dataesr/scientific-paragraphs-categorization huggingface dataset:
iEngage: A Technology-Enabled Educational Program The iEngage program targets health literacy, PA-related skills, and sugar-focused nutrition guidelines. Health literacy topics include the definition of PA, classifications of PA intensities (light, moderate, and vigorous), physical fitness parameters and their interpretation, rating one's own effort during exercise, sedentary behaviors and their effects on health, health definition, well-being (physical, mental, and social), sugar as a source of energy, and guidelines related to sugar consumption. iEngage v1. 0 was delivered via an app (powered by BePatient) comprising 8 1-hour modules; it was self-paced and had learning activities as well as goal setting and self-assessment tasks. Each module proposed learning activities, quizzes, and brief 2-to 5-min PA sessions that generally focused on a particular series of movements: sprints, jumping, walking, running, squats, sit-and-reach, or push-ups.
The total PA per adolescent over the 8 modules was 21 min; thus, these prescribed PA sessions had no significant direct impact on the MVPA levels and could not have contributed to an artificial increase in MVPA. PA was captured by the Misfit Shine 2 activity tracker and conveyed to the child via the Misfit app exclusively during the iEngage modules to support learning and provide help in self-assessing achievement against personal goals. Figure  2  presents screenshots of the app and pictures of children in activity during the program. At the end of each module, the adolescents were guided to set their own goals for the next module. These were 2-fold: first, they chose an individual "objective" in terms of number of steps and intended intensity (eg, "I plan to do 10,000 steps and 45 min of MVPA every day"), and then, they selected a "mission," which was a specific task usually involving family, friends, or health professionals (eg, "I will discuss what I've learned today with my family" or "I will play soccer with friends").
Baseline Physical Activity (GENEActiv) Baseline PA was obtained via GENEActiv activity trackers (validated in children  [18] ; Kimbolton, Cambs, PE28 0LF) positioned on the nondominant wrist for 5 days before the program. The datasets from these trackers contained 60-Hz 3-dimensional accelerometer data. Raw data were processed into 1-sec epoch signal vector magnitude datapoints of daytime activity (8 am to 8 pm daily) and were then categorized into PA levels: sedentary, light PA, and MVPA, for each second using Phillips's cut points for children  [19]. We focused on bouts of sedentary activity or MVPA occurring with a minimum duration  [18] , as these behaviors were targeted by the program. A bout was defined as a continuous episode of PA at a specific range of intensity, and the length of a bout was the number of seconds spent at that intensity during that episode.
Physical Activity During the Program (Misfit) The Misfit activity trackers were worn continuously over the 4-week program, and data were available to the researchers in a daily aggregated form for each child: this included the total daily steps and the PA sessions that were detected with duration, steps, and calories spent during each session. We reverse-engineered the PA intensities from calories spent using the following criteria, which were found empirically to match Misfit's categories: PA sessions that consumed less than 2 calories per minute were considered light, between 2 and 3. 5 calories were considered moderate, and over 3. 5 calories were considered vigorous. These empirically defined cut points are aligned with those of Colley et al  [21]  for light and moderate PA and were slightly lower for vigorous activity, suggesting that our calculation was on the conservative side.
This had no bearing on our study as we were interested in MVPA. As noted earlier, these trackers were not used here for scientific validation but for learning purposes. Nevertheless, to assess data accuracy, we compared their data with those of the GENEActiv trackers, by asking children to wear them concurrently on the same wrist during the first week: the daily totals were consistent, although the GENEActiv trackers sometimes captured more finely detailed data. This indicates that the Misfit trackers may sometimes underestimate very small activity bouts, yet still provided a close lower bound of the children's activity. Their continuous data across the 28-day program data were, therefore, useful to explore.
Analysis of Program Physical Activity (Misfit) We analyzed the PA data in several ways to gain insight into the PA behavior changes during the program. We first identified the types of daily behavior observed through daily behavioral clustering. We then devised a consistency rate function to track the regularity of the participants' healthy PA behaviors over a sliding window of 7 days. We assumed that although the adolescents might not meet the PA recommendations every day, a noteworthy sign of progress is the fact that they meet them more regularly at the end of the program than at the beginning. These methods are summarized below.
