dataesr/scientific-paragraphs-categorization huggingface dataset:
It is illustrated by the different kind of event prediction in the Fig. 5. We can notice that the model makes good predictions especially for dense regions and for hadron predictions starting in the EM part of the detector ((a) and (b) arrows in the Fig. 5 ). However, bad predictions can be observed, mainly particle misidentification (especially for EM class), and for underestimated hadronic shower size ((c) and (d) arrows in the Fig. 5 ). If the latter can be significantly improved by increasing the number of training events in the dataset, reducing the number of misidentified particles (considered as False Negative or FN) is more difficult. It will require a fine hyperparameter adjustment (especially in the RPN module) for our particular HGCAL-2D model. Globally, the number of FN (or misidentified particle number) is near 15 % of all particles of our data set.
Conclusion/perspectives This preliminary 2D study has been performed in challenging conditions mainly for technical reasons: the small datasets, the rough histogram extraction, with layers far from each other, the hit energy converted in int8 (image pixel), etc. It however gives encouraging results: the Mask-RCNN captures the scattered hits from hadronic showers, dense regions of clusters are well discriminated, however about 15 % of the particles are undetected (or not predicted) by the model. These first results are very promising if we solve the technical problems listed previously. It implies that we have to adapt the Mask RCNN model to the HGCAL topology (low number of layers, removing the energy deposit normalization for each "image", adjust the hyperparameters of the model especially those relative to the filtering, modify if required the loss function, etc.
Despite a century of experience and a great deal of effort, considering the risk of thermal instability of concrete when exposed to fire is still in many cases a very difficult task as it includes many uncertainties in real practice. Moreover, there is lack of validated numerical models for predicting realistic behaviour of concrete structural elements under fire exposure. In the framework of the international benchmark "Vulcain tests on 3 Walls", three full scale ISO fire tests on a reinforced concrete wall, simultaneously subjected to a constant uniaxial compressive load, were conducted in CSTB in Paris, France. The duration of these tests exceeded 120 minutes. In order to assess the capability of available finite elements (FE) models to represent reinforced concrete structural behaviour under fire exposure, IRSN, within the scope of the Vulcain benchmark, developed a concrete model with help of Mazars isotropic continuum damage mechanical model  [1]  ran with the CAST3M software [2].
Passive monitoring of daily activity via smartwatches, fitness trackers, and smart-home devices could provide a means of tracking behavioral changes over time to detect cognitive decline  (23, 24). Select examples of such technologies include the Mindstrong Health application, NeuraMetrix software, and the TATC algorithm. Mindstrong Health has developed a smartphone application that monitors smartphone use to detect behavioral changes associated with mood disorders, with potential to expand to cognitive decline associated with neurodegeneration  (25). Similarly, NeuraMetrix has developed software that passively monitors user typing habits to detect changes (e. g. , reduced typing speed) associated with cognitive decline  (26). The TATC algorithm uses actigraphy to detect behavioral changes associated with abnormal aging and AD  (27).
Public  grants include those from  Instituto de Salud Carlos III. Ministerio de Salud. Gobierno de España; Dirección General de Farmacia. Ministerio de Salud. Gobierno de España ; and  European Commission : H2020 program ,  Innovative Medicine Initiative  ( IMI-2 ); and  ERA-NET NEURON program ,  European Marie Sklodowska Curie. Advisory work includes that for Araclon Biotech, Biogen, Bioibérica ,  Eisai ,  Grifols ,  Lilly ,  Merck ,  Nutricia ,  Roche, Oryzon, Schwabe Farma, Servier , and  Kyowa Kirin. PMD has received research grants (through  Duke University ) from  Avid, Lilly ,  Neuronetrix, Avanir, Bauch, Alzheimer's Drug Discovery Foundation ,  Cure Alzheimer's Fund ,  Wrenn Trust ,  DOD ,  ONR , and  NIH. PMD has received speaking or advisory fees from  Anthrotronix, Neuroptix, Genomind, Clearview, Cognicity, Nutricia, Living Media, Verily, RBC, Brain Canada , and CEOs Against Alzheimers.
| Statistical analysis For each temperature treatment, a mixed model (MIXED procedure in SAS v. 9. 4; SAS Institute Inc. ) was used to explore the natural genetic variation of the disease index at each time point of phenotyping, as follows: where µ is the overall mean of the phenotypic data, "block" accounts for differences in microenvironmental conditions between the three experimental blocks, "accession" corresponds to the genetic differences among the TOU-A natural accessions, covCol is a covariate accounting for tray effects within blocks (phenotypic mean of the three Col-0 replicates per tray was used as a covariate), and "ε" is the residual term. The factor "block" was considered as a fixed factor and the factor "accession" as a random factor. The significance of the random effect was determined by likelihood ratio tests of model with and without this effect.
