloss) or that is preceded by the letter "a".
If we started from scratch, the model that results would be based only on the biblical text and thus would theoretically represent a purely biblical Greek language model whereas starting with a model trained on a general Greek corpus would produce a more mixed model. The primary question we wished to answer by manipulating this parameter is whether there is enough data in the biblical corpus itself to produce a useful language model or not. So if the pre-trained models performed better, that suggests one of two things. Either there is not enough data in the biblical corpus to produce a good model OR that the training data that we are using (the Louw-Nida Lexicon) is based to a large extent on general Greek evidence as opposed to purely biblical evidence. The Louw-Nida Lexicon uses what they call "extratextual contexts",26 i.
In this table, we see that the most important parameter for Gensim appears to be the context window size. Eight of the top ten used 5-word context windows, just as we saw above in the Gap Score results. The next most important was the size of the neural network, with 7 of 10 using a 30-neuron network and the other 3 a 50-neuron network. The next most important was the text chunk size, with 5 having used Verses, 3 having used Chapters, and then 1 each having used Books or the Full Bible. And, finally, the least important was whether a pre-trained corpus was used. Four of the top ten used a pre-trained corpus while 6 did not. The next two tables are organized the same way as the two tables above but, instead of using the whole Old and New Testament to train their language models, these are based on models trained using just the New Testament.
We include these here for two reasons. First, we wish to discover whether there are any differences in the best parameters based on corpus size. The New Testament is approximately one-fifth the size of the Septuagint and, thus, it could require different parameters to produce the best model. The second reason is that we need data on the best models for only the New Testament so that we can more easily compare the results in section 3 below. Table  5. 3 represents the top ten according to Average Gap Score and Table  5. 4 according to Gensim's doesnt_match function. These tables show the same preference for smaller input text chunks as the previous two tables, with the second, Gensim table actually having 7 of the top ten relying on the verse-level chunks. They also both show no preference for pre-trained data, with the Gap Score table having 5 pre-trained and 5 not pre-trained and the Gensim table with 4 and 6, respectively.
Semantic Information Extraction The purpose of this part of the paper is to actually go in-depth into the results produced by three different language models for the extraction of semantic information from the Greek biblical corpus. The first two language models were discussed above and both were produced by Word2Vec, one being the top scoring model according to Gap Score and the other the top-scoring model according to Gensim's doesnt_match function. The third model was produced using a different method for semantic information extraction, though one that is still based on the distributional hypothesis, and thus word co-occurrences, for its results. First we will briefly describe this differing method, which we will call the "Log-Likelihood" method, based on the hypothesis testing algorithm that sits at its heart.
Part distortion model A 3D model of part distortion was developed and simulated using SYSWELD, a commercial finite element analysis software. First, a predefined residual stress state is used in the model as input data. This initial distribution is obtained after residual stresses measurements in the part before machining. These residual stresses are determined experimentally using both layer removal and neutron diffraction techniques. One of the aims of the part distortion model is to verify the presence of a local disturbance of the residual stress distribution. In this work, the FE-model considers the clamping/unclamping conditions only. In fact, the finite elements were removed in one step without considering the depth of cut neither the toolpath effects. Then, a second step is applied in order to find a new equilibrium state after removing the clamping forces (unclamping).
Previous searches at the LHC for h → 2a include 4µ  [15] [16] [17] [18] , 4τ  [19] , 4  [20, 21] , 4 /4π  [22] , 4 /8  [23] , 4b  [24, 25] , 4γ  [26] , 2b 2τ  [27] , 2µ 2τ  [28] , and 6q  [29]  final states. A more thorough description of the NMSSM and dark SUSY models, their empirical and theoretical motivations, and constraints for their search set by previous experiments is included in Refs. [15]  and  [18]. The search presented in this Letter includes several improvements compared to the previous results published by the CMS Collaboration on light boson pair production decaying to muons given in Ref. [15]. The data used for this analysis correspond to an integrated luminosity of 35. 9 fb -1 of pp collisions at 13 TeV, compared to 20. 7 fb -1 at 8 TeV. While no dedicated analysis is performed targeting nonprompt decays, a new trigger with increased sensitivity to signatures with displaced vertices was implemented and the present search is also sensitive to signatures of this kind.
