dataesr/scientific-paragraphs-categorization huggingface dataset:
(1). A prior term is added to this  2 to account for the current precision of  13 from reactor neutrino experiments  [18] [19] [20] , which is better than the expected precision from either JUNO or ORCA. The best-fit value and uncertainty from Ref. [13] The resulting  2 map is then used to estimate the combined analysis sensitivity to the NMO by calculating the Δ 2 as the difference between the minimum  2 values obtained from the wrong and true orderings. In Fig. 3  is shown one example of this Δ 2 profile obtained for the best-fit value for normal ordering given in Ref. [13]. In this figure are also shown the results obtained by the stand-alone JUNO and ORCA analyses for comparison. From Fig. 3 , the reason why the combination of JUNO and ORCA data significantly boost the NMO sensitivity becomes clear: while both experiments obtain the same best-fit value for Δ 2   31   under the true ordering, in the wrong ordering a tension appears between these best-fit values.
Fig. 5  shows how the sensitivity, with 6 years of data from ORCA and JUNO, depends on the true value of  23. From this figure, the current best-fit value for normal ordering corresponds to one of the values where ORCA is the most sensitive, while the current best-fit value for inverted ordering corresponds to one of the cases where the sensitivity is the smallest. It is important to note that for all true values of  23 , in both orderings, the NMO is determined at 5 by the combined analysis of 6 years of data from JUNO and ORCA. Finally, as was highlighted in Sec. 2, the energy resolution is a critical parameter for the success of the JUNO determination of the NMO. While it is fully expected that JUNO will reach the desired precision  [21] , in the combined analysis the impact from the JUNO energy resolution in the result is significantly reduced.
Often the terms AI and ML are mentioned along with Big Data  [50]  or Data Science, sometimes even used interchangeably. However, neither are AI methods necessary to solve Big Data problems, nor are methods from AI only applicable to Big Data. Data Science, on the other hand, is usually considered as an intersection of computer science, statistics and the respective scientific discipline. Therefore, it is not bound to certain methods or certain data conditions. This paper aims at contributing to the current discussion by highlighting the relevance of statistical methodology in the context of AI development and application. Statistics can make important contributions to a more successful and secure use of AI systems, for example with regard to The remainder of the paper is organized as follows: First, we present an overview of AI applications and methods in Section 2.
The collection and compilation of secondary data is in general not based on a specific question. Instead, it is collected for other purposes such as accounting or storage purposes. The concept of knowledge discovery in databases  [38]  very clearly reflects the assumption that data are regarded as a given basis from which information and knowledge can be extracted by AI procedures. This notion is contrary to the traditional empirical research process, in which empirically testable research questions are derived from theoretical questions by conceptualizing and operationalizing. The resulting measurement variables are then collected for this specific purpose. In the context of AI, the process of operationalization is replaced by the ETL process: 'Extract, Transform, Load'  [138]. Relevant measurements are to be extracted from the data lake(s), then transformed and finally loaded into the (automated) analysis procedures.
Pilot projects are, for example, the 'Project Data Science and Big Data in Schools' (PRODABI; www. prodabi. de) [9] , the 'Introduction to Data Science' project (IDS; www. introdatascience. org), and the 'International Data Science in Schools Project' (IDSSP; www. idssp. org). Particularly the latter two international projects have a pioneering role in school-related projects of stimulating AI literacy. To sum up, statistics should contribute its expertise to a greater extent to school curricula to ensure a broad and sustainable increase in statistical and computer literacy as a key resource for the future development of seminal technologies. Furthermore, teachers particularly qualified in statistics and its didactics should be systematically involved in digital teaching and training offers (e.
g. , e-learning). Higher education: For the tertiary sector, it is important to provide all relevant disciplines with the scientific foundations for high-level basic and applied research in the context of AI. This applies to both, Data Science subjects, such as such as mathematics, statistics and computer science, as well as corss-sectional areas, such as medicine, engineering, social and economic sciences. This includes adapting staffing policies at the universities as well as funding programs, possibly new courses of study, doctoral programs, research associations and research programs. In this regard, we see a particular need for expanding the qualification of teachers in statistical methodology and didactics due to the growing demand. Training and interdisciplinary exchange: Continuing professional development and training should be expanded at various levels.