Anthropometry, Physical Fitness, and Physical Activity Before the iEngage Program Descriptive data collected before the iEngage program are presented in Table  1. Accelerometry results from GENEActiv show that participants spent an average of 112 min per day in sedentary behaviors lasting at least 60 sec. Daily (8 am-8 pm) baseline PA intensities expressed in minutes (sedentary, light, and MVPA) showed that they spent 122 min in light PA and 36 min in MVPA in bouts lasting at least 3 sec.
The analysis of weekly consistency rate using these clusters is presented in Figure  5. On average, the adolescents started at 0. 60 and finished at 0. 63 (with a peak at 0. 71) at the end of the iEngage program. More interestingly, the least active adolescents in week 1 (defined as being in the less active cluster more than 50% of the time) started with a consistency rate of 0. 18 and ended with 0. 45 (peak at 0. 51), indicating a 27% increase over the program for these adolescents.
Indeed, these adolescents more often met the international recommendations for daily steps and PA intensity by week 4. This approach provided better daily global analyses of PA patterns in adolescents based on the recommendations  [26]. Overall, PA steadily increased, showing levels approaching the international recommendations, especially for those who were less active at the beginning of the study. We highlight that PA was realized outside the iEngage modules, ie during the adolescents' daily life, as the program only prescribed at most 4 min per module (the remaining time in each session was spent learning via the app or discussing concepts with peers). The school educational team, including the principal, the teachers, and the school nurse, as well as the families and community (Melanesian custom) were very involved in the program, as we intended it.
Some study limitations need to be underlined. This exploratory study was conducted in 1 class with 24 children for a period of 4 weeks and no control group was included, which limits any generalization. Although Misfit data were recorded continuously during the program, enabling us to obtain unique information on the children's behavior, we did not measure the pre-post effects of the intervention via accelerometry, and this may have limited the interpretation. However, at the start of the program and for 5 consecutive days, the Misfit data were compared for data accuracy with the GENEActiv data. The daily totals were consistent, indicating that although the Misfit trackers sometimes underestimated small activity bouts, they still provided a close lower bound of the children's activity. In conclusion, an integrative approach in the sports sciences, combining user-centered and community-based education and digital technologies (wearable trackers) to build health-related skills and change PA behaviors, seems promising in the PICTs.
Acknowledgments The authors would like to thank BePatient [29] for their contribution to this project via their digital platform. The authors would also like to thank the school teaching team of Wé in Lifou Island and the administrative staff for their help and support in the investigation, especially  Dr P Zongo ,  P Monjo ,  A Mildred-Canet ,  E Cullum ,  A Le Nestour ,  J McBroom , and the  Vice-Rectorat of New Caledonia. This research program was funded by a grant from the  French Ministry of Foreign Affairs through the Pacific Fund  to CC, KY, and OG.
We describe BlackHawk, a public C program for calculating the Hawking evaporation spectra of any black hole distribution. This program allows the users to compute the primary and secondary spectra of stable or long-lived particles generated by Hawking radiation of the distribution of black holes, and to study their evolution in time. The physics of Hawking radiation is presented, and the capabilities, features and usage of BlackHawk are described here under the form of a manual. This is the BlackHawk v2.
There is therefore a strong link between HR and particle physics, particularly in the QCD domain  [28, 29]. However, before BlackHawk was released, there was no publicly available code to compute the Hawking radiation spectrum of a distribution of BHs in the universe, e. g. to derive precise constraints on the DM fraction under the form of PBHs or to predict detection signals of HR in future instruments; people usually relied on analytical approximations to compute the spectra. This was the motivation that drove the publication of the code. Moreover, since its first release, BlackHawk has received many modifications to embed interesting features: distributions of spinning BHs, emission of DM on top of the SM particles, HR rates in modified BH solutions, etc. Some of these modifications were already present in the previous version of the manual on arXiv [1905.