Each a 1 subsequently decays to a pair of oppositely charged muons. Since the h 1,2 in h 1,2 → 2a 1 might not be the observed SM Higgs boson  [40] [41] [42] , mass values of m h 1,2 between 90 and 150 GeV are simulated. This range is motivated by constraints set by the relic density measurements from WMAP  [43]  and Planck  [44] , as well as searches at LEP  [45] [46] [47] [48] [49] [50]. The light boson mass is simulated to vary between 0. 25 and 3. 55 GeV, or approximately 2m µ and 2m τ , as motivated in Ref. [51]. In the case of dark SUSY, production of SM Higgs bosons is simulated with the MC matrixelement generator MADGRAPH 4. 5. 2 [52]  at leading order. The non-SM decay of the Higgs bosons is modeled using the BRIDGE 2. 24 program  [53]. Higgs bosons are forced to decay to a pair of SUSY neutralinos n 1 via h → 2n 1.
8 cm and longitudinal decay length |L z | < 46. 5 cm. The upper limits on L xy and |L z | correspond to the dimensions of the outer layer of the CMS pixel system and define the volume in which a new light boson decay can be observed in this analysis. The parameter full is defined as the fraction of MC-generated events that pass the trigger and full offline selection described above. The insensitivity to the model used is displayed in Table  1. Scale factors are determined to correct for the differences between observed data and simulated samples. Corrections for the identification and isolation of muons and isolation of dimuons are measured using Z → µ -µ + and J/ψ → µ -µ + samples using a "tag-and-probe" technique  [55] ; the samples used are events from simulated data and from observed data control regions enriched in events from the aforementioned SM processes.
Individuals have received support from the  Marie-Curie program  and the  European Research Council and Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Technologie  ( IWT-Belgium ); the  F. R. S. -FNRS  and  FWO (Belgium)  under the " Excellence of Science -EOS "be. hproject n. 30820817; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Lend ület ("Momentum") Program  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  New National Excellence Program ÚNKP , the  NKFIA  research grants  123842 ,  123959 ,  124845 ,  124850  and  125105  (Hungary); the  Council of Science and Industrial Research, India ; the  HOMING PLUS program  of the  Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus program of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu , grant  MDM-2015-0509  and the  Programa Severo Ochoa del Principado de Asturias ; the Thalis and Aristeia programs cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
Study variables The main outcome variable was the interval between valve implantation and explantation (longevity). The main risk factor of interest was the patient's blood type (ABO and rhesus). Other known classical risk factors for structural degeneneration of bioprostheses, and for which data were prospectively collected at replacement, were as follows: patient's age at the moment of implantation, sex, valve location and number of bioprostheses implanted initially. Some additional risk factors in bioprosthesis degeneration, especially in the aortic position, have also been reported recently and include factors that increase hemodynamic stress (larger body surface area, small prosthesis size, prosthesis-patient mismatch, left ventricle hypertrophy) and cardiovascular risk factors such as smoking, hypertension, metabolic syndrome, diabetes melitus, dyslipidemia  [47, 48].
Data collection A prospective database for bioprosthetic heart valves has been developed since 1985. Thus data were collected prospectively and valve information was recorded in the operating room from 1985 to 1998. Information collected on other variables included: bioprosthetic valve degeneration and other factors resulting in bioprosthesis replacement (e. g. thrombosis, endocarditis, non-dysfunction valve); date of implantation; longevity; site of implantation; number, origin (bovine or porcine) and type of bioprosthesis. The patient's blood group information was obtained from the blood bank (2/3 of cases) and patients' records (1/3 of cases). If the information was not available in the blood bank, patients' medical records were consulted for those patients with the highest (≥13 years) and lowest longevity (≤7 years).