Acknowledgements We would like to thank  Rolf Biehler  for his valuable input on Data Science projects at schools. This paper is based on the position paper ' DAGStat Stellungnahme: Die Rolle der Statistik in der Künstlichen Intelligenz ' (https://www. dagstat. de/fileadmin/dagstat/documents/DAGStat_KI_Stellungnahme_200303. pdf), which has been drafted by a working group of the  German Consortium in Statistics (DAGStat)  and approved by the members.
Data collection and analysis Reactions on the workshop were gathered through an ad hoc questionnaire at the end (Supplementary Appendix SA1). To enhance participants' awareness of their coping responses and compare the emotional reactions arising in their art therapy process, this included issues around the workshop evaluation, the feelings and effects evoked, and the personal coping strategies in day-to-day practice. Finally, opinions about the potential of art therapy for professionals were elicited. Participants' sociodemographic data were collected. After the workshop, the study was explained to participants and informed consent requested the study was approved by the Research Ethics Committee of the Universidad de Navarra Number: 2019. 167. The organizers compiled the questionnaires and two researchers analyzed the data through a descriptive analysis of quantitative variables and a thematic analysis of the open-ended questions.
Discussion Only 74 pediatric cases of BPDCN worldwide were described in a 2017 review  [3]. However, it did not include data in Spanish or from Latin America, and its clinical progress has not been considered before. Also, this patient experienced a delay in diagnosis due to lack of resources at a hospital outside the country's capital (as seen in many low-middle income countries), which points out the indolent course of the neoplasm in this case. A systematic literature review showed the mean survival of patients under 40 years was 38 months  [3]. Similarly, another study showed the overall survival in children at 3 years was 57. 4 ± 10. 2 months  [15]. Significant lymphadenopathies in the right external iliac chain and right inguinal region were found on multi-slice spiral computed tomography scans.
Experimental Results and Analysis In order to verify the effectiveness of the proposed method, an experiment was carried out on a 230-W marine current prototype. The collected stator current signatures are used by the proposed method for verification. As shown in Figure  4 , the system is composed of five parts: a water current simulation system, a permanent magnet synchronous generator (PMSG) prototype, load, data acquisition, and a monitoring system. The specific parameters of the prototype are presented in Table  1. Different severities were simulated by attaching different masses (0, 80, and 150 g) of attachments to the blades. In this study, the blade imbalance faults were classified by the percentage of blade attachment mass and blade mass so as to generalize them to all MCTs. Therefore, the healthy state, the severity of the 8% fault, and the severity of the 15% fault can be classified as class 1, class 2, and class 3, respectively.
Bidirectional transmission allows simultaneous measurement of the gas flow (via up/down transit time differences) and sound velocity (from their average). Since the sound velocity in a gas mixture at known temperature and pressure depends on the molar concentrations of its components it can be compared in real-time to a stored concentration vs. sound velocity database previously generated using data from theoretical models  [3]  or measurements made in calibration mixtures. The gas analysis algorithm is based on the generalized equation for sound velocity, c, in a gas: 𝑐 = 𝛾𝑅𝑇 𝑀 ( 1 ) where R is the molar gas constant (8. 314 J•mol -1 •K -1 ) and T is the absolute temperature (Kelvin). The adiabatic index γm for the mixture is given by the ratio of the weighted sums of molar specific heat at constant pressure (   ) to that at constant volume (   ) of the n components: 𝛾 = 𝐶 𝐶 = ∑ 𝑤 𝐶 ∑ 𝑤 𝐶 (2) where wi are the molar fractions of the components (i = 1→n).