04268v2], but most of them have been added in the recent update presented in the BlackHawk v2. 0 release note  [1]. The latter also contains a complete overview of the publications realised since BlackHawk v1. 0 was released. The present manual is associated with BlackHawk v2. 0, which is publicly available on the webpage: http://blackhawk. hepforge. org/ It is structured as follows: in Section II we review the physics and main formulas of Hawking radiation, in Section III we provide the necessary compilation information, Section IV describes the parameters of a typical run of the code, Section V details the different routines, the two main BlackHawk programs are presented in Section VI, the output files are explained in Section VII, we briefly estimate the memory usage of a run in Section VIII, we outline some possible applications of the code in Section IX and we conclude in Section X.
III. CONTENT AND COMPILATION This Section describes the structure and file content of the code and explains its usage. BlackHawk is written in C and has been tested under Linux, Mac and Windows (using Cygwin64). A. Content a. Main directory The main directory contains: • the source codes BlackHawk inst. c and BlackHawk tot. c containing the main routines, • a pre-built parameter file parameters. txt, • a compilation file Makefile, • a README. txt file containing general information about the code, • four folders src/, results/, manual/ and scripts/ that are described in the following. b. src/ subfolder This folder contains: • a header file include. h containing the declaration of all routines along with the parameter structure struct param (see Section IV A) and the numerical values of general quantities (unit conversion factors, constants, particle masses, etc), • source files containing the definition of all the BlackHawk routines (evolution.
c, general. c, hadro herwig. c, hadro pythia. c, hadro pythianew. c, hadro hazma. c, primary. c, secondary. c, spectrum. c, technical. c), • compilation files Makefile and FlagsForMake, • a subfolder tables/ containing all the numerical tables which will be described in the following. c. results/ subfolder This folder is designed to receive subfolders of data generated by running the BlackHawk code (see Section VII). d. manual/ subfolder This folder contains an up-to-date version of the present manual. e. scripts/ subfolder This folder contains all the scripts used to compute the numerical tables mentioned in the following, as well as visualization scripts and a main program for SuperIso Relic  [53] [54] [55]. These scripts can be used to generate modified numerical tables for example (see Appendices C 1, C 2 and C 3).
B. Compilation The compilation of BlackHawk has been tested on Linux, Mac and Windows (using Cygwin64) distributions. The code is written in C99 standard. To compile the code, simply cd into the main directory and type 3  : >>> make BlackHawk * where * denotes tot or inst. This will create a library file libblackhawk. a and an executable file BlackHawk *. x. The compiler and compilation flags can be modified in Makefile if needed. To run the code, cd to the main directory and type 4  : >>>. /BlackHawk *. x parameter file where parameter file is the name of a parameter file (e. g. parameters. txt for the pre-built one). To compile only the library, just cd into the main directory and type:.
A. Parameter structure The input parameters used by BlackHawk are listed in a parameter file (e. g. parameters. txt for the pre-built one). This file can be modified by the user and is saved for each new run of the code in the results/ directory. A C structure has been defined in include. h to embed all the parameters (i. e. input parameters and default run parameters): Most routines described in Section V will use this structure as an argument in order to have an easy access to the run parameters. Depending on the choices of the parameters, some of them can be irrelevant for a given run and will therefore not be taken into account, and no error message will be displayed for the irrelevant/unused parameters. Error messages are displayed if some parameter is out of its allowed range, automatically cancelling the run.
>>> results/destination folder/parameters. txt The expected output at this stage if of the form (full output = 1, program BlackHawk inst): In this program, BlackHawk computes the time-dependent Hawking spectra of a chosen initial distribution of BHs, that is the quantities d 2 n i /dtdE at all time intervals t from the initial time t min to the end of evaporation. ############################ # BLACKHAWK v2. 0 # # HAWKING SPECTRUM # # COMPUTATION DEVICE # ############################ BlackHawk computes the initial distribution of BHs at t min using the routine spectrum or reads the user-defined BH distribution file  If the parameter primary only has been set to 0, BlackHawk reads the suitable hadronization tables (depending on the hadronization choice) with the routine read hadronization tables, filling the.