Residuals were normally distributed so no transformation was applied on raw phenotypic data. For GWA mapping analyses, we used best linear unbiased predictors (BLUPs) obtained for each natural accession. Because A. thaliana is a highly selfing species  (Platt et al. , 2010) , BLUPs correspond to genotypic values. Using a formula adapted from  Gallais (1990) , broad-sense heritabilities (H 2 ) at each time point of phenotyping were estimated from the variance component estimates of the "block" and "accession" terms obtained with the VARCOMP procedure in SAS v. 9. 4 (SAS Institute Inc. ).
| GWA mapping with local score analysis To fine map the genomic regions with additive effects associated with natural disease index variation at each time of phenotyping for each temperature treatment, a mixed model implemented in the software EMMAX was adopted (Efficient Mixed-Model Association eXpedited;  Kang et al. , 2010). To control for the effect of population structure, we included as a covariate an identity-by-state kinship matrix K based on the 1,902,592 SNPs identified in the TOU-A population  (Frachon et al. , 2017). Because rare alleles increase the rate of false positives when included in mixed models, we considered a threshold of minor allele relative frequency (MARF) >7% and ended up with 981,617 SNPs  (Brachi et al. , 2010; Kang et al. , 2010). As previously described in Frachon et al.
Implementation KeYmaera X  [17]  is an interactive theorem prover based on the sequent calculus formulation of dL. It is implemented in Scala, replacing its former system KeYmaera  [16]. It has a web-based GUI environment, and a support of automated theorem proving using computer algebra systems such as Mathematica  [27]. For the formalization of case studies in Section 6, we extended KeYmaera X version 4. 7 (available at  [17] ) with the (Sync) rule. This extension of KeYmaera X, together with our proofs in case studies, are currently available at http:// group-mmm. org/rddl tacas 2020/. The KeYmaera X implementation is structured in a flexible manner, from which we benefited. To add a rule to KeYmaera X, one has to implement a Scala program that take the conclusion of the rule and generate the premises of the rule as subgoals.
The fact that any Scala program is allowed here enabled us to implement complex algorithms, such as inductive translation of formulas. In implementing the (Sync) rule, the functions in KeYmaera X called helpers helped us, such as in the Lie derivative computation and the functionality to simplify formulas into equivalent ones. The bulk of our effort regarded the ⊗ (g,g) operator. There we did a bit more general than we stated in the paper: not only taking dynamics of form ẋ = e & Q, we also allow sequences of dynamics possibly interleaved by guards and nondeterministic choices. This feature was utilized in the case study that will be described in Section 6. 3.
Fourth, the mechanisms through which the daily food environment affects food choices (ie, food purchases and consumption) are still unknown. Individual and contextual factors may interfere in the environment-diet relationship. For example, individuals under stress may have less control over food choices and are more susceptible to unhealthy food consumption  [22, 23]. Novel smart technologies for data collection allow the aforementioned limitations and gaps in the literature to be addressed and will improve the understanding of the role of day-to-day food environments in food choices. The FoodTrack study uses a specially developed smartphone app for its main measurements. It includes a novel interface to report food purchases and consumption, and incorporates such technologies as a global positioning system (GPS) and ecological momentary assessment (EMA).
Design and Setting We used a smartphone app incorporating GPS tracking and EMA during a 7-day observational study to collect data from adults (25-45 years old) living in urban areas in the Netherlands. We set the minimum age at 25 years to exclude those undertaking full-time studies at an educational or training institute; as such, study participants were not representative of the country's population. The study did, however, include the majority of generation Y (millennials) and the youngest wave of generation X, whom we included in the study because we had hypothesized that during their daily lives, these people juggle many activities (eg, work, caregiving, leisure activities) that could interfere with health behaviors (such as food choices). The study was funded by the Netherlands Organization for Scientific Research and approved by the ethical committee of the Faculty of Social Sciences of Utrecht University, the Netherlands (FETC18-014).
Once they had completed it, they were sent an email confirming their initial participation and providing further details (eg, their log-in name and password, the start day of the research). The start day (Monday-Sunday) of the study was randomly assigned to each participant, so that data on fruit, vegetable, and snack consumption would be collected for both weekdays and weekends. Participants were sent a separate text message with a link to download the app, which was freely available from the App Store (Apple Inc, Cupertino, CA, USA) or Google Play (Google LLC, Mountain View, CA, USA). Once logged in, participants were asked to allow the app to access their location (thus allowing GPS tracking) and to receive push messages (ie, reminders). The study website provided an introductory video, as well as a video outlining the use and functionalities of the smartphone app.