For example in xenon-oxygen based anaesthesia (0 -80% Xe in O2) requires a 5 th order polynomial (fig. 2 ). The average gradient over the range is around 0. 52% per m. s -1. From eq. (5b) we see that a target precision of ±0. 1% Xe in O2 implies a sound velocity measurement precision of ± 0. 19 m. s -1. The gas mixture analysis database contains sets of polynomial fit parameters from curves of composition vs. sound velocity curves for a gas pair of primary interest calculated from eq. (  4 ) using thermodynamic data (Cp, Cv)  [3]  on a grid covering the expected process temperature, pressure and the expected concentration ranges of all known gases present in the mixture. Figure  3  illustrates the algorithm for 3-stage interpolation of parameters of sound velocity vs. {C3F8 in N2} composition generated and stored in intersection "cans" (•) on a 3-D grid of {T, P, ppm CO2} covering 13-25°C (0.
For each additional process parameter the database acquires an additional dimension, and can become tessaractic or pentaractic (4 or 5 process parameters respectively), or higher-dimensional. Figure  4  illustrates measurements of C3F8 concentration seen in 4 N2-purged environmental zones sequentially monitored in a 16-hour supercycle by aspiration through a single instrument. These volumes have varying permeability to CO2 ingress from the exterior. CO2 concentration is monitored 1 GE Telaire infra-red sensor: 10000 ppm full scale output. with an infrared monitor 1 allowing unambiguous C3F8 measurement on top of a varying known CO2 concentration. During the measurement period shown in fig. 4  the CO2 concentration in one zone underwent a step increase due to an external manipulations while the C3F8 leak rates into each zone (due to small coolant leaks) remained fairly constant.
Although ultrasonic gas analysis is primarily seen as a binary gas analysis tool, we have demonstrated that it can be successfully used with higher order gas mixtures if the concentration of the additional component(s) is known from other sources and the reference database uses their thermodynamic data in the approach of eqns (2-4) and fig. 3. This analysis approach has potential in various industrial and medical applications where simultaneous flowmetry and accurate real-time analysis of a gas pair of primary interest are required in a multi-process gas environment. One such example is in the emerging field of xenon-based anaesthesia, where ultrasonic instrumentation can simultaneously monitor flow and xenon concentration to a precision better than 0. 1%, and can help improve the postoperative recovery of expensive xenon gas for recycling.
Acknowledgments The authors acknowledge geriatricians, dieticians, medical secretaries and clinical research assistants from  Hôpital Lyon Sud, Hospices Civils de Lyon , for their involvement in the program, their participation in assessments and their help to collect data. Authors acknowledge municipalities of  Métropole de Lyon  for their collaboration to propose the care path. Authors acknowledge financial partners such as the  Agence Régionale de la Santé (Auvergne Rhône Alpes) ,  Conférence des Financeurs de la métropole de Lyon et du Rhône , and the  Mutuelle des Travailleurs de la Région Lyonnaise (MTRL)  for their support and their confidence. Also, authors acknowledge the  Caisse d'Assurance Retraite et la Santé au Travail (CARSAT)  for their implication as the main relay for participants.
Acknowledgments We are grateful to  Maurizio Fagotti ,  Frank Göhmann ,  Lorenzo Piroli ,  Tomaž Prosen ,  Tibor Rakovszky ,  Roberto Tateo ,  Miklós Werner ,  Lenart Zadnik  for useful discussions. In particular we are thankful to  Miklós Werner  for his help regarding the iTEBD program code and discussions concerning the persistent oscillations.
Data The data used in this study contain bus and subway boarding and alighting information and consist of card ID, boarding stop and time, alighting stop and time, used route, etc. We used one-year transportation card data of 2020 for the whole country to estimate the transportation card-based alighting stop operation information (alighting stop, alighting time) considering the characteristics of the trip types. However, when a passenger boarded a bus and then used a subway on the next trip, we fabricated the data using the location coordinates of the stop to construct the trip-chain. The total number of data (based on buses) used in the analysis was 4,110,205,036 (approximately 4. 1 billion/month), out of which about 17% had no alighting tag information. However, when we examine for each city or province, the percentage of the missing alighting tag data in the remaining regions after excluding the Seoul metropolitan area and Daejeon & Sejong areas is about 63%, which presents a limitation in using the data for the analysis of real travel patterns in the public transportation.