Patient and valve characteristics Between 1985 and 1998, 828 porcine bioprostheses were explanted from 641 patients. From 1998 to 2014, 32 additional porcine bioprostheses with longevity ≥13 years were removed. The longevity and blood groups were known in 426 patients (483 porcine bioprostheses) and constitute the study cohort. For these patients we do have all the variables. Types of explanted porcine bioprostheses were as follows: CE-2nd 49. 4%, 3rd 35. 4%, 1 rst 1. 8%, Hancock-II™ 7. 5%, Liotta™ 3. 2%, others 2. 7%. During the period 1987-1998, we also explanted 66 additional new generations of bovine bioprostheses (CE bovine pericardium) for degenerative reason, the majority of which (n = 64) exhibited short or intermediate longevities of b15 years. Demographic data and porcine valve characteristics are shown in Table  1A.
This paper faces the control problem of an innovative approach to combined cycle flexibility: the integration of a heat pump and a storage to supply additional heat when required and increase the working map of a combined cycle operating in CHP conditions. This has been presented already in  [6]. The adopted control is based on a hierarchical approach, mutated from the smart grid application  [7]. Such kind of control systems have found a wide diffusion in the smart grid environment: a review has been recently dedicated to the topic  [8]  but the application goes from the management of the voltage  [8]  to the decision-making process in the distribution of energy share  [7]. The approach here adopted has been already sketched in  [9]. Here the focus is placed on the implementation and the first iterations of the two involved environment: Matlab and Amesim.
MPC Embedded Model The MPC encloses a model of heat pump and storage for the correct management in real time of the process. The model of the storage is a simple integrator, which basically makes an energy balance of the flow coming from and to the storage itself. The interactions with Amesim and Matlab starts here: the model of the heat pump is derived from a detailed model developed in Amesim. Amesim linearized the model around an operative point for being embedded into the MPC -the 75% of the load. This linearized model has one input, which can vary continuously between 0 and 1, and one output, which is the compressor power in Watt. Since the thermal production is meant as an energy recovery, this is not directly controlled, whilst the electric power, which directly affect the total power output of the system, is the actual controlled variable.
Conclusions This paper introduced the control system developed for management and control of an advanced solution for flexibilization of gas turbine combined cycle via integration of a heat pump in the loop. With respect to previous works, this one describes the 15 minutes horizon function, where decision making process about operating conditions of the power plant is defined. Then, the linearized model implemented into the MPC is presented and compared to performance offered by the complete model developed in Amesim. This will give light to iterations between the control developed in Matlab and the model developed in Amesim and control system will be tested at simulation level before implementation in power plant.
Background Rapid control of cholera outbreaks is a signi cant challenge in overpopulated urban settings, and documented results on eld interventions are scarce. During the 2017-2018 period, Kinshasa, the capital of Democratic Republic of the Congo, experienced two cholera epidemics of increasing intensity. By January 2018, cholera cases were reported throughout the city. We describe the spatiotemporal evolution of the cholera outbreaks in Kinshasa from 2017 to 2018 and assess the impact of the targeted community-grid WaSH (Water, Sanitation and Hygiene) strategy to quickly interrupt cholera transmission. Methods We carried out a descriptive study of the cholera outbreaks in Kinshasa from 2017 to 2018. Cholera surveillance databases from the Ministry of Health were analyzed to assess the spatiotemporal dynamics of the cholera epidemic using epidemic curves and cartography.
Surveillance data sources The National Integrated Disease Surveillance and Response System was established in 2000 by the DRC Ministry of Health in conjunction with the World Health Organization (WHO). The Integrated Disease Surveillance and Response System targets thirteen infectious diseases with epidemic potential, including cholera, for passive surveillance  (12). Suspected cases and deaths due to moderate and severe cholera infection are documented via line list in each treatment facility. Trained Ministry of Health o cials aggregate and anonymized these data at the health zone level and report the data to the Ministry of Health in Kinshasa every week.
Epidemiological data management and analyses Secondary data was extracted from surveillance databases. Quality veri cation of the database was conducted, in which data was veri ed for consistency and analyzed to determine weekly case numbers per health zone using Microsoft Excel. Epidemic curves per health zone were drawn to assess the temporal evolution of the epidemic in Kinshasa as well as outbreaks in each affected health zone, covering the period week 1 of 2017 to week 45 of 2018 (the epidemic curve shows cases starting from week 15 of 2017, as only limited cases were reported earlier in the year). Total weekly case and death numbers per health zone were also used to perform a descriptive analysis of the epidemic as well as impact following implementation of the community-grid strategy.