An online manual (in pdf format) explaining the use and functionalities of the app in more detail was also provided. Participants were able to contact the research team by calling or messaging us during the entire study period with any study-related questions or problems. The smartphone app automatically collected GPS tracking data in the background. The participants used the app to upload information on all food purchases over the 7-day period and to answer EMA questions about contextual and individual purchase-related factors. These questions popped up whenever they uploaded data about a food purchase. Purchases were defined as food and drink products purchased for immediate consumption (eg, restaurant meals, snacks on the go), to be consumed later (eg, groceries, small packaged foods), or to also be consumed by others (eg, groceries for the entire household).
In addition, during the final 3 days of the 7-day period, participants also collected data about the consumption of fruit, vegetables, and snacks (including fast foods) and answered EMA questions about contextual and individual consumption-related factors whenever they uploaded data about food they consumed. The participants were rewarded with an online non-food shopping voucher worth €30 (about US $33) if they completed all elements of the study. At the end of the 7-day period, they completed the online closing survey. The original questionnaires were in Dutch but we translated the responses into English for this paper.
Tracking Locations and Trips The app ran in the background and used the smartphone's sensing capabilities (eg, GPS, accelerometer) to detect, record, and quantify the movements of participants, the trips they made, and the places they visited  [26]. We will use these data to calculate individual places visited and purchases made within the living environment (aim 1). The app's menu included a button labelled "see trips" that enabled participants to see the trips they had made and the locations they had visited during the day, including the date, time, and start and stop locations. They could also view each trip on a map by clicking on the trip concerned.
Tracking Food Purchases and Consumption We asked the participants to upload their purchases as soon as possible after they had paid for them. They could either take a photo of the receipt listing the products bought along with their prices and their weights, or enter their purchases manually (product and amount purchased, eg, 1 kg of sugar; or predefined serving, eg, 1 candy bar, 35 g) by means of the integrated food database. This database was built upon the Web-based food diary Eetmeter of the Netherlands Nutrition Centre  [27] , which includes food products available in the food databank of the National Institute of Health and Environment and the Netherlands Nutrition Centre  [28]. Although the databank includes many products, it does not include all products (ie, not all specific brands or types are present).
Spatiotemporal Characteristics of the Purchase or Consumption Participants reported information about the location of their purchase or consumption either immediately after the purchase or consumption by clicking on the "current location" button, or at a later point in time by entering the location data manually, although this was highly discouraged. Participants selected from multiple response options (which included an open answer box) to indicate the type of food outlet or the location where they purchased or consumed the food. Participants were asked to report the exact time and date of the food purchase or consumption. The current time and date were presented as the default.
Process Evaluation of Experiences With the App and Participants' Compliance With the Study Protocol We used 2 items to assess the app's general ease of use and likeability. We also assessed whether the participants had noticed the messages that were sent automatically by the app to remind them to register data on food purchases and consumption, and whether these had helped to improve compliance (2 items). With respect to entering food choices in the app, we also used 2 items to assess the completeness of the food product database and how easy it had been for the participants to estimate and enter the portion sizes of the products purchased or consumed, and how accurate they had been. We used 4 items to assess the use and operability of the app. We assessed whether participants had allowed it to track them throughout the research period, whether they had explored the trips that the app had tracked automatically, and whether they considered these tracks accurate.
Data Analyses In this paper, we provide insight into the descriptive statistics of the participants' compliance with the study protocol, and their experiences with and evaluation of the smartphone app and the study. To assess food environmental exposure in future studies, we will assess individual space-time paths using individual GPS tracks to extract home and other important locations. Subsequently, we will assess the individuals' accessible food environment, by extracting food events from GPS tracks and by computing corresponding space-time prisms using GISs to identify the location of food outlets that are within reach of a person. After assessing individual exposure, we will assess the association between individual food environmental exposure and food choices (purchases and consumption) using linear mixed-model analyses.
7%) with "no effort. " (n=99, 70. 0%). Of the 140 participants, 66 (47. 1%) answered the open question. They mentioned, for example, not only that it had been "fun to participate" or it was a "nice study" (n=13), but also that they had experienced struggles during the study: "not all products were available in the database" (n=11), "trips were not registered" (n=7), it was not possible to navigate back and forth in the app (n=12), and the "app was slow" (n=16). With respect to the entire study project, we are analyzing data concerning the first project aim and expect to publish the results in the spring of 2020. We plan to publish further papers providing insights into the additional aims in the next 2 years as well.
Principal Findings This paper outlines the protocol of the FoodTrack study, as well as participants' compliance with the study protocol and their experiences during study participation. We collected data over a 7-day period by means of the FoodTrack app, which uses GPS tracking and EMA to assess real-time individual and contextual variables at the moment of purchase or intake. The GPS tracking will allow us to assess participant activity-travel patterns that in turn will allow us to assess individuals' daily food environmental exposure. The results of this study indicated that participants liked the app and found it easy to use. About 90% of the participants reported that they had collected data on all or almost all purchases and intakes during the 7-day period. However, not all participants complied with the study protocol that encouraged them to enter purchases or consumption right away.