Therefore, alighting stop estimation is imperative. In this study, a trip is defined as from the time of boarding a bus to the time of alighting the bus. Furthermore, we check whether the alighting stop information is missing for individual trips, and in the case of trips with missing alighting tag information, we classify the trip type in terms of the trip continuity and recurrence based on the identifiable information of other trips of the pertinent card ID. For this, it is presumed that the same card ID is maintained for the entire time range of the utilized data. To determine the trip type, we built the basic DB using the boarding and alighting information in the sequential order of time for all trips on a pertinent day and the first trip on the next day for each ID. In the case of the first trip on the next day, the data were constructed for the estimation in the event that the passenger did not tag the card when alighting the bus on a pertinent day.
Stage 3: In the case of "non-recurrent travel" among "non-continuous travels", the alighting stop is estimated by searching the past travel history and finding the trip that seems to be similar to the alighting information-missing trip. As for the travel history of each card ID, we build an alighting stop set for each boarding stop of non-recurrent trips (less than three times per week) by using the data that contain both boarding and alighting information. The non-recurrent travel pattern data are built by classifying the weekdays and weekends since the difference in the patterns is expected to be large between each day of the week. Based on the OD pattern data for each card ID, we use the percentage of each alighting stop for each boarding stop to estimate the alighting stop. Here, if the boarding stop is not found in the pattern data, we search stops within a short distance (within 500m) to determine whether the pattern data exist or not, and if there is another route, the corresponding pattern data are used to estimate the alighting stop based on the same method.
Analysis overview We conducted an analysis using the transportation card data of the Seoul metropolitan area, in which there is almost no missing alighting tag information, to apply the proposed trip type classification and alighting stop estimation methods and examine the accuracy. The time range of the utilized data was one year in 2020. For the validation of the alighting stop estimation methodology developed in this study, we used one-month (April 202) data of the Seoul metropolitan sphere (Seoul, Gyeonggi Province, and Incheon) where the non-tagged alighting ratio was low. Since the non-tagged alighting ratio is very low in the case of subways, we selected only the bus use data as the estimation target. First, we built complete data (data with complete boarding-alighting records) for the accuracy validation of the alighting stop estimation method and deleted all alighting information from the data.
In some cases, the allowed walking distance is set to 500 m, considering that the common radius of a subway station area is 500m, or it is set to 500-1,000 m based on the distance distribution between the bus stops in the analyzed area  (Alsger et al. , 2016 , Nunes et al. , 2016 , Gordon et al. , 2013 , Yang et al. , 2019). In this study, we set the allowed walking distance to 500 m, considering the above previous studies. According to the analysis results, about 67. 68% of the bus trips are classified as continuous travels. Non-continuous travels are classified into recurrent travels and non-recurrent travels. To determine the recurrence of the trips, we analyzed one-month boarding data and counted the boarding frequency for each boarding stop in each time range for each card ID. To determine the residence and workplace of a recurrent travel, we established the "morning commute hours" and "afternoon commute hours".
We used the pattern data (weekdays/weekends) built based on the one-year boarding/alighting OD data for each card ID. In the case of route patterns, the one-year OD patterns for each route ID were constructed for all trips that could not be estimated as continuous/non-continuous trips, and the alighting stop percentages were calculated based on the boarding stop to assign the alighting stop based on the percentage. Here, we considered the moving direction on the route and assigned the alighting stop based on the probability of the corresponding stop after the route stop. As a result of the trip type classification, 13,038,870 (6. 18%) trips were counted as route patterns.