Precipitation data Precipitation levels were derived from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset (product: Daily UCSB CHIRPS v2p0 daily-improved global 0p25). The CHIRPS precipitation data is a 30-year quasi-global rainfall dataset supported by University of California Santa Barbara (CA, USA). Daily values were extracted and aggregated by health zone (R environment for statistical computing and graphics). Spatial aggregation from gridded data at the province level for Kinshasa was carried out in R. Daily precipitation levels (mm) were then aggregated by week using Microsoft Excel.
Acknowledgements The authors would like to extend our deepest gratitude to the entire rapid intervention team of the  PNECHOL-MD ,  Ministry of Health, Kinshasa. We also thank the  WHO  for their support with this study. The authors thank all community staff in affected health zones who took part in patient care, eld investigations, data reporting as well as sample collection, transport, processing and analysis. We are extremely grateful to all the village chiefs and many others who took part in the community-grid response. Finally, we also thank  Dr. Rita Colwell  for reviewing the manuscript.
Comparison to experimental data When the flame spreads up, a pyrolysis zone, in which the virgin material decomposes, forms from the flame root to the edge of preheating area. The pyrolysis zone moves up with the flame and generates gases to sustain the flame during spread. Hence, the flame spread rate can be determined from the movement of the pyrolysis zone. Instantaneous locations of the ignition and extinction fronts (denoted as respective "front" and "back" in the legends) were determined as heights where the pyrolysis mass loss rate was greater than a threshold of 0. 0001 kg/m 2 s. Figure  3 (a) shows the locations of ignition and extinction fronts from the simulations using heat conduction model Eq. (17). From Figure  3  The comparison shows that the difference of flame spread rates between the flame spread apparatus and free space is minor due to the simultaneous compensating effects of the heat confinement of flame spread apparatus and the relatively low moisture content in free space.
ACKNOWLEDGEMENT This work was supported by  National Natural Science Foundation of China (NSFC)  under grant no. 51876148 , the  Academy of Finland  under grant no. 297030  and the  European Research Council (ERC)  under the European Union's  H2020the Framework programme for Research and Innovation  ( 2014-2020 ) /  ERC  Grant Advances Agreement n°670747 -ERC 2014 AdG/FireBar-Concept. The work has been performed under the Project  HPC-EUROPA3 (INFRAIA-2016-1-730897 ), with the support of the  EC Research Innovation Action  under the  H2020 Programme ; in particular, the author gratefully acknowledges the support of  Aalto University  and the computer resources and technical support provided by  CSC -IT Center for Science, Finland.
| Classification of literature data and statistical methods As shown in Figure  1 , the initial search strategy identified 1661 records. A total of 100 articles were assessed for eligibility by full-text assessment by at least 2 reviewers. The 25 articles selected for inclusion in the review involve a total of 1610 patients, aged from 4 months to 18 years old; 1472 patients were treated with a single oral midazolam dose between 0. 25 and 1. 5 mg/kg, with a maximum dose of 20 mg used most frequently (40 mg in one of the studies),  9  and 138 patients were treated with placebo (Table  1 ). A summary description of the individual clinical efficacy studies for both contexts of sedation, including efficacy results per treatment (midazolam and placebo) and dose, is shown in Table  2. The data from the literature were tabulated, indicating the number of subjects, number of responders, and response rates per treatment.
I G U R E 1 Flow diagram of study selection process Records identified through database searching and manual searches (n = 1661) Records screened (n = 1555) systematic review of the literature was conducted in order to identify clinical studies evaluating the use of oral midazolam in pediatric patients in the context either of premedication before anesthesia for surgical procedures or during diagnostic or treatment procedures. Articles were identified from electronic resources including PubMed and ScienceDirect (eg, MEDLINE search ("midazolam"[MeSH Terms] OR "midazolam"[All Fields]) AND "sedation"[All Fields] AND ("child"[MeSH Terms] OR "child"[All Fields] OR "children"[All Fields]) AND ("pediatrics"[MeSH Terms] OR "pediatrics"[All Fields] OR "pediatric"[All Fields])) and by manual searches from further evaluation of key review articles and bibliographies of articles.