B. BlackHawk inst program Running BlackHawk inst produces three (or two if primary only is set to 1) output files: • BH spectrum. txt: this file is the same as for BlackHawk tot. • instantaneous primary spectra. txt: this file is written by write instantaneous primary spectra. It contains the emission rates of the primary particles for each simulated initial energy. The first line is the list of primary particles, the first column is the list of energies (in GeV), and each other column is the emission rate d 2 n i /dtdE of Eq. (  37 ) per unit energy and time (in GeV -1 •s -1 •cm -3 ). • instantaneous secondary spectra. txt: this file is written by write instantaneous hadronized spectra. It contains the emission rates of the secondary particles for each simulated final energy. The first line is the list of secondary particles, the first column is that of energies, and each other column is the emission rate d 2 n j /dtdE of Eq.
Masses are given in GeV (corresponding to grams) and f (M, x j ) and g(M, a * ) are in GeV 4 (corresponding to g 3 •s -1 and g 2 •GeV•s -1 , respectively). We have checked that the value of f (M, 0) is consistent with that of  [29]  in the Schwarzschild case and that the values of f (M, a * ) and g(M, a * ) are consistent with  [70]  in the Kerr case. No numerical results were ever given for the polymerized case. The C scripts used to compute the tables, a README and all the necessary greybody factors tables are provided in the subfolder: >>> scripts/greybody scripts/fM/fM. c >>> scripts/greybody scripts/fM/Makefile >>> scripts/greybody scripts/fM/README. txt >>> scripts/greybody scripts/fM/spin *. txt If these tables are recomputed, it is advisable to also modify the information in the file: >>> src/tables/fM tables/infos.
To be clear: PYHTIA and HERWIG are particle physics codes that are very efficient in their domain of validity, for energies between some GeV and some TeV (BH mass M < 10 13 g), and considers SM particles (e. g. quarks) as elementary degrees of freedom. However, Hazma is much more fitted to compute the low energy showering of (charged) particles and considers pions as fundamental degrees of freedom below the QCD scale of ∼ 200 MeV (BH mass M 10 14 g). As their is no sizeable secondary generated particles for energies below the MeV scale (BH mass M 10 17 g), all these codes should provide the same result. The scripts used to compute the pythia tables are given in the folder:  When launching BlackHawk tot with the built-in parameters. txt file, one obtains the following output files (truncated to 5 lines and 5 rows when relevant): >>> • BH spectrum.
ACKNOWLEDGMENTS We gratefully acknowledge helpful exchanges with  P. Richardson  in particular on the hadronization procedure and the HERWIG code. We are also thankful to  J. Silk  for many constructive discussions, to  P. Skands  for help with PYTHIA and hadronization, and to  G. Robbins  for the interface with SuperIso Relic. We also thank  L. Morrison ,  S. Profumo  and  A. M. Coogan  for help using the Hazma package. The authors thank the  CERN Theory Department  for its hospitality during which part of this work was done.
The minimum conductance graph partitioning problem (MC-GPP) is to partition the vertex set of a graph into two disjoint subsets while minimizing the ratio between the number of the edges crossing the two subsets and the smallest volume of the two subsets, the volume of a vertex set being the sum of degrees of its vertices. MC-GPP has a variety of relevant applications, and however, is known to be NP-hard. In this work, we present a novel metaheuristic algorithm called "stagnation-aware breakout tabu search" for approximating MC-GPP. The algorithm combines a dedicated tabu search procedure to discover high-quality solutions and a self-adaptive perturbation procedure to overcome hard-to-escape local optimum traps. We perform extensive evaluations of the algorithm on five datasets of 110 benchmark instances in the literature.
Based on efficient implementation of various multilevel heuristics, Metis is extremely fast (e. g. , it can produce a good partition in several seconds even for a graph with a half million vertices). For a given instance, we use Metis to obtain an initial partition with a minimized number of cut edges. Then we run SaBTS to improve the input solution. In this manner, SaBTS can be considered as a post-processing method to boost the solution quality that is found by Metis. With these two very different types of initialization, we can observe the impact of the initial solution on the quality of the final partition found by SaBTS. Moreover, we can verify whether partitions from a popular graph partitioning package (designed for the minimization of cut edges) can be further improved by a dedicated MC-GPP algorithm in terms of the conductance criterion.