Strengths and Limitations An important strength of the FoodTrack study is that we will be able to link collected GPS data and individual sourced data to GIS data on food outlet locations, in order to estimate individuals' daily food environment exposure. A limitation of the FoodTrack study is that we relied on self-reported data regarding food purchases and consumption (and psychosocial and contextual variables). In addition, only 3 days of data on fruit, vegetable, and snack consumption were collected, precluding an insight into weekly consumption habits. As emphasized by previous research on dietary assessments  [36] , and confirmed by our process evaluation, it is clear that the collected data on food purchases and consumption should be included in the analyses in a rather qualitative way (eg, number of items purchased), rather than trying to assess, for example, the exact amount purchased or consumed or the daily nutrients obtained.
Acknowledgments We would like to acknowledge the work of  Monique Zwinkels  during the study preparation, recruitment, and data collection periods. We are grateful to  Martin Dijst , who supported the grant application for this study. We would also like to thank Locatienet [25] ( Remco Zut  and  Sebastiaan Raaphorst ) for the development of the FoodTrack smartphone app and Locatus BV [30] for the use of their database. We would also like to acknowledge the  Netherlands Nutrition Centre  for their collaboration (sharing the food database and recruiting participants). We thank  Martin Cohen ,  Henny Ram ,  Ingrid Heersche ,  Tom Kievith ,  Margot Stoete , and  Beatrijs Voorneman  for their contribution to the developments of the recruitment materials. Finally-yet importantly-we thank all the participants for their valuable participation in this study.
Furthermore, these data can provide clinicians with a more complete picture of patient health to enable more informed and precise treatment decision making  [11]. Owing to the ubiquity of smartphones, the use of mHealth technologies to measure and record health-related behaviors and clinical parameters has become increasingly convenient and useful for research  [12]. These tools can increase access to research opportunities across diverse populations, collect near real-time data, and reduce costs for population health research by forgoing in-person visits for assessment and reducing study staff personnel. For example, the national All of Us Research Program (formerly Precision Medicine Initiative) recently selected Fitbit to pilot in up to 10,000 participants to record heart rate, physical activity, and sleep.
Overall Aim Several studies have shown promising evidence that support the feasibility, acceptability, and limited effectiveness of digital interventions for behavior change  [13] [14] [15] [16] [17] [18] [19]. However, many of these studies have been conducted for short durations and on small sample sizes  [13]. Through the use of wearable devices, this initiative aims to increase students' awareness about the importance of activity and good sleep habits through self-tracking and to develop and provide interventions and tools to help achieve healthy behaviors during and following college. We are currently conducting the first of 2 pilot studies to assess student interest, adherence, feasibility, and staffing needs and to gather baseline data through wearables and surveys on stress, diet, physical activity, and sleep behaviors.
For the pilot studies, we only developed an iPhone Operating System (iOS) app given that a large proportion of incoming students were estimated to have Apple iPhones based on the current student body data (>90%). This included creating a secure server architecture that allowed us to retrieve and store data for analyses (Figure  1 ). Access to the WearDuke app through the University App Store was restricted to students who finished the consent process. When installing the WearDuke app, students were asked to allow the app to access each category of study data stored in Apple HealthKit (eg, heart rate, steps). HealthKit is an in-built app that aggregates health-related data on the iPhone. Data from Apple Watch is transferred and stored on HealthKit. The WearDuke app retrieves and encrypts the permitted data from HealthKit and then transfers the encrypted data to a secure server on Duke's protected network.
Wearable Selection For this initiative, we will provide the enrolled students with a choice of wearable to monitor activity and heart rate; sleep behavior data will be gathered through an app. With our student team, we reviewed available wearables based on several criteria: type (wrist or ring), battery life, measurements, style, other features of interest to students, and methods of data access. We were unable to identify wearables that are compatible with both Android and iOS smartphones, have a comprehensive set of desired measures (sleep and activity) that are clinically accurate, and possess other features that are attractive to a college student population. Thus, we decided to offer the Fitbit Charge 3 and Apple Watch 3 to Android and iOS smartphone users for the first pilot study, respectively.
Pilot Study 1: Methodology and Measures For the first pilot study, we are primarily interested in ascertaining feasibility, student interest, and student experiences as well as infrastructure needs. Feasibility will be measured with study records and feedback regarding the number enrolled, number of withdrawals, frequency of wearing the wearable daily, survey completion rate, and use of information technology support services. In addition to the data on wearables (Table  1 ), we will administer weekly surveys to gather more information about students' sleep habits, caffeine use, overall health and mental health, and academic performance (Table  2 ). We administered a baseline/demographic survey following enrollment. Students with an iOS smartphone were asked to install the developed companion app to complete surveys.