According to the alighting stop estimation results, 12. 32% of the estimated stops are the same as the actual alighting stops, and 44. 92% accounts for the case where the distance (error) between the estimated and actual alighting stops is less than 1 km. The accuracy is relatively low because route patterns, not each person's patterns, are applied. Upon examining the accuracy (less than 1 km) of the alighting estimation for each stage, we found that the accuracy for the continuous travels was the highest (94. 39%), and that of the results using route patterns was the lowest (44. 92%). This study aims to ensure that more complete information is reflected when monitoring operations and making decisions by building a public transportation dataset that can be used in the field. To this end, we have designed a methodology that ensures a 100% valid tag rate by applying the route patterns in the last stage.
In this study, we classified the trip types by analyzing the characteristics of transportation card data to build 100% complete data. Furthermore, this study differs from previous studies in that it built more complete bus trip data by applying the stage-by-stage alighting estimation model. In the future, we need to conduct additional studies to apply and review more various estimation methods such as AI-based alighting stop estimation methodology to improve the alighting estimation accuracy. Addition research is also required to improve the accuracy of alighting estimation for weekend trips. This should lead to an improvement of the current public transport OD methodology and the discovery of public transportation policies and services.
Report Card Scores We obtained report card scores for providers from the seven issues of PHC4's Cardiac Surgery Report published during our study period. Table  S2  in Multimedia Appendix 1 lists the publication dates and data collection periods for each issue. Report cards provide quality scores for each surgeon who performed at least 30 CABG surgeries during the data collection period, indicating whether they had mortality rates that were greater than expected, as expected, or lower than expected based on risk-adjusted health outcomes of their patients (see Figure  S5  in Multimedia Appendix 1). We used the reported summary measure for each surgeon across his or her practice hospitals. Following the literature  [3] , we assumed that patients used the most recent report card to inform their decisions and, therefore, that the effective period of each report card was from its publication date to the publication date of the next report card.
Acknowledgments MQ gratefully acknowledges financial supports from the  National Natural Science Foundation of China  (grant  71704027 ) and the " Chenguang Program " supported by the  Shanghai Municipal Education Commission  (grant  17CG03 ). SC and XL are grateful for the support provided by  Lehigh University  ( Data X Innovation Grant ). We also thank  PK Kannan ,  Seth Richards-Shubik ,  Chad D Meyerhoefer ,  Adam Biener , and  Reagan A Baughman  for their valuable comments and suggestions. An earlier version of this article was presented at the 7th Conference of the American Society of Health Economists. PHC4, its agents, and its staff bear no responsibility or liability for the results of the analysis. The results are solely the views of the authors.
Introduction Historically, the prognosis for patients with metastatic nonsmall-cell lung cancer (NSCLC) has been poor, with 5-year survival of approximately 6%. 1  However, the introduction of immune checkpoint inhibitors that target the programmed cell death protein 1 (PD-1) receptor or its ligand (PD-L1) has significantly improved outcomes, prolonging both overall survival (OS) and progressionfree survival (PFS). ] [8]  The phase 3 MYSTIC study (NCT02453282) was a global, randomized, open-label trial comparing first-line durvalumab with or without tremelimumab versus platinum-based chemotherapy in patients with metastatic NSCLC. 9 MYSTIC did not meet its primary endpoints of improved OS or PFS with durvalumab + tremelimumab versus chemotherapy in patients with tumor cell (TC) PD-L1 expression ≥ 25%.
Study Design and Treatment MYSTIC (NCT02453282) was a phase 3, multicenter, global, randomized, open-label study. Patients were randomized 1:1:1 to receive durvalumab 20 mg/kg every 4 weeks, durvalumab 20 mg/kg every 4 weeks plus tremelimumab 1 mg/kg every 4 weeks (for up to four doses), or the investigator's choice of platinum-based doublet chemotherapy (four to six cycles). Randomization was stratified according to PD-L1 TC expression ( ≥25% vs. < 25%) and tumor histology (squamous vs. non-squamous). Maintenance therapy with pemetrexed was allowed in patients with non-squamous histology who had not progressed after four cycles of pemetrexed/platinum therapy. Crossover from the chemotherapy arm to either of the durvalumab-containing arms was not allowed. Patients continued to receive treatment until objective disease progression (per RECIST v1.