Parameter setting and experimental protocol The proposed SaBTS algorithm requires five parameters (see Table  1  (1) StS-AMA  [12] : The population-based memetic algorithm StS-AMA is among the rare and recent heuristics dedicated to MC-GPP. As indicated in  [12] , StS-AMA is the best performing algorithm among several local search and evolutionary algorithms. Since the code of this algorithm is not available, we decided to reimplement StS-AMA to make a fair comparison. It is well known that implementation details can significantly impact the performance of partitioning heuristics  [11, 20]. To implement StS-AMA, we followed faithfully the description given in  [12]  and checked that the results of our implementation are consistent with those reported in the reference paper. (2) Metis  [24] : As a popular graph partitioning package, Metis has been used to generate partitions in several studies on MC-GPP  [1, 25, 28].
Computational results In this section, we assess the performance of the SaBTS algorithm according to the two experiments explained above. To this end, we show summarized results of the studied algorithms to highlight the main findings. In the appendix, we report the detailed results of SaBTS together with the main reference algorithms on the 110 benchmark instances (Table  A. 1 and A. 2), the statistical results (p-values) from the Wilcoxon signed-rank test applied to different pairwise comparisons for all datasets (Table  A. 2) as well as a comparison using the geometric mean metric  [16, 21]  (Table  A. 3).
Using SaBTS to improve solutions given by Metis and Metis+MQI Table  3  shows the summarized results of the second experiment concerning SaBTS and MQI, when both algorithms are used to refine a partition given by Metis (entries involving Metis+SaBTS and Metis+MQI) and when SaBTS is used to refine a partition produced by Metis+MQI (entry (Metis+MQI)+SaBTS). As before, columns 1-3 indicate the studied algorithms, the information on the datasets and quality indicators respectively. Columns 4-6 count the number of instances on which each studied algorithm (Metis+SaBTS, Metis+MQI and (Metis+MQI)+SaBTS) achieves a better, equal or worse result compared to the input solution from Metis respectively (#Wins, #Ties and #Losses). Column 7 shows, for each dataset, the average improvement percentage (∆ 1 (%)) of a method over the results of Metis in terms of Φ best and Φ avg.
Specifically, we calculate the average improvement percentage (∆(%)) as follows. First, we compute the improvement percentage for each instance, which is given by Table  2  Comparative results of Greedy+SaBTS (when it is run with greedy initial solutions) with the reference algorithms StS-AMA  [12]  and Metis  [24]. (Φ -Φ M etis )/Φ × 100%, where Φ is the best (or average) conductance value of the compared algorithms and Φ M etis is the best (or average) conductance value of Metis. Then, the average improvement percentage for each dataset is given by n i=1 (Φ i -Φ M etis )/Φ i × 100%/n, where n is the number of instances of each dataset. In Table  4 , we show the improvements of SaBTS (i. e. , (Metis+MQI)+SaBTS) over the results of Metis+MQI, which are obtained by running SaBTS from the output partitions of Metis+MQI.
Columns 4-6 indicate the number of instances on which the result of (Metis+MQI)+SaBTS is better, equal or worse than the partitions provided by Metis+MQI (#Wins, #Ties and #Losses), while column 7 shows the average improvement percentage (∆ 2 (%)) of SaBTS over the results of Metis+MQI in terms of Φ best and Φ avg. From the results of Table  3  (and the detailed results of Table  A. 1 in the appendix), we first observe that both SaBTS and MQI consistently improve on the results of Metis for all five datasets. Specifically, in terms of Φ best (Φ avg resp. ), Metis+SaBTS can make an improvement for slightly more instances compared to MQI: 105 (107) out of 110 instances for Metis+SaBTS against 97 (106) for MQI. However, the last column of Table  3  shows that MQI achieves much more important average improvement percentages for the different datasets.